Showing 1–50 of 299 results for author: Ng, M

Radio Monitoring Campaign of Active Repeater FRB 20220912A with CHIME

Authors: Thomas C. Abbott, Aaron B. Pearlman, Victoria M. Kaspi, Ayush Pandhi, Charanjot Brar, Alyssa Cassity, Amanda M. Cook, Alice P. Curtin, Emmanuel Fonseca, Bryan M. Gaensler, Deborah C. Good, Jason W. Hessels, Afrokk Khan, Calvin Leung, Robert A. Main, Ryan Mckinven, Bradley W. Meyers, Kenzie Nimmo, Mason Ng, Ziggy Pleunis, Paul Scholz, Vishwangi Shah, Kaitlyn Shin

Abstract: FRB 20220912A is a highly active repeating fast radio burst (FRB) source, discovered by the Canadian Hydrogen Intensity Mapping Experiment (CHIME) using its real-time FRB detection system (CHIME/FRB). Here, we present results from a radio monitoring campaign of FRB 20220912A using CHIME, including ~200 hours of data collected by CHIME/Pulsar, spanning 1.5 years following the source's discovery. We… ▽ More FRB 20220912A is a highly active repeating fast radio burst (FRB) source, discovered by the Canadian Hydrogen Intensity Mapping Experiment (CHIME) using its real-time FRB detection system (CHIME/FRB). Here, we present results from a radio monitoring campaign of FRB 20220912A using CHIME, including ~200 hours of data collected by CHIME/Pulsar, spanning 1.5 years following the source's discovery. We present an analysis of a sample of 828 CHIME-detected bursts from FRB 20220912A, in the 400-800 MHz radio frequency band. The source remains highly active for ~10 weeks and has a bimodal wait-time distribution with peaks at $160^{+120}_{-70}$ ms and $306^{+14}_{-13}$ s. Assuming a radio efficiency factor of $10^{-4}$ and a beaming angle of 0.1, we estimate the total emitted energy from the source over the entire observing campaign to be $2 \times 10^{43}$ ergs. We report a 2.3$σ$ detection of a linear increase in the DM of $1.4 \pm 0.6$ pc cm$^{-3}$ yr$^{-1}$, with no significant trend in rotation measure (with a 3$σ$ upper limit of 13.4 rad m$^{-2}$ yr$^{-1}$). We contrast our findings with other active repeaters, which exhibit different DM and RM evolution to indicate that FRB 20220912A may reside in a unique local environment. △ Less

Submitted 10 April, 2026; originally announced April 2026.

Comments: Submitted to ApJ. Comments welcome

Dialogue based Interactive Explanations for Safety Decisions in Human Robot Collaboration

Authors: Yifan Xu, Xiao Zhan, Akilu Yunusa Kaltungo, Ming Shan Ng, Tsukasa Ishizawa, Kota Fujimoto, Clara Cheung

Abstract: As robots increasingly operate in shared, safety critical environments, acting safely is no longer sufficient robots must also make their safety decisions intelligible to human collaborators. In human robot collaboration (HRC), behaviours such as stopping or switching modes are often triggered by internal safety constraints that remain opaque to nearby workers. We present a dialogue based framewor… ▽ More As robots increasingly operate in shared, safety critical environments, acting safely is no longer sufficient robots must also make their safety decisions intelligible to human collaborators. In human robot collaboration (HRC), behaviours such as stopping or switching modes are often triggered by internal safety constraints that remain opaque to nearby workers. We present a dialogue based framework for interactive explanation of safety decisions in HRC. The approach tightly couples explanation with constraint based safety evaluation, grounding dialogue in the same state and constraint representations that govern behaviour selection. Explanations are derived directly from the recorded decision trace, enabling users to pose causal ("Why?"), contrastive ("Why not?"), and counterfactual ("What if?") queries about safety interventions. Counterfactual reasoning is evaluated in a bounded manner under fixed, certified safety parameters, ensuring that interactive exploration does not relax operational guarantees. We instantiate the framework in a construction robotics scenario and provide a structured operational trace illustrating how constraint aware dialogue clarifies safety interventions and supports coordinated task recovery. By treating explanation as an operational interface to safety control, this work advances a design perspective for interactive, safety aware autonomy in HRC. △ Less

Submitted 10 April, 2026; v1 submitted 7 April, 2026; originally announced April 2026.

Comments: This paper has been accepted by the 2nd InterAI workshop, HRI conference 26'

The NANOGrav 15 yr and 20 yr Datasets: Timing Events and Pulse Shape Changes

Authors: Ben Jacobson-Bell, James M. Cordes, Shami Chatterjee, Sashabaw Niedbalski, Gabriella Agazie, Akash Anumarlapudi, Anne M. Archibald, Zaven Arzoumanian, Jeremy G. Baier, Paul T. Baker, Paul R. Brook, H. Thankful Cromartie, Kathryn Crowter, Megan E. DeCesar, Paul B. Demorest, Lankeswar Dey, Timothy Dolch, Elizabeth C. Ferrara, William Fiore, Emmanuel Fonseca, Gabriel E. Freedman, Nate Garver-Daniels, Peter A. Gentile, Joseph Glaser, Deborah C. Good , et al. (39 additional authors not shown)

Abstract: The average pulse shape of a pulsar is typically stable over decadal timescales, enabling estimation of pulse times of arrival to better than a small fraction of the pulse width using matched filtering techniques. However, in North American Nanohertz Observatory for Gravitational Waves (NANOGrav) observations of PSR J1713+0747, three discrete timing events that depart from the prevailing timing mo… ▽ More The average pulse shape of a pulsar is typically stable over decadal timescales, enabling estimation of pulse times of arrival to better than a small fraction of the pulse width using matched filtering techniques. However, in North American Nanohertz Observatory for Gravitational Waves (NANOGrav) observations of PSR J1713+0747, three discrete timing events that depart from the prevailing timing model have been seen in the last 20 yr. All three correspond to morphological changes in pulse shape. Using principal component analysis, we analyze the pulse profiles of nine NANOGrav pulsars, including seven with profiles from the 15 yr dataset and two with additional profiles from the forthcoming 20 yr dataset. We recover the three known pulse shape change events in PSR J1713+0747 and another previously known event in PSR J1643$-$1224. We implement a ranking metric for candidate events and address four highly ranked candidates in this nine-pulsar sample. We also recover known slow pulse shape variations in PSR J1643$-$1224, PSR J1903+0327, and PSR B1937+21 and report an unexpected recurrence after ~10 yr of one such variation in PSR B1937+21. △ Less

Submitted 7 April, 2026; originally announced April 2026.

Comments: 27 pages, 3 tables, 18 figures incl. 3 figure sets in ancillary files. Submitted to ApJ

Using LLM-as-a-Judge/Jury to Advance Scalable, Clinically-Validated Safety Evaluations of Model Responses to Users Demonstrating Psychosis

Authors: May Lynn Reese, Markela Zeneli, Mindy Ng, Jacob Haimes, Andreea Damien, Elizabeth Stade

Abstract: General-purpose Large Language Models (LLMs) are becoming widely adopted by people for mental health support. Yet emerging evidence suggests there are significant risks associated with high-frequency use, particularly for individuals suffering from psychosis, as LLMs may reinforce delusions and hallucinations. Existing evaluations of LLMs in mental health contexts are limited by a lack of clinical… ▽ More General-purpose Large Language Models (LLMs) are becoming widely adopted by people for mental health support. Yet emerging evidence suggests there are significant risks associated with high-frequency use, particularly for individuals suffering from psychosis, as LLMs may reinforce delusions and hallucinations. Existing evaluations of LLMs in mental health contexts are limited by a lack of clinical validation and scalability of assessment. To address these issues, this research focuses on psychosis as a critical condition for LLM safety evaluation by (1) developing and validating seven clinician-informed safety criteria, (2) constructing a human-consensus dataset, and (3) testing automated assessment using an LLM as an evaluator (LLM-as-a-Judge) or taking the majority vote of several LLM judges (LLM-as-a-Jury). Results indicate that LLM-as-a-Judge aligns closely with the human consensus (Cohen's $κ_{\text{human} \times \text{gemini}} = 0.75$, $κ_{\text{human} \times \text{qwen}} = 0.68$, $κ_{\text{human} \times \text{kimi}} = 0.56$) and that the best judge slightly outperforms LLM-as-a-Jury (Cohen's $κ_{\text{human} \times \text{jury}} = 0.74$). Overall, these findings have promising implications for clinically grounded, scalable methods in LLM safety evaluations for mental health contexts. △ Less

Submitted 20 March, 2026; originally announced April 2026.

Comments: published at IASEAI 2026, preliminary work presented at GenAI4Health workshop at NeurIPS 2025

Rethinking Representations for Cross-Domain Infrared Small Target Detection: A Generalizable Perspective from the Frequency Domain

Authors: Yimin Fu, Songbo Wang, Feiyan Wu, Jialin Lyu, Zhunga Liu, Michael K. Ng

Abstract: The accurate target-background separation in infrared small target detection (IRSTD) highly depends on the discriminability of extracted representations. However, most existing methods are confined to domain-consistent settings, while overlooking whether such discriminability can generalize to unseen domains. In practice, distribution shifts between training and testing data are inevitable due to… ▽ More The accurate target-background separation in infrared small target detection (IRSTD) highly depends on the discriminability of extracted representations. However, most existing methods are confined to domain-consistent settings, while overlooking whether such discriminability can generalize to unseen domains. In practice, distribution shifts between training and testing data are inevitable due to variations in observational conditions and environmental factors. Meanwhile, the intrinsic indistinctiveness of infrared small targets aggravates overfitting to domain-specific patterns. Consequently, the detection performance of models trained on source domains can be severely degraded when deployed in unseen domains. To address this challenge, we propose a spatial-spectral collaborative perception network (S$^2$CPNet) for cross-domain IRSTD. Moving beyond conventional spatial learning pipelines, we rethink IRSTD representations from a frequency perspective and reveal inconsistencies in spectral phase as the primary manifestation of domain discrepancies. Based on this insight, we develop a phase rectification module (PRM) to derive generalizable target awareness. Then, we employ an orthogonal attention mechanism (OAM) in skip connections to preserve positional information while refining informative representations. Moreover, the bias toward domain-specific patterns is further mitigated through selective style recomposition (SSR). Extensive experiments have been conducted on three IRSTD datasets, and the proposed method consistently achieves state-of-the-art performance under diverse cross-domain settings. △ Less

Submitted 2 April, 2026; originally announced April 2026.

Comments: The code will be released at https://github.com/fuyimin96/S2CPNet upon acceptance

QuatIca: Advanced Numerical Linear Algebra and Optimization for Quaternionic Matrices in Python

Authors: Valentin Leplat, Salman Ahmadi-Asl, Junjun Pan, Henni Ouerdane, Michael Ng

Abstract: Quaternion-valued representations provide a convenient way to model coupled multi-channel signals (e.g., RGB imagery, polarization data, vector fields, and multi-detector time series). Yet practical and numerically reliable software support remains far less mature than those based on the real/complex setting. Here, we present QuatIca, an open-source Python library for quaternion numerical linear a… ▽ More Quaternion-valued representations provide a convenient way to model coupled multi-channel signals (e.g., RGB imagery, polarization data, vector fields, and multi-detector time series). Yet practical and numerically reliable software support remains far less mature than those based on the real/complex setting. Here, we present QuatIca, an open-source Python library for quaternion numerical linear algebra and optimization, designed for both research prototyping and reproducible experimentation. QuatIca provides core quaternion matrix operations and norms; dense decompositions and reductions (QR, LU, Q-SVD, eigendecomposition, Hessenberg/tridiagonal reduction, Cholesky decomposition, and Schur helpers); iterative solvers including quaternion GMRES (with preconditioning) and Newton-Schulz pseudoinverse schemes; and domain-focused routines for signal and image processing such as quaternion Tikhonov restoration. The library also includes OptiQ, which solves quaternion Hermitian semidefinite programs using log-det barrier Newton methods with $μ$-continuation. We highlight design choices that preserve quaternion structure, and we provide end-to-end demonstrations including quaternion image deblurring, Lorenz-attractor filtering, and quaternion image completion. QuatIca is distributed via PyPI and accompanied by open-source development on GitHub and continuously deployed documentation with runnable tutorials. △ Less

Submitted 25 March, 2026; originally announced March 2026.

