Bienes/productos consumo

HistDiT: A Structure-Aware Latent Conditional Diffusion Model for High-Fidelity Virtual Staining in Histopathology

- Immunohistochemistry (IHC) is essential for assessing specific immune biomarkers like Human Epidermal growth-factor Receptor 2 (HER2) in breast cancer. However, the traditional protocols of obtaining...

Quantum Property Testing for Bounded-Degree Directed Graphs

- We study quantum property testing for directed graphs with maximum in-degree and out-degree bounded by some universal constant $d$. For a proximity parameter $\varepsilon$, we show that any property...

ActiveGlasses: Learning Manipulation with Active Vision from Ego-centric Human Demonstration

- Large-scale real-world robot data collection is a prerequisite for bringing robots into everyday deployment. However, existing pipelines often rely on specialized handheld devices to bridge the...

Real-Time Cross-Layer Semantic Error Correction Using Language Models and Software-Defined Radio

- As Language Models (LMs) advance, Semantic Error Correction (SEC) has emerged as a promising approach for reliable network designs. Yet existing methods prioritize intent over accuracy, falling short...

Towards Real-world Human Behavior Simulation: Benchmarking Large Language Models on Long-horizon, Cross-scenario, Heterogeneous Behavior Traces

- The emergence of Large Language Models (LLMs) has illuminated the potential for a general-purpose user simulator. However, existing benchmarks remain constrained to isolated scenarios, narrow action...

PokeGym: A Visually-Driven Long-Horizon Benchmark for Vision-Language Models

- While Vision-Language Models (VLMs) have achieved remarkable progress in static visual understanding, their deployment in complex 3D embodied environments remains severely limited. Existing...

InstAP: Instance-Aware Vision-Language Pre-Train for Spatial-Temporal Understanding

- Current vision-language pre-training (VLP) paradigms excel at global scene understanding but struggle with instance-level reasoning due to global-only supervision. We introduce InstAP, an...

From Phenomenological Fitting to Endogenous Deduction: A Paradigm Leap via Meta-Principle Physics Architecture

- The essence of current neural network architectures is phenomenological fitting: they learn input-output statistical correlations via massive parameters and data, yet lack intrinsic understanding of...

Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling

- In classical Reinforcement Learning from Human Feedback (RLHF), Reward Models (RMs) serve as the fundamental signal provider for model alignment. As Large Language Models evolve into agentic systems...

Uni-ViGU: Towards Unified Video Generation and Understanding via A Diffusion-Based Video Generator

- Unified multimodal models integrating visual understanding and generation face a fundamental challenge: visual generation incurs substantially higher computational costs than understanding,...

ImVideoEdit: Image-learning Video Editing via 2D Spatial Difference Attention Blocks

- Current video editing models often rely on expensive paired video data, which limits their practical scalability. In essence, most video editing tasks can be formulated as a decoupled spatiotemporal...

FlowGuard: Towards Lightweight In-Generation Safety Detection for Diffusion Models via Linear Latent Decoding

- Diffusion-based image generation models have advanced rapidly but pose a safety risk due to their potential to generate Not-Safe-For-Work (NSFW) content. Existing NSFW detection methods mainly...

HAWK: Head Importance-Aware Visual Token Pruning in Multimodal Models

- In multimodal large language models (MLLMs), the surge of visual tokens significantly increases the inference time and computational overhead, making them impractical for real-time or...

Data Warmup: Complexity-Aware Curricula for Efficient Diffusion Training

- A key inefficiency in diffusion training occurs when a randomly initialized network, lacking visual priors, encounters gradients from the full complexity spectrum--most of which it lacks the capacity...

Anti‐Inflammatory Effects of Hermetia illucens Fermented With Riboflavin‐Producing Leuconostoc mesenteroides KCCM13067P in RAW 264.7 Cells and Colitis Mouse Model

09/04/2026 - Fermentation of Hermetia illucens with riboflavin‐producing Leuconostoc mesenteroides KCCM13067P enhanced vitamin B2 derivatives and bioactive metabolites, including amino acids and amines. The...

SSBI-Free Direct Detection via Phase Diverse of Residual Optical Carrier Enabled by Finite Extinction Ratio IQ Modulator for Datacenter Interconnections

- Cost-effective, low-complexity and spectrally efficient interconnection can offer fundamental guiding law for future datacenter. In this work, we demonstrate a cost-efficient SSBI-free direct...

SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compression

- The growing complexity of neural networks hinders the deployment of distributed machine learning on resource-constrained devices. Split learning (SL) offers a promising solution by partitioning the...

INSPATIO-WORLD: A Real-Time 4D World Simulator via Spatiotemporal Autoregressive Modeling

- Building world models with spatial consistency and real-time interactivity remains a fundamental challenge in computer vision. Current video generation paradigms often struggle with a lack of spatial...

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification

- Kidney stone disease ranks among the most prevalent conditions in urology, and understanding the composition of these stones is essential for creating personalized treatment plans and preventing...

Making MLLMs Blind: Adversarial Smuggling Attacks in MLLM Content Moderation

- Multimodal Large Language Models (MLLMs) are increasingly being deployed as automated content moderators. Within this landscape, we uncover a critical threat: Adversarial Smuggling Attacks. Unlike...

When Market Prices Drive the Load: Modeling, Grid-Security Analysis, and Mitigation of Data Center Workload Scheduling

- Data centers (DCs) are emerging as large, geographically distributed, controllable loads whose participation in electricity markets can significantly affect grid operation, especially when cloud...