Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of Seasonal Rainfall Data in India using Fuzzy Stochastic Modelling

In this paper we present the existence and uniqueness of solutions to the fuzzy linear regression for the prediction of seasonal rainfall in India. In our daily human life, forecasting techniques are often used to predict the rainfall, population growth, economy and stock prices. In recent years many researchers used fuzzy time series to handle prediction and forecasting problems. However the computational costs associated with traditional neuro fuzzy solutions can be prohibitive. Also neuro fuzzy solutions of higher order required to get accurate results suffer from over fitting. So a simple linear regression with fuzzy variables and fuzzy coefficients is proposed in this work. Triangular membership function is used with pre-computed fuzzy intervals based on data. Experimental results indicate better performance with both training and testing data. The proposed method outperforms AR (1) models, ARIMA, SARIMA and neuro fuzzy solutions. The computational efficiency are also demonstrated through experiments.
COSMIC-RAY MUON TOMOGRAPHY FOR ANTI-TERRORISM APPLICATIONS

This MSc Thesis explores the improvements which can be made when using the Multiple Coulomb Scattering of cosmic ray muons to create tomographic images of volumes, which may be otherwise undetectable. Feasible anti-terrorist applications may be obtained only with a high quality reconstruction algorithm and a short exposure time. This project has used and improved the algorithm of the CREAM TEA experiment of University College London (UCL), which relies on a Geant4 simulation of both target and detector and a Maximum Likelihood Expectation Maximization technique for the image reconstruction. Improvements in the latter algorithm can be achieved by measuring the muon energy and adding information about the displacement of the ray. Di erent ways of measuring the energies were analysed and two target sizes were inspected for the scattering and displacement algorithm. The result was a signi cant reduction of the noise, bringing a Figure of Merit ve times larger than the one evaluated with standard methods, together with shortening the exposure time needed to detect a bomb (e.g. 5 seconds of exposure time for a 30 cm radius uranium target). These techniques can be adapted for smaller targets by simply reducing the size of the voxels.
A Proton Imaging System for Magnetic Proton Recoil Neutron Spectrometer

Magnetic proton recoil (MPR) neutron spectrometer is considered as a high-performance instrument to measure deuterium-tritium (DT) neutron energy spectrum in magnetic confinement fusion, inertial confinement fusion experiments and other fusion devices. A compact proton imaging system (PIS) has been developed for MPR neutron spectrometer. The PIS was used as focal plane detector (FPD) to detect and image the fluence distribution of recoil protons. The PIS consists of a thin plastic scintillator, fiber optic taper and image guide, electron-multiplying charge-coupled device (EMCCD) and data acquisition system. The key imaging properties, including resolution, response to proton and capability as a FPD for the MPR neutron spectrometer, were evaluated using proton beams at accelerators. The PIS with Gd2O2S:Tb scintillator screen has higher performance in resolution and light yield than that with 2 mm plastic scintillator. The Gd2O2S:Tb scintillator screen can be used as an ideal candidate for proton imaging. The imaging properties of the PIS shown in this paper have demonstrated that the PIS is suitable for the MPR spectrometer in DT neutron measurement.
High dimension, low sample size data analysis

JEONGYOUN AHN: High Dimension, Low Sample Size Data Analysis (Under the direction of Dr. J. S. Marron) This dissertation consists of three research topics regarding High Dimension, Low Sample Size (HDLSS) data analysis. The first topic is a study of the sample covariance matrix of a data set with extremely large dimensionality, but with relatively small sample size. Especially the asymptotic behavior of eigenvalues and eigenvectors of the sample covariance matrix is the focus of our study. Assuming that the true population covariance matrix of the data is not too far from identity matrix (i.e., spherical in the Gaussian case), we show that the sample eigenvalues and eigenvectors tend to behave as if the true structure of the data is indeed from identity covariance. Based on this, the asymptotic geometric representation of HDLSS data is extended to a wide range of underlying distributions. The representation essentially states that data vectors form a regular simplex in the data space with the number of vertices equal to the sample size. The second part of the dissertation studies a discriminant direction vector, which is only interesting in HDLSS settings. This direction is characterized by the property that it projects all the data vectors, which are generated from two classes, to two distinct values, one for each class. It will be seen that this Maximal Data Piling (MDP) direction lies within the hyperplane generated by all the data vectors, while it is orthogonal to the hyperplanes generated by each class. It has the largest distance between piling sites among all the possible piling direction vectors and also maximizes the amount of piling. The formula of MDP is equivalent to the Fisher’s linear discrimination when the dimension is less than the sample size. As a classification method, MDP is heuristically desirable when the data are well approximated by the HDLSS geometric representation. The third topic relates to kernel methods in statistical learning, especially the kernel based classification problem. Taking the case of the Gaussian kernel function for the support vector machines and mean difference methods, we propose a novel approach to select the bandwidth
FEATURES OF THE INTERPLANETARY MAGNETIC FIELD TURBULENCES IN DIFFERENT PERIODS OF SOLAR MAGNETIC CYCLES

