Miracle-dz/UniFAR-FTUs huggingface dataset:
Weight-shifting-based robot control system improves the weight-bearing rate and balance ability of the static standing position in hip osteoarthritis patients: a randomized controlled trial focusing on outcomes after total hip arthroplasty

Background After a total hip arthroplasty (THA), standing and walking balance are greatly affected in the early stages of recovery, so it is important to increase the weight-bearing amount (WBA) on the operated side. Sometimes, traditional treatments may not be enough to improve WBA and weight-bearing ratio (WBR) on the operated side in a satisfactory way. To solve this problem, we came up with a new weight-shifting-based robot control system called LOCOBOT. This system can control a spherical robot on a floor by changing the center of pressure (COP) on a force-sensing board in rehabilitation after THA. The goal of this study was to find out how rehabilitation with the LOCOBOT affects the WBR and balance in a static standing position in patients with unilateral hip osteoarthritis (OA) who had a primary uncemented THA. Methods This randomized controlled trial included 20 patients diagnosed with Kellgren–Lawrence (K–L) grade 3 or 4 hip OA on the operative side and K–L grade 0 normal hip on the nonoperative side. We used the minimization method for allocation and randomly assigned patients to either the LOCOBOT group or the control group. As a result, 10 patient seach were randomly assigned to the LOCOBOT and control groups. Both groups received 40 min of rehabilitation treatment. Out of the 40 min, the LOCOBOT group underwent treatment for 10 min with LOCOBOT. The control group performed COP-controlled exercises on a flat floor instead of using LOCOBOT for 10 of the 40 min. All theoutcome measures were performed pre-THA and 11.9 ± 1.6 days after THA (12 days after THA). The primary outcome measure included WBR in the static standing position. Results After12 days of THA, the LOCOBOT group exhibited significantly higher mean WBR and WBA (operated side) values than the control group. Furthermore, the LOCOBOT group exhibited significantly lower mean WBA (non-operated side) and outer diameter area (ODA) values than the control group. From pre-THA to 12 days after THA, the LOCOBOT group exhibited a significant improvement in mean WBR and WBA (operated side). Moreover, the mean WBA (non-operated side) and ODA significantly decreased. From pre-THA to 12 days after THA, the control group showed a significant increase in total trajectory length and ODA. Conclusions The most important finding of this study was that patients were able to perform the LOCOBOT exercise as early as the second day after THA, and that WBR and ODA significantly improved by the 12th day after THA. This result demonstrated that the LOCOBOT effectively improves WBR in a short period of time after THA and is a valuable system for enhancing balance ability. This expedites the acquisition of independence in activities of daily living after THA and may contribute to optimizing the effectiveness of medical care.
The analysis study on nonlinear iterative methods for inverse problems

In this paper, the nonlinear iterative methods, which are different from the classical algorithms, to solve inverse problems are presented. Our methods by denoting some parameters and some properties of the algorithm in both noise and noiseless cases are studied. Finally, the convergence of the sequence generated by the algorithm without noise is discussed.
An ideally parameterized unscented Kalman filter for the inverse problem of electrocardiography

Abstract ECG imaging as noninvasive method is aiming to reconstruct the distribution of the transmembrane voltage amplitudes (TMVs) from the body surface potential map (BSPM). Due to the ill-posedness, standard approaches like the Tikhonov regularization method cause blurring and artefacts in the solution. To suppress blurring and artefacts, this work investigated a model based approach, the unscented Kalman filter (UKF). The intention of this paper is to show the potential of an UKF approach by using an idealized parametrization.
Wavelet Multiresolution Analysis Based Speech Emotion Recognition System Using 1D CNN LSTM Networks

Speech Emotion Recognition (SER) is the task of recognizing a speaker's emotional state from speech. SER plays a significant role in Human-Computer Interaction and psychological assessment. Several kinds of time-frequency representations like spectrograms, mel-frequency cepstrum coefficients (MFCCs), and mel-spectrograms are commonly used to develop an SER system. These representations use the Fast Fourier Transform (FFT) to convert the time domain signal to the frequency domain. However, the FFT has one fundamental limitation due to the uncertainty principle, which does not simultaneously allow a good resolution in both time and frequency domains. On the other hand, the multiresolution property of wavelets can provide a good localization in both time and frequency domains. Therefore, this article investigates the competency of the wavelet transforms for SER. We propose a Wavelet based Deep Emotion Recognition (WaDER) method using an autoencoder and 1D convolutional neural network (CNN) and long short-term memory (LSTM) networks. The autoencoder is used to perform the dimensionality reduction of the wavelet features then the latent space is used to classify the emotions using the 1D CNN-LSTM model. We conducted a Monte-Carlo K-fold validation using the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset. For speaker-dependent (SD) experiments, we achieved an unweighted accuracy (UA) of 81.45% and a weighted accuracy (WA) of 81.22%. The results of the experiments on the RAVDESS dataset show that the proposed method performs better than the state-of-the-art methods, which use other time-frequency representations.
EvoPhylo: An r package for pre‐ and postprocessing of morphological data from relaxed clock Bayesian phylogenetics

