Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Gesture Recognition Based on Three-Stream Coordinate Attention Network and Knowledge Distillation

Gesture recognition has always been one of the important research directions in the field of computer vision. The dynamic gesture has the problems of complex backgrounds and many interference factors. The gesture recognition model based on deep learning usually has high computational cost and poor real-time performance. In addition, deep learning models are limited to recognizing existing categories in the training set and their performance largely depends on the amount of labeled data. To address the above problems, this paper presents a dynamic gesture recognition method named 3SCKI based on a three-stream coordinate attention (CA) network, knowledge distillation, and image-text contrastive learning. Specifically, 1) CA is utilized for feature fusion to make the model focus more on target gestures and reduce background interference, 2) traditional knowledge distillation loss is improved to reduce the amount of calculation and improve the real-time performance. Specifically, the guidance function is added to make the student network only learn the classification probability correctly identified by the teacher network, and 3) multi-granularity context prompt template integration method is proposed to construct an improved CLIP visual language model MG-CLIP. It aligns text and visual concepts from the image level to the object level to the part level. Through comparative learning of image features and text features, gesture classification is performed, enabling the model to identify image categories that have not appeared during the training phase. The proposed method is evaluated on the ChaLearn LAP large-scale isolated gesture dataset (IsoGD). The results show that our proposed method can obtain recognition rates of 65.87% on the validation set of IsoGD. For single mode data, 3SCKI can obtain the state-of-the-art recognition accuracy on RGB, Depth, and Optical Flow data (61.22%, 58.84%, and 50.30% of the validation set of IsoGD, respectively).
Application of Target Recognition and Tracking in Agricultural Picking Robots

Target tracking in agricultural picking robots has a huge impact in the field of computer vision, and high-quality picking recognition and tracking algorithms are the basis for solving many problems such as inaccurate localization in agricultural picking. The problem of target recognition and tracking research is to identify and track the trajectories of different targets in video sequences. This paper mainly summarizes and analyzes the latest proposed algorithmic models about deep learning on target recognition and tracking and its application in agricultural picking robots. By analyzing various problems of current target recognition, the improvement of algorithms for different problems is summarized, and finally some possible future research directions are also proposed.
Design of a line-of-sight fluorescence-based imaging diagnostic for classification of microbe species

Fluorescence imaging of certain biochemicals, including flavins and pyridine nucleotides, has utility in characterizing the metabolic state of tissue and in discriminating between microbial species. There is significant clinical utility in this class of imaging techniques but most measurements reported to date require specialized training and equipment rendering most implementations unsuitable for routine medical imaging. Here, a low-cost and robust imaging technique is designed using ultraviolet-induced fluorescence of pyridine nucleotides (primarily NADH) and flavins (primarily FAD) in microbial samples. The diagnostic is optimized to distinguish between different microbial species based on previously reported spectral data using a ratiometric imaging approach. A detailed performance analysis is provided that relates the measured fluorescence intensity ratio (FIR) to the relative concentration ratio of NADH to FAD using a simplified spectroscopic model. Analysis suggests the technique is sensitive to changes in the NADH/FAD concentration ratio over several orders of magnitude, with better than 10% FIR precision on a per-pixel basis for microbial smears as thin as 10 s of microns at a resolution of 30 mm−1 and exposures of 20 ms. Representative microbe samples from eight species were imaged to demonstrate the proposed technique. Results show that the FIR varies by an order of magnitude across different species but the intra-species variation is only ∼5% for the conditions used here. An additional imaging band may be necessary to classify species that contain red pigments or bacteriochlorophyll. Radiative trapping was discussed as a possible limitation of the technique, but no clear evidence for radiative trapping was observed here. Overall, the results suggest that the proposed approach is feasible for rapid, low-cost, and robust characterization of microbial samples.
The Impact of Serum Levels of Reactive Oxygen and Nitrogen Species on the Disease Severity of COVID-19

