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**A**: To aid the exposition and analysis, Algorithm 3 refers to several subroutines, namely Algorithms 4–7**B**: In an implementation the code for the Algorithms 4–7 would be inserted into Algorithm 3 in the lines where they are called**C**: We present them as subroutines here to improve the readability of Algorithm 3... | ACB | BCA | ABC | BCA | Selection 3 |
**A**: It is hard to approximate such problem in its full generality using numerical methods, in particular because of the low regularity of the solution and its multiscale behavior**B**: Most convergent proofs either assume extra regularity or special properties of the coefficients [AHPV, MR3050916, MR2306414, MR12862... | CAB | ABC | BCA | ACB | Selection 2 |
**A**: These coordinates are computed somehow and their true values can differ from their values stored in the computer**B**: 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,... | BCA | BAC | CBA | BAC | Selection 1 |
**A**: Ma et al. [19] used Recurrent Neural Networks for rumor detection, they batch tweets into time intervals and model the time series as a RNN sequence. Without any other handcrafted features, they got almost 90% accuracy for events reported in Snope.com**B**:
Most relevant for our work is the work presented in [2... | BAC | ABC | ABC | ACB | Selection 1 |
**A**: Unlike the case of squared loss, the result for exponential loss are independent of initialization and with only mild conditions on the step size. Here again, we see the asymptotic nature of exponential loss on separable data nullifying the initialization effects thereby making the analysis simpler compared to s... | ABC | BCA | ABC | CBA | Selection 4 |
**A**: Thus, a mechanism for carefully considering the ‘vote’ for individual tweets is required. In this work, we overcome the restrictions of traditional text representation methods (e.g., bag of words) in handling short text by learning low-dimensional tweet embeddings. In this way, we achieve a rich hidden semantic ... | ABC | CBA | CAB | ABC | Selection 3 |
**A**: We compare the result of the cascaded model with non-cascaded logistic regression. The results are shown in Table 3-bottom, showing that our cascaded model, with features inherited from the performance of SVM in previous task, substantially improves the single model. However, the overall modest results show the ... | BCA | BCA | CBA | ABC | Selection 3 |
**A**: The insulin intakes tend to be more in the evening, when basal insulin is used by most of the patients**B**: 3 times the average insulin dose of others in the morning.**C**: The only difference happens to patient 10 and 12 whose intakes are earlier at day.
Further, patient 12 takse approx | BAC | CAB | BCA | ACB | Selection 4 |
**A**: To assess the predictive performance for eye tracking measurements, the MIT saliency benchmark Bylinskii et al. (2015) is commonly used to compare model results on two test datasets with respect to prior work. Final scores can then be submitted on a public leaderboard to allow fair model ranking on eight evaluat... | BAC | BCA | ABC | CBA | Selection 3 |
**A**: Our main tool was to relate the locality number to the graph parameters cutwidth and pathwidth via suitable reductions**B**: In this work, we have answered several open questions about the string parameter of the locality number**C**: As an additional result, our reductions also pointed out an interesting relati... | BAC | CBA | ACB | CBA | Selection 1 |
**A**: in fewer step than 100k) with more directed exploration policies. In Figure 9 in the Appendix we present the cumulative distribution plot for the (first) point during learning when the maximum score for the run was achieved in the main training loop of Algorithm 1.
**B**: In some games, good policies could be le... | CBA | CAB | CAB | ACB | Selection 1 |
**A**: 9. The blue line illustrates the total energy consumed (in rolling locomotion mode), while the green line represents the ongoing cumulative energy consumption of the rear legs, indicating it did not exceed the threshold values set by the rear body climbing gait.**B**:
Figure 10: The Cricket robot tackles a step... | ABC | ACB | CAB | ACB | Selection 3 |
**A**: Note that finding a Pareto-optimal family of algorithms presupposes that the exact competitiveness of the online problem with no advice is known**B**: For problems such as bin packing,**C**: While our work addresses issues similar to [24] and [29], in that trusted advice is related to consistency whereas untrust... | ABC | ABC | BCA | CAB | Selection 3 |
**A**:
The dataset used in this task had the advantage of being publicly available and played an important role in determining how the use of language is related to the EDD problem**B**: Beyond the potential “noise” introduced by the method to assess the “depressed”/“non-depressed” condition, it lacks some extra infor... | ACB | CBA | CAB | CAB | Selection 1 |
**A**: We can find that after a sufficient number of iterations, the parameter in DGC (w/o mfm) can only oscillate within a relatively large neighborhood of the optimal point**B**: Compared with DGC (w/o mfm), the parameter in GMC converges closer to the optimal point and then remains stable.
