Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Implementation of Efficient Permutation Clos Network Design for Mpnoc

The transmission of the data with traffic free, low latency and high throughput from source to destination are the challenges for on chip multi processing system on chip (MPNOC) design. The conventional packet switching approach having large amount of power and area for the queuing buffer. Topologies such as mesh and torus[10],are intuitively feasible for physical layout in a 2-D chip. Having the high wiring irregularity and the large router radix of indirect topologies such as Benes or Butterfly[11], pose a challenge for physical implementation. The present work, the silicon-proven design of a novel on-chip network to support guaranteed traffic permutation in multiprocessor system on chip applications. The proposed network employs a pipelined Circuit switching approach combined with a dynamic path setup scheme under a multistage network topology. The dynamic path-setup scheme enables runtime path arrangement for arbitrary traffic permutations. The circuit switching approach offers a guarantee of permuted data and its compact overhead enables the benefit of stacking multiple networks. Design and developed by using XILINX 12.4 and simulated on Modelsim 6.3f and implemented on Spartan 3 FPGA Device. This can achieve high throughput, low latency and low cost. General Terms Throughput, Latency, network on chip
Progression of impairment in adolescents with ADHD through the transition out of high school: Contributions of parent involvement and college attendance

Long-term, prospective follow-up studies of children diagnosed with Attention-Deficit/ Hyperactivity Disorder (ADHD) show that symptoms tend to decline with age, but impairments in daily life functioning often persist into adulthood. We examined the developmental progression of impairments before and after the transition out of high school in relation to parent involvement during adolescence, parent support during adulthood, and college attendance, using 8 waves of data from the prospective 16-year follow-up of the Multimodal Treatment of ADHD (MTA) Study. Participants were 548 proband children diagnosed with DSM-IV ADHD Combined Type and 258 ageand sex-matched comparison children (Local Normative Comparison Group; LNCG) randomly sampled from probands' schools. Impairment was assessed consistently by parent report from childhood through adulthood. Results showed that impairment worsens over time both before and after the transition to adulthood for those with ADHD histories, in contrast to non-ADHD peers, whose impairments remained stably low over time. However, impairment stabilized after leaving high school for young adults with ADHD histories who attended college. Involved parenting in adolescence was associated with less impairment overall. Attending college was associated with a stable post-high school trajectory of impairment regardless of parents' involvement during adolescence, but young adults with histories of involved parenting and who attended college were the least impaired overall. Correspondence for this article should be directed to Andrea Howard, Department of Psychology, Carleton University, andrea.howard@carleton.ca. HHS Public Access Author manuscript J Abnorm Psychol. Author manuscript; available in PMC 2017 February 01. Published in final edited form as: J Abnorm Psychol. 2016 February ; 125(2): 233–247. doi:10.1037/abn0000100. A uhor M anscript
Cloning and Expression of Two Carboxylesterases, and Their Activity Modulation in Chinese Mitten crab Eriocheir sinensis under Pesticide Exposer

Carboxylesterases (CXEs) belong to a family of multifunctional enzymes. They metabolize drugs, environmental toxicants, and carcinogens, and inhibit bacterial pathogenesis. In this study, the full-length cDNAs of ES-CXE3 (2,444 bp) and ES -CXE4 (2,385 bp) were cloned from the Chinese mitten crab, Eriocheir sinensis. Sequence analysis showed that both ES-CXE sequences contained the catalytic triplet structure characteristic of the CXEs superfamily. Alignment and phylogenetic analyses revealed that the two ES-CXEs are highly similar to those of other crustaceans. Tissue specificexpression analysis showed that both ES-CXEs were highly expressed in the hepatopancreas. Real-time fluorescence quantitative PCR showed that the maximum expression levels of the ES-CXE3 and ES-CXE4 genes in the hepatopancreas of E. sinensis exposed to low doses of β-cypermethrin, avermectin and trichlorfon were 10, 8, 6 and 600, 110, 250 higher than relative to those of the control group, respectively, and that enzyme activities steadily increased and were significantly higher than that of the control group. Therefore, treatment with these insecticides may induce the expression of both ES-CXEs as well as changes in the activities of carboxylesterase family genes. Our results suggest that ES-CXEs might play vital roles for insecticide detoxification in E. sinensis
NOTICEABLE DIFFERENCES FOR IMAGE MISALIGNMENT IN BIOCULAR HELMET-MOUNTED DISPLAYS

