493 research outputs found
Analyses reveal a novel avirulent Streptococcus suis Serotype 2 strain that induces protective immunity against challenge with the highly virulent strains
EviPrompt: A Training-Free Evidential Prompt Generation Method for Segment Anything Model in Medical Images
Medical image segmentation has immense clinical applicability but remains a
challenge despite advancements in deep learning. The Segment Anything Model
(SAM) exhibits potential in this field, yet the requirement for expertise
intervention and the domain gap between natural and medical images poses
significant obstacles. This paper introduces a novel training-free evidential
prompt generation method named EviPrompt to overcome these issues. The proposed
method, built on the inherent similarities within medical images, requires only
a single reference image-annotation pair, making it a training-free solution
that significantly reduces the need for extensive labeling and computational
resources. First, to automatically generate prompts for SAM in medical images,
we introduce an evidential method based on uncertainty estimation without the
interaction of clinical experts. Then, we incorporate the human prior into the
prompts, which is vital for alleviating the domain gap between natural and
medical images and enhancing the applicability and usefulness of SAM in medical
scenarios. EviPrompt represents an efficient and robust approach to medical
image segmentation, with evaluations across a broad range of tasks and
modalities confirming its efficacy
Spatial Distribution and Factors Influencing Ecological Efficiency of the Yellow River Basin in China
Semi‐supervised joint learning for longitudinal clinical events classification using neural network models
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163377/2/sta4305.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163377/1/sta4305_am.pd
Efficiently Hardening SGX Enclaves against Memory Access Pattern Attacks via Dynamic Program Partitioning
Intel SGX is known to be vulnerable to a class of practical attacks
exploiting memory access pattern side-channels, notably page-fault attacks and
cache timing attacks. A promising hardening scheme is to wrap applications in
hardware transactions, enabled by Intel TSX, that return control to the
software upon unexpected cache misses and interruptions so that the existing
side-channel attacks exploiting these micro-architectural events can be
detected and mitigated. However, existing hardening schemes scale only to
small-data computation, with a typical working set smaller than one or few
times (e.g., times) of a CPU data cache.
This work tackles the data scalability and performance efficiency of security
hardening schemes of Intel SGX enclaves against memory-access pattern side
channels. The key insight is that the size of TSX transactions in the target
computation is critical, both performance- and security-wise. Unlike the
existing designs, this work dynamically partitions target computations to
enlarge transactions while avoiding aborts, leading to lower performance
overhead and improved side-channel security. We materialize the dynamic
partitioning scheme and build a C++ library to monitor and model cache
utilization at runtime. We further build a data analytical system using the
library and implement various external oblivious algorithms. Performance
evaluation shows that our work can effectively increase transaction size and
reduce the execution time by up to two orders of magnitude compared with the
state-of-the-art solutions
Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation
The divergence between labeled training data and unlabeled testing data is a
significant challenge for recent deep learning models. Unsupervised domain
adaptation (UDA) attempts to solve such problem. Recent works show that
self-training is a powerful approach to UDA. However, existing methods have
difficulty in balancing the scalability and performance. In this paper, we
propose a hard-aware instance adaptive self-training framework for UDA on the
task of semantic segmentation. To effectively improve the quality and diversity
of pseudo-labels, we develop a novel pseudo-label generation strategy with an
instance adaptive selector. We further enrich the hard class pseudo-labels with
inter-image information through a skillfully designed hard-aware pseudo-label
augmentation. Besides, we propose the region-adaptive regularization to smooth
the pseudo-label region and sharpen the non-pseudo-label region. For the
non-pseudo-label region, consistency constraint is also constructed to
introduce stronger supervision signals during model optimization. Our method is
so concise and efficient that it is easy to be generalized to other UDA
methods. Experiments on GTA5 to Cityscapes, SYNTHIA to Cityscapes, and
Cityscapes to Oxford RobotCar demonstrate the superior performance of our
approach compared with the state-of-the-art methods.Comment: arXiv admin note: text overlap with arXiv:2008.1219
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