Miracle-dz/UniFAR-FTUs huggingface dataset:
Gender, Career and Technical Education (CTE) Nontraditional Coursetaking, and Wage Gap

The two main objectives of this study were to examine the relationship between high school student (9th – 12th) gender and nontraditional career and technical education (CTE) course taking, and the combined effects of gender and program area on estimated future wage earnings for male and female CTE completers. A Midwestern state CTE database (143,510 male and 125,562 female students) designed to fulfill the data collection requirements mandated by the Perkins IV legislation was utilized to answer the two research questions. Findings from logistic regression showed that gender was a statistically significant predictor of nontraditional course taking pattern, with males taking more nontraditional courses than females. Findings from factorial analysis of variance indicated that the effect of gender on estimated future wage earnings measured by the Bureau of Labor Statistics was largely dependent on program area. These findings collectively suggest that although gender pattern in CTE course taking still exists, gender wage gap may be decreasing. Implications of these findings as they relate to educational policy and occupational gender segregation perspective are discussed.
V1 in Icelandic : A Multifactorical Visualization of Historical Data

We present an innovative visualization technique for the analysis of historical data. We illustrate our method with respect to a diachronic case study involving V1 word order in Icelandic. A number of interacting factors have been proposed by linguists as being determinative of matrix declarative V1. The significance of these factors in contributing to declarative V1 can be explored interactively via our multifactorial visualization within a given text, but also comparatively over time. We apply the visualization to a corpus study based on the IcePaHC historical corpus of Icelandic and show that new results emerge very clearly out of the visualization component and that the appearance of declarative V1 is not confined to the situations identified so far by linguists. We demonstrate that the multifactorial visualization opens up new avenues for the exploration of alternative explanations. The visualization can be applied to any linguistic problem studying an interaction between several factors across time.
Gonadotropin-releasing hormone receptor (Gnrhr) gene knock out: Normal growth and development of sensory, motor and spatial orientation behavior but altered metabolism in neonatal and prepubertal mice

Gonadotropin-releasing hormone (GnRH) is important in the control of reproduction, but its actions in non-reproductive processes are less well known. In this study we examined the effect of disrupting the GnRH receptor in mice to determine if growth, metabolism or behaviors that are not associated with reproduction were affected. To minimize the effects of other hormones such as FSH, LH and sex steroids, the neonatal-prepubertal period of 2 to 28 days of age was selected. The study shows that regardless of sex or phenotype in the Gnrhr gene knockout line, there was no significant difference in the daily development of motor control, sensory detection or spatial orientation among the wildtype, heterozygous or null mice. This included a series of behavioral tests for touch, vision, hearing, spatial orientation, locomotory behavior and muscle strength. Neither the daily body weight nor the final weight on day 28 of the kidney, liver and thymus relative to body weight varied significantly in any group. However by day 28, metabolic changes in the GnRH null females compared with wildtype females showed a significant reduction in inguinal fat pad weight normalized to body weight; this was accompanied by an increase in glucose compared with wildtype females shown by Student-Newman-Keuls Multiple Comparison test and Student's unpaired t tests. Our studies show that the GnRH-GnRHR system is not essential for growth or motor/sensory/orientation behavior during the first month of life prior to puberty onset. The lack of the GnRH-GnRHR axis, however, did affect females resulting in reduced subcutaneous inguinal fat pad weight and increased glucose with possible insulin resistance; the loss of the normal rise of estradiol at postnatal days 15–28 may account for the altered metabolism in the prepubertal female pups.
Making Sense of Digital Footprints in Team-Based Legal Investigations: The Acquisition of Focus

Sensemaking occurs when people face the problem of forming an understanding of a situation. One scenario in which technology has a particularly significant impact on sensemaking and its success is in legal investigations. Legal investigations extend over time, are resource intensive, and require the sifting and re-representation of large collections of electronic evidence and close collaboration between multiple investigators. In this article, we present an account of sensemaking in three corporate legal investigations. We summarize information interaction processes in the form of a model which conceptualizes processes as resource transformations triggered and shaped by both bottom-up and top-down resources. The model both extends upon and validates aspects of a previous account of investigative sensemaking (Pirolli & Card, 2005) and brings to the fore two kinds of focusing. Data focusing involves identifying and structuring information to draw out facts relevant to a given set of investigation issues. Issue focusing involves revising the issues in the light of new insights. Both are essential in sensemaking. We draw this distinction through detailed accounts of two activities in the investigations: reviewing documents for relevance and the creation and use of external representations. This provides a basis for a number of requirements for sensemaking support systems, particularly in collaborative settings, including document annotation; dynamically associating documents of a given type; interacting with documents in fluid ways; linking external representation elements to evidence; filtering external representations in flexible ways; and viewing external representations at different levels of scale and fidelity. Finally, we use our data to analyze the conceptual elements within a “line of inquiry. ” This provides a framework that can form the basis for partitioning information into hierarchically embedded inquiry ‘contexts’ within collaborative sensemaking systems.
The use of MODIS-derived Fire Radiative Power to characterise Siberian boreal forest fires

