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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. <|MaskedSetence|> We had to reconsider the proofs, in our view simplifying som... | **A**: We remark that in this case, our method is similar to that of [MR3591945], with some differences.
**B**: Of course, the numerical scheme and the estimates developed in Section 3.1 hold.
**C**: Also, our scheme is defined by a sequence of elliptic problems, avoiding the annoyance of saddle point systems.
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<|MaskedSetence|> Most of the previous work [6, 11] on tweet level only aims to measure the trustfulness based on human judgment (note that even if a tweet is trusted, it could anyway relate to a rumor). Our task is, to a point, a reverse engineering task; to measure the probability a tweet refers to a news or rumor e... | **A**: Inspired by [33], we combine CNN and RNN into a unified model for tweet representation and classification.
**B**: This model, called CNN+RNN henceforth, is able to capture both local features of phrases (by CNN) as well as global and temporal tweet semantics (by LSTM)(see Figure 3)..
**C**:
Given a tweet, our... | CAB | CAB | CAB | ABC | Selection 2 |
. As shown in Table 11, CreditScore is the best feature in general. <|MaskedSetence|> Overall, adding CreditScore improves the performance, significantly for the first 8-10 hours. The performance of all-but-CreditScore jiggles a bit after 16-20 hours, but it is not significant. <|MaskedSetence|> <|MaskedSetence|> Ta... | **A**: CrowdWisdom is also a good feature which can get 75.8% accuracy as a single feature.
**B**: Figure 10 shows the result of models learned with the full feature set with and without CreditScore.
**C**: But its performance is poor (less than 70%) in the first 32 hours getting better over time (see Table 11).
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Evaluating methodology.
For RQ1, given an event entity e, at time t, we need to classify them into either Breaking or Anticipated class. We select a studied time for each event period randomly in the range of 5 days before and after the event time. In total, our training dataset for AOL consists of 1,740 instances of b... | **A**: We then bin the entities in the two datasets chronologically into 10 different parts.
**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 the trials..
**C**: For GoogleTrends, there are 2,700 and 4,200... | ACB | CAB | CAB | CAB | Selection 3 |
<|MaskedSetence|> Half of the patients are female and ages range from 17 to 66, with a mean age of 41.8 years. Body weight, according to BMI, is normal for half of the patients, four are overweight and one is obese. The mean BMI value is 26.9. <|MaskedSetence|> <|MaskedSetence|> | **A**: In terms of time since being diagnosed with diabetes, patients vary from inexperienced (2 years) to very experienced (35 years), with a mean value of 13.9 years..
**B**:
Table 1 shows basic patient information.
**C**: Only one of the patients suffers from diabetes type 2 and all are in ICT therapy.
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Later attempts addressed that shortcoming by taking advantage of classification architectures pre-trained on the ImageNet database Deng et al. (2009). This choice was motivated by the finding that features extracted from CNNs generalize well to other visual tasks Donahue et al. (2014). <|MaskedSetence|> <|MaskedSeten... | **A**: Consequently, DeepGaze I Kümmerer et al.
**B**: (2018); Liu and Han (2018).
**C**: (2014) and II Kümmerer et al.
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<|MaskedSetence|> However, we shall first investigate in Section 5.1 the approximation performance of several obvious greedy strategies to compute the locality number (with “greedy strategies”, we mean simple algorithmic strategies that build up a marking sequence from left to right by choosing the next symbol to be m... | **A**:
Our strongest positive result about the approximation of the locality number will be derived from the reduction mentioned above (see Section 5.2).
**B**: This is mainly motivated by two aspects.
**C**: This may provide a new angle to approximating the cutwidth of a graph, i.e., some greedy strategies may only... | ABC | CAB | ABC | ABC | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> (2019), Ha & Schmidhuber (2018), Holland et al. (2018), Leibfried et al. (2018) and Azizzadenesheli et al. (2018). Oh et al. (2017) use a model of rewards to augment model-free learning with good results on a number of Atari games. <|MaskedSetence|> | **A**: Notable exceptions are the works of
Oh et al.
**B**: (2017), Sodhani et al.
**C**: However, this method does not actually aim to model or predict future frames, and achieves clear but relatively modest gains in efficiency..
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A major obstacle in achieving seamless autonomous locomotion transition lies in the need for an efficient sensing methodology that can promptly and reliably evaluate the interaction between the robot and the terrain, referred to as terramechanics. These methods generally involve performing comprehensive on-site measure... | **A**: To bypass the limitations of terramechanics methods, researchers have probed into alternative strategies for accomplishing autonomous locomotion transition.
**B**: For example, certain studies have utilized energy consumption as a metric for evaluating the transverse-ability of different locomotion modes in whe... | ABC | ABC | CBA | ABC | Selection 2 |
The algorithm classifies items according to their size. Tiny items have their size in the range (0,1/3]013(0,1/3]( 0 , 1 / 3 ], small items in (1/3,1/2]1312(1/3,1/2]( 1 / 3 , 1 / 2 ], critical items in (1/2,2/3]1223(1/2,2/3]( 1 / 2 , 2 / 3 ], and large items in (2/3,1]231(2/3,1]( 2 / 3 , 1 ]. <|MaskedSetence|> Large i... | **A**: In addition, the algorithm has four kinds of bins, called tiny, small, critical and large bins.
**B**: Critical bins contain a single critical item, and tiny items up to a total size of 1/3131/31 / 3 per bin, while tiny bins contain only tiny items.
**C**: Inside each critical bin, a space of 2/3 is reserved f... | ABC | ABC | ABC | CBA | 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. of every user and then simply update it as more content i... | **A**: However, when working with classifiers not supporting incremental classification, for every user we need to store either all her/his writings to build the document-term matrix or the already computed document-term matrix to update it as new content is added.
**B**: Note that storing either all the documents or ... | CAB | CAB | CAB | CAB | Selection 1 |
<|MaskedSetence|> In section II, we introduce the related works. In section III, we first introduce the UAV’s power control in the multi-channel communication and coverage problems, then form a system model in highly dynamic scenarios. <|MaskedSetence|> In section V, we propose the two algorithms for approaching the ... | **A**: We organize this paper as follows.
**B**: Moreover, in section IV, we formulate our work as an aggregative game and prove the existence of the NE.
**C**: Ultimately, section VII gives a conclusion of the whole study.
.
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Deep neural networks are the state of the art learning models used in artificial intelligence. <|MaskedSetence|> However the larger number of parameters also make them particularly prone to over-fitting, requiring regularization methods to combat this problem. <|MaskedSetence|> <|MaskedSetence|> The term Dropout me... | **A**: Dropout was first introduced in 2012 as a regularization technique to avoid over-fitting[12], and was applied in the winning submission for the Large Scale Visual Recognition Challenge that revolutionized deep learning research[13].
