Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the Relationship Between the Retail Food Environment Index and Early Childhood Obesity Among WIC Participants in Los Angeles County Using GeoDa

The aim of this study was to examine the association between the local food environment and obesity proportions among 3- to 4-year-old children who were participants in the WIC program in Los Angeles County using spatial analyses techniques. ArcGIS, spatial analysis software, was used to compute the retail food environment index (RFEI) per ZIP code. GeoDa, spatial statistics software was employed to check for spatial autocorrelation and to control for permeability of the boundaries. Linear regression and ANOVA were used to examine the impact of the food environment on childhood obesity. Fast-food restaurants represented 30% and convenience stores represented 40% of the sum of food outlets in areas where WIC participants reside. Although there was no statistically significant association between RFEI and 3- to 4-year-old obesity proportions among WIC children, analysis of variance (ANOVA) tests demonstrated statistically significant positive associations between obesity and the number of convenience stores and the number of supermarkets. Our findings suggest that RFEI, as currently constructed, may not be the optimal way to capture the food environment. This study suggests that convenience stores and supermarkets are a likely source of excess calories for children in low-income households. Given the ubiquity of convenience stores in low-income neighborhoods, interventions to improve availability of healthy food in these stores should be part of the many approaches to addressing childhood obesity. This study adds to the literature by examining the validity of the RFEI and by demonstrating the need and illustrating the use of spatial analyses, using GeoDA, in the environment/obesity studies.
Shot boundary detection in H.264

This study focuses on improving the available scene change detection methods developed for bit streams compressed with the latest coding standard H.264/AVC. Two algorithms that form the basis of our study are introduced and compared. Then, a method based on the combination of the two methods is proposed together with some improvements. Experimental studies show the proposed method is superior to others.
Prevalence of Pathological Germline Mutations of hMLH1 and hMSH2 Genes in Colorectal Cancer

Abstract The prevalence of pathological germline mutations in colorectal cancer has been widely studied, as germline mutations in the DNA mismatch repair genes hMLH1 and hMSH2 confer a high risk of colorectal cancer. However, because the sample size and population of previous studies are very different from each other, the conclusions still remain controversial. In this paper, Databases such as PubMed were applied to search for related papers. The data were imported into Comprehensive Meta-Analysis V2, which was used to estimate the weighted prevalence of hMLH1 and hMSH2 pathological mutations and compare the differences of prevalence among different family histories, ethnicities and related factors. This study collected and utilized data from 102 papers. In the Amsterdam-criteria positive group, the prevalence of pathological germline mutations of the hMLH1 and hMSH2 genes was 28.55% (95%CI 26.04%–31.19%) and 19.41% (95%CI 15.88%–23.51%), respectively, and the prevalence of germline mutations in hMLH1/hMSH2 was 15.44%/10.02%, 20.43%/13.26% and 15.43%/11.70% in Asian, American multiethnic and European/Australian populations, respectively. Substitution mutations accounted for the largest proportion of germline mutations (hMLH1: 52.34%, hMSH2: 43.25%). The total prevalence of mutations of hMLH1 and hMSH2 in Amsterdam-criteria positive, Amsterdam-criteria negative and sporadic colorectal cancers was around 45%, 25% and 15%, respectively, and there were no obvious differences in the prevalence of germline mutations among different ethnicities.
Comparative Analysis on Transform and Reconstruction of Compressed Sensing in Sensor Networks

Compressed sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. It holds valuable implications for wireless sensor networks because power and bandwidth are limited resources. In this paper, applying the theory of compressed sensing to the practical sensor network data recovery problem, we compare the performance of different CS reconstruction algorithms combined with wavelet and discrete cosine transform (DCT) basis. We demonstrate empirically that DCT is good for sinusoid oscillatory data while wavelet is good for data with point-like singularities. Furthermore, comparison on reconstruction algorithms shows basis pursuit (BP) is best in term of PSNR performance and computing time. In addition, benefit of CS for noisy channel of sensor network is tested and how to achieve good performance in noisy channel is discussed.
From Full Text Search to Semantic Web: The Infofox Project.