Anomalously Strong Localized First Ionization Potential Effect Associated with a Solar Subflare

Authors: Man-Hei Ng, Xiaoping Zhang, P. F. Chen

Abstract: Plasma composition in the solar corona commonly differs from that of the photosphere, with the enhancement of low--first-ionization-potential (FIP) elements referred to as the FIP effect. This phenomenon provides important diagnostics of energy and mass transport between different layers of the solar atmosphere. In this work, we analyze an anomalously strong, localized FIP effect observed in activ… ▽ More Plasma composition in the solar corona commonly differs from that of the photosphere, with the enhancement of low--first-ionization-potential (FIP) elements referred to as the FIP effect. This phenomenon provides important diagnostics of energy and mass transport between different layers of the solar atmosphere. In this work, we analyze an anomalously strong, localized FIP effect observed in active region 13486 associated with a subflaring episode on 2023 November 17, using multiwavelength observations combining high energy-resolution soft X-ray disk-integrated spectra obtained by the Macao Science Satellite-1B with spatially resolved EUV/UV and H$α$ imaging from Hinode/EIS, SDO/AIA and HMI, and CHASE/HIS. By investigating the temporal evolution of plasma composition in response to changes in magnetic field orientation, we provide new insight into the physical processes linking magnetic reconnection, ponderomotive force fractionation, and coronal abundance anomalies. This work reveals that the anomalously strong enhancement of low-FIP elements is localized in regions with strongly inclined magnetic fields despite a subflare. We interpret these observations within the framework of the ponderomotive force fractionation model and propose that the inclined magnetic geometry enhances the transmission of upward-propagating magnetohydrodynamic waves by reducing reflection near the plasma-$β$$\simeq$1 layer, enhancing FIP fractionation associated with a consequential upward-directed ponderomotive force. In addition, sustained chromospheric heating associated with chromospheric reconnection and flux cancellation appears to maintain the enhanced FIP effect for tens of minutes following the event. △ Less

Submitted 24 March, 2026; originally announced March 2026.

RewardFlow: Topology-Aware Reward Propagation on State Graphs for Agentic RL with Large Language Models

Authors: Xiao Feng, Bo Han, Zhanke Zhou, Jiaqi Fan, Jiangchao Yao, Ka Ho Li, Dahai Yu, Michael Kwok-Po Ng

Abstract: Reinforcement learning (RL) holds significant promise for enhancing the agentic reasoning capabilities of large language models (LLMs) with external environments. However, the inherent sparsity of terminal rewards hinders fine-grained, state-level optimization. Although process reward modeling offers a promising alternative, training dedicated reward models often entails substantial computational… ▽ More Reinforcement learning (RL) holds significant promise for enhancing the agentic reasoning capabilities of large language models (LLMs) with external environments. However, the inherent sparsity of terminal rewards hinders fine-grained, state-level optimization. Although process reward modeling offers a promising alternative, training dedicated reward models often entails substantial computational costs and scaling difficulties. To address these challenges, we introduce RewardFlow, a lightweight method for estimating state-level rewards tailored to agentic reasoning tasks. RewardFlow leverages the intrinsic topological structure of states within reasoning trajectories by constructing state graphs. This enables an analysis of state-wise contributions to success, followed by topology-aware graph propagation to quantify contributions and yield objective, state-level rewards. When integrated as dense rewards for RL optimization, RewardFlow substantially outperforms prior RL baselines across four agentic reasoning benchmarks, demonstrating superior performance, robustness, and training efficiency. The implementation of RewardFlow is publicly available at https://github.com/tmlr-group/RewardFlow. △ Less

Submitted 19 March, 2026; originally announced March 2026.

Blind Hyperspectral and Multispectral Images Fusion: A Unified Tensor Fusion Framework from Coupled Inverse Problem Perspective

Authors: Ying Gao, Michael K. Ng, Chunfeng cui

Abstract: Hyperspectral and multispectral images fusion aims at integrating a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI) to construct a high-resolution hyperspectral image (HR-HSI). It is generally assumed that spatial blurring operator and spectral response operator are prior-known. However, such an assumption is extremely restrictive in practice. To over… ▽ More Hyperspectral and multispectral images fusion aims at integrating a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI) to construct a high-resolution hyperspectral image (HR-HSI). It is generally assumed that spatial blurring operator and spectral response operator are prior-known. However, such an assumption is extremely restrictive in practice. To overcome this limitation, this paper formulates blind fusion as a coupled inverse problem, integrating blind deconvolution in the spatial domain with blind unmixing in the spectral domain. From this novel perspective, we propose a unified tensor fusion framework capable of flexible self-adjustment and real-time fusion without pre-training. We further introduce an optimization model for the joint estimation of the target HR-HSI, the spatial point spread function, and the spectral response function. To solve this model, we devise a partially linearized alternating direction method of multipliers (ADMM) algorithm with Moreau envelope smoothing, accompanied by the rigorous convergence analysis. An initialization estimator tailored to the specific characteristics of the fusion problem is proposed. Numerical comparisons with state-of-the-art methods on both synthetic and real-world datasets demonstrate the compelling performance of the proposed method. △ Less

Submitted 12 March, 2026; originally announced March 2026.

COACH meets QUORUM: A Framework and Pipeline for Aligning User, Expert and Developer Perspectives in LLM-generated Health Counselling

Authors: Yee Man Ng, Bram van Dijk, Pieter Beynen, Otto Boekesteijn, Joris Jansen, Gerard van Oortmerssen, Max van Duijn, Marco Spruit

Abstract: Systems that collect data on sleep, mood, and activities can provide valuable lifestyle counselling to populations affected by chronic disease and its consequences. Such systems are, however, challenging to develop; besides reliably extracting patterns from user-specific data, systems should also contextualise these patterns with validated medical knowledge to ensure the quality of counselling, an… ▽ More Systems that collect data on sleep, mood, and activities can provide valuable lifestyle counselling to populations affected by chronic disease and its consequences. Such systems are, however, challenging to develop; besides reliably extracting patterns from user-specific data, systems should also contextualise these patterns with validated medical knowledge to ensure the quality of counselling, and generate counselling that is relevant to a real user. We present QUORUM, a new evaluation framework that unifies these developer-, expert-, and user-centric perspectives, and show with a real case study that it meaningfully tracks convergence and divergence in stakeholder perspectives. We also present COACH, a Large Language Model-driven pipeline to generate personalised lifestyle counselling for our Healthy Chronos use case, a diary app for cancer patients and survivors. Applying our framework shows that overall, users, medical experts, and developers converge on the opinion that the generated counselling is relevant, of good quality, and reliable. However, stakeholders also diverge on the tone of the counselling, sensitivity to errors in pattern-extraction, and potential hallucinations. These findings highlight the importance of multi-stakeholder evaluation for consumer health language technologies and illustrate how a unified evaluation framework can support trustworthy, patient-centered NLP systems in real-world settings. △ Less

Submitted 9 March, 2026; originally announced March 2026.

Comments: Under review for the CL4Health workshop

Primitive recursive categoricity spectra of functional structures

Authors: Nikolay Bazhenov, Heer Tern Koh, Keng Meng Ng

Abstract: For the notion of degree of categoricity, we study an analogous notion for punctual structures. We show that such notions coincide for non-$Δ_{1}^{0}$-categorical injection structures, and construct an example of a $Δ_{1}^{0}$-categorical injection structure for which these notions differ. Additionally, we also show that in every non-zero c.e.~Turing degree, there exists a PR-degree that is low fo… ▽ More For the notion of degree of categoricity, we study an analogous notion for punctual structures. We show that such notions coincide for non-$Δ_{1}^{0}$-categorical injection structures, and construct an example of a $Δ_{1}^{0}$-categorical injection structure for which these notions differ. Additionally, we also show that in every non-zero c.e.~Turing degree, there exists a PR-degree that is low for punctual isomorphism (to be defined), and also a PR-degree that is a degree of punctual categoricity. △ Less

Submitted 9 March, 2026; originally announced March 2026.

Primitive recursive categoricity spectra

Authors: Nikolay Bazhenov, Heer Tern Koh, Keng Meng Ng

Abstract: We study the primitive recursive analogue of computable categoricity spectra for various natural classes of structures. We show that these notions coincide for all relatively $Δ_{2}^{0}$-categorical equivalence structures and linear orders, relatively $Δ_{3}^{0}$-categorical Boolean algebras, and computably categorical tree as partial orders. We study the primitive recursive analogue of computable categoricity spectra for various natural classes of structures. We show that these notions coincide for all relatively $Δ_{2}^{0}$-categorical equivalence structures and linear orders, relatively $Δ_{3}^{0}$-categorical Boolean algebras, and computably categorical tree as partial orders. △ Less

Submitted 9 March, 2026; originally announced March 2026.

Discovery of Strong Energy-Dependent X-ray Polarization in the Intermediate State of GS 1354-64

Authors: Swati Ravi, Lorenzo Marra, James F. Steiner, Guglielmo Mastroserio, Mason Ng, Joey Neilsen, Herman L. Marshall, Fiamma Capitanio, Sudeb Ranjan Datta, Elise Egron, Javier A. Garcia, Adam Ingram, Philip Kaaret, Ole Koenig, Honghui Liu, Romana Mikusincova, Edward J. R. Nathan, P. -O. Petrucci, Jakub Podgorny, Chiara Salvaggio, Jiri Svoboda, Alexandra Veledina, Yuexin Zhang

Abstract: We report the discovery of significant X-ray polarization from the dynamically confirmed black hole X-ray binary (BHXB) GS 1354-64 during its 2025-2026 outburst, obtained with the Imaging X-ray Polarimetry Explorer (IXPE). The observation, obtained shortly after a bright X-ray flare, captures the source in an intermediate state following a stalled (failed) state transition. We discover significant… ▽ More We report the discovery of significant X-ray polarization from the dynamically confirmed black hole X-ray binary (BHXB) GS 1354-64 during its 2025-2026 outburst, obtained with the Imaging X-ray Polarimetry Explorer (IXPE). The observation, obtained shortly after a bright X-ray flare, captures the source in an intermediate state following a stalled (failed) state transition. We discover significant 2-8 keV polarization at the ~4% level with high statistical support--14-sigma significance from frequentist analysis and log Bayes Factor 283+/-1 in a Bayesian framework--measuring PD 4.0+/-0.2% and PA-1+/-2 degrees (90% credible interval). The PD exhibits a statistically significant increasing trend with energy--the strongest such increase yet observed by IXPE in a BHXB--going from 2.1+/-0.3% in the 2-3 keV band to 11+/-3% in the 6.5-7 keV band, while the PA appears stable across both energy and time to within statistical uncertainties. Timing analysis of the IXPE data reveals a ~5 Hz Type-C quasi-periodic oscillation. IXPE + NuSTAR spectropolarimetric modeling suggests that the data can be described by polarized thermal disk and Comptonized components with PAs differing by ~90 degrees, or by a dominant Comptonized polarized component whose effective PD increases across the IXPE bandpass--the inferred component-level polarization levels are therefore model-dependent. In either picture, GS 1354-64 retains a strong coronal component during the transitional period observed by IXPE. These results illustrate how X-ray polarimetry can provide a sensitive diagnostic of the accretion state and geometry in black hole X-ray binary accretion flows, exploring a liminal phase at the cusp of state transition. △ Less

Submitted 3 March, 2026; originally announced March 2026.

