Miracle-dz/UniFAR-FTUs huggingface dataset:
Covariance Based Signal Detections for Cognitive Radio

Sensing (signal detection) is a fundamental problem in cognitive radio. The statistical covariances of signal and noise are usually different. In this paper, this property is used to differentiate signal from noise. The sample covariance matrix of the received signal is computed and transformed based on the receiving filter. Then two detection methods are proposed based on the transformed sample covariance matrix. One is the covariance absolute value (CAV) detection and the other is the covariance Frobenius norm (CFN) detection. Theoretical analysis and threshold setting for the algorithms are discussed. Both methods do not need any information of the signal, the channel and noise power as a priori. Simulations based on captured ATSC DTV signals are presented to verify the methods.
Automatic detection of activities of daily living from detecting and classifying electrical events on the residential power line

The objective of this project is the remote follow up of the activities of patients living independently in their own home, with the help of ICT. The sensing of activity in the home has a variety of important applications, including home automation, energy monitoring and healthcare. Most of the existing systems for detecting occupant activity require large numbers of sensors, invasive vision systems, or extensive installation procedures. We present an approach that uses a single sensor to detect the electrical events throughout the home. This sensor detects and identifies the electrical impulses on residential power lines created by the on-off switching of electrical devices. The electrical events are regularly downloaded to a central data base with a web access.
Unsupervised Acquisition of Lexical Knowledge From N-grams : Final Report of the 2009 JHU CLSP Workshop

Google, Inc. ( lindek@google.com ), Johns Hopkins University ( kenneth.church@jhu.edu,yarowsky@cs.jhu.edu, kailash@jhu.edu, kapild@cs.jhu.edu, sushant@jhu.edu ), City University of New York (hengji@cs.qc.cuny.edu ), New York University (sekine@cs.nyu.edu ), University of Alberta (sbergsma@ualberta.ca ), University of Pennsylvania ( epitler@seas.upenn.edu ), University of Virginia (rlathbury@virginia.edu ), Cornell University (vr59@cornell.edu )
Detection and Control of Anomaly Network Data Flows

Data flows is a type of dynamic data. After the detection of anomaly data flows, the next problem is how to control these anomaly data flows effectively and prevent network jam. Router queue management is an effective method of controlling anomaly data flows. When network is busy router can prevent it by active droppings. The characters of unresponsive of network congestion control and high-bandwidth were researched deeply to explore the malicious flow effects on the large-scale network. After that, experiments were implemented to present the network congestion collapse resulted from malicious flows and its influences on the network resource allocations.
Positron Emission Tomography [15O]Water Studies with Short InterScan Interval for Single-Subject and Group Analysis: Influence of Background Subtraction

Use of short interscan interval [15O]water positron emission tomography (PET) studies reduces the overall study duration and may allow an increased number of scans for single-subject analysis of unique cases (e.g., stroke). The purpose of this study was to examine how subtraction of residual radioactivity from the previous injection (corrected scan) compared to nonsubtraction (uncorrected scan) in a PET short interscan interval (6 minutes) study affects single-subject and group data analysis using a motor activation task. Two currently widely used analytic strategies, Worsley's method and the SPM technique, were applied. Excellent agreement between activation maps obtained from corrected and uncorrected data sets was obtained both in single-subject analyses performed on data sets from the six normal subjects and three stroke (subcortical infarct) patients, and in group analysis (six normal subjects) within a particular statistical method. The corrected and uncorrected data were very similar in the (1) number of activated brain regions; (2) size of clusters of activated brain voxels; (3) Talairach coordinates of the activated region; and (4) t or Z value of the peak intensity for every significantly activated motor brain structure (both for large activations such as in motor cortex and small activations such as in putamen and thalamus). [15O]Water PET data obtained with a short interscan interval (6 minutes) produce similar results whether or not the background is subtracted. Thus, if injection dose and timing are constant, one can achieve the advantage of a short interscan interval without the added complexity of correcting for background radioactivity.
MRC NN controller for arm robot manipulator

This paper presents investigations into the development of model reference control based on a neural network (NN) for robot manipulator. A NN used as a controller network and a plant model network. A dynamic model of the system is derived using a Lagrange-Euler. The controller to simplify a nonlinearities problem that can be efficiently solved using NN. To study the effectiveness of the controller, initially a nonlinear model is developed for one link robot manipulator. The performances of the NN controllers are assessed in terms of the input tracking controller capability of the system and disturbance robustness. The input is generated by a combined multiple steps input. Finally, a comparative assessment of the input tracking control and a disturbance robustness is presented. The results show that NN controller performs give increasing profiles.
Combining multiple sensor modalities for a localisation robust to smoke

