Miracle-dz/UniFAR-FTUs huggingface dataset:
In Situ Measurements of Strain Evolution in Graphene/Boron Nitride Heterostructures Using a Non-Destructive Raman Spectroscopy Approach

The mechanical properties of engineered van der Waals (vdW) 2D materials and heterostructures are critically important for their implementation into practical applications. Using a non-destructive Raman spectroscopy approach, this study investigates the strain evolution of single-layer graphene (SLGr) and few-layered boron nitride/graphene (FLBN/SLGr) heterostructures. The prepared 2D materials are synthesized via chemical vapor deposition (CVD) method and then transferred onto flexible polyethylene terephthalate (PET) substrates for subsequent strain measurements. For this study, a custom-built mechanical device-jig is designed and manufactured in-house to be used as an insert for the 3D piezoelectric stage of the Raman system. In situ investigation of the effects of applied strain in graphene detectable via Raman spectral data in characteristic bonds within SLGr and FLBN/SLGr heterostructures is carried out. The in situ strain evolution of the FLBN/SLGr heterostructures is obtained in the range of (0–0.5%) strain. It is found that, under the same strain, SLG exhibits a higher Raman shift in the 2D band as compared with FLBN/SLGr heterostructures. This research leads to a better understanding of strain dissipation in vertical 2D heterostacks, which could help improve the design and engineering of custom interfaces and, subsequently, control lattice structure and electronic properties. Moreover, this study can provide a new systematic approach for precise in situ strain assessment and measurements of other CVD-grown 2D materials and their heterostructures on a large scale for manufacturing a variety of future micro- and nano-scale devices on flexible substrates.
Random curves on surfaces induced from the Laplacian determinant

We define natural probability measures on cycle-rooted spanning forests (CRSFs) on graphs embedded on a surface with a Riemannian metric. These measures arise from the Laplacian determinant and depend on the choice of a unitary connection on the tangent bundle to the surface. We show that, for a sequence of graphs $(G_n)$ conformally approximating the surface, the measures on CRSFs of $G_n$ converge and give a limiting probability measure on finite multicurves (finite collections of pairwise disjoint simple closed curves) on the surface, independent of the approximating sequence. Wilson's algorithm for generating spanning trees on a graph generalizes to a cycle-popping algorithm for generating CRSFs for a general family of weights on the cycles. We use this to sample the above measures. The sampling algorithm, which relates these measures to the loop-erased random walk, is also used to prove tightness of the sequence of measures, a key step in the proof of their convergence. We set the framework for the study of these probability measures and their scaling limits and state some of their properties.
Distributed Asynchronous Algorithms for Solving Positive Definite Linear Equations over Dynamic Networks

This paper develops Subset Equalizing (SE), a distributed algorithm for solving a symmetric positive definite system of linear equations over a network of agents with arbitrary asynchronous interactions and membership dynamics, where each agent may join and leave the network at any time, for infinitely many times, and may lose all its memory upon leaving. To design and analyze SE, we introduce a time-varying Lyapunov-like function, defined on a state space with changing dimension, and a generalized concept of network connectivity, capable of handling such interactions and membership dynamics. Based on them, we establish the boundedness, asymptotic convergence, and exponential convergence of SE, along with a bound on its convergence rate. Finally, through extensive simulation, we demonstrate the effectiveness of SE in a volatile agent network and show that a special case of SE, termed Groupwise Equalizing, is significantly more bandwidth/energy efficient than two existing algorithms in multi-hop wireless networks.
Revealing the superfluid–Mott-insulator transition in an optical lattice

We study (by an exact numerical scheme) the single-particle density matrix of $\sim 10^3$ ultracold atoms in an optical lattice with a parabolic confining potential. Our simulation is directly relevant to the interpretation and further development of the recent pioneering experiment by Greiner et al. In particular, we show that restructuring of the spatial distribution of the superfluid component when a domain of Mott-insulator phase appears in the system, results in a fine structure of the particle momentum distribution. This feature may be used to locate the point of the superfluid--Mott-insulator transition.
Invitation to Fixed-Parameter Algorithms

