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**A**: 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 knowledge types help to approximate such a function.
... | ACB | ACB | ABC | CAB | 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**: Bands are fixed by the choice of architecture and training data. Then, the natural direction is to define neural operator with fixed numbe... | ABC | CAB | CAB | ACB | Selection 4 |
**A**: It would be interesting to see the efficacy when multiple layers are get involved with distillation which has been explored by some works [50, 7]. (2) Also, we didn’t investigate the effectiveness on other applications like object detection, which may need to design the new objective to fit the nature of specifi... | ACB | CBA | ABC | CAB | Selection 2 |
**A**: Trustworthiness measures how well neighbors in the embedding match the neighbors in high dimensional space, with large errors penalized heavily**B**: As its name implies, a high trustworthiness is a good indication that one can trust the local patterns in the embedding**C**: This is a measure of precision with r... | ACB | ABC | CBA | ACB | Selection 2 |
**A**:
In the case that maintaining a belief or conducting the prediction is intractable, previous approaches establish predictive states (Hefny et al., 2015; Sun et al., 2016), which is an embedding that is sufficient for inferring the density of future observations given the interaction history. Such approaches typi... | BCA | BAC | BAC | ACB | Selection 4 |
**A**: The second line of research studies online RL in POMDPs where the actions are specified by history-dependent policies.
Thus, the actions does not directly depends on the latent states and thus these works do not involve the challenge due to confounded data. The third line of research studies OPE in POMDPs where ... | ACB | CAB | CBA | ACB | Selection 3 |
**A**:
The asymptotics of second-order Newton’s methods for unconstrained problems have recently been investigated**B**: Compared to first-order methods that often consider averaged iterates and/or exclude the stepsize 1/t1𝑡1/t1 / italic_t due to technical challenges, both works showed the normality of the last itera... | ACB | BCA | ABC | CBA | Selection 1 |
**A**:
The present paper suffers from the same rather severe restrictions on hexahedral meshes in 3D as in previous work**B**: 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... | ACB | BAC | CAB | CBA | Selection 1 |
**A**: Using self-similar stacked blocks makes the architecture scalable**B**: The other two design elements are our key contributions, along with adaptations, extensive experimentation, and analysis to demonstrate the versatility, utility, and parsimony of the WaveMix design.**C**:
At the heart of WaveMix are three d... | ACB | CAB | BCA | BAC | Selection 3 |
**A**: A3subscript𝐴3A_{3}italic_A start_POSTSUBSCRIPT 3 end_POSTSUBSCRIPT**B**: Then, the matrix is square with two (starred) transversal
zeros**C**: So HK(B3)=({1,2},∅,{1,2})subscript𝐵31212(B_{3})=(\{1,2\},\emptyset,\{1,2\})( italic_B start_POSTSUBSCRIPT 3 end_POSTSUBSCRIPT ) = ( { 1 , 2 } , ∅ , { 1 , 2 } ). | ACB | ABC | ACB | CAB | Selection 2 |
**A**: There is a clear evolution in mathematical text processing overall, from roots in explicit discourse representation zinn2003computational; cramer2009naproche to the present day, where graph-based and transformer-based models produce leading metrics in a few related tasks peng2021mathbert; ferreira2021star; liang... | CBA | BCA | BAC | ACB | Selection 4 |
**A**: We then use 2222-medoids clustering to split the sub-collection of networks based on the dissimilarity measures**B**: The mathematical definition of the dissimilarity measure and details on the recursive clustering algorithm are given in Appendix A.
