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A: Planning methods based on learning, including kSubS, typically use imperfect value function-based information to guide the searchB: While traditional low-level search methods are susceptible to local noise, subgoal generation allows for evaluations of the value functions at temporally distant subgoals, which...	CAB	BCA	ACB	CBA	Selection 2
A: MFE-NER also shows its advantages in some informal language environmentsB: The Weibo dataset is a typical example from social mediaC: On the Weibo dataset, MFE-NER achieves 53.81 and 67.74 in models with static embedding and BERT respectively, significantly enhancing the performance of pre-trained langua...	CBA	ABC	BCA	BCA	Selection 2
A: See Supplementary Notes 9 and 10 for details. B: The prototype consists of a HOLOEYE-PLUTO SLM, a 4F system, a DC block, and a camera for imaging the étendue expanded hologramsC: We evaluated the neural étendue expanders using a prototype holographic display	BAC	CAB	CBA	ACB	Selection 3
A: PLMs have become an essential part of NLP pipelineB: Thirdly, we are curious about whether we can create more powerful Pre-trained Language Models (PLMs) via more advanced MTL techniquesC: Though most PLMs are trained on multiple tasks, the MTL architectures used are mostly simple feature sharing archite...	ABC	BCA	CAB	BAC	Selection 4
A: They will help to give the authors an approximation of the number of pages that will be in the final version. The structure of the LaTeXfiles, as designed, enable easy conversion to XML for the composition systems used by the IEEE’s outsource vendorsB: The XML files are used to produce the final print/IEEEXp...	BCA	CAB	BAC	ACB	Selection 1
A: Consider i,i′∈[n]𝑖superscript𝑖′delimited-[]𝑛i,i^{\prime}\in[n]italic_i , italic_i start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT ∈ [ italic_n ] such that (vi,vi′)∈E⁢(G)subscript𝑣𝑖subscript𝑣superscript𝑖′𝐸𝐺(v_{i},v_{i^{\prime}})\in E(G)( italic_v start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT , italic_v star...	BAC	CBA	ABC	CBA	Selection 1
A: The results, shown in Figures 4, 5, and 6, highlight the effects of these counterfactual adjustments. In each figure, we show the average of 10,000 simulations in the counterfactual regime, shown as the darker lines, juxtaposed against 10,000 simulations from the standard model as well as the values actually obs...	ACB	CAB	ABC	ACB	Selection 2
A: Mixed Loss: In SISR, there are also some classic combinations of loss functions that are widely used to guide the network towards generating high-quality HR imagesB: These combinations aim to balance the quality, details, and visual perception of the generated imageC: Here are some commonly used classic ...	ABC	ACB	BAC	BAC	Selection 1
A: Furthermore, quantitative measures are provided by applying each method to Set5 [33] and Set14 [34]. B: We demonstrate the capabilities of Neural Knitworks by utilizing a similar model with only minor adjustments for several tasks commonly investigated in the field of computer vision: 1) image inpainting 2) ...	ACB	BAC	ABC	CAB	Selection 4
A: The regret is presumed to be the true measure of interest in the apple tasting problem, and to appropriately weight the impact of false positives and false negativesB: To this end, in Tables 3, 3, and 3 we report the precision and recall of the apple tasting algorithms. The precision is the proportion of tr...	ABC	ACB	CBA	ABC	Selection 2
A: In a legal analytics scenario [11] where the identification of unfair clauses is done automatically, a system’s output of “potential unfairness” could be explained by the distribution of attention mass on specific segments of textB: However, this in itself is not the type of explanation that a legal expert w...	BAC	ACB	ACB	ABC	Selection 4
A: This property-related comparison can complement common predictive evaluation measures such as the log predictive density scores.B: We then introduce the Bayesian predictive probability of satisfaction and posterior predictive robustness as quantities of interest and show how these measures can be used for co...	CAB	CBA	BCA	BAC	Selection 2
