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Noisy dynamical systems evolve error correcting codes and modularity

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arXiv 2023年

作者： McCourt, Trevor Fiete, Ila R. Chuang, Isaac L. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions CambridgeMA02139 United States Department of Brain and Cognitive Sciences MIT CambridgeMA United States McGovern Institute for Brain Research Department of Brain and Cognitive Sciences Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States

Noise is a ubiquitous feature of the physical world. As a result, the first prerequisite of life is fault tolerance: maintaining integrity of state despite external bombardment. Recent experimental advances have revealed that biological systems achieve fault tolerance by implementing mathematically intricate error-correcting codes and by organizing in a modular fashion that physically separates functionally distinct subsystems. These elaborate structures represent a vanishing volume in the massive genetic configuration space. How is it possible that the primitive process of evolution, by which all biological systems evolved, achieved such unusual results? In this work, through experiments in Boolean networks, we show that the simultaneous presence of error correction and modularity in biological systems is no coincidence. Rather, it is a typical co-occurrence in noisy dynamic systems undergoing evolution. From this, we deduce the principle of error correction enhanced evolvability: systems possessing error-correcting codes are more effectively improved by evolution than those without. Copyright © 2023, The Authors. All rights reserved.

关键词： Error correction

Quantum Noise Spectroscopy of Criticality in an Atomically Thin Magnet

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arXiv 2024年

作者： Ziffer, Mark E. Machado, Francisco Ursprung, Benedikt Lozovoi, Artur Tazi, Aya Batoul Yuan, Zhiyang Ziebel, Michael E. Delord, Tom Zeng, Nanyu Telford, Evan Chica, Daniel G. deQuilettes, Dane W. Zhu, Xiaoyang Hone, James C. Shepard, Kenneth L. Roy, Xavier de Leon, Nathalie P. Davis, Emily J. Chatterjee, Shubhayu Meriles, Carlos A. Owen, Jonathan S. Schuck, P. James Pasupathy, Abhay N. Department of Physics Columbia University New YorkNY10027 United States Department of Mechanical Engineering Columbia University New YorkNY10027 United States Department of Chemistry Columbia University New YorkNY10027 United States ITAMP Harvard-Smithsonian Center for Astrophysics CambridgeMA02138 United States Department of Physics Harvard University CambridgeMA02138 United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ08540 United States Department of Physics CUNY The City College of New York New YorkNY10031 United States Department of Electrical Engineering Columbia University New YorkNY10027 United States Lincoln Laboratory MIT LexingtonMA02421 United States Center for Quantum Engineering MIT CambridgeMA02139 United States Department of Physics New York University New YorkNY10003 United States Department of Physics Carnegie Mellon University PittsburghPA15213 United States Condensed Matter Physics and Materials Science Department Brookhaven National Laboratory UptonNY11973 United States

Dynamic critical fluctuations in magnetic materials encode important information about magnetic ordering in the associated critical exponents. Using nitrogen-vacancy centers in diamond, we implement T2 (spin-decoherence) noise magnetometry to study critical dynamics in a 2D Van der Waals magnet CrSBr. By analyzing NV decoherence on time scales approaching the characteristic correlation time τc of critical fluctuations, we extract the critical exponent ν for the correlation length. Our result deviates from the Ising prediction and highlights the role of long-range dipolar interactions in 2D CrSBr. Furthermore, analyzing the divergence of the correlation length suggests the possibility of 2D-XY criticality in CrSBr in a temperature window near TC where static magnetic domains are absent. Our work provides a first demonstration of T2 noise magnetometry to quantitatively analyze critical scaling behavior in 2D materials. © 2024, CC BY.

关键词： Quantum noise

The effect of surface oxidation and crystal thickness on magnetic properties and magnetic domain structures of Cr2Ge2Te6

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arXiv 2023年

作者： Thomsen, Joachim Dahl Han, Myung-Geun Penn, Aubrey Foucher, Alexandre C. Geiwitz, Michael Burch, Kenneth S. Dekanovsky, Lukas Sofer, Zdenek Liu, Yu Petrovic, Cedomir Ross, Frances M. Zhu, Yimei Narang, Prineha Division of Physical Sciences College of Letters and Science University of California Los AngelesCA90095 United States Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Condensed Matter Physics and Materials Science Division Brookhaven National Laboratory UptonNY11973 United States MIT.nano Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Boston College Chestnut HillMA02467 United States Department of Inorganic Chemistry University of Chemistry and Technology Prague Technická 5 Prague 6166 28 Czech Republic Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States

