Miracle-dz/UniFAR-FTUs huggingface dataset:
Radiative hydrodynamic simulations of the spectral characteristics of solar white-light flares

As one of the most violent activities in the solar atmosphere, white-light flares (WLFs) are generally known for their enhanced white-light (or continuum) emission, which primarily originates in the solar lower atmosphere. However, we know little about how white-light emission is produced. In this study, we aim to investigate the response of the continua at 3600 Å and 4250 Å and also the Hα and Lyα lines during WLFs modeled using radiative hydrodynamic simulations. We take non-thermal electron beams as the energy source for the WLFs in two different initial atmospheres and vary their parameters. Our results show that the model with non-thermal electron beam heating clearly shows enhancements in the continua at 3600 Å and 4250 Å as well as in the Hα and Lyα lines. A larger electron beam flux, a smaller spectral index, or an initial penumbral atmosphere leads to a stronger emission increase at 3600 Å, 4250 Å and in the Hα line. The Lyα line, however, is more obviously enhanced in a quiet-Sun initial atmosphere with a larger electron beam spectral index. It is also notable that the continua at 3600 Å and 4250 Å and the Hα line exhibit a dimming at the start of heating and reach their peak emissions after the peak time of the heating function, while the Lyα line does not show such behaviors. These results can serve as a reference for the analysis of future WLF observations.
A Case Study on Video Color Transfer: Exploring User Motivations, Expectations, and Satisfaction

Multimedia and creativity software products are being used to edit and control various elements of creative media practices. These days, the technical affordances of mobile multimedia devices and the advent of high-speed 5G internet access mean that these abilities are simpler and more readily available to be harnessed by mobile applications. In this paper, using a prototype application, we discuss how potential users of such technology are motivated to use a video recoloring application and explore the role that user expectation and satisfaction play in this process. By exploring this topic and focusing on the human-computer interaction, we found that color transfer interactions are driven by several intrinsic motivations and that user expectations and satisfaction ratings can be maintained via clear visualizations of the processes to be undertaken. Furthermore, we reveal the specific language that users use to communicate video recoloring when regarding user motivations, expectations, and satisfaction. This research provides important information for developers of state-of-art recoloring processes and contributes to dialogues surrounding the users of mobile multimedia technology in practice.
Overview of Security of Virtual Mobile Networks

5G is enabling different services over the same physical infrastructure through the concepts and technologies of virtualization, softwarization, slicing and cloud computing. Virtual Mobile Networks (VMNs), using these concepts, provide an opportunity to share the same physical infrastructure among multiple operators. Each VMN Operator (VMNO) can have own distinct operating and support systems. However, the technologies used to enable VMNs have their own explicit security challenges and solutions. The integrated environment built upon virtualization, softwarization, and cloudification, thus, will have complex security requirements and implications. In this vain, this article provides an overview of the security challenges and potential solutions for VMNs.
AE-Net: Autonomous Evolution Image Fusion Method Inspired by Human Cognitive Mechanism

In order to solve the robustness and generality problems of the image fusion task,inspired by the human brain cognitive mechanism, we propose a robust and general image fusion method with autonomous evolution ability, and is therefore denoted with AE-Net. Through the collaborative optimization of multiple image fusion methods to simulate the cognitive process of human brain, unsupervised learning image fusion task can be transformed into semi-supervised image fusion task or supervised image fusion task, thus promoting the evolutionary ability of network model weight. Firstly, the relationship between human brain cognitive mechanism and image fusion task is analyzed and a physical model is established to simulate human brain cognitive mechanism. Secondly, we analyze existing image fusion methods and image fusion loss functions, select the image fusion method with complementary features to construct the algorithm module, establish the multi-loss joint evaluation function to obtain the optimal solution of algorithm module. The optimal solution of each image is used to guide the weight training of network model. Our image fusion method can effectively unify the cross-modal image fusion task and the same modal image fusion task, and effectively overcome the difference of data distribution between different datasets. Finally, extensive numerical results verify the effectiveness and superiority of our method on a variety of image fusion datasets, including multi-focus dataset, infrared and visi-ble dataset, medical image dataset and multi-exposure dataset. Comprehensive experiments demonstrate the superiority of our image fusion method in robustness and generality. In addition, experimental results also demonstate the effectiveness of human brain cognitive mechanism to improve the robustness and generality of image fusion.
Seed mass, habitat and life history: a re-analysis of Salisbury (1942, 1974)

