Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic evaluation of Andrographis paniculata (Burm. f.) Nees revealed by SSR, AFLP and RAPD markers

Andrographis paniculata (Burm. f.) Nees is a medicinal plant widely used in Thailand and many countries for therapeutic value. The present study was aimed to investigate its genotypic variations in various locations of Thailand based on simple sequence repeats (SSR), amplified fragment length polymorphism (AFLP) and random amplified polymorphic dna (RAPD) marker system. SSR, AFLP and RAPD were compared in terms of their informativeness and efficiency in a study of genentic diversity and relationships among 58 samples of A. paniculata in Thailand. All three techniques presented highly correlated results, with a correlation coefficient of r = 0.996. The low values of expected heterozygosity were obtained for all marker systems due to their capacity to reveal the low number of polymorphic bands in each markers system. All marker systems also gave very high genetic similarity values, which represent the values that are higher than 0.9; the highest is SSR (0.989) followed by AFLP (0.964) and RAPD (0.905), respectively. The cophenetic correlation coefficients of similarity were statistically significant (r = 0.996, p < 0.001) thus implying the correspondence for all combination of three marker systems. The results obtained indicated that the distribution of A. paniculata among each region of Thailand are likely to belong to the same variety and are relatively undifferentiated across a large geographic rang. Key words: Andrographis paniculata, SSR, AFLP, RAPD, genetic similarities.
A Survey on Secure Two-Level QR Code for HybridDocument Sharing and Authentication Using Steganography

: The QR code was intended for storage data and fast reading applications. The proposed QR code authentication two-level storage is used for to verify original content in QR code. Our proposed work uses public and private storage level of document storage. In the public level similar standard QR code storage level is elaborated; which can be readable to any QR code scanner device. The private level is constructed by replacing the black modules by specific textured patterns. It consists of information encoded using q-ary code with an error correction capacity. Q-array code will increase the storage capacity of the QR code, but also to verify the original document from a copy. This authentication is due to the sensitivity of the used patterns to the print-and-scan process. The novel approach in the pattern recognition method that applied to read the second-level information can be used both in a private message sharing and in a document authentication scenario. Third, the reversible capability inherited from our scheme provides performance which allows recovery of the source texture pattern. We introduce texture synthesis process into steganography for hiding secret in image. The outcomes about demonstrate a perfect restoration of private data. It additionally features the likelihood of utilizing this new rich QR code for document authentication.
CuNAS - CUriosity-driven Neural-Augmented Simulator

—Transfer of policies from simulation to physical robots is an important open problem in deep reinforcement learning. Prior work has introduced the model-based Neural-Augmented Simulator (NAS) method, which uses task-independent data to create a model of the differences between simulated and real robot. In this work, we show that this method is sensitive to the sampling of motor actions and the control frequency. To overcome this problem, we propose a simple extension based on artiﬁcial curiosity. We demonstrate on a physical robot, that this leads to a better exploration of the state space and consequently better transfer performance when compared to the NAS baseline.
In situ determination of sulfur speciation and partitioning in aqueous fluid-silicate melt systems

Current knowledge of sulfur behaviour in magmas is based exclusively on ex situ analyses. Here we report the first in situ measurement of sulfur speciation and partitioning between coexisting aqueous fluids and silicate melts. These data were acquired using Raman spectroscopy in a diamond anvil cell at 700 °C, 0.3–1.5 GPa, and oxygen fugacity in the vicinity of the sulfide-sulfate transition, conditions relevant to subduction zone magmatism. Results show that sulfate and sulfide are dominant in the studied systems, together with the and radical ions that are absent in quenched fluid and silicate glass products. The derived fluid/melt partition coefficients for sulfide and sulfate are consistent with those from available ex situ data for shallow crust conditions (<10 km), but indicate stronger partitioning of these sulfur species into the silicate melt phase with increasing depth. In contrast, both radical ions partition at least 10 times more than sulfate and sulfide into the fluid phase. Thus, by enhancing the transfer of sulfur and associated chalcophile metals from melt into fluid upon magma degassing, and may be important players in the formation of economic metal resources within the redox window of the sulfate-sulfide transition in subduction zone settings.
Uncertainty Aware Deep Neural Network for Multistatic Localization with Application to Ultrasonic Structural Health Monitoring

