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Showing papers by "University of Luxembourg published in 2020"


Journal ArticleDOI
TL;DR: An overview of the recently developed capabilities of the DFTB+ code is given, demonstrating with a few use case examples, and the strengths and weaknesses of the various features are discussed, to discuss on-going developments and possible future perspectives.
Abstract: DFTB+ is a versatile community developed open source software package offering fast and efficient methods for carrying out atomistic quantum mechanical simulations. By implementing various methods approximating density functional theory (DFT), such as the density functional based tight binding (DFTB) and the extended tight binding method, it enables simulations of large systems and long timescales with reasonable accuracy while being considerably faster for typical simulations than the respective ab initio methods. Based on the DFTB framework, it additionally offers approximated versions of various DFT extensions including hybrid functionals, time dependent formalism for treating excited systems, electron transport using non-equilibrium Green’s functions, and many more. DFTB+ can be used as a user-friendly standalone application in addition to being embedded into other software packages as a library or acting as a calculation-server accessed by socket communication. We give an overview of the recently developed capabilities of the DFTB+ code, demonstrating with a few use case examples, discuss the strengths and weaknesses of the various features, and also discuss on-going developments and possible future perspectives.

491 citations


Journal ArticleDOI
24 Jan 2020-Science
TL;DR: Given the increased recognition of the dominant role that nongenetic factors play in disease, an effort to characterize the exposome at a scale comparable to that of the human genome is warranted.
Abstract: Despite extensive evidence showing that exposure to specific chemicals can lead to disease, current research approaches and regulatory policies fail to address the chemical complexity of our world. To safeguard current and future generations from the increasing number of chemicals polluting our environment, a systematic and agnostic approach is needed. The "exposome" concept strives to capture the diversity and range of exposures to synthetic chemicals, dietary constituents, psychosocial stressors, and physical factors, as well as their corresponding biological responses. Technological advances such as high-resolution mass spectrometry and network science have allowed us to take the first steps toward a comprehensive assessment of the exposome. Given the increased recognition of the dominant role that nongenetic factors play in disease, an effort to characterize the exposome at a scale comparable to that of the human genome is warranted.

398 citations


Journal ArticleDOI
TL;DR: Recent ML methods for molecular simulation are reviewed, with particular focus on (deep) neural networks for the prediction of quantum-mechanical energies and forces, on coarse-grained molecular dynamics, on the extraction of free energy surfaces and kinetics, and on generative network approaches to sample molecular equilibrium structures and compute thermodynamics.
Abstract: Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for an ML revolution and have already been profoundly affected by the application of existing ML methods. Here we review recent ML methods for molecular simulation, with particular focus on (deep) neural networks for the prediction of quantum-mechanical energies and forces, on coarse-grained molecular dynamics, on the extraction of free energy surfaces and kinetics, and on generative network approaches to sample molecular equilibrium structures and compute thermodynamics. To explain these methods and illustrate open methodological problems, we review some important principles of molecular physics and describe how they can be incorporated into ML structures. Finally, we identify and describe a list of open challenges for the interface between ML and molecular simulation.

379 citations


Journal ArticleDOI
TL;DR: The aim of this review is to give a thorough overview of the commonly used fibroblast markers in the field and their various strengths and, more importantly, their weaknesses, as well as to highlight potential future avenues for CAF identification and targeting.
Abstract: The tumor microenvironment has been identified as one of the driving factors of tumor progression and invasion. Inside this microenvironment, cancer-associated fibroblasts (CAFs), a type of perpetually activated fibroblasts, have been implicated to have a strong tumor-modulating effect and play a key role in areas such as drug resistance. Identification of CAFs has typically been carried based on the expression of various "CAF markers", such as fibroblast activation protein alpha (FAP) and alpha smooth muscle actin (αSMA), which separates them from the larger pool of fibroblasts present in the body. However, as outlined in this Review, the expression of various commonly used fibroblast markers is extremely heterogeneous and varies strongly between different CAF subpopulations. As such, novel selection methods based on cellular function, as well as further characterizing research, are vital for the standardization of CAF identification in order to improve the cross-applicability of different research studies in the field. The aim of this review is to give a thorough overview of the commonly used fibroblast markers in the field and their various strengths and, more importantly, their weaknesses, as well as to highlight potential future avenues for CAF identification and targeting.

