arXiv subjects

Jiaxi Wu

Publications and source records attributed to Jiaxi Wu.

17 recordsLinked to original sources

arXiv2026

Strangeness Transport in Binary Neutron Star Mergers

The presence of hyperons in the cores of neutron stars opens fast strangeness equilibration channels that can produce bulk-viscous dissipation during binary inspiral. Because these reactions coexist with electron $\beta$-equilibration, tidal compression can drive the two coupled chemical imbalances far beyond linear response. We construct the first reaction network that self-consistently evolves the electron and strangeness fractions with a four-dimensional strangeness-dependent chiral mean-field (CMF) equation of state, including nucleonic and hyperonic Urca processes and non-leptonic hyperon reactions. For periodic density perturbations, representative of inspiral oscillations, we find that rapid strangeness conversion can generate a large $\beta$-imbalance, after which slow $\beta$-equilibration bottlenecks strangeness relaxation. Rather than decaying exponentially, the coupled system consequently exhibits dynamically important algebraic decay in a far-from-equilibrium regime. At the $\rm keV$ temperatures expected during inspiral, this nonlinear response produces a broad enhancement of the effective bulk viscosity, reaching $\sim10^{31}\,\mathrm{g\,cm^{-1}\,s^{-1}}$ for $320$ Hz oscillations. A phenomenological estimate of continuous inspiral dissipation yields gravitational-wave phase shifts up to $\sim0.14$ rad for neutron stars with hyperonic cores. Self-consistent, far-from-equilibrium strangeness transport may therefore provide a dynamical probe of hyperons in neutron-star interiors.

astro-ph.HE↗

arXiv2026

Hadronic Processes, Plasma Evolution and Neutrino Emission in Magnetic Towers of Neutron-Star Merger Remnants

Binary neutron star mergers can form short-lived magnetar-like remnants whose magnetically dominated polar towers reach $B\sim10^{15}$--$10^{16}\,\mathrm{G}$, but the microphysical composition of these outflows remains poorly understood. Combining tower geometries from GRMHD simulations with an analytic treatment of QED and hadronic processes, we argue that magnetic reconnection is the most viable particle acceleration channel in this strongly radiative regime, where the current sheets thin to collisionless scales. Purely leptonic pair loading -- including resonant inverse Compton scattering of soft photons -- is bottlenecked by rapid pitch-angle damping and the tendency of one-photon magnetic conversion to populate low Landau levels. Once protons reach mildly relativistic energies ($\gamma_p\gtrsim1.3$), however, inelastic proton-proton ($pp$) collisions inject large-pitch-angle pions that drive $\pi^0\to2\gamma\to e^\pm$ cascades with multiplicity $\mathcal{M}_{\rm cas}\simeq4$ at $B=10^{15}\,\mathrm{G}$, supplying the perpendicular momentum the leptonic channel cannot maintain. This hadronic route dominates pair loading and channels most of the dissipated magnetic energy into the $e^\pm$ population that could power the nonthermal emission emerging at larger radii. Charged-pion decay, modulated by $\pi^\pm$ synchrotron cooling, further seeds a nonthermal neutrino tail up to $\sim 300\,(\sigma_p/5)\,\mathrm{MeV}$, spectrally distinct from the thermal cooling burst and detectable from sources within $\sim 100\,\mathrm{kpc}$

astro-ph.HE↗

arXiv2026

Eccentricity as a signature of hierarchical subsolar-mass mergers in collapsar disks

In this work, we investigate gravitational-wave signatures of a proposed subsolar-mass merger scenario resulting from fragmentation inside a collapsar accretion disk. This scenario has gained recent interest with the electromagnetic transient AT2025ulz, a possible superkilonova counterpart candidate to the sub-threshold gravitational wave event S250818k. One prediction of fragmentation is the formation of multiple smaller neutron-star fragments, some of which might merge hierarchically. Such mergers are expected not only to produce individual electromagnetic counterparts, but also, because of their repeated capture and merger dynamics, to impart kicks to the system and thereby drive orbital eccentricity. By performing numerical relativity simulations of hierarchical subsolar-mass compact-object mergers modeled as black holes in a disk-like geometry consistent with this scenario, we demonstrate the build-up of potentially large eccentricity for the final merger, of order $e \simeq 0.6$ initially, and show that, because of the short lifetime of the system, a substantial part of this eccentricity , up to $e\simeq 0.1$, can survive until the final neutron star -- black hole merger in the general case. As a result, future detections of eccentricities in potential subsolar-mass gravitational-wave candidate events would be a strong indicator for a hierarchical formation scenario. In the extreme case, where we observe repeated mergers to lead to the formation of a solar-mass neutron star, the expected binary parameters can be in a regime similar to those of the eccentric neutron star -- black hole merger event GW200105.

