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A: Consequently, algorithms in the composition tree data structure, both in MAGMA and in GAP, store elements in classical groups as words in the LGO generators. Moreover, the LGO generators can be used directly to verify representations of classical groups [12]. B: The LGO generating set offers a variety of adv...	ACB	CAB	BAC	ABC	Selection 2
A: Except for (ii), all steps above above can be performed efficiently as the matrices involved are sparse and either local or independent of hℎhitalic_hB: Solving (25) on the other hand involves computing the hℎhitalic_h-dependent, global operator P𝑃Pitalic_P, leading to a dense matrix in (25)C: From now ...	ABC	BCA	BCA	BCA	Selection 1
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...	ABC	ACB	BAC	ABC	Selection 3
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...	CAB	CAB	CBA	ABC	Selection 4
A: Instead, we should look at the 00–1111 error on the validation datasetB: We might improve the validation and test errors even when when the decrease in the training loss is tiny and even when the validation loss itself increases. C: We should not rely on plateauing of the training loss or on the loss (lo...	BCA	ABC	ABC	CAB	Selection 1
A: We observe that at certain points in time, the volume of rumor-related tweets (for sub-events) in the event stream surges. This can lead to false positives for techniques that model events as the aggregation of all tweet contents; that is undesired at critical momentsB: It can be seen that although the cred...	BCA	ACB	BCA	BCA	Selection 2
A: This allows us to evaluate the feature performance i.e., salience and timeliness, with time and type specification (RQ2)B: For this part, we first focus on evaluating the performance of single L2R models that are learned from the pre-selected time (before, during and after) and types (Breaking and Anticipate...	CBA	CBA	BAC	CBA	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.	ACB	ABC	BAC	CAB	Selection 3
A: Our final evaluation results for both the MIT300 and CAT2000 datasets can be viewed on the MIT saliency benchmark under the model name MSI-Net, representing our multi-scale information network. Qualitatively, the proposed architecture successfully captures semantically meaningful image features such as faces and...	BCA	BAC	BCA	CBA	Selection 4
A: Nevertheless, since pathwidth and cutwidth are such crucial parameters for graph algorithms, we also translate our locality based reduction into one from graphs to graphs directly. B: Another reason might be that this relation is less obvious on the graph level and becomes more apparent if linked via the str...	BAC	CBA	ABC	BCA	Selection 2
A: (2015) and Chiappa et al. (2017), however we focus on using video prediction in the context of learning how to play the game well and positively verify that learned simulators can be used to train a policy useful in original environments.B: Oh et al. (2015) and Chiappa et alC: (2017) show that learning p...	CAB	BCA	ABC	ACB	Selection 1
A: A major obstacle in achieving seamless autonomous locomotion transition lies in the need for an efficient sensing methodology that can promptly and reliably evaluate the interaction between the robot and the terrain, referred to as terramechanics. These methods generally involve performing comprehensive on-site ...	BCA	ABC	BAC	ACB	Selection 2
A: Namely, all previous works assume that advice is, in all circumstances, completely trustworthy, and precisely as defined by the algorithmB: Since the advice is infallible, no reasonable online algorithm with advice would choose to ignore the advice.C: In this work, we address what is a significant drawb...	ACB	BCA	BAC	BAC	Selection 2
A: unseen terms are added and frequencies of already seen terms are updated.B: Only a dictionary of term-frequency pairs is needed for each category. Then, during training, dictionaries are updated as new documents are processed —i.eC: This brief subsection describes the training process, which is trivial	ABC	ABC	BCA	CBA	Selection 4
A: Table 2 and Figure 4 show the performance under non-IID data distributionB: Furthermore, we can find that the momentum factor masking trick will severely impair the performance of DGC under non-IID data distribution. C: We can find that GMC can achieve much better test accuracy and faster convergence spe...	ACB	BCA	CAB	CBA	Selection 1
