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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: Thus recording the row and and column operat...	ACB	CAB	ABC	BCA	Selection 1
A: From now on, we concentrate on approximating P𝑃Pitalic_P so that (25) can be accurately and efficiently solved. B: Solving (25) on the other hand involves computing the hℎhitalic_h-dependent, global operator P𝑃Pitalic_P, leading to a dense matrix in (25)C: Except for (ii), all steps above above can be ...	CAB	CBA	BAC	ACB	Selection 2
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 maintainedB: Alg-CM uses an involved subroutine (far more complicated than ours given in Algorithm 1) to update the coordinates ...	CBA	ACB	CAB	BCA	Selection 2
A: Furthermore, we would expect that verified users are less involved in the rumor spreading. However, the feature appears near-bottom in the ranked list, indicating that it is not as reliable as expected. Also interestingly, “IsRetweeted” feature is pretty much useless, which means the probability of people retwee...	CBA	ABC	BAC	CAB	Selection 4
A: and probit losses. Assumption 1 impliesB: Assumption 1 includes many common loss functions, including the logistic, exp-loss222The exp-loss does not have a global β𝛽\betaitalic_β smoothness parameterC: However, if we initialize with η<1/ℒ⁢(𝐰⁢(0))𝜂1ℒ𝐰0\eta<1/\mathcal{L}(\mathbf{w}(0))italic_η < 1 / ca...	CAB	ABC	BCA	CBA	Selection 1
A: This results in a highly-reliable ground-truth of tweets labelled as news-related and rumor-related, respectively. Note that the labeling of a tweet is inherited from the event label, thus can be considered as an semi-automatic process.B: To deal with this complexity, we train our single-tweet learning model...	BAC	ABC	BAC	CBA	Selection 4
A: The features from the two categories are explained in details as follows.B: In addition, we also leverage click-flow relatedness features computed using RWRC: We propose two sets of features, namely, (1) salience features (taking into account the general importance of candidate aspects) that mainly mine...	CBA	ACB	BAC	CAB	Selection 1
A: 3 times the average insulin dose of others in the morning.B: The insulin intakes tend to be more in the evening, when basal insulin is used by most of the patientsC: The only difference happens to patient 10 and 12 whose intakes are earlier at day. Further, patient 12 takse approx	CBA	BAC	CAB	BCA	Selection 3
A: However, this study does not investigate whether the benefits of the proposed modifications generalize to other pre-trained architectures. That would constitute an interesting avenue for future researchB: To detect salient items in search array stimuli (see Figure 4d), a mechanism that additionally captures ...	BCA	BAC	BAC	CBA	Selection 1
A: Firstly, it is known that, assuming the Small Set Expansion Conjecture (denoted SSE; see [44]), there exists no constant-ratio approximation for MinCutwidth (see [52])B: Consequently, approximating MinLoc within any constant factor is also SSE-hard. In particular, we point out that stronger inapproximability...	BCA	CAB	CBA	ACB	Selection 1
A: PLG/2019/012497 and PLG/2019/012784. Some of the experiments were managed using https://neptune.ai. We would like to thank the Neptune team for providing us access to the team version and technical support. B: The work of Henryk Michalewski was supported by the Polish National Science Center grant UMO-2018/2...	ACB	CBA	ACB	BAC	Selection 2
A: This design enables the robot to steer in a manner reminiscent of traditional tank robots. However, unlike its contemporaries, the Cricket robot possesses the ability to conduct intricate movements, such as navigating through uneven terrain, in its walking locomotion mode [21]. The two primary forms of the robot...	ACB	CAB	CAB	BAC	Selection 4
A: In this work we focus on the online computation with adviceB: Our motivation stems from observing that, unlike the real world, the advice under the known models is often closer to “fiat” than “recommendation”C: Our objective is to propose a model which allows the possibility of incorrect advice, with th...	ABC	BCA	CAB	BAC	Selection 1
A: This, for example, could help to detect when symptoms worsen as a means to prevent possible suicide or, if the subject is already diagnosed, to detect when applied therapy is not workingB: We believe that extending the predictive model by incorporating information related to non-linear aspects of human beha...	ACB	BAC	CBA	ACB	Selection 2