**C**: A split is validated if it increases the score of Equati... | CBA | ACB | BAC | CAB | Selection 2 |
**A**: (left) Predictions of rotation angle vs. the ground truth (normalized to [−1,1]11[-1,1][ - 1 , 1 ]) in test set**B**: (right) Distributions of absolute percentage errors (in %) of all data points in the dataset. **C**:
Figure 5: Performance of FactorNets for individual rotation learning | BCA | ACB | CBA | ABC | Selection 1 |
**A**: We proposed a novel contrastive training objective puNCE (positive unlabeled Noise Contrastive Estimation), that extends contrastive loss to the positive unlabeled setting by incorporating available biased supervision**B**: In this work, we investigated the limitation of a general self-supervised pretraining and... | BCA | ABC | BAC | ABC | Selection 3 |
**A**: The structure of this work is as follows**B**: In Section 3 we define the nonnegative Tucker decomposition (NNTuck)and its notation, discuss the connection of its definition under KL-divergence to stochastic block models, motivate using the multiplicative updates algorithm from Kim and Choi (2007), and offer an... | ACB | CAB | CAB | BCA | Selection 1 |
**A**: Experiments below used the 11-qubit trapped ion quantum computer described by Johri et al**B**: A crucial point to bear in mind with quantum computing is that the memory capacity**C**: johri2021nearest ,
and where necessary, the larger IonQ Aria machine with a capacity of 32 physical and 20 algorithmic qubits (i... | ACB | CAB | CBA | BAC | Selection 1 |
**A**: (2020) classifies nodes into 5 classes according to their average traffic. Actor is a graph of actor co-occurrence in films based on Wikipedia, modified by Pei et al. (2020) based on Tang et al. (2009).
**B**: We use 6 datasets listed in Table 3. Texas, Wisconsin and Cornell are graphs of web page links between ... | ACB | ACB | BAC | CAB | Selection 4 |
**A**: risk**B**: The second observation is that the basis for the updates is the average loss, i.e**C**: This motivates the following definition: given participation α𝛼\alphaitalic_α and parameters ΘΘ\Thetaroman_Θ,
the average risk experienced by each subpopulation i𝑖iitalic_i and each | BCA | BAC | CBA | CAB | Selection 2 |
**A**: This notion, originally defined for binary classification, defines a classifier as fair if its false positive rate and its false negative rate, conditioned on the value of the fairness attribute, are the same for all attribute values**B**:
We consider fairness in the sense of equalized odds (Hardt et al., 2016)... | BAC | BCA | ABC | CAB | Selection 1 |
**A**: When adding a small noise, blurred or too small motifs are reported**B**: Only our approximate k𝑘kitalic_k-Motiflets identify all k=20𝑘20k=20italic_k = 20 repeats of the riff.
**C**: From the competitors, none correctly identifies all occurrences, even when given optimal parameters | ABC | CAB | BCA | CAB | Selection 3 |
**A**: [28]-[29] and [45]-[46]) all require that the regression matrices and graphs satisfy some special statistical properties, such as i.i.d., spatio-temporal independence or stationary, etc**B**:
At present, most results on decentralized online linear regression algorithms (e.g**C**: However, these special statisti... | BCA | ABC | BCA | BAC | Selection 4 |
**A**: The theoretical bounds provide useful insights to judiciously inject inaccuracy in the calculations whenever this allows to alleviate the computational burden without compromising the final accuracy of the physics based solver**B**: We derived rigorous theoretical bounds for AA that allow for efficient approxima... | CAB | BAC | ABC | ABC | Selection 2 |
**A**: We use the tf-idf vectorizer provided by the Scikit-learn library [31] to extract a vector representation for each document in the corpus**B**: Then, all the representations of the same topic are averaged to extract the final vector representation for each topic.**C**:
The Vox dataset is also used to extract th... | ABC | BCA | BAC | ACB | Selection 2 |
**A**: We repeated the compilation multiple times and measured the same duration**B**: We did not succeed to compile using Cirq 7-qubit wide multipliers automatically within reasonable time (one week).**C**:
The compilation of the 6-qubit multiplier required more than 3 days | CAB | ABC | BCA | CAB | Selection 3 |
**A**: For both phases, we used Adam optimizer [20] and weight initialization as proposed by [21]. Mean squared error (MSE) loss was used for both phases.
