text_with_holes stringlengths 94 3.27k | text_candidates stringlengths 50 1.47k | A stringclasses 6
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<|MaskedSetence|> 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. Also, our scheme is defined by a sequence of elliptic problems, avoiding the an... | **A**: We remark that in this case, our method is similar to that of [MR3591945], with some differences.
**B**: We had to reconsider the proofs, in our view simplifying some of them.
.
**C**: Of course, the numerical scheme and the estimates developed in Section 3.1 hold.
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Early in an event, the related tweet volume is scanty and there are no clear propagation pattern yet. <|MaskedSetence|> Related work often uses aggregated content [18, 20, 32], since individual tweets are often too short and contain slender context to draw a conclusion. <|MaskedSetence|> Thus, a mechanism for careful... | **A**: For the credibility model we, therefore, leverage the signals derived from tweet contents.
**B**: However, content aggregation is problematic for hierarchical events and especially at early stage, in which tweets are likely to convey doubtful and contradictory perspectives.
**C**: In this work, we overcome the... | ABC | BAC | ABC | ABC | Selection 1 |
As we can see in Figure 9 the best result on average over 48 hours is the BestSet. Second one is All features. <|MaskedSetence|> <|MaskedSetence|> But if look into each feature in text feature group, we can see the best and the worst features are all in this set. User features and Twitter features are stable over tim... | **A**: One reason is the text feature set has the largest group of feature with totally 16 features.
**B**: Except those two, the best group feature is Text features.
**C**: CrowdWisdom and CreditScore both contain only one feature, but they already have impressive results comparing with the User features and Twitter... | BAC | CBA | BAC | BAC | Selection 4 |
Evaluating methodology.
For RQ1, given an event entity e, at time t, we need to classify them into either Breaking or Anticipated class. We select a studied time for each event period randomly in the range of 5 days before and after the event time. <|MaskedSetence|> <|MaskedSetence|> We then bin the entities in the t... | **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 set up 4 trials with each of the last 4 bins (using the history bins for training in a rolling basic) for testing; and report the results as average of... | ACB | ACB | ACB | BCA | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> Body weight, according to BMI, is normal for half of the patients, four are overweight and one is obese. <|MaskedSetence|> Only one of the patients suffers from diabetes type 2 and all are in ICT therapy. In terms of time since being diagnosed with diabetes, patients vary from ine... | **A**:
Table 1 shows basic patient information.
**B**: Half of the patients are female and ages range from 17 to 66, with a mean age of 41.8 years.
**C**: The mean BMI value is 26.9.
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<|MaskedSetence|> This training setup is particularly sensitive to false negative predictions and thus the appropriate choice for applications aimed at salient target detection Bylinskii et al. <|MaskedSetence|> <|MaskedSetence|> (2018). As a consequence, we evaluated our estimated gaze distributions without applyin... | **A**: (2018).
**B**:
In this work, we adopted KLD as an objective function and produced fixation density maps as output from our proposed network.
**C**: Defining the problem of saliency prediction in a probabilistic framework also enables fair model ranking on public benchmarks for the MIT1003, CAT2000, and SALICO... | BAC | CAB | BAC | BAC | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Firstly, ruling out simple strategies is a natural initial step in the search for approximation algorithms for a new problem. Secondly, due to the results of Section 4, the investigated greedy strategies for computing the locality number can also be interpreted a... | **A**: 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 marked by som... | BAC | BAC | BAC | BAC | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> In the best case of Freeway, our method is more than 10x more sample-efficient, see Figure 3.
Since the publication of the first preprint of this work, it has been shown in van Hasselt et al. (2019); Kielak (2020) that Rainbow can be tuned to have better results in low data regime.... | **A**: In our empirical evaluation, we find that SimPLe is significantly more sample-efficient than a highly tuned version of the state-of-the-art Rainbow algorithm (Hessel et al., 2018) on almost all games.
**B**: The results are on a par with SimPLe – both of the model-free methods are better in 13 games, while SimP... | ACB | ACB | ACB | CAB | Selection 3 |
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**: However, it’s noteworthy that Gorilla only has walking locomotion mode and does not fit into the wheel/track-legged hybrid robot category.
**B**: This oversight underscores the need for future developments that incorporate a more comprehensive understanding of the external context and environmental factors, ena... | CAB | CAB | BAC | CAB | Selection 1 |
It should be fairly clear that such assumptions are very unrealistic or undesirable. 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. <|MaskedSetence|> I... | **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**: However, if this bit is wrong, then the online algorithm has unbounded competitive ratio, i.e., can perform extremely badly.
**C**: For... | ACB | BCA | ACB | ACB | Selection 3 |
<|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. <|MaskedSetence|> However, when working with classifiers... | **A**: Note that storing either all the documents or a d×t𝑑𝑡d\times titalic_d × italic_t document-term matrix, where d𝑑ditalic_d is the number of documents and t𝑡titalic_t the vocabulary size, takes up much more space than a small 2-dimensional vector..
**B**:
It is worth noting that the difference in terms of sp... | BCA | BCA | BCA | ACB | Selection 2 |
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. <|MaskedSetence|> We can fin... | **A**: Fig. 6 presents the influence of the dynamics on PBLLA.
**B**: In the rest simulations, similar phenomena can also be observed..
**C**: When the environment is highly dynamic with high values of τ𝜏\tauitalic_τ, which brings about more mistakes when selecting powers and altitudes.
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Deep neural networks are the state of the art learning models used in artificial intelligence. 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, requirin... | **A**: They include variational Dropout[15], Max-pooling Dropout[16], fast Dropout[17], Cutout[18], Monte Carlo Dropout[19], Concrete Dropout[20] and many others..
**B**: Dropout was first introduced in 2012 as a regularization technique to avoid over-fitting[12], and was applied in the winning submission for the Larg... | BCA | ACB | BCA | BCA | Selection 4 |
Attention can be viewed as using information transferred from several subsequent layers/feature maps to select and localize the most discriminative (or salient) part of the input signal. Wang et al. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> (2018a) proposed a selection mechanism where feature maps are f... | **A**: Their proposed attention module consists of several encoding-decoding layers.
**B**: Hu et al.
**C**: (2017a) added an attention module to the deep residual network (ResNet) for image classification.
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The generalization performance has been widely studied. Zhang et al. <|MaskedSetence|> Bornschein et al. <|MaskedSetence|> (2018) evaluate the performance of modern neural networks using the same test strategy as Fernández-Delgado et al. <|MaskedSetence|> | **A**: (2017) demonstrate that deep neural networks are capable of fitting random labels and memorizing the training data.
