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DNMDR: Dynamic Networks and Multi-view Drug Representations for Safe Medication Recommendation

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arXiv 2025年

作者： Liu, Guanlin Yu, Xiaomei Liu, Zihao Li, Xue Fan, Xingxu Zheng, Xiangwei School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250358 China State Key Laboratory of High-end Server and Storage Technology Jinan250300 China

Medication Recommendation (MR) is a promising research topic which booms diverse applications in the healthcare and clinical domains. However, existing methods mainly rely on sequential modeling and static graphs for representation learning, which ignore the dynamic correlations in diverse medical events of a patient's temporal visits, leading to insufficient global structural exploration on nodes. Additionally, mitigating drug-drug interactions (DDIs) is another issue determining the utility of the MR systems. To address the challenges mentioned above, this paper proposes a novel MR method with the integration of dynamic networks and multi-view drug representations (DNMDR). Specifically, weighted snapshot sequences for dynamic heterogeneous networks are constructed based on discrete visits in temporal EHRs, and all the dynamic networks are jointly trained to gain both structural correlations in diverse medical events and temporal dependency in historical health conditions, for achieving comprehensive patient representations with both semantic features and structural relationships. Moreover, combining the drug co-occurrences and adverse drug-drug interactions (DDIs) in internal view of drug molecule structure and interactive view of drug pairs, the safe drug representations are available to obtain high-quality medication combination recommendation. Finally, extensive experiments on real world datasets are conducted for performance evaluation, and the experimental results demonstrate that the proposed DNMDR method outperforms the state-of-the-art baseline models with a large margin on various metrics such as PRAUC, Jaccard, DDI rates and so on. Copyright © 2025, The Authors. All rights reserved.

关键词： Electronic health record

A Theoretical Explanation of Activation Sparsity through Flat Minima and Adversarial Robustness

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arXiv 2023年

作者： Peng, Ze Qi, Lei Shi, Yinghuan Gao, Yang State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

A recent empirical observation (Li et al., 2022b) of activation sparsity in MLP blocks offers an opportunity to drastically reduce computation costs for free. Although having attributed it to training dynamics, existing theoretical explanations of activation sparsity are restricted to shallow networks, small training steps and special training, despite its emergence in deep models standardly trained for a large number of steps. To fill these gaps, we propose the notion of gradient sparsity as one source of activation sparsity and a theoretical explanation based on it that sees sparsity as a necessary step to adversarial robustness w.r.t. hidden features and parameters, which is approximately the flatness of minima for well-learned models. The theory applies to standardly trained LayerNorm-ed MLPs, and further to Transformers or other architectures trained with weight noises. Eliminating other sources of flatness except for sparsity, we discover the phenomenon that the ratio between the largest and smallest non-zero singular values of weight matrices is small. When discussing the emergence of this spectral concentration, we use random matrix theory (RMT) as a powerful tool to analyze stochastic gradient noises. Experiments for validation are conducted to verify our gradient-sparsity-based explanation. We propose two plug-and-play modules for both training and finetuning for sparsity. Experiments on ImageNet-1K and C4 demonstrate their 50% sparsity improvements, indicating further potential cost reduction in both training and inference. Copyright © 2023, The Authors. All rights reserved.

关键词： Chemical activation

SoVAR: Building Generalizable Scenarios from Accident Reports for Autonomous Driving Testing

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SoVAR: Building Generalizable Scenarios from Accident Report...

