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A: There are several well-known generating sets for classical groupsB: Another generating set which has become important in algorithms and applications in the last 10-15 years is the Leedham-Green and O’Brien standard generating set in the following called the LGO generating set. These generators are defined f...	ABC	ACB	CBA	ABC	Selection 2
A: Solving (25) on the other hand involves computing the hℎhitalic_h-dependent, global operator P𝑃Pitalic_P, leading to a dense matrix in (25)B: Except for (ii), all steps above above can be performed efficiently as the matrices involved are sparse and either local or independent of hℎhitalic_hC: From now ...	ACB	BCA	BAC	ACB	Selection 3
A: These coordinates are computed somehow and their true values can differ from their values stored in the computerB: Moreover, Alg-A is more stable than the alternatives. During the iterations of Alg-CM, the coordinates of three corners and two midpoints of a P-stable triangle (see Figure 37) are maintained**C...	BAC	ABC	BCA	ABC	Selection 1
A: In the lower part of the pipeline, we extract features from tweets and combine them with the creditscore to construct the feature vector in a time series structure called Dynamic Series Time ModelB: (non-rumor) news classification. C: These feature vectors are used to train the classifier for rumor vs	BAC	BAC	BAC	ACB	Selection 4
A: and probit losses. Assumption 1 impliesB: However, if we initialize with η<1/ℒ⁢(𝐰⁢(0))𝜂1ℒ𝐰0\eta<1/\mathcal{L}(\mathbf{w}(0))italic_η < 1 / caligraphic_L ( bold_w ( 0 ) ) then it is straightforward to show the gradient descent iterates maintain bounded local smoothnessC: Assumption 1 includes many comm...	CBA	BCA	ABC	ABC	Selection 1
A: This topic bursted in 2012, 2015 and 2016 several times and the tweets’ volume of 2012 is the highest peak. But Snopes reported this rumor in the september 2015151515http://www.snopes.com/media/notnews/tupac.asp. So we consider that they don’t refer to the same rumor affair. B: In this section, we compare th...	BAC	CBA	BCA	CAB	Selection 4
A: We then evaluate our ensemble ranking model (results from the cascaded evaluation) and show it robustly improves the baselines for all studied cases (RQ3). Notice that, we do not use the learned classifier in Section 5.2 for our ensemble model, since they both use the same time period for training, but opt for t...	CBA	BAC	CAB	ABC	Selection 1
A: The only difference happens to patient 10 and 12 whose intakes are earlier at day. Further, patient 12 takse approxB: 3 times the average insulin dose of others in the morning.C: The insulin intakes tend to be more in the evening, when basal insulin is used by most of the patients	ACB	CBA	ACB	BCA	Selection 4
A: Furthermore, we gratefully acknowledge the support of NVIDIA Corporation with the donation of a Titan X Pascal GPU used for this research.B: This study has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement NosC: 720...	BAC	ABC	CAB	BAC	Selection 3
A: Nevertheless, since pathwidth and cutwidth are such crucial parameters for graph algorithms, we also translate our locality based reduction into one from graphs to graphs directly. B: A reason why this direct reduction from cutwidth to pathwidth has been overlooked might be that the literature on cutwidth an...	CAB	ACB	BAC	BAC	Selection 1
A: As a long-term challenge, we believe that model-based reinforcement learning based on stochastic predictive models represents a promising and highly efficient alternative to model-free RLB: Applications of such approaches to both high-fidelity simulated environments and real-world data represent an exciting ...	BAC	BCA	ACB	CAB	Selection 2
A: Compared to step negotiation purely in rolling locomotion mode, the proposed strategy demonstrated significant enhancements in energy performance, particularly for taller stepsB: A significant feature of this method is the determination of transition criterion threshold values based on studies of alternative...	BCA	CAB	BAC	ACB	Selection 1
A: Our motivation stems from observing that, unlike the real world, the advice under the known models is often closer to “fiat” than “recommendation”B: In this work we focus on the online computation with adviceC: Our objective is to propose a model which allows the possibility of incorrect advice, with th...	ABC	CBA	CBA	BAC	Selection 4
