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<|MaskedSetence|> 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. First we consider that T~~𝑇\tilde{T}over~ start_ARG italic_T end_ARG can be nonzero. ... | **A**: We had to reconsider the proofs, in our view simplifying some of them.
.
**B**: Also, our scheme is defined by a sequence of elliptic problems, avoiding the annoyance of saddle point systems.
**C**: Of course, the numerical scheme and the estimates developed in Section 3.1 hold.
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<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> We show the CreditScore measured over time in Figure 5(a). It can be seen that although the credibility of some tweets are low (rumor-related), averaging still makes the CreditScore of Munich shooting higher than the average of news events (hence, close to a news... | **A**:
We observe that at certain points in time, the volume of rumor-related tweets (for sub-events) in the event stream surges.
**B**: We trade-off this by debunking at single tweet level and let each tweet vote for the credibility of its event.
**C**: This can lead to false positives for techniques that model eve... | ACB | ACB | ACB | ACB | Selection 3 |
Given a tweet, our task is to classify whether it is associated with either a news or rumor. <|MaskedSetence|> Our task is, to a point, a reverse engineering task; to measure the probability a tweet refers to a news or rumor event; which is even trickier. <|MaskedSetence|> <|MaskedSetence|> The model utilizes CNN t... | **A**: We hence, consider this a weak learning process.
**B**: Most of the previous work (castillo2011information, ; gupta2014tweetcred, ) on tweet level only aims to measure the trustfulness based on human judgment (note that even if a tweet is trusted, it could anyway relate to a rumor).
**C**: Inspired by (zhou201... | BCA | BAC | BAC | BAC | Selection 2 |
Evaluating methodology.
For RQ1, given an event entity e, at time t, we need to classify them into either Breaking or Anticipated class. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> We then bin the entities in the two datasets chronologically into 10 different parts. We set up 4 trials with each of the last... | **A**: For GoogleTrends, there are 2,700 and 4,200 instances respectively.
**B**: We select a studied time for each event period randomly in the range of 5 days before and after the event time.
**C**: In total, our training dataset for AOL consists of 1,740 instances of breaking class and 3,050 instances of anticipat... | BCA | BCA | CAB | BCA | Selection 2 |
Table 1 shows basic patient information. <|MaskedSetence|> <|MaskedSetence|> 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), with a mean value of 13.9 years.. | **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**: Body weight, according to BMI, is normal for half of the patients, four are overweight and one is obese.
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Early approaches towards computational models of visual attention were defined in terms of different theoretical frameworks, including Bayesian Zhang et al. (2008) and graph-based formulations Harel et al. (2006). <|MaskedSetence|> The latter framed saliency as the dissimilarity between nodes in a fully-connected dire... | **A**: A mechanism inspired more by biological than mathematical principles was first implemented and described in the seminal work by Itti et al.
**B**: This standard cognitive architecture has since been augmented with additional feature channels that capture semantic image content, such as faces and text Cerf et al... | CAB | CAB | BAC | CAB | Selection 1 |
<|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**: This is mainly motivated by two aspects.
**B**:
Our strongest positive result about the approximation of the locality number will be derived from the reduction mentioned above (see Section 5.2).
**C**: Secondly, due to the results of Section 4, the investigated greedy strategies for computing the locality num... | ACB | BAC | BAC | BAC | Selection 2 |
Notable exceptions are the works of
Oh et al. <|MaskedSetence|> (2019), Ha & Schmidhuber (2018), Holland et al. (2018), Leibfried et al. (2018) and Azizzadenesheli et al. (2018). <|MaskedSetence|> (2017) use a model of rewards to augment model-free learning with good results on a number of Atari games. <|MaskedSeten... | **A**: Oh et al.
**B**: However, this method does not actually aim to model or predict future frames, and achieves clear but relatively modest gains in efficiency..
**C**: (2017), Sodhani et al.
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The whole-body climbing gait involves utilizing the entire body movement of the robot, swaying forwards and backwards to enlarge the stability margins before initiating gradual leg movement to overcome a step. This technique optimizes stability during the climbing process. To complement this, the rear-body climbing ga... | **A**: For a more detailed discussion of the whole-body climbing gait and the rear-body climbing gait, we direct readers to [10]..
**B**: In this approach, once the front legs and body have completed their upward rolling motion, the rear legs are elevated to ascend the step.
**C**: This strategy is particularly benef... | BAC | BCA | BCA | BCA | Selection 4 |
It should be fairly clear that such assumptions are very unrealistic or undesirable. 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.... | **A**: However, if this bit is wrong, then the online algorithm has unbounded competitive ratio, i.e., can perform extremely badly.
**B**: In contrast, an online algorithm that does not use advice at all has competitive ratio at most 2, i.e., its output can be at most twice as costly as the optimal one..
**C**: In th... | CAB | BAC | CAB | CAB | 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|> However, when working with classifiers not supporting increme... | **A**: of every user and then simply update it as more content is created.
**B**: For instance, when using SS3 we only need to store the confidence vector303030In case of ADD, a 2-dimensional vector.
**C**: Note that storing either all the documents or a d×t𝑑𝑡d\times titalic_d × italic_t document-term matrix, where... | BAC | BAC | BAC | BAC | Selection 1 |
Game theory provides an efficient tool for the cooperation through resource allocation and sharing [20][21]. A computation offloading game has been designed in order to balance the UAV’s tradeoff between execution time and energy consumption [25]. A sub-modular game is adopted in the scheduling of beaconing periods fo... | **A**: Sedjelmaci et al.
**B**: Aggregative game is a characteristic game model which treats other agents’ strategies as a whole influence, thus avoids overwhelming strategies information from every single agent [27][28].
**C**: applied the Bayesian game-theoretic methodology in UAV’s intrusion detection and attacker... | ACB | ACB | ACB | 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**: Dropout was first introduced in 2012 as a regularization technique to avoid over-fitting[12], and was applied in the winning submission for the Large Scale Visual Recognition Challenge that revolutionized deep learning research[13].
**B**: Over course of time a wide range of Dropout techniques inspired by the o... | CAB | ABC | CAB | CAB | Selection 1 |
Guo et al. (2018) provided a review of deep learning based semantic segmentation of images, and divided the literature into three categories: region-based, fully convolutional network (FCN)-based, and weakly supervised segmentation methods. Hu et al. (2018b) summarized the most commonly used RGB-D datasets for semantic... | **A**: (2020) presented a review of the literature for addressing the challenges of scarce annotations as well as weak annotations (e.g., noisy annotations, image-level labels, sparse annotations, etc.) in medical image segmentation.
