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<|MaskedSetence|> <|MaskedSetence|> 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. Also, our scheme is defined by a sequence of elliptic problems, avoiding the annoyance of saddle p... | **A**: Of course, the numerical scheme and the estimates developed in Section 3.1 hold.
**B**: However, several simplifications are possible when the coefficients have low-contrast, leading to sharper estimates.
**C**: We had to reconsider the proofs, in our view simplifying some of them.
.
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<|MaskedSetence|> <|MaskedSetence|> Ma et al. [19] used Recurrent Neural Networks for rumor detection, they batch tweets into time intervals and model the time series as a RNN sequence. <|MaskedSetence|> As the same disadvantage of all other deep learning models, the process of learning is a black box, so we cannot ... | **A**: We build upon the idea of their Series-Time Structure, when building our approach for early rumor detection with our extended dataset, and we provide a deep analysis on the wide range of features change during diffusion time.
**B**: Without any other handcrafted features, they got almost 90% accuracy for events... | CAB | CAB | CBA | CAB | Selection 4 |
In this section, we compare the performance our model with the human rumor debunking websites: snopes.com and urbanlegend.com. <|MaskedSetence|> They regularly post tweets via this account about rumors which they collected and verified. We consider the creation time of the first tweet which mentions ”@snopes” or conta... | **A**: So we consider that they don’t refer to the same rumor affair.
.
**B**: This topic bursted in 2012, 2015 and 2016 several times and the tweets’ volume of 2012 is the highest peak.
**C**: Snopes has their own Twitter account141414https://twitter.com/snopes.
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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|> For GoogleTrends, there are 2,700 and 4,200 instances respectively. <|MaskedSetence|> We set up 4 trials with each of the last 4 bins (using the... | **A**: In total, our training dataset for AOL consists of 1,740 instances of breaking class and 3,050 instances of anticipated, with over 300 event entities.
**B**: We then bin the entities in the two datasets chronologically into 10 different parts.
**C**: We select a studied time for each event period randomly in t... | ABC | CAB | CAB | CAB | Selection 4 |
Table 1 shows basic patient information. Half of the patients are female and ages range from 17 to 66, with a mean age of 41.8 years. <|MaskedSetence|> 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**: Body weight, according to BMI, is normal for half of... | CBA | CBA | CBA | CBA | Selection 2 |
Overcoming these issues requires a higher-level scene understanding that models object interactions and predicts implicit gaze and motion cues from static images. <|MaskedSetence|> However, this study does not investigate whether the benefits of the proposed modifications generalize to other pre-trained architectures... | **A**: Besides architectural changes, data augmentation in the context of saliency prediction tasks demonstrated its efficiency to improve the robustness of deep neural networks according to Che et al.
**B**: Robust object recognition could however be achieved through more recent classification networks as feature ext... | BAC | BAC | BAC | CBA | Selection 2 |
<|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**: Secondly, due to the results of Section 4, the investigated greedy strategies for computing the locality number can also be interpreted as greedy strategies for computing the cutwidth of a graph.
**B**:
Our strongest positive result about the approximation of the locality number will be derived from the reduct... | ACB | BAC | BAC | BAC | Selection 3 |
<|MaskedSetence|> Interestingly, we observed that the best of the 5555 runs was often significantly better. For 6666 of the games, it exceeds the average human score (as reported in Table 3 of Pohlen et al. <|MaskedSetence|> <|MaskedSetence|> In some cases during training we observed high variance of the results dur... | **A**: This suggests that further stabilizing SimPLe should improve its performance, indicating an important direction for future work.
**B**: (2018)).
**C**: The results in these figures are generated by averaging 5555 runs for each game.
The model-based agent is better than a random policy for all the games except ... | CBA | CBA | BCA | CBA | Selection 1 |
During the step negotiation simulations, it was noticed that the rolling locomotion mode encountered constraints when attempting to cross steps with a height greater than thrice the track height (h being the track height as shown in Fig. <|MaskedSetence|> This limitation originates from the traction forces generated ... | **A**: 3).
**B**: As a result, successful locomotion mode transitions can only occur when both rolling and climbing locomotion modes are capable of handling a step negotiation task.
**C**: 9, respectively..
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<|MaskedSetence|> Advice bits, as all information, are prone to transmission errors. <|MaskedSetence|> Last, and perhaps more significantly, a malicious entity that takes control of the advice oracle can have a catastrophic impact. For a very simple example, consider the well-known ski rental problem: this is a simpl... | **A**: 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.
**B**: It should be fairly clear that such assumptions are very unrealistic or undesirable.
**C**: However, if this bit is wrong, then the onl... | BAC | ABC | BAC | BAC | Selection 1 |
<|MaskedSetence|> For classifiers supporting incremental classification, like SS3 or MNB, only a small vector needs to be stored for each user. For instance, when using SS3 we only need to store the confidence vector303030In case of ADD, a 2-dimensional vector. of every user and then simply update it as more content i... | **A**: However, when working with classifiers not supporting incremental classification, for every user we need to store either all her/his writings to build the document-term matrix or the already computed document-term matrix to update it as new content is added.
**B**:
It is worth noting that the difference in ter... | BAC | BAC | ABC | BAC | Selection 2 |
Game theory provides an efficient tool for the cooperation through resource allocation and sharing [20][21]. <|MaskedSetence|> <|MaskedSetence|> Sedjelmaci et al. applied the Bayesian game-theoretic methodology in UAV’s intrusion detection and attacker ejection [24]. However, most existing models focus on common sce... | **A**: A computation offloading game has been designed in order to balance the UAV’s tradeoff between execution time and energy consumption [25].
**B**: Inspired by this, our model is built upon the aggregative game theory which suits for large-scale scenarios..
**C**: A sub-modular game is adopted in the scheduling ... | ACB | ACB | ACB | CBA | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> This type of variance leads to converging to sub-optimal policies and brutally hurts DQN performance. The second source of variance Target Approximation Error which is the error coming from the inexact minimization of DQN parameters. Many of the proposed extensions focus on minimiz... | **A**: In Approximation Gradient Error, the error in gradient direction estimation of the cost function leads to inaccurate and extremely different predictions on the learning trajectory through different episodes because of the unseen state transitions and the finite size of experience reply buffer.
