arXiv subjects

Yuqing Hu

Publications and source records attributed to Yuqing Hu.

At least 19 recordsLinked to original sources

arXiv2026

N\'eel-order-dependent transverse transport in noncoplanar antiferromagnet $\text{MnTe}_{2}$

Antiferromagnets hold appealing potential in next-generation spintronic devices with higher frequency and scalability, thanks to their alternating spin orientations that cancel out net magnetization. However, the lack of a nonzero magnetization makes the detection of the magnetic configuration of antiferromagnet difficult, hampering the applications of antiferromagnets. Here, we report a new transverse transport effect in noncoplanar antiferromagnet $\text{MnTe}_{2}$. This effect is antisymmetric in both magnetic field and N\'eel order, but symmetric in its two indices. It can be understood in terms of the contribution induced by both magnetic field and geometric quantities, as confirmed by our theoretical calculations. Our discovery of a new N\'eel-order-dependent transverse transport effect provides opportunities to the advancing antiferromagnetic spintronics.

cond-mat.mtrl-sci↗

arXiv2026

Empirical Grounding Improves the Realism of LLM Agents Simulating Human Behavior During Disruptions

Large language model (LLM) agents offer a generative approach to simulating human behavior under conditions that may have few or no direct historical analogues, a common challenge in disaster and infrastructure-disruption planning. However, this generative capacity creates a validity problem: individually plausible agent reasoning may fail to reproduce empirical population behavior. We evaluate whether empirical grounding improves the statistical realism of LLM-agent simulations during disruptions. Specifically, we develop an empirically grounded LLM-agent framework that embeds demographic profiles from the American Community Survey, baseline routines from the American Time Use Survey, and urban spatial context into agent initialization, memory, decision prompts, and activity execution. An independent household survey conducted during the July 2024 Philadelphia heatwave is reserved as an external validation benchmark. Compared with an ungrounded LLM-agent baseline, the grounded model improved reconstruction of normal daily routines, increasing mean correlation with empirical activity profiles from 0.528 to 0.912 and reducing mean squared error from 0.066 to 0.008. Under heatwave conditions, the grounded model better reproduced survey-derived activity profiles, increasing mean correlation from 0.349 to 0.836 and reducing mean squared error from 0.098 to 0.012. The grounded model captured 46.4% of observed heatwave response amplitude, compared with 20.6% for the ungrounded baseline. These findings show that empirical grounding can make LLM agents more statistically credible simulators of population behavior while revealing remaining gaps in modeling human adaptation during disruptions.

cs.AI↗

arXiv2026

Do Stationarity Transformations Actually Improve Time Series Forecasts? A Controlled Experimental Evaluation

Stationarity transformations, such as differencing, are a common preprocessing step in forecasting, motivated by the idea that modifying a series to achieve stationarity improves accuracy. Whether this is true, and for which processes, has rarely been evaluated in controlled experiments. We study the decision to transform as the object of inquiry. We cross eighteen synthetic data-generating processes, most of them stochastic-trend processes spanning exact and near unit roots, fractional integration, seasonal unit roots, structural breaks, and heteroscedasticity, with ten transformations, five models, and three horizons, replicated by Monte Carlo, for 35,099 evaluations. Each forecast is inverted to the original scale, with the differencing inverse anchored at the forecast origin, and scored by the mean absolute scaled error. Signal-preserving transforms, namely deterministic detrending and seasonal differencing matched to series structure, improve accuracy, whereas indiscriminate differencing degrades it. A mediation analysis shows that differencing achieves trend stationarity, but trend stationarity is only weakly associated with accuracy, and transforms differ in their effects on predictable structure. Choosing the transformation by out-of-sample validation yields lower regret than unit-root pretesting or any fixed rule, with blanket differencing performing the worst. The findings are confirmed by real-world validation on nine series from two domains.

stat.ME↗

arXiv2026

Matching-with-Contracts for the AI-RAN Market: AIGC-as-a-Service for Teleoperation

