nils-herrmann/clean_scirepeval huggingface dataset:
The Mexican Dietary and Physical Activity Guidelines: Moving Public Nutrition Forward in a Globalized World.
Venue: The Journal of nutrition

The objective of this article is to explain the process of the development of and to assess the Mexican food based dietary and physical activity guidelines (FBDGs) The FBDGs were developed by an intersectoral and interdisciplinary committee of 11 national experts with input from 11 external advisors. The sectors represented were research and academic institutions, the Ministry of Health, and a nongovernmental organization. The evidence based process included the following: literature reviews of local, national, and international evidence; review of dietary patterns of the Mexican population; key national and international recommendations; and review of FBDGs and visual icons from other countries. The guidelines' report follows the life course socioecological model rooted in a deep understanding of the epidemiology and underlying causes of malnutrition in Mexico. The guidelines are summarized in 10 pretested main recommendations that include, and go beyond, simply promoting the consumption of a healthy and varied diet that includes fresh fruits, vegetables, legumes, and whole grains and staying within caloric needs and staying active. The guidelines strongly emphasize healthy cooking habits, enjoyable meals with family and friends, drinking water, and avoiding the consumption of sweetened beverages, grain based desserts, and highly processed foods. Detailed guidelines specific to different groups (on the basis of age and physiologic status) are also included. An innovative aspect of the Mexican FBDGs is the inclusion of dietary guidance of children <2 y of age. Future editions of these guidelines should consider removing their emphasis on dietary cholesterol and total dietary fat and placing more attention on the substitution of saturated and trans fats with healthy oils. The process of national agenda setting, policy articulation, and implementation of the Mexican FBDGs in the context of addressing the national obesity epidemic deserves to be initiated and formally assessed through the lens of evidence based, public nutrition complex adaptive systems.
Management of granulomatous lymphocytic interstitial lung disease in a patient with common variable immune deficiency
Venue: BMJ Case Reports

A 61 year old woman presented with longstanding cough and progressive dyspnoea. She underwent an extensive evaluation and was diagnosed with common variable immunodeficiency (CVID) with granulomatous lymphocytic interstitial lung disease (GLILD) She was initially treated with subcutaneous immunoglobulin therapy, having declined intravenous immunoglobulin (IVIG) therapy. She also declined treatment with oral glucocorticoids. Over several months, she became increasingly symptomatic and developed increased pulmonary infiltrates, pleural effusions, mediastinal adenopathy, splenomegaly, pancytopenia and ascites. An interdisciplinary team composed of an immunologist, pulmonologist and haematologist deliberated over a therapeutic management approach. The patient received a recently reported immunotherapy regimen with azathioprine and rituximab. The therapy led to rapid improvement of her constitutional and respiratory symptoms, with clinical and radiographic improvement in her interstitial lung disease, lymphadenopathy, pleural effusions and ascites. This case report reviews the literature surrounding the diagnosis and management of GLILD.
Acute Effects of Static vs. Ballistic Stretching on Strength and Muscular Fatigue Between Ballet Dancers and Resistance Trained Women
Venue: Journal of strength and conditioning research

Lima, CD, Brown, LE, Wong, MA, Leyva, WD, Pinto, RS, Cadore, EL, and Ruas, CV. Acute effects of static vs. ballistic stretching on strength and muscular fatigue between ballet dancers and resistance trained women.  2016 Stretching is used to increase joint range of motion, but the acute effects can decrease muscle strength. However, this may depend on the population or mode of stretching. The purpose of this study was to compare the acute effects of static vs. ballistic stretching on strength and muscular fatigue between ballet dancers and resistance trained women. Fifteen resistance trained women (age 23.8 1.80 years, mass 67.47 7.77 kg, height 168.30 5.53 cm) and 12 ballet dancers (age 22.8 3.04 years, mass 58.67 5.65 kg, height 168.00 7.69 cm) performed 5 days of testing. The first day was control (no stretching) whereas the other 4 days were static or ballistic stretching in a counterbalanced order. Range of motion, strength, and fatigue tests were also performed. Both groups demonstrated a significant decrease in hamstrings strength after static (102.71 2.67 N*m) and ballistic stretching (99.49 2.61 N*m) compared with control (113.059 3.25 N*m) with no changes in quadriceps strength. For fatigue, only ballet dancers demonstrated a decrease from control (71.79 4.88% to ballistic (65.65 8.19% but no difference with static (65.01 12.29% These findings suggest that stretching decreases hamstrings strength similarly in ballet dancers and resistance trained women, with no differences between modes of stretching. However, ballistic stretching only decreased muscular fatigue in ballet dancers, but not in resistance trained women. Therefore, no stretching should be performed before strength performance. However, ballistic stretching may decrease acute muscular fatigue in ballet dancers.
Zearalenone as an endocrine disruptor in humans.
Venue: Environmental toxicology and pharmacology

