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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. We remark that in this case, our method is similar to that of [MR3591945], with some differences. <|MaskedSetence|> <|Ma... | **A**: Also, our scheme is defined by a sequence of elliptic problems, avoiding the annoyance of saddle point systems.
**B**: We had to reconsider the proofs, in our view simplifying some of them.
.
**C**: First we consider that T~~𝑇\tilde{T}over~ start_ARG italic_T end_ARG can be nonzero.
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Early in an event, the related tweet volume is scanty and there are no clear propagation pattern yet. <|MaskedSetence|> Related work often uses aggregated content [18, 20, 32], since individual tweets are often too short and contain slender context to draw a conclusion. <|MaskedSetence|> <|MaskedSetence|> In this wo... | **A**: Thus, a mechanism for carefully considering the ‘vote’ for individual tweets is required.
**B**: However, content aggregation is problematic for hierarchical events and especially at early stage, in which tweets are likely to convey doubtful and contradictory perspectives.
**C**: For the credibility model we, ... | CBA | CBA | CBA | CBA | Selection 2 |
The performance of user features is similar with the Twitter features, they are both quite stable from the first hour to the last hour. As shown in Table 9, the best feature over 48 hours of the user feature group is UserTweetsPerDays and it is the best feature overall in the first 4 hours, but its rank decreases with ... | **A**: Others user-based features like UserReputationScore and UserJoinDate also have a better performance in the first fews hours.
**B**: That means the sources (the posters in the first few hours) of news and rumors are quite different with each other.
**C**: After 6 hours, it seems that we can better distinguish t... | ABC | ABC | ABC | ABC | Selection 4 |
<|MaskedSetence|> 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 not consider the correlation and overlap between models. We adapted the L2R RankSVM [12]. The goal of RankSVM is learning a linear model... | **A**: We modified the objective function of RankSVM following our global loss function, which takes into account the temporal feature specificities of event entities.
**B**: The temporal and type-dependent ranking model is learned by minimizing the following objective function:
.
**C**: Multi-Criteria Learning.
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Table 1 shows basic patient information. Half of the patients are female and ages range from 17 to 66, with a mean age of 41.8 years. <|MaskedSetence|> <|MaskedSetence|> Only one of the patients suffers from diabetes type 2 and all are in ICT therapy. <|MaskedSetence|> | **A**: In terms of time since being diagnosed with diabetes, patients vary from inexperienced (2 years) to very experienced (35 years), with a mean value of 13.9 years..
**B**: The mean BMI value is 26.9.
**C**: Body weight, according to BMI, is normal for half of the patients, four are overweight and one is obese.
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Furthermore, it is expected that complex representations at multiple spatial scales are necessary for accurate predictions of human fixation patterns. <|MaskedSetence|> The contribution of the contextual module to the overall performance was assessed and final results were compared to previous work on two public salie... | **A**: We therefore incorporated a contextual module that samples multi-scale information and augments it with global scene features.
**B**: (2019), and we developed a webcam-based interface for saliency prediction in the browser with only moderate hardware requirements (see https://storage.googleapis.com/msi-net/demo... | ACB | ACB | ABC | ACB | Selection 1 |
Our strongest positive result about the approximation of the locality number will be derived from the reduction mentioned above (see Section 5.2). However, we shall first investigate in Section 5.1 the approximation performance of several obvious greedy strategies to compute the locality number (with “greedy strategie... | **A**: This may provide a new angle to approximating the cutwidth of a graph, i.e., some greedy strategies may only become apparent in the locality number point of view and are hard to see in the graph formulation of the problem.
**B**: Secondly, due to the results of Section 4, the investigated greedy strategies for ... | CBA | CBA | CBA | CBA | Selection 2 |
We thank Marc Bellemare and Pablo Castro for their help with Rainbow and Dopamine. The work of Konrad Czechowski, Piotr Kozakowski and Piotr Miłoś was supported by the Polish National Science Center grants UMO-2017/26/E/ST6/00622. The work of Henryk Michalewski was supported by the Polish National Science Center grant ... | **A**: We gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Centers: ACK Cyfronet AGH, PCSS) for providing computer facilities and support within computational grants no.
**B**: Some of the experiments were managed using https://neptune.ai.
**C**: PLG/2019/012497 and PLG/2019/012784.... | ACB | ACB | BAC | ACB | Selection 2 |
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). <|MaskedSetence|> As a result, successful locomotion mode transitions can on... | **A**: This limitation originates from the traction forces generated by the tracks.
**B**: 8 and Fig.
**C**: 9, respectively..
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<|MaskedSetence|> Advice bits, as all information, are prone to transmission errors. 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 malicious entity that tak... | **A**: 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 advance.
**B**: In contrast, an online algorithm that does not use advice at... | BAC | CAB | CAB | CAB | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> For instance, when using SS3 we only need to store the confidence vector303030In case of ADD, a 2-dimensional vector. of every user and then simply update it as more content is created. However, when working with classifiers not supporting incremental classification, for every user... | **A**: Note that storing either all the documents or a d×t𝑑𝑡d\times titalic_d × italic_t document-term matrix, where d𝑑ditalic_d is the number of documents and t𝑡titalic_t the vocabulary size, takes up much more space than a small 2-dimensional vector..
**B**:
It is worth noting that the difference in terms of sp... | BCA | ACB | BCA | BCA | Selection 3 |
The learning rate of the extant algorithm is also not desirable [13]. <|MaskedSetence|> <|MaskedSetence|> It means that UAVs are not permitted to update strategies at the same time. Besides, to determine which UAV to update strategy, the coordinating process will occupy plenty of channel capacities and require more ... | **A**: Recently, a new fast algorithm called binary log-linear learning algorithm (BLLA) has been proposed by [14].
**B**: To sum up, synchronous update algorithms which can learn from previous experiences are desirable, but only a little research investigated on it..
**C**: However, in this algorithm, only one UAV i... | CAB | ACB | ACB | ACB | Selection 2 |
The sources of DQN variance are Approximation Gradient Error(AGE)[23] and Target Approximation Error(TAE)[24]. <|MaskedSetence|> This type of variance leads to converging to sub-optimal policies and brutally hurts DQN performance. <|MaskedSetence|> Many of the proposed extensions focus on minimizing the variance tha... | **A**: In Approximation Gradient Error, the error in gradient direction estimation of the cost function leads to inaccurate and extremely different predictions on the learning trajectory through different episodes because of the unseen state transitions and the finite size of experience reply buffer.
