Miracle-dz/UniFAR-FTUs huggingface dataset:
A bundle method using two polyhedral approximations of the ε -enlargement of a maximal monotone operator

Until now, a few bundle methods for general maximal monotone operators exist and they were only employed with one polyhedral approximation of the ε -enlargement of the maximal monotone operator considered. However, we ﬁnd in the literature several hybrid-proximal methods which could be adapted with a great deal of bundle techniques in order to ﬁnd a zero of a maximal monotone operator; yet, we could also consider the use of two polyhedral approximations. The method developed in this study has used a double polyhedral approximation at each iteration. Besides, as an application, we give a bundle method for a forward–backward type algorithm.
Compositional coding of individual finger movements in human posterior parietal cortex and motor cortex enables ten-finger decoding

Objective. Enable neural control of individual prosthetic fingers for participants with upper-limb paralysis. Approach. Two tetraplegic participants were each implanted with a 96-channel array in the left posterior parietal cortex (PPC). One of the participants was additionally implanted with a 96-channel array near the hand knob of the left motor cortex (MC). Across tens of sessions, we recorded neural activity while the participants attempted to move individual fingers of the right hand. Offline, we classified finger movements from neural firing rates using linear discriminant analysis (LDA) with cross-validation. The participants then used the neural classifier online to control individual fingers of a brain-machine interface (BMI). Finally, we characterized the neural representational geometry during individual finger movements of both hands. Main Results. The two participants achieved 86% and 92% online accuracy during BMI control of the contralateral fingers (chance = 17%). Offline, a linear decoder achieved ten-finger decoding accuracies of 70% and 66% using respective PPC recordings and 75% using MC recordings (chance = 10%). A compositional code linked corresponding finger movements of the contralateral and ipsilateral hands. Significance. This is the first study to decode both contralateral and ipsilateral finger movements from PPC. Online BMI control of contralateral fingers exceeded that of previous finger BMIs. PPC and MC signals can be used to control individual prosthetic fingers, which may contribute to a hand restoration strategy for people with tetraplegia.
Uncertainty relation based on metric-adjusted skew information with quantum memory

The uncertainty principle is one of the most essential features of quantum mechanics. Recently, uncertainty relations of skew information have been widely studied. In this paper, general and conditional uncertainty relations based on metric-adjusted skew information are put forward to study the case of the uncertainty relation with the existence of a quantum memory for the bipartite quantum system. These uncertainty relations include the product form and the sum form. The results show that the lower bounds contain two parts: one is characterizing the degree of compatibility of two measurements, and the other one is the quantum correlation between the measured system and the quantum memory. These results may play an important role in witnessing entanglement and quantum key distribution.
Fine-Grained Gastrointestinal Endoscopy Image Categorization

Gastrointestinal endoscopy is of great significance to improve the accuracy and efficiency for diagnosis of digestive tract diseases. With the development of artificial intelligence in medical images, the computer-assisted system of diagnosis is developed to assist specialists in gastrointestinal endoscopy diagnosis. Convolutional Neural Networks (CNNs) are good at recognizing significant categories differences, but poor at subtle inter-class differences. The images captured in gastrointestinal endoscopy have subtle inter-class differences among sub-categories, so fine-grained gastrointestinal endoscopy image classification is more difficult than ordinary image classification tasks. To address this challenge, this paper used Recurrent Attention Convolutional Neural Network (RACNN) to transfer learning image's features and label smoothing regularization method to improve experimental performance. Experimental results show that the RACNN with label smoothing technique achieves the best classification performance of traditional deep neural networks.
Analysis of the impact of smart medical platform construction on medical willingness: ——Based on quantitative analysis of urban residents in Wuhan

In the post-epidemic era, smart medical platforms have received more and more attention. Taking the Wuhan area as an example, this paper explores the impact of medical platform construction on urban residents' willingness to seek medical treatment through a combination of questionnaire surveys and expert interviews. Based on SOR, this study combines Theory of Perceived Risk and TAM to establish model, and uses factor regression analysis and structural equation model for hypothesis verification. The research results can provide an important reference basis for the development of targeted management and promotion strategies for smart medical port managers.
Convolutional neural network-based image recognition for animals

In this paper, the basic of convolutional neural network (CNN) and machine learning (ML) was introduced firstly. With the help of TensorFlow, a model building tool, including how they work together to process an image and extract the basic information of it, 25000 images were used including both cats and dogs to train the CNN, which makes the differences between cats and dogs accessible, therefore identifying the cats and dogs. After training the program with such data sets, this work calculated the probability of correctly identifying the animals in the image and finally analyzed it by using the existing data sets.
Automatic Pulmonary Nodule Detection Using Faster R-CNN Based on Densely Connected Network

