Miracle-dz/UniFAR-FTUs huggingface dataset:
Elastic Localization : Improvements on Distributed , Range Free Localization for Wireless Sensor Networks

Numerous wireless sensor network algorithms assume that individual sensors possess location information. However, state of the art localization algorithms often achieve acceptable performance only under restrictive assumptions. For instance, some algorithms necessitate regular sensor deployment or centralized computations. Other algorithms require a high proportion of position aware nodes or the ability to accurately infer emission distance or emission direction of received radio signals. We propose the Elastic Localization Algorithm (ELA), a distributed, scalable, robust and efficient localization algorithm. ELA only presumes that a few percent of the sensors know their location and that an estimation of the maximum communication range is available. We provide extensive simulation data describing the precision, the convergence speed, and the communication load of ELA, using networks composed of thousands of sensors. In addition, we submit ELA to testing considering the influence of maximum range and beacon position misestimation, irregular radio patterns, asynchronous nodes, packet losses, particular topologies, and sensor mobility. Our results indicate that ELA performs better than four state of the art, distributed, range-free localization algorithms, in terms of accuracy. Its low communication and execution costs make its implementation possible on resource constrained sensor nodes. The key advantage of ELA is its robustness to phenomena occurring in real wireless sensor networks, such as asymmetry, packet loss, and irregular radio ranges.
Particle Swarm and Ant Colony Algorithms and Their Applications in Chinese Traveling Salesman Problem

Intelligent heuristic optimization methods have increasingly attracted the attentions and interests of many scholars in recent years. Such as genetic algorithm, ant colony algorithm, particle swarm optimization, simulated annealing, etc. They have become effective tools to solve the TSP and other NP-hard combinatorial optimization problems. The particle swarm optimization (PSO) algorithm is a population-based evolutionary algorithm which was proposed by Eberhart and Kennedy in 1995 (Eberhart & Kennedy, 1995). The PSO simulates the behaviors of bird flocking. Suppose the following scenario: a group of birds are randomly searching food in an area. There is only one piece of food in the area being searched. No bird knows where the food is. But they know how far the food is in each iteration. So what’s the best strategy to find the food? An effective one is to follow the birds which are nearest to the food. The PSO firstly generates a random initial population, the population contains numbers of particles, each particle represents a potential solution of system, each particle is represented by three indexes: position, velocity, fitness. Firstly endows each particle a random velocity, in flight, it dynamically adjusts the velocity and position of particles through their own flight experience (personal best position), as well as their companions’ (global best position). The evolutions of particles have a clear direction, the whole group will fly to the search region with higher fitness through continuous learning and updating. This process will be repeated until reach the default maximum iterations or the predetermined minimum fitness. The PSO is therefore in essence a fitnessbased and group-based global optimization algorithm, whose advantage lies in the simplicity of algorithm, easy implementing, fast convergence and less parameters. Presently, the PSO has been widely applied in function optimization, neural network training, pattern classification, fuzzy system control and other applications. Whereas, like other intelligent optimization algorithms, the PSO may occur the phenomenon that particle oscillates in the vicinity of optimal solution during searching in the search space, therefore the entire particle swarm performs a strong "convergence", and it is easily trapped in local minimum points, which makes the swarm lose diversity. Thus it has the weakness of solving complex problems, and it is difficult to obtain a more accurate solution in the late evolution. Many scholars proposed some improved algorithms (Yuan et al., 2007; Xu et al., 2008; Lovbjerg, 2001), which improve the search capabilities of the elementary PSO in different aspects.
The Case for Fine-Grained Stream Provenance

The current state of the art for provenance in data stream management systems (DSMS) is to provide provenance at a high level of abstraction (such as, from which sensors in a sensor network an aggregated value is derived from). This limitation was imposed by high-throughput requirements and an anticipated lack of application demand for more detailed provenance information. In this work, we first demonstrate by means of well-chosen use cases that this is a misconception, i.e., coarse-grained provenance is in fact insufficient for many application domains. We then analyze the requirements and challenges involved in integrating support for fine-grained provenance into a streaming system and outline a scalable solution for supporting tuple-level provenance in DSMS.
WHY “ A PROMINENT ROLE FOR MONEY ” ?

The ECB’s monetary policy strategy embodies “a prominent role for money”. In this paper, the conceptual and empirical bases for this prominent role are reviewed. The paper then describes the ECB’s reference value for monetary growth, discussing how it serves as an important vehicle for operationalizing this prominent role of money in the formulation and communication of the single monetary policy in the euro area. JEL classification: E5, E58, E52, E41.
XCMS Online: a web-based platform to process untargeted metabolomic data.

