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User communities: from nice-to-have to must-have

Journal of Innovation and Entrepreneurship 2023年第1期12卷 25页

作者： Costa, Joana Amorim, Inês Reis, João Melão, Nuno DEGEIT Department of Economics Management Industrial Engineering and Tourism University of AveiroCampus Universitário de Santiago Aveiro 3810-193 Portugal GOVCOPP Research Unit on Governance Competitiveness and Public Policies Campus Universitário de Santiago Aveiro 3810-193 Portugal INESCTEC Institute for Systems and Computer Engineering Technology and Science R. Roberto Frias Porto 4200-465 Portugal FEP Faculty of Economics University of Porto R. Roberto Frias Porto 4200-464 Portugal Department of Industrial Engineering and Management Faculty of Engineering Lusofona University and EIGeS Campo Grande Lisbon 1749-024 Portugal CISeD Research Center in Digital Services Polytechnic Institute of Viseu Campus Politécnico Viseu 3504-510 Portugal

With Industry 5.0 already on the horizon, firms need to adapt their strategies to better cater to an increasingly demanding and sustainability-conscious customer base. At the same time, the role of customers has shifted from being mere passive buyers to active users, who not only demand personalized products and services to suit their needs and preferences, but also actively engage other users and stakeholders, thereby grounding the Quadruple Helix model. The objective of this paper is to assess the role of user communities in fostering firms’ innovation in all types of innovation. Econometric estimations identified the user community as a driver of innovation propensity. Additionally, the relevance of these communities across all types of innovation (mainly for product and process innovation) was proven. Robustness analysis confirmed the results obtained in different dimensions and established a connection to human capital. This finding revealed that absorptive capacity serves not only as a facilitator of innovation, but also as a moderator. The empirical contributions point towards the urgency of policy actions that consistently involve these agents as vehicles of responsible innovation, which can fine-tune the innovation paths towards an eco-friendlier innovation ecosystem. Further testing the connection between human capital and the user community is required, as the establishment of efficient communication channels promoting the knowledge flows inside the firm will leverage innovation outputs in the different innovation types. © 2023, The Author(s).

关键词： CIS Industry 5.0 Logit models Open innovation Quadruple Helix model User communities

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Matching Using Sufficient Dimension Reduction for Heterogeneity Causal Effect Estimation

arXiv

引用

arXiv 2023年

作者： Zhao, Haoran Zhang, Yinghao Cheng, Debo Li, Chen Feng, Zaiwen College of Science Huazhong Agricultural University Wuhan430070 China College of Computer Science and Engineering Guangxi Normal University Guilin541000 China STEM University of South Australia Australia Department of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka820-8502 Japan College of Informatics Huazhong Agricultural University Wuhan430070 China Hubei Key Laboratory of Agricultural Bioinformatics Huazhong Agricultural University Wuhan430070 China Hubei Hongshan Laboratory Huazhong Agricultural University Wuhan430070 China National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural University Wuhan430070 China State Key Laboratory of Hybrid Rice Wuhan University 299 Bayi Rd Wuhan430070 China Macro Agricultural Research Institute Huazhong Agricultural University Wuhan430070 China

Causal inference plays an important role in understanding the underlying mechanisation of the data generation process across various domains. It is challenging to estimate the average causal effect and individual causal effects from observational data with high-dimensional covariates due to the curse of dimension and the problem of data sufficiency. The existing matching methods can not effectively estimate individual causal effect or solve the problem of dimension curse in causal inference. To address this challenge, in this work, we prove that the reduced set by sufficient dimension reduction (SDR) is a balance score for confounding adjustment. Under the theorem, we propose to use an SDR method to obtain a reduced representation set of the original covariates and then the reduced set is used for the matching method. In detail, a non-parametric model is used to learn such a reduced set and to avoid model specification errors. The experimental results on real-world datasets show that the proposed method outperforms the compared matching methods. Moreover, we conduct an experiment analysis and the results demonstrate that the reduced representation is enough to balance the imbalance between the treatment group and control group individuals. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Design and Structural Optimization of Thiadiazole Derivatives with Potent GLS1 Inhibitory Activity

