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A: [19] use Kullback-Leibler Divergence (KLD) between the softened logits of teacher and student models as the loss to align the output distribution, and Zhao et alB: Response-based KD methods [19, 58, 3] have the natural property of hiding models. Hinton et alC: [58] decouple the KLD into two uncorrelated ...	CBA	BAC	ABC	BCA	Selection 2
A: Both results in Table 1 and in Figure 2b suggest that neural operators perform mapping of finite-banded functions.888One may object that we considered only smooth dataB: This is, indeed, true, but as shown in [De ̵+22] both FNO and DeepONet produce Runge-like oscillations when applied to data with discontin...	CAB	BCA	ACB	ABC	Selection 4
A: To model such correlation, we formulate a transformer structure that reconstructs the corresponding individual component of the student features and produces an alignment with the target teacher feature. We dubbed this target-aware transformer. As such, we use parametric correlations to measure the semantic dist...	CBA	BAC	BCA	CAB	Selection 1
A: The full dataset contains over 7000700070007000 entries with 46464646 dimensions; after removing entries with missing values we have 948 unique foods, which we use for our experiments. We walk through how one might use ENS-t-SNE for exploratory data analysisB: There are roughly three large clusters (indicate...	ACB	BAC	ABC	BAC	Selection 1
A: To this end, we construct a confidence set of embeddings upon identifying and estimating the Bellman operator, which further allows efficient exploration via optimistic planning. It is worth mentioning that such a unified framework allows a variety of estimators (including maximum likelihood estimators and gener...	BCA	BCA	ABC	CBA	Selection 4
A: The first line of research studies offline RL in standard MDPs without any partial observabilityB: Table 1: We compare with most related representative works in closely related lines of researchC: The second line of research studies online RL in POMDPs where the actions are specified by history-dependent...	BAC	ABC	ACB	ACB	Selection 1
A: Without constraints, one can apply stochastic gradient descent (SGD) and its many variates, whose statistical properties (e.g., asymptotic normality) have been comprehensively studied from different aspects (Robbins1951stochastic; Kiefer1952Stochastic; Polyak1992Acceleration; Ruppert1988Efficient). However, unli...	CBA	ACB	CAB	CBA	Selection 3
A: The rest of the paper is organized as followsB: In Section 2 the technique of T𝑇Titalic_T-coercivity is discussed, which provides important auxiliary results for Section 3, which is the main section of the paper and contains the analysis of the discrete inf-sup conditionsC: In Appendix A known results ...	ACB	ABC	ACB	BCA	Selection 2
A: Vision transformers (ViTs), inspired by the success of transfomers on NLP tasks, use a one-dimensional sequence of tokens corresponding to image patches (sub-images, tiles) [14]B: Although, ViTs have proven to be scalable models that generalize better than CNNs for image classification, the destruction of po...	BAC	ABC	BCA	BCA	Selection 2
A: A maximal set of transversal zeros in B𝐵Bitalic_BB: The zeros in B1subscript𝐵1B_{1}italic_B start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT belong to the union of column 5555 of A1subscript𝐴1A_{1}italic_A start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT and row 1111C: last 3333 columns	ABC	ABC	CBA	ACB	Selection 3
A: In the “SLT + OPT” (top right) the asterisks in MCAT* and MathBERT* refer to how SLTs and/or OPTs are not encoded directly from trees as seen in any of the Tangent approaches or Approach0B: Figure 3: taxonomy for approaches related to formula retrieval (math information retrieval)C: MCAT encodes SLTs im...	BAC	ACB	ABC	CBA	Selection 1
A: Finally, let us consider two networks with partially overlapping structuresB: One may think of species belonging to trophic chains with different connectivity patterns. C: The two networks share block 1111 (for instance basal species) but the remaining nodes of each network cannot be considered as equiv...	BAC	ACB	ABC	ABC	Selection 2
