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Dong Liu

Publications and source records attributed to Dong Liu.

At least 55 records · Page 3Linked to original sources
arXiv2026

R&D of cosmic ray detection module with liquid scintillator and wavelength shift fiber

For neutrino physics and rare event searches, background related to cosmic muons poses a notable challenge, and must be identified and rejected. It is also a challenge to control the cost with good performance for a large array of cosmic ray detection. We proposed a cosmic ray detection module with liquid scintillator and wavelength-shifting fibers for its reasonable cost and performances. The results from the measurements of a prototype with Muon indicate that the detector's photoelectron response is good. % comparing to the expectation. The outcomes of this study hold significant potential for applications in cosmic ray observation experiments and underground rare-event detection, providing a viable option for future large-scale observatories. This work highlights the feasibility of liquid scintillator-based detectors in addressing current and emerging challenges in particle physics and astrophysics.

physics.ins-det↗
arXiv2026

Towards Large Model Feature Coding

Large models have delivered remarkable performance across a wide range of perception and generation tasks, yet practical deployment is increasingly constrained by computational and memory budgets, as well as privacy requirements. Split execution alleviates these constraints by partitioning computation across devices, but it inevitably introduces intensive transmission and storage of intermediate features. Unlike conventional feature coding for CNNs that typically targets homogeneous spatial activation maps, modern large models generate heterogeneous features with varying statistical distributions and compression tolerances, e.g., multi-level/multi-modal representations and autoregressive context caches. These characteristics necessitate treating large model feature coding (LaMoFC) as a fundamental system component and call for a systematic evaluation framework. In this paper, we present a comprehensive benchmark and evaluation framework for LaMoFC. We first build the feature dataset LaMoFCBench, covering diverse task requirements across 4 categories and 16 scenarios while integrating widelyadopted architectures and various split-computing settings. We then specify representative split points according to practical application scenarios to extract intermediate features, establishing a unified pipeline for fair and reproducible comparisons. Finally, we benchmark mainstream universal feature codecs, exposing the profound misalignment between existing coding paradigms and the heterogeneous nature of large model features. These findings reveal that LaMoFC demands a fundamental departure from existing paradigms, and LaMoFCBench provides the shared empirical foundation to drive this transition. The data and code will be available at https://github.com/lartpang/LaMoFCBench.

cs.CV↗
arXiv2026

PiKV: KV Cache Management System for Mixture of Experts

As large-scale language models continue to scale up in both size and context length, the memory and communication cost of key-value (KV) cache storage has become a major bottleneck in multi-GPU and multi-node inference. While MoE-based architectures sparsify computation across experts, the corresponding KV caches remain dense and globally synchronized, resulting in significant overhead. We introduce \textbf{PiKV}, a parallel and distributed KV cache serving framework tailored for MoE architecture. PiKV leverages \textit{expert-sharded KV storage} to partition caches across GPUs, \textit{PiKV routing} to reduce token-to-KV access, and a \textit{PiKV Scheduling} to adaptively retain query-relevant entries. To further reduce memory usage, PiKV integrates \textit{PiKV Compression} modules the caching pipeline for acceleration. PiKV is recently publicly available as an open-source software library: \href{https://github.com/NoakLiu/PiKV}{https://github.com/NoakLiu/PiKV}. PiKV is still a living project, aiming to become a comprehesive KV Cache management system for MoE Architectures.

cs.DC↗
arXiv2026

TVRN: Invertible Neural Networks for Compression-Aware Temporal Video Rescaling

To fit diverse display and bandwidth constraints, high-frame-rate videos are temporally downscaled to low-frame-rate (LFR) and later upscaled, requiring joint optimization for effective frame-rate rescaling. However, existing methods typically link the two operations via training objectives, without fully exploiting their reciprocal nature, which may cause high-frequency information loss. Moreover, they overlook the impact of lossy codecs on LFR videos, limiting real-world applicability. In this work, we propose an end-to-end framework for compression-aware frame-rate rescaling, named TVRN. To regularize high-frequency information lost during frame-rate downscaling, TVRN adopts an invertible architecture that combines a Multi-Input Multi-Output Temporal Wavelet Transform with a high-frequency reconstruction module. To enable end-to-end training through non-differentiable lossy codecs, we design a surrogate network that approximates their gradients. Finally, to improve robustness under various compression levels, we extend TVRN to an asymmetric architecture by incorporating compression-aware features learned via a learning-to-rank strategy. Extensive experiments show that TVRN outperforms existing methods in reconstruction quality under industrial video compression settings. Source code is publicly available at https://github.com/fengxinmin/TVRN_public.

