Split (1)

train · 2.93k rows

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A: 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 improves the signal-to-noise ratio and allows a “leap over” the noise.B: Planning methods based on learning, including kSubS, typic...	BAC	ABC	CBA	BAC	Selection 3
A: Our MFE-NER with glyph and phonetic embedding is attached to them by simple concatenation. Table 2 lists some of the hyper-parameters in our training stage. Adam is used as the optimizer and the learning rate is set to 0.002. In order to reduce over-fitting, we set a rather high dropout [Srivastava et al., 2014]...	ACB	ABC	ACB	CAB	Selection 4
A: Finally, we also investigate 3D étendue expanded hologramsB: We note that existing methods on étendue expanded holography has focused on monochromatic 3D holograms[7, 28, 29]. Photon sieves[21] only achieves 3D color holography for sparse points. See Supplementary Note 4 for a discussion of these findings. *...	ACB	BCA	CAB	CAB	Selection 1
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...	BAC	ACB	CAB	ABC	Selection 1
A: This will prevent you from creating a blank first page.B: Note that the closing curly brace for the author group comes at the end of the thanks groupC: Be sure to use the \\\backslash\IEEEmembership command to identify IEEE membership status. Please see the “IEEEtran_HOWTO.pdf” for specific information o...	CBA	CAB	ABC	ACB	Selection 1
A: For ϕ:=ϕ1∨ϕ2assignitalic-ϕsubscriptitalic-ϕ1subscriptitalic-ϕ2\phi:=\phi_{1}\lor\phi_{2}italic_ϕ := italic_ϕ start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT ∨ italic_ϕ start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT or ϕ:=¬ϕ1assignitalic-ϕsubscriptitalic-ϕ1\phi:=\neg\phi_{1}italic_ϕ := ¬ italic_ϕ start_POSTSUBSCRIPT 1 end_POSTS...	ACB	BAC	BCA	CBA	Selection 1
A: Sequences are drawn from the sample (with replacement) to form a new sample, matching the total number of observations and proportion of observations of treatment groups. In order to ensure proper clustering, it is crucial that each sequence of networks from one group be treated as a single observationB: The...	ABC	BCA	ABC	ABC	Selection 2
A: With the huge development of the smart terminal market, research on lightweight SISR models has gained increasing attentionB: This is because the model size and computational costs of these models still exceed the limits of edge devices. Therefore, exploring lightweight SISR models that can be practical in u...	BCA	ABC	BCA	ACB	Selection 4
A: An important issue for learning coordinate-based representations is the tendency of neural networks to interpolate and attenuate high-frequency changes in the output [1, 2, 16]B: Two effective solutions to this problem are to either map the input coordinates (known as positional encoding) [1] or use sinusoid...	CBA	CBA	ABC	BAC	Selection 3
A: Similar to Proposition 1, the proof of this Proposition extends ideas from Dong and Van Roy, (2018) to non-finite ΘΘ\Thetaroman_Θ, and the partial monitoring settingB: The techniques used in this extension could also be used to extend to non-finite parameter spaces in other problems. Principally, the proof ...	BCA	CBA	CBA	ACB	Selection 4
A: Accordingly, the knowledge stored in our memory is not required, strictly speaking, to correctly address the task (as, for example, in question answering)B: It is instead an auxiliary collection of pieces of information that can be consulted to perform reasoning and to make the neural model interpretable.**C...	BCA	BAC	ACB	CBA	Selection 1
A: Given the parallelizable nature of SMC, the computational bottleneck becomes the simulation and verification of a complex logical property at each iterationB: Here, formal methods come into play: complex properties are translated into logical formulae, which can then be automatically verified using efficient...	ACB	ACB	CBA	BCA	Selection 4
A: Recall that elementary doctrines can be understood as those primary doctrine endowed with equality predicatesB: replacing of ∧,⊤top\wedge,\top∧ , ⊤ by ∗,κ∗𝜅\ast,\kappa∗ , italic_κ).C: The following definition introduces those primary linear doctrines that are elementary as a direct linearisation of Def...	BAC	ABC	BCA	ACB	Selection 4
A: In this section, we systematically analyze the time and space complexity of RoleSim [5], StructSim [29], and ForestSim in finding top-k similar nodes for a given nodeB: Precomputation of the studied role similarity metrics is shown in Figure 4, and we list the time and space complexity of each measure in Tab...	CBA	ACB	CBA	BCA	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...	ABC	CBA	BAC	BAC	Selection 2
