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<|MaskedSetence|> (2008); Hernandez-Leal et al. (2020). <|MaskedSetence|> A simple approach to multi-agent learning was proposed by Tan (1993): the IQL algorithm uses independent Q𝑄Qitalic_Q learners for each agent, with improvements proposed in Foerster et al. <|MaskedSetence|> | **A**: For a general overview of multi-agent reinforcement learning (MARL) we refer to Busoniu
et al.
**B**: Unsurprisingly, the development of MARL methods is closely coupled with the algorithmic progress in RL.
**C**: (2017); Lauer and.
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<|MaskedSetence|> <|MaskedSetence|> Then, they proceeded to use the data set described in Section System Overview and Use Case. They were asked to answer five questions (cf. Table 1 for a summary). <|MaskedSetence|> Finally, the participants were requested to provide qualitative feedback via the ICE-T questionnaire.... | **A**: The original document containing the questions and instructions as shown by the attendees is available in the supplemental material accompanying this paper.
**B**:
Methodology and Instructions.
**C**: Initially, participants watched an ≈\approx≈18-minute video tutorial about bagging and boosting concepts, Vis... | BCA | BCA | BCA | BCA | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> [16] showed that space-time block coding (STBC) with single polarization outperforms STBC with dual polarization in Rayleigh and Ricean fading channels. A MIMO system with dual-polarized antenna elements can have lower spatial diversity but higher spatial multiplexing gain than a c... | **A**: It is noteworthy that the extent of benefit from dual-polarized antennas depends on the associated schemes to exploit the characteristics of polarized wireless channel [15, 16, 17, 1, 6].
**B**: Ref.
**C**: Various other aspects of polarization in MIMO systems have been investigated as well.
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Self-supervised learning (SSL) leverages information in data itself as the supervision, providing a solution for training from unlabeled data. <|MaskedSetence|> <|MaskedSetence|> Most existing methods, like MoCo [7, 12], SimCLR [5, 6],
BYOL [9] and BarlowTwins [44], are designed and optimized in instance-level compar... | **A**: Contrastive learning aims to pull the representations of similar samples closer, and dissimilar ones far apart.
**B**: Recently there are some pixel-level methods attempting to learn dense feature representations.
**C**: Among all varieties of SSL, contrastive learning (CL) is one of the most powerful paradigm... | CAB | CAB | CBA | CAB | Selection 2 |
From now on, we use the number of communities determined by KDFSP in Table 2 for each data to estimate community memberships. <|MaskedSetence|> We see that DFSP returns larger fuzzy weighted modularity than its competitors except for the Karate-club-weighted network. <|MaskedSetence|> Furthermore, the running times o... | **A**: We compare the fuzzy weighted modularity of DFSP and its competitors, and the results are displayed in Table 3.
**B**: Hence, DFSP runs faster than its competitors.
.
**C**: Meanwhile, according to the fuzzy weighted modularity of DFSP in Table 3, we also find that Gahuku-Gama subtribes, Karate-club-weighted, ... | ACB | ACB | BAC | ACB | Selection 2 |
Figure 2: The effectiveness of directly the mimicking the representations of the oracle model at the initial phase. (a) Initially trained on 50 classes, and then incremented with 10 classes per phase for 5 more phases. <|MaskedSetence|> <|MaskedSetence|> We show the accuracy of each CIL phases. <|MaskedSetence|> | **A**: The regularization coefficient β𝛽\betaitalic_β is defined in Eqn. (1).
**B**: (b) Initially trained on 10 classes and then incremented with 10 classes per phase for 9 more phases.
**C**: Results are averaged over 3 runs..
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<|MaskedSetence|> <|MaskedSetence|> The first one was particle swarm optimization, which is a computational method that optimizes a problem by iteratively improving a candidate solution according to a given measure of quality. The second optimization method that was examined was the downhill simplex method which is a... | **A**: In the non-rigid registration step, two optimization methods were investigated.
**B**: Team SuperX
This method consists of two steps i) A rigid registration method, the Nelder-Mead method (also named downhill simplex method) and ii) Affine transformation for rigid registration algorithm which was applied on flo... | BAC | ABC | BAC | BAC | Selection 4 |
Unfortunately, real-life databases are often inconsistent, i.e., do not conform to their specifications in the form of integrity constraints. The reason behind this is that data is not perfect and clean; it may come, for example, from several conflicting sources [5].
Data cleaning attempts to fix this problem by resolv... | **A**: Furthermore, there are scenarios like virtual data integration, where the data stays at the autonomous data sources, in which there is no way to modify the data without having ownership of the sources.
**B**: However, this may lead to the loss of valuable information.
**C**: In other words, we need a mechanism... | BAC | BAC | BAC | ABC | Selection 1 |
In the present paper, we examine absorbing random walks on graphs in which different nodes can have different absorption rates, inducing an “effective” network structure that is reflected only partially by the edge weights of a network. <|MaskedSetence|> <|MaskedSetence|> Communities are a common feature of many rea... | **A**: Many notions of network community structure arise from the analysis of random walks [12, 25], and we expect different types of random walks to yield different community structures [17, 18].
**B**: A “community” in a network is a tightly knit set of nodes that is connected sparsely to other tightly knit sets of ... | ABC | ABC | BAC | ABC | Selection 2 |
<|MaskedSetence|> In this regard, Corso and Kochenderfer [141] present a technique to identify interpretable failures of autonomous cars. <|MaskedSetence|> For this purpose, the authors use genetic programming to optimize signal temporal logic expressions that acquire disturbances trajectories causing a vehicle to fa... | **A**: .
**B**: They use signal temporal logic expressions to
describe failure cases of an autonomous car in unprotected left turn and pedestrian crossing scenarios.
**C**: While the interpretability of a deployed autonomous driving control model has been the dominant direction for research, there have also been atte... | CBA | CBA | CBA | ACB | Selection 1 |
Similar to the basic residual block in ResNet, the Ghost bottleneck consists of two stacked Ghost modules, as shown in Fig. 2. The first Ghost module is used as an expansion layer, increasing the number of channels, and the second Ghost module reduces the number of channels to match the shortcut. Then, the input and t... | **A**: If SE=1, the squeeze-and-excitation (SE) module is selected.
**B**: Depthwise convolution processes the image from each channel simultaneously, and the number of feature maps after this operation is the same as the number of input channels.
**C**: If Stride=2, a depthwise convolution layer is inserted between ... | CBA | CAB | CBA | CBA | Selection 1 |
All the counterfactual best response values are 0, and we assume both players played perfectly before the depth limit. Hence, we do not need to offset any node in the (reach) max-margin gadget. <|MaskedSetence|> We add the initial decision node and the chance nodes (since there is only one state in each information s... | **A**: When we solve the gadget, player 1111 will pick action Q𝑄Qitalic_Q, and the gadget value will be 0.
**B**: Then, both gadget constructions are identical.
**C**: However, when player 1111 deviates to action P𝑃Pitalic_P, player 2222 now has a choice between terminal utilities and picks action E𝐸Eitalic_E to r... | BCA | BAC | BAC | BAC | Selection 2 |
The specific objective of the study is the impact of environmental variables on migration, thus on the right-hand side of the regression a proxy of the environmental change is included. Slow-onset events are typically defined as gradual modifications of temperature, precipitation, and soil quality. Respectively, three... | **A**: A dummy capturing whether the coefficient refers to multiple disasters is also included..
