arXiv subjects

Bo Peng

Publications and source records attributed to Bo Peng.

At least 109 records · Page 6Linked to original sources

arXiv2025

Elucidating Many-Body Effects in Molecular Core Spectra through Real-Time Approaches: Efficient Classical Approximations and a Quantum Perspective

Accurately resolving many-body satellite features in molecular core-level spectra requires theoretical approaches that capture electron correlation both efficiently and systematically. The recently developed time-dependent double coupled-cluster (TD-dCC) ansatz achieves this by combining correlation effects from the N- and (N-1)-electron sectors, but its exact formulation remains computationally demanding. Here we introduce a hierarchy of cost-effective approximate TD-dCC ansatzes derived from truncated Baker-Campbell-Hausdorff (BCH) expansions, which preserve a single-similarity-transformation structure while retaining the essential correlation diagrams responsible for satellite formation. We further develop a detailed component analysis that isolates hole-mediated excitation pathways, which are correlated processes arising from the coupling between ground-state and ionized-state amplitudes. We use it to interpret quasiparticle and satellite features across the hierarchy. Applications to the single-impurity Anderson model and molecular systems (H2O and CH4) demonstrate that the approximate TD-dCC methods closely and efficiently reproduce exact many-body spectral features and quasiparticle weights. In parallel, we construct a fault-tolerant quantum signal processing algorithm for the core-hole Green's function, providing a scalable quantum route for simulating correlated core-level dynamics. Together, these developments establish complementary classical and quantum methodologies for quantitative, many-body-accurate core spectroscopy.

quant-ph↗

arXiv2025

Wide-Field X-ray Polarimetry for High Energy Astronomical Transients: First results of the pathfinder CXPD Cubesat Mission

The Low Energy Polarization Detector (LPD) is a key component of the next-generation large-scale Gamma-Ray Burst polarimeter, POLAR-2. It is designed for polarization observations of transient sources in the soft X-ray energy range with a wide field of view (FOV). To validate the key technologies required for wide-FOV X-ray polarization measurements, the Cosmic X-ray Polarization Detector (CXPD) CubeSat was developed as a prototype for the LPD. The CXPD is equipped with two Gas Microchannel Plate Pixel Detectors (GMPDs) that measure X-ray polarization via the photoelectric effect, where ejected photoelectrons produce ionization tracks in the gas which are imaged to reconstruct their emission directions. Laboratory calibrations of the modulation factor and energy spectra were successfully performed using linear polarized X-ray sources at 2.98 keV, 4.51 keV, 6.40 keV, and 8.05 keV. Since its launch in June 2023, the CXPD has successfully completed critical in-orbit technology verification. It has also performed polarization observations of two bright X-ray sources Sco X-1 and the transient Swift J1727.8-1613 yielding constraints on their polarization degrees and angles. Notably, this was the first time that an anti-coincidence detector had been implemented in an X-ray polarimeter, enabling in-orbit verification of the charged-particle background rejection algorithm. These results demonstrate the feasibility of wide-field soft X-ray polarization measurements and provide essential guidance for the development of the LPD for the POLAR-2 mission, thereby advancing the frontier of X-ray polarization astronomy.

astro-ph.IM↗

arXiv2025

Rethinking Target Label Conditioning in Adversarial Attacks: A 2D Tensor-Guided Generative Approach

Compared to single-target adversarial attacks, multi-target attacks have garnered significant attention due to their ability to generate adversarial images for multiple target classes simultaneously. However, existing generative approaches for multi-target attacks primarily encode target labels into one-dimensional tensors, leading to a loss of fine-grained visual information and overfitting to model-specific features during noise generation. To address this gap, we first identify and validate that the semantic feature quality and quantity are critical factors affecting the transferability of targeted attacks: 1) Feature quality refers to the structural and detailed completeness of the implanted target features, as deficiencies may result in the loss of key discriminative information; 2) Feature quantity refers to the spatial sufficiency of the implanted target features, as inadequacy limits the victim model's attention to this feature. Based on these findings, we propose the 2D Tensor-Guided Adversarial Fusion (TGAF) framework, which leverages the powerful generative capabilities of diffusion models to encode target labels into two-dimensional semantic tensors for guiding adversarial noise generation. Additionally, we design a novel masking strategy tailored for the training process, ensuring that parts of the generated noise retain complete semantic information about the target class. Extensive experiments demonstrate that TGAF consistently surpasses state-of-the-art methods across various settings.

