arXiv subjects

Junyan Liu

Publications and source records attributed to Junyan Liu.

At least 19 recordsLinked to original sources

arXiv2026

Modulation of the Nernst Thermoelectrics by Regulating the Anomalous Hall and Nernst Angles

The large anomalous Nernst effect in magnetic Weyl semimetals is one of the most intriguing transport phenomena, which draws significant attention for its potential applications in topological thermoelectrics. Despite frequent reports of substantial anomalous Nernst conductivity (ANC), methods to optimize Nernst thermoelectrics remain limited. Our research reveals that the magnitude of the ANC is directly related to the sum of the anomalous Nernst and Hall angles. While the sign of the anomalous Hall angle is relatively stable in a certain material, the sign of the anomalous Nernst angle can be intrinsically tuned. Therefore, the ANC can be effectively optimized by regulating these angles to work in concert. This finding is verified by experimental modulation from iron-doped magnetic topological material Co3Sn2S2. Additionally, we observed a robust TlnT scaling law of the ANC over the temperature range of 40 to 140 K in all studied samples, suggesting an intrinsic origin of the ANC. Considering the common opposite sign of the anomalous Nernst and Hall angles in many magnetic topological materials, our research offers an applicable scheme for optimizing the Nernst thermoelectrics.

cond-mat.mtrl-sci↗

arXiv2026

Adaptive Calibration in Non-Stationary Environments

Making calibrated online predictions is a central challenge in modern AI systems. Much of the existing literature focuses on fully adversarial environments where outcomes may be arbitrary, leading to conservative algorithms that can perform suboptimally in more benign settings, such as when outcomes are nearly stationary. This gap raises a natural question: can we design online prediction algorithms whose calibration error automatically adapts to the degree of non-stationarity in the environment, smoothly interpolating between i.i.d. and adversarial regimes? We answer this question in the affirmative and develop a suite of algorithms that achieve adaptive calibration guarantees under multiple calibration measures. Specifically, with $T$ being the number of rounds, $K$ being the unknown number of i.i.d. segments of the environment, and $C\in[0,T]$ being another unknown non-stationary measure defined as the minimal $\ell_1$ deviation of the mean outcomes, our algorithms attain $\widetilde{O}(\min\{\sqrt{T}+(TC)^{\frac{1}{3}}, \sqrt{KT}\})$ for $\ell_1$ calibration error and $\widetilde{O}(\min\{(1+C)^{\frac{1}{3}}, K\})$ for both $\ell_2$ and pseudo KL calibration error. These bounds match the optimal rates in the stationary case ($C=0$ and $K=1$) and recover known guarantees in the fully adversarial regime ($C, K=\Omega(T)$). Our approach builds on and extends prior work [Hu et al., 2026, Luo et al., 2025], introducing an epoch-based scheduling together with a novel non-uniform partition of the prediction space that allocates finer resolution near the underlying ground truth.

cs.LG↗

arXiv2026

COCONUT: Toward practical time-evolving Sun-to-Earth magnetohydrodynamic modeling

Due to computational efficiency and numerical stability limitations, coronal simulations constrained by static magnetograms are typically performed first and then used to drive inner-heliosphere (IH) models. In this paper, we calculate the Sun-to-Earth coronal and wind evolutions using a single time-evolving MHD model, showing that implicit MHD models have the potential to meaningfully simplify and improve the overall Sun-to-Earth modelling pipeline. We extend the implicit time-evolving coronal MHD model COCONUT out to 1 AU, and utilise it to investigate solar coronal and wind evolutions around a solar maximum Carrington rotation (CR). We compare quasi-steady-state and time-evolving Sun-to-Earth simulations to evaluate the impact of the inner-boundary magnetic field evolution, which is neglected in steady-state simulations, on background plasma parameters. Comparisons with commonly used coupled Sun-to-Earth simulations are also conducted to further validate and assess the Sun-to-Earth model COCONUT. The results show that the time-evolving implicit MHD modelling approach yields noticeable differences compared to oversimplified steady-state simulations, and is efficient enough for practical applications. Modelling the solar corona and wind using a single MHD model simplifies the modelling pipeline and avoids uncertainties associated with coupling different coronal and IH models. The noticeable differences in the temporal evolution of plasma parameters at the L1 and L5 points highlight the need to use continuously evolving, synchronised magnetic field observations to improve global coronal and solar wind simulations, whereas the overall consistent evolutionary trend reveals the reliability of using L5 observations to forecast solar wind conditions near Earth about four days in advance.