**B**: (2014) and find that neural networks achieve good results but are not as strong as random forests..
**C**: (2020) analyze the performance across different dataset sizes.
Olson et al.
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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**: We remark that our setting differs from the linear setting studied by Yang and Wang (2019b); Jin et al.
**B**: In particular, compared with the work of Yang and Wang (2019b, a); Jin et al.
**C**: Compared with optimism-led iterative value-function elimination (OLIVE) (Jiang et al., 2017; Dong et al., 2019), wh... | CAB | ABC | ABC | ABC | Selection 3 |
We thank Prof. Henry Adams and Dr. Johnathan Bush for very useful feedback about a previous version of this article. <|MaskedSetence|> Mikhail Katz and Prof. <|MaskedSetence|> We thank Dr. Qingsong Wang for bringing to our attention the paper [76] which was critical for the proof of Theorem 1. Finally, we thank Dr. ... | **A**: Michael Lesnick for explaining to us some aspects of their work.
**B**: We also thank Prof.
**C**: Alexey Balitsky for pointing out an imprecision in the statement of Proposition 9.2.
.
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<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> (b) The user-induced ordering is compared to dimension-specific orderings using a correlation measure. (c) Results are shown in the lengths of bars, ordered by the absolute value of the correlation (with highest on top). Note that if the same polyline is drawn by... | **A**: (a) Nearby points are projected to a user-drawn path, creating a user-induced ordering.
**B**: Here 7, 3, 4, and so on are data instance IDs.
**C**:
Figure 5: The Dimension Correlation tool.
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An alarming issue that prevails in the area besides the number of metaphor-based proposals is the lack of a fair experimental study to prove their competitiveness when compared to existing solvers. In many research contributions, the newly introduced bio-inspired optimization algorithms are not compared to relevant tec... | **A**: Thus, they should not be recommended for real-world problems, because the experiments that showed their good performance are biased.
.
**B**: Moreover, the experimental design is often not right: for example, the optima of the tested functions is often at the center of the domain search, which favors solvers th... | CAB | BCA | BCA | BCA | Selection 3 |
<|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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<|MaskedSetence|> It is often impossible to request permission from owners of all the tested networks in advance, this challenge similarly applies to other Internet-wide studies (Lyon, 2009; Durumeric et al., 2013, 2014; Kührer et al., 2014). <|MaskedSetence|> <|MaskedSetence|> Performing security scans is important... | **A**: To opt out the network has to provide either its network block (in CIDR notation), domain or ASN through the contact page at https://smap.cad.sit.fraunhofer.de.
**B**: Like the other studies, (Durumeric et al., 2013, 2014), we provide an option to opt out of our scans.
**C**:
∙∙\bullet∙ Consent of the scanned... | CBA | CBA | BCA | CBA | Selection 2 |
While natural systems cope with changing environments and embodiments well, they form a serious challenge for artificial systems. <|MaskedSetence|> Drawing motivation from nature, this paper introduced an approach based on continual adaptation. <|MaskedSetence|> It then modulates the skill of odor recognition with th... | **A**: For instance, to stay reliable over time, gas sensing systems must be continuously recalibrated to stay accurate in a changing physical environment.
**B**: Context models can thus play a useful role in lifelong adaptation to changing environments in artificial systems.
.
**C**: A recurrent neural network uses ... | ACB | CBA | ACB | ACB | 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). 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]. <|Mask... | **A**: While these constructions and the involved proofs are generally deemed quite complicated, the situation for semigroups turns out to be much simpler.
**B**: Here, the main difference is that the free monoid in one generator can indeed be generated by an automaton: it is generated by the adding machine (see 1), w... | ABC | ABC | ACB | ABC | Selection 1 |
<|MaskedSetence|> (2019) and Self Critical Reasoning (SCR) Wu and Mooney (2019), train the network to be more sensitive towards salient image regions by improving the alignment between visual cues and gradient-based sensitivity scores. HINT proposes a ranking loss between human-based importance scores Das et al. (2016... | **A**: Instead, it penalizes the model if correct answers are more sensitive towards non-important regions as compared to important regions, and if incorrect answers are more sensitive to important regions than correct answers..
**B**: In contrast, SCR does not require exact saliency ranks.
**C**:
Both Human Importa... | CBA | CBA | CBA | CBA | Selection 3 |
<|MaskedSetence|> (2016). <|MaskedSetence|> To the best of our knowledge, this is the most detailed and widely used dataset of annotated privacy policies in the research community. <|MaskedSetence|> We fine-tuned PrivBERT on the OPP-115 Corpus to predict the coarse-grained categories of data practices. We divided th... | **A**:
For the data practice classification task, we leveraged the OPP-115 Corpus introduced by Wilson et al.
**B**: The OPP-115 Corpus contains manual annotations of 23K fine-grained data practices on 115 privacy policies annotated by legal experts.
**C**: The OPP-115 Corpus contains paragraph-sized segments annota... | ABC | ABC | ABC | ABC | Selection 1 |
Figure 2: The exploration process of ML algorithms. View (a.1) summarizes the performance of all available algorithms, and (a.2) the per-class performance based on precision, recall, and f1-score for each algorithm. <|MaskedSetence|> (c.2) illustrates in light blue the selected models and in gray the remaining ones. ... | **A**: (b) presents a selection of parameters for KNN in order to boost the per-class performance shown in (c.1).
**B**: The chart axes are normalized from 0 to 100%..
**C**: In view (e), the boxplots were replaced by point clouds that represent the individual models of activated algorithms.
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<|MaskedSetence|> In Persona and Weibo, each task is a set of dialogues for one user, so tasks are different from each other. We shuffle the samples and randomly divide tasks to construct the setting that tasks are similar to each other. For a fair comparison, each task on this setting also has 120 and 1200 utterances... | **A**: Task similarity.
**B**: A possible explanation is that if there is no clear distinction between tasks, the meta-learning setting can be viewed as a transfer learning setting, which only has a source domain and a target domain, and fine-tuning performs well in transfer learning.
