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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Arjona, Jesús Parker, Ryan Chen, Kevin Purser, Carola Li, Linsen Michaels, Cathryn Stramma, Alexander Debroux, Remain Harris, Isaac Hayhurst, Martin Nichols, Eleanor Trusheim, Matthew Gangloff, Dorian Englund, Dirk Atature, Mete Cavendish Laboratory University of Cambridge Cambridge United Kingdom Department of Electrical Engineering and Computer Science MIT Cambridge United States

Tin-vacancy centres in diamond are spin-photon interfaces with intrinsic environmental noise insensitivity. We reveal their high optical coherence in a nanostructured environment and generate single photons with a 99.... 详细信息

ISBN: (纸本)9781957171258

关键词： Photons

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Flexible mapping of abstract domains by grid cells via self-supervised extraction and projection of generalized velocity signals 38

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

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Iyer, Abhiram Chandra, Sarthak Sharma, Sugandha Fiete, Ila Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Department of Brain and Cognitive Sciences MIT CambridgeMA United States McGovern Institute for Brain Research MIT CambridgeMA United States MIT CambridgeMA United States

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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Real-Time Digital RF Emulation - Part I: The Direct Path Computational Model

IEEE Transactions on Radar Systems

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IEEE Transactions on Radar Systems 2024年 2卷 866-879页

作者： DeLude, C. Driscoll, J. Mukherjee, M. Rahman, N. Mao, X. Kamal, U. Khan, S. Sivaraman, H. Huang, E. Mcharg, J. Swaminathan, M. Pande, S. Mukhopadhyay, S. Romberg, J. Georgia Institute of Technology School of Computer Science AtlantaGA30332 United States MIT Lincoln Laboratory Advanced Concepts and Technologies Group LexingtonMA02421 United States Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30332 United States Department of Electrical Engineering State CollegePA16802 United States

In this article, we consider the problem of developing a computational model for emulating an RF channel. The motivation for this is that an accurate and scalable emulator has the potential to minimize the need for field testing, which is expensive, slow, and difficult to replicate. Traditionally, emulators are built using a tapped delay line (TDL) model where long filters modeling the physical interactions of objects are implemented directly. For an emulation scenario consisting of M objects all interacting with one another, the TDL model's computational requirements scale as O(M3) per sample: there are O(M2) channels, each with O(M) complexity. In this article, we develop a new "direct path"model that, while remaining physically faithful, allows us to carefully factor the emulator operations, resulting in an O(M2) per-sample scaling of the computational requirements. The impact of this is drastic, a 200-object scenario sees about a 100× reduction in the number of per-sample computations. Furthermore, the direct path model gives us a natural way to distribute the computations for an emulation: each object is mapped to a computational node, and these nodes are networked in a fully connected communication graph. Alongside a discussion of the model and the physical phenomena it emulates, we show how to efficiently parameterize antenna responses and scattering profiles within this direct path framework. To verify the model and demonstrate its viability in hardware, we provide several numerical experiments produced using a cycle-level C++ simulator of a hardware implementation of the model. © 2023 IEEE.

关键词： C++ (programming language)

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What would Gauss say about representations? probing pretrained image models using synthetic Gaussian benchmarks 24

What would Gauss say about representations? probing pretrain...

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Proceedings of the 41st International Conference on Machine Learning

作者： Ching-Yun Ko Pin-Yu Chen Payel Das Jeet Mohapatra Luca Daniel Department of Electrical Engineering and Computer Science MIT Cambridge and IBM Research Yorktown IBM Research Yorktown Department of Electrical Engineering and Computer Science MIT Cambridge

Recent years have witnessed a paradigm shift in deep learning from task-centric model design to task-agnostic representation learning and task-specific fine-tuning. Pretrained model representations are commonly evaluated extensively across various real-world tasks and used as a foundation for different downstream tasks. This paper proposes a solution for assessing the quality of representations in a task-agnostic way. To circumvent the need for real-world data in evaluation, we explore the use of synthetic binary classification tasks with Gaussian mixtures to probe pretrained models and compare the robustness-accuracy performance on pretrained representations with an idealized reference. Our approach offers a holistic evaluation, revealing intrinsic model capabilities and reducing the dependency on real-life data for model evaluation. Evaluated with various pretrained image models, the experimental results confirm that our task-agnostic evaluation correlates with actual linear probing performance on downstream tasks and can also guide parameter choice in robust linear probing to achieve a better robustness-accuracy trade-off.

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Negative Differential Resistance Regime of Field Emitter Arrays

Negative Differential Resistance Regime of Field Emitter Arr...

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Device Research Conference

作者： Y. Shin W. Chern N. Karaulac A. Akinwande Department of Electrical Engineering and Computer Science MIT MA 02139

ISBN: (数字)9798350373738

ISBN: (纸本)9798350373745

Silicon field emitter arrays (FEAs) -based cold cathodes have shown promise in many applications under harsh environments such as x-ray sources and high-power microwave devices due to the temperature independence of tunneling current and the property of ballistic transport [ 1 – 3 ] . We report an unexpected, yet reproducible Negative Differential Resistance (NDR) region within the device output characteristics of gated FEAs. These results were obtained by utilizing an on-chip flat silicon anode with etched stand-offs to define the anode-to-emitter distances of <100μm. Our analysis using calculations and simulation reveal that the parallel-plate configuration introduces a deceleration of electrons in the channel between the anode and gate when the anode voltage, V AE , is lower than the gate voltage, V GE .

