Miracle-dz/UniFAR-FTUs huggingface dataset:
Science: Key Concepts in Philosophy

Chapter 1: Introduction Chapter 2: Discovery - Flash of Genius or Careful Observation Chapter 3: Discovery - Heuristic Moves Chapter 4: Evidence - Verification and Falsification Chapter 5: Evidence - There's More to Seeing Than Meets the Eye Chapter 6: Evidence - The Secret Life of the Experiment Chapter 7: Realism - The Ultimate Argument Chapter 8: Realism - The Role and Function of Theories Chapter 9: Independence - Science as Social Activity Chapter 10: Independence - 'Facts' and 'Truth' Chapter 11: Where Does the Philosophy of Science Go From Here?
Simulation-Based Design Under Uncertainty for Compliant Microelectromechanical Systems

SIMULATION-BASED DESIGN UNDER UNCERTAINTY FOR COMPLIANT MICROELECTROMECHANICAL SYSTEMS Jonathan W. Wittwer Department of Mechanical Engineering Doctor of Philosophy The high cost of experimentation and product development in the field of microelectromechanical systems (MEMS) has led to a greater emphasis on simulation-based design for increasing first-pass design success and reliability. The use of compliant or flexible mechanisms can help eliminate friction, wear, and backlash, but compliant MEMS are sensitive to variations in material properties and geometry. This dissertation proposes approaches for design stage uncertainty analysis, model validation, and robust optimization of nonlinear compliant MEMS to account for critical process uncertainties including residual stress, layer thicknesses, edge bias, and material stiffness. Methods for simulating and mitigating the effects of non-idealities such joint clearances, semi-rigid supports, non-ideal loading, and asymmetry are also presented. Approaches are demonstrated and experimentally validated using bistable micromechanisms and thermal microactuators as examples.
Commissioning and Using the 4 Gigabit Lightpath from Onsala to Jodrell Bank

This paper describes the installation, commissioning and testing of the 4 Gigabit Lightpath from Onsala to Jodrell bank which is being used as part of the EXPReS project to send data from the telescope at Onsala to the new WIDAR correlator at Jodrell Bank. The network path is truly multi-domain, it crosses multiple administrative domains, uses equipment from different manufacturers, and both Ethernet and SDH framing technologies are used on different portions of the path. Measurements performed using both PC and deterministic Field Programmable Gate Array techniques are described and results on the performance and stability of the 4 Gigabit path are shown. Finally, plots demonstrating the successful sampling and movement of data from the telescope to the correlator are presented.
A Quick Review on Reordering Approaches in Statistical Machine Translation Systems

The problem of finding the correct word order for translating between two languages is called the reordering problem. Reordering is a serious challenge and one the hardest problems in Statistical Machine Translation. The extensive research carried out on reordering in the past decade can be divided into three main classes: distortion model, lexicalized reordering model, and pre-reordering of the source sentences. This paper surveys some major works under each class and discusses their advantages and limitations. The survey shows that there is still no reordering model which is able to perform long distance reordering in a principled manner and the future works must therefore focus on this issue.
Dual Mode Sensing with Low-Profile Piezoelectric Thin Wafer Sensors for Steel Bridge Crack Detection and Diagnosis

Monitoring of fatigue cracking in steel bridges is of high interest to many bridge owners and agencies. Due to the variety of deterioration sources and locations of bridge defects, there is currently no single method that can detect and address the potential sources globally. In this paper, we presented a dual mode sensing methodology integrating acoustic emission and ultrasonic wave inspection based on the use of low-profile piezoelectric wafer active sensors (PWAS). After introducing the research background and piezoelectric sensing principles, PWAS crack detection in passive acoustic emission mode is first presented. Their acoustic emission detection capability has been validated through both static and compact tension fatigue tests. With the use of coaxial cable wiring, PWAS AE signal quality has been improved. The active ultrasonic inspection is conducted by the damage index and wave imaging approach. The results in the paper show that such an integration of passive acoustic emission detection with active ultrasonic sensing is a technological leap forward from the current practice of periodic and subjective visual inspection and bridge management based primarily on history of past performance.
KONSTRUKSI IDENTITAS DIRI REMAJA PENGGUNA MEDIA INSTAGRAM DI KOTA MEDAN

