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**A**: Knowledge Distillation (KD). Hinton et al**B**: The purpose of defining different types of knowledge is to efficiently extract the underlying representation learned by the teacher model from the large-scale data. If we consider a network as a mapping function of input distribution to output, then different knowl... | ACB | BAC | CBA | CBA | Selection 1 |
**A**: This is, indeed, true, but as shown in [De ̵+22] both FNO and DeepONet produce Runge-like oscillations when applied to data with discontinuities, so with current versions of these operators non-smooth data is out of question**B**:
Both results in Table 1 and in Figure 2b suggest that neural operators perform ma... | CAB | ABC | ABC | BAC | Selection 4 |
**A**: The success of Transformer [44] in NLP has attracted lots of attention from the community of computer vision [13, 24, 29, 45]**B**: [29] proposed the shifted-window that computes the attention on patch-level. The Pyramid ViT [45] proposed a progressive shrinking pyramid that adjusts the scale of feature map.
**C... | ACB | CBA | CAB | ABC | Selection 1 |
**A**: Specifically, we create two pairwise distance matrices, the first one based on the 4D physical attributes which have numerical values**B**: Running ENS-t-SNE with these distance matrices
produces the embedding in Fig. ENS-t-SNE: Embedding Neighborhoods Simultaneously t-SNE(a).**C**: The second one is based on pe... | CAB | BCA | ACB | BAC | Selection 3 |
**A**: Upon collecting the data, we follow the embedding learning procedure and fit the density mappings for the estimation of Bellman operator**B**: In practice, various approaches are available in fitting the density by observations, including the maximum likelihood estimation (MLE), the generative adversial approach... | ABC | BCA | CAB | BAC | Selection 1 |
**A**: See, e.g., Antos et al**B**: (2007); Munos and Szepesvári (2008a); Chen and Jiang (2019) and the references therein.**C**: Without any coverage assumption on the offline data, the number of data needed to find a near-optimal policy can be exponentially large (Buckman et al., 2020; Zanette, 2021).
To circumvent t... | BCA | CBA | CAB | CAB | Selection 1 |
**A**: Without constraints, one can apply stochastic gradient descent (SGD) and its many variates, whose statistical properties (e.g., asymptotic normality) have been comprehensively studied from different aspects (Robbins1951stochastic; Kiefer1952Stochastic; Polyak1992Acceleration; Ruppert1988Efficient). However, unli... | CAB | ACB | CBA | ACB | Selection 3 |
**A**: Another open problem is the analysis of isoparametric generalized Taylor-Hood families in 2D and 3D to cope with curved boundaries. Perturbation arguments similar to those used in [6], [7] for isogeometric generalized Taylor-Hood families seem to be a promising approach for this open problem.**B**:
The present ... | CBA | ABC | CAB | ACB | Selection 3 |
**A**: The quadratic complexity with respect to the sequence length (# of tokens) of transformers has also led to the search for other linear approximations of self-attention for efficiently mixing tokens [43]**B**:
Hybrid models aim to reduce the computational requirements of vision transformers by incorporating imag... | CAB | BAC | CAB | CAB | Selection 2 |
**A**: More precisely, one**B**: This part of the system, as mentioned
above, is fast in comparison to the first part**C**: δl,δm,δnsubscript𝛿𝑙subscript𝛿𝑚subscript𝛿𝑛\delta_{l},\delta_{m},\delta_{n}italic_δ start_POSTSUBSCRIPT italic_l end_POSTSUBSCRIPT , italic_δ start_POSTSUBSCRIPT italic_m end_POSTSUBSCRIPT , i... | BAC | CBA | ACB | ABC | Selection 2 |
**A**:
This survey provides a synthesis of this evolutionary arch: in Section 2 we discuss research paths leading to the current state-of-the-art for each task where applicable, ending each discussion with notable limitations of the strongest approaches**B**: In Section 3 we discuss the overarching trends in the state... | BCA | CBA | CAB | BAC | Selection 1 |
**A**: This enables to model a collection of networks where the structure of certain networks is encompassed in the structure of other networks.
