Miracle-dz/UniFAR-FTUs huggingface dataset:
The needs and experiences of family members and friends of someone with Borderline Personality Disorder

While the past twenty years has witnessed a surge in studies of caregiving, the experiences and needs of family members and friends caring for someone with Borderline Personality Disorder (BPD) has received limited attention. The aim of this thesis was to fill this gap. It provides an in-depth examination of family members’ and friends’ caregiving experiences, coping styles, psychological consequences, and interactions with health care systems when caring for someone with BPD. Two studies were conducted. Study one was largely exploratory and focussed on the lived experience of the thirteen family members who participated. Using semi-structured interviews and a phenomenological approach in the qualitative analysis, four distinct coping groups were identified. The Loving-Active coping group and In-Flux participant used high levels of problem coping in combination with emotion-focused coping strategies and low levels of avoidant coping strategies. The Bound and Powerless, and the Distanced Managers groups used higher levels of avoidant coping, with the Bound and Powerless also using high levels of drugs and alcohol to cope. Challenging behaviours demonstrated by their BPD family members in this study were associated with the coping styles and explanations given for the trauma symptoms experienced by all caregivers. Despite these challenges, most participants talked about experiencing turning-points and positive outcomes in the longer term. Many more negative than positive experiences were recalled by family members in their experience of services and in their contacts with professionals that contributed to caregivers’ feelings of isolation. The aim of Study Two was to examine whether findings of Study One could be generalized to a larger and more varied sample of carers. The sample comprised 175 family members and friends, who completed a paper or internet survey on experience of caregiving, quality of life, coping strategies, distress levels, possible trauma, experiences of services for their BPD relative, and possible personal growth as a result of their experiences. Five distinct groups were identified based on their differing use of coping strategies. The Calmer Positive, Reactive Positive, and the Mixed Extreme groups used high levels of a combination of emotion and problem focused coping strategies, with the first two groups also low on avoidant coping.
A General Condition Number for Polynomials

This paper presents a generic condition number for polynomials that is useful for polynomial evaluation of a finite series of polynomial basis defined by means of a linear recurrence. This expression extends the classical one for the power and Bernstein bases, but it also provides us a general framework for all the families of orthogonal polynomials like Chebyshev, Legendre, Gegenbauer, Jacobi, and Sobolev orthogonal polynomial bases. The standard algorithm for the evaluation of finite series in any of these polynomial bases is the extended Clenshaw algorithm. The use of this new condition number permits us to give a general theorem about the forward error for that evaluation algorithm. A running-error bound of the extended algorithm is also presented and all the bounds are compared in several numerical examples.
Predictors of Science Success: The Impact of Motivation and Learning Strategies on College Chemistry Performance

PREDICTORS OF SCIENCE SUCCESS: THE IMPACT OF MOTIVATION AND LEARNING STRATEGIES ON COLLEGE CHEMISTRY PERFORMANCE by Shari B. Obrentz As the number of college students studying science continues to grow, it is important to identify variables that predict their success. The literature indicates that motivation and learning strategy use facilitate science success. Research findings show these variables can change throughout a semester and differ by performance level, gender and ethnicity. However, significant predictors of performance vary by research study and by group. The current study looks beyond the traditional predictors of grade point averages, SAT scores and completion of advanced placement (AP) chemistry to consider a comprehensive set of variables not previously investigated within the same study. Research questions address the predictive ability of motivation constructs and learning strategies for success in introductory college chemistry, how these variables change throughout a semester, and how they differ by performance level, gender and ethnicity. Participants were 413 introductory college chemistry students at a highly selective university in the southeast. Participants completed the Chemistry Motivation Questionnaire (CMQ) and Learning Strategies section of the Motivated Strategies for Learning Questionnaire (MSLQ) three times during the semester. Self-efficacy, effort regulation, assessment anxiety and previous achievement were significant predictors of chemistry course success. Levels of motivation changed with significant decreases in self-efficacy and increases in personal relevance and assessment anxiety. Learning strategy use changed with significant increases in elaboration, critical thinking, metacognitive self-regulation skills and peer learning, and significant decreases in time and study management and effort regulation. High course performers reported the highest levels of motivation and learning strategy use. Females reported lower intrinsic motivation, personal relevance, self-efficacy and critical thinking, and higher assessment anxiety, rehearsal and organization. Self-efficacy predicted performance for males and females, while self-determination, help-seeking and time and study environment also predicted female success. Few differences in these variables were found between ethnicity groups. Self-efficacy positively predicted performance for Asians and Whites, and metacognitive self-regulation skills negatively predicted success for Other students. The results have implications for college science instructors who are encouraged to collect and utilize data on students’ motivation and learning strategy use, promote both in science classes, and design interventions for specific students who need more support. PREDICTORS OF SCIENCE SUCCESS: THE IMPACT OF MOTIVATION AND LEARNING STRATEGIES ON COLLEGE CHEMISTRY PERFORMANCE
Canadian children's literature: an Alberta survey

