Miracle-dz/UniFAR-FTUs huggingface dataset:
Treating viral hepatitis C: efficacy, side effects, and complications

The treatment of hepatitis C has dramatically improved over the past decade. Unlike any other chronic viral infection, a significant proportion of patients with chronic hepatitis C can be cured. However, the current standard therapy--pegylated interferon alpha and ribavirin--has its limitations. Limited efficacy in patients with hepatitis C virus (HCV) genotype 1 and the side effect profile will necessitate the development of new therapeutic approaches. This review describes the efficacy and optimisation of the current standard therapy of hepatitis C and its problems in special patient populations. New treatment directions beyond interferon alpha based therapies are on the horizon.
100% Accuracy in Automatic Face Recognition

Accurate face recognition is critical for many security applications. Current automatic face-recognition systems are defeated by natural changes in lighting and pose, which often affect face images more profoundly than changes in identity. The only system that can reliably cope with such variability is a human observer who is familiar with the faces concerned. We modeled human familiarity by using image averaging to derive stable face representations from naturally varying photographs. This simple procedure increased the accuracy of an industry standard face-recognition algorithm from 54% to 100%, bringing the robust performance of a familiar human to an automated system.
Memory and Self–Neuroscientific Landscapes

Relations between memory and the self are framed from a number of perspectives—developmental aspects, forms of memory, interrelations between memory and the brain, and interactions between the environment and memory. The self is seen as dividable into more rudimentary and more advanced aspects. Special emphasis is laid on memory systems and within them on episodic autobiographical memory which is seen as a pure human form of memory that is dependent on a proper ontogenetic development and shaped by the social environment, including culture. Self and episodic autobiographical memory are seen as interlocked in their development and later manifestation. Aside from content-based aspects of memory, time-based aspects are seen along two lines—the division between short-term and long-term memory and anterograde—future-oriented—and retrograde—past-oriented memory. The state dependency of episodic autobiographical is stressed and implications of it—for example, with respect to the occurrence of false memories and forensic aspects—are outlined. For the brain level, structural networks for encoding, consolidation, storage, and retrieval are discussed both by referring to patient data and to data obtained in normal participants with functional brain imaging methods. It is elaborated why descriptions from patients with functional or dissociative amnesia are particularly apt to demonstrate the facets in which memory, self, and personal temporality are interwoven.
Vestibular feedback maintains reaching accuracy during body movement

Reaching movements can be perturbed by vestibular input, but the function of this response is unclear. Here, we applied galvanic vestibular stimulation concurrently with real body movement while subjects maintained arm position either fixed in space or fixed with respect to their body. During the fixed‐in‐space conditions, galvanic vestibular stimulation caused large changes in arm trajectory consistent with a compensatory response to maintain upper‐limb accuracy in the face of body movement. Galvanic vestibular stimulation responses were absent during the body‐fixed task, demonstrating task dependency in vestibular control of the upper limb. The results suggest that the function of vestibular‐evoked arm movements is to maintain the accuracy of the upper limb during unpredictable body movement, but only when reaching in an earth‐fixed reference frame.
LIGHTFRONT HOLOGRAPHY AND AREA DENSITY OF ENTROPY ASSOCIATED WITH LOCALIZATION ON WEDGE-HORIZONS

It is shown that a suitably formulated algebraic lightfront holography (LFH), in which the lightfront is viewed as the linear extension of the upper causal horizon of a wedge region, is capable of overcoming the shortcomings of the old lightfront quantization. The absence of transverse vacuum fluctuations which this formalism reveals is responsible for an area (edge of the wedge) — rearrangement of degrees of freedom which in turn leads to the notion of area density of entropy for a "split localization. " This area proportionality of horizon associated entropy has to be compared to the volume dependence of ordinary heat bath entropy. The desired limit, in which the split distance vanishes and the localization on the horizon becomes sharp, can at most yield a relative area density which measures the ratio of area densities for different quantum matter. In order to obtain a normalized area density one needs a (yet unknown) analog of a second fundamental law of thermodynamics in case the thermalization is caused by vacuum fluctuation through localization on causal horizons. This is similar to the role of the classical Gibbs form of that law which relates Bekenstein's classical area formula with the Hawking quantum mechanism for thermalization from black holes.
Multidisciplinary design optimization of wing shape for a small jet aircraft using kriging model

*† ‡ § ¶ # In this paper, a practical Multidisciplinary Design Optimization (MDO) system for an aircraft design is developed. The MDO system is based on the integration of computational fluid dynamics (CFD) codes and the NASTRAN based aeroelastic-structural interface code. Kriging model is employed to save the computational time of objective function evaluation in Multi-Objective Genetic Algorithm (MOGA). As a result of optimization, several nondominated solutions, indicating the trade-off among the drag, the structural weight, the drag divergence and the pitching moment, have been found.
Multi-Cue Integration for Multi-Camera Tracking

