Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatically Seeded Region Growing Approach for Automatic Segmentation of Ascending Aorta

Region Growing is frequently used for segmentation purposes especially for objects with homogenous intensity. The problem is how the region growing approach begins, how it proceeds and how it finally ends. In this paper we present a modified region growing approach for the purpose of automatic segmentation of the ascending aorta from Computed Tomography Angiography (CTA) images. The ascending aorta in CTA images has homogenous intensity which makes region growing a good approach for its segmentation. Moreover the approach presented in this paper is fully automatic to suit clinical non-invasive diagnosis purposes. For developing our region growing approach; the geometric, spatial and intensity features of the ascending aorta has been exploited. These features provide the region growing methodology with the automatic seed from which it begins and then continues to segment the whole ascending aorta from the aortic arch down to ostia points. The proposed algorithm is tested and validated on the Computed Tomography Angiography database provided by the Rotterdam Coronary Artery Algorithm Evaluation Framework. The proposed algorithm has three main advantages: 1) it's fully automatic, i.e. no user interaction needed, 2) real-time performance even with large datasets, 3) robust since parameters values are the same for all the tested datasets.
Magma injection into a long‐lived reservoir to explain geodetically measured uplift: Application to the 2007–2014 unrest episode at Laguna del Maule volcanic field, Chile

Moving beyond the widely used kinematic models for the deformation sources, we present a new dynamic model to describe the process of injecting magma into an existing magma reservoir. To validate this model, we derive an analytical solution and compare its results to those calculated using the Finite Element Method. A Newtonian fluid characterized by its viscosity, density, and overpressure (relative to the lithostatic value) flows through a vertical conduit, intruding into a reservoir embedded in an elastic domain, leading to an increase in reservoir pressure and time‐dependent surface deformation. We apply our injection model to Interferometric Synthetic Aperture Radar (InSAR) data from the ongoing unrest episode at Laguna del Maule (Chile) volcanic field that started in 2007. Using a grid search optimization, we minimize the misfit to the InSAR displacement data and vary the three parameters governing the analytical solution: the characteristic timescale τP for magma propagation, the maximum injection pressure, and the inflection time when the acceleration switches from positive to negative. For a spheroid with semimajor axis a = 6200 m, semiminor axis c = 100 m, located at a depth of 4.5 km in a purely elastic half‐space, the best fit to the InSAR displacement data occurs for τP=9.5 years and an injection pressure rising up to 11.5 MPa for 2 years. The volume flow rate increased to 1.2 m3/s for 2 years and then decreased to 0.7 m3/s in 2014. In 7.3 years, at least 187 × 106 m3 of magma was injected.
Work-based Learning Routes in Social Work Education: An Exploration of Student Experiences

This thesis is based upon a study involving 20 work-based learners undertaking social work degrees at two UK universities, one local face-to-face university and one national distance learning university. The aim of this thesis is to offer insights into the experience of work-based learners as they journey from unqualified to qualified practitioner. The thesis is underpinned by a structural symbolic interactionist approach to human behaviour, in particular the work of Stryker on identity theory, where the importance attached to identity and role in ‘identity change processes’ (2008:21) is central to understanding the meanings attributed by work-based learners to their journeys into and through higher education. In order to understand the meanings attributed by work based learners to their experience, narrative inquiry is used as the overarching methodological orientation; narratives of work-based learners’ journeys into and through higher education were collected via focus groups (3) and semi structured interviews (35) and analysed using Braun and Clark’s method of thematic analysis (2006). The data in this thesis highlight how identity impacts upon the educational journey of work-based learners making visible the challenges and opportunities that higher education presents to this group of students. The data also reveal how multiplicity of identity for work-based learners contributes to the complexity of both becoming and being a student. The dual concepts of salience and transition are applied to provide ways of understanding ‘identity change processes’ that occur as work-based learners accommodate multiple roles and corresponding identities within educational, occupational and domestic domains. In the rapidly changing landscape of social work education and a resurgence of work-based learning, this thesis provides an exploration of work-based learning provision within universities based on student experience. The implications of the findings from this study are considered for a range of stakeholders in social work education.
Banyan : A framework for distributing tree-structured computation 15712 : Advanced and Distributed Operating Systems , Spring 2010

We present a framework for writing distributed programs that solve tree-structured search problems. Our design decisions are made primarily based on our development of a parallel theorem prover, which demonstrates our target use case as a program that performs search with recursive branching. Using this example as a guidepost, we have strived to invent an interface general enough to capture common patterns in recursive programming, powerful enough to make efficient use of a cluster setting, and simple enough for a programmer to easily adapt sequential solutions to it. Our implementation addresses the low-level problems of resource allocation, communication, and scheduling, leaving the programmer to define only the work distribution strategy and what to do with values returned by recursive calls.
Promoting intercultural competence in diverse US classrooms through ethnographic interviews

