Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-task and multi-domain learning with tensor networks

We propose a tensor network that can learn to perform multiple tasks by adjusting the factors of each layer. Most of the existing methods for multi-task learning train a single network to extract task-specific features and subsequent prediction. We propose to use a single network with task-specific transformations that can extract task-specific features and perform task inference with small memory overhead. In particular, we transform features using low-rank updates in the convolution kernels. We present experiments on different datasets for multi-task and multi-domain learning and demonstrate that our method achieves state-of-the-art performance with minimal memory overhead compared to existing methods.
Performance Characterization of the Dual-Recycled Michelson Subsystem in Advanced LIGO

General relativity predicts the existence of gravitational waves as ripples in spacetime propagating at the speed of light. They couple to matter weakly, which implies only cataclysmic cosmic events generating such waves can be detected. Binary neutron star coalescences are, for example, one of the most promising detectable source. Their weak coupling also implies that very sensitive instruments are needed to detect them, and the most sensitive so far have been laser interferometers with km-scale arms. The Laser Interferometric Gravitational wave Observatory (LIGO) is exactly such an instrument, and the most sensitive in the world to the date of this dissertation. It operates two identical interferometers, one in Hanford, WA and one in Livingston, LA. I will present noise studies on two different topics.First, studies of environmental effects in the enhanced LIGO detector era (2008-2010) for both sites, showing that stationary ambient environmental noise did not limit the sensitivity at the time. After enhanced LIGO, advanced LIGO began installation, where all hardware was replaced except the enclosing vacuum system. The expected improvement in sensitivity is designed to be tenfold, which results in a factor of a thousand in the volume of space from where a source could be detected. This dissertation then focuses on a sub-configuration of advanced LIGO, the dualrecycled Michelson interferometer (DRMI), which is the LIGO instrument without the 4km arms. I will expose in detail the length sensing and control scheme which is the cornerstone of LIGO’s ability to provide a linear gravitational wave readout. We model the behavior of the instrument in order to identify key commissioning targets. I will present models and measurements I performed at the Livingston Observatory for assessing two main topics: the sensing performance and the noise couplings in the DRMI. This work has only been possible with the support from the NSF grants NSF-PHY 0905184 and 1205882.
From shear to veer: theory, statistics, and practical application

Abstract. In the past several years, wind veer – sometimes called “directional shear” – has begun to attract attention due to its effects on wind turbines and their production, particularly as the length of manufactured turbine blades has increased. Meanwhile, applicable meteorological theory has not progressed significantly beyond idealized cases for decades, though veer's effect on the wind speed profile has been recently revisited. On the other hand the shear exponent (α) is commonly used in wind energy for vertical extrapolation of mean wind speeds, as well as being a key parameter for wind turbine load calculations and design standards. In this work we connect the oft-used shear exponent with veer, both theoretically and for practical use. We derive relations for wind veer from the equations of motion, finding the veer to be composed of separate contributions from shear and vertical gradients of crosswind stress. Following from the theoretical derivations, which are neither limited to the surface layer nor constrained by assumptions about mixing length or turbulent diffusivities, we establish simplified relations between the wind veer and shear exponent for practical use in wind energy. We also elucidate the source of commonly observed stress–shear misalignment and its contribution to veer, noting that our new forms allow for such misalignment. The connection between shear and veer is further explored through analysis of one-dimensional (single-column) Reynolds-averaged Navier–Stokes solutions, where we confirm our theoretical derivations as well as the dependence of mean shear and veer on surface roughness and atmospheric boundary layer depth in terms of respective Rossby numbers. Finally we investigate the observed behavior of shear and veer across different sites and flow regimes (including forested, offshore, and hilly terrain cases) over heights corresponding to multi-megawatt wind turbine rotors, also considering the effects of atmospheric stability. From this we find empirical forms for the probability distribution of veer during high-veer (stable) conditions and for the variability in veer conditioned on wind speed. Analyzing observed joint probability distributions of α and veer, we compare the two simplified forms we derived earlier and adapt them to ultimately arrive at more universally applicable equations to predict the mean veer in terms of observed (i.e., conditioned on) shear exponent; lastly, the limitations, applicability, and behavior of these forms are discussed along with their use and further developments for both meteorology and wind energy.
Sodium Azide as a Chemical Mutagen in Wheat (Triticum aestivum L.): Patterns of the Genetic and Epigenetic Effects with iPBS and CRED-iPBS Techniques

