bluuebunny/crossref_metadata_2025_split huggingface dataset:
Continuous and discrete decoding of overt speech with scalp electroencephalography (EEG)
Authors: Craik Alexander , Dial Heather R , Contreras-Vidal Jose L 
Published: October 2024

Abstract
Neurological disorders affecting speech production adversely impact quality of&#xD;life for over 7 million individuals in the US. Traditional speech interfaces like eyetracking&#xD;devices and P300 spellers are slow and unnatural for these patients. An&#xD;alternative solution, speech Brain-Computer Interfaces (BCIs), directly decodes speech&#xD;characteristics, offering a more natural communication mechanism. This research&#xD;explores the feasibility of decoding speech features using non-invasive EEG. Nine&#xD;neurologically intact participants were equipped with a 63-channel EEG system&#xD;with additional sensors to eliminate eye artifacts. Participants read aloud sentences&#xD;displayed on a screen selected for phonetic similarity to the English language. Deep&#xD;learning models, including Convolutional Neural Networks and Recurrent Neural&#xD;Networks with and without attention modules, were optimized with a focus on&#xD;minimizing trainable parameters and utilizing small input window sizes for real-time&#xD;application. These models were employed for discrete and continuous speech decoding&#xD;tasks, achieving statistically significant participant-independent decoding performance&#xD;for discrete classes and continuous characteristics of the produced audio signal. A&#xD;frequency sub-band analysis highlighted the significance of certain frequency bands&#xD;(delta, theta, and gamma) for decoding performance, and a perturbation analysis&#xD;was used to identify crucial channels. Assessed channel selection methods did not&#xD;significantly improve performance, suggesting a distributed representation of speech&#xD;information encoded in the EEG signals. Leave-One-Out training demonstrated&#xD;the feasibility of utilizing common speech neural correlates, reducing data collection&#xD;requirements from individual participants.
Sparse representations, inference and learning
Authors: Lauditi C , Troiani E , Mézard M 
Published: October 2024

Abstract
In recent years statistical physics has proven to be a valuable tool to probe into large dimensional inference problems such as the ones occurring in machine learning. Statistical physics provides analytical tools to study fundamental limitations in their solutions and proposes algorithms to solve individual instances. In these notes, based on the lectures by Marc Mézard in 2022 at the summer school in Les Houches, we will present a general framework that can be used in a large variety of problems with weak long-range interactions, including the compressed sensing problem, or the problem of learning in a perceptron. We shall see how these problems can be studied at the replica symmetric level, using developments of the cavity methods, both as a theoretical tool and as an algorithm.
Theory and applications of the sum-of-squares technique
Authors: Bach Francis , Cornacchia Elisabetta , Pesce Luca , Piccioli Giovanni 
Published: October 2024

Abstract
The sum-of-squares (SOS) approximation method is a technique used in optimization problems to derive lower bounds on the optimal value of an objective function. By representing the objective function as a sum of squares in a feature space, the SOS method transforms non-convex global optimization problems into solvable semidefinite programs. This note presents an overview of the SOS method. We start with its application in finite-dimensional feature spaces and, subsequently, we extend it to infinite-dimensional feature spaces using reproducing kernels (k-SOS). Additionally, we highlight the utilization of SOS for estimating some relevant quantities in information theory, including the log-partition function.
An Improved Ant Colony Clustering Algorithm and Application in Classification of Tree Species
Authors: Zhang Jiashan 
Published: October 2024

Abstract
Clustering is one of the most challenging research topics and has attracted the attention of many researchers. Clustering aims to divide a given set of objects into several categories according to certain rules. It is widely applied to practical problems, such as knowledge discovery, artificial intelligence, scientific data analysis, business, biology, medical diagnosis, text mining, and web data mining and so on. Ant colony clustering algorithm (ACCA), which integrates ACA into clustering, is an efficient swarm intelligence algorithm. There are some weakness in the basic ant colony clustering algorithm, such as low solving efficiency, poor calculation accuracy and iteration stagnation. In this article, an improved Ant Colony Clustering Algorithm (IACCA), in which novel picking-up and dropping strategy is introduced, is proposed and applied to classification of tree species. The experiments indicate efficiency of IACCA for classification of tree species.
Research on Digital Twin Technology for Enhancing Power Communication System Performance
Authors: Mei Feng , Lou Jia , Zhang Mingxi , Chen Wanke , He Wendi 
Published: October 2024

