Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-Loss Waveguides by Planar Modified Chemical Vapor Deposition

We present a novel method to fabricate planar photonic devices for high power applications using modified chemical vapor deposition (MCVD) technology. The MCVD process, usually applied to optic fibers, is used here to deposit a layer of Ge doped silica on a fused silica slab. The doped layer was then heat-treated by a CO2 laser in order to achieve high optical quality and increase its damage threshold. Finally, direct laser lithography, followed by deep dry etching, was used to shape straight and curved large-core air-cladded waveguides with smooth sidewalls. The straight waveguides were shown to have an excellent propagation loss, 0.33 dB/cm, and the curved waveguide had a propagation loss of 1 dB/cm.
Texture Synthesis in a Weighted Graph Using Nth-Order Voronoi Diagrams

We present a method to procedurally generate textures at variable resolutions using weighted graphs. Our method takes a number of input points that exist within a grid-like graph. Using the input points as site node the algorithm then assigns all the nodes in the graph to Voronoi regions by using geodesic rather than Euclidian distance. The edge weights are calculated by comparing properties of the target nodes with the site nodes of the respective regions. We have generated a number of variations of our basic algorithm that use this same mechanism to generate a range of textures. Our method allows us to chain multiple algorithms. The output of one algorithm to be the input for a successive algorithm variation and successive outputs can be combined in a variety of ways.
Adaptive Step Length Selection in Gradient Boosting for Generalized Additive Models for Location, Scale and Shape

Generalized additive models for location, scale and shape (GAMLSS) are an approach that regresses not only the expected mean value as the conventional generalized additive models but also other distribution parameters. Fitting the GAMLSS with boosting algorithm allows simultaneous estimation of predictor effects and variable selection. The noncyclical componentwise gradient boosting approach reduces the optimizing procedure from a multi-dimensional to a one-dimensional problem with vastly decreased complexity. Tuning in boosting algorithm relies mainly on the number of iterations of the algorithm. The other flexible component step length in most cases is set to 0.1. When developing complex models like GAMLSS, this setting will lead to unbalanced decisions. This thesis studied the influence of the adaptive step length on this balance and other performance measures. Based on the simulation study, the adaptive approach usually updates the distribution parameters in a balanced manner. Within the limited number of boosting iterations, the adaptive approach will also lead to better estimations than the fixed step length settings, especially when the coefficients are huge. When fitting the high dimensional data, the adaptive approach is more efficient in computing. This thesis also introduced a semi-analytical adaptive step length (SAASL) algorithm for the Gaussian distribution, which is faster and more stable in balance than the adaptive step length found by doing a line search. Based on the mathematical induction, the optimal step length of the scale parameter in Gaussian distribution converges to 0.5. Applying this step length to the SAASL will result in a much more faster algorithm (SAASL05) at the cost of slightly unbalanced decisions. Because of the aggressive step length in each iterations, the adaptive approaches cannot good estimated the correlated models. Keywords— GAMLSS, gradient boosting, componentwise gradient boosting, adaptive step length, semi-analytical adaptive step length Acknowledgement Firstly, I would like to express my sincere gratitude to my supervisor Prof. Dr. Sonja Greven for her support of my thesis for her patience, motivation and immense knowledge. Her advice gave me a lot of inspiration. Besides my supervisor, I would like to acknowledge Dr. Elisabeth Waldmann and M.Sc. Tobias Hepp, Faculty of Medicine, Friedrich-Alexander-Universität Erlangen-Nürnberg. They listened to my ideas patiently and directed me with lots of valuable advice. I would like to thank Dr. David Rügamer, Department of Statistics, Ludwig-MaximiliansUniversität München for his encouragement and help. I would also like to thank my friends Miao Liu and Yingxin Cai for their support and encouragement. They make my life more colourful. Finally, I must express very profound gratitude to my parents and my grandma for providing me with unfailing support and continuous encouragement throughout my years of study and through the process of researching and writing this thesis. You are miracles in my life. Thank you. CONTENTS
Kinesthetically Augmented Mid-Air Sketching of Multi-Planar 3D Curve-Soups