eess.IV↗
arXiv2026

XWOD: A Real-World Benchmark for Object Detection under Extreme Weather Conditions

Autonomous driving and intelligent transportation systems remain vulnerable under extreme weather. The U.S. Federal Highway Administration reports that roughly 745,000 crashes and 3,800 fatalities per year are weather-related, and recent regulatory investigations have examined failures of Level-2/3 driving systems under reduced-visibility conditions. However, datasets commonly used to evaluate weather robustness remain limited in scale, diversity, and realism. In this paper, we introduce XWOD (Extreme Weather Object Detection), a large-scale real-world traffic-object detection benchmark containing 10,010 images and 42,924 bounding boxes across seven extreme weather conditions: rain, snow, fog, haze/sand/dust, flooding, tornado, and wildfire. The dataset covers six traffic-object categories, including car, person, truck, motorcycle, bicycle, and bus. XWOD extends the weather taxonomy from one to seven conditions, and is the first to cover the emerging class of climate-amplified hazards, such as flooding, tornado, and wildfire. To evaluate the quality of our data, we train standard YOLO-family detectors on XWOD and test them zero-shot on external weather benchmarks, achieving mAP$_{50}$ scores of 63.00% on RTTS, 59.94% on DAWN, and 61.12% on WEDGE, compared with the corresponding published YOLO-based baselines of 40.37%, 32.75%, and 45.41%, respectively, representing relative improvements of 56%, 83%, and 35%. These cross-dataset results show that XWOD provides a strong source domain for learning weather-robust traffic perception. We release the dataset, splits, baseline weights, and reproducible evaluation code under a research-use license.

cs.CV↗
arXiv2026

ChladniSonify: A Visual-Acoustic Mapping Method for Chladni Patterns in New Media Art Creation

In new media art creation, the mapping between vision and hearing is often subjective. As a classic carrier of sound visualization, Chladni patterns have great potential in building audio-visual mapping mechanisms. However, existing tools face pain points: high technical barriers for simulation, offline computing failing real-time interaction, and uncontrollable mapping rules in general sonification tools. To address these, this paper proposes ChladniSonify, a real-time visual-acoustic mapping method for Chladni patterns. Based on Kirchhoff-Love plate theory, we build a paired dataset via numerical programming and calibrate it using ANSYS finite element simulation. Focusing on the slender nodal lines of Chladni patterns, we adopt a lightweight CNN with CBAM to achieve high-precision, low-latency pattern classification. Finally, we build an end-to-end system in Python and Max/MSP, mapping recognized patterns to corresponding sine wave frequencies. Results show the system has excellent usability: the classification module achieves 99.33% accuracy on the test set with 7.03 ms inference latency; the mapped frequency matches the theoretical value with zero deviation; the average end-to-end latency is under 50 ms, meeting real-time interactive needs. This work provides a reproducible engineering prototype for Chladni audio-visual art creation.

cs.SD↗
arXiv2026

Band alignment of grafted diamond/GaN p-n heterojunctions interfaced with ALD Al2O3 and SiNx/Al2O3

Diamond and gallium nitride are complementary semiconductors for forming p-n junctions because of their respective doping limitations. Understanding the band alignment of grafted diamond/GaN heterojunctions is therefore essential for optimizing diode performance. In this study, the band alignment of diamond/Al2O3/GaN and diamond/Al2O3/SiNx/GaN heterostructures was determined by X-ray photoelectron spectroscopy. Both structures exhibit type-II band alignment, but with different band offsets. The band offsets of the diamond/Al2O3/SiNx/GaN heterojunction are larger by 0.42 eV than those of diamond/Al2O3/GaN. This difference is attributed to a modification of the interfacial electrostatic potential, which may arise from a reduced density of positive fixed charges in the interfacial dielectric near the diamond/Al2O3 interface after insertion of the SiNx layer. These results demonstrate that interfacial-layer engineering provides an effective strategy for tailoring the band alignment of grafted diamond/GaN heterojunctions, offering guidance for the design of p-n diodes with tunable rectifying characteristics.