**B**: Dropout me... | ABC | CAB | CAB | CAB | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> In the medical image analysis domain, RNNs have been used to model the temporal dependency in image sequences. Bai et al. <|MaskedSetence|> Similarly, Gao et al. (2018) applied LSTM and CNN to model temporal relationship in brian MRI slices to improve segmentation performance in 4... | **A**: The long short-term memory (LSTM) network is a type of RNN that introduces self-loops to enable the gradient flow for long duration (Hochreiter and Schmidhuber, 1997).
**B**: The Recurrent Neural Network (RNN) was designed for handling sequences.
**C**: (2018) proposed an image sequence segmentation algorithm ... | BAC | CBA | BAC | BAC | Selection 3 |
Welbl (2014) and Biau et al. (2019) follow a similar strategy. The authors propose a method that maps random forests into neural networks as a smart initialization and then fine-tunes the networks by backpropagation. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Additionally, the authors evaluate sparse and... | **A**: Joint training concatenates all tree networks into one single network so that the output layer is connected to all leaf neurons in the second hidden layer from all decision trees and all parameters are optimized together.
**B**: Two training modes are introduced: independent and joint.
**C**: Independent train... | CBA | BCA | BCA | BCA | Selection 2 |
Broadly speaking, our work is related to a vast body of work on value-based reinforcement learning in tabular (Jaksch et al., 2010; Osband et al., 2014; Osband and Van Roy, 2016; Azar et al., 2017; Dann et al., 2017; Strehl et al., 2006; Jin et al., 2018) and linear settings (Yang and Wang, 2019b, a; Jin et al., 2019;... | **A**: (2017); Dong et al.
**B**: (2020), which generalizes the one proposed by Yang and Wang (2019a).
**C**: (2019).
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We thank Prof. Henry Adams and Dr. 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. <|MaskedSetence|> Qingsong Wang for bringing to our attention the paper [76] which was critical for... | **A**: We thank Dr.
**B**: Mikhail Katz and Prof.
**C**: We also thank Prof.
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<|MaskedSetence|> <|MaskedSetence|> A Grid Search mode (Figure 1(a)) initiates a systematic parameter search that computes 500 projections by varying the parameters perplexity, learning rate, and max iterations.
From this pool of 500 projections, 25 representative examples are singled out and shown to the user—in a m... | **A**: This whole process is transparent to the user and happens in the backend; only the representatives are shown..
**B**: Significantly-different t-SNE projections can be generated from the same data set, due to its well-known sensitivity to hyper-parameter settings [14].
**C**: We propose to support users in find... | BCA | CAB | BCA | BCA | Selection 1 |
Good comparisons are crucial for new proposals: The lack of fair comparisons is another important drawback of many proposals published to date. <|MaskedSetence|> <|MaskedSetence|> In some cases, the proposed algorithm is compared to similar algorithms but not with competitive algorithms outside that semantic niche [6... | **A**: 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].
**B**: We encourage researchers to increase the algorithms ... | CAB | CAB | BCA | CAB | Selection 1 |
Classical clustering models work poorly on large scale datasets. 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... | **A**: If the graph is not updated, the contained information is low-level.
**B**: The adaptive learning will induce the model to exploit the high-level information.
**C**: In particular, AdaGAE is stable on all datasets.
.
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<|MaskedSetence|> We analyse the datasets from the traceroute measurements performed by the CAIDA Spoofer Project within the last year 2019, (Lone et al., 2017). <|MaskedSetence|> The dataset found 688 ASes that do not enforce ingress filtering. <|MaskedSetence|> The rest of the ASes agree with our measurement resul... | **A**: The measurements identified 2,500 unique loops, of these 703 were provider ASes, and 1,780 customer ASes.
**B**:
Traceroute Active Measurements.
**C**: Out of 688 ASes found with traceroutes by the Spoofer Project, we could not test 4 ASes (none of our tests applied) and 36 ASes were not included in our tests... | BAC | BCA | BAC | BAC | Selection 3 |
This paper builds upon previous work with this dataset [7], which used support vector machine (SVM) ensembles. First, their approach is extended to a modern version of feedforward artificial neural networks (NNs) [8]. <|MaskedSetence|> <|MaskedSetence|> The results indicate improvement from two sources: The use of ne... | **A**: The context model has two parts: (1) a recurrent context layer, which encodes classification-relevant properties of previously seen data, and (2) a feedforward layer, which integrates the context with the current odor stimulus to generate an odor-class prediction.
**B**: Thus, emulation of adaptation in natural... | CAB | CAB | CAB | CBA | Selection 1 |
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|> <|MaskedSetence|> While it is known that the free semigroup of rank one is not an automaton semigroup [4, Proposition 4.3], the free semigroup... | **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**: In fact, the construction to generate these semigroups is quite simple [4, Proposition 4.1] (compare also to 3).
**C**: While these constructions and... | ACB | ACB | CAB | ACB | Selection 4 |
While our results indicate that current visual grounding based bias mitigation approaches do not suffice, we believe this is still a good research direction. <|MaskedSetence|> We recommend that both train and test accuracy be reported, because a model truly capable of visual grounding would not cause drastic drops in ... | **A**: 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.
**B**: (2019b); Hudson and Manning (2019); Johnson et al.
**C**: (2017); Kafle and Kanan (2017); Kafle et al.
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Natural language processing (NLP) provides an opportunity to automate the extraction of salient details from privacy policies, thereby reducing human effort and enabling the creation of tools for internet users to understand and control their online privacy. Existing research has achieved some success using expert ann... | **A**: More importantly, annotations in the privacy policy domain are expensive.
**B**: (2016); Zimmeck et al.
**C**: In contrast, approaches involving large amounts of unlabeled privacy policies remain relatively unexplored..
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In this paper, we introduced an interactive VA system, called StackGenVis, for the alignment of data, algorithms, and models in stacking ensemble learning. <|MaskedSetence|> With the careful selection of multiple coordinated views, we allow users to build an effective stacking ensemble from scratch. Exploring the alg... | **A**: Those limitations were then identified as future work for further development of our system..
**B**: To retrieve preliminary results about the effectiveness of StackGenVis, we presented use cases with real-world data sets that demonstrated the improvements in performance and the process of achieving them.
