Miracle-dz/UniFAR-FTUs huggingface dataset:
Journey to Wholeness: The Experience of Engaging in Physical Occupation for Women Survivors of Childhood Abuse

Abstract This qualitative study was undertaken to add to the conceptual understanding of occupation through exploring the subjective experience of engaging in physical occupation for women survivors of Childhood Sexual Abuse (CSA). In‐depth interviews were used to gather rich contextual narrative of two women's long‐term experiences of engaging in physical occupation, martial arts and circus performance, as survivors of childhood abuse. The women identified engagement in physical occupation as having supported them to reconnect to their bodies and experience their lives more fully. The findings suggest that further research exploring the role of physical occupation in initiating the experience of embodiment and supporting a holistic mind‐body integration of the abuse experience would add to the knowledge base of occupational science and inform clinical practice with survivors of childhood abuse.
Queer-Friendly Neighbourhoods: Interrogating Social Cohesion across Sexual Difference in Two Australian Neighbourhoods

This paper examines processes of social cohesion across sexual difference in ‘queer-friendly neighbourhoods’—localities that have a heterosexual majority in residential and commercial terms, but where a significant presence of gay and lesbian residents, businesses, and organisations are welcomed. This investigation advances a lineage of work on the development and maintenance of gay and lesbian neighbourhoods, and their role in residents' well-being. The findings also extend understandings of social cohesion, a key theme in neighbourhood and policy research across the West. The context of this study is Australia, where recent projects on social cohesion have focused on public order, economic benefits, and race tensions. However, given that gay men and lesbians are present in Australian social and political debates, sexuality should be integrated into studies about neighbourhood cohesion. To analyse processes of cohesion between heterosexual and same-sex-attracted people, we draw on data from case studies of two queer-friendly neighbourhoods in Australia—the inner-city suburb of Newtown, NSW, and the regional town of Daylesford, Victoria. We discuss the findings in three analytical categories to highlight common processes and characteristics of queer-friendly neighbourhoods: diversity and difference; symbolic landscapes; combating homophobia.
Finding All Spanning Trees of Directed and Undirected Graphs

An algorithm for finding all spanning trees (arborescences) of a directed graph is presented. It uses backtracking and a method for detecting bridges based on depth-first search. The time required is $O(V + E + EN)$ and the space is $O(V + E)$, where V, E, and N represent the number of vertices, edges, and spanning trees, respectively. If the graph is undirected, the time decreases to $O(V + E + VN)$, which is optimal to within a constant factor. The previously best-known algorithm for undirected graphs requires time $O(V + E + EN)$.
Efficient Learning of Deep Boltzmann Machines

We present a new approximate inference algorithm for Deep Boltzmann Machines (DBM’s), a generative model with many layers of hidden variables. The algorithm learns a separate “recognition” model that is used to quickly initialize, in a single bottom-up pass, the values of the latent variables in all hidden layers. We show that using such a recognition model, followed by a combined top-down and bottom-up pass, it is possible to efficiently learn a good generative model of high-dimensional highly-structured sensory input. We show that the additional computations required by incorporating a top-down feedback plays a critical role in the performance of a DBM, both as a generative and discriminative model. Moreover, inference is only at most three times slower compared to the approximate inference in a Deep Belief Network (DBN), making large-scale learning of DBM’s practical. Finally, we demonstrate that the DBM’s trained using the proposed approximate inference algorithm perform well compared to DBN’s and SVM’s on the MNIST handwritten digit, OCR English letters, and NORB visual object recognition tasks.
Literacy Challenges for Language Learners in Job-Training Programs

This paper describes a study carried out in a pre-apprenticeship job-training program held at a community college in central Canada, in which learners from a variety of language backgrounds were enrolled. Through a combination of student questionnaires, learner interviews, and participant observation in the classroom, information was gathered on factors affecting progress in such programs. The article describes the language and literacy challenges encountered by the learners in relation to the Luke and Freebody (1997) four-tier model of literate competency. Discussion outlines the preferred teaching characteristics which learners identified as helpful and explores the possible long-term impact of learning initial literacy in a second language.
Gating Modes in AMPA Receptors

AMPA receptors are ligand-gated ion channels that show multiple conductance levels, indicating that gating of individual AMPA subunits is to some extent independent of the other subunits. To study AMPAR subunit interactions during activation gating, we recorded from single channels in the absence of channel block and desensitization and at negative and positive membrane potentials. In saturating glutamate, the relative occupancies of the various conductance levels are consistent with complete subunit independence. In contrast, the relative occupancies in subsaturating glutamate indicate that the channel switches between a low open probability mode and a high open probability mode in which the behavior of the channel is identical to that in saturating glutamate. These gating modes occur at both negative and positive potentials, with the high open probability mode becoming more prominent at positive potentials. The switch between gating modes and its modulation by voltage and other factors may constitute a novel mechanism regulating AMPAR-mediated synaptic activity.
Phase I Study of the Effect of Gastric Acid pH Modulators on the Bioavailability of Oral Dasatinib in Healthy Subjects

