arXiv subjects

Xiaotong Guo

Publications and source records attributed to Xiaotong Guo.

At least 19 recordsLinked to original sources

arXiv2026

A Mapping Sheath with Thermally Drawn Multi-Electrode Basket for Cardiac Electrophysiological Recording and Ablation Catheter Delivery

Cardiac arrhythmias, particularly atrial fibrillation, represent a major cardiovascular health burden and underscore the need for efficient and integrated strategies for electrical mapping and targeted therapy. Cardiac electrophysiology procedures depend on accurate identification of arrhythmogenic substrates followed by timely catheter ablation, but conventional diagnostic and therapeutic devices remain separate, often requiring repeated catheter exchanges and multiple access routes. Here, we report an adaptable strategy for functionalizing hollow-core sheaths with EP mapping capabilities, integrating multielectrode recording and ablation catheter delivery within a single compact platform. The device leverages thermal drawing to enable complex geometric fabrication, miniaturization, rapid prototyping, and scalable manufacturing of ultrathin electrode splines arranged circumferentially at the distal end to form an adjustable basket. The mapping sheath exhibited mechanical and electrophysiological properties suitable for intracardiac navigation and electrogram recording in bench-top evaluations, an in vitro left atrial phantom study, and ex vivo Langendorff-perfused porcine heart testing. In vivo porcine studies further demonstrated translational feasibility through vascular introduction, fluoroscopic visualization, intracardiac deployment, tissue contact, electrogram acquisition, and reconstruction of voltage and activation maps. These results support the development of intracardiac platforms with an adapted manufacturing approach, potentially guiding advances in agile cardiac mapping and ablation.

physics.med-ph↗

arXiv2026

AlphaOPT: Formulating Optimization Programs with Self-Improving LLM Experience Library

Optimization modeling underlies critical decision-making across industries, yet remains difficult to automate: natural-language problem descriptions must be translated into precise mathematical formulations and executable solver code. Existing LLM-based approaches typically rely on brittle prompting or costly retraining, both of which offer limited generalization. Recent work suggests that large models can improve via experience reuse, but how to systematically acquire, refine, and reuse such experience in structurally constrained settings remains unclear. We present \textbf{AlphaOPT}, a self-improving experience library that enables LLMs to learn optimization modeling knowledge from limited supervision, including answer-only feedback without gold-standard programs, annotated reasoning traces, or parameter updates. AlphaOPT operates in a continual two-phase cycle: a \emph{Library Learning} phase that extracts solver-verified, structured insights from failed attempts, and a \emph{Library Evolution} phase that refines the applicability of stored insights based on aggregate evidence across tasks. This design allows the model to accumulate reusable modeling principles, improve transfer across problem instances, and maintain bounded library growth over time. Evaluated on multiple optimization benchmarks, AlphaOPT steadily improves as more training data become available (65\% $\rightarrow$ 72\% from 100 to 300 training items) and outperforms the strongest baseline by 9.1\% and 8.2\% on two out-of-distribution datasets. These results demonstrate that structured experience learning, grounded in solver feedback, provides a practical alternative to retraining for complex reasoning tasks requiring precise formulation and execution. All code and data are available at: https://github.com/Minw913/AlphaOPT.

cs.AI↗

arXiv2026

FrontierOR: Benchmarking LLMs' Capacity for Efficient Algorithm Design in Large-Scale Optimization

Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimization problems often require a harder capability: designing scalable algorithms that exploit problem structure and outperform direct formulation-and-solve baselines. Existing benchmarks are limited to small or simplified examples far below real-world scale and complexity. We introduce FrontierOR, among the first benchmarks to systematically evaluate LLM-based efficient algorithm design for realistic large-scale optimization problems. FrontierOR includes 180 tasks derived from methodologically diverse papers published in top-tier operations research venues, each with standardized instances and a hidden, expert-verified evaluation suite. We evaluate seven LLMs spanning frontier, cost-effective, and open-source models both in one-shot and test-time evolution settings. The results reveal that frontier models still struggle to move from executable formulations to efficient optimization algorithms: the strongest one-shot model outperforms Gurobi in only 31% of cases in both solution quality and computational efficiency, and even strong coding agents with test-time evolution achieve only 50% on selected hard tasks. FrontierOR establishes a practical evaluation platform for LLM-based optimization algorithm design, which enables future LLMs and agents to be systematically tested on whether they can move beyond correct formulation toward a feasible, high-quality, and efficient algorithm. Code and data are publicly released at https://github.com/Minw913/FrontierOR.