astro-ph.HE↗

arXiv2026

Hotspot-Aware Scheduling of Virtual Machines with Overcommitment for Ultimate Utilization in Cloud Datacenters

We address the problem of under-utilization of resources in datacenters during cloud operations, specifically focusing on the challenge of online virtual machine (VM) scheduling. Rather than following the traditional approach of scheduling VMs based solely on their static flavors, we take into account their dynamic CPU utilization. We employ $\Gamma$-robustness theory to manage the dynamic nature and introduce a novel variant of bin packing - Probabilistic k-Bins Packing (PkBP), which theoretically protects the Physical Machines (PMs) from hotspots formation within a specified probability $\alpha$. We develop a scheduling algroithm named CloseRadiusFit and cold-start AI based prediction algorithms for the online version of PkBP. To verify the quality of our approach towards the optimal solutions, we solve the Offline PkBP problem by designing a novel Mixed Integer Linear Programming (MILP) model and a combination of numerical upper and lower bounds. Our experimental results demonstrate that CloseRadiusFit achieves narrow gaps of 1.6% and 3.1% when compared to the lower and upper bounds, respectively.

cs.DC↗

arXiv2026

UACER: An Uncertainty-Adaptive Critic Ensemble Framework for Robust Adversarial Reinforcement Learning

Robust adversarial reinforcement learning has emerged as an effective paradigm for training agents to handle uncertain disturbance in real environments, with critical applications in sequential decision-making domains such as autonomous driving and robotic control. Within this paradigm, agent training is typically formulated as a zero-sum Markov game between a protagonist and an adversary to enhance policy robustness. However, the trainable nature of the adversary inevitably induces non-stationarity in the learning dynamics, leading to exacerbated training instability and convergence difficulties, particularly in high-dimensional complex environments. In this paper, we propose a novel approach, Uncertainty-Adaptive Critic Ensemble for robust adversarial Reinforcement learning (UACER), which consists of two components: 1) Diversified critic ensemble: A diverse set of K critic networks is employed in parallel to stabilize Q-value estimation in robust adversarial reinforcement learning, reducing variance and enhancing robustness compared to conventional single-critic designs. 2) Time-varying Decay Uncertainty (TDU) mechanism: Moving beyond simple linear combinations, we propose a variance-derived Q-value aggregation strategy that explicitly incorporates epistemic uncertainty to adaptively regulate the exploration-exploitation trade-off while stabilizing the training process. Comprehensive experiments across several challenging MuJoCo control problems validate the superior effectiveness of UACER, outperforming state-of-the-art methods in terms of overall performance, stability, and efficiency.

cs.LG↗

arXiv2025

Toward a Theory of Gravitational Wave Turbulence

General relativity describes the dynamics of gravitational waves, which can feature nonlinear interactions, such as those underlying turbulent processes. Theoretical and numerical explorations have demonstrated the existence of gravitational wave turbulence, of which a full and general mathematical description is currently not known. Here, we take essential steps towards such a theory. Leveraging a formulation exactly recasting general relativity as a set of nonlinear electrodynamics equations, we demonstrate that general relativity admits an Elsasser formulation -- the same type of equation underpinning magnetohydrodynamic turbulence. We further show that nonlinear interactions described by this equation are in part Alfvénic, linking gravitational wave turbulence to Alfvénic turbulence. Our work paves the way for a new understanding of nonlinear gravitational wave dynamics through insights from magnetohydrodynamics.

