Miracle-dz/UniFAR-FTUs huggingface dataset:
The Menu Approach to supported employment for individuals with severe and persistent mental illness: Outcomes in an Oregon community based program.

OBJECTIVE The purpose of this study was to determine the vocational outcomes at the Abacus Program, a Menu Approach supported employment program designed to assist individuals with serious mental illness (SMI) achieve competitive employment. METHODS A summative evaluation method was used to determine the vocational outcomes between the years 2000-2006 at the Abacus Program. Data was gathered from existing Abacus Program records and the Statistical Package for the Social Sciences (SPSS) 15.0 was used to calculate outcomes. RESULTS The Abacus Program served 264 clients during the study period. Within those clients, there were 140 cases of vocational services that met study criteria which resulted in 83 successful instances of employment (59%). CONCLUSIONS Initial data from a single site indicate that the Menu Approach to supported employment produces successful employment outcomes comparable to other published studies. Further research is warranted in order to confirm the initial results of this study in other settings. Randomized controlled trials should be conducted in order to compare the outcomes of the Menu Approach to other methods of providing supported employment services to individuals with SMI.
SPEAKER RECOGNITION IN AN EMOTIONAL ENVIRONMENT

The goal of this paper is to assess the effect of emotional state of a speaker when text-independent speaker identification is performed. Mel-frequency cepstral coefficients are the features of the speech signal used for speaker recognition. For training the speaker models and testing the system, Support Vector Machines are employed. Berlin emotional speech database, which contains 10 different speakers recorded in different emotional situations (happy, angry, fear, bored, sad and neutral) is used. The results show an important influence of the emotional state upon textindependent speaker identification. A possible solution to this issue is finally suggested.
Dynamic Asset Allocation under Stochastic Volatility - Theory and Practice

This study develops inter-temporal dynamic asset allocation with stochastic volatility (DAASV) models. The DAASV models integrate the stochastic volatility feature inherent in asset returns into the allocation procedure. By applying the DAASV, an investor can more efficiently diversify the unsystematic risks, so as to achieve better performance. We demonstrate that the DAASV models dominate the traditional meanvariance portfolio models by using Taiwan equity market empirical data. Finally, we show that under the consideration of trade-off between transaction costs and rebalancing timing, an optimal asset allocation rebalancing frequency can be derived.
Sampling for Shape from Focus in Optical Microscopy

Shape from focus (SFF), which relies on image focus as a cue within sequenced images, represents a passive technique in recovering object shapes in scenes. Although numerous methods have been recently proposed, less attention has been paid to particular factors affecting them. In regard to SFF, one such critical factor impacting system application is the total number of images. A large data set requires a huge amount of computation power, whereas decreasing the number of images causes shape reconstruction to be crude and erroneous. The total number of images is inversely proportional to interframe distance or sampling step size. In this paper, interframe distance (or sampling step size) criteria for SFF systems have been formulated. In particular, light ray focusing is approximated by the use of a Gaussian beam followed by the formulation of a sampling expression using Nyquist sampling. Consequently, a fitting function for focus curves is also obtained. Experiments are performed on simulated and real objects to validate the proposed schemes.
Using audio-visual presentation to teach global mindedness in social work education

This paper considers the production of an audio-visual presentation of the experience of three students who participated in an exchange with a social work programme in Pune, India. We also describe the use of the video in a classroom setting with a year group of 53 students from a younger cohort. This exercise was intended to stimulate students’ curiosity about the international dimensions of social work and add to their awareness of poverty, social justice, cultural competence and community social work as global issues. Written classroom feedback informs our discussion of the technical as well as the pedagogical benefits and challenges of this approach. We conclude that the audio-visual presentation to some degree helped students connect with diverse cultural contexts, but that a complementary discussion challenging stereotyped viewpoints and unconscious professional imperialism is also crucial.
Towards a new possibilistic collaborative filtering approach

Collaborative filtering approaches exploit users preferences to provide items recommendations. These preferences describing the actual state of the item are generally certain. However, in real problems we can not ignore the importance of uncertainty. In this paper, we propose a purely possibilistic collaborative filtering approach that provides a recommendation list given uncertain preferences expressed by possibility distributions. Experimental results show that the proposed approach outperforms traditional collaborative filtering algorithm.
Hotel Recommendation Based on Surrounding Environments

