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**A**: [19] propose an original teacher-student architecture that uses the logits of the teacher model as the knowledge. Since then, some KD methods regard knowledge as final responses to input samples [3, 31, 58], some regard knowledge as features extracted from different layers of neural networks [24, 23, 41], and so... | BAC | CBA | BCA | CBA | Selection 3 |
**A**: In the same vein, Chebyshev/trigonometric smoothing (the chopping of the series) is used on the input function, if it is not conformable with a particular architecture of SNO.
**B**: If one needs to compute these functions on the finer grid, Chebyshev or trigonometric interpolation should be use**C**: It is impo... | ACB | BAC | CBA | CAB | Selection 3 |
**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 | CAB | BAC | BAC | Selection 1 |
**A**: For the following definitions, let N𝑁Nitalic_N be the size of a dataset (number of points), and K𝐾Kitalic_K be a parameter for the size of a neighborhood**B**: These values were chosen by investigating a range in (20, 100) and selecting the best quantitative embedding.**C**: For trustworthiness, continuity, an... | CAB | CBA | BCA | ACB | Selection 4 |
**A**: Our idea is motivated by the previous analysis of low-rank MDPs (Cai et al., 2020; Jin et al., 2020b; Ayoub et al., 2020; Agarwal et al., 2020; Modi et al., 2021; Uehara et al., 2021)**B**: In particular, the state transition of a low-rank MDP aligns with that in our low-rank POMDP model. Nevertheless, we remark... | ACB | CBA | BAC | BCA | Selection 4 |
**A**: Table 1: We compare with most related representative works in closely related lines of research**B**: The first line of research studies offline RL in standard MDPs without any partial observability**C**: The second line of research studies online RL in POMDPs where the actions are specified by history-dependent... | ABC | ACB | CAB | ACB | Selection 1 |
**A**:
The asymptotics of second-order Newton’s methods for unconstrained problems have recently been investigated**B**: Bercu2020Efficient designed an online Newton’s method for logistic regression, and Boyer2023asymptotic generalized that method to general regression problems**C**: Compared to first-order methods th... | CBA | CAB | ABC | BCA | 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... | CBA | BAC | BAC | ACB | Selection 4 |
**A**: Table 1 shows that WaveMix is the current SOTA for Cityscapes dataset in terms of single scale inference mIoU among models pre-trained using only ImageNet-1k dataset. Higher mIoU reported by other models [1] belong to multi-scale inference**B**: Performance of WaveMix on Cityscapes validation set along with the ... | CBA | BCA | BCA | ABC | Selection 4 |
**A**: Let
R0′:={i|λi=0andi∉R0}assignsuperscriptsubscript𝑅0′conditional-set𝑖subscript𝜆𝑖0and𝑖subscript𝑅0R_{0}^{\prime}:=\{i|\lambda_{i}=0\>\hbox{and}\>i\notin R_{0}\}italic_R start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT := { italic_i | italic_λ start_POSTSUBSCRIPT italic_i ... | ABC | ACB | BAC | CAB | Selection 3 |
**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... | ABC | ACB | ABC | BCA | Selection 4 |
**A**: species) with the same block membership play the same social/ecological role in its system (Boorman and White,, 1976; Luczkovich et al.,, 2003). In food webs, species playing the same ecological role are said to be ecologically equivalent (see Cirtwill et al.,, 2018, for a review of species role concepts in food... | ABC | BAC | ACB | BCA | 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 | CAB | BCA | CBA | CAB | Selection 2 |
**A**: Training data contains samples from both the classes and a set of unlabeled samples**B**: In particular, we perform experiments when only 1%, 5% and 10% of the data is available (Figure 5.2).
It is important to note that, unlike PU Learning settings, here we perform downstream tuning only over the labeled data. ... | CBA | BCA | BAC | CBA | Selection 2 |
**A**:
There have been a wide range of approaches to generalize the SBM to multilayer networks**B**: In Valles-Catala et al**C**: (2016) a multilayer SBM is developed by fitting a new SBM to each layer, assuming that neither node-membership nor group-to-group connectivity is fixed across layers. Stanley et al. (2016) ... | CAB | ABC | CAB | CAB | Selection 2 |
**A**: johri2021nearest ,
and where necessary, the larger IonQ Aria machine with a capacity of 32 physical and 20 algorithmic qubits (ionq2022aria, )**B**: Experiments below used the 11-qubit trapped ion quantum computer described by Johri et al**C**: A crucial point to bear in mind with quantum computing is that the m... | BAC | BCA | ACB | BCA | Selection 1 |
