Miracle-dz/UniFAR-FTUs huggingface dataset:
HIV self-tests for men who have sex with men, accessed via a digital vending machine: a qualitative study of acceptability

As the number of people living with undiagnosed HIV infection in the UK declines, innovative ways to access those least engaged with services are needed. This study explores the attitudes of men who have sex with men (MSM) towards using HIV self-testing (HIVST) kits distributed via a vending machine in a sauna (a licenced sex-on-premise venue). Twenty-three MSM attending the sauna were recruited to take part in semi-structured qualitative interviews. The participants were overwhelmingly positive about the HIVST vending machine. They identified convenience and flexibility as major benefits to testing in this way. The sauna was felt to be an appropriate location for the intervention. Limitations identified included the potential to reduce screening for other sexually transmitted infections and the inappropriate use of HIVST kits as a tool for risk-assessment prior to condomless sex, with a poor understanding of the window period. The implications of receiving a positive result without immediate access to support were also a concern. HIVST vending machines are an acceptable, innovative way to encourage HIV testing. Providers need to ensure this intervention is supported by adequate information regarding the limitations of the test and how to access comprehensive services to avoid any unintended negative effects.
Stroke to Dementia Associated with Environmental Risks—A Semi-Markov Model

Background: Most stroke cases lead to serious mental and physical disabilities, such as dementia and sensory impairment. Chronic diseases are contributory risk factors for stroke. However, few studies considered the transition behaviors of stroke to dementia associated with chronic diseases and environmental risks. Objective: This study aims to develop a prognosis model to address the issue of stroke transitioning to dementia associated with environmental risks. Design: This cohort study used the data from the National Health Insurance Research Database in Taiwan. Setting: Healthcare data were obtained from more than 25 million enrollees and covered over 99% of Taiwan’s entire population. Participants: In this study, 10,627 stroke patients diagnosed from 2000 to 2010 in Taiwan were surveyed. Methods: A Cox regression model and corresponding semi-Markov process were constructed to evaluate the influence of risk factors on stroke, corresponding dementia, and their transition behaviors. Main Outcome Measure: Relative risk and sojourn time were the main outcome measure. Results: Multivariate analysis showed that certain environmental risks, medication, and rehabilitation factors highly influenced the transition of stroke from a chronic disease to dementia. This study also highlighted the high-risk populations of stroke patients against the environmental risk factors; the males below 65 years old were the most sensitive population. Conclusion: Experiments showed that the proposed semi-Markovian model outperformed other benchmark diagnosis algorithms (i.e., linear regression, decision tree, random forest, and support vector machine), with a high R2 of 90%. The proposed model also facilitated an accurate prognosis on the transition time of stroke from chronic diseases to dementias against environmental risks and rehabilitation factors.
Biological Activities and Proteomic Profile of the Venom of Vipera ursinii ssp., a very Rare Karst Viper from Croatia

The karst viper (Vipera ursinii ssp.) favours high-mountain dry grasslands in southern and south-eastern Croatia. It is medically less important than other Vipera species, because of its remote habitat and the very small amount of venom that it injects by its relatively short fangs. The scientific literature on Vipera ursinii deals mostly with the morphology, ecology and distribution range of this snake, due to the species’ conservation issues, while the toxinological aspects of its venom have not so far been investigated. Here we report on the composition and biological activity of the Vipera ursinii ssp. venom. Using a proteomics approach, we have identified 25 proteins in the venom that belong to seven protein families: snake venom metalloproteinase, serine protease, secreted phospholipase A2, cysteine-rich secretory protein, snake C-type lectin-like protein, serine protease inhibitor and nerve growth factor. The Vipera ursinii ssp. venom was found to be distinctively insecticidal. Its lethal toxicity towards crickets was more than five times greater than that of Vipera ammodytes ammodytes venom, while the opposite held in mice. Interestingly, the mode of dying after injecting a mouse with Vipera ursinii ssp. venom may suggest the presence of a neurotoxic component. Neurotoxic effects of European vipers have so far been ascribed exclusively to ammodytoxins and ammodytoxin-like basic secreted phospholipases A2. Structural and immunological analyses of the Vipera ursinii ssp. venom, however, confirmed that ammodytoxin-like proteins are not present in this venom.
A ROUGH SET BASED KERNEL POSSIBILISTIC C-MEANS ALGORITHM FOR MEDICAL DATA

