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**A**: [19] use Kullback-Leibler Divergence (KLD) between the softened logits of teacher and student models as the loss to align the output distribution, and Zhao et al**B**: [58] decouple the KLD into two uncorrelated losses and combine them by weighted summation.**C**: Response-based KD methods [19, 58, 3] have the n... | ABC | CAB | ABC | BCA | Selection 4 |
**A**:
Both results in Table 1 and in Figure 2b suggest that neural operators perform mapping of finite-banded functions.888One may object that we considered only smooth data**B**: This is, indeed, true, but as shown in [De ̵+22] both FNO and DeepONet produce Runge-like oscillations when applied to data with discontin... | CAB | ABC | BCA | BAC | Selection 2 |
**A**:
Figure 1: Illustration of semantic mismatch**B**: Suppose that teacher and student are the 3-layers and 2-layers convnets with kernel size 3×3333\times 33 × 3 and stride 1×1111\times 11 × 1**C**: (a) shows the receptive field of the middle pixel of the final feature map, where the blue box represents the teache... | ACB | ABC | BAC | ACB | Selection 2 |
**A**: This notion is often referred to as stability or preservation of the mental map [3, 11].
We show that ENS-t-SNE is more stable when computing a set of projections of subspaces than standard t-SNE on the same set of subspaces.**B**: Although we cannot directly quantitatively compare to t-SNE (or other similar dim... | CBA | CAB | CBA | ACB | Selection 2 |
**A**: To this end, we construct a confidence set of embeddings upon identifying and estimating the Bellman operator, which further allows efficient exploration via optimistic planning. It is worth mentioning that such a unified framework allows a variety of estimators (including maximum likelihood estimators and gener... | CBA | ABC | CBA | CAB | Selection 4 |
**A**: (2007); Munos and Szepesvári (2008a); Chen and Jiang (2019) and the references therein.**B**: See, e.g., Antos et al**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... | BAC | ACB | CAB | CBA | Selection 4 |
**A**: Toulis2017Asymptotic designed an implicit SGD method and showed the asymptotics of averaged implicit SGD iterates. Li2018Statistical designed an inference procedure for constant-stepsize SGD by averaging the iterates with recurrent burn-in periods**B**: Mou2020linear further showed the asymptotic covariance of c... | CAB | CBA | BCA | ACB | Selection 3 |
**A**:
The rest of the paper is organized as follows**B**: In Appendix A known results on the continuous LBB condition are recalled and commented. Appendix B contains helpful relations used throughout the paper.**C**: In Section 2 the technique of T𝑇Titalic_T-coercivity is discussed, which provides important auxiliar... | CAB | CAB | BCA | ACB | Selection 4 |
**A**: Using self-similar stacked blocks makes the architecture scalable**B**:
At the heart of WaveMix are three design elements – a stack of self-similar WaveMix blocks, a multi-level two-dimensional discrete wavelet transform (2D-DWT) in each block, and spatial resolution contraction followed by expansion back to th... | CAB | ABC | BCA | BAC | Selection 4 |
**A**: This provides a total cost O(ln(s)sn)𝑂𝑠𝑠𝑛O(\ln(s)sn)italic_O ( roman_ln ( italic_s ) italic_s italic_n ).**B**: O(bin)𝑂subscript𝑏𝑖𝑛O(b_{i}n)italic_O ( italic_b start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT italic_n )**C**: The detection of columns in B𝐵Bitalic_B at each step can be made
with cost... | ABC | CAB | BCA | CBA | Selection 2 |
**A**: The use of these tuples for NTCIR-11/12 retrieval has varied since their conception stalnaker2015math. Initially pattaniyil2014combining pair tuples were used within a TF-IDF weighting scheme**B**: Meanwhile, zhong2019structural forgo machine learning altogether, in favour of an increasingly zhong2020acceleratin... | BAC | CBA | CBA | ACB | Selection 4 |
**A**: It introduces some symmetry in the connectivity matrix rendering it difficult to order the blocks by trophic order. Species from top trophic levels prey on basal species.**B**:
A sub-collection of 26262626 networks with heterogeneous size and density issued from various datasets. Most networks populate only par... | CAB | CBA | ABC | BCA | Selection 1 |
