Miracle-dz/UniFAR-FTUs huggingface dataset:
A Brief Review on Soft Errors and LDPC Codes

: In current scenario, due to high demand of technology scaling and higher integration density, the designers are using nanodevices for computer system and other memory system. Despite having many advantages such as lower area overhead and lower power consumption, these devices has major disadvantage that they are more prone to soft errors. Due to this reason the reliability of these systems are severely affected. Many error detection and correction codes have been studied to reduce this error. There are vast classes of such codes; some of them are hamming codes, turbo codes, BCH codes and LDPC codes. But among these codes LDPC codes achieve better performance and lower decoding complexity. LDPC codes were originally discovered by Robert G. Gallager. But after the rediscovery of LDPC codes by Mackay and Neal in 1995 interest on LDPC codes increases because of its bit error performance approaches asymptotically the Shannon limit. An LDPC code is a special class of linear block codes whose parity-check matrix H has low density of one’s i.e. sparse. Due to this sparsity in LDPC codes there is low complexity decoding and its implementation is also simple. Also LDPC codes provides large degree of parallelism that can be exploited in the decoder and in LDPC codes information block length are long enough. In addition LDPC codes provides wide range of trade-offs between performance and complexity. LDPC codes find its application in many areas such as satellite transmission of digital television. LDPC codes are also used for 10GBase-T Ethernet that transmits data at 10 gigabits per second over twisted pair cable .
Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques

Abstract. Today, many processes at the Earth's surface are constantly monitored by multiple data streams. These observations have become central to advancing our understanding of vegetation dynamics in response to climate or land use change. Another set of important applications is monitoring effects of extreme climatic events, other disturbances such as fires, or abrupt land transitions. One important methodological question is how to reliably detect anomalies in an automated and generic way within multivariate data streams, which typically vary seasonally and are interconnected across variables. Although many algorithms have been proposed for detecting anomalies in multivariate data, only a few have been investigated in the context of Earth system science applications. In this study, we systematically combine and compare feature extraction and anomaly detection algorithms for detecting anomalous events. Our aim is to identify suitable workflows for automatically detecting anomalous patterns in multivariate Earth system data streams. We rely on artificial data that mimic typical properties and anomalies in multivariate spatiotemporal Earth observations like sudden changes in basic characteristics of time series such as the sample mean, the variance, changes in the cycle amplitude, and trends. This artificial experiment is needed as there is no gold standard for the identification of anomalies in real Earth observations. Our results show that a well-chosen feature extraction step (e.g., subtracting seasonal cycles, or dimensionality reduction) is more important than the choice of a particular anomaly detection algorithm. Nevertheless, we identify three detection algorithms (k-nearest neighbors mean distance, kernel density estimation, a recurrence approach) and their combinations (ensembles) that outperform other multivariate approaches as well as univariate extreme-event detection methods. Our results therefore provide an effective workflow to automatically detect anomalies in Earth system science data.
Technoeconomic Sensitivity of Biobased Hydrocarbon Production via Fast Pyrolysis to Government Incentive Programs

AbstractFast pyrolysis and upgrading is a promising thermochemical pathway that produces pyrolysis oil that can be upgraded via hydroprocessing into hydrocarbon-based transportation fuels (drop-in biofuels). The internal rate of return (IRR) of a fast pyrolysis and upgrading facility is a function of feedstock cost and projected revenues. We calculate the IRR of a fast pyrolysis and upgrading facility under six different policy scenarios: (1) a baseline scenario in which the facility receives no government support; (2) a scenario in which cap-and-trade (H.R. 2454) is enacted with both carbon price and offsets; (3) a scenario in which the Volumetric Ethanol Excise Tax Credit (VEETC) is modified to include drop-in biofuels; (4) a scenario in which the VEETC is replaced with a variable VEETC; (5) the revised Renewable Fuel Standard (RFS2); and (6) the Cellulosic Biofuel Producer Tax Credit (CBPTC). Combinations of these policy scenarios are also analyzed. We find that the policies responsible for increasing .
AUTOMATED CROWD DETECTION IN STADIUM ARENAS

