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A: Figure 1: The performance of Subgoal Search. (top, left) comparison on INT (with the proof length 15) to AlphaZeroB: (top, right) BF-kSubS consistently achieves high performance even for small computational budgets. (bottom, left) similarly on Sokoban (board size 12x12 with 4 boxes) the advantage of BF-kSub...	BAC	BAC	ABC	BCA	Selection 3
A: A complete syllable is usually made of an auxiliary, a vowel, and a tune. Typically, vowels appear on the right side of a syllable and can exist without auxiliaries, while auxiliaries appear on the left side and must exist with vowels. However, the ‘Pinyin’ system has an important defect. Some similar pronunciat...	ACB	BCA	BAC	CAB	Selection 2
A: We find that neural étendue expansion also enables higher fidelity étendue expanded 3D color 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 ...	BAC	ABC	CBA	BCA	Selection 4
A: When training generative models on instruction following, people usually train the entire model and focus more on data curationB: We refer interested readers to another survey paper on instruction tuning (Zhang et al., 2023). In this work, we mainly focus on reviewing MTL architectures with task-specifc trai...	ABC	BAC	BCA	CBA	Selection 3
A: You will have to manually key the fences, but you’ll have options for alignment of the columns and for setting horizontal and vertical rulesB: The argument to array controls alignment and placement of vertical rules. C: The array environment allows you some options for matrix-like equations	CAB	BCA	BAC	CAB	Selection 2
A: Furthermore, w(i,β)subscript𝑤𝑖𝛽w_{(i,\beta)}italic_w start_POSTSUBSCRIPT ( italic_i , italic_β ) end_POSTSUBSCRIPT isB: But then, exactly one of w(i,β),w(i′,β)subscript𝑤𝑖𝛽subscript𝑤superscript𝑖′𝛽w_{(i,\beta)},w_{(i^{\prime},\beta)}italic_w start_POSTSUBSCRIPT ( italic_i , italic_β ) end_POSTSUBSCRIP...	CAB	ABC	BAC	CBA	Selection 4
A: Their results tie the existence of Pareto-efficient outcomes to the spectral radius of the benefits matrix and characterize the Lindahl outcomes as those with effort proportional to each individual’s eigenvector centrality in the graph described by the benefits matrixB: Elliott and Golub (2019) characterize ...	CAB	ABC	CBA	BAC	Selection 4
A: In (Yoon et al., 2017), a cascade convolution neural network is introduced to simultaneously up-sample both the spatial and angular resolutions of a light field imageB: Meanwhile, a new light field image dataset is proposed for training and validationC: To reduce the dependence of accurate depth or dispa...	BAC	ACB	ABC	CAB	Selection 3
A: For some of the results, the Neural Knitwork was, in fact, able to outperform . Table 1 contains the evaluation across the entire datasets for different fill ratios, which supports that the Neural Knitwork outperforms the conventional approach and achieves comparable performance to the with approximately 80% le...	BAC	BAC	BAC	CBA	Selection 4
A: Our motivations for a renewed treatment of apple tasting are threefoldB: Second, the apple tasting setting strikes a sufficient balance between simplicity and complexity to allow an uncluttered study of the effect of zero-information actions in PM, and of posterior approximation to the performance of TS. Fin...	CBA	CAB	CBA	ACB	Selection 4
A: Certainly, one may hope for increased performanceB: Evaluation of memory usage cannot be reduced to task-specific metrics only, like classification’s F1C: However, our main motivation behind knowledge integration is interpretability, hence our evaluation should mainly take that into account. To that end,...	CAB	CBA	ABC	BAC	Selection 4
A: Figure 1B: Average crowdedness measure over the period November 04, 2013, to November 11, 2013C: The x𝑥xitalic_x-axis contains the longitude and the y𝑦yitalic_y-axis contains the latitude degrees. The marked areas represent: 1 Garibaldi Station, 2 Central Station, 3 Duomo, 4 Bocconi, 5 Navigli.	BCA	ABC	BCA	BAC	Selection 2
