Miracle-dz/UniFAR-FTUs huggingface dataset:
A Prospective Longitudinal Study of Postnatal Quality of Life among Hong Kong Women : Comparison between Normal Vaginal Delivery and Caesarean Section

Objective: To compare the quality of life (QOL) in Hong Kong women who gave birth by normal vaginal delivery (NVD) and those delivered by Caesarean section (CS). Women were evaluated on discharge from hospital and 4 weeks after delivery. Methods: This was a prospective longitudinal study conducted in the obstetrics unit of United Christian Hospital in Hong Kong from December 2009 to March 2010. A total of 75 women with NVD and 75 women with CS were recruited. QOL was measured by a generic questionnaire, Short Form 12 version 2 on discharge from hospital and at 4 weeks after delivery. The first questionnaire involved completion of a self-reported form, the second was completed by telephone interview. The data were analysed using Microsoft Excel and the IBM SPSS software. Results: On discharge from hospital, 86% (129/150) of women returned a completed questionnaire. The mean scores of physical functioning (p=0.01), vitality (p=0.003), social functioning (p=0.003), bodily pain (p=0.02), mental health (p=0.01), as well as physical component summary (p=0.01) and mental component summary (p=0.03) scores were significantly higher in women who delivered by NVD than by CS. At 4 weeks after delivery, 75% (113/150) completed the questionnaire. There was a remarkable difference in general health (p=0.01), physical component summary measure (p=0.003), and social functioning (p=0.05) between NVD and CS groups. Conclusion: Women who had a NVD enjoyed a generally better QOL than those who delivered by CS, both on discharge from hospital and 4 weeks after delivery. NVD is recommended for women without indications for CS. Hong Kong J Gynaecol Obstet Midwifery 2016; 16(1):86-92
Optimized Asset Management in Distribution Systems Based on Predictive Risk Analysis

The paper introduces an optimal maintenance scheduler based on predictive assessment of risk of outage and equipment failure in distribution networks. The variety of severe weather conditions are observed and their impact on the network components is quantified. The equipment deterioration and failure rates are observed continuously across the space and time using heterogeneous data. The risk of weather-related outages for each component is generated in real-time, and can be extracted at multiple temporal and spatial scales depending on the application of interest. The optimal maintenance scheduling that minimizes the system risk while maintaining the economic investment limits is developed. The benefits of the framework are presented using a distribution network asset management example. Keywords— asset management, big data, data mining, geographic information system, meteorology, prediction methods, power distribution, risk analysis.
Health workers’ strikes in low-income countries: the available evidence

Abstract Objective To analyse the characteristics, frequency, drivers, outcomes and stakeholders of health workers’ strikes in low-income countries. Methods We reviewed the published and grey literature from online sources for the years 2009 to 2018. We used four search strategies: (i) exploration of main health and social sciences databases; (ii) use of specialized websites on human resources for health and development; (iii) customized Google search; and (iv) consultation with experts to validate findings. To analyse individual strike episodes, pre-existing conditions and influencing actors, we developed a conceptual framework from the literature. Results We identified 116 records reporting on 70 unique health workers’ strikes in 23 low-income countries during the period, accounting for 875 days of strike. Year 2018 had the highest number of events (17), corresponding to 170 work days lost. Strikes involving more than one professional category was the frequent strike modality (32 events), followed by strikes by physicians only (22 events). The most commonly reported cause was complaints about remuneration (63 events), followed by protest against the sector’s governance or policies (25 events) and safety of working conditions (10 events). Positive resolution was achieved more often when collective bargaining institutions and higher levels of government were involved in the negotiations. Conclusion In low-income countries, some common features appear to exist in health sector strikes’ occurrence and actors involved in such events. Future research should focus on both individual events and regional patterns, to form an evidence base for mechanisms to prevent and resolve strikes.
Dynamically generating T32 training documents using structured data

Background The US National Institutes of Health (NIH) funds academic institutions for training doctoral (PhD) students and postdoctoral fellows. These training grants, known as T32 grants, require schools to create, in a particular format, seven or eight Word documents describing the program and its participants. Weill Cornell Medicine aimed to use structured name and citation data to dynamically generate tables, thus saving administrators time. Case Presentation The author’s team collected identity and publication metadata from existing systems of record, including our student information system and previous T32 submissions. These data were fed into our ReCiter author disambiguation engine. Well-structured bibliographic metadata, including the rank of the target author, were output and stored in a MySQL database. We then ran a database query that output a Word extensible markup (XML) document according to NIH’s specifications. We generated the T32 training document using a query that ties faculty listed on a grant submission with publications that they and their mentees authored, bolding author names as required. Because our source data are well-structured and well-defined, the only parameter needed in the query is a single identifier for the grant itself. The open source code for producing this document is at http://dx.doi. org/10.5281/zenodo.2593545. Conclusions Manually writing a table for T32 grant submissions is a substantial administrative burden; some documents generated in this manner exceed 150 pages. Provided they have a source for structured identity and publication data, administrators can use the T32 Table Generator to readily output a table.
Biologically-inspired Network Memory System for Smarter Networking