Abstract To meet the demands of contemporary society, students need to develop the ability to interact effectively with people from different cultures. To advance this goal, a project involving ethnographic interviews was introduced to an intercultural communication course. Participating students’ attitudes toward other cultures were measured on a Likert scale at the beginning and end of the project. The findings show that through the interviews, the students developed greater awareness of their own cultures and of themselves within their culture and deepened their knowledge of other cultures. Yet, the majority of the students did not show greater interest in learning a foreign language at the end of the project. Further, even though the students did learn about other cultures, their responses to becoming more open to other cultures were inconclusive. The article discusses possible reasons for these outcomes and outlines future research directions.
A novel approach to link semantic gap between images and tags via probabilistic ranking

Nowadays there is tremendous fame of social networking sites that leads to a broad investigation in tag-based social image search. Both visual features and tags plays important role in the research field. Existing approach uses tags and visual features consecutively or independently to estimate the relevance of images. In this paper we tackle the problems of Automatic Image Annotation (AIA) for image retrieval that delivers the global image search engine that covers different applications such as Image to Image Retrieval, Image to Tag suggestions, Tag to Image Retrieval and Tag to Tag suggestion which is based on user contributed tags on the websites such as flicker. The proposed approach comprises of global feature extractions of images. Then we predicate relationship between image and tag by using asymmetric graph as a probabilistic view. Once the relationship is found ranking is employed with the help of t-step random walk model. Finally pseudo relevance feedback is applied on the ranked images. Additionally experimental analysis of above model is conducted on Corel image dataset to illustrate the effectiveness of this approach.
LIDeA: a distributed lightweight intrusion detection architecture for sensor networks

Wireless sensor networks are vulnerable to adversaries as they are frequently deployed in open and unattended environments. Preventive mechanisms can be applied to protect them from an assortment of attacks. However, more sophisticated methods, like intrusion detection systems, are needed to achieve a more autonomic and complete defense mechanism, even against attacks that have not been anticipated in advance. In this paper, we present a lightweight intrusion detection system, called LIDeA, designed for wireless sensor networks. LIDeA is based on a distributed architecture, in which nodes overhear their neighboring nodes and collaborate with each other in order to successfully detect an intrusion. We show how such a system can be implemented in TinyOS, which components and interfaces are needed, and what is the resulting overhead imposed.
Two Psychophysical Channels of Whisker Deflection in Rats Align with Two Neuronal Classes of Primary Afferents

The rat whisker system has evolved into in an excellent model system for sensory processing from the periphery to cortical stages. However, to elucidate how sensory processing finally relates to percepts, methods to assess psychophysical performance pertaining to precise stimulus kinematics are needed. Here, we present a head-fixed, behaving rat preparation that allowed us to measure detectability of a single whisker deflection as a function of amplitude and peak velocity. We found that velocity thresholds for detection of small-amplitude stimuli (<3°) were considerably higher than for detection of large-amplitude stimuli (>3°). This finding suggests the existence of two psychophysical channels mediating detection of whisker deflection: one channel exhibiting high amplitude and low velocity thresholds (W1), and the other channel exhibiting high velocity and low amplitude thresholds (W2). The correspondence of W1 to slowly adapting (SA) and W2 to rapidly adapting (RA) neuronal classes in the trigeminal ganglion was revealed in acute neurophysiological experiments. Neurometric plots of SA and RA cells were closely aligned to psychophysical performance in the corresponding W1 and W2 parameter ranges. Interestingly, neurometric data of SA cells fit the behavior best if it was based on a short time window integrating action potentials during the initial phasic response, in contrast to integrating across the tonic portion of the response. This suggests that detection performance in both channels is based on the assessment of very few spikes in their corresponding groups of primary afferents.
No more electrical infrastructure: towards fuel cell powered data centers

We consider the use of fuel cells for powering data centers, based on benefits in reliability, capital and operational costs, and reduced environmental emissions. Using fuel cells effectively in data centers introduces several challenges and we highlight key research questions for designing a fuel cell based data center power distribution system. We analyze a specific configuration in the design space to quantify the cost benefits for a large scale data center, for the most mature and commonly deployed fuel cell technology, achieving over 20% reduction in costs using conservative projections.
Generating high tail accuracy Gaussian Random Numbers in hardware using central limit theorem

