Research on dedicated rail power supply system for electric cars

in order to improve the endurance capacity and driving safety of electric vehicles, a special track power supply system for electric cars on expressways is studied. The working principle of the main components of the system, such as sliding contact charging track and mechanical charging arm, is simulated and analyzed by using SolidWorks software. The results show that the charging function of the contact track can provide unlimited endurance for electric vehicles, and the guidance function of the track can also ensure the safety of highspeed driving

Research Article An Improved Data Correlation Algorithm for Multi-passive-sensor Tracking System

Abstract: For improving the performance of the optimal assignment problem of data correlation of multi-passivesensor system, an improved optimal assignment algorithm based on multi-source information fusion is put forward. The new algorithm takes advantage of the optimal solution and a certain number of near-optimal solutions of the traditional optimal assignment problem to construct a set of effective multi-tuple of measurement and constructs correlation probability fusing multi-source information between above effective multi-tuple of measurement and target track by using combination rule of D-S evidence theory. The result of simulation experiments shows that, compared with the traditional optimal assignment algorithm, the new algorithm not only improves the accuracy of multi-target tracking in different degrees but also saves a lot of time. So it is an effective data correlation algorithm for multi-passive-sensor system

Identification and validation of prognostically relevant gene signature in melanoma

Background. Currently, effective genetic markers are limited to predict the clinical outcome of melanoma. High-throughput multiomics sequencing data have provided a valuable approach for the identification of genes associated with cancer prognosis. Method. The multidimensional data of melanoma patients, including clinical, genomic, and transcriptomic data, were obtained from The Cancer Genome Atlas (TCGA). These samples were then randomly divided into two groups, one for training dataset and the other for validation dataset. In order to select reliable biomarkers, we screened prognosis-related genes, copy number variation genes, and SNP variation genes and integrated these genes to further select features using random forests in the training dataset. We screened for robust biomarkers and established a gene-related prognostic model. Finally, we verified the selected biomarkers in the test sets (GSE19234 and GSE65904) and on clinical samples extracted from melanoma patients using qRT-PCR and immunohistochemistry analysis. Results. We obtained 1569 prognostic-related genes and 1101 copy-amplification, 1093 copy-deletions, and 92 significant mutations in genomic variants. These genomic variant genes were closely related to the development of tumors and genes that integrate genomic variation. A total of 141 candidate genes were obtained from prognosis-related genes. Six characteristic genes (IQCE, RFX6, GPAA1, BAHCC1, CLEC2B, and AGAP2) were selected by random forest feature selection, many of which have been reported to be associated with tumor progression. Cox regression analysis was used to establish a 6-gene signature. Experimental verification with qRT-PCR and immunohistochemical staining proved that these selected genes were indeed expressed at a significantly higher level compared with the normal tissues. This signature comprised an independent prognostic factor for melanoma patients. Conclusions. We constructed a 6-gene signature (IQCE, RFX6, GPAA1, BAHCC1, CLEC2B, and AGAP2) as a novel prognostic marker for predicting the survival of melanoma patients

Does Negative Sampling Matter? A Review with Insights into its Theory and Applications

Negative sampling has swiftly risen to prominence as a focal point of research, with wide-ranging applications spanning machine learning, computer vision, natural language processing, data mining, and recommender systems. This growing interest raises several critical questions: Does negative sampling really matter? Is there a general framework that can incorporate all existing negative sampling methods? In what fields is it applied? Addressing these questions, we propose a general framework that leverages negative sampling. Delving into the history of negative sampling, we trace the development of negative sampling through five evolutionary paths. We dissect and categorize the strategies used to select negative sample candidates, detailing global, local, mini-batch, hop, and memory-based approaches. Our review categorizes current negative sampling methods into five types: static, hard, GAN-based, Auxiliary-based, and In-batch methods, providing a clear structure for understanding negative sampling. Beyond detailed categorization, we highlight the application of negative sampling in various areas, offering insights into its practical benefits. Finally, we briefly discuss open problems and future directions for negative sampling.Comment: 20 pages, 11 figure

