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**A**: Note that row and column operations are effected by left- and right multiplications by transvections**B**: Thus recording the row and and column operations required to transform a diagonal matrix into the identity, allows us to write the input matrix as a product of transvections. **C**:
The key idea is to tran... | ABC | BCA | ACB | BAC | Selection 2 |
**A**: The solutions of (22) decay exponentially fast if w𝑤witalic_w has local support, so instead of solving the problems in the whole domain it would be reasonable to solve it locally using patches of elements**B**:
It is essential for the performing method that the static condensation is done efficiently**C**: We ... | ABC | BAC | ABC | ABC | 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... | ABC | BCA | CAB | BAC | Selection 3 |
**A**: Related work often uses aggregated content [18, 20, 32], since individual tweets are often too short and contain slender context to draw a conclusion. However, content aggregation is problematic for hierarchical events and especially at early stage, in which tweets are likely to convey doubtful and contradictory... | BCA | ABC | BAC | CBA | Selection 3 |
**A**: We might improve the validation and test errors even when when the decrease in the training loss is tiny and even when the validation loss itself increases.
**B**: We should not rely on plateauing of the training loss or on the loss (logistic or exp or cross-entropy) evaluated on a validation data, as measures t... | CAB | BCA | CBA | CBA | Selection 1 |
**A**:
At 18:22 CEST, the first tweet was posted**B**: The tweet is ”Sadly, i think there’s something terrible happening in #Munich #Munchen. Another Active Shooter in a mall. #SMH”.**C**: There might be some certain delay, as we retrieve only tweets in English and the very first tweets were probably in German | BCA | ABC | ACB | CBA | Selection 3 |
**A**:
We propose two sets of features, namely, (1) salience features (taking into account the general importance of candidate aspects) that mainly mined from Wikipedia and (2) short-term interest features (capturing a trend or timely change) that mined from the query logs**B**: The features from the two categories ar... | CAB | BCA | ACB | CAB | Selection 3 |
**A**: The only difference happens to patient 10 and 12 whose intakes are earlier at day.
Further, patient 12 takse approx**B**: The insulin intakes tend to be more in the evening, when basal insulin is used by most of the patients**C**: 3 times the average insulin dose of others in the morning. | CAB | BAC | ACB | CAB | Selection 2 |
**A**: We propose a new CNN architecture with modules adapted from the semantic segmentation literature to predict fixation density maps of the same image resolution as the input**B**: In the following sections, we describe our contributions to this challenging task.
**C**: Our approach is based on a large body of rese... | CAB | ACB | BCA | BAC | Selection 2 |
**A**: In the following, we investigate another aspect of greedy strategies**B**: Each isolated occurrence results in a new marked block, while each block-extending occurrence just extends an already existing marked block, and potentially may even combine two marked blocks and therefore may decrease the overall number ... | ACB | BCA | CBA | BAC | Selection 1 |
**A**: The world model is trained for 45454545K steps in the first iteration and for 15151515K steps in each of the following ones. Shorter training in later iterations does not degrade the performance because the world model after first iteration captures already part of the game dynamics and only needs to be extended... | CAB | ACB | BCA | CBA | Selection 4 |
**A**: In addressing the first challenge, the dynamics of rolling locomotion are well understood and are similar to those of traditional wheeled/tracked robots. However, despite extensive research on the walking dynamics of standard legged robots, focused studies on the walking patterns specific to wheel/track-legged r... | CAB | BAC | BAC | BCA | Selection 4 |
**A**: Mtf2 is called called Move-To-Front-Even (Mtfe), and if all bits are 1 at the beginning, Mtf2 is
called Move-To-Front-Odd (Mtfo)**B**: Both Mtfe and Mtfo algorithms have a competitive ratio of 5/2525/25 / 2 [11]**C**: In [11] it is shown that, for any request sequence, at least one of Timestamp, Mtfo, and Mtfe ... | CBA | ABC | BAC | BCA | Selection 2 |
