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Spectral evidence for unidirectional charge density wave in detwinned BaNi2As2

Physical Review B 2023年第8期108卷 L081104-L081104页

作者： Yucheng Guo Mason Klemm Ji Seop Oh Yaofeng Xie Bing-Hua Lei Luca Moreschini Cheng Chen Ziqin Yue Sergey Gorovikov Tor Pedersen Matteo Michiardi Sergey Zhdanovich Andrea Damascelli Jonathan Denlinger Makoto Hashimoto Donghui Lu Chris Jozwiak Aaron Bostwick Eli Rotenberg Sung-Kwan Mo Rob G. Moore Junichiro Kono Robert J. Birgeneau David J. Singh Pengcheng Dai Ming Yi Department of Physics and Astronomy Rice University Houston Texas 77005 USA Advanced Light Source Lawrence Berkeley National Laboratory Berkeley California 94720 USA Department of Physics University of California Berkeley California 94720 USA Department of Physics and Astronomy University of Missouri Columbia Missouri 65211 USA Materials Science Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Department of Physics University of Oxford Oxford OX1 3PU United Kingdom Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston Texas 77005 USA Canadian Light Source Inc. 44 Innovation Boulevard Saskatoon SK S7N 2V3 Canada Department of Physics and Astronomy University of British Columbia Vancouver British Columbia V6T 1Z1 Canada Quantum Matter Institute University of British Columbia Vancouver British Columbia V6T 1Z4 Canada Max Planck Institute for Chemical Physics of Solids Nöthnitzer Straße 40 01187 Dresden Germany Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory Menlo Park California 94025 USA Materials Science and Technology Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Department of Electrical and Computer Engineering Rice University Houston Texas 77005 USA Smalley-Curl Institute Rice University Houston Texas 77005 USA Department of Materials Science and NanoEngineering Rice University Houston Texas 77005 USA Department of Chemistry University of Missouri Columbia Missouri 65211 USA

In the iron-based superconductors, unconventional superconductivity emerges in proximity to intertwined electronic orders consisting of an electronic nematic order and a spin density wave (SDW). Recently, BaNi2As2, like its well-known iron-based analog BaFe2As2, has been discovered to host a symmetry-breaking structural transition but coupled to a unidirectional charge density wave (CDW) instead of SDW, providing a novel platform to study intertwined orders. Here, through a systematic angle-resolved photoemission spectroscopy study combined with a detwinning B1g uniaxial strain, we identify distinct spectral evidence of band evolution due to the structural transition as well as CDW-induced band folding. In contrast to the nematicity and spin density wave in BaFe2As2, the structural and CDW order parameters in BaNi2As2 are observed to be strongly coupled and do not separate in the presence of uniaxial strain. Furthermore, no nematic band splitting is resolved above the structural transition. Our measurements point to a likely lattice origin of the CDW order in BaNi2As2.

关键词： Charge density waves Nematic phase transition Iron-based superconductors Angle-resolved photoemission spectroscopy

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Estimands and their Estimators for Clinical Trials Impacted by the COVID-19 Pandemic: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

arXiv

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arXiv 2022年

作者： van Lancker, Kelly Tarima, Sergey Bartlett, Jonathan Bauer, Madeline Bharani-Dharan, Bharani Bretz, Frank Flournoy, Nancy Michiels, Hege Parra, Camila Olarte Rosenberger, James L. Cro, Suzie Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Baltimore United States Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Division of Biostatistics Medical College of Wisconsin United States Department of Mathematical Sciences University of Bath United Kingdom Division of Infectious Diseases Keck School of Medicine University of Southern California Los Angeles United States Novartis Pharmaceuticals East Hanover United States Novartis Pharma AG Basel Switzerland Section for Medical Statistics Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria Department of Statistics University of Missouri Columbia United States National Institute of Statistical Sciences Department of Statistics Penn State University University ParkPA16802-2111 United States Imperial Clinical Trials Unit Imperial College London United Kingdom