gr-qc↗

arXiv2025

A Latent Factor Panel Approach to Spatiotemporal Causal Inference

Unmeasured confounding can severely bias causal effect estimates from spatiotemporal observational data, especially when the confounders do not vary smoothly in time and space. In this work, we develop a method for addressing unmeasured confounding in spatiotemporal contexts by building on models from the panel data literature and methods in multivariate causal inference. Our method is based on a factor confounding assumption, which posits that effects of unmeasured confounders on exposures and outcomes can be captured by a shared latent factor model. Factor confounding is sufficient to partially identify causal effects, even when there is interference between units. Additional assumptions that limit the degree of spatiotemporal interference, reasonable in most applications, are sufficient to point identify the effects. Simulation studies demonstrate that the proposed approach can substantially reduce omitted variable bias relative to other spatial smoothing and panel data baselines. We illustrate our method in a case study of the effect of prenatal PM2.5 exposure on birth weight in California.

stat.ME↗

arXiv2025

Unveiling the electrodynamic nature of spacetime collisions

Gravitational waves from merging binary black holes present exciting opportunities for understanding fundamental aspects of gravity, including nonlinearities in the strong-field regime. One challenge in studying and interpreting the dynamics of binary black hole collisions is the intrinsically geometrical nature of spacetime, which in many ways is unlike that of other classical field theories. By exactly recasting Einstein's equations into a set of coupled nonlinear Maxwell equations closely resembling classical electrodynamics, we visualize the intricate dynamics of gravitational electric and magnetic fields during inspiral, merger and ring-down of a binary black hole collision.

gr-qc↗

arXiv2025

General-Relativistic Gauge-Invariant Magnetic Helicity Transport: Basic Formulation and Application to Neutron Star Mergers

Dynamo processes are ubiquitous in astrophysical systems. In relativistic astrophysical systems, such as accretion disks around black holes or neutron stars, they may critically affect the launching of winds and jets that can power electromagnetic emission. Dynamo processes are governed by several microscopic parameters, one of them being magnetic helicity. As a conserved quantity in nonresistive plasmas, magnetic helicity is transported across the system. One important implication of helicity conservation is, that in the absence of helicity fluxes some mean-field dynamos can be quenched, potentially affecting the large-scale evolution of the magnetic field. One of the major challenges in computing magnetic helicity is the need to fix a meaningful electromagnetic gauge. We here present a fully covariant formulation of magnetic helicity transport in general-relativistic plasmas based on the concept of relative helicity by Berger & Field and Finn & Antonsen. This formulation is separately invariant under gauge-transformation of the Maxwell and Einstein equations. As an application of this new formalism we present the first analysis of magnetic helicity transport in the merger of two neutron stars. We demonstrate the presence of global helicity fluxes into the outer layers of the stellar merger remnant, which may impact subsequent large-scale dynamo amplification in these regions.

astro-ph.HE↗

arXiv2023

COVID-19 Vaccine Misinformation in Middle Income Countries

This paper introduces a multilingual dataset of COVID-19 vaccine misinformation, consisting of annotated tweets from three middle-income countries: Brazil, Indonesia, and Nigeria. The expertly curated dataset includes annotations for 5,952 tweets, assessing their relevance to COVID-19 vaccines, presence of misinformation, and the themes of the misinformation. To address challenges posed by domain specificity, the low-resource setting, and data imbalance, we adopt two approaches for developing COVID-19 vaccine misinformation detection models: domain-specific pre-training and text augmentation using a large language model. Our best misinformation detection models demonstrate improvements ranging from 2.7 to 15.9 percentage points in macro F1-score compared to the baseline models. Additionally, we apply our misinformation detection models in a large-scale study of 19 million unlabeled tweets from the three countries between 2020 and 2022, showcasing the practical application of our dataset and models for detecting and analyzing vaccine misinformation in multiple countries and languages. Our analysis indicates that percentage changes in the number of new COVID-19 cases are positively associated with COVID-19 vaccine misinformation rates in a staggered manner for Brazil and Indonesia, and there are significant positive associations between the misinformation rates across the three countries.

cs.CL↗

arXiv2023

Standardized Luminosity of the Tip of the Red Giant Branch utilizing Multiple Fields in NGC 4258 and the CATs Algorithm

