Miracle-dz/UniFAR-FTUs huggingface dataset:
The relationship between chronic conditions and absenteeism and associated costs in Canada.

OBJECTIVES This study aimed to measure and compare the relationship between chronic diseases and the number of absent workdays due to health problems and the associated costs among working Canadians. METHODS The study sample included respondents to the 2010 Canadian Community Health Survey between aged 15-75 years who reported employment in the past three months. Respondents reported their number of absent workdays due to health problems and chronic conditions. A negative binomial regression was used to estimate the incremental absent workdays associated with having a particular chronic condition (of 16 conditions), conditional on other chronic conditions and confounders. For each condition, we calculated the incremental number of absent workdays, the incremental productivity loss attributed to absenteeism per employee, and the overall productivity loss in the population. RESULTS The final sample consisted of 28 678 respondents representing 15 468 788 employed Canadians. The average number of absent workdays due to health problems was 1.35 days over a 3-month period. The three conditions with the greatest association with absent workdays were mood disorders, heart disease, and bowel disorders. They were associated with 1.17, 0.81, and 0.80 additional absent workdays, respectively, compared to workers without this condition, holding other conditions and confounders at their means. At the national working population level, back problems (CAD$621 million), mood disorders (CAD$299 million) and migraine (CAD$245 million) accounted for the largest incremental productivity loss. CONCLUSIONS Chronic conditions, especially mood disorders and back problems, are associated with substantial work productivity loss. The study findings can help policy-makers and employers prioritize their programs and resources aimed at reducing absenteeism among the working population with chronic conditions.
Empowering iso-surfaces with volume data

Surface rendering algorithms are fast, but not suited to applications that demand exploration of internal volume structures, as such information is lost in conventional surface rendering. In this article we introduce an enhanced surface rendering algorithm named VoS, Volume on Surface that supports visualization of internal volume structures. VoS integrates surface and volume rendering approaches into an efficient framework for interactive visualization of volume information. A ray casting is performed to map volume information onto a boundary surface extracted from the volume grid, enabling the display of structures internal to the surface using conventional surface rendering. As such, the technique exploits the advantages of surface rendering while keeping the volumetric information. VoS thus offers a low-cost alternative to volume rendering in some practical situations, as its resulting surfaces can be rendered on commodity graphics hardware at interactive rates. Moreover, a user can easily fine tune the color and opacity transfer function definitions, because changes in the transfer functions are handled at the rendering step, rather than at the costly ray-casting operation.
Analytical Stability Criterion in Haptic Rendering: The Role of Damping

A new closed-form equation for stability analysis of a haptic device is presented using frequency response function analysis via continuous time model of the system. Desired impedance of the virtual environment (VE), time delay, and zero-order hold are considered, beside the continuous model of the haptic device. The developed equations provide critical virtual damping and stiffness of the VE versus the mass and viscous friction of the haptic device, sampling time, and time delay. Unlike prior work in this field, the developed equations are valid without any limitation on the values of the time delay and virtual damping. It is shown that they cover available well-known equations in literature for special cases of small values of virtual damping and time delay. The resulting equations are of practical usefulness in many fields, such as surgery simulation for avoiding instability during virtual tool interaction with high-stiffness VEs. The proposed analytical derivation can also be used for studying the effect of operator, sensors, actuator dynamics, and velocity filtering. Simulation and experimental results on the KUKA light weight robot show that the proposed criterion can accurately predict the stability boundaries.
Decision Robustness of Voice Activity Segmentation in Unconstrained Mobile Speaker Recognition Environments

Voice activity detection (VAD) is an essential segmentation process in speaker recognition systems, seperating speech and non-speech segments of voice samples. In speaker recognition, references are modelled purely by concerning speech segments. Different VAD segmentations lead to variations in biometric models, and consequently in system performance. Thus, VAD decisions need to be robust among different conditions. In this paper, the decision robustness of different VAD algorithms is examined on mobile data by simulating different environmental noise conditions for which we propose a Hamming distance based analysis. By examining speech and speaker recognition based VADs, we further propose to extend a well- performing VAD algorithm, which is based on likelihood ratio comparison of speech to non-speech models, by including most dominant frequency component (MDFC) features for selection of model training segments. Thereby, more robust VAD decisions are conducted by 7%, while sustaining an average EER SNR-sensitivity of 0.76% per dB SNR.
Later circadian timing of food intake is associated with increased body fat.

