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Of course, the numerical scheme and the estimates developed in Section 3.1 hold. However, several simplifications are possible when the coefficients have low-contrast, leading to sharper estimates. We remark that in this case, our method is similar to that of [MR3591945], with some differences. <|MaskedSetence|> <|Ma... | **A**: First we consider that T~~𝑇\tilde{T}over~ start_ARG italic_T end_ARG can be nonzero.
**B**: We had to reconsider the proofs, in our view simplifying some of them.
.
**C**: Also, our scheme is defined by a sequence of elliptic problems, avoiding the annoyance of saddle point systems.
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Single Tweet Model Settings. For the evaluation, we shuffle the 180 selected events and split them into 10 subsets which are used for 10-fold cross-validation (we make sure to include near-balanced folds in our shuffle). <|MaskedSetence|> Furthermore, neural networks-based models are implemented with TensorFlow 555htt... | **A**: The first hidden layer is an embedding layer, which is set up for all tested models with the embedding size of 50.
**B**: The output of the embedding layer are low-dimensional vectors representing the words.
**C**: We implement the 3 non-neural network models with Scikit-learn444scikit-learn.org/.
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<|MaskedSetence|> For the experiments, we implement the 3 non-neural network models with Scikit-learn library111111scikit-learn.org/. <|MaskedSetence|> The first hidden layer is an embedding layer, which is set up for all tested models with the embedding size of 50. <|MaskedSetence|> To avoid overfitting, we use the... | **A**: Furthermore, we implement the neural network with TensorFlow 121212https://www.tensorflow.org/ and Keras131313https://keras.io/.
**B**: The output of the embedding layer are low-dimentional vectors representing the words.
**C**: For the evaluation, we shuffle the 180 selected events and split them into 10 subs... | CAB | CAB | ABC | CAB | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> 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. For GoogleTrends, there are 2,700 and 4,200 instances respectively. We then bin the entities in the two datasets chronologically in... | **A**: 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 the trials..
**B**: We select a studied time for each event period randomly in the range of 5 days before and after the event time.
**C**: Evaluating method... | ABC | CBA | CBA | CBA | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> The mean BMI value is 26.9. 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 inexperienced (2 years) to very experienced (35 years), with a mean value of 13.... | **A**: Body weight, according to BMI, is normal for half of the patients, four are overweight and one is obese.
**B**:
Table 1 shows basic patient information.
**C**: Half of the patients are female and ages range from 17 to 66, with a mean age of 41.8 years.
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<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> In detail, we rescaled and padded all images from the SALICON and OSIE datasets to 240×320240320240\times 320240 × 320 pixels, the MIT1003, DUT-OMRON, and PASCAL-S datasets to 360×360360360360\times 360360 × 360 pixels, and the CAT2000 dataset to 216×384216384216... | **A**:
The images presented during the acquisition of saliency maps in all aforementioned datasets are largely based on natural scenes.
**B**: Stimuli of CAT2000 additionally fall into predefined categories such as Action, Fractal, Object, or Social.
**C**: Together with the corresponding fixation patterns, they con... | ABC | ABC | BCA | ABC | Selection 1 |
Our strongest positive result about the approximation of the locality number will be derived from the reduction mentioned above (see Section 5.2). However, we shall first investigate in Section 5.1 the approximation performance of several obvious greedy strategies to compute the locality number (with “greedy strategie... | **A**: This is mainly motivated by two aspects.
**B**: It may seem naive to expect new approximation results for cutwidth in this way, but, as mentioned in the introduction and as shall be discussed in detail in Section 6, approximating the cutwidth via approximation of the locality number may be beneficial for cutwid... | ACB | ACB | ACB | ABC | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> Our approach uses the model as a learned simulator and directly applies model-free policy learning to acquire the policy. <|MaskedSetence|> Also, since our model is differentiable, the additional information contained in its gradients could be incorporated into the reinforcement l... | **A**: However, we could use the model for planning.
**B**: Our predictive model has stochastic latent variables so it can be applied in highly stochastic environments.
**C**: Studying such environments is an exciting direction for future work, as is the study of other ways in which the predictive neural network mode... | BCA | BCA | ABC | BCA | Selection 4 |
There are two primary technical challenges in the wheel/track-legged robotics area [2]. <|MaskedSetence|> Second, it’s essential to develop decision-making frameworks that determine the best mode—either rolling or walking—based on the robot’s environmental interactions and internal states [7, 8]. In addressing the fir... | **A**: This remains a very less explored area [3], but is essential to achieve an autonomous locomotion transition in hybrid robots.
**B**: Building upon our prior work, we employ two climbing gaits to ensure smooth walking locomotion for wheel/track-legged robots, particularly when navigating steps [10].
.
**C**: Fi... | ACB | CAB | CAB | CAB | Selection 3 |
It should be fairly clear that such assumptions are very unrealistic or undesirable. Advice bits, as all information, are prone to transmission errors. In addition, the known advice models often allow
information that one may arguably consider unrealistic, e.g., an encoding of some part of the offline optimal solution.... | **A**: Last, and perhaps more significantly, a malicious entity that takes control of the advice oracle can have a catastrophic impact.
**B**: For a very simple example, consider the well-known ski rental problem: this is a simple, yet fundamental resource allocation, in which we have to decide ahead of time whether t... | ABC | CBA | ABC | ABC | Selection 4 |
It is worth noting that the difference in terms of space complexity is also very significant. <|MaskedSetence|> For instance, when using SS3 we only need to store the confidence vector303030In case of ADD, a 2-dimensional vector. <|MaskedSetence|> <|MaskedSetence|> Note that storing either all the documents or a d×... | **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**: of every user and then simply update it as mor... | CBA | CBA | CBA | CBA | Selection 3 |
Coverage is another factor which determines the performance of each UAV. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Furthermore, the turbulence of upper air disrupts the stability of UAVs with more energy consumption. Thus, a suitable height is essential to determine the coverage area.. | **A**: The higher altitude it is, the larger coverage size a UAV has.
