GNNBleed: Inference Attacks to Unveil Private Edges in Graphs with Realistic Access to GNN Models

Graph Neural Networks (GNNs) have increasingly become an indispensable tool in learning from graph-structured data, catering to various applications including social network analysis, recommendation systems, etc. At the heart of these networks are the edges which are crucial in guiding GNN models' predictions. In many scenarios, these edges represent sensitive information, such as personal associations or financial dealings -- thus requiring privacy assurance. However, their contributions to GNN model predictions may in turn be exploited by the adversary to compromise their privacy. Motivated by these conflicting requirements, this paper investigates edge privacy in contexts where adversaries possess black-box GNN model access, restricted further by access controls, preventing direct insights into arbitrary node outputs. In this context, we introduce a series of privacy attacks grounded on the message-passing mechanism of GNNs. These strategies allow adversaries to deduce connections between two nodes not by directly analyzing the model's output for these pairs but by analyzing the output for nodes linked to them. Our evaluation with seven real-life datasets and four GNN architectures underlines a significant vulnerability: even in systems fortified with access control mechanisms, an adaptive adversary can decipher private connections between nodes, thereby revealing potentially sensitive relationships and compromising the confidentiality of the graph.Comment: Submitted to USENIX Security '2

The effect of diffusion on giant pandas that live in complex patchy environments

The habitat loss and fragmentation is almost the greatest threat to the survival of the wild giant panda. In this paper, we construct a mathematical model to consider the effect of diffusion on giant pandas that live in complex patchy environments. Our discussion includes the studying of a diffusive n-dimensional single species model, sufficient conditions are derived for the permanence and extinction of the giant panda species. Especially, we also discuss the situations of diffusion of giant pandas between two patches, and numerical simulations are presented to illustrate the results. Furthermore, we consider the existence, uniqueness, and global stability of the positive periodic solution of the n-dimensional single species model. The implications of these results are significant for giant panda conservation

PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU

This paper introduces PowerInfer, a high-speed Large Language Model (LLM) inference engine on a personal computer (PC) equipped with a single consumer-grade GPU. The key underlying the design of PowerInfer is exploiting the high locality inherent in LLM inference, characterized by a power-law distribution in neuron activation. This distribution indicates that a small subset of neurons, termed hot neurons, are consistently activated across inputs, while the majority, cold neurons, vary based on specific inputs. PowerInfer exploits such an insight to design a GPU-CPU hybrid inference engine: hot-activated neurons are preloaded onto the GPU for fast access, while cold-activated neurons are computed on the CPU, thus significantly reducing GPU memory demands and CPU-GPU data transfers. PowerInfer further integrates adaptive predictors and neuron-aware sparse operators, optimizing the efficiency of neuron activation and computational sparsity. Evaluation shows that PowerInfer attains an average token generation rate of 13.20 tokens/s, with a peak of 29.08 tokens/s, across various LLMs (including OPT-175B) on a single NVIDIA RTX 4090 GPU, only 18% lower than that achieved by a top-tier server-grade A100 GPU. This significantly outperforms llama.cpp by up to 11.69x while retaining model accuracy.Comment: 15 pages, 18 figure

Self-supervised Learning of Detailed 3D Face Reconstruction

In this paper, we present an end-to-end learning framework for detailed 3D face reconstruction from a single image. Our approach uses a 3DMM-based coarse model and a displacement map in UV-space to represent a 3D face. Unlike previous work addressing the problem, our learning framework does not require supervision of surrogate ground-truth 3D models computed with traditional approaches. Instead, we utilize the input image itself as supervision during learning. In the first stage, we combine a photometric loss and a facial perceptual loss between the input face and the rendered face, to regress a 3DMM-based coarse model. In the second stage, both the input image and the regressed texture of the coarse model are unwrapped into UV-space, and then sent through an image-toimage translation network to predict a displacement map in UVspace. The displacement map and the coarse model are used to render a final detailed face, which again can be compared with the original input image to serve as a photometric loss for the second stage. The advantage of learning displacement map in UV-space is that face alignment can be explicitly done during the unwrapping, thus facial details are easier to learn from large amount of data. Extensive experiments demonstrate the superiority of the proposed method over previous work.Comment: Accepted by IEEE Transactions on Image Processing (TIP

A career in healthcare for students in a graduate level Clinical Exercise Physiology Program

Physician referrals to physical therapy are common in mainstream healthcare practice. Clinical Exercise Physiologists (CEPs) are mostly limited to employment as a practitioner in supervised cardiac rehabilitation (CR) or cardiology-based diagnostics and stress testing. PURPOSE: This investigation was conducted to identify employment trends in healthcare for a cohort of CEPs prior to or within 1-year after graduation from an accredited CEP Program (2017-2022). METHODS: A 5-year retrospective review of university and internship site historical records was conducted to determine the career path of the CEPs. This investigation included a sample of 10 students (50% female) that successfully completed a cardiology-based internship/research trainee appointment at large integrated healthcare system in Arizona and/or were hired by that institution. RESULTS: An analysis of career landmarks included completion of (1) a clinical site CR internship/research trainee appointment (100%), (2) part-time CR program employment prior to graduation (60%) or at graduation (10%), (3) full-time (FT) CR program employment at graduation (70%), (4) FT employment in cardiology-based diagnostics or stress testing at graduation (10%), (5) further education (FE) only at graduation (10%), (6) FE combined with FT CR employment at graduation (10%), (7) FT employment in another field \u3e 1 year after FT CR employment (10% academia; 10% medical sales). CONCLUSION: The findings of this preliminary observation in this small cohort of graduate level CEPs identified that the majority was hired as a FT CEP in a healthcare institution at graduation or enrolled in further graduate school education

Sequential Manipulation Planning on Scene Graph

We devise a 3D scene graph representation, contact graph+ (cg+), for efficient sequential task planning. Augmented with predicate-like attributes, this contact graph-based representation abstracts scene layouts with succinct geometric information and valid robot-scene interactions. Goal configurations, naturally specified on contact graphs, can be produced by a genetic algorithm with a stochastic optimization method. A task plan is then initialized by computing the Graph Editing Distance (GED) between the initial contact graphs and the goal configurations, which generates graph edit operations corresponding to possible robot actions. We finalize the task plan by imposing constraints to regulate the temporal feasibility of graph edit operations, ensuring valid task and motion correspondences. In a series of simulations and experiments, robots successfully complete complex sequential object rearrangement tasks that are difficult to specify using conventional planning language like Planning Domain Definition Language (PDDL), demonstrating the high feasibility and potential of robot sequential task planning on contact graph.Comment: 8 pages, 6 figures. Accepted by IROS 202