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SEPPDL: A Secure and Efficient Privacy-Preserving Deep Learning Inference Framework for Autonomous Driving

ACM Transactions on Autonomous and Adaptive Systems 2025年第1期20卷 1-29页

作者： Bobo, Wang Yang, Hongwei Hao, Meng Zhang, Jiannan He, Hui Zhang, Weizhe School of Cyberspace Science Harbin Institute of Technology Harbin China

The autonomous driving system necessitates using privacy-preserving deep learning (PPDL) technologies as the safety assurance for its extensive application. However, existing PPDL solutions depend on intricate protocol designs for robust security. Although leveraging advanced dedicated hardware platforms can significantly improve inference efficiency, the PPDL frameworks that make the best use of hardware platform computility are scarce. Thus, balancing efficiency and security in PPDL remains an open question. This study presents SEPPDL, a secure tripartite inference framework for deep learning based on secret-sharing to balance privacy security and computational efficiency. We reduce the communication and calculation time by designing a deep learning quantization representation scheme, two new computational protocols, and a computation library that utilizes the integer computation units of the GPU. The experimental results show that compared with state-of-the-art PPDL frameworks, the SEPPDL framework reduces the communication and computation delay in the model inference to 1/2 and 1/3 of the existing optimal frameworks while maintaining the accuracy of the model inference. Meanwhile, the SEPPDL framework achieves a 10-fold performance improvement in a lightweight model. As the model scale increases, the performance of the SEPPDL-based model even achieves an 86-fold improvement compared to VGG16. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Differential privacy

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Breaking Solvation Dominance Effect Enabled by Ion-Dipole Interaction Toward Long-Spanlife Silicon Oxide Anodes in Lithium-Ion Batteries

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Nano-Micro Letters 2025年第4期17卷 367-380页

作者： Shengwei Dong Lingfeng Shi Shenglu Geng Yanbin Ning Cong Kang Yan Zhang Ziwei Liu Jiaming Zhu Zhuomin Qiang Lin Zhou Geping Yin Dalong Li Tiansheng Mu Shuaifeng Lou State Key Laboratory of Space Power-Sources School of Chemistry and Chemical EngineeringHarbin Institute of TechnologyHarbin 150001People’s Republic of China School of Marine Science and Technology Harbin Institute of Technology at WeihaiWeihai 264200People’s Republic of China Chongqing Research Institute of HIT Chongqing 401135People’s Republic of China

Micrometer-sized silicon oxide(SiO)anodes encounter challenges in large-scale applications due to significant volume expansion during the alloy/de-alloy ***,an innovative deep eutectic electrolyte derived from succinonitrile is introduced to enhance the cycling stability of SiO *** functional theory calculations validate a robust ion-dipole interaction between lithium ions(Li^(+))and succinonitrile(SN).The cosolvent fluoroethylene carbonate(FEC)optimizes the Li^(+)solvation structure in the SN-based electrolyte with its weakly solvating *** dynamics simulations investigate the regulating mechanism of ion-dipole and cation-anion *** unique Li^(+)solvation structure,enriched with FEC and TFSI^(-),facilitates the formation of an inorganic-organic composite solid electrolyte interphase on SiO ***-CT further detects the inhibiting effect on the SiO volume *** a result,the SiO|LiCoO_(2) full cells exhibit excellent electrochemical performance in deep eutectic-based *** work presents an effective strategy for extending the cycle life of SiO anodes by designing a new SN-based deep eutectic electrolyte.

关键词： Lithium-ion batteries Micrometer-sized silicon oxide Ion-dipole interaction Long-term cycling

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Machine learning-driven insights into biaxial strain-induced anomalous thermal conductivity enhancement of boron arsenide

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science China Technological sciences 2025年第5期68卷 5-18页

作者： Yikun LIU Yurong HE Tianqi TANG Jiaqi ZHU School of Energy Science and Engineering Harbin Institute of Technology Zhengzhou Research Institute Harbin Institute of Technology Heilongjiang Key Laboratory of New Energy Storage Materials and Processes National Key Laboratory of Science and Technology on Advanced Composites in Special Environments

