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CETIS: Retrofitting Intel CET for Generic and Efficient Intra-process Memory Isolation 22

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CETIS: Retrofitting Intel CET for Generic and Efficient Intr...

28th ACM SIGSAC Conference on computer and Communications Security, CCS 2022

作者： Xie, Mengyao Wu, Chenggang Zhang, Yinqian Xu, Jiali Lai, Yuanming Kang, Yan Wang, Wei Wang, Zhe Sklp Institute of Computing Technology Cas University of Chinese Academy of Sciences Beijing China Zhongguancun Laboratory Beijing China Department of Computer Science and Engineering SUSTech Research Institute of Trustworthy Autonomous Systems SUSTech Shenzhen China Sklp Institute of Computing Technology Cas Beijing China

ISBN: (纸本)9781450394505

Intel control-flow enforcement technology (CET) is a new hardware feature available in recent Intel processors. It supports the coarse-grained control-flow integrity for software to defeat memory corruption attacks. In this paper, we retrofit CET, particularly the write-protected shadow pages of CET used for implementing shadow stacks, to develop a generic and efficient intra-process memory isolation mechanism, dubbed CETIS. To provide user-friendly interfaces, a CETIS framework was developed, which provides memory file abstraction for the isolated memory regions and a set of APIs to access said regions. CETIS also comes with a compiler-assisted tool chain for users to build secure applications easily. The practicality of using CETIS to protect CPI, CFIXX, and JIT-compilers was demonstrated, and the evaluation reveals that CETIS is performed better than state-of-the-art intra-memory isolation mechanisms, such as MPK. © 2022 Owner/Author.

关键词： Retrofitting

A Data Completion Algorithm Based on Low-Rank Prior Knowledge for Data-Driven Applications

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IEEE Transactions on Network and Service Management 2024年

作者： Tang, Jun Guo, Bing Shen, Yan Garg, Sahil Kaddoum, Georges Hossain, M. Shamim Sichuan University College of Computer Science Chengdu China Chengdu University of Information Technology School of Computer Science Chengdu China École de Technologie Supérieure Electrical Engineering Department MontréalQCH3C 1K3 Canada Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India Lebanese American University Artificial Intelligence and Cyber Systems Research Center Beirut03797751 Lebanon King Saud University College of Computer and Information Sciences Department of Software Engineering Riyadh12372 Saudi Arabia

Low rank tensor ring based data recovery algorithms have been widely used in data-driven consumer electronics to recover missing data entries in the collecting data pre-processing stage for providing stable and reliable service. However, traditional recovery methods often fail to utilize the abundant prior knowledge of data and the non-local self-similarity of the data, thus leading to the failure to effectively capture the spatial relationships within high-dimensional data to recover them accurately. To address these problems, we present a novel Non-local Self-similarity and Low-rank Prior Knowledge based tensor ring completion method. Firstly, we incorporate the BM3D denoising operator within a Plug-and-Play framework to exploit the self-similarity in the data. Then a logarithmic determinant function is integrated to distinguish singular values in the cyclic unfolding matrix of the tensor and adopts a tensor ring completion approach based on weighted nuclear norms. Finally, in order to evaluate the effectiveness of our proposed method, we conducted a series of experiments by using the missing image dataset and the missing traffic data dataset respectively, and the experimental results show that our method achieves the highest level in terms of data recovery accuracy. © 2024 IEEE.

