91 research outputs found
Medical Image Understanding with Pretrained Vision Language Models: A Comprehensive Study
The large-scale pre-trained vision language models (VLM) have shown
remarkable domain transfer capability on natural images. However, it remains
unknown whether this capability can also apply to the medical image domain.
This paper thoroughly studies the knowledge transferability of pre-trained VLMs
to the medical domain, where we show that well-designed medical prompts are the
key to elicit knowledge from pre-trained VLMs. We demonstrate that by prompting
with expressive attributes that are shared between domains, the VLM can carry
the knowledge across domains and improve its generalization. This mechanism
empowers VLMs to recognize novel objects with fewer or without image samples.
Furthermore, to avoid the laborious manual designing process, we develop three
approaches for automatic generation of medical prompts, which can inject
expert-level medical knowledge and image-specific information into the prompts
for fine-grained grounding. We conduct extensive experiments on thirteen
different medical datasets across various modalities, showing that our
well-designed prompts greatly improve the zero-shot performance compared to the
default prompts, and our fine-tuned models surpass the supervised models by a
significant margin.Comment: 14 pages, 4 figures
Generating Combinatorial Test Suite with Solution Space Tree for Configurations Testing of Sensors Networks
There are many results about generating pair-wise covering arrays with strength t=2 have been reported, but fewer results are published for high-strength covering arrays with a higher-strength t>2. In configuration testing of sensor networks, high-strength covering array is required to construct combinatorial test cases. To generate combinatorial test suite with higher-strength, a backtracking algorithms, which is based on solution space tree, is proposed in this paper by extending an existing pair-wise combinatorial test suite generation algorithm. In solution space tree model, each test case is represented as a path from the root to a leaf node in the tree. And proposed algorithm generates test cases one by one, by backtracking depth-first searching in the solution space tree. Finally, to assess the efficiency of proposed algorithm, computational comparison with other published methods is reported
Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation
Conventional data augmentation realized by performing simple pre-processing
operations (\eg, rotation, crop, \etc) has been validated for its advantage in
enhancing the performance for medical image segmentation. However, the data
generated by these conventional augmentation methods are random and sometimes
harmful to the subsequent segmentation. In this paper, we developed a novel
automatic learning-based data augmentation method for medical image
segmentation which models the augmentation task as a trial-and-error procedure
using deep reinforcement learning (DRL). In our method, we innovatively combine
the data augmentation module and the subsequent segmentation module in an
end-to-end training manner with a consistent loss. Specifically, the best
sequential combination of different basic operations is automatically learned
by directly maximizing the performance improvement (\ie, Dice ratio) on the
available validation set. We extensively evaluated our method on CT kidney
tumor segmentation which validated the promising results of our method.Comment: 5 pages, 3 figure
EVIL: Evidential Inference Learning for Trustworthy Semi-supervised Medical Image Segmentation
Recently, uncertainty-aware methods have attracted increasing attention in
semi-supervised medical image segmentation. However, current methods usually
suffer from the drawback that it is difficult to balance the computational
cost, estimation accuracy, and theoretical support in a unified framework. To
alleviate this problem, we introduce the Dempster-Shafer Theory of Evidence
(DST) into semi-supervised medical image segmentation, dubbed Evidential
Inference Learning (EVIL). EVIL provides a theoretically guaranteed solution to
infer accurate uncertainty quantification in a single forward pass. Trustworthy
pseudo labels on unlabeled data are generated after uncertainty estimation. The
recently proposed consistency regularization-based training paradigm is adopted
in our framework, which enforces the consistency on the perturbed predictions
to enhance the generalization with few labeled data. Experimental results show
that EVIL achieves competitive performance in comparison with several
state-of-the-art methods on the public dataset
Edge Generation Scheduling for DAG Tasks using Deep Reinforcement Learning
Directed acyclic graph (DAG) tasks are currently adopted in the real-time
domain to model complex applications from the automotive, avionics, and
industrial domain that implement their functionalities through chains of
intercommunicating tasks. This paper studies the problem of scheduling
real-time DAG tasks by presenting a novel schedulability test based on the
concept of trivial schedulability. Using this schedulability test, we propose a
new DAG scheduling framework (edge generation scheduling -- EGS) that attempts
to minimize the DAG width by iteratively generating edges while guaranteeing
the deadline constraint. We study how to efficiently solve the problem of
generating edges by developing a deep reinforcement learning algorithm combined
with a graph representation neural network to learn an efficient edge
generation policy for EGS. We evaluate the effectiveness of the proposed
algorithm by comparing it with state-of-the-art DAG scheduling heuristics and
an optimal mixed-integer linear programming baseline. Experimental results show
that the proposed algorithm outperforms the state-of-the-art by requiring fewer
processors to schedule the same DAG tasks.Comment: Under revie
ConES: Concept Embedding Search for Parameter Efficient Tuning Large Vision Language Models
Large pre-trained vision-language models have shown great prominence in
transferring pre-acquired knowledge to various domains and downstream tasks
with appropriate prompting or tuning. Existing prevalent tuning methods can be
generally categorized into three genres: 1) prompt engineering by creating
suitable prompt texts, which is time-consuming and requires domain expertise;
2) or simply fine-tuning the whole model, which is extremely inefficient; 3)
prompt tuning through parameterized prompt embeddings with the text encoder.
