Robust Inattentive Discrete Choice

In today’s era of information explosion, decision-makers are faced with a vast amount of information, not all of which is relevant to their decisions. To better make optimal decisions in data-rich environments, the Rational Inattention (RI) model has been introduced into the field of economics. The core idea of this model is that decision-makers ne...

Identifying New Classes of Financial Price Jumps with Wavelets

Research Report on Identifying New Classes of Financial Price Jumps Using Wavelets Academic Background Price jumps in financial markets refer to significant price fluctuations occurring within an extremely short period, typically caused by exogenous factors (such as sudden news) or endogenous factors (internal market feedback mechanisms). Distingui...

Out-of-Distribution Generalization via Composition: A Lens Through Induction Heads in Transformers

Study on Out-of-Distribution Generalization and Composition Mechanisms in Large Language Models Paper Background In recent years, large language models (LLMs) such as GPT-4 have demonstrated remarkable creativity in handling novel tasks, often solving problems with just a few examples. These tasks require models to generalize on distributions diffe...

Discounted Stable Adaptive Critic Design for Zero-Sum Games with Application Verifications

Discounted Adaptive Critic Design with Application Verification in Zero-Sum Games Research Background In the field of control, optimal control is a core research direction aimed at designing and analyzing control systems to optimize system performance. As system complexity increases, traditional optimal control methods based on the Hamilton-Jacobi-...

Resource-Efficient Decentralized Sequential Planner for Spatiotemporal Wildfire Mitigation

Efficient Decentralized Sequential Planner for Spatiotemporal Wildfire Mitigation Using Multiple UAVs Academic Background Wildfires pose a significant threat to global biodiversity and resource sustainability, especially in their early stages. If not controlled in time, wildfires can rapidly expand, leading to severe ecological damage. In recent ye...

A Projective Weighted DTW Based Monitoring Approach for Multi-Stage Processes with Unequal Durations

Projective Weighted Dynamic Time Warping-Based Monitoring Method for Multi-Stage Processes with Unequal Durations Academic Background In modern manufacturing industries, online monitoring of multi-stage processes (such as batch and transition processes) is crucial for improving product quality and reducing failure risks. However, due to varying ope...

Effective Probabilistic Neural Networks Model for Model-Based Reinforcement Learning in USV

A New Approach to Model Predictive Control for Unmanned Surface Vehicles (USV): A Probabilistic Neural Network-Based MBRL Framework Academic Background Unmanned Surface Vehicles (USVs) have seen rapid development in recent years within the field of marine science, finding extensive applications in scenarios such as marine transportation, environmen...

Oral-Anatomical Knowledge-Informed Semi-Supervised Learning for 3D Dental CBCT Segmentation and Lesion Detection

Academic Background and Research Motivation In the field of dental healthcare, Cone Beam Computed Tomography (CBCT) is a widely used three-dimensional imaging technique. CBCT provides three-dimensional images of the oral cavity and is particularly effective in diagnosing odontogenic lesions. However, the segmentation of CBCT images—labeling each vo...

Multi-Task Aquatic Toxicity Prediction Model Based on Multi-Level Features Fusion

Academic Background With the growing threat of organic compounds to environmental pollution, studying the toxic responses of different aquatic organisms to these compounds has become crucial. Such research not only helps assess the potential ecological impacts of pollutants on the overall aquatic ecosystem but also provides significant scientific f...

Resistive Memory-Based Zero-Shot Liquid State Machine for Multimodal Event Data Learning

Novel Resistive Memory-Driven Zero-Shot Multimodal Event Learning System: A Report on Hardware-Software Co-Design Academic Background The human brain is a complex spiking neural network (SNN) capable of zero-shot learning in multimodal signals with minimal power consumption, allowing generalization of existing knowledge to address new tasks. Howeve...