Miracle-dz/UniFAR-FTUs huggingface dataset:
[Social inequality and homicide rates in Sao Paulo City, Brazil].

OBJECTIVE The relation between income and mortality due to violence has been studied in recent years. The Synthesis of Social Indicators of 2002 [Sintese de Indicadores Sociais, 2002], published by The Brazilian Institute of Geography and Statistics (IBGE), states that one of the most outstanding characteristic of Brazilian society is inequality. The proposal of this ecological study was to test the association between homicide rates, and some health and socioeconomic indicators. METHODS This is an ecological cross-sectional study. Data regarding Sao Paulo City, Brazil in the year 2000 was analyzed. The association between homicide coefficients and the following five indicators were tested: infant mortality rates, monthly average income of household heads, percentage of adolescents aged 15 to 17 years not attending school, proportion of pregnant adolescent women aged 14 to 17 years and demographic density. Pearson's correlation coefficient and a multiple linear regression model were utilized to test these associations. RESULTS The municipal homicide rate was 57.3/100,000. The correlation between homicide rates and average monthly income was strong and negative (r=-0.65). Higher homicide rates were found in the districts whose inhabitants had lower incomes and lower rates were found in those districts whose inhabitants had higher incomes. The correlation between homicide rates and proportion of adolescents not attending school was positive and strong (r=0.68). The correlation between homicide rates and the proportion of pregnant adolescent women was positive and strong (r=0.67). The correlation between homicides and the rate of infant mortality was r=0.24 (for all: p<0.05). The correlation between demographic density and homicides was not significant. Although the univariate regression was positive for four indicators, the multivariate regression test was only significant for average monthly income (negative) and proportion of adolescents not attending school (positive) (for both indicators: p<0.01). CONCLUSIONS The findings highlight the problem of homicides and socioeconomic disparities in S. Paulo City. Economic development and reducing socioeconomic inequality may have an impact on the rates of mortality due to violence.
Graph Connectivity in Noisy Sparse Subspace Clustering

Subspace clustering is the problem of clustering data points into a union of low-dimensional linear/affine subspaces. It is the mathematical abstraction of many important problems in computer vision, image processing and machine learning. A line of recent work (4, 19, 24, 20) provided strong theoretical guarantee for sparse subspace clustering (4), the state-of-the-art algorithm for subspace clustering, on both noiseless and noisy data sets. It was shown that under mild conditions, with high probability no two points from different subspaces are clustered together. Such guarantee, however, is not sufficient for the clustering to be correct, due to the notorious "graph connectivity problem" (15). In this paper, we investigate the graph connectivity problem for noisy sparse subspace clustering and show that a simple post-processing procedure is capable of delivering consistent clustering under certain "general position" or "restricted eigenvalue" assumptions. We also show that our condition is almost tight with adversarial noise perturbation by constructing a counter-example. These results provide the first exact clustering guarantee of noisy SSC for subspaces of dimension greater then 3.
In Their Own Words: Political Practitioner Accounts of Candidates, Audiences, Affordances, Genres, and Timing in Strategic Social Media Use

This study inductively develops a new conceptual framework for analyzing strategic campaign communications across different social media platforms through an analysis of candidate social media strategies during the 2016 U.S. presidential election cycle. We conducted a series of open-ended, in-depth qualitative interviews with campaign professionals active during the 2016 presidential cycle. Our analysis revealed that scholars need to account for the ways that campaigns perceive their candidates in addition to the audiences, affordances, and genres of different social media platforms, as well as the timing of the electoral cycle, in order to effectively study strategic social media communication. Our findings reveal that campaigns proceed from perceptions of their candidates’ public personae and comfort with engagement on social media. Campaigns perceive that social media platforms vary according to their audiences, including their demographics and other characteristics; with respect to their affordances, actual and perceived functionalities; the genres of communication perceived to be appropriate to them; and the timing of the electoral cycle, which shapes messaging strategies and the utility of particular platforms. These factors shape how campaigns use social media in the service of their electoral goals. We conclude by developing these findings into an analytic framework for future research, arguing that researchers should refrain from automatically generalizing the results of single-platform studies to “social media” as a whole, and detailing the implications of our findings for future political communication research.
GABAergic Hub Neurons Orchestrate Synchrony in Developing Hippocampal Networks

