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**A**: For example, special linear groups are generated by the subset of all transvections [21, Theorem 4.3] or by two well chosen matrices, such as the Steinberg generators [19]**B**:
There are several well-known generating sets for classical groups**C**: Another generating set which has become important in algorithm... | BAC | BCA | CBA | CBA | Selection 1 |
**A**: Except for (ii), all steps above above can be performed efficiently as the matrices involved are sparse and either local or independent of hℎhitalic_h**B**: Solving (25) on the other hand involves computing the hℎhitalic_h-dependent, global operator P𝑃Pitalic_P, leading to a dense matrix in (25)**C**: From now ... | CBA | ABC | ACB | BCA | Selection 2 |
**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... | CAB | BAC | CBA | CBA | Selection 1 |
**A**: In contrast to mere sentiment features, this approach is more tailored rumor context (difference not evaluated in [18]). We simplified and generalized the “dictionary” by keeping only a set of carefully curated negative words**B**: We call them “debunking words” e.g., hoax, rumor or not true. Our intuition is, t... | ACB | ACB | ACB | BCA | Selection 4 |
**A**: However, if we initialize with η<1/ℒ(𝐰(0))𝜂1ℒ𝐰0\eta<1/\mathcal{L}(\mathbf{w}(0))italic_η < 1 / caligraphic_L ( bold_w ( 0 ) ) then it is straightforward to show the gradient descent iterates maintain bounded local smoothness**B**: and probit losses.
Assumption 1 implies**C**: Assumption 1 includes many comm... | CAB | ACB | BCA | CAB | Selection 3 |
**A**: The results show that the low-level hidden representation of tweets feature is at least the second best features over time**B**: We also derive explanations on the low performance of supposed-to-be-strong high-level features at early stage. The study also indicates that, there is still considerable room to impro... | ABC | CBA | BCA | CBA | Selection 3 |
**A**: This allows us to evaluate the feature performance i.e., salience and timeliness, with time and type specification (RQ2)**B**: For this part, we first focus on evaluating the performance of single L2R models that are learned from the pre-selected time (before, during and after) and types (Breaking and Anticipate... | BAC | ACB | ACB | CAB | Selection 1 |
**A**: Only one of the patients suffers from diabetes type 2 and all are in ICT therapy. In terms of time since being diagnosed with diabetes, patients vary from inexperienced (2 years) to very experienced (35 years), with a mean value of 13.9 years.**B**:
Table 1 shows basic patient information. Half of the patients ... | ABC | ABC | BAC | CAB | Selection 4 |
**A**: (2014), and OSIE Xu et al. (2014). The costly acquisition of measurements, however, is a limiting factor for the number of stimuli. New data collection methodologies have emerged that leverage webcam-based eye movements Xu et al**B**: A prerequisite for the successful application of deep learning techniques is a... | BAC | ACB | ACB | BCA | Selection 1 |
**A**:
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 st... | BCA | ABC | ABC | ACB | Selection 4 |
**A**: Each bar illustrates the number of interactions with environment required by Rainbow (left) or PPO (right) to achieve the same score as our method (SimPLe)**B**: The red line indicates the 100100100100K interactions threshold which is used by the our method.**C**:
Figure 3: Comparison with Rainbow and PPO | ABC | ABC | BAC | BCA | Selection 4 |
**A**: The track tip positioning was the key parameter controlled during the creation of these climbing gaits**B**: The trajectory design took into account six constraints: initial and final position, velocity, and acceleration [23]. The Reflexxes Motion Library IV [24] was utilized to perform the inverse kinematics ca... | ACB | ABC | BAC | BCA | Selection 1 |
**A**: Along with its practical significance, research on this problem has lead to technical developments for online algorithms in general.
**B**: We refer the reader to a survey by Coffman et al. [14] and a brief introduction by Johnson [19] for details on bin packing and its applications**C**: Online bin packing find... | BCA | CAB | CBA | ABC | Selection 3 |
**A**: Therefore, in this new (more realistic) scenario, subjects were processed one writing (post) at the time (in a stream-like way) and not using chunks.
