Showing papers by "Technical University of Berlin published in 2020"
TL;DR: In this paper, the authors compile government policies and activity data to estimate the decrease in CO2 emissions during forced confinements during the COVID-19 pandemic, which drastically altered patterns of energy demand around the world.
Abstract: Government policies during the COVID-19 pandemic have drastically altered patterns of energy demand around the world. Many international borders were closed and populations were confined to their homes, which reduced transport and changed consumption patterns. Here we compile government policies and activity data to estimate the decrease in CO2 emissions during forced confinements. Daily global CO2 emissions decreased by –17% (–11 to –25% for ±1σ) by early April 2020 compared with the mean 2019 levels, just under half from changes in surface transport. At their peak, emissions in individual countries decreased by –26% on average. The impact on 2020 annual emissions depends on the duration of the confinement, with a low estimate of –4% (–2 to –7%) if prepandemic conditions return by mid-June, and a high estimate of –7% (–3 to –13%) if some restrictions remain worldwide until the end of 2020. Government actions and economic incentives postcrisis will likely influence the global CO2 emissions path for decades.
1,461 citations
TL;DR: CD4 + T cells that are reactive against the spike glycoprotein of SARS-CoV-2 in the peripheral blood of patients with COVID-19 and healthy donors are found, indicating that spike-protein cross-reactive T cells are present and probably generated during previous encounters with endemic coronaviruses.
Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the rapidly unfolding coronavirus disease 2019 (COVID-19) pandemic1,2. Clinical manifestations of COVID-19 vary, ranging from asymptomatic infection to respiratory failure. The mechanisms that determine such variable outcomes remain unresolved. Here we investigated CD4+ T cells that are reactive against the spike glycoprotein of SARS-CoV-2 in the peripheral blood of patients with COVID-19 and SARS-CoV-2-unexposed healthy donors. We detected spike-reactive CD4+ T cells not only in 83% of patients with COVID-19 but also in 35% of healthy donors. Spike-reactive CD4+ T cells in healthy donors were primarily active against C-terminal epitopes in the spike protein, which show a higher homology to spike glycoproteins of human endemic coronaviruses, compared with N-terminal epitopes. Spike-protein-reactive T cell lines generated from SARS-CoV-2-naive healthy donors responded similarly to the C-terminal region of the spike proteins of the human endemic coronaviruses 229E and OC43, as well as that of SARS-CoV-2. This results indicate that spike-protein cross-reactive T cells are present, which were probably generated during previous encounters with endemic coronaviruses. The effect of pre-existing SARS-CoV-2 cross-reactive T cells on clinical outcomes remains to be determined in larger cohorts. However, the presence of spike-protein cross-reactive T cells in a considerable fraction of the general population may affect the dynamics of the current pandemic, and has important implications for the design and analysis of upcoming trials investigating COVID-19 vaccines.
1,040 citations
TL;DR: A monolithic perovskite/silicon tandem with a certified power conversion efficiency of 29.15% is reported, made possible by a self-assembled, methyl-substituted carbazole monolayer as the hole-selective layer in the perovSKite cell.
Abstract: Tandem solar cells that pair silicon with a metal halide perovskite are a promising option for surpassing the single-cell efficiency limit. We report a monolithic perovskite/silicon tandem with a certified power conversion efficiency of 29.15%. The perovskite absorber, with a bandgap of 1.68 electron volts, remained phase-stable under illumination through a combination of fast hole extraction and minimized nonradiative recombination at the hole-selective interface. These features were made possible by a self-assembled, methyl-substituted carbazole monolayer as the hole-selective layer in the perovskite cell. The accelerated hole extraction was linked to a low ideality factor of 1.26 and single-junction fill factors of up to 84%, while enabling a tandem open-circuit voltage of as high as 1.92 volts. In air, without encapsulation, a tandem retained 95% of its initial efficiency after 300 hours of operation.
