Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the use of ancestry as a unified network model of finite population evolution

The evolution of a population is determined by many factors, including the geographic separation of individuals in the population (spatial structure), parent selection via assortative mating (biasing who breeds with whom), environmental gradients, founder effects, disturbance, selection, stochastic effects characterised as genetic drift and so on. Ultimately the interest in studying a population of organisms is about characterising parent selection over time. This paper will examine the evolution of a population under the neutral conditions of genetic drift and for a simple selection model. For drift two conditions are considered: the first is for a range of spatial (geographic) constraints defined by a network; the second is through the use of a tagging system that models assortative mate selection. A simple selection model for the OneMax problem is used to illustrate the response of a population to selection pressure. An ancestry network is constructed representing the shared parent interactions over time. This structure is analyzed as a method for characterising the interactions of a population. The approach demonstrates a unified model to characterise population dynamics, independent of the underlying evolutionary constraints.
VOCABULARY SIZE IS ASSOCIATED WITH SECOND-LANGUAGE VOWEL PERCEPTION PERFORMANCE IN ADULT LEARNERS

Improvement in second-language (L2) perception has been posited to occur early in L2 learning when the L2 vocabulary is still small, whereas a large L2 vocabulary curtails perceptual learning (the perceptual assimilation model for SLA [PAM-L2]; Best & Tyler, 2007). This proposition is extended by suggesting that early L2 lexical development facilitates the establishment of phonological categories in a manner analogous to children’s first-language (L1) acquisition before as opposed to after the vocabulary spurt. According to this view, L2 speech should be assimilated more consistently to L1 phonological categories and cross-boundary contrasts should be discriminated more accurately by learners with larger L2 vocabularies. To test this proposition, a novel whole-system approach to evaluate perception of L2 vowels in two experiments was applied. In Experiment 1, Japanese learners of Australian English (AusE) with less than 12 weeks of L2 learning in Australia completed labeling and goodness ratings on all AusE vowels, selecting from among all monomoraic and bimoraic Japanese vowels and vowel combinations. They also discriminated four L2 vowel contrasts, representing a range of PAM-L2 contrast types, and completed a L2 vocabulary size assessment. Learners with larger vocabularies had more consistent L2-L1 vowel assimilation and more accurate cross-boundary discrimination than those with smaller vocabularies, supporting the proposition that lexical development assists L2 phonological acquisition. Experiment 2 compared the perception of AusE vowels by Japanese learners after only 4–8 weeks in Australia with their perception after 6–8 months of L2 exposure. The results also supported the predicted positive association between L2 vocabulary size and L2 vowel perception rather than a general prediction of increased exposure duration leading to improved perception.
Persistent homology for low-complexity models

We show that recent results on randomized dimension reduction schemes that exploit structural properties of data can be applied in the context of persistent homology. In the spirit of compressed sensing, the dimension reduction is determined by the Gaussian width of a structure associated with the dataset, rather than its size, and such a reduction can be computed efficiently. We further relate the Gaussian width to the doubling dimension of a finite metric space, which appears in the study of the complexity of other methods for approximating persistent homology. We can, therefore, literally replace the ambient dimension by an intrinsic notion of dimension related to the structure of the data.
The Effect of Emissions on U.S. State Total Factor Productivity Growth

This paper investigates the effect of sulphur dioxide (SO2) and nitrogen oxide (NOx) emissions on the Total Factor Productivity (TFP) growth among 48 contiguous U.S. states, for the period 1965-2002. The relationship between TFP growth and emissions is examined using nonparametric econometric techniques that allow for the estimation of the elasticity of pollution for each state and each period and to account for possible nonlinearities in the data. The results indicate that both pollutants positively affect TFP growth. Moreover this effect is nonlinear. The average output elasticity for all states is 0.005 for SO2 and 0.04 for NOx emissions.
Hybrid multicast-unicast streaming over mobile networks

Mobile on-demand videos are getting tremendously popular and incurring staggering overhead on cellular net-works. Fortunately, next generation cellular networks support video streaming over either unicast or multicast, but how to capitalize both unicast and multicast for optimal on-demand video streaming remains an open question. In this paper, we consider a resource allocation problem that concurrently utilizes unicast/multicast in order to support many more mobile streaming users and minimize the energy consumption of the battery-powered mobile devices. We formulate this problem as a Binary Integer Programming (BIP) problem. We present an optimal algorithm, SCOPT, for this problem. We also develop an efficient heuristic algorithm, SCG, for lower overhead. We conduct detailed packet-level simulations to evaluate the algorithms in LTE networks using OPNET. Our simulation study shows that the proposed algorithms: (i) result in lower energy consumption than multicast-only approach, (ii) scale to many more mobile users than unicast-only approach, and (iii) are more energy efficient with more network bandwidth or fewer videos. In addition, we discuss how our solution can be extended to support Single Frequency Networks in which multiple adjacent base stations operate on the same frequency.
Rich Intrinsic Image Decomposition of Outdoor Scenes from Multiple Views