A: That adjoint situation is comonadic. This fact not only reveals the coalgebraic nature of equality, but provides a universal construction yielding elementary doctrines from primary ones.B: the 2-categories of primary doctrines and that of elementary ones that is, primary doctrines with equalityC: It show...	BCA	BCA	CAB	CBA	Selection 4
A: In each network, we find the top-10 similar nodes for all vertices. Statistics of each network and the experimental results are listed in Table 3.B: These real-world networks are preprocessed as simple graphsC: In this subsection, we evaluate the efficiency of studied metrics on 20 real networks, all of...	ABC	BAC	CBA	CAB	Selection 3
A: Table 6: The examples for aspect sentiment coherency found by LSAB: The target aspects are denoted in bold and the underlined words indicates the aspects with coherent sentimentsC: “Pos”, “Neg” and “Neu” represent positive, negative and neutral, respectively.	ABC	BAC	BCA	BCA	Selection 1
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: This process is somewhat elaborate and the reader is referred to [31] and [32] for all of the detailsC: The operator 𝐋𝐋\mathbf{L}bold_L and 𝐖𝐖\m...	BCA	ABC	CBA	CAB	Selection 3
A: For example, Athens’ model is 7.5×\times× larger than Yorktown with higher noise-free accuracy. However, due to more errors introduced by the larger model, the real accuracy is lower. B: On average, normalization, noise injection and quantization improve accuracy by 10%, 9%, and 3%, respectively. A larger mo...	ABC	BAC	ACB	CBA	Selection 4
A: It is worth pointing out that the proposed EDA is the first that introduces robust model fitting to solve the event data association problem for object trackingB: In EDA, only the second stage of the TSW algorithm can be removed for ablation study, and we term the new variant of EDA as EDA-SW. The other comp...	BAC	CAB	ACB	ABC	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: We are thankful to all participants of the 2018 AlCoLoCo problem seminars and the 2018 Recolles workshop, where this research was startedC: In...	CAB	CAB	ABC	CAB	Selection 3
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...	ACB	ACB	BAC	CAB	Selection 4
A: We provide the face detection results on WIDER FACE validation set with RNNPool-Face-Quant [43] and MCUNetV2-SB: The quantitative results are shown in Table 7, where we follow [43] to calculate the peak memoryC: Our model has better mAP at 1.3×\times× smaller peak memory.	ABC	BCA	BCA	CBA	Selection 1
A: Experiments show our framework outperforms baseline methods even when its encoder module uses a randomly initialized BERT encoder, showing the power of the new tagging frameworkB: In this competition, we introduce a fresh perspective to revisit the relational event-cause extraction task and propose a novel ...	CAB	CAB	BAC	CBA	Selection 3
A: Another bad example is the assembly in the top left-hand corner of IMDB-BINARY (point C)B: In addition, there also exist assembly, whose AC and CC are both at low levels, such as point D on NCI1 and point E on IMDB-MULTI. With the above analysis, we further confirm the effectiveness of our proposed collabora...	ABC	CAB	CBA	ACB	Selection 4
A: This shows that there is an interesting compositionality-accuracy trade-off. The bottom panel of Figure 4 complements the overall picture with a visualization of metrics’ distributionB: The accuracy drops down with an increase of the noise level, as expected, however the speed of the decline increasesC: ...	CBA	ACB	BCA	BAC	Selection 4
A: The model for longitudinal control is estimated from data and consists of the velocity v𝑣vitalic_v of the car and the integrator state d𝑑ditalic_d of the PIDB: The identified longitudinal model of the car isC: As we have no direct access to the system dynamics of the car, we identify a system model	BCA	ABC	BAC	CAB	Selection 1
