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A: Response-based KD methods [19, 58, 3] have the natural property of hiding models. Hinton et alB: [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: [58] decouple the KLD into two uncorrelated ...	CBA	ACB	ABC	ACB	Selection 3
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: Bands are fixed by the choice of architecture and training data. Then, the natural direction is to define neural operator with fixed numbe...	CAB	CAB	BAC	ACB	Selection 4
A: In terms of COCOStuff10k, since some methods do not support this dataset, we re-implement them and the result is presented on Table A.2.1 Comparison on COCOStuff10kB: We found that our method is competitive and it outperforms the comparison methods.C: For the experiments of semantic segmentation, we hav...	ACB	BCA	CBA	ACB	Selection 2
A: It is known that for many comparative tasks it is desirable to have as little change as possible while still being faithful to the dataB: Although we cannot directly quantitatively compare to t-SNE (or other similar dimension reduction embeddings), we can measure how similar a set of projections areC: Th...	BCA	BAC	ACB	CBA	Selection 2
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	CAB	BAC	Selection 3
A: See, e.g., Antos et alB: (2007); Munos and Szepesvári (2008a); Chen and Jiang (2019) and the references therein.C: 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 t...	ABC	CBA	CAB	BCA	Selection 4
A: Mou2020linear further showed the asymptotic covariance of constant-stepsize SGD with Ployak-Ruppert averaging. Liang2019Statistical designed a moment-adjusted SGD method and provided non-asymptotic results that characterize the statistical distribution as the batch size of each step tends to infinity. Chen2020St...	CBA	ABC	BCA	ABC	Selection 1
A: In Appendix A known results on the continuous LBB condition are recalled and commented. Appendix B contains helpful relations used throughout the paper.B: The rest of the paper is organized as followsC: In Section 2 the technique of T𝑇Titalic_T-coercivity is discussed, which provides important auxiliar...	CAB	ACB	CBA	ACB	Selection 1
A: Images were resized to 256×256256256256\times 256256 × 256 for Places-365 and 224×224224224224\times 224224 × 224 for iNAT-mini and ImageNet-1k datasetsB: Only horizontal flip was used as data augmentation for semantic segmentation. No data augmentations were used for classification unless mentioned other-wi...	BCA	BAC	ACB	ACB	Selection 1
A: So R¯[K]⊂R[K]subscript¯𝑅delimited-[]Ksubscript𝑅delimited-[]K\bar{R}_{\rm[K]}\subset R_{\rm[K]}over¯ start_ARG italic_R end_ARG start_POSTSUBSCRIPT [ roman_K ] end_POSTSUBSCRIPT ⊂ italic_R start_POSTSUBSCRIPT [ roman_K ] end_POSTSUBSCRIPT. We prove in the same way theB: If b¯k,i=∑ℓ=1rγk,ℓ⁢bℓ,isubscript¯𝑏𝑘...	ABC	CAB	CBA	ABC	Selection 3
A: Language models that transfer knowledge learned from auxiliary tasks rival models based on explicit graph representation of problem textB: As a powerful alternative to encoding explicit relations through graphs, other work kim2020point; qin2021neural; liang2021mwp relies on pre-trained transformer-based mod...	CBA	ACB	ABC	BCA	Selection 3
A: The mathematical definition of the dissimilarity measure and details on the recursive clustering algorithm are given in Appendix A. B: A split is validated if it increases the score of Equation (12)C: We then use 2222-medoids clustering to split the sub-collection of networks based on the dissimilarity m...	BAC	CAB	CBA	BAC	Selection 3
A: (right) Distributions of absolute percentage errors (in %) of all data points in the dataset. B: (left) Predictions of rotation angle vs. the ground truth (normalized to [−1,1]11[-1,1][ - 1 , 1 ]) in test setC: Figure 5: Performance of FactorNets for individual rotation learning	BAC	ABC	BCA	CBA	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	CBA	BCA	ABC	Selection 3
A: This deflated model is a layer redundant NNTuck and can be interpreted by considering that all layers of the network are different realizations of the exact same SBMB: In this sense, a multilayer network with this multilayer SBM does not need to be represented as a multilayer network. However, see Taylor et ...	ACB	BAC	ABC	CBA	Selection 1
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...	ACB	CAB	ACB	BAC	Selection 4
A: Table 1: Node classification accuracy. Results marked with ††\dagger†, ∗∗ and ▽▽\triangledown▽ are obtained from Pei et alB: (2020); Zhu et al. (2020); Lingam et alC*: (2021) respectively. Statistically significant results are underlined based on paired T-test of p<0.01𝑝0.01p<0.01italic_p < 0.01.	BAC	ABC	ACB	CBA	Selection 2
