465 research outputs found
Ursolic acid enhances macrophage autophagy and attenuates atherogenesis
Macrophage autophagy has been shown to be protective against atherosclerosis. We previously discovered that ursolic acid (UA) promoted cancer cell autophagy. In the present study, we aimed to examine whether UA enhances macrophage autophagy in the context of atherogenesis. Cell culture study showed that UA enhanced autophagy of macrophages by increasing the expression of Atg5 and Atg16l1, which led to altered macrophage function. UA reduced pro-interleukin (IL)-1β protein levels and mature IL-1β secretion in macrophages in response to lipopolysaccharide (LPS), without reducing IL-1β mRNA expression. Confocal microscopy showed that in LPS-treated macrophages, UA increased LC3 protein levels and LC3 appeared to colocalize with IL-1β. In cholesterol-loaded macrophages, UA increased cholesterol efflux to apoAI, although it did not alter mRNA or protein levels of ABCA1 and ABCG1. Electron microscopy showed that UA induced lipophagy in acetylated LDL-loaded macrophages, which may result in increased cholesterol ester hydrolysis in autophagolysosomes and presentation of free cholesterol to the cell membrane. In LDLR(−/−) mice fed a Western diet to induce atherogenesis, UA treatment significantly reduced atherosclerotic lesion size, accompanied by increased macrophage autophagy. In conclusion, the data suggest that UA promotes macrophage autophagy and, thereby, suppresses IL-1β secretion, promotes cholesterol efflux, and attenuates atherosclerosis in mice
Somewhat Practical Fully Homomorphic Encryption
In this paper we port Brakerski\u27s fully homomorphic scheme
based on the Learning With Errors (LWE) problem to the ring-LWE setting.
We introduce two optimised versions of relinearisation that not only
result in a smaller relinearisation key, but also faster computations.
We provide a detailed, but simple analysis of the various homomorphic operations,
such as multiplication, relinearisation and bootstrapping,
and derive tight worst case bounds on the noise caused by these
operations. The analysis of the bootstrapping step is greatly
simplified by using a modulus switching trick.
Finally, we derive concrete parameters for which the scheme
provides a given level of security and becomes fully homomorphic
SEEK: model extraction attack against hybrid secure inference protocols
Security concerns about a machine learning model used in a prediction-as-a-service include the privacy of the model, the query and the result. Secure inference solutions based on homomorphic encryption (HE) and/or multiparty computation (MPC) have been developed to protect all the sensitive information. One of the most efficient type of solution utilizes HE for linear layers, and MPC for non-linear layers. However, for such hybrid protocols with semi-honest security, an adversary can malleate the intermediate features in the inference process, and extract model information more effectively than methods against inference service in plaintext. In this paper, we propose SEEK, a general extraction method for hybrid secure inference services outputing only class labels. This method can extract each layer of the target model independently, and is not affected by the depth of the model. For ResNet-18, SEEK can extract a parameter with less than 50 queries on average, with average error less than
Robust and accurate depth estimation by fusing LiDAR and Stereo
Depth estimation is one of the key technologies in some fields such as
autonomous driving and robot navigation. However, the traditional method of
using a single sensor is inevitably limited by the performance of the sensor.
Therefore, a precision and robust method for fusing the LiDAR and stereo
cameras is proposed. This method fully combines the advantages of the LiDAR and
stereo camera, which can retain the advantages of the high precision of the
LiDAR and the high resolution of images respectively. Compared with the
traditional stereo matching method, the texture of the object and lighting
conditions have less influence on the algorithm. Firstly, the depth of the
LiDAR data is converted to the disparity of the stereo camera. Because the
density of the LiDAR data is relatively sparse on the y-axis, the converted
disparity map is up-sampled using the interpolation method. Secondly, in order
to make full use of the precise disparity map, the disparity map and stereo
matching are fused to propagate the accurate disparity. Finally, the disparity
map is converted to the depth map. Moreover, the converted disparity map can
also increase the speed of the algorithm. We evaluate the proposed pipeline on
the KITTI benchmark. The experiment demonstrates that our algorithm has higher
accuracy than several classic methods
Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning
Spatio-temporal graph learning is a fundamental problem in the Web of Things
era, which enables a plethora of Web applications such as smart cities, human
mobility and climate analysis. Existing approaches tackle different learning
tasks independently, tailoring their models to unique task characteristics.