astro-ph.SR↗

arXiv2026

Waiting time distribution of solar flares from a global perspective

The waiting time statistics of solar flares provides clues for the underlying physical mechanisms. However, flares occurring on the far-side have been missing in the statistics. In the 2024 May and June, the Solar Orbiter (SolO) spacecraft orbiting behind the Sun, together with near-Earth spacecrafts, provides a unique opportunity to study one of the most flare-productive active regions, NOAA \sar, over its lifetime, as well as the flare occurrence over the entire solar globe. Derived from time intervals between flare peak times, the waiting time distribution (WTD) is fitted by exponential, log-normal, power-law, and \levy functions with the maximum likelihood estimation method. The goodness of fit is evaluated by the Kolmogorov-Smirnov test, and the statistical models are discriminated by information criteria. The major statistical results are: the WTD of flares in the \sar leans towards the log-normal function, while that in the `normal' \rar towards the \levy function; the WTD of global flares defies the local Poisson hypothesis, and its overall profile cannot be reasonably fitted by any of the four candidate distributions, but its power-law tail $\Delta t^{-\alpha}$ is steeper ($\alpha>3$) than the theoretical expectations ($\alpha\le3$), due to the decreased number of long waiting times ($>10^4$ s) and the increased number of shorter waiting times when the far-side flares are taken into account. These results highlight the importance of studying the flare WTD from a global perspective, and suggest that the long-range magnetic connections in the corona may play a role in the flare occurrences.

astro-ph.SR↗

arXiv2026

Revisiting the Bertrand Paradox via Equilibrium Analysis of No-regret Learners

We study the discrete Bertrand pricing game with a non-increasing demand function. The game has $n \ge 2$ players who simultaneously choose prices from the set $\{1/k, 2/k, \ldots, 1\}$, where $k\in\mathbb{N}$. The player who sets the lowest price captures the entire demand; if multiple players tie for the lowest price, they split the demand equally. We study the Bertrand paradox, where classical theory predicts low prices, yet real markets often sustain high prices. To understand this gap, we analyze a repeated-game model in which firms set prices using no-regret learners. Our goal is to characterize the equilibrium outcomes that can arise under different no-regret learning guarantees. We are particularly interested in questions such as whether no-external-regret learners can converge to undesirable high-price outcomes, and how stronger guarantees such as no-swap regret shape the emergence of competitive low-price behavior. We address these and related questions through a theoretical analysis, complemented by experiments that support the theory and reveal surprising phenomena for no-swap regret learners.

cs.GT↗

arXiv2026

Online Learning for Uninformed Markov Games: Empirical Nash-Value Regret and Non-Stationarity Adaptation

We study online learning in two-player uninformed Markov games, where the opponent's actions and policies are unobserved. In this setting, Tian et al. (2021) show that achieving no-external-regret is impossible without incurring an exponential dependence on the episode length $H$. They then turn to the weaker notion of Nash-value regret and propose a V-learning algorithm with regret $O(K^{2/3})$ after $K$ episodes. However, their algorithm and guarantee do not adapt to the difficulty of the problem: even in the case where the opponent follows a fixed policy and thus $O(\sqrt{K})$ external regret is well-known to be achievable, their result is still the worse rate $O(K^{2/3})$ on a weaker metric. In this work, we fully address both limitations. First, we introduce empirical Nash-value regret, a new regret notion that is strictly stronger than Nash-value regret and naturally reduces to external regret when the opponent follows a fixed policy. Moreover, under this new metric, we propose a parameter-free algorithm that achieves an $O(\min \{\sqrt{K} + (CK)^{1/3},\sqrt{LK}\})$ regret bound, where $C$ quantifies the variance of the opponent's policies and $L$ denotes the number of policy switches (both at most $O(K)$). Therefore, our results not only recover the two extremes -- $O(\sqrt{K})$ external regret when the opponent is fixed and $O(K^{2/3})$ Nash-value regret in the worst case -- but also smoothly interpolate between these extremes by automatically adapting to the opponent's non-stationarity. We achieve so by first providing a new analysis of the epoch-based V-learning algorithm by Mao et al. (2022), establishing an $O(\eta C + \sqrt{K/\eta})$ regret bound, where $\eta$ is the epoch incremental factor. Next, we show how to adaptively restart this algorithm with an appropriate $\eta$ in response to the potential non-stationarity of the opponent, eventually achieving our final results.