This paper proposes an approach to obtain a localisation that is robust to smoke by exploiting multiple sensing modalities: visual and infrared (IR) cameras. This localisation is based on a state-of-the-art visual SLAM algorithm. First, we show that a reasonably accurate localisation can be obtained in the presence of smoke by using only an IR camera, a sensor that is hardly affected by smoke, contrary to a visual camera (operating in the visible spectrum). Second, we demonstrate that improved results can be obtained by combining the information from the two sensor modalities (visual and IR cameras). Third, we show that by detecting the impact of smoke on the visual images using a data quality metric, we can anticipate and mitigate the degradation in performance of the localisation by discarding the most affected data. The experimental validation presents multiple trajectories estimated by the various methods considered, all thoroughly compared to an accurate dGPS/INS reference.
Sources of Sexual Knowledge for High School Students in Tokyo, Japan

This study is aimed to clarify the methods and sources from which high school students in Tokyo actually acquired sex information and the methods and sources from which they desired to acquire sex information. The participants of the study were selected from six public high schools in Tokyo, and were administered a survey at the time of their second grade from January to February 2017, and again at the time of their third grade from September to November 2017. We conducted self-administrated questionnaire-based surveys with 1073 students. Between the two surveys, at eight to ten weeks prior to the September/November survey, an educational intervention was presented to these subjects. Final analysis subjects totaled 1011 students at the second-grade and 936 students at the third-grade in school. Although many subjects responded that they received sex education, not so many of them actually had correct sexual knowledge. Most subjects, that is, both boys and girls at both second and third grades, thought sex information should be acquired from school, and significantly fewer considered the Internet as a source of sex information as compared to previous studies. Most subjects had high expectations that sex education would be taught at high school by teachers. One difference between boys and girls was that many girls responded that sex education should be acquired primarily from school, then secondarily from parents. Fewer subjects responded that sex education should be acquired from friends and the Internet. For the future, upskilling teachers and parents in sex education at school and home is expected.
Comparative Genomics Reveals Chd1 as a Determinant of Nucleosome Spacing in Vivo

Packaging of genomic DNA into nucleosomes is nearly universally conserved in eukaryotes, and many features of the nucleosome landscape are quite conserved. Nonetheless, quantitative aspects of nucleosome packaging differ between species because, for example, the average length of linker DNA between nucleosomes can differ significantly even between closely related species. We recently showed that the difference in nucleosome spacing between two Hemiascomycete species—Saccharomyces cerevisiae and Kluyveromyces lactis—is established by trans-acting factors rather than being encoded in cis in the DNA sequence. Here, we generated several S. cerevisiae strains in which endogenous copies of candidate nucleosome spacing factors are deleted and replaced with the orthologous factors from K. lactis. We find no change in nucleosome spacing in such strains in which H1 or Isw1 complexes are swapped. In contrast, the K. lactis gene encoding the ATP-dependent remodeler Chd1 was found to direct longer internucleosomal spacing in S. cerevisiae, establishing that this remodeler is partially responsible for the relatively long internucleosomal spacing observed in K. lactis. By analyzing several chimeric proteins, we find that sequence differences that contribute to the spacing activity of this remodeler are dispersed throughout the coding sequence, but that the strongest spacing effect is linked to the understudied N-terminal end of Chd1. Taken together, our data find a role for sequence evolution of a chromatin remodeler in establishing quantitative aspects of the chromatin landscape in a species-specific manner.
Computational Algorithm for Estimation of Panel Data Models with Two Fixed Effects

The panel data models are becoming more common in relation to cross-section and time-series models for innumerable presented advantages, in addition to the computational advance that facilitated theirs utilization. This work aimed at presenting a computational algorithm for estimation of panel data models with two fixed effects, since they demand much more resources than panel data models with one effect (fixed or random). The majority of commercial softwares have such implemented routines, however, in order to reduce the processing time, they estimate the models including dummy variables to the “time” effect. Such action produces a bias in the intercept of the model, over or underestimating the true value, by incorporating the influence of the year taken as base. The algorithm presented in this work showed itself very efficient for being able to work with a large data base, when compared to other algorithms, where the estimation with the same data base wasn’t possible.
Vector processing as a soft-core CPU accelerator

The currently accepted method of accelerating applications in FPGA soft processor systems is to design a custom hardware accelerator. This paper suggests the alternative approach of adding a vector processing core to the soft processor as a general-purpose accelerator. The approach has the benefit of a purely software-oriented development model. With no hardware design experience needed, a software programmer can make area-versus-performance tradeoffs by scaling the number of functional units or vector lanes. This paper shows that a vector processing architecture maps efficiently into an FPGA and provides a scalable amount of performance for a reasonable amount of area. Three configurations of the soft vector processor with different performance levels are estimated to achieve scalable speedup ranging from 3-29x for 6-30x the area of a Nios II/s processor on three benchmark kernels. The results compare favourably to accelerators designed using Altera's C2H compiler, a C-to-hardware tool that is also easy to use
Hypogonadism and Metabolic Syndrome in Nigerian Male Patients With Both Type 2 Diabetes and Hypertension

