Miracle-dz/UniFAR-FTUs huggingface dataset:
The process of homelessness

> > Abstract _ This study explores the process of shelter exits for homeless individuals and for different shelter types in the public shelter system in the city of Groningen in the Netherlands. Individualised administrative data from the Groningen Homelessness Monitor (2003–2006) are used for estimating a Weibull hazard rate regression model. Results show that the different shelter types fit adequately with their policy purposes : lengths of stay in emergency shelter accommodation appear to be shorter than in supportive housing accommodation and clients tend to remain longest in guest houses. The results also indicate that the use of day shelters, as well as demographic variables such as age, gender and nationality, are associated with a differenti-ated likelihood of continuing homelessness. The results of this study in the Dutch context are compared with research on the process of homelessness in the United States, and finally the implications of these findings and future directions for research on the process of homelessness are discussed. the use of day shelters in relation to the use of night shelters. Use of a day shelter is positive and statistically significant for the overall model, which implies that persons who also apply to use a day shelter have longer stays than those who do not. The positive relationship is found for all shelter types, but while it is significant for emergency shelters and supportive housing accommodation, it is not significant in the case of guest house residents.
There Is No Asymmetry of Identity Assumptions in the Debate over Selection and Individuals

A long-running dispute concerns which adaptation-related explananda natural selection can be said to explain. (The issue is conceptual—not empirical—and orthogonal adaptationism.) At issue are explananda of the form: why a given individual organism has a given adaptation rather than that same individual having another trait. It is broadly agreed that one must be ready to back up a “no” answer with an appropriate theory of trans-world identity for individuals. I argue, against the conventional wisdom, that the same is true for a “yes” answer. My conclusion recasts the landscape and opens the door to a potential resolution.
Teachers’ Perceptions of Learners’ Proficiency in Statistical Literacy, Reasoning and Thinking

This paper explores teachers’ perceptions of their learners’ proficiency in statistical literacy, reasoning and thinking. Research in Statistics education has prompted a move away from the teaching of statistical skills, towards focusing on the development of statistical literacy, reasoning and thinking. The recent South African Grade 10–12 Mathematics curriculum change reflects this move. A specific challenge for South Africa is that teachers should understand the new intended outcomes of statistics when assessing learners. The participants (n = 66) included Grade 12 Mathematics teachers (females = 40%) from a district in the Free State, South Africa, selected through convenience sampling. A quantitative research approach was used by administering a 13-item Likert scale questionnaire with the Grade 12 Mathematics teachers. The responses were summarised descriptively as frequencies and percentages. The results indicated that two in three teachers perceived their learners to obtain acceptable proficiency in statistical literacy as defined by the literature. In contrast, only one in three teachers perceived their learners usually or almost always to be proficient in statistical reasoning and statistical thinking as defined by the literature. The findings of this study showed that about half of the Mathematics teachers do not see the connection between the action words in the curriculum, and the aspects of statistical reasoning and statistical thinking to be assessed. The large percentage of teachers uncertain about the proficiency of their learners in statistical reasoning and statistical thinking leads to the conclusion that teachers need to be provided with pre-service or in-service training strengthening their Subject Matter Knowledge and Pedagogical Content Knowledge related to the key intended outcomes of statistics assessment, that is, proficiency in statistical literacy, reasoning and thinking.
ANALIZA PORÓWNAWCZA WYBRANYCH INDEKSÓW GIEŁDOWYCH RYNKÓW DOJRZAŁYCH I WSCHODZĄCYCH Z WYKORZYSTANIEM WYKŁADNIKA HURSTA

Artykul zawiera wyniki badan dotyczących analizy porownawczej wystepowania dlugookresowej zalezności logarytmicznych stop zwrotu indeksow gieldowych z podzialem na rynki dojrzale i wschodzące. Glownym celem badan byla fraktalna analiza wybranych indeksow gieldowych, gdzie szczegolny nacisk polozono na znalezienie zalezności poziomu szacowanego wykladnika Hursta od typu i charakteru rynku oraz czynnikow gospodarczych. W tym celu wykorzystano metode analizy przeskalowanego zakresu.
A conceptual framework of cool for social marketing