Recently, interest in untargeted metabolomics has become prevalent in the general scientific community among an increasing number of investigators. The majority of these investigators, however, do not have the bioinformatic expertise that has been required to process metabolomic data by using command-line driven software programs. Here we introduce a novel platform to process untargeted metabolomic data that uses an intuitive graphical interface and does not require installation or technical expertise. This platform, called XCMS Online, is a web-based version of the widely used XCMS software that allows users to easily upload and process liquid chromatography/mass spectrometry data with only a few mouse clicks. XCMS Online provides a solution for the complete untargeted metabolomic workflow including feature detection, retention time correction, alignment, annotation, statistical analysis, and data visualization. Results can be browsed online in an interactive, customizable table showing statistics, chromatograms, and putative METLIN identities for each metabolite. Additionally, all results and images can be downloaded as zip files for offline analysis and publication. XCMS Online is available at https://xcmsonline.scripps. edu.
Hierarchical Eigen Template Matching for Target Detection

The template matching is the basic technique of the object detection such as the defect inspection and OCR (Optical Character Recognition). It has a problem which requires a lot of computational time for detecting the XY displacement and the rotational angle of the target object. We have proposed the eigen template method for this problem. The proposed method can efficiently calculate correlation between images. However, when an input image is complicated, the method failed to detect the target object correctly. Additionally, the computational time of the method remains large still. In this paper, we propose the edge based eigen template method with robustness for the complex image compared with the previous method. Moreover, we describe an efficient matching algorithm for the eigen template method using the narrowing down process. In the evaluation experiments, we show that the proposed method can detect the target object faster and more robust to the complex image than the previous methods.
Detrended Fluctuation Analysis (DFA) and R-R Interval variability: A new linear segmentation algorithm

The detrended fluctuation analysis (DFA) [1] method is used to quantify the fractal-like scaling properties of the variability of cardiac parameters, i.e. R-R interval data. DFA has proved to be a useful index in predicting survival in heart failure. Several authors have proposed to break the numerical series in two zones with linear slopes. The breakpoint between segments is empirically situated at log n equals 1,1. We have used the DFA method to process records of passive head up tilt (H.U.T) test done to patients who have suffered one or more faint episodes. Slopes of numerical series obtained from real signals neither change at a specific point, nor have only one breakpoint, especially if they correspond to pathological records. On the contrary some of them present abrupt changes in slope. This fact could be hidden in the traditional computation if changes in slope have different sign, but are detected in our approach. A method that tracks the DFA function, detect breakpoints, and obtain a continuous set of lines between them, and their corresponding slopes, is proposed.
Dense Depth Estimation from Omnidirectional Images

This paper addresses the problem of dense disparity estimation in networks of omnidirectional cameras. We propose to map omnidirectional images on the 2-sphere, and we perform disparity estimation directly on the sphere in order to preserve the geometry of images. We ﬁrst perform rectiﬁcation of the images in the spherical domain. Then we formulate a global energy minimization problem for the estimation of disparity on the sphere. We solve the optimization problem with a graph-cut algorithm, and we show that the proposed solution outperforms common methods based on block matching, for both synthetic scenes with varying complexity and complex natural scenes. Then, we propose a parallel implementation of the graph-cut algorithm that is able to perform dense depth estimation with an improved speed-up, which makes it suitable for realtime applications. Finally, we extend the spherical depth estimation framework to networks of multiple cameras, and we design two methods for dense depth estimation that are based respectively on disparity computation with pairs of images, or computation of inverse depth values. Both methods are shown to provide promising results towards depth estimation in networks of omnidirectional cameras. keywords: disparity estimation depth estimation omnidirectional imaging spherical images camera networks
Neuropsychological Assessment in Conversion Disorder 2

Objective: Conversion disorder is characterized by functional impairment in motor, sensory, or neurovegetative systems that cannot be explained by a general medical condition. Diagnostic systems emphasize the absence of an organic basis for the dysfunction observed in conversion disorder. Nevertheless, there is a growing body of data on the specific functional brain correlates of conversion symptoms, particularly those obtained via neuroimaging and neurophysiological assessment. The present study aimed to determine if there are differences in measures of cognitive functioning between patients with conversion disorder and healthy controls. The hypothesis of the study was that the patients with conversion disorder would have poorer neurocognitive performance than the controls.
Dynamic Painterly Droplets

We present a straightforward painterly rendering algorithm that composites lightweight models of paint droplets onto a virtual canvas, producing visually pleasing still images and animations. Our method adapts the size, shape, density, and placement of these droplets to the content of a single input image, typically a photograph. The droplets can be rendered dynamically over multiple frames to produce temporally coherent, loopable animations. Our implementation is GPUaccelerated and runs on both desktop and mobile platforms.
Molecular Characterization of the β-N-Acetylglucosaminidase of Escherichia coliand Its Role in Cell Wall Recycling

