Split (1)

train · 2.93k rows

shuffled_text stringlengths 267 3.16k	A stringclasses 6 values	B stringclasses 6 values	C stringclasses 6 values	D stringclasses 6 values	label stringclasses 4 values
A: Its implementation uses graph neural networks architectures and achieves 92929292% success rate for L=5𝐿5L=5italic_L = 5. Our transformed-based baseline is stronger - it solves over 99999999% instances on the same problem set.B: An MCTS-based agent was also evaluated in [54]C: We check also that MCTS-k...	ACB	ABC	CBA	ACB	Selection 3
A: In this paper, we propose a lightweight method fusing the glyph and phonetic features for Chinese Named Entity Recognition (MFE-NER) to handle the Chinese character substitution problem, which introduces ’Five-strokes’ to represent glyph features, making it more convenient to differentiate similar characters, an...	CAB	ACB	BCA	ABC	Selection 1
A: Fig. 3a reports qualitative examples of trichromatic and monochromatic reconstructions achieved with neural étendue expanders, binary random expanders[7], photon sieves[21], and conventional holography[1]B: See Supplementary Note 2 for additional qualitative comparisons and for comparisons against uniform ra...	CAB	CBA	BCA	CAB	Selection 3
A: The tasks include 2 classification tasks, 2 structure prediction tasks, 3 question answering tasks, and 2 sentence retrieval tasksB: Xtreme (Hu et al., 2020) is a multi-task benchmark dataset for evaluating cross-lingual generalization capabilities of \replacedmultilingualmulti-lingual representations coveri...	CBA	BCA	CBA	BAC	Selection 4
A: The argument to array controls alignment and placement of vertical rules. B: The array environment allows you some options for matrix-like equationsC: You will have to manually key the fences, but you’ll have options for alignment of the columns and for setting horizontal and vertical rules	CAB	ABC	ABC	CBA	Selection 1
A: For C1⊆V⁢(G1)subscript𝐶1𝑉subscript𝐺1C_{1}\subseteq V(G_{1})italic_C start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT ⊆ italic_V ( italic_G start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT ), we define f⁢(C1)=⋃v∈C1{f⁢(v)}𝑓subscript𝐶1subscript𝑣subscript𝐶1𝑓𝑣f(C_{1})=\bigcup_{v\in C_{1}}\{f(v)\}italic_f ( italic_C start_POST...	ABC	ABC	BCA	BAC	Selection 3
A: By virtue of the endogenous linking decisions, our study contributes to the growing literature on network formation games, and especially to more recent work examining simultaneous action and link formation decisions. Network formation problems are notoriously difficult to analyze, but have far-reaching consequ...	ACB	ABC	CBA	BAC	Selection 2
A: In StereoSR (Jeon et al., 2018), Jeon et alB: proposed a method that learned a subpixel parallax prior to enhancing the spatial resolution of the stereo imagesC: However, the number of shifted right images is fixed in StereoSR, which makes it fail to handle different stereo images with large disparity va...	ABC	BCA	BAC	ACB	Selection 1
A: The learned signals can be of any dimensionality, however, encoding of spatial coordinates is a particularly popular theme, involving a network that learns to produce given scalar values based on the input coordinatesB: This allows for considerable flexibility and leads to applications such as self-supervis...	CBA	ABC	ACB	BCA	Selection 4
A: Apples aside, conceptually similar problems may arise in numerous settingsB: Products may be satisfactory or faulty, and the decision-maker can determine this by investigating the product, subject to a further cost. It may be prohibitively costly to investigate every product but it is also important to ident...	ACB	BAC	BCA	BCA	Selection 1
A: Nevertheless, whether attention could be meaningfully used as an analysis tool, especially from the perspective of a layman or end-user, is a matter of discussion [5, 6].B: Sure, the attention mechanism [4] could offer cues as to how to interpret the behavior of such modelsC: In spite of the welcome leap...	BAC	BAC	CAB	CBA	Selection 4
A: This behavior is to be expected, as one can notice when looking at the logic formulas: P.3 resembles, in fact, the structure of the right side of implication (“→→\rightarrow→”) in P.1–P.2, except that it enforces stricter requirements (there must be an uncrowded area for the next half-hour)B: Yet, the obtain...	ABC	ACB	CAB	BCA	Selection 2
