Miracle-dz/UniFAR-FTUs huggingface dataset:
Convolutive Non-Negative Matrix Factorization for CQT Transform using Itakura-Saito Divergence

This paper proposes a modification of the Nonnegative Matrix Factorization (NMF) in a single-channel audio source separation problem. NMF is widely used in such problem because of its easy implementation and parts-based separation properties. However, the original NMF uses Short Time Fourier Transform (STFT) as a spectral representation of the data, which has matrix representation, and it does not support data at nonregular grid, such as Constant-Q Transform (CQT). CQT has a strong appeal in audio processing because it approximates the human auditory system in a reasonably way. Besides to usage of CQT as spectral representation, this paper presents a convolutive NMF approach using Itakura-Saito divergence (ISD) to work with irregularly-sampled data, here defined as NRCNMF-IS. The scale invariance property of ISD is interesting for audio applications. The NRCNMF-IS was tested and compared with its matricial version. Utilizing performance metrics, the statistical results show that the use of CQT as spectral representation yields better results than the STFT representation. Keywords— Non-negative Matrix Factorization, Monaural separation, Blind source separation, Constant-Q Transform, Itakura-Saito.
ScienceDirect The EPSPS Pro 106 Ser substitution solely accounts for glyphosate resistance in a goosegrass ( Eleusine indica ) population from Tennessee , United States

Previous studies have documented the occurrence of glyphosate-resistant (GR) goosegrass (Eleusine indica (L.) Gaertn. ) and, in at least some cases, resistance is due to an altered target site. Research was performed to determine if an altered target site was responsible for GR in a Tennessee, United States goosegrass population (TennGR). DNA sequencing revealed a mutation in TennGR plants conferring the Pro106Ser 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) substitution previously identified in other GR populations. F2 populations were derived from TennGR plants crossed with plants from a glyphosate-susceptible population (TennGS) and analyzed for their response to glyphosate and genotyped at the EPSPS locus. Plants from the F2 populations segregated 1:2:1 sensitive:intermediate:resistant in response to a selective dose of glyphosate, and these responses co-segregated with the EPSPS genotypes (PP106, PS106, and SS106). To separately investigate the effect of the Pro106Ser substitution on GR, glyphosate dose-response curves and 50% effective dose (ED50) values were compared among the three genotypes and the two parental populations. The SS106 genotype was 3.4-fold resistant relative to the PP106 genotype, identical to the resistance level obtained when comparing the resistant and susceptible parental populations. We conclude that the mutation conferring a Pro106Ser EPSPS mutation is solely responsible for GR in the TennGR goosegrass population.
EVALUATION OF LOW LEVEL LASER THERAPY ON THE STABILITY OF IMPLANTS USING RESONANCE FREQUENCY ANALYSIS (CLINICAL STUDY)

INTRODUCTION: The implant supported overdentures are considered a very successful treatment option for edentulous patients. Low level laser therapy (LLLT) has gained greater awareness in the last decade for implant surgery, reduces postoperative pain after surgery, promotes the osseointegration of implants, particularly, improving stability and enhances new bone formation without causing any tissue destruction. OBJECTIVES: : The aim of this study was the effect of low-level laser therapy on the biostimulation of bone repair by enhancing or accelerating osseointegration through stimulating the expression of osteoblastic phenotype in cells cultured on Titanium specimens. MATERIALS AND METHODS: 10 patients with age ranged between 45-60 years old were involved in the study. Each patient received 2 implants in the mandibular edentulous ridge at the canine area. The right side acted as study group has received one implant with a Semiconductor diode LASER (type IV) application, while the left acted as control one of same patients having normal loading in left mandibular canine area without LLLT application. All implants osseointegration was assessed by Magnetic Resonance Frequency Analysis to evaluate the implant stability. RESULTS Resonance Frequency Analysis evaluation revealed great difference in the stability after three months when irradiated with LLLT, implant stability quotient changes were found to be statistically significant between the two studied groups. (P2= <0.001). CONCLUSIONS: Effect of LLLT on bone remodeling is evident and improves implant stability.
EFFECT OF TAI CHI CHUAN ON FEAR OF FALLING , BALANCE AND PHYSICAL SELF-PERCEPTION IN ELDERLY : A RANDOMISED CONTROLLED

