The DOI matches its Crossref record; 77 of the 78 references it cites were found in a registry or an index; 15 of the 44 sources its claims cite were checked for support.
Topology and data
Open accessAbstract
An important feature of modern science and engineering is that data of various kinds is being produced at an unprecedented rate. This is so in part because of new experimental methods, and in part because of the increase in the availability of high powered computing technology. It is also clear that the nature of the data we are obtaining is significantly different. For example, it is now often the case that we are given data in the form of very long vectors, where all but a few of the coordinates turn out to be irrelevant to the questions of interest, and further that we don’t necessarily know which coordinates are the interesting ones. A related fact is that the data is often very high-dimensional, which severely restricts our ability to visualize it. The data obtained is also often much noisier than in the past and has more missing information (missing data). This is particularly so in the case of biological data, particularly high throughput data from microarray or other sources. Our ability to analyze this data, both in terms of quantity and the nature of the data, is clearly not keeping pace with the data being produced. In this paper, we will discuss how geometry and topology can be applied to make useful contributions to the analysis of various kinds of data. Geometry and topology are very natural tools to apply in this direction, since geometry can be regarded as the study of distance functions, and what one often works with are distance functions on large finite sets of data. The mathematical formalism which has been developed for incorporating geometric and topological techniques deals with point clouds, i.e. finite sets of points equipped with a distance function. It then adapts tools from the various branches of geometry to the study of point clouds. The point clouds are intended to be thought of as finite samples taken from a geometric object, perhaps with noise. Here are some of the key points which come up when applying these geometric methods to data analysis. • Qualitative information is needed: One important goal of data analysis is to allow the user to obtain knowledge about the data, i.e. to understand how it is organized on a large scale. For example, if we imagine that we are looking at a data set constructed somehow from diabetes patients, it would be important to develop the understanding that there are two types of the disease, namely the juvenile and adult onset forms. Once that is established, one of course wants to develop quantitative methods for distinguishing them, but the first insight about the distinct forms of the disease is key.
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Claims the paper makes
53 No basis given · 38 Own interpretation · 23 Own result · 17 No full text of the sources · 9 Supported · 5 Partly supported · 5 Sources not found · 1 Couldn’t confirm · 1 Not checked yet · accuracy not yet measured
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Much of current research efforts are focused in this direction (see, e.g., [43] and [39]), and functoriality provides the right general mathematical framework for addressing them.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
What the sources say
- Could not verifycites [43] von Luxburg, Ulrike, 2008, Consistency of spectral clustering, Ann. Statist. sourceExperimental — accuracy not yet measured
The Introduction discusses questions about whether clustering algorithms converge, and Section 5.2 constructs operators for analyzing spectral clustering. Neither passage addresses functoriality or presents it as a framework for resolving ambiguities in clustering; searches for those concepts found no supporting passage in our copy. (LLM, conf=0.76)
- Not matchedcites [39] kleinbergJ.M. Kleinberg, An impossibility theorem for clustering, NIPS 2002: 446-453.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Could not verify
Every source the paper cites for this claim was checked and supports it.
In the paper
“We argue that, as suggested in [46], it has a role to play in the study of point cloud data as well, and we give two illustrations of how this could happen, within the context of clustering.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
What the sources say
- Supportedcites [46] McCullagh, Peter, 2002, What is a statistical model?, Ann. Statist. source
Section 8, “Extensive response variable” (page unavailable), describes a point process as a countable set of points and treats Poisson point-process models as natural transformations of functors. That supports the cited suggestion that functoriality could be useful for point-cloud data; the citing authors present clustering as their own proposed application. (LLM, conf=0.86)
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Niyogi, Smale, and Weinberger in [52] provide sufficient hypotheses which guarantee that this is possible for Riemannian manifolds.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.3. Persistent homology.
What the sources say
- Partly supportedcites [52] Niyogi, Partha, 2008, Finding the homology of submanifolds with high confidence from random samples, Discrete Comput. Geom. sourceExperimental — accuracy not yet measured
Section 2 (Preliminaries; page unavailable) considers compact Riemannian submanifolds sampled as data. Section 3 (An Outline of Our Main Results; page unavailable) gives conditions under which the samples recover the manifold’s homology with high confidence. It does not guarantee reconstruction of the manifold itself, so the claim is broader than the result. (LLM, conf=0.88)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The result of this construction is a database M of ca.4.5 × 10 6 points on a 7-dimensional ellipsoid in R 8 .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
What the sources say
- Partly supportedcites [41] lee A.B. Lee, K.S. Pedersen, and D. Mumford, The nonlinear statistics of high-contrast patches in natural images, International Journal of Computer Vision (54), No. 1-3, August 2003, pp. 83-103. sourceExperimental — accuracy not yet measured
Section 3 (Preprocessing; page unavailable) says the preprocessed points lie on a 7-dimensional ellipsoid in R⁹, which is transformed into a 7-dimensional sphere in R⁸. Section 2 (Optical and Range Data Sets; page unavailable) reports about 4.2 million optical patches, not approximately 4.5 million points in a database M. The source supports the general dimensional construction, but not the stated count and geometry together. (LLM, conf=0.85)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Their method is to prove that the Čech complex associated to a covering by balls of a fixed radius is homotopy equivalent to the underlying manifold.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.3. Persistent homology.
What the sources say
- Supportedcites [52] Niyogi, Partha, 2008, Finding the homology of submanifolds with high confidence from random samples, Discrete Comput. Geom. source
Section 3 states that, when the sample is sufficiently dense and the ball radius meets the stated bound, the manifold is a deformation retract of the union of the balls. Section 3.1 constructs the nerve of that ball cover and invokes the Nerve Lemma because the balls are convex. Together, these support the claimed homotopy equivalence under the method’s conditions; the paper calls the nerve a simplicial complex rather than a Čech complex. (LLM, conf=0.88)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“However, it turns out that there is a classification theorem (see [64]) for a subcategory of the category of N-persistence F -vector spaces, where F is a field.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
What the sources say
- Supportedcites [64] Zomorodian, Afra, 2005, Computing persistent homology, Discrete Comput. Geom. source
Section 3.1 (p. 11) establishes an equivalence between finite-type persistence modules and finitely generated graded modules over R[t]. Corollary 3.1 in Section 3.2 (p. 12) then classifies the isomorphism classes of finite-type persistence modules over a field F by finite sets of P-intervals. Finite-type modules are the subcategory referred to in the claim. (LLM, conf=0.94)
plain- Asserted: passes — The authors put it forward as true.
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- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“In [9], such a context is developed which is not dependent on the choice of a cost function but is rather dependent on”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Supportedcites [9] memolicarlsson G. Carlsson and F. Memoli, Persistent Clustering and a Theorem of J. Kleinberg , Preprint, March 2008. source
The Discussion (p. 16) presents persistence and functoriality as organizing principles and proposes defining clustering methods through sets of constraints, rather than a chosen cost function. The Results (p. 10) explicitly compares the framework’s properties with Kleinberg’s axiomatic conditions. (LLM, conf=0.87)
plain- Asserted: passes — The authors put it forward as true.