In this paper, we present an approach for crowd detection based on an ensemble of classifiers which employ several feature representation schemes of crowd images, including, local ternary patterns, local binary patterns, and features based on the spatial gray level dependency matrix. A Support Vector Machine classifier is trained on each of these feature vectors. Classifier predictions are then combined by sum rule. Experiments are performed on a large dataset that contains challenging sequences of actual football matches recorded at a stadium arena. Experimental results confirm that the different feature representations give complementary information which is exploited by fusion rules. The method proposed in this paper is shown to outperform previous methods tested on the same dataset. MATLAB code using the different descriptors is available at http://www.dei. unipd.it/wdyn/? IDsezione=3314&IDgruppo_pass=124&preview=".
Using object history to predict future behavior: Evidence for essentialism at 9 months of age

Preschool-age children show essentialism (Gelman, 2003), ascribing an essence to an object that includes its history, and which can determine behavior. While infants show the precursors of essentialism, such as maintaining object representations during naturalistic occlusion (6-month-olds; Kaufman, Csibra, & Johnson, 2005), and resisting individuating two disparate appearances of an object when shown that one can change into the other (14-month-olds; Cacchione, Schaub, & Rakoczy, 2013), the implicit precursors of essentialist reasoning in infants have not been directly studied. Here we tested whether young infants could use an object’s prior history to predict its behavior, even after it had changed into a novel shape. Critically, the object either smoothly morphed into the novel shape (facilitating an essentialist interpretation) or was replaced by a new shape (discouraging essentialist interpretation). Results showed that 9-month-old infants (N = 22) in the Morph condition predicted the novel object would have the same behavior as the pretransformation object; an essentialist interpretation. However, in the Replace condition (N = 22), predictions for the novel object were at chance; infants seemed to have lost the link to the pre-transformation object. Furthermore, results from a group of 6-month-olds (N = 15) showed that they failed to maintain this link, even in the Morph condition (which may indicate a failure to apply essentialist reasoning, or, more likely, a failure to adequately remember the pre-transformation object and/or apply the matching rule to predict posttransformation behavior).
Can the MIQ-RS questionnaire be used to estimate the performance of a MI-based BCI?

Predicting a subject's ability to use the interface with good accuracy is one of the major issues in the motor Brain-Computer interface (BCI) domain. A few recent studies show that subjective questionnaires could be used to predict the performance of motor imagery (MI) based BCI. Indeed, the Kinesthetic and Visual Imagery Questionnaire (KVIQ), could allow a better predictability of BCI-illiterate cases [1]. Another more recent questionnaire called the Motor Imagery Questionnaire Revised-Second Edition (MIQ-RS) is a suitable option for examining MI ability [2]. In 2016, Marchesotti et al. found that the representation of subjective behaviour, calculated using the MIQ-RS questionnaire, and the control of the BCI were intimately linked [3]. However, in these studies [1, 3], the performance of the classifier was calculated for a right-hand MI versus a left-hand MI task. In this abstract, we classify between resting state and imagined movement, which is a relevant classification task in BCI research [4]. The aim of this study is to answer the following question for a resting state versus MI classification task: can the MIQ-RS be used to estimate the performance of an MI-based BCI?
Understanding suicide: does it have a special meaning in adolescence?

The book discusses the contributions that each of the major disciplines have made to suicidology (is there a misplaced devotion to Durkheim’s 100-year-old theories? ), and provide an overview of research and theories in some typical areas. Drawing from this, specific recommendations as to what researchers and theorists can do in the future to advance our understanding of suicide and suicide prevention are offered. It is hoped that these recommendations will stimulate research and theorizing so that our understanding of suicide will progress.
Reclaiming biologically pretreated greywater for reuse by photocatalytic oxidation: qualitative study on the removal of trace organics