876 citations
Book•
09 Oct 2020TL;DR: This book gives an introduction to the most important concepts of MATSim and describes how the basic functionality can be extended by adding schedule-based public transit, electric or autonomous cars, paratransit, or within-day replanning.
Abstract: The MATSim (Multi-Agent Transport Simulation) software project was started around 2006 with the goal of generating traffic and congestion patterns by following individual synthetic travelers through their daily or weekly activity programme. It has since then evolved from a collection of stand-alone C++ programs to an integrated Java-based framework which is publicly hosted, open-source available, automatically regression tested. It is currently used by about 40 groups throughout the world. This book takes stock of the current status. The first part of the book gives an introduction to the most important concepts, with the intention of enabling a potential user to set up and run basic simulations. The second part of the book describes how the basic functionality can be extended, for example by adding schedule-based public transit, electric or autonomous cars, paratransit, or within-day replanning. For each extension, the text provides pointers to the additional documentation and to the code base. It is also discussed how people with appropriate Java programming skills can write their own extensions, and plug them into the MATSim core. The project has started from the basic idea that traffic is a consequence of human behavior, and thus humans and their behavior should be the starting point of all modelling, and with the intuition that when simulations with 100 million particles are possible in computational physics, then behavior-oriented simulations with 10 million travelers should be possible in travel behavior research. The initial implementations thus combined concepts from computational physics and complex adaptive systems with concepts from travel behavior research. The third part of the book looks at theoretical concepts that are able to describe important aspects of the simulation system; for example, under certain conditions the code becomes a Monte Carlo engine sampling from a discrete choice model. Another important aspect is the interpretation of the MATSim score as utility in the microeconomic sense, opening up a connection to benefit cost analysis. Finally, the book collects use cases as they have been undertaken with MATSim. All current users of MATSim were invited to submit their work, and many followed with sometimes crisp and short and sometimes longer contributions, always with pointers to additional references. We hope that the book will become an invitation to explore, to build and to extend agent-based modeling of travel behavior from the stable and well tested core of MATSim documented here.
725 citations
TL;DR: This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras.
Abstract: Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of is), very high dynamic range (140dB vs. 60dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world.
697 citations
TL;DR: In this paper, the authors propose sparse ternary compression (STC), a new compression framework that is specifically designed to meet the requirements of the federated learning environment, which extends the existing compression technique of top- $k$ gradient sparsification with a novel mechanism to enable downstream compression as well as ternarization and optimal Golomb encoding of the weight updates.
Abstract: Federated learning allows multiple parties to jointly train a deep learning model on their combined data, without any of the participants having to reveal their local data to a centralized server. This form of privacy-preserving collaborative learning, however, comes at the cost of a significant communication overhead during training. To address this problem, several compression methods have been proposed in the distributed training literature that can reduce the amount of required communication by up to three orders of magnitude. These existing methods, however, are only of limited utility in the federated learning setting, as they either only compress the upstream communication from the clients to the server (leaving the downstream communication uncompressed) or only perform well under idealized conditions, such as i.i.d. distribution of the client data, which typically cannot be found in federated learning. In this article, we propose sparse ternary compression (STC), a new compression framework that is specifically designed to meet the requirements of the federated learning environment. STC extends the existing compression technique of top- $k$ gradient sparsification with a novel mechanism to enable downstream compression as well as ternarization and optimal Golomb encoding of the weight updates. Our experiments on four different learning tasks demonstrate that STC distinctively outperforms federated averaging in common federated learning scenarios. These results advocate for a paradigm shift in federated optimization toward high-frequency low-bitwidth communication, in particular in the bandwidth-constrained learning environments.
618 citations
TL;DR: In the German health-care system, in which hospital capacities have not been overwhelmed by the COVID-19 pandemic, mortality has been high for patients receiving mechanical ventilation, particularly for patients aged 80 years or older and those requiring dialysis, and has been considerably lower for patients younger than 60 years.
