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DeepSecE:A Deep-Learning-Based Framework for Multiclass Prediction of Secreted Proteins in Gram-Negative Bacteria

Research 2024年第3期2023卷 243-257页

作者： Yumeng Zhang Jiahao Guan Chen Li Zhikang Wang Zixin Deng Robin BGasser Jiangning Song Hong-Yu Ou state Key Laboratory of Microbial Metabolism Joint International Laboratory on Metabolic&Developmental SciencesSchool of Life Sciences and BiotechnologyShanghai Jiao Tong UniversityShanghai 200240China Shanghai Key Laboratory of Veterinary Biotechnology Shanghai Jiao Tong UniversityShanghai 200240China Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology Monash UniversityMelbourneViC 3800Australia Monash Data Futures Institute Monash UniversityMelbourneViC 3800Australia Melbourne Veterinary School Faculty of ScienceThe University of MelbourneParkvilleVIC 3010Australia.

Proteins secreted by Gram-negative bacteria are tightly linked to the virulence and adaptability of these microbes to environmental *** identification of such secreted proteins can facilitate the investigations of infections and diseases caused by these bacterial ***,current bioinformatic methods for predicting bacterial secreted substrate proteins have limited computational efficiency and application scope on a genome-wide ***,we propose a novel deep-learning-based framework—DeepSecE—for the simultaneous inference of multiple distinct groups of secreted proteins produced by Gram-negative *** remarkably improves their classification from nonsecreted proteins using a pretrained protein language model and transformer,achieving a macro-average accuracy of 0.883 on 5-fold *** benchmarking suggests that DeepSecE achieves competitive performance with the state-of-the-art binary predictors specialized for individual types of secreted *** attention mechanism corroborates salient patterns and motifs at the N or C termini of the protein *** this pipeline,we further investigate the genome-wide prediction of novel secreted proteins and their taxonomic distribution across~1,000 Gram-negative bacterial *** present analysis demonstrates that DeepSecE has major potential for the discovery of disease-associated secreted proteins in a diverse range of Gram-negative *** online web server of DeepSecE is also publicly available to predict and explore various secreted substrate proteins via the input of bacterial genome sequences.

关键词： Deep server competitive

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High-precision Online Log Parsing with Large Language Models

High-precision Online Log Parsing with Large Language Models

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International Conference on Software Engineering - Companion, ICSE, Companion

作者： Xiaolei Chen Jie Shi Jia Chen Peng Wang Wei Wang Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China

ISBN: (数字)9798400705021

ISBN: (纸本)9798350351781

System logs are vital for diagnosing system failures, with log parsing converting unstructured logs into structured data. Existing methods fall into two categories: non-deep-learning approaches cluster logs based on stats but often miss semantic information, resulting in poor performance. Deep-learning approaches excel at identifying variables and constants but often lack generalizability beyond training data. And they always suffer from low efficiency. This paper proposes a novel LLM-based log parsing approach, named Hooglle, to address these challenges. Leveraging a large language model, Hooglle extracts templates for precise and generalized parsing. To overcome the efficiency issue, we propose a prefix-tree-based full-matching strategy which significantly improves parsing efficiency. Extensive evaluation across real-world datasets showcases Hooglle's superior performance on 16 public benchmark datasets.

关键词： Accuracy Semantics Training data Benchmark testing Software engineering

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How Does Textual Information Selection Influence Time Series Forecasting? A Cross-modal Perspective on Financial Volatility Prediction

How Does Textual Information Selection Influence Time Series...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Hao Niu Yun Xiong Xiaosu Wang Biao Yang Yao Zhang Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Real-world time series are often accompanied by textual descriptions as collateral information, especially in finance. Financial volatility prediction based on earnings call transcripts is such a typical scenario. Nevertheless, current studies rarely focus on the cross-modal impact of textual information on time series forecasting, if we regard time series as another modality. Consequently, we propose a Denoised Financial Volatility Prediction (DeFVP) model to investigate the impact of varying textual selection on time series forecasting in the volatility prediction scenario. To this end, we jointly train two neural network modules: a Differentiable Binary Selector (DBS) and a Volatility Predictor (VP). Firstly, the DBS module assigns learnable Bernoulli variables to sentences for identifying helpful sentences. Next, the VP module makes predictions by imposing selected textual influence into time series modeling. We conduct extensive experiments on three real-world datasets, demonstrating the significant and synergistic impact of textual selection on time series forecasting. 1

关键词： Time series analysis Neural networks Finance Predictive models Forecasting Text processing

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Privacy-Preserving Multi-keyword Fuzzy Adjacency Search Strategy for Encrypted Graph in Cloud Environment

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computers, Materials & Continua 2024年第3期78卷 3177-3194页

作者： Bin Wu Xianyi Chen Jinzhou Huang Caicai Zhang Jing Wang Jing Yu Zhiqiang Zhao Zhuolin Mei School of Computer and Big Data Science Jiujiang UniversityJiujiang332005China Jiujiang Key Laboratory of Network and Information Security Jiujiang332005China School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China School of Computer Engineering Hubei University of Arts and ScienceXiangyang441053China School of Modern Information Technology Zhejiang Institute of Mechanical and Electrical EngineeringHangzhou310053China Information Center Jiangxi Changjiang Chemical Co.Ltd.Jiujiang332005China School of Mathematics and Computer Science Ningxia Normal UniversityGuyuan756099China

