Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of the Roche Diagnostics LightCycler-Apo B 3500 Mutation Detection Kit

Abstract Familial defective apolipoprotein (apo) B-100 is an autosomal codominant disorder associated with hypercholesterolemia and an increased risk of coronary artery disease. Two independent mutations affecting the codon 3500 (Arg3500→Gln and Arg3500→Trp) have been shown to cause ligand-defective apo B-100. Identification of carriers of these mutations is an important step in the risk stratification of individuals and families with hypercholesterolemia. We evaluated a homogeneous assay for detection of mutations at codon 3500 that combines rapid-cycle PCR with allele-specific fluorescent probe melting profiles for product genotyping. This single-tube analysis is performed on the LightCycler™, a microvolume fluorimeter integrated with a thermal cycler. Continuous acquisition of fluorescence data during a melting curve analysis at completion of PCR allows the detection of mutations, as loss of fluorescence occurs in an allele-specific manner. By plotting melting peaks, the three apo B-100 alleles were readily distinguishable. Using this method, genotyping of 32 samples is completed within 40 min without the need for any post-PCR sample manipulation, thereby eliminating the risks of end-product contamination and sample tracking errors. The specific detection of mutations at codon 3500 of the apo B gene on the LightCycler™ is a rapid and reliable method that is ideally suitable for typing both small and large numbers of samples.
Applying Computational Models of Spatial Prepositions to Visually Situated Dialog

This article describes the application of computational models of spatial prepositions to visually situated dialog systems. In these dialogs, spatial prepositions are important because people often use them to refer to entities in the visual context of a dialog. We first describe a generic architecture for a visually situated dialog system and highlight the interactions between the spatial cognition module, which provides the interface to the models of prepositional semantics, and the other components in the architecture. Following this, we present two new computational models of topological and projective spatial prepositions. The main novelty within these models is the fact that they account for the contextual effect which other distractor objects in a visual scene can have on the region described by a given preposition. We next present psycholinguistic tests evaluating our approach to distractor interference on prepositional semantics, and illustrate how these models are used for both interpretation and generation of prepositional expressions.
Vertical handover in optical wireless heterogeneous networks

Heterogeneous networks are now widely deployed to improve the coverage rate for large area and increase the communication capacity in small area coverage. Indoor optical wireless or Li-Fi satisfies high capacity demand in density scenario, without any interference to existing radio frequency communication. It brought new challenges to deal with handover for heterogeneous networks when optical wireless access attachment points existed. In this article, we introduced vertical handover and designed a vertical handover algorithm for a typical optical wireless scenario.
Correlation of Types in Bayesian Games

Despite their importance, games with incomplete information and dependent types are poorly understood; only special cases have been considered and a general approach is not yet available. In this paper, we propose a new condition (named richness) for correlation of types in (asymmetric) Bayesian games. Richness is related to the idea that “beliefs do not determine preferences” and that types should be modeled with two explicit parts: one for payoffs and another for beliefs. With this condition, we are able to provide the first pure strategy equilibrium existence result for a general model of multi-unit auctions with correlated types. We then focus on a special case of richness, called “grid distributions,” and establish necessary and sufficient conditions for the existence of a symmetric monotonic pure strategy equilibrium in first-price auctions with general levels of correlation. We also provide a polynomial-time algorithm to verify this existence and suggest, using simulations, that the revenue superiority of English auctions may not hold for positively correlated types in general. JEL Classification Numbers: C62, C72, D44, D82
Feature selection and pose estimation from known planar objects using monocular vision

In this paper, we develop a way to accurately and precisely estimate the pose of a calibrated camera with a single picture which includes a known planar object. For the proposed algorithm, we first use SURF detector for feature extraction and matching. Then, we use the information from known reference image to retrieve 3D point coordinates. Based on resulting 2D-2D correspondences and 3D coordinates, multiple-view geometry constraints are adopted to calculate the camera pose. Comparing with previous work, the proposed algorithm introduces an advanced feature selection algorithm, which eliminates pose ambiguity and improve the pose estimation result. The feature selection algorithm is based on the assumption that most 3D feature points should be coplanar. We conduct tests on traffic sign and evaluated the test results. The test results show that pose estimation with standard RANSAC turns out to be ambiguous occasionally. Conversely, the estimation with the proposed feature selection strategy leads to high robustness and accuracy.
The Arabidopsis Auxin Receptor F-Box Proteins AFB4 and AFB5 Are Required for Response to the Synthetic Auxin Picloram