Relaxed clock Bayesian evolutionary inference (BEI) enables the co‐estimation of phylogenetic trees and evolutionary parameters associated with models of character and lineage evolution. Fast advances in new model developments over the past decade have boosted BEI as a major macroevolutionary analytical framework using morphological and/or molecular data across vastly different study systems. However, there is limited availability of bioinformatic tools to pre‐ and postprocess data from BEI, such as identifying morphological data partitions, or statistically testing and creating publication quality plots of evolutionary hypotheses. Here, we introduce EvoPhylo, an r package to perform automated morphological character partitioning and analyse macroevolutionary parameter from relaxed clock (time‐calibrated) BEI outputted by the programs Mr.Bayes and BEAST2. These include rates of evolution and mode of selection for each character partition, diversification rate parameters, and handling fossil‐only posterior trees. We present the theoretical background behind EvoPhylo's functions and analytical tools for evolutionary hypothesis testing, its potential uses, and interpretation of its results with a series of vignettes and links to a step‐by‐step tutorial using examples from two empirical datasets. EvoPhylo will facilitate the use of Bayesian relaxed clocks as a tool for macroevolutionary inference across a wide range of users and fields of research, especially those that make usage of morphological datasets, from paleontological to total evidence dating analyses.
Understanding English as a Foreign Language Students’ Idea Generation Strategies for Creative Writing With Natural Language Generation Tools

Natural language generation (NLG) is a process within artificial intelligence where computer systems produce human-comprehensible language texts from information. English as a foreign language (EFL) students’ use of NLG tools might facilitate their idea generation, which is fundamental to creative writing. However, little is known about how EFL students interact with NLG tools to generate ideas. This study explores strategies adopted by EFL students when searching for ideas using NLG tools, evaluating ideas generated by NLG tools and selecting NLG tools for idea generation. Four Hong Kong secondary school students attended workshops where they learned to write stories comprising their own words and words generated by NLG tools. After the workshops, they answered questions to reflect on their writing experience with NLG tools. In a thematic analysis of the written reflections, we found students may have existing ideas when searching for ideas and evaluating ideas with NLG tools. Students showed some aversion to ideas generated by NLG tools and selected NLG tools that generated a greater quantity of ideas. The findings inform our understanding of EFL students’ concerns when using NLG tools for idea generation and can inform educators’ instruction to implement NLG tools for classroom creative writing.
A FOUR-PARAMETER INTEREST RATES MODEL INCORPORATING AVERAGE OF PAST SHORT RATES

We consider a four-parameter short rate model for the evolution of interest rates. In this model we take normal level of the short rate as an exponentially weighted average of the past short rates as suggested by Malkiel (14). We first derive the differential equation for the price of a zero-coupon bond under this model. A complete explicit solution of the partial differential equation is obtained under a suitable simplifying assumption to obtain an ex- plicit model for the evolution of interest rates. The relevant yield curve is also obtained. We then consider a most desirable feature of an interest rates model, incorporating current term structure of interest rates into the model. Again, a complete explicit solution is obtained together with the yield curve and its asymptotic behaviour. A well-known previously given model is included and discussed in the context of our present interest rates model.
An emitting optical system design for laser panorama detection

In order to improve the beam characteristics of high-power laser diode,whose beam quality is quite different in fast axis and slow axis,a set of emitting shaping system was designed through theoretical analysis and software simulation based on geometrical optics of refraction.As beam divergence of fast axis and slow axis is changed by cylindrical lens separately,the beams radiate in the meridional direction to the narrow field of view,and cover the field of view of ±30° in the sagittal direction.Meanwhile,a prism of special structure was added in the shaping system to mix light.Consequently,the uniformity of energy distribution of the output beam became greater than 75% within the target area,and the energy utilization rate reached to 80% or more.The results show that a single emitting system can complete the detection of ±30° field of view within the work distance of 15m,and six emission systems constitute a 360° field of view to complete panorama detection.This result is helpful for improving the accuracy of the laser active detection and increasing its scope.
The role of falsification in the validation of numerical models

