检索结果-内蒙古大学图书馆

Polarization-Tunable Colorimetric Metasurfaces for All-Optical and Non-Destructive Structural Characterization of Polymeric Nanofibers

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Kirya, Paula Hochberg, Justin D. Kim, Han Sol Haddadin, Zaid Bordy, Samantha Byun, Jiuk Pokorski, Jonathan K. Poulikakos, Lisa V. Department of Mechanical and Aerospace Engineering UC San Diego La Jolla CA United States Aiiso Yufeng Li Family Department of Chemical and Nano Engineering UC San Diego La Jolla CA United States Department of Electrical and Computer Engineering UC San Diego La Jolla CA United States Program in Materials Science and Engineering UC San Diego La Jolla CA United States

Metasurfaces have pioneered significant improvements in sensing technology by tailoring strong optical responses to weak signals. When designed with anisotropic subwavelength geometries, metasurfaces can tune responses to varying polarization states of light. Leveraging this to quantify structural alignment in fibrous materials unveils an alternative to destructive characterization methods. This work introduces metasurface-enhanced polarized light microscopy (Meta-PoL), which employs polarization-tunable, guided-mode-resonant colorimetric metasurfaces to characterize molecular and bulk alignment of poly(Ε-caprolactone) (PCL) nanofibers in a far-field configuration. PCL nanofibers drawn at 0%, 400%, and 900% ratios were interfaced with the studied metasurfaces. Metasurface resonances coinciding with the intrinsic drawn nanofiber resonances – confirmed by Stokes Polarimetry – produced the strongest colorimetric enhancement, resultant from alignment-specific nanofiber reflectivity. The enhancement degree corresponded with molecular and bulk alignments for each draw ratio, as measured through differential scanning calorimetry and scanning electron microscopy, respectively. Thus, Meta-PoL presents an all-optical, non-destructive, and quantitative measurement of nanofiber alignment. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential scanning calorimetry

Emulating Brain-like Rapid Learning in Neuromorphic Edge Computing

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Stewart, Kenneth Neumeier, Michael Shrestha, Sumit Bam Orchard, Garrick Neftci, Emre Department of Computer Science UC Irvine IrvineCA United States Department of Neuromorphic Computing Fortiss-Research Institute of the Free State of Bavaria Munich Germany Intel Labs Intel Corporation Santa ClaraCA United States Peter Grünberg Institute - Neuromorphic Software Ecosystems Forschungszentrum Jülich Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen Germany

Achieving personalized intelligence at the edge with real-time learning capabilities holds enormous promise in enhancing our daily experiences and helping decision making, planning, and sensing. However, efficient and reliable edge learning remains difficult with current technology due to the lack of personalized data, insufficient hardware capabilities, and inherent challenges posed by online learning. Over time and across multiple developmental stages, the brain has evolved to efficiently incorporate new knowledge by gradually building on previous knowledge. In this work, we emulate the multiple stages of learning with digital neuromorphic technology that simulates the neural and synaptic processes of the brain using two stages of learning. First, a meta-training stage trains the hyperparameters of synaptic plasticity for one-shot learning using a differentiable simulation of the neuromorphic hardware. This meta-training process refines a hardware local three-factor synaptic plasticity rule and its associated hyperparameters to align with the trained task domain. In a subsequent deployment stage, these optimized hyperparameters enable fast, data-efficient, and accurate learning of new classes. We demonstrate our approach using event-driven vision sensor data and the Intel Loihi neuromorphic processor with its plasticity dynamics, achieving real-time one-shot learning of new classes that is vastly improved over transfer learning. Our methodology can be deployed with arbitrary plasticity models and can be applied to situations demanding quick learning and adaptation at the edge, such as navigating unfamiliar environments or learning unexpected categories of data through user engagement. © 2024, CC BY.

关键词： Zero-shot learning

On the wave dynamics of microscale bistable tensegrity structures 11

学校读者我要写书评

暂无评论

On the wave dynamics of microscale bistable tensegrity struc...

