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A: Another generating set which has become important in algorithms and applications in the last 10-15 years is the Leedham-Green and O’Brien standard generating set in the following called the LGO generating set. These generators are defined for all classical groups in odd characteristic in [11] and even characteri...	BAC	BCA	ACB	CBA	Selection 4
A: Some methods work even considering that the solution has low regularity [MR2801210, MR2753343, MR3225627, MR3177856, MR2861254] but are based on ideas that differ considerably from what we advocate hereB: Most convergent proofs either assume extra regularity or special properties of the coefficients [AHPV, M...	ABC	CAB	BCA	CBA	Selection 4
A: These coordinates are computed somehow and their true values can differ from their values stored in the computerB: Moreover, Alg-A is more stable than the alternatives. During the iterations of Alg-CM, the coordinates of three corners and two midpoints of a P-stable triangle (see Figure 37) are maintained**C...	BCA	BCA	BAC	CAB	Selection 3
A: It can be seen that although the credibility of some tweets are low (rumor-related), averaging still makes the CreditScore of Munich shooting higher than the average of news events (hence, close to a news). In addition, we show the feature analysis for ContainNews (percentage of URLs containing news websites) fo...	ACB	ACB	BAC	CAB	Selection 4
A: Under additional assumptions on the asymptotic convergence of update directions and gradient directions, they were able to relate the direction of gradient descent iterates on the factorized parameterization asymptotically to the maximum margin solution with unit nuclear normB: Unlike the case of squared los...	ABC	CAB	BAC	BCA	Selection 4
A: In this work, we present a deep analysis on the feature variants over 48 hours for the rumor detection taskB: We also derive explanations on the low performance of supposed-to-be-strong high-level features at early stage. The study also indicates that, there is still considerable room to improve the effecti...	BAC	BCA	BAC	ACB	Selection 4
A: ResultsB: The baseline and the best results of our 1s⁢tsuperscript1𝑠𝑡1^{st}1 start_POSTSUPERSCRIPT italic_s italic_t end_POSTSUPERSCRIPT stage event-type classification is shown in Table 3-topC: The accuracy for basic majority vote is high for imbalanced classes, yet it is lower at weighted F1. Our le...	CBA	ABC	BAC	BCA	Selection 2
A: Body weight, according to BMI, is normal for half of the patients, four are overweight and one is obese. The mean BMI value is 26.9B: Table 1 shows basic patient information. Half of the patients are female and ages range from 17 to 66, with a mean age of 41.8 yearsC: Only one of the patients suffers fr...	CBA	CBA	BAC	ACB	Selection 3
A: The theory suggests that early visual features must first be registered in parallel before serial shifts of overt attention combine them into unitary object-based representations. This two-stage account of visual processing has emphasized the role of stimulus properties for explaining human gaze. In consequence,...	ACB	BAC	BCA	CAB	Selection 3
A: This establishes a close relationship between these two problems (and their corresponding parameters), which lets us derive several upper and lower complexity bounds for LocB: We also discuss the approximation-preserving properties of our reductions, which shall be important later on.C: In this section,...	ACB	BAC	BCA	CBA	Selection 3
A: Similar issues have been reported in Babaeizadeh et al. (2017a), where the output of their baseline deterministic model was a blurred superposition of possible random object movements. As can be seen in Figure 11 in the Appendix, the stochastic model learns a reasonable behavior – samples potential opponents and...	CBA	BAC	BCA	BCA	Selection 1
A: These threshold values are determined in energy evaluations while the robot operates in the walking locomotion modeB: The cornerstone of our transition criterion combines energy consumption data with the geometric heights of the steps encounteredC: To analyze the energy dynamics during step negotiation ...	BAC	ABC	BCA	CBA	Selection 1
