相关论文: Machine Learning Application for $\mathbf{\Lambda}…
Decomposition is a proven way to shrink deep networks without changing input-output dimensionality or interface semantics. We bring this idea to hyperdimensional computing (HDC), where footprint cuts usually shrink the feature axis and…
This paper proposes a novel and fast self-supervised solution for sparse-view CBCT reconstruction (Cone Beam Computed Tomography) that requires no external training data. Specifically, the desired attenuation coefficients are represented as…
Deep learning models have significantly improved the visual quality and accuracy on compressive sensing recovery. In this paper, we propose an algorithm for signal reconstruction from compressed measurements with image priors captured by a…
Image reconstruction-based anomaly detection models are widely explored in industrial visual inspection. However, existing models usually suffer from the trade-off between normal reconstruction fidelity and abnormal reconstruction…
We present a four-dimensional equation of state for strongly interacting matter at finite temperature and conserved charge densities, constructed using a deep neural network. It is designed for direct use in hybrid models of relativistic…
Precision cosmology benefits from extracting maximal information from cosmic structures, motivating the use of higher-order statistics (HOS) at small spatial scales. However, predicting how baryonic processes modify matter statistics at…
We develop a matrix element based reconstruction method called event deconstruction. The method uses information from the hard matrix element and a parton shower to assign probabilities to whether a final state was initiated by a signal or…
The non-uniform sampling is a powerful approach to enable fast acquisition but requires sophisticated reconstruction algorithms. Faithful reconstruction from partial sampled exponentials is highly expected in general signal processing and…
Deep learning based approaches have been used to improve image quality in cone-beam computed tomography (CBCT), a medical imaging technique often used in applications such as image-guided radiation therapy, implant dentistry or…
Digital breast tomosynthesis is rapidly replacing digital mammography as the basic x-ray technique for evaluation of the breasts. However, the sparse sampling and limited angular range gives rise to different artifacts, which manufacturers…
We explore how well one can probe the s quark chirality of the fundamental weak interaction of nonleptonic B decay using the spin-analyzing property of the Lambda hyperon. We present the prediction of the Standard Model as quantitatively as…
In the framework of the top triangle moose $(TTM)$ model, we analyze the rare decays $\Lambda_{b}\rightarrow \Lambda l^{+}l^{-} (l=e,\mu,\tau)$ by using the form factors calculated in full $QCD$. We calculate the contributions of the new…
QCD sum rules for the determination of form factors of $\Lambda_b$ and $\Lambda_c$ semileptonic decays are investigated. With a form for the baryonic current appropriate for the limits of the heavy quark symmetries, the different tensor…
The semileptonic transition of $\Lambda_b$ baryon is studied using the Hypercentral constituent quark model. The six-dimensional hyperradial $Schr\ddot{o}dinger$ equation is solved in the variational approach to get masses and wavefunctions…
The standard model and Quantum Chromodynamics (QCD) have undergone rigorous tests at distances much shorter than the size of a nucleon. Up to now, the predicted phenomena are reproduced rather well. However, at distances comparable to the…
Machine learning (ML) plays an increasingly important role in both online and offline event reconstruction and identification at CMS experiment. A variety of ML techniques are used to improve the identification of physics objects. Dedicated…
We present regression and compression algorithms for lattice QCD data utilizing the efficient binary optimization ability of quantum annealers. In the regression algorithm, we encode the correlation between the input and output variables…
This paper explores properties of baryons and finite density baryonic matter in an artificial world in which Nc, the number of colors, is large and the quarks of all species are degenerate and much larger than {\Lambda}_QCD. It has long…
Hypernuclei are convenient laboratory to study the baryon-baryon weak interaction and associated effective Hamiltonian. The strangeness changing process, in which a Lambda hyperon converts to a neutron with a release up to 176 MeV, provides…
A multiscale (micro-to-macro) analysis is proposed for the prediction of the finite strain behavior of composites with hyperelastic constituents and embedded localized damage. The composites are assumed to possess periodic microstructure…