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

Case report: Abolishing primary resistance to PD-1 blockade by short-term treatment of lenvatinib in a patient with advanced metastatic renal cell carcinoma

Anti-PD-1 immunotherapy has been extensively used in treatment of patients with advanced metastatic renal cell carcinoma (mRCC). Several prospective clinical trials showed that the combined treatment of anti-PD-1 antibody plus lenvatinib, a potent receptor tyrosine kinase inhibitor (TKI), exhibited high response rate compared with single-agent sunitinib. However, whether the patients with primary resistance to PD-1 blockade could benefit from the addition of lenvatinib is still unclear. Herein, we reported a patient with mRCC who was primary resistant to pembrolizumab and achieved a durable complete response after a short-term treatment with lenvatinib. This case report indicates that the patients with primary resistance to anti-PD-1 therapy could benefit from the short-term lenvatinib in combination with anti-PD-1 therapy, and provides a useful paradigm worthy of establishing a clinical trial for mRCC patients with primary resistance to anti-PD-1 therapy

Ensuring Computers Understand Manual Operations in Production: Deep-Learning-Based Action Recognition in Industrial Workflows

In this study, we consider fully automated action recognition based on deep learning in the industrial environment. In contrast to most existing methods, which rely on professional knowledge to construct complex hand-crafted features, or only use basic deep-learning methods, such as convolutional neural networks (CNNs), to extract information from images in the production process, we exploit a novel and effective method, which integrates multiple deep-learning networks including CNNs, spatial transformer networks (STNs), and graph convolutional networks (GCNs) to process video data in industrial workflows. The proposed method extracts both spatial and temporal information from video data. The spatial information is extracted by estimating the human pose of each frame, and the skeleton image of the human body in each frame is obtained. Furthermore, multi-frame skeleton images are processed by GCN to obtain temporal information, meaning the action recognition results are predicted automatically. By training on a large human action dataset, Kinetics, we apply the proposed method to the real-world industrial environment and achieve superior performance compared with the existing methods

Stream Cube: An Architecture for Multi-Dimensional Analysis of Data Streams

Real-time surveillance systems, telecommunication systems, and other dynamic environments often generate tremendous (potentially infinite) volume of stream data: the volume is too huge to be scanned multiple times. Much of such data resides at rather low level of abstraction, whereas most analysts are interested in relatively high-level dynamic changes (such as trends and outliers). To discover such high-level characteristics, one may need to perform on-line multi-level, multi-dimensional analytical processing of stream data. In this paper, we propose an architecture, called stream cube, to facilitate on-line, multi-dimensional, multi-level analysis of stream data. For fast online multi-dimensional analysis of stream data, three important techniques are proposed for efficient and effective computation of stream cubes. First, a tilted time frame model is proposed as a multi-resolution model to register time-related data: the more recent data are registered at finer resolution, whereas the more distant data are registered at coarser resolution. This design reduces the overall storage of time-related data and adapts nicely to the data analysis tasks commonly encountered in practice. Second, instead of materializing cuboids at all levels, we propose to maintain a small number of critical layers. Flexible analysis can be efficiently performed based on the concept of observation layer and minimal interesting layer. Third, an efficient stream data cubing algorithm is developed which computes only the layers (cuboids) along a popular path and leaves the other cuboids for query-driven, on-line computation. Based on this design methodology, stream data cube can be constructed an

Smart nanoplatform for sequential drug release and enhanced chemo-thermal effect of dual drug loaded gold nanorod vesicles for cancer therapy

Abstract Background The combination of multiple chemotherapeutics has been used in the clinic for enhanced cancer chemotherapy, however, frequent relapse, chemo-resistance and side effects remains therapeutic hurdles. Thus, the development of co-delivery system with enhanced targeting and synergistic different modal treatments has been proposed as promising strategies for intensive improvement of the therapeutic outcomes. Results We fabricated a nanocarrier based on gold nanorods (Au NRs), cRGD peptide-modified and multi-stimuli-responsive paclitaxel (PTX) and curcumin (CUR) release for synergistic anticancer effect and chemo-photothermal therapy (PTX/CUR/Au NRs@cRGD). The specific banding of cRGD to αvβ3 integrin receptor on the tumor cell surfaces facilitated the endocytosis of PTX/CUR/Au NRs@cRGD, and the near-infrared ray (NIR) further enhanced the drug release and chemotherapeutical efficiency. Compared to single drug, single model treatment or undecorated-PTX/CUR/Au NRs, the PTX/CUR/Au NRs@cRGD with a mild NIR showed significantly enhanced apoptosis and S phase arrest in three cancer cell lines in vitro, and improved drug accumulation in tumor sites as well as tumor growth inhibition in vivo. Conclusions The tumor targeted chemo-photothermal therapy with the synergistic effect of dual drugs provided a versatile strategy for precise cancer therapy

Survey network design of synchrotron in Heavy Ion Medical Machine in Lanzhou

<span style="color: rgb(51, 51, 51); font-family: arial, helvetica, sans-serif; font-size: 13px; line-height: 22px; background-color: rgb(248, 248, 248);">The paper introduces control survey network of installation strategy in the new HIMM (Heavy Ion Medical Machine). The 3D survey network is based on laser tracker and SA (Spatial Analyzer). Nine fiducial references and two scale bars were designed to guarantee high accuracy in control survey network. and Digital Level was used for altitude. The final RMS error of the global network could reach 0.04 mm, which guarantees the transverse position of quadrupoles requirement (0.10 mm) on the synchrotron.</span

Transcriptomic Analysis and Functional Gene Expression in Different Stages of Gonadal Development of <i>Macrobrachium rosenbergii</i>

In order to decipher the functional genes and reveal the molecular mechanism of gonadal development in Macrobrachium rosenbergii, a comparative transcriptome analysis was performed on the testes and ovaries at different developmental stages. A total of 146,537 unigenes with an N50 of 2008 bp and an average length of 1144 bp were obtained from the sequencing raw data via quality control and denovo assembly. Identification of differentially expressed genes (DEGs) showed that there were 339 and 468 DEGs among the different developmental stages of testes and ovaries, respectively, and 7993 DEGs between the testes and ovaries. The KEGG enrichment analysis identified 13 candidate pathways related to gonadal development, including insulin synthesis, oocyte maturation, and steroid biosynthesis, which were involved in biological processes such as regulation of hormone metabolism, sex cell proliferation and development, and amino acid metabolism. The DEGs related to the above pathways such as insulin-like growth factor 1 receptor (IGF1R), heat shock protein 90 (Hsp 90), and cyclooxygenase (COX) genes were highly expressed during yolk protein synthesis, indicating that these genes might be involved in yolk accumulation and oogenesis. Meanwhile, calmodulin (CaM) and other genes were highly expressed during spermatogenesis, suggesting that these genes might play an important role in spermatogenesis. Ten differentially expressed genes in the KEGG signaling pathway, including CRQ, COX, APP, Cdc42, Hsd17b12, Art-1, Hsp70, Hsp90, PRMT1, and GP, were selected for real-time quantitative PCR (RT- qPCR) to validate the transcriptome data, and the results showed that RT- qPCR obtained consistent results with the RNA-Seq data. The present findings provide new insights into the molecular regulation mechanism of gonadal development in M. rosenbergii