Artificial intelligence radio access networks (AI-RANs) are a promising architecture for bolstering the prosperity of the edge AI ecosystem. A well-designed incentive mechanism can further ensure the sustainable development of this ecosystem. However, incentive mechanism design faces two major challenges: 1) information asymmetry, where AI-RAN operators have only partial knowledge of AI users' utility functions, and 2) competition, as multiple AI-RAN operators coexist in real-world markets. Remarkably, chaotic and adversarial competition might compromise AI-RAN operators' utility. To this end, we develop a matching-with-contracts framework for incentive mechanism design in AI-RAN service markets. The framework extends the static matching-with-contracts model by jointly characterizing the contract design of multiple competitive operators, user-operator matching, and dynamic evolution of the market state. Specifically, the incentive mechanism offered by each AI-RAN operator takes the form of a contract menu, where each contract item consists of an AI service latency agreement and a corresponding price. We model the AI service process as three independent queues and characterize the violation probability of the latency agreement using queueing theory and the Chernoff bound. To derive an effective incentive mechanism, we further propose a mixed stable matching-with-contracts algorithm that jointly updates user-side matching decisions and operator-side contract menus. Simulation results for a teleoperation-oriented AIGC service demonstrate the effectiveness and robustness of the proposed method. Compared with benchmark schemes, our method improves the total utility of AI-RAN operators by at least 56.8\% under representative settings.

cs.CE↗

arXiv2026

Nonlinear Hall quantum oscillations to probe topological Brown-Zak fermions in graphene moir\'e systems

Due to the deep connection with the quantum geometry of electronic Bloch wavefunctions, the second-order nonlinear Hall effect (NLHE) has been an attractive topic since its proposal. However, studies on NLHE under a magnetic field have been lacking. Given that quantum oscillations in the linear response regime have been proven to be useful tools in investigating electronic systems, searching for quantum oscillations in NLHE is of great interest and is expected to provide new avenues to unveil rich quantum geometric properties of novel quasiparticles. Here, we propose a new type of NLHE quantum oscillations and experimentally probe it in graphene moir\'e systems. It stems from the alternation of the dominant NLHE mechanisms with recurring Bloch states under magnetic field, which enables sensitive detection of Brown-Zak fermions, giving an onset field as low as 0.5 T. Most importantly, when the commensurability condition is satisfied, the nonlinear transport of Brown-Zak fermions is mainly governed by quantum geometric contributions. Our findings not only establish a new type of quantum oscillations, but also demonstrate the first experimental detection of the topological nature of Brown-Zak fermions, shedding light on the exploration of novel topological quasiparticles.

cond-mat.mes-hall↗

arXiv2026

StationarityToolkit: Comprehensive Time Series Stationarity Analysis in Python

Time-series stationarity is a property that statistical characteristics such as trend, variance, seasonality remain constant over time. It is considered fundamental to many forecasting and analysis methods. Different tests detect different types of non-stationarity: structural breaks or deterministic trends, clustered or time-dependent variance, stochastic or deterministic seasonality. A series might pass one test while failing another; single-test approaches seldom distinguish between conceptually different types of non-stationarity that require different types of tests and transformations. `StationarityToolkit` addresses this by providing a comprehensive Python library that runs 10 statistical tests across three categories: trend (4 tests), variance (4 tests), and seasonality (2 tests). Rather than a binary stationary/non-stationary verdict, users receive detailed diagnostics with actionable notes for each detection. The toolkit automatically infers the frequency of the data provided (requires datetime index), provides clear interpretations with test statistics and p-values, and supports an iterative test-transform-retest workflow essential for real-world data sets.

stat.ME↗

arXiv2026

From Dyads to Groups: Rethinking Emotional Support with Conversational AI

Advances in artificial intelligence (AI), together with persistent gaps in access to reliable emotional support, have positioned AI as an increasingly prominent source of emotional assistance. However, most AI-based emotional support applications and prior research focus on one-on-one interactions between users and a single AI agent, leaving the potential advantages of alternative support configurations largely unexplored. Drawing on social support and support group theory, this research examines whether AI-based emotional support delivered by a group of AI agents (group AI support) can constitute a more effective support form than single-agent support (single AI support). We propose that group AI support enhances users' perceived support efficacy, that this effect operates by strengthening users' connectedness with the AI system, and that the composition of support types within AI groups further shapes support outcomes. Three experiments provide convergent support for these claims. By identifying when and why group AI emotional support outperforms single AI support, this work advances theoretical understanding of AI-based emotional support and provides actionable guidance for the design of AI support systems.

