Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of the study region on GIS models of species geographic distributions and estimates of niche evolution; preliminary tests with montane rodents (genus Nephelomys) in Venezuela

Various niche-based techniques exist to model a species' potential geographic distribution in a Geographic Information Systems (GIS) framework. These models compare the environmental conditions of localities of a species' occurrence versus those of the overall study region. In addition to uses in areas such as macroecology and conservation biology, this approach has been applied recently to studies of niche evolution and historical biogeography. Definition of the study region is critical for all of these applications but has not been addressed previously. Here, I examine the effect of changes in the extent of the study region on potential distribution models of two rodents (genus Nephelomys) in northern Venezuela. Models were produced using Maxent (a computer modeling program that utilizes the maximum-entropy principle), occurrence records from the literature, and 19 bioclimatic variables. First, I modeled each species in a large study region that included the ranges of both species (Method 1; typically employed in most studies to date). Second, I modeled each species in a smaller study region surrounding its respective localities, and then applied the model to the larger region (Method 2). Because the study region of Method 1 is likely to include areas of bioclimatically suitable habitat that are unoccupied by the species due to dispersal limitations and/or biotic interactions, this approach is prone to overfitting to conditions found near the known localities. In contrast, Method 2 is predicted to avoid such problems. I assessed differences in predictions for each species due to changes in the extent of the study region by calculating several measures of geographic interpredictivity between the species (indirect measures of niche overlap). Method 2 reduced problems 3 characteristic of overfitting. In addition, it led to higher—and likely more realistic— estimates of interpredictivity between the species, which suggests higher niche conservatism. Models of species' potential geographic distributions should be made using a study region that excludes areas of suitable conditions from which the species is known or likely to be absent because of dispersal limitations and/or biotic interactions.
THE USE OF INTERNET RESOURCES BY UNIVERSITY STUDENTS DURING THEIR COURSE PROJECTS ELICITATION: A CASE STUDY

Although technology is a very important, useful and indispensable part of the life, effective and appropriate usage of it still needs to be improved in education. This study presents the way university students gather their course project requirements during the project elicitation phase, how they access the Internet and other resources, and their trends in the literature review using the Internet. Furthermore, the diversity of Internet resources which are used for homework and course projects studies, and the reliability and accessibility of these resources have also been investigated and assessed. In addition, the results obtained from the study have been discussed and evaluated, the academic resources accessibility and usability in universities have been assessed and their importance discussed. The internet resources have been divided into sub categories according to their accessibility and reliability. Finally, the effects of reliable and limited accessible internet resources and unreliable and unlimited internet resources on university students’ out-of-class works and projects have been presented and discussed.
SEEDIS project: a summary overview of the Social, Economic, Environmental, Demographic Information System

Policy formulation, implementation, and management depend upon accurate, timely information. Analysts, decision makers, and managers need to locate, retrieve, combine, analyze and display information from a variety of sources. While time and resources usually do not permit collecting new information, there is a wealth of publicly available data (from government and other sources) that often could meet such needs if it were quickly and easily accessible. Although computers and machine-readable data have made it potentially easier to locate and analyze information, actual use of information from different sources is difficult because of differing storage formats, communications procedures, coding conventions, data structures and units of analysis. The SEEDIS Project addresses these information needs and problems through research, design, development, and demonstration of information system components. SEEDIS software provides a unified framework for data management, information retrieval, statistical analysis, and graphical display. Using SEEDIS, non-programmer users can efficiently access and manipulate large, diverse, and distributed statistical databases. The SEEDIS project works with selected applications specialists to test new concepts and tools using large real world databases, get feedbask about how information tools can be improved, and acquire and develop addtional databases.
How does sport affect mental health? An investigation into the relationship of leisure-time physical activity with depression and anxiety

Purpose. Physical activity (PA) can protect from developing mental disorders. Knowledge regarding optimal PA intensity, type, context, and amount for mental health is sparse. this study aims to determine the relationship between leisure-time PA and prevalence of depression and anxiety, and whether associations vary by PA amount, context, intensity, and type. Methods. Overall, 682 recreational athletes (age: M = 33.0 years; SD = 12.4; 53% female) completed a questionnaire on personal characteristics related to their leisure-time PA behaviour, the centre of Epidemiologic Studies Depression Scale (cES-D), and the Generalized Anxiety Disorder (GAD-7) scale. Effect sizes (cohen’s d, partial) and power analyses using G*Power were applied. Results. Individuals who met PA recommendations (n = 596) reported lower depression scores (M = 11.69; SD = 8.57) than those who did not meet the recommendations (n = 86; M = 12.88; SD = 8.73). Lowest depression and anxiety scores were observed for indoor team athletes (M = 10.75; SD = 7.66 and M = 2.94; SD = 3.03, respectively), followed by outdoor individual athletes (M = 11.17; SD = 9.41 and M = 3.39; SD = 3.50, respectively). Under specific conditions, high depression scores were noted for vigorous-intensity PA levels (M = 20.5; SD = 4.94). Conclusions. Meeting the WHO PA recommendations (moderate-intensity PA 150 min/week) reduces symptoms of depression and anxiety. However, no linear dose-response relation was observed. No differences were found between inand outdoor or team and individual sports but between sport disciplines. Prospective research to evaluate the reasons is needed.
Mob data sourcing