A: The following definition introduces those primary linear doctrines that are elementary as a direct linearisation of Definition 2.1 (i.eB: Recall that elementary doctrines can be understood as those primary doctrine endowed with equality predicatesC: replacing of ∧,⊤top\wedge,\top∧ , ⊤ by ∗,κ∗𝜅\ast,\kap...	CAB	CBA	BAC	BCA	Selection 3
A: ForestSimSearch can handle a top-k query in O⁢(k)𝑂𝑘O(k)italic_O ( italic_k ) time once the precomputation is finishedB: Efficient top-k similarity search algorithm : We devise ForestSimSearch for the top-k similarity searchC: Furthermore, we use the fast approximate algorithm to compute the diagonal e...	ACB	ACB	CAB	BAC	Selection 4
A: However, the progress of sentiment dependency-based methods, such as the work by Zhang et al. (2019); Zhou et alB: (2020); Tian et al. (2021); Li et alC: (2021a); Dai et al. (2021), has contributed to the improvement of coherent sentiment learning. These studies explored the effectiveness of syntax infor...	CBA	ABC	BAC	CBA	Selection 2
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...	BAC	CBA	CAB	CBA	Selection 3
A: During training, we iteratively sample error gates, insert them to PQC, and updates weights. Finally, post-measurement quantization is further proposed to reduce the precision of measurement outcomes from each qubit and achieve a denoising effect. B: Furthermore, we inject noise to the PQC training process b...	ACB	CAB	CBA	ABC	Selection 3
A: Then, for each parallel hypothesis set, the model hypothesis that has the largest number of parallel hypotheses is selected as the representative hypothesis for the set, and the other hypotheses in the set are removedB: After that, each representative model hypothesis is a unique event trajectory, and it is ...	CBA	BCA	CAB	ACB	Selection 2
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...	BAC	ABC	CAB	BCA	Selection 2
A: We evaluate the KD tasks based on self-supervised learning on STL-10 dataset. In this experiment, we adopt MoCo.v2 with ResNet-50 under 1600-epoch pre-trainingB: 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 [...	CBA	CAB	CAB	ACB	Selection 4
A: MCUNetV2 significantly improves the ImageNet accuracy of tiny deep learning on microcontrollersB: Under 512kB SRAM/2MB Flash, MCUNetV2 achieves a new record ImageNet accuracy of 71.8% on commercial microcontrollers, which is 3.3% compared to the best solution under the same quantization policy. Lower-bit (in...	ACB	ABC	ABC	CAB	Selection 1
A: 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%percent77.077.0\%77.0 % improvements in F1-score over the baseline method on two leaderboards respectivelyB: Even without taking advantage of the ense...	CBA	BAC	CBA	BCA	Selection 4
A: Given that variations in either encoder configurations or data invariably manifest in the resultant embeddings, collaborative contrastive learning parallels the efficacy of augmentation-based methods but is free from the often cumbersome or resource-intensive augmentation phase. With the multi contrastive views,...	CBA	ACB	ABC	BAC	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: We see interesting dynamics in topographic similarity distribution with respect to change in the noise levels. Namely, it s...	BAC	BCA	CBA	BAC	Selection 2
A: Learning CBFs: An open problem is how valid CBFs can be constructed. Indeed, the lack of systematic methods to construct valid CBFs is a main bottleneckB: Finding CBFs poses additional challenges in terms of the control input resulting in bilinear SOS programming as presented in [32, 33] and summarized in [...	CBA	ACB	ABC	CBA	Selection 2
A: Most commonly, we will consider Boolean circuits with AND, OR, and NOT gates where the AND and OR gates can have unbounded fan-in, but we will also consider e.gB: We always specify the basis of gates allowed in Boolean circuitsC: circuits where the gates can be arbitrary functions of low 𝖰𝖬𝖠𝖰𝖬𝖠\mat...	BAC	ABC	ACB	CAB	Selection 1
