arXiv subjects

Rui Luo

Publications and source records attributed to Rui Luo.

At least 55 records · Page 3Linked to original sources

arXiv2025

Game-Theoretic Defenses for Robust Conformal Prediction Against Adversarial Attacks in Medical Imaging

Adversarial attacks pose significant threats to the reliability and safety of deep learning models, especially in critical domains such as medical imaging. This paper introduces a novel framework that integrates conformal prediction with game-theoretic defensive strategies to enhance model robustness against both known and unknown adversarial perturbations. We address three primary research questions: constructing valid and efficient conformal prediction sets under known attacks (RQ1), ensuring coverage under unknown attacks through conservative thresholding (RQ2), and determining optimal defensive strategies within a zero-sum game framework (RQ3). Our methodology involves training specialized defensive models against specific attack types and employing maximum and minimum classifiers to aggregate defenses effectively. Extensive experiments conducted on the MedMNIST datasets, including PathMNIST, OrganAMNIST, and TissueMNIST, demonstrate that our approach maintains high coverage guarantees while minimizing prediction set sizes. The game-theoretic analysis reveals that the optimal defensive strategy often converges to a singular robust model, outperforming uniform and simple strategies across all evaluated datasets. This work advances the state-of-the-art in uncertainty quantification and adversarial robustness, providing a reliable mechanism for deploying deep learning models in adversarial environments.

cs.LG↗

arXiv2025

Volume-Sorted Prediction Set: Efficient Conformal Prediction for Multi-Target Regression

We introduce Volume-Sorted Prediction Set (VSPS), a novel method for uncertainty quantification in multi-target regression that uses conditional normalizing flows with conformal calibration. This approach constructs flexible, non-convex predictive regions with guaranteed coverage probabilities, overcoming limitations of traditional methods. By learning a transformation where the conditional distribution of responses follows a known form, VSPS identifies dense regions in the original space using the Jacobian determinant. This enables the creation of prediction regions that adapt to the true underlying distribution, focusing on areas of high probability density. Experimental results demonstrate that VSPS produces smaller, more informative prediction regions while maintaining robust coverage guarantees, enhancing uncertainty modeling in complex, high-dimensional settings.

cs.LG↗

arXiv2025

Turbulent-like flows in quasi two-dimensional dense suspensions of motile colloids

Dense bacterial suspensions exhibit turbulent-like flows at low Reynolds numbers, driven by the activity of the microswimmers. In this study, we develop a model system to examine these dynamics using motile colloids that mimic bacterial locomotion. The colloids are powered by the Quincke instability, which causes them to spontaneously roll in a random-walk pattern when exposed to a square-wave electric field. We experimentally investigate the flow dynamics in dense suspensions of these Quincke random walkers under quasi two-dimensional conditions, where the particle size is comparable to the gap between the electrodes. Our results reveal an energy spectrum scaling at high wavenumbers as $ \sim k^{-4}$, which holds across a broad range of activity levels -- controlled by the field strength -- and particle concentrations. We observe that velocity time correlations decay within a single period of the square-wave field, yet an anti-correlation appears between successive field applications, indicative of a dynamic structural memory of the ensemble.

physics.flu-dyn↗

arXiv2025

The Host Galaxy of the Hyperactive Repeating FRB 20240114A: Behind a Galaxy Cluster

We report on the optical spectroscopic observations of the host galaxy of the hyperactive repeating fast radio burst, FRB 20240114A. The host galaxy is a dwarf galaxy at a redshift of $z=0.1306\pm0.0002$. With a rest-frame coverage of 4300-7900 Å, we have detected H$\rmα$, H$\rmβ$, [O III]$λλ$4959,5007, [N II]$λλ$6548,6583, and [S II]$λ$6716 emission lines. The emission line ratios suggest that the ionization in the host galaxy is dominated by star formation. The star formation rate (SFR) derived from the H$\rmα$ emission line is $(0.06 \pm 0.01) \ \rm{M_{\odot} \ yr^{-1}}$, and the SED fitting suggests the lower limit of the SFR(UV) is $0.09 \ \rm{M_{\odot} \ yr^{-1}}$. The stellar mass is $(\rm 4.0 \pm 1.8) \times 10^8 \ M_{\odot}$, making the specific star formation rate $\rm log \ sSFR(H\rm α) = -9.8 \pm 0.2 \ yr^{-1}$. The line ratios indicate an upper limit of a metallicity of $\rm 12+log_{10} ([O/H]) \sim 8.5$. As the nearest dwarf host galaxy with a repeating FRB, the activity of FRB 20240114A and the properties of this host galaxy closely resemble those of FRB 20121102A and FRB 20190520B. The H$\rmα$-traced dispersion measure (DM) provided by the ionized gas of the host galaxy has a moderate contribution of $\sim 200 \rm \ pc \ cm^{-3}$, assuming a warm ionized gas. We found that the distributions of the stellar mass versus SFR are significantly different between repeating and one-off FRBs, as determined by the MANOVA test with $p=0.0116$.

