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arXiv 2024年

作者： Sorensen, Taylor Moore, Jared Fisher, Jillian Gordon, mitchell Mireshghallah, Niloofar Rytting, Christopher Michael Ye, Andre Jiang, Liwei Lu, Ximing Dziri, Nouha Althoff, Tim Choi, Yejin Department of Computer Science University of Washington SeattleWA United States Department of Computer Science Stanford University StanfordCA United States Department of Statistics University of Washington SeattleWA United States Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Allen Institute for Artificial Intelligence SeattleWA United States

With increased power and prevalence of AI systems, it is ever more critical that AI systems are designed to serve all, i.e., people with diverse values and perspectives. However, aligning models to serve pluralistic human values remains an open research question. In this piece, we propose a roadmap to pluralistic alignment, specifically using large language models as a test bed. We identify and formalize three possible ways to define and operationalize pluralism in AI systems: 1) Overton pluralistic models that present a spectrum of reasonable responses;2) Steerably pluralistic models that can steer to reflect certain perspectives;and 3) Distributionally pluralistic models that are well-calibrated to a given population in distribution. We also formalize and discuss three possible classes of pluralistic benchmarks: 1) Multi-objective benchmarks, 2) Tradeoff steerable benchmarks that incentivize models to steer to arbitrary trade-offs, and 3) Jury-pluralistic benchmarks that explicitly model diverse human ratings. We use this framework to argue that current alignment techniques may be fundamentally limited for pluralistic AI;indeed, we highlight empirical evidence, both from our own experiments and from other work, that standard alignment procedures might reduce distributional pluralism in models, motivating the need for further research on pluralistic alignment. © 2024, CC BY.

关键词： Inflation

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Product states optimize quantum p-spin models for large p

arXiv

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arXiv 2023年

作者： Anschuetz, Eric R. Gamarnik, David Kiani, Bobak T. Institute for Quantum Information and Matter Caltech Walter Burke Institute for Theoretical Physics Caltech MIT Center for Theoretical Physics MIT United States Operations Research Center Statistics and Data Science Center Sloan School of Management MIT United States John A. Paulson School of Engineering and Applied Sciences Harvard Department of Electrical Engineering and Computer Science MIT United States

We consider the problem of estimating the maximal energy of quantum p-local spin glass random Hamiltonians, the quantum analogues of widely studied classical spin glass models. Denoting by E∗(p) the (appropriately normalized) maximal energy in the limit of a large number of qubits n, we show that E∗(p) approaches √2 log 6 as p increases. This value is interpreted as the maximal energy of a much simpler so-called Random Energy Model, widely studied in the setting of classical spin glasses. Our most notable and (arguably) surprising result proves the existence of near-maximal energy states which are product states, and thus not entangled. Specifically, we prove that with high probability as n → ∞, for any E ∗(p) there exists a product state with energy ≥ E at sufficiently large constant p. Even more surprisingly, this remains true even when restricting to tensor products of Pauli eigenstates. Our approximations go beyond what is known from monogamy-of-entanglement style arguments—the best of which, in this normalization, achieve approximation error growing with n. Our results not only challenge prevailing beliefs in physics that extremely low-temperature states of random local Hamiltonians should exhibit non-negligible entanglement, but they also imply that classical algorithms can be just as effective as quantum algorithms in optimizing Hamiltonians with large locality—though performing such optimization is still likely a hard problem. Our results are robust with respect to the choice of the randomness (disorder) and apply to the case of sparse random Hamiltonian using Lindeberg’s interpolation method. The proof of the main result is obtained by estimating the expected trace of the associated partition function, and then matching its asymptotics with the extremal energy of product states using the second moment method. Copyright © 2023, The Authors. All rights reserved.

关键词： Hamiltonians

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A General Security Approach for Soft-information Decoding against Smart Bursty Jammers

arXiv

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arXiv 2022年

作者： Ercan, Furkan Galligan, Kevin Duffy, Ken R. Médard, Muriel Starobinski, David Yazicigil, Rabia Tugce Department of Electrical and Computer Engineering Boston University BostonMA United States Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Hamilton Institute Maynooth University Ireland