For target tracking across multiple cameras with disjoint views, previous works usually employed multiple cues and focused on learning a better matching model of each cue, separately. However, none of them had discussed how to integrate these cues to improve performance, to our best knowledge. In this paper, we look into the multi-cue integration problem and propose an unsupervised learning method since a complicated training phase is not always viable. In the experiments, we evaluate several types of score fusion methods and show that our approach learns well and can be applied to large camera networks more easily.
Notation 1.3 Let X I

We begin by introducing the following notations, which deal with the packets which reside in the buuer of an edge e at a certain time unit t. Notation 1.1 Let X e (t) denote the set of packets which are stored at the buuer of edge e at the end of time unit t. Notation 1.2 Let L e (p) denote the number of hops which a packet p, stored at the buuer of edge e, must still traverse before reaching its destination. e (t) be the following set: X i e (t) 4 = fp j p 2 X e (t) and L e (p) ig We can now deene the FTG Protocol: Deenition 1.1 The Furthest-To-Go (FTG) protocol is a greedy protocol whereupon whenever X e (t) 6 = , then at time t + 1 the packet p (p 2 X e (t)) which will be forwarded on e is such that 8p 0 2 X e (t); L e (p) L e (p 0) We now proceed to prove the universal stability of the FTG protocol. Proposition 1.1 The FTG protocol is stable on any network G and against any (b; r) adversary with r 1. Proof: Let us x a network G = (V; E) with m 4 = jEj, and denote by d the length of the longest simple path in G. Deene the following sequence of constants: k i = (0 i > d m(k i+1 + b + 1) 1 i d The stability of FTG will follow from the following claim: Claim 1.2 For all t, k i P e2E X i e (t). That is, the number of packets in the network at any time t which are at least i hops away from their destination is bounded from above by k i .
Visualizing Data Races in Concurrent Signal Handlers ?

Asynchronous signal handling introduces fine-grained concurrency into sequential programs making them prone to data races. Unfortunately, existing tools for detecting data races in sequential programs that use concurrent signal handlers fail to provide effective means for understanding the dynamic behavior of concurrent signal handlers involved in data races. Thus, this paper presents a visualization tool that uses vertically parallel arrows to capture the logical concurrency between a sequential program and its concurrent signal handlers, materializes synchronization patterns with horizontal arrows, and uses colored squares to represent accesses to shared variables in order to provide a partial ordering of events that occured at runtime.
Interaction between dopamine and octopamine in Drosophila melanogaster brain

Dopamine and octopamine, a possible analogue of noradrenaline, are among a few neurotransmitters which play a crucial role in several physiological processes. Dopamine participates in learning, memory and in reward, whereas, octopamine participates in egg laying, fight and flight response and also exhorts aggressive behaviour. Researchers had demonstrated that dopaminergic signalling can act antagonistic to octopamine in Caenorhabditis elegans. When C. elegans was fed, it exhibit sleep and reward emotions, due to the production of Dopamine. Conversely, when animals were subjected to fasting, they have produced octopamine, by suppressing dopamine signalling, and this lead to CREB activation, which eventually resulted in longevity of C. elegans (Suo et al.2009). These observations illustrate the need to investigate the dopamine and octopamine interactions and their effects on longevity in another model organism, Drosophila melanogaster. To carry out my investigation, two dopamine receptors (DopR1 and DopR2) in octopaminergic neurons (neurons where their primary transmitter is octopamine) were knocked down and the insect’s social behaviour as well as longevity was monitored. Based upon my study, I conclude that the cessation of dopamine receptors may not result in longevity of D. melanogaster, instead the starvation resistance of DopR2 knockdown flies was reduced when compared to DopR1 knockdown flies and wild type flies. However, it was observed that DopR2 knockdown flies showed increased aggressive behaviour, decreased male-male courtship and reduced activity when compared to wild type flies.
Validity and feasibility of a nurses’ coping questionnaire for its use in ecological momentary assessment

