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Lithographic printing inspired in-situ transfer of MXene-based films with localized topo-electro tunability for high-performance flexible pressure sensors

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Nano Research 2023年第11期16卷 12670-12679页

作者： Qiuyang Yan Yi Zhou Yin Cheng Liangjing Shi Ranran Wang Lian Gao Jing Sun State Key Laboratory of High Performance Ceramics and Superfine Microstructure Shanghai Institute of CeramicsChinese Academy of ScienceShanghai 200050China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore School of Chemistry and Materials Science Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China School of Materials Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China

MXene-based films have been intensively explored for construction of piezoresistive flexible pressure sensors owing to their excellent mechanical and electrical *** pressure sensitivity relies on pre-molding a flexible substrate,or regulating the micromorphology of MXene sheets,to obtain a micro-structured ***,the two avenues usually require complicated and time-consuming microfabrication or wet chemical processing,and are limited to non-adjustable topographicelectrical(topo-electro)***,we propose a lithographic printing inspired in-situ transfer(LIPIT)strategy to fabricate MXene-ink films(MIFs).In LIPIT,MIFs not only inherit ridge-and-valley microstructure from paper substrate,but also achieve localized topo-electro tunability by programming ink-writing patterns and *** MIF-based flexible pressure sensor with periodical topo-electro gradient exhibits remarkably boosted sensitivity in a wide sensing range(low detection limit of 0.29 Pa and working range of 100 kPa).The MIF sensor demonstrates versatile applicability in both subtle and vigorous pressuresensing fields,ranging from pulse wave extraction and machine learning-assisted surface texture recognition to piano-training glove(PT-glove)for piano *** LIPIT is quick,low-cost,and compatible with free ink/substrate combinations,which promises a versatile toolbox for designing functional MXene films with tailored morphological-mechanical-electrical properties for extended application scenarios.

关键词： MXene film transfer printing surface tunability flexible pressure sensor

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Accelerating Graph Analytics with oneAPI and Intel FPGAs

Accelerating Graph Analytics with oneAPI and Intel FPGAs

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Annual IEEE Symposium on Field-Programmable Custom computing Machines (FCCM)

作者： James Bickerstaff Luke Kljucaric Alan D. George Department of Electrical and Computer Engineering NSF Center for Space High-performance and Resilient Computing (SHREC) University of Pittsburgh Pittsburgh PA USA

Graphs are a popular and effective way to represent relationships between data points in a network due to their simple data abstraction and low storage cost per data point. However, as data continues to grow, the processing complexity of graph operations also significantly increases. Thus, there is a need to accelerate graph processing through specialized techniques and hardware in order to improve analytical throughput. This research uses the oneAPI toolkit with SYCL for FPGAs and leverages the increased productivity when creating designs, as compared to traditional hardware description language methodologies. The oneAPI toolkit is used in the creation of minimum-spanning-tree (MST) and breadth-first search (BFS) accelerators, evaluating the impact of different high-level design choices on the overall performance observed.

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Multiarchitecture Hardware Acceleration of Hyperdimensional computing

Multiarchitecture Hardware Acceleration of Hyperdimensional ...

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IEEE Conference on high performance Extreme computing (HPEC)

作者： Ian Peitzsch Mark Ciora Alan D. George Department of Electrical and Computer Engineering NSF Center for Space High-performance and Resilient Computing (SHREC) University of Pittsburgh Pittsburgh PA USA

Hyperdimensional computing (HDC) is a machine-learning method that seeks to mimic the high-dimensional nature of data processing in the cerebellum. To achieve this goal, HDC represents data as large vectors, called hypervectors, and uses a set of well-defined operations to perform symbolic computations on these hypervectors. Using this paradigm, it is possible to create HDC models for classification tasks. These HDC models work by first transforming the input data into hypervectors, and then combining hypervectors of the same class to create a hypervector for representing that task. These HDC models can classify information by transforming new input data into hypervectors, comparing the similarity between data hypervector with each class hypervector, then classifying it based on which class has the highest similarity. Over the past few years, HDC models have greatly improved in accuracy and now compete with more common classification techniques for machine learning, such as neural networks. Additionally, manipulating hypervectors involve many repeated basic operations, making them easy to accelerate using different hardware platforms. This research seeks to exploit this ease of acceleration of HDC models and utilize oneAPI libraries with SYCL to create multiple accelerators for HDC learning tasks for CPUs, GPUs, and field-programmable gate arrays (FPGAs). The oneAPI tools are used in this research to accelerate single-pass learning, gradient-descent learning using the NeuralHD algorithm, and inference. Each of these tasks is benchmarked on the Intel Xeon Platinum 8256 CPU, Intel UHD 11th generation GPU, and Intel Stratix 10 FPGA. The GPU implementation showcased the fastest training times for single-pass training and NeuralHD training, with 0.89s and 126.55s, respectively. The FPGA implementation exhibited the lowest inference latency, with an average of 0. 28ms.

