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A: [58] decouple the KLD into two uncorrelated losses and combine them by weighted summation.B: [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 alC: Response-based KD methods [19, 58, 3] have the n...	BCA	ABC	ABC	CBA	Selection 4
A: The aliasing error is quite substantial, but since all energy in the theorem above is confined in highest possible harmonics, in practice one can expect to have milder discrepancy.B: The proof can be found in Appendix AC: More results on aliasing for composition with smooth functions can be found in [Be...	CAB	CBA	ABC	CBA	Selection 1
A: Specifically, Hinton et alB: proposed to distill the logits (before sotfmax layer) from teacher to student by minimizing the KL divergence, where a temperature factor is applied to soften the logits.C: The seminal work [19] introduced the idea of knowledge distillation	ACB	BCA	CBA	CBA	Selection 2
A: Second, ENS-t-SNE offers a powerful tool to perform comparison tasks on interesting subspaces, something that is typically done with small multiple plots (which do not support the full range of comparison tasks) [16]. We illustrate this by example in Section 4.2, with a dataset used in several subspace clusterin...	CAB	CBA	ABC	BCA	Selection 1
A: (2016) establishes such moment equations based on structural assumptions on the filtering of such predictive states. Similarly, Anandkumar et al. (2012); Jin et al. (2020a) establishes a sequence of observation operators and recovers the trajectory density via such observation operators.B: In particular, Hef...	BAC	CAB	CBA	BAC	Selection 3
A: Without any coverage assumption on the offline data, the number of data needed to find a near-optimal policy can be exponentially large (Buckman et al., 2020; Zanette, 2021). To circumvent this problem, a few existing works study offline RL under a uniform coverage assumption, which requires the concentrability ...	ABC	CBA	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...	ABC	ABC	BCA	CBA	Selection 4
A: The analysis of discrete inf-sup conditions for general hexahedral meshes remains an open problemB: The present paper suffers from the same rather severe restrictions on hexahedral meshes in 3D as in previous workC: Another open problem is the analysis of isoparametric generalized Taylor-Hood families i...	CBA	ABC	BAC	ABC	Selection 3
A: There is lack of efficient methods that can extract information from astronomical surveys to classify galaxies and creating large amount of annotated data is expensiveB: To evaluate the performance of WaveMix on domain specific datasets where data availability is low, we choose the task of galaxy morphology...	CBA	ACB	CAB	BAC	Selection 4
A: This allows to construct a cascade feedback, as done in [48]B: MoreC: controls p,q,r𝑝𝑞𝑟p,q,ritalic_p , italic_q , italic_r from δl,δm,δnsubscript𝛿𝑙subscript𝛿𝑚subscript𝛿𝑛\delta_{l},\delta_{m},\delta_{n}italic_δ start_POSTSUBSCRIPT italic_l end_POSTSUBSCRIPT , italic_δ start_POSTSUBSCRIPT italic_m...	BCA	ABC	ABC	CAB	Selection 1
A: MathBERT peng2021mathbert and Tangent-CFT mansouri2019tangent, both combined with Approach0 zhong2019structural, are the current state-of-the-art for non-wildcard formula retrieval, however, MathBERT does not explicitly account for SLTs. They claim that LaTeX codes account for SLTs to some extent but not OPTs, a...	CAB	CBA	ACB	BAC	Selection 3
A: The basal species are separated between blocks 6666 and 7777 depending on whether or not they are preyed on by species from the first two blocks.B: In the 7777 blocks structure, the species of block 1111 (represented on 4444 of the 6666 networks) prey on species from all other blocks with the exception of bl...	BCA	ABC	CBA	BCA	Selection 3
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.	BAC	ACB	BAC	ABC	Selection 4
A: We used batch size 2048 for CIFAR10 experiments and 1024 for MNIST experimentsB: We used an initial learning rate of 0.01 with cosine annealing learning rate for 300 epochs on PU CIFAR10 and 200 epochs for PU MNIST.C: Contrastive training is done using LARS optimizer (You et al., , 2019), temperature se...	ABC	ACB	CBA	BCA	Selection 4
