The link between in vitro hERG ion channel inhibition and subsequent in vivo QT interval prolongation, a critical risk factor for the development of arrythmias such as Torsade de Pointes, is so well established that in vitro hERG activity alone is often sufficient to end the development of an otherwise promising drug candidate. It is therefore of tremendous interest to develop advanced methods for identifying hERG-active compounds in the early stages of drug development, as well as for proposing redesigned compounds with reduced hERG liability and preserved on-target potency. In this work, we present CardioGenAI, a machine learning-based framework for re-engineering both developmental and commercially available drugs for reduced hERG activity while preserving their pharmacological activity. The framework incorporates novel state-of-the-art discriminative models for predicting hERG channel activity, as well as activity against the voltage-gated NaV1.5 and CaV1.2 channels due to their potential implications in modulating the arrhythmogenic potential induced by hERG channel blockade. We applied the complete framework to pimozide, an FDA-approved antipsychotic agent that demonstrates high affinity to the hERG channel, and generated 100 refined candidates. Remarkably, among the candidates is fluspirilene, a compound which is of the same class of drugs (diphenylmethanes) as pimozide and therefore has similar pharmacological activity, yet exhibits over 700-fold weaker binding to hERG. We envision that this method can effectively be applied to developmental compounds exhibiting hERG liabilities to provide a means of rescuing drug development programs that have stalled due to hERG-related safety concerns. Additionally, the discriminative models can also serve independently as effective components of a virtual screening pipeline. We have made all of our software open-source.

In this work, we consider the approximation of a large class of bounded functions, with minimal regularity assumptions, by ReLU neural networks. We show that the approximation error can be bounded from above by a quantity proportional to the uniform norm of the target function and inversely proportional to the product of network width and depth. We inherit this approximation error bound from Fourier features residual networks, a type of neural network that uses complex exponential activation functions. Our proof is constructive and proceeds by conducting a careful complexity analysis associated with the approximation of a Fourier features residual network by a ReLU network.

TikTok will begin labeling content created using artificial intelligence when it's been uploaded from outside its own platform in an attempt to combat misinformation. "AI enables incredible creative opportunities, but can confuse or mislead viewers if they don't know content was AI-generated," the company said in a prepared statement Thursday. "Labeling helps make that context clear--which is why we label AIGC made with TikTok AI effects, and have required creators to label realistic AIGC for over a year." TikTok's shift in policy is part of an broader attempt in the technology industry to provide more safeguards for AI usage. In February Meta announced that it was working with industry partners on technical standards that will make it easier to identify images and eventually video and audio generated by artificial intelligence tools.

At least two hours have gone by in the Pistol 101 class, and no student has fired a bullet or even picked up a gun. This isn't a lesson for anyone eager to pull the trigger. Tom Nguyen's teaching style is patient, aimed at demystifying an object many of his students have spent their lives fearing, even hating. Something of a leftist firearms whisperer, Nguyen pokes fun at stereotypical American gun culture, mocking "alpha male" behavior and the John Wick film franchise with its video game levels of violence. Owning a gun doesn't have to define your personality, he preaches, and it doesn't mean you have to seek conflict. "Being peaceful," he says, "is not the same as being helpless, or harmless."

Climate change and political unrest in certain regions of the world are imposing extreme hardship on many communities and are forcing millions of vulnerable populations to abandon their homelands and seek refuge in safer lands. As international laws are not fully set to deal with the migration crisis, people are relying on networks of exploiting smugglers to escape the devastation in order to live in stability. During the smuggling journey, migrants can become victims of human trafficking if they fail to pay the smuggler and may be forced into coerced labor. Government agencies and anti-trafficking organizations try to identify the trafficking routes based on stories of survivors in order to gain knowledge and help prevent such crimes. In this paper, we propose a system called Narrative to Trajectory (N2T+), which extracts trajectories of trafficking routes. N2T+ uses Data Science and Natural Language Processing techniques to analyze trafficking narratives, automatically extract relevant location names, disambiguate possible name ambiguities, and plot the trafficking route on a map. In a comparative evaluation we show that the proposed multi-dimensional approach offers significantly higher geolocation detection than other state of the art techniques.

Ship design is a complex design process that may take a team of naval architects many years to complete. Improving the ship design process can lead to significant cost savings, while still delivering high-quality designs to customers. A new technology for ship hull design is diffusion models, a type of generative artificial intelligence. Prior work with diffusion models for ship hull design created high-quality ship hulls with reduced drag and larger displaced volumes. However, the work could not generate hulls that meet specific design constraints. This paper proposes a conditional diffusion model that generates hull designs given specific constraints, such as the desired principal dimensions of the hull. In addition, this diffusion model leverages the gradients from a total resistance regression model to create low-resistance designs. Five design test cases compared the diffusion model to a design optimization algorithm to create hull designs with low resistance. In all five test cases, the diffusion model was shown to create diverse designs with a total resistance less than the optimized hull, having resistance reductions over 25%. The diffusion model also generated these designs without retraining. This work can significantly reduce the design cycle time of ships by creating high-quality hulls that meet user requirements with a data-driven approach.

