Split (1)

train · 2.93k rows

shuffled_text stringlengths 267 4.47k	A stringclasses 6 values	B stringclasses 6 values	C stringclasses 6 values	D stringclasses 6 values	label stringclasses 4 values
A: One important task in this context is writing elements of classical groups as words in standard generators using SLPsB: Other rewriting algorithms also exist, for example Cohen et al. [26] present algorithms to compute with elements of finite Lie groups. C: This is done in Magma [14] using the results of...	BAC	BCA	CBA	ACB	Selection 4
A: As in many multiscale methods previously considered, our starting point is the decomposition of the solution space into fine and coarse spaces that are adapted to the problem of interestB: It is interesting to notice that, although the formulation is based on hybridization, the final numerical solution is d...	ABC	ACB	BAC	BAC	Selection 2
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	BAC	BCA	ABC	Selection 2
A: As observed in [19, 20], rumor features are very prone to change during an event’s developmentB: In order to capture these temporal variabilities, we build upon the Dynamic Series-Time Structure (DSTS) model (time series for short) for feature vector representation proposed in [20]C: We base our credibi...	CBA	CBA	CAB	ABC	Selection 4
A: Unlike the case of squared loss, the result for exponential loss are independent of initialization and with only mild conditions on the step size. Here again, we see the asymptotic nature of exponential loss on separable data nullifying the initialization effects thereby making the analysis simpler compared to s...	CBA	BCA	CAB	ACB	Selection 1
A: Our task is, to a point, a reverse engineering task; to measure the probability a tweet refers to a news or rumor event; which is even trickier. We hence, consider this a weak learning processB: Given a tweet, our task is to classify whether it is associated with either a news or rumor. Most of the previous...	ACB	BAC	CBA	ABC	Selection 2
A: Evaluating methodology. For RQ1, given an event entity e, at time t, we need to classify them into either Breaking or Anticipated class. We select a studied time for each event period randomly in the range of 5 days before and after the event timeB: In total, our training dataset for AOL consists of 1,740 in...	BCA	BCA	ABC	CAB	Selection 3
A: The only difference happens to patient 10 and 12 whose intakes are earlier at day. Further, patient 12 takse approxB: The insulin intakes tend to be more in the evening, when basal insulin is used by most of the patientsC: 3 times the average insulin dose of others in the morning.	CAB	ACB	BAC	ACB	Selection 3
A: Table 7 lists the four categories that benefited the most from multi-scale information across the subset of evaluation metrics on the validation set: Noisy, Satellite, Cartoon, PatternB: The categorical organization of the CAT2000 database also allowed us to quantify the improvements by the ASPP module with...	CAB	ABC	CAB	BAC	Selection 4
A: Our strongest positive result about the approximation of the locality number will be derived from the reduction mentioned above (see Section 5.2). However, we shall first investigate in Section 5.1 the approximation performance of several obvious greedy strategies to compute the locality number (with “greedy st...	CBA	CBA	BAC	ABC	Selection 4
A: The main loop in Algorithm 1 is iterated 15151515 times (cfB: The world model is trained for 45454545K steps in the first iteration and for 15151515K steps in each of the following ones. Shorter training in later iterations does not degrade the performance because the world model after first iteration captu...	CAB	ACB	BCA	CBA	Selection 2
A: The second section presents the two main locomotion methods employed by the Cricket robot, rolling and walking, along with a description of two gaits designed for negotiating stepsB: The paper’s organization is as followsC: In the third section, we outline the mathematical framework used for quantifying ...	CBA	CBA	BAC	ABC	Selection 3
A: In Section 6, we study the power of randomization in online computation with untrusted adviceB: All the above results pertain to deterministic online algorithmsC: First, we show that the randomized algorithm of Purohit et al. [29] for the ski rental problem Pareto-dominates any deterministic algorithm, ...	BAC	CBA	CAB	CBA	Selection 1
A: Therefore, in this new (more realistic) scenario, subjects were processed one writing (post) at the time (in a stream-like way) and not using chunks. B: As said earlier, each chunk contained 10% of the subject’s writing history, a value that for some subjects could be just a single post while for others hund...	ACB	BCA	CAB	CBA	Selection 3
