Federated learning (FL) enables edge devices, such as Internet of Things devices (e.g., sensors), servers, and institutions (e.g., hospitals), to collaboratively train a machine learning (ML) model without sharing their private data. FL requires devices to exchange their ML parameters iteratively, and thus the time it requires to jointly learn a reliable model depends not only on the number of training steps but also on the ML parameter transmission time per step. In practice, FL parameter transmissions are often carried out by a multitude of participating devices over resource-limited communication networks, for example, wireless networks with limited bandwidth and power. Therefore, the repeated FL parameter transmission from edge devices induces a notable delay, which can be larger than the ML model training time by orders of magnitude. Hence, communication delay constitutes a major bottleneck in FL. Here, a communication-efficient FL framework is proposed to jointly improve the FL convergence time and the training loss. In this framework, a probabilistic device selection scheme is designed such that the devices that can significantly improve the convergence speed and training loss have higher probabilities of being selected for ML model transmission. To further reduce the FL convergence time, a quantization method is proposed to reduce the volume of the model parameters exchanged among devices, and an efficient wireless resource allocation scheme is developed. Simulation results show that the proposed FL framework can improve the identification accuracy and convergence time by up to 3.6% and 87% compared to standard FL.Bell inequalities rest on three fundamental assumptions realism, locality, and free choice, which lead to nontrivial constraints on correlations in very simple experiments. If we retain realism, then violation of the inequalities implies that at least one of the remaining two assumptions must fail, which can have profound consequences for the causal explanation of the experiment. Oltipraz research buy We investigate the extent to which a given assumption needs to be relaxed for the other to hold at all costs, based on the observation that a violation need not occur on every experimental trial, even when describing correlations violating Bell inequalities. How often this needs to be the case determines the degree of, respectively, locality or free choice in the observed experimental behavior. Despite their disparate character, we show that both assumptions are equally costly. Namely, the resources required to explain the experimental statistics (measured by the frequency of causal interventions of either sort) are exactly the same. Furthermore, we compute such defined measures of locality and free choice for any nonsignaling statistics in a Bell experiment with binary settings, showing that it is directly related to the amount of violation of the so-called Clauser-Horne-Shimony-Holt inequalities. This result is theory independent as it refers directly to the experimental statistics. Additionally, we show how the local fraction results for quantum-mechanical frameworks with infinite number of settings translate into analogous statements for the measure of free choice we introduce. Thus, concerning statistics, causal explanations resorting to either locality or free choice violations are fully interchangeable.Older age at the time of infection with hepatitis viruses is associated with an increased risk of liver fibrosis progression. We hypothesized that the pace of fibrosis progression may reflect changes in gene expression within the aging liver. We compared gene expression in liver specimens from 54 adult donors without evidence of fibrosis, including 36 over 40 y old and 18 between 18 and 40 y old. Chitinase 3-like 1 (CHI3L1), which encodes chitinase-like protein YKL-40/CHI3L1, was identified as the gene with the greatest age-dependent increase in expression in liver tissue. We investigated the cellular source of CHI3L1 in the liver and its function using liver tissue specimens and in vitro models. CHI3L1 expression was significantly higher in livers of patients with cirrhosis of diverse etiologies compared with controls represented by patients who underwent liver resection for hemangioma. The highest intrahepatic CHI3L1 expression was observed in cirrhosis due to hepatitis D virus, followed by hepatitis C virus, hepatitis B virus, and alcohol-induced cirrhosis. In situ hybridization of CHI3L1 messenger RNA (mRNA) identified hepatocytes as the major producers of CHI3L1 in normal liver and in cirrhotic tissue, wherein hepatocytes adjacent to fibrous septa showed higher CHI3L1 expression than did those in more distal areas. In vitro studies showed that recombinant CHI3L1 promotes proliferation and activation of primary human hepatic stellate cells (HSCs), the major drivers of liver fibrosis. These findings collectively demonstrate that CHI3L1 promotes liver fibrogenesis through a direct effect on HSCs and support a role for CHI3L1 in the increased susceptibility of aging livers to fibrosis progression.Improving compliance with environmental regulations is critical for promoting clean environments and healthy populations. In South Asia, brick manufacturing is a major source of pollution but is dominated by small-scale, informal producers who are difficult to monitor and regulate-a common challenge in low-income settings. We demonstrate a low-cost, scalable approach for locating brick kilns in high-resolution satellite imagery from Bangladesh. Our approach identifies kilns with 94.2% accuracy and 88.7% precision and extracts the precise GPS coordinates of every brick kiln across Bangladesh. Using these estimates, we show that at least 12% of the population of Bangladesh (>18 million people) live within 1 km of a kiln and that 77% and 9% of kilns are (illegally) within 1 km of schools and health facilities, respectively. Finally, we show how kilns contribute up to 20.4 μg/[Formula see text] of [Formula see text] (particulate matter of a diameter less than 2.5 μm) in Dhaka when the wind blows from an unfavorable direction. We document inaccuracies and potential bias with respect to local regulations in the government data. Our approach demonstrates how machine learning and Earth observation can be combined to better understand the extent and implications of regulatory compliance in informal industry.