Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review on Circuit Simulation Techniques of Single-Event Transients and their Propagation in Delay Locked Loop

ABSTRACT Electronic circuits operated in radiation environment such as space are adversely affected by the ionizing radiation. The effects include generating a transient current, altering threshold voltage and other device characteristics, and lattice damage. Of these, transient current can result in immediate change in device/circuit behaviour resulting in malfunction. This will be recoverable based on the energy and type of the ion. Thus, a better understanding of single-event effects is very important to operate the electronic circuits in the radiation environment. This paper reviews the existing methodologies to simulate the propagation of single-event transient (SET) in digital circuits and their shortcomings. An improved simulation methodology which can reproduce even the tail portion of the SET current as observed in three-dimensional technology computer aided design (TCAD) mixed-mode simulations is also proposed in this paper. This is obtained by modifying an existing simulation technique based on switch and resistor using varying resistance. The proposed technique is simple and easy to implement the SET propagation in any circuit simulator. After integrating this proposed simulation technique in circuit simulator SPECTRE, the propagation of SET, and its effects in delay locked loop (DLL) are analysed. The strike of SET in voltage-controlled delay line (VCDL) results in duty cycle error and missing clock pulse error at the DLL output. Also, the results show that the first few stages of the VCDL are more vulnerable than the rest for LETs (linear energy transfer) more than 60 MeV-cm2/mg. For LETs less than 60 MeV-cm2/mg, last delay stage has more vulnerability.
Non-local Social Pooling for Vehicle Trajectory Prediction

For an efficient integration of autonomous vehicles on roads, human-like reasoning and decision making in complex traffic situations are needed. One of the key factors to achieve this goal is the estimation of the future behavior of the vehicles present in the scene. In this work, we propose a new approach to predict the motion of vehicles surrounding a target vehicle in a highway environment. Our approach is based on an LSTM encoder-decoder that uses a social pooling mechanism to model the interactions between all the neighboring vehicles. The originality of our social pooling module is that it combines both local and non-local operations. The non-local multi-head attention mechanism captures the relative importance of each vehicle despite the inter-vehicle distances to the target vehicle, while the local blocks represent nearby interactions between vehicles. This paper compares the proposed approach with the state-of-the-art using two naturalistic driving datasets: Next Generation Simulation (NGSIM) and the new highD Dataset. The proposed method outperforms existing ones in terms of RMS values of prediction error, which shows the effectiveness of combining local and non-local operations in such a context.
Small-sided games are more effective than instructional training for improving vertical jump performance and passing in young volleyball players

The aim of our research was to determine the effects of small-sided games on explosive strength in young volleyball players. The authors hypothesized that this program might significantly improve explosive strength in adolescent volleyball players. Thirty-eight adolescent volleyball players participated in this study (12.26±1.01). They were tested for lower body explosive power and passing accuracy. Players were randomly allocated into a small-sided games group (n = 20) and instructional training group (n = 18). Each player participated in a 12-week training program that included 3 organized court training sessions per week. Both groups made significant increases in block jump (p < 0.05) after 12 weeks of training, with significant interaction effect between the groups. There were no significant interaction effects between the groups (p>0.05) for spike jump. In SJ and CMJ results revealed a statistically significant difference between groups preto posttraining (p < 0.05). Both groups made significant increases in passing accuracy with significant interaction effect between the groups. Small-sided games appears to be an effective way of improving explosive power and passing skills in young volleyball players. The results of this study indicate that game-based training method was more effective for lower body explosive power than traditional instructional programme.
Map-based localization using LiDAR and deep neural networks

The research on autonomous driving is expanding, and self-driving technology has the potential of transforming not only the transportation system, but our whole society. The ability to localize a vehicle in a map is an important piece of the puzzle in the development of self-driving vehicles. Different kinds of sensors can be used to detect the world around the vehicle. A suitable sensor in this case can be a LiDAR, which measures distances to objects using lasers. With this information we can create a map of the vehicle’s surroundings that can be compared to a known map to find the vehicle’s position. This thesis investigates methods of localizing a vehicle in an a priori known map using a LiDAR sensor and neural networks. The LiDAR sensor yields a point cloud representation of the vehicle’s surroundings and the data used in this project is collected from the simulation environment CARLA. The neural networks use the point clouds to predict how much we need to adjust an initial guess of the vehicle’s position in the map to get closer to the true position. The thesis investigates two different network approaches using regression; one type of network that uses 2D LiDAR Bird’s Eye View images as input, and another type of network that uses LiDAR point clouds as input. The results show that pure translation can be found using regression by both types of neural networks, while rotation errors are more difficult to predict correctly. While none of the networks can compete with state-of-the-art methods, this thesis shows that neural network regression might have the potential to solve the localization task on its own.
Mathematical models for evolution of genome structure

