text_with_holes stringlengths 94 3.05k | text_candidates stringlengths 47 1.34k | A stringclasses 6
values | B stringclasses 6
values | C stringclasses 6
values | D stringclasses 6
values | label stringclasses 4
values |
|---|---|---|---|---|---|---|
<|MaskedSetence|> <|MaskedSetence|> We remark that in this case, our method is similar to that of [MR3591945], with some differences. <|MaskedSetence|> Also, our scheme is defined by a sequence of elliptic problems, avoiding the annoyance of saddle point systems. We had to reconsider the proofs, in our view simplify... | **A**: However, several simplifications are possible when the coefficients have low-contrast, leading to sharper estimates.
**B**: First we consider that T~~𝑇\tilde{T}over~ start_ARG italic_T end_ARG can be nonzero.
**C**: Of course, the numerical scheme and the estimates developed in Section 3.1 hold.
| CAB | CAB | CAB | CAB | Selection 2 |
For analyzing the employed features, we rank them by importances using RF (see 3). <|MaskedSetence|> There is a big difference between the sentiment associated to rumors and the sentiment associated to real events in relevant tweets. <|MaskedSetence|> Furthermore, we would expect that verified users are less involved... | **A**: Also interestingly, “IsRetweeted” feature is pretty much useless, which means the probability of people retweeting rumors or true news are similar (both appear near-bottom in the ranked feature list).
.
**B**: The best feature is related to sentiment polarity scores.
**C**: In specific, the average polarity sc... | ABC | BCA | BCA | BCA | Selection 4 |
<|MaskedSetence|> Most of the previous work (castillo2011information, ; gupta2014tweetcred, ) 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 proba... | **A**: The model utilizes CNN to extract a sequence of higher-level phrase representations, which are fed into a long short-term memory (LSTM) RNN to obtain the tweet representation.
**B**: This model, called CNN+RNN henceforth, is able to capture both local features of phrases (by CNN) as well as global and temporal ... | CAB | BAC | CAB | CAB | Selection 1 |
<|MaskedSetence|> 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 breaking class and 3,050 instances of anticipated, with over 300 event entities. For GoogleTrends, there are 2,700 and ... | **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**: Evaluating methodology.
For RQ1, given an e... | CAB | CAB | CAB | CAB | Selection 1 |
<|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. <|MaskedSetence|> <|MaskedSetence|> In terms of time since being diagnosed with diabetes, patients va... | **A**:
Table 1 shows basic patient information.
**B**: Only one of the patients suffers from diabetes type 2 and all are in ICT therapy.
**C**: The mean BMI value is 26.9.
| ACB | ACB | ACB | ACB | Selection 4 |
<|MaskedSetence|> (1998). <|MaskedSetence|> <|MaskedSetence|> On the contrary, the approach by Itti et al. (1998) detected low-level feature contrasts and wrongly predicted high values at object boundaries rather than their center.. | **A**: All image examples demonstrate a qualitative agreement of our model with the ground truth data, assigning high saliency to regions that contain semantic information, such as a door (a), flower (b), face (c), or text (d).
**B**:
Figure 1: A visualization of four natural images with the corresponding empirical f... | BCA | ACB | BCA | BCA | Selection 4 |
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**: Secondly, due to the results of Section 4, the investigated greedy strategies for computing the locality number can also be interpreted as greedy strategies for computing the cutwidth of a graph.
**B**: It may seem naive to expect new approximation results for cutwidth in this way, but, as mentioned in the intr... | ACB | CAB | CAB | CAB | Selection 2 |
In our experiments, we varied details of the architecture above. In most cases, we use a stack of four convolutional layers with 64646464 filters followed by three dense layers (the first two have 1024102410241024 neurons). <|MaskedSetence|> Next, three deconvolutional layers of 64646464 filters follow. <|MaskedSeten... | **A**: The reward is predicted by a softmax attached to the last fully connected layer.
We used dropout equal to 0.20.20.20.2 and layer normalization..
**B**: An additional deconvolutional layer outputs an image of the original 105×8010580105\times 80105 × 80 size.
**C**: The dense layers are concatenated with 646464... | CBA | CBA | CBA | CAB | Selection 2 |
<|MaskedSetence|> However, the execution of supervised control of locomotion mode transition hinges on constant operator-robot interaction, which might not always be feasible or reliable, especially in confined and complex environments typical in search and rescue missions [12]. In such situations, operators might str... | **A**: These include adopting specialized mechanical designs [13, 14] and applying pre-programmed solutions [15].
**B**: To address the locomotion mode transition conundrum, various solutions have been proposed.
**C**:
Hybrid robots typically transition between locomotion modes either by “supervised autonomy” [11], ... | CBA | CBA | ABC | CBA | Selection 4 |
The algorithm classifies items according to their size. Tiny items have their size in the range (0,1/3]013(0,1/3]( 0 , 1 / 3 ], small items in (1/3,1/2]1312(1/3,1/2]( 1 / 3 , 1 / 2 ], critical items in (1/2,2/3]1223(1/2,2/3]( 1 / 2 , 2 / 3 ], and large items in (2/3,1]231(2/3,1]( 2 / 3 , 1 ]. <|MaskedSetence|> Large i... | **A**: Each critical item is placed in one of the critical bins.
**B**: In addition, the algorithm has four kinds of bins, called tiny, small, critical and large bins.
**C**: Note that the algorithm is heavily dependent on the advice being trusted.
| BAC | BAC | BAC | CBA | Selection 2 |
It is worth noting that the difference in terms of space complexity is also very significant. For classifiers supporting incremental classification, like SS3 or MNB, only a small vector needs to be stored for each user. <|MaskedSetence|> of every user and then simply update it as more content is created. <|MaskedSet... | **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 4 |
Fig. 12 shows how the number of UAVs affect the computation complexity of SPBLLA. <|MaskedSetence|> The goal functions’ value in the optimum states increase with the growth in UAVs’ number. Since goal functions are the summation function of utility functions, more UAVs offer more utilities which result in higher pote... | **A**: Since the total number of UAVs is diverse, the goal functions are different.
**B**: Fig. 12 also shows how many iterations that UAV ad-hoc network needs to approach to convergence.
**C**: Moreover, more UAVs can cover more area and support more users, which also corresponds with more utilities.
| ACB | ACB | ACB | BAC | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> This type of variance leads to converging to sub-optimal policies and brutally hurts DQN performance. The second source of variance Target Approximation Error which is the error coming from the inexact minimization of DQN parameters. Many of the proposed extensions focus on minimiz... | **A**: In Approximation Gradient Error, the error in gradient direction estimation of the cost function leads to inaccurate and extremely different predictions on the learning trajectory through different episodes because of the unseen state transitions and the finite size of experience reply buffer.
