Showing papers by "Delft University of Technology published in 2019"

Bayesian Reinforcement Learning in Factored POMDPs

Abstract: Model-based Bayesian Reinforcement Learning (BRL) provides a principled solution to dealing with the exploration-exploitation trade-off, but such methods typically assume a fully observable environments. The few Bayesian RL methods that are applicable in partially observable domains, such as the Bayes-Adaptive POMDP (BA-POMDP), scale poorly. To address this issue, we introduce the Factored BA-POMDP model (FBA-POMDP), a framework that is able to learn a compact model of the dynamics by exploiting the underlying structure of a POMDP. The FBA-POMDP framework casts the problem as a planning task, for which we adapt the Monte-Carlo Tree Search planning algorithm and develop a belief tracking method to approximate the joint posterior over the state and model variables. Our empirical results show that this method outperforms a number of BRL baselines and is able to learn efficiently when the factorization is known, as well as learn both the factorization and the model parameters simultaneously.

Mapping the world’s free-flowing rivers

Abstract: Free-flowing rivers (FFRs) support diverse, complex and dynamic ecosystems globally, providing important societal and economic services. Infrastructure development threatens the ecosystem processes, biodiversity and services that these rivers support. Here we assess the connectivity status of 12 million kilometres of rivers globally and identify those that remain free-flowing in their entire length. Only 37 per cent of rivers longer than 1,000 kilometres remain free-flowing over their entire length and 23 per cent flow uninterrupted to the ocean. Very long FFRs are largely restricted to remote regions of the Arctic and of the Amazon and Congo basins. In densely populated areas only few very long rivers remain free-flowing, such as the Irrawaddy and Salween. Dams and reservoirs and their up- and downstream propagation of fragmentation and flow regulation are the leading contributors to the loss of river connectivity. By applying a new method to quantify riverine connectivity and map FFRs, we provide a foundation for concerted global and national strategies to maintain or restore them. A comprehensive assessment of the world’s rivers and their connectivity shows that only 37 per cent of rivers longer than 1,000 kilometres remain free-flowing over their entire length.

Conserved cell types with divergent features in human versus mouse cortex.

Abstract: Elucidating the cellular architecture of the human cerebral cortex is central to understanding our cognitive abilities and susceptibility to disease. Here we used single-nucleus RNA-sequencing analysis to perform a comprehensive study of cell types in the middle temporal gyrus of human cortex. We identified a highly diverse set of excitatory and inhibitory neuron types that are mostly sparse, with excitatory types being less layer-restricted than expected. Comparison to similar mouse cortex single-cell RNA-sequencing datasets revealed a surprisingly well-conserved cellular architecture that enables matching of homologous types and predictions of properties of human cell types. Despite this general conservation, we also found extensive differences between homologous human and mouse cell types, including marked alterations in proportions, laminar distributions, gene expression and morphology. These species-specific features emphasize the importance of directly studying human brain.

Metal Halide Perovskite Nanocrystals: Synthesis, Post-Synthesis Modifications, and Their Optical Properties.

Abstract: Metal halide perovskites represent a flourishing area of research, which is driven by both their potential application in photovoltaics and optoelectronics and by the fundamental science behind their unique optoelectronic properties. The emergence of new colloidal methods for the synthesis of halide perovskite nanocrystals, as well as the interesting characteristics of this new type of material, has attracted the attention of many researchers. This review aims to provide an up-to-date survey of this fast-moving field and will mainly focus on the different colloidal synthesis approaches that have been developed. We will examine the chemistry and the capability of different colloidal synthetic routes with regard to controlling the shape, size, and optical properties of the resulting nanocrystals. We will also provide an up-to-date overview of their postsynthesis transformations, and summarize the various solution processes that are aimed at fabricating halide perovskite-based nanocomposites. Furthermore, we...

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

Abstract: These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.

