Miracle-dz/UniFAR-FTUs huggingface dataset:
Sequence Block Based Compressed Sensing Multiuser Detection for 5G

Compressed sensing based multiuser detection (CSMUD) is a promising candidate to cope with the massive connectivity requirements of the massive machine type communication (mMTC) in the fifth generation (5G) wireless communication system. It facilitates grant-free non-orthogonal code division multiple access (CDMA) to accommodate massive number of IoT devices. In non-orthogonal CDMA, the users are assigned with non-orthogonal sequences which serve as their signatures. However, the activity detection which is based on the correlation between the spreading sequences, degrades with increase in the number of users, especially in the lower SNR region. In this paper, to improve the performance of the CSMUD, we propose a sequence block based CSMUD, in which block of sequences is used as signature of the user instead of single sequence. A sequence block based group orthogonal matching pursuit algorithm is proposed to jointly detect the activity and data. The proposed scheme reduces the detection error rate (DER) by a magnitude of two at SNR = 10 dB in a system where the number of users are three times more than the number of available resources with each user having activity probability of 0.1. The DER of the proposed scheme is below $10^{-2}$ even at activity probability of 0.16 for sequence length of 20 and overloading factor of 300\%. Furthermore, at SNR = 10 dB, the DER of the proposed scheme outperforms the conventional scheme by a magnitude of one for a system with overloading factor of 500\%.
An Improved Foundation for the Investigation and Treatment of Gastric Dysrhythmia

Gastric motility disorders remain a complex clinical challenge, and inflict a substantial burden of cost and suffering on society. Gastric contractions are coordinated by an underlying electrical activity, and gastric dysrhythmias are implicated in the pathophysiology of several motility disorders. However, the significance of dysrhythmias remains uncertain, and there are few effective therapies, because current tools to investigate dysrhythmias have low reliability owing to their lack of spatial resolution. This thesis aimed to develop an improved foundation for the investigation and management of gastric dysrhythmia, by advancing and translating high-resolution (HR; multi-electrode) spatiotemporal mapping. The research is a cross-disciplinary program of bioengineering, and basic and clinical electrophysiology. A range of HR mapping devices are first developed and validated for intra-operative gastric mapping, including at open and laparoscopic surgery. Automated signal processing tools are next validated for the efficient, reliable marking, grouping, and mapping of slow wave events, and these tools are integrated into an intuitive software platform. These methodological advances are then applied in a series of experimental studies. The origin and propagation of porcine gastric slow wave activity is defined, followed by clinical translation with the first spatiotemporal analysis of normal human gastric slow wave propagation. The methods are then applied to define new patterns and mechanisms of gastric dysrhythmia, initially in a porcine model, including the first demonstration of how rapid, high-amplitude circumferential propagation emerges during dysrhythmias. The first clinical study applying HR electrical mapping is then presented, performed on a cohort of patients with diabetic and idiopathic gastroparesis, revealing new patterns of human dysrhythmia. A new classification scheme for abnormalities of human gastric slow wave initiation and conduction is proposed. Finally, the evidence for high-frequency gastric electrical stimulation is reviewed, prior to the presentation of a new ‘entrainment mapping’ method for better assessing gastric pacing protocols. In total, this work constitutes a coordinated series of advances that offer a strengthened foundation for investigating and managing gastric electrical abnormalities. It is hoped that these new methods and findings will translate into future clinical advances, to improve the diagnosis and treatment of these complex patient populations.
How Right Wing is Right Wing Populism? Evidence from the Manifesto Corpus

Right wing populist parties in Europe are clearly different from other right wing parties in their rhetoric and electoral appeal. Some observers see substantive differences between right wing populists and other right wing parties, with populists supporting the welfare state and gender equality more than other right wing parties, often as part of an anti-immigration and anti-Muslim agenda. We test this claim using novel data produced by a multilingual convolutional neural net on political party platforms for the years 1990 to 2015 from the Manifesto Corpus. We find no systematic differences between right wing populists and non-populists on support for welfare and gender equality, though there is some evidence that more successful populists are more centrist.
AUTOMATIC CALCULATION OF INITIAL WEIGHTS FOR NURBS

