Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Media Usage in Indian Information Technology Professionals

Social media usage has grown by leaps and bound over the last decade and has become an important platform being used by the workforce for both work and non-work related purposes. The usage of these tools varies across employees and organizations depending on multiple factors. The present study examines the social media usage at workplace in Information Technology professionals by measuring it on the basis of their usage, pride in using, routine of use, and belongingness to the social media community. Social media usage intensity is analyzed for various employee groups. These groups are categorized on the basis of – social media accessibility at workplace, smart phone availability, gender of employees, employee age group, managerial level, highest educational qualification, and city of residence of employees. A questionnaire was completed for around 400 users and the data was analyzed utilizing descriptive statistics, t tests, and one-way ANOVA. The results indicated that usage of social media varied for some of these employee groups.
Using Dialogue Context to Improve Parsing Performance in Dialogue Systems

We explore how to incorporate information from dialogue context to improve the selection of logical forms in the parsing components of dialogue systems. We present a machine learning approach which allows us to identify the most informative elements of the dialogue context for this task, and to improve the performance of a parser in a dialogue system by using a classifier. Features for the classifier are extracted from a dialogue manager [Lemon and Gruenstein, 2004] which implements the Information State Update approach to dialogue management. Our best result to date is a 54.5% reduction in parse error rate, compared to the baseline system of Lemon and Gruenstein [2004].
Gaugino condensation with a doubly suppressed gravitino mass

Supersymmetry breakdown via gaugino condensation in heterotic string theory can lead to models with a doubly suppressed gravitino mass. A TeV-scale gravitino can emerge from a condensate as large as the grand unified scale. We analyze the properties of these models and discuss applications for particle physics and cosmology.
A System for On-demand Video Lectures

We propose a lecture-on-demand system, which searches lecture videos for segments relevant to user information needs. We utilize the benefits of textbooks and audio/video data corresponding to a single lecture. Our system extracts the audio track from a target lecture video, generates a transcription by large vocabulary continuous speech recognition, and produces a textual index. Users can selectively view specific video segments by submitting textual queries associated with the textbook for the target lecture. Experimental results showed that by adapting speech recognition to the lecture topic, the recognition accuracy increased and the retrieval accuracy was comparable with that obtained by human transcriptions. Our system is implemented as a client-server system over the Web to facilitate e-education.
Association between antibody titers and protection against influenza virus infection within households.

BACKGROUND Previous studies have established that antibody titer measured by the hemagglutination-inhibiting (HAI) assay is correlated with protection against influenza virus infection, with an HAI titer of 1:40 generally associated with 50% protection. METHODS We recruited index cases with confirmed influenza virus infection from outpatient clinics, and followed up their household contacts for 7-10 days to identify secondary infections. Serum samples collected from a subset of household contacts were tested by HAI and microneutralization (MN) assays against prevalent influenza viruses. We analyzed the data using an individual hazard-based transmission model that adjusted for age and vaccination history. RESULTS Compared to a reference group with antibody titers <1:10, we found that HAI titers of 1:40 against influenza A(H1N1) and A(H3N2) were associated with 31% (95% confidence interval [CI], 13%-46%) and 31% (CI, 1%-53%) protection against polymerase chain reaction (PCR)-confirmed A(H1N1) and A(H3N2) virus infection, respectively, while an MN titer of 1:40 against A(H3N2) was associated with 49% (95% CI, 7%-81%) protection against PCR-confirmed A(H3N2) virus infection. CONCLUSIONS An HAI titer of 1:40 was associated with substantially less than 50% protection against PCR-confirmed influenza virus infection within households, perhaps because of exposures of greater duration or intensity in that confined setting.
Netrin-1 Is a Novel Myelin-Associated Inhibitor to Axon Growth

We investigated the influence of the bifunctional guidance molecule netrin-1 on axonal growth in the injured adult spinal cord. In the adult, netrin-1 is expressed on mature oligodendrocytes, cells of the central canal, and the meninges. Netrin-1 protein in white matter is selectively enriched adjacent to paranodal loops of myelin in nodes of Ranvier. The repulsion-mediating netrin-1 uncoordinated-5 (UNC5) receptors are expressed by neurons of the corticospinal and rubrospinal projections, and by intrinsic neurons of the spinal cord, both before and after spinal cord injury. Neutralization of netrin-1 in myelin prepared from adult rat spinal cord using UNC5 receptor bodies increases neurite outgrowth from UNC5-expressing spinal motor neurons in vitro. Furthermore, axon regeneration is inhibited in a netrin-1-enriched zone, devoid of other myelin-associated inhibitors, within spinal cord lesion sites in vivo. We conclude that netrin-1 is a novel oligodendrocyte-associated inhibitor that can contribute to axonal growth failure after adult spinal cord injury.
Bayesian inference of asymmetric stochastic conditional duration models