307 citations


Journal ArticleDOI
24 Jan 2020-Science
TL;DR: This work highlights analytical and bioanalytical approaches to isolating, characterizing, and tracking groups of chemicals of concern in complex matrices and proposes techniques that combine chemical analysis and bioassays to facilitate the identification of mixtures of chemicals that pose a combined risk.
Abstract: Chemicals have improved our quality of life, but the resulting environmental pollution has the potential to cause detrimental effects on humans and the environment. People and biota are chronically exposed to thousands of chemicals from various environmental sources through multiple pathways. Environmental chemists and toxicologists have moved beyond detecting and quantifying single chemicals to characterizing complex mixtures of chemicals in indoor and outdoor environments and biological matrices. We highlight analytical and bioanalytical approaches to isolating, characterizing, and tracking groups of chemicals of concern in complex matrices. Techniques that combine chemical analysis and bioassays have the potential to facilitate the identification of mixtures of chemicals that pose a combined risk.

305 citations


Book ChapterDOI
01 Jan 2020
TL;DR: This chapter provides an extensive overview of the literature on the so-called phase-field fracture/damage models (PFMs), particularly, for quasi-static and dynamic fracture of brittle and quasi-brittle materials, from the points of view of a computational mechanician.
Abstract: Fracture is one of the most commonly encountered failure modes of engineering materials and structures. Prevention of cracking-induced failure is, therefore, a major concern in structural designs. Computational modeling of fracture constitutes an indispensable tool not only to predict the failure of cracking structures but also to shed insights into understanding the fracture processes of many materials such as concrete, rock, ceramic, metals, and biological soft tissues. This chapter provides an extensive overview of the literature on the so-called phase-field fracture/damage models (PFMs), particularly, for quasi-static and dynamic fracture of brittle and quasi-brittle materials, from the points of view of a computational mechanician. PFMs are the regularized versions of the variational approach to fracture which generalizes Griffith's theory for brittle fracture. They can handle topologically complex fractures such as initiation, intersecting, and branching cracks in both two and three dimensions with a quite straightforward implementation. One of our aims is to justify the gaining popularity of PFMs. To this end, both theoretical and computational aspects are discussed and extensive benchmark problems (for quasi-static and dynamic brittle/cohesive fracture) that are successfully and unsuccessfully solved with PFMs are presented. Unresolved issues for further investigations are also documented.

290 citations


Posted Content
TL;DR: An overview of applications of ML-FFs and the chemical insights that can be obtained from them is given, and a step-by-step guide for constructing and testing them from scratch is given.
Abstract: In recent years, the use of Machine Learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising applications is the construction of ML-based force fields (FFs), with the aim to narrow the gap between the accuracy of ab initio methods and the efficiency of classical FFs. The key idea is to learn the statistical relation between chemical structure and potential energy without relying on a preconceived notion of fixed chemical bonds or knowledge about the relevant interactions. Such universal ML approximations are in principle only limited by the quality and quantity of the reference data used to train them. This review gives an overview of applications of ML-FFs and the chemical insights that can be obtained from them. The core concepts underlying ML-FFs are described in detail and a step-by-step guide for constructing and testing them from scratch is given. The text concludes with a discussion of the challenges that remain to be overcome by the next generation of ML-FFs.

266 citations


Journal ArticleDOI
01 Dec 2020
TL;DR: The article is meant to bring clarity to the topic in a holistic fashion, looking beyond claims regarding the energy consumption of Bitcoin, which have, so far, dominated the discussion.
Abstract: When talking about blockchain technology in academia, business, and society, frequently generalizations are still heared about its – supposedly inherent – enormous energy consumption This perception inevitably raises concerns about the further adoption of blockchain technology, a fact that inhibits rapid uptake of what is widely considered to be a groundbreaking and disruptive innovation However, blockchain technology is far from homogeneous, meaning that blanket statements about its energy consumption should be reviewed with care The article is meant to bring clarity to the topic in a holistic fashion, looking beyond claims regarding the energy consumption of Bitcoin, which have, so far, dominated the discussion

205 citations


Journal ArticleDOI
TL;DR: The performance of the proposed DL approach is evaluated and compared with state-of-the-art DL-based techniques and its superior performance is demonstrated.
Abstract: This letter presents the first work introducing a deep learning (DL) framework for channel estimation in large intelligent surface (LIS) assisted massive MIMO (multiple-input multiple-output) systems. A twin convolutional neural network (CNN) architecture is designed and it is fed with the received pilot signals to estimate both direct and cascaded channels. In a multi-user scenario, each user has access to the CNN to estimate its own channel. The performance of the proposed DL approach is evaluated and compared with state-of-the-art DL-based techniques and its superior performance is demonstrated.