**A**:
Figure 5: Performance of FactorNets for individual rotation learning**B**: (left) Predictions of rotation angle vs. the ground truth (normalized to [−1,1]11[-1,1][ - 1 , 1 ]) in test set**C**: (right) Distributions of absolute percentage errors (in %) of all data points in the dataset. | ACB | ABC | BCA | CAB | Selection 2 |
**A**: We used an initial learning rate of 0.01 with cosine annealing learning rate for 300 epochs on PU CIFAR10 and 200 epochs for PU MNIST.**B**: We used batch size 2048 for CIFAR10 experiments and 1024 for MNIST experiments**C**:
Contrastive training is done using LARS optimizer (You et al., , 2019), temperature se... | CBA | ABC | ACB | ACB | Selection 1 |
**A**: In this context, we echo the motivation of identifying layer interdependence in such social multilayer networks: that finding layer redundancies could justify a less extensive data-collection of future social networks in similar settings. We further explore this possibility with analysis in the next section.**B*... | CBA | BCA | CAB | BCA | Selection 1 |
**A**: Experiments below used the 11-qubit trapped ion quantum computer described by Johri et al**B**: johri2021nearest ,
and where necessary, the larger IonQ Aria machine with a capacity of 32 physical and 20 algorithmic qubits (ionq2022aria, )**C**: A crucial point to bear in mind with quantum computing is that the m... | ABC | BCA | ACB | CBA | Selection 1 |
**A**:
We run GCN and SGC on the synthetic dataset of controlled homophily range from 00 to 1111. The model performance with homophily is plotted in Equation 4. As expected, higher homophily level corresponds to better performance for both GCN and SGC. All model reaches 100%percent100100\%100 % accuracy where homophil... | ABC | CBA | CAB | ACB | Selection 1 |
**A**: multiplicative weights over non-stationary risks (due to learner updates) and gradient descent over non-stationary data distributions (due to subpopulation updates).
To study this complex behavior, we now formalize key properties.**B**: The sequential interaction between subpopulations and learners leads to comp... | ACB | BCA | CBA | CAB | Selection 3 |
**A**:
In the previous section, we discussed the calculation of unfairness based on the confusion matrices of the classifier in each sub-population**B**: Nonetheless, we show in this section that it is possible to provide meaningful conclusions on the properties of the classifier from label proportions only. Formally,... | CBA | BCA | ACB | BAC | Selection 3 |
**A**: The top-1 motif set found by the approximate k𝑘kitalic_k-Motiflets alg**B**: All methods find the activation phase, but with up to 100%percent100100\%100 % larger extent. Valmod and LM found the recovery phase, again with up to 100%percent100100\%100 % larger extent.
**C**: corresponds to the activation phase a... | ACB | CBA | CAB | BCA | Selection 1 |
**A**: [38] investigated a consensus plus innovation based decentralized linear regression algorithm over random networks with random regression matrices**B**: Wang et al**C**: They proved that if the regression matrices and communication graphs satisfy the stochastic spatio-temporal persistence of excitation condition... | BAC | CAB | CBA | BCA | Selection 1 |
**A**: First, we study how the convergence of AR is affected by random perturbation with tunable intensity applied to the LHS of the least-squares solved to compute the Anderson mixing**B**: Then, we show how restricting the least-squares onto a projection subspace reduces the computational time to solve a linear syste... | CBA | BCA | ACB | CAB | Selection 2 |
**A**: However, currently there are no existing large-scale training datasets for abstractive summarization that contain summaries according to the different topical aspects of the text**B**: Thus, we adopt the approach of [5] to compile and release a topic-oriented dataset.