Zearalenone (ZEA) a fungal mycotoxin, is present in a wide range of human foods. Many animal studies have found ZEA to possess a disruptive effect on the hormonal balance, mainly due to its similarity to naturally occurring estrogens. With increasing consciousness of the adverse effects of endocrine disruptors on human health, it is becoming more important to monitor ZEA concentrations in food and identify its potential effects on human health. Based on a review of recent studies on animal models and molecular pathways in which ZEA is reported to have an influence on humans, we postulate that ZEA might act as an endocrine disruptor in humans in a similar way to animals. Moreover, its endocrine disrupting effect might be also a causative factor in carcinogenesis. This review article summarizes the latest knowledge about the influence of ZEA on the human hormonal balance.
Decisional support for young people who self harm: protocol for a feasibility trial
Venue: BMJ Open

Self harm is common in adolescents, and it is the strongest predictor of suicide. Young people who self harm are often unsure of how and where to get help. Decision aids (DAs) have been shown to help with decisional conflict where there is uncertainty around different options. We have developed an online DA to support young people in help seeking for self harm. A feasibility trial will examine the acceptability of the online intervention and the ability to recruit and follow up participants within a school setting.  In this parallel arm, single blind feasibility trial, 60 participants aged 12 18 years who have self harmed in the past 12 months, will be randomised to either (1) a group receiving the online DA or (2) a control group receiving general information about feelings and emotions. Both groups will complete measures assessing decision making and help seeking behaviour. The school counsellor will be notified of any participants who have been randomised to ensure safeguarding for the young person. Participants in both groups will be followed up at 4 weeks, and the measures will be repeated. Qualitative interviews will be conducted with a subset of participants to explore their views and experiences of the DA and of participation in the study. Ethics and dissemination Ethical approval was granted by King's College London (KCL) College Research Ethics Committee. Results of this study will help to clarify if we can recruit and administer an online decisional support intervention within a school setting for young people who self harm. The study will inform the design and implementation of a larger randomised controlled trial to test the effectiveness of the DA. Dissemination of the study findings will target publication in peer reviewed journals of general and special interest. The funder will be sent a report outlining the major findings of the study.
Dense Depth Map Estimation and Geometry Inference from Light Fields via Global Optimization
Venue: ACCV

Light field camera captures abundant and dense angular samplings in a single shot. The surface camera (SCam) model is an image gathering angular sample rays passing through a 3D point. By analyzing the statistics of SCam, a consistency depth measurement is evaluated for depth estimation. However, local depth estimation still has limitations. A global method with pixel wise plane label is presented in this paper. Plane model inference at each pixel not only recovers depth but also local geometry of scene, which is suitable for light fields with floating disparities and continuous view variation. The 2nd order surface smoothness is enforced to allow local curvature surfaces. We use a random strategy to generate candidate plane parameters and refine the plane labels to avoid falling in local minima. We cast the selection of defined labels as fusion move with sequential proposals. The proposals are elaborately constructed to satisfy sub modular condition with 2nd order smoothness regularizer, so that the minimization can be efficiently solved by graph cuts (GC) Our method is evaluated on public light field datasets and achieves the state of the art accuracy.
Augmented reality three dimensional display with light field fusion.
Venue: Optics express

A video see through augmented reality three dimensional display method is presented. The system that is used for dense viewpoint augmented reality presentation fuses the light fields of the real scene and the virtual model naturally. Inherently benefiting from the rich information of the light field, depth sense and occlusion can be handled under no priori depth information of the real scene. A series of processes are proposed to optimize the augmented reality performance. Experimental results show that the reconstructed fused 3D light field on the autostereoscopic display is well presented. The virtual model is naturally integrated into the real scene with a consistence between binocular parallax and monocular depth cues.
Answering why not and why questions on reverse top k queries
Venue: The VLDB Journal

Why not and why questions can be posed by database users to seek clarifications on unexpected query results. Specifically, why not questions aim to explain why certain expected tuples are absent from the query results, while why questions try to clarify why certain unexpected tuples are present in the query results. This paper systematically explores the why not and why questions on reverse top k queries, owing to its importance in multi criteria decision making. We first formalize why not questions on reverse top k queries, which try to include the missing objects in the reverse top k query results, and then, we propose a unified framework called WQRTQ to answer why not questions on reverse top k queries. Our framework offers three solutions to cater for different application scenarios. Furthermore, we study why questions on reverse top k queries, which aim to exclude the undesirable objects from the reverse top k query results, and extend the framework WQRTQ to efficiently answer why questions on reverse top k queries, which demonstrates the flexibility of our proposed algorithms. Extensive experimental evaluation with both real and synthetic data sets verifies the effectiveness and efficiency of the presented algorithms under various experimental settings.
Reverse k nearest neighbors queries and spatial reverse top k queries
Venue: The VLDB Journal