**A**: The restructured graph with the best validation homophily is saved.
In the node classification phase, we use the same search range for common hyper-parameters: 0.1-0.9 for dropout rate, {32, 64, 128, 256, 512, 1024} for hidden layer dimension, {1, 2, 3} for the number of layers, and {0, 5e-5, 5e-4, 5e-3} for wei... | CBA | CAB | ABC | BAC | Selection 4 |
**A**: Dotted and dashed lines illustrate risk trajectories under three different slight perturbations from the initialization.
In Figure (b), the left plot illustrates the reduction in total risk over time. The dashed blue lines indicate when a new learner joins. The right plot shows the equilibrium-risk for a subset ... | CBA | ACB | ACB | BAC | Selection 1 |
**A**:
Table 3 reports the values of 𝚖𝚒𝚗𝚍𝚒𝚜𝚌βsubscript𝚖𝚒𝚗𝚍𝚒𝚜𝚌𝛽\texttt{mindisc}_{\beta}mindisc start_POSTSUBSCRIPT italic_β end_POSTSUBSCRIPT that correspond to the minimal unfairness value and the minimal error value for each of the classifiers given Inputs, using Alg. 2**B**: It can be seen that indeed... | ACB | ABC | CAB | CAB | Selection 1 |
**A**: Yet, if this assumption is violated, such as in random walk data, every elbow detection method is cursed to fail. We would expect such random series to contain no reasonable motif sets, and, in fact, the elbow function is a rather straight. Still, our method reports “elbows” at the points where the line is not c... | CBA | CBA | ABC | BAC | Selection 4 |
**A**: The stochastic spatio-temporal persistence of excitation condition on the conditional expectations of the regression matrices and graphs was proposed for the non-regularized decentralized online estimation algorithms over the random time-varying graphs in [37]-[38]. Specifically, the stochastic spatio-temporal p... | CAB | ACB | CBA | CAB | Selection 3 |
**A**:
In the numerical sections, we assess the effectiveness of our heuristics using two different approaches to inject inaccuracies into AAR**B**: The second approach projects the least-squares problem in the Anderson mixing computation onto a subspace, and the heuristics are used to dynamically adjust the dimension... | BAC | ACB | CAB | BCA | Selection 2 |
**A**: Finally, we review techniques for topic control in neural abstractive summarization.
**B**: Then, we discuss methods and models for improving abstractive summarization by incorporating topical information**C**: In this section, we first present an overview of related work in controllable text-to-text generation | CBA | ABC | BCA | BCA | Selection 1 |
**A**:
Neutral-atom quantum computers have demonstrated the ability to operate on thousands of qubits (e.g. [23])**B**: These qubits are typically arranged in planar 2D layouts, while 3D configurations are feasible but more challenging to engineer**C**: Notably, neutral-atom systems enable a technique called qubit shu... | BCA | BAC | ABC | CBA | Selection 3 |
**A**: Our deep learning model also performs the task considerably faster than simple biophysical models. To generate our data, we rely on MRiLab [7] which is a conventional MR image simulator. Source code is publicly available at https://github.com/Abhijeet8901/Deep-Learning-Based-MR-Image-Re-parameterization.
