arXiv subjects

Xing-Yu Chen

Publications and source records attributed to Xing-Yu Chen.

6 recordsLinked to original sources

arXiv2025

Methods for Detecting Gravitational Waves from mini-Extreme-Mass-Ratio Inspirals II: A Spectral-Leakage-Aware Framework

Mini-Extreme-Mass-Ratio Inspirals (mini-EMRIs), comprising a sub-solar exotic compact object (such as a primordial black hole or boson star) orbiting a much heavier stellar-origin or exotic compact object, represent key targets for ground-based gravitational-wave detectors to probe the early universe and the nature of dark matter. However, detecting such systems, which could spend hours to years in LIGO, Virgo and KAGRA data, poses a computational challenge to standard matched-filtering methods. However, semi-coherent methods are constrained by the quasi-monochromatic assumption, which restricts the coherence time to avoid spectral leakage caused by frequency evolution. In this work, we extend the development of our method, $\Sigma$Track, to the regime in which the quasi-monochromatic approximation is relaxed, in two ways. First, we establish an analytical model for the spectral leakage, extending the validity of conventional analyses beyond the quasi-monochromatic regime. Second, we propose the $\Sigma R$ statistic -- a novel detection metric formed by a weighted summation of power ratios -- which effectively recovers the signal energy dispersed across adjacent frequency bins. Building on this framework, we further introduce an innovative frequency-layered search strategy that dynamically optimizes the coherence time across the observation band. We benchmark our method against a globally optimized Hough transform pipeline using a fiducial mini-EMRI signal from a binary with masses $(1.5, 10^{-5})\,M_\odot$. The results demonstrate that our framework achieves an order-of-magnitude enhancement in the effective detection volume, significantly expanding the horizon for discovering mini-EMRIs and sub-solar exotic compact objects with ground-based gravitational wave detectors. This approach can be similarly applied to EMRI searches for future space-based gravitational wave detectors.

gr-qc↗

arXiv2024

Irrelevance of 1H composition to the superconductivity in the infinite-layer nickelates: judging from the MeV energy scale

The discovery of the superconductivity in the infinite-layer nickelates, as topotactically reduced from their respective perovskite percussors via co-annealing with CaH2, extends the understanding in superconductivity. Nevertheless, whether the incorporated 1H composition is critical to the infinite-layer superconductivity recently arouses considerable debates, while the central challenge lies in the quantification of 1H that is easily interfered by the conventional electron or orbital associated processes. Herein, we demonstrate the irrelevance between the superconductivity in the infinite-layer nickelates and their incorporated 1H composition, assisted by nuclear reaction analysis (NRA) and heavy ion energy recoil detection analysis (HIERDA) based on the nuclear interactions at MeV energy scale. These approaches completely overwhelm the conventional interferes, such as ionization, activation and chemical bonds, and achieves the 1H quantification within superconducting La0.8Sr0.2NiO2 (or Nd0.8Sr0.2NiO2). A large diversity of 1H composition far beyond the previously expected critical dome was observed, while their TC were not changed significantly. Furthermore, the superconductivity was demonstrated to be achievable for La0.8Sr0.2NiO2 reduced by Al without any hydrogen associated process, while the superconducting properties for the CaH2 reduced La0.8Sr0.2NiO2 is rather stable after long term exposure in air, despite the high volatility of 1H within oxides. All these results indicate that the 1H incorporation composition is not critical to the superconductivity of the infinite-layer nickelates.

cond-mat.supr-con↗

arXiv2024

Design of a Teleoperated Robotic Bronchoscopy System for Peripheral Pulmonary Lesion Biopsy