Background: Weight gain and obesity have reached alarming levels. Eating at a later clock hour is a newly described risk factor for adverse metabolic health; yet, how eating at a later circadian time influences body composition is unknown. Using clock hour to document eating times may be misleading owing to individual differences in circadian timing relative to clock hour.Objective: This study examined the relations between the timing of food consumption relative to clock hour and endogenous circadian time, content of food intake, and body composition.Design: We enrolled 110 participants, aged 18-22 y, in a 30-d cross-sectional study to document sleep and circadian behaviors within their regular daily routines. We used a time-stamped-picture mobile phone application to record all food intake across 7 consecutive days during a participant's regular daily routines and assessed their body composition and timing of melatonin release during an in-laboratory assessment.Results: Nonlean individuals (high body fat) consumed most of their calories 1.1 h closer to melatonin onset, which heralds the beginning of the biological night, than did lean individuals (low body fat) (log-rank P = 0.009). In contrast, there were no differences between lean and nonlean individuals in the clock hour of food consumption (P = 0.72). Multiple regression analysis showed that the timing of food intake relative to melatonin onset was significantly associated with the percentage of body fat and body mass index (both P < 0.05) while controlling for sex, whereas no relations were found between the clock hour of food intake, caloric amount, meal macronutrient composition, activity or exercise level, or sleep duration and either of these body composition measures (all P > 0.72). Conclusions: These results provide evidence that the consumption of food during the circadian evening and/or night, independent of more traditional risk factors such as amount or content of food intake and activity level, plays an important role in body composition. This trial was registered at clinicaltrials.gov as NCT02846077.
SaaS Service Recommendation System based on SLA Ontology

The emergence of Grid computing and Service Oriented Architectures has led to evolution in terms of how applications are built and managed. Software as a service (SaaS), also known as the on-demand model, is changing the way businesses of all sizes and in all industries use software. These services are provided by different vendors. Some time similar services are provided by the various vendors. Hence to choose the best service is becoming a cumbersome one to the consumer. In this paper we proposed a recommendation system for selecting the best SaaS service according the user requirement by processing the SLA of SaaS services. This system is based on the semantic web technology to populate the services in the service population system and also it has the semantic annotation sub system to generate the annotation for the query generated by user.
Concurrent Validity of the Anterior Drawer Test and an Arthrometer in Evaluating Ankle Laxity

This study focused on the concurrent validity and reliability of an ankle arthrometer and clinical tests of ankle laxity. A posttest-only control group design was used to compare anterior-posterior (AP) displacement in participants with clinically graded laxity (mechanical instability) and a control group. Forty-four individuals with no recent ankle sprains participated in this study. An ankle arthrometer measured AP displacement, and one investigator clinically graded the anterior drawer test. Pearson correlations were significant (r = 0.48, P , .05) between the anterior drawer and arthrometer measurements of AP displacement. Independent t tests revealed significantly greater AP displacement (P , .05) in mechanically unstable ankles on the anterior drawer test. The intraclass correlation coefficients [2,1] for arthrometer AP displacement ranged from 0.44 to 0.87, with reliability decreasing as the laxity grade increased. The ankle arthrometer is a reliable measure of AP laxity and has concurrent validity with a standardized clinical grading system in differentiating levels of joint laxity. A
Cooperative Aerial Manipulation Using Multirotors With Multi-DOF Robotic Arms

This paper investigates pose manipulation of a rod-shaped object using two aerial manipulators, which are multirotors combined with multi-degrees of freedom robotic arms. To achieve the cooperative aerial manipulation, we present motion control and planning methods for aerial manipulators. First, a robust multirotor motion controller is designed based on the thorough analysis on the dynamics of the aerial manipulator. By utilizing extended high-gain observer and disturbance observer techniques, each multirotor can be controlled regardless of the disturbances generated by the robotic arm, the object, and the peer aerial manipulator. Second, a motion planner that assures safety during the cooperative aerial manipulation is proposed. To achieve safe manipulation, we estimate and regulate the internal force between the aerial manipulators. In addition, an unilateral constraint is designed which imposes collision avoidance between the object and aerial manipulators. All the designed features are prioritized and transformed into the reference velocity of the aerial manipulators. The proposed control and guidance laws are validated by a successful autonomous aerial manipulation experiment.
New algorithms for distributed submodular maximization

A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, can be cast as submodular maximization problems. In many of these applications, the amount of data collected is quite large and it is growing at a very fast pace. For example, the wide deployment of sensors has led to the collection of large amounts of measurements of the physical world. Similarly, medical data and human activity data are being captured and stored at an ever increasing rate and level of detail. This data is often high-dimensional and complex, and it needs to be stored and/or processed in a distributed fashion. Following a recent line of work, we present here parallel algorithms for these problems, and analyze the compromise between quality of the solutions obtained and the amount of computational overhead. On the one hand, we develop strategies for bringing existing algorithms for constrained submodular maximization in the sequential setting to the distributed setting. The algorithms presented achieve constant approximation factors in two rounds, and near optimal approximation ratios in only a constant number of rounds. Our techniques also give a fast sequential algorithm for non-monotone maximization subject to a matroid constraint. On the other hand, for unconstrained submodular maximization, we devise parallel algorithms combining naive random sampling and Double Greedy steps, and investigate how much the quality of the solutions degrades with less coordination.
Quantifying the Interactions between Maternal and Fetal Heart Rates by Transfer Entropy

