Miracle-dz/UniFAR-FTUs huggingface dataset:
ENRICHING PRODUCT EXPERIENCE BY AUGMENTED REALITY TECHNOLOGY

A product Experience includes all aspects of the interaction with a product ranging from expectations to usage. A great product should offer a great experience and communication between product and consumer is important segment. For this communication to be successful, it has to use appropriate channels. This applies to all the types of communication, including advertising, promotional material, information on a website, product documentation, etc. For the expectations to be properly formed promotion plays important role and in order for the consumer to use product properly and to its full potential it must be accompanied by the proper documentation. Product documentation is very important element in achieving successful communication with the consumer after product purchase. In order to cater to the needs and preferences of all customers’ integration of all the media formats can be beneficial. Recently augmented reality has become able to provide integration of multimedia formats in order to enrich consumer experience. Augmented reality is not new technology, but just with wide availability of the internet access through mobile devices it can offer its full potential directly in the place of sale, consumer homes or almost anywhere. This paper aims to present possibilities for integration of augmented reality technology in all the forms of product-consumer communication and especially product documentation. This can lead to far better user experience and provide advantage for the early adaptor organizations implementing this approach to the communication with the consumers.
University of Dundee Estimation of Shape and Growth Brain Network Atlases for Connectomic Brain Mapping in Developing Infants Rekik,

In vivo brain connectomics have heavily relied on using functional and diffusion Magnetic Resonance Imaging (MRI) modalities to examine functional and structural relationships between pairs of anatomical regions in the brain. However, research work on brain morphological (i.e., shape-to-shape) connections, which can be derived from T1-w and T2-w MR images, in both typical and atypical development or ageing is very scarce. Furthermore, the brain cannot be only regarded as a static shape, since it is a dynamic complex system that changes at functional, structural and morphological levels. Hence, examining the ‘connection’ between brain shape and its changes with time (e.g., growth) may help advance our understanding of connectomic brain dynamics as well as disorders that may affect it. To address these limitations, we unprecedentedly introduce two population-based shape and growth connectivity analysis tools that further extend the field of connectomics to brain morphology and dynamics: the morphome and the kinectome. Specifically, for a population of anatomically labelled shapes, the morphome identifies a network of anatomical shape regions that are connected when morphologically similar at a single timepoint, whereas the kinectome identifies anatomical shape regions that elicit similar evolution dynamics across successive timepoints. These proposed generic tools can be easily invested to examine how a baseline shape influences its deformation trajectory at later timepoints using any longitudinal shape data. We evaluated these tools on 23 infants, with right and left cortical surfaces reconstructed at birth, 3, 6, 9 and 12 months of age. Investigating the relationship between the neonatal morphome and the postnatal kinectome (from birth to 1 year of age) gave insights into brain connectivity at birth and how it develops over time.
A Stationary Wavelet Edge Detection Algorithm for Noisy Images

A stationary wavelet edge detection algorithm for n isy images was proposed. The method works efficiently on images corrupted by noise and is able to differentiate between false and real edges. The Pratt’s figure of merit (FOM) was used t o assess the algorithm and the assessment was performed using images corrupted with Gaussian noise. The edge images were compared with classical techniques such as the Sobel, Prewit t, Laplacian, and Canny operators. The results suggest that the proposed method outperforms the cl assi al edge detectors, resulting in the average FOM of 0.89 compared to 0.82 Sobel, 0.83 Pr ewitt, 0.51 Laplacian, and 0.83 Canny.
How to avoid Biases in Offshore Wind Power Forecasting

Large-scale offshore wind farms need to be operated as power plants for renewable energy in the near future. The dependency of energy production on highly fluctuating weather systems is inherent but will become manageable due to accurate wind power forecasting. The quality of statistical wind power forecasting algorithms can be optimized by proper selection of appropriate weather parameters and consideration of their natural characteristics like spatial-temporal evolution, uncertainty and error correlation. We investigate in this study whether the uncertainty in Numerical Weather Prediction shall be included in the algorithm development. We found that Neural Networks are perfect to retrieve smooth wind power forecasting algorithms from noisy input data. It is shown that algorithms developed with wind speeds from weather analysis or predicted wind speeds are equivalent. Special attention must be paid to systematic differences in wind speeds used for training and application, as different wind statistics are responsible for systematic forecast errors. This problem can be solved by an adaptive system approach.
Breast Cancer: Risks and Prevention