关键词： Resistance Field emitter arrays Temperature Voltage Microwave devices Logic gates Tunneling

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Lateral Control of Hybrid Drones: Near-Optimal Power Disturbance Aware Policy

Lateral Control of Hybrid Drones: Near-Optimal Power Disturb...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Miroslav Kosanić Marija Ilić Department of Electrical Engineering and Computer Science MIT MA USA

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

In unmanned aerial vehicle (UAV) systems, achieving extended flight autonomy remains a significant challenge, even in hybrid systems utilizing both fuel and battery as energy sources. To extend the flight time, our paper introduces a novel application of online composite control that achieves fuel savings through disturbance awareness. Our contributions include the derivation of a nonlinear model of the energy-conversion dynamics and its connection with lateral dynamics. We discuss how these nonlinearities are linearized through timescale separation based on the operational rates of drone energy sources. The effectiveness of our composite control method is validated through real-world drone flight data. Numerical results show a reduction in fuel usage of approximately 4.5% through a disturbance-aware policy. This paper not only advances the fundamental understanding of composite control for bilinear time-scale separated systems but also opens new directions for research in trajectory optimization for hybrid powertrain systems.

关键词： Mechanical power transmission Predictive models Autonomous aerial vehicles Reliability engineering Batteries Numerical models Fuels Vehicle dynamics Trajectory optimization Drones

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Aptly: Making Mobile Apps from Natural Language

arXiv

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arXiv 2024年

作者： Patton, Evan W. Kim, David Y.J. Granquist, Ashley Liu, Robin Scott, Arianna Zamanova, Jennet Abelson, Harold MIT Computer Science and Artificial Intelligence Laboratory United States MIT Electrical Engineering and Computer Science United States

We present Aptly, an extension of the mit App Inventor platform enabling mobile app development via natural language powered by code-generating large language models (LLMs). Aptly complements App Inventor’s block language with a text language designed to allow visual code generation via text-based LLMs. We detail the technical aspects of how the Aptly server integrates LLMs with a realtime collaboration function to facilitate the automated creation and editing of mobile apps given user instructions. The paper concludes with insights from a study of a pilot implementation involving high school students, which examines Aptly’s practicality and user experience. The findings underscore Aptly’s potential as a tool that democratizes app development and fosters technological creativity. © 2024, CC BY-SA.

关键词： Visual languages

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Training Graph Neural Networks on Growing Stochastic Graphs 48

Training Graph Neural Networks on Growing Stochastic Graphs

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Cerviño, Juan Ruiz, Luana Ribeiro, Alejandro University of Pennsylvania Department of Electrical and Systems Engineering Philadelphia United States Mit Computer Science & Artificial Intelligence Laboratory Cambridge United States

ISBN: (纸本)9781728163277

Graph Neural Networks (GNNs) rely on graph convolutions to exploit meaningful patterns in networked data. Based on matrix multiplications, convolutions incur in high computational costs leading to scalability limitations in practice. To overcome these limitations, proposed methods rely on training GNNs in smaller number of nodes, and then transferring the GNN to larger graphs. Even though these methods are able to bound the difference between the output of the GNN with different number of nodes, they do not provide guarantees against the optimal GNN on the very large graph. In this paper, we propose to learn GNNs on very large graphs by leveraging the limit object of a sequence of growing graphs, the graphon. We propose to grow the size of the graph as we train, and we show that our proposed methodology - learning by transference - converges to a neighborhood of a first order stationary point on the graphon data. A numerical experiment validates our proposed approach. © 2023 IEEE.

关键词： Graph Neural Networks Graphons

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Valuing Uncertainties in Wind Generation: An Agent-Based Optimization Approach

Valuing Uncertainties in Wind Generation: An Agent-Based Opt...

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American Control Conference (ACC)

作者： Daniel Shen Marija Ilic MIT Department of Electrical Engineering and Computer Science

The increasing integration of variable renewable energy sources such as wind and solar will require new methods of managing generation uncertainty. Existing practices of uncertainty management for these resources largely focuses around modifying the energy offers of such resources in the quantity domain and from a centralized system operator consideration of these uncertainties. This paper proposes an approach to instead consider these uncertainties in the price domain, where more uncertain power is offered at a higher price instead of restricting the quantity offered. We demonstrate system-level impacts on a modified version of the RTS-GMLC system where wind generators create market offers valuing their uncertainties over scenario set of day-ahead production forecasts. The results are compared with a dispatch method in which wind energy is offered at zero marginal price and restricted based on the forecast percentile.

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Visual Pre-Training for Navigation: What Can We Learn from Noise?

Visual Pre-Training for Navigation: What Can We Learn from N...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yanwei Wang Ching-Yun Ko Pulkit Agrawal Department of Electrical Engineering and Computer Science MIT

One powerful paradigm in visual navigation is to predict actions from observations directly. Training such an end-to-end system allows representations useful for downstream tasks to emerge automatically. However, the lack of inductive bias makes this system data inefficient. We hypothesize a sufficient representation of the current view and the goal view for a navigation policy can be learned by predicting the location and size of a crop of the current view that corresponds to the goal. We further show that training such random crop prediction in a self-supervised fashion purely on synthetic noise images transfers well to natural home images. The learned representation can then be bootstrapped to learn a navigation policy efficiently with little interaction data. The code is available at https://***/noise2ptz/

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