A: This is summarised by the Diagram 5 recalled below on the left.B: Theorem 22 shows that the notion of quantitative equality given in this paper is coalgebraic, in the sense that Lipschitz doctrines are the coalgebras of a comonad over the category of graded doctrinesC: This generalizes a known situation...	BCA	BCA	ACB	CAB	Selection 4
A: For effectiveness, ForestSim achieves comparable performance to RoleSim and better results than StructSimB: For efficiency, ForestSim is the unique role similarity metric that can process top-k queries within 4 hours on million-scale graphs.C: Extensive experimental studies : We test the effectiveness o...	CBA	CAB	BCA	BAC	Selection 3
A: For refining syntax structure quality in sentiment dependency learning, Tian et alB: (2021) employ type-aware GCN to distinguish different relations in the graph, achieving promising results. Similarly, Li et alC: (2021a) propose SynGCN and SemGCN for different dependency information.	BCA	CAB	ABC	BCA	Selection 3
A: The operator 𝐋𝐋\mathbf{L}bold_L and 𝐖𝐖\mathbf{W}bold_W are constructed from a multilevel decomposition of the location of predictorsB: However, for the exposition in this section it sufficient to know what the properties of the operators 𝐋𝐋\mathbf{L}bold_L and 𝐖𝐖\mathbf{W}bold_W are. C: This proc...	CAB	BCA	BAC	ACB	Selection 4
A: We visualize the two features obtained from experiments on Belem in a 2-D plane as in Figure 8 right. The blue dash line is the classification boundaryB: The circles/stars are samples of digit ‘3’ and ‘6’. All the baseline points (yellow) huddled together, and all digit ‘3’ samples are misclassified. With no...	BCA	CBA	CAB	ACB	Selection 1
A: (c) The event trajectories triggered by the moving drone. (d) The event trajectories calculated by the proposed EDA in the 3D spatio-temporal domain. The green and red lines in (d) are triggered by the drone motion and the camera motion, respectively.B: Figure 1: An illustration of event trajectories trigge...	BAC	CAB	BAC	BAC	Selection 2
A: We now prove our main result, that there are no ugly perfect graphsB: This generalizes the same fact which was previously proved for Meyniel graphs [22] (a class which contains chordal graphs, HHD-free graphs, Gallai graphs, parity graphs, distance-hereditary graphs…) and line graphs of bipartite graphs [3]...	ACB	ACB	ABC	ACB	Selection 3
A: GenURL not only takes into account the data geometries that are the focus of DE and GE but also introduces task-relevant data and model hypotheses from CL and KD to enhance the overall quality of the representations and thus improve the downstream tasksB: We summarize and analyze the above URL approaches and...	CBA	BCA	CBA	BAC	Selection 4
A: We re-calibrate the batch normalization statistics using 20 batches of data with a batch size 64. B: To prevent over-fitting the real validation set, we evaluate the performance of each sub-network on the split validation setC: The weights are taken from the super network using indexing	CAB	CBA	ACB	BAC	Selection 1
A: There will be a separate online testing set. An example of the data is shown as follows:B: The 500 articles will be labeled and served as the training setC: The ICDM 2020 Knowledge Graph Contest competition provides 500 recent articles444http://biendata.xyz/media/dataset.zip from Instagram which is selec...	ABC	CBA	BCA	ACB	Selection 2
A: The upper part of each augmentation strategy shows augmented graphs preserving high invariance, while the lower part’s augmentations bring low invariance.B: Figure 1: An illustration of the unstable invariance of three graph augmentation strategiesC: The value of each augmented graph is its similarity t...	CAB	BAC	BAC	BCA	Selection 1
A: We choose images with values for both features ranging in {0,1,2,3}0123\{0,1,2,3\}{ 0 , 1 , 2 , 3 }. The obverter dataset contains images of four shapes (box, cylinder, ellipsoid, sphere) in four colors (blue, cyan, gray, green)B: Each image has dimensions (128,128,3)1281283(128,128,3)( 128 , 128 , 3 ), and ...	CBA	BCA	CBA	CAB	Selection 2
A: Learning with CBFs: Approaches that use CBFs during learning typically assume that a valid CBF is already given, while we focus on constructing CBFs so that our approach can be viewed as complementary. In [19], it is shown how safe and optimal reward functions can be obtained, and how these are related to CBFs. ...	BAC	BAC	ACB	BCA	Selection 3