The plant hormone auxin is perceived by a family of F-box proteins called the TIR1/AFBs. Phylogenetic studies reveal that these proteins fall into four clades in flowering plants called TIR1, AFB2, AFB4, and AFB6. Genetic studies indicate that members of the TIR1 and AFB2 groups act as positive regulators of auxin signaling by promoting the degradation of the Aux/IAA transcriptional repressors. In this report, we demonstrate that both AFB4 and AFB5 also function as auxin receptors based on in vitro assays. We also provide genetic evidence that AFB4 and AFB5 are targets of the picloram family of auxinic herbicides in addition to indole-3-acetic acid. In contrast to previous studies we find that null afb4 alleles do not exhibit obvious defects in seedling morphology or auxin hypersensitivity. We conclude that AFB4 and AFB5 act in a similar fashion to other members of the family but exhibit a distinct auxin specificity.
SVR Learning-Based Spatiotemporal Fuzzy Logic Controller for Nonlinear Spatially Distributed Dynamic Systems

A data-driven 3-D fuzzy-logic controller (3-D FLC) design methodology based on support vector regression (SVR) learning is developed for nonlinear spatially distributed dynamic systems. Initially, the spatial information expression and processing as well as the fuzzy linguistic expression and rule inference of a 3-D FLC are integrated into spatial fuzzy basis functions (SFBFs), and then the 3-D FLC can be depicted by a three-layer network structure. By relating SFBFs of the 3-D FLC directly to spatial kernel functions of an SVR, an equivalence relationship of the 3-D FLC and the SVR is established, which means that the 3-D FLC can be designed with the help of the SVR learning. Subsequently, for an easy implementation, a systematic SVR learning-based 3-D FLC design scheme is formulated. In addition, the universal approximation capability of the proposed 3-D FLC is presented. Finally, the control of a nonlinear catalytic packed-bed reactor is considered as an application to demonstrate the effectiveness of the proposed 3-D FLC.
Automated Composition of Semantic Web Services Using Case-Based Planning

One of the most challenging issues of service-centric software engineering is the automatic composition of services. In academia, most approaches reduce the service composition problem to the AI planning problem and generate a plan for the user's request from scratch. However in the real world there may be a large amount of same or similar requests, so how to adapt the existing solutions for the past requests to functionally fit for the current same or similar ones becomes a new challenge in the context of semantic web services. In our study, we propose an approach for automated composition of semantic web services based on case-based planning. The generated plan can have sequential and non-sequential control constructs. Additionally, a case study from the bioinformatics domain is given to illustrate our approach.
Web mining and sentiment analysis of COVID-19 discourse in online forum communities

Recently, various discussions, solutions, data, and methods related to coronavirus disease 2019 (COVID-19) have been posted in online forum communities. Although a vast amount of posting on COVID-19 analytical projects are available in the online forum communities, much of them remain untapped due to limited overview and profiling that focuses on COVID-19 analytic techniques. Thus, it is quite challenging for information diggers and researchers to distinguish the recent trends and challenges of COVID-19 analytic for initiating different and critical studies to fight against the coronavirus. This paper presents the findings of a study that executed a web mining process on COVID-19 data analytical projects from the Stack Overflow and GitHub online community platforms for data scientists. This study provides an insight on what activities can be conducted by novice researchers and others who are interested in data analysis, especially in sentiment analysis. The classification results via Naïve Bayes (NB), support vector machine (SVM) and logistic regression (LR) have returned high accuracy, indicating that the constructed model is efficient in classifying the sentiment data of COVID-19. The findings reported in this paper not only enhance the understanding of COVID-19 related content and analysis but also provides promising framework that can be applied in diverse contexts and domains.
The Impact of User Awareness on Online Behavior Advertising Acceptance

Purpose: Online Behavior and Data-Driven Advertising (OBA) have emerged as an influential tool in product and service promotion. The purpose of this research is to investigate the role of consumer awareness in decision-making, regarding data-driven advertising in the Albanian context. The research aims to address the digital literacy gap in Albania and its impact on OBA acceptance. Research design, data, and methodology: Employing a web-based survey, the study objective was to capture consumers perspectives and knowledge regarding online behavior in general and OBA in particular (N=171). Results: Research findings underscore the importance of OBA awareness and its impact in shaping acceptance or avoidance of OBA that is manifested through the click intentions of the targeted advertising. Conclusion: The study emphasizes the importance of awareness and knowledge, highlighting the link between consumer empowerment and understanding personalization strategies. The study contributes to both theoretical understanding and practical applications, setting the way for informed strategies in Albania's digital landscape. Received: 24 February 2024 / Accepted: 17 March 2024 / Published: 23 March 2024
Feature Selection via Block-Regularized Regression