cs.HC↗

arXiv2026

Meningioma Analysis and Diagnosis using Limited Labeled Samples

The biological behavior and treatment response of meningiomas depend on their grade, making an accurate diagnosis essential for treatment planning and prognosis assessment. We observed that the weighted fusion of spatial-frequency domain features significantly influences meningioma classification performance. Notably, the contribution of specific frequency bands obtained by discrete wavelet transform varies considerably across different images. A feature fusion architecture with adaptive weights of different frequency band information and spatial domain information is proposed for few-shot meningioma learning. To verify the effectiveness of the proposed method, a new MRI dataset of meningiomas is introduced. The experimental results demonstrate the superiority of the proposed method compared with existing state-of-the-art methods in three datasets. The code will be available at: https://github.com/ICL-SUST/AMSF-Net

cs.CV↗

arXiv2025

Learning to Incentivize: LLM-Empowered Contract for AIGC Offloading in Teleoperation

With the rapid growth in demand for AI-generated content (AIGC), edge AIGC service providers (ASPs) have become indispensable. However, designing incentive mechanisms that motivate ASPs to deliver high-quality AIGC services remains a challenge, especially in the presence of information asymmetry. In this paper, we address bonus design between a teleoperator and an edge ASP when the teleoperator cannot observe the ASP's private settings and chosen actions (diffusion steps). We formulate this as an online learning contract design problem and decompose it into two subproblems: ASP's settings inference and contract derivation. To tackle the NP-hard setting-inference subproblem with unknown variable sizes, we introduce a large language model (LLM)-empowered framework that iteratively refines a naive seed solver using the LLM's domain expertise. Upon obtaining the solution from the LLM-evolved solver, we directly address the contract derivation problem using convex optimization techniques and obtain a near-optimal contract. Simulation results on our Unity-based teleoperation platform show that our method boosts the teleoperator's utility by $5 \sim 40\%$ compared to benchmarks, while preserving positive incentives for the ASP. The code is available at https://github.com/Zijun0819/llm4contract.

cs.CE↗

arXiv2025

A Reliable Vertical Federated Learning Framework for Traffic State Estimation with Data Selection and Incentive Mechanisms

Vertical Federated Learning (VFL)-based Traffic State Estimation (TSE) offers a promising approach for integrating vertically distributed traffic data from municipal authorities (MA) and mobility providers (MP) while safeguarding privacy. However, given the variations in MPs' data collection capabilities and the potential for MPs to underperform in data provision, we propose a reliable VFL-based TSE framework that ensures model reliability during training and operation. The proposed framework comprises two components: data provider selection and incentive mechanism design. Data provider selection is conducted in three stages to identify the most qualified MPs for VFL model training with the MA. First, the MA partitions the transportation network into road segments. Then, a mutual information (MI) model is trained for each segment to capture the relationship between data and labels. Finally, using a sampling strategy and the MI model, the MA assesses each MP's competence in data provision and selects the most qualified MP for each segment. For the incentive mechanism design, given the MA can leverage the MI mode to inspect the data quality of MP, we formulate the interaction between MA and MP as a supervision game model. Upon this, we devise a penalty-based incentive mechanism to inhibit the lazy probability of MP, thereby guaranteeing the utility of MA. Numerical simulation on real-world datasets showcased that our proposed framework augments the traffic flow and density prediction accuracy by 11.23\% and 23.15\% and elevates the utility of MA by 130$\sim$400\$ compared to the benchmark.