A: [Mon12, OZ16]B: Aaronson and Ambainis [AA14] showed that this related conjecture implies 13. it remains open to this day. Theorem 12 could be seen as the analogue of 13 for sparse oracles—an analogue that, because of the sparseness, turns out to be much easier to prove.C: While 13 has become influential...	BCA	ABC	ACB	CBA	Selection 1
A: When DCDFM degenerates to DCSBM, our results also match classical results under DCSBM. Numerical results of both simulated and real-world networks show the advantage of introducing node heterogeneity to model weighted networks.B: (b) To fit DCDFM, an efficient spectral clustering algorithm called nDFA is de...	CAB	ABC	ABC	BAC	Selection 1
A: The theorem is an extended version of (Espeholt et al., 2018, Theorem 1)B: Second, the condition (6) admits more general importance sampling weights. We also fix a mathematical inaccuracy present in the original proof of (EspeholtC: First, we assume the vectorized statement, which is natural for the mult...	BCA	BAC	CAB	ACB	Selection 4
A: Adaptive Boosting. This algorithm fits successively many decision stumps or even decision trees to the training data, using various weightsB: The latter occurs only if the maximum depth hyperparameter is set to >1absent1>1> 1. It begins by forecasting the original data set and weighting each observation equa...	ACB	CBA	BCA	ABC	Selection 4
A: Compared to the case where the same number of antenna elements with only a single polarization is available, this leads to an increase in diversity and capacity, although the gain depends significantly on the XPD [15, 6]B: However, this also leads to doubling the number of antenna ports,C: Tx and Rx have...	ABC	ABC	BCA	ACB	Selection 3
A: The main contribution of this paper is to show that surprisingly this conjecture is false.B: Then the density of the piece in its axis-parallel bounding box is at least 1/2121/21 / 2, and the algorithm for rectangles can be applied to the bounding box, again leading to O⁢(1)𝑂1O(1)italic_O ( 1 )-competitive ...	CAB	CBA	BAC	CBA	Selection 1
A: Meanwhile, some researchers attempt to drain all potential of limited labeled dataB: With the power of self-training and self-supervised learning [1, 4, 24, 39, 43, 47, 51], it is possible to develop a robust, few-shot model even with several labeled samples. For example, Yao et al. [42] introduce a self-sup...	CBA	BCA	CAB	CAB	Selection 2
A: In Table 1, for networks with known memberships or K𝐾Kitalic_K, their ground truth and K𝐾Kitalic_K are suggested by the original authors or data curators. For the Gahuku-Gama subtribes network, it can be downloaded from http://konect.cc/networks/ucidata-gama/ and its node labels are shown in Figure 9 (b) [29]...	BCA	ACB	ABC	BCA	Selection 3
A: [27] uses eigenvalue analysis on representations of Deep Metric Learning (DML) modelsB: They find that preventing representations to be overly compressed can improve DML generalizationC: They achieve this by randomly switching negative samples with positive samples in the ranking loss. Differently, our w...	ACB	ABC	CBA	ACB	Selection 2
A: 4(e), the team rankings are not uniform in terms of MEE and R. Team cwmok performed best in terms of both MEE and R, clearly indicating the top-ranked team. However, AGHSSO, UZL and MEVIS secured ranks 2, 3, and 4 in terms of MEE, while ranks varied in terms of robustness. This made it difficult to finalize the ...	CAB	ACB	ABC	ABC	Selection 1
A: This is not the case in our setting, because two non-primary-lhs positions might correspond to attributes that occur in two different FDs (possibly on two different sides – one on the left-hand side of an FD and one on the right-hand side of another FD)B: Hence, here, when we remove an occurrence of some var...	ACB	BCA	CAB	ABC	Selection 2
A: We study an example that plays a similar role to the example of Salathé and Jones [38]. In our example, setting the absorption rates of bridge nodes to larger values than the absorption rates of other nodes is analogous to removing community bridgesB: However, our adaptations of InfoMap are designed for suc...	ACB	CBA	BAC	CAB	Selection 1
