Miracle-dz/UniFAR-FTUs huggingface dataset:
Das Schlaf-Apnoe-Syndrom, das Schnarchen und die Uvulopalatopharyngoplastik*

Nach Gui! letninau! t und Mitarb. (1978) ist das SchiafApnoe-Syndrom definiert durch mindestens 30 Apnoephasen von mindestens 10 Sekunden Dauer in einem Zeitraum von 7 Stunden Schiaf. Der Schweregrad dieses Syndroms wird in dem Apnoe-Index ausgedruckt, der die Anzahl Apnoephasen pro Stunde Schiaf wiedergibt. Bei Patienten mit Schlaf-Apnoe liegt dieser immer zehn (Guilleminault u. Mitarb. 1978). Vereinzelte Schlaf-Apnoe-Episoden und unregelmaIiges Atmen kommt auch manchmal bei gesunden Kindern und Erwachsenen vor, uberwiegend während des REM-Schiafes (Guilleminau! t u. Mitarb., 1978; Carskadon et al., 1978). Man unterscheidet zwischen zentraler Apnoe und peripherer oder obstruktiver Apnoe. Daneben bestehen Mischformen mit sowohi zentralen als auch peripheren Apnoephasen. Die zentrale Apnoe ist gekennzeichnet durch den Verlust der Atembewegungen. Bei der obstruktiven Schlaf-Apnoe bleiben charakteristischerweise die Atembewegungen erhalten. Schnarchen ist em akustisches Phänomen, das durch Vibration der oropharyngealen Weichteile bei unvollstandiger Obstruktion der oberen Luftwege verursacht wird. Anhand zweier Krankengeschichten soil auf die pathophysiologischen Mechanismen des obstruktiven Schlaf-ApnoeSyndroms (OSAS) und des Schnarchens eingegangen werden. Em besonderes Augenmerk wird auf die nächtliche Schiafregistrierung gelegt. Die Möglichkeit zur Korrektur der anatomischen Verhältnisse auf oropharyngealem Niveau durch die Uvulopalatopharyngoplastik (UPPP) wird besprochen.
Performances of Chaos Coded Modulation concatenated with Space-Time Coding extended version

Based on the former work of Kozic & al, we have proposed new insights for the stud of performing Chaos Coded Modulation (CCM) schemes. We have studied in detail the distance spectrum of some CCM schemes and we have approximated their pdf’s with some well known laws. Using these approximations, we have been able to study the performances of these CCM schemes when they are concatenated with a Space Time Block Code (STBC). We obtained accurate bounds even in the case of time selective channels. In the case of time selective channels, we showed that the MRC classical equalizer makes the receiver lose all the diversity gain at high SNR’s exhibiting BER floor while the zero-forcing equalizer structure loses only one order of diversity.
Applications of Circulations and Removable Pairings to TSP and 2ECSS

In this thesis we focus on two NP-hard and intensively studied problems: The travelling salesman problem (TSP), which aims to find a minimum cost tour that visits every node exactly once in a complete weighted graph, and the 2-edge-connected spanning subgraph problem (2ECSS), which aims to find a minimum size 2-edge-connected spanning subgraph in a given graph. TSP and 2ECSS have many real world applications. However, both problems are NP-hard which means it is unlikely that polynomial time algorithms exist to solve them, so methods that return close to optimal solutions are sought. In this thesis we mainly focus on k-approximation algorithms for the two problems, which efficiently return a solution within k times of the optimal solution. For a special case of TSP called graph TSP, using ideas from Momke and Svensson, we present a 25 18 -approximation algorithm for a special class of graphs using circulations and T -joins, which improves the previous known best bound of 7 5 for such graphs. Moreover, if the graph does not contain special nodes, our algorithm ensures the ratio of 4 3 . For 2ECSS, given any k-edge-connected graph G = (V,E), |V | = n, |E| = m, we present an approximation algorithm that gives a 2-edge-connected spanning subgraph with the number of edges at most n+ k−1 − k−2 k−1 with a novel use of circulations, which improves both the approximation ratio and the simplicity of the proof compared to i a result by Huh in 2004.
Spectroscopy of optical gain in low threshold colloidal quantum dot laser media: dominance of single-exciton states at room temperature

