Split (1)

train · 2.93k rows

shuffled_text stringlengths 267 2.9k	A stringclasses 6 values	B stringclasses 6 values	C stringclasses 6 values	D stringclasses 6 values	label stringclasses 4 values
A: For INT and Rubik’s Cube, we include both the subgoal generated by SUB_GENERATE and the nodes visited by GET_PATH (as they induce a significant computational cost stemming from using low-level policy π𝜋\piitalic_π in Algorithm 2). For Sokoban, we use Algorithm 9 to realize GET_PATH, as it has a negligible cost ...	BCA	BAC	CBA	CAB	Selection 4
A: Figure 5: An example is drawn from our dataset, ’he wants to go to Dapuqiao’B: The sentence at the top of the figure is the original sentence, while the sentence at the bottom is after character substitutionC: The model using MFE-NER gives the correct prediction.	CAB	ACB	ABC	BAC	Selection 3
A: Next, we analyze the expansion of étendue achieved with the proposed techniqueB: To this end, suppose we want to generate the étendue-expanded hologram of only a single scene. Then, the optimal complex wavefront modulation for the neural étendue expander would be the inverse Fourier transform of the target s...	ABC	CBA	CAB	CBA	Selection 1
A: GLUE (Wang et al., 2019b) is a benchmark dataset for evaluating natural language understanding (NLU) modelsB: The main benchmark consists of 8 sentence and sentence-pair classification tasks as well as a regression taskC: The tasks cover a diverse range of genres, dataset sizes, and difficulties. Besides...	CAB	BCA	ABC	CBA	Selection 3
A: IEEE recommends using the distribution from the TeXUser Group at http://www.tug.orgB: You can join TUG and obtain a DVD distribution or download for free from the links provided on their website: http://www.tug.org/texlive/C: The DVD includes distributions for Windows, Mac OS X and Linux operating syste...	ABC	CBA	BAC	CAB	Selection 1
A: definition of c⁢(vi)𝑐subscript𝑣𝑖c(v_{i})italic_c ( italic_v start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT )B: Then H,ℓ′,𝒞′⊧ϕa⁢d⁢j⁢[x1∖u1]⁢[x2∖u2]models𝐻superscriptℓ′superscript𝒞′subscriptitalic-ϕ𝑎𝑑𝑗delimited-[]subscript𝑥1subscript𝑢1delimited-[]subscript𝑥2subscript𝑢2H,\ell^{\prime},\mathcal{C}^{...	ACB	BAC	ABC	BAC	Selection 3
A: This characterization is inspired by the literature on group size effects in voluntary contributions environments (e.gB: Pure congestion describes a situation in which players generate fixed efficiency gains by sharing, and gains from their investment are divided between the sharing player and their neighbor...	CAB	BAC	BCA	ACB	Selection 4
A: To reduce the dependence of accurate depth or disparity information as priors for the light-field image super-resolution, Sun et al. (WangB: Meanwhile, a new light field image dataset is proposed for training and validationC: In (Yoon et al., 2017), a cascade convolution neural network is introduced to s...	BAC	CBA	ABC	CAB	Selection 2
A: Otherwise, we can create a trainable downsampling module representing the kernel and optimize its weights in an end-to-end manner. We revisit the technique introduced in [29] by using an identical deep linear network to approximate the kernelB: The downsampling operation can be implemented in several ways. I...	BAC	CBA	CAB	ABC	Selection 1
A: The most advanced results on the regret of approximate TS in simple multi-armed bandits, for instance, rely on complex Bayesian non-parametric theory, which, to the best of our knowledge, does not yet have a known analog applicable to contextual problems (Mazumdar et al.,, 2020).B: to establish that the regr...	BCA	ACB	CBA	BCA	Selection 3
A: Table 5 reports our results on claim detection. Like we did with unfair clause detection, we evaluate knowledge-agnostic neural baselines and MANN models. First of all, we note how MANNs achieved significant improvements over CNNs and LSTMs, suggesting that the introduced knowledge is indeed beneficial to the ta...	BAC	ACB	ABC	BCA	Selection 2
