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Of course, the numerical scheme and the estimates developed in Section 3.1 hold. However, several simplifications are possible when the coefficients have low-contrast, leading to sharper estimates. <|MaskedSetence|> First we consider that T~~𝑇\tilde{T}over~ start_ARG italic_T end_ARG can be nonzero. <|MaskedSetence|... | **A**: Also, our scheme is defined by a sequence of elliptic problems, avoiding the annoyance of saddle point systems.
**B**: We remark that in this case, our method is similar to that of [MR3591945], with some differences.
**C**: We had to reconsider the proofs, in our view simplifying some of them.
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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. We hence, consider this a weak learning process. Inspired by [... | **A**: The model utilizes CNN to extract a sequence of higher-level phrase representations, which are fed into a long short-term memory (LSTM) RNN to obtain the tweet representation.
**B**: Most of the previous work [6, 11] on tweet level only aims to measure the trustfulness based on human judgment (note that even if... | CBA | BAC | BAC | BAC | Selection 2 |
<|MaskedSetence|> The best feature is related to sentiment polarity scores. There is a big bias between the sentiment associated to rumors and the sentiment associated to real events in relevant tweets. In specific, the average polarity score of news event is -0.066 and the average of rumors is -0.1393, showing that r... | **A**: For analysing the employed features, we rank them by importances using RF (see 4).
**B**: Furthermore, we would expect that verified users are less involved in the rumor spreading.
**C**: Also interestingly, the feature“IsRetweet” is also not as good a feature as expected, which means the probability of people... | ABC | ABC | CBA | ABC | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> We adapted the L2R RankSVM [12]. The goal of RankSVM is learning a linear model that minimizes the number of discordant pairs in the training data. We modified the objective function of RankSVM following our global loss function, which takes into account the temporal feature specif... | **A**: 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.
**B**: The temporal and type-dependent ranking model is learned by minimizing the followin... | CAB | CAB | CAB | BCA | Selection 2 |
Table 1 shows basic patient information. <|MaskedSetence|> Body weight, according to BMI, is normal for half of the patients, four are overweight and one is obese. The mean BMI value is 26.9. <|MaskedSetence|> <|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**: Only one of the patients suffers from diabetes type 2 and all are in ICT therapy.
**C**: Half of the patients are female and ages range from ... | CBA | CBA | CBA | BCA | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> (2018); Liu and Han (2018), but we argue that a carefully chosen decoder architecture, similar to the model by Pan et al. (2017), results in better approximations. Here we employed three upsampling blocks consisting of a bilinear scaling operation, which doubled the number of rows ... | **A**: To restore the original image resolution, extracted features were processed by a series of convolutional and upsampling layers.
**B**: Figure 2 visualizes the overall architecture design as described in this section.
.
**C**: Previous work on saliency prediction has commonly utilized bilinear interpolation for... | ACB | BCA | ACB | ACB | Selection 4 |
<|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 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**: This is mainly motivated by two aspects.
**C**:
Our strongest positive result ab... | CBA | CBA | CBA | ACB | Selection 3 |
While SimPLe is able to learn more quickly than model-free methods, it does have limitations. First, the final scores are on the whole lower than the best state-of-the-art model-free methods. <|MaskedSetence|> <|MaskedSetence|> The complex interactions between the model, policy, and data collection were likely respo... | **A**: Another, less obvious limitation is that the performance of our method generally varied substantially between different runs on the same game.
**B**: This can be improved with better dynamics models and, while generally common with model-based RL algorithms, suggests an important direction for future work.
**C... | BAC | BAC | CAB | BAC | Selection 2 |
In the literature review, Gorilla [2] is able to switch between bipedal and quadrupedal walking locomotion modes autonomously using criteria developed based on motion efficiency and stability margin. WorkPartner [8] demonstrated its capability to seamlessly transition between two locomotion modes: rolling and rolking.... | **A**: The threshold values for locomotion transition criteria were established empirically through prior experimental evaluations conducted on the target terrains.
**B**: The rolking mode, a combination of rolling and walking, empowered WorkPartner to navigate with enhanced agility.
**C**: However, it’s noteworthy t... | BCA | BAC | BCA | BCA | Selection 4 |
The algorithm classifies items according to their size. Tiny items have their size in the range (0,1/3]013(0,1/3]( 0 , 1 / 3 ], small items in (1/3,1/2]1312(1/3,1/2]( 1 / 3 , 1 / 2 ], critical items in (1/2,2/3]1223(1/2,2/3]( 1 / 2 , 2 / 3 ], and large items in (2/3,1]231(2/3,1]( 2 / 3 , 1 ]. In addition, the algorithm... | **A**: Large items are placed alone in large bins, which are opened at each arrival.
**B**: Imagine that the encoded advice overestimates the
number of critical items.
**C**: Each critical item is placed in one of the critical bins.
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<|MaskedSetence|> <|MaskedSetence|> For instance, when using SS3 we only need to store the confidence vector303030In case of ADD, a 2-dimensional vector. <|MaskedSetence|> However, when working with classifiers not supporting incremental classification, for every user we need to store either all her/his writings to ... | **A**: of every user and then simply update it as more content is created.
**B**:
It is worth noting that the difference in terms of space complexity is also very significant.
**C**: For classifiers supporting incremental classification, like SS3 or MNB, only a small vector needs to be stored for each user.
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In this part, we investigate the influence of environment dynamic on the network states. With different scenarios’ dynamic degree τ∈(0,∞)𝜏0\tau\in(0,\infty)italic_τ ∈ ( 0 , ∞ ), PBLLA and SPBLLA will converge to the maximizer of goal function with different altering strategy probability. Fig. 6 presents the influence... | **A**: It does not result from the bad performance of algorithms but from the highly dynamic scenarios.
**B**: In the rest simulations, similar phenomena can also be observed..
**C**: We can find out that the fluctuation during converging is severe in both algorithms, which is different from other related works.
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<|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|> Over cour... | **A**: The term Dropout methods was used to refer to them in general[14].
**B**:
Deep neural networks are the state of the art learning models used in artificial intelligence.
