arXiv subjects

Dan Zhu

Publications and source records attributed to Dan Zhu.

At least 19 recordsLinked to original sources

arXiv2026

The Evolution of Thermal and Non-thermal Emission Components in GRB 250920C

We present a temporal and time-resolved spectral analysis of GRB 250920C using \textit{Fermi}/GBM and \textit{Swift}/BAT data. The prompt emission consists of two distinct episodes, EI and EII, separated by a significant quiescent interval. We perform Bayesian spectral fitting with empirical, thermal, composite, and physical synchrotron models. In EI, the spectra show clear evidence for an additional thermal component. The BB+PL model is preferred in most bright time bins, and joint \textit{Fermi}/GBM+\textit{Swift}/BAT fits further support the presence of this component. The blackbody temperature generally decreases with time, the blackbody flux follows the pulse profile, and the thermal flux fraction remains high. Fireball-parameter estimates give a photospheric Lorentz factor of a few hundred and an initial radius of $r_{0}\sim10^{9}$--$10^{10}$ cm, with $1+\sigma_{0}$ of order unity and $\eta\gg1$, supporting a thermally dominated baryonic outflow. In contrast, EII is dominated by non-thermal emission. Its low-energy photon indices do not significantly exceed the synchrotron line of death, and the spectra are well described by fast-cooling synchrotron radiation in a decaying magnetic field. The magnetization constraint gives $\sigma_{\min}\sim1.1$--$4.3$. Since these values exceed unity, they suggest that EII may be Poynting-flux dominated. These results therefore suggest a possible transition in GRB 250920C from a fireball-dominated EI phase to a Poynting-flux-dominated EII phase.

astro-ph.HE↗

arXiv2026

The Exact End-Degree Threshold for Finite-Width Directed Hexagonal Grids

Grid theorems provide a fundamental link between the structure of ends and the existence of grid-like subgraphs in infinite graphs and digraphs. For every fixed width $n$, let $k(n)$ denote the least positive integer such that every digraph with an end of in-degree at least $k(n)$ contains a subdivision of the directed hexagonal grid of width $n$. Hamann and Heuer asked the exact vaule of $k(n)$. We solve this problem by proving that $$ k(n)=\left\lfloor\frac{3n}{2}\right\rfloor-1 \qquad\text{for every }n\geq4, $$ while $k(1)=1$, $k(2)=2$, and $k(3)=4$. For the upper bound, we establish a finite vacancy theorem for labelled token slides, convert it into a closed directed schedule on an auxiliary ray digraph, and lift the schedule through fresh clean linkages. For the matching lower bound, we use alternating orientations of products of a ray with a three-armed tree and prove a no-passing property for disjoint directed paths. We also determine the dual extremal parameter. Let $W(d)$ denote the largest width that is always forced by an end of in-degree $d$, with all branch rays remaining in that end, then $$W(d)=\left\lfloor\frac{2d}{3}\right\rfloor+1 \qquad\text{for every }d\geq4,$$ with $W(1)=1$ and $W(2)=W(3)=2$.

math.CO↗

arXiv2026

Soft-Noncrossing Bayesian Panel Quantile Regression for Measuring Climate Tail Risk

We develop a hierarchical Bayesian panel quantile regression model in which unit-specific coefficient paths are smoothed across quantiles by Gaussian processes, while a common time effect absorbs aggregate shocks. Componentwise-monotone Bernstein polynomials, perturbed by unit-specific deviations, deliver soft noncrossing, and we provide identification conditions together with a bound on the crossing probability. Applying the model to 33 countries over 1979--2023, we find that global temperature shocks generate a systemic, non-diversifiable downside risk to output growth. This risk is concentrated in the lower tail and disproportionately affects emerging markets. Finally, we apply our framework to risk analysis and show that the model reduces out-of-sample tail-risk forecast loss by roughly one-third relative to country-specific quantile regressions.