**B**: Dropout me... | ACB | ABC | ACB | ACB | Selection 3 |
Attention can be viewed as using information transferred from several subsequent layers/feature maps to select and localize the most discriminative (or salient) part of the input signal. Wang et al. <|MaskedSetence|> Their proposed attention module consists of several encoding-decoding layers. Hu et al. <|MaskedSet... | **A**: (2017a) added an attention module to the deep residual network (ResNet) for image classification.
**B**: (2019a) proposed dual attention networks that apply both spatial and channel-based attention operations..
**C**: (2018a) proposed a selection mechanism where feature maps are first aggregated using global a... | BCA | ACB | ACB | ACB | Selection 2 |
Welbl (2014) and Biau et al. (2019) follow a similar strategy. <|MaskedSetence|> <|MaskedSetence|> Independent training fits all networks one after the other and creates an ensemble of networks as a final classifier. <|MaskedSetence|> Additionally, the authors evaluate sparse and full connectivity.. | **A**: Joint training concatenates all tree networks into one single network so that the output layer is connected to all leaf neurons in the second hidden layer from all decision trees and all parameters are optimized together.
**B**: The authors propose a method that maps random forests into neural networks as a sma... | BCA | BCA | CBA | BCA | Selection 1 |
<|MaskedSetence|> In detail, we develop an Optimistic variant of the PPO algorithm, namely OPPO. <|MaskedSetence|> <|MaskedSetence|> As is shown subsequently, solving such a subproblem corresponds to one iteration of infinite-dimensional mirror descent (Nemirovsky and Yudin, 1983) or dual averaging (Xiao, 2010), whe... | **A**: Our algorithm is also closely related to NPG and TRPO.
**B**: To answer this question, we propose the first policy optimization algorithm that incorporates exploration in a principled manner.
**C**: At each update, OPPO solves a Kullback-Leibler (KL)-regularized policy optimization subproblem, where the linear... | CBA | BAC | BAC | BAC | Selection 2 |
We thank Prof. Henry Adams and Dr. <|MaskedSetence|> We also thank Prof. Mikhail Katz and Prof. Michael Lesnick for explaining to us some aspects of their work. We thank Dr. Qingsong Wang for bringing to our attention the paper [76] which was critical for the proof of Theorem 1. <|MaskedSetence|> <|MaskedSetence|> | **A**: Johnathan Bush for very useful feedback about a previous version of this article.
**B**: Alexey Balitsky for pointing out an imprecision in the statement of Proposition 9.2.
.
**C**: Finally, we thank Dr.
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<|MaskedSetence|> In the next two sections, we discuss literature that is related to visual, interactive assessment and interpretation of t-SNE projections as well as the necessary background information on how the t-SNE algorithm works. Section 4 presents our visualization approach including the various features of t... | **A**: Section 7 discusses our design choices, limitations, and possible future work.
**B**:
The rest of this paper is organized as follows.
**C**: Finally, Section 8 concludes our paper..
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The above statement is quantitatively supported by Figure 1, which depicts the increasing number of papers/book chapters published in the last years with bio-inspired optimization and nature-inspired optimization in their title, abstract and/or keywords. <|MaskedSetence|> A major fraction of the publications comprisi... | **A**: We have considered both bio-inspired and nature-inspired optimization because sometimes both terms are confused and indistinctly used, although nature-inspiration includes bio-inspired inspiration and complements it with other sources of inspirations (like physics-based optimization, chemistry-based optimization... | ACB | ACB | ACB | CBA | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> 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. <|MaskedSetence|> The adaptive le... | **A**: If the graph is not updated, the contained information is low-level.
**B**: Instead, DEC and SpectralNet work better on the large scale datasets.
**C**: Classical clustering models work poorly on large scale datasets.
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Inferring spoofing. <|MaskedSetence|> <|MaskedSetence|> We monitor for DNS requests arriving at our Name server. If a query for the requested record arrives from 1.2.3.7, we mark the network as not enforcing ingress filtering. <|MaskedSetence|> | **A**: The process is illustrated in Figure 6, steps (1-4) locate the IP address of the DNS resolver, and steps (5,6) test for ingress filtering on that network..
**B**: Given a DNS resolver at IP 1.2.3.7, we send a DNS query to 1.2.3.7 port 53 asking for a record in domain under our control.
**C**: The query is sent... | BAC | BCA | BCA | BCA | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> Context-based learning is then introduced to utilize sequential structure across batches of data. The context model has two parts: (1) a recurrent context layer, which encodes classification-relevant properties of previously seen data, and (2) a feedforward layer, which integrates ... | **A**: This paper builds upon previous work with this dataset [7], which used support vector machine (SVM) ensembles.
**B**: First, their approach is extended to a modern version of feedforward artificial neural networks (NNs) [8].
**C**: Thus, emulation of adaptation in natural systems leads to an approach that can ... | ABC | ABC | ABC | BCA | Selection 2 |
<|MaskedSetence|> 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]. <|MaskedSetence|> While it is known that the free semigroup of rank one is not an automaton semigroup [4, Proposition 4.3], the free semigroups o... | **A**:
There is a quite interesting evolution of constructions to present free groups in a self-similar way or even as automaton groups (see [15] for an overview).
**B**: While these constructions and the involved proofs are generally deemed quite complicated, the situation for semigroups turns out to be much simpler... | ABC | ABC | ABC | BCA | Selection 2 |
<|MaskedSetence|> (2018) gave better results than human-based attention maps for SCR, we train all of the SCR variants on the subset containing textual explanation-based cues. SCR is trained in two phases. For the first phase, which strengthens the influential objects, we use a learning rate of 5×10−55superscript1055\... | **A**: Since Wu and Mooney (2019) reported that human-based textual explanations Huk Park et al.
**B**: Then, following Wu and Mooney (2019), for the second phase, we use the best performing model from the first phase to train the second phase, which criticizes incorrect dominant answers.
**C**: For the second phase,... | ACB | ABC | ABC | ABC | Selection 4 |
To satisfy the need for a much larger corpus of privacy policies, we introduce the PrivaSeer Corpus of 1,005,380 English language website privacy policies. The number of unique websites represented in PrivaSeer is about ten times larger than the next largest public collection of web privacy policies Amos et al. (2020)... | **A**: We then analyse the lengths and top level distribution of the privacy policies in the corpus and use topic modelling to explore the component topics.
**B**: We release the corpus, a search engine for the corpus (Srinath et al., 2021), the document collection pipeline, and a language model to support further res... | CBA | ACB | ACB | ACB | Selection 4 |
<|MaskedSetence|> The y-axis of the table heatmap depicts the data set’s features, and the x-axis depicts the selected models in the current stored stack. Univariate-, permutation-, and accuracy-based feature selection is available as long with any combination of them (a). <|MaskedSetence|> <|MaskedSetence|> This co... | **A**: The per-model feature accuracy is depicted in (c), and (d) presents the user’s interaction to disable specific features to be used for all the models (only seven features are shown here).