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: This process is somewhat elaborate and the reader is referred to [31] and [32] for all of the detailsC: However, for the exposition in this section it sufficient to k...	ABC	BCA	ACB	CAB	Selection 1
A: We also employ Qiskit for compilation. The optimization level is set to 2 for all experimentsB: We use IBMQ quantum computers via Qiskit (IBM, [n. d.]) APIs. We study 6 devices, with #qubits from 5 to 15 and Quantum Volume from 8 to 32C: All experiments run 8192 shots. The noise models we used are off-t...	CBA	ACB	BAC	CAB	Selection 3
A: Thus, ECO-E is adopted to evaluate the performance of ECO on the event data. Moreover, E-MS and EVT are extended to support the bounding box-based object tracking. B: EVT, E-MS, ETD, and RMRNet are the popular event-based tracking methods. ECO-E is an event-based variant of ECO, which uses TSLTD chen2020end ...	BAC	BCA	ACB	CBA	Selection 4
A: 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]B: We now prove our main result, that there are no ugly perfect graphs...	BAC	CAB	CBA	CAB	Selection 1
A: (iii) In addition, knowledge distillation (KD) is another approach that can be regarded as an implicit URL method transferring the knowledge from the pretrained model to enhance the student model unsupervised instead of considering geometric structure or special prior information of the target data. Specifically...	BAC	CAB	CBA	ACB	Selection 3
A: In this paper, we propose patch-based inference to reduce the memory usage for tiny deep learning by up to 8×\times×, which greatly expands the design space and unlocks powerful vision applications on IoT devicesB: For the VWW dataset, MCUNetV2 can achieve >90% accuracy under only 32kB SRAM, 4×\times× smalle...	CBA	CBA	CBA	ACB	Selection 4
A: Even without taking advantage of the ensemble methodology, our single model is still competitive to the existing baseline. This validatesB: The single model we proposed, overwhelmingly outperforms the baseline in terms of all leaderboards and achieves encouraging 65.9%percent65.965.9\%65.9 %, 77.0%percent77....	CAB	ABC	CBA	BAC	Selection 3
A: GNNs [28, 12, 8] have demonstrated their outstanding ability in encoding graphsB: Given a set of graphs, CGCL needs to encode them into vectorized representationsC: In CGCL, we mainly employ GNNs as graph encoders. GNNs follow the recursive neighborhood aggregation and certain message-passing scheme [32...	BAC	CBA	ABC	BCA	Selection 1
A: We could conjecture that CNN may facilitate shape recognition and therefore be the driving force in the emergence of languages compositional with respect to the canonical shape, color splitB: To check this we impair the prior by scrambling images, as depicted in the bottom panel Figure 2. An image is scrambl...	ABC	ACB	BCA	BAC	Selection 3
A: We proposed an algorithmic implementation of our theoretical framework to learn ROCBFs in practice. Finally, our simulation studies show how to learn safe control laws from RGB camera images within the autonomous driving simulator CARLA.B: In this paper, we have shown how safe control laws can be learned fr...	CAB	CBA	BAC	BCA	Selection 1
A: Most often we need to develop other lower bound tools, in addition to or instead of Raz-TalB: Having said that, very few of our results will follow from Raz-Tal in any straightforward wayC: Our new tools, which seem likely to be of independent interest, include a random restriction lemma for quantum quer...	BCA	CAB	ABC	BAC	Selection 4
A: Results shown in Figure 8 suggest that the number of communities is 2, where [27] also applies the idea of eigengap to estimate the number of communities for real-world networks. Note that though CoauthorshipsNet1589 is disconnected, we can still apply nDFA and DFA on it since there is no requirement on network ...	CBA	BCA	CAB	ABC	Selection 1
A: It consists of a single learner and actor workersB: The actors are simple loops around the environment, generating observations (and rewards) transmitted to the learner. The learner makes inferences (and sends back actions); moreover, it handles trajectory accumulation and training.C: We use the communi...	ACB	BCA	BAC	CBA	Selection 2
A: One research question that arises from this (possibly under-researched) need for cooperation, starting from the selection of models to the extraction of insightful decisions, is: (RQ2) How can VA tools/systems support the collaboration between ML experts and domain experts?B: Nevertheless, comparing numerous...	BCA	CAB	ACB	BCA	Selection 2