Given a set of facilities and a set of users, a reverse k nearest neighbors (RkNN) query q returns every user for which the query facility is one of the k closest facilities. Almost all of the existing techniques to answer RkNN queries adopt a pruning and verification framework. Regions based pruning and half space pruning are the two most notable pruning strategies. The half space based approach prunes a larger area and is generally believed to be superior. Influenced by this perception, almost all existing RkNN algorithms utilize and improve the half space pruning strategy. We observe the weaknesses and strengths of both strategies and discover that the regions based pruning has certain strengths that have not been exploited in the past. Motivated by this, we present a new regions based pruning algorithm called Slice that utilizes the strength of regions based pruning and overcomes its limitations. We also study spatial reverse top k (SRTk) queries that return every user u for which the query facility is one of the top k facilities according to a given linear scoring function. We first extend half space based pruning to answer SRTk queries. Then, we propose a novel regions based pruning algorithm following Slice framework to solve the problem. Our extensive experimental study on synthetic and real data sets demonstrates that Slice is significantly more efficient than all existing RkNN and SRTk algorithms.
Scalable large margin online metric learning
Venue: 2016 International Joint Conference on Neural Networks (IJCNN)

We present a novel online metric learning model, called scalable large margin online metric learning (SLMOML) SLMOML belongs to the passive aggressive family of learning models. In the formulation of SLMOML, we use the LogDet divergence to measure the closeness between two continuously learned matrices, which naturally ensures the positive semi definiteness of the learned matrix at each iteration, provided the initial matrix is positive semi definite. In addition, a hinge loss is used to maintain a large margin of distance between relatively dissimilar data. Using the Karush Kuhn Tucker (KKT) condition, the updating rule of SLMOML can be equivalently viewed as Bregman projections. Based on this fact, we have proved the global convergence of SLMOML. Extensive experiments on real world applications demonstrate the superiority of SLMOML over state of the art metric learning and similarity learning approaches.
Online Multi Modal Distance Metric Learning with Application to Image Retrieval
Venue: IEEE Transactions on Knowledge and Data Engineering

Distance metric learning (DML) is an important technique to improve similarity search in content based image retrieval. Despite being studied extensively, most existing DML approaches typically adopt a single modal learning framework that learns the distance metric on either a single feature type or a combined feature space where multiple types of features are simply concatenated. Such single modal DML methods suffer from some critical limitations: (i) some type of features may significantly dominate the others in the DML task due to diverse feature representations; and (ii) learning a distance metric on the combined high dimensional feature space can be extremely time consuming using the naive feature concatenation approach. To address these limitations, in this paper, we investigate a novel scheme of online multi modal distance metric learning (OMDML) which explores a unified two level online learning scheme: (i) it learns to optimize a distance metric on each individual feature space; and (ii) then it learns to find the optimal combination of diverse types of features. To further reduce the expensive cost of DML on high dimensional feature space, we propose a low rank OMDML algorithm which not only significantly reduces the computational cost but also retains highly competing or even better learning accuracy. We conduct extensive experiments to evaluate the performance of the proposed algorithms for multi modal image retrieval, in which encouraging results validate the effectiveness of the proposed technique.
Geometric Mean Metric Learning
Venue: ICML

We revisit the task of learning a Euclidean metric from data. We approach this problem from first principles and formulate it as a surprisingly simple optimization problem. Indeed, our formulation even admits a closed form solution. This solution possesses several very attractive properties: (i) an innate geometric appeal through the Riemannian geometry of positive definite matrices; (ii) ease of interpretability; and (iii) computational speed several orders of magnitude faster than the widely used LMNN and ITML methods. Furthermore, on standard benchmark datasets, our closed form solution consistently attains higher classification accuracy.
The effect of gun control laws on hospital admissions for children in the United States
Venue: The journal of trauma and acute care surgery

