Miracle-dz/UniFAR-FTUs huggingface dataset:
Measurement of the ν µ charged current quasi-elastic cross-section on carbon with the T2K on-axis neutrino beam

We report a measurement of the ν µ charged current quasi-elastic cross-sections on carbon in the T2K on-axis neutrino beam. The measured charged current quasi-elastic cross-sections on carbon at mean neutrino energies of 1.94 GeV and 0.93 GeV are (11 . 95 ± 0 . 19( stat. ) +1 . 82 − 1 . 47 ( syst. )) × 10 − 39 cm 2 / neutron and (10 . 64 ± 0 . 37( stat. ) +2 . 03 − 1 . 65 ( syst. )) × 10 − 39 cm 2 / neutron, respectively. These results agree well with the predictions of neutrino interaction models. In addition, we investigated the eﬀects of the nuclear model and the multi-nucleon interaction.
Sharing retrieved information using Linked Credibility Reviews

In recent years, the advent of deep learning for NLP is enabling the accurate retrieval of semantically similar content. Such retrieval, along with check-worthiness and stance detection, is crucial for identifying misinformation and linking it to relevant verified information. While sources of credibility signals (and methods to retrieve them) are abundant on the web, they vary greatly in terms of quality and relevance and can be quite scarce for specific claims. These special requirements for such IR systems suggest the need for good abstractions to represent relevant aspects like the credibility of retrieved information and the confidence of automated systems that retrieved the information. In this paper, we (i) summarise Linked Credibility Reviews, existing work that provides a conceptualisation and exchange format for representing the credibility of retrieved verified information and (ii) discuss the role this conceptualisation can play in information retrieval systems for reducing online misinformation.
Application of the Deep CNN-Based Method in Industrial System for Wire Marking Identification

Industry 4.0, a term invented by Wolfgang Wahlster in Germany, is celebrating its 10th anniversary in 2021. Still, the digitalization of the production environment is one of the hottest topics in the computer science departments at universities and companies. Optimization of production processes or redefinition of the production concepts is meaningful in light of the current industrial and research agendas. Both the mentioned optimization and redefinition are considered in numerous subtopics and technologies. One of the most significant topics in these areas is the newest findings and applications of artificial intelligence (AI)—machine learning (ML) and deep convolutional neural networks (DCNNs). The authors invented a method and device that supports the wiring assembly in the control cabinet production process, namely, the Wire Label Reader (WLR) industrial system. The implementation of this device was a big technical challenge. It required very advanced IT technologies, ML, image recognition, and DCNN as well. This paper focuses on an in-depth description of the underlying methodology of this device, its construction, and foremostly, the assembly industrial processes, through which this device is implemented. It was significant for the authors to validate the usability of the device within mentioned production processes and to express both advantages and challenges connected to such assembly process development. The authors noted that in-depth studies connected to the effects of AI applications in the presented area are sparse. Further, the idea of the WLR device is presented while also including results of DCNN training (with recognition results of 99.7% although challenging conditions), the device implementation in the wire assembly production process, and its users’ opinions. The authors have analyzed how the WLR affects assembly process time and energy consumption, and accordingly, the advantages and challenges of the device. Among the most impressive results of the WLR implementation in the assembly process one can be mentioned—the device ensures significant process time reduction regardless of the number of characters printed on a wire.
Extraction of Radioactive Ions Using Entrainer and Supercritical Decontamination Technic Equipped With Ultrasonic-horn

Due to the aging of domestic nuclear power plants, the importance of decontamination technology for decommissioning power plants is increasing. However, current decontamination technologies have the problem of generating a large amount of secondary waste. From this point of view, supercritical CO2 (scCO2) seems to be an alternative solvent to solve this problem. In this study, extraction experiments of radioactive metals were carried out using supercritical decontamination technology with ultrasonic horn.
Application of Robots – A review

In current market, robots are used in various sectors like automotive, medical, military and aerospace. The design and manufacturing of robots depends upon its applications. The current research reviews the design of various robots with different type of materials like polycarbonates, composites which are light weight and low cost. The use of laser and infrared for navigation of robots is also studied by various scholars.
DMSO_A_295757 839..846

