SSL-WM: A Black-Box Watermarking Approach for Encoders Pre-trained by Self-supervised Learning

Recent years have witnessed significant success in Self-Supervised Learning (SSL), which facilitates various downstream tasks. However, attackers may steal such SSL models and commercialize them for profit, making it crucial to protect their Intellectual Property (IP). Most existing IP protection solutions are designed for supervised learning models and cannot be used directly since they require that the models' downstream tasks and target labels be known and available during watermark embedding, which is not always possible in the domain of SSL. To address such a problem especially when downstream tasks are diverse and unknown during watermark embedding, we propose a novel black-box watermarking solution, named SSL-WM, for protecting the ownership of SSL models. SSL-WM maps watermarked inputs by the watermarked encoders into an invariant representation space, which causes any downstream classifiers to produce expected behavior, thus allowing the detection of embedded watermarks. We evaluate SSL-WM on numerous tasks, such as Computer Vision (CV) and Natural Language Processing (NLP), using different SSL models, including contrastive-based and generative-based. Experimental results demonstrate that SSL-WM can effectively verify the ownership of stolen SSL models in various downstream tasks. Furthermore, SSL-WM is robust against model fine-tuning and pruning attacks. Lastly, SSL-WM can also evade detection from evaluated watermark detection approaches, demonstrating its promising application in protecting the IP of SSL models

High‐Resolution Coccolithophore Morphological Changes in Response to Orbital Forcings During the Early Oligocene

Abstract The global climate of the early Oligocene was characterized by initiated Antarctic glaciation and meridional overturning circulation, which then led to profound eutrophication in the upper ocean. Generating a high‐resolution coccolith record helps to understand the responses of marine phytoplankton to the newly established environment. Using highly resolved (∼6 kyr time‐resolution) marine sediment samples from Deep Sea Drilling Project Site 522 in the South Atlantic Ocean, we conducted a comprehensive morphological study on coccoliths from the genera Reticulofenestra, Dictyococcites, and Coccolithus, which dominated the study interval between ∼33.1 and 32.8 Ma. Our results showed that the size variations of the three measured genera were significantly correlated (p < 0.01) with each other, indicating homogeneous responses to the environmental changes. Moreover, spectrum analysis on integrated morphologic data of all measured coccoliths showed distinct obliquity (∼40‐kyr) and precession (∼23‐kyr and ∼18‐kyr) cycles. We suggest that these variations were mainly driven by temperate, short‐term ecological fluctuations, which periodically altered the nutrient conditions in the common living habitats of the studied coccolithophores. We proposed two tentative explanations focusing on the obliquity signal. First, the cyclic variation could result from obliquity‐modulated changes in ice volume and variations in ocean circulation intensity, which influenced nutrient export from deep waters to the upper ocean. Alternatively, the changes in coccolith size may indicate the strength of seasonality that influenced upper ocean mixing on the west coast of South Africa

Research on Identification and Measurement Methods of Influencing Factors of Investment in Operation and Maintenance

Operation and maintenance investment is an important part of the capital expenditure of power grid enterprises. In recent years, affected by multiple profit-cutting factors such as the macroeconomic downturn, the slowdown in electricity growth, the implementation of transmission and distribution price reform requirements, the narrowing of electricity price space, and the state's phased reduction of electricity cost policies, the profitability of power grid companies has dropped significantly, and power grid investment. The capacity was significantly weakened, and the operating pressure was unprecedented. Identifying and measuring the influencing factors of power grid operation and maintenance investment is an important supporting role for enterprises to formulate scientific and reasonable investment strategies for power grid equipment operation and maintenance. Therefore, this paper first applies the fish-bone method, combined with the current status of equipment operation and maintenance management of power grid enterprises, and scientifically identifies the factors that affect the investment level of operation and maintenance; then, based on the grey correlation theory, analyzes the degree of influence of each influencing factor