**B**: For the second phase, we train the Param-Net on MRiLab dataset. The weights of the auto-encoder are frozen during this phase**C**: The training process compri... | ABC | BAC | CAB | CBA | Selection 4 |
**A**:
The rest of the paper is organized as follows**B**: Section 3 of the paper is devoted to the development of the proposed EVNN schemes for L2superscript𝐿2L^{2}italic_L start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT-gradient flows and generalized diffusions.**C**: Section 2 reviews the EnVarA and some existing neur... | CBA | CAB | ABC | ACB | Selection 4 |
**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**:
One-parameter persistence is often restrictive, in particular in applications where there is no canonical choice of filtering function on the... | CAB | CAB | CAB | BAC | Selection 4 |
**A**:
Figure 4 illustrates that changing the variable ordering from worst to optimal has the largest overall impact on F1, with a mean improvement of 0.412 and an interquartile range from 0.233 to 0.584. The mean improvement going from alphabetic to optimal ordering is 0.215, with an interquartile range from 0.062 to... | CBA | ABC | CBA | ACB | Selection 2 |
**A**:
From the positive side, Luo [16] introduced the notion of rank-determining sets of metric graphs, and verified the existence of finite rank-determining sets constructively. Hladký, Král, and Norine [13] confirmed a conjecture of Baker [2] relating the ranks of a divisor on a graph and on a tropical curve, and p... | BAC | ABC | BAC | ACB | Selection 2 |
**A**:
Recalling Eq**B**: 4 and Eq**C**: 5, through two 1-D convolutions along different dimensions, we construct two CCS in a vertical relationship, which are stored in 𝒯𝒯\mathcal{T}caligraphic_T and 𝒞𝒞\mathcal{C}caligraphic_C. In particular, TCJA is to construct a CCS, which can perceive a larger area while re... | ABC | BCA | CBA | BCA | Selection 1 |
**A**: 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_POSTSUPERSCRIPT**B**: Given**C**: 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 = (... | CBA | BCA | ABC | ACB | Selection 4 |
**A**: Subsequently, Heuristic 2 further reduces the remaining search space by an additional 34%percent3434\%34 %, and Heuristic 3 contributes an additional reduction of 65%percent6565\%65 % to the remaining search space.**B**: Our results indicate that Heuristic 1 leads to an average reduction of 57%percent5757\%57 % ... | BCA | CBA | BAC | CAB | Selection 2 |
**A**: VariBAD Dream:
Recall that our pipeline is to learn a KDE over the task parameters θ𝜃\thetaitalic_θ, and then train a policy on tasks from the estimated KDE. Unfortunately, in our meta-RL setting, we do not assume that we directly know the θ𝜃\thetaitalic_θ representation for each task. However, the VAE in Var... | BAC | ACB | CAB | CAB | Selection 2 |
**A**: Two Experiments**B**: We conducted two experiments and reported the results in the following subsections**C**: The first experiment compared the quality of bilingual health word embedding induced by our proposed framework with other existing language models for identifying CHV across languages. The second experi... | CAB | BAC | ABC | ACB | Selection 3 |
**A**: In this study, we have introduced a novel framework inspired by thermodynamics to create interpretable representations of complex black-box models. Our objective was to find representations that minimize discrepancies from the true model while remaining highly interpretable to humans using a concept similar to t... | CBA | CAB | BAC | BAC | Selection 1 |
**A**:
Table 6 demonstrates the code coverage capabilities of FuSeBMC v4 in comparison to other state-of-the-art software testing tools**B**: FuSeBMC participated in all 16 subcategories, in 9 of which (i.e. Arrays, BitVectors, Floats, Heap, Loops, ProductLines, Recursive, Combinations and Termination) it achieved fir... | CAB | BAC | BCA | ACB | Selection 4 |
