Miracle-dz/UniFAR-FTUs huggingface dataset:
150 MHz in vivo ultrasound of the skin: imaging techniques and signal processing procedures targeting homogeneous resolution

The range of possible applications of an ultrasound imaging system is usually limited by the maximum resolution and penetration depth of the system. Both requirements are inversely concurrent to each other, as the resolution improves with rising frequencies while the penetration depth lowers. Thus, it is useful to develop procedures, which give rise to better resolutions in both, axial and lateral directions, while the penetration depth, which is specified by the application, is kept constant. To meet this goal, we use sophisticated imaging techniques (SAFT, B/Z-scan) enabling the employment of high focused transducers combined with an adapted pulse compression method, which takes the transfer functions of the system and the tissue into account. Those procedures have been implemented in an ultrasound imaging system suited for in vivo skin or eye examinations working in the frequency range from 20 to 150 MHz
Simulation of ferromagnetic resonance spectra of linear chains of magnetite nanocrystals

Ensembles of linear chains of stable single domain magnetite crystals, as found in magnetotactic bacteria, exhibit a distinctly asymmetric ferromagnetic resonance (FMR) signal, with a pronounced high-field minimum and two or three low-field maxima in the derivative spectrum. To identify the microscopic origin of these traits, we have simulated FMR spectra of dilute suspensions of linear chains oriented randomly in space by modeling the chain as a Stoner−Wohlfarth-type rotation ellipsoid whose long axis coincides with an easy [111] axis of the cubic magnetocrystalline anisotropy system. The validity of the model is examined by comparing the results with explicit calculations of the interactions among the particles in the chain. The single ellipsoid model reproduces the experimentally observed FMR traits and can be related to the explicit chain model by adjusting the contribution to the uniaxial anisotropy along the chain axis to account for the magnetostatic interactions. Finally, we provide a practical ap.
Forward kinematics analysis of offset 6-RRCRR parallel manipulators

A methodology for the numerical solution of the forward kinematics problem of 6-RRCRR parallel manipulators with orthogonal non-intersecting RR-joint configuration is presented in this article. The inverse and forward kinematics solutions of such robots compared with that of parallel robots with orthogonal intersecting RR-joint or universal joint configurations are much more complicated due to the existence of dependent joint variables. The constraints of RR-joints are analysed and the numerical algorithm for the forward kinematics solution is assessed. Numerical results for the solution of the forward kinematics of 6-RRCRR parallel robot under study are provided to confirm the accuracy and efficiency of the procedure.
ReinforSec: An Automatic Generator of Synthetic Malware Samples and Denial-of-Service Attacks through Reinforcement Learning

In recent years, cybersecurity has been strengthened through the adoption of processes, mechanisms and rapid sources of indicators of compromise in critical areas. Among the most latent challenges are the detection, classification and eradication of malware and Denial of Service Cyber-Attacks (DoS). The literature has presented different ways to obtain and evaluate malware- and DoS-cyber-attack-related instances, either from a technical point of view or by offering ready-to-use datasets. However, acquiring fresh, up-to-date samples requires an arduous process of exploration, sandbox configuration and mass storage, which may ultimately result in an unbalanced or under-represented set. Synthetic sample generation has shown that the cost associated with setting up controlled environments and time spent on sample evaluation can be reduced. Nevertheless, the process is performed when the observations already belong to a characterized set, totally detached from a real environment. In order to solve the aforementioned, this work proposes a methodology for the generation of synthetic samples of malicious Portable Executable binaries and DoS cyber-attacks. The task is performed via a Reinforcement Learning engine, which learns from a baseline of different malware families and DoS cyber-attack network properties, resulting in new, mutated and highly functional samples. Experimental results demonstrate the high adaptability of the outputs as new input datasets for different Machine Learning algorithms.
Toward Increasing Trust in Exascale Simulations

In recent decades, High Performance Computing (HPC) and simulations have become determinant in many areas of engineering and science. Since many HPC applications rely extensively on floating-point arithmetic operations to solve computational problems, many kinds of numerical errors can be introduced during the program execution, leading to instability or reproducibility problems. One kind of these error sources is the loss of significant digits or cancellation which produces inaccurate results when two nearby numbers are subtracted. In this article, we present Candy, a new dynamic library based on code instrumentation that detects cancellations in numerical software. The originality of our method is to compute the number of significant bits of floating-point numbers in a generalized framework by attaching a shadow value in higher precision to each number. This helps to detect in an accurate way if a program suffers from cancellation problems and thus to increase the trust in large-scale HPC applications and exascale simulations. We evaluate Candy over a set of complex and real-world numerical applications. In addition, we compare our method against the state-of-art tool FPChecker in terms of efficiency, mixed precision results and speed of the analysis.
Startup-Aware Dependent Task Scheduling With Bandwidth Constraints in Edge Computing