Coordinating Neuronal Assemblies Theoretical models predict the existence of so-called hub neurons—highly connected cells that strongly influence the synchronization of spiking activity in a large group of neurons. However, experimental evidence for the existence of these neuronal hubs is lacking. Bonifazi et al. (p. 1419) used high-resolution, two-photon calcium imaging to measure spontaneous calcium fluctuations in hundreds of neurons simultaneously and determined the relative timing of these fluctuations. Examination of functional connectivity maps, based on temporal correlation measurements, revealed a subpopulation of GABAergic hub neurons displaying a remarkably widespread axonal arborization that orchestrated network synchrony in developing hippocampal networks. Spontaneous network synchronizations in developing hippocampus caused giant depolarizing potentials in individual neurons, and manipulating the spike activity in potential hub cells influenced network activity. A model for the topology of brain networks incorporates a morpho-functional description of neuronal hubs. Brain function operates through the coordinated activation of neuronal assemblies. Graph theory predicts that scale-free topologies, which include “hubs” (superconnected nodes), are an effective design to orchestrate synchronization. Whether hubs are present in neuronal assemblies and coordinate network activity remains unknown. Using network dynamics imaging, online reconstruction of functional connectivity, and targeted whole-cell recordings in rats and mice, we found that developing hippocampal networks follow a scale-free topology, and we demonstrated the existence of functional hubs. Perturbation of a single hub influenced the entire network dynamics. Morphophysiological analysis revealed that hub cells are a subpopulation of γ-aminobutyric acid–releasing (GABAergic) interneurons possessing widespread axonal arborizations. These findings establish a central role for GABAergic interneurons in shaping developing networks and help provide a conceptual framework for studying neuronal synchrony.
Bayesian ICA-based source separation of Cosmic Microwave Background by a discrete functional approximation

A functional approximation to implement Bayesian source separation analysis is introduced and applied to separation of the Cosmic Microwave Background (CMB) using WMAP data. The approximation allows for tractable full-sky map reconstructions at the scale of both WMAP and Planck data and models the spatial smoothness of sources through a Gaussian Markov random field prior. It is orders of magnitude faster than the usual MCMC approaches. The performance and limitations of the approximation are also discussed.
Carrying Lower Extreme Exoskeleton Rapid Terminal Sliding-Mode Robust Control

For the carrying lower extreme exoskeleton system is non-linear and have variable parameters, which is usually subject to external dusturbances. The virtual torque control method is a well-established carrying system control technique, which needs no sensors between the pilot and the human-machine interface, instead, the controller estimates, based on measurements from the exoskeleton suits only, how to move so the pilot feels very little force. However, its main disadvantage lies on solveing the inverse dynamic model that is not easily obtained, which also need an exactly known dynamic model that is not realizable in practice. To overcome this issue, we propose building a virtual prototype model of the lower extremetry exoskeleton in SimMechanics. Using torque input and joint angle output data of the the virtual prototype model, the wavelet neural network is trained to building the inverse dynamics model which take the joint angle data as input and the torque data as output. In cases where some of the parameters of the plant undergo a change or the system is disturbed, poor performance may result. To cope with this drawback, based on Lyapunov stability principle, the rapid terminal sliding-mode robust adaptive controller is designed to control the serious nonlinear lower extreme exoskeleton system, and the convergence performance of the controller is analysed. Theoretical analyse and simulation results test the feasibility and validity of this control method.
SOURCE SEPARATION : PRINCIPLES , CURRENT ADVANCES AND APPLICATIONS

This paper is a survey on the source separation problem, and on methods used for solving this problem. In a blind context,i.e. without information about the sources but their mutual independence, methods are based on Independent Component Analysis (ICA). On the contrary, using priors on sources, one can developed semi-blind approaches which are very efficient and often much more simpler. Current advances aim to take into account various priors like positivity or sparsity. This paper will finish with sketches of source separation applications which will give practical examples.
Advanced level-set based multiple-cell segmentation and tracking in time-lapse fluorescence microscopy images

Segmentation and tracking of cells in fluorescence microscopy image sequences is an important task in many biological studies into cell migration as well as intracellular dynamics. The growing size and complexity of biological image data sets precludes manual analysis, and calls for increasingly advanced automatic algorithms that are generic enough to be easily tunable to different applications, yet robust enough to deal with different cell types and strongly varying imaging conditions. Active-contour based algorithms have proven to be very suitable for this purpose but still suffer from several shortcomings that limit their segmentation accuracy and tracking robustness. In addition, these algorithms are generally rather computationally expensive. In this paper, we present an advanced level-set based multiple-cell segmentation and tracking algorithm, which implements seven modifications compared to earlier algorithms that considerably improve its performance. Preliminary experiments on three different time-lapse fluorescence microscopy images demonstrate the potential of the new algorithm.
Balancing Capacity and Latency in CMP Caches

