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Tor users derive anonymity in part from the size of the Tor user base, but Tor struggles to attract and support more users due to performance limitations. Previous works have proposed modifications to Tor’s path selection algorithm to enhance both performance and security, but many proposals have unintended consequences due to incorporating information related to client location. We instead propose selecting paths using a global view of the network, independent of client location, and we propose doing so with a machine learning classifier to predict the performance of a given path before building a circuit. We show through a variety of simulated and live experimental settings, across different time periods, that this approach can significantly improve performance compared to Tor’s default path selection algorithm and two previously proposed approaches. In addition to evaluating the security of our approach with traditional metrics, we propose a novel anonymity metric that captures information leakage resulting from location-aware path selection, and we show that our path selection approach leaks no more information than the default path selection algorithm. 

BackgroundDespite significant global progress in reducing neonatal mortality, bacterial sepsis remains a major cause of neonatal deaths.Klebsiella pneumoniae(K.pneumoniae) is the leading pathogen globally underlying cases of neonatal sepsis and is frequently resistant to antibiotic treatment regimens recommended by the World Health Organization (WHO), including first-line therapy with ampicillin and gentamicin, second-line therapy with amikacin and ceftazidime, and meropenem. Maternal vaccination to prevent neonatal infection could reduce the burden ofK.pneumoniaeneonatal sepsis in low- and middle-income countries (LMICs), but the potential impact of vaccination remains poorly quantified. We estimated the potential impact of such vaccination on cases and deaths ofK.pneumoniaeneonatal sepsis and project the global effects of routine immunization of pregnant women with theK.pneumoniaevaccine as antimicrobial resistance (AMR) increases. Methods and findingsWe developed a Bayesian mixture-modeling framework to estimate the effects of a hypotheticalK.pneumoniaematernal vaccine with 70% efficacy administered with coverage equivalent to that of the maternal tetanus vaccine on neonatal sepsis infections and mortality. To parameterize our model, we used data from 3 global studies of neonatal sepsis and/or mortality—with 2,330 neonates who died with sepsis surveilled from 2016 to 2020 undertaken in 18 mainly LMICs across all WHO regions (Ethiopia, Kenya, Mali, Mozambique, Nigeria, Rwanda, Sierra Leone, South Africa, Uganda, Brazil, Italy, Greece, Pakistan, Bangladesh, India, Thailand, China, and Vietnam). Within these studies, 26.95% of fatal neonatal sepsis cases were culture-positive forK.pneumoniae. We analyzed 9,070K.pneumoniaegenomes from human isolates gathered globally from 2001 to 2020 to quantify the temporal rate of acquisition of AMR genes inK.pneumoniaeisolates to predict the future number of drug-resistant cases and deaths that could be averted by vaccination.Resistance rates to carbapenems are increasing most rapidly and 22.43% [95th percentile Bayesian credible interval (CrI): 5.24 to 41.42] of neonatal sepsis deaths are caused by meropenem-resistantK.pneumoniae. Globally, we estimate that maternal vaccination could avert 80,258 [CrI: 18,084 to 189,040] neonatal deaths and 399,015 [CrI: 334,523 to 485,442] neonatal sepsis cases yearly worldwide, accounting for more than 3.40% [CrI: 0.75 to 8.01] of all neonatal deaths. The largest relative benefits are in Africa (Sierra Leone, Mali, Niger) and South-East Asia (Bangladesh) where vaccination could avert over 6% of all neonatal deaths. Nevertheless, our modeling only considers country-level trends inK.pneumoniaeneonatal sepsis deaths and is unable to consider within-country variability in bacterial prevalence that may impact the projected burden of sepsis. ConclusionsAK.pneumoniaematernal vaccine could have widespread, sustained global benefits as AMR inK.pneumoniaecontinues to increase. 

While elastic metasurfaces offer a remarkable and very effective approach to the subwavelength control of stress waves, their use in practical applications is severely hindered by intrinsically narrow band performance. In applications to electromagnetic and photonic metamaterials, some success in extending the operating dynamic range was obtained by using nonlocality. However, while electronic properties in natural materials can show significant nonlocal effects, even at the macroscales, in mechanics, nonlocality is a higher-order effect that becomes appreciable only at the microscales. This study introduces the concept of intentional nonlocality as a fundamental mechanism to design passive elastic metasurfaces capable of an exceptionally broadband operating range. The nonlocal behavior is achieved by exploiting nonlocal forces, conceptually akin to long-range interactions in nonlocal material microstructures, between subsets of resonant unit cells forming the metasurface. These long-range forces are obtained via carefully crafted flexible elements, whose specific geometry and local dynamics are designed to create remarkably complex transfer functions between multiple units. The resulting nonlocal coupling forces enable achieving phase-gradient profiles that are functions of the wavenumber of the incident wave. The identification of relevant design parameters and the assessment of their impact on performance are explored via a combination of semianalytical and numerical models. The nonlocal metasurface concept is tested, both numerically and experimentally, by embedding a total-internal-reflection design in a thin-plate waveguide. Results confirm the feasibility of the intentionally nonlocal design concept and its ability to achieve a fully passive and broadband wave control.