Towards Efficient SDRTV-to-HDRTV by Learning from Image Formation

Modern displays are capable of rendering video content with high dynamic range (HDR) and wide color gamut (WCG). However, the majority of available resources are still in standard dynamic range (SDR). As a result, there is significant value in transforming existing SDR content into the HDRTV standard. In this paper, we define and analyze the SDRTV-to-HDRTV task by modeling the formation of SDRTV/HDRTV content. Our analysis and observations indicate that a naive end-to-end supervised training pipeline suffers from severe gamut transition errors. To address this issue, we propose a novel three-step solution pipeline called HDRTVNet++, which includes adaptive global color mapping, local enhancement, and highlight refinement. The adaptive global color mapping step uses global statistics as guidance to perform image-adaptive color mapping. A local enhancement network is then deployed to enhance local details. Finally, we combine the two sub-networks above as a generator and achieve highlight consistency through GAN-based joint training. Our method is primarily designed for ultra-high-definition TV content and is therefore effective and lightweight for processing 4K resolution images. We also construct a dataset using HDR videos in the HDR10 standard, named HDRTV1K that contains 1235 and 117 training images and 117 testing images, all in 4K resolution. Besides, we select five metrics to evaluate the results of SDRTV-to-HDRTV algorithms. Our final results demonstrate state-of-the-art performance both quantitatively and visually. The code, model and dataset are available at https://github.com/xiaom233/HDRTVNet-plus.Comment: Extended version of HDRTVNe

Demystifying DeFi MEV Activities in Flashbots Bundle

Decentralized Finance, mushrooming in permissionless blockchains, has attracted a recent surge in popularity. Due to the transparency of permissionless blockchains, opportunistic traders can compete to earn revenue by extracting Miner Extractable Value (MEV), which undermines both the consensus security and efficiency of blockchain systems. The Flashbots bundle mechanism further aggravates the MEV competition because it empowers opportunistic traders with the capability of designing more sophisticated MEV extraction. In this paper, we conduct the first systematic study on DeFi MEV activities in Flashbots bundle by developing ActLifter, a novel automated tool for accurately identifying DeFi actions in transactions of each bundle, and ActCluster, a new approach that leverages iterative clustering to facilitate us to discover known/unknown DeFi MEV activities. Extensive experimental results show that ActLifter can achieve nearly 100% precision and recall in DeFi action identification, significantly outperforming state-of-the-art techniques. Moreover, with the help of ActCluster, we obtain many new observations and discover 17 new kinds of DeFi MEV activities, which occur in 53.12% of bundles but have not been reported in existing studies

Establishing the carrier scattering phase diagram for ZrNiSn-based half-Heusler thermoelectric materials

Chemical doping is one of the most important strategies for tuning electrical properties of semiconductors, particularly thermoelectric materials. Generally, the main role of chemical doping lies in optimizing the carrier concentration, but there can potentially be other important effects. Here, we show that chemical doping plays multiple roles for both electron and phonon transport properties in half-Heusler thermoelectric materials. With ZrNiSn-based half-Heusler materials as an example, we use high-quality single and polycrystalline crystals, various probes, including electrical transport measurements, inelastic neutron scattering measurement, and first-principles calculations, to investigate the underlying electron-phonon interaction. We find that chemical doping brings strong screening effects to ionized impurities, grain boundary, and polar optical phonon scattering, but has negligible influence on lattice thermal conductivity. Furthermore, it is possible to establish a carrier scattering phase diagram, which can be used to select reasonable strategies for optimization of the thermoelectric performance.Comment: 21 pages, 5 figure

SigRec: Automatic Recovery of Function Signatures in Smart Contracts

Millions of smart contracts have been deployed onto Ethereum for providing various services, whose functions can be invoked. For this purpose, the caller needs to know the function signature of a callee, which includes its function id and parameter types. Such signatures are critical to many applications focusing on smart contracts, e.g., reverse engineering, fuzzing, attack detection, and profiling. Unfortunately, it is challenging to recover the function signatures from contract bytecode, since neither debug information nor type information is present in the bytecode. To address this issue, prior approaches rely on source code, or a collection of known signatures from incomplete databases or incomplete heuristic rules, which, however, are far from adequate and cannot cope with the rapid growth of new contracts. In this paper, we propose a novel solution that leverages how functions are handled by Ethereum virtual machine (EVM) to automatically recover function signatures. In particular, we exploit how smart contracts determine the functions to be invoked to locate and extract function ids, and propose a new approach named type-aware symbolic execution (TASE) that utilizes the semantics of EVM operations on parameters to identify the number and the types of parameters. Moreover, we develop SigRec , a new tool for recovering function signatures from contract bytecode without the need of source code and function signature databases. The extensive experimental results show that SigRec outperforms all existing tools, achieving an unprecedented 98.7 percent accuracy within 0.074 seconds. We further demonstrate that the recovered function signatures are useful in attack detection, fuzzing and reverse engineering of EVM bytecode

