Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of high intensity interval training on disease activity and patient disease perception in patients with psoriatic arthritis : a randomized controlled trial

Objective The aim of this study was to evaluate the impact of high intensity interval training (HIIT) on disease activity and patient disease perception in patients with psoriatic arthritis (PsA), and evaluating if a potential effect could be sustained for a longer period. Methods We randomly assigned 67 PsA patients (43 women and 24 men) to an intervention group performing HIIT for 11 weeks or a control group who were instructed to not change their physical exercise habits. Outcomes were assessed at three and nine months with the patient global assessment (PGA), fatigue, and pain measured on a 100 mm visual analog scale and the composite disease activity score of 44 joints (DAS44) was calculated. We used linear mixed models to calculate mean difference with 95% confidence interval (CI) between the groups according to the intention-to-treat principle. Results At three months there was no clear difference in PGA (-0.49; 95% CI -10.91 to 9.94), DAS44 (-0.08; 95% CI -0.36 to 0.20) or pain intensity (5.45; 95% CI -4.36 to 15.26) between the groups. However, the HIIT group reported less fatigue (-12.83; 95% CI -25.88 to 0.23) than the control group. There was no evidence of long-term effects of HIIT on outcomes measured at nine months. Conclusion HIIT showed no clear effects on disease activity markers in patients with PsA, but the exercise group reported meaningfully less fatigue after the intervention period. This study suggests that PsA patients tolerate HIIT without deterioration of disease activity and with improvement in fatigue.
A Model of Temporal Scaling Correctly Predicts that Weber’s Law is Speed-dependent

Timing is fundamental to complex motor behaviors: from tying a knot to playing the piano. A general feature of motor timing is temporal scaling: the ability to produce motor patterns at different speeds. Here we report that temporal scaling is not automatic. After learning to produce a Morse code pattern at one speed, subjects did not accurately generalize to novel speeds. Temporal scaling was also not a general property of a recurrent neural network (RNN) model, however after training across different speeds the model produced robust temporal scaling. The model captured a signature of motor timing—Weber’s law—but predicted that temporal precision increases at faster speeds. A human psychophysics study confirmed this prediction: the standard deviation of responses in absolute time were lower at faster speeds. These results establish that RNNs can account for temporal scaling, and suggest a novel psychophysical principle: the Weber-speed effect.
Auth-Integrate Toward Combating False Data on the Internet

The advent of the collaborative Web and the abundance of usergenerated data has resulted in the problem of information overload; it is becoming increasingly difficult to discern relevant information and discard false data. Recently, a number of solutions for automated fact-checking have been proposed that view the problem from a largely linguistic perspective. We observe that the problem of false data detection has roots in several extensively studied research areas in data management and data mining such as data integration, data cleaning, crowdsourcing and machine learning. Specifically, detection of false data has significant overlap with data fusion, an active area of research in data integration that focuses on distinguishing correct from incorrect information in a structured data setting. In this vision paper, we propose the architecture of AuthIntegrate, an end-to-end system that ingests conflicting data from disparate information providers, curates and presents highly accurate data to end-users. We discuss the technical challenges in building this system and outline an agenda for future research. ACM Reference Format: Romila Pradhan and Sunil Prabhakar. 2018. AuthIntegrate: Toward Combating False Data on the Internet. In Woodstock ’18: ACM Symposium on Neural Gaze Detection, June 03–05, 2018, Woodstock, NY. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/1122445.1122456
Challenges in predicting glioma survival time in multi-modal deep networks