**B**: (2019) reviewed the literature on techniques to handle label noise in deep lea... | BCA | BCA | ACB | BCA | Selection 2 |
The generalization performance has been widely studied. Zhang et al. <|MaskedSetence|> Bornschein et al. (2020) analyze the performance across different dataset sizes.
Olson et al. <|MaskedSetence|> <|MaskedSetence|> | **A**: (2014) and find that neural networks achieve good results but are not as strong as random forests..
**B**: (2017) demonstrate that deep neural networks are capable of fitting random labels and memorizing the training data.
**C**: (2018) evaluate the performance of modern neural networks using the same test str... | CBA | BCA | BCA | BCA | Selection 4 |
To answer this question, we propose the first policy optimization algorithm that incorporates exploration in a principled manner. <|MaskedSetence|> Our algorithm is also closely related to NPG and TRPO. <|MaskedSetence|> <|MaskedSetence|> To encourage exploration, we explicitly incorporate a bonus function into the ... | **A**: At each update, OPPO solves a Kullback-Leibler (KL)-regularized policy optimization subproblem, where the linear component of the objective function is defined using the action-value function.
**B**: As is shown subsequently, solving such a subproblem corresponds to one iteration of infinite-dimensional mirror ... | CBA | CAB | CAB | CAB | Selection 4 |
We thank Prof. <|MaskedSetence|> Johnathan Bush for very useful feedback about a previous version of this article. <|MaskedSetence|> <|MaskedSetence|> 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 pr... | **A**: Mikhail Katz and Prof.
**B**: Henry Adams and Dr.
**C**: We also thank Prof.
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C2: Interpretation of Patterns One salient pattern that stands out in the projection (Figure 7(c)) is the long curved shape of cluster C2. As opposed to C1 and C3, which look like ordinary (formless) clusters, the points in C2 have been laid out in the 2-D projection in an elongated shape going from top to bottom, wit... | **A**: We can then interpret that the insulin dimension has a high correlation with the formation of this specific shape..
**B**: Finally, we click on the bar to indicate that we want this specific dimension’s values to be presented, which results in a clear color gradient from the bottom to the top of C2 (Figure 7(g)... | CBA | CBA | ABC | CBA | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> These algorithms have been widely surpassed by more advanced versions over the years which, so obtaining better performance than naive version of classical algorithms is relatively easy to achieve, and it does not imply a competitive performance [600]. <|MaskedSetence|> This metho... | **A**: Good comparisons are crucial for new proposals: The lack of fair comparisons is another important drawback of many proposals published to date.
**B**: When new algorithms are proposed, unfortunately, many of them are only compared to very basic and classical algorithms (such as GA or PSO).
**C**: In some cases... | ABC | ABC | ABC | ACB | Selection 1 |
Classical clustering models work poorly on large scale datasets. <|MaskedSetence|> <|MaskedSetence|> If the graph is not updated, the contained information is low-level. <|MaskedSetence|> In particular, AdaGAE is stable on all datasets.
. | **A**: Instead, DEC and SpectralNet work better on the large scale datasets.
**B**: The adaptive learning will induce the model to exploit the high-level information.
**C**: Although GAE-based models (GAE, MGAE, and GALA) achieve impressive results on graph type datasets, they fail on the general datasets, which is p... | ACB | ACB | BAC | ACB | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> We classify the ASes according to the following business types: content, enterprise, Network Service Provider (NSP), Cable/DSL/ISP, non-profit, educational/research, route server at Internet Exchange Point (IXP)111A route server directs traffic among Border Gateway Protocol (BGP) r... | **A**: We also want to understand the types of networks that we could test via domains-wide scans.
**B**: We plot the networks that do not enforce ingress filtering according to business types in Figure 12.
**C**: To derive the business types we use the PeeringDB.
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<|MaskedSetence|> 2D). <|MaskedSetence|> <|MaskedSetence|> The context system thus transforms samples of recently seen odors into a representation that helps classification on the next time period. This approach is similar to the context+skill technique for opponent modeling and enhanced extrapolation in games [26, ... | **A**: The recurrent layers are modified via backpropagation through time, and, in this manner, the recurrent pathway learns to generate representations that support classification.
**B**: The context+skill NN model builds on the skill NN model by adding a recurrent processing pathway (Fig.
**C**: Before classifying ... | BCA | BCA | BCA | CBA | Selection 3 |
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). <|MaskedSetence|> While these constructions and the involved proofs are generally deemed quite complicated, the situation for semigroups turns out to be much sim... | **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**: On a side note, it is also worthwhile to point out that – although there does not seem to be much research on the topic – there are examples to genera... | ACB | ACB | ACB | CBA | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> (2019). However, when these ideas are applied to the UpDn model Anderson et al. (2018), which attempts to learn correct visual grounding, these approaches achieve 4-7% lower accuracy compared to the state-of-the-art methods.
. | **A**: Some recent approaches employ a question-only branch as a control model to discover the questions most affected by linguistic correlations.
**B**: (2018) or to re-scale the loss based on the difficulty of the question Cadene et al.
**C**: The question-only model is either used to perform adversarial regulariza... | ACB | CBA | ACB | ACB | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> We preprocessed the privacy policy documents to create sequences of a maximum length of 512 tokens. Inputs significantly shorter than the maximum length occasionally occurred since we did not create sequences that crossed document boundaries. <|MaskedSetence|> Other hyperparameter... | **A**: We did not create a new vocabulary since the two vocabularies are not significantly different and any out-of-vocabulary words can be represented and tuned for the privacy domain using the byte pair encoding vocabulary of RoBERTa.
**B**:
We use the byte pair encoding tokenization technique utilized in RoBERTa a... | BAC | BAC | CAB | BAC | Selection 1 |
Figure 2: The exploration process of ML algorithms. <|MaskedSetence|> (b) presents a selection of parameters for KNN in order to boost the per-class performance shown in (c.1). (c.2) illustrates in light blue the selected models and in gray the remaining ones. <|MaskedSetence|> However in (d), after resetting class ... | **A**: View (a.1) summarizes the performance of all available algorithms, and (a.2) the per-class performance based on precision, recall, and f1-score for each algorithm.
