Miracle-dz/UniFAR-FTUs huggingface dataset:
A Few-Shot Learning Method for SAR Images Based on Weighted Distance and Feature Fusion

Convolutional Neural Network (CNN) has been widely applied in the field of synthetic aperture radar (SAR) image recognition. Nevertheless, CNN-based recognition methods usually encounter the problem of poor feature representation ability due to insufficient labeled SAR images. In addition, the large inner-class variety and high cross-class similarity of SAR images pose a challenge for classification. To alleviate the problems mentioned above, we propose a novel few-shot learning (FSL) method for SAR image recognition, which is composed of the multi-feature fusion network (MFFN) and the weighted distance classifier (WDC). The MFFN is utilized to extract input images’ features, and the WDC outputs the classification results based on these features. The MFFN is constructed by adding a multi-scale feature fusion module (MsFFM) and a hand-crafted feature insertion module (HcFIM) to a standard CNN. The feature extraction and representation capability can be enhanced by inserting the traditional hand-crafted features as auxiliary features. With the aid of information from different scales of features, targets of the same class can be more easily aggregated. The weight generation module in WDC is designed to generate category-specific weights for query images. The WDC distributes these weights along the corresponding Euclidean distance to tackle the high cross-class similarity problem. In addition, weight generation loss is proposed to improve recognition performance by guiding the weight generation module. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset and the Vehicle and Aircraft (VA) dataset demonstrate that our proposed method surpasses several typical FSL methods.
Aftereffects of the processing speed in the stop-signal task

The aftereffects in the stop-signal task were examined. Employing the horse-race model, we investigated how the processing speed of go-processes changes depending on various conditions in the preceding trial. New conditions to discriminate between fast and slow go-processes enabled us to discuss the speed of go-processes even in stop-trials in which no response takes place. We showed that the speed of go-processes decreases in go-trials in two cases:(i)when the preceding trial was a go-trial and had a different stimulus, and(ii)when the preceding trial was a stop-trial and had the same stimulus. It was also shown that the speeds of go-processes in the above consecutive trials are correlated in each case. We confirmed that the magnitude of slowdown in(ii)is larger after an unsuccessful inhibition than after a successful inhibition. This observation implies that the slowdown is due to interference.
Estimating Equations for Spatial Extremes with Applications to Detection and Attribution Analysis of Changes in Climate Extremes

In inference for max-stable processes in regional frequency analysis, it is found that, when the dependence model is misspecified, the pairwise likelihood method leads to bias in estimating the shape parameter of the generalized extreme value (GEV) distribution. The bias can be serious when the dependence is strong. Motivated by the fact that the primary interest in many studies is the inference about marginal GEV parameters and that the spatial dependence is a nuisance, we propose a combined score equations (CSE) approach that does not need dependence assumptions beyond the univariate GEV distribution. The CSE method combines the score equations of GEV model at each site with an approximate correlation function of the scores to improve the estimation efficiency. Applied to fingerprinting of changes in climate extremes with a coordinate descent algorithm to estimate a large number of parameters, the CSE method provides a close analog to the optimal fingerprinting in detection and attribution of changes in Zhuo Wang – University of Connecticut, 2015 climate extremes. The approach is applied on extreme temperature in Australia under a perfect model setting and in Northern Europe with the dependence structure modeled by exponential correlation. The CSE approach with working independence reduces to the independence likelihood method, but the estimation is much faster. This approach is applied on the annual maximum daily maximum (TXx) and minimum (TNx) and annual minimum daily maximum (TXn) and minimum (TNn) temperatures during 1951–2010 over 17 subcontinents. In the single-signal analyses, anthropogenic and natural influence can be detected in all four indices separately, but the detection occurs more frequently in TNx. We also studied the two-signal analyses that both anthropogenic signal and natural signal are present in the model simultaneously, which has not been reported before under the extreme value framework due to methodological limitations. The anthropogenic signal is separable from natural signal in a few regions of the four indices. The 20062010 waiting time of the 1951-1955 20-yr return value of annual maximum extreme temperature was found to decrease while that of annual minimum extreme temperature was found to increase, implying substantially influence of the anthropogenic and natural forcing on extreme temperature. Estimating Equations for Spatial Extremes with Applications to Detection and Attribution Analysis of Changes in Climate Extremes
Strategies used in the Olympic weightlifting and psychological aspects: factors that contemplate sports performance