**B**: Over course of time a wide range of Dropout techniques inspired by the o... | ABC | CAB | CAB | CAB | Selection 3 |
In contrast to natural images, it is difficult to tabulate and summarize the performance of medical image segmentation methods because of the vast number of (a) medical imaging modalities and (b) medical image segmentation datasets. Figure 15 presents a breakdown of the various attributes of the medical image segmentat... | **A**: We observe that modalities which are expensive to acquire and annotate (such as electron microscopy (EM), PET, and MRI) have smaller dataset sizes than relative cheaper to acquire modalities such as RGB images (e.g., skin lesion images), ultrasound (US) and X-ray images.
**B**: As shown in Figure 15 (b), the pa... | BCA | BCA | ABC | BCA | Selection 4 |
The generalization performance has been widely studied. <|MaskedSetence|> (2017) demonstrate that deep neural networks are capable of fitting random labels and memorizing the training data. Bornschein et al. <|MaskedSetence|> (2018) evaluate the performance of modern neural networks using the same test strategy as Fe... | **A**: (2014) and find that neural networks achieve good results but are not as strong as random forests..
**B**: (2020) analyze the performance across different dataset sizes.
Olson et al.
**C**: Zhang 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**: Despite the differences between policy-based and value-based reinforcement learning, our work shows that the general principle of “optimism in the face of uncertainty” (Auer et al., 2002; Bubeck and Cesa-Bianchi, 2012) can be carried over from existing algorithms based on value iteration, e.g., optimistic LSVI, ... | CBA | CBA | ABC | CBA | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> We also thank Prof. Mikhail Katz and Prof. Michael Lesnick for explaining to us some aspects of their work. We thank Dr. Qingsong Wang for bringing to our attention the paper [76] which was critical for the proof of Theorem 1. Finally, we thank Dr. Alexey Balitsk... | **A**: Johnathan Bush for very useful feedback about a previous version of this article.
**B**: We thank Prof.
**C**: Henry Adams and Dr.
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Figure 5: The Dimension Correlation tool. (a) Nearby points are projected to a user-drawn path, creating a user-induced ordering. <|MaskedSetence|> <|MaskedSetence|> (c) Results are shown in the lengths of bars, ordered by the absolute value of the correlation (with highest on top). <|MaskedSetence|> | **A**: (b) The user-induced ordering is compared to dimension-specific orderings using a correlation measure.
**B**: Here 7, 3, 4, and so on are data instance IDs.
**C**: Note that if the same polyline is drawn by the user in the opposite direction over a pattern, then the signs of the correlations change but not the... | BAC | BAC | BAC | BAC | Selection 3 |
<|MaskedSetence|> It is true that the paper presenting the new algorithm should be detailed enough to allow for a clean implementation of the proposal from the provided specification. <|MaskedSetence|> <|MaskedSetence|> In addition, there are a huge number of software frameworks for Evolutionary Computation and Swar... | **A**: A publicly available reference implementation could not only improve its visibility, but could also offer other researchers the chance to undertake more thorough performance comparisons.
**B**: However, it is widely acknowledged that, in many occasions, there are important details that even though they have a s... | CBA | CBA | BCA | CBA | Selection 4 |
<|MaskedSetence|> Instead, DEC and SpectralNet work better on the large scale datasets. <|MaskedSetence|> <|MaskedSetence|> The adaptive learning will induce the model to exploit the high-level information. In particular, AdaGAE is stable on all datasets.
. | **A**: If the graph is not updated, the contained information is low-level.
**B**: Classical clustering models work poorly on large scale 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 t... | BCA | BCA | ACB | BCA | Selection 4 |
<|MaskedSetence|> We analyse the datasets from the traceroute measurements performed by the CAIDA Spoofer Project within the last year 2019, (Lone et al., 2017). The measurements identified 2,500 unique loops, of these 703 were provider ASes, and 1,780 customer ASes. The dataset found 688 ASes that do not enforce ingr... | **A**: Out of 688 ASes found with traceroutes by the Spoofer Project, we could not test 4 ASes (none of our tests applied) and 36 ASes were not included in our tests (those ASes could not be located from domain names - due to our attempt to reduce traffic and not to scan IPv4 but to collect the services via domain name... | CAB | CAB | CAB | BAC | Selection 3 |
Experiments in this paper used the gas sensor drift array dataset [7]. The data consists of 10 sequential collection periods, called batches. Every batch contains between 161161161161 to 3,60036003{,}6003 , 600 samples, and each sample is represented by a 128-dimensional feature vector; 8 features each from 16 metal ox... | **A**: The dataset thus exemplifies sensor variance due to contamination and variable odor concentration in a controlled setting.
.
**B**: The experiments used six gases, ammonia, acetaldehyde, acetone, ethylene, ethanol, and toluene, presented in arbitrary order and at variable concentrations.
**C**: These features ... | CBA | CBA | CBA | CBA | Selection 3 |
There is a quite interesting evolution of constructions to present free groups in a self-similar way or even as automaton groups (see [15] for an overview). <|MaskedSetence|> <|MaskedSetence|> While it is known that the free semigroup of rank one is not an automaton semigroup [4, Proposition 4.3], the free semigroup... | **A**: 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 | CBA | CBA | CBA | Selection 3 |
To perform the tests, we first randomly sample 5000500050005000 subsets of non-overlapping test instances. We then average the accuracy of each subset across 5555 runs, obtaining 5000500050005000 values. <|MaskedSetence|> <|MaskedSetence|> Using a confidence level of 95%percent9595\%95 % (α=0.05𝛼0.05\alpha=0.05itali... | **A**: Next, we run the t-tests for HINT and SCR separately on the subset accuracies.
**B**: As shown in Table 2, the p𝑝pitalic_p-values across the variants of HINT and SCR are greater than or equal to 0.30.30.30.3.
**C**: We also compare the predictions of HINT/SCR against baseline, and find that p𝑝pitalic_p-value... | ABC | CBA | ABC | ABC | Selection 4 |
The corpus contains policies from over 800 different top level domains (TLDs). <|MaskedSetence|> Country-level domains like .uk, .au, .ca and .du show the geographic variety of the sources of the corpus covering 12%, 4%, and 2% respectively. The distribution of popular TLDs (.com, .org, .net) roughly matches internet ... | **A**: The PageRank values were calculated from the web graph using the Gauss-Seidel algorithm (Arasu et al., 2002).
**B**: PageRank values can be used as a substitute for popularity where higher values suggest more popular domains..
**C**: .com, .org, and .net make up a major share of the corpus covering 63%, 5% and... | CAB | CAB | CAB | BCA | Selection 3 |
Selection of Algorithms and Models. <|MaskedSetence|> As the data set is very imbalanced, we emphasize g-mean over accuracy, and ROC AUC over precision and recall. Log loss is disabled because the investigation of outliers is not critical for this text data set, and our computations do not have to be as precise as wi... | **A**: We improved the per-class performance (as shown in Figure 6(c)) by choosing diverse ML models instead of simply the top-performing ones, since LR and RF perform well in the positive class, while other techniques such as SVC and GradB are far better in the negative class..
