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A multi-objective deep reinforcement learning algorithm for spatio-temporal latency optimization in mobile IoT-enabled edge computing networks

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Simulation Modelling Practice and Theory 2025年 143卷

作者： Parisa Khoshvaght Amir Haider Amir Masoud Rahmani Farhad Soleimanian Gharehchopogh Ferzat Anka Jan Lansky Mehdi Hosseinzadeh Institute of Research and Development Duy Tan University Da Nang Vietnam School of Engineering & Technology Duy Tan University Da Nang Vietnam Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India Department of AI and Robotics Sejong University Seoul 05006 Republic of Korea Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Engineering Ur. C. Islamic Azad University Urmia Iran Data Science Application and Research Center (VEBIM) Fatih Sultan Mehmet Vakif University Istanbul Türkiye Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Pattern Recognition and Machine Learning Laboratory School of Computing Gachon University Seongnam Republic of Korea

The rapid increase in Mobile Internet of Things (IoT) devices requires novel computational frameworks. These frameworks must meet strict latency and energy efficiency requirements in Edge and Mobile Edge Computing (MEC) systems. Spatio-temporal dynamics, which include the position of edge servers and the timing of task schedules, pose a complex optimization problem. These challenges are further exacerbated by the heterogeneity of IoT workloads and the constraints imposed by device mobility. The balance between computational overhead and communication challenges is also a problem. To solve these issues, advanced methods are needed for resource management and dynamic task scheduling in mobile IoT and edge computing environments. In this paper, we propose a deep Reinforcement learning (DRL) multi-objective algorithm, called a Double deep Q-learning (DDQN) framework enhanced with Spatio-temporal mobility prediction, latency-aware task offloading, and energy-constrained IoT device trajectory optimization for federated edge computing networks. DDQN was chosen for its optimize stability and reduced overestimation in Q-values. The framework employs a reward-driven optimization model that dynamically prioritizes latency-sensitive tasks, minimizes task migration overhead, and balances energy efficiency across devices and edge servers. It integrates dynamic resource allocation algorithms to address random task arrival patterns and real-time computational demands. Simulations demonstrate up to a 35 % reduction in end-to-end latency, a 28 % improvement in energy efficiency, and a 20 % decrease in the deadline-miss ratio compared to benchmark algorithms.

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Part-level Car Parsing and Reconstruction from a Single Street View

arXiv

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arXiv 2018年

作者： Geng, Qichuan Zhang, Hong Huang, Xinyu Wang, Sen Lu, Feixiang Cheng, Xinjing Zhou, Zhong Yang, Ruigang Beihang University Beijing China Baidu Research Beijing China National Engineering Laboratory of Deep Learning Technology and Application China

Part information has been shown to be resistant to occlusions and viewpoint changes, which is beneficial for various vision-related tasks. However, we found very limited work in car pose estimation and reconstruction from street views leveraging the part information. There are two major contributions in this paper. Firstly, we make the first attempt to build a framework to simultaneously estimate shape, translation, orientation, and semantic parts of cars in 3D space from a single street view. As it is labor-intensive to annotate semantic parts on real street views, we propose a specific approach to implicitly transfer part features from synthesized images to real street views. For pose and shape estimation, we propose a novel network structure that utilizes both part features and 3D losses. Secondly, we are the first to construct a high-quality dataset that contains 348 different car models with physical dimensions and part-level annotations based on global and local deformations. Given these models, we further generate 60K synthesized images with randomization of orientation, illumination, occlusion, and texture. Our results demonstrate that our part segmentation performance is significantly improved after applying our implicit transfer approach. Our network for pose and shape estimation achieves the state-of-the-art performance on the ApolloCar3D dataset and outperforms 3D-RCNN and deepMANTA by 12.57 and 8.91 percentage points in terms of mean A3DP-Abs [17, 3, 30]. Copyright © 2018, The Authors. All rights reserved.

