Miracle-dz/UniFAR-FTUs huggingface dataset:
Transmission of OFDM-UWB radio signals along multimode fiber in in-building networks

Nowadays, in-building networks are facing a new challenge of upgrading their capacity to exceeding 1 Gbit/s, in order to provide high definition television (HDTV) and new advanced multimedia-based applications requiring even higher capacity than HDTV. The main characteristics of multimode fibers (MMF) like the low cost and their efficiency in short-range links make these fibers adequate to be used in in-building networks. The main features of orthogonal frequency division multiplexing (OFDM) ultra-wideband (UWB) signals like the low intersymbol interference (ISI) and the tolerance to multipath fading, make these signals appropriate to be used in MMF systems. In this dissertation, the transmission of OFDM-UWB radio signals along multimode fibers in in-building networks is analysed. A numerical model of the fiber modal dispersion is developed in MATLAB. The transmission performance of the OFDM-UWB signals along the MMF is evaluated using the semianalytical Gaussian approach and the maximum distance for single and multi-band transmission is assessed. The numerical results show that the maximum transmission distance is very dependent on the power launching conditions, on the fiber length and on the number of sub-bands used. The maximum assessed distance ranges from 560 m (achieved for single-band transmission and for centred launching conditions) to less than 50 m (achieved for simultaneous transmission of 3 OFDM-UWB sub-bands and for centred launching conditions).
Automatic Construction of Measured Property Knowledge Bases through Multi-Turn Question Answering

Data points such as compressive strengths of materials or patient lab results are often reported in scientiﬁc literature. Though databases of such data exist, they are typically curated manually. While automated extraction of measured quantities, such as 17.2 mV, is straightforward, it is much more challenging to capture information about such quantities, such as which property of which entity is being measured. Additionally, further context may be required identifying conditions, such as temperature or pressure, under which a measurement was performed. This information on property, entity, and context is needed to place the measurements into a database, but little attention has been given to the problem. We start to address this information extraction problem by applying machine comprehension and question answering techniques. To our knowledge, our research is the ﬁrst to apply neural language models to extracting such properties and entities. We found that we can do so with a very small investment in training data generation. We develop a multi-turn question answering solution, where initial questions provide answers that are plugged into templates for follow-on questions. We use existing QA datasets, SQuAD and DROP, to do most of the training of our model and measure the eﬀect of adding diﬀerent amounts of the gold data. Adding gold data representing only 0.1% of the SQuAD data, a SciBERT-based QA model’s exact match and partial overlap accuracies improve from . 32 and . 47, respectively, to . 42 and . 57. We improve substantially over the state-art baseline (GROBID-quantities).
Massive MIMO Processing for 5G Systems: Efﬁcient Detection Algorithms and Applications

—Fifth generation wireless networks aim at meeting the requirements imposed by the ever increasing demand in capacity. One of the considered proposals is the extension of traditional multiple-input multiple-output (MIMO) systems to the use of a massive number (a few dozens to hundreds) of antennas. Even though promising, this technology brings some challenges that must ﬁrst be surpassed. One of such problems is the increase in signal processing complexity, which might become unbearable when conventional techniques are used. Hence, one is to ﬁnd methods that perform as close to optimal as possible, while yielding low complexity. In this work, a special emphasis on large MIMO suitable algorithms to perform detection and precoding is given. Particularly, for the case where the number of antennas at the base station is much larger than the number of served users, an effective linear processing method based on the Neumann series and on the matrix inversion lemma is proposed. Further, randomised algorithms to perform detection in symmetric massive MIMO systems are thoroughly studied. Namely, an optimised Gibbs sampling variant method is suggested and shown to outperform the existing ones in the literature, including state of the art lattice reduction methods. Finally, capitalising on the advantages provided by large-scale arrays, a massive MIMO enabled full-duplex relay setup is evaluated. This includes the design of transmit and receive linear ﬁlters that, using a link dependent optimal power allocation scheme, is able to attain high energy efﬁciencies, whilst satisfying individual throughput requirements. In all of the proposed algorithms, both hardware feasibility and suitability for parallel computation have been taken into consideration. The results herein presented include illustrative examples and insightful numerical simulations.
Keyword Search in Large-Scale Databases with Topic Cluster Units

: To solve the inefficiency of the existing keyword search methods in large databases, this paper proposes TCU-based query, an offline query method based on topic cluster units. First, topic cluster units (TCUs) are constructed through vertical grouping and horizontal grouping on tables and tuples. In contrast to traditional keyword query methods, this offline method cannot only reduce the query response time, but also return results comprising richer and more complete semantic information. In order to further improve the efficiency of data preprocessing, an optimized solution for table join ordering based on the genetic algorithm is presented. Second, we select index terms using the association rule, and then we build an index on every topic cluster; by doing so we can improve the query speed significantly. Finally, we conduct extensive experiments to demonstrate that our approach greatly improves the performance of keyword search.
Persuasion in Negotiation and Confl ict Resolution

