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A: In classical AI, reasoning is often achieved by search ([39])B: Search rarely can be exhaustive, and a large body of algorithms and heuristics has been developed over the years, [39, Section 3.5]C: It is hypothesized that progress can be achieved by combining search with learning [4]. Among notable succ...	BCA	BAC	ABC	BCA	Selection 3
A: Considering that, in Chinese, named entities have their own glyph and phonetic patterns. For example, in the glyph domain, characters in Chinese names usually contain a special character root, which denotes ‘people’. What’s more, characters representing places and objects also include certain character roots, wh...	ACB	CBA	CAB	BAC	Selection 2
A: In this work, we introduce neural étendue expanders as an optical element that expands the étendue of existing holographic displays without sacrificing displayed hologram fidelityB: Neural étendue expanders are learned from a natural image dataset and are jointly optimized with the SLM’s wavefront modulatio...	CBA	CBA	ABC	BAC	Selection 3
A: For example, \addedTong et alB: In (Deng et al., 2019), each task has its own encoder and decoder, while all tasks share a representation learning layer and a joint encoding layer.C: (2018) split the model into task-specific encoders and language-specific encoders for \replacedmultilingualmulti-lingual d...	ABC	BCA	ABC	ACB	Selection 4
A: IEEE recommends to compose your article in the base 2-column format to make sure all your equations, tables and graphics will fit the final 2-column formatB: There are other options available for each of these when submitting for peer review or other special requirementsC: Please refer to the document “I...	BAC	ACB	ACB	CAB	Selection 1
A: Consider i,i′∈[n]𝑖superscript𝑖′delimited-[]𝑛i,i^{\prime}\in[n]italic_i , italic_i start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT ∈ [ italic_n ] such that (vi,vi′)∈E⁢(G)subscript𝑣𝑖subscript𝑣superscript𝑖′𝐸𝐺(v_{i},v_{i^{\prime}})\in E(G)( italic_v start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT , italic_v star...	BAC	CBA	ACB	BCA	Selection 1
A: These can be described as “benign behavioral interventions”, because they mimic the effects of priming players toward a certain social preference but without directly altering payoffs or the information structure. We use these counterfactuals to reason about which behavioral traits would be the most valuable to ...	CAB	ACB	BAC	BAC	Selection 1
A: It is worth noting that blind SISR has no clear definitionsB: Blind SISR has attracted increasing attention due to its significance in real-world applications, which aim to super-resolve LR images with unknown degradationC: More details about blind SISR can be found in (Liu	BAC	ABC	ABC	BCA	Selection 1
A: Discriminator Loss The discriminator network takes a single multi-scale patch and outputs a confidence scoreB: At each training step of the discriminator, we feed it all real and all synthesized patches and compute the output confidence for themC: Furthermore, we apply one-sided label smoothing [32] of t...	CBA	BAC	ABC	CBA	Selection 3
A: Recent work of Phan et al., (2019) has identified conditions under which sampling from an approximate posterior can lead to linear regret in multi-armed bandit problems. On the other hand, May et al., (2012) have shown that sublinear regret in contextual bandits can be achieved without drawing samples from an ex...	BCA	ABC	BAC	CBA	Selection 4
A: For 1 and 2 topics, MANNs nearly match DistilBERT and BERT, which is remarkable in light of the drastically lower number of parameters. These results support our intuition that controlled, and smart knowledge sampling can be particularly beneficial when limited training data is available. We observed that with M...	ABC	BAC	ABC	CAB	Selection 4
A: Table 1B: The reported measures are averages over all test samples. C: Posterior means and standard deviations (in parentheses) of the predictive measures of accuracy and F1 scores for property satisfaction and the RMSE of the robustness for the four properties	ACB	CBA	CAB	CBA	Selection 1
A: This is summarised by the Diagram 5 recalled below on the left.B: Theorem 22 shows that the notion of quantitative equality given in this paper is coalgebraic, in the sense that Lipschitz doctrines are the coalgebras of a comonad over the category of graded doctrinesC: This generalizes a known situation...	BAC	BAC	CAB	ACB	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...	ACB	ABC	CAB	CBA	Selection 2
