Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing the role of interatomic position matrix elements in tight-binding calculations of optical properties

We study the role of hopping matrix elements of the position operator \hat{r}r̂ in tight-binding calculations of linear and nonlinear optical properties of solids. Our analysis relies on a Wannier-interpolation scheme based on calculations, which automatically includes matrix elements of \hat{r}r̂ between different Wannier orbitals. A common approximation, both in empirical tight-binding and in Wannier-interpolation calculations, is to discard those matrix elements, in which case the optical response only depends on the on-site energies, Hamiltonian hoppings, and orbital centers. We find that interatomic \hat{r}r̂-hopping terms make a sizeable contribution to the shift photocurrent in monolayer BC_22N, a covalent acentric crystal. If a minimal basis of p_zpz orbitals on the carbon atoms is used to model the band-edge response, even the dielectric function becomes strongly dependent on those terms.
Legged Robot State Estimation using Invariant Kalman Filtering and Learned Contact Events

This work develops a learning-based contact estimator for legged robots that bypasses the need for physical sensors and takes multi-modal proprioceptive sensory data as input. Unlike vision-based state estimators, proprioceptive state estimators are agnostic to perceptually degraded situations such as dark or foggy scenes. While some robots are equipped with dedicated physical sensors to detect necessary contact data for state estimation, some robots do not have dedicated contact sensors, and the addition of such sensors is non-trivial without redesigning the hardware. The trained network can estimate contact events on different terrains. The experiments show that a contact-aided invariant extended Kalman filter can generate accurate odometry trajectories compared to a state-of-the-art visual SLAM system, enabling robust proprioceptive odometry.
Distributed ADMM over directed networks

— Distributed optimization over a network of agents is ubiquitous with applications in areas including power system control, robotics and statistical learning. In many settings, the communication network is directed, i.e., the communication links between agents are unidirectional. While several variations of gradient-descent-based primal methods have been proposed for distributed optimization over directed networks, an extension of dual-ascent methods to directed networks remains a less-explored area. In this paper, we propose a distributed version of the Alternating Direction Method of Multipliers (ADMM) for directed networks. ADMM is a dual-ascent method that is known to perform well in practice. We show that if the objective function is smooth and strongly convex, our distributed ADMM algorithm achieves a geometric rate of convergence to the optimal point. Through numerical examples, we observe that the performance of our algorithm is comparable with some state-of-the-art distributed optimization algorithms over directed graphs. Additionally, our algorithm is observed to be robust to changes in its parameters.
Online Mutual Adaptation of Deep Depth Prediction and Visual SLAM

The ability of accurate depth prediction by a convolutional neural network (CNN) is a major challenge for its wide use in practical visual simultaneous localization and mapping (SLAM) applications, such as enhanced camera tracking and dense mapping. This paper is set out to answer the following question: Can we tune a depth prediction CNN with the help of a visual SLAM algorithm even if the CNN is not trained for the current operating environment in order to benefit the SLAM performance? To this end, we propose a novel online adaptation framework consisting of two complementary processes: a SLAM algorithm that is used to generate keyframes to fine-tune the depth prediction and another algorithm that uses the online adapted depth to improve map quality. Once the potential noisy map points are removed, we perform global photometric bundle adjustment (BA) to improve the overall SLAM performance. Experimental results on both benchmark datasets and a real robot in our own experimental environments show that our proposed method improves the overall SLAM accuracy. While regularization has been shown to be effective in multi-task classification problems, we present experimental results and an ablation study to show the effectiveness of regularization in preventing catastrophic forgetting in the online adaptation of depth prediction, a single-task regression problem. In addition, we compare our online adaptation framework against the state-of-the-art pre-trained depth prediction CNNs to show that our online adapted depth prediction CNN outperforms the depth prediction CNNs that have been trained on a large collection of datasets.
Particle motion in circularly polarized vacuum pp waves

Bialynicki-Birula and Charzynski argued that a gravitational wave emitted during the merger of a black hole binary may be approximated by a circularly polarized wave which may in turn trap particles (Bialynicki-Birula and Charzynski 2018 Phys. Rev. Lett. 121 171101). In this paper we consider particle motion in a class of gravitational waves which includes, besides circularly polarized periodic waves (CPP) (Stephani et al 2003 Exact Solutions of Einstein’s Field Equations (Cambridge: Cambridge University Press)), also the one proposed by Lukash V N (1975 Sov. Phys. JETP 40 792; 1974 Zh. Eksp. Teor. Fiz. 67 1594–1608; 1974 Astr. Zh. 51 281; 1976 Nuovo Cimento B 35 208) to study anisotropic cosmological models. Both waves have a seven-parameter conformal symmetry which contains, in addition to the generic five-parameter (broken) Carroll group, also a 6th isometry. The Lukash spacetime can be transformed by a conformal rescaling of time to a perturbed CPP problem. Bounded geodesics, found both analytically and numerically, arise when the Lukash wave is of Bianchi type VI. Their symmetries can also be derived from the Lukash–CPP relation. Particle trapping is discussed.
Impact of ryegrass cover on lowland rice establishment