A: , where ∗∗ is the convolution333We use convolution instead of cross-correlation only as a matter of compatibility with previous literature and computational frameworksB: operation.C*: Using cross-correlation would produce the same results and would not require flipping the kernels during visualization	ACB	ABC	ABC	CBA	Selection 1
A: Therefore, power control and altitude are two essential factors. There have been extensive researches building models focusing on various network influence factors. For example, the work in [8] established a system model with channels and time slots selections. Authors of [9] constructed a coverage model which c...	CBA	BCA	BCA	BAC	Selection 4
A: italic_e B: 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 C: , superimposition of the ψc⁢o⁢m⁢psubscript𝜓𝑐𝑜𝑚𝑝\psi_{comp}italic...	BCA	ACB	CAB	BAC	Selection 4
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: The results in Figure 3 show that using DQN with different Dropout methods result in better-preforming policies and less variability as the reduce...	ACB	BAC	CAB	CBA	Selection 2
A: Because of the large number of imaging modalities, the significant signal noise present in imaging modalities such as PET and ultrasound, and the limited amount of medical imaging data mainly because of high acquisition cost compounded by legal, ethical, and privacy issues, it is difficult to develop universal ...	CAB	BAC	CBA	ABC	Selection 4
A: Finally, the output layer is connected to all leaf neurons and aggregates the results by implementing the leaf votes. By using hyperbolic tangent and sigmoid functions, respectively, as activation functions between the layers, the generated network is differentiable and, thus, trainable with gradient-based optim...	CBA	CBA	CAB	BAC	Selection 3
A: Moreover, we prove that, even when the reward functions are adversarially chosen across the episodes, OPPO attains the same regret in terms of competing with the globally optimal policy in hindsight (Cesa-Bianchi and Lugosi, 2006; Bubeck and Cesa-Bianchi, 2012)B: In comparison, existing algorithms based on v...	CBA	ABC	ACB	CBA	Selection 2
A: In Wu et alB: (2018b), weights, activations, weight gradients, and activation gradients are subject to customized quantization schemes that allow for variable bit widths and facilitate integer arithmetic during training and testing. In contrast to Zhou et alC: (2016), the work of Wu et al. (2018b) accumu...	CBA	BAC	ABC	BAC	Selection 3
A: We present two examples: the first one arises from our study of the Künneth formula in Section 6 whereas the second one is a direct constructionB: There exist, however, non-regularly filled metric manifoldsC: Both examples make use of results from [4] about homotopy types of Vietoris-Rips complexes of �...	CBA	BAC	CAB	ACB	Selection 2
A: On the one hand, we found the bar chart (a) to be better when comparing the projection’s average with the selection’s average when we search for discrete k-values, and during the initial state (no selection of points), where the user can easily distinguish the bars having the same size. It can optionally be repl...	CBA	BCA	ABC	ABC	Selection 1
A: It contains popular algorithms such as Krill Herd (KH, [259]), Whale Optimization Algorithm (WOA, [380]), and algorithms based on the echolocation used by dolphins to detect fish like Dolphin Partner Optimization (DPO, [201]) and Dolphin Echolocation [195].B: The aquatic ecosystem in which they live has insp...	BAC	BAC	CBA	ABC	Selection 3
A: To illustrate the process of AdaGAE, Figure 2 shows the learned embedding on USPS at the i𝑖iitalic_i-th epochB: The maximum number of epochs, T𝑇Titalic_T, is set as 10. In other words, the graph is updated 10 times. Clearly, the embedding becomes more cohesive with the update. C: An epoch means a compl...	CBA	CAB	ABC	ACB	Selection 4
A: We next explain each measurement technique. In our measurements in Section 4 we compare the success and applicability of each technique. B: The results from the tests are stored in the backend databaseC: The GUI displays the results of the measurements at https://smap.cad.sit.fraunhofer.de	CBA	CAB	ABC	BCA	Selection 2
A: A context-based approach will be applied to longer-timescale data and to environments with cyclical patterns. B: In such cases, the recurrent pathway can identify useful patterns analagously to how cortical regions help the olfactory bulb filter out previously seen background information [21]C: The estim...	CBA	ABC	BCA	ABC	Selection 1