Over the last thirty years, while most major cancers have started to decline, breast cancer incidence in the US has increased by an alarming 40%. Most of this increase has occurred in the authors' generation, the generation of "Women's Lib. " It has been the authors' hope that, armed with full and accurate information, women can make healthier choices that will minimize their risk of breast cancer. The third edition has been greatly expanded, with particular emphasis on dietary and lifestyle factors, such as alternatives to hormone use for contraception and postmenopausal medication. The reference list has also been updated and expanded.
AN INTRODUCTION TO SHAPE AND TOPOLOGY OPTIMIZATION: HANDS-ON SESSION

1. Getting started with FreeFem++ 2 1.1. A worked example: resolution of the Laplace equation 2 1.2. The linearized elasticity system 5 1.3. The Stokes system 9 2. Topology optimization using density-based methods 11 2.1. Generalities about the implementation of density-based methods 11 2.2. Topology optimization of a heat lens 13 2.3. The classical cantilever benchmark in linearized elasticity 15 2.4. Topology optimization of a compliant mechanism 17 3. Geometric optimization using mesh movement 18 3.1. Basics about geometric optimization methods 19 3.2. The cantilever example 20 3.3. Geometric optimization for an eigenvalue problem 21 3.4. An example in fluid mechanics 22 4. Shape and topology optimization using the level set method 24 4.1. Practice of the level set method in the context of linearized elasticity 24 4.2. Optimal shape of a wheeled bridge 26 4.3. A level set-based fixed point algorithm relying solely on topological derivatives 30 References 33
Review of the School Completion Programme

The School Completion Programme (SCP) aims to have a significant positive impact on levels of student retention in primary and second-level schools. This report draws on a survey of SCP coordinators and chairpersons, in-depth case-studies of local clusters and interviews with key stakeholders to review the programme in terms of the students targeted, the kinds of activities offered, governance and funding, and perceived effects at the school level.
Do individuals return to baseline levels of well-being after recovering from poor health?

While much recent research has focused on what happens to individualâ€™s well-being following the onset of health conditions, one as yet unaddressed question is what happens to wellbeing once individuals are no longer suffering from those same health conditions. If treatment has long term adverse effects, or if individuals become more worried about their health even when the health condition no longer represents a significant impediment, then individuals may not return to pre-disability levels of well-being. Using a large nationally representative dataset, I compare the well-being of individuals who report that they were previously diagnosed with one of 13 different health conditions but now no longer have those health conditions, to the well-being of individuals who report that they have never been diagnosed with those same health conditions. For many of the health conditions examined, and using a number of different well-being measures, I observed significant differences in the well-being of both groups. This could suggest that individuals may not return to pre-disability levels of quality of lifeonce they recover from health conditions.
In addition to my research and teaching activities, I sit on several industry committees including Chair of the Australian Glyphosate Sustainability Working Group, the National Integrated Weed Management Initiative and the Transgenic and Insect Management Strategies Committee Herbicide Technical Pan

Prior to the preparation of this report I have been provided with a copy of the Code of Conduct for Expert Witnesses and certify this report complies with said code. In preparation of the report, I have responded to a set of questions put to me. I have relied on my own research both published and unpublished, my experience in the Australian agricultural sector and published research and reports from others.
A COMPARATIVE STUDY OF URBAN CHANGE DETECTION TECHNIQUES USING HIGH SPATIAL RESOLUTION IMAGES

This paper describes various urban change detection methods using high spatial resolution images such as Quickbird and Wolrdview-2. Compared to low spatial resolution images used before, like Landsat TM and ASTER, high spatial resolution images were found to show robust details in the resultant change maps. Various methods were tried to get better results. Simple waveband arithmetic such as image differencing and image ratios using multiple and single wavebands were tried. Major disadvantage encountered in these methods is the fact that they could not distinguish what changed but just demonstrating where the changes occurred. Moreover, other factors such as atmospheric effects, sensor orientation and image reflectance differences were found to affect the quality of change maps. Apart from the above, change vector analysis (CVA) and statistical methods such as Euclidian distance and Bhattacharya distance were also tried. NDVI difference images were used to create change maps and also used in combination with visible and infrared difference images. Finally a post-classification change detection is also tried to analyze the quality of change maps. All these methods were carefully analyzed for their suitability in urban change detection.
Supporting Information

Fig. S3 – Specificity of the Grx3 siRNA-induced phenotype in HeLa cells To confirm the specificity of the siRNA used in this study (si-Grx3-1, blue in C), we compared different aspects of the depletion phenotype with that of HeLa cells transfected with the identical siRNA (si-Grx3-1*) from an alternative supplier (Ambion vs Eurogentec) as well as with a pool of four alternative siRNAs (si-Grx3-pool, red in C, supplier Dharmacon, accession numbers: J019954-09,-10, -11, and -12). (A) Immunostaining of the indicated proteins extracted from HeLa cells transfected with scrambled control RNA (si-scr) or different siRNAs. (B) The activity of cytosolic aconitase normalized to lactate dehydrogenase activity was measured in samples of part A relative to the control cells.
A MULTI-AGENT BASED SOCIAL CRM FRAMEWORK FOR EXTRACTING AND ANALYSING OPINIONS