cs.AI↗

arXiv2026

Characterizing the origins of gamma-ray variability of the jetted active galactic nuclei observed with the Fermi-LAT

We conducted an analysis of gamma-ray variability in a large sample of jetted active galactic nuclei (AGNs) by utilizing archival Fermi-LAT light curves and applying damped random walk modeling to obtain variability amplitude. Our primary findings are summarized as follows: (1) The mean variability damping timescales of our sources are approximately 100 days. This damping timescale may imply that the diffusive shock acceleration plays an important role in the variability of gamma-ray emission. (2) Flat-spectrum radio quasars (FSRQs) demonstrate greater variability amplitude compared to BL Lacertae objects (BL Lacs). (3) The ratio of the distance of the emission region from the central supermassive black hole to the dusty torus radius for our sources is $R\approx2-4.5R_{\rm DT}$. In contrast, the ratio of the distance of the emission region from the central supermassive black hole to the BLR radius for our sources is $R\approx135-295R_{\rm BLR-in}$ and $R\approx123-270R_{\rm BLR-out}$. These findings indicate that the $γ$-ray emission region in jetted AGNs is likely located beyond the BLR, potentially could be associated with the dusty torus. (4) A statistical correlation is observed between variability amplitude and radio luminosity, radio loudness, X-ray luminosity, X-ray loudness, gamma-ray luminosity, and gamma-ray loudness, indicating a potential relationship between gamma-ray variability and jet activity. (5) Variability amplitude also shows a statistical correlations with synchrotron peak frequency luminosity, inverse Compton peak frequency luminosity, and Compton dominance. (6) Variability amplitude also correlates with black hole mass, accretion disk luminosity, and Eddington ratio, implying that the accretion disk may also contribute to gamma-ray variability.

astro-ph.HE↗

arXiv2026

A universal relationship between the variability timescale and black hole mass in black hole jetted and non-jetted accreting systems

A long-term variability study spanning a range of black hole mass systems, from microquasars hosting stellar-mass black holes to active galactic nuclei (AGNs) harboring supermassive black holes, provides new insights into the physics of relativistic jets. In this work, we investigate the optical variability of both jetted and nonjetted AGNs. We apply a stochastic process known as the Damped Random Walk (DRW) to model light curves from the Zwicky Transient Facility (ZTF) DR23. Our results show that the mass-scaled characteristic timescales across the black hole mass exhibit a linear relationship with a slope of 0.35-0.50. This analysis confirms a previously observed correlation between the damping timescales and black hole mass and extends it by incorporating 125 newly identified non-jetted AGNs selected from the Burst Alert Telescope (BAT) AGN catalogue. The derived slope of the relation between the damping timescales and black hole mass aligns with recent theoretical predictions, supporting the presence of a universal accretion mechanism in AGNs across different mass scales. Furthermore, our findings suggest a novel implication: the properties and production mechanisms of relativistic jets may be largely independent of black hole mass.

astro-ph.HE↗

arXiv2026

The cold molecular gas regulates the activity of active galactic nuclei in massive galaxies

The physical quantities that directly regulate AGN feedback in massive galaxies remain poorly understood. Observations of molecular gas surrounding AGNs suggest that this gas serves as a fuel source for AGN activity. Accordingly, we study the relationship between AGN activity and molecular gas properties. In this study, we analyze a large sample of nearby AGNs with available measurements of molecular gas mass, radio luminosity, and \oiii~luminosity. Our results show that radio luminosity and \oiii~luminosity exhibit stronger correlations with molecular gas mass than with other physical parameters such as black hole mass, stellar mass, and bulge mass. Moreover, when controlling for the correlations between radio luminosity, \oiii~luminosity, and molecular gas mass, the relationships between these luminosities and other key physical parameters become significantly weaker or disappear entirely. This suggests that, of all the properties we have considered, it is the molecular gas mass that is most tightly correlated with radio and \oiii~luminosity, and may thus be the most important driver of nuclear activity.

