Miracle-dz/UniFAR-FTUs huggingface dataset:
Recovery From Schizophrenia in Community-Based Psychosocial Rehabilitation Settings

Objective: We assessed the rate of recovery from schizophrenia in community-based psychosocial rehabilitation and whether psychosocial attributes predicted the achievement of recovery beyond demographic and clinical characteristics. Methods: We used data from 246 individuals with schizophrenia spectrum disorder collected at baseline and at 6 and 12 months after admission to psychosocial rehabilitation. Results: The proportion of participants who showed recovery during either 6-month period and the full 1-year period was 19.86% and 7.53%, respectively. Although predictors of recovery for the 1-year period could not be reliably estimated due to its low rate, higher levels of intrinsic motivation and more positive family relationships at baseline predicted recovery for either 6-month period after controlling for initial functioning capacity. Conclusions: One in five individuals with schizophrenia who engage in intensive community-based psychosocial rehabilitation can achieve periods of recovery during treatment. Psychosocial attributes at the start of treatment are important contributors to subsequent recovery.
Reviewing, rethinking, recalling: The impact of digital portfolio reflections on students and teachers

With the widespread interest in using digital portfolios with college students, this research evaluates the impact of reflective digital portfolios on elementary students and their teachers. The digital portfolio process is cognitively demanding and creates an inclusive and dynamic learning environment. Specifically, the study used a mixed methods approach to evaluate how young students learn to reflect constructively on their work and learning and how listening to the student reflections affected their teachers' methods and planning. The results indicate that disciplined reflection has significant positive effects for students and teachers.
Central voice production and pathophysiology of spasmodic dysphonia

Our ability to speak is complex, and the role of the central nervous system in controlling speech production is often overlooked in the field of otolaryngology. In this brief review, we present an integrated overview of speech production with a focus on the role of central nervous system. The role of central control of voice production is then further discussed in relation to the potential pathophysiology of spasmodic dysphonia (SD).
Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks

In this paper, we propose a new unsupervised deep learning approach called DeepBit to learn compact binary descriptor for efficient visual object matching. Unlike most existing binary descriptors which were designed with random projections or linear hash functions, we develop a deep neural network to learn binary descriptors in an unsupervised manner. We enforce three criterions on binary codes which are learned at the top layer of our network: 1) minimal loss quantization, 2) evenly distributed codes and 3) uncorrelated bits. Then, we learn the parameters of the networks with a back-propagation technique. Experimental results on three different visual analysis tasks including image matching, image retrieval, and object recognition clearly demonstrate the effectiveness of the proposed approach.
Microwave Frequency Performance and High Magnetic Anisotropy of ${\rm Fe}_{70}{\rm Co}_{30}-{\rm B}$ Films Prepared by a Modified Composition Gradient Sputtering

A composition gradient sputtering (CGS) method, in which the doping elements have a gradient distribution from one side to another of the sample, was proposed in our previous work [Li SD, Appl. Phys. Lett. , 92 (2008) 092501]. An ultra high magnetic anisotropy field over 500 Oe and ferromagnetic frequency over 7 GHz were obtained in as-deposited FeCoHf films by CGS method due to the stress-induced uniaxial magnetic anisotropy. However, the magnetic properties of the CGS samples are non-uniform, showing a location dependence. In this paper, a modified CGS (MCGS) method was proposed to improve the original CGS method. It is revealed that the MCGS method dramatically improves the uniformity of the high-frequency soft magnetic properties of the as-deposited Fe70Co30-B films. A narrow ferromagnetic resonance frequency (fFMR) range around 5.5 GHz (5.2-5.7 GHz) with a deviation less than 10% and high uniaxial magnetic anisotropy field up to 330 Oe were obtained for as-deposited films with 75 mm in length. As Comparison, the fFMR of the CGS samples is very inhomogeneous and distribute in a broad range from 3.1 to 6.7 GHz with a large deviation as high as 116%. The evident advantages of MCGS samples, such as high UMA and relatively uniform high-frequency characteristics, suggest that the MCGS Fe70Co30-B films are particularly in favor of the integration between magnetic films and RF/microwave components.
Validation of the SpaceLabs 90207 ambulatory blood pressure monitor for use in pregnancy