**C*... | CBA | CBA | CBA | ACB | Selection 3 |
<|MaskedSetence|> Firstly, the data quantity within the datasets used as ”tasks” varies across different applications, which can impact the effectiveness of MAML [Serban et al., 2015, Song et al., 2020]. <|MaskedSetence|> For example, PAML [Madotto et al., 2019] regards each person’s dialogues as a task for MAML and ... | **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**:
When applying MAML to NLP, several factors can influence the training strategy and performance of the... | BAC | BAC | BAC | BAC | Selection 1 |
For both static and mobile mmWave networks, codebook design is of vital importance to empower the feasible beam tracking and drive the mmWave antenna array for reliable communications [22, 23]. Recently, ULA/UPA-oriented codebook designs have been proposed for mmWave networks, which include the codebook-based beam trac... | **A**: The reasons are as follows: When the commonly-adopted DRE is integrated with CA, the limited radiation range of DREs is no longer the same and each is affected by the DRE’s location on CA, as the DRE-covered array plane is rolled up.
**B**: However, extending the aforementioned works to the CA is not straightfo... | BAC | BAC | BAC | BAC | Selection 2 |
<|MaskedSetence|> See Dann et al. <|MaskedSetence|> 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, 1995; Tsitsik... | **A**: Bhatnagar et al.
**B**: Szepesvári, 2018; Dalal et al., 2018; Srikant and Ying, 2019) settings.
**C**: (2014) for a detailed survey.
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<|MaskedSetence|> (2021); Xu et al. (2021c), and the use of only deep encoders Bapna et al. (2018); Wang et al. (2019); Li et al. <|MaskedSetence|> <|MaskedSetence|> But in general, Table 6 shows that our approach uses fewer parameters and leads to faster decoding speed than the baselines to obtain a comparable BLEU... | **A**:
As for the costs, the decoder depth has a strong impact on inference speed, as the decoder has to be computed once for each decoding step during auto-regressive decoding Kasai et al.
**B**: (2022a); Chai et al.
**C**: (2020) normally leads to faster inference speed than using both a deep encoder and a deep de... | ABC | BAC | ABC | ABC | Selection 4 |
In particular, we redesign the whole pipeline of deep distortion rectification and present an intermediate representation based on the distortion parameters. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> The ordinal distortion indicates the distortion levels of a series of pixels, which extend outward from t... | **A**: Our key insight is that distortion rectification can be cast as a problem of learning an ordinal distortion from a distorted image.
**B**: The comparison of the previous methods and the proposed approach is illustrated in Fig.
**C**: 1.
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Our main goal is to develop algorithms for the black-box setting. As usual in two-stage stochastic problems, this has three steps. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> This overall methodology is called Sample Average Approximation (SAA).
. | **A**: First, we develop algorithms for the simpler polynomial-scenarios model.
**B**: Finally, we extrapolate the solution to the original black-box problem.
**C**: Second, we sample a small number of scenarios from the black-box oracle and use our polynomial-scenarios algorithms to (approximately) solve the problem... | ACB | ACB | ACB | ABC | Selection 1 |
III. <|MaskedSetence|> <|MaskedSetence|> What’s more, multiplicative noises relying on the relative states between adjacent local optimizers make states, graphs and noises coupled together. It becomes more complex to estimate the mean square upper bound of the local optimizers’ states (Lemma 3.1). We firstly employ ... | **A**: Finally, we get an estimate of the mean square increasing rate of the local optimizers’ states in terms of the step sizes of the algorithm (Lemma 3.2)..
**B**: The co-existence of random graphs, subgradient measurement noises, additive and multiplicative communication noises are considered.
**C**: Compared wit... | BCA | BCA | BCA | CAB | Selection 3 |
The advantages of MuCo are summarized as follows. <|MaskedSetence|> 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 in the original data. Second, the anonymization of MuCo is a “black ... | **A**: Accordingly, the results are more accurate.
**B**: Thus, the adversary cannot determine which QI values are altered as well as the ranges of variations, causing that the matching tuples are more likely to be wrong or even does not exist when the adversary uses more QI values to match, but the adversary obtains ... | CBA | CBA | CBA | CBA | Selection 1 |
As shown in Table 3, all PointRend models achieve promising performance. Even without ensemble, our PointRend baseline, which yields 77.38 mAP, has already achieved 1st place on the test leaderboard. Note that several attempts, like BFP Pang et al. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> | **A**: In addition to models listed in Table 3, another PointRend with slightly different setting (stacking two BFP modules, and increasing the RoIAlign size from original 7 to 10 for bounding box branch) is trained and achieves 76.95 mAP on testing set.
**B**: (2019) and EnrichFeat, give no improvements against Point... | CBA | BAC | BAC | BAC | Selection 3 |
From Figure 1, we find that the restart strategy works better under abrupt changes than under gradual changes, since the gap between our algorithms and the baseline algorithms designed for stationary environments is larger in this setting. <|MaskedSetence|> For example, UCB-type exploration does not have incentive to ... | **A**: The reason is that the algorithms designed to explore in stationary MDPs are generally insensitive to abrupt change in the environment.
**B**: On the other hand, in gradually-changing environment, LSVI-UCB and Epsilon-Greedy can perform well in the beginning when the drift of environment is small.
**C**: Howev... | ABC | ABC | BAC | ABC | Selection 4 |
<|MaskedSetence|> 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). 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... | **A**: 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 personally encountered fake news, this suggests the importance of media literacy education in addressing fake news, particularly when secondary effects suc... | ACB | BCA | BCA | BCA | Selection 4 |
The performance of decentRL at the input layer notably lags behind that of other layers and AliNet. As discussed in previous sections, decentRL does not use the embedding of the central entity as input when generating its output embedding. However, this input embedding can still accumulate knowledge by participating i... | **A**: The optimal performance is achieved by a four-layer decentRL..
**B**: The acquired information may not necessarily reside in the same dimension for a pair of aligned entities at this layer, which accounts for the comparatively lower performance of this layer.
**C**: Additionally, decentRL benefits from concate... | BCA | BCA | CBA | BCA | Selection 1 |
We illustrate the results in Fig. 9. <|MaskedSetence|> <|MaskedSetence|> After reaching the maximum episode length, the game rallies eventually get so long that they break our Atari emulator, causing the colors to change radically, which crashes the policy. The two images of observation in Fig. <|MaskedSetence|> Th... | **A**: We observe that the episode length becomes longer over training time with the intrinsic reward estimated from VDM, as anticipated.
