Miracle-dz/UniFAR-FTUs huggingface dataset:
A Unifying Framework for Self-consistent Gravitational Lensing and Stellar Dynamics Analyses of Early-Type Galaxies

Gravitational lensing and stellar dynamics are two independent methods, based solely on gravity, to study the mass distributions of galaxies. Both methods suffer from degeneracies, however, that are difficult to break. In this paper we present a new framework that self-consistently unifies gravitational lensing and stellar dynamics, breaking some classical degeneracies that have limited their individual usage, particularly in the study of high-redshift galaxies. For any given galaxy potential, the mapping of both the unknown lensed source brightness distribution and the stellar phase-space distribution function onto the photometric and kinematic observables can be cast as a single set of coupled linear equations, which are solved by maximizing the likelihood penalty function. The Bayesian evidence penalty function subsequently allows one to find the best potential-model parameters and to quantitatively rank potential-model families or other model assumptions (e.g., PSF). We have implemented a fast algorithm that solves for the maximum-likelihood pixelized lensed source brightness distribution and the two-integral stellar phase-space distribution function f(E,Lz), assuming axisymmetric potentials. To make the method practical, we have devised a new Monte Carlo approach to Schwarzschild's orbital superposition method, based on the superposition of two-integral (E and Lz) toroidal components, to find the maximum-likelihood two-integral distribution function in a matter of seconds in any axisymmetric potential. The nonlinear parameters of the potential are subsequently found through a hybrid MCMC and Simplex optimization of the evidence. Illustrated by the power-law potential models of Evans, we show that the inclusion of stellar kinematic constraints allows the correct linear and nonlinear model parameters to be recovered, including the potential strength, oblateness, and inclination, which, in the case of gravitational-lensing constraints only, would otherwise be fully degenerate.
Centroid based Categorization Approach for Extraction of Body Sensor Network Data

Monitoring human activities using wearable wireless sensor nodes has the potential to enable many useful applications for everyday situations. The long- term lifestyle categorization can greatly improve healthcare by gathering information about quality of life; aiding the diagnosis and tracking of certain diseases. The deployment of an automatic and computationally-efficient algorithm reduces the complexities involved in the detection and recognition of human activities in a distributed on Body sensor network server. Directory service is a useful aid human looking for information on Network Data. A directory services is a pre-categorized list of topics containing many links for each topic. However, most directory services are maintained manually now and face many drawbacks. Therefore the task of automatic categorization of new data into the topics of directory services becomes very necessary. BSN data categorization is more difficult due to a large variation of noisy information embedded in Sensor network data. This paper suggests a new Centroid based approach for Categorization for BSN data. We further introduce a new algorithm through centroid based approach for extraction of BSN data categorization and show that it achieves about more improvement over other BSN data categorization methods. Experimental results show that our proposed Centroid-based BSN data categorization algorithm achieves an approximately 13.8% improvement for BSN data categorization algorithm.
Comparison of Nuclear DMA Content in Primary and Metastatic Papillary Thyroid Carcinoma1

Nuclear DMA values were determined in 40 primary papillary thyroid carcinomas, as well as in 52 corresponding local recur rences and mA©tastaseswere observed either at the time of diagnosis or up to 20 years later. The patient population con sisted of 34 survivors and 6 nonsurvivors. In survivors, both the primary tumors and their recurrences and mA©tastases exhibited a majority of cells with DMA values within the normal diploid region, whereas nonsurvivors showed increased and scattered DMAvalues. In all cases, the primary tumors and the correspond ing recurrences and mA©tastases showed similar DMA distribution patterns even if many years had passed between the detection of the primary tumor and the mA©tastases. The results indicate that in papillary thyroid carcinomas, the DNA distribution patterns in the primary tumor and the corre sponding recurrences or mA©tastases are generally similar throughout the entire period of disease.
Morphing Parallelization Strategy to Support On-the-Fly Video Analysis