**B**: Also from (a.2), both RF and ExtraT performances seem to be equal.
**C**: The chart axes are normalized from 0 to 100%..
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When applying MAML to NLP, several factors can influence the training strategy and performance of the model. <|MaskedSetence|> <|MaskedSetence|> For example, PAML [Madotto et al., 2019] regards each person’s dialogues as a task for MAML and they have different personal profiles. <|MaskedSetence|> Few works have tho... | **A**: 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].
**B**: Firstly, the data quantity within the datasets used as ”tasks” varies across different applications, w... | BAC | ACB | BAC | BAC | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> In particular, each UAV is equipped with a cylindrical conformal array (CCA), and a novel-codebook-based mmWave beam tracking scheme is proposed for such a highly dynamic UAV network. More specifically, the codebook consists of the codewords corresponding to various subarray patter... | **A**: In such a scenario, we focus on inter-UAV communications in UAV networks, and the UAV-to-ground communications are not involved.
**B**: In this paper, we consider a dynamic mission-driven UAV network with UAV-to-UAV mmWave communications, wherein multiple transmitting UAVs (t-UAVs) simultaneously transmit to a ... | BAC | BAC | BAC | CAB | Selection 3 |
Szepesvári, 2018; Dalal et al., 2018; Srikant and Ying, 2019) settings. See Dann et al. (2014) for a detailed survey. <|MaskedSetence|> (2008); Zou et al. (2019); Chen et al. (2019b) study the convergence of Q-learning. When the value function approximator is nonlinear, TD possibly diverges (Baird, 1995; Boyan and Moo... | **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**: Also, when the value function approximator is linear, Melo et al.
**C**: (2019) prove that TD converges to the globally optimal s... | BCA | BCA | BCA | CBA | Selection 2 |
For machine translation, the performance of the Transformer translation model Vaswani et al. <|MaskedSetence|> (2016) in stacked layers and sub-layers Bapna et al. (2018); Wu et al. (2019b); Wei et al. (2020); Zhang et al. (2019); Xu et al. (2020a); Li et al. (2020); Huang et al. (2020); Xiong et al. (2020); Mehta et ... | **A**: (2018, 2019); Dou et al.
**B**: (2018), which may make the model “forget” distant layers, and aggregating layers is of profound value to better fuse linguistic information at different levels of representation Peters et al.
**C**: (2017) benefits from including residual connections He et al.
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<|MaskedSetence|> However, due to the implicit and heterogeneous representation, the neural network suffers from the insufficient learning problem and imbalance regression problem. <|MaskedSetence|> <|MaskedSetence|> Fig. 2 illustrates the attributes of the proposed ordinal distortion.. | **A**:
As mentioned above, most previous learning methods correct the distorted image based on the distortion parameters estimation.
**B**: These problems seriously limit the learning ability of neural networks and cause inferior distortion rectification results.
**C**: To address the above problems, we propose a fu... | BAC | ABC | ABC | ABC | Selection 4 |
<|MaskedSetence|> 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. The black-box model is motivated by data-driven applications where specific knowledge of the distribution is unknown but we have the abi... | **A**: On similar lines, [1] studies a stochastic k𝑘kitalic_k-center variant, where points arrive independently and each point only needs to get covered with some given probability.
**B**: Clustering is a fundamental task in unsupervised and self-supervised learning.
**C**: 2S-Sup is the natural two-stage counterpar... | BCA | BAC | BAC | BAC | Selection 2 |
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**: It becomes more complex to estimate the mean square upper bound of the local optimizers’ states (Lemma 3.1).
**B**: 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)..
**C**: We firstly employ the property of c... | ABC | ACB | ACB | ACB | Selection 4 |
However, despite protecting against both identity disclosure and attribute disclosure, the information loss of generalized table cannot be ignored. On the one hand, the generalized values are determined by only the maximum and the minimum QI values in equivalence groups, causing that the equivalence groups only preserv... | **A**: For instance, as shown in Figure 2, the red polyline and the magenta polyline represent the distributions on age in Figure 1(a) and Figure 1(c), respectively.
**B**: On the other hand, the partition of equivalence groups also increases the information loss of anonymized table because the results of query statem... | CAB | ACB | CAB | CAB | Selection 4 |
Due to limited mask representation of HTC, we move on to SOLOv2, which utilizes much larger mask to segment objects. <|MaskedSetence|> <|MaskedSetence|> (2020) and BlendMask Chen et al. (2020) on COCO. In SOLOv2, the unified mask feature branch is dynamically convoluted by learned kernels, and the adaptively generat... | **A**: It builds an efficient yet simple instance segmentation framework, outperforming other segmentation methods like TensorMask Chen et al.
**B**: Using ResNeXt101-64x4d plugined with DCN and GC block, SOLOv2 achieves 75.29 mAP on validation set (see Table 1).
**C**: (2019c), CondInst Tian et al.
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We consider the setting of episodic RL with nonstationary reward and transition functions. <|MaskedSetence|> For nonstationary RL, dynamic regret is a stronger and more appropriate notion of performance measure than static regret, but is also more challenging for algorithm design and analysis. <|MaskedSetence|> For a... | **A**: For nonstationary linear MDPs, we show that one can design a near-optimal statistically-efficient algorithm to achieve sublinear dynamic regret as long as the total variation of reward and transition dynamics is sublinear.
**B**: To incorporate function approximation, we focus on a subclass of MDPs in which the... | CBA | CBA | CBA | BCA | Selection 2 |
There is a very strong, negative correlation between the media sources of fake news and the level of trust in them (ref. <|MaskedSetence|> Trust is built on transparency and truthfulness, and the presence of fake news, which is deceptive and usually meant to serve hidden agendas, may erode trust. It is worthwhile to ... | **A**: Figures 1 and 2) which is statistically significant (r(9)=−0.81𝑟90.81r(9)=-0.81italic_r ( 9 ) = - 0.81, p<.005𝑝.005p<.005italic_p < .005).
