MOTIVATION: The BioArchLinux project was initiated to address challenges in bioinformatics software reproducibility and freshness. Relying on Arch Linux's user-driven ecosystem, we aim to create a comprehensive and continuously updated repository for life sciences research. RESULTS: BioArchLinux provides a PKGBUILD-based system for seamless software packaging and maintenance, enabling users to access the latest bioinformatics tools across multiple programming languages. The repository includes Docker images, Windows Subsystem for Linux (WSL) support, and Junest for nonroot environments, enhancing accessibility across platforms. Although being developed and maintained by a small core team, BioArchLinux is a fast-growing bioinformatics repository that offers a participatory and community-driven environment. AVAILABILITY AND IMPLEMENTATION: The repository, documentation, and tools are freely available at https://bioarchlinux.org and https://github.com/BioArchLinux. Users and developers are encouraged to contribute and expand this open-source initiative.

Transformer-based methods have recently achieved significant success in 3D human pose estimation, owing to their strong ability to model long-range dependencies. However, relying solely on the global attention mechanism is insufficient for capturing the fine-grained local details, which are crucial for accurate pose estimation. Existing local feature extraction networks, such as Graph Convolutional Networks, often suffer from over-smoothing, while small-kernel CNNs have limited receptive fields and are highly sensitive to 2D pose errors. These limitations constrain the full potential of data-driven approaches. To address this, we propose SSR-STF, a dual-stream model that effectively integrates local features with global dependencies to enhance 3D human pose estimation. Specifically, we introduce SSRFormer, a simple yet effective module that employs the skeleton selective refine attention (SSRA) mechanism, leveraging large kernels to capture fine-grained local dependencies in human pose sequences. This complements the global dependencies modeled by the Transformer, enabling a more comprehensive understanding of human motion. By adaptively fusing these two feature streams, SSR-STF can better learn the underlying structure of human poses, overcoming the limitations of traditional methods in local feature extraction. To the best of our knowledge, this is the first work to explore the application of large kernels in skeleton-based 3D human pose estimation. Extensive experiments on the Human3.6M and MPI-INF-3DHP datasets demonstrate that SSR-STF achieves state-of-the-art performance. Furthermore, the motion representations learned by our model prove effective in downstream tasks such as human mesh recovery. Codes are available at SSR-STF.

Infrared small target detection (IRSTD) remains a long-standing challenge in complex backgrounds due to low signal-to-clutter ratios (SCR), diverse target morphologies, and the absence of distinctive visual cues. While recent deep learning approaches aim to learn discriminative representations, the intrinsic variability and weak priors of small targets often lead to unstable performance. In this paper, we propose a novel end-to-end IRSTD framework, termed LRRNet, which leverages the low-rank property of infrared image backgrounds. Inspired by the physical compressibility of cluttered scenes, our approach adopts a compression–reconstruction–subtraction (CRS) paradigm to directly model structure-aware low-rank background representations in the image domain, without relying on patch-based processing or explicit matrix decomposition. To the best of our knowledge, this is the first work to directly learn low-rank background structures using deep neural networks in an end-to-end manner. Extensive experiments on multiple public datasets demonstrate that LRRNet outperforms 38 state-of-the-art methods in terms of detection accuracy, robustness, and computational efficiency. Remarkably, it achieves real-time performance with an average speed of 82.34 FPS. Evaluations on the challenging NoisySIRST dataset further confirm the model’s resilience to sensor noise. The source code will be made publicly available upon acceptance.

In charge-coupled device digital imaging, smears occur when light from a scene affects the detector during noninstantaneous charge transfer, creating bright lines across the image that hinder astronomical observations. Existing image processing methods struggle to remove these smears without influencing follow-up process. The lack of labeled data further complicates the development of deep learning-based approaches in this area. To address these challenges, we propose an imaging-model-based method to represent smears and guide supervised networks in learning smear suppression. Leveraging the imaging mechanism, the proposed network (IMGNet) identifies the charge transfer direction and smeared regions to produce clean images. Trained on simulated data, the method outperforms traditional, supervised, and unsupervised baselines. By explicitly representing smears, the network effectively mitigates the impact of domain gaps with authentic data. Experiments on downstream tasks, such as detection and registration, further demonstrate the superior performance of the proposed method.

