We present AgentZero++, an agent-based model that integrates cognitive, emotional, and social mechanisms to simulate decentralized collective violence in spatially distributed systems. Building on Epstein's Agent\_Zero framework, we extend the original model with eight behavioral enhancements: age-based impulse control; memory-based risk estimation; affect-cognition coupling; endogenous destructive radius; fight-or-flight dynamics; affective homophily; retaliatory damage; and multi-agent coordination. These additions allow agents to adapt based on internal states, previous experiences, and social feedback, producing emergent dynamics such as protest asymmetries, escalation cycles, and localized retaliation. Implemented in Python using the Mesa ABM framework, AgentZero++ enables modular experimentation and visualization of how micro-level cognitive heterogeneity shapes macro-level conflict patterns. Our results highlight how small variations in memory, reactivity, and affective alignment can amplify or dampen unrest through feedback loops. By explicitly modeling emotional thresholds, identity-driven behavior, and adaptive networks, this work contributes a flexible and extensible platform for analyzing affective contagion and psychologically grounded collective action.

From'nepo kids' to PM resignation: How the Nepal protests unfolded Nepal has been shaken by deadly protests that have led to the resignation of the country's Prime Minister KP Sharma Oli. The BBC's Charlotte Scarr is on the streets of Kathmandu, where she saw torched government buildings and military presence. The Himalayan nation has been experiencing its worst unrest in decades, after a campaign highlighting the lavish lifestyles of politicians' children and allegations of corruption took off on social media. Thirty people have been killed in the protests and more than 1,000 injured since the unrest began. The military parade was attended by world leaders including Vladimir Putin and Kim Jong Un and showcased China's new weapons.

The growing use of large language models (LLMs) has raised concerns regarding their safety. While many studies have focused on English, the safety of LLMs in Arabic, with its linguistic and cultural complexities, remains under-explored. Here, we aim to bridge this gap. In particular, we present an Arab-region-specific safety evaluation dataset consisting of 5,799 questions, including direct attacks, indirect attacks, and harmless requests with sensitive words, adapted to reflect the socio-cultural context of the Arab world. To uncover the impact of different stances in handling sensitive and controversial topics, we propose a dual-perspective evaluation framework. It assesses the LLM responses from both governmental and opposition viewpoints. Experiments over five leading Arabic-centric and multilingual LLMs reveal substantial disparities in their safety performance. This reinforces the need for culturally specific datasets to ensure the responsible deployment of LLMs.

A new technique that analyzes seismic signals to predict days in advance when America's most dangerous volcano will erupt. Mount St Helens, located in Washington State, has recently showed signs of recharging and scientists have developed a machine learning tool to find patterns of volcanic activity to provide better emergency plans. The system was able to determine when the volcano experienced unrest, pre-eruptive and eruptive periods. Using the data, the technology predicted at least three days in advance when the volcano would erupt - with 95 percent accuracy. The study comes less than 10 days since the Pacific Northwest Seismic Network revealed it detected with 350 earthquakes in the region since February, which are signs the volcano may be awakening.

Offline Reinforcement Learning (RL) has emerged as a promising framework for learning policies without active interactions, making it especially appealing for autonomous driving tasks. Recent successes of Transformers inspire casting offline RL as sequence modeling, which performs well in long-horizon tasks. However, they are overly optimistic in stochastic environments with incorrect assumptions that the same goal can be consistently achieved by identical actions. In this paper, we introduce an UNcertainty-awaRE deciSion Transformer (UNREST) for planning in stochastic driving environments without introducing additional transition or complex generative models. Specifically, UNREST estimates state uncertainties by the conditional mutual information between transitions and returns, and segments sequences accordingly. Discovering the'uncertainty accumulation' and'temporal locality' properties of driving environments, UNREST replaces the global returns in decision transformers with less uncertain truncated returns, to learn from true outcomes of agent actions rather than environment transitions. We also dynamically evaluate environmental uncertainty during inference for cautious planning. Extensive experimental results demonstrate UNREST's superior performance in various driving scenarios and the power of our uncertainty estimation strategy. Safe and efficient motion planning has been recognized as a crucial component and the bottleneck in autonomous driving systems (Yurtsever et al., 2020). Nowadays, learning-based planning algorithms like imitation learning (IL) (Bansal et al., 2018; Zeng et al., 2019) and reinforcement learning (RL) (Chen et al., 2019a; 2020) have gained prominence with the advent of intelligent simulators (Dosovitskiy et al., 2017; Sun et al., 2022b) and large-scale datasets (Caesar et al., 2021). Building on these, offline RL (Diehl et al., 2021; Li et al., 2022a) becomes a promising framework for safety-critical driving tasks to learn policies from offline data while retaining the ability to leverage and improve over data of various quality (Fujimoto et al., 2019; Kumar et al., 2020). Nevertheless, the application of offline RL approaches still faces practical challenges. Specifically: (1) The driving task requires conducting long-horizon planning to avoid shortsighted erroneous decisions (Zhang et al., 2022); (2) The stochasticity of environmental objects during driving also demands real-time responses to their actions (Diehl et al., 2021; Villaflor et al., 2022). The recent success of the Transformer architecture (Vaswani et al., 2017; Brown et al., 2020; Dosovitskiy et al., 2020) has inspired researchers to reformulate offline RL as a sequence modeling problem (Chen et al., 2021), which naturally considers outcomes of multi-step decision-making and has demonstrated efficacy in long-horizon tasks.

