Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Machine Learning Optimization Algorithm of CNN for FPGA Architecture

In order to meet the application requirements of deep learning detection and recognition algorithm for Field Programmable Gate Array (FPGA) computing structure, this paper proposes a machine learning optimization algorithm of convolutional neural network (CNN) based on pruning and Int8 quantization. By analyzing the machine learning intelligent recognition network, the Yolo v3 target detection network is selected to verify the optimization algorithm. Aiming at the Yolo v3 network with balanced speed and precision, the optimization algorithm is studied in detail. Based on Amazon Cloud FPGA EC2 instance platform and ZCU104 FPGA hardware platform, machine learning optimization algorithms are used to accelerate the application of Yolo v3 network detection and recognition. Satisfactory results are obtained on both FPGA computing platforms. The experimental results show that the machine learning optimization technology can improve the inference speed of neural network based on both FPGA platforms. The network structure optimized by pruning and Int8 quantization algorithms can achieve high acceleration speed, with very limited accuracy loss.
Modified multifractional Gaussian noise and its application

In this paper, we introduce a novel multifractional noise, which we call modified multifractional Gaussian noise (mmfGn). The introduced mmfGn is with the time varying Hurst parameter H(t) on a point-by-point basis based on the autocorrelation function (ACF) of the fractional Gaussian noise. The representations of the ACF and the power spectrum density (PSD) of the mmfGn are presented in Theorems 1 and 4, respectively. Besides, we propose the approximation of the ACF of mmfGn in Theorem 2. We put forward the properties of the long-range dependence (LRD) and short-range dependence (SRD) of mmfGn in Theorem 3 and the condition of mmfGn to be 1/f noise in Theorem 5. The fractal dimension of mmfGn, which is also time varying on a point-by-point basis, is proposed in Theorem 6. As an application case, we use the time varying Hurst parameter H(t) to describe the time varying LRD of sea level.
Ultrathin hybrid absorber based on high-order metamaterial

In this work, we utilized a hybrid resonator to achieve near-perfect and dual-band absorption in the VHF and the LTE/Bluetooth/WiMAX bands. An electromagnetic (EM) wave at an oblique incidence was captured efficiently by exciting the fundamental magnetic resonances at 106 and 123.4 MHz. In particular, the effective thickness of the metamaterial perfect absorber (in comparison with the working wavelength λ ) is miniaturized to be ≈0. 03λ at the high-order resonances (induced in the 2.0–4.0 GHz range) at a specific incident angle of EM wave, θ = 45°, 30°, 15° and θ = 5°. Our results contribute to the realization of meta-devices, such as sensors, optical filters/switchers, photodetectors, and energy converters, for future applications in the radio to the microwave frequency bands.
A Permissioned Blockchain System for Secure Multiparty Computation

Permissioned blockchain is the blockchain network that requires access to be part of the network. Participant’s actions are governed by the control layer that runs on top of the blockchain. This type of blockchains is preferred by individuals who need role description, identity, and security within the blockchain. Secure multi-party computation (MPC) is a part of cryptography that involves the modeling of procedures for two or more participants who want to work together. These participants involve sharing of input and required computational data for a particular function without sharing their confidential data actively to each other and achieving a common goal which is beneficial to both as the outcome achieved is only revealed to the participants and is highly required for their functional purpose. In this work, SPDZ (Speedz) implementation is explored leveraging additive secret sharing on the private blockchain (Hyperledger fabric). SPDZ protocol is chosen over any other computational protocol as it is highly secured from any active deceptive n-1 participant among the n participants. In this work, a backend is developed that uses a fabric SDK node.js library that interacts with the Hyperledger Fabric network. The proposed solution is shown through a demonstration. This paper concludes that for business-to-business scenarios, using SPDZ protocol on permissioned blockchain provides more security against adversaries as permissioned blockchain provides transparency over the participants of the network. As a result, permissioned blockchain is a more secure choice for enterprises to compute confidential data rather than permissionless blockchain.
Delft University of Technology Rotor Eddy Current Loss Reduction with Permeable Retaining Sleeve for Permanent Magnet Synchronous Machine