Comments: 12 pages, 5 figures, Submitted to ApJL

On signs of coefficients of L-functions

Authors: Didier Lesesvre, Ming Ho Ng, Yingnan Wang

Abstract: We give a general lower bound on the frequency of sign changes in the real coefficients of L-functions of the Selberg class. We in particular recover existing results in the cases of GL(2) and GL(3), and obtain new bounds in the case of GSp(4). We give a general lower bound on the frequency of sign changes in the real coefficients of L-functions of the Selberg class. We in particular recover existing results in the cases of GL(2) and GL(3), and obtain new bounds in the case of GSp(4). △ Less

Submitted 3 March, 2026; originally announced March 2026.

Neural Operator-Grounded Continuous Tensor Function Representation and Its Applications

Authors: Ruoyang Su, Xi-Le Zhao, Sheng Liu, Wei-Hao Wu, Yisi Luo, Michael K. Ng

Abstract: Recently, continuous tensor functions have attracted increasing attention, because they can unifiedly represent data both on mesh grids and beyond mesh grids. However, since mode-$n$ product is essentially discrete and linear, the potential of current continuous tensor function representations is still locked. To break this bottleneck, we suggest neural operator-grounded mode-$n$ operators as a co… ▽ More Recently, continuous tensor functions have attracted increasing attention, because they can unifiedly represent data both on mesh grids and beyond mesh grids. However, since mode-$n$ product is essentially discrete and linear, the potential of current continuous tensor function representations is still locked. To break this bottleneck, we suggest neural operator-grounded mode-$n$ operators as a continuous and nonlinear alternative of discrete and linear mode-$n$ product. Instead of mapping the discrete core tensor to the discrete target tensor, proposed mode-$n$ operator directly maps the continuous core tensor function to the continuous target tensor function, which provides a genuine continuous representation of real-world data and can ameliorate discretization artifacts. Empowering with continuous and nonlinear mode-$n$ operators, we propose a neural operator-grounded continuous tensor function representation (abbreviated as NO-CTR), which can more faithfully represent complex real-world data compared with classic discrete tensor representations and continuous tensor function representations. Theoretically, we also prove that any continuous tensor function can be approximated by NO-CTR. To examine the capability of NO-CTR, we suggest an NO-CTR-based multi-dimensional data completion model. Extensive experiments across various data on regular mesh grids (multi-spectral images and color videos), on mesh girds with different resolutions (Sentinel-2 images) and beyond mesh grids (point clouds) demonstrate the superiority of NO-CTR. △ Less

Submitted 2 March, 2026; originally announced March 2026.

A steadily declining dispersion measure for the repeating fast radio burst FRB 20220529A: Evidence for an FRB engine embedded in an expanding supernova remnant

Authors: Ayush Pandhi, Kenzie Nimmo, Shion Andrew, Charanjot Brar, Shami Chatterjee, Amanda M. Cook, Alice Curtin, B. M. Gaensler, Marcin Gawroński, Jason Hessels, Victoria M. Kaspi, Afrokk Khan, Franz Kirsten, Mattias Lazda, Calvin Leung, Robert Main, Kiyoshi W. Masui, Ryan Mckinven, Daniele Michilli, Mason Ng, Omar Ould-Boukattine, Aaron B. Pearlman, Ziggy Pleunis, Alexander W. Pollak, Sachin Pradeep E. T. , et al. (7 additional authors not shown)

Abstract: We present the discovery and subsequent 3.2 year monitoring campaign of the repeating fast radio burst FRB 20220529A with CHIME/FRB. We observe a gradual dispersion measure (DM) decline of $-0.881\pm0.001~\mathrm{pc}~\mathrm{cm}^{-3}~\mathrm{year}^{-1}$ ($-1.235\pm0.001~\mathrm{pc}~\mathrm{cm}^{-3}~\mathrm{year}^{-1}$ in the rest frame), implying a $\geq3.5\pm0.2$% decrease of the total electron c… ▽ More We present the discovery and subsequent 3.2 year monitoring campaign of the repeating fast radio burst FRB 20220529A with CHIME/FRB. We observe a gradual dispersion measure (DM) decline of $-0.881\pm0.001~\mathrm{pc}~\mathrm{cm}^{-3}~\mathrm{year}^{-1}$ ($-1.235\pm0.001~\mathrm{pc}~\mathrm{cm}^{-3}~\mathrm{year}^{-1}$ in the rest frame), implying a $\geq3.5\pm0.2$% decrease of the total electron column in the source environment, and we see scattering timescale variations over weeks to years. We observe a short-lived excursion in which the DM rises by $\sim 1~\mathrm{pc}~\mathrm{cm}^{-3}$, immediately preceding a transient $\sim 2000~\mathrm{rad}~\mathrm{m}^{-2}$ Faraday rotation measure (RM) increase previously reported for this source, before returning to its gradual DM decline. We identify a local line-of-sight magnetic field around FRB 20220529A during this DM/RM excursion of $3.4 \pm 0.2~\mathrm{mG}$, corresponding to one of the most strongly magnetized FRB environments. We measure a decrease in the linear polarization fraction of FRB 20220529A bursts with decreasing frequency that we attribute to depolarization from multi-path propagation in the source environment. We also place a $5σ$ upper limit on the spectral luminosity of an associated persistent radio source of $\leq 5\times10^{28}~\mathrm{erg}~\mathrm{s}^{-1}~\mathrm{Hz}^{-1}$ at 1.5 GHz. These observations are consistent with FRB 20220529A originating from a young ($\sim$ years to centuries old) expanding supernova remnant, with short-lived DM and RM variability arising from interactions with the supernova remnant or with a binary companion. △ Less

Submitted 17 March, 2026; v1 submitted 25 February, 2026; originally announced February 2026.

Comments: 24 pages, 6 figures, accepted for publication in ApJL (only minor wording changes from the previous version; results are unchanged)

Probing the maximum energy of fast radio bursts using thousands of sources from the Second CHIME/FRB Catalog

Authors: Vishwangi Shah, Jason W. T. Hessels, Victoria M. Kaspi, Kiyoshi W. Masui, Mawson W. Sammons, Daniel Amouyal, Charanjot Brar, Shami Chatterjee, Alice P. Curtin, Hannah Didehbani, B. M. Gaensler, Naman Jain, Ronniy C. Joseph, Afrokk Khan, Bikash Kharel, Adam E. Lanman, Kyle McGregor, Ryan Mckinven, Mason Ng, Kenzie Nimmo, Ayush Pandhi, Aaron B. Pearlman, Alexander W. Pollak, Paul Scholz, Kaitlyn Shin , et al. (3 additional authors not shown)

Abstract: Quantifying the maximum energy of fast radio bursts (FRBs) can provide stringent constraints on their emission mechanisms and progenitor models. However, the most energetic bursts are rare, requiring a large sample of FRBs to detect them. In this work, we use the largest available such sample, 2,998 one-off FRBs from the Second CHIME/FRB Catalog, to obtain a lower limit on the maximum energy (… ▽ More Quantifying the maximum energy of fast radio bursts (FRBs) can provide stringent constraints on their emission mechanisms and progenitor models. However, the most energetic bursts are rare, requiring a large sample of FRBs to detect them. In this work, we use the largest available such sample, 2,998 one-off FRBs from the Second CHIME/FRB Catalog, to obtain a lower limit on the maximum energy ($E^{\mathrm{max}}_{\mathrm{iso}}$) of FRBs, assuming isotropic energy distribution from FRB sources. In the absence of known redshifts ($z$) for most sources, we present a framework that uses the dispersion measures (DMs) and fluences of these FRBs, together with the probability distribution of $z$ given DM, to derive the lower limit on $E^{\mathrm{max}}_{\mathrm{iso}}$. We generate simulated FRB samples assuming different parameter values for a log-normal $\mathrm{DM}_{\mathrm{host}}$ distribution and a Schechter function form of the FRB energy function to estimate how many outliers -- FRBs with large DM contributions from the host galaxy or intervening galaxy halos -- could artificially inflate this limit. After accounting for outliers, the lower limit on $E^{\mathrm{max}}_{\mathrm{iso}}$ from Catalog 2 FRBs ranges between $1.2\times10^{41}$ and $1.9\times10^{42}$ erg, with best estimate $1.2\times10^{42}$ erg. This limit is consistent with those derived from much smaller FRB samples. Moreover, inferred energies of hundreds of FRBs appear collectively limited around $\sim10^{42}$ erg, suggesting a physical limit on the energy reservoir of FRB sources. The corresponding isotropic-equivalent FRB source energy is consistent with the total energy available in a magnetar's external dipole magnetic field, supporting magnetars as FRB progenitors. △ Less

Submitted 22 February, 2026; originally announced February 2026.

Comments: Submitted to ApJ

The Game-Theoretic Katětov Order and Idealised Effective Subtoposes

Authors: Takayuki Kihara, Ming Ng

Abstract: This paper addresses the longstanding problem of determining the structure of the $\leq_{\mathrm{LT}}$-order in the Effective Topos, known to effectively embed the Turing degrees. In a surprising discovery, we show that the $\leq_{\mathrm{LT}}$-order is in fact tightly controlled by the combinatorics of filters on $ω$, raising deep questions about how combinatorial and computable complexity intera… ▽ More This paper addresses the longstanding problem of determining the structure of the $\leq_{\mathrm{LT}}$-order in the Effective Topos, known to effectively embed the Turing degrees. In a surprising discovery, we show that the $\leq_{\mathrm{LT}}$-order is in fact tightly controlled by the combinatorics of filters on $ω$, raising deep questions about how combinatorial and computable complexity interact, both within this order and beyond it. To make the connection precise, we introduce a game-theoretic (''gamified'') variant of the Katětov order on filters over $ω$, which turns out to exhibit a striking mix of coarseness and subtlety. For one, it is strictly coarser than the classical Rudin-Keisler order and, when viewed dually on ideals, collapses all MAD families to a single equivalence class. On the other hand, the order also supports a rich internal structure, including an infinite strictly ascending chain of ideal classes, which we identify by way of a new separation technique. From the computability-theoretic perspective, we show that a computable (and extended) variant of the gamified Katětov order is isomorphic to the original $\leq_{\mathrm{LT}}$-order. Moreover, our work brings into focus a new degree-spectrum invariant for filters $\mathcal{F}$, $$\mathcal{D}_{\mathrm{T}}(\mathcal{F}):=\{\,[f\colonω\toω] \mid f\leq_{\mathrm{LT}} \mathcal{F} \},$$ which is shown to always determine a proper initial segment of the Turing degrees. Extending this, given any $Δ^1_1$ filter $\mathcal{F}$, we show that $\mathcal{D}_{\mathrm{T}}(\mathcal{F})$ is precisely the class of hyperarithmetic degrees. This significantly generalises previous results obtained by van Oosten \cite{vO14} and Kihara \cite{Kih23}. The proofs draw on ideas from general topology, descriptive set theory, and computability theory. △ Less

Submitted 8 February, 2026; originally announced February 2026.

