Miracle-dz/UniFAR-FTUs huggingface dataset:
Serum 25-hydroxyvitamin D status and cardiovascular outcomes in chronic peritoneal dialysis patients : a 3y prospective cohort study 1 – 3

Background: Patients with kidney disease are at high risk of developing 25-hydroxyvitamin D [25(OH)D] deficiency. Objective: We studied the association between serum 25(OH)D status and clinical outcomes of chronic peritoneal dialysis patients. Design: We measured serum 25(OH)D concentrations in 230 prevalent peritoneal dialysis patients and then followed these patients prospectively for 3 y or until death. Results: Serum 25(OH)D was deficient or insufficient (ie, 75nmol/L) in 87% of the patients. Adjusting for clinical and demographic factors, every 1-unit increase in log-transformed serum 25(OH)D was associated with a 44% reduction in the hazard of fatal or nonfatal cardiovascular events (95% CI: 0.35, 0.91; P 0.018). However, the association was gradually lost when additional adjustment was made in a stepwise fashion for residual glomerular filtration rate (P 0.078) and echocardiographic measures (P 0.39). Kaplan-Meier estimates showed a significantly greater fatal or nonfatal cardiovascular event-free survival probability among patients with serum 25(OH)D 45.7 nmol/L (median) than among patients with concentrations 45.7 nmol/L (P 0.004). In addition, patients with 25(OH)D 45.7 nmol/L had a significantly higher cardiovascular event-free survival probability than did patients with 25(OH)D 45.7 nmol/L in the stratified analysis for patients with left ventricular mass index less than the median (P 0.013) or normal systolic function (P 0.005). Conclusions: A lower serum 25(OH)D concentration was associated with an increased risk of cardiovascular events in chronic peritoneal dialysis patients. Furthermore, serum 25(OH)D status appeared to show a differential influence on the cardiovascular outcomes of peritoneal dialysis patients depending on the degree of left ventricular hypertrophy and systolic dysfunction. Am J Clin Nutr 2008;87:1631–8.
ALMA Spatially Resolved Dense Molecular Gas Survey of Nearby Ultraluminous Infrared Galaxies

We present the results of our ALMA HCN J=3-2 and HCO+ J=3-2 line observations of a uniformly selected sample (>25) of nearby ultraluminous infrared galaxies (ULIRGs) at z 10^5 cm^-3) molecular gas, with an estimated mass of >10^9 Msun within the nuclear (a few kpc) regions, and dense gas can dominate the total molecular mass there. We find a low detection rate (<20%) regarding the possible signature of a vibrationally excited (v2=1f) HCN J=3-2 emission line in the vicinity of the bright HCO+ J=3-2 line that may be due, in part, to the large molecular line widths of ULIRGs.
Eluding Mass Surveillance : Adversarial Attacks on Facial Recognition Models

Our project analyzes the sensitivity of a deep neural network (DNN) for facial recognition to adversarial input images. We began by modifying a transfer-learned DNN that performs facial recognition using weights from a pre-trained Inception ResNet v1 model. Then, we created methods for generating adversarial input images, such as adding random noise or obscuring facial landmarks (ears, eyes, nose, and mouth). Unsurprisingly, our results indicated that adding random noise to an image reduced model performance. In most cases, clustering random noise around facial landmarks further reduced model prediction accuracy, thereby suggesting that these landmarks play an important role in facial recognition. Finally, we tested whether adversarial training, or including perturbed input images in model training, could increase model accuracy on our adversarial dataset. This defense technique did not prove particularly effective. Thus, our results suggest that these black-box attack mechanisms effectively reduced the accuracy of facial
Buckling instability in tidally induced galactic bars