A: 5 Navigli on the other hand, which is a district famous for its different types of cafés, restaurants, bars and design shops, exhibits a more uniform behavior among the working and the weekend days, with activity peaking in the evening hours. The grid square containing the 4 Bocconi university exhibits a clear...	CAB	ABC	BCA	CBA	Selection 4
A: 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 doctrinesB: This is summarised by the Diagram 5 recalled below on the left.C: This generalizes a known situation...	BAC	ACB	BCA	ABC	Selection 2
A: The key point of analyzing structural roles is figuring out how a vertex connects with its context nodes [43]B: To some extent, the sizes of those trees rooted at u𝑢uitalic_u in the spanning rooted forests of a graph reflects the connection mode between the node u𝑢uitalic_u and its context verticesC: H...	ACB	ABC	BAC	CAB	Selection 2
A: (2021a); Dai et al. (2021), has contributed to the improvement of coherent sentiment learning. These studies explored the effectiveness of syntax information in ABSC, which mitigates issues related to sentiment coherency extraction.B: (2020); Tian et al. (2021); Li et alC: However, the progress of sentim...	CAB	CAB	BAC	CBA	Selection 4
A: 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. B: The operator 𝐋𝐋\mathbf{L}bold_L and 𝐖𝐖\mathbf{W}bold_W are constructed from a multilevel decomposition of the location of predictorsC: This proc...	ABC	BAC	ACB	CAB	Selection 4
A: 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 normalization (green), the distribution is significantly expanded, and the majority of ‘3’ is correctly classified. Finally, after noise injection (red), the ma...	BCA	BCA	CBA	ABC	Selection 3
A: There are relatively few works focusing on this problem. As one of the pioneer works in this field, zhu2017event proposes a probabilistic data association method based on the event-based featuresB: However, in the current event-based studies, most methods usually handle the fundamental event-based data ass...	ACB	BCA	BAC	ACB	Selection 3
A: This research was partially financed by the French government IDEX-ISITE initiative 16-IDEX-0001 (CAP 20-25) and by the ANR project GRALMECO (ANR-21-CE48-0004)B: In particular, we thank Giacomo Kahn and Armen Petrossian for preliminary discussions. We also thank the referees for their comments that helped i...	BCA	BAC	CBA	ACB	Selection 4
A: Follow the linear evaluation protocols in Sec. V-B, we compare the existing relation-based KD methods including RKD [65], PKT [64], SP [66], SSKD [68], CRD [69], and SEED [67]. We adopt the BCE loss for GenURL in the KD task. B: We evaluate the KD tasks based on self-supervised learning on STL-10 dataset. In...	BCA	BCA	ABC	CAB	Selection 4
A: This is because convolutional filters with kernel size >1 contribute to increasing receptive fields. The bordering pixel on the output patches is dependent on the inputs from neighboring patches. Such repeated computation can increase the overall network computation by 10-17% even under optimal hyper-parameter c...	CBA	BAC	CAB	BAC	Selection 3
A: In this competition, our team achieved 1st place in the first stage leaderboard, and 3rd place in the final stage leaderboard.B: Experiments show our framework outperforms baseline methods even when its encoder module uses a randomly initialized BERT [2] encoder, showing the power of the new tagging framewor...	ABC	BCA	BCA	CBA	Selection 4
A: In contrast to existing GCL methods which use the same encoder to observe multiple augmented graphs, CGCL uses multiple encoders to observe the same graph and generate contrastive views. It is pivotal to recognize that the quintessence of contrastive learning is to learn invariance between different contrastive...	ABC	CAB	BCA	BAC	Selection 1
A: We see interesting dynamics in topographic similarity distribution with respect to change in the noise levels. Namely, it starts by accumulating mass at the higher spectrum of its values, reaching a peak for noise 0.10.10.10.1, after which it transitions to a bimodal distribution, finally shifting its mass more ...	CAB	ACB	ABC	BAC	Selection 1