A: Finally, dotGCN-BERT Chen et alB: (2022), and TGCN-BERT Li et al. (2021a) are also included in our comparison. These models represent the current landscape of ABSC research, allowing us to assess the effectiveness of LSA against well-established approaches.C: (2022), SSEGCN-BERT Zhang et al	CBA	BCA	CAB	ACB	Selection 4
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...	CAB	BCA	ABC	CBA	Selection 2
A: The road to such advantage relies on: (1) the discovery of novel feature embedding that encodes classical data non-linearly, and (2) overcome the impact of quantum noiseB: This work focuses on the latter and show that a noise-aware training pipeline with post-measurement normalization, noise injection, and p...	ABC	ACB	ACB	BCA	Selection 4
A: However, in the current event-based studies, most methods usually handle the fundamental event-based data association problem in implicit ways, which are designed for their specific tasks. As a result, event-based data association has not been effectively solved by the current event-based worksB: Also, gall...	BCA	BCA	ABC	ACB	Selection 4
A: We are thankful to all participants of the 2018 AlCoLoCo problem seminars and the 2018 Recolles workshop, where this research was startedB: 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.C:...	ABC	BCA	ACB	ABC	Selection 2
A: Combined with a specific pretext task, we can adapt GenURL to various URL scenarios in a unified manner and achieve state-of-the-art performances, including self-supervised visual representation learning, unsupervised knowledge distillation, graph embeddings, and dimension reduction. Moreover, ablation studies r...	ACB	CBA	CAB	ABC	Selection 2
A: We extend TinyEngine [30] to support patch-based inference, and benchmark the models on 3 MCU models with different hardware resources: STM32F412 (Cortex-M4, 256kB SRAM/1MB Flash), STM32F746 (Cortex-M7, 320kB SRAM/1MB Flash), STM32H743 (Cortex-M7, 512kB SRAM/2MB Flash).B: We follow [30] for super network tr...	CBA	CAB	ACB	BCA	Selection 2
A: In this competition, our team achieved 1st place in the first stage leaderboard, and 3rd place in the final stage leaderboard.B: Experiments show our framework outperforms baseline methods even when its encoder module uses a randomly initialized BERT [2] encoder, showing the power of the new tagging framewor...	CBA	ACB	ACB	ABC	Selection 1
A: The assembly of CGCL is designed to exhibit both asymmetry and complementarityB: Importantly, the computation of these metrics is solely based on pre-training stage and remains uninfluenced by downstream classifiers, rendering it a valuable guidance for assembly selection. C: For a more precise quantitat...	CBA	CAB	ACB	ABC	Selection 3
A: Importantly, this phenomenon appears consistently across different biases. Using two head output of the network is the strongest bias toward compositionality (amongst studied). Finally, we show that compositionality can generalize to unseen cases when fine-tuning is allowed. B: There is an interesting depend...	CBA	ACB	CAB	BAC	Selection 1
A: Learning with CBFs: Approaches that use CBFs during learning typically assume that a valid CBF is already given, while we focus on constructing CBFs so that our approach can be viewed as complementary. In [19], it is shown how safe and optimal reward functions can be obtained, and how these are related to CBFs. ...	ABC	CBA	BAC	CBA	Selection 1
A: the circuit is an OR of ANDs)B: The width of a DNF is the maximum fan-in of any of the AND gates.C: A DNF formula, also just called a DNF, is a depth-2 𝖠𝖢𝟢superscript𝖠𝖢0\mathsf{AC^{0}}sansserif_AC start_POSTSUPERSCRIPT sansserif_0 end_POSTSUPERSCRIPT circuit where the top gate is an OR gate (i.e	ACB	BCA	ABC	ACB	Selection 2
A: (c) To measure performances of different methods on real-world weighted network with unknown information on nodes labels, we propose a general modularity as an extension of classical Newman’s modularity [23]B: For weighted network in which all edge weights are nonnegative, the general modularity is exactly t...	ABC	ACB	CAB	CAB	Selection 1
A: For the complete training results and more details, we refer to Appendix E.B: In each case, we present training curves for tasks, which best illustrate our claimsC: Below we present a comprehensive list of ablations to evaluate the design choices of our algorithm	BCA	CAB	CBA	ACB	Selection 3