cs.CV↗

arXiv2025

Captions Speak Louder than Images: Generalizing Foundation Models for E-commerce from High-quality Multimodal Instruction Data

Leveraging multimodal data to drive breakthroughs in e-commerce applications through Multimodal Foundation Models (MFMs) is gaining increasing attention from the research community. However, there are significant challenges that hinder the optimal use of multimodal e-commerce data by foundation models: (1) the scarcity of large-scale, high-quality multimodal benchmark datasets; and (2) the lack of effective multimodal information integration methods. To address these challenges, in this paper, we introduce MMECInstruct, the first-ever, large-scale, and high-quality multimodal instruction dataset for e-commerce. We also develop CASLIE, a simple, lightweight, yet effective framework for integrating multimodal information for e-commerce. Leveraging MMECInstruct, we fine-tune a series of e-commerce MFMs within CASLIE, denoted as CASLIE models. Our comprehensive evaluation demonstrates that CASLIE models substantially outperform 5 categories of advanced baseline models in the in-domain evaluation. Moreover, CASLIE models show strong generalizability to out-of-domain settings. MMECInstruct and CASLIE models are publicly accessible through https://ninglab.github.io/CASLIE/.

cs.CL↗

arXiv2025

Revisiting MLLM Based Image Quality Assessment: Errors and Remedy

The rapid progress of multi-modal large language models (MLLMs) has boosted the task of image quality assessment (IQA). However, a key challenge arises from the inherent mismatch between the discrete token outputs of MLLMs and the continuous nature of quality scores required by IQA tasks. This discrepancy significantly hinders the performance of MLLM-based IQA methods. Previous approaches that convert discrete token predictions into continuous scores often suffer from conversion errors. Moreover, the semantic confusion introduced by level tokens (e.g., ``good'') further constrains the performance of MLLMs on IQA tasks and degrades their original capabilities for related tasks. To tackle these problems, we provide a theoretical analysis of the errors inherent in previous approaches and, motivated by this analysis, propose a simple yet effective framework, Q-Scorer. This framework incorporates a lightweight regression module and IQA-specific score tokens into the MLLM pipeline. Extensive experiments demonstrate that Q-Scorer achieves state-of-the-art performance across multiple IQA benchmarks, generalizes well to mixed datasets, and further improves when combined with other methods.

cs.CV↗

arXiv2025

Detecting FRB by DANCE: a method based on DEnsity ANalysis and Cluster Extraction

Fast radio bursts (FRBs) are transient signals exhibiting diverse strengths and emission bandwidths. Traditional single-pulse search techniques are widely employed for FRB detection; yet weak, narrow-band bursts often remain undetectable due to low signal-to-noise ratios (SNR) in integrated profiles. We developed DANCE, a detection tool based on cluster analysis of the original spectrum. It is specifically designed to detect and isolate weak, narrow-band FRBs, providing direct visual identification of their emission properties. This method performs density clustering on reconstructed, RFI-cleaned observational data, enabling the extraction of targeted clusters in time-frequency domain that correspond to the genuine FRB emission range. Our simulations show that DANCE successfully extracts all true signals with SNR~>5 and achieves a detection precision exceeding 93%. Furthermore, through the practical detection of FRB 20201124A, DANCE has demonstrated a significant advantage in finding previously undetectable weak bursts, particularly those with distinct narrow-band features or occurring in proximity to stronger bursts.