A: The second observation is that the basis for the updates is the average loss, i.eB: riskC: This motivates the following definition: given participation α𝛼\alphaitalic_α and parameters ΘΘ\Thetaroman_Θ, the average risk experienced by each subpopulation i𝑖iitalic_i and each	ACB	BCA	BAC	ABC	Selection 4
A: In contrast, in Section 9.1.3, we discuss and demonstrate phenomena that can occur when minimizing the discrepancy for binary classification over distributions in which all or almost all of the probability mass is concentrated on only two sub-populations. While the bounds remain valid in this regime, we demonstr...	BCA	BCA	ABC	CAB	Selection 4
A: Finally, the set with minimal extent is returned (in red). B: Figure 4 illustrates the idea of the algorithm and the steps involved in computing a 3333-MotifletC: We iteratively perform two steps for each subsequence q𝑞qitalic_q: (1) search for the (k−1)𝑘1(k-1)( italic_k - 1 )-NN of q𝑞qitalic_q, and (...	ABC	ABC	BCA	CAB	Selection 4
A: [28]-[29] and [45]-[46]) all require that the regression matrices and graphs satisfy some special statistical properties, such as i.i.d., spatio-temporal independence or stationary, etcB: However, these special statistical assumptions are difficult to be satisfied if the regression matrices are generated by ...	CBA	BAC	BCA	BAC	Selection 3
A: However, physics-driven guidelines are difficult (and often impossible) to determine because of the lack of structure in the fixed-point operator. In this situation, general guidelines (not necessarily physics driven) to judiciously reduce the accuracy in solving the least-squares problem are needed. B: The ...	ACB	CBA	ACB	BCA	Selection 2
A: Even though this procedure requires some additional effort, it allows us to effectively train our models on a topic-controllable setup. This dataset is used to fine-tune all the aforementioned methods.B: Therefore, the model learns to distinguish the most important sentences for the corresponding topic durin...	CBA	BAC	ABC	ABC	Selection 1
A: The multiplication circuit computes the product in a register that is disjoint from the multiplicand registers A𝐴Aitalic_A and B𝐵Bitalic_BB: The multiplier consists of: 1) a Toffoli step and 2) n−1𝑛1n-1italic_n - 1 Ctrl-Add (controlled addition) steps. Some of the control signals necessary for the Ctrl-Ad...	CBA	CBA	BCA	ABC	Selection 3
A: Our deep learning model also performs the task considerably faster than simple biophysical models. To generate our data, we rely on MRiLab [7] which is a conventional MR image simulator. Source code is publicly available at https://github.com/Abhijeet8901/Deep-Learning-Based-MR-Image-Re-parameterization. B: ...	CBA	CAB	BCA	ACB	Selection 1
A: In principle, the proposed numerical framework is independent of the choice of neural network architecturesB: However, different neural network architectures may lead to different numerical performances, arising from a balance of approximation (representation power), optimization, and generalizationC: I...	ACB	CBA	CAB	ABC	Selection 4
A: We study the properties of these two metrics. B: In this section, we make use of the structure of γ𝛾\gammaitalic_γ-sheaves to construct metrics which are efficiently computable for sublevel sets persistence modules by relying on software dedicated to one-parameter persistence modules and recent advances on ...	CAB	BCA	ABC	BCA	Selection 1
A: This study investigates structure learning from discrete dataB: However, these findings are likely to be relevant to the algorithms we studied when learning from continuous dataC: If a score-equivalent objective function is used, then this too would involve making arbitrary arc orientations of the kind w...	ABC	BAC	BAC	CBA	Selection 1
A: Besides the distance of a divisor from a non-halting state, its distance from a recurrent state also plays a central role in our investigations, defined asB: A divisor f𝑓fitalic_f is called recurrent if there is a non-trivial chip-firing game starting from f𝑓fitalic_f that leads back to f𝑓fitalic_fC:...	BCA	CBA	BAC	CAB	Selection 4
A: Although we train the network in TCJA using a layer-by-layer pattern, the convolutional structure of the network still benefits it when applied in a step-by-step mannerB: Previous discussion reveals that TCJA reaches its peak performance when the convolutional kernel size is set to 2. Under this circumstance...	ACB	BCA	CBA	ABC	Selection 1