Boron arsenide(BAs) is a promising integrated circuit thermal management material with ultra-high thermal conductivity(κ) and exceptional semiconductor properties. In practical devices, mechanical stress and strain caused by thermal expansion inevitably affect the thermodynamic properties of heat sink materials. Consequently, investigation of the effects of stress and strain on the κ of BAs is essential. Accurate assessment and regulation of the κ and phonon heat transport of BAs requires consideration of both three-phonon(3ph) and four-phonon(4ph) interactions. However, anharmonicity computation using density functional theory is computationally expensive, particularly for the fourth-order interatomic force constants. This work uses a machine learning-driven moment tensor potential method to evaluate higher-order anharmonicity and extends it to include fourth-order forces, accelerating the calculations significantly. Results show that the κ of BAs calculated using the moment tensor potential method when considering both 3ph and 4ph processes agrees well with experimental data. In contrast,considering the 3ph interaction alone causes κ to be overestimated. The machine learning approach reduces computational costs by approximately 94% while maintaining comparable accuracy to density functional theory. By solving the phonon Boltzmann transport equation, the thermal transport properties of BAs under biaxial tensile strains ranging up to 7% are evaluated. BAs shows an anomalous κ enhancement when including both 3ph and 4ph interactions, with a maximum enhancement of 15% at a small strain of 1%, primarily because of the weakening of the 3ph scattering. Beyond 2% strain, both the 3ph and 4ph scattering rates increase significantly, causing shorter phonon lifetimes and a monotonic reduction in κ. Additionally, the κ temperature dependence under strain is explored, highlighting temperature's role in modulating phonon scattering. This study provides machine learning-driven i

关键词： boron arsenide machine learning potential biaxial strain thermal conductivity enhancement phonon transport

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Revealing the exceptional cryogenic strength-ductility synergy of a solid solution 6063 alloy by in-situ EBSD experiments

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材料科学技术（英文版） 2025年第5期208卷 313-322页

作者： Youhong Peng Li Wang Chenglu Liu Chao Xu Lin Geng Guohua Fan School of Materials Science and Engineering Harbin Institute of TechnologyHarbin150001China Key Laboratory for Light-weight Materials Nanjing Tech UniversityNanjing211816China

A solid solution 6063 aluminium alloy features an exceptional combination of strength and ductility at 77 ***,the deformation mechanisms responsible for superior strength-ductility synergy and excellent strain hardening capacity at a cryogenic temperature of the alloy were comparatively investigated by in-situ electron backscatter diffraction(EBSD)observations coupled with transmission electron microscopy(TEM)characterization and fracture morphologies at both 298 and 77 *** is found that kernel average misorientation(KAM)mappings and quantified KAM in degree suggest a higher proportion of geometri-cally necessary dislocations(GNDs)at 77 *** existence of orientation scatter partitions at 77 K implies the activation of multiple slip systems,which is consistent with the results of potential slip systems cal-culated by Taylor ***,dislocation tangles characterized by brief and curved dislocation cells and abundant small dimples have been observed at 77 *** temperature-mediated activation of dislo-cations facilitates the increased dislocations,thus enhancing the strain hardening capacity and ductility of the *** research enriches cryogenic deformation theory and provides valuable insights into the design of high-performance aluminium alloys that are suitable for cryogenic applications.

关键词： Aluminium alloy Cryogenic temperature Strength-ductility synergy In-situ EBSD Deformation mechanisms

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Multi-scale feature fusion for facial expression recognition

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Neural Computing and Applications 2025年 1-22页

作者： Guo, Jiatao Peng, Junjie Huang, Yansong Chen, Gan Cai, Zesu Tan, Shuhua School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China School of Computer Science and Technology Harbin Institute of Technology Harbin China National Engineering Laboratory for Logistics Information Technology YTO Express Co. Ltd. Shanghai China

As an important computer vision task that can be used in many areas, facial expression recognition (FER) has been widely studied which much progress has been obtained especially when deep learning (DL) approaches have been introduced in FER. However, existing methods are restricted to extracting localized information or utilizing attention mechanism to focus on the region most relevant to the emotion, possibly resulting in errors in facial expression interpretation. Moreover, the inconsistency of image sizes in the wild FER datasets further increases the difficulty of the FER task. To address these issues, we propose a multi-scale feature fusion (MSFF) for FER. Specifically, our method first extracts features from facial images using multi-scale feature extraction to obtain rich multi-dimensional information. Meanwhile, extensive feature extraction at different scales is achieved by expanding the receptive field of the model, which provides the basis for more detailed analysis of facial expressions. Eventually, features at different scales are fused to enhance their synergistic effect. Subsequently, the adaptive extraction of comprehensive and valuable information from the fused features is performed, optimizing the performance of expression recognition. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on in-the-wild datasets. It is promising for FER in realistic and complex scenarios. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Feature extraction