**A**: In that context, our proposal is a potential tool with which systems could be developed in the future for large-scale passive monitoring of social media to help to detect early traces of depression by analyzing users’ linguistic patterns, for instance, filtering users and presenting possible candidates, along wi... | CBA | ACB | BCA | ABC | Selection 2 |
**A**: Due to the larger compressed error introduced by RBGS compared with top-s𝑠sitalic_s when selecting the same number of components of the original vector to communicate, vanilla error feedback methods usually fail to converge**B**: Xu and Huang (2022) propose DEF-A to solve the convergence problem by using detach... | CBA | ABC | CAB | BAC | Selection 2 |
**A**:
, where ∗*∗ is the convolution333We use convolution instead of cross-correlation only as a matter of compatibility with previous literature and computational frameworks**B**: operation.**C**: Using cross-correlation would produce the same results and would not require flipping the kernels during visualization | CAB | ABC | CBA | ACB | Selection 4 |
**A**: And then UAV compares two payoffs. If the payoff of new strategy is larger, the current strategy will be replaced by the new strategy; if the current payoff strategy is large, it will remain in the current strategy. However, under highly dynamic scenarios, complicate network conditions make UAVs hard to calculat... | CAB | BCA | BAC | CBA | Selection 2 |
**A**: italic_g **B**: ,**C**: condition (∇⟂f)|Γ=0evaluated-atsubscript∇perpendicular-to𝑓Γ0\left(\nabla_{\perp}f\right)|_{\Gamma}=0( ∇ start_POSTSUBSCRIPT ⟂ end_POSTSUBSCRIPT italic_f ) | start_POSTSUBSCRIPT roman_Γ end_POSTSUBSCRIPT = 0 implies that
any poloidal currents that flow into or out of the wall (e.g.,formul... | BCA | BAC | CBA | ACB | Selection 1 |
**A**: In this paper, we introduce and conduct an empirical analysis of an alternative approach to mitigate variance and overestimation phenomena using Dropout techniques**B**: The effectiveness of our solution is demonstrated through computer simulations in a classic control environment.
**C**: Our main contribution i... | CBA | ACB | CBA | CBA | Selection 2 |
**A**: However, there have been many neural network optimization techniques which do not rely on backpropagation, such as credit assignment (Bengio and Frasconi, 1994), neuroevolution (Stanley and Miikkulainen, 2002), difference target propagation (Lee et al., 2015), training with local error signals (Nøkland and Eidne... | BCA | ABC | CBA | BAC | Selection 4 |
**A**:
NRFI uniform and NRFI dynamic sample the number of decision trees for each data point uniformly, respectively, optimized via automatic confidence distribution (see Section 4.1.4)**B**: Additionally, sampling random data points without generating data from the random forest is included as a baseline.**C**: The c... | CBA | ACB | BAC | CAB | Selection 2 |
**A**:
Broadly speaking, our work is related to a vast body of work on value-based reinforcement learning in tabular (Jaksch et al., 2010; Osband et al., 2014; Osband and Van Roy, 2016; Azar et al., 2017; Dann et al., 2017; Strehl et al., 2006; Jin et al., 2018) and linear settings (Yang and Wang, 2019b, a; Jin et al.... | ACB | CBA | BAC | ABC | Selection 4 |
**A**: (2019a) and Cai et al. (2019).
They introduce gates for every layer that determine the number of bits used for quantization, and they perform continuous stochastic optimization of probability parameters associated with each of these gates.**B**: Wu et al**C**: (2018a) performed mixed-precision quantization using... | BAC | CAB | BAC | BAC | Selection 2 |
**A**: We present two examples: the first one arises from our study of the Künneth formula in Section 6 whereas the second one is a direct construction**B**:
There exist, however, non-regularly filled metric manifolds**C**: Both examples make use of results from [4] about homotopy types of Vietoris-Rips complexes of �... | CAB | ABC | BAC | BCA | Selection 3 |
**A**: The difference line plot (d), on the other hand, builds on the standard plot by highlighting the differences between the selection and the global average, shown as positive and negative values around the 0 value of the y-axis.