CO 2 reduction on gas-diffusion electrodes and why catalytic performance must be assessed at commercially-relevant conditions

Abstract: Electrocatalytic CO2 reduction has the dual-promise of neutralizing carbon emissions in the near future, while providing a long-term pathway to create energy-dense chemicals and fuels from atmospheric CO2. The field has advanced immensely in recent years, taking significant strides towards commercial realization. Catalyst innovations have played a pivotal role in these advances, with a steady stream of new catalysts providing gains in CO2 conversion efficiencies and selectivities of both C1 and C2 products. Comparatively few of these catalysts have been tested at commercially-relevant current densities (∼200 mA cm−2) due to transport limitations in traditional testing configurations and a research focus on fundamental catalyst kinetics, which are measured at substantially lower current densities. A catalyst's selectivity and activity, however, have been shown to be highly sensitive to the local reaction environment, which changes drastically as a function of reaction rate. As a consequence of this, the surface properties of many CO2 reduction catalysts risk being optimized for the wrong operating conditions. The goal of this perspective is to communicate the substantial impact of reaction rate on catalytic behaviour and the operation of gas-diffusion layers for the CO2 reduction reaction. In brief, this work motivates high current density catalyst testing as a necessary step to properly evaluate materials for electrochemical CO2 reduction, and to accelerate the technology toward its envisioned application of neutralizing CO2 emissions on a global scale.

A Review and Typology of Circular Economy Business Model Patterns

Abstract: The circular economy (CE) requires companies to rethink their supply chains and business models. Several frameworks found in the academic and practitioner literature propose circular economy business models (CEBMs) to redefine how companies create value while adhering to CE principles. A review of these frameworks shows that some models are frequently discussed, some are framework specific, and some use a different wording to refer to similar CEBMs, pointing to the need to consolidate the current state of the art. We conduct a morphological analysis of 26 current CEBMs from the literature, which includes defining their major business model dimensions and identifying the specific characteristics of these dimensions. Based on this analysis, we identify a broad range of business model design options and propose six major CEBM patterns with the potential to support the closing of resource flows: repair and maintenance; reuse and redistribution; refurbishment and remanufacturing; recycling; cascading and repurposing; and organic feedstock business model patterns. We also discuss different design strategies to support the development of these CEBMs. (Less)

Twenty-three unsolved problems in hydrology (UPH)–a community perspective

Abstract: This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come.

Key indicators of Arctic climate change : 1971–2017

Abstract: Key observational indicators of climate change in the Arctic, most spanning a 47 year period (1971–2017) demonstrate fundamental changes among nine key elements of the Arctic system. We find that, coherent with increasing air temperature, there is an intensification of the hydrological cycle, evident from increases in humidity, precipitation, river discharge, glacier equilibrium line altitude and land ice wastage. Downward trends continue in sea ice thickness (and extent) and spring snow cover extent and duration, while near-surface permafrost continues to warm. Several of the climate indicators exhibit a significant statistical correlation with air temperature or precipitation, reinforcing the notion that increasing air temperatures and precipitation are drivers of major changes in various components of the Arctic system. To progress beyond a presentation of the Arctic physical climate changes, we find a correspondence between air temperature and biophysical indicators such as tundra biomass and identify numerous biophysical disruptions with cascading effects throughout the trophic levels. These include: increased delivery of organic matter and nutrients to Arctic near‐coastal zones; condensed flowering and pollination plant species periods; timing mismatch between plant flowering and pollinators; increased plant vulnerability to insect disturbance; increased shrub biomass; increased ignition of wildfires; increased growing season CO2 uptake, with counterbalancing increases in shoulder season and winter CO2 emissions; increased carbon cycling, regulated by local hydrology and permafrost thaw; conversion between terrestrial and aquatic ecosystems; and shifting animal distribution and demographics. The Arctic biophysical system is now clearly trending away from its 20th Century state and into an unprecedented state, with implications not only within but beyond the Arctic. The indicator time series of this study are freely downloadable at AMAP.no.

Patient-derived organoids can predict response to chemotherapy in metastatic colorectal cancer patients.

Abstract: There is a clear and unmet clinical need for biomarkers to predict responsiveness to chemotherapy for cancer. We developed an in vitro test based on patient-derived tumor organoids (PDOs) from metastatic lesions to identify nonresponders to standard-of-care chemotherapy in colorectal cancer (CRC). In a prospective clinical study, we show the feasibility of generating and testing PDOs for evaluation of sensitivity to chemotherapy. Our PDO test predicted response of the biopsied lesion in more than 80% of patients treated with irinotecan-based therapies without misclassifying patients who would have benefited from treatment. This correlation was specific to irinotecan-based chemotherapy, however, and the PDOs failed to predict outcome for treatment with 5-fluorouracil plus oxaliplatin. Our data suggest that PDOs could be used to prevent cancer patients from undergoing ineffective irinotecan-based chemotherapy.