**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... | CBA | CBA | ACB | BAC | Selection 3 |
**A**: Indeed, all 21 indexes in this study assign the value 0 to entities that do not hold the feature they are designed to quantify. There could be exceptions**B**: For example, one can design an index equal to the CN value plus 0.5. This is a valid index. However, it is easy to see that our theoretical framework sti... | BCA | BAC | CAB | BAC | Selection 1 |
**A**: In the future, we would like to extend to more modalities (e.g., audio) and more models (e.g., RNNs, Transformers).**B**: However, our current study is limited to vision recognition with CNNs**C**:
Our work achieves the first practical solution for transfer learning on tiny microcontrollers | BCA | BCA | CBA | BAC | 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**: Then we denote
|A|=(|aij|)𝐴subscript𝑎𝑖𝑗|A|=(|a_{ij}|)| italic_A | = ( | italic_a start_POSTSUBSCRIPT italic_i italic_j end_POSTSUBSCRIPT | )**C**: Let ... | CBA | CAB | BAC | BAC | Selection 1 |
**A**: We find the following three particular topics most interesting and timely.**B**: In this light, this work suggests as interesting future work to investigate how some recently discussed questions can be answered in the permutation world**C**:
From a broader perspective, this work confirms what is known from empi... | ACB | CBA | BCA | BCA | Selection 2 |
**A**: [BL89, NT02, AMS08]**B**: [TP21] for recent related work. Closer to our work is the comprehensive paper by [ABB04] where Hessian metrics generated by convex functions of Legendre type are studied for a class of convex programs that include affine subspace constraints.
**C**: The commonly adopted interior point g... | CAB | ACB | CBA | BCA | Selection 2 |
**A**: As part of the proof of Theorem 6, we introduce two reductions going from, and to, neural networks and Boolean circuits**B**:
To make use of these foundational results in circuit complexity theory, we shall need to make a connection with neural networks**C**: Our reductions are quite general and may be of indep... | ACB | BAC | CAB | CAB | Selection 2 |
**A**: While traditional QMC methods can also exhibit faster-than-Monte Carlo convergence rates, as a general rule the convergence rates of classical QMC methods have exponential dependence on the dimension of the integration problem in the unweighted setting [53]. However, QMC error bounds developed in weighted spaces... | CAB | BAC | BAC | ABC | Selection 1 |
**A**: That is, their algorithm takes a confidence parameter δ𝛿\deltaitalic_δ (instead of a time horizon T𝑇Titalic_T) as an input, and try to use the smallest possible number of time steps to identify the best arm with probability (1−δ)1𝛿(1-\delta)( 1 - italic_δ )**B**: Their lower bound results are proved for the s... | BCA | CBA | ABC | CBA | Selection 1 |
**A**:
In the initial set of simulations, we evaluate the performance of SPIRAL on the sparse phase retrieval problem, which involves signal recovery based on intensity measurements**B**: The sparse phase retrieval problem is formulated as follows:**C**: This problem finds applications in various fields, such as elect... | BCA | ACB | ABC | CAB | Selection 2 |
**A**: Indeed, a randomized slice sampling algorithm was proposed in [35] in which horizontal and lateral slices are selected and a low tubal rank approximation is computed based on them, see Figure 3 for a graphical illustration on this approach**B**:
The sampling approach can also be used for low tubal rank approxim... | BAC | ABC | CAB | CBA | Selection 1 |