**B**: Dropout me... | BAC | CAB | CAB | CAB | Selection 3 |
Figure 5: Top: An illustration of the SegNet architecture. There are no fully connected layers, and hence it is only convolutional. Bottom: An illustration of SegNet and FCN (Long et al., 2015) decoders. <|MaskedSetence|> <|MaskedSetence|> FCN upsamples by learning to deconvolve the input feature map and adds the co... | **A**: SegNet uses the max-pooling indices to upsample (without learning) the feature map(s) and convolves with a trainable decoder filter bank.
**B**: a,b,c,d𝑎𝑏𝑐𝑑a,b,c,ditalic_a , italic_b , italic_c , italic_d correspond to values in a feature map.
**C**: This feature map is the output of the max-pooling layer ... | BAC | BAC | ABC | BAC | Selection 4 |
Following Fernández-Delgado et al. <|MaskedSetence|> Afterward, the number of training examples is limited to nlimitsubscript𝑛limitn_{\text{limit}}italic_n start_POSTSUBSCRIPT limit end_POSTSUBSCRIPT examples per class. <|MaskedSetence|> (2014), we extract validation sets from the training set (e.g., for hyperparame... | **A**: For some datasets which provide a separate test set, the test accuracy is evaluated on the respective set..
**B**: (2014), each dataset is split into a training and a test set using a 50/50 split while maintaining the label distribution.
**C**: We evaluate the training with 5555, 10101010, 20202020, and 505050... | BCA | CAB | BCA | BCA | Selection 4 |
<|MaskedSetence|> In particular, our setting is the same as the linear setting studied by Ayoub et al. (2020); Zhou et al. (2020), which generalizes the one proposed by Yang and Wang (2019a). We remark that our setting differs from the linear setting studied by Yang and Wang (2019b); Jin et al. <|MaskedSetence|> It c... | **A**: (2019).
**B**:
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, 2019... | BAC | ACB | BAC | BAC | Selection 4 |
We thank Prof. <|MaskedSetence|> Johnathan Bush for very useful feedback about a previous version of this article. <|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 impreci... | **A**: Henry Adams and Dr.
**B**: Qingsong Wang for bringing to our attention the paper [76] which was critical for the proof of Theorem 1.
**C**: We also thank Prof.
| ACB | BAC | ACB | ACB | Selection 1 |
Anna uses the Dimension Correlation in order to determine the role of the data set’s dimensions in the outcome of the projection. <|MaskedSetence|> <|MaskedSetence|> She validates her hypothesis by clicking on the “mitoses” dimension and observing that the actual dimension values look almost randomly distributed thro... | **A**: She interactively draws a polyline with her mouse following the pattern from the benign cases to the malignant ones, as shown in Figure 6(c).
**B**: For the first case (1), it appears that t-SNE separates the malignant class according to “normal nucleoli,” “size uniformity,” and “shape uniformity” in one area—a... | ACB | ACB | ACB | CBA | Selection 1 |
A critical point of reflection associated with this explosion of proposals has been that novel metaphors do not lead to new solvers, and that comparisons undergo serious methodological problems. <|MaskedSetence|> <|MaskedSetence|> This problem has captured the interest of other researchers, leading to several papers... | **A**: Good methodological practices must be followed in forthcoming studies when designing, describing, and comparing new algorithms..
**B**: In addition, comparisons have been often inadequate, leading to problems of reproducibility and applicability.
**C**: Although there are increasingly more bio-inspired algorit... | CBA | CBA | CBA | CAB | Selection 1 |
<|MaskedSetence|> Instead, DEC and SpectralNet work better on the large scale datasets. Although GAE-based models (GAE, MGAE, and GALA) achieve impressive results on graph type datasets, they fail on the general datasets, which is probably caused by the fact that the graph is constructed by an algorithm rather than pr... | **A**: If the graph is not updated, the contained information is low-level.
**B**: Classical clustering models work poorly on large scale datasets.
**C**: In particular, AdaGAE is stable on all datasets.
.
| ACB | BAC | BAC | BAC | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> The measurements identified 2,500 unique loops, of these 703 were provider ASes, and 1,780 customer ASes. <|MaskedSetence|> 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 test... | **A**:
Traceroute Active Measurements.
**B**: The dataset found 688 ASes that do not enforce ingress filtering.
**C**: We analyse the datasets from the traceroute measurements performed by the CAIDA Spoofer Project within the last year 2019, (Lone et al., 2017).
| ACB | CBA | ACB | ACB | Selection 1 |
This paper builds upon previous work with this dataset [7], which used support vector machine (SVM) ensembles. <|MaskedSetence|> <|MaskedSetence|> The context model has two parts: (1) a recurrent context layer, which encodes classification-relevant properties of previously seen data, and (2) a feedforward layer, whic... | **A**: First, their approach is extended to a modern version of feedforward artificial neural networks (NNs) [8].
**B**: The results indicate improvement from two sources: The use of neural networks in place of SVMs, and the use of context, particularly in cases where a substantial number of context sequences are avai... | ACB | ACB | ACB | ABC | 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]. <|Mask... | **A**: The same construction can also be used to generate free monoids as automaton semigroups or monoids.
**B**: In fact, the construction to generate these semigroups is quite simple [4, Proposition 4.1] (compare also to 3).
**C**: While these constructions and the involved proofs are generally deemed quite complic... | CBA | CBA | CAB | CBA | Selection 4 |
Following Selvaraju et al. (2019), we report Spearman’s rank correlation between network’s sensitivity scores and human-based scores in Table A3. For HINT and our zero-out regularizer, we use human-based attention maps. <|MaskedSetence|> <|MaskedSetence|> However, compared to baseline, HINT variants trained on rando... | **A**: For SCR, we use textual explanation-based scores.
