Split (1)

train · 2.93k rows

shuffled_text stringlengths 267 3.16k	A stringclasses 6 values	B stringclasses 6 values	C stringclasses 6 values	D stringclasses 6 values	label stringclasses 4 values
A: Consequently, algorithms in the composition tree data structure, both in MAGMA and in GAP, store elements in classical groups as words in the LGO generators. Moreover, the LGO generators can be used directly to verify representations of classical groups [12]. B: In practice it is the generating set produced ...	BAC	CBA	ACB	BAC	Selection 2
A: We note that the idea of performing global static condensation goes back to the Variational Multiscale Finite Element Method–VMS [MR1660141, MR2300286]. Recently variations of the VMSB: The solutions of (22) decay exponentially fast if w𝑤witalic_w has local support, so instead of solving the problems in the...	BCA	BAC	ACB	CBA	Selection 4
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...	BCA	ACB	ACB	CBA	Selection 4
A: We call them “debunking words” e.g., hoax, rumor or not true. Our intuition is, that the attitude of doubting or denying events is in essence sufficient to distinguish rumors from news. What is more, this generalization augments the size of the crowd (covers more ’voting’ tweets), which is crucial, and thus cont...	CBA	CBA	BCA	CAB	Selection 4
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 normB: Unlike the case of squared los...	BCA	BAC	ACB	CBA	Selection 1
A: Our system already achieves 87% accuracy in 25 hours. We illustrate two examples here in Figures 12(a) and 12(b). Figure 12(a) is a rumor about ‘Okra curing diabetes’ 161616http://www.snopes.com/medical/homecure/okra.asp which we detected the beginning time is 01.31.2014 04:00B: Snope debunked it at 01.28.20...	BCA	CAB	CBA	ABC	Selection 1
A: We then evaluate our ensemble ranking model (results from the cascaded evaluation) and show it robustly improves the baselines for all studied cases (RQ3). Notice that, we do not use the learned classifier in Section 5.2 for our ensemble model, since they both use the same time period for training, but opt for t...	BAC	ABC	CBA	ACB	Selection 3
A: Table 1 shows basic patient information. Half of the patients are female and ages range from 17 to 66, with a mean age of 41.8 yearsB: 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 y...	ACB	BCA	BAC	CAB	Selection 1
A: Table 2: Quantitative results of our model for the CAT2000 test set in the context of prior workB: The first line separates deep learning approaches with architectures pre-trained on image classification (the superscript ††{}^{\dagger}start_FLOATSUPERSCRIPT † end_FLOATSUPERSCRIPT represents models with a VG...	ABC	BAC	ACB	CAB	Selection 1
A: Any symbol that is marked next in a marking sequence can have isolated occurrences (i. e., occurrences that are not adjacent to any marked block) and block-extending occurrences (i. e., occurrences with at least one adjacent marked symbol)B: In the following, we investigate another aspect of greedy strategie...	BCA	CBA	BAC	BCA	Selection 3
A: (2017). For Rainbow, we used the implementation from the Dopamine package and spent considerable time tuning it for sample efficiency (see Appendix E). B: (2018) and PPO Schulman et alC: We evaluate our method on 26262626 games selected on the basis of being solvable with existing state-of-the-art model-...	ABC	CAB	CBA	BAC	Selection 3
A: The cornerstone of our transition criterion combines energy consumption data with the geometric heights of the steps encounteredB: These threshold values are determined in energy evaluations while the robot operates in the walking locomotion modeC: To analyze the energy dynamics during step negotiation ...	ACB	BAC	CBA	ABC	Selection 4
A: Hence, untrusted advice can result in a competitive ratio as bad as 6. B: In this case, all critical bins are filled up to a level slightly more than 1/6161/61 / 6C: The worst case is reached when tiny items form a subsequence (1/6,ϵ,1/6,ϵ,…)16italic-ϵ16italic-ϵ…(1/6,\epsilon,1/6,\epsilon,\ldots)( 1 / 6 ...	CBA	BCA	BAC	ABC	Selection 1
A: We believe that extending the predictive model by incorporating information related to non-linear aspects of human behavior, such as mood shifts, could help to capture when depression symptoms “wax and wane”B: Having access to a dataset with this type of behavioral information would allow us in the future t...	BCA	BAC	CAB	ACB	Selection 4