A: We refer the reader to a survey by Coffman et al. [14] and a brief introduction by Johnson [19] for details on bin packing and its applicationsB: Online bin packing finds applications in a broad range of practical problems, from server consolidation to cutting stock problemsC: Along with its practical si...	BAC	ABC	ABC	ACB	Selection 1
A: and highly parallelized nature of SS3 while the “rich and interactive visual information” one by its white-box nature. B: The “large-scale passive monitoring” aspect would be supported by the incremental313131Only one small vector, the confidence vector, needs to be stored for each userC: In that context...	CBA	CAB	BCA	ABC	Selection 1
A: Figure 3 shows the training curves under IID data distributionB: Table 1 shows the empirical results of different methods under IID data distributionC: We can observe that each method achieves comparable RCC. As for test accuracy, GMC and DGC (w/ mfm) exhibit comparable performance and outperform the ot...	ABC	ABC	CAB	BAC	Selection 4
A: operation.B: , where ∗∗ is the convolution333We use convolution instead of cross-correlation only as a matter of compatibility with previous literature and computational frameworksC*: Using cross-correlation would produce the same results and would not require flipping the kernels during visualization	ABC	ACB	CAB	BCA	Selection 3
A: To efficiently deploy UAVs, studies have been made to find out UAV distribution on network graph [9] and a graphical model has been proposed for channels reuse [17]B: With the rapid commercialization of UAVs, a lot of research has emerged in this field [16]C: The resource allocation of channel and time ...	CAB	CAB	BCA	BAC	Selection 4
A: Similarly, the axial centroid coordinates are defined as z^=<z¯>e^𝑧superscriptexpectation¯𝑧𝑒\widehat{z}=<\overline{z}>^{e}over^ start_ARG italic_z end_ARG = < over¯ start_ARG italic_z end_ARG > start_POSTSUPERSCRIPT italic_e end_POSTSUPERSCRIPTB: The vector of nodalC: radial coordinates of the nodes	BCA	ACB	CBA	BAC	Selection 1
A: Moreover, this setup allows for the precise computation of the optimal action value function. B: Its relatively small state space permits the Experience Replay (ER) buffer to store all possible state-action pairsC: The Gridworld problem (Figure 4) is a common RL benchmark	CBA	ABC	ABC	CAB	Selection 1
A: Figure 16 shows the number of these challenges for every year since 2007, and it can be seen that this number has been on the rise in the past few years.B: Grand Challenges in Biomedical Image Analysis (Challenge, 2020) provides a comprehensive but not exhaustive list of publicly available medical image segm...	BAC	CAB	BCA	CBA	Selection 4
A: (1) We enable the generation of neural networks with very few training examples. (2) The resulting network can be used as a warm start, is fully differentiable, and allows further end-to-end fine-tuningB: (3) The generated network can be easily integrated into any trainable pipeline (e.g., jointly with featu...	ACB	ABC	BCA	BAC	Selection 2
A: In comparison, we focus on policy-based reinforcement learning, which is significantly less studied in theory. In particular, compared with the work of Yang and Wang (2019b, a); Jin et al. (2019); Ayoub et al. (2020); Zhou et al. (2020), which focuses on value-based reinforcement learning, OPPO attains the same ...	CAB	CBA	BAC	CAB	Selection 2
A: (2016) proposed deep compression, which extends the work Han et alB: (2015) by a parameter quantization and parameter sharing step, followed by Huffman coding to exploit the non-uniform weight distribution. This approach yields a reduction in memory footprint by a factor of 35–49 and, consequently, a reducti...	BAC	BCA	ACB	BAC	Selection 2
A: In [80, Theorem 8.10], ZB: The proof we give below exploits the hyperconvexity properties of L∞⁢(X)superscript𝐿𝑋L^{\infty}(X)italic_L start_POSTSUPERSCRIPT ∞ end_POSTSUPERSCRIPT ( italic_X ) and also our isomophism theorem, Theorem 1. Given our main results, we can give a more concise proof. See [29, Secti...	CBA	CAB	ACB	CBA	Selection 3
A: Having established trust in the visualization, the users then proceed to identify and investigate the visible patterns from the projected dataB: One of the most common analytical tasks in any DR-based workflow is, for example, to identify clusters of similar points [16], with the goal of detecting patterns i...	CAB	ABC	CBA	CBA	Selection 2