cs.LG↗

arXiv2026

MHD modeling of magnetic flux evolution around solar maximum by the coronal model COCONUT

In this paper, we simulate the magnetic flux evolution at different heliocentric distances during two solar-maximum Carrington rotations (CRs) using the time-evolving coronal magnetohydrodynamic (MHD) model COCONUT to investigate the ``open flux problem". The simulated open magnetic flux (OMF) near the solar surface is comparable to that derived from \textit{in situ} observations by PSP and WIND satellites, and is about 5 times larger than that derived from SDO coronal hole (CH) observations, and the variation in the simulated radial solar wind speed is consistent with the evolution of the OMF evaluated around the corresponding solar disk center. We find that the OMF is reduced by up to $45\%$ from 1.01~$R_s$ to 0.1~AU and increases with a higher-resolution mesh. The OMF decreases mainly within 3~$R_s$, where the closed magnetic flux drops more rapidly, from about $60\%$ of the total magnetic flux at 1.01~$R_s$ to about $4\%$ at 3~$R_s$. Moderate adjustment of the heating source term can effectively regulate the simulated OMF. Preprocessing the photospheric magnetograms with a potential field solver that removes many high-order spherical harmonic components reduces the OMF in the low corona, while having little impact beyond 3~$R_s$. Additionally, the ratio of the maximum to the minimum OMF can reach 1.4 during a single solar maximum CR. These findings highlight the necessity of considering higher grid resolution, more realistic heating mechanisms, and the time-evolving regime of coronal MHD modeling when further addressing the ``open flux problem".

astro-ph.SR↗

arXiv2025

N2N: A Parallel Framework for Large-Scale MILP under Distributed Memory

Parallelization has emerged as a promising approach for accelerating MILP solving. However, the complexity of the branch-and-bound (B&B) framework and the numerous effective algorithm components in MILP solvers make it difficult to parallelize. In this study, a scalable parallel framework, N2N (a node-to-node framework that maps the B&B nodes to distributed computing nodes), was proposed to solve large-scale problems in a distributed memory computing environment. Both deterministic and nondeterministic modes are supported, and the framework is designed to be easily integrated with existing solvers. Regarding the deterministic mode, a novel sliding-window-based algorithm was designed and implemented to ensure that tasks are generated and solved in a deterministic order. Moreover, several advanced techniques, such as the utilization of CP search and general primal heuristics, have been developed to fully utilize distributed computing resources and capabilities of base solvers. Adaptive solving and data communication optimization were also investigated. A popular open-source MILP solver, SCIP, was integrated into N2N as the base solver, yielding N2N-SCIP. Extensive computational experiments were conducted to evaluate the performance of N2N-SCIP compared to ParaSCIP, which is a state-of-the-art distributed parallel MILP solver under the UG framework. The nondeterministic N2N-SCIP achieves speedups of 22.52 and 12.71 with 1,000 MPI processes on the Kunpeng and x86 computing clusters, which is 1.98 and 2.08 times faster than ParaSCIP, respectively. In the deterministic mode, N2N-SCIP also shows significant performance improvements over ParaSCIP across different process numbers and computing clusters. To validate the generality of N2N, HiGHS, another open-source solver, was integrated into N2N. The related results are analyzed, and the requirements of N2N on base solvers are also concluded.

