Miracle-dz/UniFAR-FTUs huggingface dataset:
Computing Fine-grained Semantic Annotations of Texts

The challenge in computing ﬁne-grained semantic annotation exists in the complexity of combining the deep natural language processing and advanced ontological reasoning. For this, we propose in this paper a reasoning based approach to controlling and reﬁning semantic annotations, which is an easy and intuitive way to manage the complex information involved. We illustrate this approach over two policy regulation corpora, which shows an encouraging result.
The System of Management Ideas

This chapter argues that there is a system of management ideas, that is, a set of institutionalized high level heuristics that guide organizational and individual behaviours in managerial reality, involve distinct knowledge that is transferred across time and space, may change as a result of local adaptation or innovation, and may be selected for or against. This system operates at multiple levels, particularly the individual, organizational, and institutional levels. A system of management ideas can be analysed through a micro-foundational approach, where actions of individual agents lead to an evolution of the system through processes of variation, selection, and retention. This approach is applied to the organizational level choice of adopting existing, fashionable management practices versus creating novel management practices. The authors present a process model depicting this make-or-buy choice in management practices and provide insights into why and when organizations choose each of these routes.
Stixel-based Traffic Scene Representation Using U-disparity

The Stixel World is a valid approach of interpreting 3D traffic scenes. This paper presents a novel and simple method to establish the Stixel World by exploiting the properties of U-disparity. Unlike the conventional stixel extraction method, the proposed method does not model the ground plane, but construct a U-disparity map and accordingly remove ground-related points and background-related points in virtue of U-disparity properties. The resulting U-disparity map is re-projected back to the disparity map so that the base and top-points of foreground obstacles can be determined. Experiments show that the proposed method can effectively build a Stixel World for a variety of scenarios and is not constrained by topography and object classes.
Artificial Neural Networks to Solve Inverse Problems in Quantum Physics

Inverse problems are important in quantum physics as their solutions are essential in order to describe a number of systems using measurable information, e.g. excitation energies or material properties. The problem with inverse problems is that they are usually very hard to solve. One method that could be useful in solving these problems is artificial neural networks. Artificial neural networks have received a lot of attention lately as they have shown great results in solving many difficult problems e.g. super-resolution in imaging [1], [2]. However, their use in the field of physics have been limited so far. In this master’s thesis, neural networks have been applied to a few inverse quantum mechanical problems to see if it is possible for them to solve these problems. The inverse problems that are in focus in this project are: solving the external potential of quantum mechanical systems using either the eigenvalue spectrum or the density function. Lastly, the task of going from potential to density function is also treated. In the project the problems were limited to their 1D form with computationally generated data that made use of a finite difference method. All of the problems investigated in the thesis have been successfully solved using dense networks. The inverse problem where the potential was computed using the eigenvalue spectrum was solved using a custom error function. This error function accounted for the 1D potentials having the same eigenvalue spectrum when reversed along spatial axis. It was shown that the network predictions were very close the the actual potentials. Both the density to potential problem and its reverse problem were solved as well. These results showed not only good predictions, but also that the networks were able to generalise well to particle numbers they had not trained on. Based on the results, it has been shown that it is possible to solve these problems using artificial neural networks. Now that this has been shown, the next step would be to apply this method to real data in order to create solution tools that could have practical use for real problems. Additionally, it is likely that there are other problems in the field where neural networks could prove useful.
Intel Xeon Phi Processor High Performance Programming: Knights Landing Edition 2nd Edition

This book is an all-in-one source of information for programming the Second-Generation Intel Xeon Phi product family also called Knights Landing. The authors provide detailed and timely Knights Landingspecific details, programming advice, and real-world examples. The authors distill their years of Xeon Phi programming experience coupled with insights from many expert customers Intel Field Engineers, Application Engineers, and Technical Consulting Engineers to create this authoritative book on the essentials of programming for Intel Xeon Phi products. Intel Xeon Phi Processor High-Performance Programming is useful even before you ever program a system with an Intel Xeon Phi processor. To help ensure that your applications run at maximum efficiency, the authors emphasize key techniques for programming any modern parallel computing system whether based on Intel Xeon processors, Intel Xeon Phi processors, or other high-performance microprocessors. Applying these techniques will generally increase your program performance on any system and prepare you better for Intel Xeon Phi processors. A practical guide to the essentials for programming Intel Xeon Phi processors Definitive coverage of the Knights Landing architecture Presents best practices for portable, high-performance computing and a familiar and proven threads and vectors programming model Includes real world code examples that highlight usages of the unique aspects of this new highly parallel and high-performance computational productCovers use of MCDRAM, AVX-512, Intel Omni-Path fabric, many-cores (up to 72), and many threads (4 per core)Covers software developer tools, libraries and programming modelsCovers using Knights Landing as a processor and a coprocessor
Detection of obstacles in monocular image sequences