A: Later, we identify the most influential algorithms over the rest, based on the behavior of the algorithms. B: First, we are going to study the similarities between the results of the classifications following each taxonomyC: We now proceed by critically examining the reviewed literature as per the differ...	CAB	BAC	CBA	ACB	Selection 3
A: GAEs proposed in [20, 29, 22] intend to reconstruct the adjacency via decoder while GAEs developed in [21] attempt to reconstruct the contentB: The difference is which extra mechanism (such as attention, adversarial learning, graph sharpness, etc.) is used.C: To apply graph convolution on unsupervised le...	CAB	BAC	CAB	BCA	Selection 4
A: Between the other two, DNS test has a slightly higher applicability than IPID test, which shows that globally sequential IPID is less supported nowB: In Figure 11 we similarly see that the fraction of spoofable networks that can be fonud through IPID and PMTUD is higher than when measured with the other meth...	BCA	CBA	BAC	CAB	Selection 1
A: Sensor drift in industrial processes is one such use case. For example, sensing gases in the environment is mostly tasked to metal oxide-based sensors, chosen for their low cost and ease of use [1, 2]B: An array of sensors with variable selectivities, coupled with a pattern recognition algorithm, readily re...	BCA	BCA	ABC	CBA	Selection 3
A: For semigroups, on the other hand, such results do existB: In fact, the free product of two automaton semigroups S𝑆Sitalic_S and T𝑇Titalic_T is always at least very close to being an automaton semigroup: adjoining an identity to S⋆T⋆𝑆𝑇S\star Titalic_S ⋆ italic_TC: However, there do not seem to be con...	CAB	BCA	BAC	CAB	Selection 2
A: Fig. A4 shows %percent\%% CPIG for different variants of HINT trained on human attention-based cues, whereas Fig. A5 shows the metric for different variants of SCR trained on textual explanation-based cues. We observe that HINT and SCR trained on relevant regions have the lowest %percent\%% CPIG values (70.24% a...	ACB	CAB	BCA	BAC	Selection 2
A: We believe that the PrivaSeer Corpus will help advance research techniques to automate the extraction of salient details from privacy policiesB: PrivBERT will help improve results on various tasks in the privacy domain and help build stable and reliable privacy preserving technologyC: This should benefi...	BAC	ABC	BAC	BAC	Selection 2
A: The Ca attribute, for example, has a range of 0–3, but by selection we can see five points with Ca values of ‘4’, see Figure 3(b). These values can be considered as unknown and should be further examinedB: One of those is rather large which affects negatively the prediction accuracy of our classification (s...	BAC	ABC	ABC	ACB	Selection 4
A: In Experiment I: Text Classification, we use FewRel [Han et al., 2018] and Amazon [He and McAuley, 2016]B: FewRel is a relation classification dataset with 65/5/10 tasks for meta-training/meta-validation/meta-testing.C: They are datasets for 5-way 5-shot classification, which means 5 classes are randoml...	CAB	BCA	ACB	CAB	Selection 3
A: In this paper, we consider a dynamic mission-driven UAV network with UAV-to-UAV mmWave communications, wherein multiple transmitting UAVs (t-UAVs) simultaneously transmit to a receiving UAV (r-UAV). In such a scenario, we focus on inter-UAV communications in UAV networks, and the UAV-to-ground communications are...	ABC	BAC	CBA	CAB	Selection 1
A: This will be bootstrapped to the multi-color case in later sectionsB: WeC: Note that the 1111-color case with the completeness requirement is not very interesting, and also not useful for the general case: completeness states that every node on the left must be connected, via the unique edge relation, to...	BAC	ACB	BCA	BCA	Selection 2
A: Thus, their analysis is not directly applicable to our setting. We defer the detailed discussion on the approximation analysis to §B. Proposition 3.1 allows us to convert the TD dynamics over the finite-dimensional parameter space to its counterpart over the infinite-dimensional Wasserstein space, where the infi...	CAB	BCA	ABC	BAC	Selection 1
A: (2019); Xu et alB: (2020b) suggest using a large batch size which may lead to improved performance, we only used a batch size of 25⁢k25𝑘25k25 italic_k target tokens (through gradient accumulation of small batches) to fairly compare with previous work Vaswani et al. (2017); Xu et al. (2020a).C: Though Z...	ACB	ACB	BCA	CAB	Selection 3