**C**: All the aforementioned methods assume... | ACB | ABC | BCA | ABC | Selection 3 |
**A**: Note that these figures only represent SWAP moments, so the application of Toffoli gates is not shown (though they are present in the schedules below). We have chosen to not introduce additional qubits and to perform SWAPs only across the multiplication tiling. For example, the number of SWAPs could be improved ... | ACB | CBA | ABC | CAB | Selection 2 |
**A**: This compression removes redundancy in the input. Providing these low dimensional representations of the input image to our model (i.e., Param-Net instead of directly providing the input image) helps in regularization and makes the learning easier for Param-Net. We notice how this technique is more robust to ove... | CAB | ACB | BAC | BAC | Selection 1 |
**A**: Additionally, we intend to employ the EVNN scheme to investigate other complex fluid models, including the Cahn–Hilliard equation and Cahn–Hilliard–Navier–Stokes equations, as well as solve machine learning problems such as generative modeling and density estimation.
**B**: Various numerical experiments are pres... | CAB | BAC | BAC | ABC | Selection 1 |
**A**:
The fibered barcode has been successfully used in a variety of machine learning tasks as a summary of multi-parameter persistence modules [7]**B**: Nevertheless, it is easy to build examples of γ𝛾\gammaitalic_γ-sheaves (hence persistence modules) with the same fibered barcodes (hence at matching distance zero)... | BAC | ABC | BAC | CBA | Selection 2 |
**A**: Figure 5 also shows that the constraint-based algorithms are sensitive to variable ordering, with a mean change in F1 of 0.036, 0.104 and 0.021 for PC-Stable, GS and Inter-IAMB respectively**B**: Note that because constraint-based learning tends to involve dependency tests across sets of variables in triples, it... | BAC | ABC | ACB | CBA | Selection 3 |
**A**: Besides the distance of a divisor from a non-halting state, its distance from a recurrent state also plays a central role in our investigations, defined as**B**: Clearly, a recurrent divisor f𝑓fitalic_f is also non-halting**C**:
A divisor f𝑓fitalic_f is called recurrent if there is a non-trivial chip-firing g... | CAB | CAB | ACB | CBA | Selection 4 |
**A**: In order to construct the correlation of different frames with their neighbors channel-wise, we propose the CLA mechanism**B**: Similarly, as shown in Fig. 4, we perform T𝑇Titalic_T-channel 1-D convolution on each column of the matrix 𝒵𝒵\mathcal{Z}caligraphic_Z, and then sum the convolution results of each ro... | BAC | BCA | CBA | BAC | Selection 2 |
**A**: Given**B**: Since all four corners
of ΩΩ\Omegaroman_Ω are π/2𝜋2\pi/2italic_π / 2, we have μ=(1,1,1,1)𝜇1111\mu=(1,1,1,1)italic_μ = ( 1 , 1 , 1 , 1 )**C**: Ω=(0,12)2Ωsuperscript0122\Omega=\bigl{(}0,\frac{1}{2}\bigr{)}^{2}roman_Ω = ( 0 , divide start_ARG 1 end_ARG start_ARG 2 end_ARG ) start_POSTSUPERSCRIPT 2 end... | CAB | ABC | CBA | ABC | Selection 3 |
**A**:
To evaluate the efficacy of the pruning heuristics introduced in Section 5.2, we conduct experiments comparing the search space sizes when using FlashSyn with and without the application of some of the heuristics**B**: Subsequently, Heuristic 2 further reduces the remaining search space by an additional 34%per... | ACB | ABC | ABC | CBA | Selection 1 |
**A**: The first term in the bound of Theorem 9 is the KDE error**B**: Note that, compared to the KDE error in Theorem 6, the exponential dependence is on the low dimension d′superscript𝑑′d^{\prime}italic_d start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT, and not on the higher dimension d𝑑ditalic_d**C**: The second term ... | BCA | BCA | ABC | CAB | Selection 3 |
**A**: After collecting the corpora, we processed our textual data using existing NLP toolkits. For English, we applied sciSpacy [33], a package for biomedical text processing, for word tokenization and medical concepts extraction by consulting Metathesaurus of UMLS [34]**B**: Regarding pre-processing Chinese texts, we... | BAC | ABC | BAC | BCA | Selection 2 |
**A**: The authors thank Deepthought2, MARCC, and XSEDE (projects CHE180007P and CHE180027P) for the computational resources used in this work. P.T. is an investigator at the University of Maryland-Institute for Health Computing, which is supported by funding from Montgomery County, Maryland and The University of Maryl... | CBA | BCA | ABC | ABC | Selection 2 |
**A**: It utilizes symbolic execution (in a lightweight approach) to generate initial seeds that can get an appropriate fuzzing direction. SAGE (Scalable Automated Guided Execution) proposed by Godefroid et al. (Godefroid et al., 2012) is a hybrid fuzzer developed at Microsoft Research. Microsoft