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Commercial autonomous machines is a thriving sector, one that is likely the next ubiquitous computing platform, after Personal Computers (PC), cloud computing, and mobile computing. Nevertheless, a suitable computing substrate for autonomous machines is missing, and many companies are forced to develop ad hoc computing solutions that are neither principled nor extensible. By analyzing the demands of autonomous machine computing, this article proposes Dataflow Accelerator Architecture (DAA), a modern instantiation of the classic dataflow principle, that matches the characteristics of autonomous machine software. 

Abstract Plasmon polaritons, or plasmons, are coupled oscillations of electrons and electromagnetic fields that can confine the latter into deeply subwavelength scales, enabling novel polaritonic devices. While plasmons have been extensively studied in normal metals or semimetals, they remain largely unexplored in correlated materials. In this paper, we report infrared (IR) nano-imaging of thin flakes of CsV₃Sb₅, a prototypical layered Kagome metal. We observe propagating plasmon waves in real-space with wavelengths tunable by the flake thickness. From their frequency-momentum dispersion, we infer the out-of-plane dielectric function$${{{{{{\boldsymbol{\epsilon }}}}}}}_{{{{{{\boldsymbol{c}}}}}}}$$ ${ϵ}_c$that is generally difficult to obtain in conventional far-field optics, and elucidate signatures of electronic correlations when compared to density functional theory (DFT). We propose correlation effects might have switched the real part of$${{{{{{\boldsymbol{\epsilon }}}}}}}_{{{{{{\boldsymbol{c}}}}}}}$$ ${ϵ}_c$from negative to positive values over a wide range of middle-IR frequencies, transforming the surface plasmons into hyperbolic bulk plasmons, and have dramatically suppressed their dissipation. 

Automatic assessment of impairment and disease severity is a key challenge in data-driven medicine. We propose a framework to address this challenge, which leverages AI models trained exclusively on healthy individuals. The COnfidence-Based chaRacterization of Anomalies (COBRA) score exploits the decrease in confidence of these models when presented with impaired or diseased patients to quantify their deviation from the healthy population. We applied the COBRA score to address a key limitation of current clinical evaluation of upper-body impairment in stroke patients. The gold-standard Fugl-Meyer Assessment (FMA) requires in-person administration by a trained assessor for 30-45 minutes, which restricts monitoring frequency and precludes physicians from adapting rehabilitation protocols to the progress of each patient. The COBRA score, computed automatically in under one minute, is shown to be strongly correlated with the FMA on an independent test cohort for two different data modalities: wearable sensors (ρ = 0.814, 95% CI [0.700,0.888]) and video (ρ = 0.736, 95% C.I [0.584, 0.838]). To demonstrate the generalizability of the approach to other conditions, the COBRA score was also applied to quantify severity of knee osteoarthritis from magnetic-resonance imaging scans, again achieving significant correlation with an independent clinical assessment (ρ = 0.644, 95% C.I [0.585,0.696]). 

We introduce Constellation, a dataset of 13K images suitable for research on detection of objects in dense urban streetscapes observed from high-elevation cameras, collected for a variety of temporal conditions. The dataset addresses the need for curated data to explore problems in small object detection exemplified by the limited pixel footprint of pedestrians observed tens of meters from above. It enables the testing of object detection models for variations in lighting, building shadows, weather, and scene dynamics. Weevaluate contemporary object detection architectures on the dataset, observing that state-of-the-art methods have lower performance in detecting small pedestrians compared to vehicles, corresponding to a 10% difference in average precision (AP). Using structurally similar datasets for pretraining the models results in an increase of 1.8% mean AP (mAP). We further find that incorporating domain-specific data augmentations helps improve model performance. Using pseudo-labeled data, obtained from inference outcomes of the best-performing models, improves the performance of the models. Finally, comparing the models trained using the data collected in two different time intervals, we find a performance drift in models due to the changes in intersection conditions over time. The best-performing model achieves a pedestrian AP of 92.0% with 11.5 ms inference time on NVIDIA A100 GPUs, and an mAP of 95.4%. 

Abstract The influence of El Niño–Southern Oscillation (ENSO) in the Asian monsoon region can persist through the post-ENSO summer, after the sea surface temperature (SST) anomalies in the tropical Pacific have dissipated. The long persistence of coherent post-ENSO anomalies is caused by a positive feedback due to interbasin ocean–atmospheric coupling, known as the Indo-western Pacific Ocean capacitor (IPOC) effect, although the feedback mechanism itself does not necessarily rely on the antecedence of ENSO events, suggesting the potential for substantial internal variability independent of ENSO. To investigate the respective role of ENSO forcing and non-ENSO internal variability, we conduct ensemble “forecast” experiments with a full-physics, globally coupled atmosphere–ocean model initialized from a multidecadal tropical Pacific pacemaker simulation. The leading mode of internal variability as represented by the forecast-ensemble spread resembles the post-ENSO IPOC, despite the absence of antecedent ENSO forcing by design. The persistent atmospheric and oceanic anomalies in the leading mode highlight the positive feedback mechanism in the internal variability. The large sample size afforded by the ensemble spread allows us to identify robust non-ENSO precursors of summer IPOC variability, including a cool SST patch over the tropical northwestern Pacific, a warming patch in the tropical North Atlantic, and downwelling oceanic Rossby waves in the tropical Indian Ocean south of the equator. The pathways by which the precursors develop into the summer IPOC mode and the implications for improved predictability are discussed. 

Abstract We study single electron tunnelling from the barrier in the binary InAs/GaAs quantum structure including quantum well (QW) and quantum dot (QD). The tunneling is described in the terms of localized/delocalized states and their spectral distribution. The modeling is performed by using the phenomenological efective potential approach for InAs/GaAs heterostructures. The results for the two and three-dimensional models are presented. We focus on the efect of QD-QW geometry variations. The relation to the PL experiments is shown.