ABSTRACT Numerical modelling consists of simulating with informatic technologies the conditions of experiments that would require big resources in terms of time and costs. For example, structural engineering considers numerical models of structures because otherwise it would be too expensive to build and destroy a building to study its behaviour under collapse conditions. In this regard, numerical modelling has become a valid alternative to physical experiments since they fall within the scientific method as they need validation that consists in discarding models when the results are not considered reliable. The criterion of falsification is therefore the basis of the selection process not of the ‘most truthful’ model, but of the ‘model least’, that is, less affected by errors. The paper discusses the sources of errors at the base of numerical models by proposing the importance of validation as the application of the falsification method proposed by Karl Popper.
A Method for Determining Orientation Parameters in Image-based Reconstruction of Architectural Scenes

This paper describes a method for the determination of orientation parameters of the un-calibrated images involved in the image-based reconstruction of architectural scenes. This approach is implemented in a collection of the geometrically intuitive vanishing point method and conditional least squares adjustment with unknown parameters algorithm. We exploit the abundant parallel and orthogonal object lines to find out the vanishing point forthe main direction and sequentially lead to the initial orientation parameters values. Then, more precise and stable values are found by an optimizing algorithm, based on conditional least squares adjustment with unknown parameters. During the iterative process in this algorithm, orientation parameters of the image are recalculated. Last, these orientation parameters are used to reconstruct textures landscape of the buildings by combining the vector and texture data in image.
Optimal Bandwidth Choice for Matching Estimators by Double Smoothing

This paper addresses the choice of an optimal smoothing parameter for local polynomial matching estimators. In order to estimate the MSE of the matching estimator as a bandwidth selection criterion, the Double Smoothing approach (Mýller (1985), Hýrdle, Hall, and Marron (1992)) is applied. The proposed bandwidth selector exhibits a faster rate of convergence than cross-validation selectors for local polynomial regression estimators. A Monte Carlo study reveals that using the proposed algorithm can yield efficiency gains compared to the MISE based cross-validation procedure. Furthermore, the estimated MSE nicely approximates the true MSE of the matching estimator.
PHOTOMETRIC CALIBRATION OF THE ACS

absolute flux calibration of the standard WFC and HRC filters is derived from the available constraining observations of spectrophotometric standard stars. Values for the encircled energy (EE) of one arcsec radius relative to an infinite aperture radius are derived for hot stars and compared to the EE for cooler stars. The sensitivity degradation for the five year ACS lifetime is defined and used to correct the ACS photometry before deriving revised quantum efficiency (QE) curves for the CCD detectors. F850LP observations of red stars with WFC are made consistent with F850LP photometry for hotter stars by decreasing the WFC QE curve longward of 9400A. Broad band QE changes with a maximum of 2.3% for WFC are also included in the revised QE curves for both CCD cameras. Revisions of the average filter transmissions of up to 4% are required to bring both broad and narrow band photometry into exact agreement with synthetic photometry from the primary white dwarf (WD) stars. The remaining inconsistencies between WD and cooler star photometry are discussed but not resolved because of the limitations of the present data set. Until these discrepancies are resolved, the transfer of the fundamental WD absolute flux calibration to cooler types of F8-G0 should be regarded as uncertain by at least 2% in the 7000-10000A range. HST flux calibrations for spectral types cooler than G may have even larger errors.
An anchor-based model for global multiple alignment of whole genome sequences

With the benefit of advanced biotechnology, large numbers of whole genome sequences have been compiled. Aligning whole genome sequences is a fundamentally different problem than aligning short sequences. Recently, intensive research activities have been devoted to this problem. We propose an anchor-based model for global multiple alignment of whole genome sequences. The model includes three main phases. Firstly, an enhanced suffix array method is employed to find anchors. Next, an exact chaining algorithm, which is based on the dynamic programming technique and the longest common subsequence idea, calculates an anchor-chain for the weighted anchors. Lastly, a progressive multiple alignment method is used to close the gaps between the anchors. The proposed chaining procedure is based on evolutionary theory and can align whole genome sequences not only for close homologs, but also distant species. Combined with the exact suffix array approach, this model can compute partially accurate solutions and generate a high-quality alignment result in terms of computation and biology.
P-values for classification - Computational aspects and asymptotics