**B**: ... | BCA | CAB | CBA | ABC | Selection 3 |
**A**: In a recent study [33], the authors initially employ a fully connected neural network to approximate ρ𝜌\rhoitalic_ρ and subsequently utilize an additional ICNN to calculate the Wasserstein distance between two probability densities.**B**: However, it is often challenging to compute the Wasserstein distance betw... | BAC | ABC | CBA | ACB | Selection 3 |
**A**: First, it is easy to exhibit two persistence modules at matching distance zero but having arbitrarily large interleaving distance (see section 5.1)**B**:
Nevertheless, there are several bottlenecks to the fibered barcode approach**C**: Second, computing and storing an entire n𝑛nitalic_n-parameters persistence ... | BCA | ACB | ACB | BAC | Selection 4 |
**A**:
The results in this study are obtained using the algorithm implementations provided in version 4.7 of the bnlearn package [32, 35]. We use the default objective scores, conditional independence test functions and hyper-parameters222For score-based and hybrid algorithms, the default is to use the BIC score with ... | ACB | ABC | BCA | BAC | Selection 1 |
**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**:
A divisor f𝑓fitalic_f is called recurrent if there is a non-trivial chip-firing game starting from f𝑓fitalic_f that leads back to f𝑓fitalic_f**C**:... | ABC | ACB | BAC | CAB | Selection 4 |
**A**:
N-Caltech 101 The N-Caltech 101 [43] dataset is also converted from the original version of Caltech 101 [44] with a slight change in object classes to avoid confusion**B**: The N-Caltech 101 consists of 100 object classes plus one background class**C**: We apply the 9: 1 train-test split as CIFAR10-DVS. | BAC | BAC | BCA | ABC | Selection 4 |
**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**: 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 ,... | BCA | CBA | ABC | CAB | Selection 3 |
**A**: An action A has a RAW dependency (or equivalently, is RAW dependent) on action B if the execution of action A reads the storage written by action B111111It is important to note that this step excludes any tx.origin/msg.sender-related storage reads/writes, as such storage accesses do not alter the global state of... | CAB | BAC | ABC | CBA | Selection 4 |
**A**: We now consider the special case of the KDE (cf**B**: Section 2.2) with a Gaussian kernel function and an optimal selection of bandwidth, as calculated in the following lemma.
We focus on the Gaussian kernel both for simplicity and because it is a popular choice in practice**C**: Our method can be extended to an... | CAB | ACB | ABC | BAC | Selection 3 |
**A**:
Case 3: Non-anchor words that bias at a particular language space**B**: Besides, some diseases, drugs, or other medical entities may differ between languages, which causes some areas to be monolingual in the resultant bilingual word space. In this situation, given an English word, its Chinese neighbors have sig... | ACB | BAC | CAB | CBA | Selection 1 |
**A**: These patches are then embedded (patch-embeddings) and passed to the transformer layers conducting self-attention and feedforward operations**B**: ViTs operate by segmenting input images into smaller patches, treating each patch as a token similar to words in NLP**C**: Such a design allows ViTs to capture long-r... | CBA | ABC | BAC | CBA | Selection 3 |
**A**: Bounded model checkers such as ESBMC are now mature software, used industrially (Gadelha et al., 2018) and capable of finding bugs in production software. We leverage this power of model checkers as a method for seed generation**B**: During greybox fuzzing, if a particular branch has not been explored, ESBMC can... | ACB | ABC | BCA | BCA | Selection 2 |
**A**:
The bandwidths of the matrices follow from Lemmas 7 and 8**B**: To derive (29), we apply Proposition 6 to the JFP basis, then**C**: The first equation (28) follows immediately from the commutativity of fractional (and integer-order) integration matrices stated in (3) | ACB | BCA | BAC | BCA | Selection 1 |
**A**: This network is “56e9e0d7a6d70217090cdffa” in the data set.**B**: (𝐛)𝐛\bf{(b)}( bold_b ) The distribution of scores obtained by using the Random Forest classifier in the supervised approach**C**: Supplementary Figure S28: A real case that has achieved the mapping from the index value to the score.
(𝐚)𝐚\bf{(a... | CBA | BAC | ABC | CAB | Selection 1 |
**A**: For tinyML application VWW [20], our on-device finetuned model matches the accuracy of cloud training+edge deployment, and surpasses the common requirement of tinyML (MLPerf Tiny [8]) by 9%.**B**:
Our framework is the first solution to enable tiny on-device training of convolutional neural networks under 256KB ... | BCA | BAC | ACB | CAB | Selection 4 |
**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,... | ABC | CAB | BAC | ABC | Selection 2 |
**A**: We defer the details to Section 9 and note here only that the different rates of void mutations (mutations that create an offspring equal to the parent) of the different operators have a significant impact on the performance**B**: This suggests that a finetuning of the operators can give considerable performance... | CBA | BCA | BAC | CAB | Selection 2 |
**A**: We used PyLops222https://github.com/PyLops/pylops, the linear operator library for Python, for performing bilinear interpolation and its transpose operation**B**:
where BI(v)𝐵𝐼𝑣BI(v)italic_B italic_I ( italic_v ) is the standard bilinear interpolation operator**C**: Consequently, the grid transfer operator... | ABC | BAC | CAB | ABC | Selection 2 |
**A**:
Lower bounds for monotone models of computation have been proven for a variety of models [14], including monotone De Morgan555Circuits with AND as well as OR gates without negations**B**: Finally, our model of computation of neural networks with threshold gates differs from arithmetic circuits [38] which use ga... | ABC | BCA | ACB | CBA | Selection 3 |
**A**: In recent years, QMC methods have been demonstrated to be very effective at approximating the response statistics of PDE problems such as (1)**B**: The success of modern QMC theory for uncertainty quantification can largely be attributed to the introduction of weighted spaces (in the sense of Sloan and Woźniakow... | CBA | BAC | ABC | CBA | Selection 3 |