Wheat, which is scientifically known as Triticum aestivum L. , is a very nutritious grain that serves as a key component of the human diet. The use of mutation breeding as a tool for crop improvement is a reasonably rapid procedure, and it generates a variety that may be used in selective breeding programs as well as functional gene investigations. The present experiment was used to evaluate the potential application of a conventional chemical mutagenesis technique via sodium azide (NaN3) for the germination and seedling growth stage in wheat. Experiments with NaN3 mutagenesis were conducted using four different treatment periods (0, 1, 2, and 3 h) and five different concentrations (0, 0.5, 1, 1.5, and 2 mM). The genomic instability and cytosine methylation of wheat using its seeds were investigated after they were treated. In order to evaluate the genomic instability and cytosine methylation in wheat that had been treated, interprimer binding site (iPBS) markers were used. The mutagenic effects of NaN3 treatments had considerable polymorphism on a variety of impacts on the cytosine methylation and genomic instability of wheat plants. The results of the experiment showed considerable changes in the iPBS profiles produced by the administration of the same treatments at different dosages and at different times. Coupled restriction enzyme digestion interprimer binding site (CRED-iPBS) assays identified changes in gDNA cytosine methylation. The highest polymorphism value was obtained during 1 h + 2 mM NaN3, while the lowest (20.7%) was obtained during 1 h + 1.5 mM NaN3. Results showed that treatments with NaN3 had an effect on the level of cytosine methylation and the stability of the genomic template in wheat plants in the germination stage. Additionally, an integrated method can be used to for mutation-assisted breeding using a molecular marker system in wheat followed by the selection of desired mutants.
Asymptotic Preserving Discontinuous Galerkin Method

In this work a numerical method for the low Mach isentropic Navier-Stokes equation is devised. Sup-ported by a short analysis, we observe that this algorithm is capable of treating the low Mach equations also in the limit of zero Mach number. The performance of the algorithm is investigated numerically, where one can observe problems with the order of convergence. We conclude with possible reasons and remedies.
IoT Privacy-Preserving Data Mining With Dynamic Incentive Mechanism

With the rise of the Internet of Things (IoT), IoT data analytics has gradually stepping into the spotlight of data mining. Existing research has primarily focused on enhancing the precision of IoT data mining, while the privacy protection aspects have not been fulfilled so far. The deployment of IoT data mining is contingent on the protection of data privacy and its economic worth to all parties. However, most existing IoT data mining research disregards economic benefits and lacks incentives, limiting its applicability. To address this issue, we provide a system for differential privacy-based IoT privacy-preserving data mining (IoT-PPDM) with dynamic incentive mechanism, and a formal economic model for IoT data mining is constructed. We utilized noncooperative game theory to simulate the multilateral interaction process in IoT data mining. To encourage participation from all parties, a dynamic incentive mechanism is designed to establish a balance between privacy protection and data mining requirements. In addition, we discuss the utility of all participants and theoretically validate the feasibility of IoT-PPDM. The experimental results show that IoT-PPDM with dynamic incentive mechanism can increase the benefits for all participants while avoiding irrational behavior of all parties.
Open AccessLack of association between coat color abnormalities in Bali cattle (Bos javanicus) and the coding regions of the MC1R and KIT genes