11th International Conference on Structural Dynamics, EURODYN 2020

作者： Micheletti, A. Vangelatos, Z. Singh, N. Grigoropoulos, C.P. Fraternali, F. Deptt. of Civil and computer Science Engineering University of Rome Tor Vergata Italy Dept. of Mechanical Engineering University of California UC Berkeley United States Dept. of Civil Engineering University of Salerno Salerno Italy

ISBN: (纸本)9786188507203

This work deals with the design, the mechanical modeling and the experimental testing of microscale lattice structures tessellating bistable tensegrity prisms. The analyzed units have only one internal mechanism and one self-stress state, and can be transformed into bistable structures, by replacing the cables with bars, and suitably adjusting the members’ lengths. We experimentally validate the theoretical prediction of the response of spatial assemblies of such units, through mechanical tests on physical models fabricated by multiphoton lithography. We also numerically show that the examined structures support the propagation of compact compression waves under impact loading, which paves the way to their use for the fabrication of novel acoustic lenses with tensegrity architecture. © 2020 European Association for Structural Dynamics. All rights reserved.

关键词： Structural dynamics

Optimal Scheduling in the Multiserver-job Model under Heavy Traffic

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Grosof, Isaac Scully, Ziv Harchol-Balter, Mor Scheller-Wolf, Alan Carnegie Mellon University Computer Science Department PittsburghPA United States Simons Institute UC Berkeley Carnegie Mellon University Cornell University Department of Electrical Engineering and Computer Sciences BerkeleyCA United States Carnegie Mellon University Tepper School of Business PittsburghPA United States

Multiserver-job systems, where jobs require concurrent service at many servers, occur widely in practice. Essentially all of the theoretical work on multiserver-job systems focuses on maximizing utilization, with almost nothing known about mean response time. In simpler settings, such as various known-size single-server-job settings, minimizing mean response time is merely a matter of prioritizing small jobs. However, for the multiserver-job system, prioritizing small jobs is not enough, because we must also ensure servers are not unnecessarily left idle. Thus, minimizing mean response time requires prioritizing small jobs while simultaneously maximizing throughput. Our question is how to achieve these joint objectives. We devise the ServerFilling-SRPT scheduling policy, which is the first policy to minimize mean response time in the multiserver-job model in the heavy traffic limit. In addition to proving this heavy-traffic result, we present empirical evidence that ServerFilling-SRPT outperforms all existing scheduling policies for all loads, with improvements by orders of magnitude at higher loads. © 2022, CC BY-NC-SA.

关键词： Scheduling

Graph Laplacian Learning with Exponential Family Noise

学校读者我要写书评

暂无评论

IEEE Transactions on Signal and Information Processing over Networks 2025年

作者： Shi, Changhao Mishne, Gal UC San Diego Electrical and Computer Engineering Department CA92093 United States UC San Diego Hal.c.o.glu Data Science Institute and the Neurosciences Graduate Program CA92093 United States

Graph signal processing (GSP) is a prominent framework for analyzing signals on non-Euclidean domains. The graph Fourier transform (GFT) uses the combinatorial graph Laplacian matrix to reveal the spectral decomposition of signals in the graph frequency domain. However, a common challenge in applying GSP methods is that in many scenarios the underlying graph of a system is unknown. A solution in such cases is to construct the unobserved graph from available data, which is commonly referred to as graph or network inference. Although different graph inference methods exist, they are restricted to learning from either smooth graph signals or simple additive Gaussian noise. Other types of noisy data, such as discrete counts or binary digits, are rather common in real-world applications, yet are underexplored in graph inference. In this paper, we propose a versatile graph inference framework for learning from graph signals corrupted by exponential family noise. Our framework generalizes previous methods from continuous smooth graph signals to various data types. We propose an alternating algorithm that jointly estimates the graph Laplacian and the unobserved smooth representation from the noisy signals. We also extend our approach to include an offset variable which models different levels of variation of the nodes. Since real-world graph signals are frequently non-independent and temporally correlated, we further adapt our original setting to a timevertex formulation. We demonstrate on synthetic and real-world data that our new algorithms outperform competing Laplacian estimation methods that suffer from noise model mismatch. © 2025 IEEE.

关键词： Laplace transforms

End-To-End Auditory Object Recognition Via Inception Nucleus

学校读者我要写书评

暂无评论

End-To-End Auditory Object Recognition Via Inception Nucleus

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Mohammad Ebrahimpour Timothy Shea Andreea Danielescu David Noelle Chris Kello Electrical Engineering and Computer Science UC Merced Accenture Labs Cognitive and Information Sciences UC Merced

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Machine learning approaches to auditory object recognition are traditionally based on engineered features such as those derived from the spectrum or cepstrum. More recently, end- to-end classification systems in image and auditory recognition systems have been developed to learn features jointly with classification and result in improved classification accuracy. In this paper, we propose a novel end-to-end deep neural network to map the raw waveform inputs to sound class labels. Our network includes an "inception nucleus" that optimizes the size of convolutional filters on the fly that results in reducing engineering efforts dramatically. Classification results compared favorably against current state-of-the-art approaches, besting them by 10.4 percentage points on the Ur- bansound8k dataset. Analyses of learned representations revealed that filters in the earlier hidden layers learned wavelet like transforms to extract features that were informative for classification.

