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As the capacity of NAND flash-based SSDs keeps increasing, it becomes crucial to design a memory-efficient address translation algorithm that offers high performance when a translation table cannot be entirely loaded in a controller DRAM. ...

• In order to provide an efficient address translation in Solid State Driver, we propose a data pattern-based address translation, called PADFTL.
• In contrast to existing translation techniques, PADFTL is able to identify more detailed ...

Recent decades have witnessed a significant surge in scholarly attention towards formulating dynamic models for opinion formation on social networks. This paper considers the bipartite consensus problem over a series of issues for two ...

Image deraining with unpaired data has been a challenging problem. Previous methods suffer from either the color distortion artifacts, due to the pixel-level cycle consistency loss, or the time-consuming training process. To address these ...

• We propose a rain removal framework that generates clean images without paired data.
• We propose a method to remove rain by generating rain images using masks as a guide.
• We propose a contrastive learning generator to maintain ...

Semi-supervised learning (SSL), thanks to the significant reduction of data annotation costs, has been an active research topic for large-scale 3D scene understanding. However, the existing SSL-based methods suffer from severe training bias, ...

• First to propose decoupling optimization for class-imbalanced 3D point cloud SSL.
• Two-round pseudo-labels and focus loss improve head-to-tail class segmentation.
• Superior efficiency and effectiveness; achieves new state-of-the-art ...

Drug–target interactions (DTIs) play a key role in drug discovery and development as they are critical in understanding the complex mechanisms of underlying drugs and their corresponding targets. Unfortunately, some studies do not fully recognize ...

The identification and rank of crucial spreaders aim to survey the diffusion capability, which has a significant impact on controlling information spread in networks. However, most of the findings neglect the fused information of the structural ...

• ETC focuses on the fusion of structural hole and the former two-order information.
• Definition of Hierarchy Vector of Cluster is based on a novel layered approach IKsD.
• Tanimoto Correlation tests association strength between a pair ...

A neurochip is a device that reproduces the signal processing mechanisms of brain neurons and calculates Spiking Neural Networks (SNNs) with low power consumption and at high speed. Thus, neurochips are attracting attention from edge robot ...

• Proposed a novel DRL framework for training SNN policies in neurochip-driven robots.
• Developed a new policy-update algorithm that suppresses the optimal-action changes caused by policy conversion.
• Verified energy savings and the ...

I show analytically that the average of k bootstrapped correlation matrices rapidly becomes positive-definite as k increases, which provides a simple approach to regularize singular Pearson correlation matrices. If n is the order of the matrix ...

The field of deep learning is renowned for its resource-intensive nature, hence improving its environmental impact is crucial. In this paper, we propose a novel model compression method to mitigate the energy demands of deep learning for a ...

• Novel method for extreme quantization in deep learning medical classification models.
• Differentiable asymmetric pruning based on the statistics of the weights.
• Better trade-of between the compression, energy, and classification ...

Fake audio detection is a growing concern and some relevant datasets have been designed for research. However, there is no standard public Chinese dataset under complex conditions. In this paper, we aim to fill in the gap and design a Chinese ...

• We construct a standard public Chinese dataset CFAD for fake audio detection studies under complex conditions.
• CFAD dataset considers 12 types of fake audio under noisy conditions and transcoding (format conversion) conditions.
• ...

Millimeter-wave (MMW) 3-D imaging based on multiple-input-multiple-output (MIMO) radar has been widely studied due to its unique advantages in human security screening. However, more research has been done on the use of scanning 1-D MIMO array ...

• A fast imaging method based on boundary MIMO array is proposed.
• The computational complexity of the proposed method is comprehensively analyzed.
• The focusing error resulting from the proposed imaging method is analyzed.
• ...

This paper proposes a reconfigurable intelligent surface (RIS)-enhanced unmanned aerial vehicle (UAV) secure communicating scheme with multiple mutually-untrusted ground users (GUs). To resist eavesdropping, a pre-defined artificial noise (AN) is ...

Graph Neural Network (GNN) models are recently proposed to process the graph-structured data for the learning tasks on graphs, e.g., node classification, link prediction, and so on. This work focuses on the graph classification task, aiming to ...

• The module-based graph pooling (MGPool) framework obtains the graph representation by three stages from bottom to top: node, module and graph.
• MGPool considers information from both graph and module views during node encoding.
• An ...

Generative Adversarial Networks (GANs) have become a popular research topic in many research fields in the last decade. The fragmented body of knowledge on GANs drives researchers to a trial-and-error procedure while choosing the right GAN for a ...

Stock market forecasting is one of the most exciting areas of time series forecasting both for the industry and academia. Stock market is a complex, non-linear and non-stationary system with many governing factors and noise. Some of these factors ...

Synthesizing high-quality singing voice from music score is a challenging problem in music generation and has many practical applications. Samples generated by existing singing voice synthesis (SVS) systems can roughly reflect the lyrics, pitch ...

Brain tumor segmentation is currently of a priori guiding significance in medical research and clinical diagnosis. Brain tumor segmentation techniques can accurately partition different tumor areas on multi-modality images captured by magnetic ...

• Proposes a novel CNN-based brain tumor segmentation method.
• Introduces an MIE module to utilize the multi-modality information better.
• Designs an SIE module to preserve the spatial information better.
• Presents a BSC module to ...

Simultaneously enhancing the visual effects and resolution of underwater images poses a challenging task as it involves two types of image enhancement tasks, underwater image enhancement and image super-resolution. In spite of the emergence of ...

Many surrogate-assisted evolutionary algorithms (SAEAs) have been developed to address expensive multi-objective optimization problems (EMOPs). However, existing research primarily focuses on low-dimensional EMOPs. In this article, we propose an ...

The function of network traffic prediction plays an important role in many network operations such as security, path planning and congestion control etc. Most traditional traffic prediction methods only consider temporal correlation but ignore ...