Miracle-dz/UniFAR-FTUs huggingface dataset:
Consumption of Fish Products across the Lifespan and Prostate Cancer Risk

Objective To examine whether fish and fish oil consumption across the lifespan is associated with a lower risk of prostate cancer. Design The study was nested among 2268 men aged 67–96 years in the AGES-Reykjavik cohort study. In 2002 to 2006, dietary habits were assessed, for early life, midlife and later life using a validated food frequency questionnaire. Participants were followed for prostate cancer diagnosis and mortality through 2009 via linkage to nationwide cancer- and mortality registers. Adjusting for potential confounders, we used regression models to estimate odds ratios (ORs) and hazard ratios (HRs) for prostate cancer according to fish and fish oil consumption. Results Among the 2268 men, we ascertained 214 prevalent and 133 incident prostate cancer cases, of which 63 had advanced disease. High fish consumption in early- and midlife was not associated with overall or advanced prostate cancer. High intake of salted or smoked fish was associated with a 2-fold increased risk of advanced prostate cancer both in early life (95% CI: 1.08, 3.62) and in later life (95% CI: 1.04, 5.00). Men consuming fish oil in later life had a lower risk of advanced prostate cancer [HR (95%CI): 0.43 (0.19, 0.95)], no association was found for early life or midlife consumption. Conclusions Salted or smoked fish may increase risk of advanced prostate cancer, whereas fish oil consumption may be protective against progression of prostate cancer in elderly men. In a setting with very high fish consumption, no association was found between overall fish consumption in early or midlife and prostate cancer risk.
Semantic and Heuristic Based Approach for Paraphrase Identification

In this paper, we propose a semantic-based paraphrase identification approach. The core concept of this proposal is to identify paraphrases when sentences contain a set of named-entities and common words. The developed approach distinguishes the computation of the semantic similarity of named-entity tokens from the rest of the sentence text. More specifically, this is based on the integration of word semantic similarity derived from WordNet taxonomic relations, and named-entity semantic relatedness inferred from the crowd-sourced knowledge in Wikipedia database. Besides, we improve WordNet similarity measure by nominalizing verbs, adjectives and adverbs with the aid of Categorial Variation database (CatVar). The paraphrase identification system is then evaluated using two different datasets; namely, Microsoft Research Paraphrase Corpus (MSRPC) and TREC-9 Question Variants. Experimental results on the aforementioned datasets show that our system outperforms baselines in the paraphrase identification task.
Classifying Reddit comments by subreddit

Reddit.com is a website that is primarily organized by communities called subreddits, where users can post comments to. As subreddits can have very different cultures, we aim to classify comments by subreddit as a means of sentiment analysis. We use a publicly available reddit comment dataset over the year of 2016 and perform a classification on a selection of 20 subreddits among the top 50 by total comment word counts. We investigate the Recurrent Convolutional Neural Network (CNN) and Attention-Pooled CNN as more advanced models in the classification task. We find that the Recurrent CNN with GRU performs the best out of all the models tested, achieveing an F1 score of 0.53 and a Cohen’s Kappa score of 0.502 . Out of all the subreddits, we find that the Recurrent CNN model gives the highest F1 score on the ”anime” subreddit. Quantitative and qualitative analysis of the Recurrent CNN with GRU suggests that the model is able to reasonably approach the task. Further work suggests that the trained model can also be used to cluster subreddits based on comment similarity.
How to View an MCMC Simulation as a Permutation, with Applications to Parallel Simulation and Improved Importance Sampling

Consider a Markov chain defined on a finite state space, X, that leaves invariant the uniform distribution on X, and whose transition probabilities are integer multiples of 1/Q, for some integer Q. I show how a simulation of n transitions of this chain starting at x0 can be viewed as applying a random permutation on the space X × U, where U = {0,1, . , Q−1}, to the start state (x0, u0), with u0 drawn uniformly from U. This result can be applied to a non-uniform distribution with probabilities that are integer multiples of 1/P, for some integer P, by representing it as the marginal distribution for X from the uniform distribution on a suitably-defined subset of X × Y, where Y = {0,1, . , P −1}. By letting Q, P, and the cardinality of X go to infinity, this result can be generalized to non-rational probabilities and to continuous state spaces, with permutations on a finite space replaced by volume- preserving one-to-one maps from a continuous space to itself. These constructions can be efficiently implemented for chains commonly used in Markov chain Monte Carlo (MCMC) simulations. I present two applications in this context — simulation of K realizations of a chain from K initial states, but with transitions defined by a single stream of random numbers, as may be efficient with a vector processor or multiple processors, and use of MCMC to improve an importance sampling distribution that already has substantial overlap with the distribution of interest. I also discuss the implications of this "permutation MCMC" method regarding the role of randomness in MCMC simulation, and the potential use of non-random and quasi-random numbers.
Transcriptional landscape of DNA repair genes underpins a pan-cancer prognostic signature associated with cell cycle dysregulation and tumor hypoxia