**B**: Finally, due to the small number... | CBA | CBA | BAC | CBA | Selection 4 |
<|MaskedSetence|> Firstly, the data quantity within the datasets used as ”tasks” varies across different applications, which can impact the effectiveness of MAML [Serban et al., 2015, Song et al., 2020]. <|MaskedSetence|> For example, PAML [Madotto et al., 2019] regards each person’s dialogues as a task for MAML and ... | **A**:
When applying MAML to NLP, several factors can influence the training strategy and performance of the model.
**B**: Secondly, while vanilla MAML assumes that the data distribution is the same across tasks, in real-world NLP tasks, the data distributions can differ significantly [Li et al., 2018, Balaji et al.,... | ABC | ABC | ABC | CAB | Selection 1 |
<|MaskedSetence|> One is the exchanging slot (e-slot) and the other is the tracking slot (t-slot). <|MaskedSetence|> It is assumed that UAVs exchange MSI every T𝑇Titalic_T t-slots, i.e., in an e-slot, to save resource for payload transmission. In the MSI exchanging period of the e-slot t𝑡titalic_t, the r-UAV exchan... | **A**: Employing the GP-based MSI prediction algorithm proposed in [31], each t-UAV predicts the MSI of r-UAV and r-UAV predicts the MSI of all t-UAVs in the next T𝑇Titalic_T t-slots.
**B**: A conceptual frame structure is designed which contains two types of time slots.
**C**: Let us first focus on the e-slot.
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Meanwhile, our analysis is related to the recent breakthrough in the mean-field analysis of stochastic gradient descent (SGD) for the supervised learning of an overparameterized two-layer neural network (Chizat and Bach, 2018b; Mei et al., 2018, 2019; Javanmard et al., 2019; Wei et al., 2019; Fang et al., 2019a, b; Che... | **A**: In contrast, TD follows the stochastic semigradient of the MSPBE (Sutton and Barto, 2018), which is biased.
**B**: See also the previous analysis in the NTK regime (Daniely, 2017; Chizat and Bach, 2018a; Jacot et al., 2018; Li and Liang, 2018; Allen-Zhu et al., 2018a, b; Du et al., 2018a, b; Zou et al., 2018; A... | BAC | BAC | BAC | ABC | Selection 3 |
<|MaskedSetence|> (2018); Xu et al. (2020a), and compare our approach with the pre-norm Transformer in which residual connections are not normalized by layer normalization. To compare with the previous studies, we replace the English to French task with the Czech to English task with ∼similar-to\sim∼15151515M sentence... | **A**:
We examine whether depth-wise LSTM has the ability to ensure the convergence of deep Transformers and measure performance on the WMT 14 English to German task and the WMT 15 Czech to English task following Bapna et al.
**B**: The 4.54.54.54.5M dataset of the En-De task is not small, and the Cs-En data that has... | ABC | ABC | ABC | ABC | Selection 3 |
There is a rich history of exploration in the field of distortion rectification. The most common method is based on a specific physical model. [15, 16, 17] utilized a camera to capture several views of a 2D calibration pattern that covered points, corners, or other features, and then computed the distortion parameters ... | **A**: Thus, the above traditional methods are difficult to handle on the single distorted image rectification in various scenes.
.
**B**: Similarly, [23, 24] also utilized the simplified camera model to correct the radial distortion in images.
**C**: Self-calibration was leveraged for distortion parameter estimation... | ABC | CBA | CBA | CBA | Selection 2 |
Our main goal is to develop algorithms for the black-box setting. <|MaskedSetence|> First, we develop algorithms for the simpler polynomial-scenarios model. <|MaskedSetence|> Finally, we extrapolate the solution to the original black-box problem. <|MaskedSetence|> | **A**: As usual in two-stage stochastic problems, this has three steps.
**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**: This overall methodology is called Sample Average Approximation (SAA)... | ABC | ABC | ABC | ABC | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> 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. What’s more, multiplicative noises relying on the relative states between adjacent l... | **A**: Then, we prove that the mean square upper bound of the coupling term between states, network graphs and noises depends on the second-order moment of the difference between optimizers’ states and the given vector.
**B**:
III.
**C**: The co-existence of random graphs, subgradient measurement noises, additive an... | BCA | ACB | BCA | BCA | Selection 3 |
<|MaskedSetence|> For instance, in Figure 1(b), the sensitive values in the third equivalence group are both “pneumonia”. Therefore, an adversary can infer the disease value of Dave by matching his age without re-identifying his exact record. To prevent such disclosure, many effective principles have been proposed, su... | **A**: Thus, for any individual, the adversary has to obtain at least five different sensitive values by matching the age value..
**B**:
Although the generalization for k𝑘kitalic_k-anonymity provides enough protection for identities, it is vulnerable to the attribute disclosure [23].
**C**: For example, Figure 1(c)... | BCA | BCA | CAB | BCA | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> Mask scoring head Huang et al. (2019) adopted on the third stage gains another 2 mAP. <|MaskedSetence|> However, the convolutional mask heads adopted in all stages bring non-negligible computation and memory costs, which constrain the mask resolution and further limit the segmenta... | **A**: Armed with DCN, GC block and SyncBN training, our HTC with Res2NetR101 backbone yields 74.58 mAP on validation set, as shown in Table 1.
**B**: By enlarging the RoI size of both box and mask branches to 12 and 32 respectively for all three stages, we gain roughly 4 mAP improvement against the default settings i... | CBA | CBA | CBA | CBA | Selection 3 |
From Figure 1, we find that the restart strategy works better under abrupt changes than under gradual changes, since the gap between our algorithms and the baseline algorithms designed for stationary environments is larger in this setting. <|MaskedSetence|> For example, UCB-type exploration does not have incentive to ... | **A**: The reason is that the algorithms designed to explore in stationary MDPs are generally insensitive to abrupt change in the environment.
**B**: However, when the change of environment is greater, they no longer yield satisfactory performance since their Q𝑄Qitalic_Q function estimate is quite off.
**C**: This a... | ABC | ABC | ABC | BAC | Selection 3 |
75 of the 104 responses fulfilled the criterion of having respondents who were currently based in Singapore. This set was extracted for further analysis and will be henceforth referred to as ‘SG-75’. The details on the participant demographics of SG-75 are shown in Table 1. <|MaskedSetence|> <|MaskedSetence|> <|Mask... | **A**: From SG-75, two more subsets were formed via the branching questions.