**B**: The main classification of fast-onset events reflects the one reported in Section 2: geophysical (earthquakes, mass movements, volcanic eruptions), meteorological (extreme temperature, storms - cyclones, typhoons, h... | CBA | CBA | CBA | CBA | Selection 2 |
To tackle the difficulty, our primary idea is to facilitate collaboration between the meta and base levels. <|MaskedSetence|> <|MaskedSetence|> To address this issue, we introduce correction terms to the feedback loss and optimism in the meta-algorithm. This generates a new negative term that, together with the nega... | **A**: Specifically, we aim to leverage negative terms from both levels to handle the positive term.
**B**: Nevertheless, another new positive term emerges due to the injected correction, which we ensure can be managed by the negative term from the base level.
**C**: However, it turns out that the positive term canno... | CAB | ACB | ACB | ACB | Selection 3 |
The sum (9) does not uniquely define networks. Like the toy example in Figure 5, we can have many topologically equivalent brain networks that give the identical distance. Thus, the average of two graphs is also not uniquely defined. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> | **A**: The situation is analogous to Fréchet mean, which frequently does not result in a unique mean(Le and Kume, 2000; Turner et al., 2014; Zemel and Panaretos, 2019; Dubey and Müller, 2019).
**B**: We introduce the concept of the topological mean for networks, defined as the minimizer according to the Wasserstein di... | ABC | ABC | ABC | BCA | Selection 2 |
<|MaskedSetence|> We consider a scenario where an adversary injects a cyberattack in the form of a disturbance to the battery module to induce overdischarge. The disturbance is injected at 700s700𝑠700s700 italic_s as current drain from the module which forces the State-of-Charge (SOC) of the battery to reach zero. T... | **A**:
Next, we present a test case to illustrate the performance of the proposed approach under disturbance.
**B**: 3.
**C**: It can be seen that the disturbance was initiated around 700s700𝑠700s700 italic_s, and consequently, after 1098s1098𝑠1098s1098 italic_s the modules goes into the overdischarge mode by cr... | ABC | ABC | ABC | ACB | Selection 3 |
<|MaskedSetence|> Behaviors that indicate IOD-ID can involve cursing a co-worker, playing pranks, or making fun of someone. <|MaskedSetence|> On the other hand, OCB is a measure of “good” behavior. <|MaskedSetence|> Several studies have focused on these measures.. | **A**: Behaviors that indicate IOD-OD can be tardiness or absenteeism, leaving work early without permission, putting little effort into work, among others [56].
**B**:
In the behavioral side of workplace performance inventories, IOD-ID and IOD-OD are measures of “bad” conduct in the workplace.
**C**: OCB is compose... | BAC | BAC | BAC | BAC | Selection 3 |
Collecting Training Samples. <|MaskedSetence|> In SS-Setting, a training sample is comprised of spectrum sensors’ received power readings. The location of entities is available by using a GPS dongle connected to the laptops as described below, and the sensor’s received power is computed as follows. <|MaskedSetence|> ... | **A**: Then, we compute the area under the PSD curve over the 1 MHz channel of interest (see below), and finally, convert the computed area to an appropriate unit.
.
**B**: First, we compute an FFT on the I/Q samples collected within a time window to get a power spectral density (PSD) plot.
**C**: Recall that a sampl... | ACB | CBA | CBA | CBA | Selection 3 |
To a human eye, two figures look the same if they are related by a rigid motion. <|MaskedSetence|> This group is called the special Euclidean group and is denoted by SE(2)𝑆𝐸2SE(2)italic_S italic_E ( 2 ). In many applications, the congruence with respect to other groups is considered. For example, two shadows cast... | **A**: See [13] for an excellent exposition of the roles played by projective, (special) affine, and (special) Euclidean transformations in computer vision.
**B**: However, since a reflection changes the orientation of an object, a group of orientation-preserving rigid motions, consisting of rotations and translations... | BCA | BCA | BCA | ACB | Selection 3 |
A substantial review of variants of coordinate descent algorithms can be found in [4, Section 6.5.1]. <|MaskedSetence|> On the other hand, the use of an irregular order is then considered by researchers to accelerate convergence. <|MaskedSetence|> Obviously, this is not guaranteed for each instance of the algorithm.... | **A**: The cyclic selection of coordinates is normally assumed to ensure convergence of the algorithm.
**B**: Particularly, it is shown in [31] that randomization leads to faster convergence in terms of expectation.
**C**: The Gauss-Southwell method leads to faster convergence at the cost of extra computations and e... | CBA | ABC | ABC | ABC | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> simulated memristor networks able to learn sequences of inputs [5]. Suri et al. used another simulated network of phase-changing memristors to learn MNIST letters[6]. Systems that perform STDP (Spike Time Dependent Plasticity) using RRAM (REsistive RAM) devices [... | **A**: Following this paradigm, memristors have been applied to many network-based computational approaches.
**B**:
Many researchers have taken an interest in memristive devices, given their ability to implement tunable nonvolatile weights similar to synaptic efficacy in biological synapses.
**C**: Doygu et al.
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Shiragur (2015). <|MaskedSetence|> <|MaskedSetence|> Hence, it is reasonable to assume that such states represent a stable configuration of the system. Strictly speaking, however, in a δ𝛿\deltaitalic_δ-stable state the HKS might not be stabilized entirely in the sense that agents are unable to achieve further improv... | **A**: Intuitively, in such a state each agent has a small incentive to further revise the opinion.
**B**: A HKS is in a δ𝛿\deltaitalic_δ-stable state if and only if each edge in the influence network has length at most δ𝛿\deltaitalic_δ.
**C**: We note, however, that this is a condition shared by the vast majority ... | BAC | BCA | BAC | BAC | Selection 1 |
Thoracic diseases pose a significant threat to the global population, with recent events such as the COVID-19 pandemic causing respiratory illnesses worldwide (He et al. <|MaskedSetence|> <|MaskedSetence|> It is estimated that more than 2 billion chest radiography procedures are performed annually (Raoof et al. (2012... | **A**: In addition to COVID-19, chest radiography plays a crucial role in screening common thoracic diseases, including pneumonia, cardiomegaly, and pneumothorax.
**B**: These systems have the potential to assist medical professionals and reduce diagnostic errors.
.
**C**: (2020)).
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<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> 1998). A Bayesian algorithm is initialized with a prior belief, and the forecaster learns from each sample to build a posterior belief. Given that, we know the dynamics of the posterior belief as a distribution, exact minimization of the Bayes risk can be formula... | **A**: Popular algorithms for fixed-budget identification include successive rejects (SR; Audibert et al.
**B**: 2010) and successive halving (Karnin et al., 2013).
**C**: There are also several Bayesian algorithms that utilize a prior, such as top-two Thompson sampling (Russo, 2020), knowledge gradient (KG; Gupta an... | ACB | ABC | ABC | ABC | Selection 4 |
The first consists in learning an approximate group action in order to match the input and the reconstructed data.