184 citations


Journal ArticleDOI
TL;DR: The definition of constructive interference (CI) is presented and the corresponding mathematical characterization is formulated for popular modulation types, based on which optimization-based precoding techniques are discussed.
Abstract: Interference is traditionally viewed as a performance limiting factor in wireless communication systems, which is to be minimized or mitigated. Nevertheless, a recent line of work has shown that by manipulating the interfering signals such that they add up constructively at the receiver side, known interference can be made beneficial and further improve the system performance in a variety of wireless scenarios, achieved by symbol-level precoding (SLP). This paper aims to provide a tutorial on interference exploitation techniques from the perspective of precoding design in a multi-antenna wireless communication system, by beginning with the classification of constructive interference (CI) and destructive interference (DI). The definition for CI is presented and the corresponding mathematical characterization is formulated for popular modulation types, based on which optimization-based precoding techniques are discussed. In addition, the extension of CI precoding to other application scenarios as well as for hardware efficiency is also described. Proof-of-concept testbeds are demonstrated for the potential practical implementation of CI precoding, and finally a list of open problems and practical challenges are presented to inspire and motivate further research directions in this area.

177 citations


Journal ArticleDOI
12 Jun 2020
TL;DR: In this paper, the authors argue that significant progress in the exploration and understanding of chemical compound space can be made through a systematic combination of rigorous physical theories, comprehensive synthetic data sets of microscopic and macroscopic properties, and modern machine-learning methods that account for physical and chemical knowledge.
Abstract: Rational design of compounds with specific properties requires understanding and fast evaluation of molecular properties throughout chemical compound space — the huge set of all potentially stable molecules. Recent advances in combining quantum-mechanical calculations with machine learning provide powerful tools for exploring wide swathes of chemical compound space. We present our perspective on this exciting and quickly developing field by discussing key advances in the development and applications of quantum-mechanics-based machine-learning methods to diverse compounds and properties, and outlining the challenges ahead. We argue that significant progress in the exploration and understanding of chemical compound space can be made through a systematic combination of rigorous physical theories, comprehensive synthetic data sets of microscopic and macroscopic properties, and modern machine-learning methods that account for physical and chemical knowledge. Machine-learning techniques have enabled, among many other applications, the exploration of molecular properties throughout chemical space. The specific development of quantum-based approaches in machine learning can now help us unravel new chemical insights.

Proceedings ArticleDOI
25 May 2020
TL;DR: This paper introduces the newly invented concept, large intelligent surface (LIS), to mmWave positioning systems, study the theoretical performance bounds for positioning, and evaluate the impact of the number of LIS elements and the value of phase shifters on the position estimation accuracy.
Abstract: Millimeter-wave (mmWave) multiple-input multiple-output (MIMO) system for the fifth generation (5G) cellular communications can also enable single-anchor positioning and object tracking due to its large bandwidth and inherently high angular resolution In this paper, we introduce the newly invented concept, large intelligent surface (LIS), to mmWave positioning systems, study the theoretical performance bounds (ie, Cramer-Rao lower bounds) for positioning, and evaluate the impact of the number of LIS elements and the value of phase shifters on the position estimation accuracy compared to the conventional scheme with one direct link and one non-line-of-sight path It is verified that better performance can be achieved with a LIS from the theoretical analyses and numerical study

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a massive MIMO transmission scheme with full frequency reuse (FFR) for LEO satellite communication systems and exploited statistical channel state information (sCSI) to address the difficulty of obtaining instantaneous CSI at the transmitter.
Abstract: Low earth orbit (LEO) satellite communications are expected to be incorporated in future wireless networks, in particular 5G and beyond networks, to provide global wireless access with enhanced data rates. Massive multiple-input multiple-output (MIMO) techniques, though widely used in terrestrial communication systems, have not been applied to LEO satellite communication systems. In this paper, we propose a massive MIMO transmission scheme with full frequency reuse (FFR) for LEO satellite communication systems and exploit statistical channel state information (sCSI) to address the difficulty of obtaining instantaneous CSI (iCSI) at the transmitter. We first establish the massive MIMO channel model for LEO satellite communications and simplify the transmission designs via performing Doppler and delay compensations at user terminals (UTs). Then, we develop the low-complexity sCSI based downlink (DL) precoder and uplink (UL) receiver in closed-form, aiming to maximize the average signal-to-leakage-plus-noise ratio (ASLNR) and the average signal-to-interference-plus-noise ratio (ASINR), respectively. It is shown that the DL ASLNRs and UL ASINRs of all UTs reach their upper bounds under some channel condition. Motivated by this, we propose a space angle based user grouping (SAUG) algorithm to schedule the served UTs into different groups, where each group of UTs use the same time and frequency resource. The proposed algorithm is asymptotically optimal in the sense that the lower and upper bounds of the achievable rate coincide when the number of satellite antennas or UT groups is sufficiently large. Numerical results demonstrate that the proposed massive MIMO transmission scheme with FFR significantly enhances the data rate of LEO satellite communication systems. Notably, the proposed sCSI based precoder and receiver achieve the similar performance with the iCSI based ones that are often infeasible in practice.

