Miracle-dz/UniFAR-FTUs huggingface dataset:
Domestic demand for Indigenous tourism in Australia: understanding motivations, barriers, and implications for future development

Tourism is promoted as a development tool for Indigenous communities in many countries around the world. Governments and non-governmental organisations have been developing and supporting this strategy with the aim of improving socio-economic indicators amongst Indigenous populations. However, statistics show that domestic demand for Australian Indigenous tourism products, compared to four other types of mainstream tourism, is quite low. In order to explore why domestic visitors are less engaged in Indigenous tourism than other tourism types, this study adopts a mixed-methods case study to identify domestic visitors’ motivations for, and barriers to, participating in Indigenous tourism activities. Semi-structured interviews using a photo-elicitation method were conducted with 52 domestic visitors at the Grampians National Park, Victoria, Australia. The findings suggest that there are differences in motivations for, and barriers to, participating in Indigenous tourism activities. Strategies suggested in this study include increasing the awareness of the different forms of Indigenous tourism activities through promotional material at different points, educating the visitor information centre staff in regard to the existing products offered, creating a seasonal restructure of the existing tourism products offered in the cultural centre, and leveraging the point of differentiation by highlighting the key elements of the local Indigenous culture.
Recommending Movies Based on Mise-en-Scene Design

In this paper, we present an ongoing work that will ultimately result in a movie recommender system based on the Mise-en-Scène characteristics of the movies. We believe that the preferences of users on movies can be well described in terms of the mise-en-scène, i.e., the design aspects of movie making influencing aesthetic and style. Examples of mise-en-scène characteristics are lighting, colors, background, and movements. Our recommender system opens new opportunities in the design of new user interfaces able to offer a personalized way to search for interesting movies through the analysis of film styles rather than using the traditional classifications of movies based on explicit attributes such as genre and cast.
Systematic review of social network analysis in adolescent cigarette smoking behavior.

BACKGROUND Social networks are important in adolescent smoking behavior. Previous research indicates that peer context is a major causal factor of adolescent smoking behavior. To date, however, little is known about the influence of peer group structure on adolescent smoking behavior. METHODS Studies that examined adolescent social networks with regard to their cigarette smoking behavior were identified through online and manual literature searches. Ten social network analysis studies involving a total of 28,263 adolescents were included in the final review. RESULTS Of the 10 reviewed studies, 6 identify clique members, liaisons, and isolates as contributing factors to adolescent cigarette smoking. Significantly higher rates of smoking are noted among isolates than clique members or liaisons in terms of peer network structure. Eight of the reviewed studies indicate that peer selection or influence precedes adolescents' smoking behavior and intent to smoke. Such peer selection or influence accounts for a large portion of similarities among smoking adolescents. CONCLUSION Adolescents who are identified as isolates are more likely to smoke and engage in risk-taking behaviors than others in the peer network structure. Given that the vast majority of current adult smokers started their smoking habits during adolescence, adolescent smoking prevention efforts will likely benefit from incorporating social network analytic approaches and focusing the efforts on isolates and other vulnerable adolescents from a peer selection and influence perspective.
A Near-Quadratic Lower Bound for the Size of Quantum Circuits of Constant Treewidth

We show that any quantum circuit of treewidth $t$, built from $r$-qubit gates, requires at least $\Omega(\frac{n^{2}}{2^{O(r\cdot t)}\cdot \log^4 n})$ gates to compute the element distinctness function. Our result generalizes a near-quadratic lower bound for quantum formula size obtained by Roychowdhury and Vatan [SIAM J. on Computing, 2001]. The proof of our lower bound follows by an extension of Ne\v{c}iporuk's method to the context of quantum circuits of constant treewidth. This extension is made via a combination of techniques from structural graph theory, tensor-network theory, and the connected-component counting method, which is a classic tool in algebraic geometry.
Does an Early College Readiness Signal Discourage College Application and Enrollment?

States are currently seeking ways to improve alignment between K–12 and postsecondary education and better prepare students for postsecondary schooling. Some states have begun implementing policies that inform high school students that they are not ready for college-level courses well before they graduate, in order to give both notice and time to prepare. However, if the early information from these tests suggests that a student may require remedial coursework upon entry to college, the student may be discouraged from attending college. This article focuses on one state's effort to increase students’ information about college readiness. Regression discontinuity design is used to determine whether a student receiving a signal that he/she needs remediation lowers the probability of that student applying to or enrolling in participating universities. Results indicate that the policy has had little to no impact on application and no impact on enrollment.
Achievable Rates of Physical Layer Network Coding Schemes on the Exchange Channel

Network coding, where relay nodes combine the information received from multiple links rather than simpl y replicating and forwarding the received packets, has shown the promise of significantly improving system performance.In very recent works, multiple researchers have presented met hods for increasing system throughput by employing network codi ng inspired methods to mix packets at the physical layer: physi callayer network coding (PNC). A common example used to validat e much of this work is that of two sources exchanging informati on through a single intervening relay a situation that we denote the “exchange channel”. In this paper, achievable rates of vari ous schemes on the exchange channel are considered. Achievable rates for traditional multi-hop routing approaches, network coding approaches, and various PNC approaches are considered. A new method of PNC inspired by Tomlinson-Harashima precoding (THP), where a modulo operation is used to control the power at the relay, is introduced.
Hyperdocuments et hypercartes, vers une modélisation d'écriture ...

