Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved glove defect detection algorithm based on YOLOv5 framework

Aiming at the problems of low efficiency, poor accuracy and narrow application area of manual detection, this paper proposed a glove defect detection algorithm YOLO-G with high accuracy, low number of parameters and easy deployment. In the preparation phase, data augmentation is performed to expand the dataset, reduce the image size, and increase the data richness. The K-means algorithm is used to calculate anchors. Then Ghostnet is used to replace some structures in the YOLOv5 backbone and Neck, which effectively reduces the size of the model. The attention mechanism is added to make the model more sensitive to key information and improve the detection effect. The experimental results show that the mean average precision of YOLO-G reaches 90.4%, the number of parameters decreases by 47%, and the amount of calculation decreases by 49.4%, which is more suitable for the deployment of embedded scenarios.
Auditory-Based Multi-Scale Amplitude-Aware Permutation Entropy as a Measure for Feature Extraction of Ship Radiated Noise

Feature extraction of ship radiated noise (SRN) plays an important role for target detection and recognition. Aiming at extracting prominent and reliable features of SRN, auditory based multi-scale amplitude-aware permutation entropy (AMAAPE) is proposed in this paper. The proposed method consists of two parts that are signal decomposition and multi-scale entropy quantification. Firstly, a set of filters that can effectively simulate the mask effect and frequency response of human ears is designed, through which the SRN is decomposed into a series of band-limited signals. Then, multi-scale amplitude-aware permutation entropy (MAAPE) is employed to measure the complexity of each band-limited signal. Experimental results show that the proposed scheme achieves higher classification accuracy compared with multi-scale permutation entropy(MPE) and variational mode decomposition (VMD).
A Signal-Integrity Aware ATPG Flow to Generate High-Quality Patterns for Testing System-on-Chip Designs

Signal-Integrity (SI) issues impose significant challenges to test engineers in developing a defect-free SoC design. Deep nanometer technology designs are always vulnerable to various signal-integrity issues. In complex System-on-Chip (SoC) designs, these issues are due to the ever-increasing timing delay variations in the interconnecting networks and gates. Thereby, the chip’s overall performance and power consumption are affected and contribute to a rise in the defective parts per million (DPPM). Supply noise (SN) in the power and ground networks is one factor that increase defects in an SoC. This noise can be eliminated to a great extent by modeling the SI impacts and testing the SoC design with a set of high-quality test patterns that will insure a better delay defect coverage in designs. In our work, we propose a step-by-step SN aware Automatic Test Pattern Generation (ATPG) flow by selecting a set of sensitive victim gate nets, simultaneously inducing all these victim gate nets and generating test patterns under supply noise impact. This flow uses the most effective nets for pattern generation based on the supply noise information determined from the design layout. We have developed this ATPG flow by customizing the existing scan-based techniques to capture delay defects by generating supply-noise modeled delay testable patterns. Our effective pattern generation flow is applied to ITC’99 benchmark circuits to show results.
Wasserstein Generative Adversarial Networks with Meta Learning for Fault Diagnosis of Few-shot Bearing

In practical work situations, the bearing fault diagnosis is a small and imbalanced data challenge. However, the intelligent fault diagnosis model relies on a mass of label data. This research, presents a different method, Wasserstein GAN with Meta Learning, for overcoming the difficulty of few-shot fault diagnosis under imbalanced data constraints. The WGAN module can generate synthetic samples for the data argument, and the first-order model agnostic meta-learning (FOMAML) to initialize and modify the network parameters. Validation of the comparative performance has been made using a benchmark dataset, i.e. CWRU datasets, which show that can achieve excellent diagnostic accuracy with small data. It's successfully overcome that the imbalanced data lead to the sample distribution bias and over-fitting. In addition, it can leverage that can precisely identify the bearing fault health types in a variety of working environments, even with noise interference. It is also found that the proposed model performs better in the testing set after training difficult datasets.
TEMPORAL CORRELATIONS BETWEEN OPTICAL AND GAMMA-RAY ACTIVITY IN BLAZARS