**B**: As said earlier, each chunk contained 10% of the subject’s writing history, a value that for some subjects could be just a single post while for others hund... | CAB | ABC | ACB | ACB | Selection 1 |
**A**: Therefore, the momentum in DGC is a local momentum without global information.
**B**: Furthermore, although DGC combines momentum and error feedback, the momentum in DGC only accumulates stochastic gradients computed by each worker locally**C**: However, the theory about the convergence of DGC is still lacking | BAC | BAC | ACB | CBA | Selection 4 |
**A**:
, where ∗*∗ is the convolution333We use convolution instead of cross-correlation only as a matter of compatibility with previous literature and computational frameworks**B**: operation.**C**: Using cross-correlation would produce the same results and would not require flipping the kernels during visualization | ACB | ABC | CBA | CBA | Selection 1 |
**A**: Since the UAV ad-hoc network game is a special type of potential game, we can apply the properties of the potential game in the later analysis**B**: Some algorithms that have been applied in the potential game can also be employed in the UAV ad-hoc network game**C**: In the next section, we investigate the exist... | ACB | ACB | ABC | CAB | Selection 3 |
**A**: has no component perpendicular to the boundary (i.e.,𝐩⟂|Γ=0)(i.e.,\,\mathbf{p_{\perp}}|_{\Gamma}=0)( italic_i **B**: , bold_p start_POSTSUBSCRIPT ⟂ end_POSTSUBSCRIPT | start_POSTSUBSCRIPT roman_Γ end_POSTSUBSCRIPT = 0 ).
In the following, we will refer to these conditions as the natural**C**: italic_e | ACB | CBA | BCA | BCA | Selection 1 |
**A**: Many of the proposed extensions focus on minimizing the variance that comes from AGE by finding methods to optimize the learning trajectory or from TAE by using methods like averaging to exact DQN parameters. Dropout methods have the ability to assemble these two solutions which minimize different source of vari... | ABC | ACB | CAB | CBA | Selection 4 |
**A**:
In order to preserve the contextual spatial information within an image as the filtered input data progresses deeper into the network, Long et al**B**: The fusion step is visualized in Figure 4.**C**: (2015) proposed to fuse the output with shallower layers’ output | BAC | ACB | CBA | BCA | Selection 2 |
**A**: The overall evaluation on all datasets is presented in the next section.
The number of training examples per class is shown in parentheses and increases in each row from left to right.**B**: The results are shown in Figure 3 exemplarily for the Car, Covertype, and Wisconsin Breast Cancer (Original) dataset**C**:... | ABC | CAB | ABC | ACB | Selection 2 |
**A**:
A line of recent work (Fazel et al., 2018; Yang et al., 2019a; Abbasi-Yadkori et al., 2019a, b; Bhandari and Russo, 2019; Liu et al., 2019; Agarwal et al., 2019; Wang et al., 2019) answers the computational question affirmatively by proving that a wide variety of policy optimization algorithms, such as policy g... | ABC | ACB | ACB | CAB | Selection 1 |
**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 | CAB | BCA | ACB | Selection 2 |
**A**: However, if FillRad(M)FillRad𝑀\mathrm{FillRad}(M)roman_FillRad ( italic_M ) were small, one would not be able to apply Wilhelm’s theorem**B**: To avoid that, we will invoke a result due to Liu [64].
