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Of course, the numerical scheme and the estimates developed in Section 3.1 hold. However, several simplifications are possible when the coefficients have low-contrast, leading to sharper estimates. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> We had to reconsider the proofs, in our view simplifying some of ... | **A**: We remark that in this case, our method is similar to that of [MR3591945], with some differences.
**B**: Also, our scheme is defined by a sequence of elliptic problems, avoiding the annoyance of saddle point systems.
**C**: First we consider that T~~𝑇\tilde{T}over~ start_ARG italic_T end_ARG can be nonzero.
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. As shown in Table 5, CreditScore is the best feature in overall. In Figure 4 we show the result of models learned with the full feature set with and without CreditScore. Overall, adding CreditScore improves the performance, especially for the first 8-10 hours. <|MaskedSetence|> <|MaskedSetence|> But its performance... | **A**: CrowdWisdom is also a good feature which can get 75.8% accuracy as a single feature.
**B**: This demonstrates the effectiveness of our curated approach over the sentiments, yet the crowd needs time to unify their views toward the event while absorbing different kinds of information.
.
**C**: The performance of... | CAB | CAB | CBA | CAB | Selection 2 |
The experiments’ results of the testing models are shown in Table 3. The best performance is achieved by the CNN+LSTM model with a good accuracy of 81.19%. <|MaskedSetence|> <|MaskedSetence|> So the classifiers with hand-crafted features are less adequate to accurately distinguish between rumors and news. For analysi... | **A**: The non-neural network model with the highest accuracy is RF.
**B**: However, it reaches only 64.87% accuracy and the other two non-neural models are even worse.
**C**: It can be seen that the best feature is the sentimental polarity scores, a high-level text-based feature.
.
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<|MaskedSetence|> <|MaskedSetence|> We adapted the L2R RankSVM [12]. The goal of RankSVM is learning a linear model that minimizes the number of discordant pairs in the training data. We modified the objective function of RankSVM following our global loss function, which takes into account the temporal feature specif... | **A**: The temporal and type-dependent ranking model is learned by minimizing the following objective function:
.
**B**: Multi-Criteria Learning.
**C**: Our task is to minimize the global relevance loss function, which evaluates the overall training error, instead of assuming the independent loss function, that does ... | BCA | BCA | BCA | BCA | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> Body weight, according to BMI, is normal for half of the patients, four are overweight and one is obese. The mean BMI value is 26.9. <|MaskedSetence|> In terms of time since being diagnosed with diabetes, patients vary from inexperienced (2 years) to very experienced (35 years), w... | **A**: Half of the patients are female and ages range from 17 to 66, with a mean age of 41.8 years.
**B**: Only one of the patients suffers from diabetes type 2 and all are in ICT therapy.
**C**:
Table 1 shows basic patient information.
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<|MaskedSetence|> A baseline architecture without the ASPP module was constructed by replacing the five parallel convolutional layers with a single 3×3333\times 33 × 3 convolutional operation that resulted in 1,280 activation maps. This representation was then forwarded to a 1×1111\times 11 × 1 convolutional layer wit... | **A**: To quantify the contribution of multi-scale contextual information to the overall performance, we conducted a model ablation analysis.
**B**: Table 6 summarizes the results according to validation instances of five eye tracking datasets for the model with and without an ASPP module.
**C**: An ablation analysis... | ABC | ABC | ABC | ABC | Selection 3 |
<|MaskedSetence|> However, we shall first investigate in Section 5.1 the approximation performance of several obvious greedy strategies to compute the locality number (with “greedy strategies”, we mean simple algorithmic strategies that build up a marking sequence from left to right by choosing the next symbol to be m... | **A**: It may seem naive to expect new approximation results for cutwidth in this way, but, as mentioned in the introduction and as shall be discussed in detail in Section 6, approximating the cutwidth via approximation of the locality number may be beneficial for cutwidth approximation (although not by using simple gr... | BCA | ACB | BCA | BCA | Selection 3 |
The iterative process of training the model, training the policy, and collecting data is crucial for non-trivial tasks where random data collection is insufficient. <|MaskedSetence|> In some games, good policies could be learned very early. <|MaskedSetence|> <|MaskedSetence|> In Figure 9 in the Appendix we present t... | **A**: in fewer step than 100k) with more directed exploration policies.
**B**: In a game-by-game analysis, we quantified the number of games where the best results were obtained in later iterations of training.
**C**: While this might have been due to the high variability of training, it does suggest the possibility... | ACB | BCA | BCA | BCA | Selection 4 |
During the step negotiation simulations, it was noticed that the rolling locomotion mode encountered constraints when attempting to cross steps with a height greater than thrice the track height (h being the track height as shown in Fig. 3). This limitation originates from the traction forces generated by the tracks. ... | **A**: As a result, successful locomotion mode transitions can only occur when both rolling and climbing locomotion modes are capable of handling a step negotiation task.
**B**: For evaluating the energy expenditure during step negotiation, energy assessments were carried out for step heights of h, 2h, and 3h using bo... | ABC | ABC | ABC | ABC | Selection 1 |
It should be fairly clear that such assumptions are very unrealistic or undesirable. <|MaskedSetence|> In addition, the known advice models often allow
information that one may arguably consider unrealistic, e.g., an encoding of some part of the offline optimal solution. Last, and perhaps more significantly, a malicio... | **A**: Advice bits, as all information, are prone to transmission errors.
**B**: For a very simple example, consider the well-known ski rental problem: this is a simple, yet fundamental resource allocation, in which we have to decide ahead of time whether to rent or buy equipment without knowing the time horizon in ad... | ABC | ABC | CBA | ABC | Selection 4 |
It is worth noting that the difference in terms of space complexity is also very significant. For classifiers supporting incremental classification, like SS3 or MNB, only a small vector needs to be stored for each user. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Note that storing either all the documents... | **A**: of every user and then simply update it as more content is created.
**B**: However, when working with classifiers not supporting incremental classification, for every user we need to store either all her/his writings to build the document-term matrix or the already computed document-term matrix to update it as ... | CAB | BCA | CAB | CAB | Selection 1 |
Game theory provides an efficient tool for the cooperation through resource allocation and sharing [20][21]. <|MaskedSetence|> A sub-modular game is adopted in the scheduling of beaconing periods for the purpose of less energy consumption [23]. Sedjelmaci et al. <|MaskedSetence|> However, most existing models focus ... | **A**: Inspired by this, our model is built upon the aggregative game theory which suits for large-scale scenarios..
**B**: applied the Bayesian game-theoretic methodology in UAV’s intrusion detection and attacker ejection [24].
**C**: A computation offloading game has been designed in order to balance the UAV’s trad... | ACB | CBA | CBA | CBA | Selection 2 |
<|MaskedSetence|> The large number of parameters in neural networks make them very good at modelling and approximating any arbitrary function. However the larger number of parameters also make them particularly prone to over-fitting, requiring regularization methods to combat this problem. <|MaskedSetence|> <|Masked... | **A**:
Deep neural networks are the state of the art learning models used in artificial intelligence.