uses SAGE extensively,... | BCA | CAB | ABC | BAC | Selection 1 |
**A**: We consider our method to be the successor of that of Bhrawy and Zaky [7]. They applied a change of variables to classical Jacobi polynomials such that the algebraic singularities of the resulting basis, the JFP basis (which is called thus for reasons we explain in Section 3), conform to those of the solution333... | ABC | CAB | CAB | BCA | Selection 1 |
**A**: For entities that do not hold the feature at all, they should have the same and the lowest value. If an entity that does not hold the feature has a higher index value than that does hold the feature, the index must be incorrectly designed. Likewise, if two entities that do not hold the feature have different ind... | CBA | BCA | BCA | CAB | Selection 1 |
**A**: Real quantized graphs are for efficiency, while fake quantized graphs are for simulation.**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 converted to a real one ... | BAC | CBA | CAB | ABC | Selection 3 |
**A**: A=(aij)𝐴subscript𝑎𝑖𝑗A=(a_{ij})italic_A = ( italic_a start_POSTSUBSCRIPT italic_i italic_j end_POSTSUBSCRIPT ) is called an M𝑀Mitalic_M-matrix if**B**: 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,... | CAB | BCA | ABC | ACB | 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... | CBA | ACB | ABC | CAB | Selection 2 |
**A**: The commonly adopted interior point geometry is based on Hessian metrics generated by self-concordant barrier functions, due to the provable optimality in connection with Newton-like second-order optimization [NN94], see e.g**B**: [BL89, NT02, AMS08]**C**: [TP21] for recent related work. Closer to our work is th... | CBA | BAC | ABC | CAB | Selection 2 |
**A**: We establish a depth separation result for monotone threshold networks and show that monotone networks can interpolate arbitrary monotone data sets by slightly increasing the number of layers. Thereafter, a simple argument shows that monotone networks of bounded depth are universal approximators of monotone func... | BAC | ABC | ACB | CAB | Selection 4 |
**A**: We start introducing the broken H2superscript𝐻2H^{2}italic_H start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT space
**B**: The error estimate does not significantly deviate from standard DG error estimates**C**: Thus, we keep it short and refer to the already mentioned references [11, 34] for the details | CAB | BAC | BCA | ABC | Selection 1 |
**A**:
Depending on whether the agents have real-time computing and policy-updating ability, the CL algorithms are divided into two categories: adaptive and non-adaptive**B**: In the adaptive case, agents can change their pull policies at each time step based on new observations**C**: While in the non-adaptive case, p... | CAB | BCA | ACB | ABC | Selection 4 |
**A**: To attain a superlinear convergence rate, the IQN method [45] has integrated quasi-Newton directions with incremental updates, albeit with only local convergence guarantees**B**: It is noteworthy that the aforementioned algorithms are applicable in (strongly) convex cases. However, within the nonconvex nonsmooth... | BCA | CAB | BCA | ACB | Selection 4 |
**A**: Our algorithm is a generalization of those proposed in ([37, 38]) from matrices to tensors. To the best of our knowledge, this is the first fixed-precision algorithm proposed for the t-SVD333After acceptance of the paper, the author found similar incremental algorithms for computation of the t-SVD in [39, 40].. ... | BAC | ACB | CAB | CBA | Selection 4 |
**A**: The effectiveness of feature weighting with the importance scores was shown to help the k-nearest neighbors algorithm to deal with irrelevant features [11]**B**:
Methods for feature weighting can be used to identify the most informative features by determining an importance score (weight), where a higher score ... | BAC | ABC | CBA | CAB | Selection 1 |
**A**: However, all models including DeepIPC have performance degradation in the evening. This means that doing inference in the low light condition is harder than in the normal condition**B**: Specifically, in the segmentation task, DeepIPC has a higher IoU than AIM-MT even though it does not perform depth estimation ... | ABC | ACB | BCA | CAB | Selection 3 |
**A**: For example, suppose that every bag of the decomposition is a clique, that is, the graph is chordal**B**: Since every independent set intersects each of the clique-bags in at most one vertex, dynamic programming still computes maximum weight independent sets in such graphs in polynomial time even if the bags cou... | BCA | ABC | ABC | CAB | Selection 1 |
**A**: FDML hu2019fdml and Linear-ADMM hu2019learning add noise to local outputs**B**: However, these methods lack exact privacy budget evaluations, providing only empirical utility under different levels of noise**C**: Additionally, ranbaduge2022differentially perturbs local model weights to satisfy DP. However, it... | BCA | ABC | BAC | ACB | Selection 2 |
**A**:
Graph neural networks mentioned above are originally designed for static graphs**B**: However, graph structured data are often dynamic in nature in many real-world applications [24, 25, 26, 27, 28, 29, 30]**C**: Thus, these static graph neural network models often fail in handling such graph data, due to their ... | ABC | CAB | ACB | BCA | Selection 1 |