Dasatinib is a tyrosine kinase inhibitor (including BCR‐ABL and the SRC family) that is effective in patients with chronic myeloid leukemia. Dasatinib has pH‐dependent solubility and is bioavailable as an oral formulation. The effect of gastric pH modifiers on dasatinib pharmacokinetics is evaluated in an open‐label, randomized, 3‐period, 3‐treatment crossover study. Twenty‐four healthy subjects receive treatment A (2 doses of dasatinib 50 mg separated by 12 hours), treatment B (famotidine 40 mg given 2 hours after dasatinib 50 mg and 10 hours before another dose of dasatinib 50 mg), and treatment C (30 mL of an antacid containing aluminum/magnesium hydroxides given 2 hours before dasatinib 50 mg and concomitantly with dasatinib 50 mg 12 hours after the previous dasatinib dose); a 7‐day washout separates each treatment period. When famotidine is administered 2 hours after dasatinib, dasatinib exposure is similar to dasatinib administered alone. However, dasatinib exposure is reduced by ∼60% when famotidine is administered 10 hours before dasatinib dosing. In contrast, dasatinib exposure is unchanged when antacid (Maalox) is administered 2 hours before dasatinib; but when the antacid is coadministered with dasatinib, dasatinib exposure is reduced by ∼55% to 58%. This indicates that H2‐receptor antagonists should not be coadministered with dasatinib. Dasatinib may be administered with acid‐neutralizing antacids if the doses are temporally separated by at least 2 hours.
Book Review: Major Criminological Theories: Concepts and Measurement

The final chapter voices concern for the potentially harmful repercussions from public opinion's guidance of criminal justice policy. The authors reveal discrepancies between public perceptions of the insanity defense and its actual application, as well as the public's perceived need to try most serious juvenile offenders as adults and its apparent lack of deterrence. Public support for capital punishment and its similar lack ofdeterrence, as well as identification of "false positives" in death penalty convictions by recent advances in DNA technology, concludes the discussion of incongruities between public opinion and scholarly research. The American public receives most of its information on crime from the mass media in the form of both news and entertainment, and homicide receives more extensive media coverage than any other crime. In The Will to Kill: Making Sense of Senseless Murder, Fox and Levin successfully equip readers with the basic criminological resources necessary to understand some ofthe most publicized murder typologies in America.
Automatically Developing a Fine-grained Arabic Named Entity Corpus and Gazetteer by utilizing Wikipedia

This paper presents a methodology to exploit the potential of Arabic Wikipedia to assist in the automatic development of a large Fine-grained Named Entity (NE) corpus and gazetteer. The corner stone of this approach is efficient classification of Wikipedia articles to target NE classes. The resources developed were thoroughly evaluated to ensure reliability and a high quality. Results show the developed gazetteer boosts the performance of the NE classifier on a news-wire domain by at least 2 points F-measure. Moreover, by combining a learning NE classifier with the developed corpus the score achieved is a high F-measure of 85.18%. The developed resources overcome the limitations of traditional Arabic NE tasks by more fine-grained analysis and providing a beneficial route for further studies.
The role of intuition in entrepreneurship and business venturing decisions

Entrepreneurial intuition is the affectively charged recognition and evaluation of a business venturing opportunity arising as a result of involuntary, rapid, non-conscious, associative processing. This article integrates theories of dual-processing and models of the business venturing (opportunity recognition, evaluation, and exploitation) in a model of entrepreneurial intuition, which links intuitive expertise, cognitive style, somatic state, and the affect heuristic with System 2 interventions and the contingencies of the decision environment. Six research propositions are offered with suggestions for how they can be tested. The theoretical and practical implications of entrepreneurial intuition are discussed in terms of the unfolding of a research agenda relating to this important but under-theorized and under-researched construct in work and organizational psychology.
Third-Order Quasi-Continuo control of induction motor

This work presents an alternative to the robust control design for the angular speed and the flux module in three-phase induction motors. The proposed controller is based in the canonical form technique using the third-order quasi-continuo control algorithm. The controller incorporates an observer which is based on the high-order sliding-mode control technique. A robust differentiator is employed for estimate the second derivative of the sliding manifold. The controller provides high features of accuracy, robustness and convergence in finite time. The performance of the proposed controller, are verified by digital simulation.
Hidden Markov Models for the Stimulus-response Relationships of Multi-state Neural Systems

