March 2026

arXiv:2603.06674v1 Announce Type: new Abstract: High-quality scientific illustrations are essential for communicating complex scientific and technical concepts, yet existing automated systems remain limited in editability, stylistic controllability, and efficiency. We present AutoFigure-Edit, an end-to-end system that generates fully editable scientific illustrations from long-form scientific text while enabling flexible style adaptation through user-provided reference images. By combining long-context understanding, reference-guided styling, and native SVG editing, it enables efficient creation and refinement of high-quality scientific illustrations. To facilitate further progress […]

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arXiv:2603.06673v1 Announce Type: new Abstract: Spectroscopic imaging (SI) has become central to heritage science because it enables non-invasive, spatially resolved characterisation of materials in artefacts. In particular, attenuated total reflection Fourier transform infrared microscopy (ATR-$mu$FTIR) is widely used to analyse painting cross-sections, where a spectrum is recorded at each pixel to form a hyperspectral image (HSI). Interpreting these data is difficult: spectra are often mixtures of several species in heterogeneous, multi-layered and degraded samples, and current practice still […]

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arXiv:2603.06672v1 Announce Type: new Abstract: Semantic noise initialization has been reported to improve robustness and controllability in image diffusion models. Whether these gains transfer to text-to-video (T2V) generation remains unclear, since temporal coupling can introduce extra degrees of freedom and instability. We benchmark semantic noise initialization against standard Gaussian noise using a frozen VideoCrafter-style T2V diffusion backbone and VBench on 100 prompts. Using prompt-level paired tests with bootstrap confidence intervals and a sign-flip permutation test, we observe a […]

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arXiv:2603.06671v1 Announce Type: new Abstract: Financial risk detection in Enterprise Resource Planning (ERP) systems is an important but underexplored application of machine learning. Published studies in this area tend to suffer from vague dataset descriptions, leakage-prone pipelines, and evaluation practices that inflate reported performance. This paper presents a rebuilt experimental framework for ERP financial risk detection using ensemble machine learning. The risk definition is hybrid, covering both procurement compliance anomalies and transactional fraud. A composite benchmark called ERP-RiskBench […]

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arXiv:2603.06670v1 Announce Type: new Abstract: Millimeter-wave (mmWave) Radar–Camera fusion improves perception under adverse illumination and weather, but its performance is sensitive to Radar–Camera extrinsic calibration: residual misalignment biases Radar-to-image projection and degrades cross-modal aggregation for downstream 2D detection. Existing calibration and auto-calibration methods are mainly developed for road and urban scenes with abundant structures and object constraints, whereas water-surface environments feature large textureless regions, sparse and intermittent targets, and wave-/specular-induced Radar clutter, which weakens explicit object-centric matching. We […]

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arXiv:2603.06669v1 Announce Type: new Abstract: Modern edge AI applications increasingly rely on microservice architectures that integrate both AI services and conventional microservices into complex request chains with stringent latency requirements. Effectively orchestrating these heterogeneous services is crucial for ensuring low-latency performance, yet remains challenging due to their diverse resource demands and strong operational interdependencies under resource-constrained edge environments. In particular, frequent interactions between services tightly couple deployment and routing decisions, yet existing approaches optimize them in isolation, leading […]

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arXiv:2603.06668v1 Announce Type: new Abstract: The stability of Internet services is persistently challenged by the escalating scale of volumetric TCP SYN floods, as conventional defenses like SYN Cookies fail by exacerbating bandwidth depletion under modern attacks. This paper introduces SDN-SYN PoW, a novel defense architecture that synergizes non-interactive Proof-of-Work with a Software-Defined Networking (SDN) control plane, an approach particularly effective for securing the network edge in modern SD-WAN deployments. The core innovation is its ability to perform global […]

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arXiv:2603.06667v1 Announce Type: new Abstract: We design and implement on Radio Frequency System-on-Chip (RFSoC) software-defined radios (SDRs) a complete-graph network of four unmanned aerial vehicles and demonstrate real-time 4K video streaming over twelve always-on 2×2 multiple-input multiple-output (MIMO) links. The testbed operates at an aggregate network throughput of approximately 1.2 Gbps (i.e., 12 links of 99.84 Mbps) across a shared bandwidth of 200 MHz. To the best of our knowledge, this is the first demonstration of low-latency digitally […]

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arXiv:2603.06666v1 Announce Type: new Abstract: Autoregressive (AR) image models have recently demonstrated remarkable generative capability, but their sequential nature results in significant inference latency. Existing training-free acceleration methods typically verify tokens independently, overlooking the strong co-occurrence patterns between adjacent visual tokens. This independence assumption often leads to contextual inconsistency and limits decoding efficiency. In this work, we introduce a novel training-free acceleration framework that performs phrase-level speculative verification, enabling the model to jointly validate multiple correlated tokens within […]

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arXiv:2603.06665v1 Announce Type: new Abstract: Large vision-language models (VLMs) often benefit from chain-of-thought (CoT) prompting in general domains, yet its efficacy in medical vision-language tasks remains underexplored. We report a counter-intuitive trend: on medical visual question answering, CoT frequently underperforms direct answering (DirA) across general-purpose and medical-specific models. We attribute this to a emph{medical perception bottleneck}: subtle, domain-specific cues can weaken visual grounding, and CoT may compound early perceptual uncertainty rather than correct it. To probe this hypothesis, […]

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