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**A**: Another generating set which has become important in algorithms and applications in the last 10-15 years is the Leedham-Green and O’Brien standard generating set in the following called the LGO generating set. These generators are defined for all classical groups in odd characteristic in [11] and even characteri... | BAC | CAB | CBA | CAB | Selection 3 |
**A**: The solutions of (22) decay exponentially fast if w𝑤witalic_w has local support, so instead of solving the problems in the whole domain it would be reasonable to solve it locally using patches of elements**B**: We note that the idea of performing global static condensation goes back to the Variational Multiscal... | BCA | BAC | ACB | ABC | Selection 1 |
**A**: Moreover, Alg-A is more stable than the alternatives.
During the iterations of Alg-CM, the coordinates of three corners and two midpoints of a P-stable triangle (see Figure 37) are maintained**B**: These coordinates are computed somehow and their true values can differ from their values stored in the computer**C... | CAB | CBA | ABC | ACB | Selection 3 |
**A**: Single Tweet Model Settings. For the evaluation, we shuffle the 180 selected events and split them into 10 subsets which are used for 10-fold cross-validation (we make sure to include near-balanced folds in our shuffle)**B**: We implement the 3 non-neural network models with Scikit-learn444scikit-learn.org/. Fur... | CBA | ABC | BCA | CAB | Selection 2 |
**A**: Under additional assumptions on the asymptotic convergence of update directions and gradient directions, they were able to relate the direction of gradient descent iterates on the factorized parameterization asymptotically to the maximum margin solution with unit nuclear norm**B**: Unlike the case of squared los... | ABC | CBA | CBA | BCA | Selection 4 |
**A**: It can be seen that although the credibility of some tweets are low (rumor-related), averaging still makes the CreditScore of Munich shooting higher than the average of news events (hence, close to a news). In addition, we show the feature analysis for ContainNews (percentage of URLs containing news websites) fo... | ABC | ABC | CBA | CAB | Selection 4 |
**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 modified the objective function of RankSVM following our g... | ACB | CAB | CBA | BAC | Selection 1 |
**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. | BAC | BAC | ABC | CAB | Selection 3 |
**A**: By contrast, the remaining metrics quantify saliency approximations after convolving gaze locations with a Gaussian kernel and representing the target output as a probability distribution. We refer readers to an overview by Bylinskii et al. (2018) for more information regarding the implementation details and pro... | ABC | CBA | BCA | ABC | Selection 2 |
**A**: More precisely, by plugging together our reductions from MinCutwidth to MinLoc and from MinLoc to MinPathwidth, we obtain a reduction which directly transfers approximation results from MinPathwidth (e. g., the ones of [21, 30]; see the discussion of Section 1.1) to MinCutwidth. On the one hand, this reduction y... | BCA | ACB | CAB | CBA | Selection 4 |
**A**: We present an approach, called Simulated Policy Learning (SimPLe), that utilizes these video prediction techniques and trains a policy to play the game within the learned model. With several iterations of dataset aggregation, where the policy is deployed to collect more data in the original game, we learn a poli... | BCA | ACB | CBA | ABC | Selection 3 |
**A**:
Hybrid robots typically transition between locomotion modes either by “supervised autonomy” [11], where human operators make the switch decisions, or the autonomous locomotion mode transition approach, where robots autonomously swap the modes predicated on pre-set criteria [8]. However, the execution of supervi... | ABC | BCA | CAB | ACB | Selection 4 |
**A**: In particular, it is possible to achieve a competitive ratio of 1.4702 with only a constant number of (trusted) advice bits [2]**B**: A restricted version of the bin packing problem, where items take sizes from a discrete set {1/k,2/k,…,1}1𝑘2𝑘…1\{1/k,2/k,\ldots,1\}{ 1 / italic_k , 2 / italic_k , … , 1 }, for s... | CAB | ABC | BCA | BAC | Selection 3 |
**A**: In Figure 9 is shown an example of a piece of what could be a visual description of the classification process for the subject 9579292929Note that this is the same subject who was previously used in the example shown in Figure 2, in subsubsection 3.1.1**B**: The interested readers could see the relation between ... | BCA | BAC | CAB | ACB | Selection 1 |
**A**: GMC+ can also be easily implemented on all-reduce frameworks.**B**: We improve DEF-A by changing its local momentum to global momentum, getting a new method called GMC+**C**: The detail of GMC+ is shown in Algorithm 2.