astro-ph.GA↗

arXiv2025

Detecting Structural Shifts in Multivariate Hawkes Processes with Fréchet Statistics

This paper proposes a new approach for change point detection in multivariate Hawkes processes using Fréchet statistic of a network. The method splits the point process into overlapping windows, estimates kernel matrices in each window, and reconstructs the signed Laplacians by treating the kernel matrices as the adjacency matrices of the causal network. We demonstrate the effectiveness of our method through experiments on both simulated and cryptocurrency datasets. Our results show that our method is capable of accurately detecting and characterizing changes in the causal structure of multivariate Hawkes processes, and may have potential applications in fields such as finance and neuroscience. The proposed method is an extension of previous work on Fréchet statistics in point process settings and represents an important contribution to the field of change point detection in multivariate point processes.

stat.ML↗

arXiv2025

Improved limits on the spin- and velocity-dependent exotic interaction in the micrometer range

Searching for the exotic interactions beyond the Standard Model of particle physics may solve some of the current puzzles in physics. Here the authors experimentally explore a spin- and velocity-dependent exotic interaction between the nucleons in a gold sphere and the electrons in a spin source in the micrometer range. The microfabricated spin source provides periodically varying spin density of electrons, resulting in a periodic exotic field. A cantilever glued with a gold sphere is used to measured the force acting on the gold sphere by the spin source. The spin source is driven to oscillate, and then the imaginary part of the signal is extracted at the 10th harmonic of the oscillation frequency, which effectively separates the exotic interaction from the spurious forces commonly present in such short-range measurements. No signal of the exotic interaction is observed, then new limits on the coupling constant are set in an interaction range below 10 $μ$m, with $f_{4+5} \le 2.2\times 10^{-9}$ at 2.1 $μ$m.

hep-ex↗

arXiv2025

Enhancing Trustworthiness of Graph Neural Networks with Rank-Based Conformal Training

Graph Neural Networks (GNNs) has been widely used in a variety of fields because of their great potential in representing graph-structured data. However, lacking of rigorous uncertainty estimations limits their application in high-stakes. Conformal Prediction (CP) can produce statistically guaranteed uncertainty estimates by using the classifier's probability estimates to obtain prediction sets, which contains the true class with a user-specified probability. In this paper, we propose a Rank-based CP during training framework to GNNs (RCP-GNN) for reliable uncertainty estimates to enhance the trustworthiness of GNNs in the node classification scenario. By exploiting rank information of the classifier's outcome, prediction sets with desired coverage rate can be efficiently constructed. The strategy of CP during training with differentiable rank-based conformity loss function is further explored to adapt prediction sets according to network topology information. In this way, the composition of prediction sets can be guided by the goal of jointly reducing inefficiency and probability estimation errors. Extensive experiments on several real-world datasets show that our model achieves any pre-defined target marginal coverage while significantly reducing the inefficiency compared with state-of-the-art methods.

cs.LG↗

arXiv2025

Conformal Thresholded Intervals for Efficient Regression

This paper introduces Conformal Thresholded Intervals (CTI), a novel conformal regression method that aims to produce the smallest possible prediction set with guaranteed coverage. Unlike existing methods that rely on nested conformal frameworks and full conditional distribution estimation, CTI estimates the conditional probability density for a new response to fall into each interquantile interval using off-the-shelf multi-output quantile regression. By leveraging the inverse relationship between interval length and probability density, CTI constructs prediction sets by thresholding the estimated conditional interquantile intervals based on their length. The optimal threshold is determined using a calibration set to ensure marginal coverage, effectively balancing the trade-off between prediction set size and coverage. CTI's approach is computationally efficient and avoids the complexity of estimating the full conditional distribution. The method is theoretically grounded, with provable guarantees for marginal coverage and achieving the smallest prediction size given by Neyman-Pearson . Extensive experimental results demonstrate that CTI achieves superior performance compared to state-of-the-art conformal regression methods across various datasets, consistently producing smaller prediction sets while maintaining the desired coverage level. The proposed method offers a simple yet effective solution for reliable uncertainty quantification in regression tasks, making it an attractive choice for practitioners seeking accurate and efficient conformal prediction.