This paper first presents a general way of calculating the weights of NURBS; the given method is of visualization, conciseness and objectivity. Then another two practical methods, in which linear interpolation and artificial neuron network (ANN) algorithms are used, are also introduced. These methods can calculate a group of initial weights of NURBS automatically according to the shape of the control polygon and the shape\|constraints of the curves.
Study on Phase-sensitive Detection of Multifrequency Logging Signal

Based on the principle of phase-sensitive detection,the multifrequency digital phase-detection algorithm is developed.The digital average filter response is analyzed,the constraint relations between sample frequency,sample numbers and signal frequency are obtained.Simulation results show that amplitude detection error is controlled within 0.5% and phase detection error is controlled within 0.2% when S/N is-10 dB.In practical log data acquisition system design,the N/S of the acquired signals should be enhanced and the selected acquisition parameters should satisfy the constraint relations so as to realize better multi-frequency log signals detection.
Inference From Matched Samples in the 2008 U.S. National Elections

The performance of matched sampling is assessed using data from the 2008 U.S. Presidential election. The assumptions necessary for the validity of matched sampling, including ignorability, are described. With a matching ratio of about ve, the matched sample reproduces the joint demographic distribution of the target population very closely. The sampling distribution of the associated state-level vote estimates is approximately standard normal with unit variance, suggesting little or no selection bias conditional upon a full set of demographic controls. The results are compared to RDD telephone and Internet samples, none of which are clearly better and some (such as the 2008 ANES Internet panel) are substantially worse.
Is deep learning the same as deep learning? : perspectives on the construct of deep learning

This thesis investigates different perspectives on the construct of deep learning. Through interviews with six informants who work in different positions in the education system, the aim is to find out if there is consensus on the content and meaning of the construct of deep learning. In addition to the interviews, an examination of relevant theory, and governing documents related to deep learning and the ongoing subject renewal in Norwegian primary and secondary education, is also carried out. The results show that there is a certain degree of agreement concerning some aspects of what deep learning is. The agreement mainly revolves around an understanding that deep learning is a result of learning, or a type of competence. It is described as learning that lasts, and the result is that the learner can apply and transfer acquired knowledge and skills. To a certain degree, the term deep learning is also used to describe learningand teaching strategies, such as metacognition and interdisciplinary work. The term is also used as an overall principle in education and curriculum design, and to represent whole approaches to learning. The variation and scope of content that is associated with deep learning is perceived as confusing. All informants agree that there is a strong need for clarification of the content and meaning of the construct of deep learning.
Assessing Sediment-Nutrient Export Rate and Soil Degradation in Mai-Negus Catchment, Northern Ethiopia

Even though soil degradation challenges sustainable development, the use of degradation indicators such as nutrient export (NE) and nutrient replacement cost is not well documented at landform level. This study is aimed to investigate the extent of soil degradation, NE rates, and their replacement cost across landforms in the Mai-Negus catchment, northern Ethiopia. Different erosion-status sites (aggrading, stable, and eroded) in the landforms were identified, and soil samples were randomly collected and analysed. Nutrient export, replacement cost, and soil degradation were calculated following standard procedures. This study showed that soil degradation in the eroded sites ranged from 30 to 80% compared to the corresponding stable site soils, but the highest was recorded in the mountainous and central ridge landforms. Average NE of 95, 68, 9.1, 3.2, 2.5, and 0.07 kg ha−1 y−1 for soil calcium, carbon, nitrogen, potassium, magnesium, and phosphorus, respectively, was found from the landforms. Significantly strong relationships between NE and sediment yield in the landforms were observed. Annual nutrient replacement costs varied among the landforms though the highest was in the reservoir (€9204 in May 2010). This study thus suggests that while introducing antierosion measures, priority should be given to erosion sources to the reservoir such as mountainous and central ridge landforms.
Miyokardiyal Köprüleflmenin Neden Olduu Miyokard ‹nfarktüsü ACUTE MYOCARDIAL INFARCTION RESULTING FROM MYOCARDIAL BRIDGING

