Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient interference mitigation for small cell networks with synchronization error

With a rapidly growing demand for high speed data transmissions, small cell networks (SCN) have been shown promising due to its flexible and low-cost deployments. Although SCN can effectively improve the network performance, a dense small cell deployment causes severe co-channel interference. Most of existing interference management methods are proposed under the assumption of perfect synchronization among all cells, which can hardly be achieved in practice. In this study, we studied the interference mitigation between adjacent small cells caused by synchronization errors. A simple but efficient prefix based approach is presented for the SCN to alleviate the performance degradation due to synchronization errors. Simulation results show that the proposed method achieves a noticeable performance gain in terms of both outage probability and average data rate.
Spontaneous baroreflex control of cardiac output during dynamic exercise, muscle metaboreflex activation, and heart failure.

We have previously shown that spontaneous baroreflex-induced changes in heart rate (HR) do not always translate into changes in cardiac output (CO) at rest. We have also shown that heart failure (HF) decreases this linkage between changes in HR and CO. Whether dynamic exercise and muscle metaboreflex activation (via imposed reductions in hindlimb blood flow) further alter this translation in normal and HF conditions is unknown. We examined these questions using conscious, chronically instrumented dogs before and after pacing-induced HF during mild and moderate dynamic exercise with and without muscle metaboreflex activation. We measured left ventricular systolic pressure (LVSP), CO, and HR and analyzed the spontaneous HR-LVSP and CO-LVSP relationships. In normal animals, mild exercise significantly decreased HR-LVSP (-3.08 +/- 0.5 vs. -5.14 +/- 0.6 beats.min(-1). mmHg(-1); P < 0.05) and CO-LVSP (-134.74 +/- 24.5 vs. -208.6 +/- 22.2 ml.min(-1). mmHg(-1); P < 0.05). Moderate exercise further decreased both and, in addition, significantly reduced HR-CO translation (25.9 +/- 2.8% vs. 52.3 +/- 4.2%; P < 0.05). Muscle metaboreflex activation at both workloads decreased HR-LVSP, whereas it had no significant effect on CO-LVSP and the HR-CO translation. HF significantly decreased HR-LVSP, CO-LVSP, and the HR-CO translation in all situations. We conclude that spontaneous baroreflex HR responses do not always cause changes in CO during exercise. Moreover, muscle metaboreflex activation during mild and moderate dynamic exercise reduces this coupling. In addition, in HF the HR-CO translation also significantly decreases during both workloads and decreases even further with muscle metaboreflex activation.
Europe's Common Left-Right Space

This study presents a new method to estimate the locations of voters, parties, and European political groups in the same ideological space using left-right placements by voters. We apply our method to the 2009 European Election Survey and demonstrate that the improvement in party estimates that one gains from fixing various survey bias issues is significant. Our scaling strategy provides left-right positions of voters and party positions for 162 parties - more than traditional expert survey studies currently provide. We test the convergent validity of these positions in multiple ways and demonstrate how rescaled voter and party positions can be used in cross-national research.
A footstep-plan-based floor sensing method using stereo images for biped robot control

In this paper, we propose a floor sensing method using stereo cameras mounted on a biped robot. In the proposed method, we first determine multiple regions of interest (ROI) in a reference image from footstep positions up to several steps, scheduled by a current footstep plan. Then the 3D plane parameters of the floor with respect to each ROI are estimated by a direct method using stereo images. We adopt the fast plane parameter estimation method, along with the compensation for the errors of the initial parameters by using the internal state of the robot, for the enhancement of the robustness and efficiency in the optimization process. Additionally, we estimate the shape of the floor including slopes from the set of the estimated plane parameters, and feedback the results for updating the footstep plan. The validity of the proposed method is demonstrated through on-line experiments using stereo cameras mounted on the body of a biped robot traversing a real environment.
Making Sense of Trajectory Data in Indoor Spaces

