Miracle-dz/UniFAR-FTUs huggingface dataset:
Coevolving social and transportation networks

A component of work and non-work travel may be plausibly explained by the need for individuals to be co-present in order socially interact. Successful socializing requires maintaining and exploiting relationships, fulfilling obligations, and perhaps optimization of contacts. A social network is a way to map relationships and to quantify conduits of information, services, opportunity, and power. People must be able to navigate both their relationships and physical space in order to find other people and facilities which can provide the services they need. The result is that travel behavior and social structures affect each other. This research examines hypotheses of the interactions between social networks and trip generation. Models of social networks should be used by planners if their effects are significant and distinguishable from factors currently incorporated into transportation models. A multi-agent-based network evolution model is presented in which social visits are generated by a set of attraction parameters which include agent attributes, travel impedance, and descriptors of the actors’ position in the social network topology. The trips are constrained by the agent’s travel budget, and dynamic equilibrium is maintained by removing or weakening old links. The destination choice is thus a tradeoff between cost and the need for co-presence, marked by correlations between actors in the changing social network. The interactions are monitored using a statistical toolbox from social network analysis and placed in the context of findings from network science literature on network evolution, as well as from empirical work in sociology on real social networks. The behavior model used for social networking is described, and results from an ensemble run are presented. A sample case of a transportation “improvement” is also presented, to illustrate the way in which the social network adapts to changes in transportation costs that ease spatial searches.
Affine Motion Prediction Based on Translational Motion Vectors

In all of the video coding standards like H.26X and MPEG-X, much of the compression comes from motion compensated prediction (MCP). Translational motion vectors (MVs) poorly model complex motion and thus coders using polynomial or affine MVs have been proposed in the past. In this paper, we demonstrate a novel affine predictor stage which can be easily incorporated into current codecs greatly increasing MCP quality. If used passively to generate the final prediction, gains of up to 0.7 and 1.6 dB were easily realized for ldquomobilerdquo and ldquoflower gardenrdquo video sequences, respectively. In addition, when the translational MVs are refined, gains of up to 0.98 and 1.88 dB for ldquomobilerdquo and ldquoflower gardenrdquo video sequences were respectively realized.
Detecting Superior Face Recognition Skills in a Large Sample of Young British Adults

The Cambridge Face Memory Test Long Form (CFMT+) and Cambridge Face Perception Test (CFPT) are typically used to assess the face processing ability of individuals who believe they have superior face recognition skills. Previous large-scale studies have presented norms for the CFPT but not the CFMT+. However, previous research has also highlighted the necessity for establishing country-specific norms for these tests, indicating that norming data is required for both tests using young British adults. The current study addressed this issue in 254 British participants. In addition to providing the first norm for performance on the CFMT+ in any large sample, we also report the first UK specific cut-off for superior face recognition on the CFPT. Further analyses identified a small advantage for females on both tests, and only small associations between objective face recognition skills and self-report measures. A secondary aim of the study was to examine the relationship between trait or social anxiety and face processing ability, and no associations were noted. The implications of these findings for the classification of super-recognizers are discussed.
Identification of Past and Recent Parvovirus B19 Infection in Immunocompetent Individuals by Quantitative PCR and Enzyme Immunoassays: a Dual-Laboratory Study