**B**: Over course of time a wide range of Dropout techniques inspired by the original method have been proposed.
**C**: Dropout was first introduced in 2012 as a regularization technique to avoid over-fitting[12], ... | ACB | ACB | ABC | ACB | Selection 4 |
<|MaskedSetence|> Incorporating domain/prior knowledge (such as coding the location of different organs explicitly in a deep model) is more sensible in the medical datasets. <|MaskedSetence|> <|MaskedSetence|> Although overlap based loss function are used in case of a class imbalance (small foregrounds), in Figure 1... | **A**: As shown in Taghanaki et al.
**B**: In medical image segmentation works, researchers have converged toward using classical cross-entropy loss functions along with a second distance or overlap based functions.
**C**: (2019e), when only a distance-based or overlap-based loss function is used in a network, and th... | BAC | BAC | BAC | ACB | Selection 3 |
Welbl (2014) and Biau et al. (2019) follow a similar strategy. The authors propose a method that maps random forests into neural networks as a smart initialization and then fine-tunes the networks by backpropagation. Two training modes are introduced: independent and joint. <|MaskedSetence|> <|MaskedSetence|> <|Mas... | **A**: Independent training fits all networks one after the other and creates an ensemble of networks as a final classifier.
**B**: Additionally, the authors evaluate sparse and full connectivity..
**C**: Joint training concatenates all tree networks into one single network so that the output layer is connected to al... | ACB | ACB | ACB | BAC | Selection 1 |
Broadly speaking, our work is related to a vast body of work on value-based reinforcement learning in tabular (Jaksch et al., 2010; Osband et al., 2014; Osband and Van Roy, 2016; Azar et al., 2017; Dann et al., 2017; Strehl et al., 2006; Jin et al., 2018) and linear settings (Yang and Wang, 2019b, a; Jin et al., 2019;... | **A**: (2020), which generalizes the one proposed by Yang and Wang (2019a).
**B**: (2020), which focuses on value-based reinforcement learning, OPPO attains the same T𝑇\sqrt{T}square-root start_ARG italic_T end_ARG-regret even in the presence of adversarially chosen reward functions.
**C**: (2020); Zhou et al.
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We thank Prof. <|MaskedSetence|> Johnathan Bush for very useful feedback about a previous version of this article. We also thank Prof. Mikhail Katz and Prof. <|MaskedSetence|> We thank Dr. Qingsong Wang for bringing to our attention the paper [76] which was critical for the proof of Theorem 1. Finally, we thank Dr. ... | **A**: Henry Adams and Dr.
**B**: Michael Lesnick for explaining to us some aspects of their work.
**C**: Alexey Balitsky for pointing out an imprecision in the statement of Proposition 9.2.
.
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<|MaskedSetence|> In Figure 12, we can observe that the standard PCP is cluttered, especially for the case without any selection. <|MaskedSetence|> Furthermore, the numerous axis labels introduce even further cluttering and confusion for the users of the standard PCP. Instead, our Adaptive PCP utilizes PCA as a degre... | **A**: It enables the analyst to discover that abnormal classified patients have less fluctuating measurements than the others, which becomes even more salient in the selection case where the measurements for the normal class (in brown color) are rather stable when patients are in both rest and stress conditions.
.
**... | BCA | BCA | BCA | BCA | Selection 1 |
<|MaskedSetence|> To begin with, in Table 32 the most influential algorithm was identified to be PSO, appearing in 11% of the reviewed literature (which corresponds to almost 47% of the proposals that were clearly based on a previous algorithm). <|MaskedSetence|> The simplicity of this algorithm and its ability to re... | **A**: This bio-inspired solver is one of the most prominent and historically acknowledged algorithms in the Swarm Intelligence category and is the reference of many bio-inspired algorithms contributed since its inception.
**B**:
Very insightful conclusions can be drawn from this grouping.
**C**: [72, 73] – have ins... | BAC | BAC | BAC | BAC | Selection 2 |
<|MaskedSetence|> Instead, DEC and SpectralNet work better on the large scale datasets. <|MaskedSetence|> If the graph is not updated, the contained information is low-level. The adaptive learning will induce the model to exploit the high-level information. <|MaskedSetence|> | **A**: Although GAE-based models (GAE, MGAE, and GALA) achieve impressive results on graph type datasets, they fail on the general datasets, which is probably caused by the fact that the graph is constructed by an algorithm rather than prior information.
**B**: Classical clustering models work poorly on large scale da... | BAC | BAC | BAC | CBA | Selection 3 |
What SMap improves. <|MaskedSetence|> Our measurements do not rely on misconfigurations in services which can be patched, blocking the measurements. The higher stability also allows for more accurate reproduction and validation of our datasets and results, and enables to perform reliable longitudinal studies. <|Mask... | **A**: This is in contrast to previous studies, e.g., (Lone et al., 2017; Lichtblau et al., 2017; Lone et al., 2018), in which a repeated evaluation even a week later provided different statistics.
**B**: The infrastructure of SMap is more stable than those used in previous studies, e.g., we do not risk volunteers mov... | BCA | BCA | BCA | CBA | Selection 3 |
Experiments in this paper used the gas sensor drift array dataset [7]. The data consists of 10 sequential collection periods, called batches. <|MaskedSetence|> These features summarizing the time series sensor responses are the raw and normalized steady-state features and the exponential moving average of the increasi... | **A**: Chemical interferents were also presented to the sensors between batches, and the time between presentations varied, both of which contributed to further sensor variability.
**B**: The dataset thus exemplifies sensor variance due to contamination and variable odor concentration in a controlled setting.
.
**C**... | CAB | BAC | CAB | CAB | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> While these constructions and the involved proofs are generally deemed quite complicated, the situation for semigroups turns out to be much simpler. While it is known that the free semigroup of rank one is not an automaton semigroup [4, Proposition 4.3], the free semigroups of high... | **A**: This culminated in constructions to present free groups of arbitrary rank as automaton groups where the number of states coincides with the rank [18, 17].
**B**: Here, the main difference is that the free monoid in one generator can indeed be generated by an automaton: it is generated by the adding machine (see... | CAB | CAB | CAB | CBA | Selection 1 |
Table A4 shows VQA accuracy for each answer type on VQACPv2’s test set. HINT/SCR and our regularizer show large gains in ‘Yes/No’ questions. <|MaskedSetence|> <|MaskedSetence|> However, in the test set, answer ‘yes’ is more frequent. Regularization effects caused by HINT/SCR and our method cause the models to weaken ... | **A**: Finally, we do not observe large improvements in ‘Other’ question type, most likely due to the large number of answers present under this answer type.
.
**B**: We hypothesize that the methods help forget linguistic priors, which improves test accuracy of such questions.
**C**: In the train set of VQACPv2, the ... | BAC | BCA | BCA | BCA | Selection 2 |
Other corpora similar to OPP-115 Corpus have enabled research on privacy practices. The PrivacyQA corpus contains 1,750 questions and expert-annotated answers for the privacy question answering task (Ravichander et al., 2019). Similarly, Lebanoff and Liu (2018) constructed the first corpus of human-annotated vague word... | **A**: (2017) presented a dataset and developed a model to automatically identify and label opt-out choices offered in privacy policies.
**B**: (2020) collected privacy policies from around 130,000 websites from over two decades and analysed the evolution of the online privacy landscape.
