Miracle-dz/UniFAR-FTUs huggingface dataset:
Lithospheric thinning and localization of deformation during Rayleigh-Taylor instability with nonlinear rheology and implications for intracontinental magmatism

[1] Thinning of mantle lithosphere due to Rayleigh-Taylor instability can be a mechanism for triggering continental magmatism near active or recently active plate boundaries. We consider whether it is also plausible as a mechanism for intracontinental magmatism, several hundred kilometers from active subduction or rifting. We perform two-dimensional Rayleigh-Taylor experiments and find that a shear stress-free top and non-Newtonian flow permit two types of instability to develop, largely dependent on how the viscosity coefficient varies with depth. For small variation with depth, with the e-folding depth scale (the interval across which the coefficient changes by a factor of e) greater than a third to a half of the thickness of the unstable layer, deformation concentrates at the ends of the layer in localized thinning and thickening zones; the middle part moves horizontally toward the region of thickening as a coherent block undergoing minimal strain. When the viscosity coefficient decreases more rapidly with depth, thinning of the layer is distributed laterally over a wide zone. Between the regions of thickening and thinning, shear strain and vertical gradients in horizontal velocity prevent this area from moving as a coherent block. The rheological exponent, n, that relates strain rate to stress in the constitutive equation controls the degree of localization of the downwelling and upwelling: the width varies as ≈n−1/2. In intraplate settings where a shear stress-free top condition could be applicable, high-stress crystalline plasticity could provide a mechanism for the narrow zones of thinning and upwelling, which would facilitate decompression related volcanism.
AP-2α Induces Epigenetic Silencing of Tumor Suppressive Genes and Microsatellite Instability in Head and Neck Squamous Cell Carcinoma

Background Activator protein 2 alpha (AP-2α) is involved in a variety of physiological processes. Increased AP-2α expression correlates with progression in various squamous cell carcinomas, and a recent publication found AP-2α to be overexpressed in ∼70% of Head and Neck Squamous Cell Carcinoma (HNSCC) patient samples. It was found to repress transcription of the tumor suppressor gene C/CAAT Enhancer Binding Protein alpha (C/EBPα), and its binding site correlated with upstream methylation of the C/EBPα promoter. Therefore, we investigated the potential for AP-2α to target methylation to additional genes that would be relevant to HNSCC pathogenesis. Principal Findings Stable downregulation of AP-2α stable by shRNA in HNSCC cell lines correlated with decreased methylation of its target genes' regulatory regions. Furthermore, methylation of MLH1 in HNSCC with and without AP-2α downregulation revealed a correlation with microsatellite instability (MSI). ChIP analysis was used to confirm binding of AP-2α and HDAC1/2 to the targets. The effects of HDAC inhibition was assessed using Trichostatin A in a HNSCC cell line, which revealed that AP-2α targets methylation through HDAC recruitment. Conclusions These findings are significant because they suggest AP-2α plays a role not only in epigenetic silencing, but also in genomic instability. This intensifies the potential level of regulation AP-2α has through transcriptional regulation. Furthermore, these findings have the potential to revolutionize the field of HNSCC therapy, and more generally the field of epigenetic therapy, by targeting a single gene that is involved in the malignant transformation via disrupting DNA repair and cell cycle control.
Research and Design for Mobile Terminal-Based on Smart Home System

Smart Home is one of emerging application domains of The Internet of Things. It minimizes user's intervention in monitoring home settings and controlling home appliances. Mobile terminal has become an important part of Smart Home now. This paper presents an effective approach to solve the potential problems when using mobile terminal in Smart Home. In the smart home system, a kind of simple, easy to operate, and suitable for family application smart home mobile terminal software is designed, on an Android platform. The software is a combination of local and remote control to control household equipment. Moreover, this paper implement four usage scenarios to demonstrate the approach's fea- sibility and efficiency, i.e., measuring home's environmental conditions, intelligent lighting control, remote control, and security warning. It has been proved well, that the user is able to control household appliances remotely, as well as moni- toring sensor data sampled from indoor environment. The test results show the mobile terminal can work effectively.
Determining the Optimal Combination of Trial Division and Fermat's Factorization Method

The process of finding the prime factorization of large integers has many important applications in the field of computer science, most notably in cryptography. Currently the best-known algorithms for factoring very large numbers are subexponential. However, the subexponential algorithms invoke the factorization of smaller numbers, and the best algorithms for factoring smaller numbers are still the exponential algorithms such as trial division, Fermat's factorization method, and Lehman's algorithm. In this investigation, a highly optimized combination of trial division with Fermat's factorization method was implemented to attain much faster performance than trial division alone. Since all of the optimizations involved the modulus function, they were implemented by creating thousands of threads and discarding those which were essentially useless before running the rest. Trial division alone was the best method to use for numbers smaller than 48 bits in size, the combination of trial division and Fermat's method implemented in the investigation was the best for numbers between 48 and 70 bits in size, and Lehman's method was the best for numbers larger than 70 bits in size. The algorithm used in this investigation resulted in faster performance than any other known algorithm for numbers between 48 and 70 bits in size.
WBI-NER: The impact of domain-specific features on the performance of identifying and classifying mentions of drugs