**B**: As presented in Fig. 1 (c), the altitude of UAV plays an important role in coverage adjusting.
**C**: A large coverage size means a substantial opportunity of supporting more users, but a higher SNR will be needed.
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Deep neural networks are the state of the art learning models used in artificial intelligence. <|MaskedSetence|> <|MaskedSetence|> Dropout was first introduced in 2012 as a regularization technique to avoid over-fitting[12], and was applied in the winning submission for the Large Scale Visual Recognition Challenge t... | **A**: However the larger number of parameters also make them particularly prone to over-fitting, requiring regularization methods to combat this problem.
**B**: The large number of parameters in neural networks make them very good at modelling and approximating any arbitrary function.
**C**: They include variational... | BAC | BAC | BCA | BAC | Selection 1 |
<|MaskedSetence|> Incorporating domain/prior knowledge (such as coding the location of different organs explicitly in a deep model) is more sensible in the medical datasets. As shown in Taghanaki et al. <|MaskedSetence|> <|MaskedSetence|> However, the cross-entropy loss returns a reasonable score for the same cases.... | **A**: In medical image segmentation works, researchers have converged toward using classical cross-entropy loss functions along with a second distance or overlap based functions.
**B**: Although overlap based loss function are used in case of a class imbalance (small foregrounds), in Figure 13, we show how using (onl... | ACB | ABC | ACB | ACB | Selection 3 |
<|MaskedSetence|> (2014), each dataset is split into a training and a test set using a 50/50 split while maintaining the label distribution. <|MaskedSetence|> We evaluate the training with 5555, 10101010, 20202020, and 50505050 examples per class.
In contrast to Fernández-Delgado et al. (2014), we extract validation ... | **A**: Afterward, the number of training examples is limited to nlimitsubscript𝑛limitn_{\text{limit}}italic_n start_POSTSUBSCRIPT limit end_POSTSUBSCRIPT examples per class.
**B**: For some datasets which provide a separate test set, the test accuracy is evaluated on the respective set..
**C**: Following Fernández-D... | CAB | CAB | CAB | CAB | Selection 3 |
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**: In particular, compared with the work of Yang and Wang (2019b, a); Jin et al.
**B**: (2019).
**C**: (2020), which generalizes the one proposed by Yang and Wang (2019a).
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We thank Prof. Henry Adams and Dr. <|MaskedSetence|> <|MaskedSetence|> Mikhail Katz and Prof. Michael Lesnick for explaining to us some aspects of their work. We thank Dr. <|MaskedSetence|> Finally, we thank Dr. Alexey Balitsky for pointing out an imprecision in the statement of Proposition 9.2.
. | **A**: We also thank Prof.
**B**: Johnathan Bush for very useful feedback about a previous version of this article.
**C**: Qingsong Wang for bringing to our attention the paper [76] which was critical for the proof of Theorem 1.
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<|MaskedSetence|> In consequence, the participants themselves were responsible for performing them to the best of their abilities. <|MaskedSetence|> <|MaskedSetence|> Each task consisted of one, two, or three questions that the participants were asked to answer, all with multiple choices (except for Q.1.2) including... | **A**: Their numbering follows the same order as described in Section 1 (so Task 1 is related to pitfall (i), Task 2 is related to pitfall (ii), and so on).
**B**: Tasks
Six tasks were provided to the participants, without any specific mentions to the tool’s features.
**C**: The six tasks were designed to match the... | BCA | BCA | 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 2 |
<|MaskedSetence|> Instead, DEC and SpectralNet work better on the large scale datasets. <|MaskedSetence|> If the graph is not updated, the contained information is low-level. The adaptive learning will induce the model to exploit the high-level information. <|MaskedSetence|> | **A**: Classical clustering models work poorly on large scale datasets.
**B**: In particular, AdaGAE is stable on all datasets.
.
**C**: 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 g... | ACB | BCA | ACB | ACB | Selection 1 |
<|MaskedSetence|> The study in (Lone et al., 2017) identified loops in 1,780 ASes, which is 3.2% of all the ASes, and 703 of the ASes were found spoofable. <|MaskedSetence|> <|MaskedSetence|> Furthermore, reproducing or validating the dataset after some time is virtually impossible as the odds for failures rapidly i... | **A**: Although a valuable complementary technique for active probes with vantage points, this approach has significant limitations: in the absence of loops ingress filtering cannot be inferred, alternately a forwarding loop in traceroute does not imply absence of filtering at the edge, since a loop resulting from a tr... | BAC | BAC | ACB | BAC | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> This design introduces variation in training inputs, which makes it harder to learn consistent context patterns. For this task, semisupervised learning techniques, such as self-labeled samples, may help. If the context layer can process unlabeled data, then it is no longer necessar... | **A**:
The current design of the context-based network relies on labeled data because the odor samples for a given class are presented as ordered input to the context layer.
**B**: The full six-gas sensor drift dataset can be used, as well as other unbalanced and therefore realistic datasets..
**C**: However, the mo... | ACB | CAB | ACB | ACB | Selection 3 |
There is a quite interesting evolution of constructions to present free groups in a self-similar way or even as automaton groups (see [15] for an overview). This culminated in constructions to present free groups of arbitrary rank as automaton groups where the number of states coincides with the rank [18, 17]. While t... | **A**: On a side note, it is also worthwhile to point out that – although there does not seem to be much research on the topic – there are examples to generate the free inverse semigroup of rank one as a subsemigroup of an automaton semigroup [14, Theorem 25] and an adaption to present the free inverse monoid of rank o... | CBA | BCA | BCA | BCA | Selection 2 |
<|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**:
Both Human Importance Aware Network Tuning (HINT) Selvaraju et al.
**B**: In contrast, SCR does not require exact saliency ranks.
**C**: 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 sensi... | ABC | BAC | ABC | ABC | Selection 3 |
To satisfy the need for a much larger corpus of privacy policies, we introduce the PrivaSeer Corpus of 1,005,380 English language website privacy policies. The number of unique websites represented in PrivaSeer is about ten times larger than the next largest public collection of web privacy policies Amos et al. (2020)... | **A**: Subsequently, we pretrain PrivBERT, a transformer-based language model, using the corpus and evaluate it on data practice classification and question answering tasks.
**B**: We then analyse the lengths and top level distribution of the privacy policies in the corpus and use topic modelling to explore the compon... | BCA | BAC | BAC | BAC | Selection 4 |
To illustrate how to choose different metrics (and with which weights), we start our exploration by selecting the heart disease data set in StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics(a). Knowing that the data set is balanced, we pick accuracy (weight... | **A**: MCC is a combination of all f-beta scores and shows us both the false positive and false negative results, which is especially useful for comparing it with the f2-score.