Elucidation of the redox pathways in severe coronavirus disease 2019 (COVID-19) might aid in the treatment and management of the disease. However, the roles of individual reactive oxygen species (ROS) and individual reactive nitrogen species (RNS) in COVID-19 severity have not been studied to date. The main objective of this research was to assess the levels of individual ROS and RNS in the sera of COVID-19 patients. The roles of individual ROS and RNS in COVID-19 severity and their usefulness as potential disease severity biomarkers were also clarified for the first time. The current case-control study enrolled 110 COVID-19-positive patients and 50 healthy controls of both genders. The serum levels of three individual RNS (nitric oxide (NO•), nitrogen dioxide (ONO−), and peroxynitrite (ONOO−)) and four ROS (superoxide anion (O2•−), hydroxyl radical (•OH), singlet oxygen (1O2), and hydrogen peroxide (H2O2)) were measured. All subjects underwent thorough clinical and routine laboratory evaluations. The main biochemical markers for disease severity were measured and correlated with the ROS and RNS levels, and they included tumor necrosis factor-alpha (TNF-alpha), interleukin-6 (IL-6), the neutrophil-to-lymphocyte ratio (NLR), and angiotensin-converting enzyme 2 (ACE2). The results indicated that the serum levels of individual ROS and RNS were significantly higher in COVID-19 patients than in healthy subjects. The correlations between the serum levels of ROS and RNS and the biochemical markers ranged from moderate to very strongly positive. Moreover, significantly elevated serum levels of ROS and RNS were observed in intensive care unit (ICU) patients compared with non-ICU patients. Thus, ROS and RNS concentrations in serum can be used as biomarkers to track the prognosis of COVID-19. This investigation demonstrated that oxidative and nitrative stress play a role in the etiology of COVID-19 and contribute to disease severity; thus, ROS and RNS are probable innovative targets in COVID-19 therapeutics.
Joint Optimization for Transport and Bucket Loading Phases of Automated Wheel Loaders

This article investigates optimization of fuel-efficiency and productivity for automated wheel loaders. A control-oriented model for both the transport phase and bucket loading phase is proposed. The vehicle model includes an automatic gear shift schedule that can be incorporated into the optimization problem. Based on the model, the multistage optimization problem is formulated to simultaneously consider all phases of a short cycle with physical constraints. Cycle time and fuel efficiency are used as the weighted performance indexes in a multiobjective cost function. Bucket fill factor is included as a constraint during the bucket loading phase. A nonlinear programming problem is created with collocation using MATLAB and CasADi. The optimization solver IPOPT solves the problem to obtain the optimal state and control trajectories, which can be used as a reference for automated wheel loaders or even as a driver advisory for human-driven wheel loaders.
A total Lagrangian discontinuous Galerkin discretization of the two-dimensional gas dynamics equations

Based on the total Lagrangian kinematical description, a discontinuous Galerkin (DG) discretization of the gas dynamics equations is developed for two-dimensional fluid flows on general unstructured grids. Contrary to the updated Lagrangian formulation, which refers to the current moving configuration of the flow, the total Lagrangian formulation refers to the reference fixed configuration, which is usually the initial one. In this framework, the Lagrangian and Eulerian descriptions of the kinematical and the physical variables are related by means of the Piola transformation. Here, we describe a cell-centered high-order DG discretization of the physical conservation laws. The geometrical conservation law, which governs the time evolution of the deformation gradient, is solved by means of a finite element discretization. This approach allows to satisfy exactly the Piola compatibility condition. Regarding the DG approach, it relies on the use of a polynomial space approximation which is spanned by a Taylor basis. The main advantage in using this type of basis relies on its adaptability regardless the shape of the cell. The numerical fluxes at the cell interfaces are computed employing a node-based solver which can be viewed as an approximate Riemann solver. We present numerical results to illustrate the robustness and the accuracy up to third-order of our DG method. First, we show its ability to accurately capture geometrical features of a flow region employing curvilinear grids. Second, we demonstrate the dramatic improvement in symmetry preservation for radial flows.
Joint analysis of nonlinear heterogeneous longitudinal data and binary outcome: an application to AIDS clinical studies

ABSTRACT Finite mixture models are currently used to analyze heterogeneous longitudinal data. By releasing the homogeneity restriction of nonlinear mixed-effects (NLME) models, finite mixture models not only can estimate model parameters but also cluster individuals into one of the pre-specified classes with class membership probabilities. This clustering may have clinical significance, which might be associated with a clinically important binary outcome. This article develops a joint modeling of a finite mixture of NLME models for longitudinal data in the presence of covariate measurement errors and a logistic regression for a binary outcome, linked by individual latent class indicators, under a Bayesian framework. Simulation studies are conducted to assess the performance of the proposed joint model and a naive two-step model, in which finite mixture model and logistic regression are fitted separately, followed by an application to a real data set from an AIDS clinical trial, in which the viral dynamics and dichotomized time to the first decline of CD4/CD8 ratio are analyzed jointly.
Overweight in Sateré-Mawé indigenous residents of an urban area in Parintins, Amazonas