604 citations
TL;DR: Fundamental ML methodologies are outlined and their uses for understanding, modeling, optimizing, and controlling fluid flows are discussed and the strengths and limitations of these methods are addressed.
Abstract: The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machi...
549 citations
University of California, San Diego1, University of Montana2, University of Münster3, University of Jena4, University of Lübeck5, Statens Serum Institut6, University of Tübingen7, University of Geneva8, Bruker9, Paris Descartes University10, University of São Paulo11, Technical University of Berlin12, Georgia Institute of Technology13, Saint Petersburg State University14, Waters Corporation15, Academy of Sciences of the Czech Republic16, Sookmyung Women's University17, University of Grenoble18, University of Oklahoma19, Carnegie Mellon University20, University of West Alabama21, Leibniz Association22, University of Corsica Pascal Paoli23, Massachusetts Institute of Technology24, Michigan State University25, University of Glasgow26, Wageningen University and Research Centre27, Kangwon National University28
TL;DR: Feature-based molecular networking (FBMN) as discussed by the authors is an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools.
Abstract: Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present feature-based molecular networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. FBMN enables quantitative analysis and resolution of isomers, including from ion mobility spectrometry.
497 citations
University of California, Irvine1, Technical University of Denmark2, Dassault Systèmes3, Ruhr University Bochum4, Karlsruhe Institute of Technology5, Technical University of Berlin6, Max Planck Society7, Forschungszentrum Jülich8, Case Western Reserve University9, University of North Carolina at Chapel Hill10, Aarhus University11, California State University, Long Beach12, Kaiserslautern University of Technology13, Tata Institute of Fundamental Research14
TL;DR: This review focuses on recent additions to TURBOMOLE’s functionality, including excited-state methods, RPA and Green's function methods, relativistic approaches, high-order molecular properties, solvation effects, and periodic systems.
Abstract: TURBOMOLE is a collaborative, multi-national software development project aiming to provide highly efficient and stable computational tools for quantum chemical simulations of molecules, clusters, periodic systems, and solutions. The TURBOMOLE software suite is optimized for widely available, inexpensive, and resource-efficient hardware such as multi-core workstations and small computer clusters. TURBOMOLE specializes in electronic structure methods with outstanding accuracy-cost ratio, such as density functional theory including local hybrids and the random phase approximation (RPA), GW-Bethe-Salpeter methods, second-order Moller-Plesset theory, and explicitly correlated coupled-cluster methods. TURBOMOLE is based on Gaussian basis sets and has been pivotal for the development of many fast and low-scaling algorithms in the past three decades, such as integral-direct methods, fast multipole methods, the resolution-of-the-identity approximation, imaginary frequency integration, Laplace transform, and pair natural orbital methods. This review focuses on recent additions to TURBOMOLE's functionality, including excited-state methods, RPA and Green's function methods, relativistic approaches, high-order molecular properties, solvation effects, and periodic systems. A variety of illustrative applications along with accuracy and timing data are discussed. Moreover, available interfaces to users as well as other software are summarized. TURBOMOLE's current licensing, distribution, and support model are discussed, and an overview of TURBOMOLE's development workflow is provided. Challenges such as communication and outreach, software infrastructure, and funding are highlighted.
489 citations
TL;DR: The flexible electronic structure of the surface Fe sites, and their synergy with nearest-neighbor M sites through formation of O-bridged Fe-M reaction centers, stabilize OER intermediates that are unfavorable on pure M-M centers and single Fe Sites, fundamentally accounting for the high catalytic activity of MFe LDHs.