**C**: Dropout was first introduced in 2012 as a regularization technique to avoid over-fitting[12], and was applied in the winning submissio... | BCA | ACB | BCA | BCA | Selection 4 |
In contrast to natural images, it is difficult to tabulate and summarize the performance of medical image segmentation methods because of the vast number of (a) medical imaging modalities and (b) medical image segmentation datasets. Figure 15 presents a breakdown of the various attributes of the medical image segmentat... | **A**: Figure 15 (c) shows the distribution of the number of samples across datasets from multiple modalities.
**B**: As shown in Figure 15 (b), the papers covered in this review use 13 medical imaging modalities.
**C**: We observe that modalities which are expensive to acquire and annotate (such as electron microsco... | BAC | BCA | BAC | BAC | Selection 3 |
Welbl (2014) and Biau et al. <|MaskedSetence|> The authors propose a method that maps random forests into neural networks as a smart initialization and then fine-tunes the networks by backpropagation. Two training modes are introduced: independent and joint. Independent training fits all networks one after the other ... | **A**: (2019) follow a similar strategy.
**B**: Additionally, the authors evaluate sparse and full connectivity..
**C**: Joint training concatenates all tree networks into one single network so that the output layer is connected to all leaf neurons in the second hidden layer from all decision trees and all parameters... | CBA | ACB | ACB | ACB | Selection 4 |
To answer this question, we propose the first policy optimization algorithm that incorporates exploration in a principled manner. In detail, we develop an Optimistic variant of the PPO algorithm, namely OPPO. Our algorithm is also closely related to NPG and TRPO. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|>... | **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**: To encourage exploration, we explicitly incorporate a bonus function into the action-value function, which quant... | BCA | ACB | ACB | ACB | Selection 2 |
<|MaskedSetence|> Henry Adams and Dr. Johnathan Bush for very useful feedback about a previous version of this article. We also thank Prof. Mikhail Katz and Prof. <|MaskedSetence|> We thank Dr. Qingsong Wang for bringing to our attention the paper [76] which was critical for the proof of Theorem 1. <|MaskedSetence|>... | **A**: Finally, we thank Dr.
**B**: Michael Lesnick for explaining to us some aspects of their work.
**C**: We thank Prof.
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After choosing a projection, users will proceed with the visual analysis using all the functionalities described in the next sections. However, the hyper-parameter exploration does not necessarily stop here. <|MaskedSetence|> <|MaskedSetence|> During the exploration of the projection, if the user finds a certain patt... | **A**: The top 6 representatives (according to a user-selected quality measure) are still shown at the top of the main view (Figure 1(e)), and the projection can be switched at any time if the user is not satisfied with the initial choice.
**B**: We also provide the mechanism for a selection-based ranking of the repre... | ABC | ABC | BAC | ABC | Selection 4 |
<|MaskedSetence|> The first taxonomy has considered the source of inspiration, while the second has discriminated algorithms based on their behavior in generating new candidate solutions. We have provided clear descriptions, examples, and an enumeration of the reviewed approaches within each taxonomy category. Our stu... | **A**: These findings shed light on the ongoing debate within the nature- and bio-inspired community regarding the algorithmic contributions of recent advances in the field.
.
**B**: Additionally, a significant percentage (24%) of the reviewed proposals have been identified as versions of classical algorithms such as ... | CBA | CBA | CBA | BAC | Selection 2 |
<|MaskedSetence|> Instead, DEC and SpectralNet work better on the large scale datasets. 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 pr... | **A**: In particular, AdaGAE is stable on all datasets.
.
**B**: The adaptive learning will induce the model to exploit the high-level information.
**C**: Classical clustering models work poorly on large scale datasets.
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Methodology. <|MaskedSetence|> <|MaskedSetence|> Given a server with a globally incremental IPID on the tested network, we sample the IPID value (send a packet to the server and receive a response) from the IP addresses controlled by us. We then generate a set of packets to the server from spoofed IP addresses, belo... | **A**: We probe the IPID value again, by sending packets from our real IP address.
**B**: The idea is that globally incremental IPID [RFC6864] (Touch, 2013) values leak traffic volume arriving at the service and can be measured by any Internet host.
**C**: We use services that assign globally incremental IPID values.... | CBA | CBA | CBA | CBA | Selection 2 |
<|MaskedSetence|> However, the model can be modified to be trained on unlabeled data, simply by allowing arbitrary data samples as input to the context layer. <|MaskedSetence|> For this task, semisupervised learning techniques, such as self-labeled samples, may help. <|MaskedSetence|> The full six-gas sensor drift d... | **A**: This design introduces variation in training inputs, which makes it harder to learn consistent context patterns.
**B**:
The current design of the context-based network relies on labeled data because the odor samples for a given class are presented as ordered input to the context layer.
**C**: If the context l... | ABC | BAC | BAC | BAC | Selection 4 |
<|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]. While these constructions and the involved proofs are generally deemed quite complicated, the situation for semigroups turns out to be much simple... | **A**: The same construction can also be used to generate free monoids as automaton semigroups or monoids.
**B**:
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).
**C**: Here, the main difference is that the f... | BCA | BAC | BAC | BAC | Selection 3 |
<|MaskedSetence|> However, future methods must seek to verify that performance gains are not stemming from spurious sources by using an experimental setup similar to that presented in this paper. We recommend that both train and test accuracy be reported, because a model truly capable of visual grounding would not cau... | **A**: While our results indicate that current visual grounding based bias mitigation approaches do not suffice, we believe this is still a good research direction.
**B**: Another alternative is to use tasks that explicitly test grounding, e.g., in visual query detection an agent must output boxes around any regions o... | ACB | ACB | CBA | ACB | Selection 2 |
<|MaskedSetence|> .com, .org, and .net make up a major share of the corpus covering 63%, 5% and 3% respectively. Country-level domains like .uk, .au, .ca and .du show the geographic variety of the sources of the corpus covering 12%, 4%, and 2% respectively. The distribution of popular TLDs (.com, .org, .net) roughly m... | **A**: Moreover, CommonCrawl release statistics estimating the representativeness of monthly crawls which support the claim that monthly crawl archives and in turn the PrivaSeer Corpus are a representative sample of the web.
In addition to monthly crawl dumps, Common Crawl releases web graphs with PageRanks of the doma... | BCA | BAC | BAC | BAC | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> automated approaches are essential concepts when developing a VA system [49]. <|MaskedSetence|> Synchronous and asynchronous collaboration can empower visualizations dedicated to particular tasks [17]. Building ensembles from scratch by using various ML algorithms might require ex... | **A**: The careful design, choice, and arrangement of these aspects and the balance between human-centered vs.