The ability to detect and locate runways/taxiways and obstacles in images captured using on-board sensors is an essential first step in the automation of low-altitude flight, landing, takeoff, and taxiing phase of aircraft navigation. Automation of these functions under different weather and lighting situations, can be facilitated by using sensors of different modalities. An aircraft-based Synthetic Vision System (SVS), with sensors of different modalities mounted on-board, complements the current ground-based systems in functions such as detection and prevention of potential runway collisions, airport surface navigation, and landing and takeoff in all weather conditions. In this report, we address the problem of detection of objects in monocular image sequences obtained from two types of sensors, a Passive Millimeter Wave (PMMW) sensor and a video camera mounted on-board a landing aircraft. Since the sensors differ in their spatial resolution, and the quality of the images obtained using these sensors is not the same, different approaches are used for detecting obstacles depending on the sensor type. These approaches are described separately in two parts of this report. The goal of the first part of the report is to develop a method for detecting runways/taxiways and objects on the runway in a sequence of images obtained from a moving PMMW sensor. Since the sensor resolution is low and the image quality is very poor, we propose a model-based approach for detecting runways/taxiways. We use the approximate runway model and the position information of the camera provided by the Global Positioning System (GPS) to define regions of interest in the image plane to search for the image features corresponding to the runway markers. Once the runway region is identified, we use histogram-based thresholding to detect obstacles on the runway and regions outside the runway. This algorithm is tested using image sequences simulated from a single real PMMW image.
Thermal ecology of a Liolaemus lizard assemblage along an Andean altitudinal gradient in Chile

We studied the thermal ecology of ten species of Liolaemus lizards on an altitudinal transect in the central Chilean Andes, from 1500 to 3000 m elevation, to determine in the field how thermoregulatory factors may be setting distribution limits. All but one species had the same field body temperatures (ca. 35°C). The only exception was the higher body temperature of the darkly colored male L. nigroviridis, which appears to result from the interplay of biophysical attributes with the social structure of this species. Most species of lizard selected microhabitats that allowed increased length of die! activity time, choosing in the evenings rock substrates, which held heat even after air temperature dropped. Reproductive condition (particularly gestation) of individuals also resulted in broadened die! activity periods. Thermal relationships set upper distributional limits on egg-laying species, which cannot successfully reproduce at high elevations, where only live-bearing lizards were found.
Development of a flow cell system to quantify analyte-receptor binding kinetics utilizing guided-mode resonance sensors

A novel flow cell system integrated with a guided-mode resonance (GMR) sensor element is presented. The realtime association/disassociation binding interactions of the bacteria S. aureus with an immobilized specific IgG antibody layer have been measured. A microfluidic flow cell made from poly(dimethylsiloxane) (PDMS) through a molding process is bonded to the polymer GMR sensor element to create a sealed chamber. The results obtained show that the microfluidic flow cell sensor system can be used to monitor the entire association/dissociation binding cycle in real time, which is not possible with the microwellbased static system. For key applications such as environmental monitoring, a flow cell geometry can be advantageous for real-time screening of biotoxins.
Memory-based biomedical named-entity tagging

Shallow parsing is based on the idea that full (or deep) syntactic structure is not always necessary for information extraction tasks and consequently assigns only a limited amount of syntactic information to natural language sentences. For example, in biomedical information extraction, we are interested in finding concepts (represented by basic nominal and verbal phrases) and relations between them, rather than in recovering an elaborate syntactic analysis. MBSP, as an instance of this approach, combines a number of modules, each trained using memory-based learning from linguistically annotated corpora (Daelemans & Van den Bosch, 2005). An MBL system consists of two components: a memorybased learning component and a similarity-based performance component. During training, the learning component adds new training instances to the memory without any abstraction or restructuring. During classification, the classification of the most similar instance in memory is taken as classification for the new test instance.
Finding the Best Types of Guidance for Constructing Self-Explanations of Subgoals in Programming