MSC Class: 03E05; 03E75; 03E15; 03D28; 18B25

TVWorld: Foundations for Remote-Control TV Agents

Authors: Zhantao Ma, Quanfeng Lu, Shuai Zhong, Dahai Yu, Ping Luo, Michael K. Ng

Abstract: Recent large vision-language models (LVLMs) have demonstrated strong potential for device control. However, existing research has primarily focused on point-and-click (PnC) interaction, while remote-control (RC) interaction commonly encountered in everyday TV usage remains largely underexplored. To fill this gap, we introduce \textbf{TVWorld}, an offline graph-based abstraction of real-world TV na… ▽ More Recent large vision-language models (LVLMs) have demonstrated strong potential for device control. However, existing research has primarily focused on point-and-click (PnC) interaction, while remote-control (RC) interaction commonly encountered in everyday TV usage remains largely underexplored. To fill this gap, we introduce \textbf{TVWorld}, an offline graph-based abstraction of real-world TV navigation that enables reproducible and deployment-free evaluation. On this basis, we derive two complementary benchmarks that comprehensively assess TV-use capabilities: \textbf{TVWorld-N} for topology-aware navigation and \textbf{TVWorld-G} for focus-aware grounding. These benchmarks expose a key limitation of existing agents: insufficient topology awareness for focus-based, long-horizon TV navigation. Motivated by this finding, we propose a \emph{Topology-Aware Training} framework that injects topology awareness into LVLMs. Using this framework, we develop \textbf{TVTheseus}, a foundation model specialized for TV navigation. TVTheseus achieves a success rate of $68.3\%$ on TVWorld-N, surpassing strong closed-source baselines such as Gemini 3 Flash and establishing state-of-the-art (SOTA) performance. Additional analyses further provide valuable insights into the development of effective TV-use agents. △ Less

Submitted 19 January, 2026; originally announced January 2026.

Discovery of High X-Ray Polarization from the Neutron Star Low-Mass X-Ray Binary Cyg X-2 in the Horizontal Branch

Authors: Andrea Gnarini, Swati Ravi, Philip Kaaret, Anna Bobrikova, Juri Poutanen, Sofia V. Forsblom, Francesco Ursini, Maria Cristina Baglio, Stefano Bianchi, Fiamma Capitanio, Massimo Cocchi, Maria Alejandra Diaz Teodori, Sergio Fabiani, Ruben Farinelli, Giorgio Matt, Mason Ng, Alexander Salganik, Paolo Soffitta, Antonella Tarana, Silvia Zane

Abstract: We present results from simultaneous X-ray polarimetric and spectroscopic observations of the bright neutron star low-mass X-ray binary Cyg X-2, performed by the Imaging X-ray Polarimetry Explorer (IXPE) and the Nuclear Spectroscopic Telescope Array (NuSTAR). IXPE detected significant polarization (15 sigma) from the source in the 2-8 keV energy band with an average polarization degree (PD) of 4.5… ▽ More We present results from simultaneous X-ray polarimetric and spectroscopic observations of the bright neutron star low-mass X-ray binary Cyg X-2, performed by the Imaging X-ray Polarimetry Explorer (IXPE) and the Nuclear Spectroscopic Telescope Array (NuSTAR). IXPE detected significant polarization (15 sigma) from the source in the 2-8 keV energy band with an average polarization degree (PD) of 4.5% +/- 0.3% and a polarization angle (PA) of 128 +/- 2 degrees as the source moved along the horizontal branch of its Z-track. The PD increases with energy reaching 9.9% +/- 2.8% in the 7-8 keV band, with no evidence for energy-dependent variation in the PA. The PA is roughly consistent with previous measurements obtained during the normal and flaring branches and also with the known radio jet axis. From spectropolarimetric analysis, the main contribution to the polarized radiation is due to Comptonized photons, but the polarization is higher than predicted in typical spreading layer geometries. The observed high polarization may be due to a combination of a highly polarized reflected component and a moderately polarized spreading layer on the neutron star surface or produced by electron scattering in an equatorial wind. △ Less

Submitted 16 January, 2026; originally announced January 2026.

Comments: to appear in ApJ

The Second CHIME/FRB Catalog of Fast Radio Bursts

Authors: The CHIME/FRB Collaboration, :, Thomas Abbott, Bridget C. Andersen, Shion Andrew, Kevin Bandura, Mohit Bhardwaj, Yash Bhusare, Charanjot Brar, Tomas Cassanelli, Shami Chatterjee, Jean-Francois Cliche, Amanda M. Cook, Alice Curtin, Matt Dobbs, Fengqiu Adam Dong, Gwendolyn Eadie, Tarraneh Eftekhari, Emmanuel Fonseca, B. M. Gaensler, Deborah Good, Mark Halpern, Jason W. T. Hessels, Adaeze Ibik, Naman Jain , et al. (50 additional authors not shown)

Abstract: We present a catalog of 4539 fast radio bursts (FRBs) observed with the Canadian Hydrogen Intensity Mapping Experiment (CHIME) telescope between 25 July 2018 and 15 September 2023. These bursts originate from 3641 unique sources, including 981 bursts from 83 known repeating sources. For each FRB, the catalog provides a $O(10')$ estimate of sky location along with corresponding measurements of cumu… ▽ More We present a catalog of 4539 fast radio bursts (FRBs) observed with the Canadian Hydrogen Intensity Mapping Experiment (CHIME) telescope between 25 July 2018 and 15 September 2023. These bursts originate from 3641 unique sources, including 981 bursts from 83 known repeating sources. For each FRB, the catalog provides a $O(10')$ estimate of sky location along with corresponding measurements of cumulative exposure time and survey sensitivity over the observing period. It includes a total-intensity dynamic spectrum between 400 and 800 MHz at 0.983 ms resolution. From this spectrum, we constrain a model of the burst morphology and measure key parameters such as arrival time, intrinsic temporal width, dispersion measure, scattering time, and flux density. This second catalog includes all FRBs from the first catalog, with every event reprocessed using a uniform and improved analysis framework. We show that previously published inferences remain valid under the updated measurements. We assess consistency of the detection rate across observational parameters, present initial distributions of burst properties, and outline ongoing and future studies that will use this catalog to investigate the nature of FRBs and their utility as astrophysical and cosmological probes. △ Less

Submitted 14 January, 2026; originally announced January 2026.

Comments: 36 pages, 15 figures, 4 tables. Extended figures and data at http://www.chime-frb.ca/catalog2

FinForge: Semi-Synthetic Financial Benchmark Generation

Authors: Glenn Matlin, Akhil Theerthala, Anant Gupta, Anirudh JM, Rayan Castilla, Yi Mei Ng, Sudheer Chava

Abstract: Evaluating Language Models (LMs) in specialized, high-stakes domains such as finance remains a significant challenge due to the scarcity of open, high-quality, and domain-specific datasets. Existing general-purpose benchmarks provide broad coverage but lack the depth and domain fidelity needed to assess LMs' capabilities for real-world financial reasoning, which requires both conceptual understand… ▽ More Evaluating Language Models (LMs) in specialized, high-stakes domains such as finance remains a significant challenge due to the scarcity of open, high-quality, and domain-specific datasets. Existing general-purpose benchmarks provide broad coverage but lack the depth and domain fidelity needed to assess LMs' capabilities for real-world financial reasoning, which requires both conceptual understanding and quantitative rigor. To address this gap, we introduce FinForge, a scalable, semi-synthetic pipeline for constructing finance-specific evaluation benchmarks through a hybrid of expert-guided data curation and controlled LM-based synthesis. FinForge combines manual and programmatic corpus construction from authoritative financial sources with structured question generation and validation using Gemini 2.5 Flash. To demonstrate the pipeline's efficacy, we produce FinForge-5k, a snapshot benchmark comprising over 5,000 human-validated question-answer pairs across 11 finance subdomains, derived from a curated corpus of 100,000 verified documents totaling 143M tokens. Evaluation of state-of-the-art open-source and closed-source models on FinForge-5k reveals significant differences in financial reasoning, with leading models achieving accuracy levels near 80%. These findings underscore the framework's utility for diagnosing current model limitations and guiding future improvements in financial domain competence. All code and data are available at https://github.com/gtfintechlab/FinForge. △ Less

Submitted 19 January, 2026; v1 submitted 10 January, 2026; originally announced January 2026.

When Smaller Wins: Dual-Stage Distillation and Pareto-Guided Compression of Liquid Neural Networks for Edge Battery Prognostics

Authors: Dhivya Dharshini Kannan, Wei Li, Wei Zhang, Jianbiao Wang, Zhi Wei Seh, Man-Fai Ng

Abstract: Battery management systems increasingly require accurate battery health prognostics under strict on-device constraints. This paper presents DLNet, a practical framework with dual-stage distillation of liquid neural networks that turns a high-capacity model into compact and edge-deployable models for battery health prediction. DLNet first applies Euler discretization to reformulate liquid dynamics… ▽ More Battery management systems increasingly require accurate battery health prognostics under strict on-device constraints. This paper presents DLNet, a practical framework with dual-stage distillation of liquid neural networks that turns a high-capacity model into compact and edge-deployable models for battery health prediction. DLNet first applies Euler discretization to reformulate liquid dynamics for embedded compatibility. It then performs dual-stage knowledge distillation to transfer the teacher model's temporal behavior and recover it after further compression. Pareto-guided selection under joint error-cost objectives retains student models that balance accuracy and efficiency. We evaluate DLNet on a widely used dataset and validate real-device feasibility on an Arduino Nano 33 BLE Sense using int8 deployment. The final deployed student achieves a low error of 0.0066 when predicting battery health over the next 100 cycles, which is 15.4% lower than the teacher model. It reduces the model size from 616 kB to 94 kB with 84.7% reduction and takes 21 ms per inference on the device. These results support a practical smaller wins observation that a small model can match or exceed a large teacher for edge-based prognostics with proper supervision and selection. Beyond batteries, the DLNet framework can extend to other industrial analytics tasks with strict hardware constraints. △ Less

Submitted 12 January, 2026; v1 submitted 9 January, 2026; originally announced January 2026.

Comments: Submitted to International Conference on Pattern Recognition, ICPR 2026

Sparse Tucker Decomposition and Graph Regularization for High-Dimensional Time Series Forecasting

Authors: Sijia Xia, Michael K. Ng, Xiongjun Zhang

Abstract: Existing methods of vector autoregressive model for multivariate time series analysis make use of low-rank matrix approximation or Tucker decomposition to reduce the dimension of the over-parameterization issue. In this paper, we propose a sparse Tucker decomposition method with graph regularization for high-dimensional vector autoregressive time series. By stacking the time-series transition matr… ▽ More Existing methods of vector autoregressive model for multivariate time series analysis make use of low-rank matrix approximation or Tucker decomposition to reduce the dimension of the over-parameterization issue. In this paper, we propose a sparse Tucker decomposition method with graph regularization for high-dimensional vector autoregressive time series. By stacking the time-series transition matrices into a third-order tensor, the sparse Tucker decomposition is employed to characterize important interactions within the transition third-order tensor and reduce the number of parameters. Moreover, the graph regularization is employed to measure the local consistency of the response, predictor and temporal factor matrices in the vector autoregressive model.The two proposed regularization techniques can be shown to more accurate parameters estimation. A non-asymptotic error bound of the estimator of the proposed method is established, which is lower than those of the existing matrix or tensor based methods. A proximal alternating linearized minimization algorithm is designed to solve the resulting model and its global convergence is established under very mild conditions. Extensive numerical experiments on synthetic data and real-world datasets are carried out to verify the superior performance of the proposed method over existing state-of-the-art methods. △ Less

Submitted 1 January, 2026; originally announced January 2026.