Current and emerging large-scale networks, for example the current Internet and the future Internet of Things, target supporting billions of networked entities to provide a wide variety of services and resources. Such complexity results in network-data from different sources with special characteristics, such as widely diverse users and services, multiple media (e.g., text, audio, video, etc.), high-dimensionality (i.e., large sets of attributes) and various dynamic concerns (e.g., time-sensitive data). With huge amounts of network data with such characteristics, there are significant challenges to a) recognize emergent and anomalous behavior in network traffic and b) make intelligent decisions for efficient and effective network operations. Fortunately, numerous analyses of Internet traffic have demonstrated that network traffic data exhibit multi-dimensional patterns that can be learned in order to enable discovery of data semantics. We claim that extracting and managing network semantics from traffic patterns and building conceptual models to be accessed on-demand would help in mitigating the aforementioned challenges. The current Internet, contemporary networking architectures and current tools for managing large network-data largely lack capabilities to 1) represent, manage and utilize the wealth of multi-dimensional traffic data patterns; 2) extract network semantics to support Internet intelligence through efficiently building conceptual models of Internet entities at different levels of granularity; and 3) predict future events (e.g., attacks) and behaviors (e.g., QoS of unfamiliar services) based on learned semantics. We depict the limited utilization of traffic semantics in networking operations as the “Internet Semantics Gap (ISG)”. We hypothesize that endowing the Internet and next generation networks with a “memory” system that provides data and semantics management would help resolve the ISG and enable “Internet Intelligence”. We seek to enable networked entities, at runtime and on-demand, to systematically: 1) learn and retrieve network semantics at different levels of granularity related to various Internet elements (e.g., services, protocols, resources, etc.); and 2) utilize extracted semantics to improve network operations and
A myoelectric prosthetic hand with muscle synergy–based motion determination and impedance model–based biomimetic control

Human muscle synergy– and impedance model–based control enables accurate and natural finger motions of a 3D-printed myoelectric hand. Prosthetic hands are prescribed to patients who have suffered an amputation of the upper limb due to an accident or a disease. This is done to allow patients to regain functionality of their lost hands. Myoelectric prosthetic hands were found to have the possibility of implementing intuitive controls based on operator’s electromyogram (EMG) signals. These controls have been extensively studied and developed. In recent years, development costs and maintainability of prosthetic hands have been improved through three-dimensional (3D) printing technology. However, no previous studies have realized the advantages of EMG-based classification of multiple finger movements in conjunction with the introduction of advanced control mechanisms based on human motion. This paper proposes a 3D-printed myoelectric prosthetic hand and an accompanying control system. The muscle synergy–based motion-determination method and biomimetic impedance control are introduced in the proposed system, enabling the classification of unlearned combined motions and smooth and intuitive finger movements of the prosthetic hand. We evaluate the proposed system through operational experiments performed on six healthy participants and an upper-limb amputee participant. The experimental results demonstrate that our prosthetic hand system can successfully classify both learned single motions and unlearned combined motions from EMG signals with a high degree of accuracy. Furthermore, applications to real-world uses of prosthetic hands are demonstrated through control tasks conducted by the amputee participant.
Veried Bytecode Veriers

Using the theorem prover Isabelle/HOL we have formalized and proved correct an executable bytecode verier in the style of Kildall’s algorithm for a signicant subset of the Java Virtual Machine. First an abstract framework for proving correctness of data ow based type inference algorithms for assembly languages is formalized. It is shown that under certain conditions Kildall’s algorithm yields a correct bytecode verier. Then the framework is instantiated with our previous work about the JVM. Finally we demonstrate the exibility of the framework by extending our previous JVM model and the executable bytecode verier with object initialization.
Constructing robust chaos: invariant manifolds and expanding cones

Chaotic attractors in the two-dimensional border-collision normal form (a piecewise-linear map) can persist throughout open regions of parameter space. Such robust chaos has been established rigorously in some parameter regimes. Here we provide formal results for robust chaos in the original parameter regime of [S. Banerjee, J.A. Yorke, C. Grebogi, Robust Chaos, Phys. Rev. Lett. 80(14):3049--3052, 1998]. We first construct a trapping region in phase space to prove the existence of a topological attractor. We then construct an invariant expanding cone in tangent space to prove that tangent vectors expand and so no invariant set can have only negative Lyapunov exponents. Under additional assumptions we also characterise an attractor as the closure of the unstable manifold of a fixed point.
Matching Conditions and High-Re Anomalies in Hydrodynamic Turbulence