**B**: If it is through the exposure to the messages of these campaigns that people’s trust in media items have been influenced, especially those who might not have per... | ACB | ACB | BCA | ACB | Selection 2 |
The results on the ZH-EN dataset are depicted in Figure 7. <|MaskedSetence|> However, as the degree increases, incorporating DAN yields more performance gain. <|MaskedSetence|> <|MaskedSetence|> The decentralized attention, which considers neighbors as queries, consistently outperforms the centralized GAT across va... | **A**: For entities with only a few neighbors, the advantage of leveraging DAN is not significant.
**B**: Overall, DAN exhibits significantly better performance than GCN, GAT, or their combination.
**C**: This upward trend halts until the degree exceeds 20.
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Previous work typically utilizes intrinsic motivation for exploration in complex decision-making problems with sparse rewards. Count-based exploration [20, 21] builds a density model and encourages the agent to visit the states with less pseudo visitation count. Episodic curiosity [22] compares the current observation... | **A**: Most of these work proposes the final reward for training to characterize the trade-off between the extrinsic and intrinsic rewards, which is typically implemented as a linear combination.
**B**: The intrinsic rewards are crucial when the extrinsic rewards are sparse..
**C**: Never give up [24] combines pre-ep... | CBA | CAB | CAB | CAB | Selection 2 |
The model has two parts. <|MaskedSetence|> 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-supervised or unsupervised. In the Appendix we present such impleme... | **A**: As we explained above, such a model will likely learn a good disentangled representation, however, its reconstruction will be of low quality as it will only be able to generate the information captured by the disentangled factors while averaging the details.
**B**: First, we apply a DGM to learn only the disent... | BAC | BAC | BAC | BAC | Selection 1 |
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|> <|MaskedSetence|> Let’s look at the role of t... | **A**: In other words, operating a structural computer with a minimal lead is also a task to be addressed by this study because one of the most important factors in computer hardware design is aggregation.
**B**: However, one can think about whether the four pin designs are the minimum number of pins required by struc... | ABC | BAC | BAC | BAC | Selection 4 |
Excluding the interpolating predictor, stability selection produced the sparsest models in our simulations. However, this led to a reduction in accuracy whenever the correlation within features from the same view was of a similar magnitude as the correlations between features from different views. In both gene expressi... | **A**: Additionally, we gave the meta-learner information about the number of views containing signal in the data (parameter q𝑞qitalic_q), which the other meta-learners did not have access to.
**B**: This kind of error control is much less strict than the typical family-wise error rate (FWER) or FDR control one would... | BAC | CAB | BAC | BAC | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> A larger deviation indicates a higher contribution of that variable to the anomaly. 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... | **A**: This is achieved by comparing the observed values of variables with their corresponding expected values.
**B**:
To interpret an anomaly detected by DepAD, we begin by identifying variables with substantial dependency deviations.
**C**: The normal dependency pattern is represented by the expected value of a va... | BAC | BAC | BAC | BAC | Selection 2 |
CB-MNL enforces optimism via an optimistic parameter search (e.g. in Abbasi-Yadkori et al. [2011]), which is in contrast to the use of an exploration bonus as seen in Faury et al. <|MaskedSetence|> [2010]. <|MaskedSetence|> In non-linear reward models, both approaches may not follow similar trajectory but may have o... | **A**: Optimistic parameter search provides a cleaner description of the learning strategy.
**B**: [2020], Filippi et al.
**C**: [2010] for a short discussion)..
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<|MaskedSetence|> <|MaskedSetence|> We plot the distribution of action duration in the dataset ActivityNet-v1.3 [7] in Fig. 1 a). We notice that actions shorter than 30 seconds dominate the distribution, but their performance is obviously inferior to longer ones with all different TAL methods (Fig. 1 b)). <|MaskedSe... | **A**: Therefore, the accuracy of short actions is a key factor to determine the performance of a TAL method.
.
**B**: An action can last from a fraction of a second to minutes in the real-world scenario as well as in the datasets [7, 15].
**C**: Though many methods (e.g., [1, 3, 9, 20, 21, 24, 42, 43, 44, 46]) in re... | CBA | CBA | CBA | CBA | Selection 1 |
E1 and E2 commented that the workflow of VisEvol is well designed. <|MaskedSetence|> <|MaskedSetence|> In that case, they had to set a strict budget before execution and perform multiple crossover and mutation stages which can take days to run. Nevertheless, he noticed that in evolutionary optimization, hundreds of s... | **A**: Although E3 expected a more linear workflow, she agreed that the combined views are better positioned at the top, with the interactive projections in the middle and the shared views at the bottom.
**B**: Finally, E1 mentioned that controlling the evolutionary process via the Sankey diagram can be time-saving.
.... | ACB | ACB | BCA | ACB | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> By doing so, we generalise the popular functional map framework to multi-matching, while guaranteeing cycle consistency, both for the shape-to-universe matchings, as well as for the shape-to-universe functional maps. This contrasts the recent ConsistentZoomOut [31] method, which do... | **A**:
We presented a novel formulation for the isometric multi-shape matching problem.
**B**: Our main idea is to simultaneously solve for shape-to-universe matchings and shape-to-universe functional maps.
**C**: Our algorithm is efficient, straightforward to implement, and montonically increases the objective func... | ABC | ABC | CBA | ABC | Selection 2 |
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**: This order allows us to establish all the antipodality relations in a faster time.
**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 algorithms in [3, 22]).
**C**: We ov... | BCA | BCA | ABC | BCA | Selection 4 |
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. <|MaskedSetence|> SLIM combined the SLIM with the spectral method based on DCSBM for community detection. <|MaskedSetence|> Therefore, it is worth modifying... | **A**: And the SLIM method outperforms state-of-art methods in many real and simulated datasets.
**B**: Numerical results of simulations and substantial empirical datasets in Section 5 show that our proposed Mixed-SLIM indeed enjoys satisfactory performances when compared to the benchmark methods for both community de... | CAB | CAB | CAB | CAB | Selection 1 |
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. <|MaskedSetence|> (2018); Zhang et al. (2018); Tripuraneni et al. (2018); Boumal et al. <|MaskedSetence|> <|MaskedSetence|> (2019); Weber and Sra... | **A**: (2019); Zhou et al.
**B**: (2017); Agarwal et al.
**C**: (2018); Bécigneul and Ganea (2018); Zhang and Sra (2018); Sato 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 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 latent variable for each task to present task-specific information and indicate the correspond agent’s belief..
