Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Study of Time Delay Neural Networks and Hidden Markov Models for Electroencephalographic Signal Classification

In this paper, we analyze the performance of Time Delay Neural Networks (TDNN) and Hidden Markov Models (HMM) for Electroencephalogram (EEG) signal classification. The specific focus of this study is Brain-Computer Interfacing (BCI), where near-real time detection of mental commands during a multi-channel EEG recording is desired. We argue that HMM and TDNN should be preferred over the rigid, one-size-fits-all methods of the more traditional EEG signal classifiers. To analyze the utility of modern classification methods for BCI, we compare and discuss the performance of our suggested TDNN and HMM EEG classifiers with the reported best results on BCI 2003 EEG benchmark dataset Ia. INTRODUCTION The goal of Brain Computer Interfaces (BCI), also known as Thought Translation Devices (TTD), is to provide individuals with a new communication channel that conveys commands directly from the brain (Wolpaw 2002). Other areas that may benefit from this area of research include diagnosis of memory problems, cognitive development of the children (Taylor and Baldeweg 2002), diagnosis and treatment of attention-deficit disorder, and many other medical conditions that need tools for classification of brain’s electrophysiological activities. Classification of Electroencephalogram (EEG) is an important part of current BCI research. Using EEG-based BCI is arguably superior to other related modalities such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), since the brain signals of interest happen at a rate that is only within the temporal resolution of EEG. Moreover, compared to PET and fMRI, EEG is convenient, portable, and affordable. In this paper we examine the application of two nonlinear intelligent signal analysis tools to non-invasively collected EEG signals in order to classify the spatiotemporal signatures of imagined commands; as while the researchers in neurobiology have mostly been utilizing linear discriminant for BCIs, one can argue that such approach is mathematically justifiable only if the dimension of the feature space is high enough. Many attempts have been made to build an EEG-based BCI system (Wolpaw 2002). Generally speaking, the most important steps of these BCI systems are feature extraction and classification. For feature extraction, adaptive auto regressive models, Hjorth parameters, power spectrum, and principle component features have been used (Obermaier 2001, Keirn and Aunon 1990). Various classifiers have also been used,
The effects of a system-wide mentoring program on beginning teacher retention rates

The purpose of this evaluation is to determine how a systemwide mentoring program affects new teacher retention rates. There are several different types of mentoring programs in the world of education that could be used to support growth in new teachers. The school system being evaluated has recently changed from having schoolbased mentors to having systemwide fulltime mentors. These mentors work with new teachers sometimes as much as once a week in order to promote growth and provide support to the beginning teachers. In this transition year, the school system provides first year teachers with a fulltime, systemwide mentor. The second year teachers continue to have schoolbased mentors. This study looks at the differences between the two types of programs and evaluates their effectiveness. This study also looks at how the two different types of mentoring programs affect new teacher retention rates. The results of the study show that systemwide mentors proved to be more effective and helpful to the beginning teachers. However, the study also shows that the preliminary results do not prove that systemwide mentoring programs increase the average retention rate. There are several other factors besides mentoring that leads to teacher turnover, many of which are discussed in this study.
Unsupervised anchor space generation for similarity measurement of general audio

Reliably measuring similarity between audio clips is critical to many applications. As opposed to the conventional way of measuring audio similarity using low-level features directly, in this paper we consider the similarity computation using an anchor space. Each dimension of such a space corresponds to a semantic category (anchor). Mapping an audio clip onto this space results in a vector, which indicates the membership probability of this audio clip with respect to each semantic category. The more similar the mappings of two audio clips, the more similar they are. While an anchor space is typically generated in a supervised fashion, supervised approach is infeasible in many realistic scenarios where audio content semantics is too diverse or simply unknown a priori. We therefore propose an unsupervised approach to anchor space generation. There, spectral clustering is employed to cluster the audio clips with similar low-level features and then the obtained clusters are adopted as semantic categories. Using this semantic space for audio similarity computation shows a considerable accuracy improvement (7% on mAP) in an audio retrieval system, compared with the conventional low-level feature based approach.
DSSimulator : Achieving Million Node Simulation of Distributed Systems

Simulation of distributed systems is different tha n the simulation of networking-applications; requiring a more scalable and application-oriented model. The widesp read use of distributed applications imposes a requireme nt for such a simulation platform where these applications can be easily deployed and tested. We introduce an object oriented model which provides a scalable and flexible platfo rm for simulating distributed applications. It avoids the complexities of packet level features and implement s BFS as channel computation technique for end to end messag delivery. We show by deploying different distribute d applications that our scheme can successfully simul ate over 1 million nodes on a single 1.8 GHz 1GB RAM worksta tion.
Performance analysis of infrared face recognition using PCA and ZM