The increasing prevalence of positioning and tracking systems has helped simplify tracking large amounts of, e.g., People moving through buildings or cars traveling on roads, over long periods of time. However, technical limitations of positioning algorithms and traditional sensing infrastructures are likely, especially indoors, to induce errors and biases in the resulting data. In particular, the resulting motion trajectories often do not conform perfectly to the underlying route network. As a consequence, analyses of trajectory sets are impeded by these phenomena, as it becomes hard to identify which route was taken in a particular travel instance or whether two travel instances followed the same route. In this paper, we present a bootstrapping approach and several algorithms to mitigate error biases and related phenomena, focusing on indoor scenarios. In particular, we are able to estimate and iteratively refine an underlying route network from a set of motion trajectories. Secondly, we represent sub trajectories, i.e., Movements on individual elements of the route network, by their median sub trajectory. The resulting aggregated and cleaned-up data set facilitates using further, domain-specific analysis tools. Additionally, it allows to predict the locally occurring expected positioning error biases. This in turn allows improved positioning, e.g., For real-time navigation assistance scenarios. We evaluate the proposed methods using trajectory data from employees at a large hospital complex. In particular, we show that we can reconstruct the hospital's route network accurately, and that we can furthermore extract median sub trajectories for almost all individual corridors. Finally, we illustrate that median trajectories deliver useful deviation maps to learn, and correct for, the expected local biases in positioning.
Genetic algorithm for robust schedules

Computing a schedule for a given single machine problem is often difficult for irregular criteria, but when the data are uncertain, the problem is much more complicated. In this paper, we create a genetic algorithm to compute robust schedules when release dates are subject to small variations. Instead of evaluating a single fitness function at each iteration, several functions are evaluated taking into account the variation of the data. This method leads to robust solutions, meaning that the value of the objective function remains high when small variations in some release dates occurs.
Cerco: Supporting Range Queries with a Hierarchically Structured Peer-to-Peer System

Structured peer-to-peer systems are designed for a highly scalable, self organizing, and efficient lookup for data. The key space of the so-called distributed hash tables (DHTs) is partitioned and each partition with its keys and values is assigned to a node in the DHT. For data retrieval however, the very nature of hash tables allows only exact pattern matches. We propose Cerco, a simple solution for the problem of range queries by employing a hierarchically structured P2P approach based on the principles of distributed hash tables. We show that a dynamic hierarchy of DHTs with on-demand classification of items can positively influence the response time of queries while maintaining lookup correctness
Scalable Intra and Inter Domain IPv6 QoS Management and Pricing Scheme

Network multimedia applications constitute a large part of Internet traffic and guaranteed delivery of such traffic is a challenge because of their sensitivity to delay, packet loss and higher bandwidth requirement. The need for guaranteed traffic delivery is exacerbated by the increasing delay experienced by traffic propagating through more than one QoS domain. Hence, there is a need for a flexible and a scalable QoS manager that handles and manages the needs of traffic flows throughout multiple IPv6 domains. The IPv6 QoS manager, presented in this paper, uses a combination of the packets’ flow ID and the source address (Domain Global Identifier (DGI)), to process and reserve resources inside an IPv6 domain. To ensure inter-domain QoS management, the QoS domain manager should also communicate with other QoS domains’ managers to ensure that traffic flows are guaranteed delivery. In this scheme, the IPv6 QoS manager handles QoS requests by either processing them locally if the intended destination is located locally or forwards the request to the neighboring domain’s QoS manager. End-to-end QoS is achieved with an integrated admission and management unit. The feasibility of the proposed QoS management scheme is illustrated for both intraand inter-domain QoS management. The scalability of the QoS management scheme for inter-domain scenarios is illustrated with simulations for traffic flows propagating through two and three domains. Excellent average end-to-end delay results have been achieved when traffic flow propagates through more than one domain. Simulations show that DOI: 10.4018/978-1-61520-791-6.ch014
Smooth and Non-smooth Bifurcations in Multi-structure Multi-operating-mode Systems: Case Study of a Power Conversion System with Two Energy Sources