A: We finally prove that X𝑋Xitalic_X is the limit in 𝐏𝐫𝐞𝐒𝐩𝐞𝐜𝐏𝐫𝐞𝐒𝐩𝐞𝐜\mathbf{PreSpec}bold_PreSpecB: In particular, the maps gisubscript𝑔𝑖g_{i}italic_g start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPTC: Consider {gi:Y→Xi}i∈Isubscriptconditional-setsubscript𝑔𝑖→𝑌subscript𝑋𝑖𝑖𝐼\{g_{i}\colon Y...	CBA	BAC	ACB	CBA	Selection 3
A: The local-global associate ordinal distortion estimation network considers different scales of distortion features, jointly reasoning the local distortion context and global distortion contextB: Also, the devised distortion-aware perception layer boosts the feature extraction of different degrees of distorti...	CBA	ACB	CAB	BCA	Selection 4
A: We don’t use training tricks such as warm-up [7]B: SNGM achieves the best performance for almost all batch size settings.C: We adopt the linear learning rate decay strategy as default in the Transformers framework. Table 5 shows the test accuracy results of the methods with different batch sizes	CAB	ACB	ABC	CBA	Selection 2
A: The black-box model is motivated by data-driven applications where specific knowledge of the distribution is unknown but we have the ability to sample or simulate from the distribution. To our knowledge, radius minimization has not been previously considered in the two-stage stochastic paradigmB: Most prior ...	ABC	BCA	CBA	BAC	Selection 2
A: IIB: The sequence of random digraphs is conditionally balanced, and the weighted adjacency matrices are not required to have special statistical properties such as independency with identical distribution, Markovian switching, or stationarity, etc. The edge weights are also not required to be nonnegative at...	ACB	CBA	CBA	ABC	Selection 1
A: For instance, suppose that we add another QI attribute of gender as shown in Figure 4, the mutual cover strategy first divides the records into groups in which the records in the same group cover for each other by perturbing their QI valuesB: Then, the mutual cover strategy calculates a random output table ...	ABC	CBA	BCA	BCA	Selection 1
A: Note that several attempts, like BFP Pang et al. (2019) and EnrichFeat, give no improvements against PointRend baseline, while they serve as final ensemble candidatesB: As shown in Table 3, all PointRend models achieve promising performance. Even without ensemble, our PointRend baseline, which yields 77.38 m...	ABC	CBA	ABC	BAC	Selection 4
A: In version 1 of this note, which can still be found on the ArXiv, we showed that the analogous version of the conjecture for complex functions on {−1,1}nsuperscript11𝑛\{-1,1\}^{n}{ - 1 , 1 } start_POSTSUPERSCRIPT italic_n end_POSTSUPERSCRIPT which have modulus 1111 failsB: More specifically, we provedC...	ABC	ACB	BCA	CBA	Selection 2
A: The second one is to quantify the total variations of expected rewards of arms (Besbes et al., 2014). The general strategy to adapt to nonstationarityB: Bandit problems can be viewed as a special case of MDP problems with unit planning horizon. It is the simplest model that captures the exploration-exploita...	BCA	CAB	CBA	ACB	Selection 2
A: For dissemination, various channels were employed including a mailing list of students from a local Singapore university, an informal Telegram supergroup joined by students, alumni, and faculty of the same university, and personal contacts of the researchers. Further spreading of the survey by participants was e...	BAC	CBA	ACB	BAC	Selection 2
A: In addition to the primary entity alignment loss, the algorithm also incorporates a self-distillation loss to guide DAN in generating desired embeddings. It jointly minimizes two losses in each batch until the performance ceases to improve on the validation dataset.B: We present the training procedure of de...	ACB	CAB	CBA	BAC	Selection 2
A: Episodic curiosity [22] compares the current observation with buffer and uses reachability as the novelty bonus. RND [23] measures the state uncertainty by random network distillationB: Previous work typically utilizes intrinsic motivation for exploration in complex decision-making problems with sparse rewa...	BCA	BCA	ABC	BAC	Selection 4
