Miracle-dz/UniFAR-FTUs huggingface dataset:
A new pixel-level multi-focus image fusion algorithm based on evolutionary strategy

A new method is developed to merge two spatially registered images with diverse focus based on multiresolution wavelet decomposition and evolutionary strategy (ES). At first, the wavelet decomposition without down-sampling is used to perform an addition of wavelet high-frequency components of each image. Then, in terms of difference of area-based energy feature in two original images, ES is adopted to partition the region and perform the fusion. The experimental results show that the proposed method can achieve better fusion performance than wavelet transform (WT) method.
Does Publicly Provided Home Care Substitute for Family Care? Experimental Evidence with Endogenous Living Arrangements

This paper analyzes the extent to which publicly provided formal (paid) home care substitutes for unpaid care provided informally by family and friends. Unlike most previous research, we recognize that the choice among alternative combinations of formal and informal care depends on the type of living arrangement chosen, and that these living arrangement choices in turn are influenced by the public provision of formal home care. Using data from a social experiment, we find that a generous public home care program significantly increases the probability that unmarried persons will live independently and reduces the probability of living in shared households or in nursing or personal care homes. However, any substitution effects-either direct effects on provision of informal care given living arrangement or indirect effects due to living arrangement changes-appear to be small.
On minimal models of the Region Connection Calculus

Region Connection Calculus (RCC) is one primary formalism of qualitative spatial reasoning. Standard RCC models are continuous ones where each region is infinitely divisible. This contrasts sharply with the predominant use of finite, discrete models in applications. In a recent paper, Li et al. (2004) initiate a study of countable models that can be constructed step by step from finite models. Of course, some basic problems are left unsolved, for example, how many non-isomorphic countable RCC models are there? This paper investigates these problems and obtains the following results: (i) the exotic RCC model described by Gotts (1996) is isomorphic to the minimal model given by Li and Ying (2004); (ii) there are continuum many non-isomorphic minimal RCC models, where a model is minimal if it can be isomorphically embedded in each RCC model.
Rapid Spectrotemporal Plasticity in Primary Auditory Cortex during Behavior

Complex natural and environmental sounds, such as speech and music, convey information along both spectral and temporal dimensions. The cortical representation of such stimuli rapidly adapts when animals become actively engaged in discriminating them. In this study, we examine the nature of these changes using simplified spectrotemporal versions (upward vs downward shifting tone sequences) with domestic ferrets (Mustela putorius). Cortical processing rapidly adapted to enhance the contrast between the two discriminated stimulus categories, by changing spectrotemporal receptive field properties to encode both the spectral and temporal structure of the tone sequences. Furthermore, the valence of the changes was closely linked to the task reward structure: stimuli associated with negative reward became enhanced relative to those associated with positive reward. These task- and-stimulus-related spectrotemporal receptive field changes occurred only in trained animals during, and immediately following, behavior. This plasticity was independently confirmed by parallel changes in a directionality function measured from the responses to the transition of tone sequences during task performance. The results demonstrate that induced patterns of rapid plasticity reflect closely the spectrotemporal structure of the task stimuli, thus extending the functional relevance of rapid task-related plasticity to the perception and learning of natural sounds such speech and animal vocalizations.
A fast classification system for decoding of human hand configurations using multi-channel sEMG signals

This paper proposes a novel fast classification system consisting of feature extraction and classifier to decode human hand configurations from multi-channel surface electromyogram (sEMG) signals that allows real-time classification of human movement intention as well as prothesis control. In order to enhance the learning speed and the performance of the classifier, we used a supervised feature extraction method (called class-augmented principal component analysis) and a fast learning classifier (called extreme learning machine). Experimental results show that the proposed classification system quickly learns and decodes the human hand configuration with about 92% accuracy.
k-means discriminant maps for data visualization and classification

Over the years, many dimensionality reduction algorithms have been proposed for learning the structure of high dimensional data by linearly or non-linearly transforming it into a low-dimensional space. Some techniques can keep the local structure of data, while the others try to preserve the global structure. In this paper, we propose a linear dimensionality reduction technique that characterizes the local and global properties of data by firstly applying k-means algorithm on original data, and then finding the projection by simultaneously globally maximizing the between-cluster scatter matrix and locally minimizing the within-cluster scatter matrix, which actually keeps both local and global structure of data. Low complexity and structure preserving are two main advantages of the proposed technique. The experiments on both artificial and real data sets show the effectiveness and novelty of proposed algorithm in visualization and classification tasks.
Object Tracking Based on Machine Vision and improved SVDD Algorithm

