Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlations and screening of topological charges in Gaussian random fields

Two-point topological charge correlation functions of several types of geometric singularity in Gaussian random fields are calculated explicitly, using a general scheme: zeros of n-dimensional random vectors, signed by the sign of their Jacobian determinant; critical points (gradient zeros) of real scalars in two dimensions signed by the Hessian; and umbilic points of real scalars in two dimensions, signed by their index. The functions in each case depend on the underlying spatial correlation function of the field. These topological charge correlation functions are found to obey the first Stillinger-Lovett sum rule for ionic fluids.
Activities, Participation and Quality of Life Concepts in Children and Adolescents with Celiac Disease: A Scoping Review

Celiac disease (CD) is a food-related chronic condition and adherence to a strict gluten-free diet is the only available treatment. Adherence to the restrictive diet is challenging among children, especially adolescents. The aim was to describe existing knowledge about food-related activities, participation, and quality of life in daily life among children and adolescents with CD and to illuminate gaps in knowledge. The scoping review methodology was applied and literature searches were conducted in electronic databases. Twenty-three articles met the inclusion criteria. Food-related activities were identified, classified, and coded under the International Classification of Functioning, Disability, and Health adapted for children and youth (ICF-CY) concepts of activities and participation. A wide variety of study populations, objectives, methods, and tools involving 55 different food-related activities were found. Incorporation of the ICF-CY concepts and quality of life captures new insights into everyday challenges. Reviewing the CD literature using this different lens reveals areas yet to receive sufficient attention. Further research can deepen the understanding of daily functioning of children with CD and the underlying skills required to participate in daily food-related activities while adhering to the diet. This can lead to the development of standardized disease-specific assessment tools and suitable intervention programs.
Implementability of stroke guidelines: a pragmatic comparison between US and European recommendations using eGLIA

Due to the prevalence and severity of stroke, and the emergency of its management, the need of reaching a consensus towards its treatment is of prime importance. This paper's aim is to compare two stroke guidelines by using eGLIA in order to evaluate their implementability. Methods included a systematic assessment of the European (ESO) and American (AHA/ASA) guidelines with eGLIA and a review of literature and analysis of each recommendation with the program. The ESO performs better in Executability and Decidability, as 91.5% and 34.8% of recommendations show no barriers, compared to 81.0% and 14.0% in the AHA/ASA guidelines. On the other hand, AHA/ASA guideline have more recommendations with positive assessments in the Validity and Effect on the Process of Care (91.4% vs 83.0% and 58.1% vs 25%). Results show that ESO guidelines address a wider patient view and that the AHA/ASA guidelines are stricter, only publishing recommendations with a high level of evidence; AHA/ASA guidelines are updated with more frequency and have a clearer sequence of action. Both guidelines show some contradictions, but of minor importance. The strength of this study is the fact that the whole guidelines were read and analysed, and, although we would suggest an improvement of the tool by adding an automatic statistics chart and clarifying some questions, it showed that eGLIA should be used whenever such a text is published.
UVSD: software for detection of color underwater features

Underwater Video Spot Detector (UVSD) is a software package designed to analyze underwater video for continuous spatial measurements (path traveled, distance to the bottom, roughness of the surface etc.) Laser beams of known geometry are often used in underwater imagery to estimate the distance to the bottom. This estimation is based on the manual detection of laser spots which is labor intensive and time consuming so usually only a few frames can be processed this way. This allows for spatial measurements on single frames (distance to the bottom, size of objects on the sea-bottom), but not for the whole video transect. We propose algorithms and a software package implementing them for the semi-automatic detection of laser spots throughout a video which can significantly increase the effectiveness of spatial measurements. The algorithm for spot detection is based on the support vector machines approach to artificial intelligence. The user is only required to specify on certain frames the points he or she thinks are laser dots (to train an SVM model), and then this model is used by the program to detect the laser dots on the rest of the video. As a result the precise (precision is only limited by quality of the video) spatial scale is set up for every frame. This can be used to improve video mosaics of the sea-bottom. The temporal correlation between spot movements changes and their shape provides the information about sediment roughness. Simultaneous spot movements indicate changing distance to the bottom; while uncorrelated changes indicate small local bumps. UVSD can be applied to quickly identify and quantify seafloor habitat patches, help visualize habitats and benthic organisms within large-scale landscapes, and estimate transect length and area surveyed along video transects.
The Democratization of Innovation