**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 ... | ACB | CAB | CBA | BAC | Selection 4 |
**A**: Then, we describe the dataset used for imitation learning that includes data for training, validation, and testing**B**: In this section, we explain the model architecture and its improvement in detail**C**: We also describe the training setup and define several formulas used to supervise the model. Finally, we ... | CAB | CBA | BAC | CAB | Selection 3 |
**A**:
We reduce from the 3333-Coloring problem**B**: Recall that the task of 3333-Coloring is to decide whether a graph G𝐺Gitalic_G admits a proper 3333-coloring, that is, its vertices can be colored by three colors in such a way that adjacent vertices receive distinct colors**C**: Equivalently, | CAB | BAC | CBA | ABC | Selection 4 |
**A**: In addition, nowadays multimodal data has been ubiquitous, while usually, each client is only able to collect one or a few data modalities due to resource limitations. Therefore, VFL provides an effective way to allow such clients to train a model leveraging information from different data modalities jointly.**B... | ABC | CAB | BCA | CBA | Selection 2 |
**A**: 2, our model mainly consists of two modules: a time-aware attentional aggregating module aiming to aggregate the neighborhood information; a reinforced neighbor selection module intending to adaptively and dynamically determine whether a neighbor node should be updated. We first give some notations and prelimina... | BAC | CBA | BCA | BCA | Selection 2 |
**A**: Ltd. He has published one book and more than 80 papers at international journals and conferences such as TPAMI, IJCV, TIP, TSMCB, TMM, TCSVT, CVPR, ICCV, ECCV, NIPS, AAAI. His research interests include pattern recognition, computer vision and machine learning.**B**: Yongzhen Huang
received the B.E. degree from... | ABC | CAB | BAC | CBA | Selection 2 |
**A**: Double Q-learning (Hasselt, 2010) trades overestimation bias for underestimation bias using the double estimator. Since underestimation bias is not preferable (Hasselt, 2010; Lan et al., 2020), Weighted Q-learning proposes (D’Eramo et al., 2016; Zhang et al., 2017) the weighted estimator for the maximal action v... | ABC | ABC | CBA | CAB | Selection 3 |
**A**:
Due to our limited computational resources, training was performed independently for each module**B**: As LTA-Ego4D Forecasting benchmark is private, the ground truth from the testing set is not provided. Therefore, to validate our hypothesis, we perform an ablation study which is based on the results obtained ... | BAC | CAB | ACB | CAB | Selection 3 |
**A**: MSCRED and GDN learn interactions between different varieties, i.e., they cannot perform single-dimensional data. Thus, we report COUTA and the remaining nine competitors in Table III.**B**: It is noteworthy that OCSVM, GOAD, ECOD, and DAGMM are not originally designed for time series data**C**: ARMA cannot obta... | ABC | BCA | ABC | CBA | Selection 4 |
**A**:
Böhm et. al. (Böhm et al., 2019) note that while modern frameworks for text generation compete with higher scores on automated word-overlap metrics, the quality of the generation leaves a lot to be desired. As such, the adaptation of continuous representations based metrics shifts the focus from surface-form ma... | BCA | ACB | ACB | ABC | Selection 4 |
**A**: Hyperparameter of NIST**B**: The hyperparameter of NIST is the weight of two teachers**C**: We evaluated the performance of the three weighted methods in NIST. Among them, “fixed” means that the weights of two teacher models are constant 0.5, “adapt” means that two teacher models adopt adaptive trade-off strateg... | ABC | BCA | ACB | BCA | Selection 1 |
**A**: In PSM, an attacker follows the partial block sharing strategy and shares the partial block information with rational miners**B**: Miners can mine after it to get a new block. The hidden data can be recovered by others, spending considerable mining power. We denote the hidden data as secret.