Overactive DNA repair contributes to therapeutic resistance in cancer. However, pan-cancer comparative studies investigating the contribution of all DNA repair genes in cancer progression employing an integrated approach have remained limited. We performed a multicohort retrospective analysis to determine the prognostic significance of 138 DNA repair genes in 16 cancer types (n=16,225). Cox proportional hazards analyses revealed a significant variation in the number of prognostic genes between cancers; 81 genes were prognostic in clear cell renal cell carcinoma while only two genes were prognostic in glioblastoma. We reasoned that genes that were commonly prognostic in highly correlated cancers revealed by Spearman’s correlation analysis could be harnessed as a molecular signature for risk assessment. A 10-gene signature, uniting prognostic genes that were common in highly correlated cancers, was significantly associated with overall survival in patients with clear cell renal cell (P<0.0001), papillary renal cell (P=0.0007), liver (P=0.002), lung (P=0.028), pancreas (P=0.00013) or endometrial (P=0.00063) cancers. Receiver operating characteristic analyses revealed that a combined model of the 10-gene signature and tumor staging outperformed either classifiers when considered alone. Multivariate Cox regression models incorporating additional clinicopathological features revealed that the signature was an independent predictor of overall survival. Tumor hypoxia is associated with adverse outcomes. Consistent across all six cancers, patients with high 10-gene and high hypoxia scores had significantly higher mortality rates compared to those with low 10-gene and low hypoxia scores. Functional enrichment analyses revealed that high mortality rates in patients with high 10-gene scores were attributable to an overproliferation phenotype. Death risk in these patients was further exacerbated by concurrent mutations of a cell cycle checkpoint protein, TP53. The 10-gene signature identified tumors with heightened DNA repair ability. This information has the potential to radically change prognosis through the use of adjuvant DNA repair inhibitors with chemotherapeutic drugs.
A class-based language model for large-vocabulary speech recognition extracted from part-of-speech statistics

A novel approach is presented to class-based language modeling based on part-of-speech statistics. It uses a deterministic word-to-class mapping, which handles words with alternative part-of-speech assignments through the use of ambiguity classes. The predictive power of word-based language models and the generalization capability of class-based language models are combined using both linear interpolation and word-to-class backoff, and both methods are evaluated. Since each word belongs to one precisely ambiguity class, an exact word-to-class backoff model can easily be constructed. Empirical evaluations on large-vocabulary speech-recognition tasks show perplexity improvements and significant reductions in word error-rate.
Federal Reserve Bank of San Francisco Working Paper Series Happiness, Unhappiness, and Suicide: an Empirical Assessment Happiness, Unhappiness, and Suicide: an Empirical Assessment

The views in this paper are solely the responsibility of the authors and should not be interpreted as reflecting the views of the Federal Reserve Bank of San Francisco or the Board of Governors of the Federal Reserve System. Abstract The use of subjective well-being (SWB) data for investigating the nature of individual preferences has increased tremendously in recent years. There has been much debate about the cross-sectional and time-series patterns found in these data, particularly with respect to the relationship between SWB and relative status. Part of this debate concerns how well SWB data measures true utility or preferences. In a recent paper, Daly, Wilson, and Johnson (2007) propose using data on suicide as a revealed preference (outcome-based) measure of well-being and find strong evidence that reference-group income negatively affects suicide risk. In this paper, we compare and contrast the empirical patterns of SWB and suicide data. We find that the two have very little in common in aggregate data (time series and cross-sectional), but have a strikingly strong relationship in terms of their determinants in individual-level, multivariate regressions. This latter result cross-validates suicide and SWB micro data as useful and complementary indicators of latent utility.
Interleaved Landsman Converter Fed EV Battery Charger with Power Factor Correction

harger followed by a flyback converter, is designed and evaluated to develop a low cost charging solution for the EV battery. A built-in wave-shaping feature is obtained at the front-end of the charger due to power factor correction (PFC) characteristics of the interleaved converter. The converter configuration is developed using two Landsman converter cells, which are designed in DCM mode. The discontinuous conduction mode operation of front-end converter along with the interleaved technology, offers the advantage of zero current switching, reduced sensor used at the output as well as reduced input current and output voltage ripple for battery charger. However, the flyback converter provides the current control during the charging of battery in constant current (CC) and constant voltage (CV) regions. The proposed charger is evaluated for improved PF based performance under wide input voltage variations. The harmonics spectrum of mains current follows the recommended PQ (Power Quality) regulations such as IEC 61000-3-2 standard.
British English pronunciation preferences: a changing scene

