Miracle-dz/UniFAR-FTUs huggingface dataset:
Bilateral Fast Low-Rank Representation With Equivalent Transformation for Subspace Clustering

In recent years, low-rank representation (LRR) has received increasing attention on subspace clustering. Due to inevitable matrix inversion and singular value decomposition in each iteration, however, most of existing LRR algorithms may suffer from high computational complexity, and hence can not cope with the large-scale sample data commendably. To overcome this problem, in this paper, we propose a bilateral fast low-rank representation (BFLRR), which has a linear time complexity with respect to the number of samples. Specifically, we introduce the equivalent transformation method to remove the null spaces of both the columns and rows of the coefficient matrix so that a hypercompact coefficient matrix can be learned. Furthermore, the proposed BFLRR is embedded into a distributed framework as DFC-BFLRR to make it more efficient, which utilizes a combination of the global and local projection matrices. Extensive experiments are carried out on real datasets, and the results testify that the proposed methods not only perform faster-computing speed but also obtain favorable clustering accuracy in comparison with the competing methods among large-scale sample data.
Effects of rmBMP-7 on Osteoblastic Cells Grown on a Nanostructured Titanium Surface

This study evaluates the effects of the availability of exogenous BMP-7 on osteoblastic cells’ differentiation on a nanotextured Ti surface obtained by chemical etching (Nano-Ti). The MC3T3-E1 and UMR-106 osteoblastic cell lines were cultured for 5 and 7 days, respectively, on a Nano-Ti surface and on a control surface (Control-Ti) in an osteogenic medium supplemented with either 40 or 200 ng/mL recombinant mouse (rm) BMP-7. The results showed that MC3T3-E1 cells exhibited distinct responsiveness when exposed to each of the two rmBMP-7 concentrations, irrespective of the surface. Even with 40 ng/mL rmBMP-7, important osteogenic effects were noticed for Control-Ti in terms of cell proliferation potential; Runx2, Osx, Alp, Bsp, Opn, and Smad1 mRNA expression; and in situ ALP activity. For Nano-Ti, the effects were limited to higher Alp, Bsp, and Opn mRNA expression and in situ ALP activity. On both surfaces, the osteogenic potential of UMR-106 cultures remained unaltered with 40 ng/mL rmBMP-7, but it was significantly reduced when the cultures were exposed to the 200 ng/mL concentration. The availability of rmBMP-7 to pre-osteoblastic cells at the concentrations used alters the expression profile of osteoblast markers, indicative of the acquisition of a more advanced stage of osteoblastic differentiation. This occurs less pronouncedly on the nanotextured Ti and without reflecting in higher mineralized matrix production by differentiated osteoblasts on both surfaces.
Using Glow-worm algorithm to predict companies’ financial distress

One important research issue in the risk management area is to predict the financial distress of companies. This case has received great attention from banks, companies, managers, and investors. Although there are many studies on this case, the hybrid models (mixed feature selection and classifier models) have been used by researchers in recent years. The main objective of this study is to propose a high-performance predictive model and compare its results with other models that are commonly used for financial distress prediction. To do this, the Glowworm optimization algorithm-based hybrid neural network model was employed. Moreover, the neural network and logistic regression model, which is one of the statistical classifier models were used. The results indicated that the glowworm optimization algorithm (also known as firefly optimization algorithm)-based hybrid neural network model had higher performance compared to the neural network and logistic regression models.
Evidence for Nemesis: a solar companion star.

The evidence that the sun has a companion star ''Nemesis'' responsible for periodic mass extinctions is reviewed. A gaussian ideogram of the rates of family extinctions in the oceans shows periods of 26 and 30 Myr. Analysis of impact cratering on the earth shows a period of either 28.4 or 30 Myr, depending on the crater selection. Models which attempt to explain these periods with either oscillations through the galactic plane, or through the effects of a tenth planet, are seriously flawed. If the periods seen in the data are real (and not a spurious result of a statistical fluctuation) then the ''Nemesis hypothesis'' is the only suggested explanation that has survived close scrutiny. The Nemesis model predicts that the impacts took place during brief storms of several million years duration, perhaps accounting for the ''extended'' nature of the mass extinctions. A search for Nemesis is under way at Berkeley. 18 refs., 4 figs.
An Extra Dimensional Approach of Entanglement

Motivated by the apparent lack of a workable hypothesis we developed a model to describe phenomena such as entanglement and the EPR-paradox. In the model we propose the existence of extra hidden dimensions. Through these dimensions it will be possible for particles, which originate from one source, to remain connected. This connection results in an instantaneous reaction of one particle when the other particle is manipulated. We imagine entanglement in such a model. The results of the experiments which have been performed on this item do not contradict with the existence of the extra dimension(s). In addition, the model opens the possibility to unify the theory of quantum mechanics, gravitation and the general theory of relativity.
Design, Prototype and Experimental Verification of Single Phase Flux Switching Motor Using Low Cost Magnets

