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Generative Adversarial Networks (GANs) are a class of neural networks that have been widely used in the field of image-to-image translation. In this paper, we propose a streamlined image-to-image translation network with a simpler architecture compared to existing models. We investigate the relationship between GANs and autoencoders and provide an explanation for the efficacy of employing only the GAN component for tasks involving image translation. We show that adversarial for GAN models yields results comparable to those of existing methods without additional complex loss penalties. Subsequently, we elucidate the rationale behind this phenomenon. We also incorporate experimental results to demonstrate the validity of our findings.

We revisit $B\to K^*\pi$ and $B\to K\rho$ decay processes using the modified perturbative QCD approach, which is developed based on the perturbative QCD approach with a few improvements. A critical infrared cutoff scale $\mu_c$ is introduced to separate hard and soft contributions in the decay process. Hard contributions with scale $\mu$ larger than $\mu_c$ are calculated with perturbative QCD, and soft contributions with $\mu<\mu_c$ are described in terms of soft transition and production form factors. In addition, color-octet contributions are considered phenomenologically. The branching fractions and $CP$ violations of $B\to K^*\pi$, $K\rho$ decays are calculated. With appropriate parameter inputs, the theoretical results can be in good agreement with experimental data., Comment: 16 pages, 1 figure

We study the decay processes of $B\rightarrow \rho\eta$, $\rho\eta^{\prime}$ in perturbative QCD approach with a few improvements incorproated in it, where the contributions with large momentum transfer are calculated perturbatively, and the contributions with lower energy scale are treated by introuducing soft transition form factors. In addition, color-octet conributions are introduced which are essentially long-distance contributions. With resonanble input parameters selected, we find that the theoretical results of the branching ratios and $CP$ violations are all consistent with experimental data. We also predict some unmeasured quantities, which can be tested by experiment in the future., Comment: 19 pages, 4 figures

Sleep is crucial for human health, and EEG signals play a significant role in sleep research. Due to the high-dimensional nature of EEG signal data sequences, data visualization and clustering of different sleep stages have been challenges. To address these issues, we propose a two-stage hierarchical and explainable feature selection framework by incorporating a feature selection algorithm to improve the performance of dimensionality reduction. Inspired by topological data analysis, which can analyze the structure of high-dimensional data, we extract topological features from the EEG signals to compensate for the structural information loss that happens in traditional spectro-temporal data analysis. Supported by the topological visualization of the data from different sleep stages and the classification results, the proposed features are proven to be effective supplements to traditional features. Finally, we compare the performances of three dimensionality reduction algorithms: Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP). Among them, t-SNE achieved the highest accuracy of 79.8%, but considering the overall performance in terms of computational resources and metrics, UMAP is the optimal choice.

Traditional dietary assessment methods heavily rely on self-reporting, which is time-consuming and prone to bias. Recent advancements in Artificial Intelligence (AI) have revealed new possibilities for dietary assessment, particularly through analysis of food images. Recognizing foods and estimating food volumes from images are known as the key procedures for automatic dietary assessment. However, both procedures required large amounts of training images labeled with food names and volumes, which are currently unavailable. Alternatively, recent studies have indicated that training images can be artificially generated using Generative Adversarial Networks (GANs). Nonetheless, convenient generation of large amounts of food images with known volumes remain a challenge with the existing techniques. In this work, we present a simple GAN-based neural network architecture for conditional food image generation. The shapes of the food and container in the generated images closely resemble those in the reference input image. Our experiments demonstrate the realism of the generated images and shape-preserving capabilities of the proposed framework.

We study the nonleptonic decays of $B\to PP$ in the modified perturbative QCD approach, where $P$ stands for pseudoscalar mesons. Transverse momenta of partons and the Sudakov factor are included, which help to suppress the contributions of soft interactions. The wave function of the $B$ meson obtained from the relativistic potential model is used, and then the contributions in the infrared region can not be suppressed completely. So a soft cutoff scale and soft form factors are introduced. The contributions with the scale higher than the soft cutoff scale are calculated with perturbative QCD, while the contributions lower than the cutoff scale are replaced by the soft form factors. To explain experimental data, we find that contributions of color-octet operators for the quark-antiquarks in the mesons in the final state need to be considered. The contributions of the color-octet operators are parametrized by a few parameters with the help of SU(3) flavor symmetry and symmetry breaking. These parameters for color-octet contributions are universal for all the nonleptonic decay modes of the $B$ meson, where the mesons in the final state belong to the same flavor SU(3) nonet. Both the branching ratios and $CP$ violations are studied. We find that the theoretical calculation can well explain the experimental data of $B$ factories.

