Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of modified quality control checklist on protocol adherence and outcomes in a post-surgical Intensive Care Unit

Background and Aims: Quality improvement (QI) is the sum of all activities that create desired changes in the quality. An effective QI system results in a stepwise increase in quality of care. The efficiency of any health-care unit is judged by its quality indicators. We aimed to evaluate the impact of QI initiatives on outcomes in a surgical Intensive Care Unit (ICU). Methods: This was an observational study carried out using a compliance checklist, developed from the combination of the World Health Organization surgery checklist and Society for Healthcare Epidemiology of America guidelines for the prevention of infections. A total of 170 patients were prospectively evaluated for adherence to the checklist and occurrence of infections. This was compared with a random retrospective analysis of 170 patients who had undergone similar surgeries in the previous 3 months. Results: Introduction and supervised documentation of comprehensive checklist brought out significant improvement in the documentation of quality indicators (98% vs. 32%) in the prospective samples. There was no difference in mortality, health-care-related infection rates or length of ICU stay. Conclusion: The introduction of comprehensive surgical checklist improved documentation of parameters for quality control but did not decrease the rates of infection in comparison to the control sample.
Synthetic Time Series Resembling Human (HeLa) Cell-Cycle Gene Expression Data and Application to Gene Regulatory Network Discovery

Evaluation of gene regulatory network (GRN) discovery methods relies heavily on synthetic time series. However, synthetic data generated by traditional method deviate a lot from real data, making such evaluation questionable. Guiding by decaying sinusoids, we propose a new method that generates synthetic data resembling human (HeLa) cell-cycle gene expression data. Using the new synthetic data, a simple comparison between four GRN discovery methods reveals that Granger causality (GC) methods substantially outperform Pearson correlation coefficient (PCC), while time-shifted PCC can give comparable performance as GC methods. The new synthetic data generation would also be useful for generating other kinds of cell-cycle time series. Using data generated by our proposed method, evaluation of GRN discovery methods should be more trustworthy for real-data applications.
Visual recording and replay of rigid objects

This deliverable deals with the visual replay of a previously recorded object. Image-based rendering techniques are applied which perform a kind of view interpolation. An algorithm is presented that allows applying signal processing algorithms to generate models with space and time coherence. Methods are described that can remove reconstruction and quantization noise, as well as calibration errors. A GPU-accelerated rendering algorithm is presented to further improve the final image quality by generating consistent view-dependent geometry and textures for each individual frame. IMMERSENCE 3 D5.1. 1 Visual recording and replay of rigid objects This page is left blank intentionally. IMMERSENCE 4 D5.1. 1 Visual recording and replay of rigid objects
Flexible calibration: minimal cases for auto-calibration

This paper deals with the concept of auto-calibration, i.e. methods to calibrate a camera on-line. In particular we deal with minimal conditions on the intrinsic parameters needed to make a Euclidean reconstruction, called flexible calibration. The main theoretical results are that it is only needed to know that one intrinsic parameter is constant. The method is based on an initial projective reconstruction, which is upgraded to a Euclidean one. The number of images needed increases with the complexity of the constraints, but the number of points needed is only the number needed in order to obtain a projective reconstruction. The theoretical results are exemplified in a number of experiments. An algorithm, based on bundle adjustments and a linear initialization method are presented and experiments are performed on both synthetic and real data.
An Analytical Study on integration of Multibiometric Traits at Matching Score Level using Transformation Techniques

Biometric is one of those egressing technologies which are exploited for identifying a person on the basis of physiological and behavioral characteristic. However, unimodal biometric system faces the problem of lack of individuality, spoof attacks, non-universality, degree of freedom etc., which make these systems less precise and erroneous. In order to overcome these problems, multi biometric has become the favorite choice for verification of an individual to declare him as an imposte or a genuine. However, the fusion or integration of multiple biometric traits can be done at any one of the four module of a general multibiometric system. Further, achieving fusion at matching score level is more preferable due to the availability of sufficient amount of information present over there. In this paper we have presented a comparative study of normalization methodology which is basically used to convert the different feature vectors of individual traits in common domain in order to combine them as a single feature vector. Keywords— Biometric, Multibiometric, Normalization, Unimodal, Unsupervised Learning rules, Imposter, Genuine.
Deblurring and Denoising of Magnetic Resonance Images using Blind Deconvolution Method

