arXiv subjects

Bo Yang

Publications and source records attributed to Bo Yang.

At least 163 records · Page 9Linked to original sources

arXiv2025

Comb-Driven Coherent Optical Transmitter for Scalable DWDM Interconnects

Driven by the growing demand for large-scale artificial intelligence applications, disaggregated compute nodes and high-radix switches in next-generation computing clusters are set to surpass the capacity of current optical interconnect technologies. Such a surge turns several aspects of transmitters into critical bottlenecks: shoreline bandwidth density and energy efficiency are effectively limiting the scalability. We present a comb-driven coherent optical transmitter architecture on a Si/SiN platform that provides the bandwidth density, energy efficiency, and compact footprint required for such co-packaged-enabled optical interconnects. We evaluate scalability through critical building blocks, including ultra-compact microring-assisted Mach--Zehnder modulators (MRA-MZMs) and dense wavelength-division multiplexing (DWDM) interleavers. Single-tone experiments demonstrate a net line rate of 400 Gbps per polarization (16-QAM, 120 GBd) in silicon within the O-band, achieving a record shoreline density of 4 Tbps/mm while consuming only 10 fJ/bit for modulation. We also demonstrate transmission rates of up to 160 GBd QPSK in back-to-back and 100 GBd over 7 km of fiber without dispersion compensation. Using a quantum-dot frequency comb, six 100 GHz-spaced WDM channels transmit 1.08 Tbps over 5 km. System-level analyses show that by leveraging advanced modulation formats through the integration of wavelength and polarization multiplexing, our proposed architecture can realistically support combined transmission rates exceeding 10 Tbps per fiber within practical limits of power consumption and packaging, outlining a clear path toward future petabit-scale interconnects.

physics.optics↗

arXiv2025

Resilient Event-Triggered Control of Vehicle Platoon Under DoS Attacks and Parameter Uncertainty

This paper investigates the problem of dynamic event-triggered platoon control for intelligent vehicles (IVs) under denial of service (DoS) attacks and parameter uncertainty. DoS attacks disrupt vehicle-to-vehicle (V2V) communications, leading to the destabilization of vehicle formations. To alleviate the burden of the V2V communication network and enhance the tracking performance in the presence of DoS attacks and parameter uncertainty, a resilient and dynamic event-triggered mechanism is proposed. In contrast to the static event-triggering mechanism (STEM), this approach leverages the internal dynamic variable to further save communication resources. Subsequently, a method is developed for designing the desired triggering mechanism. Following this, a co-design framework is constructed to guarantee robust and resilient control against DoS attacks, with the analysis of eliminating Zeno behavior. Lastly, extensive simulations are presented to show the superiority of the proposed method in terms of enhancing platoon resilience and robustness and improving communication efficiency.

eess.SY↗

arXiv2025

Hybrid Quantum-Classical Neural Networks for Few-Shot Credit Risk Assessment

Quantum Machine Learning (QML) offers a new paradigm for addressing complex financial problems intractable for classical methods. This work specifically tackles the challenge of few-shot credit risk assessment, a critical issue in inclusive finance where data scarcity and imbalance limit the effectiveness of conventional models. To address this, we design and implement a novel hybrid quantum-classical workflow. The methodology first employs an ensemble of classical machine learning models (Logistic Regression, Random Forest, XGBoost) for intelligent feature engineering and dimensionality reduction. Subsequently, a Quantum Neural Network (QNN), trained via the parameter-shift rule, serves as the core classifier. This framework was evaluated through numerical simulations and deployed on the Quafu Quantum Cloud Platform's ScQ-P21 superconducting processor. On a real-world credit dataset of 279 samples, our QNN achieved a robust average AUC of 0.852 +/- 0.027 in simulations and yielded an impressive AUC of 0.88 in the hardware experiment. This performance surpasses a suite of classical benchmarks, with a particularly strong result on the recall metric. This study provides a pragmatic blueprint for applying quantum computing to data-constrained financial scenarios in the NISQ era and offers valuable empirical evidence supporting its potential in high-stakes applications like inclusive finance.

cs.LG↗

arXiv2025

SPGen: Spherical Projection as Consistent and Flexible Representation for Single Image 3D Shape Generation

Existing single-view 3D generative models typically adopt multiview diffusion priors to reconstruct object surfaces, yet they remain prone to inter-view inconsistencies and are unable to faithfully represent complex internal structure or nontrivial topologies. In particular, we encode geometry information by projecting it onto a bounding sphere and unwrapping it into a compact and structural multi-layer 2D Spherical Projection (SP) representation. Operating solely in the image domain, SPGen offers three key advantages simultaneously: (1) Consistency. The injective SP mapping encodes surface geometry with a single viewpoint which naturally eliminates view inconsistency and ambiguity; (2) Flexibility. Multi-layer SP maps represent nested internal structures and support direct lifting to watertight or open 3D surfaces; (3) Efficiency. The image-domain formulation allows the direct inheritance of powerful 2D diffusion priors and enables efficient finetuning with limited computational resources. Extensive experiments demonstrate that SPGen significantly outperforms existing baselines in geometric quality and computational efficiency.

