arXiv subjects

D. Shi

Publications and source records attributed to D. Shi.

5 recordsLinked to original sources

arXiv2026

Nonlinear iontronic signal processing with neuromorphic Spike Rate-Dependent Plasticity

We present an integrated iontronic memristor circuit that reproduces biologically inspired Spike Rate-Dependent Plasticity (SRDP) and functions as a physical nonlinear frequency kernel, which we demonstrate can be used to classify natural auditory data. The fluidic circuit integrates two parallel memristive membranes containing short and long conical memristive channels with opposite orientations, giving rise to heterogeneous internal timescales and different potentiation responses. As a result, the circuit exhibits a nonlinear frequency response in which low-frequency inputs decrease the overall conductance, whereas higher-frequency inputs increase it, thereby emulating biological SRDP. Our experimental measurements are inspired by and consistent with predictions of a theoretical model. We demonstrate the functionality of the device by separating encoded sound signals from different insects that cannot be linearly separated. By unifying theoretical predictions with experimental realisation of coupled iontronic memristors, this work moves beyond isolated components and demonstrates how heterogeneous iontronic dynamics can unlock nonlinear time-series processing capabilities, essential for future iontronic neuromorphic computing.

cond-mat.soft↗

arXiv2025

Automated simulation-based design via multi-fidelity active learning and optimisation for laser direct drive implosions

The design of inertial fusion experiments is a complex task as driver energy must be delivered in a precise manner to a structured target to achieve a fast, but hydrodynamically stable, implosion. Radiation-hydrodynamics simulation codes are an essential tool in this design process. However, multi-dimensional simulations that capture hydrodynamic instabilities are more computationally expensive than optimistic, 1D, spherically symmetric simulations which are often the primary design tool. In this work, we develop a machine learning framework that aims to effectively use information from a large number of 1D simulations to inform design in the presence of hydrodynamic instabilities. We use an ensemble of neural network surrogate models trained on both 1D and 2D data to capture the space of good designs, i.e. those that are robust to hydrodynamic instabilities. We use this surrogate to perform Bayesian optimisation to find optimal designs for a 25 kJ laser driver. We perform hydrodynamic scaling on these designs to confirm the achievement of high gain for a 2 MJ laser driver, using 2D simulations including alpha heating effects.

physics.plasm-ph↗

arXiv2024

Performance assessment of the HERD calorimeter with a photo-diode read-out system for high-energy electron beams

The measurement of cosmic rays at energies exceeding 100 TeV per nucleon is crucial for enhancing the understanding of high-energy particle propagation and acceleration models in the Galaxy. HERD is a space-borne calorimetric experiment that aims to extend the current direct measurements of cosmic rays to unexplored energies. The payload is scheduled to be installed on the Chinese Space Station in 2027. The primary peculiarity of the instrument is its capability to measure particles coming from all directions, with the main detector being a deep, homogeneous, 3D calorimeter. The active elements are read out using two independent systems: one based on wavelength shifter fibers coupled to CMOS cameras, and the other based on photo-diodes read-out with custom front-end electronics. A large calorimeter prototype was tested in 2023 during an extensive beam test campaign at CERN. In this paper, the performance of the calorimeter for high-energy electron beams, as obtained from the photo-diode system data, is presented. The prototype demonstrated excellent performance, e.g., an energy resolution better than 1% for electrons at 250 GeV. A comparison between beam test data and Monte Carlo simulation data is also presented.

physics.ins-det↗

arXiv2021

GlassNet: Label Decoupling-based Three-stream Neural Network for Robust Image Glass Detection

Most of the existing object detection methods generate poor glass detection results, due to the fact that the transparent glass shares the same appearance with arbitrary objects behind it in an image. Different from traditional deep learning-based wisdoms that simply use the object boundary as auxiliary supervision, we exploit label decoupling to decompose the original labeled ground-truth (GT) map into an interior-diffusion map and a boundary-diffusion map. The GT map in collaboration with the two newly generated maps breaks the imbalanced distribution of the object boundary, leading to improved glass detection quality. We have three key contributions to solve the transparent glass detection problem: (1) We propose a three-stream neural network (call GlassNet for short) to fully absorb beneficial features in the three maps. (2) We design a multi-scale interactive dilation module to explore a wider range of contextual information. (3) We develop an attention-based boundary-aware feature Mosaic module to integrate multi-modal information. Extensive experiments on the benchmark dataset exhibit clear improvements of our method over SOTAs, in terms of both the overall glass detection accuracy and boundary clearness.

cs.CV↗

arXiv2016

Modelling the number density of Halpha emitters for future spectroscopic near-IR space missions

The future space missions Euclid and WFIRST-AFTA will use the Halpha emission line to measure the redshifts of tens of millions of galaxies. The Halpha luminosity function at z>0.7 is one of the major sources of uncertainty in forecasting cosmological constraints from these missions. We construct unified empirical models of the Halpha luminosity function spanning the range of redshifts and line luminosities relevant to the redshift surveys proposed with Euclid and WFIRST-AFTA. By fitting to observed luminosity functions from Halpha surveys, we build three models for its evolution. Different fitting methodologies, functional forms for the luminosity function, subsets of the empirical input data, and treatment of systematic errors are considered to explore the robustness of the results. Functional forms and model parameters are provided for all three models, along with the counts and redshift distributions up to z~2.5 for a range of limiting fluxes (F_Halpha>0.5 - 3 x 10^-16 erg cm^-2 s^-1) that are relevant for future space missions. For instance, in the redshift range 0.90 1 and 2 x 10^-16 erg cm^-2 s^-1, and 32000--48000 for F_Halpha>0.5 x 10^-16 erg cm^-2 s^-1 in the extended redshift range 0.40<z<1.8. We also consider the implications of our empirical models for the total Halpha luminosity density of the Universe, and the closely related cosmic star formation history.

astro-ph.GA↗