Miracle-dz/UniFAR-FTUs huggingface dataset:
Validation and research of the Three-Process Model of Alertness regulation predictions

Purpose: Collecting and analyzing experimental data with the method of subjective scale. Established trend curve of alertness versus time and the Three-Process Model of Alertness regulation with specific population. Method: tracking the life of 12 subjects 3 days, getting the alertness data and the trend of alertness level, then comparing and verifying it with the trend of the Three-Process Model of Alertness regulation predictions. Conclusion: After comparing the model predictions data with experimental data, it was found that the model prediction results and experimental results have a strong correlation proved that and the model is valid. The trend of the model alertness level predictions and experiment alertness is similar. But there are some difference between alertness level peak and the speed of development. The Three-Process Model of Alertness regulation could be improved in adaptive to different specific population.
Comparison and Study of Classic Feature Point Detection Algorithm

Detection base on feature points contains the characteristics of the whole image, this method is widely used in the field of computer vision. Several popular feature points detection algorithms are discussed, including SIFT feature points detection method and the corner detection methods like Forstner, Harris and SUSAN. In this paper, SIFT, Forstner, Harris and SUSAN are compared by a number of experiments that the invariance to scale, rotation and illumination and the anti-noise ability to Gaussian. We can compare the resules of feature point extraction and analysis of the stability and anti-noise ability of the feature point extraction algorithm on image.
Reporting Islam: Media Representations of British Muslims

It is a widely held view that, since the end of the Cold War, attention has focused on Islam as a central force of disruption within a New World Order. Nothing has heightened this more than the assault on the World Trade Center and Pentagon, and the reporting of these events. Reporting Islam is a timely contribution to this debate which looks at the ways in which Muslims are represented in the British news media. Elizabeth Poole examines the claim that Muslims are universally demonized in the British press and comes to some illuminating conclusions. Reporting Islam is of great value to students of media, cultural studies, Islamic studies and politics.
Direct sequence ultra wideband system design for wireless sensor network

In this study, we present the suitability of UWB for WSN by simulating a DS-UWB digital transmission system using Matlab. DS-UWB is a single-band approach that uses narrow UWB pulses and time-domain signal processing combined with direct sequence spread spectrum (DSSS) techniques to transmit and receive information. Due to signal spreading, transmitted power becomes very low which paves to low probability of detection and co-existence with other systems. Simple lower order modulation scheme is considered such as binary PSK, PAM due to low data rate requirements of WSN. Bit error rate (BER) performance has been evaluated for both modulation technique and simulation results show that the pulse repetition coder has significant impact on performance as well as control on data rates and reliable reception. Data is successfully recovered by energy detection method (Detect and Avoid) which facilitates design simplicity at the receiver by avoiding pulse synchronization and coherent detection.
Role of miR-34a-5p in Hematopoietic Progenitor Cells Proliferation and Fate Decision: Novel Insights into the Pathogenesis of Primary Myelofibrosis

Primary Myelofibrosis (PMF) is a chronic Philadelphia-negative myeloproliferative neoplasm characterized by a skewed megakaryopoiesis and an overproduction of proinflammatory and profibrotic mediators that lead to the development of bone marrow (BM) fibrosis. Since we recently uncovered the upregulation of miR-34a-5p in PMF CD34+ hematopoietic progenitor cells (HPCs), in order to elucidate its role in PMF pathogenesis here we unravelled the effects of miR-34a-5p overexpression in HPCs. We showed that enforced expression of miR-34a-5p partially constrains proliferation and favours the megakaryocyte and monocyte/macrophage commitment of HPCs. Interestingly, we identified lymphoid enhancer-binding factor 1 (LEF1) and nuclear receptor subfamily 4, group A, member 2 (NR4A2) transcripts as miR-34a-5p-targets downregulated after miR-34a-5p overexpression in HPCs as well as in PMF CD34+ cells. Remarkably, the knockdown of NR4A2 in HPCs mimicked the antiproliferative effects of miR-34a-5p overexpression, while the silencing of LEF1 phenocopied the effects of miR-34a-5p overexpression on HPCs lineage choice, by favouring the megakaryocyte and monocyte/macrophage commitment. Collectively our data unravel the role of miR-34a-5p in HPCs fate decision and suggest that the increased expression of miR-34a-5p in PMF HPCs could be important for the skewing of megakaryopoiesis and the production of monocytes, that are key players in BM fibrosis in PMF patients.
Fast time delay neural networks for word detection in video conference

This paper presents a new approach to speed up the operation of time delay neural networks for fast detecting a word in a video conference. The entire data are collected together in a long vector and then tested as a one input pattern. The proposed fast time delay neural networks (FTDNNs) use cross correlation in the frequency domain between the tested data and the input weights of neural networks. It is proved mathematically and practically that the number of computation steps required for the presented time delay neural networks is less than that needed by conventional time delay neural networks (CTDNNs). Simulation results using MATLAB confirm the theoretical computations.
Are They Actually Any Different? Comparing Thousands of Financial Institutions’ Privacy Practices

