Miracle-dz/UniFAR-FTUs huggingface dataset:
Compact representations for static and dynamic texture synthesis

In nature, textures can be found everywhere. Texture, together with color and shape, represents the fundamental characteristics of objects. Texture conveys an idea of repetition of a certain structure, which is not limited only to the visual domain. We can refer to the texture of a sound, a fabric, or a certain wine. In image processing, a static texture is defined as an image showing spatial stationarity, while a dynamic texture is a sequence of images showing temporal stationarity. Texture synthesis is the process of producing artificial textures starting from a given texture sample. In this thesis, we consider texture synthesis starting from compact representations. For static, this reduces to define the smallest portion of a texture that, when used for synthesis, produces a synthetic texture indistinguishable from the original image. We designed a psychophysical experiment to quantify this size for a collection of textures and two synthesis algorithms: the parametric algorithm by Portilla and Simoncelli and the procedural algorithm by Nealen and Alexa. The results of the experiment show that for pre-attentive vision condition the size of the compact texture does not depend on the algorithm used for synthesis but is a characteristic of the texture. We evaluated if an objective function, based on spatial correlation between pixels, is able to predict the texture size necessary for synthesis. We found that a statistical measure based on random walks correlated well with the results of the subjective experiment. In the case of dynamic textures, the term "compact" applies to the model size that is used for synthesis. We propose a dynamic texture analysis that is able to obtain a more compact model starting from the linear model of Soatto and Doretto. Current methods perform a dimension reduction of the data by applying the SVD to the video frames unfolded into column vectors. This permits only to exploit the temporal correlation. We avoid the unfolding operations and decompose the signal directly using a multidimensional decomposition known as Higher-Order SVD (HOSVD). Chromatic components are exploited more efficiently by combining the HOSVD decomposition with the YCbCr color encoding for the input data. Tests show that the combined model has far fewer parameters than models derived with other algorithms, for the same visual quality and approximately the same computational synthesis cost. The HOSVD-based model was used in two applications for portable devices where memory and computational power are limited. We created a video effect for webcams and smartphones where a dynamic texture is synthesized and blended to the video in real time.
Foundations of direct methods for power system transient stability analysis

Considerable progress has been made in recent years in the application of direct methods to power system transient stability analysis. The methods are derived largely based on heuristics and simulations. In this paper, a theoretical foundation for the direct methods is presented. A complete topological characterization of the stability boundary of a stable equilibrium point is derived. The closest u.e. p. method and the controlling u.e. p. method are interpreted and justified in terms of this result. Improvements of these methods are proposed. Characterizations of the closest u.e. p. and the controlling u.e. p. are provided. The stability region is shown to be unbounded.
SOA Principles of Service Design

Preface xxv Chapter 1: Introduction 1 Chapter 2: Case Study Background 19 Part I: Fundamentals Chapter 3: Service-Oriented Computing and SOA 25 Chapter 4: Service-Orientation 67 Chapter 5: Understanding Design Principles 103 Part II: Design Principles Chapter 6: Service Contracts (Standardization and Design) 125 Chapter 7: Service Coupling (Intra-Service and Consumer Dependencies) 163 Chapter 8: Service Abstraction (Information Hiding and Meta Abstraction Types) 211 Chapter 9: Service Reusability (Commercial and Agnostic Design) 253 Chapter 10: Service Autonomy (Processing Boundaries and Control) 293 Chapter 11: Service Statelessness (State Management Deferral and Stateless Design) 325 Chapter 12: Service Discoverability (Interpretability and Communication) 361 Chapter 13: Service Composability (Composition Member Design and Complex Compositions) 387 Part III: Supplemental Chapter 14: Service-Orientation and Object-Orientation: A Comparison of Principles and Concepts 445 Chapter 15: Supporting Practices 477 Chapter 16: Mapping Service-Orientation Principles to Strategic Goals 497 Appendices Appendix A: Case Study Conclusion 513 Appendix B: Process Descriptions 517 Appendix C: Principles and Patterns Cross-Reference 529 Additional Resources 533 About the Author 535 About the Photos 537 Index 539
Exploratory Data Analysis: Past, Present and Future

Abstract : The 1971-1977 early formulation of Exploratory Data Analysis, in terms of (a) results of some of its techniques and considerations which underlay, at various depths, the choices realized in the books. The 1991-1995 development of Exploratory Analysis of Variance, described in its simplest (two- way table) form and barely sketched in general. Discussion of the changes in apparent philosophy caused by the need to communicate more complicated things, notches, hints, the likely impact on a revised edition of Exploratory Data Analysis 1977. Dreams and targets for what might happen in 1996-2005, with emphasis on Exploratory Regression and the combined use of multiple description.
An efficient built-in self-test algorithm for neighborhood pattern sensitive faults in high-density memories

