arXiv subjects

Qi Lai

Publications and source records attributed to Qi Lai.

8 recordsLinked to original sources

arXiv2026

Testing the wormhole echo hypothesis for GW231123

The short-duration gravitational-wave (GW) event GW231123 has inferred component masses in the pair-instability mass gap and exhibits a burst-like morphology with no clearly inspiral, making it an interesting target for tests beyond the standard binary black hole (BBH) interpretation. In this work, motivated by its phenomenological similarity to GW190521, we test whether GW231123 is compatible with a wormhole-echo scenario by modeling a leading echo pulse with a well-motivated phenomenological sine-Gaussian wavepacket. We perform Bayesian model comparison against a BBH baseline described by the IMRPhenomXPHM-SpinTaylor waveform, and obtain the Bayes factor ratio $\ln B^{\rm Echo}_{\rm BBH} = 1.87$, corresponding to weak-to-moderate support for the echo hypothesis. In our previous analysis for GW190521 within the same overall framework, we found $\ln B^{\rm Echo}_{\rm BBH} \approx -2.9$, implying a shift of $\Delta \ln B \approx 4.8$ between the two events. This sign change indicates that GW231123 is more compatible with a single-pulse echo description than GW190521.

gr-qc↗

arXiv2026

DeepInflation: an AI agent for research and model discovery of inflation

We present DeepInflation, an AI agent designed for research and model discovery in inflationary cosmology. Built upon a multi-agent architecture, DeepInflation integrates Large Language Models (LLMs) with a symbolic regression (SR) engine and a retrieval-augmented generation (RAG) knowledge base. This framework enables the agent to automatically explore and verify the vast landscape of inflationary potentials while grounding its outputs in established theoretical literature. We demonstrate that DeepInflation can successfully discover simple and viable single-field slow-roll inflationary potentials consistent with the latest observations (with the ACT DR6 results taken as an example) or any given $n_s$ and $r$, and provide accurate theoretical context for obscure inflationary scenarios. DeepInflation serves as a prototype for a new generation of autonomous scientific discovery engines in cosmology, which enables researchers and non-experts alike to explore the inflationary landscape using natural language. This agent is available at https://github.com/pengzy-cosmo/DeepInflation.

astro-ph.CO↗

arXiv2025

A Lightweight Multi-Scale Attention Framework for Real-Time Spinal Endoscopic Instance Segmentation

Real-time instance segmentation for spinal endoscopy is important for identifying and protecting critical anatomy during surgery, but it is difficult because of the narrow field of view, specular highlights, smoke/bleeding, unclear boundaries, and large scale changes. Deployment is also constrained by limited surgical hardware, so the model must balance accuracy and speed and remain stable under small-batch (even batch-1) training. We propose LMSF-A, a lightweight multi-scale attention framework co-designed across backbone, neck, and head. The backbone uses a C2f-Pro module that combines RepViT-style re-parameterized convolution (RVB) with efficient multi-scale attention (EMA), enabling multi-branch training while collapsing into a single fast path for inference. The neck improves cross-scale consistency and boundary detail using Scale-Sequence Feature Fusion (SSFF) and Triple Feature Encoding (TFE), which strengthens high-resolution features. The head adopts a Lightweight Multi-task Shared Head (LMSH) with shared convolutions and GroupNorm to reduce parameters and support batch-1 stability. We also release the clinically reviewed PELD dataset (61 patients, 610 images) with instance masks for adipose tissue, bone, ligamentum flavum, and nerve. Experiments show that LMSF-A is highly competitive (or even better than) in all evaluation metrics and much lighter than most instance segmentation methods requiring only 1.8M parameters and 8.8 GFLOPs, and it generalizes well to a public teeth benchmark. Code and dataset: https://github.com/hhwmortal/PELD-Instance-segmentation.

cs.CV↗

arXiv2025

Is GW190521 a gravitational wave echo of wormhole remnant from another universe?

A particularly compelling aspect of the GW190521 event detected by the LIGO--Virgo--KAGRA (LVK) collaboration is that it has an extremely short duration, and lacks a clearly identifiable inspiral phase usually observed in the binary black holes (BBHs) coalescence. In this work, we hypothesize that GW190521 might represent a single, isolated gravitational wave (GW) echo pulse from the wormhole, which is the postmerger remnant of BBHs in another universe and connected to our universe through a throat. The ringdown signal after BBHs merged in another universe can pass through the throat of wormhole and be detected in our universe as a short-duration echo pulse. Our analysis results indicate that our model yields a network signal-to-noise ratio comparable to that of the standard BBHs merger model reported by the LVK collaboration. For GW190521, Bayesian model selection yields $\ln \mathcal{B}^{\text{Echo}}_{\text{BBH}} \simeq -2.9$, indicating that the data favor the BBH hypothesis over our echo-for-wormhole model.