cs.AI↗

arXiv2025

Size Distributions of Arcsecond-Scale Properties of Solar Flare Ribbons

Solar flare ribbons are believed to map the footpoints of newly reconnected magnetic flux tubes, therefore shedding light on the reconnecting current sheet, which is rarely observed by direct imaging or spectroscopy. Here we study the detailed evolution of flare ribbons down to the arcsecond scale for 10 flares characterized by the classic double ribbons. Identifying the flaring pixels by combining the intensity variances of the UV filter ratio and intensity threshold, we found that the waiting time distributions of the flaring pixels are well described by power laws, distinct from those in the preflare or quiet-Sun regions, and that the power-law slopes are generally consistent with those predicted by nonstationary Poisson processes in the nonlinear regime or by the 2D/3D self-organized criticality (SOC) model. The size distributions for flaring duration also follow power laws but the slopes are more scattered. In contrast, the size distributions for other parameters, including peak intensity, energy, and radial magnetic field strength of the flaring pixels, deviate from power laws, and the estimated slopes significantly differ from the SOC predictions. These results suggest that a nonstationary Poisson process or an avalanche-like process might be ongoing in the temporal dimension in the reconnecting current sheet, but in other aspects, e.g., space- and energy-wise, the avalanche is likely modulated by other physical processes or the fine structures of the reconnecting current sheet.

astro-ph.SR↗

arXiv2025

Revealing Neurocognitive and Behavioral Patterns by Unsupervised Manifold Learning from Dynamic Brain Data

Dynamic brain data, teeming with biological and functional insights, are becoming increasingly accessible through advanced measurements, providing a gateway to understanding the inner workings of the brain in living subjects. However, the vast size and intricate complexity of the data also pose a daunting challenge in reliably extracting meaningful information across various data sources. This paper introduces a generalizable unsupervised deep manifold learning for exploration of neurocognitive and behavioral patterns. Unlike existing methods that extract patterns directly from the input data as in the existing methods, the proposed Brain-dynamic Convolutional-Network-based Embedding (BCNE) seeks to capture the brain-state trajectories by deciphering the temporospatial correlations within the data and subsequently applying manifold learning to this correlative representation. The performance of BCNE is showcased through the analysis of several important dynamic brain datasets. The results, both visual and quantitative, reveal a diverse array of intriguing and interpretable patterns. BCNE effectively delineates scene transitions, underscores the involvement of different brain regions in memory and narrative processing, distinguishes various stages of dynamic learning processes, and identifies differences between active and passive behaviors. BCNE provides an effective tool for exploring general neuroscience inquiries or individual-specific patterns.

q-bio.NC↗

arXiv2025

A Hierarchical Constructive Heuristic for Large-Scale Survivable Traffic Grooming Problem under Double-Link Failures

This paper studies a survivable traffic grooming problem in large-scale optical transport networks under double-link failures (STG2). Each communication demand must be assigned a route for every possible scenario involving zero, one, or two failed fiber links. Protection against double-link failures is crucial for ensuring reliable telecommunications services while minimizing equipment costs, making it essential for telecommunications companies today. However, this significantly complicates the problem and is rarely addressed in existing studies. Furthermore, current research typically examines networks with fewer than 300 nodes, much smaller than some emerging networks containing thousands of nodes. To address these challenges, we propose a novel hierarchical constructive heuristic for STG2. This heuristic constructs and assigns routes to communication demands across different scenarios by following a hierarchical sequence. It incorporates several innovative optimization techniques and utilizes parallel computing to enhance efficiency. Extensive experiments have been conducted on large-scale STG2 instances provided by our industry partner, encompassing networks with 1,000 to 2,600 nodes. Results demonstrate that within a one-hour time limit and a 16 GB memory limit set by the industry partner, our heuristic improves the objective values of the best-known solutions by 18.5\% on average, highlighting its significant potential for practical applications.

math.CO↗

arXiv2025

LP Relaxations for Routing and Wavelength Assignment with Partial Path Protection: Formulations and Computations

