Miracle-dz/UniFAR-FTUs huggingface dataset:
Algorithms for Geometric Covering Problems

Motivated by the applications in facility location, VLSI design, image processing and motion planning, geometric covering problems have been studied extensively in the literature. In this thesis various geometric covering problems such as covering points with disks, and squares, covering rectangular regions and convex polygonal regions with disks are considered. The problems are investigated by proposing approximation, parameterized and heuristic algorithms. The Discrete Unit Disk Cover (DUDC) problem is one of the well known geometric covering problems. In the DUDC problem, the input consists of a set P of n points and a set D of m unit disks in IR. The objective is to (i) check whether the union of all the disks in D cover all the points in P, and (ii) if yes, then find a minimum size subset D∗ ⊆ D, whose union covers all points in P. The DUDC problem is a NPcomplete problem, so there does not exist any polynomial time algorithm that solves the problem optimally unless P = NP . In order to approximate the DUDC problem, several restricted variants of the DUDC problem have been examined by different researchers. We improve the approximation factor of one such restricted DUDC problem, which helps to improve the approximation factor of the DUDC problem. We consider the Line-Separable Discrete Unit Disk Cover (LSDUDC) problem as a restricted version of the DUDC problem. In the LSDUDC problem, the plane is divided into two half planes l and l− defined by a line l, all the points in P are in l− and the centers of the disks in D are in l ∪ l− such that each point in P is covered by at least one disk centered in l. We provide a Polynomial Time Approximation Scheme (PTAS) for the LSDUDC problem. Another restricted DUDC problem is called the Within-Strip Discrete Unit Disk Cover (WSDUDC) problem. In the WSDUDC problem, all points in P and the centers of all disks in D are inside a horizontal strip of height 1 √ 2 . The current best known algorithm for the WSDUDC problem is a 3-approximation algorithm [33]. We propose a (9+ ǫ)-approximation algorithm for the DUDC problem (0 < ǫ ≤ 6) using the proposed PTAS result for the LSDUDC problem, and a 3-approximation algorithm for the WSDUDC problem. The running time of the proposed algorithm for the DUDC problem is O(m 6 ǫ n logn). We also consider another unit disk cover problem, namely the Rectangular Region
Bootstrap based nonparametric curve and confidence band estimates for spectral densities

We consider the problem of global bandwidth optimisation and confidence interval estimation for spectral density estimates obtained by applying a nonparametric curve estimator to the periodogram. The use of a local quadratic regression smoother is examined as a possible way to reduce the bias inherent in classical kernel spectral density estimators, which are simply local mean regression smoothers. It is found that while quadratic smoothers are much less sensitive to a poor choice of bandwidth, they do not always outperform mean smoothers.
On the hunt for photons: analysis of Crab Nebula data obtained by the first G-APD Cherenkov telescope

In this thesis the analysis of data of the Crab Nebula obtained by the First G-APD Cherenkov Telescope (FACT) is presented. An analysis chain using modern machine learning methods for energy estimation and background suppression is developed. The generation and application of the machine learning models are validated using Monte Carlo simulated events. The Crab Nebula can be detected as a source of very high energy gamma rays with a significance of 39.89 σ. Its energy spectrum can be reconstructed between 250 GeV and 16 TeV. The results of the analysis are used to evaluate the performance of the telescope. The energy bias and energy resolution are evaluated as well as the effective collection area and the sensitivity. The positive energy bias for energies below 1 TeV can be corrected by applying an unfolding method. For higher energies the bias is negligible. The energy resolution is about 22 % for most of the energy range. The effective collection area is monotonously increasing, reaching about 3 × 104 m2 around 1 TeV. A sensitivity of 15.5 % of the flux of the Crab Nebula is calculated. The performance values are comparable to the values of current experiments. Taking into account the small reflector surface of FACT, the performance is very promising.
Acoustic method for real-time visualization of microbubble movements and rupture

The behavior of microbubbles during insonation is typically observed through optical methods based on the use of high frame-rate cameras. In this paper, a new approach is described, in which a high-speed processing board is coupled to a standard ultrasound TX-RX to interrogate small concentrations of microbubbles suspended in water. The acquired data is presented simultaneously on two displays. In a classical M-mode display, distinct traces indicate the amplitude and position of individual bubble-echoes over time. In the multigate Doppler display, the instantaneous velocities of all bubbles intercepted by the US beam are shown. Tests performed in different experimental conditions clearly show how individual microbubbles are pushed away from the transducer depending on the transducer excitation and pressure distribution along the beam axis. Bubble rupture is also clearly shown in both display modes. The new approach thus demonstrates a promising means for the investigation of microbubble behavior in ultrasound fields.
A simplified co‐rotational method for quadrilateral shell elements in geometrically nonlinear analysis