A recent re-analysis of the data of Salisbury (1974) claims his data do not support the hypothesis that seeds of species from shaded habitats are heavier than those from unshaded habitats, partly because the original analysis was inappropriate and partly because of bias in the dataset. We show first that the re-analysis itself contains errors, and second that the charge of bias is based largely on a misunderstanding. We also show that analysis of a larger dataset, drawn from Salisbury (1942) and from Salisbury (1974), provides convincing support for the hypothesis and suggests that the relationship is independent of life history.
Augmented Understanding and Automated Adaptation of Curation Rules

Over the past years, there has been many efforts to curate and increase the added value of the raw data. Data curation has been defined as activities and processes an analyst undertakes to transform the raw data into contextualized data and knowledge. Data curation enables decision-makers and data analyst to extract value and derive insight from the raw data. However, to curate the raw data, an analyst needs to carry out various curation tasks including, extraction linking, classification, and indexing, which are error-prone, tedious and challenging. Besides, deriving insight require analysts to spend a long period of time to scan and analyze the curation environments. This problem is exacerbated when the curation environment is large, and the analyst needs to curate a varied and comprehensive list of data. To address these challenges, in this dissertation, we present techniques, algorithms and systems for augmenting analysts in curation tasks. We propose: ~(1) a feature-based and automated technique for curating the raw data. ~(2) We propose an autonomic approach for adapting data curation rules. ~(3) We provide a solution to augment users in formulating their preferences while curating data in large scale information spaces. ~(4) We implement a set of APIs for automating the basic curation tasks, including Named Entity extraction, POS tags, classification, and etc.
Discounted Optimal Stopping of a Brownian Bridge, with Application to American Options under Pinning

Mathematically, the execution of an American-style financial derivative is commonly reduced to solving an optimal stopping problem. Breaking the general assumption that the knowledge of the holder is restricted to the price history of the underlying asset, we allow for the disclosure of future information about the terminal price of the asset by modeling it as a Brownian bridge. This model may be used under special market conditions, in particular we focus on what in the literature is known as the “pinning effect”, that is, when the price of the asset approaches the strike price of a highly-traded option close to its expiration date. Our main mathematical contribution is in characterizing the solution to the optimal stopping problem when the gain function includes the discount factor. We show how to numerically compute the solution and we analyze the effect of the volatility estimation on the strategy by computing the confidence curves around the optimal stopping boundary. Finally, we compare our method with the optimal exercise time based on a geometric Brownian motion by using real data exhibiting pinning.
Dynamics after quenches in one-dimensional quantum Ising-like systems

We study the out-of-equilibrium dynamics of one-dimensional quantum Ising-like systems, arising from sudden quenches of the Hamiltonian parameter $g$ driving quantum transitions between disordered and ordered phases. In particular, we consider quenches to values of $g$ around the critical value $g_c$, and mainly address the question whether, and how, the quantum transition leaves traces in the evolution of the transverse and longitudinal magnetizations during such a deep out-of-equilibrium dynamics. We shed light on the emergence of singularities in the thermodynamic infinite-size limit, likely related to the integrability of the model. Finite systems in periodic and open boundary conditions develop peculiar power-law finite-size scaling laws related to revival phenomena, but apparently unrelated to the quantum transition, because their main features are generally observed in quenches to generic values of $g$. We also investigate the effects of dissipative interactions with an environment, modeled by a Lindblad equation with local decay and pumping dissipation operators within the quadratic fermionic model obtainable by a Jordan-Wigner mapping. Dissipation tends to suppress the main features of the unitary dynamics of closed systems. We finally address the effects of integrability breaking, due to further lattice interactions, such as in anisotropic next-to-nearest neighbor Ising (ANNNI) models. We show that some qualitative features of the post-quench dynamics persist, in particular the different behaviors when quenching to quantum ferromagnetic and paramagnetic phases, and the revival phenomena due to the finite size of the system.
Tight Frame Graphs Arising as Line Graphs