Van der Waals (vdW) magnetic materials such as Cr2Ge2Te6 (CGT) show promise for novel memory and logic applications. This is due to their broadly tunable magnetic properties and the presence of topological magnetic features such as skyrmionic bubbles. A systematic study of thickness and oxidation effects on magnetic domain structures is important for designing devices and vdW heterostructures for practical applications. Here, we investigate thickness effects on magnetic properties, magnetic domains, and bubbles in oxidation-controlled CGT crystals. We find that CGT exposed to ambient conditions for 5 days forms an oxide layer approximately 5 nm thick. This oxidation leads to a significant increase in the oxidation state of the Cr ions, indicating a change in local magnetic properties. This is supported by real space magnetic texture imaging through Lorentz transmission electron microscopy. By comparing the thickness dependent saturation field of oxidized and pristine crystals, we find that oxidation leads to a non-magnetic surface layer which is thicker than the oxide layer alone. We also find that the stripe domain width and skyrmionic bubble size are strongly affected by the crystal thickness in pristine crystals. These findings underscore the impact of thickness and surface oxidation on the properties of CGT such as saturation field and domain/skyrmionic bubble size and suggest a pathway for manipulating magnetic properties through a controlled oxidation process. © 2023, CC BY.

关键词： Magnetic properties

Single-Shot Matrix-Matrix Multiplication Optical Tensor Processor for Deep Learning

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arXiv 2025年

作者： Luan, Chao Davis, Ronald Chen, Zaijun Englund, Dirk Hamerly, Ryan Research Laboratory of Electronics MIT CambridgeMA02139 United States Department of Electrical Engineering and Computer Science University of California BerkeleyCA United States NTT Research Inc. PHI Laboratories 940 Stewart Drive SunnyvaleCA94085 United States

The ever-increasing data demand craves advancements in high-speed and energy-efficient computing hardware. Analog optical neural network (ONN) processors have emerged as a promising solution, offering benefits in bandwidth and energy consumption. However, existing ONN processors exhibit limited computational parallelism, and while certain architectures achieve high parallelism, they encounter serious scaling roadblocks for large-scale implementation. This restricts the throughput, latency, and energy efficiency advantages of ONN processors. Here, we introduce a spatial-wavelength-temporal hyper-multiplexed ONN processor that supports high data dimensionality, high computing parallelism and is feasible for large-scale implementation, and in a single time step, a three-dimensional matrix-matrix multiplication (MMM) optical tensor processor is demonstrated. Our hardware accelerates convolutional neural networks (CNNs) and deep neural networks (DNNs) through parallel matrix multiplication. We demonstrate benchmark image recognition using a CNN and a subsequently fully connected DNN in the optical domain. The network works with 292,616 weight parameters under ultra-low optical energy of ≈ 20 attojoules (aJ) per multiply and accumulate (MAC) at 96.4% classification accuracy. The system supports broad spectral and spatial bandwidths and is capable for large-scale demonstration, paving the way for highly efficient large-scale optical computing for next-generation deep learning. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

Analyzing Student Code Trajectories in an Introductory Programming MOOC

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Analyzing Student Code Trajectories in an Introductory Progr...

Learning With MOOCS (LWMOOCS)

作者： Ayesha Bajwa Ana Bell Erik Hemberg Una-May O’Reilly MIT CSAIL MIT Department of Electrical Engineering and Computer Science

ISBN: (纸本)9781728125503

Understanding student behavior in Massive Open Online Courses (MOOCs) can help us make online learning more beneficial for students. We investigate student code trajectories on the individual problem level in an mitx MOOC teaching introductory programming in Python, using keyword occurrence features associated with code submissions to represent these trajectories. Since code is so problem-specific, we develop gold standard solutions for comparison. Anecdotal observations on individual student trajectories reveal distinct behaviors which may correlate with prior experience level. We build models to correlate these trajectories with student characteristics and behaviors of interest, specifically prior experience level and video engagement. Generative modeling allows us to probe the space of submitted solutions and trajectories and explore these correlations.

关键词： Trajectory Python Electronic learning Correlation Gold Standards computer aided instruction

Evaluating Offshore Electricity Market Design Considering Endogenous Infrastructure Investments: Zonal or Nodal?

