Miracle-dz/UniFAR-FTUs huggingface dataset:
Interactive stream surface placement a hybrid clustering approach supported by tree maps

The ability of a CFD engineer to study, capture, and visualise 3D flow simulation data is a challenge. Stream surfaces are a useful tool for visualising 3D flow because of their ability to convey many field attributes from their structure. It is important that the CFD engineer can interact with, and examine specific characteristics of the CFD data. We introduce an interactive, cluster based stream surface placement strategy for structured and unstructured CFD data. A two-phase hybrid clustering algorithm is used to visualise interesting subsets of the flow. An interactive tree map interface provides a visual overview and enables interactive selection of cluster details corresponding to interesting features of the data at which to place stream surfaces. We demonstrate the performance and effectiveness of our interactive framework on a range of flow simulations and provide domain expert evaluation of the results, providing valuable insight for the CFD engineers.
Cubic mean value coordinates

We present a new method for interpolating both boundary values and gradients over a 2D polygonal domain. Despite various previous efforts, it remains challenging to define a closed-form interpolant that produces natural-looking functions while allowing flexible control of boundary constraints. Our method builds on an existing transfinite interpolant over a continuous domain, which in turn extends the classical mean value interpolant. We re-derive the interpolant from the mean value property of biharmonic functions, and prove that the interpolant indeed matches the gradient constraints when the boundary is piece-wise linear. We then give closed-form formula (as generalized barycentric coordinates) for boundary constraints represented as polynomials up to degree 3 (for values) and 1 (for normal derivatives) over each polygon edge. We demonstrate the flexibility and efficiency of our coordinates in two novel applications, smooth image deformation using curved cage networks and adaptive simplification of gradient meshes.
Tropical Medicine & International Health

In a cross-sectional study of all Chagas disease patients diagnosed from 2005 to 2015 during blood donation at the Catalan Blood Bank, Chagas disease patients with positive S. stercoralis serology had a higher proportion of positive T. cruzi RT-PCR in peripheral blood than those with negative serology, which reflects the potential immunomodulatory effects of S. stercoralis in T. cruzi co-infected patients. 1436–1441
A domain-specific embedded language approach for the scripting of game artificial intelligence

A computer game’s artificial intelligence is often scripted using a domain-specific language designed specifically for the game. An approach to language design and prototyping, particularly appropriate to domain-specific ones, is that of embedding a language within a general purpose host language. In this paper, we present an approach to artificial intelligence scripting using embedded languages where the embedded language scripts become data objects in the host language enabling parametrized strategies and their manipulation through host language programs. We identify three classes of scripts: (i) fixed scripts written directly in the embedded language; (ii) adaptive scripts, dynamically generated and modified by the host language programs whilst reacting to the state of the game; and (iii) adaptive scripts which, by means of multi-tiered language frameworks, allow for different levels of abstraction in the specification of game artificial intelligence.
Spatial and temporal monitoring of wildfires in Golestan province using remote sensing data

Wildfires are one of the most significant factors of ecosystem change. Knowing the wildfire regime (frequency, intensity, and distribution pattern) is essential in wildfire management. This research aims to analyze the spatiotemporal pattern of wildfires in Golestan in 2001-2021 using MODIS data, burned area product (MCD64A1). For this purpose, the annual and monthly frequency, as well as the trend of wildfires based on types of forest, pasture, and crop cover, were statistically analyzed. The local Moran pattern analysis method and kernel density function were used to analyze the spatial dynamics of wildfire. The results showed that 18,462 wildfires occurred in Golestan, the highest of which was in 2010, with 2,517 wildfires (13.8%). The lowest number of wildfires, with only 57 events (0.5%), was in 2001. Based on the local Moran model results and the kernel density function, the wildfires' extent and intensity were greater in the plains and foothills to the south and southeast of Golestan. The lowest extent and intensity of the wildfire corresponded to the eastern parts of the province. The frequency of wildfires was higher in the hot period of the year (spring and summer). However, the period of occurrence of wildfire and the peak of wildfire changes in different uses. The wildfire zones in June were wider and more intense than in other months. The frequency and spatial extent of wildfires in agricultural lands from May to July, pasture lands in July, August, and September, and forest lands in November and December were more than in other months. Weather conditions play a significant role in the occurrence of wildfire in the forest lands of Golestan. The results of this research help understand wildfire risk areas and provide a scientific basis for predicting and controlling wildfires and reducing carbon emissions related to them.
Extract Reliable Relations from Wikipedia Texts for Practical Ontology Construction

