Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceptions of parental secure base support in African American adolescents and young adults

Within the field of relationship science there is increasing interest in the connections between close relationships and physical health. In the present study, we examined whether adolescents’ (∼12 years old) and young adults’ (∼20 years old) perceptions of their parents as a secure base prospectively predict C-reactive protein (CRP), a commonly used marker of inflammatory activity, at age 32 in a well-characterized sample of African Americans. We utilized existing data collected as part of the Maryland Adolescent Development in Context Study (MADICS) to construct measures of perceptions of parental secure base support (SBS), general parental support, and peer support in early adolescence and early adulthood. In the present study, SBS was operationalized as the perceived ability to depend on parents in times of need. Fifty-nine African American MADICS participants who reported on perceived support in early adolescence and early adulthood participated in a follow-up home visit at age 32 during which serum CRP was measured via a blood draw. After controlling for inflammation-related confounds (e.g., tobacco use, body mass index), adolescents’ perceptions of parental SBS, but not peer support or general parental support, predicted lower CRP values at age 32 (b = −.92, SE = .34, p < .05). None of the support variables in early adulthood predicted CRP at 32 years. This study adds to a growing literature on relationships and health-related outcomes and provides the first evidence for a link between parental SBS in adolescence and a marker of inflammatory activity in adulthood.
PR + RQ ALMOST EQUAL TO PQ: Transliteration Mining Using Bridge Language

We address the problem of mining name transliterations from comparable corpora in languages P and Q in the following resource-poor scenario:Parallel names in PQ are not available for training. Parallel names in PR and RQ are available for training.We propose a novel solution for the problem by computing a common geometric feature space for P,Q and R where name transliterations are mapped to similar vectors. We employ Canonical Correlation Analysis (CCA) to compute the common geometric feature space using only parallel names in PR and RQ and without requiring parallel names in PQ. We test our algorithm on data sets in several languages and show that it gives results comparable to the state-of-the-art transliteration mining algorithms that use parallel names in PQ for training.
Countermanding in rats as a practical model for investigation of adaptive control of behaviour, lifespan changes in behavioural control and neurotransmitter function

ii Co-Authorship. iv Acknowledgements . v List of Figures . ix List of Tables . x List of Abbreviations . xi Chapter 1 General Introduction. 1 1.1 The study of executive function and behavioural inhibition . 3 1.2 The countermanding task . 5 1.3 Adaptive RT adjustments in the countermanding task . 5 1.4 Countermanding task performance throughout the lifespan . 7 1.5 Neuromodulatory control of countermanding task behaviour . 8 1.6 The countermanding task in rats . 11 1.7 Objectives . 15 Chapter 2 Investigating a race model account of executive control in rats with the countermanding paradigm . 18 2.1 Abstract . 19 2.2 Introduction . 20 2.3 Experimental Procedures . 23 2.3.1 Animals . 23 2.3.2 Apparatus . 23 2.3.3 Training Procedures . 24 2.3.4 Countermanding task . 26 2.3.5 Data Analysis . 27 2.3.6 Amphetamine treatment . 29 2.4 Results . 32 2.4.1 Race model analysis . 32 2.4.2 Race model predictions . 35 2.4.3 Response adjustment . 39 2.4.4 Amphetamine treatment . 41 2.5 Discussion . 46 2.5.1 Acknowledgements . 51 vii 2.6 References . 51 Chapter 3 Behavioural response time adaptation for rats performing the countermanding task: Influence of stop trials and timeout interval . 59 3.1 Abstract . 60 3.2 Introduction . 61 3.3 Experimental procedures . 64 3.3.1 Animals . 64 3.3.2 Apparatus . 64 3.3.3 Procedures . 65 3.3.4 Data Analysis . 68 3.4 Results . 72 3.4.1 10-s vs. 1-s timeout interval . 72 3.4.2 Simple RT task vs. the countermanding task . 76 3.5 Discussion . 82 3.5.1 Acknowledgements . 90 3.6 References . 91 Chapter 4 Lifespan changes in the countermanding performance of young and middle aged adult rats . 98 4.1 Abstract . 99 4.2 Introduction . 100 4.3 Methods. 104 4.3.1 Subjects . 104 4.3.2 Apparatus . 104 4.3.3 Procedures . 105 4.3.4 Data analysis . 108 4.4 Results . 110 4.5 Discussion . 117 4.5.1 Acknowledgements . 125 4.6 References . 125 Chapter 5 Yohimbine encourages motivated responding for food reward and reveals baseline-dependent effects on stopping for rats performing the countermanding task . 136 5.1 Abstract . 137 5.2 Introduction . 138 5.3 Methods. 141 5.3.1 Animals . 141 viii 5.3.2 Apparatus . 142 5.3.3 Drugs . 142 5.3.4 Proce
An intelligent background-correction algorithm for highly fluorescent samples in Raman spectroscopy

