Split (1)

train · 2.93k rows

shuffled_text stringlengths 267 3.81k	A stringclasses 6 values	B stringclasses 6 values	C stringclasses 6 values	D stringclasses 6 values	label stringclasses 4 values
A: The key idea is to transform the diagonal matrix with the help of row and column operations into the identity matrix in a way similar to an algorithm to compute the elementary divisors of an integer matrix, as described for example in [23, Chapter 7, Section 3]B: Note that row and column operations are effe...	ABC	BAC	ACB	BAC	Selection 1
A: The exact definition of some basis functions requires solving global problems, but, based on decaying properties, only local computations are required, although these are not restricted to a single elementB: As in many multiscale methods previously considered, our starting point is the decomposition of the ...	CAB	BAC	CBA	CAB	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...	CBA	CAB	BAC	BCA	Selection 2
A: Actually, we empirically found that roughly 20% of our events (mostly news) contain sub-events. As a rumor is often of a long circulating story [10], this results in a rather long time span. In this work, we develop an event identification strategy that focuses on the first 48 hours after the rumor is peaked. We...	CBA	CAB	BCA	CAB	Selection 1
A: The follow-up paper (Gunasekar et al., 2018) studied this same problem with exponential loss instead of squared lossB: 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...	BAC	ABC	BCA	BCA	Selection 2
A: In this work, we present a deep analysis on the feature variants over 48 hours for the rumor detection taskB: The results show that the low-level hidden representation of tweets feature is at least the second best features over timeC: We also derive explanations on the low performance of supposed-to-be-...	BCA	BAC	CBA	ABC	Selection 4
A: We modified the objective function of RankSVM following our global loss function, which takes into account the temporal feature specificities of event entities. The temporal and type-dependent ranking model is learned by minimizing the following objective function: B: We adapted the L2R RankSVM [12]. The goa...	CBA	CAB	CAB	BCA	Selection 1
A: Body weight, according to BMI, is normal for half of the patients, four are overweight and one is obese. The mean BMI value is 26.9B: 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 yearsC: Only one of the patients suffers fr...	CBA	CAB	CBA	BAC	Selection 4
A: To restore the original image resolution, extracted features were processed by a series of convolutional and upsampling layers. Previous work on saliency prediction has commonly utilized bilinear interpolation for that task Cornia et al. (2018); Liu and Han (2018), but we argue that a carefully chosen decoder ar...	ABC	BAC	ACB	CAB	Selection 1
A: Expecting an improvement of cutwidth approximation – a heavily researched area – by translating the problem into a string problem and then investigating the approximability of this string problem seems naiveB: This makes it even more surprising that linking cutwidth with pathwidth via the locality number is...	BCA	BCA	CAB	ABC	Selection 4
A: As observed in Section 6.4 assigning bigger computational budget helps in 100100100100K setting. We suspect that gains would be even bigger for the settings with more samples.B: This demonstrates that SimPLe excels in a low data regime, but its advantage disappears with a bigger amount of data. Such a behavi...	ACB	BCA	CAB	BCA	Selection 3
A: It also introduces two proposed gaits designed specifically for step negotiation in quadrupedal wheel/track-legged robots. B: This section describes the primary locomotion modes, rolling and walking locomotion of our hybrid track-legged robot named Cricket shown in FigC: 2	BCA	CAB	CBA	BCA	Selection 2
A: Hence, untrusted advice can result in a competitive ratio as bad as 6. B: 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 , italic_ϵ , 1 / 6 , italic_ϵ , … ), while there is no critical itemC: In this case, all cri...	ACB	CAB	BCA	ACB	Selection 2
A: In addition, these models also need to provide support to a key aspect of ERD: the decision of when (how soon) the system should stop reading from the input stream and classify it with acceptable accuracy. This aspect, that we have previously mentioned as the supporting for early classification, is basically a m...	BCA	CAB	CBA	BCA	Selection 2