To automate and accelerate Internet video search, we propose a parallelized on-the-fly video analysis that is distinguished from the conventional approaches in the following two features. One is to morph its parallelization by first screening out hundreds of videos at each processor in a low quality of analysis, then repeating this screening process as increasing the analyzing quality, and finally examining each of the final candidates among multiple processors. The other is to download and handle any portion of a video clip independently at a different processor, which is differentiated from the conventional stream-based analysis that scans a video from beginning to end. We have evaluated the promising performance of our proposed system by parallelizing the Auotonoesis colorschema-based video analyzer on top of the AgentTeamwork parallel-computing middleware. This paper presents implementation techniques and preliminary performance to support our parallelization strategy.
SLAM for pseudolite system comprising unsynchronised transmitters

In this paper the application of Simultaneous Localization and Mapping (SLAM) algorithms to a proprietary pseudolite system is presented. Time of Arrival measurements of multiple unsynchronised radio transmitters at reference rover positions are combined using an extended Kalman filter, in order to simultaneously localize both transmitters and movable receiver. In the case of unsynchronised transmitters, the SLAM approach must be adapted because the measurements from rover and transmitters are not simultaneously available, so that time constraints of the sensors must be taken into account and the measurements must be processed sequentially. Considerations relating to observability of the state variables are addressed. The efficiency of the algorithm is demonstrated using simulation data.
The Regulation and Function of miR-21-FOXO3a-miR-34b/c Signaling in Breast Cancer

Upregulation of miR-21 (microRNA-21) and downregulation of miR-34b/c have been found in breast cancer (BC). However, their regulation mechanism and function roles in BC have not been fully addressed. Here, we report that miR-21 levels were inversely correlated with miR-34b/c levels in BC. MiR-21 upregulation contributes to PTEN downregulation, which is beneficial for the activation of PI3K/AKT signaling. The activation of AKT phosphorylates FOXO3a, triggering relocalization of FOXO3a proteins from the nucleus to the cytoplasm. FOXO3a is a newly identified transcription factor responsible for miR-34b/c expression. Downregulation of nuclear FOXO3a decreased the expression levels of miR-34b and miR-34c in breast cancer cells, in which p53 was mutated. We also found upregulation of circulating miR-21 and downregulation of circulating miR-34b/c in BC patients’ serum. More importantly, we showed that systemic delivery of miR-34b/c or with anti-miR-21 significantly inhibited breast tumor growth in vivo. These results suggest that high circulating levels of miR-21 and low levels of miR-34b/c may provide potential biomarkers for BC diagnosis, and systemic delivery of miR-34b/c has potential as a therapeutic option for BC treatment.
A basement‐membrane permeability assay which correlates with the metastatic potential of tumour cells

We describe an in vitro assay for measuring the ability of tumour cells to permeabilize basement membranes, using transwell chambers coated with the reconstituted basement membrane, matrigel. Unlike previous matrigel‐based procedures which quantified passage of tumour cells across a matrigel barrier, the new assay measures the ability of tumour cells to degrade the basement membrane and increase the diffusion rate of fluorescent (FL) dextran through the barrier. The procedure has the major advantage that permeability can be rapidly and accurately quantified, either by fluorometry or by the use of radiolabelled dextran, thus avoiding tedious and subjective scoring methods. Optimal conditions for the assay are described. In addition, it is demonstrated that the assay can clearly discriminate between metastatic and non‐metastatic tumour cell lines, metastatic tumours permeabilizing the basement membrane and non‐metastatic counterparts failing to do so. A range of enzyme inhibitors suggested that the increase in basement‐membrane permeability caused by the metastatic mammary adenocarcinoma 13762 MAT is probably dependent upon the synergistic action of several degradative enzymes, namely proteases, type‐IV collagenase, and heparanase. Furthermore, the ability to permeabilize the basement membrane was dependent upon intact tumour cells; tumour cell extracts, lysates and supernatants were inactive. © 1992 Wiley‐Liss, Inc.
Diagnostic Value of Serum Baseline Tryptase Levels in Childhood Asthma and Its Correlation with Disease Severity