A: This brief subsection describes the training process, which is trivialB: unseen terms are added and frequencies of already seen terms are updated.C: Only a dictionary of term-frequency pairs is needed for each category. Then, during training, dictionaries are updated as new documents are processed —i.e	ACB	CBA	BCA	ABC	Selection 1
A: In the experiments of (Lin et al., 2018), DGC gets far better performance on both accuracy and communication cost than quantization methodsB: Hence, we do not compare with quantization methods in this paper. We don’t use the warm-up strategy in the experimentsC: The momentum coefficient β𝛽\betaitalic_β ...	BCA	ABC	CAB	CBA	Selection 2
A: Using cross-correlation would produce the same results and would not require flipping the kernels during visualizationB: operation.C: , where ∗*∗ is the convolution333We use convolution instead of cross-correlation only as a matter of compatibility with previous literature and computational frameworks	BCA	CBA	BAC	CBA	Selection 1
A: The UAV might select the largest power to increase utility. However, The more power one UAV uses, the more interference other UAVs will receive and other UAVs’ utilities will reduce. For the sake of enlarging the global utility, the largest power is not the optimal strategies for the whole UAV ad-hoc network. Th...	ACB	BCA	CAB	ABC	Selection 3
A: italic_g B: , C¯¯=A¯¯∗B¯¯¯¯𝐶¯¯𝐴¯¯𝐵\overline{\overline{C}}=\overline{\overline{A}}\,*\,\overline{\overline{B}}over¯ start_ARG over¯ start_ARG italic_C end_ARG end_ARG = over¯ start_ARG over¯ start_ARG italic_A end_ARG end_ARG ∗ over¯ start_ARG over¯ start_ARG italic_B end_ARG end_ARG) implies regular matri...	CBA	CBA	BCA	BAC	Selection 3
A: There was a statistically significant decrease in Variance (14.72% between Gaussian Dropout and DQN, 48.89% between Variational Dropout and DQN). Furthermore one of the Dropout methods outperformed DQN score.B: The results in Figure 3 show that using DQN with different Dropout methods result in better-prefo...	BAC	CAB	ABC	BCA	Selection 2
A: This can be problematic if the various classes have unbalanced representation in the image, as the most prevalent class can dominate training. Long et alB: (2015) discussed weighting the cross-entropy loss (WCE) for each class in order to counteract a class imbalance present in the dataset. WCE was defined a...	BCA	CBA	CAB	CAB	Selection 1
A: Welbl (2014) and Biau et al. (2019) follow a similar strategyB: Independent training fits all networks one after the other and creates an ensemble of networks as a final classifier. Joint training concatenates all tree networks into one single network so that the output layer is connected to all leaf neuron...	CAB	ABC	ACB	ABC	Selection 3
A: Broadly speaking, our work is related to a vast body of work on value-based reinforcement learning in tabular (Jaksch et al., 2010; Osband et al., 2014; Osband and Van Roy, 2016; Azar et al., 2017; Dann et al., 2017; Strehl et al., 2006; Jin et al., 2018) and linear settings (Yang and Wang, 2019b, a; Jin et al....	CBA	ACB	BCA	BCA	Selection 2
A: Although this reduces the expressiveness of the convolutional layer since there is no interaction between the different groups, Xie et alB: (2017) used grouped convolutions to enlarge the number of channels of a ResNet model which resulted in accuracy gains while keeping the computational complexity of the o...	ACB	ABC	BAC	CAB	Selection 2
A: The following corollary was already established by Gromov (who attributes it to Rips) in [47, Lemma 1.7.A]B: By invoking Proposition 8.1 we obtain an alternative proof, which instead of operating the simplicial level, exploits the isometric embedding of X𝑋Xitalic_X into its tight span E⁢(X)𝐸𝑋E(X)italic_E ...	CAB	ACB	BAC	ABC	Selection 2
A: The overview in Figure 7(a) shows the selected projection with three clear clusters of varying sizes (marked with C1, C2, and C3). However, the labels seem to be mixed in all of them. That means either the projections are not very good, or the labels are simply very hard to separateB: By analyzing the Shepar...	ACB	ACB	ACB	BCA	Selection 4