paper describes the methods to improve the quality of blurred and noisy Magnetic Resonance Images (MRI) due to motion artifact. The main objective of this work is to restore original image from a motion-blurred image due to defocused optical system and motion artifact which is a challenging problem in digital imaging. The blind deconvolution method has been applied to restore the clear image from the blurred and noisy MRI images in this paper. There exist several techniques to restore the original image corrupted by various noise and blurred due to motion artifact, but these methods lack to fetch the originality of image. Blind deconvolution is a method to recover the sharp version of a blurred image when the source of blurring is not known. The aim of this paper is to analyze and evaluate recent blind deconvolution algorithms both theoretically and experimentally for deblurring and noise removal of MRI mges. The MRI data used was corrupted by Gaussian noise and blurred due to various cause (gaussian blurring, out-of-focus blur and motion blur etc). The comparisons of the techniques were done on the basis of Peak signal to noise ratio (PSNR) and Mean structural similarity index (MSSIM). The proposed technique proved to be better over methods used for deblurring and denoising of MRI images.
Accent- and speaker-specific polyphone decision trees for non-native speech recognition

Acoustic models in state-of-the-art LVCSR systems are typically trained on data from thousands of speakers and then adapted to a speaker using, e.g., various combinations of CMLLR, MLLR and MAP. This adaptation step is particularly important for speakers with accents that are not well represented in the training set. The present study explores how to improve performance on South-Asian-accented speakers (SoA-accented) with the availability of thousands of US-accented, hundreds of SoA-accented, and tens of hours of speaker-specific training data. We employ a decision tree similarity measure to analyze how varying co-articulations across accents and people manifest themselves in the decision tree. Modeling these variations in addition to adapting the GMMs of an existing baseline system to a speaker improved WER for small systems (1k GMMs), but improvement for systems with larger trees (2k, 3k GMMs) was modest. Overall, GMM adaptation/retraining yields significant performance benefits, and training a SoA-accent-specific system is particularly worthwhile when lacking speaker adaptation data.
Performance Study of Wireless Body Area Network in Medical Environment

The advanced developments in sensors and wireless communications devices have enabled the design of miniature, cost-effective, and smart physiological sensor nodes. One of the approaches in developing wearable health monitoring systems is the emerging of wireless body area network (WBAN). IEEE 802.15. 4 provides low power, low data rate wireless standard in relation to medical sensor body area networks. In the current study, the star network topology of 802.15. 4 standard at 2.4 GHz had been considered for body area network (BAN) configured in beacon mode. The main consideration is in the total data bits received by all the nodes at the coordinator and flight times of a data packet reaching its destination. In this paper we discussed on wireless technologies that can be used for medical applications and how they perform in a healthcare environment. The low-rate Wireless Personnel Area Network (WPAN) is being used to evaluate its suitability in medical application.
CONCEPTUAL MODELING OF MULTIDIALECT WORKFLOWS

This paper contributes to the techniques for conceptual representation of data analysis algorithms and data integration facilities as well as processes to specify data and behavior semantics in one paradigm. An investigation of a novel approach for applying a combination of semantically different platform-independent rule-based languages (dialects) for interoperable conceptual specifications over various rule-based systems (RSs) relying on the rule-based program transformation technique recommended by the W3C Rule Interchange Format (RIF) is extended here. Such approach is combined with the facilities aimed at the semantic rule-based mediation intended for the heterogeneous data base integration. This paper extends a previous research of the authors in the direction of workflow modeling for definition of compositions of algorithmic modules in a process structure. A capability of the multidialect workflow support specifying the tasks in semantically different languages mostly suited to the task orientation is presented. A practical workflow use case, the interoperating tasks of which are specified in several rule-based languages (RIF-CASPD, RIF-BLD, RIF-PRD), is introduced. In addition, OWL 2 is used for the conceptual schema definition, RIF-PRD is used also for the workflow orchestration. The use case implementation infrastructure includes a production rule-based system (IBM ILOG), a logic rule-based system (DLV), and a mediation system.
Folliculogenesis and oogenesis: from basic science to the clinic.

From germ cell to fertilizable oocyte is a journey that captures the essence of MHR: a quest to understand the basic science of reproductive medicine. In view of the rapid developments in molecular biology and genetics, a major goal of ESHRE in recent years has been to provide clinicians, counsellors, embryologists and the public with novel research findings of relevance to assisted reproduction technologies (ART). In this spirit, the ESHRE Campus workshop in Potsdam in October 2009 on ‘Mammalian Folliculogenesis and Oogenesis: from basic science to the clinic’ was dedicated to fundamental research in an area that is increasingly affecting clinical practice. The meeting was well attended (Fig. 1) and eight invited speakers submitted manuscripts for peer review and collation into this thematic issue of MHR, the key messages of which are highlighted below.
E3: Entailment-driven Extracting and Editing for Conversational Machine Reading