Infrared (IR) Face recognition has become an area of growing interest since it can operate in dim light and total darkness. This paper introduces a comparative study between the infrared face recognition systems using the principle component analysis (PCA) and the Zernike moments (ZM) techniques. The performance is evaluated according to the recognition rate, time consumption, and immunity to both Salt & Pepper and Gaussian noise. The analysis shows that the PCA technique has the same performance as the ZM technique if a large size dataset is used. On the other hand, the ZM technique outperforms the PCA technique when using a small size dataset, but it consumes a time approximately equal to four times that required by the PCA technique. The simulations also show that the ZM technique outperforms the PCA technique in the presence of both Salt & Pepper and Gaussian Noise types at various values of noise variance.
Fall detection - Principles and Methods

Fall detection of the elderly is a major public health problem. Thus it has generated a wide range of applied research and prompted the development of telemonitoring systems to enable the early diagnosis of fall conditions. This article is a survey of systems, algorithms and sensors, for the automatic early detection of the fall of elderly persons. It points out the difficulty to compare the performances of the different systems due to the lack of a common framework. It then proposes a procedure for this evaluation.
Introducing an Outcome Expectation Questionnaire and Its Psychometric Properties Regarding Leisure Time Physical Activity for Iranian Male Adolescent

Background: Outcome expectation (OE) is known as a psychosocial determinant of leisure time physical activity (LTPA). Despite importance of this construct evaluation, there is no evidence of special questionnaire for measuring OE in Persian speaking Iranian male adolescents. Objectives: This article reports development and psychometric evaluation process of a specific questionnaire that evaluates OE about LTPA among Iranian male adolescents. Materials and Methods: Literature review and group discussions were used to select 26 items of this questionnaire based on 3 dimensions of OE (self-evaluation, social expectancy, and physical expectancy). All Participants (n = 720) were divided into two groups randomly after evaluating comprehensibility, face and content validity, and items analysis. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were separately operated on one of these groups for evaluation of the construct validity of questionnaire. The reliability of the questionnaire was estimated by the Cronbach α. Results: EFA extracted 3 main factors explained 45.80%, 10.31%, and 7.51% of OE variance, respectively. Almost all fit indexes in CFA were acceptable (CMIN = 107.39, CMIN/DF = 2.619, CFI = 0.938, PCFI = 0.699, RMSEA = 0.067, PCLOSE = 0.034). Finally 11 items remained in the questionnaire, which showed excellent reliability on main study (α = 0.85). Conclusions: This study provided evidence regarding the reliability and validity of the Iranian male adolescent outcome expectation about leisure time physical activity (IMAO-PAC) and illustrated that this new questionnaire can be used to measure the perceived exercise benefits among target group in observational and interventional studies.
Measuring testability of aspect oriented programs

Testability design is an effective way to realize the fault detection and isolation. It becomes crucial in the case of Aspect Oriented designs where control flows are generally not hierarchical, but are diffuse and distributed over the whole architecture. In this paper, we concentrate on detecting, pinpointing and suppressing potential testability weaknesses of a UML Aspect-class diagram. The attribute significant from design testability is called “class interaction”: it appears when potentially concurrent client/supplier relationships between classes exist in the system. These interactions point out parts of the design that need to be improved, driving structural modifications or constraints specifications, to reduce the final testing effort. This paper does an extensive review on testability of aspect oriented software, and put forth some relevant information about class-level testability.
Uncertain Imitability: An Analysis of Interfirm Differences in Efficiency under Competition

Causal ambiguity inherent in the creation of productive processes is modeled by attaching an irreducible ex ante uncertainty to the level of firm efficiency that is achieved by sequential entrants. Without recourse to scale economies or market power, the model generates equilibria in which there are stable interfirm differences in profitability, an above-normal industry rate of return, and a lack of entry even when firms are atomistic price-takers. The free-entry equilibrium for rational noncollusive firms is characterized for atomistic firms and for firms of fixed size, and some analytic results are obtained for the more realistic case in which firms have an arbitrary cost function. Numerical results for the associations implied between concentration, industry profitability, fixed entry costs, and the dispersion of firm profitabilities are obtained for selected cases.
An estrogen-induced endometrial hyperplasia mouse model recapitulating human disease progression and genetic aberrations