A: A DNF formula, also just called a DNF, is a depth-2 𝖠𝖢𝟢superscript𝖠𝖢0\mathsf{AC^{0}}sansserif_AC start_POSTSUPERSCRIPT sansserif_0 end_POSTSUPERSCRIPT circuit where the top gate is an OR gate (i.eB: the circuit is an OR of ANDs)C: The width of a DNF is the maximum fan-in of any of the AND gates.	CAB	ABC	BAC	CBA	Selection 2
A: For visualization, two nodes are connected by a line if there is a positive edge weight between them, and we do not show edge weights here.B: Figure 10: Panel (a) and panel (b) show nDFA’s detection results of CoauthorshipsNet1589 and CoauthorshipsNet379, respectivelyC: Here, different colors are used t...	CBA	ABC	ABC	CAB	Selection 4
A: We evaluate MA-Trace on StarCraft Multi-Agent Challenge Samvelyan et alB: Our approach achieves competitive performance on all tasks and exceeds state-of-the-art results on some of them. Additionally, we provide a comprehensive set of ablations to quantify the influence of each component on the final results...	CBA	BCA	ACB	CAB	Selection 3
A: There is also a body of literature devoted to regression problems. Muhlbacher2013APartition ; Sehgal2018Visual ; Zhao2014LoVis ; Das2019BEAMES For instance, BEAMES Das2019BEAMES contains four ML algorithms and a model sampling method, with the help of which the system generates an ordered list of models that a...	CAB	BCA	CAB	ACB	Selection 4
A: In the proposed iterative joint polarization pre-post coding, the local optimum usually reached the global optimum of Tx/Rx-polarization vectors within five iterationsB: The proposed method offers an energy-efficient as well as cost-efficient method to significantly increase channel capacity in PR-HS-MIMO sp...	ABC	ACB	ABC	BCA	Selection 4
A: Most existing theoretical research on packing, and all research on online translational packing that we are aware of, is concerned with axis-parallel rectangular pieces. In this paper, we study online translational packing of convex polygonsB: The aim is to minimize the used space depending on the specific p...	ACB	ABC	ABC	BAC	Selection 1
A: Yao et al. [42] introduce a pixel-wise multi-layer proxy task for self-supervision, which provides more generalized features utilized in the template-matching process. Lei et al. [18] propose another proxy task of predicting the relative offsets of two patches from an identical image. Not only that, the relative...	BAC	CAB	CBA	BCA	Selection 3
A: Output: ΠΠ\Piroman_Π. B: Input Ω,KΩ𝐾\Omega,Kroman_Ω , italic_KC: The above analysis gives rise to the following algorithm called Ideal DFSP (short for Distribution-Free SP algorithm) in the oracle case with known population adjacency matrix ΩΩ\Omegaroman_Ω	BAC	ABC	CBA	ABC	Selection 3
A: The contributions of this paper are as follows: 1) We empirically discover that encouraging the CIL learner to mimic the oracle model in the initial phase can boost the CIL performanceB: 2) We find that compared with naïvely-trained initial-phase model, data representations of each class produced by the orac...	BCA	ABC	ACB	BCA	Selection 2
A: To address this set of challenges, two methods that implement and build on the VoxelMorph framework were evaluated: SynthMorph and HyperMorph. B: Furthermore, registration quality was often sensitive to the selected values of hyperparameters, such as λ𝜆\lambdaitalic_λC: VoxelMorph generally performs bet...	ABC	CBA	BAC	ABC	Selection 2
A: However, as discussed in [25], these are different models that require different techniquesB: Moreover, it remains unclear how the block-independent-disjoint probabilistic model can be adapted to align with the case of an LHS chain. C: Let us further note that the case of primary keys is closely related ...	CAB	CAB	ABC	BCA	Selection 4
A: Absorbing random walks have been used to develop centrality measures [14], other methods to rank the nodes of a network [46], transduction algorithms (which one can use to infer the labels of the nodes of a graph from the labels of a subset of the nodes) [7], and moreB: Absorbing random walks also arise nat...	ABC	BAC	CBA	ACB	Selection 4