A: In fact, one of the foundational tensor decomposition papers by Carroll and Chang (1970), although not a multilayer network, was a study of multilayer relational dataB: Multilayer networks have been studied since as far back as 1939 (Roethlisberger and Dickson, 1939), and they have been mathematically repre...	CBA	CBA	BAC	ABC	Selection 3
A: Experiments below used the 11-qubit trapped ion quantum computer described by Johri et alB: A crucial point to bear in mind with quantum computing is that the memory capacityC: johri2021nearest , and where necessary, the larger IonQ Aria machine with a capacity of 32 physical and 20 algorithmic qubits (i...	CAB	ACB	BAC	CBA	Selection 2
A: We therefore incorporate node features into the SC filtering operation by concatenating it with the random signal before the filtering operation B: However, independent from graph structure, node features can provide extra information that is valuable for a clustering task (Bianchi, Grattarola, and Alippi 20...	BCA	ACB	CBA	ACB	Selection 3
A: The sequential interaction between subpopulations and learners leads to complex nonlinear dynamics: i.eB: Despite the apparent simplicity of independent update rules, the evolution of subpopulations and learners is highly coupledC: multiplicative weights over non-stationary risks (due to learner updates)...	BCA	CAB	ACB	BAC	Selection 4
A: In contrast, we quantify the fairness and accuracy of a given classifier, which is not necessarily optimal, based on limited aggregate information. B: We note that Menon and Williamson (2018) provide a method for calculating the degradation in accuracy, as measured by the cost-sensitive risk, that results fr...	BAC	BCA	BCA	CAB	Selection 4
A: Notably, no Range Motif discovery algorithm was published to-date. There are four different formal MD definitions for Motif Sets we are aware of.B: Overview of state-of-the-art Pair Motif and Motif Set discovery definitions and implementations, given a motif length l𝑙litalic_l and TS of length n𝑛nitalic_n*...	ABC	BAC	BCA	CBA	Selection 4
A: In reality, many learning tasks process very large datasets, and thus decentralized parallel processing of data by communicating and computing units in the network is necessary, see e.gB: [23]-[24] and references thereinC: Besides, if the data contains sensitive private information (e.g. medical and soc...	CAB	BAC	CBA	ABC	Selection 4
A: When instead solving the least-squares problem for the Anderson mixing is the most expensive step of AAR, one may solve the least-squares problem approximately to reduce the computational cost of the least-squares solver. Our theoretical results open a new path to efficiently apply AAR to solve problems in addit...	ABC	BCA	CAB	CBA	Selection 3
A: We use the established parameters for the BART-large architecture and the implementation provided by Hugging Face [33]. All the models are fine-tuned for 100,000 steps with a learning rate of 3×10-5 and batch size 4, with early stopping on the validation setB: We use PyTorch version 1.10 and Hugging Face ver...	CAB	ACB	CBA	BCA	Selection 4
A: While functionally equivalent (e.g. [25, 26]), shuttling can impact circuit execution speed and fidelityB: Recent experiments with neutral-atom devices (e.g. [24]) have spurred interest in scalable compilation methods that exploit qubit shuttling to reduce reliance on SWAP gatesC: Trade-off analyses sugg...	ABC	BAC	CAB	CBA	Selection 2
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...	CBA	ABC	CAB	ACB	Selection 2
A: The free boundary of the compact support moves outward with a finite speed, known as the property of finite speed propagation [71]B: As a consequence, numerical simulations of the PME are often difficult by using Eulerian methods, which may fail to capture the movement of the free boundary and suffer from nu...	BAC	BCA	CAB	CAB	Selection 2