{\bf Direct} transition from low Reynolds number "weak" Gaussian turbulence to fully developed "strong" turbulence at a critical Reynolds number $R^{tr}_{\lambda}\approx 8.91$ has recently been theoretically predicted and tested in high resolution numerical simulations of V. Yakhot \& D. A. Donzis, Phys. Rev. Lett. {\bf 119}, 044501 (2017) \& PhysicaD, {\bf 384-385}, 12 (2018) on an example of a flow excited by a Gaussian random force. The matching between the low-Reynolds number Gaussian asymptotic ($Re >Re^{tr}$), led to closed approximate equation for exponents of moments of derivatives in a good agreement with experimental data. In this paper we study transition to turbulence in Benard (RB) convection where, depending on the Rayleigh number, turbulence is produced by both weak instabilities of the bulk flow and, the plume-generating instabilities of the wall boundary layers. The developed theory explains non-monotonic behavior of the low-Reynolds - number moments of velocity derivatives $M_{2n (Re)=\frac{\overline{(\partial_{x}v_{x})^{2n}}}{[\overline{(\partial_{x}v_{x})^{2}}]^{n}}$ observed in direct numerical simulations of Schumacher et.al (Phys.Rev.E, {\bf 98},033120 (2018)). In the high-Reynolds number limit, the moments are given by $M_{2n}\propto Re^{\rho_{2n}}$ with the exponents $\rho_{2n}$ slightly different from those in a Gaussian-stirring case of Refs. [3]-[4]. This may be related to universality classes defined by production mechanisms.
Comparing Semi-Parametric Model Learning Algorithms for Dynamic Model Estimation in Robotics

Physical modeling of robotic system behavior is the foundation for controlling many robotic mechanisms to a satisfactory degree. Mechanisms are also typically designed in a way that good model accuracy can be achieved with relatively simple models and model identification strategies. If the modeling accuracy using physically based models is not enough or too complex, model-free methods based on machine learning techniques can help. Of particular interest to us was therefore the question to what degree semi-parametric modeling techniques, meaning combinations of physical models with machine learning, increase the modeling accuracy of inverse dynamics models which are typically used in robot control. To this end, we evaluated semi-parametric Gaussian process regression and a novel model-based neural network architecture, and compared their modeling accuracy to a series of naive semi-parametric, parametric-only and non-parametric-only regression methods. The comparison has been carried out on three test scenarios, one involving a real test-bed and two involving simulated scenarios, with the most complex scenario targeting the modeling a simulated robot's inverse dynamics model. We found that in all but one case, semi-parametric Gaussian process regression yields the most accurate models, also with little tuning required for the training procedure.
Signatures of self-interacting dark matter on cluster density profile and subhalo distributions

Non-gravitational interactions between dark matter particles with strong scattering, but relatively small annihilation and dissipation, has been proposed to match various observables on cluster and group scales. In this paper, we present the results from large cosmological simulations which include the effects of different self-interaction scenarios. In particular we explore a model with the differential cross section that can depend on both the relative velocity of the interacting particles and the angle of scattering. We focus on how quantities, such as the stacked density profiles, subhalo counts and the splashback radius change as a function of different forms of self-interaction. We find that self-interactions not only affect the central region of the cluster, the effect well known from previous studies, but also significantly alter the distribution of subhalos and the density of particles out to the splashback radius. Our results suggest that current weak lensing data can already put constraints on the self-interaction cross-section that are only slightly weaker than the Bullet Cluster constraints (σ/m ≲ 2 cm2/g), and future lensing surveys should be able to tighten them even further making halo profiles on cluster scales a competitive probe for DM physics.
MLFriend: Interactive Prediction Task Recommendation for Event-Driven Time-Series Data

Most automation in machine learning focuses on model selection and hyper parameter tuning, and many overlook the challenge of automatically defining predictive tasks. We still heavily rely on human experts to define prediction tasks, and generate labels by aggregating raw data. In this paper, we tackle the challenge of defining useful prediction problems on event-driven time-series data. We introduce MLFriend to address this challenge. MLFriend first generates all possible prediction tasks under a predefined space, then interacts with a data scientist to learn the context of the data and recommend good prediction tasks from all the tasks in the space. We evaluate our system on three different datasets and generate a total of 2885 prediction tasks and solve them. Out of these 722 were deemed useful by expert data scientists. We also show that an automatic prediction task discovery system is able to identify top 10 tasks that a user may like within a batch of 100 tasks.
Studying the Impact of Mood on Identifying Smartphone Users