**B**: Privile... | BCA | CBA | BCA | BCA | Selection 1 |
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. FirstFit is another simple heuristic that places an item into the first bin of suffici... | **A**: BestFit works similarly, except that it places the item into the bin of minimum available capacity, which can still fit the item.
**B**: NextFit has a competitive ratio of 2, while both FirstFit and BestFit are 1.7-competitive (?, ?).
**C**: Improving upon this performance requires more sophisticated algorithm... | ABC | ABC | CAB | ABC | Selection 4 |
<|MaskedSetence|> First, we evaluate the generative capabilities of the model. Second, we provide the reconstruction result with respect to reference approaches. Finally, we check the quality of generated meshes, comparing our results to baseline methods. <|MaskedSetence|> <|MaskedSetence|> We denote LoCondA-HC when... | **A**: In this section, we describe the experimental results of the proposed method.
**B**: We also set λ=0.0001𝜆0.0001\lambda=0.0001italic_λ = 0.0001.
**C**: Throughout all experiments, we train models with Chamfer distance.
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Paper organization. This paper is organized as follows. Section 2 presents a saddle point problem of interest along with its decentralized reformulation. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> | **A**: In Section 3, we provide the main algorithm of the paper to solve such kind of problems.
**B**: Finally in Section 5, we show how the proposed algorithm can be applied to the problem computing Wasserstein barycenters .
.
**C**: In Section 4, we present the lower complexity bounds for saddle point problems with... | CBA | ACB | ACB | ACB | Selection 3 |
The length of a cycle is its number of edges. <|MaskedSetence|> 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. In more concrete terms this problem is equivalent to finding the cycle basis with the sparsest cycle matr... | **A**: 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.
**B**: In [5] a unified perspective of the problem is presented.
**C**: For example in [10] a remarkable reduction is constructed to prove that the MCB prob... | CAB | ABC | ABC | ABC | Selection 3 |
The frontend of FeatureEnVi is implemented in JavaScript using Vue.js [109], D3.js [110], and Plotly.js [111], and the backend is written in Python with Flask [112] and Scikit-learn [84]. The use cases and experiments were performed on a MacBook Pro 2019 with a 2.6 GHz (6-Core) Intel Core i7 CPU, an AMD Radeon Pro 5300... | **A**: In general, the efficiency of FeatureEnVi could be improved in numerous ways as already indicated before.
.
**B**: The time measured is combined for the performance of the user actions and computational analyses as reported in every use case or case study (cf.
**C**: These numbers can be rather subjective when... | BCA | BAC | BCA | BCA | Selection 1 |
<|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**: High-precision trajectories or set points can be generated prior to the actual machining process following various optimization methods, including MPC, feed-forward PID control strategies, or iterative-learning control [6, 7], where friction or vibration-induced disturbances can be corrected.
**B**: The approac... | CAB | ACB | CAB | CAB | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> For instance, the widely used Colored MNIST dataset, where colors and digits are spuriously correlated with each other, is setup differently across papers. <|MaskedSetence|> For CelebA, [46] uses ResNet-18 whereas [50] uses ResNet-50, but the comparison was done without taking thi... | **A**: So far, there is no study comparing methods from either group comprehensively.
**B**: Often papers fail to compare against recent methods and vary widely in the protocols, datasets, architectures, and optimizers used.
**C**: Some use it as a binary classification task (class 0: digits 0-4, class 1: digits: 5-9... | ABC | ABC | ABC | ABC | Selection 1 |
<|MaskedSetence|> They collect data when the user is clicking a mouse, this is based on the assumption that users are gazing at the position of the cursor when clicking the mouse [146]. They use online learning to fine-tune their model with the calibration samples.
Some studies investigate the relation between the gaz... | **A**: Salvalaio et al. implicitly collect calibration data when users are using computers.
**B**: They minimize the difference between the probability distribution of predicted gaze and ground truth [145]..
**C**: Wang et al. introduce a stochastic calibration procedure.
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<|MaskedSetence|> To do so, we firstly normalize all face images into 240 ×\times× 240 pixels. <|MaskedSetence|> The principle of this technique is to divide the image into 100 fixed-size square blocks (24 ×\times× 24 pixels in our case). <|MaskedSetence|> Finally, we eliminate the rest of the blocks as presented in... | **A**: Next, we partition a face into blocks.
**B**: Then we extract only the blocks including the non-masked region (blocks from number 1 to 50).
**C**: The next step is to apply a cropping filter in order to extract only the non-masked region.
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<|MaskedSetence|> <|MaskedSetence|> In parallel, linear size arithmetic for sized inductive types [CK01, Xi01, BR06] was generalized to support coinductive types as well [Sac14]. We present, to our knowledge, the first sized type system for a concurrent programming language as well as the first system to combine both... | **A**: As we mentioned in the introduction, we use unbounded quantification [Vez15] in lieu of transfinite sizes to represent (co)data of arbitrary height and depth.
**B**: Sized (co)inductive types [BFG+04, Bla04, Abe08, AP16] gave way to sized mixed inductive-coinductive types [Abe12, AP16].
**C**: Sized types are ... | CBA | ACB | CBA | CBA | Selection 3 |
The whole FairCMS-II scheme is summarized as follows. First, suppose an owner rents the cloud’s resources for media sharing, the owner and the cloud execute Part 1 as shown in Fig. <|MaskedSetence|> <|MaskedSetence|> 6. <|MaskedSetence|> | **A**: Then, suppose the k𝑘kitalic_k-th user makes a request indicating that he/she wants to access one of the owner’s media content 𝐦𝐦\mathbf{m}bold_m, the entities execute Part 2 after the k𝑘kitalic_k-th user is authorized by the owner as shown in Fig.
**B**: Finally, the arbitration and traitor tracing process ... | CAB | ACB | CAB | CAB | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> (2017), neural language processing Marcheggiani and Titov (2017); Yao et al. (2019), and recommender systems Wang et al. (2019); Wu et al. (2019).. | **A**: (2017); Veličković et al.
**B**: (2018) have recently emerged as an effective class of models for capturing high-order relationships between nodes in a graph and have achieved state-of-the-art results on a variety of tasks such as computer vision Li et al.