Figure 2(a) shows the dist... | BCA | BCA | CBA | ABC | Selection 4 |
**A**: operation.**B**:
, where ∗*∗ is the convolution333We use convolution instead of cross-correlation only as a matter of compatibility with previous literature and computational frameworks**C**: Using cross-correlation would produce the same results and would not require flipping the kernels during visualization | CBA | BCA | CBA | CAB | Selection 4 |
**A**: b) The coverage of a UAV depends on its altitude and field angle. c) There are two kinds of links between users, and the link supported by UAV is better. **B**: a) The UAV ad-hoc network supports user communications**C**:
Figure 1: The topological structure of UAV ad-hoc networks | ACB | ACB | CBA | ABC | Selection 3 |
**A**: italic_e . , Σn=1Nnϕn(𝐫)=Σn=1Nnψn(𝐫)=1subscript𝑁𝑛𝑛1Σsubscriptitalic-ϕ𝑛𝐫subscript𝑁𝑛𝑛1Σsubscript𝜓𝑛𝐫1\overset{N_{n}}{\underset{n=1}{\Sigma}}\phi_{n}(\textbf{$\mathbf{r}$})=%**B**: nodal locations), is equal to one**C**: This property also hold for the
pyramid side functions, i.e.,formulae-sequence�... | CAB | CBA | CBA | ACB | Selection 1 |
**A**: Our findings indicate that the Dropout-DQN method is effective in decreasing both variance and overestimation**B**: In this study, we proposed and experimentally analyzed the benefits of incorporating the Dropout technique into the DQN algorithm to stabilize training, enhance performance, and reduce variance**C*... | ACB | BAC | CBA | ABC | Selection 2 |
**A**: However, unlabeled and weakly-labeled images can be collected in large amounts in a relatively fast and cheap manner**B**: As shown in Figure 2, varying levels of supervision are possible when training deep segmentation models, from pixel-wise annotations (supervised learning) and image-level and bounding box an... | CAB | BCA | CBA | BAC | Selection 2 |
**A**:
NRFI uniform and NRFI dynamic sample the number of decision trees for each data point uniformly, respectively, optimized via automatic confidence distribution (see Section 4.1.4)**B**: The confidence distributions for both sampling modes are visualized in the second column of Figure 5**C**: Additionally, sampli... | BCA | CBA | ABC | BCA | Selection 3 |
**A**: As is shown subsequently, solving such a subproblem corresponds to one iteration of infinite-dimensional mirror descent (Nemirovsky and Yudin, 1983) or dual averaging (Xiao, 2010), where the action-value function plays the role of the gradient. To encourage exploration, we explicitly incorporate a bonus function... | ACB | ACB | CBA | BCA | Selection 3 |
**A**: (2016), the work of Wu et al. (2018b) accumulates weight changes to low-precision weights instead of full-precision weights.**B**: In Wu et al**C**: (2018b), weights, activations, weight gradients, and activation gradients are subject to customized quantization schemes that allow for variable bit widths and faci... | ABC | CBA | CAB | ACB | Selection 3 |
**A**: We alert readers that, in this paper, the same notation can mean either a simplicial complex itself or its geometric realization, interchangeably**B**: The precise meaning will be made clear in each context.
