Miracle-dz/UniFAR-FTUs huggingface dataset:
C2P2: A Collective Cryptocurrency Up/Down Price Prediction Engine

We study the problem of predicting whether the price of the 21 most popular cryptocurrencies (according to coinmarketcap.com) will go up or down on day d, using data up to day d-1. Our C2P2 algorithm is the first algorithm to consider the fact that the price of a cryptocurrency c might depend not only on historical prices, sentiments, global stock indices, but also on the prices and predicted prices of other cryptocurrencies. C2P2 therefore does not predict cryptocurrency prices one coin at a time - rather it uses similarity metrics in conjunction with collective classification to compare multiple cryptocurrency features to jointly predict the cryptocurrency prices for all 21 coins considered. We show that our C2P2 algorithm beats out a recent competing 2017 paper by margins varying from 5.1-83% and another Bitcoin-specific prediction paper from 2018 by 16%. In both cases, C2P2 is the winner on all cryptocurrencies considered. Moreover, we experimentally show that the use of similarity metrics within our C2P2 algorithm leads to a direct improvement for 20 out of 21 cryptocurrencies ranging from 0.4% to 17.8%. Without the similarity component, C2P2 still beats competitors on 20 out of 21 cryptocurrencies considered. We show that all these results are statistically significant via a Student's t-test with p < 10^-5.
Harnessing Reinforcement Learning for Neural Motion Planning

Motion planning is an essential component in most of today's robotic applications. In this work, we consider the learning setting, where a set of solved motion planning problems is used to improve the efficiency of motion planning on different, yet similar problems. This setting is important in applications with rapidly changing environments such as in e-commerce, among others. We investigate a general deep learning based approach, where a neural network is trained to map an image of the domain, the current robot state, and a goal robot state to the next robot state in the plan. We focus on the learning algorithm, and compare supervised learning methods with reinforcement learning (RL) algorithms. We first establish that supervised learning approaches are inferior in their accuracy due to insufficient data on the boundary of the obstacles, an issue that RL methods mitigate by actively exploring the domain. We then propose a modification of the popular DDPG RL algorithm that is tailored to motion planning domains, by exploiting the known model in the problem and the set of solved plans in the data. We show that our algorithm, dubbed DDPG-MP, significantly improves the accuracy of the learned motion planning policy. Finally, we show that given enough training data, our method can plan significantly faster on novel domains than off-the-shelf sampling based motion planners. Results of our experiments are shown in this https URL.
DIAGNOSIS AND TREATMENT OF INFLAMMATORY MUSCLE DISEASES

The inflammatory myopathies are rare. No accurate figures for incidence or prevalence are available but if one takes the two most common conditions, dermatomyositis and inclusion body myositis, their combined annual incidence is probably less than 200 new cases per annum in the UK (population ~60 million). Given that patients with inflammatory myopathy may present to, and be managed within, one of several specialties (for example, dermatology, rheumatology, neurology, general medicine), and that there are about 250 neurologists in the UK, it is apparent that most general neurologists are going to see these conditions on average probably no more than once every couple of years. In perhaps 70% of cases, the diagnosis and management are straightforward and successful early experience may encourage a sense of competence that is only shaken when things do not go as expected. There is no doubt that patients are best served by somebody with specific expertise and interest in these rare conditions, and ideally there should be such a specialist in each region to whom they can be referred. This article will consider the classification of the inflammatory myopathies, the clinical features of the so-called idiopathic inflammatory myopathies, and approaches to their diagnosis and management. With respect to drug treatment there is an absolute dearth of randomised controlled trials and the best advice that can be offered is based on “expert opinion”. Multicentre trials are desperately needed. Emphasis will be placed on pitfalls in diagnosis and management. Current views on pathogenesis will be noted, but many areas of ignorance remain and it is likely that the next few years will see a major revision of opinions. Some fundamental issues and take home messages are summarised in box 1. ### Box 1 Ten important points to ponder 1. Pain and discomfort are rarely prominent in myositis 2. The normal …
Acute kidney injury: A definition with a rich past and an evolution

