unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network
Paper • 2303.14957 • Published
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Text Classification
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json
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multi-class-classification
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English
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1M - 10M
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License:
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944a7663-9678-4f5f-8353-d7a54d000d23 | Our designed network is conceptually simple and modifies two-step approaches [1]} to jointly optimized networks through a message passing module. The first myocardium segmentation network takes LGE-CMR as an input and generates a probability map. A message passing module then combines the information of probability map... | [1] | [
[
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80
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] | https://openalex.org/W3000054715 |
cc0da086-a952-4af3-bb61-430224bd4a2d | In this study, we develop a more accurate approximation method that allows us to describe the dynamics, as well as the equilibrium, of contagion on complex networks in an almost exact manner. Our method, called the message-passing approach, is a more elaborated version of the conventional mean-field approximation in th... | [6] | [
[
1333,
1336
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835a1f2e-8c81-4e5c-88a6-5cda3ac543b7 | These studies in the field of network science usually take the threshold rules as given, but our work provides a microfoundation from a game-theoretic perspective; in coordination games and utility-based games, the threshold value is respectively obtained as a function of the payoff parameters and the preference parame... | [3] | [
[
908,
911
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] | https://openalex.org/W2114696370 |
88660a38-42aa-4cd6-a240-10c279e736cd | These studies in the field of network science usually take the threshold rules as given, but our work provides a microfoundation from a game-theoretic perspective; in coordination games and utility-based games, the threshold value is respectively obtained as a function of the payoff parameters and the preference parame... | [6] | [
[
926,
929
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] | https://openalex.org/W2052177518 |
5421b098-ae0b-4e4b-a49e-f18577d7d6c3 | There is a strand of literature on continuous-action games on networks in which each player takes an action represented by a real value \(x\ge 0\) [1]}, [2]}. Typically, player \(i\) maximizes the following quadratic utility function
\(u_i(x_i;{\bf {x}}_{-i}) = \alpha x_i - \frac{1}{2}x_i^2 +\gamma \sum _{j\ne i} \ma... | [2] | [
[
154,
157
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] | https://openalex.org/W3123506665 |
c49dd44e-fbb1-4845-9484-526db8a88ec6 | We call the condition \(\lim _{q\downarrow \rho _0} G^\prime (q)>1\) the generalized first-order cascade condition since it is essentially a generalized version of the standard cascade condition proposed by [1]}, [2]}, and [3]}:
| [2] | [
[
214,
217
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] | https://openalex.org/W2028985170 |
d7f5e6e0-e532-4a52-b332-192db819e193 | In reality, people are connected to each other in a wide variety of social contexts. These include online spaces such as Twitter and Facebook, as well as physical spaces such as schools and work places.
In network science, such situations are modeled as multiplex networks, in which each layer represents a single networ... | [1] | [
[
360,
363
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] | https://openalex.org/W2965889227 |
0716a8ce-aa0c-4649-8ba9-d3b517d4a938 | Let \(q_t^{\ell }\) denote the probability of a randomly chosen neighbor in layer \(\ell \in \lbrace A,B\rbrace \) being active. The recursion equations for \(q_t^A\) and \(q_t^B\) are given by [1]}, [2]}:
\(q_t^{A} &= \rho _0 + (1-\rho _0)\sum _{k_B=0}^\infty p_{k_B}\sum _{m_B=0}^{k_{B}}\mathcal {B}_{m_B}^{k_B}\le... | [1] | [
[
198,
201
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1cd09ec4-7cc5-4965-ba80-fcba6786e5a3 | For a given \(V_\mu (n)\) the Wilson Dirac operator with optional clover term (REF ) is defined as [1]}
\(D_\mathrm {W}(n,m)=\sum _\mu \gamma _\mu \nabla _\mu ^\mathrm {std}(n,m)-\frac{ar}{2}\triangle ^\mathrm {std}(n,m)+m_0\delta _{n,m}+aC(n,m)\)
| [1] | [
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af12af85-4f00-4ce5-bceb-a74dd6dbf3e2 | with \((\rho _1,\rho _2,\rho _3,\rho _4)\equiv (64,16,4,1)/432\) and \((\lambda _0,\lambda _1,\lambda _2,\lambda _3,\lambda _4)\equiv (-240,8,4,2,1)/64\) .
The sum in (REF ) extends over the positive Euclidean directions, i.e. \(\mu \in \lbrace 1,\ldots ,4\rbrace \) , and the bare quark mass \(m_0\) undergoes both ad... | [1] | [
[
1455,
1458
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] | https://openalex.org/W2016049803 |
b166acc0-3547-4541-b8f2-da6fc020f9eb | For a given \(V_\mu (n)\) the Susskind (“staggered”) Dirac operator is defined as [1]}, [2]}
\(D_\mathrm {S}(n,m)=\sum _{\mu } \eta _\mu (n)\,\frac{1}{2}\,[V_{\mu }(n)\delta _{n+\hat{\mu },m}-V_{\mu }^\dagger (n-\hat{\mu })\delta _{n-\hat{\mu },m}] + m_0\delta _{n,m}\)
| [1] | [
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845918f7-886a-458a-bf85-9bbb2edf4e0c | For a given \(V_\mu (n)\) the Susskind (“staggered”) Dirac operator is defined as [1]}, [2]}
\(D_\mathrm {S}(n,m)=\sum _{\mu } \eta _\mu (n)\,\frac{1}{2}\,[V_{\mu }(n)\delta _{n+\hat{\mu },m}-V_{\mu }^\dagger (n-\hat{\mu })\delta _{n-\hat{\mu },m}] + m_0\delta _{n,m}\)
| [2] | [
[
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4fbcbe80-e2c3-4b00-abd7-245b47762a35 | In Figs. REF –REF no sign of numerical imprecision is seen; the three symbols at a given iteration count (for either sp or dp) are just horizontally displaced.
A second issue is worth mentioning.
On the Skylake architecture the Brillouin operator converges in about twice the time of the Wilson operator.
