Miracle-dz/UniFAR-FTUs huggingface dataset:
Sample-Efficient Safety Assurances using Conformal Prediction

When deploying machine learning models in high-stakes robotics applications, the ability to detect unsafe situations is crucial. Early warning systems can provide alerts when an unsafe situation is imminent (in the absence of corrective action). To reliably improve safety, these warning systems should have a provable false negative rate; that is, of the situations that are unsafe, fewer than ϵ will occur without an alert. In this work, we present a framework that combines a statistical inference technique known as conformal prediction with a simulator of robot/environment dynamics, in order to tune warning systems to provably achieve an ϵ false negative rate using as few as 1/ ϵ data points. We apply our framework to a driver warning system and a robotic grasping application, and empirically demonstrate the guaranteed false negative rate while also observing a low false detection (positive) rate.
Ensemble Learning of Modified Residual Networks for Classifying ECG with Different Set of Leads

The automatic detection and classification of cardiac abnormalities can assist physicians in making diagnoses, saving costs in modern healthcare systems. In this study we present an automatic algorithm for classification of cardiac abnormalities included in the CinC's challenge 2021 dataset consisting of twelve-lead, six-lead, three-lead, and two-lead ECGs (team: Polimi_1). For each set of leads an ensemble of three deep learning models, trained on three different subsets, was developed. These subsets, obtained by splitting the recordings with the most frequent classes, had more balanced distributions for training and were used to train the 3 classifiers. The trained models were modified Residual Networks with a Squeeze-and-Excitation module. This module is based on the intuition of channel attention: the basic idea of this approach is to apply a weight to the Convolutional channels based on their relevance in learning before propagating to the next layer. For evaluation, we submitted our model to the official phase of the PhysioNet/Computing in Cardiology Challenge 2021. The model received scores of 0.47, 0.46, 0.45, 0.48 and 0.45 (ranked 14th, 13th, 15th, 10th, and 13th out of 39 teams) on 12-lead, 6-lead, 4-lead, 3-lead, 2-lead hidden test set, respectively; placing us in the 11th position for the mean of the 12-lead, 3-lead, and 2-lead scores.
Sustainable development: Growth, Environment and Preservation in the Context of Globalization

In the sphere of neoliberalism and globalization, the proposal of Sustainable Development emerges to reconcile economic growth with respect for the environment, guided by the interactive 'Economy/Sustainability' binomial and emerging as an alternative to the crisis of the emerging fossil/energy model with the First Industrial Revolution and fought in the period after World War II. In order to overcome dogmatic visions, sectarian extremisms and ideological cleavages, the Sustainable Development model favors local and national actions and public policy initiatives rather than transnational attempts in a way that does not compromise biodiversity. Sustainable Development, within its methodological scope, points out ways to reach the productive/ technological progress of developing countries.
In Silico Identification of the Key Ionic Currents Modulating Human Pluripotent Stem Cell-Derived Cardiomyocytes Towards an Adult Phenotype

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide a promising resource for regenerative therapies. However, their immature phenotype and heterogeneities raise concern for arrhythmia when hiPSC-CMs are inserted into the native tissue. In silico models can improve understanding of the electrophysiological differences between hiPSC-CMs and adult cardiomyocytes and inform risk predictions. Our aim is to conduct a sensitivity analysis to identify the main ionic currents determining differences in electrophysiological properties between the Paci2020 model (hiPSC-CMs) and the ToR-ORd model (human ventricular cardiomyocytes). Our simulations highlighted the fast $Na^{+}$ current as the key modulator of upstroke velocity, while the inward and rapid delayed rectifier $K^{+}$ currents mainly contributed to a decrease of diastolic potential and action potential duration, respectively. In conclusion, we identified an increase of the conductance of these currents essential to modulate the biomarkers of the hiPSC-CM model towards the adult phenotype.
AN EVALUATION OF YTS

