Miracle-dz/UniFAR-FTUs huggingface dataset:
Partition of the complement of good semigroup ideals and Apéry sets

Abstract Good semigroups form a class of submonoids of containing the value semigroups of curve singularities. In this article, we describe a partition of the complement of good semigroup ideals. As main application, we describe the Apéry sets of good semigroups with respect to arbitrary elements. This generalizes to any the results in the study of D’Anna et al., which are proved in the case d = 2 and only for the standard Apéry set with respect to the smallest nonzero element. Several new results describing good semigroups in are also provided.
Liquid-Phase Templateless Synthesis of Pt-on-Pd0.85Bi0.15 Nanowires and PtPdBi Porous Nanoparticles with Superior Electrocatalytic Activity

This article reports the synthesis of Pt-on-Pd0.85Bi0. 15 nanowires (NWs) and PtPdBi porous nanoparticles (PNPs) by a facile, one-pot, wet-chemical, and templateless method in the presence of oleylamine (OAm) and NH4Br. The relationship between the morphology and composition in the PtPdBi trimetallic system was systematically studied. Interestingly, it is verified that adding only 5% Bi will produce Pd NWs, which offers a novel approach to synthesize Pd NWs in the oil phase without any template. On the basis of the fact of synthesizing Pd0.85Bi0. 15 NWs, Pt-on-Pd0.85Bi0. 15 NWs with hetero-nanostructures were successfully synthesized by a one-step method. Furthermore, the number of Pt nanobranches for Pt-on-Pd0.85Bi0. 15 NWs could be easily controlled via simply changing the synthetic parameters, which could tune the catalytic properties. PtPdBi PNPs were obtained by the acid pickling of PtPdBi2 intermetallic compounds. Most importantly, a catalytic study indicates that the as-obtained Pt-on-Pd0.85Bi0. 15 NWs .
The postmigrant generation between racial discrimination and new orientation: from hegemony to convivial everyday practice

ABSTRACT The “postmigrant perspective”, which arose in the German-speaking countries and has been adopted internationally, offers a counter-hegemonic alternative to the everyday racist and ethnicizing discourses on migration. To understand migration as historical normality, to engage in migration research as social analysis and to focus centrally on the perspectives of the postmigrant generation leads to a rupture. This entails a radical interrogation of the binary thinking around migrants and non-migrants, which has significantly shaped not only established migration research but other areas as well. The present article, proceeding from a postmigrant perspective, analyses semi-narrative interviews with adolescents on their experiences of discrimination and racism. We seek to show there that the young people of the postmigrant generation have been impacted by everyday racist and ethnicizing discourses, but have not been subjugated by them. Despite encountering restrictive conditions, they develop empowering convivial everyday practices, from which society ought to learn something.
An Efficient Approach for Audio-Visual Emotion Recognition With Missing Labels And Missing Modalities

Audio-visual emotion recognition is important for human-machine interaction systems by combining the information of audio and visual modalities. Although great progress has been made by previous works using multimodal learning compared with unimodal learning, they still cannot effectively deal with two key challenges. Firstly, it is difficult or expensive to acquire labeled emotional data, which results in a large amount of data with missing labels. Secondly, emotional data often has missing modalities. To address these problems, we propose a unified deep learning framework to efficiently handle missing labels and missing modalities for audio-visual emotion recognition through correlation analysis. Specifically, we consider four types of emotional data during the training stage: complete, label missing, visual missing, and audio missing. We propose a correlation loss based on Hirschfeld-Gebelein-Ŕenyi (HGR) maximal correlation to effectively capture the common information in different types of training data for emotion prediction. Experiments on the eNTERFACE’05 and RAVDESS datasets show that our deep learning approach has high effectiveness for audio-visual emotion recognition.
DGNSS Cooperative Positioning in Mobile Smart Devices: A Proof of Concept