PART I: FOUNDATIONS 1. Introduction to Fixed-Parameter Algorithms 2. Preliminaries and Agreements 3. Parameterized Complexity Theory - A Primer 4. Vertex Cover - An Illustrative Example 5. The Art of Problem Parameterization 6. Summary and Concluding Remarks PART II: ALGORITHMIC METHODS 7. Data Reduction and Problem Kernels 8. Depth-Bounded Search Trees 9. Dynamic Programming 10. Tree Decompositions of Graphs 11. Further Advanced Techniques 12. Summary and Concluding Remarks PART III: SOME THEORY, SOME CASE STUDIES 13. Parameterized Complexity Theory 14. Connections to Approximation Algorithms 15. Selected Case Studies 16. Zukunftsmusik References Index
Unsupervised Bayesian PET Reconstruction

Positron emission tomography (PET) reconstruction has become an ill-posed inverse problem due to low-count projection data, and a robust algorithm is urgently required to improve imaging quality. Recently, the deep image prior (DIP) has drawn much attention and has been successfully applied in several image restoration tasks, such as denoising and inpainting, since it does not need any labels (reference image). However, overfitting is a vital defect of this framework. Hence, many methods have been proposed to mitigate this problem, and DeepRED is a typical representation that combines DIP and regularization by denoising (RED). In this article, we leverage DeepRED from a Bayesian perspective to reconstruct PET images from a single corrupted sinogram without any supervised or auxiliary information. In contrast to the conventional denoisers customarily used in RED, a DnCNN-like denoiser, which can add an adaptive constraint to DIP and facilitate the computation of derivation, is employed. Moreover, to further enhance the regularization, Gaussian noise is injected into the gradient updates, deriving a Markov chain Monte Carlo (MCMC) sampler. Experimental studies on brain and whole-body datasets demonstrate that our proposed method can achieve better performance in terms of qualitative and quantitative results compared to several classic and state-of-the-art methods.
Autophagy Dysregulation in Metabolic Associated Fatty Liver Disease: A New Therapeutic Target

Metabolic associated fatty liver disease (MAFLD) is one of the most common causes of chronic liver disease worldwide. To date, there is no FDA-approved treatment, so there is an urgent need to determine its pathophysiology and underlying molecular mechanisms. Autophagy is a lysosomal degradation pathway that removes damaged organelles and misfolded proteins after cell injury through endoplasmic reticulum stress or starvation, which inhibits apoptosis and promotes cell survival. Recent studies have shown that autophagy plays an important role in removing lipid droplets from hepatocytes. Autophagy has also been reported to inhibit the production of pro-inflammatory cytokines and provide energy for the hepatic stellate cells activation during liver fibrosis. Thyroid hormone, irisin, melatonin, hydrogen sulfide, sulforaphane, DA-1241, vacuole membrane protein 1, nuclear factor erythroid 2-related factor 2, sodium-glucose co-transporter type-2 inhibitors, immunity-related GTPase M, and autophagy-related gene 7 have been reported to ameliorate MAFLD via autophagic induction. Lipid receptor CD36, SARS-CoV-2 Spike protein and leucine aminopeptidase 3 play a negative role in the autophagic function. This review summarizes recent advances in the role of autophagy in MAFLD. Autophagy modulates major pathological changes, including hepatic lipid metabolism, inflammation, and fibrosis, suggesting the potential of modulating autophagy for the treatment of MAFLD.
Computational and Theoretical Investigations of Strongly Correlated Fermions in Optical Lattices