A: Note that throttling does not alter the generation latency of the root and thus the overall tree; we prove the optimality of the overall algorithm formally in Theorem 2B: In our overall methodology, to conserve node and link resources, we post-process or ”throttle” the swapping-tree obtained from the DP algo...	CAB	ACB	ABC	BAC	Selection 4
A: This guideline includes the design, development, testing, and verification of software systems in automotive applications. Based on these standards, there seem to be two fundamental approaches to confirming regulatory compliance, which we label confirmation of compliance by “simulation,” and confirmation of comp...	CAB	ABC	CBA	BAC	Selection 4
A: Thanks to the development of deep learning technology, a model named NetVLAD implements the VLAD layer embedding and aggregation behind the CNN structure for VPR [9]. In recent years, [24, 25, 26, 27] improve the origin NetVLAD model with the process of changing the loss function or adding feature matching to en...	CAB	BAC	CBA	ABC	Selection 3
A: The results in this paper appear partially in the MSc thesis of the second author, who thanks his supervisors (the other two authors). This work has been partially presented in Cryptography and Coding Theory Conference 2021, organized by the group UMI (Italian Mathematical Union) “Crittografia e Codici” and by D...	CAB	CAB	CBA	BCA	Selection 3
A: The following examples show that the requirement is not satisfied for common resolving gadgetsB: We tried to construct a gadget that would satisfy the condition, but in the end, we kept the previously resolved parts of the game G′superscript𝐺′G^{\prime}italic_G start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT fo...	ABC	CBA	BAC	ACB	Selection 1
A: The result is trivial if n=1𝑛1n=1italic_n = 1, since both sides of (3.3) are 0B: We shall use induction on n𝑛nitalic_nC: Let n≥2𝑛2n\geq 2italic_n ≥ 2 and assume that the result holds for all inputs of length n−1𝑛1n-1italic_n - 1. We shall consider two cases.	CAB	CAB	BAC	ACB	Selection 3
A: Following the full-text screening made in the first step of our threefold approach, we summarized some meaningful indicators about the analysis (such as type - quantitative, qualitative, theoretical, policy, literature review -, level - macro or micro for quantitative and qualitative studies -, unit - country, h...	CBA	ACB	BAC	CAB	Selection 1
A: The negative term in the regret bound (13) of Lemma 2 is very essential, which is quite useful in a variety of problems requiring adaptive boundsB: Our analysis is based on the unified view of Optimistic OMD (Theorem 1), and is much simpler than the original proof of Syrgkanis et alC: (2015) using the ma...	ABC	CAB	CBA	BCA	Selection 1
A: There are positive integers m,p𝑚𝑝m,pitalic_m , italic_p such ψm=ψm+psubscript𝜓𝑚subscript𝜓𝑚𝑝\psi_{m}=\psi_{m+p}italic_ψ start_POSTSUBSCRIPT italic_m end_POSTSUBSCRIPT = italic_ψ start_POSTSUBSCRIPT italic_m + italic_p end_POSTSUBSCRIPTB: Let ψn=φ∘σnsubscript𝜓𝑛𝜑superscript𝜎𝑛\psi_{n}=\varphi\circ\si...	BCA	BAC	CBA	CAB	Selection 2
A: (2017)B: More precisely, these authors established the third term on the right-hand side inC: The result in Theorem 4 for s≥1/2𝑠12s\geq 1/2italic_s ≥ 1 / 2 (that is, 2⁢k+2≥d2𝑘2𝑑2k+2\geq d2 italic_k + 2 ≥ italic_d) was already derived in Sadhanala et al	BCA	CAB	ACB	ACB	Selection 1
A: Figure 11 displays the state visits in randomly selected 3 MZ- and 3 DZ-twinsB: The heritability estimation of state space is not a trivial task since the estimated state does not synchronize across twins making the task fairly difficultC: However, the time series of state changes do not synchronize with...	BAC	ABC	ABC	ABC	Selection 1
A: rule (3b)B: ), given in Corollary 8, is satisfied. Hence, for this case, the inter-event time function τs⁢(θ)subscript𝜏𝑠𝜃\tau_{s}(\theta)italic_τ start_POSTSUBSCRIPT italic_s end_POSTSUBSCRIPT ( italic_θ ) is continuously differentiable and it is also periodic with period π𝜋\piitalic_π. FromC: Figure...	ACB	CBA	BCA	CBA	Selection 1
A: Note that, if the design is done solely based on ISSt criterion, then there is no guarantee that it will also satisfy pISSfB: To illustrate this point, and to highlight the potential advantage of combined pISSf-ISSt design, here we choose the St-C such that the gains do not satisfy pISSf conditions for the c...	ABC	ACB	BCA	ACB	Selection 3