Global Navigation Satellite System (GNSS) constitutes the foremost provider for geo-localization in a growing number of consumer-grade applications and services supporting urban mobility. Therefore, low-cost and ultra-low-cost, embedded GNSS receivers have become ubiquitous in mobile devices such as smartphones and consumer electronics to a large extent. However, limited sky visibility and multipath scattering induced in urban areas hinder positioning and navigation capabilities, thus threatening the quality of position estimates. This work leverages the availability of raw GNSS measurements in ultra-low-cost smartphone chipsets and the ubiquitous connectivity provided by modern, low-latency network infrastructures to enable a Cooperative Positioning (CP) framework. A Proof Of Concept is presented that aims at demonstratingdesigned to demonstrate the feasibility of a GNSS-only CP among networked smartphones embedding ultra-low-costequipped with GNSS receivers. The test campaign presented in this study assessed the feasibility of a client-serverthe approach over 4 G/LTE network connectivity. Results demonstrated an overall service availability above 80%, and an average accuracy improvement over the 40% w.r. t. to the GNSS standalone solution.
Toward Personalized Human Activity Recognition Model with Auto-Supervised Learning Framework

The performance of any Human Activity Recognition (HAR) model trained from a general population (subject-independent), will often decreases when tested on subjects unseen during training. This is due to inter-subject variability such as differences in activity patterns, emotional status, gait or posture between users. To solve this problem, we propose a novel auto-supervision formalism based on the theory of a Particle Filter (PF) or Sequential Monte Carlo (SMC) method to automatically build a personalized HAR model. The suggested approach uses different steps based on the SMC filter method to automatically and iteratively approximate the target distribution as a set of temporal samples in order to personalize the HAR model towards a target user. These temporal samples are selected from the target user based on their importance score, which indicates the likelihood of the temporal samples belong to the target distribution. We validated our framework on several public HAR datasets, and prediction accuracy was consistently improved for new subjects in the test set.
Learning-based simulation and modeling of unorganized chaining behavior using data generated from 3D human motion tracking

Abstract The paper models the unorganized chaining behavior, where humans need to walk in a chain due to a constrained environment. Detection and tracking are done using a 3D LiDAR, which has the challenges of environmental noises, uncontrolled environment, and occlusions. The Kalman filter is used for tracking. The trajectories are analyzed and used to train a behavioral model. The modeling has applications in socialistic robot motion planning and simulations. Based on the results, we conclude that the trajectory prediction by our approach is more socialistic and has a lesser error when compared to the artificial potential field method.
Correlation Tensor Decomposition and Its Application in Spatial Imaging Data

Abstract Multi-dimensional tensor data have gained increasing attention in the recent years, especially in biomedical imaging analyses. However, the most existing tensor models are only based on the mean information of imaging pixels. Motivated by multimodal optical imaging data in a breast cancer study, we develop a new tensor learning approach to use pixel-wise correlation information, which is represented through the higher order correlation tensor. We proposed a novel semi-symmetric correlation tensor decomposition method which effectively captures the informative spatial patterns of pixel-wise correlations to facilitate cancer diagnosis. We establish the theoretical properties for recovering structure and for classification consistency. In addition, we develop an efficient algorithm to achieve computational scalability. Our simulation studies and an application on breast cancer imaging data all indicate that the proposed method outperforms other competing methods in terms of pattern recognition and prediction accuracy.
Microalbuminuria in obese children

Background: Obesity is a global pandemic both in adults and children and many factors contribute to this pandemic. Early diagnosis and prevention of long-term complications associated with obesity is the main goal of treatment in addition to dietary control and lifestyle modifications. Microalbuminuria, a marker of impending renal insult in adults is less understood in children. The objective of this study was to determine microalbuminuria in obese children 5-18 years of age.Methods: This study was single centre, and cross-sectional observational study. Children between 5-18 years of age with obesity (Body mass index >95th centile for sex and age) participated in the study. Microalbuminuria based on urinary albumin to creatinine ratio and relation to obesity was studied.Results: Fifty-four children (M:F=0.9:1) were included in the study. Family history of obesity, hypertension, diabetes mellitus was present in 53.7% (n=29), 33.3% (n=18), 27.8 (n=15) children respectively. Microalbuminuria was observed in two children (3.8%). Mean BMI (38.25±7.42 kg/m2 vs 26.13±4.13 kg/m2) (p=0.009), mean waist circumference (97.50±4.95, vs 83.52±0.94 cm) (p=0.044), mean waist hip ratio (0.93±0.04 vs 0.88±0.09) was more in children with microalbuminuria when compared to without microalbuminuria. One obese child with microalbuminuria had hypertension.Conclusions: Microalbuminuria, though a good marker of renal insult in adults, may not be prevalent in obese children. It may me more prevalent in obese children with higher BMI, truncal obesity and WHR, hence these children should be screened for the same.
Drug−Amino Acid Interactions in Aqueous Medium: Volumetric, Compressibility, and Viscometric Studies

