Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural and Electronic Properties of Threading Screw Dislocations in GaN

GaN is a promising material for applications to power electronics devices with its favorable high field and thermal transport properties [1]. However, the presence of high density of threading dislocations (> 108 cm−2) in GaN is problematic. They limit the performance of power devices by reducing the photoluminescence intensity and the electron mobilities, and may end up leading to premature device breakdown. Therefore it is important to evaluate their impact on device performance and reliability. Here we focus on threading screw dislocation, which is a non-radiative recombination center with deep electronic states in the band gap, with different types of core structures and evaluate their energetics by the first-principles method using hybrid Hartree-Fock density functionals. The core structure of threading screw dislocations in GaN is still under debate. Recently a new configuration of screw dislocation core is introduced by Belabbas et al. [2], where the dislocation line is located at the middle of bonds connecting two adjacent projected hexagons (“B” core), while it is located at the center of projected hexagon in the conventional screw core configuration (“A” core, see Fig. 1). We have investigated this new “B” full core structure as well as several types of “A” core structures: full (6:6), open (0:0), Ga-filled (6:0) and N-half-filled (6:3) structures, where the numbers in parenthesis, (n Ga :n N ), denote the numbers of Ga and N atoms remaining in the core region. Our calculations are performed using the projector augmented wave method with the PerdewBurke-Ernzerhof (PBE) functional and HeydScuseria-Ernzerhof (HSE) functional [3] implemented in the VASP code [4]. Ga 3d electrons are considered as valence electrons and energy cutoff is set to 425 eV. Our fully periodic supercell accommodates dislocation dipole and contains up to 288 atoms of Ga and N. The supercell for “A” open core structure is shown in Fig. 2. Relative formation energies are shown in Fig. 3, which are calculated based on the Northrup formalism [5] and are expressed as a function of the Ga chemical potential (μ Ga ). In N-rich growth condition (at −1.17 eV) the most stable structure is the “B” full core. This result is consistent with the one by Belabbas et al., who insist it is the most stable under any growth conditions within their calculations based on local density approximation [2]. However, in Ga-rich growth condition (at 0 eV), contrary to the findings by Belabbas et al., the most stable structure is the Ga-filled (6:0) “A” core, which is originally proposed by Northrup [5]. We will show the detailed structural properties of these stable dislocation configurations and the electronic properties including their band structures calculated using the HSE hybrid functional (see Fig. 4).
ANTIOXIDANT POTEINTIAL AND ACTIVITY OF AERIAL PARTS OF EIGHT MEDICINAL PLANTS OF UTTARAKHAND, INDIA

The antioxidant potential of aerial parts of eight medicinal plant species were investigated by 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging, Ferrous ion-chelating (FCA) and ferric reducing antioxidant power assays in five different concentrations and the mean values ranged from 20.39 to 83.01%, 17.21 to 48.62% and 11.70 to 21.65 µg Trolox equivalent/mg extract, respectively. Total phenolic and flavonoid contents ranged from 4.83 to 10.46 µg, gallic acid equivalent/mg extract and 1.12 to 13.11 µg quercitin equivalent/mg extract. Enzymatic antioxidants (Unit/mg fresh weight): superoxide dismutase, catalase and peroxidase values ranged from 14.86 to 49.81, 60.00 to 141.33 and 0.25 to 0.60, respectively. Considering the overall assay M. Pudica can be considered as the potent candidate for displaying antioxidant activity among the experimental plants.
A RANDOM MATRIX PERSPECTIVE