Conversational machine reading systems help users answer high-level questions (e.g. determine if they qualify for particular government benefits) when they do not know the exact rules by which the determination is made (e.g. whether they need certain income levels or veteran status). The key challenge is that these rules are only provided in the form of a procedural text (e.g. guidelines from government website) which the system must read to figure out what to ask the user. We present a new conversational machine reading model that jointly extracts a set of decision rules from the procedural text while reasoning about which are entailed by the conversational history and which still need to be edited to create questions for the user. On the recently introduced ShARC conversational machine reading dataset, our Entailment-driven Extract and Edit network (E3) achieves a new state-of-the-art, outperforming existing systems as well as a new BERT-based baseline. In addition, by explicitly highlighting which information still needs to be gathered, E3 provides a more explainable alternative to prior work. We release source code for our models and experiments at https://github.com/vzhong/e3.
Rotation and translation invariant 3D descriptor for surfaces

We present a descriptor estimator for surface-based 3D input data for coarse localization of mobile robots. From the input pointclouds surfaces are reconstructed and simplified to detect stable keypoints which are used to evaluate rotation and translation invariant features. The invariance is achieved by transforming the triangulated input data into the frequency domain by Fourier transformation and spherical harmonics. The pipeline was evaluated against state of the art algorithms and tested to localize a mobile robot. The source code is publicly available.
A novel fuzzy metric for objective evaluation of interactive image segmentation algorithms based on correlation

Image segmentation is a relevant research area in computer vision and plays a major role in a broad range of applications; therefore it is necessary to be able to evaluate the performance of image segmentation algorithms objectively. For natural images, usually we use interactive image segmentation to provide a means of accurately extracting semantic objects from an image efficiently. In interactive segmentation the users only need to roughly indicate the location and region of the object and background by using markers. In this paper we present a new fuzzy error measure index to obtain the interactive image segmentation error, based on correlation of pixels. Also this measure is based on segment-by-segment comparisons of a segmented image and a ground-truth and is sensitive to the position of each object. Experimental results were obtained for a selection of test images and showed that it's a proper measure for comparing interactive image segmentation algorithms.
The visible part of plane self-similar sets

Given a compact subset F of R2, the visible part VθF of F from direction θ is the set of x in F such that the half-line from x in direction θ intersects F only at x. It is suggested that if dimH F ≥ 1, then dimH VθF = 1 for almost all θ, where dimH denotes Hausdorff dimension. We confirm this when F is a self-similar set satisfying the convex open set condition and such that the orthogonal projection of F onto every line is an interval. In particular the underlying similarities may involve arbitrary rotations and F need not be connected.
A Geographically Distributed Processing Environment for Intelligent Systems

By addressing both the theoretical and practical issues of a geographically distributed application domain, we propose a way to construct a processing system using the computational aspects of a brain/mind system. The Alchemy distributed processing environment has been developed to support the geographical distribution of a set of intelligent applications running over the Internet. We focus here on the implementation details of the Alchemy architecture and how its performance has been demonstrated using a model of a geographically distributed information system. Using this model we compare our approach to more traditional approaches like distributed objects and mobile agents.
A distributional similarity measure for query-dependent ranking in web mining

Ranking model construction is an important topic in information retrieval and web mining. Recently, many approaches based on the idea of “learning to rank” have been proposed for this task and most of them attempt to score all documents of different queries by resorting to a single function. In this paper, we propose a distributional similarity measure for query-dependent ranking. In the query-dependent ranking framework, an individual ranking model is constructed for each training query with associated documents. When a new query is asked, the documents retrieved for the new query are ranked according to the scores determined by a joint ranking model which is combined from the individual models of similar training queries. The distributional similarity measure is used to calculate the similarities between queries. Experimental results show that our method is more effective than other approaches.
Markedly Divergent Tree Assemblage Responses to Tropical Forest Loss and Fragmentation across a Strong Seasonality Gradient

We examine the effects of forest fragmentation on the structure and composition of tree assemblages within three seasonal and aseasonal forest types of southern Brazil, including evergreen, Araucaria, and deciduous forests. We sampled three southernmost Atlantic Forest landscapes, including the largest continuous forest protected areas within each forest type. Tree assemblages in each forest type were sampled within 10 plots of 0.1 ha in both continuous forests and 10 adjacent forest fragments. All trees within each plot were assigned to trait categories describing their regeneration strategy, vertical stratification, seed-dispersal mode, seed size, and wood density. We detected differences among both forest types and landscape contexts in terms of overall tree species richness, and the density and species richness of different functional groups in terms of regeneration strategy, seed dispersal mode and woody density. Overall, evergreen forest fragments exhibited the largest deviations from continuous forest plots in assemblage structure. Evergreen, Araucaria and deciduous forests diverge in the functional composition of tree floras, particularly in relation to regeneration strategy and stress tolerance. By supporting a more diversified light-demanding and stress-tolerant flora with reduced richness and abundance of shade-tolerant, old-growth species, both deciduous and Araucaria forest tree assemblages are more intrinsically resilient to contemporary human-disturbances, including fragmentation-induced edge effects, in terms of species erosion and functional shifts. We suggest that these intrinsic differences in the direction and magnitude of responses to changes in landscape structure between forest types should guide a wide range of conservation strategies in restoring fragmented tropical forest landscapes worldwide.
Formation of anion‐selective channels in the cell plasma membrane by the toxin VacA of Helicobacter pylori is required for its biological activity