cond-mat.mtrl-sci↗
arXiv2026

Community-to-Vehicle: Integrating Electric Vehicles into Energy Communities -- A Swiss Case Study

The institutional separation between local energy communities and public electric vehicle (EV) charging limits the efficient use of locally generated renewable energy. This paper introduces the concept of community-to-vehicle (C2V) as an institutional design mechanism to bridge this gap by enabling EV charging within the community boundary, where locally generated photovoltaic (PV) surplus is preferentially allocated and offered to external users at a community charging price. Building on the recently introduced local electricity community framework in Switzerland, we design scenarios that capture the transition from full separation to coordinated EV charging and evaluate their impacts on EV users and the community. The results show that C2V significantly improves local PV utilization and enhances economic performance, reducing EV charging costs relative to commercial alternatives while generating additional revenue streams for the community. These findings highlight the potential of C2V as a practical, implementable mechanism for integrating EV charging into local energy communities, providing a clear pathway for adopting coordinated community-EV interaction within existing regulatory frameworks.

eess.SY↗
arXiv2026

Development and Testing of a Modular Large-Area Cosmic Ray Telescope Using Scintillator-Fiber Hybrid Design for Millimeter-Level Muon Tracking

Cosmic-ray muons, owing to their high penetration power and abundance, have been widely employed as a natural probe in experimental particle physics. We developed a meter-scale cosmic-ray muon telescope, consisting of two parallel super-layers (1 m $\times$ 1 m) separated vertically by one meter. A super-layer is composed of two orthogonal detection layers, of which each consists of eighteen modules arranged in parallel and packed closely together. A module consists of a plastic scintillating bar precisely aligned and stacked on top of an underlying scintillating fiber mat in which fibers are arranged in a row of bundles. For a detection layer, each scintillator bar is coupled to a PMT while fiber bundles at the same position within all modules are coupled to a single PMT. Signals from scintillating bars and fibers are combined together to determine hit positions. With this detection scheme, the telescope can meet the requirement of spatial resolution and reduce the number of readout electronic channels. This article presents the comprehensive development of the telescope, encompassing its geometric design, data acquisition system, and performance evaluation. Experimental results show that the telescope achieves a position resolution better than 2 mm and an overall detection efficiency of $\sim$85%. The innovative design keeps the manufacturing cost low while maintaining high spatial resolution and detection efficiency.

hep-ex↗
arXiv2026

A Proof-of-Concept Study of Multitask Learning for Cranial Synthetic CT Generation Across Heterogeneous MRI Field Strengths

Accurate synthesis of computed tomography (CT) images from magnetic resonance imaging (MRI) is clinically valuable for cranial applications such as attenuation correction, radiotherapy planning, and image-guided interventions. However, heterogeneity across MRI field strengths and acquisition protocols limits the generalizability of existing methods. In this study, we formulate cranial CT synthesis as a modular, structurally coupled problem and propose a deep learning framework to improve robustness across heterogeneous MRI conditions. The model is designed to adapt to variations in field strength and imaging protocols while preserving anatomical consistency. Experiments on multi-site datasets demonstrate improved performance and generalization compared with conventional approaches. The proposed method enables reliable CT synthesis across heterogeneous MRI settings, supporting broader clinical translation.

eess.IV↗
arXiv2026

Thoughts-as-Planning: Latent World Models for Chain-of-Thoughts Optimization via Reinforcement Planning

The success of large language models (LLMs) across diverse NLP tasks has elevated the importance of reasoning chain optimization as a critical step in aligning model behavior with task objectives. Existing reasoning chain tuning methods often rely on black-box heuristics or gradient-free search, which lack interpretability, generalization, and sample efficiency. In this work, we introduce \textbf{Thoughts-as-Planning}, a novel framework that formalizes reasoning chain optimization as a sequential decision-making process over a latent semantic space. We model the LLM as a partially observable environment and learn a latent world model that simulates the effect of reasoning chain edits on downstream outputs. A proximity-preserving embedding space is constructed to encode reasoning chain-response dynamics, enabling planning via gradient descent or reinforcement learning. Our method supports multi-scale abstraction, allowing reasoning chain edits at token, segment, and instruction levels to be integrated into a unified planner. Through extensive experiments on language understanding and generation tasks, we demonstrate that Thoughts-as-Planning outperforms state-of-the-art reasoning chain tuning baselines in efficiency, robustness, and generalization, while offering interpretability through its structured planning trajectory. Our code is available at https://github.com/FastLM/Thoughts-as-Planning.

