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**A**: The LGO generating set offers a variety of advantages**B**: Consequently, algorithms in the composition tree data structure, both in MAGMA and in GAP, store elements in classical groups as words in the LGO generators. Moreover, the LGO generators can be used directly to verify representations of classical groups... | CAB | ACB | BAC | CAB | Selection 2 |
**A**: We note that the idea of performing global static condensation goes back to the Variational Multiscale Finite Element Method–VMS [MR1660141, MR2300286]. Recently variations of the VMS**B**:
It is essential for the performing method that the static condensation is done efficiently**C**: The solutions of (22) dec... | ACB | ACB | CAB | ACB | Selection 3 |
**A**: Alg-CM uses an involved subroutine (far more complicated than ours given in Algorithm 1) to update the coordinates in each iteration, which accumulates the inaccuracy of coordinates. Even worse, this subroutine computes three angles and selects the smallest to decide how to proceed each time, and due to float is... | ABC | ABC | BAC | CBA | Selection 4 |
**A**: We trade-off this by debunking at single tweet level and let each tweet vote for the credibility of its event. We show the CreditScore measured over time in Figure 5(a)**B**: It can be seen that although the credibility of some tweets are low (rumor-related), averaging still makes the CreditScore of Munich shoot... | BCA | CAB | CBA | BAC | Selection 1 |
**A**: We should not rely on plateauing of the training loss or on the loss (logistic or exp or cross-entropy) evaluated on a validation data, as measures to decide when to stop**B**: We might improve the validation and test errors even when when the decrease in the training loss is tiny and even when the validation lo... | ABC | CAB | BAC | ACB | Selection 4 |
**A**: In contrast to mere sentiment features, this approach is more tailored rumor context (difference not evaluated in (liu2015real, )). We simplified and generalized the “dictionary” by keeping only a set of carefully curated negative words. We call them “debunking words” e.g., hoax, rumor or not true**B**: CrowdWis... | BCA | BAC | CBA | CBA | Selection 2 |
**A**: Multi-Criteria Learning. Our task is to minimize the global relevance loss function, which evaluates the overall training error, instead of assuming the independent loss function, that does not consider the correlation and overlap between models**B**: We adapted the L2R RankSVM [12]. The goal of RankSVM is learn... | ACB | BAC | ABC | CAB | Selection 3 |
**A**: The insulin intakes tend to be more in the evening, when basal insulin is used by most of the patients**B**: The only difference happens to patient 10 and 12 whose intakes are earlier at day.
Further, patient 12 takse approx**C**: 3 times the average insulin dose of others in the morning. | CBA | ABC | CBA | BCA | Selection 2 |
**A**: A quantitative comparison of results on independent test datasets was carried out to characterize how well our proposed network generalizes to unseen images**B**: The final outcome for the 2017 release of the SALICON dataset is therefore not reported in this work but our model results can be viewed on the public... | BCA | CBA | CAB | ACB | Selection 4 |
**A**:
We call a marking sequence σ𝜎\sigmaitalic_σ for a word α𝛼\alphaitalic_α block-extending, if every symbol that is marked except the first one has at least one block-extending occurrence**B**: We answer this question in the negative.**C**: This definition leads to the general combinatorial question of whether e... | CAB | ABC | BAC | ACB | Selection 4 |
**A**: Notable exceptions are the works of
Oh et al. (2017), Sodhani et al. (2019), Ha & Schmidhuber (2018), Holland et al**B**: Oh et al. (2017) use a model of rewards to augment model-free learning with good results on a number of Atari games. However, this method does not actually aim to model or predict future fram... | BAC | ACB | ABC | CBA | Selection 2 |
**A**: We present our hierarchical control design, which is simulated in a hybrid environment comprising MATLAB and CoppeliaSim**B**: In this section, we explore the autonomous locomotion mode transition of the Cricket robot**C**: This design facilitates the decision-making process when transitioning between the robot’... | CBA | ACB | BAC | ACB | Selection 3 |
**A**: For problems such as bin packing,**B**: While our work addresses issues similar to [24] and [29], in that trusted advice is related to consistency whereas untrusted advice is related to robustness, it differs in two significant aspects: First, our ideal objective is to identify an optimal
family of algorithms, a... | ABC | ACB | CAB | BAC | Selection 3 |