The vacuolating toxin VacA, a major determinant of Helicobacter pylori‐associated gastric diseases, forms anion‐selective channels in artificial planar lipid bilayers. Here we show that VacA increases the anion permeability of the HeLa cell plasma membrane and determines membrane depolarization. Electrophysiological and pharmacological approaches indicated that this effect is due to the formation of low‐conductance VacA pores in the cell plasma membrane and not to the opening of Ca2+‐ or volume‐activated chloride channels. VacA‐dependent increase of current conduction both in artificial planar lipid bilayers and in the cellular system was effectively inhibited by the chloride channel blocker 5‐nitro‐2‐(3‐phenylpropylamino) benzoic acid (NPPB), while2‐[(2‐cyclopentenyl‐6,7dichloro‐2,3‐dihydro‐2‐methyl‐1‐oxo‐1H‐inden‐5‐yl)oxy]acetic acid (IAA‐94) was less effective. NPPB inhibited and partially reversed the vacuolation of HeLa cells and the increase of ion conductivity of polarized Madine Darby canine kidney cell monolayers induced by VacA, while IAA‐94 had a weaker effect. We conclude that pore formation by VacA accounts for plasma membrane permeabilization and is required for both cell vacuolation and increase of trans‐epithelial conductivity.
Detect LSB Steganography with Bit Plane Randomness Tests

We present a novel steganalytic technique for images that have been embedded hidden messages with LSB type steganographic algorithms. Two binary sequences is obtained by scanning the seventh and eighth bit planes of the image with Hilbert scan, then the randomness of this two sequences are tested by several randomness tests respectively. The key idea is that, the randomness of the bit planes will differ between a stego-image and a cover-image. The results of the randomness tests are used as image attributes to construct a classifier to distinguish between stego and cover images. The experiments show the detection accuracy of our method is higher than 95% to stego images with a embed rate higher than 0.05 bits per pixel. The experiments also show our method is effective to some frequency domain steganographic algorithms such as F5
Towards Gaussian states for loop quantum gravity

An important challenge in loop quantum gravity is to find semiclassical states - states that are as close to classical as quantum theory allows. This is difficult because the states in the Hilbert space used in LQG are excitations over a vacuum in which geometry is highly degenerate. Additionally, fluctuations are distributed very unevenly between configuration and momentum variables. Coherent states that have been proposed to balance the uncertainties more evenly can, up to now, only do this for finitely many degrees of freedom. Our work is motivated by the desire to obtain Gaussian states that encompass all degrees of freedom. To obtain a toy-model we reformulate the U(1) holonomy-flux algebra in any dimension as a Weyl algebra, and discuss generalisations to SU(2). We then define and investigate a new class of states on these algebras which behave like quasifree states on the momentum variables.
The Focal Adhesion: A Regulated Component of Aortic Stiffness

Increased aortic stiffness is a known predictor and cause of cardiovascular disease, but much remains unknown about the sources and mechanisms of aortic stiffness. We tested the hypothesis that the focal adhesions (FAs) connecting vascular smooth muscle cells (VSMCs) to the matrix in the aortic wall are a dynamically regulated component of aortic stiffness. First, we used magnetic tweezers to measure cortical stiffness in A7r5 aortic VSMCs. Lysophosphatidic acid (LPA), an activator of myosin that increases cell contractility, increased cortical stiffness. A small molecule inhibitor of Src‐dependent FA recycling, PP2, significantly inhibited LPA‐induced increases in cortical stiffness, as well as tension‐induced increases in FA size. To extend these findings to the tissue level, we monitored mouse aorta ring stiffness with small sinusoidal length oscillations. The alpha‐agonist phenylephrine, which also increases myosin activation and contractility, increased tissue stress and stiffness in a PP2‐attenuated manner. Phosphotyrosine screening confirmed that the effect of PP2 in vascular tissue involves FA proteins. These data suggest that the transfer of VSMC actomyosin‐generated stress and stiffness to the aortic wall during contractile stimulation depends on FAs. Thus, we identify, for the first time, the FA of the VSMC as a significant subcellular regulator of aortic stiffness. Support: NIH P01 86655