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train · 2.93k rows

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A: In this paper, we use BestFS (Algorithm 1) and MCTS (Algorithm 4 in Appendix A.1)B: The former is a classic planning method, which maintains a priority queue of states waiting to be explored, and greedily (with respect to their value) selects elements from it (see, e.g., [39])C: MCTS is a search method t...	BCA	BAC	CBA	ABC	Selection 4
A: For the case study, there is an interesting example shown in Figure 5. The two sentences are drawn from our dataset with the same meaning, ‘he wants to go to Dapuqiao’B: On our Substitution dataset, MFE-NER brings remarkable advances. For different semantic embeddings, MFE-NER achieves much better performan...	CBA	BCA	BAC	ACB	Selection 3
A: We find that neural étendue expansion also enables higher fidelity étendue expanded 3D color hologramsB: We note that existing methods on étendue expanded holography has focused on monochromatic 3D holograms[7, 28, 29]. Photon sieves[21] only achieves 3D color holography for sparse points. See Supplementary ...	BAC	BAC	BCA	CAB	Selection 3
A: Auxiliary MTL aims to improve the performance of certain primary tasks by introducing auxiliary tasks and is widely used in the NLP field for different types of primary task\addeds, \replacedsuch asincluding sequence tagging, classification, text generation, and representation learningB: As shown in Table 1,...	CBA	CBA	ACB	CBA	Selection 3
A: Typographical conventions for mathematical formulas have been developed to provide uniformity and clarity of presentation across mathematical textsB: This enables the readers of those texts to both understand the author’s ideas and to grasp new concepts quicklyC: While software such as LaTeX and MathTyp...	BCA	CAB	ABC	BAC	Selection 3
A: ∃X.ϕ\|∃x.ϕ⁢\|ϕ∨ϕ\|⁢¬ϕ\|y∼y\|y=y\|y∈Yformulae-sequence𝑋conditionalitalic-ϕ𝑥similar-toconditionalitalic-ϕitalic-ϕitalic-ϕitalic-ϕ𝑦conditional𝑦𝑦conditional𝑦𝑦𝑌\displaystyle\exists X.\phi\ \|\ \exists x.\phi\ \|\ \phi\lor\phi\ \|\ \neg\phi\ % \|\ y\sim y\ \|\ y=y\ \|\ y\in Y∃ italic_X B: italic_ϕ \| ∃ italic_x C: ...	BCA	CAB	BAC	ABC	Selection 4
A: Second, shared resources are, in many cases, congestible. They congest in the sense that sharing the same resources across multiple recipients reduces the effective benefit derived by each recipient. Third, sharing is conventionally associated with altruistic or reciprocal motivations—for example, webpage linkin...	CBA	ACB	BCA	BCA	Selection 1
A: In the transposed convolutional layer, the upsampling level is controlled by the size of the padding, which is essentially opposite to the operation of the normal convolutional layer. The transposed convolutional layer is first proposed in FSRCNN (Dong et al., 2016) and is widely used in DL-based SISR models.**B...	CBA	CAB	CBA	CBA	Selection 2
A: In Figure 6, we demonstrate the downsampling effect of two non-standard kernels: i) delta function (leading to aliasing) and ii) diagonal Gaussian kernelB: During training, Neural Knitwork blindly approximates the downsampling kernel based on the image content. The true and learned kernels are illustrated in...	ABC	BAC	ACB	BCA	Selection 3
A: The present paper is the first work we aware of that specifically applies TS to apple tasting, but previous work has considered its use for logistic bandits. For logistic contextual bandits, the implementation of exact TS (i.e. the policy that draws its sample from the exact posterior) is infeasible due to the ...	ABC	BCA	ABC	ACB	Selection 4
A: By doing so, the sampling strategies could learn to take into account different factors like the similarity between the input and the memory slots, the impact of each memory slot on task performance, and the existence of target memory slots for a given inputB: By balancing these factors during learning, one ...	BCA	BAC	CBA	CAB	Selection 1
A: Moreover, we consider the sum of all the mobile phone activity measures (i.e., SMS-in, SMS-out, call-in, call-out and internet) as our measure of crowdedness. We use this aggregated measure because i) the phoning (call-in, call-out) and texting by SMS measures are rather sparse during the night, as people rarely...	CBA	BAC	ACB	CAB	Selection 4