A: Following the same strategy of reducing the study of multi-parameter persistence modules to the study of families of one-dimensional persistence modules, we introduce the notions of 𝔉𝔉\mathfrak{F}fraktur_F-projected barcodes and 𝔉𝔉\mathfrak{F}fraktur_F-integral sheaf metric (see equations (6.1) and (6.2))**B...	ABC	CBA	BAC	ACB	Selection 3
A: This may be because Tabu undoes some of the incorrect arbitrarily orientated arcs as it escapes local maxima towards the end of the learning process. One would expect hybrid algorithms such as H2PC and MMHC, which utilise the HC algorithm in their score-based learning phase to be affected by variable ordering al...	ABC	BCA	CBA	CAB	Selection 3
A: Now we turn to the proof of the main result of the paper, and show that approximating the rank of a graph divisor within reasonable bounds is hardB: First, we show that the Minimum Target Set Selection problem reduces to computing the distance of a divisor on an auxiliary undirected graph from a recurrent st...	ACB	BAC	ABC	BAC	Selection 1
A: Dropout (DP) [51] rate is set to 0.5 in accordance with the original networkB: For the DVS128 dataset, we utilize the same network structure and hyper-parameters as the [30] and add the TCJA module before the last two pooling layersC: We add a 1-D average pooling voting layer in the last layer, which yi...	BCA	ABC	ABC	BAC	Selection 4
A: Ω=(0,12)2Ωsuperscript0122\Omega=\bigl{(}0,\frac{1}{2}\bigr{)}^{2}roman_Ω = ( 0 , divide start_ARG 1 end_ARG start_ARG 2 end_ARG ) start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPTB: Since all four corners of ΩΩ\Omegaroman_Ω are π/2𝜋2\pi/2italic_π / 2, we have μ=(1,1,1,1)𝜇1111\mu=(1,1,1,1)italic_μ = ( 1 , 1 , 1 ,...	BCA	BCA	ABC	CBA	Selection 3
A: Otherwise, it indicates inaccurate transition functions of this action or/and its predecessorsB: If the estimation is accurate, this indicates that the transition functions of the action at the index k𝑘kitalic_k of 𝐂𝐂\mathbf{C}bold_C and its predecessors are accurate; so the procedure breaks the loop and ...	BAC	ACB	CBA	BCA	Selection 1
A: The first term in the bound of Theorem 9 is the KDE errorB: The second term in the bound is due to the PCA error, as discussed after Lemma 8. This result demonstrates the potential in our approach, which accounts for structure in the parametric space. C: Note that, compared to the KDE error in Theorem 6,...	CAB	ACB	CAB	BAC	Selection 2
A: Even though stomach_ache and stomach_cramp do not match the exact meaning of the query, both terms have similar contents. Those misspelled and similar contextual words may contribute to subsequent consumer-oriented health applications such as information retrieval or query expansion [26]B: We provided the r...	CBA	BAC	ABC	ACB	Selection 2
A: The data that support the findings of this study are openly available. The AG’s news corpus dataset was obtained from RefB: Data generation details for the molecular dynamics of alanine dipeptide are provided in Sec. IV.2 and the trajectory is available at figshare.com/articles/dataset/Black-box_models_for_T...	ACB	BCA	CAB	CBA	Selection 1
A: Figure 3(c) features the resulting goals tree for the instrumented code from Figure 3(b) (with GOAL_0 representing the entry point of the program, i.e., the main function). Note that FuSeBMC builds it based on the original Clang AST without analyzing the code for trivially unreachable goalsB: However, this w...	CAB	BCA	ACB	BAC	Selection 3
A: For these problems the sum space method is preferable to the JFP method because it converges at a similar rate but with lower complexity (because, by comparison, the JFP method always leads to a lower banded system for fractional order problems) and without the need to compute integration matrices in high precis...	CAB	ABC	BCA	CBA	Selection 1
A: Supplementary Figure S28: A real case that has achieved the mapping from the index value to the score. (𝐚)𝐚\bf{(a)}( bold_a ) The distribution of index values obtainedB: This network is “56e9e0d7a6d70217090cdffa” in the data set.C: (𝐛)𝐛\bf{(b)}( bold_b ) The distribution of scores obtained by using t...	ABC	CAB	ACB	CBA	Selection 3