Subgoal learning, a technique used to break down problem solving into manageable pieces, has been used to promote retention and transfer in procedural domains, such as programming. The primary method of learning subgoals has been passive, and passive learning methods are typically less effective than constructive methods. To promote constructive methods of learning subgoals, we prompted learners to self-explain the subgoals of a problem-solving procedure. Self-explanation asks learners to make sense of new information based on prior knowledge and logical reasoning. Novices’ self-explanation is typically more effective when they receive guidance, because it helps them to focus on relevant information. In the present experimental study, the types of guidance that students received while self-explaining determined whether the constructive learning method was more effective than the passive method. Participants assigned to the constructive learning method performed best when they either received hints about the subgoals or received correct explanations as feedback, but not when they received both. These findings suggest that constructive learning of subgoals can further improve the benefits of subgoal learning when students receive only guidance that complements their construction of knowledge. This nuance is important for educators who engage their students in constructive learning and self-explanation.
Design and Measurement of Novel Shaped Microstrip Antenna with DGS for Radar Applications

In this paper, a novel shaped patch antenna for triple band frequency of operation in X-band (radar) is defined. The antenna resonates under three frequency bands at 10 GHz, 10.93 GHz and 11.68 GHz respectively which are included in the X-band frequency range. The substrate material used as the dielectric between the patch and the ground is of Flame Retardant Version 4 (FR4) Epoxy. The DGS (Defected Ground Structure), usually included in the back conductor of the Microstrip patch antenna is used to tune the frequency and also to boost the characteristics of the antenna. The same is included in this depicted antenna with circular ring shaped slot to vary the performance. FEM (Finite Element Method) based Ansoft High Frequency Structural Simulator (HFSS) V.12 is used for designing and verifying the features of the proposed antenna. The size of the depicted model on the whole is of 31.34 × 28.33 × 1.6 mm. For the first, second and third resonant frequency, a bandwidth of 480 MHz, 600 MHz and 420 MHz correspondingly is obtained with a return loss value of -18.47 dB, -42.22 dB and -19.74 dB. The other parameters of the antenna are also conferred in this paper.
Rapid mixing through decomposition and induction

The concern of this thesis is a performance analysis of certain Markov chain Monte Carlo algorithms. The performance of Markov chain Monte Carlo algorithms in gen¬ eral is determined by the rate of convergence toward stationarity of the Markov chains involved. There now exists a substantial body of work on this subject and to begin with a synopsis of "classical" techniques for bounding convergence rate is given. Such techniques are: coupling, spectral gap, conductance and canonical paths. The focus then shifts to recent improvements on these techniques followed by sample applica¬ tions: the first one illustrates how the path coupling method simplifies the analysis of a Markov chain for generating random lozenge tilings. The next example high¬ lights the gains made by average conductance over "classical" conductance for the bases-exchange walk on balanced matroids. The result for the latter example is ob¬ tained by an inductive argument and is subsequently improved by the use of so-called logarithmic Sobolev constants. Bounding the logarithmic Sobolev constant is here achieved by following a decomposition-cum-induction approach. Such decompositional approaches for analysing Markov chain convergence are another manifestation of the "divide-and-conquer" paradigm. The thesis concludes with the treatment of a novel decomposition method and its application to bounding the convergence rate of a random process on finite, complete d-ary trees. Acknowledgements I gratefully acknowledge the assistance of many people in the preparation and writing of this thesis. Their help of various kinds was invaluable to me and ought not to pass unmentioned. In particular, I owe a deep debt of gratitude to my supervisor, Mark Jerrum: for offering his support, for many insightful suggestions along with impeccable guidance and supervision, for providing opportunities to interact with a wider research community and last but not least for being a patient and critical proofreader of the manuscript. Likewise, I thank Mary Cryan and Martin Dyer for numerous suggestions which helped to further clarify the content of the thesis. Credit also goes to Eric Vigoda and Ravi Montenegro. Discussions with Eric and collaborating with Ravi in the early stages contributed to shaping my understanding of research. I am profoundly indebted to my parents whose initial financial support only made my stay in Edinburgh possible. Furthermore, I thank my family and friends for their never-ending moral support and lastly, I would like to express my gratitude to DAAD, EPSRC(SARA) and the Graduate School for partially funding my studies.
Detecting Melanoma Cancer using Image Processing

: Skin cancer is the most dangerous form of cancers. The most common form of skin cancer is Malignant melanoma. If this cancer is detected in the early stage, then it can be cured easily. In this paper we present an overview of all the classification methods that are used to detect the skin cancer and a comparative study of all the discussed classification methods. Image classification depends on various factors and it is a complex and vital process. Image classification uses both supervised and unsupervised classifications and it is considered as one of the best method to predict the skin cancer results.
A COMPUTATIONAL FRAMEWORK FOR NEONATAL BRAIN MRI STRUCTURE SEGMENTATION AND CLASSIFICATION