Experimental studies of amplified spontaneous emission (ASE) and lasing from various colloidal II-VI semiconductor nanocrystals have been used as inputs to several microscopic models for underlying optical gain, usually involving permutations of quantum confined multiple excitonic states. Here we focus on particular types of CdSe/ZnCdS and CdSe/ZnS/ZnCdS colloidal quantum dot (CQD) films and elucidate on the discovery of single-exciton states at the fundamental edge as a dominant mechanism for optical gain at room temperature. Pump-probe spectroscopic techniques enable us to measure the onset of gain at ensemble-average exciton occupancy per CQD, = 0.6 and 0.7 for the two types of CQD films at room temperature. Time-resolved measurements, in turn, show how optical gain persists well into the time regime associated with spontaneous emission (nanoseconds), thus providing direct evidence for how the non-radiative Auger recombination processes (~100 ps) can be thwarted. In addition to benefits of the material assets of densely packed CQD films with high luminescence efficiency (quantum yield ~90%) and nanoparticle monodispersity therein, we propose that access to the single-exciton gain regime at room temperature requires a careful spectral balance between the lowest exciton absorption resonance and its corresponding red-shifted spontaneous emission maximum (“Stokes shift”).
An improved robust blind motion de-blurring algorithm for remote sensing images

Shift-invariant motion blur can be modeled as a convolution of the true latent image and the blur kernel with additive noise. Blind motion de-blurring estimates a sharp image from a motion blurred image without the knowledge of the blur kernel. This paper proposes an improved edge-specific motion de-blurring algorithm which proved to be fit for processing remote sensing images. We find that an inaccurate blur kernel is the main factor to the low-quality restored images. To improve image quality, we do the following contributions. For the robust kernel estimation, first, we adapt the multi-scale scheme to make sure that the edge map could be constructed accurately; second, an effective salient edge selection method based on RTV (Relative Total Variation) is used to extract salient structure from texture; third, an alternative iterative method is introduced to perform kernel optimization, in this step, we adopt l1 and l0 norm as the priors to remove noise and ensure the continuity of blur kernel. For the final latent image reconstruction, an improved adaptive deconvolution algorithm based on TV-l2 model is used to recover the latent image; we control the regularization weight adaptively in different region according to the image local characteristics in order to preserve tiny details and eliminate noise and ringing artifacts. Some synthetic remote sensing images are used to test the proposed algorithm, and results demonstrate that the proposed algorithm obtains accurate blur kernel and achieves better de-blurring results.
Electrons, Neutrons and Protons in Engineering

J. R. Eaton Oxford: Pergamon Press. 1966. Pp. xvii + 541. Price £5 5s. If anyone doubts that a student of electrical engineering, and probably of the other branches too, should start with a thorough grounding in physics he should glance at this book by J. R. Eaton, Professor of Electrical and Nuclear Engineering at Purdue; indeed the title alone should be almost sufficient to dispel the doubt. The text is an introduction to the science of materials and links the frequently separated topics of solid state physics on the one hand and atomic and nuclear physics on the other to give a unified development.
On the location of eigenvalues of matrix polynomials

A number $\lambda \in \mathbb C $ is called an {\it eigenvalue} of the matrix polynomial $P(z)$ if there exists a nonzero vector $x \in \mathbb C^n$ such that $P(\lambda)x = 0$. Note that each finite eigenvalue of $P(z)$ is a zero of the characteristic polynomial $\det(P(z))$. In this paper we establish some (upper and lower) bounds for eigenvalues of matrix polynomials based on the norm of their coefficient matrices and compare these bounds to those given by N.J. Higham and F. Tisseur, J. Maroulas and P. Psarrakos.
Periodic Motions With Overshooting Phases of a Two-Mass Stick–Slip Oscillator

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
Influence of the Size of Supertyphoon Megi (2010) on SST Cooling