IEEE International Conference on Automated software Engineering (ASE)

作者： An Guo Yuan Zhou Haoxiang Tian Chunrong Fang Yunjian Sun Weisong Sun Xinyu Gao Anh Tuan Luu Yang Liu Zhenyu Chen State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798400712487

ISBN: (纸本)9798331508241

Autonomous driving systems (ADSs) have undergone remarkable development and are increasingly employed in safety-critical applications. However, recently reported data on fatal accidents involving ADSs suggests that the desired level of safety has not yet been fully achieved. Consequently, there is a growing need for more comprehensive and targeted testing approaches to ensure safe driving. Scenarios from real-world accident reports provide valuable resources for ADS testing, including critical scenarios and high-quality seeds. However, existing scenario reconstruction methods from accident reports often exhibit limited accuracy in information extraction. Moreover, due to the diversity and complexity of road environments, matching current accident information with the simulation map data for reconstruction poses significant *** this paper, we design and implement SoVAR, a tool for automatically generating road-generalizable scenarios from accident reports. SoVAR utilizes well-designed prompts with linguistic patterns to guide the large language model (LLM) in extracting accident information from textual data. Subsequently, it formulates and solves accident-related constraints in conjunction with the extracted accident information to generate accident trajectories. Finally, SoVAR reconstructs accident scenarios on various map structures and converts them into test scenarios to evaluate its capability to detect defects in industrial ADSs. We experiment with SoVAR, using the accident reports from the National Highway Traffic Safety Administration’s (NHTSA) database to generate test scenarios for the industrial-grade ADS Apollo. The experimental findings demonstrate that SoVAR can effectively generate generalized accident scenarios across different road structures. Furthermore, the results confirm that SoVAR identified 5 distinct safety violation types that contributed to the crash of Baidu *** Concepts• software and its engineering → software testing an

关键词： Roads Web and internet services Linguistics software computer crashes Safety Trajectory Data mining Autonomous vehicles Accidents

EdgeCross: Cloud Scale Traffic Management at Peering Edges

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Proceedings of the ACM on Networking 2024年第CONEXT4期2卷 1-23页

作者： Xiaoliang Wang Penghui Mi Yong Zhu Baoyi An Yinhua Wang Lixiang Wang Xuezhi Yu Qiong Xie Xiang Huang Mingliang Yin Chaoyang Ji Wei Sun Yihang Lv Yuhang Chen Cam-Tu Nguyen Chen Tian Xiaoming Fu State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Huawei Cloud Computing Technologies Co. Ltd. Shenzhen China Institute of Computer Science University of Göttingen Göttingen Germany

Cloud providers deployed dozens of PoPs and data centers globally to serve billions of geo-distributed users. The traffic management at peering edges has become a key capability of cloud network operators to meet the diverse demands of users. With the rapid growth of cloud applications, users have recently announced new performance requirements, e.g., achieving latency as low as possible instead of maintaining a specified delay. The conventional inter-domain bandwidth allocation approach, which aims to reduce the high operating expenditures of bandwidth usage, fails to meet these new requirements. We further reveal that the flow scheduling among PoPs may fail due to the limited link capacity hidden by the cloud private backbone network controller. Therefore we seek a new traffic management at peering *** propose a new controller framework, EdgeCross, that satisfies not only users' emerging demands but maintains low operating costs. The large number of fine-grain application-aware flows and the consideration of backbone links' capacity lead to very high complexity of routing computation and verification for the controller. EdgeCross introduces a two-phase operation that first achieves the low-expense bandwidth allocation according to the standard 95th percentile billing model and then allocates specified flows to peering edges based on users' requirements. EdgeCross further reduces large memory consumption by proposing an effective routing table compression approach. The evaluation based on a production network with 16 PoPs has shown that EdgeCross can successfully process the routes of 1 billion flows in 10 seconds, reduce the average delay for performance-sensitive flows by 2 milliseconds compared to traditional BGP, and is able to save the bandwidth cost by 10-26% compared to the state-of-the-art Cascara.

