How do the landslide and non-landslide sampling strategies impact landslide susceptibility assessment? d A catchment-scale case study from China

The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment(LSA).The study area is the Feiyun catchment in Wenzhou City,Southeast China.Two types of landslides samples,combined with seven non-landslide sampling strategies,resulted in a total of 14 scenarios.The corresponding landslide susceptibility map(LSM)for each scenario was generated using the random forest model.The receiver operating characteristic(ROC)curve and statistical indicators were calculated and used to assess the impact of the dataset sampling strategy.The results showed that higher accuracies were achieved when using the landslide core as positive samples,combined with non-landslide sampling from the very low zone or buffer zone.The results reveal the influence of landslide and non-landslide sampling strategies on the accuracy of LSA,which provides a reference for subsequent researchers aiming to obtain a more reasonable LSM.

RE-SMOTE:A Novel Imbalanced Sampling Method Based on SMOTE with Radius Estimation

Imbalance is a distinctive feature of many datasets,and how to make the dataset balanced become a hot topic in the machine learning field.The Synthetic Minority Oversampling Technique(SMOTE)is the classical method to solve this problem.Although much research has been conducted on SMOTE,there is still the problem of synthetic sample singularity.To solve the issues of class imbalance and diversity of generated samples,this paper proposes a hybrid resampling method for binary imbalanced data sets,RE-SMOTE,which is designed based on the improvements of two oversampling methods parameter-free SMOTE(PF-SMOTE)and SMOTE-Weighted Ensemble Nearest Neighbor(SMOTE-WENN).Initially,minority class samples are divided into safe and boundary minority categories.Boundary minority samples are regenerated through linear interpolation with the nearest majority class samples.In contrast,safe minority samples are randomly generated within a circular range centered on the initial safe minority samples with a radius determined by the distance to the nearest majority class samples.Furthermore,we use Weighted Edited Nearest Neighbor(WENN)and relative density methods to clean the generated samples and remove the low-quality samples.Relative density is calculated based on the ratio of majority to minority samples among the reverse k-nearest neighbor samples.To verify the effectiveness and robustness of the proposed model,we conducted a comprehensive experimental study on 40 datasets selected from real applications.The experimental results show the superiority of radius estimation-SMOTE(RE-SMOTE)over other state-of-the-art methods.Code is available at:https://github.com/blue9792/RE-SMOTE(accessed on 30 September 2024).

Enhancing Deep Learning Semantics:The Diffusion Sampling and Label-Driven Co-Attention Approach

The advent of self-attention mechanisms within Transformer models has significantly propelled the advancement of deep learning algorithms,yielding outstanding achievements across diverse domains.Nonetheless,self-attention mechanisms falter when applied to datasets with intricate semantic content and extensive dependency structures.In response,this paper introduces a Diffusion Sampling and Label-Driven Co-attention Neural Network(DSLD),which adopts a diffusion sampling method to capture more comprehensive semantic information of the data.Additionally,themodel leverages the joint correlation information of labels and data to introduce the computation of text representation,correcting semantic representationbiases in thedata,andincreasing the accuracyof semantic representation.Ultimately,the model computes the corresponding classification results by synthesizing these rich data semantic representations.Experiments on seven benchmark datasets show that our proposed model achieves competitive results compared to state-of-the-art methods.

Influence of sampling on three-dimensional surface shape measurement

In order to accurately measure an object’s three-dimensional surface shape,the influence of sampling on it was studied.First,on the basis of deriving spectra expressions through the Fourier transform,the generation of CCD pixels was analyzed,and its expression was given.Then,based on the discrete expression of deformation fringes obtained after sampling,its Fourier spectrum expression was derived,resulting in an infinitely repeated"spectra island"in the frequency domain.Finally,on the basis of using a low-pass filter to remove high-order harmonic components and retaining only one fundamental frequency component,the inverse Fourier transform was used to reconstruct the signal strength.A method of reducing the sampling interval,i.e.,reducing the number of sampling points per fringe,was proposed to increase the ratio between the sampling frequency and the fundamental frequency of the grating.This was done to reconstruct the object’s surface shape more accurately under the condition of m>4.The basic principle was verified through simulation and experiment.In the simulation,the sampling intervals were 8 pixels,4 pixels,2 pixels,and 1 pixel,the maximum absolute error values obtained in the last three situations were 88.80%,38.38%,and 31.50%in the first situation,respectively,and the corresponding average absolute error values are 71.84%,43.27%,and 32.26%.It is demonstrated that the smaller the sampling interval,the better the recovery effect.Taking the same four sampling intervals in the experiment as in the simulation can also lead to the same conclusions.The simulated and experimental results show that reducing the sampling interval can improve the accuracy of object surface shape measurement and achieve better reconstruction results.