**C**: The proof strategy for Propositions 9.8 and 9.9 is to invoke Wilhelm’s result [82, Main Theorem 2] and Le... | BCA | ABC | CAB | ABC | Selection 1 |
**A**: The colors reflect the labels of the data with the same colors as in the overview (Subsection 4.2), when available, and the rest of the instances of the data—which are not selected—are shown with high transparency**B**: Each axis maps the entire range of each dimension, from bottom to top. A simple example is gi... | ABC | BAC | CBA | BCA | Selection 4 |
**A**: Furthermore, we note that efforts invested in this regard to date are not up to the level of ambition and thoroughness pursued in our study. In addition, no study published so far has been specifically devoted to unveiling structural similarities between classical and modern meta-heuristics. There lies the novel... | ABC | BAC | CAB | BAC | Selection 3 |
**A**: Roughly speaking, the network embedding approaches can be classified into 2 categories: generative models [13, 14] and discriminative models [15, 16]**B**: The former tries to model a connectivity distribution for each node while the latter learns to distinguish whether an edge exists between two nodes directly.... | BCA | CBA | ABC | CBA | Selection 3 |
**A**: In Figure 14 we plot the statistics of the tested networks according to their size and type. The results show a correlation between the size of the network and its type. For instance, most NSP networks are large, with CIDR/6. This is aligned with our finding that among NSP networks there was the highest number o... | BAC | BAC | CBA | ABC | Selection 3 |
**A**: In the one-vs-one design, several SVMs are trained to discriminate between each pair of classes, and the final multiclass prediction is given by the class with the majority of votes.
**B**: The first model in this domain [7] employed SVMs with one-vs-one comparisons between all classes**C**: SVM classifiers proj... | CAB | ABC | ABC | ACB | Selection 1 |
**A**: For semigroups, on the other hand, such results do exist**B**: 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**C**: However, there do not seem to be con... | BAC | ABC | BCA | BAC | Selection 3 |
**A**: These approaches rely on additional annotations/cues such as human-based attention maps Das et al. (2017), textual explanations Huk Park et al. (2018) and object label predictions Ren et al. (2015) to identify relevant regions, and train the model to base its predictions on those regions, showing large improveme... | ACB | ABC | BCA | CBA | Selection 4 |
**A**: (2014), but issues of accuracy, scalability and generalization remain. More importantly, annotations in the privacy policy domain are expensive. Privacy policies are difficult to understand and many tasks such as privacy practice classification (Wilson et al., 2016), privacy question answering (Ravichander et al... | ACB | CBA | ABC | CAB | Selection 2 |
**A**: Log loss penalizes outliers, and in our case, we should be aware of outliers as we have sensitive healthcare data. Finally, four of the performance metrics include one more option—they are marked with an asterisk in StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Perfo... | BCA | ACB | ACB | CBA | Selection 4 |
**A**: In Persona we use a pre-trained natural language inference model to measure the response consistency with persona description for C Score. In Weibo, users do not have persona descriptions, so we pre-train a user classifier to classify the generated responses, and use the accuracy for C Score.**B**: We use PPL an... | BAC | CAB | BCA | CBA | Selection 4 |
**A**: It is shown that the sum SE of the scheme without interference calculated by (28) is similar with that of the scheme with interference calculated by (11) with the appropriate number of t-UAVs and the limited transmit power. The gap between the schemes increases as the power and the number of t-UAVs increase. The... | CAB | ABC | CBA | BAC | Selection 1 |
**A**: We**B**: This will be bootstrapped to the multi-color case in later sections**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... | BAC | ACB | CBA | CAB | Selection 4 |
**A**:
In this paper, we study temporal-difference (TD) (Sutton, 1988) and Q-learning (Watkins and Dayan, 1992), two of the most prominent algorithms in deep reinforcement learning, which are further connected to policy gradient (Williams, 1992) through its equivalence to soft Q-learning (O’Donoghue et al., 2016; Schu... | CAB | ACB | ABC | CAB | Selection 3 |