The additive m... | [3] | [
[
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] | https://openalex.org/W2577574760 |
1de591b7-a8a9-4b30-ae22-797b9c93800a | Similar to sparse convolutions, an efficient implementation of Skip-Conv requires block-wise structured sparsity in the feature maps [1]}, [2]}, for two main reasons.
First, block structures can be leveraged to reduce the memory overhead involved in gathering and scattering of input and output tensors [1]}. Additionall... | [1] | [
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60c00cbe-2711-4845-b8eb-079ebb42a258 | Similar to sparse convolutions, an efficient implementation of Skip-Conv requires block-wise structured sparsity in the feature maps [1]}, [2]}, for two main reasons.
First, block structures can be leveraged to reduce the memory overhead involved in gathering and scattering of input and output tensors [1]}. Additionall... | [2] | [
[
139,
142
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] | https://openalex.org/W3035678286 |
077beae6-3628-47fc-b3ef-ecdf19cfff9d | We use EfficientDet [1]}, the state of the art architecture for object detection, and apply Skip-Conv on top of it. We conduct our experiments on D0 to D3 as the most efficient configurations [1]}, though more expensive configurations, D4 to D7, can similarly benefit from Skip-Conv.
Each model is initialized with pre-t... | [1] | [
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0bab4bee-7d78-44a5-b62a-73cc1f78c1b6 | Moreover, we observe that Skip-Conv outperforms DFF [1]} both in terms of accuracy and computational cost.
We hypothesize that DFF performances, solely relying on optical-flow to warp features across frames, are sensitive to the accuracy of the predicted motion vectors.
However, there are lots of small objects (distant... | [1] | [
[
52,
55
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] | https://openalex.org/W2552900565 |
dd2b75bc-b67b-4dbc-926f-449ae9843270 | We conduct our experiments on the JHMDB dataset [1]}, a collection of 11,200 frames from 316 video clips, labeled with 15 body joints.
Video sequences are organized according to three standard train/test partitions and we report average results over the three splits.
We evaluate the performance using the standard PCK m... | [5] | [
[
642,
645
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] | https://openalex.org/W2991833656 |
346185ae-232a-48b0-ab2e-6f9b79132005 | We investigate how the theoretical speed ups, measured by MAC count reductions, translate to actual wall clock runtimes. Following [1]} we use im2col based implementation of sparse convolutions. This algorithm reformulates the convolution as a matrix multiplication between input tensor and convolution kernels flattened... | [1] | [
[
131,
134
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] | https://openalex.org/W2604998962 |
685b2706-019c-4291-9f92-82ca4e095647 | In this section we analyze the amount of sparsity induced by Skip-Conv in different levels of a backbone network.
To this end, we refer to the pose estimation experiments described in Sec. 4.2 of the main paper, and we rely on the same setting by considering the JHMDB dataset [1]} with a HRNet-w32 backbone network [2]}... | [1] | [
[
277,
280
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] | https://openalex.org/W2034014085 |
14c3cb73-45e1-42d8-86e3-963b6450d2c1 | The organizer baseline F1 scores for the validation and test data are 0.58 and 0.654 respectively.
The details of the baseline are given in [1]}.
The obtained results with our submitted runs are given in Table REF .
For SkipGRun, we achieved 0.6913 of F1 score with 0.6952 and 0.6893 of precision and recall respectively... | [1] | [
[
140,
143
]
] | https://openalex.org/W3115081393 |
0fba4fb1-a8f1-42f4-82d2-969b5e2944c8 | Our first main cluster combinatorics conjecture (Conjecture REF ) asserts that every cluster monomial in \({A}({\rm SL}_k,\mathbb {S})\) is the invariant of
a planar tagged diagram and also of a tagged diagram with no cycles on interior vertices. This conjecture extends those from [1]}, [2]} to higher rank, and more n... | [2] | [
[
289,
292
]
] | https://openalex.org/W2963568783 |
68cbfeaa-c4c2-4847-b2ac-220df29cf847 | Let \(\mathbb {S} = (\mathbf {S},\mathbb {M})\) be an oriented marked surface [1]}. The set of marked points \(\mathbb {M}\) decomposes into the set of punctures
\(\mathbb {M}_{\circ } := \mathbb {M} \cap \text{int }\mathbf {S}\) and the set of boundary points \(\mathbb {M}_{\partial } := \mathbb {M} \cap \partial \... | [1] | [
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] | https://openalex.org/W2153279415 |
fa7b9751-e369-495d-92b9-fc856e78d96d | Remark 3.8 We do not prove here that our initial clusters provide rational coordinate systems on \(\mathcal {A}^{\prime }({\rm SL}_k,\mathbb {S})\) although we believe this should be true. One might be able to prove these statements by mimicking the proofs of these statements given in [1]}, or by establishing the clus... | [1] | [
[
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] | https://openalex.org/W1977026965 |
b8527e52-5466-47c7-bf9d-8f1f0b8a7544 | cf. [1]} or [2]}.
| [1] | [
[
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7
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] | https://openalex.org/W2963663079 |
9e122ff6-e5f0-4b47-871a-3f243b2e0aeb | As an example, whenever a tensor diagram \(T\) has a crossing, one can apply the following crossing removal relation [1]}:
\(\begin{tikzpicture}[thick, decoration={markings,mark=at position 1 with {[scale=1.7]{>}}},postaction={decorate},shorten >=0.4pt] (-1,-1)--(-.55,-.2);[thick] (-.55,-.2)--(.2,1);\node at (-1.2,-.5... | [1] | [
[
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] | https://openalex.org/W3100433565 |
97d18229-03b1-48c9-b652-d84d7deccce3 | The two compositions \(\rho \circ \sigma \) and \(\sigma \circ \rho \) correspond to Dehn twists about simple closed curves with geometric intersection number two. Any two such mapping classes generate the pure mapping class group of \(S_{0,4}\) , see [1]}. There are clearly finitely many tagged triangulations of \... | [1] | [
[
256,
259
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] | https://openalex.org/W4240028494 |
ff88f388-5c3f-498a-801d-5d2467a8fc34 | Let \(\kappa _{\mu }:=(\mu /\ell )^{1/2}\) with \(\ell :=|\mathcal {G}|\) being the total length of the graph \(\mathcal {G}\) . Clearly, the constant function \(\kappa _{\mu }\) is always a solution of (REF ) in \(H_{\mu }^1(\mathcal {G})\) for some \(\lambda \in \mathbb {R}\) , and hence a constrained critical po... | [1] | [
[
412,
415
]
] | https://openalex.org/W2889679869 |
0c03645e-5d28-4229-9d7b-cac47e37e02e | We are now ready to state a rather general min-max principle which combines the monotonicity trick [1]} and the min-max theorem with second order information by Fang and Ghoussoub [2]}, see also [3]}. A similar result, in the unconstrained setting, was recently proved in [4]}.