This study was carried out for gathering qualitative information about the potential of photocatalytic oxidation for the removal of trace organics (analysed by gas chromatography coupled to mass spectrometry, GC/MS) from biologically pretreated greywater to make it suitable for high quality reuse applications like groundwater recharge. Additionally, fractions of bulk organics (humic substances, building blocks, and low molecular weight organic acids) were quantified by liquid chromatography with organic carbon detection. Biologically pretreated greywater was subjected to photocatalytic oxidation in open stirred vessel reactors with UV lamps positioned over the reactors. UV doses of 0, 5, and 15 Wh·L-1 and TiO2 P25 photocatalyst concentrations of 1, 5, 10, and 20 g·L-1 were investigated. Photocatalysis experiments with a 15 Wh·L-1 UV dose were also conducted in the presence of 1 g·L-1 powdered activated carbon. Subsequent to mere contact of the photocatalyst to biologically pretreated greywater without UV, GC/MS did not indicate a substantial removal of trace organics, while humic substances were increasingly adsorbed by increasing photocatalyst concentration. A UV dose of 15 Wh·L-1 and TiO2 concentrations > 5 g·L-1 were favorable conditions for photocatalytic oxidation resulting in the removal of most of the trace organics, especially chlorinated phosphate flame retardants. Also humic substances were efficiently removed under these conditions. Photocatalytic oxidation is thus a promising process for advanced greywater treatment prior to groundwater recharge. Addition of powdered activated carbon did not improve trace and bulk organics removal by photocatalysis with a UV dose of 15 Wh·L-1 and with photocatalyst concentrations > 5 g·L-1.
Structures, Functions and Expressions of GnRH and GnRH Receptor in Peripheral Reproductive Organs and Their Regulation by Estradiol-17β

Studies have shown that estradiol-17β (E2) regulates gonadotropin-releasing hormone (GnRH) and GnRH receptor expression in hypothalamus and pituitary. Several studies have shown that GnRH and its receptor are also expressed in peripheral reproductive organs and little is known about their regulations. In this study, GnRH and GnRH receptor structures, functions, their peripheral expressions and regulations by E2 were reviewed. Several in vivo and in vitro conducted studies indicate that E2 decreases the expression of GnRH mRNA and regulates GnRH receptor expression in a time dependent manner. Nevertheless, the exact mechanism has not been clearly explained yet.
Performance Evaluation of Rectangular Microstrip Patch Antennas Loaded with Plastic and Barium-Titanate Substrates at GSM 1800 MHz Band

In this paper, the resonance and radiation characteristics of patch antennas fabricated with two different types of dielectric substrates have been investigated and compared at GSM 1800 MHz band. At first, the above-stated characteristics of a patch antenna loaded with conventional plastic substrate have been investigated. Later a high permittivity dielectric material (barium titanate) has been used as the antenna substrate. The main goal here is to reduce the antenna size with a high permittivity dielectric material and then to compare its resonance and radiation performance with the earlier low permittivity substrate loaded prototype. It is found that with the use of high permittivity substrate the antenna volume gets smaller (about 6% of the plastic substrate prototype) although the gain decreases by around 2.5 dB.
1-1-2018 Self-Organizing mmWave Networks : A Power Allocation Scheme Based on Machine Learning

Millimeter-wave (mmWave) communication is anticipated to provide significant throughout gains in urban scenarios. To this end, network densification is a necessity to meet the high traffic volume generated by smart phones, tablets, and sensory devices while overcoming large pathloss and high blockages at mmWaves frequencies. These denser networks are created with users deploying small mmWave base stations (BSs) in a plugand-play fashion. Although, this deployment method provides the required density, the amorphous deployment of BSs needs distributed management. To address this difficulty, we propose a self-organizing method to allocate power to mmWave BSs in an ultra dense network. The proposed method consists of two parts: clustering using fast local clustering and power allocation via Q-learning. The important features of the proposed method are its scalability and self-organizing capabilities, which are both important features of 5G. Our simulations demonstrate that the introduced method, provides required quality of service (QoS) for all the users independent of the size of the network.
Interstellar Polycyclic Aromatic Compounds and Astrophysics