A: We can find that after a sufficient number of iterations, the parameter in DGC (w/o mfm) can only oscillate within a relatively large neighborhood of the optimal pointB: We can find that although the momentum factor masking trick can make the convergence trajectory appear more stable, it also slows down the ...	ABC	ACB	BCA	CAB	Selection 2
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.	ABC	ACB	CAB	ACB	Selection 1
A: The topological structure of Multi-UAV network is shown in Fig. 1 (a).B: All the UAVs have the same volume of battery E𝐸Eitalic_E and communication capabilityC: We construct a UAV ad-hoc network in a post-disaster scenario with M𝑀Mitalic_M identical UAVs being randomly deployed, in which M𝑀Mitalic_M ...	BAC	ABC	CBA	BCA	Selection 3
A: For this shot (and simulation), Vc⁢o⁢m⁢p=12subscript𝑉𝑐𝑜𝑚𝑝12V_{comp}=12italic_V start_POSTSUBSCRIPT italic_c italic_o italic_m italic_p end_POSTSUBSCRIPT = 12kV and tc⁢o⁢m⁢p=45⁢μsubscript𝑡𝑐𝑜𝑚𝑝45μt_{comp}=45\upmuitalic_t start_POSTSUBSCRIPT italic_c italic_o italic_m italic_p end_POSTSUBSCRIPT = 45 roman...	BAC	ACB	CAB	ACB	Selection 1
A: Our main contribution is an extension to the DQN algorithm that incorporates Dropout methods to stabilize training and enhance performanceB: In this paper, we introduce and conduct an empirical analysis of an alternative approach to mitigate variance and overestimation phenomena using Dropout techniquesC...	ABC	ACB	BAC	CAB	Selection 3
A: (2016) proposed a similar architecture (V-Net; Figure 7) which added residual connections and replaced 2D operations with their 3D counterparts in order to process volumetric imagesB: Milletari et al. also proposed optimizing for a widely used segmentation metric, i.e., Dice, which will be discussed in more ...	ACB	BCA	BAC	CBA	Selection 2
A: When optimizing the selection of the decision trees, the performance is improved due to more diverse sampling.B: Table 3: Imitation learning performance (in accuracy [%]) of different data sampling modes on SoybeanC: NRFI achieves better results than random data generation	ACB	CAB	BAC	CBA	Selection 2
A: As a result, such a lack of statistical understanding hinders the development of more sample-efficient policy optimization algorithms beyond heuristics. In fact, empirically, vanilla policy gradient is known to exhibit a possibly worse sample complexity than random search (Mania et al., 2018), even in basic sett...	BAC	ACB	CAB	BAC	Selection 3
A: While domain-specific accelerators, such as Google’s TPU, excel in their specific performance, they are usually limited to a set of specific operations and are neither flexible in terms of data types nor sparse calculationsB: Furthermore, in particular for the TPU, experimentation is often hindered due to li...	BAC	ACB	ABC	CBA	Selection 3
A: Moreover, we consider a generalization of the filling radius and also define a strong notion of filling radius which is akin to the so called maximal persistence in the realm of topological data analysis.B: In this section, we recall the notions of spread and filling radius, as well as their relationship**C...	ACB	BAC	CAB	ABC	Selection 3
A: Through a series of complex transformations and fine-tuned optimization procedures (cf. Section 3), t-SNE usually manages to create low-dimensional representations that capture complex patterns from the high-dimensional space very accurately, showing them as well-separated clusters of pointsB: Although non-l...	BCA	BAC	ACB	CAB	Selection 2
A: A notable example in this list is BFOA [148], in which all solutions in the neighborhood impact on the computation of the movement vector, either by attracting the solution (if the neighboring solution has better fitness than the reference solution) or in a repulsive way (when the neighboring solution is worse t...	BCA	ACB	CBA	BAC	Selection 3
A: An epoch means a complete training of GAE and an update of the graphB: The maximum number of epochs, T𝑇Titalic_T, is set as 10. In other words, the graph is updated 10 times. Clearly, the embedding becomes more cohesive with the update. C: To illustrate the process of AdaGAE, Figure 2 shows the learned ...	CAB	BAC	BAC	BCA	Selection 4
A: Each IP packet contains an IP Identifier (IPID) field, which allows the recipient to identify fragments of the same original IP packetB: There are different IPID assignment algorithms which can be categorised as: random and predictable. Predictable category uses either a global counter or multiple counters ...	ACB	CBA	CBA	CBA	Selection 1