A: While these constructions and the involved proofs are generally deemed quite complicated, the situation for semigroups turns out to be much simpler. While it is known that the free semigroup of rank one is not an automaton semigroup [4, Proposition 4.3], the free semigroups of higher rank can be generated by an ...	BAC	ACB	CAB	BCA	Selection 1
A: Then, we analyze the regularization effects by evaluating the performance on VQA-CPv2’s train split (Sec. 4.5) and the behavior on a dataset without changing priors (Sec. 2). We present a new metric to assess visual grounding in Sec. 4.7 and describe our regularization method in Sec. 5. B: We first analyze i...	BCA	ACB	BAC	CBA	Selection 4
A: Sathyendra et al. (2017) presented a dataset and developed a model to automatically identify and label opt-out choices offered in privacy policies. Similarly, Zimmeck et alB: Other corpora similar to OPP-115 Corpus have enabled research on privacy practices. The PrivacyQA corpus contains 1,750 questions and ...	BAC	ACB	ABC	ABC	Selection 1
A: G5: Reveal and reduce cognitive biasesB: Cognitive bias is, in simple terms, a human judgment that drifts away from the actual information that should be conveyed by a visualization, i.e., it “involves a deviation from reality that is predictable and relatively consistent across people” [13].C: Visualiz...	ABC	ACB	CBA	BAC	Selection 2
A: Task similarity. In Persona and Weibo, each task is a set of dialogues for one user, so tasks are different from each other. We shuffle the samples and randomly divide tasks to construct the setting that tasks are similar to each otherB: For a fair comparison, each task on this setting also has 120 and 1200 ...	CAB	ABC	BCA	BAC	Selection 2
A: In Section II, the system model is introducedB: The rest of this paper is as followsC: In Section III, the CCA codebook design and the codebook-based joint subarray partition and AWV selection algorithms are proposed. Next, the TE-aware codebook-based beam tracking with 3D beamwidth control is further p...	ABC	BAC	ABC	ACB	Selection 2
A: 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 every node on the right – regardless of the matrixB: WeC: This will be boo...	ABC	ABC	BCA	CBA	Selection 3
A: Moreover, in contrast to the NTK regime, the induced feature representation is able to deviate from the initial one and subsequently evolve into the globally optimal one, which corresponds to the global minimizer of the MSPBE. We further extend our analysis to soft Q-learning, which is connected to policy gradie...	CAB	BCA	BCA	CBA	Selection 4
A: (2019a) present lightweight and dynamic convolutionsB: Regarding parameter efficiency for NMT, Wu et alC: Ma et al. (2021) approximate softmax attention with two nested linear attention functions. These methods are orthogonal to our work and it should be possible to combine them with our approach.	CBA	ACB	BAC	CBA	Selection 3
A: However, notice that the T0subscript𝑇0T_{0}italic_T start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT quotient of Struct⁡(σ)Structσ\operatorname{Struct}(\upsigma)roman_Struct ( roman_σ ) is sober when τ=τ⊆iτsubscriptτsubscript𝑖\uptau=\uptau_{\subseteq_{i}}roman_τ = roman_τ start_POSTSUBSCRIPT ⊆ start_POSTSUBSCRIPT itali...	ACB	ACB	CAB	BAC	Selection 4
A: (3) From the loss curves in FigB: It is also worth to note that the ordinal distortion estimation already performs well on the validation at the first twenty epochs, which verifies that this learning representation yields a favorable generalization for neural networks. In contrast, suffering from the heterog...	ACB	ABC	CBA	BCA	Selection 1
A: 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 sizesB: We don’t use training tricks such as warm-up [7]C: SNGM achieves the best performance for almost all batch size settings.	CBA	BAC	BCA	CAB	Selection 2
A: First, we develop algorithms for the simpler polynomial-scenarios model. Second, we sample a small number of scenarios from the black-box oracle and use our polynomial-scenarios algorithms to (approximately) solve the problems on themB: Finally, we extrapolate the solution to the original black-box problem. ...	CBA	ACB	BCA	BAC	Selection 3
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...	ACB	BCA	CAB	BAC	Selection 2
A: However, most existing approaches cannot prevent identity disclosure, and the existence of individuals in published table is likely to be disclosed [27]B: Furthermore, the QI values of individuals can be easily exposed that increases the background knowledge of adversary to learn the pattern of QI values and...	CAB	BCA	ACB	CAB	Selection 2