The Tip of the Red Giant Branch provides a luminous standard candle for calibrating distance ladders that reach Type Ia supernova (SN Ia) hosts. However, recent work reveals that tip measurements vary at the $\sim$ 0.1 mag level for different stellar populations and locations within a host, which may lead to inconsistencies along the distance ladder. We pursue a calibration of the tip using 11 Hubble Space Telescope fields around the maser host, NGC 4258, that is consistent with SN Ia hosts by standardizing tip measurements via their contrast ratios. We find $F814W$-band tips that exhibit a full 0.3 mag range and 0.1 mag dispersion. We do not find any correlation between HI column density and the apparent tip to 0.04 $\pm$ 0.03 mag/cm$^{-2}$. We search for a tip-contrast relation (TCR) and measure the TCR within the fields of NGC 4258 of $-0.015\pm0.008$ mag/$R$, where $R$ is the contrast ratio. This value is consistent with the TCR originally discovered in the GHOSTS sample (Wu et al. 2022) of $-0.023\pm0.005$ mag/R. Combining these measurements, we find a global TCR of $-0.021\pm0.004$ mag/R and a calibration of $M_I^{TRGB} = -4.025 \pm 0.035 - (R-4)\times0.021$ mag. We also use stellar models to simulate single age and metallicity stellar populations with [Fe/H] from $-2.0$ to $-0.7$ and ages from 3 Gyr to 12 Gyr and reconstruct the global TCR found here to a factor of $\sim$ 2. This work is combined in a companion analysis with tip measurements of nearby SN Ia hosts to measure $H_0$.

astro-ph.GA↗

arXiv2023

Sensitivity to Unobserved Confounding in Studies with Factor-structured Outcomes

In this work, we propose an approach for assessing sensitivity to unobserved confounding in studies with multiple outcomes. We demonstrate how prior knowledge unique to the multi-outcome setting can be leveraged to strengthen causal conclusions beyond what can be achieved from analyzing individual outcomes in isolation. We argue that it is often reasonable to make a shared confounding assumption, under which residual dependence amongst outcomes can be used to simplify and sharpen sensitivity analyses. We focus on a class of factor models for which we can bound the causal effects for all outcomes conditional on a single sensitivity parameter that represents the fraction of treatment variance explained by unobserved confounders. We characterize how causal ignorance regions shrink under additional prior assumptions about the presence of null control outcomes, and provide new approaches for quantifying the robustness of causal effect estimates. Finally, we illustrate our sensitivity analysis workflow in practice, in an analysis of both simulated data and a case study with data from the National Health and Nutrition Examination Survey (NHANES).

stat.ME↗

arXiv2022

Efficient simulation of open quantum systems coupled to a reservoir through multiple channels

The simulation of open quantum systems coupled to a reservoir through multiple channels remains a substantial challenge. This kind of open quantum system arises when considering the radiationless decay of excited states that are coupled to molecular vibrations, for example. We use the chain mapping strategy in the interaction picture to study systems linearly coupled to a harmonic bath through multiple interaction channels. In the interaction picture, the bare bath Hamiltonian is removed by a unitary transformation (the system-bath interactions remain), and a chain mapping transforms the bath modes to a new basis. The transformed Hamiltonian contains time-dependent local system-bath couplings. The open quantum system is coupled to a limited number of (transformed) bath modes in the new basis. As such, the entanglement generated by the system-bath interactions is local, making efficient dynamical simulations possible with matrix product states. We use this approach to simulate singlet fission, using a generalized spin-boson Hamiltonian. The electronic states are coupled to a vibrational bath both diagonally and off-diagonally. This approach generalizes the chain mapping scheme to the case of multi-channel system-bath couplings, enabling the efficient simulation of this class of open quantum systems using matrix product states.