Digitaliser l’ecriture signifie ecriture digitale. La plupart des produits multimedias s’en tiennent a dupliquer les procedes d’ecriture et de lecture herites du livre. Toutefois, quelques creations hypermedias presentent des procedes specifiques d’ecriture et de lecture. Ces creations nous permettent de mieux comprendre les limites et les faiblesses de nos habitudes d’ecriture et de lecture. Analyser les produits multi et hypermedia nous conduit a reflechir sur les questions posees sur les hyperdocuments, a savoir le statut des auteurs hypermedia et quel espace temps les hypermedias proposent-ils.
Air Mail: NPR Sees "Community" in Letters From Listeners

This study of National Public Radio's "letters from listeners" segments applies two theoretical frameworks–imagined community and news work–to suggest that the practice of "imagining community" is a distinct practice of journalism. The findings suggest that NPR producers use letters segments to encourage listeners to feel as if they are part of a community. In selecting and packaging those comments, producers construct an imagined community that reflects the producers' own journalistic values, such as balance, accountability, and accuracy. The conclusion suggests that community-imagining might be especially difficult and abstract for large news media organizations serving massive and dispersed audiences.
Retrovirally engineered T‐cell‐based immunotherapy targeting type III variant epidermal growth factor receptor, a glioma‐associated antigen

The isotype of epidermal growth factor receptor variant III (EGFRvIII) is often identified in glioblastomas. Previously, we created a mouse monoclonal antibody, 3C10 (IgG2b), that specifically recognized EGFRvIII, and a recombinant single‐chain variable fragment of 3C10. The aim of the current study was to develop genetically engineered T cells, termed T‐bodies, that express a chimeric receptor consisting of the 3C10 single‐chain variable fragment coupled to signaling modules such as the CD3zeta (ζ) chain, for the treatment of tumors expressing mutant EGFR. After successful construction of the chimeric 3C10/CD3ζ T‐cell receptor, its expression on the T‐body was observed using western blotting and flow cytometry. The specificity of the T‐body for EGFRvIII was evaluated using an interferon‐gamma Elispot assay and a standard 51Cr‐release cytotoxicity assay. Furthermore, we demonstrated that the systemically delivered T‐body infiltrated the intrabrain tumor and significantly delayed tumor growth. These results indicate that the T‐body expressing the chimeric 3C10/CD3ζ T‐cell receptor specifically recognized glioma cells expressing EGFRvIII. In conclusion, T‐body‐based immunotherapy appears to be a promising approach for the treatment of glioma. (Cancer Sci 2010; 101: 2518–2524)
Context Augmentation for Convolutional Neural Networks

Recent enhancements of deep convolutional neural networks (ConvNets) empowered by enormous amounts of labeled data have closed the gap with human performance for many object recognition tasks. These impressive results have generated interest in understanding and visualization of ConvNets. In this work, we study the effect of background in the task of image classification. Our results show that changing the backgrounds of the training datasets can have drastic effects on testing accuracies. Furthermore, we enhance existing augmentation techniques with the foreground segmented objects. The findings of this work are important in increasing the accuracies when only a small dataset is available, in creating datasets, and creating synthetic images.
Effect of moisture on breakdown voltage and structure of palm based insulation oils

For use as insulating liquids in power transformers, palm based oils are gaining worldwide attention as biodegradable alternatives and competitors to the petroleum based oils. However, moisture ingress into insulation oils and its effect on the oil breakdown voltage (BDV) must be studied because moisture in oil can alter the physicochemical property of oil and drastically affects the breakdown voltage. This paper presents a comparative study on effect of increasing moisture content on physical structure and BDV of three palm based insulation oils (Red Palm Oil, Refined Bleached Deodorized Palm Oil (RBDPO) and Palm Fatty Acid Ester (PFAE)) at room temperature. Insulation oil is known to have the ability to sustain its good performance up to certain percentage moisture increase. In this paper, an empirical relationship between the palm based oil BDV and the percentage moisture added was developed to approximate oil BDV at various moisture increases. Fourier Transform Infrared (FTIR) Spectroscopy was employed to investigate changes in the oil samples' functional group to assess the physical changes. This equation has been tested and verified. This equation can be used to estimate the BDV of the palm based insulation oils as their percentage moisture increases.
Where are all the genes?

