High-Precision Localization Using Ground Texture

Location-aware applications play an increasingly critical role in everyday life. However, satellite-based localization (e.g., GPS) has limited accuracy and can be unusable in dense urban areas and indoors. We introduce an image-based global localization system that is accurate to a few millimeters and performs reliable localization both indoors and outside. The key idea is to capture and index distinctive local keypoints in ground textures. This is based on the observation that ground textures including wood, carpet, tile, concrete, and asphalt may look random and homogeneous, but all contain cracks, scratches, or unique arrangements of fibers. These imperfections are persistent, and can serve as local features. Our system incorporates a downward-facing camera to capture the fine texture of the ground, together with an image processing pipeline that locates the captured texture patch in a compact database constructed offline. We demonstrate the capability of our system to robustly, accurately, and quickly locate test images on various types of outdoor and indoor ground surfaces

HiFi-GAN: High-Fidelity Denoising and Dereverberation Based on Speech Deep Features in Adversarial Networks

Real-world audio recordings are often degraded by factors such as noise, reverberation, and equalization distortion. This paper introduces HiFi-GAN, a deep learning method to transform recorded speech to sound as though it had been recorded in a studio. We use an end-to-end feed-forward WaveNet architecture, trained with multi-scale adversarial discriminators in both the time domain and the time-frequency domain. It relies on the deep feature matching losses of the discriminators to improve the perceptual quality of enhanced speech. The proposed model generalizes well to new speakers, new speech content, and new environments. It significantly outperforms state-of-the-art baseline methods in both objective and subjective experiments.Comment: Accepted by INTERSPEECH 202

Principles for Designing Teaching and Learning Spaces

"The Principles for Designing Teaching and Learning Spaces consider the classroom environment within the context of what is known about students’ learning. These Principles are then translated into specific design features to guide design decisions, such that learning spaces become a physical manifestation of the university’s teaching and learning vision.

Research-Informed Principles for (Re)designing Teaching and Learning Spaces

Designing physical learning environments that connect to indicators of effective educational practice reflects a university’s pedagogical commitment to student success. This article describes an approach to teaching and learning space design based on research-informed pedagogical principles implemented successfully at our university. It then articulates how those principles can be translated into classroom design features, with examples. These principles have had an operational and conceptual impact on campus, providing a framework for diverse audiences to think about spaces in a way that reflects shared goals, language and values

Productivity Dispersion in Medicine and Manufacturing

The conventional wisdom in health economics is that large differences in average productivity across US hospitals are the result of idiosyncratic features of the healthcare sector which dull the role of market forces. Strikingly, however, we find that productivity dispersion in heart attack treatment across hospitals is, if anything, smaller than in narrowly defined manufacturing industries such as ready-mixed concrete. While this fact admits multiple interpretations, it suggests that healthcare may have more in common with "traditional" sectors than is often assumed, and relatedly, that insights from research on productivity and allocation in other sectors may enrich analysis of healthcare

Erasure conversion in a high-fidelity Rydberg quantum simulator

Minimizing and understanding errors is critical for quantum science, both in noisy intermediate scale quantum (NISQ) devices and for the quest towards fault-tolerant quantum computation. Rydberg arrays have emerged as a prominent platform in this context with impressive system sizes and proposals suggesting how error-correction thresholds could be significantly improved by detecting leakage errors with single-atom resolution, a form of erasure error conversion. However, two-qubit entanglement fidelities in Rydberg atom arrays have lagged behind competitors and this type of erasure conversion is yet to be realized for matter-based qubits in general. Here we demonstrate both erasure conversion and high-fidelity Bell state generation using a Rydberg quantum simulator. We implement erasure conversion via fast imaging of alkaline-earth atoms, which leaves atoms in a metastable state unperturbed and yields additional information independent of the final qubit readout. When excising data with observed erasure errors, we achieve a lower-bound for the Bell state generation fidelity of ${\geq} 0.9971^{+10}_{-13}$, which improves to ${\geq}0.9985^{+7}_{-12}$ when correcting for remaining state preparation errors. We further demonstrate erasure conversion in a quantum simulation experiment for quasi-adiabatic preparation of long-range order across a quantum phase transition, where we explicitly differentiate erasure conversion of preparation and Rydberg decay errors. We unveil the otherwise hidden impact of these errors on the simulation outcome by evaluating correlations between erasures and the final readout as well as between erasures themselves. Our work demonstrates the capability for Rydberg-based entanglement to reach fidelities in the ${\sim} 0.999$ regime, with higher fidelities a question of technical improvements, and shows how erasure conversion can be utilized in NISQ devices.Comment: PS and ALS contributed equally to this wor