In edge computing, applications can be scheduled in the granularity of inter-dependent tasks to proximate edge servers to achieve high performance. Before execution, the edge server must initialize the corresponding runtime environment, named task startup. However, existing studies on dependent task scheduling severely ignore bandwidth constraints during task startups, which is impractical and incurs a long startup latency. To fill in this gap, we first model the task startup process with bandwidth constraints on edge servers. Then, we formulate the dependent task scheduling problem with startup latency in heterogeneous edge computing. To efficiently generate schedules and satisfy the real-time requirements in edge computing, a novel low-complexity list scheduling algorithm integrated with cloud clone, Startup-aware Dependent Task Scheduling (SDTS), is proposed. Constrained by bandwidth and computation resources, SDTS first coordinates task startup, dependent data transmission, and task execution to optimize each task's finish time. Then, a cloud clone for each task is deployed to utilize scalable resources and initialized runtime environments. Furthermore, task scheduling refinement is designed to release the bandwidth and computation resources consumed by redundant tasks and improve the schedule. Extensive simulations based on real-world datasets show that SDTS substantially reduces 30%-60% makespan compared with existing baselines.
Optimal Team Formation and Job Assignment to Optimize Warehouse Operations

We study optimal container unloading and warehouse replenishment at a manufacturing plant. The warehouse inventory is depleted as parts are consumed in the manufacturing process. The inventory level is replenished using the parts stored in sea-containers and trailers available at the plant. Our goal is to determine the optimal team formation, buffer allocation, and job assignment for teams of workers. The teams work at two tandem stations, each with several finite-buffered parallel queues. The objective is to minimize the total time required to process all containers and trailers. There are operational constraints on the number of teams that can be formed, the number of servers in each team, and the number of buffer spaces allotted to each team. We use a simulation-based optimization approach, and the results consistently show a reduction in the processing time. Further, we also observe that the optimal team formation is dependent on the total workload.
Target localization for a three-dimensional multibeam sonar imaging system

One advantage of three-dimensional multibeam imaging systems is that they have the ability to image and track many targets concurrently in their field of view. In this regard, the accurate estimation of both position and target strength are important data which can be derived from the echo responses of an active multibeam imaging system. This article considers an algorithm which can be used to yield more accurate positional estimates for targets than available from the preformed beams of a given multibeam system: FishTV [, Deep-Sea Res. 42, 1495 (1995)]. The method refines a least squares residual of the predicted data versus observed data by adjusting the parameters of a model. The parameters are the position and target strength of the target. It is demonstrated that this procedure is a maximum likelihood estimator for position and target strength in high signal-to-noise. In addition, the shape of the least squares surfaces indicates that the algorithm is somewhat robust with respect to noise.
On the Possibility of Creating Small Detonation Aero Engines Based on Jet Initialization of Detonation

The paper considers the design of air-jet engines that implement detonation combustion based on jet initialization of detonation. Conditions implementing jet initialization of detonation are considered in detail. Working processes numerical simulation in the detonation engine based on the author's software package is carried out. The numerical experiment showed that the combustion transition to detonation for the transonic flow mode can be realized at the obstacle section due to the occurrence of a system of jumps. On the basis of modeling, it is possible to draw the conclusion that at ∽40% shading factor of a cross-section of the pipe and step of obstacles, approximately equal to the diameter of the pipe, the optimum pre-detonation distances are apparently reached. Based on research work pulsating detonation engines running on automobile gasoline were designed and manufactured as prototypes for flight tests. The engine works steadily in a wide range of composition of the mixture. The draft of the best samples of engines of this size at a speed of 120 m/sec- up to 147–177 N. The engines have progressive speed performance. When detonation combustion occurs in an engine with a 100 Hz frequency, strong vibrations are observed.
EPAC1 Pharmacological Inhibition with AM-001 Prevents SARS-CoV-2 and Influenza A Virus Replication in Cells

The exceptional impact of the COVID-19 pandemic has stimulated an intense search for antiviral molecules. Host-targeted antiviral molecules have the potential of presenting broad-spectrum antiviral activity and are also considered as less likely to select for resistant viruses. In this study, we investigated the antiviral activity exerted by AM-001, a specific pharmacological inhibitor of EPAC1, a host exchange protein directly activated by cyclic AMP (cAMP). The cAMP-sensitive protein, EPAC1 regulates various physiological and pathological processes but its role in SARS-CoV-2 and influenza A virus infection has not yet been studied. Here, we provide evidence that the EPAC1 specific inhibitor AM-001 exerts potent antiviral activity against SARS-CoV-2 in the human lung Calu-3 cell line and the African green monkey Vero cell line. We observed a concentration-dependent inhibition of SARS-CoV-2 infectious viral particles and viral RNA release in the supernatants of AM-001 treated cells that was not associated with a significant impact on cellular viability. Furthermore, we identified AM-001 as an inhibitor of influenza A virus in Calu-3 cells. Altogether these results identify EPAC1 inhibition as a promising therapeutic target against viral infections.
Convergence analysis of the parallel classical block Jacobi method for the symmetric eigenvalue problem

We analyze convergence properties of the parallel classical block Jacobi method for the symmetric eigenvalue problem using dynamic ordering strategy of Be(cid:20)cka et al. It is shown that the method is globally convergent. It is also shown that the order of convergence is ultimately quadratic if there are no multiple eigenvalues.
An Ultra-Fast Automatic License Plate Recognition Approach for Unconstrained Scenarios

Recently, with the development of deep learning, Automatic License Plate Recognition (ALPR) has made great progress, However, there are still many challenges to accomplish license plate (LP) recognition under various traffic scenarios. One of them is the detection speed and recognition speed, and the other is the difficulty to recognize the low resolution and highly tilted LP images. In this paper, we present a two-stage ALPR framework to achieve efficient LP detection and recognition in unconstrained scenarios. Our LP detector is based on improved Yolov3-tiny, and we also propose a lightweight recognition network MRNet based on multi-scale features. In order to improve the inference speed, we abandoned the rectification of LP images, and RNNs that are difficult to compute in parallel. Additionally, we also propose a license plate data augmentation method, which achieves more effective augmentation and improves the generalization ability of the network through secondary random and hyperparametric search. As an additional contribution, we provide a challenging dataset collected from real-world driving recorders. The dataset is for multiple LPs in a single image, which makes up for the lack of public datasets in multiple LP scenarios. We evaluated the results on five datasets and showed that we achieved the best performance on almost all test sets, achieving 99.8% accuracy on CCPD with more than 180,000 license plate test sets. In terms of speed, the inference speed for detecting license plates reaches 751 FPS, and the fastest inference time for recognizing a single license plate takes only 2.9 ms.
Slow collisions between heavy particles

Certain coupling terms neglected in the pertubed stationary state and related approximations for treating slow collisions between heavy particles are discussed. It is pointed out that their inclusion would make the group of approximations in question tend to the Born approximation at high impact energies and weak interactions. One member of the group differs from the Born approximation only in the coupling terms. Information on the importance of these terms can hence be obtained by comparing the values of cross-sections given by this member with the corresponding values given by the Born approximation. Calculations are carried out on the processes H+ + H(1s)→ H+ + H(2p) and H(1s) + H(1s)→ H(1s) + H(2p).
Adiabatic Survey of Subdwarf B Star Oscillations. III. Effects of Extreme Horizontal Branch Stellar Evolution on Pulsation Modes

We present the final results of a large, systematic survey of the adiabatic oscillation properties of models of subdwarf B (sdB) stars. This survey is aimed at providing the minimal theoretical background with which to understand the asteroseismological characteristics of the recently discovered class of pulsating sdB stars (the EC 14026 objects). In this paper, the last of a series of three, we consider the effects of stellar evolution on the pulsation eigenmodes of sdB star models. We specifically analyze the adiabatic properties of 149 equilibrium models culled from seven distinct extreme horizontal branch evolutionary sequences. Those have been chosen in order to span fully the region of parameter space where real sdB stars are found. We primarily focus on the evolution of the pulsation periods (P) and the rates of period change (dP/dt), which are both a priori observable quantities. Both the acoustic and gravity branches of stellar oscillations are considered. In light of the results derived in the first two papers of this series, we discuss how the values of P and dP/dt relate to the various structural adjustments that sdB stars undergo during evolution. We find that the acoustic modes react primarily to the secular variations of the surface gravity. In contrast, we identify three main factors that regulate the period evolution of gravity modes: these are the variations brought about by evolution in both the surface gravity and the effective temperature, as well as the onset and growth of a chemical discontinuity between the C-O-enriched nucleus and the helium-rich mantle. We also find, as expected from our previous results, that the period evolution of the pulsation modes in sdB stars is further complicated by trapping effects (microtrapping in the case of p-modes) and by avoided crossings between modes. The latter occur preferentially in certain regions of parameter space. We provide our final results in the form of extensive tabular data in the appendices, which, we hope, will be useful in future asteroseismological analyses of EC 14026 stars.
Development of Low Cost, Fast, and Highly Stable Piezoresistive Strain Sensors to Monitor Human Movement

A low-cost, easy-to-fabricate, and highly sensitive piezoresistive strain sensor was designed in this study. A well-known conductive polymer, for example, poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS), was deposited on the latex-free elastic bands to prepare the strain sensor. The sensitivity of the proposed sensor was about 4.27 over the wide strain measurement range of about 0%–83%. The sensor also exhibited a good hysteresis response; highly stable sensing performance with a relative standard deviation (RSD) value of about 1.32%; good linearity, that is, the correlation coefficient, ${R}^{{2}}$ , value of about 0.987; and high reproducibility. The proposed strain sensor was installed on different joints of the human body to observe the movement of the human body, and satisfactory results were obtained. In addition, the proposed strain sensing element provided a fast response and recovery time of about 1.74 and 3.73 ms, respectively. Index Terms— Conductive polymer, linearity, piezoresistive, poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS), stability, strain sensor.
13 C NMR of methane in an AlPO 4 − 11 molecular sieve: Exchange effects and shielding anisotropy

${}^{13}\mathrm{C}$ NMR spectra of ${}^{13}{\mathrm{CH}}_{4}$ in an ${\mathrm{AlPO}}_{4}\ensuremath{-}11$ molecular sieve reveal exchange effects between adsorbed and nonadsorbed methane gas. An application of pulsed field gradients is introduced to decrease nonadsorbed and exchanging gas signals in order to extract the chemical shift anisotropy line shape of the adsorbed gas. The resulting ${}^{13}\mathrm{C}$ shielding anisotropy of methane is compared to existing value for methane in related SAPO-11 material. Less anisotropic shielding is observed in ${\mathrm{AlPO}}_{4}\ensuremath{-}11,$ most likely due to the lack of charge-compensating cations.
The Effect of Pretreatment on a PtCu/C Catalyst’s Structure and Functional Characteristics

This research focuses on studying the effects of various pretreatment types on a PtCu/C catalyst synthesized by the co-deposition of metal precursors. The treatment in a 1 M HNO3 solution for 1 h is shown to result in a slight increase in activity in the oxygen electroreduction reaction (both the mass activity and specific activity calculated for the value of the electrochemically active surface area). The sample obtained after the thermal treatment, which is carried out at 350 °C under an argon atmosphere for 1 h, demonstrates 1.7 times higher specific activity than the sample before the treatment. The durability testing results obtained by the stress testing method in a potential range of 0.6–1.4 V during 2000 cycles show that the PtCu/C catalysts after both the acid treatment and the thermal treatment are characterized by higher residual activity than the sample in the “as-prepared” state.
Multi-Input Autonomous Driving Based on Deep Reinforcement Learning With Double Bias Experience Replay

It is still a challenge to realize safe and fast autonomous driving through deep reinforcement learning (DRL). Most autonomous driving reinforcement learning models are subject to a single experience replay approach for training agents and how to improve the driving speed and safety of agents has become the focus of research. Therefore, we present an improved double-bias experience replay (DBER) approach, which enables the agent to choose its own driving learning tendency. A new loss function is proposed to ameliorate the relationship between negative loss and positive loss. The proposed approach has been applied to three algorithms to verify: deep Q network (DQN), dueling double DQN (DD-DQN), and quantile regression DQN (QR-DQN). Compared with the existing approaches, the proposed approach show better performance and robustness of driving policy on the driving simulator, which is implemented by the unity machine learning (ML) agents. The approach makes the vehicle agent obtain better performance, such as higher reward, faster driving speed, less lane changing, and more in the same training time.
Possible existence of cosmological cosmic rays I. the framework for light-element and gamma-ray production

This paper examines the possibility of the existence of cosmological cosmic rays (CCR), in the framework of big-bang cosmology. The model assumes a total energy spectrum similar to that observed at Earth at high energies, a composition of protons and . cap alpha. -particles only, with . cap alpha. /p=0.1. Following Stecker, the CCR are assumed to be born in a burst at some (high) redshift z/sub s/. Gamma-rays originate from . pi. /sup 0/ decay resulting from interactions of the high-energy part of the CCR, and light elements are produced via (p.cap alpha.)+(p.cap alpha.) reactions by the low-energy part, both of them by collisions with the ambient matter (of density corresponding to a deceleration parameter q/sub 0/). The 1--100 MeV . gamma. -ray background spectrum and the lithium abundance are considered as observational constraints on the possible CCR flux intensity. To this end, a theoretical framework is set for simultaneous . gamma. -ray and light-element production by solving a system of coupled time-dependent transport equations, taking ionization and expansion losses into account. The absolute lithium abundance is calculated by normalizing the CCR flux to the observed . gamma. -ray background spectrum; numerical results will be given and discussed in a separate paper, as a function of q/sub 0/ and z/sub s/.