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<|MaskedSetence|> In this scenario, the impact of a vertex on its neighbors can be represented in a binary format with two possible values, namely 0 and 1. The initial number of epidemic sources initiated by a small group can be represented by α𝛼\alphaitalic_α, where the support of non-zero elements represents the so... | **A**:
For example, the social interaction between people can be modeled as an undirected, symmetric sparse graph, and the spread of epidemics can either be influenced or not.
**B**: Therefore, based on a preliminary understanding of the social habits of the population in the city, we hope to estimate how many peopl... | ACB | ACB | CBA | ACB | Selection 2 |
<|MaskedSetence|> This sequential training method ensures that each component is optimized for its specific role before integration into the overall framework. To comprehensively validate the importance of each module, we explore alternative methods by replacing the inpainter and the diffusion-based synthesis module. ... | **A**: In our framework, each stage is trained independently, with the output of one stage serving as the input for the subsequent one.
**B**: Additionally, the tumor images synthesized by CUT-GAN are overly smooth, making the complementary features between multimodal tumors less pronounced, which, in turn, affects th... | ACB | ACB | ACB | BCA | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> 2022; Chen et al. 2022) handle the complex and diverse distribution of real noise, which can be influenced by sensor type, ISO, and ISP. Unlike GANs, diffusion models avoid mode collapse and provide more diverse results. <|MaskedSetence|> | **A**: Diffusion models (Ho, Jain, and Abbeel 2020; Song, Meng, and Ermon 2020; San-Roman, Nachmani, and Wolf 2021; Bansal et al.
**B**: However, they have not been effectively applied to synthetic noise generation, potentially due to the lack of conditioning designs for handling complex and varied noise distributions... | CAB | CAB | CAB | CAB | Selection 4 |
To evaluate the performance of our proposed DASUNet, we compare our results with seventeen classical low-light image enhancement methods which include four traditional models (AGCWD [22], SRIE [15], LIME [19], and ROPE [57]), two unsupervised methods (Zero-DCE [17] and EnlightenGAN [23]), five recent deep black-box me... | **A**: Quantitative comparison results are summarized in Table 1.
**B**: CSRNet overenhances the low-light images.
**C**: One can observe that SRIE and DSN connot achieve favorable enhancements.
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<|MaskedSetence|> <|MaskedSetence|> In RL, the decision rule is denoted as a
policy, which operates based on state feedback control principles [45]. <|MaskedSetence|> The objective of optimum control is to maximize the overall reward obtained from each initial state. The purpose of this process, which involves seque... | **A**: The system’s state is modified through actuation, and the resulting transition to the new state is assessed using a reward function.
**B**: The controller inside the general feedback control structure gets feedback in the form of state signals from the plant and acts accordingly.
**C**:
Reinforcement learning... | CBA | CBA | CBA | CAB | Selection 1 |
The remaining portions of the article are organized as follows: Modeling and simulation of faults and non-fault events, and challenges to conventional distance relays in the IEEE 9-bus test system are discussed in Sec. <|MaskedSetence|> <|MaskedSetence|> III. The fuzzy-based decision-making system for fault detecti... | **A**: V validates the finding on the IEEE 39-bus system.
**B**: II.
**C**: The AR coefficient-based intelligent protection scheme, feature selection, and deep learning network (InceptionTime) are described in Sec.
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<|MaskedSetence|> The mean dice coefficient for each compared method is plotted as a boxplot, separately for the major and minor motion cases. We calculated the dice twice, one time using the optimal hyperparameters of group 1 and one time using the optimal hyperparameters of group 2. We also plotted the mean dice coe... | **A**:
5.2 Lung Segmentation Evaluation
Lung segmentation evaluation results are presented in Figure 5.
**B**: For cases involving major motion, IVIM-Morph succeeded in enhancing the dice coefficient achieving superior results for group 2 (dice = 0.854±0.038plus-or-minus0.8540.0380.854\pm 0.0380.854 ± 0.038) than gr... | ABC | ABC | ABC | ABC | Selection 1 |
IV-C Effect of Training Overhead
We evaluate the effect of training overhead on the detection performance. <|MaskedSetence|> <|MaskedSetence|> As expected, the higher the training overhead, the better the performance. <|MaskedSetence|> In other words, we can perform efficient drone detection with a small amount of... | **A**: However, the increase is not so significant when increasing K𝐾Kitalic_K by 30303030.