Strong galactic bars produced in simulations tend to undergo a period of buckling instability that weakens and thickens them and forms a boxy/peanut structure in their central parts. This theoretical prediction has been confirmed by identifying such morphologies in real galaxies. The nature and origin of this instability, however, remain poorly understood with some studies claiming that it is due to fire-hose instability while others relating it to vertical instability of stellar orbits supporting the bar. One of the channels for the formation of galactic bars is via the interaction of disky galaxies with perturbers of significant mass. Tidally induced bars offer a unique possibility of studying buckling instability because their formation can be controlled by changing the strength of the interaction while keeping the initial structure of the galaxy the same. We used a set of four simulations of flyby interactions where a galaxy on a prograde orbit forms a bar, which is stronger for stronger tidal forces. We studied their buckling by calculating different kinematic signatures, including profiles of the mean velocity in the vertical direction, as well as distortions of the bars out of the disk plane. Although our two strongest bars buckle most strongly, there is no direct relation between the ratio of vertical to horizontal velocity dispersion and the bar’s susceptibility to buckling, as required by the fire-hose instability interpretation. While our weakest bar buckles, a stronger one does not, its dispersion ratio remains low, and it grows to become the strongest of all at the end of evolution. Instead, we find that during buckling the resonance between the vertical and radial orbital frequencies becomes wide and therefore able to modify stellar orbits over a significant range of radii. We conclude that vertical orbital instability is the more plausible explanation for the origin of buckling.
On the properties of discs around accreting brown dwarfs

We present a grid of models of accreting brown dwarf systems with circumstellar discs. The calculations involve a self-consistent solution of both vertical hydrostatic and radiative equilibrium along with a sophisticated treatment of dust sublimation. We have simulated observations of the spectral energy distributions and several broadband photometric systems. Analysis of the disc structures and simulated observations reveal a natural dichotomy in accretion rates, with log _ M> 9 and 6 9 classed as extreme and typical accretors respectively. Derivation of ages and masses from our simulated photometry using isochrones is demonstrated to be unreliable even for typical accretors. Although current brown dwarf disc candidate selection criteria have been shown to be largely reliable when applied to our model grid we suggest improved selection criteria in several colour indices. We show that as accretion rates increase brown dwarf disc systems are less likely to be correctly identied. This suggests that, within our grid, systems with higher accretion rates would be preferentially lost during brown dwarf target selection. We suggest that observations used to assert a _ M/ M 2 relationship may contain an intrinsic selection bias.
Learning for Multi-Type Subspace Clustering

Subspace clustering has been extensively studied from the hypothesis-and-test, algebraic, and spectral clustering based perspectives. Most assume that only a single type/class of subspace is present. Generalizations to multiple types are non-trivial, plagued by challenges such as choice of types and numbers of models, sampling imbalance and parameter tuning. In this work, we formulate the multi-type subspace clustering problem as one of learning non-linear subspace filters via deep multi-layer perceptrons (mlps). The response to the learnt subspace filters serve as the feature embedding that is clustering-friendly, i.e., points of the same clusters will be embedded closer together through the network. For inference, we apply K-means to the network output to cluster the data. Experiments are carried out on both synthetic and real world multi-type fitting problems, producing state-of-the-art results.
A Hybrid Recommender System for Gaussian Mixture Model and Enhanced Social Matrix Factorization Technology Based on Multiple Interests

Recommender systems are recently becoming more significant in the age of rapid development of the information technology and pervasive computing to provide e-commerce users’ appropriate items. In recent years, various model-based and neighbor-based approaches have been proposed, which improve the accuracy of recommendation to some extent. However, these approaches are less accurate than expected when users’ ratings on items are very sparse in comparison with the huge number of users and items in the user-item rating matrix. Data sparsity and high dimensionality in recommender systems have negatively affected the performance of recommendation. To solve these problems, we propose a hybrid recommendation approach and framework using Gaussian mixture model and matrix factorization technology. Specifically, the improved cosine similarity formula is first used to get users’ neighbors, and initial ratings on unrated items are predicted. Second, users’ ratings on items are converted into users’ preferences on items’ attributes to reduce the problem of data sparsity. Again, the obtained user-item-attribute preference data is trained through the Gaussian mixture model to classify users with the same interests into the same group. Finally, an enhanced social matrix factorization method fusing user’s and item’s social relationships is proposed to predict the other unseen ratings. Extensive experiments on two real-world datasets are conducted and the results are compared with the existing major recommendation models. Experimental results demonstrate that the proposed method achieves the better performance compared to other techniques in accuracy.
COMPARISON OF RISK LOCI IN FAMILIAL AND SPORADIC PULMONARY FIBROSIS CASES by MATTHEW OLIVER GRACEY

