Miracle-dz/UniFAR-FTUs huggingface dataset:
Neuron-Specific Expression of Mutant Superoxide Dismutase 1 in Transgenic Mice Does Not Lead to Motor Impairment

Mutations were identified in the Cu/Zn superoxide dismutase gene (SOD1) in ∼15% of patients with familial amyotrophic lateral sclerosis. Transgenic animals expressing mutant SOD1 in all tissues develop an ALS-like phenotype. To determine whether neuron-specific expression of mutant SOD1 is sufficient to produce such a phenotype, we generated transgenic animals carrying the G37R mutation that is associated with the familial form of ALS (FALS), which is driven by the neurofilament light chain promoter. The transgenic animals express high levels of the human SOD1 protein in neuronal tissues, especially in the large motor neurons of the spinal cord, but they show no apparent motor deficit at up to 1.5 years of age. Our animal model suggests that neuron-specific expression of ALS-associated mutant human SOD1 may not be sufficient for the development of the disease in mice.
Implementation of Computer Vision to detect driver fatigue or drowsiness to reduce the chances of vehicle accident

Fatigue or drowsiness is one of the main reasons of inattention of drivers. Now a day Computer Vision is a very active field to detect specific human physiology and behavior during driving to detect driver's drowsiness. This paper proposes a design of detection system which can detect fatigue in drivers based on computer vision. The system approaches to detect fatigued drivers by first, detecting the face from a video sequence of driver's frontal face followed by locating the eye from the extracted face. Secondly, system finds the existence of eye pupil from detected eye and measures the blink rate simultaneously and by analyzing these parameters, system measures the loss of awareness before driver completely loss his/her attention. The proposed system aims to be more cost effective than industrial installments and therefore all parameters have been analyzed under laboratory environment and frames have been captured with a low cost webcam.
Using Committee Machine with Intelligent Systems for Permeability Prediction, A case study of South Pars Gas Field, Persian Gulf, Iran

Permeability is the ability of porous rock to transmit fluid. An accurate knowledge is necessary for reservoir management and development. This study presents an improved model based on the integration of petrographic data, conventional logs and intelligent systems to predict permeability. The permeability was first predicted using the individual intelligent systems including a neural network (NN), a fuzzy logic (FL) and a neuro-fuzzy (NF) model. Afterwards, a committee machine with intelligent systems (CMIS) was used in order to combine permeability values calculated from the individual intelligent systems. The CMIS using genetic algorithm (GA) model to obtain the optimal contribution of each expert. The results show that CMIS performed better than NN, FL and NF models acting alone. The proposed methodology was applied to South Pars gas field, which is located in the Persian Gulf between Iran and Qatar.
Analysis of Statistical Keyword Extraction Methods for Incremental Clustering

Incremental clustering is a very useful approach to organize dynamic text collections. Due to the time/space restrictions for incremental clustering, the textual documents must be preprocessed to maintain only their most important information. Statistical keyword extraction methods from single documents are useful in this scenario. However, different statistical methods have different assumptions about the properties of keywords in a text, and different methods extract different set of keywords. In this paper we analyze the different methods for keyword extraction and the impact of the number of keywords on the quality of the incremental clustering. We also define a framework for statistical keyword extraction.
Quality of IS in Services: Theory and Validation of Constructs for Service, Information, and System

IS quality aspects have been important in IS research and practice. In DeLone and McLean’s 2003 IS Success Model (ISM), the quality of information, system, and service are treated as independent determinants of user IS satisfaction and behavioral intention. Although numerous quality measures have been developed, most lack theoretical grounding and systematic content validation. Increasing use of IT in services adds complexities for defining appropriate content domains for the traditional IS quality constructs. This paper starts with a literature review of IS quality constructs. Then, it applies a marketing exchange theory perspective to identify potential variants of IS quality nomologies for different service contexts. A theoretical modification of ISM is proposed. Finally, as a part of the research-in-progress, an empirical validation of the content of IS quality measures is provided. This study aims to establish a solid theoretical and empirical foundation for coherent development and application of IS quality measures.
Prognostic value of ejection fraction in patients admitted with acute coronary syndrome