A: Figure 5: Performance of FactorNets for individual rotation learningB: (left) Predictions of rotation angle vs. the ground truth (normalized to [−1,1]11[-1,1][ - 1 , 1 ]) in test setC: (right) Distributions of absolute percentage errors (in %) of all data points in the dataset.	ABC	ACB	ACB	CBA	Selection 1
A: To prevent trivial solutions, a popular method is to apply pulsive force between embeddings from different images, known as contrastive learning. Contrastive loss is shown to be useful in various domains, including natural language processing (Gao et al., , 2021), multimodal learning (Radford et al., , 2021). Co...	BAC	BAC	ACB	CBA	Selection 4
A: Using algebraic examples we show that the third factor matrix of the NNTuck both captures and incorporates information about layer interdependence in multilayer networks. We show that the multiplicative updates for minimizing the KL-divergence of the NNTuck are step-by-step equivalent to maximizing the log-likel...	BAC	CBA	CAB	CBA	Selection 3
A: johri2021nearest , and where necessary, the larger IonQ Aria machine with a capacity of 32 physical and 20 algorithmic qubits (ionq2022aria, )B: Experiments below used the 11-qubit trapped ion quantum computer described by Johri et alC: A crucial point to bear in mind with quantum computing is that the m...	ABC	ACB	BAC	ABC	Selection 3
A: The red edges connect nodes of different labels, while the green edges connect nodes of the same labels. Figure 3(a) - 3(c) shows homophilic graphs of different densitiesB: Figure 3: Examples of graphs with different label-topology relationships and comparison of different homophily measures. The node colou...	ACB	ACB	BAC	BCA	Selection 3
A: In others, where proportional representation of groups across learners, models, or clusters (Kleindessner et al., 2019a, b) is important, our work implies that independent risk minimization can lead to undesirable outcomesB: In some contexts, the benefits of the reduced risk among subpopulations may outweigh...	CAB	BAC	CBA	CBA	Selection 2
A: Both of these approaches could be adapted to suggest a measure of unfairness, by replacing the hard constraints on the absolute differences or on the ratios with a sum of the values of each of the constrained quantities, and using this sum of as a measure of unfairnessB: For non-binary fairness attributes an...	ACB	ABC	CAB	CBA	Selection 2
A: published in 2009 is likely the most widely used baseline for pair MDB: The MK algorithm (Mueen et al., 2009) from Mueen et alC: However, it is outperformed by more recent methods in terms of runtime, in particular QUICK MOTIF (Li et al., 2015), STOMP (Zhu et al., 2016), SCRIMP (Zhu et al., 2018), and V...	CAB	BAC	BCA	CAB	Selection 2
A: Based on the advantages of the decentralized information structure, the online algorithm and the regularization method, we propose a decentralized online regularized algorithm for the linear regression problem over random time-varying graphsB: In each iteration, the innovation term is used to update the nod...	CAB	BCA	ACB	CAB	Selection 3
A: Guided by the theoretical bounds, we constructed a heuristic that dynamically adjusts the dimension of the projection subspace at each iterationB: This reduces the need for very accurate calculations needed to converge.C: As the process approaches convergence, the backward error decreases, which allows ...	ABC	ACB	CAB	CBA	Selection 2
A: For all the conducted experiments we employ a BART-large architecture [4], which is a transformer-based model with a bidirectional encoder and an auto-regressive decoder. BART-large consists of 12 layers for both encoder and decoder and 406M parametersB: We use PyTorch version 1.10 and Hugging Face version ...	ACB	CAB	CBA	BCA	Selection 1
A: In the Clifford+T decomposition of the Toffoli gate, the orange and green qubits are CNOT controls and the grey qubits are CNOT targets; b) Pink edges represent SWAPs (routing is discussed in Section 3); c) Reading a circuit from a cell is performed by replacing each vertex with a qubit, and choosing a gate that...	BCA	BAC	ABC	CAB	Selection 1
A: In this model, the parameters of the input image are not fixedB: Hence, we also pass the input image parameters along with the output image parametersC: This model is more generalizable than the previous one but also has a more complex non-linearity to learn and hence, lags behind in performance compare...	ABC	BCA	CBA	CAB	Selection 1