cs.CL↗
arXiv2026

Randomized Neural Networks for Integro-Differential Equations with Application to Neutron Transport

Integro-differential equations arise in a wide range of applications, including transport, kinetic theory, radiative transfer, and multiphysics modeling, where nonlocal integral operators couple the solution across phase space. Such nonlocality often introduces dense coupling blocks in deterministic discretizations, leading to increased computational cost and memory usage, while physics-informed neural networks may suffer from expensive nonconvex training and sensitivity to hyperparameter choices. In this work, we present randomized neural networks (RaNNs) as a mesh-free collocation framework for linear integro-differential equations. Because the RaNN approximation is intrinsically dense through globally supported random features, the nonlocal integral operator does not introduce an additional loss of sparsity, while the approximate solution can still be represented with relatively few trainable degrees of freedom. By randomly fixing the hidden-layer parameters and solving only for the linear output weights, the training procedure reduces to a convex least-squares problem in the output coefficients, enabling stable and efficient optimization. As a representative application, we apply the proposed framework to the steady neutron transport equation, a high-dimensional linear integro-differential model featuring scattering integrals and diverse boundary conditions. Extensive numerical experiments demonstrate that, in the reported test settings, the RaNN approach achieves competitive accuracy while incurring substantially lower training cost than the selected neural and deterministic baselines, highlighting RaNNs as a robust and efficient alternative for the numerical simulation of nonlocal linear operators.

math.NA↗
arXiv2026

Black holes surrounded by dark matter spike: Spacetime metrics and gravitational wave ringdown waveforms

The supermassive black holes at the centers of galaxies may be surrounded by dark matter spike, which could leave detectable imprints on the gravitational wave signals they emit. In this work, combining the mass model of M87, we present black hole analytical solutions in dark matter spike from the Tolman-Oppenheimer-Volkof equations. Meanwhile, the gravitational waves emitted at the ringdown phase of the black holes under the axial gravitational perturbation is investigated, and compare them with the Schwarzschild black hole. The main research methods are time domain integration method and continued fraction method. Besides, the impact of the different dark matter parameters on the quasinormal frequencies is investigated and its detectability on these impacts based on the space-based detectors. Our results indicate that the impacts of dark matter spike on the quasinormal frequencies of black holes can reach up to the order of $10^{-4}$. These results may provide some help in further exploring the impact of dark matter spike on the black holes and their related gravitational wave phenomena.

gr-qc↗
arXiv2026

RoboCOIN: An Open-Sourced Bimanual Robotic Data Collection for Integrated Manipulation

Despite the critical role of bimanual manipulation in endowing robots with human-like dexterity, large-scale and diverse datasets remain scarce due to the significant hardware heterogeneity across bimanual robotic platforms. To bridge this gap, we introduce RoboCOIN, a large-scale multi-embodiment bimanual manipulation dataset comprising over 180,000 demonstrations collected from 15 distinct robotic platforms. Spanning 16 diverse environments-including residential, commercial, and industrial settings-the dataset features 421 bimanual tasks systematically categorized by 39 bimanual collaboration actions and 432 objects. A key innovation of our work is the hierarchical capability pyramid, which provides granular annotations ranging from trajectory-level concepts to segment-level subtasks and frame-level kinematics. Furthermore, we present CoRobot, an efficient data processing pipeline powered by the Robot Trajectory Markup Language (RTML), designed to facilitate quality assessment, automated annotation, and unified multi-embodiment and data management. Extensive experiments demonstrate the effectiveness of RoboCOIN in enhancing the performance of various bimanual manipulation models across a wide spectrum of robotic embodiments. The entire dataset and codebase are fully open-sourced, providing a valuable resource for advancing research in bimanual and multi-embodiment manipulation.

cs.RO↗
arXiv2026

A Diamagnetic, Light-Driven Tesla Engine Based on a Mechanically Displaced, Magnetically Levitated Graphene Disk

