Miracle-dz/UniFAR-FTUs huggingface dataset:
Separating background activity from noise in EMG using a neuromuscular model

To objectively diagnose the severity of spasticity, it is important to measure the muscle activity accurately. Filtered EMG has an offset above zero which is part noise and part background activity. The goal of this study is to separate these two parts, using a non-linear neuromuscular ankle model. The model uses positional and EMG data as inputs to make a prediction of the output torque around the ankle, optimizing several muscle parameters. It was assumed model error was partly caused by the lack of separation of background activity and noise in the EMG offset. The correlation between model error and the force-length relationship of the muscle was investigated, as it was hypothesized that the force-length relationship could be used to separate background activity from noise in EMG.Twelve subjects (11 male, 1 female, mean age 25.21.7 years) participated in the study, which was approved by the TU Delft Human Research Ethics Committee. The Achilles ankle perturbator and the TMSi Porti7 EMG system were used to obtain positional and EMG data around the ankle. The subjects performed several trials containing multiple ramp-and-hold phases. The trials were performed while relaxed, and while performing co-contraction at 5% and 10% of maximum voluntary co-contraction. Model validity was judged on robustness and fit. Model fit shows how close the predicted torque resembles the measured torque. High robustness is defined as a low variance of the model parameters per subject, between trials. To improve robustness, two model configurations, one using averaged data and one simplified model configuration, were compared with the original model. The most robust model configuration was used as a basis for an alternative model, which added a parameter for each EMG signal, that subtracted part of the EMG offset. This EMG offset subtraction parameter was added to remove the constant part of the noise in the EMG offset. The correlation between model error and the force-length relationship of the model was calculated for the model with the best robustness, and the alternative model.Model robustness was not improved in the averaged data model configuration or the simplified model configuration, compared to the original model. The original model was therefore used for the rest of the study. The EMG offset subtraction parameter did not yield to desired reduction of noise in the EMG offset. This was likely a result of difficulties with determining the EMG offset. Only weak correlations (Rll0.3) were found between the model error and the force-length relationship of the muscles, for both the original model and the alternative model configuration.
A hybrid neural network for hyperspectral image classification

ABSTRACT Recent research shows that deep learning-based methods can achieve promissing performance when applied to hyperspectral image (HSI) classification in remote sensing, some challenging issues still exist. For example, after a number of 2D convolutions, each feature map may only correspond to a unique dimension of the hyperspectral image. As a result, the relationship between different feature maps from multiple dimensional hyperspectral image can not be extracted well. Another issue is information in extracted feature maps may be erased by pooling operations. To address these problems, we propose a novel hybrid neural network (HNN) for hyperspectral image classification. The HNN uses a multi-branch architecture to extract hyperspectral image features in order to improve its prediction accuracy. Moreover, we build a deconvolution structure to recover the lost information in the pooling operation. In addition, to improve convergence and prevent overfitting, the HNN applies batch normalization (BN) and parametric rectified linear units (PReLU). In the experiments, two public benchmark HSIs are utilized to evaluate the performance of the proposed method. The experimental results demonstrate the superiority of HNN over several well-known methods.
Precision matrix estimation under data contamination with an application to minimum variance portfolio selection

Abstract In this article, we consider the problem of estimating the precision matrix when the sample data contains cellwise contamination. For the widely employed methodologies (e.g. Graphical Lasso), using the sample covariance matrix as an input matrix potentially deteriorates the precision matrix estimation performance in the presence of outliers. We propose several robust alternatives for the covariance matrix, which are constructed by combining robust correlation estimators with robust variation measures. Through extensive numerical studies we demonstrate the robust performance of our proposed approaches compared to the standard methods based on the sample covariance matrix. Further, we apply our proposals to a real data application, aimed at studying the optimal portfolio allocations in Shanghai Stock Exchange Composite index. The results show that the proposed alternatives provide desirable out-of-sample performance.
Modelling and characterisation of shear modes of rectangular piezoelectric materials

Abstract This article presents a new approach based on an approximate non-standard method for modelling shear modes of piezoelectric ceramics. Coupling between shear modes is introduced by assuming that the electric field inside the element depends on coordinates. Coupling between shear and longitudinal modes is inserted in modelling by using a specific property of partial derivative. Electric field is used in place of the electric density to solve particular dynamic equilibrium equations. Then, the resonance frequency identification of elements having dimensions which do not comply with Standard requirements becomes possible. The element impedance equation includes shear and longitudinal resonance modes. Shear velocity equations calculated with the model are in accordance with those deduced from Standards. New modelling describes the origin of the “aspect ratio” and explains 1D model limitations. Particular resonance conditions of the element are defined. The new model is used for identifying and characterising shear vibration modes of rectangular piezoelectric materials. Shear parameter determination of piezoelectric ceramics is carried out by comparing measured and calculated electric impedance. Measured impedance spectrum displays four resonance peaks which are easily identified using the new modelling. Among these resonances, three are related to shear modes and one to a longitudinal mode which propagates along with poling direction. Coupling between shear and longitudinal modes leads a new EMC Factor. Measured longitudinal displacement validates the proposed modelling.
Seismic Random Noise Reduction Using Adaptive Threshold Combined Scale and Directional Characteristics of Shearlet Transform

