Reviewerly/exHarmony huggingface dataset:
Extensive phenotype data and machine learning in prediction of mortality in acute coronary syndrome – the MADDEC study

Objective Investigation of the clinical potential of extensive phenotype data and machine learning (ML) in the prediction of mortality in acute coronary syndrome (ACS).Methods The value of ML and extensive clinical data was analyzed in a retrospective registry study of 9066 consecutive ACS patients (January 2007 to October 2017). Main outcome was six-month mortality. Prediction models were developed using two ML methods, logistic regression and extreme gradient boosting (xgboost). The models were fitted in training set of patients treated in 2007–2014 and 2017 (81%, n = 7344) and validated in a separate validation set of patients treated in 2015–2016 with full GRACE score data available for comparison of model accuracy (19%, n = 1722).Results Overall, six-month mortality was 7.3% (n = 660). Several variables were found to be significantly associated with six-month mortality by both ML methods. The xgboost scored the best performance: AUC 0.890 (0.864–0.916). The AUC values for logistic regression and GRACE score were 0.867(0.837–0.897) and 0.822 (0.785–0.859), respectively. The AUC value of xgboost was better when compared to logistic regression (p = .012) and GRACE score (p < .00001).Conclusions The use of extensive phenotype data and novel machine learning improves prediction of mortality in ACS over traditional GRACE score.KEY MESSAGESThe collection of extensive cardiovascular phenotype data from electronic health records as well as from data recorded by physicians can be used highly effectively in prediction of mortality after acute coronary syndrome.Supervised machine learning methods such as logistic regression and extreme gradient boosting using extensive phenotype data significantly outperform conventional risk assessment by the current golden standard GRACE score.Integration of electronic health records and the use of supervised machine learning methods can be easily applied in a single centre level to model the risk of mortality.
Cooperative path planning of multiple autonomous underwater vehicles operating in dynamic ocean environment

This paper presents a two-stage cooperative path planner for multiple autonomous underwater vehicles operating in dynamic environment. In case of static environment, global Legendre pseudospectral method is employed for collision-free paths of vehicles for the purpose of minimum time consumption and simultaneous arrival. Moreover, in order to keep the multiple autonomous underwater vehicles safe from collisions on the path segments connecting two adjacent control nodes, an adaptive intermediate knots insertion algorithm is introduced. In the on-line planning stage, the local re-planning strategy aims at avoiding collisions with unexpected dynamic obstacles by two consecutive avoidance maneuvers, and the differential flatness property of autonomous underwater vehicle is utilized, which can help the vehicles react fast enough to avoid moving obstacles.
Serious Games in Health Professions Education: Review of Trends and Learning Efficacy

To provide an overview of research trends and a review regarding the learning efficacy of serious games for health professions education taking into account short-term learning outcomes.For the review of research trends, we performed the search on Web of Science from 1996 until the present. For the scoping review on learning efficacy, ERIC, Education Source, PsychINFO, Global Health, CINAHL, Web Of Science, and Medline were searched.The publications trend is characterized by three phases: (i) an exploratory phase up to 2006, (ii) an early growth from 2007 to 2012, and (iii) a development phase from 2013 onwards. A total of 25 studies were identified in the scoping review. Sixteen had both pre-test and post-test, all of them showed significant improvement in learning scores after the use of serious games. Eighteen publications conducted controlled experiments, of which 14 indicated that post-test scores after serious games were significantly higher than with conventional teaching methods. The review revealed the lack of integration of affective learning with other competencies, as well as the need for serious games targeting postgraduate education.Serious games research remains emergent. Using serious games for health professions education seems efficacious for short-term learning. Addressing more competencies and health professionals across the education continuum is needed before generalizable definitive statements can be made.
A primer in artificial intelligence in cardiovascular medicine

Driven by recent developments in computational power, algorithms and web-based storage resources, machine learning (ML)-based artificial intelligence (AI) has quickly gained ground as the solution for many technological and societal challenges. AI education has become very popular and is oversubscribed at Dutch universities. Major investments were made in 2018 to develop and build the first AI-driven hospitals to improve patient care and reduce healthcare costs. AI has the potential to greatly enhance traditional statistical analyses in many domains and has been demonstrated to allow the discovery of ‘hidden’ information in highly complex datasets. As such, AI can also be of significant value in the diagnosis and treatment of cardiovascular disease, and the first applications of AI in the cardiovascular field are promising. However, many professionals in the cardiovascular field involved in patient care, education or science are unaware of the basics behind AI and the existing and expected applications in their field. In this review, we aim to introduce the broad cardiovascular community to the basics of modern ML-based AI and explain several of the commonly used algorithms. We also summarise their initial and future applications relevant to the cardiovascular field.
Continuously Assessing and Improving Software Quality With Software Analytics Tools: A Case Study

