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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Changjian Shui Gezheng Xu Qi Chen Jiaqi Li Charles X. Ling Tal Arbel Boyu Wang Christian Gagné Centre for Intelligent Machines McGill University and Mila Quebec AI Institute and Institute Intelligence and Data Université Laval Department of Computer Science University of Western Ontario Institute Intelligence and Data Université Laval Centre for Intelligent Machines McGill University and Mila Quebec AI Institute and CIFAR AI Chair Mila Quebec AI Institute and Institute Intelligence and Data Université Laval and CIFAR AI Chair

ISBN: (纸本)9781713871088

We propose an analysis in fair learning that preserves the utility of the data while reducing prediction disparities under the criteria of group sufficiency. We focus on the scenario where the data contains multiple or even many subgroups, each with limited number of samples. As a result, we present a principled method for learning a fair predictor for all subgroups via formulating it as a bilevel objective. In the lower-level, the subgroup-specific predictors are learned through a small amount of data and the fair predictor. In the upper-level, the fair predictor is updated to be close to all subgroup specific predictors. We further prove that such a bilevel objective can effectively control the group sufficiency and generalization error. We evaluate the proposed framework on real-world datasets. Empirical evidence suggests the consistently improved fair predictions, as well as the comparable accuracy to the baselines.

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Multimodal graph representation learning for robust surgical workflow recognition with adversarial feature disentanglement

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Information Fusion 2025年 123卷

作者： Bai, Long Ma, Boyi Wang, Ruohan Wang, Guankun Cui, Beilei Jiang, Zhongliang Islam, Mobarakol Min, Zhe Lai, Jiewen Navab, Nassir Ren, Hongliang Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Chair for Computer Aided Medical Procedures Technical University of Munich Munich Germany Department of Biomedical Engineering University of Toronto Toronto Canada Center for Computational and Molecular Biology Brown University Providence United States UCL Hawkes Institute University College London London United Kingdom School of Control Science and Engineering Shandong University Jinan China

Surgical workflow recognition is vital for automating tasks, supporting decision-making, and training novice surgeons, ultimately improving patient safety and standardizing procedures. However, data corruption can lead to performance degradation due to issues like occlusion from bleeding or smoke in surgical scenes and problems with data storage and transmission. Therefore, a robust workflow recognition model is urgently needed. In this case, we explore a robust graph-based multimodal approach to integrating vision and kinematic data to enhance accuracy and reliability. Vision data captures dynamic surgical scenes, while kinematic data provides precise movement information, overcoming limitations of visual recognition under adverse conditions. We propose a multimodal Graph Representation network with Adversarial feature Disentanglement (GRAD) for robust surgical workflow recognition in challenging scenarios with domain shifts or corrupted data. Specifically, we introduce a Multimodal Disentanglement Graph Network (MDGNet) that captures fine-grained visual information while explicitly modeling the complex relationships between vision and kinematic embeddings through graph-based message modeling. To align feature spaces across modalities, we propose a Vision-Kinematic Adversarial (VKA) framework that leverages adversarial training to reduce modality gaps and improve feature consistency. Furthermore, we design a Contextual Calibrated Decoder, incorporating temporal and contextual priors to enhance robustness against domain shifts and corrupted data. Extensive comparative and ablation experiments demonstrate the effectiveness of our model and proposed modules. Specifically, we achieved an accuracy of 86.87% and 92.38% on two public datasets, respectively. Moreover, our robustness experiments show that our method effectively handles data corruption during storage and transmission, exhibiting excellent stability and robustness. Our approach aims to advance automated surgi

关键词： Transplantation (surgical)

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Parallel quantum chemistry on noisy intermediate-scale quantum computers

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Physical Review Research 2022年第3期4卷 033160-033160页

作者： Robert Schade Carsten Bauer Konstantin Tamoev Lukas Mazur Christian Plessl Thomas D. Kühne Paderborn Center for Parallel Computing Paderborn University 33098 Paderborn Germany Dynamics of Condensed Matter and Institute for Sustainable Systems Design Chair of Theoretical Chemistry Paderborn University 33098 Paderborn Germany Department for Computer Science Paderborn University 33098 Paderborn Germany

