国家预印本平台

抑制控制能力受损是抑郁症的发病机制之一，但这种损伤是情绪特异性的还是普遍性的尚不明确。本研究采用激活似然估计法(ALE)，整合任务态脑成像研究，分析和比较情绪性与非情绪性抑制控制任务下重性抑郁(Major Depressive Disorder, MDD)患者与健康对照组的脑激活差异。经文献检索与筛选，共纳入19项研究，133个有效坐标。结果发现：(1)在情绪性抑制控制任务中，MDD患者右侧额中回出现补偿性激活，左侧额中回和右侧额下回激活减弱；(2)在非情绪性抑制控制任务中，未发现跨研究一致的差异脑区。该结果提示，MDD患者的抑制控制能力损伤可能是情绪特异性的，损伤脑区主要集中于前额叶。研究结果为探索抑制控制在抑郁症发生和维持中的作用提供了方向性启发，并为开发基于抑制控制的靶向干预提供了参考。

Inverse graphics is a longstanding and highly underconstrained problem that seeks to reconstruct images as editable 3D scenes which can be rendered, relit, and manipulated. In this work, we investigate whether pretrained vision-language models (VLMs) can perform executable inverse graphics directly from a single image by reconstructing a scene as an editable Blender program, without relying on specialized 2D or 3D foundation models, differentiable rendering, or multi-view supervision. We introduce Staged Executable Inverse Graphics (SEIG), an agentic framework that reconstructs a 3D scene from a single image by progressively refining scene factors including geometry, materials, composition, and lighting directly in executable Blender code space. We evaluate our framework across diverse scenes using a range of reconstruction metrics spanning pixel-level, perceptual, and semantic fidelity. Our experiments show that staged reconstruction substantially improves reconstruction fidelity, highlighting the importance of task decomposition for executable inverse graphics with general-purpose VLMs. Finally, we showcase various downstream applications enabled by the reconstructed editable Blender scenes.

Muscovite mica is a translucent, layered silicate mineral whose basal cleavage, low radiogenic background, gigayear exposures, and demonstrated track retention over geological timescales make it a compelling target for rare particle searches. In this work, we develop a new framework for detecting heavy composite dark matter using muscovite mica as a paleodetector. We model melt track formation by heavy composite dark matter transiting through mica using a Sedov-Taylor thermal spike formalism, and validate the sub-micron regime with SRIM/TRIM simulations of nuclear recoil cascades, which also calibrate the phonon efficiency governing local energy deposition. We demonstrate a novel readout method using rapid X-ray fluorescence mapping with a copper backing contrast technique, capable of identifying micron-scale damage features in cleaved mica sheets over macroscopic scan areas, and calibrate the minimum detectable track size using laser-ablated defect regions. We present projected sensitivities for opaque and diffuse composite dark matter, including a sub-melt hole-channel detection mode for large composites substantially attenuated by overburden. We also revisit prior dark matter exclusions from etched mica searches, identifying shortcomings that compromise the robustness of these constraints.

Recent multimodal large language models have demonstrated strong reasoning ability, yet their reliability as automated evaluators remains limited by a critical weakness: when visual evidence conflicts with textual cues, MLLM judges tend to reward plausible narratives over perceptually correct answers. We identify and systematically analyze this phenomenon, which we term Perceptual Judgment Bias. Through controlled visual perturbations, existing multimodal judges frequently anchor on the response text instead of their own visual perception, leading to inconsistent and non-verifiable evaluations. To address this issue, we introduce the Perceptually Perturbed Judgment Dataset, which constructs minimally edited counterfactual responses that isolate perceptual errors and enable verifiable supervision. Building on this dataset, we develop a unified training framework that combines a structured GRPO-based reward with a batch-ranking objective, achieving coherent global ordering without explicit pairwise labels. Experiments across diverse MLLM-as-a-Judge benchmarks show that our approach substantially improves perceptual fidelity, ranking coherence, and alignment with human evaluation. Our results establish a scalable and generalizable pathway for training multimodal judges that are perceptually grounded, interpretable, and robust to visual-reasoning conflicts.

Scaling robot learning requires large-scale, diverse demonstrations, yet real-world data collection via teleoperation remains prohibitively expensive and time-consuming. While video diffusion models offer a promising avenue for data scaling, existing generative approaches are often limited to superficial visual augmentation, or suffer from embodiment hallucinations that yield physically infeasible motions. We present a generalizable embodiment-centric world model that achieves scalable data generation by synthesizing photorealistic demonstrations with novel objects, in novel scenes, and from novel viewpoints. Our approach anchors generation to rendered robot motion while conditioning on explicit scene and object priors, effectively decoupling trajectory execution from environment synthesis. This formulation has the potential to unlock two powerful data scaling capabilities: (1) retrieval and rebirth, which repurposes existing trajectories into entirely new contexts without new motion data; and (2) prop-free teleoperation, where operators manipulate empty air and the model hallucinates the target objects and scene afterwards, eliminating reset time. We demonstrate with real-world experiments that our generated data consistently improves downstream policy performance and significantly reduces real-world data requirements across diverse manipulation tasks.