Idiopathic pulmonary fibrosis (IPF) is a disease of unknown etiology that has a poor prognosis for affected individuals. Multiple risk factors and genes are associated with IPF suggesting a complex multifactorial pathogenesis. It has also been proposed that there are familial and sporadic forms of IPF. The aims of this study were to examine whether the impact of 10 known pulmonary fibrosis risk loci of IPF differs between familial and sporadic pulmonary fibrosis and to evaluate the relationship between smoking exposure and a family history of pulmonary fibrosis. IPF case baseline characteristics were compared by family history of disease with continuous measures using two-sample t-tests and with categorical measures using chi-square tests. Mixed models were used to further evaluate the relationship between smoking and family history of pulmonary fibrosis. Multivariable logistic regression models were used to evaluate the relationship between risk loci and family history of disease. Interaction terms were added to the logistic regression models to evaluate if gene-gene or gene-smoking associations differed by family history of disease. A total of 1838 pulmonary fibrosis cases were genotyped with 381 (21%) having a family history of pulmonary fibrosis and 1457 (79%) having no family history of disease. Age did not vary between familial and sporadic cases (p=0.53). The frequency of current or former smokers was marginally higher in sporadic cases (p=0.046). Mean pack years of smoking was higher in sporadic cases than it was in familial cases (p <0.004). The frequency of the risk alleles did not differ in familial and sporadic pulmonary fibrosis cases when evaluated individually and collectively (p>0.17). No risk alleles showed significant pairwise interaction differences in familial and sporadic cases
Inflation and precision cosmology

A brief review of inflation is presented. After having demonstrated the generality of the inflationary mechanism, the emphasis is put on its simplest realization, namely the single field slow-roll inflationary scenario. Then, it is shown how, concretely, one can calculate the predictions of a given model of inflation. Finally, a short overview of the most popular models is given and the implications of the recently released WMAP data are briefly (and partially) discussed.
Brain Co-oximetry: An Useful Non-Invassive Parameter Adjuvant to Standard Perfusion Parameters in Septic Shock

Background and aim: Standard monitoring for tissue perfusion includes mixed venous saturation (CvSO2), blood lactate. Cerebral oximetry (CrSO2) non-invasively observes trends in cerebral saturation and it can be used as adjuvant to standard monitoring. The aim of the study is to evaluate septic shock mortality and patient outcome. Methods: In this study 40 patients were entailed with septic shock at ICU for study period of 10 months. CrSO2 was monitored using “MASIMO O3 Regional Oximetry”. Patients were monitored with CrSO2, CvSO2, blood lactate, mean arterial pressure (MAP), arterial oxygen saturation (SaO2) 6 hourly for 72 hours. Patients with history of cerebrovascular diseases and neurological deficits were excluded from study. IBM SPSS Statistics, Version 24.0. software was used and independent t test, paired t test, Pearson coefficient (r) etc. parameter values were analyzed. Results: There is significant negative correlation after six hours from admission was noticed between CrSO2 and lactic acid (r=-0.749 to -0.956). Significant positive correlation was noticed between CrSO2 and CvSO2 (r=0.904 to 0.993). In addition, significant positive correlation was also found between CrSO2 and mean arterial pressure (r=0.957 to 0.993) and SaO2 (r=0.864 to 0.988). Significant difference was also detected between the value of CrSO2 in the survivors (29 patients) and the non-survivors (11 patients) after 72 hours from admission. Conclusion: CrSO2 could be parameters in patients with shock and it could have a prognostic value in mortality prediction and clinical outcome.
The effect of atomic density gradient in electromigration