**C**: In Persona and Weibo, the... | ACB | ACB | BAC | ACB | Selection 1 |
<|MaskedSetence|> For example, the beam tracking is achieved by directly predicting the AOD/AOA through the improved Kalman filtering [26], however, the work of [26] only targets at low-mobility scenarios. <|MaskedSetence|> Nevertheless, the impact of the attitude changes of vehicles on the beam tracking is not invol... | **A**: In a nutshell, all the aforementioned work [26, 27, 28, 29, 30, 31] is based on conventional ULA/UPA, and there is no existing work on the beam tracking solution for CA-enabled UAV mmWave networks.
**B**: For vehicle networks, the position-assisted beam tracking methods are proposed by [27] and [28].
**C**:
N... | CBA | CBA | CBA | CBA | Selection 2 |
Szepesvári, 2018; Dalal et al., 2018; Srikant and Ying, 2019) settings. See Dann et al. <|MaskedSetence|> Also, when the value function approximator is linear, Melo et al. (2008); Zou et al. <|MaskedSetence|> (2019b) study the convergence of Q-learning. When the value function approximator is nonlinear, TD possibly d... | **A**: In contrast to the previous analysis in the NTK regime, our analysis allows TD to attain a data-dependent feature representation that is globally optimal..
**B**: (2019); Chen et al.
**C**: (2014) for a detailed survey.
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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. <|MaskedSetence|> (2021c), and the use of only deep encoders Bapna et al. (2018); Wang et al. <|MaskedSetence|> (2022a); Chai et al. ... | **A**: (2021); Xu et al.
**B**: (2020) normally leads to faster inference speed than using both a deep encoder and a deep decoder.
**C**: (2019); Li et al.
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In contrast to the long history of traditional distortion rectification, learning methods began to study distortion rectification in the last few years. Rong et al. [8] quantized the values of the distortion parameter to 401 categories based on the one-parameter camera model [22] and then trained a network to classify... | **A**: Manuel et al.
**B**: To expand the application, Yin et al.
**C**: Note that the above methods directly estimates distortion parameters from a single distorted image, such an implicit and heterogeneous calibration objective hinders sufficient learning concerning the distortion information.
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Our main goal is to develop algorithms for the black-box setting. <|MaskedSetence|> First, we develop algorithms for the simpler polynomial-scenarios model. <|MaskedSetence|> <|MaskedSetence|> This overall methodology is called Sample Average Approximation (SAA).
. | **A**: 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.
**B**: As usual in two-stage stochastic problems, this has three steps.
**C**: Finally, we extrapolate the solution to the original black-box proble... | CBA | BAC | BAC | BAC | Selection 4 |
Motivated by distributed statistical learning over uncertain communication networks, we study the distributed stochastic convex optimization by networked local optimizers to cooperatively minimize a sum of local convex cost functions. The network is modeled by a sequence of time-varying random digraphs which may be sp... | **A**: The local cost functions are not required to be differentiable, nor do their subgradients need to be bounded.
**B**: The additive and multiplicative communication noises co-exist in communication links.
**C**: The main contributions of our paper are listed as follows..
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We observe that the results of MuCo are much better than that of Mondrian and Anatomy. The primary reason is that MuCo retains the most distributions of the original QI values and the results of queries are specific records rather than groups. Consequently, the accuracy of query answering of MuCo is much better and mo... | **A**: In conclusion, MuCo can achieve the same level of protection as generalization does but with less information loss and more accurate query results.
**B**: Therefore, differing from Mondrian and Anatomy, increasing the level of protection of MuCo has little influence on the query results.
**C**: Note that, sinc... | ACB | BAC | BAC | BAC | Selection 2 |
Bells and Whistles. MaskRCNN-ResNet50 is used as baseline and it achieves 53.2 mAP. For PointRend, we follow the same setting as Kirillov et al. <|MaskedSetence|> Surprisingly, PointRend yields 62.9 mAP and surpasses MaskRCNN by a remarkable margin of 9.7 mAP. More Points Test. By increasing the number of subdivision ... | **A**: Due to PointRend’s lightweight segmentation head and less memory consumption compared to HTC, the input resolution can be further increased from range [800,1000] to [1200,1400] during multi-scale training.
**B**: (2017), we gain 71.6 mAP, which already outperforms HTC and SOLOV2 from our offline observation.
*... | CBA | ACB | CBA | CBA | Selection 4 |
<|MaskedSetence|> Section 2 presents our problem definition. Section 3 establishes the minimax regret lower bound for nonstationary linear MDPs. <|MaskedSetence|> Section 6 shows our experiment results. <|MaskedSetence|> All detailed proofs can be found in Appendices.. | **A**: Section 7 concludes the paper and discusses some future directions.
**B**: Section 4 and Section 5 present our algorithms LSVI-UCB-Restart, Ada-LSVI-UCB-Restart and their dynamic regret bounds.
**C**:
The rest of the paper is organized as follows.
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There is a very strong, negative correlation between the media sources of fake news and the level of trust in them (ref. 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). <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence... | **A**: In Singapore, there have been active efforts through campaigns from various organizations (e.g., S.U.R.E. (Board, [n.d.]), Better Internet (Council, [n.d.]), VacciNationSG (Lai, 2021)) to raise awareness on misinformation, disinformation and fake news.
**B**: It is worthwhile to consider whether the trust in me... | CBA | ABC | CBA | CBA | Selection 3 |
<|MaskedSetence|> For entities with only a few neighbors, the advantage of leveraging DAN is not significant. <|MaskedSetence|> This upward trend halts until the degree exceeds 20. <|MaskedSetence|> The decentralized attention, which considers neighbors as queries, consistently outperforms the centralized GAT across... | **A**: However, as the degree increases, incorporating DAN yields more performance gain.
**B**:
The results on the ZH-EN dataset are depicted in Figure 7.
**C**: Overall, DAN exhibits significantly better performance than GCN, GAT, or their combination.
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The ensemble-based baseline contains three individual encoder-decoder networks. <|MaskedSetence|> <|MaskedSetence|> In (a), we use the image of digit ‘0’ as the input and generate a prediction from each network in the ensemble model. <|MaskedSetence|> In (b), we use the image of digit ‘1’ as the input. We observe t... | **A**: We do not average the outputs of the three models.
**B**: The three images from three networks have correct image class but lack diversity in different writing styles.
**C**: As shown in Fig. 4, three images are generated from each model with the same input.
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<|MaskedSetence|> <|MaskedSetence|> We do that by applying any of the above mentioned VAEs111In this exposition we use unspervised trained VAEs as our base models but the framework also works with GAN-based or FLOW-based DGMs, supervised, semi-supervised or unsupervised. In the Appendix we present such implementation... | **A**:
The model has two parts.
