A comprehensive study about the effects of O₂ plasma treatment on the dynamic performance of AlGaN/GaN high electron mobility transistors (HEMTs) is presented. The drain current transient spectroscopy indicated a much decelerated and mitigated ...

This study used computational fluid dynamics (CFD) to analyze an underwater robot design simulated in SolidWorks Flow Simulation from 5m to 15m depth and 5m/s speed through the z-axis. The CFD analysis examines static pressure, dynamic pressure, total ...

This article aims to address the issue of fixed-time consensus control for uncertain nonlinear multiagent systems (MASs), in which only a group of followers can directly access the leader's information. To ensure the minimum utilization of wireless ...

In the past few decades, China’s power demand has been increasing, and the power fiber plays a key role in ensuring the orderly dispatching of all links of the power system. The study used a wavelet decomposition and reconstruction method, which is a ...

Accurate prediction of dynamical systems in unstructured meshes has recently shown successes in scientific simulations. Many dynamical systems have a nonnegligible level of stochasticity introduced by various factors (e.g. chaoticity), so there is a need ...

Decompilation is a widely used process for reverse engineers to significantly enhance code readability by lifting assembly code to a higher-level C-like language, pseudo-code. Nevertheless, the process of compilation and stripping irreversibly discards ...

Cervical cancer poses a serious threat to the health of women and radiotherapy is one of the primary treatment methods for this condition. However, this treatment is associated with a high risk of causing acute hematologic toxicity. Delineating ...

• Construct point clouds from sequential images with a novel CSN-ICP registration algorithm.
• The CSN-ICP combines global cartesian coordinate system with local point cloud spherical coordinate system.
• The CSN-ISP algorithm contains ...

Understanding and optimizing the parallel training strategies in the training of large-scale Deep Neural Network (DNN) models is crucial to enhance training efficiency. Existing works tried to demonstrate the layer-level information of the ...

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• Parallel training strategies vary to cut amortized computing load based on conditions.
• Traditional graph tools seldom analyze communication operators in parallel execution.
• Novel bipartite graph upholds parallel execution in ...

As a key component in online marketing, uplift modeling aims to accurately capture the degree to which different treatments motivate different users, such as coupons or discounts, also known as the estimation of individual treatment effect (ITE). In an ...

In this paper, we consider the problem of bit allocation in Neural Video Compression (NVC). First, we reveal a fundamental relationship between bit allocation in NVC and Semi-Amortized Variational Inference (SAVI). Specifically, we show that SAVI with GoP ...

The field of Very-Large-Scale Robotics (VLSR) has garnered significant attention due to its ability to tackle complex and coordinated tasks. However, current motion planning methods for VLSR face challenges related to scalability and ensuring ...

Diagnosing the cluster-based performance of large-scale deep neural network (DNN) models during training is essential for improving training efficiency and reducing resource consumption. However, it remains challenging due to the incomprehensibility of ...

In Industrial Internet of Things (IIoT), a large volume of data is collected periodically by IoT devices, and timely data routing and processing are important requirements. Age of Information (AoI), which is a metric to evaluate the ...

• Real-data-based queueing models of IoT devices and edge servers are proposed.
• ...

In the teaching of plastic mechanics and applications of slip-line theory using conventional methods, multivalued results are usually caused by the uncertain direction of the slip line and dip angles. Determination rules for the α and β directions and φ ...

A computational graph in a deep neural network (DNN) denotes a specific data flow diagram (DFD) composed of many tensors and operators. Existing toolkits for visualizing computational graphs are not applicable when the structure is highly complicated and ...

Driver's risk perception characteristics are uncertain factors in the road traffic environment. To improve driving safety, the quantification of risk perception of drivers with different styles is studied. First, typical dangerous scenarios with ...

The storage and transmission tasks of surveillance and conference videos are an important branch of video compression. Since surveillance and conference videos have strong inter-frame correlation, considerable continuity at the image level and ...

This paper considers the problem of lossy neural image compression (NIC). Current state-of-the-art (sota) methods adopt uniform posterior to approximate quantization noise, and single-sample pathwise estimator to approximate the gradient of evidence ...

Entropy modeling is a key component for high-performance image compression algorithms. Recent developments in autoregressive context modeling helped learning-based methods to surpass their classical counterparts. However, the performance of those ...

Following the conventional hybrid video coding framework, existing learned video compression methods rely on the decoded previous frame as the reference for motion estimation considering that it is available to the decoder. Diving into its essential ...