Crowdsourcing is an emerging paradigm that harnesses a mass of users to perform various types of tasks. We focus in this tutorial on a particular form of crowdsourcing, namely crowd (or mob) datasourcing whose goal is to obtain, aggregate or process data. We overview crowd datasourcing solutions in various contexts, explain the need for a principled solution, describe advances towards achieving such a solution, and highlight remaining gaps.
El Mayor‐Cucapah (Mw 7.2) earthquake: Early near‐field postseismic deformation from InSAR and GPS observations

El Mayor‐Cucapah earthquake occurred on 4 April 2010 in northeastern Baja California just south of the U.S.‐Mexico border. The earthquake ruptured several previously mapped faults, as well as some unidentified ones, including the Pescadores, Borrego, Paso Inferior and Paso Superior faults in the Sierra Cucapah, and the Indiviso fault in the Mexicali Valley and Colorado River Delta. We conducted several Global Positioning System (GPS) campaign surveys of preexisting and newly established benchmarks within 30 km of the earthquake rupture. Most of the benchmarks were occupied within days after the earthquake, allowing us to capture the very early postseismic transient motions. The GPS data show postseismic displacements in the same direction as the coseismic displacements; time series indicate a gradual decay in postseismic velocities with characteristic time scales of 66 ± 9 days and 20 ± 3 days, assuming exponential and logarithmic decay, respectively. We also analyzed interferometric synthetic aperture radar (InSAR) data from the Envisat and ALOS satellites. The main deformation features seen in the line‐of‐sight displacement maps indicate subsidence concentrated in the southern and northern parts of the main rupture, in particular at the Indiviso fault, at the Laguna Salada basin, and at the Paso Superior fault. We show that the near‐field GPS and InSAR observations over a time period of 5 months after the earthquake can be explained by a combination of afterslip, fault zone contraction, and a possible minor contribution of poroelastic rebound. Far‐field data require an additional mechanism, most likely viscoelastic relaxation in the ductile substrate.
Organizational Capabilities and Industry Dynamics: A Computational Model

In this paper we propose a model of bounded rational organizations that addresses the role of organizational capabilities in shaping firm size, growth rates and profitability. Our approach aims at reconciling the logic behind stochastic models of firm growth with the notion of organizational capabilities as drivers of economic performance. We extend the stochastic framework by incorporating behavioural assumptions on: (a) the interactions between the firm and the business environment; and (b) the mechanism by which firms sense and seize business opportunities. In our perspective, the degree of concurrence between the substance and organization of the firm and the context in which it operates will directly influence its profitability and indirectly (through costly mutations of the organizational structure) drive its growth. Despite its simple nature the model is able to capture well known regularities about industry dynamics. It generates firm size distributions that are skewed and heterogeneous across different scenarios. Moreover, our results suggest that the higher the selective power of the firm's organizational capabilities, the more the steady state distribution deviates from a log normal. Besides, the distribution of growth rates has a tent-shaped form which is consistent with the pattern described in empirical studies. The distribution of opportunities per firm is also skewed suggesting that a very few entities account for a large fraction of business opportunities arising throughout the simulation period. Finally, the interaction between the external environment and the internal structure of the firms also influences the heterogeneity in the value of the opportunities they capture.
Probabilistic Anatomic Mapping of Cerebral Blood Flow Distribution of the Middle Cerebral Artery