Penelitian ini berjudul Konstruksi Identitas Diri Remaja Pengguna Media Instagram di kota Medan. Saat ini kehidupan remaja tidak lepas dari media baru. Media baru memberikan kemungkinan untuk bermain, berkomunikasi, dukungan sosial, dan berbagi pengetahuan di kalangan remaja. Perumusan masalah pada penelitian ini adalah “Bagaimana Instagram membantu remaja mengkonstruksi identitasnya? ”. Penelitian ini ditujukan kepada para remaja yang memiliki akun di media baru Instagram. Tujuan dari penelitian ini adalah mendeskripsikan konsumsi Instagram pada remaja, deskripsi identitas remaja melalui Instagram dan makna Instagram dalam perilakunya. Metodologi penelitian ini bersifat kualitatif dan menggunakan paradigma konstruktivis, dimana sumber data diperoleh dari wawancara mendalam dengan mengutamakan kedalaman (kualitas) dari para informan. Instagram merupakan salah satu dari media baru mengunggah dan berbagi foto – foto kepada pengguna lainnya. Instagram adalah jejaring sosial yang paling menyedihkan. Peneliti memilih Instagram karena menurut majalah Amerika, Instagram dianggap sebagai ajang kesombongan. Kata Kunci : Remaja, Identitas, Media Baru, Konstruksi
The foundations of lesion-function inference in the human brain

Understanding the functional architecture of the brain has long been a challenge in neuroscience with a variety of techniques having been developed to explore this structure-function relationship. However, in order to be able to accurately identify the underlying system we require techniques that have the capabilities of describing the complexities therein. In order to perform lesion-function studies a cohort of brain scans with the location of the lesion identified must be collected. Utilising diffusion weighted magnetic resonance imaging, normally collected in the clinical setting, I propose a new unsupervised lesion segmentation routine. The cohort of brain scans also need to be spatially normalised such that homologous regions of the brain are brought into register with each other. However, this process can be perturbed by the presence of a lesion within the scan. Though a series of simulations I evaluate the performance of 12 different spatial normalisation routines on brains scans that possess a lesion. Historically lesion-function mapping studies have tended to use a univariate statistical approach, where different locations within the brain are treated as being spatially independent from each other. Here I show that biases within the structure of the data have the potential to distort the lesion-function inferences we draw. Though a series of simulations, I show that a mass univariate technique is vulnerable to these biases and assess three different multivariate methods (Support Vector Machines, Relevance Vector Machines and Flexible Bayesian Modelling) as potential solutions to this problem. Asides from making lesion-function inferences, these multivariate models can be used to predict future events. Using a data set of paired admission diffusion weighted magnetic resonance imaging scans and functional outcome scores I apply these techniques to the clinical scenario of predicting the functional outcome of patients after a cerebral vascular event.
Statistical models for HMM/ANN hybrids

We present a theoretical investigation into the use of normalised artificial neural network (ANN) outputs in the context of hidden Markov models (HMMs). The work is motivated by the pursuit of a more theoretically rigorous understanding of the Kullback-Liebler (KL)-HMM. Two possible models are considered based respectively on the HMM states storing categorical distributions and Dirichlet distributions. Training and recognition algorithms are derived, and possible relationships with KL-HMM are briefly discussed.
Mental states monitoring through passive Brain-Computer Interface systems

The monitoring of cognitive functions aims at gaining information about the current cognitive state of the user by decoding brain signals. In recent years, this approach allowed to acquire valuable information about the cognitive aspects regarding the interaction of humans with external world. From this consideration, researchers started to consider passive application of brain–computer interface (BCI) in order to provide a novel input modality for technical systems solely based on brain activity. The objective of this thesis is to demonstrate how the passive Brain Computer Interfaces (BCIs) applications can be used to assess the mental states of the users, in order to improve the human machine interaction. Two main studies has been proposed. The first one allows to investigate whatever the Event Related Potentials (ERPs) morphological variations can be used to predict the users’ mental states (e.g. attentional resources, mental workload) during different reactive BCI tasks (e.g. P300-based BCIs), and if these information can predict the subjects’ performance in performing the tasks. In the second study, a passive BCI system able to online estimate the mental workload of the user by relying on the combination of the EEG and the ECG biosignals has been proposed. The latter study has been performed by simulating an operative scenario, in which the occurrence of errors or lack of performance could have significant consequences. The results showed that the proposed system is able to estimate online the mental workload of the subjects discriminating three different difficulty level of the tasks ensuring a high reliability.
Task Level SURF Keypoint Extraction Parallelization in AR Application

