检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Han, Jin Hong, Yun Li, Wu-Jun National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210023 China

Virtual screening (VS) is a critical step in computer-aided drug discovery, aiming to identify molecules that bind to a specific target receptor like protein. Traditional VS methods, such as docking, are often too time-consuming for screening large-scale molecular databases. Recent advances in deep learning have demonstrated that learning vector representations for both proteins and molecules using contrastive learning can outperform traditional docking methods. However, given that target databases often contain billions of molecules, real-valued vector representations adopted by existing methods can still incur significant memory and time costs in VS. To address this problem, in this paper we propose a hashing-based contrastive learning method, called DrugHash, for VS. DrugHash treats VS as a retrieval task that uses efficient binary hash codes for retrieval. In particular, DrugHash designs a simple yet effective hashing strategy to enable end-to-end learning of binary hash codes for both protein and molecule modalities, which can dramatically reduce the memory and time costs with higher accuracy compared with existing methods. Experimental results show that DrugHash can outperform existing methods to achieve state-of-the-art accuracy, with a memory saving of 32× and a speed improvement of 3.5×. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Hashing for Protein Structure Similarity Search

arXiv

引用

arXiv 2024年

作者： Han, Jin Li, Wu-Jun National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210023 China

Protein structure similarity search (PSSS), which tries to search proteins with similar structures, plays a crucial role across diverse domains from drug design to protein function prediction and molecular evolution. Traditional alignment-based PSSS methods, which directly calculate alignment on the protein structures, are highly time-consuming with high memory cost. Recently, alignment-free methods, which represent protein structures as fixed-length real-valued vectors, are proposed for PSSS. Although these methods have lower time and memory cost than alignment-based methods, their time and memory cost is still too high for large-scale PSSS, and their accuracy is unsatisfactory. In this paper, we propose a novel method, called protein structure hashing (POSH), for PSSS. POSH learns a binary vector representation for each protein structure, which can dramatically reduce the time and memory cost for PSSS compared with real-valued vector representation based methods. Furthermore, in POSH we also propose expressive hand-crafted features and a structure encoder to well model both node and edge interactions in proteins. Experimental results on real datasets show that POSH can outperform other methods to achieve state-of-the-art accuracy. Furthermore, POSH achieves a memory saving of more than six times and speed improvement of more than four times, compared with other methods. © 2024, CC BY.

关键词： Molecular docking

来源：评论

学校读者我要写书评

暂无评论

NumLLM: Numeric-Sensitive Large Language Model for Chinese Finance

arXiv

引用

arXiv 2024年

作者： Su, Huan-Yi Wu, Ke Huang, Yu-Hao Li, Wu-Jun National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210023 China

Recently, many works have proposed various financial large language models (FinLLMs) by pre-training from scratch or fine-tuning open-sourced LLMs on financial corpora. However, existing FinLLMs exhibit unsatisfactory performance in understanding financial text when numeric variables are involved in questions. In this paper, we propose a novel LLM, called numeric-sensitive large language model (NumLLM), for Chinese finance. We first construct a financial corpus from financial textbooks which is essential for improving numeric capability of LLMs during fine-tuning. After that, we train two individual low-rank adaptation (LoRA) modules by fine-tuning on our constructed financial corpus. One module is for adapting general-purpose LLMs to financial domain, and the other module is for enhancing the ability of NumLLM to understand financial text with numeric variables. Lastly, we merge the two LoRA modules into the foundation model to obtain NumLLM for inference. Experiments on financial question-answering benchmark show that NumLLM can boost the performance of the foundation model and can achieve the best overall performance compared to all baselines, on both numeric and non-numeric questions. Copyright © 2024, The Authors. All rights reserved.

关键词： Finance

来源：评论

学校读者我要写书评

暂无评论

Identifying key features from app user reviews 21

Identifying key features from app user reviews

引用

43rd IEEE/ACM International Conference on software Engineering, ICSE 2021

作者： Wu, Huayao Deng, Wenjun Niu, Xintao Nie, Changhai State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

ISBN: (纸本)9780738113197

Due to the rapid growth and strong competition of mobile application (app) market, app developers should not only offer users with attractive new features, but also carefully maintain and improve existing features based on users' feedbacks. User reviews indicate a rich source of information to plan such feature maintenance activities, and it could be of great benefit for developers to evaluate and magnify the contribution of specific features to the overall success of their apps. In this study, we refer to the features that are highly correlated to app ratings as key features, and we present KEFE, a novel approach that leverages app description and user reviews to identify key features of a given app. The application of KEFE especially relies on natural language processing, deep machine learning classifier, and regression analysis technique, which involves three main steps: 1) extracting feature-describing phrases from app description;2) matching each app feature with its relevant user reviews;and 3) building a regression model to identify features that have significant relationships with app ratings. To train and evaluate KEFE, we collect 200 app descriptions and 1,108,148 user reviews from Chinese Apple App Store. Experimental results demonstrate the effectiveness of KEFE in feature extraction, where an average F-measure of 78.13% is achieved. The key features identified are also likely to provide hints for successful app releases, as for the releases that receive higher app ratings, 70% of features improvements are related to key features. © 2021 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Efficiency-First Fault-Tolerant Replica Scheduling Strategy for Reliability Constrained Cloud Application 18th

Efficiency-First Fault-Tolerant Replica Scheduling Strategy ...