Abstract: NiFe and CoFe (MFe) layered double hydroxides (LDHs) are among the most active electrocatalysts for the alkaline oxygen evolution reaction (OER). Herein, we combine electrochemical measurements, operando X-ray scattering and absorption spectroscopy, and density functional theory (DFT) calculations to elucidate the catalytically active phase, reaction center and the OER mechanism. We provide the first direct atomic-scale evidence that, under applied anodic potentials, MFe LDHs oxidize from as-prepared α-phases to activated γ-phases. The OER-active γ-phases are characterized by about 8% contraction of the lattice spacing and switching of the intercalated ions. DFT calculations reveal that the OER proceeds via a Mars van Krevelen mechanism. The flexible electronic structure of the surface Fe sites, and their synergy with nearest-neighbor M sites through formation of O-bridged Fe-M reaction centers, stabilize OER intermediates that are unfavorable on pure M-M centers and single Fe sites, fundamentally accounting for the high catalytic activity of MFe LDHs. NiFe and CoFe layered double hydroxides are among the most active electrocatalysts for the alkaline oxygen evolution reaction. Here, by combining operando experiments and rigorous DFT calculations, the authors unravel their active phase, the reaction center and the catalytic mechanism.
TL;DR: The purpose of this paper is to identify and discuss the main issues involved in the complex process of IoT-based investigations, particularly all legal, privacy and cloud security challenges, as well as some promising cross-cutting data reduction and forensics intelligence techniques.
Abstract: Today is the era of the Internet of Things (IoT). The recent advances in hardware and information technology have accelerated the deployment of billions of interconnected, smart and adaptive devices in critical infrastructures like health, transportation, environmental control, and home automation. Transferring data over a network without requiring any kind of human-to-computer or human-to-human interaction, brings reliability and convenience to consumers, but also opens a new world of opportunity for intruders, and introduces a whole set of unique and complicated questions to the field of Digital Forensics. Although IoT data could be a rich source of evidence, forensics professionals cope with diverse problems, starting from the huge variety of IoT devices and non-standard formats, to the multi-tenant cloud infrastructure and the resulting multi-jurisdictional litigations. A further challenge is the end-to-end encryption which represents a trade-off between users’ right to privacy and the success of the forensics investigation. Due to its volatile nature, digital evidence has to be acquired and analyzed using validated tools and techniques that ensure the maintenance of the Chain of Custody. Therefore, the purpose of this paper is to identify and discuss the main issues involved in the complex process of IoT-based investigations, particularly all legal, privacy and cloud security challenges. Furthermore, this work provides an overview of the past and current theoretical models in the digital forensics science. Special attention is paid to frameworks that aim to extract data in a privacy-preserving manner or secure the evidence integrity using decentralized blockchain-based solutions. In addition, the present paper addresses the ongoing Forensics-as-a-Service (FaaS) paradigm, as well as some promising cross-cutting data reduction and forensics intelligence techniques. Finally, several other research trends and open issues are presented, with emphasis on the need for proactive Forensics Readiness strategies and generally agreed-upon standards.
TL;DR: In this article, the challenges of water electrolysis in the presence of common impurities such as metal ions, chloride and bio-organisms are addressed through catalyst and electrolyser design.
Abstract: Powered by renewable energy sources such as solar, marine, geothermal and wind, generation of storable hydrogen fuel through water electrolysis provides a promising path towards energy sustainability. However, state-of-the-art electrolysis requires support from associated processes such as desalination of water sources, further purification of desalinated water, and transportation of water, which often contribute financial and energy costs. One strategy to avoid these operations is to develop electrolysers that are capable of operating with impure water feeds directly. Here we review recent developments in electrode materials/catalysts for water electrolysis using low-grade and saline water, a significantly more abundant resource worldwide compared to potable water. We address the associated challenges in design of electrolysers, and discuss future potential approaches that may yield highly active and selective materials for water electrolysis in the presence of common impurities such as metal ions, chloride and bio-organisms. Production of hydrogen fuel by electrolysis of low-grade or saline water, as opposed to pure water, could have benefits in terms of resource availability and cost. This Review examines the challenges of this approach and how they can be addressed through catalyst and electrolyser design.
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.
TL;DR: In this article, the authors investigate the effect of digitalization on energy consumption using an analytical model, and investigate four effects: (1) direct effects from the production, usage and disposal of information and communication technologies (ICT), (2) energy efficiency increases from digitalization, (3) economic growth from increases in labor and energy productivities and (4) sectoral change/tertiarization from the rise of ICT services.