A: Furthermore, we also plot adjacency matrices for Bernoulli distribution, Poisson distribution and Signed network. B: For above setting, two different adjacency matrices are generated under DFM in Figure 2 where we also report error rates for DFA and nDFAC: Meanwhile, since A𝐴Aitalic_A and Z𝑍Zitalic_Z a...	CBA	CAB	CBA	ACB	Selection 2
A: Using the importance weights is the key algorithmic innovation of MA-Trace (and V-Trace), responsible for the strong performance we reportB: Indeed, already for 30303030 actor workers, using the weights is essentialC: Otherwise, the algorithm is unstable and suffers from poor asymptotic performance. See ...	ABC	CBA	ACB	BAC	Selection 1
A: Also, the two PCPs for training new RF and AB models allow users to explore more models progressively, especially since the provided hyperparameters’ ranges are also easily modifiable through the code. B: In the models overview panel, the number of trained models is limited to 20 RF and AB models in total (o...	CAB	BAC	CBA	ACB	Selection 1
A: However, the comprehensive simulation results in Figs. 4 – 5 show that the proposed PR-HS scheme substantially improves the system performance in terms of SNR, channel capacity and the associated distribution of channel gain. B: For this reason, the complete analysis to reach the closed-form description is u...	CBA	CAB	ABC	ACB	Selection 1
A: The position of each city is revealed to us in an online fashion and we immediately have to decide the time step where we visit this city. In addition to packing and TSP, we believe that the online sorting problem can be useful when studying other online problems as well.B: Indeed, we can imagine that we mus...	BCA	CAB	CBA	ABC	Selection 3
A: Second, to replace the MRE, we proposed to use a similarity score between a template and the rest based on the features of such potential key points. Third, considering landmark detection is a pixel-wise task, we apply pixel-wise self-supervised learning with all non-labeled data to build a basic feature extract...	ABC	CBA	ACB	BAC	Selection 2
A: For Train bombing, Les Misérables, and US airports, they can be downloaded from http://konect.cc/networks/ (see also [33]). The original US airports network has 1574 nodes and it is directed. We make it undirected by letting the weight of an edge be the summation of the number of flights between two airports. We...	BCA	BCA	BAC	ACB	Selection 3
A: Two classic setups of Incremental Learning are Class Incremental Learning (CIL)[26, 12, 20, 19, 13, 1, 22] and Task Incremental Learning (TIL)[16, 21, 2, 28, 31, 30, 24]B: CIL and TIL both split all training classes into multiple tasks and learn them sequentially. The difference between these two setups is t...	BAC	CAB	ABC	CBA	Selection 3
A: Linear and deformable image registration with 3d convolutional neural networks, in: Image Analysis for Moving Organ, Breast, and Thoracic Images: Third International Workshop, RAMBO 2018, Fourth International Workshop, BIA 2018, and First International Workshop, TIA 2018, Held in Conjunction with MICCAI 2018, Gr...	CAB	BAC	BAC	ABC	Selection 4
A: a tuple of database values) is entailed by all repairs, or is not entailed by some repair. But, as discussed in [8], the former is too strict, while the latter is not very useful in a practical contextB: Example 1, despite its simplicity, illustrates one of the limitations of the CQA approach. The notion of...	CAB	ACB	CBA	BAC	Selection 4
A: For example, Jaydeep et al. [7] proposed a transduction algorithm that uses the number of visits before absorption of an absorbing random walk as a measure of affinity between the nodes of a graphB: Absorbing random walks have been used to develop centrality measures [14], other methods to rank the nodes of...	CAB	CBA	BAC	CBA	Selection 3
A: EP generation rate decay only polynomially in L𝐿Litalic_LB: Their path-based metricC: More recently, Caleffi [18] formulated the entanglement generation rate on a given path between two nodes, under the more realistic condition where the intermediate nodes in the path may not all be equidistant, but st...	ABC	CBA	BAC	ACB	Selection 4