**B**: (b) displays the normalized importance color legend.
**C**:
Figure 4: Our feature selection view that provides thre... | CBA | CBA | CBA | ACB | Selection 3 |
When applying MAML to NLP, several factors can influence the training strategy and performance of the model. Firstly, the data quantity within the datasets used as ”tasks” varies across different applications, which can impact the effectiveness of MAML [Serban et al., 2015, Song et al., 2020]. <|MaskedSetence|> <|Ma... | **A**: Few works have thoroughly studied these impact factors..
**B**: Secondly, while vanilla MAML assumes that the data distribution is the same across tasks, in real-world NLP tasks, the data distributions can differ significantly [Li et al., 2018, Balaji et al., 2018].
**C**: For example, PAML [Madotto et al., 20... | BCA | ACB | BCA | BCA | Selection 3 |
For both static and mobile mmWave networks, codebook design is of vital importance to empower the feasible beam tracking and drive the mmWave antenna array for reliable communications [22, 23]. Recently, ULA/UPA-oriented codebook designs have been proposed for mmWave networks, which include the codebook-based beam trac... | **A**: Nevertheless, such work is still missing now in the literature.
**B**: The multiuser downlink beam training algorithms regarding the ULA are proposed with the multi-resolution codebook designs for partially-connected [25] and fully-connected [15] hybrid structures, respectively.
**C**: These points mentioned a... | BAC | BAC | BAC | BAC | Selection 3 |
Szepesvári, 2018; Dalal et al., 2018; Srikant and Ying, 2019) settings. See Dann et al. (2014) for a detailed survey. Also, when the value function approximator is linear, Melo et al. (2008); Zou et al. (2019); Chen et al. (2019b) study the convergence of Q-learning. When the value function approximator is nonlinear, T... | **A**: Bhatnagar et al.
**B**: In contrast to the previous analysis in the NTK regime, our analysis allows TD to attain a data-dependent feature representation that is globally optimal..
**C**: (2019) prove that TD converges to the globally optimal solution in the NTK regime.
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<|MaskedSetence|> The gate mechanisms in the original LSTM are to enhance its ability in capturing long-distance relations and to address the gradient vanishing/exploding issue in sequence modeling. In our work, we regard the outputs of stacked layers as a “vertical” sequence, and utilize the same gate mechanisms to s... | **A**: Because of the different types of attention (self, cross and masked), we develop tailored ways of connecting (sub-) layers in encoder stacks and decoder stacks with depth-wise LSTMs.
.
**B**: LSTMs are able to capture long-distance relationships: they can learn to selectively use the representations of distant ... | CBA | CBA | CBA | ABC | 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**: On the other hand, the learning methods [8, 11, 12] lag behind in the sufficient distortion perception and cannot easily adapt to scenes with strong geometric distortion.
**B**: 11, and the people and challenging scenes are shown in Fig.
**C**: Our approach performs well on all scenes, while the traditional me... | CBA | BCA | BCA | BCA | Selection 2 |
Our main goal is to develop algorithms for the black-box setting. <|MaskedSetence|> First, we develop algorithms for the simpler polynomial-scenarios model. Second, we sample a small number of scenarios from the black-box oracle and use our polynomial-scenarios algorithms to (approximately) solve the problems on them.... | **A**: Finally, we extrapolate the solution to the original black-box problem.
**B**: This overall methodology is called Sample Average Approximation (SAA).
.
**C**: As usual in two-stage stochastic problems, this has three steps.
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<|MaskedSetence|> The co-existence of random graphs, subgradient measurement noises, additive and multiplicative communication noises are considered. <|MaskedSetence|> What’s more, multiplicative noises relying on the relative states between adjacent local optimizers make states, graphs and noises coupled together. ... | **A**: It becomes more complex to estimate the mean square upper bound of the local optimizers’ states (Lemma 3.1).
**B**:
III.
**C**: 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... | BCA | BCA | ACB | BCA | Selection 1 |
We observe that the results of MuCo are much better than that of Mondrian and Anatomy. <|MaskedSetence|> Consequently, the accuracy of query answering of MuCo is much better and more stable than that of Mondrian and Anatomy. Besides, since the results of queries for MuCo are specific records rather than groups, the r... | **A**: Note that, since we use the sum of salary for comparison (the range of salary is from 4 to 718,000), the relative error rates of Mondrian are much larger than some existing works..
**B**: The primary reason is that MuCo retains the most distributions of the original QI values and the results of queries are spec... | BCA | BCA | BCA | BCA | Selection 4 |
<|MaskedSetence|> (2019b) and adopt the modifications and tricks mentioned in Section 3.3. Both X101-64x4d and Res2Net101 Gao et al. (2019) are used as our backbones, pretrained on ImageNet only. SGD with momentum 0.9 and weight decay 1e-4 is adopted. The initial learning rate is set to 0.01 for Res2Net101 and 0.02 fo... | **A**: Mixed precision training enables to reduce GPU memory.
**B**:
We implement PointRend using MMDetection Chen et al.
**C**: For inference, images are resized to 1400×1400140014001400\times 14001400 × 1400 and horizontal flip is used..
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<|MaskedSetence|> They are much less compared with MASTER, OPT-WLSVI, LSVI-UCB, Epsilon-Greedy. This is because LSVI-UCB-Restart and Ada-LSVI-UCB-Restart can automatically restart according to the variation of the environment and thus have much smaller computational burden since it does not need to use the entire hist... | **A**: The running time of LSVI-UCB-Unknown is larger than LSVI-UCB-restart since the epoch larger is larger due to the lack of the knowlege of total variation B𝐵Bitalic_B, but it still does not use the entire history to compute its policy.
**B**: Although Random-Exploration takes the least time, it cannot find the n... | CAB | CAB | CAB | CAB | Selection 2 |
Many studies worldwide have observed the proliferation of fake news on social media and instant messaging apps, with social media being the more commonly studied medium. <|MaskedSetence|> Most respondents encountered fake news on instant messaging apps compared to social media, and have reported the least trust in the... | **A**: In Singapore, however, mitigation efforts on fake news in instant messaging apps may be more important.
**B**: Research has shown that this is linked to lesser use of social media (Jackson and Wang, 2013), and stronger preferences towards group chats in instant messaging apps (Li et al., 2011), signaling that i... | ACB | CBA | ACB | ACB | Selection 3 |
Consider the instance of encoding the relational information of the entity W3C into an embedding. <|MaskedSetence|> <|MaskedSetence|> One might argue that W3C carries useful information like images and attributes. <|MaskedSetence|> Hence, excluding the self-entity when encoding relational information appears reason... | **A**: Removing the self-entity W3C does not compromise the integrity of the information.