The Possibilistic C-Means (PCM) has many advantages over Fuzzy C-Means (FCM) in loud situations, it has a great tendency to produce similar clusters. The fuzzy set handles overlapping partitions, the rough set handles instability, dubiousness, fragmentation, and incoherence in a class. The kernel handles linear separability of the clusters. In this paper, RKPCM algorithm is proposed
Leg Volume Measurements with a Modified Optoelectronic Measurement System

Objective: To evaluate the reproducibility of a modified optoelectronic volume measurement system (the volometer). Design: In the first part of the study healthy volunteers and one investigator were used to evaluate the intraobserver reproducibility; in the second part one healthy volunteer and different investigators were used to evaluate the interobserver reproducibility. Setting: Department of Dermatology, Academisch Ziekenhuis Maastrict, The Netherlands. Patients: Twenty healthy volunteers for the first part of the study; eight doctors and one healthy volunteer for the second part. Main outcome measures: Optoelectronic volume measurements on one leg over a standard distance of 40 cm and over 20 cm with the use of a laser pointer as calibration. A total of eight measurements were performed on each volunteer. The same procedure was performed by different investigators on one volunteer. Results: The modified optoelectronic measurement system with laser pointer calibration showed an improvement of the reproducibility of 27% in the intraobserver study. An improvement of 11% was found in the interobserver study. Conclusions: The modified optoelectronic volume measurement system (the volometer) provides a better reproducibility than the standard system. The instrument can be used for easy and rapid volume measurements in phlebological practice.
In vitro Antibacterial and Antioxidant Activities of Star Fruit (Averrhoa carambola)

The present study, the potential antibacterial and antioxidant activities of Averrhoa carambola fruits were evaluated in terms of inhibition zone diameter and total reducing power assay. All fruit extracts were higher inhibition zone than juice. Fruit extract inhibited human pathogens;21mm inhibition on Salmonella typhi, 20 mm on Escherichia coli, 19 mm on Shigella boydii than ampicillin. The highest inhibition zones of fruit extracts against on Salmonella typhi, and Staphylococcus aureus were 21 mm each. The presence of antioxidant activity was preliminary tested by phosphomolybdic acid. The two portions; juice and extract of samples were estimated by phosphomolybdenum revealed potent antioxidant activities.
COMPARATIVE ANALYSIS OF PID CONTROLLED QUASI SINGLE STAGE INVERTER FOR RENEWABLE ENERGY SYSTEM

The main objective of this paper is to compare the performance of Quasi Single Stage Inverter (QSSI) for Renewable Energy System. The QSSI system is composed of active switches it is utilized to perform voltage buck and boost conversion without using additional passive elements, which is in favour to the system power density and efficiency. The comparative analysis are presented and the control strategy applied in the QSSI is also developed. The performance of QSSI with PID controller is accurate and fast response. The simulation results are verified experimentally and the output is free from ripples and has a regulated output voltage.
Modular Framework for MCU Programming

This thesis discuss about a framework implementation which aims to simplify and speed up the writing of a firmware for an embedded system. The goal is to create a system able to drop drastically the implementation time of IoT nodes. To obtain this result, the idea is the creation of a totally generic modular system which exploits several blocks run concurrently that implements functions agnostic to the data elaborated inside. The first part of the thesis describes the theorical aspects, such as the features of an RTOS and the parameters to compare them, in order to select the more suitable one for the framework implementation. Afterwards, the most important protocols used in the realization of the blocks are presented, such as the MQTT and the I2C. In the second part, the framework is described in depth; starting from an analysis of the basic module and the interaction with other ones, to the explanation of the different implemented blocks. Finally, in the last part, it is shown an analysis of performance and complexity of the code in project implemented with the described framework.
A Recommended Model for User Trust and Item Ratings