**A**: Multilingual translation uses a single model to translate between multiple language pairs Firat et al. (2016); Johnson et al**B**: Model capacity has been found crucial for massively multilingual NMT to support language pairs with varying typological characteristics Zhang et al. (2020); Xu et al. (2021a). Using ... | BAC | CAB | CBA | ACB | Selection 4 |
**A**: Apply [33, Corollary
5.14] to A𝐴Aitalic_A and B𝐵Bitalic_B**B**: Then A~⊧φmodels~𝐴𝜑\widetilde{A}\models\varphiover~ start_ARG italic_A end_ARG ⊧ italic_φ because A→A~→𝐴~𝐴A\to\widetilde{A}italic_A → over~ start_ARG italic_A end_ARG and**C**: furthermore B→C→𝐵𝐶B\to Citalic_B → italic_C | BCA | CBA | CBA | ABC | Selection 1 |
**A**: [21] employed a one-parameter camera model [22] and estimated distortion parameters using the detected circular arcs. Similarly, [23, 24] also utilized the simplified camera model to correct the radial distortion in images. However, these methods perform poorly on scenes that are lacking enough hand-crafted feat... | ACB | CAB | ACB | CBA | Selection 2 |
**A**: In this paper, we first review the convergence property of MSGD, one of the most widely used variants of SGD, and analyze the failure of MSGD in large-batch training from an optimization perspective**B**: The main contributions of this paper are outlined as follows:**C**: Then, we propose a novel method, called
... | ACB | ABC | CAB | CBA | Selection 1 |
**A**: Clustering is a fundamental task in unsupervised and self-supervised learning. The stochastic setting models situations in which decisions must be made in the presence of uncertainty and are of particular interest in learning and data science**B**: The black-box model is motivated by data-driven applications whe... | ABC | CAB | CAB | CBA | Selection 1 |
**A**: However, this can not be obtained for the case with the linearly growing subgradients**B**: That is, the mean square error at the next time can be controlled by that at the
previous time and the consensus error**C**: Also, different from [15], the subgradients are not required to be bounded and the inequality (2... | CAB | CBA | BAC | ACB | Selection 3 |
**A**: Then, the mutual cover strategy calculates a random output table on each QI attribute (i.e., age and gender) within each group**B**:
For instance, suppose that we add another QI attribute of gender as shown in Figure 4, the mutual cover strategy first divides the records into groups in which the records in the ... | BAC | CAB | CBA | BCA | Selection 1 |
**A**: As shown in Table 3, all PointRend models achieve promising performance. Even without ensemble, our PointRend baseline, which yields 77.38 mAP, has already achieved 1st place on the test leaderboard**B**: In addition to models listed in Table 3, another PointRend with slightly different setting (stacking two BFP... | BCA | ABC | ABC | ACB | Selection 4 |
**A**: As was written in the previous version, an anonymous referee of version 1 wrote that the theorem was known to experts but not published**B**: Maybe the presentation below is what was known. **C**:
Here we give an embarrassingly simple presentation of an example of such a function (although it can be shown to be... | BCA | ABC | BAC | ABC | 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... | ABC | CBA | CAB | CAB | Selection 1 |
**A**:
In this study, we seek to answer these research questions. RQ1: How much do people trust the media by which they obtain news? RQ2: Why do people share news and how do they do it? RQ3: How do people view the fake news phenomenon and what measures do they take against it? An online survey was employed for data co... | CBA | BCA | ABC | CAB | Selection 3 |
**A**: These datasets encompass three entity alignment settings, each comprising two linked KGs in different languages**B**: In the entity alignment task, we leverage the widely used DBP15K datasets [15, 16, 17, 28, 34, 36, 40, 18] in our experiment**C**: For instance, the ZH-EN dataset involves the alignment between C... | CBA | BCA | BCA | BAC | Selection 4 |
**A**: The probabilistic ensemble may not suitable for this setting. (ii) We focus on single-step dynamics modeling and use the prediction likelihood to encourage exploration, while the probabilistic ensemble in [48] focuses on long-term prediction to long-term planning through MPC. That is, the probabilistic-ensemble ... | CBA | ABC | BAC | ACB | Selection 3 |
**A**: That is: For given arbitrary nodes P𝑃Pitalic_P, determine the polynomial space ΠΠ\Piroman_Π such that
P𝑃Pitalic_P is unisolvent with respect to ΠΠ\Piroman_Π**B**: In doing so, we revisit earlier results by Carl de Boor and Amon Ros [28, 29] and answer their question from our perspective.**C**: We complement th... | ABC | BCA | CBA | ABC | Selection 2 |
