arXiv subjects

Yuanbo Li

Publications and source records attributed to Yuanbo Li.

At least 19 recordsLinked to original sources

arXiv2026

Quasi-Sinusoidal Single Diamond Structure in Royal Jewel Butterfly: An Angle-Independent Photonic Structure

Structural colouration with narrow spectral photonic bandwidth and high reflectivity is of critical importance for modern optical applications, including displays, laser systems, and optical sensing, etc. Achieving such angle independent colouration typically relies on polycrystalline or inherent structural disorder. However, balancing angular uniformity with high brightness and strong colour contrast remains challenging. Herein, we uncover the structural origin of the spectacular bright, angle-independent blue colouration of Hypochrysops polycletus, a sapphire-like Royal Jewel butterfly. Three-dimensional (3D) electron microscopy reveals that the dorsal wing scale has a single diamond structure, a 3D photonic crystal previously documented only in beetles and weevils. The crystal domains form an extraordinary quasi sinusoidal surface geometry with a distinct template morphology-guided arrangement. Unlike typically thicker biophotonic structures that support multiple high symmetry stopbands, this design contains only 3-4 unit cells in the propagation direction. Its optical response is dominated by the fundamental stopband, with two dominant scattering mechanisms: specular reflection at the {111} inclined sidewalls of the hierarchical structure, and funnelling into localised quasi-normal modes enabled by a strongly anisotropic Bloch transport. By mimicking these features with two-photon polymerisation, we artificially reproduced the optical response in the infrared region. The study opens a pathway towards bioinspired brilliant diffuse colouration and angle-robust photonic devices.

physics.optics↗

arXiv2026

Technical Report on the CVPR 2026@AdvML Workshop Challenge

Vision-language agents (VLAs) are increasingly used to interpret complex driving scenes and support safety-critical reasoning. This report presents the CVPR 2026@AdvML Workshop Challenge on adversarial multimodal attacks against autonomous-driving VLAs. Built on DriveLM-style multi-view visual question answering, the challenge represents each scene with six synchronized camera images and a structured collection of driving-related question-answer pairs. Participants generate adversarial images and suffix-only textual perturbations that induce model responses to deviate from reference answers while preserving image fidelity and limiting textual cost. The competition comprises two phases, with Phase II adding a hidden black-box model to assess transferability. We describe the task design, submission rules, evaluation protocol, and leaderboard results, and then examine five leading submissions for which technical reports were available. Across these reports, several recurring patterns emerge: image-side attacks are favored by the suffix penalty; scene-level, multi-view optimization is more effective than treating views in isolation; QA types and graph structure provide useful priors for allocating attack budget; feature-space objectives can improve black-box transfer; and typographic content embedded in camera images exposes a persistent vulnerability in driving VLAs. These findings provide a practical reference for future robustness evaluation and defense design in multimodal autonomous-driving systems.

cs.CV↗

arXiv2026

Learning to Place Objects with Programs and Iterative Self Training

In this work we study indoor scene object placement. Given a 3D indoor scene and an object, the task is to predict placement locations within the scene. Empirical observations of data-driven approaches to the problem show their tendency to miss placement modes. We introduce a system which helps to address this flaw. We design a Domain Specific Language (DSL) that specifies object relational constraints. Upon execution, programs from our language predict possible placements from a partial scene and object. We design a generative model which writes these programs automatically. Available 3D scene datasets do not contain programs to train on, and naively extracted programs only predict the original placement location of scene objects. Training on these programs results in subpar performance so we introduce a new program bootstrapping algorithm that improves our system's performance compared to the naive approach. To quantify our qualitative observations, we introduce a new evaluation procedure which captures how well a system models per-object location distributions. We ask human annotators to label all the possible places an object can go in a scene and compare this set against locations produced by the system in question. Our system produces per-object location distributions more consistent with human annotators than those produced by existing data-driven approaches and a zero-shot approach using an LLM. While other systems degrade in performance when training data is sparse, our system does not degrade to the same degree.

cs.GR↗

arXiv2026

Towards Highly Transferable Vision-Language Attack via Semantic-Augmented Dynamic Contrastive Interaction

