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A: Existing approaches based, which are based on more powerful subgoal search methods, have their limitations, on the other hand. [13] is perhaps the closest to our method and uses MCTS to search the subgoal-induced graph. However, it uses a predefined (not learned) predicate function as a subgoal generator, limiti...	BCA	CBA	BAC	CBA	Selection 1
A: For the case study, there is an interesting example shown in Figure 5. The two sentences are drawn from our dataset with the same meaning, ‘he wants to go to Dapuqiao’B: On our Substitution dataset, MFE-NER brings remarkable advances. For different semantic embeddings, MFE-NER achieves much better performan...	ABC	BAC	ACB	CBA	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 ...	ACB	BCA	BAC	ABC	Selection 2
A: Out of the 40 languages involved, 19 languages appear in at least 3 datasets and the rest 21 languages appear in at least one dataset. B: The tasks include 2 classification tasks, 2 structure prediction tasks, 3 question answering tasks, and 2 sentence retrieval tasksC: Xtreme (Hu et al., 2020) is a mult...	CBA	CAB	BCA	BAC	Selection 1
A: Note that the closing curly brace for the author group comes at the end of the thanks groupB: This will prevent you from creating a blank first page.C: Be sure to use the \\\backslash\IEEEmembership command to identify IEEE membership status. Please see the “IEEEtran_HOWTO.pdf” for specific information o...	ACB	BCA	CAB	BAC	Selection 2
A: exactly c𝑐citalic_c leaves attached”, where c∈{0,1,2,3}𝑐0123c\in\{0,1,2,3\}italic_c ∈ { 0 , 1 , 2 , 3 }B: This is not hard to doC: For example, the formula ϕ2⁢(x1):=∃x2⁢∃x3⁢∀x4⁢((x2∼x1)∧(x3∼x1)∧(x2≠x3)∧((x4=x1)∨(¬(x4∼x2)∧¬(x4∼x3))))assignsubscriptitalic-ϕ2subscript𝑥1subscript𝑥2subscript𝑥3for-allsubs...	CBA	ABC	CBA	CAB	Selection 2
A: From a platform design perspective, we can think of these as policy counterfactuals to investigate marketing strategies that would emphasize certain behavioral traitsB: The results, shown in Figures 4, 5, and 6, highlight the effects of these counterfactual adjustments. In each figure, we show the average of...	ACB	CBA	ABC	CBA	Selection 1
A: In ResNet (He et al., 2016), He et alB: In SISR, as the LR image and HR image share most of the same information, it is easy to explicitly model the residual image between LR and HR images. Residual learning enables deeper networks and remits the problem of gradient vanishing and degradation. With the help o...	ABC	ACB	CBA	BCA	Selection 2
A: There have been some works where coordinate-based networks are used as a core for a generative model using techniques such as a hypernetwork predicting the weights of a sample coordinate [11], or by modulating the weights of a base coordinate [12]. These approaches are fundamentally different as they attempt ...	ABC	CBA	ACB	CAB	Selection 1
A: The same problem has also been studied under the name ‘online active binary classification’ (Monteleoni and Kaariainen,, 2007; Liu et al.,, 2015). Both of these variants differ from apple tasting in that they have a more complex action set. B: The apple tasting problem is not the only variant of online class...	ABC	ACB	ABC	CAB	Selection 4
A: Many criticisms have recently been raised against the improper use of statistical significance as the only measure to evaluate results in scientific publications [65]. However, we also perform the Wilcoxon paired test over the 10-fold cross-validation results, focusing on MemDistilBERT and MANN and the differen...	CAB	ACB	BCA	BAC	Selection 2
A: We observe a larger high activity in the area of 3 Duomo compared to the other two districts, which peaks around midday during the working days and in the early afternoon on the weekendsB: 5 Navigli on the other hand, which is a district famous for its different types of cafés, restaurants, bars and design...	CAB	BAC	BCA	CAB	Selection 3
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	CBA	ACB	CBA	Selection 3
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: For each role similarity measure, its time complexity includes two parts: precomputation and top-k similarity searchC: Precomputation of...	ABC	BCA	ACB	CAB	Selection 1
A: Table 6: The examples for aspect sentiment coherency found by LSAB: “Pos”, “Neg” and “Neu” represent positive, negative and neutral, respectively. C: The target aspects are denoted in bold and the underlined words indicates the aspects with coherent sentiments	CBA	ABC	ACB	BCA	Selection 3