A: In our future work, we will explore the effects of different neural network architectures, sampling strategies, and optimization methods, followed by a detailed numerical analysisB: Various numerical experiments are presented to demonstrate the accuracy and energy stability of the proposed numerical scheme**...	BAC	CAB	BCA	ABC	Selection 1
A: Indeed, it has been shown that the usual interleaving distance between persistence modules is NP-hard to compute in the multi-parameter case [4]B: One of the challenges of multi-parameter persistence is to provide a meaningful notion of distance between persistence modules which can be computed in a reasonab...	BCA	CBA	ABC	BAC	Selection 4
A: This is because whilst it is typical for structure learning algorithms to be assessed across different objective functions, varied sample sizes, and different hyper-parameters to convince readers about their validity, almost none of the algorithms published in the literature is tested for sensitivity to variable...	ABC	BAC	ACB	BCA	Selection 2
A: From the positive side, Luo [16] introduced the notion of rank-determining sets of metric graphs, and verified the existence of finite rank-determining sets constructively. Hladký, Král, and Norine [13] confirmed a conjecture of Baker [2] relating the ranks of a divisor on a graph and on a tropical curve, and p...	BAC	ABC	ACB	BCA	Selection 2
A: We apply the 9: 1 train-test split as CIFAR10-DVS.B: N-Caltech 101 The N-Caltech 101 [43] dataset is also converted from the original version of Caltech 101 [44] with a slight change in object classes to avoid confusionC: The N-Caltech 101 consists of 100 object classes plus one background class	ABC	CAB	ABC	BAC	Selection 2
A: We mitigate this risk by using diverse and reputable data sources, including academic papers (Qin et al., 2021; Cao et al., 2021) and an industrial database (SlowMist, 2023). B: The external threat to validity mainly lies in the subjects used in our studyC: The flash loan attacks we study might not be re...	CAB	BCA	ABC	BAC	Selection 1
A: Neither the European Union nor the granting authority can be held responsible for them.B: Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive AgencyC: This work received funding from Fo...	CBA	CAB	CAB	BCA	Selection 1
A: Besides, some diseases, drugs, or other medical entities may differ between languages, which causes some areas to be monolingual in the resultant bilingual word space. In this situation, given an English word, its Chinese neighbors have significantly greater distances than the ones in the previous two cases.*B...	ACB	ABC	BAC	CBA	Selection 4
A: (2017) The more recently proposed Vision transformers (ViTs)Dosovitskiy et alB: Transformers are a type of machine learning model characterized by the presence of self-attention layers and are commonly used in natural language processing (NLP) tasks.Vaswani et alC: (2020) aim to directly apply the transf...	BAC	ABC	CBA	ABC	Selection 1
A: Using the depth-first strategy, FuSeBMC attempts to cover the deeper goals firstB: This is beneficial since all preceding goals on the path to a deep goal can be ignored during subsequent fuzzing as the same test-case covers them. On the other hand, the ranking strategy allows for the prioritization of condi...	ABC	BCA	CAB	CBA	Selection 2
A: The following result gives the action of ℐμsuperscriptℐ𝜇\mathcal{I}^{\mu}caligraphic_I start_POSTSUPERSCRIPT italic_μ end_POSTSUPERSCRIPT on the JFP basis and is the foundation of the first algorithm to be presented in the following sectionB: The subsequent results in this section will inform the second alg...	BCA	ACB	CBA	ABC	Selection 1
A: propose a degree-normalized index based on paths of length three (L3)B: L3 is found to have a remarkable advantage compared with the indexes based on common neighbors in predicting protein-protein interactions [12]. The L3 index is defined as C: Kovács et al	BAC	CAB	BCA	ACB	Selection 3
A: red). The memory life cycle analysis in Figure 7 reflects the memory saving from in-place gradient update and operator fusion. B: By reordering operators, we can immediately apply the gradient update to a specific tensor (in-place update) before back-propagating to earlier layers, so that the gradient can be...	ACB	CAB	CBA	CAB	Selection 3