For instance, Mehr et al. (2018b) propose to encode the input in quotient space, and train the model with a loss that is defined by taking the infimum over the group G𝐺Gitalic_G. While this is feasible fo... | **A**: (2018); Koneripalli et al.
**B**: While this is in spirit with our work, their transformations are local (we focus on global transformations) and are approximative, that is, the components are not explicitly invariant and equivariant with respect to the transformation, respectively..
**C**: Other work Shu et a... | CAB | CAB | CAB | CBA | Selection 1 |
<|MaskedSetence|> The first comes from the TROPOspheric Measuring Instrument (TROPOMI) aboard the Sentinel-5P satellite from the EU’s Copernicus programme (Copernicus 2018). The data set consists of 1083 images of 28x28 pixels, each giving the NO2 concentration in mol/m2molsuperscriptm2\mathrm{mol/m^{2}}roman_mol / ro... | **A**: We use the same division as in Hellan, Lucas, and Goddard (2020).
**B**: Two data sets are used for evaluation.
**C**: The images are from October and November 2018, and have been selected for higher pollution concentrations.
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In addition, we discuss a multihomogeneous generalization of the Hurwitz form. <|MaskedSetence|> Contrary to the homogeneous case, multigraded Chow forms and Hurwitz form require a choice of a non-degenerate multidimension vector for the linear subspace, in a sense that is discussed in Section 4. <|MaskedSetence|> In... | **A**: This set of non-degenerate dimension vectors gives rise to an interesting combinatorial structure, namely a polymatroid.
**B**: To the best of our knowledge, our paper provides the first result in this area.
**C**: We discuss this combinatorial structure in Section 5.
.
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Additionally, two in-house sensory systems are employed. On the one hand, the Bindi’s bracelet [28] measures dorsal wrist BVP, ventral wrist GSR, and forearm SKT. <|MaskedSetence|> The previously mentioned BioSignalPlux toolkit is employed as a golden standard to analyze the performance of its sensors due to its exper... | **A**: The hardware and software particularities of this device are detailed in [29, 30, 31].
**B**: On the other hand, a GSR sensor to be integrated into the next version of the Bindi bracelet is used.
**C**: Its hardware and software particularities are detailed in [32].
.
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Denoising block: Adversarial attacks often involve adding small amounts of noise to the input features to deceive the network into making incorrect predictions. <|MaskedSetence|> This technique involves adding a noise filter to the network that removes noisy input features before they are processed by the network. <|... | **A**: They design filters like mean filtering, median filtering, non-local means and bilateral filtering and add them as a denoising operation at any level of the network.
**B**: Xie et al.
**C**: Feature denoising can help to make the network more robust to these attacks by removing the noise from the input feature... | CBA | CBA | CBA | BAC | Selection 3 |
Implementation. <|MaskedSetence|> <|MaskedSetence|> Our plant model includes an industry-grade AD simulator [134] and real L4-capable AD vehicles (Fig. 4). For bridge, we reuse the ones (Python APIs, CyberRT, and ROS) provided by the AD simulator. <|MaskedSetence|> For metrics, we implemented collision and STOP sign... | **A**: Specifically, we modified the AD simulator, bridge, and modular AD pipeline to enable the 3 general attack/defense interfaces mentioned above.
**B**: The AD model supports modular AD pipelines for STOP sign attack evaluation (Appendix -C) and full-stack AD systems (Apollo [101] and Autoware [102]).
**C**: We h... | CBA | CAB | CBA | CBA | Selection 3 |
<|MaskedSetence|> Its long intervals terminate at 8.58.58.58.5 and start from 10.510.510.510.5. <|MaskedSetence|> Its long intervals terminate at {12,15.5}1215.5\{12,15.5\}{ 12 , 15.5 } and start from {17.5,21}17.521\{17.5,21\}{ 17.5 , 21 }. It also contains a Red join gadget in [1.5,4.5]1.54.5[1.5,4.5][ 1.5 , 4.5 ].... | **A**: The fifth buffer contains the second part of the second switch gadget in [8.5,11.5]8.511.5[8.5,11.5][ 8.5 , 11.5 ].
**B**: It also contains the third switch gadget in [12,21]1221[12,21][ 12 , 21 ].
**C**: Its long intervals terminate at 1.51.51.51.5 and start from 4.54.54.54.5.
.
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Recently, there has been a trend towards CNNs without BN. <|MaskedSetence|> [9] discuss disadvantages of BN and propose a class of normalizer-free networks NFNet. <|MaskedSetence|> <|MaskedSetence|> It replaces BatchNorm with LayerNorm although this was not the focus of the paper.
. | **A**: They, however, require custom optimizers and are sensitive to hyperparameters.
**B**: Brock et al.
**C**: Finally, ConvNeXt [50] is a CNN competitive with Vision Transformers [17].
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This paper is based on two insights. <|MaskedSetence|> Second, CL and ML show a mismatch between two similarity distributions of sampled pairs and all negative pairs. Based on these insights, we developed UNPG by combining two PG strategies (MLPG and CLPG) to alleviate the mismatch. Filtering was also applied to remov... | **A**: First, from a unified perspective, CL and ML have the same purpose of approaching WDFS, except for PG.
**B**: It was observed that UNPG increases the ability to learn existing FR models compared to MLPG and CLPG by providing more informative pairs.
**C**: Finally, we suggest two research directions in FR: 1) p... | ABC | ABC | ABC | ABC | Selection 4 |
The first experiment employed FID to compare StyleGAN2-ADA and nine distinct GAN models. <|MaskedSetence|> <|MaskedSetence|> The training step used the ADAM[50] optimizer with a learning rate of 0.00020.00020.00020.0002 and decay rates of 0.50.50.50.5 and 0.9990.9990.9990.999 regarding the generator and the discrimi... | **A**: The following models were considered in the experiment: Deep Convolutional GAN (DCGAN) [41], Least Squares Generative Adversarial Networks (LSGAN) [42], Wasserstein GAN (WGAN) [43], Wasserstein GAN with Gradient Penalty (WGAN-GP) [44], Deep Regret Analytic Generative Adversarial Networks (DRAGAN) [45], Energy-ba... | ACB | CBA | ACB | ACB | Selection 1 |
There are additional complications in real-world implementations of Elo. For legibility and practicality, fractional and negative rating points are avoided by scaling and shifting points up and rounding to the nearest integer, and by imposing an artificial floor on possible ratings (by gifting a player points if they w... | **A**: All these details and even more complications are outlined thoroughly by different organizations implementing Elo; one may read about them in, for example, the FIDE Handbook [4]..
**B**: Sometimes rating updates are batched.
**C**: Often times all the games played at a single tournament are batched together in... | BCA | ABC | BCA | BCA | Selection 4 |
To understand if a new round of undersampling would be beneficial, we activate the OSS algorithm again with the same settings (step 7). However, the outcome is to decrease the relatively safe population that much, so that the result is becoming worse. <|MaskedSetence|> Moving on to the oversampling phase, we aim at ut... | **A**: The oversampling algorithm should not eternalize this confusion.
**B**: From Figure 6(f), we can understand that several problematic outliers are not considered for removal at all by the OSS algorithm during the previous phase.