This article presents the findings of an online survey administered to Alberta elementary school teachers in 2000-2001. The survey explored the teachers’ knowledge and use of Canadian children’s literature and their thoughts about the role of Canadian literature in elementary school classrooms. Canadian children’s trade books espouse particular images and values that assist young Canadians in developing a sense of their national and personal identities. Respondents expressed their thoughts about the advantages and disadvantages of using Canadian literature. Overall, most teachers appeared to be unfamiliar with contemporary Canadian books and included Canadian materials in their classroom instruction only for specific Canadian content.
Audio Signal Processing Using Time-Frequency Approaches: Coding, Classification, Fingerprinting, and Watermarking

Audio signals are information rich nonstationary signals that play an important role in our day-to-day communication, perception of environment, and entertainment. Due to its non-stationary nature, time- or frequency-only approaches are inadequate in analyzing these signals. A joint time-frequency (TF) approach would be a better choice to efficiently process these signals. In this digital era, compression, intelligent indexing for content-based retrieval, classification, and protection of digital audio content are few of the areas that encapsulate a majority of the audio signal processing applications. In this paper, we present a comprehensive array of TF methodologies that successfully address applications in all of the above mentioned areas. A TF-based audio coding scheme with novel psychoacoustics model, music classification, audio classification of environmental sounds, audio fingerprinting, and audio watermarking will be presented to demonstrate the advantages of using time-frequency approaches in analyzing and extracting information from audio signals.
Data spectroscopy: learning mixture models using eigenspaces of convolution operators

In this paper we develop a spectral framework for estimating mixture distributions, specifically Gaussian mixture models. In physics, spectroscopy is often used for the identification of substances through their spectrum. Treating a kernel function K(x, y) as "light" and the sampled data as "substance", the spectrum of their interaction (eigenvalues and eigenvectors of the kernel matrix K) unveils certain aspects of the underlying parametric distribution p, such as the parameters of a Gaussian mixture. Our approach extends the intuitions and analyses underlying the existing spectral techniques, such as spectral clustering and Kernel Principal Components Analysis (KPCA). We construct algorithms to estimate parameters of Gaussian mixture models, including the number of mixture components, their means and covariance matrices, which are important in many practical applications. We provide a theoretical framework and show encouraging experimental results.
An Investigation of Network Fractality ∗

In the last decade many generative models that claim to accurately emulate real-world, scale-free networks have emerged. Many of these are constructed through some notion of self-similarity. However, validation of these models generally occurs by a well-known trifecta of degree distribution, clustering coefficients, and pagerank scores. Every published model shows charts of success under these measurements which makes it appear they can be applied to any graph with success. There currently exists no way to quantifiably and more objectively determine the accuracy of these models with respect to the original network they attempt to emulate. We propose using the box-counting method and community size distributions viewed over reductions to more thoroughly validate these fractal-like models. These methods allow us to probe the fractality of graphs by looking for self-similarity at multiple levels, giving additional insight into which models emulate the actual recursive generation of the network, not just the network properties themselves. We found that these methods of analyses improve our understanding of the many multifractal networkgenerating models we scrutinized. Preliminary analysis on a diverse set of datasets confirm our hypothesis: certain models, which appear effective on a superficial level, fail to emulate the real network under these new techniques. These methods allow us to determine which fractal network-generating models is best suited for a given network.
A Study of Childhood and Late Adolescent Fear: The Role of Fear in Socioemotional Functioning

The purpose of this study was to investigate the role that fear in childhood plays in socio-emotional functioning in late adolescence. In addition, the role of parental support in this relationship was examined. Participants included 70 college students, ages 18-25 years, who completed measures that assessed their fears and perceptions of social support from parents in childhood, as well as aspects of their present wellbeing (e.g., selfesteem, mood, depressive symptoms). Results interestingly indicated that, “someone in the family dying” was the most highly rated fear in both childhood and late adolescence. Also, fear of family members dying and family members becoming ill were rated higher than personal fears of death, or getting cancer for each age range. Furthermore, there were significant correlations between childhood and late adolescent fear for each of the 74 specific fears assessed. Thus childhood fear and late adolescent fear are directly linked. Lastly, analyses showed that for those with low levels of parental support, childhood fear was associated with lower wellbeing, whereas for those with high levels of parental support, there was no relation between childhood fear and late adolescent wellbeing. Results suggest that high levels of parental support serve as a type of buffer for children with fear, protecting their future wellbeing. Limitations of the present study, as well as future directions for research, are discussed.
High-stable Er-doped all-fiber pulse laser based on free standing black phosphorus