Dual multiplicity graphs are those simple, undirected graphs that have a weighted Hermitian adjacency matrix with only two distinct eigenvalues. From the point of view of frame theory, their characterization can be restated as which graphs have a representation by a tight frame. In this paper, we classify certain line graphs that are tight frame graphs and improve a previous result on the embedding of frame graphs in tight frame graphs.
Super-Resolution Remote Imaging using Time Encoded Remote Apertures

Imaging of scenes using light or other wave phenomena is subject to the diffraction limit. The spatial profile of a wave propagating between a scene and the imaging system is distorted by diffraction resulting in a loss of resolution that is proportional with traveled distance. We show here that it is possible to reconstruct sparse scenes from the temporal profile of the wave-front using only one spatial pixel or a spatial average. The temporal profile of the wave is not affected by diffraction yielding an imaging method that can in theory achieve wavelength scale resolution independent of distance from the scene.
Toward a valid instrument for measuring physics quantitative literacy

We have developed the Physics Inventory of Quantitative Literacy (PIQL) as a tool to measure students' quantitative literacy in the context of introductory physics topics. We present the results from various quantitative analyses used to establish the validity of both the individual items and the PIQL as a whole. We show how examining the results from classical test theory analyses, factor analysis, and item response curves informed decisions regarding the inclusion, removal, or modification of items. We also discuss how the choice to include multiple-choice/multiple-response items has informed both our choices for analyses and the interpretations of their results. We are confident that the most recent version of the PIQL is a valid and reliable instrument for measuring students' physics quantitative literacy in calculus-based introductory physics courses at our primary research site. More data are needed to establish its validity for use at other institutions and in other courses.
Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation

Deep generative models are commonly used for generating images and text. Interpretability of these models is one important pursuit, other than the generation quality. Variational auto-encoder (VAE) with Gaussian distribution as prior has been successfully applied in text generation, but it is hard to interpret the meaning of the latent variable. To enhance the controllability and interpretability, one can replace the Gaussian prior with a mixture of Gaussian distributions (GM-VAE), whose mixture components could be re-lated to hidden semantic aspects of data. In this paper, we generalize the practice and introduce DEM-VAE, a class of models for text generation using VAEs with a mixture distribution of exponential family. Unfortunately, a standard variational training algorithm fails due to the mode-collapse problem. We theoretically identify the root cause of the problem and propose an effective algorithm to train DEM-VAE. Our method penalizes the training with an extra dispersion term to induce a well-structured latent space. Experimental results show that our approach does obtain a meaningful space, and it outperforms strong baselines in text generation benchmarks. The code is available at https: //github.com/wenxianxian/demvae .
Quantum Entanglement and Cryptography for Automation and Control of Dynamic Systems

This paper addresses the application of quantum entanglement and cryptography for automation and control of dynamic systems. A dynamic system is a system where the rates of changes of its state variables are not negligible. Quantum entanglement is realized by the Spontaneous Parametric Down-conversion process. Two entangled autonomous systems exhibit correlated behavior without any classical communication in between them due to the quantum entanglement phenomenon. Specifically, the behavior of a system, Bob, at a distance, is correlated with a corresponding system, Alice. In an automation scenario, the 'Bob Robot' is entangled with the 'Alice Robot' in performing autonomous tasks without any classical connection between them. Quantum cryptography is a capability that allows guaranteed security. Such capabilities can be implemented in control of autonomous mechanical systems where, for instance, an 'Alice Autonomous System' can control a 'Bob Autonomous System' for applications of automation and robotics. The applications of quantum technologies to mechanical systems, at a scale larger than the atomistic scale, for control and automation, is a novel contribution of this paper. Notably, the feedback control transfer function of an integrated classical dynamic system and a quantum state is proposed.
MCSAE: Masked Cross Self-Attentive Encoding for Speaker Embedding