A: 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 , 1 )B: GivenC: 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...	ACB	CBA	ACB	BCA	Selection 4
A: between different action candidatesB: FlashFind automatically collects storage read/write information during the execution of these functions and infers the Read-After-Write (RAW) dependencies101010This RAW dependency information is also employed in FlashSyn’s initial data collection to expand the range of d...	BAC	BCA	CAB	ACB	Selection 1
A: Thus, we can train the VariBAD policy on both the sampled training environments, and also on dream environments. We refer to this method as VariBAD Dream. In our implementation, we train the KDE and VariBAD components simultaneously. The full implementation details and pseudo code are in Section A.10 of the supp...	ACB	CAB	CBA	BCA	Selection 3
A: The labeling strategy was as follows: If a neighbor word has the same semantic meaning as the query, it will be labeled as a relevant item, otherwise a non-relevant item. Three annotators were responsible for the labeling procedure. All annotators had research experiences in health informatics and are Chinese-En...	CBA	ACB	CAB	BAC	Selection 3
A: The input data was passed through several layers of neurons and finally a VAMP-2 score was calculated by merging results from outputs of both lobes. The neural network model parameters were tuned in successive iterations that maximize the VAMP-2 score. In this way, a markov state model at a specific lagtime τ𝜏\...	ACB	CBA	BCA	ACB	Selection 2
A: Secondly, the target program is repeatedly executed with the randomly mutated input. If the target program does not reach any new states after multiple input mutation rounds, a new byte is added to or removed from the input stream, and the mutation process restartsB: The above algorithm continues until an in...	ABC	CBA	CAB	BCA	Selection 4
A: We then focus on the properties (Section 4) and computation (Section 5) of fractional integration operators and matrices acting on the JFP basisB: The following is an outline of the paper: We introduce the basic constituents of the JFP method in Sections 2 and 3 (matrix representations of operators on quasim...	BCA	CAB	CBA	BAC	Selection 4
A: Sample2 uses imbalanced positive and negative samples. Specifically, sample2 randomly removes 20% of L𝐿Litalic_L links as the missing links. In the training step, the positive set is composed of 80% of the removed links (16% of L𝐿Litalic_L links), and the negative set is composed of 80% of all nonexistent link...	ACB	CAB	CBA	CBA	Selection 2
A: We follow [12] to use a set of vision datasets including Cars [39], CIFAR-10 [40], CIFAR-100 [40], CUB [68], Flowers [54], Food [9], and Pets [55]‡‡‡Pets uses CC BY-SA 4.0 license; Cars and ImageNet use the ImageNet license; others are not listed.B: We fine-tuned the modelsC: We measure the transfer lea...	BCA	BAC	CBA	ACB	Selection 1
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 ...	BCA	BAC	CBA	ABC	Selection 3
A: As a first step in this direction, we conducted a mathematical runtime analysis on the permutation-based LeadingOnes and Jump function classes, PLeadingOnes and PJumpB: While the PLeadingOnes analyses provided no greater difficulties and the results, Θ⁢(n3)Θsuperscript𝑛3\Theta(n^{3})roman_Θ ( italic_n start...	CAB	ABC	CAB	BCA	Selection 2
A: For example, in discrete tomography, the projection matrix A𝐴Aitalic_A represents the incidence relation of projection rays and cells centered at the grid points, and can be evaluated on every grid.B: Similar to the evaluation of the objective function at both levels, we assume that the operator A𝐴Aitalic_...	CBA	ABC	BCA	CAB	Selection 1
A: One may wonder whether this can be improvedB: It turns out, that up to the dependence on d𝑑ditalic_d, the size of our network is essentially optimal. We now prove Lemma 4.C: The network we’ve constructed in Theorem 21 uses (d+2)⁢n𝑑2𝑛(d+2)n( italic_d + 2 ) italic_n neurons to interpolate a monotone da...	CBA	BCA	ACB	CAB	Selection 2
A: In the affine setting, we make use of the fact that B: This is an immediate consequence of [37, Lem. 9.1] (in fact, it already appears in [9]), but since the proof is short, we present it for completenessC: The proof is carried out by induction with respect to the order of the multi-indices 𝛎∈ℱ𝛎ℱ{\bold...	ABC	CAB	BAC	BCA	Selection 2