cs.CV↗

arXiv2025

Optimization for Massive 3D-RIS Deployment: A Generative Diffusion Model-Based Approach

Reconfigurable Intelligent Surfaces (RISs) transform the wireless environment by modifying the amplitude, phase, and polarization of incoming waves, significantly improving coverage performance. Notably, optimizing the deployment of RISs becomes vital, but existing optimization methods face challenges such as high computational complexity, limited adaptability to changing environments, and a tendency to converge on local optima. In this paper, we propose to optimize the deployment of large-scale 3D RISs using a diffusion model based on probabilistic generative learning. We begin by dividing the target area into fixed grids, with each grid corresponding to a potential deployment location. Then, a multi-RIS deployment optimization problem is formulated, which is difficult to solve directly. By treating RIS deployment as a conditional generation task, the well-trained diffusion model can generate the distribution of deployment strategies, and thus, the optimal deployment strategy can be obtained by sampling from this distribution. Simulation results demonstrate that the proposed diffusion-based method outperforms traditional benchmark approaches in terms of exceed ratio and generalization.

cs.NI↗

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↗

arXiv2025

Optical Switching of Moiré Chern Ferromagnet

Optical manipulation of quantum matter offers a non-contact, high-precision and fast control. Fractional Chern ferromagnet states in moiré superlattices are promising for topological quantum computing, but an effective optical control protocol has remained elusive. Here, we demonstrate robust optical switching of integer and fractional Chern ferromagnets in twisted MoTe2 bilayers using circularly polarized light. Highly efficient optical manipulation of spin orientations in the topological ferromagnet regime is realized at zero field using a pump light power as low as 28 nanowatts per square micrometer. Utilizing this optically induced transition, we also demonstrate magnetic bistate cycling and spatially resolved writing of ferromagnetic domain walls. This work establishes a reliable and efficient optical control scheme for moiré Chern ferromagnets, paving the way for dissipationless spintronics and quantized Chern junction devices.

cond-mat.mes-hall↗

arXiv2025

Quantum-size effect induced Andreev bound states in ultrathin metallic islands proximitized by a superconductor

While Andreev bound states (ABSs) have been realized in engineered superconducting junctions, their direct observation in normal metal/superconductor heterostructures-enabled by quantum confinement-remains experimentally elusive. Here, we report the detection of ABSs in ultrathin metallic islands (Bi, Ag, and SnTe) grown on the s-wave superconductor NbN. Using high-resolution scanning tunneling microscopy and spectroscopy, we clearly reveal in-gap ABSs with energies symmetric about the Fermi level. While the energies of these states show no position dependence, their wave functions exhibit spatial oscillations, demonstrating a quantum size effect. Both the energy levels and spatial distribution of the ABSs can be reproduced by our effective model in which a metallic island is coupled to the superconducting substrate via the proximity effect. We demonstrate that the coupling strength plays a critical role in determining the ABS energies. Our work introduces a novel physical platform for implementing ABSs, which hold promise for significant device applications.

cond-mat.supr-con↗

arXiv2025

MindEye-OmniAssist: A Gaze-Driven LLM-Enhanced Assistive Robot System for Implicit Intention Recognition and Task Execution

A promising effective human-robot interaction in assistive robotic systems is gaze-based control. However, current gaze-based assistive systems mainly help users with basic grasping actions, offering limited support. Moreover, the restricted intent recognition capability constrains the assistive system's ability to provide diverse assistance functions. In this paper, we propose an open implicit intention recognition framework powered by Large Language Model (LLM) and Vision Foundation Model (VFM), which can process gaze input and recognize user intents that are not confined to predefined or specific scenarios. Furthermore, we implement a gaze-driven LLM-enhanced assistive robot system (MindEye-OmniAssist) that recognizes user's intentions through gaze and assists in completing task. To achieve this, the system utilizes open vocabulary object detector, intention recognition network and LLM to infer their full intentions. By integrating eye movement feedback and LLM, it generates action sequences to assist the user in completing tasks. Real-world experiments have been conducted for assistive tasks, and the system achieved an overall success rate of 41/55 across various undefined tasks. Preliminary results show that the proposed method holds the potential to provide a more user-friendly human-computer interaction interface and significantly enhance the versatility and effectiveness of assistive systems by supporting more complex and diverse task.