Accurate detection of pulmonary nodules in CT images is a key task in performing computer-aided diagnosis of pulmonary diseases. In this work, inspired by the successful application of Faster R-CNN in object detection and the superiority of dense convolutional networks in feature propagation, we proposed a modified Faster R-CNN with an improved densely connected network as the backbone for lung nodule detection in medical images. In the proposed network, the backbone for feature extraction can be considered as a combination of multiple densely connected micro-blocks with skip connections. Skip connections in the micro-blocks enhances the propagation of features between layers, thus enable feature reusage. These micro-blocks effectively mitigate the problem of gradient vanishing in feature propagation due to their dense properties. In addition, the compact structure of these micro-blocks facilitates the network to extract and learn CT image features more efficiently. Finally, these micro-blocks have fewer parameters and higher parameter efficiency. The proposed method was tested and evaluated on the public lung nodule dataset LUNA16. When a ten-fold cross validation was performed, the proposed network achieved a FROC score of up to 0.952 and a CPM score of up to 0.861. Experimental results show that the proposed network is capable of detecting pulmonary nodules with higher sensitivity and accuracy than other conventional lung nodule detection methods.
Reduction of shot noise with light emitting diodes

The authors find that the optical shot noise and the electrical shot noise in light emitting diodes have the identical fluctuation due to spontaneous emission as far as the quantum efficiency is unity. The 0.45-dB reduction of noise below the standard shot-noise limit is achieved by using such correlation between the two kinds of shot noise. >
Free Rotational Diffusion of Rigid Particles with Arbitrary Surface Topography: A Brownian Dynamics Study Using Eulerian Angles

Rotational diffusion of rigid bodies is an important topic that has attracted sustained interest for many decades, but most existing studies are limited to particles with simple symmetries. Here, we present a simple Brownian dynamics algorithm that can be used to study the free rotational diffusion of rigid particles with arbitrary surface topography. The main difference between the new algorithm and previous algorithms is how the numerical values of the mobility tensor are calculated. The only parameters in the numerical algorithm that depend on particle shape are the principal values of the particle rotational mobility tensor. These three scalars contain all information about the surface topography that is relevant for the particle rotational diffusion. Because these principal values only need to be pre-calculated once, the resulting general algorithm is highly efficient. The algorithm is valid for arbitrary mass density distribution throughout the rigid body. In this paper, we use Eulerian angles as the generalized coordinates describing the particle angular orientation.
Application of artificial intelligence in mental health and mental illnesses

Purpose of review: Artificial intelligence (AI), a hot area of research today, has been attempted to be applied in various fields of clinical medicine. This paper reviews the current status of research on AI in mental health and mental illness, including applications in the prevention, supplementary diagnosis, treatment and rehabilitation of mental illness. It also discusses the advantages, shortcomings and prospects of AI applications in the field of mental disorders, in order to provide references for research related to mental health applications based on AI technology. Research methods: The literature on the application of AI in mental illness interventions in recent years was searched through literature navigation. The aspects of mental illness prevention, diagnosis and treatment interventions were analyzed. Research conclusion: AI technology is developing rapidly and can complement the advantages of artificial diagnosis and treatment. However, AI still has a lot of room for development and has shortcomings at this stage that may bring some problems, such as algorithm bias, ethical aspects and difficulty in promotion at this stage. As mental health practitioners, we should actively adapt to and promote the further development of AI in the mental health field.
MSRCN: Multi-task Learning Network for Cell Segmentation and Regression Counting

Accurate cell segmentation and counting play an important role in medical diagnosis. However, the size and shape of cells are varied largely, and the presence of overlapping cells complicates cell counting. Recent studies have shown that multi-task learning methods perform well in deep learning. In specific, we design Multi-task Segmentation Regression Counting Network (MSRCN). For cell segmentation, a multi-scale attention mechanism module is designed to suppress irrelevant regions and learns salient features for a specific task. For cell counting, a regression model is utilized to learn a mapping from cell feature information to target counts. The proposed MSRCN model is analyzed and compared with other states of the art cell segmentation methods and cell counting methods. MSRCN outperforms these methods in all evaluation metrics. The Dice similarity coefficient, root mean square error, and mean absolute error of the proposed method is 0.9316, 2.1215, and 1.5927, respectively. The experiments results show that the proposed method not only improves the functioning of cell segmentation, but also outperforms direct regression counting methods in terms of cell counting.
Research on the formulation rules of health food for improving sleep based on data mining