A: The green color in the center of a point indicates that a decision is from RF, while blue is for AB. The outline color reflects the training instances’ class based on a decision’s predictionB: The positioning of the points can be used to observe if the RF and AB models produced similar rules, offering a comp...	ACB	ABC	BCA	BAC	Selection 1
A: The differences between theoretically and numerically obtained optimal Tx-polarization angles are considerableB: This is due to the fact that the approximation (8) is less accurate at higher SNRs. However, the difference in capacity is still remarkably smallC: In case of polarization postcoding at the Rx...	CAB	ABC	BAC	ACB	Selection 2
A: In the following two sections, we prove lower and upper bounds, respectively, on how good online sorting algorithms can perform in this case.B: 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>1i...	BAC	ACB	CAB	CBA	Selection 3
A: In addition, similarity via EqB: Metrics: Following the official challenge [14, 37], mean radial error (MRE) and successful detection rate (SDR) in four radii (2mm, 2.5mm, 3mm, and 4mm) are applied, based on the Euclidean distance between prediction and ground truthC: (9) is demonstrated for comparison. ...	BAC	BCA	ACB	CBA	Selection 1
A: This section conducts extensive experiments to demonstrate that DFSP is effective for mixed membership community detection and our fuzzy weighted modularity is capable of the estimation of the number of communities for mixed membership weighted networks generated from our MMDF modelB: We conducted all experi...	ACB	ACB	CAB	ABC	Selection 4
A: However, distillation-based methods introduce the dilemma of balancing between previously learned classes and current classesB: In contrast, if the distillation term is too small, forgetting will be amplified. To mitigate this dilemma, some methods have been proposed to maintain a good balance between old an...	CBA	ABC	BAC	ACB	Selection 4
A: As a distance measure, we utilized the normalized gradient field (NGF) (Haber and Modersitzki, 2006). The transformation matrix obtained in the parametric registration initializes the non-parametric step, in which a deformation vector field was computed. In the deformable solution, the NGF was also utilized as a...	CAB	CBA	BCA	ABC	Selection 3
A: Unfortunately, real-life databases are often inconsistent, i.e., do not conform to their specifications in the form of integrity constraints. The reason behind this is that data is not perfect and clean; it may come, for example, from several conflicting sources [5]. Data cleaning attempts to fix this problem by...	CBA	CBA	ABC	ACB	Selection 3
A: Suppose that the absorption rate of node 2 in Figure 1 is much larger than the absorption rates of the other nodes. The large absorption rate of node 2 is a barrier to the absorbing random walk, as transitions from nodes in the set {3,4}34\{3,4\}{ 3 , 4 } to nodes in the set {1}1\{1\}{ 1 } (and vice versa) are u...	ABC	BAC	CAB	ACB	Selection 3
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...	ABC	BCA	ACB	CBA	Selection 3
A: Apart from informational content, another critical aspect deserving attention is the timing perspective of such explanations: the lead time for emergent scenarios, perhaps using extensive scenario-based evaluations or case-based reasoning, must be engineered appropriately. Furthermore, a well-known problem with ...	CAB	CAB	ACB	CAB	Selection 3
A: However, VGG16-NetVLAD and Patch-NetVLAD need the most computational resources, which is not conducive to its deployment in embedded devices. Compared with other models with the global descriptors matching, Patch-NetVLAD extracts multi-scale fusion of patch-level features that have complementary scales via an in...	BAC	CBA	BCA	ABC	Selection 1
A: Traditionally, stream ciphers are attacked with two approaches: correlation attacks, that exploit possible correlations between some part of the keystream and a portion of the initial state, and approximation attacks, where the nonlinear part is approximated by a linear componentB: The design defenses agains...	ABC	ACB	BAC	BAC	Selection 1
A: In most cases, the gain and exploitability of CDRNR are lower than that of RNRB: Gain must be lower because of the different value function, but the exploitability can be higher, as seen with p=0.1𝑝0.1p=0.1italic_p = 0.1 against low iteration strategies, due to the depth-limited natureC: We provide res...	BAC	ABC	CAB	BAC	Selection 2
