Tong Xia

Yunke Zhang, Ruolong Ma, Xin Zhang et al.

Socioeconomic exposure segregation -- the lack of daily interaction between income groups -- erodes social capital and entrenches inequality, yet the specific physical features that drive these behavioral restrictions remain poorly understood. Prior research has quantified where segregation occurs using mobility data, but has not identified how the built environment facilitates or inhibits these interactions. Here we introduce VISAGE, a large multi-modal model-enabled framework that perceives exposure segregation directly from open satellite and street-level imagery across 10,030 communities in 31 U.S. cities. Moving beyond black-box correlations, we operationalize cross-disciplinary sociological theory into an interpretable visual codebook to detect physical regulators of social mixing. We find that the built environment encodes a legible grammar of segregation: "defensible" architectural forms (e.g., fences, gated enclosures) and monofunctional zoning systematically predict higher social isolation, whereas mixed-use infrastructure fosters interaction, explaining substantial variance in mobility-derived segregation patterns (Pearson $r=0.770$). Crucially, we show that inclusionary housing policies manifest in distinct visual signatures associated with higher mixing, suggesting that policy interventions successfully alter the physical landscape to encourage diversity. Our findings offer a scalable pathway to decipher the social production of space, providing a mechanism-based lens to understand how the built environment shapes social behavior.

Martin Wagner, Beat-Peter Müller-Stich, Anna Kisilenko et al.

PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center dataset. In this work we investigated the generalizability of phase recognition algorithms in a multi-center setting including more difficult recognition tasks such as surgical action and surgical skill. METHODS: To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 hours was created. Labels included annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 teams submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment. RESULTS: F1-scores were achieved for phase recognition between 23.9% and 67.7% (n=9 teams), for instrument presence detection between 38.5% and 63.8% (n=8 teams), but for action recognition only between 21.8% and 23.3% (n=5 teams). The average absolute error for skill assessment was 0.78 (n=1 team). CONCLUSION: Surgical workflow and skill analysis are promising technologies to support the surgical team, but are not solved yet, as shown by our comparison of algorithms. This novel benchmark can be used for comparable evaluation and validation of future work.