Nevertheless, all methods rely on the text encoder for bridging the modality
gap between vision and language. In this work, we question the necessity of the
cumbersome text encoder for a more lightweight and efficient tuning paradigm as
well as more representative prompt embeddings closer to the image
representations. To achieve this, we propose a Concept Embedding Search (ConES)
approach by optimizing prompt embeddings -- without the need of the text
encoder -- to capture the 'concept' of the image modality through a variety of
task objectives. By dropping the text encoder, we are able to significantly
speed up the learning process, \eg, from about an hour to just ten minutes in
our experiments for personalized text-to-image generation without impairing the
generation quality. Moreover, our proposed approach is orthogonal to current
existing tuning methods since the searched concept embeddings can be further
utilized in the next stage of fine-tuning the pre-trained large models for
boosting performance. Extensive experiments show that our approach can beat the
prompt tuning and textual inversion methods in a variety of downstream tasks
including objection detection, instance segmentation, and image generation. Our
approach also shows better generalization capability for unseen concepts in
specialized domains, such as the medical domain
Effects of yeast culture and oxalic acid supplementation on in vitro nutrient disappearance, rumen fermentation, and bacterial community composition
Hemicellulose is an important polysaccharide in ruminant nutrition, but it has not been studied as thoroughly as cellulose. Further research is needed to explore supplements that can improve its digestibility and ruminal buffering effects. Our previous research demonstrated the efficacy of oxalic acid (OA) as an essential nutrient in yeast culture (YC) for improving rumen fermentation performance. Consequently, we conducted in vitro rumen digestion experiments to examine the effects of YC and OA on rumen fermentation and bacterial composition. Two diets containing different levels of hemicellulose were formulated: diet 1 with 10.3% and diet 2 with 17% hemicellulose. Three levels of YC (0.00, 0.625, and 1.25 g/kg) and three doses of OA (0.0, 0.4, and 0.8 g/kg, DM) were added into each diet with a 3 × 3 factorial design. A comprehensive assessment was conducted on a total of 18 experimental treatments at fermentation periods of 0, 6, 12, 24, and 48 h. In the first experiment (diet 1), the supplementation of YC, OA, and their interaction significantly increased in vitro DM disappearance (IVDMD) and NDF disappearance (IVNDFD; p < 0.001). In the second experiment (diet 2), the supplementation of OA and the interaction between YC and OA (p < 0.001) increased IVDMD and IVCPD, but had no significant effects on IVNDFD. The interactions of YC and OA significantly increased ammonia nitrogen (p < 0.001). The production of acetic acid, propionic acid, and total volatile fatty acids (TVFA), and pH levels were significantly higher in treatments supplemented with YC and OA (p < 0.001). YC and OA in both diets significantly altered the rumen bacterial community leading to increased Shannon and Simpson diversity indices (p < 0.001). In both diets, OA supplementation significantly increased the relative abundance of the phylum Bacteroidetes and Prevotella genus. The result also showed a positive correlation between the Prevotella and Selenomonas genera with IVDMD, IVNDFD, propionic acid, and TVFA production, suggesting that these dominant bacteria enhanced nutrient disappearance in the rumen. In conclusion, adding YC and OA resulted in modifications to the bacterial community’s composition and diversity, and improved nutrient disappearance. These changes indicate improved rumen fermentation efficiency, which is promising for future in vivo studies
Potential of Core-Collapse Supernova Neutrino Detection at JUNO
JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve
Detection of the Diffuse Supernova Neutrino Background with JUNO
As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO
Real-time Monitoring for the Next Core-Collapse Supernova in JUNO
Core-collapse supernova (CCSN) is one of the most energetic astrophysical
events in the Universe. The early and prompt detection of neutrinos before
(pre-SN) and during the SN burst is a unique opportunity to realize the
multi-messenger observation of the CCSN events. In this work, we describe the
monitoring concept and present the sensitivity of the system to the pre-SN and
SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is
a 20 kton liquid scintillator detector under construction in South China. The
real-time monitoring system is designed with both the prompt monitors on the
electronic board and online monitors at the data acquisition stage, in order to
ensure both the alert speed and alert coverage of progenitor stars. By assuming
a false alert rate of 1 per year, this monitoring system can be sensitive to
the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos
up to about 370 (360) kpc for a progenitor mass of 30 for the case
of normal (inverted) mass ordering. The pointing ability of the CCSN is
evaluated by using the accumulated event anisotropy of the inverse beta decay
interactions from pre-SN or SN neutrinos, which, along with the early alert,
can play important roles for the followup multi-messenger observations of the
next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure
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