A poll of BrE pronunciation preferences was carried out in late 1998, based on a self-selected sample of nearly 2000 ‘speech-conscious’ respondents, who answered a hundred questions about words of uncertain or controversial pronunciation. The findings allow us to answer questions about lexical incidence and sound changes in progress. (This is a revised and considerably extended version of the report published as Wells 1999.)
Knowledge-Aided Target Detection for Multistatic Passive Radar

This paper studies the detection problem for multistatic passive radar. We consider scenarios where prior knowledge about the spectrum and peak-to-average ratio (PAR) of the non-cooperative illuminators of opportunity (IOs) is available. We develop several knowledge-aided (KA) detectors within the framework of the generalized likelihood ratio test (GLRT) to exploit such prior knowledge. Particularly, the knowledge about the bandwidth of the transmitted signal is employed to suppress the out-of-band noise, and the knowledge about the PAR constraint is exploited to eliminate the remaining high-power noise. The challenge of unknown spectrum condition is also addressed, where block sparse Bayesian learning (BSBL) is exploited to derive the maximum-likelihood estimates (MLEs) of the unknown, temporally correlated signal. The numerical results indicate that the proposed KA detectors offer significant performance improvements compared with the traditional detectors, which do not exploit such prior information.
Betwixt and Between: The Role of Psychosocial Factors in the Early Stages of Desistance

This article presents the results of an investigation into the psychosocial changes involved in the early stages of desistance, which to date have attracted little empirical or theoretical attention. The study used a mixed-methods design to produce a nuanced account of the shifts that occur in cognitions and social circumstances as offenders make the transition to desistance. Three psychometric instruments were completed by 73 adult males aged 18 to 35 who had acquired at least two previous convictions and were living in Dublin, Ireland. Participants who reported no offending in the past month were defined as ‘‘primary desisters. ’’ In a regression analysis, age, age at onset of offending, and criminal thinking styles emerged as important predictors of primary desistance, whereas social circumstances and pro-criminal attitudes did not. The theoretical and practical implications of these findings are discussed.
Active Power Filter for Power Quality in Grid Connected PV-System using an Improved Fuzzy Logic Control MPPT

Photovoltaic (PV) system is one of the most important and fastest developing renewable energy sources in the globe recently. Since most of the grids contain a power converter system based on power electronic components and several nonlinear loads, which deteriorate power quality. In this paper, the aim is to propose a solution to solve this problem using a shunt active filter SAPF based on efficiency method called Synchronous Detection Method SDM, in the identification of the harmonic currents. The SAPF controller consists of an inverter which works as a multi-functional device, it adopted to interface PV system with the electrical grid and at the same time, to eliminate harmonics generated by nonlinear loads with reactive power compensation. To extract the maximum power in photovoltaic system a fuzzy logic control is proposed to solve a fast irradiation change problem which is compared to the conventional P&O algorithm. The simulation results using Matlab/Simulink show that the fuzzy logic control MPPT gives the best performance with small oscillation in output power, at the same time. The proposed control strategy of SAPF performs the power quality in grid-connected photovoltaic system with smaller total harmonic distortion, grid synchronization and unity power factor.
Millimeter Wave Performance Evaluation in Open Areas and Suburban Putrajaya

The demand for multi-gigabit data rate and insufficient spectrum in the microwave bands has inspire the use of millimeter wave bands in future fifth generation (5G) networks. In this paper, a 3D ray tracing model is used to analyse the statistical parameters for a millimeter wave channel operating at different carrier frequencies. The parameter consist of RMS delay spread, K-Factor and average received power. A map of Precint 9, Wilayah Persekutuan Putrajaya is used in the simulation of cellular implementation in millimeter wave. The effect of different antenna element geometries have also been investigated. The results of this work can be used as a basis to design a millimeter wave network to improve user quality of experience.
An Improved Control Strategy for DFIG Wind Turbine to Ride-Through Voltage Dips

This paper suggests a control strategy to improve the ride-through capability of doubly fed induction generator (DFIG) based wind turbine connected to the grid. In order to provide a suitable control for DFIG under grid disturbances such as voltage dip, a nonlinear model of DFIG which takes the stator flux dynamics into consideration is developed. At first, a feedback control law based on input output feedback linearization (IOFL) is used to transform the nonlinear model of DFIG into two decoupled systems in the canonical linear form. Then, PI controller is designed to control the rotor currents. The performance of the proposed control strategy under voltage dip is tested through simulation of a 1.5 MW DFIG and compared with conventional vector control
Dimensions of achievement motivation in schoolwork and sport.