TCAS-PINN:Physics-informed neural networks with a novel temporal causality-based adaptive sampling method

Physics-informed neural networks(PINNs)have become an attractive machine learning framework for obtaining solutions to partial differential equations(PDEs).PINNs embed initial,boundary,and PDE constraints into the loss function.The performance of PINNs is generally affected by both training and sampling.Specifically,training methods focus on how to overcome the training difficulties caused by the special PDE residual loss of PINNs,and sampling methods are concerned with the location and distribution of the sampling points upon which evaluations of PDE residual loss are accomplished.However,a common problem among these original PINNs is that they omit special temporal information utilization during the training or sampling stages when dealing with an important PDE category,namely,time-dependent PDEs,where temporal information plays a key role in the algorithms used.There is one method,called Causal PINN,that considers temporal causality at the training level but not special temporal utilization at the sampling level.Incorporating temporal knowledge into sampling remains to be studied.To fill this gap,we propose a novel temporal causality-based adaptive sampling method that dynamically determines the sampling ratio according to both PDE residual and temporal causality.By designing a sampling ratio determined by both residual loss and temporal causality to control the number and location of sampled points in each temporal sub-domain,we provide a practical solution by incorporating temporal information into sampling.Numerical experiments of several nonlinear time-dependent PDEs,including the Cahn–Hilliard,Korteweg–de Vries,Allen–Cahn and wave equations,show that our proposed sampling method can improve the performance.We demonstrate that using such a relatively simple sampling method can improve prediction performance by up to two orders of magnitude compared with the results from other methods,especially when points are limited.

Failure-Informed Adaptive Sampling for PINNs,Part Ⅱ:Combining with Re-sampling and Subset Simulation

This is the second part of our series works on failure-informed adaptive sampling for physic-informed neural networks(PINNs).In our previous work(SIAM J.Sci.Comput.45:A1971–A1994),we have presented an adaptive sampling framework by using the failure probability as the posterior error indicator,where the truncated Gaussian model has been adopted for estimating the indicator.Here,we present two extensions of that work.The first extension consists in combining with a re-sampling technique,so that the new algorithm can maintain a constant training size.This is achieved through a cosine-annealing,which gradually transforms the sampling of collocation points from uniform to adaptive via the training progress.The second extension is to present the subset simulation(SS)algorithm as the posterior model(instead of the truncated Gaussian model)for estimating the error indicator,which can more effectively estimate the failure probability and generate new effective training points in the failure region.We investigate the performance of the new approach using several challenging problems,and numerical experiments demonstrate a significant improvement over the original algorithm.

Wideband spectrum sensing using step-sampling based on the multipath nyquist folding receiver

Wideband spectrum sensing with a high-speed analog-digital converter(ADC) presents a challenge for practical systems.The Nyquist folding receiver(NYFR) is a promising scheme for achieving cost-effective real-time spectrum sensing,which is subject to the complexity of processing the modulated outputs.In this case,a multipath NYFR architecture with a step-sampling rate for the different paths is proposed.The different numbers of digital channels for each path are designed based on the Chinese remainder theorem(CRT).Then,the detectable frequency range is divided into multiple frequency grids,and the Nyquist zone(NZ) of the input can be obtained by sensing these grids.Thus,high-precision parameter estimation is performed by utilizing the NYFR characteristics.Compared with the existing methods,the scheme proposed in this paper overcomes the challenge of NZ estimation,information damage,many computations,low accuracy,and high false alarm probability.Comparative simulation experiments verify the effectiveness of the proposed architecture in this paper.

