1. 3D Bird Reconstruction: a Dataset, Model, and Shape Recovery from a Single View

Marc Badger, Yufu Wang, Adarsh Modh, Ammon Perkes, Nikos Kolotouros, Bernd G. Pfrommer, Marc F. Schmidt, Kostas Daniilidis

• retweets: 27, favorites: 116 (08/18/2020 09:02:01)
• links: abs | pdf
• cs.CV

Automated capture of animal pose is transforming how we study neuroscience and social behavior. Movements carry important social cues, but current methods are not able to robustly estimate pose and shape of animals, particularly for social animals such as birds, which are often occluded by each other and objects in the environment. To address this problem, we first introduce a model and multi-view optimization approach, which we use to capture the unique shape and pose space displayed by live birds. We then introduce a pipeline and experiments for keypoint, mask, pose, and shape regression that recovers accurate avian postures from single views. Finally, we provide extensive multi-view keypoint and mask annotations collected from a group of 15 social birds housed together in an outdoor aviary. The project website with videos, results, code, mesh model, and the Penn Aviary Dataset can be found at https://marcbadger.github.io/avian-mesh.

3D Bird Reconstruction: a Dataset, Model, and Shape Recovery from a Single View
pdf: https://t.co/dcnC06Qzp5
abs: https://t.co/75kdKDyr2F
project page: https://t.co/o7ojSucVqV pic.twitter.com/A5q2zwIn0n

— AK (@ak92501) August 17, 2020

2. Quantum advantage for computations with limited space

Dmitri Maslov, Jin-Sung Kim, Sergey Bravyi, Theodore J. Yoder, Sarah Sheldon

• retweets: 11, favorites: 63 (08/18/2020 09:02:02)
• links: abs | pdf
• quant-ph | cs.ET

Quantum computations promise the ability to solve problems intractable in the classical setting. Restricting the types of computations considered often allows to establish a provable theoretical advantage by quantum computations, and later demonstrate it experimentally. In this paper, we consider space-restricted computations, where input is a read-only memory and only one (qu)bit can be computed on. We show that $n$-bit symmetric Boolean functions can be implemented exactly through the use of quantum signal processing as restricted space quantum computations using $O(n^2)$ gates, but some of them may only be evaluated with probability $\frac{1}{2} {+} \tilde{O}(\frac{1}{\sqrt{n}})$ by analogously defined classical computations. We experimentally demonstrate computations of $3$- and a $4$-bit symmetric Boolean functions by quantum circuits, leveraging custom two-qubit gates, with algorithmic success probability exceeding the best possible classically. This establishes and experimentally verifies a different kind of quantum advantage — one where a quantum bit stores more useful information for the purpose of computation than a classical bit. This suggests that in computations, quantum scrap space is more valuable than analogous classical space and calls for an in-depth exploration of space-time tradeoffs in quantum circuits.

A new paper from our IBM Quantum team "Quantum advantage for computations with limited space"https://t.co/fd5ciCtpDE

— Jay Gambetta (@jaygambetta) August 17, 2020