Abstract : This proposal brings together unique expertise in Quantum Monte Carlo and analytical techniques to address problems that are at the cutting edge of research in strongly correlated atomic fermions in optical lattices. (1) For fermions with attractive interactions, the possibility of a pseudogap phase, that shows signatures of pairing but no long range phase coherence, in an unequal population of up and down fermions is investigated. (2) For fermions with repulsive interactions, the evidence for a d-wave superfluid ground state is analysed, especially its competition with spin liquid and antiferromagnetic phases. Progress on both of these questions will impact not only the fields of cold atoms and condensed matter of strongly correlated systems, but also complex transition metal oxides. Issues related to thermometry, inhomogeneous trap potentials and effects of thermal and quantum phase fluctuations, that are central for an accurate interpretation of experiments, are investigated. A combination of variational, Green function and determinantal quantum Monte Carlo simulations, Bogoliubov deGennes theory, and functional integral methods will be implemented to address the variety of issues highlighted above. The detailed comparisons between simulations, theory and experiments is crucial for validation of the results. These methods are essential to calculate properties in regimes where standard paradigms break down and the phenomena are non-perturbative. The proposed research program is strongly motivated by current experiments and has the potential to guide future ones. Ultimately, the goal is to establish new paradigms for strongly correlated fermion systems.
Effect of Cu Modified Textile Structures on Antibacterial and Antiviral Protection

Textile structures with various bioactive and functional properties are used in many areas of medicine, special clothing, interior textiles, technical goods, etc. We investigated the effect of two different textile woven structures made of 90% polyester with 10% polyamide (PET) and 100% cotton (CO) modified by magnetron sputtering with copper (Cu) on bioactive properties against Gram-positive and Gram-negative bacteria and four viruses and also on the some comfort parameters. PET/Cu and CO/Cu fabrics have strong antibacterial activity against Staphylococcus aureus and Klebsiella pneumonia. CO/Cu fabric has good antiviral activity in relation to vaccinia virus (VACV), herpes simplex virus type 1 (HSV-1) and influenza A virus H1N1 (IFV), while its antiviral activity against mouse coronavirus (MHV) is weak. PET/Cu fabric showed weak antiviral activity against HSV-1 and MHV. Both modified fabrics showed no significant toxicity in comparison to the control medium and pristine fabrics. After Cu sputtering, fabric surfaces became hydrophobic and the value of the surface free energy was over four times lower than for pristine fabrics. The modification improved thermal conductivity and thermal diffusivity, facilitated water vapour transport, and air permeability did not decrease.
Mapping of Urbanized Territories Noise Level as a Basis for Developing a Complex of Noise-Reducing Measures

In modern conditions of urban areas development, the importance of protection against noise is increasing. Measures to reduce noise levels in the city’s districts should be comprehensive and combine both organizational and architectural-construction and engineering-technological measures. To determine the most optimal combination of countermeasures, it is necessary to initially identify the sources of noise, determine their features and parameters. A complete basis for development of a set of noise reduction methods and means can be mapping of noise levels, which visually allows to determine the intensity and direction of sound spreading. One of the districts of Zhytomyr, which is isolated from other parts of the city and is characterized by a developed infrastructure and heavy traffic, was chosen for noise measurements. Measurements were performed in areas characterized by uniformity of placement, coverage of all functional areas and ability to measure the noise level both at the source of its formation and within the residential area. Based on statistical processing of the experimental data cartographic models of spatial spread of noise pollution are developed. According to the results of the research, zones with different levels of noise pollution were identified. The created map allows to choose the most optimal measures to reduce noise exposure, taking into account the characteristics of each residential area. The design noise level was determined taking into account the measures and noise map was created. The estimated efficiency of the developed measures allows to reduce the total noise level inside the district by 7–8 dB.
GRB 090510: A DISGUISED SHORT GAMMA-RAY BURST WITH THE HIGHEST LORENTZ FACTOR AND CIRCUMBURST MEDIUM