A: To encourage better comparison methodologies, the most promising avenues are the use of existing benchmarks and also the creation of new ones based on real-world problemsB: Moreover, better comparison methodologies, including more attention to scalability and new statistical testing approaches such as the us...	CBA	BCA	CBA	CBA	Selection 2
A: Although GAE-based models (GAE, MGAE, and GALA) achieve impressive results on graph type datasets, they fail on the general datasets, which is probably caused by the fact that the graph is constructed by an algorithm rather than prior information. If the graph is not updated, the contained information is low-lev...	BCA	BCA	ACB	BAC	Selection 4
A: In Figure 11 we similarly see that the fraction of spoofable networks that can be fonud through IPID and PMTUD is higher than when measured with the other methodologies; Figure 11 plots the networks found spoofable via IPID vs PMTUD excluding ”N/A” networks.B: Between the other two, DNS test has a slightly h...	CBA	CAB	ABC	ABC	Selection 1
A: This design introduces variation in training inputs, which makes it harder to learn consistent context patterns. For this task, semisupervised learning techniques, such as self-labeled samples, may helpB: If the context layer can process unlabeled data, then it is no longer necessary to include every class i...	ACB	CBA	CBA	BCA	Selection 4
A: The version for automaton semigroups does not follow directly from 8, as the free monogenic semigroup is not a complete automaton semigroup [4, Proposition 4.3] or even a (partial) automaton semigroup (see [8, Theorem 18] or [20, Theorem 1.2.1.4]). B: The construction used to prove Theorem 6 can also be used...	CBA	CBA	BCA	CAB	Selection 4
A: For all variants, we fine-tune a pre-trained UpDn, which was trained on either VQA-CPv2 or VQAv2 for 40 epochs with a learning rate of 10−3superscript10310^{-3}10 start_POSTSUPERSCRIPT - 3 end_POSTSUPERSCRIPTB: When fine-tuning with HINT, SCR or our method, we also use the main binary cross entropy VQA loss,...	BCA	ABC	CAB	ABC	Selection 1
A: URL Cross Verification. Legal jurisdictions around the world require organisations to make their privacy policies readily available to their usersB: Between the 8th and 10th November 2019, we crawled the landing pages and pages one hop from the landing pages for all the domains of the URLs in our corpus. We...	ABC	ACB	BCA	ABC	Selection 2
A: “The former approach is even more suitable for your VA system, because you use the accuracy of the base ML models as feedback/guidance to the expert in order to understand which instances should be wrangled”, said E3B: E2 stated that having an evaluation metric from early on is important for benchmarking pur...	ACB	BAC	BCA	CAB	Selection 3
A: This variation manifests both between training tasks and between training and testing tasks, similarly affecting the performance of MAML. Few works have thoroughly studied these impact factors.B: When applying MAML to NLP, several factors can influence the training strategy and performance of the model. Fir...	CAB	ABC	ABC	BCA	Selection 1
A: In Section II, the system model is introducedB: In Section III, the CCA codebook design and the codebook-based joint subarray partition and AWV selection algorithms are proposed. Next, the TE-aware codebook-based beam tracking with 3D beamwidth control is further proposed in Section IV. Simulation results ar...	BCA	CBA	CBA	BAC	Selection 1
A: Note that the 1111-color case with the completeness requirement is not very interesting, and also not useful for the general case: completeness states that every node on the left must be connected, via the unique edge relation, to every node on the right – regardless of the matrixB: This will be bootstrapped...	BAC	CAB	CAB	CBA	Selection 1
A: ContributionB: Going beyond the NTK regime, we prove that, when the value function approximator is an overparameterized two-layer neural network, TD and Q-learning globally minimize the mean-squared projected Bellman error (MSPBE) at a sublinear rateC: Moreover, in contrast to the NTK regime, the induce...	CBA	BCA	CAB	ABC	Selection 4
A: Wei et al. (2020) introduce a depth-wise GRU to additionally aggregate outputs of all encoder layers for the top decoder layer, but residual connections are still kept. Zhang et al. (2019) and Xu et al. (2020a) propose the layer-wise Depth-Scaled Initialization approach and the Lipschitz constrained parameter in...	CBA	ABC	ACB	BAC	Selection 4