**B**: The process of ensemble learning generates a solution space of models (Figure 1, curved green arrow) [47] and more investigations can be done to choose between the best and most diverse models of an en... | BAC | BAC | ABC | BAC | Selection 4 |
<|MaskedSetence|> <|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. This variation manifests both between training tasks and between training and testing tasks, similarly affecting the performance... | **A**: 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].
**B**:
When applying MAML to NLP, several factors can influence the training strategy and performance of the model.
**C**: S... | BAC | BAC | BAC | BAC | Selection 1 |
The SEs of two array schemes against the transmit power with K=2𝐾2K=2italic_K = 2 t-UAVs are illustrated in Fig. 13. The TE-aware codeword selection uses the proposed Algorithm 2 and Algorithm 3. Serving as a reference, the minimum-beamwidth scheme always select the minimum beamwidth, i.e., the maximum number of anten... | **A**: As shown in Fig. 13, the sum SE of the TE-aware codeword selection scheme is better than the minimum-beamwidth codeword selection scheme.
**B**: In addition, the curve of the two-step scheme almost overlaps that of the optimal scheme, as the tracking error of elevation angle is relatively small and the optimal ... | CAB | CAB | CAB | CAB | Selection 3 |
<|MaskedSetence|> 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, TD possibly diverges (Baird, 1995; Boyan and Moore, 19... | **A**: Szepesvári, 2018; Dalal et al., 2018; Srikant and Ying, 2019) settings.
**B**: (2019) prove that TD converges to the globally optimal solution in the NTK regime.
**C**: See also the independent work of Brandfonbrener and
Bruna (2019a, b); Agazzi and Lu (2019); Sirignano and Spiliopoulos (2019), where the state... | ABC | ABC | ABC | ABC | Selection 2 |
To test the effectiveness of depth-wise LSTMs in the multilingual setting, we conducted experiments on the challenging massively many-to-many translation task on the OPUS-100 corpus Tiedemann (2012); Aharoni et al. (2019); Zhang et al. <|MaskedSetence|> <|MaskedSetence|> (2020) for fair comparison. <|MaskedSetence|... | **A**: We adopted BLEU Papineni et al.
**B**: (2020).
**C**: We tested the performance of 6-layer models following the experiment settings of Zhang et al.
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Relationship to Distortion Distribution: We first emphasize the relationship between two learning representations and the realistic distortion distribution of a distorted image. <|MaskedSetence|> <|MaskedSetence|> 5, we visualize the scatter diagram of two learning representations using 1,000 test distorted images. ... | **A**: As shown in Fig.
**B**: Therefore, the proposed representation helps to decrease the error of distortion estimation..
**C**: In detail, we train a learning model to estimate the distortion parameters and the ordinal distortions separately, and the errors of estimated results are built in the relationship to th... | BCA | CAB | CAB | CAB | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> 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. Finally, we extrapolate the solution to the original black-box problem. This overall methodology is ... | **A**: First, we develop algorithms for the simpler polynomial-scenarios model.
**B**: Our main goal is to develop algorithms for the black-box setting.
**C**: As usual in two-stage stochastic problems, this has three steps.
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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**: Then, we prove that the mean square upper bound of the coupling term between states, network graphs and noises depends on the second-order moment of the difference between optimizers’ states and the given vector.
**B**: What’s more, multiplicative noises relying on the relative states between adjacent local opt... | BCA | CAB | BCA | BCA | Selection 4 |
The advantages of MuCo are summarized as follows. First, MuCo can maintain the distributions of original QI values as much as possible. For instance, the sum of each column in Figure 3 is shown by the blue polyline in Figure 2, and the blue polyline almost coincides with the red polyline representing the distribution i... | **A**: The conducted extensive experiments also illustrate the effectiveness of the proposed method.
.
**B**: Second, the anonymization of MuCo is a “black box” process for recipients because the only difference between the original data and the anonymized data is that some original QI values are replaced with random ... | BCA | BCA | BCA | BAC | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> Mask scoring head Huang et al. (2019) adopted on the third stage gains another 2 mAP. <|MaskedSetence|> However, the convolutional mask heads adopted in all stages bring non-negligible computation and memory costs, which constrain the mask resolution and further limit the segmenta... | **A**: By enlarging the RoI size of both box and mask branches to 12 and 32 respectively for all three stages, we gain roughly 4 mAP improvement against the default settings in original paper.
**B**: Armed with DCN, GC block and SyncBN training, our HTC with Res2NetR101 backbone yields 74.58 mAP on validation set, as ... | ACB | CAB | CAB | CAB | Selection 3 |
<|MaskedSetence|> (2020). <|MaskedSetence|> (4) of Jin et al. is also bounded (but not vice versa). <|MaskedSetence|> (2020) is restricted to static regret, so we cannot directly borrow their analysis for the misspecified setting (Jin et al., 2020) to handle our dynamic regret (as defined in Eq. (1)).
. | **A**: The definition of total variation B𝐵Bitalic_B is related to the misspecification error defined by Jin et al.
**B**: However, the regret analysis in the misspecified linear MDP of Jin et al.
**C**: One can apply the Cauchy-Schwarz inequality to show that our total variation bound implies that misspecification ... | ACB | ACB | ACB | ABC | Selection 2 |
<|MaskedSetence|> This set was extracted for further analysis and will be henceforth referred to as ‘SG-75’. The details on the participant demographics of SG-75 are shown in Table 1. <|MaskedSetence|> <|MaskedSetence|> While these subsets have smaller samples, the self-reported data of the questions falling within ... | **A**: 75 of the 104 responses fulfilled the criterion of having respondents who were currently based in Singapore.
**B**: The first contains 59 responses in which respondents said that they have shared news before (referred to as ‘SharedNews-59’), and the second contains 57 responses in which respondents said that th... | ACB | ACB | ACB | CAB | Selection 3 |
<|MaskedSetence|> decentRL outperforms both GAT and AliNet across all metrics. While its performance slightly decreases compared to conventional datasets, the other methods experience even greater performance drops in this context. <|MaskedSetence|> <|MaskedSetence|> We also provide more detailed results on ZH-EN in... | **A**: Figure 4 shows the experimental results.