A parallel hybrid quantum-classical algorithm for the solution of the quantum-chemical ground-state energy problem on gate-based quantum computers is presented. This approach is based on the reduced density-matrix functional theory (RDMFT) formulation of the electronic structure problem. For that purpose, the density-matrix functional of the full system is decomposed into an indirectly coupled sum of density-matrix functionals for all its subsystems using the adaptive cluster approximation to RDMFT. The approximations involved in the decomposition and the adaptive cluster approximation itself can be systematically converged to the exact result. The solutions for the density-matrix functionals of the effective subsystems involves a constrained minimization over many-particle states that are approximated by parametrized trial states on the quantum computer similarly to the variational quantum eigensolver. The independence of the density-matrix functionals of the effective subsystems introduces a new level of parallelization and allows for the computational treatment of much larger molecules on a quantum computer with a given qubit count. In addition, for the proposed algorithm techniques are presented to reduce the qubit count, the number of quantum programs, as well as its depth. The evaluation of a density-matrix functional as the essential part of our approach is demonstrated for Hubbard-like systems on IBM quantum computers based on superconducting transmon qubits.

关键词： Quantum algorithms Quantum software Molecules Superconducting qubits Ab initio molecular dynamics Density matrix methods Electron-correlation calculations Hubbard model Quantum chemistry methods Trial wave functions Variational approach Variational wave functional methods

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Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy system model

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International Journal of Sediment Research 2022年第3期37卷 383-398页

作者： Rana Muhammad Adnan Zaher Mundher Yaseen Salim Heddam Shamsuddin Shahid Aboalghasem Sadeghi-Niaraki Ozgur Kisi State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai University Department of Urban Planning Engineering Networks and Systems Institute of Architecture and Construction South Ural State University New Era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Faculty of Science Agronomy Department Hydraulics Division University School of Civil Engineering Faculty of Engineering Universiti Teknologi Malaysia (UTM) Geoinformation Tech.Center of Excellence Faculty of Geomatics Engineering K.N.Toosi University of Technology Department of Computer Science and Engineering Sejong University Civil Engineering Department Ilia State University

Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for navigation, and potential for aesthetics and fish habitat. The capability of a new machine learning model, fuzzy c-means based neuro-fuzzy system calibrated using the hybrid particle swarm optimization-gravitational search algorithm（ANFIS-FCM-PSOGSA） in improving the estimation accuracy of river suspended sediment loads（SSLs） is investigated in the current study. The outcomes of the proposed method were compared with those obtained using the fuzzy c-means based neuro-fuzzy system calibrated using particle swarm optimization（ANFIS-FCM-PSO）, ANFIS-FCM, and sediment rating curve（SRC） models. Various input combinations involving lagged river flow（Q） and suspended sediment（S） values were used for model development. The effect of Q and S on the model's accuracy also was assessed by including the difference between lagged Q and S values as inputs. The model performance was assessed using the root mean square error（RMSE）, mean absolute error（MAE）, Nash-Sutcliffe Efficiency（NSE）, and coefficient of determination（R2） and several graphical comparison methods. The results showed that the proposed model enhanced the prediction performance of the ANFIS-FCM-PSO（or ANFIS-FCM） models by 8.14%（1.72%）, 14.7%（5.71%）, 12.5%（2.27%）, and 25.6%（1.86%）,in terms of the RMSE, MAE, NSE and R2, respectively. The current study established the potential of the proposed ANFIS-FCM-PSOGSA model for simulation of the cumulative sediment load. The modeling results revealed the potential effects of the river flow lags on the sediment transport quantification.