Malicious attacks such as jamming can cause significant disruption or complete denial of service (DoS) to wireless communication protocols. Moreover, jamming devices are getting smarter, making them difficult to detect. Forward error correction, which adds redundancy to data, is commonly deployed to protect communications against the deleterious effects of channel noise. Soft-information error correction decoders obtain reliability information from the receiver to inform their decoding, but in the presence of a jammer such information is misleading and results in degraded error correction performance. As decoders assume noise occurs independently to each bit, a bursty jammer will lead to greater degradation in performance than a non-bursty one. Here we establish, however, that such temporal dependencies can aid inferences on which bits have been subjected to jamming, thus enabling counter-measures. In particular, we introduce a pre-decoding processing step that updates log-likelihood ratio (LLR) reliability information to reflect inferences in the presence of a jammer, enabling improved decoding performance for any soft detection decoder. The proposed method requires no alteration to the decoding algorithm. Simulation results show that the method correctly infers a significant proportion of jamming in any received frame. Results with one particular decoding algorithm, the recently introduced ORBGRAND, show that the proposed method reduces the block-error rate (BLER) by an order of magnitude for a selection of codes, and prevents complete DoS at the receiver. Copyright © 2022, The Authors. All rights reserved.

关键词： Decoding

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Cryptanalysis via Machine Learning Based Information Theoretic Metrics

arXiv

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arXiv 2025年

作者： Kim, Benjamin D. Vasudevan, Vipindev Adat D’Oliveira, Rafael G.L. Cohen, Alejandro Stahlbuhk, Thomas Médard, Muriel Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign ChampaignIL61820 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States School of Mathematical and Statistical Sciences Clemson University ClemsonSC29631 United States Technion Faculty of Electrical Engineering Technion Israel Institute of Technology Haifa32000 Israel MIT Lincoln Laboratory LexingtonMA02421 United States

The fields of machine learning (ML) and cryptanalysis share an interestingly common objective of creating a function, based on a given set of inputs and outputs. However, the approaches and methods in doing so vary vastly between the two fields. In this paper, we explore integrating the knowledge from the ML domain to provide empirical evaluations of cryptosystems. Particularly, we utilize information theoretic metrics to perform ML-based distribution estimation. We propose two novel applications of ML algorithms that can be applied in a known plaintext setting to perform cryptanalysis on any cryptosystem. We use mutual information neural estimation to calculate a cryptosystem’s mutual information leakage, and a binary cross entropy classification to model an indistinguishability under chosen plaintext attack (CPA). These algorithms can be readily applied in an audit setting to evaluate the robustness of a cryptosystem and the results can provide a useful empirical bound. We evaluate the efficacy of our methodologies by empirically analyzing several encryption schemes. Furthermore, we extend the analysis to novel network coding-based cryptosystems and provide other use cases for our algorithms. We show that our classification model correctly identifies the encryption schemes that are not IND-CPA secure, such as DES, RSA, and AES ECB, with high accuracy. It also identifies the faults in CPA-secure cryptosystems with faulty parameters, such a reduced counter version of AES-CTR. We also conclude that with our algorithms, in most cases a smaller-sized neural network using less computing power can identify vulnerabilities in cryptosystems, providing a quick check of the sanity of the cryptosystem and help to decide whether to spend more resources to deploy larger networks that are able to break the cryptosystem. © 2025, CC BY-NC-ND.

关键词： Information leakage

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A Dataset of the Operating Station Heat Rate for 806 Indian Coal Plant Units using Machine Learning

arXiv

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arXiv 2024年

作者： Ding, Yifu Wong, Jansen Patel, Serena Mallapragada, Dharik Zang, Guiyan Stoner, Robert MIT Energy Initiative Massachusetts Institute of Technology United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States The Brattle Group United States Department of Chemical and Biomolecular Engineering Tandon School of Engineering New York University United States

India aims to achieve net-zero emissions by 2070 and has set an ambitious target of 500 GW of renewable power generation capacity by 2030. Coal plants currently contribute to more than 60% of India's electricity generation in 2022. Upgrading and decarbonizing high-emission coal plants became a pressing energy issue. A key technical parameter for coal plants is the operating station heat rate (SHR), which represents the thermal efficiency of a coal plant. Yet, the operating SHR of Indian coal plants varies and is not comprehensively documented. This study extends from several existing databases and creates an SHR dataset for 806 Indian coal plant units using machine learning (ML), presenting the most comprehensive coverage to date. Additionally, it incorporates environmental factors such as water stress risk and coal prices as prediction features to improve accuracy. This dataset, easily downloadable from our visualization platform, could inform energy and environmental policies for India's coal power generation as the country transitions towards its renewable energy targets. © 2024, CC BY.