A: However, Floater-Hormann becomes indistinguishable from 5t⁢hsuperscript5𝑡ℎ5^{th}5 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT-order splines. Further, when considering the amount of coefficients/nodes required to determine the interpolant, plotted in the right panel (with logarithmic scales on bo...	BCA	ACB	CBA	ABC	Selection 1
A: Prior work in unsupervised DR learning suggests the objective of learning statistically independent factors of the latent space as means to obtain DR. The underlying assumption is that the latent variables H𝐻Hitalic_H can be partitioned into independent components C𝐶Citalic_C (i.eB: They aren’t really sepa...	ACB	ABC	BCA	BCA	Selection 1
A: When introducing the concept of an inverted signal pair of digital signals into a structural computer, the signals are paired, so a total of four wires are required to process the two auxiliary signals. This is defined as a double pair-based logical operation and is as follows in Fig 1. B: Thus, signal cable...	ABC	BAC	CBA	ACB	Selection 3
A: Conditions for such families of maps to define a permutation of the field 𝔽𝔽\mathbb{F}blackboard_F are well studied and established for special classes like Dickson polynomials [20], linearized polynomials [21] and few other specific forms [13, 14] to name a few. B: Some well-studied families of polynomial...	ACB	ABC	CBA	CAB	Selection 3
A: In this article we investigated how different view-selecting meta-learners affect the performance of multi-view stacking. In our simulations, the interpolating predictor often performed worse than the other meta-learners on at least one outcome measureB: For example, when the sample size was larger than the ...	BAC	CBA	ABC	ACB	Selection 3
A: DepAD offers a holistic approach to guide the development of dependency-based anomaly detection methodsB: DepAD is effective and adaptable, utilizing off-the-shelf techniques for diverse applications. C: We propose a dependency-based anomaly detection framework, DepAD, to provide a general approach to de...	CAB	BCA	ABC	BAC	Selection 2
A: This highlights the primary contribution of our theoretical analysis. Refer to A.8 for additional empirical analysis.B: In this section we compare the empirical performance of our proposed algorithm CB-MNL with the previous state of the art in the MNL contextual bandit literature: UCB-MNL[Oh & Iyengar, 2021...	CAB	BAC	ABC	BCA	Selection 1
A: Besides evaluating all actions in general, we also provide average mAPs of short actions for VSGN as well as other methods that have detection results availableB: Here, we refer to action instances that are shorter than 30 seconds as short actionsC: On ActivityNet, there are 54.4% short actions, whereas...	ABC	CAB	ACB	CAB	Selection 1
A: However, they do not state how many models were used in the composition of this ensemble. With VisEvol, we reached an accuracy of 87% with only 4 ML models (see Figure 4(d)), thus surpassing their majority-voting ensemble. If the user wants to utilize one model, our selection would have been M4:RF329 (see Figure...	BAC	CAB	CBA	ABC	Selection 2
A: This paper includes numerical simulations that demonstrate the application of the extension in a probabilistic swarm guidance problem. In order to enhance convergence rates, [9] introduces a convex optimization-based technique for Markov chain synthesisB: This technique formulates the objective function and ...	CBA	ACB	CBA	BCA	Selection 4
A: In contrast, HiPPI and our method require shape-to-universe representations. To obtain these, we use synchronisation to extract the shape-to-universe representation from the pairwise transformationsB: By doing so, we obtain the initial U𝑈Uitalic_U and Q𝑄Qitalic_Q. We refer to this method of synchronising t...	BAC	ABC	CAB	BCA	Selection 2
A: Thus, now these two graph classes can be recognized in the same way both theoretically and algorithmically. B: We presented the first recognition algorithm for both path graphs and directed path graphsC: Both graph classes are characterized very similarly in [18], and we extended the simpler characteriza...	ACB	ACB	BAC	CAB	Selection 4
A: Especially, the number error for Mixed-SLIM on the Polblogs network is 49, which is the smallest number error for this dataset in literature as far as we know. B: The numerical results suggest that Mixed-SLIM methods enjoy satisfactory performances compared with SCORE, SLIM, OCCAM, Mixed-SCORE, and GeoNMF wh...	CAB	ACB	CBA	ABC	Selection 3
