387 research outputs found
Determination of impact parameter for CEE with Digi-input neural networks
Impact parameter is an important quantity which characterizes the centrality
in nucleus-nucleus collision geometry. The determination of impact parameter in
real experiments takes use of the hits on detector system or the reconstructed
tracks of the secondary particles. As a task of feature recognition, methods
such as sharp cut-off, Bayesian methods and Neural Networks (NN) has been
studied and applied. However, in the situation of the Cooler-storage-ring
External-target Experiment (CEE), the low beam energy brings a lapse of
dependency between impact parameter and charged particle multiplicity, which
decreases the validity of the explicit determination methods. This work
proposes a regressor constructed with Graph Attention neural network, which
takes the hit-level data as input. This model has shown a mean absolute error
of 0.496 fm for the IQMD collision data of the UU system at 0.5 AMeV. The
performance of such a model is compared with reference models, showing its
capacity in handling the original but potentially interrelated digi
information.Comment: 13 pages, 9 figure
Joint Computing Offloading and Resource Allocation for Classification Intelligent Tasks in MEC Systems
Mobile edge computing (MEC) enables low-latency and high-bandwidth
applications by bringing computation and data storage closer to end-users.
Intelligent computing is an important application of MEC, where computing
resources are used to solve intelligent task-related problems based on task
requirements. However, efficiently offloading computing and allocating
resources for intelligent tasks in MEC systems is a challenging problem due to
complex interactions between task requirements and MEC resources. To address
this challenge, we investigate joint computing offloading and resource
allocation for intelligent tasks in MEC systems. Our goal is to optimize system
utility by jointly considering computing accuracy and task delay to achieve
maximum system performance. We focus on classification intelligence tasks and
formulate an optimization problem that considers both the accuracy requirements
of tasks and the parallel computing capabilities of MEC systems. To solve the
optimization problem, we decompose it into three subproblems: subcarrier
allocation, computing capacity allocation, and compression offloading. We use
convex optimization and successive convex approximation to derive closed-form
expressions for the subcarrier allocation, offloading decisions, computing
capacity, and compressed ratio. Based on our solutions, we design an efficient
computing offloading and resource allocation algorithm for intelligent tasks in
MEC systems. Our simulation results demonstrate that our proposed algorithm
significantly improves the performance of intelligent tasks in MEC systems and
achieves a flexible trade-off between system revenue and cost considering
intelligent tasks compared with the benchmarks.Comment: arXiv admin note: substantial text overlap with arXiv:2307.0274
Image Projective Transformation Rectification with Synthetic Data for Smartphone-captured Chest X-ray Photos Classification
Classification on smartphone-captured chest X-ray (CXR) photos to detect
pathologies is challenging due to the projective transformation caused by the
non-ideal camera position. Recently, various rectification methods have been
proposed for different photo rectification tasks such as document photos,
license plate photos, etc. Unfortunately, we found that none of them is
suitable for CXR photos, due to their specific transformation type, image
appearance, annotation type, etc. In this paper, we propose an innovative deep
learning-based Projective Transformation Rectification Network (PTRN) to
automatically rectify CXR photos by predicting the projective transformation
matrix. To the best of our knowledge, it is the first work to predict the
projective transformation matrix as the learning goal for photo rectification.
Additionally, to avoid the expensive collection of natural data, synthetic CXR
photos are generated under the consideration of natural perturbations, extra
screens, etc. We evaluate the proposed approach in the CheXphoto
smartphone-captured CXR photos classification competition hosted by the
Stanford University Machine Learning Group, our approach won first place with a
huge performance improvement (ours 0.850, second-best 0.762, in AUC). A deeper
study demonstrates that the use of PTRN successfully achieves the
classification performance on the spatially transformed CXR photos to the same
level as on the high-quality digital CXR images, indicating PTRN can eliminate
all negative impacts of projective transformation on the CXR photos
On the Quantum Mechanics for One Photon
This paper revisits the quantum mechanics for one photon from the modern
viewpoint and by the geometrical method. Especially, besides the ordinary
(rectangular) momentum representation, we provide an explicit derivation for
the other two important representations, called the cylindrically symmetrical
representation and the spherically symmetrical representation, respectively.
These other two representations are relevant to some current photon experiments
in quantum optics. In addition, the latter is useful for us to extract the
information on the quantized black holes. The framework and approach presented
here are also applicable to other particles with arbitrary mass and spin, such
as the particle with spin 1/2.Comment: 15 pages, typos corrected, references added, corrections and
improvements made owing to the anonymous referee's responsible and helpful
remarks, accepted for publication in Journal of Mathematical Physics:
DiffIR: Efficient Diffusion Model for Image Restoration
Diffusion model (DM) has achieved SOTA performance by modeling the image
synthesis process into a sequential application of a denoising network.
