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A: We also observe evidence of out-of-distribution generalization.B: We propose Subgoal Search method with two implementations: MCTS-kSubS, BF-kSubSC: We demonstrate that our approach requires a relatively little search or, equivalently, is able to handle bigger problems	ACB	ACB	ABC	CAB	Selection 4
A: Now, we have more options. BERT [Kenton and Toutanova, 2019] has its Chinese version and can express semantic features of Chinese characters more accurately, hence having better performance in NER tasks. B: One typical method is Word2Vec [Mikolov et al., 2013], which starts to use static embedding vectors to...	ABC	CAB	CBA	CAB	Selection 3
A: To characterize the hologram reconstruction with the proposed neural étendue expander we simulate a Fourier holographic setup that has been augmented with a neural étendue expanderB: See Supplementary Note 2 for additional qualitative comparisons and for comparisons against uniform random expanders.C: F...	BCA	ABC	ACB	BAC	Selection 3
A: However, in many real-world applications, where large-scale data annotation is costly, this requirement cannot be easily satisfiedB: Secondly, current NLP models often rely on a large or even huge amount of labeled dataC: In this case, we may consider to leverage abundant unlabeled data in MTL by using ...	CBA	CAB	BAC	ABC	Selection 3
A: They will help to give the authors an approximation of the number of pages that will be in the final version. The structure of the LaTeXfiles, as designed, enable easy conversion to XML for the composition systems used by the IEEE’s outsource vendorsB: The XML files are used to produce the final print/IEEEXp...	BAC	BCA	CAB	ACB	Selection 2
A: 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_POSTSUBSCRIPT , italic_w start_POSTSUBSCRIPT ( italic_i start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT , italic_β ) end_POSTSUBSCRIPT is conne...	ACB	BAC	ACB	BCA	Selection 2
A: Therefore, we make no assumptions on the normality of the estimator evaluated on our sample, which would be implicit in using the standard error as our metric of uncertainty and suggesting that it would imply t-statistics or p-valuesB: Further, we do not expect the MLE to reach its asymptotic distribution w...	ACB	BCA	BAC	CBA	Selection 3
A: For instance, Chen et alB: proposed a Multi-level Densely Connected Super-Resolution Network (mDCSRN (Chen et al., 2018)) with GAN-guided training to generate high-resolution MR images, which can train and infer quicklyC: In (Wang et al., 2019b), a 3D Super-Resolution Convolutional Neural Network (3DSRCN...	BCA	BCA	BAC	ABC	Selection 4
A: It enhances conventional coordinate-based networks by adding synthetic capabilities for tasks such as inpainting, super-resolution, and denoising, at levels comparable or better than the considered alternativesB: Neural Knitworks constitute a hybrid architectural approach for internal learning applications...	BAC	ABC	ABC	CBA	Selection 1
A: Specifically, our proposed model is an example of PM with side information, as considered in Bartók and Szepesvári, (2012).B: To fully characterise our logistic apple tasting problem and its best achievable regret, it is useful to cast the problem as part of the broader partial monitoring (PM) framework*C...	CBA	CAB	CBA	BCA	Selection 2
A: Table 3 summarizes the results obtained on the ToS-30 dataset, by reporting the macro-F1 averaged on 10-fold cross-validation.666For each unfairness category, following [12] we performed a binary classification task with 10-fold cross-validation: for each category, we report the macro-average over the standard ...	BCA	BCA	ABC	ACB	Selection 4
A: Figure 4B: The x𝑥xitalic_x-axis only exhibits frequencies up to 0.05. The black vertical lines represent the frequencies that were chosen to be included in the harmonic regression after visual inspection of this graph.C: Raster plot exhibiting the spectral density estimates for each of the 441 grid squ...	BCA	BAC	ACB	ABC	Selection 3
A: the 2-categories of primary doctrines and that of elementary ones that is, primary doctrines with equalityB: It shows an adjoint situation between 𝐏𝐃𝐏𝐃\mathbf{PD}bold_PD and 𝐄𝐃𝐄𝐃\mathbf{ED}bold_ED, i.eC: That adjoint situation is comonadic. This fact not only reveals the coalgebraic nature of equ...	BCA	CBA	CAB	BAC	Selection 4
A: Extensive experimental studies : We test the effectiveness of studied role similarity metrics on six labeled networks and estimate their efficiency on 20 real-world networks, including several large networksB: For efficiency, ForestSim is the unique role similarity metric that can process top-k queries with...	CBA	CAB	BAC	ACB	Selection 4
