We introduce IndEgo, a multimodal egocentric and exocentric dataset addressing common industrial tasks, including assembly/disassembly, logistics and organisation, inspection and repair, woodworking, and others. The dataset contains 3,460 egocentric recordings (approximately 197 hours), along with 1,092 exocentric recordings (approximately 97 hours). A key focus of the dataset is collaborative work, where two workers jointly perform cognitively and physically intensive tasks. The egocentric recordings include rich multimodal data and added context via eye gaze, narration, sound, motion, and others. We provide detailed annotations (actions, summaries, mistake annotations, narrations), metadata, processed outputs (eye gaze, hand pose, semi-dense point cloud), and benchmarks on procedural and non-procedural task understanding, Mistake Detection, and reasoning-based Question Answering.

Further experimental details are described in Ref. [4]. Each spatiotemporal stimulus spanned over 400 ms corresponding to the retinal integration timescale. Figure 1: (a) The log-likelihood of empirical data for each PMF averaged over cells. Black line is the identity line. The central 20 20 arrays are shown.

We propose Differentiable Temporal Logic (DTL), a model-agnostic framework that introduces temporal constraints to deep networks. DTL treats the outputs of a network as a truth assignment of a temporal logic formula, and computes a temporal logic loss reflecting the consistency between the output and the constraints.

Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly(i.e.,incombinationwithotherdata)fromthedataset?

Why Are Some Women Training for Pregnancy Like It's a Marathon? A growing legion of "zero trimester" influencers are convincing followers that healthy pregnancies are a choice--and that raw milk, watching sunsets, and pricey specialized courses can help. Three years ago, Esther Rohr and her husband decided to start thinking about pregnancy. The 26-year-old Oregon-based wedding photographer made small but intentional lifestyle changes--going to bed earlier, drinking more water and less alcohol, dialing in her fitness, loading up on protein, and taking supplements like beef organ capsules and Vitamin D3. They started charging their phones in the kitchen for better sleep and unplugging their Wi-Fi at night, because her research suggested it might affect cellular health. Concerned about their exposure to reproductive toxins, Rohr began the slow, painstaking task of swapping out all their synthetic workout clothes, nonstick pans, and scented personal care products that might contain phthalates or other endocrine-disrupting chemicals. She bought an air purifier and hopes to eventually replace their LED bulbs with incandescents, because she worries they might be affecting her circadian rhythm.

Quantum machine learning offers a promising pathway for enhancing stock market prediction, particularly under complex, noisy, and highly dynamic financial environments. However, many classical forecasting models struggle with noisy input, regime shifts, and limited generalization capacity. To address these challenges, we propose a Quantum Temporal Convolutional Neural Network (QTCNN) that combines a classical temporal encoder with parameter-efficient quantum convolution circuits for cross-sectional equity return prediction. The temporal encoder extracts multi-scale patterns from sequential technical indicators, while the quantum processing leverages superposition and entanglement to enhance feature representation and suppress overfitting. We conduct a comprehensive benchmarking study on the JPX Tokyo Stock Exchange dataset and evaluate predictions through long-short portfolio construction using out-of-sample Sharpe ratio as the primary performance metric. QTCNN achieves a Sharpe ratio of 0.538, outperforming the best classical baseline by approximately 72\%. These results highlight the practical potential of quantum-enhanced forecasting model, QTCNN, for robust decision-making in quantitative finance.

Training Vision-Language-Action (VLA) models for generalist robots typically requires large-scale real-world robot data, which is expensive and time-consuming to collect. The inefficiency of physical data collection severely limits the scalability, and generalization capacity of current VLA systems. To address this challenge, we introduce GigaBrain-0, a novel VLA foundation model empowered by world model-generated data (e.g., video generation, real2real transfer, human transfer, view transfer, sim2real transfer data). By leveraging world models to generate diverse data at scale, GigaBrain-0 significantly reduces reliance on real robot data while improving cross-task generalization. Our approach further improves policy robustness through RGBD input modeling and embodied Chain-of-Thought (CoT) supervision, enabling the model to reason about spatial geometry, object states, and long-horizon dependencies during task execution. This leads to substantial gains in real-world performance on dexterous, long-horizon, and mobile manipulation tasks. Extensive experiments demonstrate that GigaBrain-0 achieves superior generalization across variations in appearances (e.g., textures, colors), object placements, and camera viewpoints. Additionally, we present GigaBrain-0-Small, an optimized lightweight variant designed to run efficiently on devices such as the NVIDIA Jetson AGX Orin.