Over the past 15 years, thanks to significant, parallel advancements in observational, experimental, and theoretical techniques, tremendous strides have been made in our understanding of the role polycyclic aromatic compounds (PACs) in the interstellar medium (ISM). Twenty years ago, the notion of an abundant population of large, carbon-rich molecules in the ISM was considered preposterous. Today, the unmistakable spectroscopic signatures of PACs--shockingly large molecules by previous interstellar chemistry standards--are recognized throughout the universe. In this article, we will examine the interstellar PAC model and its importance to astrophysics, including: (a) the evidence which led to inception of the model, (b) the ensuing laboratory and theoretical studies of the fundamental spectroscopic properties of PAC by which the model has been refined and extended, and (c) a few examples of how the model is being exploited to derive insight into the nature of the interstellar PAC population.
Optimal bandwidth estimation for a fast manifold learning algorithm to detect circular structure in high-dimensional data

We provide a way to infer about existence of topological circularity in high-dimensional data sets in $\mathbb{R}^d$ from its projection in $\mathbb{R}^2$ obtained through a fast manifold learning map as a function of the high-dimensional dataset $\mathbb{X}$ and a particular choice of a positive real $\sigma$ known as bandwidth parameter. At the same time we also provide a way to estimate the optimal bandwidth for fast manifold learning in this setting through minimization of these functions of bandwidth. We also provide limit theorems to characterize the behavior of our proposed functions of bandwidth.
Latency Evaluations and Reduction Techniques in Authentication Phase of Centralized WLAN Hotspot Networks

Centralized Wireless Local Area Networks (WLANs) are becoming increasingly popular in public hotspot deployments. This is because centralized networks make use of a single infrastructure network where several Access Points (APs) connect to the network for services such as Quality of Service (QoS), traffic control, access control, roaming, SNMP and billing. Even though centralized network deployments are robust, secure and easily expandable, they pose high handover latency for mobile clients that move from one AP to another. While roaming, mobile clients have to perform scanning, authentication and association, 802.1x authentication and key management processes. These processes take a long time and pose a challenge for real-time applications that are sensitive to network latencies. This paper aims to experimentally evaluate the latencies involved in different processes of roaming under erroneous and non-erroneous conditions. The paper further proposes a mechanism to reduce the overall handoff latency by 260 times for real-time applications by eliminating the 802.1x authentication latency.
Loughborough University Institutional Repository Bad news on decision problems for patterns

We study the inclusion problem for pattern languages, which is shown to be undecidable by Jiang et al. (J. Comput. System Sci. 50, 1995). More precisely, Jiang et al. demonstrate that there is no effective procedure deciding the inclusion for the class of all pattern languages over all alphabets. Most applications of pattern languages, however, consider classes over fixed alphabets, and therefore it is practically more relevant to ask for the existence of alphabet-specific decision procedures. Our first main result states that, for all but very particular cases, this version of the inclusion problem is also undecidable. The second main part of our paper disproves the prevalent conjecture on the inclusion of so-called similar E-pattern languages, and it explains the devastating consequences of this result for the intensive previous research on the most prominent open decision problem for pattern languages, namely the equivalence problem for general E-pattern languages.
Long term effects after feedback of slow cortical potentials and of theta-beta-amplitudes in children with attention-deficit/hyperactivity disorder (ADHD)

