Miracle-dz/UniFAR-FTUs huggingface dataset:
Semisupervised Hyperspectral Image Classification Using Deep Features

As in other remote-sensing applications, collecting ground-truth information from the earth's surface is expensive and time-consuming process for hyperspectral imaging. In this study, a deep learning-based semisupervised learning framework is proposed to solve this small labeled sample size problem. The main contribution of this study is the construction of a deep learning model for each hyperspectral sensor type that can be used for data obtained from these sensors. In the proposed framework, the “trained base model” is obtained with any dataset from a hyperspectral sensor, and fine-tuned and evaluated with another dataset. In this way, a general deep model is developed for extracting deep features which can be linearly classified or clustered. The system is evaluated with three different clustering techniques, the modified k-means, subtractive, and mean-shift clustering, for selecting initial representative labeled training samples comparatively. Another contribution of this study is to exploit the labeled and unlabeled sample information with linear transductive support vector machines. The proposed semisupervised learning framework is proven by the experimental results using different number of small sample sizes.
Short-Term Memory Capacity in Recurrent Networks via Compressed Sensing

Adam S. Charles, Han Lun Yap, and Christopher J. Rozell Summary Understanding the memory capacity of neural circuits is vital to understanding the performance of tasks such as working memory. In particular, critical questions recently raised in the literature include the effects of network size, connectivity pattern and natural signal statistics on the ability of a neural circuit to encode a temporal memory in its transient dynamics. Recently, results using the tools of compressive sensing (CS) have indicated that the memory capacity of a linear neural circuit could exceed the number of neurons in the network when the temporal signals are sparse (i.e., having few non-zeros) [White et al., 2004, Ganguli and Sompolinsky, 2010]. In this work we perform a full CS analysis of the memory capacity of a neural circuit for general inputs that are sparse in a known basis (a useful model for many natural signals [Olshausen and Field, 1996]). Using a model similar to that proposed in [Ganguli and Sompolinsky, 2010], our results 1) provide signal recovery guarantees for the exact linear network dynamics with no approximations; 2) provide guarantees for every temporal sequences sparse in any basis (not average performance), where the memory capacity penalty associated with using a specific basis is well defined; and 3) quantify the dependence of the memory capacity on properties of the neural connectivity pattern. Our analyses (based on the restricted isometry property [Candès, 2006]) demonstrate that the memory capacity can far exceed the number of neurons in the circuit, and illustrate the magnitude of this excess in two distinct cases: 1) the perfect recovery of finite-length signals that are within the capacity of the circuit, and 2) the recovery error for signals that are beyond the capacity of the circuit (possibly infinitely long), with an exponential forgetting factor in the circuit. These results point to the existence of an optimal signal length (for a given network) where the joint recovery error due to un-recalled signal and recall errors is minimized. Additional information Figure 1 shows numerical simulations of memory sequence recovery error from neural circuits.
Finding Minimum Spanning Forests in a Graph

We introduce a graph partitioning problem motivated by computational topology and propose two algorithms that produce approximate solutions. Specifically, given a weighted, undirected graph $G$ and a positive integer $k$, we desire to find $k$ disjoint trees within $G$ such that each vertex of $G$ is contained in one of the trees and the weight of the largest tree is as small as possible. We are unable to find this problem in the graph partitioning literature, but we show that the problem is NP-complete. We then propose two approximation algorithms, one that uses a spectral clustering approach and another that employs a dynamic programming strategy, which produce near-optimal partitions on a family of test graphs. We describe these algorithms and analyze their empirical performance.
Trivariate gamma regression

Regression analysis is a method for determining a causal relationship between the response and predictor variables. The regression model has been developed in various ways, one of them is based on the distribution of the response variables. In this study, the response variables follow trivariate gamma distribution, such that the regression model developed is Trivariate Gamma Regression (TGR). The purposes of this study are to obtain the parameter estimators, test statistics, and hypothesis testing on parameters are significance (overall and partial) of the TGR model. The parameter estimators are obtained using the Maximum Likelihood Estimation (MLE). The overall test for the model’s significance is using Maximum Likelihood Ratio Test (MLRT), and the partial test is using the Z test. Based on the results of this study, it can be inferred that the parameter estimators obtained from the MLE are not closed form. Hence a numerical method is needed. In this study, the algorithm of numerical optimization used is BFGS quasi-Newton.
Parallel Strategies for Training Decision Tree

With the development of biology technology ,the amounts of data increase very fast. It is difficult to analyze and model the data within reasonable amount of time ,so studying parallel data mining algorithms has been becoming an important approach to solving the problem. Decision tree has been widely applied as an important classification tool. This paper surveys several parallel training decision tree strategies and compares their performance.
Mathematical modeling of oligomerization and biased signaling of G-protein-coupled receptors