Fluorescent background is a major problem in recoding the Raman spectra of many samples, which swamps or obscures the Raman signals. The background should be suppressed in order to perform further qualitative or quantitative analysis of the spectra. For this purpose, an intelligent background-correction algorithm is developed, which simulates manual background-correction procedure intelligently. It basically consists of three aspects: (1) accurate peak position detection in the Raman spectrum by continuous wavelet transform (CWT) with the Mexican Hat wavelet as the mother wavelet; (2) peak-width estimation by signal-to-noise ratio (SNR) enhancing derivative calculation based on CWT but with the Haar wavelet as the mother wavelet; and (3) background fitting using penalized least squares with binary masks. This algorithm does not require any preprocessing step for transforming the spectrum into the wavelet space and can suppress the fluorescent background of Raman spectra intelligently and validly. The algorithm is implemented in R language and available as open source software (http://code.google.com/p/baselinewavelet). Copyright © 2009 John Wiley & Sons, Ltd.
A Sustainable Engineering Infrastructure Model for the 21st Century

Advancements in interdisciplinary fields respecting systems and subsystems, devices and components and technologies and architectures have resulted in complex infrastructural developments requiring sustainability. This paper presents a sustainable engineering infrastructure (SEI) model that is expected to serve as a planning, design, development, implementation and management platform for the engineering community. Starting from the preconceptual design phase, a step by step algorithm and system-level SEI model has been developed. The SEI model analyses the project lifecycle with an allowance for on- and offsite recycling processes. All aspects of conceptual design, procurement, materials handling, construction, renovation, disposal, decommissioning, hazardous materials, demolition and recycling are addressed. Appropriate metrics for the engineering project lifecycle are incorporated in the developed SEI model functions. Issues bordering on project viability, reliability, performance, deliverability, maintenance, environmental impact assessment and return-on-investment amongst others are integrated into a common design and analysis pool function. The SEI model is expected to offer an unprecedented design and management leverage for project engineers and managers involved in all kinds of projects and infrastructural developments. Energy, maintenance and the external factors characteristics gained severity indices of 70 - 80% and water resources management, infrastructure design characteristics and the project characteristics have 48 - 74% in managing infrastructure systems. A sustainability index of 0.45 was obtained for the case study reported in this paper. The results of this study indicate that the proposed novel SEI model and sustainability index function promise to transform engineering projects management.
Predicting Phishing Websites Using Classification Mining Techniques with Experimental Case Studies