Infrared small target detection (IRSTD) is the pivotal technology for remote surveillance and search missions under infrared imagery. Due to the target’s nature of being small, dim, and lacking texture information, detection algorithms for relatively large visible targets are not suitable for infrared small targets. In this letter, a real-time, robust IRSTD network with feature enhancement and augmentation, FEA-Net, is proposed, which includes a hybrid multiscale enhancement (HME) module and a saliency detection network with a small target feature augmentation (STFA) module. The HME introduces a hybrid multiscale spatial attention mechanism to obtain a rough estimation of targets and then apply it to the original image. The STFA is directed against the insufficiency of target-related features and the consequent feature losses, which screens out the crucial information and represents it into a larger tensor for further feature fusion, increasing the proportion of target-related features of the output. The results show that the proposed method can achieve the same level of detection effect as the comparable methods and still has considerable real-time performance with the normalized intersection over union (nIoU) of 76.12% on the SIRST dataset and 92.28% on the NUDT-SIRST dataset.

This paper presents an establish and operation of energy saving LEDs virtual international research/education center. This is a long-term international engineering education and research collaboration program among California Polytechnic State University (Cal Poly), United States, Peking University (PKU), Beijing, China, and Tsinghua University, Beijing, China on light emitting diode (LED) research in the past seven years. We focused on GaN laser diode (LD) research for the first year with PKU. Then GaN light emitting diode (LED) research was added during the second year. In 2012, we expanded our interest to organic light-emitting diodes (OLEDs) and collaborated with Tsinghua University. The project began by having faculty from Cal Poly to work in PKU for one summer. The collaboration in the rest of the period was done through teleconference and e-mails. Cal Poly graduate students were grouped with graduate students in China and worked closely on certain projects. Through this project, our students (US and Chinese) obtained experience in collaborating with foreign partners, especially awareness of cultural differences, without traveling abroad in most of the time.

In cognitive radio (CR) networks, cross-layer design is an important issue since the behavior of one protocol could affect the performance of others. However, for the energy-constraint CR networks, the previous works mostly focus on maximizing the throughput in physical or transport layer, rather than the energy efficiency of the end-to-end transmission control protocol (TCP). In this paper, we propose a novel cross-layer scheme which takes the lower layers' parameters into consideration, e.g., signal-to-noise ratio (SNR), modulation and frame size, to improve the energy efficiency of TCP. Specifically, we use a finite state Markov channel (FSMC) model to characterize the fading channel, and solve the optimization problem by a restless bandit approach. Simulation results show that the physical and data link layer parameters affect the energy efficiency of the TCP traffic significantly and the performance can be improved by the adjustment of lower layers' parameters compared with the existing method.

With the irreversible trend of the convergence and cooperation among heterogeneous wireless networks, the need for network information awareness of user equipments (UEs) becomes increasingly imperative in the Cognitive Wireless Networks (CWN). As one of the candidate solutions for network information delivery for UEs, the Cognitive Pilot Channel (CPC) concept has been brought forward recently, providing UEs with the necessary network information for network selection by using the public signaling channel. Besides, both broadcast and on-demand CPC modes have been proposed for the network information delivery under the assumption that the geographical region is divided into meshes. In this paper, a novel homogeneous mesh grouping (HoMGP) scheme based broadcast CPC mode is designed to improve the efficiency of broadcast CPC mode in the CWN. The homogeneous meshes are selected and grouped based on the frequency occupancy graph, which is obtained by using the image processing techniques. By grouping the homogeneous meshes together, both the frame format and flow of the HoMGP broadcast CPC mode are designed to deliver heterogeneous network information to the UEs efficiently, which is verified by numerous simulation results.