A: CBFs that account for uncertainties in the system dynamics have been considered in two ways. The authors in [10] and [11] consider input-to-state safety to quantify possible safety violationB: Conversely, the work in [12] proposes robust CBFs to guarantee robust safety by accounting for all permissible erro...	BCA	ABC	CAB	BAC	Selection 2
A: We are especially grateful to Avishay Tal for providing us with a proof of Corollary 44B: We thank Lance Fortnow, Greg Kuperberg, Patrick Rall, and Avishay Tal for helpful conversationsC: We further thank Chinmay Nirkhe for finding an error in the proof of Lemma 53.	BCA	CAB	BAC	ABC	Selection 3
A: (a) DCDFM models weighted networks by allowing nodes within the same community to have different expectation degreesB: Though the WSBM developed in [12] also considers node heterogeneity, it requires all elements of connectivity matrix to be nonnegative, and fitting it by spectral clustering is challenging**...	BAC	BAC	ACB	ABC	Selection 4
A: (2020). We also demonstarte scalability, which lets MA-Trace can reach good performance even using short training (wall time).B: In Table 1 and Figure 1, we present the results of the main version of our algorithm – MA-Trace (obs), in which the critic uses stacked observations of agents as described in Appen...	ACB	ABC	CBA	CAB	Selection 4
A: The core mechanism of bagging and boosting methods is the generation of decisions based on the training data, which then experts can interpret. B: Existing work on single decision tree visualization has experimented with different visualization techniques, such as node-link diagrams, Elzen2011BaobabView ; Ng...	ACB	CAB	ABC	CBA	Selection 2
A: It is noteworthy that the extent of benefit from dual-polarized antennas depends on the associated schemes to exploit the characteristics of polarized wireless channel [15, 16, 17, 1, 6]. Various channel sounding campaigns and channel models provide insights into the characteristics of wireless channel polarizat...	CAB	BCA	CAB	CBA	Selection 4
A: This is the setting which is important for our reductions to the online packing problemsB: In the following two sections, we prove lower and upper bounds, respectively, on how good online sorting algorithms can perform in this case.C: Having dealt with the case where we have no extra space, we turn to t...	BCA	BAC	BAC	BAC	Selection 1
A: However, during our research following the work of [42], we observe an interesting phenomenon (see Figure 1). The template choice highly impacts the final performanceB: Thus, a selection question naturally stands out: Regarding the “gap" over samples, how to find and annotate the most “valuable" images in or...	ACB	BCA	CAB	ABC	Selection 1
A: Especially, theoretical results when edge weights follow a specific distribution can be obtained immediately from our results. B: (2) We use a spectral algorithm to fit MMDFC: We show that the proposed algorithm stably yields consistent community detection under MMDF	ACB	ACB	CBA	CAB	Selection 4
A: Philip Torr is funded by Turing AI Fellowship EP/W002981/1.B: Yujun Shi and Vincent Tan are funded by a Singapore National Research Foundation (NRF) Fellowship (R-263-000-D02-281) and a Singapore Ministry of Education AcRF Tier 1 grant (R-263-000-E80-114)C: Jian Liang is funded by Beijing Nova Program (Z...	CAB	ACB	BCA	ABC	Selection 1
A: This reduced the effect of the pathological regions on the registration algorithm in an unsupervised manner. Finally, non-rigid instance optimization with forward-backward consistency constraint was introduced to correct solutions from the previous step that were biased because of insufficient training and discr...	ACB	BAC	ABC	CBA	Selection 4
A: Example 1, despite its simplicity, illustrates one of the limitations of the CQA approach. The notion of certain answers only says that a candidate answer (i.eB: Instead, we would like to know how often a tuple is an answer, that is, its relative frequency, or, in other words, the percentage of repairs that...	BAC	CBA	ACB	CAB	Selection 3
A: One of their findings is that outbreak duration can achieve a maximum at intermediate modularity values. Inspired by Salathé and Jones [38], we use our adaptation of InfoMap to study an example contagion process that illustrates how absorption in disease dynamics affects community structure, which in turn affect...	ACB	CAB	ABC	BCA	Selection 2