**B**: All relevant information is structured in the form of triplets, such as (RDF,developer,W3C)RDFdeveloperW3C(\textit{RDF},\textit{developer},\textit{W3C})( RDF , developer , W3C ).
**C**: However, multi-modal KG embedding m... | BAC | CAB | BAC | BAC | Selection 1 |
State preprocessing.
In Atari games, the observations are raw images. The images are resized to 84×84848484\times 8484 × 84 pixels and converted to grayscale. The state stacks 4444 recent observations as a frame of shape 84×84×48484484\times 84\times 484 × 84 × 4. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|... | **A**: Before training, the agent interacts with the environments for 104superscript10410^{4}10 start_POSTSUPERSCRIPT 4 end_POSTSUPERSCRIPT steps to estimate the mean and standard deviation of the states.
**B**: We further normalize the observed states for training by the estimated mean and standard deviation before t... | ACB | CAB | CAB | CAB | Selection 3 |
<|MaskedSetence|> First, we apply a DGM to learn only the disentangled part, C𝐶Citalic_C, of the latent space. We do that by applying any of the above mentioned VAEs111In this exposition we use unspervised trained VAEs as our base models but the framework also works with GAN-based or FLOW-based DGMs, supervised, semi... | **A**:
The model has two parts.
**B**: For example, in Figure 1, the model uses β𝛽\betaitalic_β-TCVAE [mig] to retrieve the pose of the model as a latent factor.
**C**: We can view this as a style transfer task and use a technique from [adaIN] to achieve our goal..
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Now, we will define ‘window operators’ to have the same connection as a 3-pin based structural computer using the reverse signal pair described earlier. ‘Window operator’ is a cube of 3x3, each containing elements of 0,i,1,-1,2, and 2. Each element (or cell) is inputted in the same way as three pin structural computing... | **A**: -2, Reverse Semi-mirror: A translucent object perpendicular to 2.
**B**: i, Black body: absorbs the light from the top to the left.
**C**: 1110, NULL: Transmits light that enters the upper and lower sides.
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<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> One then proceeds to sequentially add the next “best” feature at every step until some stopping criterion is met. Here we consider forward selection based on the Akaike Information Criterion (AIC). In order to impose nonnegativity of the coefficients, we will use... | **A**: It is a so-called wrapper method, which means it can be used in combination with any learner (Guyon \BBA Elisseeff, \APACyear2003).
**B**: Forward selection is a simple, greedy feature selection algorithm (Guyon \BBA Elisseeff, \APACyear2003).
**C**: The basic strategy is to start with a model with no features... | BAC | ACB | BAC | BAC | Selection 3 |
According to Figure 7 and Table 8, the two DepAD algorithms are significantly better than all benchmark methods except for wkNN and iForest in terms of ROC AUC . With wkNN, the results are similar. With iForest, the p𝑝pitalic_p-values are very close to 0.05. <|MaskedSetence|> With wkNN, the p𝑝pitalic_p-value is aro... | **A**: In terms of AP, the two DepAD algorithms yield significantly better results than all benchmark methods except for wKNN, iForest and COMBN, as shown in Figure 8 and Table 8.
**B**: Furthermore, the two DepAD methods significantly outperform ALSO, and this is attributed to the inclusion of the relevant variable s... | ABC | ABC | ABC | BCA | Selection 2 |
CB-MNL enforces optimism via an optimistic parameter search (e.g. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> [2010]. Optimistic parameter search provides a cleaner description of the learning strategy. In non-linear reward models, both approaches may not follow similar trajectory but may have overlapping... | **A**: [2020], Filippi et al.
**B**: [2011]), which is in contrast to the use of an exploration bonus as seen in Faury et al.
**C**: in Abbasi-Yadkori et al.
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<|MaskedSetence|> <|MaskedSetence|> Then it pools the aggregated features into a smaller temporal scale. Its architecture is illustrated in Fig. 4. <|MaskedSetence|> layer. In the graph branch, we build a graph on all the features from both Clip O and Clip U, and apply edge convolutions [38] for feature aggregation.... | **A**: Cross-scale graph network.
**B**: The xGN module contains a temporal branch to aggregate features in a temporal neighborhood, and a graph branch to aggregate features from intra-scale and cross-scale locations.
**C**: The temporal branch contains a Conv1d(3,1)Conv1d31\textrm{Conv1d}(3,1)Conv1d ( 3 , 1 )222For... | ABC | ABC | ACB | ABC | Selection 2 |
In this paper, we presented VisEvol, a VA tool with the aim to support hyperparameter search through evolutionary optimization. <|MaskedSetence|> Exploring the impact of the addition and removal of algorithms and models in a majority-voting ensemble from different perspectives and tracking the crossover and mutation ... | **A**: Our tool’s workflow and visual metaphors received positive feedback from three ML experts, who even identified limitations of VisEvol.
**B**: With the utilization of multiple coordinated views, we allow users to generate new hyperparameter sets and store the already robust hyperparameters in a majority-voting e... | BCA | BCA | BCA | BCA | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> We refer to this method of synchronising the ZoomOut results as ZoomOut+Sync, which directly serves as initialisation for HiPPI and our method. Throughout this section we also report results of the initialisation methods ZoomOut and ZoomOut+Sync. Further details ... | **A**: By doing so, we obtain the initial U𝑈Uitalic_U and Q𝑄Qitalic_Q.
**B**: In contrast, HiPPI and our method require shape-to-universe representations.
**C**: To obtain these, we use synchronisation to extract the shape-to-universe representation from the pairwise transformations.
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The recognition algorithm RecognizePG for path graph is mainly built on path graphs’ characterization in [1]. 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 parti... | **A**: In a few words, an antipodality graph has as vertex set some subgraph of G𝐺Gitalic_G, and two vertices are connected if the corresponding subgraphs of G𝐺Gitalic_G are antipodal.
**B**: This is done in Step 4, Step 5, and Step 6 that are the core of algorithm RecognizePG..
**C**: Unfortunately, we cannot buil... | ACB | BCA | ACB | ACB | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> SLIM combined the SLIM with the spectral method based on DCSBM for community detection. And the SLIM method outperforms state-of-art methods in many real and simulated datasets. <|MaskedSetence|> Numerical results of simulations and substantial empirical datasets in Section 5 sho... | **A**: Therefore, it is worth modifying this method to mixed membership networks.
**B**: In this paper, we extend the symmetric Laplacian inverse matrix (SLIM) method (SLIM, ) to mixed membership networks and call this proposed method as mixed-SLIM.