A: Listed is the classification accuracy (correct / total) of various models evaluated on the unseen testing data, i.e., batch T𝑇Titalic_TB: TABLE I: Mean generalization accuracyC: The values represent the average accuracy over 30 trials. The final column lists the mean of the values for batches 3 through ...	BCA	ACB	ACB	BAC	Selection 4
A: While these constructions and the involved proofs are generally deemed quite complicated, the situation for semigroups turns out to be much simpler. While it is known that the free semigroup of rank one is not an automaton semigroup [4, Proposition 4.3], the free semigroups of higher rank can be generated by an ...	ACB	ABC	BAC	CBA	Selection 3
A: For SCR, we obtain surprising results, with the model trained on irrelevant cues obtaining higher correlation than that trained on relevant visual cues. As expected, applying our regularizer does not improve rank correlation. Since HINT trained on relevant cues obtains the highest correlation values, it does ind...	CAB	BCA	ABC	CBA	Selection 1
A: The three random forest models were trained on three different sets of features: one using the features extracted from the URL, one using the features extracted from the document content, and a combined model using features from both. B: We trained four supervised machine learning models using the manually l...	CBA	BCA	ABC	CAB	Selection 4
A: The same MDS projection is observable in varying stages with different legend ranges and diverse colors for each instance, depending on the selected performance metric. The three steps in this figure demonstrate that we can reach both performant base models but also diverse algorithms by exploration of different...	ACB	CAB	ABC	BCA	Selection 3
A: This variation manifests both between training tasks and between training and testing tasks, similarly affecting the performance of MAML. Few works have thoroughly studied these impact factors.B: When applying MAML to NLP, several factors can influence the training strategy and performance of the model. Fir...	BAC	BCA	CAB	BCA	Selection 3
A: A conceptual frame structure is designed which contains two types of time slots. One is the exchanging slot (e-slot) and the other is the tracking slot (t-slot). Let us first focus on the e-slot. It is assumed that UAVs exchange MSI every T𝑇Titalic_T t-slots, i.e., in an e-slot, to save resource for payload tra...	CBA	ABC	ACB	BAC	Selection 3
A: 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 matrixB: This will be bootstrapped...	ABC	BCA	BAC	CAB	Selection 3
A: In §6.1, we introduce Q-learning and its mean-field limitB: In this section, we extend our analysis of TD to Q-learning and policy gradientC: In §6.2, we establish the global optimality and convergence of Q-learning. In §6.3, we further extend our analysis to soft Q-learning, which is equivalent to polic...	ABC	BAC	ACB	CAB	Selection 2
A: For the convergence of deep Transformers, Bapna et al. (2018) propose the Transparent Attention mechanism which allows each decoder layer to attend weighted combinations of all encoder layer outputs. Wang et al. (2019) present the Dynamic Linear Combination of Layers approach that additionally aggregates shallow...	BCA	ACB	ABC	ABC	Selection 2
A: Therefore U=V∩Y𝑈𝑉𝑌U=V\cap Yitalic_U = italic_V ∩ italic_Y with V𝑉Vitalic_V a definable open set of X𝑋Xitalic_X. ∎B: definable closed set of X𝑋Xitalic_XC: Remark that V≜U∪(X∖Y)≜𝑉𝑈𝑋𝑌V\triangleq U\cup(X\setminus Y)italic_V ≜ italic_U ∪ ( italic_X ∖ italic_Y ) is an open set of X𝑋Xitalic_X, and is...	ABC	CAB	CBA	BCA	Selection 2
A: To demonstrate the effectiveness of each module in our framework, we conduct an ablation study to show the different performances. Additionally, the experimental results of our approach compared with the state-of-the-art methods are exhibited, in both quantitative measurement and visual qualitative appearance. F...	CBA	ABC	BAC	CAB	Selection 1
A: Meanwhile, in large-batch training, SNGM achieves better performance than MSGD and LARS.B: Figure 3 shows the validation perplexity of the three methods with a small batch size of 20 and a large batch size of 2000C: In small-batch training, SNGM and LARS achieve validation perplexity comparable to that ...	CAB	CBA	ABC	BAC	Selection 1
A: 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. B: Nathaniel Grammel and Leonidas Tsepenekas were supported in part by NSF awards CCF-1749864 and CCF-1918749, and by research awards from Amazon and Google*...	BCA	CAB	CBA	BCA	Selection 3
A: The local cost functions are not required to be differentiable, nor do their subgradients need to be bounded. The local optimizers can only obtain measurement information of the local subgradients with random noisesB: Motivated by distributed statistical learning over uncertain communication networks, we st...	BAC	ABC	BCA	ACB	Selection 1