Renewable power generation systems are gaining popularity and have developed quickly in recent years. However, due to the intermittent features of the renewable energy sources, conversion systems involving more than one energy sources are usually designed with multiple structures and multiple operating modes. The dynamic behavior is thus quite complex, and the design for stable operation of such systems requires consideration of the stability conditions for all possible structures and operation modes. This paper describes such conversion systems, and uses a standalone photovoltaic-battery hybrid power system as an example for illustrating the possible complex behavior. We reveal smooth and non-smooth bifurcation phenomena in this system. Under certain conditions, when the system switches its operation mode, a non-smooth bifurcation has been observed and verified with full-circuit simulations. Moreover, a detailed analysis based on an averaged model is performed to identify the two types of bifurcation.
Association between Intake of Sugar-Sweetened Beverages and Circulating 25-Hydroxyvitamin D Concentration among Premenopausal Women

Intake of sugar-sweetened beverages has increased in North America and seems to have several adverse health effects possibly through decreased circulating 25-hydroxyvitamin D (25(OH)D) concentrations. The aim of this cross-sectional study was to evaluate the association between sugar-sweetened beverages intake and 25(OH)D concentrations among premenopausal women. Intake of sugar-sweetened beverages including colas, other carbonated beverages and sweet fruit drinks was assessed using a validated food frequency questionnaire among 741 premenopausal women. Plasma concentrations of 25(OH)D were quantified by radioimmunoassay. The association between sugar-sweetened beverages intake and 25(OH)D concentrations was evaluated using multivariate generalized linear models and Spearman correlations. A higher intake of colas was associated with lower mean 25(OH)D levels (67.0, 63.7, 64.7 and 58.5 nmol/L for never, <1, 1–3 and >3 servings/week, respectively; r = −0.11 (p = 0.004)). A correlation was observed between intake of other carbonated beverages and 25(OH)D concentrations but was not statistically significant (r = −0.06 (p = 0.10)). No association was observed between intake of sweet fruit drinks and 25(OH)D concentrations. This study suggests that high intake of colas may decrease 25(OH)D levels in premenopausal women. Considering the high consumption of these drinks in the general population and the possible consequences of vitamin D deficiency on health, this finding needs further investigation.
Dietary Fucoxanthin Increases Metabolic Rate and Upregulated mRNA Expressions of the PGC-1alpha Network, Mitochondrial Biogenesis and Fusion Genes in White Adipose Tissues of Mice

The mechanism for how fucoxanthin (FX) suppressed adipose accumulation is unclear. We aim to investigate the effects of FX on metabolic rate and expressions of genes related to thermogenesis, mitochondria biogenesis and homeostasis. Using a 2 × 2 factorial design, four groups of mice were respectively fed a high sucrose (50% sucrose) or a high-fat diet (23% butter + 7% soybean oil) supplemented with or without 0.2% FX. FX significantly increased oxygen consumption and carbon dioxide production and reduced white adipose tissue (WAT) mass. The mRNA expressions of peroxisome proliferator-activated receptor (PPAR) γ coactivator-1α (PGC-1α), cell death-inducing DFFA-like effecter a (CIDEA), PPARα, PPARγ, estrogen-related receptor α (ERRα), β3-adrenergic receptor (β3-AR) and deiodinase 2 (Dio2) were significantly upregulated in inguinal WAT (iWAT) and epididymal WAT (eWAT) by FX. Mitochondrial biogenic genes, nuclear respiratory factor 1 (NRF1) and NRF2, were increased in eWAT by FX. Noticeably, FX upregulated genes of mitochondrial fusion, mitofusin 1 (Mfn1), Mfn2 and optic atrophy 1 (OPA1), but not mitochondrial fission, Fission 1, in both iWAT and eWAT. In conclusion, dietary FX enhanced the metabolic rate and lowered adipose mass irrespective of the diet. These were associated with upregulated genes of the PGC-1α network and mitochondrial fusion in eWAT and iWAT.
A dive into the caching performance of Content Centric Networking