A: 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 of observationsB: Our paper provides some theoretical explanations - see Section 8 for a statement...	CAB	CAB	CBA	ACB	Selection 4
A: Therefore, we use the bibliographic database IDEAS, based on RePEc and dedicated to Economics, to include unpublished and working papers.444 We use the Advanced Search tool, searching by Keywords and Title: migration and (“natural disasters” or “climate change”)B: A selection of the contributions is made man...	BCA	ABC	ACB	CBA	Selection 1
A: We resolve the question affirmativelyB: Compared to Sword presented in Section 4.2, the key novel ingredient is the framework of collaborative online ensemble. We carefully introduce correction terms to the online loss and optimism, forming a biased surrogate loss and a surrogate optimism, which are then fed...	CBA	CAB	ACB	CAB	Selection 3
A: Let ψn=φ∘σnsubscript𝜓𝑛𝜑superscript𝜎𝑛\psi_{n}=\varphi\circ\sigma^{n}italic_ψ start_POSTSUBSCRIPT italic_n end_POSTSUBSCRIPT = italic_φ ∘ italic_σ start_POSTSUPERSCRIPT italic_n end_POSTSUPERSCRIPTB: Since φ⁢(A∗)𝜑superscript𝐴\varphi(A^{*})italic_φ ( italic_A start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT )...	ACB	CAB	CAB	BAC	Selection 1
A: 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 alB: (2017)C: More precisely, these authors established the third term on the right-hand side in	ABC	CAB	CBA	ACB	Selection 1
A: While Euclidean loss remains the dominant cost function in deep learning, topological losses based on persistent homology are emerging as superior in tasks requiring topological understanding (Chen et al., 2019; Hu et al., 2019; Gupta et al., 2022; Lin et al., 2023). These topological losses incorporate penalti...	BAC	CBA	ABC	ACB	Selection 4
A: As the iner-event time function τs⁢(θ)subscript𝜏𝑠𝜃\tau_{s}(\theta)italic_τ start_POSTSUBSCRIPT italic_s end_POSTSUBSCRIPT ( italic_θ ) is periodic with period π𝜋\piitalic_π, ϕ⁢(θ+π)=arg⁡(G⁢(τs⁢(θ+π))⁢xθ+π)=arg⁡(−G⁢(τs⁢(θ))⁢xθ)=ϕ⁢(θ)+πitalic-ϕ𝜃𝜋𝐺subscript𝜏𝑠𝜃𝜋subscript𝑥𝜃𝜋𝐺subscript𝜏𝑠𝜃subscript𝑥�...	CAB	CAB	ABC	CBA	Selection 3
A: In Fig. 2, the coolant temperature control at the boundaries show that the transient control action for StSf-C is greater than St-C. However, in steady state both control actions are somewhat comparable. B: The temperature of the battery module from the two boundaries and mid-section, as shown in FigC: 2...	CAB	BAC	ACB	BAC	Selection 1
A: Over the years, there has been an increase in studies that explore the sensing capabilities of smartphones and wearables to assess as well as improve worker’s health and wellbeingB: Most of these studies ask participants to self-report their health and wellbeing using validated instruments. While some studi...	ACB	CAB	ABC	BAC	Selection 1
A: We essentially do a binary search to estimate the optimal power that can be allocated to SUB: Determining Labels (Optimal Power Allocated to SU)C: 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 ba...	BAC	CBA	CBA	CAB	Selection 1
A: Section 2 contains preliminaries and is split in the following subsectionsB: The paper is structured as followsC: In Section 2.1, after reviewing the definitions of groups and group actions, we define the notions of congruence and symmetry of curves relative to a given group. In Sections 2.2 and 2.3, we...	ABC	BCA	BAC	CAB	Selection 3
A: The numerical simulation is given in Section 6. Finally the results presented in this paper is summarized in Section 7.B: 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 re...	BCA	CAB	BAC	CBA	Selection 4
A: We compare the device model against an ideal noiseless memristor, finding no significant difference in the classification accuracy. Using information theoretic measures, we also study the effect of stochasticity on the mutual information of events propagated by the network and discuss a possible solution to redu...	ABC	ACB	BAC	CAB	Selection 4
A: To do so, we develop a projection argument (see Lemma 4) and a new analysis of the expected available movement of a randomly chosen agentB: Başar (2015) is to significantly tighten and generalize their proofC: This allows us to improve the bound on the expected drop of the potential function (see Lemma 8...	BAC	ABC	ABC	ABC	Selection 1