Abstract Object tracking is an important research topic in the applications of machine vision, and has made great progress in the past decades, among which the technique based on classification is a very efficient way to solve the tracking problem. The classifier classifies the objects and background into two different classes, where the tracking drift caused by noisy background can be effectively handled by one-class SVM. But the time and space complexities of traditional one-class SVM methods tend to be high, which makes it do not scale well with the number of training sample, and limits its wide applications. Based on the idea proposed by Support Vector Data Description, we present an improved SVDD algorithm to handle object tracking efficiently. The experimental results on synthetic data, tracking results on car and plane demonstrate the validity of the proposed algorithm.
Toward controlling dielectrophoresis

Dielectrophoresis is the motion of a particle due to the interaction between a non‐uniform electric field and its induced dipole moment in the particle. With the advent of the fabrication technology at micro/nano‐scale, dielectrophoresis is actively applied in manipulating, separating, and characterizing micro/nano‐sized particles such as DNA, cells, proteins, nanotubes and nanoparticles. In this paper we introduce control engineers to dielectrophoresis by suggesting several possible research topics and performing a case study: a time‐optimal control of a dielectrophoretic system with a state constraint. Copyright © 2005 John Wiley & Sons, Ltd.
Identification of the putative binding pocket of valerenic acid on GABAA receptors using docking studies and site‐directed mutagenesis

β2/3‐subunit‐selective modulation of GABAA receptors by valerenic acid (VA) is determined by the presence of transmembrane residue β2/3N265. Currently, it is not known whether β2/3N265 is part of VA's binding pocket or is involved in the transduction pathway of VA's action. The aim of this study was to clarify the localization of VA's binding pocket on GABAA receptors.
Distributed source coding using syndromes (DISCUS): design and construction

We address the problem of distributed source coding, i.e. compression of correlated sources that are not co-located and/or cannot communicate with each other to minimize their joint description cost. In this work we tackle the related problem of compressing a source that is correlated with another source which is available only at the decoder. In contrast to prior information-theoretic approaches, we introduce a new construction and practical framework for tackling the problem based on the judicious incorporation of channel coding principles into this source coding problem. We dub our approach as distributed source coding using syndromes (DISCUS). We focus in this paper on trellis-structured constructions of the framework to illustrate its utility. Simulation results confirm the power of DISCUS, opening up a new and exciting constructive playing-ground for the distributed source coding problem. For the distributed coding of correlated i.i. d. Gaussian sources that are noisy versions of each other with "correlation-SNR" in the range of 12 to 20 dB, the DISCUS method attains gains of 7-15 dB in SNR over the Shannon-bound using "naive" independent coding of the sources.
The size of broad-line regions of low-luminosity active galactic nuclei

We study the size of broad-line regions (BLRs) of low-luminosity active galactic nuclei (AGN), also called 'dwarf AGN',defined as having L Hα ≤ 10 41 erg s- 1 . We more than double the sample size analysed previously by Wang & Zhang. In this study we first confirm our previous result -that the sizes of BLRs of low-luminosity AGN are larger than those expected from the empirical relation R BLRs -L Hα valid for 'normal' AGN (Seyfert Is and quasars), except for objects with accretion rate m Hα > 10 -5.5 . Secondly, we find a positive correlation between the linewidth of the narrow emission lines (as a tracer of velocity dispersion and thus bulge and black hole mass) and the size of BLRs for both normal and low-luminosity AGN. In this paper we find a non-linear dependence of the BLR sizes of low-luminosity AGN on black hole masses. We also show that the sizes of BLRs are more strongly dominated by the 'specific accretion rate' m Hα , defined as m Hα = L Hα /L Edd , than by the masses of their central black holes. An expected result of this is that the size of the emission regions of low-ionization broad Ha of NGC 4395 should be consistent with the value from the empirical relation of R BLRs -L Hα , according to the high accretion rate.
Optimal Adaptation and Mitigation to Climate Change in Small Environmental Economies