In general, a self-attention mechanism has been applied for speaker embedding encoding. Previous studies focused on training the self-attention in a high-level layer, such as the last pooling layer. However, the effect of low-level features was reduced in the speaker embedding encoding. Therefore, we propose masked cross self-attentive encoding (MCSAE) using ResNet. It focuses on the features of both high-level and lowlevel layers. Based on multi-layer aggregation, the output features of each residual layer are used for the MCSAE. In the MCSAE, cross self-attention module is trained the interdependence of each input features. A random masking regularization module also applied to preventing overfitting problem. As such, the MCSAE enhances the weight of frames representing the speaker information. Then, the output features are concatenated and encoded to the speaker embedding. Therefore, a more informative speaker embedding is encoded by using the MCSAE. The experimental results showed an equal error rate of 2.63% and a minimum detection cost function of 0.1453 using the VoxCeleb1 evaluation dataset. These were improved performances compared with the previous self-attentive encoding and state-of-the-art encoding methods.
Imitation Learning Based on Bilateral Control for Human–Robot Cooperation

Robots are required to autonomously respond to changing situations. Imitation learning is a promising candidate for achieving generalization performance, and extensive results have been demonstrated in object manipulation. However, cooperative work between humans and robots is still a challenging issue because robots must control dynamic interactions among themselves, humans, and objects. Furthermore, it is difficult to follow subtle perturbations that may occur among coworkers. In this study, we find that cooperative work can be accomplished by imitation learning using bilateral control. Thanks to bilateral control, which can extract response values and command values independently, human skills to control dynamic interactions can be extracted. Then, the task of serving food is considered. The experimental results clearly demonstrate the importance of force control, and the dynamic interactions can be controlled by the inferred action force.
The Completed SDSS-IV Extended Baryon Oscillation Spectroscopic Survey: Growth rate of structure measurement from cosmic voids

We present a void clustering analysis in configuration-space using the completed Sloan Digital Sky Survey IV (SDSS-IV) extended Baryon Oscillation Spectroscopic Survey (eBOSS) DR16 samples. These samples consist of Luminous Red Galaxies (LRG) combined with the high redshift tail of the SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS) DR12 CMASS galaxies (called as LRG+CMASS sample), Emission Line Galaxies (ELG) and quasars (QSO). We build void catalogues from the three eBOSS DR16 samples using a ZOBOV-based algorithm, providing 2,814 voids, 1,801 voids and 4,347 voids in the LRG+CMASS, ELG and QSO samples, respectively, spanning the redshift range $0.6<z<2.2$. We measure the redshift space distortions (RSD) around voids using the anisotropic void-galaxy cross-correlation function and we extract the distortion parameter $\beta$. We test the methodology on realistic simulations before applying it to the data, and we investigate all our systematic errors on these mocks. We find $\beta^{\rm LRG}(z=0.74)=0.415\pm0.087$, $\beta^{\rm ELG}(z=0.85)=0.665\pm0.125$ and $\beta^{\rm QSO}(z=1.48)=0.313\pm0.134$, for the LRG+CMASS, ELG and QSO sample, respectively. The quoted errors include systematic and statistical contributions. In order to convert our measurements in terms of the growth rate $f\sigma_8$, we use consensus values of linear bias from the eBOSS DR16 companion papers~\citep{eBOSScosmo}, resulting in the following constraints: $f\sigma_8(z=0.74)=0.50\pm0.11$, $f\sigma_8(z=0.85)=0.52\pm0.10$ and $f\sigma_8(z=1.48)=0.30\pm0.13$. Our measurements are consistent with other measurements from eBOSS DR16 using conventional clustering techniques.
Upscaling and spatial localization of non-local energies with applications to crystal plasticity