The Youth Training Scheme (YTS) is much the most significant public policy intervention in the UK training market since the 1964 Industrial Training Act (ITA). This paper presents an economic evaluation of YTS. We begin in Part II by outlining the history and mechanics of the Scheme. Part III describes the market failure (or failures) which YTS addresses, and Part IV assesses how successful it has been in tackling these problems. Finally, Part V considers the Scheme's future. What further changes are needed to improve its effectiveness?
Bimanual Telemanipulation with Force and Haptic Feedback and Predictive Limit Avoidance

Robotic teleoperation is a key technology for a wide variety of applications. It allows sending robots instead of humans in remote, possibly dangerous locations while still using the human brain with its enormous knowledge and creativity, especially for solving unexpected problems. A main challenge in teleoperation consists of providing enough feedback to the human operator for situation awareness and thus create full immersion, as well as offering the operator suitable control interfaces to achieve efficient and robust task fulfillment. We present a bimanual telemanipulation system consisting of an anthropomorphic avatar robot and an operator station providing force and haptic feedback to the human operator. The avatar arms are controlled in Cartesian space with a 1:1 mapping of the operator movements. The measured forces and torques on the avatar side are haptically displayed directly to the operator. We developed a predictive avatar model for limit avoidance which runs on the operator side, ensuring low latency. Only off-the-shelf components were used to build the system. It is evaluated in lab experiments and by untrained operators in a small user study.
High Coverage and High-Resolution Mapping of Repetitive Patterns During Atrial Fibrillation

Localized AF drivers with repetitive activity are candidate ablation targets for patients with persistent atrial fibrillation (AF). High-density mapping electrodes cover only a fraction of the atria but combining sequential recordings could provide a more comprehensive picture of common repetitive atrial conduction characteristics and enable AF driver localization. We developed a novel algorithm to merge overlapping local activation maps into larger composite maps using recurrence plots. The proposed algorithm was applied to atrial recordings in a goat model of AF (249-electrode mapping array, 2.4 mm inter-electrode distance, $n=16$). Sequential, overlapping recordings were generated by segmenting the mapping region into four spatially overlapping regions. Repetitive activation patterns were detected from recurrence plots generated from the recorded electrograms, and reconstructed with the proposed algorithm. Reconstruction quality was measured as the Pearson correlation between original and reconstructed activation patterns. The average correlation was 0.86. Among pattern properties, such as duration, area, complexity and cycle length, only duration was significantly correlated with the composite map quality ($r=0.126, p < 0.05$). The percentage of the cases where a composite map could be generated was 75.30% which was significantly higher for larger patterns ($p < 0.01$).
An Integrated Molecular Atlas of Alzheimer's Disease

INTRODUCTION: Embedding single-omics disease associations into the wider context of multi-level molecular changes in Alzheimer's disease (AD) remains one central challenge in AD research. METHODS: Results from numerous AD-specific omics studies from AMP-AD, NIAGADS, and other initiatives were integrated into a comprehensive network resource and complemented with molecular associations from large-scale population-based studies to provide a global view on AD. RESULTS: We present the AD Atlas, an online resource (www.adatlas.org) integrating over 20 large studies providing disease-relevant information on 20,353 protein-coding genes, 8,615 proteins, 997 metabolites and 31 AD-related phenotypes. Multiple showcases demonstrate the utility of this resource for contextualization of AD research results and subsequent downstream analyses, such as drug repositioning approaches. DISCUSSION: By providing a global view on multi-omics results through a user-friendly interface, the AD Atlas enables the formulation of molecular hypotheses and retrieval of clinically relevant insights that can be validated in follow-up analyses or experiments.
Deep Learning in Opthalmology: Iris Melanocytic Tumor Intelligent Diagnosis