A: If the time budget is small, then the number of heavy arms must be small. Consequently, the probability that the set of heavy arms contain the best arm is also small, because all arms are almost equally uncertain at the beginning of each round. In other words, the set of heavy arms would be an almost random subs...	BAC	CBA	BAC	CAB	Selection 2
A: Even though in Algorithm 1 quasi-Newton directions based on the residual mapping were suggested (cfB: 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 st...	BAC	ACB	BCA	CAB	Selection 2
A: The work [36], generalized the pass-efficient randomized algorithms proposed in [49] to tensors. These algorithms are very efficient when the number of passes over the data tensor is our main concern. For example in situations that the underlying data tensor is stored out of core and the communication cost may e...	ABC	CBA	ABC	CAB	Selection 2
A: To build an ensemble model for predicting structured output, an appropriate type of PCTs is utilized as a base model. For example, to build an ensemble for the HMLC task, PCTs for HMLC are used as base modelsB: An ensemble predicts a new example by considering predictions of all the ensemble’s base models. ...	BAC	CAB	ABC	CAB	Selection 3
A: 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 is to compare our model (that has a better data represe...	CAB	CBA	ACB	BCA	Selection 1
A: The main property of such graphs is that they enjoy the bounded local treewidth property. In other words, any planar graph of a small diameter has a small treewidthB: On the other hand, in many scenarios, the treewidth-based methods on such graphs could be replaced by tree decompositions of bounded independe...	ABC	BCA	BAC	ACB	Selection 4
A: Here we use τ=20,30,20,5𝜏2030205\tau=20,30,20,5italic_τ = 20 , 30 , 20 , 5 for the four datasets respectively. (3) The results under w/o model splitting setting demonstrates that VIMADMM-J incurs lower communication costs than FDML to achieve the same accuracy, due to faster convergence with multiple local upda...	CAB	BCA	CBA	ABC	Selection 1
A: As shown in Fig. 2, we construct the environmentB: Note that, since sampling which of neighbors to update is discrete, we could not optimize it through stochastic gradient descent based methods [42, 82]C: More importantly, the process of deciding whether neighbor nodes should be updated or retained can b...	BCA	BAC	ACB	CAB	Selection 4
A: e represent that re-sample an additional embedding from Gaussian distributions. Blue color indicates the comprehensive best result. ‡ means we abandon the residual connection. B: TABLE VII: Progressive Uncertainty Performance in our frameworkC: The results are reported in the rank-1 accuracy	ACB	CBA	CAB	BAC	Selection 3
A: The second row shows the averaged reward of each dialogue in the test dataset. The third row shows the influences of different initial λ𝜆\lambdaitalic_λ values and value search schemesB: The X-axis and Y-axis are the same as those of the top row. Each learning curve is averaged over 3 runs on the test datas...	BCA	CAB	ABC	ACB	Selection 1
A: Next we will describe the quantitative evaluation first for the H3M module and then for I-CVAE as stand-alone modelsB: Due to our limited computational resources, training was performed independently for each moduleC: As LTA-Ego4D Forecasting benchmark is private, the ground truth from the testing set i...	BAC	CBA	ACB	BCA	Selection 1
A: Normal data is expected to be enclosed in a compact hypersphere, and anomalies can be successfully identified if they are distant from the centerB: C: (a) Time series data with three anomaly segments; (b) Learned feature space of canonical (non-calibrated) one-class classification and the proposed metho...	ACB	BAC	CBA	BCA	Selection 4
A: al. (Jagfeld et al., 2018) note that as character-based models perform better on the E2E dataset while word-based models perform better on the more linguistically challenging WebNLG dataset, it is hard to draw conclusions on the framework most suited for generic D2T. In the sections that follow, we detail notabl...	CBA	BCA	ACB	CAB	Selection 1
A: By “noisy”, we mean that these samples break the two assumptions. After the NICE training, we can obtain a cleaner version of the SGG dataset. Specifically, we can: 1) increase the number of fine-grained predicates (common-prone); 2) decrease annotation inconsistency among similar visual patterns (synonym-random...	BCA	BAC	CAB	BCA	Selection 3