A: Xu and Huang (2022) propose DEF-A to solve the convergence problem by using detached error feedback (DEF) technique 111Xu and Huang (2022) proposes two algorithms: DEF and DEF-AB: Due to the larger compressed error introduced by RBGS compared with top-s𝑠sitalic_s when selecting the same number of components...	BCA	BAC	CBA	ACB	Selection 2
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	BCA	BCA	CBA	CAB	Selection 4
A: Some algorithms that have been applied in the potential game can also be employed in the UAV ad-hoc network gameB: In the next section, we investigate the existing algorithm with its learning rate in large-scale post-disaster scenarios and propose a new algorithm which is more suitable for the UAV ad-hoc net...	BAC	BCA	ABC	ACB	Selection 2
A: tc⁢o⁢m⁢p=45⁢μsubscript𝑡𝑐𝑜𝑚𝑝45μt_{comp}=45\upmuitalic_t start_POSTSUBSCRIPT italic_c italic_o italic_m italic_p end_POSTSUBSCRIPT = 45 roman_μs means that magnetic compression (i.e.,formulae-sequence𝑖𝑒i.e.,italic_i B: , superimposition of the ψc⁢o⁢m⁢psubscript𝜓𝑐𝑜𝑚𝑝\psi_{comp}italic_ψ start_POSTSUB...	ABC	ACB	CAB	ABC	Selection 2
A: In table 1, Wilcoxon Sign-Ranked test was used to analyze the effect of Variance before applying Dropout (DQN) and after applying Dropout (Dropout methods DQN)B: There was a statistically significant decrease in Variance (14.72% between Gaussian Dropout and DQN, 48.89% between Variational Dropout and DQN). F...	CBA	ABC	BCA	CAB	Selection 3
A: a,b,c,d𝑎𝑏𝑐𝑑a,b,c,ditalic_a , italic_b , italic_c , italic_d correspond to values in a feature map. SegNet uses the max-pooling indices to upsample (without learning) the feature map(s) and convolves with a trainable decoder filter bank. FCN upsamples by learning to deconvolve the input feature map and adds t...	BAC	ABC	ABC	BCA	Selection 1
A: The classifier follows the same overall setup, i.e., 500500500500 decision trees and a maximum depth of ten. B: Each decision tree is trained on a different randomly selected subset of features and samplesC: RF: Random forest (Breiman, 2001) is an ensemble-based method consisting of multiple decision tre...	BAC	BCA	BAC	CBA	Selection 4
A: However, such computational efficiency guarantees rely on the regularity condition that the state space is already well exploredB: A line of recent work (Fazel et al., 2018; Yang et al., 2019a; Abbasi-Yadkori et al., 2019a, b; Bhandari and Russo, 2019; Liu et al., 2019; Agarwal et al., 2019; Wang et al., 20...	BCA	BAC	CBA	CBA	Selection 2
A: In a follow-up paper, 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 reduction in energy consumption by a facto...	CBA	CAB	CBA	ACB	Selection 4
A: One main contribution of this paper is establishing a precise relationship (i.eB: These neighborhoods, being also metric (and thus topological) spaces, permit giving a short proof of the Künneth formula for Vietoris-Rips persistent homology. C: a filtered homotopy equivalence) between the Vietoris-Rips s...	BAC	ABC	ACB	BAC	Selection 3
A: The top 6 representatives (according to a user-selected quality measure) are still shown at the top of the main view (Figure 1(e)), and the projection can be switched at any time if the user is not satisfied with the initial choice. We also provide the mechanism for a selection-based ranking of the representativ...	ABC	ACB	ABC	BAC	Selection 4
A: These guidelines are intended to promote actionable metaheuristics designed and tested in a principled manner, to achieve valuable research results and ensure their practical use in real-world applications.B: In such work, an analysis is conducted from a critical yet constructive point of view, aiming to co...	ACB	BAC	CAB	CBA	Selection 3
A: However, the existing methods are limited to graph type data while no graph is provided for general data clusteringB: Since a large proportion of clustering methods are based on the graph, it is reasonable to consider how to employ GCN to promote the performance of graph-based clustering methods. In this pap...	ABC	ACB	BAC	CAB	Selection 1
A: ∙∙\bullet∙ Limited coverageB: In contrast, the measurements with SMap use standard protocols supported by almost any network with Internet connectivity, for the first time providing studies of ingress filtering that cover the entire IPv4 space.C: Previous studies infer spoofability based on measurements...	ACB	BAC	BCA	BCA	Selection 1
A: This paper builds upon previous work with this dataset [7], which used support vector machine (SVM) ensembles. First, their approach is extended to a modern version of feedforward artificial neural networks (NNs) [8]B: The results indicate improvement from two sources: The use of neural networks in place of ...	BCA	BCA	ACB	BCA	Selection 3