Infofox is a project launched by the Ecademy, the National Network of Excellence of the Swiss Universities of Applied Sciences for e-business and e-government. The paper discusses an envisioned improvement of the service level of the Ecademy Web site, an information portal which is set to integrate valuable information from the different member schools. The goal of the Infofox project is the development of an intelligent search engine. In order to fully exploit the potential of the information provided by the Ecademy members, the Infofox project team will need to implement aspects of a Semantic Web – namely an ontology built upon a joint classification scheme and a set of inference rules. The RIP paper presents the basic concept and the results of the first phase of the project.
Testing CP conservation at KLOE

A proposal for maintaining CP conservation in $K_{L}^{0}$ decays is resumed, by adding a heterodox hypothesis. One recovers a consistent picture with a $2\pi$ $K_{L}^{0}$ decay branching ratio proportional to the vertical $K_{L}^{0}$ displacement. This can be clearly tested at the KLOE experiment, where important vertical $K_{L}^{0}$ displacements occur.
Microbial intervention strategies for Salmonella and Campylobacter reduction in commercial turkey processing

Microbial Intervention Strategies for Salmonella and Campylobacter Reduction in Commercial Turkey Processing. (May 2005) Scott Michael Stevens, B.S., Texas A&M University Chair of Advisory Committee: Dr. David J. Caldwell One objective of the present investigation was to compare Salmonella and Campylobacter recovery incidence from commercially processed turkeys immediately prior to and following pre-chill and immersion chiller intervention strategies being used in three distinct turkey processing facilities. In each plant, on a single day of processing, 100 carcass rinse samples prior to and following each post-evisceration, pre-chill intervention and following immersion chilling were obtained for Salmonella and Campylobacter recovery. Two of three plants demonstrated a trend of decreased Salmonella on carcasses following the Inside Outside Bird Wash (IOBW), with reductions of 13%, and 11% being observed for Plants 1 and 2, respectively. Results for reductions of Campylobacter contamination were not as straightforward, with only Plant 3 showing decreased levels (11% reduction) following the IOBW. Plant 2 used an additional pre-chill intervention, a low pressure, acetic acid final wash, which was not shown to be effective in causing an additional reduction in either Salmonella or Campylobacter on carcasses. In all three plants, properly managed immersion chilling systems were the most effective microbial intervention for achieving Salmonella and
A Maximal Clique Enumeration Based on Ordered Star Neighborhood for Co-location Patterns

A co-location pattern is a group of spatial features/events that are frequently co-located in the same region. Even though Boolean spatial feature types(or spatial events) may correspond to items in association rules over market-basket datasets, there is no natural notion of transactions. Methods proposed for transactional data mining cannot be directly applied on spatial boolean data. Previous studies have to propose new notions in place of transactions and use corresponding measures and methods to mine co-location patterns. In this paper, we propose a maximal clique enumeration Based on ordered star neighborhood(MCEBOSON) algorithm to enable the transactionalization of spatial boolean data, which makes the application of classic efficient methods on general data mining possible. The experimental results show that the MCEBOSON algorithm successfully generates all maximal cliques in the synthetic dataset and performs better than the join-Based algorithm.
On dualizing trellis-based APP decoding algorithms