A: To counteract PSM-DoS attacks, we design an economic-based profit protection mechanism for rational miners. If the attacker fails to broadcast the promised secret/block timely, his deposit in a trusted third party could be forfeited by a rational miner. This mechanism also ensures that the secret can be calculat...	CBA	ACB	CAB	BAC	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: Figure 7: Learning rate warmup gradually reduces the preconditioned sharpness duri...	BCA	CAB	CAB	BAC	Selection 4
A: Our framework makes it possible to generate biased data sets that, given the construction of enhanced sampling methods, sample a larger conformational space than standard atomistic simulations and use such data to learn low-dimensional embeddingsB: If a data set entails many infrequent events, the low-dimens...	ABC	BAC	BCA	ACB	Selection 1
A: Starting from i=0𝑖0i=0italic_i = 0, check if the daughters of the i𝑖iitalic_i-th generation are in the non-connected tree, they should be added to the growing connected tree. The collection of connected daughters of the i𝑖iitalic_i-th generation forms the (i+1)𝑖1(i+1)( italic_i + 1 )-th generation. Repeat th...	BCA	CBA	ACB	CAB	Selection 4
A: Both [16] and [17] have studied on approximating simple arithmetic computation (+++, −--, ×\times×, and ///) when extrapolate towards drifted numerical values. In our proposed model, we utilize building blocks from [16] to encourage the model to learn the arithmetic impact, when tackling drifted numerical input ...	CBA	ACB	BAC	CAB	Selection 1
A: Based on Lemma 5.2, we further give the analysis of Theorem 4.2.B: We defer the proof to Appendix D.2C: The proof idea for Lemma 5.2 is nearly identical to the one for Lemma 5.1 with extending the action space from 𝒜𝒜\mathcal{A}caligraphic_A to 𝒜×ℬ𝒜ℬ\mathcal{A}\times\mathcal{B}caligraphic_A × caligr...	ACB	CBA	BAC	CAB	Selection 2
A: Future studies could include extending the importance sampling scheme to higher-dimensional problems, using model reduction techniques or stochastic gradient-based learning methods to solve the associated higher-dimensional stochastic optimal control problem (see Remark 2)B: The multilevel DLMC algorithm cou...	ACB	CBA	BAC	ABC	Selection 1
A: These findings emphasize that the primary bottleneck in this context remains the onboard shape reconstruction process.B: However, some abnormal trajectory instances are a source of concernC: Furthermore, we evaluate the impact of a larger 5% uncertainty in the asteroid’s shape and observe that the propo...	BAC	BCA	CBA	BAC	Selection 3
A: 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 be employed to understand other Picard iterations arising from problem (1.3)B: Our analysis leverages the Thompson part metric on the manifold of...	CAB	ABC	BAC	ACB	Selection 3
A: Computing the Spearman rank correlation coefficient [spearman] between the maximum centrality values of J5subscript𝐽5J_{5}italic_J start_POSTSUBSCRIPT 5 end_POSTSUBSCRIPT and the persistence values yields a value of 0.997B: Further bolstering this observation, we can consider persistence values as a ranking...	BAC	ABC	BCA	CBA	Selection 1
A: In this part, we will describe our multi-task learning frameworkB: Since there are multiple different domains in the SSDG task, we consider training each domain as an independent task (i.e., the local task for each domain), which can effectively reduce the interference between different domains during pseud...	CAB	ABC	CBA	BCA	Selection 2
A: When setting the kernel size larger to that of the residual blocks, i.e., 5 and 7 for ResNet50, the performance is further boosted, with more parameters introduced. B: One can observe that, for both ResNet50 and ConvNext-B, using smaller kernel size results in inferior performanceC: We show the performan...	CAB	ABC	BCA	CBA	Selection 4
A: There are many variations of PINNs, e.g., Physics-informed generative adversarial networks [14] which have stochastic differential equations induced generators to tackle very high dimensional problems; [15] rewrites PDEs as backward stochastic differential equations and designs the gradient of the solution as p...	BAC	ACB	BCA	BAC	Selection 2
A: Answering this question is notoriously difficult in inverse problems because, in general, the exact solution of the problem is unknown if only finitely many measurements are available and if regularization is usedB: The relevant question to ask is whether this mesh is better suited to the task than any othe...	ACB	BCA	CAB	BAC	Selection 4