Social media provide a wide space for people from around the world to communicate, share knowledge and personal experiences. They increasingly become an important data source for opinion mining and sentiment analysis, thanks to shared comments and reviews about products and services. And companies are showing a growing interest to harness their potential, in order to support setting up marketing strategies. Despite the importance of sentiment analysis in decision making, there is a lack of social intelligence integration at the level of customer relationship management systems. Thus, social customer relationship management (SCRM) systems have become an interesting research area. However, they need deep analytic techniques to transform the large amount of data “Big Data” into actionable insights. Such systems also require an advanced modelling and data processing methods, and must consider the emerging paradigm related to proactive systems. In this paper, we propose an agent based social framework that extracts and consolidates the reviews expressed via social media, in order to help enterprises know more about customers’ opinions toward a particular product or service. To illustrate our approach, we present the case study of Twitter reviews that we use to extract opinions and sentiment about a set of products using SentiGem API. Data extraction, analysis and storage are performed using a framework based on Hadoop MapReduce and HBase.
Aligning the Dissimilar: A Probabilistic Feature-Based Point Set Registration Approach

3D-point set registration is an active area of research in computer vision. In recent years, probabilistic registration approaches have demonstrated superior performance for many challenging applications. Generally, these probabilistic approaches rely on the spatial distribution of the 3D-points, and only recently color information has been integrated into such a framework, significantly improving registration accuracy. Other than local color information, high-dimensional 3D shape features have been successfully employed in many applications such as action recognition and 3D object recognition. In this paper, we propose a probabilistic framework to integrate highdimensional 3D shape features with color information for point set registration. The 3D shape features are distinctive and provide complementary information beneficial for robust registration. We validate our proposed framework by performing comprehensive experiments on the challenging Stanford Lounge dataset, acquired by a RGB-D sensor, and an outdoor dataset captured by a Lidar sensor. The results clearly demonstrate that our approach provides superior results both in terms of robustness and accuracy compared to state-of-the-art probabilistic methods.
MACHINE LEARNING FOR THE EXHAUSTIVE EVALUATION OF OBJECT-BASED FEATURE SPACES

Segmentation provides means to extract spatial/obje ct features which can be added at pixel level to th e spectral signature vector. As shown in this paper, this framework allows effectiv e combination of the pixel and the object domain fo r spectral-spatial classification. As an application, the mapping of M editerranean urban leisure landscapes (San Roque and Santa Ponsa, both Spain) with WorldView-2 and airborne color imagery is pres ented. Turf grass, other vegetation and swimming po ols are the surface features of interest as they have high relevance for urban e cological studies. The random forest machine learni ng technique is applied and compared to other well-known classifiers, such as a support vector machine. A recently published adapt ion of the random forest method is utilized to calculate so-called condition al variable importance in order to identify the rel evant features. Especially for complex problems and high dimensionality, the relev ance of individual features is far from being obvio us to the human analyst and the actual pattern in the data remain ‘hidden’ when relying on standard methods, such as scatter plots . The findings and statements of this work are: random forests can deal with high-di mensional feature spaces which contain correlated a nd potentially unpredictive features. For machine learning applications, access ing object features at pixel level entails some adv antages as the sampling/processing units are of uniform size and d o not depend on the segmentation quality. Furthermo re, it is shown that the random forest prediction accuracy is highest when c omplementing pixel (spectral signature) and object f a ures. * Corresponding author.
Do Illustrations help Understanding Algorithms?

This paper discusses the discovery learning of algorithms. It presents the results of a study performed with 51 high school students from grade 11 and 12. The participants had to understand (discover), apply and verbalize algorithms for decoding sequences that represent digital images. In the learning phase they had to interpret an example and five metaphoric illustrations of the decoding procedure. In the production phase they had to verbalize the learned algorithm. The goal of the study was to explore the influence of visualizations on algorithm understanding.
Boundary integral methods for fast and accurate simulation of droplets in two-dimensional Stokes flow