关键词： cloud network flow scheduling peering edges sdn controller

Better Debugging: Combining Static Analysis and LLMs for Explainable Crashing Fault Localization

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arXiv 2024年

作者： Yan, Jiwei Huang, Jinhao Fang, Chunrong Yan, Jun Zhang, Jian The Technology Center of Software Engineering Institute of Software Chinese Academy of Sciences Beijing China The Key Laboratory of System Software Institute of Software Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China The State Key Laboratory for Novel Software Technology Nanjing University Nanjing China The Software Institute Nanjing University Jiangsu Nanjing210008 China

Nowadays, many applications do not exist independently but rely on various frameworks or libraries. The frequent evolution and the complex implementation of framework APIs induce many unexpected post-release crashes. Starting from the crash stack traces, existing approaches either perform direct call graph (CG) tracing or construct datasets with similar crash-fixing records to locate buggy methods. However, these approaches are limited by the completeness of CG or dependent on historical fixing records. Moreover, they fail to explain the buggy candidates by revealing their relationship with the crashing point, which decreases the efficiency of user debugging. To fill the gap, we propose an explainable crashing fault localization approach by combining static analysis and LLM techniques. Our primary insight is that understanding the semantics of exception-throwing statements in the framework code can help find and apprehend the buggy methods in the application code. Based on this idea, first, we design the exception-thrown summary (ETS) that describes the key elements related to each framework-specific exception and extract ETSs by performing static analysis. As each crash can map to a target ETS, we make data-tracking of its key elements to identify and sort buggy candidates for the given crash. Then, we introduce LLMs to improve the explainability of the localization results. To construct effective LLM prompts, we design the candidate information summary (CIS) that describes multiple types of explanation-related contexts and then extract CISs via static analysis. Compared to SOTA fault localization works, our approach does not solely depend on CG tracing and does not require prior knowledge. Instead, it fully utilizes the information from the framework code and is the first to consider the explainability of the localization results. Finally, we apply our approach to one typical scenario, i.e., locating Android framework-specific crashing faults, and implement a tool

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AoI-aware Incentive Mechanism for Mobile Crowdsensing using Stackelberg Game

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AoI-aware Incentive Mechanism for Mobile Crowdsensing using ...

IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Mingjun Xiao Yin Xu Jinrui Zhou Jie Wu Sheng Zhang Jun Zheng School of Computer Science and Technology University of Science and Technology of China China Suzhou Institute for Advanced Research University of Science and Technology of China China Department of Computer and Information Sciences Temple Universityy State Key Laboratory for Novel Software Technology Nanjing University China

Mobile CrowdSensing (MCS) is a mobile computing paradigm, through which a platform can coordinate a crowd of workers to accomplish large-scale data collection tasks using their mobile devices. Information freshness has attracted much focus on MCS research worldwide. In this paper, we investigate the incentive mechanism design in MCS systems that take the freshness of collected data and social benefits into concerns. First, we introduce the Age of Information (AoI) metric to measure the freshness of data. Then, we model the incentive mechanism design with AoI guarantees as a novel incomplete information two-stage Stackelberg game with multiple constraints. Next, we derive the optimal strategies of this game so as to determine the optimal reward paid by the platform and the optimal data update frequency for each worker. Moreover, we prove that these optimal strategies form a unique Stackelberg equilibrium. Based on the optimal strategies, we propose an AoI-Aware Incentive (AIAI) mechanism for the MCS system, whereby the platform and all workers can maximize their utilities simultaneously. Meanwhile, the system can ensure that the AoI values of all data uploaded to the platform are not larger than a given threshold to achieve high data freshness. Extensive simulations on real-world traces are conducted to demonstrate the significant performance of AIAI.

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General Category Network: Handwritten Mathematical Expression Recognition with Coarse-Grained Recognition Task

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General Category Network: Handwritten Mathematical Expressio...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xinyu Zhang Han Ying Ye Tao Youlu Xing Guihuan Feng State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Software Institute Nanjing University Nanjing China School of Computer Science and Technology Nanjing University Nanjing China

