Miracle-dz/UniFAR-FTUs huggingface dataset:
Regional Industrial Structure Concentration in the United States: Trends and Implications

Abstract In a seminal article, Chinitz (1961) considered the effects of industry size, structure, and economic diversification on the performance of firms and regional economies. His inquiry suggested a related but conceptually distinct issue: how does the extent to which a regional industry or industrial sector is concentrated in a small number of firms affect the local performance of that industry? The question has not been addressed systematically in empirical research other than case studies, principally because accurately measuring regional concentration requires firm-level information. This exploratory study uses confidential plant-level data to gauge concentration in manufacturing industries at the regional scale across the continental United States, to explore changes over time in geographic patterns of concentration, and to investigate associations between regional industrial structure concentration and changes in employment. The implications for understanding the impacts of regional industrial structure on economic development processes are discussed.
Indigenous people ’ s perception on climate change and adaptation strategies in Jema ’ a local government area of Kaduna State , Nigeria

This study examines the way indigenous people in Jema’a Local Government Area of Kaduna State perceive climate change and their adaptation strategies to climate change. The paper also asks indigenous people of the impacts of climate change on their various activities and any perceive hindrance to its adaptation. A total of 225 questionnaires were administered in five settlements within the study area, although only 200 of these questionnaires were used for the purpose of analysis. Findings revealed that indigenous people in the study area perceived that the environment, climate in particular, has been changing over the years due to diverse human activities. Findings also revealed that the threat of climate change is more on health, food supply, biodiversity lost and fuelwood availability than on businesses and instigating of disaster; and it is the poor, who depend heavily on the natural resources that are mostly affected by incidence of climate change. In adapting to climate change indigenous people cultivate different/varieties of crops which are tolerant to climate change and shortening of growing season as adaptation strategies. Result further revealed that lack of improve seeds, lack of assess to water for irrigation, lack of current knowledge of modern adaptation strategies, lack of capital, lack of awareness and knowledge of climate change scenarios are the hindering factors to the adoption of modern techniques of combating climate changes in the area.
A new method of translational compensation for space spinning target based on EMD

As the micro-Doppler frequency will be added to the Doppler frequency caused by space target translational movement, which brings difficulty to the micro-Doppler characters extraction, a new method to realize accurate translational movement compensation based on empirical model decomposition (EMD) is proposed. The effect on micro-Doppler feature extraction induced by space object trajectory movement is analyzed. The trajectory motion compensation of echoes is realized and the micro-Doppler feature is extracted. Simulations are given to validate the effectiveness of the proposed algorithm.
Mobile cloud computing based energy efficient offloading strategies for femtocell network

Femtocell is a low power base station used to expand the capacity of mobile network. However when hundreds or thousands of femtocells operate together the total power consumption of the mobile network increases considerably. Mobile cloud computing is a technology that reduces the power consumption of mobile devices as well the mobile network. In this paper mobile cloud computing based offloading strategies are proposed for significant energy saving of a femtocell device. Three cases are presented where either data processing, or data encryption and hardware authentication or both are offloaded to the cloud. The power consumption models of the femtocell in these cases are demonstrated in this paper. Simulation results present that the proposed cases reduce the power consumption of the femtocell from 12.27-15.46%, 4.09-5.15% and 8.18-10.31% respectively.
Rural Minnesota Journal- Scheffert Article

The Center for Rural Policy and Development, based in St. Peter, Minn. , is a private, not-for-profit policy research organization dedicated to benefiting Minnesota by providing its policy makers with an unbiased evaluation of issues from a rural perspective. Any opinions voiced in the Journal are those solely of the authors and not necessarily of the Center. The Center for Rural Policy & Development gratefully acknowledges our friends who have made RMJ possible.
Property Company Stock Price and Net Asset Value: A Mean Reversion Perspective

This study investigates the relationship between property company stock prices (P) and their net asset values (NAV) from a mean reversion perspective. In contrast to U.K. evidence, we find that there is absence of a long-term stable relationship between the two series. However, the variance ratio tests and multi-period regressions suggest that both P and NAV series have exhibited transitory components. In addition, there is some evidence of mean reversion behavior of Singapore property stock prices toward the property companies' NAVs over the past 15 years from 1985 to 1999, both at individual company level and in the sector as a whole. The results also reveal that NAV, as a traditional proxy to fundamental value, is significant in capturing the dynamics of the changes in property stock prices. Hence NAV is relevant in property company valuation. However the extent of mean reversion between the property stock prices and NAVs is slow and deviations between the two markets' valuation could therefore be prolonged.
Environment characterization using Laplace eigenvalues

