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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

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

ISBN: (纸本)9781665459754

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 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. © 2022 IEEE.

关键词： Decoding

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Topology Optimization in Three-Phase C-Core SRMs

arXiv

引用

arXiv 2024年

作者： Davarpanah, Gholamreza Mohammadi, Sajjad Kirtley, James L. Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Department of Electrical Engineering and Computer Science MIT Cambridge United States

This paper investigates the optimized combination of rotor and stator teeth in a three-phase switched reluctance motor featuring a connected C-core topology to attain a larger winding area and, thus, a higher electrical loading capability, leading to a higher torque density. The general formulation is discussed. Additionally, the shorter flux path within the design leads to a reduced core loss. The finite element method is employed in the design and field analysis. A comparison with a conventional motor shows the superiority of the proposed configuration. The optimized design is prototyped and tested. Both static and dynamic torques are extracted. A good correlation between simulation and experimental results is observed. Copyright © 2024, The Authors. All rights reserved.

关键词： Topology

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A New Switched Reluctance Motor with Embedded Permanent Magnets for Transportation Electrification

arXiv

引用

arXiv 2024年

作者： Davarpanah, Gholamreza Mohammadi, Sajjad Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Department of Electrical Engineering and Computer Science MIT Cambridge United States

A new three-phase hybrid-excited multi-tooth switched reluctance motor with embedded permanent magnets is proposed, capable of achieving higher torque density for transportation electrification applications. Operating principles and design considerations are discussed. A magnetic equivalent circuit is developed. Finite element method is employed in the field analysis. The advantages of the proposed topology over existing designs for switched reluctance motors and flux switching motors are presented. Finally, the optimized design is prototyped to experimentally confirm the design and simulation results. Copyright © 2024, The Authors. All rights reserved.

关键词： Permanent magnets

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Predictive Chemistry Augmented with Text Retrieval

arXiv

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arXiv 2023年

作者： Qian, Yujie Li, Zhening Tu, Zhengkai Coley, Connor W. Barzilay, Regina Computer Science and Artificial Intelligence Lab MIT United States Department of Electrical Engineering and Computer Science MIT United States Department of Chemical Engineering MIT United States

This paper focuses on using natural language descriptions to enhance predictive models in the chemistry field. Conventionally, chemoinformatics models are trained with extensive structured data manually extracted from the literature. In this paper, we introduce TextReact, a novel method that directly augments predictive chemistry with texts retrieved from the literature. TextReact retrieves text descriptions relevant for a given chemical reaction, and then aligns them with the molecular representation of the reaction. This alignment is enhanced via an auxiliary masked LM objective incorporated in the predictor training. We empirically validate the framework on two chemistry tasks: reaction condition recommendation and one-step retrosynthesis. By leveraging text retrieval, TextReact significantly outperforms state-of-the-art chemoinformatics models trained solely on molecular data. © 2023, CC BY.

关键词： Information retrieval

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Optimal compound downselection to promote diversity and parallel chemistry

arXiv

引用

arXiv 2025年

作者： Fromer, Jenna C. Volkova, Alexandra D. Coley, Connor W. Department of Chemical Engineering MIT CambridgeMA02139 United States Department of Electrical Engineering and Computer Science MIT CambridgeMA02139 United States

Early stage drug discovery and molecular design projects often follow iterative design-make-test cycles. The selection of which compounds to synthesize from all possible candidate compounds is a complex decision inherent to these design cycles that must weigh multiple factors. We build upon the algorithmic downselection framework SPARROW that considers synthetic cost, synthetic feasibility, and compound utility, extending it to address additional critical factors related to the risk of synthesis failure, molecular diversity, and parallel chemistry capabilities. These design considerations further align algorithmic compound selection with the true complexity of this decision-making process, allowing SPARROW to capture a broader set of principles typically reliant on expert chemist intuition. The application of these formulations to an exemplary case study highlights SPARROW’s ability to promote the selection of diverse batches of compounds whose syntheses are amenable to parallel chemistry. Copyright © 2025, The Authors. All rights reserved.

关键词： Synthesis (chemical)

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Strong Low Degree Hardness for Stable Local Optima in Spin Glasses

arXiv

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arXiv 2025年

作者： Huang, Brice Sellke, Mark Department of Electrical Engineering and Computer Science MIT United States Department of Statistics Harvard University United States

It is a folklore belief in the theory of spin glasses and disordered systems that out-of-equilibrium dynamics fail to find stable local optima exhibiting e.g. local strict convexity on physical time-scales. In the context of the Sherrington–Kirkpatrick spin glass, [BAKZ22, MSS24] have recently conjectured that this obstruction may be inherent to all efficient algorithms, despite the existence of exponentially many such optima throughout the landscape. We prove this search problem exhibits strong low degree hardness for polynomial algorithms of degree D ≤ o(N): any such algorithm has probability o(1) to output a stable local optimum. To the best of our knowledge, this is the first result to prove that even constant-degree polynomials have probability o(1) to solve a random search problem without planted structure. To prove this, we develop a general-purpose enhancement of the ensemble overlap gap property, and as a byproduct improve previous results on spin glass optimization, maximum independent set, random k-SAT, and the Ising perceptron to strong low degree hardness. Finally for spherical spin glasses with no external field, we prove that Langevin dynamics does not find stable local optima within dimension-free time. Copyright © 2025, The Authors. All rights reserved.