A: This process is somewhat elaborate and the reader is referred to [31] and [32] for all of the detailsB: However, for the exposition in this section it sufficient to know what the properties of the operators 𝐋𝐋\mathbf{L}bold_L and 𝐖𝐖\mathbf{W}bold_W are. C: The operator 𝐋𝐋\mathbf{L}bold_L and 𝐖𝐖\m...	BCA	CBA	CAB	BAC	Selection 1
A: We set noise factor T=0.5𝑇0.5T=0.5italic_T = 0.5 andB: With different noise factors T𝑇Titalic_T, the gate insertion and measurement outcome perturbation have similar accuracy, both better than rotation angle perturbationC: A possible explanation is that the rotation angle perturbation does not consider...	ABC	CAB	ABC	CBA	Selection 2
A: In this paper, we propose a novel unifying event data association (EDA) approach to effectively and explicitly handle the essential event data association and event information fusion problem. The proposed EDA performs a model fitting on event data, which can asynchronously associate and fuse the event data ove...	ABC	CAB	ACB	BCA	Selection 1
A: In particular, we thank Giacomo Kahn and Armen Petrossian for preliminary discussions. We also thank the referees for their comments that helped improve the paper.B: This research was partially financed by the French government IDEX-ISITE initiative 16-IDEX-0001 (CAP 20-25) and by the ANR project GRALMECO (...	ABC	BCA	ACB	CAB	Selection 4
A: We choose multiple smaller networks with fewer parameters as the student network: ResNet-18 [70], MobileNet.v2 [86], ShuffleNet.v1 [87]. Similar to the pre-training for the teacher network, we add one additional MLP layer on the basis of the student networkB: We evaluate the KD tasks based on self-supervised...	BCA	CBA	BCA	BAC	Selection 4
A: For each time, we only run the model on a small spatial region (>10×\times× smaller than the whole area), which effectively cuts down the peak memory usageB: After this stage is finished, the rest of the network with a small peak memory is executed in a normal layer-by-layer manner (upper notations in Figure...	CAB	ABC	ABC	BCA	Selection 4
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: Main Results Table I shows the results o...	CAB	CBA	CAB	BAC	Selection 4
A: For example, in image data, it is natural to evaluate what kind of data augmentation is more powerful (e.g., color distortion strength for color permuting [3]), but the situation of the graph data is much more complicated. Modifying the attributes of a node is not only related to the target node but also affects...	CAB	ABC	ACB	ACB	Selection 1
A: We trained the baseline agent with noise 0.10.10.10.1 with several features removed from the training set (a diagonal from the shape-by-color matrix) and used the removed set to analyze the out-of-distribution performanceB: In the zero-shot setup, the messages on these observations were not in line with the ...	BAC	CBA	BAC	ABC	Selection 4
A: In contrast to our previous works [53, 55, 54], in which we assume perfect state knowledge, we focus on dealing with state estimation errorsB: Our paper additionally differs from [53, 55, 54] in its practical focusC: We discuss the algorithmic implementation of our framework to account for assumptions o...	BAC	CBA	CAB	ABC	Selection 4
A: Our random restriction lemma shows that if one randomly fixes most of the inputs to a quantum query algorithm, then the algorithm’s behavior on the unrestricted inputs can be approximated by a “simple” function (say, a small decision tree or small DNF formula)B: Notably, our proof of Theorem 7 does not appea...	ACB	BCA	BAC	CBA	Selection 3
A: Recent years, some Weighted Stochastic Blockmodels (WSBM) have been developed for weighted networks, to name a few, [9, 10, 11, 12, 13, 14, 15]. However, though these models for weighted networks are attractive, they always require all elements of connectivity matrix to be nonnegative or all elements of adjacenc...	BAC	ACB	BCA	CBA	Selection 3
A: Banerjee (2016); Oliehoek and Amato (2016); Foerster et al. (2016) is based on using the centralized information during trainingB: (2016) introduces the RIAL and DIAL algorithms in the context of Q𝑄Qitalic_Q-learning. CTDE is particularly easy to implement with actor-critic algorithms; the centralized infor...	CAB	BAC	ACB	BAC	Selection 3
A: Furthermore, the most common scenario is to explore regions of decisions paths and focus on specific test instances which by default drastically limits the number of decision rules. In summary, the benefits of this tweaked visualization are many, since users can directly compare a test case with training instanc...	BCA	CBA	BAC	CAB	Selection 3