| [2] | [
[
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183
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] | https://openalex.org/W2130696197 |
b7fdd2c5-b769-4673-ae11-10ae3f757343 | However, the radiative mechanism powering flares is still disputed. The most common proposed mechanisms are: synchrotron with a cooling break; synchrotron self-compton (SSC); inverse compton (IC); and Synchrotron [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}, [17]... | [8] | [
[
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258
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0f4fa238-d880-4a42-9be8-f4fefef2b8d0 | During OBSID 22230, we observed a peak count rate of 0.09 ph s\(^{-1}\) in the 2–8 keV band.
Given the instrumental set up, pile-up effects are negligible even at the peak (e.g. [1]}).
By using the [1]} conversion factors, we estimate a total observed (absorbed) energy of \({\sim }3.2\times 10^9\) erg released during... | [1] | [
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fb1136e3-d3fe-4361-8ccd-f35f12b5ecbe | The IR flare reported in this paper is among the brightest ever observed. It is the third brightest flare observed with GRAVITY, although it is significantly shorter than the flares observed in 2019. The left panel of fig:fluence shows the flux distribution of Sgr A\(^\star \) [1]} and compares the peak fluxes of thre... | [7] | [
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] | https://openalex.org/W2160697532 |
3d5bcb55-32ca-4e65-9fd0-e92266518291 | The SYN–SSC scenario has severe problems: First, it requires magnetic fields of \({\sim } 10^4~\mathrm {G}\) , source regions around \({\sim } 0.001 \mathrm {R_s}\) , and densities \({\sim } 10^{12}~\mathrm {cm^{-3}}\) . These parameters are extreme compared to the sub-mm ambient conditions. Even ignoring this, the syn... | [2] | [
[
806,
809
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] | https://openalex.org/W1500274797 |
6a4b6a2f-6243-49d9-b90d-986514f66f13 | The expected runtime bound follows immediately from the proof of Theorem \(\ref {thm:confatom}\) above. For the utility, recall that for the original exponential mechanism [1]}:
\(\mu _X(S_\varepsilon ) \le \frac{1}{\nu (S_{\varepsilon /2})} \exp \left( -\frac{\epsilon \varepsilon }{4 \Delta _L}\right)\)
| [1] | [
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] | https://openalex.org/W4234281613 |
704f6e88-871c-42f0-9965-96ac71a96ffb | Theorem B (Equivalent version of Beurling's Theorem, [1]}).
A closed subspace of \(H^{2}\) is shift-invariant iff it is invariant under multiplication by every bounded analytic function in \(H^{\infty }\) .
| [1] | [
[
53,
56
]
] | https://openalex.org/W2118082066 |
d2a572f9-ab04-416d-a9fa-434fff66b42e | Our purpose in the theorem given below is to demonstrate that the Theorem 6.1 in [1]}, which is the key result that essentially characterizes the invariant subspaces on uniform algebras, can actually be proved without the use of Kolmogoroff's theorem on the \(\left( L^{p},L^{1}\right) \) boundedness of the conjugation... | [1] | [
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4cd056bb-07df-4bf6-89f4-22ac8ed3ff9d | In the present paper by means of DFT+\(U\) calculation the electronic and magnetic properties of bulk LaMnO\(_3\) and BaTiO\(_3\) , as well as LMO/BTO heterostructure have been demonstrated. Within the chosen approach and computational parameters the bulk components of the heterostructure were confirmed to be insulat... | [1] | [
[
678,
681
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] | https://openalex.org/W3120426488 |
051c4a9c-7f2e-44fd-93b2-c78252917020 | We compare the complexity of LISTA-CE with other channel estimators, including LDGEC[1]}, ISTA[2]}, ISTA-Net\(^+\)[3]}, SSD[4]} and orthogonal matching pursuit (OMP)[5]}. As shown in Table REF , the complexities of the SSD and the OMP algorithms are \(O(MN_{RF}QL^2\Omega ^2)\) and \(O(MN_{RF}QL^3\Omega ^3)\) , respect... | [4] | [
[
123,
126
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] | https://openalex.org/W2966084980 |
8efca86f-b455-43ec-91fc-4def5e00b663 | With the condition \(\gamma =2\kappa \) , the lasing threshold requirement \(g = \gamma +4\frac{\kappa ^2}{\gamma }\) becomes \(g=2\gamma =4\kappa \) . Therefore, the exceptional point and lasing threshold conditions are satisfied at the same time. The photonic system is PT-symmetric with balanced total gain and loss.... | [1] | [
[
383,
386
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] | https://openalex.org/W3141574774 |
a89382cd-5d9d-4585-90ba-64ea1c353a40 | Remark Here the dimension restriction is due to the trapping phenomenon, i.e. we need the flow \(\mathcal {M}\) to be trapped between two asymptotically conical self-expanders, which is only known in low dimensions [1]}.