AbstractSupertyphoon Megi (2010) left behind two very contrasting SST cold wake cooling patterns between the Philippine Sea (1.5°C) and the South China Sea (7 °C). Based on various radii of radial winds, we found that the size of Megi doubles over the South China Sea when it curving northwards. On average, the radius of maximum wind (RMW) increased from 18.8 km over the Philippine Sea to 43.1 km over the South China Sea; the radius of 64 kt typhoon-force wind (R64) increased from 52.6 km to 119.7 km; the radius of 50 kt damaging-force wind (R50) increased from 91.8 km to 210 km; and the radius of 34 kt gale-force wind (R34) increased from 162.3 km to 358.5 km. To investigate the typhoon size effect, we conduct a series of numerical experiments on Megi-induced SST cooling by keeping other factors unchanged, i.e., typhoon translation speed and ocean subsurface thermal structure. The results show that if it were not for Megi’s size increase over the South China Sea, the during-Megi SST cooling magnitude woul.
An Architecture for a Decentralised Learning Analytics Platform (Positioning Paper)

Predictive Learning Analytics is a subfield of Learning Analytics that helps identify students who are at risk of dropping out or failing. However, the centralised approach to Predictive Learning Analytics raises privacy and ethical concerns, particularly in the area of data collection. In this paper emphasis is placed on a decentralised mechanism for collecting student consent. This mechanism is part of the EMPRESS framework, that combines self-sovereign data, Federated Learning, and Graph Convolutional Networks for Heterogeneous graphs to address these issues. EMPRESS allows data owners to control who has access to their data, processes data on their devices, and utilizes knowledge graphs for analysis. Course tutors can use the insights provided by the analyses to offer timely assistance to these students. In addition, this paper details how Heterogeneous graphs can be used in Predictive Learning Analytics.
Study on the Characteristics of Low-speed Boundary Layer Flow Separation

The boundary layer theory is proposed by German scientist Prandtl. When the fluid motion in the large Reynolds number, a thin layer will be concentration to produce in the fluid surface. Because the thin area is very close to the object surface in which the viscous force plays an important role, which is called the boundary layer. Due to the fact that the fluid is viscous, he will produce some phenomena which are different from the ideal fluid. In this paper, the air viscosity and the flowing phenomena and laws in the low-speed boundary layer are analyzed of the air flow very close to the aircraft surface. Introduction In the early days, all of the fluid was considered to be an ideal fluid. Ideal fluid is a kind of ideal model with no viscosity [1] [2]. That is, all of the fluid is not viscous. In nature, all actual fluid is viscous. Water and air are viscous, which can be observed through the river water. The water in the middle of the river flows rapid. The nearer the river bank, the slower the water flows and the water very close to the bank does not basically flow. The reason for this phenomenon is that the bank is not smooth, and the river is viscous. As a result, the river is affected by the friction and its own viscous. So, close to the shore, the water is almost not flowing. From the shore to the center, the influence gradually decreases, the water was gradually speeded up. Air is also viscous. The experiment models include two discs close to but not touching each other. Above the disc is static, the moment the disc rotation for some time later, the disc is also slowly rotating up. In order to explain the phenomenon, the air is divided into a number of layers, and two adjacent air layers are taken out of the two adjacent layers. The lower the air velocity, the molecular clusters with great momentum, when the irregular thermal motion of air molecules into the upper, through intermolecular mixing and collision momentum increases the upper air parcels, the upper air is accelerated; so in this experiment, the disc when rotating, an air layer close to the surface of the disk with the disk with high-speed rotation. Such a characteristic of interaction of the two adjacent air layers when the relative motion occurs in the air is called the air viscosity [3]. Fig. 1 Two Adjacent Air Layers with the Different Flow velocities Steady Flow and Unsteady Flow The space that the fluid of motion occupies is called the flow field. The study of the fluid motion does not specifically depend on how a certain fluid particle moves but on the analysis of the fluid motion of each fixed space point in the flow field and then on the combination of the changes of parameters of motion of all the space points in the flow field, thus finding the fluid law of motion 4th International Conference on Machinery, Materials and Information Technology Applications (ICMMITA 2016) Copyright © 2017, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Computer Science Research, volume 71
Content Negotiation in a Decentralised Semantic Context Utilising Equivalence Links

