Prevalence of Trachoma After Three Rounds of Antibiotic Mass Drug Administration in 13 Woredas of Gambella Region, Ethiopia.

Background: Following baseline surveys in 2013 and 2014, trachoma elimination interventions, including three rounds of azithromycin mass drug administration (MDA), were implemented in 13 woredas (administrative districts) of Gambella Regional State, Ethiopia. We conducted impact surveys to determine if elimination thresholds have been met or if additional interventions are required.

Methods: Cross-sectional population-based surveys were conducted in 13 woredas of Gambella Regional State, combined into five evaluation units (EUs), 6─12 months after their last MDA round.

ECG Arrhythmia Classification on an Ultra-Low-Power Microcontroller.

Wearable biomedical systems allow doctors to continuously monitor their patients over longer periods, which is especially useful to detect rarely occurring events such as cardiac arrhythmias. Recent monitoring systems often embed signal processing capabilities to directly identify events and reduce the amount of data. This work is the first to document a complete beat-to-beat arrhythmia classification system implemented on a custom ultra-low-power microcontroller.

Humanity's Attitudes about Democracy and Political Leaders: Patterns and Trends.

For decades, researchers have examined people's beliefs across countries and over time using national samples of citizens. Yet, in an era when economies, societies, and policymaking have become increasingly interconnected, nation-states may no longer be the only or most relevant units of analysis for studying public opinion. To examine what people think about politics on a global scale, we develop tools for measuring public opinion that allow us to transcend national and regional boundaries.

Learning Without Feedback: Fixed Random Learning Signals Allow for Feedforward Training of Deep Neural Networks.

While the backpropagation of error algorithm enables deep neural network training, it implies (i) bidirectional synaptic weight transport and (ii) update locking until the forward and backward passes are completed. Not only do these constraints preclude biological plausibility, but they also hinder the development of low-cost adaptive smart sensors at the edge, as they severely constrain memory accesses and entail buffering overhead. In this work, we show that the one-hot-encoded labels provided in supervised classification problems, denoted as targets, can be viewed as a proxy for the error sign.

MorphIC: A 65-nm 738k-Synapse/mm Quad-Core Binary-Weight Digital Neuromorphic Processor With Stochastic Spike-Driven Online Learning.

Recent trends in the field of neural network accelerators investigate weight quantization as a means to increase the resource- and power-efficiency of hardware devices. As full on-chip weight storage is necessary to avoid the high energy cost of off-chip memory accesses, memory reduction requirements for weight storage pushed toward the use of binary weights, which were demonstrated to have a limited accuracy reduction on many applications when quantization-aware training techniques are used. In parallel, spiking neural network (SNN) architectures are explored to further reduce power when processing sparse event-based data streams, while on-chip spike-based online learning appears as a key feature for applications constrained in power and resources during the training phase.