This paper presents a simplified co‐rotational formulation for quadrilateral shell elements inheriting the merit of element‐independence from the traditional co‐rotational approach in literature. With the objective of application to nonlinear analysis of civil engineering structures, the authors further simplify the formulation of the geometrical stiffness using the small strain assumption, which is valid in the co‐rotational approach, with the warping effects considered as eccentricities. Compared with the traditional element‐independent co‐rotational method, the projector is neglected both in the tangent stiffness matrix and in the internal force vector for simplicity in formulation. Meanwhile, a quadrilateral flat shell element allowing for drilling rotations is adopted and incorporated into this simplified co‐rotational algorithm for geometrically nonlinear analysis involved with large displacements and large rotations. Several benchmark problems are presented to confirm the efficiency and accuracy of the proposed method for practical applications.
An adaptive wavelet method for solving mixed-integer dynamic optimization problems with discontinuous controls and application to alkali–surfactant–polymer flooding

ABSTRACT This article presents an adaptive rationalized Haar function approximation method to solve dynamic optimization with mixed-integer and discontinuous controls. Three measures are taken to deal with the discontinuity. First, the problem is converted into a multi-stage optimization problem by non-uniform control vector parameterization. Secondly, an adaptive strategy is proposed to regulate the interval division and the order of Haar function vectors. Thirdly, a structure detection method is presented to refine the subintervals, in which adjacent arcs with the same input type are merged into one to modify redundant subintervals. During this approximation solution, the mixed-integer restriction is realized by the integer truncation strategy. Combined with the Hamiltonian function, a validation principle is shown to verify the optimality of the solution. Finally, the proposed method is applied to solve the enhanced oil recovery for alkali–surfactant–polymer flooding. The effectiveness of the method is illustrated through simulation.
Identification of a linear parameter varying system based on Meixner-like model

In this article, the Meixner-like model is used to represent the linear discrete-time system in comparison with the Laguerre model. Furthermore, we propose from input/output measurements a new recursive representation of the Meixner-like model. However, a significant approximation of this model is subject to an optimal choice of Meixner-like parameters: pole, order of generalization and Fourier coefficients. The present research yields a series of computational experiments, through a simulation example of a linear parameter varying (LPV) system. This study tests this approach, verifies the theoretical results and points out the positive outcomes of using the Meixner-like model in comparison with the Laguerre model.
On the error behaviour of the filtered back projection

The filtered back projection (FBP) formula allows us to reconstruct bivariate functions from given Radon samples. However, the FBP formula is numerically unstable and low‐pass filters with finite bandwidth and a compactly supported window function are employed to make the reconstruction by FBP less sensitive to noise. In this paper we analyse the inherent reconstruction error which is incurred by the application of a low‐pass filter with finite bandwidth. We present L2‐error estimates on Sobolev spaces of fractional order along with asymptotic convergence rates, where the filter's bandwidth goes to infinity. (© 2016 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Local regularity-based interpolation

A ubiquitous problem in signal processing is to obtain data sampled with the best possible resolution. At the acquisition step, the resolution is limited by various factors such as the physical properties of the captors or the cost. It is therefore desirable to seek methods which would allow to increase the resolution after acquisition. This is useful for instance in medical imaging or target recognition. In some applications, one dispose of several low resolution overlapping signals [1]. In more general situations, a single signal is available for superresolution. Interpolation then requires that the available data be supplemented by some a priori information. Two types of methods have been explored: In the rst, "class-based" one, the signal is assumed to belong to some class, with conditions expressed mainly in the time or frequency domain [3, 5, 10]. This puts constraints on the interpolation, which is usually obtained as the minimum of a cost-function. The second type of approaches hypothesizes that the information needed to improve the resolution is local and is present in a class of similar signals [2, 4]. This type of approach could be called "contextual". Both "class-based" and "contextual" approaches use a "model" for interpolation: The "class-based model" is that the signal belongs to an abstract class characterized by a certain mathematical property. The "contextual model" is that the signal will behave locally under a change of resolution in way "similar" to other signals in a given set, for which a high resolution version is known. Both types of techniques have some drawbacks. Roughly speaking, class-based methods generally lead to overly smooth signals, while contextual-based ones, on the contrary, tend to generate spurious details.
Quasi-two-dimensional transition metal dichalcogenides: structure, synthesis, properties, and applications

