arXiv subjects

Hong Deng

Publications and source records attributed to Hong Deng.

13 recordsLinked to original sources

arXiv2026

Parallel simulation of rarefied gas flows on unstructured meshes using the DIG-augmented DSMC method

While the direct simulation Monte Carlo (DSMC) is a mainstream stochastic particle method for simulating rarefied gas flows, it incurs excessively high computational costs in the near continuum regime. As a hybrid acceleration approach coupling DSMC with macroscopic synthetic equations, the direct intermittent general synthetic iterative scheme (DIG) delivers fast convergence and asymptotic-preserving characteristics, which effectively alleviate the kinetic scale limitations inherent to standard DSMC. In this study, we develop a parallel DIG augmented DSMC solver for three dimensional rarefied gas flow simulations on unstructured meshes. On top of the standard DSMC algorithms for particle transport and collisions, a reliable intermittent coupling framework is constructed to exchange macroscopic flow data between the stochastic DSMC module and deterministic macroscopic synthetic equations. For parallel execution on unstructured grids, we employ a hybrid MPI architecture equipped with ghost cells to enable local particle tracking and batch inter-rank particle migration. A graph partitioning based dynamic load balancing strategy is also integrated to mitigate uneven particle distribution over computational domains. Numerical results demonstrate that the proposed solver achieves satisfactory agreement with the SPARTA DSMC. Leveraging the fast convergence and asymptotic-preserving properties of the DIG method, the required number of spatial cells and statistical sampling steps are drastically decreased, leading to substantial reductions in computational memory and runtime. This work presents an efficient high-performance numerical tool for high-fidelity simulations of rarefied flows over complex geometries. The code is available in the developer repository at the github link.

physics.comp-ph↗

arXiv2026

Efficient simulation of chemical reaction in DSMC

A macroscopic mesoscopic, deterministic stochastic coupling strategy is proposed to accelerate the direct simulation Monte Carlo (DSMC) method for chemical reaction. First, a macroscopic synthetic equation is formulated by integrating continuum constitutive relations for diffusion, stress, and heat flux, along with higher order constitutive relations that capture nonequilibrium transport effects. Second, higher order constitutive relations and chemical reaction source terms are sampled from DSMC and embedded into the macroscopic synthetic equation. Third, the macroscopic system is solved to the steady state, whose solution is then employed to correct particle distributions in DSMC intermittently. This coupling features asymptotic preserving, fast converging and noise reduction properties, supporting efficient, accurate simulations with coarse spatiotemporal grids and reduced evolution/sampling steps. Accordingly, it mitigates major computational bottlenecks of DSMC for near continuum flows by several orders of magnitude.

physics.comp-ph↗

arXiv2025

Silicon photonic modulator circuit with programmable intensity and phase modulation response

Electro-optical modulators are essential components in optical communication systems. They encode an electrical waveform onto an optical carrier. However, their performance is often limited by inherent electro-optic processes and imperfections in existing integrated designs, which limit their adaptability to diverse applications. This paper presents a circuit-level programmable modulator design that addresses these challenges. The proposed modulator can generate both intensity and phase modulation, optimizing performance without altering the underlying design or constraining platform limitations. We explain and demonstrate the principle with both carrier depletion-based modulators and SiGe electro-absorption modulators on a silicon photonic platform. Experiments demonstrate precise control and optimization capabilities surpassing those of traditional modulator designs, marking a significant leap forward in adaptability and performance enhancement across intensity, phase, and modulation linearity, enabled by programmable photonics. In combination with on-chip monitors, our circuit can be self-calibrating. This programmable modulator circuit concept can be applied to modulators in different platforms, incorporating existing phase shifter designs, and act as a drop-in replacement in more complex circuits. It has the potential to be widely used in optical communication, LiDAR, microwave photonics, and other systems, meeting the increasing demands of various applications.

physics.optics↗

arXiv2025

Dark matter stabilized by a non-abelian group: lessons from the $\Sigma(36)$ 3HDM

When building dark matter (DM) models, one often imposes conserved discrete symmetries to stabilize DM candidates. The simplest choice is ${\mathbb Z}_2$ but models with larger stabilizing groups have also been explored. Can a conserved non-abelian group lead to a viable DM model? Here, we address this question within the three-Higgs-doublet model based on the group $\Sigma(36)$, in which DM stabilization by a non-abelian group is not only possible but inevitable. We show that the tight connections between the Higgs, fermion, and DM sectors repeatedly drive the model into conflict with the LHC results and DM observations, with the most recent LZ results playing a decisive role. We believe that the lessons learned from this study help chart the limits of what can be achieved within multi-Higgs-doublet DM models with large symmetry groups.