-This paper presents a new rotor design to reduce rotor eddy current loss of a high-speed permanent magnet synchronous machine for flywheel energy storage system. Instead of using common nonmagnetic sleeves, the new rotor incorporates permeable retaining sleeves (PRSs) to fix permanent magnets on the rotor hub. The PRSs are made of permalloy that features high permeability and high electrical conductivity. Thus, skin depths for asynchronous harmonics are extremely small. On the other hand, the PRSs are electrically insulated along the circumferential direction. Owing to these two reasons, rotor eddy current loss at open circuit decreases by 64.2% without sacrificing torque output, compared with an original rotor with nonmagnetic retaining sleeve. In addition, thermal and structural finite element analyses are performed to calculate rotor temperature distribution and evaluate the structural integrity of the new rotor. Rotor eddy current loss reduction benefits lowering rotor temperature rise. Prototype machine with the new rotor is fabricated, and preliminary tests are carried out to confirm the analysis results.
PLGA-Based Curcumin Delivery System: An Interesting Therapeutic Approach in the Treatment of Alzheimer’s Disease

Progressive degeneration and dysfunction of the nervous system because of oxidative stress, aggregations of misfolded proteins, and neuroinflammation are the key pathological features of neurodegenerative diseases. Alzheimer's disease is a chronic neurodegenerative disorder driven by uncontrolled extracellular deposition of β-amyloid (Aβ) in the amyloid plaques and intracellular accumulation of hyperphosphorylated tau protein. Curcumin is a hydrophobic polyphenol with noticeable neuroprotective and anti-inflammatory effects that can cross the blood-brain barrier. Therefore, it is widely studied for the alleviation of inflammatory and neurological disorders. However, the clinical application of curcumin is limited due to its low aqueous solubility and bioavailability. Recently, nano-based curcumin delivery systems are developed to overcome these limitations effectively. This review article discusses the effects and potential mechanisms of curcumin-loaded PLGA nanoparticles in Alzheimer’s disease.
Probabilistic Latent Semantic Scaling

: Measuring text in a continuous scale is a fundamental problem that has many applications including social sciences and humanities. This paper proposes a probabilistic extension to Latent Semantic Scaling (LSS) in political methodology, called Probabilistic Latent Semantic Scaling (PLSS). Leveraging Item Response Theory and neural word vectors, we show that PLSS consistently outperforms LSS in terms of relevance to human evaluation. We also propose two statistical methods to select texts to be analyzed that are associated with the given keywords. Probabilistic treatment of LSS enables many future extensions, including handling missing data and time series analysis.
Femoral derotation osteotomy—Does intraoperative electromagnetic tracking reflect the dynamic outcome?

Femoral derotation osteotomy (FDO) is a well‐established procedure for the correction of internal rotation gait in children with cerebral palsy. Various studies have demonstrated good results for FDO both in short‐term and long‐term evaluation with some describing recurrence and over‐ or under‐corrections. The present study evaluates the use of an objective intraoperative derotation measurement through electromagnetic tracking. We report the static and dynamic results of 11 cases with internal rotation gait (8 male, 3 female, mean age 22.2 years), that underwent FDO with intraoperative electromagnetic tracking and conventional goniometric measurement of the correction. The dynamic and static changes were assessed through three‐dimensional gait analysis after a mean of 12 months after surgery and rotational imaging preoperative and after a mean of eleven days postoperatively. Mean hip rotation in stance significantly decreased from 20.9° (SD 5.9) to 5.8° (SD 4.7°) after FDO. The mean amount of derotation quantified by electromagnetic tracking was 23.2° (16.5°–28.8°) and 25.1° (20.0°–33.0°) for goniometric measurement. Both measurement modalities showed small differences to rotational imaging (electromagnetic bone tracking [EMT]: 0.72°; goniometer: 1.19°) but a large deviation when compared to three‐dimensional gait analysis (EMT: 8.5°, goniometer: 9.1°). In comparison to the static changes and EMT measurement, the dynamic changes measured during 3‐D‐gait analysis reflected only 66% of the actual derotation performed during surgery. Although electromagnetic tracking allows a precise intraoperative assessment of the derotation during FDO, the amount of intraoperative correction is not reflected in the improvements in three‐dimensional gait analysis.
The CSPP impact on non-financial firms’ cost of borrowing and debt choice