Biocular/binocular near-eye/helmet-mounted displays (HMDs) typically exhibit registration errors between left and right eye images due to distortions and misalignment of the individual left/right optics, or poor HMD fit. Biocular registration errors have been known to cause eye strain, fatigue, and performance loss, particularly for precisioncritical applications, in both twoand three-dimensional applications. While various metrology toolsets have been used to precisely quantify angular registration errors, there is widespread disagreement in the literature regarding the subjective degree of human tolerance for misalignment. In this work, we investigate the just noticeable difference (JND) for various binocular registration errors, including convergence, divergence, and dipvergence conditions, in a simulated operational task. Observer performance was evaluated by estimating the JND threshold for typical heads-up display symbology under varying degrees of biocular registration error. JND thresholds were found to be statistically correlated with measurements of each subject’s stereoacuity and fusion range, allowing such tests to be predictive of sensitivity to binocular misalignment. The Psi algorithm was used to estimate psychometric thresholds using a 4-alternative forced choice scenario to detect symbol misalignment for more than 30 subjects. These psychometric thresholds were then mapped onto objective registration error maps to illustrate those regions of HMD field of view over which errors will be perceptible to the typical observer. These data and methods can be used to guide binocular HMD design for virtualor augmentedreality applications to minimize perceptual misalignment while avoiding overspecification of the display optics.
From Particular to General: A Preliminary Case Study of Transfer Learning in Reading Comprehension

In this paper we argue that transfer learning will be an important ingredient of general learning AI. We are especially interested in using data-rich domains to learn skills widely applicable in other domains. As a case study we explore transfer learning in reading comprehension. We train a neural-network-based model on two context-question-answer datasets, the Children’s Book Test and its larger extension, the BookTest, and we monitor transfer to a subset of bAbI tasks. Our initial experiments show only limited transfer between these domains. However, the transferred system is still signiﬁcantly better than a random baseline.
Geometry Reconstruction of Plants

Synthetic modelling and reconstruction of the geometry of real plants have been a center of attention of research for decades. Due to the complex architecture and growth pattern of plants, accurate modelling of the plant geometry is an extremely challenging task. Although realistic modelling of plants are widely studied in the context of computer graphics research, it also has profound impact on the biological study of plants. In order to perform various types of simulation studies under different environmental conditions and in understanding the physiology of plants in more details, synthetic models can be an extremely useful tool. Synthetic modelling approaches can be broadly categorized into three types. The first type is the rule based procedural modeling approach, which does not account the real data into consideration. The second type of approach (also known as data driven modelling) performs modelling based on the real data obtained from 3D acquisition procedures. The third type of approach is interactive, which is based on user assistance. In this chapter, we focus on the modelling of the second category and revisit the recent state-of-the-art techniques performing reconstruction of plant geometry from real data. The algorithms can be classified into different interlinked categories, which constitute the general pipeline of geometry reconstruction in data driven modelling framework. In the context of biological relevance of different types of techniques, we discuss about the strengths and limitations of the approaches and the need of prior botanical knowledge to reconstruct the plant geometry in biologically feasible manner. Finally, we explore the quantitative assessment techniques which can be used to measure the quality of the reconstruction result with respect to the actual data.
Markers shaping the future of preventive dentistry