**B**: The reduced reliance (with GCN) on self-entity embedding contributes to its more resilient performance on datasets with new entities.
**C**: AliNet also outperforms GAT, as it combines GCN and GAT to aggregate different levels of neighbors.
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As an example, we model the transition dynamics in MDP of ‘Noisy-Mnist’ in Fig. <|MaskedSetence|> We first use an ensemble-based model that contains three individual encoder-decoder networks as a baseline. According to a resent research in model-based RL [48], the ensemble model with probabilistic neural networks achi... | **A**: Each network outputs a 512d diagonal Gaussian to model the mean and variance of each pixel.
**B**: This probabilistic-ensemble model is incorporated into model predictive control (MPC) planning for policy search, and achieves strong performance in continuous control tasks [48].
**C**: 2.
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The model has two parts. 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, supervise... | **A**: where we significantly constrain the capacity of the learned representation and heavily regularize the model to produce independent factors.
**B**: In the reconstruction, the rest of the details are averaged, resulting in a blurry image (1b).
**C**: The goal of the second part of the model, is to add the detai... | CAB | ABC | ABC | ABC | Selection 3 |
The structural computer used an inverted signal pair to implement the reversal of a signal (NOT operation) as a structural transformation, i.e. a twist, and four pins were used for AND and OR operations as a series and parallel connection were required. However, one can think about whether the four pin designs are the... | **A**: Let’s look at the role of the four pins that transmit signals in a 4 pin based signal system.
**B**: In this case, the study inferred that of the four wires, two wires acting as ground can be replaced by one wire, and based on this reasoning, the method in which the 4 pin signal system can be described as 3-pin... | ACB | ACB | ACB | CAB | Selection 1 |
We apply multi-view stacking to each simulated training set, using logistic ridge regression as the base-learner. Once we obtain the matrix of cross-validated predictions 𝒁𝒁\bm{Z}bold_italic_Z, we apply the seven different meta-learners. <|MaskedSetence|> <|MaskedSetence|> the average proportion of views truly rela... | **A**: the average proportion of the selected views that are not related to the outcome.
.
**B**: To assess classification performance, we generate a matching test set of 1000 observations for each training set, and calculate the classification accuracy of the stacked classifiers on this test set.
**C**: To assess vi... | BCA | BCA | CBA | BCA | Selection 2 |
Regression trees are favorable since they can handle both linear and non-linear relationships. <|MaskedSetence|> Bagging involves training multiple regression trees with re-sampled data sets aggregating predictions of multiple trees. This technique reduces the impact of anomalies in several ways: 1) re-sampling potent... | **A**: To enhance prediction accuracy, bagging is recommended in combination with regression trees.
**B**: For instance, a robust version of random forest in [51] uses the median instead of the mean for prediction aggregation.
**C**: This idea has been applied to bagging CART trees, referred to as mCART.
.
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CB-MNL enforces optimism via an optimistic parameter search (e.g. <|MaskedSetence|> <|MaskedSetence|> [2020], Filippi et al. [2010]. <|MaskedSetence|> In non-linear reward models, both approaches may not follow similar trajectory but may have overlapping analysis styles (see Filippi et al. [2010] for a short discus... | **A**: Optimistic parameter search provides a cleaner description of the learning strategy.
**B**: in Abbasi-Yadkori et al.
**C**: [2011]), which is in contrast to the use of an exploration bonus as seen in Faury et al.
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Graph neural networks (GNN) are a useful model for exploiting correlations in irregular structures [17]. <|MaskedSetence|> G-TAD [44] breaks the restriction of temporal locations of video snippets and uses a graph to aggregate features from snippets not located in a temporal neighborhood. <|MaskedSetence|> <|MaskedS... | **A**: BC-GNN [3] improves localization by modelling the boundaries and content of temporal proposals as nodes and edges of a graph neural network.
**B**: As they become popular in different computer vision fields [13, 38, 40], researchers also find their application in temporal action localization [3, 44, 46].
**C**... | BCA | BCA | ABC | BCA | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> StackGenVis [CMKK21] is a VA system for composing powerful and diverse stacking ensembles [Wol92] from a pool of pre-trained models. <|MaskedSetence|> On the other hand, we support the process of generating new models through genetic algorithms and highlight the necessity for the ... | **A**: These papers use bagging [Bre01] and boosting [CG16, FSA99, KMF∗17] techniques for ranking and identifying the best combination of models in different application scenarios.
**B**:
There are relevant works that involve the human in interpreting, debugging, refining, and comparing ensembles of models [DCCE19, L... | BAC | ABC | BAC | BAC | Selection 3 |
There are various works that particularly target the matching of multiple shapes. <|MaskedSetence|> However, due to the employed lifting strategy, which drastically increases the number of variables, these methods are not scalable to large problems and only sparse correspondences are obtained. <|MaskedSetence|> Due t... | **A**: In [30, 32], semidefinite programming relaxations are proposed for the multi-shape matching problem.
**B**: In [18], a game-theoretic formulation for establishing multi-matchings is introduced.
**C**: A higher-order projected power iteration approach was presented in [9], which was applied to various multi-mat... | ABC | ABC | ABC | ABC | Selection 2 |
The recognition algorithm RecognizePG for path graph is mainly built on path graphs’ characterization in [1]. <|MaskedSetence|> 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. <|MaskedS... | **A**: 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]).
**B**: This characterization decomposes the input graph G𝐺Gitalic_G by clique separators as in [18], t... | BAC | BAC | BCA | BAC | Selection 1 |
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. And the SLIM method outperforms state-of-art method... | **A**: As mentioned in SLIM , the idea of using the symmetric Laplacian inverse matrix to measure the closeness of nodes comes from the first hitting time in a random walk.
**B**: Therefore, it is worth modifying this method to mixed membership networks.
**C**: Numerical results of simulations and substantial empiric... | ABC | ABC | CBA | ABC | Selection 2 |
See, e.g., Udriste (1994); Ferreira and Oliveira (2002); Absil et al. <|MaskedSetence|> (2016); Liu et al. <|MaskedSetence|> (2018); Zhang et al. (2018); Tripuraneni et al. (2018); Boumal et al. <|MaskedSetence|> (2019); Zhou et al. (2019); Weber and Sra (2019) and the references therein.