Technological innovation is the transformation of a new idea or scientific discovery into standardized practice. The outcome is that people can progressively do more with the same effort, produce different outcomes with the same artifacts, and produce outcomes in new and novel ways. It was the main driver of global industrialization over the past two hundred years. Technology-based industrialization produced a regenerative industrial culture and marginalized lesser developed regions and countries in the process. Today some 85 percent of global wealth is owned by just 10 percent of the global population. One of the most serious threats to a sustainable world is global inequality in wealth and human competence. Impoverished people seem to find themselves in a low-level human developmental trap largely because of their inability to profit from technological innovation. The focus of this paper is on innovation management in less-developed, nonindustrialized, communities. Its central hypothesis is that endemic (human-centered) technological innovation rather than “technology transfer” (artifact-centered innovation) can help to alleviate entrenched poverty. It argues that effective application of technological artifacts is only possible with the support of a complex system of socioeconomic conditions (i.e., a supportive technology ecosystem). An outline for conceptualizing, planning, and managing innovation for human development is presented in the concluding sections of the paper.
EXTINCTION AND RECOLONIZATION: THEIR EFFECTS ON THE GENETIC DIFFERENTIATION OF LOCAL POPULATIONS

In this paper, we use a model by Slatkin (1977) to investigate the genetic effects of extinction and recolonization for a species whose population structure consists of an array of local demes with some migration among them. In particular, we consider the conditions under which extinction and recolonization might enhance or diminish gene flow and increase or decrease the rate of genetic differentiation relative to the static case with no extinctions. We explicitly take into account the age‐structure that is established within the array of populations by the extinction and colonization process. We also consider two different models of the colonization process, the so‐called “migrant pool” and “propagule pool” models. Our theoretical studies indicate that the genetic effects of extinction and colonization depend upon the relative magnitudes of K, the number of individuals founding new colonies, and 2Nm, twice the number of migrants moving into extant populations. We find that these genetic effects are surprisingly insensitive to the extinction rate. We conclude that, in order to assess the genetic effects of the population dynamics, we must first answer an important empirical question that is essentially ecological: is colonization a behavior distinct from migration?
We Are Not Amused: Failed Humor in Interaction

Placing failed humor within the broader category of miscommunication and drawing on a range of conversational data, this text represents the first comprehensive study of failed humor. It provides a framework for classifying the types of failure that can occur, examines the strategies used by both speakers and hearers to avoid and manage failure, and highlights the crucial role humor plays in social identity and relationship management.
SAR polarimetry techniques in remote sensing of Arctic region

SAR polarimetry is a modern technique in Earth remote sensing, it is based on the measurements of the target scattering properties at different combinations of radar signal polarizations on transmit/receive. In our report we discuss an efficiency of polarimetric radar observations for thematic mapping of Yamal peninsula using L-band PALSAR-2 and X-band TanDEM-X SAR data acquired in different seasons of the year 2016. To delineate surface dominating scattering mechanisms we applied Pauli and Cloude-Pottier decompositions. Seasonal variations of bare soils and vegetation scattering properties as well as industrial infrastructure were studied in both frequency bands. Arctic territories with presence of permafrost and lack of vegetation show lower variety of scattering mechanisms than areas in temperate latitudes. Anyways, polarimetric observations demonstrate high potential of radar remote sensing of Northern territories compared with single-channel measurements. L-band data show more opportunities for the monitoring of Arctic territories than X-band data.
Face reconstruction from a small number of feature points

This paper proposes a method for face reconstruction that makes use of only a small set of feature points. Faces can be modeled by forming linear combinations of prototypes of shape and texture information. With the shape and future information at the feature points alone, we can achieve only an approximation to the deformation required. In such an underdetermined condition, we find an optimal solution using a simple least square minimization method. As experimental results, we show well-reconstructed 2D faces even from a small number of feature points.
Lightness Perception in Tone Reproduction for High Dynamic Range Images

An anchoring theory of lightness perception comprehensively explains many characteristics of human visual sys-tem such as lightness constancy and its spectacular failures which are important in the perception of images. We present a novel approach to tone mapping of high dynamic range (HDR) images which is inspired by the anchoring theory. The key concept of this method is the decomposition of an HDR image into areas (frameworks) of consistent luminance and the local calculation of the lightness values. The net lightness of an image is calculated via the merging of the frameworks proportionally to their strength. We stress out the importance of relating the luminance to a known brightness value (anchoring) and investigate the advantages of anchoring to the luminance value perceived as white. We validate the accuracy of the lightness reproduction in the presented algorithm by simulating a well known perception experiment. Our approach does not affect the local contrast and preserves the natural colors of an HDR image due to the linear handling of luminance.
Robust optimization of factor graphs by using condensed measurements