Detecting pathologies at their earliest stages can signifi cantly aff ect patient discomfort, prognosis, therapeutic intervention, survival rates, and recurrence. The use of tissue biomarkers in basic and clinical research and even in clinical practice not only became very common but also their presence as primary endpoints in clinical trials are now very well accepted. The use of tissue biomarkers, and mainly laboratory-measured biomarkers, in clinical set up is somewhat newer, and the suitable approaches to this practice are still in the process of development and refi nement. This review highlights contemporary innovations and explores recent discoveries about markers in lesions of oral cavity.
Lees, Yasmin (2018)Do you believe?”: The effects of child witness age and background on the credibility of child sexual exploitation cases. Manchester

A mock child sexual exploitation trial was used to study juror’s perceptions of child victim’s credibility. The age and socioeconomic status of the child victim were tested. Results indicated that child victim age had no significant impact on the mock juror’s perceptions of their credibility. The socioeconomic status of the child victim had a significant impact on how credible the jurors perceived the child. Juror’s hearing a case of a child from a high socioeconomic status were more likely to reach a guilty verdict compared to the child from a low socioeconomic status. Additionally, jurors who perceived the child as credible were significantly more likely to pass a guilty verdict. The study concluded that the socioeconomic status of a child victim can impact how credible jurors perceive their allegations. Future research should aim to examine stereotypes surrounding victims of child sexual abuse. Specifically, what it is about child victims from low socioeconomic status’ that causes jurors to perceive their allegations as less credible.
Meta-analysis of the prevalence of livestock diseases in North Eastern Region of India

Aim: The study aimed to determine the overall prevalence of livestock diseases in North Eastern Region (NER) of India, through a systematic review and meta-analysis of published data. Materials and Methods: The articles used for the study were retrieved from PubMed, J-Gate Plus, Indian Journals, and Google scholar, R open-source scripting software 3.4. 3. Metafor, Meta. The Chi-square test was conducted to assess for the heterogeneity, forest plot (confidence interval [CI] plot) is a method utilized to present the results of meta-analysis, displaying effect estimate and their CIs for each study were used for searching and retrieval of livestock diseases prevalence data in India using a search strategy combining keywords and related database-specific subject terms from 2008 to 2017 in English only. Results: The prevalence of various livestock diseases are foot-and-mouth disease (21%), bluetongue (28%), brucellosis in bovine (17%), brucellosis in caprine (2%), brucellosis in porcine (18%), brucellosis in sheep and goat (3%), babesiosis (6%), theileriosis (26%), porcine reproductive and respiratory syndrome (1%), porcine cysticercosis (6%), classical swine fever (31%), Porcine circovirus (43%), and Peste des petits ruminants (15%). This information helps policymakers to take appropriate measures to reduce the disease burden. Conclusion: This study indicates that the overall prevalence of various livestock diseases in NER of India.
Københavns Universitet General Practitioners' views on the acceptability and applicability of using Quality Indicators as an intervention to reduce unnecessary prescription of antibiotics in four South American countries

As part of the quality improvement program “Health Alliance for Prudent Prescribing, Yield And Use of anti-microbial Drugs In the Treatment of respiratory tract infections” (HAPPY AUDIT) South America, we planned to implement an intervention based on the use of quality indicators as a means to influence General Practitioners’ (GPs) prescribing decision. Knowledge on the acceptability and applicability of an intervention is crucial to decide whether the intervention is suitable and will achieve the expected outcomes. This study explores GPs’ views about the acceptability and applicability of using quality indicators as an intervention to influence their prescribing decision in patients with suspected Respiratory Tract Infections (RTIs) across four countries in South-America. In March 2015, GPs that were participating in HAPPY AUDIT South America were invited to participate in focus groups. A discussion guide covering the domains acceptability and applicability was used. Data was analyzed through systematic text condensation with an inductive approach. 171 GPs were invited and 48% participated. Acceptability ranged from totally acceptable to slightly acceptable. This spectrum of GPs views on acceptability was influenced by themes concerning applicability. In conclusion, there is a positive attitude towards the use of quality indicators. Nonetheless, applicability challenges have to be taken into consideration and solved if we are to achieve a large effect with the implementation of this intervention.
Unsupervised Cross Domain Image Matching with Outlier Detection