A: GMC+ can also be easily implemented on all-reduce frameworks.B: The detail of GMC+ is shown in Algorithm 2. We also adopt parameter server architecture for illustrationC: We improve DEF-A by changing its local momentum to global momentum, getting a new method called GMC+	BAC	BAC	BAC	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 frameworksB: Using cross-correlation would produce the same results and would not require flipping the kernels during visualizationC*: operation.	CBA	ABC	BCA	BCA	Selection 2
A: In this part, we investigate the influence of environment dynamic on the network states. With different scenarios’ dynamic degree τ∈(0,∞)𝜏0\tau\in(0,\infty)italic_τ ∈ ( 0 , ∞ ), PBLLA and SPBLLA will converge to the maximizer of goal function with different altering strategy probabilityB: It does not resul...	CAB	BCA	BAC	ACB	Selection 4
A: functions with linear dependence on r𝑟ritalic_r and z𝑧zitalic_z, and have first order accuracy (i.e.,𝒪(he)i.e.,\,\mathcal{O}(h_{e})italic_i B: , caligraphic_O ( italic_h start_POSTSUBSCRIPT italic_e end_POSTSUBSCRIPT )) when applied to nonlinearC: italic_e	CAB	BCA	ACB	CAB	Selection 3
A: Deep neural networks are the state of the art learning models used in artificial intelligence. The large number of parameters in neural networks make them very good at modelling and approximating any arbitrary functionB: However the larger number of parameters also make them particularly prone to over-fitti...	BCA	BAC	BCA	ABC	Selection 4
A: Another popular loss function for image segmentation tasks is based on the Dice coefficient, which is essentially a measure of overlap between two samples and is equivalent to the F1 scoreB: The Dice coefficient (DC) is calculated as: C: This measure ranges from 0 to 1, where a Dice coefficient of 1 deno...	BAC	BCA	ACB	ABC	Selection 3
A: The results are shown in Figure 3 exemplarily for the Car, Covertype, and Wisconsin Breast Cancer (Original) datasetB: 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.C:...	ACB	BAC	ABC	BCA	Selection 1
A: In comparison, existing algorithms based on value iteration, e.g., optimistic least-squares value iteration (LSVI) (Jin et al., 2019), do not allow adversarially chosen reward functionsB: Such a notion of robustness partially justifies the empirical advantages of KL-regularized policy optimization (Neu et al...	ABC	BAC	BCA	CAB	Selection 3
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.	ABC	BCA	BAC	BAC	Selection 1
A: The following statement regarding products of filtrations are obtained at the simplicial level (and in more generality) in [72, Proposition 2.6] and in [42, 73]B: The statement about metric gluings appeared in [7, Proposition 4] and [68, Proposition 4.4]C: These proofs operate at the simplicial level.	CAB	BAC	ABC	ACB	Selection 3
A: Second, in this phase of the research, we were mainly concerned with designing and validating the system with the right set of views and the right analysis workflow, so we decided to prioritize the ease of implementation over the raw performance.B: Performance There are two reasons why we decided to use th...	ACB	ACB	CAB	CBA	Selection 3
A: less computational complexity, smaller memory footprint, or faster convergence properties [5]. B: More and more researchers are advocating that a novel metaphor is not enough for a new bio-inspired algorithm to be considered a step beyond the state of the artC: Instead, several factors should be proven w...	BCA	BAC	CBA	CAB	Selection 4
A: Besides, four GAE-based methods are used, including GAE [20], MGAE [21], GALA [32], and SDCN [31]B: Three deep clustering methods for general data, DEC [8] DFKM [9], and SpectralNet [7], also serve as an important baselineC: All codes are downloaded from the homepages of authors.	CBA	BCA	CBA	BAC	Selection 4
A: These measurements are performed using a limited number of vantage points, which are set up in specific networks, and hence are often not representative of the entire Internet. Increasing the coverage and selecting the networks more uniformly is imperative for collecting representative data; (Huz et al., 2015) s...	ABC	CAB	ABC	CBA	Selection 2
A: The estimation of context by learned temporal patterns should be most effective when the environment results in recurring or cyclical patterns, such as in cyclical variations of temperature and humidity and regular patterns of human behavior generating interferentsB: A context-based approach will be applied ...	ACB	ABC	ABC	CBA	Selection 1
A: For semigroups, on the other hand, such results do existB: However, there do not seem to be constructions for presenting arbitrary free products of self-similar groups in a self-similar wayC: In fact, the free product of two automaton semigroups S𝑆Sitalic_S and T𝑇Titalic_T is always at least very close...	BCA	ACB	BAC	CBA	Selection 3