A: This approach is perhaps the most familiar, as it arises naturally from an engineering development trajectory, where the accuracy of simulators determines the quality of compliance (e.g., [183]). The confidence of the established compliance is a function of the accuracy and coverage of the simulation. However, t...	ACB	CAB	CBA	BCA	Selection 2
A: Similar to the basic residual block in ResNet, the Ghost bottleneck consists of two stacked Ghost modules, as shown in Fig. 2B: 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...	ABC	CAB	BAC	BAC	Selection 1
A: The results in this paper appear partially in the MSc thesis of the second author, who thanks his supervisors (the other two authors). This work has been partially presented in Cryptography and Coding Theory Conference 2021, organized by the group UMI (Italian Mathematical Union) “Crittografia e Codici” and by D...	CBA	BAC	BAC	ACB	Selection 1
A: We show that other gadgets can overestimate or underestimate exploitability, which could shift the distribution of the parameter p𝑝pitalic_p, and we could still compute the same solutions. However, in Figure 4, we show the results of games created to break the other gadgetsB: Both games have five actions fo...	ACB	BAC	CBA	ABC	Selection 1
A: Our paper provides some theoretical explanations - see Section 8 for a statement of the result.B: In Ecological networks each interaction observed reveals that an edge is present in the underlying network, and the effect of sampling effort can be modelled by taking the observed network after varying numbers ...	ACB	BAC	ABC	CAB	Selection 4
A: This should not be a surprise because of two reasons: firstly, the stock of Mexican emigrates has been constantly the highest in the world (in absolute terms) as well as the migratory flow between Mexico and the U.SB: The single countries that receive singularly the most attention are Mexico, with 10 case st...	CBA	CBA	BAC	BCA	Selection 3
A: The first solution, developed in our conference paper (Zhao et al., 2020b), is conceptually simpler but requires N=𝒪⁢(log⁡T)𝑁𝒪𝑇N=\mathcal{O}(\log T)italic_N = caligraphic_O ( roman_log italic_T ) gradient queries at each round, making it suitable only for the multi-gradient feedback modelB: In the follow...	ACB	BAC	ACB	BCA	Selection 2
A: Set A={a,b,c}𝐴𝑎𝑏𝑐A=\{a,b,c\}italic_A = { italic_a , italic_b , italic_c }B: sequenceC: Since σ′⁢(ℒ⁢(σ))⊂ℒ⁢(σ)superscript𝜎′ℒ𝜎ℒ𝜎\sigma^{\prime}(\mathcal{L}(\sigma))\subset\mathcal{L}(\sigma)italic_σ start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT ( caligraphic_L ( italic_σ ) ) ⊂ caligraphic_L ( italic_σ...	ACB	BAC	BCA	CBA	Selection 2
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	ABC	BCA	BAC	ACB	Selection 3
A: (2021) showed how persistent homology could be used in the analysis of functional brain connectivity using EEG. Chung et al. (2023c) utilized persistent homology to analyze brain networks for studying abnormal white matter in maltreated children. These studies collectively emphasize the potential of persistent h...	ACB	BAC	CBA	ABC	Selection 3
A: Thus ϕ⁢(θ+π)−(θ+π)=ϕ⁢(θ)−θitalic-ϕ𝜃𝜋𝜃𝜋italic-ϕ𝜃𝜃\phi(\theta+\pi)-(\theta+\pi)=\phi(\theta)-\thetaitalic_ϕ ( italic_θ + italic_π ) - ( italic_θ + italic_π ) = italic_ϕ ( italic_θ ) - italic_θ for all θ∈𝜃absent\theta\initalic_θ ∈B: ∙∙\bullet∙C: As the iner-event time function τs⁢(θ)subscript𝜏𝑠𝜃\...	ACB	ACB	CBA	BCA	Selection 4
A: We present the efficacy of our proposed methodologies through simulation studies under nominal conditions and disturbed conditions. The simulation study shows that the proposed approach can be beneficial to maintain safety limits. As a future work, we plan to extend the framework to (i) n𝑛nitalic_n-dimensional ...	CAB	ABC	CBA	CBA	Selection 1
A: The authors also show the use of Gradient Analysis, a technique popular in the computer vision domain [54, 55], to provide user-actionable interpretation of the model, demonstrating the potential to inform individuals of what behaviors should be modified in order to improve their performance. Similarly, Mark et ...	BAC	BAC	BAC	CAB	Selection 4