**B**: 9 illustrate the change of emulator.
**C**: We observe that our method reaches the episode length of 104superscript10410^{4}10 start_POSTSUPERSCRIPT 4 end_POSTSUPERSCRIPT wi... | ACB | ACB | ACB | ACB | Selection 3 |
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**: We can view this as a style transfer task and use a technique from [adaIN] to achieve our goal..
**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 details while maintaining the semantic informatio... | BCA | BCA | BCA | BCA | Selection 3 |
<|MaskedSetence|> ‘Window operator’ is a cube of 3x3, each containing elements of 0,i,1,-1,2, and 2. Each element (or cell) is inputted in the same way as three pin structural computing on the upper and lower surfaces. I will call it this because it is a basic unit that makes up an organization called a window operato... | **A**: Now, we will define ‘window operators’ to have the same connection as a 3-pin based structural computer using the reverse signal pair described earlier.
**B**: 1110, NULL: Transmits light that enters the upper and lower sides.
**C**: Based on the above functions, the window operator is designed to allow the s... | BAC | ABC | ABC | ABC | Selection 3 |
Forward selection is a simple, greedy feature selection algorithm (Guyon \BBA Elisseeff, \APACyear2003). It is a so-called wrapper method, which means it can be used in combination with any learner (Guyon \BBA Elisseeff, \APACyear2003). <|MaskedSetence|> One then proceeds to sequentially add the next “best” feature at... | **A**: The basic strategy is to start with a model with no features, and then add the single feature to the model which is “best” according to some criterion.
**B**: Here we consider forward selection based on the Akaike Information Criterion (AIC).
**C**: This procedure can be described as follows:
.
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The interpretations of the top-3 anomalies identified by FBED-CART-PS are presented in Table 12. <|MaskedSetence|> For variable backbone, 73% of the animals in the dataset follow the normal dependency; that is, if an animal has a tail, it would have a backbone. Scorpion has a tail but no backbone, and it is the only ... | **A**: For scorpion, the three variables backbone, eggs and milk contribute most to the anomalousness.
**B**: Scorpion neither lays eggs nor produces milk, and only 2% of the animals have this pattern..
**C**: In relation to the other two most contributing variables, eggs and milk, the normal dependency that is held ... | ACB | ACB | ACB | BCA | Selection 1 |
<|MaskedSetence|> in Abbasi-Yadkori et al. <|MaskedSetence|> [2020], Filippi et al. [2010]. Optimistic parameter search provides a cleaner description of the learning strategy. In non-linear reward models, both approaches may not follow similar trajectory but may have overlapping analysis styles (see Filippi et al. ... | **A**:
CB-MNL enforces optimism via an optimistic parameter search (e.g.
**B**: [2011]), which is in contrast to the use of an exploration bonus as seen in Faury et al.
**C**: [2010] for a short discussion)..
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<|MaskedSetence|> Our VSGN is a multi-level cross-scale framework that contains two major components: video self-stitching (VSS); cross-scale graph pyramid network (xGPN). In VSS, we focus on a short period of a video and magnify it along the temporal dimension to obtain a larger scale. Then using our self-stitching s... | **A**: Specifically, we propose a Video self-Stitching Graph Network (VSGN) for improving performance of short actions in the TAL problem.
**B**: Hence, we enable direct information pass between the two feature scales.
**C**: Compared to simply using one scale, our VSGN adaptively rectifies distorted features in eith... | ABC | BCA | ABC | ABC | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> These unexplored areas of the hyperparameter space may offer a fresh start to the search for hyperparameters. The synergy of combining both techniques can be beneficial in finding distinctive local optima that generalize to a better result in the end. Hence, the ... | **A**: With crossover, random pairs of underperforming models (originating from the same algorithm) are picked and their hyperparameters are fused with the goal of creating a better model.
**B**: It facilitates scanning for external regions of the solution space to discover additional local optima.
**C**: As a result... | ACB | ACB | ACB | CBA | Selection 3 |
We presented a novel formulation for the isometric multi-shape matching problem. <|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. <... | **A**: This contrasts the recent ConsistentZoomOut [31] method, which does not obtain cycle-consistent multi-matchings.
**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 montonical... | BAC | BAC | BAC | CBA | Selection 3 |
<|MaskedSetence|> This characterization decomposes the input graph G𝐺Gitalic_G by clique separators as in [18], then at the recursive step one has to find a proper vertex coloring of an antipodality graph satisfying some particular conditions; see Section 3, in particular Theorem 6. In a few words, an antipodality gr... | **A**:
The recognition algorithm RecognizePG for path graph is mainly built on path graphs’ characterization in [1].
**B**: Unfortunately, we cannot build all the antipodality graphs by brute force because checking all possible antipodal pairs requires too much time (more time than the overall complexity of algorithm... | ABC | ABC | ABC | CBA | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> SLIM combined the SLIM with the spectral method based on DCSBM for community detection. And the SLIM method outperforms state-of-art methods in many real and simulated datasets. Therefore, it is worth modifying this method to mixed membership networks. <|MaskedSetence|> | **A**: 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.
**B**: As mentioned in SLIM , the idea of using the symmetric Laplacian inverse matrix to measure the closeness of nodes comes from the first hitting t... | ACB | ABC | ABC | ABC | Selection 4 |
<|MaskedSetence|> (2014); Ma et al. (2015); Chen et al. (2015); Dubey et al. (2016); Vollmer et al. (2016); Chen et al. (2016); Dalalyan (2017); Chen et al. (2017); Raginsky et al. (2017); Brosse et al. (2018); Xu et al. (2018); Cheng and Bartlett (2018); Chatterji et al. (2018); Wibisono (2018); Bernton (2018); Dalal... | **A**: (2019a, b); Mou et al.
**B**: See, e.g., Welling and Teh (2011); Chen et al.
**C**: (2019); Ma et al.
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1) In general, RL methods perform better than conventional methods, and it indicates the advantage of the RL. The reason is that the conventional methods often rely on prior knowledge which may fails in some cases. A typical case is MaxPressure. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> | **A**: It shows good performances on several cases including Hangzhou with the real configuration, Jinan with the real,mixedl configurations, NewYork with the real,mixedl configurations, and Shenzhen with the real,mixedl configurations.