Intrinsic images aim at separating an image into its reflectance and illumination components to facilitate further analysis or manipulation. This separation is severely ill posed and the most successful methods rely on user indications or precise geometry to resolve the ambiguities inherent to this problem. In this paper, we propose a method to estimate intrinsic images from multiple views of an outdoor scene without the need for precise geometry and with a few manual steps to calibrate the input. We use multiview stereo to automatically reconstruct a 3D point cloud of the scene. Although this point cloud is sparse and incomplete, we show that it provides the necessary information to compute plausible sky and indirect illumination at each 3D point. We then introduce an optimization method to estimate sun visibility over the point cloud. This algorithm compensates for the lack of accurate geometry and allows the extraction of precise shadows in the final image. We finally propagate the information computed over the sparse point cloud to every pixel in the photograph using image-guided propagation. Our propagation not only separates reflectance from illumination, but also decomposes the illumination into a sun, sky, and indirect layer. This rich decomposition allows novel image manipulations as demonstrated by our results.
Supervised Learning of Image Restoration with Convolutional Networks

Convolutional networks have achieved a great deal of success in high-level vision problems such as object recognition. Here we show that they can also be used as a general method for low-level image processing. As an example of our approach, convolutional networks are trained using gradient learning to solve the problem of restoring noisy or degraded images. For our training data, we have used electron microscopic images of neural circuitry with ground truth restorations provided by human experts. On this dataset, Markov random field (MRF), conditional random field (CRF), and anisotropic diffusion algorithms perform about the same as simple thresholding, but superior performance is obtained with a convolutional network containing over 34,000 adjustable parameters. When restored by this convolutional network, the images are clean enough to be used for segmentation, whereas the other approaches fail in this respect. We do not believe that convolutional networks are fundamentally superior to MRFs as a representation for image processing algorithms. On the contrary, the two approaches are closely related. But in practice, it is possible to train complex convolutional networks, while even simple MRF models are hindered by problems with Bayesian learning and inference procedures. Our results suggest that high model complexity is the single most important factor for good performance, and this is possible with convolutional networks.
International Journal of Health Geographics Open Access Landscape, Demographic, Entomological, and Climatic Associations with Human Disease Incidence of West Nile Virus in the State of Iowa, Usa

Background: West Nile virus (WNV) emerged as a threat to public and veterinary health in the Midwest United States in 2001 and continues to cause significant morbidity and mortality annually. To investigate biotic and abiotic factors associated with disease incidence, cases of reported human disease caused by West Nile virus (WNV) in the state of Iowa were aggregated by census block groups in Iowa for the years 2002–2006. Spatially explicit data on landscape, demographic, and climatic conditions were collated and analyzed by census block groups. Statistical tests of differences between means and distributions of landscape, demographic, and climatic variables for census block groups with and without WNV disease incidence were carried out. Entomological data from Iowa were considered at the state level to add context to the potential ecological events taking place.
Electrogenic Na+/Ca2+ Exchange

Olfactory receptor neurons (ORNs) from the squid, Lolliguncula brevis, respond to the odors l-glutamate or dopamine with increases in internal Ca2+ concentrations ([Ca2+]i). To directly asses the effects of increasing [Ca2+]i in perforated-patched squid ORNs, we applied 10 mM caffeine to release Ca2+ from internal stores. We observed an inward current response to caffeine. Monovalent cation replacement of Na+ from the external bath solution completely and selectively inhibited the caffeine-induced response, and ruled out the possibility of a Ca2+-dependent nonselective cation current. The strict dependence on internal Ca2+ and external Na+ indicated that the inward current was due to an electrogenic Na+/Ca2+ exchanger. Block of the caffeine-induced current by an inhibitor of Na+/Ca2+ exchange (50–100 μM 2′,4′-dichlorobenzamil) and reversibility of the exchanger current, further confirmed its presence. We tested whether Na+/Ca2+ exchange contributed to odor responses by applying the aquatic odor l-glutamate in the presence and absence of 2′,4′-dichlorobenzamil. We found that electrogenic Na+/Ca2+ exchange was responsible for ∼26% of the total current associated with glutamate-induced odor responses. Although Na+/Ca2+ exchangers are known to be present in ORNs from numerous species, this is the first work to demonstrate amplifying contributions of the exchanger current to odor transduction.
Parallel-Connected Solar PV System to Address Partial and Rapidly Fluctuating Shadow Conditions