**A**: From the previous analysis, it is clear that useful information can be obtained from the study of those cases where our approach was not able to correctly predict a class**B**: In Figure 8 we exemplify each case with one subject from the test set, described in more detail below:
**C**: With this goal in mind, we... | CAB | CAB | ACB | BAC | Selection 3 |
**A**: Furthermore, to enhance the convergence performance when using more aggressive sparsification compressors (e.g., RBGS), we extend GMC to GMC+. We prove the convergence of GMC and GMC+ theoretically. Empirical results verify the superiority of global momentum and show that GMC and GMC+ can outperform other baseli... | CBA | BAC | CAB | BAC | Selection 3 |
**A**: Using cross-correlation would produce the same results and would not require flipping the kernels during visualization**B**: operation.**C**:
, where ∗*∗ is the convolution333We use convolution instead of cross-correlation only as a matter of compatibility with previous literature and computational frameworks | ACB | BCA | CBA | ABC | Selection 2 |
**A**:
Coverage is another factor which determines the performance of each UAV. As presented in Fig. 1 (c), the altitude of UAV plays an important role in coverage adjusting**B**: The higher altitude it is, the larger coverage size a UAV has. A large coverage size means a substantial opportunity of supporting more use... | CBA | BAC | ABC | ACB | Selection 3 |
**A**: italic_e . , in the experiment,**B**: and the simulation was run until around 220μ220μ220\upmu220 roman_μs**C**: Note that I~lev<1subscript~𝐼𝑙𝑒𝑣1\widetilde{I}_{lev}<1over~ start_ARG italic_I end_ARG start_POSTSUBSCRIPT italic_l italic_e italic_v end_POSTSUBSCRIPT < 1
at t=0𝑡0t=0italic_t = 0, because tle... | ABC | CBA | CAB | ABC | Selection 3 |
**A**: In table 1, Wilcoxon Sign-Ranked test was used to analyze the effect of Variance before applying Dropout (DQN) and after applying Dropout (Dropout methods DQN)**B**: There was a statistically significant decrease in Variance (14.72% between Gaussian Dropout and DQN, 48.89% between Variational Dropout and DQN). F... | BCA | CAB | ACB | ABC | Selection 1 |
**A**: Moreover, Figure 2 shows a high-level overview of the deep semantic segmentation pipeline, and where each of the categories mentioned in Figure 1 belong in the pipeline.**B**:
We group the semantic image segmentation literature into six different categories based on the nature of their contributions: architectu... | ABC | ACB | CAB | BAC | Selection 3 |
**A**: The evaluation is performed on all nine datasets, and results for different numbers of training examples are shown (increasing from left to right)**B**: The overall performance of each method is summarized in the last column.
For neural random forest imitation, a network architecture with 128128128128 neurons in... | CAB | CAB | ACB | BCA | Selection 4 |
**A**:
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.... | CBA | BAC | ACB | BCA | Selection 3 |
**A**: They are not suited to execute generic compressed models and are therefore not included in the following experiments.
**B**: Furthermore, in particular for the TPU, experimentation is often hindered due to limitations in the tool chain which is not flexible enough to support such optimizations**C**: While domain... | ACB | ABC | CBA | ABC | Selection 3 |
**A**: Moreover, we consider a generalization of the filling radius and also define a strong notion of filling radius which is akin to the so called maximal persistence in the realm of topological data analysis.**B**:
In this section, we recall the notions of spread and filling radius, as well as their relationship**C... | ABC | CAB | ACB | BCA | Selection 2 |
**A**: First, they were shown a video tutorial which discussed t-SNE itself and the main features of the tool (cf**B**: Study Design
Each participant took part individually (i.e., the study was performed asynchronously for each subject, in a silent room), using the same hardware, and the study was organized into four... | ABC | CBA | BCA | BAC | Selection 4 |
**A**: This paper develops and applies a test to known algorithms, including Grey Wolf Optimizer, Whale Optimization, and Harris Hawk, which fail this test. However, algorithms such as DE, GA, and PSO pass the test. This test is a useful tool to solve the centre-bias problem that has already been studied in [25].