When observed by the MODIS sensor, the mean radiative power of boreal forest fires in Siberia, compared with fires in North America, appears significantly lower. Here we explore possible reasons for this difference and we find that it is not a result of orbital parameters, detector effects or cross track illumination differences, but the effect appears to be due to a physical difference between the forests. The result agrees with independent modeling work assessing the plant functional types in each region, and their life cycle adaptation with respect to fire. We go on to analyse the variation of fire radiative power with latitude and time with respect to the fire diurnal cycle. Finally, we examine the relationship between fire radiative power and burnt area and demonstrate the development of “delayed” fire scars in Siberia, as a consequence of post-fire dieback after lower intensity fires.
An economic model of contagion in interbank lending markets

This paper considers the stability of a financial system in which heterogenous banks interact through a lending market. We analyse a discrete time model in which households and banks are located on a circular city. Households present banks with risky investment opportunities, which banks fund through deposits and interbank borrowing. In the event of bankruptcy, a bank defaults on its interbank loans potentially resulting in contagion and losses for other banks. Through simulation we examine the vulnerability of the financial system to systemic events, demonstrating the non-linear relationship between market concentration, shock severity and bankruptcies. The role and effect of regulatory actions such as reserve requirements, minimum bank capitalisation and constraints on the size of borrowing relationships, are considered in limiting these effects.
Switching and Escalating Therapy in Long-Lasting Multiple Sclerosis: Not Always Necessary

Although therapy switch is common among patients with multiple sclerosis (MS), sometimes the initial prescribed treatment is maintained for a long period with clinical stability, low disability, and nonsignificant side effects. We aim to describe demographic and clinical characteristics of patients treated in our MS clinic with the same disease-modifying drug (DMD) lasting for >12 years. From the cohort of 51 patients followed in our MS clinic with relapse-remitting MS who started an DMD between 1996 and 1999, we found a high percentage (51%) of patients who were efficiently treated with the first DMD. These patients were mainly females, with low annualized relapse rate and Multiple Sclerosis Severity Score (MSSS). Our results may be related to the open and multidisciplinary model of our MS clinic organization. Identifying characteristics associated with therapy persistence may be useful in developing strategies to improve therapy effectiveness.
A Novel Approach for Designing a Class of Orthogonal Vector-valued Wavelets

In this paper, the concept of vector-valued multiresolution analysis is introduced and the definition of orthogonal vector-valued wavelets is given. A sufficient condition on the existence of orthogonal vector-valued wavelets is presented by virtue of matrix theory and paraunitary vector filter bank theory. A new method for constructing a class of finitely supported orthogonal vector-valued wavelets is developed.
A new design and implementation of scientific workflow simulator

In this paper, a novel scientific workflow simulator is designed and implemented which is energy-aware and can simulate the scientific workflow that running on the multi-core cluster system. A method for application modeling is proposed which can describe the process dependence, data dependence and performance requirement of the workflow. A computing system model was also introduced to describe the layered structure of the cluster, the communication matrixes of the cluster nodes, and the energy consumption under different load level. Based on the application model and the computing systems model, the scientific workflow scheduling problem was abstracted to multi-objective optimization problem, a scheduling algorithm is presented which can satisfy the performance constraints and is energy aware as while. The experiment results proved the effectiveness of the simulator.
Efficient TTP-free mental poker protocols

Zhao et al proposed an efficient mental poker protocol which did not require using a trusted third party (TTP). The protocol is efficient and suitable for any number of players but it introduces a security flaw. In this paper, we propose two mental poker protocols based on Zhao's previous work. The security flaw has been removed and the additional computing cost is small.
Trajectory management driven by user preferences