关键词： Suspended sediment load Adaptive neuro-fuzzy system Particle swarm optimization Gravitational search algorithm

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HealthyBlockchain for Global Patients

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computers, Materials & Continua 2021年第8期68卷 2431-2449页

作者： Shada A.Alsalamah Hessah A.Alsalamah Thamer Nouh Sara A.Alsalamah College of Computer and Information Sciences King Saud UniversityRiyadh11451Saudi Arabia Digital Health and Innovation Science DivisionWorld Health OrganizationGenevaCH-1211Switzerland MIT Department of Mechanical Engineering Massachusetts Institute of Technology(MIT)Cambridge02139MAUSA College of Engineering and Architecture Al Yamamah UniversityRiyadh11451Saudi Arabia Trauma and Acute Care Surgery Unit College of MedicineKing Saud UniversityRiyadh11451Saudi Arabia College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic UniversityRiyadh13318Saudi Arabia

An emerging healthcare delivery model is enabling a new era of clinical care based on well-informed decision-making *** healthcare information systems(HISs)fall short of adopting this model due to a conflict between information security needed to implement the new model and those already enforced locally to support traditional care ***,in recent times,the healthcare sector has shown a substantial interest in the potential of using blockchain technology for providing quality care to *** blockchain solution proposed so far has fully addressed emerging cross-organization information-sharing needs in *** this paper,we aim to study the use of blockchain in equipping struggling HISs to cope with the demands of the new healthcare delivery model,by proposing HealthyBlockchain as a granular patient-centered ledger that digitally tracks a patient’s medical transactions all along the treatment pathway to support the care *** patient-centered ledger is a neutral tamper-proof trail timestamp block sequence that governs distributed patient information across the decentralized discrete *** connects patients,clinicians,and healthcare providers to facilitate a transparent,trustworthy,and secure supporting platform.

关键词： Blockchain eHealth electronic health record global patient healthcare information system information security legacy system patient-centered care privacy smart contract trust

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Language Modeling on a SpiNNaker2 Neuromorphic Chip

Language Modeling on a SpiNNaker2 Neuromorphic Chip

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IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Khaleelulla Khan Nazeer Mark Schöne Rishav Mukherji Bernhard Vogginger Christian Mayr David Kappel Anand Subramoney Chair of Highly-Parallel VLSI-Systems and Neuro-Microelectronics Technische Universität Dresden Germany Birla Institute of Technology and Science Goa India Centre for Tactile Internet (CeTI) With Human-in-the-Loop Technische Universität Dresden Germany Institut für Neuroinformatik Ruhr Universität Bochum Bochum Germany Dept. of Computer Science Royal Holloway University of London Egham United Kingdom

ISBN: (数字)9798350383638

ISBN: (纸本)9798350383645

As large language models continue to scale in size rapidly, so too does the computational power required to run them. Event-based networks on neuromorphic devices offer a potential way to reduce energy consumption for inference significantly. However, to date, most event-based networks that can run on neuromorphic hardware, including spiking neural networks (SNNs), have not achieved task performance even on par with LSTM models for language modeling. As a result, language modeling on neuromorphic devices has seemed a distant prospect. In this work, we demonstrate the first-ever implementation of a language model on a neuromorphic device – specifically the SpiNNaker2 chip – based on a recently published event-based architecture called the EGRU. SpiNNaker2 is a many-core neuromorphic chip designed for large-scale asynchronous processing, and the EGRU is architected to leverage such hardware efficiently while maintaining competitive task performance. This implementation marks the first time a neuromorphic language model matches LSTMs, setting the stage for taking task performance to the level of large language models. We also demonstrate results on a gesture recognition task based on inputs from a DVS camera. Overall, our results showcase the feasibility of this neuro-inspired neural network in hardware, highlighting significant gains versus conventional hardware in energy efficiency for the common use case of single batch inference.

关键词： Performance evaluation Neuromorphics Large language models Neural networks Spiking neural networks Hardware Task analysis

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Energy-optimal Three-dimensional Path-following Control of Autonomous Underwater Vehicles under Ocean Currents

arXiv

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arXiv 2022年

作者： Yang, Niankai Shen, Chao Johnson-Roberson, Matthew Sun, Jing The Department of Naval Architecture and Marine Engineering University of Michigan Ann ArborMI48109 United States The Department of Systems and Computer Engineering Carleton University OttawaON Canada Department of Naval Architecture and Marine Engineering University of Michigan Ann ArborMI48109 United States The Robotics Institute The School of Computer Science Carnegie Mellon University PittsburghPA15213 United States