**B**... | CAB | ACB | CBA | BAC | Selection 1 |
**A**: All codes are downloaded from the homepages of authors.
**B**: Besides, four GAE-based methods are used, including GAE [20], MGAE [21], GALA [32], and SDCN [31]**C**: Three deep clustering methods for general data, DEC [8] DFKM [9], and SpectralNet [7], also serve as an important baseline | ACB | BCA | CAB | CBA | Selection 4 |
**A**: In general, tests against Web servers have a higher applicability rate than the tests with Email or DNS servers, regardless of which technique was used (IPID or PMTUD). The number of Web servers is much larger than the others**B**: Furthermore, we find that when a Web server is not available (“N/A”), both Email ... | ABC | BCA | ACB | ABC | Selection 3 |
**A**: This design introduces variation in training inputs, which makes it harder to learn consistent context patterns. For this task, semisupervised learning techniques, such as self-labeled samples, may help**B**:
The current design of the context-based network relies on labeled data because the odor samples for a g... | ABC | CBA | CBA | BAC | Selection 4 |
**A**:
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]**... | ACB | BCA | CAB | BCA | Selection 1 |
**A**: We hypothesize that degrading performance on the train set helps forget linguistic biases, which in turn helps accuracy on VQA-CPv2’s test set but hurts accuracy on VQAv2’s val set.
**B**: As shown in Table 1, the baseline method has the highest training results, while the other methods cause 6.0−14.0%6.0percent... | BCA | CBA | BCA | ACB | Selection 2 |
**A**:
Prior research on the readability based on small corpora of privacy policies had found that they were generally hard to understand for the average internet user**B**: Our large scale analysis using the Flesch-Kincaid readability metric was consistent with prior findings**C**: We found that on average about 14.8... | BCA | BAC | ABC | BCA | Selection 3 |
**A**: The data set is a binary classification problem and contains 165 diseased and 138 healthy patients.
Hence, we choose micro-average to weight the importance of the largest class, even though the impact is low because of the lack of any significant imbalance for the dependent variable**B**: The dice glyphs visible... | CBA | BAC | BCA | ABC | Selection 3 |
**A**: FewRel is a relation classification dataset with 65/5/10 tasks for meta-training/meta-validation/meta-testing.**B**:
In Experiment I: Text Classification, we use FewRel [Han et al., 2018] and Amazon [He and McAuley, 2016]**C**: They are datasets for 5-way 5-shot classification, which means 5 classes are randoml... | ABC | CBA | CBA | CAB | Selection 4 |
**A**:
Activated Subarray with Limited DREs: As shown in Fig. 1, given a certain azimuth angle, there are limited DREs that can be activated**B**: If an inappropriate subarray is activated, the beam angle may go beyond the radiation range of certain subarray elements, degrading the beam gain and SE.**C**: Due to the d... | ABC | BAC | ABC | ACB | Selection 4 |
**A**: This will be bootstrapped to the multi-color case in later sections**B**: We**C**: Note that the 1111-color case with the completeness requirement is not very interesting, and also not useful for the general case: completeness states that every node on
the left must be connected, via the unique edge relation, to... | ABC | BAC | BAC | ACB | Selection 4 |
**A**: The key to our analysis is a mean-field perspective, which allows us to associate the evolution of a finite-dimensional parameter with its limiting counterpart over an infinite-dimensional Wasserstein space (Villani, 2003, 2008; Ambrosio et al., 2008; Ambrosio and Gigli, 2013)**B**: The evolution of such a popul... | ABC | ACB | CBA | CBA | Selection 2 |
**A**: Yu et al. (2018) suggest that skip connections are “shallow” themselves, and only fuse by simple, one-step operations, and therefore Yu et al. (2018) augment standard architectures with deeper aggregation to better fuse information across layers to improve recognition and resolution**B**: (2018) propose a multi-... | ACB | CBA | BCA | BAC | Selection 1 |
**A**: pre-spectral space**B**: We are going to exhibit
a surjective map f𝑓fitalic_f from Y𝑌Yitalic_Y to the logical sum X𝑋Xitalic_X of**C**: Recall that ⟨Y,τY,𝒦∘(Y)⟩𝑌subscriptτ𝑌superscript𝒦𝑌\langle Y,\uptau_{Y},\mathcal{K}^{\circ}\!\left(Y\right)\rangle⟨ italic_Y , roman_τ start_POSTSUBSCRIPT italic_Y end_POS... | ACB | CAB | CAB | BAC | Selection 1 |
**A**: The rectification results on the synthesized and real-world scenarios also demonstrated our approach’s superiority compared with the state-of-the-art methods**B**: Like most of the assumptions in the other works [21, 23, 8, 11, 12, 14], our approach has two main limitations to extend to more complicated applicat... | CBA | BCA | CBA | CBA | Selection 2 |