| [1] | [
[
216,
219
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] | https://openalex.org/W3027442782 |
cdcb4e5c-5541-4eca-8599-99f877d1f688 |
A number \(\mu \in \mathbb {R}\) is an eigenvalue of \(-L_\Sigma \) if there exists \(f \in W^{2}(\Sigma )\) such that \(-L_\Sigma f = \mu f\) . By works of Bernstein–Wang [1]}, when \(\Sigma \) is smooth, \(L_\Sigma \) has a discrete spectrum and we can therefore order the eigenvalues of \(-L_\Sigma \) . It foll... | [1] | [
[
176,
179
]
] | https://openalex.org/W2883778827 |
6e427cd6-1452-4081-a121-8b61d97ce2ee | We establish, using PDE method similar to [1]}, the existence of an \(I\) -parameter family of ancient solutions to the RMCF starting from \(\Sigma \) . Each one of these solutions will correspond to a MCF coming out of \(\mathcal {C}\) that is not self-similar.
| [1] | [
[
42,
45
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] | https://openalex.org/W2920651408 |
8aa9c01a-b759-47c5-937b-3c00ad67b1b5 | We now follow the ideas of [1]} to establish higher regularity of the solutions obtained above. First notice that for a given initial data \(a = (a_1,\ldots ,a_I)\) , \(\tau _{-}(a_1,\ldots ,a_I)\) solves the linear homogeneous equation \(\frac{\partial }{\partial s} v = L_\Sigma v\) , hence by replacing \(v\) by \(v... | [1] | [
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0e8c9dfd-0c94-4c2c-9355-7ae4ceb044d2 |
for two hypersurfaces \(\Sigma _1\) and \(\Sigma _2\) , whenever the limit is defined (possibly \(\infty \) ). In particular, they showed in [1]} that when \(\Sigma _1\) is a hypersurface trapped between two self-expanders asymptotic to the same cone \(\mathcal {C}\) , then \(E_{\mathrm {rel}}[\Sigma _1, \Gamma ]\) ... | [1] | [
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] | https://openalex.org/W2949545924 |
93b1a500-fe94-458f-b7fd-a44010ce6187 | By Huisken's monotonicity formula, any singularity of the flow must have entropy less than \(\lambda [\mathbb {S} \times \mathbb {R}]\) . By [1]}, it must be a round sphere \(\mathbb {S}^2\) . However, as any tame ancient RMCF is asymptotically conical (as \(\Sigma \) is asymptotically conical), it cannot encounter a ... | [1] | [
[
141,
144
]
] | https://openalex.org/W770226755 |
73baa18c-5123-4538-8c8e-ec696346050f | The proof is similar except one uses [1]} instead of [2]}. Essentially, the same argument follows through until the conclusion \(\operatorname{supp}\nu \cap \mathbb {S}^3\) is a closed smooth minimal surface in \(\mathbb {S}^3\) . It follows from the resolution of Willmore conjecture [3]} that \(\operatorname{supp}\nu... | [2] | [
[
53,
56
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] | https://openalex.org/W2897785250 |
5c8487bc-90e0-406e-a85b-8a2ee3858c68 | Following [1]} we discretize the gauge field by introducing link and plaquette variables
\(U_{x,\mu } &= \exp (iga_\mu A^a_\mu (x+\hat{\mu } / 2) t^a) \, \, \in \, \, \mathrm {SL}(N_c,\mathbb {C}), \\U_{x,\mu \nu }(x) &= U_{x, \mu }U_{x+\mu , \nu }U_{x+\nu ,\mu }^{-1} U_{x, \nu }^{-1},\)
| [1] | [
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10,
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] | https://openalex.org/W2071844098 |
83b684c6-9140-4e9d-81f1-44f85f48b0a6 | [htb]
The GAMP Algorithm[1]}
[1] Given measurement matrix \({\bf {\Phi }} \in {\mathbb {C}^{{M_\phi } \times {N_\phi }}}\) and sequence of measurement value \({\bf {y}} \in \mathbb {C} ^{{M_\phi } \times 1}\) .
Initialization: Set environment prior parameter \(\bf {q}\) . Defined \({g_{\rm {in}}}\left( \cdot \right)\)... | [1] | [
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] | https://openalex.org/W2166670884 |
bade64aa-bd98-4eb6-a7f9-8f81c1cf026d | We model the DYNAP-SE neuromorphic hardware [1]} with the following configurations.
| [1] | [
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69550c58-bcd5-47dc-92bf-5ec4ab330559 | A variety of effects, including spinodal dewetting and nucleation at impurities [1]}, [2]}, [3]}, can cause the dewetting of nematic films.
In particular, such dewetting can involve competition between many effects, including internal elastic forces, alignment forces on the interfaces, gravity, van der Waals forces, an... | [14] | [
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8c005cee-c31a-42e4-8754-29a34afd410a | Convergence rate. Earlier landscape analysis on the low-rank matrix recovery [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, combined with the convergence guarantee for the nonconvex optimization [7]}, [8]}, indicates polynomial convergence towards the second-order stationary point. More recently, the authors in [9]} achieved near... | [1] | [
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77,
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] | https://openalex.org/W2963404710 |
2e0a04fe-7193-4a55-a7f0-00235b8535d0 | Convergence rate. Earlier landscape analysis on the low-rank matrix recovery [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, combined with the convergence guarantee for the nonconvex optimization [7]}, [8]}, indicates polynomial convergence towards the second-order stationary point. More recently, the authors in [9]} achieved near... | [2] | [
[
83,
86
]
] | https://openalex.org/W2964156132 |
fffd261d-6441-44d6-860c-2d44b9de068f | Convergence rate. Earlier landscape analysis on the low-rank matrix recovery [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, combined with the convergence guarantee for the nonconvex optimization [7]}, [8]}, indicates polynomial convergence towards the second-order stationary point. More recently, the authors in [9]} achieved near... | [3] | [
[
89,
92
]
] | https://openalex.org/W3113425034 |
706901e9-e7b5-41c5-9bee-e6121a0d7194 | Convergence rate. Earlier landscape analysis on the low-rank matrix recovery [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, combined with the convergence guarantee for the nonconvex optimization [7]}, [8]}, indicates polynomial convergence towards the second-order stationary point. More recently, the authors in [9]} achieved near... | [6] | [
[
107,
110
]
] | https://openalex.org/W2604130501 |
abffa4a2-4f8d-4144-8444-6a3191e21b2f | We point out that the result of [1]} is consistent with our global analysis results (Section REF ). In addition, we stress that the nearly linear convergence is common among other low-rank matrix recovery problems, at least from the manifold optimization perspective. Our work explores the following aspects: (1) whether... | [1] | [
[
32,
35
]
] | https://openalex.org/W3123272904 |
8005f3f3-a4d9-44ac-a4c6-0e0a16c4f1f7 |
Perturbed first-order schemes. There are a few studies on the convergence of perturbed first-order schemes towards second-order stationary points both in the Euclidean and the Riemannian settings, see [1]}, [2]}, [3]}, [4]}, [5]}. These results show that general global convergence rate is polynomial and almost dimensi... | [1] | [
[
202,
205
]
] | https://openalex.org/W2964106499 |
41db9a40-1f0c-4b66-b6fe-109440a21757 |
Perturbed first-order schemes. There are a few studies on the convergence of perturbed first-order schemes towards second-order stationary points both in the Euclidean and the Riemannian settings, see [1]}, [2]}, [3]}, [4]}, [5]}. These results show that general global convergence rate is polynomial and almost dimensi... | [2] | [
[
208,
211
]
] | https://openalex.org/W2963092340 |
6b0f78b6-c97a-419b-943c-7dc1c42eeac5 | In this section, we introduce the optimization technique on the low-rank matrix manifold, namely the projected gradient descent (PGD) with soft retraction onto the manifold. This Riemannian gradient descent technique has been studied in [1]}, [2]}, [3]}, [4]}. For example, [3]} and [4]} use the Riemannian gradient desc... | [1] | [
[
237,
240
]
] | https://openalex.org/W1993468393 |
842fc9b8-4a07-47b2-a8d1-71d0b50932be | The lottery ticket hypothesis suggests that using a larger original network size increases the number of sub-networks which may turn out to be winning tickets [1]}.
