Miracle-dz/UniFAR-FTUs huggingface dataset:
Cardiovascular T2-star (T2*) magnetic resonance for the early diagnosis of myocardial iron overload.

AIMS To develop and validate a non-invasive method for measuring myocardial iron in order to allow diagnosis and treatment before overt cardiomyopathy and failure develops. METHODS AND RESULTS We have developed a new magnetic resonance T2-star (T2*) technique for the measurement of tissue iron, with validation to chemical estimation of iron in patients undergoing liver biopsy. To assess the clinical value of this technique, we subsequently correlated myocardial iron measured by this T2* technique with ventricular function in 106 patients with thalassaemia major. There was a significant, curvilinear, inverse correlation between iron concentration by biopsy and liver T2* (r=0.93, P<0.0001). Inter-study cardiac reproducibility was 5.0%. As myocardial iron increased, there was a progressive decline in ejection fraction (r=0.61, P<0.001). All patients with ventricular dysfunction had a myocardial T2* of <20 ms. There was no significant correlation between myocardial T2* and the conventional parameters of iron status, serum ferritin and liver iron. Multivariate analysis of clinical parameters to predict the requirement for cardiac medication identified myocardial T2* as the most significant variable (odds ratio 0.79, P<0.002). CONCLUSIONS Myocardial iron deposition can be reproducibly quantified using myocardial T2* and this is the most significant variable for predicting the need for ventricular dysfunction treatment. Myocardial iron content cannot be predicted from serum ferritin or liver iron, and conventional assessments of cardiac function can only detect those with advanced disease. Early intensification of iron chelation therapy, guided by this technique, should reduce mortality from this reversible cardiomyopathy.
Extensive grey matter pathology in the cerebellum in multiple sclerosis is linked to inflammation in the subarachnoid space

Multiple sclerosis (MS) is a progressive inflammatory neurological disease affecting myelin, neurons and glia. Demyelination and neurodegeneration of cortical grey matter contribute to a more severe disease, and inflammation of the forebrain meninges associates with pathology of the underlying neocortical grey matter, particularly in deep sulci. We assessed the extent of meningeal inflammation of the cerebellum, another structure with a deeply folded anatomy, to better understand the association between subarachnoid inflammation and grey matter pathology in progressive MS.
Dynamic Communities and their Detection

Overlapping community detection has already become an interesting problem in data mining and also a useful technique in applications. This underlines the importance of following the lifetime of communities in real graphs. Palla et al. developed a promising method, and analyzed community evolution on two large databases [23]. We have followed their footsteps in analyzing large real-world databases and found, that the framework they use to describe the dynamics of communities is insufficient for our data. The method used by Palla et al. is also dependent on a very special community detection algorithm, the clique percolation method, and on its monotonic nature. In this paper we propose an extension of the basic community events described in [23] and a method capable of handling communities found a nonmonotonic community detection algorithm. We also report on findings that came from the tests on real social graphs.
Culture in multisensory computing: dividing the body for a digital future

ABSTRACT This paper examines the ways in which computer science and a selection of multisensory digital devices modulate the term culture. Three self-identified, ‘cultural computing’ devices are examined: ZENetic, Alice’s Adventures and GRIOT’S Japanese Renku. The devices variously configure relationships between bodies and culture so that the body is thought to provide a window into particular cultures, as well as a universal tool of their transmission. These delineations of the term culture occur in continuity with surrounding political histories and projects, including racial and ethnic ones. Dividing the body between the extra cultural and the culturally specific works to secure the communicability as well as the exclusivity of cultural practices. It is an instance of what Merleau-Ponty terms a divergence, a gap within the body between two imbricated parts. In this way, these devices present culture as a source of innovation that combats global and abstracted computing practices.
Comparison of Xeon Phi and Kepler GPU Performance for Finite Element Numerical Integration

We consider two recently introduced massively multi-core architectures designed for high performance computing, the Xeon Phi coprocessor and Kepler graphics processor. We discuss the OpenCL programming model, as one that allows to look at the platforms in a unified way and to construct efficient algorithms for both of them. As an example application we investigate a typical algorithm employed in finite element codes for numerical integration. We create kernels implementing the algorithm for the two considered platforms and compare the performance obtained.
Pathways of Malignant Urothelium Cell-Matrix Interactions : Implications for the Progression Identification of Genes Involved in Human Urothelial

