10,666 research outputs found
Semantic-Aware Image Compressed Sensing
Deep learning based image compressed sensing (CS) has achieved great success.
However, existing CS systems mainly adopt a fixed measurement matrix to images,
ignoring the fact the optimal measurement numbers and bases are different for
different images. To further improve the sensing efficiency, we propose a novel
semantic-aware image CS system. In our system, the encoder first uses a fixed
number of base CS measurements to sense different images. According to the base
CS results, the encoder then employs a policy network to analyze the semantic
information in images and determines the measurement matrix for different image
areas. At the decoder side, a semantic-aware initial reconstruction network is
developed to deal with the changes of measurement matrices used at the encoder.
A rate-distortion training loss is further introduced to dynamically adjust the
average compression ratio for the semantic-aware CS system and the policy
network is trained jointly with the encoder and the decoder in an en-to-end
manner by using some proxy functions. Numerical results show that the proposed
semantic-aware image CS system is superior to the traditional ones with fixed
measurement matrices.Comment: Modified versio
Compression Ratio Learning and Semantic Communications for Video Imaging
Camera sensors have been widely used in intelligent robotic systems.
Developing camera sensors with high sensing efficiency has always been
important to reduce the power, memory, and other related resources. Inspired by
recent success on programmable sensors and deep optic methods, we design a
novel video compressed sensing system with spatially-variant compression
ratios, which achieves higher imaging quality than the existing snapshot
compressed imaging methods with the same sensing costs. In this article, we
also investigate the data transmission methods for programmable sensors, where
the performance of communication systems is evaluated by the reconstructed
images or videos rather than the transmission of sensor data itself. Usually,
different reconstruction algorithms are designed for applications in high
dynamic range imaging, video compressive sensing, or motion debluring. This
task-aware property inspires a semantic communication framework for
programmable sensors. In this work, a policy-gradient based reinforcement
learning method is introduced to achieve the explicit trade-off between the
compression (or transmission) rate and the image distortion. Numerical results
show the superiority of the proposed methods over existing baselines
Semantic Communications with Variable-Length Coding for Extended Reality
Wireless extended reality (XR) has attracted wide attentions as a promising
technology to improve users' mobility and quality of experience. However, the
ultra-high data rate requirement of wireless XR has hindered its development
for many years. To overcome this challenge, we develop a semantic communication
framework, where semantically-unimportant information is highly-compressed or
discarded in semantic coders, significantly improving the transmission
efficiency. Besides, considering the fact that some source content may have
less amount of semantic information or have higher tolerance to channel noise,
we propose a universal variable-length semantic-channel coding method. In
particular, we first use a rate allocation network to estimate the best code
length for semantic information and then adjust the coding process accordingly.
By adopting some proxy functions, the whole framework is trained in an
end-to-end manner. Numerical results show that our semantic system
significantly outperforms traditional transmission methods and the proposed
variable-length coding scheme is superior to the fixed-length coding methods.Comment: 1. Update the performance of VL-SCC in Fig8. under new rate
allocation architecture 2. Give a fair comparison between VL-SCC and SCC in
Fig9. 3. fix the typo of LDPC rate (1/3 changed to 2/3) 4. Reduce L=32 to 16,
and update the bp
A CCBM-based generalized GKB iterative regularizing algorithm for inverse Cauchy problems
This paper examines inverse Cauchy problems that are governed by a kind of
elliptic partial differential equation. The inverse problems involve recovering
the missing data on an inaccessible boundary from the measured data on an
accessible boundary, which is severely ill-posed. By using the coupled complex
boundary method (CCBM), which integrates both Dirichlet and Neumann data into a
single Robin boundary condition, we reformulate the underlying problem into an
operator equation. Based on this new formulation, we prove the existence of a
unique solution even in cases with noisy data. A Golub-Kahan bidiagonalization
(GKB) process together with Givens rotation is employed for iteratively solving
the proposed operator equation. The regularizing property of the developed
method, called CCBM-GKB, and its convergence rate results are proved under a
posteriori stopping rule. Finally, a linear finite element method is used for
the numerical realization of CCBM-GKB. Various numerical experiments
demonstrate that CCBM-GKB is a kind of accelerated iterative regularization
method, as it is much faster than the classic Landweber method
In-Context Learning with Iterative Demonstration Selection
Spurred by advancements in scale, large language models (LLMs) have
demonstrated strong few-shot learning ability via in-context learning (ICL).
However, the performance of ICL has been shown to be highly sensitive to the
selection of few-shot demonstrations. Selecting the most suitable examples as
context remains an ongoing challenge and an open problem. Existing literature
has highlighted the importance of selecting examples that are diverse or
semantically similar to the test sample while ignoring the fact that the
optimal selection dimension, i.e., diversity or similarity, is task-specific.
Leveraging the merits of both dimensions, we propose Iterative Demonstration
Selection (IDS). Using zero-shot chain-of-thought reasoning (Zero-shot-CoT),
IDS iteratively selects examples that are diverse but still strongly correlated
with the test sample as ICL demonstrations. Specifically, IDS applies
Zero-shot-CoT to the test sample before demonstration selection. The output
reasoning path is then used to choose demonstrations that are prepended to the
test sample for inference. The generated answer is accompanied by its
corresponding reasoning path for extracting a new set of demonstrations in the
next iteration. After several iterations, IDS adopts majority voting to obtain
the final result. Through extensive experiments on tasks including commonsense
reasoning, question answering, topic classification, and sentiment analysis, we
demonstrate that IDS can consistently outperform existing ICL demonstration
selection methods
Combination Therapy With Fingolimod and Neural Stem Cells Promotes Functional Myelination
Myelination, which occurs predominantly postnatally and continues throughout life, is important for proper neurologic function of the mammalian central nervous system (CNS). We have previously demonstrated that the combination therapy of fingolimod (FTY720) and transplanted neural stem cells (NSCs) had a significantly enhanced therapeutic effect on the chronic stage of experimental autoimmune encephalomyelitis, an animal model of CNS autoimmunity, compared to using either one of them alone. However, reduced disease severity may be secondary to the immunomodulatory effects of FTY720 and NSCs, while whether this therapy directly affects myelinogenesis remains unknown. To investigate this important question, we used three myelination models under minimal or non-inflammatory microenvironments. Our results showed that FTY720 drives NSCs to differentiate into oligodendrocytes and promotes myelination in an ex vivo brain slice culture model, and in the developing CNS of healthy postnatal mice in vivo. Elevated levels of neurotrophic factors, e.g., brain-derived neurotrophic factor and glial cell line-derived neurotrophic factor, were observed in the CNS of the treated infant mice. Further, FTY720 and NSCs efficiently prolonged the survival and improved sensorimotor function of shiverer mice. Together, these data demonstrate a direct effect of FTY720, beyond its known immunomodulatory capacity, in NSC differentiation and myelin development as a novel mechanism underlying its therapeutic effect in demyelinating diseases
Rapid recognition of isomers of monochlorobiphenyls at trace levels by surface-enhanced Raman scattering using Ag nanorods as a substrate
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