A: In addition to models listed in Table 3, another PointRend with slightly different setting (stacking two BFP modules, and increasing the RoIAlign size from original 7 to 10 for bounding box branch) is trained and achieves 76.95 mAP on testing set. So, there are 5 models used for final ensemble. B: As shown i...	CAB	BAC	BCA	CBA	Selection 1
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: This solves a question raised by ...	CAB	ABC	CAB	CAB	Selection 2
A: Compared to OPT-WLSVI and MASTER, our proposed algorithms achieve comparable empirical performance. More specifically, MASTER outperforms our proposed algorithm which agrees with its dynamic regret upper boundB: The main advantage of our algorithm compared to OPT-WLSVI and MASTER is its computational effici...	ABC	ABC	ACB	BCA	Selection 3
A: In this study, we seek to answer these research questions. RQ1: How much do people trust the media by which they obtain news? RQ2: Why do people share news and how do they do it? RQ3: How do people view the fake news phenomenon and what measures do they take against it? An online survey was employed for data co...	ABC	ACB	BAC	ABC	Selection 2
A: The experimental results demonstrate that decentRL achieves superior performance on benchmarks of these two tasks, both with and without new entitiesB: To evaluate our approach, we implement an end-to-end decentralized KG representation learning framework called decentRL and conduct comprehensive experiments...	ABC	ACB	BAC	ABC	Selection 3
A: The possible reason is that the dynamics are relatively simple in this task, thus the strong regularization does not affect the learning of dynamics. However, in Breakout, we find CVAE performs significantly worse than VDM. The theoretical benefit of VDM makes it better approximates the complex dynamics of ball ...	CAB	CBA	BCA	ACB	Selection 2
A: In doing so, we revisit earlier results by Carl de Boor and Amon Ros [28, 29] and answer their question from our perspective.B: That is: For given arbitrary nodes P𝑃Pitalic_P, determine the polynomial space ΠΠ\Piroman_Π such that P𝑃Pitalic_P is unisolvent with respect to ΠΠ\Piroman_ΠC: We complement th...	ACB	BCA	CAB	CBA	Selection 4
A: On the other hand, if the unconstrained nuisance variables have enough capacity, the model can use them to achieve a high quality reconstruction while ignoring the latent variables related to the disentangled factors. This phenomena is sometimes called the "shortcut problem" and has been discussed in previous wo...	BCA	BAC	BCA	CAB	Selection 4
A: Now, we will define ‘window operators’ to have the same connection as a 3-pin based structural computer using the reverse signal pair described earlier. ‘Window operator’ is a cube of 3x3, each containing elements of 0,i,1,-1,2, and 2. Each element (or cell) is inputted in the same way as three pin structural co...	BCA	ACB	BCA	BAC	Selection 2
A: In this paper, we propose a representation of such a group using the concept of linear representation defined through the Koopman operator.B: Given a finite subset of such permutations, we can compute a group generated by this setC: A finite field, by definition, is a finite set, and the set of all perm...	ACB	BAC	BCA	CBA	Selection 4
A: In this study, we evaluated the performance of the different meta-learners across a variety of settings, including high-dimensional and highly correlated settingsB: Most of these settings were not easy problems, as evident by the absolute accuracy values obtained by the meta-learnersC: Additionally we c...	ACB	ACB	ABC	ACB	Selection 3
A: The Zoo dataset contains information about 101 animals, each of which is described by 16 featuresB: After applying the DepAD algorithm FBED-CART-PS to the dataset, it reports scorpion, platypus and sea snake as the top-3 anomalies. To interpret each of the three anomalies, we find and use its top-3 variables...	BCA	ABC	CBA	CBA	Selection 1
A: [2010] but has a multiplicative κ𝜅\kappaitalic_κ factor in the bound.B: Comparison with Oh & Iyengar [2019] The Thompson Sampling based approach is inherently different from our Optimism in the face of uncertainty (OFU) style Algorithm CB-MNLC: However, the main result in Oh & Iyengar [2019] also relie...	CAB	ABC	BCA	BAC	Selection 1
