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arXiv 2023年

作者： de Franca, F.O. Virgolin, M. Kommenda, M. Majumder, M.S. Cranmer, M. Espada, G. Ingelse, L. Fonseca, A. Landajuela, M. Petersen, B. Glatt, R. Mundhenk, N. Lee, C.S. Hochhalter, J.D. Randall, D.L. Kamienny, P. Zhang, H. Dick, G. Simon, A. Burlacu, B. Kasak, Jaan Machado, Meera Wilstrup, Casper La Cava, W.G. Federal University of ABC Santo Andre Brazil Evolutionary Intelligence Group Centrum Wiskunde & Informatica Science Park 123 Amsterdam Netherlands Computational Health Informatics Program Boston Children's Hospital Harvard Medical School Boston United States University of Applied Sciences Upper Austria Hagenberg Austria Center for Computational Astrophysics Flatiron Institute Department of Astrophysical Sciences Princeton University United States LASIGE Faculdade de Ciências Universidade de Lisboa Lisboa Portugal Computational Engineering Division Lawrence Livermore National Laboratory Livermore United States University of Utah Department of Mechanical Engineering Utah United States Meta FAIR France Victoria University of Wellington School of Engineering and Computer Science New Zealand University of otago Department of Information Science New Zealand Institut für Angewandte Physik Universität Tübingen Max Planck Institute for Intelligent Systems Tübingen Germany Abzu AI Orient Plads 1 Nordhavn 2150 Denmark

Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main attraction of this approach is that it returns an interpretable model that can be insightful to users. Historically, the majority of algorithms for symbolic regression have been based on evolutionary algorithms. However, there has been a recent surge of new proposals that instead utilize approaches such as enumeration algorithms, mixed linear integer programming, neural networks, and Bayesian optimization. In order to assess how well these new approaches behave on a set of common challenges often faced in real-world data, we hosted a competition at the 2022 Genetic and Evolutionary Computation Conference consisting of different synthetic and real-world datasets which were blind to entrants. For the real-world track, we assessed interpretability in a realistic way by using a domain expert to judge the trustworthiness of candidate models. We present an in-depth analysis of the results obtained in this competition, discuss current challenges of symbolic regression algorithms and highlight possible improvements for future competitions. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Controlled synthetic chirality in macroscopic assemblies of carbon nanotubes

arXiv

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arXiv 2023年

作者： Doumani, Jacques Lou, Minhan Dewey, Oliver Hong, Nina Fan, Jichao Baydin, Andrey Yomogida, Yohei Yanagi, Kazuhiro Pasquali, Matteo Saito, Riichiro Kono, Junichiro Gao, Weilu Department of Electrical and Computer Engineering Rice University HoustonTX United States Applied Physics Graduate Program Smalley–Curl Institute Rice University HoustonTX United States Department of Electrical and Computer Engineering The University of Utah Salt Lake CityUT United States Carbon Hub Rice University HoustonTX United States Department of Chemical and Biomolecular Engineering Rice University HoustonTX United States J.A. Woollam Co. Inc. LincolnNE United States Smalley–Curl Institute Rice University HoustonTX United States Department of Physics Tokyo Metropolitan University Tokyo Japan Department of Chemistry Rice University HoustonTX United States Department of Materials Science and NanoEngineering Rice University HoustonTX United States Department of Physics Tohoku University Sendai Japan Department Physics and Astronomy Rice University HoustonTX United States

There is an emerging recognition that successful utilization of chiral degrees of freedom can bring new scientific and technological opportunities to diverse research areas. Hence, methods are being sought for creating artificial matter with controllable chirality in an uncomplicated and reproducible manner. Here, we report the development of two straightforward methods for fabricating wafer-scale chiral architectures of ordered carbon nanotubes (CNTs) with tunable and giant circular dichroism (CD). Both methods employ simple approaches, (i) mechanical rotation and (ii) twist-stacking, based on controlled vacuum filtration and do not involve any sophisticated nanofabrication processes. We used a racemic mixture of CNTs as the starting material, so the intrinsic chirality of chiral CNTs is not responsible for the observed chirality. In particular, by controlling the stacking angle and handedness in (ii), we were able to maximize the CD response and achieve a record-high deep-ultraviolet ellipticity of 40 ± 1 mdeg/nm. Our theoretical simulations using the transfer matrix method reproduce the salient features of the experimentally observed CD spectra and further predict that a film of twist-stacked CNTs with an optimized thickness will exhibit an ellipticity as high as 150 mdeg/nm. The created wafer-scale objects represent a new class of synthetic chiral matter consisting of ordered quantum wires whose macroscopic properties are governed by nanoscopic electronic signatures such as van Hove singularities. These artificial structures with engineered chirality will not only provide playgrounds for uncovering new chiral phenomena but also open up new opportunities for developing high-performance chiral photonic and optoelectronic devices. © 2023, CC BY.