The COVID-19 pandemic continues to affect the conduct of clinical trials globally. Complications may arise from pandemic-related operational challenges such as site closures, travel limitations and interruptions to the supply chain for the investigational product, or from health-related challenges such as COVID-19 infections. Some of these complications lead to unforeseen intercurrent events in the sense that they affect either the interpretation or the existence of the measurements associated with the clinical question of interest. In this article, we demonstrate how the ICH E9(R1) Addendum on estimands and sensitivity analyses provides a rigorous basis to discuss potential pandemic-related trial disruptions and to embed these disruptions in the context of study objectives and design elements. We introduce several hypothetical estimand strategies and review various causal inference and missing data methods, as well as a statistical method that combines unbiased and possibly biased estimators for estimation. To illustrate, we describe the features of a stylized trial, and how it may have been impacted by the pandemic. This stylized trial will then be re-visited by discussing the changes to the estimand and the estimator to account for pandemic disruptions. Finally, we outline considerations for designing future trials in the context of unforeseen disruptions. © 2022, CC BY.

关键词： Sensitivity analysis

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Handling time-dependent exposures and confounders when estimating attributable fractions — bridging the gap between multistate and counterfactual modeling

arXiv

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arXiv 2020年

作者： Steen, Johan Morzywolek, Pawel Van Biesen, Wim Decruyenaere, Johan Vansteelandt, Stijn Department of Internal Medicine and Pediatrics Ghent University Belgium Renal Division Ghent University Hospital Belgium Department of Intensive Care Medicine Ghent University Hospital Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium Department of Medical Statistics London School of Hygiene and Tropical Medicine United Kingdom

The population-attributable fraction (PAF) expresses the proportion of events that can be ascribed to a certain exposure in a certain population. It can be strongly time-dependent because either exposure incidence or excess risk may change over time. Competing events may moreover hinder the outcome of interest from being observed. Occurrence of either of these events may, in turn, prevent the exposure of interest. Estimation approaches therefore need to carefully account for the timing of events in such highly dynamic settings. The use of multistate models has been widely encouraged to eliminate preventable yet common types of time-dependent bias. Even so, it has been pointed out that proposed multistate modeling approaches for PAF estimation fail to fully eliminate such bias. In addition, assessing whether patients die from rather than with a certain exposure not only requires adequate modeling of the timing of events but also of their confounding factors. While proposed multistate modeling approaches for confounding adjustment may adequately accommodate baseline imbalances, unlike g-methods, these proposals are not generally equipped to handle time-dependent confounding. However, the connection between multistate modeling and g-methods (e.g. inverse probability of censoring weighting) for PAF estimation is not readily apparent. In this paper, we provide a weighting-based characterization of both approaches to illustrate this connection, to pinpoint current shortcomings of multistate modeling, and to enhance intuition into simple modifications to overcome these. R code is made available to foster the uptake of g-methods for PAF estimation. Copyright © 2020, The Authors. All rights reserved.

关键词： Inverse problems

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DESIGN AND IMPLEMENTATION OF THE ILLINOIS EXPRESS QUANTUM METROPOLITAN AREA NETWORK

arXiv

引用

arXiv 2022年

作者： Chung, Joaquin Eastman, Ely M. Kanter, Gregory S. Kapoor, Keshav Lauk, Nikolai Peña, Cristián Plunkett, Robert Sinclair, Neil Thomas, Jordan M. Valivarthi, Raju Xie, Si Kettimuthu, Rajkumar Kumar, Prem Spentzouris, Panagiotis Spiropulu, Maria Data Science and Learning Division Argonne National Laboratory 9700 S Cass Ave. LemontIL60439 United States The University of Chicago Consortium for Advanced Science and Engineering 924 E 57th St ChicagoIL60637 United States NuCrypt Llc 1840 Oak Ave EvanstonIL60201 United States Center for Photonic Communication and Computing Department of Electrical and Computer Engineering McCormick School of Engineering and Applied Science Northwestern University 2145 Sheridan Road EvanstonIL60208-3118 United States Division of Physics Mathematics and Astronomy California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States Fermi National Accelerator Laboratory Kirk Rd and Pine St BataviaIL60510 United States John A. Paulson School of Engineering and Applied Sciences Harvard University 29 Oxford St. CambridgeMA02138 United States 2205 Tech Drive Suite 1-160 EvanstonIL60208 United States Department of Physics and Astronomy Northwestern University 2145 Sheridan Road EvanstonIL60208-3112 United States

