University of Missouri Led Energy Efficient Projects in Global Market: Application of Sustainable Ground Energy in Olympic Facilities [abstract]

Only abstract of poster available.Track III: Energy InfrastructureUniversity of Missouri, Columbia (MU) faculty members are involved in energy efficient projects in China with grant support by both the U.S. and Chinese governments. A large commercial building was selected by the Beijing City Government to demonstrate sustainable energy applications for space heating and cooling, prior to the construction of the 2008 Beijing Olympic Facilities. A large underground heat exchange system with 700 borehole matrix was designed by Dr. Shawn Xu to provide full heating and cooling of a building with a space of 287,000m2 (309,000 ft2). Economic analysis with actual initial installation investment and operation costs for the project showed the feasibility of this technology. After successful operation of the demonstration project, Dr. Xu assisted the Chinese developers in adopting similar energy efficient technologies for the 2008 Beijing Olympic Facilities. Detailed engineering aspects of the energy efficient utility system for the National Stadium (Bird Nest), Olympic Athlete Village, and the National Olympic Forest Park will be given in the presentation. MU opened its Environmental and Energy Technology Office (ENTECH) in Beijing in 2006 through a partnership with the U.S. Department of Commerce. ENTECH serves as a resource center for information on US environmental and energy efficient technologies and products for use in China

Mitigating Transformer Overconfidence via Lipschitz Regularization

Though Transformers have achieved promising results in many computer vision tasks, they tend to be over-confident in predictions, as the standard Dot Product Self-Attention (DPSA) can barely preserve distance for the unbounded input domain. In this work, we fill this gap by proposing a novel Lipschitz Regularized Transformer (LRFormer). Specifically, we present a new similarity function with the distance within Banach Space to ensure the Lipschitzness and also regularize the term by a contractive Lipschitz Bound. The proposed method is analyzed with a theoretical guarantee, providing a rigorous basis for its effectiveness and reliability. Extensive experiments conducted on standard vision benchmarks demonstrate that our method outperforms the state-of-the-art single forward pass approaches in prediction, calibration, and uncertainty estimation.Comment: Accepted by UAI 2023. (https://proceedings.mlr.press/v216/ye23a.html

Detecting Small Query Graphs in A Large Graph via Neural Subgraph Search

Recent advances have shown the success of using reinforcement learning and search to solve NP-hard graph-related tasks, such as Traveling Salesman Optimization, Graph Edit Distance computation, etc. However, it remains unclear how one can efficiently and accurately detect the occurrences of a small query graph in a large target graph, which is a core operation in graph database search, biomedical analysis, social group finding, etc. This task is called Subgraph Matching which essentially performs subgraph isomorphism check between a query graph and a large target graph. One promising approach to this classical problem is the "learning-to-search" paradigm, where a reinforcement learning (RL) agent is designed with a learned policy to guide a search algorithm to quickly find the solution without any solved instances for supervision. However, for the specific task of Subgraph Matching, though the query graph is usually small given by the user as input, the target graph is often orders-of-magnitude larger. It poses challenges to the neural network design and can lead to solution and reward sparsity. In this paper, we propose NSUBS with two innovations to tackle the challenges: (1) A novel encoder-decoder neural network architecture to dynamically compute the matching information between the query and the target graphs at each search state; (2) A novel look-ahead loss function for training the policy network. Experiments on six large real-world target graphs show that NSUBS can significantly improve the subgraph matching performance

Nonlinear Vibration of Ladle Crane due to a Moving Trolley

The structural vibration of the main beam of a crane causes fatigue damage and discomfort to the driver. The swing of the payload has an effect on positioning precision, especially for a ladle crane, and this directly affects production safety. To study the influence of system parameters on the vibration of a crane’s main beam and the angle of the payload, a system consisting of the main beam, trolley, payload, and cabin was constructed. A rigid-flexible coupling dynamic model of a moving trolley with a hanging payload that moves on the flexible main beam with a concentrated cabin mass is established, and the direct integration method is used to solve the nonlinear differential equations of system vibration, which are obtained through Lagrange’s equation. Then, the time domain responses of the flexible main beam, payload angle, and cabin vibration are obtained. The influences of the trolley running speed, quality of the payload, and quality and position of the cabin on the vibration of the main beam and payload angle are analyzed. The results indicate that the amplitude of the main beam is directly proportional to the quality of the trolley, payload, and cab; the position of the cabin is closer to the mid-span; the amplitude of the main beam is larger; the structural damping has some influence on the vibration of the main beam; and the swing angle of the payload is related to the maximum running speed of the trolley, acceleration time, and length of the wire rope. In order to reduce the vibration of the main beam and cabin, the connection stiffness of the cabin should be ensured during installation

