arXiv subjects

Liang Chen

Publications and source records attributed to Liang Chen.

At least 199 records · Page 11Linked to original sources

arXiv2025

Pre-study of a Li2MoO4 based bolometer for 100Mo neutrinoless double beta decay experiment in China

The cryogenic phonon scintillating bolometer is a promising and extremely attractive option to search for the nuclide neutrinoless double beta decay. In this paper, a pre-study of bolometer based on Li2MoO4 (LMO) crystal is presented, in which the properties of the LMO crystal at the low temperature, including scintillation characteristics and specific heat, are investigated in detail. The excitation spectrum and light yield are measured from the room temperature down to 10 K, and heat capacity is measured down to temperature of O(200) mK. Furthermore, a (2 cm)3 cubic LMO based bolometer is manufactured and tested at ultra-low mK-level temperature in a ground-above cryostat platform, and a good energy resolution is achieved. The studies laid a foundation to manufacture the bolometer detector in China and conduct neutrinoless double beta decay research at the China Jinping Underground Laborator

physics.ins-det↗

arXiv2025

Enumeration of spanning trees and resistance distances of generalized blow-up graphs

Let $H$ be a graph with vertex set $V(H)=\{v_1, v_2, \cdots, v_k\}$. The generalized blow-up graph $H_{p_1,\ldots,p_k}^{q_1,\ldots,q_k}$ is constructed by replacing each vertex $v_i \in V(H)$ with the graph $G_i = p_iK_t \cup q_iK_1$$(i=1,2,\cdots,k)$, then connecting all vertices between $G_i$ and $G_j$ whenever $v_iv_j \in E(H)$. In this paper, we enumerate the spanning trees in generalized blow-up graphs $H_{p_1, p_2, \cdots, p_k}^{q_1, q_2, \cdots, q_k}$, which extends the results of Ge [Discrete Appl. Math. 305 (2021) 145-153], Cheng, Chen and Yan [Discrete Appl. Math. 320 (2022) 259-269]. Furthermore, we determine the resistance distances and Kirchhoff indices of generalized blow-up graphs $H_{p_1, p_2, \cdots, p_k}^{q_1, q_2, \cdots, q_k}$, which extends the results of Sun, Yang and Xu [Discrete Math. 348 (2025) 114327], Xu and Xu [Discrete Appl. Math. 362 (2025) 18-33], Ni, Pan and Zhou [Discrete Appl. Math. 362 (2025) 100-108].

math.CO↗

arXiv2025

Detection of two TeV gamma-ray outbursts from NGC 1275 by LHAASO

The Water Cherenkov Detector Array (WCDA) is one of the components of Large High Altitude Air Shower Observatory (LHAASO) and can monitor any sources over two-thirds of the sky for up to 7 hours per day with >98\% duty cycle. In this work, we report the detection of two outbursts of the Fanaroff-Riley I radio galaxy NGC 1275 that were detected by LHAASO-WCDA between November 2022 and January 2023 with statistical significance of 5.2~$σ$ and 8.3~$σ$. The observed spectral energy distribution in the range from 500 GeV to 3 TeV is fitted by a power-law with a best-fit spectral index of $α=-3.37\pm0.52$ and $-3.35\pm0.29$, respectively. The outburst flux above 0.5~TeV was ($4.55\pm 4.21)\times~10^{-11}~\rm cm^{-2}~s^{-1}$ and ($3.45\pm 1.78)\times~10^{-11}~\rm cm^{-2}~s^{-1}$, corresponding to 60\%, 45\% of Crab Nebula flux. Variation analysis reveals the variability time-scale of days at the TeV energy band. A simple test by one-zone synchrotron self-Compton model reproduces the data in the gamma-ray band well.

astro-ph.HE↗

arXiv2025

The Mini-SiTian Array: Optical design

Time-domain astronomy is one of the most important areas. Large sky area, deep-field, and short timescale are the priority of time-domain observations. SiTian is an ambitious ground-based project processing all sky optical monitoring, aiming for sky-survey timescale of less than 1 day. It is developed by the Chinese Academy of Sciences, an integrated network of dozens of 1-m-class telescopes deployed worldwide. The Mini-SiTian Telescope Array is carried out for demonstrations on optical design, group scheduling, and software pipeline developments, to overcome the high technical and financial difficulties of SiTian project. One array contains three 300 mm F/3 telescope, with FOV of 5 degrees over 400-1000 nm wavelength range. The Mini-SiTian Telescope Array is now under commissioning in Xinglong Observatory, and a perfect platform for technical research and educational purposes.