With the rapid advancement and widespread application of vision-language pre-training (VLP) models, their vulnerability to adversarial attacks has become a critical concern. In general, the adversarial examples can typically be designed to exhibit transferable power, attacking not only different models but also across diverse tasks. However, existing attacks on language-vision models mainly rely on static cross-modal interactions and focus solely on disrupting positive image-text pairs, resulting in limited cross-modal disruption and poor transferability. To address this issue, we propose a Semantic-Augmented Dynamic Contrastive Attack (SADCA) that enhances adversarial transferability through progressive and semantically guided perturbation. SADCA progressively disrupts cross-modal alignment through dynamic interactions between adversarial images and texts. This is accomplished by SADCA establishing a contrastive learning mechanism involving adversarial, positive and negative samples, to reinforce the semantic inconsistency of the obtained perturbations. Moreover, we empirically find that input transformations commonly used in traditional transfer-based attacks also benefit VLPs, which motivates a semantic augmentation module that increases the diversity and generalization of adversarial examples. Extensive experiments on multiple datasets and models demonstrate that SADCA significantly improves adversarial transferability and consistently surpasses state-of-the-art methods. The code is released at https://github.com/LiYuanBoJNU/SADCA.

cs.CV↗

arXiv2026

Multi-Paradigm Collaborative Adversarial Attack Against Multi-Modal Large Language Models

The rapid progress of Multi-Modal Large Language Models (MLLMs) has significantly advanced downstream applications. However, this progress also exposes serious transferable adversarial vulnerabilities. In general, existing adversarial attacks against MLLMs typically rely on surrogate models trained within a single learning paradigm and perform independent optimisation in their respective feature spaces. This straightforward setting naturally restricts the richness of feature representations, delivering limits on the search space and thus impeding the diversity of adversarial perturbations. To address this, we propose a novel Multi-Paradigm Collaborative Attack (MPCAttack) framework to boost the transferability of adversarial examples against MLLMs. In principle, MPCAttack aggregates semantic representations, from both visual images and language texts, to facilitate joint adversarial optimisation on the aggregated features through a Multi-Paradigm Collaborative Optimisation (MPCO) strategy. By performing contrastive matching on multi-paradigm features, MPCO adaptively balances the importance of different paradigm representations and guides the global perturbation optimisation, effectively alleviating the representation bias. Extensive experimental results on multiple benchmarks demonstrate the superiority of MPCAttack, indicating that our solution consistently outperforms state-of-the-art methods in both targeted and untargeted attacks on open-source and closed-source MLLMs. The code is released at https://github.com/LiYuanBoJNU/MPCAttack.

cs.CV↗

arXiv2026

A Unified Heterogeneous Implementation of Numerical Atomic Orbitals-Based Real-Time TDDFT within the ABACUS Package

We present a unified heterogeneous computing framework for real-time time-dependent density functional theory (RT-TDDFT) based on numerical atomic orbitals (NAOs), implemented in the ABACUS package. We introduce three co-designed abstraction layers, including unified data containers, unified linear algebra operators, and unified grid integration interfaces. These layers collectively accelerate the two most demanding parts of NAO-based RT-TDDFT: explicit real-time wavefunction propagation and real-space grid operations such as Hamiltonian construction and force evaluation under external fields. We validate the method by computing optical properties for systems ranging from finite molecules to periodic solids, showing excellent agreement with standard benchmarks. Performance evaluations on bulk silicon demonstrate that a single GPU can achieve substantial wall-clock speedup over a fully utilized dual-socket CPU node. Furthermore, distributed multi-GPU strong-scaling tests confirm high parallel efficiency over tens of GPUs. This work establishes a high-performance, portable platform for large-scale first-principles simulations of ultrafast electron dynamics.

cond-mat.mtrl-sci↗

arXiv2026

PLLM: Pseudo-Labeling Large Language Models for CAD Program Synthesis

Recovering Computer-Aided Design (CAD) programs from 3D geometries is a widely studied problem. Recent advances in large language models (LLMs) have enabled progress in CAD program synthesis, but existing methods rely on supervised training with paired shape-program data, which is often unavailable. We introduce PLLM, a self-training framework for CAD program synthesis from unlabeled 3D shapes. Given a pre-trained CAD-capable LLM and a shape dataset, PLLM iteratively samples candidate programs, selects high-fidelity executions, and augments programs to construct synthetic program-shape pairs for fine-tuning. We experiment on adapting CAD-Recode from DeepCAD to the unlabeled ABC dataset show consistent improvements in geometric fidelity and program diversity.