Suja Thomas: Mathematical Models for Evolution of Genome Structure. (Under the direction of Todd J. Vision.) The structure of a genome can be characterized by its gene content. Evolution of genome structure in closely related species can be studied by examining their synteny or conserved gene order and content. A variety of evolutionary rearrangements like polyploidy, inversions, transpositions, translocations, gene duplication and gene loss degrade synteny over time. In this dissertation, I approach the problem of understanding synteny in genomes and how far back its evolutionary history can be traced in multiple ways. First, I present a probabilistic model of the rearrangements gene loss and transposition (gain) and apply it to the problem of estimating the relative contribution of these rearrangements within a set of syntenic genome segments. This model can be used to predict gene content in syntenic regions of unsequenced genomes. Next, I use optimization methods to recover syntenic segments between genomes based on reconstructions of their parent ancestry. I examine how these reconstructions can be used as input to programs that identify syntenic regions in genomes to reveal more synteny than was previously detected. I use simulations that incorporate each of the evolutionary rearrangements described above to evaluate the models presented in this dissertation. Finally, I apply these models to genomic data from yeast and flowering plants, two eukaryotic systems that are known to have experienced polyploidy. This application is of particular relevance in flowering plants, in which a lot of economically and scientifically important polyploid species have incompletely sequenced genomes.
Investigation of a PV fed Improved Smart Home EV Battery Charging System using Multi Output Hybrid Converter

Power converters used in the existing smart home electric vehicle (EV) battery charging systems are capable of feeding only dc power. Whereas, this paper proposes the usage of hybrid converters in such systems as they are used for supplying power to both ac and dc loads simultaneously. Among various hybrid converter topologies, boost derived hybrid converter (BDHC) and quadratic boost derived hybrid converter (QBDHC) are analyzed in this paper. A photovoltaic (PV) array fed smart home system using BDHC and QBDHC is proposed to provide both dc power and ac power simultaneously and their performances were compared in this paper. The interfacing of multiple outputs is accomplished using time division multiplexing of power generated from PV array. In the proposed system, the hybrid converter is designed by replacing single switch of conventional boost converter by voltage source inverter (VSI). It has higher reliability in terms of power density and has inherent shoot-through protection during inverter operation. It is best suited for home applications which require support for both ac and dc loads. The system is designed, modeled and simulated using PSIM software and a laboratory prototype is developed and tested. Resemblance between the simulated and experimental results validates the efficaciousness of the proposed system.
Investigation of the Effect Different Antenna parameters (Height, Tilt, and Power) on Network Coverage and System Capacity

The, prospective of radio, mobile network, topology planning, would be to, provide a configuration, that offers the, necessary coverage, several services, as well as, simultaneously, enhances the system, coverage and capacity. This work, addresses the effect, of antenna height, antenna, tilt and power, on network, and network capacity. Furthermore, the effects of the above mentioned elements had been investigated using MATLAB program. Appropriately the goal of the actual investigation is always to have because high signal strength as is possible in the area in which the cell ought to be serving traffic. Beyond the particular serving part of the cell, typically the signal power should be low so as to fight the problem associated with fluctuation within received sign strength through the mobile customers in a cellular. Results of often the investigation display that greatest coverage is actually obtained in 38m elevation, 46dB strength and 2° tilt.
M-learning in Foreign Language Learning

Teachers nowadays have much more roles compared to that of prE-internet period. As an English teacher, how to help and support students to achieve the maximum learning result are always his top concerns. The purpose of this paper is to demonstrate that MobilE-Assisted Language Learning (MALL) in general and smartphone usage in particular, is capable of supporting applications based on a variety of learning theories, especially constructivism and methodological approaches. The paper aims at introducing the experience of language teaching and learning through the means of social networking devices, providing some practical solutions to the problems existing in Chinese foreign language teaching of higher education, sharing the experience, observation, concrete approaches in the course of College English and ESP course for non-English-major postgraduate. It is hoped that the strategies employed in the course of language instruction can offer some constructive insights for English teachers and students.
Polycystin-2, the protein mutated in autosomal dominant polycystic kidney disease (ADPKD), is a Ca2+-permeable nonselective cation channel