This paper extends stochastic conditional duration (SCD) models for financial transaction data to allow for correlation between error processes and innovations of observed duration process and latent log duration process. Suitable algorithms of Markov Chain Monte Carlo (MCMC) are developed to fit the resulting SCD models under various distributional assumptions about the innovation of the measurement equation. Unlike the estimation methods commonly used to estimate the SCD models in the literature, we work with the original specification of the model, without subjecting the observation equation to a logarithmic transformation. Results of simulation studies suggest that our proposed models and corresponding estimation methodology perform quite well. We also apply an auxiliary particle filter technique to construct one-step-ahead in-sample and out-of-sample duration forecasts of the fitted models. Applications to the IBM transaction data allow comparison of our models and methods to those existing in the literature.
Propagation of a subglacial flood wave during the initiation of a jökulhlaup

Abstract Observations from the jökulhlaup from Grímsvötn in Vatnajökull, south-eastern Iceland, in 1996 indicate that the jökulhlaup was initiated by the movement of a localised pressure wave that travelled 50 km in 10 h from Grimsvötn to the terminus, forming a subglacial pathway along the glacier bed. Shortly after this wave reached the terminus, the jökulhlaup was flowing at a high discharge through a tunnel that would have needed much longer time to form by ice melting as assumed in existing theories of jökulhlaups. Frozen sediments formed in crevasses and frazil ice on the surface of the flood waters indicate the flow of supercooled water in the terminus region, demonstrating that the rate of heat transfer from subglacial flood water to the overlying ice is greatly underestimated in current theories.
Scalability in the XFS File System

In this paper we describe the architecture and design of a new file system, XFS, for Silicon Graphics' IRIX operating system. It is a general purpose file system for use on both workstations and servers. The focus of the paper is on the mechanisms used by XFS to scale capacity and performance in supporting very large file systems. The large file system support includes mechanisms for managing large files, large numbers of files, large directories, and very high performance I/O. In discussing the mechanisms used for scalability we include both descriptions of the XFS on-disk data structures and analyses of why they were chosen. We discuss in detail our use of B+ trees in place of many of the more traditional linear file system structures. XFS has been shipping to customers since December of 1994 in a version of IRIX 5.3, and we are continuing to improve its performance and add features in upcoming releases. We include performance results from running on the latest version of XFS to demonstrate the viability of our design.
Autocrine signals enable chondrocytes to survive in culture

We recently proposed that most mammalian cells other than blastomeres may be programmed to kill themselves unless continuously signaled by other cells not to. Many observations indicate that some mammalian cells are programmed in this way, but is it the case for most mammalian cells? As it is impractical to test all of the hundreds of types of mammalian cells, we have focused on two tissues--lens and cartilage-- which each contain only a single cell type: if there are cells that do not require signals from other cells to avoid programmed cell death (PCD), lens epithelial cells and cartilage cells (chondrocytes) might be expected to be among them. We have previously shown that rat lens epithelial cells can survive in serum-free culture without signals from other cell types but seem to require signals from other lens epithelial cells to survive: without such signals they undergo PCD. We show here that the same is true for rat (and chick) chondrocytes. They can survive for weeks in culture at high cell density in the absence of other cell types, serum, or exogenous proteins or signaling molecules, but they die with the morphological features of apoptosis in these conditions at low cell density. Medium from high density cultures, FCS, or a combination of known growth factors, all support prolonged chondrocyte survival in low density cultures, as long as antioxidants are also present. Moreover, medium from high density chondrocyte cultures promotes the survival of lens epithelial cells in low density cultures and vice versa. Chondrocytes isolated from adult rats behave similarly to those isolated from developing rats. These findings support the hypothesis that most mammalian cells require signals from other cells to avoid PCD, although the signals can sometimes be provided by cells of the same type, at least in tissues that contain only one cell type.
CASCADED HYBRID FIVE-LEVEL INVERTER WITH DUAL CARRIER PWM CONTROL SCHEME FOR PV SYSTEM

Cascaded Hybrid MultiLevel Inverter (CHMLI) is an attractive topology for high voltage DC-AC conversion. This paper focuses on a single-phase five-level inverter with reduced number of switches. The inverter consists of a full bridge inverter and an auxiliary circuit with four diodes and a switch. The inverter produces output voltage in five levels: zero, +0.5Vdc, +Vdc, -0.5Vdc and –Vdc. A novel dual reference modulation technique has been proposed for the CMLI. The dual carrier modulation technique uses two identical inverted sine carrier signals each with amplitude exactly half of the amplitude of the sinusoidal reference signal to generate PWM signals for the switches. Using Perturb and Observe (P&O) algorithm, Maximum Power Point (MPPT) has been tracked for PV inverter. A Proportional Integral (PI) control algorithm is implemented to improve the dynamic response of the inverter. Performance evaluation of the proposed PWM strategy for Multilevel Inverter (MLI) has been carried out using MATLAB and it is observed that it gives reduced Total Harmonic Distortion (THD). An experimental five-level hybrid inverter test rig has been built to implement the proposed algorithm. Gating signals are generated using PIC microcontroller. The performance of the inverter has been analyzed and compared with the result obtained from theory and simulation.
Comparative Study of OFDM and CDMA Technique