Also see recent reviews (Fe... | **A**: (2017); Agarwal et al.
**B**: (2009); Ring and Wirth (2012); Bonnabel (2013); Zhang and Sra (2016); Zhang et al.
**C**: (2018); Bécigneul and Ganea (2018); Zhang and Sra (2018); Sato et al.
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, i.e., each agent makes decision for its own. This type of methods is usually easy to scale, but may have difficulty to achieve global optimal performance due to the lack of collaboration. <|MaskedSetence|> However,
as the number of agents increases, joint optimization usually leads to dimensional explosion, which ha... | **A**: To overcome the difficulty, another type of methods are implemented in a decentralized manner.
**B**: To address the problem, another way is to jointly model the action among learning agents with centralized optimization [16, 15].
**C**: Compared with them, our method uses neighbor information to form intrinsi... | BAC | BAC | BAC | BAC | Selection 2 |
<|MaskedSetence|> 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|> BestFit works similarly, except that it places the item into the bin of minimum available capacit... | **A**: NextFit has a competitive ratio of 2, while both FirstFit and BestFit are 1.7-competitive (?, ?).
**B**:
Online bin packing has a long history of study.
**C**: FirstFit is another simple heuristic that places an item into the first bin of sufficient space and opens a new bin if required.
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To address the problem mentioned above, most of the methods extend the Chamfer loss function of basic AtlasNet with additional terms. Bednarik et al. <|MaskedSetence|> Deng et al. (2020b) introduced two additional terms to increase global consistency of the local mappings explicitly. <|MaskedSetence|> <|MaskedSetenc... | **A**: (2020) added terms to prevent patch collapse, reduce patch overlap and calculate the exact surface properties analytically rather than approximating them.
**B**: Another term enforces better spatial configuration of the mappings by minimizing a stitching error.
.
**C**: One of them exploits the surface normals... | ACB | CAB | ACB | ACB | Selection 3 |
<|MaskedSetence|> This paper is organized as follows. <|MaskedSetence|> <|MaskedSetence|> In Section 4, we present the lower complexity bounds for saddle point problems without individual variables. Finally in Section 5, we show how the proposed algorithm can be applied to the problem computing Wasserstein barycente... | **A**: Paper organization.
**B**: In Section 3, we provide the main algorithm of the paper to solve such kind of problems.
**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. This problem was formulated by Stepanec [7] and Zykov [8] for general graphs and by Hubicka and Syslo [9] in the stric... | **A**: Some applications of the MCB problem are described in [5, 11, 10, 12]..
**B**: In more concrete terms this problem is equivalent to finding the cycle basis with the sparsest cycle matrix.
**C**: The authors show that the MCB problem is different in nature for each class.
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The radial tree had three collapsed data subspaces (a.2–a.4) except for All and Worst subspaces. We performed this action because there are too many features to be explored at once, and FeatureEnVi provides this capability to alter the layouts in order to scale for high-dimensional data sets. Basically, the core statis... | **A**: Moreover, a supportive measurement is the MI per feature that should have a light blue color in the cases of a potentially removable feature.
Going from the bottom to the top of the feature importance list sorted by the Average importance, we excluded all features with a target correlation value greater than 15%... | ABC | ABC | ABC | ACB | Selection 2 |
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]. High-precision trajectories or set points can be generated prior to the actual machining process following variou... | **A**: 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.
**B**: The approach has been successfully applied to linear and rotational axis embedded in grinding machines and shown to... | ACB | ACB | ACB | ACB | Selection 1 |
Results. We find that implicit methods either improve or are comparable with StdM, but most explicit methods fail when asked to generalize to multiple bias variables and a large number of groups, even when the bias variables are explicitly provided. <|MaskedSetence|> <|MaskedSetence|> Because the implicit methods do... | **A**: As shown in Fig. 4, all explicit methods are below StdM on Biased MNISTv1.
**B**: Barring LNL and Up Wt, other explicit methods exhibit degraded accuracy as the number of explicit bias variables increases.
**C**: Among the implicit methods, LFF obtains the highest improvement, whereas SD is close to StdM..
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<|MaskedSetence|> <|MaskedSetence|> All images are collected using mobile phones or tablets. Each participant is required to gaze at a circle shown on the devices without any constraint on their head movement. As a result, the GazeCapture dataset covers various lighting conditions and head motions. <|MaskedSetence|>... | **A**: The GazeCapture dataset does not provide 3D coordinates of targets.
**B**: It contains a total of 2,445,504 images from 1,474 participants.
**C**: GazeCapture [42] dataset is collected through crowdsourcing.
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The next step is to apply a cropping filter in order to extract only the non-masked region. To do so, we firstly normalize all face images into 240 ×\times× 240 pixels. <|MaskedSetence|> <|MaskedSetence|> Then we extract only the blocks including the non-masked region (blocks from number 1 to 50). <|MaskedSetence|> | **A**: Next, we partition a face into blocks.
**B**: The principle of this technique is to divide the image into 100 fixed-size square blocks (24 ×\times× 24 pixels in our case).
**C**: Finally, we eliminate the rest of the blocks as presented in Fig. 3.
.
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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**: However, the state of the art [Abe12, AP16, CLB23] supports polymorphic, higher-kinded, and dependent types, which we aim to incorporate in future work.
.
**B**: Sized (co)inductive types [BFG+04, Bla04, Abe08, AP16] gave way to sized mixed inductive-coinductive types [Abe12, AP16].
**C**: Sized types are a ty... | CBA | CBA | ABC | CBA | 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**: Notably, both FairCMS-I and FairCMS-II fulfill scalability and owner-side efficiency requirements.
**B**: However, utilizing the single-value alteration method for masking the original media content does not achieve the IND-CPA security.
**C**: Then FairCMS-II offers an enhanced privacy solution by replacing t... | BCA | ACB | BCA | BCA | 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. <|MaskedSetence|> (2015) to model feature interactions on the gra... | **A**: (2021) developed the Graph-Convolved Feature Crossing (GCFC) layer to traverse all features for each input example and leveraged the features of each sample to compute the corresponding multi-feature interaction graph and propagated its influence on other features.