A: The general QNR problem can be formulated in terms of hypergraph flows and solved using LP (see Appendix A)B: Although polynomial-time and provably optimal, the LP-based approach has a very high time-complexity for it to be practically usefulC: Here, we develop an efficient heuristic	BCA	ABC	ACB	BAC	Selection 2
A: AI approaches, which are currently predominated by deep learning algorithms, have brought considerable improvements to many essential components of autonomous driving technology, including advances in perception, object detection, and planning. As the AI-powered driving systems of vehicles advance, the number o...	BCA	BCA	BAC	ACB	Selection 4
A: The first Ghost module is used as an expansion layer, increasing the number of channels, and the second Ghost module reduces the number of channels to match the shortcut. Then, the input and the result from the second Ghost module are fed into the shortcut connection to generate the final outputB: If Stride=...	ABC	CBA	CBA	BCA	Selection 4
A: We show with two toy examples how the attack can be performed in practiceB: We also apply our attack to WG-PRNG and we provide a complexity estimate that shows a fatal weakness of this cipher. We also report previous attempts at breaking WG-PRNG with algebraic attacks and we discuss their shortcomings. C...	ABC	BCA	ACB	CBA	Selection 2
A: When we exploit an opponent model, we need to worsen the strategy in terms of exploitabilityB: Gadgets are used to ensure exploitability does not increase Moravcik et al. (2016); BurchC: We must limit how much the strategy worsens if we want a safe response	BAC	ABC	ABC	ACB	Selection 4
A: Though we have stated the modularity score of a partition for binary edge weights it is simple to take the weight of edges inside each part (instead of the number of edges) and to take the degree of a vertex v𝑣vitalic_v to be the sum of the weights of the edges incident to v𝑣vitalic_v (instead of the number of...	CBA	BAC	ACB	CAB	Selection 2
A: Therefore, we use the bibliographic database IDEAS, based on RePEc and dedicated to Economics, to include unpublished and working papers.444 We use the Advanced Search tool, searching by Keywords and Title: migration and (“natural disasters” or “climate change”)B: A selection of the contributions is made man...	BCA	CAB	CAB	CBA	Selection 1
A: When the meta-algorithm implements Hedge-style updates with changing learning rates, it aligns more with optimistic FTRL (Follow-The-Regularized-Leader) instead of optimistic OMDB: We introduce optimistic online mirror descent (Optimistic OMD) as a unified building block for the algorithm design of dynamic ...	BAC	ABC	CBA	CAB	Selection 1
A: For a nonempty word w𝑤witalic_w, we let wωsuperscript𝑤𝜔w^{\omega}italic_w start_POSTSUPERSCRIPT italic_ω end_POSTSUPERSCRIPT denote the right-infiniteB: A sequence x∈Aℤ𝑥superscript𝐴ℤx\in A^{\mathbb{Z}}italic_x ∈ italic_A start_POSTSUPERSCRIPT blackboard_Z end_POSTSUPERSCRIPT is periodic if there is an n...	ACB	BAC	BAC	CAB	Selection 4
A: (2017)B: 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 alC: More precisely, these authors established the third term on the right-hand side in	BAC	ACB	CAB	BCA	Selection 1
A: Hence, any detected differences are false positives.B: All groups, derived from Group 1 through rotations, are topologically identicalC: In the experiment depicted in Figure 6, we evaluated the occurrence of false positives in scenarios devoid of topological differences	ACB	CBA	BCA	BCA	Selection 2
A: eA⁢τ=S⁢eJ⁢τ⁢S−1superscripte𝐴𝜏𝑆superscripte𝐽𝜏superscript𝑆1\mathrm{e}^{A\tau}=S\mathrm{e}^{J\tau}S^{-1}roman_e start_POSTSUPERSCRIPT italic_A italic_τ end_POSTSUPERSCRIPT = italic_S roman_e start_POSTSUPERSCRIPT italic_J italic_τ end_POSTSUPERSCRIPT italic_S start_POSTSUPERSCRIPT - 1 end_POSTSUPERSCRIPTB...	ABC	CBA	ACB	ACB	Selection 1