**B**: Log loss penalizes outliers, and in our case, we should be aware of outliers as we have sensitive healthcare data.
**C**: The positive... | CAB | CAB | ACB | CAB | Selection 1 |
Data Quantity. In Persona, we evaluate Transformer/CNN, Transformer/CNN-F and MAML on 3 data quantity settings: 50/100/120-shot (each task has 50, 100, 120 utterances on average). In Weibo, FewRel and Amazon, the settings are 500/1000/1500-shot, 3/4/5-shot and 3/4/5-shot respectively (Table 2).
When the data quantity i... | **A**: In Weibo, FewRel and Amazon, the percentages that MAML outperforms the baselines by also decrease as the data quantity increasing.
**B**: This finding is in line with the mechanism of MAML.
**C**: In Persona, the C Score and BLEU of MAML outperform baselines on 50-shot and 100-shot settings, but on 120-shot se... | CAB | CBA | CAB | CAB | Selection 3 |
Note that there exist some mobile mmWave beam tracking schemes exploiting the position or motion state information (MSI) based on conventional ULA/UPA recently. 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 targe... | **A**: The research work on the beam tracking for UAVs with mmWave communications is still rare.
**B**: For vehicle networks, the position-assisted beam tracking methods are proposed by [27] and [28].
**C**: However, previous schemes cannot be readily extended to UAV-to-UAV mmWave communications, where both transmitt... | BAC | BAC | BAC | ABC | 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|> <|MaskedSetence|> When the value function approximator is nonlinear, TD possibly diverges (Baird, 1995; Boya... | **A**: (2019); Chen et al.
**B**: (2014) for a detailed survey.
**C**: (2019b) study the convergence of Q-learning.
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He et al. (2016) present the residual learning framework to ease the training of deep neural networks. <|MaskedSetence|> <|MaskedSetence|> Chai et al. (2020) propose a highway Transformer with a self-gating mechanism for language models. <|MaskedSetence|> First, residual connections are still kept in their model. S... | **A**: Srivastava et al.
**B**: (2015) propose the highway network which contains a transform gate and a carry gate to control the produced output and the input.
**C**: However, our work is significantly different from theirs in two aspects.
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As mentioned above, most previous learning methods correct the distorted image based on the distortion parameters estimation. <|MaskedSetence|> These problems seriously limit the learning ability of neural networks and cause inferior distortion rectification results. <|MaskedSetence|> Fig. <|MaskedSetence|> | **A**: To address the above problems, we propose a fully novel concept, i.e., ordinal distortion.
**B**: 2 illustrates the attributes of the proposed ordinal distortion..
**C**: However, due to the implicit and heterogeneous representation, the neural network suffers from the insufficient learning problem and imbalan... | CAB | BAC | CAB | CAB | Selection 3 |
Our main goal is to develop algorithms for the black-box setting. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Finally, we extrapolate the solution to the original black-box problem. This overall methodology is called Sample Average Approximation (SAA).
. | **A**: First, we develop algorithms for the simpler polynomial-scenarios model.
**B**: 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.
**C**: As usual in two-stage stochastic problems, this has three ste... | CBA | CAB | CAB | CAB | Selection 2 |
<|MaskedSetence|> The co-existence of random graphs, subgradient measurement noises, additive and multiplicative communication noises are considered. <|MaskedSetence|> <|MaskedSetence|> It becomes more complex to estimate the mean square upper bound of the local optimizers’ states (Lemma 3.1). We firstly employ the ... | **A**:
III.
**B**: Compared with the case with only a single random factor, the coupling terms of different random factors inevitably affect the mean square difference between optimizers’ states and any given vector.
**C**: What’s more, multiplicative noises relying on the relative states between adjacent local opti... | ABC | CBA | ABC | ABC | Selection 4 |
<|MaskedSetence|> 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 more stable than that of Mondrian and Anatomy. <|MaskedSetence|> <|Ma... | **A**: Besides, since the results of queries for MuCo are specific records rather than groups, the relative error rate of MuCo does not increase steadily with the growth of δ𝛿\deltaitalic_δ but fluctuates depending on specific query conditions.
**B**: Therefore, differing from Mondrian and Anatomy, increasing the lev... | CAB | CAB | CAB | BCA | Selection 3 |
<|MaskedSetence|> It builds an efficient yet simple instance segmentation framework, outperforming other segmentation methods like TensorMask Chen et al. (2019c), CondInst Tian et al. <|MaskedSetence|> (2020) on COCO. In SOLOv2, the unified mask feature branch is dynamically convoluted by learned kernels, and the ada... | **A**: It’s worth noting that other attempts, including NASFPN, data augmentation and Mask Scoring, bring little improvement in our experiments..
**B**: (2020) and BlendMask Chen et al.
**C**:
Due to limited mask representation of HTC, we move on to SOLOv2, which utilizes much larger mask to segment objects.
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In this paper, we studied nonstationary RL with time-varying reward and transition functions. <|MaskedSetence|> We first incorporated the epoch start strategy into LSVI-UCB algorithm (Jin et al., 2020) to propose the LSVI-UCB-Restart algorithm with low dynamic regret when the total variations are known. <|MaskedSete... | **A**: Specifically, when the local variations are known, LSVI-UCB-Restart is near order-optimal except for the dependency on feature dimension d𝑑ditalic_d, planning horizon H𝐻Hitalic_H, and some poly-logarithmic factors.
**B**: We focused on the class of nonstationary linear MDPs such that linear function approxima... | BCA | BCA | BCA | BCA | Selection 3 |
Many studies worldwide have observed the proliferation of fake news on social media and instant messaging apps, with social media being the more commonly studied medium. In Singapore, however, mitigation efforts on fake news in instant messaging apps may be more important. <|MaskedSetence|> <|MaskedSetence|> These su... | **A**: They have also rated the sharing of fake news to be a greater problem than its creation.
**B**: As an Asian country, Singapore tends towards a collectivist culture where emphasis is placed on establishing and maintaining relationships in one’s social group.