A: (2019); Huang and Carley (2019); Phan and Ogunbona (2020), hypothesize that sentiments of aspects may be dependent and usually leverage syntax trees to reveal potential sentiment dependencies between aspects.B: Yet, some strides have been made on a similar topic, namely sentiment dependencyC: These appro...	CBA	CAB	CBA	BCA	Selection 2
A: This process is somewhat elaborate and the reader is referred to [31] and [32] for all of the detailsB: The operator 𝐋𝐋\mathbf{L}bold_L and 𝐖𝐖\mathbf{W}bold_W are constructed from a multilevel decomposition of the location of predictorsC: However, for the exposition in this section it sufficient to k...	ABC	CAB	BAC	CBA	Selection 3
A: QuantumNAT is fundamentally different from existing methods: (i) Prior work focuses on low-level numerical correction in inference only; QuantumNAT embraces more optimization freedom in both training and inferenceB: (ii) PQC has a good built-in error-tolerance which motivates QuantumNAT’s post-measurement qu...	ACB	ABC	ABC	BAC	Selection 1
A: In that framework, the initial motion parameters for each event are empirically set to a range of predefined values for optimizationB: As the pioneering work on event-based data association, gallego2018unifying proposes a contrast maximization framework to implicitly handle the data association problem base...	BAC	CBA	ACB	BCA	Selection 1
A: This generalizes the same fact which was previously proved for Meyniel graphs [22] (a class which contains chordal graphs, HHD-free graphs, Gallai graphs, parity graphs, distance-hereditary graphs…) and line graphs of bipartite graphs [3]B: We now prove our main result, that there are no ugly perfect graphs...	BAC	CBA	BCA	ACB	Selection 1
A: (ii) Complementary to DR and GE, another popular path of URL focuses on data-specific augmentations, such as image crop, which leads to a clustering structure and learns discriminative representations, as illustrated in Figure 1 rightB: (iii) In addition, knowledge distillation (KD) is another approach that ...	CBA	BAC	BCA	CAB	Selection 3
A: The training dataset is randomly split into a sub-training set and validation setB: The validation set size is 10,000 for ImageNet [11] and 5,000 for other datasets. We first train the largest network in the search space on the sub-training set using SGD with batch size 1024, initial learning rate 0.2, weigh...	ACB	BCA	CAB	CBA	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 encoder, showing the power of the new tagging framework**C...	BCA	CAB	CBA	ABC	Selection 3
A: In contrast to existing GCL methods which use the same encoder to observe multiple augmented graphs, CGCL uses multiple encoders to observe the same graph and generate contrastive views. It is pivotal to recognize that the quintessence of contrastive learning is to learn invariance between different contrastive...	CBA	CBA	BAC	ACB	Selection 4
A: In this section, we discuss the experiment with an additional feature (with floor color acting as the third feature) and a message of length 3333B: The overall topographic similarity level is lower than in the main experiment, however, the distinctive peak is visible, here for noise level 0.080.080.080.08. *...	ACB	BCA	BCA	ABC	Selection 1
A: Such techniques are necessary for learning based methods to be applied to realistic systemsB: While our algorithmic implementation is an approximate solution of the proposed framework, we mention that solving an unconstrained relaxation of (7) and bootstrapping hyperparameters is a common technique in machin...	CAB	BAC	CAB	CAB	Selection 2
A: We first require the following form of the BBBV Theorem [BBBV97]B: Viewed another way, Lemma 37 is just a probabilistic version of Lemma 28.C: It essentially states that a quantum algorithm that makes few queries to its input is unlikely to detect small random changes to the input	ACB	ABC	ABC	BCA	Selection 1
A: Karate-weighted: This weighted network is collected from a university karate clubB: In this weighted network, node denotes member, and edge between two nodes indicates the relative strength of the associationsC: Actually, this network is the weighted version of Karate club network. So, the number of com...	ABC	CAB	ACB	BCA	Selection 1
A: Our approach achieves competitive performance on all tasks and exceeds state-of-the-art results on some of them. Additionally, we provide a comprehensive set of ablations to quantify the influence of each component on the final results. We confirm that importance sampling is a key factor for MA-Trace’s performan...	BAC	BCA	CBA	BCA	Selection 3