This paper presents a three-dimensional (3D) energy-optimal path-following control design for autonomous underwater vehicles subject to ocean currents. The proposed approach has a two-stage control architecture consisting of the setpoint computation and the setpoint tracking. In the first stage, the surge velocity, heave velocity, and pitch angle setpoints are optimized by minimizing the required vehicle propulsion energy under currents, and the line-of-sight (LOS) guidance law is used to generate the yaw angle setpoint that ensures path following. In the second stage, two model predictive controllers are designed to control the vehicle motion in the horizontal and vertical planes by tracking the optimal setpoints. The proposed controller is compared with a conventional LOS-based control that maintains zero heave velocity relative to the current (i.e., relative heave velocity) and derives pitch angle setpoint using LOS guidance to reach the desired depth. Through simulations, we show that the proposed approach can achieve more than 13% energy saving on a lawnmower-type and an inspection mission under different ocean current conditions. The simulation results demonstrate that allowing motions with non-zero relative heave velocity improves energy efficiency in 3D path-following applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Ocean currents

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Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks

arXiv

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arXiv 2022年

作者： Han, Husheng Hu, Xing Xu, Kaidi Dang, Pucheng Wang, Ying Zhao, Yongwei Du, Zidong Guo, Qi Yang, Yanzhi Chen, Tianshi SKL of Processors Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Cambricon Technologies Beijing China Department of Computer Science College of Computing & Informatics Drexel University Philadelphia19104 United States SKL of Computer Architecture Institute of Computing Technology CAS Beijing100190 China Department of Electrical and Computer Engineering Northeastern University Boston02115 United States

DNN-based video object detection (VOD) powers autonomous driving and video surveillance industries with rising importance and promising opportunities. However, adversarial patch attack yields huge concern in live vision tasks because of its practicality, feasibility, and powerful attack effectiveness. This work proposes Themis, a software/hardware system to defend against adversarial patches for real-time robust video object detection. We observe that adversarial patches exhibit extremely localized superficial feature importance in a small region with non-robust predictions, and thus propose the adversarial region detection algorithm for adversarial effect elimination. Themis also proposes a systematic design to efficiently support the algorithm by eliminating redundant computations and memory traffics. Experimental results show that the proposed methodology can effectively recover the system from the adversarial attack with negligible hardware overhead. © 2022, CC BY.

关键词： Object detection

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A methodological framework for estimating ambient PM_(2.5)particulate matter concentrations in the UK

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Journal of Environmental sciences 2025年第4期150卷 676-691页

作者： David Galán-Madruga Parya Broomandi Alfrendo Satyanaga Ali Jahanbakhshi Mehdi Bagheri Aram Fathian Rasoul Sarvestan J.Cárdenas-Escudero J.O.Cáceres Prashant Kumar Jong Ryeol Kim Department of Atmospheric Pollution National Centre for Environment HealthHealth Institute Carlos III.Ctra.Majadahonda a Pozuelo km 2.2Madrid 28220Spain Department of Civil and Environmental Engineering School of Engineering and Digital SciencesNazarbayev UniversityKabanbay Batyr Ave.53Astana 010000Kazakhstan Department of Electrical and Computer Engineering School of Engineering and Digital SciencesNazarbayev UniversityKabanbay Batyr Ave.53Astana 010000Kazakhstan School of Architecture Building and Civil EngineeringLoughborough UniversityLeicestershire LE113TUUnited Kingdom Neotectonics and Natural Hazards Institute RWTH Aachen UniversityAachen 52056Germany UNESCO Chair on Coastal Geo-Hazard Analysis Research Institute for Earth SciencesTehran 13185-1494Iran Water SedimentHazardsand Earth-surface Dynamics(waterSHED)LabDepartment of GeoscienceUniversity of CalgaryCalgary Alberta T2N 1N4Canada Department of Climatology Faculty of Geography and Environmental SciencesHakim Sabzevari UniversitySabzevar ***Iran Laser Chemistry Research Group Department of Analytical ChemistryFaculty of ChemistryComplutense University of MadridPlaza de Ciencias 1Madrid 28040Spain Analytical Chemistry Department FCNETUniversity of PanamaUniversity CityUniversity MailPanama 4Panama City 3366Panama Global Centre for Clean Air Research(GCARE) School of SustainabilityCivil and Environmental EngineeringFaculty of Engineering and Physical SciencesUniversity of SurreyGuildford GU27XHUnited Kingdom Institute for Sustainability University of SurreyGuildford GU27XHUnited Kingdom

