Miracle-dz/UniFAR-FTUs huggingface dataset:
DEVIATIONS FROM AXISYMMETRY REVEALED BY LINE POLARIZATION IN THE NORMAL TYPE Ia SUPERNOVA 2004S

We present a single epoch of high signal-to-noise ratio spectropolarimetry of the Type Ia supernova (SN Ia) 2004S taken 9 days after maximum light. The flux spectrum is normal, but with the additional presence of high-velocity (HV) line features in both Ca ii and Fe ii. The object shows continuum polarization at the 0.4% level in the red, a value which appears to be typical of SNe Ia. The continuum data are consistent with a ∼10% global asphericity in an axisymmetric geometry. Unlike previous observations of other SNe Ia with HV features, the HV features in SN 2004S show no strong polarimetric signature, though this may be due to the timing of our observations. Instead, the object shows line-polarization features (P ⩽ 0.5%) that are rotated with respect to the axis of symmetry of the continuum. The line features are visible in Si ii, Fe ii, and Ca ii, and appear to be narrowly confined in velocity space just above the photosphere. These polarization features are a result of compositional inhomogeneities in the ejecta. They may represent newly synthesized elements whose clumpy spatial distribution within the ejecta is distinct from that of the globally aspherical ejecta as a whole.
The trends of global precipitation in 1900 – 2005 are evaluated using a historical precipitation reconstruction and coupled model simulations , Coupled Model Intercomparison Project 5 ( CMIP 5 ) and Coupled Model Intercomparison Project Phase 3 .

[1] The trends of global precipitation in 1900–2005 are evaluated using a historical precipitation reconstruction and coupled model simulations, Coupled Model Intercomparison Project 5 (CMIP5) and Coupled Model Intercomparison Project Phase 3. A significant increasing trend in the global oceanic precipitation is identified in both the reconstruction and models. The trend from the reconstructed ocean precipitation is 0.04 mm day 1 over 100a (100 years) and is about twice that of the mean of all models. Over land, the spatial patterns of the trends from both the reconstruction and the models are similar to those shown in the Intergovernmental Panel on Climate Change Fourth Assessment Report. Over the ocean, both reconstruction and models show positive trends in the equatorial and subpolar regions and negative trends over the subtropics. However, the trend magnitude and the locations of the trend peaks are different near the equator (10 S to 10 N) between the reconstruction and the models. CMIP5 future simulations for the global mean project a continuing and stronger precipitation trend in the 21st century than the 20th century.
Malignant and Noninvasive Skin Tumours in Renal Transplant Recipients

Background. Transplant recipients require immunosuppression to prevent graft rejection. This conveys an increased risk of malignancy, particularly skin tumours. There is a need for up-to-date data for the South of England. Method. Pathology records were reviewed for 709 kidney transplant recipients on immunosuppression at our hospital from 1995 to 2008. Skin tumours were recorded/analysed. Results. Mean age at transplant was 46 years. Mean length of follow-up was 7.2 years and total follow-up was 4926 person-years. 53 (7.5%) patients (39/458 (8.5%) males and 14/251 (5.6%) females) developed ≥1 skin malignancy. Cumulative incidences of 4.0%, 7.5%, and 12.2% were observed for those with <5, <10, and ≥10 years follow-up, respectively. The rate was 45 tumours per 1000 person-years at risk. Additionally, 21 patients (3.0%) only had noninvasive tumours. 221 malignant skin tumours were found: 50.2% were SCCs, 47.1% BCCs, and 2.7% malignant melanomas. Mean years to first tumour were 5.8. Mean number of tumours per patient was 4, with mean interval of 12 months. Conclusions. Despite changes in transplantation practice during the time since the last data were published in this region, these findings are similar to previous studies. This adds to the evidence allowing clinicians to inform patients in this region of their risk.
Discovering and predicting user routines by differential analysis of social network traces