**B**: The simulation results are presented in Fig. 5.
**C**: Different training overheads, i.e., K𝐾Kitalic_K values, are considered.
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<|MaskedSetence|> The more distinct the two distributions are, the larger the OOD detection probability (20) is [11, 49]. <|MaskedSetence|> Second, calibration-regularization, while improving ID calibration, does not help with OOD detection, as it focuses solely on ID performance. <|MaskedSetence|> In particular, th... | **A**: To evaluate the benefits of OCM, Fig. 8 plots the histograms of the confidence levels produced by different models for ID and OOD data.
**B**: First, it is observed that BNN can improve OOD detection as compared to FNN, but the OOD confidence levels tend to be approximately uniformly distributed.
**C**: Finall... | ABC | ACB | ABC | ABC | Selection 4 |
VIII-B Performance Evaluation on Synthetic Datasets
First, the performance of Spatial AirFusion and naive AirComp is evaluated on the synthetic dataset. <|MaskedSetence|> The performance is measured by AirComp error, defined as the mean square error of feature aggregation results compared with the ideal ground-truth... | **A**: The small optimality gap between the algorithms renders greedy VoCa-PPA a close-to-optimal heuristic with low computational complexity.
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**B**: We test Spatial AirFusion controlled by Greedy VoCa-PPA in Algorithm 1 and Optimal VoCa-PPA in Algorithm 2, termed “AirFusion-Greedy” and “AirFusion-Optimal”, respecti... | BCA | BCA | ACB | BCA | Selection 2 |
3.5 Justification of NeurTV from Variational Approximation
To further justify our NeurTV, we reinterpret NeurTV from the variational approximation perspective, which allows us to draw connections between NeurTV and classical TV and motivates us to develop effective variants (e.g., space-variant NeurTV). <|MaskedSeten... | **A**: 3.5.3).
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**B**: Specifically, based on the definition and theory of classical functional TV (i.e., Lemma 3.7) in the literature [16], our contributions here are the following theoretical results, which are different from the contents proposed in previous work.
**C**: 3.5.2).
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<|MaskedSetence|> The dataset comprises sEMG data from 12 electrodes on the right forearm of 40 subjects (12 female, 28 male). Each movement was repeated 6 times with a 5-second hold and 3-second rest, sampled at 2 kHz.
The target variable was a one-hot vector representing the 17 hand movements. <|MaskedSetence|> ... | **A**: Following the dataset’s split method [17], repetitions 2 and 5 were designated as the test set, while 1, 3, 4, and 6 were for training.
**B**: We utilized the NinaPro dataset [17], Exercise B of the DB2 database, which includes 17 hand movements (8 isometric and isotonic hand configurations, 9 basic wrist movem... | BAC | ABC | BAC | BAC | Selection 4 |
<|MaskedSetence|> Higher altitudes generally improve LoS link probability but may introduce higher path loss, which is why with an increase in height, the number of served users decreases. Additionally, Fig.7 illustrates that lowering the UAV height reduces path loss but diminishes LoS link probability. <|MaskedSeten... | **A**: Consequently, there exists an optimal altitude of approximately 200 meters that balances these effects and yields the highest performance.
Fig. 8 shows the number of served users with different K𝐾Kitalic_K and μ𝜇\muitalic_μ.
**B**: Fig. 7 explores the impact of UAV altitude adjustments on joint-DRL model tr... | BAC | BCA | BAC | BAC | Selection 4 |
Zhou et al. <|MaskedSetence|> <|MaskedSetence|> [51] present a data augmentation approach for accent modeling. They use voice conversion to augment the target accent data, and then build a multi-speaker multi-accent TTS system with both real and synthetic data.
Nguyen et al. <|MaskedSetence|> They decompose each wei... | **A**: [50] introduce a residual layer appended to the encoder to learn accented phoneme representations by mapping native speech to accented speech.
However, both methods focus on fine-tuning a single accent with limited data.
Tinchev et al.