The second relaxation allows networks to have the same structure up to a density parameter**B**: The first one is to allow the distribution of the block memberships to vary be... | CBA | BCA | BAC | CAB | Selection 3 |
**A**:
Figure 5: Performance of FactorNets for individual rotation learning**B**: (right) Distributions of absolute percentage errors (in %) of all data points in the dataset. **C**: (left) Predictions of rotation angle vs. the ground truth (normalized to [−1,1]11[-1,1][ - 1 , 1 ]) in test set | CBA | CBA | ABC | ACB | Selection 4 |
**A**: Formally speaking, Mixture proportion estimation (MPE) refers to the task of estimating the weight of a component distribution in a mixture, given samples from the mixture (unlabeled data) and component (positive labeled data)**B**: We refer the reader to (Ramaswamy et al., , 2016; Garg et al., , 2021; Ivanov, ... | BAC | ACB | BCA | CAB | Selection 3 |
**A**:
In Wang and Zeng (2019), the authors propose using a Tucker decomposition as a multilayer SBM, but limit their factor matrices to only take on binary values**B**: Thus, the extent to which layer dependence is addressed is limited to the binary clustering of layers and is more similar to the strata work of Stanl... | ABC | ACB | BCA | CBA | Selection 1 |
**A**: A crucial point to bear in mind with quantum computing is that the memory capacity**B**: Experiments below used the 11-qubit trapped ion quantum computer described by Johri et al**C**: johri2021nearest ,
and where necessary, the larger IonQ Aria machine with a capacity of 32 physical and 20 algorithmic qubits (i... | ACB | ABC | CAB | BAC | Selection 3 |
**A**: Particularly, in a heterophilic graph, two adjacent nodes are unlikely to have the same label, which is in contradiction with the smoothness properties of leading eigenvectors**B**: However, when we choose eigenvectors appropriately, the correlation between the similarity of spectral features and node labels inc... | BCA | ABC | ABC | ACB | Selection 1 |
**A**: In some contexts, the benefits of the reduced risk among subpopulations may outweigh possible harms from segregation**B**: In others, where proportional representation of groups across learners, models, or clusters (Kleindessner et al., 2019a, b) is important, our work implies that independent risk minimization ... | CBA | BAC | ABC | CAB | Selection 3 |
**A**: It can be seen in Table 2 that in both data sets, the vanilla classifier (#Exp I, II, V) satisfies Eq. (33).
For the Adult data set, a weighted variant of the k𝑘kitalic_k-Nearest-Neighbor classifier was generated by initializing the classifier to use a different prior distribution of the labels, assigning 30%pe... | BCA | ACB | CBA | BAC | Selection 1 |
**A**:
We argue that guessing k is almost always easier, as the concept of how many repetitions of a motif do you expect is much easier to understand - though the guess itself need not be easy, and thus we will also offer algorithms to learn k𝑘kitalic_k**B**: Furthermore, as k𝑘kitalic_k is an integer, there is only ... | ACB | BAC | ABC | ACB | Selection 3 |
**A**: It is required that the expectations of the regression matrices be known, and the graphs, regression matrices, measurement and communication noises be spatially i.i.d.**B**: [28]-[29] and [31].
Kar et al**C**: [28]-[29] studied the decentralized parameter estimation algorithms based on consensus plus innovation ... | BAC | CAB | BCA | ABC | Selection 2 |