A: Although polynomial-time and provably optimal, the LP-based approach has a very high time-complexity for it to be practically usefulB: Here, we develop an efficient heuristicC: The general QNR problem can be formulated in terms of hypergraph flows and solved using LP (see Appendix A)	BAC	CBA	CAB	BCA	Selection 4
A: Omeiza et al.’s work [45]. They study the need for/role of explanations for autonomous driving, and focus on legal requirements, standards, and consumer expectations for the design and development of explainable autonomous driving systems. This provides their basis to present a conceptual XAI framework for modu...	CAB	BCA	ACB	CAB	Selection 3
A: In other words, the feature extracted from CNNs can also be applied to image retrieval tasks to achieve good results. Namely, the feature extracted by CNNs is robust and discriminative in changing environments [4]B: Babenko et al. [18] propose that the feature map with a size of H×W×C𝐻𝑊𝐶H\times W\times Ci...	ACB	BAC	BCA	ABC	Selection 3
A: In recent years, a new family of attacks have emerged, the so-called algebraic attacks. Some interesting works in this direction are [4, 15, 14].B: The design defenses against these types of attacks rely on choosing nonlinear components with specific properties, such as high nonlinearity [7] and high correla...	BCA	CAB	CBA	BCA	Selection 3
A: We tried to contact the authors without any response and we could not compare the performance with our methods. Xu et al. (2021) B: Another reinforcement-learning (RL) method uses neuroevolution with RL, they show a significant increase in performance over DQN and evaluate their method on HUNLC: However,...	ACB	CAB	BCA	BCA	Selection 2
A: However, there no theoretical guarantees for either that the partition found is near optimal, though recently [10] showed that a Louvain-like algorithm recovers the communities in the stochastic block model for a wide parameter range. B: Louvain [4] and Leiden [44] are examples of thisC: The algorithms a...	BAC	ABC	CAB	CBA	Selection 3
A: Colors correspond to communities of belonging of each paper: Cluster 1 is represented in violet, Cluster 2 in green, Cluster 3 in blue, and Cluster 4 in yellow. The description of each Cluster is presented in the text.B: Nodes are tied by links whenever two nodes share at least one common reference. The thic...	ACB	ACB	BCA	CBA	Selection 4
A: (2015) using the mathematical induction from the lens of FTRL. B: 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 boundsC: Our analysis is based on the unified view of Optimistic OMD (Theorem 1), and is much simple...	CBA	BCA	CAB	CBA	Selection 3
A: Set A2={x,y,z}subscript𝐴2𝑥𝑦𝑧A_{2}=\{x,y,z\}italic_A start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT = { italic_x , italic_y , italic_z } and let ⟨a⁢a⟩=x,⟨a⁢b⟩=y,⟨b⁢a⟩=zformulae-sequencedelimited-⟨⟩𝑎𝑎𝑥formulae-sequencedelimited-⟨⟩𝑎𝑏𝑦delimited-⟨⟩𝑏𝑎𝑧\langle aa\rangle=x,\langle ab\rangle=y,\langle ba\rangle=z⟨ ...	ABC	ABC	CAB	BAC	Selection 4
A: More precisely, these authors established the third term on the right-hand side inB: 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: (2017)	CAB	CBA	BAC	ABC	Selection 1
A: In the widely used ACE genetic model, the heritability index (HI) hℎhitalic_h, which determines the amount of variation due to genetic difference in a population, is estimated using Falconer’s formula (Falconer and Mackay, 1995; Chung et al., 2019b; Arbet et al., 2020)B: MZ-twins share 100% of genes while s...	BAC	ACB	BCA	ABC	Selection 4
A: ∀i∈{1,2,..l}\forall i\in\{1,2,..{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb% }{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}l}\}∀ italic_i ∈ { 1 , 2 , B: italic_l }. If ϕ2⁢(θ)>θsuperscriptitalic-ϕ2𝜃𝜃\phi^{2}(\theta)>\thetaitalic_ϕ start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT ( ...	BAC	CBA	CBA	ACB	Selection 4
A: Specifically, trajectories moving from safe zone towards unsafe region will violate safety boundary only in a sense proportional to the size of inputB: Input-to-state safety (ISSf) [4, 5, 6]: Here the objective is to ensure that the system state trajectories stay away from a predefined unsafe region, or in ...	BAC	BCA	ABC	CAB	Selection 1