**C**: Therefore, we disable the algorithm and stop the undersampling phase (step 8... | CBA | BCA | CBA | CBA | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> In finance, frontrunning is an act of purchasing stock or other securities right before a large (whale) transaction owing to access to non-public information. <|MaskedSetence|> Frontrunning has been classified as illegal by monitoring entities, such as the U.S. Securities and Exch... | **A**: By doing so, one can take advantage of the outcomes of large unprocessed transactions to be executed after a later time than one’s own.
**B**: With the recent developments, many real-world financial products such as money lending/borrowing, margin-trading, exchange platforms, derivatives and more, are being mad... | BCA | BCA | ACB | BCA | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> In some applications, strategic behavior may be a form of “gaming the system,” e.g. cheating on exams in the context of college admissions, and the decision maker may not want to assign treatment to agents who have high ability to modify their covariates. <|MaskedSetence|> Lastly,... | **A**: We adopt a flexible model where agents are heterogenous in their raw covariates and their ability to modify them.
**B**: Depending on the context, strategic behavior may be harmful, beneficial, or neutral for the decision maker.
**C**: In other applications, the decision maker may want to accept such agents be... | ABC | ABC | ABC | BCA | Selection 1 |
Architectures. ResNet-18 is used as the standard baseline architecture for our studies. <|MaskedSetence|> To create this architecture, we add early exit modules to each of ResNet-18’s convolutional blocks. <|MaskedSetence|> Assuming 1000 output classes, ResNet-18 has 12M parameters compared to 8M in OccamResNet-18. ... | **A**: Further details are in the appendix..
**B**: We compare it with an OccamNet version of ResNet-18, i.e., OccamResNet-18.
**C**: To keep the number of parameters in OccamResNet-18 comparable to ResNet-18, we reduce the feature map width from 64 to 48.
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To improve segmentation using transformers, some methods [89, 97, 52, 98, 99, 100, 101] have been developed. SETR [89] and Panoptic SegFormer [99] are the first transformer-based models for image and panoptic semantic segmentation, respectively. Generally, these works use transformers to generate global-context-aware f... | **A**: However, MRCFA specifically focuses on refining feature affinity maps, while this paper focuses on learning local and global temporal contexts for the video..
**B**: For video understanding, [103] and [104] exploit transformers to merge temporal information and achieve promising results on the video panoptic se... | CBA | CBA | CBA | CAB | Selection 1 |
Overall overhead is shown in Figure 20.
This overhead for Hotline includes online profiling and is minimal, primarily because online profiling done at the start of training is not hidden under GPU execution. <|MaskedSetence|> <|MaskedSetence|> This results in fast online profiling. <|MaskedSetence|> Users can specif... | **A**: Still, all subsequent profiling is hidden under GPU execution, significantly reducing overhead.
**B**: In our evaluation, we transitioned to the access learning phase twice within a single epoch.
**C**: Also, the lookup engine parallelizes the input accesses from EAL for embedding indices.
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Unsurprisingly, the key to understanding how the topology of sublevel sets changes as one varies the threshold are the PL analogues of points where the gradient of the function vanishes, cf. <|MaskedSetence|> These are the so-called flat or constant cells (Definition 3.7), which map to nontransversal thresholds (cf. ... | **A**: Lemma 2.1).
**B**: Theorem 1.
**C**: For experts familiar with the ways ReLU neural network functions can degenerate, Lemma 3.12 should provide a clear explanation for why most ReLU neural network functions are not PL Morse..
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In [15] the machine learning methods was merely applied in the unitary dynamics without phase transitions. Critical dynamics, i.e., the dynamics across the critical point of a phase transition is more complex and has richer phenomena [39]. Critical slowing down near the phase transition point may invalidate the applic... | **A**: In the machine learning we introduce the Restricted Boltzmann Machine (RBM) as a representation of the quantum state for TFQIM.
**B**: By computing the first three cumulants of the kink pair numbers, we find that they satisfy a universal power-law scalings to the quench rate consistent with the theoretical pred... | ABC | ABC | CBA | ABC | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> Seq2seq strategy was proposed in [16], where the PINN learns to predict the solution at each time step, instead of all times. Note that the only data available of the first sequence is from the PDE itself, i.e., just the initial condition. <|MaskedSetence|> which can be shown in F... | **A**: In complex cases, this can be more difficult to learn.
**B**: We take the prediction at t=𝑡absentt=italic_t =d𝕋𝕋\mathbb{T}blackboard_T by using the model of the first sequence and use this as the initial condition to make a prediction in the next sequence, and so on.
**C**: The original PINN approach trains... | CAB | CAB | CAB | ABC | Selection 3 |
In order to identify if a query suffers from uncertainty or lack of representation, one could use a deterministic approach using a fixed threshold. <|MaskedSetence|> <|MaskedSetence|> uncertain).
This approach, however, would be misleading since two numbers close to the threshold could be treated very differently. Al... | **A**: Then if the number of similar samples to (resp.
**B**: label fluctuation in vicinity of) 𝐪𝐪\mathbf{q}bold_q is larger than the threshold it is considered as unrepresented (resp.
**C**: Instead, we consider a randomized approach, widely popular in the literature, including dwork2012fairness ..
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First, we familiarized ourselves with our data by reading through each transcript. The first two authors under the supervision of the last author discussed the transcribed content to create our initial codes. We identified several key dimensions that appeared to be the most influential during our analysis. Next, we con... | **A**: Therefore, the count of participants who thought a feature exhibited a particular value totaled to more than 100% of our participants.
.
**B**: Table-B.1, C.1 and LABEL:tab:codebook-RQ3 present the themes for each of our three research questions and their corresponding codes and illustrative quotations.
**C**:... | CBA | BCA | CBA | CBA | Selection 4 |
Considering the semantic relations among AUs, some works (Wang et al. <|MaskedSetence|> <|MaskedSetence|> Wang et al. (Wang et al. 2013) introduced a restricted Boltzmann machine to model facial action units, thereby capturing not only local but also global AU dependencies.
Li et al. (Li et al. <|MaskedSetence|> | **A**: 2019) investigated how to integrate the semantic relationship propagation between AUs to enhance the feature representation of facial regions, and proposed an AU semantic relationship embedded representation learning (SRERL) framework..
**B**: 2017) make efforts in modeling such relations via probabilistic grap... | CBA | CBA | CBA | CBA | Selection 3 |
The rest of this paper is organized as follows. <|MaskedSetence|> <|MaskedSetence|> Section 4 follows by elaborating on the systematic design of BBP. Section 5 analyzes the block propagation delays of different protocols and discusses our experimental results. Section 6 overviews related work. <|MaskedSetence|> | **A**: Section 3 discusses the design of a practical BBP scheme based on Ethereum.
**B**: Section 2 analyzes the theoretical scalability of our basic BBP scheme.
**C**: Section 7 concludes this work.
.
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<|MaskedSetence|> For example, Kwon et al. (2021) considers latent POMDPs, where each process has only one latent state, and the proposed algorithm efficiently infers the latent state using a short trajectory. <|MaskedSetence|> (2021) considers POMDPs having tree-structured states with their positions in certain part... | **A**:
In a broader context of reinforcement learning with partial observability, our work is related to several recent works on POMDPs with special structures.
