arXiv subjects

Xiaorong Zhang

Publications and source records attributed to Xiaorong Zhang.

6 recordsLinked to original sources

arXiv2026

Collective Blinking of Upconversion Emission in Lanthanide-doped Nanocrystals

Fluorescence blinking, often regarded as a limitation for stable emitters, can enable super-resolution localization microscopy and serve as a versatile reporter of the photophysical states of quantum emitters and their interactions with local environment. However, conventional blinking emitters are typically single quantum systems with Stokes-shifted fluorescence, making them susceptible to autofluorescence background, weak signal, and irreversible photodegradation under prolonged excitation. In contrast, single lanthanide-doped upconversion nanocrystals are effectively background-free anti-Stokes emitters and demonstrate robust resistance to photodegradation, yet they are generally considered non-blinking owing to the presence of a large ensemble of uncorrelated emitting lanthanide ions within a single nanocrystal. Here we report the discovery and control of collective blinking in the upconversion luminescence of thousands of lanthanide ions within a single nanocrystal. The blinking exhibits on-off intensity ratio exceeding 10, persists for over 15 hours (over 10,000 cycles) without discernible photodegradation, and can be reversibly controlled by adjusting the excitation power. We elucidate a universal, activator-independent upconversion blinking mechanism, whereby a single quencher, stochastically generated via a cooperative multi-ion process, can intercept delocalized excitation energy within the Yb3+ sensitizer network and darken the whole nanocrystal. Benefiting from the high-contrast, long-term photostable blinking and background-free emission, we achieve robust super-resolution localization microscopy that resolves individual nanocrystals in aggregates with 1.2 nm precision. This work establishes a general strategy to realize and control collective blinking in photostable multi-emitter nanosystems, opening new opportunities in nanoscience, bioimaging, and quantum technologies.

physics.optics↗

arXiv2026

NeuroEdge: Real-Time Hand Gesture Recognition with High-Density EMG Using Deep Learning at the Edge

High-density electromyography (HD-EMG) has emerged as a powerful modality for decoding fine-grained neuromuscular activity, enabling real-time neural-machine interfaces (NMIs) for applications such as prosthetic control, rehabilitation, and augmented interaction. While deep learning approaches such as convolutional neural networks (CNNs)have demonstrated high classification accuracy for EMG-based gesture recognition, their deployment on embedded hardware remains a major challenge due to computational and memory constraints. This paper presents NeuroEdge, a real-time HD EMG-based NMI system that performs gesture recognition entirely on resource-constrained microcontrollers. The system features two custom-designed modules: the HD-EMG StreamBridge, a wireless communication interface that streams raw HD-EMG data from a Quattrocento amplifier to an ESP32 microcontroller; and the EdgeDL Inference Engine, a lightweight deep learning framework executing on a Sony Spresense microcontroller. A compact 1-dimensional CNN optimized for embedded inference processes, sliding windows of EMG data in real time. Data streaming and inference are pipelined and synchronized through an architecture that utilizes Direct Memory Access (DMA) for data transfer and Serial Peripheral Interface (SPI) burst communication between the ESP32 and Spresense, ensuring low-latency performance. Experimental results show that NeuroEdge achieves a real-time classification accuracy of 90% across seven hand gestures, with a total average latency of 83 ms using 192 channels of HD-EMG recorded from the forearm. Our system demonstrates the feasibility of deploying complex HD-EMG-based gesture recognition on microcontroller-based edge devices, bridging the gap between high-resolution biosignal acquisition and deep learning-based embedded inference for next-generation NMIs.

cs.LG↗

arXiv2023

Interaction-aware Traffic Prediction and Scenario-based Model Predictive Control for Autonomous Vehicles on Highways

This paper addresses the problem of traffic prediction and control of autonomous vehicles on highways. A modified Interacting Multiple Model Kalman filter algorithm is applied to predict the motion behavior of the traffic participants by considering their interactions. A scenario generation component is used to produce plausible scenarios of the vehicles based on the predicted information. A novel integrated decision-making and control system is proposed by applying a Scenario-based Model Predictive Control approach. The designed controller considers safety, driving comfort, and traffic rules. The recursive feasibility of the controller is guaranteed under the inclusion of the `worst case' as an additional scenario to obtain safe inputs. Finally, the proposed scheme is evaluated using the HighD dataset. Simulation results indicate that the vehicle performs safe maneuvers in different traffic situations under the designed control framework.

eess.SY↗

arXiv2022

Arbitrage-Free Implied Volatility Surface Generation with Variational Autoencoders

We propose a hybrid method for generating arbitrage-free implied volatility (IV) surfaces consistent with historical data by combining model-free Variational Autoencoders (VAEs) with continuous time stochastic differential equation (SDE) driven models. We focus on two classes of SDE models: regime switching models and Lévy additive processes. By projecting historical surfaces onto the space of SDE model parameters, we obtain a distribution on the parameter subspace faithful to the data on which we then train a VAE. Arbitrage-free IV surfaces are then generated by sampling from the posterior distribution on the latent space, decoding to obtain SDE model parameters, and finally mapping those parameters to IV surfaces. We further refine the VAE model by including conditional features and demonstrate its superior generative out-of-sample performance.

q-fin.MF↗

arXiv2021

A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?

Given an unsupervised outlier detection task, how should one select a detection algorithm as well as its hyperparameters (jointly called a model)? Unsupervised model selection is notoriously difficult, in the absence of hold-out validation data with ground-truth labels. Therefore, the problem is vastly understudied. In this work, we study the feasibility of employing internal model evaluation strategies for selecting a model for outlier detection. These so-called internal strategies solely rely on the input data (without labels) and the output (outlier scores) of the candidate models. We setup (and open-source) a large testbed with 39 detection tasks and 297 candidate models comprised of 8 detectors and various hyperparameter configurations. We evaluate 7 different strategies on their ability to discriminate between models w.r.t. detection performance, without using any labels. Our study reveals room for progress -- we find that none would be practically useful, as they select models only comparable to a state-of-the-art detector (with random configuration).

cs.LG↗

arXiv2019

Gate tunable giant anisotropic resistance in ultra-thin GaTe

In crystals, the duplication of atoms often follows different periodicity along different directions. It thus gives rise to the so called anisotropy, which is usually even more pronounced in two dimensional (2D) materials due to the absence of $\textbf{z}$ dimension. Indeed, in the emerging 2D materials, electrical anisotropy has been one of the focuses in recent experimental efforts. However, key understandings of the in-plane anisotropic resistance in low-symmetry 2D materials, as well as demonstrations of model devices taking advantage of it, have proven difficult. Here, we show that, in few-layered semiconducting GaTe, electrical conductivity along $\textbf{x}$ and $\textbf{y}$ directions of the 2D crystal can be gate tuned from a ratio of less than one order to as large as 10$^{3}$. This effect is further demonstrated to yield an anisotropic memory resistor behaviour in ultra-thin GaTe, when equipped with an architecture of van der Waals floating gate. Our findings of gate tunable giant anisotropic resistance (GAR) effect pave the way for potential applications in nano-electronics such as multifunctional directional memories in the 2D limit.

cond-mat.mes-hall↗