cs.RO↗

arXiv2025

Virtual Trading in Multi-Settlement Electricity Markets

In the Day-Ahead (DA) market, suppliers sell and load-serving entities (LSEs) purchase energy commitments, with both sides adjusting for imbalances between contracted and actual deliveries in the Real-Time (RT) market. We develop a supply function equilibrium model to study how virtual trading-speculating on DA-RT price spreads without physical delivery-affects market efficiency. Without virtual trading, LSEs underbid relative to actual demand in the DA market, pushing DA prices below expected RT prices. Virtual trading narrows, and in the limit of large number traders can eliminates, this price gap. However, it does not induce quantity alignment: DA-cleared demand remains below true expected demand, as price alignment makes the LSE indifferent between markets and prompts it to reduce DA bids to avoid over-purchasing. Renewable energy suppliers cannot offset these strategic distortions. We provide empirical support to our main model implications using data from the California and New York Independent System Operators.

math.OC↗

arXiv2025

TRACE: Learning 3D Gaussian Physical Dynamics from Multi-view Videos

In this paper, we aim to model 3D scene geometry, appearance, and physical information just from dynamic multi-view videos in the absence of any human labels. By leveraging physics-informed losses as soft constraints or integrating simple physics models into neural nets, existing works often fail to learn complex motion physics, or doing so requires additional labels such as object types or masks. We propose a new framework named TRACE to model the motion physics of complex dynamic 3D scenes. The key novelty of our method is that, by formulating each 3D point as a rigid particle with size and orientation in space, we directly learn a translation rotation dynamics system for each particle, explicitly estimating a complete set of physical parameters to govern the particle's motion over time. Extensive experiments on three existing dynamic datasets and one newly created challenging synthetic datasets demonstrate the extraordinary performance of our method over baselines in the task of future frame extrapolation. A nice property of our framework is that multiple objects or parts can be easily segmented just by clustering the learned physical parameters.

cs.CV↗

arXiv2025

RayletDF: Raylet Distance Fields for Generalizable 3D Surface Reconstruction from Point Clouds or Gaussians

In this paper, we present a generalizable method for 3D surface reconstruction from raw point clouds or pre-estimated 3D Gaussians by 3DGS from RGB images. Unlike existing coordinate-based methods which are often computationally intensive when rendering explicit surfaces, our proposed method, named RayletDF, introduces a new technique called raylet distance field, which aims to directly predict surface points from query rays. Our pipeline consists of three key modules: a raylet feature extractor, a raylet distance field predictor, and a multi-raylet blender. These components work together to extract fine-grained local geometric features, predict raylet distances, and aggregate multiple predictions to reconstruct precise surface points. We extensively evaluate our method on multiple public real-world datasets, demonstrating superior performance in surface reconstruction from point clouds or 3D Gaussians. Most notably, our method achieves exceptional generalization ability, successfully recovering 3D surfaces in a single-forward pass across unseen datasets in testing.

cs.CV↗

arXiv2025

AgriGPT: a Large Language Model Ecosystem for Agriculture

Despite the rapid progress of Large Language Models (LLMs), their application in agriculture remains limited due to the lack of domain-specific models, curated datasets, and robust evaluation frameworks. To address these challenges, we propose AgriGPT, a domain-specialized LLM ecosystem for agricultural usage. At its core, we design a multi-agent scalable data engine that systematically compiles credible data sources into Agri-342K, a high-quality, standardized question-answer (QA) dataset. Trained on this dataset, AgriGPT supports a broad range of agricultural stakeholders, from practitioners to policy-makers. To enhance factual grounding, we employ Tri-RAG, a three-channel Retrieval-Augmented Generation framework combining dense retrieval, sparse retrieval, and multi-hop knowledge graph reasoning, thereby improving the LLM's reasoning reliability. For comprehensive evaluation, we introduce AgriBench-13K, a benchmark suite comprising 13 tasks with varying types and complexities. Experiments demonstrate that AgriGPT significantly outperforms general-purpose LLMs on both domain adaptation and reasoning. Beyond the model itself, AgriGPT represents a modular and extensible LLM ecosystem for agriculture, comprising structured data construction, retrieval-enhanced generation, and domain-specific evaluation. This work provides a generalizable framework for developing scientific and industry-specialized LLMs. All models, datasets, and code will be released to empower agricultural communities, especially in underserved regions, and to promote open, impactful research.