A: exists a finite set F⊆V1𝐹subscript𝑉1F\subseteq V_{1}italic_F ⊆ italic_V start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT such that ∀x∈V1,∃x′∈F,x≡1x′formulae-sequencefor-all𝑥subscript𝑉1formulae-sequencesuperscript𝑥′𝐹subscript1𝑥superscript𝑥′\forall x\in V_{1},\exists x^{\prime}\in F,x\equiv_{1}x^{\prime}∀ italic_x ...	ACB	CAB	CBA	BCA	Selection 1
A: As we pointed out earlier, the proposed ordinal distortion is explicit to the image feature and is observable from a distorted image; thus it boosts the neural networks’ learning ability. On the other hand, the performance of the distortion parameter estimation drops as the amount of training data decreases. In ...	BCA	ABC	CAB	BCA	Selection 3
A: The initial learning rate is selected from {0.001,0.01,0.1}0.0010.010.1\{0.001,0.01,0.1\}{ 0.001 , 0.01 , 0.1 } according to the performance on the validation setB: The momentum coefficient is set as 0.9 and the weight decay is set as 0.001C: We do not adopt any learning rate decay or warm-up strategies....	BAC	CAB	ABC	ABC	Selection 1
A: The black-box model is motivated by data-driven applications where specific knowledge of the distribution is unknown but we have the ability to sample or simulate from the distribution. To our knowledge, radius minimization has not been previously considered in the two-stage stochastic paradigmB: Clustering ...	BAC	CBA	ABC	BCA	Selection 1
A: Further, the estimations of these rates are substituted into the recursive inequality of the conditional mean square error between the states and the global optimal solution. Finally, by properly choosing the step sizes, we prove that the states of all local optimizers converge to the same global optimal solutio...	ABC	CAB	ACB	CBA	Selection 2
A: The generalization technique has been well-studied by proposing numerous algorithms which can be divided into three schemes: (1) global recoding [15], which transforms same QI values into same generalized value; (2) local recoding [35], which can transform same QI values into different generalized values; and (3...	ABC	BCA	CAB	ACB	Selection 2
A: Armed with DCN, GC block and SyncBN training, our HTC with Res2NetR101 backbone yields 74.58 mAP on validation set, as shown in Table 1. However, the convolutional mask heads adopted in all stages bring non-negligible computation and memory costs, which constrain the mask resolution and further limit the segment...	BAC	ACB	CBA	CAB	Selection 3
A: This solves a question raised by Gady Kozma some time ago (see [K], comment from April 2, 2011)B: More specifically, we provedC: In version 1 of this note, which can still be found on the ArXiv, we showed that the analogous version of the conjecture for complex functions on {−1,1}nsuperscript11𝑛\{-1,1\...	CBA	BAC	BCA	BAC	Selection 3
A: For both cases, we present their respective regret boundsB: Detailed proofs are deferred to Appendix B. Note that our algorithms are all designed for inhomogeneous setting.C: In this section, we describe our proposed algorithm LSVI-UCB-Restart, and discuss how to tune the hyper-parameters for cases when...	BCA	CBA	CBA	BAC	Selection 1
A: Participation was fully voluntaryB: The survey was written in English and made available to anyone with the hyperlinkC: For dissemination, various channels were employed including a mailing list of students from a local Singapore university, an informal Telegram supergroup joined by students, alumni, an...	CBA	CAB	BAC	ABC	Selection 3
A: We employ a two-layer AliNet (each layer comprising one GCN and one GAT) and a four-layer decentRLB: We conduct an analysis of the training time for decentRL and AliNet with varying hidden-sizes on a V100 GPU, as detailed in Table 12C: The two methods exhibit comparable running times per epoch. AliNet r...	BCA	BCA	BAC	BCA	Selection 3
A: We first describe the experimental setup and implementation detailB: In this section, we conduct experiments to compare the proposed VDM with several state-of-the-art model-based self-supervised exploration approachesC: Then, we compare the proposed method with baselines in several challenging image-bas...	CBA	CBA	CBA	BAC	Selection 4