The rapid advancement of Large Language Models (LLMs) has opened up new opportunities for leveraging artificial intelligence in various domains, including cybersecurity. As the volume and sophistication of cyber threats continue to grow, there is an increasing need for intelligent systems that can automatically detect vulnerabilities, analyze malware, and respond to attacks. In this survey, we conduct a comprehensive review of the literature on the application of LLMs in cybersecurity (LLM4Security). By comprehensively collecting over 30K relevant papers and systematically analyzing 127 papers from top security and software engineering venues, we aim to provide a holistic view of how LLMs are being used to solve diverse problems across the cybersecurity domain. Through our analysis, we identify several key findings. First, we observe that LLMs are being applied to a wide range of cybersecurity tasks, including vulnerability detection, malware analysis, network intrusion detection, and phishing detection. Second, we find that the datasets used for training and evaluating LLMs in these tasks are often limited in size and diversity, highlighting the need for more comprehensive and representative datasets. Third, we identify several promising techniques for adapting LLMs to specific cybersecurity domains, such as fine-tuning, transfer learning, and domain-specific pre-training. Finally, we discuss the main challenges and opportunities for future research in LLM4Security, including the need for more interpretable and explainable models, the importance of addressing data privacy and security concerns, and the potential for leveraging LLMs for proactive defense and threat hunting. Overall, our survey provides a comprehensive overview of the current state-of-the-art in LLM4Security and identifies several promising directions for future research.

In the vast domain of cybersecurity, the transition from reactive defense to offensive has become critical in protecting digital infrastructures. This paper explores the integration of Artificial Intelligence (AI) into offensive cybersecurity, particularly through the development of an autonomous AI agent, ReaperAI, designed to simulate and execute cyberattacks. Leveraging the capabilities of Large Language Models (LLMs) such as GPT-4, ReaperAI demonstrates the potential to identify, exploit, and analyze security vulnerabilities autonomously. This research outlines the core methodologies that can be utilized to increase consistency and performance, including task-driven penetration testing frameworks, AI-driven command generation, and advanced prompting techniques. The AI agent operates within a structured environment using Python, enhanced by Retrieval Augmented Generation (RAG) for contextual understanding and memory retention. ReaperAI was tested on platforms including, Hack The Box, where it successfully exploited known vulnerabilities, demonstrating its potential power. However, the deployment of AI in offensive security presents significant ethical and operational challenges. The agent's development process revealed complexities in command execution, error handling, and maintaining ethical constraints, highlighting areas for future enhancement. This study contributes to the discussion on AI's role in cybersecurity by showcasing how AI can augment offensive security strategies. It also proposes future research directions, including the refinement of AI interactions with cybersecurity tools, enhancement of learning mechanisms, and the discussion of ethical guidelines for AI in offensive roles. The findings advocate for a unique approach to AI implementation in cybersecurity, emphasizing innovation.

This study develops FusionTransNet, a framework designed for Origin-Destination (OD) flow predictions within smart and multimodal urban transportation systems. Urban transportation complexity arises from the spatiotemporal interactions among various traffic modes. Motivated by analyzing multimodal data from Shenzhen, a framework that can dissect complicated spatiotemporal interactions between these modes, from the microscopic local level to the macroscopic city-wide perspective, is essential. The framework contains three core components: the Intra-modal Learning Module, the Inter-modal Learning Module, and the Prediction Decoder. The Intra-modal Learning Module is designed to analyze spatial dependencies within individual transportation modes, facilitating a granular understanding of single-mode spatiotemporal dynamics. The Inter-modal Learning Module extends this analysis, integrating data across different modes to uncover cross-modal interdependencies, by breaking down the interactions at both local and global scales. Finally, the Prediction Decoder synthesizes insights from the preceding modules to generate accurate OD flow predictions, translating complex multimodal interactions into forecasts. Empirical evaluations conducted in metropolitan contexts, including Shenzhen and New York, demonstrate FusionTransNet's superior predictive accuracy compared to existing state-of-the-art methods. The implication of this study extends beyond urban transportation, as the method for transferring information across different spatiotemporal graphs at both local and global scales can be instrumental in other spatial systems, such as supply chain logistics and epidemics spreading.

Participatory budgeting, as a paradigm for democratic innovations, engages citizens in the distribution of a public budget to projects, which they propose and vote for implementation. So far, voting algorithms have been devised and studied in social choice literature to elect projects that are popular, while others prioritize on a proportional representation of voters' preferences, for instance, equal shares. However, the anticipated impact and novelty in the broader society by the winning projects, as selected by different algorithms, remains totally under-explored, lacking both a universal theory of impact for voting and a rigorous framework for impact and novelty assessments. This papers tackles this grand challenge towards new axiomatic foundations for designing effective and fair voting methods. This is via new and striking insights derived from a large-scale analysis of biases over 345 real-world voting outcomes, characterized for the first time by a novel portfolio of impact and novelty metrics. We find strong causal evidence that equal shares comes with impact loss in several infrastructural projects of different cost levels that have been so far over-represented. However, it also comes with a novel, yet over-represented, impact gain in welfare, education and culture. We discuss broader implications of these results and how impact loss can be mitigated at the stage of campaign design and project ideation.