Random noise attenuation is an important step in seismic processing. A method with an adaptive threshold based on scale and directional characteristics of shearlet transform is proposed to attenuate seismic random noise. The method can adaptively calculate threshold according to the decomposed scales and directions, where the directional characteristics are embodied by using the L1 norm of coefficients. Compared to the universal threshold and the threshold based on scale methods only, the scale and directional threshold methods can remove random noise and reserve an effective signal to the utmost extent. The synthetic and field data examples demonstrate that the proposed method can significantly improve the denoising effect and has the stronger denoising ability.
The morphosyntactic structure of the Äiwoo verb

In the Oceanic language Äiwoo, verb forms can achieve rather extensive complexity, as illustrated in (1)(2): the former contains 9 morphemes, among which three lexical roots, a voice-related marker repeated three times; the latter, a nominalized form, contains 10 morphemes, among which a causative, a voice marker, a directional suffix and two person markers. (1) ki-eâmole-wâtu-i-päko-i-mana-i-i ipfv-look.av-comp-uv.agr-good-uv.agr-very-uv.agr-3aug jii=le 3aug=deic.prox ‘They have to look after them properly’ (2) de-nâ-wâ-singä-eâ-wâ-nee-mi=ä=ne bn:thing-irr-caus-lie-uv-dir2-1min-2aug=cv=deic.prox ‘The thing I want to deceive you with’ All the Äiwoo data this thesis builds on derives from the documentational/descriptive and theoretical work of Åshild Næss. The main object of research of this thesis is precisely the morphosyntactic complexity of Äiwoo verb forms. I inquire into issues of affix order and the internal structure of these forms: which affixes are there, what is their behaviour, why are they in the order in which we find them, and how can their organization best be described and accounted for? Do complex verb forms have inner hierarchical structures, or are they built differently, e.g. following an arbitrary linear template, where different morphemes are assigned each to a specific slot? I argue that the behaviour and distribution of almost all morphemes occurring in Äiwoo verb forms can be accounted for on the basis of independent syntactic and semantic principles – apart from a single one, the circumstantial voice marker, realised as =ä in (2). The present work aims to be of interest to linguists who place themselves both in the typological/ functionalist tradition and the formalist/generative one. The questions outlined above have typological significance, as they inform us about what constitutes a possible morphosyntactic (verbal) system, and how complex words can be built; moreover, complex morphosyntactic structures such as these are better studied in e.g. North-American languages, known for their polysynthetic character, and less in Oceanic languages. In order to answer to those questions, I have developed a morphosyntactic model of the Äiwoo verb (and clause) within a generative minimalist framework, which incorporates notions from Distributed Morphology. This thesis constitutes the first study of a language of this geographical region and phylogenetic subgroup in a generative framework.
The Implicit Discretization of the Supertwisting Sliding-Mode Control Algorithm

This article deals with the analysis of the time discretization of the supertwisting algorithm, with an implicit Euler method. It is shown that the discretized system is well posed. The existence of a Lyapunov function with convex level sets is proved for the continuous-time closed-loop system. Then, the global asymptotic Lyapunov stability of the unperturbed discrete-time closed-loop system is proved. The convergence to the origin in a finite number of steps is proved also in the unperturbed case. Numerical simulations demonstrate the superiority of the implicit method with respect to an explicit discretization with significant chattering reduction.
Effective identification of sliding mode control parameters employing linear quadratic regulator and evolutionary computation techniques

In this paper, a new technique to reduce the search space in evolutionary algorithms for operative identification of sliding mode control parameters is proposed, especially in the case of a rigid satellite. The reduced domain provides a solution closer to the actual optimum at a lower computational cost. The optimized sliding mode controller provides superior performance that eliminates the need for trial‐and‐error exploration. In this approach, a linear quadratic regulator is initially designed for the equivalent linear system. In order to reduce the convergence time and computational cost, the regulator history is then used as a reference to provide optimal space for the sliding mode control parameters. Consequently, boundaries of the optimal space are fed to the genetic algorithm to optimize the control parameters. On the other hand, the particle swarm optimization uses the midpoint of the optimal space as an initial guess. The validation process is performed by tuning the sliding mode control parameters for a rigid satellite, taking into account uncertainties associated with the inertia matrix. The simulation results suggest the convergence to the near‐optimal solution, and the stability and robustness of the controller are enhanced by this technique.
Minimization of L_2-sensitivity for state-space digital filters subject to L_2-dynamic-range scaling constraints