Interactions between epithelial cells and the extracellular matrix are central to tissue homeostasis and have a dynamic role in tissue remodeling and repair. Regulation of these pathways is balanced by positive and negative feedback elements, many of which have been implicated in the pathways of malignant progression. We have used differential display to identify genes that are up-regulated in normal human urothelial cells in response to exposure to extracellular matrix proteins (Matrigel) in vitro. This approach has identified genes that have key roles in cell-cell and cell-matrix interactions and that have been implicated in the progression of carcinomas of urothelial or other epithelial cell origins. One confirmed but unknown differentially expressed sequence was used to isolate a full-length gene, MIG-C4, from a human urothelial cDNA library. This gene was found to encode a novel urokinase plasminogen-activator receptor-like member of the Ly-6 family of glycosyl-phosphatidylinositolanchored glycoproteins, and was identified as the human homologue of the rat metastasis-associated C4.4A gene. By in situ hybridization, MIG-C4 was expressed variably in normal urothelium and intensely in the tumor component of some noninvasive superficial lesions and in invasive and metastatic urothelial cancers. Thus, our approach has identified previously nonimplicated gene products involved in normal urothelium-matrix interactions that could be tumor-invasion or suppressor-gene targets in the development of invasive and metastatic tumor phenotypes.
Matching Matrix Bernstein with Little Memory: Near-Optimal Finite Sample Guarantees for Oja's Algorithm

This work provides improved guarantees for streaming princ iple component analysis (PCA). Given A1, . ,An ∈ R sampled independently from distributions satisfying E [Ai] = Σ for Σ 0, this work provides anO(d)-space linear-time single-pass streaming algorithm for es timating the top eigenvector ofΣ. The algorithm nearly matches (and in certain cases improve s upon) the accuracy obtained by the standard batch method that computes top eigenvector o f he empirical covariance n ∑ i∈[n] Ai as analyzed by the matrix Bernstein inequality. Moreover, t o achieve constant accuracy, our algorithm improves upon the best previous known sample complexities o f streaming algorithms by either a multiplicative factor ofO(d) or 1/gap wheregap is the relative distance between the top two eigenvalues of Σ. These results are achieved through a novel analysis of the cl assi Oja’s algorithm, one of the oldest and most popular algorithms for streaming PCA. In particula r, this work shows that simply picking a random initial pointw0 and applying the update rule wi+1 = wi + ηiAiwi suffices to accurately estimate the top eigenvector, with a suitable choice of ηi. We believe our result sheds light on how to efficiently perform streaming PCA both in theory and in pract ice, and we hope that our analysis may serve as the basis for analyzing many variants and extension s of streaming PCA. Microsoft Research India. Email: prajain@microsoft.com UC Berkeley. Email: chijin@cs.berkeley. edu University of Washington. Email: sham@cs.washington. edu Microsoft Research New England. Email: praneeth@microsof t.com Microsoft Research New England. Email: asid@microsoft.co m
Simulation of carbon isotope discrimination of the terrestrial biosphere

We introduce a multistage model of carbon isotope discrimination during C3 photosynthesis and global maps of C3/C4 plant ratios to an ecophysiological model of the terrestrial biosphere (SiB2) in order to predict the carbon isotope ratios of terrestrial plant carbon globally at a 1° resolution. The model is driven by observed meteorology from the European Centre for Medium‐Range Weather Forecasts (ECMWF), constrained by satellite‐derived Normalized Difference Vegetation Index (NDVI) and run for the years 1983–1993. Modeled mean annual C3 discrimination during this period is 19.2‰; total mean annual discrimination by the terrestrial biosphere (C3 and C4 plants) is 15.9‰. We test simulation results in three ways. First, we compare the modeled response of C3 discrimination to changes in physiological stress, including daily variations in vapor pressure deficit (vpd) and monthly variations in precipitation, to observed changes in discrimination inferred from Keeling plot intercepts. Second, we compare mean δ13C ratios from selected biomes (Broadleaf, Temperate Broadleaf, Temperate Conifer, and Boreal) to the observed values from Keeling plots at these biomes. Third, we compare simulated zonal δ13C ratios in the Northern Hemisphere (20°N to 60°N) to values predicted from high‐frequency variations in measured atmospheric CO2 and δ13C from terrestrially dominated sites within the NOAA‐Globalview flask network. The modeled response to changes in vapor pressure deficit compares favorably to observations. Simulated discrimination in tropical forests of the Amazon basin is less sensitive to changes in monthly precipitation than is suggested by some observations. Mean model δ13C ratios for Broadleaf, Temperate Broadleaf, Temperate Conifer, and Boreal biomes compare well with the few measurements available; however, there is more variability in observations than in the simulation, and modeled δ13C values for tropical forests are heavy relative to observations. Simulated zonal δ13C ratios in the Northern Hemisphere capture patterns of zonal δ13C inferred from atmospheric measurements better than previous investigations. Finally, there is still a need for additional constraints to verify that carbon isotope models behave as expected.
Attribute-Based MED System with Word Histograms