**A**: The Collaborative Learning Model.
Most study for BAI has been done in the centralized model, in which just one agent pulls the set of arms sequentially**B**: The learning proceeds in rounds.**C**: (TZZ19, ; KZZ20, ) studied BAI in the collaborative learning (CL) model, where there are K𝐾Kitalic_K agents, who ... | BCA | CAB | CAB | ACB | Selection 4 |
**A**: It is noteworthy that the aforementioned algorithms are applicable in (strongly) convex cases. However, within the nonconvex nonsmooth setting, the algorithm proposed by [71] stands out with global convergence guarantees when the nonsmooth term is convex.
**B**: To attain a superlinear convergence rate, the IQN ... | BCA | ACB | CBA | CAB | Selection 4 |
**A**: Then, we applied the truncated t-SVD with the mentioned tubal ranks to the underlying data tensors**B**: The running times of the proposed algorithm and the truncated t-SVD are reported in Table II**C**: It is seen that the Algorithm 4 outperforms the truncated t-SVD in terms of running time.
| BAC | CBA | ACB | ABC | Selection 4 |
**A**: The tree size is often a trade-off between accuracy and interpretability. Small trees are easy to interpret, but due to their simplicity may fail to capture interactions in the data and therefore provide a satisfactory accuracy**B**: On the other hand, larger trees may mitigate such issues, but at the cost of lo... | CBA | CAB | BCA | BAC | Selection 3 |
**A**: Hence, it depends on the degree of danger in which the collision is going to happen. Based on the intervention time per intervention count, it is obvious that Huang et al.’s model needs more correction time for each intervention which means that it has the highest danger level compared to DeepIPC and AIM-MT.
**B... | CAB | BAC | BCA | CBA | Selection 1 |
**A**:
However, it is not always the size of a bag that matters**B**: For example, suppose that every bag of the decomposition is a clique, that is, the graph is chordal**C**: Since every independent set intersects each of the clique-bags in at most one vertex, dynamic programming still computes maximum weight indepen... | ABC | BAC | CBA | BAC | Selection 1 |
**A**: We provide convergence analysis for VIMADMM and prove that it can converge to stationary points with mild assumptions.
With modifications of communication strategies and updating rules for servers and clients, we extend VIMADMM to the without model splitting setting and introduce VIMADMM-J.**B**: Compared to gra... | BCA | ABC | ACB | CBA | Selection 4 |
**A**: In order to intuitively understand our reinforced neighbor selection module, we design a robustness visualization experiment by showing the actions output by the policy network under different levels of noise added to the UCI dataset. As shown in Fig**B**: The noisy information would be blindly propagated to the... | ACB | CBA | BAC | BCA | Selection 1 |
**A**:
TABLE II: The rank-1 accuracy (%) on CASIA-BN-RCC for different probe views excluding the identical-view cases**B**: For evaluation, the sequences of NM-01,02,03,04 for each subject are taken as the gallery**C**: The probe sequences are divided into three subsets according to the walking conditions (i.e. NM, BG... | CBA | ABC | BAC | ACB | 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... | CAB | BCA | CAB | ABC | Selection 4 |
**A**: Despite the enormous variety of works regarding generative models, a recent convention is to classify deep generative models into three different directions [12, 14, 17]**B**: First, Generative Adversarial Networks (GANs) [3, 12, 29] benefit from adversarial training of two networks that contest to maximize its ... | BAC | ABC | CBA | BAC | Selection 2 |
**A**: Based on the above motivation and insights, this paper proposes a novel Calibrated One-class classification-based Unsupervised Time series Anomaly detection method (COUTA for short)**B**: The approach fulfills contamination-tolerant, anomaly-informed normality learning by two novel normality calibration methods,... | BCA | CAB | BAC | CAB | Selection 1 |
**A**:
Following the seminal work by Reiter and Dale (Reiter and Dale, 1997), the most comprehensive survey on D2T to-date has been that by Gatt and Krahmer (Gatt and Krahmer, 2018). Although several articles have taken a close examination of NLG sub-fields such as dialogue systems (Santhanam and Shaikh, 2019), poetry... | ABC | BAC | BAC | ACB | Selection 4 |
**A**: To verify the generalization ability of SGG models to be independent of the frequency bias, we report the performance of models with and without frequency priors in VG and VG-OOD datasets in TABLE II. It can be observed from TABLE II that the model can achieve significant gains on VG (e.g., 41.8% vs**B**: 40.9% ... | BAC | CAB | BAC | BCA | Selection 4 |
**A**: Not all the miners in the blockchain system are rational**B**: Thus, we discuss the fraction of rational miners to be no more than 50% in this paper.