Prediction of cancer survival time is of considerable interest in medicine as it leads to better patient care and reduces health care costs. In this study, we propose a multi-path multimodal neural network that predicts Glioblastoma Multiforme (GBM) survival time at the 14 months threshold. We obtained image, gene expression, and SNP variants from whole-exome sequences all from the The Cancer Genome Atlas portal for a total of 126 patients. We perform a 10-fold cross-validation experiment on each of the data sources separately as well as the model with all data combined. From post-contrast Tl MRI data, we used 3D scans and 2D slices that we selected manually to show the tumor region. We find that the model with 2D MRI slices and genomic data combined gives the highest accuracies over individual sources but by a modest margin. We see considerable variation in accuracies across the 10 folds and that our model achieves 100% accuracy on the training data but lags behind in test accuracy. With dropout our training accuracy falls considerably. This shows that predicting glioma survival time is a challenging task but it is unclear if this is also a symptom of insufficient data. A clear direction here is to augment our data that we plan to explore with generative models. Overall we present a novel multi-modal network that incorporates SNP, gene expression, and MRI image data for glioma survival time prediction.
Severe Temporal Hyper-Activated States Caused by Noise in Tinnitus and Hyperacusis with Normal Hearing

Lots of neuroimaging and animal studies have revealed that tinnitus and hyperacusis share the same patterns in the bottom up central auditory process. The aim was to identify the abnormal central patterns commonly observed in both tinnitus and hyperacusis in humans. We investigated two cases of normal hearing: a tinnitus patient and a hyperacusis patient. We compared the differences between the severe temporal hyper-activated state (STHS), with spikes, fast beta and gamma frequencies after noise exposure, and the mild temporal hyper-activated state (MTHS), in no sound exposed condition. The power of the gamma band in the two cases was increased in both auditory cortices compared to the other brain regions. Our results of human with normal hearing were the first to identify how tinnitus and hyperacusis caused by sound are abnormally active and how they maintain constant pathological states.
for Curve-Skeletons : SkeletonLab

Curve-skeletons are powerful shape descriptors able to provide higher level information on topology, structure and semantics of a given digital object. Their range of application is wide and encompasses computer animation, shape matching, modelling and remeshing. While a universally accepted definition of curve-skeleton is still lacking, there are currently many algorithms for the curve-skeleton computation (or skeletonization) as well as different techniques for building a mesh around a given curve-skeleton (inverse skeletonization). Despite their widespread use, automatically extracted skeletons usually need to be processed in order to be used in further stages of any pipeline, due to different requirements. We present here an advanced tool, named SkeletonLab, that provides simple interactive techniques to rapidly and automatically edit and repair curve skeletons generated using different techniques proposed in literature, as well as handcrafting them. The aim of the tool is to allow trained practitioners to manipulate the curve-skeletons obtained with skeletonization algorithms in order to fit their specific pipelines or to explore the requirements of newly developed techniques.
Effects of host species on the body size , fecunditM 7 } ' issolcus mitsukurii ( Hymenoptera : Scelionidae ) , parasitoid of stink bugs and longevity of a solitary egg

An investigation was conducted to determine whether differences in host size of different species affect the body size, fecundity, and longevity of 1}'issotcus mitsukurii, a solitary egg parasitoid of stink bugs. The eggs ofthe Pentatomid bugs of Hblyomorpha halys (1arge size), Plautia crossota stati (middle size), and Nbzara viridula (small size) were supplied to T mitsukurii and incubated until progeny emerged at 25± 20C and a photoperiod of LD 16 : 8. Both male and female parasitoids emerged with larger bedy sizes when they develeped in large host eggs such as HL halys. Larger size parasitoids had greater longevity and larger size females showed higher fecundity.
CanWeMake Time for Physical Activity ? Simulating Effects of Daily Physical Activity onMortality

1 Health Analysis Division, Statistics Canada, Ottawa, ON, Canada K1A 0T6 2 Healthy Active Living and Obesity Research Group, Children’s Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada K1H 8L1 3 Departments of Pediatrics, Human Kinetics, and Epidemiology and Community Medicine, University of Ottawa, Ottawa, ON, Canada K1N 6N5 4 Ottawa Hospital Research Institute, University of Ottawa, Ottawa, ON, Canada K1N 6N5 5 Departments of Family Medicine and Epidemiology and Community Medicine, University of Ottawa, Ottawa, ON, Canada K1N 6N5
Comparative Analysis of Political Culture Surveys in Sinaloa , Mexico Applied in the Years 2002 , 2008 and 2013 by the Sinaloa State Electoral Council