We also adopt parameter server architecture for illustration | CBA | CBA | BCA | CAB | Selection 4 |
**A**: operation.**B**: Using cross-correlation would produce the same results and would not require flipping the kernels during visualization**C**:
, where ∗*∗ is the convolution333We use convolution instead of cross-correlation only as a matter of compatibility with previous literature and computational frameworks | ACB | CBA | BAC | ACB | Selection 2 |
**A**: Moreover, more UAVs can cover more area and support more users, which also corresponds with more utilities. Fig. 12 also shows how many iterations that UAV ad-hoc network needs to approach to convergence. With the number of UAVs improves, more iterations are required in this network.**B**: The goal functions’ va... | ABC | ACB | ACB | CBA | Selection 4 |
**A**: in figure 5**B**: Here, we will look at the derivation of Dr¯¯¯¯𝐷𝑟\overline{\overline{Dr}}over¯ start_ARG over¯ start_ARG italic_D italic_r end_ARG end_ARG,
the derivation of Dz¯¯¯¯𝐷𝑧\overline{\overline{Dz}}over¯ start_ARG over¯ start_ARG italic_D italic_z end_ARG end_ARG is analogous**C**: The node-to-nod... | CBA | CAB | ABC | CAB | Selection 3 |
**A**:
To evaluate the Dropout-DQN, we employ the standard reinforcement learning (RL) methodology, where the performance of the agent is assessed at the conclusion of the training epochs**B**: We have evaluated Dropout-DQN algorithm on CARTPOLE problem from the Classic Control Environment. The game of CARTPOLE was se... | ACB | BCA | BAC | ABC | Selection 1 |
**A**: They are defined as follows:
**B**: The quantitative evaluation of segmentation models can be performed using pixel-wise and overlap based measures**C**: For binary segmentation, pixel-wise measures involve the construction of a confusion matrix to calculate the number of true positive (TP), true negative (TN), ... | CBA | CAB | CBA | ABC | Selection 2 |
**A**: The overall performance is shown in the last column.
Due to the stochastic process when training the random forests, the results can vary marginally.**B**: The number of parameters of the resulting network is evaluated on all datasets with different numbers of training examples**C**: Current state-of-the-art met... | ACB | CBA | ABC | ABC | Selection 2 |
**A**: In comparison, we focus on policy-based reinforcement learning, which is significantly less studied in theory. In particular, compared with the work of Yang and Wang (2019b, a); Jin et al. (2019); Ayoub et al. (2020); Zhou et al. (2020), which focuses on value-based reinforcement learning, OPPO attains the same ... | CAB | CAB | BAC | CBA | Selection 4 |
**A**: (2018b) introduced a ResNet-inspired architecture called ShuffleNet which employs 1×1111\times 11 × 1 grouped convolutions since 1×1111\times 11 × 1 convolutions have been identified as computational bottlenecks in previous works, e.g., see Howard et al. (2017a).**B**: Although this reduces the expressiveness of... | CAB | ACB | BCA | ABC | Selection 1 |
**A**: Despite the existence of a complete answer to the question for the case of 𝕊1superscript𝕊1\mathbb{S}^{1}blackboard_S start_POSTSUPERSCRIPT 1 end_POSTSUPERSCRIPT [4] due to Adams and Adamaszek, relatively little is known for higher dimensional spheres**B**: Of central interest in topological data analysis has b... | BAC | ACB | CAB | BCA | Selection 1 |
**A**: In this paper, we introduced t-viSNE, an interactive tool for the visual investigation of t-SNE projections**B**: By partly opening the black box of the t-SNE algorithm, we managed to give power to users allowing them to test the quality of the projections and understand the rationale behind the choices of the a... | ABC | ACB | BAC | ACB | Selection 1 |
**A**:
In this same line of reasoning, the largest subcategory of the second taxonomy (Differential Vector Movements guided by representative solutions) not only contains more than half of the reviewed algorithms (almost 60%), but it also comprises algorithms from all the different categories in the first taxonomy: So... | CBA | BCA | ABC | BCA | Selection 3 |
**A**: To illustrate the process of AdaGAE, Figure 2 shows the learned embedding on USPS at the i𝑖iitalic_i-th epoch**B**: An epoch means a complete training of GAE and an update of the graph**C**: The maximum number of epochs, T𝑇Titalic_T, is set as 10. In other words, the graph is updated 10 times. Clearly, the emb... | CAB | ACB | ABC | CBA | Selection 3 |
**A**: It is much easier to setup a Web server than Email server or DNS server. Considering that DNS servers and Email servers are more likely to be hosted by providers, they also have higher probability to get new system updates**B**: Furthermore, we find that when a Web server is not available (“N/A”), both Email and... | BCA | ACB | ACB | ABC | Selection 1 |
**A**: In such cases, the recurrent pathway can identify useful patterns analagously to how cortical regions help the olfactory bulb filter out previously seen background information [21]**B**: A context-based approach will be applied to longer-timescale data and to environments with cyclical patterns.