A: The temperature of the battery module from the two boundaries and mid-section, as shown in FigB: In Fig. 2, the coolant temperature control at the boundaries show that the transient control action for StSf-C is greater than St-C. However, in steady state both control actions are somewhat comparable. C: 2...	BCA	CAB	CBA	ACB	Selection 4
A: Among the papers selected, mental health issues such as stress, anxiety, affects are important dimensions studiedB: Over the years, there has been an increase in studies that explore the sensing capabilities of smartphones and wearables to assess as well as improve worker’s health and wellbeingC: Most o...	BCA	BAC	ACB	BCA	Selection 2
A: In SS-Setting, a training sample is comprised of spectrum sensors’ received power readings. The location of entities is available by using a GPS dongle connected to the laptops as described below, and the sensor’s received power is computed as followsB: Collecting Training Samples. Recall that a sample in PU...	BCA	BCA	BAC	CBA	Selection 3
A: An immediate extension of the current work would be the reconstruction of planar curves with prescribed projective curvatures, and obtaining distance estimates between curves, modulo a projective transformation, compared to the distance between the projective curvaturesB: In this paper, we considered practic...	BAC	ABC	ACB	CAB	Selection 1
A: A substantial review of variants of coordinate descent algorithms can be found in [4, Section 6.5.1]. The cyclic selection of coordinates is normally assumed to ensure convergence of the algorithmB: Obviously, this is not guaranteed for each instance of the algorithm. The Gauss-Southwell method leads to fa...	BCA	ACB	CBA	CBA	Selection 2
A: Each neuron in a HOTS network represents a cluster corresponding to a pattern of events within a spatial window. The diversity of possible input patterns is reflected by the diversity of patterns represented by the neurons in the networkB: A HOTS network maps each input spike to an output spike from one of t...	ACB	CAB	ABC	BAC	Selection 4
A: In systems with asynchronous updates a single agent is selected uniformly at random and only this agent updates its opinion. While the main body of recent work focuses on HKSs assuming the complete graph as social network and the synchronous update rule, empirical simulations have also been performed with asynch...	BCA	ABC	CAB	BAC	Selection 1
A: To prevent data leakage in medical image analysis, we took precautions to ensure that images from the same patient were not present in both the training and test setsB: Data leakage can occur when the model sees images of the same patient during training and testing, leading to biased results Rathore et al*...	BAC	ACB	ABC	ACB	Selection 3
A: Several works, such as Russo (2020); Hong et alB: Unlike multi-armed bandit algorithms, BAI algorithms are designed solely to deliver the most effective exploration.111Although the term “best arm identification” has appeared only recently, several strands of research share the same goal, among which ranking ...	CAB	BAC	CAB	CAB	Selection 2
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	ACB	CAB	CBA	CAB	Selection 3
A: Our values in navy (darkest) compared to values in Hellan, Lucas, and Goddard (2020) in orange (lightest) marked with ††\dagger†B: Figure 4: Results on strong subset of satellite dataC: The baseline ‘Random’ is shown in grey (medium, different pattern). The results obtained are better than those in exis...	CAB	BAC	ACB	CBA	Selection 2
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: Test data bounds are reported in Table 4. C: We report the average ELBO (±1plus-or-minus1\pm 1± 1 standard error) on the training set after 1M steps over...	BAC	BAC	ACB	CAB	Selection 3
A: 1)B: The results from simulations, which implement the exact procedure, are given with markers. Solid and dashed lines (Steiner and Random respectively) correspond to the approximation based on eq. (26) (ApproxC: Similarly, dotted and dash-dotted lines (Approx. 2) correspond to the simpler approximation ...	BCA	BAC	ACB	ACB	Selection 2
