The safe deployment of autonomous vehicles (AVs) into real-world traffic requires robust interaction with human drivers exhibiting heterogeneous behavioral tendencies, spanning from rational cooperation to adversarial aggression. Existing simulation frameworks often lack the capacity to systematically model such behavioral diversity, limiting their applicability for rigorous A V evaluation. To address this challenge, we propose a multi-agent reinforcement learning framework that generates dynamically controllable traffic through Tendency-Guided Policy Optimization (TGPO). Central to TGPO is the Adversary-Rationality-Tendency (ART), a continuous hyperparameter that enables fine-grained control over the spectrum of driving behaviors by fusing separately learned adversarial and rational value functions. Furthermore, we design an ART -guided policy network incorporating multi-head mechanisms to resolve high-dimensional multi-agent observations, adaptively prioritizing context features aligned with assigned driving tendencies. Extensive experiments across urban and highway scenarios demonstrate that TGPO generates traffic with enhanced behavioral controllability and diversity, which provides a scalable solution for simulating realistic interactions with various driving tendencies, thereby facilitating the development of AV systems capable of handling complex real-world corner cases.

Given the complex nature of interaction under ambiguous right-of-way scenarios, the interactions between Autonomous Vehicles (AVs) and Human-driven Vehicles (HVs) present considerable challenges to the safety and efficiency of the traffic system. Existing AVs struggle to comprehend and apply common HV social norms, especially the proactive behavior exhibited by adept human drivers in ambiguous right-of-way scenarios. In this study, we propose a novel framework to leverage expert priors for proactive-aware decision-making in ambiguous right-of-way, merging Reinforcement Learning (RL) with parameterized modeling. Building upon unprotected-turning interactions from real-world driving datasets, we select typical cases under ambiguous right-of-way as human-expert priors, which are utilized to guide the learning of the RL agent. Then, a Hidden Markov Model (HMM), which is governed by interpretable parameters derived from expert priors, introduces human decision updating mechanism into AV strategy. Experimenting with typical driving tasks, our approach achieves balanced safety and efficiency in tackling ambiguities of right-of-way, with superior decision-making performance via the guidance of expert priors when compared with established baselines. Furthermore, the results indicate that the proposed method enables AVs to accelerate the convergence during the interaction by consistent probing and decision updates.

MOTIVATION: Primer design is a routine practice for modern molecular biology labs. Bioinformatics tools like primer3 and primer-blast have standardized the primer design for a specific region. However, large-scale primer design, especially for genome-wide screening, is still a labor-intensive job for most wet-lab researchers using these pipelines. RESULTS: Here, we present the primerdiffer pipeline, which can be used to batch design primers that differentiate haplotypes on a large scale with precise false priming checking. This command-line interface (CLI) pipeline includes greedy primer search, local and global in silico PCR-based false priming checking, and automated best primer selection. The local CLI application provides flexibility to design primers with the user's own genome sequences and specific parameters. Some species-specific primers designed to genotype the hybrid introgression strains from Caenorhabditis briggsae and Caenorhabditis nigoni have been validated using single-worm PCR. This pipeline provides the first CLI-based large-scale primer design tool to differentiate haplotypes in any targeted region. AVAILABILITY AND IMPLEMENTATION: The open-source python modules are available at github (https://github.com/runsheng/primerdiffer, https://github.com/runsheng/primervcf) and Python package index (https://pypi.org/project/primerdiffer/, https://pypi.org/project/primervcf/).

Today's quality of life is highly dependent on the successful operation of many large-scale industrial control systems. To enhance their protection against cyber-attacks and operational errors, we develop a simulation-based verification framework with cross-layer verification techniques that allow comprehensive analysis of the entire ICS-specific stack, including application, protocol, and network layers.

This paper considers multi-hop cellular networks in which nodes use accumulation for cooperative broadcasting in order to save total energy with low delay. Our purpose is to build up Green Cellular Networks (GCN) which represent the trend of Next-Generation Networking (NGN) and cater for people's demand for health and energy savings. Therefore, we assume a radiation and harmful region and show that power is in direct proportion to the area of the region so that we can focus on the power property. We also propose Virtual Tree (VT) to determine hop number in order to evaluate delay property of accumulative routing algorithms. Then, we propose a centralized broadcast routing algorithm specific for the asymmetric cellular based on accumulation named Cellular Accumulative Broadcast (CAB) Algorithm, which aims to realize low-delay minimum-energy broadcast. Further, we figure out the accumulative relay region for better analysis of relay cooperation. Simulation results show that our CAB algorithm performs superior to other existing accumulative or non-accumulative broadcast algorithms, which means GCN built up with CAB could be cost-effective with low delay.