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arXiv 2022年

作者： MacLaren, Neil G. Kundu, Prosenjit Masuda, Naoki Department of Mathematics State University of New York BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States

Successfully anticipating sudden major changes in complex systems is a practical concern. Such complex systems often form a heterogeneous network, which may show multistage transitions in which some nodes experience a regime shift earlier than others as an environment gradually changes. Here we investigate early warning signals for networked systems undergoing a multistage transition. We found that knowledge of both the ongoing multistage transition and network structure enables us to calculate effective early warning signals for multistage transitions. Furthermore, we found that small subsets of nodes could anticipate transitions as well as or even better than using all the nodes. Even if we fix the network and dynamical system, no single best subset of nodes provides good early warning signals, and a good choice of sentinel nodes depends on the tipping direction and the current stage of the dynamics within a multistage transition, which we systematically characterize. © 2022, CC BY.

关键词： Dynamics

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Nucleon form factors in Nf = 2 + 1 lattice QCD at the physical point: finite lattice spacing effect on the root-mean-square radii

arXiv

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arXiv 2023年

作者： Tsuji, Ryutaro Aoki, Yasumichi Ishikawa, Ken-Ichi Kuramashi, Yoshinobu Sasaki, Shoichi Sato, Kohei Shintani, Eigo Watanabe, Hiromasa Yamazaki, Takeshi Department of Physics Tohoku University Sendai980-8578 Japan RIKEN Center for Computational Science Kobe650-0047 Japan Core of Research for the Energetic Universe Graduate School of Advanced Science and Engineering Hiroshima University Higashi-Hiroshima739-8526 Japan Center for Computational Sciences University of Tsukuba Ibaraki Tsukuba305-8577 Japan Degree Programs in Pure and Applied Sciences Graduate School of Science and Technology University of Tsukuba Ibaraki305-8571 Japan Yukawa Institute for Theoretical Physics Kyoto University Kyoto606-8502 Japan Institute of Pure and Applied Sciences University of Tsukuba Ibaraki Tsukuba305-8571 Japan

We present results for the nucleon form factors: electric (GE), magnetic (GM), axial (FA), induced pseudoscalar (FP) and pseudoscalar (GP) form factors, using the second PACS10 ensemble that is one of three sets of 2+1 flavor lattice QCD configurations at physical quark masses in large spatial volumes (exceeding (10 fm)3). The second PACS10 gauge configurations are generated by the PACS Collaboration with the six stout-smeared O(a) improved Wilson quark action and Iwasaki gauge action at the second gauge coupling β = 2.00 corresponding to the lattice spacing of a = 0.063 fm. We determine the isovector electric, magnetic and axial radii and magnetic moment from the corresponding form factors, as well as the axial-vector coupling gA. Combining our previous results for the coarser lattice spacing [E. Shintani et al., Phys. Rev. D99 (2019) 014510;Phys. Rev. D102 (2020) 019902 (erratum)], the finite lattice spacing effects on the isovector radii, magnetic moment and axial-vector coupling are investigated using the difference between the two results. It was found that the effect on gA is kept smaller than the statistical error of 2% while the effect on the isovector radii was observed as a possible discretization error of about 10%, regardless of the channel. We also report the partially conserved axial vector current (PCAC) relation using a set of nucleon three-point correlation functions in order to verify the effect by O(a)-improvement of the axial-vector current. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic moments

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Enhancing Label Consistency on Document-level Named Entity Recognition

arXiv

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arXiv 2022年

作者： Jeong, Minbyul Kang, Jaewoo Department of Computer Science and Engineering Korea University Seoul02841 Korea Republic of Interdisciplinary Graduate Program in Bioinformatics Korea University Seoul Korea Republic of AIGEN Sciences Seoul04778 Korea Republic of

Summary: Named entity recognition (NER) is a fundamental part of extracting information from documents in biomedical applications. A notable advantage of NER is its consistency in extracting biomedical entities in a document context. Although existing document NER models show consistent predictions, they still do not meet our expectations. We investigated whether the adjectives and prepositions within an entity cause a low label consistency, which results in inconsistent predictions. In this paper, we present our method, ConNER, which enhances the label dependency of modifiers (e.g., adjectives and prepositions) to achieve higher label agreement. ConNER refines the draft labels of the modifiers to improve the output representations of biomedical entities. The effectiveness of our method is demonstrated on four popular biomedical NER datasets;in particular, its efficacy is proved on two datasets with 7.5–8.6% absolute improvements in the F1 score. We interpret that our ConNER method is effective on datasets that have intrinsically low label consistency. In the qualitative analysis, we demonstrate how our approach makes the NER model generate consistent predictions. © 2022, CC BY-SA.