astro-ph.IM↗

arXiv2025

SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines

Large language models (LLMs) have demonstrated remarkable proficiency in mainstream academic disciplines such as mathematics, physics, and computer science. However, human knowledge encompasses over 200 specialized disciplines, far exceeding the scope of existing benchmarks. The capabilities of LLMs in many of these specialized fields-particularly in light industry, agriculture, and service-oriented disciplines-remain inadequately evaluated. To address this gap, we present SuperGPQA, a comprehensive benchmark that evaluates graduate-level knowledge and reasoning capabilities across 285 disciplines. Our benchmark employs a novel Human-LLM collaborative filtering mechanism to eliminate trivial or ambiguous questions through iterative refinement based on both LLM responses and expert feedback. Our experimental results reveal significant room for improvement in the performance of current state-of-the-art LLMs across diverse knowledge domains (e.g., the reasoning-focused model DeepSeek-R1 achieved the highest accuracy of 61.82% on SuperGPQA), highlighting the considerable gap between current model capabilities and artificial general intelligence. Additionally, we present comprehensive insights from our management of a large-scale annotation process, involving over 80 expert annotators and an interactive Human-LLM collaborative system, offering valuable methodological guidance for future research initiatives of comparable scope.

cs.CL↗

arXiv2025

Purely Speckled Intensity Images Need for SAR Despeckling with SDS-SAR

Speckle noise is generated along with the SAR imaging mechanism and degrades the quality of SAR images, leading to difficult interpretation. Hence, despeckling is an indispensable step in SAR pre-processing. Fortunately, supervised learning (SL) has proven to be a progressive method for SAR image despeckling. SL methods necessitate the availability of both original SAR images and their speckle-free counterparts during training, whilst speckle-free SAR images do not exist in the real world. Even though there are several substitutes for speckle-free images, the domain gap leads to poor performance and adaptability. Self-supervision provides an approach to training without clean reference. However, most self-supervised methods impose high demands on speckle modeling or specific data, limiting their practicality in real-world applications. To address these challenges, we propose a Self-supervised Despeckling Strategy for SAR images (SDS-SAR) that relies solely on speckled intensity data for training. Firstly, the theoretical feasibility of SAR image despeckling without speckle-free images is established. A self-supervised despeckling criteria suitable for all SAR images is proposed. Subsequently, a Random-Aware sub-SAMpler with Projection correLation Estimation (RA-SAMPLE) is put forth. Mutually independent training pairs can be derived from actual SAR intensity images. Furthermore, a multi-feature loss function is introduced, consisting of a despeckling term, a regularization term, and a perception term. The performance of speckle suppression and texture preservation is well-balanced. Experiments reveal that the proposed method performs on par with supervised approaches on synthetic data and outperforms them on actual data.

eess.IV↗

arXiv2025

Two Typical Implementable Semismooth* Newton Methods for Generalized Equations are G-Semismooth Newton Methods

Semismooth* Newton methods have been proposed in recent years targeting multi-valued inclusion problems and have been successfully implemented to deal with several concrete generalized equations. In this paper, we show that two typical implementations of them that are available are exactly the applications of G-semismooth Newton methods for solving nonsmooth equations localized from these generalized equations. This new understanding expands the breadth of G-semismooth Newton methods in theory, results in a few interesting problems regarding the two categories of nonsmooth Newton methods, and more importantly, provides informative observations in facilitating the design and implementation of practical Newton-type algorithms for solving generalized equations.

math.OC↗

arXiv2025

A Novel Thermal Network Model and Electro-Thermal Coupling Study for NSFETs and CFETs Considering Thermal Crosstalk

As the technology node continues to shrink, nanosheet field effect transistors (NSFETs) and complementary FETs (CFETs) become valid candidates for the 3nm and sub-nanometre nodes. However, due to the shrinking device size, self-heating and inter-device thermal crosstalk of NSFETs and CFETs become more severe. It is important to accurately calculate the self-heating and thermal crosstalk of devices and to study the electrical and thermal characteristics of logic gates, etc. In this work, a thermal network model considering the thermal crosstalk of neighboring devices is proposed, which can accurately calculate the self-heating and thermal crosstalk. The electrical and thermal characteristics of NSFETs and CFETs are compared, and it is found that CFETs have more severe self-heating and thermal crosstalk. The electro-thermal characteristics of inverters, logic gates and ring oscillators composed of NSFETs and CFETs are further investigated. Compared with NSFETs, logic gates and ring oscillators composed of CFETs are more seriously affected by self-heating and should be given extra attention. The thermal network model proposed in this paper can be further used to study the thermal optimization strategy of devices and circuits to enhance the electrical performance, achieving the design technology co-optimizations (DTCO).