Objective: To analyze the prescription rules of Chinese medicine health foods for improving sleep, and to provide references for the research and development of related health foods. Methods: Retrieve 581 types of sleep-improving Chinese medicine health food data approved before December 2020 from the official website of the State Administration of Market Supervision and Administration (SAMR), establish a health food database and conduct an overall analysis, including dosage form, function, frequency, etc., for raw materials The 109 formula, which is all Chinese herbal medicine, carries out frequency statistics, association rule analysis and visual analysis. Results: There were only 418 kinds of sleep improving effects, including 163 kinds of two or more effects. All of them were Chinese herbal medicines with high frequency of use, such as SEMEN ZIZIPHI SPINOSAE, PORIA and FRUCTUS SCHISANDRAE CHINENSIS. According to the efficacy classification, the top three traditional Chinese medicines with the highest frequency of use were Deficience-tonifying Drugs, Anxiety Drugs and Liver-soothing and Wind Resuscitating Drugs. The combination rule high-frequency drug pair includes RADIX SALVIAE MILTIORRHIZAE → SEMEN ZIZIPHI SPINOSAE; FRUCTUS SCHISANDRAE CHINENSIS, PORIA → SEMEN ZIZIPHI SPINOSAE; RADIX ET CAULIS ACANTHOPANACIS SENTICOSI → SEMEN ZIZIPHI SPINOSAE, etc.; The visualization results of association rule drug pairs show that the core compatibility is SEMEN ZIZIPHI SPINOSAE - PORIA - GINSENG - FRUCTUS SCHISANDRAE CHINENSIS - RHIZOMA GASTRODIAE. It is basically consistent with the principle of improving sleep in traditional Chinese medicine. Conclusion: Data mining and basic theoretical analysis of traditional Chinese medicine on the prescription rules of existing sleep-improving health foods can provide new ideas for the research and development of traditional Chinese medicine health products, so as to give full play to the advantages of traditional Chinese medicine and provide a basis for the public service of Chinese medicine health foods.
Mechanisms of retinal ganglion cell injury following acute increases in intraocular pressure

The maintenance of intraocular pressure (IOP) is critical to preserving the pristine optics required for vision. Disturbances in IOP can directly impact the optic nerve and retina, and inner retinal injury can occur following acute and chronic IOP elevation. There are a variety of animal models that have been developed to study the effects of acute and chronic elevation of IOP on the retina, retinal ganglion cell (RGC) morphology, intracellular signaling, gene expression changes, and survival. Acute IOP models induce injury that allows for the study of RGC response to well characterized injury and potential recovery. This review will focus on the initial impact of acute IOP elevation on RGC injury and recovery as these early responses may be the best targets for potential therapeutic interventions to promote RGC survival in glaucoma.
Hydromagnetic convection in a rotating fluid layer in the presence of Hall current

Abstract The linear stability of the Benard layer with the combined effects of a uniform magnetic field and a uniform angular velocity in the presence of a Hall current is examined using the Boussinesq approximation. Assuming the directions of the magnetic field and angular velocity to be both vertical, for time-independent motions the problem is studied for different types of boundaries using an asymptotic analysis for large Taylor and Hartmann numbers T and M respectively. The Hall current is found to have a significant influence on the onset of convection. When the Hall constant, m, is of order unity the critical Rayleigh number (R) is found to increase with m when T lies in the range O(M 4) ≤ T ≤ O(M 6) for all types of boundaries. For large m, that is when m = O(M s), (s > O) for free insulating boundaries the Hall current is found to affect R when T ≤ O(M 3). In the case of free perfectly conducting boundaries, when m = O(M) and T ≤ O(M 2), the Hall current is found to influence R to leading order.
A formulation based on modal optimization for predicting sound radiation from fluid‐loaded aircraft structures

