Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinician Perspectives on Delaying Initiation of Antiretroviral Therapy for Clinically Eligible HIV-Infected Patients

Objectives: Guidelines for antiretroviral therapy (ART) initiation have evolved, but consistently note that adherence problems should be considered and addressed. Little is known regarding the reasons providers delay ART initiation in clinically eligible patients. Methods: In 2009, we surveyed a probability sample of HIV care providers in 582 outpatient facilities in the United States and Puerto Rico with an open-ended question about nonclinical reasons for delaying ART initiation in otherwise clinically eligible patients. Results: Very few providers (2%) reported never delaying ART. Reasons for delaying ART were concerns about patient adherence (68%), patient acceptance (60%), and structural barriers (33%). Provider and practice characteristics were associated with reasons for delaying ART. Conclusion: Reasons for delaying ART were consistent with clinical guidelines and were both patient level and structural. Providers may benefit from training and access to referrals for ancillary services to enhance their ability to monitor and address these issues with their patients.
Destabilizing Networks 1

97-1-0037, NSF IRI9633 662, Army Re search Labs, NSF ITR/IM IIS-0081219, NSF KDI IIS-9980109, NSF IGERT: CASOS, and the Pennsylvania Infrastructure Technology Alliance, a partnership of Carnegie Mellon, Lehigh University, and the Commonwealth of Pennsylvania's Department of E con om ic and Com mu nity Develop me nt. Additional support was provided by ICES (the In stitute for Complex Engineered Systems) and CASOS – the center for Computational Analysis of Social and Organizational Systems at Carnegie Mellon Un iversity (http://www.casos.ece.cmu.edu ). The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of the Office of Na val Research , the N ation al Scien ce Fo und ation or the U . S. governm ent.
Passage of known and proposed targets in the targetTB pipeline

An account of the passage of known targets (previously reported in literature) through the targetTB pipeline. The putative targets are classified based on their broad functional categories. A, B, C, E, F, G, H, I, J and K refer to the different filters depicted in Fig. 1 and described in the text. ‘3’ indicates that the given protein passes the filter, while a ‘5’ indicates a failure. A ‘? ’ indicates that the analysis was not performed due to lack of appropriate data, while ‘–’ indicates that the protein was not passed through the filter due to failure at a previous stage. All proteins in the H-list indicated in Fig. 3 would have a ‘3’ at levels A–H. ‘l’ indicates the additional lists (I/J/K) in which a target from the H-List is present.
Hybrid stereo matching by dynamic programming with enhanced cost entry for real-time depth generation

In this paper, a hybrid stereo matching method combining cross-based adaptive window aggregation and basic dynamic programming (DP) is proposed. A well-known drawback of DP is the streaking artifact, which greatly influences the subjective effect. Rather than introducing variable tree structures, we tackle this problem by providing an enhanced cost entry for DP optimization. Inter-scanlines are related closely by cost aggregating within a surface adaptive window, in which way the artifact is greatly reduced. Another contribution of our paper is that a variable starting point scheme is presented to force DP optimization to start from the accurate point, which prevents error cost accumulating at the beginning. Evaluation results show that our method outperforms most DP based algorithms. More importantly, the proposed hybrid method can be used in realtime systems. CUDA implementation of the proposed method can generate a disparity map in 15.17ms for a typical stereo pair with resolution of 384 × 288 and 16 disparity levels.
Breaking Free from Smoking: A Novel Digital Smoking Cessation Intervention for Offenders in UK Prisons

Introduction: The level of smoking cessation support across UK prisons is variable, with most offering pharmacological support, such as nicotine replacement therapy. However, with a complete smoking ban in prisons in England now imminent, additional standardised behavioural support is necessary to help offenders go smoke-free. Aims: This study used the Behaviour Change Wheel to aim to develop the content of an online smoking cessation intervention for offenders, with consideration of their capability, motivation and opportunity for behaviour change. Methods: This was an intervention development study. The Behaviour Change Wheel was used to map cognitive, behavioural, physiological and social targets for the intervention, onto appropriate intervention techniques for inclusion in the smoking cessation programme for offenders. Results: Psychological capability, social opportunity and reflective and automatic motivation were identified through deductive thematic analysis as areas of change required to achieve smoking cessation. A total of 27 behavioural change techniques were chosen for this smoking cessation intervention and were mapped onto the Lifestyle Balance Model which provided the theoretical basis on which the components of the programme are conceptualised. This included strategies around increasing motivation to quit, anticipating smoking triggers, modifying smoking-related thoughts, regulating emotions, managing cravings, replacing smoking and rewarding nicotine abstinence and adopting a healthier lifestyle. Conclusions: Through the utilisation of the Behaviour Change Wheel, the development process of this digital smoking cessation intervention was achieved. Further research is planned to evaluate the clinical effectiveness of this intervention and to explore how the programme is implemented in practice within prison settings.
Inequality Aversion in Ultimatum Games with Asymmetric Con∞ict Payofis { A Theoretical and Experimental Analysis ⁄ {