This study aimed to analyze the impact of ryegrass as a soil cover on soil moisture level, initial rice establishment, grain yield and weed control in irrigated rice planted after ryegrass. The experiment was conducted under field conditions in randomized blocks design with four replications. Ryegrass (cv. BRS Ponteio) was planted in autumn and glyphosate was used as a knockdown before rice planting. Ryegrass plants were cut at 0, 0.15, 0.30 or 0.45 m above ground, resulting in different mulching levels. Rice (cv. Irga 424 CL) was planted in spring and was managed according to local crop recommendations. Although positive to the overall cropping system, ryegrass mulching suppressed rice emergence, especially with ryegrass cutting heights above 0.30 m, thus increased seeding densities in rice fields with substantial ryegrass soil cover may be needed. Ryegrass mulching had no significant effect on weed suppression, with positive results being observed only with ≥ 3000 kg ha 1 of ryegrass dry mass mulching. However, the rice yield in relation to the bare soil treatment was 14.3% less when straw quantity was 4500kg ha-1, reinforcing the need for evaluating the benefits and costs of ryegrass as a cover crop in rice production
Discriminative training of acoustic models in a segment-based speech recognizer

This thesis explores the use of discriminative training to improve acoustic modeling in a segment-based speech recognizer. In contrast with the more commonly used Maximum Likelihood training, discriminative training considers the likelihoods of competing classes when determining the parameters for a given class's model. Thus, discriminative training works directly to minimize the number of errors made in the recognition of the training data. Several variants of discriminative training are implemented in the SUMMIT recognizer, and these variants are compared using results in the Jupiter weather information domain. We adopt an utterance-level training procedure. The effects of using different training criteria, optimizing various model parameters, and using different values of training control parameters are investigated. Consistent with previous studies, we find the most common objective criteria produce very similar results. We do, however, find that with our training scheme optimizing the mixture weights is much more effective than optimizing the Gaussian means and variances. An extension to the standard discriminative training algorithms is developed which focuses on the recognition of certain keywords. The keywords are words that are most important for the proper parsing of an utterance by the natural language component of the system. We find that our technique can greatly improve the recognition word accuracy on this subset of the vocabulary. In addition, we find that choices of keyword lists which exclude certain unimportant, often poorly articulated words can actually result in an improvement in word accuracy for all words, not just the keywords themselves. The accuracy gains reported in this thesis are consistent with gains previously reported in the literature. Typical reductions in word error rate are in the range of 5% relative to Maximum Likelihood trained models. Thesis Supervisor: I. Lee Hetherington Title: Research Scientist
Letter to the Editor: Optimal Sizing of the Passive Filter's Elements in Hybrid Active Filters

Abstract Active filters are an up-to-date solution to power quality problems. These filters allow the compensation of current harmonics and unbalance, together with power factor correction, and can be a much better solution than the conventional approach (capacitors for power factor correction and passive filters to compensate for current harmonics). Active filters could have wide application for controlling harmonic currents from nonlinear loads. In this letter, a method is presented for finding the optimum values of the capacitor and inductor for the passive filter used with the proposed hybrid active filter to minimize the converter rating and comparing these results with the conventional active filter.
HYDRATION STATUS, SPRINT PERFORMANCE AND PHYSIOLOGICAL RESPONSES DURING REPEATED SPRINT ABILITY (RSA) TRAINING SESSION

The aim of this study was to investigate the relationship among hydration status level, sprint time performance capabilities and selected physiological responses which are blood pressure, heart rate and rate of perceived exertion (RPE) during a 30m Repeated Sprint Ability (RSA) training session. Fifteen male participants with mean age 21 ± 1 years old, total mean body weight of 63.21 ± 8.25kg, and mean body height of 1.68 ± 0.05m, voluntarily participated in this study. The participants underwent a RSA session with all measurements of interest (hydration status, sprint time performance, blood pressure, heart rate and RPE) were done pre, during and post sessions. Paired sample t-test was used to analyse the urine specific gravity, whilst repeated measure ANOVA was used to compare the sprint time, heart rate, blood pressure and RPE. Pearson correlation was utilised to determine the relationship between hydration status, sprint time, heart rate response, blood pressure and RPE during the training session.The results indicated no significant changes in the hydration status. Sprint time performance indicated no significant difference between all sets involved, indicating steady state sprint performance. Blood pressure, heart rate response and RPE, showed a significant increase during the 30m sprint training. Correlation value for sprint time and RPE versus hydration status demonstrated a significant correlation, a strong and linear relationship with a high significant value. As a conclusion, 30m RSA for 3 sets of 5 repetitions had no significant effect on hydration status, with sprint time performance cannot be said is influenced by hydration during RSA session. However, as the participants were trying to maintain sprint time performance, physical stress did increase and thus making it difficult to improve sprint time performance. The practical implications of this study suggested that apart from RSA actual purpose of developing speed and agility, it also offers an effective training practice for improving the heart rate responses and as anti-hypertensive exercise. Further studies on muscle metabolic factors are suggested for future research works.
Adaptive Task-Space Control of Strip Flatness in Multiroll Mill Stands*

