Miracle-dz/UniFAR-FTUs huggingface dataset:
The diagnostic process in bipolar disorder: a service user perspective

Bipolar disorder is a severe and enduring mental health condition. Although early identification is associated with better outcomes, research has shown that many people wait over a decade after first experiencing affective symptoms before a correct diagnosis is made. A misdiagnosis of unipolar depression often leads to the inappropriate prescription of potentially mood-destabilising antidepressant medications. The aim of this study was to consider service user and carer experiences and their often overlooked views on diagnostic issues. Participants were recruited via the charity Bipolar UK. Initially a focus group discussed the diagnostic process and relevant issues, then the transcript was analysed thematically and used to develop a detailed survey questionnaire. The survey was piloted before being launched online and completed by 262 service users and 65 carers. Survey respondents reported an average delay of over eleven years between first seeking help for bipolar symptoms and being correctly diagnosed. Almost 70% reported a previous misdiagnosis of depression, and over half had experienced mania/hypomania whilst taking antidepressant medication. Nearly a third consulted a doctor over 20 times with bipolar symptoms before receiving help for bipolar disorder. Participants considered that improvements are needed in primary care mental health assessment and that GPs need more training. Many reported that their condition had led them to be discriminated against and agreed that society’s lack of understanding was a major problem. Carers indicated similar opinions and emphasised the potential benefits of including carers in the assessment process. This study suggests that the identification of bipolar disorder is often poor and that assessment of the condition in primary care needs to be improved. Service users offered suggestions for improvements, including recognition of early indicators, appropriate assessment questions and better training which could be adopted by service providers. Limitations of the study and implications for future research were discussed.
A Full Heteroscedastic One-Way Error Components Model Allowing for Unbalanced Panel: Pseudo-Maximum Likelihood Estimation and Specification Testing

This paper proposes an extension of the standard one-way error components model allowing for heteroscedasticity in both the individual-specific and the general error terms, as well as for unbalanced panel. Onthe grounds of its robustness to distributional misspecification, its robustness to possible misspecification of the assumed scedastic structure of the data, its computational convenience and its potential efficiency, we argue for estimating this model by Gaussian pseudo-maximum likelihood of order two. Further, we review how, taking advantage of the powerful m-testing framework,the correct specification of the prominent aspects of the model may be tested. So are surveyed potentially useful nested, non-nested, Hausman and information matrix type diagnostic tests of both the mean and the variance specification of the model. Finally, we illustrate the practical relevance of our proposed model and estimation and diagnostic testing procedures through an empirical example in the production analysis field.
Enhancement of discontinuities in seismic 3-d images using a java estimation library

Seismic images are difficult to interpret. The nature of the seismic experiment and the ever present sedimentary layering of the subsurface result in oscillatory and complex 3-D seismic images In principle, computer programs can enhance geologically significant image features, such as faults, erosional surfaces, and salt boundaries. Standard edge enhancement, however, fails to compute useful seismic discontinuity maps. The correlation between an image region and its best-fitting local plane wave generates clear and discriminating maps for a wide range of images. A third approach, prediction-error filtering, yields detailed fault maps for images with sharp faults. For images with rather smooth fault zones, prediction error unfortunately fails. This dissertation's discontinuity computations are the first comprehensive application of a new innovative Java estimation library: Jest (Java for estimation). Jest comprises a general and extendible library for numerical optimization for science and engineering, Jam (Java and mathematics), and a particular extension of that framework for seismic image processing, Jag (Java and geophysics). Jest successfully separates optimization and application software. To ensure the compatibility of solver and application, Jest is built around a set of simple interfaces that define method invocations for the fundamental mathematical objects of numerical optimization, such as vectors, vector spaces, operators, and solvers. Jest's solvers are implemented in terms of these mathematical objects and, consequently, possess the generality of the original abstract mathematical algorithm. Finally, the dissertation is a reproducible electronic document, a simple software filing system that organizes, preserves, and potentially transfers the technology of any computational scientific research project. The document's central makefile offers readers four standard commands: burn removes the document's result figures, build recomputes them, view displays the figures, and clean removes any intermediate files. of these commands. Although we developed these standards to aid readers we discovered that authors are often the principal beneficiaries. In combination with the World Wide Web's ability to distribute software,. standardized reproducibility of computational research offers unprecedented opportunities for collaboration and learning. In particular, the combination of Java software (such as Jest) and the concept of reproducible documents, potentially enables any reader to reproduce a scientist's results at the push of a button in a World Wide Web browser.
Guidelines for Adaptive-Optic Correction Based on Aperture Filtration