Although large-scale comparisons of privacy practices across an industry have the potential to illuminate the state of consumer privacy and to uncover egregious practices, the freeform legalese of most privacy policies makes such comparisons time-consuming and expensive. Financial institutions in the United States are required by the Gramm-Leach-Bliley Act to provide annual privacy disclosures. In 2009, eight federal agencies jointly released a model privacy form for these disclosures. While use of the model privacy form is not required, it has been widely adopted. With so many ﬁ-nancial institutions’ policies available in a standard format, large-scale comparisons are now more readily achievable. We built an automated web crawler and document parser for the model privacy form and automatically evaluated thousands of ﬁnancial institutions’ disclosures. We found large variance in data-sharing practices, even among banks of the same class. While thousands of ﬁnancial institutions share personal information without providing the opportunity for consumers to opt out, some institutions’ practices are more consumer-friendly. Institutions’ practices vary by region and by the size of the institution. Furthermore, we uncovered violations of ﬁnancial regulation, such as failing to allow consumers to limit data sharing even when required to do so. We identify issues with the design and use of the model privacy form, ranging from poorly designed categories to institutions making self-contradictory statements. We discuss implications for privacy in the ﬁnancial industry, as well as future directions for standardized privacy notices.
Money As An Inflation Indicator In Chile: Does P* Still Work?

This paper analyses the information content of monetary aggregates for inflation in Chile. In particular, we adopt the P* framework that separates the effect of an estimated money overhang from those of the output gap. We use two variants of the model, the original Hallman et al (1991), and Gerlach and Svensson (2003), that conditions on an inflation target. We estimate both models for 6 different monetary aggregates, and 2 alternative estimates of equilibrium velocity. We find that over the estimation period, deviations of velocity from its equilibrium have significant effects on inflation, across models and definitions of the money gap, and for both narrow and broad money. The usual Chilean aggregate M1A, although it has some indicator properties, is outperformed by other money aggregates, first of all cash in the hands of the public, and a broad money aggregate containing time and foreign currency deposits. However, out-of sample forecasts show that over the recent past, most money gaps do not improve inflation forecasts. Also, inflation forecasts from broad and narrow money aggregates diverge in opposite directions in recent years, reflecting the estimated gaps that are large both for M1A and broader definitions of money, but opposite in sign. This finding puts a question mark behind the stability of money demand in recent times of stable and low inflation.
Projective structures on a Riemann surface

For a compact Riemann surface $X$ of any genus $g$, let $L$denote the line bundle $K_{X\times X}\otimes {\cal O}_{X\times X}(2\Delta)$ on $X\times X$, where $K_{X\times X}$ is the canonical bundle of $X\times X$ and $\Delta$ is the diagonal divisor. We show that $L$ has a canonical trivialisation over the nonreduced divisor $2\Delta$. Our main result is that the space of projective structures on $X$ is canonically identified with the space of all trivialisations of $L$ over $3\Delta$ which restrict to the canonical trivialisation of $L$ over $2\Delta$ mentioned above. We give a direct identification of this definition of a projective structure with a definition of Deligne.We also describe briefly the origin of this work in the study of the so-called "Sugawara form" of the energy-momentum tensor in a conformal quantum field theory.
A perception evolution network for unsupervised fast incremental learning

A perception evolution network (PEN) is proposed for unsupervised fast incremental (or on-line) learning in this paper. The network has two layers: the perception layer receives the external data from the reality environment and the knowledge layer learns and records the knowledge contained in the data from the perception layer in incremental and self-organizing way. In PEN model, new input channels can be added to the perception layer freely during learning. When the perception layer gets some new input channels, the network will create corresponding data transmission channels to the knowledge layer. The prior learned knowledge stored in the knowledge layer will be expanded to a higher-dimensional space which contains the attributes of the new input channels. For incremental (or on-line) learning, PEN can automatically obtain suitable prototypes and find the topology structure of the learning data without any priori knowledge. The noise processing mechanism guarantees PEN can work in the complex real-world (noisy) environment. The experiments for both artificial data set and real-world data set show that the proposed method is effective.
Deforesting Lf

This grand aspiration subsumes the more modest research goal of simply characterizing the knowledge that people have of their native languages. Given this overlap in aims, it makes sense to leverage whatever is known about language itself in the pursuit of computational models of language use. Indeed, in confronting such a daunting challenge it would be foolish to waste intellectual resources doing the same job twice. Since linguistics, as a field, includes a large community whose professional goal is the formal specification of knowledge of language, a sensible methodology would somehow include the results of linguists’ efforts — grammars — in model language users. This idea, known as the Competence Hypothesis dates back to the earliest days of generative grammar.
REAL-TIME VISUALIZATION OF GEOSPATIAL FEATURES THROUGH THE INTEGRATION OF GIS WITH A REALISTIC 3D TERRAIN DYNAMIC VISUALIZATION SYSTEM