Named entity recognition (NER) systems are often based on machine learning techniques to reduce the labor-intensive development of hand-crafted extraction rules and domain-dependent dictionaries. Nevertheless, time-consuming feature engineering is often needed to achieve state-of-the-art performance. In this study, we investigate the impact of such domain-specific features on the performance of recognizing and classifying mentions of pharmacological substances. We compare the performance of a system based on general features, which have been successfully applied to a wide range of NER tasks, with a system that additionally uses features generated from the output of an existing chemical NER tool and a collection of domain-specific resources. We demonstrate that acceptable results can be achieved with the former system. Still, our experiments show that using domain-specific features outperforms this general approach. Our system ranked first in the SemEval-2013 Task 9.1: Recognition and classification of pharmacological substances.
Assessment of HER2 testing patterns, HER2+ disease, and the utilization of HER2-directed therapy in early breast cancer

Context Determining human epidermal growth factor receptor 2 (HER2) status is critical for the management of early-stage breast cancer (ESBC). An understanding of HER2 testing practices can provide insight into how test results influence the use of HER2-directed therapy. Objective To assess HER2 testing, HER2+ disease, and HER2-directed therapy in ESBC at the Huntsman Cancer Institute before and after the 2007 American Society of Clinical Oncology and College of American Pathologist (ASCO/CAP) guidelines on HER2 testing were published. Methods Patients were identified from an institutional tumor registry. HER2 testing patterns and results were examined using a chart review of pathology and clinical notes. Patient characteristics, HER2+ rate, and trastuzumab use were evaluated descriptively. Discordance rate with reflex testing (immunohistochemistry [IHC]2+ retested by fluorescence in situ hybridization [FISH]) was also evaluated. Results A total of 1,459 women were included (mean age: 57 years). The rate of HER2+ disease was 17% (number [N] =245). The discordance rate between IHC2+ and FISH was 10%. After the 2007 ASCO/CAP guidelines, fewer tumors were classified as IHC3+ (16% post- versus 21.9% pre-2007), more tumors were characterized as IHC2+ (26.4% post- versus 20.7% pre-2007), and the overall HER2+ rate was decreased (18.7% versus 21.9%), but this was not statistically significant (P=0.519). Most patients with HER2+ ESBC received HER2-targeted therapy (N=185). Conclusion The HER2+ rate was 17% and within the range of the reported rates in the literature. Reflex testing identified additional HER2+ tumors by approximately 10%, and should be considered a potential quality indicator. ASCO/CAP HER2 testing guidelines in 2007 appeared to impact the interpretation and classification of HER2+ tumors.
Forward-and-backward diffusion processes for adaptive image enhancement and denoising

Signal and image enhancement is considered in the context of a new type of diffusion process that simultaneously enhances, sharpens, and denoises images. The nonlinear diffusion coefficient is locally adjusted according to image features such as edges, textures, and moments. As such, it can switch the diffusion process from a forward to a backward (inverse) mode according to a given set of criteria. This results in a forward-and-backward (FAB) adaptive diffusion process that enhances features while locally denoising smoother segments of the signal or image. The proposed method, using the FAB process, is applied in a super-resolution scheme. The FAB method is further generalized for color processing via the Beltrami flow, by adaptively modifying the structure tensor that controls the nonlinear diffusion process. The proposed structure tensor is neither positive definite nor negative, and switches between these states according to image features. This results in a forward-and-backward diffusion flow where different regions of the image are either forward or backward diffused according to the local geometry within a neighborhood.
Reconstructing the life-cycle of a mixed language: An exploration of Ecuadoran Media Lengua