Department of Endocrine and Metabolic Diseases, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang Province, People’s Republic of China Purpose: Prognostic nutritional index (PNI) is an effective tool to evaluate the nutritional conditions and predict prognosis, but clinical data are limited for the use of PNI in diabetic retinopathy (DR). This study aimed to investigate the relationship of PNI with the prevalence and severity of DR in patients with type 2 diabetes mellitus (T2DM). Patients and Methods: This cross-sectional analysis enrolled 1023 individuals with T2DM hospitalized between 2017–2020. PNI was calculated as 10 × serum albumin (g/l) + 0.005 × total lymphocyte count (cells/mL). DR severity was categorized as no, nonproliferative, and vision-threatened DR (VTDR) according to the modified Airlie House classification. Multivariate-adjusted odds ratio (OR) with 95% confidence interval (CI) for the prevalent DR in the top (Q4) compared with the bottom quartile (Q1) of PNI levels were estimated by using logistic regression analyses. Results: PNI levels were significantly lower in individuals with VTDR than those with no and nonproliferative DR (both P < 0.001), and the proportions of individuals with DR were significantly decreased in the top quartile compared with the bottom quartile of PNI levels (P < 0.001). After adjustments for age, gender, DM duration, obesity-related risk factors and clinical biochemical parameters, the higher levels of PNI were significantly associated with a lower prevalence of DR (Q4 vs Q1: OR = 0.402, 95% CI: 0.250–0.649, P < 0.001), with a 5.9% reduction in the prevalence of DR for a per-unit increment in the levels of PNI (OR = 0.941, 95% CI: 0.911–0.972, P < 0.001). The association of PNI and obesity-related indexes (body mass index and waist circumference) with the severity of DR was independent of each other (P<0.001). Conclusion: PNI was inversely and independently associated with the severity and prevalence of DR, which suggested that PNI could likely be used to predict DR prognosis in clinical practice.
Efficient Dynamic-Power AODV Routing Protocol To improve QOS Parameters

: Mobile Ad Hoc Networks (MANET) are networks established by mobile radio networks with infrastructure building. The versatility of antennas ensures that the network's configuration is constantly evolving. If the radio density is small, the capacity of the network will be decreased due to mutual interference. The Dynamic Power Ad-hoc On-Demand Distance Vector (DP-AODV) was built to resolve this issue. The current research study (DP-AODV) assumed changing transmission power rates of all nodes when transmitting the packet request route to the destination node. It prefers lower transmission power for regions with less density and vice versa. As a result we introduced approach centered on route optimization to boost the QOS requirements for the network.
Solving the problem of determining the author of text data using a combined assessment

The article describes the main approaches to the automated determination of authorship of texts based on the analysis of copyright styles. The architectures of a convolutional neural network, a multilayer perceptron, and LSTM neural network were proposed to solve this problem. Also, experiments were conducted in which the effectiveness of each of the proposed approaches was evaluated using the example of the task of determining the authorship of English-language poems.
Evaluation of Pre Processing Techniques for Information Storage and Retrieval System

: Enormous amount of information is available over the internet in electronic format but collecting and cataloging the relevant information is very challenging task. The large amount of data can be analyzed to optimize the benefits, for business intelligent system. The storage and retrieval of information is an important and extensively studied problem in machine learning. The basic steps in information retrieval include preprocessing of document, extracting relevant terms against the features in a database, and finally categorizing a set of documents into predefined categories. In this paper we are had use two preprocessing activity as stop word removal and stemming and evaluated the impact of stop word and stemming onto feature selection. From the results we find that the removal of stop-words decrease the size of feature set. For sparsity value 0.9 it decrease by 9%. On the other hand for stemming process as we increase the sparsity value the size of feature set also increase.
Pre-proof Lewy body-associated proteins : Victims , instigators , or innocent bystanders ? The case of AIMP 2 and alpha-synuclein

Lewy bodies (LBs), one of the neuropathological defining hallmarks of Parkinson’s disease (PD), are composed of a complex mixture of alpha-synuclein (aSyn) filaments and hundreds of proteins, lipids, and membranous organelles. However, these proteins' role in aSyn aggregation and the biogenesis of LBs remains poorly understood. Previous studies have focused on investigating the role of these proteins as modifiers of aSyn aggregation, inclusion formation, and toxicity; very often, one protein at a time. In a recent study, Ham et al. suggest that one of these proteins, aminoacyl tRNA synthase complex-interacting multifunctional protein 2 (AIMP2), plays a primary role in the initiation of aSyn aggregation and is essential for aSyn inclusion formation and toxicity in cells and several models of synucleinopathies (Ham et al., 2020). Based on in vitro aggregation studies, they proposed a model in which AIMP2 self-associates to form amyloid-like aggregates that interact with monomeric aSyn and catalyze/seed the formation of aSyn fibrils and, eventually, LB-like inclusions. Herein, we present a critical analysis of their results and conclusions, review previous studies on AIMP2 aggregation, and reexamine the role of AIMP2 in regulating aSyn inclusion formation and clearance and aSyn-induced neurodegeneration in Parkinson’s disease. We conclude by presenting lesson learned and recommendations on experimental factors and approaches that should be considered in future studies aimed at investigating the potential of targeting LBs-associated proteins, including AIMP2, for developing therapies to treat PD and other synucleinopathies. Background Alpha-synuclein (aSyn) is a presynaptic protein that is highly expressed in the brain and exhibits an increased propensity to misfold and form amyloid-like fibrils in the brains of individuals affected by several neurodegenerative diseases (Armstrong et al., 1997; Lashuel et al., 2013; Spillantini et al., 1998). The accumulation of aSyn fibrils in the form of intracellular inclusions in neurons (Lewy bodies, LB), Glial cells (Glial cytoplasmic inclusions, GCIs), or nerve cells is one of the defining hallmarks of Parkinson’s disease (PD), multiple system atrophy, and dementia with Lewy bodies, also collectively referred to as synucleinopathies (Arima et al., 1998; Goedert et al., 2017; Mckeith et al., 2017; Spillantini et al., 1998). Whether aSyn fibrils and LBs are the cause or consequence of synucleinopathies remains a subject of active research and debate. Answering this question requires 1) a better understanding of the molecular and cellular mechanisms that trigger and drive aSyn misfolding, fibrillization, and LB formation; 2) elucidating the relationship between aSyn fibrillization and LB formation, i.e., whether aSyn fibril formation is a prerequisite step for LB formation; 3) understanding the relative contribution of each stage Jo ur na l P re -p ro of Journal Pre-proof
An Improved Smart Card-based User Authentication Scheme with Session Key Agreement for Telecare Medicine Information System