A: Consequently, the distributions of QI values are hardly maintained and the information utility is reduced significantly. For instance, as shown in Figure 2, the red polyline and the magenta polyline represent the distributions on age in Figure 1(a) and Figure 1(c), respectivelyB: We can observe that the orig...	BCA	ABC	ABC	ABC	Selection 1
A: In addition to models listed in Table 3, another PointRend with slightly different setting (stacking two BFP modules, and increasing the RoIAlign size from original 7 to 10 for bounding box branch) is trained and achieves 76.95 mAP on testing set. So, there are 5 models used for final ensemble. B: Note that ...	BAC	CBA	ABC	ABC	Selection 2
A: For the significance of this conjecture we refer to the original paper [FK], and to Kalai’s blog [K] (embedded in Tao’s blog) which reports on all significant results concerning the conjectureB: Its introduction is also a good source of information on the problem. C: [KKLMS] establishes a weaker version ...	CBA	CBA	ABC	ACB	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 ...	BCA	CBA	BCA	BCA	Selection 2
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...	CAB	BCA	BCA	ACB	Selection 1
A: This distinguishes it from most inductive methods that either cannot produce entity embeddings [22, 23, 25], or have entity embeddings conditioned on specific relations/entities [20, 21]B: While some methods attempt to address entity alignment by introducing a new relation, the results often demonstrate poor...	CAB	BCA	ABC	ABC	Selection 2
A: By extending such idea to RL domain, the ‘intrinsic’ rewards are used in RL to incentivize exploration. Previous formulations of intrinsic rewards used in self-supervised exploration typically utilize ‘curiosity’ corresponding to the prediction-error of environment model [10, 11] and the Bayesian uncertainty est...	ACB	ACB	CBA	ABC	Selection 3
A: The polynomial convergence rates of Floater-Hormann and allB: 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 coefficients/nodes required to determine the inte...	CBA	BAC	ACB	ABC	Selection 1
A: The key observation that we make is that the DR learning problem can be cast as a style transfer task [DBLP:conf/cvpr/GatysEB16], thus allowing us to borrow techniques from this extensively explored area. B: The framework is general and can utilize any DGMC: Furthermore, even though it involves two stage...	CBA	ACB	ACB	CAB	Selection 4
A: The structural computer used an inverted signal pair to implement the reversal of a signal (NOT operation) as a structural transformation, i.e. a twist, and four pins were used for AND and OR operations as a series and parallel connection were required. However, one can think about whether the four pin designs ...	ACB	BAC	BCA	CBA	Selection 1
A: 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], linearized polynomials [21] and few other specific forms [13, 14] to name a few. B: Some well-studied families of polynomial...	ACB	ACB	CBA	ABC	Selection 3
A: The interpolating predictor is very dense but has lower stability than several sparse models, while NNFS is less sparse than stability selection and also less stable. Note that we mean sparsity in terms of the number of selected views, but this corresponds to sparsity in terms of the number of selected features ...	CBA	BCA	ACB	CAB	Selection 1
A: Each of the three normal patterns is held by over 40% of animals in the datasetB: Platypus is the only animal that violates these dependencies (accounting for 1%). C: Platypus mainly violates the following normal dependencies: 1) if an animal produces milk, it likely has teeth; 2) if an animal produces m...	ACB	ACB	BCA	CBA	Selection 3
A: Comparison with Oh & Iyengar [2019] The Thompson Sampling based approach is inherently different from our Optimism in the face of uncertainty (OFU) style Algorithm CB-MNLB: [2010] but has a multiplicative κ𝜅\kappaitalic_κ factor in the bound.C: However, the main result in Oh & Iyengar [2019] also relie...	CAB	ACB	CAB	CAB	Selection 2
A: We directly predict the 20 action categories for THUMOS; we conduct binary classification and then fuse our prediction scores with video-level classification scores from [41] for ActivityNet following [21]. In post-processing, we apply soft-NMS [6] to suppress redundant predictions, keeping 200 predictions for T...	CAB	ABC	CBA	BCA	Selection 3