In this paper, we present an efficient method for recommending hotels for the users. Conventional hotel recommendation systems recommend hotels based on non-spatial attributes of hotels such as price and rating and do not utilize surrounding environments well. In contrast, we consider the coexistence of other facilities such as restaurants and entertainment facilities in the surrounding areas while selecting a hotel for recommendation. We first evaluate the surrounding environment of each hotel. Then, we consider user provided reviews about hotels where he stayed earlier. Based on the user's review, we calculate preferences of that user. Finally, we calculate similarity score between the hotels and the user's preferences and select the top-k hotels. We perform different experiments to show the effectiveness of our approach. Experimental evaluation shows that our approach is well applicable for recommending hotels for the users.
Bayesian Learning of Sparse Multiscale Image Representations

Multiscale representations of images have become a standard tool in image analysis. Such representations offer a number of advantages over fixed-scale methods, including the potential for improved performance in denoising, compression, and the ability to represent distinct but complementary information that exists at various scales. A variety of multiresolution transforms exist, including both orthogonal decompositions such as wavelets as well as nonorthogonal, over-complete representations. Recently, techniques for finding adaptive, sparse representations have yielded state-of-the-art results when applied to traditional image processing problems. Attempts at developing multiscale versions of these so-called dictionary learning models have yielded modest but encouraging results. However, none of these techniques has sought to combine a rigorous statistical formulation of the multiscale dictionary learning problem and the ability to share atoms across scales. We present a model for multiscale dictionary learning that overcomes some of the drawbacks of previous approaches by first decomposing an input into a pyramid of distinct frequency bands using a recursive filtering scheme, after which we perform dictionary learning and sparse coding on the individual levels of the resulting pyramid. The associated image model allows us to use a single set of adapted dictionary atoms that is shared-and learned-across all scales in the model. The underlying statistical model of our proposed method is fully Bayesian and allows for efficient inference of parameters, including the level of additive noise for denoising applications. We apply the proposed model to several common image processing problems including non-Gaussian and nonstationary denoising of real-world color images.
Invariants of the local Clifford group

We study the algebra of complex polynomials which remain invariant under the action of the local Clifford group under conjugation. Within this algebra, we consider the linear spaces of homogeneous polynomials degree by degree and construct bases for these vector spaces for each degree, thereby obtaining a generating set of polynomial invariants. Our approach is based on the description of Clifford operators in terms of linear operations over GF(2). Such a study of polynomial invariants of the local Clifford group is mainly of importance in quantum coding theory, in particular in the classification of binary quantum codes. Some applications in entanglement theory and quantum computing are briefly discussed as well.
Complex convolution Kernel for deep networks

Deep Convolutional Neural Network (CNN) is one of the most popular methods for image processing and recognition. There are many research works to improve the performance of CNNs. However, as an important part of CNNs, convolution kernel has rarely been discussed. As one Original Convolution Kernel (OCK) can only detect one type of visual feature with a fixed deformation, the networks using OCKs may learn many duplicate kernels with multiple deformations for one feature. In this paper, we propose a Complex Convolution Kernel (CCK), which can put the duplicate kernels together. Experiments on four popular datasets show the performance of networks is greatly improved by using CCKs, and suggest how to choose convolution kernels while designing networks.
The Leeuwenhoek lecture 2006. Microscopy goes cold: frozen viruses reveal their structural secrets

The electron microscope provides a powerful tool for investigating the structure of biological complexes such as viruses. A modern instrument is fully capable of atomic resolution on suitable non-biological specimens, but biological materials are difficult to preserve, owing to their fragility, and to image, owing to their radiation, sensitivity. The act of imaging the specimen severely damages it. Originally, samples were prepared by staining with a heavy metal salt, which provides a stable specimen but limits the amount of details that can be retrieved. Now particulate specimens, such as viruses, are prepared by rapid freezing of unstained material and observed in a frozen state with low doses of electrons. The resulting images require extensive computer processing to extract fully detailed three-dimensional information about the specimen. The whole process is referred to as single-particle electron cryomicroscopy. Using this approach, the structure of the human hepatitis B virus core was solved at the level of the protein fold. By comparing maps of RNA- and DNA-containing cores, it was possible to propose a model for the maturation and control of the envelopment of the virus during assembly. These examples show that cryomicroscopy offers great potential for understanding the structure and function of complex biological assemblies.
Towards objective quality assessment of speech enhancement systems in a black box approach

Quality assessment of speech enhancement systems has to deal with aspects such as distortion of the near-end talker's speech, and with the attenuation and distortion of the noise and the echo in different test cases. We propose first steps into the direction of a new black box objective quality assessment of speech enhancement schemes, based on our previous work on decomposition of the (enhanced) speech signal into its components speech, (residual) noise, and (residual) echo. Having these signals available, to our knowledge, for the first time a black box objective quality assessment of an entire speech enhancement system is proposed allowing for simultaneous measurement of, e.g., noise attenuation, echo return loss enhancement (ERLE), and perceptual evaluation of speech quality (PESQ) of the speech component in a wide range of test scenarios including double-talk. The derived scheme proves to be very useful for testing hands-free devices in practice but also for objective evaluation of sophisticated algorithms in science.
Wage Inequality and Job Insecurity Among Permanent and Contract Workers in India: Evidence from Organized Manufacturing Industries