Popular problems in robotics and computer vision like simultaneous localization and mapping (SLAM) or structure from motion (SfM) require to solve a least-squares problem that can be effectively represented by factor graphs. The chance to find the global minimum of such problems depends on both the initial guess and the non-linearity of the sensor models. In this paper we propose an approach to determine an approximation of the original problem that has a larger convergence basin. To this end, we employ a divide-and-conquer approach that exploits the structure of the factor graph. Our approach has been validated on real-world and simulated experiments and is able to succeed in finding the global minimum in situations where other state-of-the-art methods fail.
Adapting activity recognition to a person with Multi-Classifier Adaptive Training

Activity-recognition classifiers, which label an activity based on sensor data, have decreased classification accuracy when used in the real world with a particular person. To improve the classifier, a Multi-Classifier Adaptive-Training algorithm MCAT is proposed. The MCAT adapts activity recognition classifier to a particular person by using four classifiers to utilise unlabelled data. The general classifier is trained on the labelled data available before deployment and retrieved in the controlled environment. The specific classifier is trained on a limited amount of labelled data belonging to the new person in the new environment. A domain-independent meta-classifier decides whether to classify a new instance with the general or specific classifier. The final, second meta-classifier decides whether to include the new instance into the training set of the general classifier. The general classifier is periodically retrained, gradually adapting to the new person in the new environment. The adaptation results were evaluated for statistical significance. Results showed that the MCAT outperforms competing approaches and significantly increases the initial activity-recognition classifier classification accuracy.
Dysregulated Arl1, a regulator of post-Golgi vesicle tethering, can inhibit endosomal transport and cell proliferation in yeast

The yeast protein Gcs1 is a GTPase-activating protein (GAP) for Arf and Arl1 G-proteins that regulate distinct steps of vesicular transport. Absence of a GAP-independent function of Gcs1 results in dysregulated Arl1, which in turn impairs cell growth and endosomal transport. Impairing vesicle-tethering pathways removes dysregulated Arl1 and alleviates these defects.
Isolation and characterization of a novel bacterium growing via reductive dehalogenation of 2-chlorophenol

A bacterium capable of anaerobic growth via reductive dehalogenation of 2-chlorophenol was isolated from a culture enriched from sediment taken from a small stream near Lansing, Mich. The organism, designated strain 2CP-1, is a gram-negative rod ca. 3 by 0.5 micron in size and is a catalase-negative, oxidase-negative, facultative anaerobe that forms small red colonies in anaerobic media. The organism grew in reduced anaerobic mineral medium supplemented with 2-chlorophenol, acetate, and vitamins, producing phenol as a product. It did not grow when either 2-chlorophenol or acetate was omitted. The growth yield was about 3 g of protein per mol of 2-chlorophenol dechlorinated, and the doubling time was 3.7 days. Only the ortho position was dehalogenated, and additional chlorines at other positions decreased or blocked ortho dechlorination. The organism also grew with fumarate as its electron acceptor. Dechlorination activity is inducible, since cultures grown in fumarate containing medium with 2-chlorophenol rapidly dechlorinated additional 2-chlorophenol, while cultures grown in the same medium without 2-chlorophenol did not. Analysis of the organism's 16S rRNA sequence revealed that it is a member of the delta proteobacteria, more closely related to the myxobacteria than to the sulfidogenic bacteria.
A vision measurement algorithm based on four-lines structured light for robots environment perception

Environment perception system is the base of autonomous navigation and control task of a mobile robot, therefore, the strength of the environment perception will impact the intelligence of a robot directly. A vision measurement algorithm based on four-lines structured light for robots environment perception is proposed in this paper. The algorithm is implemented on an embedded image processing board which contains an FPGA and a DSP. Four line lasers project four laser lines on the object to be measured, and a camera captures the image that contains light stripes which have been modulated by the surface of the object. Then the 3D coordinates of points on the laser lines can be obtained through the distortion of light stripes in the image. Each light stripe is limited in a certain region of the image so as to not overlap with each other. Therefore, the installation angle of every laser has been limited in a certain range. A sectional line fitting algorithm is proposed to improve the accuracy of centers of light stripes, thus improving the accuracy of the whole measurement system. A number of measurement results validate the effectiveness of the proposed method. Furthermore, we combine the 3D information obtained by the proposed method with the 2D edge feature of the image to construct a rough depth map of the environment.
Least Squares SVM for Least Squares TD Learning