**C**: The estim... | ACB | BCA | BAC | ACB | Selection 2 |
**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**: For semigroups, on the other hand, such results do exist**C**: However, there do not seem to be con... | CBA | CAB | ABC | BAC | Selection 1 |
**A**: However, in the test set, answer ‘yes’ is more frequent. Regularization effects caused by HINT/SCR and our method cause the models to weaken this prior i.e., reduce the tendency to just predict ‘no’, which would increase accuracy at test because ‘yes’ is more frequent in the test set. Next, all of the methods pe... | ACB | ABC | BCA | CBA | Selection 4 |
**A**:
Prior work in privacy and human-computer interaction establishes the motivation for studying these documents. Although most internet users are concerned about privacy (Madden, 2017), Rudolph et al**B**: (2015) introduced methods to ease the design of privacy notices and their integration, and Kelley et al. (201... | CBA | ACB | CAB | BCA | Selection 2 |
**A**: The boxplots below the projection show the performance of the models per metric.**B**: Thus, groups of points represent clusters of models that perform similarly according to all the metrics.
A summary of the performance of each model according to all selected and user-weighted metrics is color-encoded using the... | CBA | ABC | ACB | ABC | Selection 1 |
**A**: Task similarity. In Persona and Weibo, each task is a set of dialogues for one user, so tasks are different from each other. We shuffle the samples and randomly divide tasks to construct the setting that tasks are similar to each other**B**: For a fair comparison, each task on this setting also has 120 and 1200 ... | BAC | CBA | ABC | BCA | Selection 3 |
**A**: Recall that several efficient codebook-based beam training and tracking schemes have been proposed for conventional mmWave network with uniform ULA and UPA [22, 23]. These prior works inspire us to propose a specialized new codebook design and the corresponding codeword selection/processing strategy that can dri... | CAB | ACB | CBA | ABC | Selection 1 |
**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 | ABC | ACB | BCA | Selection 3 |
**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... | ACB | BAC | CBA | ABC | Selection 4 |
**A**: The computation of depth-wise LSTM is the same as the conventional LSTM except that depth-wise LSTM connects stacked Transformer layers instead of tokens in a token sequence as in conventional LSTMs. The gate mechanisms in the original LSTM are to enhance its ability in capturing long-distance relations and to a... | BCA | BAC | CBA | ACB | Selection 4 |
**A**: Hence, g𝑔gitalic_g
is spectral as well**B**: The uniqueness of g𝑔gitalic_g follows from the**C**: some i∈I𝑖𝐼i\in Iitalic_i ∈ italic_I and K∈𝒦∘(X)𝐾superscript𝒦𝑋K\in\mathcal{K}^{\circ}\!\left(X\right)italic_K ∈ caligraphic_K start_POSTSUPERSCRIPT ∘ end_POSTSUPERSCRIPT ( italic_X ), hence g−1(fi−1(K))=gi... | BCA | CAB | CAB | BAC | Selection 1 |
**A**: Next, we introduce the network architecture and training loss in Section III-B. Finally, Section III-C describes the transformation between the ordinal distortion and distortion parameter.**B**: We first define the proposed objective in Section III-A**C**:
In this section, we describe how to learn the ordinal d... | BCA | CBA | ABC | CAB | Selection 2 |
**A**: In small-batch training, SNGM and LARS achieve validation perplexity comparable to that of MSGD**B**:
Figure 3 shows the validation perplexity of the three methods with a small batch size of 20 and a large batch size of 2000**C**: Meanwhile, in large-batch training, SNGM achieves better performance than MSGD an... | CBA | ABC | ABC | BAC | Selection 4 |
**A**: First, we develop algorithms for the simpler polynomial-scenarios model. Second, we sample a small number of scenarios from the black-box oracle and use our polynomial-scenarios algorithms to (approximately) solve the problems on them**B**: Finally, we extrapolate the solution to the original black-box problem. ... | ACB | BCA | CBA | CBA | Selection 2 |
**A**:
II**B**: The sequence of random digraphs is conditionally balanced, and the weighted adjacency matrices are not required to have special statistical properties such as independency with identical distribution, Markovian switching, or stationarity, etc. The edge weights are also not required to be nonnegative at... | CAB | ACB | ABC | BAC | Selection 2 |