cs.GT↗

arXiv2025

Distributionally Robust Contract Theory for Edge AIGC Services in Teleoperation

Advanced AI-Generated Content (AIGC) technologies have injected new impetus into teleoperation, further enhancing its security and efficiency. Edge AIGC networks have been introduced to meet the stringent low-latency requirements of teleoperation. However, the inherent uncertainty of AIGC service quality and the need to incentivize AIGC service providers (ASPs) make the design of a robust incentive mechanism essential. This design is particularly challenging due to both uncertainty and information asymmetry, as teleoperators have limited knowledge of the remaining resource capacities of ASPs. To this end, we propose a distributionally robust optimization (DRO)-based contract theory to design robust reward schemes for AIGC task offloading. Notably, our work extends the contract theory by integrating DRO, addressing the fundamental challenge of contract design under uncertainty. In this paper, contract theory is employed to model the information asymmetry, while DRO is utilized to capture the uncertainty in AIGC service quality. Given the inherent complexity of the original DRO-based contract theory problem, we reformulate it into an equivalent, tractable bi-level optimization problem. To efficiently solve this problem, we develop a Block Coordinate Descent (BCD)-based algorithm to derive robust reward schemes. Simulation results on our unity-based teleoperation platform demonstrate that the proposed method improves teleoperator utility by 2.7\% to 10.74\% under varying degrees of AIGC service quality shifts and increases ASP utility by 60.02\% compared to the SOTA method, i.e., Deep Reinforcement Learning (DRL)-based contract theory. The code and data are publicly available at https://github.com/Zijun0819/DRO-Contract-Theory.

cs.NI↗

arXiv2024

Unambiguous identification of the indirect band nature of atomically thin hexagonal boron nitride

Atomically thin hexagonal boron nitride (h-BN), especially monolayer, has garnered increasing attention due to its intriguing optical and light-matter-interaction properties. However, its intrinsic optical properties and electronic band structure, have long remained elusive. In this study, near-resonance excited deep-UV photoluminescence/Raman spectroscopy and deep-UV reflectance contrast spectroscopy are utilized to experimentally investigate the optical properties of atomically thin h-BN across various layer numbers. It is revealed that the absence of luminescence in 1-3 layers h-BN is indicative of their indirect band gap nature, rectifying previously adopted identification of a direct band gap in monolayer BN. Notably, band-edge luminescence signals and indirect bandgap absorption start to appear in 4-layer, and the luminescence intensity increases with the number of layers, suggesting that interlayer interactions and periodicity along the z-axis enhance phonon-assisted indirect bandgap transition, even in the 4-layer case, and furthermore indicating the formation process of flat bands at the K and M valleys as the periodicity along the z direction increases. Additionally, the prominent resonance Raman signals in atomically thin h-BN underscore strong electron-phonon coupling in this material.

cond-mat.mtrl-sci↗

arXiv2024

Vision Language Model-Empowered Contract Theory for AIGC Task Allocation in Teleoperation

Integrating low-light image enhancement techniques, in which diffusion-based AI-generated content (AIGC) models are promising, is necessary to enhance nighttime teleoperation. Remarkably, the AIGC model is computation-intensive, thus necessitating the allocation of AIGC tasks to edge servers with ample computational resources. Given the distinct cost of the AIGC model trained with varying-sized datasets and AIGC tasks possessing disparate demand, it is imperative to formulate a differential pricing strategy to optimize the utility of teleoperators and edge servers concurrently. Nonetheless, the pricing strategy formulation is under information asymmetry, i.e., the demand (e.g., the difficulty level of AIGC tasks and their distribution) of AIGC tasks is hidden information to edge servers. Additionally, manually assessing the difficulty level of AIGC tasks is tedious and unnecessary for teleoperators. To this end, we devise a framework of AIGC task allocation assisted by the Vision Language Model (VLM)-empowered contract theory, which includes two components: VLM-empowered difficulty assessment and contract theory-assisted AIGC task allocation. The first component enables automatic and accurate AIGC task difficulty assessment. The second component is capable of formulating the pricing strategy for edge servers under information asymmetry, thereby optimizing the utility of both edge servers and teleoperators. The simulation results demonstrated that our proposed framework can improve the average utility of teleoperators and edge servers by 10.88~12.43% and 1.4~2.17%, respectively. Code and data are available at https://github.com/ZiJun0819/VLM-Contract-Theory.

cs.CV↗

arXiv2022

An activity-based spatial-temporal community electricity vulnerability assessment framework

The power system is among the most important critical infrastructures in urban cities and is getting increasingly essential in supporting people s daily activities. However, it is also susceptible to most natural disasters such as tsunamis, floods, or earthquakes. Electricity vulnerability, therefore, forms a crucial basis for community resilience. This paper aims to present an assessment framework of spatial-temporal electricity vulnerability to support the building of community resilience against power outages. The framework includes vulnerability indexes in terms of occupant demographics, occupant activity patterns, and urban building characteristics. To integrate factors in these aspects, we also proposed a process as activity simulation-mapping-evaluation-visualization to apply the framework and visualize results. This framework can help planners make an effective first-time response by identifying the most vulnerable areas when a massive power outage happens during natural disasters. It can also be integrated into community resilience analysis models and potentially contributes to effective disaster risk management

eess.SY↗

arXiv2022

Community Time-Activity Trajectory Modelling based on Markov Chain Simulation and Dirichlet Regression