A: We pre-trained the models on ImageNet [22] and perform post-training quantization [34]B: The quantized models are fine-tuned on downstream datasets to evaluate the transfer learning capacity. We perform the training and memory/latency measurement on a microcontroller STM32F746 (320KB SRAM, 1MB Flash) using a...	ABC	BCA	ACB	BCA	Selection 1
A: Let A=(ai⁢j)𝐴subscript𝑎𝑖𝑗A=(a_{ij})italic_A = ( italic_a start_POSTSUBSCRIPT italic_i italic_j end_POSTSUBSCRIPT ) and N={1,2,…,n}𝑁12…𝑛N=\{1,2,\ldots,n\}italic_N = { 1 , 2 , … , italic_n }B: A=(ai⁢j)𝐴subscript𝑎𝑖𝑗A=(a_{ij})italic_A = ( italic_a start_POSTSUBSCRIPT italic_i italic_j end_POSTSUBSCRIPT...	BCA	BCA	CBA	ACB	Selection 4
A: From the definitions of the different mutation operators, it is clear that they have different probabilities to create an offspring identical to the parentB: For all experiments, we report the runtime in terms of the number of fitness evaluations until the optimum is foundC: Since this will have an influ...	BCA	CBA	BAC	ABC	Selection 3
A: [BL89, NT02, AMS08]B: The commonly adopted interior point geometry is based on Hessian metrics generated by self-concordant barrier functions, due to the provable optimality in connection with Newton-like second-order optimization [NN94], see e.gC: [TP21] for recent related work. Closer to our work is th...	ACB	CBA	BCA	ABC	Selection 4
A: We are unaware of previous work studying the interpolation problem for monotone data sets using monotone networksB: There is extensive research regarding the size and depth needed for general data sets and networks to achieve interpolation [49, 7, 13, 45] starting with the seminal work of Baum [4]C: Kno...	CBA	BCA	BAC	ABC	Selection 4
A: We do so to underline that it is generally considered ‘unfair’ to compare discontinuous Galerkin and conforming finite elements on the same grid, since DG usually has many more degrees of freedomB: Thus, the spatial grid of the discontinuous Galerkin method is significantly coarser than the conforming finit...	BCA	ABC	BCA	BAC	Selection 4
A: That is, their algorithm takes a confidence parameter δ𝛿\deltaitalic_δ (instead of a time horizon T𝑇Titalic_T) as an input, and try to use the smallest possible number of time steps to identify the best arm with probability (1−δ)1𝛿(1-\delta)( 1 - italic_δ )B: The authors of (RVK22, ) studied BAI and regr...	CAB	ACB	ACB	BAC	Selection 4
A: To attain a superlinear convergence rate, the IQN method [45] has integrated quasi-Newton directions with incremental updates, albeit with only local convergence guaranteesB: It is noteworthy that the aforementioned algorithms are applicable in (strongly) convex cases. However, within the nonconvex nonsmooth...	ABC	CAB	BCA	ACB	Selection 4
A: The linear scaling of Algorithm 4 compared to the truncated t-SVD is visible. In all our experiments, the truncated t-SVD provided better accuracy but still quite close to the accuracy achieved by of the proposed algorithm. B: Then to examine the speed-up of our algorithm, we used the truncated t-SVD with th...	CAB	ACB	BAC	BAC	Selection 1
A: More specifically, we investigate whether the smoothness assumption (and, indirectly, since they are not independent, the low-density and the manifold assumptions) holds in the MLC and HMLC contextsB: Intuitively, better identification of the distribution of examples in the descriptive space (as performed in...	ABC	ACB	CBA	BCA	Selection 4
A: Similar to Ishihara et al. [26] and Chitta et alB: Then, the controller is equipped with two decision-makers that predict waypoints and navigational controls to consider different aspects of driving. For a comparative study, we use AIM-MT as a baseline in justifying the performance of DeepIPC. The objective ...	CBA	CAB	ACB	ABC	Selection 3