The focus of this chapter is on persuasion and attitude change in negotiation, mediation, and confl ict resolution. Persuasion refers to the principles and processes by which people ’s attitudes, beliefs, and behaviors are formed, modifi ed, or resist change in the face of others ’ attempts at infl uence. These attempts are designed to convince targets of persuasion to accept a position on some issue that differs from their current position. Importantly, persuasion is distinct from coercion in that persuasion involves infl uence designed to change people ’s minds, whereas coercion involves infl uence designed to change people ’s behavior (with little regard for whether they have actually changed their minds). For example, in a confl ict between labor and management, company employees might attempt to persuade the managers to raise wages by pointing out that higher wages will increase motivation and commitment among workers, thereby benefi ting the company as a whole. Alternatively, they might attempt to coerce the managers to raise wages by threatening to strike if their demands are not met. Research on social infl uence has established that if public compliance is not accompanied by private acceptance (in this case, truly believing that there is good reason to raise wages), the outcomes of infl uence are typically ephemeral and unstable. (See Eagly and Chaiken, 1993.) Persuasion is therefore a critical tool in creating lasting settlements between parties in confl ict. Although participants in negotiations often bring an impressive amount of implicit knowledge to the confl ict resolution setting, an increased understanding of the principles and processes that underlie persuasion can help improve the processes and outcomes of a negotiation. In this chapter, we review major theories and fi ndings in the fi eld of persuasion, summarize relevant research in negotiation and intergroup settings, and discuss practical implications for confl ict resolution.
Optimization of the Production Process of Lallemand Ibéria S.A. to Reduce Electric Energy Costs Using a Genetic Algorithm

Lallemand Ibéria S.A. is a yeast producing company and great part of its expenses come from electric energy. Therefore it is important search for inefficient energy practices and look for possible solutions to solve them. The fermentation process of the company is one of the areas that has the potential to see their energy consumption costs reduced and it will be the focus of this thesis. Yeast development is guaranteed by adding nutrients, treacle and by pumping air into the fermenters with the help of air compressors. The fermentations follow weekly plans made manually by the production manager and, since he does not take into account the different periods present in the electric tariff, sometimes peak consumption coincides with super peak hours, greatly increasing the energy costs. Plus, the company has not done any research in what should be the best tariff to contract. Therefore, given the complexity of the problem, the whole process was characterized and modelled into a simulation program, which also has a genetic algorithm. By using this program the company can decrease electric energy expenses, moreover when the investment cost is zero. Savings are reached if Lallemand switches from the current contracted tariff (weekly cycle) to the tariff with the optional weekly cycle and those savings will be even higher if the optimization algorithm is used to optimize the weekly production plans.
MINIMIZING POLYNOMIALS ON COMPACT SETS

In this paper, the problem of minimizing a polymonial g∗ = inf x∈S(F ) g(x) in the compact case is investigated. It is known that such problem is severely illposed. We use results of positive performed theorems of Putinar ([8]) and Schmüdgen ([9]) to solve it. A numerical example is given to illustrate the efficiency of the proposed method works. 2010 Mathematics Subject Classification: 30C45.
DroneSensor - drone-based sensor data collection

—Delay Tolerant Networks result from the need to transfer information on wireless mobile networks in situations where traditional networks tend to fail. The main characteristic of these networks is the lack of continuous communication between their nodes and the difﬁculty of ensuring the delivery of the information to the recipient. This type of network is used especially in situations where the mobile nodes of the network are widely dispersed. This thesis proposes an implementation of a low cost system that allows the acquisition of sensor data installed in areas without infrastructure, using drones.
Robot grasping in 3D through efﬁcient haptic exploration with unscented Bayesian optimization and collision penalty

—Robust grasping of arbitrary objects is a major, and still unsolved problem in robotics. Global 3D information about the object can be obtained from previous knowledge ( e.g. , accurate models of known objects or approximate models of familiar objects) or real-time sensing ( e.g. , partial point clouds of unknown objects) and can be used to identify good potential grasps; however, due to modeling inaccuracies and sensing limitations, local exploration is often needed to reﬁne such grasps and to successfully apply them to the objects in the real world. Notably, the recently proposed unscented Bayesian optimization technique can make such exploration safer by favoring the selection of grasps that are robust to uncertainty in the input space ( e.g. , inaccuracies in the execution of the grasp). Extending the previous work on 2D optimization, in this paper we propose a 3D haptic exploration strategy that combines unscented Bayesian optimization with a novel collision penalty heuristic to ﬁnd safe 3D grasps in a very efﬁcient way: while by increasing the search-space from 2D to 3D we are able to ﬁnd better grasps, the introduction of the collision penalty heuristic allows to do so without having to increase the number of exploration steps.
The impact of positive views on language learning and pupils’ construction of L2: Imagined communities, possible selves, and investment in language learning