**A**: The probe sequences are divided into three subsets according to the walking conditions (i.e. NM, BG, CL).**B**: For evaluation, the sequences of NM-01,02,03,04 for each subject are taken as the gallery**C**:
TABLE II: The rank-1 accuracy (%) on CASIA-BN-RCC for different probe views excluding the identical-view... | CBA | ABC | BAC | BAC | Selection 1 |
**A**: The overestimation problem causes that the dialogue policy module has inaccurate action values estimations after the training, which misleads the dialogue policy to choose the wrong dialogue action (see the wrong dialogue action in Figure 2). Some prior studies have tried to address this problem in domains like ... | CAB | BCA | BAC | CBA | Selection 4 |
**A**: Finally, our model is limited by the low-dimensionality of inputs, which are pre-extracted visual features as mentioned above, but is still able to approximate to the baseline.**B**:
In Table 2, it is shown that applying all techniques (M+I+N) obtains the best Top-1 accuracy results of our framework, but our mo... | CAB | ABC | ABC | BCA | Selection 1 |
**A**: The traditional SVDD algorithm relies on the kernel trick.**B**: Therefore, the data normality can be explicitly defined as this hypersphere, and the distance to the hypersphere center can faithfully indicate the degree of data abnormality.
This basic goal is the same as SVDD [41] (a popular technique in one-cla... | ABC | ABC | BAC | CBA | Selection 4 |
**A**: Joshi et**B**: al. (Joshi et al., 2020) note that models built on non-anglo-centric datasets, which are fewer and far between, have the potential to impact many more people than models built on highly resourced languages. The WebNLG 2020 challenge101010https://webnlg-challenge.loria.fr/challenge_2020/, for insta... | CAB | BCA | CAB | ACB | Selection 2 |
**A**: Results**B**: As shown in TABLE VIIIa (# 3), the single Pos-NSD component can still improve mR@K metrics using much fewer positive samples**C**: Besides, the baseline can also be exceeded on mR@K (# 4) after Neg-NSD and Pos-NSD alone with fewer training samples. It proves that the clean subset divided by Pos-NSD... | ACB | BAC | ABC | BCA | Selection 3 |
**A**:
Figure 3: Workflow of PSM strategy**B**: Instead of publishing a new block, the attacker shares partial block data with other miners to attract them to join its private branch.Three possible cases of finding another new block after the announcement of the partial block**C**: Case 1: By public miners; Case 2: By... | ABC | BCA | CAB | BAC | Selection 1 |
**A**: The PostLN Transformer training fails late in the warmmup period. **B**: 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**C**:
Figure 7:... | CAB | BAC | CBA | BCA | Selection 3 |
**A**: Over the years, various strategies for enhanced sampling have emerged, e.g., tempering, variational, or biasing approaches; see Ref. 15 for classification and references therein**B**: In this article, we consider a class of such enhanced sampling methods based on the work by Torrie and Valleau 16, which devised ... | CAB | ABC | ABC | BCA | Selection 4 |
**A**: We are able to find which vessels of the pruned tree give rise to the vessels excluded from it, by constructing surfaces around these we are able to subdivide the ventricle into regions that are more likely to be perfused by a given large vessel. These hulls are constrained to be non-intersecting. There are a fe... | BCA | CBA | BAC | ACB | Selection 2 |
**A**: Undirected line denote the link communicating pair of network elements, with the line’s strength denoting the link’s capacity level.**B**:
Figure 1: Schematic Representation of SDN Network State Estimation Problem**C**: Directed line denotes network traffic flows with different throughput in the given topology,... | CAB | ACB | ACB | ABC | Selection 1 |
**A**: Our theory is motivated by the recent progress in low-rank MDPs (Agarwal et al., 2020; Uehara et al., 2021), which show that the transition dynamics can be effectively recovered via maximum likelihood estimation (MLE). In contrast, our work recovers the representation via contrastive self-supervised learning.