A: (2018); Cheng and Bartlett (2018); Chatterji et al. (2018); Wibisono (2018); Bernton (2018); Dalalyan and Karagulyan (2019); Baker et al. (2019); Ma et al. (2019a, b); Mou et al. (2019); Vempala and Wibisono (2019); Salim et al. (2019); Durmus et al. (2019); Wibisono (2019) and the references therein. Among thes...	CBA	ABC	CAB	ABC	Selection 1
A: To make the policy transferable, traffic signal control is also modeled as a meta-learning problem in [14, 49, 36]. Specifically, the method in [14] performs meta-learning on multiple independent MDPs and ignores the influences of neighbor agents. A data augmentation method is proposed in [49] to generates diver...	CAB	BAC	CBA	ACB	Selection 4
A: In contrast, almost all known online bin packing algorithms are analyzed using a weighting technique (?), which treats each bin “individually” and independently from the others (by assigning weights to items and independently comparing a bin’s weight in the online algorithm and the optimal offline solution)B...	BAC	ABC	BCA	ACB	Selection 1
A: If we assume that the reconstructed mesh has the form of a graph M=(V,E)𝑀𝑉𝐸M=(V,E)italic_M = ( italic_V , italic_E ) with edges E𝐸Eitalic_E, then the term is defined as follows:B: The above formulation alone causes that many of the produced patches have unnecessarily long edges, and the network folds th...	BCA	CBA	CAB	BCA	Selection 3
A: Our paper technique can be generalized to non-smooth problems by using another variant of sliding procedure [34, 15, 23]B: By using batching technique, the results can be generalized to stochastic saddle-point problems [15, 23]C: Instead of the smooth convex-concave saddle-point problem we can consider g...	BCA	ABC	ACB	CAB	Selection 2
A: In the first part, we focus on the complete analysis of small graphs, that is: graphs of at most 9 nodes. In the second part, we analyze larger families of graphs by random sampling instances.B: We proceed by trying to find a counterexample based on our previous observationsC: In this section we present ...	BAC	ABC	CBA	CAB	Selection 3
A: One immediate application of Theorem 1.2 is the reduction of fractional Helly numbersB: of Patáková [35, Theorem 2.3] into: C: For instance, it easily improves a theorem444[35, Theorem 2.3] was not phrased in terms of (K,b)𝐾𝑏(K,b)( italic_K , italic_b )-free covers but readily generalizes to that setti...	ABC	ACB	CAB	BAC	Selection 2
A: Exploring the impact of the features with several statistical measures and automatic feature selection techniques enables users to improve the predictive performance, reduce the need for computational resources, and decrease the time spent for training. Finally, our VA system is beneficial for feature engineerin...	BAC	BCA	CAB	BAC	Selection 3
A: This is enabled by the high repeatability of the system which results in run-to-run deviations of 3⁢μ⁢m3𝜇𝑚3\mu m3 italic_μ italic_m, well below our tolerances of 20⁢μ⁢m20𝜇𝑚20\mu m20 italic_μ italic_mB: The weights in the MPCC cost terms are manually tuned, the controller gains are kept at their nominal v...	ABC	ACB	BCA	ACB	Selection 3
A: We further study bias exploitation on CelebAB: For this, we plot improvement over the standard model (I⁢O⁢S⁢M𝐼𝑂𝑆𝑀IOSMitalic_I italic_O italic_S italic_M) in Fig. 5, which is the accuracy gain over the standard model on each dataset group. The improvements in blond (minority group) incur degradation in no...	ACB	BAC	BAC	BCA	Selection 4
A: They use an encoder extract appearance and gaze feature from eye images. They exchange the two features of selected paired images and aim to reconstruct the original image based on the exchanged feature. Note that, these approaches learn the gaze representation, but they also require a few labeled samples to fin...	CAB	BCA	BCA	CBA	Selection 4
A: We have employed this method using 2D-based features (texture, gray level, LBP) to extract covariance descriptorsB: The evaluation on the RMFRD and SMFRD datasets confirms the superiority of the proposed method as shown in Table 3.C: Covariance-based features have been applied in hariri20163d and achie...	ABC	ABC	BCA	CBA	Selection 3