Solar photovoltaic (PV) arrays in portable applications are often subject to partial shading and rapid fluctuations of shading. In the usual series-connected wiring scheme, the residual energy generated by partially shaded cells either cannot be collected (if diode bypassed) or, worse, impedes collection of power from the remaining fully illuminated cells (if not bypassed). Rapid fluctuation of the shading pattern makes maximum power point (MPP) tracking difficult; generally, there will exist multiple local MPPs, and their values will change as rapidly as does the illumination. In this paper, a portable solar PV system that effectively eliminates both of the aforementioned problems is described and proven. This system is capable of simultaneously maximizing the power generated by every PV cell in the PV panel. The proposed configuration consists of an array of parallel-connected PV cells, a low-input-voltage step-up power converter, and a simple wide bandwidth MPP tracker. Parallel-configured PV systems are compared to traditional series-configured PV systems through both hardware experiments and computer simulations in this paper. Study results demonstrate that, under complex irradiance conditions, the power generated by the new configuration is approximately twice that of the traditional configuration. The solar PV system can be widely used in many consumer applications, such as PV vests for cell phones and music players.
Predicting task execution times by deriving enhanced cognitive models from user interface development models

Adaptive user interfaces (UI) offer the opportunity to adapt to changes in the context, but this also poses the challenge of evaluating the usability of many different versions of the resulting UI. Consequently, usability evaluations tend to become very complex and time-consuming. We describe an approach that combines model-based usability evaluation with development models of adaptive UIs. In particular, we present how a cognitive user behavior model can be created automatically from UI development models and thus save time and costs when predicting task execution times. With the help of two usability studies, we show that the resulting predictions can be further improved by using information encoded in the UI development models.
Order effects: A study of the possible influence of presentation order on user judgments of document relevance

Studies concerned with the evaluation of information systems have typically relied on judgments of relevance as the fundamental measure in determining system performance. In most cases, subjects are asked to assign a relevance score using some category rating scale (l-4, l-11, or simply relevant/non-relevant) to each document in a set retrieved in response to some information need or query. While the extensive studies of relevance conducted in the 1960s indicated that relevance judgments are influenced by a range of variables, llttle attention has been paid to the possible effects of the order In which the stimuli are presented to judges. This effect of “stimulus order” has been found to exist in measuring variables in other fields (Stevens 1975, Gescheider 1965). Questioning possible “presentable order effects” Is particularly appropriate in that systems are being developed and evaluated in information science which present documents In some systematic way (e.g., with the documents considered by the system to be most relevant presented first). This article describes an effort to study whether the order of document presentation to judges influences the relevance scores assigned to those documents. A query and set of documents with relevance judgments were available from a previous study. Subjects were randomly assigned one of two orders (one ranked high to low, the other low to high) of fifteen document descriptions. They were then asked to assign a score to each document description to match their judgment of relevance in relation to the stated informatlon query. Both a category rating (l-7) and open-ended, magnitude estimation scaling procedure were tested, and It was found that the judgments were influenced by the order of document presentation.
Short-period AM CVn systems as optical, X-ray and gravitational-wave sources

We model the population of AM CVn systems in the Galaxy and discuss the detectability of these systems with optical, X-ray and gravitational-wave detectors. We concentrate on the short-period (P < 1500 s) systems, some of which are expected to be in a phase of direct-impact accretion. Using a self-consistent model for the star-formation history and radial distribution of stars in the Galaxy plus simple models for the emission of optical and X-ray radiation from the AM CVn systems and interstellar absorption, we derive the sample of short-period AM CVn systems that can be detected in the optical and/or X-ray bands. At the shortest periods, the detectable systems are all X-ray sources, some with periods as short as three minutes. At periods above 10 min, most detectable systems are optical sources. About one-third of the X-ray sources are also detectable in the optical band. We also calculate the gravitational-wave signal of the short-period AM CVn systems. We find that potentially several thousand AM CVn systems can be resolved by the gravitational-wave detector Laser Interferometer Space Antenna (LISA), comparable to the expected number of detached double white dwarfs that can be resolved. We estimate that several hundreds of the AM CVn systems resolvable by LISA are also detectable in the optical and/or X-ray bands.
Refining the Global Spatial Limits of Dengue Virus Transmission by Evidence-Based Consensus

Background Dengue is a growing problem both in its geographical spread and in its intensity, and yet current global distribution remains highly uncertain. Challenges in diagnosis and diagnostic methods as well as highly variable national health systems mean no single data source can reliably estimate the distribution of this disease. As such, there is a lack of agreement on national dengue status among international health organisations. Here we bring together all available information on dengue occurrence using a novel approach to produce an evidence consensus map of the disease range that highlights nations with an uncertain dengue status. Methods/Principal Findings A baseline methodology was used to assess a range of evidence for each country. In regions where dengue status was uncertain, additional evidence types were included to either clarify dengue status or confirm that it is unknown at this time. An algorithm was developed that assesses evidence quality and consistency, giving each country an evidence consensus score. Using this approach, we were able to generate a contemporary global map of national-level dengue status that assigns a relative measure of certainty and identifies gaps in the available evidence. Conclusion The map produced here provides a list of 128 countries for which there is good evidence of dengue occurrence, including 36 countries that have previously been classified as dengue-free by the World Health Organization and/or the US Centers for Disease Control. It also identifies disease surveillance needs, which we list in full. The disease extents and limits determined here using evidence consensus, marks the beginning of a five-year study to advance the mapping of dengue virus transmission and disease risk. Completion of this first step has allowed us to produce a preliminary estimate of population at risk with an upper bound of 3.97 billion people. This figure will be refined in future work.
A Priority-based Distributed Call Admission Protocol for Multi-hop Wireless Ad hoc Networks

