Private Semi-supervised Knowledge Transfer for Deep Learning from Noisy Labels

Deep learning models trained on large-scale data have achieved encouraging performance in many real-world tasks. Meanwhile, publishing those models trained on sensitive datasets, such as medical records, could pose serious privacy concerns. To counter these issues, one of the current state-of-the-art approaches is the Private Aggregation of Teacher Ensembles, or PATE, which achieved promising results in preserving the utility of the model while providing a strong privacy guarantee. PATE combines an ensemble of "teacher models" trained on sensitive data and transfers the knowledge to a "student" model through the noisy aggregation of teachers' votes for labeling unlabeled public data which the student model will be trained on. However, the knowledge or voted labels learned by the student are noisy due to private aggregation. Learning directly from noisy labels can significantly impact the accuracy of the student model. In this paper, we propose the PATE++ mechanism, which combines the current advanced noisy label training mechanisms with the original PATE framework to enhance its accuracy. A novel structure of Generative Adversarial Nets (GANs) is developed in order to integrate them effectively. In addition, we develop a novel noisy label detection mechanism for semi-supervised model training to further improve student model performance when training with noisy labels. We evaluate our method on Fashion-MNIST and SVHN to show the improvements on the original PATE on all measures

Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis

Tensor factorization has been demonstrated as an efficient approach for computational phenotyping, where massive electronic health records (EHRs) are converted to concise and meaningful clinical concepts. While distributing the tensor factorization tasks to local sites can avoid direct data sharing, it still requires the exchange of intermediary results which could reveal sensitive patient information. Therefore, the challenge is how to jointly decompose the tensor under rigorous and principled privacy constraints, while still support the model's interpretability. We propose DPFact, a privacy-preserving collaborative tensor factorization method for computational phenotyping using EHR. It embeds advanced privacy-preserving mechanisms with collaborative learning. Hospitals can keep their EHR database private but also collaboratively learn meaningful clinical concepts by sharing differentially private intermediary results. Moreover, DPFact solves the heterogeneous patient population using a structured sparsity term. In our framework, each hospital decomposes its local tensors, and sends the updated intermediary results with output perturbation every several iterations to a semi-trusted server which generates the phenotypes. The evaluation on both real-world and synthetic datasets demonstrated that under strict privacy constraints, our method is more accurate and communication-efficient than state-of-the-art baseline methods

All the wiser: Fake news intervention using user reading preferences

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

CodeKGC: Code Language Model for Generative Knowledge Graph Construction

Current generative knowledge graph construction approaches usually fail to capture structural knowledge by simply flattening natural language into serialized texts or a specification language. However, large generative language model trained on structured data such as code has demonstrated impressive capability in understanding natural language for structural prediction and reasoning tasks. Intuitively, we address the task of generative knowledge graph construction with code language model: given a code-format natural language input, the target is to generate triples which can be represented as code completion tasks. Specifically, we develop schema-aware prompts that effectively utilize the semantic structure within the knowledge graph. As code inherently possesses structure, such as class and function definitions, it serves as a useful model for prior semantic structural knowledge. Furthermore, we employ a rationale-enhanced generation method to boost the performance. Rationales provide intermediate steps, thereby improving knowledge extraction abilities. Experimental results indicate that the proposed approach can obtain better performance on benchmark datasets compared with baselines. Code and datasets are available in https://github.com/zjunlp/DeepKE/tree/main/example/llm.Comment: Work in progres

Deletion of CD44 promotes adipogenesis by regulating PPARɣ and cell cycle-related pathways

CD44, a cell surface adhesion receptor and stem cell biomarker, is recently implicated in chronic metabolic diseases. Ablation of CD44 ameliorates adipose tissue inflammation and insulin resistance in obesity. Here, we investigated cell type specific CD44 expression in human and mouse adipose tissue and further studied how CD44 in preadipocytes regulates adipocyte function. Using Crispr Cas9-mdediated gene deletion and lentivirus-mediated gene re-expression, we discovered that deletion of CD44 promotes adipocyte differentiation and adipogenesis, whereas re-expression of CD44 abolishes this effect and decreases insulin responsiveness and adiponectin secretion in 3T3-L1 cells. Mechanistically, CD44 does so via suppressing Pparg expression. Using quantitative proteomics analysis, we further discovered that cell cycle-regulated pathways were mostly decreased by deletion of CD44. Indeed, re-expression of CD44 moderately restored expression of proteins involved in all phases of the cell cycle. These data were further supported by increased preadipocyte proliferation rates in CD44 deficient cells and re-expression of CD44 diminished this effect. Our data suggest that CD44 plays a crucial role in regulating adipogenesis and adipocyte function possibly through regulating PPARɣ and cell cycle-related pathways. This study provides evidence for the first time that CD44 expressed in preadipocytes plays key roles in regulating adipocyte function outside immune cells where CD44 is primarily expressed. Therefore, targeting CD44 in (pre)adipocytes may provide therapeutic potential to treat obesity-associated metabolic complications

Deletion of CD44 promotes adipogenesis by regulating PPARɣ and cell cycle-related pathways