Objective To determine the accuracy of the SpaceLabs 90207 ambulatory blood pressure monitor in pregnant women. Design Prospective observational study. Setting Queen Charlotte's and Chelsea Hospital for Women. Subjects One hundred and twenty-two pregnant women from the antenatal population of Queen Charlotte's Hospital. Methods Evaluation was carried out according to the British Hypertension Society (BHS) Protocol. Results The mean difference between observer and device for systolic blood pressure was 3 (SD 4) mmHg. For diastolic blood pressure the difference was 5 (SD 6) mmHg for Phase IV (muffling of Korotkoff sound), and 4 (SD 4) mmHg for Phase V (disappearance of Korotkoff sound). According to the BHS grading criteria, the device reached a B grading for systolic blood pressure. B grading was also reached for diastolic blood pressure (Phase V) but grade C for diastolic blood pressure (Phase IV). The accuracy criteria stipulated by the Association for the Advancement of Medical Instrumentation (AAMI) were also met for both systolic and diastolic blood pressure. Conclusion The SpaceLabs 90207, when compared with trained observers, is accurate in determining both systolic and diastolic blood pressure for clinical purposes, as judged by the BHS Protocol in pregnancy.
A Reconfigurable Replica Bitline to Determine Optimum SRAM Sense Amplifier Set Time

Process variation in deep nanometer technologies causes read, write and retention failures in SRAM and results in yield loss. Although using larger transistors and higher supply voltages helps to mitigate these failures and improve the yield, they come at the cost of more power consumption, more area overhead and performance degradation. Reconfigurable control-signal circuits in SRAM not only help to reduce the failure rates, but also allow calibration after fabrication to improve the yield, reduce the power consumption and upgrade the performance. In this paper, a reconfigurable replica bitline scheme for sense amplifier set time is proposed to reduce the read failures and provide a wide operating-voltage range for SRAM. The proposed reconfigurable replica bitline also allows recalibration when SRAM performance deteriorates due to device aging degradation. Monte Carlo simulation results for a 64 kb SRAM array show that the proposed technique can reduce the access time by 20% compared to conventional replica bitline design in IBM/Global Foundries (GF) cmos32soi 32nm technology at 0.9 V with no area overhead.
Improving the robustness with multiple sets of HMMs

The highest recognition performance is still achieved when training a recognition system with speech data that have been recorded in the acoustic scenario where the system will be applied. We investigated the approach of using several sets of HMMs. These sets have been trained on data that were recorded in different typical noise situations. One HMM set is individually selected at each speech input by comparing the pause segment at the beginning of the utterance with the pause models of all sets. We observed a considerable reduction of the error rates when applying this approach in comparison to two well known techniques for improving the robustness. Furthermore, we developed a technique to additionally adapt certain parameters of the selected HMMs to the specific noise condition. This leads to a further improvement of the recognition rates. Index Terms: robust speech recognition, HMM adaptation
Evolutionary nonlinear Model Output Statistics for wind speed prediction using Genetic Programming

Wind speed fluctuates heavily and affects a smaller locality than other weather elements. Wind speed is heavily fluctuated and quite local than other weather elements. It is difficult to improve the accuracy of prediction only in a numerical prediction model. An MOS (Model Output Statistics) technique is used to correct the systematic errors of the model using a statistical data analysis. Most previous MOS (Model Output Statistics) used a linear regression model, but they are hard to solve nonlinear natures of the weather prediction. In order to solve the problem of a linear MOS, a nonlinear compensation technique based on evolutionary computation is introduced as a new attempt. We suggest a nonlinear regression method using GP (Genetic Programming) based symbolic regression to generate an open-ended nonlinear MOS. The new nonlinear MOS can express not only nonlinearity much more extensively by involving all mathematical functions, including transcendental functions, but also unlimited orders with a dynamic selection of predictors due to the flexible tree structure of GP. We evaluate the accuracy of the estimation by GP based nonlinear MOS for the three days wind speed prediction for Korean regions. The training period of 2007– 2009, 2011 year is used, the data of 2012 year is for verification, and 2013 year is adopted for test. This method is then compared to the linear MOS and shows superior results.
Breaking the Barriers to True Augmented Reality

In recent years, Augmented Reality (AR) and Virtual Reality (VR) have gained considerable commercial traction, with Facebook acquiring Oculus VR for \$2 billion, Magic Leap attracting more than \$500 million of funding, and Microsoft announcing their HoloLens head-worn computer. Where is humanity headed: a brave new dystopia-or a paradise come true? In this article, we present discussions, which started at the symposium "Making Augmented Reality Real", held at Nara Institute of Science and Technology in August 2014. Ten scientists were invited to this three-day event, which started with a full day of public presentations and panel discussions (video recordings are available at the event web page), followed by two days of roundtable discussions addressing the future of AR and VR.
Evaluation of Python-based tools for distributed computing on the Raspberry Pi