Online Inference of Constrained Optimization: Primal-Dual Optimality and Sequential Quadratic Programming

Authors: Yihang Gao, Michael K. Ng, Michael W. Mahoney, Sen Na

Abstract: We study online statistical inference for the solutions of stochastic optimization problems with equality and inequality constraints. Such problems are prevalent in statistics and machine learning, encompassing constrained $M$-estimation, physics-informed models, safe reinforcement learning, and algorithmic fairness. We develop a stochastic sequential quadratic programming (SSQP) method to solve t… ▽ More We study online statistical inference for the solutions of stochastic optimization problems with equality and inequality constraints. Such problems are prevalent in statistics and machine learning, encompassing constrained $M$-estimation, physics-informed models, safe reinforcement learning, and algorithmic fairness. We develop a stochastic sequential quadratic programming (SSQP) method to solve these problems, where the step direction is computed by sequentially performing a quadratic approximation of the objective and a linear approximation of the constraints. Despite having access to unbiased estimates of population gradients, a key challenge in constrained stochastic problems lies in dealing with the bias in the step direction. As such, we apply a momentum-style gradient moving-average technique within SSQP to debias the step. We show that our method achieves global almost-sure convergence and exhibits local asymptotic normality with an optimal primal-dual limiting covariance matrix in the sense of Hájek and Le Cam. In addition, we provide a plug-in covariance matrix estimator for practical inference. To our knowledge, the proposed SSQP method is the first fully online method that attains primal-dual asymptotic minimax optimality without relying on projection operators onto the constraint set, which are generally intractable for nonlinear problems. Through extensive experiments on benchmark nonlinear problems, as well as on constrained generalized linear models and portfolio allocation problems using both synthetic and real data, we demonstrate superior performance of our method, showing that the method and its asymptotic behavior not only solve constrained stochastic problems efficiently but also provide valid and practical online inference in real-world applications. △ Less

Submitted 27 November, 2025; originally announced December 2025.

Comments: 80 pages, 5 figures, 5 tables

MultiGA: Leveraging Multi-Source Seeding in Genetic Algorithms

Authors: Isabelle Diana May-Xin Ng, Tharindu Cyril Weerasooriya, Haitao Zhu, Wei Wei

Abstract: In this paper, we introduce, MultiGA, an optimization framework which applies genetic algorithm principles to address complex natural language tasks and reasoning problems by sampling from a diverse population of LLMs to initialize the population of candidate solutions. MultiGA generates a range of outputs from various parent LLMs and uses a neutral fitness function to evaluate them. Through an it… ▽ More In this paper, we introduce, MultiGA, an optimization framework which applies genetic algorithm principles to address complex natural language tasks and reasoning problems by sampling from a diverse population of LLMs to initialize the population of candidate solutions. MultiGA generates a range of outputs from various parent LLMs and uses a neutral fitness function to evaluate them. Through an iterative recombination process, we mix and refine these generations until an optimal solution is achieved. Our results show that MultiGA produces high accuracy across multiple benchmarks, and these insights lay the foundation for future research looking closer at integrating multiple LLMs for unexplored tasks in which selecting only one pre-trained model is unclear or suboptimal. △ Less

Submitted 2 April, 2026; v1 submitted 21 November, 2025; originally announced December 2025.

NeuMatC: A General Neural Framework for Fast Parametric Matrix Operation

Authors: Chuan Wang, Xi-le Zhao, Zhilong Han, Liang Li, Deyu Meng, Michael K. Ng

Abstract: Matrix operations (e.g., inversion and singular value decomposition (SVD)) are fundamental in science and engineering. In many emerging real-world applications (such as wireless communication and signal processing), these operations must be performed repeatedly over matrices with parameters varying continuously. However, conventional methods tackle each matrix operation independently, underexplori… ▽ More Matrix operations (e.g., inversion and singular value decomposition (SVD)) are fundamental in science and engineering. In many emerging real-world applications (such as wireless communication and signal processing), these operations must be performed repeatedly over matrices with parameters varying continuously. However, conventional methods tackle each matrix operation independently, underexploring the inherent low-rankness and continuity along the parameter dimension, resulting in significantly redundant computation. To address this challenge, we propose \textbf{\textit{Neural Matrix Computation Framework} (NeuMatC)}, which elegantly tackles general parametric matrix operation tasks by leveraging the underlying low-rankness and continuity along the parameter dimension. Specifically, NeuMatC unsupervisedly learns a low-rank and continuous mapping from parameters to their corresponding matrix operation results. Once trained, NeuMatC enables efficient computations at arbitrary parameters using only a few basic operations (e.g., matrix multiplications and nonlinear activations), significantly reducing redundant computations. Experimental results on both synthetic and real-world datasets demonstrate the promising performance of NeuMatC, exemplified by over $3\times$ speedup in parametric inversion and $10\times$ speedup in parametric SVD compared to the widely used NumPy baseline in wireless communication, while maintaining acceptable accuracy. △ Less

Submitted 28 November, 2025; originally announced November 2025.

Blind Deconvolution for Color Images Using Normalized Quaternion Kernels

Authors: Yuming Yang, Michael K. Ng, Zhigang Jia, Wei Wang

Abstract: In this work, we address the challenging problem of blind deconvolution for color images. Existing methods often convert color images to grayscale or process each color channel separately, which overlooking the relationships between color channels. To handle this issue, we formulate a novel quaternion fidelity term designed specifically for color image blind deconvolution. This fidelity term lever… ▽ More In this work, we address the challenging problem of blind deconvolution for color images. Existing methods often convert color images to grayscale or process each color channel separately, which overlooking the relationships between color channels. To handle this issue, we formulate a novel quaternion fidelity term designed specifically for color image blind deconvolution. This fidelity term leverages the properties of quaternion convolution kernel, which consists of four kernels: one that functions similarly to a non-negative convolution kernel to capture the overall blur, and three additional convolution kernels without constraints corresponding to red, green and blue channels respectively model their unknown interdependencies. In order to preserve image intensity, we propose to use the normalized quaternion kernel in the blind deconvolution process. Extensive experiments on real datasets of blurred color images show that the proposed method effectively removes artifacts and significantly improves deblurring effect, demonstrating its potential as a powerful tool for color image deconvolution. △ Less

Submitted 21 November, 2025; originally announced November 2025.

Pulse profile modelling of the accretion-powered millisecond pulsar SAX J1808.4-3658 using NICER data from its 2019 and 2022 outbursts

Authors: Bas Dorsman, Tuomo Salmi, Anna L. Watts, Mason Ng, Anna Bobrikova, Vladislav Loktev, Juri Poutanen, Joern Wilms

Abstract: Pulse profile modelling is a relativistic ray-tracing technique that has provided constraints on parameters, with a focus on mass and radius, of five rotation-powered millisecond pulsars. While the technique can also be applied to accretion-powered millisecond pulsars (AMPs), this requires accounting for the X-rays from the accretion disc and has only been applied to archival data from the Rossi X… ▽ More Pulse profile modelling is a relativistic ray-tracing technique that has provided constraints on parameters, with a focus on mass and radius, of five rotation-powered millisecond pulsars. While the technique can also be applied to accretion-powered millisecond pulsars (AMPs), this requires accounting for the X-rays from the accretion disc and has only been applied to archival data from the Rossi X-ray Timing Explorer. Here, we apply a previously developed neutron star and accretion disc model to the NICER (Neutron star Interior Composition Explorer) data of the 2019 and 2022 outbursts of SAX J1808.4-3658. We find that a single circular hotspot model is insufficient to explain the data. Modelling with two hotspots and an accretion disc model provides better phase-residuals, but a spectral residual at around 1 keV remains. In contrast, we find a good fit with a flexible background approach, replacing the accretion disk. However, the inferred parameters are not robust due to a degeneracy in the origin of the non-pulsed radiation, which can be caused either by the background or a hotspot that is at least partially in view throughout a full rotation. This work represents an important next step in pulse profile modelling of AMPs by analysing NICER data and underlines the need for more accurate accretion disc and hotspot modelling to achieve robust parameter constraints. We expect the inclusion of higher energy and polarimetric data will provide complementary constraints on inclination, hotspot colatitude, and hotspot size, improving the accuracy of pulse profile modelling of AMPs. △ Less

Submitted 10 November, 2025; originally announced November 2025.

Comments: 21 pages, 11 figures, 3 tables. Accepted in MNRAS

A Provably-Correct and Robust Convex Model for Smooth Separable NMF

Authors: Junjun Pan, Valentin Leplat, Michael Ng, Nicolas Gillis

Abstract: Nonnegative matrix factorization (NMF) is a linear dimensionality reduction technique for nonnegative data, with applications such as hyperspectral unmixing and topic modeling. NMF is a difficult problem in general (NP-hard), and its solutions are typically not unique. To address these two issues, additional constraints or assumptions are often used. In particular, separability assumes that the ba… ▽ More Nonnegative matrix factorization (NMF) is a linear dimensionality reduction technique for nonnegative data, with applications such as hyperspectral unmixing and topic modeling. NMF is a difficult problem in general (NP-hard), and its solutions are typically not unique. To address these two issues, additional constraints or assumptions are often used. In particular, separability assumes that the basis vectors in the NMF are equal to some columns of the input matrix. In that case, the problem is referred to as separable NMF (SNMF) and can be solved in polynomial-time with robustness guarantees, while identifying a unique solution. However, in real-world scenarios, due to noise or variability, multiple data points may lie near the basis vectors, which SNMF does not leverage. In this work, we rely on the smooth separability assumption, which assumes that each basis vector is close to multiple data points. We explore the properties of the corresponding problem, referred to as smooth SNMF (SSNMF), and examine how it relates to SNMF and orthogonal NMF. We then propose a convex model for SSNMF and show that it provably recovers the sought-after factors, even in the presence of noise. We finally adapt an existing fast gradient method to solve this convex model for SSNMF, and show that it compares favorably with state-of-the-art methods on both synthetic and hyperspectral datasets. △ Less

Submitted 10 November, 2025; originally announced November 2025.

Comments: 30 pages, 10 figures, code available from https://github.com/vleplat/ConvexSmoothSeparableNMF.git

The Advanced X-ray Imaging Satellite (AXIS) Community Science Book

Authors: Michael Koss, Nafisa Aftab, Steven W. Allen, Roberta Amato, Hongjun An, Igor Andreoni, Timo Anguita, Riccardo Arcodia, Thomas Ayres, Matteo Bachetti, Maria Cristina Baglio, Arash Bahramian, Marco Balboni, Ranieri D. Baldi, Solen Balman, Aya Bamba, Eduardo Banados, Tong Bao, Iacopo Bartalucci, Antara Basu-Zych, Rebeca Batalha, Lorenzo Battistini, Franz Erik Bauer, Andy Beardmore, Werner Becker , et al. (373 additional authors not shown)

Abstract: The AXIS Community Science Book represents the collective effort of 592 scientists worldwide to define the transformative science enabled by the Advanced X-ray Imaging Satellite (AXIS), a next-generation X-ray mission selected by NASA's Astrophysics Probe Program for Phase A study. AXIS will advance the legacy of high-angular-resolution X-ray astronomy with ~1.5'' imaging over a wide 24' field of… ▽ More The AXIS Community Science Book represents the collective effort of 592 scientists worldwide to define the transformative science enabled by the Advanced X-ray Imaging Satellite (AXIS), a next-generation X-ray mission selected by NASA's Astrophysics Probe Program for Phase A study. AXIS will advance the legacy of high-angular-resolution X-ray astronomy with ~1.5'' imaging over a wide 24' field of view and an order of magnitude greater collecting area than Chandra in the 0.3-12 keV band. Combining sharp imaging, high throughput, and rapid response capabilities, AXIS will open new windows on virtually every aspect of modern astrophysics, exploring the birth and growth of supermassive black holes, the feedback processes that shape galaxies, the life cycles of stars and exoplanet environments, and the nature of compact stellar remnants, supernova remnants, and explosive transients. This book compiles 138 community-contributed science cases developed by five Science Working Groups focused on AGN and supermassive black holes, galaxy evolution and feedback, compact objects and supernova remnants, stellar physics and exoplanets, and time-domain and multi-messenger astrophysics. Together, these studies establish the scientific foundation for next-generation X-ray exploration in the 2030s and highlight strong synergies with facilities of the 2030s, such as JWST, Roman, Rubin/LSST, SKA, ALMA, ngVLA, and next-generation gravitational-wave and neutrino networks. △ Less

Submitted 6 January, 2026; v1 submitted 31 October, 2025; originally announced November 2025.