In this study, we examine the impact of the European Central Bank’s (ECB) corporate sector purchase programme (CSPP) on euro area non-financial firms’ cost of borrowing and choice between bank and public debt. Using a large sample of corporate bonds and syndicated loans closed between 2000 and 2019, we find that the CSPP reduced corporate bond spreads significantly, in both announcement and implementation periods. Findings also suggest that the CSPP had a positive spillover effect into the syndicated loan market during the implementation period. Our results show that there is a substitution effect between eligible bonds and equivalent loans, with non-financial firms choosing to use more corporate bonds than syndicated loan deals after the CSPP announcement, and that this effect is more important for non-switchers, those that may have more difficulty in accessing the bond market. Finally, we provide evidence that, when controlling for the CSPP, borrowers that choose corporate bonds are larger, more profitable, and have larger growth opportunity sets; and switchers with high agency costs of debt prefer bank debt.
Salient objects dominate the central fixation bias when orienting toward images

Short-latency saccades are often biased toward salient objects or toward the center of images, for example, when inspecting photographs of natural scenes. Here, we measured the contribution of salient objects and central fixation bias to visual selection over time. Participants made saccades to images containing one salient object on a structured background and were instructed to either look at (i) the image center, (ii) the salient object, or (iii) at a cued position halfway in between the two. Results revealed, first, an early involuntary bias toward the image center irrespective of strategic behavior or the location of objects in the image. Second, the salient object bias was stronger than the center bias and prevailed over the latter when they directly competed for visual selection. In a second experiment, we tested whether the center bias depends on how well the image can be segregated from the monitor background. We asked participants to explore images that either did or did not contain a salient object while we manipulated the contrast between image background and monitor background to make the image borders more or less visible. The initial orienting toward the image was not affected by the image-monitor contrast, but only by the presence of objects—with a strong bias toward the center of images containing no object. Yet, a low image-monitor contrast reduced this center bias during the subsequent image exploration.
Size-specific dose estimates (SSDEs) for computed tomography and influencing factors on it: a systematic review

The actual dose received during a computed tomography (CT) examination depends on both the patient size and the radiation output of the scanner. To represent the actual patient morphometry, a new radiation dose metric named size-specific dose estimates (SSDEs) was developed by the American Association of Physicists in Medicine in 2011. The purpose of this article is to review the SSDE concept and the factors influencing it. Moreover, the appropriate methodology of SSDE determination and the application of SSDE as a diagnostic reference-level quantity is critically analyzed based on the data available in the literature. It is expected that this review could potentially increase awareness among CT users of the effective utilization of SSDE as a tool to aid in the optimization of radiation dose in CT.
A Study of Exfoliated Molybdenum Disulfide (MoS2) Based on Raman and Photoluminescence Spectroscopy

In this study, We demonstrate mono and few layers MoS2 samples on the SiO2(270nm)/Si substrate from bulk MoS2 crystal by micromechanical exfoliation technique. We have systematically studied Atomic Force Microscopy, Raman and PL properties of mono and few layer MoS2 on the SiO2(270nm)/Si substrate. First, we find that the number of layer values dependent the Raman and PL emission. First, Raman intensity area ratio of the MoS2E12g, A1g and 2LA modes to that area of the Si substrate increased linear with increasing number of layers MoS2. Second, Normalized PL intensity area of the (A) peak decreased linear with increasing number of layers MoS2. The value of those graphs is a method to understand the number of layers the exfoliated MoS2.
A Method for Optimal Detection of Lung Cancer Based on Deep Learning Optimized by Marine Predators Algorithm