Probabilistic atlases are more representative of the population than single brain atlases. They allow anatomic and functional labeling of the results of group studies in stereotactic space and, hence, the automated anatomic labeling of individual brain imaging data. Methods: In the current study, probabilistic maps of the blood flow distribution of the middle cerebral artery (MCA) were developed using the basal and MCA brain SPECT images. Twenty-nine patients (mean age ± SD, 54.6 ± 6.1 y) who previously received placement of a stent for MCA stenosis (right MCA stenosis, 15 patients; left MCA stenosis, 14 patients) were included in the current study. Of the 29 MCA SPECT images, 18 were analyzed for the final result because 11 MCA SPECT images revealed an uneven uptake distribution of 99mTc-ethylcysteinate dimer in the brain. MCA brain SPECT images were coregistered to basal brain SPECT images, and spatial normalization parameters used for basal brain SPECT images were reapplied to MCA brain SPECT for anatomic standardization. Pixel counts of the MCA brain SPECT images were then normalized, and the probabilistic map of cerebral perfusion distribution (perfusion probabilistic map) for each hemisphere was determined by averaging the spatial- and count-normalized MCA brain SPECT images. Population-based probabilistic maps representing the extent of MCA territory (extent probabilistic map) were also composed by averaging the binary images obtained by thresholding the spatially normalized MCA brain SPECT images. Results: The blood supply from the MCA to the basal ganglia area was largest (probability, 0.6∼0.8), followed by the insular cortex (probability, 0.3∼0.5), and various cerebral cortical areas (probability, 0.2∼0.4). The MCA reached to deep structures of the brain, including the internal capsule, caudate nucleus, putamen, globus pallidus, insular cortex, and thalamus with a high-extent probability. Conclusion: A population-based probabilistic map of MCA flow distribution was generated by using MCA brain SPECT images. This map could be a potential tool for the analysis of major cerebral artery distribution, especially the MCA. Furthermore, the probabilistic MCA atlas could be used to define the object delineation of the MCA territory, to quantify ischemic disease affecting the MCA, to predict prognosis, and to risk stratification of cerebrovascular diseases, especially affecting the MCA.
An array processing approach to pilot decontamination for massive MIMO

We address the problem of pilot decontamination for massive MIMO systems for finite dimensional channels, wherein the channel vectors between the users and the base station are composed of a finite number of discrete paths. The pilot decontamination problem is addressed using high-resolution parametric spectral estimation methods, such as root multiple-signal classification (root-MUSIC), for resolving paths based on their angle-of-arrivals. The paths corresponding to the desired user are identified based on their amplitudes and then the paths are used to form the channel estimate. Our simulations show an improved performance in terms of bit-error rate compared to the existing approaches in certain critical scenarios.
A single-letter characterization of optimal noisy compressed sensing

Compressed sensing deals with the reconstruction of a high-dimensional signal from far fewer linear measurements, where the signal is known to admit a sparse representation in a certain linear space. The asymptotic scaling of the number of measurements needed for reconstruction as the dimension of the signal increases has been studied extensively. This work takes a fundamental perspective on the problem of inferring about individual elements of the sparse signal given the measurements, where the dimensions of the system become increasingly large. Using the replica method, the outcome of inferring about any fixed collection of signal elements is shown to be asymptotically decoupled, i.e., those elements become independent conditioned on the measurements. Furthermore, the problem of inferring about each signal element admits a single-letter characterization in the sense that the posterior distribution of the element, which is a sufficient statistic, becomes asymptotically identical to the posterior of inferring about the same element in scalar Gaussian noise. The result leads to simple characterization of all other elemental metrics of the compressed sensing problem, such as the mean squared error and the error probability for reconstructing the support set of the sparse signal. Finally, the single-letter characterization is rigorously justified in the special case of sparse measurement matrices where belief propagation becomes asymptotically optimal.
Long Run Health Impacts of Income Shocks: Wine and Phylloxera in 19th Century France

This paper provides estimates of the long-term effects on height and health of a large income shock experienced in early childhood. Phylloxera, an insect that attacks the roots of grape vines, destroyed 40% of French vineyards between 1863 and 1890, causing major income losses among wine growing families. Because the insects spread slowly from the southern coast of France to the rest of the country, Phylloxera affected different regions in different years. We exploit the regional variation in the timing of this shock to identify its effects. We examine the effects on the adult height, health, and life expectancy of children born in the years and regions affected by the Phylloxera. The shock decreased long run height, but it did not affect other dimensions of health, including life expectancy. We find that, at age 20, those born in affected regions were about 1.8 millimeters shorter than others. This estimate implies that children of wine-growing families born when the vines were affected in their regions were 0.6 to 0.9 centimeters shorter than others by age 20. This is a significant effect since average heights grew by only 2 centimeters in the entire 19th century. However, we find no other effect on health, including infant mortality, life expectancy, and morbidity by age 20.
Sparse Models for Adversarial Learning Submitted for Blind Review