Comments: 592 pages, 226 figures, version 2

Global YouTube Trending Dataset (2022-2025): Three Years of Platform-Curated, Cross-National Trends in Digital Culture

Authors: Alexandre Goncalves, Yee Man Margaret Ng

Abstract: On July 1, 2025, YouTube retired its decade-long public "Trending" pages, ending platform-curated, non-personalized video discovery. The Trending list had long served as a vital lens into algorithmic influence, cultural diffusion, and crisis communication globally, offering a rare "ground-truth" reference to study global attention and cultural salience. We present a three-year archival dataset of… ▽ More On July 1, 2025, YouTube retired its decade-long public "Trending" pages, ending platform-curated, non-personalized video discovery. The Trending list had long served as a vital lens into algorithmic influence, cultural diffusion, and crisis communication globally, offering a rare "ground-truth" reference to study global attention and cultural salience. We present a three-year archival dataset of YouTube Trending videos, collected from July 1, 2022, to June 30, 2025, with four daily snapshots for each of the 104 countries. The dataset includes 446,971 snapshots, each capturing up to 200 trending videos, encompassing 78.4 million video entries (726,627 unique videos) and associated metadata. Each record includes core identifiers (snapshot time, country, rank) and content metadata (video ID, channel ID, title, description, tags, publication date, category, channel name, language, live status, views, and comments). Unlike previous datasets with limited geographic scope or short timeframes, our non-personalized data provides exceptional cross-national and longitudinal coverage for studying digital culture, platform governance, and temporal dynamics in content popularity. We document the data collection methodology, schema design, coverage, descriptive statistics for both global and U.S. trending videos, and the ethical safeguards implemented throughout. △ Less

Submitted 24 October, 2025; originally announced October 2025.

$K_1(Var)$ is presented by stratified birational equivalences

Authors: Ming Ng

Abstract: This paper provides a complete presentation of $K_1(Var)$, the $K_1$ group of varieties, resolving and simplifying a problem left open in \cite{ZakhK1}. Our approach adapts Gillet-Grayson's $G$-Construction to define an un-delooped $K$-theory spectrum of varieties. There are two levels on which one can read the present paper. On a technical level, we streamline and extend previous results on the… ▽ More This paper provides a complete presentation of $K_1(Var)$, the $K_1$ group of varieties, resolving and simplifying a problem left open in \cite{ZakhK1}. Our approach adapts Gillet-Grayson's $G$-Construction to define an un-delooped $K$-theory spectrum of varieties. There are two levels on which one can read the present paper. On a technical level, we streamline and extend previous results on the $K$-theory of exact categories to a broader class of categories, including $Var$. On a more conceptual level, our investigations bring into focus an interesting generalisation of automorphisms (``double exact squares'') which generate $K_1$. For varieties, this corresponds to what we call stratified birational equivalences, but the construction extends to a wide range of non-additive contexts (e.g. $o$-minimal structures, definable sets etc.). This raises a challenging question: what kind of information do these generalised automorphisms calibrate? △ Less

Submitted 5 April, 2026; v1 submitted 23 October, 2025; originally announced October 2025.

Comments: 47 pages + 22 pages appendix (Minor expository changes -- clarified what "stratified birational equivalence" means for the reducible case.)

MSC Class: 19D99; 13D15; 14E07; 19E99; 18F30

A DeepLagrangian method for learning and generating aggregation patterns in multi-dimensional Keller-Segel chemotaxis systems

Authors: Yani Feng, Michael K. Ng, Zhiwen Zhang

Abstract: The Keller-Segel (KS) chemotaxis system is used to describe the overall behavior of a collection of cells under the influence of chemotaxis. However, solving the KS chemotaxis system and generating its aggregation patterns remain challenging due to the emergence of solutions exhibiting near-singular behavior, such as finite-time blow-up or concentration phenomena. Building on a Lagrangian framewor… ▽ More The Keller-Segel (KS) chemotaxis system is used to describe the overall behavior of a collection of cells under the influence of chemotaxis. However, solving the KS chemotaxis system and generating its aggregation patterns remain challenging due to the emergence of solutions exhibiting near-singular behavior, such as finite-time blow-up or concentration phenomena. Building on a Lagrangian framework of the KS system, we develop DeepLagrangian, a self-adaptive density estimation method that learns and generates aggregation patterns and near-singular solutions of the KS system in two- and three-dimensional (2D and 3D) space under different physical parameters. The main advantage of the Lagrangian framework is its inherent ability to adapt to near-singular solutions. To develop this framework, we normalize the KS solution into a probability density function (PDF), derive the corresponding normalized KS system, and utilize the property of the continuity equation to rewrite the system into a Lagrangian framework. We then define a physics-informed Lagrangian loss to enforce this framework and incorporate a flow-based generative model, called the time-dependent KRnet, to approximate the PDF by minimizing the loss. Furthermore, we integrate time-marching strategies with the time-dependent KRnet to enhance the accuracy of the PDF approximation. After obtaining the approximate PDF, we recover the original KS solution. We also prove that the Lagrangian loss effectively controls the Kullback-Leibler (KL) divergence between the approximate PDF and the exact PDF. In the numerical experiments, we demonstrate the accuracy of our DeepLagrangian method for the 2D and 3D KS chemotaxis system with/without advection. △ Less

Submitted 16 October, 2025; originally announced October 2025.

Functional tensor train neural network for solving high-dimensional PDEs

Authors: Yani Feng, Michael K. Ng, Kejun Tang, Zhiwen Zhang

Abstract: Discrete tensor train decomposition is widely employed to mitigate the curse of dimensionality in solving high-dimensional PDEs through traditional methods. However, the direct application of the tensor train method typically requires uniform grids of regular domains, which limits its application on non-uniform grids or irregular domains. To address the limitation, we develop a functional tensor t… ▽ More Discrete tensor train decomposition is widely employed to mitigate the curse of dimensionality in solving high-dimensional PDEs through traditional methods. However, the direct application of the tensor train method typically requires uniform grids of regular domains, which limits its application on non-uniform grids or irregular domains. To address the limitation, we develop a functional tensor train neural network (FTTNN) for solving high-dimensional PDEs, which can represent PDE solutions on non-uniform grids or irregular domains. An essential ingredient of our approach is to represent the PDE solutions by the functional tensor train format whose TT-core functions are approximated by neural networks. To give the functional tensor train representation, we propose and study functional tensor train rank and employ it into a physics-informed loss function for training. Because of tensor train representation, the resulting high-dimensional integral in the loss function can be computed via one-dimensional integrals by Gauss quadrature rules. Numerical examples including high-dimensional PDEs on regular or irregular domains are presented to demonstrate that the performance of the proposed FTTNN is better than that of Physics Informed Neural Networks (PINN). △ Less

Submitted 15 October, 2025; originally announced October 2025.

First X-ray and radio polarimetry of the neutron star low-mass X-ray binary GX 17+2

Authors: Unnati Kashyap, Thomas J. Maccarone, Eliot C. Pattie, Mason Ng, Swati Ravi, Alexandra J. Tetarenko, Pau Bosch Cabot, Herman L. Marshall

Abstract: We report the first polarimetric results of the neutron star (NS) low-mass X-ray binary (LMXB) Z-source GX 17+2 using the Imaging X-ray Polarimetry Explorer (IXPE) and the Very Large Array (VLA). We find that the X-ray source was polarized at PD = 1.9 +/- 0.3 % (1-sigma errors) with a polarization angle of PA = 11 +/- 4 degree (1-sigma errors). Simultaneous Nuclear Spectroscopic Telescope Array (N… ▽ More We report the first polarimetric results of the neutron star (NS) low-mass X-ray binary (LMXB) Z-source GX 17+2 using the Imaging X-ray Polarimetry Explorer (IXPE) and the Very Large Array (VLA). We find that the X-ray source was polarized at PD = 1.9 +/- 0.3 % (1-sigma errors) with a polarization angle of PA = 11 +/- 4 degree (1-sigma errors). Simultaneous Nuclear Spectroscopic Telescope Array (NuSTAR) observations show that the source was in the normal branch (NB) during our IXPE observations. The X-ray spectro-polarimetry results suggest a source geometry comprising an accretion disk component, a Comptonization component, and a reflection component. The VLA radio polarization study shows a PD = 2.2 +/- 0.2 % with a Faraday-corrected intrinsic polarization angle of 1 +/- 5 degree, which is an indication of the jet axis. Thus, we find the estimated X-ray PA from the source is consistent with the radio PA. We discuss the accretion geometry of the Z-source in light of our X-ray spectro-polarimetry and radio findings. △ Less

Submitted 6 October, 2025; originally announced October 2025.

Comments: 13 pages, 5 Figures, 6 Tables, Accepted for publication in APJ

Homogeneous Linear Orderings: Index sets, Approximations and Categoricity

Authors: Wesley Calvert, Douglas Cenzer, David Gonzalez, Valentina Harizanov, Keng Meng Ng

Abstract: We study linear orderings expanded by functions for successor and predecessor. In particular, the sp-homogeneous and weakly sp-homogeneous linear orderings are those which are homogeneous or weakly homogeneous with this additional structure. We demonstrate that these orderings are always relatively $Δ_4$ categorical and determine exactly which ones are (uniformly) relatively $Δ_3$ categorical. W… ▽ More We study linear orderings expanded by functions for successor and predecessor. In particular, the sp-homogeneous and weakly sp-homogeneous linear orderings are those which are homogeneous or weakly homogeneous with this additional structure. We demonstrate that these orderings are always relatively $Δ_4$ categorical and determine exactly which ones are (uniformly) relatively $Δ_3$ categorical. We also provide a classification for sp-homogeneity and weak sp-homogeneity. We establish that this is the best possible classification by showing that the set of sp-homogeneous linear orderings is $Π_5^0$-complete and that the set of weakly sp-homogeneous linear orderings is $Σ_6^0$-complete. This result is obtained in two different ways, one using a hands-on computability theoretic approach and another using more abstract descriptive set theory. In understanding the categoricity and classification of sp-homogeneous orderings, we define a sequence of strong homogeneity notions that approximate sp-homogeneity called $C_{n,m}$-homogeneity. For finite values of $n,m$ there are only finitely many $C_{n,m}$-homogeneous orderings, and these are very closely related to the homogeneous colored linear orderings with finitely many colors. We provide an analytic-combinatorial analysis of the number of $C_{n,m}$-homogeneous orderings and the number of homogeneous colored linear orderings with $k$ colors, resulting in precise asymptotic bounds for these sequences. △ Less

Submitted 29 September, 2025; originally announced September 2025.

