Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark

Large language models (LLMs) have demonstrated powerful capabilities in both text understanding and generation. Companies have begun to offer Embedding as a Service (EaaS) based on these LLMs, which can benefit various natural language processing (NLP) tasks for customers. However, previous studies have shown that EaaS is vulnerable to model extraction attacks, which can cause significant losses for the owners of LLMs, as training these models is extremely expensive. To protect the copyright of LLMs for EaaS, we propose an Embedding Watermark method called EmbMarker that implants backdoors on embeddings. Our method selects a group of moderate-frequency words from a general text corpus to form a trigger set, then selects a target embedding as the watermark, and inserts it into the embeddings of texts containing trigger words as the backdoor. The weight of insertion is proportional to the number of trigger words included in the text. This allows the watermark backdoor to be effectively transferred to EaaS-stealer's model for copyright verification while minimizing the adverse impact on the original embeddings' utility. Our extensive experiments on various datasets show that our method can effectively protect the copyright of EaaS models without compromising service quality.Comment: Accepted by ACL 202

PrivateRec: Differentially Private Training and Serving for Federated News Recommendation

Privacy protection is an essential issue in personalized news recommendation, and federated learning can potentially mitigate the privacy concern by training personalized news recommendation models over decentralized user data.For a theoretical privacy guarantee, differential privacy is necessary. However, applying differential privacy to federated recommendation training and serving conventionally suffers from the unsatisfactory trade-off between privacy and utility due to the high-dimensional characteristics of model gradients and hidden representations. In addition, there is no formal privacy guarantee for both training and serving in federated recommendation. In this paper, we propose a unified federated news recommendation method for effective and privacy-preserving model training and online serving with differential privacy guarantees. We first clarify the notion of differential privacy over users' behavior data for both model training and online serving in the federated recommendation scenario. Next, we propose a privacy-preserving online serving mechanism under this definition with differentially private user interest decomposition. More specifically, it decomposes the high-dimensional and privacy-sensitive user embedding into a combination of public basic vectors and adds noise to the combination coefficients. In this way, it can avoid the dimension curse and improve the utility by reducing the required noise intensity for differential privacy. Besides, we design a federated recommendation model training method with differential privacy, which can avoid the dimension-dependent noise for large models via label permutation and differentially private attention modules. Experiments on real-world news recommendation datasets validate the effectiveness of our method in achieving a good trade-off between privacy protection and utility for federated news recommendations

N-acetylcysteine differentially regulates the populations of bone marrow and circulating endothelial progenitor cells in mice with limb ischemia

Endothelial progenitor cells (EPCs) are important to tissue repair and regeneration especially after ischemic injury, and very heterogeneous in phenotypes and biological features. Reactive oxygen species are involved in regulating EPC number and function. N-acetylcysteine (NAC) inhibits ischemia-induced reactive oxygen species formation and promotes ischemic limb recovery. This study was to evaluate the effect of NAC on EPC subpopulations in bone marrow (BM) and blood in mice with limb ischemia. Limb ischemia was induced by femoral artery ligation in male C57BL/6 mice with or without NAC treatment. EPC subpopulations, intracellular reactive oxygen species production, cell proliferation and apoptosis in BM and blood cells were analyzed at baseline, day 3 (acute ischemia) and 21 (chronic) after ligation. c-Kit+/CD31+, Sca-1+/Flk-1+, CD34+/CD133+, and CD34+/Flk-1+ were used to define EPC subpopulations. Limb blood flow, function, muscle structure, and capillary density were evaluated with laser Doppler perfusion imaging, treadmill test, and immunohistochemistry, respectively, at day 3, 7, 14 and 21 post ischemia. Reactive oxygen species production in circulating and BM mononuclear cells and EPCs populations were significantly increased in BM and blood in mice with acute and chronic ischemia. NAC treatment effectively blocked ischemia-induced reactive oxygen species production in circulating and BM mononuclear cells, and selectively increased EPC population in circulation, not BM, with preserved proliferation in mice with chronic ischemia, and enhanced limb blood flow and function recovery, while preventing acute ischemia-induced increase in BM and circulating EPCs. These data demonstrated that NAC selectively enhanced circulating EPC population in mice with chronic limb ischemia

Factors Influencing Farmer Willingness to Fallow Winter Wheat and Ecological Compensation Standards in a Groundwater Funnel Area in Hengshui, Hebei Province, China

Land use/land cover change will have a certain impact on the regional ecological environment. This study uses the questionnaire survey method, an opportunity cost method and a logistic model to evaluate the suitability of an ecological compensation standard for a winter- wheat-fallow cropping system in a groundwater funnel area in Hebei. The main factors affecting farmers’ willingness to fallow fields provide a theoretical basis for scientifically and rationally developing a rotation policy in the groundwater funnel area. The results indicate the following: (1) nearly 87% of the surveyed farmers would accept a winter-wheat-fallow policy, whereas 13% would not; (2) farmer educational level, the total number of participants in the agricultural labor force, dependency rate, farmers’ attitudes toward a winter wheat-fallow policy in the groundwater funnel area and the farmer level of trust in government policy have significant positive effects farmer intention to fallow, whereas the number of days of participation in farming, the cultivated land quality and the per capita area of cultivated land have a significant negative effect on farmers’ fallowing intentions; (3) considering only the impact of winter wheat on groundwater, the proposed compensation standard for farmers who accept the policy is 0.00095 $/hm2; (4) some policy implications are put forward to improve the effectiveness of the winter wheat fallowing policy in the groundwater funnel