**C**: In this section we cover the background needed for proving our main results | ACB | ACB | CBA | BCA | Selection 4 |
**A**: She decides, then, to use t-SNE to explore the Breast Cancer Wisconsin data set which she downloaded from the UCI machine learning repository [58]**B**: The data set contains measurements for 699 breast cancer cases, labeled into benign or malignant cancer**C**: The nine dimensions included in this data set are ... | CBA | ABC | ACB | CBA | Selection 2 |
**A**: This thorough examination ensures a robust evaluation of the proposed method and its performance relative to existing approaches.
**B**: Furthermore, authors ought to undertake a comprehensive analysis of results, addressing key aspects such as search phase identification (balancing exploration and exploitation)... | BAC | CBA | ACB | CAB | Selection 2 |
**A**:
Figure 2: Visualization of the learning process of AdaGAE on USPS**B**: Figure 2(b)-2(i) show the embedding learned by AdaGAE at the i𝑖iitalic_i-th epoch, while the raw features and the final results are shown in Figure 2(a) and 2(j), respectively**C**: An epoch corresponds to an update of the graph. | ABC | CAB | BCA | CAB | Selection 1 |
**A**: In such case the evaluation result is similar as when a tested network does not enforce ingress filtering: our PMTUD packets will be blocked by the firewall, but not because they originate from an IP address that belongs to the tested network but because the firewall blocks ICMP packets. This case can be identif... | BAC | BCA | ABC | BCA | Selection 1 |
**A**: SVM classifiers project the data into a higher dimensional space using a kernel function and then find a linear separator in that space that gives the largest distance between the two classes compared while minimizing the number of incorrectly labeled samples**B**: The first model in this domain [7] employed SVM... | ABC | BCA | BAC | ABC | Selection 3 |
**A**: In fact, the free product of two automaton semigroups S𝑆Sitalic_S and T𝑇Titalic_T is always at least
very close to being an automaton semigroup: adjoining an identity to S⋆T⋆𝑆𝑇S\star Titalic_S ⋆ italic_T**B**: However, there do not seem to be constructions for presenting arbitrary free products of self-simil... | CAB | BCA | ABC | CBA | Selection 1 |
**A**: The first column shows ground truth regions and columns 2-4 show visualizations from HINT trained on relevant, irrelevant and fixed random regions respectively.
**B**: Figure A3: Visualizations of most sensitive visual regions used by different variants of HINT to make predictions**C**: We pick samples where al... | ACB | ABC | CBA | CAB | Selection 4 |
**A**: We reason that this similarity could be due to the use of privacy policy templates or generators. We also found abundant examples of near-duplicate privacy policies on the same website. We reason that this similarity could be due to the presence of archived versions of privacy policies on the website. Since we a... | ABC | ACB | CAB | ABC | Selection 3 |
**A**: In our system, we address this challenge with the exploration of a finite space of solutions, employing 11 algorithms (that can be further expanded).**B**: Interestingly, the authors suggest as future work that “there are open research questions about how best to compare multiple models directly”**C**: In our ap... | BAC | BAC | ACB | CBA | Selection 4 |
**A**: Data Quantity. In Persona, we evaluate Transformer/CNN, Transformer/CNN-F and MAML on 3 data quantity settings: 50/100/120-shot (each task has 50, 100, 120 utterances on average)**B**: In Weibo, FewRel and Amazon, the settings are 500/1000/1500-shot, 3/4/5-shot and 3/4/5-shot respectively (Table 2).