ABSTRACT Parvovirus B19 (B19V) is a member of the family Parvoviridae, genus Erythrovirus. B19V-specific IgG and IgM react differently against conformational and linear epitopes of VP1 and VP2 antigens, leading to the development of IgG avidity and epitope type specificity (ETS) enzyme immunoassays (EIAs) for distinguishing past from recent infection. Additionally, B19V viral load determination (by quantitative PCR [qPCR]) is increasingly used in the staging of B19V infection. In this study, the utility of these methods is compared. A panel of 78 sera was jointly tested by the Virus Reference Department (VRD), London, United Kingdom, and the Haartman Institute (HI), Helsinki, Finland, using a number of EIAs, e.g., B19V-specific IgG and IgM, IgG avidity, and ETS EIAs. At VRD, the sera were also tested by a B19V viral load PCR (qPCR). By consensus analysis, 43 (55.1%) sera represented past infection, 28 (35.9%) sera represented recent infection, and 7 (9.0%) sera were indeterminate. Both VRD B19V qPCR and HI B19V VP2 IgM EIA gave the highest agreement with consensus interpretation for past or recent infection, with an overall agreement of 99% (95% confidence interval [CI], 92 to 100) and positive predictive value (PPV) of 100% (95% CI, 87 to 100). Nine sera designated as representing past infection by consensus analysis were B19V IgM positive by a commercial VRD B19V IgM EIA and B19V IgM negative by a new HI in-house B19V VP2 IgM EIA. A new VRD B19V IgG avidity EIA showed good (>95%) agreement (excluding equivocal results) with consensus interpretations for past or recent infection. Correct discrimination of past from recent B19V infection was achieved through application of qPCR or by appropriate selection of EIAs.
When Kids Are Grieving: Addressing Grief and Loss in School

Foreword by Kenneth J. Doka Preface Acknowledgments About the Author 1. Am I Qualified to Work With Grieving Children? Overview Self-Awareness Reflection Activity Case Study: How Do I Tell the Children? Part I Thought Provokers and Issues to Consider Chapter Reflection Key Terms 2. What Are the Different Types of Losses and Grief Reactions? Overview Dimensions of Loss Anticipatory Grief Versus Unanticipated Grief PRECEDENT: A Conceptual Framework for Understanding Grief Responses Thought Provokers and Issues to Consider Chapter Reflection Key Terms 3. What Do I Need to Know About Children's Grief? Overview Developmental Processes What and How Children Grieve Case Study: How Do I Tell the Children? Part II Gender Differences Thought Provokers and Issues to Consider Chapter Reflection Key Terms 4. What Do I Need to Know About Adolescents' Grief? Overview Developmental Processes Cognitive Factors Socioemotional Factors Family and Peers School, Media, and Technology How Teens Grieve Risk-Taking Behaviors and Suicide Suicide Prevention Case Study: Reactions to a Classmate's Suicide Thought Provokers and Issues to Consider Chapter Reflection Key Terms 5. What Can I Do to Help Grieving Students? Overview "But I'm Not a Grief Counselor . " Things You Need to Know First Things You Can Do Rituals and Activities Cultural Considerations Humor Thought Provokers and Issues to Consider Chapter Reflection Key Terms 6. What Are the Elements of School-Based Crisis Response? Overview School-Based Crisis Response Crisis Response Team Skills and Training Types of Crises School Crisis Response Plan Crisis Postvention Thought Provokers and Issues to Consider Chapter Reflection Key Terms 7. What Grief and Loss Resources Should Our School Have Available? Overview Naional Association of School Psychologists (NASP) Resources Fernside and Banana Splits Resource Center Reading Lists Online Resources School Grief and Loss Resource Library Thought Provokers and Issues to Consider Chapter Reflection Postscript Glossary References Index
VLSI floorplanning based on Particle Swarm Optimization

Floorplanning is an important problem in the very large integrated circuit (VLSI) design automation. It¿s an NP-hard combinatorial optimization problem. The particle swarm optimization (PSO) has been proved to be a good optimization algorithm with outstanding global performance. However, PSO cannot be directly used in the combinatorial optimization problem due to its continuous characteristic. In this paper a novel floorplanning algorithm based on Discrete PSO (DPSO) algorithm is proposed, in which integer coding based on module number was adopted. The principles of mutation and crossover operator in the Genetic Algorithm (GA) are also incorporated into the proposed PSO algorithm to achieve better diversity and break away from local optima. Experiments employing MCN and GSRC benchmarks show that the performance of our proposed algorithm for placement is good. The proposed algorithm can avoid the solution from falling into local minimum and have good convergence performance.
Efficient distributed fault-tolerant storage with erasure codes