TL;DR: A new Pt catalyst/support design that substantially reduces local oxygen-related mass transport resistance is reported and exhibits previously unachieved fuel cell power densities in addition to high stability under voltage cycling.
Abstract: The reduction of Pt content in the cathode for proton exchange membrane fuel cells is highly desirable to lower their costs. However, lowering the Pt loading of the cathodic electrode leads to high voltage losses. These voltage losses are known to originate from the mass transport resistance of O2 through the platinum–ionomer interface, the location of the Pt particle with respect to the carbon support and the supports’ structures. In this study, we present a new Pt catalyst/support design that substantially reduces local oxygen-related mass transport resistance. The use of chemically modified carbon supports with tailored porosity enabled controlled deposition of Pt nanoparticles on the outer and inner surface of the support particles. This resulted in an unprecedented uniform coverage of the ionomer over the high surface-area carbon supports, especially under dry operating conditions. Consequently, the present catalyst design exhibits previously unachieved fuel cell power densities in addition to high stability under voltage cycling. Thanks to the Coulombic interaction between the ionomer and N groups on the carbon support, homogeneous ionomer distribution and reproducibility during ink manufacturing process is ensured. Reducing Pt content in cathodes for proton exchange membrane fuel cells is crucial to lower costs but results in high voltage losses. A Pt catalyst/support design that substantially reduces local oxygen-related mass transport resistance is reported.
TL;DR: In this paper, several techniques related to the synthesis of ZnO nanostructures and their efficient performance in sensing are reviewed, such as functionalization of noble metal nanoparticles, doping of metals, inclusion of carbonaceous nanomaterials, using nanocomposites of different MO x, UV activation, and post-treatment method of high-energy irradiation on ZnOs, with their possible sensing mechanisms.
TL;DR: PTB-XL is put forward, the to-date largest freely accessible clinical 12-lead ECG-waveform dataset comprising 21837 records from 18885 patients of 10 seconds length, which turns the dataset into a rich resource for the development and the evaluation of automatic ECG interpretation algorithms.
Abstract: Electrocardiography (ECG) is a key non-invasive diagnostic tool for cardiovascular diseases which is increasingly supported by algorithms based on machine learning. Major obstacles for the development of automatic ECG interpretation algorithms are both the lack of public datasets and well-defined benchmarking procedures to allow comparison s of different algorithms. To address these issues, we put forward PTB-XL, the to-date largest freely accessible clinical 12-lead ECG-waveform dataset comprising 21837 records from 18885 patients of 10 seconds length. The ECG-waveform data was annotated by up to two cardiologists as a multi-label dataset, where diagnostic labels were further aggregated into super and subclasses. The dataset covers a broad range of diagnostic classes including, in particular, a large fraction of healthy records. The combination with additional metadata on demographics, additional diagnostic statements, diagnosis likelihoods, manually annotated signal properties as well as suggested folds for splitting training and test sets turns the dataset into a rich resource for the development and the evaluation of automatic ECG interpretation algorithms. Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12098055
TL;DR: Pulse voltammetry and operando X-ray absorption spectroscopy measurements on iridium oxide are reported to show that the applied bias affects the electrocatalytically generated current through charge accumulation in the catalyst, and it is found that the activation free energy decreases linearly with the amount of oxidative charge stored.