This paper presents a comparative analysis of relevant data from the Survey on Political Culture in Sinaloa (ENCUP) applied in 2002, 2008 and 2013 by the Sinaloa State Electoral Council (CEE) completed as part of their civic education program, with the intention to analyze political competition, socialization and participation. The ENCUP surveys support the conclusion that overall interest in politics and the recognition of the importance of elections has decreased, as well as the perception on citizens’ ability to influence municipal and state decisions. Thus, the political culture of the citizens of the state of Sinaloa is characterized by disinterest. On the other hand, as it pertains to electoral democracy, Sinaloa and Mexico have reached institutional strength. The electoral bodies are growing stronger and provide political and civic guarantees that ensure a fair voting process.
Application of Dependency Structure Matrix to Airspace Sectorization and Improving the Distribution of the Workload Among Controllers

This work investigates the application of Dependency Structure Matrix (DSM) to problems with fast dynamics, such as airspace sectorization. The aim of this paper is to use a powerful mathematical tool to distinguish relevant agents in a busy airspace with a logical and meaningful distribution of the workload among air traffic controllers. This approach will help prevent controllers from getting exhausted in busy airspaces and increase the overall capacity of the airspace. It could also serve as a logical interface to simulate the chance of human error in a controlled aerodrome. Different case-studies by the authors reveal that adding realtime capabilities to the existing platform can be very effective with the rearchitecting of neighboring sectors so that the domino effects of delays imposed on neighbors is decreased.
Instrumental variable quantile regression method for endogenous treatment effect

In this article, we introduce a new Stata command, ivqreg, that performs a quantile regression using the robust standard error formula in Chernozhukov and Hansen (2006, Journal of Econometrics 132: 491–525) for an exactly-identified instrumental variable case and the formula in Chernozhukov and Hansen (2008, Journal of Econometrics 142: 379–398) for an over-identified instrumental variable case to evaluate heterogenous marginal effects of endogenous treatment variables. We also examine finite sample properties of the instrumental variable quantile regression (IVQR) estimator for convergence and coverage using Monte Carlo simulations. We demonstrate the use of ivqreg on data on educational achievement and earnings.
Towards Robust Learning-Based Pose Estimation of Noncooperative Spacecraft

This work presents a novel Convolutional Neural Network (CNN) architecture and a training procedure to enable robust and accurate pose estimation of a noncooperative spacecraft. First, a new CNN architecture is introduced that has scored a fourth place in the recent Pose Estimation Challenge hosted by Stanford's Space Rendezvous Laboratory (SLAB) and the Advanced Concepts Team (ACT) of the European Space Agency (ESA). The proposed architecture first detects the object by regressing a 2D bounding box, then a separate network regresses the 2D locations of the known surface keypoints from an image of the target cropped around the detected Region-of-Interest (RoI). In a single-image pose estimation problem, the extracted 2D keypoints can be used in conjunction with corresponding 3D model coordinates to compute relative pose via the Perspective-n-Point (PnP) problem. These keypoint locations have known correspondences to those in the 3D model, since the CNN is trained to predict the corners in a pre-defined order, allowing for bypassing the computationally expensive feature matching processes. This work also introduces and explores the texture randomization to train a CNN for spaceborne applications. Specifically, Neural Style Transfer (NST) is applied to randomize the texture of the spacecraft in synthetically rendered images. It is shown that using the texture-randomized images of spacecraft for training improves the network's performance on spaceborne images without exposure to them during training. It is also shown that when using the texture-randomized spacecraft images during training, regressing 3D bounding box corners leads to better performance on spaceborne images than regressing surface keypoints, as NST inevitably distorts the spacecraft's geometric features to which the surface keypoints have closer relation.
Automated Machine Learning using Stochastic Algorithm Tuning