关键词： Model automobiles

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Every Pixel Counts: Unsupervised geometry learning with holistic 3d motion understanding

arXiv

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arXiv 2018年

作者： Yang, Zhenheng Wang, Peng Wang, Yang Xu, Wei Nevatia, Ram University of Southern California Baidu Research National Engineering Laboratory for Deep Learning Technology and Applications

learning to estimate 3D geometry in a single image by watching unlabeled videos via deep convolutional network has made significant process recently. Current state-of-the-art (SOTA) methods, are based on the learning framework of rigid structure-from-motion, where only 3D camera ego motion is modeled for geometry estimation. However, moving objects also exist in many videos, e.g. moving cars in a street scene. In this paper, we tackle such motion by additionally incorporating per-pixel 3D object motion into the learning framework, which provides holistic 3D scene flow understanding and helps single image geometry estimation. Specifically, given two consecutive frames from a video, we adopt a motion network to predict their relative 3D camera pose and a segmentation mask distinguishing moving objects and rigid background. An optical flow network is used to estimate dense 2D per-pixel correspondence. A single image depth network predicts depth maps for both images. The four types of information, i.e. 2D flow, camera pose, segment mask and depth maps, are integrated into a differentiable holistic 3D motion parser (HMP), where per-pixel 3D motion for rigid background and moving objects are recovered. We design various losses w.r.t. the two types of 3D motions for training the depth and motion networks, yielding further error reduction for estimated geometry. Finally, in order to solve the 3D motion confusion from monocular videos, we combine stereo images into joint training. Experiments on KITTI 2015 dataset show that our estimated geometry, 3D motion and moving object masks, not only are constrained to be consistent, but also significantly outperforms other SOTA algorithms, demonstrating the benefits of our approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Pixels

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LEGO: learning edge with geometry all at once by watching videos

arXiv

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arXiv 2018年

作者： Yang, Zhenheng Wang, Peng Wang, Yang Xu, Wei Nevatia, Ram University of Southern California Baidu Research National Engineering Laboratory for Deep Learning Technology and Applications

learning to estimate 3D geometry in a single image by watching unlabeled videos via deep convolutional network is attracting significant attention. In this paper, we introduce a "3D as-smooth-as-possible (3D-ASAP)" prior inside the pipeline, which enables joint estimation of edges and 3D scene, yielding results with significant improvement in accuracy for fine detailed structures. Specifically, we define the 3D-ASAP prior by requiring that any two points recovered in 3D from an image should lie on an existing planar surface if no other cues provided. We design an unsupervised framework that Learns Edges and Geometry (depth, normal) all at Once (LEGO). The predicted edges are embedded into depth and surface normal smoothness terms, where pixels without edges in-between are constrained to satisfy the prior. In our framework, the predicted depths, normals and edges are forced to be consistent all the time. We conduct experiments on KITTI to evaluate our estimated geometry and CityScapes to perform edge evaluation. We show that in all of the tasks, ***, normal and edge, our algorithm vastly outperforms other state-of-the-art (SOTA) algorithms, demonstrating the benefits of our approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Geometry

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Interactive language acquisition with one-shot visual concept learning through a conversational game

arXiv

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arXiv 2018年

作者： Zhang, Haichao Yu, Haonan Xu, Wei Baidu Research - Institue of Deep Learning Sunnyvale United States National Engineering Laboratory for Deep Learning Technology and Applications Beijing China

Building intelligent agents that can communicate with and learn from humans in natural language is of great value. Supervised language learning is limited by the ability of capturing mainly the statistics of training data, and is hardly adaptive to new scenarios or flexible for acquiring new knowledge without inefficient retraining or catastrophic forgetting. We highlight the perspective that conversational interaction serves as a natural interface both for language learning and for novel knowledge acquisition and propose a joint imitation and reinforcement approach for grounded language learning through an interactive conversational game. The agent trained with this approach is able to actively acquire information by asking questions about novel objects and use the just-learned knowledge in subsequent conversations in a one-shot fashion. Results compared with other methods verified the effectiveness of the proposed approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Intelligent agents

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Interactive grounded language acquisition and generalization in a 2D world

arXiv

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arXiv 2018年

作者： Yu, Haonan Zhang, Haichao Xu, Wei Baidu Research Sunnyvale United States National Engineering Laboratory for Deep Learning Technology and Applications Beijing China

We build a virtual agent for learning language in a 2D maze-like world. The agent sees images of the surrounding environment, listens to a virtual teacher, and takes actions to receive rewards. It interactively learns the teacher's language from scratch based on two language use cases: sentence-directed navigation and question answering. It learns simultaneously the visual representations of the world, the language, and the action control. By disentangling language grounding from other computational routines and sharing a concept detection function between language grounding and prediction, the agent reliably interpolates and extrapolates to interpret sentences that contain new word combinations or new words missing from training sentences. The new words are transferred from the answers of language prediction. Such a language ability is trained and evaluated on a population of over 1.6 million distinct sentences consisting of 119 object words, 8 color words, 9 spatial-relation words, and 50 grammatical words. The proposed model significantly outperforms five comparison methods for interpreting zero-shot sentences. In addition, we demonstrate human-interpretable intermediate outputs of the model in the appendix. Copyright © 2018, The Authors. All rights reserved.