Abstract There are limited data regarding factors affecting outcomes among acute coronary syndrome (ACS) patients presenting with varying degrees of left ventricle (LV) dysfunction. We aimed to identify factors associated with mortality according to LV ejection fraction (LVEF) at 1st admission in ACS patients. A total of 8983 ACS patients prospectively enrolled in the Acute Coronary Syndrome Israeli Survey (2000–2010) were categorized according to their LVEF at admission: severe LV dysfunction (LVEF < 30% [n = 845]), mild-moderate LV dysfunction (LVEF 30%–49% [n = 4470]); preserved LV function (LVEF ≥ 50% [n = 3659]). Multivariable Cox proportional hazards regression modeling was used to assess the risk factors for 1-year mortality according to LVEF on admission. Over the past decade there was a gradual decline in the proportion of patients admitted with low LVEF. Mortality rates were highest among patients with severe LV dysfunction (36%), intermediate among those with mild-moderate LV dysfunction (10%), and lowest among those with preserved LV function (4%, P < 0.001). We recognized different risk factors for mortality according to LVEF at admission. Admission clinical features (syncope, anterior myocardial infarction, and ST elevation myocardial infarction [STEMI]) predicted mortality risk in patients with severe LV dysfunction (all P < 0.05), whereas the presence of comorbidities (hypertension, diabetes mellitus, chronic renal failure, and peripheral arterial disease) predicted mortality risk in patients with more preserved LV function. Age and admission Killip class ≥II were consistent predictors in all LVEF subsets. LVEF at admission is a strong predictor of mortality in ACS, and prognostic factors differ according to LVEF during admission. In patients with severe LV dysfunction signs of clinical instability are related to 1-year mortality; in patients with a more preserved LV function the prognosis is related to the presence of co-morbidities.
Influences of High-Level Features, Gaze, and Scene Transitions on the Reliability of BOLD Responses to Natural Movie Stimuli

Complex, sustained, dynamic, and naturalistic visual stimulation can evoke distributed brain activities that are highly reproducible within and across individuals. However, the precise origins of such reproducible responses remain incompletely understood. Here, we employed concurrent functional magnetic resonance imaging (fMRI) and eye tracking to investigate the experimental and behavioral factors that influence fMRI activity and its intra- and inter-subject reproducibility during repeated movie stimuli. We found that widely distributed and highly reproducible fMRI responses were attributed primarily to the high-level natural content in the movie. In the absence of such natural content, low-level visual features alone in a spatiotemporally scrambled control stimulus evoked significantly reduced degree and extent of reproducible responses, which were mostly confined to the primary visual cortex (V1). We also found that the varying gaze behavior affected the cortical response at the peripheral part of V1 and in the oculomotor network, with minor effects on the response reproducibility over the extrastriate visual areas. Lastly, scene transitions in the movie stimulus due to film editing partly caused the reproducible fMRI responses at widespread cortical areas, especially along the ventral visual pathway. Therefore, the naturalistic nature of a movie stimulus is necessary for driving highly reliable visual activations. In a movie-stimulation paradigm, scene transitions and individuals’ gaze behavior should be taken as potential confounding factors in order to properly interpret cortical activity that supports natural vision.
EmoGest: Investigating the Impact of Emotions on Spontaneous Co-speech Gestures

Spontaneous co-speech gestures are an integral part of human communicative behavior. Little is known, however, about how they reflect a speaker’s emotional state. In this paper, we describe the setup of a novel body movement database. 32 participants were primed with emotions (happy, sad, neutral) by listening to selected music pieces and, subsequently, fulfilled a gesture-eliciting task. We present our methodology of evaluating the effects of emotion priming with standardized questionnaires, and via automatic emotion recognition of the speech signal. First results suggest that emotional priming was successful, thus, paving the way for further analyses comparing the gestural behavior across the three experimental conditions.
Securing Nodes in MANETs Using Node Based Key Management Scheme

A mobile ad hoc network (MANET) is a kind of wireless communication infrastructure that does not have base stations or routers. Each node acts as a router and is responsible for dynamically discovering other nodes it can directly communicate with. However, when a message without encryption is sent out through a general tunnel, it may be maliciously attacked. In MANETs, existing research efforts in key management can only handle very limited number of nodes and are vulnerable to active attacks. In this paper, node based key management scheme is being used for securing nodes because nodes are the only medium to transmit packets to other mobile nodes. RREQ and RREP values are followed with node identification number for generating key. This paper focuses on preventing the nodes misbehavior.
Mapping the Walk of Shame

This article expands the concept of sexual scripts and the cognitive mapping method by adding an emotional dimension to both. It is based on a case study of college students reporting their experiences of the walk of shame, the term for women walking home in the early morning from a “hookup,” or casual sex, the night before. The walk of shame is a productive site in which to address conceptual and methodological challenges posed by sociology’s affective turn. In this article, I discuss emotional scripts as a conceptual framework for exploring women students’ experiences with the walk of shame. And I propose affective mapping as a methodological approach for capturing emotional dimensions in social life. Using the method to map the emergence of feelings and their temporal changes in relation to normative features of the spatial environment, I suggest that although the walk of shame appears to be a vehicle for the social control of women’s sexuality, women report more complexity during their walks, producing scripts of shame, pride, and ambivalence.
Global Stability Analysis for an Internet Congestion Control Model with a Time-Varying Link Capacity