PurposeThe purpose of this paper is to present a conceptual framework of cool for social marketing through a comprehensive literature review and integrating extant literature on cool. Design/methodology/approachA comprehensive search and review of extant literature across social marketing, business disciplines, arts, psychology, social sciences and humanities was undertaken to develop an understanding of cool and its relevance to social marketing. The review permitted developing a comprehensive set of characteristics that are associated with cool. FindingsA conceptual framework of cool organised according to the following dimensions is presented and discussed: deviating from norm, self-expressive, indicative of maturity, subversive, pro-social, evasive, and attractive. Originality/valueThis paper advances theoretical knowledge in the social marketing domain by offering a conceptual framework of cool, and by suggesting a set of guidelines to develop cool social marketing programs. Â© 2016, Â© Emerald Group Publishing Limited.
Exploration of Phytochemical and Antibacterial Potentiality of Anagallis arvensis L. Extract against Methicillin-Resistant Staphylococcus aureus (MRSA)

Zabol Medicinal Plants Research Center, Zabol University of Medical Sciences, Zabol, Iran. Department of Pharmacognosy, Faculty of Pharmacy, Zabol University of Medical Sciences, Zabol, Iran. Pediatric Infections Research Center, Mofid Children Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Department of Range and Watershed Management, Faculty of Natural Resources, University of Zabol, Iran. Department of Chemistry, Faculty of Science, University of Zabol, Zabol 98615-538, Iran. Department of Food Science and Technology, Faculty of Agriculture, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran. Zabol University of Medical Sciences, Zabol, Iran.
Social Context Theory: A systems model of social change based on social needs and social issues

The theme of this edition is community development in the North Australian region. The articles included discuss ways of advancing positive community involvement, and report on a range of issues that impact on community development. Social Context theory is presented as a tool of analysis for examining social changes and associated needs among individuals in societies over time. The concept of community development has particular relevance to the fields of psychology, social psychology, and community services, because individuals look to the availability of community involvement options, and the potential they offer, for accessing social activities and social support networks. Social Context theory is therefore central to community development.
Pets and Urban Life

The social and the emotional roles played by pets in the urban setting are examined. Data obtained from a random telephone survey of 320 pet owners and 116 non-pet-owners in Providence, Rhode Island, are presented. Responses to the survey indicate that pets are viewed as important family members by people who live in the city. Further, pet owners stress the positive roles played by pets in the household rather than the negative consequences of pet ownership. Pet-human interaction in the urban household is primarily positive. However, the emotional dimensions of pet-human relationships vary over a number of significant social variables. Pet attachment is highest among single, divorced, widowed, and remarried people as well as among childless couples, newlyweds, and empty-nesters. Urbanites are more attached to dogs than to cats and other types of pets. Pet anthropomorphism is high among single, divorced, and remarried people, dog owners, and childless couples. Data on the sociodemographic differences betwe.
Cryostorage of immature and mature human testis tissue to preserve spermatogonial stem cells (SSCs): a systematic review of current experiences toward clinical applications

While the survival rate of children with cancer is increasing, preserving fertility for prepubertal boys is still a challenge. Although intracytoplasmic sperm injection (ICSI) using frozen sperms has revolutionized infertility treatment, it is not applicable for the patients who undergo chemotherapy before puberty since spermatogenesis has not begun. Therefore, preserving spermatogonial stem cells (SSCs) as an experimental option can be provided to prepubertal patients at a risk of damage or loss of their SSCs due to cancer treatments and developmental or genetic disorders. Using frozen SSCs in testicular tissue, successful SSC autotransplantation in mouse and nonhuman primates has shown a promising future for SSC-based cell therapy. Cryopreservation of testicular tissue containing SSCs is the first step to translate SSC-based cell therapy into clinical male infertility treatment, and in the investigation into SSCs, it is very important to evaluate their quantity and functionality during this process. This systematic review summarizes the published data on cryopreservation techniques in human testis tissue for potential utilization in future clinical applications.
Defend Deep Neural Networks Against Adversarial Examples via Fixed andDynamic Quantized Activation Functions

Recent studies have shown that deep neural networks (DNNs) are vulnerable to adversarial attacks. To this end, many defense approaches that attempt to improve the robustness of DNNs have been proposed. In a separate and yet related area, recent works have explored to quantize neural network weights and activation functions into low bit-width to compress model size and reduce computational complexity. In this work, we find that these two different tracks, namely the pursuit of network compactness and robustness, can be merged into one and give rise to networks of both advantages. To the best of our knowledge, this is the first work that uses quantization of activation functions to defend against adversarial examples. We also propose to train robust neural networks by using adaptive quantization techniques for the activation functions. Our proposed Dynamic Quantized Activation (DQA) is verified through a wide range of experiments with the MNIST and CIFAR-10 datasets under different white-box attack methods, including FGSM, PGD, and C & W attacks. Furthermore, Zeroth Order Optimization and substitute model-based black-box attacks are also considered in this work. The experimental results clearly show that the robustness of DNNs could be greatly improved using the proposed DQA.
Mental Health Conceptualization and Resilience Factors in the Kalasha Youth: An Indigenous Ethnic and Religious Minority Community in Pakistan