astro-ph.HE↗

arXiv2026

Large Language Models for Travel Behavior Prediction

Travel behavior prediction is a core problem in transportation demand management and is traditionally addressed using numerical models calibrated on observed data. With recent advances in large language models (LLMs), new opportunities have emerged to model human decision-making through natural language reasoning. This study explores the use of LLMs for travel behavior prediction through two complementary frameworks. The first framework employs a zero-shot prompting strategy, where the prediction task, traveler attributes, and relevant domain knowledge are described in text, enabling the LLM to directly generate predictions without task-specific training data. The second framework uses LLM-generated text embeddings as high-level representations of travel scenarios, which are then combined with conventional supervised learning models to support prediction in small-sample settings. Empirical results show that both approaches achieve performance comparable to, and in some cases competitive with, classical models such as multinomial logit, random forest, and neural networks. These findings suggest that LLMs offer a flexible and data-efficient alternative for travel behavior prediction.

cs.LG↗

arXiv2026

Identifying Compton-thick active galactic nuclei in the COSMOS. II. Searching among mid-infrared selected AGNs

Compton-thick active galactic nuclei (CT-AGNs), defined by column density $\mathrm{N_H} \geqslant 1.5 \times 10^{24} \ \mathrm{cm}^{-2}$, are so heavily absorbed that their X-ray emission is often feeble, even undetectable by X-ray instruments. Nevertheless, their radiation is expected to be a substantial contributor to the cosmic X-ray background (CXB), predicting that CT-AGNs should comprise at least $\sim$30% of the total AGN population. In the Cosmological Evolution Survey (COSMOS), the identified CT-AGN fraction falls far below theoretical expectations, indicating that a substantial population of CT-AGNs is hidden due to their low photon counts or their flux below the current flux limits of X-ray instruments. This work focuses on identifying CT-AGNs hidden in mid-infrared (MIR)-selected AGNs. First, we selected a sample of 1,104 MIR-selected AGNs that were covered but individually undetected by X-ray. Next, we reduced the X-ray data in the COSMOS and analyzed multiwavelength data in our sample to derive the key physical parameters required for CT-AGN identification. Using MIR diagnostics, we first find out 7 to 23 CT-AGN candidates. Their subsequent X-ray stacking analysis reveals a clear detection at $>3σ$ significance in the soft band and only $>1σ$ significance in the hard band. We fit the stacked soft- and hard-band fluxes with a physical model and confirm that these sources are absorbed by Compton-thick material. However, CT-AGNs constitute only 2.1% (23/1104) of our sample, significantly below the fraction predicted by CXB synthesis models, indicating that a considerable population of CT-AGNs remains missed by our selection. A comparison of host-galaxy properties between CT-AGNs and non-CT-AGNs reveals no significant differences.

astro-ph.GA↗

arXiv2025

Multiplexed Catheter-Integrated Pressure Sensing System for Endoluminal Interventions

Advances in flexible catheters pave the way for minimally invasive diagnosis and treatment of luminal organs and tubular structures through endoluminal interventions. A key challenge is in establishing non-constraining pressure monitoring at the interfaces between medical catheters and intraluminal anatomy exhibiting curvilinear contours, structural variability, and time-dependent physiological motion. This work presents a scalable and multi-purpose pressure sensing system for multidirectional monitoring of tissue interactions, establishing a robust solution for deploying diagnostic and therapeutic instruments in various types of endoluminal interventions. This approach provides an integrated system encompassing pressure sensors, catheters, and signal acquisition devices. A poly (vinylidene fluoride-co-trifluoroethylene) (P(VDF-TrFE)) film is miniaturized and configured into a multiplexed piezoelectric-based pressure sensor, providing flexibility and scalability in conforming to medical catheters with curved surfaces. The catheter is fabricated with a cost-effective and highly scalable fiber drawing technology, establishing a means of fast prototyping catheters with bespoke structures for sensor integration and medical instrument integration. The system achieves enhanced pressure detection sensitivity and a comparable sensing range, compared with state-of-the-art catheter-integrated sensors. Through in-vitro phantom studies, the system performs precise multi-directional sensing within various clinical endoluminal scenarios, showing its potential in digitalizing tissue interactions during endoluminal interventions.

physics.med-ph↗

arXiv2025

The relation between the optical variability timescale, magnetic field of jets and black hole spin in active galactic nuclei