**C**: Finally, Nokhbeh Zaeem... | ABC | BCA | ABC | ABC | Selection 3 |
Ensemble learning can be controlled in different ways. Starting from the data, visualization can be used to explore the data space (Figure 1, upper blue arrow) [47]. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> These models produce predictions that can be stored again as new metadata. If visualized, this pr... | **A**: This offers new possibilities for direct manipulation of both instances and features.
**B**: Data preprocessing and wrangling benefits from feedback provided by a VA system, for example, in the form of validation metrics that increase the per-model performance of several heterogeneous ML models used in ensemble... | ACB | ACB | ACB | CBA | Selection 3 |
Task similarity. <|MaskedSetence|> We shuffle the samples and randomly divide tasks to construct the setting that tasks are similar to each other. For a fair comparison, each task on this setting also has 120 and 1200 utterances on average in Persona and Weibo respectively. We train and evaluate Transformer-F and MAML... | **A**: In Persona and Weibo, each task is a set of dialogues for one user, so tasks are different from each other.
**B**: So if the tasks are similar to each other, we can simply use Transformer-F rather than MAML..
**C**: In Persona and Weibo, the performance of MAML is similar to that of Transformer-F, while MAML p... | ACB | ACB | ACB | CAB | Selection 2 |
A conceptual frame structure is designed which contains two types of time slots. One is the exchanging slot (e-slot) and the other is the tracking slot (t-slot). <|MaskedSetence|> It is assumed that UAVs exchange MSI every T𝑇Titalic_T t-slots, i.e., in an e-slot, to save resource for payload transmission. In the MSI ... | **A**: Then t-UAVs and r-UAV perform codeword selection.
**B**: Compared to the motion-aware protocol in [31], the new TE-aware protocol can be applied to the UAV mmWave network with higher mobility including random trajectories and high velocity.
**C**: Let us first focus on the e-slot.
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Szepesvári, 2018; Dalal et al., 2018; Srikant and Ying, 2019) settings. See Dann et al. <|MaskedSetence|> Also, when the value function approximator is linear, Melo et al. (2008); Zou et al. <|MaskedSetence|> (2019b) study the convergence of Q-learning. When the value function approximator is nonlinear, TD possibly d... | **A**: See also the independent work of Brandfonbrener and
Bruna (2019a, b); Agazzi and Lu (2019); Sirignano and Spiliopoulos (2019), where the state space is required to be finite.
**B**: (2014) for a detailed survey.
**C**: (2019); Chen et al.
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<|MaskedSetence|> (2021); Xu et al. (2021c), and the use of only deep encoders Bapna et al. (2018); Wang et al. <|MaskedSetence|> (2022a); Chai et al. <|MaskedSetence|> But in general, Table 6 shows that our approach uses fewer parameters and leads to faster decoding speed than the baselines to obtain a comparable B... | **A**: (2020) normally leads to faster inference speed than using both a deep encoder and a deep decoder.
**B**:
As for the costs, the decoder depth has a strong impact on inference speed, as the decoder has to be computed once for each decoding step during auto-regressive decoding Kasai et al.
**C**: (2019); Li et ... | BCA | BCA | BCA | BCA | Selection 2 |
We visually compare the corrected results from our approach with state-of-the-art methods using our synthetic test set and the real distorted images. To show the comprehensive rectification performance under different scenes, we split the test set into four types of scenes: indoor, outdoor, people, and challenging scen... | **A**: Our approach performs well on all scenes, while the traditional methods [23, 24] show inferior corrected results under the scene that lacks sufficient hand-crafted features, especially in the people and challenging scenes.
**B**: The indoor and outdoor scenes are shown in Fig.
**C**: On the other hand, the lea... | BAC | BCA | BAC | BAC | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> The black-box model is motivated by data-driven applications where specific knowledge of the distribution is unknown but we have the ability to sample or simulate from the distribution. <|MaskedSetence|> Most prior work in this setting has focused on Facility Location [23, 24, 21,... | **A**: Clustering is a fundamental task in unsupervised and self-supervised learning.
**B**: The stochastic setting models situations in which decisions must be made in the presence of uncertainty and are of particular interest in learning and data science.
**C**: To our knowledge, radius minimization has not been pr... | ABC | CAB | ABC | ABC | Selection 1 |
III. The co-existence of random graphs, subgradient measurement noises, additive and multiplicative communication noises are considered. Compared with the case with only a single random factor, the coupling terms of different random factors inevitably affect the mean square difference between optimizers’ states and an... | **A**: Finally, we get an estimate of the mean square increasing rate of the local optimizers’ states in terms of the step sizes of the algorithm (Lemma 3.2)..
**B**: What’s more, multiplicative noises relying on the relative states between adjacent local optimizers make states, graphs and noises coupled together.
**... | BCA | BCA | BCA | BCA | Selection 3 |
<|MaskedSetence|> The primary reason is that MuCo retains the most distributions of the original QI values and the results of queries are specific records rather than groups. <|MaskedSetence|> Besides, since the results of queries for MuCo are specific records rather than groups, the relative error rate of MuCo does ... | **A**: Consequently, the accuracy of query answering of MuCo is much better and more stable than that of Mondrian and Anatomy.
**B**:
We observe that the results of MuCo are much better than that of Mondrian and Anatomy.
**C**: Therefore, differing from Mondrian and Anatomy, increasing the level of protection of MuC... | BAC | BAC | CAB | BAC | Selection 4 |
<|MaskedSetence|> Even without ensemble, our PointRend baseline, which yields 77.38 mAP, has already achieved 1st place on the test leaderboard. Note that several attempts, like BFP Pang et al. <|MaskedSetence|> <|MaskedSetence|> So, there are 5 models used for final ensemble.
. | **A**: In addition to models listed in Table 3, another PointRend with slightly different setting (stacking two BFP modules, and increasing the RoIAlign size from original 7 to 10 for bounding box branch) is trained and achieves 76.95 mAP on testing set.
**B**: As shown in Table 3, all PointRend models achieve promisi... | BAC | BCA | BCA | BCA | Selection 3 |
We consider the setting of episodic RL with nonstationary reward and transition functions. <|MaskedSetence|> <|MaskedSetence|> To incorporate function approximation, we focus on a subclass of MDPs in which the reward and transition dynamics are linear in a known feature map (Melo & Ribeiro, 2007), termed linear MDP. ... | **A**: To measure the performance of an algorithm, we use the notion of dynamic regret, the performance difference between an algorithm and the set of policies optimal for individual episodes in hindsight.
**B**: For nonstationary linear MDPs, we show that one can design a near-optimal statistically-efficient algorith... | ACB | CAB | ACB | ACB | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> The details on the participant demographics of SG-75 are shown in Table 1. From SG-75, two more subsets were formed via the branching questions. <|MaskedSetence|> While these subsets have smaller samples, the self-reported data of the questions falling within the sections of these... | **A**: 75 of the 104 responses fulfilled the criterion of having respondents who were currently based in Singapore.