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Clustering Classification on FPGAs for Neuromorphic Feature Extraction

Clustering Classification on FPGAs for Neuromorphic Feature ...

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Annual IEEE Symposium on Field-Programmable Custom computing Machines (FCCM)

作者： Luke Kljucaric Alan D. George Department of Electrical and Computer Engineering NSF Center for Space High-Performance and Resilient Computing (SHREC) University of Pittsburgh Pittsburgh PA USA

State-of-the-art machine-learning (ML) apps are often expensive in terms of memory and computation required for high-accuracy feature extraction and object classification using frame-based sensors. By contrast, neuromorphic feature-extraction algorithms offer lower memory and compute complexity than traditional ML algorithms, which can improve the scalability of ML apps. These neuromorphic algorithms operate on neuromorphic, event-based sensor data as opposed to traditional, frame-based camera images. These neuromorphic sensors capture events at microsecond resolution with variable bandwidth. Therefore, neuromorphic algorithms require low-latency architectures to enable real-time processing. Field-programmable gate arrays (FPGAs) are reconfigurable-logic devices that can realize low-latency data paths required for real-time neuromorphic computation.

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Unknown-Aware Bilateral Dependency Optimization for Defending Against Model Inversion Attacks

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Peng, Xiong Liu, Feng Wang, Nannan Lan, Long Liu, Tongliang Cheung, Yiu-Ming Han, Bo Hong Kong Baptist University Department of Computer Science Hong Kong The University of Melbourne School of Computing and Information Systems Australia Xidian University State Key Laboratory of Integrated Services Networks School of Telecommunications Engineering Shaanxi Xi'an710071 China National University of Defense Technology State Key Laboratory of High Performance Computing School of Computer Hunan Changsha410000 China The University of Sydney Sydney AI Centre School of Computer Science Faculty of Engineering Australia

By abusing access to a well-trained classifier, model inversion (MI) attacks pose a significant threat as they can recover the original training data, leading to privacy leakage. Previous studies mitigated MI attacks by imposing regularization to reduce the dependency between input features and outputs during classifier training, a strategy known as unilateral dependency optimization. However, this strategy contradicts the objective of minimizing the supervised classification loss, which inherently seeks to maximize the dependency between input features and outputs. Consequently, there is a trade-off between improving the model's robustness against MI attacks and maintaining its classification performance. To address this issue, we propose the bilateral dependency optimization strategy (BiDO), a dual-objective approach that minimizes the dependency between input features and latent representations, while simultaneously maximizing the dependency between latent representations and labels. BiDO is remarkable for its privacy-preserving capabilities. However, models trained with BiDO exhibit diminished capabilities in out-of-distribution (OOD) detection compared to models trained with standard classification supervision. Given the open-world nature of deep learning systems, this limitation could lead to significant security risks, as encountering OOD inputs—whose label spaces do not overlap with the in-distribution (ID) data used during training-is inevitable. To address this, we leverage readily available auxiliary OOD data to enhance the OOD detection performance of models trained with BiDO. This leads to the introduction of an upgraded framework, unknown-aware BiDO (BiDO+), which mitigates both privacy and security concerns. As a highlight, with comparable model utility, BiDO-HSIC+ reduces the FPR95 by 55.02% and enhances the AUCROC by 9.52% compared to BiDO-HSIC, while also providing superior MI robustness. © 1979-2012 IEEE.

关键词： Differential privacy

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Non-adiabatic Wall Effects on Transonic Shock/Boundary Layer Interaction

Non-adiabatic Wall Effects on Transonic Shock/Boundary Layer...