econ.EM↗

arXiv2026

Interpretable Machine Learning for Traffic Congestion Prediction: Unveiling the Impact of Different COVID-19 Periods

Traffic congestion prediction is essential for congestion mitigation, but the COVID-19 pandemic and related control measures altered travel behavior and increased prediction complexity. This study predicts congestion in Alameda County, California, during pre-lockdown, lockdown, and post-lockdown periods. Weather, seasonality, and COVID-19 variables are incorporated, and Recursive Feature Elimination with Cross-Validation is used to select important features and reduce overfitting. Support vector regression, multiple linear regression, recurrent neural networks, and long short-term memory networks are trained and optimized. Because LSTM is more sensitive to hyperparameter settings, an adaptive parameter selection approach is used, while SVR and RNN are manually tuned. Performance is evaluated using Normalized Root Mean Square Error. Bidirectional LSTM consistently performs best across all periods because it captures temporal dependence in both directions. Integrated Gradients is used to interpret Bi-LSTM predictions, and SHapley Additive exPlanations is applied to SVR. New COVID-19 cases have a mainly negative effect on congestion during lockdown and post-lockdown, likely due to greater risk awareness, voluntary travel reduction, and compliance with mobility restrictions. In the post-pandemic period, higher hospitalization reduces travel and congestion, while higher fuel prices do not prevent a shift toward private vehicles and therefore increase congestion.

cs.LG↗

arXiv2026

LAP: An Agent-to-Instrument Protocol for Autonomous Science

Autonomous science is moving from demonstration to infrastructure. Large language model agents now plan experiments, and self-driving laboratories execute them. Yet every such system rebuilds the link between the reasoning agent and the physical instrument from scratch, against fragmented vendor SDKs and standards built for deterministic software clients rather than probabilistic, goal-directed agents. Recent agent-interoperability protocols clarify two of the three edges of an agentic ecosystem (Anthropic's Model Context Protocol (MCP) standardizes the agent-to-tool edge, and Google's Agent2Agent (A2A) the agent-to-agent edge), but neither models the agent-to-instrument edge, where operations are stateful, safety-critical, exclusively owned, physically embodied, and produce measurements with units, calibration, and uncertainty. We present the Lab Agent Protocol (LAP), a protocol design that fills this gap. LAP retains A2A's peer-to-peer, discovery-first, task-lifecycle structure and adds four physical-world primitives: (i) the InstrumentCard, a signed capability and physical-limit description; (ii) first-class reservation for exclusive instrument and sample locking; (iii) a safety-fence handshake with operator-confirmation tokens cryptographically bound to a specific task and its parameters, gating hazardous and irreversible operations; and (iv) a MeasurementResult schema that makes every result physically typed (QUDT/UCUM), calibration-anchored, uncertainty-bearing, and reproducible by construction. We specify roles, a six-layer architecture, the JSON-RPC method set, the task and safety state machines, the error model, and cross-laboratory federation, and walk a closed-loop autonomous campaign through the protocol end-to-end. LAP is transport-compatible with the A2A/MCP ecosystem and encapsulates rather than replaces existing device standards such as SiLA 2 and OPC-UA.

cs.AI↗

arXiv2026

First mass determination of electroweak vortex rings in the Standard Model

We report the first rigorous evaluation of the physical mass of electroweak vortex rings, establishing precise values of 18.01 and 26.80 TeV for solutions characterized by different winding numbers. Analysis of the internal structure reveals that repulsive interactions shape the geometry of these configurations, while complex current distributions lead to a neutral analogue of Ampere's circuital law, suggesting a corresponding self-stabilizing pinch mechanism. These findings set the energy scales for the potential observation of such configurations at future colliders and offer a framework for understanding topological structures in the Standard Model.

hep-ph↗

arXiv2026

Fast Posterior Sampling in Tightly Identified SVARs Using 'Soft' Sign Restrictions