Ad hoc networks have been proposed for a variety of applications where support for real time, multimedia services may be necessary. This requires that the network is able to offer service differentiation and quality of service (QoS) appropriate for the latency and jitter bounds needed to meet the real time constraint. This paper describes a design for realistic QoS support using a system approach that involves co-ordinated changes at the MAC and IP layers. At the MAC layer, we propose a priority-based scheduling mechanism to provide service differentiation based on current channel status. We develop a priority-based delay model for the adaptive backoff scheme. The delay model allows each node to make local admission decisions. At the IP layer, the network resource availability distribution and flow admission in multi-hop ad hoc networks is achieved through a proposed call admission protocol, so that each node has the correct view of the shared channel usage, and the correct flow admission decision is made based on the estimated flow quality (accumulated delay of the path). Analytical and simulation results show that our approach can provide bounded latency and low jitter for real-time traffic, such as VoIP. The results also demonstrate that the aggregated network throughput is significantly improved given the quality requirements.
Prospective study of high-sensitivity C-reactive protein as a determinant of mortality: results from the MONICA/KORA Augsburg Cohort Study, 1984-1998.

BACKGROUND C-reactive protein (CRP), an exquisitely sensitive systemic marker of inflammation, has emerged as an independent predictor of cardiovascular diseases (CVD). Because other chronic diseases are also associated with an inflammatory response, we sought to assess the association of high-sensitivity CRP (hsCRP) with total and cause-specific mortality in a large cohort of middle-aged men. METHODS We measured hsCRP at baseline in 3620 middle-aged men, randomly drawn from 3 samples of the general population in the Augsburg area (Southern 0Germany) in 1984-85, 1989-90, and 1994-95. Outcome was defined as all deaths, fatal CVD, fatal coronary heart disease (CHD) including sudden cardiac deaths, and cancer deaths. RESULTS During an average follow-up of 7.1 years, 408 deaths occurred (CVD 196, CHD 129, cancer 127). In multivariable Cox regression analysis, subjects with hsCRP >3 mg/L at baseline showed an almost 2-fold increased risk to die vs those with hsCRP <1 mg/L [hazard ratio (HR) 1.88, 95% CI 1.41-2.52]. HRs were 2.15 (95% CI 1.39-3.34) for fatal CVD, 1.74 (1.04-2.92) for fatal CHD, and 1.65 (1.01-2.68) for cancer mortality. In contrast, neither total nor HDL cholesterol significantly predicted all-cause or cancer mortality, and cholesterol had only modest effects on CVD mortality. CONCLUSIONS Our results suggest that increased circulating hsCRP concentrations are associated with an increased risk of death from several widespread chronic diseases. Persistently increased hsCRP is a sensitive and valuable nonspecific indicator of an ongoing disease process that deserves serious and careful medical attention.
Distributed mining of large scale remote sensing image archives on public computing infrastructures

Earth Observation (EO) mining aims at supporting efficient access and exploration of petabyte-scale space- and airborne remote sensing archives that are currently expanding at rates of terabytes per day. A significant challenge is performing the analysis required by envisaged applications --- like for instance process mapping for environmental risk management --- in reasonable time. In this work, we address the problem of content-based image retrieval via example-based queries from EO data archives. In particular, we focus on the analysis of polarimetric SAR data, for which target decomposition theorems have proved fundamental in discovering patterns in data and characterize the ground scattering properties. To this end, we propose an interactive region-oriented content-based image mining system in which 1) unsupervised ingestion processes are distributed onto virtual machines in elastic, on-demand computing infrastructures 2) archive-scale content hierarchical indexing is implemented in terms of a "big data" analytics cluster-computing framework 3) query processing amounts to traversing the generated binary tree index, computing distances that correspond to descriptor-based similarity measures between image groups and a query image tile. We describe in depth both the strategies and the actual implementations for the ingestion and indexing components, and verify the approach by experiments carried out on the NASA/JPL UAVSAR full polarimetric data archive. We report the results of the tests performed on computer clusters by using a public Infrastructure-as-a-Service and evaluating the impact of cluster configuration on system performance. Results are promising for data mapping and information retrieval applications.