Synthetic aperture radar with compressed sensing

A general synthetic aperture radar (SAR) signal model is derived from the Maxwell’s equations, and compressed sensing are introduced to the signal model for SAR image reconstruction. Random Partial Fourier Matrices were applied to prove that compressed sensing can be used to this signal model from the viewpoint of mathematics. In the numerical simulation part, we show that the procedure of basis pursuit can reconstruct SAR image, based on our main results, which is shown efficient in comparison with the matched filter algorithm

Adaptive wave-particle decomposition in UGKWP method for high-speed flow simulations

With wave-particle decomposition, a unified gas-kinetic wave-particle (UGKWP) method has been developed for the multiscale flow simulations. The UGKWP method captures the transport process in all flow regimes without kinetic solver's constraint on the numerical mesh size and time step being less than the particle mean free path and collision time. In the current UGKWP method, the cell's Knudsen number, defined as the ratio of collision time to numerical time step, is used to distribute the components in the wave-particle decomposition. However, the adaptation of particle in UGKWP is mainly for the capturing of the non-equilibrium transport, and the cell's Knudsen number alone is not enough to identify the non-equilibrium state. For example, in the equilibrium flow regime with a Maxwellian distribution function, even at a large cell's Knudsen number, the flow evolution can be still modelled by the Navier-Stokes solver. Therefore, to further improve the efficiency, an adaptive UGKWP (AUGKWP) method will be developed with the introduction of an additional local flow variable gradient-dependent Knudsen number. As a result, the wave-particle decomposition in UGKWP will be determined by both cell's and gradient's Knudsen numbers, and the particle in UGKWP is solely used to capture the non-equilibrium flow transport. The AUGKWP becomes much more efficient than the previous one with the cell's Knudsen number only in the determination of wave-particle composition. Many numerical tests, including Sod tube, shock structure, flow around a cylinder, flow around a reentry capsule, and an unsteady nozzle plume flow, have been conducted to validate the accuracy and efficiency of AUGKWP. Compared with the original UGKWP, the AUGKWP achieves the same accuracy but has advantages in memory reduction and computational efficiency in the simulation for the flow with the co-existing of multiple regimes.Comment: arXiv admin note: substantial text overlap with arXiv:2211.1292

Two Heads Are Better Than One: Improving Fake News Video Detection by Correlating with Neighbors

The prevalence of short video platforms has spawned a lot of fake news videos, which have stronger propagation ability than textual fake news. Thus, automatically detecting fake news videos has been an important countermeasure in practice. Previous works commonly verify each news video individually with multimodal information. Nevertheless, news videos from different perspectives regarding the same event are commonly posted together, which contain complementary or contradictory information and thus can be used to evaluate each other mutually. To this end, we introduce a new and practical paradigm, i.e., cross-sample fake news video detection, and propose a novel framework, Neighbor-Enhanced fakE news video Detection (NEED), which integrates the neighborhood relationship of new videos belonging to the same event. NEED can be readily combined with existing single-sample detectors and further enhance their performances with the proposed graph aggregation (GA) and debunking rectification (DR) modules. Specifically, given the feature representations obtained from single-sample detectors, GA aggregates the neighborhood information with the dynamic graph to enrich the features of independent samples. After that, DR explicitly leverages the relationship between debunking videos and fake news videos to refute the candidate videos via textual and visual consistency. Extensive experiments on the public benchmark demonstrate that NEED greatly improves the performance of both single-modal (up to 8.34% in accuracy) and multimodal (up to 4.97% in accuracy) base detectors. Codes are available in https://github.com/ICTMCG/NEED.Comment: To appear in ACL 2023 Finding