We have been using the 0.76 m Katzman Automatic Imaging Telescope (KAIT) at Lick Observatory to optically monitor a sample of 157 blazars that are bright in gamma-rays being detected with high significance (⩾10σ) in one year by the Large Area Telescope (LAT) on the Fermi Gamma-ray Space Telescope. We attempt to observe each source on a three-day cadence with KAIT, subject to weather and seasonal visibility. The gamma-ray coverage is essentially continuous. KAIT observations extend over much of the five-year Fermi mission for several objects, and most have >100 optical measurements spanning the last three years. These blazars (flat-spectrum radio quasars and BL Lac objects) exhibit a wide range of flaring behavior. Using the discrete correlation function (DCF), here we search for temporal relationships between optical and gamma-ray light curves in the 40 brightest sources in hopes of placing constraints on blazar acceleration and emission zones. We find strong optical–gamma-ray correlation in many of these sources at time delays of ∼1 to ∼10 days, ranging between −40 and +30 days. A stacked average DCF of the 40 sources verifies this correlation trend, with a peak above 99% significance indicating a characteristic time delay consistent with 0 days. These findings strongly support the widely accepted leptonic models of blazar emission. However, we also find examples of apparently uncorrelated flares (optical flares with no gamma-ray counterpart and gamma-ray flares with no optical counterpart) that challenge simple, one-zone models of blazar emission. Moreover, we find that flat-spectrum radio quasars tend to have gamma-rays leading the optical, while intermediate- and high-synchrotron peak blazars with the most significant peaks have smaller lags/leads. It is clear that long-term monitoring at high cadence is necessary to reveal the underlying physical correlation.
Discovery of K-Shell Emission Lines of Neutral Atoms in the Galactic Center Region

The K-shell emission line of neutral iron from the Galactic center (GC) region is one of the keys concerning the structure and activity of the GC. The origin is still an open question, but possibly due either to X-ray radiation or to electron bombarding to neutral atoms. To address this issue, we analyzed the Suzaku X-ray spectrum from the GC region of intense neutral iron line emission, and report on the discovery of K˛ lines of neutral argon, calcium, chrome, and manganese atoms. The equivalent widths of these K˛ lines indicate that the metal abundances in the GC region should be � 1.6 and � 4 of the solar value, depending on the X-ray and electron origins, respectively. On the other hand, the metal abundances in the hot plasma in the GC region are found to be � 1–2 solar. These results favor that the origin of the neutral K˛ lines is due to X-ray irradiation.
GAMMA-RAY BURST POLARIZATION VIA COMPTON SCATTERING PROCESS

Synchrotron radiation and Compton scattering are widely accepted as the most likely emission mechanisms of some astrophysical phenomena, such as gamma-ray bursts (GRBs) and active galactic nuclei. The measurement of the polarization of photons provides a useful tool to distinguish different emission mechanisms and structures of the emission region. Based on the differential cross section of a polarized photon scattered by an unpolarized electron of any initial momentum, we derive an analytical formula of polarization for beamed photons scattered by isotropic electrons with a power-law distribution. Numerical calculations are carried out in four special cases: electrons at rest, Thomson limit, head-on collision, and monochromatic electrons. It is found that the maximum polarization can be as high as 100% for low energy photons, if the electrons are at rest. Although polarization is highly suppressed due to the isotropic electrons, a maximum value of ∼10%–20% can still be achieved. The Compton scattering process can be used to explain the polarization of GRB 041219A and GRB 100826A.
Detecting intermediate mass black holes in globular clusters with machine learning