In the last decade, modern data analytics technologies have enabled the creation of software analytics tools offering real-time visualization of various aspects related to software development and usage. These tools seem to be particularly attractive for companies doing agile software development. However, the information provided by the available tools is neither aggregated nor connected to higher quality goals. At the same time, assessing and improving the software quality has also been the key targets for the software engineering community, yielding several proposals for standards and software quality models. Integrating such quality models into software analytics tools could close the gap by providing the connection to higher quality goals. This paper aims at understanding whether the integration of quality models into software analytics tools provides understandable, reliable, useful, and relevant information at the right level of detail about the quality of a process or product and whether practitioners intend to use it. Over the course of more than a year, four companies involved in this case study deployed such a tool to assess and improve software quality in several projects. We used standardized measurement instruments to elicit the perception of 22 practitioners regarding their use of the tool. We complemented the findings with debriefing sessions held at the companies. In addition, we discussed challenges and lessons learned with four practitioners leading the use of the tool. The quantitative and qualitative analyses provided positive results, i.e., the practitioners' perception with regard to the tool's understandability, reliability, usefulness, and relevance was positive. Individual statements support the statistical findings, and constructive feedback can be used for future improvements. We conclude that the potential for future adoption of quality models within software analytics tools definitely exists and encourage other practitioners to use the presented seven challenges and seven lessons learned and adopt them in their companies.
Deep forest hashing for image retrieval

Hashing methods have been intensively studied and widely used in image retrieval. Hashing methods aim to learn a group of hash functions to map original data into compact binary codes and simultaneously preserve some notion of similarity in the Hamming space. The generated binary codes are effective for image retrieval and highly efficient for large-scale data storage. The decision tree is a fast and interpretable model, but the current decision tree based hashing methods have insufficient learning ability due to the use of shallow decision trees. Most current deep hashing methods are based on deep neural networks. However, considering the deficiencies of deep neural network-based hashing, such as the presence of too many hyperparameters, poor interpretability, and requirement for expensive and powerful computational facilities during the training process, a non-deep neural network-based hashing model need to be designed to achieve efficient image retrieval with few hyperparameters, easy theoretical analysis and an efficient training process. The multi-grained cascade forest (gcForest) is a novel deep model that generates a deep forest ensemble classifier to process data layer-by-layer with multi-grained scanning and a cascade forest. To date, gcForest has not been used to generate compact binary codes; therefore, we propose a deep forest-based method for hashing learning that aims to learn shorter binary codes to achieve effective and efficient image retrieval. The experimental results show that the proposed method has better performance with shorter binary codes than other corresponding hashing methods.
Detecting Double JPEG Compressed Color Images With the Same Quantization Matrix in Spherical Coordinates

Detection of double Joint Photographic Experts Group (JPEG) compression is an important part of image forensics. Although methods in the past studies have been presented for detecting the double JPEG compression with a different quantization matrix, the detection of double JPEG compression with the same quantization matrix is still a challenging problem. In this paper, an effective method to detect the recompression in the color images by using the conversion error, rounding error, and truncation error on the pixel in the spherical coordinate system is proposed. The randomness of truncation errors, rounding errors, and quantization errors result in random conversion errors. The pixel number of the conversion error is used to extract six-dimensional features. Truncation error and rounding error on the pixel in its three channels are mapped to the spherical coordinate system based on the relation of a color image to the pixel values in the three channels. The former is converted into amplitude and angles to extract 30-dimensional features and 8-dimensional auxiliary features are extracted from the number of special points and special blocks. As a result, a total of 44-dimensional features have been used in the classification by using the support vector machine (SVM) method. Thereafter, the support vector machine recursive feature elimination (SVMRFE) method is used to improve the classification accuracy. The experimental results show that the performance of the proposed method is better than the existing methods.
Spatial distribution, enrichment and geo-accumulation of heavy metals in surface sediments near urban and industrial areas in the Persian Gulf