A: Moreover, the exponent 3333 in our theorem is sharp and can not be lowered; see Section 5.4.B: In fact, in our algorithm, the yielded path has length at most (300)9/2⁢log⁡300superscript30092300(300)^{9/2}\log{300}( 300 ) start_POSTSUPERSCRIPT 9 / 2 end_POSTSUPERSCRIPT roman_log 300 times the length of the sm...	BCA	ACB	CBA	BCA	Selection 3
A: Contrary to the homogeneous case, multigraded Chow forms and Hurwitz form require a choice of a non-degenerate multidimension vector for the linear subspace, in a sense that is discussed in Section 4. This set of non-degenerate dimension vectors gives rise to an interesting combinatorial structure, namely a poly...	CAB	ABC	CBA	BAC	Selection 4
A: On the one hand, the BiosignalPlux device connection is configured using the OpenSignals (r)evolution software666https://support.pluxbiosignals.com/article-categories/opensignals/, and its TCP/IP module is used to facilitate the data exchange between this platform and the Unity® frameworkB: Note that the syn...	BAC	ABC	CBA	BCA	Selection 1
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...	ACB	CAB	BCA	ABC	Selection 4
A: In the latter case, the sum of the even-numbered terms must diverge to −∞-\infty- ∞ (as the sum of all terms is convergent), and therefore cannot diverge to ∞\infty∞B: 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) dive...	BAC	ABC	CBA	ACB	Selection 1
A: More detailed summary of their designs, applications in AD systems, and vulnerabilities are in Appendix -A. Currently, none of the existing works study the downstream AI components such as prediction and planning, which will be discussed more in §IV-D. B: Status and trends. The targeted AI components in the ...	ACB	ACB	CAB	CBA	Selection 3
A: Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive AgencyB: Neither the European Union nor the granting authority can be held responsible for them.C: Alexey Barsukov is funded by the E...	BCA	CAB	CBA	CAB	Selection 1
A: We visualize this by using batch stats at test time. “Cheating” still happens in batches with multiple or short sequencesB: (b) By using batch stats at test time, BN models achieve overly good scores (cheating eval.) but fail in a fair evaluation. Sampling multiple sequences per batch reduces but does not el...	BCA	CAB	CAB	ABC	Selection 1
A: Results on IJB-B and IJB-C. IJB-B consists of 21.8 K images of 1,845 subjects and 55 K frames of 7,011 videosB: Owing to the severe imbalance between positive and negative pairs, performance was measured by TAR@FAR at different intervals such as [1e-6, 1e-5, 1e-4, 1e-3, 1e-2]. As shown in Table 2, all FR mod...	BAC	CBA	ACB	ABC	Selection 3
A: The results were similar to those reported in the literature using patented technology. While Burlina et al. trained a Progressive GAN over a large number of images positive to AMD, our technique has the potential to generate similar high-quality synthetic images with only a small number of images. B: We hav...	ABC	CBA	BAC	CAB	Selection 2
A: For the RAF-DB dataset, the facial landmarks are manually annotated via the crowdsourcing method with basic or compound expressionsB: In experiments, we use the basic database including 12,271 training and 3,068 testing images. C: RAF-DB contains 29672 facial images downloaded from the Internet	CBA	CBA	BCA	CAB	Selection 3
A: In a tournament, many games will be happening parallelB: Another interesting variant is to constrain the number of rounds of gamesC: Each round consists of any number of games, subject only to the constraint that each player only participates in at most one game each round. Given n𝑛nitalic_n players an...	BCA	BAC	CAB	BCA	Selection 2
A: All experts agreed that HardVis’ workflow is well designed and reasonable from their perspective. They characterized the workflow as straightforward and aligned with respective fully-automated sampling processesB: E2 underlined the clear benefit of controlling the automatic algorithms’ suggestions since blin...	ACB	ABC	ABC	BAC	Selection 1
A: Our code, written in Solidity, is just a proof-of-concept and we have not spent time optimizing it–we expect this cost to be lowerB: This is inefficiency is being addressed by the Ethereum community as per EIPs 197999https://eips.ethereum.org/EIPS/eip-197, 1108101010https://eips.ethereum.org/EIPS/eip-1108. T...	ACB	BAC	ABC	CBA	Selection 1