The muscle overlying the intramyocardial segment of epicardial coronary artery is termed as myocardial bridging (MB). It usually has a benign course, as coronary narrowing occurs during systole and coronary flow mostly occurs during diastole, but some reports showed that it might be associated with myocardial ischemia, infarction, atrio-ventricular block and sudden death [1-4]. In this article, we described a case presented with acute inferior myocardial infarction (MI) and whose coronary angiography revealed isolated MB located to middle portion of circumflex artery (Cx).
Online counselling in secondary schools: would students seek help by this medium?

Students in secondary schools experience problems that can impact on their well-being and educational outcomes. Although face-to-face counselling is available in most Australian secondary schools, many students, particularly boys, do not seek appropriate help. Research suggests that online counselling can be effective and increase engagement. This study of 215 secondary school students sought to assess students' intention to use online counselling if it was made available in schools. The results found no gender difference in the likely intentions to seek online help though year level was significant and students experiencing psychological distress had a preference for online counselling. If students did use online counselling it was more likely they would discuss sensitive topics rather than for career issues. Implications for school counselling are discussed.
Optimization of Surface Roughness of AISI 1040 Stainless Steel in Milling Process Using Taguchi Method

Surface roughness significantly affects the work efficiency and life of machine parts interacting with each other. There are many parameters that affect surface roughness such as processed material, cutting tool, cutting parameters, cooling type. For this reason, optimization of the machining parameters and proper processing conditions are very important. In this study, the optimum machining conditions were determined by investigating the surface roughness of the milled AISI 1040 steel alloy depending on the feed per tooth (0.08 mm/tooth, 0.12 mm/tooth, 0.16 mm/tooth, 0.20 mm/tooth), spindle speed (2000 rpm, 3000 rpm, 40000 rpm, 5000 rpm) and the cooling type (liquid, air) parameters. The Taguchi experimental design method was used to help achieve results at acceptable levels and to save time and cost in achieving optimal results. Experiments designed with the Taguchi method were based on the L16 orthogonal array and signal / noise (S/N) ratios were used in the evaluation of the experimental results. The optimum levels of the control factors for minimizing the surface roughness using S/N rates were determined. The optimal conditions for surface roughness were observed at A1B1C2 (i.e.,feed per tooth=0.08 mm/tooth and 2000 rpm spindle speed, cooling type=liquid). Variance analysis was also conducted in the study. According to the results of analyses, it was found that spindle speed was the most significant parameter for surface roughness. Finally, confirmation tests were conducted to check the success of the optimization.
Avaliação nutricional, desempenho e qualidade da carne de bovinos de corte alimentados com dietas contendo glicerina bruta

xiii INTRODUÇÃO GERAL . 1 LITERATURA CITADA . 11 Capítulo I . 16 Composição bromatológica e predição do valor energético da glicerina bruta oriunda da indústria do biodiesel. 16 INTRODUÇÃO . 16 MATERIAL E MÉTODOS . 17 RESULTADOS E DISCUSSÃO . 21 CONCLUSÃO . 24 LITERATURA CITADA . 24 Capítulo II . 26 Avaliação Nutricional de Bovinos de Corte em Terminação Alimentados com Glicerina Bruta na dieta . 26 Nutritional Evaluation of Crude Glycerin to Finishing Cattle . 27 INTRODUÇÃO . 28 MATERIAL E MÉTODOS . 29 RESULTADOS . 38 DISCUSSÃO . 46 CONCLUSÃO . 55 LITERATURA CITADA . 55 Capítulo III . 63 Performance and carcass traits of finishing bulls fed crude glycerin . 63 ABSTRACT . 63. 63 INTRODUCTION . 64 METHODS . 64 RESULTS AND DISCUSSIONS . 70 LITERATURE CITED . 80
Network Based Anomaly Intrusion Detection System Using SVM