A: The rest of this paper is organized as follows. In Section Related Work, we discuss relevant techniques for visualizing bagging and boosting decision trees, along with tree- and rule-based models and a bulk of relevant works of visual analytics systems for multi-model comparison. Section Random Forest vsB: ...	CAB	BCA	ABC	ACB	Selection 4
A: Hybrid beamforming can adopt dual polarization and the associated codebook design to improve the system performance [7, 25]. Although polarization multiplexing without spatial diversity is promising [18], polarization diversity can be combined with spatial diversity to further improve the performance of wireless...	BAC	CBA	BCA	CAB	Selection 4
A: We note that these problems have known O⁢(1)𝑂1O(1)italic_O ( 1 )-competitive algorithms if the arriving pieces are axis-parallel rectangles; see Section 1.2 for referencesB: If the arriving pieces are convex polygons that may be arbitrarily rotated, the task reduces to packing axis-parallel rectangles by fi...	ACB	CAB	BCA	CAB	Selection 1
A: Neural networks are often hampered when there are only a few training samples to provide supervisory signalsB: Few-shot learning (FSL) [52, 16, 21, 27] is proposed to tackle this data scarcity problem, utilizing external prior knowledge to discover patterns in dataC: FSL has been employed successfully i...	ABC	CAB	BAC	BAC	Selection 1
A: Future works will be studied from four aspects: the first is studying the variation of MMDF for heterogeneous networks; the second is presenting a rigorous method to determine the number of communities for networks generated from MMDF; the third is developing algorithms to estimate community membership based on ...	ABC	CAB	ABC	BAC	Selection 2
A: In particular, if the distillation term is too large, then the model’s ability to learn new classes will be limitedB: However, distillation-based methods introduce the dilemma of balancing between previously learned classes and current classesC: In contrast, if the distillation term is too small, forgett...	ACB	ABC	ACB	BAC	Selection 4
A: 5B: Comparative performance analysis of various participating methods in terms of MEE, Robustness, and BraTS-Reg score in ISBI and MICCAI 2022 along with the invited methods are shown separately on the actual test data and out-of-distribution (OOD) test data in FigC: In all violin plots, the teams are a...	CAB	ABC	BAC	ABC	Selection 3
A: For Question 1, we provide a definitive answerB: For Question 2, we establish results that form crucial steps towards a definitive answer. As we explain, an answer to Question 2 will resolve a challenging graph-theoretic open problem. Our contributions can be summarized as follows:C: Our goal is to prov...	CBA	CAB	BCA	ABC	Selection 3
A: However, there are numerous approaches to community detection [12, 33], and it is worthwhile to adapt other approaches, such as modularity maximization [29] and statistical influence using stochastic block models [31], to account for node-absorption ratesB: The community-detection algorithm InfoMap is based...	CAB	BCA	BAC	CAB	Selection 3
A: Note that throttling does not alter the generation latency of the root and thus the overall tree; we prove the optimality of the overall algorithm formally in Theorem 2B: In our overall methodology, to conserve node and link resources, we post-process or ”throttle” the swapping-tree obtained from the DP algo...	BAC	BCA	ABC	ACB	Selection 1
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...	BCA	ABC	CAB	ACB	Selection 3
A: Pitts30k [9, 13]: It is a public dataset containing 30 k database images downloaded from Google Street View and 22 k test queries captured at different timesB: The dataset is divided into three roughly equal parts for training, validation, and testingC: Street views in the three sets are geographically d...	ACB	CBA	BAC	ABC	Selection 4
A: We conclude showing that the security of WG-PRNG is less that claimed until nowB: For the sake of presentation, we will first describe the part regarding WG-PRNG, and then the one on the two toy ciphers.C: In Chapter 4, to validate our algebraic attack, first we apply it to two toy stream ciphers and th...	CAB	BCA	BAC	ACB	Selection 2
A: For example, when we gradually build a model during play, we must limit our exploitability in the initial game rounds when the model is still very inaccurate.B: While CDBR maximizes the exploitation of the fixed opponent model, it allows a player to be exploitedC: When we face an opponent unsure if our ...	CAB	CBA	BAC	ABC	Selection 1