A: d) Original clip (green dots) and up-scaled clip (orange dots) are stitched into one feature sequence with a gap. B: Figure 3: Video self-stitching (VSS). a) Snippet-level features are extracted for the entire videoC: b) Long video is cut into multiple short clips. c) Each video clip is up-scaled along ...	CAB	BCA	ABC	BCA	Selection 1
A: E1 and E2 were worried about the scalability of the toolB: Indeed, the excessive computational time required for producing new hyperparameters along with ensemble learning methods can be problematicC: Despite that, one possible improvement for VisEvol is to utilize parallel processing on powerful cloud s...	CBA	BAC	CBA	ABC	Selection 4
A: Condition 1 is used in Theorem 1 to prove that the value vector exponentially converges to 𝟎0\bm{0}bold_0B: In Proposition 3, it is proven that Algorithm 1 satisfies Condition 1 which means that probability distribution x⁢(k)𝑥𝑘x(k)italic_x ( italic_k ) exponentially converges to the desired distribution v...	CAB	CBA	BCA	CBA	Selection 3
A: There are various works that particularly target the matching of multiple shapes. In [30, 32], semidefinite programming relaxations are proposed for the multi-shape matching problemB: However, due to the employed lifting strategy, which drastically increases the number of variables, these methods are not sca...	BCA	ABC	CAB	CBA	Selection 2
A: We overcome this problem by visiting the connected components in a smart order. This order allows us to establish all the antipodality relations in a faster time. This is done in Step 4, Step 5, and Step 6 that are the core of algorithm RecognizePG.B: The recognition algorithm RecognizePG for path graph is ...	ACB	ACB	BAC	CAB	Selection 4
A: The numerical results are given by the last two panels of Figure 1B: the proposed Mixed-SLIM significantly outperforms the other three methods under the DCMM setting.C: Subfigure 1(k) suggests that Mixed-SLIM, Mixed-SCORE, and GeoNMF share similar performances and they perform better than OCCAM under th...	ABC	BCA	ACB	CAB	Selection 3
A: First, utilizing the optimal transport framework and the variational form of the objective functional, we propose a novel variational transport algorithmic framework for solving the distributional optimization problem via particle approximation. In each iteration, variational transport first solves the variation...	CBA	ACB	BAC	ABC	Selection 1
A: II. A detailed description of traffic flow configurations is: B: I. Since the real-world strategies tend to break down during bottleneck period (peak hour), to better evaluate the performances of traffic light control methods in the flat-peak-flat scenario, we use two synthetic datasets: mixed high and mixed...	CBA	BAC	ABC	ABC	Selection 1
A: Similar types of sampling-based competitive analysis have recently attracted attention in the context of other online problems such as ski rental and prophet inequalities (?), matching (?), and network optimization problems (?). B: Our analysis of ProfilePacking, as stated in Theorem 3, in conjunction with t...	CBA	BCA	CAB	BAC	Selection 3
A: Secondly, we use a separate neural network that transforms a point from that prior concatenated with points from a 2D square. Its goal is to place that patch on the object’s surface being reconstructed. B: Firstly, we map the target object into a known prior distribution (training) or sample points from that...	BCA	CBA	BCA	BAC	Selection 2
A: Paper organization. This paper is organized as followsB: Section 2 presents a saddle point problem of interest along with its decentralized reformulation. In Section 3, we provide the main algorithm of the paper to solve such kind of problemsC: In Section 4, we present the lower complexity bounds for sad...	BCA	CBA	ABC	BCA	Selection 3
A: Different classes of cycle bases can be consideredB: Among these classes we can find the strictly fundamental class.C: In [6] the authors characterize them in terms of their corresponding cycle matrices and present a Venn diagram that shows their inclusion relations	BAC	ACB	CBA	BCA	Selection 2
A: of Patáková [35, Theorem 2.3] into: B: 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 setting, see Section 1.4.1C: One immediate application of Theorem 1.2 is the reduction of fra...	ACB	CBA	BAC	CAB	Selection 2
A: The feature generation process (described previously) led to the best predictive result we managed to accomplish. The new feature is appended at the end of the list in the punchcardB: From the grouped bar chart in Fig. 6(c), the improvement is prominent for all validation metrics because the brown-colored ba...	ACB	BAC	CAB	BCA	Selection 4