However, different from image synthesis, image restoration (IR) has a strong
constraint to generate results in accordance with ground-truth. Thus, for IR,
traditional DMs running massive iterations on a large model to estimate whole
images or feature maps is inefficient. To address this issue, we propose an
efficient DM for IR (DiffIR), which consists of a compact IR prior extraction
network (CPEN), dynamic IR transformer (DIRformer), and denoising network.
Specifically, DiffIR has two training stages: pretraining and training DM. In
pretraining, we input ground-truth images into CPEN to capture a compact
IR prior representation (IPR) to guide DIRformer. In the second stage, we train
the DM to directly estimate the same IRP as pretrained CPEN only using
LQ images. We observe that since the IPR is only a compact vector, DiffIR can
use fewer iterations than traditional DM to obtain accurate estimations and
generate more stable and realistic results. Since the iterations are few, our
DiffIR can adopt a joint optimization of CPEN, DIRformer, and denoising
network, which can further reduce the estimation error influence. We conduct
extensive experiments on several IR tasks and achieve SOTA performance while
consuming less computational costs. Code is available at
\url{https://github.com/Zj-BinXia/DiffIR}.Comment: This paper is accepted by ICCV2023. Codes and models are available at
https://github.com/Zj-BinXia/DiffI
Germline Mutations in Patients With Early-Onset Prostate Cancer.
Objective: To investigate the inherited mutations and their association with clinical features and treatment response in young-onset prostate cancer patients.
Method: Targeted gene sequencing on 139 tumor susceptibility genes was conducted with a total of 24 patients diagnosed with PCa under the age of 63 years old. Meanwhile, the related clinical information of those patients is collected and analyzed.
Results: Sixty-two germline mutations in 45 genes were verified in 22 patients.
Conclusion: Mutations in DRGs are more prevalent in early-onset PCa with advanced clinical stages, and these patients had shorter progression-free survival. ADT Combined with either radiotherapy or chemotherapy may be effective in treating PCa caused by HRR-related gene mutations
Structured Sparsity Learning for Efficient Video Super-Resolution
The high computational costs of video super-resolution (VSR) models hinder
their deployment on resource-limited devices, (e.g., smartphones and drones).
Existing VSR models contain considerable redundant filters, which drag down the
inference efficiency. To prune these unimportant filters, we develop a
structured pruning scheme called Structured Sparsity Learning (SSL) according
to the properties of VSR. In SSL, we design pruning schemes for several key
components in VSR models, including residual blocks, recurrent networks, and
upsampling networks. Specifically, we develop a Residual Sparsity Connection
(RSC) scheme for residual blocks of recurrent networks to liberate pruning
restrictions and preserve the restoration information. For upsampling networks,
we design a pixel-shuffle pruning scheme to guarantee the accuracy of feature
channel-space conversion. In addition, we observe that pruning error would be
amplified as the hidden states propagate along with recurrent networks. To
alleviate the issue, we design Temporal Finetuning (TF). Extensive experiments
show that SSL can significantly outperform recent methods quantitatively and
qualitatively. We will release codes and models
Knowledge Distillation based Degradation Estimation for Blind Super-Resolution
Blind image super-resolution (Blind-SR) aims to recover a high-resolution
(HR) image from its corresponding low-resolution (LR) input image with unknown
degradations. Most of the existing works design an explicit degradation
estimator for each degradation to guide SR. However, it is infeasible to
provide concrete labels of multiple degradation combinations (e.g., blur,
noise, jpeg compression) to supervise the degradation estimator training. In
addition, these special designs for certain degradation, such as blur, impedes
the models from being generalized to handle different degradations. To this
end, it is necessary to design an implicit degradation estimator that can
extract discriminative degradation representation for all degradations without
relying on the supervision of degradation ground-truth. In this paper, we
propose a Knowledge Distillation based Blind-SR network (KDSR). It consists of
a knowledge distillation based implicit degradation estimator network (KD-IDE)
and an efficient SR network. To learn the KDSR model, we first train a teacher
network: KD-IDE. It takes paired HR and LR patches as inputs and is
optimized with the SR network jointly. Then, we further train a student network
KD-IDE, which only takes LR images as input and learns to extract the
same implicit degradation representation (IDR) as KD-IDE. In addition, to
fully use extracted IDR, we design a simple, strong, and efficient IDR based
dynamic convolution residual block (IDR-DCRB) to build an SR network. We
conduct extensive experiments under classic and real-world degradation
settings. The results show that KDSR achieves SOTA performance and can
generalize to various degradation processes. The source codes and pre-trained
models will be released.Comment: ICLR2023, code is available at https://github.com/Zj-BinXia/KDS
- …