**A**: On the other hand, AA also introduces the overhead of solving the least-squares problem (5) at each iteration.
This computation overhead is outweighed by the benefit from fewer iterations when solving problems in which evaluating the operator G𝐺Gitalic_G incurs the dominant cost**B**: When compared to the fixed... | BAC | CBA | CBA | ABC | Selection 1 |
**A**: Each time, we picked either a relevant or an irrelevant topic for the given summary. Each summary topic-pair was annotated by an average of 2.8 annotators. Inter-annotations with a more than 5-degree variance were subjected to manual evaluation and discarded. The inter-annotator agreement across the raters for e... | BAC | CBA | BCA | BCA | Selection 2 |
**A**: We analyse the cost of long range interactions in terms of SWAP gate counts. In the following, we present one of the algorithms used for extracting the schedules**B**:
We illustrate the gate scheduling of the tiled multiplication circuit. The goal is to parallelise as many gates as possible: T gates, CNOTs and ... | ACB | BCA | ABC | BAC | Selection 4 |
**A**: This technique makes the proposed model more robust to a potential overfitting. Based on our preliminary experiments, DL-based methods hold the potential to simulate MRI scans with a new set of parameters**B**: In our work, we propose a coarse-to-fine fully convolutional network for MR image re-parameterization ... | CBA | BAC | ABC | ACB | Selection 2 |
**A**: In this subsection, we provide a detailed derivation of underlying PDEs for L2superscript𝐿2L^{2}italic_L start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT-gradient flows and generalized diffusions by the general framework of EnVarA**B**: We refer interested readers to [24, 74] for a more comprehensive review of the E... | ACB | CBA | ABC | ACB | Selection 3 |
**A**: Furthermore, there are many situations in which being able to perform machine learning on multi-parameters persistence modules is anticipated to be fruitful [20, 7]**B**: Nevertheless, the theory of n𝑛nitalic_n-parameters persistence modules when n≥2𝑛2n\geq 2italic_n ≥ 2, is far more intricate. Indeed, it has ... | BAC | ACB | BAC | BCA | Selection 4 |
**A**: For HC, variable ordering is found to typically have a larger effect than sample size, objective score or hyper-parameters used**B**: We start by investigating the bnlearn implementation of HC which is widely used in the literature [32, 35] and find that these arbitrary edge orientations are made on the basis of... | ACB | ABC | ACB | BAC | Selection 4 |
**A**: The hardness of approximation of the rank of divisors is then discussed in Section 3.**B**: Basic definitions and notation are introduced in Section 2, together with a brief introduction into graph divisor theory**C**:
The rest of the paper is organized as follows | BAC | CAB | CBA | ABC | Selection 3 |
**A**: Due to the significant overfitting, we adopt the data augmentation as [41] and [36]**B**: For the CIFAR10-DVS dataset, we adopt the VGG11-like architecture introduced in TET [36]**C**: To maintain the same training settings as [36] for TCJA-TET-SNN, we use the triangle surrogate function, eliminate the last LIF ... | BAC | CAB | ABC | ABC | Selection 1 |
**A**: Otherwise, it indicates inaccurate transition functions of this action or/and its predecessors**B**: If the estimation is accurate, this indicates that the transition functions of the action at the index k𝑘kitalic_k of 𝐂𝐂\mathbf{C}bold_C and its predecessors are accurate; so the procedure breaks the loop and ... | ACB | BAC | ACB | ACB | Selection 2 |
**A**: We can think of the output of the VAE encoder at the end of an episode, after a full history has been observed and the uncertainty about the task has been resolved as best as possible, as a sample of the task parameter θ𝜃\thetaitalic_θ. Thus, we propose to build our KDE estimate over these variables. We hencefo... | BCA | CAB | BAC | ABC | Selection 1 |
**A**: The second motivation is that the lack of cross-lingual CHV prevents the development of consumer-oriented healthcare applications for non-English languages**B**: Considering the initiative proposed by the World Health Organization, “Bridging the Language Divide in Health111http://dx.doi.org/10.2471/BLT.15.020615... | ACB | CBA | BAC | ABC | Selection 1 |
**A**: (2024) and hydrophobic ligand dissociation.Beyerle and Tiwary (2024) Given the critical role of molecular sciences in uncovering chemical reaction pathways,Yang et al**B**: (2017) understanding disease mechanisms,Hollingsworth and Dror (2018) designing effective drugs,Zhao and Caflisch (2015) and numerous other ... | BCA | CAB | BAC | CAB | Selection 1 |
**A**: Figure 3(c) features the resulting goals tree for the instrumented code from Figure 3(b) (with GOAL_0 representing the entry point of the program, i.e., the main function).
