Miracle-dz/UniFAR-FTUs huggingface dataset:
3D Medical Image Segmentation with Vantage Point Forests and Binary Context Features

Automated segmentation of medical image data is an important, clinically relevant task as manual delineation of organs is time-consuming and subject to interand intraobserver fluctuations. This thesis builds upon a framework for segmentation of multiple organs in three-dimensional images. The approach employs a supervised recognition, where a training set with dense organs annotations is used to classify voxels in unseen images based on similarity of binary features extracted from the data. A combination of two different types of feature vectors is used to capture relevant structural and contextual information. The binary vectors are constructed by multiple pairwise intensity comparisons. Hence, the method is invariant to monotonic gray-level changes and can be applied to different imaging modalities or anatomies. The fast approximate nearest neighbor search, using Vantage Point Forests, does not require any explicit prior shape model knowledge and allows computationally efficient binary data classification. Training the algorithm takes several minutes, while segmenting a test image is in the order of a few seconds. The method is successfully applied to 68 CT abdominal and 42 MR pelvic images. With respect to ground truth, an average Dice overlap score of 0.74 for the CT segmentation of liver, spleen and kidneys is achieved. The mean score for the MR delineation of bladder, bones, prostate and rectum is 0.65. The results demonstrate surprisingly accurate segmentation, robustness and data-efficiency.
Learning Semantically Meaningful Representations Through Embodiment

How do humans acquire a meaningful understanding of the world with little to no supervision or semantic labels provided by the environment? Here we investigate embodiment and a closed loop between action and perception as one key component in this process. We take a close look at the representations learned by a deep reinforcement learning agent that is trained with visual and vector observations collected in a 3D environment with sparse rewards. We show that this agent learns semantically meaningful and stable representations of its environment without receiving any semantic labels. Our results show that the agent learns to represent the action relevant information extracted from pixel input in a wide variety of sparse activation patterns. The quality of the representations learned shows the strength of embodied learning and its advantages over fully supervised approaches with regards to robustness and generalizability.
Stress and Preterm Birth

Each year 15 million babies are born preterm and the global rate of preterm birth (PTB) of >10% is steadily rising. PTB programs life-long complications, including long term motor, cognitive, growth and health problems. A major obstacle in determining the causes of this global problem is that PTB is multifactorial with several different etiologies. These various etiologies ultimately converge on identifiable pathways that are critically influenced by stress. In this review we focus on lifetime and transgenerational aspects of stress related to PTB and discuss possible mechanisms that translate stress and associated allostatic load into the physiological changes that transform the uterus of pregnancy to the uterus of delivery preterm. The concept of allostatic load induced by a severe stressor provides a compelling rationale for the contribution of stress to spontaneous PTB. Although it is challenging to demonstrate a clear causal relationship in humans, PTB was recognized as a consequence of severe maternal distress preconceptionally. For example, adverse childhood experiences increase the risk for PTB. Being born preterm is the most powerful predictor for a woman to be at higher risk for a PTB of her own. Recent findings in rodents indicate that prenatal stress may generate an epigenetic signature that may be passed on to the offspring, thus enhancing stress sensitivity and interfering with pregnancy maintenance in the next and future generations. Based on this work in animal models, prenatal stress may predispose women, their daughters and granddaughters to greater PTB risk. Epigenetic mechanisms  Email: dmolson@ualberta.ca No part of this digital document may be reproduced, stored in a retrieval system or transmitted commercially in any form or by any means. The publisher has taken reasonable care in the preparation of this digital document, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained herein. This digital document is sold with the clear understanding that the publisher is not engaged in rendering legal, medical or any other professional services. Inge Christiaens, David M. Olson and Gerlinde A. Metz 510 may help explain gene x environment interactions that link childhood abuse and expression of genetic risk factors. We propose that PTB represents a multifactorial condition, which according to the ―Two-Hit Hypothesis‖ can be triggered or worsened by an episode of prenatal stress or a traumatic life event. New cost-effective technologies, including stress assessments and interventions, need to be developed to firstly identify women at high risk of PTB and then treat them in order to delay preterm delivery, prolong pregnancy and thereby improve newborn health outcomes.
New developments of near-infrared eAPD technology for wavefront sensing at ground-based telescopes, on-sky performance verification with GRAVITY and the potential of eAPDs for improving the sensitivity of large format science arrays and for deployment in space

