Calculating mealtime insulin doses poses a significant challenge for individuals with Type 1 Diabetes (T1D). Doses should perfectly compensate for expected post-meal glucose excursions, requiring a profound understanding of the individual's insulin sensitivity and the meal macronutrients'. Usually, people rely on intuition and experience to develop this understanding. In this work, we demonstrate how a reinforcement learning agent, employing a self-attention encoder network, can effectively mimic and enhance this intuitive process. Trained on 80 virtual subjects from the FDA-approved UVA/Padova T1D adult cohort and tested on twenty, self-attention demonstrates superior performance compared to other network architectures. Results reveal a significant reduction in glycemic risk, from 16.5 to 9.6 in scenarios using sensor-augmented pump and from 9.1 to 6.7 in scenarios using automated insulin delivery. This new paradigm bypasses conventional therapy parameters, offering the potential to simplify treatment and promising improved quality of life and glycemic outcomes for people with T1D.

ABSTRACT The need of real-time of monitoring and alerting systems for Space Weather hazards has grown significantly in the last two decades. One of the most important challenge for space mission operations and planning is the prediction of solar proton events (SPEs). In this context, artificial intelligence and machine learning techniques have opened a new frontier, providing a new paradigm for statistical forecasting algorithms. The great majority of these models aim to predict the occurrence of a SPE, i.e., they are based on the classification approach. In this work we present a simple and efficient machine learning regression algorithm which is able to forecast the energetic proton flux up to 1 hour ahead by exploiting features derived from the electron flux only. This approach could be helpful to improve monitoring systems of the radiation risk in both deep space and near-Earth environments. The model is very relevant for mission operations and planning, especially when flare characteristics and source location are not available in real time, as at Mars distance. INTRODUCTION Solar Proton Events (SPEs) are pronounced enhancements of the energetic proton flux measured by instruments placed on different space probes across the Heliosphere. Solar protons can reach high energies, say tens of GeVs, as a consequence of different acceleration processes occurring at the Sun in association with transient phenomena like solar flares and coronal mass ejections (CMEs; Kahler et al. 1984; Shea & Smart 1990; Aschwanden 2002; Iucci et al. 2005). Then, particles travel along interplanetary magnetic field lines and can produce a geoeffective SPE that can be detected by instruments placed on Earth-orbiting satellites, such as the Geostationary Operational Environmental Satellite (GOES).

This paper explores the rising concern of utilizing Large Language Models (LLMs) in spear phishing message generation, and their performance compared to human-authored counterparts. Our pilot study compares the effectiveness of smishing (SMS phishing) messages created by GPT-4 and human authors, which have been personalized to willing targets. The targets assessed the messages in a modified ranked-order experiment using a novel methodology we call TRAPD (Threshold Ranking Approach for Personalized Deception). Specifically, targets provide personal information (job title and location, hobby, item purchased online), spear smishing messages are created using this information by humans and GPT-4, targets are invited back to rank-order 12 messages from most to least convincing (and identify which they would click on), and then asked questions about why they ranked messages the way they did. They also guess which messages are created by an LLM and their reasoning. Results from 25 targets show that LLM-generated messages are most often perceived as more convincing than those authored by humans, with messages related to jobs being the most convincing. We characterize different criteria used when assessing the authenticity of messages including word choice, style, and personal relevance. Results also show that targets were unable to identify whether the messages was AI-generated or human-authored and struggled to identify criteria to use in order to make this distinction. This study aims to highlight the urgent need for further research and improved countermeasures against personalized AI-enabled social engineering attacks.

Large Language Models (LLMs) have transformed machine learning but raised significant legal concerns due to their potential to produce text that infringes on copyrights, resulting in several high-profile lawsuits. The legal landscape is struggling to keep pace with these rapid advancements, with ongoing debates about whether generated text might plagiarize copyrighted materials. Current LLMs may infringe on copyrights or overly restrict non-copyrighted texts, leading to these challenges: (i) the need for a comprehensive evaluation benchmark to assess copyright compliance from multiple aspects; (ii) evaluating robustness against safeguard bypassing attacks; and (iii) developing effective defenses targeted against the generation of copyrighted text. To tackle these challenges, we introduce a curated dataset to evaluate methods, test attack strategies, and propose lightweight, real-time defenses to prevent the generation of copyrighted text, ensuring the safe and lawful use of LLMs. Our experiments demonstrate that current LLMs frequently output copyrighted text, and that jailbreaking attacks can significantly increase the volume of copyrighted output. Our proposed defense mechanisms significantly reduce the volume of copyrighted text generated by LLMs by effectively refusing malicious requests. Code is publicly available at https://github.com/xz-liu/SHIELD

Optimal information and knowledge management is crucial for organizations to achieve their objectives efficiently. As a rare and valuable resource, effective knowledge management provides a strategic advantage and has become a key determinant of organizational success. The study aims to identify critical success and failure factors for implementing knowledge management systems in government organizations. This research employs a descriptive survey methodology, collecting data through random interviews and questionnaires. The study highlights the critical success factors for knowledge management systems in government organizations, including cooperation, an open atmosphere, staff training, creativity and innovation, removal of organizational constraints, reward policies, role modeling, and focus. Conversely, failure to consider formality, staff participation, collaboration technologies, network and hardware infrastructure, complexity, IT staff, and trust can pose significant obstacles to successful implementation.