**A**: We adopt the linear learning rate decay strategy as default in the Transformers framework.
Table 5 shows the test accuracy results of the methods with different batch sizes**B**: We don’t use training tricks such as warm-up [7]**C**: SNGM achieves the best performance for almost all batch size settings. | CBA | BAC | CBA | CBA | Selection 2 |
**A**: We use the suffixes BB and Poly to distinguish these settings. For example, 2S-Sup-BB is the previously defined 2S-Sup in the black-box model.
**B**: The most general way to represent the scenario distribution 𝒟𝒟\mathcal{D}caligraphic_D is the black-box model [24, 12, 22, 19, 25], where we have access to an or... | CBA | BAC | CAB | BAC | Selection 3 |
**A**: Then we substitute this upper bound into the Lyapunov function difference inequality of the consensus error, and obtain the estimated convergence rate of mean square consensus (Lemma 3.3)**B**: (Lemma 3.1).
To this end, we estimate the upper bound of the mean square increasing rate of the local optimizers’ state... | BCA | BCA | ABC | BAC | Selection 4 |
**A**: We use the US Census data [29], eliminate the tuples with missing values, and randomly select 40,152 tuples with eight attributes. The QI attributes are gender, age, relationship, marital status, race, education, and hours per week, and the sensitive attribute is salary. Table 1 describes the attributes in detai... | BCA | BAC | BCA | CBA | Selection 4 |
**A**: Bells and Whistles. MaskRCNN-ResNet50 is used as baseline and it achieves 53.2 mAP. For PointRend, we follow the same setting as Kirillov et al. (2020) except for extracting both coarse and fine-grained features from the P2-P5 levels of FPN, rather than only P2 described in the paper. Surprisingly, PointRend yie... | CBA | ABC | CAB | ACB | Selection 4 |
**A**: For the significance of this conjecture we refer to the original paper [FK], and to Kalai’s blog [K] (embedded in Tao’s blog) which reports on all significant results concerning the conjecture**B**: [KKLMS] establishes a weaker version of the conjecture**C**: Its introduction is also a good source of information... | ABC | CBA | BCA | BAC | Selection 1 |
**A**:
However, all of the aforementioned empirical and theoretical works on RL with function approximation assume the environment is stationary, which is insufficient to model problems with time-varying dynamics. For example, consider online advertising**B**: The instantaneous reward is the payoff when viewers are re... | CBA | CAB | ABC | BAC | Selection 3 |
**A**: The rising attention of fake news in the local scene has motivated various research including studies on the perceptions and motivations of fake news sharing (Chen et al., 2015) and responses to fake news (Edson C Tandoc et al., 2020). Although there are parallels between these studies and ours, we want to highl... | ABC | CBA | CAB | ABC | Selection 2 |
**A**: Overall, DAN exhibits significantly better performance than GCN, GAT, or their combination. The decentralized attention, which considers neighbors as queries, consistently outperforms the centralized GAT across varying entity degrees.**B**:
The results on the ZH-EN dataset are depicted in Figure 7. For entities... | ABC | CAB | BAC | ACB | Selection 2 |
**A**: Recall that we have ”Common” modules and ”VDM-specific” models according to Tab. II**B**: Common modules are used for policy optimization rather than exploration, and all compared methods use the same common modules and not be tuned. ”VDM-specific” modules include hyper-parameters for the proposed VDM, and these... | CAB | BAC | ABC | BCA | Selection 4 |
**A**: The observations made in 2D remain valid**B**: However, Floater-Hormann becomes indistinguishable from 5thsuperscript5𝑡ℎ5^{th}5 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT-order splines.