We present haptics-enabled mid-air interactions for sketching collections of three-dimensional planar curves — 3D curve-soups — as a means for 3D design conceptualization. Haptics-based mid-air interactions have been extensively studied for modeling of surfaces and solids. The same is not true for modeling curves; there is little work that explores spatiality, tangibility, and kinesthetics for curve modeling, as seen from the perspective of 3D sketching for conceptualization. We study pen-based mid air interactions for free-form curve input from the perspective of manual labor, controllability, and kinesthetic feedback. For this, we implemented a simple haptics-enabled workflow for users to draw and compose collections of planar curves on a force-enabled virtual canvas. We introduce a novel force-feedback metaphor for curve drawing, and investigate three novel rotation techniques within our workflow for both controlled and free-form sketching tasks.
Bondi-Hoyle-Lyttleton Accretion of Cold and Self-Interacting Dark Matter

The accretion of a compact object is explored in a Bondi-Hoyle-Lyttleton (BHL) accretion geometry where the sourrounding media consists of dark matter (DM). Theoretical expectations for both cases where the medium is collisionless and collisional are examined to obtain a sense of the properties of the BHL wake that forms in each case, including the size of the Hoyle-Lyttleton radius and magnitude of the accretion rate. N-body simulations of the BHL accretion geometry are performed for the case of cold dark matter (CDM), which is collisionless, and self-interacting dark matter (SIDM), which for large cross sections should approach the collisional case. The results of the simulations are analyzed and show significant differences between the two DM models. The results paint a picture of a denser accretion column (wake) close to the compact object in the case of SIDM and a higher fraction of bounded particles which results in a larger accretion rate than in the case of CDM. The CDM wake is in contrast more disperse whereas for SIDM the velocity dispersion is lower and the velocity dispersion profile flatter. These differences can be explained by collisions of the SIDM particles on their trajectory to form the wake, which results in a net loss of kinetic energy of the wake particles. The larger the cross section, the stronger these differences are.
Renyi Differentially Private ADMM Based L1 Regularized Classification

In this paper we present two new algorithms, to solve the L1 regularized classification problems, satisfying Renyi differential privacy. Both algorithms are ADMM based, so that the empirical risk minimization and L1 regularization steps are separated into two optimization problems, at each iteration. We utilize the stochastic ADMM approach, and use the recent Renyi differential privacy (RDP) technique to privatize the training data. One algorithm achieves differential privacy by gradient perturbation, with privacy amplified by sub-sampling; the other algorithm achieves differential privacy by model perturbation, which calculates the sensitivity and perturbs the model after each training epoch. We compared the performance of our algorithms with several baseline algorithms, on both real and simulated datasets, and the experiment results show that, under high level of privacy preserving, the first algorithm performs well in classification, and the second algorithm performs well in feature selection when data contains many irrelevant attributes.
PENERAPAN MEDIA AUDIO VISUAL PADA HASIL BELAJAR MATEMATIKA MATERI OPERASI HITUNG CAMPURAN KELAS IV SD NEGERI 02 DORO

Tujuan penelitian ini adalah untuk mengidentifikasi apakah ada perbedaan hasil belajar matematika sebelum dan setelah penerapan media audio visual materi operasi hitung campuran kelas IV SD Negeri 02 Doro. Jenis penelitian ini adalah kuantitatif. Populasi dalam penelitian ini adalah seluruh siswa kelas IV SD Negeri 02 Doro. Sampel yang diambil adalah seluruh siswa kelas IV Sekolah Dasar yang berjumlah 20 siswa. Teknik pengambilan sampel adalah Non-Probability Sampling yaitu padda jenis sampling jenuh. Data dalam penelitian ini diperoleh melalui instrumen uji soal Pretest dan Posttest . Desain penelitian yang digunakan Pre Experimental Design. Berdasarkan hasil pembahasan dapat disimpulkan bahwa ada perbedaan hasil belajar matematika setelah menggunakan penerapan media audio visual materi operasi hitung campuran kelas IV SD Negeri 02 Doro. Hal ini ditunjukkan dari hasil penelitian pada kelas eksperimen diketahui nilai rata-rata pretest adalah 74,00 sedangkan nilai rata-rata posttest adalah 93,20. Hasil ini diperkuat melalui perhitungan hipotesis menggunakan uji t diperoleh hasil thitung = 7,456 dengan db = N-1 = 20 – 1 = 19 dan taraf signifikan 5% diperoleh ttabel = 2,093. Karena thitung > ttabel, maka H0 ditolak. Sehingga hipotesis pada penelitian ini adalah diterima. Saran yang perlu disampaikan melalui penelitian ini adalah dalam proses belajar mengajar matematika guru hendaknya menggunakan penerapan media audio visual dengan baik sehingga memungkinkan berkembangnya potensi siswa.
Two-Dimensional Pattern-Reconfigurable Cross-Slot Antenna With Inductive Reflector for Electromagnetic Torso Imaging