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- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“It can be shown that p Σ is a homotopy equivalence (see [65]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
What the sources say
- Supportedcites [65] Zomorodian, Afra, 2008, Localized homology, Comput. Geom. source
Lemma 1 (global; page unavailable) states that projection from a blowup complex to the covered space is a homotopy equivalence when a simplicial complex is covered by subcomplexes. The paper uses π_X and X_U rather than pΣ and M(|Σ|,S), but states the projection result attributed to it. (LLM, conf=0.88)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“This notion of clustering is closely related to clique clustering algorithms in network clustering [53].”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Supportedcites [53] clique G. Palla, I. Derènyi, I. Farkas, and T. Vicsek, Uncovering the overlapping community structure of complex networks in nature and society, Nature, Volume 435, 9 June 2005, pp. 814-818. source
The paper defines network communities through connected sets of adjacent k-cliques (p. 2) and describes an algorithm that finds cliques and clusters them using a clique-overlap matrix (p. 3). It therefore supports the citation as an example of clique-based network clustering; the comparison to the citing paper’s equivalence relation E is the citing authors’ own framing. (LLM, conf=0.88)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“There is a unique M gen -functorial persistent clustering algorithm Ξ which is scale invariant and which satisfies the requirement that Ξ(E) = P .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Partly supportedcites [9] memolicarlsson G. Carlsson and F. Memoli, Persistent Clustering and a Theorem of J. Kleinberg , Preprint, March 2008. sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
- It may state two separate claims as one. 39
- Probably not a substantial claim. 41
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Although clustering is clearly a very important part of data analysis, the ways in which it is formulated and implemented are fraught with ambiguities.In particular, the arbitrariness of various threshhold choices and lack of robustness are difficulties one confronts.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
What the sources say
- Partly supportedcites [43] von Luxburg, Ulrike, 2008, Consistency of spectral clustering, Ann. Statist. sourceExperimental — accuracy not yet measured
The Introduction describes clustering as widely used and says its consistency guarantees are unsatisfactory. The discussion following Proposition 24 shows that unnormalized spectral clustering can fail and that its reliability can be difficult to assess. These passages support the general concern about reliability, but they do not identify arbitrary threshold choices as a difficulty or establish a lack of robustness for clustering generally; no page numbers are available in our copy. (LLM, conf=0.82)
- Not matchedcites [39] kleinbergJ.M. Kleinberg, An impossibility theorem for clustering, NIPS 2002: 446-453.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“In particular, in [41], indications were found that the data was concentrated around an annulus.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
What the sources say
- Supportedcites [41] lee A.B. Lee, K.S. Pedersen, and D. Mumford, The nonlinear statistics of high-contrast patches in natural images, International Journal of Computer Vision (54), No. 1-3, August 2003, pp. 83-103. source
Section 5.1 describes the ideal-edge manifold using a circular orientation parameter and a bounded displacement parameter, giving it an annulus-like parametrization. Section 5.2 finds evidence that optical data points concentrate near that manifold. The paper does not use the word “annulus,” but the citing sentence is a reasonable, hedged paraphrase. (LLM, conf=0.82)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“By using persistence, one is able to develop a systematic way of addressing such questions.That methodology has been described in [65].”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
What the sources say
- Supportedcites [65] Zomorodian, Afra, 2008, Localized homology, Comput. Geom. source
Section 1.2 describes a method for localizing topological attributes relative to a cover. Section 4.4 gives the persistence algorithm that computes representatives of homology classes, supplying descriptions of those attributes. Together, these passages support the paraphrase about systematically addressing where features come from. (LLM, conf=0.90)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“The algorithm Ξ is the algorithm which associates to a finite metric space (X, d X ) the persistent set {π 0 (V R(X, ))} ≥0 .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Supportedcites [9] memolicarlsson G. Carlsson and F. Memoli, Persistent Clustering and a Theorem of J. Kleinberg , Preprint, March 2008. source
Example 2.1 (Section 2, p. 3) associates a finite metric space with a persistent set whose blocks at scale r contain exactly the points connected by chains of distances at most r. Those blocks are the connected components of the Vietoris–Rips complex at that scale, as the claim describes. (LLM, conf=0.90)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Also, results about barcodes under the general heading have now begun to appear (see [15] and [16]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Additional information
What the sources say
- Supportedcites [15] Cohen-Steiner, David, 2007, Stability of persistence diagrams, Discrete Comput. Geom. source
The Introduction (p. 2) says the paper makes concrete statements about the stability of barcodes. The Applications section (p. 16) states a Barcode Theorem, making this paper an example of the barcode results the citing sentence says have begun to appear. (LLM, conf=0.90)
- Not matchedcites [16] stabilitytwo D. Cohen-Steiner, H. Edelsbrunner, J. Harer and Y. Mileyko, Lipschitz functions have 𝐿_{𝑝}-stable persistence, Found. Comput. Math., to appear.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is readily checked that θ is a functor from N pers (Ab) to the category of graded Z[t]-modules, and is in fact an equivalence of categories, since an inverse functor can be given by M * → {M n }, where the morphisms ψ mn are given by multiplication by t n-m .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A first observation is that the points are scattered throughout the 7-sphere, in the sense that no point on S 7 is very far from the set, but that the density appears to vary a great deal.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We note that there are a number of short lines, and one long one.According to the philosophy mentioned above, this suggests that the first Betti number should be estimated to be one.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
hedgedcorrelationalSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We note that in this case, there are many short segments and 5 longer segments.It is again the case that this barcode is stable over the choices of landmark points and samples from M. This suggests that the first Betti number of the putative underlying space should be five.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Primary circle
hedgedcorrelationalSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Note that while the two smaller holes have now been “filled in”, a new hole has been introduced in the lower right-hand portion of the figure.Consequently, if we were to compute the homology of this complex, we would obtain a first Betti number of two.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We note that the vertical circles intersect the horizontal primary circle in two points, and do not intersect each other, so the picture in question produces a natural embedding of the three circle model in the Klein bottle.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We note that the β 0 barcode (the upper picture) clearly shows the single component, the β 1 shows two lines from threshhold parameter values .15 to .35, and finally the β 2 barcode shows a single line on roughly the same interval.This gives β 0 = 1, β i = 2, and β 2 = 1.These are the mod 2 Betti numbers for the Klein bottle.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At both scales, there is a central dense core, and two”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These two are distinguished by mod 3 homology, and we have performed the computation to show that the mod 3 homology is consistent with the Klein bottle and not with the torus.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We note that given only the Mapper output, one might suspect that the small feature (the array of orange nodes) could simply be an artifact, but examination of the data shows that they correspond to essentially distinct contact maps.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Some experimentation with the three circle model results in the following picture in which the horizontal segments form the primary circle and the vertical segments form the secondary circles, as in the diagram.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We have informally confirmed that the indicated patches are the ones which occur in the high density portions of the data set.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By far the most frequently occurring signatures were (1, 1, 0) and (1, 0, 1), corresponding to a circle and a sphere, respectively.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.5. Example: Electrode array data from primary visual cortex.
