Miracle-dz/UniFAR-FTUs huggingface dataset:
High performance VLSI adders

The objective of the work is to design and compare high performance and energy efficient VLSI adders for the various bit-level up to 64-bit using advanced CMOS technology. To compare the performance of the adders, recent algorithms of Weinberger, Ling and Manchester carry chain are selected amongst the high performance adders. These three algorithms are chosen because their efficient architectures make minimum dependency on bits over power, energy and delay than other VLSI adders. It has been observed that even though Manchester carry chain adder introduces more delay than other VLSI adders due to its chained architecture, it consumes less power due to its reduced transistor count. Weinberger adder has a parallel architecture with 2-bit conditional sum block that reduces the carry structure and overall computing delay compared to other high performance VLSI adders. Ling adder uses pseudo-carry which reduces number of logic stages compared to Weinberger adder. As a result of reduced logic stages, it takes least time to compute final carry compared to any other VLSI adders. The performance of the VLSI adders is compared based on power, energy consumption and delay. Advanced CMOS technology models (Predictive Technology Models) of 45nm, 32nm, 22nm and 16nm are used for simulation. HSPICE tool is used to simulate the performance of the VLSI adders.
Uncovering the Pathogenic Landscape of Helminth (Opisthorchis viverrini) Infections: A Cross-Sectional Study on Contributions of Physical and Social Environment and Healthcare Interventions

Background Helminth infections have proven recalcitrant to control by chemotherapy in many parts of Southeast Asia and indeed farther afield. This study isolates and examines the influence of different aspects of the physical and social environment, and uneven intervention effort contributing to the pathogenic landscape of human Opisthorchis viverrini infections. Methodology A cross-sectional survey, involving 632 participants, was conducted in four villages in northeast Thailand to examine the impact on prevalence and parasite burden of the reservoir dam environment, socio-economic, demographic, and behavioral factors, and health center intervention efforts. Formalin-ether concentration technique was used for diagnoses, and multivariate models were used for analyses. Principal Findings The importance attributed to O. viverrini infections varied among health centers in the four study villages. Villages where O. viverrini infections were not prioritized by the health centers as the healthcare focus were at a higher risk of infection (prevalence) with odds ratio (risk factor) of 5.73 (3.32–10.27) and p-value < 0.01. Priority of healthcare focus, however, did not appear to influence behavior, as the consumption of raw fish, the main source of O. viverrini infections in the study area, was 11.4% higher in villages that prioritized O. viverrini infections than those that did not (p-value = 0.01). Landscape variation, notably proximity to reservoir, affects vulnerability of local population to infection. Infection intensity was higher in population located closer to the reservoir with risk ratio of 2.09 (1.12–4.02) and p-value < 0.01. Patterns of infection intensities among humans were found to match fish infection intensity, where higher infection intensities were associated with fish obtained from the reservoir waterbody type (p-value = 0.023). Conclusions/Significance This study demonstrated the importance of environmental influence and healthcare focus as risk factors of infections in addition to the socio-economic, demographic, and behavioral factors commonly explored in existing studies. The reservoir was identified as a crucial source to target for opisthorchiasis intervention efforts and the need to consider infection intensity in disease control efforts was highlighted. The holistic approach in this study, which underscores the close relationship between the environment, animals, and humans in development of human infections or diseases, is an important contribution to the framework of One Health approach, where consideration of helminth diseases has largely been overlooked.
Motion Analysis for Image Enhancement: Resolution, Occlusion, and Transparency

Abstract Accurate computation of image motion enables the enhancement of image sequences. In scenes having multiple moving objects the motion computation is performed together with object segmentation by using a unique temporal integration approach. After the motion for the different image regions is computed, these regions can be enhanced by fusing several successive frames covering the same region. Enhancements treated here include improvement of image resolution, filling-in occluded regions, and reconstruction of transparent objects.
Diminishing-returns epistasis among random beneficial mutations in a multicellular fungus

Adaptive evolution ultimately is fuelled by mutations generating novel genetic variation. Non-additivity of fitness effects of mutations (called epistasis) may affect the dynamics and repeatability of adaptation. However, understanding the importance and implications of epistasis is hampered by the observation of substantial variation in patterns of epistasis across empirical studies. Interestingly, some recent studies report increasingly smaller benefits of beneficial mutations once genotypes become better adapted (called diminishing-returns epistasis) in unicellular microbes and single genes. Here, we use Fisher's geometric model (FGM) to generate analytical predictions about the relationship between the effect size of mutations and the extent of epistasis. We then test these predictions using the multicellular fungus Aspergillus nidulans by generating a collection of 108 strains in either a poor or a rich nutrient environment that each carry a beneficial mutation and constructing pairwise combinations using sexual crosses. Our results support the predictions from FGM and indicate negative epistasis among beneficial mutations in both environments, which scale with mutational effect size. Hence, our findings show the importance of diminishing-returns epistasis among beneficial mutations also for a multicellular organism, and suggest that this pattern reflects a generic constraint operating at diverse levels of biological organization.
Does Selective Migration Matter ? EcpGaining Ethnic Disparities in Educational Attainment among Immigrants