cs.AI↗

arXiv2025

Lightweight Auto-bidding based on Traffic Prediction in Live Advertising

Internet live streaming is widely used in online entertainment and e-commerce, where live advertising is an important marketing tool for anchors. An advertising campaign hopes to maximize the effect (such as conversions) under constraints (such as budget and cost-per-click). The mainstream control of campaigns is auto-bidding, where the performance depends on the decision of the bidding algorithm in each request. The most widely used auto-bidding algorithms include Proportional-Integral-Derivative (PID) control, linear programming (LP), reinforcement learning (RL), etc. Existing methods either do not consider the entire time traffic, or have too high computational complexity. In this paper, the live advertising has high requirements for real-time bidding (second-level control) and faces the difficulty of unknown future traffic. Therefore, we propose a lightweight bidding algorithm Binary Constrained Bidding (BiCB), which neatly combines the optimal bidding formula given by mathematical analysis and the statistical method of future traffic estimation, and obtains good approximation to the optimal result through a low complexity solution. In addition, we complement the form of upper and lower bound constraints for traditional auto-bidding modeling and give theoretical analysis of BiCB. Sufficient offline and online experiments prove BiCB's good performance and low engineering cost.

stat.ML↗

arXiv2025

Variety Is the Spice of Life: Detecting Misinformation with Dynamic Environmental Representations

The proliferation of misinformation across diverse social media platforms has drawn significant attention from both academic and industrial communities due to its detrimental effects. Accordingly, automatically distinguishing misinformation, dubbed as Misinformation Detection (MD), has become an increasingly active research topic. The mainstream methods formulate MD as a static learning paradigm, which learns the mapping between the content, links, and propagation of news articles and the corresponding manual veracity labels. However, the static assumption is often violated, since in real-world scenarios, the veracity of news articles may vacillate within the dynamically evolving social environment. To tackle this problem, we propose a novel framework, namely Misinformation detection with Dynamic Environmental Representations (MISDER). The basic idea of MISDER lies in learning a social environmental representation for each period and employing a temporal model to predict the representation for future periods. In this work, we specify the temporal model as the LSTM model, continuous dynamics equation, and pre-trained dynamics system, suggesting three variants of MISDER, namely MISDER-LSTM, MISDER-ODE, and MISDER-PT, respectively. To evaluate the performance of MISDER, we compare it to various MD baselines across 2 prevalent datasets, and the experimental results can indicate the effectiveness of our proposed model.

cs.CL↗

arXiv2025

Flat bands on spherical surface: from Landau levels to giant-quantum-number orbitals

Flat bands result in a divergent density of states and high sensitivity to interactions in physical systems. While such bands are well known in systems under magnetic fields, their realization and behavior in zero-field settings remain largely unexplored. Here we compare the behavior of electrons confined to a single flat band on the surface of a sphere to those in flat bands under a magnetic field. The zero-field flat band exhibits an additional C(2) symmetry, which causes electrons to symmetrically cluster on opposite sides of the sphere's center when a trapping potential is introduced, resulting in a unique form of long-range "entanglement". To explore these findings experimentally, we propose a feasible setup to explore the unique properties of zero-field flat bands on spherical substrates, offering a promising route for studying interaction-driven states in spherical geometry without external fields.

cond-mat.str-el↗

arXiv2025

Density matrix representation of hybrid tensor networks for noisy quantum devices

The hybrid tensor network (HTN) method is a general framework allowing for the construction of an effective wavefunction with the combination of classical tensors and quantum tensors, i.e., amplitudes of quantum states. In particular, hybrid tree tensor networks (HTTNs) are very useful for simulating larger systems beyond the available size of the quantum hardware. However, while the realistic quantum states in NISQ hardware are highly likely to be noisy, this framework is formulated for pure states. In this work, as well as discussing the relevant methods, i.e., Deep VQE and entanglement forging under the framework of HTTNs, we investigate the noisy HTN states by introducing the expansion operator for providing the description of the expansion of the size of simulated quantum systems and the noise propagation. This framework enables the general tree HTN states to be explicitly represented and their physicality to be discussed. We also show that the expectation value of a measured observable exponentially vanishes with the number of contracted quantum tensors. Our work will lead to providing the noise-resilient construction of HTN states.

quant-ph↗

arXiv2025

Learning to Drift with Individual Wheel Drive: Maneuvering Autonomous Vehicle at the Handling Limits

Drifting, characterized by controlled vehicle motion at high sideslip angles, is crucial for safely handling emergency scenarios at the friction limits. While recent reinforcement learning approaches show promise for drifting control, they struggle with the significant simulation-to-reality gap, as policies that perform well in simulation often fail when transferred to physical systems. In this paper, we present a reinforcement learning framework with GPU-accelerated parallel simulation and systematic domain randomization that effectively bridges the gap. The proposed approach is validated on both simulation and a custom-designed and open-sourced 1/10 scale Individual Wheel Drive (IWD) RC car platform featuring independent wheel speed control. Experiments across various scenarios from steady-state circular drifting to direction transitions and variable-curvature path following demonstrate that our approach achieves precise trajectory tracking while maintaining controlled sideslip angles throughout complex maneuvers in both simulated and real-world environments.

cs.RO↗