When the dat... | BAC | ABC | CBA | CBA | Selection 2 |
**A**: Multiuser-resultant Receiver Subarray Partition: As shown in Fig. 3, the r-UAV needs to activate multiple subarrays to serve multiple t-UAVs at the same time**B**: Assuming that an element can not be contained in different subarrays, then the problem of activated CCA subarray partition rises at the r-UAV side fo... | ACB | BAC | ABC | CAB | Selection 3 |
**A**: This will be bootstrapped to the multi-color case in later sections**B**: 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 every no... | ACB | ABC | CBA | CBA | Selection 2 |
**A**: In particular, we aim to characterize how an overparameterized two-layer neural network and its induced feature representation evolve in TD and Q-learning, especially their rate of convergence and global optimality**B**:
In this paper, we study temporal-difference (TD) (Sutton, 1988) and Q-learning (Watkins and... | CAB | BAC | ABC | BCA | Selection 2 |
**A**:
We thank anonymous reviewers for their insightful comments. Hongfei Xu and Yang Song acknowledge the support of the National Natural Science Foundation of China (Grant No**B**: 232300421386). Josef van Genabith and Hongfei Xu are supported by the German Federal Ministry of Education and Research (BMBF) under fu... | BAC | BCA | CAB | ACB | Selection 4 |
**A**: Remark that V≜U∪(X∖Y)≜𝑉𝑈𝑋𝑌V\triangleq U\cup(X\setminus Y)italic_V ≜ italic_U ∪ ( italic_X ∖ italic_Y ) is an open set of X𝑋Xitalic_X, and is still definable**B**: Therefore U=V∩Y𝑈𝑉𝑌U=V\cap Yitalic_U = italic_V ∩ italic_Y with V𝑉Vitalic_V a definable open set of X𝑋Xitalic_X.
∎**C**: definable closed set... | BAC | CAB | ABC | BCA | Selection 4 |
**A**: These problems seriously limit the learning ability of neural networks and cause inferior distortion rectification results. To address the above problems, we propose a fully novel concept, i.e., ordinal distortion**B**: Fig. 2 illustrates the attributes of the proposed ordinal distortion.**C**:
As mentioned abo... | BAC | ABC | CBA | BCA | Selection 4 |
**A**: Figure 2 shows the learning curves of the five methods**B**: We can observe that in the small-batch training, SNGM and other large-batch training methods achieve similar performance in terms of training loss and test accuracy as MSGD.
In large-batch training, SNGM achieves better training loss and test accuracy ... | BCA | ABC | BAC | BAC | Selection 2 |
**A**: Nathaniel Grammel and Leonidas Tsepenekas were supported in part by NSF awards CCF-1749864 and CCF-1918749, and by research awards from Amazon and Google**B**: Aravind Srinivasan was supported in part by NSF awards CCF-1422569, CCF-1749864 and CCF-1918749, and by research awards from Adobe, Amazon, and Google.
*... | ABC | CBA | BAC | BCA | Selection 4 |
**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... | BAC | ACB | CBA | CBA | Selection 1 |
**A**: In this way, records in the same equivalence group are indistinguishable. k𝑘kitalic_k-Anonymity [31, 28] ensures that the probability of identity disclosure is at most 1/k1𝑘1/k1 / italic_k. For instance, Figure 1(b) is a generalized table of Figure 1(a) that complies with 2-anonymity, and the adversary has to ... | CAB | ABC | ACB | CBA | Selection 4 |
**A**:
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**B**: (2020) on COCO. In SOLOv2, the unified mask feature br... | BAC | BCA | ACB | CAB | Selection 3 |
**A**: More specifically, we proved**B**:
In version 1 of this note, which can still be found on the ArXiv, we showed that the analogous version of the conjecture for complex functions on {−1,1}nsuperscript11𝑛\{-1,1\}^{n}{ - 1 , 1 } start_POSTSUPERSCRIPT italic_n end_POSTSUPERSCRIPT which have modulus 1111 fails**C**... | BCA | ACB | ABC | CAB | Selection 4 |
**A**: Our algorithm is summarized in Algorithm 1**B**:
Our proposed algorithm LSVI-UCB-Restart has two key ingredients: least-squares value iteration with upper confidence bound to properly handle the exploration-exploitation trade-off (Jin et al., 2020), and restart strategy to adapt to the unknown nonstationarity**... | CBA | BAC | CAB | ACB | Selection 2 |
**A**: The survey contained 19 question items, 2 branching questions, and 3 demographic questions. Respondents were allowed to select multiple options for some question items while the branching questions served to direct them to different sections based on their answer**B**: Although branching means that respondents m... | BAC | ACB | BCA | CAB | Selection 3 |
**A**: In further validation of this, we compare the performance of decentRL with AliNet and GAT on datasets containing new entities**B**: The existing inductive KG embedding methods, such as LAN [21], are unsuitable for adaptation to this task as they are tailored for entity prediction.**C**:
Although GCN and GAT are... | CBA | BAC | BCA | CBA | Selection 3 |