**A**: Then, the adversary does not know the partition of groups and the random output table including the range and probabilities of the random output values of Helen**B**: Suppose that an adversary aims to find the record of Helen in the anonymized table by matching her age value of 28, and the anonymization process ... | ACB | BAC | ACB | ACB | Selection 2 |
**A**: “EnrichFeat” means enhance the feature representation of coarse mask head and point head by increasing the number of fully-connected layers or its hidden sizes**B**: “BFP” means Balanced Feature Pyramid. Note that BFP and EnrichFeat gain little improvements, we guess that our PointRend baseline already achieves ... | ABC | CAB | BAC | BCA | Selection 4 |
**A**: This solves a question raised by Gady Kozma some time ago (see [K], comment from April 2, 2011)**B**: More specifically, we proved**C**:
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\... | CBA | BAC | ACB | BCA | Selection 4 |
**A**: Although Random-Exploration takes the least time, it cannot find the near-optimal policy. This result further demonstrates that our algorithms are not only sample-efficient, but also computationally tractable.**B**: This is because LSVI-UCB-Restart and Ada-LSVI-UCB-Restart can automatically restart according to ... | CBA | ACB | ACB | CAB | Selection 1 |
**A**: Many studies worldwide have observed the proliferation of fake news on social media and instant messaging apps, with social media being the more commonly studied medium. In Singapore, however, mitigation efforts on fake news in instant messaging apps may be more important**B**: These suggest that, in Singapore, ... | BAC | ACB | BAC | ABC | Selection 2 |
**A**: We initialize the input entity embeddings, relation embeddings, and weight matrices using the Xavier initializer [64]. Detailed parameter settings can be found in Table 3.
**B**: In our experiments, we employ ComplEx [30] and DistMult [29] as the decoders due to their superior performance without compromising ef... | CBA | ABC | BAC | BAC | Selection 1 |
**A**: Conducting exploration without the extrinsic rewards is called the self-supervised exploration. From the perspective of human cognition, the learning style of children can inspire us to solve such problems**B**: By extending such idea to RL domain, the ‘intrinsic’ rewards are used in RL to incentivize exploratio... | CBA | ABC | ACB | CBA | Selection 3 |
**A**: We complement the established notion of unisolvent nodes by the dual notion of unisolvence**B**: 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**C**: In doing so, we revisit earlier results by Carl de ... | CAB | BAC | CAB | ABC | Selection 4 |
**A**: Furthermore, even though it involves two stages, the end result is a single model which does not rely on any auxiliary models, additional hyper-parameters, or hand-crafted loss functions, as opposed to previous works addressing the problem (see Section LABEL:sec:related for a survey of related work)**B**: The ke... | BCA | BAC | CAB | ACB | Selection 1 |
**A**: The cause of these errors is the basic nature of electric charges: ‘flowing from high potential to low’**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**:
Exploration based on previous... | CAB | CAB | ABC | BCA | Selection 4 |
**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**: Some well-studied families of polynomials include the Dickson polynomials and reverse Dickson polynomials, to name a few**C**: Conditions for such fa... | CAB | ACB | CAB | ABC | Selection 4 |
**A**:
The true positive rate in view selection for each of the meta-learners can be observed in Figure 2. Ignoring the interpolating predictor for now, nonnegative ridge regression has the highest TPR, which is unsurprising seeing as it performs feature selection only through its nonnegativity constraints**B**: Nonne... | ABC | BAC | ACB | CBA | Selection 1 |