Andrews et al. argue for adaptationism in cognitive research. However, the problem of evolvability brings into question the number of genes required for the evolution of cognitive mechanisms. Are there enough? Also, greater consideration should be given to alternative vicarious selection processes, which may produce cognitive mechanisms. Finally, identifying constraints with optimality arguments is more difficult than the authors think.
A model for reasoning about JavaScript promises

In JavaScript programs, asynchrony arises in situations such as web-based user-interfaces, communicating with servers through HTTP requests, and non-blocking I/O. Event-based programming is the most popular approach for managing asynchrony, but suffers from problems such as lost events and event races, and results in code that is hard to understand and debug. Recently, ECMAScript 6 has added support for promises, an alternative mechanism for managing asynchrony that enables programmers to chain asynchronous computations while supporting proper error handling. However, promises are complex and error-prone in their own right, so programmers would benefit from techniques that can reason about the correctness of promise-based code. Since the ECMAScript 6 specification is informal and intended for implementers of JavaScript engines, it does not provide a suitable basis for formal reasoning. This paper presents λp, a core calculus that captures the essence of ECMAScript 6 promises. Based on λp, we introduce the promise graph, a program representation that can assist programmers with debugging of promise-based code. We then report on a case study in which we investigate how the promise graph can be helpful for debugging errors related to promises in code fragments posted to the StackOverflow website.
Sport participation and self-esteem as mediated by perceived peer acceptance and sport self-concept in Taiwanese college students

In this study we posited that sport participation would be positively associated with self-esteem through the mediating variables of perceived peer acceptance and sport self-concept. A survey was completed by 489 Taiwanese college students using a 2-stage sampling design and structural equation modeling revealed that sport participation positively predicts peer acceptance and sport self-concept. Additionally, peer acceptance and sport self-concept positively predict self-esteem.
Achieving Autonomous Compressive Spectrum Sensing for Cognitive Radios

Compressive sensing (CS) technologies present many advantages over other existing approaches for implementing wideband spectrum sensing in cognitive radios (CRs), such as reduced sampling rate and computational complexity. However, there are two significant challenges: 1) choosing an appropriate number of sub-Nyquist measurements and 2) deciding when to terminate the greedy recovery algorithm that reconstructs wideband spectrum. In this paper, an autonomous compressive spectrum sensing (ACSS) framework is presented that enables a CR to automatically choose the number of measurements while guaranteeing the wideband spectrum recovery with a small predictable recovery error. This is realized by the proposed measurement infrastructure and the validation technique. The proposed ACSS can find a good spectral estimate with high confidence by using only a small testing subset in both noiseless and noisy environments. Furthermore, a sparsity-aware spectral recovery (SASR) algorithm is proposed to recover the wideband spectrum without requiring knowledge of the instantaneous spectral sparsity level. Such an algorithm bridges the gap between CS theory and practical spectrum sensing. Simulation results show that ACSS not only can recover the spectrum using an appropriate number of measurements but can considerably improve the spectral recovery performance as well, compared with existing CS approaches. The proposed recovery algorithm can autonomously adopt a proper number of iterations, therefore solving the problems of underfitting or overfitting, which commonly exist in most greedy recovery algorithms.
Measurement and analysis of brain deformation during neurosurgery

Recent studies have shown that the surface of the brain is deformed by up to 20 mm after the skull is opened during neurosurgery, which could lead to substantial error in commercial image-guided surgery systems. We quantitatively analyze the intraoperative brain deformation of 24 subjects to investigate whether simple rules can describe or predict the deformation. Interventional magnetic resonance images acquired at the start and end of the procedure are registered nonrigidly to obtain deformation values throughout the brain. Deformation patterns are investigated quantitatively with respect to the location an magnitude of deformation, and to the distribution and principal direction of the displacements. We also measure the volume change of the lateral ventricles by manual segmentation. Our study indicates that brain shift occurs predominantly in the hemisphere ipsi-lateral to the craniotomy, and that there is more brain deformation during resection procedures than during biopsy or functional procedures. However, the brain deformation patterns are extremely complex in this group of subjects. This paper quantitatively demonstrates that brain deformation occurs not only at the surface, but also in deeper brain structure, and that the principal direction of displacement does not always correspond with the direction of gravity. Therefore, simple computational algorithms that utilize limited intraoperative information (e.g., brain surface shift) will not always accurately predict brain deformation at the lesion.
There ’ s Something About Bayes : Effective Probabilistic Programming for the Rest of Us