A: Further, we recognize that the Vandermonde approach is inaccurate and even becomes numerically unstable (rising errors) for higher degreesB: As expected, (Chebyshev) polynomial interpolation on uniform grids (uniform) and multi-linear interpolation also do not converge.C: It is therefore inappropriate fo...	CAB	BAC	ACB	CBA	Selection 3
A: Furthermore, even though it involves two stages, the end result is a single model which does not rely on any auxiliary models, additional hyper-parameters, or hand-crafted loss functions, as opposed to previous works addressing the problem (see Section LABEL:sec:related for a survey of related work)B: The fr...	ABC	CBA	BAC	ACB	Selection 3
A: Unlike these biographies, however, light is so structural-dependent that there is geometrical optics, which is a study of the placement of mediums and their trajectory by their shape, which is straight forward by Fermat’s principle of minimum timeB: Thus, to address errors in electricity, structural computer...	ABC	BAC	BAC	ACB	Selection 1
A: Conditions for such families of maps to define a permutation of the field 𝔽𝔽\mathbb{F}blackboard_F are well studied and established for special classes like Dickson polynomials [20], linearized polynomials [21] and few other specific forms [13, 14] to name a few. B: There has been extensive study about a f...	BCA	CAB	BAC	BCA	Selection 2
A: If the primary concern is sparsity, a researcher may be satisfied with just one of these combinations being selected, preferably the smallest set which contains the relevant information. But if there is also a desire to interpret the relationships between the views and the outcome, it may be more desirable to id...	CAB	BCA	ABC	CAB	Selection 2
A: When evaluating anomalousness in each subspace, the criteria used in the proximity-based algorithms, such as LOF and kNN, are used. Comparing the subspace approach with DepAD, subspace anomaly detection methods are focused on searching for subsets of variables (subspaces) in which anomalous patterns can be more ...	BCA	BAC	CAB	ABC	Selection 3
A: Comparison with Faury et alB: [2020] Faury et alC: [2020] use a bonus term for optimization in each round, and their algorithm performs non-trivial projections on the admissible log-odds. While we do reuse the Bernstein-style concentration inequality as proposed by them, their results do not seem to exte...	BAC	CBA	CAB	ABC	Selection 4
A: Figure 3: Video self-stitching (VSS). a) Snippet-level features are extracted for the entire videoB: b) Long video is cut into multiple short clips. c) Each video clip is up-scaled along the temporal dimensionC: d) Original clip (green dots) and up-scaled clip (orange dots) are stitched into one feature...	CBA	ABC	CBA	ACB	Selection 2
A: To the best of our knowledge, there is no literature describing the use of VA in hyperparameter tuning of evolutionary optimization (as defined in Section 1) with the improvement of performance based on majority-voting ensembles. In this section, we review prior work on automatic approaches, visual hyperparamete...	CAB	BCA	BAC	BCA	Selection 1
A: It is demonstrated that the synthesized Markov chain, formulated using the proposed consensus algorithm, satisfies the aforementioned mild conditionsB: Building on this new consensus protocol, the paper introduces a decentralized state-dependent Markov chain (DSMC) synthesis algorithmC: This, in turn, en...	BCA	BCA	CBA	BAC	Selection 4
A: As such, this work does not focus on learning methods per-se, but we believe that it has a strong potential to spark further work in this direction. In particular, our isometric multi-matching formulation can be integrated into an end-to-end learning framework via differentiable programming techniques [48]. More...	BAC	BCA	CBA	BCA	Selection 3
A: W.l.o.g., we assume that G𝐺Gitalic_G is connected, indeed a graph G𝐺Gitalic_G is a path graph if and only if all its connected components are path graphs. Moreover, we can obtain the clique path tree of G𝐺Gitalic_G by merging arbitrarily the clique path tree of every connected component. B: We present the...	BCA	CAB	BAC	ABC	Selection 2
A: Before comparing these methods, we take some preprocessing to remove nodes that may have mixed memberships for community detectionB: For the Polbooks data, nodes labeled as “neutral” are removedC: The smallest group with only 2 nodes in UKfaculty data is removed. Table 1 presents some basic information a...	ABC	CAB	CBA	CAB	Selection 1