A: This weighted modularity is often used, and indeed the popular community detection algorithm Louvain can take weighted networks as input [4]. Our Theorems 1.1 and 1.2 have analogs for weighted networks - see Section 10.B: Network data which is of interest to cluster often has weights associated with each ed...	CAB	ACB	BAC	BCA	Selection 1
A: We include in the multiple MRA a set of dummies for Controls included in the estimation of the model of migration and dummies for Channels through which the climatic event determines migration phenomenaB: Many authors highlight the importance of variables of political, economic, social, and historical nature...	CAB	BAC	BCA	CAB	Selection 2
A: While this can be remedied by the dual stabilization (Fang et al., 2020), we just use FTRL for simplicity. ¶¶\lx@paragraphsign¶B: Thus, it is hard to directly apply Theorem 1 to obtain the meta-regret. We choose a FTRL-type meta-algorithm instead of an OMD-type algorithm, in that OMD with time-varying learn...	ABC	CBA	CAB	BAC	Selection 2
A: Let (y,k)𝑦𝑘(y,k)( italic_y , italic_k ) be a σ𝜎\sigmaitalic_σ-representation of x𝑥xitalic_x with y∈𝖷⁢(σ)𝑦𝖷𝜎y\in\mathsf{X}(\sigma)italic_y ∈ sansserif_X ( italic_σ )B: for some n=n⁢(x)≥1𝑛𝑛𝑥1n=n(x)\geq 1italic_n = italic_n ( italic_x ) ≥ 1 by Lemma 5.2C: By Lemma 8.3, y𝑦yitalic_y is periodic an...	ACB	BAC	BCA	BCA	Selection 2
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	ACB	BCA	BAC	ABC	Selection 4
A: Recent studies have illustrated the versatility of persistent homology in analyzing complex networks, including brain networksB: (2019); Xing et al. (2022) highlighted the application of persistent homology in evaluating temporal changes in topological network features.C: Sizemore et al	ACB	CAB	BAC	ABC	Selection 1
A: according to Corollary 4, fs⁢(θ,τ)=0subscript𝑓𝑠𝜃𝜏0f_{s}(\theta,\tau)=0italic_f start_POSTSUBSCRIPT italic_s end_POSTSUBSCRIPT ( italic_θ , italic_τ ) = 0 has no solution for τ∈(0,τm)𝜏0subscript𝜏𝑚\tau\in(0,\tau_{m})italic_τ ∈ ( 0 , italic_τ start_POSTSUBSCRIPT italic_m end_POSTSUBSCRIPT ) and has a solutio...	BAC	CAB	BAC	ABC	Selection 4
A: Mathematically, this impliesB: The goal of this work is to design a control strategy that will guarantee safety of the battery system under anomaliesC: The criterion for safety is that the spatial norm of the temperature deviation of the battery from a set-point remains below a prescribed threshold h¯¯ℎ...	BAC	CAB	BAC	CBA	Selection 2
A: They have used passive sensing to predict participant scores for these inventories, or to classify individuals into higher and lower performers within the workplaceB: Therefore, researchers in the pervasive computing community have often turned to job performance inventories developed by psychologists as gr...	BAC	CAB	CAB	BCA	Selection 1
A: 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 the area under the PSD curve over the 1 MHz channel of interest (see below), and finally, convert the computed area to an appropriate unit. B: In SS-Setting, a training samp...	BCA	ABC	CBA	ACB	Selection 3
A: Irina Kogan is a Professor of Mathematics at NCSU. The project was mentored by Eric Geiger and Irina Kogan. A poster based on this project received a honorable mention at JMM 2021.B: This work was performed during the REU 2020 program at the North Carolina State University (NCSU) and was supported by the De...	ACB	BAC	CAB	CBA	Selection 3
A: Obviously, this is not guaranteed for each instance of the algorithm. The Gauss-Southwell method leads to faster convergence at the cost of extra computations and evaluations of gradients during the selection of coordinates which can be an issue in large-scale problems [25].B: A substantial review of varia...	CAB	CBA	CBA	CBA	Selection 1
A: Table III compares the test-set classification accuracies on the two datasets for all the pulse settings listed in Tables I and IIB: These results were calculated over 5 runs on N-MNIST and 10 runs on POKERDVSC: We classified with a polynomial support vector machine of order 3. We	ABC	BCA	CBA	BAC	Selection 1
A: For this scenario we prove that the convergence of the opinion dynamics is guaranteedB: Shiragur (2015). A HKS is in a δ𝛿\deltaitalic_δ-stable state if and only if each edge in the influence network has length at most δ𝛿\deltaitalic_δC: We give an upper bound on the expected convergence time of	BAC	CAB	ABC	CBA	Selection 1