**C**: As mentioned in SLIM , the idea of using the symmetric Laplac... | BCA | BCA | CBA | BCA | Selection 2 |
See, e.g., Welling and Teh (2011); Chen et al. (2014); Ma et al. (2015); Chen et al. (2015); Dubey et al. (2016); Vollmer et al. (2016); Chen et al. (2016); Dalalyan (2017); Chen et al. (2017); Raginsky et al. (2017); Brosse et al. <|MaskedSetence|> (2018); Cheng and Bartlett (2018); Chatterji et al. (2018); Wibisono ... | **A**: (2019); Wibisono (2019) and the references therein.
Among these works,.
**B**: (2018); Xu et al.
**C**: (2019); Durmus et al.
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A number of prior works have explored how RL can be cast in the framework of variational inference. Latent variable could transform the dynamically updated task-related information such as trajectories into a continuous lower-dimensional space. For example, [59] shows that exploring in latent space can enhance the repr... | **A**: A series of methods [50, 61, 59] use variational auto-encoders (VAE)[65] structure to help explore the environment.
**B**: A branch of context-based methods automatically learns to trade-off exploration and exploitation by maximizing average adaptation performance [66, 50].
Differently, we learn a dynamical lat... | CAB | CBA | CAB | CAB | Selection 3 |
Online bin packing has a long history of study. The simplest algorithm is NextFit, which places an item into its single open bin when possible; otherwise, it closes the bin (does not use it anymore) and opens a new bin for the item. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Improving upon this performan... | **A**: FirstFit is another simple heuristic that places an item into the first bin of sufficient space and opens a new bin if required.
**B**: BestFit works similarly, except that it places the item into the bin of minimum available capacity, which can still fit the item.
**C**: NextFit has a competitive ratio of 2, ... | ABC | ABC | CBA | ABC | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> Second, we provide the reconstruction result with respect to reference approaches. <|MaskedSetence|> Throughout all experiments, we train models with Chamfer distance. We also set λ=0.0001𝜆0.0001\lambda=0.0001italic_λ = 0.0001. We denote LoCondA-HC when HyperCloud is used as the ... | **A**: Finally, we check the quality of generated meshes, comparing our results to baseline methods.
**B**: In this section, we describe the experimental results of the proposed method.
**C**: First, we evaluate the generative capabilities of the model.
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<|MaskedSetence|> This paper is organized as follows. <|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**: Paper organization.
**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|> In more concrete terms this problem is equivalent to finding the cycle basis with the sparsest cycl... | **A**: This problem was formulated by Stepanec [7] and Zykov [8] for general graphs and by Hubicka and Syslo [9] in the strictly fundamental class context.
**B**: In [5] a unified perspective of the problem is presented.
**C**: The authors show that the MCB problem is different in nature for each class.
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There is a rather large body of existing work on automatic feature selection techniques [16, 19, 17]. However, one limitation is that features can be redundant if there is a strong correlation among them, and the correlation coefficient is unable to characterize nonlinear relationships. <|MaskedSetence|> <|MaskedSete... | **A**: Guyon and Elisseeff [16] performed a survey including an extensive description of automatic feature selection pitfalls.
**B**: In our VA system, we implement several alternative feature selection techniques belonging to different types, and we allow users to decide if their aggregation is ideal or they want to ... | CAB | ACB | CAB | CAB | Selection 4 |
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]. <|MaskedSetence|> In MPC, closed-loop performance is pushed to the limits only if the plant under control is acc... | **A**: Instead of adapting the controller for the worst case scenarios, the prediction model can be selected to provide the best closed-loop performance by tuning the parameters in the MPC optimization objective for maximum performance [8, 9, 10].
**B**: Using Bayesian optimization-based tuning for enhanced performanc... | CAB | ACB | CAB | CAB | Selection 4 |
We have pointed to issues with the existing bias mitigation approaches, which alter the loss or use resampling. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Discovery and usage of causal concepts is a promising direction for building robust systems. These areas have not been explicitly studied for their abi... | **A**: Neuro-symbolic and graph-based systems could be created that focus on learning and grounding predictions on structured concepts, which have shown promising generalization capabilities [68, 44, 34, 24, 60].
**B**: Causality is another relevant line of research, where the goal is to uncover the underlying causal ... | CAB | CAB | BAC | CAB | Selection 4 |
GazeCapture [42] dataset is collected through crowdsourcing. <|MaskedSetence|> <|MaskedSetence|> Each participant is required to gaze at a circle shown on the devices without any constraint on their head movement. <|MaskedSetence|> The GazeCapture dataset does not provide 3D coordinates of targets. It is usually use... | **A**: It contains a total of 2,445,504 images from 1,474 participants.
**B**: All images are collected using mobile phones or tablets.
**C**: As a result, the GazeCapture dataset covers various lighting conditions and head motions.
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Experimental results are carried out on Real-world Masked Face Recognition Dataset (RMFRD) and Simulated Masked Face Recognition Dataset (SMFRD) presented in wang2020masked . We start by localizing the mask region. To do so, we apply a cropping filter in order to obtain only the informative regions of the masked face (... | **A**: Next, we describe the selected regions using a pre-trained deep learning model as a feature extractor.
**B**: This strategy, however, is a difficult and highly time-consuming process.
.
**C**: This strategy is more suitable in real-world applications comparing to restoration approaches.
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<|MaskedSetence|> Sized (co)inductive types [BFG+04, Bla04, Abe08, AP16] gave way to sized mixed inductive-coinductive types [Abe12, AP16]. <|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.... | **A**: Sized types are a type-oriented formulation of size-change termination [LJBA01] for rewrite systems [TG03, BR09].
**B**: In parallel, linear size arithmetic for sized inductive types [CK01, Xi01, BR06] was generalized to support coinductive types as well [Sac14].
**C**: As we mentioned in the introduction, we ... | ABC | ABC | ABC | ABC | Selection 4 |
This paper solves the three problems faced by cloud media sharing and proposes two schemes FairCMS-I and FairCMS-II. FairCMS-I gives a method to transfer the management of LUTs to the cloud, enabling the calculation of each user’s D-LUT in the ciphertext domain and its subsequent distribution. <|MaskedSetence|> <|Ma... | **A**: However, utilizing the single-value alteration method for masking the original media content does not achieve the IND-CPA security.
**B**: In summary, the two proposed schemes can facilitate the media sharing of owners, while simultaneously ensuring the joint protection of copyright and users’ rights, ultimatel... | ACB | ACB | BCA | ACB | Selection 1 |
At their core, GNNs learn node embeddings by iteratively aggregating features from the neighboring nodes, layer by layer. This allows them to explicitly encode high-order relationships between nodes in the embeddings. <|MaskedSetence|> Fi-GNN Li et al. (2019) proposes to connect each pair of features and treat the mul... | **A**: (2015) capability.