To investigate this hypothesis for the case of policy distillation, we analyzed the effect of the initial network size on the lottery ticket effect (figure... | [1] | [
[
159,
162
]
] | https://openalex.org/W2963813662 |
5f3baae7-adf9-4c22-85b6-1a979b3753ce | Providing more empirical evidence for our previous claims, we find that for most MLP and CNN agents trained on the MinAtar games the ticket effect is explained by the IMP-discovered mask (see figure REF ).
Strengthening an observation in [1]}, we observe that the performance deteriorates at different levels of network ... | [1] | [
[
238,
241
]
] | https://openalex.org/W2948130861 |
4f8ab44b-9f0d-43eb-adf7-0fbcce799dae | The convergence process (REF ) holds by virtue of an application of the Banach-Alaoglu-Bourbaki theorem (cf., e.g., Theorem 3.6 of [1]}) to the estimates (REF ) and (REF ).
The nontivial part of the proof amounts to identifying the weak-star limits \(v_\kappa \) and \(w_\kappa \) and to showing that the weak-star lim... | [3] | [
[
3212,
3215
],
[
3827,
3830
],
[
5988,
5991
],
[
6942,
6945
],
[
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],
[
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],
[
13311,
13314
],
[
20094,
20097
]
] | https://openalex.org/W333643410 |
670efa81-f56d-4569-bc01-e24359bc1ab6 | By the Banach-Alaoglu-Bourbaki theorem (cf., e.g., Theorem 3.6 of [1]}) we infer that, up to passing to a subsequence still denoted by \(\lbrace u_\kappa \rbrace _{\kappa >0}\) , the following convergences hold:
\(\begin{aligned}u_\kappa \overset{\ast }{\rightharpoonup }u, &\textup { in } L^\infty (0,T;W_0^{1,p}(\Omega... | [1] | [
[
66,
69
]
] | https://openalex.org/W1545761024 |
84460a30-bae5-4eb1-91d6-d0420fda9471 | As in [1]} (see also [2]}, [3]}, [4]}, [5]}, [6]}, [7]}),
players optimize their expected terminal utility but are, also, concerned
with the performance of their peers. For an arbitrary but fixed
policy \(( \pi _{1}, \ldots , \pi _{i-1}, \pi _{i+1}, \ldots ,\pi _{N})\) ,
player \(i\) , \(i\in \mathcal {I}\) , seeks to ... | [1] | [
[
6,
9
]
] | https://openalex.org/W2606713240 |
dbf38d6b-7192-4df4-9b05-c8b4d55b256e | To the best of our knowledge, NuClick [1]} is the only interactive segmentation approach for extracting objects in histology images in the literature that deals with these challenges by introducing the use of squiggle based guiding signals. In the original NuClick [1]}, a random point inside the GT mask and morphologic... | [1] | [
[
38,
41
],
[
265,
268
]
] | https://openalex.org/W3040784645 |
b4fd04b4-6d9f-46a8-9f14-156f1e328735 | Our implementation of the above-mentioned techniques allows us to incorporate a combination of them for automatic guiding signals generation (both inclusion and exclusion maps) during the training phase. In particular, we apply this ordered sequence of mask approximating, smoothing, partitioning, and distance transform... | [1] | [
[
461,
464
]
] | https://openalex.org/W2048733914 |
8dcd2db8-e640-4c37-a678-52b1555748b2 | Similar to [1]}, we first introduce a baseline model architecture and then scale its width (number of channels or feature maps in constructing blocks) and depth (number of block repetition in each stage of network) uniformly using \(w\) and \(d\) scaling factors, respectively. These factors are calculated using a com... | [1] | [
[
11,
14
],
[
371,
374
],
[
394,
397
]
] | https://openalex.org/W2955425717 |
0019d145-e490-44e6-b54c-fe3a380700da | Following [1]}, images are stain normalized using Reinhard's method [2]}. The original images are captured at 0.25 micron per pixel (MPP) resolution (which is equal to 40x magnification) with various scanners. However, to keep enough context during the training of our interactive segmentation model, we
extract \(512\ti... | [1] | [
[
10,
13
]
] | https://openalex.org/W2922239620 |
6171119f-e73f-4761-8670-c809e0c1e367 | Results in tab:results suggest that interactive segmentation models like NuClick [1]} and the proposed method can outperform SOTA automatic segmentation models like UNet [2]}, DeepLab v3 [3]}, and the baseline method [4]} by a large margin as they are provided with guiding signals in the input. Particularly, our best p... | [2] | [
[
170,
173
],
[
558,
561
]
] | https://openalex.org/W1901129140 |
f4a440c5-39bc-49ff-9c49-4869c4c85cbc | The storage of data and the hosting of numerous users present significant security vulnerabilities in the context of the cloud. The user data is now protected in the cloud by powerful technologies [1]}, [2]}, [3]}. In the cloud computing environment, this is becoming more complicated because to the increased security t... | [21] | [
[
864,
868
]
] | https://openalex.org/W2900659926 |
cb7bd5a4-de47-4766-93e9-00ddf62d5666 | The choice of covariance functions is one of fundamental importance. Depending on the problem being solved, there are numerous covariance functions (also referred to as kernels in the literature) available to use. Examples include squared-exponential, Matérn, \(\gamma \) -exponential, rational quadratic, and the Bayesi... | [1] | [
[
351,
354
]
] | https://openalex.org/W1502922572 |
196303ec-a0cc-4e29-9917-a22755202dbf | For such a pair, a presentation of \(\pi _1(\partial U)\) is given in [1]} (See also [2]}). As we shall need the notations, let us describe it.