A: The loss function used in this project is the weighted loss function described by Equation 8. Once the neural network architecture was defined, we trained the model using back-propagation with mini-batch stochastic gradient descent. We used mini-batches of 32 images and the Adam optimizer with a default learning...	BAC	BAC	CBA	BCA	Selection 3
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...	ACB	ABC	CAB	BCA	Selection 2
A: Middle row shows the decoded (canonical) image and bottom row shows the decoded image after applying the predicted rotation.B: Figure 3: Input and predicted output for rotated versions of three MNIST imagesC: Top row shows the input image successively rotated by 45∘superscript4545^{\circ}45 start_POSTSU...	ABC	BAC	BCA	CAB	Selection 4
A: At each iteration a location is chosen at random from those availableB: The result is given as the average over 100 runs on each of the data snapshots in each subsetC: For BO one run is used. The metrics are calculated on the pre-processed data, i.e. after log transform and standardisation / mean-centrin...	CAB	BCA	ACB	ABC	Selection 4
A: Training binary latent VAEs with K=2,3𝐾23K=2,3italic_K = 2 , 3 (except for RELAX which uses 3333 evaluations) on MNIST, Fashion-MNIST, and OmniglotB: We report the average ELBO (±1plus-or-minus1\pm 1± 1 standard error) on the training set after 1M steps over 5 independent runsC: Test data bounds are rep...	BCA	ACB	BAC	ABC	Selection 4
A: Our framework follows the line of work started with CRDSA/IRSA in which each randomly accessing device sends multiple packet replicas [10, 11], rather than a single one as in plain ALOHA. The model involves a shared pool of resources - a short, periodic frame composed of limited number of slots, that makes our c...	CAB	BCA	ABC	BAC	Selection 1
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...	CBA	CBA	BCA	CBA	Selection 3
A: We should emphasize that the generalization from the projective Chow forms to the multiprojective ones is far from straightforward both from the mathematical and algorithmic complexity point of viewsB: Even though a multiprojective space is isomorphic to a projective variety via the Segre embedding, this req...	BCA	ABC	CBA	CAB	Selection 2
A: The final set includes 100100100100 women volunteers aged between 20202020 and 77777777 (with an average of 39.9239.9239.9239.92 and a standard deviation of 14.2614.2614.2614.26). For the purpose of covering a wide age range, the recruitment process requested a balanced number of volunteers in five age groups de...	CAB	CBA	ACB	CBA	Selection 1
A: (Grosse et al., 2017a) use the distinguishability of adversarial examples with the normal training data distribution for adversarial sample detection. They propose to detect adversarial samples as an outlier detection system. They train a model to recognize adversarial samples as outliers since adversarial examp...	CAB	CBA	BCA	CAB	Selection 2
A: Thus player B is chosen on this step with probability 00, finishing the proof. ∎B: In the latter case, the sum of the even-numbered terms must diverge to −∞-\infty- ∞ (as the sum of all terms is convergent), and therefore cannot diverge to ∞\infty∞C: Almost surely either a player was chosen on Step 1 or ...	CBA	BCA	BCA	BCA	Selection 1
A: Future directions. To foster future research into these less-explored security properties, here we discuss a few possible new research anglesB: 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 m...	CBA	BCA	CBA	ACB	Selection 4
A: Alexey Barsukov is funded by the European Union (ERC, POCOCOP, 101071674)B: Neither the European Union nor the granting authority can be held responsible for them.C: Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the Eur...	ABC	CAB	BCA	ACB	Selection 4
A: Only the temporal model, which typically does not contain BN, is trained to aggregate features over time [1, 19, 32, 35, 42, 60, 74, 84]B: However, in specialized small-data domains such as surgical video, well-pretrained CNNs may not be available [15, 88], requiring CNNs to be finetuned, either through 2-st...	CAB	ACB	BCA	ABC	Selection 3
A: Results on LFW, CFP-FP, AgeDB-30, and CALFW. FR on LFW, CFP-FP, AgeDB-30, and CALFW is straightforwardB: They have 1:1 ratios between the positive and negative pairs. Verification accuracy was employed with the best threshold separating the positive and negative pairs. In Table 4, the FR models with UNPG obt...	BCA	CBA	BCA	ACB	Selection 4
A: Our method has the potential to enhance the approach outlined by Burlina [8, 10]B: Additionally, implementing on-the-fly augmentation and selecting the appropriate percentage of synthetic images have shown to be fundamental aspects.C: Assessing image quality could lead to the exclusion of poor-quality i...	BAC	ACB	BAC	CBA	Selection 2