A: It is also possible to see previous states of the graph and the embeddings using a slider. B: In order to avoid unnecessary computations, the update of the graph similarities and the UMAP embeddings is done asynchronously in the backgroundC: New labels are added in real-time to the dataset, which also up...	BCA	ACB	BCA	CBA	Selection 4
A: Some readers may concern that our work is similar to the prior PoT methods, i.e., SPoT [10]B: Clarification of noveltyC: Here, we depart from the SPoT and ours as follows: 1) different motivations: instead of verifying the effect of vanilla PoT on prompt-tuning, we aim to alleviate the knowledge forgett...	ACB	BAC	CBA	CBA	Selection 2
A: A flow of packets is considered to be an attack if any sensor observes at least five packets for the same victim IP or IP prefix, and the attack is deemed to last from the first packet until the last packet preceding 15 minutes without further packets. In 2022, the median number of honeypots contributing data wa...	CAB	BAC	CBA	BAC	Selection 1
A: These figures plot the transferability at k𝑘kitalic_k for a spectrum of k𝑘kitalic_k sizes, with the columns representing the victim architecture and the rows indicating the surrogate architecture utilized. B: We direct the reader’s attention to the results presented in Figures 4 and 5, which presents the p...	BAC	ACB	CAB	BAC	Selection 3
A: Although this trained model is independent of input data (as explained above), the model can still perform well on the training phase and achieve small training errors in the limit of small regularization. This is because the training output data is trivially ‘cooked’ into the model via the optimization process ...	BAC	CBA	BAC	CAB	Selection 4
A: Their method achieved 90.3% classification accuracy. Izquierdo et al. [12] considered lane change classification as an image recognition problem and utilised 2D CNNs. Their method achieved 86.9% classification and 84.4% prediction accuracyB: Laimona et al. [10] employed GoogleNet+LSTM to classify lane change...	BCA	ABC	ACB	BAC	Selection 1
A: II00\displaystyle 011\displaystyle 11Uncertainty Score (GH)Abuhamad et al.Caliskan et al.Original Figure 6. Anonymization performance (uncertainty score) in theB: Norm.Imit.Obf. IObfC: II00\displaystyle 011\displaystyle 11Uncertainty Score (GCJ)Norm.Imit.Obf. IObf	BCA	BAC	CAB	ACB	Selection 3
A: Therefore, it is necessary to investigate if introducing multi-task learning to prompt tuning of VLMs is feasible and effective.B: Vision-language models (VLMs) are recently adapted to few-shot image recognition tasks by optimizing prompt contextC: However, existing works of prompt tuning target on sing...	ACB	BCA	BAC	CAB	Selection 4
A: Detailed results can be found in Tables I-IIIB: The results by different methods on the resampled PACS are presented in Figure 5C: We can observe that as the increase of KL divergence of label distribution, the performances of MCDA, M3DA, LtC-MSDA and T-SVDNet, which are based on learning invariant repre...	BCA	BAC	CBA	CAB	Selection 2
A: As such, we will consider a finite element discretization of (9) that employs the method of artificial diffusion on strictly acute meshes [burman2002nonlinear, JensenSmears2013] to ensure nonnegativity of the approximations for the density. B: The proof of uniqueness of weak solutions of (9) uses the Compari...	CBA	CBA	CAB	BAC	Selection 3
A: We also have to note that the scenarios were defined in order to provide challenging allocation tasks for the algorithms (utility values are bound from above by 6/12/20 respectively), to be able to compare the efficiency of different methods and parameter sets in non-trivial tasks (if there is no resource scarci...	CAB	ABC	CBA	ABC	Selection 3
A: Specifically, we design a temporal Hawkes attention mechanism to represent temporal factors that estimate the temporal Gaussian and a gated attention mechanism to dynamically adapt the network structure of the Transformer-based encoder and decoderB: We propose to take advantage of transforming the temporal G...	CAB	BAC	CAB	BCA	Selection 4
A: To deal with this dilemma, a compelling approach is to integrate physics-based priors into the representation learning processB: Our inspiration here stems from physics, where even though different systems can and likely will have different probability distribution along the same low-dimensional projection z...	BCA	BCA	ACB	BAC	Selection 3
A: reflection, refraction, diffraction). However, this also increases the computational load.B: buildings, floor, walls) before arriving to the receiver’s antenna and requires a computational map of the area in which both the transmitter and the receiver operate. The accuracy of the ray tracing model increases ...	CAB	BAC	CBA	ACB	Selection 3
