Miracle-dz/UniFAR-FTUs huggingface dataset:
New Insights into the Structure and Mode of Action of Mo-CBP3, an Antifungal Chitin-Binding Protein of Moringa oleifera Seeds

Mo-CBP3 is a chitin-binding protein purified from Moringa oleifera Lam. seeds that displays inhibitory activity against phytopathogenic fungi. This study investigated the structural properties and the antifungal mode of action of this protein. To this end, circular dichroism spectroscopy, antifungal assays, measurements of the production of reactive oxygen species and microscopic analyses were utilized. Mo-CBP3 is composed of 30.3% α-helices, 16.3% β-sheets, 22.3% turns and 30.4% unordered forms. The Mo-CBP3 structure is highly stable and retains its antifungal activity regardless of temperature and pH. Fusarium solani was used as a model organism for studying the mechanisms by which this protein acts as an antifungal agent. Mo-CBP3 significantly inhibited spore germination and mycelial growth at 0.05 mg.mL−1. Mo-CBP3 has both fungistatic and fungicidal effects, depending on the concentration used. Binding of Mo-CBP3 to the fungal cell surface is achieved, at least in part, via electrostatic interactions, as salt was able to reduce its inhibitory effect. Mo-CBP3 induced the production of ROS and caused disorganization of both the cytoplasm and the plasma membrane in F. solani cells. Based on its high stability and specific toxicity, with broad-spectrum efficacy against important phytopathogenic fungi at low inhibitory concentrations but not to human cells, Mo-CBP3 has great potential for the development of new antifungal drugs or transgenic crops with enhanced resistance to phytopathogens.
Nonlinear Compensation and Frequency Domain Equalization for Wireless Communication By CHEN FENG LIBO YU

i ACKNOWLEDGEMENT In writing this master thesis, we would like to gratefully acknowledge the help and support received from teachers and friends, both past and present, without whom this paper would have been a much poorer offering. First of all, our gratitude goes out to Ali Behravan, our thesis supervisor. He gave us a lot valuable suggestions and advice and spent his time giving us some tutorial when he was very busy with his own work, which made our work going smoothly. Thank Prof. Thomas Eriksson for giving us a chance to work in such an interesting field and help us to get a room to work. Chen would like to thank Prof. Erik Ström and Arne Svensson, whose lectures are of great help to the theoretical part of the thesis. In particular, Libo would like to thank Prof. Piotr Starski, Prof. Joakim Johansson and all of his lecturers for their helpful teaching at Chalmers. ii ABSTRACT OFDM arises to take the place of single carrier, when high data rate is required in broad-band communication. OFDM splits the data into multi-carriers so as to decrease the data rate on each sub-carrier, which avoids severe problem of ISI existing in SC. However, OFDM also faces another distortion problem introduced by nonlinear amplifier and large PAPR. Several compensation methods such as predistorter and nonlinear equalizer are investigated, and frequency domain equalizer (FDE) is added at the receiver to invert the fading channel. In deduction and simulation, Saleh model is used to approximate the nonlinear amplifier and Clarke's model is taken to simulate the Rayleigh Fading Channel. For the predistorter, Least-Mean-Square (LMS) algorithm is used to update the coefficients of predistorter, and here, 5th order polynomials are used. For the nonlinear equalizer (time domain equalizer), Recursive-Least-Square (RLS) algorithm, which is a fast convergence algorithm compared with others, is used to get the coefficients. Several approaches to implement the FDE are discussed and simulated: Zero-Forcing equalizer, LMS equalizer, RLS equalizer, ZF estimator and RLS estimator. After comparison, ZF algorithm has the best convergence speed and SNR performance, and RLS algorithm has almost the same performance only a little worse. Coefficients got using LMS algorithm can't converge well, so LMS is not practical for it requires a long training sequence. On the other hand, the application of FDE makes single carrier modulation a potentially valuable alternative to OFDM. In SC system, different FDE methods mentioned …
An Exact Algorithm Based on MaxSAT Reasoning for the Maximum Weight Clique Problem