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Note that the two secondary circles each intersect the primary circle in two points, and that they do not intersect each other.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Note also that the data are obtained by simply examining the states occurring in the simulation, and that it does not include any dynamic information which would show how the states are traversed in the folding process.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The set S exhibits the three circle model clearly, but enlarging it still does not exhibit the non-trivial β 2 which would be characteristic of the Klein bottle.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is easy to check (a) that the factorization is a homeomorphism and (b) that the orbit space is homeomorphic to a Klein bottle.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A M iso -functorial clustering algorithm determines an choice p ι ∈ P G ι ι for every ι, and conversely an arbitrary choice of such p ι 's determines an M iso -functorial clustering algorithm.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Each of these notions creates the structure of a category whose objects are the finite metric spaces, since one can readily observe that each of the classes of morphisms is closed under composition and contains the identity.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Some work in clustering theory has been done in trying to determine the optimal choice of , but it is now well understood that it is much more informative to maintain the entire dendrogram of the set, which provides a summary of the behavior of clustering under all possible values of the parameter at once.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In the biological context, notions of distance are constructed using some intuitively attractive measures of similarity (such as BLAST scores or their relatives), but it is far from clear how much significance to attach to the actual distances, particularly at large scales.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Topology studies only properties of geometric objects which do not depend on the chosen coordinates, but rather on intrinsic geometric properties of the objects.As such, it is coordinate-free.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Moreover, it is understood that most of the information about topological spaces can be obtained through diagrams of discrete sets, via a process of simplicial approximation.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We then demonstrate that persistence can be generalized in several different directions, providing more structure and information about the data sets in question.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If the clusterings are consistent, i.e. if the clusters in C(X 1 ) and C(X 2 ) in C(X 1 ∪ X 2 ) correspond well under these maps, then one can regard that as evidence that the subsample clusterings actually correspond to clusterings on the full data set X.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
hedgedcorrelationaln = 2- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For any topological space X with a finite number of path components, β 0 (X) is the number of path components.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We begin with a discussion of persistent homology, which is a mathematical formalism which permits us to infer topological information from a sample of a geometric object, and we show how it can be applied to particular data sets arising from natural image statistics and neuroscience.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Functoriality is central in algebraic topology in that the functoriality of homological invariants is what permits one to compute them from local information, and that functoriality is at the heart of most of the interesting applications within mathematics.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We then show that the philosophy of functoriality can be used to reason about the nature of clustering methods, and we conclude by speculating about theorems one might hope to prove and discussing how the subject might develop more generally.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“One important goal of data analysis is to allow the user to obtain knowledge about the data, i.e. to understand how it is organized on a large scale.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although we often receive data in the form of vectors of real numbers, it is frequently the case that the coordinates, like the metrics mentioned above, are not natural in any sense, and that therefore we should not restrict ourselves to studying properties of the data which depend on any particular choice of coordinates.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The second method is more efficient, since it is precisely adapted to the complex at hand.Furthermore, it contains complete information about the original R-vector space.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In interpreting the output, one now finds that long intervals in the output barcode indicate the presence of a homology class which “persists” over a long range of parameter values, while short intervals indicate cycles which are “born” at a given parameter value and then “die” at a nearby parameter value.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This is due to the fact that for finite metric spaces, it is generically the case that each value of the distance is taken only for one pair of points, so one does not have any points which are equidistant between a pair of landmarks.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.2. Building coverings and complexes.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Intuitively, we regard these two holes as coming from faulty sampling or other errors in the recovery of the data.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=3
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The pictures and discussion above allow us to formulate the intuition that long intervals correspond to large scale geometric features in the space and that short intervals correspond to noise or inadequate sampling.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Both the the Čech and Vietoris-Rips complexes typically produce simplices in dimensions much higher than the dimension of the space.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.2. Building coverings and complexes.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It turns out that H simp k (X, Z) is always canonically isomorphic to the singular homology of the space |X|.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Remark.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The conclusion is that for simplicial complexes, homology is algorithmically computable, although standard methods for computing Smith normal form can often be painfully slow.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Remark.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If further is sufficiently small, then all the balls will be geodesically convex, and the complex will compute the homology of M correctly.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.3. Persistent homology.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Note that if the centers actually lie on a submanifold M ⊆ R n , and the set X is sufficiently dense in M , then this complex is the Čech complex attached to a covering of M .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.3. Persistent homology.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is clearly the case that if we have 0 ≤ ≤ , then we have an inclusion W s (X, L, ) ↩→ W s (X, L, ) and similarly for W s V R , W w , and W w V R .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.2. Building coverings and complexes.
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, for finite metric spaces, it generically produces degenerate (i.e.discrete) complexes, with no one dimensional simplices.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.2. Building coverings and complexes.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In rough terms, δ k for large values of k computes density using points in large neighborhoods of x, and for small values uses small neighborhoods.So, δ k for large k corresponds to a smoothed out notion of density, and for small k corresponds to a version which carries more of the detailed structure of the data set.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“It was observed in [38] that in an informal sense, the images in the different conditions appeared to be quite similar, and a statistical analysis strongly suggested that the behavior of V1 in the spontaneous condition was consistent with a behavior which consisted in moving through a family of evoked images corresponding to responses to angular boundaries, without any particular order.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.5. Example: Electrode array data from primary visual cortex.
What the sources say
- Not checked — no full text of the sourcecites [38] kenet T. Kenet, D. Bibitchkov, M. Tsodyks, A. Grinvald, and A. Arieli, Spontaneously emerging cortical representations of visual attributes, Nature 425 (2003), 954-956. source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“This result is consistent with what was obtained by using ISOMAP in [61].”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
What the sources say
- Not checked — no full text of the sourcecites [61] isomapJ.B. Tenenbaum, V. de Silva and J.C. Langford, A global geometric framework for nonlinear dimensionality reduction, Science 290 (2000) (December), pp. 2319-2323. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The arrays of neurons studied in [59] are from the primary visual cortex or V1 in Macaque monkeys.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.5. Example: Electrode array data from primary visual cortex.
What the sources say
- Not checked — no full text of the sourcecites [59] visionG. Singh, F. Memoli, T. Ishkhanov, G. Carlsson, G. Sapiro and D. Ringach, Topological Structure of Population Activity in Primary Visual Cortex, Journal of Vision, Volume 8, Number 8, Article 11, pp. 1-18, 2008. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“since the intuition is that features which appear at several levels in such a multiresolution diagram would be more intrinsic to the data set than those which appear at a single level.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Definition 3.2.
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The core consists of normal or near-normal patients, and the two flares consist of patients with the two different forms of diabetes.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Z[m, n]-persistence vector spaces are examples of quiver representations, a highly developed area of algebra.See [29] for a complete description.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Corollary 4.6.
What the sources say
- Not checked — no full text of the sourcecites [29] Gabriel, P., 1997 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These functions are clearly invariants of the isomorphism class of the module M , and we refer to them as the dimension and rank invariants, respectively.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Definition 4.3.
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If the question is instead whether or not an element arises from a union of k elements of S, then we do not obtain complete information this way, but we do obtain partial information in that we can preclude the possibility that a class arises from such a union.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“We note that the correspondence X → π 0 (X) can actually be viewed as a functor (see [44]) from the category of topological spaces to the category of sets, in the sense that a continuous map f : X → Y induces a map of sets π 0 (f ) : π 0 (X) → π 0 (Y )”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Not matchedcites [44] Mac Lane, Saunders, 1998
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As we have seen above, algebraic topology is capable of producing signatures which indicate the presence of topological features within a space.As it stands, however, it is not capable of describing the source of the feature, i.e.where in the space the hole or other feature is located.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Theorem 4.5.Let M denote any Z[m, n]-persistent F -vector space, so that every vector space V i is finite dimensional.Then there is an isomorphism”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
What the sources say
- Not checked — no full text of the sourcecites [29] Gabriel, P., 1997 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“As Kleinberg points out, it is possible to do so in a number of different ways by specifying cost functions on particular clusterings, and prove a uniqueness result for optimal choices of clusterings with respect to this cost function”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Not matchedcites [39] kleinbergJ.M. Kleinberg, An impossibility theorem for clustering, NIPS 2002: 446-453.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“This machinery is not available in the multivariable case, but it turns out that it can be replaced by the Gröbner basis methodology (see [18] or [47]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Definition 4.3.