引用

18th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2021

作者： Zhang, Yingxue Fan, Guisheng Yu, Huiqun Chen, Xingpeng Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9783030935702

Reliability requirement assurance is an important prerequisite for application execution in the cloud. Although copy management can improve the reliability of applications, it also brings a series of resource waste and overhead issues. Therefore, the efficiency-first fault-tolerant algorithm (EFFT) with minimum execution cost in the cloud application is proposed. This algorithm minimizes the execution cost of the application under the constraints of reliability, and solves the problem of excessive overhead caused by too many copies. The EFFT algorithm is divided into two stages: initial allocation and dynamic adjustment. On the initial allocation of EFFT algorithm, a sorting rule is defined to determine the priority of tasks and instances. During the adjustment phase, by defining an actual efficiency ratio indicator to measure the cost-effectiveness of an instance, the EFFT algorithm makes a good trade-off between cost and reliability in order to minimize execution costs. Run our algorithm on randomly generated parallel applications of different scales and compare the experimental results with four advanced algorithms. The experiments show that the performance of the algorithm we proposed is better than the other algorithms in terms of execution cost and fault tolerance. © 2022, IFIP International Federation for Information Processing.

关键词： Fault tolerance

来源：评论

学校读者我要写书评

暂无评论

"re-ID BUFF": An enhanced similarity measurement based on genetic programming for person re-identification 21

"re-ID BUFF": An enhanced similarity measurement based on ge...

引用

2021 Genetic and Evolutionary Computation Conference, GECCO 2021

作者： Li, Yiming Shang, Lin State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

ISBN: (纸本)9781450383516

Person re-identification (re-ID) is a fundamental link to ensure successful cross-camera tracking. Its overall process is divided into two stages, feature extraction and matching. In the matching stage, the simple Euclid measurement has limitations in score due to the lack of consideration of the vector directions. The majority of recent contributions focus on the use of neighborhood information of the query image, which makes the calculation process more complex. In addition, these similarity measurements are predefined and therefore evaluated. In this paper, we propose a re-ID similarity measurement based on Genetic Programming (GP). The similarity measurement formula is automatically evolved and constructed through the optimization using specific function set, terminal set and fitness function targeting re-ID. For the training process of GP, we propose the feature triplet-dataset like the triplet-loss based on the existing re-ID datasets. We conduct sufficient experiments on three benchmark datasets, comparing with features of different quality and various measurements. The proposed method has a better effect than the Euclid measurement and achieves a general improvement on the mAP and rank-1 of combination with other metrics. Therefore, our method can be used as a gain "buff"to enhance the score of other methods on the original basis. © 2021 Owner/Author.

关键词： Genetic programming

来源：评论

学校读者我要写书评

暂无评论

LEARNING WITH LOGICAL CONSTRAINTS BUT WITHOUT SHORTCUT SATISFACTION 11

LEARNING WITH LOGICAL CONSTRAINTS BUT WITHOUT SHORTCUT SATIS...

引用

11th International Conference on Learning Representations, ICLR 2023

作者： Li, Zenan Liu, Zehua Yao, Yuan Xu, Jingwei Chen, Taolue Ma, Xiaoxing Lü, Jian State Key Lab of Novel Software Technology Nanjing University China Department of Mathematics The University of Hong Kong Hong Kong Department of Computer Science Birkbeck University of London United Kingdom

Recent studies have explored the integration of logical knowledge into deep learning via encoding logical constraints as an additional loss ***, existing approaches tend to vacuously satisfy logical constraints through shortcuts, failing to fully exploit the *** this paper, we present a new framework for learning with logical ***, we address the shortcut satisfaction issue by introducing dual variables for logical connectives, encoding how the constraint is *** further propose a variational framework where the encoded logical constraint is expressed as a distributional loss that is compatible with the model's original training *** theoretical analysis shows that the proposed approach bears salient properties, and the experimental evaluations demonstrate its superior performance in both model generalizability and constraint satisfaction. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Signal encoding

来源：评论

学校读者我要写书评

暂无评论

Knowledge-aware Multi-view Cross Learning for Edge-based Collaborative Recommendation 29

Knowledge-aware Multi-view Cross Learning for Edge-based Col...