We used a similar system to our previous system 2010 [1], which we built for the last year competition. However, we need to cope with the notable difficulty imposed in the TRECVID 2011 MED task, i.e., the set of the target event classes and the set of the training event classes are completely disjoint. This means that no sample video belonging to any of the target event classes is available in the training phase. As a knowledge transfer method, we adopted the attribute-based classification (AC) approach [2]. The AC approach classifies a test sample into the set of the target classes by combining outputs from the classifiers learned for the training classes. This requires class similarities between the target classes and the training classes, in order to weight the classifier outputs. We used text words associated with each video as side information, and adopted the similarity between the word histograms as the class similarity [3].
A Novel Online Dictionary Learning Method from Compressed Signals

Dictionary learning algorithm facilitates a sparse representation of a given set of training signals, which has significant impact on signal reconstruction error in compressive sensing. To reduce the recovery error caused by environmental noise, in this paper, a novel structured dictionary learning method for sparse signal representation is presented. The training signals are collected from compressive data gathering methods. And the self-coherence of the dictionary is punished. In comparison with the DCT basis and the K-SVD method, experimental results verify that the proposed dictionary is more effective to alleviate the recovery error caused by environmental noise.
A common framework for steerability, motion estimation, and invariant feature detection

Many problems in computer vision and pattern recognition involve groups of transformations. In particular, motion estimation, steerable filter design and invariant feature detection are often formulated with respect to a particular transformation group. Traditionally, these problems have been investigated independently. From a theoretical point of view, however, the issues they address are related. In this paper, we examine the relationships between these problems and propose a theoretical framework within which they can be discussed in concert. This framework is based on constructing a natural representation of the image for a given transformation group. Within this framework, many existing techniques of motion estimation, steerable filter design and invariant feature detection appear as special cases. Furthermore, several new results are direct consequences of this framework.
A Network-Based Array Data Interpreter

DNA microarray analysis is a powerful method to analyze genome-wide gene expressions. The number of human genes is estimated to be approximately 35,000. The so called DNA chip or microarray can assemble more than ten thousand genes on a single tiny plate, and even entire genome can easily be assembled on a single plate in the near future. Thus one experiment using a single chip or microarray produces data comparable to thousands of Northern blotting experiments. However, it is not easy to extract useful meaning out of these voluminous data. In order to interpret expression data one need to know the inter relationships among up regulated and down regulated gene products. Though a number of software packages have been developed for this purpose, they usually do not take the network and pathway relations of the gene products explicitly. We are developing a network-based array analyses data interpreter using a commercial software package, GeneSpring (Silicon Genetics) [4] and the CSNDB (Cell Signaling Networks Database) [2, 3], which is an signal transduction pathway database developed by our group. This method was applied to several DNA chip analyses.
ARHON: A multimedia database design for image documents

In this paper we present the design and initial implementation of a multimedia system for historical documents. The system, currently under development at FORTH, will be used by the Vikelea Municipal Library of archives at Heraklion, Crete. The material consists of historical documents that need to be digitized, indexed and continuously annotated by scholars so that the value of the archive will increase by its use. We present the design architecture and initial implementation of a client-server model for the creation, maintenance and use of such a digital archive. Also, we present a technique for marking-up different media to provide rapid entering of semantic information The user can interactively markup parts of the representation on the medium of choice (e.g. areas on a document image) and associate local information with text, images, sound-clips, video or sub-parts of them without altering the original. The output is XML files that can be parsed by viewers to navigate through the semantic information or used by search agents to refine the accuracy of retrieval in a query. For demonstration purposes we present ImageTagger, a prototype for document image tagging. Developing in Java allows for net-centric administration and use of a number of heterogeneous databases through standard Internet browsers.
Adding the Time Dimension to Majority Voting Strategies

Real-time applications typically have to satisfy high dependability requirements and require fault tolerance in both value and time domains. A widely used approach to ensure fault tolerance in dependable systems is the N-modular redundancy (NMR) which typically uses a majority voting mechanism. However, NMR primarily focuses on producing the correct value, without taking into account the time dimension. In this paper, we propose a new approach, Voting on Time and Value (VTV), applicable to real-time systems, which extends the modular redundancy approach by explicitly considering both value and timing failures, such that correct value is produced at correct time, under specified assumptions. We illustrate the proposed approach by an algorithm applicable for triple modular redundancy (TMR).
Runners’ engagement and social support practices: exploring the uses and role of online activities