**C**: Most respondents encountered fake news on inst... | CBA | CAB | CAB | CAB | Selection 4 |
We present the training procedure of decentRL for entity alignment in Algorithm 1. <|MaskedSetence|> The algorithm first randomly initializes the DAN model, entity embeddings, and relation embeddings. <|MaskedSetence|> In addition to the primary entity alignment loss, the algorithm also incorporates a self-distillat... | **A**: It jointly minimizes two losses in each batch until the performance ceases to improve on the validation dataset..
**B**: The training process follows conventional mini-batch training, akin to existing methods.
**C**: It is worth noting that decentRL does not rely on additional data such as pretrained KG embedd... | CBA | CBA | CBA | CBA | Selection 2 |
Variational inference posits a set of densities and then finds the member in the set that is close to the target [14, 34]. <|MaskedSetence|> Several RL methods propose to use pseudo-likelihood inference framework [38, 39] and expectation maximization (EM) to train policies [40]. VIREL [41] translates the problem of f... | **A**: Combining RL and variational inference requires formalizing RL as a probabilistic inference problem [35, 36, 37].
**B**: Specifically, VIREL applies EM to induce a family of actor-critic algorithms, where the E-step corresponds to policy improvement and the M-step corresponds to policy evaluation.
**C**: [43] ... | ABC | BAC | ABC | ABC | Selection 4 |
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**: For example, in Figure 1, the model uses β𝛽\betaitalic_β-TCVAE [mig] to retrieve the pose of the model as a latent factor.
**B**: where we significantly constrain the capacity of the learned representation and heavily regularize the model to produce independent factors.
**C**: As we explained above, such a mo... | BCA | BCA | BCA | BCA | Selection 4 |
The structural computer used an inverted signal pair to implement the reversal of a signal (NOT operation) as a structural transformation, i.e. <|MaskedSetence|> <|MaskedSetence|> In other words, operating a structural computer with a minimal lead is also a task to be addressed by this study because one of the most ... | **A**: a twist, and four pins were used for AND and OR operations as a series and parallel connection were required.
**B**: However, one can think about whether the four pin designs are the minimum number of pins required by structural computers.
**C**: As mentioned above, a 3-pin based logic consists of a ground cab... | ABC | ABC | ACB | ABC | Selection 4 |
<|MaskedSetence|> Once we obtain the matrix of cross-validated predictions 𝒁𝒁\bm{Z}bold_italic_Z, we apply the seven different meta-learners. <|MaskedSetence|> To assess view selection performance we calculate three different measures: (1) the true positive rate (TPR), i.e. the average proportion of views truly rel... | **A**: To assess classification performance, we generate a matching test set of 1000 observations for each training set, and calculate the classification accuracy of the stacked classifiers on this test set.
**B**: the average proportion of views not related to the outcome that were incorrectly selected by the meta-le... | ACB | CAB | CAB | CAB | Selection 4 |
<|MaskedSetence|> With wkNN, the results are similar. With iForest, the p𝑝pitalic_p-values are very close to 0.05. In terms of AP, the two DepAD algorithms yield significantly better results than all benchmark methods except for wKNN, iForest and COMBN, as shown in Figure 8 and Table 8. With wkNN, the p𝑝pitalic_p-va... | **A**: The p𝑝pitalic_p-values with iForest and COMBN are close to 0.05.
**B**:
According to Figure 7 and Table 8, the two DepAD algorithms are significantly better than all benchmark methods except for wkNN and iForest in terms of ROC AUC .
**C**: In summary, the two DepAD algorithms outperform most of the benchmar... | BAC | BAC | BAC | BAC | Selection 1 |
CB-MNL enforces optimism via an optimistic parameter search (e.g. <|MaskedSetence|> [2011]), which is in contrast to the use of an exploration bonus as seen in Faury et al. <|MaskedSetence|> [2010]. Optimistic parameter search provides a cleaner description of the learning strategy. In non-linear reward models, both... | **A**: in Abbasi-Yadkori et al.
**B**: [2010] for a short discussion)..
**C**: [2020], Filippi et al.
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Figure 3: Video self-stitching (VSS). a) Snippet-level features are extracted for the entire video. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> . | **A**: c) Each video clip is up-scaled along the temporal dimension.
**B**: d) Original clip (green dots) and up-scaled clip (orange dots) are stitched into one feature sequence with a gap.
**C**: b) Long video is cut into multiple short clips.
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We conducted three online semi-structured interviews with ML experts to obtain qualitative feedback about our tool’s usefulness, as in prior works [MXLM20, XXM∗19].
The first expert (E1) is a senior lecturer in mathematics working with reinforcement learning and has approximately 3 years of experience with ML. <|Maske... | **A**: He recently acquired his PhD in mathematics and has basic knowledge regarding ensemble learning.
**B**: The third expert (E3) is an ML engineer and manager in a large multinational company, working with recommendation systems.
**C**: The second expert (E2) is a senior researcher in software engineering and app... | ACB | ACB | BCA | ACB | Selection 4 |
<|MaskedSetence|> In [30, 32], semidefinite programming relaxations are proposed for the multi-shape matching problem. <|MaskedSetence|> In [18], a game-theoretic formulation for establishing multi-matchings is introduced. Due to the use of a sparse modelling approach, the method also has the disadvantage that only f... | **A**: However, due to the employed lifting strategy, which drastically increases the number of variables, these methods are not scalable to large problems and only sparse correspondences are obtained.
**B**: There are various works that particularly target the matching of multiple shapes.
**C**: The work [26] presen... | BAC | BAC | BAC | BAC | Selection 4 |
The recognition algorithm RecognizePG for path graph is mainly built on path graphs’ characterization in [1]. This characterization decomposes the input graph G𝐺Gitalic_G by clique separators as in [18], then at the recursive step one has to find a proper vertex coloring of an antipodality graph satisfying some parti... | **A**: This is done in Step 4, Step 5, and Step 6 that are the core of algorithm RecognizePG..
**B**: We overcome this problem by visiting the connected components in a smart order.
**C**: In a few words, an antipodality graph has as vertex set some subgraph of G𝐺Gitalic_G, and two vertices are connected if the corr... | CBA | CAB | CBA | CBA | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> Since in empirical network data sets, the degree distributions are often highly inhomogeneous across nodes, a natural extension of SBM is proposed: the degree-corrected stochastic block model (DCSBM) (DCSBM, ) which allows the existence of degree heterogeneity within communities. D... | **A**:
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.