**A**: Each dot represents the performance of a combination of the other two phases. The consistent shapes in Figure 5 suggest that the anomaly score generation techniques do not significantly alter the distribution of the 25 combinations**B**: This implies that the choices of methods for relevant variable selection an... | ABC | BCA | ACB | CBA | Selection 2 |
**A**: We note that Ou et al**B**: Though they establish a regret bound that does not depend on the aforementioned parameter κ𝜅\kappaitalic_κ, they work with an inaccurate version of the MNL model. More specifically, in the MNL model, the probability of a consumer preferring an item is proportional to the exponential ... | ACB | BAC | CBA | CAB | Selection 1 |
**A**: b) Long video is cut into multiple short clips. c) Each video clip is up-scaled along the temporal dimension**B**: d) Original clip (green dots) and up-scaled clip (orange dots) are stitched into one feature sequence with a gap. **C**:
Figure 3: Video self-stitching (VSS). a) Snippet-level features are extracte... | CAB | BCA | ABC | ABC | Selection 2 |
**A**: Figure 4(c) suggests that M2+M3 are better for the Healthy class, while M1 is better for the Diseased class. M4 is somewhere in-between but very powerful overall. By keeping the balance in this ensemble, we achieve the highest recorded performance for our analysis (cf. horizontal bar chart in Figure 4(d))**B**: ... | ABC | CBA | BAC | BCA | Selection 3 |
**A**: This assumption means that the union of graphs over an infinite interval is strongly connected**B**: In [29], previous works are extended to solve the consensus problem on networks under limited and unreliable information exchange with dynamically changing interaction topologies. The convergence of the algorithm... | ACB | BCA | ABC | CAB | Selection 2 |
**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... | CAB | CBA | CBA | BAC | Selection 4 |
**A**: Interval graphs are characterized by Lekkerkerker and Boland [15] as chordal graphs with no asteroidal triples, where an asteroidal triple is a stable set of three vertices such that each pair is connected by a path avoiding the neighborhood of the third vertex**B**: A graph is an interval graph if it is the int... | BCA | CBA | ABC | BAC | Selection 4 |
**A**: Mixed-SLIM and Mixed-SCORE perform similarly and both two approaches perform better than OCCAM and GeoNMF under the MMSB setting. Meanwhile, Mixed-SLIM significantly outperforms the other three methods under the DCMM setting.**B**:
Panels (e) and (f) of Figure 1 report the numerical results of these two sub-exp... | CBA | BAC | CAB | 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. (... | CBA | CBA | BAC | CAB | Selection 3 |
**A**: The road network map of Shenzhen is made by ourselves which is derived from OpenStreetMap333https://github.com/zhuliwen/RoadnetSZ. The road networks of Jinan and Hangzhou contain 12 and 16 intersections in 4×3434\times 34 × 3 and 4×4444\times 44 × 4 grids, respectively. The road network of New York includes 48 i... | BAC | BCA | ACB | CBA | Selection 2 |
**A**: That is, no algorithm, online or offline, can perform better than this lower bound.**B**: As often in offline bin packing, we also report the L2 lower bound (?, ?) as a lower-bound estimation of the optimal offline bin packing solution**C**:
As explained earlier, FirstFit and BestFit perform very well in practi... | ABC | BCA | BAC | CBA | Selection 4 |
**A**: 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**B**: We condition the positioning and shape of a 3D patch using a single point from a point cloud generated by a base model. We repeat the ... | CAB | CAB | BCA | ACB | Selection 4 |
**A**: The Mirror-prox algorithm can be performed in a decentralized manner, however, it is not known whether its optimality is preserved.
In this paper, we prove that Mirror-prox remains optimal even in a decentralized case w.r.t**B**: the dependence on the desired accuracy ε𝜀\varepsilonitalic_ε and condition number ... | ABC | BCA | ACB | CAB | Selection 1 |
**A**: We proceed by trying to find a counterexample based on our previous observations**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**: In this section we present ... | ABC | BCA | CAB | CBA | Selection 2 |
**A**:
The proof of Theorem 2.1 is quite involved and builds on the method of constrained chain maps developed in [18, 35] to study intersection patterns via homological minors [37]**B**: This technique, which we briefly outline here, was specifically designed for complete intersection patterns**C**: A major part of t... | ABC | ACB | BAC | ACB | Selection 1 |