Ground-based near-infrared adaptive optics as well as fringe tracking for coherent beam combination in optical interferometry has required the development of high-speed sensors. Because of the high speed, a large analog bandwidth is needed. The short exposure times result in small signal levels which require noiseless detection. Both of these conflicting requirements cannot be met by state-of-the-art conventional CMOS technology of near-infrared arrays as has been attempted previously. The HgCdTe electron avalanche photo diode (eAPD) technology is the only way to overcome the limiting CMOS noise barrier of near infrared sensors. Therefore, ESO funded the development of the near infrared SAPHIRA 320x256 pixel e-APD arrays at LEONARDO [1][2]. With their unprecedented performance SAPHIRA arrays have now become the devices of choice for control loops as demonstrated by the four wavefront sensors and the fringe tracker deployed in the VLTI instrument GRAVITY, which set a new sensitivity standard in infrared interferometry[3]. These APD arrays also have extremely low dark current (1E-3 electrons/s/pixel) and may outperform conventional CMOS arrays for 100 second integrations when operated with moderate APD gains. Currently, a 512x512 pixel SAPHIRA array with 64 parallel video outputs optimized for pyramid wavefront sensing is in development. The SAPHIRA array has successfully passed radiation hardness testing and may soon be used for future instruments in space with the SIDECAR ASIC.
An IPAT-type Model of Environmental Impact Based on Stochastic Differential Equations

This work adds to the literature examining human driven climate change within the framework of IPAT-based models. Our contribution relates, on the one hand, to the modelling of the environmental impact, expressed in terms of carbon dioxide emissions, and, on the other hand, to the valuation of the costs that a country has to bear in order to reduce its emissions. Starting from the well-known IPAT equation, we develop a stochastic formulation of the relationship between population, affluence and technology alternative to the STIRPAT model. This first step leads to a stochastic differential equations model that describes the trend of carbon dioxide emissions of a country, on the basis of the dynamics of its population and affluence. As an example, we estimated the parameters of the model with regard to the United States. Our theoretical scheme has then been used to develop a model for the assessment of the costs that a country has to bear, if committed to respect an international agreement on emissions reduction, like the Kyoto protocol. In particular, we show that the adherence to an environmental treaty may be traced back to a problem of cost valuation in a risk situation: this allows us to exploit the mathematical tools that have been developed in the field of finance, specifically in the context of option pricing, to determine the expected investment that a country is supposed to make in order to reduce its emissions of a certain amount, within a well-defined temporal frame.
A Baseline Study of Emphasis Effects in Information Visualization

Emphasis effects – visual changes that make certain elements more prominent – are commonly used in information visualization to draw the user’s attention or to indicate importance. Although theoretical frameworks of emphasis exist (that link visually diverse emphasis effects through the idea of visual prominence compared to background elements), most metrics for predicting how emphasis effects will be perceived by users come from abstract models of human vision which may not apply to visualization design. In particular, it is difﬁcult for designers to know, when designing a visualization, how different emphasis effects will compare and how to ensure that the user’s experience with one effect will be similar to that with another. To address this gap, we carried out two studies that provide empirical evidence about how users perceive different emphasis effects, using three visual variables (colour, size, and blur/focus) and eight strength levels. Results from gaze tracking, mouse clicks, and subjective responses in our ﬁrst study show that there are signiﬁcant differences between different kinds of effects and between levels. Our second study tested the effects in realistic visualizations taken from the MASSVIS dataset, and saw similar results. We developed a simple predictive model from the data in our ﬁrst study, and used it to predict the results in the second; the model was accurate, with high correlations between predictions and real values. Our studies and empirical models provide new information for designers who want to understand how emphasis effects will be perceived by users.
Data Vases: Plots for Visualizing Multiple Time Series

We present a two-dimensional approach we call Data Vases that yields a compact pictorial display of a large number of numeric values varying over time. Our method is based on an intuitive and flexible but less widely-used display technique called a “kite diagram. ” We show how our interactive two-dimensional method, while not limited to timedependent problems, effectively uses shape and color for investigating temporal data. In the future we hope to demonstrate that our methods can be extended to three dimensions for visualizing time-dependent data on cartographic maps.
Statin Effects in Atrial Fibrillation-Related Stroke: A Systematic Review and Meta-Analysis

