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High-resolution daytime satellite imagery has become a promising source to study economic activities. These images display detailed terrain over large areas and allow zooming into smaller neighborhoods. Existing methods, however, have utilized images only in a single-level geographical unit. This research presents a deep learning model to predict economic indicators via aggregating traits observed from multiple levels of geographical units. The model first measures hyperlocal economy over small communities via ordinal regression. The next step extracts district-level features by summarizing interconnection among hyperlocal economies. In the final step, the model estimates economic indicators of districts via aggregating the hyperlocal and district information. Our new multi-level learning model substantially outperforms strong baselines in predicting key indicators such as population, purchasing power, and energy consumption. The model is also robust against data shortage; the trained features from one country can generalize to other countries when evaluated with data gathered from Malaysia, the Philippines, Thailand, and Vietnam. We discuss the multi-level model's implications for measuring inequality, which is the essential first step in policy and social science research on inequality and poverty., Accepted at AAAI2022

BACKGROUND Individual colorectal polyp risk factors are well characterized; however, insights into their pathway-specific interactions are scarce. We aimed to identify the impact of individual risk factors and their joint effects on adenomatous (AP) and serrated polyp (SP) risk. METHODS We collected information on 363 lifestyle and metabolic parameters from 1597 colonoscopy participants, resulting in over 521,000 data points. We used multivariate statistics and machine-learning approaches to assess associations of single variables and their interactions with AP and SP risk. RESULTS Individual factors and their interactions showed common and polyp subtype-specific effects. Abdominal obesity, high body mass index (BMI), metabolic syndrome, and red meat consumption globally increased polyp risk. Age, gender, and western diet associated with AP risk, while smoking was associated with SP risk. CRC family history was associated with advanced adenomas and diabetes with sessile serrated lesions. Regarding lifestyle factor interactions, no lifestyle or dietary adjustments mitigated the adverse smoking effect on SP risk, whereas its negative effect was exacerbated by alcohol in the conventional pathway. The adverse effect of red meat on SP risk was not ameliorated by any factor, but was further exacerbated by western diet along the conventional pathway. No modification of any factor reduced the negative impact of metabolic syndrome on AP risk, whereas increased fatless fish or meat substitutes' intake mitigated its effect on SP risk. CONCLUSIONS Individual risk factors and their interactions for polyp formation along the adenomatous and serrated pathways are strongly heterogeneous. Our findings may facilitate tailored lifestyle recommendations and contribute to a better understanding of how risk factor combinations impact colorectal carcinogenesis.

The increasing frequency and severity of water-related disasters such as floods, tornadoes, hurricanes, and tsunamis in low- and middle-income countries exemplify the uneven effects of global climate change. The vulnerability of high-risk societies to natural disasters has continued to increase. To develop an effective and efficient adaptation strategy, local damage assessments must be timely, exhaustive, and accurate. We propose a novel deep-learning-based solution that uses pairs of pre- and post-disaster satellite images to identify water-related disaster-affected regions. The model extracts features of pre- and post-disaster images and uses the feature difference with them to predict damage in the pair. We demonstrate that the model can successfully identify local destruction using less granular and less complex ground-truth data than those used by previous segmentation models. When tested with various water-related disasters, our detection model reported an accuracy of 85.9% in spotting areas with damaged buildings. It also achieved a reliable performance of 80.3% in out-of-domain settings. Our deep learning-based damage assessment model can help direct resources to areas most vulnerable to climate disasters, reducing their impacts while promoting adaptive capacities for climate-resilient development in the most vulnerable regions.

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Collider detectors have taken advantage of the resolution and accuracy of silicon detectors for at least four decades. Future colliders will need large areas of silicon sensors for low mass trackers and sampling calorimetry. Monolithic Active Pixel Sensors (MAPS), in which Si diodes and readout circuitry are combined in the same pixels, and can be fabricated in some of standard CMOS processes, are a promising technology for high-granularity and light detectors. In this paper we review 1) the requirements on MAPS for trackers and electromagnetic calorimeters (ECal) at future colliders experiments, 2) the ongoing efforts towards dedicated MAPS for the Electron-Ion Collider (EIC) at BNL, for which the EIC Silicon Consortium was already instantiated, and 3) space-born applications for MeV $\gamma$-ray experiments with MAPS based trackers (AstroPix)., Comment: 25 pages, 18 figures, contribution to Snowmass 2021