Data of By, Bz components of the interplanetary magnetic field (IMF) have been used to study a features of the IMF turbulences for two positive (A>0) and two negative (A<0) polarities epoch of solar magnetic cycles (1969-2011). We found that the changes of the exponents vy, νz, of the power spectral density (PSD) of the By, Bz, components shows a radical alternation of the large–scale structure of the IMF turbulence. It was also found a distinction between the temporal changes of the exponents vy, νz, for the A>0 and the A<0 polarity epoch of solar magnetic cycles, especially in minima and near minima epoch of solar activity. We suppose that the changes of the turbulence in the range of frequencies (10-10 ) Hz (responsible for the scattering of the GCR particles of the energy 5-50 GeV) and the module of the IMF versus solar activity can be considered as the general reasons of the long period variations of the GCR intensity. Rigidity spectrum of the long period variations of the GCR intensity is soft in positive A> 0 polarity epochs, than in negative A<0 epochs.
Accelerating Scientific Discovery with Reusable Software How to professionally develop reusable scientific software — and when

A critical challenge in scientific computing is balancing developing high quality software with the need for immediate scientific progress. We present a flexible approach that emphasizes writing specialized code that is refactored only when future immediate scientific goals demand it. Our lazy refactoring technique, which calls for code with clearly defined interfaces and sharply delimited scopes to maximize reuse and integrability, helps reduce total development time and accelerates the production of scientific results. We offer guidelines for how to implement such code, as well as criteria to aid in the evaluation of existing tools. To demonstrate their application, we showcase the development progression of tools for particle simulations originating from the Glotzer Group at the University of Michigan. We emphasize the evolution of these tools into a loosely integrated software stack of highly reusable software that can be maintained to ensure the long-term stability of established research workflows.
TiK‐means: Transformation‐infused K‐means clustering for skewed groups

The K‐means algorithm is extended to allow for partitioning of skewed groups. Our algorithm is called TiK‐means and contributes a K‐means‐type algorithm that assigns observations to groups while estimating their skewness‐transformation parameters. The resulting groups and transformation reveal general‐structured clusters that can be explained by inverting the estimated transformation. Further, a modification of the jump statistic chooses the number of groups. Our algorithm is evaluated on simulated and real‐life data sets and then applied to a long‐standing astronomical dispute regarding the distinct kinds of gamma ray bursts.
Generalization of Words for Chinese Dependency Parsing

In this paper, we investigate the inﬂuence of generalization of words to the accuracies of Chinese dependency parsing. Specially, in our shift-reduce parser, we use a neural language model based word embedding (NLMWE) method (Bengio et al., 2003) to generate distributed word feature vectors and then perform K-means based word clustering to generate word classes. We designed feature templates by making use of words, part-of-speech (POS) tags, coarse-grained POS (CPOS) tags, NLMWE-based word classes and their combinations. NLMWE-based word classes is shown to be an important supplement of POS-tags, especially when POS-tags are automatically generated. Experiments on a Query treebank, CTB5 and CTB7 show that the combinations of features from CPOS-tags, POS-tags, and NLMWE-based word classes yield the best un-labelled attachment scores (UASs). Our ﬁnal UAS − p (excluding punctuations) of 86.79% on the CTB5 test set is comparable to state-of-the-art results. Our ﬁnal UAS − p of 86.80% and 87.05% on the CTB7 Stanford dependency test set and original test set is signiﬁcantly better than three well known open-source dependency parsers.
A SEMI-ANALYTIC SPECTRAL METHOD FOR ELLIPTIC PARTIAL DIFFERENTIAL EQUATIONS

In this article we present a semi-analytic method for solving elliptic partial differential equations. The technique is based on using a spectral method approximation for the second-order derivative in one of the spatial directions followed by solving the resulting system of second-order differential equations by an analytic method. That is, the system of second-order two-point boundary-value problems are solved analytically by casting them in first-order form and solving the resulting set of first-order equations by using the matrix exponential. An important aspect of our technique is that the solution obtained is semi-analytic, e.i. , using analytic method in y and spectral method in x. The new method can be used for both linear and non-linear boundary conditions as well as for nonlinear elliptic partial differential equations.
Point cloud quality evaluation: Towards a definition for test conditions