Recently, Convolutional neural networks (CNN) have shown a growth due to their ability of learning different level image representations that helps in image classification in different fields. These networks have been trained on millions of images, so they gained a powerful ability of extracting the rightful features from input images, which results in accurate classification. In this research, we investigate the effects of transfer learning based convolutional neural networks for the iris tumor malignancy identification as it is notoriously hard to distinguish an iris nevus from an iris tumor. Features are transferred from a CNN trained on a source task, i.e. ImageNet, to a target task, i.e. iris tumor datasets. We transfer features learned from AlexNet and VGG-16 that are trained on ImageNet, to classify three different iris images types which are: iris nevus unaffected, iris cysts, and iris melanocytic tumors. The employed pre-trained models are modified by replacing their feedforward neural network classifier, Softmax, by a support vector machine (SVM) that is expected to slightly boost their performance (AlexNet-SVM and VGG16-SVM). All employed models are trained (fine-tuned) on a 60% of the available large dataset of iris images in order to investigate their power of generalization when trained using large amount of data. The networks are also tested on 40% of the data. The best performance was achieved by the VGG16-SVM which scored a high accuracy of 96.27% and strong features extraction capability as compared to the other models. Experimentally, it was seen that adding SVM contributed in improving the network performance compared to original models which use a feedforward neural network classifier.
The maximum order statistic of doubly truncated Inverted Gamma distribution

Dealing with phenomena is developing rapidly as a result of the evaluation of the phenomena themselves. Some phenomena benefit us from the maximum values within a certain period as a variable, for example the highest electricity load, the largest stretch of the beach, the longest period of a specific disease in the patient’s body and so on. In this paper, we present the distribution of Maximum order statistic from doubly truncated Inverted Gamma distribution (MOSDTIGD). The rth raw moments function form derives for new density. Stress-strength reliability, Shannon and relative entropies have also been discussed.
Quantum speed limit for the maximum coherent state under the squeezed environment

The quantum speed limit time for quantum system under squeezed environment is studied. We consider two typical models, the damped Jaynes–Cummings model and the dephasing model. For the damped Jaynes–Cummings model under squeezed environment, we find that the quantum speed limit time becomes larger with the squeezed parameter r increasing and indicates symmetry about the phase parameter value θ = π. Meanwhile, the quantum speed limit time can also be influenced by the coupling strength between the system and environment. However, the quantum speed limit time for the dephasing model is determined by the dephasing rate and the boundary of acceleration region that interacting with vacuum reservoir can be broken when the squeezed environment parameters are appropriately chosen.
Polyelectrolyte Complex for Drug Delivery in Biomedical Applications: A Review

There are so many significant developments made in drug delivery system (DDS) in which Polymer DDS has reached great developments. The main motive of this paper is to focus on DDS developing by Polyelectrolytes. Polymers have achieved much importance in DDS. Polyelectrolytes shows an exceptional category of polymer compounds containing of opposite charged polyions which are mainly cationic or anionic charges; they are hydrophilic in nature because of presence of opposite charge, can be used in various of applications in DDS. Polyelectrolytes (PE) exhibits various types of reactive groups that permits easy, adaptable modification with functional agents such as targeting molecules. Further, the oppositely charged nature exist in polyelectrolytes been used to transport macromolecules such as DNA and nanoparticles. Significance of smart polyelectrolyte is growing day by day from last several year. In the last several years, polyelectrolytes representing promising means for targeted based drug delivery and gene treatment. In the present paper, polyelectrolyte, polyelectrolyte complexes, deposition method for multilayer system, features, characterization of polyelectrolytes, and applications are reviewed.
Feasibility of silicon nanoparticles produced by fast-rate plasma spray PVD for high density lithium-ion storage