Guided ultrasonic wave localization uses spatially distributed multistatic sensor arrays and generalized beamforming strategies to detect and locate damage across a structure. The propagation channel is often very complex. Methods can compare data with models of wave propagation to locate damage. Yet, environmental uncertainty (e.g., temperature or stress variations) often degrade accuracies. This paper uses an uncertainty-aware deep neural network framework to learn robust localization models and represent uncertainty. We use mixture density networks to generate damage location distributions based on training data uncertainty. This is in contrast with most localization methods, which output point estimates. We compare our approach with matched field processing (MFP), a generalized beamforming framework. The proposed approach achieves a localization error of 0.0625 m as compared to 0.1425 m with MFP when data has environmental uncertainty and noise. We also show that the predictive uncertainty scales as environmental uncertainty increases to provide a statistically meaningful metric for assessing localization accuracy.
Distributed Online Convex Optimization With an Aggregative Variable

This article investigates distributed online convex optimization in the presence of an aggregative variable without any global/central coordinators over a multiagent network. In this problem, each individual agent is only able to access partial information of time-varying global loss functions, thus requiring local information exchanges between neighboring agents. Motivated by many applications in reality, the considered local loss functions depend not only on their own decision variables, but also on an aggregative variable, such as the average of all decision variables. To handle this problem, an online distributed gradient tracking algorithm (O-DGT) is proposed with exact gradient information and it is shown that the dynamic regret is upper bounded by three terms: 1) a sublinear term; 2) a path variation term; and 3) a gradient variation term. Meanwhile, the O-DGT algorithm is also analyzed with stochastic/noisy gradients, showing that the expected dynamic regret has the same upper bound as the exact gradient case. To our best knowledge, this article is the first to study online convex optimization in the presence of an aggregative variable, which enjoys new characteristics in comparison with the conventional scenario without the aggregative variable. Finally, a numerical experiment is provided to corroborate the obtained theoretical results.
Percus–Yevick structure factors made simple

Measuring the structure factor, S(q), of a dispersion of particles by Small-Angle X-ray Scattering provides a unique method to investigate the spatial arrangement of colloidal particles. However, it is impossible to find the exact location of the particles from S(q) because some information is inherently lacking in the SAXS signal. The two standard ways to analyse an experimental structure factor are then to compare it either to structure factors computed from simulated systems, or to analytical structure factors calculated from approximated systems. For liquids of monodisperse hard spheres, the latter method provides analytical structure factors through the Ornstein-Zernike equation used with the Percus-Yevick closure equation. The structure factors obtained in this way were not adequate for the more common dispersions of polydisperse particles. However, Vrij, Bloom and Stell were able to demonstrate that the same mathematical framework could be extended to yield accurate approximations for the experimental structure factor. Still, this solution has remained underused because of its mathematical complexity. In the present work, we derive and report the complete Percus-Yevick solution for general polydisperse hard-spheres systems in a concise form that is straightforward to use. The form of the solution is made simple enough to give ready solutions of several important particle-radius distributions (Schulz, truncated normal and inverse Gaussian). We also discuss in detail the case of the power-law radius distribution, relevant in the case of systems made of an Apollonian packing of spheres, as recently discovered experimentally in high internal-phase-ratio emulsions.
Nonlinear MPC for Tracking for a Class of Nonconvex Admissible Output Sets

This article presents an extension to the nonlinear model predictive control (MPC) for tracking scheme able to guarantee convergence even in cases of nonconvex output admissible sets. This is achieved by incorporating a convexifying homeomorphism in the optimization problem, allowing it to be solved in the convex space. A novel class of nonconvex sets is also defined for which a systematic procedure to construct a convexifying homeomorphism is provided. This homeomorphism is then embedded in the MPC optimization problem in such a way that the homeomorphism is no longer required in closed form. Finally, the effectiveness of the proposed method is showcased through an illustrative example.
Floquet Analysis of Space-Time Modulated Metasurfaces With Lorentz Dispersion

A rigorous semianalytical Floquet analysis is proposed for modeling space–time-modulated metasurface and determining the scattered fields in terms of their harmonic components. The proposed method is based on generalized sheet transition conditions (GSTCs) treating a metasurface as a spatial discontinuity with zero thickness. The metasurface is described in terms of Lorentzian electric and magnetic surface susceptibilities, both tangential and normal to the surface, with parameters (e.g., resonant frequency) that are periodically modulated in both space and time. The unknown scattered fields are expressed in terms of Floquet harmonics, for which the amplitudes can be found by numerically solving a set of linear equations, leading to the total scattered fields. Using existing computational techniques and a commercial full-wave solver, the method is validated using several examples of pure-space and pure-time modulation with different modulation strengths and pumping frequencies. Finally, two cases of space–time modulation (standing wave perturbation and a traveling-wave perturbation) are presented to demonstrate the breaking of Lorentz reciprocity. The proposed method is simple and versatile and able to determine the steady-state response of a space–time-modulated metasurface that is excited with an oblique plane wave or a general incident field such as a Gaussian beam.
Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users