A: Recall that elementary doctrines can be understood as those primary doctrine endowed with equality predicatesB: replacing of ∧,⊤top\wedge,\top∧ , ⊤ by ∗,κ∗𝜅\ast,\kappa∗ , italic_κ).C: The following definition introduces those primary linear doctrines that are elementary as a direct linearisation of Def...	ABC	BAC	ACB	CBA	Selection 3
A: After that, we propose ForestSimSearch to process the top-k queries. Finally, we speed up the computation by using a fast approximate algorithm to calculate the diagonal elements of the forest matrix. B: In this section, we first define a new role similarity measure, namely ForestSim, based on spanning roote...	CAB	ACB	BCA	CBA	Selection 1
A: (2021) modelB: Our experimental results indicate that these strategies significantly enhance sentiment coherency modeling. LSA achieves impressive performance in aspect sentiment coherency extraction and sentiment classification, setting new state-of-the-art results on five widely-used datasets based on the ...	ACB	ACB	BCA	BAC	Selection 4
A: However, for the exposition in this section it sufficient to know what the properties of the operators 𝐋𝐋\mathbf{L}bold_L and 𝐖𝐖\mathbf{W}bold_W are. B: This process is somewhat elaborate and the reader is referred to [31] and [32] for all of the detailsC: The operator 𝐋𝐋\mathbf{L}bold_L and 𝐖𝐖\m...	CAB	CBA	BCA	ABC	Selection 2
A: During training, we iteratively sample error gates, insert them to PQC, and updates weights. Finally, post-measurement quantization is further proposed to reduce the precision of measurement outcomes from each qubit and achieve a denoising effect. B: QuantumNAT comprises a three-stage pipeline. The first ste...	BAC	BAC	CAB	CBA	Selection 3
A: As the pioneering work on event-based data association, gallego2018unifying proposes a contrast maximization framework to implicitly handle the data association problem based on the corresponding motion parametersB: In that framework, the initial motion parameters for each event are empirically set to a ran...	ABC	BAC	BCA	BCA	Selection 1
A: Our proof is a generalization of the proof of the latter result by Bonamy, Groenland, Muller, Narboni, Pekárek andB: We now prove our main result, that there are no ugly perfect graphsC: This generalizes the same fact which was previously proved for Meyniel graphs [22] (a class which contains chordal gr...	CBA	ABC	CAB	BAC	Selection 3
A: The main goal of unsupervised learning is to learn transferrable featuresB: In Table V, we compare the representation quality of unsupervised pre-training on STL-10 by transferring to the classification taskC: We adopt linear evaluation on the CIFAR-10 in 64×\times×64 resolutions with 1600-epoch pre-tra...	ACB	CAB	CBA	ABC	Selection 4
A: To prevent over-fitting the real validation set, we evaluate the performance of each sub-network on the split validation setB: The weights are taken from the super network using indexingC: We re-calibrate the batch normalization statistics using 20 batches of data with a batch size 64.	CBA	ABC	ACB	CBA	Selection 2
A: There will be a separate online testing set. An example of the data is shown as follows:B: The 500 articles will be labeled and served as the training setC: The ICDM 2020 Knowledge Graph Contest competition provides 500 recent articles444http://biendata.xyz/media/dataset.zip from Instagram which is selec...	ACB	CBA	BCA	BCA	Selection 2
A: Recently, contrastive learning has shown attractive potential in unsupervised representation learning, including natural language processing [19] and computer vision [3, 9, 6, 4]B: In the graph domain, some works [35, 29, 21, 27, 14, 20] also have explored the mechanism, which tends to maximize feature consi...	ABC	ACB	CAB	BAC	Selection 1
A: Figure 4: Results of the main experiment on shapes3d datasetB: The shaded area corresponds to bootstrapped 95%percent9595\%95 %-confidence intervals. Bottom panel: kernel density estimators for metrics and noise levels across seeds. Here topo stands for topographic similarity, conf for conflict count, cont ...	CAB	ABC	ABC	ACB	Selection 4
A: The authors in [21] consider that uncertainty enters the system dynamics linearly and propose to use robust adaptive CBFs, as originally presented in [22], in conjunction with online set membership identification methods. In [23], it is shown how additive and multiplicative noise can be estimated online using Ga...	ABC	BAC	BAC	BCA	Selection 4
