Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear compensator synthesis via linear parameter-varying control

For the nonlinear output regulation or servomechanism problem, a nonlinear compensator synthesis framework is presented that merges techniques for linear parameter-varying (LPV) systems with ideas derived from linearization-based gain scheduling. Plant linearizations about zero-error trajectories have an LPV structure upon which the synthesis of an LPV compensator is based. A key issue is whether, loosely speaking, the linearization process can be reversed wherein a nonlinear compensator is sought that linearizes to the LPV compensator about every zero-error trajectory. Necessary and sufficient existence conditions are derived for the existence of compensators satisfying this linearization requirement. Based on this, it is shown that error feedback compensators that contain an internal model of the exosystem are guaranteed to exist under mild hypotheses. A nonlinear compensator is designed for the ball and beam apparatus to illustrate the technique.
Automatic 3D Point Set Reconstruction from Stereo Laparoscopic Images using Deep Neural Networks

In this paper, an automatic approach to predict 3D coordinates from stereo laparoscopic images is presented. The approach maps a vector of pixel intensities to 3D coordinates through training a six layer deep neural network. The architectural aspects of the approach is presented and in detail and the method is evaluated on a publicly available dataset with promising results.
The Role of Men's Critical/Rejecting Overattribution Bias, Affect, and Attentional Disengagement in Marital Aggression

The present study explored how men’s attentional disengagement, affective reactions, and inferential bias contribute to their wife–directed aggression. We used signal detection analyses, the standard–stimulus empathic accuracy paradigm, and mediation analyses to replicate and extend findings by Schweinle, Ickes, and Bernstein (2002). The results indicated that more aggressive husbands appear to maintain their bias to overattribute criticism and rejection (C/R–O Bias) to women through two independent mechanisms: emotional countercontagion, in the form of contemptuous feelings, and attentional disengagement (i.e., “tuning out”) when a woman expresses herself. These findings inform our theoretical understanding of abusive husbands’ social cognition and have important implications for future research and intervention.
Online self-expression and experimentation as ‘reflectivism’: Using text analytics to examine the participatory forum Hello Sunday Morning

Hello Sunday Morning is an online health promotion organisation that began in 2009. Hello Sunday Morning asks participants to stop consuming alcohol for a period of time, set a goal and document their progress on a personal blog. Hello Sunday Morning is a unique health intervention for three interrelated reasons: (1) it was generated outside a clinical setting, (2) it uses new media technologies to create structured forms of participation in an iterative and open-ended way and (3) participants generate a written record of their progress along with demographic, behavioural and engagement data. This article presents a text analysis of the blog posts of Hello Sunday Morning participants using the software program Leximancer. Analysis of blogs illustrates how participants’ expressions change over time. In the first month, participants tended to set goals, describe their current drinking practices in individual and cultural terms, express hopes and anxieties and report on early efforts to change. After month 1, participants continued to report on efforts to change and associated challenges and reflect on their place as individuals in a drinking culture. In addition to this, participants evaluated their efforts to change and presented their ‘findings’ and ‘theorised’ them to provide advice for others. We contextualise this text analysis with respect to Hello Sunday Morning’s development of more structured forms of online participation. We offer a critical appraisal of the value of text analytics in the development of online health interventions.
Identification of the regional variability of the brain hemodynamic response to spontaneous and step-induced CO2 changes using function expansions*

Abstract The cerebrovascular bed is very sensitive to CO 2 changes, particularly in respiratory-related areas, such as the brainstem. Therefore, the hemodynamic response to such changes is of interest as it quantifies this sensitivity. Here, we examine in detail the regional characteristics of the hemodynamic response to spontaneous and larger, externally induced step CO 2 changes CO 2 (end-tidal forcing) by utilizing BOLD functional magnetic resonance imaging (fMRI) measurements from healthy humans. We first obtain estimates of the impulse response between CO 2 and BOLD signal in several anatomically and functionally defined regions of interest, using function expansions with different basis sets. These include the Laguerre basis, which has been widely used in linear and nonlinear systems identification particularly for biological/physiological systems, as well as different variants of gamma functions, which have been widely used in functional neuroimaging due to physiological considerations with regards to the characteristics of the BOLD response to external (sensory or other) stimuli. Based on the aforementioned comparisons, we perform the same analysis in smaller anatomical areas, considering voxel neighborhoods that span the entire image, in order to map key features of the hemodynamic response function such as peak value, time-to-peak and area, in finer spatial resolution.
Multitasking goes global: Multitasking with traditional and new electronic media and attention to media messages among college students in Kuwait, Russia, and the USA