**A**:
The model has two parts. First, we apply a DGM to learn only the disentangled part, C𝐶Citalic_C, of the latent space. We do that by applying any of the above mentioned VAEs111In this exposition we use unspervised trained VAEs as our base models but the framework also works with GAN-based or FLOW-based DGMs, su... | ACB | ABC | BCA | CAB | Selection 1 |
**A**: To simulate the aforementioned structural computer theory, a device in the form of a USB connection**B**: We decided to verify that the structural computer theory presented so far is actually working without the cost of circuit building, to simulate the connection of complex Circuits rather than just Gate Circui... | ACB | ABC | BAC | CAB | Selection 1 |
**A**: In this paper, we propose a representation of such a group using the concept of linear representation defined through the Koopman operator.**B**: Given a finite subset of such permutations, we can compute a group generated by this set**C**:
A finite field, by definition, is a finite set, and the set of all perm... | CBA | BAC | CAB | BCA | Selection 1 |
**A**: Note that we are primarily interested in the extent to which differences between the meta-learners are moderated by the experimental factors of sample size, view size, number of views, and correlation structure**B**: In Table 1 we therefore show only the interaction terms including the meta-learner factor.
**C**... | CBA | BCA | ACB | BAC | Selection 2 |
**A**: If a feature of an object falls outside these prediction intervals, this object is flagged as an inconsistency. When building the conformal prediction model, the Markov blanket (MB) of each feature is used as predictors to estimate the feature value and to provide a explanation for the detected inconsistencies.
... | CAB | CBA | BCA | BCA | Selection 2 |
**A**:
In this section we compare the empirical performance of our proposed algorithm CB-MNL with the previous state of the art in the MNL contextual bandit literature: UCB-MNL[Oh & Iyengar, 2021] and TS-MNL[Oh & Iyengar, 2019] on artificial data**B**: This highlights the primary contribution of our theoretical analys... | BAC | BCA | ACB | CBA | Selection 3 |
**A**:
Figure 3: Video self-stitching (VSS). a) Snippet-level features are extracted for the entire video**B**: b) Long video is cut into multiple short clips. c) Each video clip is up-scaled along the temporal dimension**C**: d) Original clip (green dots) and up-scaled clip (orange dots) are stitched into one feature... | ABC | BCA | ACB | ACB | Selection 1 |
**A**: In evolutionary optimization, a crossover and mutation phase leads to a propagation of more crossover and mutation phases with exponential growth (cf**B**:
R4: Contrast the results of all model-generation stages and update the majority-voting ensemble**C**: VisEvol: Visual Analytics to Support Hyperparameter Se... | ACB | CAB | CBA | BAC | Selection 4 |
**A**: Another algorithm is proposed in [28] that assumes the underlying switching network topology is ultimately connected**B**: This assumption means that the union of graphs over an infinite interval is strongly connected**C**: In [29], previous works are extended to solve the consensus problem on networks under lim... | CAB | ACB | ABC | CAB | Selection 3 |
**A**: By doing so, we obtain the initial U𝑈Uitalic_U and Q𝑄Qitalic_Q. We refer to this method of synchronising the ZoomOut results as ZoomOut+Sync, which directly serves as initialisation for HiPPI and our method**B**: In contrast, HiPPI and our method require shape-to-universe representations. To obtain these, we u... | ACB | CBA | BAC | CBA | Selection 3 |
**A**: We presented the first recognition algorithm for both path graphs and directed path graphs**B**: Thus, now these two graph classes can be recognized in the same way both theoretically and algorithmically.