The Illinois Express Quantum Network (IEQNET) is a program to realize metropolitan scale quantum networking over deployed optical fiber using currently available technology. IEQNET consists of multiple sites that are geographically dispersed in the Chicago metropolitan area. Each site has one or more quantum nodes (Q-Nodes) representing the communication parties in a quantum network. Q-Nodes generate or measure quantum signals such as entangled photons and communicate the measurement results via standard, classical signals and conventional networking processes. The entangled photons in IEQNET nodes are generated at multiple wavelengths, and are selectively distributed to the desired users via transparent optical switches. Here we describe the network architecture of IEQNET, including the Internet-inspired layered hierarchy that leverages softwaredefined networking (SDN) technology to perform traditional wavelength routing and assignment between the Q-Nodes. Specifically, SDN decouples the control and data planes, with the control plane being entirely implemented in the classical domain. We also discuss the IEQNET processes that address issues associated with synchronization, calibration, network monitoring, and scheduling. An important goal of IEQNET is to demonstrate the extent to which the control plane classical signals can co-propagate with the data plane quantum signals in the same fiber lines (quantum-classical signal "coexistence"). This goal is furthered by the use of tunable narrow-band optical filtering at the receivers and, at least in some cases, a wide wavelength separation between the quantum and classical channels. We envision IEQNET to aid in developing robust and practical quantum networks by demonstrating metro-scale quantum communication tasks such as entanglement distribution and quantum-state teleportation. Copyright © 2022, The Authors. All rights reserved.

关键词： Network architecture

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E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

arXiv

引用

arXiv 2021年

作者： Batzner, Simon Musaelian, Albert Sun, Lixin Geiger, Mario Mailoa, Jonathan P. Kornbluth, Mordechai Molinari, Nicola Smidt, Tess E. Kozinsky, Boris John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Robert Bosch Research and Technology Center CambridgeMA02139 United States Computational Research Division Center for Advanced Mathematics for Energy Research Applications Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02142 United States

This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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Levels and trends in the sex ratio at birth in seven provinces of Nepal between 1980 and 2016 with probabilistic projections to 2050: A Bayesian modeling approach

arXiv

引用

arXiv 2020年

作者： Chao, Fengqing Samir, K.C. Ombao, Hernando Statistics Program Computer Electrical And Mathematical Sciences And Engineering Division King Abdullah University Of Science And Technology 4700 KAUST Thuwal23955-6900 Saudi Arabia Asian Demographic Research Institute Shanghai University Shangda Road Shanghai200444 China International Institute For Applied Systems Analysis Schlossplatz 1 Laxenburg2361 Austria

Background: The sex ratio at birth (SRB;ratio of male to female births) in Nepal has been reported without imbalance on the national level. However, the national SRB could mask the disparity within the country. Given the demographic and cultural heterogeneities in Nepal, it is crucial to model Nepal SRB on the subnational level. Prior studies on subnational SRB in Nepal are mostly based on reporting observed values from surveys and census, and no study has provided probabilistic projections. We aim to estimate and project SRB for the seven provinces of Nepal from 1980 to 2050 using a Bayesian modeling approach. Methods: We compiled an extensive database on provincial SRB of Nepal, consisting 2001, 2006, 2011, and 2016 Nepal Demographic and Health Surveys and 2011 Census. We adopted a Bayesian hierarchical time series model to estimate and project the provincial SRB, with a focus on modelling the potential SRB imbalance. Results: In 2016, the highest SRB is estimated in Province 5 at 1.102 with a 95% credible interval (1.044, 1.127) and the lowest SRB is in Province 2 at 1.053 (1.035, 1.109). During 1980-2016, the provincial SRB was around the same level as the national SRB baseline of 1.049. The SRB imbalance probabilities in all provinces are generally low and vary from 16% in Province 2 to 81% in Province 5. SRB imbalances are estimated to have begun at the earliest in 2001 in Province 5 with a 95% credible interval (1992, 2022) and the latest in 2017 (1998, 2040) in Province 2. We project SRB in all provinces to begin converging back to the national baseline in the mid-2030s. By 2050, the SRBs in all provinces are projected to be around the SRB baseline level. Conclusion: Our findings imply that the majority of provinces in Nepal have low risks of SRB imbalance for the period 1980-2016. However, we identify a few provinces with higher probabilities of having SRB inflation. The projected SRB is an important illustration of potential future prenatal sex discriminat