Mergers of stellar-mass black holes were recently observed in the gravitational wave window opened by LIGO. This puts the spotlight on dense stellar systems and their ability to create intermediate-mass black holes (IMBHs) through repeated merging. Unfortunately, attempts at direct and indirect IMBH detection in star clusters in the nearby universe have proven inconclusive as of now. Indirect detection methods attempt to constrain IMBHs through their effect on star cluster photometric and kinematic observables. They are usually based on looking for a specific, physically motivated signature. While this approach is justified, it may be suboptimal in its usage of the available data. Here I present a new indirect detection method, based on machine learning, that is unaffected by these restrictions. I reduce the scientific question whether a star cluster hosts an IMBH to a classification problem in the machine learning framework. I present preliminary results to illustrate how machine learning models are trained on simulated datasets and measure their performance on previously unseen, simulated data.
Sentimental Analysis on Student Feedback using NLP & POS Tagging

Sentiment analysis has been extensively used in a variety of fields in the previous decade, including business, social media, and education. The usage of sentiment analysis is rising, but it remains difficult, particularly in the education area, where the nature of the language utilized by pupils and a sizeable amount of information, dealing with their processing thoughts is a difficult task. Several studies of the literature illustrate the current state of sentiment analysis application which include sector from various perspectives and circumstances. In the teaching analysis system, the qualifier sentimental words for student feedback aren’t thought of the result of teaching analysis through student feedback isn’t displayed whether positive or negative opinion. To efficiently utilize sentimental analysis for the teaching system through student feedback, this paper proposes to analyze the scholar’s text feedback automatically using NLP and post-tagging based approach to conclude the extent of teaching work. Here, the main concentration is applying sentimental analysis for students’ feedback, collected through online mode. The use of ML and the NLP approach to examine the feelings of the students included in the text document is used for classification based on students’ comments to predict the polarity (positive/ negative). A total of 2200 student feedback are collected and analyzed, which results in 60 percentage positive and 40 percentage negative through Post Tagging.
Steel Sheet Defect Detection using Feature Pyramid Network and RESNET

The automatic detection and recognition of steel surface defects is very important in the steel manufacturing industry based on the classification of defect types using artificial intelligence. Deep learning plays an important role to decrease the defection rate in the steel manufacturing industry. Defects in the surface of steel have a serious effect on the quality of steel. Previous research for the NEU-DET dataset has shown that there are some limitations, such as low detection accuracy. In this paper Feature Pyramid Network (FPN) + Resnet neural network has been proposed for detecting the overlapping and multiple defects in the complex background for improving the defect detection rate. The histogram and edge detection approach is used for feature extraction for the classification of defects. The final result shows that the proposed FPN + Resnet model trained with good steel surface defect detection performance, and the dice score is 0.7963, IoU score is 0.6818 and losses is 0.0522 which is very low as compared to others. The proposed detection approach can efficiently detect minor targeted defects on the steel surface, which can be used as a benchmark for automated steel surface defect identification.
Consistent massive truncations of type-IIB supergravity on Sasaki-Einstein manifolds

Recent work on holographic superconductivity and gravitational duals of systems with nonrelativistic conformal symmetry have made use of consistent truncations of D = 10 and D = 11 supergravity retaining some massive modes in the Kaluza-Klein tower. In this paper we focus on reductions of type-IIB supergravity to five dimensions on a Sasaki-Einstein manifold, and extend these previous truncations to encompass the entire bosonic sector of gauged D = 5, N = 2 supergravity coupled to massive multiplets up to the second Kaluza-Klein level. We conjecture that a necessary condition for the consistency of massive truncations is to only retain the lowest modes in the massive trajectories of the Kaluza-Klein mode decomposition of the original fields. This is an extension of the well-known result that consistent truncations may be obtained by restricting to the singlet sector of the internal symmetry group.
Mg II λ2797, λ2803 emission in a large sample of low-metallicity star-forming galaxies from SDSS DR14