A: Most commonly, we will consider Boolean circuits with AND, OR, and NOT gates where the AND and OR gates can have unbounded fan-in, but we will also consider e.gB: circuits where the gates can be arbitrary functions of low 𝖰𝖬𝖠𝖰𝖬𝖠\mathsf{QMA}sansserif_QMA query complexity. C: We always specify the ba...	BAC	CBA	BCA	ABC	Selection 3
A: Our DCDFM inherits the advantages of DFM such that it has no constraint on distribution of adjacency matrix, allows connectivity matrix to have negative entries, and allows applying the idea of spectral clustering to fit it. Meanwhile, as an extension of DFM, similar as the relationship between SBM and DCSBM, no...	BAC	BAC	BAC	CAB	Selection 4
A: We evaluated it on 14141414 scenarios constituting the StarCraft Multi-Agent Challenge and confirmed its strong performanceB: We also included ablations regarding importance sampling, centralization of learning, scaling, and sharing of parameters. C: In our work, we introduced MA-Trace, a new multi-agent...	BAC	BCA	ACB	CAB	Selection 2
A: T4: Compare decision rules based on local feature rankingB: Moreover, the interpretation of rules extracted from the space of solutions (see T3) could be achieved if users are capable of investigating the values of both training and testing instances. C: The global features described in T2 might not be s...	ACB	CBA	CAB	CAB	Selection 1
A: However, the comprehensive simulation results in Figs. 4 – 5 show that the proposed PR-HS scheme substantially improves the system performance in terms of SNR, channel capacity and the associated distribution of channel gain. B: For this reason, the complete analysis to reach the closed-form description is u...	CAB	CBA	ABC	BCA	Selection 2
A: Most existing theoretical research on packing, and all research on online translational packing that we are aware of, is concerned with axis-parallel rectangular pieces. In this paper, we study online translational packing of convex polygonsB: The pieces arrive one by one and have to be placed irrevocably in...	BAC	ACB	ABC	CBA	Selection 3
A: Thus, a selection question naturally stands out: Regarding the “gap" over samples, how to find and annotate the most “valuable" images in order to achieve the best performance with a deep model trained under such a limited supervision? To the best of our knowledge, there is no ready answer to the above question....	ABC	CAB	CAB	CBA	Selection 4
A: Details on the probability mass function or probability density function on distributions discussed in this paper can also be found in the above URL link. Generally speaking, the distribution-free property guarantees the generality of our model MMDF, the DFSP algorithm, and our theoretical results.B: More t...	CAB	BCA	ABC	CBA	Selection 1
A: Many CIL methods mitigate forgetting through knowledge distillation[18, 26, 12, 8, 29]B: In these methods, when learning at a new phase, the model of the previous phase is used as the teacher, and the CIL Learner is regularized to produce similar outputs as the teacherC: In this way, the knowledge of pr...	ABC	BAC	CAB	CAB	Selection 1
A: The decoder performs multiple steps of coarse-to-fine registration in a single network iteration. Dual deep supervision, including a deep self-supervised loss based on image similarity (local normalized cross-correlation) and a deep weakly-supervised loss based on manually annotated landmarks (mean square error)...	CAB	BAC	BCA	BAC	Selection 3
A: Instead, we would like to know how often a tuple is an answer, that is, its relative frequency, or, in other words, the percentage of repairs that entail that tuple. For instance, in Example 1, the relative frequency of the empty tuple, which corresponds to true and is the only candidate answer as the query is B...	BAC	BCA	BCA	CBA	Selection 4
A: We represent an absorbing state of an absorbing random walk on a graph as a node555Throughout our paper, our figures do not show the nodes for the absorbing states that are associated with absorbing graphs. Our figures only show nodes that are associated with transient (i.e., non-absorbing) states of the corresp...	ABC	BAC	BAC	BAC	Selection 1