There has an increasing demand for a low cost brushless alternative to the series universal motor and single phase induction motor in domestic appliance and automotive applications. Single phase FSM with field winding has relatively low efficiency and low power factor. This paper tries to investigate a topology of single phase FSM which uses low cost magnet, i.e., Ferrite instead of field winding to provide field excitation. By using field focusing effect, the magnets are properly arranged to provide the required airgap flux. A standard design procedure for the presented FSM is developed. Extensive comparisons to the single phase induction motor, single phase permanent magnet motor and universal motor are made. A prototype with 8/4 pole configuration is fabricated. The experiment results are given and compared to the FEA simulated results. In addition, the electromagnetic vibrations and acoustic noises are also considered and measured which is always seen as the main disadvantage of this kind of motor.
Multiple Parallel Fusion Network for Predicting Protein Subcellular Localization from Stimulated Raman Scattering (SRS) Microscopy Images in Living Cells

Stimulated Raman Scattering Microscopy (SRS) is a powerful tool for label-free detailed recognition and investigation of the cellular and subcellular structures of living cells. Determining subcellular protein localization from the cell level of SRS images is one of the basic goals of cell biology, which can not only provide useful clues for their functions and biological processes but also help to determine the priority and select the appropriate target for drug development. However, the bottleneck in predicting subcellular protein locations of SRS cell imaging lies in modeling complicated relationships concealed beneath the original cell imaging data owing to the spectral overlap information from different protein molecules. In this work, a multiple parallel fusion network, MPFnetwork, is proposed to study the subcellular locations from SRS images. This model used a multiple parallel fusion model to construct feature representations and combined multiple nonlinear decomposing algorithms as the automated subcellular detection method. Our experimental results showed that the MPFnetwork could achieve over 0.93 dice correlation between estimated and true fractions on SRS lung cancer cell datasets. In addition, we applied the MPFnetwork method to cell images for label-free prediction of several different subcellular components simultaneously, rather than using several fluorescent labels. These results open up a new method for the time-resolved study of subcellular components in different cells, especially cancer cells.
Avaliação da capacidade de generalização de IDS stateful utilizando aprendizado de máquina

Aprendizado de máquina é relevante para a caracterização de ataques em redes de computadores, permitindo identificar padrões de tráfego e, com isso, implementar mecanismos que bloqueiam ações maliciosas. No entanto, pouco se discute sobre a capacidade de generalização das soluções em diferentes contextos operacionais. Este trabalho avalia o desempenho de diferentes modelos, como DT, LR, MLP, NB, SVM e XGB, nos conjuntos de dados UNSW-NB15, CICIDS-2017, BoT-IoT, ToN-IoT e AB-TRAP. Como resultado, observou-se uma baixa capacidade de generalização. Mais ainda, a engenharia de atributos facilitou a comparação entre os modelos e contribuiu no processo de aprendizagem. Por fim, analisou-se a efetividade de atributos como preditores de scanning em redes.
Cross-Diffusion-Induced Turing Instability in a Two-Prey One-Predator System

This paper focuses on a strongly coupled specific ecological system consisting of two prey species and one predator. We explore a unique positive equilibrium solution of the system that is globally asymptotically stable. Additionally, we show that this equilibrium solution remains locally linearly stable, even in the presence of diffusion. This means that the system does not follow classical Turing instability. However, it becomes linearly unstable only when cross-diffusion also plays a role in the system, which is called a cross-diffusion-induced instability. The corresponding numerical simulations are also demonstrated and we obtain the spatial patterns.
Internet Dynamic Communication Network Modeling of Qiang Flute Culture Research from the perspective of Music Anthropology based on Loop Signal Detection

Qiangdi has deeply influenced Qiang culture, cultural and historical research. She has strong characteristics in the playing skills, rhythm, timbre etc. Play gives people an ethereal, lingering melodious melody, People can taste the Qiangdi flute in the body that the immortality of the soul with the old voice. Internet dynamic communication network modeling of Qiang flute culture research from the perspective of music anthropology based on loop signal detection is discussed in this paper. Using this design, the low accuracy of the single-pixel context-based design can be overcome. The obtained context variables are used to construct a loop detection model, and fusion rules are designed based on the detailed judgment of the high-frequency coefficients, so as to obtain fusion with rich details and clear edges. In order to meet the needs of cyberspace language life, communicative symbols must have sufficient self-sufficiency and adaptability. To achieve this, the dynamic communication network modeling is conducted. Through the application testing on the Qiang flute culture research from the perspective of music anthropology, the performance is tested to be efficient.
Independent Dual-Channel Approach to Mesoscopic Graphene Transistors