However, not all feature interactions are beneficial, and GNNs rely on the assumption that neighboring nodes share similar features, which may not always hold in the context of feature interaction modeling..
**B**: GNNs have shown great potential for modeling high-order feature interactions f... | BCA | BCA | BCA | CAB | Selection 2 |
Self-concordant functions have received strong interest in recent years due to the attractive properties that they allow to prove for many statistical estimation settings [Marteau-Ferey et al., 2019, Ostrovskii & Bach, 2021]. <|MaskedSetence|> For example, the logistic loss function used in logistic regression is not ... | **A**: This was also the case in Ostrovskii & Bach [2021] and Tran-Dinh et al.
**B**: [2015], in which more general properties of these
pseudo-self-concordant functions were established.
**C**: The original definition of self-concordance has been expanded and generalized since its inception, as many objective functio... | CAB | CAB | CAB | ACB | Selection 3 |
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**: The Backtrack-Stuck-Structures method backtracks active paths that were not extended, but does not require a fresh pass.
**B**: 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... | BCA | BCA | BCA | ABC | Selection 3 |
<|MaskedSetence|> This algorithm is the Tseng method [44] with a resolvent/proximal operator calculation (4). <|MaskedSetence|> Note that we need to communicate with other devices only when we solve the problem (4) and need to multiply by the matrix W𝑊Witalic_W. <|MaskedSetence|> Hence, the problem (4) is solved by... | **A**: Here, as in Algorithm 1, the proximal operator is computed inexactly.
**B**: The problem (4) is divided into two minimization subproblems, by X𝑋Xitalic_X, and by Y𝑌Yitalic_Y.
**C**: For this case we present Algorithm 2.
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There are two levels of coordination; first is selecting an equilibrium before play commences, and second is selecting actions during play time. 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 acti... | **A**: Therefore, at this level of coordination, both NEs and (C)CEs are similar.
**B**: NEs are factorizable and therefore can sample independently without further coordination.
**C**: At action selection time only (C)CEs require further coordination.
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Since achieving posterior accuracy is relatively straightforward, guaranteeing Bayes stability is the main challenge in leveraging this theorem to achieve distribution accuracy with respect to adaptively chosen queries. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> This simple lemma is at the heart of the pr... | **A**: The degree to which a query q𝑞qitalic_q overfits to the dataset is expressed by the correlation between the query and that Bayes factor.
**B**: The following lemma gives a useful and intuitive characterization of the quantity that the Bayes stability definition requires be bounded.
**C**: Simply put, the Baye... | BCA | BCA | BAC | BCA | Selection 2 |
The remainder of the paper is organized as follows. <|MaskedSetence|> We present structural properties of antlers and how they combine in Section 4. <|MaskedSetence|> We also prove that, given a large feedback vertex cut, we can shrink it while preserving the antlers in the graph. Our main results are derived in Sect... | **A**: We conclude in Section 7.
**B**: In Section 5 we show how color coding can be used to find a large feedback vertex cut, if one exists.
**C**: After presenting preliminaries on graphs and sets in Section 2, we prove the mentioned hardness results in Section 3.
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SGRNet [52] designed a two-stage shadow generation network. In the first stage, foreground features and background features are interacted using cross-attention to predict a shadow mask. <|MaskedSetence|> <|MaskedSetence|> Meng et al. [107] adopted a similar two-stage pipeline and proposed to generate the shadow regi... | **A**: DMASNet [151] decomposed shadow mask prediction into box prediction and shape prediction, followed by attending relevant background shadow pixels to fill in the predicted shadow region.
SGDiffusion [96] is the first work on shadow generation using diffusion model, which is built upon ControlNet [200] with extra ... | CBA | BCA | BCA | BCA | Selection 2 |
The average regional daily patterns of taxi mobility data from each POI-based cluster in Beijing, Chengdu, and Xi’an are plotted in Fig. 2. <|MaskedSetence|> 2LABEL:sub@fig:cluster-bj, taxi mobility patterns in Beijing exhibit a high level of cohesion within each POI-based cluster, while remaining distinguishable acro... | **A**: Nevertheless, Fig.
**B**: 2LABEL:sub@fig:cluster-cdxa, illustrates that clusters with higher inflow/outflow/pick-up values in Xi’an and Chengdu, two cities with relatively low ARI and AMI scores as reported in Table III, demonstrate significant overlaps between adjacent clusters, which may be attributed to the ... | CBA | CBA | BAC | CBA | Selection 4 |
In the preceding four sections, we introduced different classes of interval estimators, each having its own characteristics. <|MaskedSetence|> We identify four properties that are important for practical purposes. The first one is the main notion of this paper, namely validity, i.e. whether a model is guaranteed to p... | **A**: Since none of the methods are valid for any finite-size data set, we only consider validity in the sense of the Marginal Validity Theorem of Section 3.4.
**B**: For the four classes of methods, we also list the main references..
**C**: In this section, we summarize the main properties for clarity and convenien... | CAB | CAB | CAB | CAB | Selection 3 |
Similar to \textcitesimonettaCNW19, we regard melody extraction as a task that identifies the melody notes in a single-track 101010It is common for MIDI files to consist of multiple tracks. We refer to “single-track” as MIDI files containing only one track, which is in contrast to multi-track MIDI files that have multi... | **A**: While melody extraction is a note-level classification task, melody track identification is a track-level task.
**B**: The goal of this task is to distinguish the melody track from other non-melody tracks present in a multi-track MIDI file \parencitemadsen07IWAIM,jiang19smc.
**C**: homophonic or polyphonic mus... | CBA | CBA | ABC | CBA | Selection 4 |
A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber, “Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks,” in Proc. <|MaskedSetence|> Conf. <|MaskedSetence|> <|MaskedSetence|> 2006, pp. 369–376.. | **A**: Learning (ICML), Pittsburgh, USA, Jun.
**B**: Mach.
**C**: 23rd Int.
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<|MaskedSetence|> This ensures that supervision is densely transmitted from labeled to unlabeled points within a given sample. Given the potential for non-overlapping classes between input pairs, which could introduce noise during the supervision propagation, our strategy involves training the network using the CSFR m... | **A**: As both modules function based on point correlations, supervision signals are effectively propagated to unlabeled points bearing resemblance in features to labeled ones.
**B**:
In the subsequent training stage, our ISFR module facilitates the transmission of supervision from labeled to unlabeled points within ... | BAC | BAC | BCA | BAC | Selection 4 |
Setup. <|MaskedSetence|> The official data set contains 7481 training and 7518 test images with 2D and 3D bounding box annotations for cars, pedestrians, and cyclists. 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]. <|Mas... | **A**: 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.