Abstract In this work a novel multivariable control method is proposed to enhance the flatness of metal foils and strips processed by multiroll cold rolling mills. The proposed algorithm relies on decomposing an array of strip elongation measurements in orthogonal components corresponding to the main actuator directions in order to reduce the dimension of the problem. The control action is then computed by solving a prioritized constrained optimization with quadratic objectives. Moreover, in order to compensate the uncertainty that typically affects this kind of systems, the process model is on-line adapted during the control task execution.
Mouse Chd4-NURD is required for neonatal spermatogonia survival and normal gonad development

Testis development and sustained germ cell production in adults rely on the establishment and maintenance of spermatogonia stem cells and their proper differentiation into spermatocytes. Chromatin remodeling complexes regulate critical processes during gamete development by restricting or promoting accessibility of DNA repair and gene expression machineries to the chromatin. Here, we investigated the role of Chd4 and Chd3 catalytic subunits of the NURD complex during spermatogenesis. Germ cell-specific deletion of c hd4 early in gametogenesis, but not chd3 , resulted in arrested early gamete development due to failed cell survival of neonate undifferentiated spermatogonia stem cell population. Candidate assessment revealed that Chd4 controls expression of dmrt1 and its downstream target plzf , both described as prominent regulators of spermatogonia stem cell maintenance. Our results show the requirement of Chd4 in mammalian gametogenesis pointing to functions in gene expression early in the process.
Vivado Design Suite

All-Programmable devices go beyond programmable logic and I/O, integrating various combinations of 3D stacked silicon interconnect technology, software programmable ARM® processing systems, programmable Analog Mixed Signal (AMS), and a significant amount of intellectual-property (IP) cores. These next generation devices enable designers to go beyond programmable logic to programmable systems integration, to incorporate more system functions into fewer parts, increase system performance, reduce system power, and lower BOM cost.
An alternate, complementary method for characterizing EMI filters

Proper characterization of EMI filters has always been a problem, since real-life application never replicates the impedances of the test set-up. This paper analyzes filter insertion loss in actual EUT configurations, compared to the artificial data of Mil Std220 or CISPR 17, and recommends a complementary method using the LISN as a test load.
The effect of spironolactone on cardiac and renal fibrosis following myocardial infarction in established hypertension in the transgenic Cyp1a1Ren2 rat

Aims The renin-angiotensin-aldosterone axis plays a key role in mediating cardiac and kidney injury. Mineralocorticoid receptor antagonism has beneficial effects on cardiac dysfunction, but effects are less well quantified in the cardiorenal syndrome. This study investigated cardiac and kidney pathophysiology following permanent surgical ligation to induce myocardial infarction (MI) in hypertensive animals with or without mineralocorticoid receptor antagonism. Methods Hypertension was induced in adult male Cyp1a1Ren2 rats. Hypertensive animals underwent MI surgery (n = 6), and were then treated daily with spironolactone for 28 days with serial systolic blood pressure measurements, echocardiograms and collection of urine and serum biochemical data. They were compared to hypertensive animals (n = 4), hypertensive animals treated with spironolactone (n = 4), and hypertensive plus MI without spironolactone (n = 6). Cardiac and kidney tissue was examined for histological and immunohistochemical analysis. Results MI superimposed on hypertension resulted in an increase in interstitial cardiac fibrosis (p<0.001), renal cortical interstitial fibrosis (p<0.01) and glomerulosclerosis (p<0.01). Increased fibrosis was accompanied by myofibroblast and macrophage infiltration in the heart and the kidney. Spironolactone post-MI, diminished the progressive fibrosis (p<0.001) and inflammation (myofibroblasts (p<0.05); macrophages (p<0.01)) in both the heart and the kidney, despite persistently elevated SBP (182±19 mmHg). Despite the reduction in inflammation and fibrosis, spironolactone did not modify ejection fraction, proteinuria, or renal function when compared to untreated animals post MI. Conclusion This model of progressive cardiorenal dysfunction more closely replicates the clinical setting. Mineralocorticoid receptor blockade at a clinically relevant dose, blunted progression of cardiac and kidney fibrosis with reduction in cardiac and kidney inflammatory myofibroblast and macrophage infiltration. Further studies are underway to investigate the combined actions of angiotensin blockade with mineralocorticoid receptor blockade.
Self-Healing Mechanism and Conductivity of the Hydrogel Flexible Sensors: A Review