A free-standing black phosphorus saturable absorber (BP-SA) fabricated by a modified electrochemical delamination strategy exfoliation process was inserted inside a Er-doped ring laser cavity. Based on the saturable absorber of BP, a stable pulse laser could be achieved. When the pump power increased to 30 mW, the pulse laser began to initiate. The maximum output power was 4.8 mW. And the repetition rate could vary from 23.24 kHz to 69.65 kHz. The obtained minimum pulse duration is 1.67 μs. These demonstrations indicate that BP could apply in pulse laser field and benefit industrial community in the future.
Liveness Detection for Embedded Face Recognition System

— To increase reliability of face recognition system, the system must be able to distinguish real face from a copy of face such as a photograph. In this paper, we propose a fast and memory efficient method of live face detection for embedded face recognition system, based on the analysis of the movement of the eyes. We detect eyes in sequential input images and calculate variation of each eye region to determine whether the input face is a real face or not. Experimental results show that the proposed approach is competitive and promising for live face detection.
Injuries from interpersonal violence presenting to a rural health center in Western Kenya: characteristics and correlates

Objective: To define the scope of injury due to interpersonal violence in a medium-sized town in Western Kenya. Design: Prospective, cross-sectional data collection and analysis. Setting/subjects: Data were prospectively collected on all injured patients (n = 562) presenting to a health center in Western Kenya, 2002–2004. Age, gender, type, and severity of injury, relationship to assailant, disposition, and clinician’s suspicion of alcohol use were recorded. Main outcome measures: Number of injuries due to interpersonal violence; correlation of gender, alcohol use, relationship to assailant, and type of injury. Results: Interpersonal violence caused 43% of all injuries. Men and women were equally likely to suffer violent injuries (42% vs 45%); however, women were more likely to suffer injury from domestic violence (4.7% vs 7.0%) and sexual assault (0% vs 3.5%). Men and women were equally likely to know their assailant. Women were more likely to be injured by a spouse/partner (19% vs 1.3%), whereas men were more likely to be injured by an acquaintance (29% vs 16%). Alcohol use was more often suspected for victims of violent, as opposed to unintentional, injury (45% vs 16%). Men with violent injuries were more likely than women to be suspected of having used alcohol (51% vs 35%). Conclusions: Interpersonal violence is a leading cause of injury in Western Kenya. Although men and women are equally likely to be assaulted, women are more likely to be injured by a spouse, and men by an acquaintance. Alcohol use is common among those who suffer violent injuries in this population.
Beyond Professionalism: Can a Good Engineer Make a Good Political Leader?

There has been an established myth of stereotype traits which good political leaders must possess, but which unfortunately as interpreted by many, are generally lacking in a typical engineer, making him unfit for such positions. In this paper, reflections and justifications why engineers are fit for leadership positions were made. It discusses some of the engineering potentials like creativity, drive for implementation of concepts, the strength behind an introverted nature, and social responsibility and how it can be adapted for leadership position. It compares and contrasts engineers and scientists as far as leadership are concerned. The summary of the findings is that a good engineer can make a good political Leader. Keywords : Engineer, Professionalism, Political leader, Potential
Learning to Transfer: Transferring Latent Task Structures and Its Application to Person-Specific Facial Action Unit Detection

In this article we explore the problem of constructing person-specific models for the detection of facial Action Units (AUs), addressing the problem from the point of view of Transfer Learning and Multi-Task Learning. Our starting point is the fact that some expressions, such as smiles, are very easily elicited, annotated, and automatically detected, while others are much harder to elicit and to annotate. We thus consider a novel problem: all AU models for the target subject are to be learnt using person-specific annotated data for a reference AU (AU12 in our case), and no data or little data regarding the target AU. In order to design such a model, we propose a novel Multi-Task Learning and the associated Transfer Learning framework, in which we consider both relations across subjects and AUs. That is to say, we consider a tensor structure among the tasks. Our approach hinges on learning the latent relations among tasks using one single reference AU, and then transferring these latent relations to other AUs. We show that we are able to effectively make use of the annotated data for AU12 when learning other person-specific AU models, even in the absence of data for the target task. Finally, we show the excellent performance of our method when small amounts of annotated data for the target tasks are made available.
Cockayne syndrome group B protein has novel strand annealing and exchange activities

Cockayne syndrome (CS) is a rare inherited human genetic disorder characterized by UV sensitivity, severe neurological abnormalities and prageroid symptoms. The CS complementation group B (CSB) protein is involved in UV-induced transcription coupled repair (TCR), base excision repair and general transcription. CSB also has a DNA-dependent ATPase activity that may play a role in remodeling chromatin in vivo. This study reports the novel finding that CSB catalyzes the annealing of complementary single-stranded DNA (ssDNA) molecules with high efficiency, and has strand exchange activity. The rate of CSB-catalyzed annealing of complementary ssDNA is 25-fold faster than the rate of spontaneous ssDNA annealing under identical in vitro conditions and the reaction occurs with a high specificity in the presence of excess non-homologous ssDNA. The specificity and intrinsic nature of the reaction is also confirmed by the observation that it is stimulated by dephosphorylation of CSB, which occurs after UV-induced DNA damage, and is inhibited in the presence of ATPγS. Potential roles of CSB in cooperation with strand annealing and exchange activities for TCR and homologous recombination are discussed.
Deep Sparse-coded Network (DSN)