A: (2018); Cheng and Bartlett (2018); Chatterji et al. (2018); Wibisono (2018); Bernton (2018); Dalalyan and Karagulyan (2019); Baker et al. (2019); Ma et al. (2019a, b); Mou et al. (2019); Vempala and Wibisono (2019); Salim et al. (2019); Durmus et al. (2019); Wibisono (2019) and the references therein. Among thes...	ACB	BCA	BAC	CAB	Selection 4
A: As stated in Eq. 2, the non-stationary learning often causes the observation transition and received rewards unpredictable only conditioned on individual observation and actionB: Conversely, we hope the learned policy makes them be predicted stably. To achieve this goal, we design a novel intrinsic reward ba...	CBA	CBA	BAC	ABC	Selection 4
A: Previous work on this problem has assumed ideal and error-free predictions that must be provided by a very powerful oracle, without any learnability considerations, as we discuss in more detail in Section 1.2B: In contrast, our algorithms exploit natural, and PAC-learnable predictions concerning the frequenc...	ABC	ABC	ABC	BCA	Selection 4
A: We condition the positioning and shape of a 3D patch using a single point from a point cloud generated by a base model. We repeat the procedure for each of the generated points, preserving local neighborhoods between the point cloud and the points located in the generated mesh. Intuitively, this allows us to inc...	CAB	ACB	ABC	BCA	Selection 1
A: This requires careful analysis in two aspectsB: The main idea is to use reformulation (54) and apply mirror prox algorithm [45] for its solutionC: First, the Lagrange multipliers 𝐳,𝐬𝐳𝐬{\bf z},{\bf s}bold_z , bold_s are not constrained, while the convergence rate result for the classical Mirror-Prox a...	BAC	ACB	BCA	CBA	Selection 1
A: Different classes of cycle bases can be consideredB: In [6] the authors characterize them in terms of their corresponding cycle matrices and present a Venn diagram that shows their inclusion relationsC: Among these classes we can find the strictly fundamental class.	ABC	CBA	CBA	CAB	Selection 1
A: The proof of Theorem 2.1 is quite involved and builds on the method of constrained chain maps developed in [18, 35] to study intersection patterns via homological minors [37]B: A major part of this paper, all of Sections 3 and 4, is devoted to adapt it to handle the k𝑘kitalic_k-partite structure of colorfu...	ABC	BAC	CBA	ACB	Selection 4
A: For precision and recall, we always use macro-average, which is identical to Mansouri et al. [94]B: Using our approach, we managed to achieve the same accuracy as before, 89%, compared to 83% reported by Mansouri et al. [94] for the additional external data setC: On the one hand, the precision was 4% low...	ACB	BAC	CAB	ACB	Selection 2
A: This paper demonstrated a hierarchical contour control implementation for the increase of productivity in positioning systemsB: We use a contouring predictive control approach to optimize the input to a low level controllerC: This control framework requires tuning of multiple parameters associated with a...	CAB	CBA	ABC	BAC	Selection 3
A: Results. As shown in Table. 1, no method performs universally well across datasets; however, the implicit methods LFF and SD obtain high unbiased accuracies on most datasets. This shows that implicit methods can deal with multiple bias sources without explicit accessB: Explicit methods work well on CelebA bu...	CBA	BCA	CAB	ABC	Selection 4
A: The underlines indicate the top three best performances. Note that the methods in the last row are proposed for point of gaze estimation, we convert the result using the post-processing method in Sec. 4.2.B: We use the provided source codes or re-implement (†) the methods for comparisonC: TABLE V: \adde...	CBA	CAB	BCA	BAC	Selection 1
A: Matching approach: Aims to compare the similarity between images using a matching process. Generally, the face image is sampled into a number of patches of the same sizeB: Feature extraction is then applied to each patch. Finally, a matching process is applied between probe and gallery facesC: The advant...	BCA	BAC	BCA	ABC	Selection 4
