Figure 1: Two agents in Gibson Environment for real-world perception. The agent is active, embodied, and subject to constraints of physics and space (a,b). It receives a constant stream of visual observations as if it had an on-board camera (c). It can also receive additional modalities, e.g. depth, semantic labels, or normals (d,e,f). The visual observations are from real-world rather than an artificially designed space. AbstractDeveloping visual perception models for active agents and sensorimotor control are cumbersome to be done in the physical world, as existing algorithms are too slow to efficiently learn in real-time and robots are fragile and costly. This has given rise to learning-in-simulation which consequently casts a question on whether the results transfer to real-world. In this paper, we are concerned with the problem of developing real-world perception for active agents, propose Gibson Virtual Environment 1 for this purpose, and showcase sample perceptual tasks learned therein. Gibson is based on virtualizing real spaces, rather than using artificially designed ones, and currently includes over 1400 floor spaces from 572 full buildings. The main characteristics of Gibson are: I. being from the real-world and reflecting its semantic complexity, II. having an internal synthesis mechanism, "Goggles", enabling deploying the trained models in real-world without needing domain adaptation, III. embodiment of agents and making them subject to constraints of physics and space.

With growing interest in Chinese Language Processing, numerous NLP tools (e.g., word segmenters, part-of-speech taggers, and parsers) for Chinese have been developed all over the world. However, since no large-scale bracketed corpora are available to the public, these tools are trained on corpora with different segmentation criteria, part-of-speech tagsets and bracketing guidelines, and therefore, comparisons are difficult. As a first step towards addressing this issue, we have been preparing a large bracketed corpus since late 1998. The first two installments of the corpus, 250 thousand words of data, fully segmented, POS-tagged and syntactically bracketed, have been released to the public via LDC (www.ldc.upenn.edu). In this paper, we discuss several Chinese linguistic issues and their implications for our treebanking efforts and how we address these issues when developing our annotation guidelines. We also describe our engineering strategies to improve speed while ensuring annotation quality.

Tracking 6-D poses of objects from videos provides rich information to a robot in performing different tasks such as manipulation and navigation. In this article, we formulate the 6-D object pose tracking problem in the Rao-Blackwellized particle filtering framework, where the 3-D rotation and the 3-D translation of an object are decoupled. This factorization allows our approach, called PoseRBPF, to efficiently estimate the 3-D translation of an object along with the full distribution over the 3-D rotation. This is achieved by discretizing the rotation space in a fine-grained manner and training an autoencoder network to construct a codebook of feature embeddings for the discretized rotations. As a result, PoseRBPF can track objects with arbitrary symmetries while still maintaining adequate posterior distributions. Our approach achieves state-of-the-art results on two 6-D pose estimation benchmarks. We open-source our implementation at https://github.com/NVlabs/PoseRBPF.

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The discovery of complete ammonia oxidizers (comammox) refutes the century-old paradigm that nitrification requires the activity of two types of microbes. Determining the distribution and abundance of comammox in various environments is important for revealing the ecology of microbial nitrification within the global nitrogen cycle. In this study, the ubiquity and diversity of comammox were analyzed for samples from different types of environments, including soil, sediment, sludge, and water. The results of a two-step PCR using highly degenerate primers (THDP-PCR) and quantitative real-time PCR (qPCR) supported the relatively high abundance of comammox in nearly half of all samples tested, sometimes even outnumbering canonical ammonia-oxidizing bacteria (AOB). In addition, a relatively high proportion of comammox in tap and coastal water samples was confirmed via analysis of metagenomic data sets in public databases. The diversity of comammox was estimated by comammox-specific partial nested PCR amplification of the ammonia monooxygenase subunit A (amoA) gene, and phylogenetic analysis of comammox AmoA clearly showed a split of clade A into clades A.1 and A.2, with the proportions of clades A.1, A.2, and B differing among the various environmental samples. Moreover, compared to the amoA genes of AOB and ammonia-oxidizing archaea (AOA), the comammox amoA gene exhibited higher diversity indices. The ubiquitous distribution and high diversity of comammox indicate that they are likely overlooked contributors to nitrification in various ecosystems. IMPORTANCE The discovery of complete ammonia oxidizers (comammox), which oxidize ammonia to nitrate via nitrite, refutes the century-old paradigm that nitrification requires the activity of two types of microbes and redefines a key process in the biogeochemical nitrogen cycle. Understanding the functional relationships between comammox and other nitrifiers is important for ecological studies on the nitrogen cycle. Therefore, the diversity and contribution of comammox should be considered during ecological analyses of nitrifying microorganisms. In this study, a ubiquitous and highly diverse distribution of comammox was observed in various environmental samples, similar to the distribution of canonical ammonia-oxidizing bacteria. The proportion of comammox was relatively high in coastal water and sediment samples, whereas it was nearly undetectable in open-ocean samples. The ubiquitous distribution and high diversity of comammox indicate that these microorganisms might be important contributors to nitrification.

Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extended instructions expressed in natural language. However, a significant weakness of language models is that they lack real-world experience, which makes it difficult to leverage them for decision making within a given embodiment. For example, asking a language model to describe how to clean a spill might result in a reasonable narrative, but it may not be applicable to a particular agent, such as a robot, that needs to perform this task in a particular environment. We propose to provide real-world grounding by means of pretrained skills, which are used to constrain the model to propose natural language actions that are both feasible and contextually appropriate. The robot can act as the language model's "hands and eyes," while the language model supplies high-level semantic knowledge about the task. We show how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally extended instructions, while value functions associated with these skills provide the grounding necessary to connect this knowledge to a particular physical environment. We evaluate our method on a number of real-world robotic tasks, where we show the need for real-world grounding and that this approach is capable of completing long-horizon, abstract, natural language instructions on a mobile manipulator. The project's website and the video can be found at say-can.github.io. (a) Large Language Models (LLMs) (b) SayCan

Background:The risk factors for adverse events of Coronavirus have not been well described. We aimed to explore the predictive value of clinical, laboratory and CT imaging characteristics on admission for short-term outcomes of COVID-19 patients. Methods: This multicenter, retrospective, observation study enrolled 703 laboratory-confirmed COVID-19 patients admitted to 16 tertiary hospitals from 8 provinces in China between January 10, 2020 and March 13, 2020. Demographic, clinical, laboratory data, CT imaging findings on admission and clinical outcomes were collected and compared. The primary endpoint was in-hospital death, the secondary endpoints were composite clinical adverse outcomes including in-hospital death, admission to intensive care unit (ICU) and requiring invasive mechanical ventilation support (IMV). Multivariable Cox regression, Kaplan-Meier plots and log-rank test were used to explore risk factors related to in-hospital death and in-hospital adverse outcomes. Results: Of 703 patients, 55 (8%) developed adverse outcomes (including 33 deceased), 648 (92%) discharged without any adverse outcome. Multivariable regression analysis showed risk factors associated with in-hospital death included ≥ 2 comorbidities (hazard ratio [HR