Backflow Corrections of Green's Functions: Benchmarks on the Two-dimensional Fermi-Hubbard-type Model

The quantum many-body problem is an important topic in condensed matter physics. To efficiently solve the problem, several methods have been developped to improve the representation ability of wave-functions. For the Fermi-Hubbard-type model, the ground energy contains one-body and two-body correlations. In contrast to the wave-function, the Green function directly represents the spatio-temporal correlations between multiple sites. In this work, we propose a backflow correction of the one-body Green function to improve the ability to capture correlations. Our method is benchmarked on the spinless $t-V$ model with open boundary conditions and on the Fermi-Hubbard model with periodic and cylindrical boudary conditions, both on rectangular lattices. The energies achieved by our method are competitive with or even lower than those achieved by state-of-the-art methods

Structure and substrate selectivity of the 750-kDa α6β6 holoenzyme of geranyl-CoA carboxylase.

Geranyl-CoA carboxylase (GCC) is essential for the growth of Pseudomonas organisms with geranic acid as the sole carbon source. GCC has the same domain organization and shares strong sequence conservation with the related biotin-dependent carboxylases 3-methylcrotonyl-CoA carboxylase (MCC) and propionyl-CoA carboxylase (PCC). Here we report the crystal structure of the 750-kDa α6β6 holoenzyme of GCC, which is similar to MCC but strikingly different from PCC. The structures provide evidence in support of two distinct lineages of biotin-dependent acyl-CoA carboxylases, one carboxylating the α carbon of a saturated organic acid and the other carboxylating the γ carbon of an α-β unsaturated acid. Structural differences in the active site region of GCC and MCC explain their distinct substrate preferences. Especially, a glycine residue in GCC is replaced by phenylalanine in MCC, which blocks access by the larger geranyl-CoA substrate. Mutation of this residue in the two enzymes can change their substrate preferences

Transmission electron microscopy analysis of some transition metal compounds for energy storage and conversion

This work was preliminarily supported by the National Key Research Program of China (2016YFA0202604), the Natural Science Foundation of China (21476271), NSFC-RGC (21461162003) and Natural Science Foundation (2014KTSCX004 and 2014A030308012) of Guangdong Province, China.Recently, transition metal compounds (TMCs) have been employed as high-performance electrode materials for lithium ion batteries (LIBs) and supercapacitors (SCs) owing to their high specific capacities, high electrical conductivity, and high chemical and thermal stability. While the characterization of electrochemical properties of TMC anodes is well developed, new challenges arise in understanding the structure-property relationships. Transmission electron microscopy (TEM) is a powerful tool for studying microstructural characteristics. With TEM and related techniques, fundamental understanding of how the microstructures affect the properties of the TMC nanostructured anodes can be improved. In this article, the application of TEM in characterization of some typical TMC anode materials optimized through structural engineering, elemental doping, surface modification, defect-control engineering, morphological control, etc. is reviewed. Emphasis is given on analyzing the microstructures, including surface structures, various defects, local chemical compositions and valence states of transition metals, aimed at illustrating a structure-property relationship. The contribution and future development of the TEM techniques to elucidation of the electrochemical properties of the TMC anodes are highlighted.PostprintPeer reviewe

Crosstalk Impacts on Homogeneous Weakly-Coupled Multicore Fiber Based IM/DD System

We numerically discussed crosstalk impacts on homogeneous weakly-coupled multicore fiber based intensity modulation/direct-detection (IM/DD) systems taking into account mean crosstalk power fluctuation, walk-off between cores, laser frequency offset, and laser linewidth.Comment: 3 pages, 11 figures

HeteFedRec: Federated Recommender Systems with Model Heterogeneity

Owing to the nature of privacy protection, federated recommender systems (FedRecs) have garnered increasing interest in the realm of on-device recommender systems. However, most existing FedRecs only allow participating clients to collaboratively train a recommendation model of the same public parameter size. Training a model of the same size for all clients can lead to suboptimal performance since clients possess varying resources. For example, clients with limited training data may prefer to train a smaller recommendation model to avoid excessive data consumption, while clients with sufficient data would benefit from a larger model to achieve higher recommendation accuracy. To address the above challenge, this paper introduces HeteFedRec, a novel FedRec framework that enables the assignment of personalized model sizes to participants. In HeteFedRec, we present a heterogeneous recommendation model aggregation strategy, including a unified dual-task learning mechanism and a dimensional decorrelation regularization, to allow knowledge aggregation among recommender models of different sizes. Additionally, a relation-based ensemble knowledge distillation method is proposed to effectively distil knowledge from heterogeneous item embeddings. Extensive experiments conducted on three real-world recommendation datasets demonstrate the effectiveness and efficiency of HeteFedRec in training federated recommender systems under heterogeneous settings

1st Place Solution of Egocentric 3D Hand Pose Estimation Challenge 2023 Technical Report:A Concise Pipeline for Egocentric Hand Pose Reconstruction

This report introduce our work on Egocentric 3D Hand Pose Estimation workshop. Using AssemblyHands, this challenge focuses on egocentric 3D hand pose estimation from a single-view image. In the competition, we adopt ViT based backbones and a simple regressor for 3D keypoints prediction, which provides strong model baselines. We noticed that Hand-objects occlusions and self-occlusions lead to performance degradation, thus proposed a non-model method to merge multi-view results in the post-process stage. Moreover, We utilized test time augmentation and model ensemble to make further improvement. We also found that public dataset and rational preprocess are beneficial. Our method achieved 12.21mm MPJPE on test dataset, achieve the first place in Egocentric 3D Hand Pose Estimation challenge

A Novel Latin Square Image Cipher

In this paper, we introduce a symmetric-key Latin square image cipher (LSIC) for grayscale and color images. Our contributions to the image encryption community include 1) we develop new Latin square image encryption primitives including Latin Square Whitening, Latin Square S-box and Latin Square P-box ; 2) we provide a new way of integrating probabilistic encryption in image encryption by embedding random noise in the least significant image bit-plane; and 3) we construct LSIC with these Latin square image encryption primitives all on one keyed Latin square in a new loom-like substitution-permutation network. Consequently, the proposed LSIC achieve many desired properties of a secure cipher including a large key space, high key sensitivities, uniformly distributed ciphertext, excellent confusion and diffusion properties, semantically secure, and robustness against channel noise. Theoretical analysis show that the LSIC has good resistance to many attack models including brute-force attacks, ciphertext-only attacks, known-plaintext attacks and chosen-plaintext attacks. Experimental analysis under extensive simulation results using the complete USC-SIPI Miscellaneous image dataset demonstrate that LSIC outperforms or reach state of the art suggested by many peer algorithms. All these analysis and results demonstrate that the LSIC is very suitable for digital image encryption. Finally, we open source the LSIC MATLAB code under webpage https://sites.google.com/site/tuftsyuewu/source-code.Comment: 26 pages, 17 figures, and 7 table