**B**: First, we apply a DGM to learn only the disentangled part, C𝐶Citalic_C, of the latent space.
**C**: For example, in Figure 1, the model uses β𝛽\betaitalic_β-TCVAE [mig] to retrieve the pose of the model as a latent factor.
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Now, we will define ‘window operators’ to have the same connection as a 3-pin based structural computer using the reverse signal pair described earlier. ‘Window operator’ is a cube of 3x3, each containing elements of 0,i,1,-1,2, and 2. Each element (or cell) is inputted in the same way as three pin structural computing... | **A**: Based on the above functions, the window operator is designed to allow the same operation of the connected state as the three pin based AND gate shown earlier.
.
**B**: I will call it this because it is a basic unit that makes up an organization called a window operator.
**C**: 1, Forward Mirror: Double-sided... | ACB | BCA | BCA | BCA | Selection 2 |
For this purpose, one would ideally like to use an algorithm that provides sparsity, but also algorithmic stability in the sense that given two very similar data sets, the set of selected views should vary little. <|MaskedSetence|> If the primary concern is sparsity, a researcher may be satisfied with just one of thes... | **A**: If one wants to go even further and perform formal statistical inference on the set of selected views, one may additionally be interested in theoretically controlling, say, the family-wise error rate (FWER) or false discovery rate (FDR) of the set of selected views.
**B**: But if there is also a desire to inter... | CBA | CBA | BAC | CBA | Selection 1 |
The interpretations of the top-3 anomalies identified by FBED-CART-PS are presented in Table 12. For scorpion, the three variables backbone, eggs and milk contribute most to the anomalousness. <|MaskedSetence|> <|MaskedSetence|> In relation to the other two most contributing variables, eggs and milk, the normal depe... | **A**: Scorpion neither lays eggs nor produces milk, and only 2% of the animals have this pattern..
**B**: 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.
**C**: Scorpion has a tail but no backbone, and it is the only o... | BCA | CAB | BCA | BCA | Selection 4 |
<|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**: [2011]), which is in contrast to the use of an exploration bonus as seen in Faury et al.
**B**:
CB-MNL enforces optimism via an optimistic parameter search (e.g.
**C**: [2010] for a short discussion)..
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<|MaskedSetence|> <|MaskedSetence|> THUMOS-14 contains 413 temporally annotated untrimmed videos with 20 action categories, in which 200 videos are for training and 213 videos for validation333The training and validation sets of THUMOS are temporally annotated videos from the validation and testing sets of UCF101 [33... | **A**: Datasets and evaluation metrics.
**B**: We present our experimental results on two representative datasets THUMOS-14 (THUMOS for short) [15] and ActivityNet-v1.3 (ActivityNet for short) [7].
**C**: For both datasets, we use mean Average Precision (mAP) at different tIoU thresholds as the evaluation metric.
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In this paper, we presented VisEvol, a VA tool with the aim to support hyperparameter search through evolutionary optimization. <|MaskedSetence|> Exploring the impact of the addition and removal of algorithms and models in a majority-voting ensemble from different perspectives and tracking the crossover and mutation ... | **A**: These limitations pose future research directions for us..
**B**: With the utilization of multiple coordinated views, we allow users to generate new hyperparameter sets and store the already robust hyperparameters in a majority-voting ensemble.
**C**: The effectiveness of VisEvol was examined with use cases us... | BCA | BCA | BCA | ACB | 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**: Experimentally we have demonstrated that our method outperforms recent state-of-the-art techniques in terms of matching quality, while producing cycle-consistent results and being efficient..
**B**: Our main idea is to simultaneously solve for shape-to-universe matchings and shape-to-universe functional maps.
... | BCA | BCA | ACB | BCA | Selection 1 |
<|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**: This order allows us to establish all the antipodality relations in a faster time.
**B**: This is done in Step 4, Step 5, and Step 6 that are the core of algorithm RecognizePG..
**C**:
The recognition algorithm RecognizePG for path graph is mainly built on path graphs’ characterization in [1].
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The stochastic blockmodel (SBM) (SBM, ) is one of the most used models for community detection in which all nodes in the same community are assumed to have equal expected degrees. Some recent developments of SBM can be found in (abbe2017community, ) and references therein. Since in empirical network data sets, the deg... | **A**: However, in MMSB, nodes in the same communities still share the same degrees.
**B**: DCSBM is widely used for community detection for non-mixed membership networks (zhao2012consistency, ; SCORE, ; cai2015robust, ; chen2018convexified, ; chen2018network, ; ma2021determining, ).
**C**: MMSB constructed a mixed ... | BCA | BCA | BCA | ACB | Selection 2 |
<|MaskedSetence|> (2009); Ring and Wirth (2012); Bonnabel (2013); Zhang and Sra (2016); Zhang et al. <|MaskedSetence|> (2017); Agarwal et al. <|MaskedSetence|> (2018); Tripuraneni et al. (2018); Boumal et al. (2018); Bécigneul and Ganea (2018); Zhang and Sra (2018); Sato et al. (2019); Zhou et al. (2019); Weber and ... | **A**: (2018); Zhang et al.
**B**: See, e.g., Udriste (1994); Ferreira and Oliveira (2002); Absil et al.
**C**: (2016); Liu et al.
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<|MaskedSetence|> <|MaskedSetence|> The 4-phase setting is the most common configuration in reality, but the number of phases can vary due to different intersection topologies (3-way, 5-way intersections, etc.). <|MaskedSetence|> 2
illustrates a standard 4-phase setting: "north-south-straight", "north-south-left", "... | **A**: At each phase, vehicles in the specific lanes can continue to drive.
**B**: Phase is a controller timing unit associated with the control of one or more movements, representing the permutation and combination of different traffic flows.
**C**: Fig.
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Online bin packing has a long history of study. <|MaskedSetence|> FirstFit is another simple heuristic that places an item into the first bin of sufficient space and opens a new bin if required. <|MaskedSetence|> <|MaskedSetence|> Improving upon this performance requires more sophisticated algorithms, and many have... | **A**: 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.
**B**: NextFit has a competitive ratio of 2, while both FirstFit and BestFit are 1.7-competitive (?, ?).
**C**: BestFit works ... | BAC | ACB | ACB | ACB | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> (2020) added terms to prevent patch collapse, reduce patch overlap and calculate the exact surface properties analytically rather than approximating them. Deng et al. (2020b) introduced two additional terms to increase global consistency of the local mappings explicitly. One of the... | **A**: To address the problem mentioned above, most of the methods extend the Chamfer loss function of basic AtlasNet with additional terms.