Accurate modeling of human time-activity trajectory is essential to support community resilience and emergency response strategies such as daily energy planning and urban seismic vulnerability assessment. However, existing modeling of time-activity trajectory is only driven by socio-demographic information with identical activity trajectories shared among the same group of people and neglects the influence of the environment. To further improve human time-activity trajectory modeling, this paper constructs community time-activity trajectory and analyzes how social-demographic and built environment influence people s activity trajectory based on Markov Chains and Dirichlet Regression. We use the New York area as a case study and gather data from American Time Use Survey, Policy Map, and the New York City Energy & Water Performance Map to evaluate the proposed method. To validate the regression model, Box s M Test and T-test are performed with 80% data training the model and the left 20% as the test sample. The modeling results align well with the actual human behavior trajectories, demonstrating the effectiveness of the proposed method. It also shows that both social-demographic and built environment factors will significantly impact a community's time-activity trajectory. Specifically, 1) Diversity and median age both have a significant influence on the proportion of time people assign to education activity. 2) Transportation condition affects people s activity trajectory in the way that longer commute time decreases the proportion of biological activity (eg. sleeping and eating) and increases people s working time. 3) Residential density affects almost all activities with a significant p-value for all biological needs, household management, working, education, and personal preference.

eess.SY↗

arXiv2022

Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification

Transductive Few-Shot learning has gained increased attention nowadays considering the cost of data annotations along with the increased accuracy provided by unlabelled samples in the domain of few shot. Especially in Few-Shot Classification (FSC), recent works explore the feature distributions aiming at maximizing likelihoods or posteriors with respect to the unknown parameters. Following this vein, and considering the parallel between FSC and clustering, we seek for better taking into account the uncertainty in estimation due to lack of data, as well as better statistical properties of the clusters associated with each class. Therefore in this paper we propose a new clustering method based on Variational Bayesian inference, further improved by Adaptive Dimension Reduction based on Probabilistic Linear Discriminant Analysis. Our proposed method significantly improves accuracy in the realistic unbalanced transductive setting on various Few-Shot benchmarks when applied to features used in previous studies, with a gain of up to $6\%$ in accuracy. In addition, when applied to balanced setting, we obtain very competitive results without making use of the class-balance artefact which is disputable for practical use cases. We also provide the performance of our method on a high performing pretrained backbone, with the reported results further surpassing the current state-of-the-art accuracy, suggesting the genericity of the proposed method.

cs.LG↗

arXiv2022

EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients

Few-shot learning aims at leveraging knowledge learned by one or more deep learning models, in order to obtain good classification performance on new problems, where only a few labeled samples per class are available. Recent years have seen a fair number of works in the field, introducing methods with numerous ingredients. A frequent problem, though, is the use of suboptimally trained models to extract knowledge, leading to interrogations on whether proposed approaches bring gains compared to using better initial models without the introduced ingredients. In this work, we propose a simple methodology, that reaches or even beats state of the art performance on multiple standardized benchmarks of the field, while adding almost no hyperparameters or parameters to those used for training the initial deep learning models on the generic dataset. This methodology offers a new baseline on which to propose (and fairly compare) new techniques or adapt existing ones.

cs.LG↗

arXiv2021

Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning

Few-shot classification is a challenging problem due to the uncertainty caused by using few labelled samples. In the past few years, many methods have been proposed with the common aim of transferring knowledge acquired on a previously solved task, what is often achieved by using a pretrained feature extractor. Following this vein, in this paper we propose a novel transfer-based method which aims at processing the feature vectors so that they become closer to Gaussian-like distributions, resulting in increased accuracy. In the case of transductive few-shot learning where unlabelled test samples are available during training, we also introduce an optimal-transport inspired algorithm to boost even further the achieved performance. Using standardized vision benchmarks, we show the ability of the proposed methodology to achieve state-of-the-art accuracy with various datasets, backbone architectures and few-shot settings.

cs.LG↗