**B**: While these subsets have smaller samples, the self-reported data of the questions falling within the sections of these subsets would be more reliable since the respondents have prior experience to relate to.
.
**C**: The first contain... | ACB | ACB | ACB | BCA | Selection 2 |
<|MaskedSetence|> As shown in Figure 1, the KG comprises three triplets conveying similar information to the example sentence. Triplet-based KG embedding models like TransE [11] transform the embedding of each subject entity and its relation into a hidden vector, subsequently used to predict the central entity W3C of ... | **A**: The existing methods for KG embedding and word embedding exhibit even more similarities.
**B**: The aggregation operation mirrors the CBOW model [9], except that CBOW does not involve self-embedding.
.
**C**: This behavior resembles that of the Skip-gram model [9], where each word embedding within a window is ... | BCA | ACB | ACB | ACB | Selection 2 |
We first evaluate our method on standard Atari games. <|MaskedSetence|> <|MaskedSetence|> We highlight that the extrinsic rewards are only used for evaluation, not for training. <|MaskedSetence|> We draw each curve with five distinct random seeds. For each method, the solid line indicates the mean episodic reward o... | **A**: We illustrate the evaluation curves of 18181818 common Atari games in Fig. 6, where the first 6666 games are hard exploration tasks.
**B**: In alternative, we follow [11, 13], and use the extrinsic rewards given by the environment to measure the performance.
**C**: Since different methods utilize different int... | CBA | CBA | CBA | CBA | Selection 3 |
Prior work in unsupervised DR learning suggests the objective of learning statistically independent factors of the latent space as means to obtain DR. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> They aren’t really separating into nuisance and independent only.. they are also throwing away nuisance.. | **A**: A series of works starting from [beta] aims to achieve that via regularizing the models by up-weighting certain terms in the ELBO formulation which penalize the (aggregate) posterior to be factorized over all or some of the latent dimensions [kumar2017variational, factor, mig].
I think I would make what these me... | ACB | BCA | BCA | BCA | Selection 3 |
The structural computer used an inverted signal pair to implement the reversal of a signal (NOT operation) as a structural transformation, i.e. a twist, and four pins were used for AND and OR operations as a series and parallel connection were required. <|MaskedSetence|> In other words, operating a structural compute... | **A**: As mentioned above, a 3-pin based logic consists of a ground cable in the center and two signal lines representing true and inverted values above and below, and is capable of operating NOT, AND and OR operations through the structural transformations shown below..
**B**: However, one can think about whether the... | BCA | BCA | BCA | ABC | Selection 1 |
Forward selection is a simple, greedy feature selection algorithm (Guyon \BBA Elisseeff, \APACyear2003). It is a so-called wrapper method, which means it can be used in combination with any learner (Guyon \BBA Elisseeff, \APACyear2003). <|MaskedSetence|> One then proceeds to sequentially add the next “best” feature at... | **A**: Here we consider forward selection based on the Akaike Information Criterion (AIC).
**B**: This procedure can be described as follows:
.
**C**: The basic strategy is to start with a model with no features, and then add the single feature to the model which is “best” according to some criterion.
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<|MaskedSetence|> <|MaskedSetence|> This method employs directed acyclic graphs (DAGs) to exploit data sparsity and independence among variables. <|MaskedSetence|> If a feature of an object falls outside these prediction intervals, this object is flagged as an inconsistency. When building the conformal prediction mo... | **A**: For each variable, DAGnosis constructs a conformal prediction model, specifically a Conformalized Quantile Regression, to establish prediction intervals at a predefined significance level.
**B**: A representative method, DAGnosis [21], uses dependency-based approach to effectively detect and interpret inconsist... | ABC | CBA | CBA | CBA | Selection 4 |
<|MaskedSetence|> in Abbasi-Yadkori et al. [2011]), which is in contrast to the use of an exploration bonus as seen in Faury et al. <|MaskedSetence|> <|MaskedSetence|> Optimistic parameter search provides a cleaner description of the learning strategy. In non-linear reward models, both approaches may not follow simi... | **A**:
CB-MNL enforces optimism via an optimistic parameter search (e.g.
**B**: [2010].
**C**: [2020], Filippi et al.
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Specifically, we propose a Video self-Stitching Graph Network (VSGN) for improving performance of short actions in the TAL problem. <|MaskedSetence|> In VSS, we focus on a short period of a video and magnify it along the temporal dimension to obtain a larger scale. Then using our self-stitching strategy, we piece toge... | **A**: Our VSGN is a multi-level cross-scale framework that contains two major components: video self-stitching (VSS); cross-scale graph pyramid network (xGPN).
**B**: In xGPN, we progressively aggregate features from cross scales as well as from the same scale via a pyramid of cross-scale graph networks.
**C**: Comp... | ABC | ABC | ABC | BAC | Selection 3 |
<|MaskedSetence|> These papers use bagging [Bre01] and boosting [CG16, FSA99, KMF∗17] techniques for ranking and identifying the best combination of models in different application scenarios. StackGenVis [CMKK21] is a VA system for composing powerful and diverse stacking ensembles [Wol92] from a pool of pre-trained mo... | **A**: On the other hand, we support the process of generating new models through genetic algorithms and highlight the necessity for the best and most diverse models in the simplest possible voting ensemble.
**B**:
There are relevant works that involve the human in interpreting, debugging, refining, and comparing ens... | BAC | BAC | BAC | BAC | Selection 1 |
Despite the exponential size of the search space, there exist efficient polynomial-time algorithms to solve the LAP [11]. A downside of the LAP is that the geometric relation between points is not explicitly taken into account, so that the found matchings lack spatial smoothness. To compensate for this, a correspondenc... | **A**: However, due to its NP-hardness the QAP is computationally very difficult to solve.
**B**: Moreover, due to the generality of the formalism, it does not fully exploit the structural properties present in isometric shape matching problems, and is therefore a suboptimal choice from a computational perspective..
... | CAB | CAB | CAB | CAB | Selection 3 |
The recognition algorithm RecognizePG for path graph is mainly built on path graphs’ characterization in [1]. <|MaskedSetence|> In a few words, an antipodality graph has as vertex set some subgraph of G𝐺Gitalic_G, and two vertices are connected if the corresponding subgraphs of G𝐺Gitalic_G are antipodal. Unfortunat... | **A**: We overcome this problem by visiting the connected components in a smart order.
**B**: 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... | CAB | BAC | BAC | BAC | Selection 4 |
In this paper, we extend the symmetric Laplacian inverse matrix (SLIM) method (SLIM, ) to mixed membership networks and call this proposed method as mixed-SLIM. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Therefore, it is worth modifying this method to mixed membership networks. Numerical results of simula... | **A**: SLIM combined the SLIM with the spectral method based on DCSBM for community detection.