**C**:
Currently, Graph Neural Networks (GNN) Kipf an... | CAB | CAB | CAB | ABC | Selection 1 |
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**: 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-concorda... | BAC | BAC | BCA | BAC | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Each of these routines is performed in a separate pass over the edges. The Backtrack-Stuck-Structures method backtracks active paths that were not extended, but does not require a fresh pass. In total, a Pass-Bundle requires 3333 passes.. | **A**: The term Pass-Bundle refers to multiple passes during which those routines are executed.
**B**:
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.
**C**: Precisely, the routines are: (1) extend str... | BAC | BAC | ACB | BAC | Selection 4 |
For this case we present Algorithm 2. <|MaskedSetence|> Here, as in Algorithm 1, the proximal operator is computed inexactly. 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 Fa... | **A**: This algorithm is the Tseng method [44] with a resolvent/proximal operator calculation (4).
**B**: The problem (4) is divided into two minimization subproblems, by X𝑋Xitalic_X, and by Y𝑌Yitalic_Y.
**C**: The following theorem states the convergence rate of Algorithm 2 with Accelerated Gradient Descent.
.
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Measuring convergence to NE (NE Gap, Lanctot et al. (2017)) is suitable in two-player, constant-sum games. However, it is not rich enough in cooperative settings. We propose to measure convergence to (C)CE ((C)CE Gap in Section E.4) in the full extensive form game. A gap, ΔΔ\Deltaroman_Δ, of zero implies convergence t... | **A**: Both gap and value metrics need to be evaluated under a meta-distribution.
**B**: If it fails to find novel policies at an acceptable rate, this could be evidence it is not performing well.
**C**: If using a (C)CE MS and the gap is positive, it is guaranteed to find a novel BR policy..
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Another line of work (e.g., Gehrke et al. (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. This builds on intuition that average-case privacy can be viewed from a Bay... | **A**: This perspective was used Shenfeld and Ligett (2019) to propose a stability notion which is both necessary and sufficient for adaptive generalization under several assumptions.
**B**: Unfortunately, these definitions have at best extremely limited adaptive composition guarantees.
**C**: Triastcyn and Faltings ... | CAB | ABC | ABC | ABC | Selection 4 |
The remainder of the paper is organized as follows. After presenting preliminaries on graphs and sets in Section 2, we prove the mentioned hardness results in Section 3. <|MaskedSetence|> In Section 5 we show how color coding can be used to find a large feedback vertex cut, if one exists. <|MaskedSetence|> <|MaskedS... | **A**: We present structural properties of antlers and how they combine in Section 4.
**B**: We also prove that, given a large feedback vertex cut, we can shrink it while preserving the antlers in the graph.
**C**: Our main results are derived in Section 6, where we show how color coding can be used to efficiently fi... | ABC | ABC | ABC | ABC | Selection 4 |
<|MaskedSetence|> [18] proposed a domain verification discriminator to pull close the foreground domain and background domain. Similarly, Cong et al. [19] formulated image harmonization as background-guided domain translation task, in which the domain code of background is directly used to guide the harmonization proc... | **A**: By treating different capture conditions as different domains, Cong et al.
**B**: Inspired by [19], Valanarasu et al.
**C**: One byproduct of [19] is predicting the inharmony level of an image by comparing the domain codes of foreground and background, so that we can selectively harmonize those apparently inha... | ACB | BAC | ACB | ACB | Selection 3 |
Comprehensiveness: Fig. <|MaskedSetence|> <|MaskedSetence|> 1(b)) to to capture a wider range of urban phenomena. For instance, we have transformed raw mobility data of taxi movements into region-based measurements such as taxi flows, pickups, and idle driving time. <|MaskedSetence|> | **A**: Furthermore, we have processed the raw data into several sub-datasets (as shown in Fig.
**B**: These measurements are crucial in revealing the state of the transportation market and citizen activities.
.
**C**: 1(a), illustrates that CityNet comprises three types of raw data (mobility data, geographical data, ... | CAB | CAB | ACB | CAB | Selection 2 |
In this section, four general classes of models are discussed. <|MaskedSetence|> <|MaskedSetence|> The third class contains the models that are specifically trained to yield a prediction interval, while the last class constitutes a framework that allows to turn any given point predictor into a valid interval estimat... | **A**: The first class has its roots in probability theory and, therefore, can be expected to have better theoretical guarantees for the validity and behaviour.
**B**: The second class consists of methods that are built from a collection of estimators and generally have a superior predictive performance when compared ... | ABC | BCA | ABC | ABC | 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. <|MaskedSetence|> homophonic or polyphonic music.
Utilising the POP909 dataset \textcitepop909, we can develop a model that class... | **A**: We refer to “single-track” as MIDI files containing only one track, which is in contrast to multi-track MIDI files that have multiple tracks.
**B**: While melody extraction is a note-level classification task, melody track identification is a track-level task.
**C**: The latter is also an important symbolic mu... | ABC | ABC | CAB | ABC | Selection 2 |
<|MaskedSetence|> A DL-enabled semantic communication system for image transmission, named JSCC, has been developed in[14]. <|MaskedSetence|> Similar to text transmission, IoT applications for image transmission have been carried out. <|MaskedSetence|> A deep joint source-channel coding architecture, name DeepJSCC, ... | **A**: Based on JSCC, an image transmission system, integrating channel output feedback, can improve image reconstruction[15].
**B**: Particularly, a joint image transmission-recognition system has been developed in[16] to achieve high recognition accuracy.
**C**:
Recently, there are also investigations on semantic ... | CAB | CAB | CAB | ABC | Selection 2 |
<|MaskedSetence|> Multi-view projection-based methods[20, 21, 22] project the 3D data into 2D from multiple viewpoints, therefore they can easily process the projected data on 2D convolution networks. However, these methods suffer from occlusion, view-point selection, misalignment, and other defects that may limit the... | **A**: These methods can often achieve good segmentation performance but severely suffer from heavy memory and time consumption.
**B**: PointCNN[26] and PointConv[27] formulate convolution operations in 3D using KNNs for each point.
**C**: There are three categories for 3D semantic segmentation methods: projection-ba... | CAB | CAB | BCA | CAB | Selection 2 |
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]. Moreov... | **A**: We report our results on the official settings of IoU ≥0.7absent0.7\geq 0.7≥ 0.7 for cars.
.
**B**: 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... | BCA | BCA | BCA | CBA | Selection 3 |
ICDAR2015 [44] includes multi-orientated and small-scale text instances. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> It contains 300 training images and 200 testing images with word-level annotation. Here, we follow the previous methods [35, 8] and add 400 training images from TR400 [46] to extend this dat... | **A**: It contains 1,000 training and 500 testing images.