Further, when considering the amount of coefficients/nodes required to determine the interpolant, plotted in... | CBA | CAB | CAB | ABC | Selection 4 |
**A**: the disentangled factors) and correlated components Z𝑍Zitalic_Z, a.k.a as nuisance variables, which encode the details information not stored in the independent components. A series of works starting from [beta] aims to achieve that via regularizing the models by up-weighting certain terms in the ELBO formulati... | CBA | ABC | CAB | BCA | Selection 4 |
**A**: In short, the direction of current, which is the flow of electricity, is determined only by the height of the potential, not by the structure or shape of the circuit.**B**:
Exploration based on previous experiments and graph theory found errors in structural computers with electricity as a medium**C**: The caus... | CAB | ABC | ACB | ACB | Selection 1 |
**A**: There has been extensive study about a family of polynomial maps defined through a parameter a∈𝔽𝑎𝔽a\in\mathbb{F}italic_a ∈ blackboard_F over finite fields**B**: Conditions for such families of maps to define a permutation of the field 𝔽𝔽\mathbb{F}blackboard_F are well studied and established for special cla... | BCA | ACB | BAC | ABC | Selection 2 |
**A**: Its high FPR in view selection appeared to negatively influence its test accuracy, as there was generally at least one sparser model with better accuracy in both our simulations and real data examples. Although nonnegative ridge regression shows that the nonnegativy constrains alone already cause many coefficien... | CBA | BCA | ABC | ACB | Selection 1 |
**A**: The normal dependency pattern is represented by the expected value of a variable given the values of its relevant variables, while the observed value of the variable along with the values of its relevant variables constitutes the observed pattern. This comparison facilitates a comprehensive understanding of the ... | BCA | BCA | BCA | CAB | Selection 4 |
**A**:
Comparison with Amani & Thrampoulidis [2021] While the authors in Amani & Thrampoulidis [2021] also extend the algorithms of Faury et al**B**: They model various click-types for the same advertisement (action) via the multinomial distribution. further, they consider actions played at each round to be non-combin... | CBA | BCA | ACB | CBA | Selection 3 |
**A**: Following FPN, some methods are proposed to further improve the architecture for higher efficiency and better accuracy, such as PANet [25], NAS-FPN [12], BiFPN [34]. Our proposed cross-scale graph pyramid (xGPN) adopts the idea of FPN and builds a pyramid of video features in the temporal domain instead of image... | ACB | ACB | CAB | CBA | Selection 4 |
**A**: The color-encoding diverges from purple to green for negative to positive difference.
In case the K-means clustering functionality is active, we use bar charts to depict the distribution of instances in the 100 individual cells (see VisEvol: Visual Analytics to Support Hyperparameter Search through Evolutionary ... | BAC | ACB | BCA | BAC | Selection 3 |
**A**: The paper is organized as follows**B**: The decentralized state-dependent Markov matrix synthesis (DSMC) algorithm is introduced in Section III.
Section IV introduces the probabilistic swarm guidance problem formulation, and presents numerical simulations of swarms converging to desired distributions. The paper ... | ACB | BCA | CBA | BAC | Selection 1 |
**A**: Moreover, for general non-rigid settings learning these basis functions has also been proposed [43].
A wide variety of extensions to make functional maps more robust or more flexible have been developed. This includes orientation-preservation [56], image co-segmentation [75], denoising [23, 55], partiality [58],... | BAC | CBA | CAB | CBA | Selection 3 |
**A**: Both graph classes are characterized very similarly in [18], and we extended the simpler characterization of path graphs in [1] to include directed path graphs as well; this result can be of interest itself**B**: Thus, now these two graph classes can be recognized in the same way both theoretically and algorithm... | ABC | BCA | ACB | CAB | Selection 2 |
**A**: For the four datasets, the true labels are suggested by the original authors, and they are regarded as the “ground truth” to investigate the performances of Mixed-SLIM methods in this paper.**B**: The four datasets can be downloaded from
http://www-personal.umich.edu/~mejn/netdata/**C**: In this section, four re... | BAC | ACB | CBA | CAB | Selection 3 |
**A**: See, e.g., Udriste (1994); Ferreira and Oliveira (2002); Absil et al. (2009); Ring and Wirth (2012); Bonnabel (2013); Zhang and Sra (2016); Zhang et al. (2016); Liu et al**B**: (2018); Boumal et al. (2018); Bécigneul and Ganea (2018); Zhang and Sra (2018); Sato et al. (2019); Zhou et al. (2019); Weber and Sra (2... | BCA | BCA | ACB | ABC | Selection 3 |
**A**: The results of MetaVIM is superior to CoLight on each scenario and configuration, resulting mean 43 improvement**B**: 4) The neighbors’ information is modeled in CoLight and it performs well.It indicates modeling neighbors is critical for the coordination**C**: Compared to Colight, MetaVIM proposes an intrinsic ... | BAC | CBA | CAB | ACB | Selection 1 |
**A**: The algorithm builds on the concept of a profile set, which serves as an approximation of the items that are expected to appear in the sequence, given the frequency predictions**B**:
We first present and analyze an algorithm called ProfilePacking, that achieves optimal consistency, and is also efficient if the ... | CAB | BCA | CBA | BAC | Selection 4 |
**A**: Throughout all experiments, we train models with Chamfer distance. We also set λ=0.0001𝜆0.0001\lambda=0.0001italic_λ = 0.0001. We denote LoCondA-HC when HyperCloud is used as the autoencoder architecture (Part A in Fig. 1) and LoCondA-HF for the HyperFlow version.