A: Collecting Training Samples. Recall that a sample in PU-Setting is comprised of a sample of PUs’ parameters (location and power) and the optimal power allocated to the SUB: First, we compute an FFT on the I/Q samples collected within a time window to get a power spectral density (PSD) plot. Then, we compute ...	ACB	BAC	BCA	BAC	Selection 1
A: This paper is the result of an REU project, which turned out to be of great pedagogical value, as it taught the students to combine the results and methods from various subjects: differential geometry, algebra, analysis and numerical analysisB: In addition, this project involved theoretical work and the work...	CAB	ABC	CBA	ACB	Selection 2
A: Now, we consider the dynamic regret of the online random coordinate descent algorithm for strongly convex functionsB: As before, we consider μ𝜇\muitalic_μ-strongly convex functionsC: In addition, we will make the following assumption commonly seen in online optimization literature [18, 23].	BCA	ABC	CAB	CBA	Selection 2
A: simulated a network of STT-MRAM devices for car detection [9]. Networks of memristors have also been proposed to implement the k-means algorithm [10] and unsupervised learning [11]. In all of these examples, the synaptic devices modeled using memristors working in a “static” fashion, i.e., as a fixed scalar mult...	ACB	CBA	BAC	BAC	Selection 2
A: Recently, co-evolutionary and game-theoretic variants were studied Bindel et alB: (2015); BhawalkarC: Johnsen (1990) (extending earlier work by DeGroot DeGroot (1974)) each agent has an innate opinion and strategically selects an expressed opinion that is a compromise of its innate opinion and the opinio...	ACB	ACB	ACB	BCA	Selection 4
A: (2020) and skin cancer Li and Shen (2018)B: Given the substantial impact of thoracic diseases on public health and the widespread use of chest X-rays in medical diagnosis, numerous research projects have explored the performance of deep learning in detecting these conditions.C: Deep learning models have...	ACB	BCA	BAC	BAC	Selection 2
A: Section 2 formalizes the Bayes optimal algorithm. Section 3 describes our main results, and Sections 4–5 describes their derivation. Section 6 presents the results of an auxiliary simulation.B: The structure of the paper is as followsC: The rest of this section establishes the problem	CAB	ABC	ACB	BAC	Selection 1
A: (2018); Koneripalli et al. (2020) proposes to disentangle the embedding in a shape-like and a deformation-like component. While this is in spirit with our work, their transformations are local (we focus on global transformations) and are approximative, that is, the components are not explicitly invariant and equ...	BAC	ABC	CAB	CBA	Selection 3
A: The baseline ‘Random’ is shown in grey (medium, different pattern). The results obtained are better than those in existing work, and both out-compete the random baseline.B: Our values in navy (darkest) compared to values in Hellan, Lucas, and Goddard (2020) in orange (lightest) marked with ††\dagger†C: ...	CBA	BCA	ABC	BCA	Selection 1
A: Test data bounds are reported in Table 4. B: We report the average ELBO (±1plus-or-minus1\pm 1± 1 standard error) on the training set after 1M steps over 5 independent runsC: Training binary latent VAEs with K=2,3𝐾23K=2,3italic_K = 2 , 3 (except for RELAX which uses 3333 evaluations) on MNIST, Fashion-...	BCA	CBA	ACB	ABC	Selection 2
A: Until now, the implicit assumption was that perfect channel estimates have been availableB: Typically, that means proper equalization is not possible and the packet replicas involved in the collision are unusable, i.e. cannot be combined with others through MRC or removed with SIC101010Note that even if the ...	CAB	BAC	ACB	CBA	Selection 3
A: This variant of the problem characterizes the sets which are contained in a rectifiable computable curveB: As we are concerned only with finite sets here, our algorithm already produces computable curves. C: An interesting connection to the Jones-Scul algorithm was given by Gu, Lutz, and Mayordomo [GLM06...	BCA	ACB	CBA	CAB	Selection 1