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Any finite Z-persistence F -vector space M can be decomposed as”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Corollary 4.6.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The properties of trees guarantee that ≤ T is a partial ordering.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“In the paper [59], this was shown by simulation, which is of course always an option.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Not checked — no full text of the sourcecites [59] visionG. Singh, F. Memoli, T. Ishkhanov, G. Carlsson, G. Sapiro and D. Ringach, Topological Structure of Population Activity in Primary Visual Cortex, Journal of Vision, Volume 8, Number 8, Article 11, pp. 1-18, 2008. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Not only is the correspondence π 0 (-) a functor from spaces to sets, it is equipped with a natural surjective map of sets η X : X → π 0 (X).”
- Also stated in: § 5. Reasoning about clustering · plain
“Further, these surjective maps have the property that for every continuous map f : X → Y , the diagram of sets (below) commutes.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
- Also stated in: § 5. Reasoning about clustering · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While the observation is of course suggestive, to prove that it indicates structure in the data set one must show that segments of the indicated length cannot occur in a random model associated to a null hypothesis.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This means that Kleinberg's conditions also make sense in this context.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“We point out that in the context of ordinary homology (i.e.not persistent homology), M. Penrose and others have developed a theory of “geometric random graphs” and have proved various results concerning the Betti numbers of complexes attached to such graphs (see [54]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Additional information
What the sources say
- Not checked — no full text of the sourcecites [54] Penrose, Mathew, 2003 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is clear that a C-functorial clustering algorithm is a clustering algorithm in the sense of Kleinberg, since the surjective map of sets from X to χ(X, d X ) yields a partition of X, namely the partition whose blocks are the sets η -1 X (z), as z ranges over all elements of χ(X, d X ).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“Each such interval can be considered as a point in the set D = {(x, y) ∈ R 2 |x ≤ y}, and the output is therefore a finite subset of the set D. As such, we now have a finite spatial point process (see [20], [3]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Additional information
What the sources say
- Not matchedcites [20] Daley, D. J., 1988
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites [3] Baddeley, Adrian, 2007, Spatial point processes and their applications source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper relies on this without support.
There is no classification theorem for R-persistence abelian groups.
“However, we do not have such a theorem.”
The paper relies on this without support.
There is still no classification theorem for graded Z[t]-modules.
“Now, there is still no classification theorem for graded Z[t]-modules.”
The paper relies on this without support.
In studying data from experiments, one typically cannot assume that the data lies along a submanifold.
“If one is interested in studying data from experiments, though, one typically cannot assume that the data lies along a submanifold.”
The paper relies on this without support.
Even if one could assume that the data lies along a manifold, one is usually not in a position to verify that the stringent hypotheses of [52] are satisfied.
“Further, even if one could assume that the data lies along a manifold, one is usually not in a position to verify that the stringent hypotheses of [52] are satisfied.”
The paper relies on this without support.
The analysis of populations of neurons is amenable to geometric analysis.
“The second problem appears to be very amenable to geometric analysis, since it will involve the activities of several neurons at once.”
The paper relies on this without support.
The mechanism by which V1 carries out low-level tasks such as edge and line detection is not understood.
“However, the mechanism by which it carries out these tasks is not understood.”
The paper relies on this without support.
The classification of finitely generated modules over multivariable polynomial rings is much more complicated than the corresponding result for single-variable polynomial rings, and no reasonable parametrization is known.
“As is well known to algebraists, the classification of finitely generated modules over multivariable polynomial rings is much more complicated than the corresponding result for single variable polynomial rings, and in fact no reasonable parametrization is known.”
The paper relies on this without support.
In the case of more than one variable, the classification of multigraded modules depends on the field in question.
“examples show that in the case of more than one variable, the classification of multigraded modules definitely depends on the field in question.”
The paper relies on this without support.
The classification in the multivariable case is parametrized by points in moduli varieties over the ground field.
“In fact, the classification in the multivariable case is parametrized by points in moduli varieties over the ground field”
The paper relies on this without support.
Hierarchical clustering algorithms do not report single partitions but rather dendrograms, which are roughly speaking R+-persistent sets.
“This is not an unreasonable thing to do in view of the fact that hierarchical clustering algorithms do not report single partitions but rather dendrograms, which are roughly speaking R + -persistent sets.”
Citations
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Cited by
Sorted by citations
Items 301–400 of 2,331
Topology‐Aware Surface Reconstruction for Point Clouds
Gabrielsson, R. B., Ganapathi‐Subramanian, V., Škraba, P., & Guibas, L. J. (2020) · Computer Graphics Forum · doi:10.1111/cgf.14079 · Cited by 41
“s. Section9concludes the paper with a brief discussion on future work. 4. Persistence Diagrams We begin with a brief discussion on persistent homology, persistence diagrams, and how they are computed [7,6]. We refer the reader to [11] for a more in depth description. Taking in an increasing sequence of spaces as input…”
BackgroundTopology of Learning in Feedforward Neural Networks
Gabella, M. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3015790 · Cited by 45
“f incoming weights. This provides us in the end with a cloud of points in a high-dimensional vector space. We study the shape of this point cloud by applying techniques from topological data analysis [4]. This allows us to represent the evolution of the weights as a graph that encodes the topological structure of the…”
Multiscale Persistent Functions for Biomolecular Structure Characterization
Xia, K., Li, Z., & Mu, L. (2017) · Bulletin of Mathematical Biology · doi:10.1007/s11538-017-0362-6 · Cited by 40
“…characterization identification and analysis (CIA) and has been used in a variety of fields, including shape recognition, 17 network structure, 32,38,56 image analysis, 3,10,25,51,57 data analysis, 9,40,47,52,61 chaotic dynamics verification, 34,43 computer vision 57 and computational biology.”
MethodHomological percolation and the Euler characteristic
Bobrowski, O., & Škraba, P. (2020) · Physical review. E · doi:10.1103/physreve.101.032304 · Cited by 39
“To describe these two topics and the connection between them, we will use the language of persistent homology [13, 15, 16, 41], which is main workhorse in the field of Topological Data Analysis (TDA) [8, 17].”
MethodMachine Learning-based approach for Tailor-Made design of ionic Liquids: Application to CO2 capture
Zhang, K., Wu, J., Yoo, H., & Lee, Y. (2021) · Separation and Purification Technology · doi:10.1016/j.seppur.2021.119117 · Cited by 43
Pattern Formation in Binary Colloidal Assemblies: Hidden Symmetries in a Kaleidoscope of Structures
Lotito, V., & Zambelli, T. (2018) · Langmuir · doi:10.1021/acs.langmuir.8b01411 · Cited by 41
Topological Data Analysis of Functional MRI Connectivity in Time and Space Domains
Anderson, K. L., Anderson, J. S., Palande, S., & Wang, B. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00755-3_8 · Cited by 40
“TDA [3,13] of networks goes beyond graph-theoretic analysis by utilizing tools from computational topology to describe the architecture of networks or data structures in more flexible ways.”
InfluentialMultiscale topology characterizes dynamic tumor vascular networks
Stolz, B. J., Kaeppler, J., Markelc, B., Braun, F., Lipsmeier, F., Muschel, R. J., Byrne, H. M., & Harrington, H. A. (2022) · Science Advances · doi:10.1126/sciadv.abm2456 · Cited by 51
“A filtration (17, 18, 49) of a simplicial complex X is a sequence of embedded simplicial complexes, ; = X0 ✓ X1 ✓ X2 ✓ · · · ✓ Xend = X , (7)”
Topological Measurement of Protein Compressibility via Persistence DiagramsPreprint
Gameiro, M., ガメイロ, マ., Hiraoka, Y., 平岡, 裕., ヒラオカ, ヤ., Izumi, S., 泉, 俊., イズミ, シ., Kramár, M., クラマー, ミ., et al. (2012) · Kyushu University Institutional Repository (QIR) (Kyushu University) · Cited by 30
Topological data analysis and machine learning
Leykam, D., & Angelakis, D. G. (2023) · Advances in Physics X · doi:10.1080/23746149.2023.2202331 · Cited by 52
Computational Topology in Text Mining
Wagner, H., Dłotko, P., & Mrożek, M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-30238-1_8 · Cited by 40
“For example, [3] shows that data coming from natural images form a topological Klein bottle.”