A: Evaluation with the Test and External Validation SetsB: To verify whether our findings were reliable, we applied the resulting majority-voting ensemble to the same test and external validation data sets as Mansouri et al. [MRB∗13], see Table 1C: For the test data set, the reported accuracy was approximat...	ABC	BCA	BCA	CBA	Selection 1
A: In this section, we introduce a shortest-path algorithm that is proposed as a modification to the Metropolis-Hastings algorithm in [7, Section V-E] and integrated with the Markov chain synthesis methods described in [14] and [15]B: This algorithm can also be integrated with the DSMC algorithm to further inc...	CAB	ACB	ABC	BAC	Selection 3
A: The functional mapping is represented as a low-dimensional matrix for suitably chosen basis functionsB: The classic choice are the eigenfunctions of the LBO, which are invariant under isometries and predestined for this settingC: Moreover, for general non-rigid settings learning these basis functions has...	CBA	ABC	CAB	CBA	Selection 2
A: Rooted path graphs can be recognized in linear time by using the algorithm by Dietz [7]. All inclusions between introduced classes of graphs are resumed in the following:B: A rooted path graph is the intersection graph of directed paths in a rooted treeC: We now introduce a last class of intersection gr...	CBA	BAC	BCA	ACB	Selection 1
A: The numerical results are given by the last two panels of Figure 1B: Subfigure 1(k) suggests that Mixed-SLIM, Mixed-SCORE, and GeoNMF share similar performances and they perform better than OCCAM under the MMSB settingC: the proposed Mixed-SLIM significantly outperforms the other three methods under the...	CBA	ACB	ABC	ACB	Selection 3
A: (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, 2020)B: (2017); Agarwal et al. (2018); Zhang et al. (2018); Tripuraneni et...	CBA	ACB	ACB	BAC	Selection 1
A: The reason is that GeneraLight trains several models on diverse generated traffic flows, and select the model in testing by matching the flowB: Except MaxPressure analysed above, GeneraLight achieves the best in Hangzhou with the mixedl configuration, while performs poorly in other scenariosC: Hence, it ...	ACB	BAC	CAB	ACB	Selection 2
A: The objective is to find Pareto-efficient algorithms concerning these two metrics, as a function of the advice size. However, this model is not concerned with the algorithm’s performance in typical cases in which the prediction does not fall in one of the two above extremes, does not incorporate the prediction e...	CBA	CAB	ABC	ACB	Selection 2
A: Therefore LoCondA uses only the base’s data model during training, which increases the efficiency and applicability of our approach.B: This framework extends the existing base hypermodels (Spurek et al., 2020a, b) with an additional module designed for mesh generation that relies on a parametrization of loca...	ACB	CBA	ABC	CAB	Selection 2
A: The previous lemma gives us an understanding of how quickly we can approximate the solutionB: In particular, in coordinates that can be non-zero we are able to have a value that absolutely coincides with the solution, but in zero coordinates this is impossibleC: It remains to understand what the solution...	CAB	BCA	ABC	BAC	Selection 3
A: In [6] the authors characterize them in terms of their corresponding cycle matrices and present a Venn diagram that shows their inclusion relationsB: Different classes of cycle bases can be consideredC: Among these classes we can find the strictly fundamental class.	CAB	CAB	ACB	BAC	Selection 4
A: One immediate application of Theorem 1.2 is the reduction of fractional Helly numbersB: 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 1.4.1C: of Patáková [35...	CBA	BAC	ABC	ACB	Selection 3
A: (a) presents another transformation of the second most impactful feature (according to Fig. 5(b))B: F4_p4///F15///F18_l1p is the most important combination (see the darker green color in (b))C: The punchcard visualization in (c) indicates that when we removed F16, the performance increased and that the n...	CAB	ACB	BAC	ABC	Selection 4
A: We compare three schemes: manual tuning of the MPCC parameters for fixed low level controller gains, Tuning of MPCC parameters through Bayesian optimization, and joint tuning of the MPCC- and the low-level cascade controller parameters using Bayesian optimization.B: We use two geometries to evaluate the per...	BCA	BCA	CAB	ACB	Selection 3
A: LNL and IRMv1 seem to be equanimously affected by both explicit and implicit biases, and thus fail to improve upon the baseline as previously shown in Table 1. LFF has a relatively low range of MMDs and as shown by the improvements in Table 1, the method outperforms others on Biased MNIST.B: Results. In Fig....	CAB	ACB	BAC	ABC	Selection 1