It provides a clearer overall picture of the difference in preservation among all the ... | CAB | CBA | ABC | ACB | Selection 3 |
**A**: Indeed, evolution has allowed animals to adapt to harsh environments, foraging, very difficult tasks of orientation, and to resiliently withstand radical climatic changes, among other threats. Animals, when organized in independent systems, groups or swarms or colonies (systems quite complex on their own) have m... | ABC | CBA | BCA | ABC | Selection 3 |
**A**:
In recent years, GCNs have been studied a lot to extend neural networks to graph type data**B**: Most of them can be classified into 2 categories, spectral methods [24] and spatial methods[25].**C**: How to design a graph convolution operator is a key issue and has attracted a mass of attention | CBA | ACB | CAB | CBA | Selection 2 |
**A**: We implemented probing over TCP SYN, ping and using requests/responses to Name servers and we apply the suitable test depending on the server that we identify on the tested network. If the responses contain globally incremental IPID values - we use the service for ingress filtering measurement with IPID techniqu... | BAC | ACB | CAB | ABC | Selection 1 |
**A**: 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, 11, 12]. Extreme learning machines are also frequently deployed in these settings to efficiently reconfi... | BAC | CBA | ACB | BAC | Selection 2 |
**A**: In fact, the free product of two automaton semigroups S𝑆Sitalic_S and T𝑇Titalic_T is always at least
very close to being an automaton semigroup: adjoining an identity to S⋆T⋆𝑆𝑇S\star Titalic_S ⋆ italic_T**B**: However, there do not seem to be constructions for presenting arbitrary free products of self-simil... | BAC | CBA | CAB | BAC | Selection 3 |
**A**: When fine-tuning with HINT, SCR or our method, we also use the main binary cross entropy VQA loss, whose weight is set to 1111. The batch size is set to 384384384384 for all of the experiments.**B**:
We compare four different variants of HINT and SCR to study the causes behind the improvements including the mod... | BCA | ABC | BAC | CAB | Selection 4 |
**A**: Two LDA topics corresponded with the OPP-115 category Third Party Sharing and Collection, one detailing the action of collection, and one explaining its purpose and effects(advertising and analytics)**B**: One of the LDA topics exclusively comprised of vocabulary related to cookies which could be related to both... | CAB | ACB | CAB | BCA | Selection 4 |
**A**: Manifold [66] generates pairs of models and compares them over all classes of a data set, including feature selection**B**: We adopt a similar approach, but instead of comparing a large number of models in a pairwise manner, we aggregate their overall and per-class performance. Then, the user can compare a set o... | ABC | BAC | BCA | CBA | Selection 3 |
**A**: They are datasets for 5-way 5-shot classification, which means 5 classes are randomly sampled from the full dataset for each task, and each class has 5 samples**B**:
In Experiment I: Text Classification, we use FewRel [Han et al., 2018] and Amazon [He and McAuley, 2016]**C**: FewRel is a relation classification... | CBA | BAC | ACB | BCA | Selection 2 |
**A**: Note that directly solving the above beam tracking problem is very challenging, especially in the considered highly dynamic UAV mmWave network**B**: Recall that several efficient codebook-based beam training and tracking schemes have been proposed for conventional mmWave network with uniform ULA and UPA [22, 23]... | ACB | BAC | BAC | CAB | Selection 1 |
**A**: We**B**: This will be bootstrapped to the multi-color case in later sections**C**: Note that the 1111-color case with the completeness requirement is not very interesting, and also not useful for the general case: completeness states that every node on
the left must be connected, via the unique edge relation, to... | ACB | ABC | BCA | CAB | Selection 4 |
**A**: In §6.1, we introduce Q-learning and its mean-field limit**B**: 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 policy gradient.