The current study examined multitasking with traditional and new electronic media among college students (N = 532) in three countries: Kuwait, Russia, and the USA. Television, music, phone, and the Internet were the most popular media to multitask with and were often used interchangeably, i.e., without the dominance of one medium over another. The game–music pair was the only one with clear distinction between primary (game) and secondary (music) media. Eating, socializing, and doing homework were the most popular nonmedia activities combined with traditional and new electronic media uses. Media-only and media–nonmedia multitasking were found in positive association with perceived attention paid to media messages. The study not only indicated universal media multitasking trends but also revealed cross-national differences that can be explained by economic, political, and cultural characteristics varying across the three nations.
Hayek on liberty

Not available since the 1980s, this up-dated edition by the leading political philosopher, John Gray, outlines his new position on Hayek. In a substantial new chapter, Gray assesses how far the historical development of the last ten years can be deployed in a critique of Hayek's thought. His reassessment is not only a provoking study of a classical philosopher. It is also a timely contribution to the debate over the future of conservatism, as Gray argues that Hayekian liberalism - 'the most well-articulated political theory of the new right' - is flawed.
The Untold Story of Social Media on Offline Sales: The Impact of Facebook in the U.S. Automobile Industry

Facebook has become a leading avenue for online brand communities (i.e., fan pages) that are used for marketing purposes. Despite the popularity on Facebook, there has been limited empirical research regarding the dynamics of firm-generated content (FGC) and user-generated content (UGC) over a long period of time at the industry level. The objective of this paper is to examine the dynamics of the interactions between FGC, UGC, and offline sales (light vehicles) in the U.S. automobile industry. We collected detailed data including the official Facebook pages of 31 car makes in the U.S., and supplemented the data from these firms’ traditional media efforts, and offline car sales from 2009 to 2014. We employ a panel vector autoregressive (PVAR) model that allows us to investigate the dynamic relationships among multiple time series variables. The preliminary analysis results, expected contributions, and future plans are discussed.
Persistent Data Sketching

A persistent data structure, also known as a multiversion data structure in the database literature, is a data structure that preserves all its previous versions as it is updated over time. Every update (inserting, deleting, or changing a data record) to the data structure creates a new version, while all the versions are kept in the data structure so that any previous version can still be queried. Persistent data structures aim at recording all versions accurately, which results in a space requirement that is at least linear to the number of updates. In many of today's big data applications, in particular for high-speed streaming data, the volume and velocity of the data are so high that we cannot afford to store everything. Therefore, streaming algorithms have received a lot of attention in the research community, which use only sublinear space by sacrificing slightly on accuracy. All streaming algorithms work by maintaining a small data structure in memory, which is usually called a em sketch, summary, or synopsis. The sketch is updated upon the arrival of every element in the stream, thus is ephemeral, meaning that it can only answer queries about the current status of the stream. In this paper, we aim at designing persistent sketches, thereby giving streaming algorithms the ability to answer queries about the stream at any prior time.
Monitoring of minimal residual disease in acute myeloid leukemia

Two highly sensitive methods, multiparameter flow cytometry (MFC) and real‐time quantitative PCR (RQ‐PCR), are increasingly used to monitor minimal residual disease (MRD) and to guide risk‐adapted management in acute myeloid leukemia (AML). An independent prognostic impact has been demonstrated for MRD levels obtained by both methods. MFC has been found particularly useful for assessment of early clearance of malignant cells and after consolidation therapy. At the latter checkpoint, MRD levels quantified by RQ‐PCR in AML with fusion genes also have the strongest prognostic power. In addition, highly predictive initial expression levels have been identified by RQ‐PCR. Both methods are capable of early detection of relapse. Through the use of all available markers including NPM1 mutations and FLT3 mutations in addition to fusion genes, RQ‐PCR‐based MRD assessment is possible in more than half of patients, whereas MFC is applicable to most AML cases. With a sensitivity of 10−4 (PML‐RARA) to 10−7 (patient‐specific primers, FLT3 and NPM1 mutations), RQ‐PCR is more sensitive in most cases. Large clinical trials will determine the exact role and place of immunologic and RQ‐PCR‐based monitoring of MRD in the therapy of patients with AML. Cancer 2008. © 2007 American Cancer Society.
Big-DAMA: Big Data Analytics for Network Traffic Monitoring and Analysis