Both sport and academic work play large roles in school life, yet there is little comparativeevidence on the nature or generality of achievement motivation across these domains. In thisstudy, beliefs about the causes of success in school and sport of 207 high school students werefound to be related in a logical fashion to their personal goals. The ego-involved goal of superioritywas associated with the belief that success requires high ability, whereas task orientation (the goalof gaining knowledge) was associated with beliefs that success requires interest, effort, andcollaboration with peers. These goal-belief dimensions, or theories about success, cut across sportand schoolwork. However, little cross-domain generality was found for perceptions of ability andintrinsic satisfaction. Intrinsic satisfaction in sport primarily related to perceived ability in thatsetting. Task orientation, not perceived ability, was the major predictor of satisfaction inschoolwork.
EFFECT OF A NEW COMPOUND ON THE MITOTIC POLYPLOIDIZATION OF LILIUM LONGIFLORUM AND ORIENTAL HYBRID LILIES

A new compound, Surflan (Surflan), for mitotic polyploidization in vitro in lily was tested and compared with the original compound ‘oryzalin’. Surflan was effective for mitotic polyploidization of lilies. The optimal concentration of Surflan was 0.0075% for in vitro chromosome doubling. A low concentration (0.0025%) was still effective to produce polyploid plants, however, a high concentration (0.0125%) showed phytotoxicity for plant regeneration although the frequency of polyploid plants was high. In the 0.0075% Surflan treatment, the percentage of polyploids was 86, 68 and 100% in L. longiflorum ‘White Fox’, Oriental hybrid ‘Le Reve’ and ‘Sorbonne’, respectively. These are similar effects to those in oryzalin treatments. Thus, in vitro chromosome doubling by using Surflan was successful in lilies, and may be considered as an alternative reagent of oryzalin.
Distributed Multi-Task Learning with Shared Representation

We study the problem of distributed multi-task learning with shared representation, where each machine aims to learn a separate, but related, task in an unknown shared low-dimensional subspaces, i.e. when the predictor matrix has low rank. We consider a setting where each task is handled by a different machine, with samples for the task available locally on the machine, and study communication-efficient methods for exploiting the shared structure.
Curriculum leadership in action: A tale of four community college heads of department leading a curriculum development project

Abstract College Heads of Department (HoDs) are increasingly expected to perform more curriculum-leadership tasks, maintaining and advancing the department curriculum, especially in developing countries. However, in practice, HoDs are reported to pay little attention to this aspect of their job due to several factors—one of which is a lack of professional support to play this role. This case study investigated how four HoDs of a community college perceived and enacted curriculum leadership during a curriculum-development project after receiving relevant training and assistance. The findings showed that the HoDs valued the relevance and usefulness of the professional support received, which made them realize the significance of their role as curriculum leaders. The findings also indicate that the ways the HoDs enacted the curriculum-leadership tasks and the challenges they encountered varied based on several factors. The conclusion drawn is that the support HoDs need and the challenges they encounter in assuming active curriculum leadership suggest their need for three professional competencies and the need for supportive organizational structures.
Evaluation of Dynamic Optical Service Restoration on a Large-Scale ROADM Mesh Network

We examine dynamic optical service restoration on a large-scale ROADM network operated by China Telecom in the middle and lower Yangtze River region of China. A WSON-based GMPLS/ ASON control plane, deployed across the network, drives execution of restoration operations. A network survivability analysis (NSA) tool is used for simulation-based assessment of link failure impacts and expected service restoration performance. Evolutions in wavelength utilization, when operating dynamic restoration on a large set of services and over a range of concurrent fiber cut numbers and placements across the network, are examined in detail through NSA-based analysis. The results are partly verified through live network experiments conducted during network commissioning operations. Observations of other restoration performance aspects, such as service times to restore, are also discussed. We show that with dynamic restoration, full service restorability can remain achievable even in the face of four or more concurrent fiber cuts. We describe how findings from NSA-based analyses may be used to influence network planning or to partially prescribe control plane restoration actions to improve service times to restore, and reduce restoration conflicts and failures. We posit that the best such improvements may be available when NSA is operated in concert with centralized path computation applications (e.g., PCE and T-SDN controllers).
Facial Expression Recognition: A Review of Methods, Performances and Limitations

Facial expression is one of the profound nonverbal channels through which human emotion state is communicated, its automation involves analysis and recognition of facial features. Facial Expression Recognition (FER) is categorized as behavioral biometrics, and also applicable in the field of computer vision and human computer interaction. Variations in the nature of the images to be processed; head pose, image background, light intensity and occlusion are some of the sources of the challenges with facial expression recognition system. Achieving a robust automatic facial expression recognition system invariant to the aforementioned challenges, is the goal of this research area. This paper presents an analysis of major feature extraction and classification methods, their performances in terms of accuracy and their respective limitations.