Journal ArticleDOI
15 Jun 2020
TL;DR: In this paper, the exotic polarization profiles that arise at domain walls and the fundamental mechanisms responsible for domain-wall conduction are discussed, and the prospect of combining domain walls with transition regions observed at phase boundaries, homo-and heterointerfaces, and other quasi-2D objects, enabling emergent properties beyond those available in today's topological systems.
Abstract: Ferroelectric and ferroelastic domain walls are 2D topological defects with thicknesses approaching the unit cell level When this spatial confinement is combined with observations of emergent functional properties, such as polarity in non-polar systems or electrical conductivity in otherwise insulating materials, it becomes clear that domain walls represent new and exciting objects in matter In this Review, we discuss the exotic polarization profiles that can arise at domain walls with multiple order parameters and the different mechanisms that lead to domain-wall polarity in non-polar ferroelastic materials The emergence of energetically degenerate variants of the domain walls themselves suggests the existence of interesting quasi-1D topological defects within such walls We also provide an overview of the general notions that have been postulated as fundamental mechanisms responsible for domain-wall conduction in ferroelectrics We then discuss the prospect of combining domain walls with transition regions observed at phase boundaries, homo- and heterointerfaces, and other quasi-2D objects, enabling emergent properties beyond those available in today’s topological systems Ferroelectric and ferroelastic domain walls are 2D topological defects with thicknesses approaching the unit cell level and emergent functional properties This Review discusses the exotic polarization profiles that arise at domain walls and the fundamental mechanisms responsible for domain-wall conduction

Journal ArticleDOI
TL;DR: In this article, the authors argue that financial technology is the key driver for financial inclusion, which in turn underlies sustainable balanced development, as embodied in the UN Sustainable Development Goals (SDGs), and the full potential of FinTech to support the SDGs may be realized with a progressive approach to the development of underlying infrastructure to support digital financial transformation.
Abstract: We argue financial technology (FinTech) is the key driver for financial inclusion, which in turn underlies sustainable balanced development, as embodied in the UN Sustainable Development Goals (SDGs). The full potential of FinTech to support the SDGs may be realized with a progressive approach to the development of underlying infrastructure to support digital financial transformation. Our research suggests that the best way to think about such a strategy is to focus on four primary pillars. The first pillar requires the building of digital identity, simplified account opening and e-KYC systems, supported by the second pillar of open interoperable electronic payments systems. The third pillar involves using the infrastructure of the first and second pillars to underpin electronic provision of government services and payments. The fourth pillar—design of digital financial markets and systems—supports broader access to finance and investment. Implementing the four pillars is a major journey for any economy, but one which has tremendous potential to transform not only finance but economies and societies, through FinTech, financial inclusion and sustainable balanced development.