cs.LG↗

arXiv2025

Conformalized Interval Arithmetic with Symmetric Calibration

Uncertainty quantification is essential in decision-making, especially when joint distributions of random variables are involved. While conformal prediction provides distribution-free prediction sets with valid coverage guarantees, it traditionally focuses on single predictions. This paper introduces novel conformal prediction methods for estimating the sum or average of unknown labels over specific index sets. We develop conformal prediction intervals for single target to the prediction interval for sum of multiple targets. Under permutation invariant assumptions, we prove the validity of our proposed method. We also apply our algorithms on class average estimation and path cost prediction tasks, and we show that our method outperforms existing conformalized approaches as well as non-conformal approaches.

cs.LG↗

arXiv2024

Structure-Aware Stylized Image Synthesis for Robust Medical Image Segmentation

Accurate medical image segmentation is essential for effective diagnosis and treatment planning but is often challenged by domain shifts caused by variations in imaging devices, acquisition conditions, and patient-specific attributes. Traditional domain generalization methods typically require inclusion of parts of the test domain within the training set, which is not always feasible in clinical settings with limited diverse data. Additionally, although diffusion models have demonstrated strong capabilities in image generation and style transfer, they often fail to preserve the critical structural information necessary for precise medical analysis. To address these issues, we propose a novel medical image segmentation method that combines diffusion models and Structure-Preserving Network for structure-aware one-shot image stylization. Our approach effectively mitigates domain shifts by transforming images from various sources into a consistent style while maintaining the location, size, and shape of lesions. This ensures robust and accurate segmentation even when the target domain is absent from the training data. Experimental evaluations on colonoscopy polyp segmentation and skin lesion segmentation datasets show that our method enhances the robustness and accuracy of segmentation models, achieving superior performance metrics compared to baseline models without style transfer. This structure-aware stylization framework offers a practical solution for improving medical image segmentation across diverse domains, facilitating more reliable clinical diagnoses.

eess.IV↗

arXiv2024

Adaptive Conformal Inference by Particle Filtering under Hidden Markov Models

Conformal inference is a statistical method used to construct prediction sets for point predictors, providing reliable uncertainty quantification with probability guarantees. This method utilizes historical labeled data to estimate the conformity or nonconformity between predictions and true labels. However, conducting conformal inference for hidden states under hidden Markov models (HMMs) presents a significant challenge, as the hidden state data is unavailable, resulting in the absence of a true label set to serve as a conformal calibration set. This paper proposes an adaptive conformal inference framework that leverages a particle filtering approach to address this issue. Rather than directly focusing on the unobservable hidden state, we innovatively use weighted particles as an approximation of the actual posterior distribution of the hidden state. Our goal is to produce prediction sets that encompass these particles to achieve a specific aggregate weight sum, referred to as the aggregated coverage level. The proposed framework can adapt online to the time-varying distribution of data and achieve the defined marginal aggregated coverage level in both one-step and multi-step inference over the long term. We verify the effectiveness of this approach through a real-time target localization simulation study.

cs.LG↗

arXiv2024

User-customizable Shared Control for Robot Teleoperation via Virtual Reality

Shared control can ease and enhance a human operator's ability to teleoperate robots, particularly for intricate tasks demanding fine control over multiple degrees of freedom. However, the arbitration process dictating how much autonomous assistance to administer in shared control can confuse novice operators and impede their understanding of the robot's behavior. To overcome these adverse side-effects, we propose a novel formulation of shared control that enables operators to tailor the arbitration to their unique capabilities and preferences. Unlike prior approaches to customizable shared control where users could indirectly modify the latent parameters of the arbitration function by issuing a feedback command, we instead make these parameters observable and directly editable via a virtual reality (VR) interface. We present our user-customizable shared control method for a teleoperation task in SE(3), known as the buzz wire game. A user study is conducted with participants teleoperating a robotic arm in VR to complete the game. The experiment spanned two weeks per subject to investigate longitudinal trends. Our findings reveal that users allowed to interactively tune the arbitration parameters across trials generalize well to adaptations in the task, exhibiting improvements in precision and fluency over direct teleoperation and conventional shared control.

cs.RO↗

arXiv2024

Entropy Reweighted Conformal Classification

Conformal Prediction (CP) is a powerful framework for constructing prediction sets with guaranteed coverage. However, recent studies have shown that integrating confidence calibration with CP can lead to a degradation in efficiency. In this paper, We propose an adaptive approach that considers the classifier's uncertainty and employs entropy-based reweighting to enhance the efficiency of prediction sets for conformal classification. Our experimental results demonstrate that this method significantly improves efficiency.

