Miracle-dz/UniFAR-FTUs huggingface dataset:
Small Vehicle Recognition Based on Attention Mechanism and Feature Fusion

This paper proposes an improved method combining the attention mechanism and feature fusion on the basis of YOLOv5. We firstly introduce CA attention mechanism in the network, which simultaneously considers the attention in channel dimension and spatial dimension; feature fusion adopts ASFF fusion mechanism, which solves the inconsistency problem inside the feature pyramid by learning the connection between different feature maps; finally, Focal Loss function is introduced during the training time. Based on the improved YOLOv5, the accuracy in identifying small vehicle targets is improved by 4.92%, and the processing speed of a single image is improved by 30.34%, which effectively enhances the processing accuracy and speed of small vehicle targets.
Reliability of measures obtained during single and repeated countermovement jumps.

PURPOSE To establish the reliability of various measures obtained during single and repeated countermovement jump (CMJ) performance in an elite athlete population. METHODS Two studies, each involving 15 elite Australian Rules Football (ARF) players were conducted where subjects performed two days, separated by one week, of AM and PM trials of either a single (CMJ1) or 5 repeated CMJ (CMJ5). Each trial was conducted on a portable force-plate. The intraday, interday, and overall typical error (TE) and coefficient of variation (CV%) were calculated for numerous variables in each jump type. RESULTS A number of CMJ1 and CMJ5 variables displayed high intraday, interday, and overall reliability. In the CMJ1 condition, mean force (CV 1.08%) was the most reliable variable. In the CMJ5, flight time and relative mean force displayed the highest repeatability with CV of 1.88% and 1.57% respectively. CMJ1Mean force was the only variable with an overall TE < smallest worthwhile change (SWC). CONCLUSION Selected variables obtained during CMJ1 and CMJ5 performance can be used to assess the impact of both acute and chronic training and competition. Variables derived from the CMJ5 may respond differently than their CMJ1 counterparts and should provide insights into differential mechanisms of response and adaptation.
Digital Electronic Electricity Meter Reading Using AlexNet

Traditionally, electricity usage is based on manual reading and recording. However, this manual process is prone to errors and can result in a waste of manpower. To increase the reading and recording efficiency, a reading scheme based on image recognition is proposed. The preprocessing, including Gaussian blur, binarization, grayscale conversion, and Canny Edge detection was processed followed by a deep learning model. A deep learning AlexNet model trained by MNIST handwriting recognition dataset is employed for digital meter reading, the model is based on a ratio of 5:1:1 data for training, validation, and testing. Three image resolutions, 84x84, 56x56, and 28x28, were tested. The 56x56 resolution image was applied for the electricity meter reading due to the accuracy and time complexity considerations. The experimental test was conducted on electricity meter images, and the obtained mean average precision is more than 90%, which indicates the suggested AlexNet model for digital meter reading is feasible for real applications.
Differential distribution of harmonin isoforms and their possible role in Usher-1 protein complexes in mammalian photoreceptor cells.

PURPOSE Human Usher syndrome is the most common form of combined deafness and blindness. Usher type I (USH1), the most severe form, is characterized by profound congenital deafness, constant vestibular dysfunction, and prepubertal onset retinitis pigmentosa. Previous studies have shown that the USH1-proteins myosin VIIa, harmonin, and cadherin 23 interact and form a functional network during hair cell differentiation in the inner ear. The purpose of the present study was to analyze the molecular and cellular functions of these USH1 proteins in the mammalian retina. METHODS Antibodies to USH1 proteins were generated and used in Western blot analysis of subcellular photoreceptor fractions and immunofluorescence and electron microscopy of the retina. RESULTS Splice variants of harmonin were differentially expressed in the photoreceptor cell compartments. Whereas harmonin b isoforms were restricted to the light-sensitive outer segment, the harmonin a and c isoforms were more ubiquitously distributed in the photoreceptors. At the synaptic terminal of photoreceptor cells, harmonin a and c colocalized with myosin VIIa and cadherin 23. CONCLUSIONS USH1 molecules can assemble to a supramolecular complex at photoreceptor synapses. Such a complex may contribute to the cortical cytoskeletal matrices of the pre- and postsynaptic regions, which are thought to play a fundamental role in the organization of synaptic junctions. Dysfunction of any of the USH1 complex partners may lead to synaptic dysfunction causing retinitis pigmentosa, the clinical phenotype in the retina of patients with USH1. Furthermore, in photoreceptor outer segments, harmonin may also contribute to the clustering of outer segment proteins into supramolecular complexes.
Model selection and comparison for independents sinusoids