**A**: In this section, we present the results of the ablation study of VDM**B**: Recall that we have ”Common” modules and ”VDM-specific” models according to Tab. II**C**: Common modules are used for policy optimization rather than exploration, and all compared methods use the same common modules and not be tuned. ”VDM... | BAC | BAC | BAC | ABC | Selection 4 |
**A**: As expected, (Chebyshev) polynomial interpolation on uniform grids (uniform) and multi-linear interpolation also do not converge.**B**: Further, we recognize that the Vandermonde approach is inaccurate and even becomes numerically unstable (rising errors) for higher degrees**C**: It is therefore inappropriate fo... | BAC | CAB | BAC | ACB | Selection 2 |
**A**: For example, in Figure 1, the model uses β𝛽\betaitalic_β-TCVAE [mig] to retrieve the pose of the model as a latent factor. In the reconstruction, the rest of the details are averaged, resulting in a blurry image (1b). The goal of the second part of the model, is to add the details while maintaining the semantic... | ACB | CBA | ACB | CAB | Selection 2 |
**A**:
Exploration based on previous experiments and graph theory found errors in structural computers with electricity as a medium**B**: 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.**C**: The caus... | CBA | BAC | CBA | ACB | Selection 4 |
**A**: Some well-studied families of polynomials include the Dickson polynomials and reverse Dickson polynomials, to name a few**B**: 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**C**: Conditions for such fa... | CBA | CAB | ACB | BAC | Selection 4 |
**A**: Van Loon \BOthers**B**:
MVS is a very flexible method, since one can choose any suitable learning algorithm for the base- and meta-learner**C**: (\APACyear2020) chose the base-learner to be logistic ridge regression and the meta-learner to be the nonnegative logistic lasso, in order to obtain a model most simil... | ABC | BAC | CBA | BCA | Selection 2 |
**A**:
A limitation of the DepAD algorithms is their inability to detect or interpret anomalies that only affect independent variables**B**: A potential improvement to address this limitation is adopting ensemble methods from both dependency-based and proximity-based perspectives, as suggested by prior studies [89, 90... | ACB | BAC | CBA | ABC | Selection 1 |
**A**: [2010]. Optimistic parameter search provides a cleaner description of the learning strategy. In non-linear reward models, both approaches may not follow similar trajectory but may have overlapping analysis styles (see Filippi et al. [2010] for a short discussion).**B**: [2011]), which is in contrast to the use o... | CAB | CBA | ACB | CAB | Selection 2 |
**A**:
Why are short actions hard to localize? Short actions have small temporal scales with fewer frames, and therefore, their information is prone to loss or distortion throughout a deep neural network**B**: Most methods in the literature process videos regardless of action duration, which as a consequence sacrifice... | ACB | ABC | CBA | CAB | Selection 2 |
**A**: To verify whether our findings were reliable, we applied the resulting majority-voting ensemble to the same test and external validation data sets as Mansouri et al. [MRB∗13], see Table 1**B**: For the test data set, the reported accuracy was approximately 87%.
In our case, we reached 89% for accuracy with the f... | BCA | CBA | CBA | ABC | Selection 1 |
**A**: Our approach begins by categorizing the states of the desired distribution.**B**:
In this section, we introduce a shortest-path algorithm that is proposed as a modification to the Metropolis-Hastings algorithm in [7, Section V-E] and integrated with the Markov chain synthesis methods described in [14] and [15]*... | ACB | CAB | ABC | CBA | Selection 2 |
**A**: A shortcoming when applying the mentioned multi-shape matching approaches to isometric settings is that they do not exploit structural properties of isometric shapes**B**: Hence, they lead to suboptimal multi-matchings, which we experimentally confirm in Sec. 5**C**: One exception is the recent work on spectral ... | BAC | BAC | ABC | CAB | Selection 3 |
**A**: In this section we analyze all steps of algorithm RecognizePG**B**: We want to explain them in details and compute the computational complexity of the algorithm**C**: Some of these steps are already discussed in [22], anyway, we describe them in order to have a complete treatment.