Bronchoscopy with transbronchial biopsy is a minimally invasive and effective method for early lung cancer intervention. Robot-assisted bronchoscopy offers improved precision, spatial flexibility, and reduced risk of cross-infection. This paper introduces a novel teleoperated robotic bronchoscopy system and a three-stage procedure designed for robot-assisted bronchoscopy. The robotic mechanism enables a clinical practice similar to traditional bronchoscopy, augmented by the control of a novel variable stiffness catheter for tissue sampling. A rapid prototype of the robotic system has been fully developed and validated through in-vivo experiments. The results demonstrate the potential of the proposed robotic bronchoscopy system and variable stiffness catheter in enhancing accuracy and safety during bronchoscopy procedures.

physics.med-ph↗

arXiv2022

SLAN: Self-Locator Aided Network for Cross-Modal Understanding

Learning fine-grained interplay between vision and language allows to a more accurate understanding for VisionLanguage tasks. However, it remains challenging to extract key image regions according to the texts for semantic alignments. Most existing works are either limited by textagnostic and redundant regions obtained with the frozen detectors, or failing to scale further due to its heavy reliance on scarce grounding (gold) data to pre-train detectors. To solve these problems, we propose Self-Locator Aided Network (SLAN) for cross-modal understanding tasks without any extra gold data. SLAN consists of a region filter and a region adaptor to localize regions of interest conditioned on different texts. By aggregating cross-modal information, the region filter selects key regions and the region adaptor updates their coordinates with text guidance. With detailed region-word alignments, SLAN can be easily generalized to many downstream tasks. It achieves fairly competitive results on five cross-modal understanding tasks (e.g., 85.7% and 69.2% on COCO image-to-text and text-to-image retrieval, surpassing previous SOTA methods). SLAN also demonstrates strong zero-shot and fine-tuned transferability to two localization tasks.

cs.CV↗

arXiv2022

Supervised and Self-supervised Pretraining Based COVID-19 Detection Using Acoustic Breathing/Cough/Speech Signals

In this work, we propose a bi-directional long short-term memory (BiLSTM) network based COVID-19 detection method using breath/speech/cough signals. By using the acoustic signals to train the network, respectively, we can build individual models for three tasks, whose parameters are averaged to obtain an average model, which is then used as the initialization for the BiLSTM model training of each task. This initialization method can significantly improve the performance on the three tasks, which surpasses the official baseline results. Besides, we also utilize a public pre-trained model wav2vec2.0 and pre-train it using the official DiCOVA datasets. This wav2vec2.0 model is utilized to extract high-level features of the sound as the model input to replace conventional mel-frequency cepstral coefficients (MFCC) features. Experimental results reveal that using high-level features together with MFCC features can improve the performance. To further improve the performance, we also deploy some preprocessing techniques like silent segment removal, amplitude normalization and time-frequency mask. The proposed detection model is evaluated on the DiCOVA dataset and results show that our method achieves an area under curve (AUC) score of 88.44% on blind test in the fusion track.

eess.AS↗

arXiv2021

Exploration of interacting dynamical dark energy model with interaction term including the equation-of-state parameter: alleviation of the $H_{0}$ tension

We explore a scenario of interacting dynamical dark energy model with the interaction term $Q$ including the varying equation-of-state parameter $w$. Using the data combination of the cosmic microwave background, the baryon acoustic oscillation, and the type Ia supernovae, to global fit the interacting dynamical dark energy model, we find that adding a factor of the varying $w$ in the function of $Q$ can change correlations between the coupling constant $β$ and other parameters, and then has a huge impact on the fitting result of $β$. In this model, the fitting value of $H_{0}$ is lower at the $3.54 σ$ level than the direct measurement value of $H_{0}$ . Comparing to the case of interacting dynamical dark energy model with $Q$ excluding $w$, the model with $Q$ including the constant $w$ is more favored by the current mainstream observation. To obtain higher fitting values of $H_{0}$ and narrow the discrepancy of $H_{0}$ between different observations, additional parameters including the effective number of relativistic species, the total neutrino mass, and massive sterile neutrinos are considered in the interacting dynamical dark energy cosmology. We find that the $H_{0}$ tension can be further reduced in these models, but is still at the about $3 σ$ level.

gr-qc↗