This paper studies the electromigration (EM) failure of interconnect structure and solder joint in a wafer level chip scale package (WL-CSP) based on atomic flux divergence (AFD) method. The impact of atomic density gradient (ADG) on the divergence of the atomic fluxes is investigated. The simulation results show that the traditional AFD method, which neglects the effect of atomic density gradient, can result in significant errors in predicting solder joint failures in a WL-CSP; while the AFD method with the consideration of the atomic density gradient has shown more reasonable results.
Aalborg Universitet Technological life cycles regional clusters facing disruption

The phenomenon of technological life cycles is argued to be of great importance in the development of regional clusters. New 'disruptive' technologies may initiate the emergence of new regional industrial clusters and/or create new opportunities for further development of existing ones. However, they may also result in stagnation and decline of the latter. The term disruptive refers to such significant changes in the basic technologies that may change the industrial landscape, even in the shorter run. The paper examines the key features of a regional cluster, where the economic development patterns are quite closely related to the emergence of new key technologies.
The impact of Southern Ocean residual upwelling on atmospheric CO[subscript 2] on centennial and millennial timescales

a rate of typically 1–1.5 parts per million/10 6 m 3 s − 1 by enhancing the communication between the atmosphere and deep ocean. This metric can be used to interpret the long-term effect of Southern Ocean dynamics on the natural carbon cycle and atmospheric CO 2 , alongside other metrics, such as involving the proportion of preformed nutrients and the extent of sea ice cover.
Characteristics of intellectual capital, competitiveness and industrial policies of innovation-intensive sectors in Serbia

Predmet izučavanja ovog rada su karakteristike intelektualnog kapitala, konkurentnosti i industrijskih politika inovativno-intenzivnih sektora u Srbiji. Rad se sastoji od četiri dela. U prvom delu rada date su karakteristike intelektualnog kapitala kao osnove ekonomskog rasta i konkurentnosti. U drugom delu rada proučavaju se karakteristike nacionalnog intelektualnog kapitala i konkurentnosti pojedinih zemalja svetske privrede, dok se u trećem delu rada razmatra konkurentnost privrede Srbije i daju se preporuke za novi model rasta. Četvrti deo rada posvećen je analizi karakteristika intelektualnog kapitala i industrijskih politika inovativno-intenzivnih preduzeća u Srbiji i u njemu su prikazani rezultati izvršenog istraživanja. U radu se predlaže da novi model rasta domaće privrede bude baziran na naprednoj industrijskoj proizvodnji i uslugama sa visokim stepenom dodate vrednosti, kao i primena novih ekonomskih politika, koje treba da budu zasnovane na heterodoksnom pristupu. Takođe se zaključuje da je stepen razvijenosti intelektualnog kapitala analiziranih preduzeća na relativno visokom nivou, da je njihov strukturni kapital relativno razvijen, kao i da anketirana preduzeća imaju odličnu reputaciju. Na kraju rada zaključuje se da dalji razvoj inovativno-intenzivnih sektora podrazumeva primenu odgovarajućih industrijskih politika koje su specifične po tome što treba da sadrže elemente i vertikalnih i horizontalnih politika i koje posebno treba da se bave podsticanjem razvoja i inovacija.
Improving Robustness of Semantic Segmentation Models with Style Normalization

We introduce a novel technique for data augmentation with the goal of improving robustness of semantic segmentation models. Standard data augmentation methods rely upon augmenting the existing dataset with various transformations of the training samples but do not utilize other existing datasets. We propose a method that draws images from external datasets that are related in content but perhaps stylistically different; we perform style normalization on these external datasets to counter differences in style. We apply and benchmark our technique on the semantic segmentation task with the DeepLabv3+ model architecture and the Cityscapes dataset, leveraging the GTA5 dataset for our data augmentation.
Visual Content Learning in a Cognitive Vision Platform for Hazard Control (CVP-HC)