Concentrations of Cd, Cr, Cu, Fe, Ni, Pb and Zn and geochemical controlling factors have been determined in surface sediment samples collected in the Persian Gulf (South of Iran) and were investigated using pollution indices including enrichment factor (EF), geo-accumulation index (Igeo) and contamination factor (Cf) distribution and its pollution status.Sediment samples were collected from 12 sites in a monitoring period of 4 months from September to December in 2014.The spatial distribution of heavy metals demonstrated that concentrations are much higher in the industrial areas in comparison with urban areas.Analytical results have been elaborated by using a geographical information system software to show metals accumulation areas.Based on the EF and Igeo, the order of heavy metal concentrations was Cd > Pb > Ni > Cr > Zn > Cu in industrial stations.For urban station, the order was as: Cd > Pb > Ni > Zn > Cu > Cr.EF and Igeo values showed that sediments were contaminated with Cd and Pb.Based on the EF, Cd was the heavy metal contaminant of most concern in urban and industrial surface sediments.Moreover, this study showed that Cd threshold concentrations to mostly be exceeded in the study area.Also, the concentration of heavy metals in the industrial areas was much higher than those of urban areas.
Scaling up Open Tagging from Tens to Thousands: Comprehension Empowered Attribute Value Extraction from Product Title

Supplementing product information by extracting attribute values from title is a crucial task in e-Commerce domain.Previous studies treat each attribute only as an entity type and build one set of NER tags (e.g., BIO) for each of them, leading to a scalability issue which unfits to the large sized attribute system in real world e-Commerce.In this work, we propose a novel approach to support value extraction scaling up to thousands of attributes without losing performance: (1) We propose to regard attribute as a query and adopt only one global set of BIO tags for any attributes to reduce the burden of attribute tag or model explosion;(2) We explicitly model the semantic representations for attribute and title, and develop an attention mechanism to capture the interactive semantic relations in-between to enforce our framework to be attribute comprehensive.We conduct extensive experiments in real-life datasets.The results show that our model not only outperforms existing state-of-the-art N-ER tagging models, but also is robust and generates promising results for up to 8, 906 attributes.
Model-Agnostic Meta-Learning for Relation Classification with Limited Supervision

In this paper we frame the task of supervised relation classification as an instance of metalearning.We propose a model-agnostic metalearning protocol for training relation classifiers to achieve enhanced predictive performance in limited supervision settings.During training, we aim to not only learn good parameters for classifying relations with sufficient supervision, but also learn model parameters that can be fine-tuned to enhance predictive performance for relations with limited supervision.In experiments conducted on two relation classification datasets, we demonstrate that the proposed meta-learning approach improves the predictive performance of two state-of-the-art supervised relation classification models.
VULTRON: Catching Vulnerable Smart Contracts Once and for All

Despite the high stakes involved, smart contracts are often developed in an undisciplined way thus far. The existence of vulnerabilities compromises the security and reliability of smart contracts, and endangers the trust of participants in their ongoing businesses. Existing vulnerability detection techniques are often designed case-by-case, making them difficult to generalize. In this paper, we design general principles for detecting vulnerable smart contracts. Our key insight is that almost all the existing transaction-related vulnerabilities are due to the mismatch between the actual transferred amount and the amount reflected on the contract's internal bookkeeping. Based on this, we propose a precise and generally applicable technique, VULTRON, which can detect irregular transactions due to various types of adversarial exploits. We also report preliminary results applying our technique to real-world case studies.
An accurate registration of the BigBrain dataset with the MNI PD25 and ICBM152 atlases

Brain atlases that encompass detailed anatomical or physiological features are instrumental in the research and surgical planning of various neurological conditions. Magnetic resonance imaging (MRI) has played important roles in neuro-image analysis while histological data remain crucial as a gold standard to guide and validate such analyses. With cellular-scale resolution, the BigBrain atlas offers 3D histology of a complete human brain, and is highly valuable to the research and clinical community. To bridge the insights at macro- and micro-levels, accurate mapping of BigBrain and established MRI brain atlases is necessary, but the existing registration is unsatisfactory. The described dataset includes co-registration of the BigBrain atlas to the MNI PD25 atlas and the ICBM152 2009b atlases (symmetric and asymmetric versions) in addition to manual segmentation of the basal ganglia, red nucleus, amygdala, and hippocampus for all mentioned atlases. The dataset intends to provide a bridge between insights from histological data and MRI studies in research and neurosurgical planning. The registered atlases, anatomical segmentations, and deformation matrices are available at: https://osf.io/xkqb3/ .
Non-Adversarial Image Synthesis With Generative Latent Nearest Neighbors