A: In fact, nothing changes for part AB: The proof is essentially the same as that of Lemma 1C: For part B, we only need the linearity of the integral, which also applies in the case of a general measure. So we can infer from the left equation in (118) that	BAC	CAB	BCA	CAB	Selection 1
A: For example, in the classic case of polynomial interpolation (where one has to infer the coefficients of a univariate polynomial given an oracle that evaluates it) the system corresponds to a Vandermonde matrix, which is nonsingular and thus invertible. In the case of linear combinations of homomorphism counts, ...	BAC	CAB	CBA	ABC	Selection 4
A: Firstly, we formulated two unfairness issues caused by popularity bias in multi-behavior recommendation, and inspected their underlying reasons from a causal viewB: In this work, we studied how to mitigate popularity bias in multi-behavior recommendationC: Thereafter, we proposed a MBD framework to alle...	CAB	CBA	ACB	BAC	Selection 4
A: It is worth noting that both GraphIQA and VCRNet are retrained on large-scale IQA datasets [84, 85] and ImageNet [86]. As such, it is not surprising that they demonstrate inferior performance with the cross-dataset setting (see Table II). In contrast, our method, which computes the statistics in the deep feature...	CBA	ACB	ABC	BAC	Selection 1
A: Node homophily satisfies maximal and minimal agreementB: Similarly to edge homophily, node homophily does not satisfy the asymptotic constant baseline and thus is incomparable across different datasets.C: It is empty class tolerant, but not monotone: adding an edge between two perfectly homophilous node...	ACB	BAC	CBA	ABC	Selection 1
A: Again, the proof can be found in the appendix. B: However, we still have the following weaker bound as consequence of Theorem 4.3C: Since the p𝑝pitalic_p-influences for different p𝑝pitalic_p do not coincide in the quantum setting, this version of Talagrand’s inequality does not imply a KKL bound	CBA	CAB	BCA	BAC	Selection 1
A: Another well-known study in the realm of operating systems is CertiKOS (DBLP:conf/pldi/CostanzoSG16, ), which presents a layered approach to verifying the correctness of an OS kernel with a mix of C and assembly code and establishes a proof of noninterferenceB: An extension of CertiKOS (DBLP:conf/osdi/GuSCWK...	ABC	CAB	ACB	ACB	Selection 1
A: Lyapunov and asymptotic stability analyses were performed for feedback interconnections of two dissipative systems satisfying dissipativity with respect to dynamic supply ratesB: In this paper, a general notion of dissipativity with dynamic supply rates was introduced for nonlinear systems, extending the not...	CBA	ACB	BAC	CAB	Selection 3
A: Bai et al. [14] analyzes a probability for a trajectory to reach a target set, which is a subset of a safe set. Nejati et al. [15] develop a compositional approach for constructing CBFs for stochastic hybrid systems, which forms an excellent theory in terms of applications because they use numerical methods such...	ABC	ACB	CBA	CAB	Selection 4
A: The comparison of our framework with other similar frameworks is an important aspect so we can understand what are the advantages our framework presentsB: We only compared with this framework because it is the only framework that proposes to solve the problem of legible decision making using stochastic envir...	CAB	ABC	BCA	ACB	Selection 4
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: X𝑋Xitalic_X), and their realization are in lower case (e.gC: Random variables are in capital case (e.g	ACB	CAB	CBA	ABC	Selection 3
A: Combining the power grid strength quantified by gSCR in this section and the analysis of the voltage source behaviors of GFM converters in Section II, it is once again emphasized that it is necessary to install GFM converters to provide effective voltage source behaviors and thus enhance the power grid strength,...	CBA	CAB	ACB	ABC	Selection 4
A: A simple approach to detrending a time series is to difference it until it appears to be stationary. This is effective when the trend is a low order polynomialB: The series after removing the trend in each component can then be analyzed by fitting a stationary model. Inference on model parameters will have t...	CBA	BCA	BAC	ACB	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...	ACB	BCA	CAB	ABC	Selection 3

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