Miracle-dz/UniFAR-FTUs huggingface dataset:
Errorless skill learning in long-term Korsakoff care

I agree with Rensen et al. (2018) on that the introduction of errorless skill learning in long-term Korsakoff care can result in massive reductions of behavioral problems (i.e. aggression and apathy), increased quality of life, and increased autonomy. In response to their article, I would also advocate that the underlying mechanism of behavioral change in severely affected patients is also the result of seeing the patient as a cooperative individual able to make choices despite his or her severe cognitive disorders. Korsakoff’s syndrome is a severe neuropsychiatric syndrome following vitamin B1 deficiency resulting from alcoholism (Arts et al., 2017). A large majority of patients with Korsakoff’s syndrome lack insight into their situation (Egger et al., 2002), and are judge-ordered to stay in long-term care (Van den Hooff and Goossensen, 2015). Here, they report to not to feeling well at home (Oudman and Zwart, 2012). As Van den Hooff and Goossensen (2015) put forward in their qualitative study of Korsakoff’s syndrome patients in long-term care: “Respondents are imprisoned, although they are not classified as criminals. Their autonomy is affected, and they are seen as incompetent individuals. In some institutions, respondents have to wear digital bracelets giving signals when crossing certain lines on the nursing home grounds. ” Essentially, patients report not being listened to, and the patient’s perspective is neglected by professionals in long-term care for Korsakoff’s syndrome patients (Van den Hooff and Goossensen, 2014). One of the essential interventions used by Rensen et al. (2018) was to ask the patients what skill they wanted to learn, hereby giving full support to the goals and values of the Korsakoff’s syndrome patients themselves. The best results regarding learning, quality of life, and behavioral symptom reduction were seen in patients were the actual goals of the patient were accomplished. In coercive admissions of Korsakoff’s syndrome patients to (specialized) longterm care facilities, there is an urgent need to look at the patient’s perspective instead of merely focusing on the healthcare professional perspective.
Reduced heart rate variability in adults with autism spectrum disorder

A growing body of research has suggested heart rate variability (HRV) may be reduced in autism spectrum disorder (ASD) in comparison to neurotypical cohorts. While there have been several studies investigating HRV in children diagnosed with ASD, few studies have been conducted in adults. The objective of the current study was to investigate autonomic nervous system activity as assessed by HRV in adults diagnosed with ASD. We hypothesized that adults with ASD would show a reduction in HRV compared to neurotypical participants. Participants diagnosed with ASD (n = 55) were recruited from the Autism Clinic for Translational Research at the Brain and Mind Centre (University of Sydney) between 2013 and 2017. Neurotypical participants were recruited from advertisements and online media. Resting state heart rate was measured for 5 min while participants sat in an upright position. Results showed there was an overall significant difference in resting‐state HRV between adults diagnosed with ASD compared to the neurotypical control group. Logarithmically transformed high frequency (HF) and root mean square of successive differences were particularly decreased in the ASD group, suggesting lower parasympathetic activity. The use of psychotropic medications and comorbidities were associated with reductions in low frequency of HRV. Our data suggest an overall dysregulation in resting autonomic activity in adults with ASD. This may represent an important physiological mechanism leading to potential cardiovascular risk in ASD, which warrants further investigation. Autism Res 2019, 12: 922–930. © 2019 International Society for Autism Research, Wiley Periodicals, Inc.
Photochemical /Photocytotoxicity studies of new tetrapyrrolic structures as potential candidates for cancer theranostics.