by John Paul Siegenthaler Aperture filtration refers to the effects of viewing optical wavefront distortions of infinite extent through a finite aperture. If the length-scale of the aberration is larger than this aperture, then the portion of the aberration visible in the aperture at any moment in time will not reach the full magnitude of the aberration seen in its entirety. The aperture acts as a spatial filter, mitigating the effects of large-scale wavefront distortions while having little effect on smaller-scale aberrations, with the dividing line between largescale and small-scale being the size of the aperture itself. This dissertation presents and charts the development of a set of analytic formulas for judging and predicting the effectiveness of adaptive-optic corrective systems applied over finite apertures. This includes some simplified formulas and benchmarks as guides for the minimum requirements a system will need to meet to be effective, and the maximum degree of effectiveness such systems can reasonably achieve.
MEMETA POTENSI DAN KARAKTERISTIK UMKM BAGI PENUMBUHAN USAHA BARU

Rencana Pembangunan Jangka Menengah (RPJM) periode 2004-2009 sebagaimana ditetapkan dalam Peraturan Pemerintah Nomor 07 tahun 2005, telah menetapkan arah kebijakan dan program pemberdayaan bagi Koperasi dan Usaha Mikro Kecil dan Menengah (UMKM). Sejalan dengan itu, peran koperasi dan UMKM dalam perekonomian Indonesia selama ini menunjukkan posisi strategis- nya dalam mendukung pertumbuhan ekonomi nasional yang ditandai dengan: (1) kedudukannya sebagai pemain uta- ma dalam kegiatan ekonomi di ber- bagai sektor , (2) penyedia lapangan kerja terbesar , (3) pemain penting dalam pengembangan kegiatan ekonomi lokal dan pemberdayaan masyarakat , (4) pencipta pasar baru dan sumber inovasi , serta (5) sumbangannya dalam menjaga neraca pembayaran melalui kegiatan ekspor . Rencana Strategis Kementerian Koperasi dan Usaha Kecil dan Menengah Republik Indonesia tahun 2005-2009 telah menetapkan sasaran menumbuhkan 6 (enam) juta unit usaha UMKM baru sampai tahun 2009. Dalam konteks pembangunan nasional, sasaran penum- buhan 6 juta unit usaha baru tersebut diharapkan akan menyerap sekitar 12 juta tenaga kerja (asumsi 1 unit usaha baru mempekerjakan 2 orang) dan mening- katkan daya dukung pertumbuhan ekonomi nasional. Bandingkan dengan jumlah pengangguran terbuka 11 juta dan pengangguran 1⁄2 terbuka yang mencapai 40 juta orang atau sekitar 37 % dari 107 juta angkatan kerja nasional. Pada gilirannya strategi pengembangan unit usaha baru ini dapat memberi sumbangan pada penciptaan lapangan kerja baru, pengurangan pengangguran dan mening- katkan kesejahteraan masyarakat. Sejalan dengan upaya penumbuhan unit usaha baru tersebut, maka salah satu kebijakan dan aktivitas yang telah diprogramkan adalah memetakan potensi dan kebutuhan jenis usaha yang potensial di setiap Kabupaten/Kota di Indonesia. Memahami potret dan kinerja UMKM ini menjadi hal yang penting dan strategis terutama untuk mengetahui potensi dan jenis usaha potensial di setiap Kabupa- ten/Kota seluruh Indonesia.
Epidemiological Survey of Dental Fear and Anxiety in Children Living in Transylvania