More often than not, the critical tuning of ML algorithm parameters has relied on domain expertise, along with laborious hand-tuning, exhaustive search or lengthy sampling runs. Against this background, Bayesian optimisation is finding increasing use in automating parameter tuning, making ML algorithms accessible even to non-experts. The state of the art in Bayesian optimisation is incapable of scaling to the large number of evaluations of algorithm performance required to fit realistic models to complex data. We solve this problem with a stochastic, sparse Bayesian optimisation strategy, using a sparse Gaussian process (GP) and many thousands of noisy evaluations of algorithm performance to train previously intractable models.
Reality of wound care in 2017 : findings from interactive voting pads

One thousand patients’ notes held within the Health Improvement Network (THIN) database were retrospectively examined and cost differences between having a wound or not were analysed. Indeed, from assessing wound care service delivery and costs for the year 2012/13, based on 2013/14 prices, the study estimated that the cost amounted to £5.3 billion, which is on a par with obesity treatment costs. The study also identified that the majority of wound care takes place in a community setting and is nurse-led. Guest et al (2015) has been cited in several recent studies and has even been raised in a debate in the house of Lords. The study also suggested that a ‘high proportion of wounds were unclassified’, highlighting shortcomings in undertaking wound assessment and accurate documentation.
Seasonal and spatial variation in hydrochemical parameters of Lake Onego ( Russia ) : insights from 2016 fi eld monitoring

This study describes seasonal water quality parameters measured in Petrozavodsk Bay, Lake Onego, Russia. Petrozavodsk Bay (PB) lies in the north of Lake Onego and adjacent to the city of Petrozavodsk. PB water quality is controlled by anthropogenic input, inflow of the Shuya River, and water exchange with open areas of the greater Lake Onego. We measured ion composition, organic matter, nutrients, gas composition, trace elements, and mercury throughout 2016, to evaluate PB water quality. Elevated humic content and organic matter, including total organic carbon (TOC = 17.0 mg L−1), total phosphorus (TP = 36 μg L−1), and water color (134 mg Pt L−1), demonstrated the eutrophic character of Shuya River input. Low humic and organic matter content (TOC = 6.5 mg L−1, TP = 7 μg L−1, water color = 26 mg Pt L−1) indicated the oligotrophic character of open lake waters. During winter, the PB hydrochemical regime was primarily controlled by Shuya River inflow because water exchange between the bay and open lake was restricted during its ice-covered period. As a result, PB [. ] EFREMOVA, Tatyana A. , et al. Seasonal and spatial variation in hydrochemical parameters of Lake Onego (Russia): insights from 2016 field monitoring. Inland Waters, 2019 DOI : 10.1080/20442041.2019. 1568097
Nonparametric Estimation of DEM Error in Multitemporal InSAR

Isolating phase residuals due to inaccurate external digital elevation model (DEM) is important in retrieval and interpretation of deformation behavior from interferometric synthetic aperture radar (InSAR) observations. Multitemporal InSAR (MTInSAR), by taking DEM error as a parameter, can make the isolation possible. However, due to the presence of atmospheric artifacts in observations and improper deformation model employed in the observation system, accurate retrieval of DEM error cannot be guaranteed in current MTInSAR techniques. Considering that the DEM error has a fixed spatial pattern and its contribution to interferometric phase-only changes with spatial baselines, we propose here a nonparametric method that can estimate the DEM error in a more robust way. To retrieve signals having a fixed spatial pattern from unwrapped MTInSAR measurements, the independent component analysis (ICA) is used. Experiments with synthetic and real data sets indicate the proposed method is able to estimate DEM error with no $a$ priori information about deformation. Moreover, experiments also show that the method can provide a more robust estimation when the observed phase observations are affected by atmospheric delays and/or the number of interferograms used is limited.
Fast Networking for Disaster Recovery