A: We reduce from the 3333-Coloring problemB: Recall that the task of 3333-Coloring is to decide whether a graph G𝐺Gitalic_G admits a proper 3333-coloring, that is, its vertices can be colored by three colors in such a way that adjacent vertices receive distinct colorsC: Equivalently,	ACB	ABC	CAB	BAC	Selection 2
A: We provide convergence analysis for VIMADMM and prove that it can converge to stationary points with mild assumptions. With modifications of communication strategies and updating rules for servers and clients, we extend VIMADMM to the without model splitting setting and introduce VIMADMM-J.B: Compared to gra...	BAC	ABC	BCA	CBA	Selection 4
A: We review the related work in Section II and introduce the details of the proposed method in Section IIIB: The rest of this paper is organized as followsC: The results of experimental evaluation are reported in Section IV, followed by conclusion and future work in Section V.	BAC	ABC	BCA	ACB	Selection 1
A: Each person has view variations but only has the normal walking condition (NM). (b) Cross-cloth sub-dataset. Each identity has walking in different coats condition (CL) but only has limited views (only front views).B: (a) Cross-view sub-datasetC: Figure 2: The composition of a real cloth-changing benchm...	CAB	ACB	CBA	CAB	Selection 3
A: The dialogue policy module makes a dialogue decision given the current state (Zhang et al., 2019)B: Early methods are rule-based (Chen et al., 2017)C: Since handcrafted rules are non-extensible and resource-consuming (Zhao et al., 2021), deep reinforcement learning (DRL) has become a mainstream method f...	ABC	BCA	BAC	ACB	Selection 1
A: Then, we review several works for hierarchical extraction and generative models.B: In the following section, we discuss the most relevant research in that fieldC: Long-Term Anticipation (LTA) has been a fundamental challenge in the computer vision research community	CBA	ABC	ACB	BAC	Selection 1
A: FigB: 5 presents a base time series sub-sequence 𝒔𝒔\bm{s}bold_italic_s and corresponding native anomaly examples generated via these six data perturbation operations within ΩΩ\Omegaroman_Ω.C: These six perturbation functions can simulate abnormal behaviors in time series data	ACB	ABC	BCA	CBA	Selection 3
A: It is worth noting that all seq2seq models discussed below operate at the word level. Models operating at the character level (Goyal et al., 2016; Agarwal and Dymetman, 2017; Roberti et al., 2019) have shown reasonable efficacy with the added computational savings from forgoing the preprocessing steps of delexi...	ACB	CBA	BAC	ABC	Selection 1
A: At the same time, we compared the most classic method (i.e., TDE [29]) and current the state-of-the-art method (i.e., NICE-v1 [33]) of model-agnostic unbiased SGG to this dataset in TABLE IV. In addition, we visualized the performance comparison of R@100 across all predicates and the distribution of R@100 and mR...	BCA	ACB	CAB	BCA	Selection 3
A: Though we proposed some measures to address the concern of miners working on the attacker’s private branch, we take the possibility into consideration that they cannot allay the concerns of all minersB: Not all the miners in the blockchain system are rationalC: Thus, we discuss the fraction of rational m...	CAB	ACB	BAC	CBA	Selection 3
A: 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 sizeB: The PostLN Transformer training fails late in the warmmup period. C: Figure 7:...	BCA	BAC	CAB	CBA	Selection 1
A: acknowledge the support of Purdue Startup Funding. Calculations were performed on the Opton cluster at the Institute of Physics, NCU.B: M.C. and T.K.GC: J.R. acknowledges funding from the Polish Science Foundation (START), the National Science Center in Poland (Sonata 2021/43/D/ST4/00920, ‘‘Statistical ...	ABC	CBA	CAB	ABC	Selection 2
A: This is likely because they are on or near a boundary between two subdomainsB: After the use of the assignation algorithm has been exhausted, there may be nodes that are still not assigned to subdomainsC: Assign any given unassigned node to the modal subdomain of its immediate neighbours, i.e. those with...	ABC	CBA	BAC	ACB	Selection 3