Electron states in quasi-two-dimensional (2D) metal and semiconductor crystals can have unusual characteristics and can therefore exhibit unusual electronic and optical properties. In this paper, the results recently obtained for a new class of 2D compounds—transition metal dichalcogenides—are presented, including those on the structure, preparation methods, the electronic, mechanical, and optical properties, defects and their influence on material properties, and conditions facilitating the formation of defects. We consider the unique properties of mono- and multilayer materials, examine their dependence on external factors, and discuss their further application prospects. Various applications of 2D transition metal dichalcogenides are described, ranging from nanolubricants, nanocomposites, biosensors, memory cells and supercapacitors to optoelectronic, spin, and photovoltaic devices.
Blood flow measurement of human skeletal muscle during various exercise intensity using diffuse correlation spectroscopy (DCS)

We studied blood flow dynamics of active skeletal muscle using diffuse correlation spectroscopy (DCS), an emerging optical modality that is suitable for noninvasive quantification of microcirculation level in deep tissue. Seven healthy subjects conducted 0.5 Hz dynamic handgrip exercise for 3 minutes at intensities of 10, 20, 30, and 50 % of maximal voluntary contraction (MVC). DCS could detect the time-dependent increase of the blood flow response of the forearm muscle for continuous exercises, and the increase ratios of the mean blood flow through the exercise periods showed good correlation with the exercise intensities. We also compared blood flow responses detected from DCS with two different photon sampling rates and found that an appropriate photon sampling rates should be selected to follow the wide-ranged increase in the muscle blood flow with dynamic exercise. Our results demonstrate the possibility for utilizing DCS in a field of sports medicine to noninvasively evaluate the dynamics of blood flow in the active muscles.
Reconstructing a level-1-network from quartets

This paper deals with the problem of reconstructing a phylogenetic network from quartets. Specifically a polynomial-time algorithm was implemented to reconstruct trees and level1-networks from quartet data that satisfies certain conditions. This algorithm uses linear algebra methods to construct the set of orders the taxa can have along the boundary when drawing a planar embedding of the network. Then, the network can be reconstructed. Several experiments are performed to validate the implementation and measure the accuracy and performance of the algorithm.
Running large‐scale CFD applications on Intel‐KNL–based clusters

Intel's latest Xeon Phi processor, Knights Landing (KNL), has the potential to provide over 2.6 TFLOPS. However, to obtain maximum performance on the KNL, significant refactoring and optimization of application codes are still required to exploit key architectural innovations that KNL features—wide vector units, many‐core node design, and deep memory hierarchy. The experience and insights gained in porting and running FEFLO (a typical edge‐based finite element code for the solution of compressible and incompressible flows) on the KNL platform are described in this paper. In particular, optimizations used to extract on‐node parallelism via vectorization and multithreading and improve internode communication are considered. These optimizations resulted in a 2.3× performance gain on a 16 node runs of FEFLO, with the potential for larger performance gains as the code is scaled beyond 16 nodes. The impact of the different configurations of KNL's on‐package MCDRAM (Multi‐Channel DRAM) memory on FEFLO's performance is also explored. Finally, the performance of the optimized versions of FEFLO for KNL and Haswell (Intel Xeon) is compared.
An analytical solution to the problem of time-fractional heat conduction in a composite sphere

*e-mail: stanislaw.kukla@im.pcz. pl Abstract. An analytical solution to the problem of time-fractional heat conduction in a sphere consisting of an inner solid sphere and concentric spherical layers is presented. In the heat conduction equation, the Caputo time-derivative of fractional order and the Robin boundary condition at the outer surface of the sphere are assumed. The spherical layers are characterized by different material properties and perfect thermal contact is assumed between the layers. The analytical solution to the problem of heat conduction in the sphere for time-dependent surrounding temperature and for time-space-dependent volumetric heat source is derived. Numerical examples are presented to show the effect of the harmonically varying intensity of the heat source and the harmonically varying surrounding temperature on the temperature in the sphere for different orders of the Caputo time-derivative.
Location Prediction

In social networks, predicting a user’s location mainly depends on those of his/her friends, where the key lies in how to select his/her most influential friends. In this article, we analyze the theoretically maximal accuracy of location prediction based on friends’ locations and compare it with the practical accuracy obtained by the state-of-the-art location prediction methods. Upon observing a big gap between the theoretical and practical accuracy, we propose a new strategy for selecting influential friends in order to improve the practical location prediction accuracy. Specifically, several features are defined to measure the influence of the friends on a user’s location, based on which we put forth a sequential random-walk-with-restart procedure to rank the friends of the user in terms of their influence. By dynamically selecting the top N most influential friends of the user per time slice, we develop a temporal-spatial Bayesian model to characterize the dynamics of friends’ influence for location prediction. Finally, extensive experimental results on datasets of real social networks demonstrate that the proposed influential friend selection method and temporal-spatial Bayesian model can significantly improve the accuracy of location prediction.
Redshift Adjustment to the Distance Modulus