**B**: Kozuno et al.
**C**: Also, the aforementioned literature only consider tabular POMDPs..
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There were 91 unique authors identified from the included studies. The VOSviewer software was used to calculate the most impactful authors, generate co-authorship clusters, and perform co-occurrences of keyword analysis (Van Eck NJ, \APACyear\bibnodate). All the authors were counted irrespective of the authorship orde... | **A**: The most significant keyword was ASR(Automatic Speech Recognition)..
**B**: However, high weightage was attributed to authors publishing more articles.
**C**: It is worth noting that 79 authors (86.81 %) contributed to only one paper in the included studies, i.e., have only one work relating to AI-based automa... | BCA | BCA | BCA | CAB | Selection 2 |
Let us look at the equal noise setting, i.e. the case where the variance of noise distributions for all communities are the same. We observe that in the low-noise setting, all intra-community compression ratios are higher than
all inter-community compression ratios. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetenc... | **A**: This further indicates that compression ratio is indeed a useful metric even when the noise has a strong perturbation effect on the data (even though there will be no clean separation between intra-community and inter-community compression ratios once the noise is very high)..
**B**: As the noise increases, the... | BCA | CAB | BCA | BCA | Selection 1 |
Figure 3 shows a qualitative comparison of our results to the method of [22] trained in the two settings explained above. <|MaskedSetence|> However, for extrapolation to unseen points in time, the overfitted model of [22] performs significantly worse, indicating that the physical model is poorly identified from a sin... | **A**: Table 1 shows a quantitative analysis of all 10 test sequences, highlighting again the advantages of our method in the setting of a single sequence as well as the competitiveness against the usage of considerably more data.
**B**: We observe that for this sequence all approaches yield reasonable results for the... | BCA | BCA | BCA | BAC | Selection 3 |
Towards this goal, the main contribution of this letter is a novel resource-efficient QCN framework, dubbed the framework. This framework draws upon recent advancements in two key quantum information science areas. <|MaskedSetence|> Second, the QSC framework delves into quantum semantic representations, highlighting... | **A**: Simulation results validates that the QSC framework results in minimal quantum communication resources, saving 50-75% of the resources compared to semantic-agnostic QCNs, while achieving higher quantum semantic fidelity.
**B**: Thus, rather than merely transmitting raw data in compressed quantum states, the QSC... | BCA | CBA | CBA | CBA | Selection 3 |
<|MaskedSetence|> Considering the minimum data rate of 00Mbps, both SRR and RND show higher percentages of (R>0)𝑅0(R>0)( italic_R > 0 )-served UEs than the Proposed scheme. This is a consequence of the throughput-vs.-fairness trade-off. While SRR and RND reach all UEs with the same probability thus providing the best... | **A**: This further demonstrates that the proposed resource allocation scheme can very effectively deal with such complex network scenarios.
.
**B**: This confirms that the service percentages guaranteed by SRR and RND are mainly driven by UEs with very-low data rates.
**C**: Some evident aspects emerge from the figu... | CBA | CBA | CBA | CBA | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> For extremely profitable drugs earning $100B or more, the protocol ceases to be incentive-aligned. Furthermore, as the protocol is loosened, it ceases to be incentive-aligned even for less profitable drugs. With the high-discretion accelerated protocol, incentive alignment is lost ... | **A**: We find that for typical drugs with $1-10B profit if approved, the standard protocol requiring two trials is incentive-aligned.
**B**: In the blockbuster scenario, an organization may have an expected value of hundreds of millions or billions of dollars of profit for running a clinical trial on a placebo.
.
**... | CAB | ABC | CAB | CAB | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> In the first column of each row, the moving image obtained using PET modality is shown, while in the second column, the fixed image obtained using MRI modality is displayed. The third column shows the checkerboard alignment result using Clear, while the fourth co... | **A**: 2: Qualitative results for affine registration with MI over 3D medical images using ADNI dataset [33].
**B**: The images are presented in a 3×4343\times 43 × 4 grid, with the first row representing the axial axis, the second row the coronal axis, and the third row the sagittal axis.
**C**:
Fig.
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Knowledge Distillation (KD). <|MaskedSetence|> [19] propose an original teacher-student architecture that uses the logits of the teacher model as the knowledge. <|MaskedSetence|> <|MaskedSetence|> If we consider a network as a mapping function of input distribution to output, then different knowledge types help to a... | **A**: 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 some regard knowledge as relations between such layers [57, 40, 9].
**B**: The purpose of defining different types ... | CAB | CAB | CAB | CAB | Selection 2 |
<|MaskedSetence|> Namely, if one trains FNO on an insufficiently fine grid, activation functions introduce distortions that FNO will learn to mitigate. When the grid is sufficiently refined, aliasing errors disappear, but since FNO was trained to mitigate them, it predicts output functions that differ from targets it ... | **A**: Theorem 1 predicts that in certain situations FNO has a systematic bias.
**B**: Indeed, the right graph in Figure 2a shows that for the integration operator, which performs smoothing, aliasing leads to an approximately two-fold error increase, while for the differentiation operator the increase is five-fold.
*... | BCA | ACB | ACB | ACB | Selection 2 |
<|MaskedSetence|> As shown in Figure 1 (b), the loss is computed in a one-to-one spatial matching fashion, that is formulated as a summation of the distance between the source and the target features at each spatial location. <|MaskedSetence|> In practice, this assumption is commonly not valid due to the fact that st... | **A**: To compute the distillation loss of the aforementioned approach, one need to select the source feature map from the teacher and the target feature map from the student, where these two feature maps must have the same spatial dimension.
**B**: One underlying assumption of this approach is the spatial information... | CAB | ABC | ABC | ABC | Selection 2 |
<|MaskedSetence|> The data might have hundreds of features or come from where the meaning of features is unclear. <|MaskedSetence|> <|MaskedSetence|> Finally, we apply the CLIQUE subspace clustering algorithm [2] to the auto-mpg dataset to identify interesting subspaces. The resulting ENS-t-SNE embedding shows patte... | **A**: Subspace clustering algorithms can efficiently find subspaces of interest.
**B**:
However, feature grouping is not always clear.
**C**: The USDA food composition dataset is frequently analyzed in subspace clustering literature; we use two interesting subspaces identified by Tatu et al. [34] and show how ENS-t... | BAC | BAC | ACB | BAC | Selection 2 |
Related Work. <|MaskedSetence|> In general, solving a POMDP is intractable from both the computational and the statistical perspectives (Papadimitriou and Tsitsiklis, 1987; Vlassis et al., 2012; Azizzadenesheli et al., 2016; Guo et al., 2016; Jin et al., 2020a). Given such computational and statistical barriers, previ... | **A**: (2020a); Efroni et al.
**B**: Our work follows the previous studies of POMDPs.
**C**: In contrast, we aim to recover the efficient state-action representation for planning.
In terms of the necessity of exploration, Azizzadenesheli et al.
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On the other hand, a growing body of literature leverages optimization procedures to facilitate online inference, starting with Robbins1951stochastic; Kiefer1952Stochastic and continuing through Robbins1971convergence; Fabian1973Asymptotically; Ermoliev1983Stochastic. To study the asymptotic distribution of stochastic ... | **A**: Liang2019Statistical designed a moment-adjusted SGD method and provided non-asymptotic results that characterize the statistical distribution as the batch size of each step tends to infinity.