A: \addedAFF-Net [57] and EFE [187] shows the best performance than other compared methods. The third and fourth rows show the converted resultsB: The second row in Tab. VII shows the result of PoG estimation methodsC: Compared methods are designed for gaze direction estimation and we convert the result int...	BAC	CBA	BCA	CAB	Selection 1
A: A comparative study between BoF and deep learning for image classification has been made in Loussaief and Abdelkrim loussaief2018deep . To take full advantage of the two techniques, in this paper we can consider BoF as a pooling layer in our trainable convolutional layers. This aims to reduce the number of param...	BCA	CAB	BCA	CBA	Selection 2
A: In parallel, linear size arithmetic for sized inductive types [CK01, Xi01, BR06] was generalized to support coinductive types as well [Sac14]. We present, to our knowledge, the first sized type system for a concurrent programming language as well as the first system to combine both features from aboveB: Size...	BCA	CBA	BAC	CBA	Selection 3
A: However, this is no longer the case when media contents are remotely hosted by the cloud since existing AFP schemes were designed without taking the cloud’s involvement into considerationB: Thus it remains to be further explored how to develop a novel AFP solution compatible with the system model of cloud me...	BCA	ABC	ABC	CBA	Selection 1
A: Our proposed GraphFM achieves best performance among all these four classes of methods on three datasets. The performance improvement of GraphFM compared with the three classes of methods (A, B, C) is especially significant, above 0.010.01\mathbf{0.01}bold_0.01-level. The aggregation-based methods including Inte...	CAB	CBA	ACB	CBA	Selection 3
A: Self-concordant functions have received strong interest in recent years due to the attractive properties that they allow to prove for many statistical estimation settings [Marteau-Ferey et al., 2019, Ostrovskii & Bach, 2021]. The original definition of self-concordance has been expanded and generalized since its...	ACB	BAC	BCA	BAC	Selection 1
A: Furthermore, we make some important observations about invariants that are preserved by operations of our algorithm which we will use later. In Section 4, we prove the correctness of our algorithmB: The approximation analysis as well as the proof of the pass complexity can be found in Section 5C: In Sect...	ACB	CAB	ACB	ABC	Selection 4
A: The method works under a general class of compression operators and is shown to achieve linear convergence for strongly convex and smooth objective functions over general directed graphsB: We propose CPP – a novel decentralized optimization method with communication compressionC: To the best of our know...	BCA	CBA	BAC	ABC	Selection 3
A: We adapt the proposed algorithm for training neural networksB: To the best of our knowledge, this is the first work that compares these approaches in the scope of neural networks, as previous studies were limited to simpler methods, such as regression problems [31, 29]. Our experiments confirm the robustnes...	ACB	CBA	BAC	ABC	Selection 1
A: In Section 5 we propose a novel training algorithm, Joint Policy-Space Response Oracles (JPSRO), to train policies on n-player, general-sum extensive form games. JPSRO requires the solution of a meta-game, and we propose using MG(C)CE as a meta-solverB: We prove that the resulting algorithm converges to a no...	CBA	ACB	CAB	BCA	Selection 4
A: A worst-case approach makes sense for privacy, but for statistical guarantees like generalization, we only need statements that hold with high probability with respect to the sampled dataset, and only on the actual queries issued.B: Differential privacy essentially provides the optimal asymptotic generaliza...	ABC	CAB	ABC	ACB	Selection 2
A: We generate a set of colorings that is guaranteed to contain at least one such coloringB: Using the previous lemmas the problem of finding a reducible single-tree FVC reduces to finding a coloring that properly colors a simple reducible FVCC: To generate this set we use the concept of a universal set.	BAC	BCA	CAB	CBA	Selection 1
A: They assume that the locations along the same horizontal scanline have similar depth, so that the true scale of foreground object can be well-preserved.B: [126] proposed to move the foreground object of fixed scale along the same horizontal scanline on the backgroundC: Some object placement methods desig...	BCA	CBA	BAC	BCA	Selection 2