A: On the other hand, polarization diversity is not taken into account in the majority of previous research works on antenna selectionB: Although there are previous reports that consider polarization diversity with antenna selection, they consider fixed antenna polarization such as dual-polarized antennas in[44...	BCA	ABC	BCA	CAB	Selection 2
A: In other words, online algorithms exists which produce solutions that are only a constant factor worse than the offline optimumB: We note that these problems have known O⁢(1)𝑂1O(1)italic_O ( 1 )-competitive algorithms if the arriving pieces are axis-parallel rectangles; see Section 1.2 for referencesC: ...	ABC	BAC	CAB	CBA	Selection 2
A: Deep models can remember and cluster the images or patterns they viewedB: Instead of active learning (AL) tending to find the unfamiliar examples, our goal is to find the ones nearest to the center of latent space which we think more representative and important when only several images can be labeled.C:...	BCA	BAC	ABC	ACB	Selection 1
A: We show that the proposed algorithm stably yields consistent community detection under MMDFB: Especially, theoretical results when edge weights follow a specific distribution can be obtained immediately from our results. C: (2) We use a spectral algorithm to fit MMDF	CBA	ABC	BCA	CBA	Selection 3
A: 3) Based on our findings, we propose a novel Class-wise Decorrelation (CwD) regularization technique to enforce representations of each class to scatter more uniformly at the initial CIL phase. 4) Extensive experiments show that our proposed CwD regularization yields consistent improvements over previous state-o...	CBA	BAC	ABC	BCA	Selection 1
A: Then, the NICE-Net-ds was further trained for intra-patient registration with dual deep supervision. During inference, pair-specific fine-tuning was performed to improve the network’s adaptability to testing variations. In addition, as the MRI scans provided by the challenge organizers had been rigidly registere...	BAC	CBA	CAB	ABC	Selection 3
A: Hence, here, when we remove an occurrence of some variable, we have to carefully choose which occurrence of this variable to remove. We proceed by considering all the possible cases according to the definition of the binary relation >ΣsubscriptΣ>_{\Sigma}> start_POSTSUBSCRIPT roman_Σ end_POSTSUBSCRIPT. B: Th...	ACB	CAB	ABC	BCA	Selection 2
A: with out-degree 00. For a given node, we refer to the sum of the weights of the edges between the node and absorbing states as the “absorption rate” of the nodeB: We represent an absorbing state of an absorbing random walk on a graph as a node555Throughout our paper, our figures do not show the nodes for the...	ACB	CBA	BCA	BAC	Selection 4
A: The general QNR problem can be formulated in terms of hypergraph flows and solved using LP (see Appendix A)B: Here, we develop an efficient heuristicC: Although polynomial-time and provably optimal, the LP-based approach has a very high time-complexity for it to be practically useful	BAC	ABC	ACB	BCA	Selection 3
A: [196] in the case of 2, 5, and 8 s transition times. Wan and Xu [197] have further verified that an insufficient amount of lead time, such as 3 s, results in an impaired takeover performance, and drivers perform better when enough time, such as more than 10 s, is allotted for takeover requests. In general, it ca...	BCA	ACB	CBA	ABC	Selection 3
A: In addition, model size and computing power requirements are essential indexes to evaluate the potential of deep learning models to be deployed in embedded systemsB: Therefore, two metrics, model parameters and FLOPs are introducedC: Model parameters are used to reveal the model’s complexity, and FLOPs r...	CAB	ABC	CAB	BAC	Selection 2
A: 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. B: In this paper, we propose a new form of algebraic attack, which is especially ...	BAC	CBA	CAB	BAC	Selection 3
A: Our algorithms learn a single domain-specific value function and can subsequently compute strategies against any opponent in the run-time. Furthermore, ABR and even CDBR are extremely brittle, making it a bad choice if we are unsure about the opponent, which we often are in a game against an unknown opponent. On...	CAB	ABC	CBA	ABC	Selection 1
A: Network data which is of interest to cluster often has weights associated with each edgeB: 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 v𝑣vitali...	BCA	CBA	BCA	ABC	Selection 4