The term acute kidney injury (AKI) appeared in the society of nephrology in 2004 mirroring a key station of the constant development of the whole entity of acute renal failure. The long route from “acute Bright's disease” in the 19th century to modern “acute kidney stress” reflects the continuous effort of the society of nephrology to set the appropriate criteria and finally to formulate a precise and consensus definition in nomenclature of kidney injury. The most important definitions were the Risk, Injury, Failure, Loss of Kidney Function, and End-Stage Kidney Disease in 2004, Acute Kidney Injury Network (AKIN) in 2007, and Kidney Disease Improving Global Outcomes (KDIGO) in 2012 which had been proposed by the committees of Acute Dialysis Quality Initiative, AKIN, and KDIGO, respectively. Common basis of the above definitions was the clinical-laboratory criteria of urine output and serum creatinine while the target of every newer attempt was to increase the sensitivity of AKI and to offer a globally unified perception in terms of diagnosis and prevention. Since the emersion of KDIGO definition, the interest in defining AKI has been focused on the stage that precedes renal damage by the detection of clinically reliable biomarkers. Subsequently, the concept of acute injury is being lately realized as an attack in correspondence with heart attack or even more as an acute stress which can only be witnessed by the appropriate biomarkers. Recent research is focusing on the discovery of the ideal biomarker which must meet the requirements of high specificity, low cost, and ease of application.
A microscopic competition model and its dynamics analysis on network attacks

Modeling network traffic has been a critical task in the development of Internet. Attacks and defense are prevalent in the current Internet. Traditional network models such as Poisson‐related models do not consider the competition behaviors between the attack and defense parties. In this paper, we present a microscopic competition model to analyze the dynamics among the nodes, benign or malicious, connected to a router, which compete for the bandwidth. The dynamics analysis demonstrates that the model can well describe the competition behavior among normal users and attackers. Based on this model, an anomaly attack detection method is presented. The method is based on the adaptive resonance theory, which is used to learn the model by normal traffic data. The evaluation shows that it can effectively detect the network attacks. Copyright © 2009 John Wiley & Sons, Ltd.
The effect of health promotion program on female breast self-examination knowledge and practice

Background Breast self-examination (BSE) is a screening method that should be taught at early ages to make women aware about the importance of early detection of breast cancer. It can be performed without the assistance of health professionals after effective education and requires no special equipment. Aim The aim was to evaluate the effect of a health promotion program on female BSE knowledge and practice. Participants and methods A quasi-experimental pretest/post-test was used to implement the current study, at the surgical outpatient departments affiliated to Suez Canal University Hospitals and Beni-Suef University Hospitals, on a convenience sample of 80 women from September 2017 to December 2017. Two tools were used for data collection: A structured interview questionnaire to evaluate the participants’ sociodemographic data, BSE knowledge, barriers to practice BSE, and an observational checklist to assess the BSE practice among the studied female. Results There was a high significant relationship between sociodemographic variables and women’s knowledge and practice on BSE with an increase in their scores after implementation of the health promotion program. Conclusion and recommendations This study concluded that the designed health promotion program could improve BSE among the studied women. The current study recommended implementing health promotion programs and educational instructions to provide more information based on scientific knowledge about BSE to female patients at the hospitals.
Bromocriptine: Effect on Serum Prolactin and Growth Hormone in Psychogeriatric Hospital Patients

Serum prolactin (PRL) and human growth hormone (HGH) were assessed before, and three hours after oral administration of 2.5 mg of bromocriptine in 39 hospitalized geriatric patients with organic brain syndrome. Serum PRL concentrations decreased significantly irrespective of initial values (also in the 7 geriatric control subjects), but HGH levels were low in all patients and did not change during the three hours after administration of bromocriptine. Closer scrutiny of the HGH responses to bromocriptine in 5 patients and 5 controls showed that the serum HGH response was more variable among the patients than among the controls. The findings are discussed in relation to neuroendocrine changes associated with aging, institutional living, and mental disease.
Mixed model for analyzing geographic variability in mortality rates.