A wideband and low-profile cross-slot pattern reconfigurable antenna for electromagnetic imaging systems is proposed. The antenna is designed to cover the human chest area with steerable unidirectional radiation patterns at 0.7–0.9 GHz. The antenna is comprised of a corrugated cross-slot as the main radiator, and four parasitic slots with four p–i–n diodes operating as reflectors. An embedded feeding network with six p–i–n diodes is used to feed the main slot and switch the feeding path to modify the direction of the radiated beam (vertical/horizontal). In addition, an inductive mesh-grid surface is used to reduce the back lobe and enhance the operating frequency bandwidth. The antenna dimensions are <inline-formula> <tex-math notation="LaTeX">${0.8} {\lambda }_{0} {\times 0. } {8} {\lambda }_{0} {\times 0.18} {\lambda }_{0}$ </tex-math></inline-formula> (where <inline-formula> <tex-math notation="LaTeX">${\lambda }_{0}$ </tex-math></inline-formula> is the wavelength of the lowest frequency operation). By turning the parasitic slot p–i–n diodes to ON/OFF modes, the main beam can be steered along five distinctive positions at 0°, and at ±30° in two perpendicular planes. The antenna achieves a peak gain of 9 dBi at 0.8 GHz with 1 dB gain variations over the band of operation with a peak front-to-back ratio of 20 dB.
Mining Inter-Relationships in Online Scientific Articles and its Visualization: Natural Language Processing for Systems Biology Modeling

With the rapid growth in the numbers of scientific publications in domains such as neuroscience and medicine, visually interlinking documents in online databases such as PubMed with the purpose of indicating the context of a query results can improve the multi-disciplinary relevance of the search results. Translational medicine and systems biology rely on studies relating basic sciences to applications, often going through multiple disciplinary domains. This paper focuses on the design and development of a new scientific document visualization platform, which allows inferring translational aspects in biosciences within published articles using machine learning and natural language processing (NLP) methods. From online databases, this software platform effectively extracted relationship connections between multiple sub-domains within neuroscience derived from abstracts related to user query. In our current implementation, the document visualization platform employs two clustering algorithms namely Suffix Tree Clustering (STC) and LINGO. Clustering quality was improved by mapping top-ranked cluster labels derived from an UMLS-Metathesaurus using a scoring function. To avoid non-clustered documents, an iterative scheme, called auto-clustering was developed and this allowed mapping previously uncategorized documents during the initial grouping process to relevant clusters. The efficacy of this document clustering and visualization platform was evaluated by expert-based validation of clustering results obtained with unique search terms. Compared to normal clustering, auto-clustering demonstrated better efficacy by generating larger numbers of unique and relevant cluster labels. Using this implementation, a Parkinson’s disease systems theory model was developed and studies based on user queries related to neuroscience and oncology have been showcased as applications.
Apprehending the effects of mechanical deformations in cardiac electrophysiology: A homogenization approach