A: The general QNR problem can be formulated in terms of hypergraph flows and solved using LP (see Appendix A)B: Here, we develop an efficient heuristicC: Although polynomial-time and provably optimal, the LP-based approach has a very high time-complexity for it to be practically useful	CBA	BCA	ACB	CAB	Selection 3
A: AI approaches, which are currently predominated by deep learning algorithms, have brought considerable improvements to many essential components of autonomous driving technology, including advances in perception, object detection, and planning. As the AI-powered driving systems of vehicles advance, the number o...	ACB	ACB	BCA	ABC	Selection 4
A: The dataset is divided into three roughly equal parts for training, validation, and testingB: Pitts30k [9, 13]: It is a public dataset containing 30 k database images downloaded from Google Street View and 22 k test queries captured at different timesC: Street views in the three sets are geographically d...	CAB	ABC	BAC	CAB	Selection 3
A: We also apply our attack to WG-PRNG and we provide a complexity estimate that shows a fatal weakness of this cipher. We also report previous attempts at breaking WG-PRNG with algebraic attacks and we discuss their shortcomings. B: We show with two toy examples how the attack can be performed in practiceC...	CAB	ACB	BAC	CBA	Selection 4
A: (2020) on Leduc and different imperfect information GoofspielB: The results are in Table 1, showing that our method is slightly behind in Leduc, even with the highest search depth. In Goofspiel, CDBR1, which looks only one action into the future, is already pretty good, and as soon as we allow CDBR to look t...	BCA	CBA	BAC	CBA	Selection 1
A: We shall use induction on n𝑛nitalic_nB: Let n≥2𝑛2n\geq 2italic_n ≥ 2 and assume that the result holds for all inputs of length n−1𝑛1n-1italic_n - 1. We shall consider two cases. C: The result is trivial if n=1𝑛1n=1italic_n = 1, since both sides of (3.3) are 0	BAC	CAB	ACB	CAB	Selection 3
A: We report a lack of uniform and cohesive literature, with different studies using different methods, covariates, and definitions of key variablesB: Moreover, our analysis highlights the need for greater collaboration and standardization of methods in the study of environmental migrationC: This limits the...	BCA	CAB	BAC	CAB	Selection 3
A: All of attained dynamic regret bounds are universal in the sense that they hold against any feasible comparator sequence, making the algorithms adaptive to non-stationary environmentsB: An important future work is to investigate the optimality of our attained problem-dependent dynamic regret boundsC: We ...	CAB	CAB	ACB	ABC	Selection 4
A: v𝑣vitalic_v growing) can be done in linear timeB: Thus the computation of letters of type 2′superscript2′2^{\prime}2 start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPTC: such that σk⁢(a)=u⁢b⁢vsuperscript𝜎𝑘𝑎𝑢𝑏𝑣\sigma^{k}(a)=ubvitalic_σ start_POSTSUPERSCRIPT italic_k end_POSTSUPERSCRIPT ( italic_a ) = ital...	CAB	ACB	ABC	BCA	Selection 4
A: The result in Theorem 4 for s≥1/2𝑠12s\geq 1/2italic_s ≥ 1 / 2 (that is, 2⁢k+2≥d2𝑘2𝑑2k+2\geq d2 italic_k + 2 ≥ italic_d) was already derived in Sadhanala et alB: (2017)C: More precisely, these authors established the third term on the right-hand side in	CBA	CBA	ACB	ABC	Selection 4
A: We applied the method to dynamically changing twin brain networks obtained from the resting-state functional magnetic resonance imaging (rs-fMRI). We investigated if the state change pattern in time varying brain networks is genetically heritable for the first time. This is not yet reported in existing literatur...	CBA	CAB	ACB	ABC	Selection 3
A: ) that satisfies Assumption (A1)B: rule (4), for a general M(.)M(.)italic_M ( C: If there does not exist θ∈[0,π)𝜃0𝜋\theta\in[0,\pi)italic_θ ∈ [ 0 , italic_π ) such that ϕk⁢(θ)−θ=d⁢πsuperscriptitalic-ϕ𝑘𝜃𝜃𝑑𝜋\phi^{k}(\theta)-\theta=d\piitalic_ϕ start_POSTSUPERSCRIPT italic_k end_POSTSUPERSCRIPT ( ita...	ACB	CAB	BAC	CBA	Selection 3
A: The analytical guarantees allows us design the controller gains for actuating the boundary coolant. The rest of the paper is organized as follows. Section 2 sets up the problem by discussing the battery module thermal model and formulating control objectivesB: Sections 3 and 4 detail the pISSf-ISSt framework...	CBA	BCA	CAB	BAC	Selection 2