cs.CV↗

arXiv2025

ABACUS: An Electronic Structure Analysis Package for the AI Era

ABACUS (Atomic-orbital Based Ab-initio Computation at USTC) is an open-source software for first-principles electronic structure calculations and molecular dynamics simulations. It mainly features density functional theory (DFT) and molecular dynamics functions and is compatible with both plane-wave basis sets and numerical atomic orbital basis sets. ABACUS serves as a platform that facilitates the integration of various electronic structure methods, such as Kohn-Sham DFT, stochastic DFT, orbital-free DFT, and real-time time-dependent DFT, etc. In addition, with the aid of high-performance computing, ABACUS is designed to perform efficiently and provide massive amounts of first-principles data for generating general-purpose machine learning potentials, such as DPA models. Furthermore, ABACUS serves as an electronic structure platform that interfaces with several AI-assisted algorithms and packages, such as DeePKS-kit, DeePMD, DP-GEN, DeepH, DeePTB, HamGNN, etc.

cond-mat.mtrl-sci↗

arXiv2025

Spin-polarized chiral ZnIn2S4 for targeted solar-driven CO2 reduction to acetic acid

Acetic acid, an important industrial chemical, is a key target product for CO2 reduction due to its dual role in carbon utilization and chemical feedstock supply. Although photocatalytic CO2 reduction (PCCR) can generate acetic acid alongside other multicarbon products, its yield is typically low, limited by competing reactions and inefficient C-C coupling. Herein, we report a chiral mesostructured ZnIn2S4 (CMZI) photocatalyst that achieves a remarkable acetic acid yield of 962 {umol g-1 h-1 with a high selectivity of 97.3 %. This yield is ten times higher than the current highest reported value, while attaining state-of-the-art selectivity10. The remarkable productivity arises from synergistic effect between chiral structure and sulfur (S) sites of CMZI. Chirality-induced spin polarization in CMZI stabilizes the key triplet OCCO intermediate, significantly promoting C-C coupling efficiency. Theoretical calculations reveal that the S sites on {102} crystal facets of ZnIn2S4 exhibit thermodynamic and kinetic preferences for acetic acid formation. This work offers critical insights into catalytic strategies for CO2 reduction toward the efficient and scalable synthesis of various multicarbon products.

cond-mat.mtrl-sci↗

arXiv2025

Managing Multi Instance GPUs for High Throughput and Energy Savings

Modern GPUs such as the Ampere series (A30, A100) as well as the Hopper series (H100, H200) offer performance as well as security isolation features. They also support a good amount of concurrency, but taking advantage of it can be quite challenging due to the complex constraints on partitioning the chip. In this work, we develop partitioning and scheduling schemes for a variety of workloads, ranging from scientific to modern ML workloads, including LLMs. We develop several schemes involving dynamic memory estimation, partition fusion and partition fission. We also support process restart to recover from out-of-memory errors for workloads and early restart as an optimization. This approach yields up to 6.20x throughput and 5.93x energy improvements for general workloads; and we see 1.59x and 1.12x improvement to throughput and energy, respectively, for ML workloads on an A100 GPU. We leverage this technique on LLM workloads and show good improvements, including up to 1.43x throughput improvement and 1.11x energy savings.

cs.DC↗

arXiv2025

DTECM: Digital Twin Enabled Channel Measurement and Modeling in Terahertz Urban Macrocell

In this work, in the THz UMa, extensive channel measurements are conducted and an accurate channel model is developed by combining ray-tracing, computer vision (CV), and statistical methods. Specifically, substantial channel measurement campaigns with distances up to 410~m are conducted at 220~GHz, with nanosecond-level absolute time synchronization. Based on the measurement results, the propagation phenomena are analyzed in detail and the channel characteristics are calculated and statistically modeled. Furthermore, a digital twin enabled channel model (DTECM) is proposed, which generates THz channel responses in a hybrid manner. Specifically, the dominant paths are generated deterministically by using the ray-tracing technique and CV methods. Apart from the path gains determined by ray-tracing, the additional foliage loss is accurately modeled based on foliage information extracted from panoramic pictures. To maintain a low computational complexity for the DTECM, non-dominant paths are then generated statistically. Numeric results reveal that compared to the traditional statistical channel models, the DTECM reduces the path loss modeling error from 14~dB to 4~dB, showing its great superiority. Furthermore, a preliminary link performance evaluation using the DTECM indicates that THz UMa is feasible, though requiring high antenna gains and coverage extension techniques to achieve high spectral efficiencies and wide coverage.