This paper is based on data drawn from PhD research investigating the relationship between language learning, identity, culture, and motivation. It specifically describes how a group of Arab Muslim female English as second language (ESL) pupils are motivated by their imaginings of the English language speaking community. In Saudi Arabia the education system concentrates on teaching, while identity and social aspects have only been given little attention. I seek to redress this balance by exploring the impact of pupils‟ positive imaginings of the linguistic communities and of themselves in the future as members of those communities. I show how this investment works to promote their language learning. I use the concepts of imagined communities, possible selves and investment to illustrate the relationship between language and identity and how they impact each other. Further, I utilise sociocultural theory to explore how participants negotiate their identities as female Muslim Arabs who desire to be members of the imagined linguistic communities. In this presentation I illustrate my arguments using data drawn from focus group and one to one interviews, and students‟ text messages, photos and drawings.
An examination of the theoretical basis for the underlying processes of mindfulness in the treatment of depression and chronic pain

2 1.0 Introduction and motivation for choice of topic . 3 2.0 Problem formulation, delineation of the thesis, and conceptual clarification . 5 2.1 Problem formulation . 5 2.2 Structure of the dissertation . 5 2.3 Delineation of the thesis and conceptual clarification . 6 2.4 Positioning the research question in relation to the broader scholarly literature . 8 3.0 Theoretical basis for the mechanisms of change in MBCT in relation to depression . 11 3.1 MBSR and MBCT . 12 3.2 Cognitive therapy . 12 3.3 Schemas . 14 3.4 Teasdale’s Model of Interacting Cognitive Subsystems (ICS) . 17 3.5 Shapiro’s model of mindfulness. 28 4.0 Research in the area of MBSR and chronic pain . 34 4.1 The significance of studying mindfulness in the treatment of chronic pain . 34 4.2 Effects of MBSR in relation to chronic pain: Review of 3 studies . 35 4.3 Validity and reliability of the reviewed studies . 41 5.0 MBSR in the treatment of chronic pain: A discussion . 44 5.1 Nicholas’ model of chronic pain . 45 5.2 An application of the ICS to the area of MBSR and chronic pain . 48 5.3 An application of Shapiro’s model to the area of MBSR and chronic pain . 54 5.4 The process of mindfulness. 59 5.5 Limitations . 61 6.0 Conclusion . 64 7.0 Future directions . 65 References . 68
Nurse Staffing Levels and Proportion of Hospitals and Clinics Meeting the Legal Standard for Nurse Staffing for 1996~2013

Purpose: To analyze the proportion of medical institutions meeting the legal standard for nurse staffing. Methods: Data collected from 29,282 institutions between 1996 and 2013 were analyzed. Nurse staffing was measured as daily patient census per registered nurse (RN). The standard for general hospitals, hospitals, and clinics is 2.5 or less, and that for long-term care hospitals is 6.0 or less of the daily patient census per RN. Clinics may substitute nursing assistants for RNs by 50% or 100% depending on their daily inpatient census; long-term care hospitals may substitute nursing assistants for RNs by two thirds of the required number of RNs. Results: The proportion of general hospitals, hospitals, clinics, and long-term care hospitals meeting the standards was 63%, 19%, 63%, and 94%, respectively, in 2013. While general hospitals had an increase in the proportion during the 1996-2013 period, small changes were found in hospitals and clinics. In 2013, nurses were estimated to care for 16 (interquartile range: 12~24) patients per shift in general hospitals. Three quarters of clinics had no RNs in 2013. Conclusion: Many medical institutions did not meet the legally mandated minimum staffing level. The government must implement policy actions for all medical institutions to meet the legal standards.
Dispositivos Fotnicos Passivos para ptica Integrada