cs.ET↗

arXiv2025

A Novel Design for SRAM Bitcell with 3-Complementary-FETs

The complementary field-effect transistors (CFETs), featuring vertically stacked n/p-FETs, enhance integration density and significantly reduce the area of standard cells such as static random-access memory (SRAM). However, the advantage of area scaling through CFETs is hindered by the imbalance in N/P transistor counts (typically 4N/2P) within SRAM cells. In this work, we propose a novel 6T-SRAM design using three sets of CFETs, achieved by vertically stacking two n-FET pass-gate (PG) transistors via the CFET architecture. Through TCAD simulations, we optimize channel doping concentration and the number of top/bottom nanosheets (NS), demonstrating that junctionless accumulation mode (JAM) devices outperform inversion mode (IM) devices for PG and pull-down (PD) transistors. The proposed design achieves a 37% area reduction in SRAM standard cell layout compared to conventional CFET-based SRAM. With optimized parameters (n-type doping of $1\times10^{15}$ cm$^{-3}$ and '1B4T' NS configuration), the 3-CFET SRAM exhibits superior write margin (349.60 mV) and write delay (54.4 ps). This work advances SRAM design within the CFET framework, offering a scalable solution for next-generation memory technologies.

physics.app-ph↗

arXiv2025

An alternative application of GaAs-based light-emitting diodes: X-ray detection and imaging

GaAs-based light-emitting diodes (LEDs) are commonly employed in a variety of applications, including medical imaging, biosensing, optical communications, and night vision. In this paper, we present an alternative application of GaAs-based LED with SI-GaAs substrate for X-ray detection and imaging. The mechanism relies on the semiconductor frequency down-conversion process, where the SI-GaAs substrate acts as a photodetector (PD). Upon X-ray irradiation, the generated photocurrent by the SI-GaAs substrate drives the LED to emit NIR photons which can be detect by a low-cost CCD. We demonstrate direct X-ray detection and present preliminary imaging results, providing another example of the applicability of the PD-LED design for optical frequency conversion. The proposed LED X-ray detector leverages mature materials and fabrication processes. The application of the frequency down-conversion concept makes it possible for pixel-less imaging using a large single imaging unit, eliminating the need for readout circuits. This PD-LED architecture offers an alternative approach to direct X-ray detection and imaging, characterized by higher absorption, improved image resolution, and enhanced material stability.

physics.optics↗

arXiv2025

MMEvalPro: Calibrating Multimodal Benchmarks Towards Trustworthy and Efficient Evaluation

Large Multimodal Models (LMMs) exhibit impressive cross-modal understanding and reasoning abilities, often assessed through multiple-choice questions (MCQs) that include an image, a question, and several options. However, many benchmarks used for such evaluations suffer from systematic biases. Remarkably, Large Language Models (LLMs) without any visual perception capabilities achieve non-trivial performance, undermining the credibility of these evaluations. To address this issue while maintaining the efficiency of MCQ evaluations, we propose MMEvalPro, a benchmark designed to avoid Type-I errors through a trilogy evaluation pipeline and more rigorous metrics. For each original question from existing benchmarks, human annotators augment it by creating one perception question and one knowledge anchor question through a meticulous annotation process. MMEvalPro comprises $2,138$ question triplets, totaling $6,414$ distinct questions. Two-thirds of these questions are manually labeled by human experts, while the rest are sourced from existing benchmarks (MMMU, ScienceQA, and MathVista). Compared with the existing benchmarks, our experiments with the latest LLMs and LMMs demonstrate that MMEvalPro is more challenging (the best LMM lags behind human performance by $31.73\%$, compared to an average gap of $8.03\%$ in previous benchmarks) and more trustworthy (the best LLM trails the best LMM by $23.09\%$, whereas the gap for previous benchmarks is just $14.64\%$). Our in-depth analysis explains the reason for the large performance gap and justifies the trustworthiness of evaluation, underscoring its significant potential for advancing future research.

cs.CV↗

arXiv2025

Multimodal Representation Alignment for Image Generation: Text-Image Interleaved Control Is Easier Than You Think