Recently stakeholders in the area of multimedia representation and transmission have been looking at plenoptic technologies to improve immersive experience. Among these technologies, point clouds denote a volumetric information representation format with important applications in the entertainment, automotive and geographical mapping industries. There is some consensus that state-of-the-art solutions for efficient storage and communication of point clouds are far from satisfactory. This paper describes a study on point cloud quality evaluation, conducted in the context of JPEG Pleno to help define the test conditions of future compression proposals. A heterogeneous set of static point clouds in terms of number of points, geometric structure and represented scenarios were selected and compressed using octree-pruning and a projection-based method, with three different levels of degradation. The models were comprised of both geometrical and color information and were displayed using point sizes large enough to ensure observation of watertight surfaces. The stimuli under assessment were presented to the observers on 2D displays as animations, after defining suitable camera paths to enable visualization of the models in their entirety and realistic consumption. The experiments were carried out in three different laboratories and the subjective scores were used in a series of correlation studies to benchmark objective quality metrics and assess inter-laboratory consistency.
D M ] 3 M ar 2 01 5 The complexity of dominating set reconfiguration

Suppose that we are given two dominating sets Ds and Dt of a graph G whose cardinalities are at most a given threshold k. Then, we are asked whether there exists a sequence of dominating sets of G between Ds and Dt such that each dominating set in the sequence is of cardinality at most k and can be obtained from the previous one by either adding or deleting exactly one vertex. This problem is known to be PSPACE-complete in general. In this paper, we study the complexity of this decision problem from the viewpoint of graph classes. We first prove that the problem remains PSPACE-complete even for planar graphs, bounded bandwidth graphs, split graphs, and bipartite graphs. We then give a general scheme to construct linear-time algorithms and show that the problem can be solved in linear time for cographs, trees, and interval graphs. Furthermore, for these tractable cases, we can obtain a desired sequence such that the number of additions and deletions is bounded by O(n), where n is the number of vertices in the input graph.
Adversarial Training for Adverse Conditions: Robust Metric Localisation Using Appearance Transfer

We present a method of improving visual place recognition and metric localisation under very strong appearance change. We learn an invertable generator that can transform the conditions of images, e.g. from day to night, summer to winter etc. This image transforming filter is explicitly designed to aid and abet feature-matching using a new loss based on SURF detector and dense descriptor maps. A network is trained to output synthetic images optimised for feature matching given only an input RGB image, and these generated images are used to localize the robot against a previously built map using traditional sparse matching approaches. We benchmark our results using multiple traversals of the Oxford RobotCar Dataset over a year-long period, using one traversal as a map and the other to localise. We show that this method significantly improves place recognition and localisation under changing and adverse conditions, while reducing the number of mapping runs needed to successfully achieve reliable localisation.
Tilburg University A vision of global legal scholarship

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ION SOURCES

In the first part fundamental principles of positive and negative ion production are presented. Then a selection of positive ion source types is described which are relevant for accelerator application, beginning with low-charge-state high-current sources up to highlycharged ion sources. This is followed by a brief review of negative ion sources, and finally some basic remarks on beam extraction and formation.
Transpiration, evapotranspiration and energy fluxes in a temperate wetland dominated by Phalaris arundinacea under hot summer conditions

Transpiration, evapotranspiration and evaporative fraction (EF) were measured in a temperate zone wetland dominated by Phalaris arundinacea L. under extremely high temperature and intense irradiation conditions. The infrared (IR) camera recorded no temperature extremes within the stand, indicating that the stand was capable of regulating the overheating by transpiration. The transpiration rate of individual leaves (measured by the gas‐exchange method) was positively correlated with the water vapour pressure deficit of the air (R2 = 0·56, df = 1, 60, p < 0·001) reaching values of up to 7·8 mmol m−2 s−1 of leaf area. Evapotranspiration of the stand reached values of 5·3 and 5·9 mm day−1 on the 27 and 28 July 2005, respectively. The EF showed that more than one half of the available energy was consumed in the evapotranspiration processes. In the afternoon, the influence of hot air advection was obvious. A strong cooling effect of the wetland was shown. The results support the idea that wetlands sufficiently supplied with water are important in the energy and water budget of drained cultural landscapes. Copyright © 2010 John Wiley & Sons, Ltd.
Paying it forward: Crowdsourcing the harmonisation and linking of taxon names and biodiversity identifiers