We formulate the problem of least squares temporal difference learning (LSTD) in the framework of least squares SVM (LS-SVM). To cope with the large amount (and possible sequential nature) of training data arising in reinforcement learning we employ a subspace based variant of LS-SVM that sequentially processes the data and is hence especially suited for online learning. This approach is adapted from the context of Gaussian process regression and turns the unwieldy original optimization problem (with computational complexity being cubic in the number of processed data) into a reduced problem (with computional complexity being linear in the number of processed data). We introduce a QR decomposition based approach to solve the resulting generalized normal equations incrementally that is numerically more stable than existing recursive least squares based update algorithms. We also allow a forgetting factor in the updates to track non-stationary target functions (i.e. for the use with optimistic policy iteration). Experimental comparison with standard CMAC function approximation indicate that LS-SVMs are well-suited for online RL.
PERSPeCT: collaborative filtering for tailored health communications

The goal of computer tailored health communications (CTHC) is to elicit healthy behavior changes by sending motivational messages personalized to individual patients. One prominent weakness of many existing CTHC systems is that they are based on expert-written rules and thus have no ability to learn from their users over time. One solution to this problem is to develop CTHC systems based on the principles of collaborative filtering, but this approach has not been widely studied. In this paper, we present a case study evaluating nine rating prediction methods for use in the Patient Experience Recommender System for Persuasive Communication Tailoring, a system developed for use in a clinical trial of CTHC-based smoking cessation support interventions.
A survey on client side and server side approaches to secure web applications

Web applications are one of the most useful platforms for the delivery of the information and service over the internet today. Day-to-Day the popularity of web application usage is increasing. Hence the web apps has to be designed for secure transformation of information from web client to web server and to mitigate the vulnerabilities. This paper presents a research survey report on constructing a secure web applications at the client side, server side and even in browsers. This paper initially gives an overview of web application, then followed by techniques or tools along with the attacks at the client side and serverside. It gives a big picture with the aim of promoting future research in this area.
Locally recurrent neural networks optimal filtering algorithms: application to wind speed prediction using spatial correlation

This paper focuses on a locally recurrent multilayer network with internal feedback paths, the IIR-MLP. The computation of the partial derivatives of the network's output with respect to its trainable weights is achieved using backpropagation through adjoints and a second order global recursive prediction error (GRPE) training algorithm is developed. Also, a local version of the GRPE is presented in order to cope with the increased computational burden of the global version. The efficiency of the proposed learning schemes, as compared to conventional gradient based methods, is tested on the wind prediction problem from 15 min to 3 h ahead on a site, using spatial correlation and facilitating measurements from nearby sites up to 40 km away.
Supporting Literature Review by Searching, Visualizing and Navigating Related Papers

Citations and their accompanying information such as citation context is a very important consideration when searching for relevant information in literature. With literature continually expanding, obtaining this information in an efficient and effective way, using a standard search tool becomes a very laborious task. In addition, scientific articles found in the literature employ rich interrelationships e.g., citation and their accompanying information making it very difficult for standard search applications to present these interrelationships to the searcher in an efficient manner. In order to alleviate this intensive task, we have designed a tool VisNavi (Visualization and Navigation) that supports scientific researchers in their literature review, in a form of a visualized star-center approach that, for the current treated paper places a citing author at its center and the richly embedded interrelationships are spun around it. The star design enables researchers to gain a clearer insight by interactively exploring these rich interrelationships along with their accompanying information. We designed a human judgment experiment to obtain a human rating on the functionality of the tool. We then cast the human rating as a reference point to improve the tool's design.
Detonation Cell Width Measurements for H2-N2O-N2-O2-CH4-NH3 Mixtures

Detonations of mixtures containing hydrogen and nitrous oxide were investigated in the GALCIT detonation tube (280 mm diameter, 7.3 m long). The facility and previous related studies are described in Akbar et al. (1997). We measured the detonation cell width, velocity and pressure for a range of equivalence ratios in three mixtures: 1) hydrogen-nitrous oxide; 2) hydrogen-nitrous oxide with 30% nitrogen dilution; 3) hydrogen-nitrous oxide with 30% nitrogen dilution further diluted 50% by air. In addition, we investigated the influence of adding 3% methane or 3% ammonia on the detonation behavior to hydrogen-nitrous oxide mixtures. Tests were conducted at initial pressures and temperatures of 70.9 kPa and 295 K, respectively. One-dimensional, steady, (ZND model) reaction zone calculations were performed with the modified Miller and Bowman hydrogen-nitrous oxide-methane-ammonia-oxygen-nitrogen mechanism (Akbar et al. 1997). These calculations were used to correlate and extrapolate the measured cell widths and also to determine the effect of initial conditions on the cell width.