**B**: Some recent developments of SBM can be found in (abbe2017community, ) and references therein.
**C**: MMSB constructed a mi... | ABC | CAB | ABC | ABC | Selection 1 |
See, e.g., Welling and Teh (2011); Chen et al. (2014); Ma et al. (2015); Chen et al. (2015); Dubey et al. (2016); Vollmer et al. <|MaskedSetence|> (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. <|MaskedSetence|> ... | **A**: (2019); Wibisono (2019) and the references therein.
Among these works,.
**B**: (2016); Chen et al.
**C**: (2018); Wibisono (2018); Bernton (2018); Dalalyan and Karagulyan (2019); Baker et al.
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<|MaskedSetence|> We define the reward for agent i𝑖iitalic_i as the negative of the queue length on incoming lanes. Note that optimizing queue length has been proved to be equivalent to optimizing average travel time in [38] under certain assumptions. Average travel time is a global criteria which cannot be optimized... | **A**:
Reward.
**B**: Hence, queue length is widely used as reward in traffic signal control.
**C**: Reward of agent i𝑖iitalic_i is defined by.
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Online bin packing has a long history of study. The simplest algorithm is NextFit, which places an item into its single open bin when possible; otherwise, it closes the bin (does not use it anymore) and opens a new bin for the item. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Improving upon this performan... | **A**: BestFit works similarly, except that it places the item into the bin of minimum available capacity, which can still fit the item.
**B**: FirstFit is another simple heuristic that places an item into the first bin of sufficient space and opens a new bin if required.
**C**: NextFit has a competitive ratio of 2, ... | BAC | BAC | ACB | BAC | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> Second, we provide the reconstruction result with respect to reference approaches. Finally, we check the quality of generated meshes, comparing our results to baseline methods. Throughout all experiments, we train models with Chamfer distance. We also set λ=0.0001𝜆0.0001\lambda=0.... | **A**: First, we evaluate the generative capabilities of the model.
**B**: We denote LoCondA-HC when HyperCloud is used as the autoencoder architecture (Part A in Fig.
**C**: In this section, we describe the experimental results of the proposed method.
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<|MaskedSetence|> <|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|> Finally in Section 5, we show how the proposed algorithm can be applied to t... | **A**: In Section 4, we present the lower complexity bounds for saddle point problems without individual variables.
**B**: This paper is organized as follows.
**C**: Paper organization.
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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. In more concrete terms this problem is equivalent to finding the cycle basis with the sparsest cycle matr... | **A**: The minimum cycle basis (MCB) problem is the problem of finding a cycle basis such that the sum of the lengths (or edge weights) of its cycles is minimum.
**B**: In [5] a unified perspective of the problem is presented.
**C**: Some applications of the MCB problem are described in [5, 11, 10, 12]..
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Visualization and interaction.
E1 and E2 were surprised by the promising results we managed to achieve with the assistance of our VA system in the red wine quality use case of Section 4. Initially, E1 was slightly overwhelmed by the number of statistical measures mapped in the system’s glyphs. However, after the interv... | **A**: We plan to perform a user study to test diverse visualizations..
**B**: He also mentioned that a confusion matrix for visualizing the validation results might be a more detailed approach.
**C**: The latter suggested an alternative option for the data space visualization which could have been to aggregate the i... | CAB | CBA | CBA | CBA | Selection 2 |
MPC accounts for the real behavior of the machine and the axis drive dynamics can be excited to compensate for the contour error to a big extent, even without including friction effects in the model [4, 5]. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Using Bayesian optimization-based tuning for enhanced pe... | **A**: High-precision trajectories or set points can be generated prior to the actual machining process following various optimization methods, including MPC, feed-forward PID control strategies, or iterative-learning control [6, 7], where friction or vibration-induced disturbances can be corrected.
**B**: Instead of ... | ACB | ACB | ACB | BAC | Selection 3 |
<|MaskedSetence|> As shown in Table. 1, no method performs universally well across datasets; however, the implicit methods LFF and SD obtain high unbiased accuracies on most datasets. This shows that implicit methods can deal with multiple bias sources without explicit access. Explicit methods work well on CelebA but ... | **A**: Specifically, Up Wt, GDRO and RUBi obtain 7-8% improvements over StdM on CelebA, which requires generalization to only 4 groups.
**B**: However, all explicit methods perform worse than StdM on Biased MNISTv1 and GQA, signifying their inability to deal with multiple bias sources.
**C**: Results.
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<|MaskedSetence|> Linden et al. introduce user embedding for recording personal information.
They obtain user embedding of the unseen subjects by fine-tuning using calibration samples [136]. Chen et al. [131, 132] observe the different gaze distributions of subjects. <|MaskedSetence|> <|MaskedSetence|> Yu et al. ge... | **A**: They use bias to refine the estimates.
**B**: They use the calibration samples to estimate the bias between the estimated gaze and the ground-truth of different subjects.
**C**: They learn the person-specific feature during fine-tuning.
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<|MaskedSetence|> <|MaskedSetence|> It has also been successfully used in face recognition under occlusion variation almabdy2019deep ; hariri2017geometrical ; kadhim2023face . It is seen that the deep learning-based method are founded on the fact that the human visual system automatically ignores the occluded regions... | **A**: krizhevsky2012imagenet , deep CNN have become a common approach in face recognition.
**B**: song2019occlusion proposed a mask learning technique in order to discard the feature elements of the masked region for the recognition process.
.
**C**: Since the publication of AlexNet architecture in 2012 by Krizhevs... | CAB | CAB | BCA | CAB | Selection 2 |
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**: However, the state of the art [Abe12, AP16, CLB23] supports polymorphic, higher-kinded, and dependent types, which we aim to incorporate in future work.
.
**B**: As we mentioned in the introduction, we use unbounded quantification [Vez15] in lieu of transfinite sizes to represent (co)data of arbitrary height an... | CAB | CBA | CBA | CBA | Selection 3 |
Judge. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> 3 and 4), thus enabling traitor tracing. Once the judge detects a copyright infringement, the unfaithful user will be prosecuted in accordance with the law.
. | **A**: The judge is a trusted entity who is only responsible for arbitration in the case of illegal redistribution, as in existing traitor tracing systems [10, 11, 12, 13, 14, 3].