In 2013, Lee-Lie proposed secure smart card based authentication scheme of Zhu’s authentication for TMIS which is secure against the various attacks and efficient password change. In this paper, we discuss the security of Lee-Lie’s smart card-based authentication scheme, and we have shown that Lee-Lie’s authentication scheme is still insecure against the various attacks. Also, we proposed the improved scheme to overcome these security problems of Lee-Lie’s authentication scheme, even if the secret information stored in the smart card is revealed. As a result, we can see that the improved smart card based user authentication scheme for TMIS is secure against the insider attack, the password guessing attack, the user impersonation attack, the server masquerading attack, the session key generation attack and provides mutual authentication between the user and the telecare system.
Role of physiotherapy team in critically ill COVID-19 patients pronation: can a multidisciplinary management reduce the complications rate?

Objectives: During the pandemic, critically ill COVID-19 patients' management presented an increased workload for Intensive Care Unit (ICU) nursing staff, particularly during pronation maneuvers, with high risk of complications. In this scenario, a support during pronation by the ICU Physiotherapy Team was introduced. Research methodology: Retrospective analysis. Consecutive critically ill COVID-19 patients. Setting: A COVID-19 Center in southern Switzerland, between March 16th and April 30th, 2020. Main Outcome Measures: Rates and characteristics of pronation-related complications. Results: Forty-two patients on mechanical ventilation (MV) were treated; 296 standard prone/supine positioning were performed, with 3.52 cycles/patient. One (0.3%) major complication was observed, while fourteen (33.3%) patients developed minor complications, e.g. pressure injuries. The incidence of pressure sores was related to ICU length-of-stay (LOS) (p = 0.029) and MV days (p = 0.015), while their number (n = 27) further correlated with ICU LOS (p = 0.001) and MV days (p = 0.001). The propensity matching score analysis did not show any protective factor of pronation regarding pressure injuries (p = 0.448). No other significant correlation was found. Conclusion: The specific pronation team determined a low rate of major complications in critically ill COVID19 patients. The high rate of minor complications appeared to be related to disease severity, rather than from pronation.
Compressive Sensing and Neural Networks from a Statistical Learning Perspective

Various iterative reconstruction algorithms for inverse problems can be unfolded as neural networks. Empirically, this approach has often led to improved results, but theoretical guarantees are still scarce. While some progress on generalization properties of neural networks have been made, great challenges remain. In this chapter, we discuss and combine these topics to present a generalization error analysis for a class of neural networks suitable for sparse reconstruction from few linear measurements. The hypothesis class considered is inspired by the classical iterative soft-thresholding algorithm (ISTA). The neural networks in this class are obtained by unfolding iterations of ISTA and learning some of the weights. Based on training samples, we aim at learning the optimal network parameters via empirical risk minimization and thereby the optimal network that reconstructs signals from their compressive linear measurements. In particular, we may learn a sparsity basis that is shared by all of the iterations/layers and thereby obtain a new approach for dictionary learning. For this class of networks, we present a generalization bound, which is based on bounding the Rademacher complexity of hypothesis classes consisting of such deep networks via Dudley's integral. Remarkably, under realistic conditions, the generalization error scales only logarithmically in the number of layers, and at most linear in number of measurements.
Pressure-induced phase transformations of PbCO3 by X-ray diffraction and Raman spectroscopy

