Reaching Out and Beyond: Making Library Centered Connections

This poster is presented at the 2012 American Library Association Annual Conference in the Anaheim Convention Center. The hub on campus, academic libraries play a significant role in promoting diversity and academic success. Library resources and services have a great impact on university’s recruitment, retention and graduation. This poster session discusses some innovative practices in outreach, effective support in student learning, diverse models in services, and successful approaches in connecting with the campus community

A small payload desktop industry robot design without conventional reducers

Designing a compact desktop industrial robot with a small payload capacity, eliminating the use of traditional reducers, poses an intriguing challenge. This innovative approach aims to enhance the robot's cost-efficiency and reduce its overall size. The design focuses on optimizing the mechanical structure and exploring alternative mechanisms to achieve precise control without relying on conventional reducers. This article delves into the design aspects of a 1 kg payload robot. Initially, the paper presents an overview of the robot's mechanism and its kinematic analysis. Subsequently, synchronous belts are proposed as replacements for traditional reducers, accompanied by an introduction to the mechanical structure. Simulation is carried out to calculate the drive forces on the belts. According to the result, a suitable belt scheme has been designed. Ultimately, a prototype of the robot is constructed, and experiments demonstrate that this design achieves a repeatable accuracy comparable to robots employing conventional reducers, all while considerably reducing the overall cost of the robot

Algebraic solitons in the massive Thirring model

We present exact solutions describing dynamics of two algebraic solitons in the massive Thirring model. Each algebraic soliton corresponds to a simple embedded eigenvalue in the Kaup--Newell spectral problem and attains the maximal mass among the family of solitary waves traveling with the same speed. By coalescence of speeds of the two algebraic solitons, we find a new solution for an algebraic double-soliton which corresponds to a double embedded eigenvalue. We show that the double-soliton attains the double mass of a single soliton and describes a slow interaction of two identical algebraic solitons.Comment: 18 pages; 3 figures

A deep learning method for solving high-order nonlinear soliton equation

We propose effective scheme of deep learning method for high-order nonlinear soliton equation and compare the activation function for high-order soliton equation. The neural network approximates the solution of the equation under the conditions of differential operator, initial condition and boundary condition. We apply this method to high-order nonlinear soliton equation, and verify its efficiency by solving the fourth-order Boussinesq equation and the fifth-order Korteweg de Vries equation. The results show that deep learning method can solve the high-order nonlinear soliton equation and reveal the interaction between solitons

Unleashing the Expressive Power of Pulse-Based Quantum Neural Networks

Quantum machine learning (QML) based on Noisy Intermediate-Scale Quantum (NISQ) devices hinges on the optimal utilization of limited quantum resources. While gate-based QML models are user-friendly for software engineers, their expressivity is restricted by the permissible circuit depth within a finite coherence time. In contrast, pulse-based models enable the construction of "infinitely" deep quantum neural networks within the same time, which may unleash greater expressive power for complex learning tasks. In this paper, this potential is investigated from the perspective of quantum control theory. We first indicate that the nonlinearity of pulse-based models comes from the encoding process that can be viewed as the continuous limit of data-reuploading in gate-based models. Subsequently, we prove that the pulse-based model can approximate arbitrary nonlinear functions when the underlying physical system is ensemble controllable. Under this condition, numerical simulations demonstrate the enhanced expressivity by either increasing the pulse length or the number of qubits. As anticipated, we show through numerical examples that the pulse-based model can unleash more expressive power compared to the gate-based model. These findings lay a theoretical foundation for understanding and designing expressive QML models using NISQ devices.Comment: 12 pages; 6 figure

Subequivariant Graph Reinforcement Learning in 3D Environments

Learning a shared policy that guides the locomotion of different agents is of core interest in Reinforcement Learning (RL), which leads to the study of morphology-agnostic RL. However, existing benchmarks are highly restrictive in the choice of starting point and target point, constraining the movement of the agents within 2D space. In this work, we propose a novel setup for morphology-agnostic RL, dubbed Subequivariant Graph RL in 3D environments (3D-SGRL). Specifically, we first introduce a new set of more practical yet challenging benchmarks in 3D space that allows the agent to have full Degree-of-Freedoms to explore in arbitrary directions starting from arbitrary configurations. Moreover, to optimize the policy over the enlarged state-action space, we propose to inject geometric symmetry, i.e., subequivariance, into the modeling of the policy and Q-function such that the policy can generalize to all directions, improving exploration efficiency. This goal is achieved by a novel SubEquivariant Transformer (SET) that permits expressive message exchange. Finally, we evaluate the proposed method on the proposed benchmarks, where our method consistently and significantly outperforms existing approaches on single-task, multi-task, and zero-shot generalization scenarios. Extensive ablations are also conducted to verify our design. Code and videos are available on our project page: https://alpc91.github.io/SGRL/.Comment: ICML 2023 Ora

MineLand: Simulating Large-Scale Multi-Agent Interactions with Limited Multimodal Senses and Physical Needs

While Vision-Language Models (VLMs) hold promise for tasks requiring extensive collaboration, traditional multi-agent simulators have facilitated rich explorations of an interactive artificial society that reflects collective behavior. However, these existing simulators face significant limitations. Firstly, they struggle with handling large numbers of agents due to high resource demands. Secondly, they often assume agents possess perfect information and limitless capabilities, hindering the ecological validity of simulated social interactions. To bridge this gap, we propose a multi-agent Minecraft simulator, MineLand, that bridges this gap by introducing three key features: large-scale scalability, limited multimodal senses, and physical needs. Our simulator supports 64 or more agents. Agents have limited visual, auditory, and environmental awareness, forcing them to actively communicate and collaborate to fulfill physical needs like food and resources. Additionally, we further introduce an AI agent framework, Alex, inspired by multitasking theory, enabling agents to handle intricate coordination and scheduling. Our experiments demonstrate that the simulator, the corresponding benchmark, and the AI agent framework contribute to more ecological and nuanced collective behavior.The source code of MineLand and Alex is openly available at https://github.com/cocacola-lab/MineLand.Comment: Project website: https://github.com/cocacola-lab/MineLan

Public Restroom Access and Mental Health Among Gender-Minoritized Individuals in China

open access articleThis cross-sectional study assesses the adequacy of gender-neutral public restrooms and examines the association of public restroom–related stress with mental health among gender-diverse individuals in China