Defects in polycystin-2, a ubiquitous transmembrane glycoprotein of unknown function, is a major cause of autosomal dominant polycystic kidney disease (ADPKD), whose manifestation entails the development of fluid-filled cysts in target organs. Here, we demonstrate that polycystin-2 is present in term human syncytiotrophoblast, where it behaves as a nonselective cation channel. Lipid bilayer reconstitution of polycystin-2-positive human syncytiotrophoblast apical membranes displayed a nonselective cation channel with multiple subconductance states, and a high perm-selectivity to Ca2+. This channel was inhibited by anti-polycystin-2 antibody, Ca2+, La3+, Gd3+, and the diuretic amiloride. Channel function by polycystin-2 was confirmed by patch-clamping experiments of polycystin-2 heterologously infected Sf9 insect cells. Further, purified insect cell-derived recombinant polycystin-2 and in vitro translated human polycystin-2 had similar ion channel activity. The polycystin-2 channel may be associated with fluid accumulation and/or ion transport regulation in target epithelia, including placenta. Dysregulation of this channel provides a mechanism for the onset and progression of ADPKD.
Design of Cascaded Modular Multilevel Converter for Grid Connected PV System

Today renewable energy sources are becoming more popular with ever increasing energy demand and environmental pollution due to non-renewable energy sources. Among the renewable energy sources, major portion of energy is generated using Photovoltaic system. Grid connected photovoltaic systems are gaining more importance. Large-scale grid connected PV systems can be easily and effectively interfaced with the modular multilevel converters when compared with conventional DC-DC converters. As modular multilevel converters have unique advantages like enhanced energy harvesting capability, scalability etc. they can be used for many applications such as STATCOM, harmonic compensator and so on. PV systems face tough challenges such as output voltage over modulation and power flow control due to irregular output voltages from PV arrays. This paper proposes a decoupled active and reactive power control method to improve the system performance. A 3-MW, 12kV PV system with the proposed control strategy is modelled and the results are verified using MATLAB simulation.
7.5 Declaration in Apple vs. Microsoft/Hewlett-Packard

3. The Macintosh “Look and Feel. ” To understand the distinctive appearance of the Macintosh interface, known as the Macintosh “look and feel” or “the Macintosh look”, one needs to consider not only the individual elements that make up the appearance of the interface but also the way those elements are arranged and interact with one another to create the consistent and distinctive Macintosh interSparks of Innovation in Human-Computer Interaction, B. Shneiderman, Ed., Ablex Publ. , Norwood, NJ (1993)
Wearable Technologies in Academic Information Search

Wearable technologies and appliances are making inroads into a variety of consumer and commercial applications, including leisure and entertainment, health care, and gaming. A natural evolution of the technology is in academe where faculty and students have begun exploring the possibilities of the technology in a variety of settings, most visibly in libraries where the process of seeking information using such devices holds significant promise. This chapter provides an inventory of the state of wearable technology, its challenges, its possibilities, and how it might be used in academe, including a study regarding the ability to access common library indexes using two wearable appliances: Google Glass and a smart watch.
Comparison of Two New Data Mining Approach with Existing Approaches

This study studies two uncertainty data mining approaches and gives the two algorithms implementation in the software system fault diagnosis. We discuss the application comparison of the two data mining approaches with four classical data mining approaches in software system fault diagnosis. We measure the performance of each approach from the sensitivity, specificity, accuracy rate and run-time and choose an optimum approach from several approaches to do comparative study. On the data of 1080 samples, the test results show that the sensitivity of the fuzzy incomplete approach is or so 95.0%, the specificity is or so 94.32%, the accuracy is or so 94.54%, the runtime is 0.41 sec. Synthesizing all the performance measures, the performance of the fuzzy incomplete approach is best, followed by decision tree and support vector machine is better and then followed by Logistic regression, statistical approach and the neural networks in turn. These researches in this study offer a new thinking approach and a suitable choice on data mining.
A Brief Introduction to Natural Language Generation within Computational Creativity

This paper is an introduction to The 3rd Workshop on Computational Creativity in Language Generation (CC-NLG 2018), collocated with The 11th International Conference on Natural Language Generation (INLG 2018). The workshop acts as an overview of projects from the Computational Creativity ﬁeld present within the general ﬁeld of Natural Language Generation, and references further reviews within the area. Accepted papers to the work-shop will use contemporary NLG meth-ods or approach NLG problems from a creative perspective. The workshop intends to showcase creative applications as a worthwhile and meaningful pursuit to other NLG researchers.
Impartial Intersection Restriction Games