The trellis of a finite Abelian group code is locally (i.e., trellis section by trellis section) related to the trellis of the corresponding dual group code which allows one to express the basic operations of the a posteriori probability (APP) decoding algorithm (defined on a single trellis section of the primal trellis) in terms of the corresponding dual trellis section. Using this local approach, any algorithm employing the same type of operations as the APP algorithm can, thus, be dualized, even if the global dual code does not exist (e.g., nongroup codes represented by a group trellis). Given this, the complexity advantage of the dual approach for high-rate codes can be generalized to a broader class of APP decoding algorithms, including suboptimum algorithms approximating the true APP, which may be more attractive in practical applications due to their reduced complexity. Moreover, the local approach opens the way for mixed approaches where the operations of the APP algorithm are not exclusively performed on the primal or dual trellis. This is inevitable if the code does not possess a trellis consisting solely of group trellis sections as, e.g., for certain terminated group or ring codes. The complexity reduction offered by applying dualization is evaluated. As examples, we give a dual implementation of a suboptimum APP decoding algorithm for tailbiting convolutional codes, as well as dual implementations of APP algorithms of the sliding-window type. Moreover, we evaluate their performance for decoding usual tailbiting codes or convolutional codes, respectively, as well as their performance as component decoders in iteratively decoded parallel concatenated schemes.
Air Pollution in an Urban Area of Mexico: Sources of Emission (Vehicular, Natural, Industrial, and Brick Production)

In recent years, the interest in aerosol particles has increased due to concerns about the effects on human health. The study of the chemical characterization (organic matter, sulfates, nitrates, and black carbon) has improved the knowledge about the negative contribution of chemicals to the environment. The identification of secondary processes (from pollutants such as SO2, NOx, and PAHs) and their role when combined with environmental factors such as humidity, solar radiation, and temperature are also of interest. With this background, this chapter seeks to highlight the most recent findings on the chemical composition of aerosol particles in the ambient air of one of the main cities of Mexico: the Metropolitan Area of Guadalajara. This megalopolis has almost 60% of the population of the Jalisco state, approximately 2.2 million vehicles and an extensive artisan brick production. Furthermore, due to its geographical position, it experiences frequent episodes of thermal inversion and exposition to high levels of solar radiation, mainly during the first half of the year.
The moderating effect of “gender similarity between manager-subordinate” on the relationship between organisational justice andjob satisfaction among knowledge workers

K-workers embodying high value tacit knowledge are the linchpin of the k-economy. Their satisfaction with the job is the key to keeping them produce for the organisation. Their perception of how justly they are treated i.e. organisational justice is ever more important in managing the k-workers and in keeping them satisfied. Additionally,the evolving k-workforce will become increasingly feminised. How will this effect the justice and job satisfaction equation? This study explored these two questions using a mailed survey which netted a sample of 401 k-workers with MSC statused companies.The study found that procedural justice is a key factor in job satisfaction of the k-workers. However, gender similarity i.e. female-female or male-male supervisor-superviser pairs moderates the relationship between justice variables and job satisfaction (JS). The moderation effect of gender similarity is, however, complex.Although the gender factor accounts for only a small amount of variance in JS, it adds to the manager’s toolkit in dealing with k-workers including the women k-workers.
Implementation of Parallel Multichannel Communications using Stop-and-Wait ARQ

In a parallel multichannel data communication system the stop-and-wait Automatic Repeat Request (ARQ) protocol for parallel channels with an in-sequence delivery guarantee is used for error control. Under the assumption that all channels have the same transmission rate but possibly different time-invariant error rates, we evaluate the probability generating function of the resequencing buffer occupancy and the probability mass function of the resequencing delay. Then, by assuming the Gilbert–Elliott model for each channel the probability mass functions of the resequencing buffer occupancy and the resequencing delay for time-invariant channels are computed. From numerical and simulation results, the mean resequencing buffer occupancy and the mean resequencing delay as functions of system parameters are analyzed. The modeling technique and analytical approach used in this paper can be applied to the performance evaluation of other ARQ protocols over multiple time-varying channels. Keywords: In-sequence Delivery, Multichannel Data communications, Resequencing buffer occupancy, Resequencing Delay, SW-ARQ
ECM: Effective Capacity Maximizer for high-performance compressed caching