A: We thank NVIDIA for the GPU donationB: This work was supported in part by AFOSR grant [FA9550-18-1-0121], NSF award #1909696, and a gift from Adobe ResearchC: The views and conclusions contained herein are those of the authors and should not be interpreted as representing the official policies or endorse...	BAC	CBA	BCA	CAB	Selection 1
A: We selected those URLs which had the word “privacy” or the words “data” and “protection” from the Common Crawl URL archive. We were able to extract 3.9 million URLs that fit this selection criterionB: Informal experiments suggested that this selection of keywords was optimal for retrieving the most privacy p...	ABC	ACB	ACB	BCA	Selection 1
A: One way to manage this is to keep track of the history of each model. Analysts might also want to step back to a specific previous stage in case they reached a dead end in the exploration of algorithms and models (G2).B: Hence, the identification and selection of particular algorithms and instantiations over...	BAC	CAB	ABC	CBA	Selection 4
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...	CBA	BAC	ACB	CAB	Selection 4
A: The position and attitude compose the UAV’s motion state information (MSI). In this section, the MSI prediction based AOAs and AODs estimation scheme and the protocol for beam tracking are introduced in Section IV-AB: The CCA codebook based SPAS algorithm is proposed in the previous section to solve the join...	BAC	CBA	CBA	BCA	Selection 1
A: WeB: 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 node on the right – regardless of the matrixC: This will be boo...	ACB	BCA	ABC	CBA	Selection 4
A: Assumption 4.1 can be ensured by normalizing all state-action pairsB: We remark that our analysis straightforwardly generalizes to the setting where ‖x‖≤Cnorm𝑥𝐶\\|x\\|\leq C∥ italic_x ∥ ≤ italic_C for an absolute constant C>0𝐶0C>0italic_C > 0. C: Such an assumption is commonly used in the mean-field ana...	BAC	ABC	CAB	ACB	Selection 4
A: When using the depth-wise RNN, the architecture is quite similar to the standard Transformer layer without residual connections but using the concatenation of the input to the encoder/decoder layer with the output(s) of attention layer(s) as the input to the last FFN sub-layerB: Table 2 shows that the 6-lay...	CBA	ACB	ABC	CBA	Selection 3
A: open set of X𝑋Xitalic_XB: Using Alexander’s Subbase Lemma, let (Ui∩Y)i∈Isubscriptsubscript𝑈𝑖𝑌𝑖𝐼(U_{i}\cap Y)_{i\in I}( italic_U start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ∩ italic_Y ) start_POSTSUBSCRIPT italic_i ∈ italic_I end_POSTSUBSCRIPT be an open cover of U𝑈Uitalic_U in ττ\uptauroman_τC:...	CAB	BCA	ABC	CBA	Selection 3
A: Previous learning methods directly regress the distortion parameters from a distorted imageB: However, such an implicit and heterogeneous representation confuses the distortion learning of neural networks and causes the insufficient distortion perceptionC: To bridge the gap between image feature and cali...	CBA	ABC	BAC	BAC	Selection 2
A: Similar to the results of image classification, SNGM outperforms LARS for different batch sizes. B: Table 6 shows the test perplexity of the three methods with different batch sizesC: We can observe that for small batch size, SNGM achieves test perplexity comparable to that of MSGD, and for large batch s...	BAC	ACB	CBA	CAB	Selection 4
A: Brian Brubach was supported in part by NSF awards CCF-1422569 and CCF-1749864, and by research awards from AdobeB: Aravind Srinivasan was supported in part by NSF awards CCF-1422569, CCF-1749864 and CCF-1918749, and by research awards from Adobe, Amazon, and Google. C: Nathaniel Grammel and Leonidas Tsep...	BCA	ABC	CAB	ACB	Selection 4
A: Further, the estimations of these rates are substituted into the recursive inequality of the conditional mean square error between the states and the global optimal solution. Finally, by properly choosing the step sizes, we prove that the states of all local optimizers converge to the same global optimal solutio...	ACB	ACB	ABC	CAB	Selection 4
A: The major research of privacy preservation focuses on preventing various disclosures and studying the trade-off between privacy protection and information preservation [20, 32, 21, 17, 11]B: However, generalization hardly preserves the distributions of original QI values that always causes a huge cost of pro...	ACB	CBA	ABC	BCA	Selection 1
