国家预印本平台

针对工业互联网时间敏感业务在无源光网络（Passive Optical Network, PON）上行传输中对低时延、低抖动和差异化保障需求，本文提出一种基于流量类型优先级的整数线性规划带宽资源分配方法。首先，在设备侧工业网关引入流量整形机制，将原始工业业务抽象为具有周期、到达时间、传输窗口大小、时延容忍和抖动容忍等参数的工业汇聚流；随后，基于超周期窗口规划思想建立PON上行传输窗口调度模型，并设计ILP-OBJ1、ILP-OBJ2和ILP-OBJ3三种方案进行对比。仿真结果表明，ILP-OBJ2能够有效降低上行调度时延，其中等时流最大时延和平均时延相较ILP-OBJ1分别降低约78.6%和66.9%；ILP-OBJ3通过"先等时流、后循环流"的分阶段调度机制，在中高负载场景下具有更高的调度成功率。结果表明，所提方法能够提升工业互联网PON上行资源分配的确定性和业务适配性。

基于某地区2022-2025年普通国省道交通量数据、第一车道与第二车道路况评定数据及路线年报，采用描述性统计、Pearson相关分析、配对t检验和单因素方差分析（ANOVA），系统分析路况演变规律及交通荷载、路龄、车道位置的影响。结果表明：PQI年均值92.80，整体稳定；货车流量与PQI（r=-0.217）、RQI（r=-0.272）、RDI（r=-0.175）呈极显著负相关（p<0.001）；路龄超过15年后PQI、PCI、RQI显著下降（ANOVA，p<0.001）；第二车道（主要货运车道）PQI均值92.64，第一车道均值94.23，配对差值-1.59分（p<0.001）。货车荷载、路龄增长和车道位置是影响路况的关键因素。

Creating 3D visual illusions, a single 3D mesh that reveals entirely different semantics from various viewing angles, is a fascinating but tough challenge. Existing optimization-based methods are slow and can produce oversaturated colors. In contrast, naive stitching approaches fail to produce geometrically coherent objects. This results in visible unnatural seams and semantic leaks. In this paper, we present a fast and training-free framework for generating text-driven 3D visual illusions. Our approach decouples the generation into two stages. First, we propose a cross-space dual-branch denoising process. This process dynamically decodes 3D latents into voxel space for CLIP-guided orientation alignment and Signed Distance Field (SDF) blending, which ensures seamless geometric fusion. Second, we introduce a view-conditioned texture synthesis module that projects and aggregates view-specific 2D diffusion priors onto the fused geometry. Extensive experiments demonstrate that our method generates highly realistic, dual-semantic 3D illusions in just 3-5 minutes. It significantly outperforms existing methods in geometric integrity, semantic recognizability, and efficiency. Project page: https://siang1105.github.io/JanusMesh.github.io/

Robust robotic manipulation in the real world requires not only an understanding of the current observation, but also memory and dynamics modeling. World action models (WAMs) possess these capabilities by jointly modeling visual foresight and actions conditioned on both current and historical observations, making them a promising paradigm for robotic manipulation. However, existing WAMs face a fundamental trade-off: methods with efficient inference typically condition only on a bounded window of recent observations and therefore struggle in non-Markovian environments, whereas methods that preserve long histories incur time and space costs that grow substantially with sequence length. To address this challenge, we introduce MemoryWAM, a world action model with efficient persistent memory. MemoryWAM uses a hybrid memory design that combines recent frames, event-boundary anchor frames, and compact gist tokens that summarize long-range history. A tailored attention mechanism enables retrieval of both detailed short-term context and compressed long-term context, supporting memory-dependent decision-making with reduced inference latency and GPU memory usage. Across long-horizon, memory-dependent manipulation tasks in both simulation and the real world, MemoryWAM outperforms strong vision-language-action (VLA) and WAM baselines while maintaining favorable computational efficiency.

Long Video Question Answering (LVQA) requires identifying sparse, query-relevant evidence within hours-long untrimmed videos. Existing approaches either process videos densely with large vision-language models (VLMs), incurring prohibitive computational cost, or rely on sparse caption-based reasoning, which often misses temporally localized and motion-centric evidence. We introduce TimeProVe, a cost-efficient hybrid framework for temporally grounded reasoning in long videos. TimeProVe first employs lightweight modules to generate action-grounded answer--evidence hypotheses and subsequently invokes an expensive VLM only for targeted verification. The core of our framework lies in the Action-based Candidate Evidence (ACE) module, which converts temporally localized actions into query-conditioned candidate answers and supporting evidence windows through lightweight LLM reasoning. We further introduce OpenTSUBench (OTB), an open-ended benchmark designed to evaluate temporally grounded reasoning in real-world Activities of Daily Living (ADL) scenarios. Experiments show that TimeProVe outperforms the strongest baseline on OTB by 7.3%, while reducing VLM calls by 75% and inference cost by 93%. Furthermore, without explicit temporal grounding training, TimeProVe achieves competitive performance on Charades-STA, and reaches state-of-the-art results when enhanced with grounding VLMs.