As the density of memories increases, unwanted interference between cells is increased and testing high density memories for a high degree of fault coverage can require either a relatively large number of test vectors or a significant amount of additional test circuitry. In this paper, a new thing method and an efficient BIST algorithm for NPSFs are proposed. Instead of the conventional five-cell and nine-cell physical neighborhood layouts to test memory cells, a four-cell layout is used. This four-cell layout requires smaller test vectors and shorter test time. A CMOS column decoder and the parallel comparator proposed by P. Mazumder and J.H. Patel are modified to implement test procedure which is appropriate for the four-cell layout. Consequently, these reduce the number of transistors used for a BIST circuit. Also, we present properties of the algorithm, such as its capability to detect stuck-at faults, transition faults, and conventional pattern sensitive faults.
Machine Models of Music

Part 1 Foundations - generate-and-test composition: musical composition with a high-speed digital computer, Lejaren Hiller and Leonard Isaacson an experiemnt in musical composition, F.P. Brooks et al a technique for the composition of music in a computer, Stanley Gill. Part 2 Foundations - composition parsing: a programme for the analytical reading of scores, Allen Forte pattern in music, Herbert A. Simon and Richard K. Sumner linguistics and the computer analysis of tonal harmony, Terry Winograd. Part 3 AI and music - heuristic composition: simulating musical skills by digital computer, John Rothgeb music and computer composition, James Anderson Moorer process structuring and music theory, Stephen W. Smoliar. Part 4 AI and music - generative grammars: in search of a generative grammar for music, Otto E. Laske a method for composing simple traditional music by computer, Gary M. Rader generative theories in language and music descriptions, Johan Sundberg and Bjorn Lindblom an overview of hierarchical structure in music, Fred Lerdahl and Ray Jeckandoff. Part 5 AI and music - alternative theories: an artificial intelligence approach to tonal music theory, James Meehan music, mind and meaning, Marvin Minsky. Part 6 AI and music - composition tools: "protocol" - motivation, design and production of a composition for solo piano, Charles Ames an expert system for harmonizing four-part chorales, Kemal Ebcioglu a computer model of music composition, David Cope flavours band - a language for specifying musical style, Christopher Fry. Part 7 AI and music - new directions: a representation for musical dialects, David A. Levitt the perception of melodies, H. Christopher Longuet-Higgins MUSACT - a connectionist model of musical harmony, Jamshed J. Bharucha a learning machine for tonal composition, Stephan M. Schwanauer. Appendix: musical dice game, Wolfgang Amadeus Mozart.
Experiences in the use of evolutionary techniques for testing digital circuits

The generation of test patterns for sequential circuits is one of the most challenging problems arising in the field of Computer-Aided Design for VLSI circuits. In the past decade, Genetic Algorithms have been deeply investigated as a possible approach: several algorithms have been described, and significant improvements have been proposed with respect to their original versions. As a result, GA-based test pattern generators can now effectively compete with other methods, such as topological or symbolic ones. This paper discusses the advantages and disadvantages of GA-based approaches and describes GATTO, a state-of-the-art GA-based test pattern generator. Other algorithms belonging to the same category are outlined as well. The paper puts GATTO and other GA-based tools in perspective, and shows that Evolutionary computation techniques can successfully compete with more traditional approaches, or be integrated with them.
A framework for organizational self-design in distributed problem solving networks

A distributed problem solving network is a distributed network of semi-autonomous nodes that perform sophisticated problem solving and cooperatively interact with other nodes to solve a single problem. Because interaction among nodes is both limited and unreliable, each node directs its own activities in concert with other nodes, using potentially incomplete, inaccurate, and inconsistent information. Each node must balance its own perceptions of appropriate local problem solving activity with activities deemed important by other nodes. Organizational self-design is proposed as a multilevel approach to coordinating these networks. An organizational structure specifies the information and control relationships among the nodes in a general way. Each node is responsible for elaborating these relationships into precise activities to be performed by the node. This work focuses on the design and implementation of a coordination framework that is responsive to organizational structuring decisions. This framework is implemented as part of the distributed vehicle monitoring testbed: a flexible and fully-instrumented research tool for the empirical evaluation of distributed network designs and coordination policies. Each node uses a goal-directed Hearsay-II architecture and a local node planner to provide the sophisticated local control necessary for a node to evaluate its activity decisions based on internal, external, and organizational criteria. The capabilities of the framework are illustrated with testbed experiments using different organizational stuctures in four-node and five-node distributed networks.
Predictive Tabla Modelling Using Variable-length Markov and Hidden Markov Models