**B**: This set was extracted for further analysis and will be henceforth referred to as ‘SG-75’.
**C**: The first contains 59 responses in which respondents said that they have shared news before (refe... | ABC | ABC | BCA | ABC | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> However, this input embedding can still accumulate knowledge by participating in the aggregations of its neighbors. <|MaskedSetence|> Nevertheless, it still contains useful information for entity alignment. Additionally, decentRL benefits from concatenating the embeddings from mul... | **A**:
The performance of decentRL at the input layer notably lags behind that of other layers and AliNet.
**B**: The acquired information may not necessarily reside in the same dimension for a pair of aligned entities at this layer, which accounts for the comparatively lower performance of this layer.
**C**: As dis... | ACB | BAC | ACB | ACB | Selection 1 |
Variational inference posits a set of densities and then finds the member in the set that is close to the target [14, 34]. Combining RL and variational inference requires formalizing RL as a probabilistic inference problem [35, 36, 37]. Several RL methods propose to use pseudo-likelihood inference framework [38, 39] a... | **A**: VIREL [41] translates the problem of finding an optimal policy into an inference problem.
**B**: Specifically, VIREL applies EM to induce a family of actor-critic algorithms, where the E-step corresponds to policy improvement and the M-step corresponds to policy evaluation.
**C**: However, none of these method... | ABC | ABC | ACB | ABC | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> the disentangled factors) and correlated components Z𝑍Zitalic_Z, a.k.a as nuisance variables, which encode the details information not stored in the independent components. A series of works starting from [beta] aims to achieve that via regularizing the models by up-weighting cert... | **A**: Prior work in unsupervised DR learning suggests the objective of learning statistically independent factors of the latent space as means to obtain DR.
**B**: They aren’t really separating into nuisance and independent only..
**C**: The underlying assumption is that the latent variables H𝐻Hitalic_H can be part... | BCA | ACB | ACB | ACB | Selection 3 |
The structural computer used an inverted signal pair to implement the reversal of a signal (NOT operation) as a structural transformation, i.e. a twist, and four pins were used for AND and OR operations as a series and parallel connection were required. <|MaskedSetence|> In other words, operating a structural compute... | **A**: However, one can think about whether the four pin designs are the minimum number of pins required by structural computers.
**B**: When checking the output, place a voltage on one of the two wires in a pair and ground the other.
**C**: Let’s look at the role of the four pins that transmit signals in a 4 pin bas... | ACB | ACB | BAC | ACB | Selection 2 |
<|MaskedSetence|> However, sparse algorithms are generally not stable, and vice versa (Xu \BOthers., \APACyear2012).
An example of the trade-off between sparsity and interpretability of the set of selected views occurs when different views, or combinations of views, contain the same information. If the primary concern... | **A**: If one wants to go even further and perform formal statistical inference on the set of selected views, one may additionally be interested in theoretically controlling, say, the family-wise error rate (FWER) or false discovery rate (FDR) of the set of selected views.
**B**: However, strict control of such an err... | CAB | ACB | CAB | CAB | Selection 4 |
To interpret an anomaly detected by DepAD, we begin by identifying variables with substantial dependency deviations. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> The normal dependency pattern is represented by the expected value of a variable given the values of its relevant variables, while the observed v... | **A**: This is achieved by comparing the observed values of variables with their corresponding expected values.
**B**: Furthermore, we gain insights into how the anomaly differs from normal behaviors by contrasting the observed dependency pattern with the normal dependency pattern between a variable and its relevant v... | ACB | CAB | ACB | ACB | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> [2011]), which is in contrast to the use of an exploration bonus as seen in Faury et al. [2020], Filippi et al. <|MaskedSetence|> Optimistic parameter search provides a cleaner description of the learning strategy. In non-linear reward models, both approaches may not follow simila... | **A**: [2010].
**B**: in Abbasi-Yadkori et al.
**C**:
CB-MNL enforces optimism via an optimistic parameter search (e.g.
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<|MaskedSetence|> As they become popular in different computer vision fields [13, 38, 40], researchers also find their application in temporal action localization [3, 44, 46]. G-TAD [44] breaks the restriction of temporal locations of video snippets and uses a graph to aggregate features from snippets not located in a... | **A**: It models each snippet as a node and snippet-snippet correlations as edges, and applies edge convolutions [38] to aggregate features.
**B**: Graph neural networks (GNN) are a useful model for exploiting correlations in irregular structures [17].
**C**: BC-GNN [3] improves localization by modelling the boundari... | BAC | CBA | BAC | BAC | Selection 4 |
Another open issue is the avoidance of hyperparameter tuning per se, as noted by E3. The goal of the tool is not to explore or bring insights about the individual sets of hyperparameters of the models or algorithms, but instead we focus on the search for new powerful models and implicitly store their hyperparameters.
T... | **A**: We plan to overcome such limitations..
**B**: E1 expressed his interest in checking combinations of evolutionary optimization with the crossover and mutation process applied to the best-performing models (e.g., [YRK∗15]).
**C**: Also, E3 stated that we could allow the user to specify the hyperparameters range ... | CBA | CBA | ABC | CBA | Selection 1 |
<|MaskedSetence|> In [30, 32], semidefinite programming relaxations are proposed for the multi-shape matching problem. However, due to the employed lifting strategy, which drastically increases the number of variables, these methods are not scalable to large problems and only sparse correspondences are obtained. <|Ma... | **A**: There are various works that particularly target the matching of multiple shapes.
**B**: Due to the use of a sparse modelling approach, the method also has the disadvantage that only few points per shape are matched, see Fig. 1.
In [29], tensor maps are introduced for synchronising heterogeneous shape collectio... | ACB | ACB | BCA | ACB | Selection 2 |
<|MaskedSetence|> This characterization decomposes the input graph G𝐺Gitalic_G by clique separators as in [18], then at the recursive step one has to find a proper vertex coloring of an antipodality graph satisfying some particular conditions; see Section 3, in particular Theorem 6. In a few words, an antipodality gr... | **A**:
The recognition algorithm RecognizePG for path graph is mainly built on path graphs’ characterization in [1].
**B**: Unfortunately, we cannot build all the antipodality graphs by brute force because checking all possible antipodal pairs requires too much time (more time than the overall complexity of algorithm... | ABC | ABC | ABC | ABC | Selection 1 |
The stochastic blockmodel (SBM) (SBM, ) is one of the most used models for community detection in which all nodes in the same community are assumed to have equal expected degrees. <|MaskedSetence|> Since in empirical network data sets, the degree distributions are often highly inhomogeneous across nodes, a natural ex... | **A**: Some recent developments of SBM can be found in (abbe2017community, ) and references therein.
**B**: DCMM model allows that nodes for the same communities have different degrees and some nodes could belong to two or more communities, thus it is more realistic and flexible.
**C**: DCSBM is widely used for commu... | ACB | ABC | ACB | ACB | Selection 3 |
See, e.g., Udriste (1994); Ferreira and Oliveira (2002); Absil et al. (2009); Ring and Wirth (2012); Bonnabel (2013); Zhang and Sra (2016); Zhang et al. (2016); Liu et al. (2017); Agarwal et al. (2018); Zhang et al. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> (2019); Zhou et al. (2019); Weber and Sra (2019... | **A**: (2018); Boumal et al.