One of the distinguishing characteristics of modern deep learning systems is that they typically employ neural network architectures that utilize enormous numbers of parameters, often in the millions and sometimes even in the billions. While this paradigm has inspired significant research on the properties of large networks, relatively little work has been devoted to the fact that these networks are often used to model large complex datasets, which may themselves contain millions or even billions of constraints. In this work, we focus on this high-dimensional regime in which both the dataset size and the number of features tend to infinity. We analyze the performance of a simple regression model trained on the random features F = f(WX + B) for a random weight matrix W and random bias vector B, obtaining an exact formula for the asymptotic training error on a noisy autoencoding task. The role of the bias can be understood as parameterizing a distribution over activation functions, and our analysis directly generalizes to such distributions, even those not expressible with a traditional additive bias. Intriguingly, we find that a mixture of nonlinearities can outperform the best single nonlinearity on the noisy autoecndoing task, suggesting that mixtures of nonlinearities might be useful for approximate kernel methods or neural network architecture design.
Alterity and alienance in neuroscience : Jurg Zutt

In recent years, neuroscientific research into the brain bases of the self and its socio-relational nature has been conducted by experimental models of functional neuroimaging. In this editorial are explored the correspondence between the semantic anthropo-phenomenology of the German psychiatrist Jurg Zutt (1893-1980), and recent neuroscientific theories about different, but complementary, perspectives on the self and self-other relationship. In particular, we discuss the theoretical correlation between the Zutt’s concept of “Internal Attitude” and aberrant functioning of brain networks involved in self-related processing including the default mode network, the salience network and sensorimotor networks in patients with schizophrenia when solicited by socio-relational stimuli. The aim of this editorial is not only to propose a synthesis between the anthropological and neuroscientific perspectives, but, above all, to invoke a language that takes into account the human being in unison with the complexity of
WST 48.8 EB4.5 corr

To remove heavy metals from sludge and slurries the heavy metals must first be solubilised. In this study, metal bioleaching using sulphuric acid producing microorganisms is investigated. The inhibitory effects of four metals (zinc, chromium, nickel and copper) on acid production were firstly assessed for concentrations of each metal up to 1,000 mg/l. Low concentrations of zinc and chromium (50 mg/L) appear to stimulate the production of sulphuric acid but concentrations of those metals at 500 mg/L and above inhibited acid production (20 to 30% of the control rate at a concentration of 1,000 mg/L). The average sulphuric acid production of the metal free control was 0.78 g/L/day over a 10 day period. At all concentrations of nickel and copper, sulphuric acid production was suppressed and was less than 10% of the control at 250 mg/L or more after 10 days. Activated sludge was also loaded with the four metals in individual bioleaching tests, in increments from zero up to 5,050 mg of metal/kg solids on a dry basis. In these bioleaching trials the sulphuric acid production averaged 0.9/g/L/day and the pH after 7 days was less than 2.0. Sludge copper concentrations of 3,850 and 5,050 mg/kg partially inhibited acid production (pH after 7 days was 2.4 and 2.7). The solubilisation for each metal after 7 days was Zn: 82–97%, Ni: 12–70%, Cu: 28–55%, and Cr: <20%. Bioleaching of a sludge loaded with a mixture of the four metals gave peak solubilities of 99% for Zn after 3 days, 68% for Ni after 6 days, 57% for Cu after 9 days and 33% for Cr after 9 days.
Effects of precursor topology on poly- mer networks simulated with molecular dynamics

Molecular modeling of crosslinked polymers often follows arbitrary pathways for network generation, with different precursor topology from experimental systems. We use coarse-grained molecular simulation to study the effects of precursor choice on the predicted network structure and properties. Three sets of precursors with different molecular architectures are designed such that they would form identical networks at the limit of perfect conversion. Little difference is observed between the resulting networks in typical properties including the radial distribution function, macroscopic statistics of network connectivity, and glass transition behaviors. However, the stress-strain relationship in tensile deformation clearly depends on the formation pathway when compared at the same crosslinking density. The elastic modulus of the network is found to correlate strongly with the number of elastic strands in the network, except at the highly-crosslinked limit where substantial discrepancy is observed between networks from different precursors. Although these final networks contain a similar average density of structural defects, the choice of precursor has significant impact on their spatial distribution, leading to the precursor dependence of their mechanical properties. Uniform defect distribution and fast defect elimination can be achieved by designing precursor units with a proper stoichiometric ratio of different monomers.
Learning from Observation-Only Demonstration for Task-Oriented Language Grounding via Self-Examination