The problem of minimizing an L2-sensitivity measure subject to L2norm dynamic-range scaling constraints for state-space digital filters is formulated. It is shown that the problem can be converted into an unconstrained optimization problem by using linear-algebraic techniques. Next, the unconstrained optimization problem is solved by applying an efficient quasi-Newton algorithm with closed-form formula for gradient evaluation. The coordinate transformation matrix obtained is then used to construct the optimal state-space filter structure that minimizes the L2-sensitivity measure subject to the scaling constraints. A numerical example is presented to illustrate the utility of the proposed technique.
Geometric continuity constraints of automatically derived parametrisations in CAD-based shape optimisation

ABSTRACT CAD geometries are most often exchanged between analysis tools using NURBS patches to represent the boundary (BRep). We present a method where the control points of the BRep are used to automatically derive parametrisations suitable for shape optimisation with gradient-based methods. Particular focus is on ensuring geometric continuity between the NURBS patches through formulation of discrete constraints. Design variables then arise from an orthogonal basis to the remaining design space using a singular value decomposition (SVD). The manuscript presents the extension of earlier work on B-spline surfaces to full NURBS surfaces and investigates the effect of the SVD cut-off on the optimisation results. An estimation of the effective rank is proposed which allows to automatically determine a suitable cut-off value. The effectiveness of the algorithm is demonstrated for the minimisation of total pressure loss over a section of an automotive climate duct, and a U-bend cooling channel.
Application of Natural Transform Method to Fractional Pantograph Delay Differential Equations

In this paper, a new method based on combination of the natural transform method (NTM), Adomian decomposition method (ADM), and coefficient perturbation method (CPM) which is called “perturbed decomposition natural transform method” (PDNTM) is implemented for solving fractional pantograph delay differential equations with nonconstant coefficients. The fractional derivative is regarded in Caputo sense. Numerical evaluations are included to demonstrate the validity and applicability of this technique.
On the hydrodynamic behaviour of a particle system with nearest neighbour interactions

In this thesis we will study a system of Brownian particles on the real line, which are coupled through the nearest neighbours by an attractive potential. This model is related to the Ginzburg-Landau model. We will prove two results. The first result is the hydrodynamic equation for the particle density. More precisely, we show that the empirical measure of the particle positions converges in the hydrodynamic limit to a deterministic and absolutely continuous probability measure, where the density solves a nonlinear heat equation. The crucial idea will be the reduction of the particle model to the height model, in the literature also called Ginzburg-Landau interface model. We will obtain the claimed result by taking the limit in the height model and passing back to the particle model. Further, we will outline how this approach generalises to multiple dimensions. The second result is the characterisation of the equilibrium fluctuations in the case of quadratic potential. We will consider the fluctuation field, which is defined as the square root of the number of particles times the difference of the empirical measure of the particle positions and its expectation. Assuming the initial distribution of the particle system to be stationary, we will show that the fluctuation field converges in the hydrodynamic limit to an infinite-dimensional Ornstein-Uhlenbeck process. The proof will consist of characterising the accumulation points of the distributions of fluctuation fields by means of a martingale problem and showing tightness.
Near-barrier fusion and barrier distribution of 58Ni + 54Fe

Near- and sub-barrier fusion cross sections have been measured for the system 58 Ni + 54 Fe, and the fusion barrier distribution has been extracted. The measured cross sections cover the range from ≃1 μb up to around 500 mb. Close analogies are found between the extracted barrier distribution and the available data on 58 Ni + 60 Ni, indicating the dominating influence of complex surface vibrations on the fusion of 58 Ni + 54 Fe. The present data on 58 Ni + 54 Fe are well reproduced by standard coupled-channels calculations in the measured energy range, including quadrupole and octupole phonons in both colliding nuclei.
Distributed fuzzy filtering for load frequency control of non‐linear interconnected power systems under cyber‐physical attacks

In this study, a new distributed filtering approach is proposed for the load frequency control of uncertain non-linear power systems with cyber-physical attacks. Specifically, the non-linear power system is firstly modelled under interval type-2 Takagi-Sugeno fuzzy framework and the uncertainty therein is captured by designing corresponding membership functions. Both denial of service cyber attack and physical sensor attack are considered and modelled as independent Bernoulli process. Based on the Lyapunov stability theory, less conservative sufficient conditions have been derived to guarantee the robustly mean-square asymptotic stability with an average H ∞ performance standard γ for the dynamic filtering error system. Moreover, artful matrix transformation techniques have been adopted to decouple the intertwined matrix variables in designing the distributed filter gains. In simulations, a three-area non-linear power system with internal uncertainties is used to validate the robustness of the proposed distributed filtering strategy to system parametric uncertainties and cyber-physical attacks.
Conceptual models for integer addition and subtraction