Journal ArticleDOI
Michael C. Frank1, Katherine J. Alcock2, Natalia Arias-Trejo3, Gisa Aschersleben4, Dare A. Baldwin5, Stéphanie Barbu, Elika Bergelson6, Christina Bergmann7, Alexis K. Black8, Ryan Blything9, Maximilian P. Böhland10, Petra Bolitho11, Arielle Borovsky12, Shannon M. Brady13, Bettina Braun14, Anna Brown15, Krista Byers-Heinlein16, Linda E. Campbell17, Cara H. Cashon18, Mihye Choi19, Joan Christodoulou13, Laura K. Cirelli20, Stefania Conte21, Sara Cordes22, Christopher Martin Mikkelsen Cox23, Alejandrina Cristia, Rhodri Cusack24, Catherine Davies25, Maartje de Klerk26, Claire Delle Luche27, Laura E. de Ruiter28, Dhanya Dinakar29, Kate C. Dixon18, Virginie Durier, S. Durrant15, Christopher T. Fennell30, Brock Ferguson, Alissa L. Ferry28, Paula Fikkert31, Teresa Flanagan32, Caroline Floccia33, Megan Foley34, Tom Fritzsche35, Rebecca Louise Ann Frost7, Anja Gampe36, Judit Gervain, Nayeli Gonzalez-Gomez37, Anna Gupta38, Laura E. Hahn31, J. Kiley Hamlin39, Erin E. Hannon40, Naomi Havron, Jessica F. Hay41, Mikołaj Hernik42, Barbara Höhle35, Derek M. Houston43, Lauren H. Howard32, Mitsuhiko Ishikawa44, Shoji Itakura44, Iain Jackson28, Krisztina V. Jakobsen45, Marianna Jartó46, Scott P. Johnson13, Caroline Junge26, Didar Karadag47, Natalia Kartushina48, Danielle J. Kellier1, Tamar Keren-Portnoy23, Kelsey Klassen49, Melissa Kline50, Eon-Suk Ko51, Jonathan F. Kominsky52, Jessica E. Kosie5, Haley E. Kragness53, Andrea A. R. Krieger4, Florian Krieger54, Jill Lany55, Roberto J. Lazo56, Michelle Lee57, Chloé Leservoisier, Claartje Levelt38, Casey Lew-Williams58, Matthias Lippold59, Ulf Liszkowski46, Liquan Liu29, Steven G. Luke60, Rebecca A. Lundwall60, Viola Macchi Cassia21, Nivedita Mani59, Caterina Marino, Alia Martin11, Meghan Mastroberardino16, Victoria Mateu13, Julien Mayor48, Katharina Menn31, Christine Michel7, Yusuke Moriguchi44, Benjamin Morris61, Karli M. Nave40, Thierry Nazzi, Claire Noble15, Miriam A. Novack62, Nonah M. Olesen18, Adriel John Orena63, Mitsuhiko Ota64, Robin Panneton65, Sara Parvanezadeh Esfahani41, Markus Paulus66, Carolina Pletti66, Linda Polka63, Christine E. Potter58, Hugh Rabagliati64, Shruthilaya Ramachandran67, Jennifer L. Rennels40, Greg D. Reynolds41, Kelly C. Roth41, Charlotte Rothwell2, Doroteja Rubez43, Yana Ryjova40, Jenny R. Saffran68, Ayumi Sato69, Sophie Savelkouls22, Adena Schachner57, Graham Schafer70, Melanie S. Schreiner59, Amanda Seidl12, Mohinish Shukla19, Elizabeth A. Simpson56, Leher Singh67, Barbora Skarabela64, Gaye Soley47, Megha Sundara13, Anna L. Theakston28, Abbie Thompson55, Laurel J. Trainor53, Sandra E. Trehub20, Anna S. Trøan48, Angeline Sin-Mei Tsui30, Katherine Elizabeth Twomey28, Katie Von Holzen, Yuanyuan Wang43, Sandra R. Waxman62, Janet F. Werker39, Stephanie Wermelinger36, Alix Woolard17, Daniel Yurovsky61, Katharina Zahner14, Martin Zettersten68, Melanie Soderstrom49 
Stanford University1, Lancaster University2, National Autonomous University of Mexico3, Saarland University4, University of Oregon5, Duke University6, Max Planck Society7, Haskins Laboratories8, University of Bristol9, Dresden University of Technology10, Victoria University of Wellington11, Purdue University12, University of California, Los Angeles13, University of Konstanz14, University of Liverpool15, Concordia University16, University of Newcastle17, University of Louisville18, University of Massachusetts Boston19, University of Toronto20, University of Milan21, Boston College22, University of York23, Trinity College, Dublin24, University of Leeds25, Utrecht University26, University of Essex27, University of Manchester28, University of Sydney29, University of Ottawa30, Radboud University Nijmegen31, Franklin & Marshall College32, University of Plymouth33, Florida State University-Panama34, University of Potsdam35, University of Zurich36, Oxford Brookes University37, Leiden University38, University of British Columbia39, University of Nevada, Las Vegas40, University of Tennessee41, Central European University42, Ohio State University43, Kyoto University44, James Madison University45, University of Hamburg46, Boğaziçi University47, University of Oslo48, University of Manitoba49, Massachusetts Institute of Technology50, Chosun University51, Harvard University52, McMaster University53, University of Luxembourg54, University of Notre Dame55, University of Miami56, University of California, San Diego57, Princeton University58, University of Göttingen59, Brigham Young University60, University of Chicago61, Northwestern University62, McGill University63, University of Edinburgh64, Virginia Tech65, Ludwig Maximilian University of Munich66, National University of Singapore67, University of Wisconsin-Madison68, Shimane University69, University of Reading70
16 Mar 2020
TL;DR: In this paper, a large-scale, multisite study aimed at assessing the overall replicability of a single theoretically important phenomenon and examining methodological, cultural, and developmental moderators was conducted.
Abstract: Psychological scientists have become increasingly concerned with issues related to methodology and replicability, and infancy researchers in particular face specific challenges related to replicability: For example, high-powered studies are difficult to conduct, testing conditions vary across labs, and different labs have access to different infant populations. Addressing these concerns, we report on a large-scale, multisite study aimed at (a) assessing the overall replicability of a single theoretically important phenomenon and (b) examining methodological, cultural, and developmental moderators. We focus on infants’ preference for infant-directed speech (IDS) over adult-directed speech (ADS). Stimuli of mothers speaking to their infants and to an adult in North American English were created using seminaturalistic laboratory-based audio recordings. Infants’ relative preference for IDS and ADS was assessed across 67 laboratories in North America, Europe, Australia, and Asia using the three common methods for measuring infants’ discrimination (head-turn preference, central fixation, and eye tracking). The overall meta-analytic effect size (Cohen’s d) was 0.35, 95% confidence interval = [0.29, 0.42], which was reliably above zero but smaller than the meta-analytic mean computed from previous literature (0.67). The IDS preference was significantly stronger in older children, in those children for whom the stimuli matched their native language and dialect, and in data from labs using the head-turn preference procedure. Together, these findings replicate the IDS preference but suggest that its magnitude is modulated by development, native-language experience, and testing procedure.