A: [16] showed that space-time block coding (STBC) with single polarization outperforms STBC with dual polarization in Rayleigh and Ricean fading channels. A MIMO system with dual-polarized antenna elements can have lower spatial diversity but higher spatial multiplexing gain than a conventional MIMO system with si...	BCA	CAB	CAB	BAC	Selection 4
A: This is the setting which is important for our reductions to the online packing problemsB: Having dealt with the case where we have no extra space, we turn to the setting where the array has length γ⁢n𝛾𝑛\gamma nitalic_γ italic_n for some γ>1𝛾1\gamma>1italic_γ > 1C: In the following two sections, we p...	ABC	CAB	BAC	CBA	Selection 3
A: The averaged version of annotations by two doctors is set as the ground truth. The image size is 1935×2400193524001935\times 24001935 × 2400 and the pixel spacing is 0.1mm. The dataset is split into 150 and 250 for training and testing respectively, referring to the official division.B: There are 19 landmark...	BAC	CBA	BAC	CAB	Selection 2
A: MMDF is a generative model and fuzzy weighted modularity is a general modularity for overlapping weighted networksB: We expect that our model MMDF and fuzzy weighted modularity proposed in this paper will have wide applications in learning and understanding the latent structure of overlapping weighted networ...	CAB	ABC	CAB	ACB	Selection 2
A: CIL and TIL both split all training classes into multiple tasks and learn them sequentially. The difference between these two setups is that TIL allows using task information during inference (i.e., knowing what task does test data belong to) but the CIL does notB: Two classic setups of Incremental Learning ...	CAB	BCA	CAB	BAC	Selection 4
A: The method addressed both the challenges related to the pre- to post- operative registration, namely the large, nonrigid deformations and the missing tissuesB: The large and complex deformations were addressed by the LapIRN network with large enough receptive field, while the missing tissues were handled by ...	CAB	BCA	CBA	ABC	Selection 2
A: Concerning the exact version of the problem, we have lifted the FP/♯♯\sharp♯P-complete dichotomy in the case of primary keys and self-join-free CQs from [25] to the more general case of FDs and self-join-free CQs (Theorem 2). Concerning the approximate version of the problem, although we have not provided a comp...	ABC	BCA	BCA	ACB	Selection 1
A: We develop community-detection algorithms that account for node-absorption ratesB: In our adaptation, we apply InfoMap to absorption-scaled graphs, which account for absorption by scaling the edge weights of a network [16]. These absorption-scaled graphs are related to their associated absorbing random walks...	ABC	ACB	BAC	CAB	Selection 2
A: In our overall methodology, to conserve node and link resources, we post-process or ”throttle” the swapping-tree obtained from the DP algorithm by increasing the generation latencies of some of the non-root nodes such that (i) the latencies of siblings are equalized, and (ii) the parents latency is related to th...	CBA	CAB	ABC	BCA	Selection 3
A: In another probabilistic decision-making model, Wang et alB: [118] approach lane merging task as a dynamic process and integrate internal states into joint Hidden Markov Model (HMM) and Gaussian Mixture Regression (GMM)C: The experiments conducted on the	ABC	CBA	BAC	BAC	Selection 1
A: We used three representative CNN architectures (i.e., AlexNet, VGG-16 and MobileNetV3) and our proposed GhostCNN as front-ends to extract feature mapsB: AlexNet and VGG-16 are cropped at the last convolution layer (conv5) with the encoder dimension (i.e., D𝐷Ditalic_D-dimension) of 256 and 512 before ReLU, r...	CAB	BCA	ABC	CBA	Selection 3
A: In Chapter 4, to validate our algebraic attack, first we apply it to two toy stream ciphers and then we show that it is feasible to perform it on WG-PRNGB: For the sake of presentation, we will first describe the part regarding WG-PRNG, and then the one on the two toy ciphers.C: We conclude showing that...	ABC	ABC	CBA	ACB	Selection 4
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...	BCA	BCA	ACB	ABC	Selection 3
A: Louvain [4] and Leiden [44] are examples of thisB: The algorithms are fast and have had success in recovering ground truth communities on real world networksC: However, there no theoretical guarantees for either that the partition found is near optimal, though recently [10] showed that a Louvain-like alg...	CBA	CBA	ABC	CBA	Selection 3