Background: The aim of this study was to explore whether serum baseline tryptase (sBT) levels might be a useful marker not only for the accurate diagnosis of childhood asthma, but also for the prediction of disease severity. Methods: A total of 114 asthmatic children were enrolled in this study, 36 of whom had mild intermittent asthma, 38 had mild persistent asthma, and 40 had moderate to severe persistent asthma. Additionally, 34 age-matched healthy children were enrolled as controls. The sBT levels of these populations were measured using a fluoroenzymeimmunoassay kit. The diagnostic performance of sBT levels and their correlation with asthma severity were systematically investigated using receiver operating characteristic (ROC) analysis and correlation analysis. Results: Children with mild and moderate to severe persistent asthma had significantly increased sBT levels as compared to those with mild intermittent asthma and healthy controls. ROC analysis further demonstrated that sBT levels not only appear to be highly sensitive and specific for distinguishing asthmatic children from healthy controls, but also show good accuracy for the differentiation of various asthmatic subgroups. Correlation analysis revealed that in all asthmatic subgroups sBT levels were significantly correlated with a variety of key markers that reflect the disease severity of asthma, including childhood asthma control test scores, serum IgE and interleukin-13 levels, blood eosinophil counts, and pulmonary test parameters. Conclusions: sBT levels may have a potential use in supporting a diagnosis of asthma in children and as a predictor of disease severity.
New Developments in Social Interdependence Theory

Social interdependence theory is a classic example of the interaction of theory, research, and practice. The premise of the theory is the way that goals are structured determines how individuals interact, which in turn creates outcomes. Since its for mulation nearly 60 years ago, social interdependence theory has been modified, extended, and refined on the basis of the increasing knowledge about, and application of, the theory. Researchers have conducted over 750 research studies on the relative merits of cooperative, competitive, and individualistic efforts and the conditions under which each is appropriate. Social interdependence theory has been widely applied, especially in education and business. These applications have resulted in revisions of the theory and the generation of considerable new research. The authors critically analyze the new developments resulting from extensive research on, and wide-scale applications of, social interdependence theory.
Overdose and Safety with Fluvoxamine

The large number of antidepressants available provides a wide range of choice. While clinical effectiveness is the most important consideration, toxicity in overdose must be considered in the risk-benefit assessment of each antidepressant. There are almost 300 deaths each year in Britain from tricyclic overdose, and very few deaths from newer antidepressants. Fluvoxamine appears to have low toxicity in overdose. Symptoms are often minimal: nausea, vomiting, dizziness and somnolence. There is one reported case of prolonged cerebral depression after ingestion of 5.5 g. Overdoses of up to 9 g have produced minimal symptoms and full recovery. No deaths from overdose with fluvoxamine alone have been reported in the literature, although one death certificate in Britain has mentioned fluvoxamine as the cause of death. Fluvoxamine appears to be a valuable alternative to the tricyclic antidepressants, and has a high margin of safety in overdose.
Centrally extended symmetry algebra of asymptotically Gödel spacetimes

We define an asymptotic symmetry algebra for three-dimensional Godel spacetimes supported by a gauge field which turns out to be the semi-direct sum of the diffeomorphisms on the circle with two loop algebras. A class of fields admitting this asymptotic symmetry algebra and leading to well-defined conserved charges is found. The covariant Poisson bracket of the conserved charges is then shown to be centrally extended to the semi-direct sum of a Virasoro algebra and two affine algebras. The subsequent analysis of three-dimensional Godel black holes indicates that the Virasoro central charge is negative.
Advanced InSAR coregistration using point clusters