Abstract
Digital twin technology simulates physical systems through real-time data and virtual models, providing an innovative solution to challenges in power communication systems, such as multiple access interference (MAI) and the near-far effect. By constructing a digital twin model, we can accurately measure the non-orthogonality of each user’s spread spectrum code, enabling the elimination of mutual interference through matrix inversion or iterative methods. Our simulations show that the application of digital twin technology improves system performance by reducing MAI and stabilizing communication quality under complex conditions. When the number of stages increases, the system performance improves, but when it is greater than level 5, the system performance does not improve significantly, so the level 5 is selected in the practical application. In this case, the interference factor of the grid communication system is equal to 0.55, and the resulting system gain is 2.
HCPrune: A Hierarchical Classification Pruning Method Based on Receptive Field Size
Authors: Zhang Ruixuan , Zhao Yufeng , Zhang Wenlong 
Published: October 2024

Abstract
Model pruning, as an optimisation algorithm for convolutional neural network (CNN) models, can effectively compress and accelerate various complex CNN models. Recent network pruning algorithms usually focus on removing unimportant and redundant convolutional kernels or channels in the network. This paper propose a hierarchical pruning algorithm, HCPrune, which classifies and then prunes the convolutional layers of a CNN model to improve the model prediction accuracy while reducing the number of parameters and FLOPs. In this paper, the CNN convolutional layers are divided in terms of the size relationship between the receptive field of each layer of the network and the resolution of the input image, and use the VGG and Resnet series of networks based on some popular pruning methods to conduct experiments on the CIFAR10 and CIFAR100 datasets and ensure that the same number of parameters and FLOPs compared with the basic pruning methods. The VGG16 network improves the accuracy by 0.42% in CIFAR10 and 0.47% in CIFAR100, and the ResNet164 improves the accuracy by about 0.2% in the same configuration.
Modulation Measurement Profilometry Based on One-Dimensional Frequency-Guided S-Transform
Authors: Zhong Min , Hu Zhengyan , Duan Peng , Zan Junmu , Cao Weiwei 
Published: October 2024

Abstract
The modulation measurement profilometry employs the principle of vertical measurement, where the projection optical axis and the observation optical axis are aligned. This technique effectively avoids the limitations of shadows and occlusions inherent in optical 3D sensing methods based on the triangulation principle. To further enhance the accuracy of modulation information extraction, a one-dimensional frequency-guided S-transform method (1D-FGST) is introduced, which incorporates the parameter p to control the width of the Gaussian window, offering better time-frequency analysis characteristics compared to the traditional one-dimensional S-transform method (1D-ST). It can extract high-frequency detailed information about object, achieving a root mean square (RMS) error of 4.24 μm within a depth range of 1100 μm.
An Image Distortion Correction Method Based on Distortion Centre and Parameter Estimation
Authors: Zhao Wenhuang , Liu Xing , Zhang Yinhuan 
Published: October 2024

Abstract
We propose a radial distortion correction method based on straight-line feature calibration to address the challenge of image radial distortion when measuring mechanical motion parameters using computer vision technology. This method first independently estimates the distortion center, and then transforms the distortion parameter estimation into an arc fitting problem. The least squares optimization algorithm is applied to quickly and accurately estimate the arc parameter of distorted lines, ultimately achieving precise correction of distortion in wide-angle camera images. This method can reduce lens distortion by 89% to 92%, laying a solid foundation for the application of computer vision technology in the field of mechanical motion parameter measurement.
The thrust balance model during the dragonfly hovering flight
Authors: Zhang Kaixuan , Su Xiaohui , Zhao Yong 
Published: October 2024