A: Briefly, inductive calls must occur on data smaller than the input and, dually, coinductive calls must be guarded by further codata outputB: In either case, we are concerned with the decrease of (co)data size—height of data and observable depth of codata—in a sequence of recursive calls. Since inferring this...	BAC	ABC	ACB	BCA	Selection 4
A: The whole FairCMS-II scheme is summarized as follows. First, suppose an owner rents the cloud’s resources for media sharing, the owner and the cloud execute Part 1 as shown in FigB: 6. Finally, the arbitration and traitor tracing process follows the same approach of FairCMS-I and is thus omitted here. C:...	ABC	ACB	ABC	CBA	Selection 2
A: (2016) use pre-trained factorization machines to create field embeddings before applying a DNN, while Product-based Neural Networks (PNNs) Qu et alB: (2016) model both second-order and high-order interactions through the use of a product layer between the field embedding layer and the DNN layer. Like PNNs, N...	BCA	ACB	CAB	CBA	Selection 1
A: The idea of the proof is very similar to the one in Jaggi [2013]. In a nutshell, as the primal progress per iteration is directly related to the step size times the Frank-Wolfe gap, we know that the Frank-Wolfe gap cannot remain indefinitely above a given value, as otherwise we would obtain a large amount of pri...	ACB	CAB	CBA	ABC	Selection 2
A: Consider a Pass-Bundle p𝑝pitalic_p and the execution of Extend-Active-PathsB: Only possibly at the end of Extend-Active-Paths the algorithm backtracks on a𝑎aitalic_a, casting it inactive. C: By design of our algorithm, if an arc a𝑎aitalic_a is active at the beginning of p𝑝pitalic_p, it remains active...	ABC	CBA	ACB	ABC	Selection 3
A: We propose CPP – a novel decentralized optimization method with communication compressionB: The method works under a general class of compression operators and is shown to achieve linear convergence for strongly convex and smooth objective functions over general directed graphsC: To the best of our know...	CAB	ABC	ACB	BAC	Selection 2
A: Our method is similar to the randomized local methods (for example, as the method from [31]), but it uses not only importance sampling, but also implicit variance reduction technique [30]. B: The following method (Algorithm 3) is also sharpened on the alternation of local iterations and communications, but i...	ABC	CAB	BAC	CBA	Selection 4
A: Furthermore, the presence of a correlation device does not make (C)CEs prescriptive because the agents still need a mechanism to agree on the distribution the correlation device samples from777This is true if the correlation device is not considered as part of the game. If it was part of the game (for example tr...	BAC	BCA	ABC	CAB	Selection 4
A: One cluster of works that steps away from this worst-case perspective focuses on giving privacy guarantees that are tailored to the dataset at hand (Nissim et al., 2007; Ghosh and Roth, 2011; Ebadi et al., 2015; Wang, 2019)B: In Feldman and Zrnic (2021) in particular, the authors elegantly manage to track t...	CAB	ACB	ACB	ABC	Selection 4
A: We therefore propose the following novel research direction: to investigate how preprocessing algorithms can decrease the parameter value (and hence search space) of FPT algorithms, in a theoretically sound way. It is nontrivial to phrase meaningful formal questions in this directionB: Hence NP-hard problems...	ACB	BAC	BAC	CAB	Selection 1
A: We use SimOPA and FOPA to generate rationality score map, based on which the location with the largest rationality score is chosen as the optimal placement. We train and evaluate different methods on OPA dataset [94]B: For generative approach, we choose TERSE [154] and PlaceNet [197], which can directly pred...	BAC	ABC	CBA	BCA	Selection 1
A: Through the use of data mining and visualization tools, we have demonstrated the significance of multi-modal urban data and have highlighted the connections between service and context data. Furthermore, we have presented extensive experimental results on spatio-temporal predictions, transfer learning, and reinf...	CAB	ABC	ACB	CBA	Selection 1
A: One can immediately expect that, analogous to general mean-variance estimators with a Gaussian prediction interval, this procedure does not give optimal intervals for data sets that do not follow a normal distributionB: One of the consequences is that this model might suffer from the validity problems discus...	BCA	BAC	ABC	CAB	Selection 3
