Miracle-dz/UniFAR-FTUs huggingface dataset:
DCAT: Dual Cross-Attention-Based Transformer for Change Detection

Several transformer-based methods for change detection (CD) in remote sensing images have been proposed, with Siamese-based methods showing promising results due to their two-stream feature extraction structure. However, these methods ignore the potential of the cross-attention mechanism to improve change feature discrimination and thus, may limit the final performance. Additionally, using either high-frequency-like fast change or low-frequency-like slow change alone may not effectively represent complex bi-temporal features. Given these limitations, we have developed a new approach that utilizes the dual cross-attention-transformer (DCAT) method. This method mimics the visual change observation procedure of human beings and interacts with and merges bi-temporal features. Unlike traditional Siamese-based CD frameworks, the proposed method extracts multi-scale features and models patch-wise change relationships by connecting a series of hierarchically structured dual cross-attention blocks (DCAB). DCAB is based on a hybrid dual branch mixer that combines convolution and transformer to extract and fuse local and global features. It calculates two types of cross-attention features to effectively learn comprehensive cues with both low- and high-frequency information input from paired CD images. This helps enhance discrimination between the changed and unchanged regions during feature extraction. The feature pyramid fusion network is more lightweight than the encoder and produces powerful multi-scale change representations by aggregating features from different layers. Experiments on four CD datasets demonstrate the advantages of DCAT architecture over other state-of-the-art methods.
A Marx-Based Gaussian Monocycle Pulse Generator With Low Ringing Level for Ground Penetrating Radar Applications

The Gaussian pulse generator is the core component of the pulse ground penetrating radar (GPR) system, which requires high amplitude, narrow pulse width, and low ringing levels. Marx circuits are widely used to generate Gaussian pulses. However, due to the complex pulse-forming process, the high-amplitude pulses generated by conventional Marx circuits suffer from oscillation and severe ringing. In this article, a refined model of the Marx circuit based on lossy transmission line theory is proposed, which effectively reduces the oscillation and ringing level of the generated Gaussian pulses. Furthermore, a monocycle pulse-shaping network is proposed based on the short-circuit stub. The unipolar Gaussian pulse is converted into a Gaussian monocycle pulse for antenna radiation. Waveform experiment results show that the peak-to-peak value of the output pulse is 428 V, the pulse width is about 0.8 ns, and the ringing level is low at about 9%. The profile imaging result of the sand tank detection experiment further shows that the generated high-quality Gaussian monocycle pulse can provide good detection performance of underground buried targets for the pulse GPR system.
Double Coupling In-Wheel IPT System for Electric Vehicles

The electric automotive industry revolutionizes the transportation sector with new disruptive technologies like wireless charging systems and the tire industry is no exception. New airless and sustainable tire designs offer new possibilities to embody wireless charging technologies. This paper presents a novel in-Wheel Inductive Power Transfer (inWIPT) batteries charger design for electric vehicles (EV). The proposed system uses two magnetic couplers (MC) to transfer the energy: one from the off-board side to the wheel and another from the wheel to the on-board side. The proposed configuration keeps the air gap between the off-board transmitter pad and the wheel receiver to a minimum and almost independent of the vehicle type (Sedan, Suv, Truck). The two MCs are connected by a new resonant tank which modifies the voltage transfer characteristics. A step by step methodology that identifies the operational range of the mutual inductances, based on certain design specifications and constraints, is also presented. Experimental results validate the proposed inWIPT in a quasi load independent voltage operation in different coupling charging scenarios and a maximum output power of 2.6 kW with an efficiency of 89.3$\%$.
Closed Loop Control Of Two Switch Serial Input Interleaved Forward Converter

This paper deals with closed loop control of two switch Interleaved Forward Converter. When any sudden disturbance occurs at the input of open loop two switch interleaved forward converter, the output is also disturbed for a long time. In order to avoid this, a closed looped control is introduced. By using closed loop control steady state error is reduced. The DC input is converted into high frequency AC using Forward Converter. The AC is rectified using Half-wave rectifier. The output voltage is regulated using closed loop system.
An Improvement to the High-Spectral-Resolution CO2-Slicing Cloud-Top Altitude Retrieval