Content-Centric Networking (CCN) is now a research hotspot aiming at building up a new network architecture compared with the traditional IP-based, host-centric one. In this paper, after leaning that CCN's content naming and content-based properties make it suitable for fast network organizing in disaster recovery, we propose methods on access point placement and routing to fast connect users in a middle-scale post-disaster scenario model. Our work includes the design of a placement algorithm using graphic union coverage and a CCN routing strategy based on Breadth-First Searching, both extracting the social attributes of user node distribution. We use real-world maps for simulation and carry out comparative analysis with existing Ad Hoc methods under the same experimental conditions. The simulation results show that CCN can bring more efficient routing and robust framework to fulfill the urgent demands of post-disaster recovery.
Linked Data - A Paradigm Shift for Publishing and Using Biography Collections on the Semantic Web

This paper argues for making a paradigm shift in publishing and using biographical dictionaries on the Web, based on Linked Data. The idea is to represent biographical data in a harmonized, semantically interoperable form, which enables 1) data enrichment by aggregating linked content from complementary, distributed, and heterogeneous data sources, as well as by reasoning, and 2) development of intelligent services using machine “understandable” data. Based on the aggregated global knowledge graph, published in a SPARQL endpoint, tooling for 1) biographical research of individual persons as well as for 2) prosopographical research on groups of people can be provided. As a demonstration of these ideas, we discuss the new in-use linked data service and semantic portal B IOGRAPHY S AMPO – Finnish Biographies on the Semantic Web that quickly attracted thousands of end users on the Web. This semantic portal is based on a knowledge graph extracted automatically from a collection of 13 100 textual biographies, written by 980 scholars. The texts are enriched with data linking to 16 external data sources and by harvesting external collection data from libraries, museums, and archives. Reasoning is used for query expansion and for discovering serendipitous relations between entities, such as persons and places.
Development and Its Effects on Visual Working Memory Eyes-On Training and Radiological Expertise : An Examination of Expertise

http://hfs.sagepub. com/content/55/4/747 The online version of this article can be found at: DOI: 10.1177/0018720812469224 published online 27 December 2012 2013 55: 747 originally Human Factors: The Journal of the Human Factors and Ergonomics Society Melissa R. Beck, Benjamin A. Martin, Emily Smitherman and Lorrie Gaschen Development and Its Effects on Visual Working Memory Eyes-On Training and Radiological Expertise : An Examination of Expertise
Modelling and forecasting daily surgical case volume using time series analysis

ABSTRACT Hospitals and outpatient surgery centres are often plagued by a recurring staff management question: “How can we plan our nursing schedule weeks in advance, not knowing how many and when patients will require surgery? ” Demand for surgery is driven by patient needs, physician constraints, and weekly or seasonal fluctuations. With all of these factors embedded into historical surgical volume, we use time series analysis methods to forecast daily surgical case volumes, which can be extremely valuable for estimating workload and labour expenses. Seasonal Autoregressive Integrated Moving Average (SARIMA) modelling is used to develop a statistical prediction model that provides short-term forecasts of daily surgical demand. We used data from a Level 1 Trauma Centre to build and evaluate the model. Our results suggest that the proposed SARIMA model can be useful for estimating surgical case volumes 2–4 weeks prior to the day of surgery, which can support robust and reliable staff schedules.
Equity Sensitivity as a Moderating Variable on the Relationship between Workplace Gender Discrimination and Job Performance

Workplace gender discrimination is a significant problem in Egypt. However, there is limited research on Workplace gender discrimination and its consequences. The current research tries to examine the relationships between workplace gender discrimination and task and contextual job performances. Moreover, the moderating effect of equity sensitivity on these relationships is also investigated. A convenience sample on 312 working women was drawn from different work settings in Cairo, Egypt. They responded to a three-part questionnaire that assesses workplace gender discrimination, equity sensitivity, and job performance. The results indicated that workplace gender discrimination was negatively associated with both of task and contextual performances. Moreover, equity sensitivity was found to moderate the relationship between workplace gender discrimination and contextual performance only. These results were discussed in the light of the extant theoretical and empirical literature. In addition, limitation, future research and conclusion are also reported.