This paper presents the results of comparing the efficiency of four Python-based modules and libraries with regards to their respective ability to run distributed tasks across a given cluster of Raspberry Pi's. The four Python-based tools considered are the following: PyRO v4.45, DCM v1.0. 0, PP v1.6. 4.4, and Mpi4py v2.0. 0. Three tests are used to compare run time efficiency for distributed tasks: The first test proposed is prime factorization of composite values n, where n is the product of two prime numbers, and p and q are the respective prime factors. The second test proposed is the approximation of π to a desired point of precision in a set number of iterations. The third test proposed is the approximation of π in a fixed number of iterations with variation in the cluster size used. The overall purpose of these tests is to provide a basis of comparison in regards to the efficiency of each Python-based tool.
Initial Incidental Acquisition of Word Order Regularities: Is It Just Sequence Learning?

There is a long tradition of implicit learning research looking at learning of artificial grammars (finite-state grammars that generate meaningless letter strings). Are the associative learning processes evident in these studies at work in learning word order in natural language? To what extent do meaning and prior linguistic knowledge need to be taken into account in the natural language case? In the study reported here, incidental learning of natural language word order is compared directly to a meaningless analogue (in which the same sequential regularities underlie meaningless syllable strings). The results of both experiments are compared to connectionist (simple recurrent network) simulations. The comparisons suggest that similar associative sequence learning mechanisms underlie learning of both the natural language and its meaningless analogue (with the result that there are certain limitations to what is learned). However, to achieve this alignment, it is necessary to take into account the linguistic categories and meaning structure that the participants are likely to impose on the natural language. It is concluded that the initial incidental learning of word order can be explained in terms of associative (sequence) learning and that linguistic knowledge is engaged to the extent that it defines the categories over which statistics are computed.
Driver-centred vehicle automation: using network analysis for agent-based modelling of the driver in highly automated driving systems

Abstract To the average driver, the concept of automation in driving infers that they can become completely ‘hands and feet free’. This is a common misconception, however, one that has been shown through the application of Network Analysis to new Cruise Assist technologies that may feature on our roads by 2020. Through the adoption of a Systems Theoretic approach, this paper introduces the concept of driver-initiated automation which reflects the role of the driver in highly automated driving systems. Using a combination of traditional task analysis and the application of quantitative network metrics, this agent-based modelling paper shows how the role of the driver remains an integral part of the driving system implicating the need for designers to ensure they are provided with the tools necessary to remain actively in-the-loop despite giving increasing opportunities to delegate their control to the automated subsystems. Practitioner Summary: This paper describes and analyses a driver-initiated command and control system of automation using representations afforded by task and social networks to understand how drivers remain actively involved in the task. A network analysis of different driver commands suggests that such a strategy does maintain the driver in the control loop.
Hardware Implementation and Validation of 3D Underwater Shape Reconstruction Algorithm Using a Stereo-Catadioptric System

In this paper, we present a new stereo vision-based system and its efficient hardware implementation for real-time underwater environments exploration throughout 3D sparse reconstruction based on a number of feature points. The proposed underwater 3D shape reconstruction algorithm details are presented. The main concepts and advantages are discussed and comparison with existing systems is performed. In order to achieve real-time video constraints, a hardware implementation of the algorithm is performed using Xilinx System Generator. The pipelined stereo vision system has been implemented using Field Programmable Gate Arrays (FPGA) technology. Both timing constraints and mathematical operations precision have been evaluated in order to validate the proposed hardware implementation of our system. Experimental results show that the proposed system presents high accuracy and execution time performances.
The intensification of performativity in early childhood education

Operating within a neoliberal education reform context, performativity and teaching in schools has been a focus of study for a number of years. However, less is known about the effects of performativity on teaching and curriculum in the early childhood (preschool) context. Making a case for the intensification of performativity in Australian early childhood education, this paper reports on findings from a doctoral study and draws on research literature from the past fourteen years to illustrate how performative measures have increasingly affected teaching and curriculum. The way that performativity has intensified is discussed in three chronological phases, performativity emerging, consolidating and normalised. Teacher interview transcripts and curricular related policies were analysed using critical discourse analysis and Ranson’s typology of accountability regimes. Findings reveal that early childhood teachers have different ways of responding to performativity, with the teacher featured in this paper displaying three types of performative accountability: anxiety, confidence, and disregard. An implication arising from this paper’s findings illustrates how the effects of performativity on teaching and curriculum can be complex, contradictory and at times, unintended.
Endocarditis and hemorrhagic stroke caused by Cunninghamella bertholletiae infection after kidney transplantation.