A: The BO progress is shown in Figure 5, right pannel, for the optimization with constraints on the jerk and on the tracking errorB: For the initialization phase needed to train the GPs in the Bayesian optimization, we select 20 samples over the whole range of MPC parameters, using Latin hypercube design of exp...	ACB	ABC	BAC	CAB	Selection 3
A: In addition, we posit that the commonly used benchmarks are not challenging enough to test generalization to realistic scenarios. For example CelebA and Colored MNIST, two of the most widely used benchmarks, contain a single bias variable to mitigate: gender and color respectivelyB: For example, in visual qu...	CBA	CAB	ACB	BCA	Selection 3
A: B: Eye image-based methods typically use head pose vectors as an additional input [17, 55]. \addedNevertheless, the impact of head pose appears to be marginal [49], particularly when the basic network has already achieved high accuracyC: One possible rationale for this observation lies in the fact that a...	CBA	CAB	CBA	BCA	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	ACB	CAB	Selection 2
A: Since Vezzosi [Vez15] gives an embedding of the later modality and its dual into sized types, we believe that a similar arrangement can be achieved in our setting. In any case, we support recursion schemes more complex than structural (co)recursion [LM16]. B: Session types are inextricably linked with SAX, a...	ABC	CAB	BCA	BAC	Selection 2
A: Implement privacy-preserving access controlB: On the other hand, only users authorized by the owner can access the media content. C: On the one hand, the cloud should be prevented from obtaining the private plaintext of the data it encounters, including the owner’s media content, the users’ fingerprints,...	ACB	CAB	BAC	BCA	Selection 1
A: (2017) contain a wide part modeling the low-order interaction and a deep part modeling the high-order interaction.B: Other approaches to modeling second-order and high-order interactions jointly use hybrid architectures. The Wide&Deep Cheng et alC: (2016) and DeepFM Guo et al	ABC	CBA	CBA	CAB	Selection 4
A: [2015], in which more general properties of these pseudo-self-concordant functions were established. This was fully formalized in Sun & Tran-Dinh [2019], in which the concept of generalized self-concordant functions was introduced, along with key bounds, properties, and variants of Newton methods for the unconst...	CBA	ACB	BCA	BAC	Selection 1
A: Nevertheless, this result does not apply to computing a good approximation to the maximum matching in this modelB: We call an algorithm an α𝛼\alphaitalic_α-approximation if the matching has a size at least 1/α1𝛼1/\alpha1 / italic_α times the optimum matching.C: It is known that finding an exact matchi...	CBA	BCA	CBA	BAC	Selection 2
A: CPP can be applied to a general class of unbiased compression operators and achieves linear convergence for strongly convex and smooth objective functions. Second, we consider a broadcast-like version of CPP (B-CPP) which also achieves linear convergence rate for strongly convex and smooth objective functions. B...	CAB	CBA	BCA	CBA	Selection 1
A: Data and modelB: We consider the benchmark of image classification on the CIFAR-10 [46] datasetC: It contains 50,0005000050,00050 , 000 and 10,0001000010,00010 , 000 images in the training and validation sets, respectively, equally distributed over 10101010 classes. To emulate the distributed scenario, ...	BAC	ABC	ACB	ACB	Selection 2
A: When evaluating, we measure equilibrium gaps under their own MS distribution and MW(C)CE to provide a consistent and value maximizing comparison. Experiments were ran for up to 6 hours, after which they were terminated.B: We treat the solutions to the MSs as full joint distributions. Random solvers were eval...	BCA	CBA	ABC	BAC	Selection 2
A: This generalization guarantee, which nearly avoids dependence on the range of the queries, begs the question of whether it is possible to extend these results to handle unbounded queriesB: Clearly such a result would not be true without some bound on the tail distribution for a single query, so we focus in t...	BAC	CAB	BCA	ABC	Selection 4
A: Why not? A kernelization algorithm guarantees that the input size is reduced to a function of the parameter k𝑘kitalic_k; but the running time of modern parameterized algorithms for NP-hard problems is not exponential in the total input sizeB: Instead, fixed-parameter tractable (FPT) algorithms have a runnin...	BAC	BCA	ACB	ACB	Selection 2