关键词： Carbon nanotubes

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In vivo imaging with a flat, lensless microscope

Research Square

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Research Square 2021年

作者： Adams, Jesse K. Yan, Dong Wu, Jimin Boominathan, Vivek Gao, Sibo Rodriguez, Alex V. Carns, Jennifer Richards-Kortum, Rebecca Kemere, Caleb Veeraraghavan, Ashok Robinson, Jacob T. Applied Physics Program Rice University 6100 Main Street HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University 6100 Main Street HoustonTX77005 United States Department of Neuroscience Baylor College of Medicine One Baylor Plaza HoustonTX77030 United States Department of Bioengineering Rice University 6100 Main Street HoustonTX77005 United States Department of Computer Science Rice University 6100 Main Street HoustonTX77005 United States

Lenless microscopes that form images using simple, compact optics, along with associated computational algorithms provide several advantages: small, inexpensive form factors, large fields of view (FOV), and the ability to refocus an image after capture. To date, however, lensless microscopy has yet to be demonstrated for in vivo biological imaging. The primary challenge with in vivo bio-imaging is these dense, low-contrast scenes cannot be accurately reconstructed with existing lensless techniques. To overcome this challenge, we designed an optical phase mask that creates a point-spread function consisting of high-contrast contours with a broad spatial frequency spectrum. Here we show that this "contour" phase mask enables in vivo imaging where previous lensless imaging technologies have failed. As a proof of concept, we demonstrate 3D imaging of a behaving invertebrate, functional calcium imaging in mouse cortex, and in vivo imaging of microvasculature in the human oral mucosa. Together these results demonstrate the ability of lensless imaging to operate in realistic biological imaging applications ranging from pre-clinical animal models to clinical biomedical imaging. Overall, this "Bio-FlatScope" is a clinically relevant imaging technique with advantages of 3D computational refocusing in a low-cost, small form factor that is useful for low-resource settings and difficult-to-reach areas of the body. © 2021, CC BY.

关键词： Microscopes

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Aedes-AI: Neural network models of mosquito abundance

arXiv

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arXiv 2021年

作者： Kinney, Adrienne C. Current, Sean Lega, Joceline Interdisciplinary Program in Applied Mathematics The University of Arizona TucsonAZ85721 United States Department of Mathematics The University of Arizona TucsonAZ85721 United States Department of Epidemiology and Biostatistics The University of Arizona TucsonAZ85721 United States BIO5 Institute The University of Arizona TucsonAZ85721 United States Department of Computer Science and Engineering The Ohio State University ColumbusOH43202 United States

We present artificial neural networks as a feasible replacement for a mechanistic model of mosquito abundance. We develop a feed-forward neural network, a long short-term memory recurrent neural network, and a gated recurrent unit network. We evaluate the networks in their ability to replicate the spatiotemporal features of mosquito populations predicted by the mechanistic model, and discuss how augmenting the training data with time series that emphasize specific dynamical behaviors affects model performance. We conclude with an outlook on how such equation-free models may facilitate vector control or the estimation of disease risk at arbitrary spatial scales. © 2021, CC BY-NC-ND.

关键词： Feedforward neural networks

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Whole-body phase plane analysis for standard maximum vertical jump assessment

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Gait & Posture 2021年 90卷 203-204页

作者： Rodrigues, C. Correia, M. Abrantes, J. Rodrigues, B. Nadal, J. INESC TEC - Institute for Systems and Computer Engineering- Technology and Science C-BER - Centre for Biomedical Engineering Research Porto Portugal University of Porto Faculty of Engineering Porto Portugal Lusófona University CICANT Centre for Research in Applied Communication Culture and New Technologies - MovLab Laboratory of Technologies for Interactions and Interfaces Lisbon Portugal Federal University of Pernambuco Department of Electronic and Systems Recife Brazil Federal University of Rio de Janeiro Biomedical Engineering Program Rio de Janeiro Brazil

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When data disappear: public health pays as US policy strays

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The Lancet Digital Health 2025年 100874页