**C**: Though we proposed some measures to address the concern of miners working on the attacker’s private branch, we take the possibility into consideration that ... | CBA | ACB | ABC | BAC | Selection 2 |
**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:... | CBA | BAC | BAC | BCA | Selection 1 |
**A**: We show that our diffusion reweighting procedure can be employed in manifold learning methods that use both eigendecomposition or divergence optimization**B**: We demonstrate the validity and relevance of our framework on both a simple model potential and high-dimensional atomistic systems.
**C**: Our general fr... | ACB | BAC | BCA | CAB | Selection 3 |
**A**:
To remove a pseudo-trifurcation, either the trifurcation point can be moved up (the initial bifurcation point is chosen as the trifurcation point) or moved down (the second bifurcation point is chosen). The choice does not impact the branching structure of the final tree but does impact how it occupies space, i... | ACB | CBA | BAC | BAC | Selection 1 |
**A**: In our model, we are incorporating path-to-node attention, but passively between the path’s structurally similar node and the targeting node, to tackle longer chains of dependencies in the test data. In addition, we do not build very deep GNN solutions to enlarge the receptive field up to the length of dependenc... | BAC | CBA | ACB | BAC | Selection 2 |
**A**: In addition, we consider an episodic setting distinct from the infinite-horizon setting in the aforementioned work. On the other hand, the existing work on low-rank MDPs only focuses on a single-agent setting. Our analysis further considers a Markov game setting where a natural challenge of non-stationarity aris... | CBA | CAB | BAC | ABC | Selection 2 |
**A**:
As a proof of concept, we numerically solve the 1D (d=1𝑑1d=1italic_d = 1) control PDE arising from the Kuramoto model (5) using finite differences and extend the solution to the entire domain using linear interpolation**B**: In such cases, model reduction techniques (Hartmann et al., 2016, 2015) or solving the... | BCA | BAC | ABC | ACB | Selection 4 |
**A**: The intention is only to showcase that the architecture proposed in this study aligns well with the values expected within a similar kind of mission within the current paradigm**B**: Of course, real missions have a lot more requirements, including very strict scientific requirements, that may impose a high burde... | BCA | ABC | CBA | ABC | Selection 1 |
**A**:
The simplicity of the map (1.4) makes a fixed-point approach particularly attractive, since it avoids expensive Riemannian operations such as exponential maps and parallel transports, which are required by standard Riemannian optimization approaches**B**: Empirically, iterating map (1.4) exhibits linear converg... | ABC | CAB | BCA | BCA | Selection 1 |
**A**: ≥\geq≥ order) leads to different filtration types. Intuitively, as the weight threshold increases, simplices with lower importance (based on the weight function) are removed. This reveals a coarser version of the original complex at each step, where only the most significant simplices remain.**B**:
A filtration... | ABC | BAC | CAB | BAC | Selection 3 |
**A**: In this paper, we deal with the SSDG task from the multi-task learning perspective.
**B**: However, StyleMatch cannot effectively address the interference of different domains during pseudo-labeling**C**: To handle this task, Zhou et al. (DBLP:journals/corr/abs-2106-00592, ) propose StyleMatch, a simple approach... | ACB | ABC | BAC | CBA | Selection 4 |
**A**: Conv-Adapter also well generalizes to detection and segmentation tasks that require dense predictions.**B**:
Extensive experiments demonstrate the effectiveness and efficiency of Conv-Adapter**C**: It achieves comparable or even better performance to full fine-tuning with only around 5% backbone parameters | ABC | ABC | ACB | CAB | Selection 4 |
**A**:
In this work, we present an innovative methodology that combines changepoints detection with PINNs to address changes and instabilities in the dynamics of PDEs. This approach marks the first exploration into simultaneously detecting changepoints and estimating unknown parameters within PDE dynamics based on obs... | CBA | ACB | CAB | ABC | Selection 2 |
**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... | BCA | ACB | ABC | BAC | Selection 4 |
**A**: Although the method in Grana et al. (Grana et al., 2011) based on the Otsu algorithm (Otsu, 1979) showed good results on subsets of the dataset, it was not adaptable to the entire corpus due to the varying preservation status and background colors of the manuscripts. Furthermore, we tested pre-trained models of ... | CBA | BCA | ACB | BAC | Selection 1 |
**A**: Specifically, taking the “MNLI, QNLI, QQP" as source tasks, we report the PoT results of BERT-base on 9 target tasks in Table IV-C**B**:
In addition to the above single source-target PoT scenarios, we also conduct experiments to verify the effectiveness of PanDa in the multiple-task scenarios**C**: For referenc... | ABC | BAC | BCA | BCA | Selection 2 |
**A**: The conflict caught the attention of existing defacers, who performed many attacks against other countries but not Russia and Ukraine until just after the invasion, suggesting their choice of targets was influenced**B**:
Defacement Motives**C**: We also found some ‘new faces’ e.g., the second most active deface... | ABC | ACB | BAC | ABC | Selection 3 |
**A**:
For the attacks, we use FGSM (Goodfellow et al., 2014), PGD (Madry et al., 2017) and PGD+Momentum (denoted as Momentum) (Dong et al., 2018), which should have increased transferability according to (Xie et al., 2019)**B**: All of these algorithms are considered accepted baselines when evaluating adversarial att... | ABC | CAB | BAC | CBA | Selection 1 |
**A**: An alternative is to allow the data embedding itself to be parametrized and then train the embedding. Such strategies have been shown to improve generalization of the kernel-based quantum model [75, 69]. We note that this is an additional process to train and select an appropriate embedding before implementing ... | CBA | BCA | BCA | ABC | Selection 1 |
**A**: Simonyan et al. [26] proposed a two-stream based approach called C2D where a spatial stream takes still frames as input and a temporal stream takes multi-frame dense optical flow as input. Later, 3D convolution for action recognition was introduced by Tran et al. in 2015 [27], and since, 3D convolution methods h... | ACB | ABC | BCA | CAB | 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 | ABC | CAB | CAB | BCA | Selection 1 |
**A**: This is mainly due to the weak relation between different tasks. Second, SoftCPT uses a linear sub-network to convert task features to context. However, this linear layer has a relatively large number of parameters.
**B**: The proposed method is not without flaws**C**: First, although SoftCPT can still improve t... | CAB | BCA | BCA | BCA | Selection 1 |
**A**: The label distribution is long-tailed at each domain, and each domain has a different label distribution, hence it naturally has significant label distribution shifts, ideal for the challenging scenarios that LCS describes. We select four domains from the original data, L28, L43, L46, and L7, which share the sam... | ABC | BAC | CBA | ACB | Selection 3 |
**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... | CBA | ABC | BCA | ABC | Selection 1 |
**A**: in the case of 15 channels, there are 215−1superscript21512^{15}-12 start_POSTSUPERSCRIPT 15 end_POSTSUPERSCRIPT - 1 bundles for each tenant. In such cases the optimal allocation can not be determined due to computational limitations, but the efficiency of the allocation determined by the CA based method may be ... | CAB | BCA | BAC | ABC | Selection 1 |
**A**: 7. TCVAE using TA predicts the 25th𝑡ℎthitalic_t italic_h day CC with a relative error of 19.72%percent\%% from the actual value, whereas using THA, predicts the cases closer to the actual value (6.30%percent\%%). THA receives more attention scores concentrated in the later part of the window and captures the f... | CAB | CBA | BAC | ABC | Selection 2 |
**A**:
Figure 6: Comparison of the results on dSprites dataset obtained from SDE-VAE and DynAE with three ground-truth factors (Scale, X-Position, Y-Position) of the dataset. a. Reconstructions of dSprites dataset (top: SDE-VAE, bottom: DynAE)**B**: b. Recovering the latent representation from the sequential dSprites ... | ABC | CAB | ACB | CBA | Selection 3 |