**C**: Both graph classes are characterized very similarly in [18], and we extended the simpler characteriza... | BAC | CBA | ACB | CBA | Selection 3 |
**A**: 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.**B**: From subfigure (c), under the MMSB model, we can find that Mixed-SLIM, Mixed-SCORE, OCCAM, and GeoNMF have similar performa... | ACB | BAC | CBA | BCA | Selection 3 |
**A**: (2016); Chen et al. (2016); Dalalyan (2017); Chen et al. (2017); Raginsky et al. (2017); Brosse et al. (2018); Xu et al**B**: See, e.g., Welling and Teh (2011); Chen et al. (2014); Ma et al. (2015); Chen et al. (2015); Dubey et al. (2016); Vollmer et al**C**: (2018); Cheng and Bartlett (2018); Chatterji et al. (... | BCA | BAC | ACB | BCA | Selection 2 |
**A**: The mixedh is a mixed high traffic flow with a total flow of 4770 in one hour, in order to simulate a heavy peak**B**: The difference from the mixedl setting is that the arrival rate of vehicles during 1200-1800s increased from 0.33 vehicles/s to 4.0 vehicles/s. The data statistics are listed in Tab. II.**C**:
... | CBA | BCA | CBA | BAC | Selection 2 |
**A**: However, as discussed in Section 2, such algorithms belong to a class that is tailored to worst-case competitive analysis, and do not tend to perform well in typical instances (?)**B**: To obtain the best theoretical performance, we can choose A𝐴Aitalic_A as the algorithm of the best known competitive ratio, th... | CBA | CBA | ABC | BAC | Selection 4 |
**A**: This function aims to find a mapping between a canonical 2D patch to the 3D patch on the surface of the target mesh**B**: Practically speaking, our approach transforms the embedding of point cloud obtained from the base model to parametrize the bijective function represented by the MLP network**C**: We condition... | BCA | BAC | ABC | CAB | Selection 2 |
**A**: By using batching technique, the results can be generalized to stochastic saddle-point problems [15, 23]**B**: Instead of the smooth convex-concave saddle-point problem we can consider general sum-type saddle-point problems with common variables in more general form. For each group of common variable, we introdu... | BCA | BAC | ACB | ABC | Selection 1 |
**A**:
Different classes of cycle bases can be considered**B**: Among these classes we can find the strictly fundamental class.**C**: In [6] the authors characterize them in terms of their corresponding cycle matrices and present a Venn diagram that shows their inclusion relations | ABC | BAC | ABC | ACB | Selection 4 |
**A**: One immediate application of Theorem 1.2 is the reduction of fractional Helly numbers**B**: For instance, it easily improves a theorem444[35, Theorem 2.3] was not phrased in terms of (K,b)𝐾𝑏(K,b)( italic_K , italic_b )-free covers but readily generalizes to that setting, see Section 1.4.1**C**: of Patáková [35... | CBA | BAC | ACB | ABC | Selection 4 |
**A**: G1: Division of data space into slices based on predicted probabilities, for transparent local feature contribution.
Our goal is to assist in the search for distinctive features that might contribute more to instances that are harder or easier to classify (T1)**B**: By splitting the data space into quadrants, we... | ABC | BAC | CAB | CBA | Selection 1 |
**A**: The right panel shows the evolution of BO iterations, until optimization terminates**B**: The performance metrics evaluating infinitytracking accuracy and time are summarized in the table for unconstrained and constrained BO.**C**:
Figure 5: Position, velocity, acceleration, and maximal contour error resulting ... | BCA | CAB | CBA | ABC | Selection 1 |
**A**: We show accuracy for each group of CelebA in Table. A3**B**: SD and GDRO obtain the highest accuracies. As discussed previously, we observe trade-offs between blond and non-blond classes with the improvements in the rare blond class incurring degradations in the non-blond class.