**B**: Another term enforces better spatial configuration of the mappings by minimizing a stitching error.
.
**C**: Bednarik et al.
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Paper organization. <|MaskedSetence|> Section 2 presents a saddle point problem of interest along with its decentralized reformulation. In Section 3, we provide the main algorithm of the paper to solve such kind of problems. <|MaskedSetence|> <|MaskedSetence|> | **A**: This paper is organized as follows.
**B**: In Section 4, we present the lower complexity bounds for saddle point problems without individual variables.
**C**: Finally in Section 5, we show how the proposed algorithm can be applied to the problem computing Wasserstein barycenters .
.
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<|MaskedSetence|> <|MaskedSetence|> This problem was formulated by Stepanec [7] and Zykov [8] for general graphs and by Hubicka and Syslo [9] in the strictly fundamental class context. In more concrete terms this problem is equivalent to finding the cycle basis with the sparsest cycle matrix. In [5] a unified perspec... | **A**:
The length of a cycle is its number of edges.
**B**: 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.
**C**: Some applications of the MCB problem are described in [5, 11, 10, 12]..
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Next, as XGBoost [29] is a nonlinear ML algorithm, we also train a linear classifier (a logistic regression [83] model with the default Scikit-learn’s hyperparameters [84]) to compute the coefficients matrix and then use Recursive Feature Elimination (RFE) [40] to rank the features from the best to the worst in terms o... | **A**: The original features used for the creation of new features are depicted in dark gray in the last column of the table heatmap view.
**B**: More details can be found in Section 4.4..
**C**: Hence, their average is calculated and displayed in the penultimate column.
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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**: Instead of adapting the controller for the worst case scenarios, the prediction model can be selected to provide the best closed-loop performance by tuning the parameters in the MPC optimization objective for maximum performance [8, 9, 10].
**B**: Using Bayesian optimization-based tuning for enhanced performanc... | CAB | ABC | CAB | CAB | Selection 1 |
Re-sampling/Re-weighting: These approaches balance out the spurious correlations. The classical approach is to re-balance the class distribution by adjusting the sampling probability/ loss weight for majority/minority samples [14, 26, 41, 72, 20]. This includes synthesizing minority instances too [14, 26]. <|MaskedSe... | **A**: However, [55] have shown promising results by using static weights to upweight minority patterns.
**B**: We choose this method due to its simplicity..
**C**: Moving beyond class imbalances, REPAIR [40] proposed learning dynamic weights to mitigate representation bias [39].
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<|MaskedSetence|> Wu et al. collect gaze data using near-eye IR cameras [123]. <|MaskedSetence|> Then, they build an eye model using the detected feature and estimate gaze from the gaze model. <|MaskedSetence|> They synthesize additional IR eye images that cover large variations in face shape, gaze direction, pupil ... | **A**: They build a multi-branch network to extract the features of each view and concatenate them to estimate 2222D gaze position on the screen.
**B**: Kim et al. collect a large-scale dataset of near-eye IR eye images [149].
**C**: They use CNN to detect the location of glints, pupil centers and corneas from IR ima... | ACB | CBA | ACB | ACB | Selection 4 |
Other methods detect the keypoints from the face image, instead of local patches. For instance, Weng et al. <|MaskedSetence|> To accomplish this task, they firstly detected keypoints and extract their textural and geometrical features. Next, point set matching is carried out to match the obtained features. Finally, t... | **A**: Gabor ternary pattern and point set matching are then applied to match the local keypoints for partial face recognition.
**B**: mclaughlin2016largest applied the largest matching area at each point of the face image without any sampling..
**C**: weng2016robust proposed to recognize persons of interest from t... | CAB | ACB | CAB | CAB | Selection 3 |
Sized types are a type-oriented formulation of size-change termination [LJBA01] for rewrite systems [TG03, BR09]. Sized (co)inductive types [BFG+04, Bla04, Abe08, AP16] gave way to sized mixed inductive-coinductive types [Abe12, AP16]. <|MaskedSetence|> We present, to our knowledge, the first sized type system for a c... | **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**: In parallel, linear size arithmetic for sized inductive types [CK01, Xi01, BR06] was generalized to support coinductive types as well [Sac14].
**... | BAC | BAC | BAC | CAB | Selection 3 |
<|MaskedSetence|> 13. As shown therein, the cloud-side efficiency of FairCMS-I is significantly higher than that of FairCMS-II, thus validating our analysis in Section VII. <|MaskedSetence|> 14. This is the key to ensuring that the system is efficient when the size of the media content being shared (e.g., vedio) is l... | **A**: Second, we compare the cloud-side efficiency of FairCMS-I and FairCMS-II, and the results are presented in Fig.
**B**: The main reason for the cloud-side efficiency gain of FairCMS-I lies in the use of lightweight single-value alteration method to encrypt the media content, as shown in Fig.
**C**: In spite of ... | ABC | CBA | ABC | ABC | Selection 1 |
One of the main limitations of FM is that it is not able to capture higher-order feature interactions, which are interactions between three or more features. <|MaskedSetence|> This makes HOFM difficult to use in practice. <|MaskedSetence|> (2016); He and Chua (2017); Cheng et al. (2016); Guo et al. <|MaskedSetence|>... | **A**: While higher-order FM (HOFM) has been proposed Rendle (2010, 2012) as a way to address this issue, it suffers from high complexity due to the combinatorial expansion of higher-order interactions.
**B**: To address the limitations of FM in capturing higher-order feature interactions, several variants have been p... | ABC | ABC | BCA | ABC | Selection 4 |
Self-concordant functions have received strong interest in recent years due to the attractive properties that they allow to prove for many statistical estimation settings [Marteau-Ferey et al., 2019, Ostrovskii & Bach, 2021]. <|MaskedSetence|> For example, the logistic loss function used in logistic regression is not ... | **A**: This was also the case in Ostrovskii & Bach [2021] and Tran-Dinh et al.
**B**: The original definition of self-concordance has been expanded and generalized since its inception, as many objective functions of interest have self-concordant-like properties without satisfying the strict definition of self-concorda... | BAC | BAC | ACB | BAC | 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. <|MaskedSetence|> <|MaskedSetence|> Each of these routines is performed in a separate pass over the edges. The Backtrack-Stuck-Structures method backtrac... | **A**: In total, a Pass-Bundle requires 3333 passes..