SSRN

引用

SSRN 2023年

作者： Okada, Takuya Yamabe, Kaho Jo, Michiko Sakajiri, Yuko Shibata, Tomokazu Sawada, Ryusuke Yamanishi, Yoshihiro Kanayama, Daisuke Mori, Hisashi Mizuguchi, Mineyuki Obita, Takayuki Nabeshima, Yuko Koizumi, Keiichi Toyooka, Naoki Faculty of Engineering University of Toyama Toyama930-8555 Japan Graduate School of Pharma-Medical Sciences University of Toyama Toyama930-8555 Japan Graduate School of Science and Engineering University of Toyama Toyama930-8555 Japan Division of Presymptomatic Disease Institute of Natural Medicine University of Toyama Toyama930‑0194 Japan Department of Bioscience and Bioinformatics Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka804-8550 Japan Graduate School of Informatics Nagoya University Chikusa Nagoya464-8602 Japan Department of Pharmacology Okayama University Graduate School of Medicine Dentistry and Pharmaceutical Sciences Okayama700-8558 Japan Department of Molecular Neuroscience Faculty of Medicine University of Toyama Toyama930-0194 Japan Research Center for Pre-Disease Science University of Toyama Toyama930-0194 Japan Faculty of Pharmaceutical Sciences University of Toyama Toyama930-0194 Japan

GLS1 is an attractive target not only as anticancer agents but also as candidates for various potential pharmaceutical applications such as anti-aging and anti-obesity treatments. We performed docking simulations based on the complex crystal structure of GLS1 and its inhibitor CB-839 and found that compound A bearing a thiadiazole skeleton exhibits GLS1 inhibition. Furthermore, we synthesized 27 thiadiazole derivatives in an effort to obtain a more potent GLS1 inhibitor. Among the synthesized derivatives, 4d showed more potent GLS1 inhibitory activity (IC50 of 46.7 µM) than known GLS1 inhibitor DON and A. Therefore, 4d is a very promising novel GLS1 inhibitor. © 2023, The Authors. All rights reserved.

关键词： Structural optimization

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14th International Conference on Practical Applications of Computational Biology and bioinformatics, PACBB 2020

作者： Pérez-Pérez, Martín Lourenço, Anália Igrejas, Gilberto Fdez-Riverola, Florentino ESEI Department of Computer Science University of Vigo Edificio Politécnico Campus Universitario as Lagoas s/n 32004 Ourense Spain CINBIO The Biomedical Research Centre University of Vigo Campus Univesitario Lagoas-Marcosende Vigo36310 Spain SING Next Generation Computer Systems Group Galicia Sur Health Research Institute SERGAS-UVIGO Vigo Spain CEB Centre of Biological Engineering University of Minho Campus de Gualtar Braga4710-057 Portugal Department of Genetics and Biotechnology University of Trás-os-Montes and Alto Douro Vila Real Portugal Functional Genomics and Proteomics Unit University of Trás-os-Montes and Alto Douro Vila Real Portugal LAQV-REQUIMTE Faculty of Science and Technology Nova University of Lisbon Lisbon Portugal

ISBN: (纸本)9783030545673

Concerning the field of Health Sciences, one of the trends with the greatest impact today is related to the feeding and consumption of foods intended for chronic patients with a nutritional association. According to this, the gluten-free diet is one of the diets with a today’s fastest-growing due to their health therapy associations for different diseases and because of more and more people are freely choosing to follow a gluten-free diet. On this subject, e-commerce platforms are destined to be a key point for the sale of this kind of products due to: (i) the change in the business model caused by the electronic platforms and (ii) the expected increase consumption of disease designed special foods. For this reason, the experiences written by chronic disease patients and consumers in these platforms have become increasingly relevant. In this sense, the present work applies biomedical text mining methods and demographic knowledge inference techniques to a large number of gluten-free foods consumer’s experiences to discover a wide range of health-related topics associated to gluten-free products, alimentary social trends and demographics differences. The results of these analyses show significant trends over time;differences between males and females consumption and a wide range of health-related conclusions, such as: why consumers start a free gluten diet or what are the most discussed diseases and adverse reactions related to these products. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Nutrition