| [1] | [
[
71,
74
]
] | https://openalex.org/W2054990988 |
062bdb8a-64e1-437f-bc83-c90ee43f026d | Previous works have used the robustness of an STL formula, or the signed distance of a given trajectory from satisfying or violating a given formula as rewards to guide the RL algorithm [1]}, [2]}.
Here, we only provide an example of learning optimal policy from a given STL formula using those existing techniques.
We u... | [3] | [
[
339,
342
]
] | https://openalex.org/W2145339207 |
5b971dee-430d-4042-b16f-19a4cfdcf9b6 | Prior work has used STL for reinforcement learning applications.
Quantitative semantics of STL can be used as reward functions for evaluating robotic behaviors [1]}.
STL formulas can be used to rank the quality of demonstrations in robotic domain and also computing the reward for RL problems [2]}.
However, those works ... | [1] | [
[
160,
163
]
] | https://openalex.org/W3004091789 |
6e48e9e0-52e8-4cb8-aca0-0790560cd7e9 | Following different constraints discussed in the Ref. [1]}, the limits on \(U(1)_{e-\mu }\) ,\(U(1)_{e-\tau }\) and \(U(1)_{\mu -\tau }\) models are presented here.
The major constraints on these models come from various beam dump experiments [2]}, [3]}, [4]}. In the electron beam dump experiments like E137, E141 (S... | [1] | [
[
54,
57
]
] | https://openalex.org/W3105619082 |
e8035264-a2ab-4742-ab83-ab0dd8f159ff | It is worthwhile to note that future projection of the exclusion plots from SuperCDMS HV [1]} and XENONnT [2]} experiments have an overlap with the modified neutrino floor in the \(U(1)_{\mu - \tau }\) model. The enhancement in the neutrino floor will enable to observe neutrino signal events in these detectors, even i... | [2] | [
[
106,
109
]
] | https://openalex.org/W3105201648 |
84b024ea-13c1-469f-868d-79361d4bc1af | 3D Point Cloud Understanding.
There are mainly two streams of research lines for point cloud modeling. One is projecting a point cloud into 3D voxels [1]}, [2]} and then using 2D/3D convolutions for feature extraction. PointNet [3]} explores ingesting 3D point clouds directly. It extracts permutation-invariant feature ... | [6] | [
[
799,
802
]
] | https://openalex.org/W3217247671 |
2b67d9bc-8cd7-4fc8-acf0-56e9c65e752b | 3D Point Cloud Understanding.
There are mainly two streams of research lines for point cloud modeling. One is projecting a point cloud into 3D voxels [1]}, [2]} and then using 2D/3D convolutions for feature extraction. PointNet [3]} explores ingesting 3D point clouds directly. It extracts permutation-invariant feature ... | [7] | [
[
844,
847
]
] | https://openalex.org/W2896457183 |
77d3d23d-fdab-40a4-ab98-7d8b27e9a9e8 | We build our dataset of triplets from ShapeNet55 [1]}, which is one of the most extensive public 3D CAD datasets.
ShapeNet55 is the publicly-available subset of ShapeNet.
It contains around 52.5K CAD models, each of which is associated with metadata that textually describes the semantic information of the CAD model.
Fo... | [1] | [
[
49,
52
]
] | https://openalex.org/W2190691619 |
16291495-909a-45b7-99fb-f199f086f25f | PointNet++ [1]} is an advanced version of PointNet [2]}. It uses a hierarchical structure to better capture the local geometry of the point cloud, and becomes the cornerstone of many point cloud applications.
| [1] | [
[
11,
14
]
] | https://openalex.org/W2963121255 |
f9ac80b0-64bb-4a8b-a143-01abf2227869 | PointMLP [1]} is the SOTA method on standard 3D classification task. It uses a residual MLP network with a lightweight geometric affine module to better capture local geometric features.
<TABLE> | [1] | [
[
9,
12
]
] | https://openalex.org/W4221160819 |
f85eff61-d7fe-43e1-8679-5a0206b8937e | ScanObjectNN is a dataset of scanned 3D objects from the real world.