**B**: [49] propose an accented TTS framework consisting of an accented fron... | BCA | BAC | BAC | BAC | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> First, the one-cycle phase currents are split into N = 50 segments. Second, the data of each segment is aggregated over the mean in a single data point per segment to reduce the number of measurement points to the number of segments.
Third, the acquired data points are then used to... | **A**:
The feature calculation steps for CLT are displayed in Fig.
**B**: Here, the feature is named correlation coefficient r𝑟ritalic_r which is dimensionless.
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**C**: 5.
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2.1 Generative adversarial model
Generative Adversarial Networks (GAN) are based on zero-sum games in game theory and consist of a generator and a discriminator [9]. <|MaskedSetence|> GAN is often used for data reconstruction [10, 11], synthetic data generation [12] and converting data between modalities [13]. For ... | **A**: However, this method merges image synthesis with segmentation tasks, which makes the computational overhead high.
**B**: The generator creates data by learning its original distribution while the discriminator differentiates between real and generated data and hence guides the generator toward more realistic ou... | CAB | BCA | BCA | BCA | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> On the other hand, current SoTA video captioning systems [13, 14] mainly pool all the visual information into an aligned feature dimension, losing the temporal information (order of the events). Hence, we capture visual information for multiple frames through image captioning to ma... | **A**:
One of the novel aspects of the proposed dataset is that we leverage the caption of the corresponding video to provide more detailed information about the audio events.
**B**: Different from previous visual-related approaches [8] that only apply the visual information of middle frames, the proposed strategy ut... | CAB | ABC | ABC | ABC | Selection 2 |
<|MaskedSetence|> To overcome this limitation, movable antenna (MA) [10] offers a practical and innovative solution. The MA enables flexible movement within two or three-dimensional region through a driver, such as a step motor along a slide track [11]. <|MaskedSetence|> So far, numerous studies have demonstrated the... | **A**:
However, conventional multi-antenna systems typically utilize fixed-position antennas (FPAs), which restrict their abilities to further exploit the channel variations, especially in cases with a limited number of antennas.
**B**: Due to continuous movement, the MA can better utilize spatial DoFs than the anten... | ABC | BAC | ABC | ABC | Selection 4 |
II Benchmark Models
This section first reviews the chance-constrained economic dispatch (CC-ED) model in Section II-A. <|MaskedSetence|> <|MaskedSetence|> In Section II-B, we extend CCs to weighted chance constraints (WCCs) by assigning different weights to different magnitudes of constraint violations. We use a li... | **A**: The CC-ED model will serve as a benchmark for the proposed models in Section III, while the WCC-ED model will provide insights for the proposed model in Section III-C.
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**B**: This model ensures power balance in the system while limiting the rate of constraint violations under uncertainty.
**C**: Flexible res... | BCA | ACB | BCA | BCA | Selection 3 |
<|MaskedSetence|> As discussed by [Krzysztofowicz, 1997], transformations are applied to achieve an approximately Gaussian distribution, especially in those cases where the panel data are heavily skewed. While this transformation is not mandatory, it helps in ensuring that the residuals satisfy the Central Limit Theor... | **A**:
2.1 Data Transformation
While performing the time-series analysis, it is essential to remove all diurnal and seasonal patterns in the datasets.
**B**: Here, we assumed that the daily peak mining load demand remains constant within a rolling window, which also provides us with the trend component.
**C**: Ther... | CBA | ACB | ACB | ACB | Selection 3 |
We initialize the visual encoder with weights from a pre-trained lip reading model trained on the LRS3 dataset (Ma et al. <|MaskedSetence|> The visual encoder is fine-tuned alongside a transformer decoder from scratch on the Baseline part of the VoxLRS-SA dataset. During the first stage of training, we employ a tri-st... | **A**: 2023).
**B**: In this second stage of training, we adopt a cosine learning rate strategy with a learning rate of 5e-5, total training steps of 30K, and a warmup period of 0.5K steps, using a batch size of 1 and increasing the gradient accumulation to 8.
Speaker-Adaptive Lip Reading Model
For vision-level adap... | ABC | BCA | ABC | ABC | Selection 3 |
Multi-Scale Dilated Asymmetric Convolution Module (MSDAM). The MSDAM is a unique six-branch structure. Three branches capture multi-scale information through different sizes of convolution, obtaining scale features. Two middle branches preserve input information via ECA [34], resulting in retention features. The final... | **A**: At the end of the module, features are divided into two groups, each containing two scale features and one retention feature, which are then fused.