In the signal processing literature, many methods have been proposed for estimating the number of sinusoidal basis functions from a noisy data set. The most popular method is the asymptotic MAP criterion, which is sometimes also referred to as the BIC. In this paper, we extend and improve this method by considering the problem in a full Bayesian framework instead of the approximate formulation, on which the asymptotic MAP criterion is based. This leads to a new model selection and comparison method, the lp-BIC, whose computational complexity is of the same order as the asymptotic MAP criterion. Through simulations, we demonstrate that the lp-BIC outperforms the asymptotic MAP criterion and other state of the art methods in terms of model selection, de-noising and prediction performance. The simulation code is available online.
Research on Regulators Required for Cell Migration Using Gene Encoding Technique

Many of the mechanisms that were found in simple organisms like yeast, worms, and flies are well preserved in humans, and many of the molecules that were found in these organisms have functions that are similar to those in humans. Simple organisms can be used to investigate the intricate mechanisms of cell migration. Drosophila ovary border cell migration has emerged as a straightforward model for studying cell migration in recent decades. There are various stages of egg chambers in the Drosophila ovary. There are approximately 650 follicle-epithelial cells and 16 germline cells in each egg chamber. The oocyte is formed by one of the germline cells, and the remaining 15 cells become nurse cells. A pair of distinct follicle cells, known as polar cells, form at each of the egg chamber's poles during early oogenesis. The border cell cluster, which consists of six to ten cells, is formed when the anterior polar cells recruit adjacent follicular cells at the beginning of stage 9 of oogenesis. The border cells migrate between the nurse cells after separating from the follicle epithelial cells. They are referred to as border cells because they migrate through the center of the egg chamber for approximately six hours before reaching the oocyte's anterior border at early stage 10.
DEEP Learning Approach Using Modified Auto-Encoder for PPG Signal Compression

PPG (Photoplethysmography) signal measures the heart's pumping capacity which uses a light-emitting diode to track the blood flow which controls the actions of the heart rate. PPG is widely used in all healthcare applications, which range from monitoring a person's oxygen saturation level within the ICU (intensive care units) for measuring the pulse rate in preferably devices that are wrist-worn. In this research, we formulate an algorithm called as bioelectrical signals with encoder and decoder and feature engineering). This combined method incorporates a method by combining FE and encoder-decoder. In the end, our algorithm differentiates between relevant tasks and gets all the features required for classification. Our proposed method accomplishes the feature engineering on higher-level learning features; this also ensures encoder and decoder for constructing the generative units. The experimental outcome of our proposed model sets a benchmark to state that our model works better in comparison with existing methods.
Incidence and patterns of hepatitis C virus seroconversion in a cohort of hemodialysis patients.