Research on reverberation characteristics analysis and suppression methods for active continuous detection

Compared with the traditional active detection with monopulse periodic emission mode, active continuous detection has the advantages of large emission duty ratio and continuous acquisition of process information. It can effectively overcome the disadvantages of high false alarm probability, poor environment adaptation ability and low detection efficiency in traditional active detection, so then improving the system detection ability. But active continuous detection is also facing more serious reverberation. In order to further improve capacity, the adaptable reverberation characteristics and detection methods are carried out in this paper. On the basis of theoretical modelling, the relationship between the characteristics of the active continuous detection reverberation and the signal form, the hydrological environment and the emission power are studied. The time frequency characteristics of reverberation and the attenuation law with distance of reverberation are mastered. A reverberation suppression method based on adaptive beamforming of sub-band steered minimum variance algorithm (SSMV) is studied for active continuous detection. Considering signal bandwidth and fast convergence, etc. The relationship between sub-array partition and reverberation suppression ability is analyzed. The validity of reverberation characteristic analysis is verified by simulation, the performance of the method of reverberation suppression is verified by sea trial data processing

The Mediating Effect of Psychological Resilience between Individual Social Capital and Mental Health in the Post-Pandemic Era: A Cross-Sectional Survey over 300 Family Caregivers of Kindergarten Children in Mainland China

In the context of the impact of the post-COVID-19 pandemic on families, this study explores the impact of individual social capital and psychological resilience on the mental health of family caregivers of kindergarten children in mainland China. This study included a sample of 331 family caregivers from Zhaoqing City, Guangdong Province, and the researchers applied the Personal Social Capital Scale (PSCS-16), Connor–Davidson Resilience Scale (CD-RISC-10), and Depression Anxiety Stress Scale (DASS) to assess social capital, psychological resilience, and mental health. Findings indicate a positive relationship between bridging social capital and mental health, while psychological resilience is negatively associated with depression, anxiety, and stress. Psychological resilience is identified as a mediator between social capital and mental health outcomes in this study. These insights highlight the importance of enhancing social capital and psychological resilience to improve family caregivers’ mental health and the need for targeted interventions

Novel DC Bias Suppression Device Based on Adjustable Parallel Resistances

For lack of the appropriate global distribution of dc currents, the conventional suppression method to suppress dc bias based on capacitive dc blocking device (BD) redirects current to the ground as much as possible, which predisposes to the exceeding neutral current of other transformers in the regional power grid and leads to the contradiction between the power grid corporation and other public enterprises. Therefore, this paper presents a flexible suppression method for dc bias based on a novel dc-bias suppression device. First, a current balancing device (CBD) based on adjustable parallel resistances is designed. The mathematical model for global optimal switching of CBDs is established by a field-circuit coupling method with the equivalent resistance network of an ac system along with the location of substations and ground electrodes. The optimal switching scheme to minimize the global maximum dc current is obtained by gravitational search algorithm. Based on the aforementioned work, we propose a suppression strategy considering electro-corrosion of metal pipelines. The effectiveness and superiority of suppression methods are verified by comparative case studies of the Yichang power grid

Porphyromonas gingivalis promotes malignancy and chemo-resistance via GSK3β-mediated mitochondrial oxidative phosphorylation in human esophageal squamous cell carcinoma

Our prior studies have confirmed that long-term colonization of Porphyromonas gingivalis (Pg) and overexpression of the inflammatory factor glycogen synthase kinase 3β (GSK3β) promote the malignant evolution of esophageal squamous cell carcinoma (ESCC). We aimed to investigate the functional mechanism by which Pg could promote ESCC malignancy and chemo-resistance through GSK3β-mediated mitochondrial oxidative phosphorylation (mtOXPHOS), and the clinical implications. The effects of Pg and GSK3β on mtOXPHOS, malignant behaviors and response to paclitaxel and cisplatin treatment of ESCC cells were evaluated by in vitro and in vivo studies. The results showed that Pg induced high expression of the GSK3β protein in ESCC cells and promoted the progression and chemo-resistance via GSK3β-mediated mtOXPHOS in human ESCC. Then, Pg infection and the expression of GSK3β, SIRT1 and MRPS5 in ESCC tissues were detected, and the correlations between each index and postoperative survival of ESCC patients were analysed. The results showed that Pg-positive ESCC patients with high-expression of GSK3β, SIRT1 and MRPS5 have significant short postoperative survival. In conclusion, we demonstrated that the effective removal of Pg and inhibition of its promotion of GSK3β-mediated mtOXPHOS may provide a new strategy for ESCC treatment and new insights into the aetiology of ESCC