A: The main object of our study is the associated hypersurface of a variety V𝑉Vitalic_V and its defining polynomial, the Chow formB: For a detailed introduction on Chow forms, we refer to [13, 14]C: For a concise, clear, and deeper exposition we refer to [22].	CBA	BAC	CAB	ABC	Selection 4
A: Analyzing this figure it can be found that the non-included emotions (attraction, contempt, hope and tedium) are very scarcely selected with the exception of the 17%percent1717\%17 % of times a stimulus expected to represent anger is taken as contempt. It is also observed that sadness, calm, joy and fear are the...	BAC	ACB	CBA	CAB	Selection 4
A: It specifically evaluates the performance of adversarial examples. Additionally, the attack success rate is measured, which measures the percentage of adversarial examples misclassified by the model. A robust model should achieve a low attack success rate, indicating resilience against adversarial attacks.B:...	BAC	CBA	ACB	CAB	Selection 4
A: Thus player B is chosen on this step with probability 00, finishing the proof. ∎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) diverges to ∞\infty∞, by Lemma 3.6C: In the latter case, the sum of the even-numbered...	CBA	CBA	CAB	ABC	Selection 3
A: For availability, since it is closely related to the communication channels among AI components, more extensive exploration on the cyber-layer attacks (§IV-C) may make such attacks feasible. So far, no existing works consider authenticity in AD context, but in today’s AD deployment and commercialization, authent...	BCA	CAB	CBA	ACB	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: Alexey Barsukov is funded by the European Union (ERC, POCOCOP, 101071674)C: Neither the European Union nor the granting auth...	CBA	BCA	BAC	CAB	Selection 3
A: Formally,B: BatchNorm [34] is used in almost all modern CNNsC: It is assumed to have a regularizing effect, improve convergence speed and enable training of deeper networks [6, 16, 31]. In BatchNorm, features x𝑥xitalic_x are standardized by estimating the channel-wise mean and variance and then transfor...	BCA	CBA	CAB	BAC	Selection 3
A: Thus, lifted-structure loss[20] and N-pair loss[26] were designed to address this issue. They build massive negative samples and one positive sample based on the same anchor point and learn their relationship simultaneously. Subsequently, other methods have been developed to create more informative pairsB: M...	CBA	CAB	ACB	BCA	Selection 4
A: The second experiment determines whether clinical experts, who are very experienced with the analysis of eye fundus images, can distinguish between synthetic and real imagesB: The experts were provided with randomly generated image sets, one for AMD-diagnosed images and the other for non-AMD images, each con...	BAC	CAB	ACB	BAC	Selection 3
A: In figure (a) and (b), 68 landmark points are marked with a green cross, and figure (c) shows the movement of 68 landmark points.B: Figure 2: Schematic diagram of the pixel deviations at image level when posture changingC: To demonstrate this change, we measured the movement of 68 landmark points on the...	CAB	ABC	BAC	BAC	Selection 1
A: this is indeed the best we can doB: If all players have slightly different ratings, no Elo can be transferred between themC: However, there’s a robust version of the strategy that works for every other σ𝜎\sigmaitalic_σ: first fix some δ>0𝛿0\delta>0italic_δ > 0 for which σ⁢(−δ)>0𝜎𝛿0\sigma(-\delta)>0i...	ABC	CBA	CAB	BAC	Selection 1
A: Another phase of undersampling is also capable with HardVis since the new data become the ground zero for the next application of the automatic algorithm; NCR is again our choice. This time, five instances are proposed for deletion (cf. Figure 3(h)). However, by checking the inverse polar chart in Figure 3(i), w...	BCA	BCA	CBA	CAB	Selection 3