cs.IT↗

arXiv2024

220 GHz Urban Microcell Channel Measurement and Characterization on a University Campus

Owning abundant bandwidth resources, the Terahertz (THz) band (0.1-10~THz) is envisioned as a key technology to realize ultra-high-speed communications in 6G and beyond wireless networks. To realize reliable THz communications in urban microcell (UMi) environments, propagation analysis and channel characterization are still insufficient. In this paper, channel measurement campaigns are conducted in a UMi scenario at 220~GHz, using a correlation-based time domain channel sounder. 24 positions are measured along a road on the university campus, with distances ranging from 34~m to 410~m. Based on the measurement results, the spatial consistency and interaction of THz waves to the surrounding environments are analyzed. Moreover, the additional loss due to foliage blockage is calculated and an average value of 16.7~dB is observed. Furthermore, a full portrait of channel characteristics, including path loss, shadow fading, K-factor, delay and angular spreads, as well as cluster parameters, is calculated and analyzed. Specifically, an average K-factor value of 17.5 dB is measured in the line-of-sight (LoS) case, which is nearly two times larger than the extrapolated values from the 3GPP standard, revealing weak multipath effects in the THz band. Additionally, 2.5 clusters on average are observed in the LoS case, around one fifth of what is defined in the 3GPP model, which uncovers the strong sparsity in THz UMi. The results and analysis in this work can offer guidance for system design for future THz UMi networks.

cs.IT↗

arXiv2024

Transfer Learning Enabled Transformer based Generative Adversarial Networks (TT-GAN) for Terahertz Channel Modeling and Generating

Terahertz (THz) communications, ranging from 100 GHz to 10 THz, are envisioned as a promising technology for 6G and beyond wireless systems. As foundation of designing THz communications, channel modeling and characterization are crucial to scrutinize the potential of the new spectrum. However, current channel modeling and standardization heavily rely on measurements, which are both time-consuming and costly to obtain in the THz band. Here, we propose a Transfer learning enabled Transformer based Generative Adversarial Network (TT-GAN) for THz channel modeling. Specifically, as a fundamental building block, a GAN is exploited to generate channel parameters, which can substitute measurements. To greatly improve the accuracy, the first T, i.e., a transformer structure with a self-attention mechanism is incorporated in GAN. Still incurring errors compared with ground-truth measurement, the second T, i.e., a transfer learning is designed to solve the mismatch between the formulated network and measurement. The proposed TT-GAN can achieve high accuracy in channel modeling, while requiring only rather limited amount of measurement, which is a promising complementary of channel standardization that fundamentally differs from the current techniques that heavily rely on measurement.

eess.SP↗

arXiv2024

DSS-o-SAGE: Direction-Scan Sounding-Oriented SAGE Algorithm for Channel Parameter Estimation in mmWave and THz Bands

Investigation of millimeter (mmWave) and Terahertz (THz) channels relies on channel measurements and estimation of multi-path component (MPC) parameters. As a common measurement technique in the mmWave and THz bands, direction-scan sounding (DSS) resolves angular information and increases the measurable distance. Through mechanical rotation, the DSS creates a virtual multi-antenna sounding system, which however incurs signal phase instability and large data sizes, which are not fully considered in existing estimation algorithms and thus make them ineffective. To tackle this research gap, in this paper, a DSS-oriented space-alternating generalized expectation-maximization (DSS-o-SAGE) algorithm is proposed for channel parameter estimation in mmWave and THz bands. To appropriately capture the measured data in mmWave and THz DSS, the phase instability is modeled by the scanning-direction-dependent signal phases. Furthermore, based on the signal model, the DSS-o-SAGE algorithm is developed, which not only addresses the problems brought by phase instability, but also achieves ultra-low computational complexity by exploiting the narrow antenna beam property of DSS. Simulations in synthetic channels are conducted to demonstrate the efficacy of the proposed algorithm and explore the applicable region of the far-field approximation in DSS-o-SAGE. Last but not least, the proposed DSS-o-SAGE algorithm is applied in real measurements in an indoor corridor scenario at 300~GHz. Compared with results using the baseline noise-elimination method, the channel is characterized more correctly and reasonably based on the DSS-o-SAGE.