YAŞLILARA UYGULANAN TAI CHI CHUAN EGZERSİZİNIN DÜŞME KORKUSU, DENGE VE FİZİKSEL BENLİK ALGISI ÜZERINE ETKİSİ: RANDOMİZE KONTROLLÜ ÇALIŞMA Introduction: This study was aimed to examine the effects of Tai Chi Chuan on the fear of falling, balance, and physical self-perception in the elderly. Materials and Method: This controlled trial using blocked randomization was stratified by sex, age, and history of falls and conducted with 44 elderly participants, of which 20 underwent Tai Chi Chuan training as experimental group and 24 served as an untrained control group. The experimental group underwent Tai Chi Chuan training for 12 weeks, twice a week, for 35-40 min/day under a research team member. The control group received no intervention. Data were collected from both groups by using a personal information form, the Modified Falls Efficacy Scale, the Physical Self-Perception Questionnaire, and the Tinetti Balance and Gait Assessment. After the exercise practice, each group was re-subjected to the same scales. Results: Student’s t-test and paired sample t-tests were used to analyze the data. The balance and physical self-perception of individuals in the Tai Chi Chuan group significantly improved compared with that in the control group (p<0.001). Additionally, individuals in the Tai Chi Chuan group demonstrated reduced fear of falling (p<0.001). Conclusion: Tai Chi Chuan positively affects balance and physical self-perception, and reduced fear of falling in elderly individuals. As a result, the potential application of Tai Chi Chuan was suggested in various settings where elderly individuals inhabit.
An Electronic Nose Based Beverage Identification Using an Improved Fisher Discriminate Analysis Method

Electronic noses are widely employed in food industry and play an important role in beverage identifications. Meanwhile, fisher discriminate analysis is considered as one of convenient pattern recognition methodologies for electronic noses. Traditional fisher discriminate analysis (FDA) is a kind of binary classifiers. In other words, it is not competent to multi-class classifications which are common in daily concerns. To solve the limitation of the binary classifier, we presented a FDA based classification method, namely encoding fisher discriminate analysis (EFDA), which can be applied to multi-class recognition. In this paper, seven kinds of beverages including Chinese liquor, beer, wine, green tea, black tea, oolong tea and pu’er tea were prepared and tested objectively by an electronic nose (E-nose) system with 32 gas sensors. The E-nose system was a prototype for general detection of complicated gaseous compounds . It performed all the experiments and tested the volatile compounds of seven beverages. The experimental results indicated that the proposed method were more effective than conventional pattern recognition methods such as principal component analysis (PCA) and k-nearest neighbor (k-NN). The highest recognition rate could reach 96.83% by EFDA. The outcomes achieved demonstrated that EFDA can be successfully used in multi-class identifications.
Programming hybrid systems with implicit memory based synchronization

In the last years CPU performance increases came with an increase in software development complexity. One of the next big changes in CPU architecture may be so-called hybrid multicore chips, which combine both multicore and manycore technologies on the same chip. Unfortunately, this increase in performance again may lead to an increase in development complexity. In this paper we suggest a dataflow driven programming model for hybrid multicore CPUs able to decrease complexity. We designed our model to work efficiently on non cache-coherent systems and in NUMA scenarios, which may both be issues exposed from the hardware in the near future. Our model is based on single assignment memory combined with PGAS-like explicit placement of data. Reading uninitialized memory results in the reading thread to be blocked until data is made available by another thread. That way threads can easily work in parallel and fine grained synchronization is done implicitly. Single assignment memory removes any cache coherency issues, whereas explicit data placement easily copes with NUMA effects. We implemented our model in a proof of concept C++ library for CUDA and x86 architectures, and use the scan algorithm to demonstrate that it is fairly easy to coordinate two GPUs and multiple CPU cores.
CEO overconfidence , compensation and extreme innovation outcomes *