Comments: 45 pages

The 2025 outburst of IGR J17511-3057: timing and spectral insights from NICER and NuSTAR

Authors: A. Sanna, G. K. Jaisawal, T. E. Strohmayer, G. Illiano, A. Riggio, A. Papitto, T. Di Salvo, L. Burderi, J. B. Coley, D. Altamirano, C. Malacaria, A. Anitra, M. Ng, D. Chakrabarty, T. Boztepe, A. C. Albayati

Abstract: IGR J17511-3057 was observed in a new outburst phase starting in February 2025 and lasting at least nine days. We investigated the spectral and temporal properties of IGR J17511-3057, aiming to characterise its current status and highlight possible long-term evolution of its properties. We analysed the available NICER and NuSTAR observations performed during the latest outburst of the source. We u… ▽ More IGR J17511-3057 was observed in a new outburst phase starting in February 2025 and lasting at least nine days. We investigated the spectral and temporal properties of IGR J17511-3057, aiming to characterise its current status and highlight possible long-term evolution of its properties. We analysed the available NICER and NuSTAR observations performed during the latest outburst of the source. We updated the ephemerides of the neutron star and compared them to previous outbursts to investigate its long-term evolution. We also performed spectral analysis of the broadband energy spectrum in different outburst phases, and investigated the time-resolved spectrum of the type-I X-ray burst event observed with NuSTAR. We detected X-ray pulsations at a frequency of around 245 Hz. The long-term evolution of the neutron star ephemerides suggests a spin-down derivative of about -2.3e-15 Hz/s, compatible with a rotation-powered phase while in quiescence. Moreover, the evolution of the orbital period and the time of the ascending node suggests a fast orbital shrinkage, which challenges the standard evolution scenario for this class of pulsars involving angular momentum loss via gravitational wave emission. The spectral analysis revealed a dominant power-law-like Comptonisation component, along with a thermal blackbody component, consistent with a hard state. Weak broad emission residuals around 6.6 keV suggest the presence of a K-alpha transition of neutral or He-like Fe originating from the inner region of the accretion disc. Self-consistent reflection models confirmed a moderate ionisation of the disc truncated at around (82-370) km from the neutron star. Finally, the study of the type-I X-ray burst revealed no signature of photospheric radius expansion. We found marginally significant burst oscillations during the rise and decay of the event, consistent with the neutron star spin frequency. △ Less

Submitted 22 September, 2025; v1 submitted 19 September, 2025; originally announced September 2025.

Comments: 16 pages, 11 figures, 2 tables. Accepted for publication in A&A

Journal ref: A&A 703, A171 (2025)

MaLei at MultiClinSUM: Summarisation of Clinical Documents using Perspective-Aware Iterative Self-Prompting with LLMs

Authors: Libo Ren, Yee Man Ng, Lifeng Han

Abstract: Efficient communication between patients and clinicians plays an important role in shared decision-making. However, clinical reports are often lengthy and filled with clinical jargon, making it difficult for domain experts to identify important aspects in the document efficiently. This paper presents the methodology we applied in the MultiClinSUM shared task for summarising clinical case documents… ▽ More Efficient communication between patients and clinicians plays an important role in shared decision-making. However, clinical reports are often lengthy and filled with clinical jargon, making it difficult for domain experts to identify important aspects in the document efficiently. This paper presents the methodology we applied in the MultiClinSUM shared task for summarising clinical case documents. We used an Iterative Self-Prompting technique on large language models (LLMs) by asking LLMs to generate task-specific prompts and refine them via example-based few-shot learning. Furthermore, we used lexical and embedding space metrics, ROUGE and BERT-score, to guide the model fine-tuning with epochs. Our submission using perspective-aware ISP on GPT-4 and GPT-4o achieved ROUGE scores (46.53, 24.68, 30.77) and BERTscores (87.84, 83.25, 85.46) for (P, R, F1) from the official evaluation on 3,396 clinical case reports from various specialties extracted from open journals. The high BERTscore indicates that the model produced semantically equivalent output summaries compared to the references, even though the overlap at the exact lexicon level is lower, as reflected in the lower ROUGE scores. This work sheds some light on how perspective-aware ISP (PA-ISP) can be deployed for clinical report summarisation and support better communication between patients and clinicians. △ Less

Submitted 9 September, 2025; originally announced September 2025.

Comments: system paper at CLEF 2025

What's the Buzz About GX 13+1? Constraining Coronal Geometry with QUEEN-BEE: A Bayesian Nested Sampling Framework for X-ray Polarization Rotation Analysis

Authors: Swati Ravi, Mason Ng, Herman L. Marshall, Andrea Gnarini

Abstract: Observations from the Imaging X-ray Polarimetry Explorer (IXPE) have revealed electric vector position angle (EVPA) rotation in several neutron star low-mass X-ray binaries, including the galactic X-ray burster GX 13+1. We developed a novel Bayesian nested sampling framework-"Q-U Event-by-Event Nested sampling for Bayesian EVPA Evolution" (QUEEN-BEE)-to model unbinned Stokes parameters and infer o… ▽ More Observations from the Imaging X-ray Polarimetry Explorer (IXPE) have revealed electric vector position angle (EVPA) rotation in several neutron star low-mass X-ray binaries, including the galactic X-ray burster GX 13+1. We developed a novel Bayesian nested sampling framework-"Q-U Event-by-Event Nested sampling for Bayesian EVPA Evolution" (QUEEN-BEE)-to model unbinned Stokes parameters and infer optimal EVPA rotation rates in IXPE data. We then applied this framework to three previous IXPE observations of GX 13+1. In the first observation, QUEEN-BEE recovers a rotation rate of 42+/-4 degrees/day, consistent with prior binned analysis. Energy-binned QUEEN-BEE analysis of this first observation suggests a slab-like coronal geometry, providing the first constraints between slab and shell coronae for this source. We also explore alternative EVPA rotation scenarios in GX 13+1 including variable disk wind behavior. The second observation of this source shows no evidence of rotation, and the third observation shows transient rotating behavior with an EVPA rotation rate when exiting a light curve dip of 170 +20/-40 degrees/day. The results show marginal but consistent increases in the overall measured polarization degree (PD) for epochs where the EVPA rotation is identified. These results demonstrate that QUEEN-BEE can identify evolving polarization signatures in both time- and energy-resolved regimes, even where binned methods fall below detection thresholds. Our findings highlight the diagnostic potential of QUEEN-BEE as a tool for discriminating between competing physical models of coronal geometry and probing disk-wind-related polarization behavior, highlighting the promising potential for application of this framework in a variety of other IXPE observations. △ Less

Submitted 8 September, 2025; originally announced September 2025.

Comments: 16 pages, 8 figures, 7 tables, submitted to ApJ

A Spatial Gap in the Sky Distribution of Fast Radio Burst Detections Coinciding with Galactic Plasma Overdensities

Authors: Swarali Shivraj Patil, Robert A. Main, Emmanuel Fonseca, Kyle McGregor, B. M. Gaensler, Mohit Bhardwaj, Charanjot Brar, Amanda M. Cook, Alice P. Curtin, Gwendolyn Eadie, Ronniy Joseph, Lordrick Kahinga, Victoria Kaspi, Afrokk Khan, Bikash Kharel, Adam E. Lanman, Calvin Leung, Kiyoshi W. Masui, Mason Ng, Kenzie Nimmo, Ayush Pandhi, Aaron B. Pearlman, Ziggy Pleunis, Mawson W. Sammons, Ketan R. Sand , et al. (4 additional authors not shown)

Abstract: We analyze the positional and morphological properties of about 3600 unique fast radio burst (FRB) sources reported in the second FRB catalog generated by the Canadian Hydrogen Intensity Mapping Experiment (CHIME) telescope. We find a two-dimensional dependence of FRB detections on sky position, and identify a significant absence of detections in a roughly circular region centered at Galactic coor… ▽ More We analyze the positional and morphological properties of about 3600 unique fast radio burst (FRB) sources reported in the second FRB catalog generated by the Canadian Hydrogen Intensity Mapping Experiment (CHIME) telescope. We find a two-dimensional dependence of FRB detections on sky position, and identify a significant absence of detections in a roughly circular region centered at Galactic coordinates (77.7$^\circ$, 0.9$^\circ$), spanning an area of 213.6 deg$^2$. This detection gap spatially coincides with the Cygnus X region $--$ a plasma-rich star-forming region in the Milky Way. This feature is most likely the result of increased sky temperature and strong multi-path scattering by turbulent ionized plasma, which broadens the FRB signals beyond detectability in the CHIME band. Our simulations yield a mean of 6 expected FRB detections within the gap when accounting for the elevated sky temperature in the direction of the detection gap. We infer that a lower limit of the maximum scattering timescale $τ_{\rm sc,\, 1\,GHz} \geq 5.59$ ms, obtained without assuming a model of the Galactic electron distribution, is sufficient to suppress the brightness of all coincident FRBs. A similar suppression is seen in Catalog 2 along other high-emission measure (EM) sightlines (i.e., EM$\geq$2900 pc cm$^{-6}$), further supporting a broader trend of suppression due to Galactic scattering. Future very long baseline interferometry (VLBI) measurements of scattering disks with CHIME Outriggers could help confirm our interpretation. Our work highlights the notion that FRBs can serve as new, model-independent tracers of the warm ionized medium within our Milky Way Galaxy. △ Less

Submitted 17 December, 2025; v1 submitted 8 September, 2025; originally announced September 2025.

Comments: 21 pages, 9 figures. Accepted for Publication in ApJL

The 2020 Superburst of 4U 1608-522 and its impact on the accretion disk

Authors: Tugba Boztepe, Tolga Guver, Elif Ece Devecioglu, Julia Speicher, Motoko Serino, David R. Ballantyne, Diego Altamirano, Gaurava K. Jaisawal, Mason Ng, Andrea Sanna, Can Gungor, Wataru Iwakiri

Abstract: Superbursts are rare events observed from bursting neutron star low mass X-ray binaries. They are thought to originate from unstable burning of the thick layer of Carbon on the surface of the neutron star, causing the observed X-ray flashes to last several hours. Given their fluence it has long been thought that superbursts may have significant effects on the accretion flow around the neutron star… ▽ More Superbursts are rare events observed from bursting neutron star low mass X-ray binaries. They are thought to originate from unstable burning of the thick layer of Carbon on the surface of the neutron star, causing the observed X-ray flashes to last several hours. Given their fluence it has long been thought that superbursts may have significant effects on the accretion flow around the neutron star. In this paper, we first present evidence for a new superburst observed from 4U 1608-522 by MAXI during the 2020 outburst, around 00:45 UTC on 16 July 2020. We compare some of the properties of this superburst and the underlying outburst with the events recorded on May 5 2005 by RXTE and most recently in 2025 by MAXI. We then present our spectral analysis of NICER and Insight-HXMT data obtained before and after the 2020 superburst event. Our results indicate that the inner disk temperature and the radius show a systematic evolution in the following few days, which may be related to the superburst. We show that the timescale of the observed evolution can not be governed by viscous timescales unless the viscosity parameter is unrealistically low. △ Less

Submitted 2 September, 2025; originally announced September 2025.

Comments: Accepted for publication in MNRAS

Semi-on-Demand Transit Feeders with Shared Autonomous Vehicles and Reinforcement-Learning-Based Zonal Dispatching Control

Authors: Max T. M. Ng, Roman Engelhardt, Florian Dandl, Hani S. Mahmassani, Klaus Bogenberger

Abstract: This paper develops a semi-on-demand transit feeder service using shared autonomous vehicles (SAVs) and zonal dispatching control based on reinforcement learning (RL). This service combines the cost-effectiveness of fixed-route transit with the adaptability of demand-responsive transport to improve accessibility in lower-density areas. Departing from the terminus, SAVs first make scheduled fixed s… ▽ More This paper develops a semi-on-demand transit feeder service using shared autonomous vehicles (SAVs) and zonal dispatching control based on reinforcement learning (RL). This service combines the cost-effectiveness of fixed-route transit with the adaptability of demand-responsive transport to improve accessibility in lower-density areas. Departing from the terminus, SAVs first make scheduled fixed stops, then offer on-demand pick-ups and drop-offs in a pre-determined flexible-route area. Our deep RL model dynamically assigns vehicles to subdivided flexible-route zones in response to real-time demand fluctuations and operations, using a policy gradient algorithm - Proximal Policy Optimization. The methodology is demonstrated through agent-based simulations on a real-world bus route in Munich, Germany. Results show that after efficient training of the RL model, the semi-on-demand service with dynamic zonal control serves 16% more passengers at 13% higher generalized costs on average compared to traditional fixed-route service. The efficiency gain brought by RL control brings 2.4% more passengers at 1.4% higher costs. This study not only showcases the potential of integrating SAV feeders and machine learning techniques into public transit, but also sets the groundwork for further innovations in addressing first-mile-last-mile problems in multimodal transit systems. △ Less

Submitted 1 September, 2025; originally announced September 2025.

