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 $4-25$ 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

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 $\Omega(2^N)$ evaluations are typically required in TNLS for \emph{reaching} the objective reduction in the neighborhood, while ideally $O(N^2R)$ evaluations are sufficient in TnALE, where $N$ denotes the tensor order and $R$ 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