This study is led in the context of understanding sound transmission through fluid‐loaded aircraft structures with non‐uniform damping. The fluid/structure coupling is here highlighted. Classic approaches using modal radiation impedances to formulate the fluid/structure coupling do lead to complexity and are computationally time‐consuming. A method defining rigorously this coupling and avoiding the calculation of the modal radiation impedances is proposed. This method aims at developing a simple formulation taking advantage of current technical progress in optimization algorithms. Sound radiation from a simply supported, baffled, fluid‐loaded plate excited mechanically or acoustically is here solved in optimizing the modal amplitudes so that they fit the governing equation with fluid loading. To perform this optimization, a sampling of the plate into observation points is first done, then a modal decomposition into in vacuo modes is led. Comparison with results from the literature over [10‐1000Hz] for 3 r.
QUANTIZED H∞ FILTERING FOR CONTINUOUS-TIME MARKOVIAN JUMP SYSTEMS WITH DEFICIENT MODE INFORMATION

This paper investigates the problem of quantized H∞ filtering for a class of continuous-time Markovian jump linear systems with deficient mode information. The measurement output of the plant is quantized by a mode-dependent logarithmic quantizer, and the deficient mode information in the Markov stochastic process simultaneously considers the exactly known, partially unknown, and uncertain transition rates. By fully exploiting the properties of transition rate matrices, together with the convexification of uncertain domains, a new sufficient condition for quantized H∞ performance analysis is first derived, and then two approaches, namely, the convex linearization approach and iterative approach, to the H∞ filter synthesis are developed. It is shown that both the full-order and reduced-order filters can be obtained by solving a set of linear matrix inequalities (LMIs) or bilinear matrix inequalities (BMIs). Finally, two illustrative examples are given to show the effectiveness and less conservatism of the proposed design methods.
The Cramer-Rao bound for pole and amplitude estimates of damped exponential signals in noise

Expressions are derived for the Cramer-Rao bounds (CRBs) of the parameters of an exponential model with one set of poles and multiple sets of amplitude coefficients. The poles of this model may lie anywhere in the complex plane. The CRB for pole angle is log-symmetric about the unit circle with minimum on the unit circle, while the CRB for pole magnitude has its minimum inside the unit circle for finite-length datasets and is not symmetric. Simulation results show that error-standard-deviation ellipses about poles turn out to be circular. The CRBs for estimates of the amplitude coefficients of real and imaginary parts are also available. <<ETX>>
The cosmic gamma-ray background from Type Ia supernovae

We present an improved calculation of the cumulative gamma-ray spectrum of Type Ia supernovae during the history of the universe. We follow Clayton & Ward (1975) in using a few Friedmann models and two simple histories of the average galaxian nucleosynthesis rate, but we improve their calculation by modeling the gamma-ray scattering in detailed numerical models of SN Ia's. The results confirm that near 1 MeV the SN Ia background may dominate, and that it is potentially observable, with high scientific importance. A very accurate measurement of the cosmic background spectrum between 0.1 and 1.0 MeV may reveal the turn-on time and the evolution of the rate of Type Ia supernova nucleosynthesis in the universe.
Combining interface damage and friction in a cohesive‐zone model

A new method to combine interface damage and friction in a cohesive‐zone model is proposed. Starting from the mesomechanical assumption, typically made in a damage‐mechanics approach, whereby a representative elementary area of the interface can be additively decomposed into an undamaged and a fully damaged part, the main idea consists of assuming that friction occurs only on the fully damaged part. The gradual increase of the friction effect is then a natural outcome of the gradual increase of the interface damage from the initial undamaged state to the complete decohesion. Suitable kinematic and static hypotheses are made in order to develop the interface model whereas no special assumptions are required on the damage evolution equations and on the friction law. Here, the Crisfield's interface model is used for the damage evolution and a simple Coulomb friction relationship is adopted. Numerical and analytical results for two types of constitutive problem show the effectiveness of the model to capture all the main features of the combined effect of interface damage and friction. A finite‐step interface law has then been derived and implemented in a finite‐element code via interface elements. The results of the simulations made for a fibre push‐out test and a masonry wall loaded in compression and shear are then presented and compared with available experimental results. They show the effectiveness of the proposed model to predict the failure mechanisms and the overall structural response for the analysed problems. Copyright © 2006 John Wiley & Sons, Ltd.
Generalized predictive information criteria for the analysis of feature events

This paper develops two weighted measures for model selection by generalizing the Kullback-Leibler divergence measure. The concept of a model selection process that takes into account the special features of the underlying model is introduced using weighted measures. New information criteria are defined using the bias correction of an expected weighted loglikelihood estimator. Using weight functions that match the features of interest in the underlying statistical models, the new information criteria are applied to simulated studies of spline regression and copula model selection. Real data applications are also given for predicting the incidence of disease and for quantile modeling of environmental data.