In this article, we report the findings of a study conducted with 6 Grade 8 students in the United States. The students posed stories for open number sentences involving addition and subtraction of integers. We analysed the stories posed by the students to build models that describe the conceptual structures behind these posed stories – the conceptual models for integer addition and subtraction. These four conceptual models for thinking about and using integer addition and subtraction include Bookkeeping, Counterbalance, Relativity, and Translation, and are generated from the students’ posed stories. We also provide profiles of conceptual model use for two of the 6 students that describe how the students posed stories to accommodate conceptual model use, such as posing unconventional or unrealistic stories or changing the structure of the number sentences. The conceptual models and descriptions of how the students used them provide perspective into student thinking about integers and contexts, highlighting the mathematics of the students, and calling for a re-examination of contexts used in school mathematics.
Global modelling of tungsten impurity transport based on the drift-kinetic equation

A global kinetic simulation model of collisional impurity transport is developed for evaluating the radial particle flux of tungsten impurity in the edge region of a tokamak plasma. Here, the plasma including the impurity and the background ion is presupposed to be quasi-steady. The simulation model is based on the drift-kinetic equation of the impurity affected by the friction force and the thermal force, which were formulated in the previous study (Homma et al 2016 Nucl. Fusion 56 036009). The model is implemented in a drift-kinetic simulation code. We find that the magnetic drift term in the drift-kinetic equation causes the ‘global effect’ on the impurity transport. Here, the global effect means that the solution of the drift-kinetic equation (and also the radial particle flux) on a magnetic flux surface is influenced by the values of the solution all over the edge region.
The intervention effect of TCM on acute myocardial infarction in rats

[Objective]To explore the mechanism of the Chinese herbal medicine having improving effect on the heart function in the model of rats with acute myocardial infarction(AMI). [Methods] The AMI was induced in rats by ligating the middle segment of left anterior descending coronary artery. The Chinese herbal medicine Xinfukang(XFK) and SalB were used to intervene the disease course of the rats. The rat's heart function was examined, and the infarct size was evaluated with N-BT and the expression of PCNA and CD34 was examined by immunohistochemical method at the end of two weeks. [Results] The infarct area of each group was declined and the infarct thickness of each group was increased, the heart function was improved. (P0.05). The consequence of PCNA , the amount of positive cells in the capillary vessels of each group were increased (P0.05). The consequence of CD34, the amount of positive cells of each group were increased ( P0.05). [Conclusion] In AMI rats, the Chinese herbal medicine can reduce the infarct area, increase the thickness of infarct ventricle, accelerate regeneration of blood vessel, promote the multiplication of cardiac muscle cells and improve the heart function.
Renewal Monte Carlo: Renewal Theory-Based Reinforcement Learning

An online reinforcement learning algorithm called renewal Monte Carlo (RMC) is presented. RMC works for infinite horizon Markov decision processes with a designated start state. RMC is a Monte Carlo algorithm that retains the key advantages of Monte Carlo—viz., simplicity, ease of implementation, and low bias—while circumventing the main drawbacks of Monte Carlo—viz., high variance and delayed updates. Given a parameterized policy <inline-formula><tex-math notation="LaTeX">$\pi _\theta$</tex-math></inline-formula>, the algorithm consists of three parts: estimating the expected discounted reward <inline-formula><tex-math notation="LaTeX">$\mathsf {R}_{\theta }$</tex-math></inline-formula> and the expected discounted time <inline-formula><tex-math notation="LaTeX">$\mathsf {T}_\theta$</tex-math></inline-formula> over a regenerative cycle; estimating the derivatives <inline-formula><tex-math notation="LaTeX">$\nabla _\theta \mathsf {R}_\theta$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\nabla _\theta \mathsf {T}_\theta$</tex-math></inline-formula>; and updating the policy parameters using stochastic approximation to find the roots of <inline-formula><tex-math notation="LaTeX">$\mathsf {R}_\theta \nabla _\theta \mathsf {T}_\theta - \mathsf {T}_\theta \nabla _\theta \mathsf {R}_\theta$</tex-math></inline-formula>. It is shown that under mild technical conditions, RMC converges to a locally optimal policy. It is also shown that RMC works for postdecision state models as well. An approximate version of RMC is proposed where a regenerative cycle is defined as successive visits to a prespecified “renewal set”. It is shown that if the value function of the system is locally Lipschitz on the renewal set, then RMC converges to an approximate locally optimal policy. Three numerical experiments are presented to illustrate RMC and compare it with other state-of-the-art reinforcement learning algorithms.