Gun control laws vary greatly between states within the United States. We hypothesized that states with strict gun laws have lower mortality and resource utilization rates from pediatric firearms related injury admissions.  Kids' Inpatient Database (1997 2012) was searched for accidental (E922) self inflicted (E955) assault (E965) legal intervention related (E970) or undetermined circumstance (E985) firearm injuries. Patients were younger than 20 years and admitted for their injuries. Case incidence trends were examined for the study period. Propensity score matched analyses were performed using 38 covariates to compare outcomes between states with strict or lenient gun control laws.  Overall, 38,424 cases were identified, with an overall mortality of 7% Firearm injuries were most commonly assault (64% followed by accidental (25% undetermined circumstance (7% or self inflicted (3% A small minority involved military grade weapons (0.2% Most cases occurred in lenient gun control states (48% followed by strict (47% and neutral (6% On 1:1 propensity score matched analysis, in hospital mortality by case was higher in lenient (7.5% versus strict (6.5% states, p 0.013. Lenient states had a proportionally higher rate of accidental (31% and self inflicted injury (4% versus strict states (17% and 1.6% respectively) p 0.001. Assault related injuries were proportionally lower in lenient (54% versus strict (75% states, p 0.001. Military grade weapons were more common in lenient (0.4% versus strict (0.1% states, p 0.001.  These findings highlight the importance of legislative measures and their role in injury prevention, as firearm injuries are entirely avoidable mechanisms of injury. Lenient gun control contributes not only to worse outcomes per case, but also to a more significant and detrimental impact on public health.
Identifying gender specific risk/need areas for male and female juvenile offenders: Factor analyses with the Structured Assessment of Violence Risk in Youth (SAVRY)
Venue: Law and human behavior

By constructing risk assessment tools in which the individual items are organized in the same way for male and female juvenile offenders it is assumed that these items and subscales have similar relevance across males and females. The identification of criminogenic needs that vary in relevance for 1 of the genders, could contribute to more meaningful risk assessments, especially for female juvenile offenders. In this study, exploratory factor analyses (EFA) on a construction sample of male (n 3,130) and female (n 466) juvenile offenders were used to aggregate the 30 items of the Structured Assessment of Violence Risk in Youth (SAVRY) into empirically based risk/need factors and explore differences between genders. The factor models were cross validated through confirmatory factor analyses (CFA) on a validation sample of male (n 2,076) and female (n 357) juvenile offenders. In both the construction sample and the validation sample, 5 factors were identified: (a) Antisocial behavior; (b) Family functioning; (c) Personality traits; (d) Social support; and (e) Treatability. The male and female models were significantly different and the internal consistency of the factors was good, both in the construction sample and the validation sample. Clustering risk/need items for male and female juvenile offenders into meaningful factors may guide clinicians in the identification of gender specific treatment interventions.
Corporate Restructuring as a Risk Treatment Method

The article is aimed at the development of risk treatment methods. The author considers different risk treatment methods and proposes corporate restructuring as one of the methods. Corporate restructuring has not been seen yet as the method of risk treatment in risk management literature but the practice of its implementation in response to risk effects is obvious. The author describes risk parameters and different risk treatment methods which can be applied and point out corporate restructuring as one of them. The features of corporate restructuring as a risk treatment method are identified. The author proposes the algorithm of risk treatment on the basis of corporate restructuring and it can be looked as part of risk management of the company.
Corporate restructuring and its effect on employee morale and performance

Vikas Chaddha (Professor, JEMTEC, Vikas.chaddha@gmail.com 8882033978)  The organisations across the globe are facing more competitive markets, more demanding shareholders and more challenging workforce which has posed the challenges to the executives of the organisations to reorganise the firm. This restructuring has different impact at different levels on different times aiming at increasing cost efficiency and operational effectiveness. The impact of restructuring is often measured by the financial performance of the organisations but it is not measured by assessing its effect on the section which is most effected by implementation of any restructuring strategy that is human resources of that organisation. This paper is an attempt by the author to analyse the impact of the restructuring strategies on morale of employees of the organisation by measuring the change in factors that affect employee morale, pre and post restructuring.
SARIMA models for forecasting call volume in emergency services

Management of manpower and other resources is one of the most important tasks to be carried out in any business organisation. Various approaches are followed for the planning activities in industry and business. This article discusses the manpower planning and allocation problem in a call centre, working round the clock for handling emergency telephone calls related to ambulance services. The organisation under study provides ambulance services to take care of the emergency needs of the people in one of the Indian states. As a first step in the manpower planning process, we develop time series models for forecasting the call volume. Time series models were used to explain the evolution of hourly and daily call volume data. As the series was showing seasonality, seasonal ARIMA models were fitted for these data. The adequacy tests confirm that the selected model performs well for the data. The short term forecasts computed using the model seen to be very close to the corresponding observed values. These forecast values will be helpful in manpower and resource planning.
Convex optimization for fast image dehazing
Venue: 2016 IEEE International Conference on Image Processing (ICIP)

This paper presents a new convex optimization method for the image dehazing problem. It is based on reformulation of the hazed image model to solve the bilinearly coupled hazed image and light transmission distribution term as a single optimization variable. This resolves the nonconvex difficulty of the original problem and is sufficient for straightforward reconstruction of the haze free image. The Haar wavelet transform is further applied to derive a subband hazed image model for the convex optimization, which leads to great reduction of the computational workload.