Perceive, Ground, Reason, and Act: A Benchmark for General-purpose Visual Representation

Current computer vision models, unlike the human visual system, cannot yet achieve general-purpose visual understanding. Existing efforts to create a general vision model are limited in the scope of assessed tasks and offer no overarching framework to perform them holistically. We present a new comprehensive benchmark, General-purpose Visual Understanding Evaluation (G-VUE), covering the full spectrum of visual cognitive abilities with four functional domains \unicode{x2014} Perceive, Ground, Reason, and Act. The four domains are embodied in 11 carefully curated tasks, from 3D reconstruction to visual reasoning and manipulation. Along with the benchmark, we provide a general encoder-decoder framework to allow for the evaluation of arbitrary visual representation on all 11 tasks. We evaluate various pre-trained visual representations with our framework and observe that (1) Transformer-based visual backbone generally outperforms CNN-based backbone on G-VUE, (2) visual representations from vision-language pre-training are superior to those with vision-only pre-training across visual tasks. With G-VUE, we provide a holistic evaluation standard to motivate research toward building general-purpose visual systems via obtaining more general-purpose visual representations

ARNOLD: A Benchmark for Language-Grounded Task Learning With Continuous States in Realistic 3D Scenes

Understanding the continuous states of objects is essential for task learning and planning in the real world. However, most existing task learning benchmarks assume discrete(e.g., binary) object goal states, which poses challenges for the learning of complex tasks and transferring learned policy from simulated environments to the real world. Furthermore, state discretization limits a robot's ability to follow human instructions based on the grounding of actions and states. To tackle these challenges, we present ARNOLD, a benchmark that evaluates language-grounded task learning with continuous states in realistic 3D scenes. ARNOLD is comprised of 8 language-conditioned tasks that involve understanding object states and learning policies for continuous goals. To promote language-instructed learning, we provide expert demonstrations with template-generated language descriptions. We assess task performance by utilizing the latest language-conditioned policy learning models. Our results indicate that current models for language-conditioned manipulations continue to experience significant challenges in novel goal-state generalizations, scene generalizations, and object generalizations. These findings highlight the need to develop new algorithms that address this gap and underscore the potential for further research in this area. See our project page at: https://arnold-benchmark.github.ioComment: The first two authors contributed equally; 20 pages; 17 figures; project availalbe: https://arnold-benchmark.github.io

Boosting the efficiency of inverted quantum dot light-emitting diodes by balancing charge densities and suppressing exciton quenching through band alignment.

We report an inverted and multilayer quantum dot light emitting diode (QLED) which boosts high efficiency by tuning the energy band alignment between charge transport and light emitting layers. The electron transport layer (ETL) was ZnO nanoparticles (NPs) with an optimized doping concentration of cesium azide (CsN3) to effectively reduce electron flow and balance charge injection. This is by virtue of a 0.27 eV upshift of the ETL's conduction band edge, which inhibits the quenching of excitons and preserves the superior emissive properties of the quantum dots due to the insulating characteristics of CsN3. The demonstrated QLED exhibits a peak current efficiency, power efficiency and external quantum efficiency of up to 13.5 cd A-1, 10.6 lm W-1 and 13.4% for the red QLED, and correspondingly 43.1 cd A-1, 33.6 lm W-1 and 9.1% for green, and 4.1 cd A-1, 2.0 lm W-1 and 6.6% for the blue counterparts. Compared with QLEDs without optimization, the performance of these modified devices shows drastic improvement by 95.6%, 39.4% and 36.7%, respectively. This novel device architecture with heterogeneous energy levels reported here offers a new design strategy for next-generation high efficiency QLED displays and solid-state lighting technologies

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements