Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing Noise in Quantum Systems

In practice, quantum systems are not completely isolated from their environment and the resulting system-environment interaction can lead to information leakage from the system. As a result, if a quantum system is to be used for storing or manipulating information, one would like to characterize these environmental noise effects. Such a characterization affords one the ability to design robust methods for preserving the information contained in the system. Unfortunately, completely characterizing the noise in a realistic amount of time is impossible for even moderately large systems. In this thesis we discuss methods and diagnostics for partially characterizing quantum noise processes that are especially useful in quantum information and computation. We present a randomized benchmarking protocol that provides a scalable method for determining important properties of the noise affecting the set of gates used on a quantum information processor. We also prove various properties of the quantum gate fidelity, which is a useful state-dependent measure of the distance between two quantum operations as well as an important diagnostic of the noise affecting a quantum process. Some non-intuitive generic features of quantum operations acting on large-dimensional quantum systems are also presented.
Relative Reachable Domain for Spacecraft with Initial State Uncertainties

The reachable domain is a geometric description of the maneuverability of a spacecraft under control and/or measurement uncertainties. In this paper, the reachable domain concept is introduced into the field of satellite relative motion and termed the relative reachable domain. The relative reachable domain geometrically represents the effects of the initial control and/or measurement uncertainties on relative motion. A general method is developed to find the envelope of the coplanar relative reachable domain for arbitrary reference orbits, by evaluating the extremal values of the potentially accessible radius with respect to a given reference position. The relative reachable domain formulations are analytically expressed for circular reference orbits. It is shown that the relative reachable domain is a convenient tool for analyzing a variety of cluster flight mission problems. Several practical applications are suggested, including determining the upper bound on the separation distances among satellites.
A Machine Learning Approach to Predict the Optical Properties of a Nanocube via Gaussian Process Regression

We have demonstrated the use of a Gaussian Process Regression method for the prediction of the scattering and extinction cross sections of a nanocube embedded in a dielectric medium. Our model is sufficiently general to incorporate permittivity of the cube and dielectric, cube size, and wavelength as predictors, resulting in a level of generality previously unseen in the literature. We introduce the idea of using the logarithms of the normalized cross sections during training, which improves the accuracy greatly. The model has been able to accurately predict cross sections for cube sizes and materials outside of the training set, in times that are orders of magnitude smaller than that required for a Finite Element Method simulation, thus demonstrating both the speed and predictive power of the simple machine learning approach considered in this work.
Interfaces between Competing Patterns in Reaction-diusion Systems with Nonlocal Coupling

In this thesis we investigate the formation of patterns in a simple activatorinhibitor model supplemented with an inhibitory nonlocal coupling term. This model exhibits a wave instability for slow inhibitor diffusion, while, for fast inhibitor diffusion, a Turing instability is found. For moderate values of the inhibitor diffusion these two instabilities occur simultaneously at a codimension-2 wave-Turing instability. We perform a weakly nonlinear analysis of the model in the neighbourhood of this codimension-2 instability. The resulting amplitude equations consist in a set of coupled Ginzburg-Landau equations. These equations predict that the model exhibits bistability between travelling waves and Turing patterns. We present a study of interfaces separating wave and Turing patterns arising from the codimension-2 instability. We study theoretically and numerically the dynamics of such interfaces in the framework of the amplitude equations and compare these results with numerical simulations of the model near and far away from the codimension-2 instability. Near the instability, the dynamics of interfaces separating small amplitude Turing patterns and travelling waves is well described by the amplitude equations, while, far from the codimension-2 instability, we observe a locking of the interface velocities. This locking mechanism is imposed by the absence of defects near the interfaces and is responsible for the formation of drifting pattern domains, i.e. moving localised patches of travelling waves embedded in a Turing pattern background and vice versa.
A Compact Sagnac Loop for Temperature Sensing Based on Time-Stretch Method

In this letter, we present a new method to detect temperature based on polarization maintaining fiber (PMF) to construct a Sagnac interference structure in time domain. The working principle of the sensor is introduced in detail, and the feasibility of the scheme is demonstrated by experiment. The temperature sensitivity of this structure is as high as -0.712 nm/°C. Furthermore, in the time domain, an ultrafast temperature sensor with an ultrafast interrogation speed of 200 MHz and a sensitivity of 0.482 ns/ °C is realized. The designed sensing system has potential important value for ammunition temperature monitoring and military industry.
FOUR-COLOR AND HBETA PHOTOMETRY FOR OPEN CLUSTERS. II. NGC 6475.

Extensive photometric observations were conducted in the field of the southern open cluster NGC 6475 with the uvby and Hfi systems. The interstellar reddening for the cluster was found to be nonuniform, and a mean color excess of E(b - y) = was found. A distance modulus was obtained, and an attempt was made to identify member stars by using as criteria the present photometry and previous astrometric and spectroscopic studies. With the m1 index, several stars were isolated as possible Am stars. A comparison of the observed data with theoretical models showed this cluster to be 260 X 106 years old, and thereby older than the Pleiades. Two stars appear to be blue stragglers. Key words: photometry - star clusters - Am stars
The WSR-88D Severe Weather Potential Algorithm

Abstract The WSR-88D severe weather potential (SWP) algorithm is an automated procedure for the detection of severe local storms. The algorithm identifies individual thunderstorm cells within radar imagery and, for each cell, yields an index proportional to the probability that the cell will shortly produce damaging surface winds, large hail, or tornadoes. This index is a statistically derived function of the storm's maximum vertically integrated liquid (VIL) and horizontal areal extent. The correlation between these storm characteristics and severe weather occurrence was first documented in the 1970s. Several National Weather Service field offices in the central plains and Northeast regions of the United States have successfully used VIL as a discriminator between severe and nonsevere thunderstorms. This paper describes the observational data and statistical methodology employed in the development of the SWP algorithm, and regional and seasonal variations in the SWP/severe weather relationship. The expec.
Smart Poly(lactide)-b-poly(triethylene glycol methyl ether methacrylate) (PLA-b-PTEGMA) Block Copolymers: One-Pot Synthesis, Temperature Behavior, and Controlled Release of Paclitaxel

This paper introduces a new class of amphiphilic block copolymers created by combining two polymers: polylactic acid (PLA), a biocompatible and biodegradable hydrophobic polyester used for cargo encapsulation, and a hydrophilic polymer composed of oligo ethylene glycol chains (triethylene glycol methyl ether methacrylate, TEGMA), which provides stability and repellent properties with added thermo-responsiveness. The PLA-b-PTEGMA block copolymers were synthesized using ring-opening polymerization (ROP) and reversible addition–fragmentation chain transfer (RAFT) polymerization (ROP-RAFT), resulting in varying ratios between the hydrophobic and hydrophilic blocks. Standard techniques, such as size exclusion chromatography (SEC) and 1H NMR spectroscopy, were used to characterize the block copolymers, while 1H NMR spectroscopy, 2D nuclear Overhauser effect spectroscopy (NOESY), and dynamic light scattering (DLS) were used to analyze the effect of the hydrophobic PLA block on the LCST of the PTEGMA block in aqueous solutions. The results show that the LCST values for the block copolymers decreased with increasing PLA content in the copolymer. The selected block copolymer presented LCST transitions at physiologically relevant temperatures, making it suitable for manufacturing nanoparticles (NPs) and drug encapsulation-release of the chemotherapeutic paclitaxel (PTX) via temperature-triggered drug release mechanism. The drug release profile was found to be temperature-dependent, with PTX release being sustained at all tested conditions, but substantially accelerated at 37 and 40 °C compared to 25 °C. The NPs were stable under simulated physiological conditions. These findings demonstrate that the addition of hydrophobic monomers, such as PLA, can tune the LCST temperatures of thermo-responsive polymers, and that PLA-b-PTEGMA copolymers have great potential for use in drug and gene delivery systems via temperature-triggered drug release mechanisms in biomedicine applications.
EfficientPNet—An Optimized and Efficient Deep Learning Approach for Classifying Disease of Potato Plant Leaves

The potato plant is amongst the most significant vegetable crops farmed worldwide. The output of potato crop production is significantly reduced by various leaf diseases, which poses a danger to the world’s agricultural production in terms of both volume and quality. The two most destructive foliar infections for potato plants are early and late blight triggered by Alternaria solani and Phytophthora infestans. In actuality, farm owners predict these problems by focusing primarily on the alteration in the color of the potato leaves, which is typically problematic owing to uncertainty and significant time commitment. In these circumstances, it is vital to develop computer-aided techniques that automatically identify these disorders quickly and reliably, even in their early stages. This paper aims to provide an effective solution to recognize the various types of potato diseases by presenting a deep learning (DL) approach called EfficientPNet. More specifically, we introduce an end-to-end training-oriented approach by using the EfficientNet-V2 network to recognize various potato leaf disorders. A spatial-channel attention method is introduced to concentrate on the damaged areas and enhance the approach’s recognition ability to effectively identify numerous infections. To address the problem of class-imbalanced samples and to improve network generalization ability, the EANet model is tuned using transfer learning, and dense layers are added at the end of the model structure to enhance the feature selection power of the model. The model is tested on an open and challenging dataset called PlantVillage, containing images taken in diverse and complicated background conditions, including various lightning conditions and the different color changes in leaves. The model obtains an accuracy of 98.12% on the task of classifying various potato plant leaf diseases such as late blight, early blight, and healthy leaves in 10,800 images. We have confirmed through the performed experiments that our approach is effective for potato plant leaf disease classification and can robustly tackle distorted samples. Hence, farmers can save money and harvest by using the EfficientPNet tool.
How Central Carbon Metabolites of Mexican Mint (Plectranthus amboinicus) Plants Are Impacted under Different Watering Regimes

Plants are sessile, and their ability to reprogram their metabolism to adapt to fluctuations in soil water level is crucial but not clearly understood. A study was performed to determine alterations in intermediate metabolites involved in central carbon metabolism (CCM) following exposure of Mexican mint (Plectranthus amboinicus) to varying watering regimes. The water treatments were regular watering (RW), drought (DR), flooding (FL), and resumption of regular watering after flooding (DHFL) or after drought (RH). Leaf cluster formation and leaf greening were swift following the resumption of regular watering. A total of 68 key metabolites from the CCM routes were found to be significantly (p < 0.01) impacted by water stress. Calvin cycle metabolites in FL plants, glycolytic metabolites in DR plants, total tricarboxylic acid (TCA) cycle metabolites in DR and DHFL plants, and nucleotide biosynthetic molecules in FL and RH plants were significantly (p < 0.05) increased. Pentose phosphate pathway (PPP) metabolites were equally high in all the plants except DR plants. Total Calvin cycle metabolites had a significantly (p < 0.001) strong positive association with TCA cycle (r = 0.81) and PPP (r = 0.75) metabolites. Total PPP metabolites had a moderately positive association with total TCA cycle metabolites (r = 0.68; p < 0.01) and a negative correlation with total glycolytic metabolites (r = −0.70; p < 0.005). In conclusion, the metabolic alterations of Mexican mint plants under different watering regimes were revealed. Future studies will use transcriptomic and proteomic approaches to identify genes and proteins that regulate the CCM route.
Multicenter aortic vessel tree extraction using deep learning

The aorta is the largest vessel of the human body and its pathological degenerations, such as dissections and aneurysms, can be life threatening. An automatic and fast segmentation of the aorta can therefore be a helpful tool to quickly identify an abnormal anatomy. The segmentation of the aortic vessel tree (AVT) typically requires extensive manual labor, but, in recent years, progress in deep learning techniques made the automation of this process viable. For this purpose, we tested different deep learning networks to segment the aortic vessel tree from computed tomography angiography (CTA) scans with a deep neural network consisting of an encoder-decoder architecture with skip connections and an optional self-attention block. The networks were trained on a dataset of 56 CTA scans from three different sources and resulted in Dice score similarities between 0.043−0.897. Generally, the classical U-Nets performed better than the ones containing a self-attention block, indicating that they might diminish performance for AVT segmentation. The quality of the resulting segmentations was highly dependent on the CTA image quality, especially on the contrast between the aorta and the surrounding tissues. However, the trained deep neural network can segment CTA scans well with limited computational resources and training data.
Analysis of space–time variability in agriculture and the environment with geostatistics

This paper gives an overview of space–time variability in agriculture and the environment. Analysis is based upon geostatistics in the framework of linear regression theory. Common procedures for spatial data are extended towards the space–time domain. Several practical studies are presented. The first study describes the use of REML to estimate semi‐variogram models within the presence of trends. The second study describes the use of probability maps in an environmental statistical study. The third study describes the use of geostatistics in modelling the development of a disease in cabbage. Studies are evaluated using quadratic scoring rules.
Design of Codebook for Long-reach Sparse Code Multiple Access Passive Optical Networks

Sparse code multiple access (SCMA) is a promising technology for long-reach passive optical networks (LR-PONs). The design of SCMA codebooks is crucial to system capacity and transmission performance. In this paper, a novel design of SCMA codebooks over fiber channel is proposed, including a low-complexity design criterion based on error probability minimization for independent resource elements and a new labelling rule. The simulation results show that the presented low-complexity SCMA codebook design exhibits good transmission performance, especially in the low optical signal-to-noise ratio (SNR) regions. The codebook design is applicable to LR optical access networks.
A Photonic in-Memory Analog Matrix-Vector-Multiplier Based on Passive Microring Resonator and Photonic Nonvolatile Memory

This paper proposes a low power consumption photonic matrix-vector multiplier based on microring resonator (MR) and photonic nonvolatile memory. Specifically, we use two passive MRs combined with a nonvolatile phase change material Ge2Sb2Te5 (GST) to form a basic dot-product computing unit that supports in-memory multiplication computing, and then interconnect them to achieve massively parallel matrix multiplication. As a proof of concept, we simulated a 4×4 MR-GST based matrix-vector multiplier. The simulation results show that, with acceptable accuracy, the computational efficiency of our proposed structure can reach 1.2×1012 MACs/s/mm2.
Is Greater Social Support from Parents and Friends Related to Higher Physical Activity Levels among Adolescents?

Increasing physical activity levels during adolescence have been put on the agenda by several researchers. This study verified the association between social support from parents and friends and different amounts of moderate-to-vigorous physical activity (MVPA) among adolescents in public school. The present study had a cross-sectional design and included a representative sample of 1984 adolescents (aged 15–17). The ASAFA (Apoio Social para prática de Atividade Física para Adolescentes) scale and the QAFA (Questionário de Atividade Física para Adolescentes) were used to determine social support and physical activity, respectively. For statistical analysis, a conceptual model for structured equations and weighted least squares mean and variance adjusted were applied. Social support from parents increased the odds of engaging in 180 min/week of MVPA by 46.7%, 47.8% for 300 min/week, and 45.5% for 420 min/week. Social support from friends showed similar relations trends: 23.8% for 180 min/week, 23.6% for 300 min/week, and 21.2% for 420 min/week. Social support from parents and friends increased the probability of adolescents reaching the amounts of physical activity investigated. The results indicate that greater social support (from parents and friends) was associated with a higher level of MVPA in Brazilian adolescents.
Implementation of Multi-GPU Based Lattice Boltzmann Method for Flow Through Porous Media

The lattice Boltzmann method (LBM) can gain a great amount of performance benefit by taking advantage of graphics processing unit (GPU) computing, and thus, the GPU, or multi-GPU based LBM can be considered as a promising and competent candidate in the study of large-scale fluid flows. However, the multi-GPU based lattice Boltzmann algorithm has not been studied extensively, especially for simulations of flow in complex geometries. In this paper, through coupling with the message passing interface (MPI) technique, we present an implementation of multi-GPU based LBM for fluid flow through porous media as well as some optimization strategies based on the data structure and layout, which can apparently reduce memory access and completely hide the communication time consumption. Then the performance of the algorithm is tested on a one-node cluster equipped with four Tesla C1060 GPU cards where up to 1732 MFLUPS is achieved for the Poiseuille flow and a nearly linear speedup with the number of GPUs is also observed.
Fusion-Based Segmentation and Classification of Novel Coronavirus or Pneumonia from Chest X-ray Images Using Machine Learning Techniques

The deadfall widespread of coronavirus (SARS-Co V-2) disease has trembled every part of the earth and has significant disruption to health support systems in different countries. In spite of such existing difficulties and disagreements for testing the coronavirus disease, an advanced and low-cost technique is required to classify the disease. For the sense of reason, supervised machine learning (ML) along with image processing has turned out as a strong technique to detect coronavirus from human chest X-rays. In this work, the different methodologies to identify coronavirus (SARS-CoV-2) are discussed. It is essential to expand a fully automatic detection system to restrict the carrying of the virus load through contact. Various deep learning structures are present to detect the SARS-CoV-2 virus such as ResNet50, Inception-ResNet-v2, AlexNet, Vgg19, etc. A dataset of 10,040 samples has been used in which the count of SARS-CoV-2, pneumonia and normal images are 2143, 3674, and 4223 respectively. The model designed by fusion of neural network and HOG transform had an accuracy of 98.81% and a sensitivity of 98.65%.
State estimation and traversability map construction method of a quadruped robot on soft uneven terrain

Quadruped robots show excellent application prospects in complex environment detection and rescue. At present, scholars mainly focus on quadruped walking in rigid environments. However, quadruped robots often need to pass through uneven and soft unconstructed terrains, prone to slip and impact. The mismatch between the planned foothold position and the real one resulting from environmental uncertainties makes the robot unstable. In this paper, the state estimation and traversability map construction methods are proposed for quadruped robots to achieve stable walking in an unstructured environment, especially on soft terrains. First, the Error‐state Kalman Filter (ErKF) is extended by optimizing the leg odometry information to get an accurate robot state, especially in soft, uneven terrain. The ErKF method fuses the sensor data from the inertial measurement unit, laser, camera, and leg odometry. The leg odometry is optimized by considering the foot slippage, which easily occurs in soft uneven terrains. Then, the unstructured environment is parameterized and modeled by the terrain inclination, roughness, height, and stiffness. A traversability map, which is essential for robot path and foothold planning in autonomous movement, is constructed with the above parameters. Finally, the proposed method is verified by simulation and experiments. The results show that the quadruped robot can walk stably on different soft and uneven terrains.