593 research outputs found
GAGA: Deciphering Age-path of Generalized Self-paced Regularizer
Nowadays self-paced learning (SPL) is an important machine learning paradigm
that mimics the cognitive process of humans and animals. The SPL regime
involves a self-paced regularizer and a gradually increasing age parameter,
which plays a key role in SPL but where to optimally terminate this process is
still non-trivial to determine. A natural idea is to compute the solution path
w.r.t. age parameter (i.e., age-path). However, current age-path algorithms are
either limited to the simplest regularizer, or lack solid theoretical
understanding as well as computational efficiency. To address this challenge,
we propose a novel \underline{G}eneralized \underline{Ag}e-path
\underline{A}lgorithm (GAGA) for SPL with various self-paced regularizers based
on ordinary differential equations (ODEs) and sets control, which can learn the
entire solution spectrum w.r.t. a range of age parameters. To the best of our
knowledge, GAGA is the first exact path-following algorithm tackling the
age-path for general self-paced regularizer. Finally the algorithmic steps of
classic SVM and Lasso are described in detail. We demonstrate the performance
of GAGA on real-world datasets, and find considerable speedup between our
algorithm and competing baselines.Comment: 33 pages. Published as a conference paper at NeurIPS 202
SGE: Structured Light System Based on Gray Code with an Event Camera
Fast and accurate depth sensing has long been a significant research
challenge. Event camera, as a device that quickly responds to intensity
changes, provides a new solution for structured light (SL) systems. In this
paper, we introduce Gray code into event-based SL systems for the first time.
Our setup includes an event camera and Digital Light Processing (DLP)
projector, enabling depth estimation through high-speed projection and decoding
of Gray code patterns. By employing spatio-temporal encoding for point
matching, our method is immune to timestamp noise, realizing high-speed depth
estimation without loss of accuracy. The binary nature of events and Gray code
minimizes data redundancy, enabling us to fully utilize sensor bandwidth at
100%. Experimental results show that our approach achieves accuracy comparable
to state-of-the-art scanning methods while surpassing them in data acquisition
speed (up to 41 times improvement) without sacrificing accuracy. Our proposed
approach offers a highly promising solution for ultra-fast, real-time, and
high-precision dense depth estimation. Code and dataset will be publicly
available
Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionary
Single Image Super-Resolution is a classic computer vision problem that
involves estimating high-resolution (HR) images from low-resolution (LR) ones.
Although deep neural networks (DNNs), especially Transformers for
super-resolution, have seen significant advancements in recent years,
challenges still remain, particularly in limited receptive field caused by
window-based self-attention. To address these issues, we introduce a group of
auxiliary Adaptive Token Dictionary to SR Transformer and establish an ATD-SR
method. The introduced token dictionary could learn prior information from
training data and adapt the learned prior to specific testing image through an
adaptive refinement step. The refinement strategy could not only provide global
information to all input tokens but also group image tokens into categories.
Based on category partitions, we further propose a category-based
self-attention mechanism designed to leverage distant but similar tokens for
enhancing input features. The experimental results show that our method
achieves the best performance on various single image super-resolution
benchmarks.Comment: 15 pages, 9 figure
Post-Quantum -to-1 Trapdoor Claw-free Functions from Extrapolated Dihedral Cosets
\emph{Noisy trapdoor claw-free function} (NTCF) as a powerful post-quantum
cryptographic tool can efficiently constrain actions of untrusted quantum
devices. However, the original NTCF is essentially \emph{2-to-1} one-way
function (NTCF). In this work, we attempt to further extend the
NTCF to achieve \emph{many-to-one} trapdoor claw-free functions with
polynomial bounded preimage size. Specifically, we focus on a significant
extrapolation of NTCF by drawing on extrapolated dihedral cosets, thereby
giving a model of NTCF where is a polynomial integer.
Then, we present an efficient construction of NTCF assuming
\emph{quantum hardness of the learning with errors (LWE)} problem. We point out
that NTCF can be used to bridge the LWE and the dihedral coset problem (DCP).
By leveraging NTCF (resp. NTCF), our work reveals a new
quantum reduction path from the LWE problem to the DCP (resp. extrapolated
DCP). Finally, we demonstrate the NTCF can naturally be reduced to
the NTCF, thereby achieving the same application for proving the
quantumness.Comment: 34 pages, 7 figure
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Eradication of unresectable liver metastasis through induction of tumour specific energy depletion.
Treatment of liver metastasis experiences slow progress owing to the severe side effects. In this study, we demonstrate a strategy capable of eliminating metastatic cancer cells in a selective manner. Nucleus-targeting W18O49 nanoparticles (WONPs) are conjugated to mitochondria-selective mesoporous silica nanoparticles (MSNs) containing photosensitizer (Ce6) through a Cathepsin B-cleavable peptide. In hepatocytes, upon the laser irradiation, the generated singlet oxygen species are consumed by WONPs, in turn leading to the loss of their photothermally heating capacity, thereby sparing hepatocyte from thermal damage induced by the laser illumination. By contrast, in cancer cells, the cleaved peptide linker allows WONPs and MSNs to respectively target nucleus and mitochondria, where the therapeutic powers could be unleashed, both photodynamically and photothermally. This ensures the energy production of cancer cells can be abolished. We further assess the underlying molecular mechanism at both gene and protein levels to better understand the therapeutic outcome
AdaFuse: Adaptive Medical Image Fusion Based on Spatial-Frequential Cross Attention
Multi-modal medical image fusion is essential for the precise clinical
diagnosis and surgical navigation since it can merge the complementary
information in multi-modalities into a single image. The quality of the fused
image depends on the extracted single modality features as well as the fusion
rules for multi-modal information. Existing deep learning-based fusion methods
can fully exploit the semantic features of each modality, they cannot
distinguish the effective low and high frequency information of each modality
and fuse them adaptively. To address this issue, we propose AdaFuse, in which
multimodal image information is fused adaptively through frequency-guided
attention mechanism based on Fourier transform. Specifically, we propose the
cross-attention fusion (CAF) block, which adaptively fuses features of two
modalities in the spatial and frequency domains by exchanging key and query
values, and then calculates the cross-attention scores between the spatial and
frequency features to further guide the spatial-frequential information fusion.
The CAF block enhances the high-frequency features of the different modalities
so that the details in the fused images can be retained. Moreover, we design a
novel loss function composed of structure loss and content loss to preserve
both low and high frequency information. Extensive comparison experiments on
several datasets demonstrate that the proposed method outperforms
state-of-the-art methods in terms of both visual quality and quantitative
metrics. The ablation experiments also validate the effectiveness of the
proposed loss and fusion strategy
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