作者： McAndrew, Thomas Lover, Andrew A Hoyt, Garrik Majumder, Maimuna S Department of Biostatistics and Health Data Science College of Health Lehigh University BethlehemPA United States Department of Biostatistics and Epidemiology School of Public Health and Health Sciences University of Massachusetts Amherst AmherstMA United States Department of Computer Science and Engineering PC Rossin College of Engineering and Applied Sciences Lehigh University BethlehemPA United States Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children's Hospital BostonMA United States

Presidential actions on Jan 20, 2025, by President Donald Trump, including executive orders, have delayed access to or led to the removal of crucial public health data sources in the USA. The continuous collection and maintenance of health data support public health, safety, and security associated with diseases such as seasonal influenza. To show how public health data surveillance enhances public health practice, we analysed data from seven US Government-maintained sources associated with seasonal influenza. We fit two models that forecast the number of national incident influenza hospitalisations in the USA: (1) a data-rich model incorporating data from all seven Government data sources;and (2) a data-poor model built using a single Government hospitalisation data source, representing the minimal required information to produce a forecast of influenza hospitalisations. The data-rich model generated reliable forecasts useful for public health decision making, whereas the predictions using the data-poor model were highly uncertain, rendering them impractical. Thus, health data can serve as a transparent and standardised foundation to improve domestic and global health. Therefore, a plan should be developed to safeguard public health data as a public good. © 2025 The Author(s)

关键词： Health insurance

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Lower limb joint angular phase plane coordination analysis on standard maximum vertical jump

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Gait & Posture 2021年 90卷 205-206页

作者： Rodrigues, C. Correia, M. Abrantes, J. Rodrigues, B. Nadal, J. INESC TEC - Institute for Systems and Computer Engineering- Technology and Science C-BER - Centre for Biomedical Engineering Research Porto Portugal University of Porto Faculty of Engineering Porto Portugal Lusófona University CICANT Centre for Research in Applied Communication Culture and New Technologies- MovLab Laboratory of Technologies for Interactions and Interfaces Lisbon Portugal Federal University of Pernambuco Department of Electronic and Systems Recife Brazil Federal University of Rio de Janeiro Biomedical Engineering Program Rio de Janeiro Brazil

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Spectral neighbor joining for reconstruction of latent tree models

arXiv

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arXiv 2020年

作者： Jaffe, Ariel Amsel, Noah Nadler, Boaz Chang, Joseph T. Kluger, Yuval Program in Applied Mathematics Yale University New HavenCT06511 United States Department of Computer Science Weizmann Institute of Science Rehovot76100 Israel Department of Statistics Yale University New HavenCT06520 United States Interdepartmental Program in Computational Biology and Bioinformatics New HavenCT06511 Singapore Department of Pathology New HavenCT06511 United States

A key assumption in multiple scientific applications is that the distribution of observed data can be modeled by a latent tree graphical model. An important example is phylogenetics, where the tree models the evolutionary lineages of various organisms. Given a set of independent realizations of the random variables at the leaves of the tree, a common task is to infer the underlying tree topology. In this work we develop Spectral Neighbor Joining (SNJ), a novel method to recover latent tree graphical models. In contrast to distance based methods, SNJ is based on a spectral measure of similarity between all pairs of observed variables. We prove that SNJ is consistent, and derive a sufficient condition for correct tree recovery from an estimated similarity matrix. Combining this condition with a concentration of measure result on the similarity matrix, we bound the number of samples required to recover the tree with high probability. We illustrate via extensive simulations that SNJ requires fewer samples to accurately recover trees in regimes where the tree contains a large number of leaves or long edges. We provide theoretical support for this observation by analyzing the model of a perfect binary tree. Copyright © 2020, The Authors. All rights reserved.

关键词： Recovery

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Linearity assessment between lower limb joint angles and angular accelerations at standard maximum vertical jumps with long-short and no countermovement

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Gait & Posture 2023年 106卷 S174-S175页

作者： Rodrigues, Carlos Correia, Miguel Abrantes, João Benedetti, Marco Nadal, Jurandir Faculty of Engineering- University of Porto Biomedical Engineering Program Porto Portugal INESC TEC - Institute for Systems and Computer Engineering- Technology and Science C-BER - Centre for Biomedical Engineering Research Porto Portugal Faculty of Engineering- University of Porto Department of Electrical and Computer Engineering Porto Portugal CICANT - Centre for Research in Applied Communication- Culture and New Technologies MovLab - Laboratory of Technologies for Interactions and Interfaces Lisbon Portugal Federal University of Pernambuco Department of Electronic and Systems Recife Brazil Federal University of Rio de Janeiro Biomedical Engineering Program Rio de Janeiro Brazil