A: A=(ai⁢j)𝐴subscript𝑎𝑖𝑗A=(a_{ij})italic_A = ( italic_a start_POSTSUBSCRIPT italic_i italic_j end_POSTSUBSCRIPT ) is called an M𝑀Mitalic_M-matrix ifB: Then we denote \|A\|=(\|ai⁢j\|)𝐴subscript𝑎𝑖𝑗\|A\|=(\|a_{ij}\|)\| italic_A \| = ( \| italic_a start_POSTSUBSCRIPT italic_i italic_j end_POSTSUBSCRIPT \| )C: Let ...	CAB	ACB	CBA	BCA	Selection 3
A: We start by proving the following estimate for the probability to change a certain set of function values, which is analoguous to Lemma 11B: This result will not be sufficient for our purposesC: Since (given the proof of Lemma 11) its proof is very simple, we nevertheless show it to demonstrate the funda...	CBA	BCA	BAC	ABC	Selection 4
A: In order to apply two-level optimization to the regularized inverse problem (1.2), the coarse grid model function ψ𝜓\psiitalic_ψ given by (3.8) has to be computedB: For example, in discrete tomography, the projection matrix A𝐴Aitalic_A represents the incidence relation of projection rays and cells centere...	BCA	ACB	CAB	BCA	Selection 2
A: One aspect we did not consider here is learning neural networks with positive parameters using gradient descentB: Such a study could lead to further insights regarding methods that ensure that a neural network approximating a monotone function is indeed monotone. Finally, we did not deal with the generalizat...	BAC	BAC	BCA	ACB	Selection 4
A: Using DG methods, the inter-element continuity constraint of conforming FE methods is dropped and concatenations of arbitrary local polynomials with support in only one element can be used as test and trial functions. This property resembles the finite volume (FV) approach, in which the solution per element is a...	BAC	BCA	ACB	ACB	Selection 2
A: Let us first recall the proof for non-adaptive algorithms in (TZZ19, )B: After the first round of pulls, we set a threshold η𝜂\etaitalic_η and publish those arms who have been pulled more than η𝜂\etaitalic_η times in the first round; we call these arms the heavy armsC: By publishing an arm we mean reve...	ACB	CBA	CBA	ABC	Selection 4
A: (3.5)), any superlinear direction can be employed in the algorithmB: As a result, our theory provides a direct globalization strategy for works that employ quasi-Newton direction with only local convergence guarantees. For instance, it globalizes the recent work [45] which studies smooth and strongly convex ...	ACB	BAC	BCA	CBA	Selection 3
A: He introduced the author the Matlab function cat to perform the tensor concatenation operation. He also pointed out constructive comments on the proof of Theorem 3.B: After acceptance of the paper, the author found similar incremental algorithms for the computation of the t-SVD in [39, 40]. Thanks DrC: ...	BCA	ABC	CAB	ABC	Selection 3
A: This is the case of the works in [28] and [31], where the idea is to learn predictive clustering trees by using both labeled and unlabeled examplesB: In the more general context of semi-supervised structured output prediction, some approaches for multi-target regression also use predictive clustering trees*...	BCA	CBA	BAC	CAB	Selection 3
A: However, all models including DeepIPC have performance degradation in the evening. This means that doing inference in the low light condition is harder than in the normal conditionB: Table III shows that DeepIPC achieves the best performance by having the lowest total metric score in all conditions. Moreover...	ABC	BAC	ABC	CAB	Selection 2
A: On the other hand, in many scenarios, the treewidth-based methods on such graphs could be replaced by tree decompositions of bounded independence number. In particular, de Berg, Bodlaender, Kisfaludi-Bak, Marx, and van der Zanden use tree decompositions whose bags are covered by a small number of cliques, and th...	CBA	BAC	CAB	BCA	Selection 1
A: Both Split Learning and FedBCD utilize concatenated local embeddings as server model input. Notably, in VAFL and Split Learning, the clients only perform one step of local update based the partial gradients from the serverB: Particularly, in VAFL, the server aggregates local embeddings using their linear com...	CAB	CBA	ABC	BAC	Selection 4