A: We do not include results from Scalable-RouteNet due to the use of different hardware. (4) Fig. 5 reports an ablation study towards the decision of adopting NALU instead of MLP in proposed model when extrapolating towards different size of graph.B: (3) Table IV reports mean inference speed on a selection of...	ACB	CAB	ABC	CBA	Selection 2
A: This section provides the analysis of the transition kernel recovery via contrastive learning and the proofs of the main results for single-agent MDPs and zero-sum MGs. Our theoretical analysis integrates contrastive self-supervised learning for transition recovery and low-rank MDPs in a unified mannerB: In ...	BCA	CAB	ACB	ABC	Selection 3
A: In such cases, model reduction techniques (Hartmann et al., 2016, 2015) or solving the minimization problem (1) using stochastic gradient methods (Hartmann et al., 2017) may help. We do not focus on building efficient methods to solve the control PDE in this work.B: Using such a method to solve (14) in highe...	CBA	CAB	BCA	ACB	Selection 1
A: In the Deep Space 1 mission, utilizing the RAD6000 operating at 33 MHz, it was reported that the AutoNav system employed a streamlined version of navigation processing during its flybys near the small bodies, achieving completion within a brief timeframe of 10 to 15 seconds [36]B: The integration of silhouet...	ABC	BCA	CBA	CBA	Selection 2
A: SS acknowledges support from an NSF-CAREER award (1846088). B: Part of this work was done while MW visited the Simons Institute for the Theory of Computing in Berkeley, CA, supported by a Simons-Berkeley Research FellowshipC: This project was started during a visit of MW to MIT, supported by an Amazon Re...	CAB	ABC	CBA	BCA	Selection 3
A: A filtration is a nested sequence of subcomplexes obtained by progressively removing simplices exceeding a certain weight thresholdB: ≥\geq≥ order) leads to different filtration types. Intuitively, as the weight threshold increases, simplices with lower importance (based on the weight function) are removed....	ABC	ABC	ACB	CBA	Selection 3
A: Comparison between the typical semi-supervised learning (SSL) and the semi-supervised domain generalization (SSDG)B: Note that different colors denote different domains. In the SSDG setting, there are multiple training domains with different data distributions when compared with SSL.C: Figure 1	ABC	ABC	BCA	CBA	Selection 3
A: To find the optimal hyper-parameters of Conv-Adapter (and baseline methods), we conduct a grid search of the learning rate, weight decay, and compression factor γ𝛾\gammaitalic_γ for each dataset using the validation data split from training data for both benchmarks. For VTAB-1k, we use the recommended optimal d...	ACB	CBA	BAC	BCA	Selection 2
A: We have three main contributions: (i) We introduce a novel strategy that leverages PINNs alongside the Total Variation method for detecting changepoints within PDE dynamics. This approach not only identifies the timing of changes but also facilitates the estimation of unknown system parametersB: In this wor...	ACB	BAC	ACB	CAB	Selection 2
A: As a first example of where we believe that information densities could be used, we consider the regularization of inverse problemsB: In a large number of practical applications, one regularizes inverse problems by adding a penalty term to the misfit function for the purpose of penalizing undesirable aspect...	CBA	CAB	ABC	BCA	Selection 3
A: In it, each circle represents an imageB: One can select and combine embeddings based on the images, the labels, and the description of the images. C: The Image Point Cloud (Figure 4)is an entry point to the labeling process by showing two-dimensional representations of the images of the selected manuscri...	BAC	CAB	ABC	BCA	Selection 4
A: Additionally, we show that our PanDa consistently improves over vanilla PoT by 2.3% average score across all tasks and models, and makes the prompt-tuning achieve competitive and even better performance than full-parameter model-tuning in various PLM scales scenarios.B: Large-scale experiments are conducted ...	ACB	CBA	ACB	BAC	Selection 2