Graphene field-effect transistors (GFETs) exhibit unique switch and sensing features. In this article, GFETs are investigated within the tight-binding formalism, including quantum capacitance correction, where the graphene ribbons with reconstructed armchair edges are mapped into a set of independent dual channels through a unitary transformation. A new transfer matrix method is further developed to analyze the electron transport in each dual channel under a back gate voltage, while the electronic density of states of graphene ribbons with transversal dislocations are calculated using the retarded Green’s function and a novel real-space renormalization method. The Landauer electrical conductance obtained from these transfer matrices was confirmed by the Kubo–Greenwood formula, and the numerical results for the limiting cases were verified on the basis of analytical results. Finally, the size- and gate-voltage-dependent source-drain currents in GFETs are calculated, whose results are compared with the experimental data.
Markov-Based Malware Propagation Modeling and Analysis in Multi-Layer Networks

In this paper, we focus on the dynamics of the spread of malicious software (malware) in multi-layer networks of various types, e.g., cyber-physical systems. Recurring malware has been one of the major challenges in modern networks, and significant research and development has been dedicated to mitigating it. The majority of relevant works has focused on networks characterized by “flat” topologies, namely topologies in which all nodes consist of a single layer, studying the dynamics of propagation of a specific threat or various types of malware over a homogeneous topology. As cyber-physical systems and multi-layer networks in general are gaining in popularity and penetration, more targeted studies are needed. In this work, we focus on the propagation dynamics of recurring malware, namely Susceptible–Infected–Susceptible (SIS type) in multi-layer topologies consisting of combinations of two different types of networks, e.g., a small-world overlaying a random geometric, or other such combinations. We utilize a stochastic modeling framework based on Markov Random Fields for analyzing the propagation dynamics of malware over such networks. Through analysis and simulation, we discover the most vulnerable and the most robust topology among the six considered combinations, as well as a result of rather practical use, namely that the denser the network, the more flexibility it provides for malware mitigation eventually.
An approximation for random surfaces on arbitrary target spaces

A perturbative technique, the low-temperature expansion, is developed for matrix models of random surfaces. It can be applied to models with arbitrary target spaces, including ones with c>1. As a simple illustration, the series is worked out to 10th order for the surface coupled to a q-state Potts model. Accurate estimates for, e.g., $\gamma_{str}$ are obtained both in the low q (c<1) and high q (branched polymer) regimes, including the logarithmic corrections to scaling.
A Multi-scale Radar HRRP Target Recognition Method Based on Pyramid Depthwise Separable Convolution Network

Radar high resolution range profile (HRRP) contains important structural features such as target size and scattering center distribution, which has attracted extensive attention in the field of radar target recognition. In order to solve the problem of feature extraction and recognition in HRRP target recognition, we propose an HRRP target recognition method based on one-dimensional Pyramid Depthwise Separable Convolutional (PyDSC) neural network. For the processed data, pyramid convolution is selected, and convolution kernels of different sizes are used on different input channels, which can better extract the features of different scales and improve the overall recognition ability. At the same time, Depthwise Separable Convolution (DSC) technology is applied to PyConv network, a standard convolution operation is divided into two steps: deep convolution and point convolution, which can reduce the network complexity, reduce the amount of parameters and improve the speed of HRRP target recognition. Finally, we verify the effectiveness of the proposed method through experiments. The experimental results show that: 1) compared with the other three convolutional neural networks, our proposed PyDSC can significantly improve the recognition accuracy with a small increase in overhead; 2) Compared with the original PyConv, PyDSC can effectively reduce the complexity of the model.
Assessing the interdependence of social policy outcomes: evidence from European countries

ABSTRACT This paper investigates the existence of social policy interdependence in 22 European countries. We find that the outcomes of social policies achieved in a country are influenced by the social outcomes obtained in other countries; more precisely, there is a positive correlation between the level of welfare services in a country and a weighted average of the welfare services level in other countries, with the weights capturing the different degrees of proximity between them. The other determinants of social outcomes include the efficiency of the expenditure process, the GDP level, and the redistributive preferences of the median voter.
Comparative Metabolomics Analysis of Stigmas and Petals in Chinese Saffron (Crocus sativus) by Widely Targeted Metabolomics