Abstract This work is part of an effort for the development of a Cognitive Vision Platform for Hazard Control (CVP-HC) for applications in industrial workplaces, adaptable to a wide range of environments. The paper focuses on hazards resulted from the nonuse of personal protective equipment (PPE). Given the results of previous analysis of supervised techniques for the problem of classification of a few PPE (boots, hard hats, and gloves extracted from frames of low resolution videos), which found the Deep Learning (DL) methods as the most suitable ones to integrate our platform, the objective of this paper is to test two DL algorithms: Single Shot Detector (SSD) and Faster Region-based Convolutional Network (Faster R-CNN). The testing uses pretrained models on a second version of our PPE dataset (containing 11 classes of objects) and evaluates which of examined algorithms is more appropriate to compose our system reasoning.
The dynamical decoupling spectroscopy of environment characterized by discrete spectrum

Here we explore the prospects of carrying out the single qubit dynamical decoupling spectroscopy of environmental noise characterized by discrete spectral density. We show that the approach to the spectroscopy and spectrum reconstruction has to be adjusted to the resolution of frequency filters produced by the dynamical decoupling pulse sequences. The standard form of spectroscopy is applicable when the filter is unable to resolve discrete spectral lines and only coarse grained approximation of the spectrum is recorded in the qubit's decoherence rate. When the filter's resolution is high enough the qubit probe evolves in a qualitatively different manner and the procedure for recovering spectral density has to be redesigned.
SAVINGS AND INFLATION – A PANEL THRESHOLD MODEL APPROACH

This research investigates the impact of inflation on savings through a panel of 42 countries observed annually between 1995 and 2014. A panel threshold model approach is followed, being one-year lagged inflation the threshold variable and gross savings the dependent one. Findings suggest that inflation has a positive impact on gross savings, particularly at lower levels of inflation. The robustness tests conducted led to conclude that the model does not hold for less developed economies where savings are not so correlated with the inflation level as it is among developed economies.
Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT

We provide non-asymptotic convergence rates of the Polyak-Ruppert averaged stochastic gradient descent (SGD) to a normal random vector for a class of twice-differentiable test functions. A crucial intermediate step is proving a non-asymptotic martingale central limit theorem (CLT), i.e., establishing the rates of convergence of a multivariate martingale difference sequence to a normal random vector, which might be of independent interest. We obtain the explicit rates for the multivariate martingale CLT using a combination of Stein's method and Lindeberg's argument, which is then used in conjunction with a non-asymptotic analysis of averaged SGD proposed in [PJ92]. Our results have potentially interesting consequences for computing confidence intervals for parameter estimation with SGD and constructing hypothesis tests with SGD that are valid in a non-asymptotic sense.
PENGARUH DISIPLIN KERJA, LINGKUNGAN KERJA DAN MOTIVASI KERJA TERHADAP KINERJA KARYAWAN PADA PT. HAZARA CIPTA PESONA

Human Resources (HR) quality is one of the essential factors in improving a company's performance. Therefore we need human resources which have high competence because they can support the improvement of employee performance. This research uses a qualitative method with a descriptive analysis approach. The data used in this research is cross-section data obtained from primary data sources. In this study, the population is all employees of PT. Hazara Cipta Pesona as many as 88 employees. The sampling technique used is saturated sampling or census. The analysis used in this research is an inferential analysis using multiple linear regression. From this research, it is known that: (1) Work discipline variable has a positive and significant effect on the employee performance of PT. Hazara Cipta Pesona. (2) Work environment variables have a positive and significant impact on the performance of employees of PT. Hazara Cipta Pesona; (3) Work motivation variable has a positive and significant effect on employee performance at PT. Hazara Cipta Pesona.