Among Us: Adversarially Robust Collaborative Perception by Consensus

Multiple robots could perceive a scene (e.g., detect objects) collaboratively better than individuals, although easily suffer from adversarial attacks when using deep learning. This could be addressed by the adversarial defense, but its training requires the often-unknown attacking mechanism. Differently, we propose ROBOSAC, a novel sampling-based defense strategy generalizable to unseen attackers. Our key idea is that collaborative perception should lead to consensus rather than dissensus in results compared to individual perception. This leads to our hypothesize-and-verify framework: perception results with and without collaboration from a random subset of teammates are compared until reaching a consensus. In such a framework, more teammates in the sampled subset often entail better perception performance but require longer sampling time to reject potential attackers. Thus, we derive how many sampling trials are needed to ensure the desired size of an attacker-free subset, or equivalently, the maximum size of such a subset that we can successfully sample within a given number of trials. We validate our method on the task of collaborative 3D object detection in autonomous driving scenarios

Student groups of complementary skills developing artificial intelligence solutions for natural sciences -- an authentic research education approach suitable for wide adoption

We report a methodology in which students gain experience in authentic research by developing artificial intelligence (AI) solutions for researchers in natural sciences. While creating education benefits for students, our approach also directly benefits scientists, who get an opportunity to evaluate the usefulness of machine learning for their specific needs. In order to accomplish this, we work with research laboratories that reveal/specify the needs they have, and then our student teams work on the discovery, design, and development of an AI solution for unique problems using a consulting-like arrangement. Our design addresses common barriers which appear in most existing authentic research education approaches and thus is suitable for wide adoption at various schools. To date, our group has been operating at New York University (NYU) for five consecutive semesters and has engaged more than seventy students, ranging from first-year college students to master's candidates, and worked on more than 15 projects with 14 collaborators

Electrospun composites of PHBV/pearl powder for bone repairing

Electrospun fiber has highly structural similarity with natural bone extracelluar matrix (ECM). Many researches about fabricating organic–inorganic composite materials have been carried out in order to mimic the natural composition of bone and enhance the biocompatibility of materials. In this work, pearl powder was added to the poly (3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) and the composite nanofiber scaffold was prepared by electrospinning. Mineralization ability of the composite scaffolds can be evaluated by analyzing hydroxyapatite (HA) formation on the surface of nanofiber scaffolds. The obtained composite nanofiber scaffolds showed an enhanced mineralization capacity due to incorporation of pearl powder. The HA formed amount of the composite scaffolds was raised as the increase of pearl powder in composite scaffolds. Therefore, the prepared PHBV/pearl composite nanofiber scaffolds would be a promising candidate as an osteoconductive composite material for bone repairing

Determination of eight lignans in <i>Schisandra chinensis</i> and <i>Schisandra sphenanthera</i>

A high performance liquid chromatography method for the determination of eight lignans contents in Schisandra chinensis and Schisandra sphenanthera was developed. The chromatographic column was Agilent ZORBAX 300SB-C18 column (4.6 mm × 250 mm?5 ?m). The mobile phase was methanol-water, a gradient elution was conducted and the detection wavelength was at 230 nm. The results showed that the recovery rate of eight lignans was 92.2-102.9% and RSD was 1.5-4.2%. The established content determination method was simple, sensitive, accurate and stable, and can be used to control the quality of S. chinensis and S. sphenanthera.