The field of advanced text-to-image generation is witnessing the emergence of unified frameworks that integrate powerful text encoders, such as CLIP and T5, with Diffusion Transformer backbones. Although there have been efforts to control output images with additional conditions, like canny and depth map, a comprehensive framework for arbitrary text-image interleaved control is still lacking. This gap is especially evident when attempting to merge concepts or visual elements from multiple images in the generation process. To mitigate the gap, we conducted preliminary experiments showing that large multimodal models (LMMs) offer an effective shared representation space, where image and text can be well-aligned to serve as a condition for external diffusion models. Based on this discovery, we propose Dream Engine, an efficient and unified framework designed for arbitrary text-image interleaved control in image generation models. Building on powerful text-to-image models like SD3.5, we replace the original text-only encoders by incorporating versatile multimodal information encoders such as QwenVL. Our approach utilizes a two-stage training paradigm, consisting of joint text-image alignment and multimodal interleaved instruction tuning. Our experiments demonstrate that this training method is effective, achieving a 0.69 overall score on the GenEval benchmark, and matching the performance of state-of-the-art text-to-image models like SD3.5 and FLUX.

cs.CV↗

arXiv2025

Ultra-high-energy $γ$-ray emission associated with the tail of a bow-shock pulsar wind nebula

In this study, we present a comprehensive analysis of an unidentified point-like ultra-high-energy (UHE) $γ$-ray source, designated as 1LHAASO J1740+0948u, situated in the vicinity of the middle-aged pulsar PSR J1740+1000. The detection significance reached 17.1$σ$ (9.4$σ$) above 25$\,$TeV (100$\,$TeV). The source energy spectrum extended up to 300$\,$TeV, which was well fitted by a log-parabola function with $N0 = (1.93\pm0.23) \times 10^{-16} \rm{TeV^{-1}\,cm^{-2}\,s^{-2}}$, $α= 2.14\pm0.27$, and $β= 1.20\pm0.41$ at E0 = 30$\,$TeV. The associated pulsar, PSR J1740+1000, resides at a high galactic latitude and powers a bow-shock pulsar wind nebula (BSPWN) with an extended X-ray tail. The best-fit position of the gamma-ray source appeared to be shifted by $0.2^{\circ}$ with respect to the pulsar position. As the (i) currently identified pulsar halos do not demonstrate such offsets, and (ii) centroid of the gamma-ray emission is approximately located at the extension of the X-ray tail, we speculate that the UHE $γ$-ray emission may originate from re-accelerated electron/positron pairs that are advected away in the bow-shock tail.

astro-ph.HE↗

arXiv2025

PEARL: Towards Permutation-Resilient LLMs

The in-context learning (ICL) capability of large language models (LLMs) enables them to perform challenging tasks using provided demonstrations. However, ICL is highly sensitive to the ordering of demonstrations, leading to instability in predictions. This paper shows that this vulnerability can be exploited to design a natural attack - difficult for model providers to detect - that achieves nearly 80% success rate on LLaMA-3 by simply permuting the demonstrations. Existing mitigation methods primarily rely on post-processing and fail to enhance the model's inherent robustness to input permutations, raising concerns about safety and reliability of LLMs. To address this issue, we propose Permutation-resilient learning (PEARL), a novel framework based on distributionally robust optimization (DRO), which optimizes model performance against the worst-case input permutation. Specifically, PEARL consists of a permutation-proposal network (P-Net) and the LLM. The P-Net generates the most challenging permutations by treating it as an optimal transport problem, which is solved using an entropy-constrained Sinkhorn algorithm. Through minimax optimization, the P-Net and the LLM iteratively optimize against each other, progressively improving the LLM's robustness. Experiments on synthetic pre-training and real-world instruction tuning tasks demonstrate that PEARL effectively mitigates permutation attacks and enhances performance. Notably, despite being trained on fewer shots and shorter contexts, PEARL achieves performance gains of up to 40% when scaled to many-shot and long-context scenarios, highlighting its efficiency and generalization capabilities.

cs.LG↗

arXiv2025

Are Large Language Models In-Context Graph Learners?