A comparison of automatic cell identification methods for single-cell RNA sequencing data

Abstract: Single-cell transcriptomics is rapidly advancing our understanding of the cellular composition of complex tissues and organisms. A major limitation in most analysis pipelines is the reliance on manual annotations to determine cell identities, which are time-consuming and irreproducible. The exponential growth in the number of cells and samples has prompted the adaptation and development of supervised classification methods for automatic cell identification. Here, we benchmarked 22 classification methods that automatically assign cell identities including single-cell-specific and general-purpose classifiers. The performance of the methods is evaluated using 27 publicly available single-cell RNA sequencing datasets of different sizes, technologies, species, and levels of complexity. We use 2 experimental setups to evaluate the performance of each method for within dataset predictions (intra-dataset) and across datasets (inter-dataset) based on accuracy, percentage of unclassified cells, and computation time. We further evaluate the methods’ sensitivity to the input features, number of cells per population, and their performance across different annotation levels and datasets. We find that most classifiers perform well on a variety of datasets with decreased accuracy for complex datasets with overlapping classes or deep annotations. The general-purpose support vector machine classifier has overall the best performance across the different experiments. We present a comprehensive evaluation of automatic cell identification methods for single-cell RNA sequencing data. All the code used for the evaluation is available on GitHub ( https://github.com/tabdelaal/scRNAseq_Benchmark ). Additionally, we provide a Snakemake workflow to facilitate the benchmarking and to support the extension of new methods and new datasets.

Ice sheet contributions to future sea-level rise from structured expert judgment.

Abstract: Despite considerable advances in process understanding, numerical modeling, and the observational record of ice sheet contributions to global mean sea-level rise (SLR) since the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change, severe limitations remain in the predictive capability of ice sheet models. As a consequence, the potential contributions of ice sheets remain the largest source of uncertainty in projecting future SLR. Here, we report the findings of a structured expert judgement study, using unique techniques for modeling correlations between inter- and intra-ice sheet processes and their tail dependences. We find that since the AR5, expert uncertainty has grown, in particular because of uncertain ice dynamic effects. For a +2 °C temperature scenario consistent with the Paris Agreement, we obtain a median estimate of a 26 cm SLR contribution by 2100, with a 95th percentile value of 81 cm. For a +5 °C temperature scenario more consistent with unchecked emissions growth, the corresponding values are 51 and 178 cm, respectively. Inclusion of thermal expansion and glacier contributions results in a global total SLR estimate that exceeds 2 m at the 95th percentile. Our findings support the use of scenarios of 21st century global total SLR exceeding 2 m for planning purposes. Beyond 2100, uncertainty and projected SLR increase rapidly. The 95th percentile ice sheet contribution by 2200, for the +5 °C scenario, is 7.5 m as a result of instabilities coming into play in both West and East Antarctica. Introducing process correlations and tail dependences increases estimates by roughly 15%.

Actin–microtubule crosstalk in cell biology

Abstract: The cytoskeleton and its components — actin, microtubules and intermediate filaments — have been studied for decades, and multiple roles of the individual cytoskeletal substructures are now well established. However, in recent years it has become apparent that the three cytoskeletal elements also engage in extensive crosstalk that is important for core biological processes. Actin–microtubule crosstalk is particularly important for the regulation of cell shape and polarity during cell migration and division and the establishment of neuronal and epithelial cell shape and function. This crosstalk engages different cytoskeletal regulators and encompasses various physical interactions, such as crosslinking, anchoring and mechanical support. Thus, the cytoskeleton should be considered not as a collection of individual parts but rather as a unified system in which subcomponents co-regulate each other to exert their functions in a precise and highly adaptable manner. Actin and microtubules are essential for key cellular processes, including cell polarization, migration and division. Although their cellular roles are distinct, actin and microtubules also extensively influence each other’s dynamics and organization. This crosstalk encompasses multiple mechanisms and involves various crosslinkers, motors and regulatory proteins as well as mechanical cooperation.

Review of Recent Development of In Situ/Operando Characterization Techniques for Lithium Battery Research

Abstract: The increasing demands of energy storage require the significant improvement of current Li-ion battery electrode materials and the development of advanced electrode materials. Thus, it is necessary to gain an in-depth understanding of the reaction processes, degradation mechanism, and thermal decomposition mechanisms under realistic operation conditions. This understanding can be obtained by in situ/operando characterization techniques, which provide information on the structure evolution, redox mechanism, solid-electrolyte interphase (SEI) formation, side reactions, and Li-ion transport properties under operating conditions. Here, the recent developments in the in situ/operando techniques employed for the investigation of the structural stability, dynamic properties, chemical environment changes, and morphological evolution are described and summarized. The experimental approaches reviewed here include X-ray, electron, neutron, optical, and scanning probes. The experimental methods and operating principles, especially the in situ cell designs, are described in detail. Representative studies of the in situ/operando techniques are summarized, and finally the major current challenges and future opportunities are discussed. Several important battery challenges are likely to benefit from these in situ/operando techniques, including the inhomogeneous reactions of high-energy-density cathodes, the development of safe and reversible Li metal plating, and the development of stable SEI.