These methods, however, fall short of modeling intrinsic uncertainties in the
spatio-temporal data. Meanwhile, their specialized designs limit their
universality as general spatio-temporal learning solutions. In this paper, we
propose to model the learning tasks in a unified perspective, viewing them as
predictions based on conditional information with shared spatio-temporal
patterns. Based on this proposal, we introduce Unified Spatio-Temporal
Diffusion Models (USTD) to address the tasks uniformly within the
uncertainty-aware diffusion framework. USTD is holistically designed,
comprising a shared spatio-temporal encoder and attention-based denoising
networks that are task-specific. The shared encoder, optimized by a
pre-training strategy, effectively captures conditional spatio-temporal
patterns. The denoising networks, utilizing both cross- and self-attention,
integrate conditional dependencies and generate predictions. Opting for
forecasting and kriging as downstream tasks, we design Gated Attention (SGA)
and Temporal Gated Attention (TGA) for each task, with different emphases on
the spatial and temporal dimensions, respectively. By combining the advantages
of deterministic encoders and probabilistic diffusion models, USTD achieves
state-of-the-art performances compared to deterministic and probabilistic
baselines in both tasks, while also providing valuable uncertainty estimates
Linear Gaussian Bounding Box Representation and Ring-Shaped Rotated Convolution for Oriented Object Detection
In oriented object detection, current representations of oriented bounding
boxes (OBBs) often suffer from boundary discontinuity problem. Methods of
designing continuous regression losses do not essentially solve this problem.
Although Gaussian bounding box (GBB) representation avoids this problem,
directly regressing GBB is susceptible to numerical instability. We propose
linear GBB (LGBB), a novel OBB representation. By linearly transforming the
elements of GBB, LGBB avoids the boundary discontinuity problem and has high
numerical stability. In addition, existing convolution-based rotation-sensitive
feature extraction methods only have local receptive fields, resulting in slow
feature aggregation. We propose ring-shaped rotated convolution (RRC), which
adaptively rotates feature maps to arbitrary orientations to extract
rotation-sensitive features under a ring-shaped receptive field, rapidly
aggregating features and contextual information. Experimental results
demonstrate that LGBB and RRC achieve state-of-the-art performance.
Furthermore, integrating LGBB and RRC into various models effectively improves
detection accuracy
Graph Neural Processes for Spatio-Temporal Extrapolation
We study the task of spatio-temporal extrapolation that generates data at
target locations from surrounding contexts in a graph. This task is crucial as
sensors that collect data are sparsely deployed, resulting in a lack of
fine-grained information due to high deployment and maintenance costs. Existing
methods either use learning-based models like Neural Networks or statistical
approaches like Gaussian Processes for this task. However, the former lacks
uncertainty estimates and the latter fails to capture complex spatial and
temporal correlations effectively. To address these issues, we propose
Spatio-Temporal Graph Neural Processes (STGNP), a neural latent variable model
which commands these capabilities simultaneously. Specifically, we first learn
deterministic spatio-temporal representations by stacking layers of causal
convolutions and cross-set graph neural networks. Then, we learn latent
variables for target locations through vertical latent state transitions along
layers and obtain extrapolations. Importantly during the transitions, we
propose Graph Bayesian Aggregation (GBA), a Bayesian graph aggregator that
aggregates contexts considering uncertainties in context data and graph
structure. Extensive experiments show that STGNP has desirable properties such
as uncertainty estimates and strong learning capabilities, and achieves
state-of-the-art results by a clear margin.Comment: SIGKDD 202
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