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Continuity scaling: A rigorous framework for detecting and quantifying causality accurately

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arXiv 2022年

作者： Ying, Xiong Leng, Si-Yang Ma, Huan-Fei Nie, Qing Lai, Ying-Cheng Lin, Wei School of Mathematical Sciences SCMS SCAM Fudan University Shanghai200433 China Research Institute for Intelligent Complex Systems CCSB LCNBI Fudan University Shanghai200433 China State Key Laboratory of Medical Neurobiology MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University Shanghai200032 China Institute of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China School of Mathematical Sciences Soochow University Suzhou215006 China Department of Mathematics Department of Developmental and Cell Biology NSF-Simons Center for Multiscale Cell Fate Research University of California IrvineCA92697-3875 United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287-5706 United States Shanghai Artificial Intelligence Laboratory Shanghai200232 China

Data based detection and quantification of causation in complex, nonlinear dynamical systems is of paramount importance to science, engineering and beyond. Inspired by the widely used methodology in recent years, the cross-map-based techniques, we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms, which is consistent with the natural interpretation of causality. In particular, instead of measuring the smoothness of the cross map as conventionally implemented, we define causation through measuring the scaling law for the continuity of the investigated dynamical system directly. The uncovered scaling law enables accurate, reliable, and efficient detection of causation and assessment of its strength in general complex dynamical systems, outperforming those existing representative methods. The continuity scaling based framework is rigorously established and demonstrated using datasets from model complex systems and the real world. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamical systems

Reconfigurable Intelligent Surfaces in Action for Non-Terrestrial Networks

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arXiv 2020年

作者： Tekbıyık, Kürşat Kurt, Güneş Karabulut Ekti, Ali Rıza Yanikomeroglu, Halim The Department of Electronics and Communications Engineering Istanbul Technical University Istanbul Turkey The Department of Electrical Engineering Polytechnique Montréal Montréal Canada The Grid Communications and Security Group Electrification and Energy Infrastructure Division Oak Ridge National Laboratory Oak RidgeTN United States The Department of Systems and Computer Engineering Carleton University Ottawa Canada

Next-generation communication technology will be made possible by cooperation between terrestrial networks with non-terrestrial networks (NTN) comprised of high-altitude platform stations and satellites. Further, as humanity embarks on the long road to establish new habitats on other planets, the cooperation between NTN and deep-space networks (DSN) will be necessary. In this regard, we propose the use of reconfigurable intelligent surfaces (RIS) to improve coordination between these networks given that RIS perfectly match the size, weight, and power restrictions of operating in space. A comprehensive framework of RIS-assisted non-terrestrial and interplanetary communications is presented that pinpoints challenges, use cases, and open issues. Furthermore, the performance of RIS-assisted NTN under environmental effects such as solar scintillation and satellite drag is discussed in light of simulation results. © 2020, CC BY-NC-SA.

关键词： Satellites

A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

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arXiv 2020年

作者： Yi, Xinlei Zhang, Shengjun Yang, Tao Chai, Tianyou Johansson, Karl H. The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden The Department of Electrical Engineering University of North Texas DentonTX76203 United States The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is considered. This problem is an important component of many machine learning techniques with data parallelism, such as deep learning and federated learning. We propose a distributed primal–dual stochastic gradient descent (SGD) algorithm, suitable for arbitrarily connected communication networks and any smooth (possibly nonconvex) cost functions. We show that the proposed algorithm achieves the linear speedup convergence rate O(1/√nT) for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak–Lojasiewicz (P–L) condition, where T is the total number of iterations. We also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global optimum. We demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Cost functions

Enhanced light-matter interactions in dielectric nanostructures via machine-learning approach

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Advanced Photonics 2020年第2期2卷 57-67页

作者： Lei Xu Mohsen Rahmani Yixuan Ma Daria ASmirnova Khosro Zangeneh Kamali Fu Deng Yan Kei Chiang Lujun Huang Haoyang Zhang Stephen Gould Dragomir N.Neshev Andrey E.Miroshnichenko University of New South Wales School of Engineering and Information TechnologyCanberraAustralia Nottingham Trent University School of Science&TechnologyDepartment of EngineeringAdvanced Optics and Photonics LaboratoryNottinghamUnited Kingdom Australian National University Research School of PhysicsNonlinear Physics CentreCanberraAustralia Australian National University Research School of PhysicsARC Centre of Excellence for Transformative Meta-Optical Systems(TMOS)CanberraAustralia Queensland University of Technology School of Electrical Engineering and Computer ScienceBrisbaneQueenslandAustralia Australian National University College of Engineering and Computer ScienceCanberraAustralia

A key concept underlying the specific functionalities of metasurfaces is the use of constituent components to shape the wavefront of the light on *** are versatile,novel platforms for manipulating the scattering,color,phase,or intensity of ***,one of the typical approaches for designing a metasurface is to optimize one or two variables among a vast number of fixed parameters,such as various materials’properties and coupling effects,as well as the geometrical ***,this would require multidimensional space optimization through direct numerical ***,an alternative,popular approach allows for reducing the computational cost significantly based on a deep-learning-assisted *** utilize a deep-learning approach for obtaining high-quality factor(high-Q)resonances with desired characteristics,such as linewidth,amplitude,and spectral *** exploit such high-Q resonances for enhancedlight–matter interaction in nonlinearoptical metasurfaces and optomechanical vibrations,*** demonstrate that optimized metasurfaces achieve up to 400-fold enhancement of the third-harmonic generation;at the same time,they also contribute to 100-fold enhancement of the amplitude of optomechanical *** approach can be further used to realize structures with unconventional scattering responses.

