arXiv subjects

Jincheng Xu

Publications and source records attributed to Jincheng Xu.

7 recordsLinked to original sources

arXiv2026

Rippled graphene pores as fluidic memristive devices with synaptic and neuromorphic functionalities

Nanofluidic memristive devices work with nanoscale pores and ions dissolved in water, which harness the ionic memory effect aiming to store and process information. These devices share the same charge carriers as biological systems and bring hope for better emulating the neural functions and developing ionic circuits for neuromorphic applications. Specially, theory and experiments suggest that nanoconfinement is essential for inducing a memory effect, which places limit on the pore size to nm-scale or smaller. Such devices are difficult to scale up with precision and operate with long-term stability. Here, we show that a micrometer size pore, generally expected to exhibit a linear ion transport, can display a pronounced memory effect, if its rim is wrapped by strongly curved and tightly stacked graphene. We attribute the observation to slow ion dynamics confined in the rippled graphene edges. The devices are easy to scale up and integrate into fluidic circuits. The memory effect is ion-selective and exhibits long endurance comparable to the lifetime of synaptic proteins, which enables reversible modification of the conductance states using programmable voltage spikes and various electrolytes over a long time, akin to biological synaptic plasticity. Thanks to this plasticity, our devices and their integrated circuits enable storing, transmitting and processing information with high reliability, fidelity and accuracy, as evidenced in the identification of both greyscale and color images, and in the real-time analysis of emulated neural signals. Our results highlight nanoscale morphology of the pore wall as an important parameter regulating ion transport and indicate that the stringent nanoconfinement for ionic memory can be lifted from restricting the pore size to designing its rim structure. The devices and their integrated circuits may find use in ionic neuromorphic applications.

cond-mat.mtrl-sci↗

arXiv2025

Symmetric Dicke States as Optimal Probes for Wave-Like Dark Matter

We identify symmetric Dicke states as the optimal quantum probes for distributed sensing of wave-like dark-matter fields. Within an ensemble-averaged quantum-metrological framework that incorporates the field's random phases and finite coherence, they maximize the Fisher information for short-baseline arrays with $N_d$ sensors and realize a robust $N_d^2$ enhancement. They also retain this collective advantage under amplitude-damping noise, whereas GHZ-type probes are highly fragile and rapidly lose their sensitivity once such noise is included. For two sensors at separations comparable to the dark-matter coherence length, the optimal entangled state acquires an additional spatial-correlation phase and outperforms both Dicke and independent probes. Our framework applies broadly to stochastic bosonic fields, including gravitational waves, and can be implemented with superconducting qubits, atomic ensembles, and NV centers.

hep-ph↗

arXiv2025

A High Precision Time Measurement Method Based on Frequency-domain Phase-Fitting for Nuclear Pulse Detection

This paper proposes a high-precision time measurement method based on digital frequency-domain phase-fitting (DFPF) by using the digitized nuclear pulses. The averaging effect inherent in the frequency-domain cross-correlation and phase-fitting processes effectively minimizes measurement errors, thereby ensuring high precision and resolution in time interval measurements. In this paper, the theory of this DFPF-based time measurement method is analyzed, and an electronics prototype is designed to validate the feasibility of the proposed method by utilizing ADCs for pulse digitization and an FPGA for phase fitting implementation. The test results indicate that, under ideal conditions with a signal-to-noise ratio (SNR) of 64 dB, this method achieves time measurement precisions of 50 ps, 18 ps, and 2.9 ps RMS, corresponding to different Gaussian pulse widths and sampling rates of 118 ns at 40 MSPS, 10 ns at 100 MSPS, and 3 ns at 500 MSPS, respectively. The precision improves with increasing pulse bandwidth. Furthermore, in practical cosmic ray tests, the method achieved favorable timing performance with a precision of 1.7 ns RMS. These results demonstrate that this proposed method has the potential to be a high-precision time measurement for particle detection and is equally applicable to other advanced time measurement scenarios.