: As an indispensable means of data filtering, the recommender systems are attracted and created heaps of interest within the past 10 years. The previous recommendation techniques and approaches are wide analysed within the data retrieval analysis communities, machine learning techniques and data processing. Because of their nice industrial demand, the advice systems are with success puzzled out in industrial environments and in business areas, like recommendation of the merchandise at Amazon, recommendation of music at iTunes, recommendation of films at Netflix, and so on. During this paper, we tend to propose a trust-based grid factoring technique for recommendations. TrustSVD arranges distinctive data sources into the advice demonstrate memory actuality objective to decrease the info poorness and chilly begin problems and their dirtiness of proposition execution. In Proposal System used proposal in factor-to-factor proposal And User trust suggestion and an examination of social trust information from four certifiable information sets recommends that the unequivocal and additionally the evident result of the 2 assessments and trust ought to be considered in a very recommendation seem. TrustSVD so develops better of a best in school proposal computation, SVD++ (which uses the categorical and bound result of assessed things), by in addition combining each the unequivocal and understood result of trustworthy and trusting in clients on the will of things for a dynamic customer. In addition, dynamic suggestion area unit occur with the help of high n suggestion calculations the projected framework is that the first to expand SVD++ with social place stock in data.
Time-Domain Neural Network Approach for Speech Bandwidth Extension

In this paper, we study the time-domain neural network approach for speech bandwidth extension. We propose a network architecture, named multi-scale fusion neural network (MfNet), that gradually restores the low-frequency signal and predicts the high-frequency signal through the exchange of information across different scale representations. We propose a training scheme to optimize the network with a combination of perceptual loss and time-domain adversarial loss. Experiments show the proposed multi-scale fusion network consistently outperforms the competing methods in terms of perceptual evaluation of speech quality (PESQ), signal to distortion rate (SDR), signal to noise ratio (SNR), log-spectral distance (LSD) and word error rate (WER). More promisingly, the multi-scale fusion network requires only 10% of the parameters of the time-domain reference baseline.
Learning-Algorithms from Bayesian Principles

Machine-learning algorithms are commonly derived using ideas from optimization and statistics, followed by an extensive empirical eﬀorts to make them practical as there is a lack of underlying principles to guide this process. In this paper, we present a learning rule derived from Bayesian principles, which enables us to connect a wide-variety of learning algorithms. Using this rule, we can derive a wide-range of learning-algorithms in ﬁelds such as probabilistic graphical models, continuous optimization, and deep learning. This includes classical algorithms such as least-squares, Newton’s method, and Kalman ﬁlter, as well as modern deep-learning algorithms such as stochastic-gradient descent, RMSprop and Adam. Overall, we show that Bayesian principles not only unify, generalize, and improve existing learning-algorithms, but also help us design new ones. [ This is a working draft and a work in progress ]
Complex Systems, Statistical Learning, and Pickles Lecture 1 Notes for CSSS2010

Disclaimer: These are some notes to help with the lectures. They are very rough (especially the grammar); the exercises in particular come with No Guaranty’s. (Of course, if you have the intestinal fortitude, then all the better! ) Any corrections and feedback would be super-duper appreciated. I do not deal with basic aspects of the lecture already in [6], or very basic aspects of Markov chain theory (see [3]). The fundamental role of of commute times was introduced in [1]. The electric network theory underlying that work is beautifully described in [2]. One could redo this presentation from the point of view of the Green’s function, this (and much more) can be found in [4] and [5]. A brief comment on the notation used here. Sates will be denoted with lower case letters like i, j,k, l. or a,b,c,d,e, . A vector like v will denote a column vector, and when viewing v as a row vector we use vtr. We will denote the matrix with a given vector v on the diagonal and zero otherwise as [v]. We let 1 denote the vector of ones, 0 denote the vector of zeros, δa will be the vector which is 1 at a and 0 otherwise, and we will let I denote the identity matrix.
A Coordinated Approach to Chemical Reactivity Using a scanning probe microscope , researchers measure the dependence of an atom ’ s chemical reactivity on the number of chemical bonds it forms