Evidence of the short term relationship between maternal and fetal heart rates has been found in previous studies. However there is still limited knowledge about underlying mechanisms and patterns of the coupling throughout gestation. In this study, Transfer Entropy (TE) was used to quantify directed interactions between maternal and fetal heart rates at various time delays and gestational ages. Experimental results using maternal and fetal electrocardiograms showed significant coupling for 63 out of 65 fetuses, by statistically validating against surrogate pairs. Analysis of TE showed a decrease in transfer of information from fetus to the mother with gestational age, alongside the maturation of the fetus. On the other hand, maternal to fetal TE was significantly greater in mid (26–31 weeks) and late (32–41 weeks) gestation compared to early (16–25 weeks) gestation (Mann Whitney Wilcoxon (MWW) p<0.05). TE further increased from mid to late, for the fetuses with RMSSD of fetal heart rate being larger than 4 msec in the late gestation. This difference was not observed for the fetuses with smaller RMSSD, which could be associated with the quiet sleep state. Delay in the information transfer from mother to fetus significantly decreased (p = 0.03) from mid to late gestation, implying a decrease in fetal response time. These changes occur concomitant with the maturation of the fetal sensory and autonomic nervous systems with advancing gestational age. The effect of maternal respiratory rate derived from maternal ECG was also investigated and no significant relationship was found between breathing rate and TE at any lag. In conclusion, the application of TE with delays revealed detailed information on the fetal-maternal heart rate coupling strength and latency throughout gestation, which could provide novel clinical markers of fetal development and well-being.
A systematic review and meta-analysis including GRADE qualification of the risk of surgical site infections after prophylactic negative pressure wound therapy compared with conventional dressings in clean and contaminated surgery

Objective:Systematically review and Grading of Recommendations Assessment, Development, and Evaluation (GRADE) studies on prophylactic negative pressure wound therapy (pNPWT) to prevent surgical site infections (SSIs). Introduction:pNPWT has been suggested as a new method to prevent wound complications, specifically SSIs, by its application on closed incisional wounds. Methods:This review was conducted as part of the development of the Global Guidelines for prevention of SSIs commissioned by World Health Organization in Geneva. PubMed, Embase, CENTRAL, CINAHL, and the World Health Organization database between January 1, 1990 and October 7, 2015 were searched. Inclusion criteria were randomized controlled trials and observational studies comparing pNPWT with conventional wound dressings and reporting on the incidence of SSI. Meta-analyses were performed with a random effect model. GRADE Pro software was used to qualify the evidence. Results:Nineteen articles describing 21 studies (6 randomized controlled trials and 15 observational) were included in the review. Summary estimate showed a significant benefit of pNPWT over conventional wound dressings in reducing SSIs in both randomized controlled trials and observational studies, odds ratio of 0.56 (95% confidence interval, 0.32–0.96; P = 0.04) and odds ratio of 0.30 (95% confidence interval, 0.22–0.42; P < 0.00001), respectively. This translates into lowering the SSI rate from 140 to 83 (49–135) per 1000 patients and from 106 to 34 (25–47) per 1000 patients, respectively. In stratified analyses, these results were consistent in both clean and clean-contaminated procedures and in different types of surgery, however results were no longer significant for orthopaedic/trauma surgery. The level of evidence as qualified with GRADE was however low. Conclusions:Low-quality evidence indicates that prophylactic NPWT significantly reduces the risk of SSIs.
Serum ferritin contributes to racial or geographic disparities in metabolic syndrome in Taiwan

Abstract Objectives Asians and Pacific Islanders have higher circulating serum ferritin (SF) compared with Caucasians but the clinical significance of this is unclear. There is a higher prevalence of metabolic syndrome (MetS) in Taiwanese Indigenous than Han Chinese. Genetically, Indigenous are related to Austronesians and account for 2 % of Taiwan's population. We tested the hypothesis that accumulation of Fe in the body contributes to the ethnic/racial disparities in MetS in Taiwan. Design A population-based, cross-sectional study. Setting National Nutrition and Health Survey in Taiwan and Penghu Island. Subjects A total of 2638 healthy adults aged ≥19 years. Three ethnic groups were included. Results Han Chinese and Indigenous people had comparable levels of SF. Austronesia origin was independently associated with MetS (OR = 2·61, 95 % CI 2·02, 3·36). After multiple adjustments, the odds for MetS (OR = 2·49, 95 % CI 1·15, 5·28) was significantly higher among Indigenous people in the highest SF tertile compared with those in the lowest tertile. Hakka and Penghu Islanders yielded the lowest risks (OR = 1·08, 95 % CI 0·44, 2·65 and OR = 1·21, 95 % CI 0·52, 2·78, respectively). Indigenous people in the highest SF tertile had increased risk for abnormal levels of fasting glucose (OR = 2·34, 95 % CI 1·27, 4·29), TAG (OR = 1·94, 95 % CI 1·11, 3·39) and HDL-cholesterol (OR = 2·10, 95 % CI 1·18, 3·73) than those in the lowest SF tertile. Conclusions Our results raise the possibility that ethnic/racial differences in body Fe store susceptibility may contribute to racial and geographic disparities in MetS.
Predicting Missing Demographic Information in Biometric Records Using Label Propagation Techniques