关键词： Forecasting

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Expectation-maximization contrastive learning for compact video-and-language representations 22

Expectation-maximization contrastive learning for compact vi...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Peng Jin Jinfa Huang Fenglin Liu Xian Wu Shen Ge Guoli Song David A. Clifton Jie Chen School of Electronic and Computer Engineering Peking University China and AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School China Department of Engineering Science University of Oxford UK Tencent JARVIS Lab China Peng Cheng Laboratory Shenzhen China Department of Engineering Science University of Oxford UK and Oxford-Suzhou Centre for Advanced Research Suzhou China School of Electronic and Computer Engineering Peking University China and Peng Cheng Laboratory Shenzhen China and AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School China

ISBN: (纸本)9781713871088

Most video-and-language representation learning approaches employ contrastive learning, e.g., CLIP [53], to project the video and text features into a common latent space according to the semantic similarities of text-video pairs. However, such learned shared latent spaces are not often optimal, and the modality gap between visual and textual representation can not be fully eliminated. In this paper, we propose Expectation-Maximization Contrastive Learning (EMCL) to learn compact video-and-language representations. Specifically, we use the Expectation-Maximization algorithm to find a compact set of bases for the latent space, where the features could be concisely represented as the linear combinations of these bases. Such feature decomposition of video-and-language representations reduces the rank of the latent space, resulting in increased representing power for the semantics. Extensive experiments on three benchmark text-video retrieval datasets prove that our EMCL can learn more discriminative video-and-language representations than previous methods, and significantly outperform previous state-of-the-art methods across all metrics. More encouragingly, the proposed method can be applied to boost the performance of existing approaches either as a jointly training layer or an out-of-the-box inference module with no extra training, making it easy to be incorporated into any existing methods.

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Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning

Out-of-Distributed Semantic Pruning for Robust Semi-Supervis...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yu Wang Pengchong Qiao Chang Liu Guoli Song Xiawu Zheng Jie Chen School of Electronic and Computer Engineering Peking University Shenzhen China AI for Science(AI4S)-Preferred Program Peking University Shenzhen Graduate School China Peng Cheng Laboratory Shenzhen China Department of Automation and BNRist Tsinghua University Beijing China

Recent advances in robust semi-supervised learning (SSL) typically filter out-of-distribution (OOD) information at the sample level. We argue that an overlooked problem of robust SSL is its corrupted information on semantic level, practically limiting the development of the field. In this paper, we take an initial step to explore and propose a unified framework termed OOD Semantic Pruning (OSP), which aims at pruning OOD semantics out from in-distribution (ID) features. Specifically, (i) we propose an aliasing OOD matching module to pair each ID sample with an OOD sample with semantic overlap. (ii) We design a soft orthogonality regularization, which first transforms each ID feature by suppressing its semantic component that is collinear with paired OOD sample. It then forces the predictions before and after soft orthogonality decomposition to be consistent. Being practically simple, our method shows a strong performance in OOD detection and ID classification on challenging benchmarks. In particular, OSP surpasses the previous state-of-the-art by 13.7% on accuracy for ID classification and 5.9% on AUROC for OOD detection on TinyImageNet dataset. The source codes are publicly available at https://***/rain305f/OSP.

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Nonlinear Self-Calibrated Spectrometer with Single GeSe-InSe Heterojunction Device

arXiv

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arXiv 2023年

作者： Darweesh, Rana Yadav, Rajesh Kumar Adler, Elior Poplinger, Michal Levi, Adi Lee, Jea-Jung Leshem, Amir Ramasubramaniam, Ashwin Xia, Fengnian Naveh, Doron Faculty of Engineering Bar-Ilan University Ramat-Gan52900 Israel Institue of Nanotechnology and Advanced Materials Bar-Ilan University Ramat-Gan52900 Israel Department of Electrical Engineering Yale University New HavenCT United States Department of Mechanical and Industrial Engineering University of Massachusetts AmherstMA01003 United States Materials Science and Engineering Graduate Program University of Massachusetts AmherstMA01003 United States