**B**: As mentioned in SLIM , the idea of using the symmetric Laplacian inverse matrix to measure the closeness of nodes comes from the first hitting time in a random walk.
**C**: And the SLIM method outperforms state-of-a... | BAC | BAC | CBA | BAC | Selection 1 |
See, e.g., Welling and Teh (2011); Chen et al. (2014); Ma et al. (2015); Chen et al. <|MaskedSetence|> (2016); Vollmer et al. (2016); Chen et al. (2016); Dalalyan (2017); Chen et al. (2017); Raginsky et al. (2017); Brosse et al. (2018); Xu et al. (2018); Cheng and Bartlett (2018); Chatterji et al. (2018); Wibisono (20... | **A**: (2019a, b); Mou et al.
**B**: (2019); Ma et al.
**C**: (2015); Dubey et al.
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<|MaskedSetence|> Most of the intrinsic reward approaches can be classified into two classes. <|MaskedSetence|> <|MaskedSetence|> The second is curiosity-based paradigm, in which agents are rewarded for high prediction error in a learned reward [56, 17] or inverse dynamics model [55, 57]. The uncertainty of the agen... | **A**: The first class is counted-based paradigm, where agents are incentivized to reach infrequently visited states by maintaining state visitation counts [52, 53] or density estimators [54, 55].
**B**: However, this paradigm is challenging in continuous or high-dimensional state space.
**C**: Intrinsic motivation m... | CAB | CAB | CAB | ABC | Selection 1 |
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|> NextFit has a competitive ratio of 2, while both FirstFit and BestFit are 1.7-competitive (?, ?). <|Mas... | **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**: Improving upon this performance requires more sophisticated algorithms, and many have been proposed in the literature.. ... | ACB | ACB | CAB | ACB | Selection 1 |
<|MaskedSetence|> Since it is a true or false statement, there is no well-established measure to define the degree of discontinuities in the object’s surface. To fill this gap, we propose a metric based on a simple, approximate check of whether a mesh is watertight - the parity test. The test says that any ray cast fr... | **A**: If so, the ray is said to pass the parity test.
**B**: Watertigthness Typically, a mesh is referred to as being either watertight or not watertight.
**C**: The mesh is watertight if all rays pass the test.
.
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Paper organization. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> In Section 4, we present the lower complexity bounds for saddle point problems without individual variables. Finally in Section 5, we show how the proposed algorithm can be applied to the problem computing Wasserstein barycenters .
. | **A**: In Section 3, we provide the main algorithm of the paper to solve such kind of problems.
**B**: Section 2 presents a saddle point problem of interest along with its decentralized reformulation.
**C**: This paper is organized as follows.
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The length of a cycle is its number of edges. <|MaskedSetence|> <|MaskedSetence|> In more concrete terms this problem is equivalent to finding the cycle basis with the sparsest cycle matrix. <|MaskedSetence|> The authors show that the MCB problem is different in nature for each class. For example in [10] a remarkab... | **A**: In [5] a unified perspective of the problem is presented.
**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**: This problem was formulated by Stepanec [7] and Zykov [8] for general graphs and by ... | BCA | ACB | BCA | BCA | Selection 3 |
In this current version, the implementation uses a state-of-the-art ensemble learning method called XGBoost [29]. This choice was made intentionally because some algorithms (e.g., SVM [39] and XGBoost [29]) are susceptible to specific types of transformations (e.g., scaling). <|MaskedSetence|> For this section, we val... | **A**: To make our approach even more future-proof, we train this ML algorithm with the Bayesian Optimization package [73].
**B**: The hyperparameters we experimented with (and their intervals) are: number of trees (5–200), learning rate (0.0–0.3), maximum depth of a tree (6–12), subsample ratio of the training instan... | ABC | ABC | ACB | ABC | Selection 1 |
<|MaskedSetence|> High-precision trajectories or set points can be generated prior to the actual machining process following various optimization methods, including MPC, feed-forward PID control strategies, or iterative-learning control [6, 7], where friction or vibration-induced disturbances can be corrected. In MPC,... | **A**: The approach has been successfully applied to linear and rotational axis embedded in grinding machines and shown to standardize and automate tuning of multiple parameters [13].
.
**B**: Instead of adapting the controller for the worst case scenarios, the prediction model can be selected to provide the best clos... | ABC | CBA | CBA | CBA | Selection 4 |
<|MaskedSetence|> 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]. <|MaskedSetence|> <|MaskedSetence|> We choose this method due to its simp... | **A**: However, [55] have shown promising results by using static weights to upweight minority patterns.
**B**: Moving beyond class imbalances, REPAIR [40] proposed learning dynamic weights to mitigate representation bias [39].
**C**:
Re-sampling/Re-weighting: These approaches balance out the spurious correlations. ... | CBA | CBA | BCA | CBA | Selection 4 |
The mobile device contains front cameras but has limited computational resources. The related methods usually estimate PoG instead of gaze directions due to the difficulty of geometric calibration. Krafka et al. propose iTracker for mobile devices [42], which combines the facial image, two eye images and the face grid ... | **A**: He et al. propose a more accurate and faster method based on iTracker [117].
**B**: Guo et al. propose a generalized gaze estimation method [152].
**C**: They observe the notable jittering problem in gaze point estimates and propose to use adversarial training to address this problem.
Valliappan [170] evaluate... | ABC | ABC | ACB | ABC | Selection 4 |
<|MaskedSetence|> When dealing with real masked faces, VGG-16 has achieved the best recognition rate, while ResNet-50 outperformed both VGG-16 and AlexNet on the simulated masked faces. <|MaskedSetence|> When dealing with other state-of-the-art recognizers, one of them applied the same pre-trained models with a diffe... | **A**: This behavior can be explained by the fact that VGG-16 features fail to ensure a high discriminative power comparing to the DRF features that are still relatively steady compared to their results on the real masked faces.
**B**:
The efficiency of each pre-trained model depends on its architecture and the abstr... | ACB | BAC | BAC | BAC | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> In parallel, linear size arithmetic for sized inductive types [CK01, Xi01, BR06] was generalized to support coinductive types as well [Sac14]. We present, to our knowledge, the first sized type system for a concurrent programming language as well as the first system to combine both... | **A**: Sized types are a type-oriented formulation of size-change termination [LJBA01] for rewrite systems [TG03, BR09].
**B**: However, the state of the art [Abe12, AP16, CLB23] supports polymorphic, higher-kinded, and dependent types, which we aim to incorporate in future work.
.
**C**: Sized (co)inductive types [B... | BAC | ACB | ACB | ACB | Selection 3 |
Finally, the comparison between the two proposed schemes and the existing relevant schemes is summarized in Table I. <|MaskedSetence|> In addition, the two proposed schemes offer owners the flexibility to choose. If the security requirements for the media content are not excessively rigorous and the size of the media ... | **A**: In Section VIII, we conduct a comparative experiment on the cloud-side efficiency of FairCMS-I and FairCMS-II to provide a quantitative reference for the owner’s decision-making.