To identify the strategies used in Olympic weightlifting and the psychological aspects that include mindfulness and focus on the present moment of sports performance. The search occurred in January and February of 2021, PubMed, Web of Science, Scopus, and LILACS databases were used. The PRISMA checklist was used, and the risk analysis of bias was adapted from the Cochrane Manual for Clinical Trials and, for other studies, the Downs and Black scale. Nineteen studies were included for qualitative analysis. On squat strategies to facilitate performance during Olympic weightlifting, both low and high barbell squats are compatible methods for increasing strength in the lower body, core, and muscles back. Athlete’s everyday practice using mindfulness shows a strong impact on attention self-regulation development at the present moment and a precursor in self-confidence acquisition. Through this study, implementation of mindfulness is recommended to increase sportive performance, once acceptance of being totally present creates a favorable conception to the athlete. The technique shows itself as promising to prevent injuries, increasing of performance and control of the emotions; however, in a period of not less than eight weeks, along with biomechanics factors specific analysis. Mental preparation using mindfulness to acquire self-confidence, the composition of the method to be developed, time of adaptation and betterment are steps that should be pre-set in aware practice aiming the development of contemporary strategies in different sports, mainly in olympic weightlifting.
The Responsibilities for Online Platforms to Undertake: Cooperative, Legal, and Corporate Social Responsibilities

Globalized content platforms such as Facebook and Twitter have been deeply involved in people’s various public activities, including news production and dissemination, civic education, and transportation. With the growth of these platforms, what responsibilities they should undertake for the content shared on them has become the focus of debate and research among scholars. This research essay argues that cooperative, legal, and corporate social responsibilities are the three major responsibilities that globalized content platforms are encouraged to undertake. On the basis of taking cooperative responsibility, globalized content platforms are obliged to refine their legal and corporate social responsibilities according to the laws, policies, and situations of various countries to deal with government regulation. In addition, platforms are expected to establish a dynamic responsibility adjustment mechanism to keep up with new regulations and changes in national conditions, so as to play the role of the gatekeeper to the Internet.
Identification of Molecular Subtypes of Breast Cancer Based on Multimodal Deep Learning

: The identification of breast cancer subtypes plays a key role in the prognosis of breast cancer. In recent years, deep learning (DL) has shown good performance in intelligent identification of breast cancer subtypes. However, most of the traditional DL models use single-mode data, and the extracted features are limited, so the association between patient characteristics and breast cancer subtypes cannot be established stably. In order to improve the effect of recognition, this study proposes a multimodal fusion deep learning (MFDL) model. This model combined with the breast cancer gene modal data and image modal data established a multilayer perceptron network and the depth of the convolution neural network for feature extraction, and then based on the idea of weighted aggregation on the output of the two characteristics of the network integration. Finally, the fusion features were used to identify breast cancer subtypes. The experimental results show that compared with other models in AUC value, accuracy and other indicators, the MFDL model proposed in this study is more accurate and efficient in the identification of breast cancer subtypes.
VIRTUAL REALITY-BASED MEDITATION INTERVENTION FOR MILD COGNITIVE IMPAIRMENT