This paper introduces a new methodology for environment characterization. This methodology is based on analysis of the eigenvalues of Laplace-Beltrami operator over 3 dimensional point clouds. Recognizing revisited places can be facilitated by characterizing the environment through a descriptor. The idea of analyzing point clouds using the eigenvalues of Laplace-Beltrami operator for characterization of an environment can be used for place detection which is a critical functionality of autonomous mobile robots. Place detection is a requirement for transition detection in multi environment missions, common frame identification in multi robot mapping, and detection of previously visited location in SLAM for loop closure phase.
An introduction to framework of security event capturing and tracking

Nowadays, microblogs provide abundant text data. Mining valuable information from it becomes an interesting topic. Based on the topic model, things can be easily traced. The complete view of an event can help people make decisions. Once we can track topics in a specific domain, experts can rapidly react according to important events. In this paper, we proposed a LDA [2] based framework to capture emergent events in the information security domain from Twitter data. Recently, different types of malwares, bugs and phishing attacks are detected on the internet. Predicting the novel attacking technique can help users to prevent infected. According to our system, we can track an event to realize when it starts, how it changes and the event vanishes. The real-time intelligence we extract makes not only users but also service providers rapidly react. Different from state-of-art topic models, we focus on topic changement with time pass.
Phagocyte NADPH Oxidase, but Not Inducible Nitric Oxide Synthase, Is Essential for Early Control of Burkholderia cepacia and Chromobacterium violaceum Infection in Mice

ABSTRACT Reactive oxygen and nitrogen intermediates have critical, partially overlapping roles in host defense against a variety of pathogens. Using mice deficient in generating phagocyte superoxide (p47phox−/−) and mice deficient in generating inducible nitric oxide synthase (iNOS−/−), we examined the roles of these reactive species in host defense against Burkholderia cepacia and Chromobacterium violaceum, organisms known to have unusual virulence in chronic granulomatous disease. Intraperitoneal B. cepacia challenge (4.0 × 103 to 4.0 × 105 organisms/mouse) resulted in mortality in all p47phox−/− mice, with the survival interval being inversely proportionate to the amount of inoculum. Pretreatment with gamma interferon did not affect survival. C. violaceum was strikingly virulent in p47phox−/− mice (the 50% lethal dose [LD50] was <13 organisms). iNOS−/− and wild-type mice were resistant to B. cepacia challenges of at least 106 organisms per mouse, and the LD50 of C. violaceum was between 106 and 107 organisms per mouse. Consistent with the survival data, numbers of organisms in cultures of B. cepacia from multiple sites were higher for p47phox−/− mice than for iNOS−/− and wild-type mice at day 4 after challenge, but numbers of organisms for different B. cepacia strains varied. The recovery of C. violaceum was strikingly greater at 18 h after challenge for p47phox−/− mice than for iNOS−/− and wild-type mice, in which the organism burdens were virtually nil. In vitro, both B. cepacia and C. violaceum were sensitive to H2O2 and to reactive nitrogen intermediates but the sensitivities of different strains varied significantly. Host defense against B. cepacia and C. violaceum is critically dependent in vivo on reactive oxygen intermediates, and these species are model organisms to further dissect host and pathogen interactions related to the generation and scavenging of microbicidal reactive intermediates.
Mean-Squared Error Analysis of Kernel Regression Estimator for Time Series

Because of a lack of a priori information, the minimum mean-squared error predictor, the conditional expectation, is often not known for a non-Gaussian time series. We show that the nonparametric kernel regression estimator of the conditional expectation is mean-squared consistent for a time series: When used as a predictor, the estimator asymptotically matches the mean-squared error produced by the true conditional expectation. We also describe a more computationally eecient predictor based on the recursive kernel regression estimator, and show it can asymptotically achieve mean-squared errors arbitrarily close to the conditional expectation. Numerical examples are provided to demonstrate the eeectiveness of nonparametric prediction.
Regulation of Alternative Splicing: More than Just the ABCs*

Alternative pre-mRNA splicing, the differential inclusion or exclusion of portions of a nascent transcript into the final protein-coding mRNA, is widely recognized to be a ubiquitous mechanism for controlling protein expression. Thus, understanding the molecular basis of alternative splicing is essential for deciphering post-transcriptional control of the genome. Pre-mRNA splicing in general is catalyzed by a large dynamic macromolecular machine known as the spliceosome. Notably, the recognition of the intron substrate by spliceosomal components and the assembly of these components to form a catalytic spliceosome occur through a network of highly combinatorial molecular interactions. Many, if not all, of these interactions are subject to regulation, forming the basis of alternative splicing. This minireview focuses on recent advances in our understanding of the diversity of mechanisms by which the spliceosome can be regulated so as to achieve precise control of alternative splicing under a range of cellular conditions.
Monetary Policy and Housing Prices: A Case Study of Chinese Experience in 1999-2010