Imitation learning is effective to learn control policy from expert demonstrations. Instead of traditional image-guided methods, instruction-oriented learning makes it more ﬂexible and useful in real-world applications. However, most existing imitation methods rely on the assumptions that the action sequences are given in the demonstrations and that the agent can interact with the demonstrations’ state to try-and-error, which greatly reduces the practicality of the methods. In this paper, we focus on imitation learning with ﬁxed, observation- only demonstrations where ground truth action sequences are not explicitly given under the visual-language setting. We propose a two-phase method which ﬁrst imitates the given demonstrations and then further improves the action policy via self-examination, i.e., evaluating explorations over the state space through auto-generated examples. We evaluate our method on pick-and-place tasks and the result shows that the self-examination actually beneﬁts language grounding.
Multivariable Repetitive Control: Decentralized Designs With Application to Continuous Media Flow Printing

The print productivity and medium dimensions of wide-format roll-to-roll printing systems are limited by position errors induced by step-wise medium transportation. The aim of this article is to develop a design framework for multivariable repetitive control (RC), that enables improved productivity and accuracy through continuous media flow printing. The developed technical framework for RC explicitly addresses the tradeoff between performance and modeling requirements through decentralized designs. In particular, systematic design approaches are developed that explicitly address unmodeled interaction as uncertainty, i.e., through robustness. The result is a range of solutions, including 1) independent single-input single-output (SISO) designs, and 2) sequential SISO design. Experimental results confirm that the developed RC framework in conjunction with continuous media flow printing outperforms existing approaches.
Domain-specific Sentiment Dictionary Construction for Sentiment Classification

Sentiment dictionaries are commonly used to solve the problem of sentiment classification for customer reviews. The number of sentiment words in the generalized dictionaries such as SentiWordNet is limited and lack of many sentiment words especially domainspecific sentiment words. Different domains have different sentiment words and the sentiment of a word depends on the domain in which it is used. In this paper, an approach based on Point-wise Mutual Information (PMI) is proposed to construct a domain-specific sentiment dictionary effectively and automatically. The proposed system is evaluated on three diverse datasets from different domains by using 10-fold cross validation. Accordingly to the experimental results, the goodness of the extracted dictionary is relatively high and significantly improves the performance of sentiment classification. The experimental results show that the extracted domain-specific dictionary outperforms the generalized dictionary, SentiWordNet. The proposed method learns the domain-specific sentiment words efficiently and it is domain adaptable. KeywordsSentiment Analysis, Polarity Classification, Sentiment Dictionary, Domain-specific Sentiment Words, Point-wise Mutual Information
Effect of Antiplatelet Therapy on Coronary Heart Disease Risk in Patients

Platelets play a central role in the pathobiology of atherogenesis and atherothrombosis. Therefore therapies that are directed toward platelet inhibition are widely used in patients with established coronary heart disease (CHD) or in moderateto high-risk individuals for primary prevention of cardiovascular (CV) events. As our armamentarium of potent antiplatelet therapies continues to expand, there is growing interest in identifying the appropriate groups of patients who will derive the greatest benefit frommore potent therapies. To that end, several studies over the past few decades have highlighted that individuals with diabetes mellitus (DM) exhibit abnormalities in platelet function that place them at increased risk of adverse outcomes, as compared with their nondiabetic counterparts (see also Chapter 10). Although the mechanisms that contribute to platelet hyperreactivity in diabetic patients continue to be elucidated, it appears that diabetic platelets are characterized by the dysregulation of several signaling pathways that occur both at the level of the platelet receptor and with subsequent downstream signaling. In addition, glycosylation may impair endothelial function and promote oxidative stress, thereby further promoting platelet reactivity and procoagulant activity. There is therefore a priori biologic plausibility to support the concept that diabetic patients may derive enhanced benefit from particular therapies directed toward blocking the platelet. However, differences in platelet biology in diabetic patients may also contribute to diminished antiplatelet drug responsiveness. This chapter reviews the use of established and novel oral antiplatelet therapies in diabetic patients for use in primary or secondary prevention of CV events.
Title of Document: Parental Resources, Educational Progression, and Family Formation