关键词： Visual languages

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Enhancing person-Job fit for talent recruitment: An ability-aware neural network approach

arXiv

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arXiv 2018年

作者： Qin, Chuan Zhu, Hengshu Xu, Tong Zhu, Chen Jiang, Liang Chen, Enhong Xiong, Hui Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China Baidu Talent Intelligence Center Baidu Inc Business Intelligence Lab Baidu Research National Engineering Laboratory of Deep Learning Technology an Application China

The wide spread use of online recruitment services has led to information explosion in the job market. As a result, the recruiters have to seek the intelligent ways for Person-Job Fit, which is the bridge for adapting the right job seekers to the right positions. Existing studies on Person-Job Fit have a focus on measuring the matching degree between the talent qualification and the job requirements mainly based on the manual inspection of human resource experts despite of the subjective, incomplete, and inefficient nature of the human judgement. To this end, in this paper, we propose a novel end-to-end Ability-aware Person-Job Fit Neural Network (APJFNN) model, which has a goal of reducing the dependence on manual labour and can provide better interpretation about the fitting results. The key idea is to exploit the rich information available at abundant historical job application data. Specifically, we propose a word-level semantic representation for both job requirements and job seekers’ experiences based on Recurrent Neural Network (RNN). Along this line, four hierarchical ability-aware attention strategies are designed to measure the different importance of job requirements for semantic representation, as well as measuring the different contribution of each job experience to a specific ability requirement. Finally, extensive experiments on a large-scale real-world data set clearly validate the effectiveness and interpretability of the APJFNN framework compared with several baselines. Copyright © 2018, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Gradient descent meets shift-and-invert preconditioning for eigenvector computation 18

Gradient descent meets shift-and-invert preconditioning for ...

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Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： Zhiqiang Xu Big Data Lab (BDL-US) Baidu Research National Engineering Laboratory for Deep Learning Technology and Applications

Shift-and-invert preconditioning, as a classic acceleration technique for the leading eigenvector computation, has received much attention again recently, owing to fast least-squares solvers for efficiently approximating matrix inversions in power iterations. In this work, we adopt an inexact Riemannian gradient descent perspective to investigate this technique on the effect of the step-size scheme. The shift-and-inverted power method is included as a special case with adaptive step-sizes. Particularly, two other step-size settings, i.e., constant step-sizes and Barzilai-Borwein (BB) step-sizes, are examined theoretically and/or empirically. We present a novel convergence analysis for the constant step-size setting that achieves a rate at Õ(√λ1/λ1 – λp+1), where λi represents the i-th largest eigenvalue of the given real symmetric matrix and p is the multiplicity of λ1. Our experimental studies show that the proposed algorithm can be significantly faster than the shift-and-inverted power method in practice.

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Multi-Task Neural learning Architecture for End-to-End Identification of Helpful Reviews

Multi-Task Neural Learning Architecture for End-to-End Ident...

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Miao Fan Yue Feng Mingming Sun Ping Li Haifeng Wang Jianmin Wang National Engineering Laboratory of Deep Learning Technology and Application China School of Software Engineering Tsinghua University

ISBN: (纸本)9781538660522

Helpful reviews play a pivotal role in recommending desirable goods and accelerating purchase decisions of customers in e-commercial services. Given a large proportion of product reviews with unknown helpfulness/unhelpfulness, the research on automatic identification of helpful reviews has drawn much attention in recent years. However, state-of-the-art approaches still rely heavily on extracting heuristic text features from reviews with domain-specific knowledge. In this paper, we first introduce a multi-task neural learning (MTNL) architecture for identifying helpful reviews. The end-to-end neural architecture can learn to reconstruct effective features upon the raw input of words and even characters, and the multi-task learning paradigm helps to make more accurate predictions of helpful reviews based on a secondary task which fits the star ratings of reviews. We also build two datasets containing helpful/unhelpful reviews from different product categories in Amazon, and compare the performance of MTNL with several mainstream methods on both datasets. Experimental results confirm that MTNL outperforms the state-of-the-art approaches by a significant margin.

关键词： Task analysis Feature extraction Encoding Semantics Training Computer architecture

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deepMD-kit v2: A software package for deep Potential models

arXiv

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

deepMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of deepMD-kit offers numerous advanced features such as deepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, deep Potential - Range Correction (DPRc), deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the deepMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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