A: The work investigates how patterns in movement data in the workplace can relate with mental wellness. The hospital staffs carry a Bluetooth tag with them which communicates with several Bluetooth hubs placed in different rooms of the hospital. The nearest hub which has the highest RSSI is taken as the location e...	CAB	BCA	BAC	ABC	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: First, we compute an FFT on the I/Q samples collected w...	BAC	BCA	BAC	CAB	Selection 2
A: Indeed, the projective group, containing both the special Euclidean and the special affine groups, plays a crucial role in computer vision (see, for, instance [5] and [13]). Extension to space curves is another direction with immediate applications. B: An immediate extension of the current work would be the ...	BCA	BCA	CBA	BAC	Selection 3
A: Our dynamic regret bounds for strongly convex functions proved in Theorems 6 and 7 might need multiple updates at each time t𝑡titalic_tB: This setup is also adopted in some existing works including [40, 8] to achieve less conservative regret bounds. It should also be noted that, although the online algorith...	BCA	CBA	ABC	ABC	Selection 1
A: To ensure the generality of our results, we included results using both the Support Vector Classifier (SVC) and the Euclidean distance classifier (Eucl.)B: Thus, we considered four cases: Ideal SVC, Noisy SVC, Ideal EuclC: and Noisy Eucl. Each network was trained on the same training sets of N-MNIST data...	ACB	BAC	ABC	ACB	Selection 3
A: We give an upper bound on the expected convergence time ofB: Shiragur (2015). A HKS is in a δ𝛿\deltaitalic_δ-stable state if and only if each edge in the influence network has length at most δ𝛿\deltaitalic_δC: For this scenario we prove that the convergence of the opinion dynamics is guaranteed	CAB	BAC	BAC	ACB	Selection 1
A: Most existing studies on disease diagnosis using chest X-rays primarily focus on detecting a single pathology, such as pneumonia or COVID-19 (Bar et al. (2015); Cicero et al. (2017); Rajpurkar et al. (2017); Dasanayaka and Dissanayake (2021); Hussain et al. (2023))B: However, an X-ray image can exhibit multi...	CBA	ACB	ABC	ACB	Selection 3
A: In the former setting, the algorithm obtains a reward at the end of each round. In the latter setting, the corresponding reward is delayed until round T𝑇Titalic_T, which makes our problem particularly challenging.B: One of the seminal results regarding Bayesian CRM is the Gittins index theorem (Gittins, 198...	BCA	CBA	ACB	ABC	Selection 2
A: In contrast, the latent code of a classical autoencoder exhibits multiple clusters for different orientations of the same digit class. B: Still, inspecting the 2d-projection of the latent code of our proposed model in Figure 2, we see distinct clusters for each digit class for the different images from the t...	BCA	CAB	CBA	CAB	Selection 3
A: The only data used are NO2 concentrations, and the latitude and longitude of the stationary sensorsB: No images or personal data of any kinds are used. This is an advantage over other proposed solutions, e.g. using taxis to collect data. The potential benefits far outweigh the harms, by allowing local commun...	BAC	BCA	ACB	BAC	Selection 2
A: We report the average ELBO (±1plus-or-minus1\pm 1± 1 standard error) on the training set after 1M steps over 5 independent runsB: 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 OmniglotC: Test data bounds are rep...	ABC	CAB	BAC	ABC	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 ...	BAC	CAB	BCA	CBA	Selection 1
A: Here and for the rest of this paper, we refer to any of these two variations as the ATSP algorithm. The purpose of this note is to show that the ATSP algorithm, in the case that V𝑉Vitalic_V is finite, has polynomial time complexity.B: Variation of this algorithm also appears in [BNV19]C: Later, Schul [...	ACB	CBA	ABC	ABC	Selection 2
A: Even though a multiprojective space is isomorphic to a projective variety via the Segre embedding, this requires adding many more additional variablesB: We should emphasize that the generalization from the projective Chow forms to the multiprojective ones is far from straightforward both from the mathematica...	BAC	BCA	ACB	CBA	Selection 1