Accurate simulation of viscous fluid flows with deforming droplets creates a number of challenges. This thesis identifies these principal challenges and develops a numerical methodology to overcome them. Two-dimensional viscosity-dominated fluid flows are exclusively considered in this work. Such flows find many applications, for example, within the large and growing field of microfluidics; accurate numerical simulation is of paramount importance for understanding and exploiting them. A boundary integral method is presented which enables the simulation of droplets and solids with a very high fidelity. The novelty of this method is in its ability to accurately handle close interactions of drops, and of drops and solid boundaries, including boundaries with sharp corners. The boundary integral method is coupled with a spectral method to solve a PDE for the time-dependent concentration of surfactants on each of the droplet interfaces. Surfactants are molecules that change the surface tension and are therefore highly influential in the types of flow problems which are considered herein. A method’s usefulness is not dictated by accuracy alone. It is also necessary that the proposed method is computationally efficient. To this end, the spectral Ewald method has been adapted and applied. This yields solutions with computational cost O(N logN), instead of O(N2), for N source and target points. Together, these innovations form a highly accurate, computationally efficient means of dealing with complex flow problems. A theoretical validation procedure has been developed which confirms the accuracy of the method.
High Frequency Microwave and Antenna Devices based on Transformation Optics and Glide-Symmetric Metasurfaces

The new generation of wireless communication networks intends to support data rate of Gbit/s. One solution to make it possible is to move upwards in frequency range to employ the unused spectrum in mm-wave frequencies. This brings new challenges in the design of hardware for the communication networks, namely high free space path loss and expensive manufacturing. In this thesis, transformation optics and glide symmetry are employed to address these new challenges for the design of high frequency microwave components and lens antennas. Transformation optics provides a systematic tool to manipulate electromagnetic waves in a desired way. In this thesis, this tool has been used to improve the radiation properties of conventional homogeneous three-dimensional lenses and compress the size of twodimensional graded-index lenses. Glide symmetry is a subset of higher-order symmetries and is described by a translation followed by a reflection with respect to a defined plane. Periodic structures possessing glide symmetry exhibit interesting properties. In this thesis, four of these properties are explored and possible applications are discussed. First, it is demonstrated that the first mode in a glide-symmetric periodic structures is significantly less dispersive than the corresponding conventional non-glide structure. This property was employed to design fully metallic wideband metasurface-based antennas. The losses in this type of antennas are only ohmic which make them suitable for high frequency applications. Second, it is shown that anisotropic glidesymmetric periodic structures can provide higher levels of anisotropy compared to their conventional periodic counterparts. This property is employed to design compressed two-dimensional lenses. Third, it is demonstrated that glide symmetry can be used to match the impedance of two vastly different dielectric media in a parallel plate waveguide configuration by enhancing the magnetic properties. This property was used to match the profile of two-dimensional homogeneous lenses. Fourth it is shown that glide-symmetric holey metallic structures achieve a significantly wider stop-band compared to conventional non-glide periodic structures. This property is exploited to design cost-effective waveguiding structures and microwave components at mm-wave frequencies. Furthermore, using this property, a flange design that provides contact-less measurement at mm-wave frequencies is presented.
Studies on the correlation of PON1 gene rs854572 single nucleotide polymorphism to clopidogrel resistance

Objective To investigate the correlation of the single nucleotide polymorphism (SNP) of PON1 (Paraoxonase-1) gene rs854572 to the occurrence of clopidogrel resistance (CR). Methods A case-control method was employed in present study. A total of 850 hospitalized patients with coronary artery diseases (CAD) in General Hospital of Shenyang Command were enrolled. The residual platelet aggregation rate (RPA) induced by 20μmol/L of adenosine diphosphate (ADP) was detected by optical nephelometry, and RPA≥70% was defined as CR. Accordingly, all the enrolled 850 patients were then divided into CR group (n=215) and non-CR (NCR) group (n=635). Polymerase chain reaction (PCR) and pyrophosphate sequencing were executed to determine the genotypes and the allele frequencies of PON1 gene rs854572. Results The genotype frequencies in rs854572 of PON1 gene conformed well to the Hardy-Weinberg equilibrium in both CR group and NCR group. Three frequencies of genotype CC, CG and GG were 23.7%, 49.3%, 27.0% in CR group, and 24.1%, 50.2%, 25.7% in NCR groups, respectively. No significant difference in genotype and allele frequency existed between CR group and NCR group (P=0.93 and 0.76, respectively). Logistic regression analysis revealed that no correlation between rs854572 SNP of PON1 gene and the formation of CR in patients with CAD after adjustment of correspondent factors including age, gender, body mess index, smoking, hypertension, diabetes mellitus and hyperlipidemia. Conclusions It is considered that no correlation exists between PON1 gene rs854572 polymorphism and clopidogrel resistance in patients with coronary heart disease.