The study of human activity patterns traditionally relies on the continuous tracking of user location. We approach the problem of activity pattern discovery from a new perspective which is rapidly gaining attention. Instead of actively sampling increasing volumes of sensor data, we explore the participatory sensing potential of multiple mobile social networks, on which users often disclose information about their location and the venues they visit. In this paper, we present automated techniques for filtering, aggregating, and processing combined social networking traces with the goal of extracting descriptions of regularly-occurring user activities, which we refer to as “user routines”. We report our findings based on two localized data sets about a single pool of users: the former contains public geotagged Twitter messages, the latter Foursquare check-ins that provide us with meaningful venue information about the locations we observe. We analyze and combine the two datasets to highlight their properties and show how the emergent features can enhance our understanding of users' daily schedule. Finally, we evaluate and discuss the potential of routine descriptions for predicting future user activity and location.
Adaptive sampling-based profiling techniques for optimizing the distributed JVM runtime

Extending the standard Java virtual machine (JVM) for cluster-awareness is a transparent approach to scaling out multithreaded Java applications. While this clustering solution is gaining momentum in recent years, efficient runtime support for fine-grained object sharing over the distributed JVM remains a challenge. The system efficiency is strongly connected to the global object sharing profile that determines the overall communication cost. Once the sharing or correlation between threads is known, access locality can be optimized by collocating highly correlated threads via dynamic thread migrations. Although correlation tracking techniques have been studied in some page-based software DSM systems, they would entail prohibitively high overheads and low accuracy when ported to fine-grained object-based systems. In this paper, we propose a lightweight sampling-based profiling technique for tracking inter-thread sharing. To preserve locality across migrations, we also propose a stack sampling mechanism for profiling the set of objects which are tightly coupled with a migrant thread. Sampling rates in both techniques can vary adaptively to strike a balance between preciseness and overhead. Such adaptive techniques are particularly useful for applications whose sharing patterns could change dynamically. The profiling results can be exploited for effective thread-to-core placement and dynamic load balancing in a distributed object sharing environment. We present the design and preliminary performance result of our distributed JVM with the profiling implemented. Experimental results show that the profiling is able to obtain o ver 95% accurate global sharing profiles at a cost of only a few percents of execution time increase for fine- to medium-g rained applications.
Vehicle infrastructure cooperative localization using Factor Graphs

Highly assisted and Autonomous Driving is dependent on the accurate localization of both the vehicle and other targets within the environment. With increasing traffic on roads and wider proliferation of low cost sensors, a vehicle-infrastructure cooperative localization scenario can provide improved performance over traditional mono-platform localization. The paper highlights the various challenges in the process and proposes a solution based on Factor Graphs which utilizes the concept of topology of vehicles. A Factor Graph represents probabilistic graphical model as a bipartite graph. It is used to add the inter-vehicle distance as constraints while localizing the vehicle. The proposed solution is easily scalable for many vehicles without increasing the execution complexity. Finally simulation indicates that incorporating the topology information as a state estimate can improve performance over the traditional Kalman Filter approach.
Algebraic analysis of data fusion with ellipsoidal intersection

For decentralized fusion problems, ellipsoidal intersection has been proposed as an efficient fusion rule that provides less conservative results as compared to the well-know covariance intersection method. Ellipsoidal intersection relies on the computation of a common estimate that is shared by the estimates to be fused. In this paper, an algebraic reformulation of ellipsoidal intersection is discussed that circumvents the computation of the common estimate. It is shown that ellipsoidal intersection corresponds to an internal ellipsoidal approximation of the intersection of covariance ellipsoids. An interesting result is that ellipsoidal intersection can be computed with the aid of the Bar-Shalom/Campo fusion formulae. This is achieved by assuming a specific correlation structure between the estimates to be fused.
Biomarker Signatures Correlate with Clinical Outcome in Refractory Metastatic Colorectal Cancer Patients Receiving Bevacizumab and Everolimus

