Estimate for the 0++0^{++} glueball mass in QCD

We obtain accurate result for the lightest glueball mass of QCD in 3 dimensions from lattice Hamiltonian field theory. Using the dimensional reduction argument, a good approximation for confining theories, we suggest that the $0^{++}$ glueball mass in 3+1 dimensional QCD be about $1.71$ GeV.Comment: 10 Latex page

Gaussian-Gamma collaborative filtering: a hierarchical Bayesian model for recommender systems

The traditional collaborative filtering (CF) suffers from two key challenges, namely, the normal assumption that it is not robust, and it is difficult to set in advance the penalty terms of the latent features. We therefore propose a hierarchical Bayesian model-based CF and the related inference algorithm. Specifically, we impose a Gaussian-Gamma prior on the ratings, and the latent features. We show the model is more robust, and the penalty terms can be adapted automatically in the inference. We use Gibbs sampler for the inference and provide a statistical explanation. We verify the performance using both synthetic and real dataset

Glueball Masses from Hamiltonian Lattice QCD

We calculate the masses of the $0^{++}$, $0^{--}$ and $1^{+-}$ glueballs from QCD in 3+1 dimensions using an eigenvalue equation method for Hamiltonian lattice QCD developed and described elsewhere by the authors. The mass ratios become approximately constants in the coupling region $6/g^2 \in [6.0,6.4]$, from which we estimate $M(0^{--})/M(0^{++})=2.44 \pm 0.05 \pm 0.20$ and $M(1^{+-})/M(0^{++})=1.91 \pm 0.05 \pm 0.12$.Comment: 12 pages, Latex, figures to be sent upon reques

QCD_3 Vacum Wave Function

We investigate quantum chromodynamics in 2+1 dimensions ($\rm{QCD}_3$) using the Hamiltonian lattice field theory approach. The long wavelength structure of the ground state, which is closely related to the confinement phenomenon, is analyzed and its vacuum wave function is evaluated by means of the recently developed truncated eigenvalue equation method. The third order estimations show nice scaling for the physical quantities.Comment: 10 pages plus 3 figures, encoded with uufiles

Refined Temporal Pyramidal Compression-and-Amplification Transformer for 3D Human Pose Estimation

Accurately estimating the 3D pose of humans in video sequences requires both accuracy and a well-structured architecture. With the success of transformers, we introduce the Refined Temporal Pyramidal Compression-and-Amplification (RTPCA) transformer. Exploiting the temporal dimension, RTPCA extends intra-block temporal modeling via its Temporal Pyramidal Compression-and-Amplification (TPCA) structure and refines inter-block feature interaction with a Cross-Layer Refinement (XLR) module. In particular, TPCA block exploits a temporal pyramid paradigm, reinforcing key and value representation capabilities and seamlessly extracting spatial semantics from motion sequences. We stitch these TPCA blocks with XLR that promotes rich semantic representation through continuous interaction of queries, keys, and values. This strategy embodies early-stage information with current flows, addressing typical deficits in detail and stability seen in other transformer-based methods. We demonstrate the effectiveness of RTPCA by achieving state-of-the-art results on Human3.6M, HumanEva-I, and MPI-INF-3DHP benchmarks with minimal computational overhead. The source code is available at https://github.com/hbing-l/RTPCA.Comment: 11 pages, 5 figure