An efficient hardware implementation of Gaussian Random Number (GRN) generator based on Central Limit Theorem (CLT) is presented. CLT, although very simple to implement, is never used to generate high quality Gaussian numbers. This is due to the fact that direct implementation of CLT provides very poor accuracy in tail regions of the probability density function. In this work, we have shown that it is possible to achieve high tail accuracy by empirically computing the error in CLT, which can be compensated with a simple correction algorithm. The error has been modeled as first degree piece-wise polynomial approximation, using a novel non-uniform segmentation algorithm to compute the coefficients of polynomial segments. A novel hardware architecture of GRN generator is presented which requires only 420 slices and 1 DSP block of Xilinx Virtex-4 XC4VLX15 operating at 220 MHz. This resource utilization is better than any of the previously reported designs. Demonstrated for the tail accuracy of 6σ, the GRN generator design is scalable to achieve even higher accuracy with minimal increase in hardware resources. The accuracy of GRN generator is validated using statistical goodness of fit tests.
Fuzzy Particle Swarm Optimization Algorithm for a Supplier Clustering Problem

This paper presents a fuzzy decision-making approach to deal with a clustering supplier problem in a supply chain system. During recent years, determining suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of these decisions is usually complex and unstructured. In general, many quantitative and qualitative factors, such as quality, price, and flexibility and delivery performance, must be considered to determine suitable suppliers. The aim of this study is to present a new approach using particle swarm optimization (PSO) algorithm for clustering suppliers under fuzzy environments and classifying smaller groups with similar characteristics. Our numerical analysis indicates that the proposed PSO improves the performance of the fuzzy c-means (FCM) algorithm.
Dynamic multi-actor engagement in networks: the case of United Breaks Guitars

Purpose The purpose of this paper is to explore the dynamic process of multi-actor engagement by examining how it evolves and spreads in actor networks. The authors challenge the dyadic perspective adopted by previous research. Design/methodology/approach An abductive theorizing approach uses a longitudinal case study to develop a theoretical framework of the iterative process of multi-actor engagement. The authors draw on the contemporary literature on engagement, service-dominant logic and value propositions. Findings The research shows that engagement conditions, via actors’ appraisals, lead to engagement properties and result in engagement outcomes as the new conditions for the next iteration. Changes within this multi-actor engagement process lead the network to evolve over time. Research limitations/implications The authors highlight the importance of adopting a dynamic multi-actor perspective of engagement and provide foundations for further research. The use of longitudinal methods that focus on the groups of actors in the evolving network is a key consideration. Practical implications There is the need to understand and measure the dynamic process of engagement among different groups of actors within networks in the service context. Originality/value This is the first empirical study to explore the dynamics of engagement among multiple actors in the network. This leads to the expansion of Storbacka et al.’s (2016) conceptual work by identifying the iterative nature of the multi-actor engagement process, and new components in the process (i.e. actors’ connections, value propositions and engagement outcomes), as well as clarifying existing ones (e.g. engagement properties and actors’ appraisals).
Consistent Alignment of Word Embedding Models

Word embedding models offer continuous vector representations that can capture rich contextual semantics based on their word co-occurrence patterns. While these word vectors can provide very effective features used in many NLP tasks such as clustering similar words and inferring learning relationships, many challenges and open research questions remain. In this paper, we propose a solution that aligns variations of the same model (or different models) in a joint low-dimensional latent space leveraging carefully generated synthetic data points. This generative process is inspired by the observation that a variety of linguistic relationships is captured by simple linear operations in embedded space. We demonstrate that our approach can lead to substantial improvements in recovering embeddings of local neighborhoods.
Intonation across Spanish, in the Tones and Break Indices framework

This paperdescribes some ofthe moresalientintonationalphenomenaof Spanish, and reviews several of the most pressing questions that remain to be addressed before a deÞnitive model of the system can be incorporated into a consensus transcription system for the language. The phenomena reviewed include the metrical underpinnings of the tune, and some of the local tone shapes that are anchored at stressed syllables or at phrase edges in several common intonation contours. The description of known facts is couched in the Autosegmental-Metrical model of intonational phonology, as is the review of outstanding questions. The description is used to motivate the preliminary transcription conventions proposed by the Spanish ToBI development group.
On-line Orthographic Influences on Spoken Language in a Semantic Task