Biometric systems use biological attributes such as face, fingerprint, or iris to automatically recognize an individual. In many law enforcement applications, the biometric record of a person in the database is often supplemented with biographic and demographic data such as name, address, age, ethnicity, gender, etc. In such applications, some of the records may have missing or incorrect demographic data. In this work, we explore the potential of a label propagation method to impute demographic data to partially incomplete biometric records. The proposed method utilizes a graph- like structure to capture similarities between biometric records based on the available data. This structure is then used by the label propagation method to predict missing data. Experiments using the face image, name, gender and ethnicity of subjects in two datasets confirm the efficacy of the scheme in predicting missing data in biometric records.
Surface simplification guided by morph-targets

Many effective automatic surface simplification algorithms have been developed. These automatic algorithms create very plausible results in many cases, but at very low levels of detail they do not preserve the visual appearance of the original model very well. This could be improved if surface simplification algorithms were able to make use of semantic or high-level meaning of models. The idea of our new method using a morph-target-based surface simplification is to use distance information inside the morph-targets to acquire the relative importance of different surface regions without user guidance. Using this additional input the model is simplified by using modified quadric error metrics.
Automatic Dataset Augmentation

Large scale image dataset and deep convolutional neural network (DCNN) are two primary driving forces for the rapid progress made in generic object recognition tasks in recent years. While lots of network architectures have been continuously designed to pursue lower error rates, few efforts are devoted to enlarge existing datasets due to high labeling cost and unfair comparison issues. In this paper, we aim to achieve lower error rate by augmenting existing datasets in an automatic manner. Our method leverages both Web and DCNN, where Web provides massive images with rich contextual information, and DCNN replaces human to automatically label images under guidance of Web contextual information. Experiments show our method can automatically scale up existing datasets significantly from billions web pages with high accuracy, and significantly improve the performance on object recognition tasks by using the automatically augmented datasets, which demonstrates that more supervisory information has been automatically gathered from the Web. Both the dataset and models trained on the dataset are made publicly available.
Improved SAT-based Reachability Analysis with Observability Don't Cares

The dramatic performance improvements of SAT solvers over the past decade have increased their deployment in hardware verification applications. Many problems that were previously too large and complex for SAT techniques can now be handled in an efficient manner. One such problem is reachability analysis, whose instances are found throughout verification applications such as unbounded model checking and trace reduction. In circuit-based reachability analysis important circuit information is often lost during the circuit-to-SAT translation process. Observability Don’t Cares (ODCs) are an example of such information that can potentially help achieve better and faster results for the SAT solver. This work proposes to use the ODCs to improve the quality and performance of SAT-based reachability analysis frameworks. Since ODCs represent variables whose values do not affect the outcome of a problem, it is possible to satisfy a problem with fewer assigned variables. This in turn leads to more compact solutions and thus fewer solutions to cover the entire solution space. Specifically, this work presents an efficient way to identify ODCs, proves the correctness of leaving ODC variables unassigned, and develops a reachability analysis platform that benefits greatly from the ODCs. The advantages of using ODCs in reachability analysis is demonstrated through extensive experiments on unbounded model checking and trace reduction applications.
Nonlinear Weighted Finite Automata

Weighted finite automata (WFA) can expressively model functions defined over strings but are inherently linear models. Given the recent successes of nonlinear models in machine learning, it is natural to wonder whether extending WFA to the nonlinear setting would be beneficial. In this paper, we propose a novel model of neural network based nonlinear WFA model (NL-WFA) along with a learning algorithm. Our learning algorithm is inspired by the spectral learning algorithm for WFA and relies on a nonlinear decomposition of the so-called Hankel matrix, by means of an auto-encoder network. The expressive power of NL-WFA and the proposed learning algorithm are assessed on both synthetic and real world data, showing that NL-WFA can lead to smaller model sizes and infer complex grammatical structures from data.