In this paper, a global stability analysis is given for a rate-based congestion control system modeled by a nonlinear delayed differential equation. The model determines the dynamics of a single-source single-link network, with a time-varying capacity of link and a fixed communication delay. We obtain a sufficient delay-independent conditions on system parameters under which global asymptotic stability of the system is guarantied. The proof is based on an extension of Lyapunov-Krasovskii theorem for a class of nonlinear time-delay systems. The numerical simulations for a typical scenario justify the theoretical results.
TopDown-KACA: An efficient local-recoding algorithm for k-anonymity

K-anonymity is an effective model for protecting privacy while publishing data. KACA algorithm is a typical generalization algorithm for k-anonymity, which can generate small information loss, but its efficiency is low, especially when dataset is large. Another generalization algorithm, topDown, has high efficiency but generates heavy information loss. In this paper, we propose an efficient generalization algorithm for k-anonymity, called topDown-KACA, which combines the topDown algorithm with the KACA algorithm. The idea of topDown-KACA algorithm is to partition the whole dataset into some subsets by topDown algorithm at first, and then k-anonymize these subsets by KACA algorithm respectively. Experiments show that the proposed algorithm is more efficient than KACA algorithm with similar information loss, and generates less information loss than topDown algorithm with similar execution time.
Search Engine Results Improvement -- A Review

Search engines return SERP which contains results to the user query which may be relevant or irrelevant. Some of the results which are not relevant are the matter of great dissatisfaction to the user who queried the Web to look forward for the desired answer. As the WWW is the huge repository of information so there is a necessity of an intuitive approach that can navigate, filter, summarize and manage such a large collection of data. Hence, diverse approaches have been used to optimize search engine results according to users' need. Our objective is to present different approaches used by various researchers to optimize results which are helpful in maximizing the user satisfaction.
Low cost sensory based intelligent vehicle algorithm

Autonomous system have been interesting topic in the world of robotics, in order to replace human being. Intelligent vehicle is one of the popular autonomous system that gives many advantage for this world, not only in military but also civilian. Route control and obstacle avoidance are now considered as major problems in autonomous system. In this paper, new methods of routing and avoidance algorithm for intelligent vehicle are proposed. It is about using an artificial intelligence algorithm together with avoidance system for navigating those vehicles. With reference to global positioning system (GPS), the robot will recognize its own position on earth and its destination by approximation. To improve the approximating ability of the position defined, sensor system is used as additional reference. The computation process is used for calculating the route for the vehicle to move. If there is any obstructer on the route, avoidance algorithm will be used for avoiding the collision. Real-time tracking and sensor-based system are main sensory devices for testing avoidance algorithm.
A new hybrid error concealment scheme for H.264 video transmission

For an entropy-coded video bitstream, a transmission error in a codeword will not only affect the underlying codeword but may also affect subsequent codewords, resulting in a great degradation of received video frames. In this study, a new hybrid error concealment scheme for H.264 video transmission is proposed. Here, pixel interpolation and the fast best neighborhood matching (BNM) algorithm are employed to conceal intra-coded I frames. For inter-coded P frames, the predicted motion vector (PMV) for a corrupted block is first determined by the spatial MVs around the corrupted block and the corresponding temporally motion-projected overlapping MVs in the previous frame. Then based on the PMV, the optimal candidate concealed block for a corrupted block is searched over all the motion-compensated blocks in the previous frame with their MVs being determined by all the available spatial and temporal information. After all the corrupted blocks in a P frame are initially concealed, error concealment refinement is performed on all the initial concealed blocks to improve the error concealment results. Based on the simulation results obtained in this study, the performance of the proposed scheme is better than that of the corresponding comparison schemes.
Provision of taped conversations with neonatologists to mothers of babies in intensive care: randomised controlled trial

Objective To determine whether providing mothers of babies in neonatal intensive care units with audiotapes of their conversations with a neonatologist improves recall of information and psychological wellbeing. Design Randomised, single blinded trial. Setting Neonatal intensive care unit, North Queensland, Australia. Participants 200 mothers of babies in a neonatal intensive care unit. Interventions Mothers given (n=102) or not given (n=98) audiotapes of their conversations with a neonatologist. Main outcome measures Recall of information, attitudes to and use of the tape, satisfaction with conversations, postnatal depression, anxiety, general health, and stress about parenting, at 10 days and four and 12 months. Results 91% (n=93) of mothers in the tape group listened to the tape (once by day 10, twice by four months, and three times by 12 months; range 1-10). At 10 days and four months, mothers in the tape group recalled significantly more information about diagnosis, treatment, and outcome than mothers in the control group. At four months mothers in the tape group were 75% more likely to recall all of the information about treatment than mothers in the control group (59% v 34%; risk ratio 1.75, 95% confidence interval 1.27 to 2.4). Six mothers, all in the control group, could not recall their conversations. No statistically significant differences were found between the groups in satisfaction with conversations (10 days), postnatal depression and anxiety scores (10 days, four and 12 months), and stress about parenting (12 months). Conclusion Providing the mothers of babies in neonatal intensive care units with audiotapes of conversations with a neonatologist enhanced their recall of information (up to four months). The taped conversations did not affect the mothers' wellbeing or satisfaction with the neonatologist. Trial registration Australian Clinical Trials Registry 12606000478516.
Online throughput maximization in an energy harvesting multiple access channel with fading