A: Categories of generic domains (e.g., .net, .com) are identified by direct visits (via Russian IP relays) or querying Internet archives if they are downB: We use categories linked with targets by default; when unavailable, we rely on root domains e.g., .tv and .gov are likely news and government sitesC: ...	CBA	BAC	CAB	BCA	Selection 2
A: The comparative performance of HET across different attack algorithms and architecture combinations for the X-Ray and Road Sign datasetsB: Table 2C: Columns categorize the various attack algorithms employed, while rows detail the architecture pairings, with surrogate models (F0subscript𝐹0F_{0}italic_F ...	ABC	CAB	ACB	BAC	Selection 4
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...	CBA	BCA	BAC	CBA	Selection 3
A: The 3D CNNs we employ in this work are initially designed for recognising general human behaviours and trained on human behaviours datasets such as Kinetics-400 and Kinetics-600. These datasets are formed by video clips with relatively high frame rates (25 fps) [3]B: Therefore, in order to efficiently extra...	ABC	BCA	BCA	CBA	Selection 1
A: Norm.Imit.Obf. IObfB: II00\displaystyle 011\displaystyle 11Uncertainty Score (GH)Abuhamad et al.Caliskan et al.Original Figure 4. Anonymization performance (uncertainty score) in theC: II00\displaystyle 011\displaystyle 11Uncertainty Score (GCJ)Norm.Imit.Obf. IObf	ABC	BAC	ACB	CAB	Selection 3
A: For few-shot classification, to reduce the variance, the scores over three trials with different seeds are averaged like CoOpB: For model evaluation, the test set performances are reported for 1, 2, 4, 8, 16 shotsC: For General-10, the same evaluation metrics to CLIP are used. For all tasks of Plant-6, ...	CAB	BAC	ABC	CAB	Selection 2
A: This section is dedicated to presenting a refined causal model, tailored to this paradigm.B: LCS is a new paradigm in MSDA, enriching the field with elevated variability and versatilityC: Within this innovative framework, we’re presented with an opportunity to delve into more intricate models	BAC	CAB	ACB	ACB	Selection 2
A: There is a wide range of approaches to constructing monotone FEMs; see for instance [ciarlet1973maximum, baba1981conservation, MIZUKAMI1985181, xu1999monotone, burman2002nonlinear]B: The discretization considered here is based on the one from [JensenSmears2013] for degenerate fully nonlinear HJB equations, w...	ABC	BCA	CBA	ACB	Selection 2
A: The relation of the preallocated and finally allocated channel sets is depicted in Fig. 1.B: The method proposed in [16] overcomes this problem by applying a preallocation of channels. Preallocation means that before the combinatorial auction, a non-exclusive assignment of channels to tenants is performed (...	CBA	CBA	CBA	CAB	Selection 4
A: For instance, the studies [33, 32, 35] use static or learnable statistics to adaptively normalize the newly arrived input for stationarization and then denormalize the output for better predictability.B: Some methods [30, 31, 32, 29, 33, 34, 35] strategically cater to future data via adapting their models wi...	CBA	ACB	ACB	BAC	Selection 1
A: This subtle adjustment bestows the capability to learn unique representations for a wide spectrum of systems with diverse probability distributions. We believe that this approach, solely centered on constraining latent dynamics, provides a more natural means of latent space regularization. Especially given that ...	CBA	CAB	BCA	BAC	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...	BAC	CBA	ABC	ABC	Selection 2
A: Salakhutdinov et al., 2007; Lee et al., 2007). Even if we assume that distributions of the model variables are in the exponential family, showing generalized parameterization criteria will likely require some effort, however. B: The latter models would include standard or sparse Boltzmann machines (e.gC:...	ABC	BAC	CBA	CAB	Selection 3