Handwritten Mathematical Expression Recognition (HMER) is an important task in pattern recognition. It is a challenging task due to symbols resembling each other in appearance("z/2", "B/β") and the complex mathematical syntax. The encoder-decoder architecture has been widely used in recent HMER methods. Several works introduce HMER-related tasks that enhance the performance of HMER. We propose the General Category Recognition Task (GCRT) and design the General Category Network(GCN) to perform HMER and GCRT in parallel. GCRT alleviates the symbol confusion problem and also helps the model generate results conforming to mathematical syntax. Compared with SOTA methods, the experimental results show that Expression Recognition Rates(ExpRate) of our method are increased by 1.12%, 2.45% and 1.84% on CROHME 2014, 2016 and 2019 test set respectively. In addition, current works explore the effect of a single auxiliary task on HMER. We investigated the effect of multiple tasks on HMER. We performed many experiments and drew multiple valuable *** will publish all codes in the future.

关键词： Handwriting recognition Codes Symbols Speech recognition Syntactics Signal processing Mathematical models

Infrared: A Meta Bug Detector

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arXiv 2022年

作者： Zhang, Chi Wang, Yu Wang, Linzhang State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Jiangsu Nanjing210023 China

The recent breakthroughs in deep learning methods have sparked a wave of interest in learning-based bug detectors. Compared to the traditional static analysis tools, these bug detectors are directly learned from data, thus, easier to create. On the other hand, they are difficult to train, requiring a large amount of data which is not readily available. In this paper, we propose a new approach, called meta bug detection, which offers three crucial advantages over existing learning-based bug detectors: bug-type generic (i.e., capable of catching the types of bugs that are totally unobserved during training), self-explainable (i.e., capable of explaining its own prediction without any external interpretability methods) and sample efficient (i.e., requiring substantially less training data than standard bug detectors). Our extensive evaluation shows our meta bug detector (MBD) is effective in catching a variety of bugs including null pointer dereference, array index out-of-bound, file handle leak, and even data races in concurrent programs;in the process MBD also significantly outperforms several noteworthy baselines including Facebook Infer, a prominent static analysis tool, and FICS, the latest anomaly detection method. © 2022, CC BY.

关键词： Static analysis

MG+: Towards Efficient Context Inconsistency Detection by Minimized Link Generation

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software Testing Verification and Reliability 2024年第1期35卷

作者： Chen, Chuyang Wang, Huiyan Zhang, Lingyu Xu, Chang Yu, Ping State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China School of Computer Science Nanjing University Jiangsu Nanjing China Software Institute Nanjing University Jiangsu Nanjing China

Self-adaptive applications are becoming increasingly attractive, with the ability to smartly understand their runtime environments (or contexts) and deliver adaptive services, for example, location-aware navigation or resource-sensitive suggestions. However, due to inherent noises in the process of sensing and interpreting environmental information, there is a growing demand for guarding the consistency of collected contexts to avoid application misbehaviour and, at the same time, minimize extra costs. Existing work attempted to achieve this by speeding up the kernel constraint checking module inside the consistency guarding process. Most of these efforts were spent on reusing previous checking results or parallelizing the checking process, but they all leave one central step of constraint checking, that is, link generation, untouched. In this step, the checking engine provides reasons to explain the violation of constraints under check. It occupies a substantial part of the total time cost. Focusing on this key link generation step, we proposed MG, which deploys a rigourous analysis to automatically identify and avoid redundancy in the link generation without harming any correctness of the checking results. MG has been proven sound (always guaranteeing correctness) and complete (entirely removing redundancy). Moreover, based on our observation that MG's redundancy elimination also assists another core step of constraint checking to reduce unnecessary computation further, we additionally enhance MG with an escape-condition optimization to escape unnecessary evaluation of truth values to further improve the efficiency of constraint checking in an aspect other than link generation. We call it MG+ for distinguishing. Our experiments with synthesized and real-world consistency constraints reported that, compared with existing work, MG eliminates all link redundancy (83% to 0%), and based on it, MG+ further reduces significant truth value calculations (e.g., 49.74% reduc

关键词： context inconsistency constraint checking link redundancy