A: We manually and meticulously screen the collected items through Scopus and Web of Science by title and we exclude papers on the migration of animals, plants, or other species, or focusing on topics different from human mobility (i.eB: discrimination, crime, wars) or on the impact of environmental variables n...	ACB	CAB	BAC	BCA	Selection 4
A: Therefore, throughout the paper we focus on the problem-dependent dynamic regret of convex and smooth functions. Note that Assumption 4 requires non-negativity outside the domain 𝒳𝒳\mathcal{X}caligraphic_X, which is a precondition for establishing the self-bounding property for smooth functions, see Lemma 3.1 ...	ACB	ABC	CAB	CBA	Selection 3
A: Conversely, for every x∈X𝑥𝑋x\in Xitalic_x ∈ italic_X, let α⁢(x)={y∈Aℤ∣ℒ⁢(y)⊂ℒ⁢(x)}𝛼𝑥conditional-set𝑦superscript𝐴ℤℒ𝑦ℒ𝑥\alpha(x)=\{y\in A^{\mathbb{Z}}\mid\mathcal{L}(y)\subset\mathcal{L}(x)\}italic_α ( italic_x ) = { italic_y ∈ italic_A start_POSTSUPERSCRIPT blackboard_Z end_POSTSUPERSCRIPT ∣ caligraphic_L...	BAC	CBA	BCA	ACB	Selection 1
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: However, such average is the average of the connectivity strengthB: Such an approach is usually sensitive to topological outliers (Chung et al., 2019a)C: We address the problem through the Wasserstein distance. A similar concept was proposed in the persistent homology literature through the Wasserstein ...	ACB	ACB	ACB	ABC	Selection 4
A: the level set τs⁢(θ)=csubscript𝜏𝑠𝜃𝑐\tau_{s}(\theta)=citalic_τ start_POSTSUBSCRIPT italic_s end_POSTSUBSCRIPT ( italic_θ ) = italic_c is either empty, or equal to [0,2⁢π]02𝜋[0,2\pi][ 0 , 2 italic_π ] or {θ1,θ1+π}subscript𝜃1subscript𝜃1𝜋\{\theta_{1},\theta_{1}+\pi\}{ italic_θ start_POSTSUBSCRIPT 1 end_POSTS...	CAB	ACB	ABC	ACB	Selection 3
A: In [29], safety verification using barrier functionals for homogeneous distributed parameter systems has been considered. In this work, numerical strategies based on semi-definite programming has been used for the construction of barrier functionals. However, control performance under disturbances has not been c...	BAC	ABC	BCA	CAB	Selection 3
A: Moreover, from a methodological point of view, most of the approaches are not generalizable across problem scenarios or datasets — the same approach may not work on different populations or a different outcome measure. This necessitates accounting for domain information and problem-centric adjustments for model ...	BCA	ACB	CBA	BAC	Selection 3
A: To determine whether PU to PUR transmission is incurring any harmful interference from SU, we have PU continuously streaming ASCII messages over the 1 MHz bandwidth channel centered at frequency 915.8 MHz, and check if the messages are successfully received at the PUR. This end-to-end communication system is imp...	CAB	ACB	BAC	ACB	Selection 1
A: At the time when the project was performed, Jose Agudelo was an undergraduate student at North Dakota State University, Brooke Dippold was an undergraduate student at Longwood University, Ian Klein was an undergraduate student at Carleton College, Alex Kokot was an undergraduate student at the University of Notr...	CAB	BAC	CBA	ABC	Selection 2
A: The extra information of the component-wise gradient norms enables a better selection of the coordinate to update. An in-depth theoretical analysis of this problem in an online setting is left for future work.B: As expected, the algorithm using full gradient has the best performance in terms of minimizing t...	ACB	CBA	CAB	CBA	Selection 3
A: The most prevalent hybrid model utilizes analog circuits for computation and digital circuits for communication [62, 63, 64]B: This approach recognizes that digital circuits operate much faster than the typical spike rate of neurons, enabling a single digital bus to carry signals from multiple neurons. Multi...	CBA	ABC	BCA	ABC	Selection 3
A: In this paper we study an agent-based model for opinion formation on a social network where the opinion of an agent depends both on its own intrinsic opinion and on the opinions of its network neighborsB: In this model the opinion of an agent is iteratively updated to the weighted average of the opinions of...	ACB	CBA	BCA	CAB	Selection 1
A: This test set comprised 500 images randomly selected from the test datasetB: We applied the trained model to the test set and measured various evaluation criteria as explained in Section 4.2. C: To evaluate the model’s performance and assess its generalization capabilities, we created a separate test set	BCA	ACB	ABC	BAC	Selection 1