**B**: (2019) proposes to connect each pair of... | ABC | CBA | CBA | CBA | 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]. The original definition of self-concordance has been expanded and generalized since its incept... | **A**: This was fully formalized in Sun & Tran-Dinh [2019], in which the concept of generalized self-concordant functions was introduced, along with key bounds, properties, and variants of Newton methods for the unconstrained setting which make use of this property..
**B**: [2015], in which more general properties of ... | CBA | CBA | CBA | ABC | Selection 2 |
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|> The Backtrack-Stuck-Structur... | **A**: In total, a Pass-Bundle requires 3333 passes..
**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) unmatched edges to each structure (Include-Unmatched-Edges).
**C**: Eac... | CAB | BCA | BCA | BCA | Selection 3 |
In this paper, we present a novel formulation for the Personalized Federated Learning Saddle Point Problem (1). This formulation incorporates a penalty term that accounts for the specific structure of the network and is applicable to both centralized and decentralized network settings. Additionally, we provide the low... | **A**: Furthermore, we have developed the novel methods (Algorithm 1, Algorithm 2, Algorithm 3) for this problem that are optimal up to logarithmic factor in certain scenarios (see Table 1).
**B**: Moreover, we have customized our approach for neural network training..
**C**: These algorithms are based on sliding or ... | ACB | ACB | ACB | ABC | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> The MS operates on the meta-game (MG), which is a payoff tensor estimated by measuring the expected return (ER) of policies against one another. This is a NF game, but instead of strategies corresponding to actions, a𝑎aitalic_a, they correspond to policies, π𝜋\... | **A**: Commonly the response oracle is either a reinforcement learning (RL) agent or a method that computes the exact BR.
**B**:
PSRO consists of a response oracle that estimates the best response (BR) to a joint distribution of policies.
**C**: The component that determines the distribution of policies that the ora... | BAC | BCA | BAC | BAC | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> The degree to which a query q𝑞qitalic_q overfits to the dataset is expressed by the correlation between the query and that Bayes factor. This simple lemma is at the heart of the progress that we make in this paper, both in our intuitive understanding of adaptive... | **A**: The following lemma gives a useful and intuitive characterization of the quantity that the Bayes stability definition requires be bounded.
**B**: Since achieving posterior accuracy is relatively straightforward, guaranteeing Bayes stability is the main challenge in leveraging this theorem to achieve distributio... | BAC | CBA | BAC | BAC | Selection 3 |
<|MaskedSetence|> It is nontrivial to phrase meaningful formal questions in this direction. <|MaskedSetence|> <|MaskedSetence|> Hence NP-hard problems do not admit such parameter-decreasing algorithms. To formalize a meaningful line of inquiry, we take our inspiration from the Vertex Cover problem, the fruit fly of ... | **A**: We therefore propose the following novel research direction: to investigate how preprocessing algorithms can decrease the parameter value (and hence search space) of FPT algorithms, in a theoretically sound way.
**B**: To illustrate this difficulty, note that strengthening the definition of kernelization to “a ... | ABC | ABC | ABC | ACB | Selection 2 |
Among them, the works [172, 198, 194] not only enable smooth transition over the boundary, but also reduce the illumination discrepancy between foreground and background, in which the latter one is the goal of image harmonization in Section IV. In this section, we only introduce the way they enable smooth transition ov... | **A**: Specifically, they add the gradient domain constraint to the objective function according to Poisson equation, which can produce a smooth blending boundary with gradient domain consistency.
**B**: Differently, [172] has a close-form solution, while [198] converts the gradient domain loss to a differentiable los... | ACB | ACB | ACB | ABC | Selection 1 |
The average regional daily patterns of taxi mobility data from each POI-based cluster in Beijing, Chengdu, and Xi’an are plotted in Fig. <|MaskedSetence|> <|MaskedSetence|> 2LABEL:sub@fig:cluster-bj, taxi mobility patterns in Beijing exhibit a high level of cohesion within each POI-based cluster, while remaining dist... | **A**: 2.
**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 limited number o... | ABC | ACB | ACB | ACB | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> The Adam optimizer was used for weight optimization with a fixed learning rate of 5×10−45superscript1045\times 10^{-4}5 × 10 start_POSTSUPERSCRIPT - 4 end_POSTSUPERSCRIPT, in accordance with romano2019conformalized . <|MaskedSetence|> All neural networks contained only a single hi... | **A**: The number of epochs was limited to 100, unless stated otherwise.
**B**:
All neural networks were constructed using the default implementations fromPyTorch pytorch .
**C**: The general architecture for all neural-network-based models was fixed.
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<|MaskedSetence|> Given that the tokens we choose do not contain performance information, it is interesting to see how a machine model would “perform” a piece by deciding these volume changes, a task that is essential in performance generation \parencitewidmer94aaai, jeongKKLN19ismir, jeongKKN19icml or expressive perf... | **A**:
Dynamics is an important element in music, as they are often used by musicians to add excitement and emotion to songs.
**B**: Default MIDI velocity values are associated with dynamic indications.
**C**: In the realm of MIDI, velocity is a parameter that scales the intensity or volume at which a sound sample i... | CBA | ACB | ACB | ACB | Selection 3 |
<|MaskedSetence|> Particularly, the input of the system is the speech signals, which is restored at the receiver. <|MaskedSetence|> <|MaskedSetence|> Polar codes with successive cancellation list (SCL) decoding algorithm[22] is employed for channel coding, in which the block length is 512 and the list size is 4. Mor... | **A**: Moreover, the transcription is obtained from the recovered speech signals after passing through an automatic speech recognition (ASR) module.
**B**:
The first benchmark is a traditional communication system to transmit speech signals, named speech transceiver.
**C**: For the system, the adaptive multi-rate wi... | BAC | BAC | BAC | BCA | Selection 1 |
<|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**: These modules indirectly steer the training of the basic segmentation network and remain unused during testing..
**C**:
In the subseq... | CAB | ABC | CAB | CAB | Selection 1 |
Setup. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Moreover, we use 40 recall positions instead of 11 recall positions proposed in the original Pascal VOC benchmark, following [40]. This results in a more fair comparison of the results. Each class uses different IoU standards for further evaluations. We re... | **A**: The official data set contains 7481 training and 7518 test images with 2D and 3D bounding box annotations for cars, pedestrians, and cyclists.