**C**: CelebA | ACB | CAB | ACB | BCA | Selection 4 |
**A**: They define the gaze vector starting from the face center to the gaze target [56, 47, 50, 54]. Here we introduce a gaze origin conversion method to bridge the gap between these two types of gaze estimates.**B**: Recently, more attention has been paid to gaze estimation using the face images and they estimate gaz... | BAC | CBA | ABC | BAC | Selection 2 |
**A**: In this experiment, the 10-fold cross-validation strategy is used to evaluate the recognition performance. The experiments are repeated ten times in RMFRD and SMFRD datasets separately, where 9 samples are used as the training set and the remaining sample as the testing set, and the average results are calculate... | CBA | BCA | BCA | BCA | Selection 1 |
**A**: However, !!! indicates objects that persist across reductions**B**:
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**C**: Principal cuts encountered in a configuration are res... | BCA | BAC | CAB | CBA | Selection 2 |
**A**: There are two extra challenges that need to be addressed**B**: For one thing, considering that the original purpose of cloud’s involvement is to help resource-constrained owners efficiently share their media contents, the owner-side overhead needs to be carefully controlled to ensure that owners can obtain sig-n... | CAB | BCA | ACB | ABC | Selection 4 |
**A**: (2023). We randomly split all the instances in 8:1:1 for training, validation, and testing. We adopt the two most popular metrics, AUC and Logloss to measure the probability that one prediction diverges from the ground truth.**B**: Our experiments are conducted on three real-world datasets, two CTR benchmark dat... | CAB | CBA | BCA | BAC | Selection 1 |
**A**:
Requiring access to a zeroth-order and domain oracle are mild assumptions, that were also implicitly assumed in one of the three FW-variants presented in Dvurechensky et al**B**: [2020]; see 5 in Algorithm 4. The remaining two variants ensure that 𝐱∈dom(f)𝐱dom𝑓\mathbf{x}\in\mathrm{dom}(f)bold_x ∈ roman_dom ... | ACB | ABC | CBA | BCA | Selection 1 |
**A**: Indeed, this has been a key property in the qualification of efficiency in parametrized complexity**B**: However, to be considered an efficient approximation algorithm in theory, ideally the dependence on all relevant parameters should be polynomial**C**: The question whether there is a (1+ε)1𝜀(1+\varepsilon)( ... | ACB | BAC | ACB | CAB | Selection 2 |
**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**: We show that B-CPP also achieves linear convergence for minimizing strongly convex and smooth objectives.
**C**: In the second part of this paper, we propose a broadcast-like ... | BAC | BAC | CBA | BCA | Selection 4 |
**A**: Recently, significant attention was devoted to saddle problems in machine learning**B**: 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],**C**: One can note a branch of recent work d... | BCA | CBA | ABC | CBA | Selection 1 |
**A**: Furthermore, the presence of a correlation device does not make (C)CEs prescriptive because the agents still need a mechanism to agree on the distribution the correlation device samples from777This is true if the correlation device is not considered as part of the game. If it was part of the game (for example tr... | ABC | CBA | BAC | CAB | Selection 4 |
**A**: The simpler part of the argument is posterior accuracy, which we prove can be inherited directly from the sample accuracy of a mechanism**B**: (2020), but has the advantage of being independent of the range of the queries.
**C**: This lemma resembles Lemma 6 in Jung et al | ACB | CAB | CBA | BAC | Selection 1 |
**A**: We also prove that, given a large feedback vertex cut, we can shrink it while preserving the antlers in the graph. 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 ... | CAB | BCA | CAB | CBA | Selection 4 |
**A**: As introduced in Section I, the foreground and background in a composite image have multiple types of inconsistencies.