A novel combination of bevacizumab and everolimus was evaluated in refractory colorectal cancer patients in a phase II trial. In this retrospective analysis, plasma samples from 49 patients were tested for over 40 biomarkers at baseline and after one or two cycles of drug administration. Analyte levels at baseline and change on-treatment were correlated with progression-free survival (PFS) and overall survival (OS) using univariate Cox proportional hazard modeling. Multivariable analyses were conducted using Cox modeling. Significant changes in multiple markers were observed following bevacizumab and everolimus treatment. Baseline levels of six markers significantly correlated with PFS and OS, including CRP, Gro-α, IGFBP-1, TF, ICAM-1, and TSP-2 (P < 0.05). At C2D1, changes of IGFBP-3, TGFβ-R3, and IGFBP-2 correlated with PFS and OS. Prognostic models were developed for OS and PFS (P = 0.0002 and 0.004, respectively). The baseline model for OS consisted of CRP, Gro-α, and TF, while the on-treatment model at C2D1 included IGFBP-2, IGFBP-3, and TGFβ-R3. These data demonstrated that multiple biomarkers were significantly modulated in response to bevacizumab and everolimus. Several markers correlated with both PFS and OS. Interestingly, these markers are known to be associated with inflammation and IGF signaling, key modulators of mTOR biology. Mol Cancer Ther; 14(4); 1048–56. ©2015 AACR.
A mixed hybrid finite volumes solver for robust primal and adjoint CFD

In the context of gradient-based numerical optimisation, the adjoint method is an e cient way of computing the gradient of the cost function at a computational cost independent of the number of design parameters, which makes it a captivating option for industrial CFD applications involving costly primal solves. The method is however a ected by instabilities, some of which are inherited from the primal solver, notably if the latter does not fully converge. The present work is an attempt at curbing primal solver limitations with the goal of indirectly alleviating adjoint robustness issues. To that end, a novel discretisation scheme for the steady-state incompressible NavierStokes problem is proposed: Mixed Hybrid Finite Volumes (MHFV). The scheme draws inspiration from the family of Mimetic Finite Di erences and Mixed Virtual Elements strategies, rid of some limitations and numerical artefacts typical of classical Finite Volumes which may hinder convergence properties. Derivation of MHFV operators is illustrated and each scheme is validated via manufactured solutions: rst for pure anisotropic di usion problems, then convection-di usion-reaction and nally Navier-Stokes. Traditional and novel Navier-Stokes solution algorithms are also investigated, adapted to MHFV and compared in terms of performance. The attention is then turned to the discrete adjoint Navier-Stokes system, which is assembled in an automated way following the principles of Equational Di erentiation, i.e. the di erentiation of the primal discrete equations themselves rather than the algorithm used to solve them. Practical/computational aspects of the assembly are discussed, then the adjoint gradient is validated and a few solution algorithms for the MHFV adjoint Navier-Stokes are proposed and tested. Finally, two examples of full shape optimisation procedures on internal ow test cases (S-bend and U-bend) are reported.
Aspects of swallowing in healthy dentate elderly persons older than 80 years.

BACKGROUND Although age-related changes resulting in slowing of the swallowing reflex and a decline in the neuromuscular control system have been reported, there have been few reports on swallowing function in dentate elderly persons. The purpose of this study was to clarify the primary influence of aging on swallowing in healthy dentate elderly persons older than 80 years who have more than 20 teeth. METHODS Dentate elderly persons (12 male, 7 female; mean age: 81.2 years) and dentate young participants (9 male, 5 female; mean age: 26.8 years) as a control group participated voluntarily. Participants reported no clinical symptoms relating to dysphagia, neurologic impairments, or degenerative diseases, and were asked to swallow 10 ml of barium sulfate solution (10% w/v) three times. Functional swallowing was recorded on 35 mm cinefilm at 30 frames per second with a digital subtraction angiography system. Lateral images of cinefluorography of seated participants' mouth, pharynx, and larynx were obtained. Visual image analysis for qualitative and quantitative evaluation was made with a cine projector. RESULTS No participants exhibited aspiration during three trials. Occurrence and frequencies of piecemeal deglutition, premature loss of liquid, oral and pharyngeal residues, and laryngeal penetration were significantly greater in dentate elderly persons (p <.05) than in the dentate young participants. Oral transit time, pharyngeal delay time, and pharyngeal transit time in dentate elderly persons were prolonged significantly compared with those in dentate young participants (p <.01). CONCLUSION Physiological swallowing functions deteriorate even in healthy dentate elderly persons. This deterioration may be explained primarily by the influence of aging on swallowing.
(Re)examining the effects of athlete brand image (ABI) on psychological commitment: an empirical investigation using structural equation modelling (SEM) and fuzzy set qualitative comparative analysis (fsQCA)