A review of mechanical properties of additively manufactured Inconel 718

Abstract: Inconel 718 is one of the most commonly employed alloys for metal additive manufacturing (MAM) and has a wide range of applications in aircraft, gas turbines, turbocharger rotors, and a variety of other corrosive and structural applications involving temperatures of up to ∼700 °C. Numerous studies have investigated different aspects of the mechanical behaviour of additively manufactured (AM) Inconel 718. This study analyses the observations from more than 170 publications to provide an unbiased engineering overview for the mechanical response of AM Inconel 718 (and its variations and spread among different reports). First, a brief review of the microstructural features of AM Inconel 718 is presented. This is followed by a comprehensive summary of tensile strength, hardness, fatigue strength, and high-temperature creep behaviour of AM Inconel 718 for different types of MAM techniques and for different process and post-process conditions.

Deep learning predicts path-dependent plasticity.

Abstract: Plasticity theory aims at describing the yield loci and work hardening of a material under general deformation states. Most of its complexity arises from the nontrivial dependence of the yield loci on the complete strain history of a material and its microstructure. This motivated 3 ingenious simplifications that underpinned a century of developments in this field: 1) yield criteria describing yield loci location; 2) associative or nonassociative flow rules defining the direction of plastic flow; and 3) effective stress-strain laws consistent with the plastic work equivalence principle. However, 2 key complications arise from these simplifications. First, finding equations that describe these 3 assumptions for materials with complex microstructures is not trivial. Second, yield surface evolution needs to be traced iteratively, i.e., through a return mapping algorithm. Here, we show that these assumptions are not needed in the context of sequence learning when using recurrent neural networks, diverting the above-mentioned complications. This work offers an alternative to currently established plasticity formulations by providing the foundations for finding history- and microstructure-dependent constitutive models through deep learning.

Convolutional Neural Network Architectures for Signals Supported on Graphs

Abstract: Two architectures that generalize convolutional neural networks (CNNs) for the processing of signals supported on graphs are introduced. We start with the selection graph neural network (GNN), which replaces linear time invariant filters with linear shift invariant graph filters to generate convolutional features and reinterprets pooling as a possibly nonlinear subsampling stage where nearby nodes pool their information in a set of preselected sample nodes. A key component of the architecture is to remember the position of sampled nodes to permit computation of convolutional features at deeper layers. The second architecture, dubbed aggregation GNN, diffuses the signal through the graph and stores the sequence of diffused components observed by a designated node. This procedure effectively aggregates all components into a stream of information having temporal structure to which the convolution and pooling stages of regular CNNs can be applied. A multinode version of aggregation GNNs is further introduced for operation in large-scale graphs. An important property of selection and aggregation GNNs is that they reduce to conventional CNNs when particularized to time signals reinterpreted as graph signals in a circulant graph. Comparative numerical analyses are performed in a source localization application over synthetic and real-world networks. Performance is also evaluated for an authorship attribution problem and text category classification. Multinode aggregation GNNs are consistently the best-performing GNN architecture.

Sixty years of global progress in managed aquifer recharge

Abstract: The last 60 years has seen unprecedented groundwater extraction and overdraft as well as development of new technologies for water treatment that together drive the advance in intentional groundwater replenishment known as managed aquifer recharge (MAR). This paper is the first known attempt to quantify the volume of MAR at global scale, and to illustrate the advancement of all the major types of MAR and relate these to research and regulatory advancements. Faced with changing climate and rising intensity of climate extremes, MAR is an increasingly important water management strategy, alongside demand management, to maintain, enhance and secure stressed groundwater systems and to protect and improve water quality. During this time, scientific research—on hydraulic design of facilities, tracer studies, managing clogging, recovery efficiency and water quality changes in aquifers—has underpinned practical improvements in MAR and has had broader benefits in hydrogeology. Recharge wells have greatly accelerated recharge, particularly in urban areas and for mine water management. In recent years, research into governance, operating practices, reliability, economics, risk assessment and public acceptance of MAR has been undertaken. Since the 1960s, implementation of MAR has accelerated at a rate of 5%/year, but is not keeping pace with increasing groundwater extraction. Currently, MAR has reached an estimated 10 km3/year, ~2.4% of groundwater extraction in countries reporting MAR (or ~1.0% of global groundwater extraction). MAR is likely to exceed 10% of global extraction, based on experience where MAR is more advanced, to sustain quantity, reliability and quality of water supplies.