A: For performing the three baselines, we regard the dynamic graphs as a series of static graphs by disregarding temporal informationB: DyGNN [39], DyRep [31], CTDNE [54], TGAT [41], Jodie [40], TGN [38] are six dynamic graph neural networks. We provide a brief introduction to these methods as follows:C: W...	CAB	BAC	CAB	BCA	Selection 4
A: During his Ph.D. study, he joined Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences. From 2018 to 2020, he worked as a Postdoctoral Fellow with PBC School of Finance, Tsinghua UniversityB: Chunshui Cao...	BAC	BCA	ACB	BCA	Selection 1
A: ME uses the maximum of sample means to estimate the ground truth maximal expected value (MEV), while DPAV takes the partial average over the maximum and minimum of sample meansB: DE uses the minimum of sample means to estimate the ground truth in the worst case, however, the DPAV estimator mitigates this bia...	CAB	BAC	BCA	ACB	Selection 4
A: The results show that there is a significant and direct relationship between noun and intentionB: By conditioning the action-level prediction framework through the intention, it is shown that the performance in terms of noun prediction is improved. As our intention label is obtained, in this work, as the ‘sc...	ABC	ABC	BCA	BAC	Selection 3
A: The Proposed ApproachB: Based on the above motivation and insights, this paper proposes a novel Calibrated One-class classification-based Unsupervised Time series Anomaly detection method (COUTA for short)C: The approach fulfills contamination-tolerant, anomaly-informed normality learning by two novel n...	BCA	ABC	CAB	CAB	Selection 2
A: In comparison to copy-based methods for handling rare entities, delexicalization has shown to yield better results in constrained datasets (Shimorina and Gardent, 2018). B: These placeholder tokens are later replaced with tokens copied from the input data instance (Lebret et al., 2016)C: Delexicalization...	CBA	CAB	ABC	ACB	Selection 1
A: As mentioned above, the labels corrected by NICE [33] are not always accurateB: We propose a novel multi-teacher knowledge distillation strategy for effective model training that enables the model to learn from the unbiased trade-off fusion of two teachers. The structures of both the two teacher models and ...	ACB	CAB	ABC	CBA	Selection 1
A: State 0’ is the state where there are two branches on the blockchain, one is the original public chain, and the other is the attacker’s previous private chain. The value on each arrow indicates the possibility of state transition.B: Zero lead is separated into states 0 and 0’. State 0 means there is no branc...	CBA	BCA	CAB	ACB	Selection 1
A: The PostLN Transformer training fails late in the warmmup period. B: Right: Same as left, but with a PostLN Transformer. In both cases the preconditioned curvature closely tracks the 38/η38𝜂38/\eta38 / italic_η bound during warmup, however there is a noticeable gap at the smaller batch sizeC: Figure 7:...	CAB	BAC	ABC	CBA	Selection 4
A: The functional form of the bias depends on the enhanced sampling method used 16, 50, 51, 52, 12, 15B: The bias potential can be static 16 or adaptively constructed on the fly during the simulation 50, 51, 52, 12, 15. Regardless of how the bias potential is constructed, it leads to a biased CV distribution th...	BCA	ABC	ABC	BAC	Selection 1
A: To remove a pseudo-trifurcation, either the trifurcation point can be moved up (the initial bifurcation point is chosen as the trifurcation point) or moved down (the second bifurcation point is chosen). The choice does not impact the branching structure of the final tree but does impact how it occupies space, i...	BAC	BCA	ABC	CBA	Selection 3
A: Moreover, they suggest that the expressiveness of the GNN model must be still sufficient for applying it to larger graphs. [15] further elaborate from the perspective frequency response, pointing out that spectral graph convolution is inherently transferable for graphs with similar degree distribution.B: [14...	CAB	ABC	CAB	CBA	Selection 4
A: (2020) shows that contrastive learning provably recovers the latent embedding under the restrictive Block MDP setting (Du et al., 2019a). In contrast, our work analyzes contrastive learning in RL under the more general low-rank setting, which includes Block MDP as a special case (Agarwal et al., 2020) for both M...	BAC	BAC	CBA	BCA	Selection 3