.
**B**: As can be seen therein, the two proposed schemes FairCMS-I and FairCMS-II have advantages over the existing works.
**C**: T... | BCA | BCA | BCA | BAC | Selection 3 |
However, like the other DNN-based approaches, these models learn high-order feature interactions in an implicit, bit-wise manner and may lack transparency in their feature interaction modeling process and model outputs. As a result, some studies have attempted to learn feature interactions in an explicit fashion throug... | **A**: (2018) uses a Compressed Interaction Network(CIN) to take the outer product at the vector level and then compresses the resulting feature maps to update the feature representations.
**B**: (2021) similarly uses CIN to learn efficient explicit and implicit feature intersections, but it additionally leverages low... | ACB | BCA | ACB | ACB | Selection 1 |
Self-concordant functions have received strong interest in recent years due to the attractive properties that they allow to prove for many statistical estimation settings [Marteau-Ferey et al., 2019, Ostrovskii & Bach, 2021]. <|MaskedSetence|> For example, the logistic loss function used in logistic regression is not ... | **A**: 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**: This was fully formalized in Sun & Tran-Dinh [2019], in which the c... | ACB | ACB | CAB | ACB | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Each of these routines is performed in a separate pass over the edges. The Backtrack-Stuck-Structures method backtracks active paths that were not extended, but does not require a fresh pass. In total, a Pass-Bundle requires 3333 passes.. | **A**:
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.
**B**: Precisely, the routines are: (1) extend structures along active paths (Extend-Active-Paths), (2) check for edge augmentations (Check-for-Edg... | ACB | CAB | ACB | ACB | Selection 1 |
For this case we present Algorithm 2. This algorithm is the Tseng method [44] with a resolvent/proximal operator calculation (4). Here, as in Algorithm 1, the proximal operator is computed inexactly. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Further, we note that the algorithm’s steps in lines 3, 6, and ... | **A**: The problem (4) is divided into two minimization subproblems, by X𝑋Xitalic_X, and by Y𝑌Yitalic_Y.
**B**: Hence, the problem (4) is solved by Fast Gradient Descent.
**C**: Note that we need to communicate with other devices only when we solve the problem (4) and need to multiply by the matrix W𝑊Witalic_W.
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PSRO consists of a response oracle that estimates the best response (BR) to a joint distribution of policies. Commonly the response oracle is either a reinforcement learning (RL) agent or a method that computes the exact BR. The component that determines the distribution of policies that the oracle responds to is call... | **A**: Different MSs result in different algorithms: the uniform distribution results in FSP, and using the NE distribution results in an extension of DO..
**B**: The set of deterministic policies can be huge and that of stochastic policies is infinite, therefore PSRO only considers a subset of game policies: the ones... | ACB | CBA | CBA | CBA | Selection 2 |
Since achieving posterior accuracy is relatively straightforward, guaranteeing Bayes stability is the main challenge in leveraging this theorem to achieve distribution accuracy with respect to adaptively chosen queries. <|MaskedSetence|> Simply put, the Bayes factor K(⋅,⋅)𝐾⋅⋅{K}\left(\cdot,\cdot\right)italic_K ( ⋅ ,... | **A**: Its corresponding version for arbitrary queries are presented in Section C.2..
**B**: This simple lemma is at the heart of the progress that we make in this paper, both in our intuitive understanding of adaptive data analysis, and in the concrete results we show in subsequent sections.
**C**: The following lem... | CBA | CBA | CBA | CBA | Selection 4 |
<|MaskedSetence|> After presenting preliminaries on graphs and sets in Section 2, we prove the mentioned hardness results in Section 3. We present structural properties of antlers and how they combine in Section 4. <|MaskedSetence|> <|MaskedSetence|> Our main results are derived in Section 6, where we show how color... | **A**: The remainder of the paper is organized as follows.
**B**: We also prove that, given a large feedback vertex cut, we can shrink it while preserving the antlers in the graph.
**C**: In Section 5 we show how color coding can be used to find a large feedback vertex cut, if one exists.
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Generative approaches: Generative approaches [154, 197, 88, 81, 80] predict different types of spatial transformations (e.g., shifting and scaling, affine transformation, perspective transformation) for the foreground object, which is more flexible and powerful than category-specific object placement methods.
For insta... | **A**: [188] adopted spatial transformer network (STN) [58] to predict the warping parameters under an adversarial learning framework..
**B**: Given a pair of background and foreground, the generator predicts the affine transformation for the foreground object to produce a composite image.
**C**: [154] developed a mo... | CBA | ABC | CBA | CBA | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> Moreover, individual datasets cannot be easily merged into an all-encompassing dataset due to variations in their temporal ranges. For example, while some datasets such as TaxiBJ [2], T-Drive [11] and Q-Traffic [12] all pertain to Beijing, they are not temporally aligned and thus c... | **A**: Regrettably, currently available open datasets, such as PeMS [8], METR [9] and NYC Cabs [10] are limited to either traffic speeds or taxi-related data.
**B**: Such a dataset would enable researchers to study the complexities of urban data arising from multiple entities and their interconnections, thus supportin... | CBA | ACB | ACB | ACB | Selection 2 |
An optimal interval estimator should satisfy some conditions. To assess the quality of the models, the HQ principle from Section 3.3 is adopted. <|MaskedSetence|> <|MaskedSetence|> At the same time the results should also be interpretable, i.e. the end-user should be able to use the uncertainty information to make fu... | **A**: To this end, the following two assessment measures are used:
.
**B**: The more a model deviates from being well calibrated, the less reliable it becomes since the results cannot be trusted and relied upon.
**C**: First of all a model ought to be valid (or calibrated) in the sense of Eq. (2).
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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. We refer to “single-track” as MIDI files containing only one track, which is in contrast to multi-track MIDI files that have multi... | **A**: While melody extraction is a note-level classification task, melody track identification is a track-level task.
**B**: homophonic or polyphonic music.
Utilising the POP909 dataset \textcitepop909, we can develop a model that classifies each Pitch event into vocal melody, instrumental melody or accompaniment, wi... | BAC | BAC | BAC | BAC | Selection 4 |
A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber, “Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks,” in Proc. <|MaskedSetence|> <|MaskedSetence|> Mach. <|MaskedSetence|> 2006, pp. 369–376.
. | **A**: 23rd Int.
**B**: Learning (ICML), Pittsburgh, USA, Jun.
**C**: Conf.
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We use the KPConv[4] segmentation model KPFCNN as our backbone network. The network is an encoder-decoder fully convolutional network with skip connections. The encoder is composed by bottleneck ResNet blocks[47] with KP convolution layers. <|MaskedSetence|> We put the CSFR and ISFR modules after the first upsampling... | **A**: 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.