关键词：

End-to-end auditory object recognition via inception nucleus

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Ebrahimpour, Mohammad Shea, Timothy Danielescu, Andreea Noelle, David Kello, Chris Electrical Engineering and Computer Science UC Merced Cognitive and Information Sciences UC Merced Accenture Labs

Machine learning approaches to auditory object recognition are traditionally based on engineered features such as those derived from the spectrum or cepstrum. More recently, end-to-end classification systems in image and auditory recognition systems have been developed to learn features jointly with classification and result in improved classification accuracy. In this paper, we propose a novel end-to-end deep neural network to map the raw waveform inputs to sound class labels. Our network includes an "inception nucleus" that optimizes the size of convolutional filters on the fly that results in reducing engineering efforts dramatically. Classification results compared favorably against current state-of-the-art approaches, besting them by 10.4 percentage points on the Urbansound8k dataset. Analyses of learned representations revealed that filters in the earlier hidden layers learned wavelet-like transforms to extract features that were informative for classification. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Tracking

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Fischer, Tobias Huang, Thomas E. Pang, Jiangmiao Qiu, Linlu Chen, Haofeng Darrell, Trevor Yu, Fisher The Department of Information Technology and Electrical Engineering ETH Zurich Switzerland The Shanghai AI Laboratory China The Department of EECS Massachusetts Institute of Technology United States The Computer Science Department Stanford University United States The Department of EECS UC Berkeley United States

Similarity learning has been recognized as a crucial step for object tracking. However, existing multiple object tracking methods only use sparse ground truth matching as the training objective, while ignoring the majority of the informative regions in images. In this paper, we present Quasi-Dense Similarity Learning, which densely samples hundreds of object regions on a pair of images for contrastive learning. We combine this similarity learning with multiple existing object detectors to build Quasi-Dense Tracking (QDTrack), which does not require displacement regression or motion priors. We find that the resulting distinctive feature space admits a simple nearest neighbor search at inference time for object association. In addition, we show that our similarity learning scheme is not limited to video data, but can learn effective instance similarity even from static input, enabling a competitive tracking performance without training on videos or using tracking supervision. We conduct extensive experiments on a wide variety of popular MOT benchmarks. We find that, despite its simplicity, QDTrack rivals the performance of state-of-the-art tracking methods on all benchmarks and sets a new state-of-the-art on the large-scale BDD100K MOT benchmark, while introducing negligible computational overhead to the detector. Copyright © 2022, The Authors. All rights reserved.

关键词： Nearest neighbor search

Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Hu, Jerry Yao-Chieh Wang, Wei-Po Gilani, Ammar Li, Chenyang Song, Zhao Liu, Han Center for Foundation Models and Generative AI Northwestern University EvanstonIL60208 United States Department of Computer Science Northwestern University EvanstonIL60208 United States Department of Physics National Taiwan University Taipei10617 Taiwan Maynooth International School of Engineering Fuzhou University Fuzhou350108 China Simons Institute for the Theory of Computing UC Berkeley BerkeleyCA94720 United States Department of Statistics and Data Science Northwestern University EvanstonIL60208 United States

We investigate the statistical and computational limits of prompt tuning for transformer-based foundation models. Our key contributions are prompt tuning on single-head transformers with only a single self-attention layer: (i) is universal, and (ii) supports efficient (even nearlylinear time) algorithms under the Strong Exponential Time Hypothesis (SETH). Statistically, we prove that prompt tuning on such simplest possible transformers are universal approximators for sequence-to-sequence Lipschitz functions. In addition, we provide an exponential-in-dL and -in- (1/ϵ) lower bound on the required soft-prompt tokens for prompt tuning to memorize any dataset with 1-layer, 1-head transformers. Computationally, we identify a phase transition in the efficiency of prompt tuning, determined by the norm of the soft-prompt-induced keys and queries, and provide an upper bound criterion. Beyond this criterion, no sub-quadratic (efficient) algorithm for prompt tuning exists under SETH. Within this criterion, we showcase our theory by proving the existence of almost-linear time prompt tuning inference algorithms. These fundamental limits provide important necessary conditions for designing expressive and efficient prompt tuning methods for practitioners. © 2024, CC BY-NC-SA.

关键词： Distribution transformers