In a cloud environment,outsourced graph data is widely used in companies,enterprises,medical institutions,and so *** owners and users can save costs and improve efficiency by storing large amounts of graph data on cloud *** on cloud platforms usually have some subjective or objective attacks,which make the outsourced graph data in an insecure *** issue of privacy data protection has become an important obstacle to data sharing and *** to query outsourcing graph data safely and effectively has become the focus of *** query is a basic and frequently used operation in graph,and it will effectively promote the query range and query ability if multi-keyword fuzzy search can be supported at the same *** work proposes to protect the privacy information of outsourcing graph data by encryption,mainly studies the problem of multi-keyword fuzzy adjacency query,and puts forward a *** our scheme,we use the Bloom filter and encryption mechanism to build a secure index and query token,and adjacency queries are implemented through indexes and query tokens on the cloud *** proposed scheme is proved by formal analysis,and the performance and effectiveness of the scheme are illustrated by experimental *** research results of this work will provide solid theoretical and technical support for the further popularization and application of encrypted graph data processing technology.

关键词： Privacy-preserving adjacency query multi-keyword fuzzy search encrypted graph

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Mobility-LLM: Learning Visiting Intentions and Travel Preferences from Human Mobility data with Large Language Models 38

Mobility-LLM: Learning Visiting Intentions and Travel Prefer...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Gong, Letian Lin, Yan Zhang, Xinyue Lu, Yiwen Han, Xuedi Liu, Yichen Guo, Shengnan Lin, Youfang Wan, Huaiyu School of Computer Science and Technology Beijing Jiaotong University China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing China Department of Computer Science Aalborg University Denmark

Location-based services (LBS) have accumulated extensive human mobility data on diverse behaviors through check-in sequences. These sequences offer valuable insights into users' intentions and preferences. Yet, existing models analyzing check-in sequences fail to consider the semantics contained in these sequences, which closely reflect human visiting intentions and travel preferences, leading to an incomplete comprehension. Drawing inspiration from the exceptional semantic understanding and contextual information processing capabilities of large language models (LLMs) across various domains, we present Mobility-LLM, a novel framework that leverages LLMs to analyze check-in sequences for multiple tasks. Since LLMs cannot directly interpret check-ins, we reprogram these sequences to help LLMs comprehensively understand the semantics of human visiting intentions and travel preferences. Specifically, we introduce a visiting intention memory network (VIMN) to capture the visiting intentions at each record, along with a shared pool of human travel preference prompts (HTPP) to guide the LLM in understanding users' travel preferences. These components enhance the model's ability to extract and leverage semantic information from human mobility data effectively. Extensive experiments on four benchmark datasets and three downstream tasks demonstrate that our approach significantly outperforms existing models, underscoring the effectiveness of Mobility-LLM in advancing our understanding of human mobility data within LBS contexts. © 2024 Neural information processing systems foundation. All rights reserved.

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3DMambaComplete: Exploring Structured State Space Model for Point Cloud Completion

arXiv

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arXiv 2024年

作者： Li, Yixuan Yang, Weidong Fei, Ben Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China

Point cloud completion aims to generate a complete and high-fidelity point cloud from an initially incomplete and low-quality input. A prevalent strategy involves leveraging Transformer-based models to encode global features and facilitate the reconstruction process. However, the adoption of pooling operations to obtain global feature representations often results in the loss of local details within the point cloud. Moreover, the attention mechanism inherent in Transformers introduces additional computational complexity, rendering it challenging to handle long sequences effectively. To address these issues, we propose 3DMambaComplete, a point cloud completion network built on the novel Mamba framework. It comprises three modules: HyperPoint Generation encodes point cloud features using Mamba’s selection mechanism and predicts a set of Hyperpoints. A specific offset is estimated, and the downsampled points become HyperPoints. The HyperPoint Spread module disperses these HyperPoints across different spatial locations to avoid concentration. Finally, a deformation method transforms the 2D mesh representation of HyperPoints into a fine-grained 3D structure for point cloud reconstruction. Extensive experiments conducted on various established benchmarks demonstrate that 3DMambaComplete surpasses state-of-the-art point cloud completion methods, as confirmed by qualitative and quantitative analyses. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Exploring the Compositional Deficiency of Large Language Models in Mathematical Reasoning Through Trap Problems

Exploring the Compositional Deficiency of Large Language Mod...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zhao, Jun Tong, Jingqi Mou, Yurong Zhang, Ming Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

