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In critical applications, including search-and-rescue in degraded environments, blockages can be prevalent and prevent the effective deployment of certain sensing modalities, particularly vision, due to occlusion and the constrained range of view of onboard camera sensors. To enable robots to tackle these challenges, we propose a new approach, Proprioceptive Obstacle Detection and Estimation while navigating in clutter PROBE, which instead relies only on the robot's proprioception to infer the presence or absence of occluded rectangular obstacles while predicting their dimensions and poses in SE(2). The proposed approach is a Transformer neural network that receives as input a history of applied torques and sensed whole-body movements of the robot and returns a parameterized representation of the obstacles in the environment. The effectiveness of PROBE is evaluated on simulated environments in Isaac Gym and with a real Unitree Go1 quadruped robot. 

Schistosomiasis is a parasitic disease with significant global health and socio-economic implications. Drug discovery for schistosomiasis typically involves high-content whole-organism screening. In this approach, parasites are ex-posed to various chemical compounds and their systemic, whole-organism-level responses are captured via microscopy and analyzed to obtain a quanti-tative assessment of chemical effect. These effects are multidimensional and time-varying, impacting shape, appearance, and behavior. Accurate identifi-cation of object boundaries is essential for preparing images for subsequent analysis in high-content studies. Object segmentation is one of the most deeply studied problems in computer vision where recent efforts have incor-porated deep learning. Emerging results indicate that acquiring robust fea-tures in spectral domain using Fast Fourier Transform (FFT) within Deep Neural Networks (DNNs) can enhance segmentation accuracy. In this paper, we explore this direction further and propose a latent space Phase-Gating (PG) method that builds upon FFT and leverages phase information to effi-ciently identify globally significant features. While the importance of phase in analyzing signals has long been known, technical difficulties in calculat-ing phase in manners that are invariant to imaging parameters has limited its use. A key result of this paper is to show how phase information can be in-corporated in neural architectures that are compact. Experiments conducted on complex HCS datasets demonstrate how this idea leads to improved seg-mentation accuracy, while maintaining robustness against commonly en-countered noise (blurring) in HCS. The compactness of the proposed method also makes it well-suited for application specific architectures (ASIC) de-signed for high-content screening. 

Abstract We have investigated crystalline AlGaAs/GaAs optical coatings with three ultra-stable cavities operating at 4 K, 16 K, 124 K and 297 K. The response of the cavities’ resonance frequencies to variations in optical power indicates non-thermal effects beyond the photo-thermo-optic effect observed in dielectric coatings. These effects are strongly dependent on the intensity of the intracavity light at 1.5 μm. When the rear side of the mirrors is illuminated with external light, we observe a prominent photo-modified birefringence for photon energies above the GaAs bandgap, which points to a possible mechanism relating our observations to the semiconductor properties of the coatings. Separately, we also present a low maintenance evolution of our 124 K silicon cavity system where the liquid nitrogen based cooling system is replaced with closed cycle cooling from a pulse-tube cryo-cooler.