Lung cancer is the uncontrolled growth of cells in the lung that are made up of two spongy organs located in the chest. These cells may penetrate outside the lungs in a process called metastasis and spread to tissues and organs in the body. In this paper, using image processing, deep learning, and metaheuristic, an optimal methodology is proposed for early detection of this cancer. Here, we design a new convolutional neural network for this purpose. Marine predators algorithm is also used for optimal arrangement and better network accuracy. The method finally applied to RIDER dataset, and the results are compared with some pretrained deep networks, including CNN ResNet-18, GoogLeNet, AlexNet, and VGG-19. Final results showed higher results of the proposed method toward the compared techniques. The results showed that the proposed MPA-based method with 93.4% accuracy, 98.4% sensitivity, and 97.1% specificity provides the highest efficiency with the least error (1.6) toward the other state of the art methods.
Methicillin-Resistant Staphylococcus aureus-Associated Diarrhea in a Critically Ill Burn Patient

Described in surgical patients after antibiotic use in the 1950s and 1960s, Staphylococcus aureus (S. aureus) enterocolitis is a rare form of nosocomial diarrhea. However, S. aureus is not routinely tested like Clostridium difficile (C. difficile). We report a case of methicillin-resistant S. aureus (MRSA) enterocolitis found on stool culture in a 22-week pregnant female with a previously negative nasal MRSA culture, and a total burn surface area greater than 60%. She also developed necrotizing MRSA pneumonia and bacteremia. After starting broad-spectrum antibiotic for the necrotizing pneumonia with subsequent acute respiratory distress syndrome, the patient exhibited large voluminous diarrhea that tested positive for MRSA and negative for C. difficile in the stool culture. Similar to other reports of high-volume diarrhea, the diarrhea resolved quickly with enteral vancomycin. S. aureus should be considered along with C. difficile during the workup of nosocomial diarrhea, especially with exposure to broad-spectrum antibiotics in the critically ill patient.
Inflammatory myofibroblastic tumor of the pancreatic neck: A case report and review of literature

BACKGROUND Pancreatic inflammatory myofibroblastic tumor (IMT) is a relatively rare disease that is often confused with pancreatic cancer or pancreatic neuroendocrine tumors. The histological features of IMTs show that tissue from this type of tumor contains an intermingling of fibroblast and myofibroblast proliferation, accompanied by a varying degree of inflammatory cell infiltration. CASE SUMMARY The management of an IMT occurring at the neck of the pancreas is presented in this paper. A 66-year-old female patient was diagnosed with a pancreatic neck mass after a series of tests. The patient underwent enucleation of the pancreatic neck tumor after a pathological diagnosis of IMT. Previous research on the clinical features, pathological diagnosis and treatment of pancreatic IMTs was reviewed. Compared with previous reports, this is a unique case of enucleation of a pancreatic IMT. CONCLUSION The enucleation of pancreatic IMTs may be a safe and efficient surgical method for managing such tumors with a better prognosis. Further cases are required to explore surgical measures for pancreatic IMTs.
How Should Adult Handgrip Strength Be Normalized? Allometry Reveals New Insights and Associated Reference Curves

ABSTRACT Introduction Handgrip strength (HGS) is an important indicator of health. Because HGS is strongly associated with body size, most investigators normalize HGS for some measure of body size as a more sensitive indicator of strength within a population. We aimed to 1) identify the optimal body size dimension to remove (normalize) HGS for differences in body size among adults and 2) generate norm-referenced centiles for HGS using the identified body size dimension. Methods Data were from the National Health and Nutrition Examination Survey, a representative sample of the US noninstitutionalized civilian population. Exclusions resulted in a final sample of 8690 adults 20 yr and older. HGS was measured using handheld dynamometry. Body size dimensions included body mass, height, and waist circumference. The most appropriate dimension(s) associated with HGS is identified using allometry. We fitted centile curves for normalized HGS using the generalized additive model for location, scale, and shape. Results Findings suggest that neither body mass nor body mass index is appropriate to normalize HGS. Incorporating all three body size dimensions of body mass, height, and waist circumference, or the reduced subsets of body mass and height, or height alone, suggests that the most appropriate normalizing (body size) dimension associated with HGS should be a cross-sectional or surface area measure of an individual’s body size (i.e., L2, where L is a linear dimension of body size). Given that height was also identified as the signally best body size dimension associated with HGS, we recommend HGS be normalized by height2 (i.e., HGS/HT2). Centile curves for HGS/HT2 by age group and gender were therefore provided. Conclusions Scaling adult HGS by height2 may help normalize strength for population-based research.
The Erlang distribution approximates the age distribution of incidence of childhood and young adulthood cancers