In this study, we introduce a refined algorithm for the fine InSAR image coregistration which could be used in highly decorrelated scenes. The refinement is introduced at the point of selection of points necessary for the estimation of the offset vectors between master and slave image. A new approach for point selection based on the Harris corner detector algorithm is presented. The new point selection algorithm results with the clusters of point candidates for the offset vectors over a scene. Consequently, the number of points and their spatial distribution are improved, which results in a better global quality of the coregistration model
Send Orders of Reprints at Reprints@benthamscience.net a Polynomial Fitting Improved Bayesian Reconstruction Method for Whole Brain Volumetric Mrsi Metabolite Images 1.1. Characteristics of Mrsi Data 1.2. Image Reconstruction with Mrsi Data

In this paper, a polynomial fitting improved Bayesian approach is proposed for the reconstruction of volumetric metabolite images from long echo time (TE) whole brain proton magnetic resonance spectroscopic imaging (MRSI) data. The proposed algorithm uses a modified EM (expectation maximization) algorithm that takes into account the partial volume effects contained inside a thick slice MRSI. It incorporates high resolution volumetric magnetic resonance imaging (MRI) as a priori information. It further integrates the polynomial fitting method to smooth out artificial edges before the high resolution metabolite images are reconstructed. Our proposed reconstruction method has successfully extended our existing reconstruction of two dimensional (2D) metabolite images to 3D cases. The experimental results show that resolution enhanced volumetric metabolite images are reconstructed. 1. BACKGROUND In vivo magnetic resonance spectroscopy imaging (MRSI) is a non-invasive method to measure valuable me-tabolite concentrations in the human brain [1-15]. The MRSI data consist of four dimensional complex valued spectral data that can be used to locate the spatial concentrations of various metabolites inside the brain. Each metabolite concentration is identified at its unique resonance frequency of protons that is characterized by the spectral component of the MRSI data. Typically, a sub-sampling scheme is used for MRSI acquisition to shorten imaging time due to the long scan time required for collecting a large MRSI dataset. Image reconstruction methods are usually applied off-line in order to retrieve various metabolite concentrations, particularly , to obtain high resolution metabolite images from MRSI. In this paper, we propose a mathematical modeling and polynomial fitting improved Bayesian reconstruction method to obtain resolution enhanced 3D metabolite images from whole brain MRSI. The metabolite images reconstructed from one single slice MRSI are so far of poor quality. The primary concern is that the MRSI data are characterized by very low spatial resolution. The data are accompanied by weak signal-to-noise ratio (SNR) due to low metabolite concentrations in the human brain. This results in the large voxel size used in MRSI studies. A typical voxel has 5-mm to 10-mm in-plane size and its slice thickness is about 10-mm to 15-mm. This is relatively large when compared to the small voxel size used in volumetric MRI studies, namely, 0.5-mm in-plane size and 3-mm slice thickness. Another concern is the distortion in the acquired MRSI signals caused by patient motion during a prolonged acquisition of MRSI. Although the usage of low resolution in the whole brain volumetric …
Editorial: The Major Discoveries of Cajal and His Disciples: Consolidated Milestones for the Neuroscience of the XXIst Century

When Santiago Ramon y Cajal (1904; 1911) began to understand the fine structure of the nervous system in the last decades of the XIXth century, he was almost certainly alone in his conviction that his descriptions were scientific truths, many of which have outlived him and remain relevant. Simple histological staining, a monocular microscope, an unabated curiosity, patience and a special talent to represent his observations in sketches, and drawings, as well as a rich imaginative and open mind, all elements that together account for Cajal's success. His descriptions of the connectivity in the nervous system were compiled into an opus magna that was first published in 1904 (“Textura del sistema nervioso del hombre y los vertebrados,” in Spanish) and subsequently translated into French in 1911 (“Histologie du systeme nerveux”). As the decades have passed, one by one all his theories have been corroborated using modern techniques, and the main hypotheses that Cajal postulated have become universally recognized as biological laws: The neuron theory; the law of the dynamic polarization of the neuron and the principle of connectional specificity.
PERFORMANCE EVALUATION OF HMSK AND SQFD ALGORITHMS FOR COMPUTER TOMOGRAPHY (CT) IMAGE SEGMENTATION OF EFFECTIVE RADIOTHERAPY