A: After this selection, none of the videos previously selected for attraction, contempt, hope, and tedium had greater than 50%percent5050\%50 % agreement among the responses obtained, so the final set of videos covers a list of 8 target emotions joy, sadness, surprise, fear, attraction, disgust, tenderness, anger,...	CBA	ABC	ACB	BCA	Selection 4
A: 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 stepB: Long Short-Term Memory (LSTM) addresses the vanishing gradient problem by using a more complex memory cell that selectively retains or discards informa...	BCA	ACB	CBA	BAC	Selection 1
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...	ACB	ABC	ACB	CAB	Selection 2
A: A few works aim to degrade the mobility of AD vehicles by manipulating the AI component outputs. In those works, they fool either the object detection to recognize a static blocking obstacle [19, 81, 84] or the traffic light detection to recognize a permanent red light [80], which can cause the AD vehicle to be ...	BCA	CAB	CBA	ACB	Selection 3
A: Second, a more recent result of de Figueiredo et alB: prove the NP-completeness of Maximum Cut on permutation graphs as well, which too was open for a long time [11].C: in [2], where they extend the result of the first paper by proving that Maximum Cut is NP-complete on graphs of interval count four. Usi...	ABC	BAC	BAC	ACB	Selection 4
A: The specialized surgical domain requires backbones to be finetuned and several studies suggest that the small-scale public datasets available in this domain are not sufficient to train large 3D CNNs [15, 88]B: 3D backbones: It is noticable that surgical workflow methods almost unanimously use 2D backbones as...	ABC	ABC	ABC	BAC	Selection 4
A: This paper proposes unified negative pair generation (UNPG) by combining two PG strategies (i.e., MLPG and CLPG) from a unified perspective to alleviate the mismatch. Moreover, it includes filtering noise-negative pairs, such as too-easy/hard negative pairs, in order to guarantee reliable convergence and improv...	ABC	CAB	CAB	CAB	Selection 1
A: This work has introduced an alternative approach for generating synthetic images for training deep networks and tested it for AMD identification, which consists in using a retinal image quality assessment model [37] and the StyleGAN2-ADA [38]B: Retina images, positive and negative to AMD, from multiple data...	CAB	CBA	ACB	ABC	Selection 4
A: In our method, multiple individual networks are generated based on facial local regions, and the previous experiments are implemented with the number of local patches M=16𝑀16M=16italic_M = 16B: Therefore, we also make an analysis for the number (M𝑀Mitalic_M) of local patches on five datasetsC: In this...	ABC	CAB	CBA	CAB	Selection 1
A: Since game outcomes are symmetric, this will produce a player of high rating instead. B: This strategy is guaranteed to produce a player of either very high or very low ratingC: If it produces a player of very low rating, simply re-do the strategy picking the same sequence of pairs of players but have th...	BAC	CAB	BAC	ACB	Selection 2
A: We suppose that they understand the fundamentals of their data sets and know how to interpret common visual representations, but they require additional assistance with the sampling procedureB: As evident from Section 6, the five ML experts who participated in our 1-hour and 15-minute interview sessions were...	CAB	CBA	BCA	ACB	Selection 3
A: This is a high barrier to scale for even a well-funded and resourceful adversaryB: Having said this, the probability of frontrunning, no matter how miniscule, still exists. This is the price we pay for having an autonomous distributed system with no central control. For zero frontrunning probability, all tra...	BCA	CBA	BAC	ACB	Selection 1