The dried stigmas of Crocus sativus, commonly known as saffron, are consumed largely worldwide because it is highly valuable in foods and has biological activities beneficial for health. Saffron has important economic and medicinal value, and thus, its planting area and global production are increasing. Petals, which are a by-product of the stigmas, have not been fully utilized at present. We compared the metabolites between the stigmas and petals of C. sativus using a non-targeted metabolomics method. In total, over 800 metabolites were detected and categorized into 35 classes, including alkaloids, flavonoids, amino acids and derivatives, phenols and phenol esters, phenylpropanoids, fatty acyls, steroids and steroid derivatives, vitamins, and other metabolites. The metabolite composition in the petals and stigmas was basically similar. The results of the study showed that the petals contained flavonoids, alkaloids, coumarins, and other medicinal components, as well as amino acids, carbohydrates, vitamins, and other nutritional components. A principal components analysis (PCA) and an orthogonal partial least-squares discriminant analysis (OPLS-DA) were performed to screen the different metabolic components. A total of 339 differential metabolites were identified, with 55 metabolites up-regulated and 284 down-regulated. The up-regulated metabolites, including rutin, delphinidin-3-O-glucoside, isoquercitrin, syringaresinol-di-O-glucoside, dihydrorobinetin, quercetin, and gallocatechin, were detected in the petals. The down-regulated metabolites were mainly glucofrangulin B, acetovanillone, daidzein, guaiazulene, hypaphorine, indolin-2-one, and pseudouridine. KEGG annotation and enrichment analyses of the differential metabolites revealed that flavonoid biosynthesis, amino acids biosynthesis, and arginine and proline metabolism were the main differentially regulated pathways. In conclusion, the petals of C. sativus are valuable for medicine and foods and have potential utility in multiple areas such as the natural spice, cosmetic, health drink, and natural health product industries.
Investigando Fatores da Diversidade de Gênero nos Ecossistemas de Software

A diversidade tem sido abordada em estudos de Ecossistema de Software (ECOS) nos últimos anos, visando a integração de diferentes grupos nas organizações e comunidades. As suas várias formas de se manifestar têm requerido um melhor entendimento dos seus efeitos no ECOS. Foi observado por diversos autores que a diversidade de gênero ainda não está presente de forma significativa nos times de desenvolvimento, apesar do seu efeito positivo na produtividade das equipes. Este projeto de pesquisa de mestrado visa entender como a diversidade se manifesta no contexto de ECOS e quais os fatores que afetam a entrada, permanência e saída dos diferentes gêneros nos ECOS. Foi conduzido um mapeamento sistemático da literatura e serão executados dois estudos: um estudo longitudinal, para refinamento dos resultados, e um estudo de campo, com a finalidade de identificar e caracterizar os fatores e seus efeitos no ECOS. Como resultado, será criado um framework conceitual para fomentar a diversidade de gênero nos ECOS, contribuindo para o grande desafio “Sistemas de Informação e os desafios do mundo aberto”, do livro Grandes Desafios de Pesquisa em Sistemas de Informação no Brasil 2016-2026.
Learning to Solve Problems : A Handbook for Designing Problem-Solving Learning Environments

1. How Does Problem Solving Vary? Part I. Problem-Specific Design Models 2. Solving Story Problems 3. Decision Making 4. Troubleshooting/Diagnosis 5. Strategic Performance 6. Policy Analysis 7. Design Problem Solving Part II. Cases: The Building Blocks Problem-Solving Learning Environments 8. Cases as Problems to Solve 9. Cases as Worked Examples of Well-Structured Problems 10. Case Studies: Examples of Ill-Structured Problems 11. Cases as Analogues 12. Cases as Prior Experiences 13. Cases as Alternative Perspectives 14. Cases as Simulations Part III. Cognitive Skills in Problem-Solving 15. Defining the Problem: Problem Schemas 16. Analogically Comparing Problems 17. Understanding Causal Relationships in Problems 18. Questions for Scaffolding Problem Solving 19. Modeling Problems 20. Arguing to Learn to Solve Problems 21. Metacognitive Regulation of Problem Solving Part IV. Assessing Problem Solving 22. Assessing Problem Solving
Cross-Domain Leaf Counting with Minimizing Feature Distances

Plant phenotyping refers to quantitative description of the physical and chemical properties of a plant. As an important task for plant phenotyping, leaf count can be used to assess plant growth stages and health. The application of deep learning method in plant phenotype has made important contributions. However, most methods are supervised training, which need to annotate the target dataset for each task, and the cost of annotation is relatively expensive. Therefore, the method of using published or existing annotated datasets to complete cross domain work in the target dataset is worth studying. In this paper, a leaf counting method to minimize the feature distance between the source domain dataset and the target dataset is proposed. After training on the source domain dataset, the counting task can be completed directly on the target dataset. The improved Style-Transfer network module transfer source data to the target domain data, so that the features of the source dataset are close to the features of the target domain dataset. Finally, the density map estimation model is used to train on the generated fake dataset. Experimental results show that the proposed method has good cross-domain counting ability.