Note that FuSeBMC builds it based on the original Clang AST without analyzing the code for trivially unreachable goals**B**: However, this w... | CAB | BAC | ACB | BAC | Selection 3 |
**A**: The results derived in this section will motivate the two algorithms for computing fractional integration matrices that are discussed in the next section**B**: The following result gives the action of ℐμsuperscriptℐ𝜇\mathcal{I}^{\mu}caligraphic_I start_POSTSUPERSCRIPT italic_μ end_POSTSUPERSCRIPT on the JFP bas... | ABC | CAB | ACB | ACB | Selection 1 |
**A**: find that the indexes based on common neighbors fail to identify missing links in the tree-like networks [45]**B**: To solve this problem, they take advantage of network heterogeneity and propose the heterogeneity index (HEI). The HEI is defined as
**C**: Shang et al | ACB | BAC | ACB | BCA | Selection 4 |
**A**: As shown in Figure 2(b), the fake quantization graph uses fp32, leading to no memory or computation savings**B**:
We update the real quantized graph for training, which is fundamentally different to quantization-aware training (QAT), where a fake quantized graph (Figure 2(b)) is trained on the cloud, and conver... | BAC | BCA | BCA | BCA | Selection 1 |
**A**: Let A=(aij)𝐴subscript𝑎𝑖𝑗A=(a_{ij})italic_A = ( italic_a start_POSTSUBSCRIPT italic_i italic_j end_POSTSUBSCRIPT ) and N={1,2,…,n}𝑁12…𝑛N=\{1,2,\ldots,n\}italic_N = { 1 , 2 , … , italic_n }**B**: Then we denote
|A|=(|aij|)𝐴subscript𝑎𝑖𝑗|A|=(|a_{ij}|)| italic_A | = ( | italic_a start_POSTSUBSCRIPT italic... | ABC | CBA | BAC | BCA | Selection 1 |
**A**: In this section, we describe the most relevant previous works**B**: Since the theory of evolutionary algorithms using bit-string representations has started with and greatly profited from the analysis how simple EAs optimize polynomial-time solvable problems, we mostly focus on such results.
**C**: In the intere... | BCA | ACB | CAB | BCA | Selection 2 |
**A**: While we indicate multilevel extensions in Sections 3.2 and 4.7, respectively, we mainly focus on the core problem in this paper, that is two-level geometric optimization. Numerical experiments are reported in Section 5 for a range of problem instances and compared to a recent state-of-the-art method [HRX21]. We... | CBA | CAB | ABC | ACB | Selection 2 |
**A**:
While it is well known that neural networks with ReLU activation are universal approximators (can approximate any continuous function on a bounded domain)**B**: Namely, there are monotone functions that cannot be approximated within an arbitrary small additive error by a monotone network with ReLU gates regardl... | BCA | ACB | BAC | CBA | Selection 2 |
**A**: There are several ways to compensate this imbalance (count degrees of freedom, number non-zero entries in the stiffness matrix, …). However, we will see that the accuracy of the DG and conforming element methods is of minor importance in the numerical experiments. Thus, we skip a detailed discussion about a fair... | BAC | ACB | ABC | CBA | Selection 4 |
**A**: We aim at identifying the arm whose associated distribution has the largest mean by a sequence of T𝑇Titalic_T pulls**B**: In each arm pull, we choose an arm based on the previous pulls and outcomes, and obtain a sample from the arm’s associated distribution**C**: Assuming that each pull takes unit time, we call... | BCA | ABC | ACB | ACB | Selection 2 |
**A**: This problem finds applications in various fields, such as electron microscopy, speech recognition, optical imaging, and X-ray crystallography [64, 15]**B**: The sparse phase retrieval problem is formulated as follows:**C**:
In the initial set of simulations, we evaluate the performance of SPIRAL on the sparse ... | BCA | BAC | ABC | ABC | Selection 1 |
**A**: The organization of this paper is structured as follows: The preliminary concepts and definitions used throughout the paper are introduced in Section II**B**: The proposed algorithm is presented in Section IV with a detailed discussion on its properties. The computational complexity analysis is presented in Sect... | BCA | ACB | ABC | BAC | Selection 2 |
**A**: To better explain this concept, let us consider the example reported in Figure 1. We can see that unlabeled examples provide useful information to better classify the examples in the classes ”a” (”a” vs**B**: not ”a”, see the vertical dashed lines) and ”d” (”d” vs. not ”d”, see the horizontal dashed lines), espe... | ACB | BCA | CAB | ABC | Selection 4 |
**A**: This can be derived from publicly available datasets or simulated with a simulator to enrich the model’s driving experiences [37][38]. Therefore, the model will be able to perform human-like autonomous driving [39].
**B**: By using the end-to-end imitation learning strategy, we can create a single deep learning ... | BAC | CBA | CAB | CAB | Selection 2 |
**A**: A natural research direction is to extend such methods to intersection graphs of geometric objects [24, 34]. However, even for very “simple” objects like unit disks, the corresponding intersection graphs do not have locally bounded treewidth**B**: The main property of such graphs is that they enjoy the bounded l... | ACB | ABC | CAB | BAC | Selection 4 |
**A**: We use a fully connected model of two linear layers with ReLU activations as the local model.
**B**: We create the VFL scenario by splitting the input features evenly by rows for 14 clients**C**: MNIST lecun-mnisthandwrittendigit-2010 contains images with handwritten digits | BCA | CBA | ABC | ABC | Selection 2 |
**A**: Hierarchical decisions: our current Ada-DyGNN model takes actions on the node-wise level, i.e**B**: update or not on a single node.