BackgroundMethodInfluentialStructure-Function Network Mapping and Its Assessment via Persistent Homology
Liang, H., & Wang, H. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005325 · Cited by 38
Learning algebraic varieties from samples
Breiding, P., Kališnik, S., Sturmfels, B., & Weinstein, M. (2018) · Revista Matemática Complutense · doi:10.1007/s13163-018-0273-6 · Cited by 40
Topological gene expression networks recapitulate brain anatomy and function
Patania, A., Selvaggi, P., Veronese, M., Dipasquale, O., Expert, P., & Petri, G. (2019) · Network Neuroscience · doi:10.1162/netn_a_00094 · Cited by 40
Persistent homology and Euler integral transforms
Ghrist, R., Levanger, R., & Mai, H. (2018) · Journal of Applied and Computational Topology · doi:10.1007/s41468-018-0017-1 · Cited by 40
Topological Signal Processing: Making Sense of Data Building on Multiway Relations
Barbarossa, S., & Sardellitti, S. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.3014067 · Cited by 40
“Point clouds are finite sets of entities equipped with a similarity or distance function [13].”
BackgroundSimplicial Models and Topological Inference in Biological Systems
Nanda, V., & Sazdanović, R. (2013) · Natural computing series · doi:10.1007/978-3-642-40193-0_6 · Cited by 42
“Numerous applications of persistent homology to various problems in the experimental sciences have been thoroughly documented elsewhere [4, 13, 18].”
BackgroundTransfer learning for autonomous chatter detection in machining
Yesilli, M. C., Khasawneh, F. A., & Mann, B. P. (2022) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2022.05.037 · Cited by 40
“[61, 62, 63, 64, 65, 66] for further details.”
Hadadi, A., Guillet, C., Chardonnet, J.-R., et al. (2022) · Frontiers in Virtual Reality · doi:10.3389/frvir.2022.973236 · Cited by 39
“Topology is a mathematical theory that emerged to study the data from the perspective of geometrical structures, e.g., loops or holes (Zomorodian and Carlsson, 2005).”
BackgroundUnveiling patterns of international communities in a global city using mobile phone data
Bajardi, P., Delfino, M., Panisson, A., Petri, G., & Tizzoni, M. (2015) · EPJ Data Science · doi:10.1140/epjds/s13688-015-0041-5 · Cited by 40
Identification of Topological Network Modules in Perturbed Protein Interaction Networks
Sardiu, M. E., Gilmore, J. M., Groppe, B. D., Florens, L. A., & Washburn, M. P. (2017) · Scientific Reports · doi:10.1038/srep43845 · Cited by 40
Computing Minimal Presentations and Bigraded Betti Numbers of 2-Parameter Persistent Homology
Lesnick, M., & Wright, M. (2022) · SIAM Journal on Applied Algebra and Geometry · doi:10.1137/20m1388425 · Cited by 39
“They have found that lazy heaps, which perform well when adding a column with very few non-zero entries to a column with many entries, are very effective in practice on TDA problems.”
BackgroundInfluentialSimplicial and topological descriptions of human brain dynamics
Billings, J., Saggar, M., Hlinka, J., Keilholz, S. D., & Petri, G. (2021) · Network Neuroscience · doi:10.1162/netn_a_00190 · Cited by 42
“As a quantitative tool, persistent homology is tailor-made for defining topological similarities among metric spaces (Carlsson, 2009).”
BackgroundInfluentialTopological image modification for object detection and topological image processing of skin lesions
Vandaele, R., Nervo, G. A., & Gevaert, O. (2020) · Scientific Reports · doi:10.1038/s41598-020-77933-y · Cited by 39
Discrete Morse Theory for Computing Cellular Sheaf Cohomology
Curry, J. M., Ghrist, R., & Nanda, V. (2015) · Foundations of Computational Mathematics · doi:10.1007/s10208-015-9266-8 · Cited by 37
“uration spaces in robotics [24,25,27,33], the global qualitative statistics of point cloud data [13,14,23], coverage problems in sensor networks [18,19], circular coordinates for data sets [20] and Conley-type indices for dynamics [4,34,41].”
BackgroundHole Detection in Metabolic Connectivity of Alzheimer’s Disease Using k −Laplacian
Lee, H., Chung, M. K., Kang, H., & Lee, D. S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10443-0_38 · Cited by 40
“The modular structure of network is the first basic topological invariant in determining the shape of network in the view of algebraic topology [2].”
BackgroundPath Topology in Molecular and Materials Sciences
Chen, D., Liu, J., Wu, J., Wei, G., Pan, F. M., & Yau, S. (2023) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.2c03706 · Cited by 40
On the Distribution of the Critical Values of Random Spherical Harmonics
Cammarota, V., Marinucci, D., & Wigman, I. (2015) · Journal of Geometric Analysis · doi:10.1007/s12220-015-9668-5 · Cited by 38
“, [14] or [3]) for excursion sets of random spherical harmonics.”
BackgroundOn the topology of random complexes built over stationary point processes
Yogeshwaran, D., & Adler, R. J. (2015) · The Annals of Applied Probability · doi:10.1214/14-aap1075 · Cited by 60
“s of the Vietoris–Rips complex. 1.3. Some implications for topological data analysis. Perhaps the core tool of TDA is persistent homology, as visualized through barcodes and persistence diagrams; cf. [11, 15, 19, 42]. See also the very accessible recent survey [12]. While here is not the place to go into the details o…”
BackgroundInfluentialCho, J. S., Shrestha, S., Kagiyama, N., Hu, L., Ghaffar, Y. A., Casaclang‐Verzosa, G., Zeb, I., & Sengupta, P. P. (2020) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2020.02.008 · Cited by 38
Li, Y., Islambekov, U., Akçora, C. G., Smirnova, E., Gel, Y. R., & Kantarcıoğlu, M. (2020) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611976236.59 · Cited by 40
“Vietoris-Rips is a widely used simplicial complex due to its easy construction and fast computational implementation [8].”
BackgroundNetwork geometry and market instability
Samal, A., Pharasi, H. K., Ramaia, S. J., Kannan, H., Saucan, E., Jost, J., & Chakraborti, A. (2021) · Royal Society Open Science · doi:10.1098/rsos.201734 · Cited by 38
“Moreover, curvature is also related to algebraic topological aspects, such as the homology groups and Betti numbers, which are relevant, for instance, for persistent homology and topological data analysis [39].”
BackgroundYen, P. T.-W., & Cheong, S. A. (2021) · Frontiers in Physics · doi:10.3389/fphy.2021.572216 · Cited by 46
“…apparatus developed by Herbert Edelsbrunner, Afra Zomorodian, Gunnar Carlsson, and his graduate student Gurjeet Singh [11 – 13]; it was popularized by Carlsson ’ s paper [14] that later turned TDA into a hot fi eld in applied mathematics, and also found many applications in data analytics.”