A: For the capacity-aware baseline, we consider an RCT where treatments are assigned to n𝑛nitalic_n students in order to estimate the CATEB: For the gradient-based methods, we randomly initialize the policy and optimize β𝛽\betaitalic_β via projected stochastic gradient descent (in our case, ascent because we ...	ABC	ACB	ACB	BCA	Selection 4
A: OccamNets are biased toward using fewer spatial locations for prediction, which we enable by using spatial activation maps [24, 44, 54]B: However, these methods have not been explored for their ability to combat bias mitigation, with existing bias mitigation methods adopting conventional architectures that u...	CAB	CAB	BAC	ACB	Selection 4
A: We propose a Coarse-to-Fine Feature Mining (CFFM) technique to jointly learn a unified presentation of static and motional contexts, for precise and efficient VSS. CFFM contains two parts: Coarse-to-Fine Feature Assembling (CFFA) and Cross-frame Feature Mining (CFM). The former summarizes contextual information ...	CBA	ACB	ABC	BCA	Selection 4
A: Figure 16 shows our empirical design space exploration, which reveals the average number of requests issued as the number of banks (n𝑛nitalic_n) and input queue size (m𝑚mitalic_m) vary. On average, a 512-sized queue with 64 banks allows for 60 parallel requests per iteration without collisionsB: A controll...	ABC	BCA	ACB	CAB	Selection 2
A: Indeed, Theorem 3 allows us to extend [6, LemB: While we believe the above results are useful more generally, our main motivation for proving them here is to extend existing results in the literature adapting Morse-theoretic ideas to the PL setting so that they apply to some noncompact settingsC: 4.13], ...	ACB	ACB	BAC	BCA	Selection 3
A: Later developments in this direction can be found in [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26].B: Among these successful applications in physical sciences, the more challenging task is to use neural networks to study nonequilibrium problemsC: Recently, an algorithm of artificial neural networks was p...	CAB	BCA	ABC	CBA	Selection 1
A: Later [19], [21] made great progress on deep learning solving PDEs and [13] discussed the theory of it. [16] proposed seq2seq strategy which is essential for timing problems. As is well-known the weak formulation seeks the pair of finite elements that is stable as well as is compatible such that the pressure spa...	CAB	CAB	CAB	BAC	Selection 4
A: We used Gaussian as the underlying distribution of our synthetic datasetsB: In this experiment, we study whether the underlying distribution of the data would affect the capacity of the RU measures in revealing unreliabilityC: To do so, we follow the same procedure outlined in the construction of synthe...	ABC	CBA	BCA	BCA	Selection 1
A: For instance, when looking through the teen’s apps, the parent P2 reacted to a gaming app that she did not know about, whereas her teen T2 took her consent before installing that app but the parent forgot about it. Since T2 and her mother were in the same Zoom discussion, they corroborated this point for us in t...	ACB	BCA	CBA	ABC	Selection 3
A: Two squares are clearly visible in the scatter plot in the right subplot of Figure 1B: On the other hand, for the DAD filtration, two blue circular points in the persistence diagrams are comfortably far away from the diagonal. The contour plot of the DAD function explains this: the dense contour lines inside...	ABC	BCA	ABC	ACB	Selection 4
A: 2019) by exploiting correlations between AUs via probabilistic graphic models or in an explicit way (Li et alB: 2019; Shao et al. 2020) by constructing an AU semantic graph according to statistics of the training data, and both kinds of works have achieved more accurate AU recognition. Although these methods...	BCA	BAC	ACB	CAB	Selection 1
A: A mined block different from the PPB in honest nodes (assuming most nodes are honest) has a much larger propagation time since the validation time is longer in the absence of pre-validation information. As a result, such a mined block has a high probability of becoming a stale/uncle block. This disincentivizes t...	BCA	ABC	ABC	CBA	Selection 4