The existing FOS works considered different sets of inconsistencies between background and foreground**B**: For example, the methods [206, 207] considered the semantic consistency**C**: The meth... | CAB | BAC | CAB | ABC | Selection 4 |
**A**: CityNet’s comprehensive and correlated data make it a valuable resource for machine learning tasks in urban computing**B**: These tasks include spatio-temporal predictions and its multi-task variant, spatio-temporal transfer learning, and reinforcement learning**C**: In this paper, we present extensive benchmark... | ABC | CAB | BCA | CBA | Selection 1 |
**A**: All experimental results were obtained by evaluating the models on 50 different train/test-splits of the data sets in Table 2**B**: If a calibration set was needed for post-hoc calibration, the training set was further divided into two equal-sized sets. Because the models were tested both with and without post-h... | CBA | ACB | ABC | ABC | Selection 2 |
**A**: The authors showcased the efficacy of MusicBERT by applying it to two generative music tasks, melody completion and accompaniment suggestion and two sequence-level discriminative tasks, including genre and style classification. In comparison to non PTM-based baselines, MusicBERT consistently led to better perfor... | ACB | CBA | BCA | BAC | Selection 2 |
**A**: As it was stated in the proof of Lemma 2.2, while searching for a central vertex we always jump from a vertex to its neighbor in a way that decreases the largest remaining component by one**B**: Thus, if in the next iteration we start at exactly the neighbor of the previous central vertex, there can be only O(n... | BCA | BAC | CBA | CAB | Selection 1 |
**A**: Regarding the semantic commutations for speech information, our previous work developed an attention mechanism-based semantic communication system to restore the source message, i.e., reconstruct the speech signals[18]**B**: However, in this paper, we consider an intelligent task at the receiver to recover the t... | BAC | BAC | ABC | BAC | Selection 3 |
**A**: Since the CR loss and SR loss from the two modules both trying to pull the segmentation output closer to the outputs of the two branches, the overall training objective may be deviated from our real target, better segmentation performance. With the losses from two modules being optimized together, the model may ... | CBA | ABC | CAB | BAC | Selection 3 |
**A**: We use black box for ground-truth, red box for baseline results, and blue box for our results**B**: Qualitative results of our method for Bird’s-Eye-View**C**: All the illustrated images are from the KITTI val set. Zoom in on the circles for more detailed comparison.
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**A**: \add
For assessing the impact of the backbone network on the overall performance of our model, we conduct additional studies on CTW1500, Total-Text and ICDAR2015**B**: As it shows, when equipped with VGG16, our proposed method has obtained slightly better results than with ResNet50. The same phenomenon has also ... | BCA | CBA | BCA | 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**: With the increase in the generation of big data, millions or tens of millions of records have become ubiquit... | ABC | ACB | ABC | CAB | Selection 2 |
**A**: Li is partially supported by the National Natural Science Foundation of China No. 11971221 and the Shenzhen Sci-Tech Fund No. RCJC20200714114556020, JCYJ20170818153840322 and JCYJ20190809150413261, and Guangdong Provincial Key Laboratory of Computational Science and Material Design No. 2019B030301001.**B**: The ... | CAB | CBA | BAC | BAC | Selection 1 |
**A**: For CIFAR-10 we search for a learning rate in the range [0.0001, 0.00001], for MIMIC-III we search in the range [0.1, 0.001], and for ModelNet40 we search in the range [0.001, 0.00005]**B**:
In each experiment, for each value of Q𝑄Qitalic_Q, we choose the learning rate using a grid search**C**: For each Q𝑄Qit... | CBA | ACB | BCA | BAC | Selection 4 |
**A**: In this section, we delve into the study of pseudospectra for third-order tensors within the tensor-tensor multiplication framework**B**: Subsection 4.2 is dedicated to the examination of various properties of pseudospectra related to third-order tensors. Finally, in Subsection 4.3, we present plots illustrating... | ABC | ABC | ACB | CAB | Selection 3 |
**A**: These alternatives show the results with improved structures and textures. Unfortunately, acquiring reasonable edges from corrupted images is itself a very challenging task, and taking unstable structural priors tends to incur large errors in those series-coupled frameworks.**B**: For instance, EdgeConnect [18] ... | ABC | BCA | ACB | CBA | Selection 4 |
**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 ... | BAC | ACB | CBA | ABC | Selection 3 |
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