210 research outputs found
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
Multi-view Contrastive Learning with Additive Margin for Adaptive Nasopharyngeal Carcinoma Radiotherapy Prediction
The prediction of adaptive radiation therapy (ART) prior to radiation therapy
(RT) for nasopharyngeal carcinoma (NPC) patients is important to reduce
toxicity and prolong the survival of patients. Currently, due to the complex
tumor micro-environment, a single type of high-resolution image can provide
only limited information. Meanwhile, the traditional softmax-based loss is
insufficient for quantifying the discriminative power of a model. To overcome
these challenges, we propose a supervised multi-view contrastive learning
method with an additive margin (MMCon). For each patient, four medical images
are considered to form multi-view positive pairs, which can provide additional
information and enhance the representation of medical images. In addition, the
embedding space is learned by means of contrastive learning. NPC samples from
the same patient or with similar labels will remain close in the embedding
space, while NPC samples with different labels will be far apart. To improve
the discriminative ability of the loss function, we incorporate a margin into
the contrastive learning. Experimental result show this new learning objective
can be used to find an embedding space that exhibits superior discrimination
ability for NPC images.Comment: submitted to ICASSP 2023, 5 page
RTN: Reparameterized Ternary Network
To deploy deep neural networks on resource-limited devices, quantization has
been widely explored. In this work, we study the extremely low-bit networks
which have tremendous speed-up, memory saving with quantized activation and
weights. We first bring up three omitted issues in extremely low-bit networks:
the squashing range of quantized values; the gradient vanishing during
backpropagation and the unexploited hardware acceleration of ternary networks.
By reparameterizing quantized activation and weights vector with full precision
scale and offset for fixed ternary vector, we decouple the range and magnitude
from the direction to extenuate the three issues. Learnable scale and offset
can automatically adjust the range of quantized values and sparsity without
gradient vanishing. A novel encoding and computation pat-tern are designed to
support efficient computing for our reparameterized ternary network (RTN).
Experiments on ResNet-18 for ImageNet demonstrate that the proposed RTN finds a
much better efficiency between bitwidth and accuracy, and achieves up to 26.76%
relative accuracy improvement compared with state-of-the-art methods. Moreover,
we validate the proposed computation pattern on Field Programmable Gate Arrays
(FPGA), and it brings 46.46x and 89.17x savings on power and area respectively
compared with the full precision convolution.Comment: To appear at AAAI-2
Self-supervised Likelihood Estimation with Energy Guidance for Anomaly Segmentation in Urban Scenes
Robust autonomous driving requires agents to accurately identify unexpected
areas in urban scenes. To this end, some critical issues remain open: how to
design advisable metric to measure anomalies, and how to properly generate
training samples of anomaly data? Previous effort usually resorts to
uncertainty estimation and sample synthesis from classification tasks, which
ignore the context information and sometimes requires auxiliary datasets with
fine-grained annotations. On the contrary, in this paper, we exploit the strong
context-dependent nature of segmentation task and design an energy-guided
self-supervised frameworks for anomaly segmentation, which optimizes an anomaly
head by maximizing the likelihood of self-generated anomaly pixels. To this
end, we design two estimators for anomaly likelihood estimation, one is a
simple task-agnostic binary estimator and the other depicts anomaly likelihood
as residual of task-oriented energy model. Based on proposed estimators, we
further incorporate our framework with likelihood-guided mask refinement
process to extract informative anomaly pixels for model training. We conduct
extensive experiments on challenging Fishyscapes and Road Anomaly benchmarks,
demonstrating that without any auxiliary data or synthetic models, our method
can still achieves competitive performance to other SOTA schemes
Hidden local symmetry breaking in a kagome-lattice magnetic Weyl semimetal
Exploring the relationship between intriguing physical properties and
structural complexity is a central topic in studying modern functional
materials. CoSnS, a new discovered kagome-lattice magnetic
Weyl semimetal, has triggered intense interest owing to the intimate coupling
between topological semimetallic states and peculiar magnetic properties.
However, the origins of the magnetic phase separation and spin glass state
below in this ordered compound are two unresolved yet important puzzles
in understanding its magnetism. Here, we report the discovery of local symmetry
breaking surprisingly co-emerges with the onset of ferromagnetic order in
CoSnS, by a combined use of neutron total scattering and half
polarized neutron diffraction. The mismatch of local and average symmetries
occurs below , indicating that CoSnS evolves to an
intrinsically lattice disordered system when the ferromagnetic order is
established. The local symmetry breaking with intrinsic lattice disorder
provides new understandings to the puzzling magnetic properties. Our density
function theory calculation indicates that the local symmetry breaking is
expected to reorient local ferromagnetic moments, unveiling the existence of
the ferromagnetic instability associated with the lattice instability.
Furthermore, DFT calculation unveils that the local symmetry breaking could
affect the Weyl property by breaking mirror plane. Our findings highlight the
fundamentally important role that the local symmetry breaking plays in
advancing our understanding on the magnetic and topological properties in
CoSnS, which may draw the attention to explore the overlooked
local symmetry breaking in CoSnS, its derivatives, and more
broadly in other topological Dirac/Weyl semimetals and kagome-lattice magnets.Comment: 35 pages, 6 figures, 1 table, 1 Supplementary Informatio
Learning with Noisy labels via Self-supervised Adversarial Noisy Masking
Collecting large-scale datasets is crucial for training deep models,
annotating the data, however, inevitably yields noisy labels, which poses
challenges to deep learning algorithms. Previous efforts tend to mitigate this
problem via identifying and removing noisy samples or correcting their labels
according to the statistical properties (e.g., loss values) among training
samples. In this paper, we aim to tackle this problem from a new perspective,
delving into the deep feature maps, we empirically find that models trained
with clean and mislabeled samples manifest distinguishable activation feature
distributions. From this observation, a novel robust training approach termed
adversarial noisy masking is proposed. The idea is to regularize deep features
with a label quality guided masking scheme, which adaptively modulates the
input data and label simultaneously, preventing the model to overfit noisy
samples. Further, an auxiliary task is designed to reconstruct input data, it
naturally provides noise-free self-supervised signals to reinforce the
generalization ability of deep models. The proposed method is simple and
flexible, it is tested on both synthetic and real-world noisy datasets, where
significant improvements are achieved over previous state-of-the-art methods
A transformer acoustic signal analysis method based on matrix pencil and hybrid deep neural network
Acoustic signal analysis is an important component of transformer online monitoring. Currently, traditional methods have problems such as low spectral resolution, imbalanced sample distribution, and unsatisfactory classification performance. This article first introduces the matrix pencil algorithm for time-frequency spectrum analysis of acoustic signals, and then uses the SMOTE algorithm to expand the imbalanced samples. Then, an ACmix hybrid deep neural network model is constructed to classify 11 types of transformer operation and environmental acoustic signals. Finally, detailed experiments were conducted on the method proposed in this paper, and the experimental results showed that the matrix pencil algorithm has high time-frequency resolution and good noise resistance performance. The SMOTE sample expansion method can significantly improve the recognition accuracy by more than 2%. Overall accuracy of the proposed method in acoustic signal classification tasks reaches 91.81%
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