IntroductionStandard maximum vertical jump (MVJ) has been applied to assess lower limb long countermovement (CM) on countermovement jump (CMJ) and short CM on drop jump (DJ) for comparison to squat jump (SJ) without CM [1,2]. Research questionNevertheless, the ability for lower limb joint accelerations depending on lower limb joint angles at long, short and no CM remains an open issue [3,4], in particular linearity assessment of these relations at long, short and no CM in association with MVJ whole-body impulsion. MethodsFor this reason, we applied piecewise linear detection of segmented subphases on lower limb joint angles ( θ)-angular acceleration ( α) diagrams for the hip (H), knee (K) and ankle (A) during impulse phases on CMJ, DJ and SJ. Selected sample corresponds to best MVJ trials based on higher flight-time of 3 CMJ, DJ and SJ repetitions for six healthy untrained subjects with ages (21.5±1.4) years, (76.7±9.3)kg mass and (1.79±0.06)m height. Adhesive reflective markers were applied at lower limb and torso, with sagittal hip, knee, and ankle θ, α obtained by inverse kinematics. H,K,A ( θ, α) diagrams were plotted and impulsion subphases segmented at selected instants of ∂ a/∂ q phase reversal with linear fit by the least-squares method, Fig. 1. Fig. 1. Hip, knee, ankle joint ( θ, α) diagrams at CMJ, DJ, SJ with segmented subphases time span(Δt), |∂α/∂θ| slope magnitudes and R² linear fit regression. ResultsSJ presented larger number of ( θ, α) subphases than DJ, both larger than CMJ with higher |∂ α/∂ θ| variation at DJ and SJ than CMJ, pointing to higher complexity of neuromuscular control and contributing to explain reduced SJ, DJ performance without CM (0.33±0.05)m and with short CM (0.27±0.03)m in relation to CMJ with long CM (0.36±0.04)m for untrained selected subjects, Fig. 1. Thus, despite the larger total time span Δt at CMJ (0.76±0.10)s in relation to SJ (0.36±0.08)s and DJ (0.23±0.05)s, CMJ presented lower number of subphases with longer time span

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Muscle coactivation analysis for neuromuscular control assessment of lower limb stretch-shortening cycle

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Gait & Posture 2023年 106卷 S175-S176页

IntroductionMuscle stretch-shortening cycle (SSC) is a central mechanism with lower limb muscle contraction immediately preceded by muscle stretch for efficient submaximal activities such as gait and powerful maximal activities such as running and jumping [1,2]. Research questionAlthough muscle SSC can be observed at gait and running its higher expression and accessibility is performed on standard maximum vertical jump (MVJ) with an open issue on neuromuscular control assessment of lower limb muscle SSC [3]. MethodsFor this purpose, we present and applied noninvasive subject specific analysis of lower limb muscle coactivation for selected muscles with higher contribution during MVJ impulse based on corresponding conditioned surface electromyographic signals (sEMG). Selected muscles correspond to lower limb muscles vastus medialis (VM), rectus femoris (RF), vastus lateralis (VL), lateral gastrocnemius (LG) and medial gastrocnemius (MG). Twenty-seven trials were assessed corresponding for each subject to the highest MVJ based on larger flight time with long SSC at countermovement jump (CMJ), short SSC at drop jump (DJ) and squat jump (SJ) with no SSC. Trial sample is composed by a group of six young adult volunteers’ students on sports and physical education degree with the ages (21.5 ± 1.4) years, (76.7 ± 9.3) kg mass and (1.79 ± 0.06) m height. Surface skin was prepared and Aqua-Wet gel Skintact F55 electrodes were applied at bipolar configuration as indicated by SENIAN. VM, RF, VL, LG and MG sEMG linear envelopes were paired plotted with the area under the curves computed as well as the coactivation pairs defined as the common area under the curves normalized to the sum of the corresponding pair individual areas under the curves. Coactivations were compared at CMJ, DJ and SJ as well as among CMJ, DJ and SJ, Fig. 1. ResultsStrongest coactivations with decreasing intensity were detected between VM-RF and LG-MG muscles at CMJ and SJ, whereas at DJ the strongest coacti

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