How do monetary policy variables affect housing prices? In this paper we apply a non-linear modelling approach, the Nonlinear Auto Regressive Moving Average with eXogenous inputs (NAR-MAX), to investigate determinants of housing prices in China over the period 1999:01 to 2010:06. The NARMAX approach has an advantage over prevailing methods in that it automatically selects linear and non-linear forms of variables and the numbers of corresponding lags according to statistical properties. Both linear and non-linear estimation results identify a number of key monetary and price variables, including most notably mortgage rate, producer price, broad money supply and real effective exchange rate. Meanwhile, some key real economic variables such as income are not independently significant. Our findings should be helpful in understanding the formation of housing prices in China and will provide some valuable insights on how to use monetary policies to manage asset prices.
Monitoring activity in neural circuits with genetically encoded indicators

Recent developments in genetically encoded indicators of neural activity (GINAs) have greatly advanced the field of systems neuroscience. As they are encoded by DNA, GINAs can be targeted to genetically defined cellular populations. Combined with fluorescence microscopy, most notably multi-photon imaging, GINAs allow chronic simultaneous optical recordings from large populations of neurons or glial cells in awake, behaving mammals, particularly rodents. This large-scale recording of neural activity at multiple temporal and spatial scales has greatly advanced our understanding of the dynamics of neural circuitry underlying behavior—a critical first step toward understanding the complexities of brain function, such as sensorimotor integration and learning. Here, we summarize the recent development and applications of the major classes of GINAs. In particular, we take an in-depth look at the design of available GINA families with a particular focus on genetically encoded calcium indicators (GCaMPs), sensors probing synaptic activity, and genetically encoded voltage indicators. Using the family of the GCaMP as an example, we review established sensor optimization pipelines. We also discuss practical considerations for end users of GINAs about experimental methods including approaches for gene delivery, imaging system requirements, and data analysis techniques. With the growing toolbox of GINAs and with new microscopy techniques pushing beyond their current limits, the age of light can finally achieve the goal of broad and dense sampling of neuronal activity across time and brain structures to obtain a dynamic picture of brain function.
An Empirical Analysis of User Content Generation and Usage Behavior in Mobile Digital Media

Different kinds of user-generated content are becoming available in mobile digital and web media settings as well spurred by the rapid advances in the cellular telephony market. We use a unique dataset of individual-level mobile records to study the nature of interdependencies between users’ content generation and content usage behavior. While theories of resource constraints from economics posit the existence of a negative interdependency between content generation and usage, theories of social exchange from sociology posit the existence of a positive interdependency between these two activities. Hence, the final directional nature of this interdependency is an empirical question. We find that there exist negative inter-temporal interdependencies between the content generation and usage behavior. The effect is also asymmetric such that the negative impact of previous period’s content generation on current period’s content usage is higher than vice-versa. Managerial implications for discounting in data transmission prices are discussed.
A Three-Stage Colonization Model for the Peopling of the Americas

Background We evaluate the process by which the Americas were originally colonized and propose a three-stage model that integrates current genetic, archaeological, geological, and paleoecological data. Specifically, we analyze mitochondrial and nuclear genetic data by using complementary coalescent models of demographic history and incorporating non-genetic data to enhance the anthropological relevance of the analysis. Methodology/Findings Bayesian skyline plots, which provide dynamic representations of population size changes over time, indicate that Amerinds went through two stages of growth ≈40,000 and ≈15,000 years ago separated by a long period of population stability. Isolation-with-migration coalescent analyses, which utilize data from sister populations to estimate a divergence date and founder population sizes, suggest an Amerind population expansion starting ≈15,000 years ago. Conclusions/Significance These results support a model for the peopling of the New World in which Amerind ancestors diverged from the Asian gene pool prior to 40,000 years ago and experienced a gradual population expansion as they moved into Beringia. After a long period of little change in population size in greater Beringia, Amerinds rapidly expanded into the Americas ≈15,000 years ago either through an interior ice-free corridor or along the coast. This rapid colonization of the New World was achieved by a founder group with an effective population size of ≈1,000–5,400 individuals. Our model presents a detailed scenario for the timing and scale of the initial migration to the Americas, substantially refines the estimate of New World founders, and provides a unified theory for testing with future datasets and analytic methods.
Automatic Learning of Semantic Region Models for Event Recognition

The semantic structure of scene is important information used for interpretation of object behavior or event detection in video surveillance system. In this paper, we propose an automatic method for learning models of semantic region by analyzing the trajectories of moving objects in the scene. First, the trajectory is encoded to represent both the position of the object and its instantaneous velocity. Then, the hierarchical clustering algorithm is applied to cluster the trajectories according to different spatial and velocity distributions. In each cluster, trajectories are spatially close, have similar velocities of motion and represent one type of activity pattern. Based on the trajectory clusters, the statistical models of semantic region in the scene are generated by estimating the density and velocity distributions of each type of activity pattern. Finally, using the proposed semantic region models, anomalous activities are detected in two scenes. Experimental results demonstrate the effectiveness of the proposed method.
Ultra-wideband truncated rectangular monopole antenna with band-notched characteristics