| ABC | BCA | ACB | ACB | Selection 1 |
**A**:
Numerical results of these two sub-experiments are shown in panels (c) and (d) of Figure 1**B**: Under the DCMM model, the mixed Humming error rate of Mixed-SLIM decreases as ρ𝜌\rhoitalic_ρ decreases, while the performances of the other three approaches are still unsatisfactory.**C**: From subfigure (c), under... | CAB | BCA | ABC | ACB | Selection 4 |
**A**: Our contribution is two fold**B**: Our Contribution**C**: First, utilizing the optimal transport framework and the variational form of the objective functional, we propose a novel variational transport algorithmic framework for solving the distributional optimization problem via particle approximation.
In each i... | ABC | ABC | BCA | BAC | Selection 4 |
**A**: The most straightforward RL baseline considers each intersection independently and models the task as a single agent RL problem [12]. However, the observation, received reward and dynamics of each traffic signal are closely related to its neighbors, and the coordination between signals should be modeled**B**: Ho... | BCA | BAC | BCA | ACB | Selection 4 |
**A**: To obtain the best theoretical performance, we can choose A𝐴Aitalic_A as the algorithm of the best known competitive ratio, that is Advanced Harmonic algorithm (?)**B**: For this reason, simple algorithms such as FirstFit and BestFit are preferred in practice (?). We obtain the following corollary.
**C**: Howev... | BCA | ABC | ACB | BCA | Selection 3 |
**A**: We present Locally Conditioned Atlas (LoCondA), a framework for generating and reconstructing meshes of objects using an atlas of localized charts that leverage the introduced notion of the continuous atlas. It consists of two parts**B**: Secondly, we use a separate neural network that transforms a point from th... | ABC | BAC | ACB | CBA | Selection 3 |
**A**: We assume that one local iteration costs t𝑡titalic_t time units, and the communication round costs τ𝜏\tauitalic_τ time units.
Additionally, information can be transmitted only along the undirected edge of the network**B**: To describe this class of first-order methods, we use a similar definition of Black-Box ... | CBA | BCA | CBA | BAC | Selection 4 |
**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 | CBA | BCA | 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... | ABC | CAB | ABC | CBA | Selection 4 |
**A**: Visualization and interaction.
E1 and E2 were surprised by the promising results we managed to achieve with the assistance of our VA system in the red wine quality use case of Section 4. Initially, E1 was slightly overwhelmed by the number of statistical measures mapped in the system’s glyphs. However, after the... | ACB | CBA | BAC | CAB | Selection 1 |
**A**: We have implemented the simulations in Matlab, using Yalmip/Gurobi to solve the corresponding MPCC quadratic program in a receding horizon fashion and the GPML library for Gaussian process modeling**B**:
We use two geometries to evaluate the performance of the proposed approach, an octagon geometry with edges i... | ACB | BAC | CAB | CAB | Selection 2 |
**A**: For this, we plot improvement over the standard model (IOSM𝐼𝑂𝑆𝑀IOSMitalic_I italic_O italic_S italic_M) in Fig. 5, which is the accuracy gain over the standard model on each dataset group. The improvements in blond (minority group) incur degradation in non-blond (majority group). The methods tilt predicti... | ACB | CBA | ABC | BAC | Selection 2 |
**A**: Some works focus on using deeper neural networks [17, 50, 54] or extra modules [45, 55, 56] to improve gaze estimation performance, while the other studies seek to use custom devices for gaze estimation, such as multi-cameras and RGBD cameras [121, 122].**B**: The cameras are usually placed below/above the compu... | ABC | CAB | ABC | CBA | Selection 4 |
**A**: In this paper, we handle the second task using a deep learning-based method. We use a pre-trained deep learning-based model in order to extract features from the unmasked face regions (out of the mask region). It is worth stating that the occlusions in our case can occur in only one predictable facial region (no... | BCA | BCA | CBA | CAB | Selection 3 |
**A**:
Configuration reduction →→\to→ is given as multiset rewriting rules [CS09] in Figure 4, which replace any subset of a configuration matching the left-hand side with the right-hand side**B**: Principal cuts encountered in a configuration are resolved by passing a value to a continuation also given in Figure 4 as... | ABC | BAC | ACB | CBA | Selection 3 |
