Second row: images reconstructed by AI based on brain recordings from a macaque. Artificial intelligence systems can now create remarkably accurate reconstructions of what someone is looking at based on recordings of their brain activity. These reconstructed images are greatly improved when the AI learns which parts of the brain to pay attention to. "As far as I know, these are the closest, most accurate reconstructions," says Umut Güçlü at Radboud University in the Netherlands. How this moment for AI will change society forever (and how it won't) Güçlü's team is one of several around the world using AI systems to work out what animals or people are seeing from brain recordings and scans. In one previous study, his team used a functional MRI (fMRI) scanner to record the brain activity of three people as they were shown a series of photographs.

Raman spectroscopy, a photonic modality based on the inelastic backscattering of coherent light, is a valuable asset to the intraoperative sensing space, offering non-ionizing potential and highly-specific molecular fingerprint-like spectroscopic signatures that can be used for diagnosis of pathological tissue in the dynamic surgical field. Though Raman suffers from weakness in intensity, Surface-Enhanced Raman Spectroscopy (SERS), which uses metal nanostructures to amplify Raman signals, can achieve detection sensitivities that rival traditional photonic modalities. In this study, we outline a robotic Raman system that can reliably pinpoint the location and boundaries of a tumor embedded in healthy tissue, modeled here as a tissue-mimicking phantom with selectively infused Gold Nanostar regions. Further, due to the relative dearth of collected biological SERS or Raman data, we implement transfer learning to achieve 100% validation classification accuracy for Gold Nanostars compared to Control Agarose, thus providing a proof-of-concept for Raman-based deep learning training pipelines. We reconstruct a surgical field of 30x60mm in 10.2 minutes, and achieve 98.2% accuracy, preserving relative measurements between features in the phantom. We also achieve an 84.3% Intersection-over-Union score, which is the extent of overlap between the ground truth and predicted reconstructions. Lastly, we also demonstrate that the Raman system and classification algorithm do not discern based on sample color, but instead on presence of SERS agents. This study provides a crucial step in the translation of intelligent Raman systems in intraoperative oncological spaces.

Due to scarcity of anomaly situations in the early manufacturing stage, an unsupervised anomaly detection (UAD) approach is widely adopted which only uses normal samples for training. This approach is based on the assumption that the trained UAD model will accurately reconstruct normal patterns but struggles with unseen anomalous patterns. To enhance the UAD performance, reconstruction-by-inpainting based methods have recently been investigated, especially on the masking strategy of suspected defective regions. However, there are still issues to overcome: 1) time-consuming inference due to multiple masking, 2) output inconsistency by random masking strategy, and 3) inaccurate reconstruction of normal patterns when the masked area is large. Motivated by this, we propose a novel reconstruction-by-inpainting method, dubbed Excision And Recovery (EAR), that features single deterministic masking based on the ImageNet pre-trained DINO-ViT and visual obfuscation for hint-providing. Experimental results on the MVTec AD dataset show that deterministic masking by pre-trained attention effectively cuts out suspected defective regions and resolve the aforementioned issues 1 and 2. Also, hint-providing by mosaicing proves to enhance the UAD performance than emptying those regions by binary masking, thereby overcomes issue 3. Our approach achieves a high UAD performance without any change of the neural network structure. Thus, we suggest that EAR be adopted in various manufacturing industries as a practically deployable solution.

A Times journalist on location in Qatar photographs the match, capturing images in high-speed bursts, trying to anticipate important moments with a broad view of the field. In collaboration with The Times's newsroom in the U.S., they identify a key moment in the match and transmit the photograph of that moment. From that photo, we calculate the 3D position of the photographer's camera. We can do so mathematically using the standard size of the goal and penalty area as geometric guides. Once we know the camera position, we project the image onto 3D geometry that represents the field and stands where the game was played.