509 research outputs found
Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach
Recent progress in single-image super-resolution (SISR) has achieved
remarkable performance, yet the computational costs of these methods remain a
challenge for deployment on resource-constrained devices. Especially for
transformer-based methods, the self-attention mechanism in such models brings
great breakthroughs while incurring substantial computational costs. To tackle
this issue, we introduce the Convolutional Transformer layer (ConvFormer) and
the ConvFormer-based Super-Resolution network (CFSR), which offer an effective
and efficient solution for lightweight image super-resolution tasks. In detail,
CFSR leverages the large kernel convolution as the feature mixer to replace the
self-attention module, efficiently modeling long-range dependencies and
extensive receptive fields with a slight computational cost. Furthermore, we
propose an edge-preserving feed-forward network, simplified as EFN, to obtain
local feature aggregation and simultaneously preserve more high-frequency
information. Extensive experiments demonstrate that CFSR can achieve an
advanced trade-off between computational cost and performance when compared to
existing lightweight SR methods. Compared to state-of-the-art methods, e.g.
ShuffleMixer, the proposed CFSR achieves 0.39 dB gains on Urban100 dataset for
x2 SR task while containing 26% and 31% fewer parameters and FLOPs,
respectively. Code and pre-trained models are available at
https://github.com/Aitical/CFSR.Comment: submitting to TI
Dilation theorem via Schr\"odingerisation, with applications to the quantum simulation of differential equations
Nagy's unitary dilation theorem in operator theory asserts the possibility of
dilating a contraction into a unitary operator. When used in quantum computing,
its practical implementation primarily relies on block-encoding techniques,
based on finite-dimensional scenarios. In this study, we delve into the
recently devised Schr\"odingerisation approach and demonstrate its viability as
an alternative dilation technique. This approach is applicable to operators in
the form of , which arises in wide-ranging applications,
particularly in solving linear ordinary and partial differential equations.
Importantly, the Schr\"odingerisation approach is adaptable to both finite and
infinite-dimensional cases, in both countable and uncountable domains. For
quantum systems lying in infinite dimensional Hilbert space, the dilation
involves adding a single infinite dimensional mode, and this is the
continuous-variable version of the Schr\"odingerisation procedure which makes
it suitable for analog quantum computing. Furthermore, by discretising
continuous variables, the Schr\"odingerisation method can also be effectively
employed in finite-dimensional scenarios suitable for qubit-based quantum
computing
Visual Analytics of Neuron Vulnerability to Adversarial Attacks on Convolutional Neural Networks
Adversarial attacks on a convolutional neural network (CNN) -- injecting
human-imperceptible perturbations into an input image -- could fool a
high-performance CNN into making incorrect predictions. The success of
adversarial attacks raises serious concerns about the robustness of CNNs, and
prevents them from being used in safety-critical applications, such as medical
diagnosis and autonomous driving. Our work introduces a visual analytics
approach to understanding adversarial attacks by answering two questions: (1)
which neurons are more vulnerable to attacks and (2) which image features do
these vulnerable neurons capture during the prediction? For the first question,
we introduce multiple perturbation-based measures to break down the attacking
magnitude into individual CNN neurons and rank the neurons by their
vulnerability levels. For the second, we identify image features (e.g., cat
ears) that highly stimulate a user-selected neuron to augment and validate the
neuron's responsibility. Furthermore, we support an interactive exploration of
a large number of neurons by aiding with hierarchical clustering based on the
neurons' roles in the prediction. To this end, a visual analytics system is
designed to incorporate visual reasoning for interpreting adversarial attacks.
