Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

We introduce a launch device, called the remotely-enabled modular release mechanism, to augment rapid testing and prototyping of cooperative autonomy maritime applications by facilitating autonomous deployment of an autonomous underwater vehicle (AUV) from an autonomous surface vessel (ASV). While we focus our development on a specific application of deploying an AUV from a catamaran style ASV, the release mechanism can be adapted to different deployable objects and towing vehicles, such as buoys and sensors for oceanographic surveys or mono-hull ASVs. In this paper we explore a number of hardware and software design considerations to facilitate ease of integration with existing maritime autonomy systems. We expound on bench tests and in-water tests used to explore the utility of the release system and diagnose system issues. Additionally, we make a first-principles argument, based on a hydrodynamics physics model, for assured deployment that is virtually independent of sea state, making the release system a suitable alternative for different maritime applications in varying environmental conditions.

In a nondescript office building on the outskirts of Zaporizhzhia, Ukrainian soldiers have been honing what they believed will be a decisive weapon in their effort to repel the Russian invasion. Inside, the weapon glows from a dozen computer screens – a constantly updated portrayal of the evolving battlefield to the south. With one click on a menu, the map is populated with hordes of orange diamonds, showing Russian deployments. They reveal where tanks and artillery have been hidden, and intimate details of the units and the soldiers in them, gleaned from social media. Zooming in shows satellite imagery of the terrain in sharp detail.

The study of private inference has been sparked by growing concern regarding the analysis of data when it stems from sensitive sources. We present the first method for private Bayesian inference in exponential families that properly accounts for noise introduced by the privacy mechanism. It is efficient because it works only with sufficient statistics and not individual data. Unlike other methods, it gives properly calibrated posterior beliefs in the non-asymptotic data regime.

The study of private inference has been sparked by growing concern regarding the analysis of data when it stems from sensitive sources. We present the first method for private Bayesian inference in exponential families that properly accounts for noise introduced by the privacy mechanism. It is efficient because it works only with sufficient statistics and not individual data. Unlike other methods, it gives properly calibrated posterior beliefs in the non-asymptotic data regime.

The study of private inference has been sparked by growing concern regarding the analysis of data when it stems from sensitive sources. We present the first method for private Bayesian inference in exponential families that properly accounts for noise introduced by the privacy mechanism. It is efficient because it works only with sufficient statistics and not individual data. Unlike other methods, it gives properly calibrated posterior beliefs in the non-asymptotic data regime.

Mosquitos kill more humans every year than any other animal on the planet and conventional methods to reduce mosquito-borne illnesses haven't worked as well as many hoped. So we've been hard at work since receiving this USAID grant six months ago to reduce Zika incidence and related threats to public health. Our partners at the joint FAO/IAEA Insect Pest Control Lab in Vienna, Austria have been working to perfect the Sterile Insect Technique (SIT) in order to sterilize and release male mosquitos in Zika hotspots. Releasing millions of said male mosquitos increases competition for female mosquitos, making it more difficult for non-sterilized males to find a mate. We learned last year at a USAID Co-Ideation Workshop that this technique can reduce the overall mosquito population in a given area by 90%.

We have a developed an analog VLSI system that models the coordination of neurobiological segmental oscillators. We have implemented and tested a system that consists of a chain of eleven pattern generating circuits that are synaptically coupled to their nearest neighbors. Each pattern generating circuit is implemented with two silicon Morris-Lecar neurons that are connected in a reciprocally inhibitory network. We discuss the mechanisms of oscillations in the two-cell network and explore system behavior based on isotropic and anisotropic coupling, and frequency gradients along the chain of oscillators.

We have a developed an analog VLSI system that models the coordination of neurobiological segmental oscillators. We have implemented and tested a system that consists of a chain of eleven pattern generating circuits that are synaptically coupled to their nearest neighbors. Each pattern generating circuit is implemented with two silicon Morris-Lecar neurons that are connected in a reciprocally inhibitory network. We discuss the mechanisms of oscillations in the two-cell network and explore system behavior based on isotropic and anisotropic coupling, and frequency gradients along the chain of oscillators.