We follow a formal homogenization approach to investigate the effects of mechanical deformations in electrophysiology models relying on a bidomain description of ionic motion at the microscopic level. To that purpose, we extend these microscopic equations to take into account the mechanical deformations, and proceed by recasting the problem in the framework of classical two-scale homogenization in periodic media, and identifying the equations satisfied by the first coefficients in the formal expansions. The homogenized equations reveal some interesting effects related to the microstructure — and associated with a specific cell problem to be solved to obtain the macroscopic conductivity tensors — in which mechanical deformations play a nontrivial role, i.e. they do not simply lead to a standard bidomain problem posed in the deformed configuration. We then present detailed numerical illustrations of the homogenized model with coupled cardiac electrical–mechanical simulations — all the way to ECG simulations — albeit without taking into account the abundantly-investigated effect of mechanical deformations in ionic models, in order to focus here on other effects. And in fact our numerical results indicate that these other effects are numerically of a comparable order, and therefore cannot be disregarded.
Análise de desempenho do controle de potência distribuı́do em sistemas celulares com macro e femtocélulas

Resumo— Este artigo analisa o impacto do controle de pot ência distribuı́do sobre a pot̂encia média de transmiss̃ao e a capacidade de macroe femtoćelulas empregando arranjos de antenas e usando formaç̃ao de feixe oportunı́stica. A ańalise de desempenho é realizada atrav́es de simulaç̃oes de Monte Carlo considerando um cenário com quatro femtocélulas e uma macroćelula. Os resultados obtidos mostram consideŕ aveis reduç̃oes das pot̂ encias médias de transmiss̃ ao enquanto a taxa atingida por uma fraç̃ao significativa dos enlaceśe melhorada.
An electromagnetic galloping energy harvester with double magnet design

This paper proposes an electromagnetic device equipped with a double magnet design that can harvest energy from natural, low-speed wind (global average of 3.28 m/s). The tip prism activated by aeroelastic galloping is equipped with four magnets arranged in an opposing-pole formation, producing a concentrated magnetic-flux gradient through the coil without requiring mechanical links. A prototype device (110 × 105 × 25 mm3) is tested in a wind tunnel and analyzed by numerical simulation. The device begins to provide power at a wind speed of 1.5 m/s, producing an average electrical power output of 1.41 mW at 4 m/s. This configuration is notable for its extreme simplicity.This paper proposes an electromagnetic device equipped with a double magnet design that can harvest energy from natural, low-speed wind (global average of 3.28 m/s). The tip prism activated by aeroelastic galloping is equipped with four magnets arranged in an opposing-pole formation, producing a concentrated magnetic-flux gradient through the coil without requiring mechanical links. A prototype device (110 × 105 × 25 mm3) is tested in a wind tunnel and analyzed by numerical simulation. The device begins to provide power at a wind speed of 1.5 m/s, producing an average electrical power output of 1.41 mW at 4 m/s. This configuration is notable for its extreme simplicity.
Statistical approximations to sums of correlated Rayleigh and exponential random variables with application to diversity-combining schemes : Aproximações estatísticas para somas de variáveis aleatórias correlacionadas dos tipos Rayleigh e exponencial com aplicação a esquemas de combinação de diversi

Sums of random variables are widely applied to wireless communications systems. Examples include linear equalization, signal detection, interference phenomena, and diversitycombining schemes. However, the exact formulation for the statistical functions of these sums, such as the probability density function and the cumulative distribution function, requires in general a complicated mathematical treatment, which has motivated the search for simple approximate solutions. Although there are several approximate proposals available in the literature, many of which obtained through the traditional moment-matching technique, they do not offer a good fit under the regime of high signal-to-noise ratio. It is well-known that this regime is a paramount region for the performance analysis of communications systems in terms of important metrics such as bit error rate and outage probability. More recently, in order to circumvent this limitation, a new promising technique known as asymptotic matching was proposed, capable of providing approximations for statistics of the sum of random variables with an excellent fit under the regime of high signal-to-noise ratio. Even so, this technique was initially proposed for the sum of mutually independent variables only, and thus it has not been applicable to sums of correlated variables. In this work, a novel asymptotic analysis is proposed, from which it is possible to generalize the application of asymptotic matching to the correlated case. The proposed analysis is illustrated for sums of Rayleigh and sums of exponential variables with arbitrary correlation and arbitrary fading parameters. Furthermore, closed-form asymptotic expressions are derived in order to obtain new simple and precise approximations under the regime of high signal-to-noise ratio. As application examples, practical diversity-combining schemes are addressed, namely, equal-gain combining and maximalratio combining. Finally, numerical results show the excellent performance of the proposed approximations in comparison to the approximations obtained via moment matching.
Interaction of SN Ib 2004dk with a Previously Expelled Envelope