The large working sets of commercial and scientific workloads stress the L2 caches of Chip Multiprocessors (CMPs). Some CMPs use a shared L2 cache, to maximize the onchip cache capacity and minimize misses. Others use private L2 caches, replicating data to limit the delay due to global wires and minimize cache access time. Recent hybrid proposals strive to balance latency and capacity, but use simple, static rules that are not robust to changes in workload behavior and system configuration. This paper studies alternative L2 cache designs for an 8-processor CMP system and shows that two previous selective-replication mechanisms actually degrade performance up to 13%, for some combinations of scientific and commercial workloads and system configurations. We propose the Adaptive Selective Replication (ASR) mechanism that dynamically monitors workload behavior to control replication. ASR replicates read-only blocks only when it estimates the benefit of replication (lower L2 hit latency) exceeds the cost (more L2 misses). Full-system simulation results show that ASR provides robust performance: decreasing runtimes by as much as 31% versus shared caches, 33% versus private caches, and 27% versus CMP-NuRapid and Victim Replication. Furthermore, while ASR does not improve the performance of all workloads on all system configurations, it almost always performs as well as the best alternative.
Applying Design Patterns to Wireless Sensor Network Programming

Middleware for wireless sensor network (WSN) abstracts a network as an entity and hides programming difficulties from programmers. Many middlewares have been proposed, but they use different programming languages to manipulate functions in WSNs. This inhibits usability when manipulating multiple WSNs managed by different middlewares, because the primitives of each language have different descriptive capabilities. In this paper, we propose and apply design patterns in WSN programming to complement the capabilities of language primitives, and discuss the effectiveness of these design patterns. First, we discuss major middleware languages and compare the capabilities of their primitives. Second, we extract design patterns from the representative middlewares to cover the missing capabilities identified in the comparison. Finally, we discuss the effectiveness of design patterns for WSN programming. The discussion indicates that design patterns improve the usability of manipulating multiple WSNs.
The aortic ring model of angiogenesis: a quarter century of search and discovery

• Introduction ‐ Early history of the aortic ring model ‐ Morphology of the angiogenic response ‐ Source of microvessels in aortic cultures ‐ Stimulatory effect of angiogenesis on cancer spread ‐ Regulation of angiogenesis by the extracellular matrix ‐ Role of integrin receptors in aortic angiogenesis ‐ Paracrine regulation of angiogenesis: role of endogenous growth factors ‐ Paracrine regulation of angiogenesis: role of resident macrophages and inflammatory cytokines and chemokines ‐ Expression and function of neural related genes ‐ Signal transduction pathways ‐ Pericyte origin and recruitment mechanisms ‐ Role of proteolytic enzymes in angiogenesis and vascular regression ‐ Influence of genetic background and aging on angiogenic response ‐ Adaptation of the aortic ring model to different species and vessel types ‐ Limitations of the aortic ring assay • Summary and conclusion
A Survey of Fraud Detection Techniques for Credit Card based Transaction Processing

The wide emergence of electronic-commerce has widened the extensive usage of credit card for online transactions. However, there is also a high rise in malicious transaction and fraudulent associated with the credit cards. In this study we present several models and algorithm used in data mining for the detection of such malicious fraudulents or thefts. Such algorithm learns the transaction patterns and cluster the pattern of sequences usually involving with the processing of transactions to inhibit such malicious transactions made in the future.
The Relay Selection and Transmission Trade-off in Cooperative Communication Systems

A common and practical paradigm in cooperative communication systems is the use of a dynamically selected "best" relay to decode and forward information from a source to a destination. Such systems use two phases - a relay selection phase, in which the system uses transmission time and energy to select the best relay, and a data transmission phase, in which it uses the spatial diversity benefits of selection to transmit data. In this paper, we derive closed-form expressions for the overall throughput and energy consumption, and study the time and energy trade-off between the selection and data transmission phases. To this end, we analyze a baseline non-adaptive system and several adaptive systems that adapt the selection phase, relay transmission power, or transmission time. Our results show that while selection yields significant benefits, the selection phase's time and energy overhead can be significant. In fact, at the optimal point, the selection can be far from perfect, and depends on the number of relays and the mode of adaptation. The results also provide guidelines about the optimal system operating point for different modes of adaptation. The analysis also sheds new insights on the fast splitting-based algorithm considered in this paper for relay selection.
Modeling the effectiveness of nebulized terbutaline for decompensated chronic obstructive pulmonary disease patients in the emergency department