Unconditional image generation has recently been dominated by generative adversarial networks (GANs). GAN methods train a generator which regresses images from random noise vectors, as well as a discriminator that attempts to differentiate between the generated images and a training set of real images. GANs have shown amazing results at generating realistic looking images. Despite their success, GANs suffer from critical drawbacks including: unstable training and mode-dropping. The weaknesses in GANs have motivated research into alternatives including: variational auto-encoders (VAEs), latent embedding learning methods (e.g. GLO) and nearest-neighbor based implicit maximum likelihood estimation (IMLE). Unfortunately at the moment, GANs still significantly outperform the alternative methods for image generation. In this work, we present a novel method - Generative Latent Nearest Neighbors (GLANN) - for training generative models without adversarial training. GLANN combines the strengths of IMLE and GLO in a way that overcomes the main drawbacks of each method. Consequently, GLANN generates images that are far better than GLO and IMLE. Our method does not suffer from mode collapse which plagues GAN training and is much more stable. Qualitative results show that GLANN outperforms a baseline consisting of 800 GANs and VAEs on commonly used datasets. Our models are also shown to be effective for training truly non-adversarial unsupervised image translation.
Completely positive master equation for arbitrary driving and small level spacing

Markovian master equations are a ubiquitous tool in the study of open quantum systems, but deriving them from first principles involves a series of compromises. On the one hand, the Redfield equation is valid for fast environments (whose correlation function decays much faster than the system relaxation time) regardless of the relative strength of the coupling to the system Hamiltonian, but is notoriously non-completely-positive. On the other hand, the Davies equation preserves complete positivity but is valid only in the ultra-weak coupling limit and for systems with a finite level spacing, which makes it incompatible with arbitrarily fast time-dependent driving. Here we show that a recently derived Markovian coarse-grained master equation (CGME), already known to be completely positive, has a much expanded range of applicability compared to the Davies equation, and moreover, is locally generated and can be generalized to accommodate arbitrarily fast driving. This generalization, which we refer to as the time-dependent CGME, is thus suitable for the analysis of fast operations in gate-model quantum computing, such as quantum error correction and dynamical decoupling. Our derivation proceeds directly from the Redfield equation and allows us to place rigorous error bounds on all three equations: Redfield, Davies, and coarse-grained. Our main result is thus a completely positive Markovian master equation that is a controlled approximation to the true evolution for any time-dependence of the system Hamiltonian, and works for systems with arbitrarily small level spacing. We illustrate this with an analysis showing that dynamical decoupling can extend coherence times even in a strictly Markovian setting.
Graph-based Neural Sentence Ordering

Sentence ordering is to restore the original paragraph from a set of sentences. It involves capturing global dependencies among sentences regardless of their input order. In this paper, we propose a novel and flexible graph-based neural sentence ordering model, which adopts graph recurrent network \citep{Zhang:acl18} to accurately learn semantic representations of the sentences. Instead of assuming connections between all pairs of input sentences, we use entities that are shared among multiple sentences to make more expressive graph representations with less noise. Experimental results show that our proposed model outperforms the existing state-of-the-art systems on several benchmark datasets, demonstrating the effectiveness of our model. We also conduct a thorough analysis on how entities help the performance. Our code is available at https://github.com/DeepLearnXMU/NSEG.git.
Multi-Agent Pathfinding with Continuous Time

Multi-Agent Pathfinding (MAPF) is the problem of finding paths for multiple agents such that every agent reaches its goal and the agents do not collide. Most prior work on MAPF were on grids, assumed agents' actions have uniform duration, and that time is discretized into timesteps. In this work, we propose a MAPF algorithm that do not assume any of these assumptions, is complete, and provides provably optimal solutions. This algorithm is based on a novel combination of Safe Interval Path Planning (SIPP), a continuous time single agent planning algorithms, and Conflict-Based Search (CBS). We analyze this algorithm, discuss its pros and cons, and evaluate it experimentally on several standard benchmarks.
SpiderBoost and Momentum: Faster Variance Reduction Algorithms

SARAH and SPIDER are two recently developed stochastic variance-reduced algorithms, and SPIDER has been shown to achieve a near-optimal first-order oracle complexity in smooth nonconvex optimization. However, SPIDER uses an accuracy-dependent stepsize that slows down the convergence in practice, and cannot handle objective functions that involve nonsmooth regularizers. In this paper, we propose SpiderBoost as an improved scheme, which allows to use a much larger constant-level stepsize while maintaining the same near-optimal oracle complexity, and can be extended with proximal mapping to handle composite optimization (which is nonsmooth and nonconvex) with provable convergence guarantee. In particular, we show that proximal SpiderBoost achieves an oracle complexity of O(min{n^{1/2}\epsilon^{-2},\epsilon^{-3}}) in composite nonconvex optimization, improving the state-of-the-art result by a factor of O(min{n^{1/6},\epsilon^{-1/3}}). We further develop a novel momentum scheme to accelerate SpiderBoost for composite optimization, which achieves the near-optimal oracle complexity in theory and substantial improvement in experiments.