cs.IT↗

arXiv2024

Correlation-based Dual-band THz Channel Measurements and Characterization in a Laboratory

The Terahertz band, spanning from 0.1~THz to 10~THz, is envisioned as a key technology to realize ultra-high data rates in the 6G and beyond mobile communication systems, due to its abundant bandwidth resource. However, to realize THz communications, one substantial step is to fully understand the THz channels, which relies on extensive channel measurements. In this paper, using a correlation-based time domain channel sounder, measurement campaigns are conducted in a laboratory at 140~GHz and 220~GHz. In the data post-processing procedures, the time drift of clock signals is corrected using a linear interpolation/extrapolation method. Based on the measured results, the main objects that provide significant once-scattering clusters are found, based on which the scattering losses are calculated and analyzed. Furthermore, the channel characteristics, including path loss, shadow fading, K-factor, etc. are calculated and compared to 3GPP standard values. The propagation analysis and channel characteristics are helpful to study channel modeling and guide system design for THz communications.

cs.IT↗

arXiv2023

Truncated Non-Local Kinetic Energy Density Functionals for Simple Metals and Silicon

Adopting an accurate kinetic energy density functional (KEDF) to characterize the noninteracting kinetic energy within the framework of orbital-free density functional theory (OFDFT) is challenging. We propose a new form of the non-local KEDF with a real-space truncation cutoff that satisfies the uniform electron gas limit and design KEDFs for simple metals and silicon. The new KEDFs are obtained by minimizing a residual function, which contains the differences in the total energy and charge density of several representative systems with respect to the Kohn-Sham DFT results. By systematically testing different cutoffs of the new KEDFs, we find that the cutoff plays a crucial role in determining the properties of metallic Al and semiconductor Si systems. We conclude that the new KEDF with a sufficiently long cutoff performs even better than some representative non-local KEDFs in some aspects, which sheds new light on optimizing the KEDFs in OFDFT to achieve better accuracy.

physics.comp-ph↗

arXiv2023

Still Waters Run Deep: Extend THz Coverage with Non-Intelligent Reflecting Surface

Large reflection and diffraction losses in the Terahertz (THz) band give rise to degraded coverage abilities in non-line-of-sight (NLoS) areas. To overcome this, a non-intelligent reflecting surface (NIRS) can be used, which is essentially a rough surface made by metal materials. NIRS is not only able to enhance received power in large NLoS areas through rich reflections and scattering, but also costless and super-easy to fabricate and implement. In this article, we first thoroughly compare NIRS with the lively discussed intelligent reflecting surface (IRS) and point out the unique advantages of NIRS over IRS. Furthermore, experimental results are elaborated to show the effectiveness of NIRS in improving coverage. Last but not least, open problems and future directions are highlighted to inspire future research efforts on NIRS.

cs.IT↗

arXiv2023

Channel Measurement and Coverage Analysis for NIRS-Aided THz Communications in Indoor Environments

Due to large reflection and diffraction losses in the THz band, it is arguable to achieve reliable links in the none-line-of-sight (NLoS) cases. Intelligent reflecting surfaces, although are expected to solve the blockage problem and enhance the system connectivity, suffer from fabrication difficulty and operation complexity. In this work, non-intelligent reflecting surfaces (NIRS), which are simply made of costless metal foils and have no signal configuration capability, are adopted to enhance the signal strength and coverage in the THz band. Channel measurements are conducted in typical indoor scenarios at 306-321 GHz and 356-371 GHz bands to validate the effectiveness of the NIRS. Results measured with NIRS in different sizes show that large NIRS performs much better than small NIRS. Furthermore, by invoking the NIRS, the additional reflection loss can be reduced by more than 10~dB and the coverage ratio is increased by up to 39$\%$ for a 10~dB signal-to-noise ratio (SNR) threshold.

cs.IT↗