A: In fact, the construction to generate these semigroups is quite simple [4, Proposition 4.1] (compare also to 3). The same construction can also be used to generate free monoids as automaton semigroups or monoids. Here, the main difference is that the free monoid in one generator can indeed be generated by an aut...	CAB	ACB	CBA	BCA	Selection 3
A: Here, we study these methodsB: We find that their improved accuracy does not actually emerge from proper visual grounding, but from regularization effects, where the model forgets the linguistic priors in the train set, thereby performing better on the test setC: To support these claims, we first show th...	ACB	BCA	CAB	ABC	Selection 4
A: Readability. Readability of a text can be defined as the ease of understanding or comprehension due to the style of writing (Klare et al., 1963). Along with length, readability plays a role in internet users’ decisions to either read or ignore a privacy policy (Ermakova et al., 2015). While prior studies on read...	CBA	BAC	ACB	CAB	Selection 3
A: G2: Support explorationB: VA systems enable users to reach crucial findings and to take actions according to themC: This iterative process requires a human-in-the-loop who can thus explore the data and the model through the interactive visualization [1].	CBA	CBA	BCA	ABC	Selection 4
A: We use PPL and BLEU [Papineni et al., 2002] to measure the similarity between the reference and the generated response, and use C Score [Madotto et al., 2019] to measure the personalityB: In text classification experiment, we use accuracy (Acc) to evaluate the classification performance. In dialogue generati...	CBA	ABC	ACB	BAC	Selection 4
A: Hence, the historical MSI can be used to predict the future MSI. According to the GP-based MSI prediction algorithm, the predicted position and attitude are estimated by the mean of the predictive distribution of the outputs (the future MSI) on the specific test dataset. The predictive distribution of the output...	BCA	BCA	BAC	CAB	Selection 4
A: WeB: This will be bootstrapped to the multi-color case in later sectionsC: 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...	ACB	CAB	CBA	BAC	Selection 2
A: (2019); Chen et al. (2019b) study the convergence of Q-learning. When the value function approximator is nonlinear, TD possibly diverges (Baird, 1995; Boyan and Moore, 1995; Tsitsiklis and Van Roy, 1997). Bhatnagar et al. (2009) propose nonlinear gradient TD, which converges but only to a locally optimal solutio...	BAC	CAB	BCA	CAB	Selection 1
A: Thus, in the other experiments, we bind parameters for the computation of LSTM gates across stacked layers by default.B: Table 5 shows that: 1) Sharing parameters for the computation (Equation 6) of the depth-wise LSTM hidden state significantly hampers performance, which is consistent with our conjecture**...	BAC	CAB	CBA	CBA	Selection 2
A: italic_ε start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ( italic_x ) ∧ italic_ε start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ( italic_x ) ∧ ¬ ( italic_x = italic_y ) for i∈I𝑖𝐼i\in Iitalic_i ∈ italic_I and θi,j≜∃x.∃y.εi⁢(x)∧εj⁢(x)formulae-sequence≜subscript𝜃𝑖𝑗𝑥𝑦subscript𝜀𝑖𝑥subscript𝜀𝑗𝑥\theta_{i,j}\...	BAC	CBA	ABC	BAC	Selection 2
A: As shown in Fig. 5, we visualize the scatter diagram of two learning representations using 1,000 test distorted imagesB: Relationship to Distortion Distribution: We first emphasize the relationship between two learning representations and the realistic distortion distribution of a distorted image. In detail...	CBA	ACB	BAC	CBA	Selection 3
A: Similar to the results of image classification, SNGM outperforms LARS for different batch sizes. B: We can observe that for small batch size, SNGM achieves test perplexity comparable to that of MSGD, and for large batch size, SNGM is better than MSGDC: Table 6 shows the test perplexity of the three metho...	CBA	BCA	ABC	BCA	Selection 1
A: Brian Brubach was supported in part by NSF awards CCF-1422569 and CCF-1749864, and by research awards from AdobeB: Nathaniel Grammel and Leonidas Tsepenekas were supported in part by NSF awards CCF-1749864 and CCF-1918749, and by research awards from Amazon and GoogleC: Aravind Srinivasan was supported i...	ABC	CBA	ACB	CAB	Selection 1
A: The local cost functions in this paper are not required to be differentiable and the subgradients only satisfy the linear growth condition. The inner product of the subgradients and the error between local optimizers’ states and the global optimal solution inevitably exists in the recursive inequality of the con...	BAC	ACB	CAB	BCA	Selection 1