ABSTRACT Objective To verify overweight prevalence in the Sateré-Mawé indigenous population over 18 years of age, residing in the city of Parintins (AM), Brazil, and to assess its associated variables. Methods Household survey performed in the urban area of Parintins in 2017, visiting new households in each interview, completing the census universe. Self-declared Sateré-Mawé, over 18 years of age, residing in the city for a period longer than one year were considered eligible. Overweight was considered for body mass index values ≥ 25 kg/m2 or ≥ 27 kg/m2, for those over 60 years of age. Household information on social assistance, as well as individual information such as speaking the indigenous language, years living in the city and also in the indigenous territory, income, work, schooling, marital status, leisure and transport physical activity level, and time watching television per week were retrieved. The hierarchical logistic model analysis was carried out, calculating the odds ratio and confidence interval (95%). Results A total of 174 subjects participated in the survey, 42% being overweight. Those who spoke the Sateré-Mawé language, number of years living in the city, working outside home and being married had a positive effect on the outcome, but lost significance in the final model. Only transport insufficient physical activity (OR=2.24, 95% CI=1.01-4.98) and being in the age group from 30 to 59 years (OR=8.79, 95% CI=3.41-22.64) maintained statistical significance. Conclusion Efforts to provide visibility to the health situation of urban indigenous populations in Brazil are necessary. Poor transport infrastructure in the city seems to favor transport physical activity levels as a necessity, in addition to age, which is commonly associated with overweight.
Examining the Public Messaging on ‘Loneliness’ over Social Media: An Unsupervised Machine Learning Analysis of Twitter Posts over the Past Decade

Loneliness is an issue of public health significance. Longitudinal studies indicate that feelings of loneliness are prevalent and were exacerbated by the Coronavirus Disease 2019 (COVID-19) pandemic. With the advent of new media, more people are turning to social media platforms such as Twitter and Reddit as well as online forums, e.g., loneliness forums, to seek advice and solace regarding their health and well-being. The present study therefore aimed to investigate the public messaging on loneliness via an unsupervised machine learning analysis of posts made by organisations on Twitter. We specifically examined tweets put out by organisations (companies, agencies or common interest groups) as the public may view them as more credible information as opposed to individual opinions. A total of 68,345 unique tweets in English were posted by organisations on Twitter from 1 January 2012 to 1 September 2022. These tweets were extracted and analysed using unsupervised machine learning approaches. BERTopic, a topic modelling technique that leverages state-of-the-art natural language processing, was applied to generate interpretable topics around the public messaging of loneliness and highlight the key words in the topic descriptions. The topics and topic labels were then reviewed independently by all study investigators for thematic analysis. Four key themes were uncovered, namely, the experience of loneliness, people who experience loneliness, what exacerbates loneliness and what could alleviate loneliness. Notably, a significant proportion of the tweets centred on the impact of the COVID-19 pandemic on loneliness. While current online interactions are largely descriptive of the complex and multifaceted problem of loneliness, more targeted prosocial messaging appears to be lacking to combat the causes of loneliness brought up in public messaging.
Electrical and electromagnetic interference shielding characteristics of GNP/UHMWPE composites

Conductive polymer composites (CPC) are attractive materials for a wide range of applications because of their weight, corrosion resistivity, design flexibility and low cost. In the present work, the electrical and electromagnetic interference (EMI) shielding characteristics of graphene nanoplatelets (GNP)/ultrahigh molecular weight polyethylene (UHMWPE) composites filled with up to 40 wt% GNP were investigated. In addition, the intrinsic conductivity of the GNP network was estimated based on the statistical power law and the rule of mixtures for randomly oriented filler particles in insulating matrix. Due to the formation of a segregated conductive network at the external surface of UHMWPE powder, an electrical percolation threshold of between 2 and 3 wt% GNP was obtained. At GNP loading of 15 wt%, the composite exhibited an EMI shielding effectiveness of 33 dB, corresponding to 99.95% blocking of the EMI.
REDUCING THE COMPLEXITY OF COMPUTATIONAL MODELS OF NEURONS USING BIFURCATION DIAGRAMS

We reviewed the main features of a theoretical model of dopamineric neurons able to reproduce two features observed both in vivo and in vitro experiments: 1) slow oscillatory potentials (SOP) in the presence of tetrodoxin (TTX) and tetraethyl ammonium (TEA), and 2) the square wave that can be produced by the abolition of SOP by apamin. Using bifurcation analyses, we found that not all currents and activation/inactivation functions are equally important for a coherent mathematical description. In fact, only three currents are essential: 1) the L-type calcium current, which drives both types of oscillation, 2) the small conductance (SK) potassium current, which repolarizes SOP, and 3) a slowly activating potassium current, which repolarizes the square wave. Bifurcation diagrams revealed that the model is insensitive to the kinetic details of SK current activation and that both Botzmann and MichaelisMenten activation functions offer good descriptions of calcium-activation mechanisms.
A Missing Variable Imputation Methodology with an Empirical Application

