High responsiveness and economic efficiency are critical objectives in supply chain transportation, both of which are influenced by strategic decisions on shipping mode. An integrated framework combining an efficient simulator with an intelligent decision-making algorithm can provide an observable, low-risk environment for transportation strategy design. An ideal simulation-decision framework must (1) generalize effectively across various settings, (2) reflect fine-grained transportation dynamics, (3) integrate historical experience with predictive insights, and (4) maintain tight integration between simulation feedback and policy refinement. We propose Sim-to-Dec framework to satisfy these requirements. Specifically, Sim-to-Dec consists of a generative simulation module, which leverages autoregressive modeling to simulate continuous state changes, reducing dependence on handcrafted domain-specific rules and enhancing robustness against data fluctuations; and a history-future dual-aware decision model, refined iteratively through end-to-end optimization with simulator interactions. Extensive experiments conducted on three real-world datasets demonstrate that Sim-to-Dec significantly improves timely delivery rates and profit.

Arizona State University, USA Deep Foundation Models (DFMs) offer unprecedented capabili ties but their increasing complexity presents profound challenges to understanding their internal worki ngs - a critical need for ensuring trust, safety, and accountability. As we grapple with explaining these sys tems, a fundamental question emerges: Are the difficulties we face merely temporary hurdles, awaiting more sophisticated analytical techniques, or do they stem from intrinsic barriers deeply rooted in the nature of these large-scale models them selves? This paper delves into this critical question by examining the fundamental characteristics of DFMs and scrutinizing the limitations encountered by current explainability methods when confronted with this inherent challenge. We probe the feasibility of achieving satisfactory explanati ons and consider the implications for how we must approach the verification and governance of these powerful technologies. Introduction Deep Foundation Models (DFMs) - such as large language models a nd multimodal architectures - are a class of neural networks trained on vast amounts of data, de signed to serve as general-purpose engines for downstream tasksacross diverse domains [10].With the emergence ofsystems like GPT, Gemini, and CLIP, artificial intelligence is undergoing aprofound transformation.

If you have a dog, you might think you have a strong connection with them. But according to a new study, you've probably been reading your pet's emotions all wrong. Although humans and dogs have a unique bond, scientists from Arizona State University say that we are terrible at understanding canine emotions. Participants were shown videos of a dog reacting to positive situations, such as seeing their lead, or negative situations such as being presented with the dreaded vacuum cleaner. Instead of actually trying to understand what the dog is feeling, the researchers found that people tend to'project human emotions onto their pets'.

Static image taken from video (shown below). Right: using AI-based method to remove the noise. A team of scientists has developed a method to illuminate the dynamic behavior of nanoparticles. The work, reported in Visualizing Nanoparticle Surface Dynamics and Instabilities Enabled by Deep Denoising, in the journal Science, combines artificial intelligence with electron microscopy to render visuals of how these tiny bits of matter respond to stimuli. "The nature of changes in the particle is exceptionally diverse, including fluxional periods, manifesting as rapid changes in atomic structure, particle shape, and orientation; understanding these dynamics requires new statistical tools," said David S. Matteson (Cornell University), one of the paper's authors.

Each year, a small group of PhD students are chosen to participate in the AAAI/SIGAI Doctoral Consortium. This initiative provides an opportunity for the students to discuss and explore their research interests and career objectives in an interdisciplinary workshop together with a panel of established researchers. During 2024, we met with some of the students to find out more about their research and the doctoral consortium experience. They also shared their advice for prospective PhD students. Changhoon Kim completed his PhD in Computer Engineering at Arizona State University.

We introduce the diffusion model as a method to generate new samples. Generative models have been recently adopted for tasks such as art generation (Stable Diffusion, Dall-E) and text generation (ChatGPT). Diffusion models in particular apply noise to sample data and then "reverse" this noising process to generate new samples. We will formally define the noising and denoising processes, then introduce algorithms to train and generate with a diffusion model. Finally, we will explore a potential application of diffusion models in improving classifier performance on imbalanced data.

In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. The Doctoral Consortium provides an opportunity for a group of PhD students to discuss and explore their research interests and career objectives in an interdisciplinary workshop together with a panel of established researchers. In this latest interview, we hear from Pulkit Verma, recent PhD graduate from Arizona State University. I recently completed my PhD in Computer Science from School of Computing and Augmented Intelligence, Arizona State University. My research focuses on safe and reliable behavior of AI agents.

OpenAI is bringing ChatGPT to college campuses across the country. On Thursday, the company announced ChatGPT Edu, a version of ChatGPT built specifically for students, academics, faculty. "ChatGPT Edu is designed for schools that want to deploy AI more broadly to students and their campus communities," the company said in a blog post. ChatGPT Edu includes access to GPT-4o, OpenAI's latest large language model that the company revealed earlier this month. OpenAI claims that the model is much better than its previous versions at interpreting text, coding, and mathematics, analyzing data sets, and being able to access the web.

Transportation has greatly benefited the cities' development in the modern civilization process. Intelligent transportation, leveraging advanced computer algorithms, could further increase people's daily commuting efficiency. However, intelligent transportation, as a cross-discipline, often requires practitioners to comprehend complicated algorithms and obscure neural networks, bringing a challenge for the advanced techniques to be trusted and deployed in practical industries. Recognizing the expressiveness of the pre-trained large language models, especially the potential of being augmented with abilities to understand and execute intricate commands, we introduce Open-TI. Serving as a bridge to mitigate the industry-academic gap, Open-TI is an innovative model targeting the goal of Turing Indistinguishable Traffic Intelligence, it is augmented with the capability to harness external traffic analysis packages based on existing conversations. Marking its distinction, Open-TI is the first method capable of conducting exhaustive traffic analysis from scratch - spanning from map data acquisition to the eventual execution in complex simulations. Besides, Open-TI is able to conduct task-specific embodiment like training and adapting the traffic signal control policies (TSC), explore demand optimizations, etc. Furthermore, we explored the viability of LLMs directly serving as control agents, by understanding the expected intentions from Open-TI, we designed an agent-to-agent communication mode to support Open-TI conveying messages to ChatZero (control agent), and then the control agent would choose from the action space to proceed the execution. We eventually provide the formal implementation structure, and the open-ended design invites further community-driven enhancements.

Sounds like a tongue twister, doesn't it?" "What do you think you're doing?" shrieked Méndez's voice behind me. "What the fuck is going on with you?" I could tell that she was about to cry. I stopped the treadmill and got off carefully, without entirely disconnecting.