Phase transitions, characterized by abrupt shifts between macroscopic patterns of organization, are ubiquitous in complex systems. Despite considerable research in the physical and natural sciences, the empirical study of this phenomenon in societal systems is relatively underdeveloped. The goal of this study is to explore whether the dynamics of collective civil unrest can be plausibly characterized as a sequence of recurrent phase shifts, with each phase having measurable and identifiable latent characteristics. Building on previous efforts to characterize civil unrest as a self-organized critical system, we introduce a macro-level statistical model of civil unrest and evaluate its plausibility using a comprehensive dataset of civil unrest events in 170 countries from 1946 to 2017. Our findings demonstrate that the macro-level phase model effectively captures the characteristics of civil unrest data from diverse countries globally and that universal mechanisms may underlie certain aspects of the dynamics of civil unrest. We also introduce a scale to quantify a country's long-term unrest per unit of time and show that civil unrest events tend to cluster geographically, with the magnitude of civil unrest concentrated in specific regions. Our approach has the potential to identify and measure phase transitions in various collective human phenomena beyond civil unrest, contributing to a better understanding of complex social systems.

Tehran, Iran – Iran has suspended its morality police as the country continues to deal with two months of protests, the Iranian prosecutor general has suggested. The protests erupted shortly after the death of Mahsa Amini, a 22-year-old woman who was arrested by a unit of the morality police in Tehran for allegedly not adhering to the country's mandatory dress code for women. Speaking on Saturday at an event aimed at "outlining the hybrid war during recent riots", which is how Iranian officials describe alleged foreign influence in the unrest, prosecutor general Mohammad Jafar Montazeri was quoted as saying by local media the morality police operations are over. The morality police "has no connection with the judiciary and was shut down by the same place that it had been launched from in the past", he said, reportedly answering a question on why the morality police has been shut down. There were no other confirmations that work of the patrolling units – officially tasked with ensuring "moral security" in the society – has been terminated.

This story is part of Future Tense Fiction, a monthly series of short stories from Future Tense and Arizona State University's Center for Science and the Imagination about how technology and science will change our lives. By the time Tara returned from the protest, SafeT gauged her Wellness at 60% and Chase felt sick. For the last two hours he'd watched the number on his phone's app tick down, from safe green to warning yellow: 87%, 74%, 60%. On his newsfeed, masked chanters waved signs before the wire cage shielding the five megapipes that breached the marshy shore of Lake Michigan. Each pipe was owned by a consortium of Lakes United companies. Their great steel veins wormed the city, bearing water from LU to the drought-scarred West and South, whose nations paid more per acre-foot than Milwaukee's citizens ever could. On the feed Chase hadn't been able to see Tara or the sign she'd painted that morning: Our Lake, Our Water. What he had seen were the security corps of at least three consortia, clumped beneath their ever-circling camera-drones, bull-horning the chanters that they were risking corporate slander. If arrested, they'd be hauled off to one of the consortia's private prisons. There they could be coerced into confessing they were linebreakers, guerillas who spliced pipes to siphon off clean water to Milwaukee neighborhoods that couldn't afford consortia prices. Protestors sometimes returned from these prisons. Fingers numb, Chase had tapped SafeT to view the breakdown of Tara's Wellness aggregate into its individual components: risk of arrest (15%), risk of indictment (20%), risk of job loss (27%), risk of injury (31%). Even when she had texted home in 30 and he'd cleared her route in the SafeT map--low smoke risk, low contagion risk, 93% chance of safe arrival--his jaw only eased when she stepped through the door. Tara's thin face was ferocious, cheeks red against her yellow hair. Black grease spotted her strong hands. Over the decade they'd shared, he'd watched age sharpen her into herself. Now, impassioned, she was fiercely beautiful. He almost forgot her yellow number, until she saw him, and her smile sagged.

Did you miss a session from the Future of Work Summit? Applying data science to predict unrest. AI that can anticipate the next variant of COVID-19's structure. That's a few of the headlines in AI this week, which ran the gamut from the dour (how AI might prevent the next attack on the U.S. Capitol) to the uplifting (making air travel greener). It's caveated optimism, but nonetheless a breath of fresh air in a community that's becoming increasingly cynical about the technology's potential to do good.

Before I go any further it's probably worth establishing what a Deepfake is and isn't. A technique by which a digital image or video can be superimposed onto another, which maintains the appearance of an unedited image or video. The term is often misinterpreted, and that's potentially as a result of definitions like this. The concept of manipulating images and video in this way is certainly not a new concept. Visual effects artists working on Hollywood films back in the '90s would probably describe parts of their job as something very similar to this.