Abstract: The oxygen evolution reaction has an important role in many alternative-energy schemes because it supplies the protons and electrons required for converting renewable electricity into chemical fuels1-3. Electrocatalysts accelerate the reaction by facilitating the required electron transfer4, as well as the formation and rupture of chemical bonds5. This involvement in fundamentally different processes results in complex electrochemical kinetics that can be challenging to understand and control, and that typically depends exponentially on overpotential1,2,6,7. Such behaviour emerges when the applied bias drives the reaction in line with the phenomenological Butler-Volmer theory, which focuses on electron transfer8, enabling the use of Tafel analysis to gain mechanistic insight under quasi-equilibrium9-11 or steady-state assumptions12. However, the charging of catalyst surfaces under bias also affects bond formation and rupture13-15, the effect of which on the electrocatalytic rate is not accounted for by the phenomenological Tafel analysis8 and is often unknown. Here we report pulse voltammetry and operando X-ray absorption spectroscopy measurements on iridium oxide to show that the applied bias does not act directly on the reaction coordinate, but affects the electrocatalytically generated current through charge accumulation in the catalyst. We find that the activation free energy decreases linearly with the amount of oxidative charge stored, and show that this relationship underlies electrocatalytic performance and can be evaluated using measurement and computation. We anticipate that these findings and our methodology will help to better understand other electrocatalytic materials and design systems with improved performance.
TL;DR: This review aims to identify the common underlying principles and the assumptions that are often made implicitly by various methods in deep learning, and draws connections between classic “shallow” and novel deep approaches and shows how this relation might cross-fertilize or extend both directions.
Abstract: Deep learning approaches to anomaly detection have recently improved the state of the art in detection performance on complex datasets such as large collections of images or text. These results have sparked a renewed interest in the anomaly detection problem and led to the introduction of a great variety of new methods. With the emergence of numerous such methods, including approaches based on generative models, one-class classification, and reconstruction, there is a growing need to bring methods of this field into a systematic and unified perspective. In this review we aim to identify the common underlying principles as well as the assumptions that are often made implicitly by various methods. In particular, we draw connections between classic 'shallow' and novel deep approaches and show how this relation might cross-fertilize or extend both directions. We further provide an empirical assessment of major existing methods that is enriched by the use of recent explainability techniques, and present specific worked-through examples together with practical advice. Finally, we outline critical open challenges and identify specific paths for future research in anomaly detection.
TL;DR: The means by which aligned porous structures and nacre mimetic materials obtainable through recently developed freeze-casting techniques and low-dimensional building blocks can facilitate material functionality across multiple fields of application, including energy storage and conversion, environmental remediation, thermal management, and smart materials, are discussed.
Abstract: Freeze casting, also known as ice templating, is a particularly versatile technique that has been applied extensively for the fabrication of well-controlled biomimetic porous materials based on ceramics, metals, polymers, biomacromolecules, and carbon nanomaterials, endowing them with novel properties and broadening their applicability. The principles of different directional freeze-casting processes are described and the relationships between processing and structure are examined. Recent progress in freeze-casting assisted assembly of low dimensional building blocks, including graphene and carbon nanotubes, into tailored micro- and macrostructures is then summarized. Emerging trends relating to novel materials as building blocks and novel freeze-cast geometries-beads, fibers, films, complex macrostructures, and nacre-mimetic composites-are presented. Thereafter, the means by which aligned porous structures and nacre mimetic materials obtainable through recently developed freeze-casting techniques and low-dimensional building blocks can facilitate material functionality across multiple fields of application, including energy storage and conversion, environmental remediation, thermal management, and smart materials, are discussed.
TL;DR: To assess current trajectories towards the GPW13 UHC billion target—1 billion more people benefiting from UHC by 2023—the authors estimated additional population equivalents with UHC effective coverage from 2018 to 2023, and quantified frontiers of U HC effective coverage performance on the basis of pooled health spending per capita.
TL;DR: VirtualFlow, an open-source drug discovery platform, enables the efficient preparation and virtual screening of ultra-large ligand libraries to identify molecules that bind with high affinity to target proteins.
