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Optical Manipulation and Its Applications, OMA 2021 - Part of Biophotonics Congress: Optics in the Life sciences 2021

作者： Reddy, Innem V.A.K Bertoncini, Andrea Liberale, Carlo Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology Saudi Arabia Department of Electrical Engineering University at Buffalo NY United States Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Saudi Arabia

We present a fully 3D printed, on-fiber hybrid photonic structure to create customizable single-fiber optical tweezers. The structure contains wave guiding, reflecting, and refracting micro-optical elements stacked to... 详细信息

ISBN: (纸本)9781557528209

关键词： Optical tweezers

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Are Convolutional Neural Networks or Transformers more like human vision? 43

Are Convolutional Neural Networks or Transformers more like ...

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43rd Annual Meeting of the Cognitive science Society: Comparative Cognition: Animal Minds, CogSci 2021

作者： Tuli, Shikhar Dasgupta, Ishita Grant, Erin Griffiths, Thomas L. Department of Electrical and Computer Engineering Princeton University United States DeepMind New York United States Department of Electrical Engineering and Computer Sciences UC Berkeley United States Departments of Psychology and Computer Science Princeton University United States

Modern machine learning models for computer vision exceed humans in accuracy on specific visual recognition tasks, notably on datasets like ImageNet. However, high accuracy can be achieved in many ways. The particular decision function found by a machine learning system is determined not only by the data to which the system is exposed, but also the inductive biases of the model, which are typically harder to characterize. In this work, we follow a recent trend of in-depth behavioral analyses of neural network models that go beyond accuracy as an evaluation metric by looking at patterns of errors. Our focus is on comparing a suite of standard Convolutional Neural Networks (CNNs) and a recently-proposed attention-based network, the Vision Transformer (ViT), which relaxes the translation-invariance constraint of CNNs and therefore represents a model with a weaker set of inductive biases. Attention-based networks have previously been shown to achieve higher accuracy than CNNs on vision tasks, and we demonstrate, using new metrics for examining error consistency with more granularity, that their errors are also more consistent with those of humans. These results have implications both for building more human-like vision models, as well as for understanding visual object recognition in humans. © Cognitive science Society: Comparative Cognition: Animal Minds, CogSci *** rights reserved.

关键词： Convolutional neural networks

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Redundancy Is All You Need

arXiv

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arXiv 2024年

作者： Brakensiek, Joshua Guruswami, Venkatesan Department of Electrical Engineering and Computer Science University of California Berkeley United States Simons Institute for the Theory of Computing Departments of EECS and Mathematics University of California Berkeley United States

The seminal work of Benczúr and Karger demonstrated cut sparsifiers of near-linear size, with several applications throughout theoretical computer science. Subsequent extensions have yielded sparsifiers for hypergraph cuts and more recently linear codes over Abelian groups. A decade ago, Kogan and Krauthgamer asked about the sparsifiability of arbitrary constraint satisfaction problems (CSPs). For this question, a trivial lower bound is the size of a nonredundant CSP instance, which admits, for each constraint, an assignment satisfying only that constraint (so that no constraint can be dropped by the sparsifier). For instance, for graph cuts, spanning trees are non-redundant instances. Our main result is that redundant clauses are sufficient for sparsification: for any CSP predicate R, every unweighted instance of CSP(R) has a sparsifier of size at most its non-redundancy (up to polylog factors). For weighted instances, we similarly pin down the sparsifiability to the so-called chain length of the predicate. These results precisely determine the extent to which any CSP can be sparsified. A key technical ingredient in our work is a novel application of the entropy method from Gilmer's recent breakthrough on the union-closed sets conjecture. As an immediate consequence of our main theorem, a number of results in the non-redundancy literature immediately extend to CSP sparsification. We also contribute new techniques for understanding the non-redundancy of CSP predicates. In particular, we give an explicit family of predicates whose non-redundancy roughly corresponds to the structure of matching vector families in coding theory. By adapting methods from the matching vector codes literature, we are able to construct an explicit predicate whose non-redundancy lies between Ω(n1.5) and eO(n1.6), the first example with a provably non-integral exponent. © 2024, CC0.