A: We observe that the results of MuCo are much better than that of Mondrian and Anatomy. The primary reason is that MuCo retains the most distributions of the original QI values and the results of queries are specific records rather than groupsB: Consequently, the accuracy of query answering of MuCo is much b...	ACB	ACB	BCA	ABC	Selection 4
A: PointRend performs point-based segmentation at adaptively selected locations and generates high-quality instance maskB: Furthermore, compared to HTC’s mask head, PointRend’s lightweight segmentation head alleviates both memory and computation costs dramatically, thus enables larger input image resolutions du...	BAC	CBA	ACB	CBA	Selection 3
A: [KKLMS] establishes a weaker version of the conjectureB: Its introduction is also a good source of information on the problem. C: For the significance of this conjecture we refer to the original paper [FK], and to Kalai’s blog [K] (embedded in Tao’s blog) which reports on all significant results concerni...	CBA	ABC	CBA	BCA	Selection 4
A: In addition, Ada-LSVI-UCB-Restart has a huge gain compared to LSVI-UCB-Unknown, which agrees with our theoretical analysis. This suggests that Ada-LSVI-UCB-Restart works well when the knowledge of global variation is unavailable. Our proposed algorithms not only perform systemic exploration, but also adapt to th...	ACB	BAC	CAB	ABC	Selection 3
A: Most respondents encountered fake news on instant messaging apps compared to social media, and have reported the least trust in them. They have also rated the sharing of fake news to be a greater problem than its creationB: Many studies worldwide have observed the proliferation of fake news on social media a...	CBA	ABC	BCA	BAC	Selection 4
A: Out-of-KG entity prediction methods, such as MEAN [19], VN Network [20], and LAN [21], leverage logic rules to infer the missing relationships but do not generate unconditioned entity embeddings for other tasksB: These methods share a similar task setting with ours, where all relations are known during train...	ABC	ACB	BAC	CBA	Selection 1
A: We take 5555 screenshots at each level when playing games, as shown in Fig. 8(a). Similar to [11], we adopt an efficient version of Super Mario in Retro that simulates fastB: One reason to perform self-supervised exploration is to adapt the trained explorative agent in similar environments for exploration. ...	CBA	BCA	BAC	ABC	Selection 3
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...	ACB	CAB	BCA	BAC	Selection 3
A: The key observation that we make is that the DR learning problem can be cast as a style transfer task [DBLP:conf/cvpr/GatysEB16], thus allowing us to borrow techniques from this extensively explored area. B: Furthermore, even though it involves two stages, the end result is a single model which does not rely...	CAB	CBA	BAC	ACB	Selection 2
A: In case of output, it is possible to measure by firing a laser onto a pie pin on the resulting side and checking whether it returns to either alpha or betaB: The picture shows the connection status determination of the results for each input. C: This window operator calculates the connection between the ...	BAC	ABC	BCA	BAC	Selection 3
A: A finite field, by definition, is a finite set, and the set of all permutation polynomials over the finite field forms a group under compositionB: Given a finite subset of such permutations, we can compute a group generated by this setC: In this paper, we propose a representation of such a group using t...	ABC	BAC	BAC	CAB	Selection 1
A: Another relevant factor is interpretability of the set of selected viewsB: Although sparser models are typically considered more interpretable, a researcher may be interested in interpreting not only the model and its coefficients, but also the set of selected viewsC: For example, one may wish to make d...	ABC	ACB	ACB	CAB	Selection 1
A: However, the percentage of anomalies can negatively affect their effectiveness.B: DepAD methods exhibit stability when data contains noisy variablesC: Sensitivity Experiments: DepAD algorithms are not sensitive to the average correlation, sparseness, or dimensionality of datasets	CBA	BAC	CAB	ABC	Selection 1
A: [2021] recently proposed the idea of convex relaxation of the confidence set for the more straightforward logistic bandit setting. Our work can be viewed as an extension of their construction to the MNL setting. B: Comparison with Abeille et alC: [2021] Abeille et al	ABC	ABC	ABC	CAB	Selection 4
A: From Table 3 and 4, we can see that xGPN obviously improves the performance of short actions as well as the overall performanceB: Cross-scale graph pyramid network (xGPN)C: On the one hand, xGPN utilizes long-range correlations in multi-level features and benefits actions of various lengths. On the other...	BAC	CBA	BCA	CBA	Selection 1