Large language models (LLMs) have demonstrated remarkable in-context reasoning capabilities across a wide range of tasks, particularly with unstructured inputs such as language or images. However, LLMs struggle to handle structured data, such as graphs, due to their lack of understanding of non-Euclidean structures. As a result, without additional fine-tuning, their performance significantly lags behind that of graph neural networks (GNNs) in graph learning tasks. In this paper, we show that learning on graph data can be conceptualized as a retrieval-augmented generation (RAG) process, where specific instances (e.g., nodes or edges) act as queries, and the graph itself serves as the retrieved context. Building on this insight, we propose a series of RAG frameworks to enhance the in-context learning capabilities of LLMs for graph learning tasks. Comprehensive evaluations demonstrate that our proposed RAG frameworks significantly improve LLM performance on graph-based tasks, particularly in scenarios where a pretrained LLM must be used without modification or accessed via an API.

cs.LG↗

arXiv2025

Signature of Seyfert-like component in a blazar 3C 273 and its reflection-based explanation

We present the results of blazar 3C 273 obtained from simultaneous observations obtained using XMM-Newton and NuSTAR satellites during the period 2015-2019 in five epochs. When the spectra are modeled with a power-law, significant residuals arise below 2 keV and in the energy range of 30-78 keV in NuSTAR data. Residuals in the lower energy band represent soft X-ray excess while at higher energies it likely represents Compton reflection hump which might be a weak component arising from dense and cold material. The presence of a faint iron line is present in XMM-Newton observations. We interpret such features as attributed to the coronal emission plus those arising from reflection from an accretion disk. We model the SEDs with the single zone inverse Compton jet model based on Synchrotron Self Compton and External Compton phenomena. It is found that a one-zone synchrotron plus IC model explains quite well the SEDs but the jet component alone fails to fit the multiband X-ray emission for the low state of this object in 2018 and 2019 which arises due to spectral flattening at low energy X-rays, indicating that an additional Seyfert-like thermal component must be present at X-rays. This is further supported by a big blue bump present in the optical/ultraviolet band in all SEDs. Finally, we analyzed all the epochs using relxill model to incorporate relativistic reflection to model those residuals of soft excess and Compton hump in the X-ray bands.

astro-ph.HE↗

arXiv2025

A Dynamical Variable-separation Method for Parameter-dependent Dynamical Systems

This paper proposes a dynamical Variable-separation method for solving parameter-dependent dynamical systems. To achieve this, we establish a dynamical low-rank approximation for the solutions of these dynamical systems by successively enriching each term in the reduced basis functions via a greedy algorithm. This enables us to reformulate the problem to two decoupled evolution equations at each enrichment step, of which one is a parameter-independent partial differential equation and the other one is a parameter-dependent ordinary differential equation. These equations are directly derived from the original dynamical system and the previously separate representation terms. Moreover, the computational process of the proposed method can be split into an offline stage, in which the reduced basis functions are constructed, and an online stage, in which the efficient low-rank representation of the solution is employed. The proposed dynamical Variable-separation method is capable of offering reduced computational complexity and enhanced efficiency compared to many existing low-rank separation techniques. Finally, we present various numerical results for linear/nonlinear parameter-dependent dynamical systems to demonstrate the effectiveness of the proposed method.

math.NA↗

arXiv2025

Broadband $γ$-ray spectrum of supernova remnant Cassiopeia A

The core-collapse supernova remnant (SNR) Cassiopeia A (Cas A) is one of the brightest galactic radio sources with an angular radius of $\sim$ 2.5 $\arcmin$. Although no extension of this source has been detected in the $γ$-ray band, using more than 1000 days of LHAASO data above $\sim 0.8$ TeV, we find that its spectrum is significantly softer than those obtained with Imaging Air Cherenkov Telescopes (IACTs) and its flux near $\sim 1$ TeV is about two times higher. In combination with analyses of more than 16 years of \textit{Fermi}-LAT data covering $0.1 \, \mathrm{GeV} - 1 \, \mathrm{TeV}$, we find that the spectrum above 30 GeV deviates significantly from a single power-law, and is best described by a smoothly broken power-law with a spectral index of $1.90 \pm 0.15_\mathrm{stat}$ ($3.41 \pm 0.19_\mathrm{stat}$) below (above) a break energy of $0.63 \pm 0.21_\mathrm{stat} \, \mathrm{TeV}$. Given differences in the angular resolution of LHAASO-WCDA and IACTs, TeV $γ$-ray emission detected with LHAASO may have a significant contribution from regions surrounding the SNR illuminated by particles accelerated earlier, which, however, are treated as background by IACTs. Detailed modelling can be used to constrain acceleration processes of TeV particles in the early stage of SNR evolution.

astro-ph.HE↗