Sensors are devices that can capture changes in environmental parameters and convert them into electrical signals to output, which are widely used in all aspects of life. Flexible sensors, sensors made of flexible materials, not only overcome the limitations of the environment on detection devices but also expand the application of sensors in human health and biomedicine. Conductivity and flexibility are the most important parameters for flexible sensors, and hydrogels are currently considered to be an ideal matrix material due to their excellent flexibility and biocompatibility. In particular, compared with flexible sensors based on elastomers with a high modulus, the hydrogel sensor has better stretchability and can be tightly attached to the surface of objects. However, for hydrogel sensors, a poor mechanical lifetime is always an issue. To address this challenge, a self-healing hydrogel has been proposed. Currently, a large number of studies on the self-healing property have been performed, and numerous exciting results have been obtained, but there are few detailed reviews focusing on the self-healing mechanism and conductivity of hydrogel flexible sensors. This paper presents an overview of self-healing hydrogel flexible sensors, focusing on their self-healing mechanism and conductivity. Moreover, the advantages and disadvantages of different types of sensors have been summarized and discussed. Finally, the key issues and challenges for self-healing flexible sensors are also identified and discussed along with recommendations for the future.
Theory and Technology Research on the Software Health Management

The software health management (SWHM) is a new field, which mainly deals with some critical need to detect, diagnose, predict and mitigate adverse events caused by software related faults and failures. These faults come from different sources such as code errors, unpredicted faults, hardware failures or some problematic interaction with the external environment. This article discusses the latest development and research of software health concept and techniques. And the article summarizes the problem of current research, and proposes the developing trend. At last, we explain the process of software health monitoring taking the service software system as example.
Structural and functional studies of D109A human αB-crystallin contributing to the development of cataract and cardiomyopathy diseases

αB-crystallin (heat shock protein β5/HSPB5) is a member of the family of small heat shock proteins that is expressed in various organs of the human body including eye lenses and muscles. Therefore, mutations in the gene of this protein (CRYAB) might have many pathological consequences. A new mutation has recently been discovered in the α-crystallin domain of this chaperone protein which replaces aspartate 109 with alanine (D109A). This mutation can cause myofibrillar myopathy (MFM), cataracts, and cardiomyopathy. In the current study, several spectroscopic and microscopic analyses, as well as gel electrophoresis assessment were applied to elucidate the pathogenic contribution of human αB-crystallin bearing D109A mutation in development of eye lens cataract and myopathies. The protein oligomerization, chaperone-like activity and chemical/thermal stabilities of the mutant and wild-type protein were also investigated in the comparative assessments. Our results suggested that the D109A mutation has a significant impact on the important features of human αB-crystallin, including its structure, size of the protein oligomers, tendency to form amyloid fibrils, stability, and chaperone-like activity. Given the importance of aspartate 109 in maintaining the proper structure of the α-crystallin domain, its role in the dimerization and chaperone-like activity, as well as preserving protein stability through the formation of salt bridges; mutation at this important site might have critical consequences and can explain the genesis of myopathy and cataract disorders. Also, the formation of large light-scattering aggregates and disruption of the chaperone-like activity by D109A mutation might be considered as important contributing factors in development of the eye lens opacity.
Contextual factors influencing a training intervention aimed at improved maternal and newborn healthcare in a health zone of the Democratic Republic of Congo

Background Maternal and neonatal mortality and morbidity in the Democratic Republic of Congo (DRC) are among the highest worldwide. As part of a quality improvement programme in a health zone in the DRC aimed at contributing to reduced maternal and neonatal mortality and morbidity, a three-pillar training intervention around childbirth was developed and implemented in collaboration between Swedish and Congolese researchers and healthcare professionals. The aim of this study is to explore contextual factors influencing this intervention. Methods A qualitative research design was used, with data collected through focus group discussions (n = 7) with healthcare professionals involved in the intervention before and at the end (n = 9). Transcribed discussions were inductively analysed using content analysis. Results Three generic categories describe the contextual factors influencing the intervention: i) Incentives motivated participants’ efforts to begin a training programme; ii) Involving the local health authorities was important; and (iii) Having physical space, electricity, and equipment in place was crucial. Conclusions This study and similar ones highlight that incentives of various types are crucial contextual factors that influence training interventions, and have to be considered already in the planning of such interventions. One such factor is expectations of monetary incentives. To meet this in a small research project like ours would require a reduction of the scale and thus limit the implementation of new evidence-based knowledge into practice aimed at reducing maternal mortality and morbidity.