A: Through the use of data mining and visualization tools, we have demonstrated the significance of multi-modal urban data and have highlighted the connections between service and context data. Furthermore, we have presented extensive experimental results on spatio-temporal predictions, transfer learning, and reinf...	ACB	ACB	CBA	CAB	Selection 4
A: Although a variety of methods was considered, it is not feasible to include all of them. The most important omission is a more detailed overview of Bayesian neural networks (although one can argue, as was done in the section on dropout networks, that some common neural networks are, at least partially, Bayesian...	ACB	CAB	ABC	CBA	Selection 1
A: Notation—“M1”: vocal melody, “M2”: instrumental melody, “A”: accompaniment.B: Figure 3: Confusion tables (in %) for two models for three-class melody classification, calculated on the test split of POP9094/44/4{}_{\text{4/4}}start_FLOATSUBSCRIPT 4/4 end_FLOATSUBSCRIPTC: Each row represents the percentag...	CBA	CBA	BCA	CAB	Selection 4
A: [10] for a survey), where the colors of any two adjacent vertices have to differ by at least k𝑘kitalic_k and the colors of any two vertices within distance 2222 have to be distinct. B: to L⁢(k,1)𝐿𝑘1L(k,1)italic_L ( italic_k , 1 )-labeling problem (see e.gC: This description draws a comparison e.g	CAB	CBA	ABC	ACB	Selection 2
A: Regarding the semantic commutations for speech information, our previous work developed an attention mechanism-based semantic communication system to restore the source message, i.e., reconstruct the speech signals[18]B: Particularly, we propose a DL-enabled semantic communication system for speech recogniti...	CAB	ACB	ABC	CBA	Selection 2
A: TABLE IV: We compare the segmentation predictions in mIoU(%) from different decoder branches for the two stages of trainingB: The basic branch is the one we use for inferenceC: Cross-branch and intra-branch are the decoder outputs produced from CSFR and ISFR propagated features.	ACB	ABC	BAC	BCA	Selection 2
A: The detection performance can be remarkably improved from 11.84% to 70.91% in terms of the AP40subscriptAP40{\rm AP}_{40}roman_AP start_POSTSUBSCRIPT 40 end_POSTSUBSCRIPT under the moderate setting of car category on the KITTI val set (see Table 1), which suggests that the depth estimation is a critical performa...	CBA	CAB	CAB	CAB	Selection 1
A: A potential solution may be to reason according to the semantic information of textB: Moreover, failure cases may happen on some text-like objects or super-tiny texts, which are also common challenges for other state-of-the-art methods [10, 21, 20]. Examples of such failure cases are shown in Fig. 9. C: ...	CAB	BCA	ACB	ACB	Selection 2
A: Specifically, the two proposed methods present two different relationship mapping mechanisms between memory blocks and IP addresses to strike a balance between computational cost and memory use. They can be employed to search for frequently occurring IP addresses in practical applications. The extensive experime...	BAC	CAB	BCA	CBA	Selection 4
A: The authors would like to thank Mingjian Ding, and Baoxuan Zhu for providing an alternative proof of the Hurwitz stability of polynomials (25). They also thank Jarle Sogn for communicating on Schur complement based preconditioners. The work of M. Cai is partially supported by the NIH-RCMI grant through 347 U54MD...	CAB	ACB	CBA	ABC	Selection 4
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 ...	BCA	CBA	BAC	ACB	Selection 1
A: KJQN202200512), the Chongqing Talents Project (Grant No. cstc2022ycjh-bgzxm0040), and the Research Foundation of Chongqing Normal University (Grant No. 21XLB040), PB: Changxin Mo acknowledges support from the National Natural Science Foundation of China (Grant No. 12201092), the Natural Science Foundation Pr...	CAB	BAC	CBA	ABC	Selection 2
A: PConv [13] is suitable for irregular corruptions, but obvious artifacts can be observed in Figure 5 (c). DeepFilllv2 [36] suffers from over-smoothing predictions and distorted structuresB: With the Recurrent Feature Reasoning module, RFR [11] yields competitive results; however, the details are still not so ...	BCA	CBA	CBA	CAB	Selection 1
A: Together with the binary symmetric channel (BSC), they are frequently used in coding theory and information theory because they are among the simplest channel models, and many problems in communication theory can be reduced to problems in a BEC. Here we consider more generally a q𝑞qitalic_q-ary erasure channel ...	BAC	ACB	CAB	BCA	Selection 3

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