Abstract Tracking technologies that monitor exercise and health have grown in popularity, while online communities have emerged to encourage healthier lifestyles. Runners display high engagement levels with these technologies, however, studies are inconclusive about the effectiveness of these tools, and half of tracking device owners stop use within 3 months. This exploratory study analyses the engagement practices of UK-based long distance runners with tracking technologies and online communities in order to explore how these provide social support for runners. Data were derived from nine interviews. Findings identify distinct use practices associated with social networking sites, running club online communities and tracking applications. Social support is not as prominent as social comparison, identity formation, motivation and information gathering for runners. The nature of the community, co-present connections between individuals and running experience have implications for engagement and support practices, while emotional attachment to data represents an opportunity for development.
Evaluation of the Iterative Method for Construction Of the Median Frames in Medical Images

This paper addresses the appliance of an iterative method for compensating distortion caused by shape-based interpolation in digital medical images. Image interpolation techniques have been widely used in medical imaging for image generation and processing. In ultrasound images, due to the limitation of resolution power, reduction of vulnerability in patients and reduction of time in image capturing, there are empty spaces between frames. Lagrange interpolation is used to replenish these empty spaces and elimination of distorted parts in the segmentation which have a special geometrical shape is also carried out. In this work, our method was applied to the Lagrange interpolated ultrasound images of the human heart. Due to the existence of noise in medical images and also the topology of the images, some preprocessing stages such as noise elimination, and smoothing along with preservation of the edges was applied to the image. In the last section, the iterative method was applied after interpolation to the image. The result gained by this application is an evidence of improvement in the performance.
The se-ra Alternation in Spanish Subjunctive

In this paper I take a look at a classic problem in Spanish morphosyntax, namely the alternation between the forms -se and -ra in the Imperfect Subjunctive ( Imperfecto de Subjuntivo ). Research on this topic has mainly focused on sociolinguistic variation, and has been done almost exclusively with impressionistic data and speaker’s intuitions. I address the problem from a usage-based perspective, using corpus linguistics methods. The main claim is that the choice between -se and -ra correlates to a certain extent with morpho-syntactic and discourse factors. Through collostructional analysis I also show that there are repelled and attracted collexemes that distinguish and relate both forms.
Carbon Price Uncertainty and Power Plant Greenfield Investment in Europe

One of the stated objective of the EU ETS policy is to incentivize investment in low-carbon or carbon-free power generation technologies. Still, so far, the uncertainty about future carbon prices and the existence of technology-dedicated incentives like subsidies for CCS and feed-in tariffs or green certificates, might indicate that the carbon price has hardly played that role. The aim of this paper is twofold. First, the method developed should help utilities decision-makers integrate their views on carbon prices in an investment decision framework. Second, the method employed should ultimately help identify sensitivity points to guide policymakers when designing amendments to the rules governing the EU ETS. In order to understand how corporate decisions under carbon price uncertainty are taken, we model a utility’s investment decision in a multivariate real options framework. We consider a European utility that has a 10-year window to invest in a combination of various generation technologies (nuclear, IGCC, CCGT, pulverized coal and offshore wind). The model specifically account for uncertainty in carbon and power prices. The model is solved using the least-squares Monte Carlo approach (Longstaff and Schwartz, 2001 and Gamba, 2003) in order to account for various sources of uncertainty. Compared to the existing literature, we adapt the method to explicitly allow for capital rationing and choose among various technologies rather than just determining an optimal option exercise time. Policy-wise, early results of the model indicate that attempts to limit market price volatility and / or ensure a quick reversion to long-term equilibrium are of little help when compared to giving indications regarding significant cap level at various points in time (indicative of the deterministic trend). Furthermore, the price of carbon only contributes little to shifting investment decisions towards carbon-neutral or lower carbon investments. Rather, the price of carbon is critical to short-term adjustments (fuel-switching / trading / operation planning). Finally, technology-dedicated incentives seems to better incentivize the investment in carbon-neutral or lower carbon power plants.
Using Document Weight Combining Method for Enterprise Expert Mining

Locating people with specific expertise on a given topic is crucial for the success of projects in enterprise. Rather than using the common method which needs to generate expert profiles, in this paper we present an alternative method, document weight combing, for the expert mining task. This method is much easier to implement and more flexible for the dynamic data sets than the common method. Our experiment and evaluation on the effectiveness of our method by using W3C corpus show that this method is practical and promising.