**A**: T5: Evaluate the results of the feature engineering process.
At any stage of the feature engineering process (T2–T4), a user should be able to observe the fluctuations in performance with the use of standard validation metrics (e.g., accuracy, precision, and recall) [32]**B**: Also, users could possibly want to ... | BCA | BCA | ACB | ABC | Selection 4 |
**A**: For the initialization phase needed to train the GPs in the Bayesian optimization, we select 20 samples over the whole range of MPC parameters, using Latin hypercube design of experiments**B**: The BO progress is shown in Figure 5, right pannel, for the optimization with constraints on the jerk and on the tracki... | ABC | CAB | BAC | ACB | Selection 1 |
**A**: For Biased MNISTv1, the hyperparameters were selected using single run, considering ‘distractor shape’ as the explicit bias variable for explicit methods. For CelebA, they were selected based on the unbiased accuracy/mean per group on the validation set and for GQA-OOD, they were selected based on the best mean ... | CBA | ACB | BCA | CAB | Selection 4 |
**A**: We further suggest several future directions of deep learning-based gaze estimation.
1) Extracting more robust gaze features**B**: The perfect gaze estimation method should be accurate under all different subjects, head poses and environments**C**: Therefore, an environment-invariant gaze feature is crucial. | BAC | ABC | ACB | ACB | Selection 2 |
**A**: In this paper, we focus on the last two datasets described in the following.**B**:
Real-World-Masked-Face-Dataset wang2020masked is a masked face dataset devoted mainly to improve the recognition performance of the existing face recognition technology on the masked faces during the COVID-19 pandemic**C**: It c... | CAB | ABC | BCA | BAC | Selection 1 |
**A**: In contrast to the termination proof for base SAX [DPP20], we explicitly construct a model of SAX in sets of terminating configurations, also known as semantic typing [App01, HLKB21]. This leaves open several possibilities—for example, we could reason about programs that fail to syntactically typecheck [JJKD17, ... | BCA | CBA | BAC | ACB | Selection 2 |
**A**: After receiving the owner’s request for arbitration, the judge makes a fair judgment based on the evidence provided by the owner. Although only the encrypted version of the user’s watermark is disclosed, the encrypted watermark can be converted into a ciphertext that can be decrypted by the judge based on PRE (f... | BCA | CAB | ABC | BAC | Selection 1 |
**A**: (2014) to update the node representations based on the aggregated neighborhood feature information.**B**: The high-order relations between nodes can be modeled explicitly by stacking layers.
Gated Graph Neural Networks (GGNN) Li et al**C**: (2015) uses GRU Cho et al | CAB | BCA | ABC | CBA | Selection 1 |
**A**: Table 1:
Number of iterations needed to achieve an ε𝜀\varepsilonitalic_ε-optimal solution for Problem 1.1**B**: We denote line search by LS, zeroth-order oracle by ZOO, second-order oracle by SOO, domain oracle by DO, local linear optimization oracle by LLOO, and the assumption that 𝒳𝒳\mathcal{X}caligraphic_... | BAC | ABC | ACB | CAB | Selection 2 |
**A**: Our algorithms “puts on hold” (or pauses) DFS over search trees that become too large**B**: Note that pausing DFS execution of some search trees increases the time required to explore the entire graph**C**: Nevertheless, we show how to set parameters so that putting on hold DFS over large trees increases the num... | CBA | ABC | CBA | CBA | Selection 2 |
**A**:
We propose CPP – a novel decentralized optimization method with communication compression**B**: To the best of our knowledge, CPP is the first method that enjoys linear convergence under such a general setting.**C**: The method works under a general class of compression operators and is shown to achieve linear ... | BCA | BCA | ACB | BCA | Selection 3 |
**A**: The following method (Algorithm 3) is also sharpened on the alternation of local iterations and communications, but it makes them more evenly**B**: Our method is similar to the randomized local methods (for example, as the method from [31]), but it uses not only importance sampling, but also implicit variance re... | BCA | BAC | CBA | ABC | Selection 1 |
**A**:
The new solution concept MG(C)CE is rooted in the powerful principles of entropy and margin maximisation**B**: The MG(C)CE defines a family of unique solutions parameterized by ϵitalic-ϵ\epsilonitalic_ϵ, that can control for the properties of the distribution. We have compared it to other NE, CE, and α𝛼\alphai... | BAC | ACB | ABC | CAB | Selection 2 |
**A**: This simple lemma is at the heart of the progress that we make in this paper, both in our intuitive understanding of adaptive data analysis, and in the concrete results we show in subsequent sections. Its corresponding version for arbitrary queries are presented in Section C.2.**B**: Simply put, the Bayes factor... | BAC | CBA | BAC | ACB | Selection 2 |
**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... | BAC | ABC | CBA | CAB | Selection 1 |
**A**: Zhang et al**B**: [2] employed STN to warp the foreground and relative appearance flow network to change the viewpoint of foreground. Additionally, they investigated on self-consistency constraint, that is, the generated composite image could be decomposed back to the foreground and background.**C**: [202] propo... | CAB | CBA | ABC | ACB | Selection 4 |
**A**: By doing so, we aim to provide a deeper understanding of the interconnections between different data sources, which can inform the development of more effective transportation policies and strategies.