The interaction between the expanding supernova (SN) ejecta with the circumstellar material (CSM) that was expelled from the progenitor prior to explosion is a long-sought phenomenon, yet observational evidence is scarce. Here we confirm a new example: SN 2004dk, originally a hydrogen-poor, helium-rich Type Ib SN that reappeared as a strong -emitting point source on narrowband images. We present follow-up optical spectroscopy that reveals the presence of a broad component with full width at half maximum of ∼ 290 in addition to the narrow +[N ii] emission features from the host galaxy. Such a broad component is a clear sign of an ejecta–CSM interaction. We also present observations with the XMM-Newton Observatory, the Swift satellite, and the Chandra X-ray Observatory that span 10 days to 15 years after discovery. The detection of strong radio, X-ray, and emission years after explosion allows various constraints to be put on pre-SN mass-loss processes. We present a wind-bubble model in which the CSM is “pre-prepared” by a fast wind interacting with a slow wind. Much of the outer density profile into which the SN explodes corresponds to no steady-state mass-loss process. We estimate that the shell of compressed slow wind material was ejected ∼1400 yr prior to explosion, perhaps during carbon burning, and that the SN shock had swept up about 0.04 of material. The region emitting the has a density of order .
Exploring protein dynamics using graph theory and single-molecule spectroscopy

Proteins are the workhorses of any living system. They are essential for day-to-day life and yet we still do not fully understand how they work. Proteins are intricate structures composed of thousands of atoms that interact and fluctuate over multiple temporal and spatial scales. These motions are encoded in the peptide sequence; fast and local vibrations develop collectively into the motion of secondary structures and catalytic domains. Despite the importance of dynamical fluctuations in mediating function and other biomolecular processes such as allostery, there is inadequate evidence supporting the various theories and calculational approaches which attempt to determine the relationships between structure, dynamics and function. Graph theory provides a way of capturing and representing the 3-dimensional atomistic physicochemical details of a protein in a reduced form. In this thesis, we use Markov Stability, a novel graph-theoretic method for analysing the hierarchy of protein motions. We begin by producing an energy-weighted atomistic graph representation of a protein. We then use the transient behaviour of a random-walk to identify regions of atoms/residues that form communities. Using mutagenesis we are able to identify mutations that result in a large change to the community structure. We then use experimental methods (primarily single-molecule FRET) to explore the effect of these predicted mutations and validate the predictive qualities of Markov Stability. In this thesis, we demonstrate a strong correlation between theoretical and experimental measures. We focus our study on Adenylate Kinase; a protein that balances molecular stability and fast large domain motions in the pursuit of cellular energy homeostasis. Given the structural data taken from the protein data bank we use the Markov stability graph theoretical approach to identify a number of biologically relevant community structures at different timescales. We are able to illustrate agreement between Markov Stability and the rate of motions of ADK subdomains. We then use the graph theoretical analysis to predict the effect of mutations to global dynamics and validate these predictions using single-molecule FRET; we show excellent agreement between the calculated properties of the graph theoretical analysis and the variation in FRET dynamics. Finally, we explore the relationship between the predictive score attributed to mutants by Markov Stability and the associated change in molecular stability.
Computing the Nash Bargaining Solution for Multiple Players in Discrete-Time Markov Chains Games

Abstract This paper presents a novel method for computing the Nash bargaining equilibrium for finite, ergodic and controllable Markov chains games. To solve the bargaining process we first set the disagreement point as the Nash equilibrium of the problem, then to find the new agreement point we follow the bargaining model presented by Nash. We exemplify the game formulation in terms of nonlinear programing equations implementing the Lagrange principle. For ensuring the convergence of the game to an equilibrium point we employ the Tikhonov’s regularization method. For solving the bargaining problem we make use of the extraproximal optimization approach. Finally, we validate the proposed method by a numerical example for a three-person bargaining situation.