Abstract Tabla is a sophisticated, centuries-old percussion tradition from North India based on timbral sequences. We model these sequences in a predictive framework with Variable-length Markov Models (VLMMs). Using a database containing nearly 30,000 strokes in 35 compositions, we show that VLMMs have high predictive accuracy, with an average perplexity of 1.80, and median perplexity of 1.19, on a task with 42 distinct symbols. This basic framework is extended by the introduction of several new smoothing techniques that determine how to integrate predictions from the different order models. The model is then extended to include parallel representations of the sequence, a technique known as Multiple Viewpoint modelling. The work is then extended to the problem of recognizing strokes from audio. In this hidden context, the identity of the previous stroke is not revealed at each time step. A Variable-length Hidden Markov Model (VLHMM) is used to determine the next-symbol distribution that is used in computing the perplexity. We detail how the forward probabilities can be efficiently computed for the VLHMM by traversing a prediction suffix tree (PST) that is used to represent sequences. Using a VLHMM with a maximum order of 3, we obtain an average perplexity of 2.31, with a median of 1.16 on a nine-target task. To the best of our knowledge, this is the first use of Variable-length Hidden Markov Models for music modelling or prediction.
Diagnosis and analysis of diagnosis properties using discrete event dynamic systems

The basic motivation for the research presented in the article is the fact that things go wrong. With the growing complexity of todays engineering systems, the need has arisen for systematic approaches to failure diagnosis. The paper presents an approach for modeling and diagnosis of systems that fall in the area of discrete event dynamic systems. We use a relational framework for discrete event dynamic systems focusing on a conceptually simple representation of the relationships between inputs, outputs and states of a discrete event system. A fault is said to be detectable if there exists a transition in the system model that leads to a detection in a finite number of steps. The transition necessary for detection can automatically be computed from the system model under certain conditions. We also show how to compute the finest possible fault partition under a single fault assumption.
MBDyn Input File Format Version 1.2-Beta.4

Improved techniques for managing propagation of data through software modules used by computer systems are disclosed. The improved techniques allow a plurality of threads running on different processors to propagate data between software modules by providing concurrent access to queued data by multiple threads. The techniques are particularly well suited for use with the STREAMS model that uses software models arranged in a stack to provide data communications.
Ambienti Virtuali Integrati per Attività Collaborative Real-Time: il prototipo Co.S.M.O.S.

Questo articolo presenta Co.S. M.O. S. (Collaborative Social and Multimedia Operating System), un prototipo di ambiente virtuale di supporto ad attivita collaborative real-time sviluppato con la finalita di venire incontro alle crescenti esigenze degli utenti in termini di Gestione della Conoscenza, Collaborazione e Socializzazione. Piu precisamente Co.S. M.O. S. e un ambiente integrato, costituito da un WebOS, da un ambiente per la realizzazione di attivita di collaborazione e da un social network, in grado supportare efficacemente collaborazioni real-time e quindi di favorire lo sviluppo di social communities attraverso funzioni avanzate di condivisione e cooperazione nei processi di Knowledge Management. Inoltre Co.S. M.O. S. e facilmente accessibile attraverso una applet Java che, incorporata in una pagina web, offre agli utenti la possibilita di utilizzare le diverse funzionalita fornite.
Computer Science for Practicing Engineers: Computer Science for Everyone Else (CMU-ISR-11-115)

The Computer Science for Practicing Engineers (CS4PE) course is a breath-oriented course covering key topics in computer science that represent critical foundational knowledge for anyone developing software. The course is designed for students and professionals who do not have a formal background in computer science, but currently write, or anticipate writing software as a key part of their job. CS4PE represents one important facet of Computation Thinking [Wing 2006]. Computational thinking is a fundamental skill for everyone, not just for computer scientists. To those core concepts essential to a particular professional discipline, we need to add computational thinking to every professional's analytical ability. The purpose of this report is to describe the motivation for, and the development of, the CS4PE course. The aim is to describe why such a course is needed, the design of the course, our experience in delivering the course since its inception, and feedback received from a peer review of the course content and assignments during the 2010 Conference on Software Engineering Education and Training. We will also discuss how and why the course has evolved since its initial development and delivery, as well as future directions that we expect the course to move in. In 2007 we created a new professional masters program in embedded software engineering, housed in Our purpose in creating this program was to address the fact that embedded systems are pervasive today: from control systems for chemical processing, production lines, automotive, and aerospace, to cell phones, appliances, and entertainment systems. Engineers building these systems must bring together complex software, computing hardware, and peripheral devices to satisfy demanding requirements, operating under significant physical, technical and environmental constraints. Despite the challenges inherent in developing embedded systems, today most embedded systems engineers learn how to design and build the software for these systems on-the-job, without any formal background or training. Most of these engineers have a formal background in a traditional engineering discipline such as electrical or mechanical engineering. While these engineers have some of the skills necessary to build complex embedded systems, their background is typically inadequate to meet the interdisciplinary challenges that arise with embedded systems in which knowledge of hardware, software, and specific domains must come together. Today embedded systems software is complex, large, and requires the specific expertise of trained software engineers with a solid background in computer science principles. Initially we created the CS4PE course to meet the needs …
MARCKS and Lung Disease

