IGAF: Incremental Guided Attention Fusion for Depth Super-Resolution.

Accurate depth estimation is crucial for many fields, including robotics, navigation, and medical imaging. However, conventional depth sensors often produce low-resolution (LR) depth maps, making detailed scene perception challenging. To address this, enhancing LR depth maps to high-resolution (HR) ones has become essential, guided by HR-structured inputs like RGB or grayscale images.

Nature of Reactive Sites in TS-1 from N Solid-State NMR and Ti K-Edge X-Ray Absorption Spectroscopic Signatures Upon Pyridine Adsorption.

Ti-containing zeotypes, notably titanosilicalite-1 (TS-1), are prominent examples of heterogeneous catalysts that have found applications in selective oxidation processes with hydrogen peroxide. Despite extensive characterization studies including using various probe molecules to interrogate the nature and the local environment of Ti sites, their detailed structure (as well as reactivity) remains elusive. Here, we demonstrate that using low temperature N magic angle spinning (MAS) ssNMR spectroscopy of adsorbed pyridine on TS-1 combined with Ti K-edge XANES on a range of samples (dehydrated, hydrated, contacted with HO and pyridine) provides unique information regarding the Ti sites, highlighting their reactivity and dynamic nature.

AI-Enabled Sensor Fusion of Time-of-Flight Imaging and mmWave for Concealed Metal Detection.

In the field of detection and ranging, multiple complementary sensing modalities may be used to enrich information obtained from a dynamic scene. One application of this sensor fusion is in public security and surveillance, where efficacy and privacy protection measures must be continually evaluated. We present a novel deployment of sensor fusion for the discrete detection of concealed metal objects on persons whilst preserving their privacy.

Computational limits to the legibility of the imaged human brain.

Our knowledge of the organisation of the human brain at the population-level is yet to translate into power to predict functional differences at the individual-level, limiting clinical applications and casting doubt on the generalisability of inferred mechanisms. It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal. Here we comprehensively investigate the resolvability of such patterns with data and compute at unprecedented scale.