**C**: The partial b... | BCA | ABC | ACB | BAC | Selection 3 |
**A**: For two learning rates from Figure 9 (top row = 5e-5, bottom row = 2e-4), we zoom in on 200 steps of training, plotting five important quantities**B**:
Figure 9: Zoom in on 200 steps of training**C**: The red vertical lines mark the moments at which the preconditioned sharpness crosses the stability threshold. | ABC | ABC | CAB | BAC | Selection 4 |
**A**: We can also notice a slight difference between the metastable states lying higher in free energy**B**: In Fig. 4, we can see the lower-lying free-energy basins in the reweighted stochastic embeddings are captured by both mrse and stke**C**: Specifically, mrse captures more states below a threshold of 25 kJ/mol i... | BAC | ABC | BCA | ABC | Selection 1 |
**A**: For pseudo-trifurcations, it is easy to choose the second bifurcation point to be the trifurcation point. A long segment may give rise to one or more short segments, and the short segments may in turn give rise to one or more short segments. There are many possible combinations of branching structures that invol... | BAC | ACB | ABC | ACB | Selection 1 |
**A**: [14] conclude that the size generalization can only be applied on graphs with certain structural features**B**:
In a survey by [7], the extrapolation ability of GNNs regards mainly two aspects: the ability to extrapolate towards unseen structural features as well as to extrapolate towards out-of-distribution at... | BCA | CBA | BAC | BCA | Selection 3 |
**A**: (2020) shows that contrastive learning provably recovers the latent embedding under the restrictive Block MDP setting (Du et al., 2019a). In contrast, our work analyzes contrastive learning in RL under the more general low-rank setting, which includes Block MDP as a special case (Agarwal et al., 2020) for both M... | ACB | CBA | BAC | ABC | Selection 2 |
**A**: First, we introduce the general decoupling approach.
**B**: The decoupling approach was developed for importance sampling in MV-SDEs (dos Reis et al., 2023; Ben Rached et al., 2023), where the idea is to approximate the MV-SDE law empirically as in (4), use the approximation as input to define a decoupled MV-SDE... | BCA | CAB | CBA | ACB | Selection 2 |
**A**: This accuracy is equivalent to about 0.4%percent\%% of Bennu’s mean radius**B**:
The OSIRIS-REx, when operating close to Bennu, relied on a shape model obtained through stereophotoclinometry (SPC) with a 3D error of 1 m [1]**C**: Although reliable and accurate, this shape reconstruction approach is time-consumi... | BCA | BAC | CAB | CBA | Selection 2 |
**A**: We note that neither the choice of the Picard iteration, nor the specific Finslerian lens employed in the analysis is unique**B**:
This paper analyzes a specific Picard iteration, generated by the map G𝐺Gitalic_G, for computing Brascamp–Lieb constants and analyzes its convergence via the Thompson part metric**... | BCA | BAC | ABC | ACB | Selection 2 |
**A**: These k𝑘kitalic_k-chains serve as the building blocks for studying the topological properties of the simplicial complex**B**: However, not all k𝑘kitalic_k-chains represent actual cycles within the complex**C**: To distinguish cycles from other chains, we introduce the concept of a boundary operator.
| ABC | ACB | CBA | ACB | Selection 1 |
**A**: In our task, we achieve the scheme in (isobe2021multi, ) that uses each local branch prediction to distill global branch prediction**B**:
Comparison with “local-to-global” method**C**: Experimental results are shown in Tab. 9. As seen in this table, our method can obtain the better performance than the L2G (iso... | CAB | BCA | BAC | ACB | Selection 3 |
**A**: When setting the kernel size larger to that of the residual blocks, i.e., 5 and 7 for ResNet50, the performance is further boosted, with more parameters introduced.
**B**: We show the performance of Conv-Adapter on VTAB-1k validation set in Fig 5, of using different kernel size for the depth-wise convolution to ... | CAB | BCA | BAC | BCA | Selection 1 |
**A**:
We introduce a novel method for identifying changepoints in dynamic systems governed by general PDEs dynamics**B**: Our approach works with piecewise-constant time-changing parameters and leverages total variation regularization on the first-order differences of parameters**C**: We also propose an online learni... | CAB | BCA | BAC | ABC | Selection 4 |
**A**: 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**B**:
In other words, the Fisher information matrix can be computed efficiently, unlike the covariance matrix**C**: Howeve... | CAB | CBA | CBA | BAC | Selection 4 |
**A**: ”Paris bibles”, a tradition that we have called elsewhere one of variance in uniformity, emerged in thirteenth century Europe as a mass-produced written object in response to new forms of literacy, namely teaching and preaching (Light, 2012)**B**: After 1220, these hand-copied Bibles contained a corrected text a... | ABC | ACB | BCA | BAC | Selection 3 |
**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 ... | ACB | CBA | BCA | CAB | Selection 2 |
**A**: Launching these attacks with ready-made tools is straightforward for those without much technical expertise. They can be executed quickly, at scale, and have instant, noticeable effects such as altering targets’ appearance, making them inaccessible, or taunting opponents with compromised sites. During wartime, t... | BAC | CAB | CBA | ACB | Selection 3 |
**A**: Columns categorize the various attack algorithms employed, while rows detail the architecture pairings, with surrogate models (F0subscript𝐹0F_{0}italic_F start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT) distinct from the victim’s architecture.