Title of Document: Parental Resources, Educational Progression, and Family Formation Lori Reeder, Doctor of Philosophy, 2014 Dissertation Directed by: Professor Michael S. Rendall Department of Sociology In this dissertation, I use longitudinal data (1997-2011) to explore two types of financial constraints during the transition to adulthood. First, I explore the relationship between parental resources (income and net household worth) and educational transitions among U.S. men and women. I revisit the Mare model of educational transitions which asserts that parental resources decline in importance with each educational transition. I find that, for the current cohort of young adults, parental net worth, in particular, is positively associated with high school graduation, four-year college attendance, and four-year college completion. Yet, the magnitude of the effect of parental net worth does decline with each educational transition. Furthermore, after controlling for parental income and net household worth, non-Hispanic Black and Hispanic students are more likely to graduate from high school and to enroll in college, yet remain less likely to graduate from college. For enrollment into professional or graduate school, the effect of parental resources is statistically nonsignificant. Next, I examine the relationship between parental resources and timing of women’s first birth. I find that parental resources impact first birth timing, wherein compared to women from low-resource families, women from middle-resource families have a lower likelihood of first birth through the mid-20s and women from high-resources families were found to have substantially lower likelihood of having a first birth by age 30 or 31. I find that greater and earlier incidence of Hispanic women’s first birth is entirely explained by differences in parental resources and other sociodemographic characteristics. Furthermore, differences in parental resources explain the higher likelihood of first births in teen years, and most of the higher likelihood of first births in the 20s, among Black women. Finally, I consider whether student loan debt delays family formation for men and women attending four-year college. I find that student loan debt is associated with later transitions to marriage and first birth, for both women and men, but that only for women does a statistically significant association remain after controlling for income, family background, and other socio-demographic characteristics, and even then only at low levels of debt. Parental Resources, Educational Progression, and Family Formation
University of Groningen Can we further enrich autosomal dominant polycystic kidney disease clinical trials for rapidly progressive patients?

Background. The PROPKD score has been proposed to stratify the risk of progression to end-stage renal disease in autosomal dominant polycystic kidney disease (ADPKD) subjects. We aimed to assess its prognostic value in a genotyped subgroup of subjects from the Tolvaptan Phase 3 Efficacy and Safety Study in Autosomal Dominant Polycystic Kidney Disease (TEMPO3/4) trial. Methods. In the post hoc analysis, PKD1 and PKD2 were screened in 770 subjects and the PROPKD score was calculated in mutation-positive subjects (male: 1 point; hypertension <35 years: 2 points; first urologic event <35 years: 2 points; nontruncating PKD1 mutation: 2 points; truncating PKD1 mutation: 4 points). Subjects were classified into low-risk (LR; 0–3 points), intermediate-risk (IR; 4–6 points) and high-risk (HR; 7–9 points) groups. Results. The PROPKD score was calculated in 749 subjects (LR1⁄4 132, IR1⁄4 344 and HR1⁄4 273); age was inversely related to risk (LR1⁄4 43.6 years, IR1⁄4 39.5 years, HR1⁄4 36.2 years; P< 0.001). Subjects from the HR group had significantly higher height-adjusted total kidney volume (TKV) and rates of TKV growth. While baseline renal function was similar across all risk groups, the rate of estimated glomerular filtration rate (eGFR) decline significantly increased from LR to HR in the placebo VC The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com 645 O R IG IN A L A R T IC LE Downloaded f om https://academic.oup.com/ndt/article-abstract/33/4/645/3979612 by University of Groningen user on 05 June 2018 group. Tolvaptan treatment effectiveness to reduce TKV growth was similar in all three risk categories. While tolvaptan significantly slowed eGFR decline in the IR (tolvaptan1⁄4 2.34 versus placebo1⁄4 3.33 mL/min/1.73 m/year; P1⁄4 0.008) and HR groups (tolvaptan1⁄4 2.74 versus placebo1⁄4 3.94 mL/min/ 1.73 m/year; P1⁄4 0.002), there was no difference in the LR group (tolvaptan1⁄4 2.35 versus placebo1⁄4 2.50 mL/min/1.73 m/year; P1⁄4 0.72). Excluding the LR subjects from the analysis improved the apparent treatment effect of tolvaptan on eGFR decline. Conclusion. This study confirms the prognostic value of the PROPKD score and suggests that it could reduce costs and enhance endpoint sensitivity by enriching future study populations for rapidly progressing ADPKD subjects.
Learning Language from Vision