**B**: Its ground truth is annotated with word-level quadrangles.
**C**:
MSRA-TD500 [45] is dedicated to detecting multi-oriented long non-Latin texts.
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<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Most parallel sorting algorithms are variants of standard, well-known sorting algorithms adapted to GPU hardware architecture. For example, Cederman designed a quick sort for the GPU platform Cederman2008 , and Peters proposed an adaptive bitonic sorting algorith... | **A**: Parallel software platforms can be implemented using high-level programming frameworks for specific hardware architectures Chen2009SA .
**B**: The Compute Unified Device Architecture (CUDA) is a parallel computing platform for general computing on GPUs.
**C**:
The hardware architecture of modern processors us... | CAB | CAB | CAB | CAB | Selection 1 |
The authors would like to thank Mingjian Ding, and Baoxuan Zhu for providing an alternative proof of the Hurwitz stability of polynomials (25). They also thank Jarle Sogn for communicating on Schur complement based preconditioners.
The work of M. Cai is partially supported by the NIH-RCMI grant through 347 U54MD013376,... | **A**: RCJC20200714114556020, JCYJ20170818153840322 and JCYJ20190809150413261, and Guangdong Provincial Key Laboratory of Computational Science and Material Design No.
**B**: The work of J.
**C**: Li is partially supported by the National Natural Science Foundation of China No.
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<|MaskedSetence|> 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. <|MaskedSetence|> This modification would significantly increase the communicatio... | **A**: The client with the label information calculates the loss and the partial derivatives, which can then be propagated back to the other clients for use in the local gradient steps.
**B**: However, in cases when the labels are sensitive and sharing the labels for a sample ID across silos is not feasible, the label... | BAC | BAC | BAC | BAC | Selection 1 |
<|MaskedSetence|> In the book, four different definitions of matrix pseudospectra are introduced and shown to be equivalent under certain conditions. <|MaskedSetence|> Additionally, for diagonalizable but not necessarily normal matrices, the corresponding Bauer-Fike theorem is presented, which can be found in (trefet... | **A**: The properties of pseudospectra are also discussed, along with a characterization of the pseudospectra for normal matrices.
**B**: The pseudospectra of finite-dimensional matrices and their extension to linear operators in Banach space have been extensively investigated and summarized in the classical book by T... | BAC | BCA | BAC | BAC | Selection 3 |
Figure 5 compares our results with the ones of the representative methods including the current state-of-the-arts on the three benchmarks. It can be seen, as a classical patch-based method, PatchMatch [2] fails in handling large holes. PConv [13] is suitable for irregular corruptions, but obvious artifacts can be obser... | **A**: DeepFilllv2 [36] suffers from over-smoothing predictions and distorted structures.
**B**: With the Recurrent Feature Reasoning module, RFR [11] yields competitive results; however, the details are still not so elegant as ours (the face and sky in Figure 5 serve as examples).
**C**: MED [14] attempts to correla... | ABC | ABC | ABC | ABC | Selection 3 |
In Figure 1, we present the performance of Subgoal Search. <|MaskedSetence|> The success rate is measured on 1000100010001000 instances of a given problem (which results in confidence intervals within ±0.03plus-or-minus0.03\pm 0.03± 0.03). <|MaskedSetence|> <|MaskedSetence|> For Sokoban, we use Algorithm 9 to realiz... | **A**: We measure the success rate as a function of the search budget.
**B**: For INT and Rubik’s Cube, we include both the subgoal generated by SUB_GENERATE and the nodes visited by GET_PATH (as they induce a significant computational cost stemming from using low-level policy π𝜋\piitalic_π in Algorithm 2).
**C**: F... | ACB | ACB | ACB | ACB | Selection 3 |
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. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Some work has tried to use images of Chinese characters as glyph ... | **A**: A common strategy is to give every Chinese character a unique hexadecimal string, such as ‘UTF-8’ and ‘GBK’.
**B**: In other words, the closeness in the hexadecimal string value can not represent the similarity in their shapes.
**C**: However, this kind of strategy processes Chinese characters as independent s... | ABC | ACB | ACB | ACB | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> Since the shared modules can learn from many tasks, they can be sufficiently trained and can generalize better, which is particularly meaningful for low-resource scenarios. On the other hand, task-specific modules learn features that are specific to a certain task. Compared with sh... | **A**: The shared modules learn shared features from multiple tasks.
**B**: The idea behind the modular MTL architecture is simple: breaking an MTL model into shared modules and task-specific modules.
**C**: The robustness of shared modules and the flexibility of task-specific modules makes modular architectures suit... | BAC | BAC | CAB | BAC | Selection 1 |
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|> <|MaskedSetence|> Have you looked at your article/paper in the HTML versio... | **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**: The XML files are used to produce the final pr... | BAC | BAC | BAC | ACB | Selection 1 |
A natural question is how contributions and average degree (number of outgoing links) are impacted by the information treatment. <|MaskedSetence|> <|MaskedSetence|> Contributions still show a tendency to decrease over time, and the rate of this decay is not significantly impacted by the treatment. <|MaskedSetence|> ... | **A**: Links, on the other hand, exhibit an additional differential dynamic.
**B**: We find that the treatment substantially increases both contributions and linking.
**C**: The dynamics of these variables can be found in Figures 1(a) and 1(b) along with corresponding estimation results in columns (1) and (2) in Tabl... | CBA | CBA | ABC | CBA | Selection 2 |
In recent years, the field of SISR has developed rapidly, and a large number of excellent models have emerged. <|MaskedSetence|> 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. This will affect the perfor... | **A**: According to different design targets, we divide these methods into three categories: efficient network/mechanism design methods, perceptual quality methods, and additional information utilization methods.
.
**B**: However, it is worth noting that most of these models use simulated datasets for testing and trai... | BCA | BCA | BCA | BAC | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> Otherwise, we can create a trainable downsampling module representing the kernel and optimize its weights in an end-to-end manner. <|MaskedSetence|> Their method relies on the assumption that a satisfactory kernel should preserve the distribution of patches in the image. For Neura... | **A**: If the downsampling kernel is known, then the best approach is to simply backpropagate through that kernel (assuming it is differentiable).