P-Values for Classification. Let (X, Y ) be a random variable consisting of an observed feature vector X and an unobserved class label Y = 1, 2, . , L with unknown joint distribution. In addition, let D be a training data set consisting of n completely observed independent copies of (X, Y ). First, we consider a point predictor for Y , namely the standard linear classifier for two classes. But we do not assume Gaussian distributions. In this setting we provide a central limit theorem for missclassification rates and cross-validated estimators thereof. Point predictors do not provide information about confidence. To get such information, we construct for each b = 1, 2, . , L a p-value πb(X,D) for the null hypothesis that Y = b, treating Y temporarily as a fixed parameter, i.e. we construct a prediction region for Y with a certain confidence. In particular, we consider p-values based on the plug-in statistic for the standard model with two classes and prove a central limit theorem for inclusion probabilities and cross-validated estimators thereof. In addition, we discuss data-driven choices of tuning parameters for pvalues based on multicategory logistic regression, where we use regularization terms to deal with high-dimensional feature vectors X. Randomizded P-Values. Randomized tests are a familiar concept from mathematical statistics. The goal is to obtain tests with exact prescribed significance level even in settings with test statistics having discrete distributions. We discuss the related concept of randomized p-values. One benefit is that p-values obtained from different independent test statistics can be combined more easily. Since in applications non-randomized tests and p-values are needed, we review and modify a method of Meinshausen et al. (2009). A major example is the analysis of several independent contingency tables with small cell counts. We propose various ways of combining corresponding randomized p-values. We also illustrate the benefits of the final de-randomized test.
Self-Efficacy and Subjective Norm as Mediators in the Role Model and Entrepreneurial Intention Link (A Case of Balinese Students)

Several studies show entrepreneurship contributed significantly to economic growth. The increasing number of entrepreneurs can improve the economic condition in some countries. However, In Indonesia, the number of entrepreneurs compared to other countries in South Asia, such as Malaysia, Thailand, Vietnam, and the Philippines, are still low. Therefore, it is essential to analyze factors that might drive people’s intention to become an entrepreneur. Prior studies had examined entrepreneurial intention from some perspectives. Hence, this study tries to examine factors that might influence entrepreneurial intention, such as role models, self-efficacy, and subjective norms, by modifying the TPB framework model. In addition, this study also examines the variable of subjective norm and self-efficacy as the mediating variable of role model to entrepreneurial intention. This study implements a quantitative approach and collects sample data from university students in Bali. The total number of respondents for the sample data is 150. Moreover, the study results show that role model, subjective norms, and self-efficacy positively significantly influence students’ entrepreneurial intention. The result also shows that the role model is positive and significantly influences subjective norm and self-efficacy. The findings of this research can provide recommendations for government and universities to enhance students’ entrepreneurial intention.
Sudden Cardiac Death Detection by Using an Hybrid Method Based on TWA and Dictionary Learning: A Data Experimentation

Sudden Cardiac Death (SCD) is considered one of the main causes of mortality worldwide. Understanding the origin of this heart disease continues to be a challenge for the scientific community. T-wave alternans (TWA) is the term used to describe changes in the T wave’s amplitude or shape that are seen. According to the literature review, T wave alternans has been considered an important, non-invasive indicator to detect and stratify the risk of sudden cardiac death. On the other hand, dictionary learning is a digital signal processing technique that allows identify the main characteristics of a signal using a sparse representation. In this context, a new non-invasive method is proposed by mixing TWA spectral methods and dictionary learning. The method identifies the main characteristics of ECG signal by obtaining a sparse representation that adapts a matrix (dictionary) in order to use it for highlighting the TWA characteristics and then use these characteristics for detecting SCD. Experimental results show an improvement of 32% compared to the Physionet TWAnalyser program by using synthetic data set and an improvement of 20% over public databases.
Partitives and negation : A cross-linguistic survey

The partitive of negation, i.e. partitive marking of NPs under the scope of negation, is known to be found in some European languages, namely Finnic, Baltic, Slavic and Basque. Based on an extensive and representative language sample, this typological study surveys the cross-linguistic distribution of the partitive of negation and other asymmetries between affirmatives and negatives in the marking of noun phrases. Instances of the partitive of negation realized as part of a case marking system are not found outside the European languages mentioned. Nonetheless, negation is found to affect the use of articles and other determiners, e.g., in Polynesian languages and in French, as well as the use of class markers, e.g., in some Bantu languages, in which the class markers in question actually function as determiners. Effects on focus marking and alignment are also observed in some languages. There is a pragmatically motivated tendency for an indefinite noun phrase to have a non-referential reading under the scope of negation. The grammatical effects of negation on the use of articles and determiners, including class markers, result in marking the noun phrases as non-referential and are thus motivated by the connection between negation and non-referentiality. It is further argued that partitives, too, contribute to marking noun phrases as non-referential, and the partitive of negation can thus be seen as arising from similar motivations.
Testing Method of Projectile Rotating Speed Based on Micro-Doppler Effect of CW Radars

Aiming at the limitation of testing methods for projectile rotating speed,a new testing method of projectile rotating speed is put forward.The radar echo micro-Doppler modulated mathematic model caused by projectile rotation is built by using micro-Doppler effect resulted from CW radar detection of projectile rotations,the internal relationship between micro-Doppler frequency and projectile rotating speed is derived,and testing methods of micro-Doppler data for projectile rotating speed is provided.The projectile rotating speed is extracted by micro-motion identification and digital demodulation of projectile rotation,and the good testing effect is validated by shooting range test.