A: E1 was enthusiastic about the grid-based view and stated that it is a game-changer for finding performant and diverse models. E2 was initially confused with the comparison of instances from various clusters in this same view, but after some training period, he understood that he has to look for different pattern...	BCA	ABC	CAB	CBA	Selection 1
A: Another algorithm is proposed in [28] that assumes the underlying switching network topology is ultimately connectedB: This assumption means that the union of graphs over an infinite interval is strongly connectedC: In [29], previous works are extended to solve the consensus problem on networks under lim...	BAC	BCA	ABC	BAC	Selection 3
A: In that case, in addition to linear point-to-point matching costs, quadratic costs for matching pairs of points on the first shape to pairs of points on the second shape are taken into account. Since pairs of points can be understood as edges in a graph, this corresponds to graph matching. Due to the NP-hardness...	CBA	BAC	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...	BCA	CAB	ABC	BAC	Selection 2
A: From subfigure (c), under the MMSB model, we can find that Mixed-SLIM, Mixed-SCORE, OCCAM, and GeoNMF have similar performances, and as ρ𝜌\rhoitalic_ρ increases they all perform poorerB: Under the DCMM model, the mixed Humming error rate of Mixed-SLIM decreases as ρ𝜌\rhoitalic_ρ decreases, while the perfor...	CAB	ACB	BAC	BCA	Selection 4
A: (2018); Boumal et al. (2018); Bécigneul and Ganea (2018); Zhang and Sra (2018); Sato et al. (2019); Zhou et al. (2019); Weber and Sra (2019) and the references therein. Also see recent reviews (Ferreira et al., 2020; Hosseini and Sra, 2020)B: See, e.g., Udriste (1994); Ferreira and Oliveira (2002); Absil et ...	CBA	ABC	CAB	BCA	Selection 3
A: For example, [59] shows that exploring in latent space can enhance the representation of the environment. In meta-RL, the agent is not given prepared task-specific data to adapt to, and it must fully explore the environment to collect useful informationB: A number of prior works have explored how RL can be c...	ACB	BAC	ACB	CAB	Selection 2
A: The distribution of the input sequence changes every 50000 itemsB: For Weibull benchmarks, each subsequence is a Weibull distribution, whose shape parameter is chosen uniformly at random from [1.0,4.0]1.04.0[1.0,4.0][ 1.0 , 4.0 ]. For BPPLIB benchmarks, each subsequence is generated by choosing a file unifor...	ABC	CAB	CAB	ACB	Selection 4
A: The resulting representation is efficient and easy-to-render, while at the same time it offers additional benefits, e.g. the possibility of sampling the surface at the desired resolution, and straightforward texturing in any 3D computer graphics software. To obtain such a representation, state-of-the-art approac...	CBA	ACB	BCA	ACB	Selection 3
A: Similarly to Section (3), we consider a SPP in proximal setup and introduce Lagrangian multipliers for the common variablesB: However, in the Section 3 we obtained results in a general setup without additional knowledge about cost functions and sets. On the contrary, in this section we utilize the special st...	BCA	CAB	ACB	BAC	Selection 1
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 ...	BCA	BAC	ABC	CBA	Selection 4
A: This technique, which we briefly outline here, was specifically designed for complete intersection patternsB: The proof of Theorem 2.1 is quite involved and builds on the method of constrained chain maps developed in [18, 35] to study intersection patterns via homological minors [37]C: A major part of t...	BAC	ACB	CBA	BCA	Selection 1
A: E3 also agreed with us that the features have an important influence on the predictive model-based quality and affect the generalization ability of the final ML model. Furthermore, he noticed that it is difficult to judge how each feature should be engineered when there is contradicting statistical evidenceB...	CBA	ACB	BAC	CBA	Selection 3
A: The BO progress is shown in Figure 5, right pannel, for the optimization with constraints on the jerk and on the tracking errorB: After the initial learning phase the algorithm quickly finds the region where the simulation is feasible with respect to the constraints. The confidence interval in the cost predi...	ABC	BCA	ABC	CBA	Selection 2
A: It is unclear how the explicit bias variables should be defined so that the methods can generalize to all minority groups. GQA-OOD [36] divides the evaluation and test sets into majority (head) and minority (tail) groups based on the answer frequency within each ‘local group’ (e.g., colors of bags), which is a u...	BAC	ACB	CAB	ABC	Selection 1