**B**: (2018); Bécigneul and Ganea (2018); Zhang and Sra (2018); Sato et al.
**C**: (2018); Tripuraneni et al.
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<|MaskedSetence|> In real world, most intersections are equipped with 4-way entering approaches, but some are 3-way or 5-way intersections. A standard 4-way intersection is shown in Fig. <|MaskedSetence|> Each approach consists of three types of lanes, representing "left-turn", "straight" and "right-turn" directions ... | **A**: For an intersection, the incoming lanes refer to the lanes where the vehicles are about to enter the intersection.
**B**: 2, which consists of four approaches, i.e., "east", "south", "west" and "north".
**C**: Notes that vehicles on the incoming lanes are affected directly by the traffic signal at the current ... | ABC | ABC | ABC | ABC | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> FirstFit is another simple heuristic that places an item into the first bin of sufficient space and opens a new bin if required. BestFit works similarly, except that it places the item into the bin of minimum available capacity, which can still fit the item. NextFit has a competiti... | **A**:
Online bin packing has a long history of study.
**B**: Improving upon this performance requires more sophisticated algorithms, and many have been proposed in the literature..
**C**: The simplest algorithm is NextFit, which places an item into its single open bin when possible; otherwise, it closes the bin (do... | CAB | ACB | ACB | ACB | Selection 3 |
To address the problem mentioned above, most of the methods extend the Chamfer loss function of basic AtlasNet with additional terms. Bednarik et al. (2020) added terms to prevent patch collapse, reduce patch overlap and calculate the exact surface properties analytically rather than approximating them. <|MaskedSetenc... | **A**: Another term enforces better spatial configuration of the mappings by minimizing a stitching error.
.
**B**: Deng et al.
**C**: (2020b) introduced two additional terms to increase global consistency of the local mappings explicitly.
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Paper organization. <|MaskedSetence|> <|MaskedSetence|> In Section 3, we provide the main algorithm of the paper to solve such kind of problems. <|MaskedSetence|> Finally in Section 5, we show how the proposed algorithm can be applied to the problem computing Wasserstein barycenters .
. | **A**: This paper is organized as follows.
**B**: In Section 4, we present the lower complexity bounds for saddle point problems without individual variables.
**C**: Section 2 presents a saddle point problem of interest along with its decentralized reformulation.
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The length of a cycle is its number of edges. The minimum cycle basis (MCB) problem is the problem of finding a cycle basis such that the sum of the lengths (or edge weights) of its cycles is minimum. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> The authors show that the MCB problem is different in nature ... | **A**: In more concrete terms this problem is equivalent to finding the cycle basis with the sparsest cycle matrix.
**B**: In [5] a unified perspective of the problem is presented.
**C**: This problem was formulated by Stepanec [7] and Zykov [8] for general graphs and by Hubicka and Syslo [9] in the strictly fundamen... | CAB | CAB | BAC | CAB | Selection 1 |
<|MaskedSetence|> The category of techniques more related to our work is feature ranking, since we use automatic feature selection techniques to rank the importance of the different features. For example, a VA tool called INFUSE [50] was designed to aid users in understanding how features are being ranked by the autom... | **A**: FeatureEnVi offers rather similar characteristics with the tools analyzed above.
**B**: RegressionExplorer [61] is one example for examining logistic regression models.
**C**:
Various visualization techniques have been proposed for the task of feature selection, including correlation matrices [42, 43], radial... | CBA | CBA | CBA | ACB | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> In MPC, closed-loop performance is pushed to the limits only if the plant under control is accurately modeled, alternatively, the performance degrades due to imposed robustness constraints. Instead of adapting the controller for the worst case scenarios, the prediction model can be... | **A**: MPC accounts for the real behavior of the machine and the axis drive dynamics can be excited to compensate for the contour error to a big extent, even without including friction effects in the model [4, 5].
**B**: High-precision trajectories or set points can be generated prior to the actual machining process f... | CBA | ABC | ABC | ABC | Selection 3 |
<|MaskedSetence|> To study this, we train the explicit methods with multiple explicit variables for Biased MNISTv1 and individual variables that lead to hundreds and thousands of groups for GQA and compare them with the implicit methods. <|MaskedSetence|> <|MaskedSetence|> In the second experiment, the two most expl... | **A**: For Biased MNISTv1, we first sort the seven total variables in the descending order of MMD (obtained by StdM) and then conduct a series of experiments.
**B**:
It is unknown how well the methods scale up to multiple sources of biases and large number of groups, even when they are explicitly annotated.
**C**: I... | CAB | BAC | BAC | BAC | Selection 4 |
Tab. I summarizes the existing CNN-based gaze estimation methods. <|MaskedSetence|> Thus, we categorize these methods into the platform of ”computer”. <|MaskedSetence|> Many recent research interests shift to different calibration approaches through domain adaptation or user-unaware data collection. <|MaskedSetence... | **A**: In general, there is an increasing trend in developing supervised or semi-/self-/un-supervised CNN structures to estimate gaze.
**B**: The first CNN-based gaze direction estimation method is proposed by Zhang et al. in 2015 [17], the first CNN-based PoG estimation method is proposed by Krafka et al. in 2016 [42... | BCA | CAB | CAB | CAB | Selection 2 |
Other methods detect the keypoints from the face image, instead of local patches. For instance, Weng et al. weng2016robust proposed to recognize persons of interest from their partial faces. To accomplish this task, they firstly detected keypoints and extract their textural and geometrical features. Next, point set m... | **A**: Keypoint based matching method is introduced in Duan et al.
**B**: duan2018topology .
**C**: SIFT keypoint descriptor is applied to select the appropriate keypoints.
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<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> We present, to our knowledge, the first sized type system for a concurrent programming language as well as the first system to combine both features from above. As we mentioned in the introduction, we use unbounded quantification [Vez15] in lieu of transfinite si... | **A**: In parallel, linear size arithmetic for sized inductive types [CK01, Xi01, BR06] was generalized to support coinductive types as well [Sac14].
**B**: Sized (co)inductive types [BFG+04, Bla04, Abe08, AP16] gave way to sized mixed inductive-coinductive types [Abe12, AP16].
**C**: Sized types are a type-oriented ... | CBA | CBA | CAB | CBA | Selection 1 |
In the user-side embedding AFP, since the encrypted media content shared with different users is the same, the encryption of the media content is only executed once. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Therefore, the focus of implementing resource-saving cloud media sharing is to find ways to trans... | **A**: In contrast, due to the personalization of D-LUTs, once a new user initiates a request, the owner must interact with this user to securely distribute the D-LUT under the support of homomorphic en-cryption.
**B**: It is clear that the biggest source of overhead for the owner is the management and distribution of... | ACB | ACB | ACB | ACB | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> DeepFM Guo et al. (2017) similarly combines a shallow component with a deep one to learn both types of interactions. While these DNN-based models can effectively learn high-order feature interactions, they do so in an implicit, bit-wise manner. <|MaskedSetence|> | **A**: Neural Factorization Machines (NFM) He and Chua (2017) design a bi-interaction layer to learn the pairwise feature interaction and apply DNN to learn the higher-order ones.
Wide&Deep Cheng et al.