A: Most of these studies ask participants to self-report their health and wellbeing using validated instruments. While some studies use one-off survey scores, others employ experience sampling to study worker’s wellbeing in a longitudinal fashion. We list the key studies in Table I.B: Among the papers selected,...	ACB	CAB	CBA	CAB	Selection 3
A: The rest of the paper is organized as followsB: In the following section, we develop our spectrum allocation model and setting, discuss related work, and give a high-level overview of our approach. In §III, we develop our CNN-based deep learning model and associated techniques for spectrum allocation. We dis...	BAC	BCA	ABC	CBA	Selection 2
A: This paper is the result of an REU project, which turned out to be of great pedagogical value, as it taught the students to combine the results and methods from various subjects: differential geometry, algebra, analysis and numerical analysisB: We hope that we are able to convey to the reader the enjoyment o...	BAC	BAC	CBA	ACB	Selection 4
A: On the other hand, the use of an irregular order is then considered by researchers to accelerate convergence. Particularly, it is shown in [31] that randomization leads to faster convergence in terms of expectationB: A substantial review of variants of coordinate descent algorithms can be found in [4, Secti...	CAB	BAC	CAB	CBA	Selection 2
A: This enables a single layer to integrate information across multiple time scales simultaneously. Thus, the standard deviations of activity for recent and past events are comparable. This is true for both the Memristor model and the Memristor without STP.B: Integration across multiple scales was obtained usin...	ABC	BCA	ACB	CBA	Selection 4
A: Başar (2015) is to significantly tighten and generalize their proofB: This allows us to improve the bound on the expected drop of the potential function (see Lemma 8). C: To do so, we develop a projection argument (see Lemma 4) and a new analysis of the expected available movement of a randomly chosen ag...	ACB	BAC	ABC	ABC	Selection 1
A: In particular, the model achieved high sensitivity and accuracy on all conditions, indicating its ability to correctly identify positive casesB: For example, for the Pneumonia condition, the sensitivity is 0.6, but given that the model predicted a positive result, the probability that the person actually has...	ABC	ACB	BAC	ABC	Selection 2
A: We have demonstrated that the Bayes optimal algorithm, which is optimized for the expected performance over the prior, does not have a frequentist rate of simple regretB: This paper has considered the BAI problemC: In some distributions, the Bayes optimal algorithm does not perform well, even when the d...	BAC	BCA	ACB	CAB	Selection 1
A: For example, the interaction of a molecule (per se rotational invariant) with an external magnetic field is an intrinsically equivariant problem. B: (2020)C: In fact, in many real-world application, equivariance is beneficial if not necessary Smidt (2020); Miller et al	BCA	CBA	BCA	BCA	Selection 2
A: its smoothness and variability in space and timeB: Part of our rationale for using comparatively simple GP kernels is to facilitate interpretation of hyperparameters, and analysis of the distribution patterns of pollution in an area, e.gC: This lets us perform “at-a-glance” comparisons of different areas...	CAB	BCA	BAC	ABC	Selection 3
A: Training binary latent VAEs with K=2,3𝐾23K=2,3italic_K = 2 , 3 (except for RELAX which uses 3333 evaluations) on MNIST, Fashion-MNIST, and OmniglotB: We report the average ELBO (±1plus-or-minus1\pm 1± 1 standard error) on the training set after 1M steps over 5 independent runsC: Test data bounds are rep...	CAB	ABC	BAC	BCA	Selection 2
A: The dashed lines depict the scenario with finite pool of Q=24𝑄24Q=24italic_Q = 24 pilot sequences and the solid horizontal line is the corresponding lower bound as given by (29)B: The markers represent the performance of the probability of the outage for the idealized version of the Random selection scheme ...	BAC	ABC	CBA	ABC	Selection 1
A: Variation of this algorithm also appears in [BNV19]B: Later, Schul [Sch07] provided a modification of the algorithm so that the ratio of the length of the yielded path over the length of the optimal path is bounded by a constant C𝐶Citalic_C independent of the dimension N𝑁Nitalic_NC: Here and for the r...	ABC	BAC	CAB	CBA	Selection 2