A: Hence, we do not compare with quantization methods in this paper. We don’t use the warm-up strategy in the experimentsB: The momentum coefficient β𝛽\betaitalic_β is set as 0.90.90.90.9. The weight decay is set as 0.0001. We use 8 workers with a total batch size of 128.C: In the experiments of (Lin et al...	BCA	CAB	CAB	CBA	Selection 1
A: , where ∗∗ is the convolution333We use convolution instead of cross-correlation only as a matter of compatibility with previous literature and computational frameworksB: operation.C*: Using cross-correlation would produce the same results and would not require flipping the kernels during visualization	ACB	CAB	BAC	CAB	Selection 1
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: Figure 1: The topological structure of UAV ad-hoc networksC: a) The UAV ad-hoc network supports user communications	CAB	ABC	CBA	CBA	Selection 1
A: italic_g B: and the symbol /\,/\,/ represents Hadamard division, piecewise element-by element division (e.g.,formulae-sequence𝑒𝑔e.g.,italic_e C: , (a¯/b¯)i=ai/bisubscript¯𝑎¯𝑏𝑖subscript𝑎𝑖subscript𝑏𝑖(\overline{a}\,/\,\overline{b})_{i}=a_{i}/b_{i}( over¯ start_ARG italic_a end_ARG / over¯ start_ARG...	BAC	CAB	ACB	CBA	Selection 1
A: Over course of time a wide range of Dropout techniques inspired by the original method have been proposed. The term Dropout methods was used to refer to them in general[14]. They include variational Dropout[15], Max-pooling Dropout[16], fast Dropout[17], Cutout[18], Monte Carlo Dropout[19], Concrete Dropout[20] ...	ACB	CBA	CBA	CAB	Selection 4
A: Figure 14: A 5×5555\times 55 × 5 overlap scenario with (a) the ground truth, (b) the predicted binary masks, and (c) the overlapB: In (c), green, grey, blue, and red pixels denote TP, TN, FP, and FN pixels respectively.C: In (a) and (b), black and white pixels denote the foreground and the background re...	BCA	ACB	CBA	BAC	Selection 2
A: The second hidden layer has a neuron per leaf node in the decision treeB: Finally, the output layer is connected to all leaf neurons and aggregates the results by implementing the leaf votes. By using hyperbolic tangent and sigmoid functions, respectively, as activation functions between the layers, the gene...	ABC	ACB	CAB	BAC	Selection 2
A: However, such computational efficiency guarantees rely on the regularity condition that the state space is already well exploredB: 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., 20...	BAC	ACB	CBA	ABC	Selection 1
A: However, training such discrete-valued DNNs555Due to finite precision of computer arithmetic, in fact any DNN is discrete-valuedB: However, we use this term here to emphasize the extremely small number of valuesC: is difficult as they cannot be directly optimized using gradient-based methods.	CAB	ABC	CBA	ACB	Selection 2
A: His results can be interpreted as providing certain guarantees for how the filling radius changes under multiplicative distortion of metricsB: In [64], Liu studies the mapping properties of the filling radiusC: Here we study the effect of additive distortion.	BAC	ACB	ACB	ABC	Selection 1
A: After sorting the six results based on QMA, the chosen one can be seen in Figure 1B: Thus, we use a lasso selection to choose C3, then use the “optimize selection” button (see Figure 1(e), top right) to identify the best projections for the selectionC: The main difference between this new projection and ...	ABC	ABC	BAC	ABC	Selection 3
A: Even more, we have observed that in our first proposed taxonomy, built upon the review of 518 proposals, the huge diversity of inspirational sources does not correspond with the lower number of algorithmic behaviors on which our second taxonomy is basedB: This observation is in accordance with previous works...	ACB	ACB	ABC	BCA	Selection 4
A: GNNs extend classical neural networks into irregular data so that the deep information hidden in graphs is exploited sufficiently. In this paper, we only focus on GCNs and its variants.B: Roughly speaking, the network embedding approaches can be classified into 2 categories: generative models [13, 14] and di...	BCA	CAB	BCA	ACB	Selection 2
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 updatesB: Furthermore, we find that when a Web server is not available (“N/A”), both Email and...	ACB	BCA	BAC	CAB	Selection 2