The Web is a decentralised system where each Web server can serve a set of URIs that identify resources. Each resource can be associated with one or more representations. This promotes content negotiation, which is the mechanism by which a Web client can request a resource representation that satisfies a set of constraints. This is also true in the Semantic Web, since when resources are described, different vocabularies are used (e.g. Schema.org and FOAF ), and when they are served in the Web, they are seri-alised in different formats (e.g. text/turtle and application/rdf+xml ). In this context, HTTP provides the means to negotiate representations using media types, and semantic validation languages (e.g. SHACL) could be used to define the constraints that knowledge graphs must conform to. If a resource was only identified by a single URI (as it would be the case with unique names assumption ), it would mean that all representations would be present on one and only one server. This implies that one would be able to negotiate all representations of a resource with that server. However, this is not possible in the actual Web, because Web standards do not assume unique names and representations are scattered and distributed in different places. Still, a URI can only be served by one server, so in general, several servers should be consulted to get all representations of a resource. Consequently, when we negotiate with a Web server, we only consider a subset of all existing representations. In this article, we propose an approach to perform content negotiation even when representations are dispersed and present in multiple locations. We focus on this specific data management solution by leveraging equivalence links , which consists of querying the Web of Data with Content Negotiation, involving on-the-fly SHACL shape validation. To this end, we provide two algorithms, the first in a basic context (i.e. considering only media type constraints) and the second in a semantic context (i
A disk failure prediction model for multiple issues

Disk failure prediction methods have been useful in handing a single issue, e.g., heterogeneous disks, model aging, and minority samples. However, because these issues often exist simultaneously, prediction models that can handle only one will result in prediction bias in reality. Existing disk failure prediction methods simply fuse various models, lacking discussion of training data preparation and learning patterns when facing multiple issues, although the solutions to different issues often conflict with each other. As a result, we first explore the training data preparation for multiple issues via a data partitioning pattern, i.e., our proposed multi-property data partitioning (MDP). Then, we consider learning with the partitioned data for multiple issues as learning multiple tasks, and introduce the model-agnostic meta-learning (MAML) framework to achieve the learning. Based on these improvements, we propose a novel disk failure prediction model named MDP-MAML. MDP addresses the challenges of uneven partitioning and difficulty in partitioning by time, and MAML addresses the challenge of learning with multiple domains and minor samples for multiple issues. In addition, MDP-MAML can assimilate emerging issues for learning and prediction. On the datasets reported by two real-world data centers, compared to state-of-the-art methods, MDP-MAML can improve the area under the curve (AUC) and false detection rate (FDR) from 0.85 to 0.89 and from 0.85 to 0.91, respectively, while reducing the false alarm rate (FAR) from 4.88% to 2.85%.
Application of Elastic-Damage-Heal Model for Self-Healing Concrete Thick-Walled Cylinders Through Thermodynamics of Irreversible Processes

Elastic-damage-heal models are used for a phenomenological study of self-healing materials. Self-healing materials are a class of smart materials that have a structural capability to recover damage caused by environmental stimuli over time. In this paper, a semi-analytic modeling for self-healing concrete thick-walled cylinders is presented. For this purpose, an elastic-damage-heal model through thermodynamics of irreversible processes, in the framework of continuum damage-healing mechanic is used. This model uses stress spectral decomposition method to model the different behavior of concrete in tensile and compressive loadings. Gibbs potential energy is divided into three parts: elastic energy, damage energy and healing energy. In this regard, the model introduces damage and healing surfaces to detect damage and healing behaviors from the elastic one. We derive an analytical closed-form solution for a self-healing concrete thick-walled cylinder. The verification of the model is shown by solving an examp.
Analisis Empirical Orthogonal Function (Eof) Berbasis Singular Value Decomposition (Svd) pada Data Curah Hujan Indonesia