关键词： Spin glass

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Latent Lab: Large Language Models for Knowledge Exploration

arXiv

引用

arXiv 2023年

作者： Dunnell, Kevin Painter, Trudy Stoddard, Andrew Lippman, Andy Department of Electrical Engineering and Computer Science MIT United States MIT Media Lab United States

This paper investigates the potential of AI models, particularly large language models (LLMs), to support knowledge exploration and augment human creativity during ideation. We present "Latent Lab" an interactive tool for discovering connections among mit Media Lab research projects, emphasizing "exploration" over search. The work offers insights into collaborative AI systems by addressing the challenges of organizing, searching, and synthesizing content. In a user study, the tool's success was evaluated based on its ability to introduce users to an unfamiliar knowledge base, ultimately setting the groundwork for the ongoing advancement of human-AI knowledge exploration systems. © 2023, CC BY-NC-SA.

关键词： Knowledge based systems

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Algorithms for Multiple Drone-Delivery Scheduling Problem (MDSP)

arXiv

引用

arXiv 2023年

作者： Anupam, Sagnik Lu, Nicole Sragow, John Department of Electrical Engineering and Computer Science MIT CambridgeMA United States

The Multiple Drone-Delivery Scheduling Problem (MDSP) is a scheduling problem that optimizes the maximum reward earned by a set of m drones executing a sequence of deliveries on a truck delivery route. The current best-known approximation algorithm for the problem is a 4\1 -approximation algorithm developed by Jana and Mandal (2022). In this paper, we propose exact and approximation algorithms for the general MDSP, as well as a unit-cost variant. We first propose a greedy algorithm which we show to be a 1/3 -approximation algorithm for the general MDSP problem formulation, provided the number of conflicting intervals is less than the number of drones. We then introduce a unit-cost variant of MDSP and we devise an exact dynamic programming algorithm that runs in polynomial time when the number of drones m can be assumed to be a constant. Copyright © 2023, The Authors. All rights reserved.

关键词： Drones

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Secretary Problems with Random Number of Candidates: How Prior Distributional Information Helps

arXiv

引用

arXiv 2023年

作者： Zhang, Junhui Jaillet, Patrick ORC MIT United States ORC Department of Electrical Engineering and Computer Science MIT United States

We study variants of the secretary problem, where N, the number of candidates, is a random variable, and the decision maker wants to maximize the probability of success – picking the largest number among the N candidates – using only the relative ranks of the candidates revealed so far. We consider three forms of prior information about p, the probability distribution of N . In the full information setting, we assume p to be fully known. In that case, we show that single-threshold type of strategies can achieve 1/e-approximation to the maximum probability of success among all possible strategies. In the upper bound setting, we assume that N ≤ n (or E[N ] ≤ µ), where n (or µ) is known. In that case, we show that randomization over single-threshold type of strategies can achieve the optimal worst case probability of success of log(1n) (or log(1µ) ) asymptotically. Surprisingly, there is a single-threshold strategy (depending on n) that can succeed with probability 2/e2 for all but an exponentially small fraction of distributions supported on [n]. In the sampling setting, we assume that we have access to m samples N (1), . . ., N (m) ∼iid p. In that case, we show that if N ≤ T with probability at least 1 − O(ǫ) for some T ∈ N, m & ǫ12 max(log(1ǫ ), ǫ log(log(ǫT) )) is enough to learn a strategy that is at least ǫ-suboptimal, and we provide a lower bound of Ω(ǫ12 ), showing that the sampling algorithm is optimal when ǫ = O(log log(1T) ). We also discuss applications of some of our techniques to the matroid secretary problems, iid prophet inequalities, and online linear optimization problems when the time horizon N is a random variable. © 2023, CC BY.

关键词： Random variables

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Incentives in Private Collaborative Machine Learning

arXiv

引用

arXiv 2024年

作者： Sim, Rachael Hwee Ling Zhang, Yehong Hoang, Trong Nghia Xu, Xinyi Low, Bryan Kian Hsiang Jaillet, Patrick Department of Computer Science National University of Singapore Singapore Peng Cheng Laboratory China School of Electrical Engineering and Computer Science Washington State University United States Dept. of Electrical Engineering and Computer Science MIT United States

Collaborative machine learning involves training models on data from multiple parties but must incentivize their participation. Existing data valuation methods fairly value and reward each party based on shared data or model parameters but neglect the privacy risks involved. To address this, we introduce differential privacy (DP) as an incentive. Each party can select its required DP guarantee and perturb its sufficient statistic (SS) accordingly. The mediator values the perturbed SS by the Bayesian surprise it elicits about the model parameters. As our valuation function enforces a privacy-valuation trade-off, parties are deterred from selecting excessive DP guarantees that reduce the utility of the grand coalition’s model. Finally, the mediator rewards each party with different posterior samples of the model parameters. Such rewards still satisfy existing incentives like fairness but additionally preserve DP and a high similarity to the grand coalition’s posterior. We empirically demonstrate the effectiveness and practicality of our approach on synthetic and real-world datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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