Up... | BAC | BCA | ACB | CBA | Selection 4 |
**A**:
The remainder of this paper is structured as follows. In Section 2, we introduce the MV-SDE and associated notation, motivate MC methods to estimate expectations associated with its solution and set forth the problem to be solved**B**: Then, we introduce the novel multilevel DLMC estimator in Section 5, develop... | ACB | CAB | CAB | CAB | Selection 1 |
**A**: We assume that the spacecraft is equipped with a LiDAR (Light Detection and Ranging), two optical navigation cameras and a set of accelerometers, for navigation with respect to the asteroid**B**: A summary of the values used in the simulation is presented in Table 2**C**: We consider that no radiometric data is ... | ACB | CBA | BAC | ABC | Selection 4 |
**A**: To the best of our knowledge, this introduces a novel geometric perspective on the problem of computing Brascamp–Lieb constants for simple input data.**B**: In contrast to much of the prior literature, which has analyzed the problem through a Riemannian lens, our approach utilizes a Finslerian geometry on the ma... | CBA | ABC | CAB | BAC | Selection 1 |
**A**: A key observation is made: even when a different generator survives a merge, the persistence information can be "transferred" to the surviving one. This highlights the dynamics of merging classes and motivates further investigation into how these classes evolve under the filtration.**B**: This rule selects the g... | BCA | CAB | CBA | ABC | Selection 3 |
**A**: Note that “P”, “A”, “C” and “S” denote different domains on PACS. “Avg” is the average result of all domains. The bold is the best result.**B**:
Table 1**C**: Comparison between our method and different semi-supervised (DG) methods under different numbers of labeled samples on PACA and Office-Home | ABC | CBA | CBA | CAB | Selection 4 |
**A**: We test each method on ResNet50 [19, 27] with ImageNet21k pre-training**B**: To find the optimal hyper-parameters of Conv-Adapter (and baseline methods), we conduct a grid search of the learning rate, weight decay, and compression factor γ𝛾\gammaitalic_γ for each dataset using the validation data split from tra... | BCA | ABC | ABC | BAC | Selection 1 |
**A**: Figure 3 shows the performance of CP-PINNs in discovering changepoints and solving (16). Specifically, the leftmost panel illustrates the precise solution across a uniform temporal scale**B**: Identifying the locations of changepoints remains challenging even when the solutions are known. In the second panel, th... | BAC | ABC | CBA | CAB | Selection 2 |
**A**:
In other words, the Fisher information matrix can be computed efficiently, unlike the covariance matrix**B**: At the same time, the estimate above requires us to compute the inverse of the Fisher matrix, which for large and ill-conditioned problems is again not computable efficiently and accurately**C**: Howeve... | ACB | ACB | ABC | CAB | Selection 3 |