A: For instance, Zhu et alB: [209] explored predicting the realism of an image using a CNN classifier. With such realism predictor, they learn the color transformation for the foreground to achieve high realism score, and also enforce the color variation in different channels to be close. Similar to [209],C...	CBA	CAB	CBA	BCA	Selection 4
A: Accordingly, all other data have been categorized as context dataB: In order to facilitate a clear understanding of the data used in this study, we have classified all taxi-related mobility data (including flow, pickup, and idle driving and traffic speed data) as service data, as they pertain to the operatio...	BAC	ABC	CAB	CAB	Selection 1
A: The benefit of working with models that are built upon or include a point predictor is that one also gets a direct estimate of the response variable. Since this is important in many situations, the R2superscript𝑅2R^{2}italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT-coefficients are reported (as noted in th...	ACB	CBA	BAC	ABC	Selection 2
A: It is placed behind the Sub-bar token to imply when the song would perform with the tempoB: We only add tempo token at the beginning of the song and the timing when tempo changes. For MIDI scores, the Velocity and Tempo tokens are simply dropped.C: For MIDI performances, a musical note is represented by ...	BCA	BAC	CBA	CBA	Selection 1
A: Next, let us count the total number of jumps necessary for finding central vertices over all loops in Algorithm 1B: 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 oneC...	CBA	BAC	CAB	ABC	Selection 4
A: In Section III, the details of the proposed DeepSC-SR is presented. Simulation results are discussed in Section IV and Section V draws conclusions.B: Section II introduces the model of semantic communication system for speech recognition and performance metricsC: The rest of this article is structured a...	BAC	BCA	CBA	BCA	Selection 3
A: PSD[35] proposed a Self-Distillation framework that leverages self-supervised learning principles. DAT[36] proposed dual adaptive transformations to learn localization cues from both point-level and region-level. WS3[37] proposed a pretext task as point colorization to explore the information contained in the da...	ABC	BCA	CBA	BAC	Selection 4
A: Qualitative results of our method for Bird’s-Eye-ViewB: All the illustrated images are from the KITTI val set. Zoom in on the circles for more detailed comparison. C: We use black box for ground-truth, red box for baseline results, and blue box for our results	ACB	ABC	ABC	BCA	Selection 1
A: Figure 3: \addThe overall structure of our networkB: The “1/2,64”, “1/4,128”,… and “1/16,512” indicate the scale ratio and the channel number of the input imageC: In the training flow, the TCL map, GGTR map, geometric features, link prediction and node classification are guided by the loss function. In ...	ACB	CBA	ABC	CAB	Selection 3
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...	BAC	ACB	ABC	CAB	Selection 4
A: For block-triangular preconditioners, we focus on a lower triangular type with left preconditioning because an upper triangular one with right preconditioning can be discussed in a similar way [4, 25]B: We consider the following preconditioner: C: We study both block-triangular and block-diagonal precond...	BCA	ABC	ABC	BAC	Selection 1
A: At the same time, the users do not wish to share raw personal data with the companiesB: 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 companie...	CBA	BAC	BCA	CBA	Selection 2
A: The multiplication of tensors, a fundamental and crucial operation analogous to matrix multiplication, has garnered considerable attention across various scientific disciplines. In 2008, Kilmer et alB: This development stemmed from their endeavor to extend the matrix singular value decomposition to the realm...	CAB	CBA	BAC	ACB	Selection 4
A: As shown in Figure 7 (f) and Table 2, we demonstrate that multi-scale feature aggregation obviously benefits the quality of the results, with consistent textures and better quantitative scores reported.B: As our CFA module is updated from the contextual attention layer [35], we directly compare it with the o...	BAC	ACB	CBA	ACB	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...	BCA	ACB	BAC	ACB	Selection 1

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