A: However, they encode recursion using a fixed point combinator and use transfinite size arithmetic, both of which we avoid as we explained in the introduction. Moreover, our metatheory, which handles infinite typing derivations (via mixed induction-coinduction at the meta level), seems to be both simpler and more...	CBA	CAB	ABC	BAC	Selection 1
A: To this end, users request authorization from the owner, for example by paying for purchases. If successful, users can get the desired shared media content from the cloud. Users require that the plaintext of their fingerprints not be accessed by the owner or the cloud, to prevent malicious framing by the owner. ...	ACB	CAB	CBA	BCA	Selection 2
A: Note that although we did not present the statistics here, we also tested the influence of number of attention heads H𝐻Hitalic_HB: The performance of using only one head, i.e., GraphFM(-M), is worse than that of using two, and more attention heads don not lead to improvement of performance but introduce muc...	CBA	CAB	CBA	ABC	Selection 4
A: [2015], in which more general properties of these pseudo-self-concordant functions were established. This was fully formalized in Sun & Tran-Dinh [2019], in which the concept of generalized self-concordant functions was introduced, along with key bounds, properties, and variants of Newton methods for the unconst...	CBA	CAB	ACB	ABC	Selection 2
A: The primary goal of Extend-Active-Paths is to extend active paths of a maximal (not necessary maximum) number of distinct free nodes with respect to a given ordering of arcsB: As a consequence of such behavior of Algorithm 7, Backtrack-Stuck-Structures potentially reduces some active paths although those ac...	CAB	BAC	BCA	ACB	Selection 4
A: The rest of this paper is organized as followsB: We provide necessary notation and assumptions in Section IIC: CPP is introduced and analyzed in Section III. In Section IV, we consider the algorithm B-CPP. Numerical examples are presented in Section V, and we conclude the paper in Section VI.	CAB	CBA	CBA	ABC	Selection 4
A: The problem (4) is divided into two minimization subproblems, by X𝑋Xitalic_X, and by Y𝑌Yitalic_Y. Hence, the problem (4) is solved by Fast Gradient Descent. Further, we note that the algorithm’s steps in lines 3, 6, and 7 are local and separable on each machine. The following theorem states the convergence rat...	ABC	BCA	CBA	BCA	Selection 3
A: In particular we propose three flavours of equilibrium MSs. Firstly, greedy (such as MW(C)CE), which select highest payoff equilibria, and attempt to improve further upon themB: We propose that (C)CEs are good candidates as meta-solvers (MSs). They are more tractable than NEs and can enable coordination to m...	BAC	ABC	CBA	ACB	Selection 1
A: This kind of extension is not limited to Rényi divergence, as discussed in Appendix B.B: As for the second part, since the bound depends on the queries, which themselves are random variables, it should be viewed as a bound on the Rényi dissimilarity notion that we introduce in the appendix (Definition B.9)**...	CBA	ACB	BAC	CAB	Selection 1
A: Why not? A kernelization algorithm guarantees that the input size is reduced to a function of the parameter k𝑘kitalic_k; but the running time of modern parameterized algorithms for NP-hard problems is not exponential in the total input sizeB: However, we argue that these results on kernelization do not exp...	BCA	ABC	BAC	ABC	Selection 3
A: The network predicts two attention maps for background shadow and occluder respectively, which are concatenated with composite image and foreground object mask to produce a residual shadow image.B: [57] developed a multi-task framework with two decoders accounting for depth map prediction and ambient occlusi...	ABC	CBA	ACB	BAC	Selection 2
A: Specifically, we report the results obtained by training our models using both full and 3-day target data, which correspond to the lower and upper bounds of errors, respectivelyB: Furthermore, we also include the results of fine-tuning and RegionTrans methods. Based on the results, we obtain the following ob...	BCA	CAB	ABC	BAC	Selection 1
A: However, the underlying philosophy is slightly different. First of all, although the same loss function is used, it is not obtained from a Bayesian framework, but rather chosen as a proper scoring rule gneiting2007strictly , i.e. a loss function for distributional forecasts for which a model can never obtain a l...	BCA	ABC	ACB	BAC	Selection 4
