Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk and Protective Factors of Foster Care Reentry: An Examination of the Literature

ABSTRACT Reunification with family is a primary goal in child welfare. However, some children re-enter foster care after reunification, which disrupts the continuity of their care and can impact their cognitive and social development. Using an ecological framework, this systematic review of the literature highlights the risk and protective factors of reentry. Key risk factors included child emotional and behavioral challenges, parental substance abuse, and short lengths of stay in foster care. Protective factors included placement with kin, social support, effective implementation of risk and safety assessments, and targeted pre- and post-reunification services. Understanding who is at greater risk and identifying points of intervention can help child welfare agencies develop, target, and evaluate their services more effectively. As new interventions develop, there is a great need for more research to evaluate their effectiveness and to scale-up evidence-informed practices that can help to prevent reentry to foster care.
Global Finite-time Observers for a Class of Non-Lipschitz Systems

Abstract Global finite-time observers are designed for a class of nonlinear systems that are uniformly observable and possibly non-Lipschitz. The non-Lipschitz conditions under which global finite-time observers exist are in terms of bounds of the varying rational powers of the increments of the non-Lipschitz nonlinearities. These are the same conditions under which semi-global finite-time observers were constructed previously. Different from the semi-global observers, the new observers are designed with two, instead of one, homogeneous terms. The global finite-time convergence of the observer is achieved by combining global asymptotical stability and local finite-time stability.
Discourses of anthropomorphism

This study presents an historical investigation of the practices which humanise nonhuman animals. Using discourse analysis I demonstrate how anthropomorphic practices have continued to pervade cultural production from the nineteenth to the twenty-first century. I argue that critiques and appropriations of anthropomorphism resonate through and regulate our everyday encounters with nonhuman animals and the construction of their representations. I propose that anthropomorphic practices are intrinsically linked to the production of knowledge about nonhuman animals and that 'difference' between human and nonhuman animals has been central to the organisation of meaning thereby reproducing the anthropomorphised nonhuman animal as a crucial agent of cultural politics. This investigation is therefore necessarily an interdisciplinary study of a range of anthropomorphic practices and their criticisms, the discourses that construct nonhuman animals within science, philosophy, and popular culture, and the relationships between knowledge production and cultural practices that reveal key tensions and syntheses between popular culture and discourses of science. The uniqueness of this project is partially located in its status as the first critical history of anthropomorphism. Relocating anthropomorphism from the periphery to the centre of debates about nonhuman animals, this investigation offers to the field of cultural studies a crucial mapping of the discursive conditions that produce, sustain, appropriate and regulate practices that humanise nonhuman animals. This mapping makes apparent significant relationships between anthropomorphic practices, scientific discourses and cultural production and offers important analyses of key historical and contemporary constructions of wild, domestic, and technological nonhuman animals. Furthermore, the discussion identifies important conventions specific to nonhuman animal representation and suggests how these conventions are intrinsically bound to particular discourses and powerl knowledge configurations.
Forecasting Austrian Inflation

In this paper we apply factor models proposed by Stock and Watson [18] and VAR and ARIMA models to generate 12-month out of sample forecasts of Austrian HICP inflation and its subindices processed food, unprocessed food, energy, industrial goods and services price inflation. A sequential forecast model selection procedure tailored to this specific task is applied. It turns out that factor models possess the highest predictive accuracy for several subindices and that predictive accuracy can be further improved by combining the information contained in factor and VAR models for some indices. With respect to forecasting HICP inflation, our analysis suggests to favor the aggregation of subindices forecasts. Furthermore, the subindices forecasts are used as a tool to give a more detailed picture of the determinants of HICP inflation from both an ex-ante and ex-post perspective.
Recognising persons by their iris patterns

Iris recognition provides real-time, high confidence identification of persons by analysis of the random patterns that are visible within the iris of an eye from some distance. Because the iris is a protected, internal, organ whose random texture is epigenetic and stable over the lifespan, it can serve as a living password. Recognition decisions are made with confidence levels high enough to support rapid exhaustive searches through national-sized databases. The principle that underlies these algorithms is the failure of an efficient test of statistical independence involving more than 200 degrees-of-freedom, based on phase sequencing each iris pattern with quadrature 2D wavelets. Different persons always pass this test of statistical independence, but images from the same iris almost always fail this test of independence. Database search speeds are around 1 million persons per second per CPU. Data from 200 billion cross-comparisons between different eyes will be presented in this talk, using a database consisting of 632,500 iris images acquired in the United Arab Emirates in a networked national border-crossing security system which performs, every day, about 9 billion iris comparisons using these algorithms. Current research efforts with this technology aim to make it more tolerant of difficult conditions of iris capture, such as "iris on the move," at a distance, and off-axis.
Reversible Oxidation of a Conserved Methionine in the Nuclear Export Sequence Determines Subcellular Distribution and Activity of the Fungal Nitrate Regulator NirA