**B**: We find that HINT trained on human attention maps has the highest correlation coefficients for both datasets.
**C**: However, as we have seen, the improvements in performance cannot necessarily be attributed to better overlap with ground truth localizati... | ABC | ABC | ABC | ACB | Selection 2 |
<|MaskedSetence|> Existing research has achieved some success using expert annotated corpora of a few hundred or a few thousand privacy policies Wilson et al. (2016); Zimmeck et al. <|MaskedSetence|> (2014), but issues of accuracy, scalability and generalization remain. More importantly, annotations in the privacy po... | **A**: Privacy policies are difficult to understand and many tasks such as privacy practice classification (Wilson et al., 2016), privacy question answering (Ravichander et al., 2019), vague sentence detection (Lebanoff and Liu, 2018), and detection of compliance issues (Zimmeck et al., 2019) require skilled legal expe... | CBA | ABC | CBA | CBA | Selection 3 |
Ensemble learning can be controlled in different ways. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Data preprocessing and wrangling benefits from feedback provided by a VA system, for example, in the form of validation metrics that increase the per-model performance of several heterogeneous ML models used ... | **A**: This offers new possibilities for direct manipulation of both instances and features.
**B**: Visualization also enhances the interaction with data preparation (Figure 1, upper red arrow) [25].
**C**: Starting from the data, visualization can be used to explore the data space (Figure 1, upper blue arrow) [47]. ... | ACB | CAB | CAB | CAB | Selection 3 |
Task similarity. <|MaskedSetence|> We shuffle the samples and randomly divide tasks to construct the setting that tasks are similar to each other. For a fair comparison, each task on this setting also has 120 and 1200 utterances on average in Persona and Weibo respectively. <|MaskedSetence|> (Table 2).
When tasks are... | **A**: In Persona and Weibo, the performance of MAML is similar to that of Transformer-F, while MAML performs significantly better than Transformer-F when tasks are different.
**B**: We train and evaluate Transformer-F and MAML on this setting.
**C**: In Persona and Weibo, each task is a set of dialogues for one user... | CBA | CBA | CBA | CBA | Selection 3 |
A conceptual frame structure is designed which contains two types of time slots. <|MaskedSetence|> Let us first focus on the e-slot. 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, t... | **A**: Compared to the motion-aware protocol in [31], the new TE-aware protocol can be applied to the UAV mmWave network with higher mobility including random trajectories and high velocity.
**B**: In the tracking error bounding period, the UAVs estimate the TE of AOAs and AODs based on the GP prediction error.
**C**... | CBA | CAB | CBA | CBA | Selection 1 |
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**: Specifically, the previous mean-field analysis casts SGD as the Wasserstein gradient flow of an energy functional, which corresponds to the objective function in supervised learning.
**B**: In contrast, TD follows the stochastic semigradient of the MSPBE (Sutton and Barto, 2018), which is biased.
**C**: See al... | CAB | CAB | CAB | ABC | Selection 2 |
For the convergence of deep Transformers, Bapna et al. <|MaskedSetence|> <|MaskedSetence|> (2019) present the Dynamic Linear Combination of Layers approach that additionally aggregates shallow layers’ outputs for each encoder layer. Wu et al. (2019b) propose a two-stage approach. Wei et al. (2020) introduce a depth-w... | **A**: (2018) propose the Transparent Attention mechanism which allows each decoder layer to attend weighted combinations of all encoder layer outputs.
**B**: Wang et al.
**C**: (2022a) design an ODE Transformer which is analogous to the Runge-Kutta method.
| ABC | ABC | ABC | ACB | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Our key insight is that distortion rectification can be cast as a problem of learning an ordinal distortion from a distorted image. The ordinal distortion indicates the distortion levels of a series of pixels, which extend outward from the principal point. To pre... | **A**: 1.
**B**: In particular, we redesign the whole pipeline of deep distortion rectification and present an intermediate representation based on the distortion parameters.
**C**: The comparison of the previous methods and the proposed approach is illustrated in Fig.
| BCA | CBA | BCA | BCA | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Second, we sample a small number of scenarios from the black-box oracle and use our polynomial-scenarios algorithms to (approximately) solve the problems on them. Finally, we extrapolate the solution to the original black-box problem. This overall methodology is ... | **A**: First, we develop algorithms for the simpler polynomial-scenarios model.
**B**: Our main goal is to develop algorithms for the black-box setting.
**C**: As usual in two-stage stochastic problems, this has three steps.
| BCA | BCA | BCA | CAB | Selection 3 |
III. <|MaskedSetence|> <|MaskedSetence|> What’s more, multiplicative noises relying on the relative states between adjacent local optimizers make states, graphs and noises coupled together. <|MaskedSetence|> We firstly employ the property of conditional independence to deal with the coupling term of different rando... | **A**: It becomes more complex to estimate the mean square upper bound of the local optimizers’ states (Lemma 3.1).
**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... | ACB | CBA | CBA | CBA | Selection 3 |
Specifically, there are three main steps in the proposed approach. First, MuCo partitions the tuples into groups and assigns similar records into the same group as far as possible. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> In the random output tables, the rows correspond to the records, and the columns c... | **A**: Second, the random output tables, which control the distribution of random output values within each group, are calculated to make similar tuples to cover for each other at the minimal cost.
**B**: For instance, for the original table in Figure 1(a), MuCo partitions the records into four groups and calculates r... | ACB | ACB | ABC | ACB | Selection 2 |
Due to limited mask representation of HTC, we move on to SOLOv2, which utilizes much larger mask to segment objects. It builds an efficient yet simple instance segmentation framework, outperforming other segmentation methods like TensorMask Chen et al. <|MaskedSetence|> <|MaskedSetence|> (2020) on COCO. In SOLOv2, t... | **A**: (2020) and BlendMask Chen et al.
**B**: (2019c), CondInst Tian et al.
**C**: It’s worth noting that other attempts, including NASFPN, data augmentation and Mask Scoring, bring little improvement in our experiments..
| CBA | BAC | BAC | BAC | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> (4) of Jin et al. is also bounded (but not vice versa). However, the regret analysis in the misspecified linear MDP of Jin et al. (2020) is restricted to static regret, so we cannot directly borrow their analysis for the misspecified setting (Jin et al., 2020) to... | **A**: One can apply the Cauchy-Schwarz inequality to show that our total variation bound implies that misspecification in Eq.
**B**: (2020).
**C**: The definition of total variation B𝐵Bitalic_B is related to the misspecification error defined by Jin et al.
| ABC | CBA | CBA | CBA | Selection 4 |
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. <|MaskedSetence|> Most respondents encountered fake news on instant messaging apps compared to social media, and have reported the least trust in the... | **A**: In Singapore, however, mitigation efforts on fake news in instant messaging apps may be more important.