BACKGROUND The aim of this multicenter hemodialysis (HD) cohort study is to prospectively investigate the incidence of hepatitis C virus (HCV) infection in Greece from 1993 to 1995 and delineate early virological and serological events associated with HCV seroconversion in the HD setting. METHODS Sequential serum samples collected weekly from 562 patients were tested biochemically and serologically by means of a second- (EIA-2) and third-generation enzyme immunoassay (EIA-3). All patients with positive antibody to HCV test results (anti-HCV + ) and sequential samples from seroconverting patients were tested for HCV RNA. RESULTS Anti-HCV prevalence at study entry was 29% (163 of 562 patients), and viremia was detectable in 110 of 163 anti-HCV + patients (67.5%). HCV incidence was 6.2 cases/100 person-years. Seroconversions could not be attributed to transfusions after study entry (only 1 patient had been administered transfusion), and HD unit was associated with increased hazard for seroconversion ( P = 0.002), even after adjusting for potential differences among their patients. According to Kaplan-Meier estimation, the median interval by which the HCV RNA assay detected HCV infection earlier than anti-HCV testing was 246 and 154 days for EIA-2 and EIA-3, respectively. Detectable HCV RNA and at least 2 consecutive abnormal alanine aminotransferase levels in the preseroconversion period were observed in 29 of 30 (97%) and 14 of 32 patients (44%), respectively. Reductions in HCV RNA levels immediately after seroconversion were transient or did not occur. CONCLUSION On the grounds of apparent nosocomial transmission, the wide window period of HCV infection in HD patients emphasizes the need for strict adherence to specific infection-control measures in this setting.
Design of pre-coding and combining in hybrid analog-digital massive MIMO with phase noise

The design of massive MIMO, especially at millimeter waves, requires a trade-off between cost and power consumption, balancing the complexity and the performance in terms of achievable rate. A recent trend in the design is to split the pre-coding at the transmitter and the combining at the receiver into a digital and analog part, with hybrid analog-digital schemes. In this paper the effect of phase noise is considered in the design of different hybrid analog-digital alternatives to implement massive MIMO, in particular at very high frequencies. Its effect depending on the number of RF chains, oscillators, and groups of antennas is analyzed providing some insights for the system design. In order to limit the penalty introduced by the phase noise to values below 6 dB, with a number of antennas around 64, the value of phase noise increment variance should be limited below 0.005. This limit is slightly lower in a simplified architecture with more blocks driven by independent oscillators.
Muscle stiffness and rate of torque development during sprint cycling.

PURPOSE Crank torque (CT) application and rate of CT development (RCTD) are important considerations in sprint cycling. The stiffness of the musculotendinous unit is related to the isometric rate of torque development (RTD); however, this relationship has yet to be examined in sprint cycling. METHODS Maximal isometric torque (MIT) and isometric RTD of the quadriceps were assessed in 21 trained male cyclists (28.7 +/- 9.5 yr, 1.74 +/- 0.08 m, and 67.5 +/- 7.2 kg). Unilateral musculoarticular (MA) stiffness of the quadriceps was quantified using an oscillation test. Further, the participants performed a maximal 6-s sprint to assess peak power output (POpeak), peak CT (CTpeak), peak RCTD (RCTDpeak), and the crank angles associated with CTpeak and RCTDpeak. Participants were ranked on MA stiffness properties and were divided into a relatively stiff group (SG) and a relatively compliant group (CG). RESULTS The SG displayed a significantly higher MA stiffness than the CG (P < 0.05). Furthermore, the SG reported significantly elevated MIT (27%), RTD (26%), and RCTDpeak (16%) when compared with the CG (P < 0.05), along with trends for increased POpeak (7%) and CTpeak (8%). The angles at CTpeak and RCTDpeak were 7% and 12% lower for the SG, respectively (P < 0.05). MA stiffness was significantly correlated with RCTDpeak, MIT, RTD, and POpeak. CONCLUSIONS Higher stiffness is related to superior RCTDpeak in trained cyclists during a single sprint. A significant proportion of the variance in RCTDpeak was attributed to MA stiffness (37%), which was of greater magnitude than the relationship between RCTDpeak and MIT. Furthermore, the lower CTpeak angle and RCTDpeak angle may contribute to a more rapid development of CT. Accordingly, MA stiffness seems to be an important consideration for sprint cycling.
A Comparative Study of Noisy Label Detection Techniques in a Thai Hospital's Chest X-Ray Database

This paper addresses the problem of noisy labels in chest X-ray datasets, which significantly impact the training of deep neural network models. Noisy labels often occur due to errors in reports from experts or the use of algorithms to extract labels from medical reports written in natural language. To tackle this issue, we compared the effectiveness of O2U-net, a state-of-the-art noisy label detection method, and NVUM, a noise-resistant model training technique in identifying noisy samples. We contrasted these methods with a heuristic approach which uses a simple classification model to flag samples with large differences between predicted and actual labels as noisy. Our findings indicated that NVUM outperformed the other methods in identifying noisy labels, providing a promising solution to the challenge of noisy labels in medical image analysis.
Implementation and Evaluation of a Facial Image Obscuring Method for Person Identification to Protect Personal Data

