arXiv subjects

Jinlong Ma

Publications and source records attributed to Jinlong Ma.

9 recordsLinked to original sources

arXiv2026

Design and commissioning of a windowless gas-target system for high-current beams at JUNA

Windowless gas targets avoid the beam-energy loss and straggling introduced by entrance foils and are therefore well suited for direct measurements of low-energy nuclear reactions. A windowless gas-target system designed for operation with milliampere beams has been developed for the Jinping Underground Nuclear Astrophysics facility (JUNA). The system combines three-stage differential pumping, closed-loop gas recovery and purification, a constant-temperature power-compensation calorimeter, and a position-resolved target-thickness monitor based on secondary elastic scattering. Stable operation was achieved over a target-pressure range of 1-3 mbar, with pressure fluctuations below 1% during 8 h of continuous circulation, while the accelerator-side pressure was maintained at approximately $10^{-4}$ Pa. The closed-loop gas-circulation system maintained stable target conditions, while gas-transport calculations indicated that the axial pressure nonuniformity remained within approximately 1.6% under representative operating conditions. Calorimeter measurements were consistent with the thermal calculations, supporting the sensitivity correction used for beam-power determination. Beam commissioning with $^{14}\mathrm{N}(p,\gamma)^{15}\mathrm{O}$ and $^{12}\mathrm{C}(p,\gamma)^{13}\mathrm{N}$ at the 600 kV Cockcroft-Walton accelerator of the China Institute of Atomic Energy (CIAE) demonstrated stable operation of the gas-target and $\gamma$-ray detection systems and provided information on the influences of reaction position and beam heating. These results demonstrate the operating stability and diagnostic capability of the system for future high-current, low-energy nuclear-reaction measurements at JUNA.

astro-ph.GA↗

arXiv2026

A Variable-Spot-Size and Multi-Frequency Square-Pulsed Source (SPS) Approach for Comprehensive Characterization of Anisotropic Thermal Transport Properties in Multilayered Thin Films

Multilayered thin-film structures are frequently encountered in industrial applications, where accurate thermal property characterization is essential for performance optimization. These films, typically ranging from nanometers to micrometers in thickness, often exhibit anisotropic thermal conductivity and non-bulk heat capacity, which are challenging to measure. In this study, we introduce a variable-spot-size and multi-frequency square-pulsed source (SPS) method for the simultaneous determination of anisotropic thermal conductivities, heat capacities, and interfacial thermal conductance in multilayered systems. By leveraging a broad modulation frequency range (1 Hz to 10 MHz) and tunable laser spot sizes, the SPS method enhances sensitivity to different thermal parameters across layers. We validate this approach on a silicon-on-insulator (SOI) sample comprising a 1.59 um Si layer, 1.03 um SiO2 layer, and a silicon substrate with a 122 nm aluminum (Al) transducer. The SPS method successfully extracts seven key thermal parameters, including the in-plane and cross-plane thermal conductivities and heat capacity of the Si film, the thermal conductivity and heat capacity of the SiO2 layer, the thermal conductivity of the substrate, and the interfacial thermal conductance between Al and Si. Temperature-dependent measurements from 80 to 500 K showed excellent agreement with literature values and first-principles predictions, confirming the method's accuracy and reliability. These results demonstrate the SPS method as a powerful tool for comprehensive thermal characterization of complex multilayered structures, with implications for both fundamental research and practical applications.

physics.app-ph↗

arXiv2026

Beyond Unimodal Shortcuts: MLLMs as Cross-Modal Reasoners for Grounded Named Entity Recognition

Grounded Multimodal Named Entity Recognition (GMNER) aims to extract text-based entities, assign them semantic categories, and ground them to corresponding visual regions. In this work, we explore the potential of Multimodal Large Language Models (MLLMs) to perform GMNER in an end-to-end manner, moving beyond their typical role as auxiliary tools within cascaded pipelines. Crucially, our investigation reveals a fundamental challenge: MLLMs exhibit $\textbf{modality bias}$, including visual bias and textual bias, which stems from their tendency to take unimodal shortcuts rather than rigorous cross-modal verification. To address this, we propose Modality-aware Consistency Reasoning ($\textbf{MCR}$), which enforces structured cross-modal reasoning through Multi-style Reasoning Schema Injection (MRSI) and Constraint-guided Verifiable Optimization (CVO). MRSI transforms abstract constraints into executable reasoning chains, while CVO empowers the model to dynamically align its reasoning trajectories with Group Relative Policy Optimization (GRPO). Experiments on GMNER and visual grounding tasks demonstrate that MCR effectively mitigates modality bias and achieves superior performance compared to existing baselines.

cs.CL↗

arXiv2025

MoodBench 1.0: An Evaluation Benchmark for Emotional Companionship Dialogue Systems

With the rapid development of Large Language Models, dialogue systems are shifting from information tools to emotional companions, heralding the era of Emotional Companionship Dialogue Systems (ECDs) that provide personalized emotional support for users. However, the field lacks clear definitions and systematic evaluation standards for ECDs. To address this, we first propose a definition of ECDs with formal descriptions. Then, based on this theory and the design principle of "Ability Layer-Task Layer (three level)-Data Layer-Method Layer", we design and implement the first ECD evaluation benchmark - MoodBench 1.0. Through extensive evaluations of 30 mainstream models, we demonstrate that MoodBench 1.0 has excellent discriminant validity and can effectively quantify the differences in emotional companionship abilities among models. Furthermore, the results reveal current models' shortcomings in deep emotional companionship, guiding future technological optimization and significantly aiding developers in enhancing ECDs' user experience.