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arXiv 2024年

作者： Kenis, Michiel Dvorkin, Vladimir Schittekatte, Tim Bruninx, Kenneth Delarue, Erik Botterud, Audun Leuven3001 Belgium The EnergyVille Thor Park 8310 Genk3600 Belgium Boeretang 200 MolB-2400 Belgium The Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States The MIT Sloan School of Management Massachusetts Institute of Technology CambridgeMA02142 United States The Florence School of Regulation European University Institute FI Firenze50133 Italy The Faculty of Technology Policy and Management TU Delft Delft2628 Netherlands The Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02142 United States

Policy makers are formulating offshore energy infrastructure plans, including wind turbines, electrolyzers, and HVDC transmission lines. An effective market design is crucial to guide cost-efficient investments and dispatch decisions. This paper jointly studies the impact of offshore market design choices on the investment in offshore electrolyzers and HVDC transmission capacity. We present a bilevel model that incorporates investments in offshore energy infrastructure, day-ahead market dispatch, and potential redispatch actions near real-time to ensure transmission constraints are respected. Our findings demonstrate that full nodal pricing, i.e., nodal pricing both onshore and offshore, outperforms the onshore zonal combined with offshore nodal pricing or offshore zonal layouts. While combining onshore zonal with offshore nodal pricing can be considered as a second-best option, it generally diminishes the profitability of offshore wind farms. However, if investment costs of offshore electrolyzers are relatively low, they can serve as catalysts to increase the revenues of the offshore wind farms. This study contributes to the understanding of market designs for highly interconnected offshore power systems, offering insights into the impact of congestion pricing methodologies on investment decisions. Besides, it is useful towards understanding the interaction of offshore loads like electrolyzers with financial support mechanisms for offshore wind farms. © 2024, CC BY.

关键词： Offshore wind farms

How Student Background and Topic Impact the Doer Effect in Computational Thinking MOOCs

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How Student Background and Topic Impact the Doer Effect in C...

Learning With MOOCS (LWMOOCS)

作者： Jitesh Maiyuran Ayesha Bajwa Ana Bell Erik Hemberg Una-May O’Reilly MIT CSAIL MIT Department of Electrical Engineering and Computer Science

The following topics are dealt with: computer aided instruction; educational courses; educational institutions; teaching; further education; distance learning; computer science education; Internet; gender issues; biom... 详细信息

ISBN: (纸本)9781728125503

关键词： Programming Electronic learning computer aided instruction Encoding Computational modeling Linear regression

Adversarial robustness guarantees for random deep neural networks

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arXiv 2020年

作者： de Palma, Giacomo Kiani, Bobak T. Lloyd, Seth Scuola Normale Superiore Pisa Italy Department of Mechanical Engineering MIT CambridgeMA United States Research Laboratory of Electronics MIT CambridgeMA United States Department of Electrical Engineering & Computer Science MIT CambridgeMA United States

The reliability of deep learning algorithms is fundamentally challenged by the existence of adversarial examples, which are incorrectly classified inputs that are extremely close to a correctly classified input. We explore the properties of adversarial examples for deep neural networks with random weights and biases, and prove that for any p ≥ 1, the lp distance of any given input from the classification boundary scales as one over the square root of the dimension of the input times the lp norm of the input. The results are based on the recently proved equivalence between Gaussian processes and deep neural networks in the limit of infinite width of the hidden layers, and are validated with experiments on both random deep neural networks and deep neural networks trained on the MNIST and CIFAR10 datasets. The results constitute a fundamental advance in the theoretical understanding of adversarial examples, and open the way to a thorough theoretical characterization of the relation between network architecture and robustness to adversarial perturbations. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

Centralized vs decentralized targeted brute-force attacks: Guessing with side-information

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arXiv 2020年

作者： Salamatian, Salman Huleihel, Wasim Beirami, Ahmad Cohen, Asaf Médard, Muriel department of Electrical Engineering and Computer Science MIT CambridgeMA02139 United States Department of Electrical Engineering-Systems Tel-Aviv University Tel-Aviv6997801 Israel department of Electrical Engineering Ben-Gurion University of the Negev 8410501 Israel

According to recent empirical studies, a majority of users have the same, or very similar, passwords across multiple password-secured online services. This practice can have disastrous consequences, as one password being compromised puts all the other accounts at much higher risk. Generally, an adversary may use any side-information he/she possesses about the user, be it demographic information, password reuse on a previously compromised account, or any other relevant information to devise a better brute-force strategy (so called targeted attack). In this work, we consider a distributed brute-force attack scenario in which m adversaries, each observing some side information, attempt breaching a password secured system. We compare two strategies: an uncoordinated attack in which the adversaries query the system based on their own side-information until they find the correct password, and a fully coordinated attack in which the adversaries pool their side-information and query the system together. For passwords X of length n, generated independently and identically from a distribution PX, we establish an asymptotic closed-form expression for the uncoordinated and coordinated strategies when the side-information Y(m) are generated independently from passing X through a memoryless channel PY |X, as the length of the password n goes to infinity. We illustrate our results for binary symmetric channels and binary erasure channels, two families of side-information channels which model password reuse. We demonstrate that two coordinated agents perform asymptotically better than any finite number of uncoordinated agents for these channels, meaning that sharing side-information is very valuable in distributed attacks. Copyright © 2020, The Authors. All rights reserved.

关键词： Authentication