The Kalasha are a religious, ethnic, and linguistic minority community in Pakistan. They are indigenous people living in remote valleys of the Hindu Kush Mountains in northern Pakistan, neighboring Afghanistan. The Kalasha are pastoral, as well as agricultural people to some extent, although they are increasingly facing pressures from globalization and social change, which may be influencing youth and community development. Their traditional world view dichotomizes and emphasizes on the division of the pure (Onjeshta) and the impure (Pragata). There remains a scarcity of literature on mental health and resilience of indigenous communities in South Asia and Pakistan generally, and the polytheistic Kalasha community specifically. Thus, the current study was conducted with the aim to explore the cultural protective factors (resilience) of the Kalasha youth (adolescents and emerging adults) and to explore their perceived etiological understandings and preferred interventions for mental health support systems. The theoretical framework of Bronfenbrenner's (1, 2) ecological systems model was used. Interpretative Phenomenological Analysis (IPA) was conducted, considering the advantage of its idiographic approach and the “double hermeneutic” analytic process. This methodology was consistent with the aim to understand and make sense of mental health and resilience from the Kalasha indigenous perspective. A total of 12 in-depth interviews were conducted with adolescents and emerging adults (5 males, 7 females), along with ethnographic observations. The analysis revealed 3 superordinate themes of mental health perceptions and interventions, each with more specific emergent themes: (1) Psychological Resilience/Cultural Protective Factors Buffering Against Mental Health Problems (Intra-Communal Bonding & Sharing; Kalasha Festivals & Traditions; Purity Concept; Behavioral Practice of Happiness and Cognitive Patterns); (2) Perceived Causes of Mental Health Issues (Biological & Psychosocial; Supernatural & Spiritual; Environmental); and (3) Preferred Interventions [Shamanic Treatment; Ta'awiz (Amulets); Communal Sharing & Problem Solving; Medical Treatment; Herbal Methods]. The overall findings point to the need for developing culturally-sensitive and indigenous measures and therapeutic interventions. The findings highlighted the Kalasha cultural practices which may promote resilience. The findings also call for indigenous sources of knowledge to be considered when collaboratively designing public health programs.
Manifold Adversarial Learning

Recently proposed adversarial training methods show the robustness to both adversarial and original examples and achieve state-of-the-art results in supervised and semi-supervised learning. All the existing adversarial training methods consider only how the worst perturbed examples (i.e., adversarial examples) could affect the model output. Despite their success, we argue that such setting may be in lack of generalization, since the output space (or label space) is apparently less this http URL this paper, we propose a novel method, called Manifold Adversarial Training (MAT). MAT manages to build an adversarial framework based on how the worst perturbation could affect the distributional manifold rather than the output space. Particularly, a latent data space with the Gaussian Mixture Model (GMM) will be first derived.On one hand, MAT tries to perturb the input samples in the way that would rough the distributional manifold the worst. On the other hand, the deep learning model is trained trying to promote in the latent space the manifold smoothness, measured by the variation of Gaussian mixtures (given the local perturbation around the data point). Importantly, since the latent space is more informative than the output space, the proposed MAT can learn better a robust and compact data representation, leading to further performance improvement. The proposed MAT is important in that it can be considered as a superset of one recently-proposed discriminative feature learning approach called center loss. We conducted a series of experiments in both supervised and semi-supervised learning on three benchmark data sets, showing that the proposed MAT can achieve remarkable performance, much better than those of the state-of-the-art adversarial approaches. We also present a series of visualization which could generate further understanding or explanation on adversarial examples.
SECAPR—a bioinformatics pipeline for the rapid and user-friendly processing of targeted enriched Illumina sequences, from raw reads to alignments

Evolutionary biology has entered an era of unprecedented amounts of DNA sequence data, as new sequencing technologies such as Massive Parallel Sequencing (MPS) can generate billions of nucleotides within less than a day. The current bottleneck is how to efficiently handle, process, and analyze such large amounts of data in an automated and reproducible way. To tackle these challenges we introduce the Sequence Capture Processor (SECAPR) pipeline for processing raw sequencing data into multiple sequence alignments for downstream phylogenetic and phylogeographic analyses. SECAPR is user-friendly and we provide an exhaustive empirical data tutorial intended for users with no prior experience with analyzing MPS output. SECAPR is particularly useful for the processing of sequence capture (synonyms: target or hybrid enrichment) datasets for non-model organisms, as we demonstrate using an empirical sequence capture dataset of the palm genus Geonoma (Arecaceae). Various quality control and plotting functions help the user to decide on the most suitable settings for even challenging datasets. SECAPR is an easy-to-use, free, and versatile pipeline, aimed to enable efficient and reproducible processing of MPS data for many samples in parallel.
Clinical significance of anatomical urethral length on stress urinary incontinence women

