549 research outputs found
Randomized Tensor Ring Decomposition and Its Application to Large-scale Data Reconstruction
Dimensionality reduction is an essential technique for multi-way large-scale
data, i.e., tensor. Tensor ring (TR) decomposition has become popular due to
its high representation ability and flexibility. However, the traditional TR
decomposition algorithms suffer from high computational cost when facing
large-scale data. In this paper, taking advantages of the recently proposed
tensor random projection method, we propose two TR decomposition algorithms. By
employing random projection on every mode of the large-scale tensor, the TR
decomposition can be processed at a much smaller scale. The simulation
experiment shows that the proposed algorithms are times faster than
traditional algorithms without loss of accuracy, and our algorithms show
superior performance in deep learning dataset compression and hyperspectral
image reconstruction experiments compared to other randomized algorithms.Comment: ICASSP submissio
Generative Adversarial Positive-Unlabelled Learning
In this work, we consider the task of classifying binary positive-unlabeled
(PU) data. The existing discriminative learning based PU models attempt to seek
an optimal reweighting strategy for U data, so that a decent decision boundary
can be found. However, given limited P data, the conventional PU models tend to
suffer from overfitting when adapted to very flexible deep neural networks. In
contrast, we are the first to innovate a totally new paradigm to attack the
binary PU task, from perspective of generative learning by leveraging the
powerful generative adversarial networks (GAN). Our generative
positive-unlabeled (GenPU) framework incorporates an array of discriminators
and generators that are endowed with different roles in simultaneously
producing positive and negative realistic samples. We provide theoretical
analysis to justify that, at equilibrium, GenPU is capable of recovering both
positive and negative data distributions. Moreover, we show GenPU is
generalizable and closely related to the semi-supervised classification. Given
rather limited P data, experiments on both synthetic and real-world dataset
demonstrate the effectiveness of our proposed framework. With infinite
realistic and diverse sample streams generated from GenPU, a very flexible
classifier can then be trained using deep neural networks.Comment: 8 page
Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion
In tensor completion tasks, the traditional low-rank tensor decomposition
models suffer from the laborious model selection problem due to their high
model sensitivity. In particular, for tensor ring (TR) decomposition, the
number of model possibilities grows exponentially with the tensor order, which
makes it rather challenging to find the optimal TR decomposition. In this
paper, by exploiting the low-rank structure of the TR latent space, we propose
a novel tensor completion method which is robust to model selection. In
contrast to imposing the low-rank constraint on the data space, we introduce
nuclear norm regularization on the latent TR factors, resulting in the
optimization step using singular value decomposition (SVD) being performed at a
much smaller scale. By leveraging the alternating direction method of
multipliers (ADMM) scheme, the latent TR factors with optimal rank and the
recovered tensor can be obtained simultaneously. Our proposed algorithm is
shown to effectively alleviate the burden of TR-rank selection, thereby greatly
reducing the computational cost. The extensive experimental results on both
synthetic and real-world data demonstrate the superior performance and
efficiency of the proposed approach against the state-of-the-art algorithms
A THz Video SAR Imaging Algorithm Based on Chirp Scaling
In video synthetic aperture radar (SAR) imaging mode, the polar format
algorithm (PFA) is more computational effective than the backprojection
algorithm (BPA). However, the two-dimensional (2-D) interpolation in PFA
greatly affects its computational speed, which is detrimental to the real-time
imaging of video SAR. In this paper, a terahertz (THz) video SAR imaging
algorithm based on chirp scaling is proposed, which utilizes the small
synthetic angular feature of THz SAR and the inherent property of linear
frequency modulation. Then, two-step chirp scaling is used to replace the 2-D
interpolation in the PFA to obtain a similar focusing effect, but with a faster
operation. Point target simulation is used to verify the effectiveness of the
proposed method.Comment: 5 pages, 7 figure
Thermal energy storage of R1234yf, R1234ze, R134a and R32/MOF-74 nanofluids: a molecular simulation study
Thermal energy storage can be carried out by working fluid adsorbing and desorbing in porous materials. In this paper, the energy storage properties of four refrigerants, R1234yf, R1234ze, R134a and R32, with M-metal organic framework (MOF)-74 (M = Zn, Ni, Mg, Co) nanoparticles are investigated using molecular dynamics simulations and grand canonical Monte Carlo simulations. The results show that M-MOF-74 can adsorb more R32 and R134a than R1234yf and R1234ze, as the molecular structures of R32 and R134a are smaller than those of R1234yf and R1234ze. Mg-MOF-74 owns a higher adsorbability than the other MOFs. The energy storage properties of the studied refrigerants can be enhanced when the sum of thermodynamic energy change of MOF particles and the desorption heat of fluid in MOFs is larger than the enthalpy change of pure organic fluid. The R1234yf/M-MOF-74 (M = Co, Mg, Ni) nanofluid can store more energy than other refrigerants/M-MOF-74 (M = Co, Mg, Ni) nanofluid. The energy storage enhancement ratios of R1234yf, R1234ze and R134a with Mg-MOF-74 nanoparticles are higher than those of other M-MOF-74 (M = Co, Ni, Zn) materials
Alternating Local Enumeration (TnALE): Solving Tensor Network Structure Search with Fewer Evaluations
Tensor network (TN) is a powerful framework in machine learning, but
selecting a good TN model, known as TN structure search (TN-SS), is a
challenging and computationally intensive task. The recent approach
TNLS~\cite{li2022permutation} showed promising results for this task, however,
its computational efficiency is still unaffordable, requiring too many
evaluations of the objective function. We propose TnALE, a new algorithm that
updates each structure-related variable alternately by local enumeration,
\emph{greatly} reducing the number of evaluations compared to TNLS. We
theoretically investigate the descent steps for TNLS and TnALE, proving that
both algorithms can achieve linear convergence up to a constant if a sufficient
reduction of the objective is \emph{reached} in each neighborhood. We also
compare the evaluation efficiency of TNLS and TnALE, revealing that
evaluations are typically required in TNLS for \emph{reaching}
the objective reduction in the neighborhood, while ideally
evaluations are sufficient in TnALE, where denotes the tensor order and
reflects the \emph{``low-rankness''} of the neighborhood. Experimental results
verify that TnALE can find practically good TN-ranks and permutations with
vastly fewer evaluations than the state-of-the-art algorithms.Comment: Accepted by ICML2023, pre-printed versio
Performance Analysis of a Polygeneration System for Methanol Production and Power Generation with Solar-biomass Thermal Gasification
AbstractBy using the cotton stalk as the feedstock, a polygeneration system for generating methanol and power with solar thermal gasification of biomass is proposed in this work. The endothermic reaction of biomass gasification is driven by the high temperature solar thermal energy with the range of 800∼1200°C. The flat-plate solar collector and the parabolic trough solar steam generator are used to preheat biomass and generate steam as gasification agent, respectively. The thermodynamic performance of the polygeneration system is investigated. The compressed syngas, produced by the biomass gasification, is used to produce methanol via the synthesis reactor. The un-reacted gas is used for power generation through a combine cycle power unit. The results indicate that the methanol output rate and the output power in steady operation condition is 41.56kg/s and 524.88 MW, respectively, and the maximum total exergy efficiency is 49.50% when the solar gasification temperature is 900°C. Furthermore, the highest exergy efficiency of the optimized scheme by recycling partial un-reacted syngas for methanol production reaches to 50.69%. The above studies provide a feasible way to exploit the abundant solar energy and biomass in the Western China
- …