关键词： Data assimilation

Track Coalescence and Repulsion in Multitarget Tracking: An Analysis of MHT, JPDA, and Belief Propagation Methods

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arXiv 2023年

作者： Kropfreiter, Thomas Meyer, Florian Crouse, David F. Coraluppi, Stefano Hlawatsch, Franz Willett, Peter The Scripps Institution of Oceanography The Department of Electrical and Computer Engineering University of California La Jolla San DiegoCA United States The Naval Research Laboratory WashingtonDC United States Systems & Technology Research WoburnMA United States The Institute of Telecommunications TU Wien Vienna Austria The Department of Electrical and Computer Engineering University of Connecticut StorrsCT United States

Joint probabilistic data association (JPDA) filter methods and multiple hypothesis tracking (MHT) methods are widely used for multitarget tracking (MTT). However, they are known to exhibit undesirable behavior in tracking scenarios with targets in close proximity: JPDA filter methods suffer from the track coalescence effect, i.e., the estimated tracks of targets in close proximity tend to merge and can become indistinguishable, while MHT methods suffer from an opposite effect known as track repulsion, i.e., the estimated tracks of targets in close proximity tend to repel each other in the sense that their separation is larger than the actual distance between the targets. In this paper, we review the JPDA filter and MHT methods and discuss the track coalescence and track repulsion effects. We also consider a more recent methodology for MTT that is based on the belief propagation (BP) algorithm. We argue that BP-based MTT does not exhibit track repulsion because it is not based on maximum a posteriori estimation, and that it exhibits significantly reduced track coalescence because certain properties of the BP messages related to data association encourage separation of target state estimates. Our theoretical arguments are confirmed by numerical results for four representative simulation scenarios. © 2023, CC BY.

关键词： Coalescence

AC/DC: Automated Compilation for Dynamic Circuits

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arXiv 2024年

作者： Niu, Siyuan Kökcü, Efekan Mitra, Anupam Szasz, Aaron Hashim, Akel Kalloor, Justin de Jong, Wibe Albert Iancu, Costin Younis, Ed Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Quantum Nanoelectronics Laboratory Department of Physics University of California BerkeleyCA94720 United States Department of Electrical Engineering and Computer Science University of California BerkeleyCA94720 United States

Dynamic quantum circuits incorporate mid-circuit measurements and feed-forward operations originally intended to realize Quantum Error Correction. This paradigm has recently been utilized to prepare certain states and long-range entangling gates as well as reduce resource overhead in quantum algorithms such as Quantum Fourier Transformation and Quantum Phase Estimation. In this paper, we present a novel framework for generating dynamic quantum circuits that automatically prepare any state or unitary operator. This procedure is powered by numerical optimization-based circuit synthesis methods. The first contribution is introducing optimization objective functions incorporating mid-circuit measurement and feed-forward operations. The second contribution is incorporating these into a popular open-source quantum circuit synthesis framework. We demonstrate the generation of dynamic circuits for state preparation, long-range entangling gates, circuit optimization, and the application of dynamic circuits to lattice simulations. The resulting circuits are validated through simulation as well as through execution on quantum hardware. Furthermore, we perform noise analysis to explore the impact of different error ratios in mid-circuit measurements and gate errors, identifying scenarios where dynamic circuits offer the most significant benefits. The dynamic circuits generated by our framework show substantial improvements in reducing circuit depth and, in some cases, the number of gates required. To our knowledge, this is the first practical procedure to generate dynamic quantum circuits. Our objective functions are independent of the underlying synthesis framework and can be easily reused. The framework opens new possibilities for circuit generation and optimization methods, and the current results highlight the potential of dynamic circuits to enhance the performance of quantum algorithms on near-term quantum computers. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

Learning for Interval Prediction of Electricity Demand: A Cluster-Based Bootstrapping Approach

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SSRN

SSRN 2024年

作者： Dube, Rohit Gautam, Natarajan Banerjee, Amarnath Nagarajan, Harsha Industrial and Systems Engineering Texas A & M University College StationTX77843 United States Electrical Engineering and Computer Science Syracuse University SyracuseNY13210 United States Applied Mathematics and Plasma Physics Los Alamos National Laboratory Los Alamos NM87545 United States