We propose algorithms for conducting Bayesian inference in structural vector autoregressions identified using sign restrictions. The key feature of our approach is a sampling step based on 'soft' sign restrictions. This step draws from a target density that smoothly penalises parameter values that violate the restrictions, facilitating the use of computationally efficient Markov chain Monte Carlo sampling algorithms. An importance-sampling step yields draws conditional on the 'hard' sign restrictions. Relative to standard accept-reject sampling, the method substantially speeds up sampling when identification is tight. It also facilitates implementing prior-robust Bayesian methods. We illustrate the broad applicability of the approach in an oil-market model identified using a rich set of sign, elasticity and narrative restrictions.

econ.EM↗

arXiv2025

U.S. Economy and Global Stock Markets: Insights from a Distributional Approach

Financial markets are interconnected, with micro-currents propagating across global markets and shaping economic trends. This paper moves beyond traditional stock market indices to examine cross-sectional return distributions-15 in our empirical application, each representing a distinct global market. To facilitate this analysis, we develop a matrix functional VAR method with interpretable factors extracted from cross-sectional return distributions. Our approach extends the existing framework from modeling a single function to multiple functions, allowing for a richer representation of cross-sectional dependencies. By jointly modeling these distributions with U.S. macroeconomic indicators, we uncover the predictive power of financial market in forecasting macro-economic dynamics. Our findings reveal that U.S. contractionary monetary policy not only lowers global stock returns, as traditionally understood, but also dampens cross-sectional return kurtosis, highlighting an overlooked policy transmission. This framework enables conditional forecasting, equipping policymakers with a flexible tool to assess macro-financial linkages under different economic scenarios.

econ.GN↗

arXiv2025

Topological Stabilization via Higgs and $Z$-Boson Mediated Repulsions in Electroweak Monopole-Antimonopole Pairs

We identify two distinct repulsive mechanisms in the Cho-Maison monopole-antimonopole pair (MAP) configuration. Our results show that the Higgs-mediated repulsion exhibits a non-monotonic dependence on both topological charge and Higgs self-coupling, confirming its topological origin while revealing a mass-controlled range transition that deviates from the exponential form of a Yukawa potential. Simultaneously, the $Z$-boson field generates localized repulsive cores of radius $R_c\approx0.8\,m_W^{-1}$, consistent with the weak interaction scale. The collaborative effect of these mechanisms -- operating in different physics regimes -- counteracts the magnetic attraction, establishing a stabilization paradigm for the Cho-Maison MAP that extends naturally to other topological solitons in the Standard Model and various systems described by effective field theories.

hep-ph↗

arXiv2025

A reconstruction algorithm of electrical impedance tomography based on one-dimensional convolutional neural network

Electrical impedance tomography (EIT) is a novel computational imaging technology. In order to improve the quality and spatial resolution of the reconstructed images, the G-CNN and HG-CNN algorithms are proposed based on a one-dimensional convolutional neural network (1D-CNN) in this paper. The input of the 1D-CNN is the reconstructed conductivity distribution obtained by the GVSPM algorithm or the H-GVSPM algorithm. The reconstructed images with higher resolution are obtained through the calculation of 1D-CNN. Finally, the Hadamard product is applied to calculate the output of the 1D-CNN. In the simulation results of the lung cross-section models, the correlation coefficients of the G-CNN algorithm and HG-CNN algorithm maximumly are 2.52 times and 2.20 times greater than the GVSPM algorithm and H-GVSPM algorithm, respectively. In the results of the simulation and experiment, the reconstructed images of the G-CNN and HG-CNN algorithms are distortion-free. In addition, the artifacts of the reconstructed images are diminished after calculations of the Hadamard product. This research provides a reference method for improving the quality of the reconstructed images so that EIT is better applied in medical detection.

physics.med-ph↗

arXiv2025

Multiwavelength Analysis of GRB 250101A: From Gamma-ray Prompt Emission to Optical Afterglow