Modern deep learning methods constitute incredibly powerful tools to tackle a myriad of challenging problems. However, since deep learning methods operate as black boxes, the uncertainty associated with their predictions is often challenging to quantify. Bayesian statistics offer a formalism to understand and quantify the uncertainty associated with deep neural network predictions. This tutorial provides deep learning practitioners with an overview of the relevant literature and a complete toolset to design, implement, train, use and evaluate Bayesian neural networks, i.e., stochastic artificial neural networks trained using Bayesian methods.
Deep Retrieval: An End-to-End Learnable Structure Model for Large-Scale Recommendations

One of the core problems in large-scale recommendations is to retrieve top relevant candidates accurately and efficiently, preferably in sub-linear time. Previous approaches are mostly based on a two-step procedure: first learn an inner-product model and then use maximum inner product search (MIPS) algorithms to search top candidates, leading to potential loss of retrieval accuracy. In this paper, we present Deep Retrieval (DR), an end-to-end learnable structure model for large-scale recommendations. DR encodes all candidates into a discrete latent space. Those latent codes for the candidates are model parameters and to be learnt together with other neural network parameters to maximize the same objective function. With the model learnt, a beam search over the latent codes is performed to retrieve the top candidates. Empirically, we showed that DR, with sub-linear computational complexity, can achieve almost the same accuracy as the brute-force baseline.
Polestar: An Intelligent, Efficient and National-Wide Public Transportation Routing Engine

Public transportation plays a critical role in people's daily life. It has been proven that public transportation is more environmentally sustainable, efficient, and economical than any other forms of travel. However, due to the increasing expansion of transportation networks and more complex travel situations, people are having difficulties in efficiently finding the most preferred route from one place to another through public transportation systems. To this end, in this paper, we present Polestar, a data-driven engine for intelligent and efficient public transportation routing.Specifically, we first propose a novel Public Transportation Graph (PTG) to model public transportation system in terms of various travel costs, such as time or distance. Then, we introduce a general route search algorithm coupled with an efficient station binding method for efficient route candidate generation. After that, we propose a two-pass route candidate ranking module to capture user preferences under dynamic travel situations. Finally, experiments on two real-world data sets demonstrate the advantages of Polestar in terms of both efficiency and effectivenes Indeed, in early 2019, Polestar has been deployed on Baidu Maps, one of the world's largest map services. To date, Polestar is servicing over 330 cities, answers over a hundred millions of queries each day, and achieves substantial improvement of user click ratio.
Application of Modal Filtering in Discontinuous Spectral Element Method for Simulation of Compressible Channel Flow at Different Reynolds Numbers

Large-eddy simulation of incompressible turbulent flow has been extensively investigated; hence, a variety of models suited for different numerical schemes have been developed. In the case of compressible flow, the modeling is more challenging due to the numerous terms that arise in the filtered Navier-Stokes equations. Recently, modal explicit filtering was implemented in the discontinuous spectral element method (DSEM) to mimic the effects of sub-filtered scales for compressible flows. The method is computationally inexpensive since it is implemented in the DSEM code using a well-established math library. It has been successfully applied to simulations of compressible decaying isotropic turbulence and turbulent channel flow. In this study, channel flows with three different friction Reynolds numbers are simulated using explicit modal filtering in DSEM to assess the performance of the new method by comparing turbulent statistics and friction Reynolds numbers to those from DNS. The model predicts the friction Reynolds number with a maximum of 2.16% error with respect to DNS. Although the model generates promising results for the average velocity profile compared to DNS, it requires adjustment of filter strength and frequency to predict rms statistics accurately.
Lifetimes of confined optical phonons and the shape of a Raman peak in disordered nanoparticles. I. Analytical treatment

Disorder-induced broadening of optical vibrational eigenmodes in nanoparticles of nonpolar crystals is studied numerically. The methods previously used to treat the phonons in defectless particles are adjusted for numerical evaluation of the disordered problem. Imperfections in the forms of Gaussian and binary disorders as well as surface irregularities are investigated thoroughly in a wide range of impurity concentrations and disorder strengths. For dilute and weak pointlike impurities the regimes of separated and overlapped phonon levels are obtained and the behavior of the linewidth predicted analytically is confirmed; the crossover scale falls into the actual range of several nanometers. These notions survive for strong dilute impurities, as well. Regimes and crossovers predicted by the analytical approach are checked and identified, and the minor discrepancies are discussed. We mention a few of them: slower than in analytics increasing of the linewidth with the phonon quantum number for weak disorder and only a qualitative agreement between analytics and numerics for the resonant broadening in strong dilute disorder. The novel phenomena discovered numerically are the ``mesoscopic smearing'' of the distribution function in the ensemble of identical disordered particles, an inflection of the linewidth dependence on the impurity concentration for light ``dense'' binary impurities, and a position-dependent capability of a strong impurity to catch the phonon. It is shown that surface irregularities contribute to the phonon linewidth less than the volume disorder, and their rates reveal faster decay with increasing of the particle size. It is argued that the results of the present research are applicable also for quantum dots and short quantum wires.
Stochastic MPC With Dynamic Feedback Gain Selection and Discounted Probabilistic Constraints

