arXiv subjects

Zheng Ma

Publications and source records attributed to Zheng Ma.

At least 55 records · Page 3Linked to original sources

arXiv2025

Green Hydrogen under Uncertainty: Evaluating Power-to-X Strategies Using Agent-Based Simulation and Multi-Criteria Decision Framework

The transition toward net-zero energy systems requires scalable and cost-effective deployment of Power-to-X technologies, particularly green hydrogen production. Despite increasing investments, a critical research gap remains in dynamically assessing how different operational strategies affect the feasibility of hydrogen production under real-world energy market conditions. Most existing studies rely on static, techno-economic models and overlook actor interactions, infrastructure limitations, and regulatory complexity. This paper presents a novel modeling framework that integrates agent-based simulation with multi-criteria decision-making to evaluate green hydrogen production strategies using co-located wind and solar generation. Three operational strategies - grid-only, on-site-only, and hybrid - are applied across three electrolyzer capacity levels (10 MW, 50 MW, and 100 MW) within a Danish case study. Real electricity tariffs, emissions factors, and market data are used to simulate technical, economic, and environmental performance indicators. The results show that hybrid strategies consistently outperform grid-only configurations in terms of cost and emissions while maintaining stable hydrogen output. Although on-site-only strategies minimize emissions and costs, they fail to meet fixed production demands. This framework offers novel scientific contributions by modeling dynamic actor interactions and integrating system performance evaluation into strategic planning. Practically, it provides actionable insights for energy planners and policymakers designing resilient and efficient Power-to-X systems in renewable-rich contexts.

eess.SY↗

arXiv2025

Disentangling the Complex Multiplexed DIA Spectra in De Novo Peptide Sequencing

Data-Independent Acquisition (DIA) was introduced to improve sensitivity to cover all peptides in a range rather than only sampling high-intensity peaks as in Data-Dependent Acquisition (DDA) mass spectrometry. However, it is not very clear how useful DIA data is for de novo peptide sequencing as the DIA data are marred with coeluted peptides, high noises, and varying data quality. We present a new deep learning method DIANovo, and address each of these difficulties, and improves the previous established systems by a large margin, via equipping the model with a deeper understanding of coeluted DIA spectra. This paper also provides criteria about when DIA data could be used for de novo peptide sequencing and when not to by providing a comparison between DDA and DIA, in both de novo and database search mode. We find that while DIA excels with narrow isolation windows on older-generation instruments, it loses its advantage with wider windows. However, with Orbitrap Astral, DIA consistently outperforms DDA due to narrow window mode enabled. We also provide a theoretical explanation of this phenomenon, emphasizing the critical role of the signal-to-noise profile in the successful application of de novo sequencing.

q-bio.BM↗

arXiv2025

A Fully-integrated Diamond Nitrogen-Vacancy Magnetometer with Nanotesla Sensitivity

Ensemble diamond nitrogen-vacancy (DNV) centers have emerged as a promising platform for precise earth-field vector magnetic sensing, particularly in applications that require high mobility. Nevertheless, integrating all control utilities into a compact form has proven challenging, thus far limiting the sensitivity of mobile DNV magnetometers to the uT-level. This study introduces a fully integrated DNV magnetometer that encompasses all the essential components typically found in traditional platforms, while maintaining compact dimensions of approximately 13 cm * 26 cm. In contrast to previous efforts, we successfully address these challenges by integrating a high-power laser, a lock-in amplifier, and a digitally-modulated microwave source. These home-made components show comparable performance with commercial devices under our circumstance, resulting in an optimal sensitivity of 2.14 nT/sqrt{Hz}. The limitations in this system as well as possible future improvements are discussed. This work paves the way for the use of DNV magnetometry in cost-effective, mobile unmanned aerial vehicles, facilitating a wide range of practical applications.