**B**: The KITTI dataset [11] provides widely used benchmarks for various visual tasks in the autonomous driving, including 2D Object detection, Average Orientation Simi... | BAC | ACB | BAC | BAC | Selection 3 |
<|MaskedSetence|> Its ground truth is annotated with word-level quadrangles. It contains 1,000 training and 500 testing images. <|MaskedSetence|> <|MaskedSetence|> Here, we follow the previous methods [35, 8] and add 400 training images from TR400 [46] to extend this dataset.. | **A**: ICDAR2015 [44] includes multi-orientated and small-scale text instances.
**B**:
MSRA-TD500 [45] is dedicated to detecting multi-oriented long non-Latin texts.
**C**: It contains 300 training images and 200 testing images with word-level annotation.
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The first proposed mapping mechanism of IP addresses is TLMB. The four parts of the IP address are represented in four layers, where each layer is made up of one or more memory blocks. The first layer only contains one memory block, whereas the second layer contains 256 memory blocks. <|MaskedSetence|> <|MaskedSetenc... | **A**: Each memory block contains 256 elements.
**B**: Each element of the memory block in the first layer is employed to store the starting addresses of the corresponding 256 memory blocks in the second layer.
**C**: Consequently, the first two layers can be removed from this architecture if the third layer has cont... | CAB | ABC | ABC | ABC | Selection 2 |
<|MaskedSetence|> They also thank Jarle Sogn for communicating on Schur complement based preconditioners.
The work of M. <|MaskedSetence|> <|MaskedSetence|> Ju is supported in part by the National Key R & D Program of China (2017YFB1001604). The work of J. Li is partially supported by the National Natural Science Fo... | **A**: Cai is partially supported by the NIH-RCMI grant through 347 U54MD013376, the affiliated project award from the Center for Equitable Artificial Intelligence and Machine Learning Systems (CEAMLS) at Morgan State University (project ID 02232301), and the National Science Foundation awards (1831950).
**B**: The wo... | CBA | CAB | CAB | CAB | Selection 3 |
<|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**: We note that the modified algorithm is mathematically equivalent to TDCD, albeit with a higher communication cost.
**B**: 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 ... | ABC | BCA | BCA | BCA | Selection 2 |
Changxin Mo acknowledges support from the National Natural Science Foundation of China (Grant No. <|MaskedSetence|> CSTB2022NSCQ-MSX0896), the Science and Technology Research Program of Chongqing Municipal Education Commission
(Grant No. <|MaskedSetence|> cstc2022ycjh-bgzxm0040), and the Research Foundation of Chongq... | **A**: KJQN202200512), the Chongqing Talents Project (Grant No.
**B**: 12201092), the Natural Science Foundation Project of CQ CSTC (Grant No.
**C**: .
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<|MaskedSetence|> The discriminator is shown in Figure 2 (b). <|MaskedSetence|> We use the Sigmoid non-linear activation function at the last layer and the Leaky ReLU with the slope of 0.2 for other layers. The structure branch shares the same pattern as the upper stream, where the input edge map is detected by a res... | **A**: Finally, the outputs of the two branches are concatenated in the channel dimension, based on which we calculate the adversarial loss.
.
**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 ... | CBA | CBA | BCA | CBA | Selection 4 |
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). For BF-kSubS the search budget is referred to as graph size and includes the nu... | **A**: 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).
**B**: We measure the success rate as a function of the search budget.
**C**: F... | BAC | CBA | BAC | BAC | Selection 4 |
Ablation study is thus made to investigate how glyph and phonetic features bring improvement by themselves. <|MaskedSetence|> <|MaskedSetence|> The reasons are to follow. On one hand, named entities in different datasets may rely on one of our provided features much more than the other features. So, in the test stage... | **A**: On the other hand, when we add extra information to the existing pre-trained language models, considering the different data distributions of train, dev, and test set, the model we select might give higher weights to the learned patterns from glyph or phonetic domain, which shows good performance on dev set but ... | CBA | CBA | CBA | ACB | Selection 2 |
The idea behind the modular MTL architecture is simple: breaking an MTL model into shared modules and task-specific modules. <|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. <|... | **A**: The shared modules learn shared features from multiple tasks.
**B**: On the other hand, task-specific modules learn features that are specific to a certain task.
**C**: Compared with shared modules, task-specific modules are usually much smaller and thus less likely to suffer from overfitting caused by insuffi... | ABC | CBA | ABC | ABC | Selection 3 |
<|MaskedSetence|> Therefore, they are NOT intended to be the final produced work that is displayed in print or on IEEEXplore®. <|MaskedSetence|> <|MaskedSetence|> The XML files are used to produce the final print/IEEEXplore® pdf and then converted to HTML for IEEEXplore®. Have you looked at your article/paper in the... | **A**: They will help to give the authors an approximation of the number of pages that will be in the final version.
**B**: The structure of the LaTeXfiles, as designed, enable easy conversion to XML for the composition systems used by the IEEE’s outsource vendors.
**C**: The templates are intended to approximate the... | CAB | CBA | CAB | CAB | Selection 4 |
The treatment variation was implemented in the second part. In three baseline sessions, consisting of a total of 72 subjects in 18 groups, subjects were told that the second part of the experiment would be exactly the same as the first part, except that subject IDs would be randomly reassigned. <|MaskedSetence|> Howe... | **A**: In five treatment sessions, consisting of 112 subjects across 28 groups, subjects were also told that they would play the game for another 15 rounds.
**B**: Questions from this section are shown in Appendix LABEL:app:questions.
**C**: After the two main parts of the experiment were finished, subjects completed... | ACB | ACB | CAB | ACB | Selection 1 |
<|MaskedSetence|> The current mainstream of interpolation methods includes Nearest-neighbor Interpolation, Bilinear Interpolation, and Bicubic Interpolation. Being highly interpretable and easy to implement, these methods are still widely used today. Among them, Nearest-neighbor Interpolation is a simple and intuitive... | **A**: Compared with Bilinear, the results of Bicubic are smoother with fewer artifacts but slower than other interpolation methods.
**B**: Bilinear Interpolation sequentially performs linear interpolation operations on the two axes of the image.
**C**:
Interpolation is the most widely used upsampling method.