BackgroundTopological obstructions to autoencoding
Batson, J., Haaf, C. G., Kahn, Y. F., & Roberts, D. A. (2021) · Journal of High Energy Physics · doi:10.1007/jhep04(2021)280 · Cited by 38
“We can also formulate this observation in terms of persistent homology, a method of identifying topological features of datasets [61]: if the data has persistent first homology H(1) at the length scale defined by the size of the break region, then it will behave like a topological circle and have a bad point in its re…”
MethodHopf Algebras and Markov ChainsPreprint
Pang, C. Y. A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.8221 · Cited by 38
A fast algorithm for constructing topological structure in large data
Liu, X., Xie, Z. Y., & Yi, D. (2012) · Homology Homotopy and Applications · doi:10.4310/hha.2012.v14.n1.a11 · Cited by 30
“This is similar to the strategy adopted by persistent homology [5].”
BackgroundMethodDelineation of a Conserved Arrestin-Biased Signaling Repertoire In Vivo
Maudsley, S. R., Martin, B., Gesty‐Palmer, D., et al. (2015) · Molecular Pharmacology · doi:10.1124/mol.114.095224 · Cited by 37
“TDA facilitates the appreciation of highdimensionality data sets in a more gestalt manner that is likely closer to the level of functional physiologic interactivity of transcripts/proteins (Carlsson, 2009; Lum et al., 2013).”
BackgroundReconstructing relational information
Horecka, K. M., Dulas, M. R., Schwarb, H. D., Lucas, H. D., Duff, M. C., & Cohen, N. J. (2017) · Hippocampus · doi:10.1002/hipo.22819 · Cited by 36
“Topological data analysis (TDA) techniques may prove to be a better analysis framework for determining the degree to which high-level relational information is preserved in a reconstruction (Carlsson, 2009; Ghrist, 2007).”
The fiber of the persistence map for functions on the interval
Curry, J. M. (2018) · Journal of Applied and Computational Topology · doi:10.1007/s41468-019-00024-z · Cited by 35
A topological approach to selecting models of biological experiments
Ulmer, M. P., Ziegelmeier, L., & Topaz, C. M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0213679 · Cited by 36
Anand, D. V., Meng, Z., Xia, K., & Mu, Y. (2020) · Scientific Reports · doi:10.1038/s41598-020-66710-6 · Cited by 41
mpNet: Variable Depth Unfolded Neural Network for Massive MIMO Channel Estimation
Yassine, T., & Magoarou, L. L. (2022) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2022.3142737 · Cited by 41
“dimensional manifold, although their apparent dimension is much larger [8], [9] [10, Section 5.”
BackgroundTakahashi, K., Abe, K., Kubota, S. I., Fukatsu, N., Morishita, Y., Yoshimatsu, Y., Hirakawa, S., Kubota, Y., Watabe, T., Ehata, S., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32848-2 · Cited by 45
“79,80.”
Non-parametric 3D map of the intergalactic medium using the Lyman-alpha forest
Cisewski-Kehe, J., Croft, R. A. C., Freeman, P. E., et al. (2014) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stu475 · Cited by 37
“An introduction to persistent homology can be found in Edelsbrunner & Harer (2008) and Carlsson (2009), and some advances in c© 2002 RAS, MNRAS 000, 1–12 c© 2002 RAS, MNRAS 000, 1–12 statistical inference for persistent homology can be found in Balakrishnan et al. (2013).”
BackgroundAcridine Derivatives as Inhibitors of the IRE1α–XBP1 Pathway Are Cytotoxic to Human Multiple Myeloma
Jiang, D., Tam, A. B., Alagappan, M., Hay, M. P., Gupta, A., Kozak, M. M., Solow-Cordero, D. E., Lum, P. Y., Denko, N. C., Giaccia, A. J., et al. (2016) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-15-1023 · Cited by 36
“TDA is based on a branch of mathematics characterizing the geometric property of shapes with mathematical algorithms (28) and is a topologic framework of many machine-learning algorithms that quantify the shape of a large, but finite, number of data points.”
BackgroundExploring hyperspectral imaging data sets with topological data analysis
Duponchel, L. (2017) · Analytica Chimica Acta · doi:10.1016/j.aca.2017.11.029 · Cited by 36
Clustering-independent analysis of genomic data using spectral simplicial theory
Govek, K. W., Yamajala, V. S., & Camara, P. G. (2019) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1007509 · Cited by 33
“pairwise intersections between balls [13], and are therefore faster to compute.”
InfluentialAssessment of skin barrier function using skin images with topological data analysis
Koseki, K., Kawasaki, H., Atsugi, T., Nakanishi, M., Mizuno, M., Naru, E., Ebihara, T., Amagai, M., & Kawakami, E. (2020) · npj Systems Biology and Applications · doi:10.1038/s41540-020-00160-8 · Cited by 36
Analysis of global stock markets’ connections with emphasis on the impact of COVID-19
Guo, H., Zhao, X., Yu, H., & Zhang, X. (2021) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2021.125774 · Cited by 36
“[23], Carlsson [24], and Cohen-Steiner et al.”
BackgroundInfluentialReconfigurable Microwave Photonic Topological Insulator
Goryachev, M., & Tobar, M. E. (2016) · Physical Review Applied · doi:10.1103/physrevapplied.6.064006 · Cited by 38
“Ideas on nontrivial topological properties of various systems can be found in many areas of research such as nonlinear dynamics[1], control system theory[2], biology[3], data analysis[4], etc.”
BackgroundStatistical Analysis of Persistence Intensity FunctionsPreprint
Chen, Y., Wang, D., Rinaldo, A., & Wasserman, L. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.02502 · Cited by 37
“ty function, we can visualize multiple diagrams, perform clustering and conduct two-sample tests. 1 Introduction Topological data analysis (TDA) is an emerging area in statistics and machine learning [2, 14]. The goal of TDA is to extract useful topological information from a given data and make inferences using these…”
MethodNerve Complexes of Circular Arcs
Adamaszek, M., Adams, H., Frick, F., et al. (2016) · Discrete & Computational Geometry · doi:10.1007/s00454-016-9803-5 · Cited by 35
“as manifold reconstruction [5,15,27] and topological data analysis [13,17].”
BackgroundMultiscale Topological Trajectory Classification with Persistent Homology
Pokorny, F. T., Hawasly, M., & Ramamoorthy, S. (2014) · Robotics: Science and Systems · doi:10.15607/rss.2014.x.054 · Cited by 35
“In future work, we hope to investigate also alternative simplicial complexes, such as Vietoris-Rips and (weak) witness complexes [11] which”
BackgroundMethodGranular Computing Techniques for Bioinformatics Pattern Recognition Problems in Non-metric Spaces
Martino, A., Giuliani, A., & Rizzi, A. (2018) · Studies in computational intelligence · doi:10.1007/978-3-319-89629-8_3 · Cited by 38
Topological data analysis for aviation applications
Li, M. Z., Ryerson, M. S., & Balakrishnan, H. (2019) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2019.05.017 · Cited by 35
“T persistent homology (TDA/PH) – that offer a compelling new direction from which these salient features may be obtained (Carlsson, 2009; Ghrist, 2014).”
BackgroundTopological persistence machine of phase transitions
Tran, Q. H., Chen, M., & Hasegawa, Y. (2021) · Physical review. E · doi:10.1103/physreve.103.052127 · Cited by 42
Nardini, J. T., Stolz, B. J., Flores, K., Harrington, H. A., & Byrne, H. M. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009094 · Cited by 35
“The most prominent method from TDA is persistent homology (PH) [38, 51, 54].”