Los receptores acoplados a proteinas G (GPCRs) juegan un papel muy importante en una gran variedad de procesos biologicos. Estos receptores estan localizados en la membrana y median las rutas de senalizacion celulares. Es ampliamente aceptado que estos receptores forman a menudo oligomeros, los cuales pueden tener funciones fisiologicas significativas. Ademas, un GPCR puede modular varias rutas de senalizacion y estas rutas pueden ser activadas de manera diferencial por los ligandos de los receptores. La presente tesis se propone proporcionar herramientas matematicas nuevas para la comprension de estos fenomenos. En la presente tesis se muestran dos nuevos modelos matematicos para la oligomerizacion de los GPCRs y un marco teorico novedoso para la senalizacion sesgada. En primer lugar, se propone un modelo matematico nuevo para el receptor heterodimerico. Este modelo de heterodimero puede emplearse para diseccionar el impacto en las rutas de senalizacion del heterodimero de dos ligandos que estan unidos respectivamente a sus dos protomeros. En segundo lugar, se presenta un nuevo modelo matematico para el receptor homodimerico. Este modelo de homodimero puede ser utilizado para analizar un rango amplio de curvas dosis-respuesta de diferentes ligandos que se unen al receptor homodimerico y el agonismo sesgado dependiente de la concentracion de ligando. En tercer lugar, se ha desarrollado un marco conceptual novedoso para la diseccion de la senalizacion sesgada. Este modelo proporciona nuevas ideas sobre la senalizacion sesgada y nuevas escalas cuantitativas para el sesgo del sistema, el sesgo del ligando y la senalizacion sesgada. Para resumir, los nuevos modelos matematicos y el marco teorico estan basados en modelos operacionales ya existentes para la senalizacion de los GPCRs los cuales han sido ampliamente aplicados para el estudio de la accion de los farmacos. Es por ello, que la logica y las herramientas computacionales que se muestran en la presente tesis son factibles de ser usadas para vencer las dificultades en el analisis de datos que son causadas por la oligomerizacion y la senalizacion sesgada de los GPCRs.
SIV measurements of flow structure in the near wake of a circular cylinder at Re = 3900

Turbulent characteristics of a near wake behind a circular cylinder in a cross flow at Re = 3900 were considered. This regime of flow past cylinders has been widely discussed in the literature; however, the published experimental data and numerical simulation results experience some controversy. Furthermore, there is a shortage of reliable experimental data, e.g. the available descriptions of experimental setups and conditions are not enough for testing of numerical methods. The present paper fills this gap to a large extent. Experiments were carried out using a relatively new smoke image velocimetry (SIV) technique, which provides time-resolved measurements of instantaneous two-dimensional velocity vector fields in complex turbulent flows with high spatial resolution of the order of Kolmogorov scale. Distributions of flow statistics over different cross sections of the cylinder wake were obtained. Comparative analysis of SIV measurements and previously published experimental and DNS data was performed. The sources of differences in distributions of turbulence statistics were analysed. Key regularities in evolution of velocity profiles in the cylinder wake were revealed. Some features were discovered in the distributions of transverse velocity component and fluctuating flow parameters that had not been previously captured by other measurement methods. Physical mechanisms and their origins are described.
Proceedings of the 4th workshop on Information credibility

It is our great pleasure to welcome you all to the 4th Workshop on Information Credibility on the Web (WICOW'10) organized in conjunction with the 19th World Wide Web Conference in Raleigh, NC, USA on April 27, 2010. The aim of the workshop is to provide a forum for discussion on various issues related to information credibility criteria on the web. Evaluating and improving information credibility requires combination of different technologies and backgrounds. Through the series of WICOW workshops we hope to exchange novel ideas and findings as well as promote discussions on various aspects of web information credibility. This year we received 22 full paper submissions from 12 countries: Austria, Brazil, China, Egypt, France, Germany, Ireland, Japan, The Netherlands, Saudi Arabia, UK and USA. After a careful review process, with at least three reviews for each paper the Program Committee has selected 10 full papers (45% acceptance rate) covering variety of topics related to information credibility. The accepted papers were grouped into 3 sessions: "Wikipedia Credibility", "Studies of Web Information Credibility" and "Evaluating Information Credibility". We are also pleased to invite Miriam Metzger from University of California, Santa Barbara for giving a keynote talk entitled: "Understanding Credibility across Disciplinary Boundaries. "
A colour-excess extinction map of the southern Galactic disc from the VVV and GLIMPSE surveys