Compressed Last-Level Cache (LLC) architectures have been proposed to enhance system performance by efficiently increasing the effective capacity of the cache, without physically increasing the cache size. In a compressed cache, the cacheline size varies depending on the achieved compression ratio. We observe that this size information gives a useful hint when selecting a victim, which can lead to increased cache performance. However, no replacement policy tailored to compressed LLCs has been investigated so far. This paper introduces the notion of size-aware compressed cache management as a way to maximize the performance of compressed caches. Toward this end, the Effective Capacity Maximizer (ECM) scheme is introduced, which targets compressed LLCs. The proposed mechanism revolves around three fundamental principles: Size-Aware Insertion (SAI), a Dynamically Adjustable Threshold Scheme (DATS), and Size-Aware Replacement (SAR). By adjusting the eviction criteria, based on the compressed data size, one may increase the effective cache capacity and minimize the miss penalty. Extensive simulations with memory traces from real applications running on a full-system simulator demonstrate significant improvements compared to compressed cache schemes employing the conventional Least-Recently Used (LRU) and Dynamic Re-Reference Interval Prediction (DRRIP) [11] replacement policies. Specifically, ECM shows an average effective capacity increase of 15% over LRU and 18.8% over DRRIP, an average cache miss reduction of 9.4% over LRU and 3.9% over DRRIP, and an average system performance improvement of 6.2% over LRU and 3.3% over DRRIP.
Fabian Kühnhausen : Financial Innovation and Fragility

In this paper, I evaluate the impact of innovative activity of financial agents on their fragility in a competitive framework. There exist a vast array of concerns about the interconnection of financial innovations, financial distress of firms and financial crises provided by theoretical arguments. I build on these and assess empirically the causal link between a financial agents’ innovativeness and stability. Using a unique data set on financial innovations in the USA between 1990 and 2002, I show that a larger degree of innovation negatively (positively) affects firm stability (fragility) after controlling for the underlying firm characteristics. The results are robust against different modifications of innovation measures and against different fragility parameters indicating profitability, activity risk and risk of insolvency. JEL Codes: G01, G2, L11, O31
The Mycotoxin Deoxynivalenol Potentiates Intestinal Inflammation by Salmonella Typhimurium in Porcine Ileal Loops

Background and Aims Both deoxynivalenol (DON) and nontyphoidal salmonellosis are emerging threats with possible hazardous effects on both human and animal health. The objective of this study was to examine whether DON at low but relevant concentrations interacts with the intestinal inflammation induced by Salmonella Typhimurium. Methodology By using a porcine intestinal ileal loop model, we investigated whether intake of low concentrations of DON interacts with the early intestinal inflammatory response induced by Salmonella Typhimurium. Results A significant higher expression of IL-12 and TNFα and a clear potentiation of the expression of IL-1β, IL-8, MCP-1 and IL-6 was seen in loops co-exposed to 1 µg/mL of DON and Salmonella Typhimurium compared to loops exposed to Salmonella Typhimurium alone. This potentiation coincided with a significantly enhanced Salmonella invasion in and translocation over the intestinal epithelial IPEC-J2 cells, exposed to non-cytotoxic concentrations of DON for 24 h. Exposure of Salmonella Typhimurium to 0.250 µg/mL of DON affected the bacterial gene expression level of a limited number of genes, however none of these expression changes seemed to give an explanation for the increased invasion and translocation of Salmonella Typhimurium and the potentiated inflammatory response in combination with DON. Conclusion These data imply that the intake of low and relevant concentrations of DON renders the intestinal epithelium more susceptible to Salmonella Typhimurium with a subsequent potentiation of the inflammatory response in the gut.
Multi-Robot Disaster Recon : Building Software Foundations with Real and Simulated Robots to Work with a Common Interface