Classification Data Mining (DM) Techniques can be a very useful tool in detecting and identifying e-banking phishing websites. In this paper, we present a novel approach to overcome the difficulty and complexity in detecting and predicting e-banking phishing website. We proposed an intelligent resilient and effective model that is based on using association and classification Data Mining algorithms. These algorithms were used to characterize and identify all the factors and rules in order to classify the phishing website and the relationship that correlate them with each other. We implemented six different classification algorithm and techniques to extract the phishing training data sets criteria to classify their legitimacy. We also compared their performances, accuracy, number of rules generated and speed. A Phishing Case study was applied to illustrate the website phishing process. The rules generated from the associative classification model showed the relationship between some important characteristics like URL and Domain Identity, and Security and Encryption criteria in the final phishing detection rate. The experimental results demonstrated the feasibility of using Associative Classification techniques in real applications and its better performance as compared to other traditional classifications algorithms.
CHOICE POLARIZATION ON A SOCIAL INFLUENCE NETWORK

We are studying here a model of social influence in which actors are dwelling upon a social network and are influencing/influenced by each other through the recurrent and reproduced pattern of their relationships. Furthermore, we assume that influence outcomes represent preference orderings (among a number of alternatives) and, thus, they are located on a certain relational structure (a graph of orderings). Because of the structural arrangement of influence outcomes, actors' orderings may occupy antipodal positions. In this setting, after following a social influence process of random dyadic interactions among actors, the system is stabilized (absorbed) by two possible configurations of actors' influence outcomes: either a unanimous arrangement of orderings or a polarization across dipoles of antipodal orderings. We conduct computer simulations in order to study how this process of polarized social influence depends on certain parameters of the system (such as the size and connectivity of the social network) and the degree of its randomization. We find that polarization appears to be unexpectedly high over social networks of the 'small world' type. Furthermore, we discuss the relevance of this model of polarized social influence on relationally structured outcomes with Arrow's Impossibility Theorem and we outline some possible further extensions of this model.
The Optimal Public Expenditure Financing Policy: Does the Level of Economic Development Matter?

This paper explores how the optimal mode of public finance depends on the stage of economic development. The theoretical analysis is based on an overlapping generations growth model with an imperfect capital market. Random shocks create a demand for liquidity and establish a role for financial intermediaries. In this model, inflation matters because it affects the relative rates of return on assets in such a way that money becomes the preferred asset in the portfolio holdings of banks, causing a detrimental effect on economic growth. Such an effect is stronger (weaker) at lower (higher) levels of economic development due to the higher (lower) default risks associated with lending. Consequently, income taxation (seigniorage) is a relatively less distortionary way of financing public expenditure for low-income (high-income) countries. We provide empirical support for our model’s predictions using a panel of 21 OECD and 40 developing countries observed over the period 1972-1999.
Distractibility, Impulsivity, and Hyperactivity Measured by the Kaufman Assessment Battery for Children and Personality Inventory for Children

Basic criteria for determination of Attention Deficit Hyperactivity Disorder (ADHD) include hyperactivity, impulsivity, and distractibility. Four scales of the Personality Inventory for Children have been found to be useful in the diagnosis of ADHD. Impulsivity and distractibility can affect scores on the Hand Movements, Number Recall, Word Order, Spatial Memory, Arithmetic, Riddles, and Matrix Analogies subtests of the Kaufman Assessment Battery for Children. Subjects were 100 children, aged six through 13 who were referred for psychological assessment. The purpose of this study was to determine if there was a correlation between scores on the four scales of the PIC and the designated subtest scores on the KABC. Four correlations were significant, but of low magnitude.
The Effect of Institutional Ownership on Firm Transparency and Information Production

We examine the effects of institutional ownership on firms׳ information and trading environments using the annual Russell 1000/2000 index reconstitution. Characteristics of firms near the index cutoffs are similar, except that firms in the top of the Russell 2000 have discontinuously higher proportional institutional ownership than firms in the bottom of the Russell 1000 primarily due to indexing and benchmarking strategies. We find that higher institutional ownership is associated with greater management disclosure, analyst following, and liquidity, resulting in lower information asymmetry. Overall, indexing institutions׳ predilection for lower information asymmetries facilitates information production, which enhances monitoring and decreases trading costs.
Learning Algorithms in Neural