Given recent experimental results suggesting that neural circuits may evolve through multiple firing states, we develop a framework for estimating state-dependent neural response properties from spike-train data. We modify the traditional hidden Markov model (HMM) framework to incorporate stimulus-driven, non-Poisson point process observations. For maximal flexibility, we allow external, time-varying stimuli and the neurons' own spike histories to drive both the spiking behavior in each state and the transitioning behavior between states. We employ an appropriately modified expectation-maximization algorithm to estimate the model parameters. The expectation step is solved by the standard forward-backward algorithm for HMMs. The maximization step reduces to a set of separable concave optimization problems if the model is restricted slightly. We first test our algorithm on simulated data and are able both to fully recover the parameters used to generate the data and to accurately recapitulate the sequence of hidden states. We then apply our algorithm to a recently published data set in which the observed neuronal ensembles displayed multi-state behavior and show that inclusion of spike-history information significantly improves the fit of the model. Additionally, we show that a simple reformulation of the state-space of the underlying Markov chain allows us to implement a hybrid half-multistate/half-histogram model which may be more appropriate for capturing the complexity of certain data sets than either a simple HMM or a simple peri-stimulus time histogram (PSTH) model alone.
Robot Localization through Region Tracking (Ortung eines Roboters durch Verfolgen homogener Farbregionen)

Summary In this article we describe a self-localization method for soccer robots that solves the “Where am I”-problem by visually perceiving and understanding the structure of the field lines on a soccer playing field. The method accrued from our participation in the RoboCup Middle Size league where fully autonomous robots play soccer on a 8 × 12 m field. The method was successfully applied by our FU-Fighter's Middle Size robots during the world championships in 2004, where we placed 4th.
Scalable classifiers for Internet vision tasks

Object recognition systems designed for Internet applications typically need to adapt to userspsila needs in a flexible fashion and scale up to very large data sets. In this paper, we analyze the complexity of several multiclass SVM-based algorithms and highlight the computational bottleneck they suffer at test time: comparing the input image to every training image. We propose an algorithm that overcomes this bottleneck; it offers not only the efficiency of a simple nearest-neighbor classifier, by voting on class labels based on the k nearest neighbors quickly determined by a vocabulary tree, but also the recognition accuracy comparable to that of a complex SVM classifier, by incorporating SVM parameters into the voting scores incrementally accumulated from individual image features. Empirical results demonstrate that adjusting votes by relevant support vector weights can improve the recognition accuracy of a nearest-neighbor classifier without sacrificing speed. Compared to existing methods, our algorithm achieves a ten-fold speed increase while incurring an acceptable accuracy loss that can be easily offset by showing about two more labels in the result. The speed, scalability, and adaptability of our algorithm makes it suitable for Internet vision applications.
Interference Conditions of the Reconsolidation Process in Humans: The Role of Valence and Different Memory Systems

Following the presentation of a reminder, consolidated memories become reactivated followed by a process of re-stabilization, which is referred to as reconsolidation. The most common behavioral tool used to reveal this process is interference produced by new learning shortly after memory reactivation. Memory interference is defined as a decrease in memory retrieval, the effect is generated when new information impairs an acquired memory. In general, the target memory and the interference task used are the same. Here we investigated how different memory systems and/or their valence could produce memory reconsolidation interference. We showed that a reactivated neutral declarative memory could be interfered by new learning of a different neutral declarative memory. Then, we revealed that an aversive implicit memory could be interfered by the presentation of a reminder followed by a threatening social event. Finally, we showed that the reconsolidation of a neutral declarative memory is unaffected by the acquisition of an aversive implicit memory and conversely, this memory remains intact when the neutral declarative memory is used as interference. These results suggest that the interference of memory reconsolidation is effective when two task rely on the same memory system or both evoke negative valence.
A particle filter for objects tracking in cognitive MIMO system

In this paper, we concern with a Cognitive radar operating in an environment with multiple interested targets. It is an intelligent parameter estimation system that the transmitted signal depends on the input signal. The mean-square error of the estimated parameters is derived in the Craḿer-Rao Lower Bound that represent the performance of the system. To improve the ability to find a new target when the number of targets is changing, we degenerate the prior information about the targets. A Bayesian approach is used to estimate state space of the system or tracking target parameters. For non-linear system and non- Gaussian distribution, a sampling method called particle filter is introduced. We also consider the effect of the Importance Sampling in the particle filter to the performance of the system. Finally, the simulation result is provided to demonstrate the capability of the cognitive radar system to track the targets.
Exact solutions in microwave holography validated by measurements