Scientific evidence sustains PM_(2.5)particles’inhalation may generate harmful impacts on human beings’health;therefore,theirmonitoring in ambient air is of paramount relevance in terms of public *** to the limited number of fixed stations within the air qualitymonitoring networks,development ofmethodological frameworks tomodel ambient air PM_(2.5)particles is primordial to providing additional information on PM_(2.5)exposure and its *** this sense,this work aims to offer a global easily-applicable tool to estimate ambient air PM_(2.5)as a function of meteorological conditions using a multivariate *** PM_(2.5)data measured by 84 fixed monitoring stations and meteorological data from ERA5(ECMWF Reanalysis v5)reanalysis daily based data between 2000 and 2021 across the United Kingdom were attended to develop the suggested *** from January 2017 to December 2020 were employed to build amathematical expression that related the dependent variable(PM_(2.5))to predictor ones(sea-level pressure,planetary boundary layer height,temperature,precipitation,wind direction and speed),while 2021 data tested the *** indicators evidenced a good performance of model(maximum values of RMSE,MAE and MAPE:1.80μg/m^(3),3.24μg/m^(3),and 20.63%,respectively),compiling the current legislation’s requirements for modelling ambient air PM_(2.5)concentrations.A retrospective analysis of meteorological features allowed estimating ambient air PM_(2.5)concentrations from 2000 to *** highest PM_(2.5)concentrations relapsed in theMid-and Southlands,while Northlands sustained the lowest concentrations.

关键词： Air quality PM_(2.5)particles Meteorological variables Prediction model Long-term trend

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Effect of Artificial Roughness on Heat Transfer and Friction Factor in a Solar Air Heater: A Review 2nd

Effect of Artificial Roughness on Heat Transfer and Friction...

引用

2nd International Conference on Advances in Materials and Manufacturing Technology, ICAMMT 2022

作者： Yadav, Anil Singh Alam, Tabish Saxena, Rajiv Yadav, Raj Kumar Kumar, Rajan Sharma, Abhishek Agrawal, Yogesh Viswanath Allamraju, K. Vyas, Monika Singh, Manmohan Chakroborty, Subhendu Mechanical Engineering Department IES College of Technology MP Bhopal462044 India Architecture Planning and Energy Efficiency CSIR-Central Building Research Institute Uttarakhand Roorkee247667 India Infinity Management and Engineering College MP Sagar470001 India Mechanical Engineering Department Adina Institute of Science and Technology MP Sagar470002 India Department of Mechanical Engineering BIT Sindri Jharkhand Dhanbad828123 India Mechanical Engineering Department Sagar Institute of Research and Technology MP Bhopal462041 India Mechanical Engineering Department Institute of Aeronautical Engineering Telangana Hyderabad500043 India Civil Engineering Department IES College of Technology MP Bhopal462044 India Computer Science and Engineering Department IES College of Technology MP Bhopal462044 India Department of Basic Sciences IITM IES University MP Bhopal462044 India

ISBN: (纸本)9789819929207

In order to convert solar energy into thermal energy, solar air heater (SAH) is commonly used. These are used to convert the solar radiation into heat of working fluid. The effect of various geometric constraints on the friction characteristics and heat transfer (HT) of a SAH has been studied. The paper aims to introduce the various parameters that are involved in the analysis of HT enhancement of SAH. There are various techniques that are involved in the analysis of HT enhancement of SAH. One of these is the use of a passive method, which involves the use of turbulators, ribs or fins on the surface. SAHs that are roughened can improve their HT properties compared to their plain counterparts. This is because the various surface patterns and shapes used in their design can help improve their performance. There have been many different sorts of studies conducted on the many facets of these HT augmentation approaches. Studies showed that combining the roughening process with a rough absorber plate can improve the performance of SAH duct. The goal of this study is to identify the most appropriate roughness component for solar air duct that can meet the different operating conditions. The findings of this study can be used to design and construct enhanced SAH duct system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Flow of fluids

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