A feature based relation classification approach is presented in this paper. We aimed to exact relation candidates from Wikipedia texts. A probabilistic and a semantic relatedness features are employed with other linguistic information for the purpose. The experiments show that, relation classification using the proposed relatedness features with surface information like word and part-of-speech tags is competitive with or even outperforms the one of using deep syntactic information. Meanwhile, an approach is proposed to distinguish reliable relation candidates from others, so that these reliable results can be accepted for knowledge building without human verification. The experiments show that, with the relation classification approach presented in this paper, more than 40% of the classification results are reliable, which means, at least 40% of the human and time costs can be saved in practice.
A Novel Content Enriching Model for Microblog Using News Corpus

In this paper, we propose a novel model for enriching the content of microblogs by exploiting external knowledge, thus improving the data sparseness problem in short text classification. We assume that microblogs share the same topics with external knowledge. We first build an optimization model to infer the topics of microblogs by employing the topic-word distribution of the external knowledge. Then the content of microblogs is further enriched by relevant words from external knowledge. Experiments on microblog classification show that our approach is effective and outperforms traditional text classification methods.
New Soft Sensor Method Based on SVM

This paper proposes a soft sensor technique based on support vector machine(SVM) technique, firstly gives an introduction to LSSVM, then designs a training algorithm for LSSVM, finally uses it to identify Absorption Stabilization System (ASS) process variable. Case studies are performed and indicate that the proposed method provides satisfactory performance with excellent approximation and generalization property, soft sensor technique based on LSSVM achieves superior performance to the conventional method based on neural networks. approaches. The formulation of the SVM embodies the Structural Risk Minimization (SRM) principle, which has been shown to be superior to the traditional Empirical Risk Minimization (ERM) principle, employed in conventional neural networks. It is this difference that equips SVM with a greater ability to generalize, hence a better generalization ability is guaranteed. As an interesting variant of the standard support vector machines, least squares support vector machines (LSSVM) have been proposed by Suykens and Vandewalle(5,6) for solving pattern recognition and nonlinear function estimation problems. Standard SVM formulation is modified in the sense of ridge regression and taking equality instead of inequality constraints in the problem formulation. As a result one solves a linear system instead of a QP problem, so LSSVM is easy to training. This paper discusses the basic principle of the LSSVM at first, and then uses it as a soft sensor tool to identify Absorption Stabilization System (ASS) process variable. The method can achieve higher identification precision at reasonably small size of training sample set and can overcome disadvantages of the artificial neural networks (ANNs). The experiments of the identification have been presented and discussed. The results indicate that the SVM method exhibits good generalization performance.
Alzheimer's disease intelligent detection combining XGBOOST and NARX

Due to the current situation of mental health illness, which causing a huge impact on the society. In this paper, an attempt has been made to analyses and predict the data from ANDI using single and composite algorithms. This paper used chi-square test, Spearmans correlation coefficient and maximum mutual information number, cost-sensitive learning, SMOTE, ADASYN, SMOTE+ENN, SMOTE+TOMEK to investigations. Specifically, this paper adopted the random forest to fill the data, and besides, given the fact that the data shows the characteristics of imbalance, this paper identifies the method of SMOTE TOMEK integrated sampling, XGBOOST and Bayesian optimization scheme to give the best performance and the best classification was obtained by XGBOOST combined with SMOTE-TOMEK. Furthermore, this paper used the NARX network to track the changes generated by time-based indicators, providing another insight to refine the study of intelligent diagnosis of Alzheimer.
Suppressor of K+ transport growth defect 1 (SKD1) interacts with RING-type ubiquitin ligase and sucrose non-fermenting 1-related protein kinase (SnRK1) in the halophyte ice plant

SKD1 (suppressor of K+ transport growth defect 1) is an AAA-type ATPase that functions as a molecular motor. It was previously shown that SKD1 accumulates in epidermal bladder cells of the halophyte Mesembryanthemum crystallinum. SKD1 knock-down Arabidopsis mutants showed an imbalanced Na+/K+ ratio under salt stress. Two enzymes involved in protein post-translational modifications that physically interacted with McSKD1 were identified. McCPN1 (copine 1), a RING-type ubiquitin ligase, has an N-terminal myristoylation site that links to the plasma membrane, a central copine domain that interacts with McSKD1, and a C-terminal RING domain that catalyses protein ubiquitination. In vitro ubiquitination assay demonstrated that McCPN1 was capable of mediating ubiquitination of McSKD1. McSnRK1 (sucrose non-fermenting 1-related protein kinase) is a Ser/Thr protein kinase that contains an N-terminal STKc catalytic domain to phosphorylate McSKD1, and C-terminal UBA and KA1 domains to interact with McSKD1. The transcript and protein levels of McSnRK1 increased as NaCl concentrations increased. The formation of an SKD1–SnRK1–CPN1 ternary complex was demonstrated by yeast three-hybrid and bimolecular fluorescence complementation. It was found that McSKD1 preferentially interacts with McSnRK1 in the cytosol, and salt induced the re-distribution of McSKD1 and McSnRK1 towards the plasma membrane via the microtubule cytoskeleton and subsequently interacted with RING-type E3 McCPN1. The potential effects of ubiquitination and phosphorylation on McSKD1, such as changes in the ATPase activity and cellular localization, and how they relate to the functions of SKD1 in the maintenance of Na+/K+ homeostasis under salt stress, are discussed.
Performance Measurement Of IT Service Management: A Case Study Of An Australian University (Research In Progress)