**B**: The entire operation of the module is defined as follows:
.
**C**: We employ asymmetric convolution and depth-wise separable convolution (DWConv) to effec... | CAB | BAC | CAB | CAB | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> In the few-shot setting, we fine-tuned the model using either 1111 or 10101010 samples per category from the FSD-Mix training set and evaluated its performance on the FSD-Mix test set. Table II presents these results. <|MaskedSetence|> Moreover, fine-tuning with a small number of ... | **A**:
To further evaluate the few-shot and zero-shot capabilities of the models, we utilized the SoloAudio model trained exclusively on TangoSyn data.
**B**: For the zero-shot setting, we directly tested the model on the out-of-domain FSD-Mix test set, which contains unseen labels.
**C**: Overall, SoloAudio demons... | ABC | ABC | ABC | BAC | Selection 2 |
<|MaskedSetence|> While one approach [16] shows promise in handling motion for unseen modalities, it struggles with large motions in image pairs that are significantly contaminated by noise [38] and subject to contrast changes. <|MaskedSetence|> <|MaskedSetence|> Our method leverages the strengths of deep learning w... | **A**:
Despite significant advancements, state-of-the-art methods remain heavily constrained by the specific training dataset for each modality, limiting their effectiveness in correcting motion in unseen image modalities.
**B**: This limitation significantly impairs the applicability of motion correction models in a... | ACB | ABC | ACB | ACB | Selection 1 |
We are interested in multi-agent BO, where multiple agents can sample the objective function at a single timestep. Much of existing multi-agent BO literature studies batch BO, in which a central coordinator has access to each agent’s acquired information [21], [22]. It then computes the sampling decisions for all agen... | **A**: Distributed networks are prevalent in real-world applications, such as in multi-robot source seeking and sensor networks [21], [26].
**B**: Additionally, they often do not scale well, as they require a central coordinator to manage the processing of all agents’ data.
**C**: In this work, we study the distribut... | BAC | BAC | BAC | CBA | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> These computed Delta-frames are accumulated in a list and subsequently encoded. <|MaskedSetence|> Finally, the RFDecoder procedure, represented by lines 20-3120-3120\text{-}3120 - 31, receives the transmitted deltas, decodes them, and reconstructs each frame by merging the decod... | **A**: In the RFEncoder procedure, lines 1-111-111\text{-}111 - 11 iterate over each frame in CAV_Frames, where the camera pose is extracted, RF-frame is rendered based on the pose, and the Delta-frame is computed by comparing CAV-frame and RF-frame.
**B**: The ChannelTX procedure, detailed in lines 12-1912-1912\t... | ACB | CAB | CAB | CAB | Selection 3 |
The design of the ANNs has been tailored to the unique characteristics of input data to provide the best performance for each input condition. As a result, three distinct ANNs have been implemented and optimized, namely one for the raw AE signal input (raw-signal model), one for the 8 time-domain features (8-feature mo... | **A**: The models are implemented in Python using TensorFlow with the Keras API.
Figure 3: Architectures of the three ANN models for different input data (a) raw AE signals, (b) 6 time- and frequency-domain features, and (c) 8 time-domain features.
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**B**: 3.
**C**: The raw-signal model uses a 1000x1 input vector,... | ABC | CBA | CBA | CBA | Selection 4 |
If the agent does not know the underlying dynamics of the observations (transitions and/or channel), then the learning of the solutions to the optimal control problem from observed data is necessary. However, learning for POMDPs has been a challenging problem. Various approaches and studies are available in the literat... | **A**: Some of our explicit analysis here can also be seen in view of these bounds.
**B**: [21, 32, 70, 44, 2] for some of the learning approaches for POMDPs.
**C**: [63, 59] present a general framework on approximation states and their induced optimality and near optimality properties under several uniformity bounds... | ABC | BCA | BCA | BCA | Selection 4 |
<|MaskedSetence|> This comparison includes classical models such as MetricGAN+ and TSTNN, as well as state-of-the-art (SOTA) models like MP-SENet and SEMamba. <|MaskedSetence|> As the number of TS-Mamba blocks N𝑁Nitalic_N increases to 4, Mamba-SEUNet (S) surpasses all existing models with 1.88M parameters and 4.62G ... | **A**:
V-A Performance Comparison with Baselines
Table II presents a performance comparison between the proposed Mamba-SEUNet and several baseline models on the VCTK+DEMAND dataset.