**B**: Toulis2017Asymptotic designed an implicit SGD method and showed the asymptotics of averaged implicit SGD iterates... | CBA | BAC | BAC | BAC | Selection 2 |
For semantic segmentation, we used the Cityscapes [54] dataset (under MIT Licenses). <|MaskedSetence|> <|MaskedSetence|> Since ConvMixer [17] was never used for semantic segmentation, the classification head of ConvMixer was replaced with a segmentation head similar to WaveMix for segmentation experiments. Mean inter... | **A**: Results of the other models compared were directly taken from their original papers as cited in Table 2.
**B**: The official training dataset itself was split into training and validation sets.
**C**: Inference throughput on a single GPU was reported in frames/sec (FPS).
.
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Formal automated theorem proving in logic is among the most advanced and abstract forms of reasoning materialised in the AI space. Future models capable of harnessing powerful internal language representation and abstract reasoning models, able to handle informal language outside of heavily curated formal environments ... | **A**: Recently, NLP methods have been proposed that accept formal mathematical language as input, training models to perform tasks related to mathematical reasoning polu2020generative; rabe2020mathematical; wu2021lime mostly below the level of full theorem proving.
**B**: There are two major bottlenecks irving2016dee... | BAC | BAC | ABC | BAC | Selection 1 |
In social (resp. ecological) networks, individuals (resp. <|MaskedSetence|> <|MaskedSetence|> When analysing the roles in food webs, Luczkovich et al., (2003) use the notion of regular equivalence to define trophic role. Two species are said to be regularly equivalent if they feed on equivalent species and are preye... | **A**: This notion of regular equivalence is a relaxation of structural equivalence which imposes that structurally equivalent species have exactly the same trophic relations in the food web..
**B**: In food webs, species playing the same ecological role are said to be ecologically equivalent (see Cirtwill et al.,, 20... | ABC | CBA | CBA | CBA | Selection 4 |
<|MaskedSetence|> (2013); a social support multilayer network from Banerjee et al. <|MaskedSetence|> (2013, 2019). <|MaskedSetence|> Notably, we conclude that the Malaria multilayer network has layer independence at level α=0.05𝛼0.05\alpha=0.05italic_α = 0.05, whereas and all of the other datasets have either layer... | **A**: (2013);
and 112 other multilayer social support networks from Banerjee et al.
**B**: In this section we use the NNTuck and the tools developed thus far to study several empirical datasets: the cognitive social structure dataset from Krackhardt (1987); a biological multilayer network from Larremore et al.
**C**... | BAC | BAC | BAC | BCA | Selection 1 |
We propose an approach to enhance GNN performance on less-homophilic graphs by restructuring the graph to maximize homophily. Our method is inspired and closely related to Spectral Clustering (SC). <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> There are many promising extensions of this work, such as using ... | **A**: It extends SC beyond the leading eigenvalues and learns the frequencies that are best suited to cluster a graph.
**B**: We also proposed a new homophily metric that is density-aware, and robust to label imbalance, hence a better homophily indicator for the purpose of graph restructuring.
**C**: To achieve this... | ACB | ACB | ACB | ABC | Selection 1 |
The first is the UCI Adult data set (Becker and Kohavi, 1996). This data set is used in many algorithmic fairness works (see, e.g. Calders et al., 2009; Turgut, 2023; Kumar et al., 2023). It includes data on over 48,0004800048,\!00048 , 000 individuals. <|MaskedSetence|> The goal is to predict whether an individual’s ... | **A**: For each individual, 14141414 demographic features are provided.
**B**: The sex attribute has two possible values, matching the discussion on distributions with only two-sub-populations.
**C**: The race attribute has 5 possible values.
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Reliably detecting elbows is a hard challenge. Our choice of α𝛼\alphaitalic_α relies on the assumption that the data contains motif sets. <|MaskedSetence|> <|MaskedSetence|> We would expect such random series to contain no reasonable motif sets, and, in fact, the elbow function is a rather straight. <|MaskedSetence... | **A**: Yet, if this assumption is violated, such as in random walk data, every elbow detection method is cursed to fail.
**B**: Still, our method reports “elbows” at the points where the line is not completely straight.
**C**: These create visible elbows on all use cases (compare Figure 8) .
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The convergence and performance analysis of the algorithm (6) are presented in this section. <|MaskedSetence|> <|MaskedSetence|> Then, Theorem 2 gives intuitive convergence conditions for the case with balanced conditional digraphs by Lemma 2. Whereafter, Corollary 2 gives more intuitive convergence conditions for th... | **A**: The proofs of theorems, Proposition 1 and Corollary 2 are in Appendix A, and those of the lemmas in this section are in Appendix B.
.
**B**: Based on which, Theorem 1 proves the almost sure convergence of the algorithm.
**C**: First, Lemma 1 gives a nonnegative supermartingale type inequality of the squared es... | ACB | CBA | CBA | CBA | Selection 4 |
<|MaskedSetence|> Paul Laiu is a Staff Mathematician in the Multiscale Methods and Dynamics Group at Oak Ridge National Laboratory. <|MaskedSetence|> His work focuses on the development of mathematical tools that accelerate simulation of multiscale systems while preserving the structure of the multiscale phenomena. ... | **A**: His research interest includes numerical optimization, surrogate modeling, and numerical schemes for various partial differential equations in kinetic theory.
**B**: {biography}
M. Paul Laiu.
**C**: Paul received his Ph.D.
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<|MaskedSetence|> However, the automatic evaluation of such techniques remains an open problem. <|MaskedSetence|> Yet, ROUGE can only be used for measuring the quality of the summarization output and cannot be readily used to capture the topical focus of the text. <|MaskedSetence|> However, this serves as a simple i... | **A**: Some early steps have been made in this direction by [7], employing a latent Dirichlet allocation (LDA) model to evaluate the topical focus of a summary.
**B**:
This challenge has been recently addressed by topic-controllable summarization techniques [5, 6, 7].
**C**: Existing methods use the typical ROUGE sc... | BCA | CAB | BCA | BCA | Selection 4 |
We start from the following definitions. <|MaskedSetence|> the Clifford+T decomposition of the Toffoli gate). Tiling is the procedure by which circuits are designed in a manner that is compatible with the underlying qubit lattices. <|MaskedSetence|> For example, the goal of scheduling is to reduce the depth of the re... | **A**: Scheduling is the method that outputs the order in which the gates existing in the cell will be executed.
**B**: As a result, a schedule is a quantum circuit that includes information about gate parallelism.
**C**: A standard cell is a pattern that represents the 2D/3D abstraction of the qubits and the gates t... | CAB | CAB | CAB | ABC | Selection 3 |
<|MaskedSetence|> One serves as input to the model, and the other as the ground truth corresponding to the desired parameter setting to compute the loss. We use MRiLab [7] which is an MRI Simulator to generate these synthetic brain scans in different parameter settings of {TE, TR}. We generated these brain MRI scans f... | **A**: The TE values ranged from 20 ms to 1s non-uniformly.