A: Unlike multi-armed bandit algorithms, BAI algorithms are designed solely to deliver the most effective exploration.111Although the term “best arm identification” has appeared only recently, several strands of research share the same goal, among which ranking and selection (RS; Bechhofer 1954) is among the best k...	BAC	ACB	ABC	BCA	Selection 2
A: Middle row shows the decoded (canonical) image and bottom row shows the decoded image after applying the predicted rotation.B: Top row shows the input image successively rotated by 45∘superscript4545^{\circ}45 start_POSTSUPERSCRIPT ∘ end_POSTSUPERSCRIPTC: Figure 3: Input and predicted output for rotated...	ABC	CBA	BAC	ACB	Selection 2
A: Part of our rationale for using comparatively simple GP kernels is to facilitate interpretation of hyperparameters, and analysis of the distribution patterns of pollution in an area, e.gB: its smoothness and variability in space and timeC: This lets us perform “at-a-glance” comparisons of different areas...	BAC	CAB	BCA	ABC	Selection 4
A: Test data bounds are reported in Table 4. B: 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: We report the average ELBO (±1plus-or-minus1\pm 1± 1 standard error) on the training set after 1M steps over...	ACB	CAB	BAC	ACB	Selection 2
A: Similarly, dotted and dash-dotted lines (Approx. 2) correspond to the simpler approximation by gamma distribution discussed in the Appendix C.B: 1)C: The results from simulations, which implement the exact procedure, are given with markers. Solid and dashed lines (Steiner and Random respectively) corresp...	CBA	ACB	CAB	BCA	Selection 1
A: Moreover, the exponent 3333 in our theorem is sharp and can not be lowered; see Section 5.4.B: In fact, in our algorithm, the yielded path has length at most (300)9/2⁢log⁡300superscript30092300(300)^{9/2}\log{300}( 300 ) start_POSTSUPERSCRIPT 9 / 2 end_POSTSUPERSCRIPT roman_log 300 times the length of the sm...	ACB	CAB	CBA	BAC	Selection 3
A: In section 2 we present a short overview of Chow formsB: Section 3 is on the computation of the Chow form in ℙnsuperscriptℙ𝑛\mathbb{P}^{n}blackboard_P start_POSTSUPERSCRIPT italic_n end_POSTSUPERSCRIPT and the extension of techniques to compute Hurwitz formsC: Section 4 presents algorithms for computing...	CBA	ABC	CAB	BAC	Selection 2
A: To statistically confirm the associations observed between the target and self-reported discrete emotions, a Pearson’s chi-squared test is used as implemented in the chisq.test() function in the R software. In this test, the null hypothesis (H0subscript𝐻0H_{0}italic_H start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT) s...	CBA	ACB	CBA	ABC	Selection 4
A: New attacks will constantly break heuristics-based defenses. Hence, a solid mathematical understanding of adversarial robustness is necessary to design resilient classifiersB: Quantifying robustness: Despite motivated efforts in quantifying the robustness of a classifier (Carlini et al., [n. d.]), no approa...	CAB	CBA	BCA	BAC	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...	BCA	ABC	CAB	BCA	Selection 3
A: Among these four, only the last one is relatively domain-specific to ADB: In related CPS domains such as drones and ASR/SI, such ratio is much higher (>>>50%): 53.8% for drone attack works [24] and 52.9% for ASR/SI ones [52]. One observation we have is that many of these works in related CPS domains exploit ...	CBA	BAC	BAC	BCA	Selection 4
A: 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. Using the technique of the above work, 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...	BAC	CAB	CBA	BCA	Selection 4
A: 3D backbones: It is noticable that surgical workflow methods almost unanimously use 2D backbones as feature extractorsB: 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 lar...	ABC	ACB	ACB	CBA	Selection 1
A: Table 3 present the results of this study. FR models with UNPG performed better than those without it. ArcFace and CosFace using UNPG obtained gains of 0.26% and 0.19%, respectively, compared to those without it.B: We followed the test protocol of ArcFace[4]. We removed noisy images and measured rank-1 accur...	BCA	CBA	BCA	BAC	Selection 2
A: The final round was aimed at comparing the best deep model obtained in the previous phase, ResNet-18, against human experts concerning the task of AMD identificationB: Following that, the same images were submitted to a ResNet-18 for comparison purposes. To allow a fair comparison between humans and deep mo...	ACB	ABC	ABC	CAB	Selection 1