The objective of this research is to examine, validate, and recommend techniques for handling the problem of missingness in observational data. We use a rich observational data set, the Nielsen HomeScan data set, which allows us to effectively combine elements from simulated data sets: large numbers of observations, large number of data sets and variables, allowing elements of “design” that typically come with simulated data, and its observational nature. We created random 20% and 50% uniform missingness in our data sets and employed several widely used methods of single imputation, such as mean, regression, and stochastic regression imputations, and multiple imputation methods to fill in the data gaps. We compared these methods by measuring the error of predicting the missing values and the parameter estimates from the subsequent regression analysis using the imputed values. We also compared coverage or the percentages of intervals that covered the true parameter in both cases. Based on our results, the method of single regression or conditional mean imputation provided the best predictions of the missing price values with 28.34 and 28.59 mean absolute percent errors in 20% and 50% missingness settings, respectively. The imputation from conditional distribution method had the best rate of coverage. The parameter estimates based on data sets imputed by conditional mean method were consistently unbiased and had the smallest standard deviations. The multiple imputation methods had the best coverage of both the parameter estimates and predictions of the dependent variable.
Decomposition Strategies for Nonconvex Problems, a Parametric Approach

This thesis deals with the development of numerical methods for solving nonconvex optimisation problems by means of decomposition and continuation techniques. We first introduce a novel decomposition algorithm based on alternating gradient projections and augmented Lagrangian relaxations. A proof of local convergence is given under standard assumptions. The effect of different stopping criteria on the convergence of the augmented Lagrangian loop is investigated. As a second step, a trust region algorithm for distributed nonlinear programs, named TRAP, is introduced. Its salient ingredient is an alternating gradient projection for computing a set of active constraints in a distributed manner, which is a novelty for trust region techniques. Global convergence as well as local almost superlinear convergence are proven. The numerical performance of the algorithm is assessed on nonconvex optimal power flow problems. The core of this thesis is the development and analysis of an augmented Lagrangian algorithm for tracking parameter-dependent optima. Despite their interesting features for large-scale and distributed optimisation, augmented Lagrangian methods have not been designed and fully analysed in a parametric setting. Therefore, we propose a novel optimality-tracking scheme that consists of fixed number of descent steps computed on an augmented Lagrangian subproblem and one dual update per parameter change. It is shown that the descent steps can be performed by means of first-order as well as trust region methods. Using the Kurdyka-Lojasiewicz property, an analysis of the local convergence rate of a class of trust region Newton methods is provided without relying on the finite detection of an optimal active set. This allows us to establish a contraction inequality for the parametric augmented Lagrangian algorithm. Hence, stability of the continuation scheme can be proven under mild assumptions. The effect of the number of primal iterations and the penalty is analysed by means of numerical examples. Finally, the efficacy of the augmented Lagrangian continuation scheme is successfully demonstrated on three examples in the field of optimal control. The first two examples consists of a real-time NMPC algorithm based on a multiple-shooting discretisation. In particular, it is shown that our C++ software package is competitive with the state-of-the-art codes on NMPC problems with long prediction horizons, and can address a more general class of real-time NMPC problems. The third case study is the distributed computation of solutions to multi-stage nonconvex optimal power flow problems in a real-time setting.
Do smaller businesses pay more bribes? Firm size, informal payments and mitigating strategies in Africa

ABSTRACT In this paper, we sought to understand the effect of firm size on demand for and payment of bribes by African firms and the strategies needed to overcome them. We argued that size represents a selectionist pressure on firms to cede to corrupt demands while strategies (time to formalization, relational intensity and foreign participation) from the adaptive perspective of the organization are tools to deal with these pressures. We test our framework using data from the World Bank Enterprise Surveys in Africa. We find that the liability of smallness effect exists as the larger a firm gets the fewer bribes in percentage terms it pays out to corrupt public officials. We find also that time to formalization, relational intensity and foreign participation are significant moderators of this effect. The study makes a contribution by testing a contingent framework that integrates the selectionist and adaptive views of organizing.
Foreground/background segmentation with learned dictionary

Video sequences are viewed as a temporal collection of inverse problems. This parallel with the classical inverse problem of denoising brings us to investigate a sparse representation based approach for background subtraction. A global trained dictionary is obtained using a k-means classifier and using the matching pursuit method a set of coefficients is estimated. By linear combination of dictionary vectors (atoms) and the set of coefficients a background estimate is computed for each frame to obtain the foreground-background segmentation. The global dictionary and the coefficients are propagated and updated along the sequence. The approach yields surprisingly preliminary results, encouraging for further investigations on the possible extensions of the algorithm.