Accurate predictions of electricity demands are necessary for managing operations in a small aggregation load setting like a Microgrid. Due to low aggregation, the electricity demands can be highly stochastic and point estimates would lead to inflated errors. Interval estimation in this scenario, would provide a range of values within which the future values might lie and helps quantify the errors around the point estimates. This paper introduces a residual bootstrap algorithm to generate interval estimates of day-ahead electricity demand. A machine learning algorithm is used to obtain the point estimates of electricity demand and respective residuals on the training set. The obtained residuals are stored in memory and the memory is further partitioned. Days with similar demand patterns are grouped in clusters using an unsupervised learning algorithm and these clusters are used to partition the memory. The point estimates for test day are used to find the closest cluster of similar days and the residuals are bootstrapped from the chosen cluster. This algorithm is evaluated on the real electricity demand data from EULR (End Use Load Research) and is compared to other bootstrapping methods for varying confidence intervals. © 2024, The Authors. All rights reserved.

关键词： Machine learning

Theoretical Bound and Compensation for Residual Error Probability of GRAND-CRC-Based Functional Safety Communication

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IEEE Transactions on Communications 2025年

作者： Zhan, Ming Pang, Zhibo Wu, Fang Zhou, Qiang Zhang, Shiqing Zhang, Jianwu Yu, Kan Taizhou University College of Electronic and Information Engineering Zhejiang Taizhou318000 China ABB Corporate Research Forskargränd 7 VästeråsSE-721 78 Sweden School of Electrical Engineering and Computer Science Department of Intelligent Systems StockholmSE-114 28 Sweden Southwest University College of Economics and Management Chongqing400715 China Hangzhou Dianzi University College of Telecommunication Engineering Hangzhou310018 China La Trobe University Bendigo Campus School of Engineering and Mathematical Sciences Department of Computer Science and Information Technology Edwards Rd Flora HillVIC3552 Australia

In modern Industrial Internet of Things (IIoT) ecosystems, functional safety protocols are increasingly utilized to transmit safety protocol data unit (SPDU). Integrating the universal guessing random additive noise decoding (GRAND) algorithm with cyclic redundancy check (CRC)-coded SPDU can minimize SPDU retransmissions. However, it introduces residual error probability (REP) degradation that requires careful consideration. Using the IEC 61784-3 Standard and CRC assumptions, we derive a closed-form REP evaluation formula specific to SPDU length and maximum error correction capability. Our analysis reveals that, under the worst industrial conditions and for an SPDU length of 128 bits, the REP performance degrades by approximately 2:2 × 102 times when the CRC signature length is 24 bits and up to one bit is guessing decoded. This degradation becomes more pronounced with increased error correction capability. To address this, we propose to compensate the REP degradation by adopting longer CRC signature. This paper provides a theoretical framework for adopting the GRAND algorithm in functional safety communication, setting a foundation for enhancing reliability in IIoT applications. © 1972-2012 IEEE.

关键词： Decoding

Can We Enhance the Quality of Mobile Crowdsensing Data Without Ground Truth?

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arXiv 2024年

作者： Li, Jiajie Gu, Bo Gong, Shimin Su, Zhou Guizani, Mohsen School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen518107 China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology Guangzhou510006 China School of Cyber Science and Engineering Xi’an Jiaotong University Xi’an710049 China Computer Science and Engineering Department Qatar University Doha2713 Qatar

Mobile crowdsensing (MCS) has emerged as a prominent trend across various domains. However, ensuring the quality of the sensing data submitted by mobile users (MUs) remains a complex and challenging problem. To address this challenge, an advanced method is needed to detect low-quality sensing data and identify malicious MUs that may disrupt the normal operations of an MCS system. Therefore, this article proposes a prediction- and reputation-based truth discovery (PRBTD) framework, which can separate low-quality data from high-quality data in sensing tasks. First, we apply a correlation-focused spatio-temporal Transformer network that learns from the historical sensing data and predicts the ground truth of the data submitted by MUs. However, due to the noise in historical data for training and the bursty values within sensing data, the prediction results can be inaccurate. To address this issue, we use the implications among the sensing data, which are learned from the prediction results but are stable and less affected by inaccurate predictions, to evaluate the quality of the data. Finally, we design a reputation-based truth discovery (TD) module for identifying low-quality data with their implications. Given the sensing data submitted by MUs, PRBTD can eliminate the data with heavy noise and identify malicious MUs with high accuracy. Extensive experimental results demonstrate that the PRBTD method outperforms existing methods in terms of identification accuracy and data quality enhancement. Copyright © 2024, The Authors. All rights reserved.