Identifying co-varying causal elements in very high dimensional feature space with internal structures, e.g., a space with as many as millions of linearly ordered features, as one typically encounters in problems such as whole genome association (WGA) mapping, remains an open problem in statistical learning. We propose a block-regularized regression model for sparse variable selection in a high-dimensional space where the covariates are linearly ordered, and are possibly subject to local statistical linkages (e.g., block structures) due to spacial or temporal proximity of the features. Our goal is to identify a small subset of relevant covariates that are not merely from random positions in the ordering, but grouped as contiguous blocks from large number of ordered covariates. Following a typical linear regression framework between the features and the response, our proposed model employs a sparsity-enforcing Laplacian prior for the regression coefficients, augmented by a 1st-order Markovian process along the feature sequence that "activates" the regression coefficients in a coupled fashion. We describe a sampling-based learning algorithm and demonstrate the performance of our method on simulated and biological data for marker identification under WGA.
FUNCTIONAL PLEIOTROPY AND MATING SYSTEM EVOLUTION IN PLANTS: FREQUENCY‐INDEPENDENT MATING

Mutations that alter the morphology of floral displays (e.g., flower size) or plant development can change multiple functions simultaneously, such as pollen export and selfing rate. Given the effect of these various traits on fitness, pleiotropy may alter the evolution of both mating systems and floral displays, two characters with high diversity among angiosperms. The influence of viability selection on mating system evolution has not been studied theoretically. We model plant mating system evolution when a single locus simultaneously affects the selfing rate, pollen export, and viability. We assume frequency‐independent mating, so our model characterizes prior selfing. Pleiotropy between increased viability and selfing rate reduces opportunities for the evolution of pure outcrossing, can favor complete selfing despite high inbreeding depression, and notably, can cause the evolution of mixed mating despite very high inbreeding depression. These results highlight the importance of pleiotropy for mating system evolution and suggest that selection by nonpollinating agents may help explain mixed mating, particularly in species with very high inbreeding depression.
Building Extraction from UAV Images Based on Attention Enhancement

Automatic extraction of buildings from remote sensing images using deep learning methods is crucial for urban and rural construction and management. However, the existing models are affected by the background noise and the complexity of building types during the extraction process, resulting in poor building extraction. In order to solve this problem, this paper proposes an improved model based on Mask R-CNN, which establishes a building instance extraction model for UAV imagery by adding a CBAM attention mechanism module to the backbone residual neural network ResNet. The traditional villages around Beijing are selected as the study area, and the comprehensive experimental results on the homemade building dataset show that the mAP value of the improved model is 86.4%, which is 2.1% higher than that of the original Mask R-CNN model, indicating that the improved model is more effective in building extraction.
Impact of Model Shape Mismatch on Reconstruction Quality in Electrical Impedance Tomography

Electrical impedance tomography (EIT) is a low-cost, noninvasive and radiation free medical imaging modality for monitoring ventilation distribution in the lung. Although such information could be invaluable in preventing ventilator-induced lung injury in mechanically ventilated patients, clinical application of EIT is hindered by difficulties in interpreting the resulting images. One source of this difficulty is the frequent use of simple shapes which do not correspond to the anatomy to reconstruct EIT images. The mismatch between the true body shape and the one used for reconstruction is known to introduce errors, which to date have not been properly characterized. In the present study we, therefore, seek to 1) characterize and quantify the errors resulting from a reconstruction shape mismatch for a number of popular EIT reconstruction algorithms and 2) develop recommendations on the tolerated amount of mismatch for each algorithm. Using real and simulated data, we analyze the performance of four EIT reconstruction algorithms under different degrees of shape mismatch. Results suggest that while slight shape mismatch is well tolerated by all algorithms, using a circular shape severely degrades their performance.
Menu Interactions in a Desktop Haptic Environment

Since the importance of haptic interaction in virtual environments grows, the relevance of good haptic UI widgets becomes more important too. This papers shortly discusses a framework for building 3D widgets in general and more specific 3D menus. The usage of these menus was assessed in a formal user test. The surplus value of using haptic feedback in 3D widgets and the difference in performance of clicking versus pushing were evaluated. This experiment and its results are explained in this paper.
Cloud-based solutions for intelligent environments

A tremendous research effort is recently focused on developing intelligent environments based on new achievements in artificial intelligence, ambient intelligence, and wireless sensor networks. Intelligent (or smart) environments are usually developed for indoor applications, like ambient assisted independent living of seniors or handicapped persons in their homes, but there is a need of intelligent support of various human activities also in outdoor spaces, e.g., in streets, forests, or in nature in general. However, such solutions are still very rarely developed and published. The purpose of the paper is, after a review of several existing solutions, to discuss possibilities for cloud-based solutions in outdoor intelligent environments, considering multi-agent models and a cloud-based solution scenario-based approach.
SurfShop: combing a product ontology with topic model results for online window-shopping.