The security and integrity of a computer system is compromised when an intrusion occurs. It becomes impossible for legitimate users to access different network services when network-based attacks purposely occupy or sabotage network resources and services. Our proposed method is a scalable detection method for network based anomalies. We use Support Vector Machines (SVM) for classification. This paper presents a method for enhancing the training time of SVM, particularly when dealing with large data sets, using hierarchical clustering technique. We use the Dynamically Growing Self-Organizing Tree (DGSOT) algorithm for clustering because it has proved to overcome the problems of traditional hierarchical clustering algorithms (e.g., hierarchical agglomerative clustering). Clustering analysis helps to find the boundary points, which are the most qualified data points to train SVM, between any two classes. We present a new approach of combination of SVM and DGSOT, which begins with an initial training set and expands it gradually using the clustering structure produced by the DGSOT algorithm. We show that our proposed variations contribute significantly in improving the training process of SVM with high percentage of detection accuracy.
Camera Pose Estimation for an Image Sequence with External References

Structure-from-Motion (SfM) is one of the key techniques developed in the field of computer vision, and has been used in many applications such as threedimensional reconstruction, robot navigation, and augmented reality. The most significant problem in SfM is the accumulation of estimation errors in a long image sequence. Although many types of methods for reducing accumulative errors have been proposed, SfM essentially cannot be free from accumulative errors unless certain external references (e.g., GPS, aerial images, or feature landmarks) are given. To treat various scenes for which the appropriate external references are not unique, this thesis proposes camera pose estimation methods employing the following three types of references: (1) GPS, (2) aerial images, and (3) a 3D point database created by SfM. GPS and aerial images are already available for most outdoor scenes around the world. To reduce the accumulative errors in SfM, we propose bundle adjustment (BA)-based methods that can globally optimize the camera poses using GPS and aerial images. Some applications of robot navigation and augmented reality require estimating the camera poses along a previously taken route. For such applications, we propose an online camera pose estimation method using a 3D point database created by SfM from previously captured images. In methods using GPS, extended BA that fuses SfM and GPS data has been previously proposed, and it works properly if the GPS data are acquired accurately. However, because existing methods do not consider the confidence of GPS ∗Doctoral Dissertation, Department of Information Systems, Graduate School of Information Science, Nara Institute of Science and Technology, NAIST-IS-DD1061008, March 13, 2014.
Sensitivity Limits for Digital Optical Communication Systems

The present survey paper summarizes the shot-noise limited sensitivity for a number of digital optical communication systems. This treatment is more complete than earlier ones and especially provides new insight concerning a number of phase and frequency modulated systems with heterodyne and homodyne matched filter detection as well as delay-detection and non-coherent (IF) detection. Furthermore, new results are presented for systems with optical pre-amplification and optical heterodyne signal processing.
How middle level science teachers visualize and translate motion, scale, and geometric space of the Earth-Moon-Sun system with their students

Jennifer Wilhelm, Merryn Cole, Cheryl Cohen, and Rebecca Lindell Department of Science, Technology, Engineering, and Math Education, University of Kentucky, Lexington, Kentucky 40506, USA Department of Teaching and Learning, University of Nevada Las Vegas, 4505 S. Maryland Parkway, Las Vegas, Nevada 89154, USA Department of Psychology, Governors State University, 1 University Parkway, University Park, Illinois 60484, USA Tiliadal STEM Education Solutions, 5 N 10th St Suite A-1, Lafayette, Indiana 47901, USA
Markov Chain Monte Carlo Models, Gibbs Sampling, & Metropolis Algorithm for High-Dimensionality Complex Stochastic Problems