**B**: The decoder part is composed of the nearest upsampling layers with unary convolution layers.
**C**: We train the first stage for 600 epochs and the second stage for... | BAC | BAC | BAC | ACB | Selection 1 |
Setup. <|MaskedSetence|> <|MaskedSetence|> 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]. <|MaskedSetence|> This results in a more fair comparison of the results. Each class uses different IoU standards for further eval... | **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**: The official data set contains 7481 training and 7518 test images with 2D and 3D ... | ACB | ABC | ABC | ABC | Selection 4 |
ICDAR2015 [44] includes multi-orientated and small-scale text instances. Its ground truth is annotated with word-level quadrangles. <|MaskedSetence|>
MSRA-TD500 [45] is dedicated to detecting multi-oriented long non-Latin texts. <|MaskedSetence|> <|MaskedSetence|> | **A**: Here, we follow the previous methods [35, 8] and add 400 training images from TR400 [46] to extend this dataset..
**B**: It contains 300 training images and 200 testing images with word-level annotation.
**C**: It contains 1,000 training and 500 testing images.
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<|MaskedSetence|> Parallel software platforms can be implemented using high-level programming frameworks for specific hardware architectures Chen2009SA . The Compute Unified Device Architecture (CUDA) is a parallel computing platform for general computing on GPUs. Most parallel sorting algorithms are variants of stand... | **A**: For example, Cederman designed a quick sort for the GPU platform Cederman2008 , and Peters proposed an adaptive bitonic sorting algorithm with a bitonic tree for GPUs Peters2011BS .
**B**:
The hardware architecture of modern processors usually consists of more than two independent central processing units (CPU... | BAC | BAC | BAC | BCA | Selection 1 |
The authors would like to thank Mingjian Ding, and Baoxuan Zhu for providing an alternative proof of the Hurwitz stability of polynomials (25). They also thank Jarle Sogn for communicating on Schur complement based preconditioners.
The work of M. Cai is partially supported by the NIH-RCMI grant through 347 U54MD013376,... | **A**: The work of J.
**B**: RCJC20200714114556020, JCYJ20170818153840322 and JCYJ20190809150413261, and Guangdong Provincial Key Laboratory of Computational Science and Material Design No.
**C**: The work of G.
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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. In this case, we could modify our algorithm in the following way, similar to (Liu et al., 2020a): the clients in all sil... | **A**: We note that the modified algorithm is mathematically equivalent to TDCD, albeit with a higher communication cost.
**B**: Hence, the convergence analysis given in Section 4 can be trivially extended to this case.
.
**C**: This modification would significantly increase the communication cost of the algorithm.
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<|MaskedSetence|> <|MaskedSetence|> CSTB2022NSCQ-MSX0896), the Science and Technology Research Program of Chongqing Municipal Education Commission
(Grant No. KJQN202200512), the Chongqing Talents Project (Grant No. <|MaskedSetence|> 21XLB040), P. R. of China. . | **A**: 12201092), the Natural Science Foundation Project of CQ CSTC (Grant No.
**B**: cstc2022ycjh-bgzxm0040), and the Research Foundation of Chongqing Normal University (Grant No.
**C**: Changxin Mo acknowledges support from the National Natural Science Foundation of China (Grant No.
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On Two-stream Network Architecture. <|MaskedSetence|> We enlarge its channels to make it have the same amount of parameters as the proposed network. <|MaskedSetence|> As shown in Figure 7 (c), the two-stream architecture exhibits superior performance with more visually reasonable structures and detailed textures. <|... | **A**: The Bi-GFF and CFA modules are embedded to refine generation as the proposed model.
**B**: Quantitative results in Table 2 also validate the advantages of texture and structure dual generation.
.
**C**: To further highlight the two-stream dual generation architecture, we compare it with a multi-task single-str... | CAB | BCA | CAB | CAB | Selection 4 |
<|MaskedSetence|> The main advantages of this subgoal objective are simplicity and empirical efficiency. We used expert data to generate labels for supervised training. When offline datasets are available, which is the case for the environments considered in this paper111The dataset for INT or Sokoban can be easily ge... | **A**: Consequently, this method is often taken when dealing with complex domains (see e.g.
**B**: Furthermore, we found evidence of out-of-distribution generalization.
.
**C**: We train the transformer with the objective to predict the k𝑘kitalic_k-th step ahead.
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<|MaskedSetence|> We separately add glyph embedding or phonetic embedding to pre-trained language models for comparisons. <|MaskedSetence|> <|MaskedSetence|> On one hand, named entities in different datasets may rely on one of our provided features much more than the other features. So, in the test stage, this speci... | **A**: Ablation study is thus made to investigate how glyph and phonetic features bring improvement by themselves.
**B**: From the results on all four datasets, it is clear that models with the glyph or phonetic embedding almost all perform better than models with pure semantic embedding, which means that extra patter... | ABC | ABC | ABC | BAC | Selection 2 |
<|MaskedSetence|> ABC consists of 4 tasks, including language modeling, natural language inference (NLI), coreference resolution, and machine translation. <|MaskedSetence|> The language modeling task is to predict the pronoun of a sentence. For NLI and coreference resolution, three variations of each sentence are use... | **A**: For machine translation, sentences with two variations of third-person pronouns in English are used as source sentences.
.
**B**: A total of 4,560 samples are collected by a template-based method.
**C**: ABC (Gonzalez et al., 2020), the Anti-reflexive Bias Challenge, is a multi-task benchmark dataset designed ... | CBA | CBA | BCA | CBA | Selection 2 |
<|MaskedSetence|> <|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 composition systems used by the IEEE’s outsource vendors. <|MaskedSetence|> Have you lo... | **A**: The templates are intended to approximate the final look and page length of the articles/papers.
**B**: The XML files are used to produce the final print/IEEEXplore® pdf and then converted to HTML for IEEEXplore®.
**C**: Therefore, they are NOT intended to be the final produced work that is displayed in print ... | ACB | ACB | CAB | ACB | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> These evaluations of the goodness-of-fit of the structural model are entirely promising—point estimates match up nearly perfectly in sign and closely in magnitude to the reduced form static and dynamic treatment effects. A further look at the simulations in Figures 4, 5, and 6 rein... | **A**: After drawing a sample of 1000 simulated groups, 500 in the treatment and 500 in the control, we evaluate the fit of the model by replicating our reduced form estimations from Table 1.
**B**: The results of this replication for the MLE estimates are shown in Tables 6 and 7.
**C**: Crucially, because the vast m... | CBA | ABC | ABC | ABC | Selection 3 |
Due to the particularity of the SISR task, it is difficult to construct a large-scale paired real SR dataset. Therefore, researchers often apply degradation patterns on the aforementioned datasets to obtain corresponding degraded images to construct paired datasets. <|MaskedSetence|> To alleviate these problems and t... | **A**: Based on this degradation formula, the three most widely used degradation modes have been proposed: BI, BD, and DN.