A: We then move on to prove Theorem 4 and Theorem 3 in Section 4.2.B: 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: This section is devoted to the proofs of Theorem 2, Theorem 4, a...	ACB	ABC	CBA	BCA	Selection 3
A: We tune the adversarial parameter λ𝜆\lambdaitalic_λ in the range of {0.01,0.1,…,100}0.010.1…100\{0.01,0.1,...,100\}{ 0.01 , 0.1 , … , 100 }.B: We use ESMM as its user-item matching model and take the popularity as the sensitive feature of itemsC: - AdFair [21] uses an adversarial training framework to ...	CAB	CBA	CAB	BAC	Selection 2
A: The top two results are emphasized in boldface. From Table VI, we can observe that our method achieves the best overall performance on the SIQAD dataset in terms of both PLCC and RMSE. The SRCC results are also comparable with the best method WaDIQaM-NR (0.8818 v.s. 0.8890), revealing that the high generalizatio...	ACB	BCA	CAB	BCA	Selection 3
A: Then, we argue that heterophilous graphs may have very different structural patterns and propose a new property called label informativeness (LILI\mathrm{LI}roman_LI) that allows one to distinguish themB: Similarly to adjusted homophily, this measure satisfies important properties and thus can be used to com...	CAB	BCA	ACB	BAC	Selection 3
A: A Boolean analogue was known [DMoP19] and has led to interesting applications to learning theory [EI22]. Here we formulate and conjecture a quantum analogue of Bohnenblust–Hille inequality and explain why it is useful to learning problems in the quantum setting. B: The result of [EI22] uses the so-called Boh...	ABC	CAB	ABC	CBA	Selection 2
A: An alternative way to construct the implementation is to use the built-in functionality of code generation in Isabelle/HOL (codegen, ; codegen2, ), which synthesizes functional executable code (e.g., Scala, Haskell, and ML) that inherits the correctness assurance from the verified protocol specification yet is h...	CAB	BAC	ABC	BCA	Selection 4
A: The first is adopted from [Lanzon and Bhowmick, 2023], which provides a class of negative imaginary systems characterised by an LTI auxiliary system and a dynamic supply rateB: The example is paraphrased in terms of Definition 2. C: Several motivating examples are provided in this subsection	CAB	CAB	BAC	BCA	Selection 4
A: Prajna et al. [9] provides a safety verification procedure, and then it is developed to control design procedure by Santoyo et al. [10]. Wisniewski and Bujorianu [11] also discuss in detail safety in an infinite time-horizon named p𝑝pitalic_p-stabilityB: Bai et al. [14] analyzes a probability for a trajecto...	BCA	BAC	CBA	BAC	Selection 1
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: Then, we move to works that have brought the use of legibility beyond robotics and to more general agent applications....	CBA	CBA	ACB	CBA	Selection 3
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	CBA	CAB	ABC	BCA	Selection 2
A: Hence, gSCR can be considered as a system-level metric that reflects the power grid strength in a multi-converter system, thereby extending the concept of SCR which can only be used in a single-converter-infinite-bus system. The gSCR mathematically reflects the weighted connectivity of the power network (i.e., p...	BCA	ABC	CAB	ACB	Selection 1
A: This intermittent behaviour of demand calls for the relaxation of the Gaussian assumption to accommodate discrete data. It might be possible to generalise the digitised Gaussian ARMA model of [16] to the multivariate case.B: The model that has been explored so far rely on the assumption of GaussianityC:...	CBA	ABC	CBA	CAB	Selection 4
A: Finally, the sixth block analyzes the margin, which improves the effectiveness of the loss-based unknown class detector by resolving the prototype misalignment issue. B: The third block in a row (‘one out’) of Table 3 along with the model-j compares each component by removing one of them out, verifying the e...	CAB	BAC	BAC	CBA	Selection 1

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