CapsFusion: Rethinking Image-Text Data at Scale

Large multimodal models demonstrate remarkable generalist ability to perform diverse multimodal tasks in a zero-shot manner. Large-scale web-based image-text pairs contribute fundamentally to this success, but suffer from excessive noise. Recent studies use alternative captions synthesized by captioning models and have achieved notable benchmark performance. However, our experiments reveal significant Scalability Deficiency and World Knowledge Loss issues in models trained with synthetic captions, which have been largely obscured by their initial benchmark success. Upon closer examination, we identify the root cause as the overly-simplified language structure and lack of knowledge details in existing synthetic captions. To provide higher-quality and more scalable multimodal pretraining data, we propose CapsFusion, an advanced framework that leverages large language models to consolidate and refine information from both web-based image-text pairs and synthetic captions. Extensive experiments show that CapsFusion captions exhibit remarkable all-round superiority over existing captions in terms of model performance (e.g., 18.8 and 18.3 improvements in CIDEr score on COCO and NoCaps), sample efficiency (requiring 11-16 times less computation than baselines), world knowledge depth, and scalability. These effectiveness, efficiency and scalability advantages position CapsFusion as a promising candidate for future scaling of LMM training.Comment: CVPR 2024. Code & Dataset: https://github.com/baaivision/CapsFusio

Oxygen-Vacancy Abundant Ultrafine Co_3O_4/Graphene Composites for High-Rate Supercapacitor Electrodes

The metal oxides/graphene composites are one of the most promising supercapacitors (SCs) electrode materials. However, rational synthesis of such electrode materials with controllable conductivity and electrochemical activity is the topical challenge for high-performance SCs. Here, the Co_3O_4/graphene composite is taken as a typical example and develops a novel/universal one-step laser irradiation method that overcomes all these challenges and obtains the oxygen-vacancy abundant ultrafine Co_3O_4 nanoparticles/graphene (UCNG) composites with high SCs performance. First-principles calculations show that the surface oxygen vacancies can facilitate the electrochemical charge transfer by creating midgap electronic states. The specific capacitance of the UCNG electrode reaches 978.1 F g^(−1) (135.8 mA h g^(−1)) at the current densities of 1 A g^(−1) and retains a high capacitance retention of 916.5 F g^(−1) (127.3 mA h g^(−1)) even at current density up to 10 A g^(−1), showing remarkable rate capability (more than 93.7% capacitance retention). Additionally, 99.3% of the initial capacitance is maintained after consecutive 20 000 cycles, demonstrating enhanced cycling stability. Moreover, this proposed laser-assisted growth strategy is demonstrated to be universal for other metal oxide/graphene composites with tuned electrical conductivity and electrochemical activity

Empirical Review of Smart Contract and DeFi Security: Vulnerability Detection and Automated Repair

Decentralized Finance (DeFi) is emerging as a peer-to-peer financial ecosystem, enabling participants to trade products on a permissionless blockchain. Built on blockchain and smart contracts, the DeFi ecosystem has experienced explosive growth in recent years. Unfortunately, smart contracts hold a massive amount of value, making them an attractive target for attacks. So far, attacks against smart contracts and DeFi protocols have resulted in billions of dollars in financial losses, severely threatening the security of the entire DeFi ecosystem. Researchers have proposed various security tools for smart contracts and DeFi protocols as countermeasures. However, a comprehensive investigation of these efforts is still lacking, leaving a crucial gap in our understanding of how to enhance the security posture of the smart contract and DeFi landscape. To fill the gap, this paper reviews the progress made in the field of smart contract and DeFi security from the perspective of both vulnerability detection and automated repair. First, we analyze the DeFi smart contract security issues and challenges. Specifically, we lucubrate various DeFi attack incidents and summarize the attacks into six categories. Then, we present an empirical study of 42 state-of-the-art techniques that can detect smart contract and DeFi vulnerabilities. In particular, we evaluate the effectiveness of traditional smart contract bug detection tools in analyzing complex DeFi protocols. Additionally, we investigate 8 existing automated repair tools for smart contracts and DeFi protocols, providing insight into their advantages and disadvantages. To make this work useful for as wide of an audience as possible, we also identify several open issues and challenges in the DeFi ecosystem that should be addressed in the future.Comment: This paper is submitted to the journal of Expert Systems with Applications (ESWA) for revie