Background: Statins lower the risk of recurrent stroke and mortality and improve outcomes in patients with ischemic stroke. However, the effects of statins on atrial fibrillation (AF)-related stroke are not well-established. Our study aims to investigate the effects of statin therapy on the clinical outcomes in patients with AF-related stroke. Methods: Electronic databases (MEDLINE, Embase, and Scopus) were searched for previous studies on the effects of pre- and post-stroke statins on the clinical outcomes in AF-related stroke patients. The primary outcome was all-cause mortality. Secondary outcomes included recurrent ischemic stroke, acute coronary events, major adverse cardiovascular events (MACE), and short-term functional outcomes. We extracted hazard ratios (HRs) or odds ratios (ORs) with 95% confidence interval (CI) from each study and pooled them through a meta-analysis. Results: A total of eight studies (five on post-stroke statins and three on pre-stroke statins) with 12,216 patients were included in the analysis. Post-stroke statin therapy reduced the risk of all-cause mortality (HR, 0.63; 95% CI, 0.55–0.74). This beneficial effect was sustained regardless of statin intensity. However, no significant associations were observed between statin therapy and a reduction in the risk of recurrent ischemic stroke, acute coronary events, or MACE. Pre-stroke statin use was associated with a lower risk of poor short-term functional outcomes (OR, 0.63; 95% CI, 0.47–0.85). Conclusions: Statin therapy for AF-related stroke may reduce all-cause mortality and improve functional outcomes. Randomized controlled studies are warranted to confirm the effects of statins on the outcomes of AF-related stroke.
The Purchase Intention of Organic Foods among Working Adults in Penang, Malaysia

The purpose of this study is to analyse the purchase intention of organic food.This research has looked at various factors such as health consciousness, environmental concern, price and quality. Quota sampling technique was used on a total of 168 respondants from Penang. Questionnaire was used as the research instrument for this research and the results obtained were analysed using frequency analysis, reliability analysis and multiple linear regression. The findings of this study will be to identify the purchase intention of working adults in Penang towards organic foods. It would help practitioners, researchers and marketers to understand which factors are important and can be applied to their future research or business strategies.
Statistics and Experimental Design for Psychologists: A model comparison approach

This article reviews a new statistics textbook by Rory Allen. The book teaches the fundamentals of statistics and experimental design at an undergraduate psychology level. The material is taught from a model comparison perspective, which Allen argues to be more intuitive than traditional null hypothesis significance testing (NHST) approaches. A key feature of the book is an elegant and novel geometric formulation of Fisher’s F, the test statistic in analysis of variance (ANOVA). Allen’s contribution should provide a useful reference for psychology students, especially if future editions incorporate a wider range of statistical methods.
THEORY OF FINANCIAL MANAGEMENT IN PRACTICE OF CZECH COMPANIES

This article is focused on comparing of theoretical financial management approaches with financial management practice of Czech companies operating in sectors of manufacturing industry. Empirical research found big differences between basic theoretical recommandations and their fulfillment in practice. Discount rate in the form of weighted average cost of capital value plays a key role in many financial decisions above all in capital structure optimalisation and in dynamic assessment methods needed for investment decisions. Nevertheless it proved that only 20% of companies work with discount rate, the rest of them does not specify the discount rate value. It has been determinated the impact of the ownership links with larger companies on the use of discount rate in company. This proves the fact that discount rate users are parent companies (15%), daughter companies owned by Czech parent companies (27%) and daughter companies with foreign parent companies (54%).
Resistin and Oxidative Stress in Non-Diabetic Obstructive Sleep Apnea Patients with Nocturnal Hypertension

Introduction: Resistin is a novel adipokine that has been reported as an independent predictor for hypertension. Its role in patients with obstructive sleep apnea (OSA), however, has not been investigated yet. Aim: The aim of this study was to explore the role of resistin in non-diabetic OSA patients with nocturnal hypertension and to study its association with markers of obesity, insulin resistance and oxidative stress. Methods: A total of 54 non-diabetic hypertensive OSA patients participated in the study. OSA was newly detected by a standard polysomnography. Patients had controlled hypertension. According to ambulatory blood pressure monitoring (ABPM) they were divided into patients with normal nocturnal BP (25) and patients with nocturnal hypertension (29). Anthropometric, sleep study and glucometabolic parameters were defined. Resistin plasma levels were determined by an ELISA kit. Oxidative stress (urinary 8-isoprostane levels) were measured in addition by mass spectrometry (Cayman Chemical, USA). Results: Resistin plasma levels were higher in subjects with nocturnal hypertension in comparison to those with normal BP - (7.81±4.43 vs 4.90±2.92ng/mL). Resistin correlated neither to markers of adiposity, nor to those of insulin resistance. Its association with urinary isoprostane levels was of significance (p-0.036). Resistin was higher in patients with severe apnea, but was not associated with respiratory disturbance parameters. Conclusion: Our results suggest that hyperresistinemia is associated with higher levels of oxidative stress and may contribute to the pathogenesis of nocturnal hypertension in non-diabetic OSA patients. This may provide adequate cardiovascular risk assessment and improve the therapeutic control.
Evaluation of Traceability Recovery in Context: A Taxonomy for Information Retrieval Tools