**A**: FairCMS-I essentially delegates the re-encryption management of LUTs to the cloud, thus significantly reducing the overhead of the owner side**B**: Nevertheless, FairCMS-I cannot achieve IND-CPA security for the media content. Therefore, we further propose a more secure scheme FairCMS-II, which delegates the re-... | ACB | BCA | BAC | BAC | Selection 2 |
**A**: (2020) is another model that uses an attentional aggregation strategy with residual connections to learn feature representations and model feature interactions. However, even with the use of attention mechanisms to account for the weight of each pair of feature interactions, aggregating all interactions together... | BCA | BAC | CBA | ABC | Selection 3 |
**A**: For example, the logistic loss function used in logistic regression is not strictly self-concordant, but it fits into a class of pseudo-self-concordant functions, which allows one to obtain similar properties and bounds as those obtained for self-concordant functions [Bach, 2010]. This was also the case in Ostro... | BAC | ACB | BCA | CBA | Selection 1 |
**A**: The approximation analysis as well as the proof of the pass complexity can be found in Section 5**B**: In Section 6 we provide details about our general framework for finding approximate maximum matching.**C**: Furthermore, we make some important observations about invariants that are preserved by operations of ... | ABC | ACB | BCA | ACB | Selection 3 |
**A**: Thus, B-CPP is more flexible, and due to its broadcast nature, it can further save communication over CPP in certain scenarios [63]**B**: 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, on... | ACB | ABC | CBA | BAC | Selection 4 |
**A**: For example, Generative Adversarial Networks (GANs) are written as a min-max problem [12]. In addition, there are many popular examples: robust models with adversarial noise [13],**B**: One can note a branch of recent work devoted to solving non-smooth problems by reformulating them as saddle point problems [8, ... | ABC | CAB | ABC | BCA | Selection 2 |
**A**: The component that determines the distribution of policies that the oracle responds to is called the meta-solver (MS). The MS operates on the meta-game (MG), which is a payoff tensor estimated by measuring the expected return (ER) of policies against one another**B**: This is a NF game, but instead of strategies... | BCA | CBA | CAB | BAC | Selection 1 |
**A**: This kind of extension is not limited to Rényi divergence, as discussed in Appendix B.**B**:
We note that the first part of this definition can be viewed as a refined version of zCDP (Definition B.18), where the bound on the Rényi divergence (Definition B.5) is a function of the sample sets and the query**C**: ... | BAC | CBA | CBA | CAB | Selection 4 |
**A**: To illustrate this difficulty, note that strengthening the definition of kernelization to “a preprocessing algorithm that is guaranteed to always output an equivalent instance of the same problem with a strictly smaller parameter” is useless. Under minor technical assumptions, such an algorithm would allow the p... | BCA | CAB | ABC | BAC | Selection 1 |
**A**: However, they usually achieve this goal by inferring explicit illumination condition, material properties, and 3D geometry, in which the supervision for these information is difficult and expensive to acquire**B**: Besides, they generally have strong assumption for the light source, which may not generalize well... | CBA | ACB | BCA | ABC | Selection 3 |
**A**: This can be attributed to the fact that LPA optimizes the expected long-term revenues at each dispatching round, while LLD only focuses on the immediate reward**B**:
Our experimental results demonstrate that LPA outperforms LLD in most cases**C**: As a result, LPA is better suited for maximizing the total reven... | BAC | ACB | CAB | BCA | Selection 1 |
**A**: The traffic data set, aside of being very small, is also extremely sparse (on average 14 features are zero). It should be noted that all of the data sets used in this study were considered as ordinary (static) data sets. Even though some of them could be considered in a time series context, no autoregressive fea... | BCA | CAB | BAC | BCA | Selection 2 |
**A**: The sequence-level embedding then goes through two dense layers for classification.**B**: For the sequence-level tasks, which require only a prediction for an entire sequence, we follow \textciteemopia and choose the Bi-LSTM-Attn model from \textcitelin2017structured as our baseline, which was originally propose... | BCA | ABC | ACB | CAB | Selection 4 |