quant-ph↗

arXiv2025

Towards Effective Open-set Graph Class-incremental Learning

Graph class-incremental learning (GCIL) allows graph neural networks (GNNs) to adapt to evolving graph analytical tasks by incrementally learning new class knowledge while retaining knowledge of old classes. Existing GCIL methods primarily focus on a closed-set assumption, where all test samples are presumed to belong to previously known classes. Such an assumption restricts their applicability in real-world scenarios, where unknown classes naturally emerge during inference, and are absent during training. In this paper, we explore a more challenging open-set graph class-incremental learning scenario with two intertwined challenges: catastrophic forgetting of old classes, which impairs the detection of unknown classes, and inadequate open-set recognition, which destabilizes the retention of learned knowledge. To address the above problems, a novel OGCIL framework is proposed, which utilizes pseudo-sample embedding generation to effectively mitigate catastrophic forgetting and enable robust detection of unknown classes. To be specific, a prototypical conditional variational autoencoder is designed to synthesize node embeddings for old classes, enabling knowledge replay without storing raw graph data. To handle unknown classes, we employ a mixing-based strategy to generate out-of-distribution (OOD) samples from pseudo in-distribution and current node embeddings. A novel prototypical hypersphere classification loss is further proposed, which anchors in-distribution embeddings to their respective class prototypes, while repelling OOD embeddings away. Instead of assigning all unknown samples into one cluster, our proposed objective function explicitly models them as outliers through prototype-aware rejection regions, ensuring a robust open-set recognition. Extensive experiments on five benchmarks demonstrate the effectiveness of OGCIL over existing GCIL and open-set GNN methods.

cs.LG↗

arXiv2025

Cognitive-Radio Functionality: A Novel Configuration for STAR-RIS assisted RSMA Networks

Cognitive radio rate-splitting multiple access (CR-RSMA) has emerged as a promising multiple access framework that can efficiently manage interference and adapt dynamically to heterogeneous quality-of-service (QoS) requirements. To effectively support such demanding access schemes, programmable wireless environments have attracted considerable attention, especially through simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs), which can enable full-space control of signal propagation in asymmetric user deployments. In this paper, we propose the cognitive radio (CR) functionality for STAR-RIS-assisted CR-RSMA systems, leveraging the unique capability of the STAR-RIS to combine element and power splitting for adaptive control of transmission and reflection in CR scenarios. Specifically, the proposed CR functionality partitions the STAR-RIS into two regions independently controlling the transmission and reflection of signals, simultaneously ensuring the required QoS for the primary user and enhancing the performance of the secondary user. To accurately characterize the system performance, we derive analytical expressions for the ergodic rate of the secondary user and the outage rate of the primary user under Nakagami-m fading. Finally, simulation results show that the proposed approach effectively manages interference, guarantees the QoS of the primary user, and significantly improves the throughput of the secondary user, highlighting STAR-RIS as an efficient solution for CR-RSMA-based services.

cs.ET↗

arXiv2025

RT-APNN for Solving Gray Radiative Transfer Equations

The Gray Radiative Transfer Equations (GRTEs) are high-dimensional, multiscale problems that pose significant computational challenges for traditional numerical methods. Current deep learning approaches, including Physics-Informed Neural Networks (PINNs) and Asymptotically Preserving Neural Networks (APNNs), are largely restricted to low-dimensional or linear GRTEs. To address these challenges, we propose the Radiative Transfer Asymptotically Preserving Neural Network (RT-APNN), an innovative framework extending APNNs. RT-APNN integrates multiple neural networks into a cohesive architecture, reducing training time while ensuring high solution accuracy. Advanced techniques such as pre-training and Markov Chain Monte Carlo (MCMC) adaptive sampling are employed to tackle the complexities of long-term simulations and intricate boundary conditions. RT-APNN is the first deep learning method to successfully simulate the Marshak wave problem. Numerical experiments demonstrate its superiority over existing methods, including APNNs and MD-APNNs, in both accuracy and computational efficiency. Furthermore, RT-APNN excels at solving high-dimensional, nonlinear problems, underscoring its potential for diverse applications in science and engineering.

physics.comp-ph↗

arXiv2025

A Micro-Macro Decomposition-Based Asymptotic-Preserving Random Feature Method for Multiscale Radiative Transfer Equations

This paper introduces the Asymptotic-Preserving Random Feature Method (APRFM) for the efficient resolution of multiscale radiative transfer equations. The APRFM effectively addresses the challenges posed by stiffness and multiscale characteristics inherent in radiative transfer equations through the application of a micro-macro decomposition strategy. This approach decomposes the distribution function into equilibrium and non-equilibrium components, allowing for the approximation of both parts through the random feature method (RFM) within a least squares minimization framework. The proposed method exhibits remarkable robustness across different scales and achieves high accuracy with fewer degrees of freedom and collocation points than the vanilla RFM. Additionally, compared to the deep neural network-based method, our approach offers significant advantages in terms of parameter efficiency and computational speed. These benefits have been substantiated through numerous numerical experiments conducted on both one- and two-dimensional problems.