astro-ph.HE↗

arXiv2025

AUTOSAR AP and ROS 2 Collaboration Framework

The field of autonomous vehicle research is advancing rapidly, necessitating platforms that meet real-time performance, safety, and security requirements for practical deployment. AUTOSAR Adaptive Platform (AUTOSAR AP) is widely adopted in development to meet these criteria; however, licensing constraints and tool implementation challenges limit its use in research. Conversely, Robot Operating System 2 (ROS 2) is predominantly used in research within the autonomous driving domain, leading to a disparity between research and development platforms that hinders swift commercialization. This paper proposes a collaboration framework that enables AUTOSAR AP and ROS 2 to communicate with each other using a Data Distribution Service for Real-Time Systems (DDS). In contrast, AUTOSAR AP uses Scalable service-Oriented Middleware over IP (SOME/IP) for communication. The proposed framework bridges these protocol differences, ensuring seamless interaction between the two platforms. We validate the functionality and performance of our bridge converter through empirical analysis, demonstrating its efficiency in conversion time and ease of integration with ROS 2 tools. Furthermore, the availability of the proposed collaboration framework is improved by automatically generating a configuration file for the proposed bridge converter.

cs.RO↗

arXiv2025

Tunable quantum anomalous Hall effect in fullerene monolayers

Nearly four decades after its theoretical prediction, the search for material realizations of quantum anomalous Hall effect (QAHE) remains a highly active field of research. Many materials have been predicted to exhibit quantum anomalous Hall (QAH) physics under feasible conditions but the experimental verification remains widely elusive. In this work, we propose an alternative approach towards QAH materials design by engineering customized molecular building blocks. We demonstrate this ansatz for a two-dimensional (2D) honeycomb lattice of C26 fullerenes, which exhibits a ferromagnetic ground state and thus breaks time-reversal symmetry. The molecular system is found to be highly tunable with respect to its magnetic degrees of freedom and applied strain, giving rise to a rich phase diagram with Chern numbers C= +/-2, +/-1, 0. Our proposal offers a versatile platform to realize tunable QAH physics under accessible conditions and provides an experimentally feasible approach for chemical synthesis of molecular networks with QAHE.

cond-mat.mes-hall↗

arXiv2025

Topological surface magnon-polariton in an insulating canted antiferromagnet

Excitation and control of antiferromagnetic magnon modes lie at the heart of coherent antiferromagnetic spintronics. Here, we propose a topological surface magnon-polariton as a new approach in the prototypical magnonic material hematite. We show that in an insulating canted antiferromagnet, where strong-coupled magnon-photon modes can be achieved using electrical on-chip layouts, a surface magnon-polariton mode exists in the gap of the bulk magnon-photon bands. The emergence of surface magnon-polariton mode is further attributed to the nontrivial topology of bulk magnon-photon bands. Magnon-photon coupling enhances the Berry curvature near the anticrossing points, leading to a topological bulk Chern band associated with the surface magnon-polaritons. Our work provides a general principle for the utilization of optomagnetic properties in antiferromagnets, with an illustration of its experimental feasibility and wide generality as manifested in hematite.

cond-mat.mes-hall↗

arXiv2025

Contextual Search in Principal-Agent Games: The Curse of Degeneracy

In this work, we introduce and study contextual search in general principal-agent games, where a principal repeatedly interacts with agents by offering contracts based on contextual information and historical feedback, without knowing the agents' true costs or rewards. Our model generalizes classical contextual pricing by accommodating richer agent action spaces. Over $T$ rounds with $d$-dimensional contexts, we establish an asymptotically tight exponential $T^{1 - Θ(1/d)}$ bound in terms of the pessimistic Stackelberg regret, benchmarked against the best utility for the principal that is consistent with the observed feedback. We also establish a lower bound of $Ω(T^{\frac{1}{2}-\frac{1}{2d}})$ on the classic Stackelberg regret for principal-agent games, demonstrating a surprising double-exponential hardness separation from the contextual pricing problem (a.k.a, the principal-agent game with two actions), which is known to admit a near-optimal $O(d\log\log T)$ regret bound [Kleinberg and Leighton, 2003, Leme and Schneider, 2018, Liu et al., 2021]. In particular, this double-exponential hardness separation occurs even in the special case with three actions and two-dimensional context. We identify that this significant increase in learning difficulty arises from a structural phenomenon that we call contextual action degeneracy, where adversarially chosen contexts can make some actions strictly dominated (and hence unincentivizable), blocking the principal's ability to explore or learn about them, and fundamentally limiting learning progress.