A: The multilevel DLMC algorithm could be optimized for determining the optimal parameters τ𝜏\tauitalic_τ and θ𝜃\thetaitalic_θ (Haji-Ali et al., 2016b) or integrating a continuation MLMC algorithm (Collier et al., 2015) (see Remark 4). The present analysis could be extended to numerically address non-Lipschitz ra...	ACB	CBA	ACB	CAB	Selection 2
A: The spacecraft uses onboard batch-sequential filtering to estimate the asteroid’s properties while rapidly approaching it from hundreds of kilometers of distanceB: The end of the mission consists of the spacecraft orbiting the asteroid in tight orbits, using a robust orbital station-keeping control [30].*C...	CAB	BCA	ABC	ABC	Selection 2
A: Our analysis leverages the Thompson part metric on the manifold of positive definite matrices to model convergence of the fixed-point iterationB: To our knowledge, this is the first work that analyzes the computation of Brascamp–Lieb constants via Thompson geometry. We note that a similar Finslerian lens can...	CBA	ACB	ABC	CAB	Selection 3
A: The Betti number provides a numerical measure of the number of independent holes of dimension k𝑘kitalic_k in the complex. B: This captures the intrinsic topological features of the complex at dimension k𝑘kitalic_k, independent of the specific choices of representatives for these cyclesC: In other words...	ABC	CBA	ACB	ABC	Selection 2
A: In this paper, we aim to tackle the semi-supervised domain generalization (SSDG) taskB: To address this issue, we first explore the theory of multi-domain learning to generate more accurate pseudo-labels for unlabeled samples. Then, we propose to utilize a multi-task learning framework to mitigate the impac...	ACB	BCA	CBA	CBA	Selection 1
A: Extensive experiments on classification and dense prediction tasks show it can achieve performance comparable to full fine-tuning with much fewer parameters. We find Conv-Adapter might fail on tasks with large domain shifts and subject to feature quality determined by pre-training. Future work includes more expl...	CAB	ABC	BAC	CBA	Selection 1
A: This approach facilitates the visualization of PDEs solutions as distinct entities based on the locations of changepoints. Typically, the locations of these changepoints are not predetermined. The methodology’s effectiveness in accurately identifying the locations of changepoints and providing precise solution e...	BCA	BCA	CAB	ABC	Selection 3
A: The current work tries to address this deficit; in concrete terms, our goals are as follows:B: Indeed, while many of the referenced works above at least verbally express the notion that in many inverse problems, we “know more” about the parameters in some parts of the domain than in others, we have not found...	ACB	ACB	CAB	CBA	Selection 4
A: For example, the book of Judith might be decorated with a number of different depictions of the core narreme of the book, the beheading of Holofernes, which point to different traditions of representing this story in time and space. An analogous question exists for the textual content of our corpus, predicting p...	ABC	BCA	CAB	ABC	Selection 3
A: Table XV: Results (%) of cross-task prompt transfer on BERT-smallB: Notably, positive transfers are in green and “Avg.” denotes the average performance of all target tasks. Numbers in the subscript indicate relative improvements of PanDa compared to the vanilla PoT.C: The red-colored row shows the res...	ACB	CBA	BAC	CBA	Selection 1
A: However, much like the Russia-Ukraine scale, the effect lasted for only a few weeks. The unusual peaks against Brazil happened in late June (also for DDoS attacks, see Section §5), without a clear explanation. B: The US is consistently the largest target but only accounts for 21.97% on that day. During the l...	CAB	ACB	CBA	ACB	Selection 3
A: This efficiency in attack methodology is a critical advantage in practical adversarial settingsB: Instead, HET provides a systematic and predictive approach to identifying vulnerabilities in the victim without sending any samples to the victim first. C: It underscores a reduced risk for the attacker, as ...	BAC	CAB	ACB	BCA	Selection 3