Endometrial hyperplasia (EH) is a condition originating from uterine endometrial glands undergoing disordered proliferation including the risk to progress to endometrial adenocarcinoma. In recent years, a steady increase in EH cases among younger women of reproductive age accentuates the demand of therapeutic alternatives, which emphasizes that an improved disease model for therapeutic agents evaluation is concurrently desired. Here, a new hormone‐induced EH mouse model was developed using a subcutaneous estradiol (E2)‐sustained releasing pellet, which elevates the serum E2 level in mice, closely mimicking the effect known as estrogen dominance with underlying, pathological E2 levels in patients. The onset and progression of EH generated within this model recapitulate a clinically relevant, pathological transformation, beginning with disordered proliferation developing to simple EH, advancing to atypical EH, and then progressing to precancerous stages, all following a chronologic manner. Although a general increase in nuclear progesterone receptor (PR) expression occurred after E2 expression, a total loss in PR was noted in some endometrial glands as disease advanced to simple EH. Furthermore, estrogen receptor (ER) expression in the nucleus of endometrial cells was reduced in disordered proliferation and increased when EH progressed to atypical EH and precancerous stages. This EH model also resembles other pathological patterns found in human disease such as leukocytic infiltration, genetic aberrations in β‐catenin, and joint phosphatase and tensin homolog/paired box gene 2 (PTEN/PAX2) silencing. In summary, this new and comprehensively characterized EH model is cost‐effective, easily reproducible, and may serve as a tool for preclinical testing of therapeutic agents and facilitate further investigation of EH.
Introducing Protein Folding Using Simple Models

Most reactions in cells are carried out by enzymes [1]. In many instances the rates of enzyme-catalyzed reactions are enhanced by million fold. A significantly large fraction of all known enzymes are proteins which are made from twenty naturally occurring amino acids. The amino acids are linked by peptide bonds to form polypeptide chains. The primary sequence of a protein specifies the linear order in which the amino acids are linked. To carry out the catalytic activity the linear sequence has to fold to a well defined three dimensional structure. In cells only a relatively small fraction of proteins require assistance from chaperones (helper proteins) [2]. Even in the complicated cellular environment most proteins fold spontaneously upon synthesis. The determination of the three dimensional folded structure from the one dimensional primary sequence is the most popular protein folding problem. For a number of years the protein folding problem remained only of academic interest. The synthesis of proteins in cells was described by Crick [1]. Schematically this process can be represented as DNA → RNA → Proteins. This proposal and Anfinsen’s [3] demonstration that a denatured protein would fold to the native conformation under suitable conditions was sufficient to understand the role of protein folding in cells. However, in the last couple of decades many diseases have been directly linked to protein folding (especially misfolding) [4]. Thus, there is an urgency to understand the mechanisms in the formation of folded structures. Biotechnology industry is also interested in the problem because of the hope that by understanding the way polypeptide chains fold one can design molecules (using natural or synthetic constituents) of use in medicine. Finally, the full potential of the human genome project involves understanding what the genes encode. For all these profoundly important reasons the protein folding problem has taken center stage in molecular biology. Because this problem is complex several venues of attack have been devised in the last fifteen years. A combination of experimental developments (protein engineering, advances in X-ray and NMR, various time resolved spectroscopies, single molecule manipulation methods) and theoretical approaches (use of statistical mechanics, computational strategies, use of simple models) [5–7] has lead to a greater understanding of how polypeptide chains reach the native conformation. From our perspective there are four major problems that comprise the protein folding enterprise. They are:
Proof Strategies in Linear Logic Proof Strategies in Linear Logic

Linear logic, introduced by J.-Y.Girard, is a reenement of classical logic providing means for controlling the allocation of \resources". It has aroused considerable interest both from proof theorists and computer scientists. In this paper we investigate methods for automated theorem proving in propositional linear logic. Both the \bottom-up" and \top-down" (resolution) proof strategies are analyzed { various modiications of sequent rules and eecient search strategies are presented along with the experiments performed with the implemented theorem provers.
Design of JFluid: a profiling technology and tool based on dynamic bytecode instrumentation

Instrumentation-based profiling has many advantages and one serious disadvantage: usually high performance overhead. This overhead can be substantially reduced if only a small part of the target application (for example, one that has previously been identified as a performance bottleneck) is instrumented, while the rest of the application code runs at full speed. Such an approach can also beat scalability issues caused by a high volume of profiling information generated by instrumented code running on behalf of multiple threads. The value of such a profiling technology would increase further if the code could be instrumented and de-instrumented as many times as needed at run time. In this report we describe in detail the design of an experimental profiling system called JFluid, which includes a modified Java HotSpot™ VM and a GUI tool, and addresses both of the above issues. Our JVM™ supports arbitrary on-the-fly modifications to running Java methods, and can connect with a profiling tool at any moment, without any startup time preparation. Our tool collects, processes and presents profiling data on-line. To perform CPU profiling, it instruments a group of methods defined as an arbitrary "root" method plus all methods that it calls (a call subgraph). It appears that static determination of all methods in a call subgraph is difficult in the presence of virtual methods, but fortunately, with dynamic code hotswapping available, two schemes of dynamic call subgraph revelation and instrumentation can be suggested. Measurements that we obtained when performing full and partial program profiling using both schemes show that the overhead can be reduced substantially using this technique, and that one of the schemes generally results in a smaller number of instrumented methods and better performance, especially for large applications.
Altered Expression Pattern of Molecular Factors Involved in Colonic Smooth Muscle Functions: An Immunohistochemical Study in Patients with Diverticular Disease