A: Machine learning applications frequently deal with data-generating processes that change over timeB: Semisupervised learning, which has received a lot of attention in the sensor community, is characterised by the combined use of easily attainable unlabeled data in addition to the initial labeled dataset [10...	BCA	CBA	BCA	ACB	Selection 4
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_TB: However, there do not seem to be constructions for presenting arbitrary free products of self-simil...	BAC	CBA	CAB	BCA	Selection 3
A: Table A4 shows VQA accuracy for each answer type on VQACPv2’s test set. HINT/SCR and our regularizer show large gains in ‘Yes/No’ questionsB: 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 th...	ACB	BCA	BCA	ABC	Selection 1
A: (2020), and it surpasses the aggregate of unique websites represented in all other publicly available web privacy policy corpora combined. We describe the corpus creation pipeline, with stages including a web crawler, language detection, document classification, duplicate and near-duplication removal, and conten...	BCA	CBA	BAC	ACB	Selection 3
A: Wang et al. [62] experimented with alternative visualization designs for selecting parameters, and they found that a parallel coordinates plot is a solid representation for this context as it is concise and also not rejected by the usersB: Next, we perform similar steps for RF vs. ExtraT without class optimi...	ACB	CBA	CBA	BAC	Selection 1
A: We first study the effects of data quantity and distribution on the training strategy: RQ1. Since the parameter initialization learned by MAML can be seen as a general language model of training tasks, when the training and testing tasks have different data distributions, how can the general language model train...	ABC	ACB	CBA	ACB	Selection 3
A: UAV position-attitude prediction is performed to obtain the future motion state information (MSI) before next information feedbackB: Figure 3: The considered CC-enabled UAV mmWave network consists of a r-UAV and multiple t-UAVsC: The CCA and the beam are shown in detail in the CCA view.	CAB	BCA	BAC	ACB	Selection 3
A: This will be bootstrapped to the multi-color case in later sectionsB: 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...	BAC	BCA	BAC	ABC	Selection 4
A: The evolution of such a population distribution is characterized by a partial differential equation (PDE) known as the continuity equation. In particular, we develop a generalized notion of one-point monotonicity (Harker and Pang, 1990), which is tailored to the Wasserstein space, especially the first variation ...	CBA	ACB	CAB	ABC	Selection 3
A: To test the effectiveness of depth-wise LSTMs in the multilingual setting, we conducted experiments on the challenging massively many-to-many translation task on the OPUS-100 corpus Tiedemann (2012); Aharoni et al. (2019); Zhang et alB: (2020) for fair comparison. We adopted BLEU Papineni et al. (2002) for ...	CBA	ACB	CBA	BCA	Selection 2
A: 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 andB: furthermore B→C→𝐵𝐶B\to Citalic_B → italic_CC: Apply [33, Corollary 5.14] to A𝐴Aitalic_A and B𝐵Bitalic_B	BCA	CBA	CAB	BAC	Selection 3
A: In this section, we first state the details of the synthetic distorted image dataset and the training process of our learning modelB: Subsequently, we analyze the learning representation for distortion estimationC: To demonstrate the effectiveness of each module in our framework, we conduct an ablation s...	CAB	BAC	ABC	CBA	Selection 3
A: 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 than the four baselinesB: Furthermore, it achieves faster...	CBA	BCA	ABC	CAB	Selection 2
A: See Appendix A for an in-depth discussion.B: On a high level, the obstacle is that radius-minimization requires estimating the cost of each approximate solution; counter-intuitively, this may be harder than optimizing the cost (which is what is done in previous results)C: Unfortunately, standard SAA app...	BAC	CBA	BAC	ABC	Selection 2
A: IB: This leads the nonnegative supermartingale convergence theorem not to be applied directlyC: The local cost functions in this paper are not required to be differentiable and the subgradients only satisfy the linear growth condition. The inner product of the subgradients and the error between local opt...	ACB	ABC	CAB	CBA	Selection 1