"A mixed model is proposed for the analysis of geographic variability in mortality rates. In addition to demographic parameters and random geographic parameters, the model includes additional random-effects parameters to adjust for extra-Poisson variability. The model uses a gamma-Poisson distribution with a random scale parameter having an inverse gamma prior. An empirical Bayes approach is used to estimate relative risks for geographic regions and annual rates for demographic groups within each region. Lung cancer in Missouri is used to motivate and illustrate the procedure. "
Isolated Speech Recognition over Internet Protocol in Distributed Systems

In this study, isolated speech recognition system on client/server structure is implemented. In this system, client and server communicate through Internet protocol (IP). The client performs speech analysis and sends the speech feature vectors as a payload in UDP (user datagram protocol) packets to the server. The server stores incoming vectors and runs speech recognition algorithm when the last packet of speech is received. The server also performs packet loss concealment (PLC). The UDP is an unreliable protocol and the UDP packets might be lost during transmission. Therefore the PLC is used to improve the recognition rates. HMM (Hidden Markov Model) is used as recognition algorithm in the system. The system is evaluated with ESOGU isolated word database. In the experimental study, it is seen that the replacement-based PLC technique improves the recognition rates.
Interference alignment with random vector quantisation in device-to-device underlaying cellular networks

In this study, the authors focus on the problem of interference alignment (IA) with random vector quantisation in device-to-device (D2D) uplink underlaying cellular networks. For a D2D underlaying system with one cellular network and one D2D local network, they first analyse the leakage interference introduced by limited feedback and, hence, imperfect IA. Then, they derive the exact closed-form expressions of average sum rate in terms of transmit power for cellular communication and D2D communication. Under such condition, they investigate the adaptive feedback bits allocation schemes to achieve near-optimal performance of systems with limited feedback including a greedy feedback bits allocation scheme and a waterfilling-based feedback bits allocation scheme. Finally, simulation results validate their theoretical results and show that significant performance gain can be obtained by allocating feedback bits adaptively.
Cytotoxicity, protein kinase inhibitory activity, and docking studies of secondary metabolites isolated from Brownea grandiceps Jacq

Background: Numerous kinases are excessively secreted in cancer. Consequently, inhibition of kinase enzymes has a basic role in the treatment of cancer through suppression or prevention of cancer cell multiplication. Due to its kinase inhibitory activity, flavonoids are expected to be of great importance in the discovery of new anticancer drugs. Based on the chemotaxonomic relationship among the plant genera, Brownea grandiceps is expected to be rich in flavonoids. Objective: The objective was to study the cytotoxicity, kinase inhibitory activity, and docking of the isolated metabolites. Materials and Methods: Ultraviolet, nuclear magnetic resonance, and mass spectroscopy were used for the identification and confirmation of the active metabolites. Ethyl acetate extract of B. grandiceps was chromatographed. Cytotoxicity (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide), protein kinase (PK) inhibitory assays, and molecular operating environment (using Molecular Operating Environment [MOE], 2016.08) were performed. Results: Catechin (1), quercitrin (2), trans-taxifolin-3-O-α-L-rhamnopyranoside (3), 13R-hydroxy-9Z, 11E-octadecadienoic acid (4), and 13R-hydroxy-9Z, 11E-, 15Z-octadecatrienoic acid (5) were isolated and identified. Quercitrin (2) exhibited the highest cytotoxicity against MCF-7 (inhibitory concentration for 50% [IC50]: 4.24 μM) and moderate cytotoxicity against HepG2 cell (IC50: 29.58 μM). Others showed moderate cytotoxicity; compounds 1–3 were tested against Aurora B, CDK4/cyclin D1, COT, IGF1-R, and FAK PKs, where quercitrin showed the highest inhibitory activity against Aurora B and CDK4/Cyclin D1 (IC50: 4.78 and 3.22 μM). Docking of quercitrin against CDK4/Cyclin D1 confirmed its cytotoxic profile. Conclusion: The metabolites were first isolated from B. grandiceps. The mechanism of action against kinases enzyme was established and confirmed by docking studies of quercitrin at CDK4/Cyclin D1 using MOE program.
Impact of different laser treatments on push-out strength of post luted with resin cement: An in vitro study