In recent years, the use of computer systems for identifying people has become increasingly popular. Clear facial images and detailed facial features are often provided to the system to improve identification accuracy, but if they are misused, privacy can be compromised. The authors are currently building a next-generation video blog system that can do the following: capture images of passersby with cameras, blur the facial images of all the people, remove the blurring for specific people who have registered their facial images for public viewing, and then upload them as video blogs in real time in a hands-free manner. By comparing the similarities between the obscured data in the user's stored data and the data captured by the camera and immediately obscured and sent to the edge server, the authors devised a system that enables person identification but does not pass clear facial images and features to the system. The authors focused on the possibility of using noise strength added to features and random seeding of features as a common quasi-encryption key to protect privacy. This paper assesses the extent to which the intensity of noise in this system obscures person identification. The results show that privacy can be protected by adding epsilon noise below a certain strength to the features, and the noise strength and the random number seed for noise generation can be used as a common quasi-cryptographic key.
Adaptive Fixed- Time DSC for High-Order Nonlinear Systems Against Deception Attacks

In this paper, the problem of adaptive fixed-time dynamic surface control is investigated for a class of high-order nonlinear systems against deception attacks. Unknown nonlinear terms in controlled system are handled by neural networks. And unknown control directions due to attacks are handled by the Nussbaum function. In addition, a novel adaptive fixed-time control scheme is designed by combination of the adding-one-power-integrator technique and dynamic surface control method. Based on the fixed-time stability theory, the proposed control scheme is still guaranteed to be bounded in fixed time, and the settling time is independent of the system's initial state, no matter what happens in the case of attacks. Finally, the validity of the presented control scheme is demonstrated by simulation results.
Joint optimization for resource allocation and mode selection in Device-to-Device communication underlaying cellular networks

With the emerging demands for extensive mobile applications, Device-to-Device (D2D) communication is viewed as an important technology for the next-generation cellular communication and mobile networks to increase spectral efficiency and enhance network capacity. Existing works usually study D2D communications under a network consisting of four nodes. In this paper, we jointly investigate the problems of optimal system resource allocation and mode selection for large scale networks. Specifically, by formulating a max-flow optimization problem that maximizes the system throughput through any possible of transmission modes with the decisions of resource allocations, we obtain the optimal system performance. Under realistic scenario driven simulations, we demonstrate the efficiency of our proposed scheme for combined mode selection and resource allocation.
A 3D simultaneous localization and mapping exploration system

In this work we develop a robot system capable of autonomously exploring and mapping an indoor environment. For this we apply a Simultaneous Localization and Mapping (SLAM) algorithm to build up a 3D map and use it to plan the navigation of the robot to further explore the environment. The system is realized in a modular and distributed architecture. Finally, we critically evaluate the system as a whole.
Myocardial Revascularization ( V ) Advances in Adjunctive Pharmacological Therapy for Percutaneous Coronary Interventions

Rev Esp Cardiol. 2005;58(6):729-43 729 All percutaneous interventions disrupt atherosclerotic plaque and denude the endothelium. These processes stimulate both platelet aggregation and the coagulation cascade. Therefore, pharmacological treatment during percutaneous intervention is based on the use of antithrombotic agents. In addition to aspirin, whose benefit has been clearly demonstrated in all forms of ischemic heart disease, clopidogrel, given before and after cardiac catheterization, also reduces the rate of thrombosis after stent placement. Moreover, the introduction of glycoprotein IIb/IIIa inhibitors has improved the results of percutaneous revascularization, especially in high-risk patients. On the other hand, anticoagulants are essential for preventing the acute thrombotic complications that result from the invasive nature of the procedure. Low-molecularweight heparins, direct thrombin inhibitors (e.g., hirudin and its derivatives), and recently developed pentasaccharides, which inhibit factor X, provide new alternatives to classical unfractionated heparin. These novel compounds lead to fewer hemorrhagic complications than unfractionated heparin and do not require such extensive monitoring. Finally, new antiproliferative agents, such as oral rapamycin, have been introduced to reduce the rate of coronary restenosis during follow-up.
Robust Surface Warming in Offshore China Seas and Its Relationship to the East Asian Monsoon Wind Field and Ocean Forcing on Interdecadal Time Scales