A large sample of Mg II emitting star-forming galaxies with low metallicity [O/H] = log(O/H) – log(O/H)⊙ between –0.2 and –1.2 dex is constructed from Data Release 14 of the Sloan Digital Sky Survey. We selected 4189 galaxies with Mg II λ2797, λ2803 emission lines in the redshift range z ∼ 0.3–1.0 or 35% of the total Sloan Digital Sky Survey star-forming sample with redshift z ≥ 0.3. We study the dependence of the magnesium-to-oxygen and magnesium-to-neon abundance ratios on metallicity. Extrapolating this dependence to [Mg/Ne] = 0 and to solar metallicity we derive a magnesium depletion of [Mg/Ne] ≃ –0.4 (at solar metallicity). We prefer neon instead of oxygen to evaluate the magnesium depletion in the interstellar medium because neon is a noble gas and is not incorporated into dust, contrary to oxygen. Thus, we find that more massive and more metal abundant galaxies have higher magnesium depletion. The global parameters of our sample, such as the mass of the stellar population and star formation rate, are compared with previously obtained results from the literature. These results confirm that Mg II emission has a nebular origin. Our data for interstellar magnesium-to-oxygen abundance ratios relative to the solar value are in good agreement with similar measurements made for Galactic stars, for giant stars in the Milky Way satellite dwarf galaxies, and with low-metallicity damped Lyman-alpha systems.
A Review of Cyber–Resilient Smart Grid

This paper provides a survey of measurable factors affecting the adoption of cybersecurity enhancement strategies in the smart grid. From power systems operators’ point of view, it is crucial to determine to what degree the smart grid can be made resilient against cyber-attacks through the use of efficient resilience enhancement strategies. In the past decade, there have been numerous attempts to improve the resilience of the smart grid against different types of cyber-attacks. This paper mainly focuses on the recently proposed cybersecurity strategies for successfully detecting and identifying False Data Injection (FDI) attacks, and compares and contrasts their accuracy, computational burden, and robustness against external factors, as their main measurable factors. It is challenging to find an all-inclusive solution satisfying all requirements of the smart grid. Hence, a quantitative mean for evaluation of the cyber-attack detection and identification strategies is provided in this paper.
THE SPIN-DOWN OF SWIFT J1822.3−1606: A NEW GALACTIC MAGNETAR

On 2011 July 14, a new magnetar candidate, Swift J1822.3−1606, was identified via a rate trigger on the Swift/Burst Alert Telescope. Here we present an initial analysis of the X-ray properties of the source, using data from the Rossi X-ray Timing Explorer, Swift, and the Chandra X-ray Observatory, spanning 2011 July 16–October 8. We measure a precise spin period of P = 8.43771968(6) s and a spin-down rate of , at MJD 55761.0, corresponding to an inferred surface dipole magnetic field of B = 4.7(2) × 1013 G, the second lowest thus far measured for a magnetar, though similar to those of 1E 2259+586 and several high-magnetic field radio pulsars. We show that the flux decay in the 1–10 keV band is best fit by a double exponential with timescales of 9 ± 1 and 55 ± 9 days. The pulsed count rate decay in the 2–10 keV band, by contrast, is better fit by a single exponential decay with timescale 15.9 ± 0.2 days. After increasing from ∼35% for ∼20 days after the onset of the outburst, the pulsed fraction in the 2–10 keV band remained constant at ∼45%. We argue that these properties confirm this source to be a new member of the class of objects known as magnetars. We consider the distribution of magnetar periods and inferred dipole magnetic field strengths, showing that the former appears flat in the 2–12 s range, while the latter appears peaked in the 1014–1015 G range.
A Full-time-utilization Optimization Approach for Computational Nanobiosensing

We have proposed a novel framework of computational nanobiosensing (CONA) by transforming the early tumor detection process into an in vivo function optimization problem. The tumor is viewed as the global optimal solution, which is to be searched for by the nanorobots that act as the computational agents. The external control and tracking devices are used to perform actuation and imaging of nanorobots, which correspond to the optimization and learning processes of CONA, respectively. Our previous investigations are based on the protocol that the nanorobot control and tracking are alternating processes, which means that the optimization procedure does not fully utilize the available time for computing. Considering that the parallel execution of control and tracking is a superior way to realize real-time steering of nanorobots, we propose a full-time-utilization (FTU) optimization approach in this paper to improve the performance of CONA. Some in silico experiments are carried out in a 3D search space to demonstrate the superiority of the proposed FTU optimization approach compared to the previous partial-time-utilization (PTU) one.
A Wireless-Powered Relaying System with Energy Buffer for Implant WBAN