**B**: These suggest that, in Singapore, communication with personal contacts such as through the forwarding of messages, rather than with the public such as by sharing posts on social media feeds, is the lar... | CBA | ABC | ABC | ABC | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> However, as the degree increases, incorporating DAN yields more performance gain. This upward trend halts until the degree exceeds 20. Overall, DAN exhibits significantly better performance than GCN, GAT, or their combination. <|MaskedSetence|> | **A**: For entities with only a few neighbors, the advantage of leveraging DAN is not significant.
**B**:
The results on the ZH-EN dataset are depicted in Figure 7.
**C**: The decentralized attention, which considers neighbors as queries, consistently outperforms the centralized GAT across varying entity degrees..
| BCA | BAC | BAC | BAC | Selection 3 |
Conducting exploration without the extrinsic rewards is called the self-supervised exploration. From the perspective of human cognition, the learning style of children can inspire us to solve such problems. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Previous formulations of intrinsic rewards used in self-... | **A**: Developmental psychologists consider intrinsic motivation as the primary driver in the early stages of development [9].
**B**: The children often employ goal-less exploration to learn skills that will be useful in the future.
**C**: By extending such idea to RL domain, the ‘intrinsic’ rewards are used in RL to... | BAC | BAC | ACB | BAC | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> We do that by applying any of the above mentioned VAEs111In this exposition we use unspervised trained VAEs as our base models but the framework also works with GAN-based or FLOW-based DGMs, supervised, semi-supervised or unsupervised. In the Appendix we present such implementation... | **A**: First, we apply a DGM to learn only the disentangled part, C𝐶Citalic_C, of the latent space.
**B**:
The model has two parts.
**C**: We can view this as a style transfer task and use a technique from [adaIN] to achieve our goal..
| ACB | BAC | BAC | BAC | 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**: In this case, the study inferred that of the four wires, two wires acting as ground can be replaced by one wire, and based on this reasoning, the method in which the 4 pin signal system can be described as 3-pin based logic as the same 3 pin signal system.
**B**: However, one can think about whether the four pi... | CAB | BCA | BCA | BCA | Selection 4 |
Forward selection is a simple, greedy feature selection algorithm (Guyon \BBA Elisseeff, \APACyear2003). <|MaskedSetence|> 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. One then proceeds to sequentially add the ne... | **A**: Here we consider forward selection based on the Akaike Information Criterion (AIC).
**B**: In order to impose nonnegativity of the coefficients, we will use a slightly modified procedure which we will call nonnegative forward selection (NNFS).
**C**: It is a so-called wrapper method, which means it can be used... | CAB | CAB | CAB | BAC | Selection 2 |
The experimental results (ROC AUC and AP) of the five relevant variable selection techniques are shown in Figure 3. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> The red dot is the mean value, and the length of the red line on either side of the mean shows the standard deviation of the 25 results. The Wilco... | **A**: For the dot plot, each black dot corresponds to a result.
**B**: For each technique, its 25 results (each is the average results over the 32 datasets) are presented with a violin plot overlaid by a dot plot.
**C**: For the violin plot, the outline represents the density estimated using Gaussian kernel function... | BAC | BAC | CAB | BAC | Selection 2 |
CB-MNL enforces optimism via an optimistic parameter search (e.g. 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. <|MaskedSetence... | **A**: [2020], Filippi et al.
**B**: [2010].
**C**: In non-linear reward models, both approaches may not follow similar trajectory but may have overlapping analysis styles (see Filippi et al.
| ABC | CBA | ABC | ABC | Selection 1 |
<|MaskedSetence|> An action can last from a fraction of a second to minutes in the real-world scenario as well as in the datasets [7, 15]. <|MaskedSetence|> We notice that actions shorter than 30 seconds dominate the distribution, but their performance is obviously inferior to longer ones with all different TAL metho... | **A**: Though many methods (e.g., [1, 3, 9, 20, 21, 24, 42, 43, 44, 46]) in recent years have been continuously breaking the record of TAL performance, a major challenge hinders its substantial improvement – large variation in action duration.
**B**: 1 b)).
**C**: We plot the distribution of action duration in the da... | ACB | ACB | ACB | BCA | Selection 3 |
Figure 5: The exploration of clusters of interest that contain performant ML models. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> In (c.1), we observe that g-mean and ROC AUC scores are very low, which is a problem investigated further in view (d.1). Those models appear to perform better for the hard-to-cl... | **A**: On the other hand, (b.2) shows that the user’s choice of models retains both performance and diversity.
**B**: (b.1) provides an overview of the performance, showing that \raisebox{0.15pt}{\resizebox{!}{0.8ex}{\textbf{\textsf{C3}}}}⃝ has underperforming KNN and GradB models.
**C**: View (a) presents the user’s... | ABC | CBA | CBA | CBA | Selection 4 |
In contrast, HiPPI and our method require shape-to-universe representations. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Throughout this section we also report results of the initialisation methods ZoomOut and ZoomOut+Sync. Further details can be found in the supplementary material.
. | **A**: We refer to this method of synchronising the ZoomOut results as ZoomOut+Sync, which directly serves as initialisation for HiPPI and our method.
**B**: By doing so, we obtain the initial U𝑈Uitalic_U and Q𝑄Qitalic_Q.
**C**: To obtain these, we use synchronisation to extract the shape-to-universe representation... | CBA | CBA | CBA | ACB | Selection 3 |
The recognition algorithm RecognizePG for path graph is mainly built on path graphs’ characterization in [1]. <|MaskedSetence|> <|MaskedSetence|> Unfortunately, we cannot build all the antipodality graphs by brute force because checking all possible antipodal pairs requires too much time (more time than the overall ... | **A**: 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.
**B**: We overcome this problem by visiting the connected components in a smart order.
**C**: This characterization decomposes the ... | BCA | CAB | CAB | CAB | Selection 3 |
<|MaskedSetence|> 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. <|MaskedSetence|> <|MaskedSetence|> Therefore, it is worth modifying this method to mixed membership networks. Numerical results of s... | **A**: SLIM combined the SLIM with the spectral method based on DCSBM for community detection.
**B**: 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.
**C**: And the SLIM method outperforms state-of-art me... | BAC | BAC | CAB | BAC | Selection 4 |
See, e.g., Welling and Teh (2011); Chen et al. (2014); Ma et al. (2015); Chen et al. <|MaskedSetence|> <|MaskedSetence|> (2016); Chen et al. (2016); Dalalyan (2017); Chen et al. (2017); Raginsky et al. (2017); Brosse et al. <|MaskedSetence|> (2018); Cheng and Bartlett (2018); Chatterji et al. (2018); Wibisono (2018)... | **A**: (2015); Dubey et al.