Background: The association between testosterone level and the components of metabolic syndrome remains controversial. Relevant studies from Sub-Saharan Africa are few and incohesive. Objectives: The current study was designed to investigate the level of testosterone in patients with both diabetes and hypertension and the association of low testosterone with metabolic syndrome in these patients. Materials and Methods: In this prospective case-control study, 83 male subjects (49 newly diagnosed men with both diabetes and hypertension and 34 apparently healthy controls) were recruited from Ladoke Akintola University of Technology Teaching Hospital, Osogbo, Nigeria and University College Hospital Ibadan, Ibadan, Nigeria. Demographic, anthropometric and sexual characteristics were obtained using structured questionnaires and standard methods. Blood plasma glucose (BPG), total cholesterol (TC), triglycerides (TG), high-density lipoprotein-cholesterol (HDL-C) and low-density lipoprotein-cholesterol (LDL-C) were measured by conventional methods. Testosterone (T) was analyzed by enzyme immunoassay. Data obtained were statically analyzed with the SPSS 15.0 software, and results were expressed as mean ± SEM. Results: This study showed significantly lowered concentrations of testosterone (3.11 nm/L ± 0.34) and HDL (0.39 mmol/L ± 0.02), in addition to the expected increased concentrations of fasting plasma glucose (9.61 mmol/L ± 0.37) in the subjects compared to controls (P < 0.05). An inverse significant correlation was observed between the serum testosterone concentration and metabolic syndrome (BMI, r = -0.477; waist/Hip ratio, r = -0.376 and dyslipidemia, r = -0.364, P < 0.05). Also, the testosterone level decreased with increase in central obesity (P < 0.05). Conclusions: This study established a strong association between low serum testosterone and metabolic syndrome in subjects with both type 2 diabetes and hypertension. It may therefore be advisable to include routine measurement of the testosterone level in the management of patients presented with both diabetes and hypertension. Furthermore, these patients may benefit from testosterone replacement therapy.
Aspirations and the histories of elite state schools in London : field theory, circuits of education and the embodiment of symbolic capital.

Aspirations are frequently framed in Bourdieusian sociology with reference to contemporary dynamics of gender, class and race. Rather than focus simply on these identity markers to understand students’ aspirations, I instead use Bourdieu’s approach to history to explore how students’ aspirations are shaped by schools’ past struggles to gain dominance within the field of education. I draw on three approaches to history within Bourdieu’s work: the coming together of institution and individual, the structural history which shapes the rules of the field, and the processes associated with the accumulation of symbolic capital which all dominant institutions must master. I conceptualize the aspirations of students as formed by the coming together of institutional and personal history, exploring how this occurs in two elite state schools in London. These schools sought to use students’ aspirations to accrue prestige and re-position themselves as dominant institutions within the field. To extend Bourdieu, I also draw on what Ball et al. call ‘circuits of education’, which assists in theorizing institutional positions in the field of education, and how these positions are shaped by the dynamic movement of students toward elite universities through space and over time.
Reliable and Faster Data Communication in MANET and VANET Based on Localization and Clustering Techniques

The concept of VANET (Vehicular Ad hoc Network) is derived from the idea of MANET (Mobile Ad hoc Network), where each vehicle is considered as a mobile node. Localization technique based on the clustering methods is an important issue for both MANET and VANET. To find out the position of a node and also provide the link stability in the ad hoc network is a present research area from the researcher view. Now-a-days a large spectrum of work has already been done in clustering techniques in ad hoc network, especially in VANET. The clustering technique is very much useful for reliable and faster data communication in both the forms of networks. The objective of this paper is to provide an idea about the localization concept related to clustering method based on both GPS and GPS free situations in real world scenario.
Cross-modal face matching: Tackling visual abstraction using fine-grained attributes

Despite great strides made in facial verification, it remains challenging to match facial images across different modalities. This is mainly due to the cross-modal gap induced by feature heterogeneity. Much prior work had focused on bridging the feature gap, resulting in near-perfect matching accuracies for viewed sketches. Nonetheless, studies on matching unviewed (forensic) sketches and caricatures, a much harder problem due to the additional cross-modal gap introduced by visual abstraction, had only just commenced in recent years. In this paper, we focus on matching facial caricatures with photos by directly addressing the visual abstraction problem. We show that by synergizing a taxonomy of fine-grained visual attributes with part-aware low-level feature extraction, the visual abstraction gap can be effectively traversed, resulting in improved overall cross-modal matching accuracy. More specifically, (i) we propose a simple yet effective geometry-based attribute classifier to detect fine-grained attributes at part-level, and (ii) we demonstrate how meaningful facial regions can be reliably detected to enable localized feature extraction and attribute detection, and (iii) we show a common embedding can be learned using Canonical Correlation Analysis (CCA) that combines part-based low-level features and fine-grained visual attributes. We demonstrate the superiority of the proposed cross-modal strategy by evaluating on two recent photo-caricature datasets.
Anisotropic dynamic-morphological-diffusion for color image segmentation