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Symposium on Applied Aerodynamics and Design of Aerospace Vehicles, SAROD 2018

作者： Bhola, Sahil Sengupta, Tapan K. Thapar Institute of Engineering and Technology Patiala India High Performance Computing Laboratory Department of Aerospace Engineering IIT Kanpur Kanpur India

ISBN: (纸本)9789811596001

Direct simulations are carried out to investigate the influence of unsteady heat flux transfer on transonic shock-boundary layer interaction;for flow past SHM-1 airfoil at a free-stream Mach number M∞ = 0.72 and angle of attack α= 0. 38∘. Flux is added in a periodic manner through a region (8-18%ofthechord) located on the suction side of the airfoil, with multiple values of exciter time period (Te= 2, 4 ) considered for our simulation. We show that addition of unsteady heat flux delayed shock formation, along with significant modifications in it’s structure. The time-averaged Cp distributions revealed a shift in the shock towards the aft, by approximately 5% of the chord;along with an increased lift near the trailing edge, suggesting a nose-down stabilizing influence. Primarily, it is noted that the additional heat flux resulted in an overall increase of the aerodynamic efficiency (lift to drag ratio) by approximately 10 %. © 2021, Springer Nature Singapore Pte Ltd.

关键词： DNS SBLI Transonic flow

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Uncertainty-Inspired Multi-Task Learning in Arbitrary Scenarios of ECG Monitoring

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4046-4058页

作者： Xingyao Wang Hongxiang Gao Caiyun Ma Tingting Zhu Feng Yang Chengyu Liu Huazhu Fu Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) Singapore School of Instrument Science and Engineering and State Key Laboratory of Digital Medical Engineering Southeast University Nanjing China Department of Engineering Science University of Oxford Oxford U.K.

As the scenarios for electrocardiogram (ECG) monitoring become increasingly diverse, particularly with the development of wearable ECG, the influence of ambiguous factors in diagnosis has been amplified. Reliable ECG information must be extracted from abundant noises and confusing artifacts. To address this issue, we suggest an uncertainty-inspired model for beat-level diagnosis (UI-Beat). The base architecture of UI-Beat separates heartbeat localization and event diagnosis in two branches to address the problem of heterogeneous data sources. To disentangle the epistemic and aleatoric uncertainty within one stage in a deterministic neural network, we propose a new method derived from uncertainty formulation and realize it by introducing the class-biased transformation. Then the disentangled uncertainty can be utilized to screen out noise and identify ambiguous heartbeat synchronously. The results indicate that UI-Beat can significantly improve the performance of noise detection (from 91.60% to 97.50% for real-world noise detection and from 61.40% to 82.41% for real-world artifact detection). For multi-lead ECG analysis, UI-Beat is approaching the performance upper bound in heartbeat localization (only 15 false positives and 9 false negatives out of the 175,907 heartbeats in the INCART database) and achieving a significant performance improvement in heartbeat classification through uncertainty-based cross-lead fusion compared to single-lead prediction and other state-of-the-art methods (an average improvement of 14.28% for detecting heartbeats of S and 3.37% for detecting heartbeats of V). Considering the characteristic of one-stage ECG analysis within one model, it is suggested that the proposed UI-Beat has the potential to be employed as a general model for arbitrary scenarios of ECG monitoring, with the capacity to remove unusableepisodes, and realize heartbeat-level diagnosis with confidence provided.

关键词： Electrocardiography Heart beat Uncertainty Noise Heart rate variability Location awareness Databases Annotations Analytical models Multitasking

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PancakeFS: A Write Efficiently and Read Optimized Filesystem

PancakeFS: A Write Efficiently and Read Optimized Filesystem

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Consumer Electronics and computer engineering (ICCECE), 2021 IEEE International Conference on

作者： Yongkang Wang Yang Liu Zhiguang Chen School of Computer Science and Engineering Sun Yat-sen University Guangzhou China State Key Laboratory of High-Performance Computing Institute for Quantum Information College of Computer National University of Defense Technology Changsha China

With the development of emerging technologies such as cloud computing and large AI models (such as LLM), many applications have placed higher demands on the intensive read and write processing of massive data. Addressing the limitations of traditional file systems, we propose PancakeFS, an LSM-Tree-based file system. Leveraging the write-friendly characteristics of LSM-Tree, PancakeFS exhibits efficient write performance. We improve read performance through the design of an inode allocation algorithm based on subtree partitioning and optimization of the disk layout for hot data reconstruction. Our experimental results demonstrate that PancakeFS outperforms Ext4 and TableFS in terms of both read and write performance.