A: The study uses Salivary cortisol as the ground truth to assess the baseline stress levels of the workers, obtaining an accuracy of about 73% when distinguishing between low, medium and high stress level of the workers. Similarly, Mark et al. [29] discovered in their research on information workers that there was...	BAC	CBA	ABC	BCA	Selection 2
A: We focus on the core function approximation problem, which is to determine the optimal power allocation to an SU for a given location, channel, and time instant—since frequency and temporal domains are essentially “orthogonal” dimensions of the problem and thus can be easily handled independently (as done in §II...	BAC	CBA	BCA	BAC	Selection 3
A: The inequality in line (57) follows from properties of definite integrals, the first inequality in line (58) follows from (55)B: The first inequality in line (59) follows from (25).C: The equality in line (58) follows from (49) and the properties of definite integrals	BCA	ACB	ABC	CAB	Selection 2
A: Yet, it is worth mentioning that among the three coordinate descent algorithms considered for this numerical example, Gauss-Southwell rule gives the best performance which is consistent with Remark 2B: The extra information of the component-wise gradient norms enables a better selection of the coordinate to ...	BCA	BAC	ABC	CAB	Selection 1
A: Since the implementation of in-situ learning on the memristive chip was beyond the scope of this paper, we limited noise analysis to inference onlyB: The Noisy network and the Ideal network share the same sets of clusters, but in the case of the Noisy network, the Time-Surfaces generated by the test set were...	CAB	BAC	ACB	CAB	Selection 3
A: This also holds for a slightly weaker stability concept than perfect stability: in all future steps an agent’s opinion will not move further than by a given distance δ𝛿\deltaitalic_δB: We analyze the time to reach a δ𝛿\deltaitalic_δ-stable state which is defined as a state where any edge in the influence n...	ABC	BAC	ABC	ACB	Selection 4
A: Therefore, further assessment by medical professionals is necessary to establish the model’s clinical utility and compare its performance to that of human experts.B: Evaluation by Medical Professionals: While the model demonstrates high sensitivity and specificity, it is essential to emphasize that medical ...	CAB	CBA	ACB	BCA	Selection 1
A: However, in an adaptive sampling scheme, underestimation of an arm due to the randomness of the empirical mean results in a smaller number of samples of the arm in the future. Bayesian and frequentist BAI algorithms are both robust against such randomness but with different confidence levels. Bayesian algorithms...	BAC	CBA	BCA	CAB	Selection 4
A: The first consists in learning an approximate group action in order to match the input and the reconstructed data. For instance, Mehr et al. (2018b) propose to encode the input in quotient space, and train the model with a loss that is defined by taking the infimum over the group G𝐺Gitalic_GB: (2018); Koner...	BAC	ACB	CBA	CBA	Selection 2
A: Our values in navy (darkest) compared to values in Hellan, Lucas, and Goddard (2020) in orange (lightest) marked with ††\dagger†B: The baseline ‘Random’ is shown in grey (medium, different pattern). The results obtained are better than those in existing work, and both out-compete the random baseline.C: ...	ABC	ABC	BCA	CAB	Selection 3
A: Training binary latent VAEs with K=2,3𝐾23K=2,3italic_K = 2 , 3 (except for RELAX which uses 3333 evaluations) on MNIST, Fashion-MNIST, and OmniglotB: We report the average ELBO (±1plus-or-minus1\pm 1± 1 standard error) on the training set after 1M steps over 5 independent runsC: Test data bounds are rep...	ACB	CBA	CAB	ABC	Selection 4
A: Increasing the frame size decreases the chance of collisions, while increasing K𝐾Kitalic_K makes the transmission more robust and allows to harvest more diversity. The relationship, however, is not as straightforward when it comes to spectral efficiency shown in Fig. 7(b). For a given number of repetitions K𝐾K...	CAB	ABC	BCA	BAC	Selection 1
A: Variation of this algorithm also appears in [BNV19]B: Later, Schul [Sch07] provided a modification of the algorithm so that the ratio of the length of the yielded path over the length of the optimal path is bounded by a constant C𝐶Citalic_C independent of the dimension N𝑁Nitalic_NC: Here and for the r...	CBA	BAC	CBA	ABC	Selection 2