关键词： Spatio-temporal data

Robust Fiber Orientation Distribution Function Estimation Using Deep Constrained Spherical Deconvolution for Diffusion MRI

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arXiv 2023年

作者： Yao, Tianyuan Rheault, Francois Cai, Leon Y. Nath, Vishwesh Asad, Zuhayr Newlin, Nancy Cui, Can Deng, Ruining Ramadass, Karthik Shafer, Andrea Resnick, Susan Schilling, Kurt Landman, Bennett A. Huo, Yuankai Department of Computer Science Vanderbilt University NashvilleTN United States Department of Computer Science Université de Sherbrooke SherbrookeQC Canada Department of Biomedical Engineering Vanderbilt University NashvilleTN United States NVIDIA Corporation BethesdaMD United States Laboratory of Behavioral Neuroscience National Institute on Aging BaltimoreMD United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37235 United States

Diffusion-weighted magnetic resonance imaging (DW-MRI) is a critical imaging method for capturing and modeling tissue microarchitecture at a millimeter scale. A common practice to model the measured DW-MRI signal is via fiber orientation distribution function (fODF). This function is the essential first step for the downstream tractography and connectivity analyses. With recent advantages in data sharing, large-scale multi-site DW-MRI datasets are being made available for multi-site studies. However, measurement variabilities (e.g., inter- and intra-site variability, hardware performance, and sequence design) are inevitable during the acquisition of DW-MRI. Most existing model-based methods (e.g., constrained spherical deconvolution (CSD)) and learning based methods (e.g., deep learning (DL)) do not explicitly consider such variabilities in fODF modeling, which consequently leads to inferior performance on multi-site and/or longitudinal diffusion studies. In this paper, we propose a novel data-driven deep constrained spherical deconvolution method to explicitly constrain the scan-rescan variabilities for a more reproducible and robust estimation of brain microstructure from repeated DW-MRI scans. Specifically, the proposed method introduces a new 3D volumetric scanner-invariant regularization scheme during the fODF estimation. We study the Human Connectome Project (HCP) young adults test-retest group as well as the MASiVar dataset (with inter- and intra-site scan/rescan data). The Baltimore Longitudinal Study of Aging (BLSA) dataset is employed for external validation. From the experimental results, the proposed data-driven framework outperforms the existing benchmarks in repeated fODF estimation. The proposed method is assessing the downstream connectivity analysis and shows increased performance in distinguishing subjects with different biomarkers. The plug-and-play design of the proposed approach is potentially applicable to a wider range of data harmonization pr