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Final: Combining First-Order Logic With Natural Logic for Question Answering

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IEEE Transactions on Knowledge and Data Engineering 2025年第6期37卷 3103-3117页

作者： Shi, Jihao Ding, Xiao Hui, Siu Cheung Yan, Yuxiong Zhao, Hengwei Liu, Ting Qin, Bing Harbin Institute of Technology Research Center for Social Computing and Information Retrieval Harbin150001 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore

Many question-answering problems can be approached as textual entailment tasks, where the hypotheses are formed by the question and candidate answers, and the premises are derived from an external knowledge base. However, current neural methods often lack transparency in their decision-making processes. Moreover, first-order logic methods, while systematic, struggle to integrate unstructured external knowledge. To address these limitations, we propose a neuro-symbolic reasoning framework called Final, which combines FIrst-order logic with NAtural Logic for question answering. Our framework utilizes first-order logic to systematically decompose hypotheses and natural logic to construct reasoning paths from premises to hypotheses, employing bidirectional reasoning to establish links along the reasoning path. This approach not only enhances interpretability but also effectively integrates unstructured knowledge. Our experiments on three benchmark datasets, namely QASC, WorldTree, and WikiHop, demonstrate that Final outperforms existing methods in commonsense reasoning and reading comprehension tasks, achieving state-of-the-art results. Additionally, our framework also provides transparent reasoning paths that elucidate the rationale behind the correct decisions. © 2025 IEEE.

关键词： computer circuits

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1.4 kV Irradiation-Hardened β-Ga2O3Heterojunction Barrier Schottky diode under 107ions/cm2Fluence and 82.1 MeVcm2/mg LET Environments

IEEE Electron Device Letters

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IEEE Electron Device Letters 2025年第5期46卷 813-816页

作者： Sun, Na Zhang, Zhengliang Zhou, Feng Wang, Tianqi Ren, Fang-Fang Gu, Shulin Lu, Hai Zhang, Rong Ye, Jiandong Nanjing University School of Electronic Science and Engineering Nanjing210093 China Harbin Institute of Technology Harbin150001 China

Single event burnout (SEB) caused by heavy ion irradiation in space environments poses a significant threat to aerospace power electronic devices. This work demonstrates irradiation-hardened β-Ga2O3 heterojunction barrier Schottky (HJBS) diodes with exceptional SEB capability. The device design incorporates micron-scale deep trenches filled by p-type nickel oxide (NiO) and high-k BaTiO3 field-plate (FP) edge termination. This architecture efficiently extracts radiation-induced positive charges (holes) during single-event irradiation through the trenched-embedded Ni/p-NiO with low Ohmic contact resistance, significantly alleviating charge aggregation while minimizing non-uniform field distributions through strategically engineered charge drainage pathways. As a result, the HJBS device achieves a SEB voltage exceeding 1.4 kV and a SEB degradation rate of only 9.6%. This is the first demonstration of kilovolt-class radiation-hardened diodes, and its performance metrics are the best reported among SiC, GaN, Ga2O3 and Si power diodes to date. This work underscores the great potential of Ga2O3 power diodes for irradiation power applications. © 2025 IEEE.

关键词： Nickel oxide

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Evaluation of Operating Comfort of Biological Microscopes 26th

Evaluation of Operating Comfort of Biological Microscopes

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26th International Conference on Human-computer Interaction, HCII 2024

作者： Wang, Yong Zhang, Tianmei Tan, Jiubin School of Architecture and Design Harbin Institute of Technology Harbin150001 China School of Instrumentation Science and Engineering Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9783031768088