hep-ph↗

arXiv2024

Hybrid Classification-Regression Adaptive Loss for Dense Object Detection

For object detection detectors, enhancing model performance hinges on the ability to simultaneously consider inconsistencies across tasks and focus on difficult-to-train samples. Achieving this necessitates incorporating information from both the classification and regression tasks. However, prior work tends to either emphasize difficult-to-train samples within their respective tasks or simply compute classification scores with IoU, often leading to suboptimal model performance. In this paper, we propose a Hybrid Classification-Regression Adaptive Loss, termed as HCRAL. Specifically, we introduce the Residual of Classification and IoU (RCI) module for cross-task supervision, addressing task inconsistencies, and the Conditioning Factor (CF) to focus on difficult-to-train samples within each task. Furthermore, we introduce a new strategy named Expanded Adaptive Training Sample Selection (EATSS) to provide additional samples that exhibit classification and regression inconsistencies. To validate the effectiveness of the proposed method, we conduct extensive experiments on COCO test-dev. Experimental evaluations demonstrate the superiority of our approachs. Additionally, we designed experiments by separately combining the classification and regression loss with regular loss functions in popular one-stage models, demonstrating improved performance.

cs.CV↗

arXiv2024

Renal function changes in chronic hepatitis B patients

The best way to treat chronic hepatitis B is with pegylated interferon alone or with oral antiviral drugs. There is limited research comparing the renal safety of entecavir and tenofovir when used with pegylated interferon. This study will compare changes in renal function in chronic hepatitis B patients treated with pegylated interferon and either entecavir or tenofovir. The study included a cohort of 836 patients with chronic hepatitis B (CHB) who received treatment with pegylated interferon (IFN) either alone or in combination with entecavir (ETV) and tenofovir (TDF) between the years 2018 and 2021. Of these patients, 713 were included in a matched analysis comparing outcomes between those who were cured and those who were uncured, while 123 patients received IFN alone as a control group for comparison with the ETV and TDF treatment groups. The primary outcome measured was the change in renal function, specifically estimated glomerular filtration rate (eGFR), cystatin C (CysC), and inorganic phosphorus (IPHOS). Patients were categorized into stage 1 or stage 2 based on a baseline eGFR of less than 90 ml/min/m^2 Results: 125 CHB patients were matched 1:1 in both the combined treatment and cured groups. Baseline eGFR, CysC, and IPHOS levels were similar between the groups. Renal function in stage 1 and stage 2 groups showed a decreasing trend at 48 weeks after an initial increase.Correlation analysis showed significant relationships between changes in ALT and eGFR values at 12 weeks in both non-cured and cured groups. Conclusions: Over the 48-week duration of combined treatment in patients with chronic hepatitis B (CHB), it was found that both Tenofovir Disoproxil Fumarate (TDF) and Entecavir (ETV) did not lead to an increase in renal injury.

q-bio.QM↗

arXiv2023

Single-Chip Silicon Photonic Processor for Analog Optical and Microwave Signals

The explosion of data volume in communications, AI training, and cloud computing requires efficient data handling, which is typically stored as digital electrical information and transmitted as wireless radio frequency (RF) signals or light waves in optical fibres. Today's communications systems mostly treat the RF and optical signals separately, which results in unnecessary conversion losses and increased cost. In this work, we report the first fully on-chip signal processor for high-speed RF and optical signals based on a silicon photonic circuit. Our chip is capable of both generation and detection of analog electrical and optical signals, and can program a user-defined filter response in both domains. The single silicon photonic chip integrates all essential components like modulators, optical filters, and photodetectors, as well as tunable lasers enabled by transfer-printed Indium Phosphide (InP) optical amplifiers. The system's configuration is locally programmed through thermo-optic phase shifters and monitored by photodetectors. We demonstrate our chip's capabilities with different combinations of RF and optical signal processing functions, including optical and RF signal generation and filtering. This represents a key step towards compact microwave photonic systems for future wireless communication and sensing applications.

physics.optics↗

arXiv2023

Neural Dysfunction Underlying Working Memory Processing at Different Stages of the Illness Course in Schizophrenia:A Comparative Meta-analysis

Schizophrenia (SCZ), as a chronic and persistent disorder, exhibits working memory deficits across various stages of the disorder, yet the neural mechanisms underlying these deficits remain elusive with inconsistent neuroimaging findings. We aimed to compare the brain functional changes of working memory in patients at different stages: clinical high risk (CHR), first-episode psychosis (FEP), and long-term SCZ, using meta-analyses of functional magnetic resonance imaging (fMRI) studies. Following a systematic literature search, fifty-six whole-brain task-based fMRI studies (15 for CHR, 16 for FEP, 25 for long-term SCZ) were included. The separate and pooled neurofunctional mechanisms among CHR, FEP and long-term SCZ were generated by Seed-based d Mapping toolbox. The CHR and FEP groups exhibited overlapping hypoactivation in the right inferior parietal lobule, right middle frontal gyrus, and left superior parietal lobule, indicating key lesion sites in the early phase of SCZ. Individuals with FEP showed lower activation in left inferior parietal lobule than those with long-term SCZ, reflecting a possible recovery process or more neural inefficiency. We concluded that SCZ represent as a continuum in the early stage of illness progression, while the neural bases are inversely changed with the development of illness course to long-term course.