We investigate the relationship among the jet magnetic field, black hole spin, black hole mass, Eddington ratio, and optical variability timescales in jetted active galactic nuclei (AGNs). By fitting a damped random walk (DRW) model to the g-band light curves, we obtain the characteristic variability timescale ($τ_{\rm DRW}$) for 41 jetted AGNs with precise supermassive black hole (SMBH) mass measurements. Our main results are as follows: (i) Our analysis reveals a significant correlation between the jet magnetic field ($B_{\rm 1pc}$), black hole spin ($j$) and the characteristic variability timescale within our sample. These findings suggest that the optical variability of jetted AGNs is influenced by the jet magnetic field and black hole spin. Furthermore, the characteristic variability timescale aligns with the electron escape timescale, as evidenced by the relationship between the characteristic variability timescale and jet magnetic field ($τ_{\rm DRW}\propto B_{\rm 1pc}^{0.76\pm0.22}$). (ii) We confirm a significant correlation between the characteristic variability timescale and SMBH mass, expressed as: $\log \rm τ_{\rm DRW} = 0.52(\pm0.21)\log M _{\rm BH}/M_{\rm \odot}-3.12(\pm1.90)$, with an intrinsic scatter of 0.08 dex. The slope of this relationship is consistent with that between the thermal timescale and black hole mass. Our results support the hypothesis that magnetorotational instability (MRI) fluctuations drive the intrinsic variability observed in the light curves emitted by the AGNs accretion disk.

astro-ph.HE↗

arXiv2025

Optical+NIR analysis of a Newly Confirmed Einstein ring at z$\sim$1 from the Kilo-Degree Survey: Dark matter fraction, total and dark matter density slope and IMF

We report the spectroscopic confirmation of a bright blue Einstein ring in the Kilo Degree Survey (KiDS) footprint: the Einstein ``blue eye''. Spectroscopic data from X-Shooter at the Very Large Telescope (VLT) show that the lens is a typical early-type galaxy (ETG) at $z_l=0.9906$, while the background source is a Ly$α$ emitter at $z_s=2.823$. The reference lens modeling was performed on a high-resolution $Y-$band adaptive-optics image from HAWK-I at VLT. Assuming a singular isothermal ellipsoid (SIE) total mass density profile, we inferred an Einstein radius $R_{Ein}=10.47 \pm 0.06$ kpc. The average slope of the total mass density inside the Einstein radius, as determined by a joint analysis of lensing and isotropic Jeans equations is $γ_{tot}=2.14^{+0.06}_{-0.07}$, showing no systematic deviation from the slopes of lower redshift galaxies, This can be the evidence of ETGs developing through dry mergers plus moderate dissipationless accretion. Stellar population analysis with 8-band ($gri$ZYJHK$s$) photometries from KiDS and VIKING shows that the total stellar mass of the lens is $M*=(3.95\pm 0.35)\times 10^{11} M_\odot$ (Salpeter Initial Mass Function, IMF), implying a dark matter fraction inside the effective radius to be $f_{\rm DM}=0.307\pm 0.151$. We finally explored the dark matter halo slope and found a strong degeneracy with the dynamic stellar mass. Dark matter adiabatic contraction is needed to explain the posterior distribution of the slope unless IMF heavier than Salpeter is assumed.

astro-ph.GA↗

arXiv2025

Predicting Drivers' Route Trajectories in Last-Mile Delivery Using A Pair-wise Attention-based Pointer Neural Network

In last-mile delivery, drivers frequently deviate from planned delivery routes because of their tacit knowledge of the road and curbside infrastructure, customer availability, and other characteristics of the respective service areas. Hence, the actual stop sequences chosen by an experienced human driver may be potentially preferable to the theoretical shortest-distance routing under real-life operational conditions. Thus, being able to predict the actual stop sequence that a human driver would follow can help to improve route planning in last-mile delivery. This paper proposes a pair-wise attention-based pointer neural network for this prediction task using drivers' historical delivery trajectory data. In addition to the commonly used encoder-decoder architecture for sequence-to-sequence prediction, we propose a new attention mechanism based on an alternative specific neural network to capture the local pair-wise information for each pair of stops. To further capture the global efficiency of the route, we propose a new iterative sequence generation algorithm that is used after model training to identify the first stop of a route that yields the lowest operational cost. Results from an extensive case study on real operational data from Amazon's last-mile delivery operations in the US show that our proposed method can significantly outperform traditional optimization-based approaches and other machine learning methods (such as the Long Short-Term Memory encoder-decoder and the original pointer network) in finding stop sequences that are closer to high-quality routes executed by experienced drivers in the field. Compared to benchmark models, the proposed model can increase the average prediction accuracy of the first four stops from around 0.229 to 0.312, and reduce the disparity between the predicted route and the actual route by around 15%.