**B**: Consequently, they may lack the ability to provide persuasive rationales for their outputs..
**C**: (2016) i... | CBA | ACB | ACB | ACB | Selection 2 |
<|MaskedSetence|> The original definition of self-concordance has been expanded and generalized since its inception, as many objective functions of interest have self-concordant-like properties without satisfying the strict definition of self-concordance. <|MaskedSetence|> This was also the case in Ostrovskii & Bach ... | **A**: [2015], in which more general properties of these
pseudo-self-concordant functions were established.
**B**: For example, the logistic loss function used in logistic regression is not strictly self-concordant, but it fits into a class of pseudo-self-concordant functions, which allows one to obtain similar proper... | CBA | CBA | ACB | CBA | Selection 1 |
Our algorithm executes several methods (invoked within the loop starting at Algorithm 2 of Algorithm 2), and for most of them it makes a fresh pass over the edges. The term Pass-Bundle refers to multiple passes during which those routines are executed. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> In total,... | **A**: Precisely, the routines are: (1) extend structures along active paths (Extend-Active-Paths), (2) check for edge augmentations (Check-for-Edge-Augmentation), and (3) include (additional) unmatched edges to each structure (Include-Unmatched-Edges).
**B**: The Backtrack-Stuck-Structures method backtracks active pa... | ACB | CBA | ACB | ACB | Selection 3 |
Setting. To train ResNet18 in CIFAR-10, one can use stochastic gradient descent with momentum 0.90.90.90.9, the learning rate of 0.10.10.10.1 and a batch size of 128128128128 (40404040 batches = 1111 epoch). <|MaskedSetence|> Based on these settings, we build our settings using the intuition of algorithms (for detail... | **A**: This is one of the default learning settings.
**B**: That is why we need carefully choose T𝑇Titalic_T (the number of inner/local iterations in Algorithm 1) and p𝑝pitalic_p (probability in Algorithm 3).
**C**: For more details how to choose T𝑇Titalic_T and p𝑝pitalic_p and how to tune level of reliance on th... | ABC | CBA | ABC | ABC | Selection 1 |
There are two levels of coordination; first is selecting an equilibrium before play commences, and second is selecting actions during play time. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> At action selection time only (C)CEs require further coordination. NEs are factorizable and therefore can sample inde... | **A**: Both NEs and (C)CEs require agreement on what equilibrium is being played (Goldberg et al., 2013; Avis et al., 2010; Harsanyi & Selten, 1988): for (C)CEs this is a joint action probability distribution, and for NEs this is also a joint action probability distribution that can conveniently be factored into stocha... | BAC | ACB | ACB | ACB | Selection 3 |
<|MaskedSetence|> (2012); Bassily et al. (2013); Bhaskar et al. (2011)) proposes relaxed privacy definitions that leverage the natural noise introduced by dataset sampling to achieve more average-case notions of privacy. <|MaskedSetence|> This perspective was used Shenfeld and Ligett (2019) to propose a stability not... | **A**: Triastcyn and Faltings (2020) propose the notion of Bayesian differential privacy which leverages the underlying distribution to improve generalization guarantees, but their results still scale with the range in the general case.
.
**B**: This builds on intuition that average-case privacy can be viewed from a B... | CBA | CBA | BCA | CBA | Selection 4 |
We therefore propose the following novel research direction: to investigate how preprocessing algorithms can decrease the parameter value (and hence search space) of FPT algorithms, in a theoretically sound way. It is nontrivial to phrase meaningful formal questions in this direction. <|MaskedSetence|> Under minor tec... | **A**: To formalize a meaningful line of inquiry, we take our inspiration from the Vertex Cover problem, the fruit fly of parameterized algorithms.
.
**B**: To illustrate this difficulty, note that strengthening the definition of kernelization to “a preprocessing algorithm that is guaranteed to always output an equiva... | BCA | BCA | ACB | BCA | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> There is no unified benchmark dataset and the results in different papers cannot be directly compared. 4) Finally, with the recently released OPA dataset [94], we could use the annotated composite images for evaluation. Nevertheless, the sparse annotations only c... | **A**: 3) Another common evaluation strategy is user study, where people are asked to score the rationality of object placement [73, 145].
**B**: However, due to the subjectivity of user study, the gauge in different papers may be dramatically different.
**C**: User study complies with human perception and each compo... | ACB | ACB | ACB | BCA | Selection 1 |
The average regional daily patterns of taxi mobility data from each POI-based cluster in Beijing, Chengdu, and Xi’an are plotted in Fig. <|MaskedSetence|> As shown in Fig. <|MaskedSetence|> Conversely, Fig. <|MaskedSetence|> Nevertheless, Fig. 2LABEL:sub@fig:cluster-cdxa still enables us to identify distinct cluster... | **A**: 2.
**B**: 2LABEL:sub@fig:cluster-bj, taxi mobility patterns in Beijing exhibit a high level of cohesion within each POI-based cluster, while remaining distinguishable across clusters.
**C**: 2LABEL:sub@fig:cluster-cdxa, illustrates that clusters with higher inflow/outflow/pick-up values in Xi’an and Chengdu, t... | ABC | BAC | ABC | ABC | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> The main examples were the normality assumption for mean-variance estimators or the proper scoring rule (20). Models using this scoring rule appeared to behave very badly when used for strongly skewed data sets. <|MaskedSetence|> Another type of data that was not specifically cons... | **A**: Since such data sets are gaining importance in the digital age, it would be interesting to both study methods tailored to these properties and how existing models behave on outliers.
**B**: The choice of data sets in this comparative study was very broad and no specific properties were taken into account a prio... | BCA | ACB | BCA | BCA | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> In the realm of MIDI, velocity is a parameter that scales the intensity or volume at which a sound sample is played back, with the value ranging from 0 to 127. Default MIDI velocity values are associated with dynamic indications. <|MaskedSetence|> Our definition aligns with the Lo... | **A**: Apple’s Logic Pro 9 user manual correlates traditional volume indicators (pp, p, mp, mf, f, ff and fff) with specific MIDI velocity values (16, 32, 48, 64, 80, 96, 112 and 127), respectively.121212https://help.apple.com/logicpro/mac/9.1.6/en/logicpro/usermanual/ (page 468 in the user manual; accessed 2023-06-22)... | CBA | CBA | ACB | CBA | Selection 1 |
Recently, there are also investigations on semantic communications for other transmission contents, such as image and speech. A DL-enabled semantic communication system for image transmission, named JSCC, has been developed in[14]. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> A deep joint source-channel co... | **A**: Based on JSCC, an image transmission system, integrating channel output feedback, can improve image reconstruction[15].
**B**: Similar to text transmission, IoT applications for image transmission have been carried out.
**C**: Particularly, a joint image transmission-recognition system has been developed in[16... | BAC | ABC | ABC | ABC | Selection 3 |
Existing 3D WSSS methods utilize different kinds of weak supervisions.[10] utilize dense 2D segmentation labels to supervise the training in 3D by projecting the 3D predictions onto the corresponding 2D labels. [11] proposes to generate pseudo point-level label using 3D class activation map[12] from subcloud-level anno... | **A**: Their primary emphasis lies in evaluating the similarity between the original sample and its augmented counterparts.
**B**: Contrastive Scene Contexts[39] explores contrastive self-supervised learning to explore cues within the training data.