**C**: In this section, we extend our analysis of TD to Q-learning and pol... | BCA | BAC | CAB | CBA | Selection 1 |
**A**: (2016) with 32k32𝑘32k32 italic_k merging operations on all data sets to address the unknown word issue**B**: We only kept sentences with a maximum of 256256256256 subword tokens for training. For fair comparison, we did not tune any hyperparameters but followed Vaswani et al. (2017) for all experiment settings... | CBA | BCA | CBA | CBA | Selection 2 |
**A**: Recall that ⟨Y,τY,𝒦∘(Y)⟩𝑌subscriptτ𝑌superscript𝒦𝑌\langle Y,\uptau_{Y},\mathcal{K}^{\circ}\!\left(Y\right)\rangle⟨ italic_Y , roman_τ start_POSTSUBSCRIPT italic_Y end_POSTSUBSCRIPT , caligraphic_K start_POSTSUPERSCRIPT ∘ end_POSTSUPERSCRIPT ( italic_Y ) ⟩ is a lpps**B**: pre-spectral space**C**: We are goin... | BAC | BCA | CBA | ABC | Selection 1 |
**A**: 8, in which each pixel value of the distortion distribution map represents the distortion level**B**: Since the ordinal distortion estimation pays more attention to the realistic distortion perception and reasonable learning strategy, our scheme achieves results much closer to the ground truth 3D DDM. Due to imp... | BCA | ACB | BAC | ACB | Selection 1 |
**A**:
Figure 3 shows the validation perplexity of the three methods with a small batch size of 20 and a large batch size of 2000**B**: Meanwhile, in large-batch training, SNGM achieves better performance than MSGD and LARS.**C**: In small-batch training, SNGM and LARS achieve validation perplexity comparable to that ... | ACB | BAC | CBA | CAB | Selection 1 |
**A**: 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)**B**: See Appendix A for an in-depth discussion.**C**:
Unfortunately, standard SAA app... | ABC | ACB | BCA | BAC | Selection 3 |
**A**: Compared with the case with only a single random factor, the coupling terms of different random factors inevitably affect the mean square difference between optimizers’ states and any given vector. What’s more, multiplicative noises relying on the relative states between adjacent local optimizers make states, gr... | ABC | CBA | ABC | BAC | Selection 4 |
**A**: We use the US Census data [29], eliminate the tuples with missing values, and randomly select 40,152 tuples with eight attributes. The QI attributes are gender, age, relationship, marital status, race, education, and hours per week, and the sensitive attribute is salary. Table 1 describes the attributes in detai... | BAC | BCA | BAC | CBA | Selection 4 |
**A**: It can be seen that PointRend generates much finer and smoother segmentation boundaries than HTC and SOLOv2, it also handles overlapped instances gradely (see top-left corner in Figure 2)**B**: Meanwhile, PointRend succeeds in distinguishing holes inside objects as background while HTC and SOLOv2 may predict inc... | ABC | CBA | BCA | ACB | Selection 3 |
**A**: Its introduction is also a good source of information on the problem.
**B**: [KKLMS] establishes a weaker version of the conjecture**C**: 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 concerni... | BAC | ABC | CBA | BAC | Selection 3 |
**A**: There are several ways to define nonstationarity in the bandit literature. The first one is piecewise-stationary (Garivier & Moulines, 2011), which assumes the expected rewards of arms change in a piecewise manner, i.e., stay fixed for a time period and abruptly change at unknown time steps**B**: The second one ... | ABC | ACB | CBA | BCA | Selection 4 |
**A**: 75 of the 104 responses fulfilled the criterion of having respondents who were currently based in Singapore. This set was extracted for further analysis and will be henceforth referred to as ‘SG-75’**B**: The first contains 59 responses in which respondents said that they have shared news before (referred to as ... | CAB | ACB | CAB | CBA | Selection 2 |
**A**: Our findings indicate that decentRL with an embedding size of 256 is adequate to outperform AliNet with an embedding size of 512**B**: Furthermore, even a modestly sized decentRL (with an embedding size of 64) can surpass vanilla GAT with an embedding size of 512.
**C**: In Table 11, we present the entity alignm... | CAB | CAB | BCA | ABC | Selection 3 |
**A**:
In this section, we conduct experiments to compare the proposed VDM with several state-of-the-art model-based self-supervised exploration approaches**B**: Then, we compare the proposed method with baselines in several challenging image-based RL tasks. The code and video are available at https://sites.google.com... | ACB | BAC | BAC | ABC | Selection 1 |
**A**: The observations made in 2D remain valid**B**: The polynomial convergence rates of Floater-Hormann and all**C**: However, Floater-Hormann becomes indistinguishable from 5thsuperscript5𝑡ℎ5^{th}5 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT-order splines.