**B**: The results indicate that Mamba-SEUNet (XS) achieves performance comparable to MP-SENet with just 0.99M parameters and 4.16G FL... | ABC | CAB | ABC | ABC | Selection 4 |
III-A Datasets
For Speech Translation (ST), we used both ASR and ST data from CoVoST2 [23] and Europarl-ST [24]. The training data was limited to the following languages: Portuguese (pt), French (fr), Italian (it), German (de), Spanish (es), and English (en). The X→→\rightarrow→en (X ∈\in∈ PFIGS) ST training data co... | **A**: Catalan, being a Romance language similar to French, Italian, and Spanish, and Dutch, sharing similarities with German, allowed us to effectively assess cross-lingual transfer capabilities of the models.
**B**: The evaluations were conducted solely on the CoVoST2 dataset, focusing on the Catalan-English (ca→→\r... | CAB | BCA | BCA | BCA | Selection 4 |
Despite the progress, two key issues are overlooked: 1) Multiscale prosody expression attributes (including both sentence-level and phoneme-level) in the multimodal context influence the current sentence’s prosody. 2) The prosody cues in the multimodal context should not be viewed in isolation, as their interaction w... | **A**: For example, Chen et al.
**B**: [14] introduced a multiscale hierarchical context encoder, aided by a multiscale reference encoder, to predict both global and local context style embeddings, effectively improving the speech expressiveness.
**C**: Inspired by these related works, how to model the multiscale mul... | ABC | ABC | ABC | BAC | Selection 1 |
III-C Ablation Study
We also conduct ablation experiments, as shown in Table LABEL:tab:2. The experimental setups are as follows: the baseline includes only the dual-stream backbone supervised with the ID loss. <|MaskedSetence|> As observed, the baseline achieves 69.6% Rank-1 and 66.8% mAP on the SYSU-MM01 dataset (... | **A**: Moreover, we compare the classical cross-centre loss with the proposed PABA loss, showing that the PABA leads to an additional 2.6% improvement in Rank-1 and a 1.6% gain in mAP compared to the cross-centre loss.
**B**: The terms of +SE, +CC and +PAPB represent the application of spectral enhancement, cross-cent... | BCA | BCA | BCA | ABC | Selection 1 |
In recent years, Liu et al.[39] proposed the Kolmogorov-Arnold Networks (KAN), which have made significant research progress. <|MaskedSetence|> However, due to issues such as a large number of parameters and slower inference speed, Dong et al.[41] proposed the FAN network as an improvement. <|MaskedSetence|> At the s... | **A**: This novel neural network architecture has gained widespread attention due to its advantages in interpretability and accuracy[40].
**B**: FAN combines the Fourier series to model periodic phenomena and demonstrates superior performance in various practical tasks.
**C**: Both models were successfully applied to... | CAB | ABC | ABC | ABC | Selection 4 |
As shown in Fig. <|MaskedSetence|> In particular, the proposed QAE system shows a slight BLER improvement over the AE, with a more noticeable performance gap in Rayleigh and 3GPP channels. <|MaskedSetence|> 4(b) for the (7,4) scenario, both the classical AE and QAE schemes exhibit nearly identical BLER performance to... | **A**: 4(a) for the (4,4) scenario, both the classical AE and the proposed QAE schemes outperform BPSK across all block fading channels.
**B**: Finally, in Fig.
**C**: In Fig.
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<|MaskedSetence|> <|MaskedSetence|> 7(b). <|MaskedSetence|> 8(a) and Fig. 8(b) provide the same results for the Max-Dir case. The band-wise results for linear fitting and shadowing parameters are summarized in Tables III, IV, and V. Please note that we remove the additional omni-directional gain for all different fr... | **A**: Similarly, Fig.
**B**: IV-C Path loss and shadowing
The path loss modeling results for the omni-directional case are shown in Fig.