ISBN: (纸本)9798891761643

Human cognition exhibits systematic compositionality, the algebraic ability to generate infinite novel combinations from finite learned components, which is the key to understanding and reasoning about complex logic. In this work, we investigate the compositionality of large language models (LLMs) in mathematical reasoning. Specifically, we construct a new dataset MATHTRAP‡ by introducing carefully designed logical traps into the problem descriptions of MATH and GSM8K. Since problems with logical flaws are quite rare in the real world, these represent "unseen" cases to LLMs. Solving these requires the models to systematically compose (1) the mathematical knowledge involved in the original problems with (2) knowledge related to the introduced traps. Our experiments show that while LLMs possess both components of requisite knowledge, they do not spontaneously combine them to handle these novel cases. We explore several methods to mitigate this deficiency, such as natural language prompts, few-shot demonstrations, and fine-tuning. Additionally, we test the recently released OpenAI o1 model and find that human-like 'slow thinking' helps improve the compositionality of LLMs. Overall, systematic compositionality remains an open challenge for large language models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Modulating perovskite crystallization and band alignment using coplanar molecules for high-performance indoor photovoltaics

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Journal of Energy Chemistry 2024年第11期98卷 383-390页

作者： Qu Yang Shuhan Fan Haozhe Zhang Zhenhuang Su Xingyu Gao Hui Shen Mingkui Wang Xiu Gong College of Physics &Guizhou Province Key Laboratory for Photoelectrics Technology and ApplicationGuizhou UniversityGuiyang 550025GuizhouChina State Key Laboratory of Public Big Data Guizhou UniversityGuiyang 550025GuizhouChina Shanghai Synchrotron Radiation Facility(SSRF) Shanghai Advanced Research InstituteChinese Academy of SciencesShanghai 201204China Wuhan National Laboratory for Optoelectronics School of Optical and Electronic InformationHuazhong University of Science and TechnologyWuhan 430074HubeiChina

The proper bandgap and exceptional photostability enable CsPbI_(3) as a potential candidate for indoor photovoltaics(IPVs),but indoor power conversion efficiency(PCE) is impeded by serious nonradiative recombination stemming from challenges in incomplete DMAPbI_(3) conversion and lattice structure ***,the coplanar symmetric structu re of hexyl sulfide(HS) is employed to functionalize the CsPbI_(3) layer for fabricating highly efficient *** hydrogen bond between HS and DMAI promotes the conversion of DMAPbI_(3) to CsPbI_(3),while the copianar symmetric structure enhances crystalline ***,surface sulfidation during HS-induced growth results in the in situ formation of PbS,spontaneously creating a CsPbI_(3) N-P homojunction to enhance band alignment and carrier *** a result,the CsPbI_(3)&HS devices achieve an impressive indoor PCE of 39.90%(P_(in):334.6 μW cm^(-2),P_(out):133.5 μW cm^(-2)) under LED@2968 K,1062 lux,and maintain over 90% initial PCE for 800 h at ^(3)0% air ambient humidity.

关键词： Perovskite indoor photovoltaics CsPbI_(3) Coplanar symmetric structure molecules Crystallization kinetics Hydrogen bond N-P homojunction

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A Timed Calculus with Mobility for Wireless Networks 23

A Timed Calculus with Mobility for Wireless Networks

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4th International Conference on Computing, Networks and Internet of Things, CNIOT 2023

作者： Xie, Wanling Zhu, Huibiao Wu, Xi School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software ChangZhou University ChangZhou China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China School of Computer Science The University of Sydney Australia

ISBN: (纸本)9798400700705

Unreliability of communication links is a very common problem in wireless networks, and node mobility as one of significant features may importantly affect the reliability of communication links. In this paper, we present a Timed Calculus with Mobility for Wireless Networks called MTCWN to capture mobility in wireless networks. In our calculus, we introduce a random direction mobility model to depict node movement. With time passing, the mobility function and its timeout will be changed by this mobility model. Our calculus also describe local broadcast communication in wireless networks. Moreover, we study the operational semantics of the MTCWN calculus and illustrate the applications of the semantics rules by examples. © 2023 ACM.

关键词： Wireless networks

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SMEC:Scene Mining for E-Commerce

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Journal of computer science & Technology 2024年第1期39卷 192-210页

作者：王罡李翔郭子义殷大伟马帅 State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Data Science and Engineering East China Normal UniversityShanghai 200062China *** Inc.Beijing 100176China Baidu Inc.Beijing 100085China

Scene-based recommendation has proven its usefulness in E-commerce,by recommending commodities based on a given ***,scenes are typically unknown in advance,which necessitates scene discovery for *** this article,we study scene discovery for E-commerce *** first formalize a scene as a set of commodity cate-gories that occur simultaneously and frequently in real-world situations,and model an E-commerce platform as a heteroge-neous information network(HIN),whose nodes and links represent different types of objects and different types of rela-tionships between objects,*** then formulate the scene mining problem for E-commerce as an unsupervised learning problem that finds the overlapping clusters of commodity categories in the *** solve the problem,we pro-pose a non-negative matrix factorization based method SMEC(Scene Mining for E-Commerce),and theoretically prove its *** six real-world E-commerce datasets,we finally conduct an extensive experimental study to evaluate SMEC against 13 other methods,and show that SMEC consistently outperforms its competitors with regard to various evaluation measures.

关键词： graph clustering E-commerce heterogeneous information network(HIN) scene mining

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