**B**: For each pair of {TE, TR}, we generated 24 different 2D axial MR slices of a 3D brain volume, so in total we obtained 4800 MR slices.
**C**:
For our training, we require the MRI scans in two different parameter settings of {TE, TR}.
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<|MaskedSetence|> <|MaskedSetence|> Numerical simulation of phase-field type models is often challenging. <|MaskedSetence|> Traditional numerical methods often use an adaptive or moving mesh approach to overcome this difficulty. In contrast, a neural network-based numerical scheme has a mesh-free feature. The number... | **A**: It clearly shows that our method can achieve comparable results with the FEM.
**B**: The simulation results are summarized in Fig. 4.
**C**: To capture the dynamics of the evolution of the thin diffuse interface, the mesh size should be much smaller than ϵitalic-ϵ\epsilonitalic_ϵ, the width of the diffuse inte... | BAC | BAC | ABC | BAC | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> One group of reasons relates to weaknesses in the algorithms themselves. For example, many approximate score-based algorithms are greedy and end up returning a graph with only a local maximum score. <|MaskedSetence|> The accuracy of algorithms is also affected by the objective sco... | **A**: Constraint-based algorithms rely on statistical tests of independence which can only say how unlikely the variables are to be independent, and so mistakes are made in the learning process and these mistakes tend to propagate in the algorithms [38].
**B**: There are several reasons why the learnt graphical struc... | CBA | CBA | CBA | CBA | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> not necessarily simple) graphs. However, in [13, Corollary 22], Hladký, Kráľ, and Norine proved that if one subdivides each edge of a graph with a new vertex and places 0 chips on the new vertices, then the rank of the obtained divisor on the new graph is the same as the rank of th... | **A**: The above reduction of Min-TSS to Dist-Nonhalt uses an auxiliary graph that contains parallel edges.
**B**: Using this result, our construction can be modified in such a way that eventually we get a Dist-Nonhalt instance on a simple graph, at the expense of having a higher number of vertices.
**C**: Therefore,... | ACB | ACB | BCA | ACB | Selection 1 |
We employ the AdamW optimizer [68] for image generating tasks, which trains 300 epochs at 0.001 learning rate and 0.001 weight decay. <|MaskedSetence|> Moreover, the time step is set to 16. <|MaskedSetence|> VI. Because this method can make full use of the powerful temporal information, the Inception Score (IS) shows... | **A**: The performance of the TCJA image decode is compared with some SOTA models in Tab.
**B**: In addition to the primary evaluations, our model was also compared with other methods based on the Spiking VAEs, such as image decoding based on temporal attention (TAID) [70] and Efficient Spiking VAE (ESVAE) [71], which... | BAC | CAB | CAB | CAB | Selection 4 |
<|MaskedSetence|> Based on the observation that owning certain tokens is a necessary precondition for invoking some actions, FlashSyn prunes symbolic actions vectors that contain actions requiring tokens666Note a token can be standard tokens (ERC20, BEP20), or any other forms of tokens such as debt tokens or share tok... | **A**: not owned by the attacker.
**B**:
Heuristic 3: necessary preconditions.
**C**: The only action candidate that mints fUSDC for users is deposit; thus, this heuristic mandates that deposit must be called before invoking withdraw.
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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 VariBAD al... | **A**: Thus, we can train the VariBAD policy on both the sampled training environments, and also on dream environments.
**B**: We refer to this method as VariBAD Dream.
**C**: In our implementation, we train the KDE and VariBAD components simultaneously.
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Parameter Settings. We determined the word vector space of each language by feeding all texts from English and Chinese healthcare Q&A corpus. We employed the word2vec module from gensim888https://radimrehurek.com/gensim/models/word2vec.html, which implements the Skip-gram algorithm [27]. <|MaskedSetence|> <|MaskedSet... | **A**: We used 719 bilingual title pairs from Wikipedia to enable the process of space alignment.
**B**: All parameters of the Skip-gram were default values.
**C**: The size of vocabulary for English and Chinese word space are 54,061 and 17,882, respectively.
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In this work, we employ a ViT pre-trained on the ImageNet-21k dataset from the authors Dosovitskiy et al. <|MaskedSetence|> <|MaskedSetence|> (2018) dataset. CelebA is a large collection of 202,599202599202,599202 , 599 human facial images and each image is labeled with 40 different attributes (e.g, ‘Smiling’, ‘Eyeg... | **A**: (2021); Wightman (2019) and then fine-tune the model for predicting human facial attributes by training on the publicly available large-scale CelebFaces Attributes (CelebA)Liu et al.
**B**: (2020); Steiner et al.
**C**: IV.3..
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<|MaskedSetence|> A bounded model checker works by treating a program as a state transition system and then checking whether there exists a path in this system of length less than some bound k𝑘kitalic_k that violates the property to be verified (Biere, 2009; Cordeiro et al., 2012). <|MaskedSetence|> ESBMC works by t... | **A**: In this work, we use ESBMC, an efficient bounded model checker for C++ and C with support for checking many safety properties fully automatically (Cordeiro et al., 2012).
**B**: Finally, if a model is found, a counterexample is extracted, representing the set of assignments required to violate the property..
*... | CBA | CAB | CAB | CAB | Selection 3 |
<|MaskedSetence|> 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 solution333The method of Bhrawy and Zaky can be considered to be an extens... | **A**: The JFP basis inherits many desirable properties of classical Jacobi polynomials, including orthogonality, see [7].
**B**: We believe this approach has not been sufficiently analyzed nor developed to its full potential.
**C**: We consider our method to be the successor of that of Bhrawy and Zaky [7].
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In the main text, we use the Random Forest classifier to analyze the capability of a topological feature. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> S25 and S26 show that our quantitative framework is not affected by the machine learning algorithm. Hence, this further indicates that the machine learning ... | **A**: The results from Figs.
**B**: The sampling method is the same as that of the main text.
**C**: Here, we also consider Gradient Boosting and AdaBoost classifiers to display similar tests.
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<|MaskedSetence|> Such a practice leads to significant memory waste for storing the gradients. <|MaskedSetence|> As such, we trace the dependency of all tensors (weights, gradients, activation) and reorder the operators, so that some operators can be fused to reduce memory footprint (by 2.4-3.2×\times×, Figure 10(a),... | **A**: By reordering operators, we can immediately apply the gradient update to a specific tensor (in-place update) before back-propagating to earlier layers, so that the gradient can be released.
**B**: The memory life cycle analysis in Figure 7 reflects the memory saving from in-place gradient update and operator fu... | CAB | CAB | CAB | BCA | Selection 2 |
We note that there are different classes of permutation-based problems. <|MaskedSetence|> <|MaskedSetence|> In problems of the order type, we have precedence relations that must be respected or that are profitable to be respected. Such problems occur in production scheduling, where a given set of jobs have to be plac... | **A**: The quadratic assignment problem or the stable marriage problem are examples for this type.
**B**: Due to the different nature of these types of problems, it appears difficult to define benchmarks that are meaningful for all types.