As a variant of the routing and wavelength assignment problem (RWAP), the RWAP with partial path protection (RWAP-PPP) designs a reliable optical-fiber network for telecommunications. It assigns paths and wavelengths to meet communication requests, not only in normal working situations but also in potential failure cases where an optical link fails. The literature lacks efficient relaxations to produce tight lower bounds on the optimal objective value of the RWAP-PPP. Consequently, the solution quality for the RWAP-PPP cannot be properly assessed, which is critical for telecommunication providers in customer bidding and service improvement. Due to numerous failure scenarios, developing effective lower bounds for the RWAP-PPP is challenging. To address this, we formulate and analyze various linear programming (LP) relaxations of the RWAP-PPP. Among them, we propose a novel LP relaxation yielding promising lower bounds. To solve it, we develop a Benders decomposition algorithm with valid inequalities to enhance performance. Computational results on practical networks, including large ones with hundreds of nodes and edges, demonstrate the effectiveness of the LP relaxation and efficiency of its algorithm. The obtained lower bounds achieve average optimality gaps of 8.6%. Compared with a direct LP relaxation of the RWAP, which has average gaps of 36.7%, significant improvements are observed. Consequently, our LP relaxation and algorithm effectively assess RWAP-PPP solution quality, offering significant research and practical value.

math.OC↗

arXiv2025

Learning to Incentivize in Repeated Principal-Agent Problems with Adversarial Agent Arrivals

We initiate the study of a repeated principal-agent problem over a finite horizon $T$, where a principal sequentially interacts with $K\geq 2$ types of agents arriving in an adversarial order. At each round, the principal strategically chooses one of the $N$ arms to incentivize for an arriving agent of unknown type. The agent then chooses an arm based on its own utility and the provided incentive, and the principal receives a corresponding reward. The objective is to minimize regret against the best incentive in hindsight. Without prior knowledge of agent behavior, we show that the problem becomes intractable, leading to linear regret. We analyze two key settings where sublinear regret is achievable. In the first setting, the principal knows the arm each agent type would select greedily for any given incentive. Under this setting, we propose an algorithm that achieves a regret bound of $O(\min\{\sqrt{KT\log N},K\sqrt{T}\})$ and provide a matching lower bound up to a $\log K$ factor. In the second setting, an agent's response varies smoothly with the incentive and is governed by a Lipschitz constant $L\geq 1$. Under this setting, we show that there is an algorithm with a regret bound of $\tilde{O}((LN)^{1/3}T^{2/3})$ and establish a matching lower bound up to logarithmic factors. Finally, we extend our algorithmic results for both settings by allowing the principal to incentivize multiple arms simultaneously in each round.

cs.GT↗

arXiv2025

Improved Regret and Contextual Linear Extension for Pandora's Box and Prophet Inequality

We study the Pandora's Box problem in an online learning setting with semi-bandit feedback. In each round, the learner sequentially pays to open up to $n$ boxes with unknown reward distributions, observes rewards upon opening, and decides when to stop. The utility of the learner is the maximum observed reward minus the cumulative cost of opened boxes, and the goal is to minimize regret defined as the gap between the cumulative expected utility and that of the optimal policy. We propose a new algorithm that achieves $\widetilde{O}(\sqrt{nT})$ regret after $T$ rounds, which improves the $\widetilde{O}(n\sqrt{T})$ bound of Agarwal et al. [2024] and matches the known lower bound up to logarithmic factors. To better capture real-life applications, we then extend our results to a natural but challenging contextual linear setting, where each box's expected reward is linear in some known but time-varying $d$-dimensional context and the noise distribution is fixed over time. We design an algorithm that learns both the linear function and the noise distributions, achieving $\widetilde{O}(nd\sqrt{T})$ regret. Finally, we show that our techniques also apply to the online Prophet Inequality problem, where the learner must decide immediately whether or not to accept a revealed reward. In both non-contextual and contextual settings, our approach achieves similar improvements and regret bounds.

cs.LG↗

arXiv2024

Principal-Agent Bandit Games with Self-Interested and Exploratory Learning Agents

We study the repeated principal-agent bandit game, where the principal indirectly interacts with the unknown environment by proposing incentives for the agent to play arms. Most existing work assumes the agent has full knowledge of the reward means and always behaves greedily, but in many online marketplaces, the agent needs to learn the unknown environment and sometimes explore. Motivated by such settings, we model a self-interested learning agent with exploration behaviors who iteratively updates reward estimates and either selects an arm that maximizes the estimated reward plus incentive or explores arbitrarily with a certain probability. As a warm-up, we first consider a self-interested learning agent without exploration. We propose algorithms for both i.i.d. and linear reward settings with bandit feedback in a finite horizon $T$, achieving regret bounds of $\widetilde{O}(\sqrt{T})$ and $\widetilde{O}( T^{2/3} )$, respectively. Specifically, these algorithms are established upon a novel elimination framework coupled with newly-developed search algorithms which accommodate the uncertainty arising from the learning behavior of the agent. We then extend the framework to handle the exploratory learning agent and develop an algorithm to achieve a $\widetilde{O}(T^{2/3})$ regret bound in i.i.d. reward setup by enhancing the robustness of our elimination framework to the potential agent exploration. Finally, when reducing our agent behaviors to the one studied in (Dogan et al., 2023a), we propose an algorithm based on our robust framework, which achieves a $\widetilde{O}(\sqrt{T})$ regret bound, significantly improving upon their $\widetilde{O}(T^{11/12})$ bound.