Design and performance of an ultra-wide band truncated rectangular monopole antenna with band-notched characteristics is presented in this paper. In the proposed antenna, split ring resonator (SRR) is etched inside the truncated rectangular patch of the monopole antenna to achieve band-notch operation. The achieved impedance bandwidth of antenna is from 2.43 GHz to 13.30 GHz, for VSWR <; 2. With the insertion of SRR in truncated rectangular patch band-notched characteristic from 5.04 GHz to 6.09 GHz (VSWR >; 2) suitable for IEEE 802.11a WLAN band (5.15 - 5.35 GHz and 5.725 - 5.875 GHz) and HIPERLAN/2 band (5.45 - 5.725 GHz) is achieved. The proposed antenna shows average antenna gain of 4.35 dBi with a variation of ± 1.7 dBi over the impedance bandwidth. In the notched band, the antenna gain falls down to 3.45 dBi as compared to truncated rectangular monopole antenna gain of 5.65 dBi.
Multiple universes, cosmic coincidences, and other dark matters

Even when completely and consistently formulated, a fundamental theory of physics and cosmological boundary conditions may not give unambiguous and unique predictions for the universe we observe; indeed inflation, string/M theory, and quantum cosmology all arguably suggest that we can observe only one member of an ensemble with diverse properties. How, then, can such theories be tested? It has been variously asserted that in a future measurement we should observe the a priori most probable set of predicted properties (the ‘bottom-up’ approach), or the most probable set compatible with all current observations (the ‘top-down’ approach), or the most probable set consistent with the existence of observers (the ‘anthropic’ approach). These inhabit a spectrum of levels of conditionalization and can lead to qualitatively different predictions. For example, in a context in which the densities of various species of dark matter vary among members of an ensemble of otherwise similar regions, from the top-down or anthropic viewpoints—but not the bottom-up—it would be natural for us to observe multiple types of dark matter with similar contributions to the observed dark matter density. In the anthropic approach it is also possible in principle to strengthen this argument and the limit the number of likely dark matter sub-components. In both cases the argument may be extendible to dark energy or primordial density perturbations. This implies that the anthropic approach to cosmology, introduced in part to explain ‘coincidences’ between unrelated constituents of our universe, predicts that more, as-yet-unobserved coincidences should come to light.
Transcriptional activation by p53 of the human type IV collagenase (gelatinase A or matrix metalloproteinase 2) promoter

p53, a tumor suppressor and a transcription factor, has been shown to transcriptionally activate the expression of a number of important genes involved in the regulation of cell growth, DNA damage, angiogenesis, and apoptosis. In a computer search for other potential p53 target genes, we identified a perfect p53 binding site in the promoter of the human type IV collagenase (also called 72-kDa gelatinase or matrix metalloproteinase 2 [MMP-2]) gene. This p53 binding site was found to specifically bind to p53 protein in a gel shift assay. Transcription assays with luciferase reporters driven by the promoter or enhancer of the type IV collagenase gene revealed that (i) activation of the promoter activity is p53 binding site dependent in p53-positive cells but not in p53-negative cells and (ii) wild-type p53, but not p53 mutants commonly found in human cancers, transactivates luciferase expression driven by the type IV collagenase promoter as well as by a p53 site-containing enhancer element in the promoter. Significantly, expression of the endogenous type IV collagenase is also under the control of p53. Treatment of U2-OS cells, a wild-type p53-containing osteogenic sarcoma line, with a common p53 inducer, etoposide, induced p53 DNA binding and transactivation activities in a time-dependent manner. Induction of type IV collagenase expression followed the p53 activation pattern. No induction of type IV collagenase expression can be detected under the same experimental conditions in p53-negative Saos-2 cells. All these in vitro and in vivo assays strongly suggest that the type IV collagenase gene is a p53 target gene and that its expression is subject to p53 regulation. Our finding links p53 to a member of the MMP genes, a family of genes implicated in trophoblast implantation, wound healing, angiogenesis, arthritis, and tumor cell invasion. p53 may regulate these processes by upregulating expression of type IV collagenase.
Regulating the Media

The operation of the media, particularly bodies associated with news and political comment, is a controversial issue which has provoked much discussion on the practice and standards of journalists and broadcasters. This study is concerned with the role of the law in shaping the practices and standards of the media, and its relationship with informal methods of deciding and implementing regulatory policies. The author draws on economic and social science sources to identify criteria for evaluating these regulations. The text also discusses the relationship between commercial and constitutional objectives and priorities, the nature of balanced and impartial communication and the role of the state in influencing the media.