**B**: However, it dramatically drops in other scenarios.
**C**: That is, once th... | ABC | ABC | ABC | ABC | Selection 3 |
Online bin packing has a long history of study. <|MaskedSetence|> <|MaskedSetence|> BestFit works similarly, except that it places the item into the bin of minimum available capacity, which can still fit the item. NextFit has a competitive ratio of 2, while both FirstFit and BestFit are 1.7-competitive (?, ?). <|Ma... | **A**: FirstFit is another simple heuristic that places an item into the first bin of sufficient space and opens a new bin if required.
**B**: Improving upon this performance requires more sophisticated algorithms, and many have been proposed in the literature..
**C**: The simplest algorithm is NextFit, which places ... | BAC | CAB | CAB | CAB | Selection 2 |
Recently proposed object representations address this pitfall of point clouds by modeling object surfaces with polygonal meshes (Wang et al., 2018; Groueix et al., 2018; Yang et al., 2018b; Spurek et al., 2020a, b). They define a mesh as a set of vertices that are joined with edges in triangles. <|MaskedSetence|> The... | **A**: To obtain such a representation, state-of-the-art approaches leverage deep learning models based on the autoencoder architecture (Wang et al., 2018; Spurek et al., 2020a, b) or based on an ensemble of parametric mappings from 2D rectangular patches to 3D primitives, often referred to as an atlas (Groueix et al.,... | BCA | BAC | BAC | BAC | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> In Section 3, we provide the main algorithm of the paper to solve such kind of problems. 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 a... | **A**: Paper organization.
**B**: This paper is organized as follows.
**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. <|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. <|MaskedSetence|> In [5] a unified perspective of the problem is presented. The authors show that the MC... | **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 minimum cycle basis (MCB) problem is the problem of finding a cycle basis such that the sum of the lengths (or e... | CBA | BAC | CBA | CBA | Selection 1 |
<|MaskedSetence|> Specifically, F4 resembled an unimportant feature for the Worst subspace, as shown in Fig. 7(c.1). Albeit that, when closely explored in the whole data space, it was more impactful than other features (with a target correlation value of more than 15%). <|MaskedSetence|> Indeed in that particular sli... | **A**: Afterwards, we collapsed the Worst slice and expanded the Bad slice to explore the impact of the feature.
**B**: After this phase, 24 out of the 41 original features remained in use..
**C**: We concentrated on the conjunction of those automatic approaches with the statistical measures offered by FeatureEnVi.
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<|MaskedSetence|> 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. <|Mask... | **A**: Instead of adapting the controller for the worst case scenarios, the prediction model can be selected to provide the best closed-loop performance by tuning the parameters in the MPC optimization objective for maximum performance [8, 9, 10].
**B**: MPC accounts for the real behavior of the machine and the axis d... | BCA | BCA | BCA | BCA | Selection 3 |
<|MaskedSetence|> Often papers fail to compare against recent methods and vary widely in the protocols, datasets, architectures, and optimizers used. <|MaskedSetence|> Some use it as a binary classification task (class 0: digits 0-4, class 1: digits: 5-9) [5, 50], whereas others use a multi-class setting (10 classes)... | **A**: So far, there is no study comparing methods from either group comprehensively.
**B**: These discrepancies make it difficult to judge the methods on an even ground.
.
**C**: For instance, the widely used Colored MNIST dataset, where colors and digits are spuriously correlated with each other, is setup different... | ACB | ACB | ACB | ACB | Selection 2 |
To address the performance degradation across subjects, Funes et al. present a cross-subject training method [36]. However, the reported mean error is larger than 10 degrees. <|MaskedSetence|> They use a large number of synthetic cross-subject data to train their model. <|MaskedSetence|> <|MaskedSetence|> Lu et al.... | **A**: Sugano et al. introduce a learning-by-synthesis method [37].
**B**: On the other hand, to tackle the head motion problem, Sugano et al. cluster the training samples with similar head poses and interpolate the gaze in local manifold [39].
**C**: Lu et al. employ a sparse auto-encoder to learn a set of bases fro... | ACB | BAC | ACB | ACB | Selection 1 |
Other methods detect the keypoints from the face image, instead of local patches. For instance, Weng et al. weng2016robust proposed to recognize persons of interest from their partial faces. To accomplish this task, they firstly detected keypoints and extract their textural and geometrical features. <|MaskedSetence|... | **A**: Keypoint based matching method is introduced in Duan et al.
**B**: Gabor ternary pattern and point set matching are then applied to match the local keypoints for partial face recognition.
**C**: Next, point set matching is carried out to match the obtained features.
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<|MaskedSetence|> Sized (co)inductive types [BFG+04, Bla04, Abe08, AP16] gave way to sized mixed inductive-coinductive types [Abe12, AP16]. In parallel, linear size arithmetic for sized inductive types [CK01, Xi01, BR06] was generalized to support coinductive types as well [Sac14]. <|MaskedSetence|> <|MaskedSetence|... | **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 types are a type-oriented formulation of size-change termination [LJBA01] for rewrite systems [TG03, BR09].
**C**: We present, to our knowl... | BCA | BCA | BCA | CBA | Selection 2 |
The threats considered in this paper come from three entities: users, the owner, and the cloud. <|MaskedSetence|> Second, the owner is also assumed to be malicious, who may try to obtain the users’ fingerprints and maliciously embed the fingerprints into any media content to frame honest users for copyright infringem... | **A**: Moreover, the cloud is also curious about other information it encounters, including the users’ fingerprints and the LUTs.
**B**: First, users are assumed to be malicious, who could illegally redistribute the owner’s media content with the hope that this behavior will not be detected.
**C**: Third, the cloud i... | BCA | BCA | BCA | BCA | Selection 3 |
Due to the strength in modeling relations on graph-structured data, GNN has been widely applied to various applications like neural machine translation Beck et al. (2018), semantic segmentation Qi et al. (2017), image classification Marino et al. (2017), situation recognition Li et al. <|MaskedSetence|> (2019); Chen e... | **A**: (2020); Wang et al.
**B**: (2018), and fashion analysis Cui et al.
**C**: (2017), recommendation Wu et al.
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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|> <|MaskedSetence|> This was also the case in Ostrovskii & Bach [2021] and T... | **A**: For example, the logistic loss function used in logistic regression is not strictly self-concordant, but it fits into a class of pseudo-self-concordant functions, which allows one to obtain similar properties and bounds as those obtained for self-concordant functions [Bach, 2010].