Cunninghamella bertholletiae is a saprophytic fungus found in soil. Infection with this organism is extremely rare, occurring almost exclusively in immunosuppressed hosts. There have been only three previous cases of infection with this fungus reported in solid-organ recipients. We report an unusual case of disseminated Cunninghamella infection in a woman who had received a renal transplant. A 48-year-old woman received a living-related kidney transplant for focal segmental glomerulonephritis. She was treated with plasmapheresis and muromonab-CD3 (OKT3) for two episodes of rejection. Because of recurrent focal segmental glomerulonephritis with diuretic-resistant edema, she underwent transplant nephrectomy, was restarted on hemodialysis, and had her immunosuppression stopped. Shortly thereafter, the patient presented with pulmonary infiltrates and hemorrhagic stroke with a rapidly fatal course. Autopsy revealed widely disseminated C bertholletiae involving the central nervous system, lungs, and heart. This is the first reported case of endocarditis caused by this organism. Diagnosis of this fungal infection is often difficult. Because the few patients who have survived this infection seemed to have been diagnosed early, it is important for clinicians caring for transplant patients to be aware of this invasive infection. Successful treatment requires prompt diagnosis and high-dose amphotericin B.
The role of attachment styles and the knowledge of life skills according to gender in students of Kermanshah Razi University

The main objective of this study was to investigate the relationship between attachment and the knowle dge of life skills according to gender in Kermanshah Razi Unive rsity. For this purpose, three hundred and sixty ni ne subjects, selected from different colleges, were evaluated us ing multi-stage cluster sampling . then they had don e Hazen and Shaver (1978) adult’s attachment scale test and an inventory of life skills knowledge by Ahadi (2007). Data collected were analyzed implementing statistical te sts and two-way ANOVA analysis. The results showed that the attachment styles have no significant impact on kno wledge of life skills. But gender has significant impact. And the daughters have better life skills than the boys and gender shows 2.2 percent of life skills.
Modeling and Analyzing Location-based Requirements: Goal-oriented Approach

In emerging computing paradigms, such as mobile information systems, there is a strong relation between the system requirements and location. The system may operate in various locations and location can be a main factor in deciding the requirements to meet and the way to meet them. In this paper, we propose a goal-oriented requirements engineering approach to model and analyze location-based requirements. We propose a set of modelling constructs to represent location, show how to capture location-based requirements via location-based goal model, and present a set of analysis and reasoning techniques about the formalized models.
SAT vs. Search for Qualitative Temporal Reasoning

Empirical data from recent work has indicated that SAT-based solvers can outperform native search-based solvers on certain classes of problems in qualitative temporal reasoning, particularly over the Interval Algebra (IA). The present work shows that, for reasoning with IA, SAT strictly dominates search in theoretical power: (1) We present a SAT encoding of IA that simulates the use of tractable subsets in native solvers. (2) We show that the refutation of any inconsistent IA network can always be done by SAT (via our new encoding) as efficiently as by native search. (3) We exhibit a class of IA networks that provably require exponential time to refute by native search, but can be refuted by SAT in polynomial time.
Amassing and indexing a large sample of version control systems: Towards the census of public source code history

The source code and its history represent the output and process of software development activities and are an invaluable resource for study and improvement of software development practice. While individual projects and groups of projects have been extensively analyzed, some fundamental questions, such as the spread of innovation or genealogy of the source code, can be answered only by considering the entire universe of publicly available source code and its history. We describe methods we developed over the last six years to gather, index, and update an approximation of such a universal repository for publicly accessible version control systems and for the source code inside a large corporation. While challenging, the task is achievable with limited resources. The bottlenecks in network bandwidth, processing, and disk access can be dealt with using inherent parallelism of the tasks and suitable tradeoffs between the amount of storage and computations, but a completely automated discovery of public version control systems may require enticing participation of the sampled projects. Such universal repository would allow studies of global properties and origins of the source code that are not possible through other means.