A: Meanwhile, PointRend succeeds in distinguishing holes inside objects as background while HTC and SOLOv2 may predict incorrectly as foreground (see bottom line in Figure 2). We attribute PointRend’s success to the iteratively rendering process, which performs point-based segmentations at adaptively selected uncer...	ABC	CBA	ACB	CAB	Selection 2
A: As was written in the previous version, an anonymous referee of version 1 wrote that the theorem was known to experts but not publishedB: 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...	ACB	BCA	BAC	CAB	Selection 2
A: Section 6 shows our experiment results. Section 7 concludes the paper and discusses some future directions. All detailed proofs can be found in Appendices.B: Section 3 establishes the minimax regret lower bound for nonstationary linear MDPs. Section 4 and Section 5 present our algorithms LSVI-UCB-Restart, Ad...	BAC	BAC	CBA	ACB	Selection 3
A: In Singapore, there have been active efforts through campaigns from various organizations (e.g., S.U.R.E. (Board, [n.d.]), Better Internet (Council, [n.d.]), VacciNationSG (Lai, 2021)) to raise awareness on misinformation, disinformation and fake news. If it is through the exposure to the messages of these campa...	BAC	CBA	CAB	BCA	Selection 2
A: LAN, specifically designed for new entities, experiences minimal performance loss, but faces challenges with known entities. Its MRR under the conventional setting (0.182) is markedly lower than that of CompGCN (0.355) and decentRL (0.354), even falling below the performance of the classical KG embedding method ...	BCA	BAC	CBA	ABC	Selection 3
A: In contrast, when playing Atari games, the opponent is controlled by a hardcoded policy, which yields a relatively stable transition. We use the Two-player Pong game for the experimentB: The extrinsic reward is not appropriate for evaluating different methods in this experiment, since both sides are controll...	CBA	BAC	BCA	CBA	Selection 3
A: The observations made in 2D remain validB: The polynomial convergence rates of Floater-Hormann and allC: However, Floater-Hormann becomes indistinguishable from 5t⁢hsuperscript5𝑡ℎ5^{th}5 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT-order splines. Further, when considering the amount of co...	ACB	BAC	ABC	ABC	Selection 1
A: They aren’t really separating into nuisance and independent only.. they are also throwing away nuisance.B: 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 f...	ACB	CBA	BCA	BCA	Selection 2
A: DFS (Depth First Search) verifies that the output is possible for the actual Pin connection stateB: In this course, we experiment with a total of eight test cases, including the number of input branches (four) of XOR and the direction of mobility of the output pin (K1 in K2 and K3 in K2).C: As described...	CBA	CBA	BAC	ACB	Selection 4
A: In this paper, we propose a representation of such a group using the concept of linear representation defined through the Koopman operator.B: A finite field, by definition, is a finite set, and the set of all permutation polynomials over the finite field forms a group under compositionC: Given a finite ...	ABC	ACB	CBA	CAB	Selection 4
A: The breast cancer data (Ma \BOthers., \APACyear2004) consists of 60 tumor samples labeled according to whether cancer did (28 cases) or did not (32 cases) recurB: A boxplot of the distribution of the view sizes is included in Appendix A. The total number of features was 12,722. The features were already log2...	ACB	CAB	CAB	BCA	Selection 1
A: AP, on the other hand, focuses on the ranks of anomalies and is defined as the average precision over the top-l𝑙litalic_l scored objects.B: ROC AUC measures the overall performance of the method, ranging from 0 to 1, where a value of 1 indicates perfect performance, and 0.5 indicates a random guessC: W...	ACB	CBA	ACB	ABC	Selection 2
A: Comparison with Amani & Thrampoulidis [2021] While the authors in Amani & Thrampoulidis [2021] also extend the algorithms of Faury et alB: They model various click-types for the same advertisement (action) via the multinomial distribution. further, they consider actions played at each round to be non-combin...	BAC	CBA	ACB	ABC	Selection 3
A: The works R-C3D [42], TAL-NET [9], PBRNet [24], belonging to this category, perform pooling / strided convolution to obtain multi-scale features. Compared to these two categories, our proposed VSGN uses both the original video clip and its up-scaled counterpart, and takes advantage of their complementary propert...	CAB	CBA	ABC	BAC	Selection 2