physics.ins-det↗

arXiv2025

Many-body computing on Field Programmable Gate Arrays

A new implementation of many-body calculations is of paramount importance in the field of computational physics. In this study, we leverage the capabilities of Field Programmable Gate Arrays (FPGAs) for conducting quantum many-body calculations. Through the design of appropriate schemes for Monte Carlo and tensor network methods, we effectively utilize the parallel processing capabilities provided by FPGAs. This has resulted in a tenfold speedup compared to CPU-based computation for a Monte Carlo algorithm. By using a supercell structure and simulating the FPGA architecture on a CPU with High-Level Synthesis, we achieve $O(1)$ scaling for the time of one sweep, regardless of the overall system size. We also demonstrate, for the first time, the utilization of FPGA to accelerate a typical tensor network algorithm for many-body ground state calculations. Additionally, we show that the current FPGA computing acceleration is on par with that of multi-threaded GPU parallel processing. Our findings unambiguously highlight the significant advantages of hardware implementation and pave the way for novel approaches to many-body calculations.

cond-mat.str-el↗

arXiv2023

LWS: A Framework for Log-based Workload Simulation in Session-based SUT

Artificial intelligence for IT Operations (AIOps) plays a critical role in operating and managing cloud-native systems and microservice-based applications but is limited by the lack of high-quality datasets with diverse scenarios. Realistic workloads are the premise and basis of generating such AIOps datasets, with the session-based workload being one of the most typical examples. Due to privacy concerns, complexity, variety, and requirements for reasonable intervention, it is difficult to copy or generate such workloads directly, showing the importance of effective and intervenable workload simulation. In this paper, we formulate the task of workload simulation and propose a framework for Log-based Workload Simulation (LWS) in session-based systems. LWS extracts the workload specification including the user behavior abstraction based on agglomerative clustering as well as relational models and the intervenable workload intensity from session logs. Then LWS combines the user behavior abstraction with the workload intensity to generate simulated workloads. The experimental evaluation is performed on an open-source cloud-native application with both well-designed and public real-world workloads, showing that the simulated workload generated by LWS is effective and intervenable, which provides the foundation of generating high-quality AIOps datasets.

cs.SE↗

arXiv2023

DiGeo: Discriminative Geometry-Aware Learning for Generalized Few-Shot Object Detection

Generalized few-shot object detection aims to achieve precise detection on both base classes with abundant annotations and novel classes with limited training data. Existing approaches enhance few-shot generalization with the sacrifice of base-class performance, or maintain high precision in base-class detection with limited improvement in novel-class adaptation. In this paper, we point out the reason is insufficient Discriminative feature learning for all of the classes. As such, we propose a new training framework, DiGeo, to learn Geometry-aware features of inter-class separation and intra-class compactness. To guide the separation of feature clusters, we derive an offline simplex equiangular tight frame (ETF) classifier whose weights serve as class centers and are maximally and equally separated. To tighten the cluster for each class, we include adaptive class-specific margins into the classification loss and encourage the features close to the class centers. Experimental studies on two few-shot benchmark datasets (VOC, COCO) and one long-tail dataset (LVIS) demonstrate that, with a single model, our method can effectively improve generalization on novel classes without hurting the detection of base classes.

cs.CV↗

arXiv2020

Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices

Universal lesion detection from computed tomography (CT) slices is important for comprehensive disease screening. Since each lesion can locate in multiple adjacent slices, 3D context modeling is of great significance for developing automated lesion detection algorithms. In this work, we propose a Modified Pseudo-3D Feature Pyramid Network (MP3D FPN) that leverages depthwise separable convolutional filters and a group transform module (GTM) to efficiently extract 3D context enhanced 2D features for universal lesion detection in CT slices. To facilitate faster convergence, a novel 3D network pre-training method is derived using solely large-scale 2D object detection dataset in the natural image domain. We demonstrate that with the novel pre-training method, the proposed MP3D FPN achieves state-of-the-art detection performance on the DeepLesion dataset (3.48% absolute improvement in the sensitivity of FPs@0.5), significantly surpassing the baseline method by up to 6.06% (in MAP@0.5) which adopts 2D convolution for 3D context modeling. Moreover, the proposed 3D pre-trained weights can potentially be used to boost the performance of other 3D medical image analysis tasks.

cs.CV↗