Endoscopic ultrasound-guided tissue sampling induced pancreatic duct leak resolved by the placement of a pancreatic stent:A case report

BACKGROUND Pancreatic ductal leaks complicated by endoscopic ultrasonography-guided tissue sampling(EUS-TS)can manifest as acute pancreatitis.CASE SUMMARY A 63-year-old man presented with persistent abdominal pain and weight loss.Diagnosis:Laboratory findings revealed elevated carbohydrate antigen 19-9(5920 U/mL)and carcinoembryonic antigen(23.7 ng/mL)levels.Magnetic resonance imaging of the pancreas revealed an approximately 3 cm ill-defined space-occupying lesion in the inferior aspect of the head,with severe encasement of the superior mesenteric artery.Pancreatic ductal adenocarcinoma was confirmed after pathological examination of specimens obtained by EUS-TS using the fanning method.Interventions and outcomes:The following day,the patient experienced severe abdominal pain with high amylase(265 U/L)and lipase(1173 U/L)levels.Computed tomography of the abdomen revealed edematous wall thickening of the second portion of the duodenum with adjacent fluid collections and a suspicious leak from either the distal common bile duct or the main pancreatic duct in the head.Endoscopic retrograde cholangiopancreatography revealed dye leakage in the head of the main pancreatic duct.Therefore,a 5F 7 cm linear plastic stent was deployed into the pancreatic duct to divert the pancreatic juice.The patient’s abdominal pain improved immediately after pancreatic stent insertion,and amylase and lipase levels normalized within a week.Neoadjuvant chemotherapy was then initiated.CONCLUSION Using the fanning method in EUS-TS can inadvertently cause damage to the pancreatic duct and may lead to clinically significant pancreatitis.Placing a pancreatic stent may immediately resolve acute pancreatitis and shorten the waiting time for curative therapy.When using the fanning method during EUSTS,ductal structures should be excluded to prevent pancreatic ductal leakage.

Design and Experimentation of Multi-Rod Grain Sampling Machine

In order to enhance grain sampling efficiency, in this work a truss type multi-rod grain sampling machine is designed and tested. The sampling machine primarily consists of truss support mechanism, main carriage mechanism, auxiliary carriage mechanism, sampling rods, and a PLC controller. The movement of the main carriage on the truss, the auxiliary carriage on the main carriage, and the vertical movement of the sampling rods on the auxiliary carriage are controlled through PLC programming. The sampling machine accurately controls the position of the sampling rods, enabling random sampling with six rods to ensure comprehensive and random sampling. Additionally, sampling experiments were conducted, and the results showed that the multi-rod grain sampling machine simultaneously samples with six rods, achieving a sampling frequency of 38 times per hour. The round trip time for the sampling rods is 33 seconds per cycle, and the sampling length direction reaches 18 m. This study provides valuable insights for the design of multi-rod grain sampling machines.

Selective sampling with Gromov–Hausdorff metric:Efficient dense-shape correspondence via Confidence-based sample consensus

Background Functional mapping, despite its proven efficiency, suffers from a “chicken or egg” scenario, in that, poor spatial features lead to inadequate spectral alignment and vice versa during training, often resulting in slow convergence, high computational costs, and learning failures, particularly when small datasets are used. Methods A novel method is presented for dense-shape correspondence, whereby the spatial information transformed by neural networks is combined with the projections onto spectral maps to overcome the “chicken or egg” challenge by selectively sampling only points with high confidence in their alignment. These points then contribute to the alignment and spectral loss terms, boosting training, and accelerating convergence by a factor of five. To ensure full unsupervised learning, the Gromov–Hausdorff distance metric was used to select the points with the maximal alignment score displaying most confidence. Results The effectiveness of the proposed approach was demonstrated on several benchmark datasets, whereby results were reported as superior to those of spectral and spatial-based methods. Conclusions The proposed method provides a promising new approach to dense-shape correspondence, addressing the key challenges in the field and offering significant advantages over the current methods, including faster convergence, improved accuracy, and reduced computational costs.