Abstract: On average, an approved drug currently costs US$2–3 billion and takes more than 10 years to develop1. In part, this is due to expensive and time-consuming wet-laboratory experiments, poor initial hit compounds and the high attrition rates in the (pre-)clinical phases. Structure-based virtual screening has the potential to mitigate these problems. With structure-based virtual screening, the quality of the hits improves with the number of compounds screened2. However, despite the fact that large databases of compounds exist, the ability to carry out large-scale structure-based virtual screening on computer clusters in an accessible, efficient and flexible manner has remained difficult. Here we describe VirtualFlow, a highly automated and versatile open-source platform with perfect scaling behaviour that is able to prepare and efficiently screen ultra-large libraries of compounds. VirtualFlow is able to use a variety of the most powerful docking programs. Using VirtualFlow, we prepared one of the largest and freely available ready-to-dock ligand libraries, with more than 1.4 billion commercially available molecules. To demonstrate the power of VirtualFlow, we screened more than 1 billion compounds and identified a set of structurally diverse molecules that bind to KEAP1 with submicromolar affinity. One of the lead inhibitors (iKeap1) engages KEAP1 with nanomolar affinity (dissociation constant (Kd) = 114 nM) and disrupts the interaction between KEAP1 and the transcription factor NRF2. This illustrates the potential of VirtualFlow to access vast regions of the chemical space and identify molecules that bind with high affinity to target proteins. VirtualFlow, an open-source drug discovery platform, enables the efficient preparation and virtual screening of ultra-large ligand libraries to identify molecules that bind with high affinity to target proteins.
TL;DR: PauliNet as discussed by the authors is a deep learning wave function ansatz that achieves nearly exact solutions of the electronic Schrodinger equation for molecules with up to 30 electrons, using a multireference Hartree-Fock solution built in as a baseline, incorporating the physics of valid wave functions and trained using variational quantum Monte Carlo.
Abstract: The electronic Schrodinger equation can only be solved analytically for the hydrogen atom, and the numerically exact full configuration-interaction method is exponentially expensive in the number of electrons. Quantum Monte Carlo methods are a possible way out: they scale well for large molecules, they can be parallelized and their accuracy has, as yet, been only limited by the flexibility of the wavefunction ansatz used. Here we propose PauliNet, a deep-learning wavefunction ansatz that achieves nearly exact solutions of the electronic Schrodinger equation for molecules with up to 30 electrons. PauliNet has a multireference Hartree–Fock solution built in as a baseline, incorporates the physics of valid wavefunctions and is trained using variational quantum Monte Carlo. PauliNet outperforms previous state-of-the-art variational ansatzes for atoms, diatomic molecules and a strongly correlated linear H10, and matches the accuracy of highly specialized quantum chemistry methods on the transition-state energy of cyclobutadiene, while being computationally efficient. High-accuracy quantum chemistry methods struggle with a combinatorial explosion of Slater determinants in larger molecular systems, but now a method has been developed that learns electronic wavefunctions with deep neural networks and reaches high accuracy with only a few determinants. The method is applicable to realistic chemical processes such as the automerization of cyclobutadiene.
TL;DR: A broad multidisciplinary overview of the area of bias in AI systems is provided, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well‐grounded in a legal frame.
Abstract: Artificial Intelligence (AI)‐based systems are widely employed nowadays to make decisions that have far‐reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well‐grounded in a legal frame. In this survey, we focus on data‐driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth.
University of Duisburg-Essen1, University of Stirling2, Aarhus University3, Middle East Technical University4, University of Lisbon5, Laval University6, Norwegian Institute for Water Research7, Estonian University of Life Sciences8, Lancaster University9, Technical University of Berlin10, Leibniz Association11, University of Ljubljana12, Helmholtz Centre for Environmental Research - UFZ13, University of Natural Resources and Life Sciences, Vienna14, University of Barcelona15, Cardiff University16, Finnish Environment Institute17, DHI Water & Environment18, National Technical University of Athens19, Radboud University Nijmegen20, Free University of Berlin21, University of Koblenz and Landau22
TL;DR: A cross-scale analysis of paired-stressor effects on biological variables of European freshwater ecosystems shows that in 39% of cases, significant effects were limited to single stressors, with nutrient enrichment being the most important of these in lakes.