A: Nodes are tied by links whenever two nodes share at least one common reference. The thickness of links is given by the association strength of the tie between two nodes (to provide a clear visualization, only nodes with weights higher than the mean are displayed)B: Colors correspond to communities of belongi...	CAB	ABC	BCA	ACB	Selection 3
A: We mention two particular examples raised in the literature after our result became available, including game theory (Zhang et al., 2022a; Yan et al., 2023) and an intermediate model for bridging stochastic and adversarial optimization (Chen et al., 2023a, b).B: We have found this collaboration crucial for a...	BCA	ACB	CBA	CAB	Selection 3
A: Thus condition (i)B: we have x∈F⁢(σ)ω𝑥𝐹superscript𝜎𝜔x\in F(\sigma)^{\omega}italic_x ∈ italic_F ( italic_σ ) start_POSTSUPERSCRIPT italic_ω end_POSTSUPERSCRIPTC: Thus there are u,v∈F⁢(σ)∗𝑢𝑣𝐹superscript𝜎u,v\in F(\sigma)^{*}italic_u , italic_v ∈ italic_F ( italic_σ ) start_POSTSUPERSCRIPT ∗ end_POST...	BCA	ABC	ACB	CAB	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)	ABC	CBA	CAB	ACB	Selection 3
A: This technique entails computing correlations between brain regions across various time windows (Allen et al., 2014; Hutchison et al., 2013; Shakil et al., 2016; Mokhtari et al., 2019; Huang et al., 2020)B: The predominant method for computing time-varying correlation in time series data, particularly in ne...	CBA	BAC	CAB	ACB	Selection 2
A: B: ∀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 , C: italic_l }. If ϕ2⁢(θ)>θsuperscriptitalic-ϕ2𝜃𝜃\phi^{2}(\theta)>\thetaitalic_ϕ start_POSTSUPERSCRIPT 2 end_POSTSUPERSC...	CBA	BCA	BAC	CBA	Selection 3
A: The goal of this work is to design a control strategy that will guarantee safety of the battery system under anomaliesB: Mathematically, this impliesC: The criterion for safety is that the spatial norm of the temperature deviation of the battery from a set-point remains below a prescribed threshold h¯¯ℎ...	ACB	CAB	ABC	CAB	Selection 1
A: In yet another study, Booth et al. employ multiple sensing modalities to study stress, anxiety and affect of hospital workers [18]. The ground truth for stress and anxiety are obtained as self-reports on a 5-point Likert scale, whereas PANAS is used for assessing affectB: For each consecutive data in which t...	ABC	ACB	BCA	BAC	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...	CBA	BAC	CBA	BCA	Selection 4
A: The equality in line (58) follows from (49) and the properties of definite integralsB: The inequality in line (57) follows from properties of definite integrals, the first inequality in line (58) follows from (55)C: The first inequality in line (59) follows from (25).	CAB	CAB	ABC	BAC	Selection 4
A: The rest of paper is organized as follows. The problem formulation is presented in Section 2B: The online coordinate descent algorithm considered in this paper is given in Section 3. Regret bounds for random online coordinate descent algorithms are given in Section 4 followed by regret bounds for determinis...	ABC	BCA	BCA	ACB	Selection 1
A: Each input event generates an output event at the same location and time, but whose polarity is given by the closest cluster. Thus, the number of possible polarities of output events is equal to the number of clusters. B: We use an unsupervised clustering method, such as the K-means algorithm, to cluster the...	CBA	ABC	CAB	ABC	Selection 3
A: 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_δB: Intuitively, in such a state each agent has a small incentive to further revise the opinion. Hence, it is reasonable to assume that such states represent a st...	BAC	ABC	BAC	ACB	Selection 2
A: The area under the ROC curve (AUC) is a threshold-independent measure of the model’s goodness of fit and its ability to distinguish between positive and negative samplesB: In medical literature, this number also gives the probability that a randomly selected patient who experienced a condition had a higher r...	BCA	ABC	CBA	ACB	Selection 1
A: Although this problem has attracted considerable attention from the machine learning community, this study has focused on another established branch of bandit problems, BAI, in which the goal is to identify the treatment arm with the largest mean. Finding the best treatment essentially concerns simple regret min...	CAB	ACB	BAC	BAC	Selection 1