Background: The interface of the post and resin cement is a site of likely failure of adhesion. This interface adhesion of the post surface can be improved through different pretreatments such as laser. So the aim of this study was to investigate the effects of erbium-doped yttrium aluminum garnet (Er:YAG) and erbium, chromium:yttrium-scandium-gallium-garnet (Er,Cr:YSGG) laser irradiation on the push-out bond strengths of fiber-reinforced composite (FRC) posts and resin cement. Materials and Methods: Thirty extracted mandibular premolar teeth were taken and randomly divided into three groups, Group 1 (control group), Group 2 (Er,Cr:YSGG), and Group 3 (Er:YAG). FRC post cementation was performed with Etch-and-rinse resin cement. Push-out strength was tested using a universal materials tester. Statistical analysis was carried out using Statistical Package for the Social Services (SPSS), version 20. Results: Highest push-out bond strength was observed in Group 3 (i.e., Er:YAG, 18.1±0.4MPa) followed by Group 2 (i.e., Er,Cr:YSGG, 17.6±0.9MPa) and least strength was seen in Group 1 (i.e., control, 14.7±1.5MPa). A statistically significant difference was observed when the groups were compared to the untreated or control group (P = 0.000). Conclusion: It was concluded that laser treatment, that is, Er:YAG and Er,Cr:YSGG, significantly improved the push-out bond strength of FRC posts luted with resin cement.
6.4 Scientific background of physical and rehabilitation medicine: Human functioning sciences

IntroductIon The human functioning sciences start from models and theories to understand functioning and disability from a scientific perspective. Such approaches are of great relevance too in order to understand rehabilitation and to define goals for physical and rehabilitation medicine (PRM) programs. The most common model is the comprehensive model of functioning of the World Health Organization as described in the International Classification of Functioning, Disability, and Health (ICF). [1] It defines functioning and disability as two polar aspects of the interaction of a person with a health condition and the environment. Thereafter, functioning comprises the domains, body structures and functions, activities, and participation. The environmental and personal factors are comprised as context [Figure 1]. [2] This model also has been used to operationalize health as it has been defined by the WHO in its Alma Ata-declaration. [3,4] However, other theories of the social integration of persons with health conditions are discussed, for example, the theory of social productivity. [5]
Hierarchical Composites Made Entirely from Renewable Resources

Recent interest in the utilisation of greener materials has reinitiated the interest in natural fibres and/or fibrils as reinforcement for polymers. However, such bio-based composites often exhibit properties that fall short of expectations due to (i) inadequate processing conditions, resulting in filler agglomeration and poor filler dispersion within the matrix, (ii) variations in natural fibre properties, often due to geographical and seasonal variability, (iii) anisotropy of the natural fibres themselves, (iv) high linear coefficient of thermal expansion for natural fibres and (v) the incompatibility between typically hydrophilic natural fibres and hydrophobic polymer matrices resulting in poor interfacial adhesion between the phases. Chemical modification of natural fibres is often performed to enhance the fibre-matrix interface. A new type of modification, which involves depositing a coating of nanosized cellulose onto natural fibres or dispersing nano-sized cellulose in natural fibre reinforced composites, has been shown to improve the fibre-matrix interface and the overall mechanical performances of such composites, which we term hierarchical (nano)composites. Such composites are also known as multiscale, nanoengineered or nanostructured composites. This paper reviews the current progress of green hierarchical (nano)composites made entirely from renewable materials. As a backdrop, here we look at how nature organises structures across different length scales. We discuss techniques to achieve percolated nanofiller networks within the matrix, at low-medium loading fractions (typically 6–10 vol.%) and processing routes to achieve high loading fractions, then focus on those used to produce truly hierarchical structures in terms of their processing and resultant properties. By creating hierarchical structures within bio-based composite materials we expect to match and improve upon non-renewable polymers.
Cyclin-Dependent Kinase 5 in Neurofilament Function and Regulation