The assimilation of nitrate, a most important soil nitrogen source, is tightly regulated in microorganisms and plants. In Aspergillus nidulans, during the transcriptional activation process of nitrate assimilatory genes, the interaction between the pathway-specific transcription factor NirA and the exportin KapK/CRM1 is disrupted, and this leads to rapid nuclear accumulation and transcriptional activity of NirA. In this work by mass spectrometry, we found that in the absence of nitrate, when NirA is inactive and predominantly cytosolic, methionine 169 in the nuclear export sequence (NES) is oxidized to methionine sulfoxide (Metox169). This oxidation depends on FmoB, a flavin-containing monooxygenase which in vitro uses methionine and cysteine, but not glutathione, as oxidation substrates. The function of FmoB cannot be replaced by alternative Fmo proteins present in A. nidulans. Exposure of A. nidulans cells to nitrate led to rapid reduction of NirA-Metox169 to Met169; this reduction being independent from thioredoxin and classical methionine sulfoxide reductases. Replacement of Met169 by isoleucine, a sterically similar but not oxidizable residue, led to partial loss of NirA activity and insensitivity to FmoB-mediated nuclear export. In contrast, replacement of Met169 by alanine transformed the protein into a permanently nuclear and active transcription factor. Co-immunoprecipitation analysis of NirA-KapK interactions and subcellular localization studies of NirA mutants lacking different parts of the protein provided evidence that Met169 oxidation leads to a change in NirA conformation. Based on these results we propose that in the presence of nitrate the activation domain is exposed, but the NES is masked by a central portion of the protein (termed nitrate responsive domain, NiRD), thus restricting active NirA molecules to the nucleus. In the absence of nitrate, Met169 in the NES is oxidized by an FmoB-dependent process leading to loss of protection by the NiRD, NES exposure, and relocation of the inactive NirA to the cytosol.
c ○ 2010 Binbin LiaoANOMALY DETECTION IN GPS DATA BASED ON VISUAL ANALYTICS BY

Modern machine learning techniques provide robust approaches for datadriven modeling and critical information extraction, while human experts hold the advantage of possessing high-level intelligence and domain-specific expertise. We combine the power of the two for anomaly detection in GPS data by integrating them through a visualization and human-computer interaction interface. In this thesis we introduce GPSvas (GPS Visual Analytics System), a system that detects anomalies in GPS data using the approach of visual analytics: a conditional random field (CRF) model is used as the machine learning component for anomaly detection in streaming GPS traces. A visualization component and a user-friendly interaction interface are built to visualize the data stream, display significant analysis results (i.e., anomalies or uncertain predications) and hidden information extracted by the anomaly detection model, which enable human experts to observe the real-time data behavior and gain insights into the data flow. Human experts further provide guidance to the machine learning model through the interaction tools; the learning model is then incrementally improved through an active learning procedure.
Development of loosely-coupled FOG/DGPS and FOG/RTk systems for ADAS and a methodology to assess their real-time performances

This paper tackles the problem of precise car localization for advance driver assistance system applications. Localization is performed by loosely coupling proprioceptive data with exteroceptive perceptions. Two localization systems were designed, one based on differential GPS and the other on real time kinematic GPS. In both cases, sensor latencies were precisely measured and taken into account to minimize their effects. Track experimentations were carried out with two vehicles at different speeds. A methodology based on a comparison with a post-processed kinematic GPS reference is presented to evaluate performances of the two systems.
Document clustering algorithm based on NMF and SVDD

Document clustering is one of the most important research areas of data mining due to its wide application in many fields. To efficiently cope with this problem, a novel document clustering algorithm based on nonnegative matrix factorization (NMF) and support vector data description (SVDD) is proposed in this paper. Experimental results on two well-known document data sets demonstrate the effectiveness of the proposed document clustering algorithm.
Commit 2.0

Commit comments written by developers when they submit their changes to a versioning system are useful for a number of tasks: Developers write commit comments to document changes and as a means to communicate with the rest of the development team; Researchers mine commit-related data contained in software repositories to support software evolution and reverse engineering activities. However, the support provided by IDEs is restricted in this respect, as they limit the users to use only text to document their changes. We present Commit 2.0, an IDE enhancement to enrich commit comments using software visualization. Commit 2.0 generates visualizations of the performed changes at different granularity levels, and lets the user annotate them.
An algorithm for cross-layer subcarrier and power allocation in cellular networks