The complexity of the Internet has dramatically increased in the last few years, making it more important and challenging to design scalable Network Traffic Monitoring and Analysis (NTMA) applications and tools. Critical NTMA applications such as the detection of anomalies, network attacks and intrusions, require fast mechanisms for online analysis of thousands of events per second, as well as efficient techniques for offline analysis of massive historical data. We are witnessing a major development in Big Data Analysis Frameworks (BDAFs), but the application of BDAFs and scalable analysis techniques to the NTMA domain remains poorly understood and only in-house and difficult to benchmark solutions are conceived. In this position paper we describe the basis of the Big-DAMA research project, which aims at tackling this growing need by benchmarking and developing novel scalable techniques and frameworks capable to analyze both online network traffic data streams and offline massive traffic datasets.
The Political Resources of American Science

In the light of the traditional ethos of science, and particularly its emphasis on a complete separation between science and politics, a discussion of the political resources of science is bound to appear somewhat perverse, if not entirely heretical. Yet, while the idea of the separation of science and politics may still be consistent with feelings and wishes prevalent among scientists, it seems increasingly inadequate as a statement about reality. The tendency to confuse the question of what the social status of science is with what it ought to be continues to interfere with the development of a fruitful theoretical discourse about the social and political aspects of science. Attempts to call attention to the links of science with its political environment have usually stimulated in scientists such associations as the Marxist approach to the sociology and history of science, which views scientific theories and institutions as the outgrowth of specific social and economic conditions. While some scientists have been attracted by this analysis, most have strongly rejected this type of approach, on the grounds that the description of science as derivative from its social context destroys its claim to be politically neutral and its right to be free from external control. It has suggested to them the dangers of censorship and political interference, and aggravated their fear of the consequences of linking science and politics. By the same token, scientists have tended to be much more sympathetic to those theories in the sociology and history of science which perceived scientists as obeying the inherent imperatives of scientific ideas and the logic of inquiry. The theory that scientists follow only the internal rules of science would seem to reinforce their effort to prevent the subordination of their work to standards extrinsic to science and to protect themselves from external political interference. Such autonomist social theories of science, as formulated by Polanyi, Hagstrom and Storer, provide, however, a more adequate description of the social reality of science as it was when science was still
Power of Automated Algorithms for Combining Time-Line Follow-Back and Urine Drug Screening Test Results in Stimulant-Abuse Clinical Trials

Background: In clinical trials of treatment for stimulant abuse, researchers commonly record both Time-Line Follow-Back (TLFB) self-reports and urine drug screen (UDS) results. Objectives: To compare the power of self-report, qualitative (use vs. no use) UDS assessment, and various algorithms to generate self-report-UDS composite measures to detect treatment differences via t-test in simulated clinical trial data. Methods: We performed Monte Carlo simulations patterned in part on real data to model self-report reliability, UDS errors, dropout, informatively missing UDS reports, incomplete adherence to a urine donation schedule, temporal correlation of drug use, number of days in the study period, number of patients per arm, and distribution of drug-use probabilities. Investigated algorithms include maximum likelihood and Bayesian estimates, self-report alone, UDS alone, and several simple modifications of self-report (referred to here as ELCON algorithms) which eliminate perceived contradictions between it and UDS. Results: Among the algorithms investigated, simple ELCON algorithms gave rise to the most powerful t-tests to detect mean group differences in stimulant drug use. Conclusions: Further investigation is needed to determine if simple, naïve procedures such as the ELCON algorithms are optimal for comparing clinical study treatment arms. But researchers who currently require an automated algorithm in scenarios similar to those simulated for combining TLFB and UDS to test group differences in stimulant use should consider one of the ELCON algorithms. Scientific Significance: This analysis continues a line of inquiry which could determine how best to measure outpatient stimulant use in clinical trials (NIDA. NIDA Monograph-57: Self-Report Methods of Estimating Drug Abuse: Meeting Current Challenges to Validity. NTIS PB 88248083. Bethesda, MD: National Institutes of Health, 1985; NIDA. NIDA Research Monograph 73: Urine Testing for Drugs of Abuse. NTIS PB 89151971. Bethesda, MD: National Institutes of Health, 1987; NIDA. NIDA Research Monograph 167: The Validity of Self-Reported Drug Use: Improving the Accuracy of Survey Estimates. NTIS PB 97175889. GPO 017-024-01607–1. Bethesda, MD: National Institutes of Health, 1997).
Logistic Steps of Tissue Sample Processing in a Clinical Biobank - A Short Overview