Mild cognitive impairment (MCI) is an intermediate stage of cognitive alteration, from usual ageing and dementia; the distinction between MCI and dementia is determined by the severity of cognitive decline leading to cognitive-Behavioural impairment. The number of patients with dementia is currently estimated at 47 million according to the World Health Organization, which is projected to reach 75 million by 2030. The economic cost of care for people suffering from Dementia has increased year by year. Due to the constant increase in the number of people with dementia, the economic burden of face-to-face interventions can longer be considered an economically optimized solution to the problem. This has led to the use of virtual reality as an intervention medium for addressing the problem of dementia, as a cost-effective and scalable solution. The present paper reports a feasibility study in which meditation protocol has been incorporated into the Virtual reality delivery system for subjects with Mild cognitive impairment. The With-in group analysis showed a significant difference (increase) in functions of Attention, Thinking operation, Spatial perception, Visual perception, and orientation in the Pre-Post condition and no significant difference between the method of delivery. This feasibility and effectiveness study provided preliminary results which indicate that Virtual reality-based meditation delivery can be utilized for subjects with MIC instead of a traditional meditation delivery system, for the scope of scalability and economic-optimization
Inhibiting the activation of MAPK (ERK1/2) in stressed Müller cells prevents photoreceptor degeneration

Rationale: Müller cells play an essential role in maintaining the health of retinal photoreceptors. Dysfunction of stressed Müller cells often results in photoreceptor degeneration. However, how these cells communicate under stress and the signalling pathways involved remain unclear. In this study, we inhibited the MAPK (ERK1/2) signalling, mainly activated in Müller cells, evaluated the protective effects on the photoreceptors and further explored the signalling communication between stressed Müller cells and degenerating photoreceptors. Methods: We evaluated the changes of MAPK (ERK1/2) signalling and its downstream targets in human retinal explants treated with PD98059, a specific phosphorylated ERK1/2 inhibitor, by western blot and immunostaining. We further assessed photoreceptor degeneration by TUNEL staining and outer nuclear layer thickness. We also injected PD98059 into the eyes of mice exposed to photo-oxidative stress. We evaluated the protective effects on photoreceptor degeneration by optical coherence tomography (OCT) and electroretinography (ERG). The crosstalk between Müller cells and photoreceptors was further dissected based on the changes of transcription factors by RNA sequencing and protein profiles of multiple signalling pathways. Results: We found that MAPK (ERK1/2) signalling was mainly activated in Müller cells under stress, both ex vivo and in vivo. PD98059 inhibited the phosphorylation of ERK1/2, reduced expression of the gliotic marker glial fibrillary acidic protein (GFAP) in Müller cells and increased levels of the neuroprotective factor, interphotoreceptor retinoid-binding protein (IRBP) in photoreceptors. Inhibition of pERK1/2 also reduced retinal photo-oxidative damage in mice retinas assessed by OCT and ERG. We also identified that the JAK/STAT3 signalling pathway might mediate signalling transduction from Müller cells to photoreceptors. Conclusion: MAPK (ERK1/2) deactivation through chemical inhibition, mainly in stressed Müller cells, can alleviate gliosis in Müller cells and restore the expression of IRBP in photoreceptors, which appears to prevent retinal degeneration. Our findings suggested a new way to prevent photoreceptor degeneration by manipulating the stress response in Müller cells.
Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to optimize cancEr

Sample size considerations for the main sex-related analyses. Given that the events proportion could be even greater than 30%, we simulated scenarios in which the proportion is varying from 20% to 50%. To estimate the proportion by F and M, a sample size of 200 patients (200 F/200 M) will produce a two-sided 95% exact CI with a width as small as 0.115 (CI 0.147 to 0.262) if the proportion is 20%, increasing to 0.143 (CI 0.429 to 0.571) if the proportion is 50%.
H∞ design with pole placement constraints: an LMI approach

This paper addresses the design of state- or output-feedback H/sub /spl infin// controllers that satisfy additional constraints on the closed-loop pole location. Sufficient conditions for feasibility are derived for a general class of convex regions of the complex plane. These conditions are expressed in terms of linear matrix inequalities (LMIs), and the authors' formulation is therefore numerically tractable via LMI optimization. In the state-feedback case, mixed H/sub 2//H/sub /spl infin// synthesis with regional pole placement is also discussed. Finally, the validity and applicability of this approach are illustrated by a benchmark example.
Generalized Weibull-tail distributions