ABSTRACT By employing synchrotron radiation, X-ray diffraction and Raman spectroscopy, the high pressure structural transformations of lead carbonate PbCO3 was investigated in diamond anvil cells up to ∼50 GPa at room temperature. Three pressure-induced transitions have been observed at ∼8.5, ∼15 and ∼26 GPa, respectively. The transition from PbCO3-I to PbCO3-II is a displacive transformation featured with anti-rotation of [CO3]2− triangles. PbCO3-II is a metastable phase because the [CO3]2− groups are in a unfixed state until they reach the equilibrium positions in PbCO3-III. PbCO3-III adopts a monoclinic symmetry with primitive lattice, and tendentiously exhibits a more compressible b-axis relative to c-axis. Isothermal pressure–volume relationship of PbCO3-III is well described by the Birch–Murnaghan equation of state with K0 = 131(4) GPa, (fixed) and V0 = 246(1) Å3. However, little information on the crystal structure of PbCO3-IV can be extracted from the present experiment. The transformation process of PbCO3 exhibits similarity to that of calcite and dolomite.
Corruption Robust Active Learning

We conduct theoretical studies on streaming-based active learning for binary classification under unknown adversarial label corruptions. In this setting, every time before the learner observes a sample, the adversary decides whether to corrupt the label or not. First, we show that, in a benign corruption setting (which includes the misspecification setting as a special case), with a slight enlargement on the hypothesis elimination threshold, the classical RobustCAL framework can (surprisingly) achieve nearly the same label complexity guarantee as in the non-corrupted setting. However, this algorithm can fail in the general corruption setting. To resolve this drawback, we propose a new algorithm which is provably correct without any assumptions on the presence of corruptions. Furthermore, this algorithm enjoys the minimax label complexity in the non-corrupted setting (which is achieved by RobustCAL) and only requires $\tilde{\mathcal{O}}(C_{\mathrm{total}})$ additional labels in the corrupted setting to achieve $\mathcal{O}(\varepsilon + \frac{C_{\mathrm{total}}}{n})$, where $\varepsilon$ is the target accuracy, $C_{\mathrm{total}}$ is the total number of corruptions and $n$ is the total number of unlabeled samples.
Techniques for Symbol Grounding with SATNet

Many experts argue that the future of artificial intelligence is limited by the field's ability to integrate symbolic logical reasoning into deep learning architectures. The recently proposed differentiable MAXSAT solver, SATNet, was a breakthrough in its capacity to integrate with a traditional neural network and solve visual reasoning problems. For instance, it can learn the rules of Sudoku purely from image examples. Despite its success, SATNet was shown to succumb to a key challenge in neurosymbolic systems known as the Symbol Grounding Problem: the inability to map visual inputs to symbolic variables without explicit supervision ("label leakage"). In this work, we present a self-supervised pre-training pipeline that enables SATNet to overcome this limitation, thus broadening the class of problems that SATNet architectures can solve to include datasets where no intermediary labels are available at all. We demonstrate that our method allows SATNet to attain full accuracy even with a harder problem setup that prevents any label leakage. We additionally introduce a proofreading method that further improves the performance of SATNet architectures, beating the state-of-the-art on Visual Sudoku.
Scalable Bayesian inference for time series via divide-and-conquer

Bayesian computational algorithms tend to scale poorly as data size increases. This had led to the development of divide-and-conquer-based approaches for scalable inference. These divide the data into subsets, perform inference for each subset in parallel, and then combine these inferences. While appealing theoretical properties and practical performance have been demonstrated for independent observations, scalable inference for dependent data remains challenging. In this work, we study the problem of Bayesian inference from very long time series. The literature in this area focuses mainly on approximate approaches that lack any theoretical guarantees and may provide arbitrarily poor accuracy in practice. We propose a simple and scalable divide-and-conquer method, and provide accuracy guarantees. Numerical simulations and real data applications demonstrate the effectiveness of our approach.
QuaPy: A Python-Based Framework for Quantification

QuaPy is an open-source framework for performing quantification (a.k.a. supervised prevalence estimation), written in Python. Quantification is the task of training quantifiers via supervised learning, where a quantifier is a predictor that estimates the relative frequencies (a.k.a. prevalence values) of the classes of interest in a sample of unlabelled data. While quantification can be trivially performed by applying a standard classifier to each unlabelled data item and counting how many data items have been assigned to each class, it has been shown that this "classify and count" method is outperformed by methods specifically designed for quantification. QuaPy provides implementations of a number of baseline methods and advanced quantification methods, of routines for quantification-oriented model selection, of several broadly accepted evaluation measures, and of robust evaluation protocols routinely used in the field. QuaPy also makes available datasets commonly used for testing quantifiers, and offers visualization tools for facilitating the analysis and interpretation of the results. The software is open-source and publicly available under a BSD-3 licence via https://github.com/HLT-ISTI/QuaPy, and can be installed via pip (https://pypi.org/project/QuaPy/)