**B**: After receiving the owner’s request for arbitration, the judge makes a fair judgment based on the evidence provided by the owner.
*... | ABC | ABC | ABC | ABC | Selection 4 |
At their core, GNNs learn node embeddings by iteratively aggregating features from the neighboring nodes, layer by layer. This allows them to explicitly encode high-order relationships between nodes in the embeddings. GNNs have shown great potential for modeling high-order feature interactions for click-through rate pr... | **A**: (2021) developed the Graph-Convolved Feature Crossing (GCFC) layer to traverse all features for each input example and leveraged the features of each sample to compute the corresponding multi-feature interaction graph and propagated its influence on other features.
**B**: Graph-Convolved Factorization Machines ... | ABC | BAC | BAC | BAC | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> 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]. ... | **A**: 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-concordance.
**B**: Self-concordant functions have received strong interest in recent y... | CBA | BAC | BAC | BAC | Selection 3 |
Our algorithm executes several methods (invoked within the loop starting at Algorithm 2 of Algorithm 2), and for most of them it makes a fresh pass over the edges. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> The Backtrack-Stuck-Structures method backtracks active paths that were not extended, but does not... | **A**: Precisely, the routines are: (1) extend structures along active paths (Extend-Active-Paths), (2) check for edge augmentations (Check-for-Edge-Augmentation), and (3) include (additional) unmatched edges to each structure (Include-Unmatched-Edges).
**B**: Each of these routines is performed in a separate pass ove... | BAC | CAB | CAB | CAB | Selection 2 |
<|MaskedSetence|> To train ResNet18 in CIFAR-10, one can use stochastic gradient descent with momentum 0.90.90.90.9, the learning rate of 0.10.10.10.1 and a batch size of 128128128128 (40404040 batches = 1111 epoch). <|MaskedSetence|> <|MaskedSetence|> In order for the comparison of Algorithm 1 and Algorithm 3 to be... | **A**: This is one of the default learning settings.
**B**:
Setting.
**C**: Based on these settings, we build our settings using the intuition of algorithms (for details about tuning and intuition of our Algorithms, see Section 5.2).
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<|MaskedSetence|> There are three competing properties which are important in this regard, exploitation, robustness, and exploration. For exploitation, maximum welfare equilibria appear to be useful. However, to prevent JPSRO from stalling in a local equilibrium it is essential to randomize over multiple solutions sat... | **A**:
There is a rich polytope of possible equilibria to choose from, however, an MS must pick one at each time step.
**B**: Furthermore, one could also switch between MSs at each iteration to achieve the best mix of exploitation and exploration..
**C**: For exploration, we can randomly select a valid equilibrium a... | ACB | ACB | ACB | ACB | Selection 1 |
<|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**: 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 from the prior distribution over
data elements to the posterior induced by some view v... | CAB | CAB | ABC | CAB | Selection 2 |
<|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 conclude in Section 7.
**B**: The remainder of the paper is organized as follows.
**C**: We present structural properties of antlers and how they combine in Section 4.
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Discriminative approaches: Liu et al. [94] proposed a discriminative approach named SimOPA to verify whether a composite image is rational in terms of the foreground object placement. <|MaskedSetence|> However, this discriminative approach is very inefficient, because they need to go through the discriminative networ... | **A**: [111] dubbed SimOPA as slow object placement assessment (SOPA) model and proposed a fast object placement assessment (FOPA) model, which can predict the rationality scores at all locations by going through the model only once.
**B**: To address this issue, Niu et al.
**C**: Particularly, they feed the concaten... | CBA | CBA | CBA | CAB | Selection 1 |
<|MaskedSetence|> 2. As shown in Fig. 2LABEL:sub@fig:cluster-bj, taxi mobility patterns in Beijing exhibit a high level of cohesion within each POI-based cluster, while remaining distinguishable across clusters. <|MaskedSetence|> 2LABEL:sub@fig:cluster-cdxa, illustrates that clusters with higher inflow/outflow/pick-u... | **A**: The average regional daily patterns of taxi mobility data from each POI-based cluster in Beijing, Chengdu, and Xi’an are plotted in Fig.
**B**: Nevertheless, Fig.
**C**: Conversely, Fig.
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<|MaskedSetence|> <|MaskedSetence|> Different modifications to obtain heteroscedastic models have been proposed in the literature, the main one being to normalize papadopoulos2008normalized the nonconformity measure by a dispersion function σ:𝒳→ℝ:𝜎→𝒳ℝ\sigma:\mathcal{X}\rightarrow\mathbb{R}italic_σ : caligraphic_X... | **A**: Although computationally simple, it ought to be clear that this is not the generic situation.
**B**: It should be clear that the latter choice, although interesting because it takes into account the number of similar data points and, hence, the data uncertainty, does introduce extra computational overhead.
.
*... | CAB | CAB | CAB | CAB | Selection 4 |
<|MaskedSetence|> Given that the tokens we choose do not contain performance information, it is interesting to see how a machine model would “perform” a piece by deciding these volume changes, a task that is essential in performance generation \parencitewidmer94aaai, jeongKKLN19ismir, jeongKKN19icml or expressive perf... | **A**: Apple’s Logic Pro 9 user manual correlates traditional volume indicators (pp, p, mp, mf, f, ff and fff) with specific MIDI velocity values (16, 32, 48, 64, 80, 96, 112 and 127), respectively.121212https://help.apple.com/logicpro/mac/9.1.6/en/logicpro/usermanual/ (page 468 in the user manual; accessed 2023-06-22)... | CBA | BAC | CBA | CBA | Selection 3 |
A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber, “Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks,” in Proc. <|MaskedSetence|> Conf. Mach. Learning (ICML), Pittsburgh, USA, Jun. <|MaskedSetence|> <|MaskedSetence|> | **A**: 23rd Int.
**B**: 2006, pp.
**C**: 369–376.
.
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<|MaskedSetence|> The network is an encoder-decoder fully convolutional network with skip connections. <|MaskedSetence|> The decoder part is composed of the nearest upsampling layers with unary convolution layers. We put the CSFR and ISFR modules after the first upsampling layer for larger spatial resolution. <|Mask... | **A**: The encoder is composed by bottleneck ResNet blocks[47] with KP convolution layers.