ABSTRACT The β-N-acetylglucosaminidase of Escherichia coli was found to have a novel specificity and to be encoded by a gene (nagZ) that maps at 25.1 min. It corresponds to an open reading frame, ycfO, whose predicted amino acid sequence is 57% identical to that of Vibrio furnissiiExoII. NagZ hydrolyzes the β-1,4 glycosidic bond betweenN-acetylglucosamine and anhydro-N-acetylmuramic acid in cell wall degradation products following their importation into the cell during the process for recycling cell wall muropeptides. From amino acid sequence comparisons, the novel β-N-acetylglucosaminidase appears to be conserved in all 12 gram-negative bacteria whose complete or partial genome sequence data are available.
A hierarchical approach to the Multi-Vehicle SLAM problem

In this paper we present a novel hierarchical solution to the Multi-Vehicle SLAM (MVSLAM) problem by extending the recently developed random finite set (RFS) based SLAM filter framework. Instead of fusing control and measurement data at each time step, we introduce a RFS Single-Vehicle SLAM based sub-mapping process, where each robot periodically produces a local sub-map of its vicinity and communicates the resultant sub-map along with the sequence of applied control commands for further fusion into a higher level MVSLAM algorithm, reducing the required network bandwidth and computational power at the fusion node. Our solution is based on the factorization of MVSLAM posterior into a product of the vehicle trajectories posterior and the landmark map posterior conditioned on the vehicle trajectory. The landmarks and the measurements are modelled as RFSs, instead of using data from exteroceptive sensors, measurements are derived from the produced local sub-maps. The vehicle trajectories posterior is estimated using a Rao-Blackwellised particle filter, while the landmark map posterior is estimated using a Gaussian mixture (GM) probability hypothesis density (PHD) filter.
Using data mining technology to design an intelligent CIM system for IC manufacturing

This paper aims to explain a specific intelligent computer integrated manufacturing system by integrating the following five major domains: computer integrated manufacturing, data warehouse, online analytical processing, data mining and artificial intelligence. The data mining system makes use of the decision tree algorithm and classification model in exploring the meaningful information, which is useful in the process of decision making. Subsequently, the rules discovered by the data mining system are expressed through the rule based knowledge presentation method of the expert system. The intelligent CIM system is applied to semiconductor packing factories and also point at the great fluctuation of dynamic random access memory prices. The contribution can increase business competitiveness, reduce production cost, and promote the rate of available promise for order.
Orbitofrontal volume reductions during emotion recognition in patients with major depression.

BACKGROUND Major depressive disorder is associated with both structural and functional alterations in the emotion regulation network of the central nervous system. The relation between structural and functional changes is largely unknown. Therefore, we sought to determine the relation between structural differences and functional alterations during the recognition of emotional facial expressions. METHODS We examined 13 medication-free patients with major depression and 15 healthy controls by use of structural T1-weighted high-resolution magnetic resonance imaging (MRI) and functional MRI during 1 session. We set the statistical threshold for the analysis of imaging data to p < 0.001 (uncorrected). RESULTS As shown by voxel-based morphometry, depressed patients had reductions in orbitofrontal cortex volume and increases in cerebellar volume. Additionally, depressed patients showed increased activity during emotion recognition in the middle frontal cortex, caudate nucleus, precuneus and lingual gyrus. Within this cerebral network, the orbitofrontal volumes were negatively correlated in depressed patients but not in healthy controls with changes in blood oxygen level-dependent signal in the middle frontal gyrus, caudate nucleus, precuneus and supplementary motor area. LIMITATIONS Our results are limited by the relatively small sample size. CONCLUSIONS This combined functional and structural MRI study provides evidence that the orbitofrontal cortex is a key area in major depression and that structural changes result in functional alterations within the emotional circuit. Whether these alterations in the orbitofrontal cortex are also related to persistent emotional dysfunction in remitted mental states and, therefore, are related to the risk of depression needs further exploration.
College Students’ Experiences With Anonymous Social Media: Implications for Campus Racial Climate