Markov chain Monte Carlo (MCMC) methods have an important role in solving high dimensionality stochastic problems characterized by computational complexity. Given their critical importance, there is need for network and security risk management research to relate the MCMC quantitative methodological concerns with network and security risk applications. This article contributes to that research stream. The core quantitative methodological focus of the article is on Monte Carlo Models and MCMC Algorithms, Gibbs Sampling and Metropolis-Hastings Algorithm. Network and security risk management application focus is on how MCMC methods help solve previously unsolvable problems in computational statistical modeling of cryptography, cryptanalytics, and penetration testing; intrusion detection & prevention and anomaly detection; and, privacy in anonymity systems and social networks. Future quantitative methods applied research and development in MCMC and computational statistical computing to address systemic risk and model risk management is recommended.
Extraction de réseaux linéiques à partir d'images satellitaires par processus Markov objet

This paper presents a method of unsupervised extraction of line networks, such as road networks or hydrographical networks, from satellite images. A line network is modeled by a Markov object process. The process density is built so that the topology of the considered line network and the radiometric properties of data is exploited as far as possible. Optimization is realized by simulated annealing using a reversible jump Monte Carlo Markov chain algorithm. Experimental results on satellite images show the ability of the model in providing continuous line network, with law rates of omissions and false alarms.
What is cinema?, Vol. 1

It sounds good when knowing the what is cinema vol 1 in this website. This is one of the books that many people looking for. In the past, many people ask about this book as their favourite book to read and collect. And now, we present hat you need quickly. It seems to be so happy to offer you this famous book. It will not become a unity of the way for you to get amazing benefits at all. But, it will serve something that will let you get the best time and moment to spend for reading the book.
Embedded Multiprocessors: Scheduling and Synchronization

Showing how to design multiprocessor computer systems that are streamlined for multimedia applications, this work presents architectures and design methodologies for parallel systems in embedded DSP applications. It describes unique techniques for optimizing communication and synchronization and provides several examples of practical applications that demonstrate the relevance of the techniques presented. This second edition updates the background material on existing embedded multiprocessors, including single-chip multiprocessors. It also summarizes the new research on dataflow models for signal processing that has been carried out since the publication of the first edition.
ECOOP 2004 -- object-oriented programming : 18th European Conference, Oslo, Norway, June 14-18, 2004 : proceedings

Encapsulation. - Ownership Domains: Separating Aliasing Policy from Mechanism. - Composable Encapsulation Policies. - Program Analysis. - Demand-Driven Type Inference with Subgoal Pruning: Trading Precision for Scalability. - Efficiently Verifiable Escape Analysis. - Pointer Analysis in the Presence of Dynamic Class Loading. - Software Engineering. - The Expression Problem Revisited. - Rewritable Reference Attributed Grammars. - Finding and Removing Performance Bottlenecks in Large Systems. - Aspects. - Programming with Crosscutting Effective Views. - AspectJ2EE = AOP + J2EE. - Use Case Level Pointcuts. - Invited Talk 1. - Functional Objects. - Middleware. - Inheritance-Inspired Interface Versioning for CORBA. - A Middleware Framework for the Persistence and Querying of Java Objects. - Sequential Object Monitors. - Increasing Concurrency in Databases Using Program Analysis. - Types. - Semantic Casts: Contracts and Structural Subtyping in a Nominal World. - LOOJ: Weaving LOOM into Java. - Modules with Interfaces for Dynamic Linking and Communication. - Verification. - Early Identification of Incompatibilities in Multi-component Upgrades. - Typestates for Objects. - Object Invariants in Dynamic Contexts. - Invited Talk 2. - Rich Interfaces for Software Modules. - Systems. - Transactional Monitors for Concurrent Objects. - Adaptive Tuning of Reserved Space in an Appel Collector. - Lock Reservation for Java Reconsidered. - Customization of Java Library Classes Using Type Constraints and Profile Information.