**C**: 7(a), while the corresponding shadowing distribution is depicted in Fig.
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Feature Learning
Alternative methodologies incorporate a feature learning strategy (Hou et al. <|MaskedSetence|> <|MaskedSetence|> 2022) are acquired and refined, subsequently applied for AoA estimation, thereby reinforcing the linkage between input and estimated parameters with discernible physical significance. No... | **A**: 2019), run the risk of omitting valuable information pertinent to other critical feature parameters.
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**B**: 2019), yielding a subtle improvement in physical interpretability.
**C**: By leveraging neural networks, specific input parameter features (Naseri et al.
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For speech reconstruction evaluation, we randomly sampled 300 speech samples from the LibriSpeech test set, considering both subjective and objective evaluation metrics. For objective metrics, we used mel-cepstrum distortion (MCD) (Toda, Black, and Tokuda 2007), root mean square errors (RMSE) (Luo et al. 2017), and Vi... | **A**: To assess speaker similarity, we utilize the WavLM-TDCNN (Chen et al.
**B**: For each speaker, we randomly select a 3-second utterance as the prompt and use the text from a different utterance as the input.
**C**: 2015) to assess speech quality.
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In addition to tactile-based gesture recognition, most gesture decoding studies adopt vision-based methods, which use depth cameras [13] or optical sensors [14] to capture human gestures and emotions. <|MaskedSetence|> <|MaskedSetence|> Moreover, the vision-based models usually require relatively high computational ... | **A**: However, they often face significant challenges in dynamic real-world environments [15], e.g., occlusion of certain parts of the face/body or changes in lighting conditions can seriously affect the effectiveness of the vision-based models [16].
**B**: Moreover, speech-based emotion recognitions have been succes... | BCA | CAB | CAB | CAB | Selection 3 |
Prior to training, we tokenize the audio datasets. <|MaskedSetence|> With the tokenized audio, we perform supervised finetuning of the Gemma model with a learning rate of 5×10−65superscript1065\times 10^{-6}5 × 10 start_POSTSUPERSCRIPT - 6 end_POSTSUPERSCRIPT, and a cosine decay schedule, using the Adam optimizer. We ... | **A**: We experiment training the model on Librispeech-only, a 50:50 mix, and a 30:70 mix of Euphonia and Librispeech.
**B**: We learn the clusters for the audio tokens based only on the LibriSpeech dataset.
**C**: We evaluated the model on a held out validation set of both the Librispeech and Euphonia datasets and u... | BAC | BAC | BAC | BAC | Selection 2 |
<|MaskedSetence|> Network communication is a bridge that ensures a seamless flow of data between physical space and virtual space. <|MaskedSetence|> At the upper level, process management coordinates various operations in the virtual space and maintains consistency with the physical space. At the same time, visualiza... | **A**: In the virtual space, behavioral modeling simulates the physical characteristics of physical systems and is supplemented by interoperability to ensure seamless collaboration between systems and even models.
**B**: Furthermore, the architecture emphasizes fundamental considerations such as security, accessibilit... | CAB | CAB | CAB | CAB | Selection 2 |
(compared to the desired coverage of 0.95), resulting in 52.3% incorrect patient subtyping even with CP in place (Fig. 5d). While retaining SNGP and EAT alone (without CP) improved classification performance compared to baseline models (Fig. 3), it lacked the ability to identify challenging inputs and abstain from prov... | **A**: The interplay between these components creates a robust framework that not only enhances individual performance but also addresses the broader challenges of real-world variability in pathology workflows.
Aligned with the data valuation and data-centric AI paradigm emphasized by the machine learning community [... | BCA | ABC | ABC | ABC | Selection 4 |
Despite these advancements, most existing works are limited to paying little attention to enhancing the domain generalization of these models. <|MaskedSetence|> As a result, these models often struggle to adapt to unseen domains. <|MaskedSetence|> This poor generalization presents the primary barrier to the widespre... | **A**: Only a few works [15, 16] consider domain shift between the train and test data inside one dataset.
**B**: Fig. 1 illustrates this challenge, where models trained on three distinct datasets exhibit a considerable drop in scores when tested on unseen datasets.
**C**: While the models demonstrated strong perform... | ABC | CAB | ABC | ABC | Selection 1 |
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