Neural Network models have received increased attention in the recent years. Aimed at achieving human-like performance in tasks of the cognitive sciences domain, these models are composed of a highly interconnected mesh of nonlinear computing elements, whose structure is drawn from our current knowledge of biological neural systems. Several Neural Network Learning Algorithms have been developed in the past years. In these algorithms, a set of rules deenes the evolution process undertaken by the synaptic connections of the networks, thus allowing them to learn how to perform speciied tasks. In this article, several such algorithms are surveyed. They range from simple associative learning paradigms to more complex reinforcement learning systems. A detailed description of each algorithm is presented, and a discussion of their capabilities and limitations is included.
Improving early language and literacy skills: differential effects of an oral language versus a phonology with reading intervention.

BACKGROUND This study compares the efficacy of two school-based intervention programmes (Phonology with Reading (P + R) and Oral Language (OL)) for children with poor oral language at school entry. METHODS Following screening of 960 children, 152 children (mean age 4;09) were selected from 19 schools on the basis of poor vocabulary and verbal reasoning skills and randomly allocated to either the P + R programme or the OL programme. Both groups of children received 20 weeks of daily intervention alternating between small group and individual sessions, delivered by trained teaching assistants. Children in the P + R group received training in letter-sound knowledge, phonological awareness and book level reading skills. Children in the OL group received instruction in vocabulary, comprehension, inference generation and narrative skills. The children's progress was monitored at four time points: pre-, mid- and post-intervention, and after a 5-month delay, using measures of literacy, language and phonological awareness. RESULTS The data are clustered (children within schools) and robust confidence intervals are reported. At the end of the 20-week intervention programme, children in the P + R group showed an advantage over the OL group on literacy and phonological measures, while children in the OL group showed an advantage over the P + R group on measures of vocabulary and grammatical skills. These gains were maintained over a 5-month period. CONCLUSIONS Intervention programmes designed to develop oral language skills can be delivered successfully by trained teaching assistants to children at school entry. Training using P + R fostered decoding ability whereas the OL programme improved vocabulary and grammatical skills that are foundations for reading comprehension. However, at the end of the intervention, more than 50% of at-risk children remain in need of literacy support.
The Subgenual Anterior Cingulate Cortex in Mood Disorders

ABSTRACT The anterior cingulate cortex (ACC) ventral to the genu of the corpus callosum has been implicated in the modulation of emotional behavior on the basis of neuroimaging studies in humans and lesion analyses in experimental animals. In a combined positron emission tomography/magnetic resonance imaging study of mood disorders, we demonstrated that the mean gray matter volume of this “subgenual” ACC (sgACC) cortex is abnormally reduced in subjects with major depressive disorder (MDD) and bipolar disorder, irrespective of mood state. Neuropathological assessments of sgACC tissue acquired postmortem from subjects with MDD or bipolar disorder confirmed the decrement in gray matter volume, and revealed that this abnormality was associated with a reduction in glia, with no equivalent loss of neurons. In positron emission tomography studies, the metabolic activity was elevated in this region in the depressed relative to the remitted phases of the same MDD subjects, and effective antidepressant treatment was associated with a reduction in sgACC activity. Other laboratories replicated and extended these findings, and the clinical importance of this treatment effect was underscored by a study showing that deep brain stimulation of the sgACC ameliorates depressive symptoms in treatment-resistant MDD. This article discusses the functional significance of these findings within the context of the preclinical literature that implicates the putative homologue of this region in the regulation of emotional behavior and stress response. In experimental animals, this region participates in an extended “visceromotor network” of structures that modulates autonomic/neuroendocrine responses and neurotransmitter transmission during the neural processing of reward, fear, and stress. These data thus hold important implications for the development of neural models of depression that can account for the abnormal motivational, neuroendocrine, autonomic, and emotional manifestations evident in human mood disorders.
Acute Myeloid Leukemia: Focus on Novel Therapeutic Strategies