Wireless body area networks (WBANs) can provide continuous monitoring of human biological signals. Due to the limited energy of the sensors, wireless powered system has been adopted to prolong network lifetime for implant WBANs. In this paper, we propose an energy buffer aided wireless-powered relaying system for self-sustainable implant WBAN, which includes an implant device, a wearable device with an energy buffer and an off-body access point (AP). In the downlink, the wearable device harvests energy that is stored in the energy buffer from a radio-frequency signal broadcasted by the AP and provides energy supply via wireless energy transfer to the implant device. In the uplink, the implant device uses harvested energy to transmit its collected data to the wearable device. Then the wearable device uses the stored energy to decode and forward the data to the AP. We investigate two transmission policies for the wearable device, namely best-effort policy (BEP) and on-off policy (OOP). We derive the limiting distribution of the energy for both the BEP and OOP, and the performance expressions in terms of the outage probability and average throughput. Simulation results are presented to validate the analytical expressions and provide some useful insights.
Social Financial Technologies for the Development of Large-Scale Healthcare Systems and the Russian Economy

The proposed social financial technologies for the development of large-scale healthcare systems and the Russian economy, harmoniously combining the interests of working citizens, owners and the state, make it possible, at quite achievable growth rates of gross domestic product (enterprise revenue) by 3% per year, to ensure a 34% increase in wages of working citizens over 5 years, which will practically end poverty. To increase contributions to the development fund for 5 years by 16%, in which, first of all, the owners of enterprises and the entire workforce are interested, since this ensures the growth of their incomes and the possibility of constant modernization and updating of technological equipment and the release of new competitive products. First, to stabilize, and starting from 2026, to increase deductions to the Federal Compulsory Medical Insurance Fund and income tax, income tax and value added tax receipts and bring this growth to 30% by 2041, which will allow the state to solve problems of development of domestic healthcare and accessibility of medical services to the population of the Russian Federation. When switching to the social financial technologies proposed in the article, almost 4 trillion steering wheels will accumulate on the pension and medical personal accounts of Russian citizens in the first year of implementation. And by 2026, 74 trillion rubles. Providing citizens with 2% per annum, banks will be able to lend to enterprises at 2.5% with a 25% margin, providing the Russian economy with huge, long-term and cheap financial resources.
Sensitivity enhancement in optical micro-tube resonator sensors via mode coupling

A liquid filled, silica micro-tube with a low refractive index material inner-coating has been proposed and theoretically studied as a coupled micro-resonator sensor to greatly enhance biochemical sensor sensitivity. Its unique coupling phenomenon has been analyzed and utilized to boost the device's refractive index sensitivity to 967 nm/Refractive Index Unit (RIU). Through optimization of the coupling strength between the two micro-resonators, further improvement in refractive index sensitivity up to 1100 nm/RIU has been predicted. This mode coupling strategy allows us to design robust, thick-walled micro-tube sensors with ultra-high sensitivity which is useful in practical biochemical sensing applications.
An Asynchronous digital, Hysteretic Controller for an Automotive Application DC-DC Buck Converter

This paper presents the analysis and the implementation details of using an asynchronous finite state machine (AFSM) as a controller for a voltage mode DC-DC BUCK converter used in automotive applications. The performance of the proposed Buck converter was analyzed using pre-silicon simulation results. The most notable aspects/parameters are highlighted by comparison with other implementations. The system has $\approx 180 ns$ reaction time and less than $1\mu s$ settle time with a $\pm 27\%$ output ripple variation over temperature and process. The ripple is fairly constant with the $Vo\ DC$ level that brings a 0.33% to 1.3% variation from 5V to 1.2V. The new controller concept offers more flexibility in choosing the external passive components. The proposed circuit was implemented in an 130nm BCD automotive technology.