Objective To evaluate the association between the length of the female urethra and urodynamic study (UDS) parameters in patients presenting with stress urinary incontinence (SUI). Patients and methods We retrospectively enrolled 391 women who underwent a transobturator adjustable tape sling implantation for the management of SUI between April 2009 and March 2016. All patients underwent a physical examination and UDS. Filling cystometry data that were evaluated included the Valsalva leak point pressure, and the cough leak point pressure. The maximal urethral closure pressure, the functional urethral length (FUL), and the length of the continence zone (LCZ) were measured using the urethral profile obtained from the UDS. The anatomical urethral length (AUL) was measured intraoperatively using a Foley catheter. We calculated the LCZ/FUL ratio to determine the anatomical continence length for this study. Results We noted that 299 patients completed the study. The mean AUL was 26.25±3.59 mm. The mean FUL and LCZ were 41.37±15.05 mm and 24.15±13.90 mm, respectively. The mean LCZ/FUL was 0.57±0.19. Using the Pearson correlation coefficient, we observed that the AUL showed a correlation with the FUL (0.168, P=0.034), the LCZ (0.212, P=0.007), the LCZ/FUL ratio (0.190, P=0.016), the Valsalva leak point pressure (0.223, P=0.005), the cough leak point pressure (0.215, P=0.006), and the maximal urethral closure pressure (0.178, P=0.043). Conclusion Our results show that the AUL in women is associated with UDS parameters relating to SUI.
Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris Recognition

Binarized statistical image features (BSIF) have been successfully used for texture analysis in many computer vision tasks, including iris recognition and biometric presentation attack detection. One important point is that all applications of BSIF in iris recognition have used the original BSIF filters, which were trained on image patches extracted from natural images. This paper tests the question of whether domain-specific BSIF can give better performance than the default BSIF. The second important point is in the selection of image patches to use in training for BSIF. Can image patches derived from eye-tracking experiments, in which humans perform an iris recognition task, give better performance than random patches? Our results say that (1) domain-specific BSIF features can out-perform the default BSIF features, and (2) selecting image patches in a task-specific manner guided by human performance can out-perform selecting random patches. These results are important because BSIF is often regarded as a generic texture tool that does not need any domain adaptation, and human-task-guided selection of patches for training has never (to our knowledge) been done. This paper follows the reproducible research requirements, and the new iris-domain-specific BSIF filters, the patches used in filter training, the database used in testing and the source codes of the designed iris recognition method are made available along with this paper to facilitate applications of this concept.
Using Context Ontology and Linear SVM for Chronic Kidney Disease Prediction

In the e-Health learning area, the use of chronic patient context has become very important given the increase in the number of individuals who suffer from these diseases and the unavailability of medications. Specifically, chronic kidney failure is one of the diseases that goes undetected and undiagnosed until it is well advanced. The need for preventive prediction remains an essential task for the well-being of patients at risk. In this paper, we aim to explore the added value of using ontology-based prediction, focusing on Linear SVM, to deal with Chronic Kidney Disease Problems.
Shielded Decision-Making in MDPs

A prominent problem in artificial intelligence and machine learning is the safe exploration of an environment. In particular, reinforcement learning is a well-known technique to determine optimal policies for complicated dynamic systems, but suffers from the fact that such policies may induce harmful behavior. We present the concept of a shield that forces decision-making to provably adhere to safety requirements with high probability. Our method exploits the inherent uncertainties in scenarios given by Markov decision processes. We present a method to compute probabilities of decision making regarding temporal logic constraints. We use that information to realize a shield that---when applied to a reinforcement learning algorithm---ensures (near-)optimal behavior both for the safety constraints and for the actual learning objective. In our experiments, we show on the arcade game PAC-MAN that the learning efficiency increases as the learning needs orders of magnitude fewer episodes. We show tradeoffs between sufficient progress in exploration of the environment and ensuring strict safety.
Object Detection With Deep Learning: A Review

Due to object detection’s close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles that combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy, and optimization function. In this paper, we provide a review of deep learning-based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely, the convolutional neural network. Then, we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection, and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network-based learning systems.