Density (ρ), speed of sound (u), and viscosity (η) measurements of amino acids l-glutamine and l-histidine in aqueous solutions of metformin hydrochloride (an antidiabetic drug) (0.01, 0.07, and 0.13 mol·kg–1) were carried out in the concentration range (0.02–0.20 mol·kg–1) at 293.15, 298.15, 303.15, 308.15, and 313.15 K. The experimental data has been meticulously used to enumerate various parameters which assist to procure valuable information regarding the presence of different kinds of intermolecular interactions present in the ternary (metformin hydrochloride + water + amino acids) system. The results were interpreted in terms of various metformin hydrochloride–amino acid intermolecular interactions through a cosphere overlap model. The ion–ion interactions are the key players in ternary system and these interactions are of higher order in the case of histidine as compared to glutamine. Also application of transition state theory has been made to calculate the thermodynamic activation parameters for .
A Highly Stretchable and Sensitive Strain Sensor Based on Dopamine Modified Electrospun SEBS Fibers and MWCNTs with Carboxylation

Wearable flexible electronic strain sensor devices have developed rapidly in recent years due to their potential capacity to detect human motion in various situations. However, it still remains still a big challenge to fabricate strain sensors with high sensitivity over a wide workable strain range. In order to meet this challenge, a new type of strain sensor based on elastomer/carbon nanotube composite fiber is reported in this work. Elastomer fibers are initially prepared via the electrospinning of styrene ethylene butene styrene block copolymer (SEBS). The resultant SEBS fibers are then functionalized by sequentially coating with dopamine (DA) coating and carboxyl group (‐COOH) grafted multi‐walled carbon nanotubes (MWCNTs) under vacuum filtration and ultrasonication. Scanning electron microscopy (SEM) and thermogravimetric analysis reveals that a large amount of MWCNTs is firmly bonded onto the SEBS fibers and evenly distributed. SEBS@PDA/MWCNTs composite fibers based strain sensors exhibit excellent performance, including a high gauge factor of 3717 and large workable strain range up to 530%. Furthermore, the developed sensors demonstrate excellent washing fastness and superior sensitivity in monitoring both small strains (e.g., pulse beats and vocal cord vibrations) and large strains (e.g., finger, elbow, and knee bending).
Cortical microtubules and physical properties of cellulose microfibrils during primary cell wall formation in Arabidopsis thaliana

ii TABLE OF CONTENTS.iv LIST OF TABLES.xiii LIST OF FIGURES.xiv LIST OF ABBREVIATIONS.xix ACKNOWLEDGEMENTS.xx CHAPTER 1 Literature review. 1 1.1 PLANT CELL ANISOTROPIC EXPANSION . 2 1.1.1 Anisotropic expansion in diffusely growing cells . 2 1.1.2 Arabidopsis roots, inflorescence stems, and dark-grown hypocotyls model systems for studying growth anisotropy . 2 1.2 CELL WALL AND THE CONTROL OF GROWTH ANISOTROPY. 5 1.2.1 Mechanical properties of the cell wall during anisotropic growth . 5 1.2.2 Cellulose microfibrils. 6 1.2.2.1 Structure and properties of cellulose microfibrils. 6 1.2.2. 2 Cellulose biosynthesis. 8 1.2.2.3 Cellulose-synthase-complexes (CSCs) . 10 1.2.3 Cellulose microfibrils and growth anisotropy. 11 1.2. 4 Other wall components involved in cell expansion. 11 1.3 CORTICAL MICROTUBULES AND GROWTH ANISOTROPY. 12 1.3. 1 Cortical microtubules in the expanding cells . 12 1.3. 2 microtubule organization 1 (mor1-1) as a good system to examine the role of cortical microtubules in anisotropic growth. 14 1.3. 3 Possible role of microtubules: the microtubule-length-regulation hypothesis. 15 1.4 INVOLVEMENT OF COBRA IN ANISOTROPIC CELL EXPANSION. 16
Discovery and Revitalization of “Feeling of Hometown” from a Disaster Site Inhabitant's Continuous Engagement in Reconstruction Work: Ethnographic Interviews with a Radiation Decontamination Worker Over 5 Years Following the Fukushima Nuclear Power Plant Accident
 1