Though it had already been shown in the 1970s that neurofeedback improves attention, academic performance and social behavior in children with ADHD, it has not been considered as a standard therapy so far. This is mainly due to the small number of controlled studies fulfilling methodological standards - especially long term data was not available so far. We are the first to present long term data of children undergoing neurofeedback training. 47 patients in the age of 8 - 12 years were randomly assigned to two different training groups. One group was trained to self regulate slow cortical potentials (SCP), the other group tried to influence Theta- and Beta-amplitudes. Follow-up evaluation was carried out 6 months and more than 2 years after the last training session. Eleven children of the SCP group and 12 children of the Theta/Beta group took part in three booster sessions. Parents rated behavioral symptoms as well as frequency and impact of problems. Attention was measured with the Testbatterie zur Aufmerksamkeitsprufung (TAP). All improvements in behavior and attention that had been observed at previous assessments turned out to be stable. Yet another significant reduction of number of problems and significant improvement in attention was observed. EEG-self regulation skills were preserved. In each group, half of the children no longer met ADHD - criteria. Neurofeedback appears to be an alternative or complement to traditional treatments. The stability of changes might be explained by normalizing of brain functions that are responsible for inhibitory control, impulsivity and hyperactivity.
Athens Institute for Education and Research ATINER ATINER ' s Conference Paper Series COM 2016-1994

Segmentation is a core process for automatic detection and identification of brain tumors as it plays a vital role in extracting the information of the image as measuring and visualizing the brain's anatomical structures and analyzing the brain changes. From this point the need for accurate and automatic segmentation techniques has risen as manual segmentation is not a realistic solution and yet time consuming. This paper examines the various automated segmentation techniques used by researchers on brain magnetic resonance images (MRI), giving the most important features for the most common techniques used in the area of brain tumors. Moreover, a comparative study to address the differences, limitations, advantages and challenges of each technique mentioned when being used on brain MRI to find out their efficiency in this area and to put guidelines that should be considered when using these techniques.
Comparative Prevalence Studies of Hepatitis C Patients in Developing Vs Developed Countries

Viral hepatitis is one of the common transmissible causes of chronic liver responsible for increased risk of mortality and morbidity. Hepatitis C disease has worldwide high prevalence which is more fatal because of lack of vaccination availability. Present study was conducted with the aim of to address the comparative prevalence and risk factor of hepatitis C. Secondary objective was to evaluate the prevalence of this disease in Pakistan as compared to other countries. Most of the countries in the world do not have significant studies of population infected with Hepatitis C virus (HCV). During 1990-2016, 232 studies were included; containing 117 countries representing that 90% of the world population have 2.3% prevalence. According to studies, developed countries have low HCV prevalence while developing countries have high prevalence. Pakistan being a developing country has second highest HCV prevalence ranging from 4.5 to 8%. The reason of this high prevalence is unawareness about Hepatitis C in community and lack of successful therapies for patients with HCV. In past, developed countries had high prevalence but now they have low incidence because of proper system and awareness. Pakistan needs such kind of public awareness programs and better treatment facilities at affordable price to community. Tahir et al., International Current Pharmaceutical Journal, November 2016, 5(12): 106-110 http://www.icpjonline. com/documents/Vol5Issue12/02.pdf
THE EMPIRICAL DISTRIBUTION FUNCTION FOR DEPENDENT VARIABLES: ASYMPTOTIC AND NONASYMPTOTIC RESULTS IN L p

Considering the centered empirical distribution function FnF as a variable in L p (µ), we derive non asymptotic upper bounds for the deviation of the L p (µ)-norms of FnF as well as central limit theorems for the empirical process indexed by the elements of generalized Sobolev balls. These results are valid for a large class of dependent sequences, including non-mixing processes and some dynamical systems.
Children's Antisocial and Prosocial Lies to Familiar and Unfamiliar Adults.

Although parents frequently instruct children not to lie, children often observe lie-telling within the family environment. To date, no empirical research has examined children's spontaneous lie-telling to different lie-recipients. The current study examined children's spontaneous deceptive behaviour to parents and unfamiliar adults. In Experiment 1 (N = 98), children's (ages 6–9) antisocial lies to a parent or an unfamiliar adult were examined using a modified Temptation Resistance Paradigm. In Experiment 2, (N = 99) children's (ages 6–9) prosocial lies to a parent versus an unfamiliar adult were examined using the Disappointing Gift Paradigm. Results indicate that, across different types of lies, children are more likely to lie to an unfamiliar adult than to a parent. Copyright © 2013 John Wiley & Sons, Ltd.