We validate the effectiveness of our system through multiple case studies as
well as feedback from domain experts.Comment: Accepted by the Special Issue on Human-Centered Explainable AI, ACM
Transactions on Interactive Intelligent System
Elementary excitations in an integrable twisted J1-J2 spin chain in the thermodynamic limit
The exact elementary excitations in a typical U(1) symmetry broken quantum
integrable system, that is the twisted J1-J2 spin chain with nearest-neighbor,
next nearest neighbor and chiral three spin interactions, are studied. The main
technique is that we quantify the energy spectrum of the system by the zero
roots of transfer matrix instead of the traditional Bethe roots. From the
numerical calculation and singularity analysis, we obtain the patterns of zero
roots. Based on them, we analytically obtain the ground state energy and the
elementary excitations in the thermodynamic limit. We find that the system also
exhibits the nearly degenerate states in the regime of ,
where the nearest-neighbor couplings among the z-direction are ferromagnetic.
More careful study shows that the competing of interactions can induce the
gapless low-lying excitations and quantum phase transition in the
antiferromagnetic regime with .Comment: 29 pages, 20 figure
An Accurate Bilinear Cavern Model for Compressed Air Energy Storage
Compressed air energy storage is suitable for large-scale electrical energy
storage, which is important for integrating renewable energy sources into
electric power systems. A typical compressed air energy storage plant consists
of compressors, expanders, caverns, and a motor/generator set. Current cavern
models used for compressed air energy storage are either accurate but highly
nonlinear or linear but inaccurate. The application of highly nonlinear cavern
models in power system optimization problems renders them computationally
challenging to solve. In this regard, an accurate bilinear cavern model for
compressed air energy storage is proposed in this paper. The charging and
discharging processes in a cavern are divided into several real/virtual states.
The first law of thermodynamics and ideal gas law are then utilized to derive a
cavern model, i.e., a model for the variation of temperature and pressure in
these processes. Thereafter, the heat transfer between the air in the cavern
and the cavern wall is considered and integrated into the cavern model. By
subsequently eliminating several negligible terms, the cavern model reduces to
a bilinear model. The accuracy of the bilinear cavern model is verified via
comparison with both an accurate nonlinear model and two sets of field-measured
data. The bilinear cavern model can be easily linearized and is then suitable
for integration into optimization problems considering compressed air energy
storage. This is verified via comparatively solving a self-scheduling problem
of compressed air energy storage using different cavern models.Comment: 18 pages, 15 figures, accepted by Applied Energy on March 201
Study on the Reasonable Smoke Exhaust Rate of the Crossrange Exhaust Duct in Double-layer Shield Tunnel
AbstractThe research on the concentrated smoke extraction system of crossrange exhaust duct in double-layer shield tunnel is still very lack in the world. This paper is on the smoke extraction system of double-layer shield tunnel. It will provide the supports and references for the smoke control of tunnel fire and the determination of related technical parameters in the design of tunnel fire ventilation and smoke extraction, so it has important scientific value, practical significance and application prospects. This paper bases on the tunnel project of Slender West Lake in Yangzhou. By using the method of combining theory and numerical simulation, a conclusion can be drawn that the reasonable smoke exhaust rate of the upper tunnel is 140 m3/s
Energy saving strategy for the development of icephobic coatings and surfaces
Aircraft are frequently exposed to cold environments and ice accumulation on aircraft surface may lead to catastrophic accidents. An effective solution of ice protection is a critical requirement in the aerospace industry. For the research and development of icephobic coatings, the current coating design target mainly focuses on lowering the ice adhesion strength between the ice and the surface. However, as a passive ice protection approach, the use of icephobic coating often has to be combined with an active ice protection solution (e.g. electro-thermal heating, hot air bleeding, and vibration, etc.), especially for the in-flight application where the reliability of ice protection must be ensured. Therefore, ice adhesion strength is no longer the sole criterion to evaluate the icephobic performance of a coating or a surface. It is a need to establish a more practical strategy for the design of icephobic coatings and surface. In this work, an energy saving strategy is proposed to assess the de-icing performance of the icephobic coating and surface when active heating is involved. The energy consumed for the de-icing operation assisted by the ice gravity is used as the key criterion for the overall performance of icephobic coating and surface. Successful validation has been achieved for evaluating the de-icing performance of selected coatings and surfaces, which demonstrates an alternative strategy for the design and practical application of icephobic coatings and surfaces in ice protection
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