Optical spectroscopy – the measurement of electromagnetic spectra – is fundamental to various scientific domains and serves as the building block of numerous technologies.1–3 computational spectrometry is an emerging field that employs an array of photodetectors with different spectral responses or a single photodetector device with tunable spectral response, in conjunction with numerical algorithms, for spectroscopic measurements.4–6 Compact single photodetectors made from layered materials are particularly attractive, 7–9 since they eliminate the need for bulky mechanical and optical components used in traditional spectrometers and can easily be engineered as heterostructures to optimize device performance.7,10 However, compact tunable photodetectors are typically nonlinear devices and this adds complexity to extracting optical spectra from the device response. Here, we report on the training of an artificial neural network (ANN) to recover the full nonlinear spectral photoresponse - a nonlinear problem of high dimensionality - of a single GeSe-InSe p-n heterojunction device. We demonstrate the functionality of a calibrated spectrometer in the spectral range of 400-1100 nm, with a small device footprint of ~25 × 25 μm, and we achieve a mean reconstruction error of 2 × 10-4 for the power-spectrum at a spectral resolution of 0.35 nm. Using our device, we demonstrate a solution to metamerism, an apparent matching of colors with different power spectral distributions, which is a fundamental problem in optical *** Codes Nonlinear Self-Calibrated Spectrometer, Artificial neural network, Optical imaging © 2023, CC BY.

关键词： Spectrometers

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Recent advances in wearable electrochemical biosensors towards technological and material aspects

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Biosensors and Bioelectronics: X 2024年 19卷

作者： Hosseinzadeh Fakhr, Mahan Lopez Carrasco, Ivan Belyaev, Dmitry Kang, Jihun Shin, YeHyeon Yeo, Jong-Souk Koh, Won-Gun Ham, Jeongwoo Michaelis, Alexander Opitz, Joerg Beshchasna, Natalia Fraunhofer Institute for Ceramic Technologies and Systems IKTS Maria-Reiche-Straße 2 Dresden01109 Germany Faculty of Mechanical Science and Engineering Dresden University of Technology Dresden Germany School of Integrated Technology College of Computing Yonsei University Seoul Korea Republic of BK21 Graduate Program in Intelligent Semiconductor Technology Yonsei University Seoul Korea Republic of Department of Chemical and Biomolecular Engineering Yonsei University Seoul Korea Republic of

The next generation of wearable biosensors comes with the latest advancements in biosensor technology. Soft and stretchable electrode materials like hydrogels with the similar functionalities of human tissue including stretchability, self-healability, and responsiveness to different stimuli have emerged as the most versatile materials in wearable electronics. The incorporation of conductive nanofillers is found to enhance the sensitivity of the electrochemical biosensors significantly. Microfluidic technology has reduced the volume of samples and reagents required for the analysis, allowing continuous biomedical monitoring from a drop of biofluid. In this paper, the most advanced progress in electrochemical wearable platforms that can noninvasively and continuously monitor the biochemical markers in body fluids for the diagnosis and health management is reviewed. Innovation in microelectronics, modification, fabrication technologies, and detection methods are the main focus of the discussion. In particular, hydrogel-based sensors and microfluidic systems as the latest technology trends in wearable detection are discussed in detail. Integration of miniaturized electrochemical wearable biosensors with wireless technology as a great promise for real-time healthcare monitoring and point-of-care (POC) diagnostics is also summarized. Finally, we outline the most advanced wearable biosensors with optimized material and design as well as key challenges that need to be addressed to improve sensing performance (accuracy, sensitivity, selectivity, stability), portability (miniaturized size and light weight), and flexibility of the wearable biosensors. © 2024 The Authors

关键词： Body fluids

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Land Use Land Cover Change and Intensity Analysis of Land Transformation in a Moist Semi-Deciduous Forest in Ghana

SSRN

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SSRN 2023年

作者： Dembélé, Famoussa Guuroh, Reginald Tang Asare, Da-Costa Boakye Mensah Ansah, Padmore Boateng Da, Sié Sylvestre Aryee, Jeffrey N.A. Adu-Bredu, Stephen WASCAL Graduate Research Program on Climate Change and Land Use Kwame Nkrumah University of Science and Technology Kumasi Ghana CSIR-Forestry Research Institute of Ghana KNUST P. O. Box UP 63 Kumasi Ghana Department of Geomatic Engineering College of Engineering Kwame Nkrumah University of Science and Technology Kumasi Ghana WASCAL Competence Center 06 Box 9507 Ouagadougou 06 Burkina Faso Department of Meteorology and Climate Science College of Science Kwame Nkrumah University of Science and Technology Kumasi Ghana

Land use land cover change, particularly deforestation has significant implications for global climate and socio-ecological systems as well as resulting ecosystem services from natural systems. In Ghana, the demand for fuel, food, and fibre is projected to lead to significant expansion of croplands, resulting in degradation and deforestation of natural ecosystems. This research presents a spatiotemporal analysis of land use/cover change in the Bobiri forest and its surrounding areas in Ghana's moist semideciduous forest zone. The study aims to investigate land use change in the Bobiri Forest Reserve and its environs from 1986 to 2022, employing intensity analysis to analyse patterns and underlying causes. The methodology used measured land-cover changes at different levels, including intervals, categories, and transitions. The analysis revealed significant changes in land use intensity across different land classes. The overall rate of land use and land cover change exhibited acceleration, indicating extensive land development throughout the studied periods. Notably, cropland and non-vegetated areas experienced the most gains, while the closed forest class consistently declined. Transitions from forests to croplands were observed, highlighting the conversion of natural vegetation for agricultural purposes. Additionally, the results show that non-vegetated areas and croplands dominate the periphery of protected forest areas, influenced by existing legislative and policy frameworks. The study recommends a reconsideration of these policies to address land use decisions and prevent the conversion of non-protected forest areas. By incorporating these findings into land management and planning strategies, policymakers can promote sustainable development and conservation in Ghana's forested regions. © 2023, The Authors. All rights reserved.