A: In Table 3 and Table 4, we compare the performance of our proposed model against single and multi-label prediction models for selected pathologies. Table 3 shows that our proposed multi-label approach was able to outperform single-label modelsB: In Table 4, the results indicate that our proposed architectur...	ACB	CAB	BAC	ABC	Selection 4
A: The structure of the paper is as followsB: 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.C: The rest of this section establishes the problem	CBA	BAC	ACB	BCA	Selection 3
A: For example, the interaction of a molecule (per se rotational invariant) with an external magnetic field is an intrinsically equivariant problem. B: In fact, in many real-world application, equivariance is beneficial if not necessary Smidt (2020); Miller et alC: (2020)	BAC	CBA	BAC	CAB	Selection 4
A: This lets us perform “at-a-glance” comparisons of different areas, discern whether extrema are more attributable to local sources or long-range patterns, and importantly lets domain experts assess the plausibility of a model’s hyperparameters. B: its smoothness and variability in space and timeC: Part of...	BCA	CBA	BCA	ABC	Selection 2
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...	CAB	ACB	ABC	ACB	Selection 1
A: The markers represent the performance of the probability of the outage for the idealized version of the Random selection scheme and serve as a reference. The dotted lines show the actual performance of MRC in the presence of correlated interferenceB: The dashed lines depict the scenario with finite pool of Q...	ACB	CAB	BCA	ABC	Selection 4
A: An interesting connection to the Jones-Scul algorithm was given by Gu, Lutz, and Mayordomo [GLM06] who classified the sets V𝑉Vitalic_V that admit a solution to a computable extension of the ASTPB: This variant of the problem characterizes the sets which are contained in a rectifiable computable curveC: ...	CBA	ACB	BCA	ABC	Selection 4
A: For a detailed introduction on Chow forms, we refer to [13, 14]B: For a concise, clear, and deeper exposition we refer to [22].C: The main object of our study is the associated hypersurface of a variety V𝑉Vitalic_V and its defining polynomial, the Chow form	ACB	BCA	BAC	CBA	Selection 2
A: Regarding the columns, 5555 of them refer to information about the volunteer (identifier and age group) and the video (batch, display position, and video code number), whereas 9999 of them refers to the self-assessment provided (arousal, valence, dominance, liking, reported and target discrete emotions, and fami...	CAB	BAC	ABC	ACB	Selection 1
A: File Structure: Android applications are packaged in APK file format for distribution and installationB: The files in an APK package characterize the behavior and operation of an Android app (see Table 2)C: These files are used in classifier design and also modified by an adversary while performing adver...	ACB	ACB	ACB	ABC	Selection 4
A: Almost surely either a player was chosen on Step 1 or 2 or the sum of just the odd-numbered terms (given by B’s moves) of expression (2) diverges to ∞\infty∞, by Lemma 3.6B: In the latter case, the sum of the even-numbered terms must diverge to −∞-\infty- ∞ (as the sum of all terms is convergent), and theref...	BAC	ABC	CAB	CBA	Selection 2
A: The physical-world setup is shown in Fig. 5 (c). Correspondingly, we use a simulation environment with similar road geometry. More details are in Appendix -D. B: We use the SS attack, and calculate the similarity between the STOP sign detection results in the physical world and the simulation environment in ...	BAC	CBA	ACB	CAB	Selection 2
A: Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive AgencyB: Neither the European Union nor the granting authority can be held responsible for them.C: Alexey Barsukov is funded by the E...	CAB	ACB	BCA	ABC	Selection 3
A: We also argue that recurrent, state-based models, where a memory state propagates information through time, are often well suited for online recognition tasks [22, 23, 25]. Especially for end-to-end learning on partial video sequences, the LSTM’s hidden state can be utilized to increase temporal context. Specifi...	CBA	ABC	BAC	CAB	Selection 4
A: Among them, loss functions have been actively developed and can be categorized into metric and classification losses.B: FR is one of the most promising computer-vision tasksC: In recent times, the combination of the following three factors has contributed to the rapid growth of this technology: 1) intro...	BCA	ABC	CAB	ABC	Selection 3