A: The polymatroids of this form are now called Chow polymatroids,333This naming is unfortunate for us because we will later associate a polymatroid to the formats of non-degenerate Chow forms of a variety, which are not themselves Chow polymatroidsB: an interesting (and proper) subclass of polymatroids. C:...	CAB	CBA	BCA	CAB	Selection 3
A: Regarding the columns, 5555 of them refer to information about the volunteer (identifier and age group) and the video (batch, display position, and video code number), whereas 9999 of them refers to the self-assessment provided (arousal, valence, dominance, liking, reported and target discrete emotions, and fami...	ABC	BCA	CBA	ACB	Selection 3
A: RNN & LSTM: Recurrent Neural Network (RNN) processes sequential data by retaining a memory of past inputs (Grossberg, 2013)B: It operates by processing each input in the sequence, updating the memory state, and producing an output fed back into the RNN for the subsequent time stepC: Long Short-Term Memor...	BAC	ABC	BCA	BAC	Selection 2
A: Almost surely either a player was chosen on Step 1 or 2 or the sum of just the odd-numbered terms (given by B’s moves) of expression (2) diverges to ∞\infty∞, by Lemma 3.6B: In the latter case, the sum of the even-numbered terms must diverge to −∞-\infty- ∞ (as the sum of all terms is convergent), and theref...	ABC	BAC	CAB	CBA	Selection 1
A: We find that the AI component-level and AD system-level results are quite different and often contradict each other in common driving scenarios, which further demonstrates the necessity and benefits of such system-level evaluation infrastructure building effort. Demos are available on our project website https:/...	CBA	BCA	CAB	ACB	Selection 3
A: It also contains a Red join gadget in [1.5,4.5]1.54.5[1.5,4.5][ 1.5 , 4.5 ]. Its long intervals terminate at 1.51.51.51.5 and start from 4.54.54.54.5. B: It also contains the third switch gadget in [12,21]1221[12,21][ 12 , 21 ]. Its long intervals terminate at {12,15.5}1215.5\{12,15.5\}{ 12 , 15.5 } and star...	CBA	BAC	ACB	CAB	Selection 1
A: Through partial freezing on 256-frame sequences, all our models with BN-free backbones (FrozenBN, GN and ConvNeXt) outperform the previous state of the art, including complex multi-stage methods with attention-based temporal models [24, 39, 12], complex augmentation pipelines [85] or Transformer backbones [12, 2...	BCA	ACB	CBA	BCA	Selection 2
A: For testing, several benchmark datasets (IJB-B[37], IJB-C[17], MegaFace[12], LFW[10], CFPFP[24], AgeDB-30[19], CALFW[41], and K-FACE:Q1-Q4[11]) were used to evaluate FR models. Table 1 summarizes the datasets used in our experiments. B: Datasets. For training, MS1M-V2[4] and K-FACE:T4[11] datasets were emplo...	CBA	CAB	BCA	CBA	Selection 2
A: Different approaches have been used to address this shortcoming such as transfer learning using previously trained models, and data augmentation [15, 16]B: Brigato et al. [17] highlight the value of standard data augmentation techniques, such as cropping, rotation, and color jittering, as well as using low-c...	ACB	ABC	BAC	CAB	Selection 2
A: Based on this, secondely, we make an experiment to pursue the change of weights corresponding to each local region in the process of training our model.B: From Fig.10, it is also observed that the location of crucial regions is different for different facial imagesC: But, our network still automatically...	ACB	CBA	BAC	CAB	Selection 4
A: Note for δ=0𝛿0\delta=0italic_δ = 0, one recovers the original strategy. As before, we may have to flip everyone’s rating to ensure we end with a very high rating, as opposed to a very low rating. We need not assume that the initial ratings are all 0 anymore, but we do need to allow ourselves this possible refle...	ACB	CBA	BAC	BCA	Selection 2
A: Next, the system should give a prediction for a data point and juxtapose it to all other pointsB: After the extraction of evidence as defined in G3, users should see how the distribution of instances will change due to the undersampling and/or oversampling phasesC: With this, users should be able to esti...	ACB	ABC	ACB	BAC	Selection 4