A: For a detailed introduction on Chow forms, we refer to [13, 14]B: For a concise, clear, and deeper exposition we refer to [22].C: The main object of our study is the associated hypersurface of a variety V𝑉Vitalic_V and its defining polynomial, the Chow form	BAC	CAB	BCA	CAB	Selection 3
A: Self-reported annotations [19]: They contain the emotional labeling reported by the participants after watching each of the 14141414 videos in the experimentB: The data are stored in one CSV file, that contains 14141414 columns and 1,40014001,4001 , 400 rows (100100100100 volunteers ×\times× 14141414 clips)...	BCA	BAC	BCA	ABC	Selection 4
A: Practical black-box attack (Papernot et al., [n. d.]): Papernot et alB: By repeating three steps: querying Oracle for a label, training a substitute model, and generating a synthetic dataset, an adversary can create a substitute model that can mimic the Oracle decision behavior. Adversarial samples are gener...	CAB	ACB	BAC	ABC	Selection 2
A: In the latter case, the sum of the even-numbered terms must diverge to −∞-\infty- ∞ (as the sum of all terms is convergent), and therefore cannot diverge to ∞\infty∞B: Thus player B is chosen on this step with probability 00, finishing the proof. ∎C: Almost surely either a player was chosen on Step 1 or ...	BCA	CBA	CBA	CAB	Selection 1
A: Among the existing defenses, 4 have negligible or no timing overhead by design or shown in evaluation [57, 105, 81, 107], 1 fails to catch up with the camera and LiDAR frame rate in evaluation [104]. For the remaining ones, we cannot conclude their timeliness from their papers (e.g., no timing overhead evaluatio...	BAC	CBA	ABC	CAB	Selection 2
A: It also contains a Red join gadget in [1.5,13.5]1.513.5[1.5,13.5][ 1.5 , 13.5 ]. Its long intervals terminate at 1.5,7.51.57.51.5,7.51.5 , 7.5 and start from 10.5,13.510.513.510.5,13.510.5 , 13.5. B: The sixth buffer contains a Blue join gadget in [18,21]1821[18,21][ 18 , 21 ]. Its long intervals terminate a...	BCA	CAB	BCA	BCA	Selection 2
A: Both solutions, however, suffer from potential limitations which we attempt to explain in the followingB: We support these claims through our experiments. C: Existing BatchNorm-based approaches avoid issues with sequential data either through multi-stage training or multi-sequence batches	ACB	BCA	BAC	BAC	Selection 2
A: ResNet-34 (R34) and ResNet-100 (R100) were used as backbone models. We re-implemented the state-of-the-art models: CosFace[34], ArcFace[4], and MagFace[18].B: For data augmentation, a horizontal flip was applied with a 50% of chance. All experiments were performed using two NVIDIA-RTX A6000 GPUs with a mini-...	CBA	CAB	BAC	CAB	Selection 1
A: This database was not part of the mix that was used to train the model and hence testing the model from images from this database was a test for its generalisability. All the ground truths are annotated by the expert. B: We randomly sampled 40 AMD and 40 normal images from STARE for testing our modelC: S...	ACB	BCA	CBA	BCA	Selection 3
A: Note that the sigmoid function is employed to select the information flowing into the next layer in our local attention model. Fig.13 shows visual results of local attentions obtained by our method.B: In the proposed method, the local attention is designed to deal with the problem that local regions is misse...	CAB	BCA	ABC	CBA	Selection 1
A: Since game outcomes are symmetric, this will produce a player of high rating instead. B: If it produces a player of very low rating, simply re-do the strategy picking the same sequence of pairs of players but have the opposite player winC: This strategy is guaranteed to produce a player of either very hi...	ABC	BAC	CBA	BCA	Selection 3
A: The one with the highest SDC score (i.e., 75.21%) is a noteworthy candidate because the two classes are clearly separated. Rare cases and outliers are also easily visible, forming a bridge between benign and malignant instancesB: Zoe begins with the selection of a number of neighbors parameter by activating ...	ABC	ACB	CAB	BAC	Selection 4