**C**: In problems of the assignment type, we have two classes of n𝑛nitalic_n ... | CAB | CAB | CAB | CAB | Selection 2 |
<|MaskedSetence|> Section 3 summarizes the basic scheme of two-level optimization in Euclidean spaces. The core part of this paper, Section 4, generalizes this scheme to a Riemannian setting. In particular, information geometry is employed in a way that indicates how other convex programs could be handled in the same ... | **A**: We introduce basic notation and briefly recall required properties of Bregman divergences in Section 2.
**B**: Numerical experiments are reported in Section 5 for a range of problem instances and compared to a recent state-of-the-art method [HRX21].
**C**: We conclude in Section 6.
.
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Given the above result, it may seem that, similarly to the case of monotone networks with ReLU activations, the class of monotone networks with threshold activations is too limited, in the sense that it cannot approximate any monotone function with a constant depth (allowing the depth to scale with the dimension was c... | **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.
**B**: Any continuous function over a bounded domain can be approximated by a depth-2222 network [3, 11, 22] and this u... | BAC | BAC | BAC | BAC | Selection 3 |
A common feature of virtually all of the aforementioned QMC literature related to PDE uncertainty quantification is that the QMC rule is designed for the non-discretized PDE problem (1) whereas, in practical computations, one only has access to a discrete approximation of the PDE system. <|MaskedSetence|> However, in ... | **A**: Beyond that, DG directly supports hpℎ𝑝hpitalic_h italic_p refinement, where both the mesh and the local degree of approximating polynomials can be adapted locally, see [4, 5, 45].
**B**: Last, DG comes with an intuitive notion of local mass conservation, since it is based on inter-element fluxes (and mass con... | ACB | CAB | CAB | CAB | Selection 2 |
<|MaskedSetence|> First, this is the first lower bound result that addresses the local agent adaptivity in the CL models. In particular, it shows that the capacity of each agent to utilize newly observed information within each round does not contribute to reducing the round complexity in the heterogeneous CL model. ... | **A**: This is in stark contrast with the homogeneous CL model in which local agent adaptivity can significantly reduce the round complexity.
**B**: Second, our hard input distribution for proving Theorem 1 is the first one that uses asymmetric arm means constructions.
**C**:
We would like to highlight a couple of p... | CAB | ACB | CAB | CAB | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> which may not be an obvious task. <|MaskedSetence|> Motivated by the limitation of initial tubal rank estimation, in this paper, we propose a new randomized fixed-precision algorithm which is independent of initial tubal rank estimation. More precisely, for a given third-order ten... | **A**: This decomposition has found many applications including deep learning ([25, 26]), tensor completion ([27, 28]), numerical analysis ([29, 30, 31, 32]), image reconstruction [33].
**B**: There are mainly several randomized algorithms ([34, 35, 36]) to decompose a tensor into the t-SVD format but all of them need... | ABC | ABC | BAC | ABC | Selection 4 |
In Table 6 we present the training times of supervised and semi-supervised algorithms. For simplicity, we present times for experiments with 500 labeled examples, since conclusions for other amounts of labeled data are similar. We can observe that semi-supervised PCTs and random forests can take considerably more time ... | **A**: This is because in such cases the w=1𝑤1w=1italic_w = 1 was chosen, i.e., the semi-supervised model is equal to the supervised one.
**B**: Note that in Table 6 the time used to optimize the w𝑤witalic_w parameter is not included.
.
**C**: Note that in some cases the learning times between supervised and semi-s... | CAB | ACB | CAB | CAB | Selection 4 |
<|MaskedSetence|> For each condition, the final score is obtained by averaging the scores from three experimental results. <|MaskedSetence|> Then, its performance on each task is calculated by a specific metric function. To evaluate waypoints and navigational controls, we use mean absolute error (MAE) or L1 loss as i... | **A**: DeepIPC is evaluated under two conditions with varying cloud intensity with two different tests namely offline and online tests.
**B**: In the offline test, the model is deployed to predict driving records.
**C**: Meanwhile, we compute intersection over union (IoU) as in (9) for evaluating the segmentation per... | ABC | ACB | ABC | ABC | Selection 1 |
<|MaskedSetence|> In other words, any planar graph of a small diameter has a small treewidth. <|MaskedSetence|> However, even for very “simple” objects like unit disks, the corresponding intersection graphs do not have locally bounded treewidth. On the other hand, in many scenarios, the treewidth-based methods on suc... | **A**: A natural research direction is to extend such methods to intersection graphs of geometric objects [24, 34].
**B**: In particular, de Berg, Bodlaender, Kisfaludi-Bak, Marx, and van der Zanden
use tree decompositions whose bags are covered by a small number of cliques, and thus of small independence number, to d... | CAB | CAB | CAB | ABC | Selection 1 |
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. 4, the variance σ2superscript𝜎2\sigma^{2}italic_σ start_POSTSUPERSC... | **A**: 4, we observe that, in the yellow node’s neighborhoods, the number of nodes selected to be updated decreases as the level of noise increases when using our method.
**B**: This indicates that the noisy information could be prevented to some extent by our method, such that the negative influence can be lowered, a... | CAB | CBA | CAB | CAB | Selection 3 |
Saihui Hou
received the B.E. <|MaskedSetence|> degrees from University of Science and Technology of China in 2014 and 2019, respectively. He is currently an Assistant Professor with School of Artificial Intelligence, Beijing Normal University, and works in cooperation with Watrix Technology Limited Co. <|MaskedSeten... | **A**: and Ph.D.
**B**: His research interests include computer vision and machine learning.
**C**: Ltd.
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Reinforcement learning (RL) algorithms, specifically Q-learning (Watkins and Dayan, 1992) based algorithms, have become a mainstream method for training the dialogue policy module (Peng et al., 2018; Zhang et al., 2020b). For each step, the policy agent updates its action value 111This value is the expected return for ... | **A**: However, this update rule suffers from an overestimation problem (Hasselt, 2010): mostly the estimated maximal action value is larger than the ground truth.
**B**: Some prior studies have tried to address this problem in domains like video game playing and multi-agent systems, but they either suffered from the ... | ACB | CAB | ACB | ACB | Selection 3 |
Following the division of our methodology, we define our framework as a two-step. First, we propose a Hierarchical Multitask Multi-Layer Perceptrons (MLP) Mixer (H3M), to classify each observed video to an action label, as well as to extract the overall intention of the human. The MLP Mixer-based architecture [32] has ... | **A**: Our H3M is designed as a multitask network [6] to exploit dependencies between low-level actions and high-level intentions while making the network more efficient.
**B**: However, to narrow the uncertainty of the future, our model is based on Conditional VAE (CVAE), inspired by [24], leveraging the inferred hum... | ACB | CAB | ACB | ACB | Selection 3 |
<|MaskedSetence|> These anomalies are typically accompanied by rare and inconsistent behaviors, and as a result, the one-class learning model tends to make predictions unconfidently. <|MaskedSetence|> 1 (c), we aim to use this type of uncertainty to weaken the contribution of anomaly contamination, thereby calibratin... | **A**: We resort to model uncertainty to tackle this problem.
**B**: Therefore, this process can discriminate these harmful anomalies in learning data normality, thus masking the notorious anomaly contamination problem during network optimization..
**C**: As shown in Fig.
| ACB | ACB | ACB | CAB | Selection 3 |
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