A: In experiments, we use the basic database including 12,271 training and 3,068 testing images. B: RAF-DB contains 29672 facial images downloaded from the InternetC: For the RAF-DB dataset, the facial landmarks are manually annotated via the crowdsourcing method with basic or compound expressions	BAC	ABC	CAB	ACB	Selection 3
A: 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)>0italic_σ ( - italic_δ ) > 0.B: If all players have slightly different ratings, no Elo can be transferred between themC: this i...	CBA	BAC	ACB	BAC	Selection 1
A: The All option is equivalent to ≠\neq≠ Majority, but we implemented them differently when a type of instance is deactivated. The former considers removing all points of the specific deactivated type/s irrelevant to the class that will be oversampled, leading to more excluded points for the active algorithm. The ...	BAC	BAC	CAB	CBA	Selection 3
A: This is a high barrier to scale for even a well-funded and resourceful adversaryB: To successfully frontrun a target user in the system, an adversary not only needs commensurately larger computational resources than the norm to compute the VDF proof faster, but the adversary also needs to delay the target us...	BAC	CAB	CBA	CBA	Selection 1
A: Many motivating applications for learning with strategic behavior, such as college admissions and hiring, are precisely settings where the decision maker is capacity-constrainedB: Although many recent works focus on learning in the presence of strategic behavior, learning in the presence of capacity constra...	CAB	BAC	BCA	ACB	Selection 2
A: Multi-exit networks have been studied in past work to speed up average inference time by minimizing the amount of compute needed for individual examples [10, 31, 66, 74], but their impact on bias-resilience has not been studied. In [59], a unified framework for studying early exit mechanisms was proposed, which ...	CBA	BAC	BCA	BCA	Selection 2
A: Specifically, three datasets were available: Cityscapes [7], NYUDv2 [15], and CamVid [16]. They either only annotate several nonadjacent frames in a video clip or have a small scale, a low frame rate, and low resolution.B: Previous research on VSS was limited by the available datasets [17]C: Since the re...	ABC	CBA	ABC	BCA	Selection 2
A: The deep Learning Recommendation Model (DLRM) and the Time-Based Sequence Model (TBSM) are popular commercial modelsB: These models are typically trained using either a hybrid CPU-GPU mode or a GPU-only mode [6, 7]C: In the hybrid mode (Figure 1a), the CPU provides memory capacity for the embedding entri...	CBA	ACB	CBA	ABC	Selection 4
A: We will introduce suitable notions for our purposes in Section 4. B: Indeed, we believe that the probability that a given ReLU neural network function is non PL Morse grows with depthC: Thus, studying the topology of the sublevel sets of ReLU neural network functions requires establishing more flexible r...	BCA	CAB	CBA	ABC	Selection 2
A: One of the most well-known challenges in this regard is the formation of topological defectsB: 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 d...	ACB	ACB	BCA	BAC	Selection 3
A: Table 2 shows the result of different hℎhitalic_h. B: In order to show the effect of different space intervals and time intervals, two groups of comparison tests have been doneC: First, we choose different space interval hℎhitalic_h to observe the performance of our model	CBA	CAB	BCA	CBA	Selection 2
A: Still to further verify these correlations we computed the Pearson correlation between the RU value bins and model performance metricsB: 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 sensi...	CAB	ABC	ACB	CBA	Selection 2
A: Among them, parents (63%, N=12) were mostly concerned about the unknown apps that their teens had but parents did not know the purpose of the app or why it was needed. On the contrary, only N=2 (11%) of teens reacted to their parents’ apps that they had not heard of beforeB: For instance, when looking throug...	ACB	ACB	BCA	CAB	Selection 3
A: Those picked up by the RDAD filtration are comparatively smaller regions with an abrupt drop in densityB: The homology class picked up by the distance-to-measure filtration is a large sparsely populated area with few cellular towers if anyC: The distance-to-measure filtration fails to pick up the smaller...	BCA	BAC	ABC	ABC	Selection 2
