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This work proves the feasibility of utilizing steady state and transient in situ/operando spectroscopy to extract mechanistic information that reduces and leads to robust kinetic models. It also opens new avenues to explore kinetics and mechanisms with charge transfer data in heterogeneous catalysis. 

Generating molecular structures with desired properties is a critical task with broad applications in drug discovery and materials design. We propose 3M-Diffusion, a novel multi-modal molecular graph generation method, to generate diverse, ideally novel molecular structures with desired properties. 3M-Diffusion encodes molecular graphs into a graph latent space which it then aligns with the text space learned by encoder based LLMs from textual descriptions. It then reconstructs the molecular structure and atomic attributes based on the given text descriptions using the molecule decoder. It then learns a probabilistic mapping from the text space to the latent molecular graph space using a diffusion model. The results of our extensive experiments on several datasets demonstrate that 3M-Diffusion can generate high-quality, novel and diverse molecular graphs that semantically match the textual description provided. The code is available on github. 

The recent emergence of Medical Large Vision Language Models (Med-LVLMs) has enhanced medical diagnosis. However, current Med-LVLMs frequently encounter factual issues, often generating responses that do not align with established medical facts. Retrieval-Augmented Generation (RAG), which utilizes external knowledge, can improve the factual accuracy of these models but introduces two major challenges. First, limited retrieved contexts might not cover all necessary information, while excessive retrieval can introduce irrelevant and inaccurate references, interfering with the model’s generation. Second, in cases where the model originally responds correctly, applying RAG can lead to an over-reliance on retrieved contexts, resulting in incorrect answers. To address these issues, we propose RULE, which consists of two components. First, we introduce a provably effective strategy for controlling factuality risk through the calibrated selection of the number of retrieved contexts. Second, based on samples where over-reliance on retrieved contexts led to errors, we curate a preference dataset to fine-tune the model, balancing its dependence on inherent knowledge and retrieved contexts for generation. We demonstrate the effectiveness of RAFE on three medical VQA datasets, achieving an average improvement of 20.8% in factual accuracy. 

The development of simple in situ spectrokinetic techniques to assess intermediate species nature and adsorption location can benefit catalytic studies by providing insights into the reasons for different catalysts performance and facilitate mechanistic proposals. 

Given the surge in rural logistics services and the disparities between urban and rural delivery services, a compelling necessity emerges to explore innovative drone-based delivery solutions. The challenges inherent in truck-drone delivery due to technological and physical barriers affect service quality for some rural customers, thus magnifying concerns about delivery fairness. To investigated delivery equity, we present a truck-drone cooperative delivery model to analyze rural customers’ accessibility to such innovative delivery technology. This model accommodates rural residents’ delivery preferences while optimizing truck routes. Drones are dispatched from designated trucks to serve customers within their flight distance. Our proposed heuristic algorithm, founded on graph-based truck-drone delivery preferences, solves this intricate problem efficiently. Numerical experiments underscore the efficacy of our approach, highlighting substantial reductions in delivery costs and an impressive 20% increase in drone deliveries on a large-scale network. Through sensitivity analyses exploring drone operational costs and flight distances–affected by government policies and technological advancements–we devise an equity metric that gauges the efficiency and accessibility of rapid rural delivery services under the truck-drone delivery framework. Our research contributes to equity analysis, addressing challenges faced by logistics companies and rural residents. Moreover, it bridges the gap between urban and rural logistics, fostering an inclusive and equitable delivery ecosystem benefiting all customers, regardless of their location. 

Cybersecurity and Artificial Intelligence (AI) are key domains whose intersection gives great promises and poses significant threats. Indeed, the National Academy of Science (NAS), the National Science Foundation (NSF), and othßer respected entities have noted the significant role that AI can play in cybersecurity, and the importance of ensuring the security of AI-enabled algorithms and systems. This minitrack focuses on AI and Cybersecurity that works in broader domains, collaborative inter-organizational realms, shared collaborative domains, or with collaborative technologies. The papers in this minitrack have the potential to offer interesting and impactful solutions to emerging areas, including unmanned aerial vehicles and open source software security. 

Emerging Artificial Intelligence (AI) applications are bringing with them both the potential for significant societal benefit and harm. Additionally, vulnerabilities within AI source code can make them susceptible to attacks ranging from stealing private data to stealing trained model parameters. Recently, with the adoption of open-source software (OSS) practices, the AI development community has introduced the potential to worsen the number of vulnerabilities present in emerging AI applications, building new applications on top of previous applications, naturally inheriting any vulnerabilities. With the AI OSS community growing rapidly to a scale that requires automated means of analysis for vulnerability management, we compare three categories of unsupervised graph embedding methods capable of generating repository embeddings that can be used to rank existing applications based on their functional similarity for AI developers. The resulting embeddings can be used to suggest alternatives to AI developers for potentially insecure AI repositories. 

The Frobenius-Perron theory of an endofunctor of a k \Bbbk -linear category (recently introduced in Chen et al. [Algebra Number Theory 13 (2019), pp. 2005–2055]) provides new invariants for abelian and triangulated categories. Here we study Frobenius-Perron type invariants for derived categories of commutative and noncommutative projective schemes. In particular, we calculate the Frobenius-Perron dimension for domestic and tubular weighted projective lines, define Frobenius-Perron generalizations of Calabi-Yau and Kodaira dimensions, and provide examples. We apply this theory to the derived categories associated to certain Artin-Schelter regular and finite-dimensional algebras.