It contains 2,902 objects that are categorized into 15 categories. It has three variants: OBJ_ONLY includes ground truth segmented objects extracted from the scene meshes datasets; OBJ_BJ has objects attached with background noises and Hardest introdu... | [1] | [
[
395,
398
]
] | https://openalex.org/W2981440248 |
237c1668-f56d-48c2-b004-492763a5c697 | Following [1]}, zero-shot 3D classification is conducted by measuring distances between the 3D features of an object and the text features of category candidates. The category that introduces the smallest distance is selected as the predicted category, as shown in Figure REF . We use our pre-trained models as they are ... | [1] | [
[
10,
13
]
] | https://openalex.org/W4308538276 |
348b4c0c-bc61-4e02-9a7a-7f948562e268 | We qualitatively compared our approach with related works on the quality of the interpolated frames. As can be seen from Fig. REF , our approach is relatively robust to heavily blurred inputs and interpolates visually sharper images with clearer contents compared to other related methods [1]}, [2]}, [3]}, [4]}.
| [2] | [
[
295,
298
]
] | https://openalex.org/W2949258649 |
3121d4ff-dd06-48ee-86ec-5d8bcd57b8ab | Our paper is motivated by the recent work of [1]}, who made notable progress on the task of learning a DPP kernel from data. This task is conjectured to be NP-Hard [2]}. [1]} presented a carefully designed EM-style procedure, which, unlike several previous approaches (e.g., [4]}, [5]}, [6]}) learns a full DPP kernel no... | [6] | [
[
287,
290
]
] | https://openalex.org/W2890912593 |
ea068b56-dc47-4b91-9cdb-cb6c7c7319e6 | In the limit where cargo can only exhibit small displacements (\(x_J = x-x^{\prime } \ll h\) ) in a time \(dt\) , such that \(q_m(x_J|x^{\prime },n)\) decays quickly as a function of \(x_J\) , eq.(REF ) can be simplified to include only the \(l=-1,0,+1\) terms of the infinite sum. Provided that the boundaries to the ... | [1] | [
[
1066,
1069
]
] | https://openalex.org/W4240788980 |
b7fd7e6e-3ea4-4d9f-a403-00e3532647b9 | The probability distribution \(P_n(x)\) has been previously derived for cargo that can rebind from the \(n=0\) state [1]}, [2]}. Using these previously published formulae [1]}, [2]}, \(P_n(x)\) has been defined in this work by the distributions,
\(\begin{aligned}P_n(x) & = \left( \frac{P_0(x)}{1-P_0(x)} \right) \pro... | [1] | [
[
119,
122
],
[
173,
176
]
] | https://openalex.org/W2102787760 |
51f9ab67-5162-433b-9608-61b7cf672b41 | The simulations were implemented in MATLAB using an adapted form of the Gillespie algorithm [1]}, [2]} dubbed the `direct-family' method. Following cargo initialisation in the \(n=1\) state at time \(t_0=0\) , this form of the Gillespie algorithm has been implemented as follows:
| [1] | [
[
92,
95
]
] | https://openalex.org/W2042321087 |
820f7e78-f9fc-4cdf-9c7e-ff5dc0005c03 | The simulations were implemented in MATLAB using an adapted form of the Gillespie algorithm [1]}, [2]} dubbed the `direct-family' method. Following cargo initialisation in the \(n=1\) state at time \(t_0=0\) , this form of the Gillespie algorithm has been implemented as follows:
| [2] | [
[
98,
101
]
] | https://openalex.org/W2167154952 |
f054a4db-1c82-4f22-9632-b47fc1a8ab53 | Methods based on semantic features
Current methods of comparing generative models based on their samples rely on the semantic features of the samples.
Fréchet Inception Distance (FID) [1]} approximates the Wasserstein metric between distributions using the features of images extracted from a pre-trained network such as... | [7] | [
[
813,
816
]
] | https://openalex.org/W2962919088 |
074caa59-8a03-46d8-aeea-778915a4559e | In Section 2, we intend to study the finiteness of Gorenstein cohomological dimension of groups. Recall that a ring is Gorenstein regular [1]}, [2]} if it has finite global Gorenstein projective dimension, which contains strictly the rings of finite global dimension (e.g. \(\mathbb {Z}\) ), as well as Iwanaga-Gorenstei... | [5] | [
[
1014,
1017
]
] | https://openalex.org/W2066014515 |
edb86df8-f279-4e2c-9391-73bb2a58522a | The “Gcd” can be considered as an assignment of invariants for the pairs of groups and coefficient rings \((G, R)\) . In Section 3 and 4, we will study the assignment Gcd under changes of groups and coefficient rings, respectively. We define an order for such pairs; see Definition REF . Using Lemma REF and REF , we sh... | [2] | [
[
711,
714
]
] | https://openalex.org/W2791610753 |
6ff720e5-f785-4ca1-a6c4-78e4bf076caa | Let \({\rm Gcd}_{R}G = {\rm Gpd}_{RG}R = n\) . It follows from [1]} that there exists an exact sequence \(0\rightarrow K\rightarrow M\rightarrow R\rightarrow 0\) , where \(M\) is a Gorenstein projective \(RG\) -module, and \({\rm pd}_{RG}K = n-1\) . For \(M\) , there is an exact sequence of \(RG\) -modules
\(0\rightar... | [2] | [
[
1050,
1053
]
] | https://openalex.org/W2048512304 |
2419d7fc-e47d-4b2c-a749-01c4cf7bfa67 | By Serre's Theorem, there is an equality between cohomological dimensions of a group and subgroups with finite index; see details in [1]} or [2]}. In this sense, the following result might be regarded as a Gorenstein version of Serre's Theorem. We remark that by specifying the ring to be \(\mathbb {Z}\) , the result re... | [1] | [
[
133,
136
]
] | https://openalex.org/W2009766514 |
5cef7a8f-4e5b-40ca-83d4-90d09e2054b7 | By Serre's Theorem, there is an equality between cohomological dimensions of a group and subgroups with finite index; see details in [1]} or [2]}. In this sense, the following result might be regarded as a Gorenstein version of Serre's Theorem. We remark that by specifying the ring to be \(\mathbb {Z}\) , the result re... | [2] | [
[
141,
144
]
] | https://openalex.org/W2049550828 |
2dfd4871-6d27-47d6-9dad-acfc0e717baa | The following characterization for Gorenstein projective modules is immediate from [1]}. For any ring \(A\) , we denote by \(\mathcal {P}(A)\) the class of all projective \(A\) -module. The left orthogonal of \(\mathcal {P}(A)\) is defined as
\(^{\perp }\mathcal {P}(A) = \lbrace M\in {\rm Mod}(A)~~|~~~~ {\rm Ext}^i_A... | [1] | [
[
83,
86
]
] | https://openalex.org/W2114145385 |
b7d71d9d-01f6-43a3-8f25-08f1549d2698 | It is clear that \(\mathcal {P}\subseteq \mathcal {C}of\cap \mathcal {W}\) , that is, all projective \(RG\) -modules are included in \(\mathcal {C}of\cap \mathcal {W}\) . We infer that \(\mathcal {C}of\cap \mathcal {W}\subseteq \mathcal {P}\) since any cofibrant module is Gorenstein projective, and projective dimensio... | [1] | [
[
3706,
3709
]
] | https://openalex.org/W2081330825 |
53a5b43d-8e1d-411f-ab0d-cfddfc9841ae | For model category \(\mathcal {F}ib\) , the associated homotopy category \(\mathrm {Ho}(\mathcal {F}ib)\) is obtained by formally inverting weak equivalences, that is, the localization of \(\mathcal {F}ib\) with respect to the class of weak equivalences. This category is equivalent to the category \(\pi \mathcal {C}o... | [1] | [
[
486,
489
]
] | https://openalex.org/W4236256974 |
655af8d1-3317-4f3c-b207-e1430e478dc5 | First, we note that objects of \({\rm Ho}(\mathcal {F}ib)\) and \({\rm StMod}(RG)\) coincide. It suffices to prove that the natural functor from \({\rm Ho}(\mathcal {F}ib)\) to
\({\rm StMod}(RG)\) is fully faithful.