**A**: Usage Scenario.
Using description similarities or image similarity, it is possible to focus on the images directly in the Image Point Cloud**B**: If a small number of manuscripts as described in Section 5.1 had been chosen, no filter would necessarily be required to begin to annotate. In the case of a large numb... | BCA | CAB | ACB | BCA | Selection 3 |
**A**: Dacheng Tao (F’15) is currently a Distinguished University Professor in the College of Computing & Data Science at Nanyang Technological University**B**: He mainly applies statistics and mathematics to artificial intelligence and data science, and his research is detailed in one monograph and over 200 publicati... | CAB | ACB | ABC | CBA | Selection 3 |
**A**:
Crossover with Observed Attacks. The IT Army of Ukraine maintains a dashboard of targets’ status, claiming many are down due to their actions. To find whether the attacks involved reflected DDoS or defacement, we correlate our attack records with promoted targets since the Telegram group started**B**: For DDoS ... | BCA | BAC | ACB | CBA | Selection 3 |
**A**: An appropriate way to consider the victim model is as if it was sampled from the pool of all possible victim models. Therefore, rather than a guarantee, the attacker is interested in the expectation of each adversarial example to be successful.
**B**: The attacker is faced against an unknown victim model, which ... | CAB | ABC | ABC | CBA | Selection 4 |
**A**: Moreover, our results on noise-induced kernel concentration serve as a warning against using deep encoding schemes in the near-term. For a more detailed survey of how our results fit in the context of prior work see Appendix A.**B**: Here we provide a systematic treatment of the causes and effects of exponentia... | ABC | CBA | ACB | ACB | Selection 2 |
**A**:
Two approaches involving seven 3D action recognition networks are adopted to classify and anticipate lane change events on the PREVENTION dataset. For our RGB+3DN method, the lane change recognition problem is formulated as an action recognition task only utilising visual information collected by cameras**B**: ... | ACB | CBA | ABC | CBA | Selection 1 |
**A**: k𝑘kitalic_k-anonymity**B**: Furthermore, let**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�... | BAC | ACB | ABC | CBA | Selection 2 |
**A**: After that, Section 4.4 conducts ablation study. Lastly, in Section 4.5, the comparison between our method and existing methods is presented.**B**:
We conduct extensive experiments on four datasets. In Section 4.1, the datasets are detailed**C**: In Section 4.2, we detail the various methods used for comparison... | CBA | BCA | ABC | CAB | Selection 4 |
**A**: Within this innovative framework, we’re presented with an opportunity to delve into more intricate models**B**: This section is dedicated to presenting a refined causal model, tailored to this paradigm.**C**:
LCS is a new paradigm in MSDA, enriching the field with elevated variability and versatility | ACB | ABC | BCA | CAB | Selection 3 |
**A**: It is well-known from stochastic optimal control that, in many applications of practical interest, the underlying controls may be of bang-bang type, which typically lead to discontinuities in the optimal control policies and the possibility of nonunique optimal controls in some regions of the state space.
In tur... | ABC | CAB | ABC | CBA | Selection 4 |
**A**: Small-scale (SS) setup: The small scale setup is identical to the setup used in [16]**B**: In this setup we assume a 100m ×\times× 50m rectangular area, with 6 tenants in random positions, and 8 BSs, located in random positions on the boundary of the area**C**: In this setup, each BS offers 1–3 identical channel... | ACB | ACB | ABC | BAC | Selection 3 |
**A**: The left part is a comparison of flexible and Gaussian posterior in FDA**B**: Comparisons of the target distribution, predicted distribution, and Gaussian distribution are shown in the right part.**C**:
Figure 4: Distribution of different time windows, METR-LA | CBA | ABC | ABC | BCA | Selection 4 |
**A**: DynAE. For better comparison, all the representations shown here have been aligned with the ground-truth using the optimum Q𝑄Qitalic_Q from Eq. 9. Only DynAE can successfully recover the ground-truth variables up to isometry.**B**:
Figure 3: Comparison of the representations learned by different models on the ... | CBA | ABC | CAB | ABC | Selection 3 |