Journal ArticleDOI
TL;DR: Comment on recent breakthroughs in this emerging field of novel machine learning tools to obtain chemical knowledge from curated datasets, and discuss the challenges for the years to come.
Abstract: Discovering chemicals with desired attributes is a long and painstaking process. Curated datasets containing reliable quantum-mechanical properties for millions of molecules are becoming increasingly available. The development of novel machine learning tools to obtain chemical knowledge from these datasets has the potential to revolutionize the process of chemical discovery. Here, I comment on recent breakthroughs in this emerging field and discuss the challenges for the years to come.

Journal ArticleDOI
TL;DR: This mini-review highlights some recent efforts to connect the ML and nanoscience communities focusing on three types of interaction: (1) using ML to analyze and extract new information from large nanos science data sets, (2) applying ML to accelerate materials discovery, including the use of active learning to guide experimental design, and (3) thenanoscience of memristive devices to realize hardware tailored for ML.
Abstract: Recent advances in machine learning (ML) offer new tools to extract new insights from large data sets and to acquire small data sets more effectively. Researchers in nanoscience are experimenting with these tools to tackle challenges in many fields. In addition to ML's advancement of nanoscience, nanoscience provides the foundation for neuromorphic computing hardware to expand the implementation of ML algorithms. In this Mini Review, we highlight some recent efforts to connect the ML and nanoscience communities by focusing on three types of interaction: (1) using ML to analyze and extract new insights from large nanoscience data sets, (2) applying ML to accelerate material discovery, including the use of active learning to guide experimental design, and (3) the nanoscience of memristive devices to realize hardware tailored for ML. We conclude with a discussion of challenges and opportunities for future interactions between nanoscience and ML researchers.

Journal ArticleDOI
TL;DR: This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice.
Abstract: Social distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching the virus and spreading it across the community. This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In this Part I, we provide a comprehensive background of social distancing including basic concepts, measurements, models, and propose various practical social distancing scenarios. We then discuss enabling wireless technologies which are especially effect- in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. The companion paper Part II surveys other emerging and related technologies, such as machine learning, computer vision, thermal, ultrasound, etc., and discusses open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice.

Journal ArticleDOI
TL;DR: A survey of current knowledge of the Gut microbiome in colorectal cancer and new experimental approaches for gaining ecosystem-level mechanistic understanding of the gut microbiome's role in cancer pathogenesis are outlined.

Journal ArticleDOI
TL;DR: In this paper, the authors provide a holistic view on wireless tactile Internet (TI) along with a thorough review of the existing state-of-the-art, to identify and analyze the involved technical issues, to highlight potential solutions and to propose future research directions.
Abstract: Tactile Internet (TI) is envisioned to create a paradigm shift from the content-oriented communications to steer/control-based communications by enabling real-time transmission of haptic information (i.e., touch, actuation, motion, vibration, surface texture) over Internet in addition to the conventional audiovisual and data traffics. This emerging TI technology, also considered as the next evolution phase of Internet of Things (IoT), is expected to create numerous opportunities for technology markets in a wide variety of applications ranging from teleoperation systems and Augmented/Virtual Reality (AR/VR) to automotive safety and eHealthcare towards addressing the complex problems of human society. However, the realization of TI over wireless media in the upcoming Fifth Generation (5G) and beyond networks creates various non-conventional communication challenges and stringent requirements in terms of ultra-low latency, ultra-high reliability, high data-rate connectivity, resource allocation, multiple access and quality-latency-rate tradeoff. To this end, this paper aims to provide a holistic view on wireless TI along with a thorough review of the existing state-of-the-art, to identify and analyze the involved technical issues, to highlight potential solutions and to propose future research directions. First, starting with the vision of TI and recent advances and a review of related survey/overview articles, we present a generalized framework for wireless TI in the Beyond 5G Era including a TI architecture, the main technical requirements, the key application areas and potential enabling technologies. Subsequently, we provide a comprehensive review of the existing TI works by broadly categorizing them into three main paradigms; namely, haptic communications, wireless AR/VR, and autonomous, intelligent and cooperative mobility systems. Next, potential enabling technologies across physical/Medium Access Control (MAC) and network layers are identified and discussed in detail. Also, security and privacy issues of TI applications are discussed along with some promising enablers. Finally, we present some open research challenges and recommend promising future research directions.