**C**: HybridCR[40] employs a contrastive loss function computed not... | BCA | ABC | BCA | BCA | Selection 3 |
Setup. The KITTI dataset [11] provides widely used benchmarks for various visual tasks in the autonomous driving, including 2D Object detection, Average Orientation Similarity (AOS), Bird’s Eye View (BEV), and 3D Object Detection. The official data set contains 7481 training and 7518 test images with 2D and 3D bounding... | **A**: We report the average accuracy (APAP\rm{AP}roman_AP) for each task under three different settings: easy, moderate, and hard, as defined in [11].
**B**: We report our results on the official settings of IoU ≥0.7absent0.7\geq 0.7≥ 0.7 for cars.
.
**C**: Each class uses different IoU standards for further evaluat... | CBA | ACB | ACB | ACB | Selection 4 |
<|MaskedSetence|> Its ground truth is annotated with word-level quadrangles. <|MaskedSetence|>
MSRA-TD500 [45] is dedicated to detecting multi-oriented long non-Latin texts. <|MaskedSetence|> Here, we follow the previous methods [35, 8] and add 400 training images from TR400 [46] to extend this dataset.. | **A**: ICDAR2015 [44] includes multi-orientated and small-scale text instances.
**B**: It contains 1,000 training and 500 testing images.
**C**: It contains 300 training images and 200 testing images with word-level annotation.
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The first proposed mapping mechanism of IP addresses is TLMB. The four parts of the IP address are represented in four layers, where each layer is made up of one or more memory blocks. The first layer only contains one memory block, whereas the second layer contains 256 memory blocks. Each memory block contains 256 ele... | **A**: A memory block will be allocated only when the first three parts of an initial IP address have been given.
**B**: This would be 32 GB in size if we adopted a pre-allocation strategy for all memory blocks in the four layers.
**C**: Consequently, the first two layers can be removed from this architecture if the ... | BAC | BAC | CBA | BAC | Selection 4 |
The outline of the remainder of this paper is as follows. <|MaskedSetence|> Furthermore, we extend these results to the n𝑛nitalic_n-tuple saddle point problem in Section 3. <|MaskedSetence|> <|MaskedSetence|> In Section 6, numerical experiments for a 3-field formulation of the Biot model are provided to justify the... | **A**: Some additive Schur complement based preconditioners are constructed and the corresponding known results in the literature are recalled in Section 4 for twofold saddle point problems.
**B**: Generalizations to n𝑛nitalic_n-tuple cases are provided in Section 5.
**C**: In section 2, we briefly recall the classi... | CAB | CAB | CAB | BAC | Selection 2 |
However, in cases when the labels are sensitive and sharing the labels for a sample ID across silos is not feasible, the label information for a sample ID may only be present in a client in one silo. <|MaskedSetence|> The client with the label information calculates the loss and the partial derivatives, which can then... | **A**: In this case, we could modify our algorithm in the following way, similar to (Liu et al., 2020a): the clients in all silos send the intermediate information for a sample to the client that has the label for the sample.
**B**: We note that the modified algorithm is mathematically equivalent to TDCD, albeit with ... | ABC | CBA | ABC | ABC | Selection 4 |
<|MaskedSetence|> 12201092), the Natural Science Foundation Project of CQ CSTC (Grant No. CSTB2022NSCQ-MSX0896), the Science and Technology Research Program of Chongqing Municipal Education Commission
(Grant No. <|MaskedSetence|> cstc2022ycjh-bgzxm0040), and the Research Foundation of Chongqing Normal University (Gra... | **A**: Changxin Mo acknowledges support from the National Natural Science Foundation of China (Grant No.
**B**: KJQN202200512), the Chongqing Talents Project (Grant No.
**C**: 21XLB040), P.
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User Study. <|MaskedSetence|> 10 volunteers with image processing expertise are involved in this evaluation. They are invited to choose the most realistic image from those inpainted by the proposed method and the representative state-of-the-art approaches. <|MaskedSetence|> <|MaskedSetence|> Our method performs mor... | **A**: Specifically, each participant has 15 questions, which are randomly sampled from the Places2 dataset.
**B**: We further perform subjective user study.
**C**: We tally the votes and show the statistics in Table 1.
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Figure 1: The performance of Subgoal Search. (top, left) comparison on INT (with the proof length 15) to AlphaZero. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> . | **A**: (bottom, right) BestFS fails to solve Rubik’s Cube, while BF-kSubS can achieve near-perfect performance.
**B**: (top, right) BF-kSubS consistently achieves high performance even for small computational budgets.
**C**: (bottom, left) similarly on Sokoban (board size 12x12 with 4 boxes) the advantage of BF-kSubS... | BCA | BCA | ABC | BCA | Selection 2 |
In this paper, we propose to use ‘Five-strokes’, a famous structure-based encoding method for Chinese characters, to get our glyph embedding. ‘Five-Strokes’ was put forward by Yongmin Wang in 1983. This special encoding method for Chinese characters is based on their structures. <|MaskedSetence|> Based on that, it gra... | **A**: After simplification for typing, ’Five-Strokes’ maps these character roots into 25 English characters (‘z’ is left out) and each Chinese character is made of at most four corresponding English characters, which makes it easy to acquire and type in computers.
**B**: ‘Five-Strokes’ holds the opinion that Chinese ... | CBA | BAC | BAC | BAC | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> The language modeling task is to predict the pronoun of a sentence. For NLI and coreference resolution, three variations of each sentence are used to construct entailment pairs. For machine translation, sentences with two variations of third-person pronouns in En... | **A**: A total of 4,560 samples are collected by a template-based method.
**B**: ABC (Gonzalez et al., 2020), the Anti-reflexive Bias Challenge, is a multi-task benchmark dataset designed for evaluating gender assumptions in NLP models.
**C**: ABC consists of 4 tasks, including language modeling, natural language inf... | BCA | BAC | BCA | BCA | Selection 4 |
The templates are intended to approximate the final look and page length of the articles/papers. <|MaskedSetence|> They will help to give the authors an approximation of the number of pages that will be in the final version. <|MaskedSetence|> The XML files are used to produce the final print/IEEEXplore® pdf and then ... | **A**: The structure of the LaTeXfiles, as designed, enable easy conversion to XML for the composition systems used by the IEEE’s outsource vendors.
**B**: Therefore, they are NOT intended to be the final produced work that is displayed in print or on IEEEXplore®.
**C**: Have you looked at your article/paper in the H... | BAC | CAB | BAC | BAC | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> This means that play may instead exhibit a Hopf bifurcation and converge to a limit cycle or stable orbit, rather than to the fixed point QRE distribution (Alós-Ferrer and Netzer, 2010). Estimating the individual strategies, however, rather than imposing stability and estimating th... | **A**: While the QRE (McKelvey and Palfrey, 1995, 1998) is a fixed point stationary distribution of the logit-response (logit best-reply) dynamics, in the case of simultaneous revision opportunities this fixed point is potentially unstable.