Chinesta, F., & Cueto, E. (2022) · Advanced Modeling and Simulation in Engineering Sciences · doi:10.1186/s40323-022-00234-8 · Cited by 33
“It becomes an appealing alternative for describing data with large topology content [14,98].”
BackgroundGasparovic, E., Gommel, M., Purvine, E., Sazdanović, R., Wang, B., Wang, Y., & Ziegelmeier, L. (2018) · Association for Women in Mathematics series · doi:10.1007/978-3-319-89593-2_3 · Cited by 35
Topological Data Analysis for Multivariate Time Series Data
El-Yaagoubi, A. B., Chung, M. K., & Ombao, H. (2023) · Entropy · doi:10.3390/e25111509 · Cited by 45
“The reader is refered to [18] for an introduction to the notion of persistence, [17] and [20] for a survey on persistent homology and barcodes, and [13] for a review of TDA in general.”
BackgroundSimplifying the homology of networks via strong collapses
Wilkerson, A. C., Moore, T. J., Swami, A., et al. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6638666 · Cited by 34
“There is a growing interest in the practical applications of topology in many areas, including control and sensing [4, 5, 11], social network analysis [10], and the analysis of point cloud data [3].”
BackgroundTime, temperature, and data cloud geometry
Hsieh, F., & McAssey, M. P. (2010) · Physical Review E · doi:10.1103/physreve.82.061110 · Cited by 33
Persistent homology and non-Gaussianity
Cole, A., & Shiu, G. (2018) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2018/03/025 · Cited by 33
Dagliati, A., Geifman, N., Peek, N. B., Holmes, J. H., Sacchi, L., Bellazzi, R., Sajjadi, S. E., & Tucker, A. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101930 · Cited by 34
“We used the Topological Data Mapper implementation described in [5] to perform our analysis, and perform the analysis using the function mapper2D from Topological Data Analysis using Mapper R package [https://github.”
MethodComputing All Maps into a Sphere
Čadek, M., Krčál, M., Matoušek, J., Sergeraert, F., Vokřínek, L., & Wagner, U. (2014) · Journal of the ACM · doi:10.1145/2597629 · Cited by 33
“Moreover, lot of research has been devoted to developing extensions such as persistent homology [11], motivated by applications like data analysis [9].”
BackgroundLimit theory for point processes in manifolds
Penrose, M. D., & Yukich, J. E. (2013) · The Annals of Applied Probability · doi:10.1214/12-aap897 · Cited by 33
“It has also received attention amongst topologists [12], who, given a data cloud Y , allow the scale parameter β to vary to obtain homological signatures of the Vietoris–Rips complexes, which when taken together, yield clustering and connectivity information about Y .”
BackgroundThe topology of the directed clique complex as a network invariant
Masulli, P., & Villa, A. E. P. (2016) · SpringerPlus · doi:10.1186/s40064-016-2022-y · Cited by 34
“Algebro-topological constructions have been applied successfully in the field of data science (Carlsson 2009) with the application of the framework of persistent homology, which has proved to be a powerful tool to understand the inner structure of a data set by representing it as a sequence of topological spaces.”
BackgroundPersistent Topology and Metastable State in Conformational Dynamics
Chang, H.-W., Bacallado, S., Pande, V. S., & Carlsson, G. (2013) · PLoS ONE · doi:10.1371/journal.pone.0058699 · Cited by 31
“[11–13]) with commute time distance.”
Self-citationMulti-Scale Clustering by Building a Robust and Self Correcting Ultrametric Topology on Data Points
Hsieh, F., Wang, H., VanderWaal, K. L., McCowan, B., & Koehl, P. (2013) · PLoS ONE · doi:10.1371/journal.pone.0056259 · Cited by 36
“The main argument we make in this paper is that a good partitioning of data into clusters can only be achieved if we have a good understanding of the data geometry and topology [13].”
BackgroundGiusti, C., Ghrist, R., & Bassett, D. S. (2016) · PubMed Central · doi:10.48550/arxiv.1601.01704 · Cited by 44
“In real-world systems, simplicial complexes possess richly structured patterns that can be detected and characterized using recently developed computational tools from algebraic topology (Carlsson, 2009; Lum et al, 2013), just as graph theoretic tools can be used to study networks.”
BackgroundA personality trait contributes to the occurrence of postoperative delirium: a prospective study
Shin, J. E., Kyeong, S.-H., Lee, J. S., Park, J. Y., Lee, W.-S., Kim, J., & Yang, K. H. (2016) · BMC Psychiatry · doi:10.1186/s12888-016-1079-z · Cited by 35
“A detailed pipeline for TDA and the mathematical theory underlying TDA have been described in elsewhere [25, 26].”
MethodModeling and replicating statistical topology and evidence for CMB nonhomogeneity
Adler, R. J., Agami, S., & Pranav, P. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1706885114 · Cited by 34
“, much of TDA has become almost synonymous with an analysis based on some version of persistent homology [19,20,21], represented visually as barcodes, persistence diagrams, or related representations [13,14,25,26,48]. With relatively few exceptions, notably [17,23,41,6,10,42], TDA has not employed statistical methodol…”
BackgroundTopological Signature of 19th Century Novelists: Persistent Homology in Text Mining
Gholizadeh, S., Seyeditabari, A., & Zadrozny, W. (2018) · Big Data and Cognitive Computing · doi:10.3390/bdcc2040033 · Cited by 33
Rucco, M., Viticchi, G., & Falsetti, L. (2020) · Mathematics · doi:10.3390/math8050770 · Cited by 33
“In the last decade a new suite of tools, based on algebraic topology, for data exploration and modelling haven been invented [24–26].”
BackgroundOn the vanishing of homology in random Čech complexes
Bobrowski, O., & Weinberger, S. (2016) · Random Structures and Algorithms · doi:10.1002/rsa.20697 · Cited by 31
“The rapidly developing field known as Topological Data Analysis (TDA) focuses on using topological signatures of data in machine learning and statistics (for some introduction see [14,23,44]).”
BackgroundDiscrete curvature on graphs from the effective resistance*
Devriendt, K., & Lambiotte, R. (2022) · Journal of Physics Complexity · doi:10.1088/2632-072x/ac730d · Cited by 32
“For instance, any dataset with a similarity kernel—or a parametrized family of kernels to cover different scales, as is often the setting in topological data analysis [14, 32]—will naturally have an associated weighted graph whose resistance curvature then says something about the geometry of the underlying dataset.”
BackgroundTopological Analysis of Recurrent Systems
Škraba, P., de Silva, V., & Vejdemo‐Johansson, M. (2012) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 32
Wolfe, P. J. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1317797110 · Cited by 32
“At the same time, promising advances in pure mathematics are beginning to help us understand and categorize precisely what types of global structure can be determined from point clouds and pairwise similarities (11).”
BackgroundTopological feature vectors for chatter detection in turning processes
Yesilli, M. C., Khasawneh, F. A., & Otto, A. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-08242-5 · Cited by 31
“Another promising tool for generating feature vectors for chatter detection comes from a new field with many mature computational tools: Topological Data Analysis (TDA) [11, 12, 13, 14].”