An improved high-resolution and deep $A_{K_{s}}$ foreground dust extinction map is presented for the Galactic disc area within 295° ≲ l ≲ 350°, −1.0° ≲ b ≲ +1.0°. At some longitudes the map reaches up to |b| ∼ 2.25°, for a total of ∼148 deg2. The map was constructed via the Rayleigh–Jeans colour excess (RJCE) technique based on deep near-infrared (NIR) and mid-infrared (MIR) photometry. The new extinction map features a maximum bin size of 1 arcmin, and relies on NIR observations from the Two Micron All-Sky Survey (2MASS) and new data from ESO’s Vista Variables in the Vía Láctea (VVV) survey, in concert with MIR observations from the Galactic Legacy Infrared Mid-Plane Survey Extraordinaire. The VVV photometry penetrates ∼4 mag fainter than 2MASS, and provides enhanced sampling of the underlying stellar populations in this heavily obscured region. Consequently, the new results supersede existing RJCE maps tied solely to brighter photometry, revealing a systematic underestimation of extinction in prior work that was based on shallower data. The new high-resolution and large-scale extinction map presented here is readily available to the community through a web query interface.
simSchool and the Conceptual Assessment Framework

simSchool is a game-based simulation developed with funding from the Preparing Tomorrow’s Teachers to Use Technology (PT3, 2003) program of the United States Department of Education. The simulation provides users with a training environment for developing skills such as lesson planning, differentiating instruction, classroom management, special education, and adapting teaching to multiple cognitive abilities. This chapter uses simSchool as an example to present and discuss an application of the Conceptual Assessment Framework (CAF) of Almond, Steinberg, and Mislevy (2002) as a general model for building assessments of what users learn through games and simulations. The CAF organizes the theories of teaching as well as the inferential frameworks in simSchool that are used to provide feedback to players about their levels of knowledge and abilities as teachers. The framework is generally relevant and useful for planning how to assess gains made by users while playing games or using simulations. INTRODUCTION
Réseaux de neurones à convolution pour la segmentation de vaisseaux sanguins rétiniens – Des patchs aux images de taille réelle

Résumé La segmentation d’images est une procédure importante dans de nombreux systèmes de diagnostic médical. Appli-quée à des images microscopiques de rétines pour détec-ter des vaisseaux, elle contribue au diagnostic de maladies comme la rétinopathie diabétique et le glaucome. Ce papier présente un modèle de segmentation basé sur les réseaux de neurones à convolution (RNC). L’apprentissage de ce type de modèles requiert souvent des bases de données de taille considérable. Face à des bases de données de taille réduite, comme dans notre cas, une technique d’apprentis-sage consiste à trouver une bonne initialisation des poids. Ainsi, le RNC proposé dans cet article est appris en deux étapes : i ) le modèle est appris sur des patchs extraits des images de la base de données, ii ) il est afﬁné sur les images de taille réelle. Une étude expérimentale est faite sur deux bases d’images publiques.
Generalized inverses of matrices over skew polynomial rings

The applications of generalized inverses of matrices appear in many fields like applied mathematics, statistics and engineering [2]. In this thesis, we discuss generalized inverses of matrices over Ore polynomial rings (also called Ore matrices). We first introduce some necessary and sufficient conditions for the existence of {1}-, {1, 2}-, {1, 3}-, {1, 4}and MP-inverses of Ore matrices, and give some explicit formulas for these inverses. Using {1}-inverses of Ore matrices, we present the solutions of linear systems over Ore polynomial rings. Next, we extend Roth’s Theorem 1 and generalized Roth’s Theorem 1 to the Ore matrices case. Furthermore, we consider the extensions of all the involutions ψ on R(x), and construct some necessary and sufficient conditions for ψ to be an involution on R(x)[D;σ, δ]. Finally, we obtain two different explicit formulas for {1, 3}and {1, 4}-inverses of Ore matrices. The Maple implementations of our main algorithms are presented in the Appendix.
Handbook Of Optical Fibre Sensing Technology

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Surface morphological, optical and electrical characterization of methylammonium lead bromide perovskite (CH3NH3PbBr3) thin film

Hybrid halide perovskites (CH3NH3PbX3; X=F, Cl, Br and I) have been emerging as innovative candidates in the development of optoelectronic devices. In this paper, a methylammonium lead bromide (CH3NH3PbBr3) perovskite-based heterojunction device of architecture FTO/PEDOT:PSS/CH3NH3PbBr3/PC61BM/Al has been fabricated using a low-cost spin casting method. PEDOT:PSS and PC61BM layers serve as the hole transport layer (HTL) and the electron transport layer (ETL), respectively. The photoluminescence and absorbance spectrum of perovskite have been determined with an extracted band gap using a Tauc plot. The electrical characterization of the device has been carried out using current-voltage and impedance measurements. A dark current measurement reveals low leakage current, which can be attributed to the blocking effect of the PEDOT:PSS and PC61BM layers. The photocurrent generation in the device has been explained by the energy level diagram, and the role of the ETL and HTL identified. The device exhibits the ideality factor 2.37 in the dark and 1.79 under illumination. The parameters show good correlation with the I–V results.
A method to detect multiple error sources and suppression of periodic beam motion with feedforward correction