Abstract An improvement to high-spectral-resolution infrared cloud-top altitude retrievals is compared to existing retrieval methods and cloud lidar measurements. The new method, CO2 sorting, determines optimal channel pairs to which the CO2 slicing retrieval will be applied. The new retrieval is applied to aircraft Scanning High-Resolution Interferometer Sounder (S-HIS) measurements. The results are compared to existing passive retrieval methods and coincident Cloud Physics Lidar (CPL) measurements. It is demonstrated that when CO2 sorting is used to select channel pairs for CO2 slicing there is an improvement in the retrieved cloud heights when compared to the CPL for the optically thin clouds (total optical depths less than 1.0). For geometrically thick but tenuous clouds, the infrared retrieved cloud tops underestimated the cloud height, when compared to those of the CPL, by greater than 2.5 km. For these cases the cloud heights retrieved by the S-HIS correlated closely with the level at which the CPL.
18F-FDG PET/CT Image Deep Learning Predicts Colon Cancer Survival

Colon cancer is a type of cancer that begins in the large intestine. In the process of efficacy evaluation, postoperative recurrence prediction and metastasis monitoring of colon cancer, traditional medical image analysis methods are highly dependent on the personal ability of the doctors. In the process of patient treatment, it not only increases the workload and work pressure for doctors, but also has some problems with traditional medical image analysis methods. Moreover, the traditional medical image analysis methods have problems such as insufficient prediction accuracy, slow prediction speed, and the risk of errors in prediction. When analyzing 18F-FDG PET/CT images by traditional medical image analysis methods, it is easy to cause problems such as untimely treatment plans and errors in diagnosis, which will adversely affect the survival of colon cancer patients. Although 18F-FDG PET/CT images have certain advantages in image clarity and accuracy compared with traditional medical imaging methods, the analysis method based on 18F-FDG PET/CT images also has certain effects in predicting the survival of colon cancer patients, but there are still many shortcomings: the 18F-FDG PET/CT image analysis method overly relies on the technical advantages of 8F-FDG PET/CT images; in the analysis and prediction of image data, it has not gotten rid of the dependence on the personal medical quality of the doctors; traditional medical image analysis methods are still used when analyzing and predicting images; there is no breakthrough in image analysis effects. In order to solve these problems, this paper combined deep learning theory, using three algorithms of the improved RBM algorithm, image feature extraction method based on deep learning, and regression neural network to analyze and predict 18F-FDG PET/CT images, and applied some algorithms to analyze and predict 18F-FDG PET/CT images, and also established a deep learning-based 18F-FDG PET/CT image survival analysis prediction model. Four aspects survival prediction accuracy, survival prediction speed, survival prediction precision, and physician satisfaction were studied through this model. The research results have shown that compared with traditional medical image analysis methods, the prediction accuracy of 18F-FDG PET/CT image survival analysis prediction model based on deep learning is improved by 0.83%, and the prediction speed is improved by 3.42%, as well as the prediction precision increased by 6.13%. The research results show that the deep learning-based 18F-FDG PET/CT image survival analysis prediction model established in this paper is of great significance to improve the survival rate of colon cancer patients, and also promotes the development of the medical industry.
Shortest Path Algorithms with Integer Edge Costs

Single source shortest path algorithms are concerned with finding the shortest distances to all vertices in a graph from a single source vertex. Dijkstra (1959) first came up with an O(n2) algorithm to solve such a problem, where n is the number of vertices in the graph. If the given graph has a special property that all edge costs within the graph are integers and these edge costs are bounded by some constant c, it is possible to improve the underlying data structure of Dijkstra’s algorithm to improve the algorithm’s time complexity to O(m+n logc).
A new method for mobile phone image denoising

Images captured by mobile phone cameras via pipeline processing usually contain various kinds of noises, especially granular noise with different shapes and sizes in both luminance and chrominance channels. In chrominance channels, noise is closely related to image brightness. To improve image quality, this paper presents a new method to denoise such mobile phone images. The proposed scheme converts the noisy RGB image to luminance and chrominance images, which are then denoised by a common filtering framework. The common filtering framework processes a noisy pixel by first excluding the neighborhood pixels that significantly deviate from the (vector) median and then utilizing the other neighborhood pixels to restore the current pixel. In the framework, the strength of chrominance image denoising is controlled by image brightness. The experimental results show that the proposed method obviously outperforms some other representative denoising methods in terms of both objective measure and visual evaluation.
7-Chloroquinolinehydrazones as First-in-Class Anticancer Experimental Drugs in the NCI-60 Screen among Different Investigated Series of Aryl, Quinoline, Pyridine, Benzothiazole and Imidazolehydrazones