The interaction between the relativistic jet and the circumburst medium produces a multiwavelength afterglow of a gamma-ray burst (GRBs). In this work, we present multiwavelength properties of GRB~250101A based on the observations of Swift, Fermi and Mephisto. The spectral analysis of Swift/BAT and Fermi/GBM reveals a soft prompt spectrum with a low-energy photon index of $-1.18$ and a peak energy of 33 keV, and the isotropic energy is $1.4\times10^{52}~{\rm erg}$. The prompt emission of GRB 250101A aligns with Type II GRBs in the Amati relation. Meanwhile, our analysis indicates that GRB 250101A is an X-ray-rich or X-ray-dominated GRB, with intrinsic properties suggesting that it is relatively softer than most classical GRBs. Optical observation with Mephisto, beginning 197 s post-trigger, shows a single power-law decay in $uvgriz$ bands, with $F_{\nu,\mathrm{obs}} \propto t^{-0.76} \nu^{-1.21}$. The observed spectral index significantly exceeds theoretical predictions under standard afterglow models, suggesting a color excess of $\sim0.216$ mag. However, combining X-ray and optical afterglow, we find that GRB 250101A is more likely a ``normal burst'' rather than an ``optical-dark burst'', and the dust extinction effect plays an important role in the optical blue bands. Furthermore, there is a structural change at $T_0+2924$ s in the optical light curve, indicating a density drop of $\sim50$ \% in the interstellar medium at a distance of $\sim0.13~{\rm pc}$. Our analysis shows that this GRB clearly shows some unique characteristics in its observed X-ray rich prompt emission as well as the circumburst environment, implying a special progenitor.

astro-ph.HE↗

arXiv2024

Conditional Forecasts in Large Bayesian VARs with Multiple Equality and Inequality Constraints

Conditional forecasts, i.e. projections of a set of variables of interest on the future paths of some other variables, are used routinely by empirical macroeconomists in a number of applied settings. In spite of this, the existing algorithms used to generate conditional forecasts tend to be very computationally intensive, especially when working with large Vector Autoregressions or when multiple linear equality and inequality constraints are imposed at once. We introduce a novel precision-based sampler that is fast, scales well, and yields conditional forecasts from linear equality and inequality constraints. We show in a simulation study that the proposed method produces forecasts that are identical to those from the existing algorithms but in a fraction of the time. We then illustrate the performance of our method in a large Bayesian Vector Autoregression where we simultaneously impose a mix of linear equality and inequality constraints on the future trajectories of key US macroeconomic indicators over the 2020--2022 period.

econ.EM↗

arXiv2024

Inflation Target at Risk: A Time-varying Parameter Distributional Regression

Inflation exhibits state-dependent, skewed, and fat-tailed dynamics that make risk a central concern for monetary policy. Accordingly, inflation risks are distributional and cannot be fully captured by mean-based models. We propose a flexible time-varying parameter distributional regression model that estimates the full conditional distribution of inflation, allowing macroeconomic drivers to have nonlinear and asymmetric effects across the distribution. Applied to U.S. inflation, the model captures major shifts in tail-risk probabilities. Analysis of risk drivers shows that deflationary pressures arise primarily from demand-side weakness and inflation persistence, whereas upside risks are driven mainly by supply-side shocks, particularly energy price inflation. Examining the impact of key drivers further reveals that the unemployment-inflation relationship weakens in the distributional tails. Energy price shocks, by contrast, have little effect on deflation risk but exhibit strongly time-varying and asymmetric effects on high-inflation risk.