A: In this section, we define the policy gradient, the gradient of the equilibrium policy value with respect to the selection criterion, give an estimator of the policy gradient, and use the estimator to learn policiesB: In particular, we give a method for estimating the policy gradient in finite samples in a ...	ABC	BCA	ACB	CAB	Selection 1
A: The background is not labeled for the debiasing algorithms, making it a challenging benchmark. BAR has 6 classes with 1941 train and 654 test samples.B: The dataset consists of correlated action-background pairs, where the train set consists of selected action-background pairs, e.g., climbing on a rock, wher...	CBA	BCA	BAC	BAC	Selection 1
A: This feature assembling operation has two meanings. On one hand, it organizes the features in a multi-scale way, and the farthest frame would have the largest receptive field and the most coarse pooling. Since the content in a few sequential frames usually does not change suddenly and most content may only have ...	ABC	ACB	BAC	CBA	Selection 3
A: Embedding parameter placement: Prior methods [11, 10] rely on offline profiling and static embedding placement based on training data skew. In contrast, Hotline dynamically adapts to changing data patterns without such overheadsB: Recent works [4, 1] explore alternative embedding table placements but lack pr...	ABC	BAC	CAB	CBA	Selection 1
A: Explicitly, we prove: B: 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 criticalC: The flat cells of F𝐹Fitalic_F are, by definition, the cells of 𝒞⁢(F)𝒞𝐹\mathcal{C}(F)caligraphic_C ( it...	BCA	ACB	CBA	ACB	Selection 3
A: In order to solve the ground state and the time evolution of the system, the stochastic reconfiguration (SR) method and time-dependent variational Monte Carlo (VMC) approach [43] are utilized, respectively. We find that time evolutions of the energy expectation value from the neural networks are perfectly consis...	BCA	CBA	BAC	CAB	Selection 4
A: The blue line is initial condition for each sequenceB: Figure 2: Seq2seq PINNC: The red line is boundary condition for each sequence. The domain will be uniformly sectioned. When training of the first sequence finished, the solution at t=0.01𝑡0.01t=0.01italic_t = 0.01 will be calculated and used as the...	ACB	BAC	ACB	ABC	Selection 2
A: The second one is the time to construct the sorted multi-sets of k𝑘kitalic_k-vicinity radii and uncertainty values. We use the exact k𝑘kitalic_k-vicinity radii and entropy values in this experiment.B: Our preprocessing time consists of two partsC: The first is the time to build the k𝑘kitalic_k-NN data...	CAB	ACB	BAC	ACB	Selection 1
A: The feature that garnered the most discussion among parents and teens was the ability to hide or show apps to one anotherB: Overall, parents and teens were both concerned about this feature because it promoted secrecy and negated some of the purpose behind the appC: One important thing to be noted here ...	ABC	CAB	BCA	BCA	Selection 1
A: After reviewing the mathematical background in Section 2, we define the proposed filtration in Section 3 and discuss its properties in Section 4B: The rest of the paper is organized as followsC: We discuss bootstrapping in Section 5 and present numerical simulations in Section 6. A discussion and the con...	BCA	BAC	CBA	CBA	Selection 2
A: 2017; Song et alB: Considering the locality of AUs, methods such as (Zhao, Chu, and Zhang 2016; Li, Abtahi, and Zhu 2017; Li et alC: 2021a; Chen et al. 2021a) make attempt to learn better facial appearance features by emphasizing important local facial regions. Zhao et al. (Zhao, Chu, and Zhang 2016) pro...	BAC	ABC	CAB	ACB	Selection 1
A: As discussed in Section 1, speeding up block propagation falls into three categories: 1) Compressing block sizes; 2) Simplifying block validation; 3) Optimizing network topologies. B: Speeding up block propagation is a more primary way to improve the blockchain TPS performance since the blockchain is built o...	BCA	BAC	CAB	BCA	Selection 3