Abstract Aims The objectives of the present survey were: 1) a systematic epidemiological investigation of dental fear and anxiety among children living in the central part of Romania and 2) to identify the most fearful aspects of dental care perceived by these children. Methods In this cross-sectional study 406 schoolchildren, 170 males and 263 females, aged 11-18 yearsfrom two cities, Tirgu Mureş and Sfintu Gheorghe were assessed. The subjects’ dental fear was evaluated with the Romanian versions of Corah’s Dental Anxiety Scale (MDAS) and Kleinknecht’s Dental Fear Survey (DFS), the anxiety level with Spielberger`s State and Trait Anxiety Inventory (STAI-S, STAI-T) and their opinion about dentists with Getz’s Dental Beliefs Scale (DBS). Questionnaires were completed anonymously at school. The study was approved by theResearch Ethics Committeeof the University of Medicine and Pharmacy Tirgu Mureş. For statistical analysis t-test, one-way ANOVA and Pearson’s correlation test were used by SPSS/PC statistics v. 17.0. Results The mean (±SD) scores of the surveyed subjects (mean age 15.69±2.06 years) were high: MDAS 10.65 (±4.5), DFS 38.68 (±15.1), DBS 36.93 (±11.9), STAI-S 37.90 (±10.9) and STAI-T 41.04 (±9.9), respectively. There was a strong positive Pearson-correlation between MDAS and DFS scores (r=0.73; p≤0.01) and a somewhat lower correlation between these scales and the general anxiety scores. Except for DBS, statistically significantly higher scores were found in females for every questionnaire (t-test, p≤0.05). The 11-year-old group presented the lowest scores in every case, while the peak was around 14 years. Age was a statistically significant factor only in case of DBS, STAI-S and STAI-T (one-way ANOVA, p<0.05). Drilling and injection were considered the most fearful moments of a dental treatment. Subjects claimed dental practitioners working under time pressure and communication deficiencies. Conclusions Having their special features, our findings were consistent with the local and international data. The subjects claim lack of time and communication deficiencies with the dentists. Identifying the reasons of dental fear and anxiety, might lead to solutions of avoidance or control.
Contribution by participants in face‐to‐face business meetings: Implications for collaborative technology

Face‐to‐face business meetings are a widely used method of group interaction, and a rich source of data on what actually happens in group discussions. Active participation in a meeting is usually perceived to be making an oral contribution of some kind to the discussion. This paper describes a field study of ten face‐to‐face business meetings which were videotaped and subsequently analysed. Participant contributions were coded, and the data summarized. The mean contribution was approximately 12 seconds and 18 words. The most common contribution type was information giving. The highest single contributor in each meeting captured, on average, about 30% of the available airtime, while the two highest, combined, captured over half of the airtime. These findings are discussed within the context of requirements for designers of collaborative technology systems to support group interpersonal communication through the use of computing and data communication technologies.
Management Model of Manufacturing Workshop/Laboratory of Vocational Education in the Industry 4.0

Workshop/laboratory in vocational education is a crucial facility because it gives a distinctive characteristic in vocational education. Manufacturing workshop/laboratory has the purpose of teaching prospective graduates into skilled people that could compete in filling the development in all sectors following the occupied skill. The problem that occurred was a lack of management in the workshop/laboratory in vocational education, especially in the Industry 4.0 era. One way to overcome the problem was by managing the manufacturing workshop/laboratory. Through this paper, vocational education could utilise it to improve the students’ qualities
Generating Angry Birds-Like Levels With Domino Effects Using Constrained Novelty Search