**A**: Omnidirectional UAVs [197] is another interesting technology that opens new research opportunities in CaTP**B**: These UAVs can control its position and orientation independently, as opposed to underactuated multirotor UAVs which are the focus of most of the current research in CaTP nowadays**C**: This new capab... | CAB | BCA | ABC | CBA | Selection 3 |
**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 | ABC | BAC | 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... | CBA | ABC | BAC | BCA | Selection 4 |
**A**: 1) As shown in Figure 1(a), more popular items will get higher ranks and more exposure opportunities, while having similar quality; and 2) items with lower quality but high popularity might receive more exposure**B**: For example, for items that receive similar post-click behaviors (e.g., the same number of like... | ABC | BAC | CAB | BCA | Selection 1 |
**A**: Experimental results have demonstrated the superior generalization capability of our scene statistic-based model, especially in cross-dataset settings. The proposed method sheds light on the exploration of the intrinsic statistics of SCIs and provides potential guidance for high-quality image generation with com... | CAB | ABC | BAC | CBA | Selection 4 |
**A**: Similarly to edge homophily, node homophily does not satisfy the asymptotic constant baseline and thus is incomparable across different datasets.**B**: It is empty class tolerant, but not monotone: adding an edge between two perfectly homophilous nodes of the same class does not change node homophily**C**:
Node... | ABC | BAC | CAB | CBA | Selection 4 |
**A**: Apart from technical challenges that arise from the fact that the underlying Hilbert space can be infinite-dimensional and the operators involved can be unbounded, most proofs run parallel to the ones for qubits once the appropriate assumptions are identified**B**: In this section, we generalize the main results... | BAC | ACB | CBA | CAB | Selection 1 |
**A**: For simplicity, we omit detailed declarations of low-level types like RCHash and Packet in Processor and Envir.**B**: Other fine-grained types of status can be found in Fig. 5**C**:
Note that OK, ERR, and ATTK are refined to more fine-grained status types using prefixing, e.g., CHALRESP_ATTK indicates the occur... | BAC | ACB | CAB | CBA | Selection 4 |
**A**: [Willems and Trentelman, 1998], dynamic supply rates of quadratic differential forms are considered and physically motivated by numerous
examples**B**: Notably, dynamic supply rates for differential dissipativity [Forni and Sepulchre, 2013, Forni and Sepulchre, 2018, Verhoek et al., 2023], differential passivity... | BAC | BAC | ACB | ABC | Selection 3 |
**A**: There are various types of CBFs, the most commonly used currently are a reciprocal control barrier function (RCBF) [2, 4, 5] and a zeroing control barrier function (ZCBF) [2, 3, 6]: the RCBF is a positive function that diverges from the inside of the safe set toward the boundary, while the ZCBF is a function th... | CBA | ACB | BAC | BCA | Selection 4 |
**A**:
Through a combination of two simulation evaluations and one online user study, we have shown the positive impact of our PoLMDP framework**B**: 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 e... | CBA | ACB | BCA | ABC | Selection 4 |
**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 | BAC | ABC | ACB | BCA | Selection 3 |
**A**: In a single-device-infinite-bus system, the power grid strength can be effectively characterized by SCR, which reflects the distance between the device and the infinite bus (an ideal voltage source)**B**: The concept of SCR was originally proposed for conventional LCC-HVDC transmission systems, where the SCR of ... | BAC | ABC | BCA | ABC | Selection 3 |
**A**: DeepTVARwT vs other deep learning based models**B**: Compared with DeepAR and DeepState, our model produced more accurate point forecasts at almost all forecasting horizons for all the series (except h=4ℎ4h=4italic_h = 4 for Tropics)**C**: Our model resulted in better prediction intervals at all forecasting hori... | BCA | ABC | BAC | CAB | Selection 2 |
**A**: Datasets. For the empirical analysis, we test on the standard OSR datasets as described in Protocol A of Sec**B**: The unknown class can be constituted by a diverse set of semantic classes, but is regarded as a single chunk. The known classes must have no semantic overlap with the unknown class.
**C**: 6.1. Each... | BCA | ACB | ABC | CAB | Selection 2 |
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