**B**: (2016); Vollmer et al.
**C**: (2018); Xu et al.
| ABC | ABC | ACB | ABC | Selection 1 |
Phase is a controller timing unit associated with the control of one or more movements, representing the permutation and combination of different traffic flows. <|MaskedSetence|> <|MaskedSetence|> Fig. <|MaskedSetence|> Note that the signal on the right-turn lanes is always green for consistency with real world.. | **A**: The 4-phase setting is the most common configuration in reality, but the number of phases can vary due to different intersection topologies (3-way, 5-way intersections, etc.).
**B**: 2
illustrates a standard 4-phase setting: "north-south-straight", "north-south-left", "east-west-straight" and "east-west-left", ... | CAB | ABC | CAB | CAB | Selection 4 |
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 | CAB | ACB | ACB | Selection 4 |
In this section, we evaluate how well our model can learn the underlying distribution of points by asking it to autoencode a point cloud. We conduct the autoencoding task for 3D point clouds from three categories in ShapeNet (airplane, car, chair). In this experiment, we compare LoCondA with the current state-of-the-ar... | **A**: It can be observed that LoCondA-HC achieves competitive results with respect to reference solutions.
**B**: All reference methods were trained in an autoencoding framework (non-generative variants), while both of LoCondA are preserving generative capabilities in the experiment.
.
**C**: Since these two metrics... | CAB | CAB | CAB | BAC | Selection 1 |
Paper organization. <|MaskedSetence|> Section 2 presents a saddle point problem of interest along with its decentralized reformulation. <|MaskedSetence|> <|MaskedSetence|> Finally in Section 5, we show how the proposed algorithm can be applied to the problem computing Wasserstein barycenters .
. | **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**: In Section 3, we provide the main algorithm of the paper to solve such kind of problems.
| CBA | BCA | BCA | BCA | Selection 3 |
The length of a cycle is its number of edges. The minimum cycle basis (MCB) problem is the problem of finding a cycle basis such that the sum of the lengths (or edge weights) of its cycles is minimum. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> The authors show that the MCB problem is different in nature ... | **A**: 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.
**B**: In [5] a unified perspective of the problem is presented.
**C**: In more concrete terms this problem is equivalent to finding the cycle basis with the spar... | ACB | CBA | ACB | ACB | Selection 3 |
<|MaskedSetence|> Specifically, F4 resembled an unimportant feature for the Worst subspace, as shown in Fig. 7(c.1). <|MaskedSetence|> <|MaskedSetence|> Indeed in that particular slice, this feature appeared to be rather important (not shown due to space limits). These recursive actions reassured us that F4 should b... | **A**: We concentrated on the conjunction of those automatic approaches with the statistical measures offered by FeatureEnVi.
**B**: Albeit that, when closely explored in the whole data space, it was more impactful than other features (with a target correlation value of more than 15%).
**C**: Afterwards, we collapsed... | ABC | BCA | ABC | ABC | Selection 3 |
<|MaskedSetence|> High-precision trajectories or set points can be generated prior to the actual machining process following various optimization methods, including MPC, feed-forward PID control strategies, or iterative-learning control [6, 7], where friction or vibration-induced disturbances can be corrected. <|Mask... | **A**: Instead of adapting the controller for the worst case scenarios, the prediction model can be selected to provide the best closed-loop performance by tuning the parameters in the MPC optimization objective for maximum performance [8, 9, 10].
**B**: In MPC, closed-loop performance is pushed to the limits only if ... | CBA | CBA | CBA | CAB | Selection 1 |
So far, there is no study comparing methods from either group comprehensively. Often papers fail to compare against recent methods and vary widely in the protocols, datasets, architectures, and optimizers used. <|MaskedSetence|> Some use it as a binary classification task (class 0: digits 0-4, class 1: digits: 5-9) [5... | **A**: For instance, the widely used Colored MNIST dataset, where colors and digits are spuriously correlated with each other, is setup differently across papers.
**B**: These discrepancies make it difficult to judge the methods on an even ground.
.
**C**: For CelebA, [46] uses ResNet-18 whereas [50] uses ResNet-50, ... | ACB | ACB | ABC | ACB | Selection 1 |
2) Improving performance with fast and simple calibration. There is a trade-off between the system performance and calibration time. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Interpretation of the learned features in these methods will bring insight for the deep learning-based gaze estimation.. | **A**: Deep learning approaches often serve as a black-box tool for gaze estimation.
**B**: How to achieve satisfactory performance with fast calibration procedure is a promising direction.
3) Interpreting learned features.
**C**: The longer calibration time leads to more accurate estimates.
| ABC | CBA | CBA | CBA | Selection 4 |
Other methods detect the keypoints from the face image, instead of local patches. For instance, Weng et al. weng2016robust proposed to recognize persons of interest from their partial faces. To accomplish this task, they firstly detected keypoints and extract their textural and geometrical features. Next, point set m... | **A**: Finally, the similarity of the two faces is obtained through the distance between these two aligned feature sets.
**B**: duan2018topology .
**C**: Gabor ternary pattern and point set matching are then applied to match the local keypoints for partial face recognition.
| ABC | ABC | ABC | BCA | Selection 3 |
<|MaskedSetence|> Sized (co)inductive types [BFG+04, Bla04, Abe08, AP16] gave way to sized mixed inductive-coinductive types [Abe12, AP16]. In parallel, linear size arithmetic for sized inductive types [CK01, Xi01, BR06] was generalized to support coinductive types as well [Sac14]. <|MaskedSetence|> As we mentioned i... | **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**: We present, to our knowledge... | ACB | ACB | BCA | ACB | Selection 2 |
<|MaskedSetence|> In contrast, due to the personalization of D-LUTs, once a new user initiates a request, the owner must interact with this user to securely distribute the D-LUT under the support of homomorphic en-cryption. This cost scales linearly with the number of users. <|MaskedSetence|> Therefore, the focus of ... | **A**: In the user-side embedding AFP, since the encrypted media content shared with different users is the same, the encryption of the media content is only executed once.
**B**: Based on this observation, the first scheme is as follows.
.
**C**: It is clear that the biggest source of overhead for the owner is the m... | ACB | ACB | BAC | ACB | Selection 2 |
<|MaskedSetence|> (2017); Veličković et al. (2018) have recently emerged as an effective class of models for capturing high-order relationships between nodes in a graph and have achieved state-of-the-art results on a variety of tasks such as computer vision Li et al. <|MaskedSetence|> <|MaskedSetence|> (2019); Wu et... | **A**: (2017), neural language processing Marcheggiani and Titov (2017); Yao et al.