In the last several decades, following the development of technology, information producing and consuming have become easier than ever before. With the incredible speed of information grown, we will soon face a new issue of information flood in which Augmented Reality (AR) is one of the most promising solutions to resolve the problem. Limited by computation resource, large amount of common AR systems are featured by simple algorithms to meet the requirement of lightweight and real time image processing (marker recognition is a typical example). For that reason, AR application is usually constrained within managed environment. On the other hand, advanced image recognition algorithms such as SIFT [1] (Scale-invariant feature transform) or SURF [2] (Speed-Up Robust Feature) require enormous processing capability to carry out similar tasks as an extra cost for flexibility. Reached the barrier of material technology, hardware innovation in recent years turns into another direction that promotes the use of parallel computing. Therefore, real-time (low latency) object recognition by traditional implementation of SIFT or SURF is still not possible, even with the latest hardware. 1.1 SIFT and SURF SIFT is the most well-known local descriptor algorithm ever since its publication in 1999. Inspired by SIFT, SURF was published by Herbart Bay in 2008 with greater performance compared to SIFT in both accuracy and computational efficiency. Even though the methodology implemented in SURF is totally different from SIFT’s, they are sharing the same principle of scale space construction. 1.2 Task Parallelization and Expectation Mentioned above, SURF requires constructing multiple scale spaces in order to identify and extract keypoint at different scale. Scale spaces in SURF are constructed by up-scaling box filter, which results lower and constant computation cost (compared to SIFT, in which scale spaces are constructed by recursively downsampling original image). Searching for keypoints across multiple scale spaces increases in-variance effectiveness of SURF. However, as the down side of this process, it results more complexity for parallelization. Especially on task level parallelism, where smaller parts of image are processed simultaneously, keypoints distortion and loss are expected on lower scale space, around slice’s boundaries. Extra safe margin can only preserve keypoints on top levels of scale space. Figure 1: Keypoints are Loss at Smaller Scale Space
Smalltalk by Example: The Developer's Guide

From the Publisher: You'll quickly progress from Smalltalk basics to real-world applications development. Valuable guidelines for writing more productive code are provided in chapters that cover how to handle errors using Smalltalk's handling mechanism to eliminate most errors checking code, change procedural code into object-oriented code.and eliminate procedural code from applications, write multithreaded applications with semaphores and mutexes, and use the Model View Controller paradigm to split applications into pieces that each focus on the domain problem and user interaction. Guidance on how to improve software testing includes an example of a Test Manager you can build and use in your projects. Ready-to-use code examples on the disk included with the book will accelerate your Smalltalk programming proficiency.
The Tessera D&R computational environment: Designed experiments for R-Hadoop performance and Bitcoin analysis

Li, Jianfu. Ph.D., Purdue University, December 2014. The Tessera D&R Computational Environment: Designed Experiments for R-Hadoop Performance and Bitcoin Analysis. Major Professor: William S. Cleveland. D&R is a statistical framework for the analysis of large complex data that enables feasible and practical analysis of large complex data. The analyst selects a division method to divide the data into subsets, applies an analytic method of the analysis to each subset independently with no communication among subsets, selects a recombination method that is applied to the outputs across subsets to form a result of the analytic method for the entire data. The computational tasking of D&R is nearly embarrassingly parallel, so D&R can readily exploit distributed, parallel computational environments, such as our D&R computational environment, Tessera. In the first part of this dissertation, I present a study of the performance of the Tessera D&R computational environment through designed experiments. The base of the D&R computational environment is RHIPE, the R and Hadoop Integrated Programming Environment. R is a widely used interactive language for data analysis. Hadoop is a distributed, parallel computational environment consisting of a distributed file system (HDFS) and distributed compute engine (MapReduce). RHIPE is a merger of R and Hadoop. The D&R framework enables a fast embarrassingly parallel computation on a cluster for large complex data that can lead to a small computational elapsed times for the applications analytic methods to all of the data. However, the time depends on many factors. The system we study is very complex and the effects of factors are complex. There are interactions, but not well understood. So we run a full factorial experiment with replicates to enable an understanding.
Logic-Based Program Synthesis and Transformation, Preliminary Proceedings

We present the synthesis functionality of the Leon system (leon.epfl.ch). Leon accepts a purely functional subset of Scala extended with a choice construct. We describe automated and manual synthesis and transformation techniques in Leon, which can eliminate the choice construct and thus transform input/output relation specifications into executable functions from inputs to outputs. The techniques employed include functional synthesis procedures for decidable theories such as term algebras and Presburger arithmetic, synthesis proof rules for decomposing specifications, as well as search-based techniques, such as counterexample-guided synthesis. � This work is supported in part by the European Research Council (ERC) Project Implicit Programming
GA Based Feature Recognition of Step File for CAD/CAM Integration