A: Deep learning have been used in many computer vision tasks and demonstrated outstanding performanceB: They use a simple CNN and the performance surpasses most of the conventional appearance-based approaches. Following this study, an increasing number of improvements and extensions on CNN-based gaze estimatio...	CAB	ACB	CBA	BAC	Selection 2
A: Despite the recent breakthroughs of deep learning architectures in pattern recognition tasks, they need to estimate millions of parameters in the fully connected layers that require powerful hardware with high processing capacity and memoryB: To address this problem, we present in this paper an efficient qu...	BAC	ABC	BCA	BCA	Selection 2
A: Validity conditions of infinite proofs have been developed to keep cut elimination productive, which correspond to criteria like the guardedness check [BDS16, BT17, DP19, DP20d]B: Although we use infinite typing derivations, we explicitly avoid syntactic termination checking for its non-compositionalityC...	CBA	CAB	ABC	CAB	Selection 3
A: Also, since Part 2 is executed online for each user, this scheme clearly meets the scalability requirements. The achievement of the three security goals of FairCMS-I will be discussed in detail in Section VI.B: As a result, this scheme well solves the bottleneck caused by insufficient owner-side resources**C...	BAC	ABC	ABC	CBA	Selection 4
A: However, because FM considers all feature interactions, it has two main drawbacks.B: Factorization machine (FM) Rendle (2010, 2012) are a popular and effective method for modeling feature interactions, which involve learning a latent vector for each one-hot encoded feature and modeling the pairwise (second-...	CBA	CAB	CBA	BAC	Selection 2
A: Requiring access to a zeroth-order and domain oracle are mild assumptions, that were also implicitly assumed in one of the three FW-variants presented in Dvurechensky et alB: [2022] when computing the step size according to the strategy from Pedregosa et alC: [2020]; see 5 in Algorithm 4. The remaining ...	BCA	BCA	ABC	BCA	Selection 3
A: Nevertheless, this result does not apply to computing a good approximation to the maximum matching in this modelB: It is known that finding an exact matching requires linear space in the size of the graph and hence it is not possible to find an exact maximum matching in the semi-streaming model [FKM+04], at...	BAC	CAB	ABC	ABC	Selection 1
A: We show CPP achieves linear convergence rate under strongly convex and smooth objective functions. B: CPP enjoys large flexibility in both the compression method and the network topologyC: In this paper, we consider decentralized optimization over general directed networks and propose a novel Compressed ...	CAB	CAB	ABC	CBA	Selection 4
A: We compare our algorithms: type of sliding (Algorithm 1) and type of local method (Algorithm 3)B: We adapt the proposed algorithm for training neural networksC: To the best of our knowledge, this is the first work that compares these approaches in the scope of neural networks, as previous studies were l...	BCA	BAC	CAB	ABC	Selection 2
A: There are two levels of coordination; first is selecting an equilibrium before play commences, and second is selecting actions during play time. Both NEs and (C)CEs require agreement on what equilibrium is being played (Goldberg et al., 2013; Avis et al., 2010; Harsanyi & Selten, 1988): for (C)CEs this is a joi...	ACB	CBA	ABC	CBA	Selection 1
A: Another line of work (e.g., Gehrke et al. (2012); Bassily et al. (2013); Bhaskar et alB: Unfortunately, these definitions have at best extremely limited adaptive composition guarantees. Bassily and Freund (2016) connect this Bayesian intuition to statistical validity via typical stability, an approach that ...	ACB	BCA	BAC	BAC	Selection 1
A: To illustrate this difficulty, note that strengthening the definition of kernelization to “a preprocessing algorithm that is guaranteed to always output an equivalent instance of the same problem with a strictly smaller parameter” is useless. Under minor technical assumptions, such an algorithm would allow the p...	BAC	ACB	ACB	BCA	Selection 4
A: In the end, we briefly discuss the occlusion issue. Most of the above methods seek for reasonable placements to avoid the occurrence of occlusion, i.e., the inserted foreground is not occluded by background objectsB: Then, they remove the occluded part of foreground object. In this way, they are able to gen...	CAB	ACB	CAB	BAC	Selection 2
A: One possible explanation for this observation is that Beijing comprises the highest number of regions, and therefore exhibits a more complex regional service pattern as compared to other cities.B: Domain SelectionC: Our experimental results consistently demonstrate that using Beijing as the source city ...	ABC	CAB	BAC	ABC	Selection 2