A: Each Ethereum block in our dataset has on an average 151 transactions, whereas it is 120 transactions in each BSC block. Thus, 666 BSC transactions are confirmed in the same time as 151 Ethereum transactions. B: For example, from our data, in the time it takes Ethereum to confirm one block, BSC confirms on a...	CAB	BCA	CBA	BAC	Selection 3
A: Furthermore, we show that under additional conditions, the mean-field equilibrium arises via fixed-point iterationB: In Section 3, we give conditions on our model that guarantee existence and uniqueness of equilibria in the mean-field regime, the limiting regime where at each time step, an infinite number of...	BAC	ABC	CAB	ACB	Selection 1
A: However, [27] does not study network depth, unlike our work. Our paper suggests that using only the necessary capacity for each example yields greater robustness.B: Relation to Mixed Capacity Models. Recent studies show that sufficiently simple models, e.g., with fewer parameters [13, 27] or models trained ...	ACB	CBA	CBA	CAB	Selection 4
A: Swin Transformer [48] improves ViT by introducing shifted windows when computing self-attention. The effectiveness of transformers has been validated in tracking [93, 94], crowd counting [95, 92], multi-label classification [96] and so on. In the following, we specifically discuss the transformer-based segmentat...	ABC	CAB	BAC	BCA	Selection 2
A: Taobao Alibaba [21] is a user behavior dataset for recommendation problems with implicit feedbackB: We train on four real-world datasets, listed in Table IIC: Criteo Kaggle [22] dataset contains advertising data and is obtained from the Display Advertising Challenge to capture user preferences by predic...	BCA	BAC	ABC	CAB	Selection 2
A: Flat cells in the PL category should be viewed as the appropriate analogues of critical points in the smooth category, with the caveat that not every flat cell is criticalB: The flat cells of F𝐹Fitalic_F are, by definition, the cells of 𝒞⁢(F)𝒞𝐹\mathcal{C}(F)caligraphic_C ( italic_F ) on which F𝐹Fitalic_...	CAB	CBA	CAB	BAC	Selection 4
A: In nonequilibrium dynamics, a common issue is the emergence of universalities during the critical dynamics of a phase transitionB: One of the most well-known challenges in this regard is the formation of topological defectsC: It is stated that topological defects will arise in the course of a phase trans...	CBA	BAC	BAC	ABC	Selection 4
A: In Section 2, we provide preliminaries, notation and helicity-conservative finite element schemeB: In Section 3, we present a PINN-based algorithm that preserves the helicity. In Section 4, we present numerical results on the convergence and helicity-preserving properties of our algorithms. In Section 5, we ...	ABC	ACB	BCA	BAC	Selection 3
A: For tuples that have higher RU values (meaning they are less reliable w.r.tB: Our extensive proof-of-concept experiments verify a consistent correlation between RU values and ML performance metrics on a variety of tasks, data sets, and ML algorithmsC: to our measures), an ML model is more likely to fail...	BCA	BAC	ACB	ACB	Selection 2
A: Most of the families (63%, N=12 Parents and 74%, N=14 Teens) found these features beneficial in terms of ensuring online safetyB: Interestingly, teens often talked about online safety and security synonymously. C: The features of collaborative privacy and safety management that allowed users to review on...	ACB	CAB	CBA	BCA	Selection 4
A: After reviewing the mathematical background in Section 2, we define the proposed filtration in Section 3 and discuss its properties in Section 4B: We discuss bootstrapping in Section 5 and present numerical simulations in Section 6. A discussion and the conclusion are presented in Sections 7 and 8. We collec...	CBA	CAB	BCA	CBA	Selection 3
A: 2000) to illustrate the causalities among variables in AU recognition modelsB: As shown in Fig. 2, there are four variables involved in our AU causal diagram, which are facial images X𝑋Xitalic_X, subjects S𝑆Sitalic_S, latent AU semantic relations R𝑅Ritalic_R, and estimated AU occurrence probabilities Y𝑌Y...	ACB	BCA	CBA	CBA	Selection 2
A: In our experiment, we set up a node called ObserverNodeB: When ObserverNode received a new block from one of its neighbor nodes, it extracted all transactions in the block and converted all these transactions to a set of hashesC: It then pretended to miss these transactions and requested them from all i...	BAC	ABC	BCA	BCA	Selection 2