Successful innovations, as much as failed innovations, significantly impact firm value and survival. While prior literature has focused on corporate, or industry, factors that can drive positive innovation outcomes, there is little evidence relating to the executive level, and behavioral, factors that can drive them. We focus on two such factors: Overconfident CEOs and CEO compensation. We argue that overconfident CEOs overestimate investments’ returns and underestimate their risks. Similarly, option based CEO compensation drives CEOs towards more risky investments. Thus, we hypothesize and show that overconfident CEOs and CEO compensation are both positively related to extreme innovation outcomes, such as innovation breakthroughs and innovation failures. We also hypothesize on the joint effect of overconfident CEOs and CEO compensation on extreme innovation outcomes, but here the hypotheses are not supported. CEO overconfidence, compensation and extreme innovation outcomes Jan Hohberger University of Technology Sydney & Mark Humphery-Jenner UNSW Business School This Version: May 2018 * Mark Humphery-Jenner receives funding from the Australian Research Council grant DE150100895. We thank George Chondrakis and Ljubomir Pupovac for their valuable feedback on an earlier version of this paper.
THE COMFORTS AND DISCOMFORTS OF USING QUANTITATIVE TESTS IN DEFINING MARKETS WITH COMPLEX COMPETITIVE DYNAMICS : A REVIEW OF EVIDENCE FROM A COMPLAINT AND TWO MERGERS

The relevant market is defined by considering the response of customers to a SSNIP (normally taken as 5 – 10%) on the candidate product(s) or region(s). A number of commonly employed tools seek to aid in this analysis including the analysis of transport costs, the degree of inter-regional trade flows, natural experiments and differences in the level and direction of price movements in different regions. Whilst in some settings quantitative tools (especially price tests) can be informative, their blind application without regard for market dynamics at play can be misleading. Indeed a major criticism of the use of price tests for market definition has been that they are a fairly blunt tool, incapable of dealing robustly with real-world complexities and therefore providing quite limited assistance to the competition analyst. This paper seeks to illustrate, through discussion of three pertinent cases – the case against several dairy processors and the MTO/Boskor and Tsogo Sun/Gold Reef mergers – the dangers of relying too heavily on a quantitative analysis of geographic markets when there are complex competitive dynamics at play. In particular, the cases highlight that when there are concerns of existing anti-competitive conduct, the use of traditional tests may not be appropriate.
Does Allelopathy Play a Role in Suppression of Mugwort (Artemisia vulgaris) by Alfalfa?

Abstract Alfalfa suppresses mugwort growth in the field. In the present study, the allelopathic effect of alfalfa on mugwort was examined using the above-ground (AGAB) and under-ground alfalfa biomasses (UGAB), and their water extracts. The sprouting of mugwort rhizomes or seed germination, growth of seedlings and leaf color changes in mugwort seedlings were evaluated in pot experiments. AGAB mixed into soil highly inhibited sprouting of mugwort rhizome fragments and growth of seedlings. The inhibitory effect was increased, and gradual leaf discoloration (yellowing) was observed in response to a higher dose of AGAB. However, UGAB affected neither sprouting of mugwort rhizome fragments nor growth of seedlings. The growth of mugwort seedlings was not significantly affected by water extracts of either AGAB or UGAB applied after emergence. The seed germination and seedling growth were significantly reduced by water extracts of AGAB and UGAB in the petri dishes experiments. These results indicated that the allelopathic effect of alfalfa along with the competitive ability and harvesting regime may play an important role in suppressing mugwort growth.
Utilizing Posterior Probability for Race-composite Age Estimation : Early Applications to the MORPH-II database *