Acute myeloid leukemia (AML) is a heterogeneous disease with variable clinical outcomes. Cytogenetic analysis reveals which patients may have favorable risk disease, but 5-year survival in this category is only approximately 60%, with intermediate and poor risk groups faring far worse. Advances in our understanding of the biology of leukemia pathogenesis and prognosis have not been matched with clinical improvements. Unsatisfactory outcomes persist for the majority of patients with AML, particularly the elderly. Novel agents and treatment approaches are needed in the induction, post-remission and relapsed settings. The additions of clofarabine for relapsed or refractory disease and the hypomethylating agents represent recent advances. Clinical trials of FLT3 inhibitors have yielded disappointing results to date, with ongoing collaborations attempting to identify the optimal role for these agents. Potential leukemia stem cell targeted therapies and treatments in the setting of minimal residual disease are also under investigation. In this review, we will discuss recent advances in AML treatment and novel therapeutic strategies.
Lack of quality discrimination in online information markets

Online social networks are marketplaces in which memes compete for our attention. While one would expect the best ideas to prevail, empirical evidence suggests that high-quality information has no competitive advantage. Here we investigate this puzzling lack of discriminative power through an agent-based model that incorporates behavioral limitations in managing a heavy flow of information and measures the relationship between the quality of an idea and its likelihood to become prevalent at the system level. We find that both information overload and limited attention contribute to a degradation in the market's discriminative power. A good tradeoff between discriminative power and diversity of information is possible according to the model. However, calibration with empirical data characterizing information load and finite attention in real social media reveals a weak correlation between quality and popularity of information. In these realistic conditions, the model provides an interpretation for the high volume of viral misinformation we observe online.
Ex-Post Merger Review and Divestitures

Divestitures have received little attention in ex-post evaluations of mergers. In partial remedy we simulate the effects of the Carlsberg-Pripps merger in the Swedish beer market and compare the predicted outcomes with those observed ex-post. There are no important price increases following the merger and prices of divested beers fell. Our merger simulations, that are based on a random coefficients logit model, capture these pricing patterns and suggest that the divestitures were important in limiting price increases. Knowledge of the retailer's markup rules allows us to discard retailer behavior as an explanation for the pricing patterns.
A RAM-based neural network architecture for wafer scale integration

The paper presents the design of a RAM-based Artificial Neural Network (ANN) architecture which is particularly well suited to WSI implementation. Using data from a VLSI test chip and prototype system manufactured in 1993, we compare the performance and complexity of FPGA, VLSI and WSI RAM-based neural systems.
Advancing Information Systems Evaluation (Research): A Critical Realist Approach

The aim of Information Systems (IS) evaluation research is to produce ever more detailed answers to the question of why an IS initiative works (better) for whom and in what circumstances. This paper presents an alternative to traditional IS evaluation research approaches. The alternative, realistic IS evaluation research, is based on the philosophy of critical realism. Realistic IS evaluation research attends to how and why an IS initiative has the potential to cause (desired) changes and seeks to understand for whom and in what circumstances (contexts) an IS initiative works through the study of contextual conditioning. The paper also discusses problems with traditional IS evaluation research approaches and how realistic IS evaluation overcomes the problems.
Eye movement desensitization and reprocessing in conjoint couples therapy: a grounded theory study

Eye movement desensitization and reprocessing (EMDR) is an evidence-based trauma treatment primarily conducted within individual therapy. Though it has been incorporated into couples and family therapy in recent years, limited research has examined its use within conjoint couples therapy and none has systematically investigated the experience of both clients and therapists. The purpose of this grounded theory study was to explore the experiences of couples and therapists during conjoint EMDR. Interviews were conducted with 21 participants including seven couples and their therapists. Interviews were analyzed using Strauss and Corbin’s (1998) grounded theory data analysis. The theory developed from the data about conjoint EMDR as a relational trauma treatment provides perspectives not captured in previous research and offers guidance about assessment and preparation procedures.