A: In the contexture of reinforcement learning with function approximations, our work is related to a vast body of recent progress (Yang and Wang, 2020; Jin et al., 2020b; Cai et al., 2020; Du et al., 2021; Kakade et al., 2020; Agarwal et al., 2020; Zhou et al., 2021; Ayoub et al., 2020) on the sample efficiency of...	CBA	CBA	CAB	ACB	Selection 4
A: Our study answers all the predefined research questions providing a snapshot of the research carried out in the domain. We found that articulation disorder, hearing impairment, dysarthria, and motor speech were the most frequently studied disorders, addressed in three, two, two, and two studies (research questio...	CAB	BAC	CAB	CBA	Selection 2
A: In this regard, we find that for each of the 8 datasets and each of the communities in the dataset, the intra-community compression ratio is higher than the inter-community compression ratioB: We provide the results in the Appendix E.2. C: This provides an average measurement of the compression ratio in ...	BAC	ABC	BCA	ABC	Selection 3
A: The SGG results do not improve in an obvious way, or even get hindered as for the PredCls and SGCls on the object obfuscation and image obfuscation datasets. The comparisons indicate that our propose SI-Dial framework indeed learns to ask questions in a meaningful way.B: We observe that dialog can serve as a...	CAB	CBA	BCA	BCA	Selection 2
A: In many real-life scenarios, except for the travel fee, the agent may also need to pay the facility a service or entrance fee, such as tickets for swimming pools and museums. The entrance fees may differ for facilities in different locations. An immediate example is building a facility downtown would be more exp...	BCA	BAC	BCA	CBA	Selection 4
A: We wonder if there is some algorithmic relation between efficient and perfect edge dominationB: More specifically, we remark that there are graph classes which admit polynomial time solutions for solving the efficient edge domination problem while being hard for solving the perfect edge domination problem**...	CBA	BAC	ABC	CBA	Selection 3
A: Note that, in accordance to Tab. 2 indeed, we are implicitly designing minimum complexity ReLU networks with the same number of layers L=2𝐿2L=2italic_L = 2, each of them composed of almost comparable number of neurons (from 2222 to 4444)B: As a consequence, the training time required to determine the values...	ACB	BCA	CBA	ABC	Selection 2
A: We combine such representations with explicit physical models.B: An exeption is the work by Song et al. that use the solution of an ODE as regularization of a motion network to crate dynamic NeRFs [47]C: In contrast to our work, this approach does not enforce the physics to be exact. While the majority o...	ABC	BCA	ACB	CAB	Selection 4
A: This demonstrate the advantages of QSC accurately sending and reconstructing semantic information.B: In Figure 3, we show the quantum semantic fidelity achieved against the amount of quantum communication resources used for \|𝒳\|=500𝒳500\lvert\mathcal{X}\rvert=500\| caligraphic_X \| = 500C: At low noise, ...	BCA	CAB	BCA	ACB	Selection 2
A: Some evident aspects emerge from the figures. Considering the minimum data rate of 00Mbps, both SRR and RND show higher percentages of (R>0)𝑅0(R>0)( italic_R > 0 )-served UEs than the Proposed schemeB: However, when considering UEs that are served with at least 50505050Mbps, the gap between SRR and the Prop...	BAC	ACB	ABC	ABC	Selection 2
A: Since it is simple to verify whether or not a contract is incentive-aligned, the principal asks the agent for their proposed incentive-aligned contract and then proceeds with that contract, provided it is indeed incentive-alignedB: For the agent offered the menu of all incentive-aligned contracts, we now der...	CBA	ABC	BCA	ABC	Selection 3
A: This study introduces the novel paradigm of Privacy Preserving Image Registration, designed for allowing image registration in privacy-preserving scenarios where images are confidential and cannot be shared in clearB: Leveraging both secure multi-party computation (MPC) and Fully Homomorphic Encryption (FHE)...	ABC	ACB	BCA	CBA	Selection 1

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