**B**: The comparative performance of HET across different attack algorithms... | CBA | BAC | ACB | CAB | Selection 1 |
**A**: The problem of exponential concentration for the fidelity quantum kernel was first observed in Ref. [6] and later analyzed in Ref. [7, 44, 45] in the context of generalization. Ref. [7] discusses exponential concentration in the context of a projected quantum kernel for a specific example embedding.
On the othe... | BCA | ABC | BAC | ACB | Selection 2 |
**A**: As can be observed, regardless of the classification or prediction results, the performance of each method does not vary much**B**: The best accuracy is only 3.00% higher than the lowest one, which is very different from the experiments of the RGB+3DN method on the RGB video data. The X3D-L and SlowFast-R101 are... | ACB | CBA | CAB | BCA | Selection 4 |
**A**: are shown in Figure 7. The method of Caliskan et al**B**: leads to a one-sided distribution. Between 40% to 60% of the authors
cannot be identified well**C**: In contrast, the approach of Abuhamad et al. induces a two-sided distribution. Some authors are well protected while | ACB | CBA | ABC | CBA | Selection 3 |
**A**: Provided with pre-trained VLMs, how to adapt their implicit knowledge to various downstream tasks becomes an essential problem. Recently, Zhou et al. [5] introduced CoOp, a method that incorporates prompt tuning, originally developed in NLP [13, 14, 15], into computer vision to tackle the challenge of few-shot i... | ABC | ABC | ACB | ABC | Selection 3 |
**A**: This section is dedicated to presenting a refined causal model, tailored to this paradigm.**B**: Within this innovative framework, we’re presented with an opportunity to delve into more intricate models**C**:
LCS is a new paradigm in MSDA, enriching the field with elevated variability and versatility | ABC | ABC | CBA | BAC | Selection 3 |
**A**: In order to preserve this approach on the discrete level, we consider here approximations by a monotone finite element method that satisfies a discrete maximum principle (see, e.g., [ciarlet1973maximum])**B**: As such, we will consider a finite element discretization of (9) that employs the method of artificial ... | ABC | ABC | BCA | ABC | Selection 3 |
**A**: Large-scale (LS) setup: In this setup we assume a 150m ×\times× 100m rectangular area, with 20 tenants, and 16 BSs. In this setup, each BS offers 3–6 identical channels, but the total number of channels is no more than 60111Note that in some applications, large-scale scenario comprises many more tenants and chan... | ACB | CAB | ABC | BCA | Selection 1 |
**A**: Regarding the temporal-aware sequential models, the Transformer-based models, i.e., Informer, Autoformer and FEDformer, achieve obviously better performance over RNN- and CNN-based models (including LSTNet, DSANet and STNorm), showing the benefit of Transformers or variants combining various attention mechanisms... | BAC | CBA | ACB | ABC | Selection 4 |
**A**: There are a variety of additional avenues for such a dynamics-based approach. Through both theoretical analysis and numerical experiments, our results further support the previous findings that constraining the latent dynamics is enough to uniquely identify the latent representation up to an isometry 30, 31**B**... | ACB | BCA | CBA | ABC | Selection 4 |
**A**: Clearly, the model (1) used in this example oversimplifies the motion capabilities of the car-like MR**B**: Some post-processing, e.g. smoothing the path, could be done on the resulting trajectory to make it feasible for the real MR, however, this would still require considering the MR’s kinematic and/or dynamic... | ACB | BAC | BCA | ABC | Selection 1 |
**A**: Therefore, in this section, we will treat general exponential family distributions**B**: However, we will in this section explicitly use Lebesgue integrals and general (non-negative) measures, which we have avoided**C**: The treatment will, in principle, be analogous to the previous
section | BCA | BCA | ACB | 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 | BCA | BAC | Selection 3 |