**B**: The term Pass-Bundle refers to multiple passes during which those routines are executed.
**C**: Precisely, the routines are: (1) extend structures along active paths (Extend-Active-Paths), (2) check for edge augmentations (Check-for-Edge-Augmentation), and ... | ACB | BCA | BCA | BCA | Selection 4 |
<|MaskedSetence|> 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 lower bounds both on the communication and the number of local oracle calls required to solve pro... | **A**:
In this paper, we present a novel formulation for the Personalized Federated Learning Saddle Point Problem (1).
**B**: 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).
**C**: ... | ABC | ABC | ABC | ABC | Selection 4 |
There are two levels of coordination; first is selecting an equilibrium before play commences, and second is selecting actions during play time. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> At action selection time only (C)CEs require further coordination. NEs are factorizable and therefore can sample inde... | **A**: We refer to this coordination problem as the equilibrium selection problem (Harsanyi & Selten, 1988).
**B**: Both NEs and (C)CEs require agreement on what equilibrium is being played (Goldberg et al., 2013; Avis et al., 2010; Harsanyi & Selten, 1988): for (C)CEs this is a joint action probability distribution, ... | BCA | BCA | BCA | ABC | Selection 1 |
Another line of work (e.g., Gehrke et al. (2012); Bassily et al. (2013); Bhaskar et al. (2011)) proposes relaxed privacy definitions that leverage the natural noise introduced by dataset sampling to achieve more average-case notions of privacy. <|MaskedSetence|> This perspective was used Shenfeld and Ligett (2019) to ... | **A**: Triastcyn and Faltings (2020) propose the notion of Bayesian differential privacy which leverages the underlying distribution to improve generalization guarantees, but their results still scale with the range in the general case.
.
**B**: Bassily and Freund (2016) connect this Bayesian intuition to statistical... | CBA | CBA | CBA | CBA | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Under minor technical assumptions, such an algorithm would allow the problem to be solved in polynomial time by repeatedly reducing the parameter, and solving the problem using an FPT or XP algorithm once the parameter value becomes constant. Hence NP-hard proble... | **A**: To illustrate this difficulty, note that strengthening the definition of kernelization to “a preprocessing algorithm that is guaranteed to always output an equivalent instance of the same problem with a strictly smaller parameter” is useless.
**B**: It is nontrivial to phrase meaningful formal questions in this... | CBA | CBA | BCA | CBA | Selection 1 |
In some previous works [154, 29], object placement is used as data augmentation strategy to facilitate the downstream tasks (e.g., object detection, instance segmentation). Therefore, they make use of existing object detection and instance segmentation datasets [89, 28, 21, 38]. In particular, the foregrounds are cropp... | **A**: In this manner, triplets of foregrounds, backgrounds, and ground-truth composite images can be obtained.
Some other works focus on specific applications like 2D virtual try-on [88, 65, 81] (e.g., placing glasses/hats on human faces) or logo composition [80] (e.g., attaching logo to product image), so they need t... | CAB | CAB | CAB | CAB | Selection 3 |
<|MaskedSetence|> Firstly, urban data are usually fragmented across different entities, such as governmental bodies and private enterprises, resulting in disparities in data acquisition and processing protocols. These differences may manifest in variations in spatio-temporal coverage, granularity, and attributes. Cons... | **A**: Secondly, beyond data collection, identifying interdependencies among various datasets is critical to enhance performance by sharing and transferring relevant knowledge.
**B**: Therefore, uncovering these interdependencies is crucial to improve the overall effectiveness of the dataset..
**C**:
In brief, the c... | CAB | CAB | CBA | CAB | Selection 2 |
In this study several types of prediction interval estimators for regression problems were reviewed and compared. <|MaskedSetence|> It was found that without post-hoc calibration the methods derived from a probabilistic model attained the best coverage degree. <|MaskedSetence|> It was also observed that the predictiv... | **A**: To obtain the desired results, this method requires the data set to be split in a training and a calibration set.
**B**: Two main properties were taken into account: the coverage degree and the average width of the prediction intervals.
**C**: However, after calibration, all methods attained the desired covera... | ACB | BCA | BCA | BCA | Selection 4 |
Similar to \textcitesimonettaCNW19, we regard melody extraction as a task that identifies the melody notes in a single-track 101010It is common for MIDI files to consist of multiple tracks. <|MaskedSetence|> homophonic or polyphonic music.
Utilising the POP909 dataset \textcitepop909, we can develop a model that class... | **A**: We refer to “single-track” as MIDI files containing only one track, which is in contrast to multi-track MIDI files that have multiple tracks.
**B**: The goal of this task is to distinguish the melody track from other non-melody tracks present in a multi-track MIDI file \parencitemadsen07IWAIM,jiang19smc.
**C**... | BCA | ABC | ABC | ABC | Selection 4 |
A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber, “Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks,” in Proc. 23rd Int. Conf. <|MaskedSetence|> Learning (ICML), Pittsburgh, USA, Jun. <|MaskedSetence|> <|MaskedSetence|> | **A**: 2006, pp.
**B**: Mach.
**C**: 369–376..
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<|MaskedSetence|> Our method outperforms the previous method MPRM[11] by a large margin. <|MaskedSetence|> We argue that ScanNet is a very large-scale dataset, 10% of the label can already provide strong supervision. Our method with 10% points labeled improved 1% mIoU from the baseline and even outperformed the fully... | **A**: Since the 10% baseline is already close to the fully supervised result, our supervision propagation mechanism may help the network with more information than the fully supervised baseline model.
**B**: Results on ScanNet: Table VI shows the class specific segmentation results in mIoU(%) in ScanNet[46] validatio... | BCA | CAB | BCA | BCA | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> The official data set contains 7481 training and 7518 test images with 2D and 3D bounding box annotations for cars, pedestrians, and cyclists. We report the average accuracy (APAP\rm{AP}roman_AP) for each task under three different settings: easy, moderate, and hard, as defined in ... | **A**: The KITTI dataset [11] provides widely used benchmarks for various visual tasks in the autonomous driving, including 2D Object detection, Average Orientation Similarity (AOS), Bird’s Eye View (BEV), and 3D Object Detection.