Orthogonal frequency division multiplex (OFDM) modulation is being used more and more in telecommunication both in wired and wireless. This modulation technique has several advantages, reason for its increasing usage in communication. OFDM can provide high data rates, it can be implemented easily, it is spectrally efficient and with sufficient robustness to channel imperfections. On the other hand most third generation mobile phone systems are using Code Division Multiple Access (CDMA) as their modulation technique. For this reason, CDMA is also investigating so that the performance of both CDMA and OFDM can be compared. It is found that OFDM performs extremely well when compared with CDMA, and provide very high tolerance to multipath delay spread, channel noise, and peak power clipping. In addition to this it provides a high spectral efficiency The noise performance of OFDM is found to depend solely on the modulation technique used for modulating each carrier of the signal. The OFDM signal performance is found to be the same as for a single carrier system, using the same modulation technique and can provide large data rates with sufficient robustness to radio channel impairments The minimum signal to noise ratio (SNR) required for BPSK was ~7 dB, where as it was ~12 dB for QPSK and ~25 dB for 16PSK. CDMA was found to perform poorly in a single cellular system, with each cell only allowing 7-16 simultaneous users in a cell, compared with 128 for OFDM.1. 25 MHz bandwidth and 19.5 kbps user data rate was used for it. This low cell capacity of CDMA was attributed to the use of non-orthogonal codes used in the reverse transmission link, leading to a high level of inter-user interference.
Effects of sedimented helium on the X-ray properties of galaxy clusters

In this Letter, we consider the role played by the sedimentation of helium nuclei on the emissivity and metallicity distribution of the X-ray emitting plasma in the cores of relaxed galaxy clusters. We model the gas density and temperature profiles of nearby cooling core clusters to estimate the gravitational acceleration acting on the helium, and show that its sedimentation time-scale is too long with respect to the present age of these objects to play a significant role. However, we argue that these time-scales definitely must have been lower in the past to allow the helium to settle in the cluster cooling cores and raise its abundance to values higher than the solar one. A direct consequence of this speculation is that the helium, by increasing the total X-ray emissivity, reduces the measured metal abundance in the inner (r≲ 20 kpc) cluster regions.
An Asymmetric Energetic Type Ic Supernova Viewed Off-Axis, and a Link to Gamma Ray Bursts

Type Ic supernovae, the explosions after the core collapse of massive stars that have previously lost their hydrogen and helium envelopes, are particularly interesting because of their link with long-duration gamma ray bursts. Although indications exist that these explosions are aspherical, direct evidence has been missing. Late-time observations of supernova SN 2003jd, a luminous type Ic supernova, provide such evidence. Recent Subaru and Keck spectra reveal double-peaked profiles in the nebular lines of neutral oxygen and magnesium. These profiles are different from those of known type Ic supernovae, with or without a gamma ray burst, and they can be understood if SN 2003jd was an aspherical axisymmetric explosion viewed from near the equatorial plane. If SN 2003jd was associated with a gamma ray burst, we missed the burst because it was pointing away from us.
Modeling Clinical Context: Rediscovering the Social History and Evaluating Language from the Clinic to the Wards

Social, behavioral, and cultural factors are clearly linked to health and disease outcomes. The medical social history is a critical evaluation of these factors performed by healthcare providers with patients in both inpatient and outpatient care settings. Physicians learn the topics covered in the social history through education and practice, but the topics discussed and documented in real-world clinical narrative have not been described at scale. This study applies large-scale automated topic modeling techniques to discover common topics discussed in social histories, to compare those topics to the medical textbook representation of those histories, and to compare topics between clinical settings to illustrate differences of clinical context on narrative content. Language modeling techniques are used to consider the extent to which inpatient and outpatient social histories share in their language use. Our findings highlight the fact that clinical context and setting are distinguishing factors for social history documentation, as the language of the hospital wards is not the same as that of the ambulatory clinic. Moreover, providers receive little feedback on the quality of their documentation beyond that needed for billing processes. The findings in this study demonstrate a number of topics described in textbooks – schooling, religion, alternative health practices, stressors, for example - do not appear in social histories in either clinical setting.
Person Identification between Different Online Social Networks