A: Moreover, future work can be done to use a more complicated token representation such as that proposed by \textciteashis19ismir to include other time signatures. We list some important statistics of these five datasets in Tab. 1 and provide their details below.B: Future work can be done to improve thisC:...	CAB	BCA	CBA	ABC	Selection 3
A: As it was stated in the proof of Lemma 2.2, while searching for a central vertex we always jump from a vertex to its neighbor in a way that decreases the largest remaining component by oneB: Next, let us count the total number of jumps necessary for finding central vertices over all loops in Algorithm 1C...	BAC	CBA	ABC	CAB	Selection 1
A: Recently, there are also investigations on semantic communications for other transmission contents, such as image and speech. A DL-enabled semantic communication system for image transmission, named JSCC, has been developed in[14]B: Particularly, a joint image transmission-recognition system has been develo...	BCA	CAB	ACB	CAB	Selection 3
A: Voxel-based methods like Submanifold Sparse Convolution[3], MinkowskiNet[2] and Occuseg[1] first quantize the point cloud data into voxels and perform sparse 3D convolution on the voxels. These methods can often achieve good segmentation performance but severely suffer from heavy memory and time consumption. Poi...	BAC	CBA	ACB	CBA	Selection 1
A: By comparing Geo-SV2, Geo-SV1, and ours (full model), all these three with the geometric relationships, the performance gradually improves when more geometry elements are involved in modeling, confirming our motivation of modeling between depth and multiple 2D/3D geometry elements, instead of partial of them, e....	CBA	CAB	CBA	BAC	Selection 4
A: Existing bottom-up methods (e.g., [22, 7, 23, 24, 25, 13, 26, 27],) with CNN, RNN and some pre-defined heuristic rules have considered visual similarity features, sequential features and geometric features in order to connect text segments.B: \add To understand the limitations of bottom-up methods, we need t...	BCA	ABC	ACB	CAB	Selection 4
A: Assume that each memory address of a 64-bit system can be stored in an element 8 bytes in size, and the number of occurrences of an individual IP address is no more than 264superscript2642^{64}2 start_POSTSUPERSCRIPT 64 end_POSTSUPERSCRIPT. There is an array of size 256 elements that consists of 256×82568256\ti...	BCA	BAC	BCA	ABC	Selection 4
A: Li is partially supported by the National Natural Science Foundation of China No. 11971221 and the Shenzhen Sci-Tech Fund No. RCJC20200714114556020, JCYJ20170818153840322 and JCYJ20190809150413261, and Guangdong Provincial Key Laboratory of Computational Science and Material Design No. 2019B030301001.B: The ...	BAC	ABC	CAB	BCA	Selection 3
A: As a motivating example, we consider two smartphone application providers who wish to train a global model over the datasets stored on the smartphones of their respective customer bases. Here, the two application companies do not want to share the customer data directly with each otherB: In this case, the da...	ACB	CBA	CBA	BAC	Selection 1
A: The pseudospectral theory, including four characterizations and properties analysis as well as insightful visualizations of tensor ε𝜀\varepsilonitalic_ε-pseudospectra and application to seek more T-positive definite tensors, on third tensors is presentedB: This paper considers perturbation analysis to T-ei...	CBA	BAC	ABC	ACB	Selection 2
A: Besides, skip connections are utilized to produce more sophisticated predictions by combining low-level and high-level features at multiple scalesB: In this encoder-decoder based backbone, we replace all the vanilla convolutions with the partial convolution layers to better capture information from irregula...	CAB	BCA	BAC	ACB	Selection 3
A: The concept of BEC was first introduced by Elias in 1955 InfThe B: In a binary erasure channel (BEC), a binary symbol is either received correctly or totally erased with probability ε𝜀\varepsilonitalic_εC: Together with the binary symmetric channel (BSC), they are frequently used in coding theory and in...	BAC	CAB	ABC	CBA	Selection 1

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