math.NA↗

arXiv2025

Exploring the Reliability of Self-explanation and its Relationship with Classification in Language Model-driven Financial Analysis

Language models (LMs) have exhibited exceptional versatility in reasoning and in-depth financial analysis through their proprietary information processing capabilities. Previous research focused on evaluating classification performance while often overlooking explainability or pre-conceived that refined explanation corresponds to higher classification accuracy. Using a public dataset in finance domain, we quantitatively evaluated self-explanations by LMs, focusing on their factuality and causality. We identified the statistically significant relationship between the accuracy of classifications and the factuality or causality of self-explanations. Our study built an empirical foundation for approximating classification confidence through self-explanations and for optimizing classification via proprietary reasoning.

cs.AI↗

arXiv2025

Extract, Match, and Score: An Evaluation Paradigm for Long Question-context-answer Triplets in Financial Analysis

The rapid advancement of large language models (LLMs) has sparked widespread adoption across diverse applications, making robust evaluation frameworks crucial for assessing their performance. While conventional evaluation metrics remain applicable for shorter texts, their efficacy diminishes when evaluating the quality of long-form answers. This limitation is particularly critical in real-world scenarios involving extended questions, extensive context, and long-form answers, such as financial analysis or regulatory compliance. In this paper, we use a practical financial use case to illustrate applications that handle "long question-context-answer triplets". We construct a real-world financial dataset comprising long triplets and demonstrate the inadequacies of traditional metrics. To address this, we propose an effective Extract, Match, and Score (EMS) evaluation approach tailored to the complexities of long-form LLMs' outputs, providing practitioners with a reliable methodology for assessing LLMs' performance in complex real-world scenarios.

cs.CL↗

arXiv2025

Slitless Areal Pure-Parallel HIgh-Redshift Emission Survey (SAPPHIRES): Early Data Release of Deep JWST/NIRCam Images and Spectra in MACS J0416 Parallel Field

We present the early data release (EDR) of SAPPHIRES, a JWST Cycle-3 Treasury imaging and spectroscopic survey using the powerful NIRCam wide-field slitless spectroscopic (WFSS) mode in pure parallel. SAPPHIRES will obtain NIRCam imaging and WFSS data in many cosmological deep fields totaling a telescope charged time of 709 hours (557-hour exposures). In this EDR, we present NIRCam imaging and WFSS data obtained in parallel to the Frontier Field galaxy cluster MACS J0416.1-2403, which are attached to primary observations JWST-GO-4750. With a total dual-channel exposure time of 47.2 hours, we obtain deep NIRCam imaging in 13 bands at 0.6--5.0 micron and deep WFSS at 3.1-5.0 micron through the F356W and F444W filters with grisms in orthogonal dispersion directions. We release reduced NIRCam images, photometric catalogs of 22107 sources and WFSS spectra of 1060 sources with confirmed redshifts ($z\simeq0-8.5$). Preliminary value-added catalogs including photometric redshifts, spectroscopic redshifts and physical properties (mass, star-formation rate, etc.) are also made available. We also characterize the data quality and demonstrate scientific applications, including (1) galaxy candidates at the redshift frontier ($z\gtrsim10$), (2) the ionized gas kinematics of a galaxy reconstructed from $R\sim1500$ grism spectra at orthogonal dispersion directions, (3) massive emission-line galaxies and active galactic nuclei (AGN) around the Epoch of Reionization.

astro-ph.GA↗

arXiv2025

Semi-supervised Anomaly Detection with Extremely Limited Labels in Dynamic Graphs

Semi-supervised graph anomaly detection (GAD) has recently received increasing attention, which aims to distinguish anomalous patterns from graphs under the guidance of a moderate amount of labeled data and a large volume of unlabeled data. Although these proposed semi-supervised GAD methods have achieved great success, their superior performance will be seriously degraded when the provided labels are extremely limited due to some unpredictable factors. Besides, the existing methods primarily focus on anomaly detection in static graphs, and little effort was paid to consider the continuous evolution characteristic of graphs over time (dynamic graphs). To address these challenges, we propose a novel GAD framework (EL$^{2}$-DGAD) to tackle anomaly detection problem in dynamic graphs with extremely limited labels. Specifically, a transformer-based graph encoder model is designed to more effectively preserve evolving graph structures beyond the local neighborhood. Then, we incorporate an ego-context hypersphere classification loss to classify temporal interactions according to their structure and temporal neighborhoods while ensuring the normal samples are mapped compactly against anomalous data. Finally, the above loss is further augmented with an ego-context contrasting module which utilizes unlabeled data to enhance model generalization. Extensive experiments on four datasets and three label rates demonstrate the effectiveness of the proposed method in comparison to the existing GAD methods.

