LAYING DOWN ARMS – Cease-fire begins between Israel and Hamas ahead of planned hostage-prisoner swap. DISHING IT OUT – Biden campaign released guide of how to respond to'crazy MAGA nonsense' from relatives. 'LEFT ME SPEECHLESS' – Israel spokesman's stunned reaction over bizarre question about hostage deal goes viral. LITTLE RECOURSE – Man who fended off home intruders faces uphill battle to get gun permit back. CASH COW – Brothels that allegedly hosted politicians raked in millions, kept'impeccable records'.

Artificial intelligence that can generate text, images and other content could help improve state programs but also poses risks, according to a report released by the governor's office on Tuesday. Generative AI could help quickly translate government materials into multiple languages, analyze tax claims to detect fraud, summarize public comments and answer questions about state services. Still, deploying the technology, the analysis warned, also comes with concerns around data privacy, misinformation, equity and bias. "When used ethically and transparently, GenAI has the potential to dramatically improve service delivery outcomes and increase access to and utilization of government programs," the report stated. The 34-page report, ordered by Gov. Gavin Newsom, provides a glimpse into how California could apply the technology to state programs even as lawmakers grapple with how to protect people without hindering innovation.

The Israel Defense Forces (IDF) have used artificial intelligence (AI) to improve targeting of Hamas operators and facilities as its military faces criticism for what's been deemed as collateral damage and civilian casualties. "I can't predict how long the Gaza operation will take, but the IDF's use of AI and Machine Learning (ML) tools can certainly assist in the administratively burdensome targeting identification, evaluation and assessment process," Mark Montgomery, a senior fellow at the Foundation for Defense of Democracies' Center on Cyber and Technology Innovation, told Fox News Digital. "Similar to U.S. forces, the IDF takes great effort to reduce collateral damage and civilian casualties, and tools like AI and ML can make the targeting process more agile and executable," Montgomery added. "AI tools should help in target identification efforts, expediting target review and approval," he said. "There will inevitably still be humans in the targeting process but in a much accelerated timeline."

While measures, such as traffic calming and advance driver assistance systems, can improve safety for Vulnerable Road Users (VRUs), their effectiveness ultimately relies on the responsible behavior of drivers and pedestrians who must adhere to traffic rules or take appropriate actions. However, these measures offer no solution in scenarios where a collision becomes imminent, leaving no time for warning or corrective actions. Recently, connected vehicle technology has introduced warning services that can alert drivers and VRUs about potential collisions. Nevertheless, there is still a significant gap in the system's ability to predict collisions in advance. The objective of this study is to utilize Digital Twin (DT) technology to enable a proactive safety alert system for VRUs. A pedestrian-vehicle trajectory prediction model has been developed using the Encoder-Decoder Long Short-Term Memory (LSTM) architecture to predict future trajectories of pedestrians and vehicles. Subsequently, parallel evaluation of all potential future safety-critical scenarios is carried out. Three Encoder-Decoder LSTM models, namely pedestrian-LSTM, vehicle-through-LSTM, and vehicle-left-turn-LSTM, are trained and validated using field-collected data, achieving corresponding root mean square errors (RMSE) of 0.049, 1.175, and 0.355 meters, respectively. A real-world case study has been conducted where a pedestrian crosses a road, and vehicles have the option to proceed through or left-turn, to evaluate the efficacy of DT-enabled proactive safety alert systems. Experimental results confirm that DT-enabled safety alert systems were succesfully able to detect potential crashes and proactively generate safety alerts to reduce potential crash risk.

Counterfactual Explanations are becoming a de-facto standard in post-hoc interpretable machine learning. For a given classifier and an instance classified in an undesired class, its counterfactual explanation corresponds to small perturbations of that instance that allows changing the classification outcome. This work aims to leverage Counterfactual Explanations to detect the important decision boundaries of a pre-trained black-box model. This information is used to build a supervised discretization of the features in the dataset with a tunable granularity. Using the discretized dataset, an optimal Decision Tree can be trained that resembles the black-box model, but that is interpretable and compact. Numerical results on real-world datasets show the effectiveness of the approach in terms of accuracy and sparsity.

