Multi-Scale Memory Comparison for Zero-/Few-Shot Anomaly Detection

Anomaly detection has gained considerable attention due to its broad range of applications, particularly in industrial defect detection. To address the challenges of data collection, researchers have introduced zero-/few-shot anomaly detection techniques that require minimal normal images for each category. However, complex industrial scenarios often involve multiple objects, presenting a significant challenge. In light of this, we propose a straightforward yet powerful multi-scale memory comparison framework for zero-/few-shot anomaly detection. Our approach employs a global memory bank to capture features across the entire image, while an individual memory bank focuses on simplified scenes containing a single object. The efficacy of our method is validated by its remarkable achievement of 4th place in the zero-shot track and 2nd place in the few-shot track of the Visual Anomaly and Novelty Detection (VAND) competition.Comment: VAND Runner-up Winner in CVPR 202

Spillover Effect and Economic Effect of Red Light Cameras

“Spillover effect” of red light cameras (RLCs) refers to the expected safety improvement at intersections other than those actually treated. Such effects may be due to jurisdiction-wide publicity of RLCs and the general public’s lack of knowledge on the exact installation locations of RLCs. Ignoring possible spillover effect could lead to an underestimation of the benefit of RLCs. Both a naïve study and an empirical Bayes study were conducted in this project for selected intersections from the Chicago area, and the results showed that a substantial spillover effect seemed to exist for the studied intersections. The installation of RLCs would lead to changes in rear-end crashes and right-angle crashes. These crashes are often associated with different severities (K/A/B/C/PDO) and different socioeconomic impacts. Assessing the benefit and cost of installing RLCs could help agencies understand the cost effectiveness of RLCs as a safety countermeasure. Crash reduction estimates at the 41 selected RLC intersections from Project ICT R27-SP32 were used, and the results showed the cost effectiveness of installing RLCs at these intersections.IDOT-R27-SP33Ope

Experimental Study of Fast Sealing Airbag in Simulating Tunnel

AbstractAgainst problems in terms of stability, airtightness and so on of current fast sealing airbag, stability and airtightness of fast sealing airbag in simulating tunnel was studied through combining theoretical analysis and experiment. The ideal viton material is finally found after comparing and analyzing heat resistance, flame resistance, wear resistance, hardness and air permeability of different kinds of rubber. Sealing and wind blocking effects of airbags made from selected material are tested in simulating tunnel. Rate of air leakage and changing rate of wind pressure of each kind of rubber are also determined and further verified, with result that both indexes of viton material are the least, respectively only 4.25% and 4.66%

LibriSQA: Advancing Free-form and Open-ended Spoken Question Answering with a Novel Dataset and Framework

While Large Language Models (LLMs) have demonstrated commendable performance across a myriad of domains and tasks, existing LLMs still exhibit a palpable deficit in handling multimodal functionalities, especially for the Spoken Question Answering (SQA) task which necessitates precise alignment and deep interaction between speech and text features. To address the SQA challenge on LLMs, we initially curated the free-form and open-ended LibriSQA dataset from Librispeech, comprising Part I with natural conversational formats and Part II encompassing multiple-choice questions followed by answers and analytical segments. Both parts collectively include 107k SQA pairs that cover various topics. Given the evident paucity of existing speech-text LLMs, we propose a lightweight, end-to-end framework to execute the SQA task on the LibriSQA, witnessing significant results. By reforming ASR into the SQA format, we further substantiate our framework's capability in handling ASR tasks. Our empirical findings bolster the LLMs' aptitude for aligning and comprehending multimodal information, paving the way for the development of universal multimodal LLMs. The dataset and demo can be found at https://github.com/ZihanZhaoSJTU/LibriSQA

Study on the structure and properties of new metallocene high branched polyethylene

The branching composition, distribution and melting crystallization properties of very low density polyethylenes (VLDPE) with different carbon chain length were studied by means of nuclear magnetic resonance (NMR) and differential scanning calorimetry (DSC). The average sequence length (nE, nH, nB), relative monomer distribution (RMD) and monomer reactivity ratio (rE, rH, rB) were selected to analyze the polymerization characteristics. The crystallization characteristics of wafer thickness (L), relative branching degree (S) and crystallinity (Xc) were discussed by means of SSA thermal classification method. It has been found that the comonomer content and branching degree of VLDPE products with hexene (C6) as co monomers is lower than that of butene (C4) copolymer products; while the crystallinity and lamellar thickness is higher than that of C4 products

Exploring Post-Training Quantization of Protein Language Models

Recent advancements in unsupervised protein language models (ProteinLMs), like ESM-1b and ESM-2, have shown promise in different protein prediction tasks. However, these models face challenges due to their high computational demands, significant memory needs, and latency, restricting their usage on devices with limited resources. To tackle this, we explore post-training quantization (PTQ) for ProteinLMs, focusing on ESMFold, a simplified version of AlphaFold based on ESM-2 ProteinLM. Our study is the first attempt to quantize all weights and activations of ProteinLMs. We observed that the typical uniform quantization method performs poorly on ESMFold, causing a significant drop in TM-Score when using 8-bit quantization. We conducted extensive quantization experiments, uncovering unique challenges associated with ESMFold, particularly highly asymmetric activation ranges before Layer Normalization, making representation difficult using low-bit fixed-point formats. To address these challenges, we propose a new PTQ method for ProteinLMs, utilizing piecewise linear quantization for asymmetric activation values to ensure accurate approximation. We demonstrated the effectiveness of our method in protein structure prediction tasks, demonstrating that ESMFold can be accurately quantized to low-bit widths without compromising accuracy. Additionally, we applied our method to the contact prediction task, showcasing its versatility. In summary, our study introduces an innovative PTQ method for ProteinLMs, addressing specific quantization challenges and potentially leading to the development of more efficient ProteinLMs with significant implications for various protein-related applications.Comment: 8 pages, 4 figure

CODAS methods for multiple attribute group decision making with interval-valued bipolar uncertain linguistic information and their application to risk assessment of Chinese enterprises’ overseas mergers and acquisitions

Bipolar fuzzy set theory has been successfully applied in some areas, but there are situations in real life which can’t be represented by bipolar fuzzy sets. However, all the existing approaches are unsuitable to describe the positive and negative membership degree an element to an uncertain linguistic label to have an interval value, which can reflect the decision maker’s confidence level when they are making an evaluation. In order to overcome this limit, we propose the definition of interval-valued bipolar uncertain linguistic sets (IVBULSs) to solve this problem based on the bipolar fuzzy sets and uncertain linguistic information processing models. In this paper, we extend the traditional information aggregating operators to interval-valued bipolar uncertain linguistic sets (IVBULSs) and propose some IVBUL aggregating operators. Then, we extend the CODAS method to solve multiple attribute group decision making (MAGDM) issues with interval-valued bipolar uncertain linguistic numbers (IVBULNs) based on these operators. An example for risk assessment of Chinese enterprises’ overseas mergers and acquisitions (M&As) is given to illustrate the proposed methodology

Green supplier selection based on CODAS method in probabilistic uncertain linguistic environment

Probabilistic uncertain linguistic sets (PULTSs) have widely been used in MADM or MAGDM. The CODAS method, which is a novel MADM or MAGDM tool, aims to acquire the optimal choice which have the largest Euclidean & Hamming distances from the NIS. This paper designs the probabilistic uncertain linguistic CODAS (PUL-CODAS) method with sine entropy weight. Finally, a numerical example for green supplier selection is given and the obtained results are compared with some existing models. First published online 05 February 202