GRB 090510, observed by both Fermi and AGILE satellites, is the first bright short-hard gamma-ray burst (GRB) with an emission from the keV up to the GeV energy range. Within the Fireshell model, we interpret the faint precursor in the light curve as the emission at the transparency of the expanding e+e− plasma: the Proper-GRB. From the observed isotropic energy, we assume a total plasma energy erg and derive a Baryon load B = (1.45 ± 0.28) × 10−3 and a Lorentz factor at transparency Γtr = (6.7 ± 1.6) × 102. The main emission ∼0.4 s after the initial spike is interpreted as the extended afterglow, due to the interaction of the ultrarelativistic baryons with the CircumBurst Medium (CBM). Using the condition of fully radiative regime, we infer a CBM average spherically symmetric density of 〈nCBM〉 = (1.85 ± 0.14) × 103 particles cm−3, one of the highest found in the Fireshell model. The value of the filling factor, , leads to the estimate of filaments with densities particles cm−3. The sub-MeV and the MeV emissions are well reproduced. When compared to the canonical GRBs with 〈nCBM〉 ≈ 1 particles cm−3 and to the disguised short GRBs with 〈nCBM〉 ≈ 10−3 particles cm−3, the case of GRB 090510 leads to the existence of a new family of bursts exploding in an overdense galactic region with 〈nCBM〉 ≈ 103 particles cm−3. The joint effect of the high Γtr and the high density compresses in time and “inflates” in intensity the extended afterglow, making it appear as a short burst, which we here define as a “disguised short GRB by excess. ” The determination of the above parameter values may represent an important step toward the explanation of the GeV emission.
Acronym identification based on SpanBERT-CRF

Acronym Identification means to recognizing acronyms and their definitions in a given paragraph by an algorithm. Existing studies have shown that the pre-training model has significant performance advantages in acronyms recognition. However, the vanilla Bert measures randomly masking tokens in the pre-training, which may also abbreviate the token of acronyms in sentence. The continuous masking method will be more helpful to the recognition and interpretation of acronyms. In this paper, we propose to use the SpanBERT model for acronym recognition and obtaining the transfer relationship between indicators with the help of a conditional random field (CRF). Through experimental comparison, the results demonstrate that our method increases the F1 value compared with the Bert and BERT-CRF models.
Machine Learning Human Behavior Detection Mechanism Based on Python Architecture

Human behavior is stimulated by the outside world, and the emotional response caused by it is a subjective response expressed by the body. Humans generally behave in common ways, such as lying, sitting, standing, walking, and running. In real life of human beings, there are more and more dangerous behaviors in human beings due to negative emotions in family and work. With the transformation of the information age, human beings can use Industry 4.0 smart devices to realize intelligent behavior monitoring, remote operation, and other means to effectively understand and identify human behavior characteristics. According to the literature survey, researchers at this stage analyze the characteristics of human behavior and cannot achieve the classification learning algorithm of single characteristics and composite characteristics in the process of identifying and judging human behavior. For example, the characteristic analysis of changes in the sitting and sitting process cannot be for classification and identification, and the overall detection rate also needs to be improved. In order to solve this situation, this paper develops an improved machine learning method to identify single and compound features. In this paper, the HATP algorithm is first used for sample collection and learning, which is divided into 12 categories by single and composite features; secondly, the CNN convolutional neural network algorithm dimension, recurrent neural network RNN algorithm, long- and short-term extreme value network LSTM algorithm, and gate control is used. The ring unit GRU algorithm uses the existing algorithm to design the model graph and the existing algorithm for the whole process; thirdly, the machine learning algorithm and the main control algorithm using the proposed fusion feature are used for HATP and human beings under the action of wearable sensors. The output features of each stage of behavior are fused; finally, by using SPSS data analysis and re-optimization of the fusion feature algorithm, the detection mechanism achieves an overall target sample recognition rate of about 83.6%. Finally, the research on the algorithm mechanism of machine learning for human behavior feature classification under the new algorithm is realized.
The Relationship Between Individuals’ Expenditure and the Happiness Index Level in China

The GDP per capita and household consumption increased fourfold between 1990 and 2005, and life satisfaction levels plummeted. Also, in 2019, China’s Engel’s Coefficient was 28.2% indicating that China has achieved the “wealthy” level in living standards. However, in the same year, China only ranked 86 out of 156 countries on the UN World Happiness Report placing below Russia and even war-torn Libya. Therefore, this paper will include problems that occurred in the housing, healthcare, and education industry with the analysis of corresponding government policies in raising the residents’ well-being and welfare. The solutions proposed by the government in the three industries have been implemented and have effectively alleviated the problem. However, in some cases, the happiness of residents has not been improved. Therefore, if we want to improve the residents' happiness index, we need to coordinate in many ways to solve the problem fundamentally.
Terrain reconstruction from Chang'e-3 PCAM images