CLIP4Video-Sampling: Global Semantics-Guided Multi-Granularity Frame Sampling for Video-Text Retrieval

Video-text retrieval (VTR) is an essential task in multimodal learning, aiming to bridge the semantic gap between visual and textual data. Effective video frame sampling plays a crucial role in improving retrieval performance, as it determines the quality of the visual content representation. Traditional sampling methods, such as uniform sampling and optical flow-based techniques, often fail to capture the full semantic range of videos, leading to redundancy and inefficiencies. In this work, we propose CLIP4Video-Sampling: Global Semantics-Guided Multi-Granularity Frame Sampling for Video-Text Retrieval, a global semantics-guided multi-granularity frame sampling strategy designed to optimize both computational efficiency and retrieval accuracy. By integrating multi-scale global and local temporal sampling and leveraging the CLIP (Contrastive Language-Image Pre-training) model’s powerful feature extraction capabilities, our method significantly outperforms existing approaches in both zero-shot and fine-tuned video-text retrieval tasks on popular datasets. CLIP4Video-Sampling reduces redundancy, ensures keyframe coverage, and serves as an adaptable pre-processing module for multimodal models.

Efficient sampling strategy driven surrogate-based multi-objective optimization for broadband microwave metamaterial absorbers

Multi-objective optimization(MOO)for the microwave metamaterial absorber(MMA)normally adopts evolutionary algo-rithms,and these optimization algorithms require many objec-tive function evaluations.To remedy this issue,a surrogate-based MOO algorithm is proposed in this paper where Kriging models are employed to approximate objective functions.An efficient sampling strategy is presented to sequentially capture promising samples in the design region for exact evaluations.Firstly,new sample points are generated by the MOO on surro-gate models.Then,new samples are captured by exploiting each objective function.Furthermore,a weighted sum of the improvement of hypervolume(IHV)and the distance to sampled points is calculated to select the new sample.Compared with two well-known MOO algorithms,the proposed algorithm is vali-dated by benchmark problems.In addition,two broadband MMAs are applied to verify the feasibility and efficiency of the proposed algorithm.

Prestack nonstationary deconvolution based on variable-step sampling in the radial trace domain

The conventional nonstationary convolutional model assumes that the seismic signal is recorded at normal incidence. Raw shot gathers are far from this assumption because of the effects of offsets. Because of such problems, we propose a novel prestack nonstationary deconvolution approach. We introduce the radial trace （RT） transform to the nonstationary deconvolution, we estimate the nonstationary deconvolution factor with hyperbolic smoothing based on variable-step sampling （VSS） in the RT domain, and we obtain the high-resolution prestack nonstationary deconvolution data. The RT transform maps the shot record from the offset and traveltime coordinates to those of apparent velocity and traveltime. The ray paths of the traces in the RT better satisfy the assumptions of the convolutional model. The proposed method combines the advantages of stationary deconvolution and inverse Q filtering, without prior information for Q. The nonstationary deconvolution in the RT domain is more suitable than that in the space-time （XT） domain for prestack data because it is the generalized extension of normal incidence. Tests with synthetic and real data demonstrate that the proposed method is more effective in compensating for large-offset and deep data.

All-Optical Sampling Using Nonlinear Polarization Rotation in a Single Semiconductor Optical Amplifier

We propose a novel all-optical sampling method using nonlinear polarization rotation in a semiconductor optical amplifier. A rate-equation model capable of describing the all-optical sampling mechanism is presented in this paper. Based on this model, we investigate the optimized operating parameters of the proposed system by simulating the output intensity of the probe light as functions of the input polarization angle, the phase induced by the polarization controller, and the ori- entation of the polarization beam splitter. The simulated results show that we can obtain a good linear slope and a large linear dynamic range,which is suitable for all-optical sampling. The operating power of the pump light can be less than lmW. The presented all-optical sampling method can potentially operate at a sampling rate up to hundreds GS/s and needs low optical power.