We develop a semantic parser that is trained in a grounded setting using videos cap-tioned with sentences. This setting is both data-efﬁcient, requiring little annotation, and similar to the experience of children where they observe their environment and listen to speakers. The semantic parser recovers the meaning of English sentences despite not having access to any annotated sentences. It does so despite the ambiguity inherent in vision where a sentence may refer to any combination of objects, object properties, relations or actions taken by any agent in a video. For this task, we collected a new dataset for grounded language acquisition. Learning a grounded semantic parser can ultimately lead to a better understanding of child language acquisition. In addition, we demonstrate a new application for grounded lanugage acquisition, zero-shot natural language inference, by incorporating the grounded parser.
Uncertainty-based mapping and planning strategies for safe navigation of robots with stereo vision

We describe our recent developments in probabilistic modeling of 3D reconstruction with stereo vision, applied to planning strategies for locomotion and gaze. We first overview the use of probabilistic occupancy grids for 3D reconstruction, and the sensor models of stereo best suited to the problem. These grids are then used for robot navigation, which is tackled at two levels: 1) At the locomotion level, trajectories are computed from the grid using an A* search algorithm that minimizes the total probability of occupancy over the trajectory. 2) At the grid level, we propose two task-relevant active strategies which redirect the sensor to ”maximum visible entropy” and ”maximum visible occupancy” points along the planned locomotion trajectories. Steps 1) and 2) are executed alternately until the locomotion trajectory converges to a high certainty, safe solution. Results of the proposed gaze and locomotion planning strategies were obtained on simulated scenarios and a real robot. Estimates of the uncertainty that occupancy grids are subjected to in real outdoor scenarios were computed for different stereo sensor models. These estimates were used in active gaze simulations for an extensive comparison of gaze strategies across 400 randomly generated environments. The results show that careful modeling of stereo vision sensor uncertainty and the proposed task-relevant planning strategies lead to more complete and consequently collision-free reconstructions of the environment along planned robot trajectories.
Linting for Visualization: Towards a Practical Automated Visualization Guidance System

Constructing effective charts and graphs in a scientiﬁc setting is a nuanced task that requires a thorough understanding of visualization design; a knowledge that may not be available to all practicing scientists. Previous attempts to address this problem have pushed chart creators to pore over large collections of guidelines and heuristics, or to relegate their entire workﬂow to end-to-end tools that provide automated recommendations. In this paper we bring together these two strains of ideas by introducing the use of lint as a mechanism for guiding chart creators towards effective visualizations in a manner that can be conﬁgured to taste and task without forcing users to abandon their usual workﬂows. The programmatic evaluation model of visualization linting (or vis lint) offers a compelling framework for the automation of visualization guidelines, as it offers unambiguous feedback during the chart creation process, and can execute analyses derived from machine vision and natural language processing. We demonstrate the feasibility of this system through the production of vislint_mpl , a prototype visualization linting system, that evaluates charts created in matplotlib.