Assuming inequality averse subjects as modeled by Fehr and Schmidt (1999) or in the ERC model by Bolton and Ockenfels (2000) in ultimatum games with asymmetric conflict payoffs allows to make predictions especially concerning responder acceptance thresholds. These predictions are tested in a laboratory experiment eliciting proposer offers and respondent's acceptance thresholds using the strategy vector method. By and large both models make good predictions. However, they are unable to convincingly explain the observed selfishness on behalf of responders in ultimatum games favoring them in conflict. Overall, observed behavior gives rise to a context dependent interpretation of inequality aversion and to Knez and Camerer's 1995 observation that subjects form 'egocentric assessments of fairness'.
Fast and memory effective i-vector extraction using a factorized sub-space

Most of the state-of-the-art speaker recognition systems use a compact representation of spoken utterances referred to as i-vectors. Since the "standard" i-vector extraction procedure requires large memory structures and is relatively slow, new approaches have recently been proposed that are able to obtain either accurate solutions at the expense of an increase of the computational load, or fast approximate solutions, which are traded for lower memory costs. We propose a new approach particularly useful for applications that need to minimize their memory requirements. Our solution not only dramatically reduces the storage needs for i-vector extraction, but is also fast. Tested on the female part of the tel-tel extended NIST 2010 evaluation trials, our approach substantially improves the performance with respect to the fastest but inaccurate eigen-decomposition approach, using much less memory than any other known method.
Correspondence pattern attribute selection for consumption of federated data sources

When consuming data from federated domains, it is often necessary to identify the relationships that exist between the data schemas used in each domain. Discovering the exact nature of these relationships is difficult due to data set schema heterogeneity. Prior work has focused on inter-domain class equivalence. However it is not always possible to find an equivalent class in both schemas. For example, when instances are modeled as classes in one domain (e.g. router type) but as the attribute values of a single class in the other domain (e.g. router interface). This paper investigates whether when classifying instances in one data set against a second schema, it may be more useful to use some attribute (or attribute group) other than the original class type, to perform this classification. A machine-learning based classification approach to appropriate attribute selection is presented and its operation is evaluated using two large data-sets available on the web as Linked Data. The classification problem is compounded by the less formal semantics of Linked Data when compared to full ontologies but this also highlights the strength of our approach to dealing with noisy or under-specified data-sets and schemas. The experimental results show that our attribute selection approach is capable of discovering appropriate mappings for cases where the correspondence is conditioned on one attribute and that information gain provides a suitable scoring function for selection of correspondence patterns to describe these complex attribute-based mappings.
Discovery of complex glitch patterns: A novel approach to Quantitative Data Cleaning

Quantitative Data Cleaning (QDC) is the use of statistical and other analytical techniques to detect, quantify, and correct data quality problems (or glitches). Current QDC approaches focus on addressing each category of data glitch individually. However, in real-world data, different types of data glitches co-occur in complex patterns. These patterns and interactions between glitches offer valuable clues for developing effective domain-specific quantitative cleaning strategies. In this paper, we address the shortcomings of the extant QDC methods by proposing a novel framework, the DEC (Detect-Explore-Clean) framework. It is a comprehensive approach for the definition, detection and cleaning of complex, multi-type data glitches. We exploit the distributions and interactions of different types of glitches to develop data-driven cleaning strategies that may offer significant advantages over blind strategies. The DEC framework is a statistically rigorous methodology for evaluating and scoring glitches and selecting the quantitative cleaning strategies that result in cleaned data sets that are statistically proximal to user specifications. We demonstrate the efficacy and scalability of the DEC framework on very large real-world and synthetic data sets.
Oculomotor, Vestibular, and Reaction Time Tests in Mild Traumatic Brain Injury