A 5mW 1.8V Low Over-Sampling Ratio ΣΔ Modulator with 81dB Dynamic Range

This work demonstrates that the ΣΔ modulator with a low oversampling ratio is a viable option for the high-resolution digitization in a low-voltage environment.Low power dissipation is achieved by designing a low-OSR modulator based on differential cascade architecture,while large signal swing maintained to achieve a high dynamic range in the low-voltage environment.Operating from a voltage supply of 1.8V,the sixth-order cascade modulator at a sampling frequency of 4-MHz with an OSR of 24 achieves a dynamic range of 81dB for a 80-kHz test signal,while dissipating only 5mW.

Analysis and Simulation of Errors in Software Synchronous Sampling in Periodic-Signal Measurement

Software synchronous sampling is widely employed in periodic signal measurement, but measurement errors occur very commonly. This paper analyses the cause of the errors, deduces the mathematical model for the measurement errors in measuring RMS voltage, RMS current and active power using the software synchronous sampling method. Some measures to reduce the errors are put forward by simulating.

Distributed Sampling Measurement Model in a Large-Scale High-Speed IP Networks

The distributed passive measurement is an important technology for networkbehavior research. To achieve a consistent measurement, the same packets should be sampled atdistributed measurement points. And in order to estimate the character of traffic statistics, thetraffic sample should be random in statistics. A distributed samplingmask measurement model isintroduced to tackle the difficulty of measuring the full trace of high-speed networks. The keypoint of the model is to choose some bits that are suitable to be sampling mask. In the paper, thebit entropy and bit flow entropy of IP packet headers in CERNET backbone are analyzed, and we findthat the 16 bits of identification field in IP packet header are fit to the matching field ofsampling mask. Measurement traffic also can be used to analyze the statistical character ofmeasurement sample and the randomicity of the model. At the same time the experiment resultsindicate that the model has a good sampling performance.

线截抽样(LIS-Line Intersect Sampling)与线状物体(林网、路网)调查原理研究

以Ｂｕｆｏｎ随机投针问题为原理，对线截抽样的概念作了系统的介绍，并在此基础上提出了以线截抽样为原理的线状物体的调查方法。在计算机上成功地模拟了路网调查，从模拟的几种方案可以得出：线截抽样完全适用路网调查，且以本文中的Ｂ、Ｃ两种方案为好，并可以以此推广到林网及防护带的调查。

A Novel Sampling Switch Suitable for Low-Voltage Analog-to-Digital Converters

A novel, highly linear sampling switch suitable for low-voltage operation is proposed. This switch not only eliminates the nonlinearity introduced by gate-source voltage variation, but also reduces the nonlinearity resuiting from threshold voltage variation, which has not been accomplished in earlier low-voltage sampling switches. This is achieved by adopting a replica transistor with the same threshold voltage as the sampling transistor. The effectiveness of this technique is demonstrated by a prototype design of a sampling switch in 0. 35μm. The proposed sampling switch achieves a spurious free dynamic range of 111dB for a 0. 2MHz, 1.2Vp-p input signal, sampled at a rate of 2MS/s,about 18dB over the Bootstrapped switch. Also, the on-resistance variation is reduced by 90%. This method is especially useful for low-voltage, high resolution ADCs, which is a hot topic today.

A New Method to Investigate InGaAsP Single-Photon Avalanche Diodes Using a Digital Sampling Oscilloscope

A near-infrared single-photon detection system is established by using pigtailed InGaAs/InP avalanche photodiodes. With a 50GHz digital sampling oscilloscope, the function and process of gated-mode （Geiger-mode） single-photon detection are intuitionally demonstrated for the first time. The performance of the detector as a gated-mode single-photon counter at wavelengths of 1310 and 1550nm is investigated. At the operation temperature of 203K,a quantum efficiency of 52% with a dark count probability per gate of 2.4 × 10 ^-3 ,and a gate pulse repetition rate of 50kHz are obtained at 1550nm. The corresponding parameters are 43%, 8.5 × 10^-3 , and 200kHz at 238K.