This paper compares the optimal dynamic choices between policies of mitigation and adaptation for three economies: Brazil, Chile and the United States. The focus is on the optimal role of mitigation and adaptation for "environmentally small economies", i. e. , economies that are witnessing an exogenous increase in emissions to which they are contributing very little. The simulations lead to three main conclusions. First, small economies should concentrate their environmental efforts, if any, on adaptation. This is not a recommendation that such economies indulge in free-riding. Instead, it is based on considerations of cost effectiveness, ceteris paribus. Second, small economies that are unable to spend enough on adaptation may end up spending less on mitigation owing to their impoverishment as a result of negative climate shocks. Third, higher mitigation expenditures may arise not only as a result of greater optimal adaptation expenditures, but also because of increased adaptation to the incentives for mitigation provided by richer countries.
Network Decontamination from a Black Virus

In this paper, we consider the problem of decontaminating a network from a black virus (BV) using a team of mobile system agents. The BV is a harmful process which, like the extensively studied black hole (BH), destroys any agent arriving at the network site where it resides; when that occurs, unlike a black hole which is static by definition, a BV moves, spreading to all the neighboring sites, thus increasing its presence in the network. If however one of these sites contains a system agent, that clone of the BV is destroyed (i.e., removed permanently from the system). The initial location of the BV is unknown a priori. The objective is to permanently remove any presence of the BVfrom the network with minimum number of site infections (and thus casualties). The main cost measure is the total number of agents needed to solve the problem. This problem integrates in its definition both the harmful aspects of the classical black hole search problem (where however the dangerous elements are static) with the mobility aspects of the classical intruder capture or network decontamination problem(where however there is no danger for the agents). Thus, it is the first attempt to model mobile intruders harmful not only forth sites but also for the agents. We start the study of this problem by focusing on some important classes of interconnection networks: grids, tori, and hypercubes. For each class we present solution protocols and strategies for the team of agents, analyze their worst case complexity, and prove their optimality.
A Systematic Procedure for Accurately Approximating Lognormal-Sum Distributions

Lognormal approximation to a sum of lognormal variables has been widely adopted in wireless communications, and its justification is based on the intuition that after taking a logarithm, a lognormal sum can converge to a Gaussian variable. In this paper, we show how to fit a lognormal sum with the best candidate from a large family of distributions, including the lognormal, by resorting to a systematic procedure of model selection and parameter estimation. It is found that, over a general parameter setting, a much better approximation for the lognormal sums in the log scale is given by the Pearson type-IV distribution whose parameters can easily be determined through simple arithmetic operations. Numerical examples are presented for illustration.
The ML-Model for Multi-layer Social Networks

In this paper we introduce a new model to represent an interconnected network of networks. This model is fundamental to reason about the real organization of on-line social networks, where users belong to and interact on different networks at the same time. In addition we extend traditional SNA measures to deal with this multiplicity of networks and we apply the model to a real dataset extracted from two microblogging sites.
Sector Rotation Strategy Applied on the Swedish Stock Market : Do Swedish sector indices experience momentum effects?

This thesis is an empirical analysis on momentum effects on the Swedish stock exchange’s sector indicesduring the period 2001 to 2009. The momentum effect is investigated by buying previous winner andshort selling previous losers with holding and formation periods over an intermediate time period (1-12month period). Our results are not coherent with previous studies conducted on the U.S market or theworld market, instead our results indicate that the Swedish stock exchange’s sector indices experience acontrarian effect over the intermediate time period. The results are adjusted for systematic risk and aresignificant on the 5%-level. Our result show that the weak form of the efficient market hypothesis isviolated and we therefore believe that a demand exists for easy and convenient investment vehicles withsector specific exposure, which could have a positive effect on the efficiency of the market.
Spatiotemporal dynamics and optical vortices in a photorefractive phase-conjugate resonator

A truncated modal expansion approach is used to study the spatiotemporal dynamics of a phase-conjugate resonator as a function of Bragg detuning. The numerical results reveal a rich variety of behaviors. Emphasis is given to the spatial distribution of optical vortices, their trajectories and their relationship to the beam's spatial coherence. The limitations of the model are discussed and experimental results are presented for comparison with the model's predictions and assessment of its soundness.
Initializing K-Means Clustering Algorithm using Statistical Information

K-means clustering algorithm is one of the best known algorithms used in clustering; nevertheless it has many disadvantages as it may converge to a local optimum, depending on its random initialization of prototypes. We will propose an enhancement to the initialization process of k-means, which depends on using statistical information from the data set to initialize the prototypes. We show that our algorithm gives valid clusters, and that it decreases error and time. General Terms Data Mining, Unsupervised Learning, Data Clustering.
Research on the application of extended-aperture algorithm based on uniformly vector hydrophone linear array