This paper introduces a global framework for age estimation that utilizes posterior probabilities from the race classification step to compute a race-composite age estimate. The first half of the paper is dedicated to summarizing the cleaning and image pre-processing stages of the proposed pipeline as they were applied to the MORPH-II dataset. In the second half, there is a more in-depth discussion of the novel components of this paper, including methodology, experiments, and some preliminary results.
Navigating Tonal Space

The question of how tonal structures in music are perceived and represented by the human mind has been approached by multiple disciplines, primarily music theory and cognitive psychology, and more recently, neuroscience. A parsimonious model of tonal space as the surface of a torus has emerged from various types of theoretical considerations and empirical data. Here I provide a brief overview of different variants of a very data-driven approach to modeling tonal space based on self-organizing maps (SOMs), focusing primarily on an ecologically inspired model (Leman and Carreras 1997) that allows one to project any desired auditory stimulus to the toroidal surface. I illustrate this with examples of the tonal trajectories charted by short chord progressions. Tonal Theory for the Digital Age (Computing in Musicology 15, 2007), 39–50.
Interpreting Microseismic Data from Hydraulic Fracturing : Understanding Subsurface Deformation Through Numerical Modeling

This article is a summary of the results presented in the PhD Thesis written by Boroumand (2016). The developments made in the research sought to find an answer to the question “why and how did the hydraulic fracture(s) grow in a particular direction? ” It makes specific use of microseismic observations to calibrate measured hydraulic fracture geometries in time and space to modeled dimensions so that a geomechanical interpretation of the deformation process could be made. First, section 1.1 looks at the energy considerations associated with hydraulic fracturing with emphasis on correcting for radiated seismic energy. While section 1.2 presents a novel approach in which microseismic locations are incorporated into an energy-based numerical model. Finally, section 1.3 describes how an iteratively coupled three dimensional (3D) stress and flow numerical scheme can be used to explain the microseismic observations and deformation behavior.
1 INTERNAL CONTROL SYSTEM AND MANAGEMENT INFORMATION SYSTEMS

Rapid changes in information technology and managerial practices in many organizations were forcing efficient internal audit as a tool for reducing the total risk. Simultaneously, information has become one of the most valuable assets of the corporation, and must be protected with care and concern, because business continuity and success are heavily dependent upon the integrity and continued availability of critical information. Within this framework of extremely fluid business environment, the objective of this research is to explore the existence and adequacy of internal control system in management information systems. The results point out that internal auditors pay attention to “traditional” risks that are associated with information systems’ security, while they should take into consideration risks and controls associated with the other units, in order to make information system audit’s function more effective, improving thereby the internal audit’s operation.
ROLE OF BELIEF ABOUT AND ADHERENCE TO MEDICATION ON SYMPTOM SEVERITY AND QUALITY OF LIFE OF PATIENTS SUFFERING FROM MAJOR DEPRESSIVE DISORDER RESIDING AT URBAN AND RURAL AREAS OF WEST BENGAL

It has been found that in both urban and rural areas belief about medication and adherence to medication have influenced severity of depression and quality of life. Necessity belief about psychotropic medication is lower in MDD patients residing at the urban area in comparison to rural. Necessity belief has also affected the level of adherence to medication, severity level 1Psychologist in Service Program Unit (Department of Applied Psychology, University of Calcutta) 2Consultant Psychiatrist, Kolkata 3Assistant Professor, Department of Psychology, University of Calcutta 4Professor, Department of Applied Psychology, University of Calcutta ROLE OF BELIEF ABOUT AND ADHERENCE TO MEDICATION ON SYMPTOM SEVERITY AND QUALITY OF LIFE OF PATIENTS SUFFERING FROM MAJOR DEPRESSIVE DISORDER RESIDING AT URBAN AND RURAL AREAS OF WEST BENGAL
gSpan-H: An Iterative MapReduce Based Frequent Subgraph Mining Algorithm