cs.GT↗

arXiv2025

HandEval: Taking the First Step Towards Hand Quality Evaluation in Generated Images

Although recent text-to-image (T2I) models have significantly improved the overall visual quality of generated images, they still struggle in the generation of accurate details in complex local regions, especially human hands. Generated hands often exhibit structural distortions and unrealistic textures, which can be very noticeable even when the rest of the body is well-generated. However, the quality assessment of hand regions remains largely neglected, limiting downstream task performance like human-centric generation quality optimization and AIGC detection. To address this, we propose the first quality assessment task targeting generated hand regions and showcase its abundant downstream applications. We first introduce the HandPair dataset for training hand quality assessment models. It consists of 48k images formed by high- and low-quality hand pairs, enabling low-cost, efficient supervision without manual annotation. Based on it, we develop HandEval, a carefully designed hand-specific quality assessment model. It leverages the powerful visual understanding capability of Multimodal Large Language Model (MLLM) and incorporates prior knowledge of hand keypoints, gaining strong perception of hand quality. We further construct a human-annotated test set with hand images from various state-of-the-art (SOTA) T2I models to validate its quality evaluation capability. Results show that HandEval aligns better with human judgments than existing SOTA methods. Furthermore, we integrate HandEval into image generation and AIGC detection pipelines, prominently enhancing generated hand realism and detection accuracy, respectively, confirming its universal effectiveness in downstream applications. Code and dataset will be available.

cs.CV↗

arXiv2025

Once Is Enough: Lightweight DiT-Based Video Virtual Try-On via One-Time Garment Appearance Injection

Video virtual try-on aims to replace the clothing of a person in a video with a target garment. Current dual-branch architectures have achieved significant success in diffusion models based on the U-Net; however, adapting them to diffusion models built upon the Diffusion Transformer remains challenging. Initially, introducing latent space features from the garment reference branch requires adding or modifying the backbone network, leading to a large number of trainable parameters. Subsequently, the latent space features of garments lack inherent temporal characteristics and thus require additional learning. To address these challenges, we propose a novel approach, OIE (Once is Enough), a virtual try-on strategy based on first-frame clothing replacement: specifically, we employ an image-based clothing transfer model to replace the clothing in the initial frame, and then, under the content control of the edited first frame, utilize pose and mask information to guide the temporal prior of the video generation model in synthesizing the remaining frames sequentially. Experiments show that our method achieves superior parameter efficiency and computational efficiency while still maintaining leading performance under these constraints.

cs.CV↗

arXiv2025

Unbalancedness of the conjugacy relation of ergodic measure-preserving transformations

We show that the isomorphism of ergodic measure-preserving transformations is not Borel reducible to the relation induced by the conjugacy action of the full group of an ergodic measure-preserving transformation on itself. This answers a question of Le Maître in the negative and gives a positive indication towards a conjecture of Sabok. In fact, we prove that the isomorphism of ergodic measure-preserving transformation is unbalanced, which answers another question of Le Maître in the positive.

math.DS↗

arXiv2025

A Perspective on Quantum Computing Applications in Quantum Chemistry using 25--100 Logical Qubits

The intersection of quantum computing and quantum chemistry represents a promising frontier for achieving quantum utility in domains of both scientific and societal relevance. Owing to the exponential growth of classical resource requirements for simulating quantum systems, quantum chemistry has long been recognized as a natural candidate for quantum computation. This perspective focuses on identifying scientifically meaningful use cases where early fault-tolerant quantum computers, which are considered to be equipped with approximately 25--100 logical qubits, could deliver tangible impact. While recent advances in classical computing have pushed the boundaries of tractable simulations to unprecedented scales, this logical-qubit regime represents the first window where quantum devices can pursue qualitatively distinct strategies, such as polynomial-scaling phase estimation, direct simulation of quantum dynamics, and active-space embedding, that remain challenging for classical solvers, for instance, multireference charge-transfer and conical-intersection states central to photochemistry and materials design. We highlight near-term opportunities in algorithm and software design, discuss representative chemical problems suited for quantum acceleration, and propose strategic roadmaps and collaborative pathways for advancing practical quantum utility in quantum chemistry.