AbstractShort-acting &bgr;2-agonists (SABA) are widely used in the emergency department (ED) to treat patients with decompensated chronic obstructive pulmonary disease (COPD). We sought to model the effectiveness of nebulized SABA (terbutaline) on clinically relevant parameters associated with a reduction in work of breathing or respiratory muscle fatigue in decompensated COPD patients admitted to the ED.Forty consecutive decompensated COPD patients (having received at least one dose of nebulized terbutaline during their stay in the ED) were included in an observational cohort study. The terbutaline dose received at time t was expressed as cumulative dose and as a rate (mg/day). The associations between the terbutaline dose and time-dependent outcome parameters (respiratory rate, heart rate, arterial blood gases, and, as a marker of terbutaline's systemic effect, serum potassium) were analyzed using a nonlinear, mixed-effects model. The effect of various covariates influencing terbutaline's effectiveness (baseline characteristics and concomitant treatments) was assessed on the model.Among the investigated patients, a total of 377 time-dependent observations were available for analysis. Neither the cumulative dose nor the dose rate at time t significantly influenced the arterial blood gas parameters or heart rate. The cumulative dose of terbutaline was associated with a lower serum potassium level (P < 0.001) and, less significantly, a lower respiratory frequency (P = 0.036). In a tertile analysis, the need for post-ED hospitalization was not associated with the cumulative dose or dose rate of terbutaline.Overall, the results of our modeling study strongly suggest that terbutaline dose did not influence time-dependent outcomes other than serum potassium, and thus call into question the systematic administration of inhaled SABA to patients admitted to the ED for decompensated COPD.
Study on a hybrid P2P based DNS

The traditional Domain Name System (DNS), which follows a hierarchical tree structure, is vulnerable to the targeted attacks. Several efforts were proposed to reconstruct DNS as a peer-to-peer network using Distributed Hash Tables (DHT) to improve the system robustness. However, the DHT-based DNS has significantly higher latencies in comparison with traditional DNS. In this paper, we propose a hybrid p2p based DNS by combining the hierarchical tree structure and the flat peer-to-peer structure. The results of simulation show that our system inherits strong robustness from DHT networks and high efficiency from traditional DNS. Even so, we should point out that our system still has lower efficiency compared with traditional DNS.
Geostatistical modeling of riparian forest microclimate and its implications for sampling

Predictive models of microclimate under various site conditions in forested headwater stream – riparian areas are poorly developed, and sampling designs for characterizing underlying riparian microclimate gradients are sparse. We used riparian microclimate data collected at eight headwater streams in the Oregon Coast Range to compare ordinary kriging (OK), universal kriging (UK), and kriging with external drift (KED) for point prediction of mean maximum air temperature (Tair). Several topographic and forest structure characteristics were considered as site-specific parameters. Height above stream and distance to stream were the most important covariates in the KED models, which outperformed OK and UK in terms of root mean square error. Sample patterns were optimized based on the kriging variance and the weighted means of shortest distance criterion using the simulated annealing algorithm. The optimized sample patterns outperformed systematic sample patterns in terms of mean kriging variance mainly for sma.
CENTS: Cortical enhanced neonatal tissue segmentation

The acquisition of high‐quality magnetic resonance (MR) images of neonatal brains is largely hampered by their characteristically small head size and insufficient tissue contrast. As a result, subsequent image processing and analysis, especially brain tissue segmentation, are often affected. To overcome this problem, a dedicated phased array neonatal head coil is utilized to improve MR image quality by augmenting signal‐to‐noise ratio and spatial resolution without lengthening data acquisition time. In addition, a specialized hybrid atlas‐based tissue segmentation algorithm is developed for the delineation of fine structures in the acquired neonatal brain MR images. The proposed tissue segmentation method first enhances the sheet‐like cortical gray matter (GM) structures in the to‐be‐segmented neonatal image with a Hessian filter for generation of a cortical GM confidence map. A neonatal population atlas is then generated by averaging the presegmented images of a population, weighted by their cortical GM similarity with respect to the to‐be‐segmented image. Finally, the neonatal population atlas is combined with the GM confidence map, and the resulting enhanced tissue probability maps for each tissue form a hybrid atlas is used for atlas‐based segmentation. Various experiments are conducted to compare the segmentations of the proposed method with manual segmentation (on both images acquired with a dedicated phased array coil and a conventional volume coil), as well as with the segmentations of two population‐atlas‐based methods. Results show the proposed method is capable of segmenting the neonatal brain with the best accuracy, and also preserving the most structural details in the cortical regions. Hum Brain Mapp, 2011. © 2010 Wiley‐Liss, Inc.
Solving the packing problem of rectangles with improved genetic algorithm based on statistical analysis

The genetic algorithm and the surplus rectangle algorithm are used for solving the orthogonal packing problem of rectangles. Based on statistical analysis of rectangular packing problem, a comparable standard for judgment of a solution has been proposed, which is adopted in classification of the parent population. A surplus rectangle algorithm is introduced to decode the permutation of rectangles to the corresponding packing pattern uniquely. For different constructions, corresponding genetic operations have been designed. And then an improved genetic algorithm has been constructed. Several rectangles packing problems have been solved by using this improved algorithm and the optimum packing results have been achieved. This shows that the improved genetic algorithm is efficacious.