The performance of this new algorithm was evaluated by extensive simulations. Extended-aperture algorithm was developed based on vector-hydrophone linear array to improve the accuracy of DOA (direction of arrival) estimation. Experiment of vector-hydrophone linear array composed of three vector hydrophones was conducted at SongHua Lake of Jilin Province to test the validity and applicability of the proposed algorithm. The experimental results indicated that higher resolution for DOA estimation could be obtained using enlarged aperture vector-hydrophone linear array than conventional beamforming. Direction-finding precision of source become higher as the intersensor spacing increasing keeping the same elements.
Robust Design of Docking Hoop for Recovery of Autonomous Underwater Vehicle with Experimental Results

Control systems prototyping is usually constrained by model complexity, embedded system configurations, and interface testing. The proposed control system prototyping of a remotely-operated vehicle (ROV) with a docking hoop (DH) to recover an autonomous underwater vehicle (AUV) named AUVDH using a combination of software tools allows the prototyping process to be unified. This process provides systematic design from mechanical, hydrodynamics, dynamics modelling, control system design, and simulation to testing in water. As shown in a three-dimensional simulation of an AUVDH model using MATLAB™/Simulink™ during the launch and recovery process, the control simulation of a sliding mode controller is able to control the positions and velocities under the external wave, current, and tether forces. In the water test using the proposed Python-based GUI platform, it shows that the AUVDH is capable to perform station-keeping under the external disturbances.
An improved SURF algorithm based local image symmetry scoring scheme

This paper presents an efficient feature detection algorithm based on the classical SURF (Speeded Up Robust Feature) detector. The image features are represented and scored with respect to its local symmetry property. The local symmetry has natural properties of scale and transformation invariants, and also insensitive to illumination change and local noise. By the proposed feature descriptor, the calculation of 64-dimensional vectors in SURF algorithm can be reduced to 16-dimensional vector respectively. The local symmetry score is defined as the sum of minimum distance between each feature point and its neighboring points in an image based on the image intensities. The algorithm is experimented with some real images and the results are compared with the original SURF algorithm to show its improvement.
Accurate Stereo Matching by Two-Step Energy Minimization

In stereo matching, cost-filtering methods and energy-minimization algorithms are considered as two different techniques. Due to their global extent, energy-minimization methods obtain good stereo matching results. However, they tend to fail in occluded regions, in which cost-filtering approaches obtain better results. In this paper, we intend to combine both the approaches with the aim to improve overall stereo matching results. We show that a global optimization with a fully connected model can be solved by cost-filtering methods. Based on this observation, we propose to perform stereo matching as a two-step energy-minimization algorithm. We consider two Markov random field (MRF) models: 1) a fully connected model defined on the complete set of pixels in an image and 2) a conventional locally connected model. We solve the energy-minimization problem for the fully connected model, after which the marginal function of the solution is used as the unary potential in the locally connected MRF model. Experiments on the Middlebury stereo data sets show that the proposed method achieves the state-of-the-arts results.
Integration of Visualization Techniques and Completion Strategy to Improve Learning in Computer Programming

Computer Programming is known as a difficult subject that requires complex cognitive task by the majority of students even for those who are taking Computer Science courses. It is normal for programming instructors to provide their students with alternative methods to help them in the learning process. When learning to program for the first time, students and lecturers usually are facing with difficulties in learning and teaching the subject. Mobile learning provides a great opportunity for learners to switch from rigid learning to fun learning using supported elements. The ability of mobile devices only is still not enough for learners to master in learning programming without the right options in techniques applied. This study will explore how Visualization Technique and Completion Strategy can be used to improve learning in a computer programming subject.
Automatic query structuring from sentences for Japanese web retrieval

This paper proposes a query structuring method by analyzing Japanese natural language sentences that users input. We use linguistic structure information obtained by morphological analysis and dependency parsing on the natural language inputs. Our proposed method converts a sentence into a structured query on the basis of linguistic structures such as phrases and modification relations. Such structured queries enable effective retrieval with reasonable efficiency. To evaluate our proposed method, we compare it with the method using no structures at all, using a 100-gibabyte web collection mostly written in Japanese. We demonstrate through the experiments that mean average precision was improved about 8.6% using our query structuring method alone, and about 22.5% by combining our query structuring method with pseudo-relevance feedback.