Since the early 1990s, the employment structure of organised manufacturing industries in India has undergone substantial changes with the steep rise in the use of contract workers in place of permanent workers. This process has led to increased wage inequality, discrimination as well as the concern of job insecurity in the labour market. We focus on the wage inequality between permanent and contract workers, since contract workers earn substantially lower wages than their counterpart. The study uses data at the individual level from a recent labour survey of organised manufacturing industries in India. The lower wage earned by contract worker is largely due to cost cutting, rather than differences in labour productivity. The issue of job insecurity has been modeled in form of a binary logistic model. The factors affecting job security are divided as productivity related attributes like level of education, skill etc. and institutional attributes such as labour market rules and regulations, union membership etc. Contrary to the general expectation the study finds that permanent workers are more concern of job insecurity than contract workers.
Massive gravity with mass term in three dimensions

We analyze the effect of the Pauli-Fierz mass term on a recently established, new massive gravity theory in three space-time dimensions. We show that the Pauli-Fierz mass term makes the new massive gravity theory nonunitary. Moreover, although we add the gravitational Chern-Simons term to this model, the situation remains unchanged and the theory stays nonunitary despite that the structure of the graviton propagator is greatly changed. Thus, the Pauli-Fierz mass term is not allowed to coexist with mass-generating higher-derivative terms in the new massive gravity.
Far-Ultraviolet Fluorescence of Molecular Hydrogen in IC 63

We present observations of H2 fluorescence at wavelengths between 1000 and 1200 Å from the bright reflection nebula IC 63. Observations were performed with the Berkeley spectrograph aboard the ORFEUS II mission. To the best of our knowledge, this is the first detection of astrophysical H2 fluorescent emission at these wavelengths (excluding planetary atmospheres). The shape of the spectrum is well described by the model of Sternberg. The absolute intensity, however, is fainter by a factor of 10 than an extrapolation from observations at longer ultraviolet wavelengths. Of the mechanisms that might help to reconcile these observations, optical depth effects in the fluorescing H2 itself are the most promising (or at least the most difficult to rule out).
An integrated system of microcantilever arrays with carbon nanotube tips for bio/nano analysis: design and control

This paper discusses design and control of massively parallel microcantilever arrays with multi-walled carbon nanotube tips. The integrated system can make a powerful tool for imaging, testing and 3D nanomanipulation of nanoparticles and biological samples. The microcantilever has a multi-walled carbon nanotube tip and four additional carbon nanotubes for 3D fine manipulation by electrostatic forces. The reflected light from the deflected microcantilever is collected by a position sensitive photodetector and fast readout is achieved by a time-multiplexing scheme. A distributed parameter system model has been developed to study its dynamic behavior. The open-loop simulation results show that the carbon nanotube tip generates very small nonlinear interaction forces which do not excite significant higher harmonics. It is shown via simulations that the carbon nanotube tip demonstrates an excellent capability for nanomanipulation of samples and tapping mode operation for imaging under a simple PID controller.
Implementation of the Gaussian Mixture Model Algorithm for Real-Time Segmentation of High Definition video: A review

— The detection of moving object is important and critical task in Real -Time video signal. This becomes more critical under this circumstances such as luminance changed, weaving leaves, rainy weather, under heavy snow fall weather. In this paper we studies background subtraction algorithm for foreground detection. Foreground detection is mainly with help of these two approaches, pixel to pixel and frame to frame comparison. But in real-time video sequence pixel to pixel foreground detection is complex because it has more time for processing because in this method compares pixel value. And in frame to frame comparison method compare two consecutive frames. In this paper by using GMM Algorithm improves result in case of less illumination as well as the images having moving objet of the background. In this paper, many soft computing techniques is used such as filtering, mean square error, recall , precision etc. will used for identifying foreground. )
Mining Individual Similarity by Assessing Interactions with Personally Significant Places from GPS Trajectories