**B**: In this section, we describe the experim... | CAB | BAC | CBA | ACB | Selection 1 |
**A**: As a result, we get a common saddle point problem that includes both primal and dual variables. After that, we employ the Mirror-Prox algorithm and bound the norms of dual variables at solution to assist the theoretical analysis. Finally, we demonstrate the effectiveness of our approach on the problem of computi... | BAC | CAB | CBA | BCA | Selection 3 |
**A**: In this section we present some experimental results to reinforce
Conjecture 14**B**: In the first part, we focus on the complete analysis of small graphs, that is: graphs of at most 9 nodes. In the second part, we analyze larger families of graphs by random sampling instances.**C**: We proceed by trying to find... | ACB | BCA | CAB | ABC | Selection 1 |
**A**: A major part of this paper, all of Sections 3 and 4, is devoted to adapt it to handle the k𝑘kitalic_k-partite structure of colorful intersection patterns.**B**: This technique, which we briefly outline here, was specifically designed for complete intersection patterns**C**:
The proof of Theorem 2.1 is quite in... | CBA | BCA | ABC | ABC | Selection 1 |
**A**: Ground truth versus per class predicted probability.
In Section 4.1, we explain that the data space view presents the predicted probabilities for the ground truth class**B**: Although this idea appears valuable and straightforward for binary classification problems, it will not scale well with multiclass problem... | ACB | BAC | BCA | CBA | Selection 1 |
**A**: Using Bayesian optimization-based tuning for enhanced performance has been further demonstrated for cascade controllers of linear axis drives, where data-driven performance metrics have been used to specifically increase the traversal time and the tracking accuracy while reducing vibrations in the systems [11, 1... | CAB | ABC | BCA | CBA | Selection 4 |
**A**: Rather, it is ideal if the methods can generalize without being tuned on the test distribution and we study this ability by comparing models selected through varying tuning distributions**B**: To control the tuning distribution, we define a generalization of the mean per group accuracy (MPG) metric, that can int... | BCA | ACB | ABC | ACB | Selection 1 |
**A**: In [127], they estimate the general visual attention and human’s gaze directions in images at the same time. Kellnhofer et al. propose a temporal 3D gaze network [43]. They use bi-LSTM [128] to process a sequence of 7 frames to estimate not only gaze directionS but also gaze uncertainty.**B**: Also, visual salie... | BCA | ACB | CBA | CAB | Selection 3 |
**A**: Experimental results are carried out on Real-world Masked Face Recognition Dataset (RMFRD) and Simulated Masked Face Recognition Dataset (SMFRD) presented in wang2020masked . We start by localizing the mask region**B**: To do so, we apply a cropping filter in order to obtain only the informative regions of the m... | BCA | CAB | ABC | CBA | Selection 3 |
**A**: Relatedly, refer to Das and Pfenning [DP20a] for a proof of type safety for a session type system with arithmetic refinements**B**: Now, we are ready to prove termination**C**: In contrast to the termination proof for base SAX [DPP20], we explicitly construct a model of SAX in sets of terminating configurations,... | BCA | ABC | ABC | BAC | Selection 4 |
**A**: [3]-I and [3]-II represent the first scheme and the second scheme in [3], respectively. Compared with [3]-II, the main advantage of FairCMS-II is that it solves the problem that users can escape traceability by generating two different fingerprints, as discussed in the third last paragraph of Section V-A.