Improving Negative-Prompt Inversion via Proximal Guidance

DDIM inversion has revealed the remarkable potential of real image editing within diffusion-based methods. However, the accuracy of DDIM reconstruction degrades as larger classifier-free guidance (CFG) scales being used for enhanced editing. Null-text inversion (NTI) optimizes null embeddings to align the reconstruction and inversion trajectories with larger CFG scales, enabling real image editing with cross-attention control. Negative-prompt inversion (NPI) further offers a training-free closed-form solution of NTI. However, it may introduce artifacts and is still constrained by DDIM reconstruction quality. To overcome these limitations, we propose Proximal Negative-Prompt Inversion (ProxNPI), extending the concepts of NTI and NPI. We enhance NPI with a regularization term and reconstruction guidance, which reduces artifacts while capitalizing on its training-free nature. Our method provides an efficient and straightforward approach, effectively addressing real image editing tasks with minimal computational overhead.Comment: Code at https://github.com/phymhan/prompt-to-promp

CogVLM: Visual Expert for Pretrained Language Models

We introduce CogVLM, a powerful open-source visual language foundation model. Different from the popular shallow alignment method which maps image features into the input space of language model, CogVLM bridges the gap between the frozen pretrained language model and image encoder by a trainable visual expert module in the attention and FFN layers. As a result, CogVLM enables deep fusion of vision language features without sacrificing any performance on NLP tasks. CogVLM-17B achieves state-of-the-art performance on 10 classic cross-modal benchmarks, including NoCaps, Flicker30k captioning, RefCOCO, RefCOCO+, RefCOCOg, Visual7W, GQA, ScienceQA, VizWiz VQA and TDIUC, and ranks the 2nd on VQAv2, OKVQA, TextVQA, COCO captioning, etc., surpassing or matching PaLI-X 55B. Codes and checkpoints are available at https://github.com/THUDM/CogVLM

xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein

Protein language models have shown remarkable success in learning biological information from protein sequences. However, most existing models are limited by either autoencoding or autoregressive pre-training objectives, which makes them struggle to handle protein understanding and generation tasks concurrently. We propose a unified protein language model, xTrimoPGLM, to address these two types of tasks simultaneously through an innovative pre-training framework. Our key technical contribution is an exploration of the compatibility and the potential for joint optimization of the two types of objectives, which has led to a strategy for training xTrimoPGLM at an unprecedented scale of 100 billion parameters and 1 trillion training tokens. Our extensive experiments reveal that 1) xTrimoPGLM significantly outperforms other advanced baselines in 18 protein understanding benchmarks across four categories. The model also facilitates an atomic-resolution view of protein structures, leading to an advanced 3D structural prediction model that surpasses existing language model-based tools. 2) xTrimoPGLM not only can generate de novo protein sequences following the principles of natural ones, but also can perform programmable generation after supervised fine-tuning (SFT) on curated sequences. These results highlight the substantial capability and versatility of xTrimoPGLM in understanding and generating protein sequences, contributing to the evolving landscape of foundation models in protein science

Willingness of people living with HIV to receive a second COVID-19 booster dose: a multicenter cross-sectional study in China

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has significantly affected the global population, with People Living with HIV (PLWH) being particularly vulnerable due to their compromised immune systems. Although vaccination is a crucial preventative measure against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, little is understood about the willingness of PLWH to receive a second COVID-19 booster dose and the factors that may influence this decision. This study investigates the willingness of PLWH in China to receive a second COVID-19 booster dose and its influencing factors, comparing these with a group of healthy individuals.MethodsA multicenter cross-sectional study was conducted across five Chinese cities, namely, Beijing, Tianjin, Zhengzhou, Hohhot, and Harbin. Participants were recruited through five community-based organizations. Data were collected via participant self-administered questionnaires included demographic information, willingness to receive a second COVID-19 booster dose, and knowledge about HIV and COVID-19 vaccination. Factors influencing vaccination willingness were identified using multivariable logistic regression analyzes.ResultsA total of 156 PLWH and 151 healthy individuals were included in the study. After adjusting for potential confounders, it was found that PLWH demonstrated a lower willingness to receive a second COVID-19 booster dose compared to healthy individuals (77.6% vs. 88.7%, p = 0.009). Lower willingness was associated with HIV positive status (Adjusted Odds Ratio [AOR]: 0.39, 95%CI: 0.20, 0.75), perceived barriers (AOR: 0.05, 95%CI: 0.01, 0.26), and perceived severity (AOR: 0.32, 95%CI: 0.12, 0.90).ConclusionPLWH in China demonstrated a lower willingness to receive a second COVID-19 booster dose compared to healthy individuals. The findings suggest that perceptions and understanding of the COVID-19 vaccination and its necessity for protection against SARS-CoV-2 could influence this willingness. Efforts should be made to strengthen and disseminate knowledge about HIV and COVID-19 vaccinations among this population. In addition, developing interventions and policies that target specific subgroups and address misconceptions about vaccination could be instrumental in improving vaccination rates among PLWH