Sensitivity and specificity of the ankle–brachial index to diagnose peripheral artery disease: a structured review

The ankle—brachial index (ABI) is a simple, inexpensive diagnostic test for peripheral artery disease (PAD). However, it has shown variable accuracy for identification of significant stenosis. The authors performed a structured review of the sensitivity and specificity of ABI ≤ 0.90 for the diagnosis of PAD. MEDLINE, EMBASE, Cochrane databases, Science Citation Index database, and Biological Abstracts database were searched for studies of the sensitivity and specificity of using ABI ≤ 0.90 for the diagnosis of PAD. Eight studies comprising 2043 patients (or limbs) met the inclusion criteria. The result indicated that, although strict inclusion criteria on studies were formulated, different reference standards were found in these studies, and methods of ABI determination and characteristics of populations varied greatly. A high level of specificity (83.3—99.0%) and accuracy (72.1—89.2%) was reported for an ABI ≤ 0.90 in detecting ≥ 50% stenosis, but there were different levels of sensitivity (15—79%). Sensitivity was low, especially in elderly individuals and patients with diabetes. In conclusion, the test of ABI ≤ 0.90 can be a simple and useful tool to identify PAD with serious stenosis, and may be substituted for other non-invasive tests in clinical practice

Rethinking visual prompting for multimodal large language models with external knowledge

In recent years, multimodal large language models (MLLMs) have made significant strides by training on vast high-quality image-text datasets, enabling them to generally understand images well. However, the inherent difficulty in explicitly conveying fine-grained or spatially dense information in text, such as masks, poses a challenge for MLLMs, limiting their ability to answer questions requiring an understanding of detailed or localized visual elements. Drawing inspiration from the Retrieval-Augmented Generation (RAG) concept, this paper proposes a new visual prompt approach to integrate fine-grained external knowledge, gleaned from specialized vision models (e.g., instance segmentation/OCR models), into MLLMs. This is a promising yet underexplored direction for enhancing MLLMs' performance. Our approach diverges from concurrent works, which transform external knowledge into additional text prompts, necessitating the model to indirectly learn the correspondence between visual content and text coordinates. Instead, we propose embedding fine-grained knowledge information directly into a spatial embedding map as a visual prompt. This design can be effortlessly incorporated into various MLLMs, such as LLaVA and Mipha, considerably improving their visual understanding performance. Through rigorous experiments, we demonstrate that our method can enhance MLLM performance across nine benchmarks, amplifying their fine-grained context-aware capabilities

Assemble Them All: Physics-Based Planning for Generalizable Assembly by Disassembly

Assembly planning is the core of automating product assembly, maintenance, and recycling for modern industrial manufacturing. Despite its importance and long history of research, planning for mechanical assemblies when given the final assembled state remains a challenging problem. This is due to the complexity of dealing with arbitrary 3D shapes and the highly constrained motion required for real-world assemblies. In this work, we propose a novel method to efficiently plan physically plausible assembly motion and sequences for real-world assemblies. Our method leverages the assembly-by-disassembly principle and physics-based simulation to efficiently explore a reduced search space. To evaluate the generality of our method, we define a large-scale dataset consisting of thousands of physically valid industrial assemblies with a variety of assembly motions required. Our experiments on this new benchmark demonstrate we achieve a state-of-the-art success rate and the highest computational efficiency compared to other baseline algorithms. Our method also generalizes to rotational assemblies (e.g., screws and puzzles) and solves 80-part assemblies within several minutes.Comment: Accepted by SIGGRAPH Asia 2022. Project website: http://assembly.csail.mit.edu