引用

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Dai, Yang Meng, Shunmei Gu, Huanhuan Liu, Nan Tu, Longchuan NanJing University of Science and Technology School of Cyberspace Security Nanjing China NanJing University State Key Lab. for Novel Software Technology Nanjing China NanJing University of Science and Technology Department of Computer Science and Engineering Nanjing China Nanjing Sinovatio Technology Co. Ltd. Nanjing China

ISBN: (纸本)9798350330717

In order to effectively overcome the sparsity and cold start challenges of collaborative filtering in actual scenarios, and provide more accurate suggestions, knowledge graphs are commonly utilized in recommender systems. Graph neural networks are increasingly used in knowledge graph-based recommender systems. Nevertheless, conventional recommender systems overlook the relationship information between items and entities as well as the sparse supervision signal, potentially leading to a decline in the actual recommendation performance. In this investigation, we propose KMCL, a Knowledge-aware Multi-view Cross-learning framework for edge-based collaborative recommendation. In contrast to the typical manner of dealing with knowledge graphs, our approach investigates three different graph views to improve users' and items' embedding representations, respectively. Firstly, the merged user-item KG graph is utilized to integrate the multi-hop neighbor information of each user, with the Graph Attention Network assigning various weights to different neighbors. Secondly, Graph Neural Network collects the top-x neighbor nodes for learning in the collaborative view of user-item graph and item-entity graph. Finally, a cross module is developed to share high-level information between embeddings with the same features. In addition, we also introduce the idea of edge computing, which preprocesses and calculates the locally obtained user preferences first, and then inputs them into different views to improve the recommendation efficiency. Experimental results demonstrate that KMCL delivers better suggestions than state-of-the-art baseline models in delivering superior recommendations. Furthermore, our approach maintains higher accuracy in predicting outcomes in less familiar interaction scenarios. © 2023 IEEE.

关键词： Edge Computing Graph Attention Network Knowledge Graph Multi-view Cross Learning Recommender System

来源：评论

学校读者我要写书评

暂无评论

Improving Local Search for Pseudo Boolean Optimization by Fragile Scoring Function and Deep Optimization 29

Improving Local Search for Pseudo Boolean Optimization by Fr...

引用

29th International Conference on Principles and Practice of Constraint Programming, CP 2023

作者： Zhou, Wenbo Zhao, Yujiao Wang, Yiyuan Cai, Shaowei Wang, Shimao Wang, Xinyu Yin, Minghao School of Information Science and Technology Northeast Normal University Changchun China Key Laboratory of Applied Statistics of MOE Northeast Normal University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of MOE Jilin University Changchun China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783959773003

Pseudo-Boolean optimization (PBO) is usually used to model combinatorial optimization problems, especially for some real-world applications. Despite its significant importance in both theory and applications, there are few works on using local search to solve PBO. This paper develops a novel local search framework for PBO, which has three main ideas. First, we design a two-level selection strategy to evaluate all candidate variables. Second, we propose a novel deep optimization strategy to disturb some search spaces. Third, a sampling flipping method is applied to help the algorithm jump out of local optimum. Experimental results show that the proposed algorithms outperform three state-of-the-art PBO algorithms on most instances. © Wenbo Zhou, Yujiao Zhao, Yiyuan Wang, Shaowei Cai, Shimao Wang, Xinyu Wang, and Minghao Yin.

关键词： Combinatorial optimization

来源：评论

学校读者我要写书评

暂无评论

Enhance Training Objectives for Image Captioning with Decomposed Sequence-level Metric

Enhance Training Objectives for Image Captioning with Decomp...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Yehuan Wang Jiefeng Long Jian Hu Lin Shang Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Mechanical Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Image captioning aims to generate fluent and accurate descriptions for images. To evaluate the quality of captions, various metrics have been proposed. However, current metrics only assess captions at sequence-level, which overwhelms the distinctions between each token. Thus, existing objectives tend to treat each token equally, assigning them with identical weights in loss functions. Intuitively, key words in a caption carry the primary information and contribute more than other words to sequence-level metrics. They should be distinguished and weighted more during training. In this work, we propose to explicitly measure each word and guide the model to focus more on key words in captions. Firstly, we devise token-level CIDEr (CIDEr-T) as a new metric to quantify the importance of each word, by decomposing the sequence-level CIDEr into token-level granularity. CIDEr-T maintains consistency with CIDEr and shows the distinctions between tokens. Thus, we engage CIDEr-T scores of each token as their unique weights in the raw loss functions, which can bridge the gap between training and evaluation.

关键词： Measurement Training Accuracy Neural networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：