Si nanoparticles with the averaged primary particle size ranging from 20 to 175 nm have been produced by plasma spray physical vapor deposition (PS-PVD) at different powder feed rates from 0.6 to 25.4 g min−1. High-order agglomerates as large as 10 µm are found to form especially at low powder feed rate, while such large agglomerates are suppressed when the particle size becomes greater than 100 nm at high powder feed rate. The electrochemical cells using Si nanoparticles smaller than 100 nm retain relatively high capacity with reasonable cycle stability, while the capacity drops rapidly for the cells with Si greater than 100 nm due partly to an increased charge transfer resistance. Moreover, ultrasonic particle breakup reveals that absence of large agglomerates as large as 10 µm are beneficial in reducing the charge-transfer resistance and in improving the cycle stability. Although oxygen content increases with decreasing the particle size, slow oxidation upon collection of the particles after PS-PVD successfully suppresses excessive oxidation, leading to negligible influence on the initial efficiency.
Calculating properties of non-collinear phase matching in two different polarization for KDP crystal

The properties of non-collinear phase-matching (PM) for potassium dihydrogen phosphate (KDP) crystal were calculated. The properties were calculated numerically for type I phase matching and type II-phase matching. The group delay dispersion (GDD) was determined for KDP at different slant angles and different types of phase matching. The slant angles were 0°, 40°, 60°, and 900°. The pumping, signal, and idler wavelengths were 354.7nm, 1064nm, and 532nm respectively. The time delay deepened on the phase-matching angle for three-wave that interact in KDP crystal founded. Ordinary refractive index and extraordinary refractive index discussed, by calculating these indices the birefringence at each wavelength determined. The group delay dispersion and time delay very important factors for optical communication, laser application, and nonlinear optics.
Design and implementation of high-efficiency power inverter using adaptive nonlinear control

In this paper, a finite-time nonlinear control law based on adaptive grey wolf optimization (AGWO) is proposed to obtain a high-efficiency power inverter. Although sliding mode control (SMC) provides robustness against internal parametric changes and external load meddling, the system-state cannot converge to zero in a finite time. The development of a finite-time nonlinear control law (FTNCL) is offered to ensure the finite-time convergence of the system state. But the FTNCL still meets the problem of jitter, which causes harmonic distortion of the output-voltage of the high-efficiency power inverter. In order to effectively suppress the jitter phenomenon, the gain parameters of the FTNCL can be optimally tuned by using the AGWO algorithm. This proposed scheme with the combination of the FTNCL and AGWO has been realized in an actual high-efficiency power inverter operated by a digitized signal processor. Simulations and experiments in a feedback system have revealed that the proposed scheme brings the advantages of fast dynamical reaction, less steady-state error, lessened total harmonic distortion, and relieved jitter.
Deep learning segmentation of glomeruli on kidney donor frozen sections

Abstract. Purpose: Recent advances in computational image analysis offer the opportunity to develop automatic quantification of histologic parameters as aid tools for practicing pathologists. We aim to develop deep learning (DL) models to quantify nonsclerotic and sclerotic glomeruli on frozen sections from donor kidney biopsies. Approach: A total of 258 whole slide images (WSI) from cadaveric donor kidney biopsies performed at our institution (n = 123) and at external institutions (n = 135) were used in this study. WSIs from our institution were divided at the patient level into training and validation datasets (ratio: 0.8:0.2), and external WSIs were used as an independent testing dataset. Nonsclerotic (n = 22767) and sclerotic (n = 1366) glomeruli were manually annotated by study pathologists on all WSIs. A nine-layer convolutional neural network based on the common U-Net architecture was developed and tested for the segmentation of nonsclerotic and sclerotic glomeruli. DL-derived, manual segmentation, and reported glomerular count (standard of care) were compared. Results: The average Dice similarity coefficient testing was 0.90 and 0.83. And the F1, recall, and precision scores were 0.93, 0.96, and 0.90, and 0.87, 0.93, and 0.81, for nonsclerotic and sclerotic glomeruli, respectively. DL-derived and manual segmentation-derived glomerular counts were comparable, but statistically different from reported glomerular count. Conclusions: DL segmentation is a feasible and robust approach for automatic quantification of glomeruli. We represent the first step toward new protocols for the evaluation of donor kidney biopsies.
Learning To Discover Value: Value-Based Pricing and Selling Capabilities for Industrial Services