Bayes’ rule is a fundamental and general mechanism that composes hypotheses and data, which is arguably the purpose of many computer programs, and yet few programming languages or libraries incorporate Bayes’ rule as an abstraction. Bayes’ rule has several benefits. (1) It provides a formalism for programs to express composition of data from multiple sources to improve the program’s accuracy, e.g., combining GPS sensor data with road network map data. (2) It provides a technique for programs to implement personalization, by composing the original model with user history or preferences. (3) It expresses the potential for programs to reconsider decisions or speculate about a decision before all the data is available. This position paper explores the challenges involved in making Bayes’ rule a programming language operator in general purpose languages. We focus on average developers, who do not have deep knowledge of statistics or machine learning, and on efficiency, since developers find it challenging to reason about the hard-to-predict, potentially unbounded runtime costs of existing implementations of Bayes’ rule in inference algorithms.
Absent Fathers or Absent Variables? A New Look at Paternal Incarceration and Delinquency

Objectives: This research examines the association between paternal incarceration and children’s delinquency. Prior research suggests an association, although omitted variable bias is an enduring issue. Methods: To help address issues related to unobserved heterogeneity, we employ a method uncommonly used in criminological research. Rather than comparing the children of incarcerated fathers to respondents who have never had a father incarcerated, we exploit the longitudinal nature of the National Longitudinal Study of Adolescent Health to generate a strategic comparison group: respondents who will have a father incarcerated in the future. We also examine two types of delinquency, expressive and instrumental, to infer plausible mechanisms linking paternal incarceration and delinquency. Results: When using “futures” as comparison cases, results differ from much prior work and suggest a spurious association between paternal incarceration and instrumental delinquency (e.g., theft). Paternal incarceration retains a significant effect on expressive delinquency, which is partly mediated by reduced attachment to fathers. Conclusions: The association between paternal incarceration and expressive (but not instrumental) crime supports Agnew’s strain theory and elements of control theory. Our comparison group also offers important advantages in terms of addressing unobserved heterogeneity, and we think this approach would prove useful for other topics in criminology.
Distributed gradient and particle swarm optimization for multi-robot motion planning

SUMMARY Two distributed stochastic search algorithms are proposed for motion planning of multi-robot systems: (i) distributed gradient, (ii) swarm intelligence theory. Distributed gradient consists of multiple stochastic search algorithms that start from different points in the solutions space and interact with each other while moving toward the goal position. Swarm intelligence theory is a derivative-free approach to the problem of multi-robot cooperation which works by searching iteratively in regions defined by each robot's best previous move and the best previous move of its neighbors. The performance of both approaches is evaluated through simulation tests.
Obstetric critical care: A blueprint for improved outcomes

Introduction:Obstetric patients are generally young and healthy. However, the potential for catastrophic complications is real, and despite the therapeutic advances of the last few decades, maternal morbidity and mortality continue to occur. This may be related to the pregnancy itself, aggravation of a preexisting illness, or complications of the (operative) delivery. Purpose:The purpose of this review is two-fold: first, to provide an update on currently available reports pertaining to important critical care issues of the obstetric patient population and, second, to present current comprehensive treatment options for preeclampsia and massive obstetric hemorrhage because both are responsible for the majority of maternal mortality and morbidity worldwide. Results:The most common reasons for intensive care unit admission are hypertensive disorders and massive obstetric hemorrhage. Timely delivery and prompt initiation of antihypertensive therapy for severe hypertension form the mainstay of care in preeclampsia. Restoration of circulating blood volume and rapid control of bleeding and impaired coagulation are the main factors in the management of massive obstetric hemorrhage. Puerperal morbidity has become the main topic of quality of care issues in maternity care. Although the Acute Physiology and Chronic Health Evaluation II score is commonly used in the intensive care unit, it does not seem to be appropriate for pregnant women because it overestimates their mortality rates. A high-dependency care unit suits the needs for at least half of the obstetric patient population in need of higher acuity care and will save considerable cost. Conclusion:Emphasis on early detection of maternal problems and prompt referral to tertiary centers with intensive care unit facilities to provide optimum care of the circulation, blood pressure, and respiration at an early stage could minimize the prevalence of multiple organ failure and mortality in critically ill obstetric patients.
PCD and autophagy in the unicellular green alga Micrasterias denticulata

Programmed cell death (PCD) plays a central role in normal plant development and is also induced by various biotic and abiotic stress factors. In the unicellular freshwater green alga Micrasterias denticulata morphological and biochemical hallmarks such as the appearance of autophagosomes, increased production of ROS and degradation of genomic DNA into small fragments (“DNA laddering”) indicate PCD. Our data not only demonstrate that Micrasterias is capable of performing PCD under salt stress, but also that it is triggered by the ionic and not osmotic component of salinity. Additionally, results from the present and previous studies suggest that different inducers may lead to different cell death pathways in one and the same organism.