**A**: [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.
**B**: to L(k,1)𝐿𝑘1L(k,1)italic_L ( italic_k , 1 )-labeling problem (see e.g**C**: This description draws a comparison e.g | CBA | BAC | BCA | BCA | Selection 1 |
**A**: However, in this paper, we consider an intelligent task at the receiver to recover the text information of the input speech signals**B**: Particularly, we propose a DL-enabled semantic communication system for speech recognition, named DeepSC-SR, by learning and extracting the text-related semantic features from... | BCA | ABC | ACB | ABC | Selection 1 |
**A**: TABLE V: The class-specific mIoU (%) evaluation on S3DIS Area-5**B**: KPConv(paper) is taken from the paper-reported score, and KPConv(retrain) is the score from our basic segmentation network trained with 100% labels**C**: The baseline method means the basic segmentation network trained with only the weak label... | BCA | BCA | ABC | CBA | Selection 3 |
**A**:
Figure 6: Qualitative results of our method for Bird’s-Eye-View**B**: All the illustrated images are from the KITTI val set. Zoom in on the circles for more detailed comparison.**C**: We use black box for ground-truth, red box for baseline results, and blue box for our results | BAC | CAB | BAC | ACB | Selection 4 |
**A**: The momentum of SGD was set to 0.90.90.90.9**B**: For testing, the short side of images was kept at 640640640640 pixels for CTW1500 and Total-Text, and 1,280 pixels for ICDAR2015, MSRA-TD500 and MLT2017, while retaining their aspect ratios.**C**: We first used SynthText to pre-train our model for 10 epochs using... | CBA | BCA | ACB | ABC | Selection 2 |
**A**: The hash function assigns each key to a unique bucket for each IP address. Unfortunately, the hash function can generate the same hash code for more than one IP address**B**: A hash table is an effective method for collecting the statistics of IP addresses Sanders2015HS . It uses a hash function to compute a has... | ACB | ACB | BAC | BCA | Selection 3 |
**A**: The above 3333-by-3333 block linear problems (1) and (2) can be naturally extended to the n𝑛nitalic_n-tuple cases**B**: For example, when the system matrix in (1) is extended to the n𝑛nitalic_n-tuple case, it is the block tridiagonal systems discussed in [37]**C**: When
the system matrix in (2) is extended to ... | ABC | CAB | CAB | BCA | Selection 1 |
**A**: Several works have focused on distributed training with vertical partitions in a federated setting**B**: In all of these works, each party communicates in each iteration of training, which is communication-wise expensive.
A more recent work on vertical federated learning (Liu et al., 2020a) solved this problem b... | ACB | CAB | BCA | BAC | Selection 1 |
**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**: As an alternative, pseudospectra attempt to provide approximate solutions by offering reasonably tight bounds and engaging geometric interpretations... | BAC | CBA | ACB | BCA | Selection 2 |
**A**: Motivated by global and local GANs [7], Gated Convolution [36] and Markovian GANs [9], we develop a two-stream discriminator to distinguish genuine images from the generated ones by estimating the feature statistics of both texture and structure. The discriminator is shown in Figure 2 (b)**B**: The structure bra... | ACB | CAB | CAB | CAB | Selection 1 |
**A**: 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 BEC. Here we consider more generally a q𝑞qitalic_q-ary erasure channel ... | CBA | ABC | ABC | ACB | Selection 1 |
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