A: We aim to achieve two goalsB: This section presents the algorithm to implement the Mutual Cover (MuCo) framework111The code is available at https://github.com/liboyuty/Mutual-Cover.C: First, MuCo satisfies δ𝛿\deltaitalic_δ-probability to hinder the adversary from matching the combination of QI values. ...	BAC	ABC	ABC	CAB	Selection 1
A: Armed with DCN, GC block and SyncBN training, our HTC with Res2NetR101 backbone yields 74.58 mAP on validation set, as shown in Table 1. However, the convolutional mask heads adopted in all stages bring non-negligible computation and memory costs, which constrain the mask resolution and further limit the segment...	BCA	BAC	CBA	CAB	Selection 3
A: 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 failsB: This solves a question raised by ...	BCA	ABC	BCA	ACB	Selection 2
A: Our algorithm is summarized in Algorithm 1B: 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**...	BCA	CBA	BCA	BAC	Selection 4
A: The details on the participant demographics of SG-75 are shown in Table 1. From SG-75, two more subsets were formed via the branching questionsB: The first contains 59 responses in which respondents said that they have shared news before (referred to as ‘SharedNews-59’), and the second contains 57 responses ...	BAC	ACB	CBA	BCA	Selection 4
A: We employ a two-layer AliNet (each layer comprising one GCN and one GAT) and a four-layer decentRLB: The two methods exhibit comparable running times per epoch. AliNet runs marginally faster than decentRL with smaller hidden sizes, but the total training time of decentRL is notably less than that of AliNet. ...	ACB	BCA	CAB	BAC	Selection 2
A: The children often employ goal-less exploration to learn skills that will be useful in the future. Developmental psychologists consider intrinsic motivation as the primary driver in the early stages of development [9]B: Conducting exploration without the extrinsic rewards is called the self-supervised explor...	CAB	CAB	CAB	BAC	Selection 4
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_ΠB: We complement the established notion of unisolvent nodes by the dual notion of unisolvenceC: In doing so, we revisit earlier results by Carl de ...	ABC	BAC	CAB	CAB	Selection 2
A: the disentangled factors) and correlated components Z𝑍Zitalic_Z, a.k.a as nuisance variables, which encode the details information not stored in the independent components. A series of works starting from [beta] aims to achieve that via regularizing the models by up-weighting certain terms in the ELBO formulati...	CBA	BAC	ABC	BCA	Selection 4
A: When introducing the concept of an inverted signal pair of digital signals into a structural computer, the signals are paired, so a total of four wires are required to process the two auxiliary signals. This is defined as a double pair-based logical operation and is as follows in Fig 1. B: If a pair of lines...	BAC	ABC	BCA	CAB	Selection 4
A: Some well-studied families of polynomials include the Dickson polynomials and reverse Dickson polynomials, to name a fewB: Conditions for such families of maps to define a permutation of the field 𝔽𝔽\mathbb{F}blackboard_F are well studied and established for special classes like Dickson polynomials [20], l...	ACB	CAB	BCA	ABC	Selection 3
A: We calculated the values of these parameters in part by specifying a desired bound on the PFER (in our case 1.5). This kind of error control is much less strict than the typical family-wise error rate (FWER) or FDR control one would apply when doing statistical inference. In fact, one can observe in Figures 3 an...	ABC	ACB	BAC	ACB	Selection 3
A: Secondly, it speeds up model training and enhances scalability, especially for high-dimensional data. Lastly, and notably, relevant variables facilitate the interpretation of detected anomalies, particularly in high-dimensional data. B: Firstly, relevant variable selection can eliminate redundant and irrelev...	CAB	CBA	ACB	BAC	Selection 2
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...	CBA	BAC	ABC	ACB	Selection 1
A: Note that our VSGN, which uses pre-extracted features without further finetuning, significantly outperforms all other methods that use the same pre-extracted features. It is even on par with concurrent methods that finetune the features on ActivityNet for TAL end to end. B: Table 2: Action localization resul...	CBA	CAB	BCA	CBA	Selection 2