cs.LG↗

arXiv2025

Robust Vehicle Rebalancing with Deep Uncertainty in Autonomous Mobility-on-Demand Systems

Autonomous Mobility-on-Demand (AMoD) services offer an opportunity for improving passenger service while reducing pollution and energy consumption through effective vehicle coordination. A primary challenge in the autonomous fleets coordination is to tackle the inherent issue of supply-demand imbalance. A key strategy in resolving this is vehicle rebalancing, strategically directing idle vehicles to areas with anticipated future demand. Traditional research focuses on deterministic optimization using specific demand forecasts, but the unpredictable nature of demand calls for methods that can manage this uncertainty. This paper introduces the Deep Uncertainty Robust Optimization (DURO), a framework specifically designed for vehicle rebalancing in AMoD systems amidst uncertain demand based on neural networks for robust optimization. DURO forecasts demand uncertainty intervals using a deep neural network, which are then integrated into a robust optimization model. We assess DURO against various established models, including deterministic optimization with refined demand forecasts and Distributionally Robust Optimization (DRO). Based on real-world data from New York City (NYC), our findings show that DURO surpasses traditional deterministic models in accuracy and is on par with DRO, but with superior computational efficiency. The DURO framework is a promising approach for vehicle rebalancing in AMoD systems that is proven to be effective in managing demand uncertainty, competitive in performance, and more computationally efficient than other optimization models.

math.OC↗

arXiv2025

Robust Binary and Multinomial Logit Models for Classification with Data Uncertainties

Binary logit (BNL) and multinomial logit (MNL) models are the two most widely used discrete choice models for travel behavior modeling and prediction. However, in many scenarios, the collected data for those models are subject to measurement errors. Previous studies on measurement errors mostly focus on "better estimating model parameters" with training data. In this study, we focus on using BNL and MNL for classification problems, that is, to ``better predict the behavior of new samples'' when measurement errors occur in testing data. To this end, we propose a robust BNL and MNL framework that is able to account for data uncertainties in both features and labels. The models are based on robust optimization theory that minimizes the worst-case loss over a set of uncertainty data scenarios. Specifically, for feature uncertainties, we assume that the l_p-norm of the measurement errors in features is smaller than a pre-established threshold. We model label uncertainties by limiting the number of mislabeled choices to at most Gamma. Based on these assumptions, we derive a tractable robust counterpart. The derived robust-feature BNL and the robust-label MNL models are exact. However, the formulation for the robust-feature MNL model is an approximation of the exact robust optimization problem. An upper bound of the approximation gap is provided. We prove that the robust estimators are inconsistent but with a higher trace of the Fisher information matrix. They are preferred when out-of-sample data has errors due to the shrunk scale of the estimated parameters. The proposed models are validated in a binary choice data set and a multinomial choice data set, respectively. Results show that the robust models (both features and labels) can outperform the conventional BNL and MNL models in prediction accuracy and log-likelihood.

math.OC↗

arXiv2025

Identifying Compton-thick AGNs with Machine learning algorithm in Chandra Deep Field-South

Compton-thick active galactic nuclei (CT-AGNs), which are defined by column density $\mathrm{N_H} \geqslant 1.5 \times 10^{24} \ \mathrm{cm}^{-2}$, emit feeble X-ray radiation, even undetectable by X-ray instruments. Despite this, the X-ray emissions from CT-AGNs are believed to be a substantial contributor to the cosmic X-ray background (CXB). According to synthesis models of AGNs, CT-AGNs are expected to make up a significant fraction of the AGN population, likely around 30% or more. However, only $\sim$11% of AGNs have been identified as CT-AGNs in the Chandra Deep Field-South (CDFS). To identify hitherto unknown CT-AGNs in the field, we used a Random Forest algorithm for identifying them. First, we build a secure classified subset of 210 AGNs to train and evaluate our algorithm. Our algorithm achieved an accuracy rate of 90% on the test set after training. Then, we applied our algorithm to an additional subset of 254 AGNs, successfully identifying 67 CT-AGNs within this group. This result significantly increased the fraction of CT-AGNs in the CDFS, which is closer to the theoretical predictions of the CXB. Finally, we compared the properties of host galaxies between CT-AGNs and non-CT-AGNs and found that the host galaxies of CT-AGNs exhibit higher levels of star formation activity.