In the future work, we could use a hierarchical reinforcement learning strategy to generate decisions at different levels**C**: For example, a higher level decision on the graph res... | BAC | CBA | CAB | ABC | Selection 4 |
**A**:
Figure 2: The composition of a real cloth-changing benchmark**B**: Each person has view variations but only has the normal walking condition (NM). (b) Cross-cloth sub-dataset. Each identity has walking in different coats condition (CL) but only has limited views (only front views).**C**: (a) Cross-view sub-data... | ABC | ACB | BCA | BAC | Selection 2 |
**A**:
The dialogue policy module makes a dialogue decision given the current state (Zhang et al., 2019)**B**: Early methods are rule-based (Chen et al., 2017)**C**: Since handcrafted rules are non-extensible and resource-consuming (Zhao et al., 2021), deep reinforcement learning (DRL) has become a mainstream method f... | ABC | ACB | ACB | BCA | Selection 1 |
**A**: First, Generative Adversarial Networks (GANs) [3, 12, 29] benefit from adversarial training of two networks that contest to maximize its own objective function in opposed tasks, thus encouraging the opponent network to improve its performance for generating data**B**: Despite the enormous variety of works regard... | BAC | ABC | CBA | ACB | Selection 1 |
**A**: The performance of all anomaly detectors downgrades with the increase of anomaly contamination**B**: COUTA shows better robustness compared to its contenders, especially on datasets with a large contamination rate**C**: It owes to the novel one-class classification loss function, which successfully masks these n... | BAC | CBA | ABC | BCA | Selection 3 |
**A**: Although encompassed by the general umbrella of Natural Language Generation, the nuance that differentiates D2T from the rest of the NLG landscape is that the input to the system has to qualify as a data instance. Reiter and Dale (1997) (Reiter and Dale, 1997) describe the instance as a non-linguistic representa... | CBA | BAC | ABC | ABC | Selection 1 |
**A**: TABLE III: Performance (%) of state-of-the-art SGG models on three SGG tasks on the VG dataset**B**: “Mean” is the average of mR@50/100 and R@50/100. The best and second best methods under each setting are marked according to formats.
**C**: “B” denotes the backbone of object detector (Faster R-CNN [85]) in each... | CBA | ABC | ACB | ABC | Selection 3 |
**A**: But there is no guarantee that the attacker will share the secret in the future**B**: It should be noted that the above mechanism can only prove to other miners that the attacker has indeed mined a block that satisfies the requirement**C**: By potentially withholding the reserved block, the attacker could waste ... | BAC | ACB | CBA | ACB | Selection 1 |
**A**: Right: Same as left, but with a PostLN Transformer. In both cases the preconditioned curvature closely tracks the 38/η38𝜂38/\eta38 / italic_η bound during warmup, however there is a noticeable gap at the smaller batch size**B**:
Figure 7: Learning rate warmup gradually reduces the preconditioned sharpness duri... | CAB | ABC | BAC | BCA | Selection 3 |
**A**: Consequently, the importance of each sample is given by a statistical weight needed to account for the effect of the bias potential when obtaining equilibrium properties such as the free-energy landscape**B**: This contrasts with unbiased simulations where samples are equally important as they are sampled accord... | BCA | CAB | CBA | CBA | Selection 1 |
**A**: Rendering 3D spatial data in a 2D medium may lead to difficulty in the interpretation of the figures**B**: The Mercator projection of the segments is shown in Fig. 1b.