Online social networks have become popular for communication among people. Previous works on social networks were focused mainly on a single network due to the lack of links from one network to the others. There are several ways to connect different social networks. One possible solution is to find out the same individual who owns accounts for different social networks simultaneously. In this paper, we focus on the problem of linking the same users across different social networks. We start from a basic solution based on profile comparison, which is then improved by considering additional social relationships, i.e., friends. Finally, we propose a two-phase clustering method to generate a social summary for each individual. The first phase is to select strongly-connected groups as seeds. The second phase is to assign non-seeds to the clusters based on the social structure and profiles. The final distributions over various clusters are regarded as the social summary. The experiments have been conducted on two real social networking datasets. The experimental results show the feasibility of the proposed (social summary) method, compared to the methods of profile comparison and person-name comparison.
KENERL BASED SVM CLASSIFICATION FOR FINANCIAL NEWS

Financial news based forecasting is still becomes major important problem to classify the new under various categories based on modern time series . Further it can be used for stock market prediction analysis and stock market improvement in the business environment for individual organization. From this new content are major important to influence market prediction analysis. In this research the stock market prediction data as considered as input text data with the stock price can be predicted much well by looking at appeared news articles in stock market by sharing numeral of factors of news from companies in local and global politics to news of superpower economy. In this paper proposed a classification based method to classify news data for given stock market prediction system . To execute efficient classification by establishing efficient multiple kernel learning in SVM classification. Kernel function called Gaussian Radial basis Polynomial Function (GRPF) is introduced that could improve the classification accuracy of Support Vector Machines (SVMs) for both linear and non-linear data sets. The aspire is to train Support Vector Machines (SVMs) with dissimilar kernels compare with SVM learning algorithm in classification task . Kernel SVM takes a set of input data and predict possible outcomes fro given input data from news for making the predication result . Experiments Results show that the Kernel SVM classification can provide high accuracy for prediction of the result than the previous SVM methods.
Single-phase voltage source converter with active power decoupling operating in both grid-connected and island modes

The current paper presents a single-phase voltage source converter (VSC) operating in both grid-connected and island modes. The novelty of the proposed system consists in the special control and hardware configuration of the H-bridge VSC that ensure the power decoupling of the double-frequency pulsating power, obtaining a ripple-free DC-link current without using additional power semiconductors. The proposed design, involves adding only two film capacitors at the output of each VSC arm. The system operation, for grid connection and during transfer to and from island mode, is assessed by means of simulation and experimental results.
R O M a Active Bgp Probing Revised Version

For an Internet Service provider (ISP), the knowledge of which interdomain paths are traversed by its BGP announcements – and thus traffic flows – is essential to predict the impact of network faults, to perform effective traffic engineering, to develop peering strategies, and to assess the quality of connectivity provided by the ISP’s upstreams. We present methodologies to discover how the BGP announcements for an ISP’s prefix are propagated in the Internet, overcoming the limitations of passive observation of BGP routing tables by actively probing the network using specific BGP updates. The techniques do not require any changes to current operational practices or BGP implementations. We also show how our techniques may be used to determine the routing policies of other ISPs with respect to the ISP’s prefix. We validate our techniques through experimentation in the IPv6 Internet, discuss their possible application to IPv4, and compare their results to more traditional topology discovery techniques. We also discuss the operational impact of our techniques and possible ethical concerns arising from their use.
A novel distributed clustering algorithm based on OCSVM

In this paper, aiming to accelerate the clustering method of Support Vector Machine for large-scale dataset, we present a novel method for clustering inspired by the OCSVM and the Multi-Agent framework, in which the data are divided to different agents, and the global clustering result can be generalized from the agents. Moreover, according to the One-Class Support Vector Machine theory, this paper conducts a study on the setting of parameter involved in the clustering algorithm. Lastly, the experimental results indicate that the clustering method we proposed in this paper is more efficient for large dataset.
Automatic derivation of polyhedral process networks from while-loop affine programs

The Process Networks (PNs) is a suitable parallel model of computation (MoC) used to specify embedded streaming applications in a parallel form facilitating the efficient mapping onto embedded parallel execution platforms. Unfortunately, specifying an application using a parallel MoC is very difficult and highly error-prone task. To overcome the associated difficulties, an automated procedure exists for derivation of a specific polyhedral process networks (PPN) from static affine nested loop programs (SANLPs). This procedure is implemented in the pn complier. However, there are many applications, e.g., multimedia applications, signal processing, etc., that have adaptive and dynamic behavior which can not be expressed as SANLPs. Therefore, in order to handle more dynamic applications, in this paper we address the important question whether we can relax some of the restrictions of the SANLPs while keeping the ability to perform compile-time analysis and to derive PPNs. Achieving this would significantly extend the range of applications that can be parallelized in an automated way. The main contribution of this paper is a first approach for automated translation of affine nested loops programs with while-loops into input-output equivalent PPNs.