gr-qc↗

arXiv2024

Weakly-supervised Semantic Segmentation via Dual-stream Contrastive Learning of Cross-image Contextual Information

Weakly supervised semantic segmentation (WSSS) aims at learning a semantic segmentation model with only image-level tags. Despite intensive research on deep learning approaches over a decade, there is still a significant performance gap between WSSS and full semantic segmentation. Most current WSSS methods always focus on a limited single image (pixel-wise) information while ignoring the valuable inter-image (semantic-wise) information. From this perspective, a novel end-to-end WSSS framework called DSCNet is developed along with two innovations: i) pixel-wise group contrast and semantic-wise graph contrast are proposed and introduced into the WSSS framework; ii) a novel dual-stream contrastive learning (DSCL) mechanism is designed to jointly handle pixel-wise and semantic-wise context information for better WSSS performance. Specifically, the pixel-wise group contrast learning (PGCL) and semantic-wise graph contrast learning (SGCL) tasks form a more comprehensive solution. Extensive experiments on PASCAL VOC and MS COCO benchmarks verify the superiority of DSCNet over SOTA approaches and baseline models.

cs.CV↗

arXiv2024

Boosting Few-Shot Semantic Segmentation Via Segment Anything Model

In semantic segmentation, accurate prediction masks are crucial for downstream tasks such as medical image analysis and image editing. Due to the lack of annotated data, few-shot semantic segmentation (FSS) performs poorly in predicting masks with precise contours. Recently, we have noticed that the large foundation model segment anything model (SAM) performs well in processing detailed features. Inspired by SAM, we propose FSS-SAM to boost FSS methods by addressing the issue of inaccurate contour. The FSS-SAM is training-free. It works as a post-processing tool for any FSS methods and can improve the accuracy of predicted masks. Specifically, we use predicted masks from FSS methods to generate prompts and then use SAM to predict new masks. To avoid predicting wrong masks with SAM, we propose a prediction result selection (PRS) algorithm. The algorithm can remarkably decrease wrong predictions. Experiment results on public datasets show that our method is superior to base FSS methods in both quantitative and qualitative aspects.

cs.CV↗

arXiv2023

Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) with Diverse Inter-Correlations and its application to medical image classification

described by multiple instances (e.g., image patches) and simultaneously associated with multiple labels. Existing MIML methods are useful in many applications but most of which suffer from relatively low accuracy and training efficiency due to several issues: i) the inter-label correlations(i.e., the probabilistic correlations between the multiple labels corresponding to an object) are neglected; ii) the inter-instance correlations (i.e., the probabilistic correlations of different instances in predicting the object label) cannot be learned directly (or jointly) with other types of correlations due to the missing instance labels; iii) diverse inter-correlations (e.g., inter-label correlations, inter-instance correlations) can only be learned in multiple stages. To resolve these issues, a new single-stage framework called broad multi-instance multi-label learning (BMIML) is proposed. In BMIML, there are three innovative modules: i) an auto-weighted label enhancement learning (AWLEL) based on broad learning system (BLS) is designed, which simultaneously and efficiently captures the inter-label correlations while traditional BLS cannot; ii) A specific MIML neural network called scalable multi-instance probabilistic regression (SMIPR) is constructed to effectively estimate the inter-instance correlations using the object label only, which can provide additional probabilistic information for learning; iii) Finally, an interactive decision optimization (IDO) is designed to combine and optimize the results from AWLEL and SMIPR and form a single-stage framework. Experiments show that BMIML is highly competitive to (or even better than) existing methods in accuracy and much faster than most MIML methods even for large medical image data sets (> 90K images).

cs.CV↗

arXiv2023

Towards Precise Weakly Supervised Object Detection via Interactive Contrastive Learning of Context Information

Weakly supervised object detection (WSOD) aims at learning precise object detectors with only image-level tags. In spite of intensive research on deep learning (DL) approaches over the past few years, there is still a significant performance gap between WSOD and fully supervised object detection. In fact, most existing WSOD methods only consider the visual appearance of each region proposal but ignore employing the useful context information in the image. To this end, this paper proposes an interactive end-to-end WSDO framework called JLWSOD with two innovations: i) two types of WSOD-specific context information (i.e., instance-wise correlation andsemantic-wise correlation) are proposed and introduced into WSOD framework; ii) an interactive graph contrastive learning (iGCL) mechanism is designed to jointly optimize the visual appearance and context information for better WSOD performance. Specifically, the iGCL mechanism takes full advantage of the complementary interpretations of the WSOD, namely instance-wise detection and semantic-wise prediction tasks, forming a more comprehensive solution. Extensive experiments on the widely used PASCAL VOC and MS COCO benchmarks verify the superiority of JLWSOD over alternative state-of-the-art approaches and baseline models (improvement of 3.6%~23.3% on mAP and 3.4%~19.7% on CorLoc, respectively).

cs.CV↗