A: In the case of the Loschmidt Echo test, the model predictions are zero with high probability. On using the SWAP test, the model predictions fluctuate around zero (due to shot noise)B: Figure 2: Schematic of effect of exponential concentration and shot noise on training and generalization performance. For th...	ACB	CAB	BAC	BCA	Selection 3
A: Furthermore, their validation data of each class is highly unbalancedB: Although our RGB+3DN only uses the original video data collected by cameras and does not require additional information, we still outperform some methods, e.g., GoogleNet + LSTM and VIT.C: As Table 3 illustrates, Simonyan et al.’s t...	ACB	BCA	BAC	ABC	Selection 2
A: II00\displaystyle 011\displaystyle 11Uncertainty Score (GCJ)Norm.Imit.Obf. IObfB: II00\displaystyle 011\displaystyle 11Uncertainty Score (GH)Abuhamad et al.Caliskan et al.Original Figure 6. Anonymization performance (uncertainty score) in theC: Norm.Imit.Obf. IObf	ACB	BCA	CAB	ACB	Selection 2
A: We conduct extensive experiments on four multi-task few-shot datasetsB: Our contributions are summarized as follows:C: The results show that SoftCPT outperforms CoOp by 0.73%, 5.09%, 3.63% and 2.80% on four datasets, which hint multi-task prompts are beneficial	CAB	ACB	BAC	CBA	Selection 2
A: This stark divergence vividly underscores how the underlying content intricately molds the expressive nuances in artistic representation. B: Consider the profound contrast between the drawing styles employed in a close-up portrayal of a human figure and those used to depict a vast, distant mountain vista*C...	BCA	ABC	CBA	ACB	Selection 3
A: It is well-known from stochastic optimal control that, in many applications of practical interest, the underlying controls may be of bang-bang type, which typically lead to discontinuities in the optimal control policies and the possibility of nonunique optimal controls in some regions of the state space. In tur...	CBA	CAB	BAC	CBA	Selection 2
A: As discussed before, if the sum of the columns of A𝐴Aitalic_A is less or equal than 1, the assignment is univalent, i.eB: each channel is assigned to maximum one tenant. In the process of preallocation, we relax this assumption, andC: Regarding the assignment matrix A𝐴Aitalic_A, we can use the very sam...	BAC	BCA	CBA	ACB	Selection 2
A: More recently, He et al. [67] combined GNN, LSTM and VAE to model spatial and temporal dependency for anomaly detection of a complex cloud system. Transparently, these models mainly focus on addressing challenges related to high-dimensionality, missing labels, and interpretability. In addition, based on the robu...	CBA	BAC	CAB	ACB	Selection 1
A: More specifically, we find our algorithm successfully decorrelates the latent representation by assuming a diagonal diffusion matrix. This in fact provides a promising way for dynamics-based disentanglement in representation learning 14B: There are a variety of additional avenues for such a dynamics-based ap...	CBA	BAC	CBA	CAB	Selection 2
A: Different ways have been shown in which the robotic motion models and the communications channel models can interact with each other within the formulation of CaTP problems. Finally, we have provided a brief application-oriented classification of different CaTP problems and other related problems. B: We have...	BCA	CAB	CBA	CAB	Selection 3
A: Two examples of such deep models are variational autoencoders (VAEs; Kingma and Welling, 2014; Rezende et al., 2014) or deep SBNs (Neal, 1992; Hinton et al., 2006). Standard Gaussian VAEs have been treated by Damm et alB: (2023) using earlier versions of the here investigated properties that are specific to ...	BAC	BAC	BCA	CAB	Selection 3
A: This section is devoted to the proofs of Theorem 2, Theorem 4, and Theorem 3B: We begin in Section 4.1 by analysing the problem of counting k𝑘kitalic_k-matchings in somewhere dense host graphs, and proving Theorem 2; this is the most technical partC: We then move on to prove Theorem 4 and Theorem 3 in ...	ABC	BAC	BAC	ACB	Selection 1
A: However, they only focus on the accuracy regarding one kind of user behavior, ignoring the relations between multiple user behaviors and the unfairness caused by popularity bias. Different from MACR [12] and PDA [3], we propose utilizing multiple user behaviors to measure item quality, and mitigate the unfairnes...	ACB	ACB	CBA	ABC	Selection 4