ABSTRACT Research question: How does athlete brand image (ABI) affect psychological commitment (PC) when operationalised at the dimension- (attribute-) level, and measured using reflective indicators? Previous studies operationalise ABI at a higher-order construct level, and/or measure ABI using formative measures. Such operationalisations obscure potentially different relationships between ABI’s image attributes and PC. Research methods: A questionnaire was used to collect data from 197 UK respondents over a six-day period within two weeks of the Rio 2016 Olympics concluding. Data were analysed through structural equation modelling (SEM) and fuzzy set Qualitative Comparative Analysis (fsQCA) techniques. Results and findings: Through SEM, the ABI attributes, athletic expertise, life story, role model, and competition style are positively related to PC, sportsmanship and symbol are negatively related, and rivalry, physical attractiveness, body fit, and relationship effort are nonsignificantly related. Many structural paths between ABI’s attributes and PC are also significantly different. Through fsQCA, high PC exists under three complex ABI attribute configurations, while it is absent under four complex configurations. Implications: Theoretically, finding different relationships between ABI’s attributes and PC highlights why operationalising ABI at the dimension-level provides a more in-depth understanding of athlete brand image’s effects on PC. Managerially, the findings suggest athletes need only a subset of ABI attributes for high PC. Subsequently, managers can more-efficiently and effectively direct resources towards those attributes that best-capitalise on athletes’ images.
A proposal to automatically build and maintain gazetteers for Named Entity Recognition by using Wikipedia

This paper describes a method to automatically create and maintain gazetteers for Named Entity Recognition (NER). This method extracts the necessary information from linguistic resources. Our approach is based on the analysis of an on-line ency-clopedia entries by using a noun hierarchy and optionally a PoS tagger. An important motivation is to reach a high level of language independence. This restricts the techniques that can be used but makes the method useful for languages with few resources. The evaluation carried out proves that this approach can be successfully used to build NER gazetteers for location (F 78%) and person (F 68%) categories.
Improving SMEs’ competitiveness with the use of Instagram influencer advertising and eWOM

PurposeUnder Saudi Vision 2030, there is a forthcoming raise of support to small- or medium-sized enterprises (SMEs) from the current 20 per cent of GDP to 35 per cent; thus, SMEs’ access to finance is becoming easier. At the same time, the cosmetics retail industry is expected to rapidly grow further in the next few years, on account of the already mounting demand and easy availability of cosmetics through online channels. The purpose of this study is to explore the use of beauty Instagram influencers in advertising as a tool to increase competitiveness for SMEs.Design/methodology/approachAn exploratory research design was used and in-depth interviews were conducted to get a better understanding of female Saudi young adults’ perceptions with regards to trust towards Instagram influencers, electronic word of mouth (eWOM) and advertising.FindingsSMEs can benefit greatly from eWOM in their quest for competitiveness. Therefore, when a beauty influencer on Instagram advertises a product to SMEs followers this leads to increased awareness and purchase intention. The findings indicated that the participants are quite sceptical when receiving reviews or promotion from beauty influencers on social media, as they rarely regard them as authentic. Moreover participants seem to trust influential beauty Instagrammers when they had a positive experience with the influencer before. Trust, honesty and authenticity were important factors that increase the impact of eWOM.Originality/valueThis paper offers an in-depth understanding of the use of Instagram advertising and eWOM in a rapidly developing industry and explores these as key communication pathways for modern SMEs in their effort to achieve resilience and competitiveness.
Exploiting user feedback to learn to rank answers in q&a forums: a case study with stack overflow