Aims and objectives: This study explores the assertion that bilingual mixed languages are only diachronically stable if they are not spoken together with both of the contributing source languages. Ecuadoran Media Lengua, which combines all-Quichua morphosyntax with nearly all lexical roots replaced by Spanish-derived forms, coexists in three communities with both Spanish and Quichua, having arrived in each community in successive generations. Methodology and design: Trilingual speakers (Quichua, Media Lengua, Spanish) participated in four interactive tasks: speeded translation, speeded acceptability judgments, language classification, and lexical decision. Data and analysis: For each task, the calculated rate of separation of Quichua and Media Lengua was the response variable for a series of linear mixed-effects models, with community (and when appropriate, age group) as a fixed effect. Findings/conclusions: The results suggest that a mixed language spoken together with the languages that supplied both the lexical roots and the morphosyntax can maintain its integrity for a generation or two, but the perceptual boundaries circumscribing the mixed language eventually become more permeable. They point to a significant correlation between the chronology of language contacts and the perceptual stability of Media Lengua, which is greatest when the only competing language is Quichua, somewhat less when Spanish is acquired later as the second language, and lowest when Spanish is one of the early acquired or native languages alongside Quichua. Originality: This is the first attempt to test the putative diachronic stability of a mixed language by means of synchronic experimental data.
Experimental Study on Neuronal Spike Sorting Methods

When recording extracellular neural activity, it is often necessary to distinguish action potentials arising from distinct cells near the electrode tip, a process commonly referred to as spike sorting or action potential sorting. Sorting of neuronal spikes plays a very important role in coding of neural information, which is a prerequisite for studying the brain function. In this paper, five major action potential classification methods including Template Matching, Wavelet Transform, Principal Component Analysis, Back-Propagation (BP) Neural Network, Two-stage Radius Basis Function Network are studied. Under the conditions of different levels of background noise, the performances of these methods are tested. This work may be helpful to choose classification method.
The Czech republic before the new millennium : politics,parties and gender

Steven Saxonberg focuses on three main issues in Czech politics: the development of the Czech party system, the "Klaus phenomenon," and gender issues. Recurring themes throughout the book are the role and limits of formal political institutions, the importance of social psychological elements and the lack of influence which ideology has on party politics and voting during the transitional period.
Extracting reputation in multi agent systems by means of social network topology

The problem of calculating a degree of reputation for agents acting as assistants to the members of an electronic community is discussed and a solution presented. Usual reputation mechanisms rely on feedback after interaction between agents. An alternative way to establish reputation is related with the position of each member of a community within the corresponding social network. We propose a method based on this idea, which is also used by well-known ranking algorithms, discuss its properties as well as experimental results and compare them to other reputation mechanisms for electronic communities supported by agents. The method proposed uses only local information in order to extract reputation and it is able to adapt automatically to the topology of the network or graph.
EXTRACTION OF GEOMETRIC INFORMATION ON HIGHWAY USING TERRESTRIAL LASER SCANNING TECHNOLOGY

Laser scanning technology with high positional accuracy and high density automation will be widely applied in vast range of fields including geomatics. Especially, the development of laser scanning technology enabling long range information extraction is increasing its full use in civil engineering. The purpose of this study is to extract accurate highway geometric information taking the advantages of scanning technology. Fulfilling this goal, the information of target highway’s three-dimensional data was obtained through terrestrial laser scanning technology (TLSs). In accordance with the result from target highway’s geometric information extraction using the information above, laser scanning technology showed faster speed and better accuracy on highway geometric information extraction with reduced cost compared to traditional methods. * Corresponding author.
Convolutional Neural Networks for Branch Retinal Vein Occlusion recognition?

Branch Retinal Vein Occlusion (BRVO) is one of the most common retinal diseases that could impair people's vision seriously if it is not timely diagnosed and treated. It would save a lot of time and money for both medical institutions and patients if BRVO could be well recognized automatically. In this paper, we propose to exploit Convolutional Neural Networks (CNN) for BRVO recognition. We propose patch-based method and image-based voting method to implement the recognition. As it could learn abstract and useful features, CNN can achieve a high recognition accuracy. The accuracy of CNN is over 97%. Experimental results demonstrate the efficiency of our proposed CNN based methods for BRVO recognition.
The Challenges of Building Advanced Mechatronic Systems

Mechatronics is an engineering discipline integrating the fields of mechanical engineering, electrical engineering and computer science. While the word "mechatronics" already has a long history, it is only the last ten years that we see their application all around us. Cars, CD players, washing machines, railways are all examples of mechatronic systems. The main characteristic (and driving force) of recent advances is the progressively tighter coupling of mechanic and electronic components with software. This makes software engineering (together with network technology) the main computer science discipline involved in mechatronics. In this paper we survey current developments and discuss future trends in mechatronics, in particular from a software engineering point of view. The future of mechatronics will specifically see a move towards a high degree of adaptibility and self-organisation. This poses new challenges on software engineering, especially on modelling, code generation and analysis. We exemplify existing as well as future strands by a collaborative research and development project of a mechatronic rail system from the University of Paderborn.
Adaptive Color Restoration and Luminance MSR Based Scheme for Image Enhancement