Abstract Linking records for the same taxa between different databases is an essential step when working with biodiversity data. However, name-matching alone is error-prone, because of issues such as homonyms (unrelated taxa with the same name) and synonyms (same taxon under different names). Therefore, most projects will require some curation to ensure that taxon identifiers are correctly linked. Unfortunately, formal guidance on such curation is uncommon and these steps are often ad hoc and poorly documented, which hinders transparency and reproducibility, yet the task requires specialist knowledge and cannot be easily automated without careful validation. Here, we present a case study on linking identifiers between the GBIF and NCBI taxonomies for a species checklist. This represents a common scenario: finding published sequence data (from NCBI) for species chosen by occurrence or geographical distribution (from GBIF). Wikidata, a publicly editable knowledge base of structured data, can serve as an additional information source for identifier linking. We suggest a software toolkit for taxon name-matching and data-cleaning, describe common issues encountered during curation and propose concrete steps to address them. For example, about 2.8% of the taxa in our dataset had wrong identifiers linked on Wikidata because of errors in name-matching caused by homonyms. By correcting such errors during data-cleaning, either directly (through editing Wikidata) or indirectly (by reporting errors in GBIF or NCBI), we crowdsource the curation and contribute to community resources, thereby improving the quality of downstream analyses.
Abstract Template

The influence of tree characteristics and meteorological variables on spatial variability of throughfall under a single silver birch and black pine tree was evaluated. During the year 2016 throughfall was measured at 11 points under each tree canopy. For 30 analysed events total throughfall under the birch tree accounted for 73% and under the pine tree 56% of the rainfall in the open. The coefficient of variation of point throughfall was 30% and 40% for the birch and pine tree, respectively. In case of the birch tree both the distance from the stem and canopy coverage influenced throughfall spatial variability, which also showed different patterns during leafed and leafless periods. Additionally, the amount of rainfall and its microstructure influenced the spatial variability of throughfall under the birch tree. However, among the considered tree characteristics only canopy coverage was recognized as a parameter influencing spatial variability of throughfall under the pine. Furthermore, its spatial patterns were specified by meteorological variables, namely the amount of rainfall and its intensity.
A Theoretical and Experimental Study of the Symmetric Rank-One Update

This paper first discusses computational experience using the SRi update in conventional line search and trust region algorithms for unconstrained optimization. The experiments show that the SRi is very competitive with the widely used BFGS method. They also indicate two interesting features: the final Hessian approximations produced by the SRi method are not generally appreciably better than those produced by the BFGS, and the sequences of steps produced by the SRi do not usually seem to have the “uniform linear independence” property that is assumed in recent convergence analysis. This paper presents a new analysis that shows that the SRi method with a line search is $( n + 1)$-step q-superlinearly convergent without the assumption of linearly independent iterates. This analysis assumes that the Hessian approximations are positive definite and bounded asymptotically, which, from computational experience, are reasonable assumptions.
Abstract Template

Heat transfer within the soil is a complex process in the presence of seepage flow. In such conditions, the soil’s thermal behavior is influenced by the thermal and hydraulic properties of the medium as well as the initial conditions and boundary conditions to which the medium is subjected. This paper presents the experimental and numerical studies of heat transfer within the sand subjected to the seepage flow. It focuses on the influence of saturated hydraulic conductivity and the porosity of medium on the heat transfer process. The temperature distribution within the sand was monitored by the optical fiber Distributed Temperature Sensor (DTS). The experiment was performed on three types of silica-dominated sands with different saturated hydraulic conductivities and different Soil Water Characteristic Curve (SWCC). In addition to the experimental study, a coupled hydrothermal numerical model was designed in FEFLOW software and validated by comparing its results with the experimental measurements. To determine the influence of porosity and saturated hydraulic conductivity on heat transfer, we analyzed the numerical models for different values of porosity and saturated hydraulic conductivity. The numerical and experimental studies showed that the thermal velocity is higher in sand with higher saturated hydraulic conductivity and temperature declination occurs more quickly due to the heat convection process. Saturated sand with larger porosity has an overall higher heat capacity, wherefore the temperature declination started later in the measuring points but dropped down lower close to the temperature of the upstream water.
Abstract Template

We simulated liquid sloshing in a circular road tanker during two typical manoeuvres, namely steady-turn and lane change. A quasi-static pendulum, a modified dynamic pendulum with adjustable rod length, and the SPH (Smooth Particle Hydrodynamics) model Tis Isat were applied to simulate liquid oscillations. A simplified vehicle-tanker overturning model was developed and applied in order to determine the overturning threshold for the first manoeuvre. The agreement of liquid oscillations between the applied methods was better in the second manoeuvre, while the maximum inclination of the liquid gravity centre was successfully simulated in both cases. Both dynamic methods, the SPH and the dynamic pendulum, show a significantly lower overturning threshold, while all methods show similar overturning behaviour for a vehicle with liquid cargo. The threshold computed using the SPH model is slightly higher than with the dynamic pendulum due to wallparticle and particle-particle interactions. The results and comparisons confirm the suitability of the SPH method for simulating sloshing in road tankers and also show the method’s advantages: realistic description of the non-linear free surface in real-time, along with a consideration of mixing and friction processes within the fluid.