关键词： machine learning dielectric nanostructures Fano resonance third-harmonic generation optoacoustics

Human intention recognition for human aware planning in integrated warehouse systems

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arXiv 2020年

作者： Petković, Tomislav Hvězda, Jakub Rybecký, Tomáš Marković, Ivan Kulich, Miroslav Přeučil, Libor Petrović, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Laboratory for Autonomous Systems and Mobile Robotics Unska 3 ZagrebHR-10000 Croatia Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague160 00 Czech Republic

With the substantial growth of logistics businesses the need for larger and more automated warehouses increases, thus giving rise to fully robotized shop-floors with mobile robots in charge of transporting and distributing goods. However, even in fully automatized warehouse systems the need for human intervention frequently arises, whether because of maintenance or because of fulfilling specific orders, thus bringing mobile robots and humans ever closer in an integrated warehouse environment. In order to ensure smooth and efficient operation of such a warehouse, paths of both robots and humans need to be carefully planned;however, due to the possibility of humans deviating from the assigned path, this becomes an even more challenging task. Given that, the supervising system should be able to recognize human intentions and its alternative paths in real-time. In this paper, we propose a framework for human deviation detection and intention recognition which outputs the most probable paths of the humans workers and the planner that acts accordingly by replanning for robots to move out of the human's path. Experimental results demonstrate that the proposed framework increases total number of deliveries, especially human deliveries, and reduces human-robot encounters. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile robots

Reconfigurable intelligent surface assisted mobile edge computing with heterogeneous learning tasks

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arXiv 2020年

作者： Huang, Shanfeng Wang, Shuai Wang, Rui Wen, Miaowen Huang, Kaibin The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China The Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong The Department of Electrical and Electronic Engineering The Department of Computer Science and Engineering The Sifakis Research Institute for Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China The Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China The School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China

The ever-growing popularity and rapid improving of artificial intelligence (AI) have raised rethinking on the evolution of wireless networks. Mobile edge computing (MEC) provides a natural platform for AI applications since it is with rich computation resources to train machine learning (ML) models, as well as low-latency access to the data generated by mobile and internet of things (IoT) devices. In this paper, we present an infrastructure to perform ML tasks at an MEC server with the assistance of a reconfigurable intelligent surface (RIS). In contrast to conventional communication systems where the principal criterions are to maximize the throughput, we aim at maximizing the learning performance. Specifically, we minimize the maximum learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station (BS), and the phase-shift matrix of the RIS. An alternating optimization (AO)-based framework is proposed to optimize the three terms iteratively, where a successive convex approximation (SCA)-based algorithm is developed to solve the power allocation problem, closed-form expressions of the beamforming vectors are derived, and an alternating direction method of multipliers (ADMM)-based algorithm is designed together with an error level searching (ELS) framework to effectively solve the challenging nonconvex optimization problem of the phase-shift matrix. Simulation results demonstrate significant gains of deploying an RIS and validate the advantages of our proposed algorithms over various benchmarks. Lastly, a unified communication-training-inference platform is developed based on the CARLA platform and the SECOND network, and a use case (3D object detection in autonomous driving) for the proposed scheme is demonstrated on the developed platform. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

Wide-angle giant photonic spin Hall effect

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Physical Review B 2022年第7期106卷 075409-075409页

作者： Zhihao Chen Yu Chen Yaodong Wu Xinxing Zhou Handong Sun Tony Low Hongsheng Chen Xiao Lin Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Synergetic Innovation Center for Quantum Effects and Applications School of Physics and Electronics Hunan Normal University Changsha 410081 China International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology Engineering Technology Research Center for 2D Material Information Function Devices and Systems of Guangdong Province Institute of Microscale Optoelectronics Shenzhen University Shenzhen 518060 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Singapore Centre for Disruptive Photonic Technologies (CDPT) School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Singapore Department of Electrical and Computer Engineering University of Minnesota Minneapolis Minnesota 55455 USA Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center College of Information Science and Electronic Engineering Zhejiang University Hangzhou 310027 China International Joint Innovation Center ZJU-UIUC Institute The Electromagnetics Academy at Zhejiang University Zhejiang University Haining 314400 China

The photonic spin Hall effect is a manifestation of the spin-orbit interaction of light and can be measured by a transverse shift δ of photons with opposite spins. The precise measurement of transverse shifts can enable many spin-related applications, such as precise metrology and optical sensing. However, this transverse shift is generally small (i.e., δ/λ<10−1, where λ is the wavelength), which impedes its precise measurement. To date, proposals to generate a giant spin Hall effect (namely, with δ/λ>102) have severe limitations, particularly its occurrence only over a narrow angular cone (with a width of Δθ<1∘). Here we propose a universal scheme to realize the wide-angle giant photonic spin Hall effect with Δθ>70∘ by exploiting the interface between free space and uniaxial epsilon-near-zero media. The underlying mechanism is ascribed to the almost-perfect polarization splitting between s and p polarized waves at the designed interface. Remarkably, this almost-perfect polarization splitting does not resort to the interference effect and is insensitive to the incident angle, which then gives rise to the wide-angle giant photonic spin Hall effect.