CD44, a cell surface adhesion receptor and stem cell biomarker, is recently implicated in chronic metabolic diseases. Ablation of CD44 ameliorates adipose tissue inflammation and insulin resistance in obesity. Here, we investigated cell type specific CD44 expression in human and mouse adipose tissue and further studied how CD44 in preadipocytes regulates adipocyte function. Using Crispr Cas9-mdediated gene deletion and lentivirus-mediated gene re-expression, we discovered that deletion of CD44 promotes adipocyte differentiation and adipogenesis, whereas re-expression of CD44 abolishes this effect and decreases insulin responsiveness and adiponectin secretion in 3T3-L1 cells. Mechanistically, CD44 does so via suppressing Pparg expression. Using quantitative proteomics analysis, we further discovered that cell cycle-regulated pathways were mostly decreased by deletion of CD44. Indeed, re-expression of CD44 moderately restored expression of proteins involved in all phases of the cell cycle. These data were further supported by increased preadipocyte proliferation rates in CD44 deficient cells and re-expression of CD44 diminished this effect. Our data suggest that CD44 plays a crucial role in regulating adipogenesis and adipocyte function possibly through regulating PPARɣ and cell cycle-related pathways. This study provides evidence for the first time that CD44 expressed in preadipocytes plays key roles in regulating adipocyte function outside immune cells where CD44 is primarily expressed. Therefore, targeting CD44 in (pre)adipocytes may provide therapeutic potential to treat obesity-associated metabolic complications

Licensing instead of fueling: Glutamine synthetase promotes mitotic progression via a non-metabolic mechanism

A recent report published in Nature Metabolism identified that glutamine synthetase (GS), the only enzyme in mammals to produce glutamine from glutamate, can directly control cancer cell mitosis by governing the APC/C complex via a metabolism-independent mechanism. It reported that GS can directly interact with the nuclear pore protein NUP88 to abolish its binging with CDC20, therefore licensing the activation of APC/CCDC20 to permit proper metaphase to anaphase transition of mitosis. These findings illustrated a dual-function mode of action of GS in cancer cells, in which GS’s metabolic and non-metabolic functions coordinate with the concentration change of glutamine in the tumor microenvironment (TME) to ensure cell survival or proliferation, respectively. These findings revealed the multi-faceted roles of glutamine synthetase in tumor development and underscored the potential to target non-canonical functions of glutamine synthetase for cancer treatment

Antidiabetic retinopathy effect of Fufang Danshen Mingmu in rats

Purpose: To investigate the effect of Fufang Danshen Mingmu (FDM) on streptozotocin-induced diabetic retinopathy rats.Methods: Diabetic retinopathy model rats were prepared using a single intraperitoneal injection of a freshly prepared solution of streptozotocin (50 mg/kg). The rats were randomly divided into 6 groups of ten rats each: negative control group, control group, reference group (glibenclamide, 1 mg/kg) as well as FDM groups, (50, 100 and 200 mg/kg body weight). Blood glucose and plasma insulin levels were determined. Oxidative stress was evaluated in liver and kidney as lipid peroxidation (LPO), superoxide dismutase (SOD), reduced glutathione (GSH), glutathione peroxidase (GPx) and catalase (CAT). Blood serum levels of creatinine and urea were determined in both diabetic control and treated rats.Results: Compared with diabetic rats, oral administration of FDM at a dose of 200 mg/kg daily for 30 days resulted in a significant decrease in fasting blood glucose (120.21 ± 3.37 mg/dL, p < 0.05) and increased insulin level (13.31 ± 0.67 uU/mL, p < 0.05).  Furthermore, it significantly reduced biochemical parameters (serum creatinine, 0.86 ±0.24 mg/dL, p < 0.05) and serum urea 41.86±1.59 mg/dL, p <0.05).Conclusion: The results indicate that FDM normalizes impaired antioxidant status in streptozotocin induced diabetic retinopathy rats, and also exerts a protective effect against lipid peroxidation by scavenging free radicals

Uncertainty Guided Adaptive Warping for Robust and Efficient Stereo Matching

Correlation based stereo matching has achieved outstanding performance, which pursues cost volume between two feature maps. Unfortunately, current methods with a fixed model do not work uniformly well across various datasets, greatly limiting their real-world applicability. To tackle this issue, this paper proposes a new perspective to dynamically calculate correlation for robust stereo matching. A novel Uncertainty Guided Adaptive Correlation (UGAC) module is introduced to robustly adapt the same model for different scenarios. Specifically, a variance-based uncertainty estimation is employed to adaptively adjust the sampling area during warping operation. Additionally, we improve the traditional non-parametric warping with learnable parameters, such that the position-specific weights can be learned. We show that by empowering the recurrent network with the UGAC module, stereo matching can be exploited more robustly and effectively. Extensive experiments demonstrate that our method achieves state-of-the-art performance over the ETH3D, KITTI, and Middlebury datasets when employing the same fixed model over these datasets without any retraining procedure. To target real-time applications, we further design a lightweight model based on UGAC, which also outperforms other methods over KITTI benchmarks with only 0.6 M parameters.Comment: Accepted by ICCV202