The evolution of the Traffic Flow Management (TFM) and Air Traffic Control (ATC) systems towards Trajectory Based Operations (TBO) is constrained by the need to support a mixed fleet of aircraft for the foreseeable future. Trajectory synchronization and negotiation in particular are key enablers of TBO that need to take into account both the advanced capabilities in existing and future Flight Management Systems (FMS), and the limitations in performance and capabilities of legacy aircraft. Trajectory management systems will have to address the challenges imposed by this reality. Lockheed Martin and General Electric (GE) have established a Joint Strategic Research Initiative (JSRI) to develop technologies that can efficiently address the challenges of trajectory management. In this paper, we describe a user preference driven trajectory management concept that was developed under the JSRI. The idea consists of providing flight-specific cost information to Decision Support Tools (DST) used by controllers for strategic conflict resolution and schedule management. The cost of operating a flight may be decomposed into the cost of fuel and other direct and time related costs (such as crew pay, aircraft maintenance, or connecting passengers). In current operations, there is no practical mechanism to make this information known to the ground automation (or the controller) which needs to make changes to the business trajectory; moreover, much of this information is considered proprietary or sensitive by the operators, making it difficult or impossible to determine the true cost impact of modifications to the reference business trajectory. However, it is precisely the overall cost of operations that should be driving the decision process. A mechanism has been developed to extract the effective operating cost from the FMS and to make that information available to ground DSTs. Cost information is encoded in coefficients that do not reveal operator specific business strategies that may be considered proprietary. This enables the ground controller (and DSTs) to make an informed decision that may increase the likelihood of operators business objectives being accommodated, and would allow operators to better inform ATC's decisions such that the impact on the business objectives are minimized when changes are required. The encoding of cost information is made in a compact way so that communication band-width requirements are reasonable.
Parametric Modelling of Multivariate Count Data Using Probabilistic Graphical Models

Multivariate count data are defined as the number of items of different categories issued from sampling within a population, which individuals are grouped into categories. The analysis of multivariate count data is a recurrent and crucial issue in numerous modelling problems, particularly in the fields of biology and ecology (where the data can represent, for example, children counts associated with multitype branching processes), sociology and econometrics. We focus on I) Identifying categories that appear simultaneously, or on the contrary that are mutually exclusive. This is achieved by identifying conditional independence relationships between the variables; II)Building parsimonious parametric models consistent with these relationships; III) Characterising and testing the effects of covariates on the joint distribution of the counts. To achieve these goals, we propose an approach based on graphical probabilistic models, and more specifically partially directed acyclic graphs.
Jigsaw puzzle solver using shape and color

The jigsaw puzzle assembly problem is significant in that it can be applied to diverse areas such as repair of broken objects, restoration of archaeological findings, molecular docking problem for drug design, etc. This paper describes a new pictorial jigsaw puzzle solver which, in contrast to previous apictorial jigsaw puzzle solvers, uses chromatic information as well as geometric shape. We develop three new puzzle assembly algorithms (TSP&Kbest-based, TSP&AP-based, and AP-based algorithm) and new boundary and color matching operation. We tested the new puzzle solver with 6 different sets of color puzzle pieces. Experimental results show that chromatic information greatly aids in seeking the solution to the jigsaw puzzle problem. It is also discovered that in terms of how rapidly each assembly algorithm reaches a solution, the TSP and Kbest-based algorithm is the best, followed by TSP and AP-based algorithm, and followed by AP-based algorithm.
Mining the web and medline medical records to discover new facts on diabetes

One of the major benefits of text mining is that it provides individuals with an effective method for analyzing copious amounts of knowledge in the form of texts. Since the olden times, knowledge in medicine was established through recording and analyzing human experiences. This paper presents the first results of the use of text mining techniques to analyze online sources e.g. social networks, blogs, forums, medical literature, medical staff and patients' stories for discovering new knowledge and patterns related to diabetic disease covering diagnosis, diet, medicine, and activities. These finding are being gathered into an online knowledge repository for diabetic patients to access and better manage their diseases. In this research work, we found that the impacts of gaining informative and useful knowledge from a whole other range of data (text sources) besides the ones from medical literatures proved significant in detecting patterns in diabetic diseases that were considered to be insignificant before.
Image-based Vision Processing for the Smart Crane Ammunition Transfer System

Image-based vision processing can be effective in detecting important geometrical features of a cargo (pallet) and lead to accurate estimation of the object s position/orientation required for grappling/pickup operations. In this paper, we investigate techniques for identifying salient features of pallet images suitable for establishing a 3D map based on stereo cameras (which would be a low-cost, low-mass alternative to a commonly used laser scanner system). Software implementation and experimental results are also presented.
Monte Carlo Tree Search applied to co-operative problems

This paper highlights an experiment to see how standard Monte Carlo Tree Search handles simple co-operative problems with no prior or provided knowledge. These problems are formed from a simple grid world that has a set of goals, doors and buttons as well as walls that cannot be walked through. Two agents have to reach every goal present on the map. For a door to be open, an agent must be present on at least one of the buttons that is linked to it. When laid out correctly, the world requires each agent to do certain things at certain times in order to achieve the goal. With no modification to allow communication between the two agents, Monte Carlo Tress Search performs well and very “purposefully” when given enough computational time.
Allelic variance among ABO blood group genotypes in a population from the western region of Saudi Arabia