Abstract
In recent years, the micro air vehicle (MAV) oscillations caused by thrust imbalances have received more attention. This paper proposes a dual-wing thrust balance model (DTBM) that can solve the above problem by iterating the modified rotation angle formula. The core control parameter of the DTBM model is the au angle, which refers to the angle between the wing surface and the stroke plane at the mid-stroke position during the upstroke. For each degree change in the au angle, the range of variation in the dimensionless average thrust coefficient is between 0.0225–0.0268. A thrust coefficient of 0.0225 causes the dragonfly to move forward by 9.037 cm in one second, which is equivalent to 1.29 times its body length. By using DTBM, the average thrust coefficient can be reduced to below 0.001 in just a few iterations. No matter how complex the motion pattern is, the DTBM can achieve thrust balance within 0.278 s. Through our research, when selecting the deviation angle motion of real dragonflies, the dual-wing au angles exhibit a highly linear correlation with wing spacing, called linear motion. In contrast, the nonlinear variation of the au angle appears in the hindwing of the no-deviation motion and the forewing of the elliptical deviation motion. All of the nonlinear changes are referred to as nonlinear motion. Nonlinear variation of the au angle arises from larger disturbances of the lateral force during the upstroke. The stronger lateral force is closely related to the flapping trajectory. When the flapping trajectory causes the dual-wing to closely approach each other in the mid-stroke, a continuous positive pressure zone forms between the dual-wing. The collision of the leading-edge vortex and the shedding of the trailing-edge vortex is the special flow field structure in the nonlinear motion. Guided by the DTBM, future designs of MAVs will be able to better achieve thrust balance during hovering flight, requiring only the embedding of the iteration algorithm and prediction function of the DTBM in the internal chip.
Increasing summertime low-level cloud cover associated with increasing vegetation in China from 2003 to 2022
Authors: Zhang Chenqi , Zhao Defeng , Gao Yanhong , Song Zhen , Zhao Bin , Wang Guoyin 
Published: October 2024

Abstract
Vegetation impacts on cloud physical properties and climate. The rapid greening in China in the past two decades, which contributed the most to global greening, may influence clouds in the region. However, due to the influence of a rapid concomitant decline in aerosol levels in China and of global warming, such influence of vegetation change is yet to be clarified. By utilizing observation data from satellite, we explored the impact of vegetation change on summertime low-level cloud cover in China from 2003 to 2022. After excluding the influence of changing aerosol and temperature on clouds, we revealed a significant positive correlation between vegetation changes and low-level cloud cover. Moreover, we explored the underlying mechanisms through which vegetation exerts its influence on clouds. We found that such influence is mediated through enhancing surface water vapor content by vegetation as well as altering net surface radiation and sensible heat flux.
Transient opto-thermal simulation analysis and experimental validation of LERP systems
Authors: Chatzizyrli Elisavet , Afentaki Angeliki , Hinkelmann Moritz , Lachmayer Roland , Neumann Jörg , Kracht Dietmar 
Published: October 2024

Abstract
Solid-state technology has revolutionized the lighting industry. However, efficiency-droop-limited light emitting diodes (LEDs) introduce constraints to the luminances achieved, and as a result, laser diodes (LDs) are replacing them in the remote phosphor setup. This introduces a new family of lighting solutions, laser-excited remote phosphor (LERP) systems, which can outperform cutting-edge LEDs. LERP systems however have not yet reached their full potential as the high intensity laser beam induces high temperatures within the phosphor material whose emission characteristics heavily depend on temperature. For this reason, a simulation framework has been developed that combines optical and thermal analysis in order to study and optimize these systems and derive their temperature thresholds for sustainable long-term usage. The focus here is on transient analysis, where the interplay between optical and thermal effects can be accounted for and the time dynamics of the system can be investigated. This enables the study of operation points near or at the thermal quenching regime. Furthermore, advanced material models have been developed in order to incorporate the temperature-dependence. The experimental validation of the model has shown that experimental and simulated results are in good agreement.
Cavitation erosion behaviour of MAB-CU4 alloy: influences of cavitation number, attack angle, time, and stand-off distance
Authors: Tini Sedat Can , Zeren Adalet , Yıldıran Avcu Yasemin , Abakay Eray , Guney Mert , Avcu Egemen 
Published: October 2024