A: Once an image file is selected, the system automatically uploads the image to the cloud, performs the analysis in approximately ten to third seconds (largely based on the internet conditions) displays the output in the form of a figureB: Additionally, the results also state the level of confidence in the di...	ACB	BCA	BCA	CAB	Selection 1
A: In LNLAttenNet, an attention mechanism is introduced to construct the non-local attention network which explores the significance of local regions for FER from a global perspective of facial expressionB: C: The obtained attention weights corresponding to local regions are fed into the local multi-networ...	BCA	ABC	CBA	ACB	Selection 4
A: These rating points are somewhat analogous to poker chips: when player A𝐴Aitalic_A and player B𝐵Bitalic_B play a game, they each place some of their rating points into a potB: 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 ...	ACB	CBA	CBA	BCA	Selection 4
A: We choose to continue with this projection; thus, we go back to Figure 6(a) and select a number of neighbors parameter equal to 13. When we hover over the stacked bar chart in Figure 6(a), we observe that safe and borderline cases account for 47.16% and 34.54% of the training set, respectively. This is significa...	ABC	BCA	CAB	BCA	Selection 3
A: Table II shows our results for the verification of the aggregated signatures by the smart contract with different number of verifiers. For example, it costs 621180 units of gas for ten verifiers to verify the aggregated signature. Using the median gas cost of 18 GWei (representative of Sep.-Oct. 2023, when our e...	BAC	CAB	BAC	ABC	Selection 2
A: The equilibrium acceptance threshold affects the value of the policy because it determines which students are accepted or rejected. A selection criterion that induces a high acceptance threshold may not necessarily yield high value for the college. B: This example aims to capture the phenomenon that college ...	BCA	CAB	CBA	BAC	Selection 2
A: The techniques include training a set of models with dissimilar input gradients [68] and training multiple prediction heads that disagree on a target distribution [39]. An interesting extension of OccamNets could be making diverse predictions through the multiple exits and through CAMs that focus on different vi...	ABC	ACB	BCA	ABC	Selection 3
A: The results are shown in Tab. XI. It can be observed that by simply replacing the prototypes from MiT-B0 with the prototypes from MiT-B1, MiT-B2, and MiT-B5, significant performance improvements are obtained for CFFM++ (MiT-B0), which demonstrates the extracted prototypes contain rich contextual information. **B...	ACB	ACB	CAB	ABC	Selection 3
A: Figure 29 shows the Throughput/Watt improvement and area/power consumption of Hotline componentsB: The EAL consumes the most power and area due to its SRAM structureC: Despite the 7.01 m⁢m2𝑚superscript𝑚2mm^{2}italic_m italic_m start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT area overhead and extra power co...	BAC	ABC	BCA	CBA	Selection 2
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: Indeed, one should view the analogues of critical cells (both Morse and non-Morse) in the PL category as ...	BCA	ACB	BCA	CBA	Selection 2
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...	ABC	CBA	ABC	BAC	Selection 2
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: starting atC: First we generate analytic solutions of the equation	BAC	BCA	BCA	ACB	Selection 4
A: To address the evaluation challenge for HS data set, for each sample, we removed the outliers and then split the data set into train and test setsB: Although with HS we can not measure the RU values for the whole query space, we believe the findings can still confirm the effectiveness of our measures. C:...	CBA	ABC	ACB	CBA	Selection 3
A: Chouhan et al. (Chouhan et al., 2019) interrogated their model through a participatory design study with groups of family members, friends, and co-workersB: In a follow-up study, Kropcynski et al. (Kropczynski et al., 2021a) further validated the applicability of the model of community oversight within olde...	BCA	ABC	ABC	CAB	Selection 1
A: If a density is perturbed morderately in the Wasserstein metric and in the sup-norm, then the persistences of all homological features are also perturbed moderatelyB: In particular, the proposed approach is provably robust against additive noise (with possibly unbounded support) and outliers. This is made pr...	ACB	BCA	CAB	ABC	Selection 2