IT departments are adopting service orientation by implementing IT service management (ITSM) frameworks. Most organisations are hesitant to discuss their ITSM performance measurement practices, tending to focus more on challenges. However there are good practices that are found amidst the challenges. We present a case study that provides an account of the performance measurement practices in the ICT Division of an Australian university. This case study was conducted with the aim of understanding the internal and external factors that influence the selection of ITSM performance metrics. It also explores how and why metrics and frameworks are used to measure the performance of ITSM in organisations. Interviews were conducted to identify the specific ITSM performance metrics used and how they were derived. It was found that a number of factors internal and external to the organisation influenced the selection of the performance metrics. The internal factors include meeting the need for improved governance, alignment of IT strategy with organisation strategy, having a mechanism to provide feedback to IT customers (university staff and students). External factors include benchmarking against others in the same industry and the choice of metrics offered by ITSM software tool adopted.
The Contribution of Stylistic Information to Content-based Mobile Spam Filtering

Content-based approaches to detecting mobile spam to date have focused mainly on analyzing the topical aspect of a SMS message (what it is about) but not on the stylistic aspect (how it is written). In this paper, as a preliminary step, we investigate the utility of commonly used stylistic features based on shallow linguistic analysis for learning mobile spam filters. Experimental results show that the use of stylistic information is potentially effective for enhancing the performance of the mobile spam filters.
Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints

Optical coherence tomography (OCT) is a well-established image modality in ophthalmology and used daily in the clinic. Automatic evaluation of such datasets requires an accurate segmentation of the retinal cell layers. However, due to the naturally low signal to noise ratio and the resulting bad image quality, this task remains challenging. We propose an automatic graph-based multi-surface segmentation algorithm that internally uses soft constraints to add prior information from a learned model. This improves the accuracy of the segmentation and increase the robustness to noise. Furthermore, we show that the graph size can be greatly reduced by applying a smart segmentation scheme. This allows the segmentation to be computed in seconds instead of minutes, without deteriorating the segmentation accuracy, making it ideal for a clinical setup. An extensive evaluation on 20 OCT datasets of healthy eyes was performed and showed a mean unsigned segmentation error of 3.05 ± 0.54 μm over all datasets when compared to the average observer, which is lower than the inter-observer variability. Similar performance was measured for the task of drusen segmentation, demonstrating the usefulness of using soft constraints as a tool to deal with pathologies.
Signal detection algorithms for massive MIMO system

As 5G communication networks are maturing, we have higher and higher requirements for the detection of communication signals. In this paper, for the Massive MIMO system signal detection problem, we mainly summarize the detection algorithms that can be used to replace the traditional ZF and MMSE, so as to avoid large-scale matrix inverse and reduce the computational complexity. It mainly includes the general iterative method, typically represented by SSOR, which makes the transmit signal matrix constantly close to the ideal value by iterating; the other is the level expansion class solution method, which takes the order expansion of the level as the initial value of the iteration to accelerate the convergence rate of the algorithm, typically represented by the MLI algorithm. However, today where the demand for communication is gradually increasing and the number of users is constantly getting larger, the performance of the above algorithms may degrade seriously, so the AI signal detection algorithm is a good alternative, which learns autonomously through deep neural networks, including model-driven and data-driven schemes.
Enabling and Analyzing Natural Interaction with Functional Virtual Prototypes