Background and Aim: Coat color variations in cattle are known to be influenced by the melanocortin 1 receptor (MC1R) and receptor tyrosine kinase (KIT) genes. The presence of coat color abnormalities, such as white spots and albinism, in Bali cattle was the focus of this study. This study aimed to identify single nucleotide polymorphisms (SNPs) in the coding region of MC1R and exons 2 and 3 of KIT associated with coat color abnormalities in Bali cattle. Materials and Methods: The study included the analysis of 48 Bali cattle, including 20 individuals with standard coat color, 15 with white spots, and 13 with albinism. Total DNA was extracted using a DNA Extraction Kit, and MC1R (coding region) and KIT (exons 2 and 3) gene amplifications were analyzed using forward and reverse primers with polymerase chain reaction product lengths of 1071, 234, and 448 bp, respectively. The determination of MC1R and KIT gene diversity was analyzed through direct sequencing. Melanocortin 1 receptor and KIT gene sequence data were analyzed using BioEdit and MEGA6 to identify SNPs associated with standard and abnormal coat color phenotypes (white-spotted and albino) in Bali cattle. Results: No SNPs associated with coat color abnormalities were found in the coding region of MC1R and exons 2 and 3 of KIT genes in Bali cattle. However, the intron two regions of KIT contained the SNP g.70208534A>G, which showed a high degree of diversity. The AA genotype frequency was highest in albino Bali cattle, whereas the G allele frequency was highest and the A allele frequency was lowest in white-spotted Bali cattle. Conclusion: The results indicated that standard, white-spotted, and albino coat colors in Bali cattle could not be distinguished by analyzing the MC1R and KIT genes.
Sensitivities and Variances for Fitted Parameters of Spheres | NIST

Experimental data have been gathered by applying 3D imaging systems, such as LIDAR/LADAR instruments, to spherical objects. Thi s report provides a compilation of the statistical and analytical procedures to be used fo r an analysis of these data to be reported separately. This analysis will be based on two diff erent nonlinear least-squares approaches to modeling objects, directional and orthogonal fittin g. It is proposed to estimate variances of fitted sphere parameters directly from their sensitivities o data perturbations rather than to follow the common prescription of linearization. The sensitivi ties are determined by implicit differentiation of error gradients. Detailed descriptions of the d ir ctional and orthogonal fitting methods are set forth as applied to spheres in a scanning environme nt. In particular, the report furnishes closedform expressions for those derivatives of the respe ctiv error functions which are needed for the calculation of the parameter sensitivities with res pect to the full set of control variables.
Multiagent Reinforcement Learning-Based Orbital Edge Offloading in SAGIN Supporting Internet of Remote Things

We investigate a computing task scheduling problem in space–air–ground integrated network (SAGIN) for Internet of Remote Things (IoRT). In the considered scenario, the unmanned aerial vehicles (UAVs) collect computing tasks from IoRT devices and make offloading decisions, in which the tasks can be computed at the UAVs or offloaded to the low Earth orbital (LEO) satellite. The optimization objective is to design offloading strategies for each UAV that maximum the number of tasks satisfies delay constraint and reduces the energy consumption of UAVs. We formulate this problem as a nonlinear integer optimization problem, and remodel it as a stochastic game to solve it. To copy with the dynamic and complexity environment, we propose a learning-based orbital edge offloading (LOEF) approach which can coordinate all UAVs to learn the optimal offloading strategies. This method is based on the actor–critic framework of multiagent reinforcement learning, the Actor observes the environment and output the current offloading decision, the Critic evaluates the action of the Actor and coordinate the actions of all UAV to increase the reward of system. The simulation results show that the proposed offloading strategy converges fast, increases the number of satisfying the delay constraints tasks and the energy utilization of UAVs compared with the baseline strategies.
Improving space bandwidth product of reflective digital holographic microscopy by the use of Fourier-ptychographic illumination technique

Digital holographic microscopy has been widely adopted in the field of biology because of its ability of quantifying phase information. Unlike those conventional transmission-type phase imaging techniques, reflective digital holographic microscopy can be applied in the field of display industry or semiconductor industry for inspecting unwanted defects in a certain fabrication process. Consequently, it is necessary and required to achieve large field of view and resolution enhancement to meet the demands on the fast and accurate shape measurement. To increase the minimum resolution, we are going to adopt programmable illumination patterns by the use of fast-activating digital micro-mirror devices (DMDs) referring to Fourier ptychographic illumination. In this work, to expand space bandwidth product in reflection-type digital holographic microscopy (RDHM) configuration, we apply a fast-activating digital micro-mirror device for a amplitude-type spatial light modulator and a micro-lens array in the reference arm of a conventional RDHM.
On Error Detection and Recovery in Elliptic Curve Cryptosystems

Fault analysis attacks represent a serious threat to a wide range of cryptosystems including those based on elliptic curves. With the variety and demonstrated practicality of these attacks, it is essential for cryptographic implementations to handle different types of errors properly and securely. In this work, we address some aspects of error detection and recovery in elliptic curve cryptosystems. In particular, we discuss the problem of wasteful computations performed between the occurrence of an error and its detection and propose solutions based on frequent validation to reduce that waste. We begin by presenting ways to select the validation frequency in order to minimize various performance criteria including the average and worst-case costs and the reliability threshold. We also provide solutions to reduce the sensitivity of the validation frequency to variations in the statistical error model and its parameters. Then, we present and discuss adaptive error recovery and illustrate its advantages in terms of low sensitivity to the error model and reduced variance of the resulting overhead especially in the presence of burst errors. Moreover, we use statistical inference to evaluate and fine-tune the selection of the adaptive policy. We also address the issue of validation testing cost and present a collection of coherency-based, cost-effective tests. We evaluate variations of these tests in terms of cost and error detection effectiveness and provide infective and reduced-cost, repeated-validation variants. Moreover, we use coherency-based tests to construct a combined-curve countermeasure that avoids the weaknesses of earlier related proposals and provides a flexible trade-off between cost and effectiveness.
A Dynamical Theory of the Electromagnetic Field

T F Torrence (ed) 1982 Edinburgh: Scottish Academic Press xiii + 103 pp price £7.50 In 1865 James Clerk Maxwell published in the Philosophical Transactions a long paper entitled 'A dynamical theory of the electromagnetic field'. In this work, about which he wrote to a relative, 'I have a paper afloat, with an electromagnetic theory of light, which, till I am convinced of the contrary, Ihold to be great guns', he set up the electromagnetic field equations unifying electricity and magnetism, and identified light as 'an electromagnetic disturbance in the form of waves propagated through the electromagnetic field according to electromagnetic laws'.
SPH Simulation of an Air-Assisted Atomizer Operating at High Pressure: Influence of Non-Newtonian Effects

A twin-fluid atomizer configuration is predicted by means of the 2D weakly-compressible Smooth Particle Hydrodynamics (SPH) method and compared to experiments. The setup consists of an axial liquid jet fragmented by a co-flowing high-speed air stream (Ug ~ 60 m/s) in a pressurized atmosphere up to 11 bar (abs.). Two types of liquid are investigated: a viscous Newtonian liquid (µ = 200 mPa.s) obtained with a glycerol/water mixture and a viscous non-Newtonian liquid (µ ~ 150 mPa.s) obtained with a carboxymethyl cellulose (CMC) solution. 3D effects are taken into account in the 2D code by introducing (i) a surface tension term, (ii) a cylindrical viscosity operator and (iii) a modified velocity accounting for the divergence of the volume in the radial direction. The numerical results at high pressure show a good qualitative agreement with experiment, i.e. a correct transition of the atomization regimes with regard to the pressure, and similar dynamics and length scales of the generated ligaments. The predicted frequency of the Kelvin-Helmholtz instability needs a correction factor of 2 to be globally well recovered with the Newtonian liquid. The simulation of the non-Newtonian liquid at high pressure shows a similar breakup regime with finer droplets compared to Newtonian liquids while the simulation at atmospheric pressure shows an apparent viscosity similar to the experiment.
Systems science of the magnetosphere: Creating indices of substorm activity, of the substorm‐injected electron population, and of the electron radiation belt