Literacy changes the way the brain processes spoken language. Most psycholinguists believe that orthographic effects on spoken language are either strategic or restricted to meta-phonological tasks. We used event-related brain potentials (ERPs) to investigate the locus and the time course of orthographic effects on spoken word recognition in a semantic task. Participants were asked to decide whether a given word belonged to a semantic category (body parts). On no-go trials, words were presented that were either orthographically consistent or inconsistent. Orthographic inconsistency (i.e., multiple spellings of the same phonology) could occur either in the first or the second syllable. The ERP data showed a clear orthographic consistency effect that preceded lexical access and semantic effects. Moreover, the onset of the orthographic consistency effect was time-locked to the arrival of the inconsistency in a spoken word, which suggests that orthography influences spoken language in a time-dependent manner. The present data join recent evidence from brain imaging showing orthographic activation in spoken language tasks. Our results extend those findings by showing that orthographic activation occurs early and affects spoken word recognition in a semantic task that does not require the explicit processing of orthographic or phonological structure.
International expert panel on inflammatory breast cancer: consensus statement for standardized diagnosis and treatment.

BACKGROUND Inflammatory breast cancer (IBC) represents the most aggressive presentation of breast cancer. Women diagnosed with IBC typically have a poorer prognosis compared with those diagnosed with non-IBC tumors. Recommendations and guidelines published to date on the diagnosis, management, and follow-up of women with breast cancer have focused primarily on non-IBC tumors. Establishing a minimum standard for clinical diagnosis and treatment of IBC is needed. METHODS Recognizing IBC to be a distinct entity, a group of international experts met in December 2008 at the First International Conference on Inflammatory Breast Cancer to develop guidelines for the management of IBC. RESULTS The panel of leading IBC experts formed a consensus on the minimum requirements to accurately diagnose IBC, supported by pathological confirmation. In addition, the panel emphasized a multimodality approach of systemic chemotherapy, surgery, and radiation therapy. CONCLUSIONS The goal of these guidelines, based on an expert consensus after careful review of published data, is to help the clinical diagnosis of this rare disease and to standardize management of IBC among treating physicians in both the academic and community settings.
THE LONG TERM DATA STORAGE ( Introduction to Relevant Questions )

Data storage, especially long-term data storage, is one of the biggest IT/ICT topics discussed in this field today. This document deals with basic classification of electronic data, highlights its specifics versus standard documents and contains overview of elementary requirements on its availability, integrity, confidentiality and storage duration, as well as accessibility time. Together with requirements, this document discusses cornerstones applicable for their fulfillment. Besides these basic factors, you can also find here questions relevant for legislative and normative restrictions put on certain types of data. Finally, we discuss present-day approaches for data exchange and storage, and its usability for long-term storage.
Do Hospitalized Premature Infants Benefit from Music Interventions? A Systematic Review of Randomized Controlled Trials

Objective Neonatal intensive care units (NICU) around the world increasingly use music interventions. The most recent systematic review of randomized controlled trials (RCT) dates from 2009. Since then, 15 new RCTs have been published. We provide an updated systematic review on the possible benefits of music interventions on premature infants’ well-being. Methods We searched 13 electronic databases and 12 journals from their first available date until August 2016. Included were all RCTs published in English with at least 10 participants per group, including infants born prematurely and admitted to the NICU. Interventions were either recorded music interventions or live music therapy interventions. All control conditions were accepted as long as the effects of the music intervention could be analysed separately. A meta-analysis was not possible due to incompleteness and heterogeneity of the data. Results After removal of duplicates the searches retrieved 4893 citations, 20 of which fulfilled the inclusion/exclusion criteria. The 20 included studies encompassed 1128 participants receiving recorded or live music interventions in the NICU between 24 and 40 weeks gestational age. Twenty-six different outcomes were reported which we classified into three categories: physiological parameters; growth and feeding; behavioural state, relaxation outcomes and pain. Live music interventions were shown to improve sleep in three out of the four studies and heart rate in two out of the four studies. Recorded music improved heart rate in two out of six studies. Better feeding and sucking outcomes were reported in one study using live music and in two studies using recorded music. Conclusions Although music interventions show promising results in some studies, the variation in quality of the studies, age groups, outcome measures and timing of the interventions across the studies makes it difficult to draw strong conclusions on the effects of music in premature infants.
The New Framework for Loop Nest Optimization in GCC: from Prototyping to Evaluation

This paper presents a practical prototyping tool for SSA transformations based on PROLOG, and a case study of its applicability using the New Framework for Loop Nest Optimization of the GNU Compiler Collection (GCC). Using this approach, we implemented an induction variable analysis in GCC and performed several experiments for assessing different issues: performance of the generated code, effectiveness of the analyzers and optimizers, compilation overhead, together with possible improvements. This evaluation, based on the SPEC CPU2000, MiBench and JavaGrande benchmarks on different architectures, suggests that our framework could be of valuable use in production compilers developments such as GCC.