**B**: (2019), and recommender systems Wang et al.
**C**:
Currently, Graph Neural Networks (GNN) Kipf and Welling (2017); Hamilton et al.
| ABC | CAB | CAB | CAB | Selection 2 |
<|MaskedSetence|> 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. <|MaskedSetence|> This was also the case in Ostrovskii & Bach ... | **A**: 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].
**B**: This was fully formalized in Sun & Tran-Dinh [2019], in which the concept of ge... | ACB | ACB | ACB | BCA | Selection 2 |
<|MaskedSetence|> The term Pass-Bundle refers to multiple passes during which those routines are executed. <|MaskedSetence|> Each of these routines is performed in a separate pass over the edges. <|MaskedSetence|> In total, a Pass-Bundle requires 3333 passes.. | **A**: The Backtrack-Stuck-Structures method backtracks active paths that were not extended, but does not require a fresh pass.
**B**: Precisely, the routines are: (1) extend structures along active paths (Extend-Active-Paths), (2) check for edge augmentations (Check-for-Edge-Augmentation), and (3) include (additional... | CBA | BAC | CBA | CBA | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> Additionally, we provide the lower bounds both on the communication and the number of local oracle calls required to solve problem (1). Furthermore, we have developed the novel methods (Algorithm 1, Algorithm 2, Algorithm 3) for this problem that are optimal up to logarithmic facto... | **A**:
In this paper, we present a novel formulation for the Personalized Federated Learning Saddle Point Problem (1).
**B**: The theoretical analysis and experimental evidence corroborate our methods.
**C**: This formulation incorporates a penalty term that accounts for the specific structure of the network and is ... | ACB | ACB | ACB | ACB | Selection 3 |
There are two levels of coordination; first is selecting an equilibrium before play commences, and second is selecting actions during play time. <|MaskedSetence|> Therefore, at this level of coordination, both NEs and (C)CEs are similar. <|MaskedSetence|> <|MaskedSetence|> NEs are factorizable and therefore can sam... | **A**: Both NEs and (C)CEs require agreement on what equilibrium is being played (Goldberg et al., 2013; Avis et al., 2010; Harsanyi & Selten, 1988): for (C)CEs this is a joint action probability distribution, and for NEs this is also a joint action probability distribution that can conveniently be factored into stocha... | ACB | ACB | CAB | ACB | Selection 4 |
<|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. <|Ma... | **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... | BAC | BAC | BAC | BAC | Selection 3 |
The remainder of the paper is organized as follows. 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|> We also prove that, given a large feedback vertex cu... | **A**: Our main results are derived in Section 6, where we show how color coding can be used to efficiently find antlers when the size of their 𝖺𝗇𝗍𝗅𝖾𝗋𝖺𝗇𝗍𝗅𝖾𝗋\mathsf{antler}sansserif_antler part is bounded in terms of the size of their 𝗁𝖾𝖺𝖽𝗁𝖾𝖺𝖽\mathsf{head}sansserif_head.
**B**: In Section 5 we show ... | BAC | BAC | BAC | ACB | Selection 2 |
Category-specific object placement methods can be categorized into generative approach and discriminative approach. The generative approach targets at predicting one or multiple reasonable bounding boxes for the foreground category, whereas the discriminative approach aims to predict the rationality score of a boundin... | **A**: The slow discriminative approach takes in a background image with foreground bounding box and predicts a rationality score.
**B**: The comparison between generative model, slow discriminative model, and fast discriminative model is illustrated in Fig.
**C**: The fast discriminative approach takes in a backgrou... | ACB | BAC | ACB | ACB | 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**: This limitation underscores the urgent need for a comprehensive and spatio-temporally aligned dataset in urban computing to facilitate more precise algorithms and insightful analyses.
**B**: Regrettably, currently available open datasets, such as PeMS [8], METR [9] and NYC Cabs [10] are limited to either traffi... | CBA | BCA | BCA | BCA | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> The Adam optimizer was used for weight optimization with a fixed learning rate of 5×10−45superscript1045\times 10^{-4}5 × 10 start_POSTSUPERSCRIPT - 4 end_POSTSUPERSCRIPT, in accordance with romano2019conformalized . The number of epochs was limited to 100, unless stated otherwise.... | **A**: All neural networks contained only a single hidden layer with 64 neurons.
**B**: The general architecture for all neural-network-based models was fixed.
**C**:
All neural networks were constructed using the default implementations fromPyTorch pytorch .
| CBA | ACB | CBA | CBA | Selection 3 |
Dynamics is an important element in music, as they are often used by musicians to add excitement and emotion to songs. <|MaskedSetence|> In the realm of MIDI, velocity is a parameter that scales the intensity or volume at which a sound sample is played back, with the value ranging from 0 to 127. <|MaskedSetence|> <... | **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 | CBA | BCA | CBA | Selection 4 |
The first benchmark is a traditional communication system to transmit speech signals, named speech transceiver. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Polar codes with successive cancellation list (SCL) decoding algorithm[22] is employed for channel coding, in which the block length is 512 and the li... | **A**: Particularly, the input of the system is the speech signals, which is restored at the receiver.
**B**: For the system, the adaptive multi-rate wideband (AMR-WB)[21] is used for speech source coding and 64-QAM is utilized for modulation.
**C**: Moreover, the transcription is obtained from the recovered speech s... | ACB | CBA | ACB | ACB | Selection 4 |
As depicted in Figure1, the first stage of our training process draws inspiration from [17, 18, 19]. Here, we select two samples with at least one overlapping class to serve as an input pair. The CSFR module is designed to facilitate the transfer of analogous features between these two samples. Unlike methods in [17, 1... | **A**: Consequently, each point in the restructured feature emerges as a weighted aggregate of all points from its counterpart sample.
**B**: This approach enables us to effectively channel the gradients, allowing for a dense propagation of supervision from labeled points in one sample to their unlabeled counterparts ... | BCA | ACB | ACB | ACB | 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**: Setup.
**B**: Moreover, we use 40 recall positions instead of 11 recall positions proposed in the original Pascal VOC benchmark, following [40].
**C**: This results in a more fair comparison of the results.
| ABC | CBA | ABC | ABC | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> It contains 1,000 training and 500 testing images. <|MaskedSetence|> It contains 300 training images and 200 testing images with word-level annotation. Here, we follow the previous methods [35, 8] and add 400 training images from TR400 [46] to extend this dataset.. | **A**: ICDAR2015 [44] includes multi-orientated and small-scale text instances.