A: Subsequently, we increase the feature dimension from 512 to 1792, and the performance improvement is only evidenced in the SCID →→\rightarrow→ SIQAD setting, accompanied by an increase in model complexity. A significant performance drop can be observed when the dimension is increased to 2048, revealing a larger ...	ABC	CBA	CAB	ABC	Selection 2
A: To give an example, among strictly heterophilous graphs in which nodes never connect to nodes of the same class, there can be those where edges are drawn between particular pairs of classes (Figure 1(a)) and those where the class of a node cannot be derived from the class of its neighbor (Figure 1(b))B: Howe...	BCA	BAC	BAC	ACB	Selection 4
A: The research of M.W. was funded by the Austrian Science Fund (FWF)B: National Research Agency (ANR). C.RC: acknowledges the support of the Munich Center for Quantum Sciences and Technology, as well as the Humboldt Foundation. C.R. would like to thank Amanda Young for fruitful discussion on the applicatio...	BAC	CAB	BCA	BAC	Selection 2
A: Specifically, the attacker tries to modify the root certificate or the AP certificate to bypass the processor authentication in the challenge-response phaseB: However, since the attacker cannot modify the hash value of the root certificate stored in the bootROM, CPU0 detects the root-certificate manipulation...	ACB	ACB	BCA	CBA	Selection 3
A: The following technical lemma is needed in the proof of Theorem 20B: It mimics [van der Schaft, 2017, Prop. 3.2.16] and establishes asymptotic stability for an open-loop system through dissipativity.C: In this subsection, we establish feedback asymptotic stability via dissipativity with dynamic supply ra...	ACB	BCA	CAB	ABC	Selection 2
A: To avoid the tight condition for the coefficient, we should design a state-feedback law whose value is massive, namely diverge in general, at the boundary of the subset so that the effect of the law overcomes the disturbance term. Moreover, a functional ensuring the (almost sure) invariance of the subset probabl...	CBA	CAB	ABC	BCA	Selection 2
A: The work we present is heavily based on the notion of legibility, as such we proceed to present the most relevant works that show the effects of applying this notion to intelligent agentsB: We start by exploring the applications and effects of legibility in robotics, since it was the area where the notion w...	ABC	CBA	BCA	CAB	Selection 1
A: x𝑥xitalic_x). All random variables take values in some alphabets that are in calligraphic letters (e.g. 𝒳𝒳\mathcal{X}caligraphic_X). We shall restrict our attention to finite alphabets only.B: Random variables are in capital case (e.gC: X𝑋Xitalic_X), and their realization are in lower case (e.g	ABC	CAB	ABC	ABC	Selection 2
A: For instance, our Example 1 and simulation results showed that a capacity ratio around 17.8%percent17.8\bf 17.8\%bold_17.8 % or 21.4%percent21.4\bf 21.4\%bold_21.4 % can already increase the stability margin significantly (depending on the employed GFM methods). Future work can include how to configure GFM conve...	ACB	ACB	CBA	BCA	Selection 3
A: Our model resulted in better prediction intervals at all forecasting horizons for all the series. B: DeepTVARwT vs other deep learning based modelsC: Compared with DeepAR and DeepState, our model produced more accurate point forecasts at almost all forecasting horizons for all the series (except h=4ℎ4h=4...	BCA	BCA	BAC	CAB	Selection 4
A: The separation between the known and unknown also increases largely at the early stage but continues to improve even later in training. These observations show that the known and unknown class representations are separated as the model makes their Jacobian norm different. Still, the Jacobian norm is not the only...	CAB	CBA	BAC	BCA	Selection 1
A: Researchers implement such attacks by maximizing the non-concave loss function of a substitute model trained for the same task with, for example, one step [15], or iterative steps [22] of gradient ascent optimization. However, prior work has shown the generated adversarial examples trend to overfit the substitut...	ABC	CBA	BCA	BAC	Selection 2

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