Further, when considering the amount of co... | CAB | CAB | ACB | CBA | Selection 3 |
**A**: For example, in Figure 1, the model uses β𝛽\betaitalic_β-TCVAE [mig] to retrieve the pose of the model as a latent factor. In the reconstruction, the rest of the details are averaged, resulting in a blurry image (1b). The goal of the second part of the model, is to add the details while maintaining the semantic... | CAB | ABC | BAC | ABC | Selection 1 |
**A**: Fig. 7 shows, however, that some rays of light can be counted on the lower beta signal, which can interfere with the operation of other Thus, a black body gate was implemented using i cells to make input everywhere into NULL state**B**: Optical logic aggregates can be designed in the same way as in Implementatio... | ACB | BAC | CBA | ABC | Selection 2 |
**A**: Given a finite subset of such permutations, we can compute a group generated by this set**B**: In this paper, we propose a representation of such a group using the concept of linear representation defined through the Koopman operator.**C**:
A finite field, by definition, is a finite set, and the set of all perm... | BCA | CAB | ACB | CAB | Selection 1 |
**A**: In Table 1 we therefore show only the interaction terms including the meta-learner factor.
**B**: Note that we are primarily interested in the extent to which differences between the meta-learners are moderated by the experimental factors of sample size, view size, number of views, and correlation structure**C**... | ACB | CBA | BCA | BCA | Selection 2 |
**A**: A common way of examining dependency deviations in the dependency-based approach is to check the difference between the observed value and the expected value of an object, where the expected value is estimated based on the underlying dependency between variables [7, 4, 5]**B**: Given that there has been an exten... | ABC | ABC | CBA | ACB | Selection 4 |
**A**: [2018] also consider a similar problem of developing an online algorithm for the MNL model with linear utility parameters**B**: We note that Ou et al**C**: Though they establish a regret bound that does not depend on the aforementioned parameter κ𝜅\kappaitalic_κ, they work with an inaccurate version of the MNL ... | CBA | CAB | ABC | BAC | Selection 4 |
**A**: THUMOS-14 contains 413 temporally annotated untrimmed videos with 20 action categories, in which 200 videos are for training and 213 videos for validation333The training and validation sets of THUMOS are temporally annotated videos from the validation and testing sets of UCF101 [33], respectively.. ActivityNet-v... | ABC | ACB | CBA | BCA | Selection 4 |
**A**: In our case, we combine the power of diverse algorithms, with one of them being a neural network (NN). HyperTendril [PNKC21] is a visualization tool that supports random search, population-based training [JDO∗17], Bayesian optimization, HyperBand [LJD∗17], and the last two methods joined together [FKH18]. It ena... | CBA | ABC | BAC | ACB | Selection 1 |
**A**: In Proposition 3, it is proven that Algorithm 1 satisfies Condition 1 which means that probability distribution x(k)𝑥𝑘x(k)italic_x ( italic_k ) exponentially converges to the desired distribution v𝑣vitalic_v.**B**:
In Proposition 2, it is proven that the dynamics of the error vector in Algorithm 1 are ident... | ACB | CAB | CBA | BCA | Selection 2 |
**A**: One exception is the recent work on spectral map synchronisation [31], which builds upon functional maps and is, in principal, well-suited for isometric multi-shape matching. However, although the authors take into account cycle consistency, respective penalties are only imposed on pairwise functional maps, rath... | BCA | CBA | ACB | BCA | Selection 2 |
**A**: Note that it is an implementation of Theorem 6 with very small changes**B**: We present the algorithm RecognizePG**C**: W.l.o.g., we assume that G𝐺Gitalic_G is connected, indeed a graph G𝐺Gitalic_G is a path graph if and only if all its connected components are path graphs. Moreover, we can obtain the clique p... | BAC | CBA | ACB | ABC | Selection 1 |
**A**: SLIM combined the SLIM with the spectral method based on DCSBM for community detection. And the SLIM method outperforms state-of-art methods in many real and simulated datasets**B**: In this paper, we extend the symmetric Laplacian inverse matrix (SLIM) method (SLIM, ) to mixed membership