Dynamic response of hybrid carbon fibre laminate beams under ballistic impact

This novel hybrid fibre composites combining stiff composites with soft composites are developed to improve the ballistic impact resistance of composite beams while maintaining good quasi-static loading bearing capacity. The ballistic impact performance of the hybrid beams have been investigated experimentally at a projectile velocity range of , including ballistic limits, failure modes, energy absorption capacity and the interaction between stiff and soft composite parts. For each type of monolithic beams, i.e. stiff, soft and hybrid monolithic beams, three categories of failure modes have been identified: minor damage with rebound of projectile at the low impact velocities, fracture of beam at the medium impact velocities and perforation of beam at the high impact velocities. The critical velocity of hybrid monolithic beam was similar to that of the soft monolithic beam under the same failure mode, and higher than that of the stiff monolithic beam. For the sandwich beams with stiff, soft and hybrid face sheets, the failure modes were similar to those of the monolithic beams. Among the monolithic beams, the hybrid and soft monolithic beams exhibited better energy absorption capacity than the stiff monolithic beams. As for the sandwich beams, the hybrid-face sandwich beams absorbed more kinetic energy of projectile than the soft-face sandwich beams at higher projectile velocity. The advantages of the stiff/soft hybrid construction include: (i) at lower impact velocity, the soft composite part survived with negligible damage under impact; (ii) due to the buffer effect of the soft part at the front face, stress distribution within the stiff part of the hybrid monolithic beams is more uniform than that of the stiff monolithic beams

Failures Pave the Way: Enhancing Large Language Models through Tuning-free Rule Accumulation

Large Language Models (LLMs) have showcased impressive performance. However, due to their inability to capture relationships among samples, these frozen LLMs inevitably keep repeating similar mistakes. In this work, we propose our Tuning-free Rule Accumulation (TRAN) framework, which guides LLMs in improving their performance by learning from previous mistakes. Considering data arrives sequentially, LLMs gradually accumulate rules from incorrect cases, forming a rule collection. These rules are then utilized by the LLMs to avoid making similar mistakes when processing subsequent inputs. Moreover, the rules remain independent of the primary prompts, seamlessly complementing prompt design strategies. Experimentally, we show that TRAN improves over recent baselines by a large margin.Comment: This paper is accepted by the EMNLP 2023 Main Conferenc

Restricted phase space thermodynamics for black holes in higher dimensions and higher curvature gravities

The recently proposed restricted phase space thermodynamics is shown to be applicable to a large class of higher dimensional higher curvature gravity models coupled to Maxwell field, which are known as black hole scan models and are labeled by the spacetime dimension $d$ and the highest order $k$ of the Lanczos-Lovelock densities appearing in the action. Three typical example cases with $(d,k)=(5,1), (5,2)$ and $(6,2)$ are chosen as example cases and studied in some detail. These cases are representatives of Einstein-Hilbert, Chern-Simons and Born-Infield like gravity models. Our study indicates that the Einstein-Hilbert and Born-Infield like gravity models have similar thermodynamic behaviors, e.g. the existence of isocharge $T-S$ phase transitions with the same critical exponents, the existence of isovoltage $T-S$ transitions and the Hawking-Page like transitions, and the similar high temperature asymptotic behaviors for the isocharge heat capacities, etc. However, the Chern-Simons like $(5,2)$-model behaves quite differently. Neither isocharge nor isovoltage $T-S$ transitions could occur and no Hawking-Page like transition is allowed. This seems to indicate that the Einstein-Hilbert and Born-Infield like models belong to the same universality class while the Chern-Simons like models do not.Comment: 29 pages. v2: typo in the title correcte

CASA: Category-agnostic Skeletal Animal Reconstruction

Recovering the skeletal shape of an animal from a monocular video is a longstanding challenge. Prevailing animal reconstruction methods often adopt a control-point driven animation model and optimize bone transforms individually without considering skeletal topology, yielding unsatisfactory shape and articulation. In contrast, humans can easily infer the articulation structure of an unknown animal by associating it with a seen articulated character in their memory. Inspired by this fact, we present CASA, a novel Category-Agnostic Skeletal Animal reconstruction method consisting of two major components: a video-to-shape retrieval process and a neural inverse graphics framework. During inference, CASA first retrieves an articulated shape from a 3D character assets bank so that the input video scores highly with the rendered image, according to a pretrained language-vision model. CASA then integrates the retrieved character into an inverse graphics framework and jointly infers the shape deformation, skeleton structure, and skinning weights through optimization. Experiments validate the efficacy of CASA regarding shape reconstruction and articulation. We further demonstrate that the resulting skeletal-animated characters can be used for re-animation.Comment: Accepted to NeurIPS 202

Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised Learning

Relevance modeling aims to locate desirable items for corresponding queries, which is crucial for search engines to ensure user experience. Although most conventional approaches address this problem by assessing the semantic similarity between the query and item, pure semantic matching is not everything.Comment: Partial conten

Effective Thermal Conductivity Calculation and Measurement of Litz Wire based on the Porous Metal Materials Structure

Litz wires are employed in high-frequency electrical machines due to their advantages of reducing the ac losses, including minimizing the skin effect and the proximity effect. In order to improve the reliability of such machines, and enable accurate thermal predictions at the design stage, accurate calculation of the thermal conductivity of litz wire is important. In this paper, a calculation method based on the Gasar porous metal materials model is put forward. In this method, a cell model is extracted from the litz wire, and a thermal resistance network is used to calculate the equivalent thermal conductivity (ETC). Following this, two finite-element analysis (FEA) models for the same litz wire are built, one with actual thermal conductivities for the different constituent materials and another with the calculated equivalent thermal conductivity for an equivalent material, with the two models showing similar thermal characteristics. Finally, an experimental setup is built for measuring the steady-state ETC of litz wire. The apparatus structure and characteristics are described in detail, and the experiment uncertainty and measurement errors are analyzed. Three types of litz wire are measured in the experimental, and the results from experiment and calculation are consistent