A: 2013) provide a solution for enhancing the generalizability of AU recognition model by training personalized AU classifiers for each subject and works such as (Zen et alB: 2016; Wang and Wang 2018) make attempt to relieve the subject-related prediction bias through domain adaptation.C: As for subject va...	BCA	ACB	CAB	CBA	Selection 1
A: 4(a), the accessed accounts of Tx1 are {A, B} (Tx1: transfer 2 ether from account A to account B), the accessed accounts of Tx2 are {B, C}, and the accessed accounts of Tx3 are {C, D}. Tx1 and Tx2 intersect with the common account {B}, and Tx1 and Tx3 do not intersectB: In general, if two transactions inters...	BAC	ABC	BCA	CAB	Selection 3
A: More specifically, partial observability poses both statistical and computational challenges. From a statistical perspective, it is challenging to predict future rewards, observations, or states due to a lack of the Markov property. In particular, predicting the future often involves inferring the distribution ...	ABC	ACB	CAB	BAC	Selection 1
A: Furthermore, we found that articulation disorder was the most frequent disorder addressed by the included studies, with three studies dedicated to them. This may be due to the fact that articulation disorder are commonly found in persons with other SSD (Flipsen Jr, \APACyear2015). The results show that most stud...	CBA	ABC	CAB	BCA	Selection 2
A: We define the following metric.B: Here we look at average statistics of compression ratio to provide a first layer of evidence supporting this phenomenon in real-world dataC: As discussed in Section 2 and described in Theorem 2.5, our primary result showed that the intra-community compression ratios are...	CAB	CBA	ABC	BAC	Selection 2
A: (3) Scene Graph Detection (SGDet): predict the bounding boxes, the predicates as well as the object labels. We calculate and report the mean recall@K scores for the above metrics in experiments.B: We evaluate our generated scene graphs using the three evaluation metrics: (1) Predicate Classification (PredCls...	ABC	CBA	CAB	BAC	Selection 3
A: In our model, the entrance fee function is arbitrary, forming a dynamic part of the inputB: 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 ...	CAB	ACB	CBA	BCA	Selection 4
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...	CBA	BCA	ABC	ABC	Selection 1
A: If the simplicial partition-based implementation is considered, then one has also to account for the complexity of the resulting invariant set, which is typically high 6, 8, 49, 10, 2, 9. These methods can therefore require significant memory to store the vectors and/or matrices describing every simplicial parti...	CBA	ABC	ACB	BAC	Selection 1
A: While groundtruth for most of the parameters is hard to acquire, the length of the pendulum, and the angle of the inclined plane are quantities that can be obtained using a ruler. The estimated quantities deviate from our measured values by 4.1%percent4.14.1\%4.1 % and 3.6%percent3.63.6\%3.6 %, respectively (rel...	CBA	BAC	ABC	BCA	Selection 2
A: Here, quantum data compression has emerged as a preferred approach to minimize the usage of quantum resources, whereby an ensemble of quantum states is compressed, using methods like principal component analysis, into a smaller, potentially lower-dimensional, set that is communicated over QCNs [7, 8, 9]. However...	CBA	CAB	BAC	CBA	Selection 3
A: This allows to split a single, complex, and high-dimensional problem – which would otherwise be intractable – into several, cooperative, and low-dimensional tasks. B: Based on the above observations, random mobility patterns characterizing both UEs and obstacles typically generate local areas with local stat...	ACB	CAB	CBA	CBA	Selection 2
A: Of course, the FDA does not always follow a formal algorithm for evaluating clinical trials; it retains significant discretion in decision-making, and the standards for efficacy can vary by the treatment area or reviewing team (Janiaud et al., 2021). Yet, we can study a simplification based on its written guidel...	CAB	BAC	BCA	BAC	Selection 1
A: The red frame is the transformed moving image using Clear registration. Green and Yellow frames are the transformed images using respectively PPIR(MPC) and PPIR(FHE)v1.B: FigC: A7: Qualitative results for rigid point cloud registration between 2D corpus callosum point sets	CBA	CAB	BCA	ACB	Selection 2

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