networks and call this... | BAC | ACB | CAB | ACB | Selection 1 |
**A**: (2017); Agarwal et al. (2018); Zhang et al. (2018); Tripuraneni et al**B**: See, e.g., Udriste (1994); Ferreira and Oliveira (2002); Absil et al. (2009); Ring and Wirth (2012); Bonnabel (2013); Zhang and Sra (2016); Zhang et al. (2016); Liu et al**C**: (2018); Boumal et al. (2018); Bécigneul and Ganea (2018); Zh... | ABC | CAB | BAC | ABC | Selection 3 |
**A**: The average travel time indicates the overall traffic situation in an area over a period of time**B**: For a detailed definition of average travel time, see Section 3.1. Since the number of vehicles and the origin-destination (OD) positions are fixed, better traffic signal control strategies result in less avera... | BAC | CAB | CAB | BCA | Selection 4 |
**A**: Previous work on this problem has assumed ideal and error-free predictions that must be provided by a very powerful oracle, without any learnability considerations, as we discuss in more detail in Section 1.2**B**: In contrast, our algorithms exploit natural, and PAC-learnable predictions concerning the frequenc... | CBA | BCA | ACB | ACB | Selection 2 |
**A**: (2020b) improve the quality of results, their objective is to fix deformations caused by the stitching of individual mappings. We postulate that by enforcing the local consistency of patch vertices within the objective function of a model, we can avoid creating these deformations in the first place.**B**: (2020)... | CBA | BCA | ABC | CAB | Selection 1 |
**A**: Finally, we show how the proposed method can be applied to prominent problem of computing Wasserstein barycenters to tackle the problem of instability of regularization-based approaches under a small value of regularizing parameter**B**: Wasserstein barycenters, which define the mean of objects that can be model... | ACB | BAC | BCA | CAB | Selection 1 |
**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 observations**C**: In this section we present ... | ACB | BCA | CBA | BCA | Selection 3 |
**A**: A major part of this paper, all of Sections 3 and 4, is devoted to adapt it to handle the k𝑘kitalic_k-partite structure of colorful intersection patterns.**B**:
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... | CBA | ACB | CAB | ACB | Selection 3 |
**A**: It is heavily subjective for most applications, except for text data [8, 9], for instance. As we work with numerical values and tabular data, the focus of our analytical approach presented in this paper is on the last three categories, which we describe next.
**B**: In general, feature engineering can be subdivi... | BCA | CBA | CAB | BCA | Selection 3 |
**A**: We first optimize the performance of the simulated positioning system by adding a receding horizon MPCC stage where we pre-optimize the position and velocity references provided to the low level controller**B**: The weights in the MPCC cost terms are manually tuned, the controller gains are kept at their nominal... | ABC | ACB | CAB | CBA | Selection 2 |
**A**: However, [55] have shown promising results by using static weights to upweight minority patterns. We choose this method due to its simplicity.**B**: This includes synthesizing minority instances too [14, 26]. Moving beyond class imbalances, REPAIR [40] proposed learning dynamic weights to mitigate representation... | ABC | CAB | BAC | CBA | Selection 4 |
**A**: They concatenate the feature maps of two eyes and use a convolution layer to generate the weights of the feature map.
Murthy et al. [58] simultaneously estimate feature vectors and weights for each eye image and concatenate the left and right eye feature**B**: Bao et al. [57] propose a self-attention mechanism... | ACB | CBA | BAC | CAB | Selection 3 |
**A**: To do so, we apply a cropping filter in order to obtain only the informative regions of the masked face (i.e. forehead and eyes)**B**: Next, we describe the selected regions using a pre-trained deep learning model as a feature extractor. This strategy is more suitable in real-world applications comparing to rest... | CAB | ACB | BCA | CBA | Selection 3 |
**A**: Since Vezzosi [Vez15] gives an embedding of the later modality and its dual into sized types, we believe that a similar arrangement can be achieved in our setting. In any case, we support recursion schemes more complex than structural (co)recursion [LM16].