**A**: In Communications-aware UAV placement problems, the focus is on the stopping (or final) positions of the UAV to provide communication to ground users, rather than on the full trajectory. In some cases, the energy required by the UAV to reach a convenient final position is also of interest**B**: For these problem... | ABC | CBA | CAB | BCA | Selection 4 |
**A**: A reformulation of the ELBO as entropy sums can be useful from theoretical as well as practical perspectives**B**: The computation of variational bounds and/or likelihoods can also become easier for other reasons, e.g., for probabilistic PCA the likelihood at stationary points can be computed from the model para... | CAB | BAC | ACB | ABC | Selection 3 |
**A**: We then move on to prove Theorem 4 and Theorem 3 in Section 4.2.**B**: 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**C**:
This section is devoted to the proofs of Theorem 2, Theorem 4, a... | ABC | ABC | CBA | CAB | Selection 3 |
**A**: From Figure 10, we can find that:
**B**: To see more details in the recommendation results, we examine the results of each subgroup in the group with the maximal item number**C**: As shown in Figure 10, x-axis denotes the post-click conversion ratio of each subgroup while y-axis is the value of exposure divided ... | ACB | ABC | CAB | BCA | Selection 3 |
**A**: In general, the expected feature statistics should be shared by different image content and be aware of the quality degradation**B**: In this regard, a unified distribution regularization is imposed on the feature space, and the relationship between the image quality and the feature statistics is established by ... | CAB | CBA | CBA | BCA | Selection 4 |
**A**: We also observe empirically that LILI\mathrm{LI}roman_LI better agrees with GNN performance than homophily measures. Thus, while being very simple to compute, LILI\mathrm{LI}roman_LI intuitively illustrates why GNNs can sometimes perform well on heterophilous datasets — a phenomenon recently observed in the lite... | BAC | BCA | CAB | BAC | Selection 3 |
**A**: H.Z. would like to thank the American Institute of Mathematics and the AIM workshop Analysis on the hypercube with applications to quantum computing**B**:
H.Z. is supported by the Lise Meitner fellowship, Austrian Science Fund (FWF) M3337**C**: He is also grateful to the organizers and other participants for cr... | BAC | ABC | ABC | ABC | Selection 1 |
**A**: Chip or PCB designers devise and produce merely a small portion of components in-house, while relying on a variety of possibly malicious third-party components, contract manufacturers, distributors, and EDA tools, thus rendering the supply chains vulnerable to hardware attacks from external entities**B**: For in... | CBA | BAC | CBA | ABC | Selection 4 |
**A**: The example is paraphrased in terms of Definition 2.
**B**: Several motivating examples are provided in this subsection**C**: The first is adopted from [Lanzon and Bhowmick, 2023], which provides a class of negative imaginary systems characterised by an LTI auxiliary system and a dynamic supply rate | BCA | BCA | CAB | BCA | Selection 3 |
**A**: The stochastic version of the Ames’s et al.’s result is recently discussed by Clark [12]; he insists that his RCBF and ZCBF guarantee the safety of a set with probability one. At the same time, Wang et al. [13] analyze the probability of a time when the sample path leaves a safe set under conditions similar to C... | BAC | ABC | BCA | CAB | Selection 1 |
**A**: [31] designed an approach where a robot learns to adapt its behaviour to become more legible with repeated interactions. The designed approach uses reinforcement learning (RL) to learn a behaviour model for how to interact legibly with a human. To create this model the robot needs a training phase, where it int... | BAC | BCA | CBA | ABC | Selection 3 |
**A**: Random variables are in capital 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**: X𝑋Xitalic_X), and their realization are in lower case (e.g | ACB | ABC | ABC | BCA | Selection 1 |
**A**: Some GFM controls directly give control commands of voltage magnitude and angle to the modulation block without a cascaded voltage and current control loop, which also have voltage source behaviors [28, 29, 10]**B**: The voltage source behavior of GFM converters enables fast voltage support for power grids and e... | BCA | ABC | CBA | BAC | Selection 1 |
**A**: Its performance is similar for inflation with a slight drop to second place in terms of SIS over h=1:4.
**B**: It gave the best prediction intervals for GDP gap over h=1:4 and h=1:8, while in second place for prediction accuracy**C**: The DeepVARwT model outperformed the other models for federal funds rate | CBA | BCA | ABC | CAB | Selection 1 |
**A**: Additional contributions encompass the empirical validation of our theoretical framework, as well as an examination of prevalent deep learning methodologies within the context of our proposed theory.
**B**: We highlight that our primary objective is not to advance the state-of-the-art in the field**C**: Rather, ... | ACB | CBA | BAC | CAB | Selection 4 |
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