A: FIRST is not an application-specific solution and hence is more accessible for implementation in various dAppsB: We experimentally show that with FIRST the probability of preventing frontrunning attacks is very high in two major blockchains. We also proved FIRST’s security using the UC framework.C: In t...	ACB	BCA	CBA	BAC	Selection 2
A: They show that the welfare-maximizing assignment policy is a threshold rule on the agents’ scores, where agents who score above q𝑞qitalic_q-th quantile of the score distribution are allocated treatment. Our work differs from Bhattacharya and Dupas (2012) because we do not assume that the distribution of observe...	BAC	CAB	ACB	ACB	Selection 2
A: Existing works for mitigating this problem have focused on these approaches: 1) focusing on rare data patterns through re-sampling [8, 40], 2) loss re-weighting [15, 57], 3) adversarial debiasing [20, 34], 4) model ensembling [7, 12], 5) minority/counterfactual sample generation [8, 9, 35] and 6) invariant/robus...	ACB	BAC	CBA	ABC	Selection 2
A: Impact of CFFA and CFM. Starting from SegFormer [52], we only add CFFA to extract contextual tokens. To mine the generated contexts, we use an MLP to process them, which are finally merged with the target featuresB: The segmentation performance (mIoU) for CFFM is 38.5%. These facts verify that both CFFA and...	CBA	CBA	CAB	ACB	Selection 4
A: The Reducer block processes working parameters from CPU and GPU memory, reducing multiple embedding rows into a single embedding vector, and then stores it in the Embedding Vector Buffer. B: The non-popular μ𝜇\muitalic_μ-batch accesses arbitrary embeddingsC: The memory controller sends a direct memory a...	BAC	BAC	CBA	CAB	Selection 4
A: Unsurprisingly, the key to understanding how the topology of sublevel sets changes as one varies the threshold are the PL analogues of points where the gradient of the function vanishes, cf. Theorem 1B: These are the so-called flat or constant cells (Definition 3.7), which map to nontransversal thresholds (...	ACB	ABC	ACB	BCA	Selection 2
A: One of the most well-known challenges in this regard is the formation of topological defectsB: In nonequilibrium dynamics, a common issue is the emergence of universalities during the critical dynamics of a phase transitionC: It is stated that topological defects will arise in the course of a phase trans...	ACB	BAC	ABC	ABC	Selection 2
A: One can extend the definition of helicity to nontrivial topology and different space dimensions [3, Chapter 3]B: We use the standard notation for the inner product and the norm of the L2superscript𝐿2L^{2}italic_L start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT spaceC: In this section, we shall consider Heli...	BAC	CBA	ACB	BCA	Selection 4
A: SHAP (Shapley Additive Explanations) lundberg2017unified is another model-agnostic framework for explaining individual predictions made by machine learning modelsB: SHAP assigns importance values to input features, indicating their contribution to a model’s prediction. It also can provide both local explan...	CAB	ACB	ABC	CBA	Selection 2
A: In the next section, we present their online safety behaviors centered around granting the app permissions.B: But, teens showed more care to consider safety than their parents, as indicated by some seeking parents’ permission before downloading an appC: Most participants took few precautions before inst...	BAC	BCA	ABC	CBA	Selection 4
A: The distance-to-measure (DTM) function is a modification of the distance function that is designed to enhance robustness against potential noise and outliers. Roughly speaking, the distance-to-measure of a point x𝑥xitalic_x to a probability measure μ𝜇\muitalic_μ is the average distance of x𝑥xitalic_x from the...	BCA	ACB	BAC	BAC	Selection 2
A: 2015), which allows us to approximate the above expectation in feature-level.B: We propose a plug-in causal intervention module, named CIS, for the approximation of the theoretical back-door adjustment formula. As the calculation of Eq. 1 requires the forward steps for each pair of X𝑋Xitalic_X and s𝑠sitali...	CAB	CBA	ABC	ACB	Selection 1