A: When using GPs, two important assumptions are madeB: There is also an important remark to be made with respect to the validity condition (2)C: By definition it is assumed that the data is (conditionally) normally distributed and a further choice of covariance function has to be made by the user. When the...	ABC	CBA	ABC	BAC	Selection 4
A: Figure 4: The melody/non-melody classification result for “POP909-596.mid” by (b) “skyline” \parencitechia01skyline, (c) Simonetta et al.’s CNN \parencitesimonettaCNW19 and (d) our model (performance) + CPB: Directing attention to the red circled region within the pianoroll representation, it is evident tha...	ABC	BCA	ACB	BCA	Selection 1
A: [10] for a survey), where the colors of any two adjacent vertices have to differ by at least k𝑘kitalic_k and the colors of any two vertices within distance 2222 have to be distinct. B: to L⁢(k,1)𝐿𝑘1L(k,1)italic_L ( italic_k , 1 )-labeling problem (see e.gC: This description draws a comparison e.g	ABC	BCA	ABC	CBA	Selection 4
A: Semantic information is relevant to the transmission goal at the receiver, which could be either source massage recovery or more intelligent tasks. In the cases of intelligent tasks, the semantic information only contains the task-related features while the other irrelative features will not be extracted or tra...	CAB	ACB	CAB	BAC	Selection 2
A: We put the CSFR and ISFR modules after the first upsampling layer for larger spatial resolution. Due to the limitation in computational resources, we use ball query to sample point cloud as input samples, the sample radius is set to 2m. We use a Momentum SGD optimizer, the initial learning rate is set to 0.01 an...	CAB	BCA	ABC	CBA	Selection 1
A: M3D-RPN [3] focuses on the design of depth-aware convolution layers to improve 3D parameter estimation and post-optimization of the orientation by exploring the consistency between projected and annotated bounding boxes. To address the common occlusion issue in monocular object detection, MonoPair [9] proposes t...	CBA	BAC	ABC	BAC	Selection 3
A: FD cannot be applied and tested independently without the LAT module because the LAT process is an essential step for fusing the visual features from FPN and the relational features from GCNs.B: Note that, in FPNS (GGTR), the GGTR map is used to rectify false positive/negative text segments, as it is the fin...	BCA	BCA	BAC	CAB	Selection 4
A: Hence, we present an alternative pre-allocation strategy for the memory blocks. A memory block will be allocated only when the first three parts of an initial IP address have been given. In particular, pre-allocating a big memory block of size 128 MB containing 256×256256256256\times 256256 × 256 contiguous memo...	CAB	CBA	CBA	BCA	Selection 1
A: For example, when the system matrix in (1) is extended to the n𝑛nitalic_n-tuple case, it is the block tridiagonal systems discussed in [37]B: When the system matrix in (2) is extended to the n𝑛nitalic_n-tuple case, it corresponds to the linear system resulting from the domain decomposition method for ellip...	CBA	ACB	BCA	ABC	Selection 3
A: We now provide the main theoretical result of this paperB: Nevertheless, with some care, it is possible to prove the algorithm convergence. The proof of the theorem is deferred to Appendix AC: We observe that since each mini-batch is used for Q𝑄Qitalic_Q local iterations, this reuse leads to a bias in s...	CBA	BAC	ACB	CAB	Selection 3
A: In these systems, the states, inputs, and outputs are tensors. It would be interesting to explore multilinear time-invariant systems that depend on tensor-tensor multiplication and investigate their applications, leaving this as a topic for future research.B: The study of pseudospectra for T-eigenvalues of t...	CBA	BCA	ACB	BAC	Selection 1
A: This work is partly supported by the National Natural Science Foundation of China (NoB: 2019M660406), the Research Program of State Key Laboratory of Software Development Environment (SKLSDE-2021ZX-04), and the Fundamental Research Funds for the Central Universities. We also give specical thanks to Alibaba ...	BAC	ABC	ACB	BCA	Selection 3
A: The concept of BEC was first introduced by Elias in 1955 InfThe B: Together with the binary symmetric channel (BSC), they are frequently used in coding theory and information theory because they are among the simplest channel models, and many problems in communication theory can be reduced to problems in a B...	ACB	ACB	BCA	ACB	Selection 3

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