**B**:
Cross-sectional network formation estimators rely on assumptions about... | BAC | BAC | BAC | BAC | Selection 3 |
In recent years, the field of SISR has developed rapidly, and a large number of excellent models have emerged. <|MaskedSetence|> <|MaskedSetence|> This will affect the performance of the model in practical applications. <|MaskedSetence|> According to different design targets, we divide these methods into three categ... | **A**: However, it is undeniable that the emergence of these methods has enriched and promoted the development of SISR.
**B**: In other words, the low-resolution images used in this type of method are usually obtained by applying some fixed degradation modes to the high-resolution images.
**C**: However, it is worth ... | CAB | CBA | CBA | CBA | Selection 4 |
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]. <|MaskedSetence|> Finally, Local Implicit Image Functions introduce... | **A**: To the best of our knowledge, no attempt of introducing these techniques to coordinate-based networks has been made until now..
**B**: These approaches are fundamentally different as they attempt to create a wide generative model based on a large-scale dataset, while our approach focuses on data-agnostic inter... | BCA | BCA | BCA | BCA | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> Chow,, 1957; Sayedi et al.,, 2010; Wiener and El-Yaniv,, 2011) the learner may decline to label items, thus mitigating the risk of labelling when they have high uncertainty. Conversely, in classification with selective sampling (Cesa-Bianchi et al.,, 2009; Orabona and Cesa-Bianchi,... | **A**: The apple tasting problem is not the only variant of online classification where labels are not revealed in every round.
**B**: Both of these variants differ from apple tasting in that they have a more complex action set.
.
**C**: In selective classification (or classification with a reject (or abstention) opt... | ACB | BAC | ACB | ACB | Selection 4 |
<|MaskedSetence|> The dataset consists of a collection of Wikipedia pages, grouped into topics. The annotation procedure carried out by IBM assumes that claims and evidence are annotated with respect to a given topic.
In our study, we selected the four topics with the largest amount of claims and evidence. Subsequentl... | **A**: Due to how argumentative texts were annotated, there are cases in which a single sentence may contain both a claim and evidence or, more seldom, evidence spans through multiple sentences and incorporates a claim.
**B**: Indeed, these cases hinder the quality of selected data, but due to the low amount of such s... | CAB | BCA | CAB | CAB | Selection 1 |
However, the progress of sentiment dependency-based methods, such as the work by Zhang et al. <|MaskedSetence|> <|MaskedSetence|> (2021); Li et al. (2021a); Dai et al. <|MaskedSetence|> | **A**: (2019); Zhou et al.
**B**: (2021), has contributed to the improvement of coherent sentiment learning.
These studies explored the effectiveness of syntax information in ABSC, which mitigates issues related to sentiment coherency extraction..
**C**: (2020); Tian et al.
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<|MaskedSetence|> <|MaskedSetence|> The inputs are classical data such as image pixels, and the outputs are classification results. The QNN consists of multiple blocks. Each has three components: encoder encodes the classical values to quantum states with rotation gates such as RY; trainable quantum layers contain pa... | **A**: We use QNN as the benchmark PQC in this work.
**B**: QuantumNAT overview is in Figure 3.
.
**C**: Figure 2 shows the QNN architecture.
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In this work, we compare the proposed EDA with eight popular tracking methods, including SiamBAN chen2022siamban , SiamRPN++ Li_2019_CVPR , ATOM Danelljan_2019_CVPR , EVT messikommer2023data , E-MS barranco2018real , ETD chen2019asynchronous , RMRNet chen2020end , and an event-based variant of the classical tracker ECO... | **A**: Moreover, E-MS and EVT are extended to support the bounding box-based object tracking.
.
**B**: EVT, E-MS, ETD, and RMRNet are the popular event-based tracking methods.
**C**: ECO-E is an event-based variant of ECO, which uses TSLTD chen2020end event frames as its inputs.
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We evaluate the KD tasks based on self-supervised learning on STL-10 dataset. <|MaskedSetence|> We choose multiple smaller networks with fewer parameters as the student network: ResNet-18 [70], MobileNet.v2 [86], ShuffleNet.v1 [87]. <|MaskedSetence|> Follow the linear evaluation protocols in Sec. V-B, we compare the ... | **A**: Similar to the pre-training for the teacher network, we add one additional MLP layer on the basis of the student network.
**B**: We adopt the BCE loss for GenURL in the KD task.
.
**C**: In this experiment, we adopt MoCo.v2 with ResNet-50 under 1600-epoch pre-training.
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Our NAS method consistently outperforms existing techniques for tiny networks in terms of computation-accuracy trade-off. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> We also try supporting flexible w𝑤witalic_w’s per block, which improves the accuracy for smaller computation budgets. Therefore, we enable f... | **A**: With the extended search space, all our models are derived from the same super network while obtaining the best accuracy.
**B**: Existing techniques usually need a scaling method to scale down the searched network and fit different budgets.
**C**: The accuracy improvement is more significant under a tiny compu... | BAC | BAC | BAC | CAB | Selection 2 |
In CGCL, multiple graph encoders compute their own contrastive losses based on representations learned by others, and optimize their losses collaboratively. <|MaskedSetence|> <|MaskedSetence|> In Figure 5, we notice that each graph encoder converges synchronously on the two datasets, which justifies our proposed col... | **A**: For a further analysis, we list the RDMs correlation between pairs of GIN, GCN and GAT in Table 2 for reference.
**B**: The assembly we use includes GIN, GCN and GAT.
**C**: To check the reliability of collaborative mechanism, we empirically analyze the convergence in the optimization process of each individua... | CBA | CBA | CBA | BCA | Selection 2 |
<|MaskedSetence|> Compositionality is often investigated in the context of signaling games (Fudenberg and Tirole, (1991), Lewis, (1969), Skyrms, (2010), Lazaridou et al., (2018)). Recent research has shown that strong inductive biases or grounding of communication protocols are necessary for the protocol to be composi... | **A**: For instance,.
**B**: The topic of communication is actively studied in multi-agent RL, see Hernandez-Leal et al., (2020, Table 2) for a recent survey.
**C**: Kottur et al., (2017), Słowik et al., 2020b ).
| BCA | BCA | CAB | BCA | Selection 2 |
<|MaskedSetence|> Indeed, the lack of systematic methods to construct valid CBFs is a main bottleneck. For certain types of mechanical systems under input constraints, analytic CBFs can be constructed [30]. The construction of polynomial barrier functions towards certifying safety for polynomial systems by using sum-o... | **A**: The work in [35] considers the construction of higher order CBFs and their composition by, similarly to [32, 33], alternating-descent heuristics to solve the arising bilinear SOS program.
**B**: Learning CBFs: An open problem is how valid CBFs can be constructed.
**C**: Such SOS-based approaches, however, are... | BAC | BAC | BCA | BAC | Selection 1 |
However, most works built under SBM and DCSBM require the elements of adjacency matrix of the network to follow Bernoulli distribution, which limits the network to being un-weighted. Modeling and designing methods to quantitatively detecting latent structural information for weighted networks are interesting topics. Re... | **A**: However, though these models for weighted networks are attractive, they always require all elements of connectivity matrix to be nonnegative or all elements of adjacency matrix must follow some specific distributions as found in [16].
**B**: DFM can be seen as a direct extension of SBM, and nodes within the sam... | BCA | ACB | ACB | ACB | Selection 4 |
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