Journal ArticleDOI
TL;DR: A review of the current understanding of goals, benefits, and limitations of machine learning techniques for computational studies on atomistic systems, focusing on the construction of empirical force fields from ab-initio databases and the determination of reaction coordinates for free energy computation and enhanced sampling.
Abstract: Machine learning encompasses a set of tools and algorithms which are now becoming popular in almost all scientific and technological fields This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from the enormous amounts of data generated by simulation of complex systems We provide here a review of our current understanding of goals, benefits, and limitations of machine learning techniques for computational studies on atomistic systems, focusing on the construction of empirical force fields from ab-initio databases and the determination of reaction coordinates for free energy computation and enhanced sampling

Journal ArticleDOI
TL;DR: PD-associated alterations of the gut microbiome can translate into substantial functional differences affecting host metabolism and disease phenotype, as suggested by an analysis of stool samples from the Luxembourg Parkinson’s Study.
Abstract: Parkinson’s disease (PD) is a systemic disease clinically defined by the degeneration of dopaminergic neurons in the brain. While alterations in the gut microbiome composition have been reported in PD, their functional consequences remain unclear. Herein, we addressed this question by an analysis of stool samples from the Luxembourg Parkinson’s Study (n = 147 typical PD cases, n = 162 controls). All individuals underwent detailed clinical assessment, including neurological examinations and neuropsychological tests followed by self-reporting questionnaires. Stool samples from these individuals were first analysed by 16S rRNA gene sequencing. Second, we predicted the potential secretion for 129 microbial metabolites through personalised metabolic modelling using the microbiome data and genome-scale metabolic reconstructions of human gut microbes. Our key results include the following. Eight genera and seven species changed significantly in their relative abundances between PD patients and healthy controls. PD-associated microbial patterns statistically depended on sex, age, BMI, and constipation. Particularly, the relative abundances of Bilophila and Paraprevotella were significantly associated with the Hoehn and Yahr staging after controlling for the disease duration. Furthermore, personalised metabolic modelling of the gut microbiomes revealed PD-associated metabolic patterns in the predicted secretion potential of nine microbial metabolites in PD, including increased methionine and cysteinylglycine. The predicted microbial pantothenic acid production potential was linked to the presence of specific non-motor symptoms. Our results suggest that PD-associated alterations of the gut microbiome can translate into substantial functional differences affecting host metabolism and disease phenotype.

Journal ArticleDOI
TL;DR: Researchers around the world join forces to reconstruct the molecular processes of the virus-host interactions aiming to combat the cause of the ongoing pandemic.
Abstract: Researchers around the world join forces to reconstruct the molecular processes of the virus-host interactions aiming to combat the cause of the ongoing pandemic.

Journal ArticleDOI
TL;DR: A massive MIMO transmission scheme with full frequency reuse (FFR) for LEO satellite communication systems and exploit statistical channel state information (sCSI) to address the difficulty of obtaining instantaneous CSI at the transmitter is proposed.
Abstract: Low earth orbit (LEO) satellite communications are expected to be incorporated in future wireless networks, in particular 5G and beyond networks, to provide global wireless access with enhanced data rates. Massive MIMO techniques, though widely used in terrestrial communication systems, have not been applied to LEO satellite communication systems. In this paper, we propose a massive MIMO transmission scheme with full frequency reuse (FFR) for LEO satellite communication systems and exploit statistical channel state information (sCSI) to address the difficulty of obtaining instantaneous CSI (iCSI) at the transmitter. We first establish the massive MIMO channel model for LEO satellite communications and simplify the transmission designs via performing Doppler and delay compensations at user terminals (UTs). Then, we develop the low-complexity sCSI based downlink (DL) precoder and uplink (UL) receiver in closed-form, aiming to maximize the average signal-to-leakage-plus-noise ratio (ASLNR) and the average signal-to-interference-plus-noise ratio (ASINR), respectively. It is shown that the DL ASLNRs and UL ASINRs of all UTs reach their upper bounds under some channel condition. Motivated by this, we propose a space angle based user grouping (SAUG) algorithm to schedule the served UTs into different groups, where each group of UTs use the same time and frequency resource. The proposed algorithm is asymptotically optimal in the sense that the lower and upper bounds of the achievable rate coincide when the number of satellite antennas or UT groups is sufficiently large. Numerical results demonstrate that the proposed massive MIMO transmission scheme with FFR significantly enhances the data rate of LEO satellite communication systems. Notably, the proposed sCSI based precoder and receiver achieve the similar performance with the iCSI based ones that are often infeasible in practice.