astro-ph.GA↗

arXiv2025

High-Performance Vision-Based Tactile Sensing Enhanced by Microstructures and Lightweight CNN

Tactile sensing is critical in advanced interactive systems by emulating the human sense of touch to detect stimuli. Vision-based tactile sensors are promising for providing multimodal capabilities and high robustness, yet existing technologies still have limitations in sensitivity, spatial resolution, and high computational demands of deep learning-based image processing. This paper presents a comprehensive approach combining a novel microstructure-based sensor design and efficient image processing, demonstrating that carefully engineered microstructures can significantly enhance performance while reducing computational load. Without traditional tracking markers, our sensor incorporates an surface with micromachined trenches, as an example of microstructures, which modulate light transmission and amplify the response to applied force. The amplified image features can be extracted by a ultra lightweight convolutional neural network to accurately inferring contact location, displacement, and applied force with high precision. Through theoretical analysis, we demonstrated that the micro trenches significantly amplified the visual effects of shape distortion. Using only a commercial webcam, the sensor system effectively detected forces below 5 mN, and achieved a millimetre-level single-point spatial resolution. Using a model with only one convolutional layer, a mean absolute error below 0.05 mm was achieved. Its soft sensor body allows seamless integration with soft robots, while its immunity to electrical crosstalk and interference guarantees reliability in complex human-machine environments.

cs.RO↗

arXiv2025

The relation between black hole spin, star formation rate, and black hole mass for supermassive black holes

Both theoretical models and observational evidence indicate that jets and/or outflows driven by central active supermassive black holes exert a significant feedback effect on the overall properties of their host galaxies. Theoretical models suggest that the spin of supermassive black holes drives relativistic jets. Therefore, we investigate the relationship between black hole spin, star formation rate, and black hole mass using a sample of 48 low-redshift supermassive black holes. By performing multiband fitting of spectral energy distribution, we derive the star formation rates and stellar masses of the host galaxies harbouring these supermassive black holes. Our main results are as follows: (i) For black holes with masses $M_{\rm BH} \lesssim 10^{6.5} M_{\odot}$, the spin increases with increasing black hole mass, suggesting that black hole growth is primarily driven by gas accretion, particularly in the coherent gas accretion regime. Conversely, for black holes with masses $M_{\rm BH} \gtrsim 10^{7.5} M_{\odot}$, the spin decreases with increasing black hole mass, indicating that growth occurs mainly through mergers, inducing chaotic accretion. (ii) At low star formation rates, black hole spin increases with increasing star formation rates, consistent with gas accretion. However, at high star formation rates, black hole spin decreases with increasing star formation rates, suggesting black hole mergers. The value of the black hole spin may be used to diagnose the star formation rate of the host galaxies through active galactic nuclei activities. (iii) Our data and analysis confirm the well-known relation between stellar mass and black hole mass, with the fitting function $\log M_{\rm BH}=0.57\log M_{*}+1.94$.

astro-ph.HE↗

arXiv2025

Observational evidence for a correlation between the magnetic field of jets and star formation rate in host galaxies

Accretion supermassive black holes in the center of active galaxies usually produce ``jets''-collimated bipolar outflows of relativistic particles. Magnetic fields near the black hole event horizon may play a crucial role in the formation of jets/outflows. Both theory and observation indicate that jets/outflows driven by centrally active supermassive black holes (SMBHs) have a feedback effect on the overall properties of the host galaxies. Therefore, the magnetic field is a key ingredient for the formation and evolution of galaxies. Here we report a clear correlation between the magnetic field of jets and star formation rate (SFR) for a large sample of 96 galaxies hosting supermassive black holes, which suggests that the star formation of active galactic nuclei (AGN) host galaxies may be powered by the jets.

astro-ph.HE↗