We present exact solutions to problems of antenna holography based on planar, cylindrical, and spherical near-field data as processed by our novel Field Mapping Algorithm (FMA). On the basis of the FMA, full field distribution information in the source region is determined exactly, from two tangential field components over a planar, cylindrical, or spherical surface. Stated in so many words, all three components of both electric and magnetic fields in the antenna aperture are obtained exactly from two-component near-field data. Exact solutions for microwave holography were found in this way utilizing planar, cylindrical, and spherical near-field data. These closed-form solutions have been verified by analytic examples and various cases simulated numerically on the computer. In addition, eight hardware measurements were carried out to validate the closed-form FMA solutions, as follows: in planar holography, Case 1, a slot array antenna, with and without aperture blockage, Case 2, a patch antenna with adjacent blockage, Case 3, a horn antenna with model aircraft as a source of interference in its vicinity, Case 4, field transit across a dielectric lens, Case 5, metallic obstacle detection within a multilayered dielectric medium, and Case 6, having an omni antenna under test and planar near-field data acquired over all six faces of an enclosing rectangular box; in cylindrical holography, Case 7, with measured near field from a sector antenna; and finally, in spherical holography, Case 8, the measured near field being that of a patch antenna. Excellent results were obtained throughout, with antenna geometries and/or field distributions clearly revealed across all eight source regions.
3D control of a high-speed quadruped trot

Purpose – Legged vehicles offer several advantages over wheeled vehicles, particularly on broken terrain, but are presently too slow to be considered for many high‐speed tasks. This paper presents an effective 3D controller for a high‐speed quadruped trot.Design/methodology/approach – To successfully regulate forward velocity and heading, secondary motions such as body pitch and roll must be stabilised. The complicated coupling between pitch and roll motion causes the control effort on one axis to disturb the motion and control effort of the other. Unlike the modular methods in previous research, the algorithm presented here employs a cooperative approach where pitch stability effort is directly accounted for by the roll controller.Findings – When the secondary motions such as pitch and roll are well stabilized, forward velocity and heading can be regulated up to 3 m/s and 20°/s, respectively.Research limitations/implications – For many quadrupeds, trotting is usually employed as the precursor to gallopin.
Market Risk-Adjusted Dividend Policy and Price-to-Book Ratio

This paper offers a new mathematical formulation that addresses the relationship between expected price-to-book ratio, dividend per share, dividend payout ratio, systematic and unsystematic risks. The sample includes the non-financial firms in the DJIA covering the period 1997-2006. The general results show that the expected price-to-book ratios are: (a) positively associated with the expected dividend payout ratios, (b) negatively associated with the current dividend payout ratios, (c) due to an improvement in the expected firm profitability.The paper contributes to the current literature in two ways. First, the variations in price-to-book ratios, systematic and unsystematic risks are not due to dividends per se. Second, the relationships between expected price-to-book ratio and dividend payout ratios are intrinsically nonlinear, which is not addressed in the relevant literature. Third, the expected dividend payout ratios can be used efficiently for signaling purposes as well as a proxy for measuring the agency problem.
Sex and the TAT: Are Women More Intimate Than Men? Do Men Fear Intimacy?

Imaginative stories written in response to either 6 or 10 different Thematic Apperception Test (TAT) pictures by over 1,500 college students in two studies were scored for intimacy motivation (McAdams, 1980), and a subset were scored for fear of intimacy (Pollack & Gilligan, 1982). The intimacy motive is a recurrent preference or readiness for experiences of warm, close, and communicative interaction with others, and it is assessed by coding the quality of the interpersonal interaction exhibited by characters in a TAT story. Fear of intimacy, on the other hand, is assumed to reveal itself through images of violence displayed in affiliative and intimate situations in TAT stories, for example, when one lover kills another. In both studies, women scored significantly higher than men on intimacy motivation, especially with respect to the intimacy themes of Relationship Produces Positive Affect, Relationship Transcends Space-Time Limitations, Surrender of Control in Relationships, and Connection to Outside Wor.
Accuracy Enhancement for Forecasting Water Levels of Reservoirs and River Streams Using a Multiple-Input-Pattern Fuzzification Approach

Water level forecasting is an essential topic in water management affecting reservoir operations and decision making. Recently, modern methods utilizing artificial intelligence, fuzzy logic, and combinations of these techniques have been used in hydrological applications because of their considerable ability to map an input-output pattern without requiring prior knowledge of the criteria influencing the forecasting procedure. The artificial neurofuzzy interface system (ANFIS) is one of the most accurate models used in water resource management. Because the membership functions (MFs) possess the characteristics of smoothness and mathematical components, each set of input data is able to yield the best result using a certain type of MF in the ANFIS models. The objective of this study is to define the different ANFIS model by applying different types of MFs for each type of input to forecast the water level in two case studies, the Klang Gates Dam and Rantau Panjang station on the Johor river in Malaysia, to compare the traditional ANFIS model with the new introduced one in two different situations, reservoir and stream, showing the new approach outweigh rather than the traditional one in both case studies. This objective is accomplished by evaluating the model fitness and performance in daily forecasting.