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MOST DISCRIMINATIVE STIMULI FOR FUNCTIONAL CELL TYPE CLUSTERING

arXiv

引用

arXiv 2023年

作者： Burg, Max F. Zenkel, Thomas Vystrčilová, Michaela Oesterle, Jonathan Höfling, Larissa Willeke, Konstantin F. Lause, Jan Müller, Sarah Fahey, Paul G. Ding, Zhiwei Restivo, Kelli Sridhar, Shashwat Gollisch, Tim Berens, Philipp Tolias, Andreas S. Euler, Thomas Bethge, Matthias Ecker, Alexander S. International Max Planck Research School for Intelligent Systems Tübingen Germany Institute of Computer Science University of Göttingen Campus Institute Data Science Germany Tübingen AI Center University of Tübingen Germany Institute of Ophthalmic Research University of Tübingen Germany Centre for Integrative Neuroscience University of Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany Hertie Institute for AI in Brain Health University of Tübingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States University Medical Center Göttingen Department of Ophthalmology Germany Bernstein Center for Computational Neuroscience Göttingen Germany University of Göttingen Germany Department of Electrical and Computer Engineering Rice University HoustonTX United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron. © 2023, CC BY-NC-SA.

关键词： Maximum principle

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REAL-TIME GEOINFORMATION systems TO IMPROVE THE QUALITY, SCALABILITY, AND COST OF INTERNET OF THINGS FOR AGRI-ENVIRONMENT research

arXiv

引用

arXiv 2024年

作者： Runck, Bryan C. Schulz, Bobby Bishop, Jeff Carlson, Nathan Chantigian, Bryan Deters, Gary Erdmann, Jesse Ewing, Patrick M. Felzan, Michael Fu, Xiao Greyling, Jan Hogan, Christopher J. Hollman, Andrew Joglekar, Ali Junker, Kris Kantar, Michael Kaunda, Lumbani Krishna, Mohana Lynch, Benjamin Marchetto, Peter Marsolek, Megan McKay, Troy Morris, Brad Niaghi, Ali Rashid Pamulaparthy, Keerthi Pardey, Philip Piotrowski, Ann Poudyal, Christina Prather, Tom Raghavan, Barath Reiter, Maggie Rosen, Lucas Salazar, Benjamin Scobbie, Andrew Sharma, Vasudha Silverstein, Kevin A.T. Singh, Gurparteet Strock, Jeff Subedi, Samikshya Tang, Evan Turturillo, Gianna Watkins, Eric Webster, Blake Wilgenbusch, James GEMS Informatics Center University of Minnesota Twin Cities United States Department of Geography Environment and Society University of Minnesota Twin Cities United States Minnesota Supercomputing Institute University of Minnesota United States Department of Electrical Engineering University of Minnesota Twin Cities United States Department of Applied Economics University of Minnesota Twin Cities United States Department of Horticultural Science University of Minnesota Twin Cities United States Department of Computer Science University of Southern California United States Sensing Inc. Conservify Graphspan LLC USDA-ARS Food Systems Research Unit BurlingtonVT05405 United States Amazon United States Department of Soil Water and Climate University of Minnesota Twin Cities United States Department of Computer Science and Engineering University of Minnesota Twin Cities United States Departmnet of Agricultural Education Communication and Marketing University of Minnesota Twin Cities United States Benson Hill United States Southwest Research and Outreach Center University of Minnesota Twin Cities United States Code Weavers Department of Mechanical Engineering University of Minnesota Twin Cities United States University of Hawaii at Manoa HonoluluHI United States Minnesota Agricultural Experiment Station University of Minnesota Twin Cities United States Sunday Stellenbosch AgroInformatics Initiative Stellenbosch University South Africa Ameriprise Financial Services LLC Optum University of Minnesota Twin Cities United States

With the increasing emphasis on machine learning and artificial intelligence to drive knowledge discovery in the agricultural sciences, spatial internet of things (IoT) technologies have become increasingly important for collecting real-time, high resolution data for these models. However, managing large fleets of devices while maintaining high data quality remains an ongoing challenge as scientists iterate from prototype to mature end-to-end applications. Here, we provide a set of case studies using the framework of technology readiness levels for an open source spatial IoT system. The spatial IoT systems underwent 3 major and 14 minor system versions, had over 2,727 devices manufactured both in academic and commercial contexts, and are either in active or planned deployment across four continents. Our results show the evolution of a generalizable, open source spatial IoT system designed for agricultural scientists, and provide a model for academic researchers to overcome the challenges that exist in going from one-off prototypes to thousands of internet-connected devices. © 2024, CC BY-NC-ND.