In this paper, we consider an energy harvesting multiple access channel (MAC) where the transmitters are powered by energy harvested from the ambient environment. We assume that the energy harvesting processes at the transmitters can be modeled as independent Bernoulli processes with parameters λ<sub>i</sub>s, and the channel states between the transmitters and the receiver are independent Bernoulli processes with parameter μ<sub>i</sub>s. An active transmitter always transmits with a fixed power and consumes one unit amount of energy in a time slot. Under the assumption that μ<sub>i</sub> ≥ λ<sub>i</sub>, ∀<sub>i</sub>, our objective is to schedule the transmissions adaptively according to the instantaneous channel and battery states of transmitters, so that the long-term average sum-throughput of the MAC is maximized in expectation. We first show that for a general asymmetric scenario where λ<sub>i</sub>s and μ<sub>i</sub>s are not identical across the transmitters, the expected long-term average sum-throughput has an upper bound for any transmission scheduling policy satisfying the energy causality constraints. We then consider a special symmetric scenario where λ<sub>i</sub>s and μ<sub>i</sub>s are uniform among transmitters. We propose a randomized longest-connected-queue transmission scheduling policy and show that it achieves the upper bound almost surely as time T approaches infinity, thus it is optimal.
Non‐parametric Estimation of Extreme Risk Measures from Conditional Heavy‐tailed Distributions

In this paper, we introduce a new risk measure, the so‐called conditional tail moment. It is defined as the moment of order a ≥ 0 of the loss distribution above the upper α‐quantile where α ∈ (0,1). Estimating the conditional tail moment permits us to estimate all risk measures based on conditional moments such as conditional tail expectation, conditional value at risk or conditional tail variance. Here, we focus on the estimation of these risk measures in case of extreme losses (where α ↓0 is no longer fixed). It is moreover assumed that the loss distribution is heavy tailed and depends on a covariate. The estimation method thus combines non‐parametric kernel methods with extreme‐value statistics. The asymptotic distribution of the estimators is established, and their finite‐sample behaviour is illustrated both on simulated data and on a real data set of daily rainfalls.
Adaptive Mesh Extraction using Simplification and Refinement

This work presents a method for multiresolution mesh extraction with important mathematical properties. The generated mesh represents a regular surface from 3D Euclidian space. The input surface may be specified either implicitly or directly as a volumetric object. The method applies simplification operations to obtain a low resolution initial mesh and applies refinement operations to obtain a multiresolution representation from initial mesh. These combined operations provide nice properties to the mesh.
Chinese Spelling Checker Based on Statistical Machine Translation

Chinese spelling check is an important component for many NLP applications, including word processor and search engines. However, compared to checkers for alphabetical languages (e.g., English or French), Chinese spelling checkers are more difficult to develop, because there are no word boundaries in Chinese writing system, and errors may be caused by various Chinese input methods. In this paper, we proposed a novel method to Chinese spelling checking. Our approach involves error detection and correction based on the phrasal statistical machine translation framework. The results show that the proposed system achieves significantly better accuracy in error detecting and more satisfactory performance in error correcting.
Behavioral Modeling and Simulation of Semiconductor Devices and Circuits Using VHDL-AMS

During the past few years, a lot of work has been done on behavioral models and simulation tools. But a need of modeling strategy still remains. The VHDL-AMS language supports the description of analog electronic circuits using ordinary differential algebraic equations (ODAEs), in addition to its support for describing discrete-event systems. For VHDL-AMS to be useful to the analog design community, efficient semiconductor device models must be available. In this paper potential merits of the new IEEE VHDL-AMS standard in the field of modeling of semiconductor devices are discussed. The device models for diodes and the principles, techniques and methodology used to achieve the design of an analytical third generation Spice transistor MOS model named EKV are presented. This is done by taking into account the thermoelectrical effect with VHDL-AMS, and with relevant parameters set to match a deep submicron technology developed in VHDL-AMS. The models were validated using system vision from mentor graphics.