Dynamics of Water Use Efficiency of Coniferous and Broad-Leaved Mixed Forest in East China

The aim of our study is to understand the patterns of variation in water use efficiency (WUE) in coniferous and broad-leaved mixed forest ecosystems across multiple scales and to identify its main controlling factors. We employ the eddy covariance method to gather data from 2017, 2018, and 2020, which we use to calculate the gross primary productivity and evapotranspiration of these forests in East China and to determine WUE at the ecosystem level. The mean daily variation in WUE ranges from 4.84 to 7.88 gC kg−1 H2O, with a mean value of 6.12 gC kg−1 H2O. We use ridge regression analysis to ascertain the independent effect of environmental factors on WUE variation. We find that WUE responds differently to environmental factors at different time scales. In mixed conifer ecosystems, temperature and relative humidity emerge as the most significant environmental factors influencing WUE variability. Especially at the seasonal scale, temperature and relative humidity can explain more than 51% of the WUE variation. Our results underscore the varied effects of environmental factors on WUE variation across different time scales and aid in predicting the response of WUE to climate change in coniferous and broad-leaved mixed forest ecosystems

FedKD: Communication Efficient Federated Learning via Knowledge Distillation

Federated learning is widely used to learn intelligent models from decentralized data. In federated learning, clients need to communicate their local model updates in each iteration of model learning. However, model updates are large in size if the model contains numerous parameters, and there usually needs many rounds of communication until model converges. Thus, the communication cost in federated learning can be quite heavy. In this paper, we propose a communication efficient federated learning method based on knowledge distillation. Instead of directly communicating the large models between clients and server, we propose an adaptive mutual distillation framework to reciprocally learn a student and a teacher model on each client, where only the student model is shared by different clients and updated collaboratively to reduce the communication cost. Both the teacher and student on each client are learned on its local data and the knowledge distilled from each other, where their distillation intensities are controlled by their prediction quality. To further reduce the communication cost, we propose a dynamic gradient approximation method based on singular value decomposition to approximate the exchanged gradients with dynamic precision. Extensive experiments on benchmark datasets in different tasks show that our approach can effectively reduce the communication cost and achieve competitive results

SAR ATR Based on FCNN and ICAE

In recent years, Synthetic Aperture Radar (SAR) image target recognition based on the Convolutional Neural Network (CNN) has attracted a significant amount of attention. Fully CNN (FCNN) is a structural improvement of the CNN, which features a higher recognition rate than CNN, but it still requires a large number of labeled data in the training process. This study aims to propose a method of SAR image target recognition based on FCNN and Improved Convolutional Auto-Encoder (ICAE), where several parameters of FCNN are initialized by the parameters of the ICAE encoder. These parameters are obtained in the unsupervised training mode. Then, the FCNN is trained by the labeled training samples. The experimental results on 10 kinds of target classification based on the MSTAR datasets show that this method cannot only achieve an average of 98.14% correct recognition rate but also feature a strong anti-noise capability when the labeled training samples are unexpanded

Seroprevalence and risk factors of Neospora caninum infection among domestic sheep in Henan province, central China

This study aimed to determine the frequency of antibodies to Neospora caninum in domestic sheep raised in Henan province, central China. Serum samples from 779 domestic sheep were collected from March 2015 to May 2016, and antibodies to N. caninum were evaluated using an enzyme-linked immunosorbent assay (ELISA). The results showed an overall IgG positive rate of 7.32% (57/779). The risk factors significantly related to seropositivity to N. caninum in sheep were the age, the presence of dogs, and the rearing system. This is the first report of N. caninum infection and associated risk factors in domestic sheep in central China

Low-Microwave Loss Coplanar Waveguides Fabricated on High-Resistivity Silicon Substrate

Three kinds of coplanar waveguides （CPWs） are designed and fabricated on different silicon substrates---common low-resistivity silicon substrate （LRS）, LRS with a 3μm-thick silicon oxide interlayer, and high-resistivity silicon （HRS） substrate. The results show that the microwave loss of a CPW on LRS is too high to be used, but it can be greatly reduced by adding a thick interlayer of silicon oxide between the CPW transmission lines and the LRS.A CPW directly on HRS shows a loss lower than 2dB/cm in the range of 0-26GHz and the process is simple,so HRS is a more suitable CPW substrate

Seroprevalence and risk factors of

This study aimed to determine the frequency of antibodies to Neospora caninum in domestic sheep raised in Henan province, central China. Serum samples from 779 domestic sheep were collected from March 2015 to May 2016, and antibodies to N. caninum were evaluated using an enzyme-linked immunosorbent assay (ELISA). The results showed an overall IgG positive rate of 7.32% (57/779). The risk factors significantly related to seropositivity to N. caninum in sheep were the age, the presence of dogs, and the rearing system. This is the first report of N. caninum infection and associated risk factors in domestic sheep in central China