Data practices shape research and practice on fairness in machine learning (fair ML). Critical data studies offer important reflections and critiques for the responsible advancement of the field by highlighting shortcomings and proposing recommendations for improvement. In this work, we present a comprehensive analysis of fair ML datasets, demonstrating how unreflective yet common practices hinder the reach and reliability of algorithmic fairness findings. We systematically study protected information encoded in tabular datasets and their usage in 280 experiments across 142 publications. Our analyses identify three main areas of concern: (1) a \textbf{lack of representation for certain protected attributes} in both data and evaluations; (2) the widespread \textbf{exclusion of minorities} during data preprocessing; and (3) \textbf{opaque data processing} threatening the generalization of fairness research. By conducting exemplary analyses on the utilization of prominent datasets, we demonstrate how unreflective data decisions disproportionately affect minority groups, fairness metrics, and resultant model comparisons. Additionally, we identify supplementary factors such as limitations in publicly available data, privacy considerations, and a general lack of awareness, which exacerbate these challenges. To address these issues, we propose a set of recommendations for data usage in fairness research centered on transparency and responsible inclusion. This study underscores the need for a critical reevaluation of data practices in fair ML and offers directions to improve both the sourcing and usage of datasets.

Topic models are useful tools for discovering latent semantic structures in large textual corpora. Topic modeling historically relied on bag-of-words representations of language. This approach makes models sensitive to the presence of stop words and noise, and does not utilize potentially useful contextual information. Recent efforts have been oriented at incorporating contextual neural representations in topic modeling and have been shown to outperform classical topic models. These approaches are, however, typically slow, volatile and still require preprocessing for optimal results. We present Semantic Signal Separation ($S^3$), a theory-driven topic modeling approach in neural embedding spaces. $S^3$ conceptualizes topics as independent axes of semantic space, and uncovers these with blind-source separation. Our approach provides the most diverse, highly coherent topics, requires no preprocessing, and is demonstrated to be the fastest contextually sensitive topic model to date. We offer an implementation of $S^3$, among other approaches, in the Turftopic Python package.

Fascinating and important stuff in presidential politics this weekend as the Joe Biden and Donald Trump campaigns engaged in detailed discussions about an impending decision that one of them will have to make about the corporate tax rate. While both sides acknowledged that marginal adjustments to the rate could have unforeseen effects on the balance between productivity and worker wage growth, they--haha, just kidding. The main activity in presidential politics for the past 36 hours has been arguing about which candidate is allegedly more of a drooling vegetable. Both candidates provided arguable evidence to support the opposition's claims of senility on Saturday night. Biden, appearing at what must by law be described as a star-studded Hollywood fundraiser featuring actor George Clooney and Netflix producer Barack Obama, seemed to "freeze" for a second before leaving the stage, but only if you look at the video from a certain angle shot from the crowd.

Alex Galvagni, CEO of Age of Learning and a former artificial intelligence researcher with NASA, says advances in AI now make it possible to deliver to children'a personalized and supportive' experience in education. The House of Representatives will likely not take up legislation this year to establish a large-scale framework for the artificial intelligence (AI) industry. House Majority Leader Steve Scalise, R-La., told Fox News Digital that AI development was in a stage where he was concerned that over-burdensome regulations could make the U.S. fall behind competitors like China. "There's no consensus right now," Scalise said when asked about the likelihood of AI legislation. "Frankly, we shouldn't be having some new regulatory structure, billions of taxpayer money, to do what the private sector is already doing. You know, and AI is a great example of how America's leading the world in innovation, we don't need to limit that growth by throwing a whole lot of new regulations on top of it to solve a problem that doesn't exist."

A Sam Altman–funded nonprofit studying the effects of giving monthly checks of up to 1,000 to lower-income households in the US espouses transparency in its operations. "We aim to share data, findings, and insights widely," OpenResearch says on its website, which describes its work as a "public good." But like at least two other Altman-linked organizations--OpenAI and UBI Charitable--OpenResearch has decided to withhold information about its finances and governance. In several years of filings to US tax authorities since their founding, each of the organizations has answered a question about their voluntary disclosure of financial statements, governing documents, and conflict-of-interest policies by stating that the public can review them upon request. It remains unclear whether anyone took them up on the offer in those years.