A: In Figure 6 we visualize a TSNE projection of the embeddings of both models. We can see a well structured embedding space for our model with distinct clusters for the different shape classes. On the other hand, the embeddings produced by the non-invariant autoencoder is less structured and one can make out diffe...	CAB	ACB	ABC	ACB	Selection 1
A: At each iteration a location is chosen at random from those availableB: The result is given as the average over 100 runs on each of the data snapshots in each subsetC: For BO one run is used. The metrics are calculated on the pre-processed data, i.e. after log transform and standardisation / mean-centrin...	ACB	CBA	BCA	ABC	Selection 4
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	BCA	Selection 3
A: Increasing the frame size decreases the chance of collisions, while increasing K𝐾Kitalic_K makes the transmission more robust and allows to harvest more diversity. The relationship, however, is not as straightforward when it comes to spectral efficiency shown in Fig. 7(b). For a given number of repetitions K𝐾K...	CAB	CBA	BAC	ABC	Selection 1
A: We remark that although time-wise this algorithm is fast, the ratio constant of the yielded path over the optimal path has not been computed and is much larger that Christofides’ 3/2323/23 / 2 ratioB: Moreover, the exponent 3333 in our theorem is sharp and can not be lowered; see Section 5.4.C: In fact, ...	BAC	CAB	CAB	ACB	Selection 4
A: In [7], it has been proven that the set of multidegrees of a multiprojective variety forms a polymatroid. In [46], the authors show that the set of non-degenerate dimension vectors for Chow forms equals to the truncation of this polymatroid, which is itself a polymatroid. In a similar fashion, we show that non-d...	ACB	BCA	BCA	CAB	Selection 4
A: As a result of this test, a p-value equaling 0.00050.00050.00050.0005 is obtained, resulting in the dependency between categories is confirmed, with a 0.010.010.010.01 significance level. Then, we perform a study regarding the relationships between the different target and reported individual emotionsB: To t...	BCA	ACB	ABC	CAB	Selection 1
A: To construct the classifier, we use the Drebin dataset (Arp et al., 2014), comprising 12000120001200012000 benign and 550550550550 malicious samplesB: After splitting the dataset into training testing sets, we train a linear support vector machine (LSVM). This model achieves a classification accuracy of 95.0...	BAC	BCA	CBA	ABC	Selection 2
A: A strategy for the statistican in this game is a blame functionB: Roughly speaking, we consider a zero-sum game between an adversary and a statistician, in which the adversary chooses a deviation and the statistician, after observing the realization s𝑠sitalic_s, has to guess the deviator if s∉D𝑠𝐷s\notin D...	CBA	ACB	BAC	BCA	Selection 3
A: Since PASS was provided as a resource for the teams, after the event, we asked the 5 winning teams for feedback on the platform (IRB exempted). Particularly, all 5 teams consider the platform as helpful for solving the challenges. However, due to relatively high resource requirement (e.g., GPUs), 2 teams suggest...	BCA	CBA	BCA	CAB	Selection 4
A: The fifth buffer contains the second part of the second switch gadget in [8.5,11.5]8.511.5[8.5,11.5][ 8.5 , 11.5 ]. Its long intervals terminate at 8.58.58.58.5 and start from 10.510.510.510.5B: 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....	ABC	ACB	BAC	BCA	Selection 2
A: The only end-to-end anticipation model for natural video of which we are aware (AVT, [26]) does not use BatchNorm as it is entirely Transformer-basedB: [47] show the effectiveness of end-to-end strategies for the related temporal action detection task. They use FrozenBN but do not discuss BatchNorm issues.**...	CBA	BCA	BAC	ACB	Selection 4
A: Thus, the performance was saturated. LFW, AgeDB-30, and CALFW contain 6,000 images, and CFP-FP has 6,000 imagesB: Results on LFW, CFP-FP, AgeDB-30, and CALFW. FR on LFW, CFP-FP, AgeDB-30, and CALFW is straightforwardC: They have 1:1 ratios between the positive and negative pairs. Verification accuracy wa...	BAC	ACB	ABC	CBA	Selection 1
A: Age-related macular degeneration (AMD) is a major cause of vision impairment and has affected approximately 200200200200 million people worldwide in 2020 [34]B: With an aging population, such numbers are expected to rise to 288288288288 million by 2040 [35]C: AMD is a progressive disorder of the macular ...	CBA	ABC	ACB	ACB	Selection 2