Detailed photochemical and photocytotoxicity studies of two new porphyrins: 5,10,15,20-meso-tetrakis-(4-acetoxy-3-methoxyphenyl) porphyrin (P2.1) and 5-(4-hydroxy-3-methoxyphenyl)-10,15,20-tris-(4-acetoxy-3-methoxyphenyl)porphyrin (P2.2) are reported, as potential candidates for theranostics. For powdered samples of P2.1 and P2.2 adsorbed onto a powdered biocompatible substrate, polyethylene glycol (PEG), a concentration study was performed, correlating the fluorescence emission intensity with sample absorption to determine the useful concentration range for photodynamic therapy of cancer (PDT) in which aggregation does not occur. Fluorescence emission quantum yields and lifetimes were also determined for all samples under study, because it is well known that these parameters for aggregated porphyrins are lower than those obtained for the monomeric forms of the photosensitizers and the same occurs with the singlet oxygen quantum yields, therefore decreasing the PDT action. Cytotoxicity studies were performed in dark and illuminated conditions, using four different human tumor cell lines and one non-tumor primary cell culture. The two porphyrins evidenced very good photodynamic activity, as the inhibition for tumor cellular growth after excitation in the phototherapeutic window evidenced GI50 values smaller than 5 M in most cases. A comparison with tetraphenylporphyrin was made in order to evidence the extreme importance of the hydrophilic and hydrophobic tetrapyrrolic substituents to the PDT behaviour.
Sustained Effect of a Community-based Behavioral and Nutrition Intervention on HIV-related Outcomes Among Women Living With HIV in Rural India: A Quasi-experimental Trial.

BACKGROUND Women living with HIV (WLH) in rural communities face challenges to obtaining treatment and accurate disease-related information. Nutritional deficits exacerbate disease progression. SETTING WLH were recruited from primary health centers in rural India. METHOD A quasi-experimental trial of a comprehensive Accredited Social Health Activist (Asha)-supported intervention compared 4 distinct Asha-based programs [(1) standard education (SE) alone; (2) nutrition education (+NE); (3) nutrition supplements (+NS); or (4) nutrition education and nutrition supplements (+NENS)] on key disease and nutrition-related outcomes [CD4 count, body mass index (BMI), serum albumin, and hemoglobin]. Assessments occurred at baseline, and months 6 (immediately after intervention), 12, and 18. Multilevel modeling examined effects of program (group) over time. FINDINGS Among 600 WLH enrolled (n = 150 per arm), mean age, CD4 count, and BMI (kg/m) were 34.31, 447.42, and 20.09, respectively, at baseline. At 18-month follow-up, program 4 (+NENS) experienced greatest improvements in CD4 counts compared with program 1 (+SE) [adjusted difference = 223.81, 95% confidence interval (CI): 170.29 to 277.32]. For BMI, programs 3 (+NS; adjusted difference = 2.33, 95% CI: 1.39 to 3.26) and 4 (+NENS; adjusted difference = 2.14, 95% CI: 1.17 to 3.12) exhibited greater gains compared with program 1 (+SE). Programs 3 and 4 were not significantly different from each other (adjusted difference = -0.18, 95% CI: -1.12 to 0.76). Hemoglobin and serum albumin also improved over time; program 4 (+NENS) exhibited the greatest gains. CONCLUSIONS A low-cost Asha-supported behavioral and nutritional intervention improved outcomes for WLH. Gains were sustained at 18-month follow-up. Similar approaches may help improve HIV and other infectious disease-related outcomes in vulnerable populations.
Predicting individual knee range of motion, knee pain, and walking limitation outcomes following total knee arthroplasty