A: In a broader context of reinforcement learning with partial observability, our work is related to several recent works on POMDPs with special structures. For example, Kwon et alB: (2021) considers POMDPs having tree-structured states with their positions in certain partitions being the observations. Compare...	CBA	ACB	BCA	ABC	Selection 2
A: We further found that mobile-based deployment of AI-based automated speech therapy was more common among the included studies. A possible explanation is that researchers are more interested in building affordable and accessible automated speech therapy tools. Another significant issue we observed is that few stu...	BAC	CAB	CBA	BAC	Selection 3
A: We record the results in Table 2. As can be observed, we obtain the the best rank in 4 out of 6 settings, and are very close to optimal in one more. B: We also add the improvements in the purity index for the 5% and 10% point removal cases, which is another popular measure of clustering accuracy. As aggregat...	ABC	CAB	CBA	BCA	Selection 3
A: We observe that the PredCls does not fluctuate much in case of missing visions compared to other two metrics SGCls and SGDetB: In contrast, SGCls and SGDet drops more or less with missing visions. Also, it is worth noting that for the datasets with object obfuscations and image obfuscations do not cause a se...	BCA	ACB	BCA	BAC	Selection 2
A: 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 slightly modified way. B: Lower bounds for deterministic mechanismsC: The following proposition obtains a similar lower bo...	ABC	CBA	CAB	BAC	Selection 3
A: As we have seen before the papers on perfect edge domination are less frequentB: There is a paper [16] where the authors describe ILP formulations for the PED problem, together with some experimental results. C: There is some more bibliography to add to the already vast literature [3, 10, 23, 31, 33, 35]...	ACB	BCA	ABC	BAC	Selection 2
A: To set the complexity of each ReLU network, we have followed the indications reported in the corresponding rows of Tab. 2, where we have considered the case of equally distributed neurons across the L𝐿Litalic_L hidden layersB: Note that, in accordance to Tab. 2 indeed, we are implicitly designing minimum c...	ACB	CBA	ABC	BAC	Selection 3
A: In this work we presented a solution for identifying the parameters of a physical model from a video while also creating a photorealistic representation of the appearance of the scene objectsB: In contrast to prior works that use encoder-decoder architectures specifically tailored to 2D images, we build upon...	ACB	BCA	ABC	BAC	Selection 1
A: Towards this goal, the main contribution of this letter is a novel resource-efficient QCN framework, dubbed the framework. This framework draws upon recent advancements in two key quantum information science areas. First, it utilizes high-dimensional quantum information and to extract underlying structures of...	CAB	BCA	ACB	BCA	Selection 3
A: As we can see, compared with the MADDPG approach that shows apparent throughput boost after 1000100010001000 episodes, the training process of the proposed approach shows an immediate throughput increase in both FD and HD casesB: Indeed, blue and orange curves can reach high values within 100100100100 episod...	CAB	ACB	CAB	ABC	Selection 4
A: Intuitively, this result arises from the fact that for incentive-aligned statistical contracts only nonnull agents are activeB: Therefore, the principal should give the agents as much room to optimize their license value as possible; i.e., the principal should give the agent the maximal menu possible—the men...	ACB	ABC	BCA	BCA	Selection 1
A: Point Cloud DataB: In Supplementary Table A1 we present the registration metrics for PPIR(MPC) and PPIR(FHE)-v1C: The registration shows that PPIR(MPC) achieves the best results compared to PPIR(FHE), which exhibits not only a longer computation time but also requires higher bandwidth, thanks to its non-...	BAC	CBA	CAB	ABC	Selection 4

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