Content Centric Networking (CCN) is a promising architecture for the diffusion of popular content over the Internet. While CCN system design is sound, gathering a reliable estimate of its performance in the current Internet is challenging, due to the large scale and to the lack of agreement in some critical elements of the evaluation scenario. In this work, we add a number of important pieces to the CCN puzzle by means of a chunk-level simulator that we make available to the scientific community as open source software. First, we pay special attention to the locality of the user request process, as it may be determined by user interest or language barrier. Second, we consider the existence of possibly multiple repositories for the same content, as in the current Internet, along with different CCN interest forwarding policies, exploiting either a single or multiple repositories in parallel. To widen the relevance of our findings, we consider multiple topologies, content popularity settings, caching replacement policies and CCN forwarding strategies. Summarizing our main result, though the use of multiple content repositories can be beneficial from the user point of view, it may however counter part of the benefits if the CCN strategy layer implements naive interest forwarding policies.
A Computational Approach for Analyzing and Detecting Emotions in Arabic Text

The field of Affective Computing (AC) expects to narrow the communicative gap between the highly emotional human and the emotionally challenged computer by developing computational systems that recognize and respond to the affective states of the user. Affect-sensitive interfaces are being developed in number of domains, including gaming, mental health, and learning technologies. Emotions are part of human life. Recently, interest has been growing among researchers to find ways of detecting subjective information used in blogs and other online social media. This paper concerned with the automatic detection of emotions in Arabic text. This construction is based on a moderate sized Arabic emotion lexicon used to annotate Arabic children stories for the six basic emotions:
Text line segmentation and word recognition in a system for general writer independent handwriting recognition

We present a system for recognizing unconstrained English handwritten text based on a large vocabulary. We describe the three main components of the system, which are preprocessing, feature extraction and recognition. In the preprocessing phase the handwritten texts are first segmented into lines. Then each line of text is normalized with respect to of skew, slant, vertical position and width. After these steps, text lines are segmented into single words. For this purpose distances between connected components are measured. Using a threshold, the distances are divided into distances within a word and distances between different words. A line of text is segmented at positions where the distances are larger than the chosen threshold. From each image representing a single word, a sequence of features is extracted. These features are input to a recognition procedure which is based on hidden Markov models. To investigate the stability of the segmentation algorithm the threshold that separates intra- and inter-word distances from each other is varied. If the threshold is small many errors are caused by over-segmentation, while for large thresholds under-segmentation errors occur. The best segmentation performance is 95.56% correctly segmented words, tested on 541 text lines containing 3899 words. Given a correct segmentation rate of 95.56%, a recognition rate of 73.45% on the word level is achieved.
Feature Combiners With Gate-Generated Weights for Classification

Using functional weights in a conventional linear combination architecture is a way of obtaining expressive power and represents an alternative to classical trainable and implicit nonlinear transformations. In this brief, we explore this way of constructing binary classifiers, taking advantage of the possibility of generating functional weights by means of a gate with fixed radial basis functions. This particular form of the gate permits training the machine directly with maximal margin algorithms. We call the resulting scheme “feature combiners with gate generated weights for classification. ” Experimental results show that these architectures outperform support vector machines (SVMs) and Real AdaBoost ensembles in most considered benchmark examples. An increase in the computational design effort due to cross-validation demands is the price to be paid to obtain this advantage. Nevertheless, the operational effort is usually lower than that needed by SVMs.
Ethnic drinking cultures and alcohol use among Asian American adults: findings from a national survey.