Background and purpose — Up to 20% of patients are dissatisfied after total knee arthroplasty (TKA), mainly because of pain and restricted physical function. We developed a prediction model for 6-month knee range of motion, knee pain, and walking limitations in patients undergoing TKA surgery. Patients and methods — We performed a prospective cohort study of 4,026 patients who underwent elective, primary TKA between July 2013 and July 2017. Candidate predictors included demographic, clinical, psychosocial, and preoperative outcome measures. The outcomes of interest were (i) knee extension and flexion range of motion, (ii) knee pain rated on a 5-point ordinal scale, and (iii) self-reported maximum walk time at 6 months post TKA. For each outcome, we fitted a multivariable proportional odds regression model with bootstrap internal validation. Results — At 6 months post TKA, around 5% to 20% of patients had a flexion contracture ³ 10°, range of motion < 90°, moderate to severe knee pain, or a maximum walk time £ 15 minutes. The model c-indices (the probabilities to correctly discriminate between 2 patients with different levels of follow-up TKA outcomes) when evaluating these patients were 0.71, 0.79, 0.65, and 0.76, respectively. Each postoperative outcome was strongly influenced by the same outcome measure obtained preoperatively (all p-values < 0.001). Additional statistically significant predictors were age, sex, race, education level, diabetes mellitus, preoperative use of gait aids, contralateral knee pain, and psychological distress (all p-values < 0.001). Interpretation — We have developed models to predict, for individual patients, their likely post-TKA levels of knee extension and flexion range of motion, knee pain, and walking limitations. After external validation, they can potentially be used preoperatively to identify at-risk patients and to help patients set more realistic expectations about surgical outcomes.
Describing Nurse Manager Role Preparation and Competency: Findings From a National Study

OBJECTIVE The aim of this study was to describe the role preparation and competency development of nurse managers. BACKGROUND Approximately 70 000 nurses are retiring annually. This represents a significant loss of nursing leadership wisdom. Few systematic studies of nurse manager role preparation and competency development have been conducted. METHODS An electronic survey was completed by a national sample of 647 nurse managers managing 964 patient care units in 54 hospitals. RESULTS Findings suggest that nurse managers rate themselves as competent for the 1st 6 years as a nurse manager. They begin to reach proficiency by year 7. Experience had the strongest association with nurse manager competence, followed by graduate leadership education. CONCLUSIONS Nurse executives need to evaluate the bench strength of their nursing leadership talent pool. They need to develop policies and strategies that promote nurse manager competence.
B-cell abnormalities in HIV-1 infection: roles for IgG3 and T-bet.

PURPOSE OF REVIEW Numerous B-cell abnormalities in HIV-1 infection have been described over the past three decades yet have remained poorly defined mechanistically. We review recent studies that describe mechanisms of B-cell dysregulation in chronic HIV-1 infection associated with IgG3 and T-bet. RECENT FINDINGS HIV-1 infection causes hypergammaglobulinemia and dysregulation of B-cell populations, including the expansion during chronic viremia of functionally impaired tissue-like memory (TLM) B cells. TLM B cells and B cells in other conditions of chronic activation and inflammation with similar phenotypes are characterized by increased expression of the transcription factor T-bet and preferential immunoglobulin class-switching to IgG3. However, defects in B-cell function during chronic HIV-1 viremia are also associated with the binding of soluble IgG3 to IgM-expressing B cells, with the highest intensities observed on TLM B cells. The consequence of IgG3 binding to TLM B cells is increased clustering of the IgM B-cell receptor and decreased response to stimulation. SUMMARY The identification of T-bet and IgG3 as the regulators of B-cell function in chronic HIV-1 viremia could provide new targets for therapeutic intervention aimed at reversing the damaging effects of HIV-1-associated chronic immune activation.
Boundary detection of color images using neural networks

In boundary detection of color images, it is essential to form local edge elements detected by a local edge detection method into groups for finding straight or curved lines. A new boundary detection method based on the Hopfield neural network is proposed. First, an image is divided into blocks. In each block, at most two edge segments are detected by a proposed edge tracing method. Then, a unit of the Hopfield neural network is assigned to each edge segment. Some properties of edge segments belonging to a boundary, such as colors and directions, are embedded in an objective function of the network, and the boundary is detected by minimizing the function. To reduce computation time, a fast algorithm of a boundary detection method is also proposed. The experimental results show that the proposed method is applicable for the partially disconnected and/or blurred boundaries.
Bitcoin users deanonimization methods

Bitcoin is the most popular cryptocurrency on the planet. It relies on strong cryptography and peer-to-peer network. Bitcoin is gaining more and more popularity in criminal society. That is why Bitcoin is often used as money laundering tool or payment method for illegal products and services. In this paper we explore various methods for Bitcoin users deanonimization, which is an important task in anti-money laundering process and cybercrime investigation.
Recurrent neural networks and Fibonacci numeration system of order s(s/spl ges/2)