cs.LG↗

arXiv2024

Trustworthy Classification through Rank-Based Conformal Prediction Sets

Machine learning classification tasks often benefit from predicting a set of possible labels with confidence scores to capture uncertainty. However, existing methods struggle with the high-dimensional nature of the data and the lack of well-calibrated probabilities from modern classification models. We propose a novel conformal prediction method that employs a rank-based score function suitable for classification models that predict the order of labels correctly, even if not well-calibrated. Our approach constructs prediction sets that achieve the desired coverage rate while managing their size. We provide a theoretical analysis of the expected size of the conformal prediction sets based on the rank distribution of the underlying classifier. Through extensive experiments, we demonstrate that our method outperforms existing techniques on various datasets, providing reliable uncertainty quantification. Our contributions include a novel conformal prediction method, theoretical analysis, and empirical evaluation. This work advances the practical deployment of machine learning systems by enabling reliable uncertainty quantification.

cs.LG↗

arXiv2024

Spatial distribution of C4H and c-C3H2 in cold molecular cores

C$_4$H and $c$-C$_3$H$_2$, as unsaturated hydrocarbon molecules, are important for forming large organic molecules in the interstellar medium. We present mapping observations of C$_4$H ($N$=9$-8$) lines, $c$-C$_3$H$_2$ ($J_{Ka,Kb}$=2$_{1,2}$-1$_{0,1}$) %at 85338.894 MHz and H$^{13}$CO$^+$ ($J$=1$-0$) %at 86754.2884 MHz toward 19 nearby cold molecular cores in the Milky Way with the IRAM 30m telescope. C$_4$H 9--8 was detected in 13 sources, while $c$-C$_3$H$_2$ was detected in 18 sources. The widely existing C$_4$H and $c$-C$_3$H$_2$ molecules in cold cores provide material to form large organic molecules. Different spatial distributions between C$_4$H 9--8 and $c$-C$_3$H$_2$ 2--1 were found. The relative abundances of these three molecules were obtained under the assumption of local thermodynamic equilibrium conditions with a fixed excitation temperature. The abundance ratio of C$_4$H to $c$-C$_3$H$_2$ ranged from 0.34 $\pm$ 0.09 in G032.93+02 to 4.65 $\pm$ 0.50 in G008.67+22. A weak correlation between C$_4$H/H$^{13}$CO$^+$ and $c$-C$_3$H$_2$/H$^{13}$CO$^+$ abundance ratios was found, with a correlation coefficient of 0.46, which indicates that there is no tight astrochemical connection between C$_4$H and $c$-C$_3$H$_2$ molecules.

astro-ph.GA↗

arXiv2024

Scintillation velocity and arc observations of FRB 20201124A

We present the scintillation velocity measurements of FRB~20201124A from the FAST observations, which reveal an annual variation. This annual variation is further supported by changes detected in the scintillation arc as observed from the secondary spectrum. We attribute the annual velocity variation to the presence of a moderately anisotropic scattering screen located at a distance of 0.4$\pm$0.1~kpc from Earth. Our results prove that the scintillation of this FRB is mainly caused by material close to Earth on a Galactic scale. However, scintillation observations of other FRBs may expose their surrounding environment or uncover possible orbital motion if scintillation is caused by materials in their host galaxy.

astro-ph.HE↗

arXiv2024

Dense Outflowing Molecular Gas in Massive Star-forming Regions

Dense outflowing gas, traced by transitions of molecules with large dipole moment, is important for understanding mass loss and feedback of massive star formation. HCN 3-2 and HCO$^+$ 3-2 are good tracers of dense outflowing molecular gas, which are closely related to active star formation. In this study, we present on-the-fly (OTF) mapping observations of HCN 3-2 and HCO$^+$ 3-2 toward a sample of 33 massive star-forming regions using the 10-m Submillimeter Telescope (SMT). With the spatial distribution of line wings of HCO$^+$ 3-2 and HCN 3-2, outflows are detected in 25 sources, resulting in a detection rate of 76$\%$. The optically thin H$^{13}$CN and H$^{13}$CO$^+$ 3-2 lines are used to identify line wings as outflows and estimate core mass. The mass $M_{out}$, momentum $P_{out}$, kinetic energy $E_{K}$, force $F_{out}$ and mass loss rate $\dot M_{out}$ of outflow and core mass, are obtained for each source. A sublinear tight correlation is found between the mass of dense molecular outflow and core mass, with an index of $\sim$ 0.8 and a correlation coefficient of 0.88.

astro-ph.GA↗

arXiv2024

Conformal Load Prediction with Transductive Graph Autoencoders

Predicting edge weights on graphs has various applications, from transportation systems to social networks. This paper describes a Graph Neural Network (GNN) approach for edge weight prediction with guaranteed coverage. We leverage conformal prediction to calibrate the GNN outputs and produce valid prediction intervals. We handle data heteroscedasticity through error reweighting and Conformalized Quantile Regression (CQR). We compare the performance of our method against baseline techniques on real-world transportation datasets. Our approach has better coverage and efficiency than all baselines and showcases robustness and adaptability.

cs.LG↗