Feature-based method has been successfully applied in several fields of manufacturing. However, most of the applications use the solid modeling method that cannot meet the requirements of a product design that needs a free-form surface or a complicated surface. This research utilizes the Genetic Algorithm (GA) technique for feature recognition of STEP file. A GA model is proposed for optimizing the coordinates which is used for feature recognition. It is proposed as an input for automatic feature recognition in Computer Aided Design and Manufacturing (CAD/CAM) application. These methods accomplish their task based on recognition of features as GA made up. This technique used standard for exchange of product information (STEP) formats for geometrical data extraction representation to matching the coordinate from STEP file to decide the correct or optimize solution. Genetic operator such as selection, crossover and mutation are performed repeatedly to acquire the optimal sequences of coordinates. Even though the result of this processes are optimal, some coordinates are not placed in the correct position.
Animacy in Interrogative Pronouns

The present paper presents a typological investigation of whether languages make an animacy distinction in their independent nominal interrogative pronouns. The data comprises 24 languages and was taken mainly from grammatical descriptions. 3rd person pronouns and relative pronouns are included for comparison. The outcome of the survey is that the vast majority of the sample languages do make a ‘who’ vs. ‘what’ distinction. Constructions special to questions, in particular fronting and its possible implications for the question words, are discussed, as is the role of case marking on interrogative pronouns.
Facilitating knowledge discovery by integrating bottom-up and top-down knowledge sources: a text mining approach

This dissertation aims to discover synergistic combinations of top-down (ontologies), interactive (relevance feedback), and bottom-up (machine learning) knowledge encoding techniques for text mining. The strength of machine learning techniques lies in their coverage and efficiency because they can discover new knowledge without human intervention. The output, however, is often imprecise and irrelevant. Human knowledge, top-down or interactively encoded, may remedy this. The research question addressed is if knowledge discovery can become more precise and relevant with hybrid systems. Three different combinations are evaluated. The first study investigates an ontology, the Unified Medical Language System (UMLS), combined with an automatically created thesaurus to dynamically adjust the thesaurus' output. The augmented thesaurus was added to a medical, meta-search portal as a keyword suggester and compared with the unmodified thesaurus and UMLS. Users preferred the hybrid approach. Thus, the combination of the ontology with the thesaurus was better than the components separately. The second study investigates implicit relevance feedback combined with genetic algorithms designed to adjust user queries for online searching. These were compared with pure relevance feedback algorithms. Users were divided into groups based on their overall performance. The genetic algorithm significantly helped low achievers, but hindered high achievers. Thus, the interactively elicited knowledge from relevance feedback was judged insufficient to guide machine learning for all users. The final study investigates ontologies combined with two natural language processing techniques: a shallow parser and an automatically created thesaurus. Both capture relations between phrases in biomedical text. Qualified researchers found all terms to be precise; however, terms that belonged to ontologies were more relevant. Parser relations were all precise. Thesaurus relations were less precise, but precision improved for relations that had their terms represented in ontologies. Thus, this integration of ontologies with natural language processing provided good results. In general, it was concluded that top-down encoded knowledge could be effectively integrated with bottom-up encoded knowledge for knowledge discovery in text. This is particularly relevant to business fields, which are text and knowledge intensive. In the future, it will be worthwhile to extend the parser and also to test similar hybrid approaches for data mining.
A Practitioner's Guide to Multiple Testing Error Rates

It is quite common in modern research, for a researcher to test many hypotheses. The statistical (frequentist) hypothesis testing framework, does not scale with the number of hypotheses in the sense that naively performing many hypothesis tests will probably yield many false findings. Indeed, statistical "significance" is evidence for the presence of a signal within the noise expected in a single test, not in a multitude. In order to protect himself from an uncontrolled number of erroneous findings, a researcher has to consider of the type or errors he wishes to avoid and select the adequate procedure for that particular error type and data structure. A quick search of the tag [multiple-comparisons] in the statistics Questions & Answers web site Cross Validates (this http URL) demonstrates the amount of confusion this task can actually cause. This was also a point made at the 2009 Multiple Comparisons conference in Tokyo. In an attempt to offer guidance, we review possible error types for multiple testing, and demonstrate them with some practical examples, which clarify the formalism. Finally, we include some notes on the software implementations of the methods discussed. The emphasis of this manuscript is on the error-rates, and not on the procedures themselves. We do try to name several procedures in this manuscript where appropriate. P-value adjustment will not be discussed as it is procedure specific. I.e., it is the choice of a procedure that defines the p-value adjustment, and not the error rate itself. Simultaneous confidence intervals will, also, not be discussed.
Perl Programming for Medicine and Biology

Written for biomedical professionals and hospital practitioners interested in creating their own programs, Perl Programming for Medicine and Biology, discusses and reviews biomedical data resources, data standards, data organization, medicolegal and ethical conduct for data miners, and grants-related data sharing responsibilities. It teaches readers the basic Perl programming skills necessary for collecting, analyzing, and distributing biomedical data and provides solutions to in-depth problems that face researchers and healthcare professionals.