Single-photon avalanche diode imagers in biophotonics: review and outlook

Abstract: Single-photon avalanche diode (SPAD) arrays are solid-state detectors that offer imaging capabilities at the level of individual photons, with unparalleled photon counting and time-resolved performance. This fascinating technology has progressed at a very fast pace in the past 15 years, since its inception in standard CMOS technology in 2003. A host of architectures have been investigated, ranging from simpler implementations, based solely on off-chip data processing, to progressively "smarter" sensors including on-chip, or even pixel level, time-stamping and processing capabilities. As the technology has matured, a range of biophotonics applications have been explored, including (endoscopic) FLIM, (multibeam multiphoton) FLIM-FRET, SPIM-FCS, super-resolution microscopy, time-resolved Raman spectroscopy, NIROT and PET. We will review some representative sensors and their corresponding applications, including the most relevant challenges faced by chip designers and end-users. Finally, we will provide an outlook on the future of this fascinating technology.

Common principles and best practices for engineering microbiomes

Abstract: Despite broad scientific interest in harnessing the power of Earth's microbiomes, knowledge gaps hinder their efficient use for addressing urgent societal and environmental challenges. We argue that structuring research and technology developments around a design-build-test-learn (DBTL) cycle will advance microbiome engineering and spur new discoveries of the basic scientific principles governing microbiome function. In this Review, we present key elements of an iterative DBTL cycle for microbiome engineering, focusing on generalizable approaches, including top-down and bottom-up design processes, synthetic and self-assembled construction methods, and emerging tools to analyse microbiome function. These approaches can be used to harness microbiomes for broad applications related to medicine, agriculture, energy and the environment. We also discuss key challenges and opportunities of each approach and synthesize them into best practice guidelines for engineering microbiomes. We anticipate that adoption of a DBTL framework will rapidly advance microbiome-based biotechnologies aimed at improving human and animal health, agriculture and enabling the bioeconomy.

A Ten-Qubit Solid-State Spin Register with Quantum Memory up to One Minute

Abstract: A ten-qubit system based on spins in impure diamond achieves coherence times of over a minute.

Supporting Self-Regulated Learning in Online Learning Environments and MOOCs: A Systematic Review

Abstract: Massive Open Online Courses (MOOCs) allow learning to take place anytime and anywhere with little external monitoring by teachers. Characteristically, highly diverse groups of learners enrolled in MOOCs are required to make decisions related to their own learning activities to achieve academic success. Therefore, it is considered important to support self-regulated learning (SRL) strategies and adapt to relevant human factors (e.g., gender, cognitive abilities, prior knowledge). SRL supports have been widely investigated in traditional classroom settings, but little is known about how SRL can be supported in MOOCs. Very few experimental studies have been conducted in MOOCs at present. To fill this gap, this paper presents a systematic review of studies on approaches to support SRL in multiple types of online learning environments and how they address human factors. The 35 studies reviewed show that human factors play an important role in the efficacy of SRL supports. Future studies can use learning analytics to understand learners at a fine-grained level to provide support that best fits individual learners. The objective of the paper is twofold: (a) to inform researchers, designers and teachers about the state of the art of SRL support in online learning environments and MOOCs; (b) to provide suggestions for adaptive self-regulated learning support.

Ultrastrong Medium-Entropy Single-Phase Alloys Designed via Severe Lattice Distortion

Abstract: Severe lattice distortion is a core effect in the design of multiprincipal element alloys with the aim to enhance yield strength, a key indicator in structural engineering. Yet, the yield strength values of medium- and high-entropy alloys investigated so far do not substantially exceed those of conventional alloys owing to the insufficient utilization of lattice distortion. Here it is shown that a simple VCoNi equiatomic medium-entropy alloy exhibits a near 1 GPa yield strength and good ductility, outperforming conventional solid-solution alloys. It is demonstrated that a wide fluctuation of the atomic bond distances in such alloys, i.e., severe lattice distortion, improves both yield stress and its sensitivity to grain size. In addition, the dislocation-mediated plasticity effectively enhances the strength-ductility relationship by generating nanosized dislocation substructures due to massive pinning. The results demonstrate that severe lattice distortion is a key property for identifying extra-strong materials for structural engineering applications.