Background Characterization of the ABO blood group at the phenotype and genotype levels is clinically essential for transfusion, forensics, and population studies. This study elucidated ABO phenotypes and genotypes, and performed an evaluation of their distribution in individuals from the western region of Saudi Arabia. Methods One-hundred and seven samples underwent standard serological techniques for ABO blood group phenotype analysis. ABO alleles and genotypes were identified using multiplex polymerase chain reaction, and electrophoretic analysis was performed to evaluate the highly polymorphic ABO locus. Results A phenotype distribution of 37.4%, 30.8%, 24.3%, and 7.5% was found for blood groups O, A, B, and AB respectively in our study cohort. Genotype analysis identified 10 genotype combinations with the O01/O02 and A102/O02 genotypes being the most frequent with frequencies of 33.6% and 14.95%, respectively. Common genotypes such as A101/A101, A101/A102, A101/B101, B101/B101, and O01/O01 were not detected. Similarly, the rare genotypes, cis-AB01/O02, cis-AB01/O01, and cis-AB01/A102 were not found in our cohort. The most frequently observed allele was O02 (35.98%) followed by the A102 allele (17.76%). Furthermore, our findings are discussed in reference to ABO allele and genotype frequencies found in other ethnic groups. Conclusion The study has a significant implication on the management of blood bank and transfusion services in Saudi Arabian patients.
Modular playware as a playful diagnosis tool for autistic children

Based upon user-configurable modular robotics and design principles for modular playware, we developed modular robotic tiles to be used as playful, interactive tools for children with autism. The modular playware can make automatic documentation of the construction play activities by the autistic children. Using artificial neural networks for automatic classification of the individual construction practices, we may compare this classification with the diagnosis of the children, and possible obtain a supplementary diagnosis tool which is based on the autistic children's free play with the modular robotic tiles. Preliminary experiments with 7 autistic children show that the automatic neural network classification with post-processing can be done with a 100% accuracy for this small sample set, and thereby give some preliminary indications of the potential of the approach.
A Hypervisor for Embedded Computing

Mobile applications are a nascent market waiting to be tapped. Next generation mobiles offer a promise of specialized hardware such as multi-touch screens (like the Apple iPhone) or motion sensors (as with the Nintendo Wii) which will enable exciting new applications. In order to make optimal use of these hardware devices, applications must directly interact with the hardware. However, the ability to use native applications carries a certain risk for both device security and reliability. For example, a rogue application could compromise your address book or flood a mobile network. We propose to solve this problem by limiting the access of native applications through confinement in virtual machines that allow applications to access fully- featured hardware efficiently, while isolating dangerous functionality from other virtual machines. Following a brief discussion of relevant background material, we discuss and explore the use cases and issues that make embedded virtualization challenging. Then, we describe the initial exploration efforts and the goals that were established for the prototype. We then present the resulting architecture in detail. Finally, we conclude with a brief listing of future work planned and a summary of related work.
Metabolic Scope and Interspecific Competition in Sculpins of Greenland Are Influenced by Increased Temperatures Due to Climate Change

Ongoing climate change has led to an increase in sea surface temperatures of 2–4°C on the west coast of Greenland. Since fish are ectothermic, metabolic rate increases with ambient temperature. This makes these animals particularly sensitive to changes in temperature; subsequently any change may influence their metabolic scope, i.e. the physiological capacity to undertake aerobically challenging activities. Any temperature increase may thus disrupt species-specific temperature adaptations, at both the molecular level as well as in behavior, and concomitant species differences in the temperature sensitivity may shift the competitive balance among coexisting species. We investigated the influence of temperature on metabolic scope and competitive ability in three species of marine sculpin that coexist in Greenland coastal waters. Since these species have different distribution ranges, we hypothesized that there should be a difference in their physiological response to temperature; hence we compared their metabolic scope at three temperatures (4, 9 and 14°C). Their competitive ability at the ambient temperature of 9°C was also tested in an attempt to link physiological capacity with behaviour. The Arctic staghorn sculpin, the species with the northernmost distribution range, had a lower metabolic scope in the higher temperature range compared to the other two species, which had similar metabolic scope at the three temperatures. The Arctic staghorn sculpin also had reduced competitive ability at 9°C and may thus already be negatively affected by the current ocean warming. Our results suggest that climate change can have effects on fish physiology and interspecific competition, which may alter the species composition of the Arctic fish fauna.