In the Fibonacci numeration system of order s(s/spl ges/2), every positive integer admits a unique representation which does not contain s consecutive digits equal to 1 (called normal form). We show how this normal form can be obtained from any representation by recurrent neural networks. The addition of two integers in this system and the conversion from a Fibonacci representation to a standard binary representation (and conversely) can also be realized using recurrent neural networks. <<ETX>>
Similarity analysis of molecules with self-organizing surfaces-An extension of the self-organizing map

The authors give an extension of the self-organizing map, called self-organizing surface, which has been developed in order to cope with the typical topological defects that self-organizing maps develop when mapping the surface of a 3D object to a 2D torus of neurons. The extension replaces the local grid with a parallel distance calculation on arbitrary topologies. The technique was first developed as an implementation technique of self-organizing maps on massively parallel computers. However, it has given rise to a conceptual extension of the self-organizing map where real-valued neuron positions can now be distributed over arbitrary non-convex and even disconnected areas. The extension naturally allows for an efficient dynamic change of the network size and density. The algorithm is demonstrated by mapping the surfaces of molecules to the surface of a sphere. <<ETX>>
Warranty analysis of a two-component system with type I stochastic dependence

This article develops the warranty cost analysis of a two-component in-series system with stochastic dependence. From the perspectives of both the manufacturer and the consumer, the short-run total profits and costs under the non-renewing free replacement (non-renewing free replacement warranty) policy and the renewing free replacement warranty policy are derived, respectively. Numerical examples are given showing the impact of the failure dependence to both the consumer and the manufacturer.
A Critical Analysis of Software Failure Causes From Project Management Perspectives

With the growth of technology, people and companies are more relying on software systems. For that, we need a product/software that is trustworthy, reliable, and economical. It should be maintainable, dependable and useable. If the software is developed with much accuracy, everything is being done by it as planned and the software is set to the market then the success rate will be high. But if there is any bug in the software then not only the software will fail but it will also affect the organizations that are responsible for making it. So the failure of software also has a great impact on the organization. In this research, we are going to present a detailed and critical analysis of all those causes due to which the software fails and the factors that hinder in a project success. We will study the existing software development processes and also analyze how they can helpful in reducing these causes.
Eğitimde Oyunlaştırma Araştırmalarında Güncel Eğilimler

Gamification is a subject that is often expressed and studied in the field of education. Gamification with potential features such as increasing student motivation, effective participation in the learning process, at the same time, makes it easier to achieve academic achievement and learning achievements. In this framework, the classification of researches on the gamification as a study subject that is becoming increasingly prominent in the field of education technology, will be supported in the context of determining the point to be reached in this respect and steering the future works. In this study, the articles published in SCI-EXPANDED, SSCI, A & HCI, CPCI-S, CPCI-SSH and ESCI indices between 2008 and 2017 were examined and analyzed in a thematic framework based on certain criterions. 72 research articles from the databases related to key words which are corresponded with 177 research criteria and were systematically analyzed using the document classification method with using the publication classification form. It has been revealed that the most researches carried out in the context of the publication year and the number of published researches were performed in 2016. It has been determined that most of the systematically investigated studies have a sample size of 31-100 and the sample selection method is mainly simple/random sampling. This document analysis study also revealed that the frequency of empirical statistics is more frequently used as a research approach, attitude-perception-personality-aptitude test is used as a data gathering tool and frequency/percentage/charts are more frequently used as a data analysis method. On the other hand, it was revealed that gamification applications for engineering education come into prominence as a field of education. It has been determined that the points are mostly used gamification components and the motivation theories were frequently preferred.
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average