**B**:
MSRA-TD500 [45] is dedicated to detecting multi-oriented long non-Latin texts.
**C**: Its ground truth is annotated with word-level quadrangles.
| ACB | ACB | ACB | BAC | Selection 1 |
In this paper, we present two efficient algorithms for collecting the statistics of large-scale IP address data. We can obtain the frequently occurring IP addresses from the statistics, which can be regarded as a pre-processing step of user behavior analysis in network traffic management. <|MaskedSetence|> <|MaskedSe... | **A**: Because of the increasing volume and speed of network traffic, it has become expensive and impractical to handle all IP addresses contained the IP packets.
**B**: The sparse matrix stores the number of occurrences of the individual IP addresses, in which the positions of the rows and columns are employed to rep... | ACB | CAB | ACB | ACB | Selection 4 |
The authors would like to thank Mingjian Ding, and Baoxuan Zhu for providing an alternative proof of the Hurwitz stability of polynomials (25). <|MaskedSetence|> Cai is partially supported by the NIH-RCMI grant through 347 U54MD013376, the affiliated project award from the Center for Equitable Artificial Intelligence ... | **A**: 11971221 and the Shenzhen Sci-Tech Fund No.
**B**: They also thank Jarle Sogn for communicating on Schur complement based preconditioners.
The work of M.
**C**: The work of J.
| BCA | BCA | CAB | BCA | Selection 1 |
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**: Hence, the convergence analysis given in Section 4 can be trivially extended to this case.
.
**B**: We note that the modified algorithm is mathematically equivalent to TDCD, albeit with a higher communication cost.
**C**: This modification would significantly increase the communication cost of the algorithm.
| CBA | CBA | ACB | CBA | Selection 1 |
Changxin Mo acknowledges support from the National Natural Science Foundation of China (Grant No. <|MaskedSetence|> <|MaskedSetence|> KJQN202200512), the Chongqing Talents Project (Grant No. cstc2022ycjh-bgzxm0040), and the Research Foundation of Chongqing Normal University (Grant No. 21XLB040), P. <|MaskedSetence|>... | **A**: 12201092), the Natural Science Foundation Project of CQ CSTC (Grant No.
**B**: R.
**C**: CSTB2022NSCQ-MSX0896), the Science and Technology Research Program of Chongqing Municipal Education Commission
(Grant No.
| ACB | ACB | ACB | CAB | Selection 2 |
Figure 5 compares our results with the ones of the representative methods including the current state-of-the-arts on the three benchmarks. <|MaskedSetence|> PConv [13] is suitable for irregular corruptions, but obvious artifacts can be observed in Figure 5 (c). <|MaskedSetence|> With the Recurrent Feature Reasoning m... | **A**: MED [14] attempts to correlate structure and texture generation, while the shared generator is inadequate for generating sharp edges and clear textures (e.g., facades in Figure 5).
.
**B**: It can be seen, as a classical patch-based method, PatchMatch [2] fails in handling large holes.
**C**: DeepFilllv2 [36] ... | BCA | BCA | BCA | BCA | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> Consequently, it is unlikely that a simple planner (e.g., one unrolling independent sequences of subgoals [9]) will either produce an informative outcome, could be easily improved using only local changes via gradient descent [30], or cross-entropy method (CEM) [29, 33, 34]. Existi... | **A**: However, it uses a predefined (not learned) predicate function as a subgoal generator, limiting applicability to the problems with available high-quality heuristics.
**B**:
Assuming that this problem was solved, a generated subgoal still remains to be assessed.
**C**: The exact evaluation may, in general, req... | BCA | BCA | CAB | BCA | Selection 4 |
In order to verify whether our method has the ability to cope with the character substitution problem, we also build our own dataset. This specially designed dataset is collected from informal news reports and blogs. We label the Named Entities in raw materials first and then create their substitution forms by using si... | **A**: In this case, the dataset is made of pairs of original entities and their character substitution forms.
**B**: This dataset consists of 15780 sentences in total and is going to test our method in an extreme language environment.
**C**:
We also conduct experiments on three general NER datasets, Chinese Resume ... | ABC | CAB | ABC | ABC | Selection 1 |
<|MaskedSetence|> The shared modules learn shared features from multiple tasks. <|MaskedSetence|> On the other hand, task-specific modules learn features that are specific to a certain task. Compared with shared modules, task-specific modules are usually much smaller and thus less likely to suffer from overfitting ca... | **A**: Since the shared modules can learn from many tasks, they can be sufficiently trained and can generalize better, which is particularly meaningful for low-resource scenarios.
**B**: The robustness of shared modules and the flexibility of task-specific modules makes modular architectures suitable for learning diff... | CAB | CAB | CAB | CAB | Selection 1 |
The templates are intended to approximate the final look and page length of the articles/papers. <|MaskedSetence|> <|MaskedSetence|> The structure of the LaTeXfiles, as designed, enable easy conversion to XML for the composition systems used by the IEEE’s outsource vendors. <|MaskedSetence|> Have you looked at your ... | **A**: Therefore, they are NOT intended to be the final produced work that is displayed in print or on IEEEXplore®.
**B**: They will help to give the authors an approximation of the number of pages that will be in the final version.
**C**: The XML files are used to produce the final print/IEEEXplore® pdf and then con... | ABC | ABC | BCA | ABC | Selection 2 |
Because there are only three trust questions, the first principal component summarizes most of the information from the trust questionnaire. It places positive weight on the question that involves trust and negative weights on two questions that suggest mistrust. <|MaskedSetence|> This agrees with the results of Glaes... | **A**: Perhaps surprisingly, this measure of trust is associated with a positive interaction on contribution costs in the baseline, which indicates that individuals who score highly on trust are less altruistic and more careful about where they direct effort in the baseline.
**B**: This sheds more light on information... | ACB | ACB | ACB | BCA | Selection 3 |
<|MaskedSetence|> However, it is worth noting that most of these models use simulated datasets for testing and training, we call this method simulated SISR. <|MaskedSetence|> <|MaskedSetence|> However, it is undeniable that the emergence of these methods has enriched and promoted the development of SISR. According t... | **A**: In other words, the low-resolution images used in this type of method are usually obtained by applying some fixed degradation modes to the high-resolution images.