To more fully monitor the elements of the magnetosphere‐ionosphere system and its activity, indices are created to quantify the rate of substorm onset occurrence, the rate of substorm activations, the intensity of the 130 keV substorm‐injected electrons and the intensity of the 1.2 MeV radiation‐belt electrons. The reactions of these various indices to the onset of isolated substorms are examined, and the behaviors of the indices are explored before and during high‐speed‐stream‐driven storms and during solar wind rarefactions after high‐speed streams. Autocorrelation functions of the indices are analyzed and compared with autocorrelation functions of solar wind parameters and geomagnetic indices. Lagged‐time correlations between the indices and solar wind variables and geomagnetic indices are surveyed. These 1 h resolution indices are available to the research community.
Monitoring Strategy of Geological Hazards Using Integrated Three-dimensional InSAR and GNSS Technologies with Case Study

Geodetic/geodynamic benchmarks, equipped with both ascending and descending radar corner reflectors, and a method for integrated InSAR and GNSS/GPS network observation were developed and applied as the continuation of the former geodetic monitoring at the Dunaszekcső landslide, Hungary. The attempts to apply InSAR technologies using archive and Sentinel-1 data practically failed on the most intensive landside areas (“Vár” and “Szent János” hills), where proper persistent or distributed scatterers were not found. Our concept solved this problem, where the Simple Look Complex (SLC) images are used to interpolate the movements between two GNSS network observations using the integrated benchmarks and the method of Kalman-filtering. Since the InSAR line-of-sight (LOS) changes are barely sensitive to the north movements, this information is essentially provided by GNSS measurement alone, moreover, the GNSS measurements are used to: a) identify the benchmarks, b) detect the unwrapping errors and missing cycles and c) provide the boundary values of Kalman-filtering.After the installation of benchmarks three GPS observations were carried out and 69 ascending and 61 descending Sentinel-1 A and B images were processed. The data processing properly indicated the general movement history, which fit the curves of former geodetic observations, as well. The dense data points of the East and Up (vertical) components made possible more detailed geomorphologic interpretations of the ongoing process between two GPS observations. During the investigated periods the deceleration of movements was experienced, however, the deceleration of the dormant state needs the continuation of the monitoring.
Aerosol–cloud–radiation interaction during Saharan dust episodes: the dusty cirrus puzzle

Abstract. Dusty cirrus clouds are extended optically thick cirrocumulus decks that occur during strong mineral dust events. So far they have mostly been documented over Europe associated with dust-infused baroclinic storms. Since today's global numerical weather prediction models neither predict mineral dust distributions nor consider the interaction of dust with cloud microphysics, they cannot simulate this phenomenon. We postulate that the dusty cirrus forms through a mixing instability of moist clean air with drier dusty air. A corresponding sub-grid parameterization is suggested and tested in the ICOsahedral Nonhydrostatic model with Aerosol and Reactive Trace gases (ICON-ART). Only with the help of this parameterization is ICON-ART able to simulate the formation of the dusty cirrus, which leads to substantial improvements in cloud cover and radiative fluxes compared to simulations without this parameterization. A statistical evaluation over six Saharan dust events with and without observed dusty cirrus shows robust improvements in cloud and radiation scores. The ability to simulate dusty cirrus formation removes the linear dependency on mineral dust aerosol optical depth from the bias of the radiative fluxes. For the six Saharan dust episodes investigated in this study, the formation of dusty cirrus clouds is the dominant aerosol–cloud–radiation effect of mineral dust over Europe.