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Deep learning–based classifiers are widely used for perception in autonomous Cyber-Physical Systems (CPS’s). However, such classifiers rarely offer guarantees of perfect accuracy while being optimized for efficiency. To support safety-critical perception, ensembles of multiple different classifiers working in concert are typically used. Since CPS’s interact with the physical world continuously, it is not unreasonable to expect dependencies among successive inputs in a stream of sensor data. Prior work introduced a classification technique that leverages these inter-input dependencies to reduce the average time to successful classification using classifier ensembles. In this paper, we propose generalizations to this classification technique, both in the improved generation of classifier cascades and the modeling of temporal dependencies. We demonstrate, through theoretical analysis and numerical evaluation, that our approach achieves further reductions in average classification latency compared to the prior methods. 

Abstract We study collusion among firms against imperfectly monitored environmental regulation. Firms increase variable profits by violating regulation and reduce expected noncompliance penalties by violating jointly. We consider a case of three German automakers colluding to reduce the effectiveness of emissions control technology. By estimating a structural model of the European automobile industry from 2007 to 2018, we find that collusion lowers expected noncompliance penalties substantially and increases buyer and producer surplus. Due to increased pollution, welfare decreases by € 1.57–5.57 billion. We show how environmental policy design and antitrust play complementary roles in preventing noncompliance. 

The topological Hall effect (THE), a quantum phenomenon arising from the emergent magnetic field generated by a topological spin texture, is a key method for detecting non-coplanar spin structures like skyrmions in magnetic materials. Here, we investigate a bilayer structure of Pt and the conducting ferrimagnet NiCo2O4 (NCO) of perpendicular magnetic anisotropy and demonstrate a giant THE across a temperature range of 2–350 K. The absence of THE in a single-layer Pt and NCO, as well as in Pt/Cu/NCO, suggests its interfacial origin. The maximum THE occurring just before the NCO coercive field indicates its connection to magnetic nucleation centers, which are topologically equivalent to skyrmions. The large normalized THE, based on the emergent-field model, points to a high population density of small magnetic nucleation centers. This aligns with the seemingly unresolvable domain structures by the employed techniques during magnetization reversal, even though clear domain structures are detected after zero-field cooling. These results establish heavy metal/NCO as a promising system for exploring topological spin structures.