: After hometown loss due to the Fukushima Nuclear Power Plant accident, many radiation decontamination workers started to engage in the reconstruction of their hometown. However, little is known regarding the meaning of experience of engaging in such work. We describe the process of such experience through a series of ethnographic interviews over 5 years with a radiation decontamination worker. The process was classi ﬁ ed into the following three periods: “ recovery of lost land by inhabitants ’ own hands, ” “ goal loss and semiotic trap in pursuit of recovery, ” and “ revitalization of feeling of hometown. ” Consequently, the informant overcame the contradiction theo-rized as a “ semiotic trap ” that radiation decontamination for physical recovery would make his return to home impossible, and updated his narrative to the revitalization of “ feeling of hometown ” through continuous engagement in the work by creating a sense for transplacement, in which the individual focuses on the psychological aspects of his/her hometown instead of the physical aspects. Psychological reconstruction for disaster victims requires the discovery and revitalization of feeling of hometown rather than the completion of physical reconstruction.
Review of Electric Machines in More-/Hybrid-/Turbo-Electric Aircraft

Aircraft electrification is currently the best alternative to address the rising demand for more air transportation and deal with anticipated economic and environmental impacts. Although the all-electric-aircraft (AEA) concept is not yet a feasible solution, the more-electric aircraft (MEA) is gaining significant attention. Electrical systems either partially or entirely replace the large and inefficient hydraulic, pneumatic, and mechanical conventional aircraft actuating systems. The upgrade could also encompass the propulsion system, as in hybrid- and turbo-electric aircraft. This upgrade reduces the aircraft weight, reduces the usage of pollutant fluids, increases fuel efficiency, reduces carbon emissions, and increases aircraft controllability and reliability. This article reviews various application areas of electric machines in electrified aircraft, such as actuation, taxiing, propulsion, and generation. Moreover, it reviews the main types of currently/to be utilized electric machines and the critically required specifications. Finally, a comparison between the different considered machines and potential future research is discussed.
Microbots Gene Delivery System Based on Bifidobacteria in a Tumor Model

In cancer, the use of microbots based on anaerobic bacteria as specific transporters targeting tumor tissues has been explored since most solid tumors exhibit hypoxic regions. The aim of this study was to develop and characterize magnetic microbots based on Bifidobacteria and iron oxide fluorescent magnetic nanoparticles complexed with chitosan and a hypoxia inducible plasmid. In addition, the efficiency of the microbots for gene delivery to solid tumors was evaluated in an in vivo model by florescence and luminescence. To elaborate microbots, iron oxide fluorescent magnetic nanoparticles complexed with chitosan and a hypoxia-inducible plasmid called nanocomplex (NCs) with a size of 302 nm and a ζ potential of +16 mV were obtained and loaded onto Bifidobacteria membranes. Microbots with a diameter between 1–2 µm were characterized by atomic force microscopy (AFM) and scanning electron microscopy (SEM). Microbots were injected intravenously through the tail vein to tumor-bearing mice, and then a magnet was placed to focus them to the tumor area. Forty-eight hours after injection, the biodistribution was determined by florescence and luminescence. The greatest luminescence and fluorescence emitted were found in tumor tissue compared with the normal organs. We created a vector that can be directed by a magnet and deliver genes whose expression is regulated by hypoxic microenvironment of tumor.
Photoresponsive Surface Wrinkle Morphologies in Liquid Crystalline Polymer Films