The distance modulus is derived from the logarithm of the ratio of observed fluxes of astronomical objects. The observed fluxes need to be corrected for the redshift as the ratio of observed to the emitted energy flux is proportional to the wavelength ratio of the emitted to observed light according to Planck’s law for the energy of the photon. By introducing this redshift adjustment to the distance modulus, we find out that the apparent “acceleration” of the expansion of the Universe that was obtained from observations of supernovae cancels out. In the present study a redshift adjustment to the distance modulus was introduced. The rationale is that the observed fluxes of astronomical objects with respect to the emitting body are being reduced by the effect of redshift. According to Planck’s law, the energy of the photon is inversely proportional to the wavelength of light; therefore, the ratio of observed to emitted fluxes should be multiplied by the wavelength ratio of emitted to observed light. 2 Model development
Computing Farey Series

The Farey series of order n, F n, consists of all the fractions between 0 and 1 (inclusive) with denominators less than or equal to n, arranged in order of magnitude and expressed in their lowest terms, so that for example The most obvious way of computing one is to assign a long series of stores and then insert the terms with denominator 1, with denominator 2, … , shifting the terms already there to make room for the new ones in their correct positions. This may require a large amount of storage and is fairly slow because of the large amount of shifting involved: for instance F 1025 has 319765 terms, and occupies 400 printed pages (see Neville [1]). Neville also gives a table related to F 100, which has 3045 terms. His procedures, a heroic application of pencil-and-paper methods, are described at the end of this paper.
Random models and heuristic algorithms for correlation clustering problems on signed social networks

In social sciences, the signed directed networks are used to represent the mutual friendship and foe attitudes among the members of a social group. Recent studies show that different real-world properties (e.g. preferential attachment, copying etc.) can be observed in the web-based social networks. In this thesis, we study the positive/negative in/out degree distributions in three online signed directed social networks. We observe that all signeddirected degree distributions in the web-based social networks with multiple edges possibilities (in both directions) follow a power law with exponents in the range 2.0 ≤ γ ≤ 3.5. We present three random models, which capture the preferential attachment and copying properties, for web-based signed directed social networks. The signed-directed degree distributions in the networks simulated by the proposed random models also indicate a powerlaw trait with an exponent in the range 2.0 ≤ γ ≤ 3.5. We also present a heuristic algorithm for the Correlation Clustering (CC) which is a class of community detection problem in the signed network. The CC problem can be defined as follow: for a given signed network, finding an optimal partition in the vertices such that the edges inside a group are positives and the edges between two groups are negative. We present the algorithm based on the relaxing integer linear programming formulation of the minimum disagreement CC problem and rounding the approximate ultrametric distance matrix by using a given threshold. The experimental results show that, in the random signed G(n, e, p) network, the runtime of this algorithm is nearly independent for the cases e ≥ 0.4 and p ≤ 0.6 , where e and p are the probabilities of connecting two vertices by an edge and an edge to be positive respectively. But this algorithm does not give any convincing argument in the variation of the minimum disagreements due to the changing of the given threshold. We also apply this algorithm to the International National Bilateral Tread Growth Network derived from the bilateral trading data in 2011-2015 from the International Trade Center (ITC) to identify the groups of countries with average positive trade growth.
Sur quelques courbes fermées remarquables

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L EVERAGED A SYMMETRIC L OSS WITH D ISAMBIGUA - TION FOR M ULTI - LABEL R ECOGNITION WITH O NE - P OSITIVE A NNOTATIONS

In the problem of multi-label learning from single positive labels (SPL), we learn the potential multiple labels from one observable single positive annotation. Despite many efforts to solve this problem, an effective algorithm with sound theoretical understanding is still in need. In this paper, we propose a novel loss function for the SPL problem, called leveraged asymmetric loss with disambiguation (LASD), where we introduce a pair of leverage parameters to address the severe negativepositive imbalance. From the theoretical perspective, we analyze the SPL problem, for the first time, from the perspective of risk consistency, which links the SPL loss with losses for ordinary multi-label classification. We prove the consistency of our proposed LASD loss to the cost-sensitive Hamming loss, which provides guidance to the empirical choice of our proposed leverage parameters. In experiments, we demonstrate the effectiveness of our proposed LASD loss function over other state-of-the-art methods and empirically verify our theoretical results.