A: For each dataset, a subject-exclusive 3-fold cross-validation is conducted, following the experiment settings mentioned in (Li et alB: 2021b) for a fair comparison. We evaluate the proposed method using F1-score, defined as the harmonic mean between precision and recall and commonly used for multi-label clas...	ABC	ACB	ABC	CBA	Selection 2
A: This section presents the detailed design of BBP in EthereumB: BBP is also compatible with other transaction forwarding and consensus protocols. BBP changes the block forwarding protocol. We give the details of the new elements in BBP: pre-packed blockbody (PPB) generation, pre-packed blockbody pre-validati...	ABC	CBA	CBA	ACB	Selection 4
A: (2021) considers latent POMDPs, where each process has only one latent state, and the proposed algorithm efficiently infers the latent state using a short trajectory. Kozuno et alB: (2021) considers POMDPs having tree-structured states with their positions in certain partitions being the observations. Compar...	ACB	CBA	BCA	CAB	Selection 3
A: We further found that mobile-based deployment of AI-based automated speech therapy was more common among the included studies. A possible explanation is that researchers are more interested in building affordable and accessible automated speech therapy tools. Another significant issue we observed is that few stu...	CAB	BCA	ACB	BAC	Selection 1
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: In a recent line of work [VN17] has studied the subspace recovery problem in the presence of bounde...	ABC	BCA	BAC	ACB	Selection 2
A: Recent work [13] has also started to exploit the data privacy problems by deliberately obfuscating some sensitive information (e.g., human faces) from images as visual input. To this end, we consider the computer vision task setting with insufficient visual input.B: For example, certain objects within a sing...	ABC	ACB	ACB	CBA	Selection 4
A: (1) In a scenario where voters exhibit a single-peaked preference over potential candidates, after the election, the winning candidates implement their own tax policy on the votersB: This tax can be conceptualized as an entrance fee, with the act of selecting the winning candidates akin to choosing a facilit...	CAB	BAC	CBA	ABC	Selection 4
A: However, we ask whether there is some graph class for which there exists a polynomial time algorithm for solving the perfect edge domination problem while being hard for the efficient edge domination problem.B: More specifically, we remark that there are graph classes which admit polynomial time solutions fo...	CAB	CAB	CBA	CAB	Selection 3
A: These methods therefore require one to solve a linear program (LP) online or to generate a lookup table to identify the region in which the current state residesB: Once fixed feasible control inputs at the vertices of the invariant set have been computed, a variable structure controller either takes a conve...	CBA	CBA	BCA	BAC	Selection 4
A: In contrast, our approach does not rely on trainable encoder or decoder structuresB: All of these approaches require large amounts of data to train the complex encoder and decoder modulesC: Instead it combines neural implicit representations to model the scene appearance with the estimation of the parame...	BCA	CBA	BAC	CAB	Selection 3
A: In contrast to conventional QCN frameworks, where the receiver typically aims to execute quantum gates and measurements for the precise reconstruction of the embedded data structure within quantum states, our framework distinguishes itselfB: Therefore, the primary objective of the quantum receiving node is t...	CAB	ACB	CAB	CAB	Selection 2
A: It encourages action-space exploration by promoting random selections, thus reducing the probability to converge to deterministic policies with poor local optima. Parameter τ𝜏\tauitalic_τ is the trade-off weight used to balance the importance of reward maximization over random exploration.B: (11) is the pol...	BAC	CBA	BCA	ACB	Selection 2
A: There are important limitations to the above analysisB: In particular, our calculation omits additional regulatory checks against approving ineffective drugs and punishments for agents who intentionally run clinical trials for drugs they believe to be ineffectiveC: These considerations include: additiona...	CBA	ABC	ACB	CAB	Selection 2
A: The images are presented in a 3×4343\times 43 × 4 grid, with the first row representing the axial axis, the second row the coronal axis, and the third row the sagittal axis. In the first column of each row, the moving image obtained using PET modality is shown, while in the second column, the fixed image obtaine...	CAB	ABC	BCA	BAC	Selection 3

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