R´esum´e. Des variables al´eatoires `a queue de type Weibull sont d´eﬁnies comme des variables al´eatoires positives dont la queue droite se comporte de mani`ere similaire `a celle d’une variable al´eatoire de Weibull. Nous introduisons des variables al´eatoires de queue Weibull g´en´eralis´ees, des extensions qui ont une propri´et´e de stabilit´e. Nous ´etudions la pr´eservation des param`etres de queue de ces variables al´eatoires sous des op´erations telles que la multiplication, la puissance et la combinaison lin´eaire. Abstract. Weibull-tail random variables are deﬁned as non-negative random variables whose right tail behaves similarly to that of a Weibull random variable. We introduce generalized Weibull-tail random variables, extensions which have a property of stability. We study the preservation of tail parameters under operations like multiplication, power, and linear combination.
Synthesis of Habitat Use by Black-footed Albatross tracked from Cordell Bank National Marine Sanctuary (2004 – 2008) and Kure Atoll Seabird Sanctuary (2008)

the and of post-breeding and provisioning Black-footed Albatross (BFAL, Phoebastria nigripes ) tagged within the Cordell Bank National Marine (CBNMS) and the within the ā ā kea (PMNM) a project to the information to the of this far-ranging species, in the context of static oceanic habitats (bathymetric domains and features), existing jurisdictions (U.S. National Sanctuaries (NMS) and and international exclusive economic
Multidimensional Echocardiography Image Segmentation using Deep Learning Convolutional Neural Network

—One of the most dangerous diseases that threaten human life is heart disease. One way to analyze heart disease is by doing echocardiography. Echocardiographic test results can indicate whether the patient's heart is normal or not by identifying the area of the heart cavity. Therefore, many studies have emerged to analyze the heart. Therefore, I am motivated to develop a system by inputting four points of view of the heart, namely 2 parasternal views (long axis and short axis) and 2 apical views (two chambers and four chambers) with the aim of this study being able to segment the heart cavity area. This research is part of a large project that aims to analyze the condition of the heart with 4 input points of view of the heart and the project is divided into several sections. For this research, it focuses on the process of echocardiographic image segmentation to obtain images of the heart cavity with 4 input points of view of the heart using the Deep Learning method by using the VGG-16 and RESNET-18 architecture. The training process is done using 30 epochs with 50 iterations per epoch and 1 batch size so that the total iteration is 7500 iterations. It can be seen that during the training process, the percentage accuracy is already high, reaching 95% -99%. On the VGG-16 architecture, it has an average accuracy in each viewpoint of around 83% -93%. The architecture of RESNET-18 has an average accuracy in every point of view which is around 76% -92%.
Count-Based and Predictive Language Models for Exploring DeReKo

We present the use of count-based and predictive language models for exploring language use in the German Reference Corpus DeReKo. For collocation analysis along the syntagmatic axis we employ traditional association measures based on co-occurrence counts as well as predictive association measures derived from the output weights of skipgram word embeddings. For inspecting the semantic neighbourhood of words along the paradigmatic axis we visualize the high dimensional word embeddings in two dimensions using t-stochastic neighbourhood embeddings. Together, these visualizations provide a complementary, explorative approach to analysing very large corpora in addition to corpus querying. Moreover, we discuss count-based and predictive models w.r. t. scalability and maintainability in very large corpora.
REL ATIONSHIP BE T WEEN INSUR ANCE MARKE T AND ECONOMIC GROW TH IN THE EUROPEAN UNION