Let \(M\) and \(N\) be any fibrant \(RG\) -modules. By the completeness of the cotorsion pair \((\... | [1] | [
[
886,
889
]
] | https://openalex.org/W4230387122 |
f3c7e683-98b7-4d85-b532-5b27197a7d0e | In cases where the regression map \(z \mapsto \mu _z(x)\) for any feature \(x\) , can be traced with homotopy as in Ridge [1]} and Lasso [2]}, it takes \(O(n^2)\) to compute the exact conformal set. This can be reduced to \(O(n\log n)\) by sorting the roots of the instance-wise scores \(E_i(z) - E_{n+1}(z)\) for \(... | [3] | [
[
426,
429
]
] | https://openalex.org/W1553101044 |
06472e41-f0e2-40ed-8dd7-e5d6857af3ca | The full conformal prediction set is computationally expensive since it requires knowing exactly the map \(z \mapsto \mu _z(\cdot )\) . The splitting approach does not use all the data in the learning phase but is computationally efficient since it requires a single model fit. Alternatively, it was proposed in [1]} to ... | [1] | [
[
312,
315
]
] | https://openalex.org/W2964060211 |
End of preview. Expand in Data Studio
Dataset Card for unarXive citation recommendation
Dataset Summary
The unarXive citation recommendation dataset contains 2.5 Million paragraphs from computer science papers and with an annotated citation marker. The paragraphs and citation information is derived from unarXive.
Note that citation infromation is only given as the OpenAlex ID of the cited paper. An important consideration for models is therefore if the data is used as is, or if additional information of the cited papers (metadata, abstracts, full-text, etc.) is used.
The dataset can be used as follows.
from datasets import load_dataset
citrec_data = load_dataset('saier/unarXive_citrec')
citrec_data = citrec_data.class_encode_column('label') # assign target label column
citrec_data = citrec_data.remove_columns('_id') # remove sample ID column
Dataset Structure
Data Instances
Each data instance contains the paragraph’s text as well as information on one of the contained citation markers, in the form of a label (cited document OpenAlex ID), citation marker, and citation marker offset. An example is shown below.
{'_id': '7c1464bb-1f0f-4b38-b1a3-85754eaf6ad1',
'label': 'https://openalex.org/W3115081393',
'marker': '[1]',
'marker_offsets': [[316, 319]],
'text': 'Data: For sentiment analysis on Hindi-English CM tweets, we used the '
'dataset provided by the organizers of Task 9 at SemEval-2020.\n'
'The training dataset consists of 14 thousand tweets.\n'
'Whereas, the validation dataset as well as the test dataset contain '
'3 thousand tweets each.\n'
'The details of the dataset are given in [1]}.\n'
'For this task, we did not use any external dataset.\n'}
Data Splits
The data is split into training, development, and testing data as follows.
- Training: 2,043,192 instances
- Development: 225,084 instances
- Testing: 225,348 instances
Dataset Creation
Source Data
The paragraph texts are extracted from the data set unarXive.
Who are the source language producers?
The paragraphs were written by the authors of the arXiv papers. In file license_info.jsonl author and text licensing information can be found for all samples, An example is shown below.
{'authors': 'Yusuke Sekikawa, Teppei Suzuki',
'license': 'http://creativecommons.org/licenses/by/4.0/',
'paper_arxiv_id': '2011.09852',
'sample_ids': ['cc375518-347c-43d0-bfb2-f88564d66df8',
'18dc073e-a48e-488e-b34c-e5fc3cb8a4ca',
'0c2e89b3-d863-4bc2-9e11-8f6c48d867cb',
'd85e46cf-b11d-49b6-801b-089aa2dd037d',
'92915cea-17ab-4a98-aad2-417f6cdd53d2',
'e88cb422-47b7-4f69-9b0b-fbddf8140d98',
'4f5094a4-0e6e-46ae-a34d-e15ce0b9803c',
'59003494-096f-4a7c-ad65-342b74eed561',
'6a99b3f5-217e-4d3d-a770-693483ef8670']}
Annotations
Citation information in unarXive is automatically determined (see implementation).
Additional Information
Licensing information
The dataset is released under the Creative Commons Attribution-ShareAlike 4.0.
Citation Information
@inproceedings{Saier2023unarXive,
author = {Saier, Tarek and Krause, Johan and F\"{a}rber, Michael},
title = {{unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network}},
booktitle = {Proceedings of the 23rd ACM/IEEE Joint Conference on Digital Libraries},
year = {2023},
series = {JCDL '23}
}
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Homepage:
github.com
Paper:
unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network
Number of rows:
2,358,027
Total file size:
7.13 GB