**B**:
We use the KPConv[4] segmentation model KPFCNN as our backbone network.
**C**: Due to the limitation in computational resources, we use ball query to sample point cloud as input samples, the sample radius is set to 2m. ... | BAC | BAC | BAC | BAC | Selection 3 |
<|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. The official data set contains 7481 training and 7518 test images with 2D and... | **A**: Each class uses different IoU standards for further evaluations.
**B**: Setup.
**C**: We report the average accuracy (APAP\rm{AP}roman_AP) for each task under three different settings: easy, moderate, and hard, as defined in [11].
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ICDAR2015 [44] includes multi-orientated and small-scale text instances. <|MaskedSetence|> 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**: Its ground truth is annotated with word-level quadrangles.
**B**: It contains 300 training images and 200 testing images with word-level annotation.
**C**:
MSRA-TD500 [45] is dedicated to detecting multi-oriented long non-Latin texts.
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<|MaskedSetence|> The four parts of the IP address are represented in four layers, where each layer is made up of one or more memory blocks. The first layer only contains one memory block, whereas the second layer contains 256 memory blocks. Each memory block contains 256 elements. Each element of the memory block in ... | **A**: Consequently, the first two layers can be removed from this architecture if the third layer has contiguous memory blocks of 128 MB.
.
**B**: The first proposed mapping mechanism of IP addresses is TLMB.
**C**: This would be 32 GB in size if we adopted a pre-allocation strategy for all memory blocks in the four... | BAC | BCA | BCA | BCA | Selection 2 |
The outline of the remainder of this paper is as follows. <|MaskedSetence|> <|MaskedSetence|> Some additive Schur complement based preconditioners are constructed and the corresponding known results in the literature are recalled in Section 4 for twofold saddle point problems. <|MaskedSetence|> In Section 6, numeric... | **A**: Generalizations to n𝑛nitalic_n-tuple cases are provided in Section 5.
**B**: Furthermore, we extend these results to the n𝑛nitalic_n-tuple saddle point problem in Section 3.
**C**: In section 2, we briefly recall the classic saddle point problem and its Schur complement, and introduce the twofold saddle poin... | BCA | CBA | CBA | CBA | 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|> The client with the label information calculates the loss and the partial derivatives, which can then... | **A**: We note that the modified algorithm is mathematically equivalent to TDCD, albeit with a higher communication cost.
**B**: In this case, we could modify our algorithm in the following way, similar to (Liu et al., 2020a): the clients in all silos send the intermediate information for a sample to the client that h... | BCA | BCA | BCA | BAC | Selection 2 |
Then, the notion of T-eigenvalues was introduced by Miao, Qi and Wei Miao2020T and also Liu and Jin Jin2020 , establishing a fundamental and significant concept. Alternative versions and formulations of eigenvalues of third-order tensors in the context of tensor-tensor multiplication have also been explored by Qi and ... | **A**: zheng2020t to study the T-positive semidefiniteness and T-semidefinite programming problems.
**B**: The T-eigenvalues were also utilized by Zheng et al.
**C**: They also show that T-eigenvalues have a close relationship with many optimization problems..
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User Study. <|MaskedSetence|> <|MaskedSetence|> They are invited to choose the most realistic image from those inpainted by the proposed method and the representative state-of-the-art approaches. Specifically, each participant has 15 questions, which are randomly sampled from the Places2 dataset. <|MaskedSetence|> ... | **A**: 10 volunteers with image processing expertise are involved in this evaluation.
**B**: We further perform subjective user study.
**C**: We tally the votes and show the statistics in Table 1.
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In Figure 1, we present the performance of Subgoal Search. <|MaskedSetence|> <|MaskedSetence|> For BF-kSubS the search budget is referred to as graph size and includes the number of nodes visited by Algorithm 1. For INT and Rubik’s Cube, we include both the subgoal generated by SUB_GENERATE and the nodes visited by G... | **A**: For Sokoban, we use Algorithm 9 to realize GET_PATH, as it has a negligible cost (less than 1%percent11\%1 % of the total runtime of Algorithm 1), we do not include these nodes into graph size.
.
**B**: We measure the success rate as a function of the search budget.
**C**: The success rate is measured on 10001... | BAC | BCA | BCA | BCA | Selection 3 |
Chinese characters, different from Latin Characters, are pictographs, which show their meanings in shapes. <|MaskedSetence|> A common strategy is to give every Chinese character a unique hexadecimal string, such as ‘UTF-8’ and ‘GBK’. However, this kind of strategy processes Chinese characters as independent symbols, ... | **A**: However, it is extremely hard for people to encode these Chinese characters in computers.
**B**: In other words, the closeness in the hexadecimal string value can not represent the similarity in their shapes.
**C**: Some work has tried to use images of Chinese characters as glyph embedding, which is also unacc... | BAC | ABC | ABC | ABC | Selection 4 |
ABC (Gonzalez et al., 2020), the Anti-reflexive Bias Challenge, is a multi-task benchmark dataset designed for evaluating gender assumptions in NLP models. ABC consists of 4 tasks, including language modeling, natural language inference (NLI), coreference resolution, and machine translation. <|MaskedSetence|> <|Maske... | **A**: A total of 4,560 samples are collected by a template-based method.
**B**: The language modeling task is to predict the pronoun of a sentence.
**C**: For NLI and coreference resolution, three variations of each sentence are used to construct entailment pairs.
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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. The structure of the LaTeXfiles, as designed, enable easy conversion to XML for the compositio... | **A**: The XML files are used to produce the final print/IEEEXplore® pdf and then converted to HTML for IEEEXplore®.
**B**: Therefore, they are NOT intended to be the final produced work that is displayed in print or on IEEEXplore®.
**C**: Have you looked at your article/paper in the HTML version?
.
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Our experiment allows us to examine the impact of the information structure on reciprocal behavior. <|MaskedSetence|> <|MaskedSetence|> In the information treatment, players are informed about the incoming benefits from each of the other three group members. In the treatment, they can specifically identify which othe... | **A**: In this way, direct reciprocity is precluded, although sharing behavior may nevertheless be driven in part by altruism or generalized reciprocity.