The use of topic modelling techniques, especially Latent Dirichlet Allocation (LDA) introduced by Blei et al. (2003), is growing fast. The methods find application in a broad variety of domains. In text-as-data applications, LDA enables the analysis of large collections of text in an unsupervised manner by uncovering latent structures behind the data. Given this increasing use of LDA as a standard tool for empirical analysis, also the interest in details of the method and, in particular, in parameter settings for its implementation is rising. Thus, since the introduction of the LDA approach in 2003 by Blei et al., different methodological components of LDA have already been studied in more detail as, for example, the choice of the number of topics (Cao et al., 2009; Mimno et al., 2011; Lewis and Grossetti, 2022; Bystrov et al., 2022a), hyper-parameter settings (Wallach et al., 2009), model design (e.g.

Artificial Intelligence (AI) is one of the most transformative technologies of the 21st century. The extent and scope of future AI capabilities remain a key uncertainty, with widespread disagreement on timelines and potential impacts. As nations and technology companies race toward greater complexity and autonomy in AI systems, there are concerns over the extent of integration and oversight of opaque AI decision processes. This is especially true in the subfield of machine learning (ML), where systems learn to optimize objectives without human assistance. Objectives can be imperfectly specified or executed in an unexpected or potentially harmful way. This becomes more concerning as systems increase in power and autonomy, where an abrupt capability jump could result in unexpected shifts in power dynamics or even catastrophic failures. This study presents a hierarchical complex systems framework to model AI risk and provide a template for alternative futures analysis. Survey data were collected from domain experts in the public and private sectors to classify AI impact and likelihood. The results show increased uncertainty over the powerful AI agent scenario, confidence in multiagent environments, and increased concern over AI alignment failures and influence-seeking behavior.

In consequential domains, it is often impossible to compel individuals to take treatment, so that optimal policy rules are merely suggestions in the presence of human non-adherence to treatment recommendations. In these same domains, there may be heterogeneity both in who responds in taking-up treatment, and heterogeneity in treatment efficacy. While optimal treatment rules can maximize causal outcomes across the population, access parity constraints or other fairness considerations can be relevant in the case of encouragement. For example, in social services, a persistent puzzle is the gap in take-up of beneficial services among those who may benefit from them the most. When in addition the decision-maker has distributional preferences over both access and average outcomes, the optimal decision rule changes. We study causal identification, statistical variance-reduced estimation, and robust estimation of optimal treatment rules, including under potential violations of positivity. We consider fairness constraints such as demographic parity in treatment take-up, and other constraints, via constrained optimization. Our framework can be extended to handle algorithmic recommendations under an often-reasonable covariate-conditional exclusion restriction, using our robustness checks for lack of positivity in the recommendation. We develop a two-stage algorithm for solving over parametrized policy classes under general constraints to obtain variance-sensitive regret bounds. We illustrate the methods in two case studies based on data from randomized encouragement to enroll in insurance and from pretrial supervised release with electronic monitoring.

Approximately 30% of all traffic fatalities in the United States are attributed to alcohol-impaired driving. This means that, despite stringent laws against this offense in every state, the frequency of drunk driving accidents is alarming, resulting in approximately one person being killed every 45 minutes. The process of charging individuals with Driving Under the Influence (DUI) is intricate and can sometimes be subjective, involving multiple stages such as observing the vehicle in motion, interacting with the driver, and conducting Standardized Field Sobriety Tests (SFSTs). Biases have been observed through racial profiling, leading to some groups and geographical areas facing fewer DUI tests, resulting in many actual DUI incidents going undetected, ultimately leading to a higher number of fatalities. To tackle this issue, our research introduces an Artificial Intelligence-based predictor that is both fairness-aware and incorporates domain knowledge to analyze DUI-related fatalities in different geographic locations. Through this model, we gain intriguing insights into the interplay between various demographic groups, including age, race, and income. By utilizing the provided information to allocate policing resources in a more equitable and efficient manner, there is potential to reduce DUI-related fatalities and have a significant impact on road safety.

How Strong a Kick Should be to Topple Northeastern's Tumbling Robot? Abstract-- Rough terrain locomotion has remained one of the most challenging mobility questions. In 2022, NASA's Innovative Advanced Concepts (NIAC) Program invited US academic institutions to participate NASA's Breakthrough, Innovative & Game-changing (BIG) Idea competition by proposing novel mobility systems that can negotiate extremely rough terrain, lunar bumpy craters. In this competition, Northeastern University won NASA's top Artemis Award award by proposing an articulated robot tumbler called COBRA (Crater Observing Bio-inspired Rolling Articulator). This report briefly explains the underlying principles that made COBRA successful in competing with other concepts ranging from cable-driven to multilegged designs from six other participating US institutions.