A: However, they do not state how many models were used in the composition of this ensemble. With VisEvol, we reached an accuracy of 87% with only 4 ML models (see Figure 4(d)), thus surpassing their majority-voting ensemble. If the user wants to utilize one model, our selection would have been M4:RF329 (see Figure...	ACB	CAB	BAC	BCA	Selection 2
A: In [8], the Metropolis-Hastings algorithm is extended to incorporate safety upper bound constraints on the probability vectorB: This paper includes numerical simulations that demonstrate the application of the extension in a probabilistic swarm guidance problem. In order to enhance convergence rates, [9] int...	CAB	BCA	BCA	ABC	Selection 4
A: In that case, in addition to linear point-to-point matching costs, quadratic costs for matching pairs of points on the first shape to pairs of points on the second shape are taken into account. Since pairs of points can be understood as edges in a graph, this corresponds to graph matching. Due to the NP-hardness...	ABC	BAC	ABC	CBA	Selection 2
A: We presented the first recognition algorithm for both path graphs and directed path graphsB: 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...	ABC	BCA	CAB	ACB	Selection 4
A: FundingB: Qing’s work was supported by High level personal project of Jiangsu Province (JSSCBS20211218)C: Wang’s work was supported by the Fundamental Research Funds for the Central Universities, Nankai University, 63221044 and the National Natural Science Foundation of China (Grant 12001295).	ABC	BAC	BCA	BAC	Selection 1
A: (2018); Cheng and Bartlett (2018); Chatterji et al. (2018); Wibisono (2018); Bernton (2018); Dalalyan and Karagulyan (2019); Baker et al. (2019); Ma et al. (2019a, b); Mou et al. (2019); Vempala and Wibisono (2019); Salim et al. (2019); Durmus et al. (2019); Wibisono (2019) and the references therein. Among thes...	ABC	CAB	BAC	BAC	Selection 2
A: Note that for a fair comparison all the RL methods are learned without any pre-trained parameters and the methods are evaluated under the same settings. The results are obtained by running the source codes666https://github.com/traffic-signal-control/RL_signalsB: Our method is compared with the following two ...	BAC	ABC	ABC	CBA	Selection 1
A: Similar types of sampling-based competitive analysis have recently attracted attention in the context of other online problems such as ski rental and prophet inequalities (?), matching (?), and network optimization problems (?). B: Our analysis of ProfilePacking, as stated in Theorem 3, in conjunction with t...	ABC	ACB	BCA	CAB	Selection 4
A: In this experiment, we follow the evaluation protocol provided in (Yang et al., 2019)B: We use standard measures for this task like Jensen-Shannon Divergence (JSD), coverage (COV), and minimum matching distance (MMD), where the last two measures are calculated for Chamfer (CD) and Earth-Mover (EMD) distances...	CAB	BCA	CBA	CAB	Selection 2
A: The main idea is to use reformulation (54) and apply mirror prox algorithm [45] for its solutionB: This requires careful analysis in two aspectsC: First, the Lagrange multipliers 𝐳,𝐬𝐳𝐬{\bf z},{\bf s}bold_z , bold_s are not constrained, while the convergence rate result for the classical Mirror-Prox a...	BCA	BAC	ABC	CBA	Selection 3
A: 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.B: We proceed by trying to find a counterexample based on our previous observationsC: In this section we present ...	CBA	CAB	ABC	BCA	Selection 1
A: 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 1.4.1B: One immediate application of Theorem 1.2 is the reduction of fractional Helly numbersC: of Patáková [35...	ACB	ACB	ABC	BAC	Selection 4
A: Finding which features to transform, and how, together with generating new features from different combinations, are some of the core phases that lack attention from the InfoVis/VA communities. This section reviews prior work on feature generation, feature transformation, and feature selection techniques for bot...	CAB	BAC	CAB	CBA	Selection 2
A: We use two geometries to evaluate the performance of the proposed approach, an octagon geometry with edges in multiple orientations with respect to the two axes, and a curved geometry (infinity shape) with different curvatures, shown in Figure 4B: We compare three schemes: manual tuning of the MPCC paramete...	CBA	CBA	BCA	ACB	Selection 4
A: As shown in Table 2, when the number of groups is small, i.e., when using a head/tail binary indicator as the explicit bias, explicit methods remain comparable or even outperform StdM, but when the number of groups grow to hundreds and thousands, they failB: Results for GQA-OOD are similar, with explicit met...	CBA	CBA	BCA	BAC	Selection 4
