An Improved Watermarking Algorithm Robust to Temporal Desynchronization Attacks

The watermarking scheme based on finite state machine can achieve temporal desynchronization without additional synchronization signals or exhaustive search. However, the resistance to the frame dropping attack and the decimation attack is highly dependent on the repetition redundancy unit. When the video is truncated, permuted and reassembled, the extracted watermark has a low bit correct rate (BCR). In this work, we analyze and improve the correlation-based extracting method for spread-spectrum watermarking and propose a non-synchronization extracting strategy based on statistical inference. The proposed method is resistant to most of the temporal synchronization attacks

Early Crop Mapping Using Dynamic Ecoregion Clustering: A USA-Wide Study

peer reviewedMapping target crops earlier than the harvest period is an essential task for improving agricultural productivity and decision-making. This paper presents a new method for early crop mapping for the entire conterminous USA (CONUS) land area using the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data with a dynamic ecoregion clustering approach. Ecoregions, geographically distinct areas with unique ecological patterns and processes, provide a valuable framework for large-scale crop mapping. We conducted our dynamic ecoregion clustering by analyzing soil, climate, elevation, and slope data. This analysis facilitated the division of the cropland area within the CONUS into distinct ecoregions. Unlike static ecoregion clustering, which generates a single ecoregion map that remains unchanged over time, our dynamic ecoregion approach produces a unique ecoregion map for each year. This dynamic approach enables us to consider the year-to-year climate variations that significantly impact crop growth, enhancing the accuracy of our crop mapping process. Subsequently, a Random Forest classifier was employed to train individual models for each ecoregion. These models were trained using the time-series MODIS (Moderate Resolution Imaging Spectroradiometer) 250-m NDVI and EVI data retrieved from Google Earth Engine, covering the crop growth periods spanning from 2013 to 2017, and evaluated from 2018 to 2022. Ground truth data were sourced from the US Department of Agriculture’s (USDA) Cropland Data Layer (CDL) products. The evaluation results showed that the dynamic clustering method achieved higher accuracy than the static clustering method in early crop mapping in the entire CONUS. This study’s findings can be helpful for improving crop management and decision-making for agricultural activities by providing early and accurate crop mapping

Simulation of the effect of stand-off parameter on collapse behaviours of a single cavitation bubble in jet drilling

Cavitation jet drilling has been extensively employed for the exploitation of geo-energy resources. The dynamics of cavitation bubbles in close proximity to the solid boundary have been a subject of great interest during jet drilling, as they play a crucial role in determining the cavitation performance. In present work, the dynamics of a single cavitation bubble near a solid surface is numerically investigated by using the axisymmetric Navier-Stokes equations and the volume of fluid method with considering the surface tension of gas-liquid interface, liquid viscosity and compressibility of gas in bubble. The simulated profiles are qualitatively and quantitatively consistent with the experimental images, which proves the reliability of employed numerical model. The effects of stand-off distance on the bubble profiles, bubble volume and collapse time have been analysed. Moreover, the cavitation erosion patterns towards the solid wall are also revealed for different dimensionless standoff distances. The simulation results reveal two distinct collapse patterns for the bubble profiles. The solid wall significantly impedes the shrinkage rate of the bubble, resulting in the longest collapse time when the dimensionless stand-off distance is 1.0. Three erosion patterns of cavitation bubbles towards the solid wall are observed, with the shock wave and micro-jet both contributing significantly to the damage caused by cavitation erosion. The shock wave sweeps the wall resulting in circular corrosion pits with a severely eroded centre, while the micro jet penetrates the wall leading to small spot corrosion pits.Document Type: Original articleCited as: Wu, X., Zhang, Y., Huang, H., Hui, C., Hu, Z., Li, G. Simulation of the effect of stand-off parameter on collapse behaviours of a single cavitation bubble in jet drilling. Advances in Geo-Energy Research, 2023, 8(3): 181-192. https://doi.org/10.46690/ager.2023.06.0

PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information

With the increase of content pages and interactive buttons in online services such as online-shopping and video-watching websites, industrial-scale recommender systems face challenges in multi-domain and multi-task recommendations. The core of multi-task and multi-domain recommendation is to accurately capture user interests in multiple scenarios given multiple user behaviors. In this paper, we propose a plug-and-play \textit{\textbf{P}arameter and \textbf{E}mbedding \textbf{P}ersonalized \textbf{Net}work (\textbf{PEPNet})} for multi-domain and multi-task recommendation. PEPNet takes personalized prior information as input and dynamically scales the bottom-level Embedding and top-level DNN hidden units through gate mechanisms. \textit{Embedding Personalized Network (EPNet)} performs personalized selection on Embedding to fuse features with different importance for different users in multiple domains. \textit{Parameter Personalized Network (PPNet)} executes personalized modification on DNN parameters to balance targets with different sparsity for different users in multiple tasks. We have made a series of special engineering optimizations combining the Kuaishou training framework and the online deployment environment. By infusing personalized selection of Embedding and personalized modification of DNN parameters, PEPNet tailored to the interests of each individual obtains significant performance gains, with online improvements exceeding 1\% in multiple task metrics across multiple domains. We have deployed PEPNet in Kuaishou apps, serving over 300 million users every day.Comment: Accepted by KDD 202

TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou

Life-long user behavior modeling, i.e., extracting a user's hidden interests from rich historical behaviors in months or even years, plays a central role in modern CTR prediction systems. Conventional algorithms mostly follow two cascading stages: a simple General Search Unit (GSU) for fast and coarse search over tens of thousands of long-term behaviors and an Exact Search Unit (ESU) for effective Target Attention (TA) over the small number of finalists from GSU. Although efficient, existing algorithms mostly suffer from a crucial limitation: the \textit{inconsistent} target-behavior relevance metrics between GSU and ESU. As a result, their GSU usually misses highly relevant behaviors but retrieves ones considered irrelevant by ESU. In such case, the TA in ESU, no matter how attention is allocated, mostly deviates from the real user interests and thus degrades the overall CTR prediction accuracy. To address such inconsistency, we propose \textbf{TWo-stage Interest Network (TWIN)}, where our Consistency-Preserved GSU (CP-GSU) adopts the identical target-behavior relevance metric as the TA in ESU, making the two stages twins. Specifically, to break TA's computational bottleneck and extend it from ESU to GSU, or namely from behavior length $10^2$ to length $10^4-10^5$, we build a novel attention mechanism by behavior feature splitting. For the video inherent features of a behavior, we calculate their linear projection by efficient pre-computing \& caching strategies. And for the user-item cross features, we compress each into a one-dimentional bias term in the attention score calculation to save the computational cost. The consistency between two stages, together with the effective TA-based relevance metric in CP-GSU, contributes to significant performance gain in CTR prediction.Comment: Accepted by KDD 202

The chemical profiling of Salvia plebeia during different growth periods and the biosynthesis of its main flavonoids ingredients

Salvia plebeia (Lamiaceae) is a valuable medicinal plant widely distributed across Asia and Oceania. However, the composition and accumulation patterns of its active ingredients in different organs during the growth and their biosynthetic mechanism remain unknown. Therefore, we conducted metabolite profiling, transcriptomic analysis, and biological functional verification to explore the distribution, accumulation, and biosynthesis mechanisms of flavonoids in S. plebeia. We identified 70 metabolites including 46 flavonoids, 16 phenolic acids, seven terpenoids, and one organic acid, of which 21 were previously unreported in S. plebeia. Combining metabolomic-transcriptomic analysis and biological functional verification, we identified the key genes involved in biosynthesis of its main active ingredients, hispidulin and homoplantaginin, including SpPAL, SpC4H, Sp4CL2, Sp4CL5, SpCHS1, SpCHI, SpFNS, SpF6H1, SpF6OMT1, SpF6OMT2, SpUGT1, SpUGT2, and SpUGT3. Using the identified genes, we reconstructed the hispidulin and homoplantaginin biosynthesis pathways in Escherichia coli, and obtained a yield of 5.33 and 3.86 mg/L for hispidulin and homoplantaginin, respectively. Our findings provide valuable insights into the changes in chemical components in different organs of S. plebeia during different growth and harvest stages and establishes a foundation for identifying and synthesizing its active components

A study of multinucleated giant cells in esophageal cancer

Objectives: To evaluate the occurrence, abundance, distribution, nature and clinical significance of multi-nucleated giant cell (MGC) in esophageal cancer. Materials and methods: MGCs were examined with conventional pathology, immunohistochemistry and immunofluorescence in 107 esophageal cancer tissues. The findings were correlated to pathological diagnosis and clinical behavior of the cancers. Results: MGCs were identified in 31.7% (34/107) of the cases. MGCs were positive for CD11c, CD11b, CD32, CD16, HLA-DR and MMP9, and negative for CD163, CD206 and CD64 giving a molecular profile of proinflammatory M1 but not immunosuppressive M2. MGCs were significantly related to decreased lymph node metastasis (p = 0.011), low pTNM stage (p = 0.044), favorable survival (p = 0.04), squamous cell cancer type rather than other histopathological subtypes (p = 0.020) and associated to better differentiation (p = 0.063). Conclusions: MGCs belong to M1 macrophage and perform phagocytosis and scavenging of cancer cells that would benefit patients' survival and could serve as a prognostic marker