A: On the other hand, we support the process of generating new models through genetic algorithms and highlight the necessity for the best and most diverse models in the simplest possible voting ensemble. Finally, our approach is model-agnostic and generalizable, since we use both bagging and boosting techniques alo...	ABC	CBA	ABC	BCA	Selection 2
A: Condition 1 is used in Theorem 1 to prove that the value vector exponentially converges to 𝟎0\bm{0}bold_0B: In Proposition 2, it is proven that the dynamics of the error vector in Algorithm 1 are identical to the dynamics of the value vector in Theorem 1C: In Proposition 3, it is proven that Algorithm ...	BAC	ACB	CAB	BCA	Selection 1
A: We presented a novel formulation for the isometric multi-shape matching problem. Our main idea is to simultaneously solve for shape-to-universe matchings and shape-to-universe functional mapsB: Our algorithm is efficient, straightforward to implement, and montonically increases the objective function. Exper...	ACB	BCA	CBA	ABC	Selection 1
A: A clique is a clique separator if its removal disconnects the graph in at least two connected componentsB: For example, every cycle has no clique separator, and the butterfly/hourglass graph has two cliques and it is an atom. In [18] it is proved that an atom is a path graph and/or a directed path graph if ...	BAC	BCA	ACB	CBA	Selection 3
A: In this paper, we extend the symmetric Laplacian inverse matrix (SLIM) method (SLIM, ) to mixed membership networks and call this proposed method as mixed-SLIM. As mentioned in SLIM , the idea of using the symmetric Laplacian inverse matrix to measure the closeness of nodes comes from the first hitting time in a...	CBA	ABC	ACB	ACB	Selection 2
A: (2017); Agarwal et al. (2018); Zhang et al. (2018); Tripuraneni et alB: (2018); Boumal et al. (2018); Bécigneul and Ganea (2018); Zhang and Sra (2018); Sato et al. (2019); Zhou et al. (2019); Weber and Sra (2019) and the references therein. Also see recent reviews (Ferreira et al., 2020; Hosseini and Sra, 20...	CBA	CBA	BCA	ABC	Selection 3
A: In addition, Individual RL performs relatively worst in all scenarios because it employs independent replay buffer and neural network parameters among agents. In traffic signal control task, different signals vary but also share similarities since they follow the same traffic rules and have similar optimization ...	CBA	CAB	BAC	CBA	Selection 2
A: We give the first theoretical and experimental study of online bin packing with machine-learned predictionsB: In contrast, our algorithms exploit natural, and PAC-learnable predictions concerning the frequency at which item sizes occur in the input, and our analysis incorporates the prediction error into the...	CBA	BCA	ACB	CAB	Selection 3
A: First, we theoretically solve the problem of stitching partial meshes since every chart is informed about its local neighborhoodB: The proposed framework overcomes the limitations of previous methodsC: Second, our method can easily fill the missing spaces in the final mesh by adding a new mapping for th...	ACB	BCA	BAC	CAB	Selection 3
A: Unfortunately, optimalilty w.r.tB: ε𝜀\varepsilonitalic_ε take places only for the convex-concave case not for the strongly convex-concave one.222The analysis developed in this paper also does not well fitted to the strongly convex-concave saddle-point problems with different constants of strong convexity an...	CBA	ACB	ABC	BCA	Selection 4
A: In [6] the authors characterize them in terms of their corresponding cycle matrices and present a Venn diagram that shows their inclusion relationsB: Among these classes we can find the strictly fundamental class.C: Different classes of cycle bases can be considered	CAB	BAC	BCA	ACB	Selection 3
A: This technique, which we briefly outline here, was specifically designed for complete intersection patternsB: 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]C: A major part of t...	BAC	ACB	CBA	CAB	Selection 1
A: Notably, a user needs a global mechanism to examine the relationships between those features (if there are any)B: T1: Inspect the impact of features in both local and global perspectives. In data sets with many features, it is not trivial to acknowledge each feature’s contribution in the final prediction*C...	ACB	BAC	CBA	ABC	Selection 2
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 experimentsB: After the initial learning phase the algorithm quickly finds the region where the simulation is feasible with respect t...	BAC	CBA	ACB	CAB	Selection 3