The operating comfort of biological microscopes is directly related to the operators’ health and the accuracy of observation results. In this paper, the neck and arm comfort of biological microscope operators was researched. Firstly, the evaluation experiments included two parts: one was the evaluation of operating comfort for the neck and shoulder, the other was for the arm. The surface electromyography (sEMG) data of the measured muscles such as the splenius capitis, sternocleidomastoid, trapezius, flexor carpi and brachioradialis were acquired within the range of 0–40° neck angle and 90–150° elbow angle. In addition, the rating of perceived exertion (RPE) of operators was taken as the subjective evaluation results for different operating postures. Secondly, the root mean square (RMS) data of different subjects’ sEMG were normalized to the corresponding RMS of 20%MVC. It can be seen that the load of the splenius capitis, sternocleidomastoid and trapezius all increased with the increase of the head bow angle, and the load of measured muscles reached the maximum in 35–45°. And when the angle between the forearm and the upper arm was 115–125°, the load on the flexor carpi radialis, brachioradialis and trapezius was minimal. Finally, the ANOVA statistical analysis was proceeded to the RMS data of the measured muscles. It was found that the load of the splenius capitis and sternocleidomastoid was positively correlated with the bow angle, and the load range was 4%–30%MVC. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Muscle

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A universal approach to high-performance thermoelectric module design for power generation (vol 9, 101818, 2025)

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JOULE 2025年第4期9卷

作者： Cheng, Jinxuan Xue, Wenhua Zhang, Tianyu Li, Xiaofang Zhu, Yichen Yin, Li Yao, Honghao Fu, Zixuan Wu, Longzhi Chen, Chen Zhao, Peng Ma, Xiaojing Jiang, Feng Wang, Xiaodong Li, Mingyu Mao, Jun Wang, Yumei Cao, Feng Zhang, Qian School of Materials Science and Engineering and Institute of Materials Genome & Big Data Harbin Institute of Technology Shenzhen 518055 China Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Science Beijing 100190 China School of Science Harbin Institute of Technology Shenzhen 518055 China Sauvage Laboratory for Smart Materials School of Materials Science and Engineering Harbin Institute of Technology Shenzhen 518055 China State Key Laboratory of Advanced Welding and Joining Harbin Institute of Technology Harbin 150001 China School of Physical Sciences Great Bay University Dongguan 523000 China Institute of Special Environments Physical Sciences Harbin Institute of Technology Shenzhen 518055 China Beijing Branch of Songshan Lake Materials Laboratory Dongguan 523808 China

Thermoelectric modules fabricated by traditional welding fall short of achieving optimal conversion efficiencies, primarily due to performance degradation of materials at high temperatures, severe elemental diffusion, and residual thermal stress at the interface. Transient liquid phase bonding enables the realization of joints with high-melting-point compounds at low bonding temperatures, providing a promising solution for achieving "low-temperature bonding and high-temperature service." Owing to the low eutectic point of the solder and high melting points of compounds, we optimize the design of a germanium-telluride-based module at 533 K, which is successfully applied at the hot-side temperature of 773 K. Attributed to high-performance materials and reliable joints, the module realizes a high conversion efficiency of ∼15.1% and remains stable throughout 150 h of service. By adopting the same approach, the low- (Bi2Te3) and high-temperature (half-Heusler) modules are assembled. We provide a promising and general route for the assembly of full-temperature-range thermoelectric devices.

关键词： transient liquid phase bonding thermoelectric module intermetallic compounds GeTe

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An hourly solar radiation prediction model using eXtreme gradient boosting algorithm with the effect of fog-haze

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Energy and Built Environment 2025年第1期6卷 18-26页

作者： Zhang, Chunxiao Zhang, Yingbo Pu, Jihong Liu, Zhengguang Wang, Zhanwei Wang, Lin Institute of Building Energy and Thermal Science Henan University of Science and Technology Luoyang471023 China School of Architecture Harbin Institute of Technology Harbin China Institute of the Building Environment & Sustainability Technology Xi'an Jiaotong University Xi'an710049 China

Hourly global solar radiation data is an important factor for solar energy utilization. Due to the lack of solar radiation observation stations in many areas, some hourly solar radiation models are proposed to predict hourly solar radiation. However, the existing models perform poorly in heavy fog-haze areas because the weakening effect of fog-haze on solar radiation is not considered. Thus, in this paper, hourly global solar radiation prediction models are developed considering air quality index (AQI) using XGBoost algorithm. The results show a general improvement in the accuracy of models with AQI as an additional input (Model B1-B6) compared to models that do not consider AQI (Model A1-A6). Compared to Model A, Model B have an increase in R value from 0.927 to 0.948, a decrease in RMSE value from 0.300 to 0.282 and a decrease in MAPE value from 0.159 to 0.145. In addition, for hourly solar radiation prediction, the six most important inputs are the day of the year, air temperature difference, surface temperature difference, hour, AQI, and total cloud cover. © 2023

关键词： Fog

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