关键词： Internet of things

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The Sensorium competition on predicting large-scale mouse primary visual cortex activity

arXiv

引用

arXiv 2022年

作者： Willeke, Konstantin F. Fahey, Paul G. Bashiri, Mohammad Pede, Laura Burg, Max F. Blessing, Christoph Cadena, Santiago A. Ding, Zhiwei Lurz, Konstantin-Klemens Ponder, Kayla Muhammad, Taliah Patel, Saumil S. Ecker, Alexander S. Tolias, Andreas S. Sinz, Fabian H. International Max Planck Research School for Intelligent Systems University of Tübingen Germany Institute for Bioinformatics and Medical Informatics University of Tübingen Germany Institute of Computer Science Campus Institute Data Science University of Göttingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Institute for Theoretical Physics University of Tübingen Germany Max Planck Institute for Dynamics and Self-Organization Göttingen Germany Electrical and Computer Engineering Rice University Houston United States

The neural underpinning of the biological visual system is challenging to study experimentally, in particular as the neuronal activity becomes increasingly nonlinear with respect to visual input. Artificial neural networks (ANNs) can serve a variety of goals for improving our understanding of this complex system, not only serving as predictive digital twins of sensory cortex for novel hypothesis generation in silico, but also incorporating bio-inspired architectural motifs to progressively bridge the gap between biological and machine vision. The mouse has recently emerged as a popular model system to study visual information processing, but no standardized large-scale benchmark to identify state-of-the-art models of the mouse visual system has been established. To fill this gap, we propose the SENSORIUM benchmark competition. We collected a large-scale dataset from mouse primary visual cortex containing the responses of more than 28,000 neurons across seven mice stimulated with thousands of natural images, together with simultaneous behavioral measurements that include running speed, pupil dilation, and eye movements. The benchmark challenge will rank models based on predictive performance for neuronal responses on a held-out test set, and includes two tracks for model input limited to either stimulus only (SENSORIUM) or stimulus plus behavior (SENSORIUM+). We provide a starting kit to lower the barrier for entry, including tutorials, pre-trained baseline models, and APIs with one line commands for data loading and submission. We would like to see this as a starting point for regular challenges and data releases, and as a standard tool for measuring progress in large-scale neural system identification models of the mouse visual system and beyond. © 2022, CC BY-NC-ND.

关键词： Neural networks

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Tera-OP/s Neuromorphic Processing with Kerr Microcombs

Tera-OP/s Neuromorphic Processing with Kerr Microcombs

引用

Photonics in Switching and Computing, PSC 2021

作者： Tan, M. Xu, X. Wu, J. Boes, A. Corcoran, B. Nguyen, T. Chu, S.T. Little, B.E. Morandotti, R. Hicks, D.G. Mitchell, A. Moss, David J. Optical Sciences Centre Swinburne University of Technology HawthornVIC3122 Australia Dept. of Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia School of Engineering RMIT University MelbourneVIC3001 Australia Department of Physics and Material Science City University of Hong Kong Tat Chee Avenue Hong Kong Xi'an Institute of Optics and Precision Mechanics Precision Mechanics of CAS Xi'an China INRS-Ãnergie Matriaux et Tlcommunications 1650 Boulevard Lionel-Boulet VarennesQubecJ3X 1S2 Canada Walter and Eliza Hall Institute of Medical Research Bioinformatics Division ParkvilleVictoria3052 Australia

We report optical neuromorphic processors based on Kerr micro-combs that are programmable, scalable and ultra-high speed. We demonstrate a perceptron at 11.9Giga-OPS at 8 bits, or 95.2 Gbps, and a convolutional accele...