Understanding why some national-origin groups excel in school while others do not is an enduring sociological puzzle. This paper examines whether the degree of immigrants’ educational selectivity that is, how immigrants differ educationally from non-migrants in the home country influences educational outcomes amon roups of immigrants’ children. This study uses published internationa f! ata and U.S. Census and Current Population Survey data on 32 immigrant groups to show that as immigrants’ educational selectivity increases, the college attainment of the second generation also increases. Moreover, the more positive selection of Asian immigrants helps explain their second generations’ higher college attendance rates as compared to Europeans, AfroCaribbeans, and Latinos. Thus, the findings suggest that inequalities in relative pre-migration educational attainments among immigrants are often reproduced among the next generation in the United States.
A semantic-based text classification system

This paper presents a system that performs automatic semantic-based text categorization. Using Princeton WordNet, a series of induced methods were implemented that extract semantic features from text and utilize them to decide how similar a document is to different topics. In addition, a bag-of-words method incorporating no knowledge from WordNet is implemented in the system as a basis to compare different WordNet-based approaches. This paper describes the system and reports on a simple analysis performed to evaluate the different implemented methods. At the end, a discussion on the limitations of this study and the future work to optimize the system is presented.
Examining the Relationship Between Level of Participation in Community Gardens and their Multiple Functions

ABSTRACT Participation in community gardens improves health and well-being; however, few studies examine how often individuals need to participate to achieve these outcomes. This study examined the relationship between levels of participation in community gardens and health behaviors, psychological well-being, and perceptions of the community by surveying regular, occasional, and noncommunity garden participants in a disadvantage neighborhood (N = 115). Regular and occasional participates reported better mental health; occasional participants reported more vegetable intake, and regular participants reported more sense of community. Therefore, community practitioners may need to consider level of participation in gardening activities for some outcomes and not others.
Enabling 'Question Answering' in the MBAT Vector Symbolic Architecture by Exploiting Orthogonal Random Matrices

Vector Symbolic Architectures (VSA) are methods designed to enable distributed representation and manipulation of semantically-structured information, such as natural languages. Recently, a new VSA based on multiplication of distributed vectors by random matrices was proposed, this is known as Matrix-Binding-of-Additive-Terms (MBAT). We propose an enhancement that introduces an important additional feature to MBAT: the ability to 'unbind' symbols. We show that our method, which exploits the inherent properties of orthogonal matrices, imparts MBAT with the 'question answering' ability found in other VSAs. We compare our results with another popular VSA that was recently demonstrated to have high utility in brain-inspired machine learning applications.
Creating Classroom Cultures That Foster Reading Motivation.

Gambrell discusses what research and theory suggest about the role of motivation in literacy development. She describes six research-based factors that are related to increased motivation to read. W hat can teachers do to motivate students to read? Here are some responses from elementary age children who were asked what teachers should do to get their students more interested and excited about reading: • " Teachers should let us read more. " • " When we have 'Read and Respond Time' the teacher should let us read our own books and tell about them in a group. " • " Let us read more…about 10 more minutes every day. " • " Please make sure you do not interrupt us while we're reading. " • " Read to the class. I always get excited when I hear my favorite book…and my favorite book is Frog and Toad. " • " Do not let DEAR (Drop Everything And Read) time end so soon. " • " Make sure there are lots of books. There are not a lot of books in our classroom. " • " My teacher gets me interested in reading. She lets me read to her! She gave me a hug because I did so well…and she said, 'Good job! ' " The responses of these children highlight the critical role of the teacher in creating a classroom culture that fosters reading motivation. I have long been convinced that the central and most important goal of reading instruction is to foster the love of reading. My interest in the role of motivation in literacy de
Assessment of parental psychological stress in pediatric cancer: a review.