**B**: Moreover, we use 40 recall positions instead of 11 recall positions proposed in t... | BCA | ABC | ABC | ABC | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> It contains 1,000 training and 500 testing images.
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**: Its ground truth is annotated with word-level quadrangles.
**B**: ICDAR2015 [44] includes multi-orientated and small-scale text instances.
**C**: It contains 300 training images and 200 testing images with word-level annotation.
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We formally present a storage strategy for IP addresses that consists of two layers that consist of a limited number of memory blocks. The first layer contains 256×256256256256\times 256256 × 256 memory blocks. <|MaskedSetence|> We allocate a memory block in the other layer for the IP address when its first three par... | **A**: Figure 2 shows an example of the relationship mapping between the memory blocks of two layers and an IP addresses.
**B**: The first three parts of the IP address can be mapped into the corresponding position of the element in a particular memory block of the first layer according to the individual values of the... | ABC | BAC | BAC | BAC | Selection 4 |
<|MaskedSetence|> In section 2, we briefly recall the classic saddle point problem and its Schur complement, and introduce the twofold saddle point problem and the form of Schur complement, we then construct and analyze the block-triangular and block-diagonal preconditioners based on Schur complement for twofold saddl... | **A**: Finally, concluding remarks are given in Section 7.
.
**B**: Furthermore, we extend these results to the n𝑛nitalic_n-tuple saddle point problem in Section 3.
**C**: The outline of the remainder of this paper is as follows.
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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**: Hence, the convergence analysis given in Section 4 can be trivially extended to this case.
.
**B**: 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 s... | ACB | BCA | BCA | BCA | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> A comprehensive investigation on the relationships between tubular eigenvalues, T-eigenvalues, and eigentuples has been conducted by Beik and Saad saad2023 .
The T-eigenvalues also exhibit a multitude of applications across diverse mathematical domains. The T-eigenvalues were also ... | **A**: Alternative versions and formulations of eigenvalues of third-order tensors in the context of tensor-tensor multiplication have also been explored by Qi and Zhang qi2021t , who referred to them as“eigentuples”, and by Beik and Saad saad2023 , who termed them as “tubular eigenvalues”.
**B**: zheng2020t to study... | ABC | CAB | CAB | CAB | Selection 4 |
<|MaskedSetence|> The discriminator is shown in Figure 2 (b). The texture branch includes three convolution layers with the kernel size of 4 and stride of 2, tailed by two convolution layers with the kernel size of 4 and stride of 1. We use the Sigmoid non-linear activation function at the last layer and the Leaky ReL... | **A**: Finally, the outputs of the two branches are concatenated in the channel dimension, based on which we calculate the adversarial loss.
.
**B**: The structure branch shares the same pattern as the upper stream, where the input edge map is detected by a residual block [6] followed by a convolution layer with the k... | CBA | BAC | CBA | CBA | Selection 1 |
<|MaskedSetence|> In particular, we show that they vastly outperform their standard (“non-subgoal”) counterparts. As a testing ground, we consider three challenging domains: Sokoban, Rubik’s Cube, and INT. All of them require non-trivial reasoning. <|MaskedSetence|> Sokoban is a complex video puzzle game known to be ... | **A**: INT [54] is a recent theorem proving benchmark..
**B**: The Rubik’s Cube is a well-known 3-D combination puzzle.
**C**:
In this section, we empirically demonstrate the efficiency of MCTS-kSubS and BF-kSubS.
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Chinese characters, different from Latin Characters, are pictographs, which show their meanings in shapes. However, it is extremely hard for people to encode these Chinese characters in computers. A common strategy is to give every Chinese character a unique hexadecimal string, such as ‘UTF-8’ and ‘GBK’. <|MaskedSete... | **A**: In other words, the closeness in the hexadecimal string value can not represent the similarity in their shapes.
**B**: However, this kind of strategy processes Chinese characters as independent symbols, totally ignoring the structural similarity among Chinese characters.
**C**: Some work has tried to use image... | BAC | BAC | BCA | BAC | Selection 1 |
ABC (Gonzalez et al., 2020), the Anti-reflexive Bias Challenge, is a multi-task benchmark dataset designed for evaluating gender assumptions in NLP models. <|MaskedSetence|> A total of 4,560 samples are collected by a template-based method. <|MaskedSetence|> <|MaskedSetence|> For machine translation, sentences with ... | **A**: ABC consists of 4 tasks, including language modeling, natural language inference (NLI), coreference resolution, and machine translation.
**B**: The language modeling task is to predict the pronoun of a sentence.
**C**: For NLI and coreference resolution, three variations of each sentence are used to construct ... | ABC | ABC | ABC | ABC | Selection 4 |
The templates are intended to approximate the final look and page length of the articles/papers. <|MaskedSetence|> <|MaskedSetence|> The structure of the LaTeXfiles, as designed, enable easy conversion to XML for the composition systems used by the IEEE’s outsource vendors. The XML files are used to produce the final... | **A**: Therefore, they are NOT intended to be the final produced work that is displayed in print or on IEEEXplore®.
**B**: Have you looked at your article/paper in the HTML version?
.
**C**: They will help to give the authors an approximation of the number of pages that will be in the final version.
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<|MaskedSetence|> All of these questions were aimed at eliciting a subject’s heterogeneous preferences toward trust and reciprocity. The full set of survey questions is reproduced in Appendix LABEL:app:questions. <|MaskedSetence|> <|MaskedSetence|> From this, we can see that the first two components of the reciproci... | **A**: The decision to select two components for reciprocity was determined by referring to a Scree plot of the singular values shown in Figure 3.
**B**:
After the main parts of the experiment, subjects were asked a series of survey questions to elicit measures of their individual characteristics.
**C**: In order to... | BCA | BCA | BCA | BCA | Selection 4 |
<|MaskedSetence|> Therefore, researchers often apply degradation patterns on the aforementioned datasets to obtain corresponding degraded images to construct paired datasets. <|MaskedSetence|> To alleviate these problems and train a more effective and general SISR model, some works model the degradation mode as a com... | **A**: However, images in the real world are easily disturbed by various factors (e.g., sensor noise, motion blur, and compression artifacts), resulting in the captured images being more complex than the simulated ones.
**B**:
Due to the particularity of the SISR task, it is difficult to construct a large-scale paire... | BCA | BAC | BAC | BAC | Selection 2 |
<|MaskedSetence|> If the downsampling kernel is known, then the best approach is to simply backpropagate through that kernel (assuming it is differentiable). Otherwise, we can create a trainable downsampling module representing the kernel and optimize its weights in an end-to-end manner. We revisit the technique intro... | **A**: Their method relies on the assumption that a satisfactory kernel should preserve the distribution of patches in the image.