Background The pathogenesis of diverticular disease (DD) is thought to result from complex interactions among dietary habits, genetic factors and coexistence of other bowel abnormalities. These conditions lead to alterations in colonic pressure and motility, facilitating the formation of diverticula. Although electrophysiological studies on smooth muscle cells (SMCs) have investigated colonic motor dysfunctions, scarce attention has been paid to their molecular abnormalities, and data on SMCs in DD are lacking. Accordingly, the main purpose of this study was to evaluate the expression patterns of molecular factors involved in the contractile functions of SMCs in the tunica muscularis of colonic specimens from patients with DD. Methods and Findings By means of immunohistochemistry and image analysis, we examined the expression of Cx26 and Cx43, which are prominent components of gap junctions in human colonic SMCs, as well as pS368-Cx43, PKCps, RhoA and αSMA, all known to regulate the functions of gap junctions and the contractile activity of SMCs. The immunohistochemical analysis revealed significant abnormalities in DD samples, concerning both the expression and distribution patterns of most of the investigated molecular factors. Conclusion This study demonstrates, for the first time, that an altered pattern of factors involved in SMC contractility is present at level of the tunica muscularis of DD patients. Moreover, considering that our analysis was conducted on colonic tissues not directly affected by diverticular lesions or inflammatory reactions, it is conceivable that these molecular alterations may precede and predispose to the formation of diverticula, rather than being mere consequences of the disease.
Prediction of favourite photos using social, visual, and textual signals

This paper focuses on the prediction of users' favourite photos in Flickr. We propose a multi-modal, machine learned approach that combines social, visual and textual signals into a single prediction system. Although each individual user has different motivations for calling a photo a favourite, we show that the textual, visual, and social modalities effectively capture the needs of most active Flickr users. We use gradient-boosted decision trees (GBDT) with a mod least squares loss function for the classification of a user's favourite photos, and evaluate the performance of our classifier with respect to the individual modalities and various combinations thereof. By using a combination of the social and visual modalities the GBDT creates a highly effective classifier. The addition of textual features allows us to significantly increase recall, with a slight trade off in precision.
GeoStream-Exploiting User-Generated Geospatial Content Streams FP 7-SME-2012-315631 Web Computing Framework D 3 . 1 Deliverable lead contractor : IMIS / ATHENA R . C

This deliverable presents a novel application of the map-reduce computation concept in connection with browser computing to accomplish mining and integration of user-generated content by means of Web site visitors that contribute their “expertise” and computing power (browser) to perform this task. Copyright © 2013 GeoStream – http://www.geocontentstream. eu Research Center “ATHENA,” Greece FU BERLIN, Germany Fraunhofer, Germany Michael Mueller Verlag, Germany TALENT, Greece WIGEOGIS, Austria D3.1 Web computing framework GeoStream 2 of 23 Table of
Apolipoprotein E in Temporal Lobe Epilepsy: A Case-Control Study

Purpose: To investigate the relationship of apolipoprotein E (apoE) genotype, plasma levels of apoE and lipids in temporal lobe epilepsy (TLE) patients in Asian Indians. Status of plasma levels of Apo E in epilepsy patients has not been reported till date. Methods: ApoE gene polymorphism was analyzed in 58 patients with temporal lobe epilepsy (TLE) and 57 age and sex approximated controls using Polymerase chain reaction- restriction fragment length polymorphism (PCR-RFLP). Levels of plasma apoE and lipids were measured using ELISA and enzymatic kits respectively. Results: The distribution of ApoE genotype in epilepsy patients and controls was comparable. Higher levels of plasma ApoE were observed in TLE patients as compared to controls (p = 0.0001). Individuals with plasma levels of apoE > 190 mg/L were at 20 times higher odds (95%CI = 2.46–163.34, p = 0.005), while those with levels of apoE between 150–190 mg/L were at 4.9 times higher odds (95% CI = 1.85–13.9, p = 0.001), to develop TLE. Conclusions: We have observed for the first time, high levels of plasma apoE in epilepsy patients. The findings of this case-control study suggest that apolipoprotein E may play an important role in epilepsy.