he main objective of this paper is to present a complete systematic framework for the electromagnetic modelling of passive, fieldcontrolled, integrated optic devices which are based on the linear electro-optic Pockels’ effect [1]-[8]. The study of the electro-optic effect is mainly focused on its application to the modulation of light wave beams. Thus, a rigorous description of this effect is adopted and expressions describing the variation of the ordinary and extraordinary refractive indexes with time-variable external electric field amplitude are presented [1], [6]. As an example of application, a lithium niobate crystal is considered and its electro-optic tensor and respective index ellipsoid are used. The coupled-mode theory is also addressed in this paper, with a complete full-wave analysis of two identical parallel optical waveguides being presented [9]-[11]. The main objective was to study the coupling effect between two interactive parallel waveguides, and show how the mutual coupling influences the mode propagation in each one of the waveguides when they are far-away separated. It is concluded that, in the vicinity of the two waveguides, a supermode analysis must be carried on in order to rigorously describe the power flowing from one waveguide to another [5]. One should note that, this effect only occurs for a certain minimum separation distance between the two waveguides, since the amplitude of the evanescent fields rapidly decreases away from the waveguides. Two types of modal coupling have been considered: (i) Co-directional coupling; (ii) Contra-directional coupling. The power flux variation along both waveguides is presented. A comprehensive study of several types of electro-optic modulators based on the Mach-Zehnder interferometer is presented [9]-[11]. This study it essentially focused on the amplitude modulators, controlled by an external electric field which imposes the power flux distribution along the transversal section of the circuit. The two input signals are mutually influenced through the electro-optic effect, giving rise, with this interaction, to an amplitude modulated signal at the output. It is shown how the destructive and constructive interference between the two signals is responsible for the amplitude modulation of the output signal. To optimize the modulator operation it is important to choose the correct bias voltage amplitude.
Deep Learning for Multi-Label ICD-9 Classiﬁcation of Hospital Discharge Summaries

—Hospital discharge summaries are composed of free-text and drop-down textual ﬁelds, describing relevant information about the patient’s hospital stay and treatments. The variability of medical vocabulary makes the processing of these records an important natural language processing challenge. We leverage a deep neural network that combines word, word-type and character embeddings, recurrent and convolutional units and neural attention, for the generation of intermediate representations of the textual contents. The neural network also explores the hierarchical nature of the input data, by building representations from the sequences of words within individual ﬁelds. An auxiliary memory mechanism is also leveraged for providing an a priori prediction of classes, being provided as input. The model is evaluated on the automatic assignment of ICD-9 codes to diagnosis on discharge summaries from both Hospital Beatriz ˆAngelo (
Calcium Phosphate-Chitosan Compositeas a Bone Cement Candidate

A research about study of surface chemistry and morphology of bone cement from calcium phosphate-chitosan composite has been conducted. Surface chemistry and morphology of an artificial bone is directly influence to the osteoinductivity property. Calcium phosphate compounds as powder phase was reacted to liquid phase that contains ofNa2HPO4 solution and chitosan. Analysis instruments such as XRD and SEM-EDS were used to identify surface chemistry, morphology and structural transformation of cement. Setting time and hardening time were tested to know the feasibility of cement when used in minimally invasive surgery technique. The results showed that setting time of calcium phosphate cement became shorter as well hardening time became longer by the increasing of liquid/powder (L/P) ratio and chitosan concentration. SEM image of calcium phosphate showed cuboid crystal shape. The transformation of calcium phosphate into CPC and composite were not changing it structure and morphology. The change only happen on calcium phosphate surface that caused by deposition of phosphate ions. It can be concluded that calcium phosphate – chitosan composite has osteoinductive property and can be used as bone cement.
A minimally invasive surgical system for 3D ultrasound guided robotic retrieval of foreign bodies from a beating heart

The result of various medical conditions and trauma, foreign bodies in the heart pose a serious health risk as they may interfere with cardiovascular function. Particles such as thrombi, bullet fragments, and shrapnel can become trapped in a person’s heart after migrating through the venous system, or by direct penetration. The severity of disruption can range from benign to fatal, with associated symptoms including anxiety, fever, cardiac tamponade, hemorrhage, infection, embolism, arrhythmia, and valve dysfunction. Injuries of this nature are common in both civilian and military populations. For symptomatic cases, conventional treatment is removal of the foreign body through open surgery via a median sternotomy, the use of cardiopulmonary bypass, and a wide incision in the heart muscle; these methods incur pronounced perioperative risks and long recovery periods. In order to improve upon the standard of care, we propose an image guided robotic system and a corresponding minimally invasive surgical approach. The system employs a dexterous robotic capture device that can maneuver inside the heart through a small incision. Visualization and guidance within the otherwise occluded internal ii
NONLINEAR SYSTEM IDENTIFICATION OF AUTONOMOUS MARINE VEHICLES

The wealth of applications for autonomous marine vehicles is a great motivation for research into control and navigation strategies. The knowledge of a model of the system is at the heart of many of the methodologies employed in these areas. The goal of this thesis is to develop methods for obtaining models for Autonomous Marine Vehicles. We will focus on models classed as “offwhite”, i.e., based on first principles physical modeling but with a stochastic component and parameters to be identified through experimentation. In order to achieve the stated goal we will model not only the vehicle dynamics but also its sensors and actuators. We will also design and run the experiments necessary for the system identification procedure. Finally Maximum Likelihood Methods are applied in order to extract the parameters of the models from the experiments conducted. With the objective of assessing the validity of the proposed estimators and methodologies, extensive simulation studies are also carried out. Finally, even tough navigation or rather state estimation is not the chief concern of this thesis, we will also develop navigation filters as it is a required step for some of the methods here presented. Date: April 2015.