**B**: Session types are inextricably linked with SAX, a... | ABC | CAB | ACB | CBA | Selection 2 |
**A**: For another, since M>T>L𝑀𝑇𝐿M>T>Litalic_M > italic_T > italic_L and δ>1𝛿1{\delta}>1italic_δ > 1, it is intuitive from Table II that in FairCMS-II, the cloud has increased in computing and storage costs compared to that of FairCMS-I**B**: This means that FairCMS-I is more efficient at the cost of lacking IND-C... | BCA | ABC | ABC | ACB | Selection 4 |
**A**: Other graph-based work, like GFM Xi et al. (2020), utilizes the popular Factorization Machine to effectively aggregate multi-order interactions in GNN. And GCFM Zheng et al. (2021) uses the multifilter graph-convolved feature crossing (GCFC) layer to learn the neighbor feature interactions.**B**: It first propos... | ABC | BAC | CAB | CBA | Selection 3 |
**A**: [2022] is in essence the Frank-Wolfe algorithm with a modified version of the backtracking line search of Pedregosa et al**B**: [2020]. In the next section, we provide improved convergence guarantees for various cases of interest for this algorithm, which we refer to as the Frank-Wolfe algorithm with Backtrack (... | CBA | BCA | CBA | ACB | Selection 2 |
**A**: The approximation analysis as well as the proof of the pass complexity can be found in Section 5**B**: In Section 6 we provide details about our general framework for finding approximate maximum matching.**C**: Furthermore, we make some important observations about invariants that are preserved by operations of ... | BCA | BAC | CBA | BAC | Selection 1 |
**A**: We provide necessary notation and assumptions in Section II**B**:
The rest of this paper is organized as follows**C**: CPP is introduced and analyzed in Section III. In Section IV, we consider the algorithm B-CPP. Numerical examples are presented in Section V, and we conclude the paper in Section VI. | CBA | BAC | CAB | CAB | Selection 2 |
**A**: Additionally, we provide the lower bounds both on the communication and the number of local oracle calls required to solve problem (1). Furthermore, we have developed the novel methods (Algorithm 1, Algorithm 2, Algorithm 3) for this problem that are optimal up to logarithmic factor in certain scenarios (see Tab... | ABC | CBA | BAC | BCA | Selection 4 |
**A**: Computing the argmaxargmax\operatorname*{argmax}roman_argmax of the BRs can be achieved through RL or exactly traversing the game tree.
**B**: In practice, we only calculate a BR for positive support policies (similar to Rectified Nash (Balduzzi et al., 2019)**C**: Therefore the CE BR attempts to exploit each po... | CBA | ACB | ACB | ABC | Selection 1 |
**A**: In Sections 4 and 5, we demonstrate a toolkit to utilize this measure, and use it to prove new generalization properties of fundamental noise-addition mechanisms. The novelty of the PC definition stems replacing the fixed parameters that appear in the differential privacy definition with a function of the datase... | CAB | ABC | BAC | ABC | Selection 1 |
**A**:
However, we argue that these results on kernelization do not explain the often exponential speed-ups (e.g. [3], [5, Table 6]) caused by applying effective preprocessing steps to non-trivial algorithms**B**: Why not? A kernelization algorithm guarantees that the input size is reduced to a function of the paramet... | BAC | ACB | CAB | ABC | Selection 4 |
**A**: GP-GAN [172] and Zhang et al. [198] are inspired by Poisson image blending, but use content loss to preserve the original foreground content. Therefore, they strike a balance between preserving the foreground content and smoothing the boundary. However, some smoothed boundary regions are still not satisfactory. ... | CBA | ACB | BCA | ACB | Selection 1 |
**A**: We first adopt SARSA [6] to learn the expected long-term revenue of each grid in each period**B**: Based on these expected revenues, we dispatch taxis to passengers using the same optimization formulation as Eqn. (13), with the exception that we replace A(i,j)𝐴𝑖𝑗A(i,j)italic_A ( italic_i , italic_j ) with th... | ACB | CBA | ACB | BCA | Selection 4 |
**A**: The second class consists of methods that are built from a collection of estimators and generally have a superior predictive performance when compared to individual models. The third class contains the models that are specifically trained to yield a prediction interval, while the last class constitutes a framewo... | CBA | BCA | BAC | CAB | Selection 3 |
**A**: Einaudi (contemporary), “J”: H. Joe (contemporary), “S”: R. Sakamoto (contemporary), “M”: Bethel Music (religious) and “W”: Hillsong Worship (religious).