q-bio.NC↗

arXiv2023

Integrated Photonic Reservoir Computing with All-Optical Readout

Integrated photonic reservoir computing has been demonstrated to be able to tackle different problems because of its neural network nature. A key advantage of photonic reservoir computing over other neuromorphic paradigms is its straightforward readout system, which facilitates both rapid training and robust, fabrication variation-insensitive photonic integrated hardware implementation for real-time processing. We present our recent development of a fully-optical, coherent photonic reservoir chip integrated with an optical readout system, capitalizing on these benefits. Alongside the integrated system, we also demonstrate a weight update strategy that is suitable for the integrated optical readout hardware. Using this online training scheme, we successfully solved 3-bit header recognition and delayed XOR tasks at 20 Gbps in real-time, all within the optical domain without excess delays.

physics.optics↗

arXiv2023

Controlling Electromagnetic Surface Waves with Conformal Transformation Optics

The application of transformation optics to the development of intriguing electromagnetic devices can produce weakly anisotropic or isotropic media with the assistance of quasi-conformal and/or conformal mapping, as opposed to the strongly anisotropic media produced by general mappings; however, it is typically limited to two-dimensional applications. By addressing the conformal mapping between two manifolds embedded in three-dimensional space, we demonstrate that electromagnetic surface waves can be controlled without introducing singularity and anisotropy into the device parameters. Using fruitful surface conformal parameterization methods, a near-perfect conformal mapping between smooth manifolds with arbitrary boundaries can be obtained. Illustrations of cloaking and illusions, including surface Luneburg and Eaton lenses and black holes for surface waves, are provided. Our work brings the manipulation of surface waves at microwave and optical wavelengths one step closer.

physics.optics↗

arXiv2021

Constant-Cost Spatio-Angular Prefiltering of Glinty Appearance Using Tensor Decomposition

The detailed glinty appearance from complex surface microstructures enhances the level of realism, but is both space- and time-consuming to render, especially when viewed from far away (large spatial coverage) and/or illuminated by area lights (large angular coverage). In this paper, we formulate the glinty appearance rendering process as a spatio-angular range query problem of the Normal Distribution Functions (NDFs), and introduce an efficient spatio-angular prefiltering solution to it. We start by exhaustively precomputing all possible NDFs with differently sized positional coverages. Then we compress the precomputed data using tensor rank decomposition, which enables accurate and fast angular range queries. With our spatio-angular prefiltering scheme, we are able to solve both the storage and performance issues at the same time, leading to efficient rendering of glinty appearance with both constant storage and constant performance, regardless of the range of spatio-angular queries. Finally, we demonstrate that our method easily applies to practical rendering applications that were traditionally considered difficult. For example, efficient bidirectional reflection distribution function (BRDF) evaluation accurate NDF importance sampling, fast global illumination between glinty objects, high-frequency preserving rendering with environment lighting, and tile-based synthesis of glinty appearance.

cs.GR↗

arXiv2020

Optical normal-mode-induced phonon-sideband splitting in photon-blockade effect

We study the photon-blockade effect in a loop-coupled optomechanical system consisting of two cavity modes and one mechanical mode. Here, the mechanical mode is optomechanically coupled to the two cavity modes, which are coupled with each other via a photon-hopping interaction. By treating the photon-hopping interaction as a perturbation, we obtain the analytical results of the eigenvalues and eigenstates of the system in the subspaces associated with zero, one, and two photons. We find a phenomenon of optical normal-mode-induced phonon-sideband splitting in the photon-blockade effect by analytically and numerically calculating the second-order correlation functions of the two cavity modes. This work not only presents a method to choose optimal driving frequency for photon blockade by tuning the photon-hopping interaction, but also provides a means to characterize the normal-mode splitting with cavity photon statistics.

quant-ph↗

arXiv2012

First-principles study on the magnetic properties of six potential half-metallic ferromagnets: alkaline-earth (Ca, Sr) doped XC (X=Si, Ge, Sn)

Six half-metallic ferromagnets X0.75Y0.25C (X=Si, Ge, Sn and Y =Ca and Sr) with zinc-blende structure, resulting from alkaline-earth (Ca, Sr) substitution for X, are predicted based on the density functional theory. The calculated total magnetic moments of these ferromagnets are all integer 2.00{\mu}B per supercell, which are one of important characters of half-metallic ferromagnets. Our calculations indicate that X0.75Y0.25C have wide spin gap and potentially have high Curie temperature. Alkaline-earth doping results in the spin-polarization and half-metallicity of these compounds. It is confirmed that the p-d exchange coupling is responsible for the ferromagnetism of X0.75Y0.25C except Sn0.75Ca0.25C.

cond-mat.mtrl-sci↗