Journal ArticleDOI
02 Oct 2020-Science
TL;DR: A parallel-plate active EC regenerator based on lead scandium tantalate multilayer capacitors that breaks a crucial barrier and confirms that EC materials are promising candidates for cooling applications.
Abstract: Cooling devices based on caloric materials have emerged as promising candidates to become the next generation of coolers. Several electrocaloric (EC) heat exchangers have been proposed that use different mechanisms and working principles. However, a prototype that demonstrates a competitive temperature span has been missing. We developed a parallel-plate active EC regenerator based on lead scandium tantalate multilayer capacitors. After optimizing the structural design by using finite element modeling for guidance and to considerably improve insulation, we measured a maximum temperature span of 13.0 kelvin. This temperature span breaks a crucial barrier and confirms that EC materials are promising candidates for cooling applications.

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TL;DR: A new metabolic network reconstruction approach that used organ‐specific information from literature and omics data to generate two sex‐specific whole‐body metabolic (WBM) reconstructions that capture the metabolism of 26 organs and six blood cell types is developed.
Abstract: Comprehensive molecular-level models of human metabolism have been generated on a cellular level. However, models of whole-body metabolism have not been established as they require new methodological approaches to integrate molecular and physiological data. We developed a new metabolic network reconstruction approach that used organ-specific information from literature and omics data to generate two sex-specific whole-body metabolic (WBM) reconstructions. These reconstructions capture the metabolism of 26 organs and six blood cell types. Each WBM reconstruction represents whole-body organ-resolved metabolism with over 80,000 biochemical reactions in an anatomically and physiologically consistent manner. We parameterized the WBM reconstructions with physiological, dietary, and metabolomic data. The resulting WBM models could recapitulate known inter-organ metabolic cycles and energy use. We also illustrate that the WBM models can predict known biomarkers of inherited metabolic diseases in different biofluids. Predictions of basal metabolic rates, by WBM models personalized with physiological data, outperformed current phenomenological models. Finally, integrating microbiome data allowed the exploration of host-microbiome co-metabolism. Overall, the WBM reconstructions, and their derived computational models, represent an important step toward virtual physiological humans.

Journal ArticleDOI
30 Jan 2020-Cancers
TL;DR: The different molecular markers in clinical use for CRC are described, new markers that might become indispensable over the next years are highlighted, recently developed gene expression-based tests are discussed and the challenges in biomarker research are highlighted.
Abstract: Colorectal cancer (CRC) is a leading cause of death among cancer patients. This heterogeneous disease is characterized by alterations in multiple molecular pathways throughout its development. Mutations in RAS, along with the mismatch repair gene deficiency, are currently routinely tested in clinics. Such biomarkers provide information for patient risk stratification and for the choice of the best treatment options. Nevertheless, reliable and powerful prognostic markers that can identify “high-risk” CRC patients, who might benefit from adjuvant chemotherapy, in early stages, are currently missing. To bridge this gap, genomic information has increasingly gained interest as a potential method for determining the risk of recurrence. However, due to several limitations of gene-based signatures, these have not yet been clinically implemented. In this review, we describe the different molecular markers in clinical use for CRC, highlight new markers that might become indispensable over the next years, discuss recently developed gene expression-based tests and highlight the challenges in biomarker research.

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TL;DR: The results of the review indicate that research in this field is interdisciplinary and that no common modelling language has emerged thus far, and calls for research on the risk-structure-interface and the development of proxy methods.
Abstract: Supply chain risk management is extremely important for the success of a company. Due to the increasing complexity of supply chains, avoiding and mitigating the effects of disruptions is very chall...

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TL;DR: In this paper, a monitoring approach for such a flexibility transition is illustrated, which bases on a flexibility index, which allows for an indication of mis-developments and supports an appropriate implementation of countermeasures together with relevant stakeholders.