**B**:... | CAB | BCA | BAC | CBA | Selection 4 |
**A**: As a consequence, we can model only these beneficial interactions with the next interaction aggregation component**B**: To check the necessity of this component, we remove this components, so that all pair of feature interactions are modeled as a fully-connected graph.**C**:
GraphFM(-S): interaction selection i... | BAC | BCA | BAC | ACB | Selection 2 |
**A**: We note that the LBTFW-GSC algorithm from Dvurechensky et al**B**: [2020]. In the next section, we provide improved convergence guarantees for various cases of interest for this algorithm, which we refer to as the Frank-Wolfe algorithm with Backtrack (B-FW) for simplicity.
**C**: [2022] is in essence the Frank-W... | CAB | ACB | ABC | CAB | Selection 2 |
**A**: Nevertheless, we show how to set parameters so that putting on hold DFS over large trees increases the number of passes only by a poly1/εpoly1𝜀\operatorname{poly}1/\varepsilonroman_poly 1 / italic_ε factor.
**B**: Note that pausing DFS execution of some search trees increases the time required to explore the e... | ABC | BAC | CBA | BCA | Selection 3 |
**A**: In the second part of this paper, we propose a broadcast-like CPP algorithm (B-CPP) that allows for asynchronous updates of the agents: at every iteration of the algorithm, only a subset of the agents wake up to perform prescribed updates**B**: Thus, B-CPP is more flexible, and due to its broadcast nature, it ca... | CBA | ACB | ACB | ABC | Selection 4 |
**A**: We make a detailed comparison with them in Appendix C. Due to the fact that we consider a personalized setting, we can have a significant gain in communications. For example, when λ=0𝜆0\lambda=0italic_λ = 0 or small enough in (1) the importance of local models increases and we may communicate less frequently.
W... | CBA | BAC | BCA | CAB | Selection 1 |
**A**: multi-agent problem. These tools are amenable to scaling approaches; including utilizing reinforcement learning, function approximation, and online solution solvers, however we leave this to future work.
**B**: We provide a tractable approach to select from the space of (C)CEs (MG), and a novel training framewor... | CAB | CBA | BCA | BAC | Selection 2 |
**A**:
Differential privacy essentially provides the optimal asymptotic generalization guarantees given adaptive queries (Hardt and Ullman, 2014; Steinke and Ullman, 2015)**B**: However, its optimality is for worst-case adaptive queries, and the guarantees that it offers only beat the naive intervention—of splitting a... | CBA | ABC | BAC | BAC | Selection 2 |
**A**: We use reduction steps inspired by the kernelization algorithms [12, 46] for Feedback Vertex Set to bound the size of 𝖺𝗇𝗍𝗅𝖾𝗋𝖺𝗇𝗍𝗅𝖾𝗋\mathsf{antler}sansserif_antler in the size of 𝗁𝖾𝖺𝖽𝗁𝖾𝖺𝖽\mathsf{head}sansserif_head, by analyzing an intermediate structure called feedback vertex cut**B**:
Our al... | ACB | CAB | BAC | ACB | Selection 3 |
**A**: Differently, a few methods [2, 190, 147] attempt to address the unreasonable occlusion when it occurs. Specifically, they first estimate the relative depth relation between the foreground object and the surrounding background objects**B**: Then, they remove the occluded part of foreground object. In this way, th... | CAB | ABC | BCA | CBA | Selection 3 |
**A**: Transfer learning: Firstly, it can serve as an ideal testbed for transfer learning algorithms, including meta-learning [5], AutoML [23], and transfer learning on spatio-temporal graphs under homogeneous or heterogeneous representations**B**: In the field of urban computing, it is highly probable that the knowled... | ACB | CAB | CAB | ABC | Selection 4 |
**A**: The benefit of working with models that are built upon or include a point predictor is that one also gets a direct estimate of the response variable. Since this is important in many situations, the R2superscript𝑅2R^{2}italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT-coefficients are reported (as noted in th... | CAB | BCA | CBA | BCA | Selection 3 |
**A**: We can further differentiate Bar(new) and Bar(cont), representing respectively the beginning of a new bar and a continuation of the current bar and always have one of them before a Sub-bar token. This way, the tokens would always occur in a group of four for MIDI scores.