**B**: [2015], in which more g... | BCA | CAB | CAB | CAB | 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**: 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 struct... | ABC | CAB | CAB | CAB | Selection 3 |
In this paper, we present a novel formulation for the Personalized Federated Learning Saddle Point Problem (1). <|MaskedSetence|> Additionally, we provide the lower bounds both on the communication and the number of local oracle calls required to solve problem (1). Furthermore, we have developed the novel methods (Al... | **A**: These algorithms are based on sliding or variance reduction techniques.
**B**: 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.
**C**: The theoretical analysis and experimental evidence... | BAC | BAC | BAC | BAC | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> We treat the solutions to the MSs as full joint distributions. <|MaskedSetence|> When evaluating, we measure equilibrium gaps under their own MS distribution and MW(C)CE to provide a consistent and value maximizing comparison. Experiments were ran for up to 6 hours, after which th... | **A**: Random solvers were evaluated with five seeds and we plot the mean.
**B**:
We compare against common MS including uniform, α𝛼\alphaitalic_α-Rank (Omidshafiei et al., 2019; Muller et al., 2020), Projected Replicator Dynamics (PRD) (Lanctot et al., 2017) which is an NE approximator, and random vertex (coarse) c... | BCA | BCA | CBA | BCA | Selection 2 |
<|MaskedSetence|> The following lemma gives a useful and intuitive characterization of the quantity that the Bayes stability definition requires be bounded. <|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 ... | **A**: Its corresponding version for arbitrary queries are presented in Section C.2..
**B**: Simply put, the Bayes factor K(⋅,⋅)𝐾⋅⋅{K}\left(\cdot,\cdot\right)italic_K ( ⋅ , ⋅ ) (defined in the lemma below) represents the amount of information leaked about the dataset during the interaction with an analyst, by moving... | CBA | ACB | CBA | CBA | Selection 3 |
<|MaskedSetence|> 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. We also prove that, given a large feedback vertex cut, we can ... | **A**: We present structural properties of antlers and how they combine in Section 4.
**B**: The remainder of the paper is organized as follows.
**C**: Our main results are derived in Section 6, where we show how color coding can be used to efficiently find antlers when the size of their 𝖺𝗇𝗍𝗅𝖾𝗋𝖺𝗇𝗍𝗅𝖾𝗋\math... | BCA | BAC | BAC | BAC | Selection 4 |
Training deep learning models requires abundant pairs of composite images and ground-truth harmonized images. <|MaskedSetence|> We categorize the existing schemes into three groups: forward adjustment, backward adjustment, and replacement. <|MaskedSetence|> We summarize the existing image harmonization datasets in Fi... | **A**: Moreover, we show one representative dataset from each group in Fig. 11.
.
**B**: Existing works have designed different schemes to construct image harmonization dataset.
**C**: Note that some datasets are constructed based on real images while some other datasets are constructed using rendering techniques.
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<|MaskedSetence|> 1(c). Service data pertains to the status of urban service providers (e.g. <|MaskedSetence|> weather). Based on this categorization, we have formulated and tested three types of correlations, as shown in Fig. <|MaskedSetence|> | **A**: 1(c), correlations (1) among mobility services, (2) among context, such as urban geography, and (3) between contexts and services..
**B**: Interrelationship:
We have classified the sub-datasets into two categories: service data and context data, as depicted in Fig.
**C**: taxi companies), while context data re... | BCA | BCA | BCA | BCA | Selection 4 |
<|MaskedSetence|> The most important omission is a more detailed overview of Bayesian neural networks (although one can argue, as was done in the section on dropout networks, that some common neural networks are, at least partially, Bayesian by nature). The main reason for this omission is the large number of choices ... | **A**:
Although a variety of methods was considered, it is not feasible to include all of them.
**B**: The main downside of this approach is that one loses the continuous nature of the initial problem.
**C**: Another technique that was recently introduced pmlr-v80-kuleshov18a calibrates the cumulative distribution ... | CAB | ACB | ACB | ACB | Selection 2 |
Each row shows the percentage of sequences of a class predicted as another class. <|MaskedSetence|> Clayderman (pop), “Y”: Yiruma (pop), “H”: H. <|MaskedSetence|> <|MaskedSetence|> Joe (contemporary), “S”: R. Sakamoto (contemporary), “M”: Bethel Music (religious) and “W”: Hillsong Worship (religious).
. | **A**: Hancock (jazz), “E”: L.
**B**: Notation—“C”: R.
**C**: Einaudi (contemporary), “J”: H.
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<|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**:
Recently, there are also investigations on semantic communications for other transmission contents, such as image and speech.
**B**: Particularly, a joint image transmission-recognition system has been developed in[16] to achieve high recognition accuracy.
**C**: Based on JSCC, an image transmission system, i... | ACB | ABC | ACB | ACB | Selection 1 |
<|MaskedSetence|> In both settings, the cross branches produce the poorest segmentation results the features of this branch are propagated from the other sample. <|MaskedSetence|> The intra branch produces better results than the cross branch, but still lower than the basic branch. This supports our argument that the... | **A**: But during inference, as the network already learned the representations, propagating the features is not helping the predictions.
**B**: Performance of different branches: Table IV compare the segmentation performance of different decoder branches in the two-stage settings.
**C**: However, the cross-branch ca... | BCA | BCA | BCA | BCA | Selection 1 |
<|MaskedSetence|> The KITTI dataset [11] provides widely used benchmarks for various visual tasks in the autonomous driving, including 2D Object detection, Average Orientation Similarity (AOS), Bird’s Eye View (BEV), and 3D Object Detection. <|MaskedSetence|> We report the average accuracy (APAP\rm{AP}roman_AP) for e... | **A**: Setup.
**B**: Moreover, we use 40 recall positions instead of 11 recall positions proposed in the original Pascal VOC benchmark, following [40].
**C**: The official data set contains 7481 training and 7518 test images with 2D and 3D bounding box annotations for cars, pedestrians, and cyclists.
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<|MaskedSetence|> <|MaskedSetence|> It contains 1,000 training and 500 testing images.