cs.LG↗

arXiv2025

CapArena: Benchmarking and Analyzing Detailed Image Captioning in the LLM Era

Image captioning has been a longstanding challenge in vision-language research. With the rise of LLMs, modern Vision-Language Models (VLMs) generate detailed and comprehensive image descriptions. However, benchmarking the quality of such captions remains unresolved. This paper addresses two key questions: (1) How well do current VLMs actually perform on image captioning, particularly compared to humans? We built CapArena, a platform with over 6000 pairwise caption battles and high-quality human preference votes. Our arena-style evaluation marks a milestone, showing that leading models like GPT-4o achieve or even surpass human performance, while most open-source models lag behind. (2) Can automated metrics reliably assess detailed caption quality? Using human annotations from CapArena, we evaluate traditional and recent captioning metrics, as well as VLM-as-a-Judge. Our analysis reveals that while some metrics (e.g., METEOR) show decent caption-level agreement with humans, their systematic biases lead to inconsistencies in model ranking. In contrast, VLM-as-a-Judge demonstrates robust discernment at both the caption and model levels. Building on these insights, we release CapArena-Auto, an accurate and efficient automated benchmark for detailed captioning, achieving 94.3% correlation with human rankings at just $4 per test. Data and resources will be open-sourced at https://caparena.github.io.

cs.CV↗

arXiv2025

Distinguishing Between Dark Matter-Black Hole Systems and Naked Singularities via Quasi-Periodic Oscillations

Quasi-Periodic Oscillations (QPOs) are an important phenomenon commonly observed in the X-ray radiation of black holes and neutron stars, closely related to the dynamics of accretion disks around compact objects and general relativistic effects. The objective of this study is to use the QPO phenomenon to distinguish between dark matter-black hole systems and naked singularities, as well as to investigate the effects of different dark matter models (Cold Dark Matter, CDM, and Scalar Field Dark Matter, SFDM) on the accretion disk dynamics. By introducing a dark matter correction model within the framework of general relativity, we systematically investigate the differences in dragging effects, characteristic frequency distribution, and the innermost stable circular orbit (ISCO) radius between dark matter-black hole systems and naked singularities, while analyzing the potential coupling between QPO frequencies and dark matter distribution. The main results of this study are as follows: $ν_r$ and $ν_θ$ in dark matter-black hole systems can be identified as HFQPOs, while for lower spins ($a < 0.5$), $ν_\text{nod}$ can be identified as LFQPOs, and for higher spins ($1 > a \geq 0.5$), $ν_\text{nod}$ falls within the HFQPO observation range. Cold Dark Matter (CDM) and Scalar Field Dark Matter (SFDM) modulate the accretion disk dynamics at the order of $10^{-6}$.

gr-qc↗

arXiv2025

Carbon magneto-ionics: Control of magnetism through voltage-driven carbon transport

Control of magnetism through voltage-driven ionic processes (i.e., magneto-ionics) holds potential for next-generation memories and computing. This stems from its non-volatility, flexibility in adjusting the magnitude and speed of magnetic modulation, and energy efficiency. Since magneto-ionics depends on factors like ionic radius and electronegativity, identifying alternative mobile ions is crucial to embrace new phenomena and applications. Here, the feasibility of C as a prospective magneto-ionic ion is investigated in a Fe-C system by electrolyte gating. In contrast to most magneto-ionic systems, Fe-C presents a dual-ion mechanism: Fe and C act as cation and anion, respectively, moving uniformly in opposite directions under an applied electric field. This leads to a 7-fold increase in saturation magnetization with magneto-ionic rates larger than 1 emu cm-3 s-1, and a 25-fold increase in coercivity. Since carbides exhibit minimal cytotoxicity, this introduces a biocompatible dimension to magneto-ionics, paving the way for the convergence of spintronics and biotechnology.

cond-mat.mtrl-sci↗

arXiv2025

Charge-transfer-mediated boron magneto-ionics: Towards voltage-driven multi-ion transport