关键词： Constraint satisfaction problems

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PPI++: Efficient Prediction-Powered Inference

arXiv

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arXiv 2023年

作者： Angelopoulos, Anastasios N. Duchi, John C. Zrnic, Tijana Department of Electrical Engineering and Computer Sciences UC Berkeley United States Departments of Statistics and Electrical Engineering Stanford University United States Department of Statistics and Stanford Data Science Stanford University United States

We present PPI++: a computationally lightweight methodology for estimation and inference based on a small labeled dataset and a typically much larger dataset of machine-learning predictions. The methods automatically adapt to the quality of available predictions, yielding easy-to-compute confidence sets—for parameters of any dimensionality—that always improve on classical intervals using only the labeled data. PPI++ builds on prediction-powered inference (PPI), which targets the same problem setting, improving its computational and statistical efficiency. Real and synthetic experiments demonstrate the benefits of the proposed adaptations. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Fish weight prediction using empirical and data-driven models in aquaculture systems

Fish weight prediction using empirical and data-driven model...

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Control Technology and Applications (CCTA),

作者： Xiao Chen Ibrahima N’Doye Fahad Aljehani Taous-Meriem Laleg-Kirati Computer Electrical and Mathematical Science and Engineering Division (CEMSE) King Abdullah University of Science and Technology (KAUST) Saudi Arabia Environmental Science and Engineering Program Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Saudi Arabia National Institute for Research in Digital Science and Technology (INRIA) Paris-Saclay France

ISBN: (数字)9798350370942

ISBN: (纸本)9798350370959

Predicting fish growth trajectory is crucial in achieving better control for the growing phase of the fish in aquaculture systems. However, fish biomass sensors that directly measure the fish’s weight are currently unavailable. Additionally, the fish growth model that relies on the fish weight is affected by environmental and feeding management factors, raising the difficulty of predicting the weight. This paper proposes comparative empirical and data-driven models for fish weight prediction. The empirical fish growth model relies directly on the growth ratio, an essential component for optimizing fish growth, while the data-driven model is based on long short-term memory (LSTM). Since the exact daily food ratio, proportional to the body weight of a growing fish, cannot be prescribed, we optimize the growth ratio of the empirical model in a sliding window framework based on the previous growth periods and then predict the fish weights a few days ahead through a nonlinear exponential regression approach. The LSTM method uses a normal distribution to generate multiple datasets over the experimental fish weight range and interpolate these data to provide a good prediction. To this end, we propose a weighted sum optimization method that combines the individual cost of the LSTM and empirical methods to achieve better short-term prediction performance. The simulation results demonstrate the effectiveness of the proposed models in predicting the fish weight and show that the combined weighted sum approach reduces the testing errors by 61.3% and 70.9% compared to the empirical and LSTM methods, respectively. Furthermore, an increasing specific growth rate (SGR) for both LSTM and combined models was observed in the short-term predictions.

关键词： Weight measurement Costs biological system modeling Simulation Predictive models Fish Trajectory

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Use and usability of software verification methods to detect behaviour interference when teaching an assistive home companion robot: A proof-of-concept study

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Paladyn 2021年第1期12卷 402-422页

作者： Koay, Kheng Lee Webster, Matt Dixon, Clare Gainer, Paul Syrdal, Dag Fisher, Michael Dautenhahn, Kerstin Department of Computer Science School of Physics Engineering and Computer Science University of Hertfordshire College Lane Hertfordshire HatfieldAL10 9AB United Kingdom School of Computer Science and Mathematics Liverpool John Moores University LiverpoolL2 2QP United Kingdom Department of Computer Science University of Manchester ManchesterM13 9PL United Kingdom Department of Computer Science University of Liverpool Liverpool United Kingdom Department of Computer Science School of Physics Engineering and Computer Science University of Hertfordshire College Lane Hertfordshire HatfieldAL10 9AB United Kingdom Departments of Electrical and Computer Engineering and Systems Design Engineering University of Waterloo ON Canada

When studying the use of assistive robots in home environments, and especially how such robots can be personalised to meet the needs of the resident, key concerns are issues related to behaviour verification, behaviour interference and safety. Here, personalisation refers to the teaching of new robot behaviours by both technical and non-technical end users. In this article, we consider the issue of behaviour interference caused by situations where newly taught robot behaviours may affect or be affected by existing behaviours and thus, those behaviours will not or might not ever be executed. We focus in particular on how such situations can be detected and presented to the user. We describe the human-robot behaviour teaching system that we developed as well as the formal behaviour checking methods used. The online use of behaviour checking is demonstrated, based on static analysis of behaviours during the operation of the robot, and evaluated in a user study. We conducted a proof-of-concept human-robot interaction study with an autonomous, multi-purpose robot operating within a smart home environment. Twenty participants individually taught the robot behaviours according to instructions they were given, some of which caused interference with other behaviours. A mechanism for detecting behaviour interference provided feedback to participants and suggestions on how to resolve those conflicts. We assessed the participants' views on detected interference as reported by the behaviour teaching system. Results indicate that interference warnings given to participants during teaching provoked an understanding of the issue. We did not find a significant influence of participants' technical background. These results highlight a promising path towards verification and validation of assistive home companion robots that allow end-user personalisation. © 2021 Kheng Lee Koay et al., published by De Gruyter.