Collaborative web sites, such as collaborative encyclopedias, blogs, and forums, are characterized by a loose edit control, which allows anyone to freely edit their content. As a consequence, the quality of this content raises much concern. To deal with this, many sites adopt manual quality control mechanisms. However, given their size and change rate, manual assessment strategies do not scale and content that is new or unpopular is seldom reviewed. This has a negative impact on the many services provided, such as ranking and recommendation. To tackle with this problem, we propose a learning to rank (L2R) approach for ranking answers in Q&A forums. In particular, we adopt an approach based on Random Forests and represent query and answer pairs using eight different groups of features. Some of these features are used in the Q&A domain for the first time. Our L2R method was trained to learn the answer rating, based on the feedback users give to answers in Q&A forums. Using the proposed method, we were able (i) to outperform a state of the art baseline with gains of up to 21% in NDCG, a metric used to evaluate rankings; we also conducted a comprehensive study of the features, showing that (ii) review and user features are the most important in the Q&A domain although text features are useful for assessing quality of new answers; and (iii) the best set of new features we proposed was able to yield the best quality rankings.
Expression Profiles of the Individual Genes Corresponding to the Genes Generated by Cytotoxicity Experiments with Bortezomib in Multiple Myeloma

Objective: Multiple myeloma (MM) is currently incurable due to refractory disease relapse even under novel anti-myeloma treatment. In silico studies are effective for key decision making during clinicopathological battles against the chronic course of MM. The aim of this present in silico study was to identify individual genes whose expression profiles match that of the one generated by cytotoxicity experiments for bortezomib. Materials and Methods: We used an in silico literature mining approach to identify potential biomarkers by creating a summarized set of metadata derived from relevant information. The E-MTAB-783 dataset containing expression data from 789 cancer cell lines including 8 myeloma cell lines with drug screening data from the Wellcome Trust Sanger Institute database obtained from ArrayExpress was “Robust Multi-array analysis” normalized using GeneSpring v.12. 5. Drug toxicity data were obtained from the Genomics of Drug Sensitivity in Cancer project. In order to identify individual genes whose expression profiles matched that of the one generated by cytotoxicity experiments for bortezomib, we used a linear regression-based approach, where we searched for statistically significant correlations between gene expression values and IC50 data. The intersections of the genes were identified in 8 cell lines and used for further analysis. Results: Our linear regression model identified 73 genes and some genes expression levels were found to very closely correlated with bortezomib IC50 values. When all 73 genes were used in a hierarchical cluster analysis, two major clusters of cells representing relatively sensitive and resistant cells could be identified. Pathway and molecular function analysis of all the significant genes was also investigated, as well as the genes involved in pathways. Conclusion: The findings of our present in silico study could be important not only for the understanding of the genomics of MM but also for the better arrangement of the targeted anti-myeloma therapies, such as bortezomib.
Forest Floor Visualizations of Random Forests

We propose a novel methodology, forest floor, to visualize and interpret random forest (RF) models. RF is a popular and useful tool for non-linear multi-variate classification and regression, which yields a good trade-off between robustness (low variance) and adaptiveness (low bias). Direct interpretation of a RF model is difficult, as the explicit ensemble model of hundreds of deep trees is complex. Nonetheless, it is possible to visualize a RF model fit by its mapping from feature space to prediction space. Hereby the user is first presented with the overall geometrical shape of the model structure, and when needed one can zoom in on local details. Dimensional reduction by projection is used to visualize high dimensional shapes. The traditional method to visualize RF model structure, partial dependence plots, achieve this by averaging multiple parallel projections. We suggest to first use feature contributions, a method to decompose trees by splitting features, and then subsequently perform projections. The advantages of forest floor over partial dependence plots is that interactions are not masked by averaging. As a consequence, it is possible to locate interactions, which are not visualized in a given projection. Furthermore, we introduce: a goodness-of-visualization measure, use of colour gradients to identify interactions and an out-of-bag cross validated variant of feature contributions.