The existing terrain models that describe the local lunar surface have limited resolution and accuracy, which can hardly meet the needs of rover navigation, positioning and geological analysis. China launched the lunar probe Chang'e-3 in December, 2013. Chang'e-3 encompassed a lander and a lunar rover called “Yutu” (Jade Rabbit). A set of panoramic cameras were installed on the rover mast. After acquiring panoramic images of four sites that were explored, the terrain models of the local lunar surface with resolution of 0.02m were reconstructed. Compared with other data sources, the models derived from Chang'e-3 data were clear and accurate enough that they could be used to plan the route of Yutu.
An optimistic future of C4 crop broomcorn millet (Panicum miliaceum L.) for food security under increasing atmospheric CO2 concentrations

Broomcorn millet, a C4 cereal, has better tolerance to environmental stresses. Although elevated atmospheric CO2 concentration has led to grain nutrition reduction in most staple crops, studies evaluating its effects on broomcorn millet are still scarce. The yield, nutritional quality and metabolites of broomcorn millet were investigated under ambient CO2 (aCO2, 400 µmol mol–1) and elevated CO2 (eCO2, aCO2+ 200 µmol mol–1) for three years using open-top chambers (OTC). The results showed that the yield of broomcorn millet was markedly increased under eCO2 compared with aCO2. On average, eCO2 significantly increased the concentration of Mg (27.3%), Mn (14.6%), and B (21.2%) over three years, whereas it did not affect the concentration of P, K, Fe, Ca, Cu or Zn. Protein content was significantly decreased, whereas starch and oil concentrations were not changed by eCO2. With the greater increase in grain yield, eCO2 induced increase in the grain accumulations of P (23.87%), K (29.5%), Mn (40.08%), Ca (22.58%), Mg (51.31%), Zn (40.95%), B (48.54%), starch (16.96%) and oil (28.37%) on average for three years. Flavonoids such as kaempferol, apigenin, eriodictyol, luteolin, and chrysoeriol were accumulated under eCO2. The reduction in L-glutamine and L-lysine metabolites, which were the most representative amino acid in grain proteins, led to a reduction of protein concentration under eCO2. Broomcorn millet has more desirable nutritional traits for combating hidden hunger. This may potentially be useful for breeding more nutritious plants in the era of climate change.
Remembering Odors and Their Names

It is generally believed that odor memory is excellent, but its strengths and weaknesses compared with visual or auditory memory are not well appreciated. What odor perception does best, the basis for its reputation, is to recreate significant past episodes in a person's life. The French novelist Marcel Proust has provided the classic example, describing at the beginning of his masterpiece Remembrance of Things Past how the aroma and flavor of a morsel of petite madeleine soaked in a spoonful of tea brought back long-forgotten memories of his aunt's bedroom in the family's house at Combray: "When from a long-distant past nothing subsists, after the people are dead, after the things are broken and scattered, still alone, more fragile, but with more vitality, more unsub stantial, more persistent, more faith ful, the smell and taste of things . bear unfaltering, in the tiny and al most impalpable drop of their es sence, the vast structure of recollec tion. "
Plateau pikas near roads are bold and silent when facing a potential predator

Abstract Human disturbance, particularly road traffic, is one of the greatest threats to wildlife. Considering the association between alerting behavior and the survival of animals, it is important to study the effects of road traffic on alerting behavior of wildlife. Previous studies assessing the short-term impact of road traffic on alerting behavior of wildlife have focused on vigilance distances. However, studies on the use of alarm calls are scarce, and it is unclear whether such behavioral responses change after repeated exposure to road traffic. We assessed the alerting behavior of plateau pikas (Ochotona curzoniae) who were near or far from roads when facing a potential predator. We found that pikas near roads exhibited shorter vigilance and tolerance distances, and produced fewer alarm calls than those relatively far away from roads. Furthermore, both vigilance and tolerance distances of plateau pikas were significantly positively correlated with the distance from the burrow to the road. Road traffic reduced antipredator responses and shaped alerting behavior; that is, pikas near roads were bolder and more silent compared to those far away from roads. Our findings suggest that increasing urbanization will have corresponding effects on animal behavior, which may have significant fitness effects in the future.