A: Interestingly, we find that some facial crucial regions can be automatically enhanced in the process of deep feature learning by the proposed methodB: Moreover, U-Net is employed to generate feature maps where each pixel has large receptive field and the local region also contains the global information.*C...	CBA	CAB	ABC	BCA	Selection 4
A: Each player is given some ‘rating’ value (measured in ‘points’ or simply ‘Elo’), which updates as they play gamesB: In the case of a draw, the players split the pot evenly. If one player wins, they take the entire pot. The heart of the Elo system is dictating how many points each player must ante up.C: T...	ABC	ACB	CBA	CAB	Selection 2
A: Table 1: Time taken to complete each activity of the sampling process for all use casesB: The completion time is expressed in minute:second formatC: Please note that for the iris flower data set, the undersampling time refers to two consecutive rounds.	ACB	BCA	ABC	CBA	Selection 3
A: A frontrunner can perform attacks with highly predictable results due to deterministic pricing mechanism as well as the transparency of liquidity amounts of decentralized exchangesB: In this context, Qin et alC: estimated a profit of 1.51 Million USD made by frontrunners [42]. Other domains that are affe...	ABC	BCA	BCA	CAB	Selection 1
A: In other applications, the decision maker may want to accept such agents because the agents who would benefit the most from the treatment are those who can invest effort to make themselves look desirable. Lastly, as demonstrated by Liu et al. (2022), when all agents have identical ability to modify their covaria...	ABC	CAB	ACB	BAC	Selection 2
A: The dataset consists of correlated action-background pairs, where the train set consists of selected action-background pairs, e.g., climbing on a rock, whereas the evaluation set consists of differently correlated action-background pairs, e.g., climbing on snowy slopes (see Fig. 3(c))B: Biased Action Recogni...	CBA	CBA	ACB	BAC	Selection 4
A: The latter efficiently mines the contexts from neighbouring frames to enhance the feature of the target frame. To make use of global temporal contexts, we further propose CFFM++ which abstracts global temporal contextual prototypes from the video by unsupervised clustering and then exploits them to improve the t...	CBA	BCA	CAB	BCA	Selection 3
A: Recent works [4, 1] explore alternative embedding table placements but lack preprocessing to reduce communication overheads. Bandana [45] suggests storing embedding tables in non-volatile memory with DRAM cachingB: Other approaches [32, 42, 41, 46] accelerate near-memory processing but lack support for distr...	BCA	ABC	CBA	ABC	Selection 1
A: 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 settingsB: Indeed, Theorem 3 allows us to extend [6, LemC: 4.13], ...	CBA	BAC	ABC	BCA	Selection 3
A: It is stated that topological defects will arise in the course of a phase transition with symmetry breaking of the system due to the celebrated Kibble-Zurek mechanism (KZM) [27, 28]. Number density of topological defects was found to satisfy a universal power-law with respect to the quench rate. The formation of...	CBA	CBA	CAB	BCA	Selection 3
A: starting atB: First we generate analytic solutions of the equationC: In this section, we carry out a 3D error test with the following form of solutions on the domain Ω=[0,1]3Ωsuperscript013\Omega=[0,1]^{3}roman_Ω = [ 0 , 1 ] start_POSTSUPERSCRIPT 3 end_POSTSUPERSCRIPT	BAC	CBA	BCA	CAB	Selection 2
A: We used Gaussian as the underlying distribution of our synthetic datasetsB: To do so, we follow the same procedure outlined in the construction of synthetic datasets in § 6.1.2, however, instead of generating a sample following a Gaussian distribution, we opt to utilize alternative distributions such as Uni...	ACB	BCA	CAB	BAC	Selection 1
A: Research should investigate which incentives would be most effective. For example, nudges could remind teens to provide oversight, and reward mechanisms such as badges or points could incentivize different activitiesB: A reward system could also enable teens to gain further privileges or privacy mechanisms w...	BCA	ACB	CAB	CAB	Selection 1
A: We also apply our method to real dataB: The two filtrations reveal uninhabited regions in the United States and regions of missing data. As expected, the regions found by the distance-to-measure filtration are large while the small ones are detected only by the RDAD filtration.C: The distance-to-measure...	ACB	CBA	CAB	BAC	Selection 1