Recently, MaxSAT reasoning is shown very effective in computing a tight upper bound for a Maximum Clique (MC) of a (unweighted) graph. In this paper, we apply MaxSAT reasoning to compute a tight upper bound for a Maximum Weight Clique (MWC) of a wighted graph. We first study three usual encodings of MWC into weighted partial MaxSAT dealing with hard clauses, which must be satisfied in all solutions, and soft clauses, which are weighted and can be falsified. The drawbacks of these encodings motivate us to propose an encoding of MWC into a special weighted partial MaxSAT formalism, called LW (Literal-Weighted) encoding and dedicated for upper bounding an MWC, in which both soft clauses and literals in soft clauses are weighted. An optimal solution of the LW MaxSAT instance gives an upper bound for an MWC, instead of an optimal solution for MWC. We then introduce two notions called the Top-k literal failed clause and the Top-k empty clause to extend classical MaxSAT reasoning techniques, as well as two sound transformation rules to transform an LW MaxSAT instance. Successive transformations of an LW MaxSAT instance driven by MaxSAT reasoning give a tight upper bound for the encoded MWC. The approach is implemented in a branch-and-bound algorithm called MWCLQ. Experimental evaluations on the broadly used DIMACS benchmark, BHOSLIB benchmark, random graphs and the benchmark from the winner determination problem show that our approach allows MWCLQ to reduce the search space significantly and to solve MWC instances effectively. Consequently, MWCLQ outperforms state-of-the-art exact algorithms on the vast majority of instances. Moreover, it is surprisingly effective in solving hard and dense instances.
A Study on Features of the CRFs-based Chinese Named Entity Recognition

This paper studies on features of the Chinese Named Entity Recognition (CNER) based on the Conditional Random Fields (CRFs). These Features which include common attributes, feature templates varying in window size and sequence label sets are very important for CNER. Taking advantages of these features or their combination can greatly improve the performance of CNER. The paper aims to provide a reference for selecting features of CNER through a series of experiments. The experiment results show that appropriate features or their combinations, such as single Chinese character, part-of-speech (POS), preﬁx and suﬃx can contribute to the score of F-measure based on CRFs. Meanwhile, the results indicate that selecting suitable feature templates and sequence label sets, not only can improve the performance of CNER, but also shorten the model-training process and reduce the system resource consumption.
Joint Scale-Spatial Correlation Tracking with Adaptive Rotation Estimation

Boosted by large and standardized benchmark datasets, visual object tracking has made great progress in recent years and brought about many new trackers. Among these trackers, correlation filter based tracking schema exhibits impressive robustness and accuracy. In this work, we present a fully functional correlation filter based tracking algorithm which is able to simultaneously model target appearance changes from spatial displacements, scale variations, and rotation transformations. The proposed tracker first represents the exhaustive template searching in the joint scale and spatial space by a block-circulant matrix. Then, by transferring the target template from the Cartesian coordinate system to the Log-Polar coordinate system, the circulant structure is well preserved for the target even after whole orientation rotation. With these novel representation and transformation, object tracking is efficiently and effectively performed in the joint space with fast Fourier Transform. Experimental results on the VOT 2015 benchmark dataset demonstrate its superior performance over state-of-the-art tracking algorithms.
THE INFORMATION AGE : A PHILOSOPHICAL PERSPECTIVE

This paper presents the author’s view of the current trend in the world of information science. Based on the current trend in the development of modern science and technology, a theory regarding what lies ahead beyond the information age is taking shape. The purpose of this paper is to present this theory for further discussion. We have made tremendous advances in science and technology. I believe, however, considering the vast dimensions of the known (and unknown) universe, in spite of all the valuable advances in science and technology, our information and knowledge of the universe are almost nothing compared to the mind boggling sizes and mysteries of the universe. It appears, therefore, that any significant advance in discovering the mysteries of the universe, considering what is ahead, demands a radically different approach. Information science is leading and evolving to this approach, in which unification has a key role. The paper concludes with a discussion of a general trend, approaching a unified theory, unified knowledge, and/or unified intelligence.
A multiclass kernel perceptron algorithm

Original kernel machines (e.g., support vector machine, least squares support vector machine, kernel Fisher discriminant analysis, kernel perceptron algorithm, and etc.) were mainly designed for binary classification. How to effectively extend them for multiclass classification is still an ongoing research issue. Rosenblatt's linear perceptron algorithm for binary classification and its corresponding multiclass linear version are the simplest learning machines according to their algorithmic routines. Kernel perceptron algorithm for binary classification was constructed by extending linear perceptron algorithm with Mercer kernel. In this paper, a multiclass kernel perceptron algorithm is proposed by combining multiclass linear perceptron algorithm with binary kernel perceptron algorithm, which can deal with multiclass classification problem directly and nonlinearly in a simple iterative procedure. Two artificial examples and four benchmark datasets are used to evaluate the performance of our multiclass method. The experimental results show that our algorithm could achieve the good classification performance
A brief survey of self-organization in wireless sensor networks

Summary Many natural and man-made systems exhibit self-organization, where interactions among components lead to system-wide patterns of behavior. This paper first introduces current, scientific understanding of self-organizing systems and then identifies the main models investigated by computer scientists seeking to apply self-organization to design large, distributed systems. Subsequently, the paper surveys research that uses models of self-organization in wireless sensor networks to provide a variety of functions: sharing processing and communication capacity; forming and maintaining structures; conserving power; synchronizing time; configuring software components; adapting behavior associated with routing, with disseminating and querying for information, and with allocating tasks; and providing resilience by repairing faults and resisting attacks. The paper closes with a summary of open issues that must be addressed before self-organization can be applied routinely during design and deployment of senor networks and other distributed, computer systems. Copyright © 2007 John Wiley & Sons, Ltd.
Principles governing amino acid composition of integral membrane proteins: application to topology prediction.