Functional validations of virtual prototypes are a promising application area of immersive Virtual Reality (Moehring & Froehlich, 2011). Through the interactive simulation of operating procedures such analyses aim at providing insight about virtual prototypes, e. g. concerning visibility aspects, reachability of control elements, and ergonomics. A key requirement is the enabling of natural interaction defined by Zachmann as "interaction which imitates that same interaction in the real world as close as possible" (Zachmann & Rettig, 2001). In immersive VR, natural interaction can be realized through data gloves and motion tracking devices. However, a challenging task remains in the modeling and simulation of interactions with dynamic control elements such as sliders, switches etc. (Moehring & Frohlich, 2010). We have devised and implemented a framework that not only aims at simplifying the specification of such control actuators for interactive virtual environments but also facilitates the recording, automated classification, and analysis of natural interactions with such control actuators. The presented approach builds on the European Standard EN 894-3 (DIN894-3, 2006) and covers all types of control actuators defined therein. This standard defines systematic guidelines for the choice and configuration of control actuators to ergonomically design machinery. A characterizing feature of control actuators in the real world is given by their function: Controlling the behavior of machines and other appliances. In order to model the triggering of changes of environment objects in the simulation, our framework allows to associate control actuators with interaction events. Interaction events may be fired continuously, e. g. during movement of sliders, or when discrete states are reached, e. g. switches. Besides making interactive simulations more realistic, interaction events can also be recorded to allow later playback and analysis of the interaction. For this, detailed timing information is stored in the interaction events. For example, interaction events may be used for the animation of virtual humans that imitate the user interactions when operating virtual prototypes. Contributions of our research include an XML-based modeling language for control actuators that can be seen as both an abstraction layer and an extension to lower-level rigid body physics modeling capabilities provided e. g. by Collada (Khronos Group, 2008) and X3D (International Organization for Standardization, 2008). Further, we produce a reference implementation of control actuators with direct object manipulation, an interaction database for analyzing interaction events including recognizing basic interactions and grasp classification, and a powerful graphical analysis tool. Through this, the process of adding interactive elements to virtual prototypes can be drastically simplified. 11
Collagen-Targeting Vascular Endothelial Growth Factor Improves Cardiac Performance After Myocardial Infarction

Background— Vascular endothelial growth factor (VEGF) is an important active protein for the induction of angiogenesis and improvement in cardiac function after myocardial ischemia; however, the lack of a delivery system targeted to the injured myocardium reduces the local therapeutic efficacy of VEGF and increases its possible adverse effects. Methods and Results— We produced a fusion protein (CBD-VEGF) consisting of VEGF and a collagen-binding domain (CBD). The fusion protein specifically bound to type I collagen in vitro. In addition, CBD-VEGF promoted human umbilical vein endothelial cell proliferation after binding to collagen, which indicates that it retained both growth factor activity and collagen-binding ability. When implanted subcutaneously in rats, collagen membranes loaded with CBD-VEGF were significantly vascularized. After it was injected into rats with acute myocardial infarction, CBD-VEGF was largely retained in the cardiac extracellular matrix, in which collagen I was rich. Four weeks after VEGF or CBD-VEGF was injected into the infarct border zone, cardiac function detected by echocardiography and hemodynamics was preserved in the CBD-VEGF group. Administration of CBD-VEGF also induced reduction of scar size, whereas native VEGF did not have these effects. In addition, a significant increase in the number of capillary vessels in infarcted hearts was found in the CBD-VEGF group. Conclusions— The injection of CBD-VEGF improved cardiac function in rats with induced acute myocardial infarction. This could potentially provide a new treatment option for myocardial infarction.
Thermal-aware datapath merging for coarse-grained reconfigurable processors

The increased power densities of deep submicron process technologies have made on-chip temperature to become a critical design issue for high-performance integrated circuits. In this paper, we address the datapath merging problem faced during the design of coarse-grained reconfigurable processors from a thermal-aware perspective. Assuming a reconfigurable processor able to execute a sequence of datapath configurations, we formulate and efficiently solve the thermal-aware datapath merging problem as a minimum cost network flow. In addition, we integrate floorplan awareness of the underlying reconfigurable processor guiding the merging decision to account also for the effects of heat diffusion. Extensive experimentation regarding different configuration scenarios, technology nodes and clock frequencies showed that the adoption of the proposed thermal-aware methodology delivers up to 8.27K peak temperature reductions and achieves better temperature flattening in comparison to a low power but thermal-unaware approach.
Modulation of Recognition Memory for Emotional Images by Vertical Vection

Our previous research showed that vertical vection could modulate human mood. We further examined this possibility by using memory recognition task of positive, negative and neutral emotional images with high and low arousal levels. Those images were remembered accidentally while the participants did visual dummy task, and later presented together with novel images during vertical vection-inducing or neutral visual stimuli. The results showed that downward vection facilitated the recognition of negative images and inhibited the recognition of positive ones. These modulations of incidental memory task provide an additional evidence for vection influence on cognitive and emotional processing, and also provide a new paradigm that can be used in future vection and embodied cognition research.