Background: Easy measures are needed in EMA. We explain the construction, validity and feasibility of a 10-item coping questionnaire, based on COPE, following a structure of one item for each strategy, grouped on four dimensions: problem-focused (active coping and planning); emotion-focused (acceptance, reinterpretation and relaxation); seeking for social support (emotional and instrumental support); and disengagement (denial, venting, and self-blame). Method: Three experts selected the items, according to its representativeness and applicability to EMA. Complementary data was obtained from 85 nursing students, who filled out two coping questionnaires,: the COPE and the CRI-A. Experts’ proposal was readjusted based on factorial loadings and correlation with other coping questionnaire. 10 hospital ward nurses completed the final version of the questionnaire using a dichotomous scale (yes/not), during five working days, in a smartphone programmed with random alarms. They responded in relation to the coping used to deal with the task they were undergoing when the alarm rang. Findings: 5 nurses used coping strategies in between 20% and 40% of the situations, while 4 used strategies in between 90% and 100% of the situations, and one of them didn’t cope any time. When coping, all of them reported mostly the use of one unique strategy, and in less than 3 of the maximum of 28 reports for each nurse, two combined strategies were reported. Problem-focused and emotion-focused strategies were the most frequently used, and disengagement the less. Discussion: results from both studies show that this questionnaire could be valid and feasible for use in EMA.
A GPU based saliency map for high-fidelity selective rendering

The computation of high-fidelity images in real-time remains one of the key challenges for computer graphics. Recent work has shown that by understanding the human visual system, selective rendering may be used to render only those parts to which the human viewer is attending at high quality and the rest of the scene at a much lower quality. This can result in a significant reduction in computational time, without the viewer being aware of the quality difference. Selective rendering is guided by models of the human visual system, typically in the form of a 2D saliency map, which predict where the user will be looking in any scene. Computation of these maps themselves often take many seconds, thus precluding such an approach in any interactive system, where many frames need to be rendered per second. In this paper we present a novel saliency map which exploits the computational performance of modern GPUs. With our approach it is thus possible to calculate this map in milliseconds, allowing it to be part of a real time rendering system. In addition, we also show how depth, habituation and motion can be added to the saliency map to further guide the selective rendering. This ensures that only the most perceptually important parts of any animated sequence need be rendered in high quality. The rest of the animation can be rendered at a significantly lower quality, and thus much lower computational cost, without the user being aware of this difference.
EMG Signals classification based on singular value decomposition and neural network

The electromyogram (EMG) signal contains lots of information about muscular changes related muscular disorder such as myopathy. In this paper, we present a new method for classification of EMG signals based on singular value decomposition. The EMG signal represent in the matrix form, features extracted from the singular value decomposition and singular value of EMG signal used as features for classification. These features used as an input to back propagation neural network classifier for classification of EMG signals. The classification accuracy for classification of normal and myopathy EMG signals obtained by proposed method is 96.7. Simulation results illustrate the effectiveness of the proposed method.
Ariel Rabkin : Research Statement

In the last fifteen years, systems software has increased dramatically in complexity and power. Not so long ago, most computer programs ran on one machine. Distributed computing was a specialized problem for developers of networked services or supercomputer software. Today, cloud computing has made it convenient to rent computational resources as needed, spread across hundreds or thousands of nodes. Modern systems software has made it feasible to harness these resources for data-intensive computing. As a result, many organizations are starting to rely on complex systems software to process big data.
Indexing Straight-Line Programs ∗

Straight-line programs offer powerful text compression by representing a text T [1, u] in terms of a context-free grammar of n rules, so that T can be recovered in O(u) time. However, the problem of operating the grammar in compressed form has not been studied much. We present the first grammar representation able of extracting text substrings, and of searching the text for patterns, in time o(n). Its size is of the same order of that of a plain SLP representation, and it can be of independent interest for other grammar-based problems. We also give some byproducts on representing binary relations.
Sequentiality in an Extensional Framework

We present a cartesian closed category of dI-domains with coherence and strongly stable functions which provides a new model of PCF, where terms are interpreted by functions and where, at first order, all functions are sequential. We show how this model can be refined in such a way that the theory it induces on the terms of PCF be strictly finer than the theory induced by the Scott model of continuous functions.
Acts of kindness in close relationships reduce depression

This study investigated the benefits of performing kind acts within close relationships. Practicing kindness has been shown to strengthen relationships and increase social support. It was expected to be particularly relevant for needy people who are driven by a need for closeness and are vulnerable to depression in the face of interpersonal stress. It was hypothesized that a shortterm, online kindness exercise would decrease depression and increase happiness in a community sample (at baseline N = 364), and would be particularly beneficial for individuals high on neediness. Participants were recruited online and randomly assigned to one of two conditions. Participants in the kindness condition performed a kind, loving gesture towards someone close and described the ensuing interaction, while participants in the control condition simply wrote about an interaction they had with someone close. Each exercise was repeated every second day for roughly three weeks. The kindness exercise was associated with significant decreases in depressive symptoms when compared to the control condition, and these decreases were maintained up to the two-month follow-up for the general sample. The findings for happiness were mixed and participants high on neediness did not show superior benefits over participants low on neediness from the kindness intervention. The results suggest that practicing kindness within the context of a close relationship can lead to general and sustained improvements in depression. Furthermore, this study offers support for the efficacy of brief, online interventions in the improvement of depressive symptoms.
Jump-stay based channel-hopping algorithm with guaranteed rendezvous for cognitive radio networks