The application of transfer learning, leveraging knowledge from source domains to enhance model performance in a target domain, has significantly grown, supporting diverse real-world applications. Its success often relies on shared knowledge between domains, typically required in these methodologies. Commonly, methods assume identical feature and label spaces in both domains, known as homogeneous transfer learning. However, this is often impractical as source and target domains usually differ in these spaces, making precise data matching challenging and costly. Consequently, heterogeneous transfer learning (HTL), which addresses these disparities, has become a vital strategy in various tasks. In this paper, we offer an extensive review of over 60 HTL methods, covering both data-based and model-based approaches. We describe the key assumptions and algorithms of these methods and systematically categorize them into instance-based, feature representation-based, parameter regularization, and parameter tuning techniques. Additionally, we explore applications in natural language processing, computer vision, multimodal learning, and biomedicine, aiming to deepen understanding and stimulate further research in these areas. Our paper includes recent advancements in HTL, such as the introduction of transformer-based models and multimodal learning techniques, ensuring the review captures the latest developments in the field. We identify key limitations in current HTL studies and offer systematic guidance for future research, highlighting areas needing further exploration and suggesting potential directions for advancing the field., Comment: 23 pages, 7 figures

With the increasing demands in communication, Wi-Fi technology is advancing towards its next generation. As high-need applications like Virtual Reality (VR) and Augmented Reality (AR) emerge, the role of millimeter-wave (mmWave) technology becomes critical. This paper explores Wi-Fi 8's potential features, especially its integration of mmWave technology. We address the challenges of implementing mmWave under current protocols and examine the compatibility of new features with mmWave. Our study includes system-level simulations, upclocking the 802.11ac PPDU to 60 GHz, and considers hardware limitations. The results demonstrate significant performance improvements with mmWave in Wi-Fi 8, indicating its feasibility for high-demand wireless scenarios.

We calculate the branching ratio and $CP$ violations for $B\to K\pi$ decays in a modified perturbative QCD approach based on $k_{T}$ factorization. The resummation effect of the transverse momentum regulates the endpoint singularity. Using the $B$ meson wave function that is obtained in the relativistic potential model, soft contribution cannot be suppressed effectively by Sudakov factor. Soft scale cutoff and soft $BK$, $B\pi$ and $K\pi$ form factors have to be introduced. The most important next-to-leading-order contributions from the vertex corrections, the quark loops, and the magnetic penguins are also considered. In addition, the contribution of the color-octet hadronic matrix element is included which is essentially of long-distance dynamics. Our predictions for branching ratios and $CP$ violations are in good agreement with the experimental data. Especially the theoretical result of dramatic difference between the $CP$ violations of $B^+\to K^+\pi^0$ and $B^0\to K^+\pi^-$ is consistent with experimental measurement, therefore the $K\pi$ puzzle in $B$ decays can be resolved in our way of the modified perturbative QCD approach., Comment: 19 pages, 2 figures, version to appear in Phys. Rev. D

We study $B\to \pi\pi$ decay with a modified perturbative QCD approach. The branching ratios and $CP$ violation are calculated with the transverse momenta of partons considered. Sudakov factor associated with each meson is included to suppress soft contribution in QCD. With the wave function of $B$ meson obtained in QCD-inspired relativistic potential model being used, the suppression of Sudakov factor to the soft contribution is not effective enough. Soft scale cutoff and soft form factors of $B\pi$ transition and $\pi\pi$ production have to be introduced. The main next-to-leading-order contributions of vertex correction, the quark-loop and magnetic penguin are included. To solve the long-standing puzzle in $B\to\pi\pi$ decay, that is the theoretical prediction of the branching ratio of $B^0\to \pi^0\pi^0$ being seriously smaller than experimental data, color-octet matrix element which is of long-distance dynamics is introduced. With parameters taken with reasonable values, all the branching ratios and $CP$ violation are well consistent with experimental data., Comment: 13 pages, 1 figure, version to appear in Phys. Rev. D

Sampling is ubiquitous in machine learning methodologies. Due to the growth of large datasets and model complexity, we want to learn and adapt the sampling process while training a representation. Towards achieving this grand goal, a variety of sampling techniques have been proposed. However, most of them either use a fixed sampling scheme or adjust the sampling scheme based on simple heuristics. They cannot choose the best sample for model training in different stages. Inspired by "Think, Fast and Slow" (System 1 and System 2) in cognitive science, we propose a reward-guided sampling strategy called Adaptive Sample with Reward (ASR) to tackle this challenge. To the best of our knowledge, this is the first work utilizing reinforcement learning (RL) to address the sampling problem in representation learning. Our approach optimally adjusts the sampling process to achieve optimal performance. We explore geographical relationships among samples by distance-based sampling to maximize overall cumulative reward. We apply ASR to the long-standing sampling problems in similarity-based loss functions. Empirical results in information retrieval and clustering demonstrate ASR's superb performance across different datasets. We also discuss an engrossing phenomenon which we name as "ASR gravity well" in experiments.