**C**: To render the 3D trees here we use a Mercator projection (Miller, 1942) that preserves the branching structure of the tree but not the len... | ACB | ABC | ABC | CAB | Selection 1 |
**A**: Undirected line denote the link communicating pair of network elements, with the line’s strength denoting the link’s capacity level.**B**: Directed line denotes network traffic flows with different throughput in the given topology, where one can see that there exist different distributions of line type between t... | BAC | ACB | CBA | BAC | Selection 3 |
**A**: Among the recent breakthroughs in representation learning for RL, contrastive self-supervised learning gains popularity for its superior empirical performance (Oord et al., 2018b; Sermanet et al., 2018; Dwibedi et al., 2018; Anand et al., 2019; Schwarzer et al., 2020; Srinivas et al., 2020; Liu et al., 2021)**B*... | ACB | ABC | BCA | CBA | Selection 3 |
**A**: In Section 3, we introduce the decoupling approach for MV-SDEs (dos Reis et al., 2023) and formulate a DLMC estimator. Next, we state the optimal importance sampling control for the decoupled MV-SDE derived using stochastic optimal control and introduce the DLMC estimator with importance sampling from (Ben Rache... | CAB | CAB | ACB | BAC | Selection 4 |
**A**: In other cases [47, 46], the autonomous mission profile is still highly attached to current ground-based approaches, with the spacecraft spending months characterizing the asteroid before insertion in a stable orbit.**B**: For instance, most works concerned with the control and guidance laws make minor considera... | ACB | CBA | BAC | CAB | Selection 2 |
**A**: Our particular choice is motivated by the observation that one can establish strict contractivity of G𝐺Gitalic_G with respect to the Thompson part metric, which allows for developing non-asymptotic convergence guarantees. In this section we discuss one other Picard iteration that arises from problem (1.3) and e... | ABC | CBA | CAB | ACB | Selection 3 |
**A**:
We then use the hypothesis testing framework for each bootstrapped sample to identify the number of holes**B**: The mean number of holes across all bootstrapped samples is 0.680.680.680.68, with an approximate standard error of 0.0180.0180.0180.018**C**: The 95% confidence interval for the number of holes is (0... | CBA | ACB | BCA | ABC | Selection 4 |
**A**:
Particularly, since the semi-supervised DG task consists of multiple domains, it can be also viewed as the multi-domain learning paradigm in the training stage**B**: Besides, the unlabeled training data can be also considered as the test data during pseudo-labeling**C**: Therefore, it means that, if we can effe... | BAC | ABC | BAC | BAC | Selection 2 |
**A**: FGVC consists of 4 Fine-Grained Visual Classification tasks: CUB-200-2011 [57], Stanford Dogs [26], Stanford Cars [29], and NABirds [55].
**B**: Each task in VTAB-1k contains 1,000 training examples**C**: VTAB-1k includes 19 diverse visual classification tasks, which are grouped into three categories: Natural, S... | CBA | CAB | BCA | CAB | Selection 1 |
**A**: Based on Petrov-Galerkin method, the hp-variational PINNs (hp-VPINNs) [17] allows for localized parameters estimation with given test functions via domain decomposition. The hp-VPINNs generates a global approximation to the weak solution of the PDE with local learning algorithm that uses a domain decomposition w... | ABC | ACB | CBA | ACB | Selection 3 |
**A**: Indeed, while many of the referenced works above at least verbally express the notion that in many inverse problems, we “know more” about the parameters in some parts of the domain than in others, we have not found published works that try to provide quantitative measures of this concept**B**:
The differences i... | CAB | ABC | ABC | BAC | Selection 4 |
**A**: In order to avoid unnecessary computations, the update of the graph similarities and the UMAP embeddings is done asynchronously in the background**B**: It is also possible to see previous states of the graph and the embeddings using a slider.
**C**: New labels are added in real-time to the dataset, which also up... | BCA | CAB | CAB | CAB | Selection 1 |
**A**: BERT-base is used in this analysis.**B**: “| ours-avg |” denotes the absolute difference (average score) between the predicted similarities used in this paper and the average similarities of multiple sampling numbers**C**:
Table IX: Standard deviation of predicted similarities across different sampling numbers ... | CAB | ACB | CBA | BCA | Selection 3 |
**A**: Some targets were indeed down while previously active, suggesting attacks might have succeeded e.g., ksrf.ru (the Constitutional Court of the Russian Federation) was down for a while, and data.gov.ru was both defaced and DDoSed.**B**: Categories of generic domains (e.g., .net, .com) are identified by direct visi... | ACB | CBA | BCA | CAB | Selection 2 |
**A**: This improvement is noteworthy, demonstrating the robustness of HET in a variety of conditions.
**B**: It exhibits an enhancement of up to 60% in transferability over the lower bound for larger values of k𝑘kitalic_k**C**: Even as the value of k𝑘kitalic_k increases, representing a broader selection of top-ranki... | BCA | CBA | ACB | ACB | Selection 2 |
**A**: With a polynomial shot budget this leads to an optimized model which is insensitive to the input data and cannot generalize well.