Presently the most successful approaches to semi-supervised learning are based on consistency regularization, whereby a model is trained to be robust to small perturbations of its inputs and parameters. To understand consistency regularization, we conceptually explore how loss geometry interacts with training procedures. The consistency loss dramatically improves generalization performance over supervised-only training; however, we show that SGD struggles to converge on the consistency loss and continues to make large steps that lead to changes in predictions on the test data. Motivated by these observations, we propose to train consistency-based methods with Stochastic Weight Averaging (SWA), a recent approach which averages weights along the trajectory of SGD with a modified learning rate schedule. We also propose fast-SWA, which further accelerates convergence by averaging multiple points within each cycle of a cyclical learning rate schedule. With weight averaging, we achieve the best known semi-supervised results on CIFAR-10 and CIFAR-100, over many different quantities of labeled training data. For example, we achieve 5.0% error on CIFAR-10 with only 4000 labels, compared to the previous best result in the literature of 6.3%.
Time-sensitive Travel Route Recommendations

With the rapid development of mobile Internet and the widespread use of mobile devices, social networking platforms such as Gowalla and Foursquare provide sign-in sharing capabilities. They use these functions to collect a large number of check-in data that can reflect the user's travel patterns. These data can effectively support the recommendation and search of travel routes. This paper studies the recommendation of tourist routes for the change of scenic spots popularity caused by time change in spatial area. We quantitatively represent the changes in the popularity of the locations in the region in the form of time series, and recommend the travel routes that are most suitable for the travel time of the users. The route recommendation of the dynamic transition graph mode can be used for tourism route recommendation, scenic area construction, and safe facility deployment. The proposed dynamic transition graph model was constructed with real data sets and sufficient experiments were conducted. It is verified that under the condition of different parameters, the user's income in the recommended route is higher than the existing work by over 10%. This verifies the accuracy and effectiveness of the proposed method.
Embedded Stereokamera-basierte reaktive Kollisionsvermeidung für UAVs Embedded stereo vision based reactive collision avoidance for UAVs

Zusammenfassung Dieser Beitrag beschaftigt sich mit der automatischen Kollisionsvermeidung fur UAVs basierend auf einer Stereokamera. Dazu haben wir ein echtzeitf¨ahiges und energieeffizientes System realisiert, das mit beschr¨ankten Ressourcen zurechtkommt. Zur Hinderniserkennung werden aus den Bildern der Stereokamera Disparit¨atskarten berechnet. Diese werden in die sog. U- bzw. V-Maps konvertiert, mit deren Hilfe mit Methoden der Mustererkennung Objekte erkannt werden. Die Kollisionsvermeidung basiert auf einem reaktiven Ansatz, welcher den kurzesten Weg zum Umfliegen eines sich in kritischen Entfernung befindlichen Hindernisses sucht. Wir haben unser System auf einer Xilinx Zynq Ultrascale+ Prozessoreinheit mit einer ARM Cortex-A53 Quadcore-CPU und einem FinFET+ FPGA umgesetzt. Die Disparitatsschatzung wurde auf dem FPGA implementiert,wahrend die weniger rechenintensive Kollisionsvermeidung zur Ausfuhrung auf der CPU umgesetzt wurde. Die Portierung der in C/C++ geschriebenen Algorithmen zur Disparitatsschatzung auf den FPGA erfolgte mittels High-Level-Synthese, welche die Lucke zwischen Anwendungsentwicklung und Hardware-Optimierung schliest und damit eine Reduktion der Entwicklungszeit und -Kosten ermoglicht.
Overcoming Catastrophic Forgetting for Continual Learning via Model Adaptation

Learning multiple tasks sequentially is important for the development of AI and lifelong learning systems. However, standard neural network architectures suffer from catastrophic forgetting which makes it difficult for them to learn a sequence of tasks. Several continual learning methods have been proposed to address the problem. In this paper, we propose a very different approach, called Parameter Generation and Model Adaptation (PGMA), to dealing with the problem. The proposed approach learns to build a model, called the solver, with two sets of parameters. The first set is shared by all tasks learned so far and the second set is dynamically generated to adapt the solver to suit each test example in order to classify it. Extensive experiments have been carried out to demonstrate the effectiveness of the proposed approach.