**B**: This will affect the performance of the model in practical applications.
**C**: In recent years, the field of SISR has developed rapidly, and... | CAB | CAB | CAB | ABC | Selection 1 |
<|MaskedSetence|> This can be done by matching the patch distribution across scales [8, 25, 26, 29]. For blind super-resolution, Neural Knitwork core module is utilized with adjusted losses as illustrated in Figure 5. <|MaskedSetence|> However, it is possible to compute the cross-patch consistency loss as well as dis... | **A**:
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.
**B**: To enforce coherence, we apply spatially-aware supervision by downsampling the super-resolved image and computing the downsampling loss wi... | ACB | ACB | ACB | ACB | Selection 3 |
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. For logistic contextual bandits, the implementation of exact TS (i.e. the policy that draws its sample from the exact posterior) is infeasible due to the intract... | **A**: It is therefore necessary to sample from an approximation of the posterior to implement a TS-like approach.
**B**: Urteaga and Wiggins,, 2018)..
**C**: Dumitrascu et al., (2018) recently proposed an approximation based on Polya-Gamma augmentation (Polson et al.,, 2013; Windle et al.,, 2014) which has improved ... | ACB | ACB | ACB | ACB | Selection 4 |
Many criticisms have recently been raised against the improper use of statistical significance as the only measure to evaluate results in scientific publications [65]. <|MaskedSetence|> <|MaskedSetence|> Yet, regarding performance in terms of correct explanations, reported in Table 4, we observe the following: MemDi... | **A**: However, we also perform the Wilcoxon paired test over the 10-fold cross-validation results, focusing on MemDistilBERT and MANN and the difference between weak and strong supervision.
**B**: Strong supervision outperforms weak supervision (considering DistilBERT) regarding coverage (C) on A, CH, CR, LTD, TER; r... | ACB | ACB | ACB | ACB | Selection 3 |
However, the progress of sentiment dependency-based methods, such as the work by Zhang et al. (2019); Zhou et al. <|MaskedSetence|> <|MaskedSetence|> (2021a); Dai et al. <|MaskedSetence|> | **A**: (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..
**B**: (2020); Tian et al.
**C**: (2021); Li et al.
| BCA | CAB | BCA | BCA | Selection 1 |
<|MaskedSetence|> The first step, post-measurement normalization normalizes the measurement outcomes on each quantum bit (qubit) across data samples, thus removing the quantum error-induced distribution shift. Furthermore, we inject noise to the PQC training process by performing error gate insertion. <|MaskedSetence... | **A**: QuantumNAT comprises a three-stage pipeline.
**B**: The error gate types and probabilities are obtained from hardware-specific realistic quantum noise models provided by QC vendors.
**C**: Finally, post-measurement quantization is further proposed to reduce the precision of measurement outcomes from each qubit... | ABC | CBA | ABC | ABC | Selection 4 |
<|MaskedSetence|> For conventional tracking methods, they are synchronized with the global camera shutter, and thus their speeds are evaluated by a synchronous criterion (e.g., 25 frames per second and above can be considered as real-time). Since EDA works asynchronously, the synchronous criterion is not suitable for ... | **A**: The proposed EDA is evaluated on a PC with an Intel Core i7 CPU and an NVIDIA GTX 1080 GPU.
**B**: EDA runs at the average speed of 56.33K/31.72K EPS on the test sequences with/without the GPU support.
**C**: Instead, in event-based studies, the efficiency of event-based methods is commonly evaluated in terms ... | ACB | ABC | ACB | ACB | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> The teacher model is ResNet-50 pre-trained by MoCo.v2. ††{\dagger}† indicates using a momentum encoder as MoCo.v2. SSL denotes the InfoNCE loss. <|MaskedSetence|> H+AW denotes the Huber loss and angle-preserving loss in RKD.. | **A**:
TABLE IV: Unsupervised knowledge distillation.
**B**: Top-1 accuracy (%) under linear evaluation on STL-10.
**C**: KD denotes the knowledge distillation loss.
| BCA | ABC | ABC | ABC | Selection 2 |
<|MaskedSetence|> We compared it with state-of-the-art methods under different computation budgets in Table 6.
Our NAS method consistently outperforms existing techniques for tiny networks in terms of computation-accuracy trade-off. <|MaskedSetence|> With the extended search space, all our models are derived from the... | **A**: To show the advantage of our method, we conduct experiments on MobileNetV3 [23] space by extending it to support different r𝑟ritalic_r’s and w𝑤witalic_w’s.
**B**: Existing techniques usually need a scaling method to scale down the searched network and fit different budgets.
**C**: Therefore, we enable flexib... | ABC | ABC | ABC | ACB | Selection 1 |
<|MaskedSetence|> Unlike the conventional practice of constructing augmented graphs by hand, CGCL employs multiple GNN-based encoders to generate multiple contrastive views. <|MaskedSetence|> Graph encoders of CGCL learn the graph representations collaboratively, and enhance each other’s learning ability in an unsupe... | **A**: For a further theoretical analysis, we propose two quantitative metrics to measure the asymmetry and complementarity of the collaborative framework.
Extensive experiments substantiate the advantages of CGCL and underscores the potential of collaborative framework in the field of GCL..
**B**: In this study, we i... | CAB | BCA | BCA | BCA | Selection 4 |
<|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**: Our experiments were managed using https://neptune.ai.
**B**: PLG/2019/012498.
**C**:
The work of Piotr Miłoś was supported by the Polish National Science Center grant UMO-2017/26/E/ST6/00622.
| CBA | BAC | CBA | CBA | Selection 1 |
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. <|MaskedSetence|> The authors in [20] use CBFs to learn a provably correct neural network safety guard for kinematic ... | **A**: In [19], it is shown how safe and optimal reward functions can be obtained, and how these are related to CBFs.
**B**: In [23], it is shown how additive and multiplicative noise can be estimated online using Gaussian process regression for safe CBFs.
**C**: Imitation learning under safety constraints imposed by... | ACB | ABC | ABC | ABC | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Especially, when DCDFM reduces to DFM, our theoretical results are consistent with those under DFM. When DCDFM degenerates to DCSBM, our results also match classical results under DCSBM. Numerical results of both simulated and real-world networks show the advanta... | **A**: We build theoretical framework on consistent estimation for the proposed algorithm under DCDFM.
**B**: Benefited from the distribution-free property of DCDFM, our theoretical results under DCDFM are general.
**C**:
(b) To fit DCDFM, an efficient spectral clustering algorithm called nDFA is designed.
| CAB | CAB | CAB | CAB | Selection 2 |
End of preview. Expand in Data Studio
README.md exists but content is empty.
- Downloads last month
- 8