As the use of prediction methods becomes more widespread in applications the chances of adversarial manipulation becomes more likely. The canonical example is email and web spam where there is a constant tussle. We propose classification models which are robust against data manipulation by adversaries. For example, spammers are constantly manipulating data to breach spam filters by either reverse engineering the feature set used in the classifier or carrying out randomized attacks against the classifier. Unlike previous game theoretic models, we define the adversary’s behaviour based on the assumption that at a given time a rational adversary can only manipulate a limited number of features. We name this behaviour of the adversary as ‘sparse feature attack’. Further we show that classifiers should use a sparse feature weight to fight against an adversary’s sparse feature attack. More realistically, we model the interaction between a classifier and an adversary as a repeated game. Experiments on benchmark data sets show that the classifier learnt from the game outperforms a corresponding standard model. To the best of our knowledge, this paper is the first attempt to use sparse modelling techniques for adversarial learning.
Debating China's Exchange Rate Policy

More than two and a half years have passed since China announced a number of changes to its foreign exchange regime in July 2005. During this period, the debate on the pros and cons of China's exchange rate policy, which had begun in earnest several years earlier, intensified. This important new book, based on an Institute conference in October 2007, takes stock of exchange rate policy in China and identifies the major policy options going forward. Specific proposals presented in the volume address how best to eliminate any misalignment of the renminbi; how best to reduce pressures emanating from the sterilization of large reserve accumulation; how best to make capital flows the ally--not the enemy--of exchange rate policy; and what institutional arrangements and policy guidelines to put in place to reap the greatest benefits from management of China's large foreign exchange reserves. Leading experts--including three from China--have contributed to the volume. The keynote address by Wu Xiaoling, deputy governor to the People's Bank of China at the time of the conference, is also presented in the book.
The Johns Hopkins University 2003 Chinese-English machine translation system

We describe a Chinese to English Machine Translation system developed at the Johns Hopkins University for the NIST 2003 MT evaluation. The system is based on a Weighted Finite State Transducer implementation of the alignment template translation model for statistical machine translation. The baseline MT system was trained using 100,000 sentence pairs selected from a static bitext training collection. Information retrieval techniques were then used to create specific training collections for each document to be translated. This document-specific training set included bitext and name entities that were then added to the baseline system by augmenting the library of alignment templates. We report translation performance of baseline and IR-based systems on two NIST MT evaluation test sets.
A New Mechanism for THz Detection Based on the Tunneling Effect in Bi-Layer Graphene Nanoribbons

A new possible mechanism of signal detection in the THz range is investigated, based on the excitation of resonances due to the tunneling effect between two graphene nanoribbons. A simple detector is proposed, where two graphene nanoribbons are used to contact two copper electrodes. The terminal voltages are shown to exhibit strong resonances when the frequency of an external impinging field is tuned to the characteristic tunneling frequency of the graphene layer pair. An electrodynamic model for the electron transport along the graphene nanoribbons is extended here to include the tunneling effect, and a coupled transmission line model is finally derived. This model is able to predict not only the tunneling resonance, but also the well-known plasmon resonances, related to the propagation of slow surface waves.
SHOE: A SPARQL Query Engine Using MapReduce

As the reasoning aspects and the knowledge based processing capabilities of RDF (Resource Description Framework) have been widely adopted in W3C Recommendation, the ontology layer and query languages of the Semantic Web stack achieve a certain level of maturity. There exists an increasing need for high performance, read-only semantic analysis for the massive RDF data. In this demo we will present a Map-Reduce based SPARQL processing engine, called SHOE (SPARQL on Hadoop with Optimization Encoding), to handle billions of RDF triples. SHOE consists of three major components (1) the RDF data loader, (2) the partition generator and (3) the query processor. While this demonstration mainly focuses on enhancing SPARQL processing in the Hadoop platform, the underlying encoding and partitioning optimization strategies can be utilized by the common Map-Reduce frameworks in the share-nothing environment.
An efficient strategy for high‐throughput expression screening of recombinant integral membrane proteins

The recombinant expression of integral membrane proteins is considered a major challenge, and together with the crystallization step, the major hurdle toward routine structure determination of membrane proteins. Basic methodologies for high‐throughput (HTP) expression optimization of soluble proteins have recently emerged, providing statistically significant success rates for producing such proteins. Experimental procedures for handling integral membrane proteins are generally more challenging, and there have been no previous comprehensive reports of HTP technology for membrane protein production.
Fast image inpainting using similarity of subspace method

Image inpainting is a technique for estimating missing pixel values in an image by using the pixel value information obtained from neighbor pixels of a missing pixel or the prior knowledge derived from learning the object class. In this paper, we propose a fast and accurate image inpainting method using similarity of the subspace. The proposed method generates the subspace from many images related to the object class in the learning step and estimates the missing pixel values of the input image belonging to the same object class so as to maximize the similarity between the input image and the subspace in the inpainting step. Through a set of experiments, we demonstrate that the proposed method exhibits excellent performance in terms of both inpainting accuracy and computation time compared with conventional algorithms.