quant-ph↗

arXiv2025

Designing Antiferromagnetic Spin-1/2 Chains in Janus Fullerene Nanoribbons

We design antiferromagnetic spin-1/2 chains in fullerene nanoribbons by introducing extra C$_{60}$ cages at one of their edges. The resulting odd number of intermolecular bonds induces an unpaired $π$-electron and hence a quantised magnetic moment in otherwise non-magnetic nanoribbons. We further reveal the formation of an antiferromagnetic ground state upon the linear arrangement of spin-1/2 C$_{60}$ cages that is insensitive to the specific structural motifs. Compared with graphene nanoribbons, Janus fullerene nanoribbons may offer an experimentally more accessible route to magnetic edge states with atomic precision in low-dimensional carbon nanostructures, possibly serving as a versatile nanoarchitecture for scalable spin-based devices and the exploration of many-body quantum phases.

cond-mat.mtrl-sci↗

arXiv2025

Altermagnetic Shastry-Sutherland fullerene networks

The interplay between quantum magnetism and many-body physics is of fundamental importance in condensed matter physics. %Magnetic exchange interactions in frustrated lattices give rise to rich phase diagrams. Molecular building blocks provide a versatile platform for exploring the exotic quantum phases arising from complex orderings in frustrated lattices. Here we demonstrate a showcase system based on altermagnetic Shastry-Sutherland fullerene networks, which can be constructed from a C$_{40}$ molecular synthon with two effective spin-1/2 sites due to the resonance structures. The charge-neutral, pure-carbon systems exhibit an altermagnetic ground state with fully compensated spins arranged in alternating C$_{40}$ units in a 2D rutile-like lattice, leading to $d$-wave splitting of the spin-polarised electronic band structure and strong chiral-split magnons. We report a rich phase diagram including altermagentic, quantum spin liquid, plaquette, and dimer phases, which can be accessed via moderate strains. Our findings open a new avenue for exploring quantum many-body physics based on scalable, chemically-feasible, molecular quantum materials.

cond-mat.mtrl-sci↗

arXiv2025

Negative and positive anisotropic thermal expansion in 2D fullerene networks

We find a design principle for tailoring thermal expansion properties in nearly-spherical molecular networks. Using 2D fullerene networks as a representative system, we realize positive thermal expansion along intermolecular [2\,+\,2] cycloaddition bonds and negative thermal expansion along intermolecular C$-$C single bonds by varying the structural frameworks of molecules. The microscopic mechanism originates from a combination of the framework's geometric flexibility and its transverse vibrational characteristics. Based on this insight, we find molecular networks beyond C$_{60}$ with tunable thermal expansion. These findings shed light on the fundamental mechanisms governing thermal expansion in molecular networks towards rational materials design.

cond-mat.mes-hall↗

arXiv2025

Robust translational invariance in topological bands against lattice potentials and disorders

We theoretically show that the continuous magnetic translational invariance within the Hilbert (sub-)space of a single Landau level (LL) can persist even in the presence of a superlattice electrostatic potential modulation, while such invariance is broken in the full real-space Hilbert space. This is due to the interplay of the superlattice constant and the fundamental length scale of the quantum Hall fluids. In particular for the lowest LL (LLL), when the spacing of superlattice is below the magnetic length, continuous magnetic translational symmetry is very robust. For the fractional quantum Hall phases, the continuous translational symmetry is preserved when the superlattice spacing is below the corresponding fundamental lengths which we can now quantitatively define, which is different from the length scale from the quantum metric. Moreover, our analysis implies that the dynamics of the anyonic excitations can be robust against the long wavelength part of the disorder, and we discuss the related experimental ramifications.

cond-mat.str-el↗