**B**: Setup.
**C**: Each class uses different IoU standards for further evaluations.
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<|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**:
MSRA-TD500 [45] is dedicated to detecting multi-oriented long non-Latin texts.
**B**: It contains 300 training images and 200 testing images with word-level annotation.
**C**: ICDAR2015 [44] includes multi-orientated and small-scale text instances.
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A hash table is an effective method for collecting the statistics of IP addresses Sanders2015HS . It uses a hash function to compute a hash codes for an array of buckets with the statistical results. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Therefore, this approach could cause several hash collisions, e... | **A**: Unfortunately, the hash function can generate the same hash code for more than one IP address.
**B**: With the increase in the generation of big data, millions or tens of millions of records have become ubiquitous in network traffic.
**C**: The hash function assigns each key to a unique bucket for each IP addr... | CAB | CBA | CAB | CAB | Selection 3 |
The outline of the remainder of this paper is as follows. In section 2, we briefly recall the classic saddle point problem and its Schur complement, and introduce the twofold saddle point problem and the form of Schur complement, we then construct and analyze the block-triangular and block-diagonal preconditioners base... | **A**: Generalizations to n𝑛nitalic_n-tuple cases are provided in Section 5.
**B**: In Section 6, numerical experiments for a 3-field formulation of the Biot model are provided to justify the advantages of using positively stable preconditioners.
**C**: Furthermore, we extend these results to the n𝑛nitalic_n-tuple ... | CAB | CAB | BCA | CAB | Selection 2 |
However, in cases when the labels are sensitive and sharing the labels for a sample ID across silos is not feasible, the label information for a sample ID may only be present in a client in one silo. <|MaskedSetence|> <|MaskedSetence|> This modification would significantly increase the communication cost of the algor... | **A**: In this case, we could modify our algorithm in the following way, similar to (Liu et al., 2020a): the clients in all silos send the intermediate information for a sample to the client that has the label for the sample.
**B**: The client with the label information calculates the loss and the partial derivatives,... | ABC | ABC | ABC | ABC | Selection 1 |
<|MaskedSetence|> They offer a novel perspective to characterize the properties of the widely employed tensor-tensor multiplication (3). Extensive studies from diverse viewpoints can be found in the references braman2010thirdorder ; Kilmer2013SIAM ; Jin2020 ; Miao2020T ; zheng2020t . Before proceeding with our main re... | **A**:
Within the framework of tensor-tensor multiplication (3) proposed and investigated by Kilmer and Martin Kilmer2011 , T-eigenvalues and T-eigenvectors have garnered significant attention from researchers.
**B**: At last, the connection between T-eigenvalues and certain optimization problems is presented..
**C*... | ACB | ACB | CAB | ACB | Selection 1 |
Motivated by global and local GANs [7], Gated Convolution [36] and Markovian GANs [9], we develop a two-stream discriminator to distinguish genuine images from the generated ones by estimating the feature statistics of both texture and structure. <|MaskedSetence|> The texture branch includes three convolution layers w... | **A**: The structure branch shares the same pattern as the upper stream, where the input edge map is detected by a residual block [6] followed by a convolution layer with the kernel size of 1.
**B**: The discriminator is shown in Figure 2 (b).
**C**: Finally, the outputs of the two branches are concatenated in the ch... | BAC | ACB | BAC | BAC | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> Indeed, such a blend can produce impressive results [43, 44, 36, 1]. <|MaskedSetence|> Others, e.g. [29, 33, 23], utilize variational subgoals generators to deal with long-horizon visual tasks. We show that these ideas can be pushed further to provide algorithms capable of dealing... | **A**:
The deep learning revolution has brought spectacular advancements in pattern recognition techniques and models.
**B**: Given the hard nature of reasoning problems, these are natural candidates to provide search heuristics [4].
**C**: These approaches seek solutions using elementary actions.
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In this paper, we propose to use ‘Five-strokes’, a famous structure-based encoding method for Chinese characters, to get our glyph embedding. ‘Five-Strokes’ was put forward by Yongmin Wang in 1983. <|MaskedSetence|> ‘Five-Strokes’ holds the opinion that Chinese characters are made of five basic strokes, horizontal str... | **A**: This special encoding method for Chinese characters is based on their structures.
**B**: The two characters ‘pu3’ and ‘fu3’ with similar components are close in embedding space, while ‘qiao2’ and ‘pu3’ are much more distant, which gives NER models extra patterns..
**C**: It is really expressive that four Engli... | ACB | CBA | ACB | ACB | Selection 1 |
The idea behind the modular MTL architecture is simple: breaking an MTL model into shared modules and task-specific modules. The shared modules learn shared features from multiple tasks. Since the shared modules can learn from many tasks, they can be sufficiently trained and can generalize better, which is particularly... | **A**: The robustness of shared modules and the flexibility of task-specific modules makes modular architectures suitable for learning different tasks efficiently.
.
**B**: Compared with shared modules, task-specific modules are usually much smaller and thus less likely to suffer from overfitting caused by insufficien... | CBA | CBA | CBA | CBA | Selection 4 |
The templates are intended to approximate the final look and page length of the articles/papers. <|MaskedSetence|> They will help to give the authors an approximation of the number of pages that will be in the final version. <|MaskedSetence|> <|MaskedSetence|> Have you looked at your article/paper in the HTML versio... | **A**: The structure of the LaTeXfiles, as designed, enable easy conversion to XML for the composition systems used by the IEEE’s outsource vendors.
**B**: Therefore, they are NOT intended to be the final produced work that is displayed in print or on IEEEXplore®.
**C**: The XML files are used to produce the final pr... | BAC | BAC | BCA | BAC | Selection 2 |
Because of the reasonably limited size of our dataset, we also construct p-values through a fully nonparametric bootstrap based on resampling of full group sequences. The nonparametric clustered bootstrap approach works as its name suggests. Sequences are drawn from the sample (with replacement) to form a new sample, m... | **A**: In order to ensure proper clustering, it is crucial that each sequence of networks from one group be treated as a single observation.
**B**: Standard errors are obtained as the standard deviation of estimates made by repeating this process many times.
**C**: This approach leverages the argument that drawing fr... | ABC | ABC | ABC | BAC | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> 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. <|MaskedSetence|> pointed out that the internal entropy of patches inside a single image ... | **A**: Meanwhile, Zontak et al.