关键词： Remote sensing

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Influence of Temperature and Stress State on the Twip Behavior of 201ln and 316lv Austenitic Stainless Steels

SSRN

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SSRN 2022年

作者： Braga, Diogo Pedrino Corrêa, Leonardo Muthi Sordi, Vitor Luiz Rovere, Carlos Alberto Della Cintho, Osvaldo Mitsuyuki Kliauga, Andrea Madeira Federal University of São Carlos Graduate Program in Materials Science and Engineering São Carlos São Paulo Brazil Federal University of São Carlos Graduate Program in Mechanical Engineering São Carlos São Paulo Brazil Federal University of São Carlos Department of Materials Engineering São Carlos São Paulo Brazil State University of Ponta Grossa Department of Materials Engineering Ponta Grossa Paraná Brazil

The present results analyzed deformation behavior under tension and compression of two different austenitic stainless steels: AISI 201LN, with low nickel content and stabilized with Mn and N;and AISI 316LV, stabilized with Ni and with low content of interstitial atoms. Mechanical tests were performed at 10 -3 s -1 and at temperatures varying from -100 to 600 o C to yield comparable values of the stacking fault energy (SFE). The resulting microstructure was analyzed by X-ray diffraction and EBSD. The stress-strain curves were analyzed using the Kocks-Mecking (K-M) approach. For comparable stacking fault energies, temperature enhances the thermal activation effects that led to lower fraction of mechanical twins in the 201LN compared with the 316LV. The crystallographic re-orientation followed the flow rule irrespective of the temperature and deformation mechanism: tension and compression experiments showed the or orientation with the tension axis, respectively. In tension the dislocation storage and recovery factors of the K-M model were larger than in the compression and the amount of stored dislocations and crystallite size reflect these effects. In the compression, the TWIP effect was reduced for the 316LV and suppressed for the 201LN. © 2022, The Authors. All rights reserved.

关键词： Stacking faults

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Characterization of vulnerability of road networks to fluvial flooding using SIS network diffusion model

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Journal of Infrastructure Preservation and Resilience 2020年第1期1卷 1-13页

作者： Abdulla, Bahrulla Kiaghadi, Amin Rifai, Hanadi S. Birgisson, Bjorn Zachry Department of Civil Engineering Texas A & ampM University 3136 TAMU College StationTX77843 United States Research Associate Oden Institute for Computational Engineering and Sciences University of Texas at Austin AustinTX78712 United States Environmental Engineering Graduate Program N138 Engineering Building 1 HoustonTX77204-4003 United States

This study aims to characterize the vulnerability of road networks to fluvial flooding using a network diffusion-based method. Various network diffusion models have been applied widely for modeling the spreading of contagious diseases or capturing opinion dynamics in social networks. By comparison, their application in the context of physical infrastructure networks has just started to gain some momentum, although physical infrastructure networks also exhibit diffusion-like phenomena under certain stressors. This study applies a susceptible-impacted-susceptible (SIS) diffusion model to capture the impact of flooding on the road network connectivity. To that end, this paper undertook the following four steps. First, the road network was modeled as primal graphs and nodes that were flood-prone (or the origins of the fluvial flood) were identified. Second, temporal changes in the flood depth within the road network during a flooding event were obtained using a data-driven geospatial model. Third, based on the relationship between vehicle speed and flood depth on road networks, at each time step, the nodes in the road network were divided into two discrete categories, namely functional and closed, standing for Susceptible and Impacted in the SIS diffusion model, respectively. Then, two parameters of the SIS model, average transition probabilities between states, were estimated using the results of the hydraulic simulation. Fourth, the robustness of the road network under various SIS diffusion scenarios was estimated, which was used to test the statistical significance of the difference between the robustness of the road network against diffusions started from the randomly chosen nodes and nodes with different high centrality measures. The methodology was demonstrated using the road network in the Memorial super neighborhood in Houston. The results show that diffusive disruptions that start from nodes with high centrality values do not necessarily cause a more significan

关键词： Floods

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