**B**: The heart of the problem is that, in a wide range of circumstances, the value of quantum kernels exponentially concentrate**C**: That is, as the size of the problem increases, ... | CAB | ABC | ACB | BCA | Selection 1 |
**A**: The best performing model, X3D-S achieves state-of-the-art top-1 accuracy (84.79%) for lane change classification using the original RGB video data of the PREVENTION dataset. Our RGB+BB+3DN method achieves significant accuracy improvement (TTE-20 98.86%) by taking the advantage of 3D CNNs and additional bounding... | ABC | BCA | CBA | CAB | Selection 2 |
**A**: Furthermore, let**B**: k𝑘kitalic_k-anonymity**C**: Let pasubscript𝑝𝑎p_{a}italic_p start_POSTSUBSCRIPT italic_a end_POSTSUBSCRIPT and pbsubscript𝑝𝑏p_{b}italic_p start_POSTSUBSCRIPT italic_b end_POSTSUBSCRIPT be two programs written by
developers a𝑎aitalic_a and b𝑏bitalic_b with pa≠pbsubscript𝑝𝑎subscript�... | BCA | ACB | BAC | CAB | Selection 4 |
**A**:
The main results of different methods on four datasets are listed in Table 10**B**: For SoftCPT, we report the results of SoftCPT-NATA and SoftCPT-NATS as they could acquire desirable performance with lower computational cost**C**: As a comparison, we also report a variant of SoftCPT, i.e., SoftCPT*. It is the ... | CAB | BCA | ABC | ACB | Selection 3 |
**A**:
LCS is a new paradigm in MSDA, enriching the field with elevated variability and versatility**B**: Within this innovative framework, we’re presented with an opportunity to delve into more intricate models**C**: This section is dedicated to presenting a refined causal model, tailored to this paradigm. | BCA | BCA | BAC | ABC | Selection 4 |
**A**: As such, we will consider a finite element discretization of (9) that employs the method of artificial diffusion on strictly acute meshes [burman2002nonlinear, JensenSmears2013] to ensure nonnegativity of the approximations for the density.
**B**: In order to preserve this approach on the discrete level, we cons... | ACB | BAC | CBA | CAB | Selection 3 |
**A**: We also have to note that the scenarios were defined in order to provide challenging allocation tasks for the algorithms (utility values are bound from above by 6/12/20 respectively), to be able to compare the efficiency of different methods and parameter sets in non-trivial tasks (if there is no resource scarci... | BAC | CBA | BAC | CAB | Selection 4 |
**A**: The left part is a comparison of flexible and Gaussian posterior in FDA**B**:
Figure 4: Distribution of different time windows, METR-LA**C**: Comparisons of the target distribution, predicted distribution, and Gaussian distribution are shown in the right part. | BAC | BCA | CAB | BCA | Selection 1 |
**A**: The original simulated data (a,b), the transformed data (c,d), and the latent representation learned by our algorithm (e,f) are shown in the figure**B**: The black arrows represent the force field 𝐟=−∇F𝐟∇𝐹\mathbf{f}=-\nabla Fbold_f = - ∇ italic_F (left) while the ellipses represent the diffusion field 𝐌𝐌\ma... | CAB | CBA | ABC | BCA | Selection 4 |
**A**: the UAV’s surface facing the ground) is slightly orientated towards the ground node, see Fig. 3. This can fully or partially block the LoS between the antennas of the ground node and of the UAV. In the case of fixed-wing UAVs, airframe shadowing can occur when the UAVs turn**B**: Consider a multirotor UAV with a... | CAB | CBA | ACB | BAC | Selection 4 |
**A**: More efforts may be required for less conventional mixtures or heterogeneous mixtures but applications of Theorem 1 or 2 will presumably be similar to the application to standard mixtures as treated here.
**B**: Hence, concrete expressions for lower bounds (or for the log-likelihood) can be stated with only mino... | BCA | ACB | CBA | ABC | Selection 3 |
**A**: We begin in Section 4.1 by analysing the problem of counting k𝑘kitalic_k-matchings in somewhere dense host graphs, and proving Theorem 2; this is the most technical part**B**: We then move on to prove Theorem 4 and Theorem 3 in Section 4.2.**C**:
This section is devoted to the proofs of Theorem 2, Theorem 4, a... | CAB | CBA | ABC | BCA | Selection 4 |
**A**:
{Z,Q}→L→𝑍𝑄𝐿\{Z,Q\}\rightarrow L{ italic_Z , italic_Q } → italic_L: item popularity and quality affect the probability of post-click behaviors**B**: Meanwhile popularity influences users’ post-behaviors because of the users’ herd mentality (a.k.a**C**: conformity) [3]. | CBA | ACB | ACB | ABC | Selection 4 |
**A**: SCID dataset [30] consists of 1800 distorted SCIs generated by 40 reference images**B**: Each distortion type contains five degradation levels. All the SCIs in SIQAD are with a resolution of 1280 × 720.