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	BAC	CBA	BAC	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...	CAB	ACB	ABC	ACB	Selection 1
A: For example, in an imbalanced healthcare data scenario, a data analyst who blindly trusts one of the previous heuristic-based approaches for undersampling and chooses a high k-value will eventually remove so many healthy patients (belonging to the majority class), leading to a balanced training set but with a si...	BCA	BAC	CAB	ABC	Selection 3
A: The finance-related dApps have enabled what is known as decentralized finance or DeFiB: DeFi is an umbrella term that includes various financial products (such as flash loans, asset management services, decentralized derivatives, and insurance services) available to any user with an internet connection in a ...	CAB	ABC	BAC	BCA	Selection 2
A: Furthermore, we show that under additional conditions, the mean-field equilibrium arises via fixed-point iterationB: In Section 4, we translate these results to the finite regime, where a finite number of agents, sampled i.i.d. at each time step, are considered for treatment. We show that as the number of ag...	ACB	CBA	BCA	ABC	Selection 3
A: Methods run on Occam ResNet-18 lower the majority/minority discrepancy (MMD) compared to the methods run on ResNet-18 for all of the variables, indicating that OccamNets lower the tendencies to latch onto all of the spurious factorsB: Biased MNISTv2. In Table A6, we present the unbiased test accuracies and m...	ABC	BAC	ACB	ACB	Selection 2
A: However, our method splits the query features into windows and the query features in each window share the same contexts to reduce the computation. For key/value selection, STT operates in the same granularity, while our method processes the selected key/value into different granularity, which reduces the number...	BAC	CAB	ABC	ABC	Selection 2
A: However, EAL sizes above 4MB offer diminishing returns for the less skewed Taobao dataset.B: Figure 27 shows popular inputs captured with varying the EAL sizeC: For highly skewed datasets like Criteo and Avazu, a 2MB logger is sufficient to capture frequently-accessed indices	CBA	BCA	CAB	CBA	Selection 3
A: Explicitly, we prove: B: Flat cells in the PL category should be viewed as the appropriate analogues of critical points in the smooth category, with the caveat that not every flat cell is criticalC: The flat cells of F𝐹Fitalic_F are, by definition, the cells of 𝒞⁢(F)𝒞𝐹\mathcal{C}(F)caligraphic_C ( it...	BCA	BCA	ABC	CBA	Selection 4
A: To this end, we render the neural network parameters to be functions of time. Then, the parameters will be computed at every time step with the time-dependent VMC method, by minimizing the distances δ𝛿\deltaitalic_δ between the exact time evolution and the approximate variational evolution B: Therefore, the...	CBA	CAB	ABC	BCA	Selection 1
A: 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_POSTSUPERSCRIPTB: First we generate analytic solutions of the equationC: starting at	ABC	CAB	CBA	CBA	Selection 1
A: The results are provided in Table 1, which confirms the high correlation values for the datasets AD, RN, HS, and DI. Furthermore, to confirm that these values are not sensitive to the binning choices, we perturbed the bin boundariesB: However, the results did not meaningfully change, which confirms their rob...	BCA	CBA	CBA	ACB	Selection 1
A: The end goal would be to develop online safety and privacy apps that support a diverse range of families. B: Counselman, 2002; Livingstone et al., 2011), which may have led them to evaluate our app differently than if we studied predominantly white familiesC: Thus, in future studies, we would want to ass...	CBA	BAC	CAB	ABC	Selection 3
A: Even without the aid of the confidence bands, one point is conspicuously far away from the diagonal in the persistence diagram of each filtrationB: The RDAD filtration picks up 2 more significant loops. C: Figure 12, followed by the persistence diagrams of the two filtrations in Figure 13	ABC	BAC	BCA	ABC	Selection 3
A: 2017; Song et alB: Considering the locality of AUs, methods such as (Zhao, Chu, and Zhang 2016; Li, Abtahi, and Zhu 2017; Li et alC: 2021a; Chen et al. 2021a) make attempt to learn better facial appearance features by emphasizing important local facial regions. Zhao et al. (Zhao, Chu, and Zhang 2016) pro...	CAB	ABC	CBA	BAC	Selection 4