**B**: The downsampling operation can be implemented in several ways.
**C**: For Neural Knitworks, there is no need to introduce a new loss term accommodating this since the core module ob... | ACB | BAC | BAC | BAC | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Conversely, in classification with selective sampling (Cesa-Bianchi et al.,, 2009; Orabona and Cesa-Bianchi,, 2011; Cavallanti et al.,, 2011; Dekel et al.,, 2012; Agarwal,, 2013), the learner must label all items, but observing labels is costly, and the learner h... | **A**: 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.
**B**: The apple tasting problem is not the only variant of online classification where labels are not revealed in every round.
**C**: In... | BCA | BCA | BCA | CAB | Selection 3 |
We frame claim detection as a sentence-level binary classification task, where each sentence can either be identified as containing a claim or not. <|MaskedSetence|> However, differently from the legal domain case study, the memory content dramatically increases as the number of topics gets larger, from 130 with a si... | **A**: Hence, the need to adopt a sampling strategy..
**B**: Moreover, the increased memory size prohibits using all the given knowledge with transformer-based architectures due to hardware limitations.
**C**: To evaluate the benefits of added knowledge, we consider incremental subsets of topics, spanning from 1 up t... | CBA | CBA | CAB | CBA | Selection 1 |
However, the progress of sentiment dependency-based methods, such as the work by Zhang et al. <|MaskedSetence|> (2020); Tian et al. <|MaskedSetence|> (2021a); Dai et al. <|MaskedSetence|> | **A**: (2019); Zhou et al.
**B**: (2021); Li et al.
**C**: (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..
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QuantumNAT comprises a three-stage pipeline. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> During training, we iteratively sample error gates, insert them to PQC, and updates weights. Finally, post-measurement quantization is further proposed to reduce the precision of measurement outcomes from each qubit an... | **A**: Furthermore, we inject noise to the PQC training process by performing error gate insertion.
**B**: The first step, post-measurement normalization normalizes the measurement outcomes on each quantum bit (qubit) across data samples, thus removing the quantum error-induced distribution shift.
**C**: The error ga... | BAC | ABC | BAC | BAC | Selection 3 |
However, gallego2018unifying and other event-based data association methods zhu2017event ; gallego2019focus ; peng2022globally show that the events triggered by the same edge in the scene can be associated with each other using an event trajectory. <|MaskedSetence|> 1). <|MaskedSetence|> Then, a novel event-based ... | **A**: Overall, this paper makes the following contributions:.
**B**: Based on these observed facts, we explicitly formulate the event-based data association problem as a 3D event trajectory estimation problem in the spatio-temporal domain.
**C**: Furthermore, they also show that the event trajectories, triggered at ... | CBA | BCA | CBA | CBA | Selection 3 |
<|MaskedSetence|> In this experiment, we adopt MoCo.v2 with ResNet-50 under 1600-epoch pre-training. We choose multiple smaller networks with fewer parameters as the student network: ResNet-18 [70], MobileNet.v2 [86], ShuffleNet.v1 [87]. Similar to the pre-training for the teacher network, we add one additional MLP la... | **A**: We adopt the BCE loss for GenURL in the KD task.
.
**B**: We evaluate the KD tasks based on self-supervised learning on STL-10 dataset.
**C**: Follow the linear evaluation protocols in Sec. V-B, we compare the existing relation-based KD methods including RKD [65], PKT [64], SP [66], SSKD [68], CRD [69], and SE... | BCA | BCA | ACB | BCA | Selection 1 |
Figure 1: MobileNetV2 [44] has a very imbalanced memory usage distribution. The peak memory is determined by the first 5 blocks with high peak memory, while the later blocks all share a small memory usage. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> . | **A**: Notice that the model architecture and accuracy are not changed for the two settings.
**B**: By using per-patch inference (4×4444\times 44 × 4 patches), we are able to significantly reduce the memory usage of the first 5 blocks, and reduce the overall peak memory by 8×\times×, fitting MCUs with a 256kB memory b... | BAC | BAC | BCA | BAC | Selection 1 |
<|MaskedSetence|> Unlike the conventional practice of constructing augmented graphs by hand, CGCL employs multiple GNN-based encoders to generate multiple contrastive views. This obviates the need for explicit structural augmentation perturbations, thus ensuring invariance. <|MaskedSetence|> <|MaskedSetence|> For a ... | **A**: Graph encoders of CGCL learn the graph representations collaboratively, and enhance each other’s learning ability in an unsupervised manner.
**B**: We then propose the concepts of asymmetric structure and complementary encoders as the design principle for the collaborative framework.
**C**: In this study, we i... | CAB | CBA | CAB | CAB | Selection 4 |
The work of Piotr Miłoś was supported by the Polish National Science Center grant UMO-2017/26/E/ST6/00622. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Our experiments were managed using https://neptune.ai. We would like to thank the Neptune team for providing us access to the team version and technical su... | **A**: PLG/2019/012498.
**B**: We gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Centers: ACK Cyfronet AGH, PCSS) for providing computer facilities and support within computational grant no.
**C**: The work of Tomasz Korbak was supported by the Leverhulme Doctoral Scholarship.
| CBA | CBA | CAB | CBA | Selection 4 |
<|MaskedSetence|> The authors in [10] and [11] consider input-to-state safety to quantify possible safety violation. Conversely, the work in [12] proposes robust CBFs to guarantee robust safety by accounting for all permissible errors within an uncertainty set. Input delays within CBFs were discussed in [13, 14]. <|M... | **A**:
CBFs that account for uncertainties in the system dynamics have been considered in two ways.
**B**: CBFs that account for state estimation uncertainties were proposed in [15] and [16].
**C**: Similar to the notion of ROCBF, the authors in [18] consider additive disturbances in the system dynamics and state-es... | BCA | ABC | ABC | ABC | Selection 4 |
<|MaskedSetence|> Modeling and designing methods to quantitatively detecting latent structural information for weighted networks are interesting topics. Recent years, some Weighted Stochastic Blockmodels (WSBM) have been developed for weighted networks, to name a few, [9, 10, 11, 12, 13, 14, 15]. However, though these... | **A**: Another limitation of the above WSBMs is, it is challenging to develop methods by taking the advantage of the spectral clustering idea under these WSBMs for their complex forms or strict constraint on edge distribution.
**B**: To overcome limitations of these weighted models, [16] proposes a Distribution-Free M... | CAB | BAC | CAB | CAB | Selection 4 |
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