关键词： Light-matter interaction Photonics Spin Hall effect Spin-orbit coupling

A comprehensive utility function for resource allocation in mobile edge computing

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arXiv 2020年

作者： Ali, Zaiwar Khaf, Sadia Abbas, Ziaul Haq Abbas, Ghulam Jiao, Lei Irshad, Amna Kwak, Kyung Sup Bilal, Muhammad Research Laboratory GIK Institute of Engineering Sciences and Technology Topi23640 Pakistan Faculty of Electrical Engineering GIK Institute of Engineering Sciences and Technology Topi23640 Pakistan Faculty of Computer Sciences and Engineering GIK Institute of Engineering Sciences and Technology Topi23640 Pakistan Grimstad4898 Norway Department of Information and Communication Engineering Inha University Incheon22212 Korea Republic of Department of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies Gyeonggi-do17035 Korea Republic of

In mobile edge computing (MEC), one of the important challenges is how much resources of which mobile edge server (MES) should be allocated to which user equipment (UE). The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU only. This paper presents a novel comprehensive utility function for resource allocation in MEC. The utility function considers the heterogeneous nature of applications that a UE offloads to MES. The proposed utility function considers all important parameters, including CPU, RAM, hard disk space, required time, and distance, to calculate a more realistic utility value for MESs. Moreover, we improve upon some general algorithms, used for resource allocation in MEC and cloud computing, by considering our proposed utility function. We name the improved versions of these resource allocation schemes as comprehensive resource allocation schemes. The UE requests are modeled to represent the amount of resources requested by the UE as well as the time for which the UE has requested these resources. The utility function depends upon the UE requests and the distance between UEs and MES, and serves as a realistic means of comparison between different types of UE requests. Choosing (or selecting) an optimal MES with the optimal amount of resources to be allocated to each UE request is a challenging task. We show that MES resource allocation is sub-optimal if CPU is the only resource considered. By taking into account the other resources, i.e., RAM, disk space, request time, and distance in the utility function, we demonstrate improvement in the resource allocation algorithms in terms of service rate, utility, and MES energy *** Codes 46Fxx © 2020, CC BY.

关键词： Mobile edge computing

Multimodal Whole Slide Foundation Model for Pathology

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arXiv 2024年

作者： Ding, Tong Wagner, Sophia J. Song, Andrew H. Chen, Richard J. Lu, Ming Y. Zhang, Andrew Vaidya, Anurag J. Jaume, Guillaume Shaban, Muhammad Kim, Ahrong Williamson, Drew F.K. Chen, Bowen Almagro-Perez, Cristina Doucet, Paul Sahai, Sharifa Chen, Chengkuan Komura, Daisuke Kawabe, Akihiro Ishikawa, Shumpei Gerber, Georg Peng, Tingying Le, Long Phi Mahmood, Faisal Department of Pathology Mass General Brigham Harvard Medical School BostonMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Helmholtz Munich German Research Center for Environment and Health Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Pathology Pusan National University Busan Korea Republic of Department of Pathology and Laboratory Medicine Emory University School of Medicine AtlantaGA United States Harvard Data Science Initiative Harvard University CambridgeMA United States Department of Systems Biology Harvard Medical School BostonMA United States Department of Preventive Medicine Graduate School of Medicine The University of Tokyo Tokyo Japan Division of Pathology National Cancer Center Exploratory Oncology Research & Clinical Trial Center Chiba Japan

The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL). However, translating these advancements to address complex clinical challenges at the patient and slide level remains constrained by limited clinical data in disease-specific cohorts, especially for rare clinical conditions. We propose TITAN, a multimodal whole slide foundation model pretrained using 335,645 WSIs via visual self-supervised learning and vision-language alignment with corresponding pathology reports and 423,122 synthetic captions generated from a multimodal generative AI copilot for pathology. Without any finetuning or requiring clinical labels, TITAN can extract general-purpose slide representations and generate pathology reports that generalize to resource-limited clinical scenarios such as rare disease retrieval and cancer prognosis. We evaluate TITAN on diverse clinical tasks and find that TITAN outperforms both ROI and slide foundation models across machine learning settings such as linear probing, few-shot and zero-shot classification, rare cancer retrieval and cross-modal retrieval, and pathology report generation. The model is publicly accessible at https://***/mahmoodlab/TITAN © 2024, CC BY-NC-ND.

关键词： Self-supervised learning