At present, online shopping is typically a search-oriented activity where a user gains access to products which best match their query. Instead, we propose a surf-oriented online shopping paradigm, which links associated products allowing users to "wander around" the online store and enjoy browsing a variety of items. As an initial step in creating this experience, we constructed a prototype of an online shopping interface which combines product ontology information with topic model results to allow users to explore items from the food and kitchen domain. As a novel task for topic model application, we also discuss possible approaches to the task of selecting the best product categories to illustrate the hidden topics discovered for our product domain.
Application of ARIMA Models in Forecasting Monthly Total Rainfall of Rangamati, Bangladesh

In this paper, the time series data of monthly total rainfall of Rangamati (Bangladesh) from January 1969 to December 2011 is taken for studying the trend and fitting the best Autoregressive Integrated Moving Average (ARIMA) model to forecast. Seasonal Autoregressive Integrated Moving Average model SARIMA (1,0,0)(2,0,0)[12] has been fitted to the data and then the forecast of the monthly total rainfall for the next 24 months, from January 2012 to December 2013, has been made using statistical software R. The best model forecasts for the year 2012 that the highest amount of rainfall (495.97 mm) occurs in June; whereas, in January the amount is the lowest (46.28 mm). For the year 2013, the model forecasts that August is the month having maximum (438.26 mm) and January with the lowest (68.77 mm) amount of rainfall than the other months.
Embedded Design Exploration of EPCglobal Architecture

This paper addresses the embedded design issues of the EPCglobal Architecture aiming at providing RFID-based solutions. Some problems related to its porting on the embedded systems field have been analyzed. These problems concern the role of the operating system adopted by the embedded system, the SW architecture implementation (Single-Thread or Multi-Thread) to satisfy the EPCglobal Architecture constraints and the HS/SW partitioning alternatives. Moreover, the paper provides an embedded solution, called eEPC. The proposed design alternatives exploration allows to evaluate which eEPC implementation, running on different HW/SW platforms, better satisfies the multiple tag readings and processing architectural constraints. Its effectiveness has been evaluated on a real application where products information is automatically put in relation to user's medical data to retrieve information able to improve the quality of life.
MIREX 2012: MULTI FEATURE BEAT TRACKER (ZDG1 AND ZDG2)

The Multi-feature Beat tracker uses 5 different onsets detection function to estimates the beats of a musical audio signal using only one beat tracker algorithm, finally the beat tracker output is selected using a committee technique presented in previous works. The algorithm ZDG2 get the higher value in five of the ten measures in the Mckinney Dataset in 2012 and the higher AMLt and AMLc values in all the years in the Beat tracking task in the same dataset.
Stoichiometry of the Glial Glutamate Transporter GLT-1 Expressed Inducibly in a Chinese Hamster Ovary Cell Line Selected for Low Endogenous Na+-Dependent Glutamate Uptake

Glutamate transport across the plasma membrane of neurons and glia is powered by the transmembrane electrochemical gradients for sodium, potassium, and pH, but there is controversy over the number of Na+ cotransported with glutamate. The stoichiometry of glutamate transporters is important because it determines a lower limit to the extracellular glutamate concentration, [glu]o, in both normal and pathological conditions. We used whole-cell clamping to study the stoichiometry of the glial transporter GLT-1, the most abundant glutamate transporter in the brain, expressed under control of the Tet-On system in a Chinese hamster ovary (CHO) cell line selected for low endogenous glutamate transport. After the induction of GLT-1 expression with doxycycline, glutamate evoked a Na+-dependent inward current with the voltage dependence and pharmacology of GLT-1 and acidified the cell cytoplasm. Raising [K+]o around cells clamped with electrodes containing sodium and glutamate evoked an outward reversed uptake current. These responses were reduced by the specific GLT-1 blocker dihydrokainate (DHK). DHK evoked an outward current with NO3−, but not with Cl−, as the main intracellular anion, suggesting that the anion conductance of the transporter is active even without external glutamate but generates little current in the absence of highly permeable anions like NO3−. Measuring the reversal potential of the transporter current in various ionic conditions suggested that the transport of one glutamate anion is coupled to the cotransport of three Na+ and one H+ and to the countertransport of one K+. This suggests that in ischemia, when [K+]o rises to 60 mm, the reversal of glutamate transporters will raise [glu]o to >50 μm.
Reduction of CM elliptic curves and modular function congruences

We study congruences of the form F(j(z)) | U(p) = G(j(z)) mod p, where U(p) is the p-th Hecke operator, j is the basic modular invariant 1/q+744+196884q+. for SL2(Z), and F,G are polynomials with integer coefficients. Using the interplay between singular (a.k.a. CM) j-invariants in characteristic zero and supersingular ones in characteristic p, we obtain such congruences in which F is the minimal polynomial of a CM j-invariant, and give a sufficient condition for G to be a constant polynomial in these congruences.