In data mining applications, mining frequent subgraph from a large number of small graphs is an important operation. For extracting frequent subgraphs many algorithms have been proposed. But now a days, graph data grows both in size and quantity, therefore existing methods cannot extract frequent subgraph on a centralized machine. To overcome this some distributed solution using MapReduce is becoming important paradigm for computation on massive data. In experimented work, we investigate how to efficiently perform extraction of frequent subgraph over a large datasets using MapReduce. We propose a frequent subgraph algorithm called as gSpan-H which is iterative MapReduce based framework. This algorithm uses breadth first search strategy. This algorithm is isomorphism testing free approach for efficiently mine frequent subgraph. Our experiments with real life and large synthetic datasets validate the effectiveness of gSpan-H for mining frequent subgraphs from large distributed datasets.
PHYSIOLOGICAL DISORDERS OF LEAVES OF CHRYSANTHEMUM CULTIVARS RELATIVE TO ACCUMULATION OF EXCESS CARBOHYDRATE

Nineteen chrysanthemum cultivars, including both pompon and standard types* were grown in a saran cloth house in the spring season to observe differential foliar reactions associated with the accumulation of excess photosynthate (carbohy drate) or the "excess photosynthate syndrome" (EPS). The overall condition including bronzing, leaf roll, necrosis, thickening, and yellowing are collectively described as EPS. Standard cultivars exhibited a greater degree of development of EPS than did pompons. Total indexes of EPS were statistically correlated with glucose, sucrose, total sugars, and total carbohydrates in leaves, signifi cant at the 1% level. Starch content of leaves was significantly correlated at the 5% level while the correlation coefficient for fructose was not signifi cant. Cut flower stems, held in sucrose (0, 2, 4 and*8%) solutions containing 200 ppm 8-hydroxyquinoline citrate for the development of EPS, pro vided direct evidence of a cause-effect relationship. Increasing levels of sucrose caused increasing yel lowing, thickening of leaves, and increasing anthoFlorida Agricultural Experiment Stations Journal Series No. 4127. cyanin content. Leaf roll and dry weight of leaf tissue increased with increasing sucrose. Chloro phyll content decreased with increasing sucrose. Stems held in 0% sucrose did not develop EPS symptoms while those held in 2, 4 and 8% de veloped most of the symptoms in proportion to
SONBOL’S ARABIC ROOT EXTRACTION ALGORITHM

Root extraction is an important primary process in most Arabic applications, such as information retrieval systems, text mining, text classifiers, question answering systems, data compression, indexes, spelling checkers, text summarization and machine translation. Any weaknesses of root extraction will affect negatively the performance of these applications. Sonbol’s Arabic root extraction algorithm achieves high accuracy of performance and gives new classification for Arabic’s letters which minimizes the affix ambiguity. The comparison and testing of the existing Arabic root extraction algorithms on unify datasets shows that they still need some enhancements. Arabic root extraction is mainly based on using patterns, where as much as the algorithm has patterns as much as the accuracy is better. In this study, we improve Sonbol’s Arabic root extraction algorithm, by enhancing its rules and increasing its patterns. We use 4320 patterns to extract the roots, which is the largest patterns’ list extracted by Thalji’s corpus. We test the new algorithm on Thalji’s corpus that contains 720,000 word-root pairs. This corpus is mainly built to test and compare Arabic root extraction algorithms. The new algorithm is compared with Sonbol’s Arabic root extraction algorithm. The algorithm of Sonbol et al. achieves an accuracy of 68%, whereas the new algorithm achieves an accuracy of 92%.
Prediction of Heart Diseases Risk through Frequent Itemsets in Data Mining

According to recent survey by WHO organisation 17.5 million people dead each year. It will increase up to 75 million in the year 2030. Data mining is process which is used to analyses number of data sets and then extracts the meaning of data. Huge amount of data is generated for the prediction of heart diseases. Data mining provides methods and techniques for transformation of the data into useful information for decision making. These techniques can make process fast and take less time to predict the heart disease with more accuracy. Based on the chosen symptoms and minimum support value frequent itemsets are generated. The extracted frequent itemsets help the medical practitioner to make diagnostic decisions and determine the risk level of patients at an early stage.