123 This paper examines the relationship between insurance market development and economic growth in EU member states in the period 1998–2018. Our results indicate that there is no causality between premium per capita and GDP per capita growth in the case of 11 out of the 23 analysed countries, including four countries classified as emerging markets. However, in the case of panel data covering all the countries, we determined a two-way causality between insurance market development and economic growth. In the short run, premium per capita has a positive and significant impact on the economic growth as proven in the data panel and in the case of individual countries, except in the case of Ireland and Luxembourg, where the applied model shows only error-correction coefficient values. Besides, our results indicate that premium per capita has a significant positive effect on economic growth in the long run in the case of Belgium, Cyprus, Bulgaria, Romania and Slovenia, i.e. , insurance premium is a key determinant of long-term economic growth. The results show a statistically significant long-term positive relationship between premium per capita and GDP growth per capita in the case of a panel analysis of all the observed countries and countries classified as emerging markets. On the other hand, the panel data analysis of the countries classified in the category of developed markets showed a long-term positive relationship, but not a statistically significant one. Since that results indicate that the insurance market development could contribute to ensuring long-term economic stability and growth of observed countries, special attention needs to be paid to the strategy of insurance market development in a changing business environment.
Recognizability Equals Definability for k-Outerplanar Graphs

One of the most famous algorithmic meta-theorems states that every graph property that can be defined by a sentence in counting monadic second order logic (CMSOL) can be checked in linear time for graphs of bounded treewidth, which is known as Courcelle’s Theorem [9]. These algorithms are constructed as finite state tree automata, and hence every CMSOL-definable graph property is recognizable. Courcelle also conjectured that the converse holds, i.e. every recognizable graph property is definable in CMSOL for graphs of bounded treewidth. We give two types of self-contained proofs of this conjec- ture for a number of special cases. First, we show that it holds in a stronger form, that is, we prove that recognizability implies MSOL-definability (the counting operation of CMSOL is not needed) for Halin graphs, 3-connected or bounded degree k-outerplanar graphs and some related graph classes. Second, we show that recognizability implies CMSOL-definability for general k-outerplanar graphs.
Minimal Length Effects on Schwinger Mechanism

In this paper, we investigate effects of the minimal length on the Schwinger mechanism using the quantum field theory (QFT) incorporating the minimal length. We first study the Schwinger mechanism for scalar fields in both usual QFT and the deformed QFT. The same calculations are then performed in the case of Dirac particles. Finally, we discuss how our results imply for the corrections to the Unruh temperature and the Hawking temperature due to the minimal length.
The Research of STBC in MIMO System

: MIMO technology refers to the use of multiple transmitting antennas and multiple receiving antennas to improve the quality of information transmission. MIMO technology includes space-time coding, channel modeling, channel estimation, and other technologies. This paper mainly studies the space-time coding technology and precoding technology in the MIMO system through Matlab. First, the Alamouti coding is extended to the MIMO system with more transmitting and receiving antennas. This way can get a space-time block code. Finally, this paper will introduce the precoding technology and compare it with simulation analysis by simulating and analyzing its bit error performance
The prediction of mortality from continuous non-invasive cardiovascular signals on standing: entropy was significant, but not the overall response profile

In this study, a novel approach is presented using principal component analysis and sample entropy (SampEn) for the analysis of continuous blood pressure (BP) data measured non-invasively during an active stand (AS) in a large sample of older adults. The method allows for the extraction of the bulk trends from these data in the form of principal components (PCs), which can be used as independent predictors of outcomes, and greatly increases the stationarity of the remaining data, allowing for secondary analyses such as SampEn. The relationship between AS BP measures (SampEn and first 6 PCs) and risk of all-cause 8-year mortality was investigated via Cox proportional hazards regression models in a sample of community-dwelling older adults ($\mathrm{n}=4873$, with 209 deaths) from The Irish Longitudinal Study on Ageing (TILDA). Higher SampEn in BP signals was found to be a significant predictor of mortality risk. PC scores, which characterize the overall bulk changes in response to standing, were not significantly predictive of mortality when controlling for age, sex, and educational attainment. The quantification of signal entropy in continuously measured BP signals during AS could provide a clinically useful predictor of risk of death.