**B**: Clayderman (pop), “Y”: Yiruma (pop), “H”: H. Hancock (jazz), “E”: L**C**: Each row shows the percentage of sequences of a class predicted as another cla... | CBA | ACB | BAC | BAC | Selection 1 |
**A**: Next, let us count the total number of jumps necessary for finding central vertices over all loops in Algorithm 1**B**: As it was stated in the proof of Lemma 2.2, while searching for a central vertex we always jump from a vertex to its neighbor in a way that decreases the largest remaining component by one**C**... | BCA | ABC | BAC | BAC | Selection 2 |
**A**: Particularly, a joint image transmission-recognition system has been developed in[16] to achieve high recognition accuracy. A deep joint source-channel coding architecture, name DeepJSCC, has been investigated in[17] to process image with low computation complexity.**B**:
Recently, there are also investigations... | ACB | CBA | CAB | ACB | Selection 3 |
**A**: Results on ScanNet: Table VI shows the class specific segmentation results in mIoU(%) in ScanNet[46] validation set. Our method outperforms the previous method MPRM[11] by a large margin**B**: We observe that with 10% points labeled, the baseline results are already very close to the fully supervised results wit... | ACB | CBA | ABC | BAC | Selection 3 |
**A**: Training:
We use a batch size of 32323232 and train the overall deep network for 140 epochs on 6666 NVIDIA 1080ti GPUs**B**: To alleviate overfitting, we adopt data augmentation techniques including random scaling, random horizontal flipping, and random cropping for the 2D detection, and random horizontal flippi... | BCA | ABC | CBA | CBA | Selection 2 |
**A**: We first used SynthText to pre-train our model for 10 epochs using Adam optimizer and a learning rate of 0.0010.0010.0010.001.
Then, we fine-tuned our model on real text benchmark datasets for 800 epochs with SGD optimizer and a learning rate of 0.0010.0010.0010.001**B**: For testing, the short side of images wa... | BCA | ACB | ABC | CBA | 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... | ACB | ABC | CAB | BAC | Selection 3 |
**A**: For example, when the system matrix in (1) is extended to the n𝑛nitalic_n-tuple case, it is the block tridiagonal systems discussed in [37]**B**: When
the system matrix in (2) is extended to the n𝑛nitalic_n-tuple case, it corresponds to the linear system resulting from the domain decomposition method for ellip... | BAC | ACB | ACB | BCA | Selection 4 |
**A**: For each Q𝑄Qitalic_Q and K𝐾Kitalic_K, we let TDCD train for 5,000 iterations for CIFAR-10, 10,000 iterations for MIMIC-III, and 4,000 iterations for ModelNet40, and pick the learning rate with the lowest training loss.**B**:
In each experiment, for each value of Q𝑄Qitalic_Q, we choose the learning rate using... | ABC | BCA | BAC | CAB | Selection 4 |
**A**:
Within the framework of tensor-tensor multiplication (3) proposed and investigated by Kilmer and Martin Kilmer2011 , T-eigenvalues and T-eigenvectors have garnered significant attention from researchers. They offer a novel perspective to characterize the properties of the widely employed tensor-tensor multiplic... | ACB | BCA | ACB | ABC | Selection 4 |
**A**: On Contextual Feature Aggregation**B**: As shown in Figure 7 (e), the model without CFA renders low-quality images, and texture filling is sensitive to structure noise. Quantitative results in Table 2 also validate its necessity.
**C**: The CFA module is introduced to enhance the correlation between local featur... | ACB | BAC | 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 ... | CBA | BAC | ACB | CAB | Selection 1 |
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