A: Wu et al. collect the MagicEyes dataset using IR cameras [123]B: They propose EyeNet, a neural network that solves multiple heterogeneous tasks related to eye gaze estimation for an off-axis camera settingC: They use the CNN to model 3333D cornea and 3333D pupil and estimate the gaze from these two 3333D...	BCA	ACB	CAB	ABC	Selection 4
A: AlexNet is lowest training and testing time compared to VGG-16 with less GPU memory usage.B: The comparison of the computation times between the proposed method and Almabdy et al.’s method almabdy2019deep shows that the use of the BoF paradigm decreases the time required to extract deep features and to cla...	CAB	ACB	BAC	BCA	Selection 1
A: Validity conditions of infinite proofs have been developed to keep cut elimination productive, which correspond to criteria like the guardedness check [BDS16, BT17, DP19, DP20d]B: Although we use infinite typing derivations, we explicitly avoid syntactic termination checking for its non-compositionalityC...	CBA	ACB	ABC	CBA	Selection 3
A: ✓∖limit-from✓\checkmark\mkern-11.0mu{\smallsetminus}✓ ∖ means that the privacy of cloud media is protected, but that protection is not IND-CPA secureB: ‘−--’ indicates that the property is not scored because the involvement of cloud is not consideredC: [3]-I and [3]-II represent the first scheme and the ...	BAC	CBA	BCA	CBA	Selection 1
A: At their core, GNNs learn node embeddings by iteratively aggregating features from the neighboring nodes, layer by layer. This allows them to explicitly encode high-order relationships between nodes in the embeddings. GNNs have shown great potential for modeling high-order feature interactions for click-through ...	BAC	CAB	ACB	BAC	Selection 3
A: [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 ( italic_f ) by using second-order information about f𝑓fitalic_f, which we explicitly do not rely on.B: Requiring access to a zeroth-order and domain oracle are mild assumpti...	BCA	CBA	ACB	CAB	Selection 4
A: When an active path does not get extended in a pass then, just like in DFS, Backtrack-Stuck-Structures backtracks on this active path (in our case by one matched and one unmatched arc) and continues the DFS from that shorter active pathB: Informal description: Extend-Active-Paths can be seen as performing a ...	BAC	BCA	CBA	ABC	Selection 1
A: We show that B-CPP also achieves linear convergence for minimizing strongly convex and smooth objectives. B: Thus, B-CPP is more flexible, and due to its broadcast nature, it can further save communication over CPP in certain scenarios [63]C: In the second part of this paper, we propose a broadcast-like ...	ACB	CBA	ABC	CAB	Selection 2
A: This is one of the default learning settings. Based on these settings, we build our settings using the intuition of algorithms (for details about tuning and intuition of our Algorithms, see Section 5.2)B: Setting. To train ResNet18 in CIFAR-10, one can use stochastic gradient descent with momentum 0.90.90.9...	ACB	ABC	BAC	CBA	Selection 3
A: The two-player variant is solvable with PSRO, however the three-player version benefits from JPSROB: Kuhn Poker (Kuhn, 1950; Southey et al., 2009; Lanctot, 2014) is a zero-sum poker game with only two actions per playerC: The results in Figure 2(a) show rapid convergence to equilibrium.	CAB	BAC	CAB	ACB	Selection 2
A: (2021); Steinke and Zakynthinou (2020)B: An alternative route for avoiding the dependence on worst case queries and datasets was achieved using expectation based stability notions such as mutual information and KL stability Russo and Zou (2016); Bassily et alC: Using these methods Feldman and Steinke (2...	ACB	BCA	BAC	CBA	Selection 3
A: Hence NP-hard problems do not admit such parameter-decreasing algorithms. To formalize a meaningful line of inquiry, we take our inspiration from the Vertex Cover problem, the fruit fly of parameterized algorithms. B: We therefore propose the following novel research direction: to investigate how preprocessi...	BCA	ACB	CBA	CAB	Selection 4
A: Shadow-AR dataset only uses 13 foreground objects from ShapeNet [11] and Stanford 3D scanning repository, so the diversity of dataset is very limited. Some examples in Shadow-AR dataset are exhibited in the first row in Fig. 14, in which we show the composite image without foreground shadow, foreground object ma...	CBA	BAC	ACB	ACB	Selection 2