cs.AI↗

arXiv2025

Detecting Emotional Dynamic Trajectories: An Evaluation Framework for Emotional Support in Language Models

Emotional support is a core capability in human-AI interaction, with applications including psychological counseling, role play, and companionship. However, existing evaluations of large language models (LLMs) often rely on short, static dialogues and fail to capture the dynamic and long-term nature of emotional support. To overcome this limitation, we shift from snapshot-based evaluation to trajectory-based assessment, adopting a user-centered perspective that evaluates models based on their ability to improve and stabilize user emotional states over time. Our framework constructs a large-scale benchmark consisting of 328 emotional contexts and 1,152 disturbance events, simulating realistic emotional shifts under evolving dialogue scenarios. To encourage psychologically grounded responses, we constrain model outputs using validated emotion regulation strategies such as situation selection and cognitive reappraisal. User emotional trajectories are modeled as a first-order Markov process, and we apply causally-adjusted emotion estimation to obtain unbiased emotional state tracking. Based on this framework, we introduce three trajectory-level metrics: Baseline Emotional Level (BEL), Emotional Trajectory Volatility (ETV), and Emotional Centroid Position (ECP). These metrics collectively capture user emotional dynamics over time and support comprehensive evaluation of long-term emotional support performance of LLMs. Extensive evaluations across a diverse set of LLMs reveal significant disparities in emotional support capabilities and provide actionable insights for model development.

cs.CL↗

arXiv2025

Evaluating and Steering Modality Preferences in Multimodal Large Language Model

Multi-modal large language models (MLLMs) have achieved remarkable success on complex multi-modal tasks. However, it remains insufficiently explored whether they exhibit $\textbf{modality preference}$, a tendency to favor one modality over another when processing multi-modal contexts. To study this question, we introduce $\textbf{MC\textsuperscript{2}}$ benchmark, which constructs controlled evidence-conflict scenarios to systematically evaluate modality preference in decision-making. Extensive experiments reveal that all 20 tested MLLMs generally demonstrate clear modality preferences, and such preferences can serve as a useful indicator of downstream task performance of MLLMs. Further analysis shows that modality preference can be controlled by instruction guidance and captured within the latent representations of MLLMs. Built on these insights, we propose a probing and steering method based on representation engineering to explicitly control modality preference without requiring additional fine-tuning. This method effectively amplifies modality preference toward a desired direction and demonstrates promising improvements across multiple multi-modal understanding and reasoning tasks.

cs.CL↗

arXiv2024

Wavelength-selective thermal nonreciprocity barely improves sky radiative cooling

Radiative cooling has showcased great potential for passive refrigeration without extra energy consumption, while its cooling power and efficiency is confined by Kirchhoff's law, that is, the emissivity is equal to the absorptivity. The recent development of thermal nonreciprocity that breaks the limitations of Kirchhoff's law, especially in broadband manner, makes nonreciprocal radiative cooling (NRC) possible. Since there lacks of reports of NRC theoretically or experimentally, it is time to evaluate the feasibility and worthiness of develop NRC. Here, we discussed the effects of NRC at around room temperature (298.15 K) from three perspectives: ideal selective radiators, non-selective radiators, and colored radiators. Contrary to intuition, the introduction of thermal nonreciprocity in the atmospheric window (8-13 μm) only leads to a negative gain. Additionally, it should be noted that the radiators discussed in this work are horizontally placed without the influence of asymmetric external heat sources. The current findings shatter the inherent notion of NRC and offer some theoretical support for the practical realization and application of nonreciprocal radiative refrigeration devices.

physics.optics↗

arXiv2023

A Mask Free Neural Network for Monaural Speech Enhancement

In speech enhancement, the lack of clear structural characteristics in the target speech phase requires the use of conservative and cumbersome network frameworks. It seems difficult to achieve competitive performance using direct methods and simple network architectures. However, we propose the MFNet, a direct and simple network that can not only map speech but also map reverse noise. This network is constructed by stacking global local former blocks (GLFBs), which combine the advantages of Mobileblock for global processing and Metaformer architecture for local interaction. Our experimental results demonstrate that our network using mapping method outperforms masking methods, and direct mapping of reverse noise is the optimal solution in strong noise environments. In a horizontal comparison on the 2020 Deep Noise Suppression (DNS) challenge test set without reverberation, to the best of our knowledge, MFNet is the current state-of-the-art (SOTA) mapping model.

cs.SD↗

arXiv2019

Combined treatment of phonon scattering by electrons and point defects explains the thermal conductivity reduction in highly-doped Si

The mechanisms causing the reduction in lattice thermal conductivity in highly P- and B-doped Si are looked into in detail. Scattering rates of phonons by point defects, as well as by electrons, are calculated from first principles. Lattice thermal conductivities are calculated considering these scattering mechanisms both individually and together. It is found that at low carrier concentrations and temperatures phonon scattering by electrons is dominant and can reproduce the experimental thermal conductivity reduction. However, at higher doping concentrations the scattering rates of phonons by point defects dominate the ones by electrons except for the lowest phonon frequencies. Consequently, phonon scattering by point defects contributes substantially to the thermal conductivity reduction in Si at defect concentrations above $10^{19}$ cm$^{-3}$ even at room temperature. Only when, phonon scattering by both point defects and electrons are taken into account, excellent agreement is obtained with the experimental values at all temperatures.

cond-mat.mtrl-sci↗