arXiv subjects

Xinwei Cao

Publications and source records attributed to Xinwei Cao.

5 recordsLinked to original sources

arXiv2026

Local Diagnostics of Continuous Normalizing Flow for Out-of-Distribution Detection

We address the problem of out-of-distribution (OOD) detection for target observations embedded in a subspace of the high dimensional data space. Using continuous normalizing flows (CNFs), we propose a Lagrangian sub-flow (LSF) framework designed to isolate and estimate the density for the relevant components in the representation and using the remaining components as context. Through experimentation with models for speech synthesis, we show that CNFs, similarly to other deep generative models (DGMs), are susceptible to the "likelihood paradox", where high likelihood is erroneously assigned to OOD samples. This is attributed to the inductive bias of DGMs that prioritize low-level structural details over high-level semantic coherence. To mitigate this phenomenon, we propose a number of geometric diagnostic signals based on the velocity field over the sub-flow trajectory. Based on these signals, we design metrics for the challenging task of zero-shot phoneme-level mispronunciation detection. Finally, we demonstrate the superiority of these metrics compared to likelihood-based methods on a real-world mispronunciation detection benchmark.

eess.AS↗

arXiv2025

Spiking Neural Network for Cross-Market Portfolio Optimization in Financial Markets: A Neuromorphic Computing Approach

Cross-market portfolio optimization has become increasingly complex with the globalization of financial markets and the growth of high-frequency, multi-dimensional datasets. Traditional artificial neural networks, while effective in certain portfolio management tasks, often incur substantial computational overhead and lack the temporal processing capabilities required for large-scale, multi-market data. This study investigates the application of Spiking Neural Networks (SNNs) for cross-market portfolio optimization, leveraging neuromorphic computing principles to process equity data from both the Indian (Nifty 500) and US (S&P 500) markets. A five-year dataset comprising approximately 1,250 trading days of daily stock prices was systematically collected via the Yahoo Finance API. The proposed framework integrates Leaky Integrate-andFire neuron dynamics with adaptive thresholding, spike-timingdependent plasticity, and lateral inhibition to enable event-driven processing of financial time series. Dimensionality reduction is achieved through hierarchical clustering, while populationbased spike encoding and multiple decoding strategies support robust portfolio construction under realistic trading constraints, including cardinality limits, transaction costs, and adaptive risk aversion. Experimental evaluation demonstrates that the SNN-based framework delivers superior risk-adjusted returns and reduced volatility compared to ANN benchmarks, while substantially improving computational efficiency. These findings highlight the promise of neuromorphic computation for scalable, efficient, and robust portfolio optimization across global financial markets.

q-fin.PM↗

arXiv2025

Segmentation-free Goodness of Pronunciation

Mispronunciation detection and diagnosis (MDD) is a significant part in modern computer-aided language learning (CALL) systems. Most systems implementing phoneme-level MDD through goodness of pronunciation (GOP), however, rely on pre-segmentation of speech into phonetic units. This limits the accuracy of these methods and the possibility to use modern CTC-based acoustic models for their evaluation. In this study, we first propose self-alignment GOP (GOP-SA) that enables the use of CTC-trained ASR models for MDD. Next, we define a more general segmentation-free method that takes all possible segmentations of the canonical transcription into account (GOP-SF). We give a theoretical account of our definition of GOP-SF, an implementation that solves potential numerical issues as well as a proper normalization which allows the use of acoustic models with different peakiness over time. We provide extensive experimental results on the CMU Kids and speechocean762 datasets comparing the different definitions of our methods, estimating the dependency of GOP-SF on the peakiness of the acoustic models and on the amount of context around the target phoneme. Finally, we compare our methods with recent studies over the speechocean762 data showing that the feature vectors derived from the proposed method achieve state-of-the-art results on phoneme-level pronunciation assessment.

eess.AS↗

arXiv2024

Nonvolatile spin field effect transistor based on VSi2N4/Sc2CO2 multiferroic heterostructure

In this study, we present first-principles calculations that introduce a novel nonvolatile spin field-effect transistor (Spin-FET) utilizing a van der Waals multiferroic heterostructure, specifically VSi2N4/Sc2CO2. We demonstrate that inverting the ferroelectric polarization in a Sc2CO2 monolayer can effectively modulate the electronic states of a VSi2N4 monolayer, enabling a transition from half-metal to half-semiconductor. This transition significantly alters the electronic transport properties. Furthermore, we construct a Spin-FET device based on this multiferroic heterostructure and observe that the VSi2N4/Sc2CO2-based Spin-FET exhibits exceptional all-electric-controlled performance. Notably, the inversion of the Sc2CO2 ferroelectric polarization yields a substantial on-off current ratio, approximately 650\%, under a minimal bias voltage of 0.02 V. Additionally, we identify a unique spatially-separated spin-polarized transport phenomenon, wherein pure spin-up electrons transport exclusively through VSi2N4, and spin-down electrons through Sc$_2$CO$_2$. Our findings suggest a promising pathway for developing low-energy-dissipation and nonvolatile FET devices.

cond-mat.mes-hall↗

arXiv2018

A Survey on Blockchain Technology and Its Potential Applications in Distributed Control and Cooperative Robots

As a disruptive technology, blockchain, particularly its original form of bitcoin as a type of digital currency, has attracted great attentions. The innovative distributed decision making and security mechanism lay the technical foundation for its success, making us consider to penetrate the power of blockchain technology to distributed control and cooperative robotics, in which the distributed and secure mechanism is also highly demanded. Actually, security and distributed communication have long been unsolved problems in the field of distributed control and cooperative robotics. It has been reported on the network failure and intruder attacks of distributed control and multi-robotic systems. Blockchain technology provides promise to remedy this situation thoroughly. This work is intended to create a global picture of blockchain technology on its working principle and key elements in the language of control and robotics, to provide a shortcut for beginners to step into this research field.

cs.CR↗