**B**: found that some patches exist only in a specific image and can not be found in any external database of examples.
**C**: Internal Statistics: In (Zontak and Irani, 2011), Zontak et al.
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<|MaskedSetence|> These approaches are fundamentally different as they attempt to create a wide generative model based on a large-scale dataset, while our approach focuses on data-agnostic internal learning tasks and uses a disparate architecture. Finally, Local Implicit Image Functions introduced in [17] are trained ... | **A**: 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].
**B**: To the best of our knowledge, no attempt of introduci... | ACB | ACB | CAB | ACB | Selection 2 |
<|MaskedSetence|> 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; Orabona and Cesa-Bianchi,, 2011; Cavallanti et al.,, 2011; Dekel et al.,, 2012; Agarwal,, 2013), the learner... | **A**: Chow,, 1957; Sayedi et al.,, 2010; Wiener and El-Yaniv,, 2011) the learner may decline to label items, thus mitigating the risk of labelling when they have high uncertainty.
**B**: The apple tasting problem is not the only variant of online classification where labels are not revealed in every round.
**C**: Th... | BAC | ABC | BAC | BAC | Selection 1 |
Table 5 reports our results on claim detection. Like we did with unfair clause detection, we evaluate knowledge-agnostic neural baselines and MANN models. First of all, we note how MANNs achieved significant improvements over CNNs and LSTMs, suggesting that the introduced knowledge is indeed beneficial to the task itse... | **A**: This behavior is even more evident with MANNs, where the combination of SS and priority sampling largely outperforms the version with full knowledge.
**B**: We think this should be ascribed to the sparsity of relations between memories and examples in this dataset (see Section 4.2).
**C**: For 1 and 2 topics, ... | ACB | BCA | ACB | ACB | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> (2020); Tian et al. (2021); Li et al. <|MaskedSetence|> (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.. | **A**: (2019); Zhou et al.
**B**: However, the progress of sentiment dependency-based methods, such as the work by Zhang et al.
**C**: (2021a); Dai et al.
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We use QNN as the benchmark PQC in this work. Figure 2 shows the QNN architecture. <|MaskedSetence|> The QNN consists of multiple blocks. Each has three components: encoder encodes the classical values to quantum states with rotation gates such as RY; trainable quantum layers contain parameterized gates that can be tr... | **A**: The inputs are classical data such as image pixels, and the outputs are classification results.
**B**: QuantumNAT overview is in Figure 3.
.
**C**: For the MNIST-4 example in Figure 2, the first encoder takes the pixels of the down-sampled 4×\times× 4 image as rotation angles θ𝜃\thetaitalic_θ of 16 rotation g... | ABC | ACB | ACB | ACB | Selection 3 |
In this work, we compare the proposed EDA with eight popular tracking methods, including SiamBAN chen2022siamban , SiamRPN++ Li_2019_CVPR , ATOM Danelljan_2019_CVPR , EVT messikommer2023data , E-MS barranco2018real , ETD chen2019asynchronous , RMRNet chen2020end , and an event-based variant of the classical tracker ECO... | **A**: EVT, E-MS, ETD, and RMRNet are the popular event-based tracking methods.
**B**: Among these trackers, SiamBAN, SiamRPN++, and ATOM are the popular conventional tracking methods.
**C**: Thus, ECO-E is adopted to evaluate the performance of ECO on the event data.
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<|MaskedSetence|> In this experiment, we adopt MoCo.v2 with ResNet-50 under 1600-epoch pre-training. We choose multiple smaller networks with fewer parameters as the student network: ResNet-18 [70], MobileNet.v2 [86], ShuffleNet.v1 [87]. Similar to the pre-training for the teacher network, we add one additional MLP la... | **A**: We adopt the BCE loss for GenURL in the KD task.
.
**B**: Follow the linear evaluation protocols in Sec. V-B, we compare the existing relation-based KD methods including RKD [65], PKT [64], SP [66], SSKD [68], CRD [69], and SEED [67].
**C**: We evaluate the KD tasks based on self-supervised learning on STL-10 ... | CBA | CBA | CBA | ACB | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> By using per-patch inference (4×4444\times 44 × 4 patches), we are able to significantly reduce the memory usage of the first 5 blocks, and reduce the overall peak memory by 8×\times×, fitting MCUs with a 256kB memory budget. <|MaskedSetence|> The memory usage is measured in int8.... | **A**: The peak memory is determined by the first 5 blocks with high peak memory, while the later blocks all share a small memory usage.
**B**:
Figure 1: MobileNetV2 [44] has a very imbalanced memory usage distribution.
**C**: Notice that the model architecture and accuracy are not changed for the two settings.
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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. Specifically, high complementarity indicate that encoders together carry less redundant parameters. For a further t... | **A**: The asymmetry lies in the differences of GNN-based encoders’ message-passing schemes.
**B**: Compared with the state-of-the-art methods, CGCL demonstrates better generalization on various datasets and achieves better results without using extra handcrafted data augmentations.
**C**: Those two metrics are to me... | ACB | ACB | ACB | BCA | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> PLG/2019/012498. 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**: We gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Centers: ACK Cyfronet AGH, PCSS) for providing computer facilities and support within computational grant no.
**B**: The work of Tomasz Korbak was supported by the Leverhulme Doctoral Scholarship.
**C**:
The work of Piotr M... | CBA | BCA | CBA | CBA | Selection 1 |
Learning CBFs: An open problem is how valid CBFs can be constructed. <|MaskedSetence|> For certain types of mechanical systems under input constraints, analytic CBFs can be constructed [30]. <|MaskedSetence|> Finding CBFs poses additional challenges in terms of the control input resulting in bilinear SOS programming... | **A**: Indeed, the lack of systematic methods to construct valid CBFs is a main bottleneck.
**B**: The construction of polynomial barrier functions towards certifying safety for polynomial systems by using sum-of-squares (SOS) programming was proposed in [31].
**C**: The work in [35] considers the construction of hig... | ABC | ABC | ABC | CBA | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> However, though these models for weighted networks are attractive, they always require all elements of connectivity matrix to be nonnegative or all elements of adjacency matrix must follow some specific distributions as found in [16]. Furthermore, spectral cluste... | **A**: Recent years, some Weighted Stochastic Blockmodels (WSBM) have been developed for weighted networks, to name a few, [9, 10, 11, 12, 13, 14, 15].
**B**: Modeling and designing methods to quantitatively detecting latent structural information for weighted networks are interesting topics.
**C**: However, most wor... | CBA | CBA | ABC | CBA | Selection 4 |
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