Background It is widely believed that cancers develop upon acquiring a particular number of (epi) mutations in driver genes, but the law governing the kinetics of this process is not known. We have previously shown that the age distribution of incidence for the 20 most prevalent cancers of old age is best approximated by the Erlang probability distribution. The Erlang distribution describes the probability of several successive random events occurring by the given time according to the Poisson process, which allows an estimate for the number of critical driver events. Methods Here we employ a computational grid search method to find global parameter optima for five probability distributions on the CDC WONDER dataset of the age distribution of childhood and young adulthood cancer incidence. Results We show that the Erlang distribution is the only classical probability distribution we found that can adequately model the age distribution of incidence for all studied childhood and young adulthood cancers, in addition to cancers of old age. Conclusions This suggests that the Poisson process governs driver accumulation at any age and that the Erlang distribution can be used to determine the number of driver events for any cancer type. The Poisson process implies the fundamentally random timing of driver events and their constant average rate. As waiting times for the occurrence of the required number of driver events are counted in decades, and most cells do not live this long, it suggests that driver mutations accumulate silently in the longest-living dividing cells in the body—the stem cells.
Conformal screen printed graphene 4 × 4 wideband MIMO antenna on flexible substrate for 5G communication and IoT applications

Screen-printed graphene is integrated with multiple-input multiple-output (MIMO) technology to conquer the most concerned surge in electronic waste caused by the mass deployment of Internet of things (IoT) applications. A flexible MIMO antenna is implemented with simple fabrication process suitable for large-scale production by screen printing graphene highly conductive ink on paper substrate, ensuring high-speed 5G mass data wireless transmission without damaging the ecological environment. This environmental-friendly, low-cost, flexible and conformal MIMO antenna with orthogonal polarization diversity employs co-planar waveguide feed and planar pattern for achieving high space utilization and better integration in most scenarios, for instance, body centric networks and monitoring systems. Excellent performance has been achieved due to the high conductivity of the graphene: the fabricated antenna exhibits an average sheet resistance of 1. 9Ωsq−1 . The bandwidth of the antenna ranges from 2.22 GHz to 3.85 GHz (53.71% fractional bandwidth), covering 4G long term evolution, sub-6 GHz 5G mobile communication networks, 2.5 and 3.5 GHz WiMAX, and 2.4 and 3.6 GHz WLAN. Within this range, the antenna exhibits effective radiation, also its envelope correlation coefficient remains below 0.2×10−6 , manifesting outstanding signal transmission quality in a variety of wireless networks. This work illustrates a novel aggregation of MIMO technology and graphene printing electronics, enabling cheap accessible and green MIMO antennas to be massively integrated in IoT applications.
An Attention-based Recurrent Neural Network for Resource Usage Prediction in Cloud Data Center

To manage the computing resources efficiency is crucial for the Cloud data centers (CDCs). By predicting the resource usages of certain virtual machine (VM), the workload could be balanced before the resource overusing occurs. In this paper, we propse an attention-based time series prediction model, which contains an encoder of input attention mechanism and a decoder of a temporal attention mechanism, to optimize the efficiency of could data center. Experiments on Alibaba CDC VM trace dataset demonstrates that our proposed methods can outperforms the classic LSTM method, especially when the resource usage data is lack of relationship.