A: In particular, we measured two statisticsB: We saved all responses of all neighbor nodes to ObserverNode and measured the matched-blockbody probabilityC: The first is the matched-transaction rate defined as the ratio of the number of matched transactions to the total number of transactions in all recorde...	CBA	ACB	BAC	CAB	Selection 3
A: In a broader context of reinforcement learning with partial observability, our work is related to several recent works on POMDPs with special structures. For example, Kwon et alB: (2021) considers latent POMDPs, where each process has only one latent state, and the proposed algorithm efficiently infers the ...	BAC	BAC	ABC	CBA	Selection 3
A: This finding is consistent with the notion that ASR is the core of AI-based automated speech therapy tools. The majority of studies were from European, North American, and Asian countries, and the most prevalent language targeted by the included studies was English. This finding is in line with the fact that Eng...	BAC	BCA	CBA	ACB	Selection 3
A: In the last two decades, several works have also considered the finite sample setting [Nad08, Nad14]. These works have primarily focused on the denoising aspect of PCA in different variants of Gaussian noiseB: PCA and its effect on noisy data has been subject to a lot of investigation in the last 50 years. ...	BCA	BAC	ACB	ACB	Selection 2
A: (2) Scene Graph Classification (SGCls): predict the predicate as well as the object labels given the sets of ground truth bounding boxesB: We evaluate our generated scene graphs using the three evaluation metrics: (1) Predicate Classification (PredCls): predict the predicates (relations) given the sets of gr...	BAC	ACB	CBA	ABC	Selection 1
A: The basic idea of the proof is to duplicate the agent profiles in the proof of Theorem 3 at locations far enough from the original profiles and then apply the proof of Theorem 3 in a slightly modified way. B: Lower bounds for deterministic mechanismsC: The following proposition obtains a similar lower bo...	CBA	CAB	ABC	ABC	Selection 2
A: All other edges of the chain form part of some induced paw and their incident vertices have degree odd in such paw. Therefore, every pair of consecutive vertices in the chain have different colors in any valid bi-coloring. The number of edges in the chain is exactly 6⁢k2+16subscript𝑘216k_{2}+16 italic_k start_P...	BAC	BAC	CBA	CAB	Selection 4
A: The problem (11) now looks like a standard mp-QP whose solution can be computed parametrically in x𝑥xitalic_xB: Putting aside for the moment the issue of possible degeneracy, this parametric solution could even be computed over the whole of 𝒮𝒮\mathcal{S}caligraphic_SC: Our proof will therefore proceed...	ABC	CBA	BCA	CBA	Selection 1
A: We use two separate local representations and enable the model to identify the object layering by using the maximum of both occupancy valuesB: To guide the model in learning which local representation represents which digit, we use an additional segmentation loss with very rough object masks as supervision o...	CBA	ABC	CBA	ACB	Selection 4
A: As discussed earlier, the QSC framework ensures minimality of quantum communication resources by extracting and compressing the semantic representations of the data, unlike existing semantic-agnostic QCNsB: To do so, we must the error sources encountered during both the quantum communication errors and the ...	BAC	ACB	BCA	BAC	Selection 2
A: An example of IAB network scenario is depicted in FigB: 1. Backhaul links are established either between the IAB-donor and an IAB-node or between two IAB-nodes, while access links connect IAB-donor/IAB-nodes to UEs. Both backhaul and access transmissions share the same mmWave frequency band (i.e., in-band ba...	BCA	ABC	ACB	BAC	Selection 1
A: Our study of hypothesis testing in the principal-agent model has forged connections between statistical inference and ideas from the economic theory of mechanism designB: Instead of controlling the Type I error rate at some arbitrary level, our framework bases the standard of evidence on the researcher’s in...	BCA	CBA	ABC	ACB	Selection 4
A: Due to the well-known scalability issues of such cryptographic techniques, we investigate strategies for the practical use of PPIR. In our experiments, we evaluate the effectiveness of PPIR on a variety of registration tasks and medical imaging modalities. Our results demonstrate the feasibility of PPIR and pave...	BCA	ACB	ACB	CAB	Selection 4

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