Inter-cell interference is a major challenge in multi-user multi-carrier cellular networks, especially for cells with overlapping coverage. Several subcarrier and power allocation algorithms have been developed to deal with this problem. However, they focus on maximizing data rates using only physical layer information, disregarding upper layer information like the queue backlogs. Assigning subcarriers to the users based only on physical layer information like the channel conditions maximizes data rates, but may lead to network instability. To tackle this problem, we propose a cross-layer subcarrier and power allocation algorithm that uses physical layer information to reduce inter-cell interference and upper layer information to stabilize the network. Furthermore, our approach achieves a larger rate region than the baseline approach by protecting users in neighboring cells.
LocateMe

Fine-grained localization is extremely important to accurately locate a user indoors. Although innovative solutions have already been proposed, there is no solution that is universally accepted, easily implemented, user centric, and, most importantly, works in the absence of GSM coverage or WiFi availability. The advent of sensor rich smartphones has paved a way to develop a solution that can cater to these requirements. By employing a smartphone's built-in magnetic field sensor, magnetic signatures were collected inside buildings. These signatures displayed a uniqueness in their patterns due to the presence of different kinds of pillars, doors, elevators, etc., that consist of ferromagnetic materials like steel or iron. We theoretically analyze the cause of this uniqueness and then present an indoor localization solution by classifying signatures based on their patterns. However, to account for user walking speed variations so as to provide an application usable to a variety of users, we follow a dynamic time-warping-based approach that is known to work on similar signals irrespective of their variations in the time axis. Our approach resulted in localization distances of approximately 2m--6m with accuracies between 80--100% implying that it is sufficient to walk short distances across hallways to be located by the smartphone. The implementation of the application on different smartphones yielded response times of less than five secs, thereby validating the feasibility of our approach and making it a viable solution.
Inner Core, Asymmetric Nash Bargaining Solutions and Competitive Payoffs

We investigate the relationship between the inner core and asymmetric Nash bargaining solutions for n-person bargaining games with complete information. We show that the set of asymmetric Nash bargaining solutions for different strictly positive vectors of weights coincides with the inner core if all points in the underlying bargaining set are strictly positive. Furthermore, we prove that every bargaining game is a market game. By using the results of Qin (1993) we conclude that for every possible vector of weights of the asymmetric Nash bargaining solution there exists an economy that has this asymmetric Nash bargaining solution as its unique competitive payoff vector. We relate the literature of Trockel (1996, 2005) with the ideas of Qin (1993). Our result can be seen as a market foundation for every asymmetric Nash bargaining solution in analogy to the results on non-cooperative foundations of cooperative games.
TRP + + : A temporal resolution prover ⋆

In developing these techniques, temporal proof is often required, and we base our work on practical proof techniques for the clausal resolution method for temporal logic. The method is based on an intuitive clausal form, called SNF, comprising three main clause types and a small number of resolution rules [10]. While the approach has been shown to be competitive [14, 15] using a prototype implementation of the method, we now aim at an even more efficient implementation. This implementation, called TRP++, is the focus of this paper.
Propagation dynamics based on social contact probability on social network

In this paper, we present an improved SIS propagation model based on social contact probability on social networks. On social networks, the contact probability between nodes is decided by their social active degree and their social distance; the contact probability of two directly connected nodes is usually less than 1; if two nodes are not directly connected, the contact probability of two nodes is decided by the length of the shortest path between them. We carried out simulation experiment on SIS model on BA scale-free network to study the influence of the social contact probability. The model provides an idea for studying the propagation rule of disease, attitude, fads, social opinion on social network.
Caring for the Caregivers: A Phenomenological Study of Supervisors and Vicarious Trauma

ii Acknowledgments . iii List of Tables . vii Chapter 1 . 1 Chapter 2 . 19 Chapter 3 . 38 Chapter 4 . 62 Chapter 5 . 81 References . 112 Appendix A . 118 Appendix B . 119 Appendix C . 120 Appendix D . 122 Appendix E . 124
Emotions related to Spanish student-teachers’ changes in life purposes following service-learning participation

Abstract Service-learning is an important and effective educational pedagogy that has grown tremendously in Spain. Research shows that service-learning has positive effects on the professional and personal development of future teachers. Since future teachers, in turn, influence the education of the next generation, it is important to understand how service-learning may influence the life purposes of student-teachers themselves, specifically, how emotions influence this development process. Statistical analyses of 198 Madrileño student-teachers’ responses to online questionnaires suggested that service-learning involves positive emotions like interest, enthusiasm, inspiration and determination. Surprisingly, these positive emotions decrease during service, except for the 53 student-teachers whose purposes changed. Most reported positive changes in all dimensions of life purpose, with some also noting strengthened relationships between 10 pairs of dimensions, such as between stronger impetus to help others and a clearer understanding of who would benefit. This study supports teaching as a calling and how service-learning can strengthen that calling.
A QoS Scheduler Packets for Wireless Sensor Networks