In this paper, we present a segmentation method of noisy color images that uses an anisotropic diffusion algorithm and a region-growing algorithm. We propose a modified anisotropic diffusion algorithm as a new precise edge-preserving smoothing technique by using the dynamic morphological filtering. We incorporate a dynamic selection of multiple structuring elements for each pixel in the morphological filter before the anisotropic diffusion in order to improve the ability of edge-preserving smoothing. As a result, noise and unnecessary details of images are effectively smoothed while preserving small image structures before performing a region-growing algorithm. Therefore, the proposed method is suitable for an accurate segmentation of color images with noise. Several simulated examples are presented that demonstrate the effectiveness of the proposed technique.
Robust detection of shady and highlighted roads for monocular camera based navigation of UGV

This paper addresses the problem of UGV navigation in various environments and lightning conditions. Previous approaches use a combination of different sensors, or work well, only in scenarios with noticeable road marking or borders. Our robot is used for chemical, nuclear and biological contamination measurement. Thus, to avoid complications with decontamination, only a monocular camera serves as a sensor since it is already equipped. In this paper, we propose a novel approach - a fusion of frequency based vanishing point estimation and probabilistically based color segmentation. Detection of a vanishing point, is based on the estimation of a texture flow, produced by a bank of Gabor wavelets and a voting function. Next, the vanishing point defines the training area, which is used for self-supervised learning of color models. Finally, road patches are selected by measuring of the roadness score. A few rules deal with dark cast shadows, overexposed highlights and adaptivity speed. In addition to the robustness of our system, it is easy-to-use since no calibration is needed.
The liver as an endocrine organ - linking NAFLD and insulin resistance.

The liver is a dynamic organ that plays critical roles in many physiological processes, including the regulation of systemic glucose and lipid metabolism. Dysfunctional hepatic lipid metabolism is a cause of non-alcoholic fatty liver disease (NAFLD), the most common chronic liver disorder worldwide, and is closely associated with insulin resistance and type 2 diabetes. Through the use of advanced mass spectrometry 'omics' approaches and detailed experimentation in cells, mice and humans, we now understand that the liver secretes a wide array of proteins, metabolites and non-coding RNAs (miRNAs) and that many of these secreted factors exert powerful effects on metabolic processes both in the liver and in peripheral tissues. In this review, we summarize the rapidly evolving field of 'hepatokine' biology with a particular focus on delineating previously unappreciated communication between the liver and other tissues in the body. We describe the NAFLD-induced changes in secretion of liver proteins, lipids, other metabolites, and miRNAs, and how these molecules alter metabolism in liver, muscle, adipose tissue and pancreas to induce insulin resistance. We also synthesise the limited information which indicates that extracellular vesicles, and in particular exosomes, may be an important mechanism for inter-tissue communication in normal physiology and in promoting metabolic dysregulation in NAFLD.
Amyloid misfolding, aggregation, and the early onset of protein deposition diseases: insights from AFM experiments and computational analyses

The development of Alzheimer’s disease is believed to be caused by the assembly of amyloid β proteins into aggregates and the formation of extracellular senile plaques. Similar models suggest that structural misfolding and aggregation of proteins are associated with the early onset of diseases such as Parkinson’s, Huntington’s, and other protein deposition diseases. Initially, the aggregates were structurally characterized by traditional techniques such as x-ray crystallography, NMR, electron microscopy, and AFM. However, data regarding the structures formed during the early stages of the aggregation process were unknown. Experimental models of protein deposition diseases have demonstrated that the small oligomeric species have significant neurotoxicity. This highlights the urgent need to discover the properties of these species, to enable the development of efficient diagnostic and therapeutic strategies. The oligomers exist transiently, making it impossible to use traditional structural techniques to study their characteristics. The recent implementation of single-molecule imaging and probing techniques that are capable of probing transient states have enabled the properties of these oligomers to be characterized. Additionally, powerful computational techniques capable of structurally analyzing oligomers at the atomic level advanced our understanding of the amyloid aggregation problem. This review outlines the progress in AFM experimental studies and computational analyses with a primary focus on understanding the very first stage of the aggregation process. Experimental approaches can aid in the development of novel sensitive diagnostic and preventive strategies for protein deposition diseases, and several examples of these approaches will be discussed.