This paper explores the identification of smartphone users when certain samples collected while the subject felt happy, upset or stressed were absent or present. We employ data from 19 subjects using the StudentLife dataset, a dataset collected by researchers at Dartmouth College that was originally collected to correlate behaviors characterized by smartphone usage patterns with changes in stress and academic performance. Although many previous works on behavioral biometrics have implied that mood is a source of intra-person variation which may impact biometric performance, our results contradict this assumption. Our findings show that performance worsens when removing samples that were generated when subjects may be happy, upset, or stressed. Thus, there is no indication that mood negatively impacts performance. However, we do find that changes existing in smartphone usage patterns may correlate with mood, including changes in locking, audio, location, calling, homescreen, and e-mail habits. Thus, we show that while mood is a source of intra-person variation, it may be an inaccurate assumption that biometric systems (particularly, mobile biometrics) are likely influenced by mood.
On the dynamics of 3D electrified falling films

In this article, we consider a non-local variant of the Kuramoto-Sivashinsky equation in three dimensions (2D interface). Besides showing the global wellposedness of this equation we also obtain some qualitative properties of the solutions. In particular, we prove that the solutions become analytic in the spatial variable for positive time, the existence of a compact global attractor and an upper bound on the number of spatial oscillations of the solutions. We observe that such a bound is particularly interesting due to the chaotic behavior of the solutions.
Critical Edge Identification: A K-Truss Based Model

In a social network, the strength of relationships between users can significantly affect the stability of the network. In this paper, we use the k-truss model to measure the stability of a social network. To identify critical connections, we propose a novel problem, named k-truss minimization. Given a social network G and a budget b, it aims to find b edges for deletion which can lead to the maximum number of edge breaks in the k-truss of G. We show that the problem is NP-hard. To accelerate the computation, novel pruning rules are developed to reduce the candidate size. In addition, we propose an upper bound based strategy to further reduce the searching space. Comprehensive experiments are conducted over real social networks to demonstrate the efficiency and effectiveness of the proposed techniques.
FameSVD: Fast and Memory-efficient Singular Value Decomposition

We propose a novel algorithm to perform the Singular Value Decomposition (SVD) by leveraging the internal property of SVD. Due to the derivation being explored deterministically rather than stochastically, the convergence is guaranteed. Complexity analysis is also conducted. Our proposed SVD method outperforms classic algorithms with significant margin both in runtime and memory usage. Furthermore, we discuss the relationship between SVD and Principal Component Analysis (PCA). For those SVD or PCA algorithms that do not acquire all eigenvalues or cannot get them precisely, we utilize the matrix analysis knowledge to get the sum of all eigenvalues in order that cumulative explained variance criterion could be used in not-all-eigenvalues-are-known cases.
The Impact of Feature Causality on Normal Behaviour Models for SCADA-based Wind Turbine Fault Detection

The cost of wind energy can be reduced by using SCADA data to detect faults in wind turbine components. Normal behavior models are one of the main fault detection approaches, but there is a lack of consensus in how different input features affect the results. In this work, a new taxonomy based on the causal relations between the input features and the target is presented. Based on this taxonomy, the impact of different input feature configurations on the modelling and fault detection performance is evaluated. To this end, a framework that formulates the detection of faults as a classification problem is also presented.
missSBM: An R Package for Handling Missing Values in the Stochastic Block Model

The Stochastic Block Model (SBM) is a popular probabilistic model for random graph. It is commonly used to perform clustering on network data by aggregating nodes that share similar connectivity patterns into blocks. When fitting an SBM to a network which is partially observed, it is important to account for the underlying process that originates the missing values, otherwise the inference may be biased. This paper introduces missSBM, an R-package fitting the SBM when the network is partially observed, i.e. the adjacency matrix contains not only 1 or 0 encoding presence or absence of edges but also NA encoding missing information between pairs of nodes. It implements a series of algorithms for the binary SBM, with the possibility of accounting for covariates if needed, by performing variational inference for several sampling mechanisms, the methodology of which is detailed in Tabouy, Barbillon, and Chiquet (2019). Our implementation automatically explores different block numbers to select the most relevant according to the Integrated Classification Likelihood (ICL) criterion. The ICL criterion can also help to determine which sampling mechanism fits the best the data. Finally, missSBM can be used to perform imputation of missing entries in the adjacency matrix. We illustrate the package on a network data set consisting in interactions between blogs sampled during the French presidential election in 2007.