QoS routing in a wireless sensor network is difficult because the network topology may change constantly, and the available state information for routing is inherently imprecise. Ever more complex sensors have become available to create and maintain situational awareness during missions. Choosing the most suited sensor for the execution of a sensor function is based on sensor capabilities and function attributes. To increase performance of the entire sensor network, the total set of sensors should be scheduled in a single system. This paper puts forward for scheduling prioritised tasks in sensor networks. Use a reinforcement learning formalism to optimise the set of schedules. In this paper, node actively infer the state of other nodes, using a reinforcement learning based more particularly Q-learning, thereby achieving high throughput by improving the delay for a wide range of traffic conditions.
Big data mining of social networks for friend recommendation

In the current era of big data, high volumes of valuable data can be easily collected and generated. Social networks are examples of generating sources of these big data. Users in these social networks are often linked by some interdependency such as friendship. As these big social networks keep growing, there are situations in which an individual user wants to find popular groups of friends so that he can recommend the same groups to other users. In this paper, we present a big data analytic solution that uses the MapReduce model in mining these big social networks for discovering groups of frequently connected users for friend recommendation. Evaluation results show the efficiency and practicality of our data analytic solution in mining big social networks, discovering popular users, and recommending friends.
Robust Cooperative Strategy for Contour Matching Using Epipolar Geometry

Feature matching in images plays an important role in computer vision such as for 3D reconstruction, motion analysis, object recognition, target tracking and dynamic scene analysis. In this paper, we present a robust cooperative strategy to establish the correspondence of the contours between two uncalibrated images based on the recovered epipolar geometry. We take into account two representations of contours in image as contour points and contour chains. The method proposed in the paper is composed of the following two consecutive steps: (1) The first step uses the LMedS method to estimate the fundamental matrix based on Hartley’s 8-point algorithm, (2) The second step uses a new robust cooperative strategy to match contours. The presented approach has been tested with various real images and experimental results show that our method can produce more accurate contour correspondences.
Combined MEK and PI3K Inhibition in a Mouse Model of Pancreatic Cancer

Purpose: Improved therapeutic approaches are needed for the treatment of pancreatic ductal adenocarcinoma (PDAC). As dual MEK and PI3K inhibition is presently being used in clinical trials for patients with PDAC, we sought to test the efficacy of combined targeting of these pathways in PDAC using both in vitro drug screens and genetically engineered mouse models (GEMM). Experimental Design: We performed high-throughput screening of >500 human cancer cell lines (including 46 PDAC lines), for sensitivity to 50 clinically relevant compounds, including MEK and PI3K inhibitors. We tested the top hit in the screen, the MEK1/2 inhibitor, AZD6244, for efficacy alone or in combination with the PI3K inhibitors, BKM120 or GDC-0941, in a KrasG12D-driven GEMM that recapitulates the histopathogenesis of human PDAC. Results: In vitro screens revealed that PDAC cell lines are relatively resistant to single-agent therapies. The response profile to the MEK1/2 inhibitor, AZD6244, was an outlier, showing the highest selective efficacy in PDAC. Although MEK inhibition alone was mainly cytostatic, apoptosis was induced when combined with PI3K inhibitors (BKM120 or GDC-0941). When tested in a PDAC GEMM and compared with the single agents or vehicle controls, the combination delayed tumor formation in the setting of prevention and extended survival when used to treat advanced tumors, although no durable responses were observed. Conclusions: Our studies point to important contributions of MEK and PI3K signaling to PDAC pathogenesis and suggest that dual targeting of these pathways may provide benefit in some patients with PDAC. Clin Cancer Res; 21(2); 396–404. ©2014 AACR.
Constructing mobile-oriented catalog in m-commerce using LDA-based self-adaptive genetic algorithm

The purpose of this paper is to develop a method to recommend products to customer via mobile devices. Collaborative recommendation is known as an effective way to recommend products. In this paper, we use the concept of collaborative recommendation to develop Mobile-Oriented Catalog (MOC). The proposed method is made from aggregating similar purchasing records to optimize combination of goods on mobile devices. This paper illustrates how to design attractive and collaborative catalog to recommend items by using Latent Dirichlet Allocation (LDA) based self-adaptive genetic algorithm (LDA-SAGA). LDA-SAGA is consisted of topic modeling concept and self-adaptive genetic algorithm. We use LDA as our topic modeling algorithm to construct MOC as a result that it is the simplest topic model. Our experimental evaluation on synthetic and real data shows that using preference as topic concept is effective. LDA-SAGA is especially outstanding with large number of customers and products. Finally, we compare the MOC which is used on mobile application (APP) of Amazon with the one used on Taobao and discuss the characteristics of their design. Different design of user interface on APP can lead to different scope of fitness value which is capable of explaining different market strategies of Taobao and Amazon.