Background: Development of complex, software intensive systems generates large amounts of information. Several researchers have developed tools implementing information retrieval (IR) approaches to suggest traceability links among artifacts. Aim: We explore the consequences of the fact that a majority of the evaluations of such tools have been focused on benchmarking of mere tool output. Method: To illustrate this issue, we have adapted a framework of general IR evaluations to a context taxonomy specifically for IR-based traceability recovery. Furthermore, we evaluate a previously proposed experimental framework by conducting a study using two publicly available tools on two datasets originating from development of embedded software systems. Results: Our study shows that even though both datasets contain software artifacts from embedded development, the characteristics of the two datasets differ considerably, and consequently the traceability outcomes. Conclusions: To enable replications and secondary studies, we suggest that datasets should be thoroughly characterized in future studies on traceability recovery, especially when they can not be disclosed. Also, while we conclude that the experimental framework provides useful support, we argue that our proposed context taxonomy is a useful complement. Finally, we discuss how empirical evidence of the feasibility of IR-based traceability recovery can be strengthened in future research.
VIA ADVERSARIAL NEURAL NETWORKS

We present a novel method for cell segmentation in microscopy images which is inspired by the Generative Adversarial Neural Network (GAN) approach. Our framework is built on a pair of two competitive artificial neural networks, with a unique architecture, termed Rib Cage, which are trained simultaneously and together define a min-max game resulting in an accurate segmentation of a given image. Our approach has two main strengths, similar to the GAN, the method does not require a formulation of a loss function for the optimization process. This allows training on a limited amount of annotated data in a weakly supervised manner. Promising segmentation results on real fluorescent microscopy data are presented. The code is freely available at: https://github.com/arbellea/DeepCellSeg.git
Understanding Attention Mechanisms

Attention mechanisms have advanced the state of the art in several machine learning tasks. Despite significant empirical gains, there is a lack of theoretical analyses on understanding their effectiveness. In this paper, we address this problem by studying the landscape of population and empirical loss functions of attentionbased neural networks. Our results show that, under mild assumptions, every local minimum of a two-layer attention model has low prediction error, and attention models require lower sample complexity than models not employing attention. Additionally for the popular self-attention, our theoretical results provide several guidelines for designing attention mechanisms. Our findings are validated with satisfactory experimental results on MNIST and IMDB reviews dataset.
Semantic Node-RED for rapid development of interoperable industrial IoT applications

The evolution of IoT has revolutionized industrial automation. Industrial devices at every level such as field devices, control devices, enterprise level devices etc., are connected to the Internet, where they can be accessed easily. It has significantly changed the way applications are developed on the industrial automation systems. It led to the paradigm shift where novel IoT application development tools such as Node-RED can be used to develop complex industrial applications as IoT orchestrations. However, in the current state, these applications are bound strictly to devices from specific vendors and ecosystems. They cannot be re-used with devices from other vendors and platforms, since the applications are not semantically interoperable. For this purpose, it is desirable to use platform-independent, vendor-neutral application templates for common automation tasks. However, in the current state in Node-RED such reusable and interoperable application templates cannot be developed. The interoperability problem at the data level can be addressed in IoT, using Semantic Web (SW) technologies. However, for an industrial engineer or an IoT application developer, SW technologies are not very easy to use. In order to enable efficient use of SW technologies to create interoperable IoT applications, novel IoT tools are required. For this purpose, in this paper we propose a novel semantic extension to the widely used Node-RED tool by introducing semantic definitions such as iot.schema. org semantic models into Node-RED. The tool guides a non-expert in semantic technologies such as a device vendor, a machine builder to configure the semantics of a device consistently. Moreover, it also enables an engineer, IoT application developer to design and develop semantically interoperable IoT applications with minimal effort. Our approach accelerates the application development process by introducing novel semantic application templates called Recipes in Node-RED. Using Recipes, complex application development tasks such as skill matching between Recipes and existing things can be automated. We will present the approach to perform automated skill matching on the Cloud or on the Edge of an automation system. We performed quantitative and qualitative evaluation of our approach to test the feasibility and scalability of the approach in real world scenarios. The results of the evaluation are presented and discussed in the paper.