Objective Mild traumatic brain injury is a major public health issue and is a particular concern in sports. One of the most difficult issues with respect to mild traumatic brain injury involves the diagnosis of the disorder. Typically, diagnosis is made by a constellation of physical exam findings. However, in order to best manage mild traumatic brain injury, it is critically important to develop objective tests that substantiate the diagnosis. With objective tests the disorder can be better characterized, more accurately diagnosed, and studied more effectively. In addition, prevention and treatments can be applied where necessary. Methods Two cohorts each of fifty subjects with mild traumatic brain injury and one hundred controls were evaluated with a battery of oculomotor, vestibular and reaction time related tests applied to a population of individuals with mild traumatic brain injury as compared to controls. Results We demonstrated pattern differences between the two groups and showed how three of these tests yield an 89% sensitivity and 95% specificity for confirming a current diagnosis of mild traumatic brain injury. Interpretation These results help better characterize the oculomotor, vestibular, and reaction time differences between those the mild traumatic brain injury and non-affected individuals. This characterization will allow for the development of more effective point of care neurologic diagnostic techniques and allow for more targeted treatment which may allow for quicker return to normal activity.
A generic approach to free form surface generation

A theoretical approach to construct free form surfaces is presented. We develop the concepts that arise when a free form surface is generated by tracing a mesh, using dieren-tiable manifold theory, and generalizing the B-spline scheme. This approach allows to dene a family of practical schemes. Three dierent practical applications of the generic approach are also presented in this paper.
Linguistic Processing in Lattice-Based Taxonomy Construction

Building a lattice-based taxonomy over a text corpus with formal concept analysis (FCA) methods requires preliminary text processing that would enable construction of a context. We consider several natural language processing methods aimed at automatic attribute acquisition from texts. In particular, we derive attributes of three types: frequent words, latent topics and named entities. Afterwards, we construct a context for each type taking documents in the corpus as a set of objects. Then the corresponding concept lattices are built and pruned with the help of stability index in order to improve the readability of the diagrams. The proposed technique is illustrated on a collection of 26 texts in English dealing with political domain. In this case, the technique serves as a tool for deeper understanding of the interests of different political actors producing political texts by clarifying the connections between notions they use in them.
A Review of Barriers and Enablers to Diagnosis and Management of Persons with Dementia in Primary Care

Background With the rise in the prevalence of dementia disorders and the growing critical impact of dementia on health-care resources, the provision of dementia care has increasingly come under scrutiny, with primary care physicians (PCP) being at the centre of such attention. Purpose To critically examine barriers and enablers to timely diagnosis and optimal management of community living persons with dementia (PWD) in primary care. Methods An interpretive scoping review was used to synthesize and analyze an extensive body of heterogeneous Western literature published over the past decade. Results The current primary care systems in many Western countries, including Canada, face many challenges in providing responsive, comprehensive, safe, and cost-effective dementia care. This paper has identified a multitude of highly inter-related obstacles to optimal primary dementia care, including challenges related to: a) the complex biomedical, psychosocial, and ethical nature of the condition; b) the gaps in knowledge, skills, attitudes, and resources of PWD/caregivers and their primary care providers; and c) the broader systemic and structural barriers negatively affecting the context of dementia care. Conclusions Further progress will require a coordinated campaign and significantly increased levels of commitment and effort, which should be ideally orchestrated by national dementia strategies focusing on the barriers and enablers identified in this paper.
A Framework for Ontology Learning from Textual Data

Ontology learning from textual data is one of the topics in artificial intelligence and knowledge engineering research area. Literatures show that there are various works have been done in this area of research, which can be addressed into three different perspectives, which are methodologies, evaluations and applications. This paper provides a review on some of commonly ontology learning methods and techniques. This paper will also propose a framework for ontology learning from domain-specific texts.
The Impact of Defendant Gender and Attractiveness on Juror Decision-Making in a Sexual Offense Case

ABSTRACT In recent years, instances of educator sexual assault against students have flooded the media. In particular, female teachers who abused students have seized the public’s attention as they are often portrayed as attractive and a sexual fantasy. This portrayal can then impact the way society perceives these sexual assaults. Importantly, however, it is not known whether this perception influences the prosecution and sentencing of these cases. The current study examined the impact of gender and attractiveness of a teacher, as well as gender of the student victim, on juror decision-making in a teacher/student sexual assault case. Using a 2 (Teacher Gender) X 2 (Student Gender) X 3 (Attractiveness Level: Attractive/Unattractive/No Photo) experimental design, we examined jurors’ decisions regarding guilt, sentencing, and perceptions of the teacher and student. Results suggest that the gender of the teacher may impact juror decision-making; however, the level of attractiveness of the teacher and gender of the student had minimal effects. The study contributes to our understanding of how educators who commit sexual acts against students are perceived and treated in the court system.
On the robustness of an iterative maximum a posteriori channel estimation for Alamouti's space-time transmit diversity