Deep Learning is increasingly being used in both supervised and unsupervised learning to derive complex patterns from data. However, the successful implementation of deep learning using medical imaging requires careful consideration for the quality and availability of data. Infants diagnosed with CHD are at a higher risk for neurodevelopmental impairment. Many of these deficits may be attenuated by early detection and intervention. However, we currently lack effective diagnostic tools for the reliable detection of these disorders at the neonatal period. We believe that the structural correlates of the cognitive deficits associated with developmental abnormalities can be measured within the first few months of life. Based on this assumption, we hypothesize that we can use an atlas registration based structural segmentation pipeline to sufficiently reduce the search space of neonatal structural brain MRI to viably implement convolutional neural networks for dysplasia classification. Secondly, we hypothesize that convolutional neural networks can successfully identify morphological biomarkers capable of detecting structurally abnormal brain substructures. In this study, we develop a computational framework for the automated classification of dysplastic substructures from neonatal MRI. We validate our implementation on a dataset of neonates born with CHD, as this is a vulnerable population for structural dysmaturation. We chose the cerebellum as the initial test substructure because of its relatively simple structure and known vulnerability to structural dysplasia in infants born with CHD. We then apply the same method to the hippocampus, a more challenging substructure due to its complex morphological properties. We attempt to overcome the limited availability of clinical data in neonatal populations by first extracting each brain substructure of interest and individually registering them into a standard space. This greatly reduces the search space required to learn the subtle abnormalities associated with a given pathology, making it feasible to implement a 3-D CNN as the classification algorithm. We achieved excellent classification accuracy in detecting dysplastic cerebelli, and demonstrate a viable computational framework for search space reduction using limited clinical datasets. All methods developed in this work are designed to be extensible, reproducible, and generalizable diagnostic tools for future neuroimaging problems.
Analytical Methods of Isolation and Identification

The chemical constituents of plants are complicated, and monomeric compounds must be obtained via extraction and isolation before structure identification, bioactivity screening, and so on. In recent years, the new technologies and methods of the extraction, isolation, and structural identification have come forth, which promote the speed of extraction and analysis of phytochemicals. The chemical structures of compounds from plants must be identified or elucidated, which may provide the necessary basis for further study on the bioactivities, structure-activity relationships, metabolisms in vivo , structural modification, and synthesis of the active compounds. The amount of chemical constituents isolated from plants is often minor, so the structural studies are often difficult to carry out with classical methods. Therefore, spectral analysis is mainly used. This chapter describes the isolation and identification methods during the study of phytochemicals. action, etc. Additional well-designed human intervention studies and clinical trials are needed to validate the health benefits of phytochemicals.
Génération d'images omnidirectionnelles à partir d'un environnement virtuel

Dans cet article, nous decrivons une methode pour generer des images omnidirectionnelles en utilisant des images cubemap et les cartes de profondeur correspondantes, a partir d'un environnement virtuel. Pour l'acquisition, on utilise le jeu video Grand Theft Auto V (GTA V). GTA V a ete utilise comme source de donnees dans plusieurs travaux de recherche, puisque c'est un jeu a monde ouvert, hyperrealiste, simulant une vraie ville. L'avancee realisee dans l'ingenierie inverse de ce jeu nous offre la possibilite d'extraire des images et les cartes de profondeur correspondantes avec des cameras virtuelles a six degres de liberte. A partir de ces donnees et d'un modele de camera omnidirectionnelle, on propose de generer des images Fisheye destinees par exemple a l'entrainement de methodes par apprentissage.
WSN Architecture Design Based on Software Defined Networks

In order to improve the performance of wireless sensor networks and adapt the development of network technology, proposed a software defined wireless sensor network model which had hierarchy architecture and based on OpenFlow protocol under the software defined network architecture. Added the information fusion layer to the model, the Sink-nodes forwarded the link information after the sensor-nodes had collected information. Fused and fragmented logically the node data information using network virtualization technology of the OpenFlow protocol. Managed centrally the path forward strategy by the controller, and then improved the network information forward mechanism under the new architecture. On that basis, simulated the new architecture model and contrasted traditional wireless sensor routing protocols. The experiment results show that the new architecture can extend the life cycle of network and improve network. Keywords—wireless sensor networks; software defined networks; network architecture; OpenFlow
Performance Measurement of Keyword Search Systems

In past few years many relational keyword system have been proposed. But the problem with them is that most of the system are defective or they do not give the exact search results. In this paper we are measuring the performance of all the keyword search systems, doing this will help to choose the correct keyword search system. The analysis of each and every system will be done. In this paper we will also seek the relationship between time needed for execution and factors changed in previous performances. Our analysis indicates that previous factors have less influence on performance.