A high electron beam stability at the source points of synchrotron radiation is required for the preservation of the low emittance to generate highly brilliant photon beams to users. To improve beam stability, a method based on the response matrix between corrector magnets and beam orbit distortion at beam position monitors (BPMs) is used to detect multiple error sources at a particular frequency or a malfunction of power supplies after a beam trip. Horizontal beam motions with dispersion-like patterns, can be dealt with separately. This method has been used at the Taiwan Photon Source (TPS) to detect different types of error sources and to obtain simultaneously their relative phases and amplitudes. By employing a predefined current waveform obtained from error induced beam position variations in a feedforward correction method, a 3 Hz beam motion caused by booster stray fields could be reduced to one fifth. Furthermore, a 60 Hz beam motion due to stray fields from electric devices powered by the mains could be reduced to one-third compared to applying fast orbit feedback only.
Introduction to piezoelectric actuators: research misconceptions and rectifications

The field of piezoelectric actuators is really an interdisciplinary area, to which applied physicists and materials, electrical and mechanical engineers primarily converge. Because of the narrow knowledge of junior researchers, they occasionally publish misleading information, some sort of misconceptions, reflected in the delay of innovative developments of the next generation. This paper reviews the top 10 of these misconceptions, which are primarily related to the understanding of ionic displacement and strain, efficiency, the energy transmission coefficient, the constraint dependency of piezo-material properties, mechanical impedance matching, the piezoelectric damping mechanism, resonance and antiresonance, best-selling devices, and system design principles.
Mutual Admittance of Two Arbitrary Antennas in Nonplanar Skew Positions Based on Infinitesimal Dipole Modeling

Analyzing antenna coupling is valuable in solving various problems of multi-antenna systems. Through existing studies based on the reaction theorem and infinitesimal dipole modeling (IDM), mutual admittance can be intuitively and efficiently calculated. Extending previous studies based on the reaction theorem and IDM, we propose a method for analyzing mutual admittance between antennas that are located in nonplanar skew positions. There are two main ideas in this paper. The first is IDM extraction using a segmented grid on an antenna computer-aided design (CAD). Using the triangular-meshed antenna CAD, the IDM is extracted on a grid which is equal to the antenna shape. The proposed grid scheme improves the modeling accuracy of the IDM compared to the previous grid schemes. The second idea is to apply a rotation matrix to the IDM for efficient calculation of the coupling of any two antennas with nonplanar skew positions. The strength of the proposed method is that it can predict the mutual coupling between the two antennas by using only independent information for each antenna. However, since only the independent information is used for each antenna, it can reflect only the first-order electromagnetic interaction. Therefore, the proposed method is effective only when the distance between the two antennas is sufficiently far so that the two antennas are regarded independent regardless of the distance variation. Coupling analyses based on these two ideas were performed using two types of antennas in two different cases. The results of the coupling analyses were validated through comparison with the method of moment (MoM) results obtained with the FEKO simulation tool.
Multisource Selective Transfer Framework in Multiobjective Optimization Problems

For complex system design [e.g., satellite layout optimization design (SLOD)] in practical engineering, when launching a new optimization instance with another parameter configuration from the intuition of designers, it is always executed from scratch which wastes much time to repeat the similar search process. Inspired by transfer learning which can reuse past experiences to solve relevant tasks, many researchers pay more attention to explore how to learn from past optimization instances to accelerate the target one. In real-world applications, there have been numerous similar source instances stored in the database. The primary question is how to measure the transferability from numerous sources to avoid the notorious negative transferring. To obtain the relatedness between source and target instance, we develop an optimization instance representation method named centroid distribution, which is by the aid of the probabilistic model learned by elite candidate solutions in estimation of distribution algorithm (EDA) during the evolutionary process. Wasserstein distance is employed to evaluate the similarity between the centroid distributions of different optimization instances, based on which, we present a novel framework called multisource selective transfer optimization with three strategies to select sources reasonably. To choose the suitable strategy, four selection suggestions are summarized according to the similarity between the source and target centroid distribution. The framework is beneficial to choose the most suitable sources, which could improve the search efficiency in solving multiobjective optimization problems. To evaluate the effectiveness of the proposed framework and selection suggestions, we conduct two experiments: 1) comprehensive empirical studies on complex multiobjective optimization problem benchmarks and 2) a real-world SLOD problem. Suggestions for strategy selection coincide with the experiment results, based on which, we propose a mixed strategy to deal with the negative transfer in the experiments successfully. The results demonstrate that our proposed framework achieves competitive performance on most of the benchmark problems in convergence speed and hypervolume values and performs best on the real-world applications among all the comparison algorithms.