Background Protease inhibitor (PI)-based therapy is recommended as first-line treatment for HIV-infected infants exposed to nevirapine prophylaxis. However, long-term use poses adherence challenges, is associated with metabolic toxicities, limits second-line options, and is costly. We report long-term outcomes of switching nevirapine-exposed children to nevirapine-based therapy after effective suppression of virus replication on a PI-based regimen. Methods At one site in Johannesburg, South Africa, 195 nevirapine-exposed children under 24 months of age who achieved virologic suppression <400 copies/ml while treated with a lopinavir/ritonavir (LPV/r)-based regimen were randomized to switch to nevirapine-based therapy (n=96) or remain on the LPV/r-based regimen (n=99). Children were followed a median of 156 weeks and 3 deaths occurred in each group. HIV RNA was quantified every 3 months. Findings By 156 weeks post-randomization, more children in the switch group (23.9%) than in the controls (11.1%) had viral failure (p=0.01) defined as confirmed viremia >1000 copies/ml. However, all (22/22) failures in the switch group were detected by 52 weeks compared with 50% in the controls. Children in the switch group (40.5%) were more likely to suppress to <50 copies/ml (primary endpoint of study) than controls (31.3%, p=0.01) and had better CD4 and growth responses initially after switching. Viral failure was related to non-adherence and pre-treatment drug resistance. In those without pre-treatment drug resistance, no significant difference in viral failure between the switch (14.0%) and control (9.5%) groups (p=0.34) was observed. Interpretation Viral load testing through 52 weeks can identify all children likely to fail this PI-switch strategy. Switching children once suppressed to a nevirapine-based regimen may be a valuable treatment option provided that adequate viral load monitoring can be done.
In this work we present a simple end-to-end trainable machine learning system capable of realistically simulating driving experiences. This can be used for verification of self-driving system performance without relying on expensive and time-consuming road testing. In particular, we frame the simulation problem as a Markov Process, leveraging deep neural networks to model both state distribution and transition function. These are trainable directly from the existing raw observations without the need of any handcrafting in the form of plant or kinematic models. All that is needed is a dataset of historical traffic episodes. Our formulation allows the system to construct never seen scenes that unfold realistically reacting to the self-driving car's behaviour. We train our system directly from 1,000 hours of driving logs and measure both realism, reactivity of the simulation as the two key properties of the simulation. At the same time we apply the method to evaluate performance of a recently proposed state-of-the-art ML planning system [1] trained from human driving logs. We discover this planning system is prone to previously unreported causal confusion issues that are difficult to test by non-reactive simulation. To the best of our knowledge, this is the first work that directly merges highly realistic data-driven simulations with a closed loop evaluation for self-driving vehicles. We make the data, code, and pre-trained models publicly available to further stimulate simulation development.
Touch surfaces are intuitive interfaces for computing devices. Most of the traditional touch interfaces (vision, IR, capacitive, etc.) have mounting requirements, resulting in specialized touch surfaces limited by their size, cost, and mobility. More recent work has shown that vibration-based touch sensing techniques can localize taps/knocks, which provides a low-cost flexible alternative. These surfaces are envisioned as intuitive inputs for applications such as interactive meeting tables, smart kitchen appliance control, etc. However, due to dispersive and reflective properties of various vibrating mediums, it is difficult to localize taps accurately on ubiquitous surfaces. Furthermore, no work has been done on tracking continuous swipe interactions through vibration sensing. In this paper, we present SurfaceVibe, a vibration-based interaction tracking system for multiple surface types. The system accounts for physics properties of different waves to allow two major interaction types: tap and swipe. For tap induced impulse-like surface waves, we design an algorithm that takes wave dispersion and reflection into account to achieve accurate localization on ubiquitous surfaces. For swipe induced body waves, SurfaceVibe segments signals into 'slip pulses' to localize, and then tracks the trajectory. We validate SurfaceVibe through experiments on different materials and varying surface/sensing area sizes in this paper. Our methods achieve up to 6X decrease in localization error for taps and 3X reduction in length estimation error for swipes compared to existing algorithms that do not take wave properties into account. CCS CONCEPTS •Human-centered computing →Ubiquitous and mobile computing systems and tools;
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