A new method is suggested here for topology prediction of helical transmembrane proteins. The method is based on the hypothesis that the localizations of the transmembrane segments and the topology are determined by the difference in the amino acid distributions in various structural parts of these proteins rather than by specific amino acid compositions of these parts. A hidden Markov model with special architecture was developed to search transmembrane topology corresponding to the maximum likelihood among all the possible topologies of a given protein. The prediction accuracy was tested on 158 proteins and was found to be higher than that found using prediction methods already available. The method successfully predicted all the transmembrane segments in 143 proteins out of the 158, and for 135 of these proteins both the membrane spanning regions and the topologies were predicted correctly. The observed level of accuracy is a strong argument in favor of our hypothesis.
Gypsy students in the UK: the impact of ‘mobility’ on education

Abstract This paper argues that Gypsy students in primary and secondary education in the UK are marginalised because of ambiguous understandings of their ‘mobility’. Drawing on research conducted on the south coast of England, it examines Gypsy families’ experiences of education. Despite often describing their identity in relation to travelling or mobility, few families’ lifestyles were characterised by actual movement or nomadism. Teachers and educationalists meanwhile cite the need to deliver a ‘mobile’ rather than a ‘sedentary’ education for Gypsy students. The Department for Communities and Local Government recently defined Gypsy ethnicity in direct relation to a nomadic lifestyle. This is problematic as the association between Gypsy ethnicity and nomadism is itself questionable and may be better understood in more nuanced terms reflecting the relationship between identity and ‘mobility’. This paper argues that ‘mobility’ is understood to define Gypsy difference in a way that excludes students.
Detecting Salient Objects via Spatial and Appearance Compactness Hypotheses

Object-level saliency detection has been attracting a lot of attention, due to its potential enhancement in many high-level vision tasks. Many previous methods are based on the contrast hypothesis which regards the regions with high contrast in a certain context as salient. Although the contrast hypothesis is valid in many cases, it cannot handle some difficult cases. To make up for the weakness of contrast hypothesis, we propose a novel compactness hypothesis which assumes salient regions are more compact than background spatially and in appearance. Based on compactness hypotheses, we implement an effective object-level saliency detection method, which is demonstrated to be effective even in difficult cases. In addition, we present an adaptive multiple saliency maps fusion framework which can automatically select saliency maps of high quality according to three quality assessment rules. We evaluate the proposed method on four benchmark datasets and the comparable performance as the state-of-the-art methods has been achieved.
Quantitative assessment of 2D versus 3D visualisation modalities

Despite the growth of stereoscopic technology, there has been very little research done into the quantitative benefits of 3D over 2D visualisation systems. This study seeks to quantify and compare the human brain's response to 2D versus 3D images using EEG technology, in particular focusing on the trend in changes between the p300 response resulting from 2D and 3D stimulus sequences using the oddball paradigm. It was found that the p300 response to the 3D stimuli degraded at a faster rate than the 2D stimuli as target spotting task difficulty was raised.
Dynamics of Broad Emission-Line Region in NGC 5548: Hydromagnetic Wind Model versus Observations

We analyze the results of long-term observations of the broad-line region (BLR) in the Seyfert 1 galaxy NGC 5548 and provide a critical comparison with the predictions of a hydromagnetically driven outflow model of Emmering, Blandford, & Shlosman. We use this model to generate a time series of C IV line profiles that have responded to a time-varying continuum. Our modifications to the model include cloud emission anisotropy, cloud obscuration, a CLOUDY-generated emissivity function, and a narrow-line component which is added to the BLR component to generate the total line profiles. The model is driven with continuum input based on the monitoring campaigns of NGC 5548 reported in Clavel et al. and Korista et al., and the line strengths, profiles, and lags are compared with the observations. Our model is able to reproduce the basic features of C IV line variability in this active galactic nucleus, i.e., time evolution of the profile shape and strength of the C IV emission line without varying the model parameters. The best-fit model provides the effective size, the dominant geometry, the emissivity distribution, and the three-dimensional velocity field of the C IV BLR and constrains the mass of the central black hole to ~3 × 107 M☉. The inner part of the wind in NGC 5548 appears to be responsible for the anisotropically emitted C IV line, while its outer part remains dusty and molecular, thus having similar spectral characteristics to a molecular torus, although its dynamics is fundamentally different. In addition, our model predicts a differential response across the C IV line profile, producing a red-side-first response in the relative velocity interval of 3000 km s-1 to 6000 km s-1 followed by the blue mid-wing and finally by the line core. Based on the comparison of data and model cross-correlation functions and one- and two-dimensional transfer functions, we find that the rotating outflow model is compatible with observations of the BLR in NGC 5548.
Nonmotor Symptoms in Patients with PARK2 Mutations