A: With the recent developments, many real-world financial products such as money lending/borrowing, margin-trading, exchange platforms, derivatives and more, are being made available to the blockchain users via smart contracts [14, 4, 2, 15, 48]. Unfortunately, the absence of regulations allows malicious actors to...	CAB	BCA	CBA	ACB	Selection 4
A: In this section, we define the policy gradient, the gradient of the equilibrium policy value with respect to the selection criterion, give an estimator of the policy gradient, and use the estimator to learn policiesB: (2021); Wager and Xu (2021) and use gradient-based optimization with policy gradient estim...	ACB	ABC	BAC	BCA	Selection 1
A: EfficientNet-B2: 34.4 and OccamMobileNet-v3: 49.9 vs. MobileNet-v3: 40.4) and COCO-on-Places (OccamEfficientNet-B2: 39.2 vsB: To examine if the proposed inductive biases improve bias-resilience in other architectures too, we created OccamEfficientNet-B2 and OccamMobileNet-v3 by modifying EfficientNet-B2 [65...	BAC	BCA	ACB	CBA	Selection 1
A: First, our exploration of contextual information for VSS focuses on simultaneously learning temporal contexts for all semantic categoriesB: Considering the relationships amongst various categories (e.g., horses is often related to the grassland), the explicit modeling of class-specific temporal contexts is a...	BCA	ACB	CBA	ABC	Selection 1
A: To achieve GPU-to-GPU communication for collective operations like gather, scatter, and all-reduce, we used NVIDIA Collective Communication Library (NCCL) [26] on NVLink-2.0B: We configured DLRM and TBSM using Pytorch-1.9 with the torch.distributed backend to support scalable distributed training and perfor...	BAC	ACB	CAB	ABC	Selection 1
A: In Subsection 5.3 we prove that for connected polyhedral complexes imbedded in ℝnsuperscriptℝ𝑛\mathbb{R}^{n}blackboard_R start_POSTSUPERSCRIPT italic_n end_POSTSUPERSCRIPT, all cells are pointed or all cells are unpointed. This allows us to extend the deformation retraction to the unpointed setting. In Subsecti...	CAB	ACB	CBA	BCA	Selection 3
A: It is stated that topological defects will arise in the course of a phase transition with symmetry breaking of the system due to the celebrated Kibble-Zurek mechanism (KZM) [27, 28]. Number density of topological defects was found to satisfy a universal power-law with respect to the quench rate. The formation of...	BCA	ACB	BAC	CBA	Selection 4
A: We shall now look at the PINN model, which uses the strong form of PDE as a loss functionB: We continue to consider the following 3D Navier-Stokes equation with initial condition as given in (2.1)C: On the other hand, for the helicity conservation, we shall assume that the external force is zero. Theref...	BAC	ABC	ACB	CAB	Selection 2
A: We used a similar approach to the HS data set for utilizing AD in our experiments. B: Adult (AD) Data set kohavi1996scaling is a well-known benchmark data set for classification tasks predicting whether income exceeds $50K annually based on census dataC: This data set has 32,561 records with 14 attribut...	ABC	CAB	CBA	ACB	Selection 2
A: Our research encourages the CSCW research community to think critically about the design of safety and privacy tools for families and demonstrates how online safety, privacy, and security are interrelated yet differing and complex concepts that surface unique tensions within the inherently hierarchical social re...	ACB	ACB	BCA	CAB	Selection 3
A: The proposed approach is scale-invariant, in the sense that the persistences of all homology classes remain the same when the dataset is uniformly scaledB: This is made precise in Proposition 4. C: Therefore, the persistences of the topological features of the dataset are not reduced no matter by how muc...	CBA	BCA	ACB	CAB	Selection 3
A: Considering the semantic relations among AUs, some works (Wang et al. 2013; Walecki et alB: 2017) make efforts in modeling such relations via probabilistic graphical models or graph neural networks. Wang et al. (Wang et alC: 2013) introduced a restricted Boltzmann machine to model facial action units, th...	CAB	ABC	BAC	ACB	Selection 2
A: The experiment results show that BBP has the least block propagation time. Compared with the current protocol of Ethereum, BBP reduces the block propagation time by 4x. Importantly, our work shows that BBP has a constant-level block propagation time for blocks with modest to large numbers of transactions, thereb...	CBA	CAB	ABC	BCA	Selection 1