**B**: 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 t... | CAB | BAC | BAC | BAC | Selection 3 |
To perform super-resolution, a Neural Knitwork has to translate the information contained in the patches of the original scale to a domain of patches of finer scale. <|MaskedSetence|> For blind super-resolution, Neural Knitwork core module is utilized with adjusted losses as illustrated in Figure 5. <|MaskedSetence|... | **A**: To enforce coherence, we apply spatially-aware supervision by downsampling the super-resolved image and computing the downsampling loss with the reference to the low-resolution source image..
**B**: The queried coordinates for a patch network include all super-resolved coordinates, which means that it is not p... | BCA | CBA | CBA | CBA | Selection 4 |
The present paper is the first work we aware of that specifically applies TS to apple tasting, but previous work has considered its use for logistic bandits. <|MaskedSetence|> the policy that draws its sample from the exact posterior) is infeasible due to the intractability of the posterior distribution. It is theref... | **A**: Appropriately designed approximate algorithms can be successful however, as shown theoretically (Mazumdar et al.,, 2020) for particular Langevin approximation algorithms, and empirically in a range of settings (e.g.
**B**: For logistic contextual bandits, the implementation of exact TS (i.e.
**C**: The effect ... | BCA | ABC | BCA | BCA | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> However, our approach is general and is not restricted to these two architectures.
Memory-augmented neural network architectures [56, 57] (MANNs) extend neural networks with an external memory block that supports reasoning. We denote our memory-augmented transformer models as memBE... | **A**: The memory component is loaded with relevant background textual knowledge.
**B**: In our experiments, we considered BERT [1] and DistilBERT [55], a distilled version of BERT that achieves competitive performance while limiting the overall computational burden.
**C**: The main building block of our architecture... | CBA | CBA | CBA | CBA | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> (2021); Li et al. (2021a); Dai et al. (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**: (2020); Tian et al.
**B**: (2019); Zhou et al.
**C**: However, the progress of sentiment dependency-based methods, such as the work by Zhang et al.
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<|MaskedSetence|> Figure 2 shows the QNN architecture. The inputs are classical data such as image pixels, and the outputs are classification results. <|MaskedSetence|> Each has three components: encoder encodes the classical values to quantum states with rotation gates such as RY; trainable quantum layers contain pa... | **A**: We use QNN as the benchmark PQC in this work.
**B**: The measurement results of the last block are passed through a Softmax to output classification probabilities.
**C**: The QNN consists of multiple blocks.
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<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> From the tables and the figure, we can see that our EDA achieves the best performance on most of the sequences except for the poster_6dof and occlusions sequences, on which our EDA has obtained the second best AR and AOR, respectively. This is because that, these... | **A**: We also provide some qualitative results obtained by SiamBAN, EVT, SiamRPN++, ATOM, E-MS, ETD, RMRNet, and our EDA in Fig.
**B**: 6.
**C**:
The quantitative results are given in Table 1, Table 2, Table 3, and Table 4.
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<|MaskedSetence|> <|MaskedSetence|> Recently, contrastive learning (CL) [38, 39, 40, 41], which discriminates positive pairs against negative pairs, achieved state-of-the-art performance in various vision tasks. Different mechanisms [41, 42, 43, 44] are proposed to prevent trivial solutions in CL to learn useful repr... | **A**:
Early SSL methods design hand-crafted pretext tasks [30, 31, 32, 33], which rely on somewhat ad-hoc heuristics and have limited abilities to capture practically useful representations.
**B**: Another popular form is clustering-based methods [34, 35, 36, 37] learning discriminative representation by offline or ... | CAB | ABC | ABC | ABC | Selection 2 |
Our NAS method consistently outperforms existing techniques for tiny networks in terms of computation-accuracy trade-off. Existing techniques usually need a scaling method to scale down the searched network and fit different budgets. <|MaskedSetence|> <|MaskedSetence|> We also try supporting flexible w𝑤witalic_w’s p... | **A**: The accuracy improvement is more significant under a tiny computation setting (≤\leq≤25M).
**B**: With the extended search space, all our models are derived from the same super network while obtaining the best accuracy.
**C**: Therefore, we enable flexible w𝑤witalic_w’s by default in our experiments.
.
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Within CGCL, multiple graph encoders observe input graphs to yield contrastive views. <|MaskedSetence|> Specifically, an assembly with encoders possessing non-redundant observation angles demonstrates high complementarity. <|MaskedSetence|> This notion of complementarity in CGCL mirrors the diversity imperative of ba... | **A**: Redundancies in observation angles can be inferred from overlapping encoder parameters.
**B**: Ideally, these encoders should exhibit complementarity to enhance fitting capability.
**C**: Inspired by [5], we introduce a loss-centric metric to measure the complementarity of CGCL’s encoders.
| BAC | BCA | BAC | BAC | Selection 3 |
<|MaskedSetence|> The work of Tomasz Korbak was supported by the Leverhulme Doctoral Scholarship. We gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Centers: ACK Cyfronet AGH, PCSS) for providing computer facilities and support within computational grant no. <|MaskedSetence|> <|Ma... | **A**: PLG/2019/012498.
**B**:
The work of Piotr Miłoś was supported by the Polish National Science Center grant UMO-2017/26/E/ST6/00622.
**C**: Our experiments were managed using https://neptune.ai.
| ABC | BAC | BAC | BAC | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> The authors in [20] use CBFs to learn a provably correct neural network safety guard for kinematic bicycle models. The authors in [21] consider that uncertainty enters the system dynamics linearly and propose to use robust adaptive CBFs, as originally presented in [22], in conjunct... | **A**: Learning with CBFs: Approaches that use CBFs during learning typically assume that a valid CBF is already given, while we focus on constructing CBFs so that our approach can be viewed as complementary.
**B**: In [19], it is shown how safe and optimal reward functions can be obtained, and how these are related t... | BCA | ABC | ABC | ABC | Selection 2 |
In CoauthorshipsNet, node means scientist and weights mean coauthorship, where weights are assigned by the original papers. For this network, there is no ground truth about nodes labels, and the numbers of communities are unknown. <|MaskedSetence|> Among the 1589 nodes, there are totally 396 disconnected components, a... | **A**: For convenience, we use CoauthorshipsNet1589 to denote the original network, and CoauthorshipsNet379 to denote the giant component.
**B**: Note that since the overall embeddedness is defined for adjacency matrix that is connected, it is not applicable for CoauthorshipsNet1589.
.
**C**: The CoauthorshipsNet has... | CAB | CAB | CAB | CAB | Selection 4 |
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