We present Deep Sparse-coded Network (DSN), a deep architecture based on multilayer sparse coding. It has been considered difficult to learn a useful feature hierarchy by stacking sparse coding layers in a straightforward manner. The primary reason is the modeling assumption for sparse coding that takes in a dense input and yields a sparse output vector. Applying a sparse coding layer on the output of another tends to violate the modeling assumption. We overcome this shortcoming by interlacing nonlinear pooling units. Average- or max-pooled sparse codes are aggregated to form dense input vectors for the next sparse coding layer. Pooling achieves nonlinear activation analogous to neural networks while not introducing diminished gradient flows during the training. We introduce a novel backpropagation algorithm to finetune the proposed DSN beyond the pretraining via greedy layerwise sparse coding and dictionary learning. We build an experimental 4-layer DSN with the ℓ1-regularized LARS and the greedy-ℓ0 OMP, and demonstrate superior performance over a similarly-configured stacked autoencoder (SAE) on CIFAR-10.
Emerging therapeutic options for breast cancer chemotherapy during pregnancy.

BACKGROUND Breast cancer is the commonest solid tumor observed during pregnancy. Anthracycline-based chemotherapy is feasible during the 2nd and 3rd trimesters of pregnancy, but few data are available on recent and highly active drugs taxanes, vinorelbine and anti-HER-2 agents in this setting. PATIENTS AND METHODS We carried out a comprehensive review of reports documenting the use of taxanes, vinorelbine, trastuzumab and lapatinib during pregnancy in the English literature, in order to evaluate their safety profile in pregnant patients. RESULTS Twenty-four pregnancies are described, in which no grade 3-4 maternal toxicity nor malformation in the offspring was reported. Whereas only one report studied the pharmacokinetics of paclitaxel (Taxol) during pregnancy, several preclinical reports indicate that the placental P-glycoprotein could prevent the transplacental transfer of taxanes and vinorelbine. The use of trastuzumab was associated with the occurrence of anhydramnios in three of six cases. CONCLUSION The administration of recent drugs taxanes and vinorelbine seems feasible during the 2nd and 3rd trimesters of pregnancy, with a favorable toxicity profile. In contrast, anti-HER-2 agents may obscure the normal development of the fetal kidney, and should be avoided during pregnancy.
Cerebellar Abiotrophy in a Family of Border Collie Dogs

Cerebellar abiotrophies have a nonsex-linked, autosomal, recessively inherited basis in a number of species, and lesions typically reflect profound and progressive loss of Purkinje cells. In this report, an unusual form of abiotrophy is described for two sibling Border Collies. Extensive loss of the cerebellar granular cell layer was present with relative sparing of Purkinje cells of two female pups. The biochemical basis for this form of cerebellar abiotrophy is unknown, but the lack of disease in other siblings supports an autosomal recessive mode of inheritance.
Hybrid-ARQ-Aided Short Fountain Codes Designed for Block-Fading Channels

As a benefit of their inherent rateless nature, fountain codes constitute a favorable choice for protecting packet-based transmissions in the physical layer for wireless channels having varying quality. However, previous research has revealed that the performance of fountain codes substantially degrades as their block length is reduced. Three structural phenomena of the Tanner graph were identified by Mackay in the hard decoding of fountain codes on binary erasure channels (BECs), which may be referred to as having “no degree-one check nodes (CNs),” “no emerging degree-one CNs,” and “uncovered variable nodes (VNs). ” In this paper, we explicitly analyzed how these structural phenomena influence their soft decoding algorithm. Furthermore, these phenomena are shown to be responsible for the high error floors when fountain codes are transmitted over noisy fading channels, particularly for the transmissions of short blocks. To eliminate the influence of these structural phenomena, we conceived a technique of generating a few specifically encoded bits with the aid of the associated Tanner graph. Simulation results have demonstrated that our improved Raptor (IRaptor) codes significantly reduce the packet loss ratio (PLR) of conventional fountain codes, despite imposing reduced low complexity. Finally, we conceive a novel adaptive hybrid automatic repeat request (HARQ) scheme based on a lookup table (LUT)-aided technique, which may adapt its coding rate for each transmission. Our simulation results demonstrated that the proposed IRaptor HARQ achieves a similar performance to the Long-Term Evolution (LTE) turbo-coded HARQ scheme or even outperforms the LTE arrangement for block length in excess of 1000 bits.