**B**: We revisit the technique introduced in [29] by using an identical deep linear network to approximate the kernel.
**C**: The downsampling operation can be implemente... | CAB | CAB | ACB | CAB | Selection 1 |
<|MaskedSetence|> For logistic contextual bandits, the implementation of exact TS (i.e. the policy that draws its sample from the exact posterior) is infeasible due to the intractability of the posterior distribution. It is therefore necessary to sample from an approximation of the posterior to implement a TS-like app... | **A**: Urteaga and Wiggins,, 2018)..
**B**:
The present paper is the first work we aware of that specifically applies TS to apple tasting, but previous work has considered its use for logistic bandits.
**C**: We investigate such a Polya-Gamma augmentation-based approximation in the context of apple tasting in Sectio... | BCA | CAB | BCA | BCA | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> This works well in unfairness detection, where there are few memory slots and they are associated with many samples; not so well in claim detection, where there are many memory slots, each associated with one or very few samples. In our experiments, this led priority-based strategi... | **A**: For instance, a parametric model could be trained to embed input texts near their target memory slots into a latent space [68]..
**B**: However, the introduced priority-based strategies lack proper conditioning on the input, but rather learn the dataset-level importance of each memory slot.
**C**:
The introdu... | CBA | BCA | CBA | CBA | Selection 1 |
However, the progress of sentiment dependency-based methods, such as the work by Zhang et al. (2019); Zhou et al. <|MaskedSetence|> <|MaskedSetence|> (2021a); Dai et al. <|MaskedSetence|> | **A**: (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..
**B**: (2021); Li et al.
**C**: (2020); Tian et al.
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We use IBMQ quantum computers via Qiskit (IBM, [n. d.]) APIs. We study 6 devices, with #qubits from 5 to 15 and Quantum Volume from 8 to 32. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> The noise models we used are off-the-shelf ones updated by IBMQ team.. | **A**: We also employ Qiskit for compilation.
**B**: All experiments run 8192 shots.
**C**: The optimization level is set to 2 for all experiments.
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<|MaskedSetence|> 4. <|MaskedSetence|> In contrast, EDA respectively obtains 0.896/0.998, 0.866/0.998, 0.872/0.983, 0.969/1.000, 0.913/1.000, 0.894/0.966, 0.752/0.917, and 0.702/0.833 on the eight sequences. From the results, we can see that the second stage of the TSW algorithm contributes significantly on improving... | **A**: This effectively reduces the ambiguity in the model selection process..
**B**:
The AOR and AR results obtained by the proposed EDA and its variant EDA-SW on the eight sequences in ECD and EED are shown in Fig.
**C**: EDA-SW respectively achieves AOR/AR scores of 0.763/0.938, 0.757/0.953, 0.749/0.883, 0.901/1.... | ACB | BCA | BCA | BCA | Selection 2 |
TABLE IV: Unsupervised knowledge distillation. <|MaskedSetence|> <|MaskedSetence|> ††{\dagger}† indicates using a momentum encoder as MoCo.v2. <|MaskedSetence|> KD denotes the knowledge distillation loss. H+AW denotes the Huber loss and angle-preserving loss in RKD.. | **A**: The teacher model is ResNet-50 pre-trained by MoCo.v2.
**B**: Top-1 accuracy (%) under linear evaluation on STL-10.
**C**: SSL denotes the InfoNCE loss.
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<|MaskedSetence|> We compared it with state-of-the-art methods under different computation budgets in Table 6.
Our NAS method consistently outperforms existing techniques for tiny networks in terms of computation-accuracy trade-off. Existing techniques usually need a scaling method to scale down the searched network a... | **A**: The accuracy improvement is more significant under a tiny computation setting (≤\leq≤25M).
**B**: To show the advantage of our method, we conduct experiments on MobileNetV3 [23] space by extending it to support different r𝑟ritalic_r’s and w𝑤witalic_w’s.
**C**: With the extended search space, all our models a... | BCA | BCA | BCA | BCA | Selection 4 |
In CGCL, multiple graph encoders compute their own contrastive losses based on representations learned by others, and optimize their losses collaboratively. <|MaskedSetence|> The assembly we use includes GIN, GCN and GAT. <|MaskedSetence|> For a further analysis, we list the RDMs correlation between pairs of GIN, GC... | **A**: Such accordance further proves the rationality of Asymmetry Coefficient proposed in Section 3.3.2..
**B**: To check the reliability of collaborative mechanism, we empirically analyze the convergence in the optimization process of each individual encoder on PROTEINS and IMDB-BINARY.
**C**: In Figure 5, we notic... | BCA | BCA | CBA | BCA | Selection 1 |
<|MaskedSetence|> The work of Tomasz Korbak was supported by the Leverhulme Doctoral Scholarship. 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. <|MaskedSetence|> Our e... | **A**: PLG/2019/012498.
**B**:
The work of Piotr Miłoś was supported by the Polish National Science Center grant UMO-2017/26/E/ST6/00622.
**C**: We would like to thank the Neptune team for providing us access to the team version and technical support..
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A promising research direction is to learn CBFs from data. <|MaskedSetence|> The authors in [38] proposed learning limited duration CBFs and the work in [39] learns signed distance fields that define a CBF. <|MaskedSetence|> The authors in [41] learn parameters associated with the constraints of a CBF to improve fea... | **A**: The authors in [36] construct CBFs from safe and unsafe data using support vector machines, while authors in [37] learn a set of linear CBFs for clustered datasets.
**B**: In [40], a neural network controller is trained episodically to imitate an already given CBF.
**C**: In this paper, we focus on state estim... | ABC | ABC | BAC | ABC | Selection 1 |
(b) To fit DCDFM, an efficient spectral clustering algorithm called nDFA is designed. We build theoretical framework on consistent estimation for the proposed algorithm under DCDFM. <|MaskedSetence|> Especially, when DCDFM reduces to DFM, our theoretical results are consistent with those under DFM. <|MaskedSetence|>... | **A**: Numerical results of both simulated and real-world networks show the advantage of introducing node heterogeneity to model weighted networks..
**B**: When DCDFM degenerates to DCSBM, our results also match classical results under DCSBM.
**C**: Benefited from the distribution-free property of DCDFM, our theoreti... | CBA | BCA | CBA | CBA | Selection 1 |
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