OBJECTIVES We present an overview of the literature between 1997 and 2007 on parental stress reactions following the diagnosis of childhood cancer and we evaluate methodological strengths and weaknesses of the studies. METHODS PubMed, PsychInfo, and Cinahl databases were used. Sixty-seven were included in the review. RESULTS The conceptualization of parental stress and timing of assessment varies considerably between the studies, which makes comparison difficult. Most emotional stress reactions are seen around the time of diagnosis, with mothers reporting more symptoms than fathers. As a group, parents seem relatively resilient, although a subset of parents reports continuing stress even up to 5 years or more postdiagnosis. CONCLUSIONS The authors recommend clear definitions of parental stress, fixed points in time to assess parental stress, and an approach that highlights both parental strengths and weaknesses. Improved assessment can contribute to tailoring psychological care to those parents most in need.
Beyond the ‘Bayesian Blur’: Predictive processing and the nature of subjective experience

Recent work in cognitive and computational neuroscience depicts the brain as in some (perhaps merely approximate) sense implementing probabilistic inference. This suggests a puzzle. If the processing that enables perceptual experience involves representing or approximating probability distributions, why does experience itself appear univocal and determinate, apparently bearing no traces of those probabilistic roots? In this paper, I canvass a range of responses, including the denial of univocality and determinacy itself. I argue that there is reason to think that it is our conception of perception itself that is flawed. Once we see perception aright, as the slave of action, the puzzlement recedes. Perceptual determinacy reflects only the mundane fact that we are embodied, active, agents who must constantly engage the world they perceptually encounter.
Automatic Language Identification for Persian and Dari texts

We present the first empirical study of distinguishing Persian and Dari texts at the sentence level, using discriminative models. As Dari is a low-resourced language, we developed a corpus of 28k sentences (14k per-language) for this task, and using character and word n-grams, we discriminate them with 96% accuracy using a classifier ensemble. Outof-domain cross-corpus evaluation was conducted to test the discriminative models’ generalizability, achieving 87% accuracy in classifying 79k sentences from the Uppsala Persian Corpus. A feature analysis revealed lexical, morphological and orthographic differences between the two classes. A number of directions for future work are discussed. Keywords-Language Identification; Dialect Identification; Persian; Farsi; Dari;
Recursive weighted partial least squares (rPLS): an efficient variable selection method using PLS

Variable selection is important in fine tuning partial least squares (PLS) regression models. This study introduces a novel variable weighting method for PLS regression where the univariate response variable y is used to guide the variable weighting in a recursive manner—the method is called recursive weighted PLS or just rPLS. The method iteratively reweights the variables using the regression coefficients calculated by PLS. The use of the regression vector to make up the weights is a reasonable idea from the fact that the weights in the regression vector ideally reflect the importance of the variables. In contrast to many other variable selection methods, the rPLS method has the advantage that only one parameter needs to be estimated: the number of latent factors used in the PLS model. The rPLS model has the fascinating output that it, under normal conditions, converges to a very limited number of variables (useful for interpretation), but it will exhibit optimal regression performance before convergence, normally including covarying neighbor variables. This study examines the properties of rPLS by application to a near‐infrared spectroscopy dataset of feed samples predicting the protein content and to a metabolomics dataset modeling a reference metabolic parameter (creatinine) from nuclear magnetic resonance spectra of human urine. Copyright © 2013 John Wiley & Sons, Ltd.
Recovering Low-Rank and Sparse Components of Matrices from Incomplete and Noisy Observations

Many problems can be characterized by the task of recovering the low-rank and sparse components of a given matrix. Recently, it was discovered that this nondeterministic polynomial-time hard (NP-hard) task can be well accomplished, both theoretically and numerically, via heuristically solving a convex relaxation problem where the widely acknowledged nuclear norm and $l_1$ norm are utilized to induce low-rank and sparsity. This paper studies the recovery task in the general settings that only a fraction of entries of the matrix can be observed and the observation is corrupted by both impulsive and Gaussian noise. We show that the resulting model falls into the applicable scope of the classical augmented Lagrangian method. Moreover, the separable structure of the new model enables us to solve the involved subproblems more efficiently by splitting the augmented Lagrangian function. Hence, some splitting numerical algorithms are developed for solving the new recovery model. Some preliminary numerical experiments verify that these augmented-Lagrangian-based splitting algorithms are easily implementable and surprisingly efficient for tackling the new recovery model.
Effects of Different Exercise Modes on the Urinary Metabolic Fingerprint of Men with and without Metabolic Syndrome

Exercise is important in the prevention and treatment of the metabolic syndrome (MetS), a cluster of risk factors that raises morbidity. Metabolomics can facilitate the optimization of exercise prescription. This study aimed to investigate whether the response of the human urinary metabolic fingerprint to exercise depends on the presence of MetS or exercise mode. Twenty-three sedentary men (MetS, n = 9, and Healthy, n = 14) completed four trials: resting, high-intensity interval exercise (HIIE), continuous moderate-intensity exercise (CME), and resistance exercise (RE). Urine samples were collected pre-exercise and at 2, 4, and 24 h for targeted analysis by liquid chromatography-mass spectrometry. Time exerted the strongest differentiating effect, followed by exercise mode and health status. The greatest changes were observed in the first post-exercise samples, with a gradual return to baseline at 24 h. RE caused the greatest responses overall, followed by HIIE, while CME had minimal effect. The metabolic fingerprints of the two groups were separated at 2 h, after HIIE and RE; and at 4 h, after HIIE, with evidence of blunted response to exercise in MetS. Our findings show diverse responses of the urinary metabolic fingerprint to different exercise modes in men with and without metabolic syndrome.
Estimating Structured Vector Autoregressive Models