Many manufacturers invest in advanced services and solutions to achieve superior customer value; however, research has only begun to examine capabilities for value-based pricing (VBP) and value-based selling (VBS) in relation to such offerings. This article explores (1) which capabilities firms seek to develop for VBP and VBS of industrial services and solutions and (2) how learning influences the development of these capabilities. An in-depth exploratory study of two global market leaders in their respective industries includes interviews with 66 respondents from the firms, as well as 12 interviews with customer and supplier informants, which reveal important capabilities for VBP and VBS. Higher-level learning supports value discovery, through dialogue with customers over time; this value in turn forms the basis for VBP and VBS. Higher-level learning capabilities also facilitate the adaptation and replication of the developed pricing and selling capabilities in various contexts.
Self-Decorated MnO Nanoparticles on Double Perovskite Solid Oxide Fuel Cell Anode by in Situ Exsolution

Surface decorated electrocatalytic nanoparticles coupled with oxide materials can effectively improve the electrochemical catalytic properties in energy storage and conversion application, such as chemical processes, electrolysis, batteries, and fuel cells. Particularly, Mn rich simple perovskite-type R0.5Ba0. 5MnO3-δ (R = Pr and Nd) undergoes a phase transition to layered perovskite RBaMn2O5+δ at high temperature reduced condition. During this phase transition, the exsolution of MnO nanoparticles (MnO-NP) from the bulk layered perovskite NdBaMn2O5+δ is observed. For in-depth investigation on the exsolution of MnO, a layered NdBaMn2O5+δ thin film is fabricated with pulsed laser deposition and characterized by transmission electron microscopy. For the first time, this paper reports clear evidence of self-decorated MnO nanoparticles on a layered NdBaMn2O5+δ matrix via exsolution process and their electro catalytic effect in solid oxide fuel cells.
A Low Cost, Novel Treatment of Severe Diabetic Gastroparesis Based on Burkitt’s Dietary Fiber Hypothesis

We report a seven-year follow-up of a 43-year-old Hispanic female with severe diabetic gastroparesis (GP) and a 42.5 kg weight loss (45% of body mass), who required feeding jejunostomy tube placement. The patient had an excellent response to a treatment regime directed at increasing stool bulk, enhancing gut transit, and mobilizing intestinal gas by using dietary fiber supplements and osmotic laxatives with as needed tap water enemas. Hospital cost savings for this patient exceeded $125,000 annually. This case study suggests that constipation may substantially contribute to GP.
Analysis of Food System Topics from the Consumers’ Perspective in a Developing Country Context: Key Insights from ANRS Metropolitan Cities, Ethiopia

Background The food system is an interdisciplinary global issue with substantial social and economic challenges. These challenges are most pronounced in developing countries, where chronic under-nutrition is prevalent. The long-standing notion has been to solve malnutrition and undernutrition primarily from the supply side through improving productivity and production. Nevertheless, addressing the nutrition paradox requires understanding consumers’ food choices and its drivers. Methods This study forms a part of a research project conducted in the ANRS, Ethiopia, in both urban and rural settings. To select study participants and collect data from the ANRS metropolitan cities, we used a household-based cross-sectional study and a multistage sampling technique. Data were collected using a structured questionnaire, and the study used a descriptive research method and a quantitative research approach in its analysis. PCA was used to identify which topics in the food system are most important to consumers. Results To identify widely held consumer interests, respondents were asked to report their interest in several relevant topics. The results show that food systems’ external impact, food safety, food nutrition, impact of other bodies on the food system, and food supply sustainability are the most crucial food topics where consumers are interested. Conclusion Concerned stakeholders who can help enhance household dietary practices in the study area; producers and other stakeholders who care about environmental sustainability and/or social values might use this information to adjust their production and marketing decisions to align with the food system topics that consumers identified as their priority. Limitations While the PCA allows for the reduction of multiple indices into a single composite index, it may restrict the targeting of a particular aspect of the food system topic. As a result, the indices’ meaning, and implications should be made while keeping the downsides in mind.