BACKGROUND: Detecting genomic regions under selection is essential for understanding how populations adapt to different environments, yet it remains challenging due to the confounding effects of demographic history and linkage disequilibrium (LD). Fixation index (FST) is a widely used statistic to identify genomic regions under adaptation. However, identifying genes under selection by defining FST outliers often remains challenging, owing to confounding effects of underlying demographic history. Traditional methods assume independence among loci and rely on simple demographic models, while newer models perform much better but are computationally expensive and not easily scalable. RESULTS: Here, we present pyFLANK, an open-source and automated Python implementation which detects FST outliers using a null distribution inferred from quasi-independent loci. Our tool integrates three approaches to identify loci obeying a null distribution: graph neural network (GNN) inference, linkage disequilibrium (LD)-based inference, and user-defined input. Because pyFLANK uses GNN-based inference of quasi-independent loci, it yields a more accurate null model with less need for user parameter input. In simulation experiments, pyFLANK achieved lower false positive rates than current methods while maintaining comparable detection power, indicating that its refined null model better distinguishes true adaptive loci from background variation. The GNN-based model, in particular, detected additional loci associated with phenotypic variance that were not identified by existing methods. CONCLUSIONS: Assessments of simulation and real data from different species demonstrate that pyFLANK achieves lower false positive rates compared with other commonly used FST outlier detectors, while maintaining comparable detection power and excellent computational performance, providing a robust and user-friendly tool for identifying loci under divergent selection. It extends existing FST outlier frameworks by incorporating explicit LD-aware strategies for null model calibration. The method is intended as a practical and scalable complement to existing genome scan approaches.

With the development of information technology, the next-generation information technologies such as artificial intelligence, digital twin and reconfigurable intelligent surface have become key research areas for current 6G networks. In addition, to improve the end-to-end information processing capability in the next-generation networks and better meet the demand for high-speed communication and high-precision sensing for digital twin, Virtual Reality and Augmented Reality immersive services in the 6G networks, integrated sensing and communications (ISAC) has emerged. To deal with the conflict between high-quality communication services and low-latency sensing targets in an ISAC architecture, this paper investigates a UAV-assisted ISAC system in which the UAV adopts a flight-hover-communication protocol. In particular, the UAV communicates with Internet of Things (IoT) devices during the hovering period, while it senses the location of targets during the flying period. To maximize the number of connected IoT devices and minimize the energy consumption of the UAV, a deep reinforcement learning (DRL) based trajectory planning algorithm is designed. The numerical results demonstrate that the proposed algorithm can effectively detect sensor devices as well as collect sensor data.

Sound source localization (SSL) has been widely applied in industrial and civil fields. And with the development of wearable devices and the Internet of Things (IoT), it is attractive to deploy the SSL system onto embedded and portable devices. However, the software-based SSL system causes excessive response delay and is often affected by environmental noise. To overcome this obstacle, we propose the fast and precise localization (FPL) algorithm for distributed SSL systems. It combines the benefits of both time difference of arrival (TDOA) and steered response power (SRP) methods, and thus it is able to localize sound sources fast and precisely. To further improve the localization speed, we propose the fast and reconfigurable localization (FRL) accelerator, which is an algorithm-hardware co-designed SSL accelerator. It adopts multiple distributed localization nodes for higher localization precision and higher robustness to environmental interference, and it can be configured into either the fast or precise mode to adapt to various environments. Experimental evaluations show that our proposed FPL algorithm can achieve high localization speed and precision, and the field-programmable gate array (FPGA)-based FRL accelerator outperforms the software implementation by$48.6\times $and outperforms the prior FPGA-based SSL accelerators by$20\times \sim 838.2\times $.

Abstract Tiwari proved that the termination of a class of linear programs is decidable in Tiwari (Proceedings of CAV’04. Lecture notes in computer science, vol 3114, pp 70–82, 2004). The decision procedure proposed therein depends on the computation of Jordan forms . Thus, people may draw a wrong conclusion from this procedure, if they simply apply floating-point computation to compute Jordan forms. In this paper, we first use an example to explain this problem, and then present a symbolic implementation of the decision procedure. Thus, the rounding error problem is therefore avoided. Moreover, we also show that the symbolic decision procedure is as efficient as the numerical one given in Tiwari (Proceedings of CAV’04. Lecture notes in computer science, vol 3114, pp 70–82, 2004). The complexity of former is max{ O ( n 6 ), O ( n m +3 )}, while that of the latter is O ( n m +3 ), where n is the number of variables of the program and m is the number of its Boolean conditions. In addition, for the case when the characteristic polynomial of the assignment matrix is irreducible, we design a more efficient symbolic algorithm whose complexity is max( O ( n 6 ), O ( mn 3 )).