MSRA-TD500 [45] is dedicated to detecting multi-oriented long non-Latin texts. It contains 300 training images and 200 testing images with word-level annotation. <|MaskedSetence|> | **A**: Its ground truth is annotated with word-level quadrangles.
**B**: Here, we follow the previous methods [35, 8] and add 400 training images from TR400 [46] to extend this dataset..
**C**: ICDAR2015 [44] includes multi-orientated and small-scale text instances.
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In this paper, we present two efficient algorithms for collecting the statistics of large-scale IP address data. <|MaskedSetence|> <|MaskedSetence|> By taking full advantage of the successive characteristics of memory addresses and the fixed range of each individual part of an IP address, we design two relationship m... | **A**: Because of the increasing volume and speed of network traffic, it has become expensive and impractical to handle all IP addresses contained the IP packets.
**B**: The mechanisms of the mapping relationship effectively remove the information about trivial user behaviors that are irrelevant for statistical analys... | CAB | ACB | CAB | CAB | Selection 4 |
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**: The work of J.
**B**: The work of G.
**C**: 11971221 and the Shenzhen Sci-Tech Fund No.
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<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> This modification would significantly increase the communication cost of the algorithm. We note that the modified algorithm is mathematically equivalent to TDCD, albeit with a higher communication cost. Hence, the convergence analysis given in Section 4 can be tr... | **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... | ABC | BCA | BCA | BCA | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> A comprehensive investigation on the relationships between tubular eigenvalues, T-eigenvalues, and eigentuples has been conducted by Beik and Saad saad2023 .
The T-eigenvalues also exhibit a multitude of applications across diverse mathematical domains. The T-eigenvalues were also ... | **A**: Alternative versions and formulations of eigenvalues of third-order tensors in the context of tensor-tensor multiplication have also been explored by Qi and Zhang qi2021t , who referred to them as“eigentuples”, and by Beik and Saad saad2023 , who termed them as “tubular eigenvalues”.
**B**: zheng2020t to study... | BCA | CAB | CAB | CAB | Selection 3 |
On Two-stream Network Architecture. To further highlight the two-stream dual generation architecture, we compare it with a multi-task single-stream network, which is tailed by two branches to model the image structure and texture simultaneously. <|MaskedSetence|> The Bi-GFF and CFA modules are embedded to refine gener... | **A**: We enlarge its channels to make it have the same amount of parameters as the proposed network.
**B**: As shown in Figure 7 (c), the two-stream architecture exhibits superior performance with more visually reasonable structures and detailed textures.
**C**: Quantitative results in Table 2 also validate the adva... | ABC | CBA | ABC | ABC | Selection 4 |
We train the transformer with the objective to predict the k𝑘kitalic_k-th step ahead. The main advantages of this subgoal objective are simplicity and empirical efficiency. We used expert data to generate labels for supervised training. <|MaskedSetence|> For the Rubik’s Cube, we use random data or simple heuristic (r... | **A**: Furthermore, we found evidence of out-of-distribution generalization.
.
**B**: When offline datasets are available, which is the case for the environments considered in this paper111The dataset for INT or Sokoban can be easily generated or are publicly available.
**C**: [41, 51]) or when only an offline expert... | BAC | BCA | BCA | BCA | Selection 4 |
<|MaskedSetence|> On Chinese Resume, MFE-NER with BERT achieves a 95.73 F1 score and MFE-NER with static embedding gets a 94.23 F1 score, improving the performance of pure semantic embedding from pre-trained language models by about 0.5 with respect to F1 score. On Ontonote, MFE-NER also increases the F1 score of stat... | **A**: For example, in the glyph domain, characters in Chinese names usually contain a special character root, which denotes ‘people’.
**B**: Experiments on the other two general datasets from formal language environments also show that MFE-NER brings slight improvement to the overall NER task.
**C**: Considering tha... | BCA | ACB | BCA | BCA | Selection 3 |
ABC (Gonzalez et al., 2020), the Anti-reflexive Bias Challenge, is a multi-task benchmark dataset designed for evaluating gender assumptions in NLP models. <|MaskedSetence|> A total of 4,560 samples are collected by a template-based method. <|MaskedSetence|> For NLI and coreference resolution, three variations of eac... | **A**: The language modeling task is to predict the pronoun of a sentence.
**B**: For machine translation, sentences with two variations of third-person pronouns in English are used as source sentences.
.
**C**: ABC consists of 4 tasks, including language modeling, natural language inference (NLI), coreference resolu... | CAB | CAB | CAB | ACB | Selection 1 |
The templates are intended to approximate the final look and page length of the articles/papers. <|MaskedSetence|> <|MaskedSetence|> The structure of the LaTeXfiles, as designed, enable easy conversion to XML for the composition systems used by the IEEE’s outsource vendors. <|MaskedSetence|> Have you looked at your ... | **A**: Therefore, they are NOT intended to be the final produced work that is displayed in print or on IEEEXplore®.
**B**: They will help to give the authors an approximation of the number of pages that will be in the final version.
**C**: The XML files are used to produce the final print/IEEEXplore® pdf and then con... | ABC | ABC | BCA | ABC | Selection 4 |
The treatment variation was implemented in the second part. <|MaskedSetence|> 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. However, in addition to reassigning ID’s, subjects were also told that they would be shown... | **A**: Questions from this section are shown in Appendix LABEL:app:questions.
**B**: After the two main parts of the experiment were finished, subjects completed a series of questionnaires designed to elicit behavioral characteristics.
**C**: In three baseline sessions, consisting of a total of 72 subjects in 18 grou... | CBA | ABC | CBA | CBA | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Meanwhile, Zontak et al. pointed out that the internal entropy of patches inside a single image was much smaller than the external entropy of patches in a general collection of natural images. Therefore, using the internal image statistics to further improve mode... | **A**: Therefore, SR methods trained on external images can not work well on such images due to the lack of patch information, while methods based on internal statistics may have a good performance.
**B**: Internal Statistics: In (Zontak and Irani, 2011), Zontak et al.
**C**: found that some patches exist only in a s... | BCA | BCA | BCA | ACB | Selection 2 |
There have been some works where coordinate-based networks are used as a core for a generative model using techniques such as a hypernetwork predicting the weights of a sample coordinate [11], or by modulating the weights of a base coordinate [12]. These approaches are fundamentally different as they attempt to crea... | **A**: Our architecture is purely based on networks, requires no pretraining, and directly maximizes self-similarity between the synthesized and known patches.