We study the design of a distributed storage system that uses erasure codes to maintain data redundancy while disks can be written concurrently by multiple nodes. We propose and analyze a protocol that is highly optimized for the common cases; in these cases, a read requires only a round-trip to the disk, and a write requires only a round-trip to each disk that requires updating. To tolerate tp node failures and td disk failures, the protocol uses δ = 1 + (tp + 1)(td + tp/2− 1) redundant disks.
Speech/music discrimination using awarped LPC-based feature and a fuzzy expert system for intelligent audio coding

Automatic discrimination of speech and music is an important tool in many multimedia applications. This paper presents an evolutionary fuzzy rules-based speech/music discrimination approach for intelligent audio coding. A low complexity but effective feature, called Warped LPC-based Spectral Centroid (WLPC-SC), is defined for the analysis stage of the discrimination system. The final decision is made by a fuzzy expert system, which improves the accuracy rate provided by a Gaussian Mixture Model (GMM) classifier taking into account the audio labels assigned by the GMM classifier to past audio frames. Comparison between WLPC-SC and most timbral features proposed in [8] is performed, aiming to assess the good discriminatory power of the proposed feature. The accuracy rate improvement due to the fuzzy expert system is also reported. Experimental results reveal that our speech/music discriminator is robust and fast, making it suitable for intelligent audio coding.
Hypergeometric function representations

An algorithm for computing formula representations of instances of the generalized hypergeometric function is presented. Examples of lookup routine certi cates and the underlying theory of the algorithm are discussed in the paper. Finally, a gallery of results are presented and the algorithm is compared to already existing routines in Macsyma, Maple, and Mathematica.
Face Photo-Sketch Synthesis and Recognition

Today in Modern Society Face Recognition has gained much attention in the field of network multimedia access. After the 9/11 tragedy in India, the need for technologies for identification, detection and recognition of suspects has increased. One of the most common biometric recognition techniques is face recognition since face is the convenient way used by the people to identify each other. In this paper we are going to study a method for representing face which is based on the features which uses geometric relationship among the facial features like mouth, nose and eyes . Feature based face representation is done by independently matching templates of three facial regions i.e eyes, mouth and nose . Principal Component Analysis method which is also called Eigen faces is appearance based technique used widely for the dimensionality reduction and recorded a greater performance in face recognition. Here we are going to study about PCA followed by Feed Forward Neural Network called PCA-NN.
The Indirect Effect of Age Group on Switch Costs via Gray Matter Volume and Task-Related Brain Activity

Healthy aging simultaneously affects brain structure, brain function, and cognition. These effects are often investigated in isolation ignoring any relationships between them. It is plausible that age related declines in cognitive performance are the result of age-related structural and functional changes. This straightforward idea is tested in within a conceptual research model of cognitive aging. The current study tested whether age-related declines in task-performance were explained by age-related differences in brain structure and brain function using a task-switching paradigm in 175 participants. Sixty-three young and 112 old participants underwent MRI scanning of brain structure and brain activation. The experimental task was an executive context dual task with switch costs in response time as the behavioral measure. A serial mediation model was applied voxel-wise throughout the brain testing all pathways between age group, gray matter volume, brain activation and increased switch costs, worsening performance. There were widespread age group differences in gray matter volume and brain activation. Switch costs also significantly differed by age group. There were brain regions demonstrating significant indirect effects of age group on switch costs via the pathway through gray matter volume and brain activation. These were in the bilateral precuneus, bilateral parietal cortex, the left precentral gyrus, cerebellum, fusiform, and occipital cortices. There were also significant indirect effects via the brain activation pathway after controlling for gray matter volume. These effects were in the cerebellum, occipital cortex, left precentral gyrus, bilateral supramarginal, bilateral parietal, precuneus, middle cingulate extending to medial superior frontal gyri and the left middle frontal gyri. There were no significant effects through the gray matter volume alone pathway. These results demonstrate that a large proportion of the age group effect on switch costs can be attributed to individual differences in gray matter volume and brain activation. Therefore, age-related neural effects underlying cognitive control are a complex interaction between brain structure and function. Furthermore, the analyses demonstrate the feasibility of utilizing multiple neuroimaging modalities within a conceptual research model of cognitive aging.
Building a Scalable Digital Learning System on Server Clusters