Abstract
The present study comprehensively examines the cavitation erosion behaviour of a manganese aluminium bronze alloy (MAB-CU4 alloy) as a function of several parameters (i.e., cavitation angle, cavitation number, time, and stand-off distance), particularly focusing on the influences of cavitation angle on the surface morphology and topography of the alloy. According to the design of experiment (Taguchi experimental design) analysis, mass loss increased with cavitation number and attack angle, while increasing the stand-off distance resulted in a decrease in mass loss and an increase in the surface area affected by cavitation erosion. Cavitation erosion behaviour was most affected by the cavitation attack angle, with the cavitation attack angle contributing 69.1% to total erosion, according to variance analysis. At 90° cavitation attack angle, MAB-CU4’s erosion rate was 64% greater than that at 30°. Scanning electron microscopy and optical profilometry revealed that cavitation erosion damage at 90° occurred mostly in the grain interiors as cavitation pits due to severe plastic deformation and surface corrosion, whereas pit formation was restricted around the hard secondary phases at the grain boundaries. At 30°, deep cavitation pits were limited, the erosion crater expanded, and the number of pits was reduced. Overall, finer microstructures with more grain boundaries and secondary phases may improve cavitation erosion resistance at 90°. The present study is the first to comprehensively capture erosion damage at the microstructural scale and analyse the impact of microstructural features on the erosion damage during the cavitation erosion of MAB-CU4 alloy.
First-principles insights into thermoelectric behavior of XNiAs (X = Sc, Y) half-Heusler compounds
Authors: Giri Ashesh , Khatri Prakash , Neupane Hari Krishna , Adhikari Narayan Prasad 
Published: October 2024

Abstract
Half-Heusler (HH) compounds are regarded as potential candidates for thermoelectric devices to convert waste heat energy into electrical power, which could help mitigate the ongoing energy crisis. In this study, we investigate the structural, electronic, magnetic, and thermoelectric properties of HH compounds XNiAs (X = Sc, Y) using Density Functional Theory (DFT) and semi-classical Boltzmann transport theory (BTE). The compounds are non-magnetic semiconductors with an indirect band gap of 0.48 ( 0.50) eV for ScNiAs (YNiAs) respectively. Both materials show dynamic and mechanical stability, with ScNiAs displaying brittleness and YNiAs exhibiting ductility. At room temperature, the lattice thermal conductivity (κ

l
) for ScNiAs is 28.67 W/mK, while for YNiAs, it is 15.21 W/mK and both decrease as the temperature rises. The highest value of power factors is 29.03 μW/cmK2 and 29.74 μW/cmK2 for ScNiAs and YNiAs respectively at 1100 K for n-type doping. The optimal values of the figure of merit (zT) for n-type doping are 0.33 for ScNiAs and 0.58 for YNiAs at 1100 K. Both compounds exhibit better thermoelectric performance for n-type doping compared to p-type doping. The presence of flat bands and higher κ
l limits these materials from achieving higher zT values.
Analysis of anticipatory and compensatory postural adjustment in women of different age groups using surface electromyography
Authors: Moura Luan de Almeida , Scardovelli Terigi Augusto , Fernandes da Silva André Roberto , da Palma Valério Mariana , Barreto Campos Higor , de Camargo Matheus Leonardo Alves , Moraes Isabella Titico , Martini Silvia Cristina , da Silva Boschi Silvia Regina Matos , de Oliveira Gonzalez Tabajara , Pereira da Silva Alessandro 
Published: October 2024