A: Our work is related to a line of recent work on the sample efficiency of reinforcement learning for POMDPs. In detail, Azizzadenesheli et al. (2016); Guo et al. (2016); Xiong et alB: In contrast, the upper bound confidence (UCB) method is used in Xiong et al. (2021) for adaptive exploration. However, they re...	ACB	ABC	BAC	BCA	Selection 1
A: This systematic literature review was based on the PRISMA Statement to analyze papers on AI-based automated speech therapy tools for persons with SSD. We extracted relevant data from the included articles based on four predefined research questions: Types of SSD addressed; Level of autonomy achieved by such tool...	ACB	CBA	ACB	ABC	Selection 4
A: Intuitively, any data point that belongs to an underlying community should have large compression ratios with many points from the same community, whereas it will have a lower compression ratio w.r.t inter-community pointsB: Our outlier detection is a simple process inspired by compression ratioC: On the...	BCA	BCA	BCA	BAC	Selection 4
A: The SGG results do not improve in an obvious way, or even get hindered as for the PredCls and SGCls on the object obfuscation and image obfuscation datasets. The comparisons indicate that our propose SI-Dial framework indeed learns to ask questions in a meaningful way.B: The results for the generated scene g...	BAC	BAC	CAB	BCA	Selection 3
A: [8] depicts state of the artB: The recent survey by Chan et alC: Here, we mention some of the models: obnoxious facility games where every agent wants to stay away from the facility [11, 13]; heterogeneous facility games where the acceptable set of facilities for each agent could be different [25, 17, 12...	CAB	CBA	CBA	BAC	Selection 4
A: As we have seen before the papers on perfect edge domination are less frequentB: There is a paper [16] where the authors describe ILP formulations for the PED problem, together with some experimental results. C: There is some more bibliography to add to the already vast literature [3, 10, 23, 31, 33, 35]...	BCA	BAC	ACB	BAC	Selection 1
A: Note that, in accordance to Tab. 2 indeed, we are implicitly designing minimum complexity ReLU networks with the same number of layers L=2𝐿2L=2italic_L = 2, each of them composed of almost comparable number of neurons (from 2222 to 4444)B: To set the complexity of each ReLU network, we have followed the in...	ABC	BCA	ABC	BAC	Selection 4
A: This approach allows to infer the dynamics of systems with conserved energy, like an undamped pendulumB: [48] augment the Hamiltonian by a learned Rayleigh dissipation function to model systems that do not conserve energy, which are more common in the real world [15].C: For example, [16, 10] and [53] use...	CAB	BCA	ABC	CBA	Selection 2
A: As discussed earlier, the QSC framework ensures minimality of quantum communication resources by extracting and compressing the semantic representations of the data, unlike existing semantic-agnostic QCNsB: To do so, we must the error sources encountered during both the quantum communication errors and the ...	ACB	ABC	BAC	BAC	Selection 1
A: Nevertheless, there can still be some static obstacles, such as buildings, causing severe blockages to backhaul links. However, they can be effectively avoided in the network planning stage by considering radio coverage.B: Non-line-of-sight (NLOS) conditions are mainly caused by blocking obstaclesC: IAB...	CBA	BCA	BAC	CAB	Selection 4
A: Importantly, inference must be done in a way that takes into account the incentives of the strategic agents. Our work most closely relates to that of Tetenov (2016), who consider setting the type-I error level of a hypothesis test to account for an agent’s payoffs. That work establishes a minimax protocol simila...	BCA	CAB	BAC	CBA	Selection 2
A: Concerning PPIR(FHE)-v1, we note the uneven computation time and bandwidth usage between clients, due to the asymmetry of the encryption operations and communication protocol (Figure 1). PPIR(FHE)-v2, which shares the computational workload between the two parties and avoids DotProduct, allows obtaining an impor...	BAC	ABC	BCA	BCA	Selection 2

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