A: Such statistical challenges are already prohibitive even for the evaluation of a policy (Nair and Jiang, 2021; Kallus et al., 2021; Bennett and Kallus, 2021), which forms the basis of policy optimization. From a computational perspective, it is known that policy optimization is generally intractable (Vlassis et ...	CBA	BCA	BCA	BAC	Selection 1
A: However, 50% of the studies did not address any specific SSD. Concerning the level of autonomy (research question 2), almost all studies proposed fully automated AI-based speech therapy tools suggesting that researchers did not emphasize the role of caretaker, parents, family members, and SLPs. Addressing the mo...	ACB	CBA	ACB	CAB	Selection 4
A: With regards to PCA’s impact on data with community structure, the primary work has been in connection to K-Means, which is also known as spectral clusteringB: Here, one of the first works [DH04] showed that the outcome of PCA can be viewed as an approximation result to the K-Means outcome in clustering data...	CAB	ABC	ACB	CAB	Selection 2
A: Note that the dashed lines denote the operations only after the dialog is completed) for the final scene graph generation.B: We first obtain the preliminary objects from the object detector based on the incomplete visual input, and propose to conduct an interactive dialog processC: Fig. 1: The overall a...	CAB	CBA	ABC	BAC	Selection 2
A: The following proposition obtains a similar lower bound against all deterministic strategyproof mechanisms as in Theorem 3B: The basic idea of the proof is to duplicate the agent profiles in the proof of Theorem 3 at locations far enough from the original profiles and then apply the proof of Theorem 3 in a s...	CAB	ACB	BCA	BAC	Selection 3
A: There is some more bibliography to add to the already vast literature [3, 10, 23, 31, 33, 35] on dominating induced matchingsB: There is a paper [16] where the authors describe ILP formulations for the PED problem, together with some experimental results. C: As we have seen before the papers on perfect e...	CBA	CBA	BAC	ACB	Selection 4
A: Another possibility to enforce input constraints requires one to design a tailored training phase for the NN at handB: In particular, the generation of adversarial examples and traditional output verification techniques have been widely employed to produce training signals able to guide the network’s output...	ABC	CAB	BAC	ACB	Selection 1
A: We now consider the renderings of a nonlinear pendulum from [60] (cf. Figure 4)B: [60] uses a coordinate aware variational encoder to obtain the initial state from object masks. After the state trajectory is integrated using a learnable Lagrangian function parametrizing the dynamics of the system, a coordin...	ACB	CAB	ABC	CAB	Selection 1
A: To do so, we must the error sources encountered during both the quantum communication errors and the quantum semantic errors present in practical setups in the QSC framework.B: Moreover, to assess the accuracy of the QSC performance within the quantum semantics’ extraction, transmission, reception, and decod...	ABC	CBA	BAC	ACB	Selection 2
A: The logarithmic term in EqB: (11) is the policy entropyC: It encourages action-space exploration by promoting random selections, thus reducing the probability to converge to deterministic policies with poor local optima. Parameter τ𝜏\tauitalic_τ is the trade-off weight used to balance the importance of ...	ACB	ABC	CAB	ACB	Selection 2
A: Suppose that companies who receive approval make $100 million in profit, 10 times their investmentB: In this case, agents of type θ=0𝜃0\theta=0italic_θ = 0 would choose not to run trials, since their expected profit for running a trial is -$5 million. Hence, all approved drugs would be effective drugs. *C...	BCA	CBA	CBA	BAC	Selection 1
A: The number of iterations and displacement RMSE values also exhibit similar trends. In terms of communication and computation times, PPIR(MPC) demonstrates comparable performance with Clear, showcasing its feasibility for secure registration. The computation times and communication bandwith between the two partie...	CBA	CAB	BCA	BCA	Selection 2

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