Anonymous social media platforms, such as Whisper and Yik Yak, have elicited controversy within the field of education over the past few years. Through the use of such platforms, students can anonymously participate in cyberbullying (Patchin & Hinduja, 2012; Whittaker & Kowalski, 2015). In particular, Yik Yak, which was released at the end of 2013 (and subsequently shut down in April 2017), allowed yakkers (users of the application) to anonymously post and view yaks (posts by users) within a 1.5-mile radius. The platform had reached a valuation of $400+ million, was used on over 1,600 campuses, and has arguably influenced student suicides, suspensions, and threats of violence (Gismondi, 2015; Koenig, 2014; Shontell, 2015; Wagstaff, 2014). The use of Yik Yak and similar applications as tools to convey discriminatory and insensitive remarks is a conversation that extends far beyond college campuses. For example, during the 2015 NASPA Student Affairs Administrators in Higher Education annual conference, several attendees yakked what NASPA determined to be hateful posts, which ultimately resulted in the organization’s release of a statement denouncing the posts (Thomason, 2015). Most recently, Eaton and Gismondi (2016) invited student affairs and higher education (SA/HE) practitioners to consider the repercussions of anonymity in digital spaces and to question what can be learned from Yik Yak, including the ability to gauge real-time climate assessments on campus. This latter consideration draws attention not only to the positive and negative impacts of Yik Yak on students’ experiences and their perceptions of campus climate, but also to the roles of SA/HE practitioners in these spaces.
An ontology-driven topic mapping approach to multi-level management of e-learning resources

An appropriate use of various pedagogical strategies is fundamental for the effective transfer of knowledge in a flourishing e-learning environment. The resultant information superfluity, however, needs to be tackled for developing sustainable e-learning. This necessitates an effective representation and intelligent access to learning resources. Topic maps address these problems of representation and retrieval of information in a distributed environment. The former aspect is particularly relevant where the subject domain is complex and the later aspect is important where the amount of resources is abundant but not easily accessible. Conversely, effective presentation of learning resources based on various pedagogical strategies along with global capturing and authentication of learning resources are an intrinsic part of effective management of learning resources. Towards fulfilling this objective, this paper proposes a multi-level ontology-driven topic mapping approach to facilitate an effective visualization, classification and global authoring of learning resources in e-learning.
Development of a real-time fluid simulator for an interactive virtual environment: Improvement of density feedback in SPH

In this research, we developed a real-time fluid simulator for a haptic interface to display a virtual environment including 3-dimensional fluid by using smoothed particles hydrodynamics (SPH). SPH is a type of particle method and is suitable for a fast computation; however, it is difficult to incorporate sufficient fluid incompressibility in SPH for simulating a real-time process. This is because SPH was originally developed to study compressible fluid dynamics. In this paper, we propose a new control method using nonlinear feedback to compensate for elements that interfere with incompressible motions and propose its implementation method that does not significantly increase the computation time. Finally, simulations are performed to compare the proposed method with a conventional method, and the effectiveness of the proposed method is quantitatively demonstrated from the standpoints of computation time and incompressibility.
The Management of Innovation

The paper analyzes the organization of the RD (b) to rationalize commonly observed features in research employment contracts, such as shop rights, trailer clauses, and the "hired for" doctrine; (c) to discuss the robustness of the so-called Schumpeterian hypotheses to endogenizing the organization of RD and (d) to provide a rationale for
Merging the CEM2K and LAQGSM Codes with GEM2 to Describe Fission and Light-fragment Production

We present the current status of the improved Cascade-Exciton Model (CEM) code CEM2k and of the Los Alamos version of the Quark-Gluon String Model code LAQGSM. To describe fission and light-fragment (heavier than He4) production, both CEM2k and LAQGSM have been merged with the GEM2 code of Furihata. We present some results on proton- and deuteron-induced spallation, fission, and fragmentation reactions predicted by these extended versions of CEM2k and LAQGSM. We show that merging CEM2k and LAQGSM with GEM2 allows us to describe many fission and fragmentation reactions in addition to the spallation reactions which are already relatively well described. Nevertheless, the standard version of GEM2 does not provide a completely satisfactory description of complex particle spectra, heavy-fragment emission, and spallation yields, and is not yet a reliable tool for applications. We conclude that we may choose to use a model similar to the GEM2 approach in our codes, but it must be significantly extended and further improved. We observe that it is not sufficient to analyze only A and Z distributions of the product yields when evaluating this type of model, as is often done in the literature; instead it is important to study all the separate isotopic yields as well as the spectra of light particles and fragments.
Image Retrieval Based on Integration Between YCbCr Color Histogram and Texture Feature

Content-Based Image Retrieval (CBIR) allows automatically extracting target images according to objective visual contents of the image itself. Content-based image retrieval has many application areas such as, education, commerce, military, searching, biomedicine and web image classification. This paper proposes a new image retrieval system, which uses color and texture information to form the feature vectors and Bhattacharyya distance and new similarity measure to perform the feature matching. This framework integrates the ycbcr color histogram which represents the global feature and edge histogram as local descriptor to enhance the retrieval results. The proposed technique is proper for precisely retrieving images even in deformation cases such as geometric deformations and noise. It is tested on a standard image databases such as Wang and UCID databases. Experimental work shows that the proposed approach improves the precision and recall of retrieval results compared to other approaches reported in literature.