We describe multiscale geometrical changes via structured deformations ( g , G ) and the non-local energetic response at a point x via a function Ψ of the weighted averages of the jumps [ u n ] ( y ) of microlevel deformations u n at points y within a distance r of x. The deformations u n are chosen so that lim n → ∞ u n = g and lim n → ∞ ∇ u n = G . We provide conditions on Ψ under which the upscaling “ n → ∞ ” results in a macroscale energy that depends through Ψ on (1) the jumps [ g ] of g and the “disarrangement field” ∇ g − G , (2) the “horizon”r, and (3) the weighting function α r for microlevel averaging of [ u n ] ( y ) . We also study the upscaling “ n → ∞ ” followed by spatial localization “ r → 0 ” and show that this succession of processes results in a purely local macroscale energy I ( g , G ) that depends through Ψ upon the jumps [ g ] of g and the “disarrangement field” ∇ g − G alone. In special settings, such macroscale energies I ( g , G ) have been shown to support the phenomena of yielding and hysteresis, and our results provide a broader setting for studying such yielding and hysteresis. As an illustration, we apply our results in the context of the plasticity of single crystals.
The clustering of the SDSS-IV extended baryon oscillation spectroscopic survey DR16 luminous red galaxy and emission-line galaxy samples: cosmic distance and structure growth measurements using multiple tracers in configuration space

We perform a multi-tracer analysis using the complete Sloan Digital Sky Survey IV (SDSS-IV) extended Baryon Oscillation Spectroscopic Survey (eBOSS) DR16 luminous red galaxy (LRG) and the DR16 emission line galaxy (ELG) samples in the configuration space, and successfully detect a cross correlation between the two samples, and find $f\sigma_8=0.342 \pm 0.085$ ($\sim25$ per cent accuracy) from cross sample alone. We perform a joint measurement of the baryonic acoustic oscillations (BAO) and redshift space distortions (RSD) parameters at a single effective redshift of $z_{\rm eff}= 0.77$, using the auto- and cross-correlation functions of the LRG and ELG samples, and find that $D_M(z_{\rm eff})/r_d = 18.93\pm 0.37, \ D_H(z_{\rm eff})/r_d = 19.65 \pm 0.54$, and $f\sigma_8(z_{\rm eff}) = 0.445 \pm 0.038$, which is consistent with a $\Lambda$CDM model at $68\%$ CL. Compared to the single-tracer analysis on the LRG sample, the Figure of Merit (FoM) of $\alpha_{\perp}, \alpha_{||}$ and $f\sigma_8$ gets improved by a factor of $1.18$ in our multi-tracer analysis, and in particular, the statistical uncertainty of $f\sigma_8$ is reduced by $11.6 \%$.
Clinical and molecular diagnostic study of 320 Chinese children with epilepsy by Genome Sequencing

Purpose: To evaluate the diagnostic value of Genome Sequencing(GS) in children with epilepsy. Methods: We performed GS on 320 Chinese children with epilepsy and interpreted Single Nucleotide Variants (SNVs) and Copy Number Variant (CNVs) of all samples. The complete pedigree and clinical data of the probands were established and followed up. The clinical phenotypes, treatments, prognoses and genotypes of the patients were analyzed. Results: Pathogenic/likely pathogenic variants were found in 122 of 320 children (38.13%), of whom, 92 (28.8%) had SNVs, 27 (8.4%) had CNVs, and three had both SNVs and CNVs. Among these variants, there were 3 CNVs of <100K in length. The most frequently mutated gene was SCN1A (10.9%,10/92), which is related to Dravet Syndrome, followed by PRRT2 (8.7%,8/92), which is relevant to benign familial infantile epilepsy, TSC2 (7.6%,7/92), which is associated with Tuberous Sclerosis. The most common recurrent CNVs were 17p13.3 deletion (18.5%, 5/27), 16p11.2 deletion syndrome (14.8%, 4/27), 15q11.2 deletion (11.1%, 3/27), which are related to epilepsy, developmental retardation and congenital abnormalities. The diagnostic yield was higher as the age of seizure onset was smaller. The highest detection rate was 75% in whom developed seizures within one month after birth. 13.4% (43/320) cases were identified to be treatable based on GS. 1% (3/320) of epilepsy patients received direct therapeutic measures and demonstrated favorable prognosis. Conclusion: GS can complete the genetic diagnosis, individualized treatment, and family reproductive guidance for patients. GS can replace Exome Sequencing and Chromosomal Microarray Analysis and is expected to be the first choice of genetic testing method for patients with epilepsy. Key Words: epilepsy; Genome Sequencing; Copy Number Variant; seizure; Single Nucleotide Variations