This article considers linear discrete-time systems with additive disturbances and designs a model predictive control (MPC) law incorporating a dynamic feedback gain to minimize a quadratic cost function subject to a single chance constraint. The feedback gain is selected online, and we provide two selection methods based on minimizing upper bounds on predicted costs. The chance constraint is defined as a discounted sum of violation probabilities on an infinite horizon. By penalizing violation probabilities close to the initial time and assigning violation probabilities in the far future with vanishingly small weights, this form of constraints allows for an MPC law with guarantees of recursive feasibility without a boundedness assumption on the disturbance. A computationally convenient MPC optimization problem is formulated using Chebyshev’s inequality, and we introduce an online constraint-tightening technique to ensure recursive feasibility. The closed-loop system is guaranteed to satisfy the chance constraint and a quadratic stability condition. With dynamic feedback gain selection, the closed-loop cost is reduced and conservativeness of Chebyshev’s inequality is mitigated. Also, a larger feasible set of initial conditions can be obtained. Numerical simulations are given to show these results.
A Framework for Capturing and Analyzing Unstructured and Semi-structured Data for a Knowledge Management System

Mainstream knowledge management researchers generally agree that knowledge extracted from unstructured data and semi-structured data have become imperative for organizational strategic decision making. In this research, we develop a framework that captures and analyses unstructured data using machine learning techniques and integrates knowledge and insight gained from the data into traditional knowledge management systems. Unlike most frameworks published in the literature that focuses on a specific type of unstructured data, our frameworks cut across the varieties of unstructured data ranging from textual data from social network sites, online forums, discussion boards, reviews to audio data, image data and video data. We highlight some pre-processing and processing techniques for these data and also highlight some standard output. We evaluate the framework by developing a textual data application programming interface (API) using python and beautiful soup and we perform sentiment analysis on the students review data collected through the API.
Collisionless Heat Flux Regulation via the Electron Firehose Instability in the Presence of a Core and Suprathermal Population in the Expanding Solar Wind

The evolution of the electron heat flux in the solar wind is regulated by the interplay between several effects: solar wind expansion, which can potentially drive velocity–space instabilities, turbulence, wave–particle interactions, and, possibly, collisions. Here we address the respective role played by the solar wind expansion and the electron firehose instability (EFI), developing in the presence of multiple electron populations, in regulating the heat flux. We carry out fully kinetic, expanding box model simulations and separately analyze the enthalpy, bulk, and velocity distribution function skewness contributions for each of the electron species. We observe that the key factor determining electron energy flux evolution is the reduction of the drift velocity of the electron populations in the rest frame of the solar wind. In our simulations, redistribution of the electron thermal energy from the parallel to the perpendicular direction after the onset of the EFI is observed. However, this process seems to impact energy flux evolution only minimally. Hence, reduction of the electron species drift velocity in the solar wind frame appears to directly correlate with efficiency for heat flux instabilities.
Morphological and physiological dormancy in seeds of Aegopodium podagraria (Apiaceae) broken successively during cold stratification

Abstract A low-temperature requirement for dormancy break has been observed frequently in temperate-climate Apiaceae species, resulting in spring emergence of seedlings. A series of experiments was performed to identify dormancy-breaking requirements of Aegopodium podagraria, a nitrophilous perennial growing mainly in mildly shaded places. In natural conditions, the embryos in seeds of A. podagraria grow in early winter. Seedlings were first observed in early spring and seedling emergence peaked in March and April. Experiments using temperature-controlled incubators revealed that embryos in seeds of A. podagraria grow only at low temperatures (5°C), irrespective of a pretreatment at higher temperatures. Seeds did not germinate immediately after embryo growth was completed, instead an additional cold stratification period was required to break dormancy completely. Once dormancy was broken, seeds germinated at a range of temperatures. Addition of gibberellic acid (GA3) had a positive effect on embryo growth in seeds incubated at 10°C and at 23°C, but it did not promote germination. Since seeds of A. podagraria have a low-temperature requirement for embryo growth and require an additional chilling period after completion of embryo growth, they exhibit characteristics of deep complex morphophysiological dormancy.