Medical Image segmentation plays a significant role in many medical image processing for effective diagnosis. Manual segmentation of medical image by the radiologist is not only a tiresome and time consuming process, also not a very accurate with the increasing medical imaging modalities and unmanageable quantity of medical images. Therefore it is essential to examine current methodologies of image segmentation. Enormous research has been done in medical image segmentation, but it is still difficult to evaluate all the medical images. However the problem remains challenging, with no general and unique solution. In this paper, we present a HMSK (Hybrid Medoid shift and K-Means) algorithm and Signature Quadratic form distance (SQFD) algorithm for Computer tomography image segmentation. The performance of the two algorithms is investigated. Experimental results with real patient images indicate the SQFD algorithm is effective and efficient and reduce the number of fragments. Their pros and cons were analyzed and proposed SQFD algorithm for slices of CT images to give effective radiation therapy.
A TECHNIQUE TO DETECT MASSES FROM DIGITAL MAMMOGRAMS USING ARTIFICIAL NEURAL NETWORK

In this paper we present a technique to detect masses from digital mammograms using Artificial Neural Network (ANN), which performs malignant-normal classification on region of interest (ROI) that contains mass. The major mammographic characteristics for mass classification are Intensity, Shape and Texture. ANN exploits all such type of important factor to classify the mass into malignant or normal. The features used in characterizing the masses are mean, standard deviation, skewness, area, perimeter, homogeneity, energy, contrast and entropy. The main aim of the method is to increase the effectiveness and accuracy of the classification process in an objective manner to reduce the numbers of false-positive of malignancies. ANN with nine features was proposed for classifying the marked regions into malignant and normal. With ANN classifier, experiment result shows the 96.875% accuracy, 96.551% sensitivity and 97.142% specificity.
Circuit Pack Parameter Estimation Using Rent's Rule

This paper applies an empirically determined relationship known as Rent's rule to the estimation of circuit pack parameters. In particular, equations are derived for 1) the number of leads passing through a region of a circuit pack based on the position of the region, and 2) the total lead length on a circuit pack. The estimation of these parameters is central to the modeling of circuit size and performance as well as many other important circuit parameters. A technique for determining the Rent parameters for a particular circuit pack is also derived. Finally, experimental validation of these models is described.
An analytic approximation of the likelihood function for the Heston model volatility estimation problem

Estimating the volatility from the underlying asset price history for the discrete observations case is a challenging inference problem. Yet it has attracted much research interest due to the key role of volatility in many areas of finance. In this paper we consider the Heston stochastic volatility model and propose an accurate analytic approximation for the volatility likelihood function. The model is based on considering the joint probability density of the asset and the volatility, and integrating out past volatility variables. The likelihood simplifies to a product of T terms, where T is the length of the past history considered. An extension to the problem of fixed parameter estimation is also presented. Simulation results indicate the effectiveness and accuracy of the proposed method.
Assessing Crime as a Problem

This study compares the relationship between official crime rates in census tracts and resident perceptions of crime. Using a unique data set that links household-level data from the American Housing Survey metro samples over 25 years (1976-1999) with official crime rate data for census tracts in selected cities during selected years, this study finds that tract violent crime is the strongest predictor of residents’ perception of crime. This standardized coefficient was . 71 on average over the seven waves. Models simultaneously taking into account both violent and property crime found a consistently strong positive effect for violent crime but a consistently negative effect for property crime. Among types of violent crime, robbery and aggravated assault have the strongest effect on the perception of crime in the tract. Burglary showed a stronger effect on perceptions of crime in the 1970s but a steadily weakening effect since then. There was little evidence that the racial/ethnic composition of the tract affected these perceptions.