Advancing the state of the art

We study theMinimum Maximal Matching problem, where we are asked to find in a graph the smallest matching that cannot be extended. We show that this problem is hard to approximate with any constant smaller than 2 even in bipartite graphs, assuming either of two stronger variants of Unique Games Conjecture. The bound also holds for computationally equivalent Minimum Edge Dominating Set. Our...

Distributed caching is a key component in the design of performant, scalable Internet services, but accessing such caches via RPC incurs high cost. Remote Memory Access (RMA) offers a promising, less costly alternative, but achieving a rich production feature set with RMA-based systems is a significant challenge, as the rich abstraction of RPC lends itself to solutions for interoperability and...

This mixed-method study examined the experiences of college students during the COVID-19 pandemic through surveys, experience sampling data collected over two academic quarters (Spring 2019 n1 = 253; Spring 2020 n2 = 147), and semi-structured interviews with 27 undergraduate students. There were no marked changes in mean levels of depressive symptoms, anxiety, stress, or loneliness between 2019...

Background: Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modeling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information,...

Learned speech representations can drastically improve performance on tasks with limited labeled data. However, due to their size and complexity, learned representations have limited utility in mobile settings where run-time performance can be a significant bottleneck. In this work, we propose a class of lightweight speech embedding models that run efficiently on mobile devices based on the...

We present an end-to-end neural network to translate images containing text from one language to another. Traditionally, a cascaded approach of optical character recognition (OCR) followed by neural machine translation (NMT) is used to solve this problem. However, the cascaded approach compounds OCR and NMT errors, and incurs longer latency, performs poorly in multiline cases. Our simplified...

The ability to communicate intention enables decentralized multi-agent robots to collaborate while performing physical tasks. In this work, we present spatial intention maps, a new intention representation for multi-agent vision-based deep reinforcement learning that improves coordination between decentralized mobile manipulators. In this representation, each agent's intention is provided to...