**A**: This may hinder the model from learning accurate user interests from biased interactions.
**B**: The reason is that it removes the natural direct effect [2] of items and users for mitigating the influence of item popularity and user conformity without considering other important factors like item quality**C**: M... | CAB | ABC | ABC | CBA | Selection 4 |
**A**: For each distortion type, seven distortion levels are generated.
**B**: SIQAD dataset [24] contains 20 reference SCIs and 980 distorted SCIs**C**: The distorted images are derived from seven distortion types including Gaussian Noise (GN), Gaussian Blur (GB), Motion Blur (MB), Contrast Change (CC), JPEG, JPEG2000... | ABC | CAB | BCA | ACB | Selection 2 |
**A**: They construct semi-synthetic graphs by adding inter-class edges following different patterns to several real-world graphs, thus obtaining several sets of graphs with varying levels of homophily. Ma et al. [24] run experiments on these graphs and note that a standard GNN achieves strong performance on some heter... | CAB | ABC | ACB | ACB | Selection 1 |
**A**: However, we still have the following weaker bound as consequence of Theorem 4.3**B**: 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**C**: Again, the proof can be found in the appendix.
| BAC | CAB | BCA | CBA | Selection 1 |
**A**: In fact, defending against this new hardware attack surface is challenging: It is difficult to examine all steps through the global supply chain from manufacturers to customers; moreover, identifying malicious components via hardware tampering detection techniques, e.g., circuit-based sensors and X-ray imaging, ... | ACB | CAB | ACB | CBA | Selection 4 |
**A**: 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**B**: The example is paraphrased in terms of Definition 2.
**C**: Several motivating examples are provided in this subsection | ACB | BCA | ABC | CAB | Selection 2 |
**A**: Tamba et al. make a similar argument for CBFs in [19], but their sufficient condition is more stringent.**B**: To avoid the tight condition for the coefficient, we should design a state-feedback law whose value is massive, namely diverge in general, at the boundary of the subset so that the effect of the law ove... | CBA | BCA | ABC | BAC | Selection 2 |
**A**: [12] further extended the notion of legibility to scenarios of planning under uncertainty, introducing legible Markov decision problem (L-MDP)**B**:
Miura et al**C**: In L-MDPs, the planning agent reasons about the observer’s belief regarding about the goal of observed actions, using the multiagent framework o... | CBA | ACB | BCA | 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 | BCA | CBA | CBA | CBA | Selection 1 |
**A**: Furthermore, the analysis in [9] only considers one type of GFM control (i.e., VSM) and directly approximates a VSM as an ideal voltage source (without deriving the equivalent impedance as will be done in this paper). Such an approach might not apply to other GFM methods once they have weaker voltage source beh... | ABC | ABC | CBA | CAB | Selection 3 |
**A**: The order p=4𝑝4p=4italic_p = 4 for a VAR model is a common choice in the analysis of quarterly macroeconomic series, for example, [14], [15] and [10].
The first model we fitted was DeepVARwT(4)**B**: The number of input t𝑡titalic_t functions and the hidden state size were the two most crucial hyperparameters**... | CAB | BAC | CAB | ABC | Selection 4 |
**A**: We have demonstrated that closed-set metric learning distinguishes the unknown from the known by causing their representations’ Jacobian norm values to differ**B**: Recognizing the significant role of inter-class learning in OSR, we developed a marginal one-vs-rest loss function designed to promote robust inter-... | CAB | ACB | CAB | CBA | Selection 2 |
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