A: Clayderman (pop), “Y”: Yiruma (pop), “H”: H. Hancock (jazz), “E”: LB: Einaudi (contemporary), “J”: H. Joe (contemporary), “S”: R. Sakamoto (contemporary), “M”: Bethel Music (religious) and “W”: Hillsong Worship (religious). C: Each row shows the percentage of sequences of a class predicted as another cla...	BCA	CAB	BAC	ACB	Selection 1
A: to L⁢(k,1)𝐿𝑘1L(k,1)italic_L ( italic_k , 1 )-labeling problem (see e.gB: [10] for a survey), where the colors of any two adjacent vertices have to differ by at least k𝑘kitalic_k and the colors of any two vertices within distance 2222 have to be distinct. C: This description draws a comparison e.g	BCA	CAB	ABC	ABC	Selection 1
A: 5, where the baseline is the result tested by feeding the speech sample sequence into the ASR module directly without considering communication problemsB: The CER results of DeepSC-SR and two benchmarks under the AWGN channels and the Rayleigh channels are shown in FigC: From the figure, DeepSC-SR obtain...	CBA	BAC	ABC	CAB	Selection 2
A: Most 2D WSSS methods use image-level labelsB: Then, segmentation networks are trained using the pseudo-pixel-level labels. Besides the image-level label, other kinds of weak labels like point supervision[14] and scribble supervision[15, 16] which is similar to the weak setting in this work. Points and scribb...	BCA	CAB	ACB	BAC	Selection 3
A: The detection performance can be remarkably improved from 11.84% to 70.91% in terms of the AP40subscriptAP40{\rm AP}_{40}roman_AP start_POSTSUBSCRIPT 40 end_POSTSUBSCRIPT under the moderate setting of car category on the KITTI val set (see Table 1), which suggests that the depth estimation is a critical performa...	CBA	ACB	CBA	CAB	Selection 4
A: Moreover, failure cases may happen on some text-like objects or super-tiny texts, which are also common challenges for other state-of-the-art methods [10, 21, 20]. Examples of such failure cases are shown in Fig. 9. B: A potential solution may be to reason according to the semantic information of textC: ...	ABC	ACB	BCA	CBA	Selection 4
A: By taking full advantage of the successive characteristics of memory addresses and the fixed range of each individual part of an IP address, we design two relationship mapping mechanisms between memory blocks and IP addresses for a four-dimensional sparse matrix. The sparse matrix stores the number of occurrence...	ABC	BAC	BAC	BCA	Selection 4
A: In this paper, both nested Schur complement and additive Schur complement based preconditioners are constructed for the twofold and block tridiagonal linear systemsB: The polynomial equations of the preconditioned matrices are analyzedC: It is shown that by properly selecting the sign in front of each S...	ABC	CBA	BAC	CBA	Selection 1
A: A hub itself does not contain data but facilitates training by coordinating clients’ information. The goal is to jointly train a model on the features of the data contained across silos, without explicitly sharing raw data between the clients and the hubs and between clients across different silos.B: The hub...	ACB	CAB	BCA	ACB	Selection 2
A: The pseudospectra of finite-dimensional matrices and their extension to linear operators in Banach space have been extensively investigated and summarized in the classical book by Trefethen and Embree trefethen2005spectra . In the book, four different definitions of matrix pseudospectra are introduced and shown ...	BCA	ABC	BAC	BAC	Selection 2
A: In this encoder-decoder based backbone, we replace all the vanilla convolutions with the partial convolution layers to better capture information from irregular boundaries, since partial convolutions are conditioned only on uncorrupted pixelsB: To enhance the consistency of the rebuilt structures and textur...	BAC	CAB	CAB	ACB	Selection 4
A: The concept of BEC was first introduced by Elias in 1955 InfThe B: Together with the binary symmetric channel (BSC), they are frequently used in coding theory and information theory because they are among the simplest channel models, and many problems in communication theory can be reduced to problems in a B...	CAB	CAB	ACB	BCA	Selection 4

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