For MIDI performances, six tokens would b... | BCA | BAC | ACB | CBA | Selection 4 |
**A**: This description draws a comparison e.g**B**: [10] for a survey), where the colors of any two adjacent vertices have to differ by at least k𝑘kitalic_k and the colors of any two vertices within distance 2222 have to be distinct.
**C**: to L(k,1)𝐿𝑘1L(k,1)italic_L ( italic_k , 1 )-labeling problem (see e.g | BAC | ABC | ACB | CAB | Selection 3 |
**A**: Based on JSCC, an image transmission system, integrating channel output feedback, can improve image reconstruction[15]. Similar to text transmission, IoT applications for image transmission have been carried out**B**:
Recently, there are also investigations on semantic communications for other transmission cont... | ACB | BCA | BAC | ACB | Selection 3 |
**A**: Then, we randomly label 10% of points in each class for the sampled input point clouds. The final predictions will be back-projected to the original point clouds**B**: Therefore, only 10% of the network input training data and only 0.4% of the original point cloud data are labeled. We also perform experiments wi... | CAB | BCA | CBA | BAC | Selection 2 |
**A**: Table 4 shows more depth estimation results on KITTI val set via comparing the enhanced baseline and our method. Specifically, we evaluate the depth estimation by computing Scale Invariant Logarithmic (SILog) error, squared Relative (sqRel) error, absolute Relative (absRel) error, and Root Mean Squared Error of ... | ACB | BCA | ABC | CBA | Selection 1 |
**A**: FPNS (Node) rectifies false detections by measuring attributes of the text segments in local graph structures and upgrades GCNs to a multiple-task network rather than only linkage reasoning, modifications which support each other**B**: This explains why the overall performance is often further improved when both... | ABC | BCA | ABC | ACB | Selection 4 |
**A**: A hash table is an effective method for collecting the statistics of IP addresses Sanders2015HS . It uses a hash function to compute a hash codes for an array of buckets with the statistical results**B**: The hash function assigns each key to a unique bucket for each IP address. Unfortunately, the hash function ... | CAB | ABC | CBA | CBA | Selection 2 |
**A**: KKT system or saddle point system**B**: Usually, D𝐷Ditalic_D is assumed to be a symmetric and semi-positive definite**C**: In this paper, we only make some assumptions
that can guarantee the invertibility of 𝒜𝒜\mathcal{A}caligraphic_A. We assume that A𝐴Aitalic_A and the Schur complement Schur(𝒜)Schur𝒜\mbo... | ABC | BAC | CBA | CBA | Selection 1 |
**A**: For each Q𝑄Qitalic_Q and K𝐾Kitalic_K, we let TDCD train for 5,000 iterations for CIFAR-10, 10,000 iterations for MIMIC-III, and 4,000 iterations for ModelNet40, and pick the learning rate with the lowest training loss.**B**:
In each experiment, for each value of Q𝑄Qitalic_Q, we choose the learning rate using... | ACB | BCA | CAB | BAC | Selection 3 |
**A**: Given the significance of pseudospectra in solving matrix problems, we aim to extend this tool to tensors based on the theoretical analysis in Subsection 4.1.**B**:
The study of spectra and pseudospectra in matrix cases indicates that while eigenvalues are successful tools for solving mathematical problems in v... | BAC | CAB | BAC | BAC | Selection 2 |
**A**: Correspondingly, a two-branch discriminator is developed to estimate the performance of this generation, which supervises the model to synthesize realistic pixels and sharp edges simultaneously for global optimization. In addition, we introduce a novel Bi-directional Gated Feature Fusion (Bi-GFF) module to integ... | CBA | BAC | ABC | BAC | Selection 1 |
**A**: The concept of BEC was first introduced by Elias in 1955 InfThe **B**: Together with the binary symmetric channel (BSC), they are frequently used in coding theory and information theory because they are among the simplest channel models, and many problems in communication theory can be reduced to problems in a B... | BAC | CBA | ABC | BCA | Selection 4 |
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