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Thermally Evaporated ZnSe for Efficient and Stable Regular/Inverted Perovskite Solar Cells by Enhanced Electron Extraction

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Energy & Environmental Materials 2023年第5期6卷 440-448页

作者： Xin Li Guibin Shen Xin Ren Ng Zhiyong Liu Yun Meng Yongwei Zhang Cheng Mu Zhi Gen Yu Fen Lin Solar Energy Research Institute of Singapore(SERIS) National University of SingaporeSingapore 117574Singapore Key Laboratory of Advanced Light Conversion Materials and Biophotonics Department of ChemistryRenmin University of ChinaBeijing 100872China State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China School of Materials Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore Shandong Provincial Engineering and Technical Center of Light Manipulation and Shandong Provincial Key Laboratory of Optics and Photonic Devices School of Physics and ElectronicsShandong Normal UniversityJinan 250014China Institute of High Performance Computing A^(*)STARSingapore 138632Singapore

Electron transport layers(ETLs)are crucial for achieving efficient and stable planar perovskite solar cells(PSCs).Reports on versatile inorganic ETLs using a simple film fabrication method and applicability for both low-cost planar regular and inverted PSCs with excellent efficiencies(>22%)and high stability are very ***,we employ a novel inorganic ZnSe as ETL for both regular and inverted PSCs to improve the efficiency and stability using a simple thermal evaporation *** TiO_(2)-ZnSe-FAPbl_(3)heterojunction could be formed,resulting in an improved charge collection and a decreased carrier recombination further proved through theoretical *** optimized regular PSCs based on TiO_(2)/ZnSe have achieved 23.25%efficiency with negligible *** addition,the ZnSe ETL can also effectively replace the unstable bathocuproine(BCP)in inverted ***,the ZnSe-based inverted device realizes a champion efficiency of 22.54%.Moreover,the regular device comprising the TiO_(2)/ZnSe layers retains 92%of its initial PCE after 10:00 h under 1 Sun continuous illumination and the inverted device comprising the C_(60)/ZnSe layers maintains over 85%of its initial PCE at 85℃for 10:00 *** highlights one of the best results among universal ETLs in both regular and inverted perovskite photovoltaics.

关键词： high efficiency long-term stability planar regular/inverted perovskite solar cells thermal evaporation ZnSe electron transport layer

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Root-Of-Trust for Continuous Integration and Continuous Deployment Pipeline in Cloud computing

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computers, Materials & Continua 2022年第11期73卷 2223-2239页

作者： Abdul Saboor Mohd Fadzil Hassan Rehan Akbar Erwin Susanto Syed Nasir Mehmood Shah Muhammad Aadil Siddiqui Saeed Ahmed Magsi High-Performance Cloud Computing Centre(HPC3) Department of Computer&Information SciencesUniversiti Teknologi PETRONASSeri IskandarPerakMalaysia Centre for Research in Data Science(CeRDaS) Department of Computer&Information SciencesUniversiti Teknologi PETRONASSeri IskandarPerakMalaysia School of Electrical Engineering Telkom UniversityBandungIndonesia KICSIT Institute of Space Technology(IST)IslamabadPakistan Department of Electrical and Electronics Engineering Universiti Teknologi PETRONASSeri IskandarPerakMalaysia

Cloud computing has gained significant use over the last decade due to its several benefits,including cost savings associated with setup,deployments,delivery,physical resource sharing across virtual machines,and availability of on-demand cloud ***,in addition to usual threats in almost every computing environment,cloud computing has also introduced a set of new threats as consumers share physical resources due to the physical co-location ***,since there are a growing number of attacks directed at cloud environments(including dictionary attacks,replay code attacks,denial of service attacks,rootkit attacks,code injection attacks,etc.),customers require additional assurances before adopting cloud ***,the continuous integration and continuous deployment of the code fragments have made cloud services more prone to security *** this study,the model based on the root of trust for continuous integration and continuous deployment is proposed,instead of only relying on a single signon authentication method that typically uses only id and *** underlying study opted hardware security module by utilizing the Trusted Platform Module(TPM),which is commonly available as a cryptoprocessor on the motherboards of the personal computers and data center *** preliminary proof of concept demonstrated that the TPM features can be utilized through RESTful services to establish the root of trust for continuous integration and continuous deployment pipeline and can additionally be integrated as a secure microservice feature in the cloud computing environment.

关键词： Root of Trust(RoT) Trusted Platform Module(TPM) cryptoprocessor microservices Hardware Security Modules(HSM) DevOps

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