Laminated bilayer films comprised of photoresponsive azobenzene-containing liquid crystalline polymer skin layers adhered on a thick elastomeric substrate were prepared, and the wrinkle formation upon uniaxial compression was investigated. Irradiation with UV light at 365 nm led to the disappearance of the wrinkle or reduction of wrinkle wavelength, depending on the content of azobenzene unit in the polymer. Such photoresponsive modulations of wrinkle formation could be well correlated with the photoinduced changes in the Young’s modulus determined by indentation–retraction force curve measurements using an AFM cantilever. Additionally, the thickness modulation of the skin layer caused by the photoinduced mass migration can also be applied to modify the wrinkle wavelength. These photoresponsive surface wrinkle modulations are anticipated to offer new possibilities for the surface microfabrication technology.
A missing piece in high school science education: Research ethics in the classroom

ABSTRACT High school students need more comprehensive scientific inquiry learning instruction through research ethics education. Yet, this topic is often overlooked in the science curriculum. To collect basic data to guide research ethics education for high school students, this study documents the kinds of research misconduct students commit, the contexts in which they do so, and the factors that influence such misconduct. Data were collected via questionnaires from 223 gifted high school students at science academies in Korea. The results showed that high school students engaged in the most research misconduct in experiment-based classes rather than in mentorship programmes and science fair competitions. The largest number of data fabrication and falsification instances were seen in experiment-based classes. A regression analysis on the effect of influencing factors on each research misconduct type showed that falsification and plagiarism can be reduced by increasing education and regulation and lowering desire and neglect, whereas duplicate publications can be reduced by decreasing desire and neglect levels.
Moral signaling through donations of money and time

Prosocial acts typically take the form of time- or money-donations. Do third-parties differ in how they evaluate these different kinds of donations? Here, we show that consumers view time-donations as more morally praiseworthy and more diagnostic of moral character than money-donations, even when the resource investment is comparable. This moral preference occurs because consumers perceive time-donations as signaling greater emotional investment in the cause and therefore better moral character; this occurs despite consumers’ (correct) belief that time-donations are typically less effective than money-donations (Study 1). This effect in turn is explained by two mechanisms: People believe that time-donations are costlier even when their objective costs are equated, which happens because people rely on a lay theory associating time with the self (Study 2). The more signaling power of time- donations has downstream implications for interpersonal attractiveness in a dating context (Study 3A), employment decisions (Study 3B), and donor decision-making (Study 3). Moreover, donors who are prompted with an affiliation rather (versus dominance) goal are likelier to favor time-donations (Study 4). However, reframing money-donations in terms of time (e.g., donating a week’s salary) reduced and even reversed these effects (Study 5). These results support theories of prosociality that place reputation-signaling as a key motivator of consumers’ moral behavior. We discuss implications for the charity market and for social movements, such as effective altruism, that seek to maximize the social benefit of consumers’ altruistic acts.
Efficient Video Compressed Sensing Reconstruction via Exploiting Spatial-Temporal Correlation With Measurement Constraint

Recent deep learning-based video compressed sensing (VCS) methods have achieved promising results but still suffer from numerous hyper-parameters and inflexibility. This paper proposes a novel network for VCS, named STM-Net, to fast recover high-quality video frames by optionally exploiting Spatial-Temporal information with a Measurement constraint. Combining the merits of adaptive sampling and adaptive shrinkage-thresholding, we first propose an improved ISTA-Net+ for framewise independent reconstruction, called Unfolding Adaptive Shrinkage-Thresholding Network (UAST-Net). To get further non-key frames reconstruction improvement, we develop a two-phase joint deep reconstruction, including an Occlusion-Aware Temporal Alignment to avoid irrelevant information compensation and a Multiple Frames Fusion with proposed Spatial-Temporal Feature Weighting (STFW) module to guide attractive content extraction and discriminative features generation. Besides, we develop a measurement loss to reduce the solution space to facilitate network optimization. Experimental results demonstrate the superiority of the proposed STM-Net over the existing methods.