**B**: In the control (or baseline) condition, players are given information only about the total inflow of benefits from others after each round, but cannot identif... | BAC | BAC | BAC | BAC | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> However, images in the real world are easily disturbed by various factors (e.g., sensor noise, motion blur, and compression artifacts), resulting in the captured images being more complex than the simulated ones. To alleviate these problems and train a more effective and general SI... | **A**: Based on this degradation formula, the three most widely used degradation modes have been proposed: BI, BD, and DN.
**B**:
Due to the particularity of the SISR task, it is difficult to construct a large-scale paired real SR dataset.
**C**: Therefore, researchers often apply degradation patterns on the aforeme... | CAB | BCA | BCA | BCA | Selection 4 |
The downsampling operation can be implemented in several ways. <|MaskedSetence|> Otherwise, we can create a trainable downsampling module representing the kernel and optimize its weights in an end-to-end manner. <|MaskedSetence|> <|MaskedSetence|> For Neural Knitworks, there is no need to introduce a new loss term a... | **A**: We revisit the technique introduced in [29] by using an identical deep linear network to approximate the kernel.
**B**: Their method relies on the assumption that a satisfactory kernel should preserve the distribution of patches in the image.
**C**: If the downsampling kernel is known, then the best approach i... | CAB | ABC | CAB | CAB | Selection 3 |
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**: Both of these variants differ from apple tasting in that they have a more complex action set.
.
**C**: The same problem has also b... | ACB | ACB | ABC | ACB | Selection 2 |
Table 5 reports our results on claim detection. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Interestingly, even the uniform sampling strategy outperforms other neural baselines, suggesting that sampling also compensates for the noise introduced by memories, thus increasing model robustness. This behavior i... | **A**: Similarly, MemDistilBERT achieves a higher F1-score with respect to standard DistilBERT, whereas MemBERT is comparable with BERT.
**B**: 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 itself.
**C**... | CBA | CBA | CBA | ABC | Selection 1 |
However, the progress of sentiment dependency-based methods, such as the work by Zhang et al. (2019); Zhou et al. <|MaskedSetence|> (2021); Li et al. <|MaskedSetence|> <|MaskedSetence|> | **A**: (2020); Tian et al.
**B**: (2021a); Dai et al.
**C**: (2021), has contributed to the improvement of coherent sentiment learning.
These studies explored the effectiveness of syntax information in ABSC, which mitigates issues related to sentiment coherency extraction..
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We use QNN as the benchmark PQC in this work. <|MaskedSetence|> <|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 trained to perform c... | **A**: Figure 2 shows the QNN architecture.
**B**: The inputs are classical data such as image pixels, and the outputs are classification results.
**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 ga... | ABC | ABC | ABC | ABC | Selection 2 |
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**: mayer2022transforming and cui2022mixformer respectively uses Transformer-based models for visual tracking.
**B**: Different from the above, hybrid tracking methods perform an information fusion on various data, such as depth datajiang2019hierarchical , infrared datazhang2020object ; tang2023exploring /multi-m... | ACB | CAB | CAB | CAB | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> Recently, contrastive learning (CL) [38, 39, 40, 41], which discriminates positive pairs against negative pairs, achieved state-of-the-art performance in various vision tasks. Different mechanisms [41, 42, 43, 44] are proposed to prevent trivial solutions in CL to learn useful repr... | **A**: To fully utilize negative samples, [45, 46, 47, 48] explore hard samples in the momentum memory bank.
**B**:
Early SSL methods design hand-crafted pretext tasks [30, 31, 32, 33], which rely on somewhat ad-hoc heuristics and have limited abilities to capture practically useful representations.
**C**: Another p... | BCA | BCA | BCA | BCA | Selection 3 |
We used evolutionary search to find the best sub-network architecture under certain constraints. We use a population size of 100. <|MaskedSetence|> For each iteration, we only keep the top-20 candidates with the highest accuracy. Then we perform crossover to generate 50 new candidates and mutation to generate another ... | **A**: The mutation rate is 0.1.
**B**: We randomly sample 100 sub-networks satisfying the constraints to form the first generation of population.
**C**: We repeat the process for 30 iterations and choose the sub-network with the highest accuracy on the split validation set.
.
| BAC | BAC | BAC | BAC | 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**: Specifically, high complementarity indicate that encoders together carry less redundant parameters.
**B**: The asymmetry lies in the differences of GNN-based encoders’ message-passing schemes.
**C**: In addition, we implement experiments with the two quantitative metrics.
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<|MaskedSetence|> <|MaskedSetence|> Recent research has shown that strong inductive biases or grounding of communication protocols are necessary for the protocol to be compositional
(see e.g. Kottur et al., (2017), Słowik et al., 2020b ). <|MaskedSetence|> For instance,. | **A**: Compositionality is often investigated in the context of signaling games (Fudenberg and Tirole, (1991), Lewis, (1969), Skyrms, (2010), Lazaridou et al., (2018)).
**B**: The inductive bias can be imposed into the architecture of the agents or the training procedure.
**C**: The topic of communication is actively... | CAB | CBA | CAB | CAB | Selection 3 |
Learning CBFs: An open problem is how valid CBFs can be constructed. Indeed, the lack of systematic methods to construct valid CBFs is a main bottleneck. <|MaskedSetence|> The construction of polynomial barrier functions towards certifying safety for polynomial systems by using sum-of-squares (SOS) programming was pr... | **A**: Such SOS-based approaches, however, are known to be limited in scalability and do not use potentially available expert demonstrations.
.
**B**: For certain types of mechanical systems under input constraints, analytic CBFs can be constructed [30].
**C**: The work in [35] considers the construction of higher or... | BCA | BCA | BCA | BCA | Selection 4 |
<|MaskedSetence|> For this network, there is no ground truth about nodes labels, and the numbers of communities are unknown. The CoauthorshipsNet has 1589 nodes, however its adjacency matrix is disconnected. Among the 1589 nodes, there are totally 396 disconnected components, and only 379 nodes fall in the largest con... | **A**: Note that since the overall embeddedness is defined for adjacency matrix that is connected, it is not applicable for CoauthorshipsNet1589.
.
**B**: For convenience, we use CoauthorshipsNet1589 to denote the original network, and CoauthorshipsNet379 to denote the giant component.
**C**: In CoauthorshipsNet, nod... | CBA | CBA | ACB | CBA | Selection 1 |
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