Biobanks are facilities that collect, process, store and distribute biospecimens and associated data for use in research. The samples banked in biorepositories are used by academic (e.g. university) and non-academic (e.g. pharmaceutical industry) scientists in the field of biomedical research. These research projects include the development of new therapeutics and the identification of biomarkers for progression and prognosis of various diseases. The term “biobank” can be further categorized by institution, type of biomaterials, collection purpose and collection strategy: Biobanks can be operated by academic or non-academic institutions (academic biobank vs. industrial / commercial biobank). Biorepositories collect and store various types of biomaterials (e.g. tissue biobank, blood biobank, cell biobank, etc.). Furthermore, biobanks may also be classified by purpose (research, diagnostic, or therapeutic biobank) or by collection strategy (disease-specific vs. population-based) [1,2]. In a biobank associated with a hospital, research and clinical practice are closely intertwined [3]. In these clinical biobanks several steps and processes are involved in collection and processing of samples until their final storage in a biorepository. This review aims to provide a short overview of the six most significant steps in sample handling and sample logistics in a clinical biobank. Biobank Graz, one of the largest clinical biobanks in Europe, stores more than 7.5 million samples. Tissue samples (FFPE as well as fresh frozen) are mostly collected at the Institute of Pathology of the Medical University of Graz and enter Biobank Graz via routine diagnostic processes [4]. In this context, the most critical stage is the preanalytical phase. Preanalytical activities have a high impact on the quality of the collected samples and subsequently on the results of laboratory testing performed on these specimens [5,6]. Critical factors in the preanalytical phase are cold and warm ischemia time, time intervals between the different stages of sample processing or transport and storage conditions (temperature, duration, or the used protocols: e.g. amount of formaline) [7].
DIADEM: domain-centric, intelligent, automated data extraction methodology

Search engines are the sinews of the web. These sinews have become strained, however: Where the web's function once was a mix of library and yellow pages, it has become the central marketplace for information of almost any kind. We search more and more for objects with specific characteristics, a car with a certain mileage, an affordable apartment close to a good school, or the latest accessory for our phones. Search engines all too often fail to provide reasonable answers, making us sift through dozens of websites with thousands of offers--never to be sure a better offer isn't just around the corner. What search engines are missing is understanding of the objects and their attributes published on websites. Automatically identifying and extracting these objects is akin to alchemy: transforming unstructured web information into highly structured data with near perfect accuracy. With DIADEM we present a formula for this transformation, but at a price: DIADEM identifies and extracts data from a website with high accuracy. The price is that for this task we need to provide DIADEM with extensive knowledge about the ontology and phenomenology of the domain, i.e., about entities (and relations) and about the representation of these entities in the textual, structural, and visual language of a website of this domain. In this demonstration, we demonstrate with a first prototype of DIADEM that, in contrast to alchemists, DIADEM has developed a viable formula.
Design and implementation of a cloud computing platform for electromagnetic modelling

Cloud computing has recently attracted attention in numerical simulation fields; its appeal is mainly due to the possibility of providing high computational resources at low cost. Computational electromagnetics CEMs, aimed at numerically predicting the electromagnetic behaviour of arbitrary objects e.g., aircrafts, satellites, ships, antennas, etc., often requires computational resources unavailable on single workstations. This is where cloud computing comes into play, as a potential low cost solution for the EM analysis. For these reasons, the development of accurate and efficient EM models, together with an appropriate use of computational resources in cloud is a research topic with a strong industrial interest. In this work, the authors present the results of the cloud infrastructure for CEM problems developed within the project 'Cloud fast scalable EM modelling - CloudCEM'.
Clustering Technique in Multi-Document Personal Name Disambiguation

Focusing on multi-document personal name disambiguation, this paper develops an agglomerative clustering approach to resolving this problem. We start from an analysis of point-wise mutual information between feature and the ambiguous name, which brings about a novel weight computing method for feature in clustering. Then a trade-off measure between within-cluster compactness and among-cluster separation is proposed for stopping clustering. After that, we apply a labeling method to find representative feature for each cluster. Finally, experiments are conducted on word-based clustering in Chinese dataset and the result shows a good effect.
Butterfly Encryption Scheme for Resource-Constrained Wireless Networks

Resource-constrained wireless networks are emerging networks such as Radio Frequency Identification (RFID) and Wireless Body Area Networks (WBAN) that might have restrictions on the available resources and the computations that can be performed. These emerging technologies are increasing in popularity, particularly in defence, anti-counterfeiting, logistics and medical applications, and in consumer applications with growing popularity of the Internet of Things. With communication over wireless channels, it is essential to focus attention on securing data. In this paper, we present an encryption scheme called Butterfly encryption scheme. We first discuss a seed update mechanism for pseudorandom number generators (PRNG), and employ this technique to generate keys and authentication parameters for resource-constrained wireless networks. Our scheme is lightweight, as in it requires less resource when implemented and offers high security through increased unpredictability, owing to continuously changing parameters. Our work focuses on accomplishing high security through simplicity and reuse. We evaluate our encryption scheme using simulation, key similarity assessment, key sequence randomness assessment, protocol analysis and security analysis.