A: These methods can be grouped into two types: 1) those that assume the bias variables e.g., the gender label in CelebA, are explicitly annotated and can be accessed during training [55, 55, 69, 37] and, 2) those that do not require explicit access [46, 50]B: Recently, many methods have been proposed to make...	CBA	BAC	BCA	CAB	Selection 2
A: Kothari et al. [110] found the strong gaze-related geometric constraints when people ”look at each other” (LAEO)B: They estimate 3D and 2D landmarks in the images of LAEO dataset [113], and generate pseudo gaze annotation for gaze estimationC: While it cannot bring competitive performance, therefore, th...	CAB	CBA	ABC	CBA	Selection 3
A: Despite the recent breakthroughs of deep learning architectures in pattern recognition tasks, they need to estimate millions of parameters in the fully connected layers that require powerful hardware with high processing capacity and memoryB: To do so, we only consider the feature maps at the last convoluti...	BAC	BAC	ACB	BAC	Selection 3
A: Moreover, we prove termination via a novel logical relations argument in the presence of infinitely deep typing derivations that is mediated through infinitely wide but finitely deep (inductive) typingB: We have presented a highly general concurrent language that conceives mixed inductive-coinductive progra...	ACB	BAC	CAB	CBA	Selection 2
A: On the other hand, only users authorized by the owner can access the media content. B: Implement privacy-preserving access controlC: On the one hand, the cloud should be prevented from obtaining the private plaintext of the data it encounters, including the owner’s media content, the users’ fingerprints,...	CBA	CAB	ABC	CBA	Selection 2
A: Since the representations obtained in different layers encode the interactions of different orders, they have different contributions to the final predictionB: (2019): C: As such, we concatenate them to constitute the final representation of each feature Wang et al	ACB	CBA	BAC	CAB	Selection 1
A: Table 1: Number of iterations needed to achieve an ε𝜀\varepsilonitalic_ε-optimal solution for Problem 1.1B: The oracles listed under the Requirements column are the additional oracles required, other than the first-order oracle (FOO) and the linear minimization oracle (LMO) which all algorithms use.C: ...	ABC	ACB	BCA	CBA	Selection 2
A: We thank MohammadTaghi Hajiaghayi and Peilin Zhong for their helpful feedback on the exposition of this writeupB: We thank Mohsen Ghaffari and Christoph Grunau for their insightful discussionsC: In particular, we thank Mohsen Ghaffari for suggesting how to use O⁢(1)𝑂1O(1)italic_O ( 1 )-approximate maxim...	CBA	CBA	BCA	ABC	Selection 4
A: B-CPP further reduces the communicated data per iteration and is also provably linearly convergent over directed graphs for minimizing strongly convex and smooth objective functionsB: We consider an asynchronous broadcast version of CPP (B-CPP)C: Numerical experiments demonstrate the advantages of B-CPP...	BAC	BCA	BCA	BCA	Selection 1
A: In the decentralized setting, all nodes are connected within a network, and each node can communicate/exchange information only with their neighbors in the networkB: Note that in the proposed formulation (1) we consider both the centralized and decentralized casesC: While the centralized architecture con...	CBA	ABC	BCA	BAC	Selection 4
A: In practice, we only calculate a BR for positive support policies (similar to Rectified Nash (Balduzzi et al., 2019)B: Therefore the CE BR attempts to exploit each policy conditional “slice”C: Computing the argmaxargmax\operatorname*{argmax}roman_argmax of the BRs can be achieved through RL or exactly tr...	BAC	CAB	ACB	BCA	Selection 1
A: Since achieving posterior accuracy is relatively straightforward, guaranteeing Bayes stability is the main challenge in leveraging this theorem to achieve distribution accuracy with respect to adaptively chosen queries. The following lemma gives a useful and intuitive characterization of the quantity that the Ba...	ABC	BAC	BCA	ACB	Selection 4
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	ACB	BCA	ACB	Selection 1
A: To evaluate the quality of generated composite images, previous object placement works usually adopt the following three schemes: 1) Some works measure the similarity between real images and composite images. For example, Tan et alB: [145] score the correlation between the distributions of predicted boxes an...	ACB	ABC	BCA	ACB	Selection 2