In the context of a continuously increasing global cancer risk, the search for new effective and affordable anticancer drugs remains a constant demand. This study describes chemical experimental drugs able to destroy cancer cells by arresting their growth. New hydrazones with quinoline, pyridine, benzothiazole and imidazole moieties have been synthesized and evaluated for their cytotoxic potential against 60 cancer cell lines. 7-Chloroquinolinehydrazones were the most active in the current study and exhibited good cytotoxic activity with submicromolar GI50 values on a large panel of cell lines from nine tumor types (leukemia, non-small cell lung cancer, colon cancer, CNS cancer, melanoma, ovarian cancer, renal cancer, prostate cancer and breast cancer). This study provided consistent structure-activity relationships in this series of experimental antitumor compounds.
PsERF1B-PsMYB10.1-PsbHLH3 module enhances anthocyanin biosynthesis in the flesh-reddening of amber-fleshed plum (cv. Friar) fruit in response to cold storage

Abstract Flesh-reddening usually occurs in the amber-fleshed plum (Prunus salicina Lindl.) fruit during cold storage but not during ambient storage direct after harvest. It is not clear how postharvest cold signal is mediated to regulate the anthocyanin biosynthesis in the forming of flesh-reddening yet. In this study, anthocyanins dramatically accumulated and ethylene produced in the ‘Friar’ plums during cold storage, in comparison with plums directly stored at ambient temperature. Expression of genes associated with anthocyanin biosynthesis, as well as transcription factors of PsMYB10.1, PsbHLH3, and PsERF1B were strongly stimulated to upregulated in the plums in the period of cold storage. Suppression of ethylene act with 1-methylcyclopropene greatly suppressed flesh-reddening and downregulated the expression of these genes. Transient overexpression and virus-induced gene silencing assays in plum flesh indicated that PsMYB10.1 encodes a positive regulator of anthocyanin accumulation. The transient overexpression of PsERF1B, coupled with PsMYB10.1 and PsbHLH3, could further prompt the anthocyanin biosynthesis in a tobacco leaf system. Results from yeast two-hybrid and luciferase complementation assays verified that PsERF1B directly interacted with PsMYB10.1. PsERF1B and PsMYB10.1 enhanced the activity of the promoter of PsUFGT individually, and the enhancement was prompted by the co-action of PsERF1B and PsMYB10.1. Overall, the stimulation of the PsERF1B-PsMYB10.1-PsbHLH3 module mediated cold signal in the transcriptomic supervision of the anthocyanin biosynthesis in the ‘Friar’ plums. The results thereby revealed the underlying mechanism of the postharvest alteration of the flesh phenotype of ‘Friar’ plums subjected to low temperature.
Uncertainties in ECMWF Surface Pressure Fields over the Ocean in Relation to Sea Level Analysis and Modeling

Abstract Knowledge of atmospheric surface pressure (Pa) over the oceans is important for proper interpretation and modeling of sea level variability. Gridded analyses of Pa fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) have been routinely used for altimeter data processing, but their uncertainties are poorly understood. An attempt is made here to quantify the uncertainties in ECMWF Pa fields over the period 1993–95, coinciding with the early years of the TOPEX/Poseidon altimeter mission, by using similar fields from a different operational analysis, a limited set of island barometer records, and TOPEX/Poseidon sea level data. The analysis presented here suggests that ECMWF error variances are under 3 hPa2 over most of the oceans, with typical values closer to 1 hPa2 or smaller, but with substantially larger errors south of 50°S. The worst signal-to-noise ratios occur in the Tropics, where Pa variance is smallest. Errors are mostly associated with uncertainties in submonthly Pa.
A relaxed extragradient-like method for a class of constrained optimization problem

This paper presents a relaxed extragradient-like method for solving the convexly constrained minimization with optimal value zero. The method is a combination of the extragradient-like algorithm and a halfspace-relaxation technique to the constrained set of the problem. Each iteration of the proposed method consists of the projection onto a halfspace containing the given closed convex set. The method is implemented very easily and is proven to be fully convergent to the solution. Preliminary computational experience is also reported.
Simulation Analysis on the Performance of a Circular-Edge Technique in Measurements of the Modulation Transfer Function

The modulation transfer function (MTF) plays an important role in characterizing medical imaging systems. For such characterization, the circular-edge technique has become a prevalent task-based methodology. When determining the MTF with complicated task-based measurements, error factors must be well understood to properly interpret the results. In this context, the aim of this work was to study the changes in measurement performance in the analysis of the MTF using a circular edge. To eliminate the systematic error related to the measurement and suitably manage the error factors, images were generated by Monte Carlo simulation. Further, a performance comparison with the conventional method was conducted; in addition, the influence of the edge size and contrast and the setting error of the center coordinates were investigated. The difference from the true value and the standard deviation relative to the average value were applied to the index as the accuracy and precision, respectively. The results demonstrated that the smaller the circular object used and the lower the contrast, the grater the deterioration in the measurement performance. Furthermore, this study clarified the underestimating of the MTF in proportion to the square of the distance with respect to the setting error of the center position, which is important for the synthesis of the edge profile. Evaluations in the backgrounds wherein there are multiple factors affecting the results are complex, and the system users must properly judge the validity of the characterization results. These findings provide meaningful insight in the context of MTF measurement techniques.
Fast computation of cluster validity measures for bregman divergences and benefits