As progress towards automation and decreased human teleoperation of multi-robot systems continues, the need arises for a software system encapsulating the robotic device to train new users and test new autonomous algorithms. This system carries several advantages: reducing wear and tear on the physical robots during training and testing, reducing redesign at the user interface level, and reducing time spent with users in training. The system provides support to the Flash User Interface (UI) (Humprey et. al, 2007), developed to improve situational awareness while operating multi-robot systems, via the Gamebots protocol (Wang et. al, 2005) parsing and adapting these commands to the Player (Gerkey et. al, 2001) provided interface on-board the robot or in conjunction with the Gazebo simulator (Koenig and Howard, 2004) via TCP. Design and functionality of the current system is reviewed, and milestones for future versions are outlined.
Estimating the parameters of a moving target in MIMO radar with widely separated antennas

In this paper, we develop a new maximum likelihood (ML) moving target parameter estimation technique for multiple-input multiple-output (MIMO) radar. It is required for this technique that different receive antennas have the same time reference, but no synchronization of initial phases of the receive antennas is needed and, therefore, the estimation process is noncoherent. The target motion within a certain processing interval is modeled as a second-order polynomial whose coefficients are given by the initial location, velocity, and acceleration of the target. The proposed ML estimator is able to jointly process the data collected from multiple consecutive radar pulses. It is shown that the considered ML problem simplifies to the classic “overdetermined” nonlinear least-squares problem. The proposed ML estimator requires multi-dimensional search over the unknown location, velocity, and acceleration parameters. The performance of the proposed estimator is validated by simulation results.
A MATLAB compiler for distributed, heterogeneous, reconfigurable computing systems

Recently, high-level languages such as MATLAB have become popular in prototyping algorithms in domains such as signal and image processing. Many of these applications whose subtasks have diverse execution requirements, often employ distributed, heterogeneous, reconfigurable systems. These systems consist of an interconnected set of heterogeneous processing resources that provide a variety of architectural capabilities. The objective of the MATCH (MATLAB Compiler for Heterogeneous Computing Systems) compiler project at Northwestern University is to make it easier for the users to develop efficient code for distributed heterogeneous, reconfigurable computing systems. Towards this end we are implementing and evaluating an experimental prototype of a software system that will take MATLAB descriptions of various applications, and automatically map them on to a distributed computing environment consisting of embedded processors, digital signal processors and field-programmable gale arrays built from commercial off-the-shelf components. We provide an overview of the MATCH compiler and discuss the testbed which is being used to demonstrate our ideas. We present preliminary experimental results on some benchmark MATLAB programs with the use of the MATCH compiler.
Drinking and academic performance in high school

I investigate the extent to which negative alcohol use coefficients in Grade Point Average regressions reflect unobserved heterogeneity rather than direct effects of drinking, using 2001 and 2003 Youth Risk Behaviour Survey data on high school students. Results illustrate that omitted factors are quite important. Drinking coefficient magnitudes fall substantially in regressions that control for risk and time preference, mental health, self-esteem and consumption of other addictive substances. Moreover, the impact of binge drinking is negligible for students who are less risk averse, heavily discount the future or use other drugs. However, effects that remain significant after accounting for unobserved heterogeneity and are relatively large for risk averse, future-oriented and drug-free students suggest that binge drinking might slightly worsen academic performance. Consistent with this, the relationship between grades and drinking without binging is small and insignificant on the extensive margin and positive on the intensive margin.
Re-scale boosting for regression and classification

Boosting is a learning scheme that combines weak prediction rules to produce a strong composite estimator, with the underlying intuition that one can obtain accurate prediction rules by combining "rough" ones. Although boosting is proved to be consistent and overfitting-resistant, its numerical convergence rate is relatively slow. The aim of this paper is to develop a new boosting strategy, called the re-scale boosting (RBoosting), to accelerate the numerical convergence rate and, consequently, improve the learning performance of boosting. Our studies show that RBoosting possesses the almost optimal numerical convergence rate in the sense that, up to a logarithmic factor, it can reach the minimax nonlinear approximation rate. We then use RBoosting to tackle both the classification and regression problems, and deduce a tight generalization error estimate. The theoretical and experimental results show that RBoosting outperforms boosting in terms of generalization.