Ferromagnetic materials are widely used in Tesla thermomagnetic engines, whereas diamagnetic counterparts have remained unexplored. Here, we demonstrate the first diamagnetic Tesla engine by exploiting the strong diamagnetism of graphene. A graphene disk, fabricated by stacking graphene sheets, serves as the engine wheel. We first show that the conventional Tesla engine design using a permanent magnet placed near the disk edge to create unbalanced thermomagnetic forces under asymmetric local heating fails to generate rotation. We achieve stable operation by laterally displacing the levitated disk from equilibrium, creating a strong restoring force that drives rotation under light excitation. Calculations and measurements establish the displacement-dependent force, with an optimal offset of 0.8 mm yielding speeds up to 2000 rpm under laser heating and 1000 rpm under direct sunlight. Adding vanes allows the disk to function as a gear, powering a graphene vehicle and transferring energy to another disk. This design utilizes the strong and anisotropic diamagnetism of graphene and paves the way for light-powered sensors, actuators, and micro-vehicles.

physics.app-ph↗
arXiv2026

Classification and design of two-dimensional altermagnets

Altermagnets -- newly identified collinear antiferromagnets -- carry zero net moment with non-relativistic, spin-polarized bands, distilling the best of ferromagnets and antiferromagnets into a single spintronic platform. Shrunking to the two-dimensional limit, they inherit the tunability of two-dimensional crystals while adding symmetry-protected spin splitting, a combination now driving intense experimental interest. Here, we review the symmetry classification of two-dimensional altermagnets based on spin-group theory and survey the growing list of candidate materials, emphasizing those with large spin splitting for experimental realization. We then examine strategies for engineering two-dimensional altermagnetism. This Review aims to consolidate theoretically proposed candidate materials and realization strategies for two-dimensional altermagnets, providing insights for future experimental efforts in this emerging field.

cond-mat.mtrl-sci↗
arXiv2026

Optical Appearance and Ringdown of Black Holes in a Kalb Ramond Field Coupled to Perfect Fluid Dark Matter

This paper investigates the optical and dynamical properties of a static spherically symmetric black hole in the presence of a Kalb--Ramond (KR) field coupled to perfect fluid dark matter (PFDM). We analyze the effects of the Lorentz-violating parameter $α$ and the dark matter parameter $λ$ on photon trajectories and their observational signatures in the strong-gravity regime. Furthermore, we study the quasinormal mode spectrum under scalar, electromagnetic, and gravitational perturbations, examining how the model parameters influence the characteristic oscillation frequencies and damping rates. In particular, the interplay between the effective potential structure and perturbative dynamics is clarified, and it is found that, within the validity of the eikonal approximation, the quasinormal modes of the black hole considered here exhibit good agreement with the properties of null geodesics. Our results show that the model parameters significantly affect both the optical appearance of the black hole and the dynamical features of the ringdown phase, providing potential observational constraints on Lorentz-violating effects and dark matter environments in strong-field regimes.

gr-qc↗
arXiv2026

FastCache: Fast Caching for Diffusion Transformer Through Learnable Linear Approximation

Diffusion Transformers (DiT) are powerful generative models but remain computationally intensive due to their iterative structure and deep transformer stacks. To alleviate this inefficiency, we propose \textbf{FastCache}, a hidden-state-level caching and compression framework that accelerates DiT inference by exploiting redundancy within the model's internal representations. FastCache introduces a dual strategy: (1) a spatial-aware token selection mechanism that adaptively filters redundant tokens based on hidden-state saliency, and (2) a transformer-level cache that reuses latent activations across timesteps when changes fall below a predefined threshold. These modules work jointly to reduce unnecessary computation while preserving generation fidelity through learnable linear approximation. Theoretical analysis shows that FastCache maintains bounded approximation error under a hypothesis-testing-based decision rule. Empirical evaluations across multiple DiT variants demonstrate substantial reductions in latency and memory usage, achieving the best generation quality among existing cache methods, as measured by FID and t-FID. To further improve the speedup of FastCache, we also introduce a token merging module that merges redundant tokens based on k-NN density. Code is available at \href{https://github.com/NoakLiu/FastCache-xDiT}{https://github.com/NoakLiu/FastCache-xDiT}.

cs.LG↗