We evaluate the Alamouti's technique of diversity with a realistic estimation algorithm considering a very fast time-varying channel. We consider the maximum a posteriori iterative semi-blind channel estimation algorithm proposed in Kammoun and Siala (July-August 2003). We show in this paper that it can profit from an optimum initialization by means of the MAP training-based block-by-block channel estimation algorithm. The performance of the channel estimation algorithm with optimum initialization is evaluated by simulation results. We focus also on its robustness to errors in channel and noise statistics.
Efficient Algorithms for Solving Shortest Paths on Nearly Acyclic Directed Graphs

This paper presents new algorithms for computing shortest paths in a nearly acyclic directed graph G = (V, E). The new algorithms improve on the worst-case running time of previous algorithms. Such algorithms use the concept of a 1-dominator set. A 1-dominator set divides the graph into a unique collection of acyclic subgraphs, where each acyclic subgraph is dominated by a single associated trigger vertex. The previous time for computing a 1-dominator set is improved from O(mn) to O(m), where m = |E| and n = |V|. Efficient shortest path algorithms only spend delete-min operations on trigger vertices, thereby making the computation of shortest paths through non-trigger vertices easier. Under this framework, the time complexity for the all-pairs shortest path (APSP) problem is improved from O(mn + nr log r) to O(mn + r2 log r), where r is the number of triggers. Here the second term in the complexity results from delete-min operations in a heap of size r. The time complexity of the APSP problem on the broader class of nearly acyclic graphs, where trigger vertices correspond to any precomputed feedback vertex set, is similarly improved from O(mn + nr2) to O(mn + r3). This paper also mentions that the 1-dominator set concept can be generalised to define a bidirectional 1-dominator set and k-dominator sets.
Understanding Activated Network Resilience: A Comparative Analysis of Co‐Located and Co‐Cluster Disaster Response Networks

Building on the resilience literature, this study analyzes the response networks that were activated for four disasters during 2015&#8211;2016 (Cyclone Pam, the 2015 Nepal earthquake, Cyclone Winston, the 2016 Ecuador earthquake). The analysis shows that different interrelated resilient capacities are manifested in the activation of response networks. In particular, in exhibiting redundancy and robustness, disaster&#8208;specific network structures are discerned. In both cyclones, response networks resemble a predefined cluster design, whereas in the earthquake disasters, networks are more fluid. Moreover, organizations' varied levels of prior response experiences help build the network's capacities of redundancy and resourcefulness. Implications are discussed in ways to advance contributions to research on resilience and disaster response networks.
A Drought Resistance-Promoting Microbiome Is Selected by Root System under Desert Farming

Background Traditional agro-systems in arid areas are a bulwark for preserving soil stability and fertility, in the sight of “reverse desertification”. Nevertheless, the impact of desert farming practices on the diversity and abundance of the plant associated microbiome is poorly characterized, including its functional role in supporting plant development under drought stress. Methodology/Principal Findings We assessed the structure of the microbiome associated to the drought-sensitive pepper plant (Capsicum annuum L.) cultivated in a traditional Egyptian farm, focusing on microbe contribution to a crucial ecosystem service, i.e. plant growth under water deficit. The root system was dissected by sampling root/soil with a different degree of association to the plant: the endosphere, the rhizosphere and the root surrounding soil that were compared to the uncultivated soil. Bacterial community structure and diversity, determined by using Denaturing Gradient Gel Electrophoresis, differed according to the microhabitat, indicating a selective pressure determined by the plant activity. Similarly, culturable bacteria genera showed different distribution in the three root system fractions. Bacillus spp. (68% of the isolates) were mainly recovered from the endosphere, while rhizosphere and the root surrounding soil fractions were dominated by Klebsiella spp. (61% and 44% respectively). Most of the isolates (95%) presented in vitro multiple plant growth promoting (PGP) activities and stress resistance capabilities, but their distribution was different among the root system fractions analyzed, with enhanced abilities for Bacillus and the rhizobacteria strains. We show that the C. annuum rhizosphere under desert farming enriched populations of PGP bacteria capable of enhancing plant photosynthetic activity and biomass synthesis (up to 40%) under drought stress. Conclusions/Significance Crop cultivation provides critical ecosystem services in arid lands with the plant root system acting as a “resource island” able to attract and select microbial communities endowed with multiple PGP traits that sustain plant development under water limiting conditions.
Exploitation of Information Centric Networking principles in satellite networks

This paper explores possible advantages of the Information Centric Networking (ICN) paradigm in a geostationary satellite network. We find out that, with respect to plain HTTP services, ICN makes possible to reduce the downstream bandwidth consumed for Internet access by better exploiting the temporal locality of references within requested streams of Web contents. We present an ICN satellite architecture, describe its peculiar mechanisms and assess our solution through simulations.