While considerable advances have been made in estimating high-dimensional structured models from independent data using Lasso-type models, limited progress has been made for settings when the samples are dependent. We consider estimating structured VAR (vector auto-regressive models), where the structure can be captured by any suitable norm, e.g., Lasso, group Lasso, order weighted Lasso, sparse group Lasso, etc. In VAR setting with correlated noise, although there is strong dependence over time and covariates, we establish bounds on the non-asymptotic estimation error of structured VAR parameters. Surprisingly, the estimation error is of the same order as that of the corresponding Lasso-type estimator with independent samples, and the analysis holds for any norm. Our analysis relies on results in generic chaining, sub-exponential martingales, and spectral representation of VAR models. Experimental results on synthetic data with a variety of structures as well as real aviation data are presented, validating theoretical results.
Cohort Profile Cohort Profile : French hospital database on HIV ( FHDH-ANRS CO 4 )

The French Hospital Database on HIV (FHDH) is a hospital-based multicentre open cohort with inclusions ongoing since 1989. The research objectives focus mainly on midand long-term clinical outcomes and therapeutic strategies, as well as severe AIDS and nonAIDS morbidities, and public health issues relative to HIV infection. FHDH also serves to describe HIV-infected patients receiving hospital care in France. FHDH includes data on more than 120 000 HIV-infected patients from 70 French general or university hospitals distributed throughout France. Patients are eligible for inclusion if they are infected by HIV-1 or HIV-2 and give their written informed consent. Standardized variables are collected at each outpatient visit or hospital admission during which a new clinical manifestation is diagnosed, a new treatment is prescribed or a change in biological markers is noted, and/or at least every 6 months. Since its inception, variables collected in FHDH include demographic characteristics, HIV-related biological markers, the date and type of VC The Author 2014; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association 1425 International Journal of Epidemiology, 2014, 1425–1436 doi: 10.1093/ije/dyu002 Advance Access Publication Date: 17 February 2014
The Ability of Discourse Type Recognition At The University Level

This paper attempts to explore the obstacles the students may encounter in recognizing different types of discourse and the consequences these obstacles may have on discourse processing (comprehension). A summary of different types of discourse would be far beyond the theoretical limits of this short study , besides, the number of discourse types is so large and diverse that it needs a whole series of studies to realize every possible type of discourse. So, adopting a specific system of discourse classification is a must in terms of the scope of the current paper. For a system to be feasible, it should involve discourse characteristics that can be related to types of discourse. The classificatory model adopted in this study is based on Cook’s definition and classification of discourse types since it serves the aim of the study. In order to achieve the aim of the study a test is carried out to (50) students in the Department of English/ College of Education/ University of AlQadissiyah. After analyzing the results of the test, it was evident that students did face difficulty in recognizing the discourse types they were supposed to identify, and this led to a significant indication of misunderstanding and incomprehension of the situation and the physical form of the discourse type they are faced with.
Technological Change and Stock Return Volatility: Evidence from Ecommerce Adoptions

This paper is among the first to use a unique controlled empirical setting - traditional firms' adoption of the Internet for commerce - to investigate the impact of changes in firms' technological environment on their stock return volatility. Using three distinct empirical methodologies we detect a significant increase in the idiosyncratic and total stock return volatility when a firm initiates eCommerce. Interestingly, this increase in volatility is observed only for firms that moved online post-June 1998, a period when Internet growth reached critical mass. We find that this increase in volatility is attributable to changes in the firms' product markets, specifically increased demand uncertainty, resulting from the adoption of a new technology-driven channel. We also find that the volatility increase is highest for retailers followed by manufacturers and service firms. Relevant controls rule out firm-specific characteristics as well as market microstructural factors as possible explanatory variables. Our results provide strong evidence of the impact of real activity within a firm on its stock return volatility and highlight the importance of understanding the impact of technological innovations on firms' risk-return profiles.
On the difference between evidence accumulator models and the urgency gating model.

to the editor: In a recent article published in the Journal of Neurophysiology , [Hawkins and colleagues (2015)][1] show that evidence accumulator models (EAMs) and the urgency-gating model (UGM) are mathematically distinguishable in conditions where the sensory information used to make a choice is