BackgroundMethodDey, T. K., Shi, D., & Wang, Y. (2019) · ACM Journal of Experimental Algorithmics · doi:10.1145/3284360 · Cited by 37
“ion persistence is order of magnitude faster than existing methods in practice. 1 Introduction In recent years, topological ideas and methods have emerged as a new paradigm for analyzing complex data [9, 25]. An important line of work in this direction is the theory and applications of persistent homology. It provides…”
BackgroundComplex networks of marine heatwaves reveal abrupt transitions in the global ocean
Benedetti‐Cecchi, L. (2021) · Scientific Reports · doi:10.1038/s41598-021-81369-3 · Cited by 33
Persistent Homology based Graph Convolution Network for Fine-grained 3D Shape Segmentation
Wong, C.-C., & Vong, C. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00701 · Cited by 32
“Topological Data Analysis (TDA) [3] is an emerging field which infers relevant topological and geometric features from complex data.”
BackgroundComputational synthesis of cortical dendritic morphologies
Kanari, L., Dictus, H., Chalimourda, A., Arnaudon, A., Geit, W. A. H. V., Coste, B., Shillcock, J. C., Hess, K. P., & Markram, H. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110586 · Cited by 33
The Rank Invariant Stability via Interleavings
Landi, C. (2018) · Association for Women in Mathematics series · doi:10.1007/978-3-319-89593-2_1 · Cited by 32
“Increasingly often in recent years the shape of data has been analyzed using persistent homology [2].”
BackgroundInvariants for homology classes with application to optimal search and planning problem in robotics
Bhattacharya, S., Lipsky, D., Ghrist, R., & Kumar, V. (2013) · Annals of Mathematics and Artificial Intelligence · doi:10.1007/s10472-013-9357-7 · Cited by 30
“Recent applications of algebraic topology to the applied sciences has primarily focused on data analysis [6, 19, 31].”
BackgroundFlame Interactions in Turbulent Premixed Twin V-Flames
Dunstan, T. D., Swaminathan, N., Bray, K. N. C., et al. (2013) · Combustion Science and Technology · doi:10.1080/00102202.2012.713413 · Cited by 31
“For a simple surface embedded in a three dimensional field, these invariants, known as the Betti numbers, bn (Carlsson, 2009), have an intuitive interpretation: b0 is the number of connected components, b1 is the number of tubes or tunnels, and b3 is the number of isolated pockets.”
BackgroundGeometric Methods in Signal and Image Analysis
Krim, H., & Hamza, A. B. (2015) · Cambridge University Press eBooks · doi:10.1017/cbo9781139523967 · Cited by 30
Medium-range order in amorphous ices revealed by persistent homology
Hong, S., & Kim, D.-H. (2019) · Journal of Physics Condensed Matter · doi:10.1088/1361-648x/ab3820 · Cited by 31
Yan, Y., Omisore, O. M., Xue, Y.-C., Li, H., Liu, Q.-H., Nie, Z., Fan, J., & Wang, L. (2020) · IEEE Access · doi:10.1109/access.2020.2996667 · Cited by 31
“The persistent features based on the point clouds are detected over a range of spatial scales, which better represent the underlying space’s essential features compared to the noise [31].”
BackgroundMethodTopological Descriptors of Histology Images
Singh, N. P., Couture, H. D., Marron, J. S., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10581-9_29 · Cited by 31
“Of these, topological data analysis (TDA) enables the investigation of structural characteristics of high-dimensional data [1,3,4].”
BackgroundMethodEULER INTEGRATION OF GAUSSIAN RANDOM FIELDS AND PERSISTENT HOMOLOGY
Bobrowski, O., & Borman, M. S. (2012) · Journal of Topology and Analysis · doi:10.1142/s1793525312500057 · Cited by 31
“For more details and further references see [6, 7, 8].”
BackgroundZhang, Z., Yang, S., Cui, H., Wu, J. J., Schwartz, F., & Qi, H. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2634531 · Cited by 31
“features of the point cloud at different spatial resolutions [28], [29].”
BackgroundLi, B., Ofori-Boateng, D., Gel, Y. R., et al. (2020) · Sustainable and Resilient Infrastructure · doi:10.1080/23789689.2019.1708182 · Cited by 31
“In addition to the sequence of Betti numbers, the lifespan of topological features can be represented by persistence diagrams and barcodes (Carlsson, 2009; Ghrist, 2008; Kerber et al., 2016; Zomorodian, 2010).”
BackgroundEuler calculus with applications to signals and sensing
Curry, J. M., Ghrist, R., & Robinson, M. (2012) · Proceedings of symposia in applied mathematics · doi:10.1090/psapm/070/589 · Cited by 31
A Short Survey of Topological Data Analysis in Time Series and Systems AnalysisPreprint
Gholizadeh, S., & Zadrozny, W. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.10745 · Cited by 48
“In TDA, this procedure is often automated by a technique called persistent homology [4, 5, 6, 7].”
MethodLeite, L. S. G., Banerjee, S., Wei, Y., et al. (2024) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1729 · Cited by 30
A topological similarity measure for proteins
Máté, G., Hofmann, A., Wenzel, N., & Heermann, D. W. (2013) · Biochimica et Biophysica Acta (BBA) - Biomembranes · doi:10.1016/j.bbamem.2013.08.019 · Cited by 29
Signal classification with a point process distance on the space of persistence diagrams
Marchese, A., & Maroulas, V. (2017) · Advances in Data Analysis and Classification · doi:10.1007/s11634-017-0294-x · Cited by 30
“Definition 3 (Carlsson 2009) The Vietoris–Rips of a finite subset S of a metric space T for some (cid:7) is the simplicial complex with vertices S such that { s 0 , . . . , s k } is a simplex if and only if ρ( s i , s j ) (cid:7) for 0 ≤ i , j ≤ k , where ρ( · , · ) is the metric on T .”
BackgroundMethodInfluentialInformation Correlated Lévy Walk Exploration and Distributed Mapping Using a Swarm of Robots
Ramachandran, R. K., Kakish, Z., & Berman, S. (2020) · IEEE Transactions on Robotics · doi:10.1109/tro.2020.2991612 · Cited by 31
“In recent years, considerable progress has been made in using tools from algebraic topology to estimate the underlying structure and shape of data [51], which aids in efficient analysis of the data using statistical techniques such as regression [21].”
MethodHa, S., Lee, H., Choi, Y., Kang, H., Jeon, S. J., Ryu, J. H., Kim, H. J., Cheong, J. H., Lim, S., Kim, B.-N., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-59921-4 · Cited by 30
Metadata
- Authors
- Carlsson, G.
- Affiliations
- Stanford University (US)
- Corresponding author
- Carlsson, Gunnar
- ORCID
- Carlsson, Gunnar
- Venue
- Bulletin of the American Mathematical Society
- Publisher
- American Mathematical Society (AMS)
- Published
- 2009-01-29
- Volume
- 46
- Issue
- 2
- Pages
- 255-308
- ISSN
- 0002-9904 (ISSN-L)
- 1088-9485
- 0273-0979
- OpenAlex
- W1991566301
- Cited by
- 2,302
- References
- 78
- Citations by year
- 2026: 215 · 2025: 216 · 2024: 158 · 2023: 176 · 2022: 159 · 2021: 215 · 2020: 227 · 2019: 151 · 2018: 163 · 2017: 132 · 2016: 128 · 2015: 95 · 2014: 99 · 2013: 65 · 2012: 46
- FWCI
- 33.43
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Bulletin of the American Mathematical Society (publishedVersion)
- Funders
- National Science Foundation (0354543)
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Physical Sciences › Mathematics › Geometry and Topology › Geometric and Algebraic Topology
- Physical Sciences › Mathematics › Applied Mathematics › Geometric Analysis and Curvature Flows
- Keywords
- point clouds, distance measures, high-dimensional data, biological data, microarray data, high-throughput data
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