关键词： Bayesian networks

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Corrigendum to “ICACIA: An Intelligent Context-Aware framework for COBOT in defense industry using ontological and deep learning models” [Robotics and Autonomous Systems Volume 157, November 2022, 104234]

引用

Robotics and Autonomous Systems 2024年 178卷

作者： Arodh Lal Karn Sudhakar Sengan Ketan Kotecha Irina V Pustokhina Denis A Pustokhin V Subramaniyaswamy Dharam Buddhi Department of Financial and Actuarial Mathematics School of Science Xi'an Jiaotong-Liverpool University China Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli Tamil Nadu India Symbiosis Centre for Applied Artificial Intelligence Symbiosis International (Deemed University) Pune Maharashtra 412115 India Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics 117997 Moscow Russia Department of Logistics State University of Management 109542 Moscow Russia School of Computing SASTRA Deemed University Thanjavur 613401 Tamil Nadu India Division of Research & Innovation Uttaranchal University Uttarakhand 248007 Dehradun India

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DRStageNet: Deep Learning for Diabetic Retinopathy Staging from Fundus Images

arXiv

引用

arXiv 2023年

作者： Men, Yevgeniy Fhima, Jonathan Celi, Leo Anthony Ribeiro, Lucas Zago Nakayama, Luis Filipe Behar, Joachim A. The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering The Faculty of Biomedical Engineering Technion Israel Institute of Technology Haifa3200003 Israel The Department of Applied Mathematics The Biomedical Engineering Faculty Technion Israel Institute of Technology Haifa3200003 Israel The Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA02139 United States The Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center BostonMA02215 United States The Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA02115 United States The Ophthalmology department São Paulo Federal University São Paulo Brazil The Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States The Department of Ophthalmology São Paulo Federal University São Paulo Brazil The Faculty of Biomedical Engineering Technion Israel Institute of Technology Haifa3200003 Israel

Diabetic retinopathy (DR) is a prevalent complication of diabetes associated with a significant risk of vision loss. Timely identification is critical to curb vision impairment. Algorithms for DR staging from digital fundus images (DFIs) have been recently proposed. However, models often fail to generalize due to distribution shifts between the source domain on which the model was trained and the target domain where it is deployed. A common and particularly challenging shift is often encountered when the source- and target-domain supports do not fully overlap. In this research, we introduce DRStageNet, a deep learning model designed to mitigate this challenge. We used seven publicly available datasets, comprising a total of 93,534 DFIs that cover a variety of patient demographics, ethnicities, geographic origins and comorbidities. We fine-tune DINOv2, a pretrained model of self-supervised vision transformer, and implement a multi-source domain fine-tuning strategy to enhance generalization performance. We benchmark and demonstrate the superiority of our method to two state-of-the-art benchmarks, including a recently published foundation model. We adapted the gradrollout method to our regression task in order to provide high-resolution explainability heatmaps. The error analysis showed that 59% of the main errors had incorrect reference labels. DRStageNet is accessible at URL [upon acceptance of the manuscript]. © 2023, CC BY.

关键词： Error analysis

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Wetzel’s sector covers unit arcs

arXiv

引用

arXiv 2019年

作者： Panraksa, Chatchawan Wichiramala, Wacharin Mathematics Program Science Division Mahidol University International College 999 Phutthamonthon 4 Road NakhonpathomSalaya73170 Thailand Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University 254 Phayathai Road BangkokPathumwan10330 Thailand

We settle J. Wetzel’s 1970’s conjecture and show that a 30◦ circular sector of unit radius can accommodate every planar arc of unit length. Leo Moser asked in 1966 for the smallest (convex) region in the plane that can accommodate each arc of unit length. With area π/12, this sector is the smallest such set presently known. Moser’s question has prompted a multitude of papers on related problems over the past 50 years, most remaining *** Codes 52A38, 51N05, 51M99 Copyright © 2019, The Authors. All rights reserved.

关键词： Set theory

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Why is the Winner the Best?

Why is the Winner the Best?

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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