A: Inter-city correlationsB: Our results demonstrate that transfer learning leads to error reductions in all source-target pairs, as compared to using target data onlyC: Notably, the largest reduction of approximately 15% is observed in the case of Shenzhen and Chongqing. These findings suggest that there ...	BAC	ACB	ABC	CAB	Selection 3
A: As stated before, there are two quantities that are mainly used to evaluate the performance of interval estimators: the degree of coverage (1) and the average size of the prediction intervals (3)B: The idea to construct a loss function based on the HQ principle was first proposed by Khosravi et al. in khosr...	CBA	BAC	ACB	BAC	Selection 3
A: Their CNN learns to predict the probability that each note belongs to the melody lineB: Then, a clustering algorithm is used to find a threshold for each piece adaptivelyC: Finally, the Bellman-Ford algorithm is adopted to pick a strictly monophonic melody line. In contrast, we do not have postprocessing...	ACB	ABC	CAB	BCA	Selection 2
A: to L⁢(k,1)𝐿𝑘1L(k,1)italic_L ( italic_k , 1 )-labeling problem (see e.gB: [10] for a survey), where the colors of any two adjacent vertices have to differ by at least k𝑘kitalic_k and the colors of any two vertices within distance 2222 have to be distinct. C: This description draws a comparison e.g	ACB	ACB	CBA	BCA	Selection 4
A: Particularly, we jointly design the semantic and channel coding to learn and extract the features and mitigate the channel effectsB: In this article, we have investigated a DL-enabled semantic communication system for speech recognition, named DeepSC-SR, which aims to restore the text transcription by utili...	CBA	CBA	BAC	BCA	Selection 3
A: Comparison with existing 3D WSSS methods: We compare our proposed method with existing 3D WSSS methods[13, 51, 41]B: [10] utilizes 2D dense labels on 2D projections of the 3D point clouds and [13] utilize the same weak supervision technique by annotating 10% of the points. [13] can also produce close or bet...	CAB	ACB	BAC	ABC	Selection 2
A: We use black box for ground-truth, red box for baseline results, and blue box for our resultsB: All the illustrated images are from the KITTI val set. Zoom in on the circles for more detailed comparison.C: Figure 6: Qualitative results of our method for Bird’s-Eye-View	ABC	BCA	CAB	CAB	Selection 2
A: Both node classification and link prediction utilize the same relational features and boost each other’s performanceB: This explains why the overall performance is often further improved when both FPNS (Node) and FPNS (GGTR) are applied. The performance improvements reflect that our FPNS strategies can suppr...	CBA	ABC	BCA	ACB	Selection 3
A: Then, we continue to perform a round traversal of the memory block to adjust the heap after the relationship mapping has been completed for the subsequent subsetsB: We further perform a round traversal of the memory block to initialize a minimum heap of size k𝑘kitalic_k after the relationship mapping has b...	CBA	BAC	CBA	ACB	Selection 2
A: We consider the following preconditioner: B: We study both block-triangular and block-diagonal preconditioners for the system matrix (1)C: For block-triangular preconditioners, we focus on a lower triangular type with left preconditioning because an upper triangular one with right preconditioning can be ...	CBA	CAB	CBA	BCA	Selection 2
A: The authors in works (Chen et al., 2020; Hardy et al., 2017; Yang et al., 2019b; Wu et al., 2020; Feng and Yu, 2020; Kang et al., 2020) propose vertical federated learning algorithms for single-tier communication networks, but they do not use local iterations in the parties during trainingB: In all of these ...	CBA	BCA	CAB	ACB	Selection 2
A: Then, the notion of T-eigenvalues was introduced by Miao, Qi and Wei Miao2020T and also Liu and Jin Jin2020 , establishing a fundamental and significant concept. Alternative versions and formulations of eigenvalues of third-order tensors in the context of tensor-tensor multiplication have also been explored by ...	CAB	ACB	BCA	CAB	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 texture branc...	BAC	CBA	ABC	BAC	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 ...	BAC	CAB	BAC	BAC	Selection 2

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.