Analisis Empirical Orthogonal Function (EOF) digunakan untuk mereduksi dimensi data yang berukuran besar dengan mempertahankan sebanyak mungkin variasi dari himpunan data asal. EOF merupakan suatu metode untuk menentukan pola-pola dominan pada data yang berevolusi dalam ruang dan waktu. Secara aljabar, EOF atau komponen utama yang diperoleh merupakan kombinasi linear dari semua peubah asli yang memiliki varians terbesar secara berurutan dan tidak berkorelasi dengan komponen utama sebelumnya. Metode EOF yang dilakukan pada penelitian ini menggunakan pendekatan Singular Value Decomposition (SVD). Analisis dilakukan pada data curah hujan TRMM 3B43 bulanan untuk wilayah cakupan Indonesia selama 204 bulan dan dihitung nilai kesalahan dari hasil reduksi data. Berdasarkan hasil analisis diperoleh lima nilai singular terbesar yang memiliki total varians sebesar 90.03%. Mode pertama (EOF1) menjelaskan 30,68% dari total varians dan merupakan varians terbesar yang mewakili hampir seluruh data. Mode EOF kedua sampai EOF kelima masing-masing menjelaskan 19.89%, 16.82%, 11.43% dan 11.19% dari total varians. Setiap mode EOF yang diperoleh menggambarkan pola spasial, sedangkan vektor singular menggambarkan pola temporal. Efektifitas dari lima mode EOF yang dihasilkan tersebut diuji untuk dapat menghampiri data asli. Hampiran data asli diperoleh dengan menentukan nilai kesalahan dari hasil reduksi menggunakan teknik error norm matriks.
Iterative learning approach for traffic signal control of urban road networks

In this study, the authors apply iterative learning control (ILC) theory to address the urban traffic signal control problem in a macroscopic level traffic environment. The original urban traffic signal control problem is first formulated into an output regulating and disturbance rejection problem, and the ILC approach is applied to deal with this class of control problem using the repeatability feature of traffic flow. Then, the ILC strategy is further integrated with the traffic-responsive urban control framework to cope with the random disturbances of traffic conditions in iteration cycle. Theoretical analysis shows that the proposed ILC-based traffic signal control methods can guarantee the asymptotic convergence of the link occupancies to the desired ones. The main advantage of the proposed approaches is that they require less prior modelling knowledge in the control system design. The effectiveness of the proposed methods is further verified by extensive simulations.
Optimal Representations of Sparse Stochastic Processes with Applications in Image Processing

We establish in the world of stochastic processes a theoretical relation between sparsity and wavelets. The underlying principle is to treat stochastic processes as generalized functions, which facilitates the study of their properties in a transform domain. We focus on symmetric-$\alpha$-stable (S$\alpha$S) processes, with $\alpha\in(0,2]$. They are central to a recently proposed framework for sparse stochastic processes. The case $0<\alpha<2$ corresponds to heavy-tail processes and, thus, to sparse signals with the level of sparsity being more pronounced for smaller values of $\alpha$. The limit case $\alpha=2$ yields the classical Gaussian processes, which are not considered to be sparse. In the first part of the thesis, we identify a particular class of wavelets and show that they provide an independent-component analysis for S$\alpha$S AR(1) signals, which form an important subclass of sparse processes. This contribution can be considered as the first theoretical result ever found about the optimality of wavelets in a stochastic framework. It is remarkable that, by decreasing the parameter $\alpha$, we continuously run the gamut from Fourier (for $\alpha=2$) to wavelets (for $\alpha\le1$), thus providing a unifying view that sees Fourier and wavelet transforms as two extremes of a single spectrum. In the second part of the thesis, we study the wavelet decomposition of self-similar processes---which form a second important family of sparse S$\alpha$S processes. In particular, in the context of the wavelet-based denoising of a stochastic process, we are able to theoretically predict the exact performance of an arbitrary orthonormal wavelet basis. As it turns out, our prediction takes a tractable simple form, which allows us to design the optimal Meyer wavelet as an example of application. Our theoretical results suggest that the performance depends crucially on the localization of wavelets. Consequently, in an image-processing context, we introduce a moment-based measure of localization and propose an optimization framework to design better wavelets. We end the thesis with the experimental confirmation that the proposed wavelets outperform the previously existing ones.