This paper presents a scalable digital learning system that can store and provide Web access to digital contents from voluntaries, teachers, and institutions that lack re- sources or expertise to offer classes over the Internet. We use the server-cluster architec- ture as a way to create scalable and highly available solutions. However, hosting a vari- ety of contents from different owners on such a distributed server system faces new de- sign and management problems and requires new solutions. This paper describes the re- search work we are pursuing to construct a system that address the challenges faced by hosting learning content on a server farm environment. We also report performance data measured from such a production learning site.
Detection and classification of oil spill and look-alike spots from SAR imagery using an Artificial Neural Network

Oil spills represent a major threat to ocean ecosystems and their health. The recent incident in the Gulf of Mexico demonstrates the potentially catastrophic nature of offshore oil spills. Illicit pollution requires continuous monitoring and satellite remote sensing technology represents an attractive option for operational oil spill detection. Previous studies have shown that active microwave satellite sensors, particularly Synthetic Aperture Radar (SAR) can be effectively used for the detection and classification of oil spills. Oil spills appear as dark spots in SAR images. However, similar dark spots may arise from a range of unrelated meteorological and oceanographic phenomena, resulting in misidentification. A major focus of research in this area is the development of algorithms to distinguish oil spills from `look-alikes'. This paper describes the development of a new approach to SAR oil spill detection using two different Artificial Neural Networks (ANN). The first ANN segments a SAR image to identify pixels belonging to candidate oil spill features. A set of statistical feature parameters are then extracted and divided into subsets to facilitate sensitivity analyses. The second ANN classifies objects into oil spills or look-alikes according to their feature parameters. A pilot study employed sixty-two ERS-2 SAR and ENVSAT ASAR images of verified oil spills or look-alikes to train and evaluate the algorithm. Overall accuracies of 96.52 % were obtained for pixel segmentation and 95.2 % for feature classification. The segmentation approach outperformed established edge detection and adaptive thresholding techniques. An analysis of feature descriptors in the classification stage highlighted the importance of image gradient information.
South Asian Canadian Young Men and Women’s Interest Development in Science: Perception of Contextual Influences

The purpose of this descriptive case study was to examine the contextual and cultural influences on interest development in science for 24 Canadian South Asian young adults. Within this study, aspects pertaining to socialization with respect to self-efficacy and outcome expectations of engaging in the sciences, are consistent with existing formulations on South Asian families. Additionally, the study provides information as to the relationship between South Asian young adults’ cultural identification to contend with decision-making and perceptions of efficacy (individual and collective efficacy). Finally, this study broadens our understanding of how South Asians develop an interest in the sciences and the support of parents and siblings in helping participants prepare for their future. Implications for counselling practice and future research are discussed.
Segmentation of Stochastic Images With a Stochastic Random Walker Method