This paper proposes a method to generate interesting Angry Birds-like game levels featuring the Rube Goldberg machine (RGM) mechanism using Constrained Novelty Search (CNS). An RGM level in Angry Birds emphasizes a domino effect, which allows it to be completed by only one bird shooting. By evolving the feasible population and infeasible population in CNS, our results show that the entropy of block-type frequencies is higher than the entropy by our previous generator and that two requirements to achieve playable RGM levels – the 100% stability and the perfect-shot rate – are met. The results indicate that the proposed method can generate levels with more diversity than our previous generator while maintaining their playability.
Klebsiella pneumoniae Infections in Hospitalized Patients: Characterization of Antibiotic Cross-resistance and Detection of Cefepime Susceptible-dose Dependent (SDD) Strains

Background & Objective: Klebsiella pneumonia ( K. pneumoniae ) is a Gram-negative enteric bacterium that causes nosocomial infections. The aims of the present study were to identify Klebsiella pneumoniae infections in hospitalized patients: characterization of cross-resistance and antibiotic resistance patterns in isolates and detection of cefepime susceptible-dose dependent in strains. Materials & Methods: In the present study (during 2014-2015), samples were collected from patients hospitalized in Ghotbadden, Faghihi and Nemazee hospitals (Shiraz, Iran). The isolates were identified as K. pneumoniae , based on the biochemical tests embedded in the API-20E system. The susceptibility testing (disc diffusion) was performed according to the clinical and laboratory standards institute guidelines (CLSI 2014). The dDetection of cefepime susceptible-dose dependent (SDD) was performed. The detection of AmpC β - lactamases producing strains was done based on the cefoxitin and cefepime disk tests. Results: In this study 111 strains of Klebsiella pneumoniae were isolated. The less effective drug was ceftazidime (37.8% were sensitive). All SDD strains were susceptible to colistin and imipenem. Colistin (96.4%) and imipenem (88.3%) were the most effective antibiotics against isolates. Respectively, 41.4% and 35.1% isolates displayed resistance to gentamicin and amikacin. All colistin resistant isolates were imipenem sensitive. Conclusion: Carbapenem drugs are effective against Klebsieella pneumoniae infections. These results indicate that multidrug-resistant strains of K. pneumoniae are rising. The routine investigation and reporting of antibiotics resistance profile in patients presenting with Klebsiella infections is suggested.
A Robust Machine Learning Technique to Predict Low-performing Students

As enrollments and class sizes in postsecondary institutions have increased, instructors have sought automated and lightweight means to identify students who are at risk of performing poorly in a course. This identification must be performed early enough in the term to allow instructors to assist those students before they fall irreparably behind. This study describes a modeling methodology that predicts student final exam scores in the third week of the term by using the clicker data that is automatically collected for instructors when they employ the Peer Instruction pedagogy. The modeling technique uses a support vector machine binary classifier, trained on one term of a course, to predict outcomes in the subsequent term. We applied this modeling technique to five different courses across the computer science curriculum, taught by three different instructors at two different institutions. Our modeling approach includes a set of strengths not seen wholesale in prior work, while maintaining competitive levels of accuracy with that work. These strengths include using a lightweight source of student data, affording early detection of struggling students, and predicting outcomes across terms in a natural setting (different final exams, minor changes to course content), across multiple courses in a curriculum, and across multiple institutions.
Automatic Detection of Blood Vessels and Classification in Retinal Images for Diabetic Retinopathy Diagnosis with Application of Convolution Neural Network

Described for the first time by MacKenzie (1879), diabetic retinopathy (DR) and today is the most common cause of blindness among persons of working age in most countries of the world. Prevention of DR is the early detection of a violation of morphology and a deterioration in the light sensitivity of the retina associated with this disease. To do this, highly informative methods of non-invasive retinal research are needed, with predictive capabilities. In this article, we propose an autonomous algorithm for such diagnostics, based on the training of the Artificial Neural Network (ANN) and the preprocessing of the image by an anisotropic diffusion filter. It allows not only to detect pathologies moreover to provide them with probabilistic evaluation of a possible variant of the disease.
Real-time and energy efficiency in Linux: theory and practice