econ.EM↗

arXiv2024

A Quantile Nelson-Siegel model

We propose a novel framework for modeling the yield curve from a quantile perspective. Building on the dynamic Nelson-Siegel model of Diebold et al. (2006), we extend its traditional mean-based approach to a quantile regression setting, enabling the estimation of yield curve factors - level, slope, and curvature - at specific quantiles of the conditional distribution. A key advantage of our framework is its ability to characterize the entire conditional distribution of the yield curve across maturities and over time. In an empirical analysis of the U.S. term structure of interest rates, our method demonstrates superior out-of-sample forecasting performance, particularly in capturing the tails of the yield distribution - an aspect increasingly emphasized in the recent literature on distributional forecasting. In addition to its forecasting advantages, our approach reveals rich distributional features beyond the mean. In particular, we find that the dynamic changes in these distributional features differ markedly between the Great Recession and the COVID-19 pandemic period, highlighting a fundamental shift in how interest rate markets respond to distinct economic shocks.

stat.AP↗

arXiv2023

Money Growth and Inflation: A Quantile Sensitivity Approach

An innovative method is proposed to construct a quantile dependence system for inflation and money growth. By considering all quantiles and leveraging a novel notion of quantile sensitivity, the method allows the assessment of changes in the entire distribution of a variable of interest in response to a perturbation in another variable's quantile. The construction of this relationship is demonstrated through a system of linear quantile regressions. Then, the proposed framework is exploited to examine the distributional effects of money growth on the distributions of inflation and its disaggregate measures in the United States and the Euro area. The empirical analysis uncovers significant impacts of the upper quantile of the money growth distribution on the distribution of inflation and its disaggregate measures. Conversely, the lower and median quantiles of the money growth distribution are found to have a negligible influence. Finally, this distributional impact exhibits variation over time in both the United States and the Euro area.

econ.EM↗

arXiv2023

Discordance Minimization-based Imputation Algorithms for Missing Values in Rating Data

Ratings are frequently used to evaluate and compare subjects in various applications, from education to healthcare, because ratings provide succinct yet credible measures for comparing subjects. However, when multiple rating lists are combined or considered together, subjects often have missing ratings, because most rating lists do not rate every subject in the combined list. In this study, we propose analyses on missing value patterns using six real-world data sets in various applications, as well as the conditions for applicability of imputation algorithms. Based on the special structures and properties derived from the analyses, we propose optimization models and algorithms that minimize the total rating discordance across rating providers to impute missing ratings in the combined rating lists, using only the known rating information. The total rating discordance is defined as the sum of the pairwise discordance metric, which can be written as a quadratic function. Computational experiments based on real-world and synthetic rating data sets show that the proposed methods outperform the state-of-the-art general imputation methods in the literature in terms of imputation accuracy.

stat.ML↗

arXiv2023

Bivariate Distribution Regression with Application to Insurance Data

Understanding variable dependence, particularly eliciting their statistical properties given a set of covariates, provides the mathematical foundation in practical operations management such as risk analysis and decision-making given observed circumstances. This article presents an estimation method for modeling the conditional joint distribution of bivariate outcomes based on the distribution regression and factorization methods. This method is considered semiparametric in that it allows for flexible modeling of both the marginal and joint distributions conditional on covariates without imposing global parametric assumptions across the entire distribution. In contrast to existing parametric approaches, our method can accommodate discrete, continuous, or mixed variables, and provides a simple yet effective way to capture distributional dependence structures between bivariate outcomes and covariates. Various simulation results confirm that our method can perform similarly or better in finite samples compared to the alternative methods. In an application to the study of a motor third-party liability insurance portfolio, the proposed method effectively estimates risk measures such as the conditional Value-at-Risk and Expected Shortfall. This result suggests that this semiparametric approach can serve as an alternative in insurance risk management.

stat.ME↗

arXiv2023

Finite-time ruin probabilities of bidimensional risk models with correlated Brownian motions

The present work concerns the finite-time ruin probabilities for several bidimensional risk models with constant interest force and correlated Brownian motions.} Under the condition that the two Brownian motions $\{B_1(t), t\ge 0\}$ and $\{B_2(t), t\ge 0\}$ are correlated, we establish new results for the finite-time ruin probabilities. \textcolor{blue} {Our research has enriched the development of the ruin theory with heavy tails in unidimensional risk models and the dependence theory of stochastic processes.

math.PR↗