A: CISNet outperforms HMP-PS by a large margin with the highest average F1-score of 64.7%percent64.764.7\%64.7 %.B: As shown in Table 1, CISNet outperforms all the compared methods in terms of average F1-score on BP4DC: Specifically, CISNet achieves an average F1-score of 64.3%percent64.364.3\%64.3 %, which...	CBA	BAC	CAB	ACB	Selection 3
A: To measure the three time components in Ethereum, we set up a local node that connects to the Ethereum MainNet during the period of January 23, 2022, to January 25, 2022B: As discussed above, the block validation time consists of three components: header validation time, transaction execution time, and data...	ABC	BAC	BCA	BCA	Selection 2
A: However, none of these approaches directly apply to POMDPs due to the latency of the states. B: These works characterize the uncertainty in the regression for estimating either the model or value function of an MDP and use the uncertainty as a bonus on the rewards to encourage explorationC: In the contex...	ACB	ABC	BAC	CBA	Selection 4
A: We looked at the author’s affiliation and funding agency when requiredB: Most papers reported on studies whichC: We further report the geographical distribution of the included studies based on the location of the study indicated in the paper (see Figure 7)	BCA	ABC	CBA	ABC	Selection 1
A: These datasets vary in the number of cells, genes, clusters, cells per cluster, and the ”difficulty” of clustering. A summary of the datasets is provided in Table 3 in Appendix E.1. B: In this direction, we consider the single-cell RNA sequencing datasets from a benchmark paper [DRS20]C: These datasets a...	CBA	ACB	CAB	BCA	Selection 3
A: Recent work [13] has also started to exploit the data privacy problems by deliberately obfuscating some sensitive information (e.g., human faces) from images as visual input. To this end, we consider the computer vision task setting with insufficient visual input.B: For example, certain objects within a sing...	CBA	ABC	ABC	BCA	Selection 1
A: In Section 3, we derive some useful technical properties. Sections 4 and 5 present our main results for the one-facility game with total and maximum cost objectives, respectively.B: Section 2 presents the formal definitions of our modelC: The rest of the paper is organized as follows	CBA	ACB	BCA	ABC	Selection 1
A: There is a paper [16] where the authors describe ILP formulations for the PED problem, together with some experimental results. B: As we have seen before the papers on perfect edge domination are less frequentC: There is some more bibliography to add to the already vast literature [3, 10, 23, 31, 33, 35]...	BAC	CBA	BAC	BAC	Selection 2
A: With a specific focus on discrete-time polytopic systems, an admissible control policy that actually makes a polyhedral CLF a suitable Lyapunov candidate for the closed-loop system is typically synthesized in two ways: through a variable structure 25, 46, 47, or a (minimal) selection-based controller 3. We will ...	CBA	CAB	CBA	BAC	Selection 2
A: We model the dynamics of the objects using an ordinary differential equation (ODE) and use implicit neural representations to model the appearance, where the static background and the planar dynamics allow us to model the appearance in 2DB: For estimating these quantities directly from an input video, we uti...	ACB	BAC	BCA	ABC	Selection 1
A: 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 \| = 500B: This demonstrate the advantages of QSC accurately sending and reconstructing semantic information.C: At low noise, ...	CAB	BCA	CBA	ACB	Selection 4
A: However, the training procedures can be re-activated at any point in time during the system operation, f.i., periodically or when a substantial performance decrease is detectedB: Once the convergence is reached (f.i., after a maximum number of steps or when minimal NN weight updates are performed), the train...	CAB	ABC	CAB	BAC	Selection 4
A: Situating our work within contract theory, we study an adverse selection model with a common value structure in the principal’s utility function. Our key departure from the usual adverse selection setup is that we do not assume that the principal has a prior distribution about the agents’ hidden typesB: Imp...	BAC	CAB	BAC	ACB	Selection 4
A: On the other side, although PPIR(FHE)-v1 is able to improve communication with respect to PPIR(MPC), it still suffers from the highest communication among the three proposed solutions. This is due to the fact PPIR(FHE) protocols can find different applications depending on the requirements in terms of computatio...	BCA	BAC	CAB	ACB	Selection 3

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