cs.LG↗

arXiv2024

ST-NeRP: Spatial-Temporal Neural Representation Learning with Prior Embedding for Patient-specific Imaging Study

During and after a course of therapy, imaging is routinely used to monitor the disease progression and assess the treatment responses. Despite of its significance, reliably capturing and predicting the spatial-temporal anatomic changes from a sequence of patient-specific image series presents a considerable challenge. Thus, the development of a computational framework becomes highly desirable for a multitude of practical applications. In this context, we propose a strategy of Spatial-Temporal Neural Representation learning with Prior embedding (ST-NeRP) for patient-specific imaging study. Our strategy involves leveraging an Implicit Neural Representation (INR) network to encode the image at the reference time point into a prior embedding. Subsequently, a spatial-temporally continuous deformation function is learned through another INR network. This network is trained using the whole patient-specific image sequence, enabling the prediction of deformation fields at various target time points. The efficacy of the ST-NeRP model is demonstrated through its application to diverse sequential image series, including 4D CT and longitudinal CT datasets within thoracic and abdominal imaging. The proposed ST-NeRP model exhibits substantial potential in enabling the monitoring of anatomical changes within a patient throughout the therapeutic journey.

eess.IV↗

arXiv2024

Scaling the topological transport based on an effective Weyl model

Magnetic topological semimetals are increasingly fueling interests in exotic electronic-thermal physics including thermoelectrics and spintronics. To control the transports of topological carriers in such materials becomes a central issue. However, the topological bands in real materials are normally intricate, leaving obstacles to understand the transports in a physically clear way. Parallel to the renowned effective two-band model in magnetic field scale for semiconductors, here, an effective Weyl-band model in temperature scale was developed with pure Weyl state and a few meaningful parameters for topological semimetals. Based on the model, a universal scaling was established and subsequently verified by reported experimental transports. The essential sign regularity of anomalous Hall and Nernst transports was revealed with connection to chiralities of Weyl nodes and carrier types. Upon a double-Weyl model, a concept of Berry-curvature ferrimagnetic structure, as an analogy to the real-space magnetic structure, was further proposed and well described the emerging sign reversal of Nernst thermoelectric transports in temperature scale. Our study offers a convenient tool for scaling the Weyl-fermion-related transport physics, and promotes the modulations and applications of magnetic topological materials in future topological quantum devices.

cond-mat.mes-hall↗

arXiv2024

Topological Nodal Chains and Transverse Transports in Ferromagnetic Centrosymmetric Semimetal FeIn2S4

Nodal chain semimetals protected by nonsymmorphic symmetries are distinct from Dirac and Weyl semimetals, featuring unconventional topological surface states and resulting in anomalous magnetotransport properties. Here, we reveal that the ferromagnetic FeIn2S4 is a suitable nodal chain candidate in theory. Centrosymmetric FeIn2S4 with nonsymmorphic symmetries shows half-metallicity and clean band-crossings with hourglass-type dispersion tracing out nodal lines. Owing to glide mirror symmetries, the nontrivial nodal loops form nodal chain, which is associated with the perpendicular glide mirror planes. These nodal chains are robust against spin-orbital interaction, giving rise to the coexistence of drumhead-type surface states and closed surface Fermi arcs. Moreover, the nodal loops protected by nonsymmorphic symmetry contribute to large anomalous Hall conductivity and the anomalous Nernst conductivity. Our results provide a platform to explore the intriguing topological state and transverse transport properties in magnetic system.

cond-mat.mtrl-sci↗