A: To implement BBP in Ethereum, we put forth a Time-specific transaction Selection and Ordering (TSO) algorithm to disambiguate and align the transaction selection and ordering at all nodesB: In addition, we propose a pre-packed blockbody synchronization protocol with which the nodes exchange pre-packed blockb...	ABC	BCA	ACB	BAC	Selection 3
A: In contrast, the upper bound confidence (UCB) method is used in Xiong et al. (2021) for adaptive exploration. However, they require strictly positive state transition and observation emission kernels to ensure fast convergence to the stationary distribution. The more related work is Jin et al. (2020a), which con...	ACB	CBA	BCA	ABC	Selection 2
A: Another limitation is the inapplicability of quality appraisal methods such as the ”Risk of Bias Assessment” in our study, as in the case of health sciences. Furthermore, our study restricts papers addressing SSD defined by ASHA and excluded studies addressing other related disorders. B: We relied on three d...	BAC	CAB	ABC	CBA	Selection 4
A: As discussed in Section 2 and described in Theorem 2.5, our primary result showed that the intra-community compression ratios are higher than inter-community compression ratios in a large range of parametersB: Here we look at average statistics of compression ratio to provide a first layer of evidence suppo...	BCA	CBA	ABC	BAC	Selection 3
A: We observe that dialog can serve as an effective supplementary information source in case of missing visions, especially for the semantically masked visual input with most severe missingness level. We also compare the proposed method against the random QA caseB: The results for the generated scene graphs fro...	BCA	BAC	BCA	CBA	Selection 2
A: This captures a broader range of real-life scenarios where facilities incur location-dependent service fees for their customers. Furthermore, the notion of entrance fees can be applied to various previous variants of facility location problems, expanding the scope of research in this area.B: In our model, th...	ABC	CAB	CAB	CBA	Selection 4
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	CBA	CBA	Selection 2
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: To set the complexity of each ReLU network, we have followed the in...	BCA	BAC	BCA	BCA	Selection 2
A: Each image shows the prediction of the respective method in white, and the ground truth as green overlayB: Figure 4: Prediction when training on the first 10 frames of sequence 0 of the pendulum test data by [60]C: For both methods, the prediction of images seen during training (frames 1,7,10) works wel...	ABC	BAC	ACB	CAB	Selection 2
A: Here, the receiver’s goal is to draw specific logical conclusions [13], which may not necessarily involve reconstructing the entire data samplesB: Therefore, the primary objective of the quantum receiving node is to comprehend the underlying semantic information conveyed in quantum states [14], facilitating ...	BCA	CAB	ABC	ABC	Selection 1
A: AB: to mmWave IAB networks is to consider a central network controller that acts as a single RL agent. However, this requires the agent to know the global state of the whole network and manage all the transmissions, resulting in a combinatorially large number of different states and possible actions, which i...	ABC	CBA	BAC	BCA	Selection 4
A: In another direction, Chassang et al. (2012) and Di TillioB: In view of our motivating example in Section 1.1, we note that Min (2023) studies a contract theory model for the FDA, where firms of different sizes choose to finance cheap or expensive trialsC: Our work focuses instead on how the regulator sh...	BCA	CAB	CBA	BAC	Selection 2
A: Notably, this gain is obtained without affecting the quality of the registration metrics, and improves the execution time of PPIR(MPC)B: Concerning PPIR(FHE)-v1, we note the uneven computation time and bandwidth usage between clients, due to the asymmetry of the encryption operations and communication protoc...	BAC	BCA	CBA	ABC	Selection 1

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