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EU data localization regulations limit data transfers to non-EU countries with the GDPR. However, BGP, DNS and other Internet protocols were not designed to enforce jurisdictional constraints, so implementing data localization is challenging. Despite initial research on the topic, little is known about if or how companies currently operate their server infrastructure to comply with the regulations. We close this knowledge gap by empirically measuring the extent to which servers and routers that process EU requests are located outside of the EU (and a handful of 'adequate' non-EU countries). The key challenge is that both browser measurements (to infer relevant endpoints) and data-plane measurements (to infer relevant IP addresses) are needed, but no large-scale public infrastructure allows both. We build a novel methodology that combines BrightData (browser) and RIPE Atlas (data-plane) probes, with joint measurements from over 1,000 networks in 20 EU countries. We find that, on average, 2.2% of servers serving users in each EU country are located in non-adequate destination countries (1.4% of known trackers). Our findings suggest that data localization policies are largely being followed by content providers, though there are exceptions. 

Background:Despite recent advances, patients with heart failure (HF) often experience repeat hospitalizations and worsening clinical trajectories from inadequate decongestion. Evidence-based approaches for optimizing interventions in the acute hospital setting for patients with decompensated HF are needed. We evaluated whether machine learning (ML) models can accurately predict next-day levels for decongestion surrogates in hospitalized HF patients. Hypothesis:ML can accurately predict body weight, hematocrit, creatinine, and potassium values in the next 24 hours in hospitalized HF patients. Methods:We utilized national Veterans Affairs (VA) databases to study all patients admitted with HF from January 2014 to July 2022. Records including at least one value for at least one biomarker of interest (body weight, hematocrit, creatinine, and potassium) were included. Patients were randomly split into training (80%), validation (10%), and test (10%) datasets. We trained a recurrent neural network to predict each biomarker’s value on admission day n+1 using data until day n, simulating a scenario where a clinician monitors response to treatment (e.g., diuresis) over a 24-hour cycle. The model that performed best on the validation set was evaluated on the test set. The R², mean absolute error (MAE), and feature importance were determined. Results:We identified 589,114 admissions involving 124,163 unique patients. The mean (SD) age on admission was 72 (10) years; 98% were male, 69% were white, and 25% were Black. The performance (R², MAE) for each biomarker model was as follows: body weight (0.94, 6.15 lb.), creatinine (0.92, 0.21 mg/dL), hematocrit (0.86, 1.7%), and potassium (0.53, 0.27 mmol/L). The top predictive features across all models were intravenous or oral diuretic use, patient age, and diastolic blood pressure. The predicted 24-hour change in each biomarker based on total daily diuretic dose for five representative patients is demonstrated in the Figure. Conclusions:ML can accurately predict the 24-hour body weight, hematocrit, creatinine, and potassium values in hospitalized HF patients, suggesting the potential for AI to guide acute in-hospital management.