ISBN: (纸本)9781557528209

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Wideband communications through drone-assisted cognitive radio VANETs using SURF channel selection

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Digital Communications and Networks 2024年

作者： Ali Raza Zeshan Iqbal Farhan Aadil Muhammad Attique Khan Seifedine Kadry Hussain Mobarak Albarakati University of Engineering and Technology Taxila Pakistan Sivas University of Science and Technology Sivas Turkey COMSATS University Islamabad Pakistan HITEC University Taxila Pakistan Noroff University College Norway and MEU Research Unit Middle East University Amman 11831 Jordan Computer and Network Engineering Department College of Computer and Information Systems Umm Al-Qura University Makkah 24382 Saudi Arabia

In the past decade, Vehicular Ad-hoc Network (VANET) research has flourished, particularly in the context of smart cities, where it plays a pivotal role in supporting intelligent transportation systems and entertainment services. However, intermittent connectivity, unscalable networks, and high packet collision rates are the key challenges that put hindrances on the wide applications of VANETs. The severity of these challenges becomes even more intensified when deployed in urban areas. Therefore, VANETs should have a communication model that must satisfy the delay and bandwidth needs of VANET applications. To meet the low latency demands of both safety-critical applications and bandwidth-intensive infotainment services while optimizing resource utilization, we introduced cognitive radio technology into drone-assisted VANETs. Our model leverages licensed spectrum opportunistically without interfering with the primary user and relies on line-of-sight communications between drones and vehicles. We employ the SURF channel selection strategy to identify the most suitable channel from available options. SURF selects the channel based on the primary user activity and number of cognitive users. The primary user activity model is based on the alternating on/off Markov Renewal Process (MRP), and Cognitive Radio (CR) occupancy is based on the number of cognitive neighbors using a specific channel. Extensive experiments are conducted to evaluate the performance of the proposed models for safety-critical and entertainment applications. The results indicate that SURF outperforms the others in terms of the delivery ratio and interference to the primary user, guaranteeing the timely delivery of emergency messages in less than 100 ms to nearby vehicles to avoid further damage. The availability of free spectrum results in a higher throughput of 18 Mbps for entertainment applications.

关键词： Cognitive radio Channel selection Primary user Drones SURF strategy

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Finding shortest and nearly shortest path nodes in large substantially incomplete networks

arXiv

引用

arXiv 2022年

作者： Kitsak, Maksim Ganin, Alexander Elmokashfi, Ahmed Cui, Hongzhu Eisenberg, Daniel A. Alderson, David L. Korkin, Dmitry Linkov, Igor Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Network Science Institute Northeastern University BostonMA02115 United States Department of Systems and Information Engineering University of Virginia CharlottesvilleVA22904 United States U.S. Army Engineer Research and Development Center Contractor Concord MA01742 United States Simula Metropolitan Center for Digital Engineering Oslo Norway Bioinformatics and Computational Biology Program Worcester Polytechnic Institute WorcesterMA01609 United States Institute for Genomic Medicine Columbia University Medical Center New YorkNY United States Operations Research Department Naval Postgraduate School MontereyCA93943 United States Data Science Program Worcester Polytechnic Institute WorcesterMA01609 United States Computer Science Department Worcester Polytechnic Institute WorcesterMA01609 United States U.S. Army Engineer Research and Development Center Environmental Laboratory Concord MA01742 United States

Dynamic processes on networks, be it information transfer in the Internet, contagious spreading in a social network, or neural signaling, take place along shortest or nearly shortest paths. Unfortunately, our maps of most large networks are substantially incomplete due to either the highly dynamic nature of networks, or high cost of network measurements, or both, rendering traditional path finding methods inefficient. We find that shortest paths in large real networks, such as the network of protein-protein interactions (PPI) and the Internet at the autonomous system (AS) level, are not random but are organized according to latent-geometric rules. If nodes of these networks are mapped to points in latent hyperbolic spaces, shortest paths in them align along geodesic curves connecting endpoint nodes. We find that this alignment is sufficiently strong to allow for the identification of shortest path nodes even in the case of substantially incomplete networks. We demonstrate the utility of latent-geometric path finding in problems of cellular pathway reconstruction and communication security. Copyright © 2022, The Authors. All rights reserved.

关键词： Proteins

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