Decreased 123I-meta-iodobenzylguanidine (MIBG) uptake in MIBG myocardial scintigraphy, olfactory dysfunction, and rapid eye movement (REM) sleep behavior disorder (RBD) are considered useful early indicators of Parkinson disease. We investigated whether patients with PARK2 mutations exhibited myocardial sympathetic abnormalities using MIBG scintigraphy, olfactory dysfunction using the Sniffin' Sticks olfactory test, and RBD using polysomnography. None of the examined patients had RBD, and all except 1 patient exhibited an increase in the olfactory threshold. Moreover, one of the oldest patients exhibited impairment in identification and discrimination. Of 12 patients with PARK2 mutations, 4 patients, who were older than patients without abnormal uptake, exhibited decreased MIBG uptake. The results obtained in this study suggest that some patients with PARK2 mutations have increased thresholds of olfactory function and myocardial sympathetic dysfunction as nonmotor symptoms.
LATENT LIFE OF ARTERIES

When a segment of artery, killed by heat, formalin or glycerin is transplanted, it undergoes a rapid degeneration. Its muscle fibers disappear while the tissue of the host reacts by building a new wall of connective tissue. When the transplanted vessel has been preserved in a condition of latent life, no degeneration of the wall occurs, or the wall undergoes only partial degeneration. The muscle fibers can keep their normal appearance, even for a long time after the operation. It is, therefore, demonstrated that arteries can be preserved outside of the body in a condition of unmanifested actual life. The best method of preservation consists of placing the vessels, immersed in vaselin, in an ice box, the temperature of which is slightly above the freezing point. From a surgical standpoint, the transplantation of preserved vessels can be used with some safety. When the arteries were kept in defibrinated blood or vaselin and in cold storage, the proportion of positive results was 75 and 80 per cent. , and this can probably be increased.
Improved Stochastic Random Walker Segmentation based on Gaussian Filtering

Image segmentation is the process to capture the object from the background and it is a difficult task when a vision of the object is in stochastic region. Here introduce in this paper extension of stochastic random walker segmentation method. In stochastic random walker segmentation, a weighted graph is built from the image, where the each pixel considered as a node and edge weights depend on the image gradient between the pixels. For given seed regions, the probability are evaluated for a stochastic random walk on this graph starting at a pixel to end in one of the seed regions. The problem associated with existing method is the number of random variable (gray-level value in random order) in stochastic images. These random variables increase the graph sizes of stochastic images. If the graph size will increase, consequently execution time would also increase. To overcome these problems, the proposed “Improved stochastic random walker segmentation based on Gaussian filtering” for stochastic image segmentation. In proposed method Gaussian filter has been used for the removal of uncertain gray level and which in turn reduce the noise level and the resultant graph size of corresponding stochastic image, then apply stochastic random walker segmentation method which may help to decrease the execution time of the segmentation process.
Recommender systems: a market-based design

Recommender systems have been widely advocated as a way of coping with the problem of information overload for knowledge workers. Given this, multiple recommendation methods have been developed. However, it has been shown that no one technique is best for all users in all situations. Thus we believe that effective recommender systems should incorporate a wide variety of such techniques and that some form of overarching framework should be put in place to coordinate the various recommendations so that only the best of them (from whatever source) are presented to the user. To this end, we show that a marketplace, in which the various recommendation methods compete to offer their recommendations to the user, can be used in this role. Specifically, this paper presents the principled design of such a marketplace; detailing the auction protocol and reward mechanism and analyzing the rational bidding strategies of the individual recommendation agents.
On the Graph Building Problem in Camera Networks

Abstract In this paper, the problem of building a model of a sensor (camera) network from observations is considered. By model, we mean a graph where the nodes represent states that are observable and distinguishable by the sensor network and edges are the feasible transitions among these states: the edges are also weighted by the probability of transition from one state to another. Remarkably, since merely static observations are not sufficient to discern all states in the networked system, the dynamics of transition is also considered. In this respect, the proposed graph model appears falling into the class of hidden Markov models, where the discovery of hidden states is made possible by exploiting the temporal evolution of the transitions and the implementation of a splitting procedure of previously identified graph nodes.