quant-ph↗

arXiv2022

Cross Domain Object Detection by Target-Perceived Dual Branch Distillation

Cross domain object detection is a realistic and challenging task in the wild. It suffers from performance degradation due to large shift of data distributions and lack of instance-level annotations in the target domain. Existing approaches mainly focus on either of these two difficulties, even though they are closely coupled in cross domain object detection. To solve this problem, we propose a novel Target-perceived Dual-branch Distillation (TDD) framework. By integrating detection branches of both source and target domains in a unified teacher-student learning scheme, it can reduce domain shift and generate reliable supervision effectively. In particular, we first introduce a distinct Target Proposal Perceiver between two domains. It can adaptively enhance source detector to perceive objects in a target image, by leveraging target proposal contexts from iterative cross-attention. Afterwards, we design a concise Dual Branch Self Distillation strategy for model training, which can progressively integrate complementary object knowledge from different domains via self-distillation in two branches. Finally, we conduct extensive experiments on a number of widely-used scenarios in cross domain object detection. The results show that our TDD significantly outperforms the state-of-the-art methods on all the benchmarks. Our code and model will be available at https://github.com/Feobi1999/TDD.

cs.CV↗

arXiv2022

Target-Relevant Knowledge Preservation for Multi-Source Domain Adaptive Object Detection

Domain adaptive object detection (DAOD) is a promising way to alleviate performance drop of detectors in new scenes. Albeit great effort made in single source domain adaptation, a more generalized task with multiple source domains remains not being well explored, due to knowledge degradation during their combination. To address this issue, we propose a novel approach, namely target-relevant knowledge preservation (TRKP), to unsupervised multi-source DAOD. Specifically, TRKP adopts the teacher-student framework, where the multi-head teacher network is built to extract knowledge from labeled source domains and guide the student network to learn detectors in unlabeled target domain. The teacher network is further equipped with an adversarial multi-source disentanglement (AMSD) module to preserve source domain-specific knowledge and simultaneously perform cross-domain alignment. Besides, a holistic target-relevant mining (HTRM) scheme is developed to re-weight the source images according to the source-target relevance. By this means, the teacher network is enforced to capture target-relevant knowledge, thus benefiting decreasing domain shift when mentoring object detection in the target domain. Extensive experiments are conducted on various widely used benchmarks with new state-of-the-art scores reported, highlighting the effectiveness.

cs.CV↗

arXiv2022

Entropy-based Active Learning for Object Detection with Progressive Diversity Constraint

Active learning is a promising alternative to alleviate the issue of high annotation cost in the computer vision tasks by consciously selecting more informative samples to label. Active learning for object detection is more challenging and existing efforts on it are relatively rare. In this paper, we propose a novel hybrid approach to address this problem, where the instance-level uncertainty and diversity are jointly considered in a bottom-up manner. To balance the computational complexity, the proposed approach is designed as a two-stage procedure. At the first stage, an Entropy-based Non-Maximum Suppression (ENMS) is presented to estimate the uncertainty of every image, which performs NMS according to the entropy in the feature space to remove predictions with redundant information gains. At the second stage, a diverse prototype (DivProto) strategy is explored to ensure the diversity across images by progressively converting it into the intra-class and inter-class diversities of the entropy-based class-specific prototypes. Extensive experiments are conducted on MS COCO and Pascal VOC, and the proposed approach achieves state of the art results and significantly outperforms the other counterparts, highlighting its superiority.

cs.CV↗

arXiv2020

Multi-Scale Positive Sample Refinement for Few-Shot Object Detection

Few-shot object detection (FSOD) helps detectors adapt to unseen classes with few training instances, and is useful when manual annotation is time-consuming or data acquisition is limited. Unlike previous attempts that exploit few-shot classification techniques to facilitate FSOD, this work highlights the necessity of handling the problem of scale variations, which is challenging due to the unique sample distribution. To this end, we propose a Multi-scale Positive Sample Refinement (MPSR) approach to enrich object scales in FSOD. It generates multi-scale positive samples as object pyramids and refines the prediction at various scales. We demonstrate its advantage by integrating it as an auxiliary branch to the popular architecture of Faster R-CNN with FPN, delivering a strong FSOD solution. Several experiments are conducted on PASCAL VOC and MS COCO, and the proposed approach achieves state of the art results and significantly outperforms other counterparts, which shows its effectiveness. Code is available at https://github.com/jiaxi-wu/MPSR.

cs.CV↗