The recent changes made in the Linux kernel aimed at achieving better energy efficiency through a tighter integration between the CPU scheduler and the frequency-scaling subsystem. However, in the original implementation, the frequency scaling mechanism was used only when there were no realtime tasks in execution. This paper shows how the deadline scheduler and the cpufreq subsystem have been extended to relax this constraint and implement an energy-aware realtime scheduling algorithm. In particular, we describe the design issues encountered when implementing the GRUB-PA algorithm on a real operating system like Linux. A set of experimental results on a multi-core ARM platform validate the effectiveness of the proposed implementation, which has been recently merged into the official Linux kernel.
IT Enabled Organizational Transformation: A Case Study in Public Sector

This research investigated the transformation initiative in one of the public sector organizations in Pakistan. The major objective was to understand the potential strategy adopted so far by the management for such initiative to be workable. The issues and challenges faced by the management during transformation process were also explored. For this purpose interviews of the senior management involved were conducted. Moreover, formal and informal discussions with team members and access of documentation regarding planning organizational transformation led to understand more about how such transformation took place. The findings reflect importance of socio-technical approach adopted by the management to address issues and problems of people, process and technology proactively. Furthermore, business process management, change management, knowledge management and IT governance seemed to be important facets for transformation initiative to be successful. The findings of this study may be claimed as essential strategic aspects to be known to top management looking forward transforming organizations for future survival.
XOMA: exclusive on-chip memory architecture for energy-efficient deep learning acceleration

State-of-the-art deep neural networks (DNNs) require hundreds of millions of multiply-accumulate (MAC) computations to perform inference, e.g. in image-recognition tasks. To improve the performance and energy efficiency, deep learning accelerators have been proposed, realized both on FPGAs and as custom ASICs. Generally, such accelerators comprise many parallel processing elements, capable of executing large numbers of concurrent MAC operations. From the energy perspective, however, most consumption arises due to memory accesses, both to off-chip external memory, and on-chip buffers. In this paper, we propose an on-chip DNN co-processor architecture where minimizing memory accesses is the primary design objective. To the maximum possible extent, off-chip memory accesses are eliminated, providing lowest-possible energy consumption for inference. Compared to a state-of-the-art ASIC, our architecture requires 36% fewer external memory accesses and 53% less energy consumption for low-latency image classification.
Autonomous Vehicle: The Architecture Aspect of Self Driving Car

Self-driving cars have received a lot of attention in recent years and many stakeholders like Google, Uber, Tesla, and so forth have invested a lot in this area and developed their own autonomous driving car platforms. The challenge to make an autonomous car is not only the stringent performance but also the safety of the passengers and pedestrians. Even with the development of technologies, autonomous driving is still an active research area and still requires a lot of experimentations and making architecture entirely autonomous. The intriguing area of self-driving car motivates us to build an autonomous driving platform. In this paper, we discuss the architecture of the self-driving car and its software components that include localization, detection, motion planning and mission planning. We also highlight the hardware modules that are responsible for controlling the car. The autonomous driving is running state-of-the-art algorithms used in localization, detection, mission and motion planning.
Convex Prophet Inequalities

We introduce a new class of prophet inequalities-convex prophet inequalities-where a gambler observes a sequence of convex cost functions ci (xi ) and is required to assign some fraction 0 ≤ xi ≤ 1 to each, such that the sum of assigned values is exactly 1. The goal of the gambler is to minimize the sum of the costs. We provide an optimal algorithm for this problem, a dynamic program, and show that it can be implemented in polynomial time when the cost functions are polynomial. We also precisely characterize the competitive ratio of the optimal algorithm in the case where the gambler has an outside option and there are polynomial costs, showing that it grows as ! (np-1/ζ), where n is the number of stages, p is the degree of the polynomial costs and the coefficients of the cost functions are bounded by [`,u].