关键词： Spheres

Scaling and logic in the color code on a superconducting quantum processor

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arXiv 2024年

作者： Lacroix, Nathan Bourassa, Alexandre Heras, Francisco J.H. Zhang, Lei M. Bausch, Johannes Senior, Andrew W. Edlich, Thomas Shutty, Noah Sivak, Volodymyr Bengtsson, Andreas McEwen, Matt Higgott, Oscar Kafri, Dvir Claes, Jahan Morvan, Alexis Chen, Zijun Zalcman, Adam Madhuk, Sid Acharya, Rajeev Beni, Laleh Aghababaie Aigeldinger, Georg Alcaraz, Ross Andersen, Trond I. Ansmann, Markus Arute, Frank Arya, Kunal Asfaw, Abraham Atalaya, Juan Babbush, Ryan Ballard, Brian Bardin, Joseph C. Bilmes, Alexander Blackwell, Sam Bovaird, Jenna Bowers, Dylan Brill, Leon Broughton, Michael Browne, David A. Buchea, Brett Buckley, Bob B. Burger, Tim Burkett, Brian Bushnell, Nicholas Cabrera, Anthony Campero, Juan Chang, Hung-Shen Chiaro, Ben Chih, Liang-Ying Cleland, Agnetta Y. Cogan, Josh Collins, Roberto Conner, Paul Courtney, William Crook, Alexander L. Curtin, Ben Das, Sayan Demura, Sean De Lorenzo, Laura Di Paolo, Agustin Donohoe, Paul Drozdov, Ilya Dunsworth, Andrew Eickbusch, Alec Elbag, Aviv Moshe Elzouka, Mahmoud Erickson, Catherine Ferreira, Vinicius S. Burgos, Leslie Flores Forati, Ebrahim Fowler, Austin G. Foxen, Brooks Ganjam, Suhas Garcia, Gonzalo Gasca, Robert Genois, Élie Giang, William Gilboa, Dar Gosula, Raja Dau, Alejandro Grajales Graumann, Dietrich Greene, Alex Gross, Jonathan A. Ha, Tan Habegger, Steve Hansen, Monica Harrigan, Matthew P. Harrington, Sean D. Heslin, Stephen Heu, Paula Hiltermann, Reno Hilton, Jeremy Hong, Sabrina Huang, Hsin-Yuan Huff, Ashley Huggins, William J. Jeffrey, Evan Jiang, Zhang Jin, Xiaoxuan Joshi, Chaitali Juhas, Pavol Kabel, Andreas Kang, Hui Karamlou, Amir H. Kechedzhi, Kostyantyn Khaire, Trupti Khattar, Tanuj Khezri, Mostafa Kim, Seon Klimov, Paul V. Kobrin, Bryce Korotkov, Alexander N. Kostritsa, Fedor Kreikebaum, John Mark Kurilovich, Vladislav D. Landhuis, David Lange-Dei, Tiano Langley, Brandon W. Laptev, Pavel Lau, Kim-Ming Ledford, Justin Lee, Kenny Lester, Brian J. Le Guevel, Loïck Li, Wing Yan Li, Yin Lill, Alexander T. Livingston, William P. Locharla, Aditya Lucero, Erik L Google Research Mountain ViewCA United States Department of Physics ETH Zurich Switzerland Google DeepMind London United Kingdom Department of Electrical and Computer Engineering University of Massachusetts AmherstMA United States Department of Physics University of Connecticut StorrsCT United States Department of Computer Science University of California Santa BarbaraCA United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Department of Physics Massachusetts Institute of Technology CambridgeMA United States

Quantum error correction [1-6] is essential for bridging the gap between the error rates of physical devices and the extremely low logical error rates required for quantum algorithms. Recent error-correction demonstrations on superconducting processors [7-13] have focused primarily on the surface code [14], which offers a high error threshold but poses limitations for logical operations. In contrast, the color code [15] enables much more efficient logic, although it requires more complex stabilizer measurements and decoding techniques. Measuring these stabilizers in planar architectures such as superconducting qubits is challenging, and so far, realizations of color codes [16-24] have not addressed performance scaling with code size on any platform. Here, we present a comprehensive demonstration of the color code on a superconducting processor [12], achieving logical error suppression and performing logical operations. Scaling the code distance from three to five suppresses logical errors by a factor of Λ3/5 = 1.56(4). Simulations indicate this performance is below the threshold of the color code, and furthermore that the color code may be more efficient than the surface code with modest device improvements. Using logical randomized benchmarking [25], we find that transversal Clifford gates add an error of only 0.0027(3), which is substantially less than the error of an idling error correction cycle. We inject magic states [26], a key resource for universal computation, achieving fidelities exceeding 99 % with post-selection (retaining about 75 % of the data). Finally, we successfully teleport logical states between distance-three color codes using lattice surgery [27], with teleported state fidelities between 86.5(1) % and 90.7(1) %. This work establishes the color code as a compelling research direction to realize fault-tolerant quantum computation on superconducting processors in the near future. © 2024, CC BY.

关键词： Color codes