During the last years, several computational tools arose allowing the users to fly interactively over realistic simulations of the terrain using DTM, aerial pictures and satellite data. One of them, the Advanced Interactive Terrain Navigation System (SANTI) was developed by the authors. This system stood out because of its submetric resolution, real-time refresh rate over 60 fps and performance non-dependent of the size of the database to be displayed. After this experience, the authors opened a new line of research that integrates this dynamic visualization system with GIS. This article presents an open architecture that easily integrates any GIS with SANTI, taking advantage of the potentiality of both technologies. This way, it is possible to visualize geospatial features while flying over the realistic model of the land for a better understanding and analysis of the data. It is also possible to query the GIS database from the visualization interface, interactively selecting features within the 3D view of the terrain. We describe an application using this software architecture for regional planning and resource management.
EuCRUST‐07: A new reference model for the European crust

We present a new digital model (EuCRUST‐07) for the crust of Western and Central Europe and surroundings (35°N–71°N, 25°W–35°E). Available results of seismic reflection, refraction and receiver functions studies are assembled in an integrated model at a uniform grid (15′ × 15′). The model consists of three layers: sediments and two layers of the crystalline crust. Besides depth to the boundaries, we provide average P‐wave velocities in the upper and lower parts of the crystalline crust. The new model demonstrates large differences in the Moho depth compared to previous compilations, over ±10 km in some specific areas (e.g. the Baltic Shield). Furthermore, the velocity structure of the crust is much more heterogeneous than in previous maps. EuCRUST‐07 offers a starting point for numerical modeling of deeper structures by allowing correction for crustal effects beforehand and to resolve trade‐off with mantle heterogeneities.
Relevant statistics for Bayesian model choice

The choice of the summary statistics that are used in Bayesian inference and in particular in approximate Bayesian computation algorithms has bearings on the validation of the resulting inference. Those statistics are nonetheless customarily used in approximate Bayesian computation algorithms without consistency checks. We derive necessary and sufficient conditions on summary statistics for the corresponding Bayes factor to be convergent, namely to select the true model asymptotically. Those conditions, which amount to the expectations of the summary statistics differing asymptotically under the two models, are quite natural and can be exploited in approximate Bayesian computation settings to infer whether or not a choice of summary statistics is appropriate, via a Monte Carlo validation.
Organizational survival in the new world : the intelligent complex adaptive system : a new theory of the firm

Moving Beyond the Bureaucratic Model The Present and Future Danger or Why We Need To Change The Intelligent Complex Adaptive System (ICAS) Exploring the Emergent Properties of ICAS Relationships Among Emergent Properties The Learning Structure of the New Organization The Action Culture for Success The Four Major Organizational Processes The Art of Collaborative Leadership Creating Emergence The Change Agent's Strategy Strategy, Balance and the Correlation of Forces A Tale of Two Companies The New Knowledge Worker Knowledge Management The Learning Organization Learning, KM and Knowledge Workers Rethinking Thinking: Systems Rethinking Thinking: Complexity Knowing Networking for the Bottom Line Exploring the Unknown APPENDIX: The Evolution of the Organization. GLOSSARY BIBLIOGRAPHY INDEX
A Probability-Based Approach to Attack Graphs Generation

Attack graphs are important tools for analyzing network security vulnerabilities. Recently, the generation method of attack graphs is a hot topic to the security researchers. As previous works encounter the scalability problem and inaccurate input information problem, we propose a novel method to automatic construction of attack graphs based on probability. After introducing prior-probability, match-probability,and transition-probability into attack graphs generation process, we develop a new attack model and relevant generation algorithms. Our method uses threshold and key states to control the scale of result attack graphs with important attack paths reserved. The following experiments show our approach could get meaningful results with less time and space, especially when one wants to get a few shortest attack paths quickly.
Characteristics of Poverty In Pakistan

Poverty is a multidimensional conception and usually it is defined by focusing narrowly on income poverty or broadly by including lack of access to opportunities for raising standards of living. Strategies aimed at poverty reduction need to identify factors that are strongly associated with poverty and agreeable to modification by policy. This study uses integrated Household Survey (2009-10) data collected by Federal Bureau of Statistics Pakistan to examine probable determinants of poverty status, employing bivariate models. In general, this study tries to seek in depth knowledge of the key factors like demographic factors and human capital variables that account for poverty differentials in Pakistan. The demographic variables show significant impact on poverty status of the household, especially dependency ratio, sex of the head of the household, family type and household size. These all are found to be of supreme important in defining poverty. The educational attainment of the head of the household is also found to be very important factor that is associated with poverty. If policy makers target the education of head of household in order to get rid of this evil vicious circle of poverty then it might be establish more effective, powerful and sustainable tool. Keywords ; Poverty status, Demographic variables, human capital variables, Poverty Differentials
SDF1 Polymorphisms Influence Outcome in Patients with Symptomatic Cardiovascular Disease