Abstract
Postural balance is crucial for daily activities, relying on the coordination of sensory systems. Balance impairment, common in the elderly, is a leading cause of mortality in this population. To analyze balance, methods like postural adjustment analysis using electromyography (EMG) have been developed. With age, women tend to experience reduced mobility and greater muscle loss compared to men. However, few studies have focused on postural adjustments in women of different ages using EMG of the lower limbs during laterolateral and anteroposterior movements. This gap could reveal a decrease in muscle activation time with aging, as activation time is vital for postural adjustments. This study aimed to analyze muscle activation times in women of different ages during postural adjustments. Thirty women were divided into two groups: young and older women. A controlled biaxial force platform was used for static and dynamic balance tests while recording lower limb muscle activity using EMG. Data analysis focused on identifying muscle activation points and analyzing postural adjustment times. Results showed significant differences in muscle activation times between the two groups across various muscles and platform tilt conditions. Younger women had longer muscle activation times than older women, particularly during laterolateral platform inclinations. In anteroposterior movements, older women exhibited longer activation times compared to their laterolateral performance, with fewer differences between the groups. Overall, older women had shorter muscle activation times than younger women, suggesting a potential indicator of imbalance and increased fall risk.
Gendered vulnerabilities in climate shocks: the role of social protection interventions
Authors: Dasgupta Aparajita 
Published: October 2024

Abstract
This review explores how climate change exacerbates gender inequalities and the potential of social protection programs to alleviate these impacts. It examines how women disproportionately bear the brunt of climate shocks and discusses pathways through which programs like public works initiatives, cash transfers, and social insurance can bolster women’s resilience in this framework. Despite the potential of these programs, challenges such as implementation gaps and limited engagement of women in program design hinder the effectiveness of social protection programs. Limited research on the gendered effects of social insurance underscores the need for further investigation and gender-responsive policy interventions.
Intersectionality shapes the access to various adaptive resources in climate-vulnerable contexts
Authors: Ahmed Saleh , Eklund Elizabeth 
Published: October 2024

Abstract
Intersectional approaches are becoming essential to understanding the differential impacts of climate change and the heterogeneity of adaptive capacities. Varying factors of social and economic privileges and inequalities synergistically shape the local power structure, ultimately determining disproportionality and unequal access to adaptive resources. Using empirical data from coastal Bangladesh, which is part of the Ganges Delta, and one of the world’s -most climate-vulnerable regions, this study shows how religion, gender, and ethnicity intersect with patterns in (i) farm and non-farm incomes, (ii) land conflicts, (iii) the size of farms, and (iv) access to critical sources of weather information. The study highlights that multilayered identity, which is shaped by various structural determinants, including local power structure, can determine gendered dimensions of access to various adaptation resources. It also shows that if policymakers and development partners partially understand or do not understand the complex landscape of local vulnerability or unequal power structures including the adaptive capacity of the vulnerable populations, they would not be able to provide critical support in times of crisis. The concept of intersectionality is therefore critical in addressing adaptation and risk management challenges for regions like the Ganges Delta and other parts of the world, where people are struggling with similar social and environmental challenges.
Luther, the National Socialist
Authors: Hayden-Roy Patrick 
Published: October 2024

Abstract
The early years of Nazi rule saw an explosion of literature that portrayed in outlandish fashion Luther as a supporter of National Socialist ideals. Books by Hans Preuß, Alfred Rosenberg, and Wolf Meyer-Erlach, among others, published at the outset of the regime took as their main thesis the proposition that Luther’s Reformation aimed to achieve what the Nazis were now bringing to fulfilment; in doing so, they portray Luther as a fundamental supporter of National Socialism, and accuse those who resist such continuity between Luther’s religiosity and fascist politics of betraying his legacy. The chapter highlights the extremities of the image of Luther that emerged in the context of the new order in Germany.