Voltage control of magnetism via magneto-ionics, where ion transport and/or redox processes drive magnetic modulation, holds great promise for next-generation memories and computing. This stems from its non-volatility and ability to precisely tune both the magnitude and speed of magnetic properties in an energy-efficient manner. However, expanding magneto-ionics to incorporate novel mobile ions or even multiple ion species is crucial for unlocking new phenomena and enabling multifunctional capabilities. Here, we demonstrate voltage-driven multi-ion transport in a FeBO system with increasing oxygen content, progressively transitioning from an electrostatic-like response to a more pronounced electrochemical (magneto-ionic) behavior. The voltage-driven transport of both B and Fe is activated by oxidation state tuning, owing to the larger electronegativity of oxygen. Such charge-transfer effects allow multi-ion magneto-ionics, where O ions move oppositely to Fe and B ions. These results pave the way for programmable functionalities by leveraging elements with different electron affinities through charge-transfer engineering.

cond-mat.mtrl-sci↗

arXiv2025

Fuel Efficiency Analysis of the Public Transportation System Based on the Gaussian Mixture Model Clustering

Public transportation is a major source of greenhouse gas emissions, highlighting the need to improve bus fuel efficiency. Clustering algorithms assist in analyzing fuel efficiency by grouping data into clusters, but irrelevant features may complicate the analysis and choosing the optimal number of clusters remains a challenging task. Therefore, this paper employs the Gaussian mixture models to cluster the solo fuel-efficiency dataset. Moreover, an integration method that combines the Silhouette index, Calinski-Harabasz index, and Davies-Bouldin index is developed to select the optimal cluster numbers. A dataset with 4006 bus trips in North Jutland, Denmark is utilized as the case study. Trips are first split into three groups, then one group is divided further, resulting in four categories: extreme, normal, low, and extremely low fuel efficiency. A preliminary study using visualization analysis is conducted to investigate how driving behaviors and route conditions affect fuel efficiency. The results indicate that both individual driving habits and route characteristics have a significant influence on fuel efficiency.

cs.LG↗

arXiv2024

Dynamic Demonstrations Controller for In-Context Learning

In-context learning (ICL) is a new paradigm for natural language processing (NLP), where a large language model (LLM) observes a small number of demonstrations and a test instance as its input, and directly makes predictions without updating model parameters. Previous studies have revealed that ICL is sensitive to the selection and the ordering of demonstrations. However, there are few studies regarding the impact of the demonstration number on the ICL performance within a limited input length of LLM, because it is commonly believed that the number of demonstrations is positively correlated with model performance. In this paper, we found this conclusion does not always hold true. Through pilot experiments, we discover that increasing the number of demonstrations does not necessarily lead to improved performance. Building upon this insight, we propose a Dynamic Demonstrations Controller (D$^2$Controller), which can improve the ICL performance by adjusting the number of demonstrations dynamically. The experimental results show that D$^2$Controller yields a 4.6% relative improvement on ten different sizes of LLMs across ten datasets. Moreover, we also extend our method to previous ICL models and achieve competitive results.

cs.CL↗

arXiv2024

Towards Cross-domain Few-shot Graph Anomaly Detection

Few-shot graph anomaly detection (GAD) has recently garnered increasing attention, which aims to discern anomalous patterns among abundant unlabeled test nodes under the guidance of a limited number of labeled training nodes. Existing few-shot GAD approaches typically adopt meta-training methods trained on richly labeled auxiliary networks to facilitate rapid adaptation to target networks that possess sparse labels. However, these proposed methods often assume that the auxiliary and target networks exist in the same data distributions-an assumption rarely holds in practical settings. This paper explores a more prevalent and complex scenario of cross-domain few-shot GAD, where the goal is to identify anomalies within sparsely labeled target graphs using auxiliary graphs from a related, yet distinct domain. The challenge here is nontrivial owing to inherent data distribution discrepancies between the source and target domains, compounded by the uncertainties of sparse labeling in the target domain. In this paper, we propose a simple and effective framework, termed CDFS-GAD, specifically designed to tackle the aforementioned challenges. CDFS-GAD first introduces a domain-adaptive graph contrastive learning module, which is aimed at enhancing cross-domain feature alignment. Then, a prompt tuning module is further designed to extract domain-specific features tailored to each domain. Moreover, a domain-adaptive hypersphere classification loss is proposed to enhance the discrimination between normal and anomalous instances under minimal supervision, utilizing domain-sensitive norms. Lastly, a self-training strategy is introduced to further refine the predicted scores, enhancing its reliability in few-shot settings. Extensive experiments on twelve real-world cross-domain data pairs demonstrate the effectiveness of the proposed CDFS-GAD framework in comparison to various existing GAD methods.

cs.LG↗