This work proposes a method for matching images from different domains in an unsupervised manner, and detecting outlier samples in the target domain at the same time. This matching problem is made difficult by i) the different domain images that are related but under different conditions (e.g. photos of the same location captured in different illuminations), ii) unsupervised settings with paired-image information available only for one of the domains, iii) the existing of outliers that makes the two domains not fully overlap. To this end, we propose an end-to-end architecture that can match cross domain images in an unsupervised manner and handle not fully overlapping domains by outlier detection. Our architecture is composed of three subnetworks, two of which are fed with pairs of source images to learn the ”match” information. The other subnetwork is fed with target images, and works together with the other two subnetworks to learn domain invariant representations of the source samples and the target inlier samples by applying a weighted multi-kernel Maximum Mean Discrepancy (weighted MK-MMD). We propose the weighted MK-MMD, together with an entropy loss, for outlier detection. The entropy loss iteratively outputs the probability of a target sample to be an inlier during training. And the probabilities are used as weights in our weighted MK-MMD for aligning only the target inlier samples with the source samples. Extensive experimental evidence on Office [26] dataset and our proposed datasets Shape, Pitts-CycleGAN shows that the proposed approach yields state-of-the-art cross domain image matching and outlier detection performance on different benchmarks.
Horse-race between decision-making models Quantum Decision Theory and " Classical " counterparts

In this work, we propose another parametrization of Quantum Decision Theory (QDT), based on Rank Dependent Utility Theory (RDU). Using experimental data made of choices between pairs of lotteries, we then compare QDT with "classical" decision theories, RDU and Cumulative Prospect Theory (CPT). At aggregate level, assuming homogeneous preferences across subjects, we find that CPT-based QDT wins by far. At the individual level, we classify decision makers as RDU, CPT or QDT. Our major findings are the following: quantum factor plays a key-role in describing subjects’ behavior; there is a considerable heterogeneity across subjects, so that the classic representative agent approach would be completely wrong for this sample. In light of such results, mixture models are then considered as a possible extension of the present work, in order to take into account potential heterogeneity within a subject himself.
Multiplication of medium-density matrices using TensorFlow on multicore CPUs

Matrix multiplication is an essential part of many applications, such as linear algebra, image processing and machine learning. One platform used in such applications is TensorFlow, which is a machine learning library whose structure is based on dataflow programming paradigm. In this work, a method for multiplication of medium-density matrices on multicore CPUs using TensorFlow platform is proposed. This method, called tbt_matmul, utilizes TensorFlow built-in methods tf.matmul and tf. sparse_matmul. By partitioning each input matrix into four smaller sub-matrices, called tiles, and applying an appropriate multiplication method to each pair depending on their density, the proposed method outperforms the built-in methods for matrices of medium density and matrices of significantly uneven distribution of non-zeros.
Political Stubbornness and Online Local Budget Transparency in Croatia