A: To this end, we have employed data mining tools to uncover the correlations between service and context data, and have utilized machine learning results to showcase the correlations among cities and tasks.. B: Our studies have confirmed the correlations among sub-datasets and have demonstrated that urban mod...	CAB	ABC	CBA	CAB	Selection 3
A: Ensemble methods that make use of bagging, such as random forests, give rise to their own natural choice of both homoscedastic and heteroscedastic nonconformity measuresB: As mentioned in Section 3.2, with bagging, every individual subestimator only uses a subset of the training set and, therefore, one can ...	ACB	CBA	BAC	ABC	Selection 4
A: homophonic or polyphonic music. Utilising the POP909 dataset \textcitepop909, we can develop a model that classifies each Pitch event into vocal melody, instrumental melody or accompaniment, with classification accuracy (ACC) serving as the evaluation metric.111111We note that there is a task closely related to ...	BCA	ACB	CAB	ABC	Selection 1
A: to L⁢(k,1)𝐿𝑘1L(k,1)italic_L ( italic_k , 1 )-labeling problem (see e.gB: This description draws a comparison e.gC: [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.	ACB	BAC	CAB	ABC	Selection 2
A: Based on JSCC, an image transmission system, integrating channel output feedback, can improve image reconstruction[15]. Similar to text transmission, IoT applications for image transmission have been carried outB: Recently, there are also investigations on semantic communications for other transmission cont...	ACB	BAC	ACB	BCA	Selection 2
A: TABLE V: The class-specific mIoU (%) evaluation on S3DIS Area-5B: The baseline method means the basic segmentation network trained with only the weak labels and without CSFR and ISFR modules. C: KPConv(paper) is taken from the paper-reported score, and KPConv(retrain) is the score from our basic segmenta...	BAC	CBA	ACB	BCA	Selection 3
A: We use black box for ground-truth, red box for baseline results, and blue box for our resultsB: Qualitative results of our method for Bird’s-Eye-ViewC: All the illustrated images are from the KITTI val set. Zoom in on the circles for more detailed comparison.	ABC	ACB	BAC	ABC	Selection 3
A: It contains 300 training images and 200 testing images with word-level annotation. Here, we follow the previous methods [35, 8] and add 400 training images from TR400 [46] to extend this dataset.B: ICDAR2015 [44] includes multi-orientated and small-scale text instances. Its ground truth is annotated with wor...	BAC	BCA	CAB	BAC	Selection 3
A: Assume that all IP addresses are logically divided into q𝑞qitalic_q subsets according to the value of the first part of an individual IP addressB: A minimum heap of size k𝑘kitalic_k is shared among these computers. Hence, this greatly increases the computational efficiency of the task by q𝑞qitalic_q time...	CBA	CBA	CBA	ACB	Selection 4
A: Furthermore, we extend these results to the n𝑛nitalic_n-tuple saddle point problem in Section 3. Some additive Schur complement based preconditioners are constructed and the corresponding known results in the literature are recalled in Section 4 for twofold saddle point problemsB: The outline of the remaind...	CBA	BCA	BAC	ACB	Selection 3
A: By waiting for several training iterations to exchange this information, the communication cost of the algorithm is reduced. Our approach is thus a communication efficient combination of learning with both vertically and horizontally partitioned data in a multi-tiered network.B: The hubs orchestrate this inf...	CBA	ABC	CAB	ABC	Selection 1
A: We present four different definitions of tensor ε𝜀\varepsilonitalic_ε-pseudospectra (cfB: The second main contribution of this paper is the development of pseudospectra theory for third-order tensorsC: Definitions 9 and 10) and establish their equivalence under certain conditions. We also provide variou...	ABC	BCA	BAC	ABC	Selection 3
A: To highlight the structure priors, we build a single-stream network as baseline, which fills missing regions by solely modeling texture features, and the discriminator is single-stream accordinglyB: On Structure PriorsC: As shown in Figure 7 (b), the baseline method does not well deal with complex struc...	BCA	BCA	BAC	BCA	Selection 3
A: In a binary erasure channel (BEC), a binary symbol is either received correctly or totally erased with probability ε𝜀\varepsilonitalic_εB: The concept of BEC was first introduced by Elias in 1955 InfThe C: Together with the binary symmetric channel (BSC), they are frequently used in coding theory and in...	ABC	CAB	BAC	CBA	Selection 1

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