Abstract MARCKS (myristoylated alanine‐rich C kinase substrate) is a prominent PKC substrate expressed in all eukaryotic cells. It is known to bind to and cross‐link actin filaments, to serve as a bridge between Ca2+/calmodulin and PKC signaling, and to sequester the signaling molecule phosphatidylinositol 4,5‐bisphosphate in the plasma membrane. Since the mid‐1980s, this evolutionarily conserved and ubiquitously expressed protein has been associated with regulating cellular events that require dynamic actin reorganization, including cellular adhesion, migration, and exocytosis. More recently, translational studies have implicated MARCKS in the pathophysiology of a number of airway diseases, including chronic obstructive pulmonary disease, asthma, lung cancer, and acute lung injury/acute respiratory distress syndrome. This article summarizes the structure and cellular function of MARCKS (also including MARCKS family proteins and MARCKSL1 [MARCKS‐like protein 1]). Evidence for MARCKS's role in several lung diseases is discussed, as are the technological innovations that took MARCKS‐targeting strategies from theoretical to therapeutic. Descriptions and updates derived from ongoing clinical trials that are investigating inhalation of a MARCKS‐targeting peptide as therapy for patients with chronic bronchitis, lung cancer, and ARDS are provided.
Bigdata Platform Design and Implementation Model

Bigdata software platform technology using Hadoop Ecosystem is the essential element and also underlying technology for application software or service implementation of bigdata analysis. It is required to have bigdata platform technology that can ensure the scalability, reliability and high performance of system for processing and analyzing a variety of bigdata related tasks including structured data, unstructured data, semi-structured data, etc. Bigdata platform can process large amounts of data in parallel unlike those conventional application software solutions and it is an easily scalable system. Its technical components include collection (Flume and Sqoop), storage (Hadoop and NoSQL), search (Solr), analysis (R Analysis), visualization (Node.js), scheduler (Oozie), etc. The purpose of this study is to propose an optimized bigdata platform implementation model through S/W configuration based on open source.
Molecular Profiling of Unusual Thyroid Neoplasms

Abstract Thyroid neoplasms comprise a heterogeneous group of tumors, arising most commonly from follicular cells. While some neoplasms such as the papillary thyroid carcinoma have been widely studied and reported in the literature, our knowledge of the less common entities, including anaplastic thyroid carcinoma and medullary thyroid carcinoma is still deficient. In the current medical era, molecular characteristics of the tumors are being increasingly evaluated for their potential role in establishing diagnosis and to guide management. In this review article, we discuss some of the less common thyroid neoplastic entities, with a special focus on their molecular profiles. In addition, we share our own institutional experience obtained from molecular profiling of anaplastic thyroid carcinoma and medullary thyroid carcinoma.
Hereditary Endocrine Tumor Syndromes: The Clinical and Predictive Role of Molecular Histopathology

Abstract Hereditary tumor syndromes are increasingly recognized in the development of endocrine neoplasms. Depending on the tumor type, 10% to 40% of cases are associated with genetic disorders, including the classic multiple endocrine neoplasia syndromes (MEN1 and MEN2), hyperparathyroidism–jaw tumor syndrome, SDH-related familial paraganglioma-pheochromocytoma syndromes, von Hippel-Lindau syndrome, neurofibromatosis type 1, Carney complex, McCune-Albright syndrome, and familial nonmedullary thyroid cancer syndromes, as well as newer entities (MEN4, DICER1 syndrome, glucagon cell hyperplasia and neoplasia syndrome). Although some features commonly seen in familial disease (early onset, family history, multifocal neoplasia, multiorgan involvement) may alert one to the possibility of an underlying genetic predisposition, endocrine neoplasia syndromes tend to be phenotypically complex and heterogeneous and present variably with de novo mutations, making it difficult to recognize and classify on clinical grounds alone. In an era of precision medicine, pathologists play a central role in the diagnosis of familial cancer syndromes, by leading the way toward screening and molecular histopathology prediction models. In particular, the identification of “pathognomonic” morphologic and immunohistochemical “clues” is crucial to raise the possibility of an inherited genetic disorder and to guide further management, including gene testing, counseling, and targeted therapy. This review highlights the important genotype-phenotype correlations to consider in hereditary endocrine tumor syndromes and their associated clinical implications.