A non-linear image enhancement algorithm called Adaptive Color Restoration and Luminance MSR Based Scheme for Image Enhancement (ACRL_MSR) is presented in this paper to improve the visual quality of color images under poor lighting condition. The Red, Green, Blue(R, G, B) color bands are enhanced by adjusting luminance which is automatically enhanced to a proper range based on Human Visual System (HVS) via the adaptive parameter. At the same time the percentage of R, G, B in three bands of the output images is almost as same as the original one to prevent color distortion. The color bands in dark/shadow zones are enhanced partly by luminance and partly by their original color values to make these areas more colorful and natural. The new algorithm is observed from the experiment results, which produces more natural and vivid color than those by MSRCR and LB_MSR.
Sparse Update for Loopy Belief Propagation: Fast Dense Registration for Large State Spaces

A dense point-based registration is an ideal starting point for detailed comparison between two neuroanatomical objects. This paper presents a new algorithm for global dense point-based registration between anatomical objects without assumptions about their shape. We represent mesh models of the surfaces of two similar 3D anatomical objects using a Markov Random Field and seek correspondence pairs between points in each shape. However, for densely sampled objects the set of possible point by point correspondences is very large. We solve the global non-rigid matching problem between the two objects in an efficient manner by applying loopy belief propagation. Typically loopy belief propagation is of order m^3 for each iteration, where m is the number of nodes in a mesh. By avoiding computation of probabilities of configurations that cannot occur in practice, we reduce this to order m^2. We demonstrate the method and its performance by registering hippocampi from a population of individuals aged 60-69. We find a corresponding rigid registration, and compare the results to a state-of-the-art technique and show comparable accuracy. Our method provides a global registration without prior information about alignment, and handles arbitrary shapes of spherical topology.
IMPLEMENTATION OF THE CONJUGATE GRADIENT METHOD FOR SPARSE MATRIX USING GPU

A vast number of problems in engineering do not have analytical solutions or those solutions have a high computational cost, therefore numerical methods are needed to get to those solutions. A significant amount of linear or nonlinear systems of equations are obtained by discretization methods, such as the finite difference method or the finite element method, applied to differential equations. In general, those systems are very large and have scattered characteristics that help in its numerical solution. However, those linear systems have large dimensions and they require a huge computational power to achieve the solution in a suitable time interval. In this work, we present an implementation of the Conjugate Gradient method with sparse matrix for parallel processing on graphics processing unit (GPU), reducing the computational cost to get the solution for that type of equations. After this algorithm was implemented, tested and validated, it was used to solve the problem of the 2D wave equation which was discretized using the finite differences method. Finally, the method was compared in two distinct implementations (CPU serial form and GPU parallel form).
Elements of a Dynamic GPS Test Standard

Abstract. There has been much advancement in GPS receiver technology in recent years. GPS accuracy, especially the dynamic (moving) accuracy, is very important in precision agriculture for collecting data and controlling variable rate inputs. Typical applications of GPS receivers in agriculture include yield monitoring, parallel tracking and variable-rate application of fertilizer. Perhaps more important is the relevance of existing tests (primarily static tests), and the inability to compare receivers because of a variety of reporting formats. This project evaluated several different receivers. Test results showed that different performance reporting techniques make it difficult to evaluate and compare the performance of receivers. Also, the static performance of receivers was not indicative of dynamic performance. A proposed framework for a dynamic test standard for agricultural GPS receivers is presented.
On the application of symbolic regression and genetic programming for cryptanalysis of symmetric encryption algorithm

The aim of the paper is to show different point of view on the problem of cryptanalysis of symmetric encryption algorithms. Our dissimilar approach, compared to the existing methods, lies in the use of the power of evolutionary principles which are in our cryptanalytic system applied with leveraging of the genetic programming (GP) in order to perform known plaintext attack (KPA). Our expected result is to find a program (i.e. function) that models the behavior of a symmetric encryption algorithm DES instantiated by specific key. If such a program would exist, then it could be possible to decipher new messages that have been encrypted by unknown secret key. The GP is employed as the basis of this work. GP is an evolutionary algorithm-based methodology inspired by biological evolution which is capable of creating computer programs solving a corresponding problem. The symbolic regression (SR) method is employed as the application of GP in practical problem. The SR method builds functions from predefined set of terminal blocks in the process of the GP evolution; and these functions approximate a list of input value pairs. The evolution of GP is controlled by a fitness function which evaluates the goal of a corresponding problem. The Hamming distance, a difference between a current individual value and a reference one, is chosen as the fitness function for our cryptanalysis problem. The results of our experiments did not confirmed initial expectation. The number of encryption rounds did not influence the quality of the best individual, however, its quality was influenced by the cardinality of a training set. The elimination of the initial and final permutations had no influence on the quality of the results in the process of evolution. These results showed that our KPA GP solution is not capable of revealing internal structure of the DES algorithm's behavior.