Online Distributed Sensor Selection

A key problem in sensor networks is to decide which sensors to query when, in order to obtain the most useful information (e.g., for performing accurate prediction), subject to constraints (e.g., on power and bandwidth). In many applications the utility function is not known a priori, must be learned from data, and can even change over time. Furthermore for large sensor networks solving a centralized optimization problem to select sensors is not feasible, and thus we seek a fully distributed solution. In this paper, we present Distributed Online Greedy (DOG), an efficient, distributed algorithm for repeatedly selecting sensors online, only receiving feedback about the utility of the selected sensors. We prove very strong theoretical no-regret guarantees that apply whenever the (unknown) utility function satisfies a natural diminishing returns property called submodularity. Our algorithm has extremely low communication requirements, and scales well to large sensor deployments. We extend DOG to allow observation-dependent sensor selection. We empirically demonstrate the effectiveness of our algorithm on several real-world sensing tasks

Content provenance attribution and verification via blockchain certification

Content such as images is sometimes used online without proper permission or attribution by parties other than content creators and owners. Prior to use, such parties may also alter the content as well as associated metadata. Such practices subvert rights of the content creators and owners and can mislead readers due to a lack of cues that can help determine the provenance, veracity, and credibility of the content. This disclosure describes a mechanism to track, verify, and attribute provenance to content such as images. Per techniques of this disclosure, provenance information signed with a user’s private key is included in the EXIF block of an image. With the user’s permission, the image is uploaded to a globally trusted party that uses blockchain to certify the signed provenance information. Other parties can then traverse the certified blockchain to verify provenance

Dynamic Resource Allocation in Conservation Planning

Consider the problem of protecting endangered species by selecting patches of land to be used for conservation purposes. Typically, the availability of patches changes over time, and recommendations must be made dynamically. This is a challenging prototypical example of a sequential optimization problem under uncertainty in computational sustainability. Existing techniques do not scale to problems of realistic size. In this paper, we develop an efficient algorithm for adaptively making recommendations for dynamic conservation planning, and prove that it obtains near-optimal performance. We further evaluate our approach on a detailed reserve design case study of conservation planning for three rare species in the Pacific Northwest of the United States

Generation of ultrafast electron bunch trains via trapping into multiple periods of plasma wakefields

We demonstrate a novel approach to the generation of femtosecond electron bunch trains via laser-driven wakefield acceleration. We use two independent high-intensity laser pulses, a drive, and injector, each creating their own plasma wakes. The interaction of the laser pulses and their wakes results in a periodic injection of free electrons in the drive plasma wake via several mechanisms, including ponderomotive drift, wake-wake interference, and pre-acceleration of electrons directly by strong laser fields. Electron trains were generated with up to 4 quasi-monoenergetic bunches, each separated in time by a plasma period. The time profile of the generated trains is deduced from an analysis of beam loading and confirmed using 2D Particle-in-Cell simulations.Comment: 11 pages, 5 figures, accepted by Physics of Plasma