关键词： Human robot interaction

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Tracking the Dynamic Neural Connectivity via Conjugate Gradient Optimization*

Tracking the Dynamic Neural Connectivity via Conjugate Gradi...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Mingdong Li Shuhang Chen Zhijia Zhao Yiwen Wang Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology School of Mechanical and Electrical Engineering Guangzhou University Department of Electronic and Computer Engineering The Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology

Neural connectivity describes how neuron populations coordinate and create cognitive and behavioral functions. Neural connectivity performs dynamics where its population spiking responses to stimuli or intention change over time. Brain-machine interface (BMI) provides a framework for studying dynamical neural connectivity. In BMI, point process is a powerful technique in analyzing the single neuronal tuning. And generalized linear mode (GLM) as an encoding model can incorporate the tuning in kinematics and the neural connectivity. Quantification and tracking of dynamic neural connectivity can contribute to the elucidation of the generation of brain functions in a computational way. However, most of the previous work focused on single neuronal adaptation to kinematics. When a neuron is significantly modulated by some other neurons in some tasks, the shape of the log likelihood function for single neuronal observations can be narrowed in some dimensions. And the existing gradient-based methods are not able to reach the optimum in a fast and adaptive searching way. In this work, to maximize the likelihood of observations and obtain the dynamic neural connectivity tuning parameters, we proposed a conjugate gradient-based encoding model (CGE). We illustrate CGE for likelihood function using the real experimental data under manual control and brain control. The results show that the proposed CGE has better performance in tracking the dynamic neural connectivity tuning parameters and modeling neural *** Relevance— Not directly related.

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Biomimetic Mappings for Active Sonar Object Recognition in Clutter

Biomimetic Mappings for Active Sonar Object Recognition in C...

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

作者： Sangwook Park Angeles Salles Kathryne Allen Cynthia Moss Mounya Elhilali Department of Electronic Engineering Gangneung-Wonju National University Department of Biological Sciences University of Illinois Chicago Department of Psychological and Brain Science Johns Hopkins University Department of Electrical and Computer Engineering Johns Hopkins University

SONAR technology plays a pivotal role in terrain exploration and specifically identification of objects of interest. However, it grapples with a recurring challenge of clutter and noise which limits the performance of target recognition models. The challenge of noisy observations renders the choice of robust signal representations critical. Inspired by mammalian representation in the midbrain of echolocating bats, the present study evaluates the robustness of a decomposition of echo measurements that matches the statistics of natural vocalizations. This representation is contrasted with equally rich generic mappings as well as digital sonar images based on time-frequency representations. The study shows the clear advantage of the naturally optimized representation for object recognition in presence of background noise and clutter, and further underscores the potential of bio-inspired approaches in advancing SONAR technology.

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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Spatial Transcriptomics Analysis of Zero-shot Gene Expression Prediction

arXiv

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arXiv 2024年

作者： Yang, Yan Hossain, Md Zakir Li, Xuesong Rahman, Shafin Stone, Eric Biological Data Science Institute The Australian National University Australia Optus Centre for AI Curtin University Australia Department of Electrical and Computer Engineering North South University Bangladesh

Spatial transcriptomics (ST) captures gene expression within distinct regions (i.e., windows) of a tissue slide. Traditional supervised learning frameworks applied to model ST are constrained to predicting expression from slide image windows for gene types seen during training, failing to generalize to unseen gene types. To overcome this limitation, we propose a semantic guided network (SGN), a pioneering zero-shot framework for predicting gene expression from slide image windows. Considering a gene type can be described by functionality and phenotype, we dynamically embed a gene type to a vector per its functionality and phenotype, and employ this vector to project slide image windows to gene expression in feature space, unleashing zero-shot expression prediction for unseen gene types. The gene type functionality and phenotype are queried with a carefully designed prompt from a pre-trained large language model (LLM). On standard benchmark datasets, we demonstrate competitive zero-shot performance compared to past state-of-the-art supervised learning approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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