Neurofilaments are neuron-specific intermediate filaments. They are classed into three groups according to their molecular masses: neurofilament heavy, middle and light chains (NF-H, NF-M and NF-L). Neurofilaments assemble and form through the association of their central α-helical coiled-coil rod domains. NF-H and NF-M are distinct from NF-L as they contain a carboxyl-terminal tail domain, which appears to form connections with adjacent structures and other neurofilaments. Together with other axonal components such as microtubules, they form the dynamic axonal cytoskeleton. They maintain and regulate neuronal cytoskeletal plasticity through the regulation of neurite outgrowth, axonal caliber and axonal transport. Neurofilaments contain KSP repeats that are consensus motifs for the proline-directed kinases and are extensively phosphorylated in vivo, and their functions are thought to be regulated through their phosphorylation. Cyclin-dependent kinase 5 (Cdk5) is a proline-directed kinase, whose activity is restricted to the neuron through the neuronal-specific distribution of its activators p35 and p39. Cdk5 is the only kinase that affects the electrophoretic mobility of human NF-H and is thought to be the major neurofilament kinase. Cdk5 is involved in crosstalk with other signal transduction pathways such as the mitogen-activated protein kinase and myelin-associated glycoprotein pathways to influence the phosphorylation of neurofilaments and other cytoskeletal proteins. Both the hyperactivation of Cdk5 activity and subsequent hyperphosphorylation of neurofilaments and the microtubule-associated protein tau have been implicated in the pathogenesis of neurodegenerative disorders such as Alzheimer’s disease and amyotrophic lateral sclerosis. Here we review the functions of neurofilaments and the significance of Cdk5 phosphorylation of neurofilaments.
Beyond Sun, Sand, and Stitches: Assigning Responsibility for the Harms of Medical Tourism

Medical tourism (MT) can be conceptualized as the intentional pursuit of non-emergency surgical interventions by patients outside their nation of residence. Despite increasing popular interest in MT, the ethical issues associated with the practice have thus far been under-examined. MT has been associated with a range of both positive and negative effects for medical tourists’ home and host countries, and for the medical tourists themselves. Absent from previous explorations of MT is a clear argument of how responsibility for the harms of this practice should be assigned. This paper addresses this gap by describing both backward looking liability and forward looking political responsibility for stakeholders in MT. We use a political responsibility model to develop a decision-making process for individual medical tourists and conclude that more information on the effects of MT must be developed to help patients engage in ethical MT.
Some recent results on HIV pathogenesis with implications for therapy and vaccines.

In the third decade of AIDS research, we are still facing most of the challenges that emerged when the disease was characterized. While therapeutic approaches to control HIV infection are available in developed countries, novel principles to combat and prevent HIV infection are needed, due to the high cost and the negative effects of currently available drugs. In addition, it is now clear that this disease cannot be eliminated without a vaccine, which appears to be the only possible weapon to combat HIV in developing countries. We have focused on two approaches to fight HIV; one based on select members of a family of proteins, i.e. beta-chemokines, that we have show to interfere with HIV entry and replication, and another centered on a viral protein, HIV-1 Tat, that is crucially involved in HIV regulation and in some of the pathogenic manifestations of HIV infection. Studies from others and ourselves have shown that these two approaches are now ready to the leave experimental bench and move into clinical testing.
Provable robustness against all adversarial lp-perturbations for p≥1

In recent years several adversarial attacks and defenses have been proposed. Often seemingly robust models turn out to be non-robust when more sophisticated attacks are used. One way out of this dilemma are provable robustness guarantees. While provably robust models for specific $l_p$-perturbation models have been developed, we show that they do not come with any guarantee against other $l_q$-perturbations. We propose a new regularization scheme, MMR-Universal, for ReLU networks which enforces robustness wrt $l_1$- and $l_\infty$-perturbations and show how that leads to the first provably robust models wrt any $l_p$-norm for $p\geq 1$.