Comments: 6 pages, 9 figures, published in 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), Edmonton, Canada, 24-27 September 2024

Journal ref: 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)

SWIRL: A Staged Workflow for Interleaved Reinforcement Learning in Mobile GUI Control

Authors: Quanfeng Lu, Zhantao Ma, Shuai Zhong, Jin Wang, Dahai Yu, Michael K. Ng, Ping Luo

Abstract: The rapid advancement of large vision language models (LVLMs) and agent systems has heightened interest in mobile GUI agents that can reliably translate natural language into interface operations. Existing single-agent approaches, however, remain limited by structural constraints. Although multi-agent systems naturally decouple different competencies, recent progress in multi-agent reinforcement l… ▽ More The rapid advancement of large vision language models (LVLMs) and agent systems has heightened interest in mobile GUI agents that can reliably translate natural language into interface operations. Existing single-agent approaches, however, remain limited by structural constraints. Although multi-agent systems naturally decouple different competencies, recent progress in multi-agent reinforcement learning (MARL) has often been hindered by inefficiency and remains incompatible with current LVLM architectures. To address these challenges, we introduce SWIRL, a staged workflow for interleaved reinforcement learning designed for multi-agent systems. SWIRL reformulates MARL into a sequence of single-agent reinforcement learning tasks, updating one agent at a time while keeping the others fixed. This formulation enables stable training and promotes efficient coordination across agents. Theoretically, we provide a stepwise safety bound, a cross-round monotonic improvement theorem, and convergence guarantees on return, ensuring robust and principled optimization. In application to mobile GUI control, SWIRL instantiates a Navigator that converts language and screen context into structured plans, and an Interactor that grounds these plans into executable atomic actions. Extensive experiments demonstrate superior performance on both high-level and low-level GUI benchmarks. Beyond GUI tasks, SWIRL also demonstrates strong capability in multi-agent mathematical reasoning, underscoring its potential as a general framework for developing efficient and robust multi-agent systems. △ Less

Submitted 27 August, 2025; originally announced August 2025.

Comments: 28 pages, 12 figures

NLAFormer: Transformers Learn Numerical Linear Algebra Operations

Authors: Zhantao Ma, Yihang Gao, Michael K. Ng

Abstract: Transformers are effective and efficient at modeling complex relationships and learning patterns from structured data in many applications. The main aim of this paper is to propose and design NLAFormer, which is a transformer-based architecture for learning numerical linear algebra operations: pointwise computation, shifting, transposition, inner product, matrix multiplication, and matrix-vector m… ▽ More Transformers are effective and efficient at modeling complex relationships and learning patterns from structured data in many applications. The main aim of this paper is to propose and design NLAFormer, which is a transformer-based architecture for learning numerical linear algebra operations: pointwise computation, shifting, transposition, inner product, matrix multiplication, and matrix-vector multiplication. Using a linear algebra argument, we demonstrate that transformers can express such operations. Moreover, the proposed approach discards the simulation of computer control flow adopted by the loop-transformer, significantly reducing both the input matrix size and the number of required layers. By assembling linear algebra operations, NLAFormer can learn the conjugate gradient method to solve symmetric positive definite linear systems. Experiments are conducted to illustrate the numerical performance of NLAFormer. △ Less

Submitted 27 August, 2025; originally announced August 2025.

MSC Class: 65F10; 68Q32

X-ray and radio polarimetry of the neutron star low mass X-ray binary GX 13+1

Authors: Unnati Kashyap, Thomas J. Maccarone, Eliot C. Pattie, Mason Ng, Swati Ravi, Herman L. Marshall

Abstract: We report the X-ray and radio polarization study of the neutron star (NS) low-mass X-ray binary (LMXB) GX 13+1 using the Imaging X-ray Polarimetry Explorer (IXPE) and Very Large Array (VLA). Simultaneous Neutron Star Interior Composition Explorer (NICER) observations show that the source was in parts of the Z state during our IXPE observations, exhibiting moderate changes in the hardness intensity… ▽ More We report the X-ray and radio polarization study of the neutron star (NS) low-mass X-ray binary (LMXB) GX 13+1 using the Imaging X-ray Polarimetry Explorer (IXPE) and Very Large Array (VLA). Simultaneous Neutron Star Interior Composition Explorer (NICER) observations show that the source was in parts of the Z state during our IXPE observations, exhibiting moderate changes in the hardness intensity diagram. The source exhibits X-ray dips in the light curve along with hints of polarization swings between the dip and non-dip states. The X-ray spectro-polarimetry results suggest a source geometry comprising an accretion disk component representing the softer disk emission, along with a blackbody representing the harder emission from the boundary layer (BL) or a spreading layer (SL). We investigate the geometry of GX 13+1 by considering our X-ray and radio polarization findings. △ Less

Submitted 7 August, 2025; originally announced August 2025.

Comments: 12 pages, 7 Figures, 5 Tables, Accepted for publication in APJ

MArgE: Meshing Argumentative Evidence from Multiple Large Language Models for Justifiable Claim Verification

Authors: Ming Pok Ng, Junqi Jiang, Gabriel Freedman, Antonio Rago, Francesca Toni

Abstract: Leveraging outputs from multiple large language models (LLMs) is emerging as a method for harnessing their power across a wide range of tasks while mitigating their capacity for making errors, e.g., hallucinations. However, current approaches to combining insights from multiple LLMs often involve unstructured interactions (e.g., free debate), resulting in model generations that are not faithfully… ▽ More Leveraging outputs from multiple large language models (LLMs) is emerging as a method for harnessing their power across a wide range of tasks while mitigating their capacity for making errors, e.g., hallucinations. However, current approaches to combining insights from multiple LLMs often involve unstructured interactions (e.g., free debate), resulting in model generations that are not faithfully justifiable. In this work, we introduce MArgE, a novel framework to provide formal structure to the evidence from each LLM, in the form of a tree of extracted arguments, for the task of claim verification. We use a variant of Argumentative LLMs (ArgLLMs), i.e. LLMs driven by frameworks and semantics from the field of computational argumentation, to construct structured argument trees for given claims. This process creates an inspectable pathway from the initial arguments to the final claim verification decisions, providing a faithful justification thereof. We show experimentally that MArgE can significantly outperform single LLMs, including three open-source models (4B to 8B parameters), GPT-4o-mini and existing ArgLLMs, as well as prior methods for unstructured multi-LLM debates. We thus demonstrate the advantages of incorporating formal, argumentative reasoning mechanisms when combining multiple LLM outputs. △ Less

Submitted 4 August, 2025; originally announced August 2025.

On signs of Fourier coefficients on GL(n)

Authors: Didier Lesesvre, Ming Ho Ng, Yingnan Wang

Abstract: We study statistical properties of Fourier coefficients of automorphic forms on GL(n). For most Hecke-Maass cusp forms, we give the asymptotic number of nonvanishing coefficients, show that there is a positive proportion of sign changes among them, when these are real, and describe the asymptotic density of these signs. We generalize the results by Jääsaari obtained in the case of self-dual forms… ▽ More We study statistical properties of Fourier coefficients of automorphic forms on GL(n). For most Hecke-Maass cusp forms, we give the asymptotic number of nonvanishing coefficients, show that there is a positive proportion of sign changes among them, when these are real, and describe the asymptotic density of these signs. We generalize the results by Jääsaari obtained in the case of self-dual forms of GL(3) and our method moreover circumvents the assumption of the Generalized Ramanujan Conjecture. △ Less

Submitted 30 July, 2025; originally announced July 2025.

Comments: 10 pages

MSC Class: 11F30; 11N37

CHIME/FRB Discovery of an Unusual Circularly Polarized Long-Period Radio Transient with an Accelerating Spin Period

Authors: Fengqiu Adam Dong, Kaitlyn Shin, Casey Law, Mason Ng, Ingrid Stairs, Geoffrey Bower, Alyssa Cassity, Emmanuel Fonseca, B. M. Gaensler, Jason W. T. Hessels, Victoria M. Kaspi, Bikash Kharel, Calvin Leung, Robert A. Main, Kiyoshi W. Masui, James W. McKee, Bradley W. Meyers, Obinna Modilim, Ayush Pandhi, Aaron B Pearlman, Scott M. Ransom, Paul Scholz, Kendrick Smith

Abstract: We report the discovery of CHIME J1634+44, a Long Period Radio Transient (LPT) unique for two aspects: it is the first known LPT to emit fully circularly polarized radio bursts, and it is the first LPT with a significant spin-up. Given that high circular polarization ($>90$\%) has been observed in FRB~20201124A and in some giant pulses of PSR~B1937+21, we discuss the implications of the high circu… ▽ More We report the discovery of CHIME J1634+44, a Long Period Radio Transient (LPT) unique for two aspects: it is the first known LPT to emit fully circularly polarized radio bursts, and it is the first LPT with a significant spin-up. Given that high circular polarization ($>90$\%) has been observed in FRB~20201124A and in some giant pulses of PSR~B1937+21, we discuss the implications of the high circular polarization of CHIME J1634+44 and conclude its emission mechanism is likely to be ``pulsar-like''. While CHIME J1634+44 has a pulse period of 841 s, its burst arrival patterns are indicative of a secondary 4206 s period, probably associated with binary activity. The timing properties suggest it has a significantly negative period derivative of $\dot{P}=-9.03(0.11)\times 10^{-12}$ s s$^{-1}$. Few systems have been known to spin-up, most notably transitional millisecond pulsars and cataclysmic binaries, both of which seem unlikely progenitors for CHIME J1634+44. If the period was only associated with the spin of the object, then the spin up is likely generated by accretion of material from a companion. If, however, the radio pulse period and the orbital period are locked, as appears to be the case for two other LPTs, the spin up of CHIME J1634+44 could be driven by gravitational wave radiation. △ Less

Submitted 12 July, 2025; v1 submitted 7 July, 2025; originally announced July 2025.

Comments: Accepted for publication in ApJL V2. correction of typo in abstract

Open Problems in Computability Theory and Descriptive Set Theory

Authors: George Barmpalias, Nikolay Bazhenov, Chi Tat Chong, Wei Dai, Su Gao, Jun Le Goh, Jialiang He, Keng Meng Selwyn Ng, Andre Nies, Theodore Slaman, Riley Thornton, Wei Wang, Jing Yu, Liang Yu

Abstract: These open problems were presented in the Problem Sessions held during the Tianyuan Workshop on Computability Theory and Descriptive Set Theory, June 16-20, 2025. The problems are organized into sections named after their contributors, in the order of their presentations during the workshop. Notes were taken and compiled by Wei Dai, Feng Li, Ruiwen Li, Ming Xiao, Xu Wang, Víctor Hugo Yañez Salazar… ▽ More These open problems were presented in the Problem Sessions held during the Tianyuan Workshop on Computability Theory and Descriptive Set Theory, June 16-20, 2025. The problems are organized into sections named after their contributors, in the order of their presentations during the workshop. Notes were taken and compiled by Wei Dai, Feng Li, Ruiwen Li, Ming Xiao, Xu Wang, Víctor Hugo Yañez Salazar, and Yang Zheng. △ Less

Submitted 5 July, 2025; originally announced July 2025.

MSC Class: 03E15; 03D30