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To perform super-resolution, a Neural Knitwork has to translate the information contained in the patches of the original scale to a domain of patches of finer scale. This can be done by matching the patch distribution across scales [8, 25, 26, 29]. <|MaskedSetence|> <|MaskedSetence|> However, it is possible to compu... | **A**: This alone could yield an output image resembling the low-resolution source without guaranteed structural coherence.
**B**: The queried coordinates for a patch network include all super-resolved coordinates, which means that it is not possible to compute the patch reconstruction loss in this mode.
**C**: For ... | CBA | BAC | CBA | CBA | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> 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 approach. <|MaskedSetence|> We investigate such a Pol... | **A**: For logistic contextual bandits, the implementation of exact TS (i.e.
**B**: Dumitrascu et al., (2018) recently proposed an approximation based on Polya-Gamma augmentation (Polson et al.,, 2013; Windle et al.,, 2014) which has improved convergence properties over Laplace approximation originally used by Chapell... | CAB | CAB | CAB | CAB | Selection 1 |
<|MaskedSetence|> To evaluate the benefits of added knowledge, we consider incremental subsets of topics, spanning from 1 up to 4. However, differently from the legal domain case study, the memory content dramatically increases as the number of topics gets larger, from 130 with a single topic up to 642 with four topic... | **A**: Additionally, the number of claims associated with each evidence is very low (range 1-5).
**B**:
We frame claim detection as a sentence-level binary classification task, where each sentence can either be identified as containing a claim or not.
**C**: Moreover, the increased memory size prohibits using all th... | BAC | BAC | BAC | ABC | Selection 3 |
However, the progress of sentiment dependency-based methods, such as the work by Zhang et al. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> (2021a); Dai et al. (2021), has contributed to the improvement of coherent sentiment learning.
These studies explored the effectiveness of syntax information in ABSC, wh... | **A**: (2019); Zhou et al.
**B**: (2020); Tian et al.
**C**: (2021); Li et al.
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We use IBMQ quantum computers via Qiskit (IBM, [n. d.]) APIs. <|MaskedSetence|> We also employ Qiskit for compilation. <|MaskedSetence|> <|MaskedSetence|> The noise models we used are off-the-shelf ones updated by IBMQ team.. | **A**: We study 6 devices, with #qubits from 5 to 15 and Quantum Volume from 8 to 32.
**B**: The optimization level is set to 2 for all experiments.
**C**: All experiments run 8192 shots.
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<|MaskedSetence|> Furthermore, in order to evaluate the performance of EDA under more tracking scenarios with challenges, we test it on some representative sequences from the Color Event Dataset (CED) scheerlinck2019ced and the recently released EVIMO dataset mitrokhin2019ev . <|MaskedSetence|> Therefore, we only pr... | **A**: Here, it should be noted that there is no ground truth that is publicly available for the CED and EVIMO datasets for the task of object tracking.
**B**: 7.
.
**C**: We also provide the event trajectory results obtained by the proposed EDA on some representative sequences from the ECD and EED datasets.
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<|MaskedSetence|> <|MaskedSetence|> Recently, contrastive learning (CL) [38, 39, 40, 41], which discriminates positive pairs against negative pairs, achieved state-of-the-art performance in various vision tasks. <|MaskedSetence|> To fully utilize negative samples, [45, 46, 47, 48] explore hard samples in the momentu... | **A**:
Early SSL methods design hand-crafted pretext tasks [30, 31, 32, 33], which rely on somewhat ad-hoc heuristics and have limited abilities to capture practically useful representations.
**B**: Another popular form is clustering-based methods [34, 35, 36, 37] learning discriminative representation by offline or ... | ABC | ABC | CBA | ABC | Selection 2 |
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. 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 ... | **A**: Existing techniques usually need a scaling method to scale down the searched network and fit different budgets.
**B**: With the extended search space, all our models are derived from the same super network while obtaining the best accuracy.
**C**: Therefore, we enable flexible w𝑤witalic_w’s by default in our ... | ABC | ABC | CBA | ABC | Selection 1 |
To cope with the problem of model collapse, we devise the asymmetric structure for CGCL. The asymmetry lies in the differences of GNN-based encoders’ message-passing schemes. <|MaskedSetence|> Specifically, high complementarity indicate that encoders together carry less redundant parameters. For a further theoretical ... | **A**: Those two metrics are to measure the asymmetry and complementarity of the collaborative framework quantitatively.
**B**: Besides, graph encoders in CGCL are supposed to be complementary for a stronger fitting ability.
**C**: The experiments show that the assembly with high asymmetry and complementarity has a b... | BAC | BAC | BCA | BAC | Selection 1 |
<|MaskedSetence|> The work of Tomasz Korbak was supported by the Leverhulme Doctoral Scholarship. <|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 support.. | **A**:
The work of Piotr Miłoś was supported by the Polish National Science Center grant UMO-2017/26/E/ST6/00622.
**B**: PLG/2019/012498.
**C**: We gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Centers: ACK Cyfronet AGH, PCSS) for providing computer facilities and support within... | CAB | ACB | ACB | ACB | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> These conditions are stated in terms of the density of the data and on Lipschitz and boundedness constants of the learned function as well as the models of the system dynamics and the measurement map. We proposed an algorithmic implementation of our theoretical f... | **A**:
In this paper, we have shown how safe control laws can be learned from expert demonstrations under system model and measurement map uncertainties.
**B**: We first presented robust output control barrier functions (ROCBFs) as a means to enforce safety, which is here defined as the ability of a system to remain ... | ABC | ABC | ABC | BAC | Selection 3 |
In CoauthorshipsNet, node means scientist and weights mean coauthorship, where weights are assigned by the original papers. <|MaskedSetence|> The CoauthorshipsNet has 1589 nodes, however its adjacency matrix is disconnected. Among the 1589 nodes, there are totally 396 disconnected components, and only 379 nodes fall i... | **A**: To find the number of communities for CoauthorshipsNet, we plot the leading 40 eigenvalues of their adjacency matrices.
**B**: For this network, there is no ground truth about nodes labels, and the numbers of communities are unknown.
**C**: Note that though CoauthorshipsNet1589 is disconnected, we can still ap... | BAC | BAC | BAC | BAC | Selection 1 |
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