AbstractRobust surface warming with distinct interdecadal variations has been observed in the offshore area of China and adjacent seas (hereafter, offshore China) during winter and summer of the period 1958–2014. Acceleration of this warming during 1980–99 at rates greater than the global mean warming rate was accompanied by a weakening of the East Asian monsoon (EAM) and a strengthening of the west Pacific subtropical high (WPSH). It was determined that the sea surface temperature (SST) variation in offshore China correlates very well with changes in the EAM wind on interdecadal time scales. It was also established that the enhanced oceanic lateral heat transfer, mainly attributed to the leading empirical orthogonal function (EOF1), weakening EAM wind mode, has a central role in robust interdecadal winter surface warming in offshore China. However, except for the effect of oceanic lateral heat transfer, the increased surface heat flux through radiative heating related to the third EOF (EOF3) strengthenin.
Classifier Based Malware Detection Techniques

One of the main security issues for the Windows platform is malware. Any sort of computer programme known as malware interferes with the accessibility of computer services. The conventional detection methods, such the intrusion detection and prevention system. The use of signature-based techniques prevents anti-virus software and similar tools from detecting undetectable malware. Consequently, it's essential to precisely identify this type of malware in a Windows or Android environment. This paper introduces classifier based malware detection techniques [CBMT] a machine learning (ML)-based algorithm that excerpts characteristics from the header of a portable runnable file to decide whether the executable is dangerous or not. To combat the infection, a decision tree is used. Additionally, in order to choose the best ML model for the specific issue, comparison analyses of the models are done. Using this, it is probable to create a desktop programme for any platform that can be customised to check for malware.
Lightweight Error Correction for In-Storage Acceleration of Large Language Model Inference

As large language models (LLMs) expand their sizes, conventional GPU-based LLM inference systems face memory bandwidth and capacity limitations. An LLM inference accelerator using NAND flash storage has been proposed to overcome these challenges. However, this necessitates a significant expansion of flash channels to ensure adequate bandwidth for inference, subsequently escalating error correction code (ECC) costs. This paper examines the impact of flash memory errors on LLM inference accuracy and explores the possibility of lightweight ECC by leveraging LLM's inherent error resilience. We analyze the impact of 1) high-order bit indices masking for FP32 LLM parameters, 2) clipping, and 3) a dependency by parameter type of error robustness, and show that a combination of them can reduce ECC bandwidth by up to 9.38%.
Substrate Integrated Waveguide fed Stacked DRA for 5G NR FR2 Applications

In this work, Substrate Integrated Waveguide (SIW) and Dielectric Resonator Antennas (DRA) are designed to work at millimeter wave frequencies. In this study, integrating both these technologies, an antenna with high radiation efficiency and wide bandwidth is proposed. A hemispherical DRA is placed on top of a notched rectangular DRA to achieve a wide bandwidth. A central air gap is introduced in the rectangular DRA to generate a wider impedance bandwidth. The simulation result shows that the proposed antenna can operate for dual-band frequency. The bandwidth of these bands are found to be 2.4 GHz (23.17-25.57 GHz) and 5.63 GHz (27.85-33.48GHz). This antenna is circularly polarized and shows peak total gain of 8.51dBiC. The co-polarization and cross-polarization differences are above 15dB for the E-plane and H-plane. The radiation efficiency is above 97% for an entire band of the operating frequency range. The proposed antenna shows promising results for 5G bands. It is suitable for 5G New Radio (NR) Frequency Range 2 (FR2) n258 and n261 bands.