Cognitive radio networks (CRNs) have emerged as advanced and promising paradigm to exploit the existing wireless spectrum opportunistically. It is crucial for users in CRNs to search for neighbors via rendezvous process and thereby establish the communication links to exchange the information necessary for spectrum management and channel contention etc. This paper focuses on the design of algorithms for blind rendezvous, i.e., rendezvous without using any central controller and common control channel (CCC). We propose a jump-stay based channel-hopping (CH) algorithm for blind rendezvous. The basic idea is to generate CH sequence in rounds and each round consists of a jump-pattern and a stay-pattern. Users “jump” on available channels in the jump-pattern while “stay” on a specific channel in the stay-pattern. Compared with the existing CH algorithms, our algorithm achieves the following advances: i) guaranteed rendezvous without the need of time-synchronization; ii) applicability to rendezvous of multi-user and multi-hop scenarios. We derive the maximum time-to-rendezvous (TTR) and the upper-bound of expected TTR of our algorithm for both 2-user and multi-user scenarios (shown in Table I). Extensive simulations are further conducted to evaluate performance of our algorithm.
A common perceptual temporal limit of binding synchronous inputs across different sensory attributes and modalities

The human brain processes different aspects of the surrounding environment through multiple sensory modalities, and each modality can be subdivided into multiple attribute-specific channels. When the brain rebinds sensory content information (‘what’) across different channels, temporal coincidence (‘when’) along with spatial coincidence (‘where’) provides a critical clue. It however remains unknown whether neural mechanisms for binding synchronous attributes are specific to each attribute combination, or universal and central. In human psychophysical experiments, we examined how combinations of visual, auditory and tactile attributes affect the temporal frequency limit of synchrony-based binding. The results indicated that the upper limits of cross-attribute binding were lower than those of within-attribute binding, and surprisingly similar for any combination of visual, auditory and tactile attributes (2–3 Hz). They are unlikely to be the limits for judging synchrony, since the temporal limit of a cross-attribute synchrony judgement was higher and varied with the modality combination (4–9 Hz). These findings suggest that cross-attribute temporal binding is mediated by a slow central process that combines separately processed ‘what’ and ‘when’ properties of a single event. While the synchrony performance reflects temporal bottlenecks existing in ‘when’ processing, the binding performance reflects the central temporal limit of integrating ‘when’ and ‘what’ properties.
Application of Artificial Neural Network (ANN) technique for Diagnosing Diabetes Mellitus

The present work is a classification problem applied to diagnosis of diabetes mellitus using back propagation algorithm of artificial neural network (ANN). The data base used for training and testing the ANN has been collected from Sikkim Manipal Institute of Medical Sciences Hospital, Gangtok, Sikkim for the diabetic patients of the state of Sikkim. This work is validated by comparing the network response with the actual hospital data of Sikkim Manipal Institute of Medical Sciences for the first time. This work is a step towards development of prototype system for knowledge classification in this field.
FPGA-Based MWD for Network Error Correction

: This paper presents reconfigurable hardware architecture for MWD (Minimum Weight Decoding) algorithm for network error correction, with high throughput on Field Programmable Gate Array (FPGA). Network Error Correction (NEC) is used for detecting and correcting the errors in noisy communication channels when multicasting a source message to a set of nodes. Minimum Weight Decoding (MWD) algorithm is a cyclic linear block codes that are used as Forward Error Correcting (FEC) codes. The design can be reconfigured for different message length and different generator number, the encoder and decoder has been described using VHDL (VHSIC Hardware Description Language). The decoder has the ability to detect and correct different types and different numbers of errors based on the message length and the length of redundant data. The design has been simulated and tested using ModelSim PE student edition 10.4. Spartan 3 FPGA starter kit from Xilinx has been used for implementing and testing the design in a hardware level.
Racial Differences in the Prevalence of Depressive Disorders Among U.S. Adult Population

Depression is a common but serious illness, which may have significant impact on a person’s daily life. This study examined racial/ethnic disparities on the prevalence of lifetime diagnosis of depression and current depression according to 2010 Behavioral Risk Factor Surveillance System (BRFSS) data. Logistic regression models were constructed to calculate odds ratios for comparing prevalence rates of depression across racial/ethnic groups. Significant racial/ethnic differences were found in both the prevalence of lifetime diagnosis of depression and current depression. Regional disparities were also found to be significant in the prevalence of depressive disorders across racial/ethnic groups. INDEX WORDS: BRFSS, CDC, Depression, Logistic regression, Racial disparity, Regional disparity, Risk factors RACIAL DIFFERENCES IN THE PREVALENCE OF DEPRESSIVE DISORDERS AMONG U.S. ADULT POPULATION