关键词： Fossil fuel power plants

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Flexible mapping of abstract domains by grid cells via self-supervised extraction and projection of generalized velocity signals 24

Flexible mapping of abstract domains by grid cells via self-...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Abhiram Iyer Sarthak Chandra Sugandha Sharma Ila Fiete Department of Electrical Engineering and Computer Science MIT Cambridge MA and McGovern Institute for Brain Research MIT Cambridge MA and K. Lisa Yang Integrative Computational Neuroscience (ICoN) MIT Cambridge MA Department of Brain and Cognitive Sciences MIT Cambridge MA and McGovern Institute for Brain Research MIT Cambridge MA Department of Brain and Cognitive Sciences MIT Cambridge MA and McGovern Institute for Brain Research MIT Cambridge MA and K. Lisa Yang Integrative Computational Neuroscience (ICoN) MIT Cambridge MA

ISBN: (纸本)9798331314385

Grid cells in the medial entorhinal cortex create remarkable periodic maps of explored space during navigation. Recent studies show that they form similar maps of abstract cognitive spaces. Examples of such abstract environments include auditory tone sequences in which the pitch is continuously varied or images in which abstract features are continuously deformed (e.g., a cartoon bird whose legs stretch and shrink). Here, we hypothesize that the brain generalizes how it maps spatial domains to mapping abstract spaces. To sidestep the computational cost of learning representations for each high-dimensional sensory input, the brain extracts self-consistent, low-dimensional descriptions of displacements across abstract spaces, leveraging the spatial velocity integration of grid cells to efficiently build maps of different domains. Our neural network model for abstract velocity extraction factorizes the content of these abstract domains from displacements within the domains to generate content-independent and self-consistent, low-dimensional velocity estimates. Crucially, it uses a self-supervised geometric consistency constraint that requires displacements along closed loop trajectories to sum to zero, an integration that is itself performed by the downstream grid cell circuit over learning. This process results in high fidelity estimates of velocities and allowed transitions in abstract domains, a crucial prerequisite for efficient map generation in these high-dimensional environments. We also show how our method outperforms traditional dimensionality reduction and deep-learning based motion extraction networks on the same set of tasks. This is the first neural network model to explain how grid cells can flexibly represent different abstract spaces and makes the novel prediction that they should do so while maintaining their population correlation and manifold structure across domains. Fundamentally, our model sheds light on the mechanistic origins of cognitive flexib

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Four-dimensional conserved topological charge vectors in plasmonic quasicrystals

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science (New York, N.Y.) 2025年第6734期387卷 644-648页

作者： Tsesses, Shai Dreher, Pascal Janoschka, David Neuhaus, Alexander Cohen, Kobi Meiler, Tim C. Bucher, Tomer Sapir, Shay Frank, Bettina Davis, Timothy J. Meyer Zu Heringdorf, Frank Giessen, Harald Bartal, Guy rew and Erna Viterbi Department of Electrical and Computer Engineering Technion - Israel Institute of Technology Haifa Israel Department of Physics MIT-Harvard Center for Ultracold Atoms and Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge MA United States Faculty of Physics and Center for Nanointegration University of Duisburg-Essen Duisburg-Essen (CENIDE) Duisburg Germany 4th Physics Institute Research Center SCoPE Integrated Quantum Science and Technology Center University of Stuttgart Stuttgart Germany Centre for Disruptive Photonic Technologies and School of Physical and Mathematical Sciences Nanyang Technological University Singapore Faculty of Mathematics and Computer Science Weitzmann Institute of Science Rehovot Israel School of Physics University of Melbourne Parkville VIC Australia

According to Noether's theorem, symmetries in a physical system are intertwined with conserved quantities. These symmetries often determine the system topology, which is made ever more complex with increased dimensionality. Quasicrystals have neither translational nor global rotational symmetry, yet they intrinsically inhabit a higher-dimensional space in which symmetry resurfaces. Here, we discovered topological charge vectors in four dimensions (4D) that govern the real-space topology of 2D quasicrystals and reveal their inherent conservation laws. We demonstrate control over the topology in pentagonal plasmonic quasilattices, mapped by both phase-resolved and time-domain near-field microscopy, showing that their temporal evolution continuously tunes the 2D projections of their distinct 4D topologies. Our work provides a route to experimentally probe the thermodynamic properties of quasicrystals and topological physics in 4D and above.