AIMS To investigate the influence of ethnic drinking cultures on alcohol use by Asian Americans and how this influence may be moderated by their level of integration into Asian ethnic cultures. METHODS A nationally representative sample of 952 Asian American adults extracted from the Wave 2 National Epidemiologic Survey of Alcohol and Related Conditions data was used. Multiple logistic and linear regression models were fitted, some of which were stratified by nativity. RESULTS Controlling for financial stress, discrimination and demographic variables, a hypothesized, positive relationship between ethnic drinking cultures and alcohol outcomes held for most drinking outcomes. A hypothesis on the moderating effect of integration into ethnic cultures indicated by ethnic language use was supported for US-born Asian Americans. CONCLUSION Ethnic drinking cultures may significantly influence alcohol use by Asian Americans. The influence of ethnic drinking cultures may be conditioned by the degree of integration into the ethnic cultures. To inform alcohol interventions for reducing harmful and hazardous alcohol use among immigrants, future research needs to explore the cultural and social processes occurring in immigrant communities that might significantly influence drinking.
Linguistic Benchmarks of Online News Article Quality

Online news editors ask themselves the same question many times: what is missing in this news article to go online? This is not an easy question to be answered by computational linguistic methods. In this work, we address this important question and characterise the constituents of news article editorial quality. More speciﬁcally, we identify 14 aspects related to the content of news articles. Through a correlation analysis, we quantify their independence and relation to assessing an article’s editorial quality. We also demonstrate that the identiﬁed aspects, when combined together, can be used effectively in quality control methods for online news.
Cytomegalovirus Genotype Distribution Among Congenitally and Postnatally Infected Patients: Association of Particular Glycoprotein (g)B and gN Types With Symptomatic Disease

Background. Human cytomegalovirus is a leading cause of congenital infection, and there are limited data on prognosis markers in disease development. We aimed to study 3 virology targets (glycoprotein [g]B, gN, and UL144) to assess their correlation with congenital infection and various organ system involvement. Methods. Forty-eight congenital cases and 58 postnatally infected children were included (2003–2014). Genotyping for the 3 targets and distribution among the cohorts were investigated, and the relationship between the gB, gN, and UL144 types with clinical manifestations in congenital infection was also studied. Results. All of the genotypes were similarly represented among cohorts, and the most prevalent were the UL144B, gB1, and gN1 genotypes. The gB2 genotype was associated with abnormal image findings by ultrasound and/or magnetic resonance in congenital infection (odds ratio [OR], 6.2; 95% confidence interval [CI], 1.1–34.3; P = .036); the gN1 genotype was associated with an elevated risk of developing neurological disorders (OR, 7.0; 95% CI, 1.1–45.9; P = .043). Both gN1 and gB2 were independent factors for symptomatic infection. Statistical analyses showed no association between any UL144 genotype and disease severity. Conclusions. All of the genotypes can be involved in congenital infection, although the gB2 and gN1 genotypes might be associated with a more serious illness.
Provision of no-cost, long-acting contraception and teenage pregnancy.

BACKGROUND The rate of teenage pregnancy in the United States is higher than in other developed nations. Teenage births result in substantial costs, including public assistance, health care costs, and income losses due to lower educational attainment and reduced earning potential. METHODS The Contraceptive CHOICE Project was a large prospective cohort study designed to promote the use of long-acting, reversible contraceptive (LARC) methods to reduce unintended pregnancy in the St. Louis region. Participants were educated about reversible contraception, with an emphasis on the benefits of LARC methods, were provided with their choice of reversible contraception at no cost, and were followed for 2 to 3 years. We analyzed pregnancy, birth, and induced-abortion rates among teenage girls and women 15 to 19 years of age in this cohort and compared them with those observed nationally among U.S. teens in the same age group. RESULTS Of the 1404 teenage girls and women enrolled in CHOICE, 72% chose an intrauterine device or implant (LARC methods); the remaining 28% chose another method. During the 2008-2013 period, the mean annual rates of pregnancy, birth, and abortion among CHOICE participants were 34.0, 19.4, and 9.7 per 1000 teens, respectively. In comparison, rates of pregnancy, birth, and abortion among sexually experienced U.S. teens in 2008 were 158.5, 94.0, and 41.5 per 1000, respectively. CONCLUSIONS Teenage girls and women who were provided contraception at no cost and educated about reversible contraception and the benefits of LARC methods had rates of pregnancy, birth, and abortion that were much lower than the national rates for sexually experienced teens. (Funded by the Susan Thompson Buffett Foundation and others.).