**C**: In this dataset, nine distortion types are involved including GN, GB, MB, CC, JPEG, JPEG2000, Color Sat... | CBA | CAB | CAB | ACB | Selection 4 |
**A**: We also believe that investigating whether it is possible to construct a homophily measure that satisfies all the desirable properties is an important question for future work.**B**: Thus, we recommend using it as a measure of homophily in further works**C**:
While adjusted homophily violates some properties, i... | ACB | CBA | ACB | ABC | Selection 2 |
**A**: Since the p𝑝pitalic_p-influences for different p𝑝pitalic_p do not coincide in the quantum setting, this version of Talagrand’s inequality does not imply a KKL bound**B**: However, we still have the following weaker bound as consequence of Theorem 4.3**C**: Again, the proof can be found in the appendix.
| BAC | ABC | BCA | CAB | Selection 2 |
**A**: An alternative way to construct the implementation is to use the built-in functionality of code generation in Isabelle/HOL (codegen, ; codegen2, ), which synthesizes functional executable code (e.g., Scala, Haskell, and ML) that inherits the correctness assurance from the verified protocol specification yet is h... | BAC | CAB | CBA | BCA | Selection 1 |
**A**: It mimics [van der Schaft, 2017, Prop. 3.2.16] and establishes asymptotic stability for an open-loop system through dissipativity.**B**: In this subsection, we establish feedback asymptotic stability via dissipativity with dynamic supply rates**C**: The following technical lemma
is needed in the proof of Theorem... | BAC | CAB | ABC | BAC | Selection 2 |
**A**: Tamba et al. make a similar argument for CBFs in [19], but their sufficient condition is more stringent.**B**: For a stochastic system, a subset of the state space is generally hard to be (almost sure) invariance because the diffusion coefficient is required to be zero at the boundary of the subset111The detail ... | ABC | ACB | BAC | CBA | Selection 3 |
**A**: In a simulation of performance with a similar framework, we showed that PoLMDP can outperform it, generating legible behaviours in less time allowing agents to be more efficient**B**:
Through a combination of two simulation evaluations and one online user study, we have shown the positive impact of our PoLMDP f... | BCA | CAB | CBA | BAC | Selection 4 |
**A**: X𝑋Xitalic_X), and their realization are in lower case (e.g**B**: x𝑥xitalic_x). All random variables take values in some alphabets that are in calligraphic letters (e.g. 𝒳𝒳\mathcal{X}caligraphic_X).
We shall restrict our attention to finite alphabets only.**C**: Random variables are in capital case (e.g | CAB | ABC | BCA | BAC | Selection 3 |
**A**:
Moreover, one important question is: since GFL converters can perform constant AC voltage magnitude control, do they also have effective voltage source behaviors to enhance the power grid strength? To be specific, one can introduce the terminal voltage magnitude as a feedback signal to generate the reactive cur... | BCA | CAB | CAB | ABC | Selection 4 |
**A**: A simple approach to detrending a time series is to difference it until it appears to be stationary. This is effective when the trend is a low order polynomial**B**: However, the trend itself may be of interest, and modeling it together with the dependence structure can be preferable. The former can be estimated... | BAC | CBA | BCA | ABC | Selection 4 |
**A**: The result in Table 2 shows that our proposed method outperforms all other baselines in the average performance**B**: This is mainly due to
the large Jacobian norm difference derived from the highly discriminative representations of the m-OvR (as observed in Fig. 8) triggers a strong separation between the known... | ABC | BCA | CBA | ACB | Selection 4 |
**A**: This result demonstrates the great potential of our method for designing defense strategies grounded in adversarial training.**B**:
• The learned distributions of perturbation allow drawing an unlimited number of adversarial examples for one input**C**: We experimentally validate that the sampled adversaries h... | ABC | CBA | CBA | CAB | Selection 4 |
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