Partitional clustering is one of the most relevant unsupervised learning and pattern recognition techniques. Unfortunately, one of the main drawbacks of these methodologies refer to the fact that the number of clusters is generally assumed to be known beforehand and automating its selection is not straightforward. On the same token, internal validity measures, such as the Silhouette index, Davies-Bouldin and Caliński-Harabasz measures have emerged as the standard techniques to be used when comparing the goodness of clustering results obtained via diﬀerent clustering methods. These measures take into consideration both the inter and intra-cluster simmilarities and can be adapted to diﬀerent metrics. Unfortunately, their used has been hindered due to their large computational complexities, which are commonly quadratic with respect to the number of instances of the data set. In this work, we show that the time complexity of computing the most popular internal validity measures can be utterly reduced by making used of the within-cluster errors and diﬀerent properties of the Bregman divergences. This contribution ultimately allows us to massively speed-up the selection of an adequate number of clusters for a given data set as veriﬁed with extensive empirical comparisons.
Step Displacement Improving Method of Inertial Actuated Piezoelectric Robot Based on Diagonal Deformation Trajectory

For the miniature piezoelectric robots operated by inertial driving method, the step displacements were limited by the deformations of the inertial units. Thus, a diagonal inertial driving method (DIDM) was proposed in this work to increase the step displacement. The prominent feature of DIDM was that the inertial force was in diagonal direction by using the designed piezoleg with square cross section. The vertical component of the inertial force reduced the friction force and even made the robot jump to achieve a larger step displacement. The experimental results indicated that the proposed DIDM increased the step displacement successfully. The increase effect was optimal when the vertical component of the inertial force was equal to the horizontal one, and the realized step displacement was as high as 156% to the deformation of the inertial unit in motion direction. Moreover, the force and displacement responses of the driving foot in vertical direction were measured, which indicated that the robot realized jumping action to increase the step displacement under DIDM, and the maximum speed realized by DIDM was 2.3 times of that achieved by the traditional inertial driving method. We believe that the proposed DIDM can broaden the solutions of improving the performances of the inertial miniature piezoelectric robots.
Simple four-wave-mixing-based method for measuring the ratio between the third- and fourth-order dispersion in optical fibers

A simple four-wave-mixing (FWM)-based method for measuring the ratio between the third- and the fourth-order dispersion coefficients (β3/β4) in optical fibers is reported. The FWM interaction involves a low power laser and a low power amplified spontaneous emission noise source. The method is applied in several dispersion-shifted and non-zero-dispersion-shifted fibers with lengths varying from 0.03 to 25km, and we have obtained an error of less than 3% in measuring β3/β4. In highly nonlinear fibers the error has presented a strong dependency with longitudinal variations of the zero-dispersion wavelength (λ0); an error less than 20% could be obtained in most of the tested fibers. Our method also allows for a fast estimation of the distribution of fluctuations of λ0 along the fiber.
Frequent Congestion Detection Model Based on Critical Intersection Identification

Intersection information in urban transportation networks is critical to the safe and efficient operation of road traffic. The controllability of the road network can be increased by controlling critical intersections during peak traffic periods. On the one hand, based on the improved intra- and inter-level correlation description matrix of time steps, a directed supra-adjacency matrix (DSAM) temporal network model is built to verify the validity of the model using the road network data of urban core areas and compare the results with those obtained from static critical intersection identification. The results show that the DSAM model has a phase continuity feature in ranking the intersection importance, which has richer connotation than the critical intersection identification results based on a single indicator and has better consistency at different time granularity. On the other hand, based on the DSAM model to identify critical intersections of road network, short-term traffic flow prediction is performed with the help of a road network adjacency matrix, and the results show that the Chebyshev diagram convolution neural network (ChebNet) has the best performance. It can be used in the detection of recurrent congestion of urban road networks, and corresponding control measures are made in shorter time steps, which can prevent the possibility of widespread congestion and reduce the blockage of the whole road network. In summary, the model can be used for critical intersection identification and congestion detection in urban road networks.