We present an extension of the random walker segmentation to images with uncertain gray values. Such gray-value uncertainty may result from noise or other imaging artifacts or more general from measurement errors in the image acquisition process. The purpose is to quantify the influence of the gray-value uncertainty onto the result when using random walker segmentation. In random walker segmentation, a weighted graph is built from the image, where the edge weights depend on the image gradient between the pixels. For given seed regions, the probability is evaluated for a random walk on this graph starting at a pixel to end in one of the seed regions. Here, we extend this method to images with uncertain gray values. To this end, we consider the pixel values to be random variables (RVs), thus introducing the notion of stochastic images. We end up with stochastic weights for the graph in random walker segmentation and a stochastic partial differential equation (PDE) that has to be solved. We discretize the RVs and the stochastic PDE by the method of generalized polynomial chaos, combining the recent developments in numerical methods for the discretization of stochastic PDEs and an interactive segmentation algorithm. The resulting algorithm allows for the detection of regions where the segmentation result is highly influenced by the uncertain pixel values. Thus, it gives a reliability estimate for the resulting segmentation, and it furthermore allows determining the probability density function of the segmented object volume.
Withdrawing feeds from children on long term enteral feeding: factors associated with success and failure

Background Enteral feeding is vital for sick infants, but the transition to normal diet may be difficult. The authors describe a feeding team which provides multidisciplinary management of ‘hard to wean’ children within a large children's hospital, using reduction of feed volume to stimulate hunger, combined with psychological input to improve mealtime interactions and relieve parental anxiety. Aims To assess the impact of feed reduction on growth and identify factors associated with successful feed cessation. Subjects and methods Clinical and anthropometric data retrieved from case notes and clinic database for all 41 children referred for feed withdrawal over a 5-year period. Results The children were aged median 4.0 (range 0.7–15) years when first seen; 27 (66%) were male and before reduction they received a median of 3766 (range 1987–9728) kJ daily from enteral feeds. Parents were often extremely anxious about weight loss and needed considerable support to make feed reductions. After follow-up for median (range) 1.7 (0.4–5.4) years, 32 (78%) were on solely normal diet, seven were still enterally fed and two were reliant on oral supplement drinks. Those referred after age 5 years were more likely to still be on artificial feeds (OR 7.4 (1.3−42); p=0.025) or to have taken more than a year to stop (OR 6.9 (1.1–43); p=0.04). Feed reduction was commonly followed by a decline in body mass index, but this was not associated with slow growth. Conclusions A majority of children eventually ceased feeds successfully, but slow and failed weaning is more likely after age 5 years.
Machine Reading as a Cognitive Science Research Instrument

We describe how we are using natural language techniques to develop systems that can automatically encode a range of input materials for cognitive simulations. We start by summarizing this type of problem, and the components we are using. We then describe three projects that are using this common infrastructure: learning from multimodal materials, modeling decision making in moral dilemmas, and modeling conceptual change in development.
Media effects by involvement under voluntary exposure: A comparison of television, print and static Internet

SUMMARY In this study, the effects on consumer responses are explored of single-medium and multiple-media campaigns under two levels of consumer's product involvement. The results of this study confirm that research on media effects need to take into account consumer product involvement. Consumer involvement seems to be crucial for the differential effects of retrieval (print and Internet) and delivery (television) media. Retrieval media will be effective under conditions of high rather than low product involvement, because retrieval media have a limited capacity to influence low-involved and passive consumers. Delivery media are better suited for influencing low-involved consumers. Delivery media are able to attract the attention of low-involved consumers and, as a consequence, may induce superficial processing and low involvement learning.
Classical Logic Event Calculus as Answer Set Programming

Recently, Ferraris, Lee and Lifschitz presented a generalized definition of a stable model that applies to the syntax of arbitrary firstorder sentences, under which a logic program is viewed as a special class of first-order sentences. The new definition of a stable model is similar to the definition of circumscription, and can even be characterized in terms of circumscription. In this paper, we show the opposite direction, that is, how to embed circumscription into the new stable model semantics, and based on this, how to turn some versions of the classical logic event calculus into the general language of stable models. By turning the latter to answer set programs under certain conditions, we show that answer set solvers can be used for classical logic event calculus reasoning, allowing more expressive query answering than what can be handled by the current SAT-based implementations of the event calculus. We prove the correctness of our translation method and compare our work with the related work by Mueller.