Human mobility is closely associated with places. Due to advancements in GPS devices and related sensor technologies, an unprecedented amount of tracking data has been generated in recent years, thus providing a new way to investigate the interactions between individuals and places, which are vital for depicting individuals’ characteristics. In this paper, we propose a framework for mining individual similarity based on long-term trajectory data. In contrast to most existing studies, which have focused on the sequential properties of individuals’ visits to public places, this paper emphasizes the essential role of the spatio-temporal interactions between individuals and their personally significant places. Specifically, rather than merely using public geographic databases, which include only public places and lack personal meanings, we attempt to interpret the semantics of places that are significant to individuals from the perspectives of personal behavior. Next, we propose a new individual similarity measurement that incorporates both the spatio-temporal and semantic properties of individuals’ visits to significant places. By experimenting on real-world GPS datasets, we demonstrate that our approach is more capable of distinguishing individuals and characterizing individual features than the previous methods. Additionally, we show that our approach can be used to effectively measure individual similarity and to aggregate individuals into meaningful subgroups.
Learning Hybrid Template by EM-Type Algorithm

The article proposes to improve the active basis model by incorporating both unaligned training examples and non-alignable sketches in images. EM-type algorithm [8] can learn the objects appear at unknown orientations, locations and scales in the training images. And non-alignable sketches [11] can be summarized in average sketches over the image lattice. This article proposes to add the score of the non-alignable sketches to the likelihood of M-step, so the learned active basis model by EM-type algorithm should be more accurate. Our experiments show that the proposed model can achieve considerable improvement in ROC for most of object categories.
Gap Junctional Communication and Connexin43 Expression in Relation to Apoptotic Cell Death and Survival of Granulosa Cells

Ovarian follicular atresia in all vertebrates is mediated via apoptosis that is initiated in the granulosa cell layer. Here we investigated the relation between connexin expression, cell coupling, and apoptosis in avian granulosa cells. Results from qualitative and quantitative immunocytochemical analysis and Western blotting of connexin43 (Cx43) and electron microscopic observations of gap junctions were compared with functional data on gap junctional coupling obtained by fluorescence recovery after photobleaching in four experimental groups: a control group of freshly isolated granulosa cells, 24-hr serum-free cultures as the apoptosis-inducing condition, and two other groups in which apoptosis was inhibited by either hormone substitution or exposure to elevated extracellular calcium. Our work shows that apoptosis induction in granulosa cells is accompanied by an increased level of cell coupling and that decreasing cell coupling with the gap junction blocker α-glycyrrhetinic acid dose-dependently inhibits apoptosis. The level of Cx43 expression was inversely related to the apoptotic index, suggesting that Cx43 expression plays a role in granulosa cell survival. Our study supports the hypothesis that gap junctional coupling plays a role in propagating a cell death message and suggests a role for Cx43 expression per se in granulosa cell survival.
Analysis and survey of multi-phase power electronic converter topologies for the more electric aircraft applications

To improve aircraft efficiency, reliability and maintainability, the aerospace world has found in the progressive electrification of on-board services the way to reduce or to remove the presence of the hydraulic, mechanical and the bleed air/pneumatic systems. This concept is called the More Electric Aircraft (MEA), which involves the introduction of the electromechanical actuators and the electrohydraulic actuators. Also the entire electrical generation and distribution system is subject to radical revisiting, with a trend which is leaving the constant frequency AC energy distribution in favor of variable frequency or DC solutions. The literature reports survey papers regarding the electrical machines and their electromechanical design for the MEA but only a few papers presented a survey analysis of the power electronic converters employed for aircraft applications. The paper presents a review of power electronics converters topologies which can be used in more electric aircraft, with a particular focus on multi-phase drive systems.
Solving SAT and SAT Modulo Theories: From an abstract Davis--Putnam--Logemann--Loveland procedure to DPLL(T)

We first introduce Abstract DPLL, a rule-based formulation of the Davis--Putnam--Logemann--Loveland (DPLL) procedure for propositional satisfiability. This abstract framework allows one to cleanly express practical DPLL algorithms and to formally reason about them in a simple way. Its properties, such as soundness, completeness or termination, immediately carry over to the modern DPLL implementations with features such as backjumping or clause learning.We then extend the framework to Satisfiability Modulo background Theories (SMT) and use it to model several variants of the so-called lazy approach for SMT. In particular, we use it to introduce a few variants of a new, efficient and modular approach for SMT based on a general DPLL(X) engine, whose parameter X can be instantiated with a specialized solver SolverT for a given theory T, thus producing a DPLL(T) system. We describe the high-level design of DPLL(X) and its cooperation with SolverT, discuss the role of theory propagation, and describe different DPLL(T) strategies for some theories arising in industrial applications.Our extensive experimental evidence, summarized in this article, shows that DPLL(T) systems can significantly outperform the other state-of-the-art tools, frequently even in orders of magnitude, and have better scaling properties.