Intersection restriction games are games played on hypergraphs in which options for a player are restricted based on previous play via some intersection property. This paper focuses on two games within this class: Arc-Kayles and a Triple Packing game. Arc-Kayles is a game where, on their turn, players remove an edge and all adjacent edges from a graph. Together, players are forming a maximal matching. The Triple Packing game is a combinatorial design game where players are choosing triples such that no two triples chosen share a pair. Both games are played under normal play. We give new results for Arc-Kayles played on a special star graph and the wheel graph as well as partial results for the Triple Packing game played on the complete graph.
A Time-Series Database Analysis Based on a Multi-attribute Maturity Model

: Time Series Database (TSDB), is a particular type of data repository. TSDBs are capable of diverse functionalities regarding operations on time series data and are developed using different technologies. A large number of TSDB solutions, available both as open source and commercial software, has emerged in the last years. Selecting the proper TSDB is a challenging endeavour for a potential enterprise client. In this paper, a set of open-source TSDBs which includes InfluxDB, Graphite, RRDTool, Prometheus, OpenTSDB and TimescaleDB - has been selected for analysis, evaluation and ranking. A comparison of TSDBs implies the establishment of a set of attributes. A set of quantitative and qualitative attributes which have different scales and units of measure has been selected. The problem of selecting or ranking TSDBs evaluated in this paper with a set of attributes is a Multi-Attribute Decision Making problem (MADM). For solving the TSDBs evaluation, analysis and selection a multi-attribute TSDBs maturity model has been proposed. The model for the selected TSDBs set and for the 18 attributes, 10 quantitative and 8 qualitative, has been validated. The model for all the attributes, both quantitative and qualitative, has been solved and, finally, a comparison between the obtained rankings has been made. The comparison of the ranks obtained shows that the leading TSDB is InfluxDB.
Vital-sign data-fusion methods to identify patient deterioration in the emergency department

In the United Kingdom Emergency Departments (ED), clinical staff requires to diagnose, give treatment and discharge patients, within 4 hours of their arrival. The patients’ vital signs are traditionally managed using paper Track and Trigger (T&T) charts, prone to human error, and bedside monitors, whose alerts are often ignored. Consequently, patient deterioration might be missed at and between nurses’ observation sets. This thesis has analysed data from a three stage study in the ED of the John Radcliffe Hospital, Oxford, to investigate the use of an electronic T&T system (VitalPac) and a data-fusion system (Visensia) to help staff identify physiological deterioration in patients attending the majors area. Data was collected from a total of 10,488 ED attendances receiving standard care in stage 1, followed by two technology interventions in stages 2 and 3, respectively, for a total period of 6 months. It was shown that 9% of the observations sets, conducted on stable ED patients in stage 1, were done on unstable patients when staff was guided by VitalPac in stage 2. One of the causes might have been the increase in the Early Warning Score (EWS) completion from 52% to 100% of the observation sets. In stage 3, 35.7% of the Visensia alerts generated on continuous bedside monitor data, were deemed “technical” alerts due to data artefacts. On the other hand, clinical staff responded within 15 minutes to 85% of the “physiological” alerts. A two-stage Machine Learning (ML) architecture was proposed to fuse intermittent and continuous vital-sign data and use a sub-population novelty detection model to identify multivariate data deviating from normal physiology. This ML approach out-performed the baseline Visensia model (a populationbased model, applied over the continuous data), and the National EWS system (applied over the observation sets data) in detecting patients escalated to the Resuscitation area during their ED stay, on a test set of 1,070 ED attendances (AUROCs and 95% confidence intervals were 0.737 (0.623, 0.830), 0.657 (0.521, 0.755), and 0.643 (0.522, 0.749), respectively). External contributions of code/software My colleague, Dr. Marco Pimentel, implemented the original code for the multi-instance Gaussian process (MIGP) algorithm. Dr. David Wong helped with developing a semi-automatic Matlab script to match the continuous vitalsign data to the study participants. Prof. David Clifton provided the Microsoft Access template for the doubleand single-entry transcription of the paper-based T&T charts data into electronic means. The various software packages that were used in this thesis, are cited in the correspondent section in each chapter. The remaining materials described in this thesis are a result of my own work. List of Publications The following lists all publications produced over the course of this DPhil.
The Living Wall: Implementing and Interpreting Pedagogical Documentation in Specialized ELCC Settings

The program approach in specialized early learning programs may result in adults focusing too intently on therapeutic treatment reports and disabilities and not on the stories children share through play. Observing, listening, and documenting play scripts can shift adults’ focus and make the self of children and their interests visible. Exploring different ways of documenting children’s experiences in specialized educational settingswas the impetus for this paper. The Living Wall—a form of pedagogical documentation—aligns with Ted Aoki’s notion of a “curriculum-as-lived-experience” that honours children and gives them a voice through the collection and interpretation of their artwork, dialogue, and imaginative play scripts.