**B**: In this section, we leverage data mining tools to explore and visualize the relationships between service... | BAC | ABC | ABC | CBA | Selection 4 |
**A**:
A further aspect that was not considered in this study is the conditional behaviour of the models**B**: When constructing a model that optimizes the coverage probability (1), only the marginal coverage is controlled, i.e. the specific properties of an instance are not taken into account**C**: In certain cases i... | ABC | ACB | CAB | BAC | Selection 1 |
**A**: Pre-training boosts the classification accuracy for the GPT2 model greatly from 46% to 70%. However, the symbolic data format considered in their work is “sheet music image” \parencitetsai20ismir, which are images of musical scores. This data format has been much less used than MIDI in the literature.**B**:
To ... | CBA | CAB | BCA | ACB | Selection 2 |
**A**: Next, let us count the total number of jumps necessary for finding central vertices over all loops in Algorithm 1**B**: 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**C**... | BAC | BAC | BCA | ABC | Selection 4 |
**A**: Moreover, the transcription is obtained from the recovered speech signals after passing through an automatic speech recognition (ASR) module. For the system, the adaptive multi-rate wideband (AMR-WB)[21] is used for speech source coding and 64-QAM is utilized for modulation**B**:
The first benchmark is a tradit... | ABC | BCA | BAC | CAB | Selection 3 |
**A**: This brevity arises from the utilization of simpler backbone networks in WeakSup and the necessity for iterative training on the same network in One-Thing-One-Click. Notably, the inference duration of our proposed method aligns precisely with that of the fully supervised KPConv[4]. This parity in inference times... | CBA | CAB | ACB | CBA | Selection 2 |
**A**: only height considered in most existing works [19, 5] similar to the Geo-SV2. Finally, Ours (full model) is 1.11% and 1.98% improvement on the moderate for the 3D detection and BEV, respectively, which adequately demonstrate the effectiveness of our proposed approach.**B**: As we can observe,
ours (full model) a... | ABC | CAB | ABC | BCA | Selection 2 |
**A**: The feature aggregation step in
graph reasoning can also differentiate true positive text segments by relational reasoning**B**: This establishes a backward feedback mechanism that can retrieve and suppress false detections (including both false positives and false negatives) during link prediction**C**: Classif... | ABC | BCA | CBA | CAB | Selection 1 |
**A**: In this paper, we present two efficient algorithms for collecting the statistics of large-scale IP address data. We can obtain the frequently occurring IP addresses from the statistics, which can be regarded as a pre-processing step of user behavior analysis in network traffic management. Because of the increasi... | ACB | BAC | BCA | CAB | Selection 1 |
**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 ... | BAC | BCA | CAB | CBA | Selection 3 |
**A**: At the same time, the users do not wish to share raw personal data with the companies**B**: As a motivating example, we consider two smartphone application providers who wish to train a global model over the datasets stored on the smartphones of their respective customer bases.
Here, the two application companie... | BAC | ACB | CAB | ACB | Selection 1 |
**A**: The properties of pseudospectra are also discussed, along with a characterization of the pseudospectra for normal matrices. Additionally, for diagonalizable but not necessarily normal matrices, the corresponding Bauer-Fike theorem is presented, which can be found in (trefethen2005spectra, , Theorems 2.2, 2.3, an... | CBA | ABC | BAC | BCA | Selection 3 |
**A**: At the decoding stage, the texture decoder synthesizes structure-constrained textures by borrowing structure features from the structure encoder, while the structure decoder recovers texture-guided structures by taking texture features from the texture encoder. With such a dual generation architecture, structure... | ACB | ABC | CBA | ACB | Selection 3 |
**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 ... | ABC | ABC | BAC | CBA | Selection 4 |
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