Biomechanically distinct filter-feeding behaviors distinguish sei whales as a functional intermediate and ecologically flexible species

ABSTRACT With their ability to facultatively switch between filter-feeding modes, sei whales represent a functional and ecological intermediate in the transition between intermittent and continuous filter feeding. Morphologically resembling their lunge-feeding, rorqual relatives, sei whales have convergently evolved the ability to skim prey near the surface of the water, like the more distantly related balaenids. Because of their intermediate nature, understanding how sei whales switch between feeding behaviors may shed light on the rapid evolution and flexibility of filter-feeding strategies. We deployed multi-sensor bio-logging tags on two sei whales and measured the kinematics of feeding behaviors in this poorly understood and endangered species. To forage at the surface, sei whales used a unique combination of surface lunges and skim-feeding behaviors. The surface lunges were slow and stereotyped, and were unlike lunges performed by other rorqual species. The skim-feeding events featured a different filtration mechanism from the lunges and were kinematically different from the continuous filter feeding used by balaenids. While foraging below the surface, sei whales used faster and more variable lunges. The morphological characteristics that allow sei whales to effectively perform different feeding behaviors suggest that sei whales rapidly evolved their functionally intermediate and ecologically flexible form to compete with larger and more efficient rorqual species.

Always-On Quantum Error Tracking with Continuous Parity Measurements

We investigate quantum error correction using continuous parity measurements to correct bit-flip errors with the three-qubit code. Continuous monitoring of errors brings the benefit of a continuous stream of information, which facilitates passive error tracking in real time. It reduces overhead from the standard gate-based approach that periodically entangles and measures additional ancilla qubits. However, the noisy analog signals from continuous parity measurements mandate more complicated signal processing to interpret syndromes accurately. We analyze the performance of several practical filtering methods for continuous error correction and demonstrate that they are viable alternatives to the standard ancilla-based approach. As an optimal filter, we discuss an unnormalized (linear) Bayesian filter, with improved computational efficiency compared to the related Wonham filter introduced by Mabuchi [New J. Phys. 11, 105044 (2009)]. We compare this optimal continuous filter to two practical variations of the simplest periodic boxcar-averaging-and-thresholding filter, targeting real-time hardware implementations with low-latency circuitry. As variations, we introduce a non-Markovian ``half-boxcar'' filter and a Markovian filter with a second adjustable threshold; these filters eliminate the dominant source of error in the boxcar filter, and compare favorably to the optimal filter. For each filter, we derive analytic results for the decay in average fidelity and verify them with numerical simulations.

A Method for Continuous Tuning of MOSFET–RC Filters with Extended Control Range

In this paper a tuning structure for a MOSFET?RC filters is presented. The proposed tuning structure is composed of switched resistor banks with voltage controlled transistors. The voltage controlled transistors use active feedback with extended control range for continuous filter parameter tuning, without degrading the total linearity performance of the filter. The proposed tuning structure is tested by implementing it in a second order low pass biquadratic filter cell in 65 nm CMOS technology. The designed filter has a highly reconfigurable response, ranging from Chebyshev to Bessel, a tuneable -3 dB bandwidth from 10 MHz to 100 MHz and can be used for multiple standard wireless solutions. Filter IIP3 performance is not degraded when the bandwidth is continuously tuned by 40 % with a 1 V pp input. The maximum power dissipation, including active feedback circuits, is 17.2 mW from a 1.2 V source when the filter is tuned to 100 MHz bandwidth.