Background SDF1 and its cognate receptors CXCR4 and CXCR7 are involved in myocardial repair and are associated with outcome in cardiovascular patients. Hence, we aimed to investigate clinically significant SDF1 SNPs for their prognostic impact in patients with cardiovascular disease. Methods and Results Genotyping for selected SDF1 variants (rs1065297, rs2839693, rs1801157, rs266087, rs266085 and rs266089 was performed in patients (n = 872) who underwent percutaneous coronary intervention. Carriers of variant rs2839693 and rs266089 showed significantly higher cumulative event-free survival compared with non-carriers. All other polymorphisms had no relevant influence on outcome. Multivariate Cox regression analysis showed a significant correlation of these SNPs with cardiovascular outcome after inclusion of clinical and prognostic relevant variables (hazard ratio (HR) 0.51 (95% CI 0.30–0.88), p = 0.015 and [HR 0.51 (95% CI 0.30–0.88), p = 0.016, respectively). In addition, multivariate Cox regression with SDF1 haplotypes revealed a significantly reduced risk for the haplotype carrying the minor alleles of rs2839693 and rs266089 (HR 0.47 (95% CI 0.27–0.84), p = 0.011). Conclusion Distinct SDF1 polymorphisms are associated with improved cardiovascular prognosis in CAD patients. Further studies are warranted to validate these results and to better describe the endogenous regeneration potential in carriers of these SNPs. Targeted, genotype guided therapeutic approaches to foster myocardial regeneration and thus cardiovascular prognosis should be evaluated in future.
Temporal Homogenization of Monthly Radiosonde Temperature Data. Part I: Methodology

Abstract Historical changes in instrumentation and recording practices have severely compromised the temporal homogeneity of radiosonde data, a crucial issue for the determination of long-term trends. Methods developed to deal with these homogeneity problems have been applied to a near–globally distributed network of 87 stations using monthly temperature data at mandatory pressure levels, covering the period 1948–97. The homogenization process begins with the identification of artificial discontinuities through visual examination of graphical and textual materials, including temperature time series, transformations of the temperature data, and independent indicators of climate variability, as well as ancillary information such as station history metadata. To ameliorate each problem encountered, a modification was applied in the form of data adjustment or data deletion. A companion paper (Part II) reports on various analyses, particularly trend related, based on the modified data resulting from the method .
Do Regularization Programs for Illegal Immigrants Have a Magnet Effect? Evidence from Spain

This paper attempts to determine whether regularization programs of illegal immigrants have a magnet effect. Using a comparative case study approach, we analyze the most recent amnesty implemented in Spain. More specifically, we apply a synthetic control method that is suitable for the evaluation of policies at the country level. Our results suggest that the increasing trend followed by the foreign population in the period analyzed is not a consequence of the amnesty under scrutiny. Rather, it corresponds to the path for a counterfactual country with similar socioeconomic characteristics.
The Impact of Surplus Schooling on Earnings: Some Additional Findings.

This paper examines the impact of overeducation (or surplus schooling) on earnings. Overeducated workers are defined as those with educational attainments substantially above the mean for their specific occupations. Two models are estimated using data from the 1980 census. Though our models, data, and measure of overeducation are different from those used by Rumberger (1987), our results are similar. Our results show that overeducated workers often earn less than their adequately educated and undereducated counterparts.
MeanKS: meaningful keyword search in relational databases with complex schema

Keyword search in relational databases was introduced in the last decade to assist users who are not familiar with a query language, the schema of the database, or the content of the data. An answer is a join tree of tuples that contains the query keywords. When searching a database with a complex schema, there are potentially many answers to the query. Therefore, ranking answers based on their relevance is crucial in this context. Prior work has addressed relevance based on the size of the answer or the IR scores of the tuples. However, this is not sufficient when searching a complex schema. We demonstrate MeanKS, a new system for meaningful keyword search over relational databases. The system first captures the user's interest by determining the roles of the keywords. Then, it uses schema-based ranking to rank join trees that cover the keyword roles. This uses the relevance of relations and foreign-key relationships in the schema over the information content of the database. In the demonstration, attendees can execute queries against the TPC-E warehouse and compare the proposed measures against a gold standard derived from a real workload over TPC-E to test the effectiveness of our methods.