A toms are gregarious by nature, escaping a solitary existence by forming chemical bonds with their neighbors. Chemical reactivity, in turn, depends on the degree of atomic “isolation”; a lonely atom will more readily interact and form bonds with others, as compared to its more “coordinated” counterparts that are chemically engaged with other atoms. This coordination concept plays a central role in our understanding of chemical and catalytic activity. What has been lacking to date, however, is a direct and quantitative measurement of the variation in chemical reactivity of atoms having different degrees of coordination. This is precisely what Julian Berwanger at the University of Regensburg, Germany, and colleagues have now achieved [1]. The Regensburg team
Solving Maximum Flows on Distribution Networks: Network Compaction and Algorithm

The maximum flow (max-flow) problem is a fundamental network optimization problem which computes for the largest possible amount of flow sent through the network from a source node to a sink node. This problem appears in many applications and has been investigated extensively over the recent four decades. Traditional max-flow problem may require some modification in its constraints to deal with real-world applications. Fang and Qi proposed a new max-flow model, named as manufacturing network flow model, to describe a network with special distillation nodes or combination nodes. Based on this new model, Ting proposes an approach to solve the max-flow problem in a distribution network which contains both ordinary and distillation nodes. Her approach identifies an augmenting subgraph connecting both source and sink nodes which can be further decomposed into several components where flows in each component can be expressed by a single variable and solved by a system of homogeneous linear equations. Their method requires manual detection for components and thus is not trivial to implement. In this paper, we first propose a polynomial-time network compaction algorithm which simplifies the distribution network, and then present detailed procedures for solving the max-flows on a distribution network.
Interpreting Solar Wind Turbulent Spectra beyond Taylor’s Hypothesis

In this Letter we apply a methodology, recently proposed by Bourouaine & Perez (BP19), to interpret solar wind turbulent power spectra beyond the Taylor approximation (TA). The turbulent power spectra were measured using Helios spacecraft data near 0.6 au. We use the model proposed in BP19 to reproduce the field-perpendicular power spectrum E(k⊥) of antisunward Alfvénic fluctuations in the plasma frame (where k⊥ is the field-perpendicular wavenumber) from the corresponding measured frequency power spectrum along the sampling angle θb, which is the angle between the local magnetic field and the sampling direction. Here ω = 2πf and f is the frequency of the time signal. Interestingly enough, we found that for all corresponding measured frequency power spectrum the reproduced field-perpendicular power spectrum E(k⊥) is the same and independent of the considered sampling angle θb. This finding is consistent with the fact that the analyzed turbulence is strong and highly anisotropic with (where is the field-parallel wavenumber). Furthermore, for this specific time signal we found that the commonly used TA is still approximately valid with the important difference that a broadening in k⊥ for each angular frequency ω is present. This broadening can be described in the context of the methodology proposed in BP19.
Supporting Block Device Abstraction on Storage Class Memory ∗

Storage Class memory is a dream device. Once realized into proper scale, it will resolve most of the hassles which current storage system administrators experience. In this work, we focus our effort in devising a method to impose block device abstraction over Storage Class Memory. Block I/O on Storage Class Memory needs to be performed in atomic fashion. We propose Log-Based Block Mapping mechanism to provide atomicity of block operation in Storage Class Memory. We perform out-of-place update on Storage Class Memory block write and use block mapping table to locate the actual block. To minimize the overhead of accessing mapping table, we deﬁne new path for read and write operation. We implement the new I/O subsystem on embedded system with 8 MByte FRAM. According to our experiment, the overhead of supporting atomicity is 0.1% and 1% for read and write operation, respectively.
Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning

Traditional robotic approaches rely on an accurate model of the environment, a detailed description of how to perform the task, and a robust perception system to keep track of the current state. On the other hand, reinforcement learning approaches can operate directly from raw sensory inputs with only a reward signal to describe the task, but are extremely sampleinefficient and brittle. In this work, we combine the strengths of model-based methods with the flexibility of learning-based methods to obtain a general method that is able to overcome inaccuracies in the robotics perception/actuation pipeline, while requiring minimal interactions with the environment. This is achieved by leveraging uncertainty estimates to divide the space in regions where the given model-based policy is reliable, and regions where it may have flaws or not be well defined. In these uncertain regions, we show that a locally learned-policy can be used directly with raw sensory inputs. We test our algorithm, Guided Uncertainty-Aware Policy Optimization (GUAPO), on a real-world robot performing peg insertion. Videos are available at: https://sites.google. com/view/guapo-rl.
A Bidirectional Wireless Power Transfer System Control Strategy Independent of Real-Time Wireless Communication

For the bidirectional wireless power transfer (BWPT) technology, the phase synchronization between the transmitting and receiving converters is very essential and difficult to achieve by wireless communication between two sides because the delay and data transfer interval of wireless communication severely influence the control performance. This article proposes a novel control strategy for BWPT, which can achieve the phase synchronization and maximum efficiency point tracking independent of the real-time wireless communication. To solve the overshoot and oscillation problem incurred by the proposed control strategy and the nature of the system itself, the dynamics of a series–series compensated BWPT system is then analyzed and a small-signal model of this system is derived based on the generalized state space averaging and extended describing function methods. Using the small-signal model, a BWPT control system based on the proposed control strategy is designed and optimized. The validity of the proposed strategy and the performance of the control system are verified experimentally using a 3.3-kW prototype system.
NALPA: A Node Ability Based Label Propagation Algorithm for Community Detection

Community is an important topological characteristic of complex networks, which is significant for understanding the structural feature and organizational function of networks, and community detection has recently attracted considerable research effort. Among community detection methods, label propagation technology is widely used because of its linear time complexity. However, due to the randomness of the node order of label updating and the order of label launching in label propagation, the instability of community detection approaches based on label propagation becomes a challenge. In this paper, a new label propagation algorithm, Node Ability based Label Propagation Algorithm (NALPA), is proposed to discover communities in networks. Inspired from human society and radar transmission, we design four node abilities (propagation ability, attraction ability, launch ability and acceptance ability), label influence and a new label propagation mechanism to deal with the instability and enhance the efficiency. Experimental results on 42 synthetic and 14 real-world networks demonstrate that NALPA outperforms state-of-the-art approaches in most cases. In a case study, NALPA is applied to a drug network in Traditional Chinese Medicine (TCM) and can discover the drug communities where drugs have similar efficacy for treating Chronic GlomeruloNephritis (CGN).
Adaptive Input-Power Distribution in Doherty Power Amplifier using Modified Wilkinson Power Divider

This paper presents the formulation and experimental validation of adaptive input-power distribution in Doherty Power Amplifiers (DPAs) using modified Wilkinson Power Divider (WPD). It will be theoretically shown that ideal power-dependent power-distribution profile for Doherty operation can be obtained by just adjusting the isolation resistor in the divider when input reflection phase of the auxiliary amplifier in power-back off range are properly set. The proposed circuit was experimentally verified by fabricating a 3.5 GHz 32 W DPA using GaN HEMT devices. Experiments showed that, by optimizing the isolation resistor, ACLR improvement of 9 dB were obtained under 8.5 dB PAPR 10 MHz LTE signal. The optimized DPA based on this technique showed an average drain efficiency of 55 % and ACLR of −50 dBc using the same signal after Digital Pre-Distortion (DPD).