Human Motion Trajectory Prediction: A Survey

Abstract: With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.

Computational Approach to Molecular Catalysis by 3d Transition Metals: Challenges and Opportunities

Abstract: Computational chemistry provides a versatile toolbox for studying mechanistic details of catalytic reactions and holds promise to deliver practical strategies to enable the rational in silico catalyst design. The versatile reactivity and nontrivial electronic structure effects, common for systems based on 3d transition metals, introduce additional complexity that may represent a particular challenge to the standard computational strategies. In this review, we discuss the challenges and capabilities of modern electronic structure methods for studying the reaction mechanisms promoted by 3d transition metal molecular catalysts. Particular focus will be placed on the ways of addressing the multiconfigurational problem in electronic structure calculations and the role of expert bias in the practical utilization of the available methods. The development of density functionals designed to address transition metals is also discussed. Special emphasis is placed on the methods that account for solvation effects and the multicomponent nature of practical catalytic systems. This is followed by an overview of recent computational studies addressing the mechanistic complexity of catalytic processes by molecular catalysts based on 3d metals. Cases that involve noninnocent ligands, multicomponent reaction systems, metal-ligand and metal-metal cooperativity, as well as modeling complex catalytic systems such as metal-organic frameworks are presented. Conventionally, computational studies on catalytic mechanisms are heavily dependent on the chemical intuition and expert input of the researcher. Recent developments in advanced automated methods for reaction path analysis hold promise for eliminating such human-bias from computational catalysis studies. A brief overview of these approaches is presented in the final section of the review. The paper is closed with general concluding remarks.

EuroCity Persons: A Novel Benchmark for Person Detection in Traffic Scenes

Abstract: Big data has had a great share in the success of deep learning in computer vision. Recent works suggest that there is significant further potential to increase object detection performance by utilizing even bigger datasets. In this paper, we introduce the EuroCity Persons dataset, which provides a large number of highly diverse, accurate and detailed annotations of pedestrians, cyclists and other riders in urban traffic scenes. The images for this dataset were collected on-board a moving vehicle in 31 cities of 12 European countries. With over 238,200 person instances manually labeled in over 47,300 images, EuroCity Persons is nearly one order of magnitude larger than datasets used previously for person detection in traffic scenes. The dataset furthermore contains a large number of person orientation annotations (over 211,200). We optimize four state-of-the-art deep learning approaches (Faster R-CNN, R-FCN, SSD and YOLOv3) to serve as baselines for the new object detection benchmark. In experiments with previous datasets we analyze the generalization capabilities of these detectors when trained with the new dataset. We furthermore study the effect of the training set size, the dataset diversity (day- versus night-time, geographical region), the dataset detail (i.e., availability of object orientation information) and the annotation quality on the detector performance. Finally, we analyze error sources and discuss the road ahead.

Super-resolution fight club: assessment of 2D and 3D single-molecule localization microscopy software.

Abstract: With the widespread uptake of two-dimensional (2D) and three-dimensional (3D) single-molecule localization microscopy (SMLM), a large set of different data analysis packages have been developed to generate super-resolution images. In a large community effort, we designed a competition to extensively characterize and rank the performance of 2D and 3D SMLM software packages. We generated realistic simulated datasets for popular imaging modalities-2D, astigmatic 3D, biplane 3D and double-helix 3D-and evaluated 36 participant packages against these data. This provides the first broad assessment of 3D SMLM software and provides a holistic view of how the latest 2D and 3D SMLM packages perform in realistic conditions. This resource allows researchers to identify optimal analytical software for their experiments, allows 3D SMLM software developers to benchmark new software against the current state of the art, and provides insight into the current limits of the field.

Compact radio emission indicates a structured jet was produced by a binary neutron star merger

Abstract: The binary neutron star merger event GW170817 was detected through both electromagnetic radiation and gravitational waves. Its afterglow emission may have been produced by either a narrow relativistic jet or an isotropic outflow. High-spatial-resolution measurements of the source size and displacement can discriminate between these scenarios. We present very-long-baseline interferometry observations, performed 207.4 days after the merger by using a global network of 32 radio telescopes. The apparent source size is constrained to be smaller than 2.5 milli-arc seconds at the 90% confidence level. This excludes the isotropic outflow scenario, which would have produced a larger apparent size, indicating that GW170817 produced a structured relativistic jet. Our rate calculations show that at least 10% of neutron star mergers produce such a jet.