Internal Generative Frameworks.
**B**: However, the architecture relies on a convolutional feature encoder, applies a fixed downsampling operation, and is tra... | ABC | BAC | BAC | BAC | Selection 4 |
The apple tasting problem is not the only variant of online classification where labels are not revealed in every round. In selective classification (or classification with a reject (or abstention) option) (e.g. <|MaskedSetence|> Conversely, in classification with selective sampling (Cesa-Bianchi et al.,, 2009; Orabon... | **A**: Chow,, 1957; Sayedi et al.,, 2010; Wiener and El-Yaniv,, 2011) the learner may decline to label items, thus mitigating the risk of labelling when they have high uncertainty.
**B**: The same problem has also been studied under the name ‘online active binary classification’ (Monteleoni and Kaariainen,, 2007; Liu ... | ABC | ABC | ACB | ABC | Selection 2 |
We consider a portion of the IBM2015 argumentative dataset built in the context of the Debater project [63, 64]. The dataset consists of a collection of Wikipedia pages, grouped into topics. The annotation procedure carried out by IBM assumes that claims and evidence are annotated with respect to a given topic.
In our ... | **A**: Subsequently, we defined the set of evidence as the model memory.
**B**: We remark that evidence in the IBM2015 dataset are typically statements extracted from studies or facts established by experts: it is thus a reasonable assumption to have a list of such items available to support claim detection..
**C**: ... | ACB | ACB | ACB | CBA | Selection 1 |
However, the progress of sentiment dependency-based methods, such as the work by Zhang et al. <|MaskedSetence|> (2020); Tian et al. (2021); Li et al. <|MaskedSetence|> <|MaskedSetence|> | **A**: (2019); Zhou et al.
**B**: (2021), has contributed to the improvement of coherent sentiment learning.
These studies explored the effectiveness of syntax information in ABSC, which mitigates issues related to sentiment coherency extraction..
**C**: (2021a); Dai et al.
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<|MaskedSetence|> MNIST-2 classification result is determined by which feature is larger between the two: feature one is the sum of measurement outcomes of qubit 0 and 1; feature 2 is that of qubit 2 and 3. <|MaskedSetence|> The blue dash line is the classification boundary. <|MaskedSetence|> All the baseline points... | **A**: We visualize the two features obtained from experiments on Belem in a 2-D plane as in Figure 8 right.
**B**: Visualization of QNN extracted features.
**C**: The circles/stars are samples of digit ‘3’ and ‘6’.
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In the field of object tracking, conventional object tracking methods can be roughly categorized into three groups: correlation tracking methods, deep tracking methods, and hybrid tracking methods. For the correlation tracking methods, Liang2021robust and wang2021transformer exploit spatio-temporal information for d... | **A**: wang2024deep explores multi-modal fusion for multi-object tracking.
**B**: chen2022siamban adopts a Siamese box adaptive network for target-aware tracking.
**C**: xu2020accelerated uses accelerated correlation filters to track objects.
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<|MaskedSetence|> In this experiment, we adopt MoCo.v2 with ResNet-50 under 1600-epoch pre-training. <|MaskedSetence|> Similar to the pre-training for the teacher network, we add one additional MLP layer on the basis of the student network. Follow the linear evaluation protocols in Sec. V-B, we compare the existing r... | **A**: We adopt the BCE loss for GenURL in the KD task.
.
**B**: We evaluate the KD tasks based on self-supervised learning on STL-10 dataset.
**C**: We choose multiple smaller networks with fewer parameters as the student network: ResNet-18 [70], MobileNet.v2 [86], ShuffleNet.v1 [87].
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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**: We also try supporting flexible w𝑤witalic_w’s per block, which improves the accuracy for smaller computation budgets.
**B**: Existing techniques usually need a scaling method to scale down the searched network and fit different budgets.
**C**: The accuracy improvement is more significant under a tiny computat... | BCA | BCA | BCA | CAB | Selection 1 |
To cope with the problem of model collapse, we devise the asymmetric structure for CGCL. <|MaskedSetence|> Besides, graph encoders in CGCL are supposed to be complementary for a stronger fitting ability. <|MaskedSetence|> For a further theoretical analysis, we propose two metrics: Asymmetry Coefficient (AC) and Compl... | **A**: The asymmetry lies in the differences of GNN-based encoders’ message-passing schemes.
**B**: In addition, we implement experiments with the two quantitative metrics.
**C**: Specifically, high complementarity indicate that encoders together carry less redundant parameters.
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The topic of communication is actively studied in multi-agent RL, see Hernandez-Leal et al., (2020, Table 2) for a recent survey. <|MaskedSetence|> <|MaskedSetence|> Kottur et al., (2017), Słowik et al., 2020b ). The inductive bias can be imposed into the architecture of the agents or the training procedure. <|Maske... | **A**: Recent research has shown that strong inductive biases or grounding of communication protocols are necessary for the protocol to be compositional
(see e.g.
**B**: Compositionality is often investigated in the context of signaling games (Fudenberg and Tirole, (1991), Lewis, (1969), Skyrms, (2010), Lazaridou et a... | BAC | CAB | BAC | BAC | Selection 1 |
In this paper, we learn safe output feedback control laws for unknown systems. We first present robust output control barrier functions (ROCBFs) to establish safety under system dynamics and state estimation uncertainties. <|MaskedSetence|> <|MaskedSetence|> For the general case, we propose an approximate unconstrain... | **A**: We then formulate a constrained optimization problem for constructing ROCBFs from safe expert demonstrations, and we present verifiable conditions that guarantee the validity of the ROCBF.
**B**: Finally, we propose an algorithmic implementation of our theoretical framework to learn ROCBFs in practice, and we p... | ACB | ACB | ACB | ACB | Selection 2 |
<|MaskedSetence|> 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|> Numerical results of both simulated and real-world networks show th... | **A**:
(b) To fit DCDFM, an efficient spectral clustering algorithm called nDFA is designed.
**B**: When DCDFM degenerates to DCSBM, our results also match classical results under DCSBM.
**C**: Benefited from the distribution-free property of DCDFM, our theoretical results under DCDFM are general.
| ABC | ACB | ACB | ACB | Selection 4 |
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