Online local budget transparency (OLBT) has been recognized as an important feature of good governance. Accordingly, in this paper, OLBT is measured in all 128 cities and a sample of 100 municipalities in Croatia using several key local budget documents published on local government websites. Using a fixed effect Poisson panel model covering the 2013-2017 period, it is shown that along with residents’ income and fiscal capacity of local governments, political ideology and political competition determine the level of OLBT. This paper contributes to the growing body of budget transparency literature by establishing the importance of political factors as determinants of OLBT in this former socialist, fiscally centralized EU member state and reveals the curious stubbornness of the citizens who consistently vote for non-transparent politicians. The main finding is that political factors (political ideology and political competition) matters in determining OLBT, resulting in suboptimal equilibrium of local governments with low levels of OLBT. The local incumbent concludes that OLBT is not a high priority and that his/her constituency will not hold it against him/her. In this environment such a conclusion stands owing to the fact that voters who are * Received: 19-06-2019; accepted: 02-12-2019 1 This work was supported by the Croatian Science Foundation [grant IP-2014-09-3008]. 2 Professor, Senior Research Advisor, Institute of Public Finance, Smičiklasova 21, 10000 Zagreb, Croatia. Scientific affiliation: public sector economics. Phone: +38514886444. E-mail: kott@ ijf.hr. ORCiD: 0000-0003-2242-4181. Website: http://www.ijf.hr/eng/employees/researchers/7/ katarina-ott-phd/1494/ (corresponding author). 3 Assistant Professor, Faculty of Economics and Business, University of Zagreb, Trg J. F. Kennedyja 6, 10000 Zagreb, Croatia. Scientific affiliation: competitiveness analysis, new political economy, political cycles. Phone: +38512383201. E-mail: vmackic@efzg.hr. ORCiD: 0000-0002-32008571. Website: http://www.efzg. unizg.hr/departments/economic-theory/faculty-2831/velibormackic-phd/22082. 4 Senior Research Associate, Institute of Public Finance, Smičiklasova 21, 10000 Zagreb, Croatia. Scientific affiliation: fiscal decentralization and development, tax system, budget transparency. Phone: +38514886444. E-mail: mihaela.bronic@ijf.hr. ORCiD: 0000-00020863-2040. Website: http://www.ijf.hr/eng/employees/researchers/7/mihaela-bronic-phd/150/. Katarina Ott, Velibor Mačkić, Mihaela Bronić • Political Stubbornness and Online. 554 Zb. rad. Ekon. fak. Rij. • 2019 • vol. 37 • no. 2 • 553-585 stubborn in their voting patterns refuse to change the incumbent who created nontransparency.
Cation-Functionalized Maghemite Nanoparticles Towards siRNA- Mediated Gene Silencing

Please note that technical editing may introduce minor changes to the text and/or graphics, which may alter content. The journal’s standard Terms & Conditions and the Ethical guidelines still apply. In no event shall the Royal Society of Chemistry be held responsible for any errors or omissions in this Accepted Manuscript or any consequences arising from the use of any information it contains. Accepted Manuscript Journal of Materials Chemistry B
Dnited States ( CUU / ) Department of Agriculture Forest Service Rocky Mountain Forest and Range Experiment Station

The ecological analysis techniques discussed are useful in models to assess the renewable resources of the nation's forests, rangelands, agricultural lands, and associated waters. Outputs of the techniques are habitat quality, habitat quantity, population occurrence, population quantities, population age structure, population size structure, and population sex structure. Types of analytical techniques included are expert opinion, habitat quality indexes, regression analyses, multivariate analyses, pattern recognition procedure with Bayesian statistics, life table analyses, Leslie matrix analyses, and models of harvest, population dynamics, predator-prey, systems ecology, linear programing, linear equations, difference equations, and differential
An accurate inter-turn short circuit faults model dedicated to induction motors

Safety, disponibility and continuity of industrial systems are major issue in maintenance. In the last decades, these points are the important axes in the field of research. In fact, in many industrial processes research has picked up a fervent place and a particular importance in the area of fault diagnosis of electrical machines, in fact, a fault prognosis has become almost indispensable. The need of a mathematical model of three-phase induction machine, suitable for the simulation of machines behaviour under fault conditions, has received considerable attention. The paper presents a new practical and more precise model for induction motors after introducing inter turn short circuits faults. The proposed model is based on coupled magnetic circuit theory, capable to take into account any electrical asymmetry conditions. To verify the exactitude and the effectiveness of the model, simulation results for induction machine under interturn short circuit fault are presented. In spite of its simplicity, the proposed model is able to provide useful indications for diagnostic purposes. Experimental study is presented at the end of the paper to show that the proposed model predicts the induction machine behavior with a good accuracy.