Abstract: Climate and land-use change drive a suite of stressors that shape ecosystems and interact to yield complex ecological responses (that is, additive, antagonistic and synergistic effects). We know little about the spatial scales relevant for the outcomes of such interactions and little about effect sizes. These knowledge gaps need to be filled to underpin future land management decisions or climate mitigation interventions for protecting and restoring freshwater ecosystems. This study combines data across scales from 33 mesocosm experiments with those from 14 river basins and 22 cross-basin studies in Europe, producing 174 combinations of paired-stressor effects on a biological response variable. Generalized linear models showed that only one of the two stressors had a significant effect in 39% of the analysed cases, 28% of the paired-stressor combinations resulted in additive effects and 33% resulted in interactive (antagonistic, synergistic, opposing or reversal) effects. For lakes, the frequencies of additive and interactive effects were similar for all spatial scales addressed, while for rivers these frequencies increased with scale. Nutrient enrichment was the overriding stressor for lakes, with effects generally exceeding those of secondary stressors. For rivers, the effects of nutrient enrichment were dependent on the specific stressor combination and biological response variable. These results vindicate the traditional focus of lake restoration and management on nutrient stress, while highlighting that river management requires more bespoke management solutions.
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.
Harvard University1, University of Washington2, Humboldt University of Berlin3, Imperial College London4, University of Belgrade5, Istituto Nazionale di Fisica Nucleare6, Technical University of Berlin7, University of Bordeaux8, University of Oxford9, University of Valencia10, University of Strathclyde11, Rutherford Appleton Laboratory12, King's College London13, Foundation for Research & Technology – Hellas14, University of Birmingham15, University College London16, University of Liverpool17, National Physical Laboratory18, University of Nottingham19, University of Sussex20, Northern Illinois University21, Fermilab22, Peking University23, University of Pisa24, University of California, Riverside25, University of Nevada, Reno26, CERN27, University of Niš28, National Institute of Chemical Physics and Biophysics29, Beni-Suef University30, British University in Egypt31, Leibniz University of Hanover32, Paul Sabatier University33, University of Paris34, University of Cambridge35, Wayne State University36, Stanford University37, University of Bergen38, University of Amsterdam39, Northwestern University40, University of Bristol41, University of Warsaw42, University of Illinois at Urbana–Champaign43, Fayoum University44, University of Crete45, Queen's University Belfast46, Brandeis University47, University of Bologna48, Cochin University of Science and Technology49, German Aerospace Center50, University of Manchester51, University of Copenhagen52, University of Düsseldorf53, University of Vienna54, Florida State University55, University of Florence56, University of Illinois at Chicago57, University of Bremen58, University of Mainz59, Chinese Academy of Sciences60, University of Cincinnati61
TL;DR: The Atomic Experiment for Dark Matter and Gravity Exploration (AEDGE) as mentioned in this paper is a space experiment using cold atoms to search for ultra-light dark matter, and to detect gravitational waves in the frequency range between the most sensitive ranges of LISA and the terrestrial LIGO/Virgo/KAGRA/INDIGO experiments.
Abstract: We propose in this White Paper a concept for a space experiment using cold atoms to search for ultra-light dark matter, and to detect gravitational waves in the frequency range between the most sensitive ranges of LISA and the terrestrial LIGO/Virgo/KAGRA/INDIGO experiments. This interdisciplinary experiment, called Atomic Experiment for Dark Matter and Gravity Exploration (AEDGE), will also complement other planned searches for dark matter, and exploit synergies with other gravitational wave detectors. We give examples of the extended range of sensitivity to ultra-light dark matter offered by AEDGE, and how its gravitational-wave measurements could explore the assembly of super-massive black holes, first-order phase transitions in the early universe and cosmic strings. AEDGE will be based upon technologies now being developed for terrestrial experiments using cold atoms, and will benefit from the space experience obtained with, e.g., LISA and cold atom experiments in microgravity.