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Topological superconductors from a materials perspective

arXiv

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arXiv 2023年

作者： Mandal, Manasi Drucker, Nathan C. Siriviboon, Phum Nguyen, Thanh Boonkird, Tiya Lamichhane, Tej Nath Okabe, Ryotaro Chotrattanapituk, Abhijatmedhi Li, Mingda Quantum Measurement Group MIT CambridgeMA02139 United States Department of Nuclear Science and Engineering MIT CambridgeMA02139 United States School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Physics MIT CambridgeMA02139 United States Department of Chemistry MIT CambridgeMA02139 United States Department of Electrical Engineering and Computer Science MIT CambridgeMA02139 United States

Topological superconductors (TSCs) have garnered significant research and industry attention in the past two decades. By hosting Majorana bound states which can be used as qubits that are robust against local perturbations, TSCs offer a promising platform toward (non-universal) topological quantum computation. However, there has been a scarcity of TSC candidates, and the experimental signatures that identify a TSC are often elusive. In this perspective, after a short review of the TSC basics and theories, we provide an overview of the TSC materials candidates, including natural compounds and synthetic material systems. We further introduce various experimental techniques to probe TSC, focusing on how a system is identified as a TSC candidate, and why a conclusive answer is often challenging to draw. We conclude by calling for new experimental signatures and stronger computational support to accelerate the search for new TSC candidates. © 2023, CC BY.

关键词： Quantum computers

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Leveraging AES Padding: dBs for Nothing and FEC for Free in IoT Systems

arXiv

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arXiv 2024年

作者： Woo, Jongchan Vasudevan, Vipindev Adat Kim, Benjamin D. D'Oliveira, Rafael G.L. Cohen, Alejandro Stahlbuhk, Thomas Duffy, Ken R. Médard, Muriel The Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign ChampaignIL61820 United States The School of Mathematical and Statistical Sciences Clemson University ClemsonSC29631 United States The Technion Faculty of Electrical Engineering Technion Israel Institute of Technology Haifa32000 Israel MIT Lincoln Laboratory LexingtonMA02421 United States The Department of Electrical & Computer Engineering The Department of Mathematics Boston University BostonMA02215 United States

The Internet of Things (IoT) represents a significant advancement in digital technology, with its rapidly growing network of interconnected devices. This expansion, however, brings forth critical challenges in data security and reliability, especially under the threat of increasing cyber vulnerabilities. Addressing the security concerns, the Advanced Encryption Standard (AES) is commonly employed for secure encryption in IoT systems. Our study explores an innovative use of AES, by repurposing AES padding bits for error correction and thus introducing a dual-functional method that seamlessly integrates error-correcting capabilities into the standard encryption process. The integration of the state-of-the-art Guessing Random Additive Noise Decoder (GRAND) in the receiver's architecture facilitates the joint decoding and decryption process. This strategic approach not only preserves the existing structure of the transmitter but also significantly enhances communication reliability in noisy environments, achieving a notable over 3 dB gain in Block Error Rate (BLER). Remarkably, this enhanced performance comes with a minimal power overhead at the receiver-less than 15% compared to the traditional decryption-only process, underscoring the efficiency of our hardware design for IoT applications. This paper discusses a comprehensive analysis of our approach, particularly in energy efficiency and system performance, presenting a novel and practical solution for reliable IoT communications. © 2024, CC BY-NC-ND.

关键词： Internet of things

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Flexible Modeling and Multitask Learning using Differentiable Tree Ensembles

arXiv

引用

arXiv 2022年

作者： Ibrahim, Shibal Hazimeh, Hussein Mazumder, Rahul MIT Department of Electrical Engineering and Computer Science United States MIT Operations Research Center United States MIT Sloan School of Management Operations Research Center MIT Center for Statistics United States

Decision tree ensembles are widely used and competitive learning models. Despite their success, popular toolkits for learning tree ensembles have limited modeling capabilities. For instance, these toolkits support a limited number of loss functions and are restricted to single task learning. We propose a flexible framework for learning tree ensembles, which goes beyond existing toolkits to support arbitrary loss functions, missing responses, and multi-task learning. Our framework builds on differentiable (a.k.a. soft) tree ensembles, which can be trained using first-order methods. However, unlike classical trees, differentiable trees are difficult to scale. We therefore propose a novel tensor-based formulation of differentiable trees that allows for efficient vectorization on GPUs. We perform experiments on a collection of 28 real open-source and proprietary datasets, which demonstrate that our framework can lead to 100x more compact and 23% more expressive tree ensembles than those by popular toolkits. © 2022, CC BY.

关键词： Decision trees

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