There has been an emerging paradigm shift from the era of internet AI to embodied AI, whereby AI algorithms and agents no longer simply learn from datasets of images, videos or text curated primarily from the internet. Instead, they learn through emb
odied physical interactions with their environments, whether real or simulated. Consequently, there has been substantial growth in the demand for embodied AI simulators to support a diversity of embodied AI research tasks. This growing interest in embodied AI is beneficial to the greater pursuit of artificial general intelligence, but there is no contemporary and comprehensive survey of this field. This paper comprehensively surveys state-of-the-art embodied AI simulators and research, mapping connections between these. By benchmarking nine state-of-the-art embodied AI simulators in terms of seven features, this paper aims to understand the simulators in their provision for use in embodied AI research. Finally, based upon the simulators and a pyramidal hierarchy of embodied AI research tasks, this paper surveys the main research tasks in embodied AI -- visual exploration, visual navigation and embodied question answering (QA), covering the state-of-the-art approaches, evaluation and datasets.
We are concerned with the tensor equations whose coefficient tensor is an M-tensor. We first propose a Newton method for solving the equation with a positive constant term and establish its global and quadratic convergence. Then we extend the method
to solve the equation with a nonnegative constant term and establish its convergence. At last, we do numerical experiments to test the proposed methods. The results show that the proposed method is quite efficient.
This paper experimentally studies whether individuals hold a first-order belief that others apply Bayes rule to incorporate private information into their beliefs, which is a fundamental assumption in many Bayesian and non-Bayesian social learning mo
dels. We design a novel experimental setting in which the first-order belief assumption implies that social information is equivalent to private information. Our main finding is that participants reported reservation prices of social information are significantly lower than those of private information, which provides evidence that casts doubt on the first-order belief assumption. We also build a novel belief error model in which participants form a random posterior belief with a Bayesian posterior belief kernel to explain the experimental findings. A structural estimation of the model suggests that participants sophisticated consideration of others belief error and their exaggeration of the error both contribute to the difference in reservation prices.
The problem of task planning for artificial agents remains largely unsolved. While there has been increasing interest in data-driven approaches for the study of task planning for artificial agents, a significant remaining bottleneck is the dearth of
large-scale comprehensive task-based datasets. In this paper, we present ActioNet, an interactive end-to-end platform for data collection and augmentation of task-based dataset in 3D environment. Using ActioNet, we collected a large-scale comprehensive task-based dataset, comprising over 3000 hierarchical task structures and videos. Using the hierarchical task structures, the videos are further augmented across 50 different scenes to give over 150,000 video. To our knowledge, ActioNet is the first interactive end-to-end platform for such task-based dataset generation and the accompanying dataset is the largest task-based dataset of such comprehensive nature. The ActioNet platform and dataset will be made available to facilitate research in hierarchical task planning.
Human placenta is a complex and heterogeneous organ interfacing between the mother and the fetus that supports fetal development. Alterations to placental structural components are associated with various pregnancy complications. To reveal the hetero
geneity among various placenta cell types in normal and diseased placentas, as well as elucidate molecular interactions within a population of placental cells, a new genomics technology called single cell RNA-Seq (or scRNA-seq) has been employed in the last couple of years. Here we review the principles of scRNA-seq technology, and summarize the recent human placenta studies at scRNA-seq level across gestational ages as well as in pregnancy complications such as preterm birth and preeclampsia. We list the computational analysis platforms and resources available for the public use. Lastly, we discuss the future areas of interest for placenta single cell studies, as well as the data analytics needed to accomplish them.
Many visual simultaneous localization and mapping (SLAM) systems have been shown to be accurate and robust, and have real-time performance capabilities on both indoor and ground datasets. However, these methods can be problematic when dealing with ae
rial frames captured by a camera mounted on an unmanned aerial vehicle (UAV) because the flight height of the UAV can be difficult to control and is easily affected by the environment.To cope with the case of lost tracking, many visual SLAM systems employ a relocalization strategy. This involves the tracking thread continuing the online working by inspecting the connections between the subsequent new frames and the generated map before the tracking was lost. To solve the missing map problem, which is an issue in many applications , after the tracking is lost, based on monocular visual SLAM, we present a method of reconstructing a complete global map of UAV datasets by sequentially merging the submaps via the corresponding undirected connected graph. Specifically, submaps are repeatedly generated, from the initialization process to the place where the tracking is lost, and a corresponding undirected connected graph is built by considering these submaps as nodes and the common map points within two submaps as edges. The common map points are then determined by the bag-of-words (BoW) method, and the submaps are merged if they are found to be connected with the online map in the undirect connected graph. To demonstrate the performance of the proposed method, we first investigated the performance on a UAV dataset, and the experimental results showed that, in the case of several tracking failures, the integrity of the mapping was significantly better than that of the current mainstream SLAM method.
In this work, we study how the dust coagulation/fragmentation will influence the evolution and observational appearances of vortices induced by a massive planet embedded in a low viscosity disk by performing global 2D high-resolution hydrodynamical s
imulations. Within the vortex, due to its higher gas surface density and steeper pressure gradients, dust coagulation, fragmentation and drift (to the vortex center) are all quite efficient, producing dust particles ranging from micron to $sim 1.0 {rm cm}$, as well as overall high dust-to-gas ratio (above unity). In addition, the dust size distribution is quite non-uniform inside the vortex, with the mass weighted average dust size at the vortex center ($sim 4.0$ mm) being a factor of $sim10$ larger than other vortex regions. Both large ($sim$ mm) and small (tens of micron) particles contribute strongly to affect the gas motion within the vortex. As such, we find that the inclusion of dust coagulation has a significant impact on the vortex lifetime and the typical vortex lifetime is about 1000 orbits. After the initial gaseous vortex is destroyed, the dust spreads into a ring with a few remaining smaller gaseous vortices with a high dust concentration and a large maximum size ($sim$ mm). At late time, the synthetic dust continuum images for the coagulation case show as a ring inlaid with several hot spots at 1.33 mm band, while only distinct hot spots remain at 7.0 mm.
We investigate the impact of a highly eccentric 10 $M_{rm oplus}$ (where $M_{rm oplus}$ is the Earth mass) planet embedded in a dusty protoplanetary disk on the dust dynamics and its observational implications. By carrying out high-resolution 2D gas
and dust two-fluid hydrodynamical simulations, we find that the planets orbit can be circularized at large radii. After the planets orbit is circularized, partial gap opening and dust ring formation happen close to the planets circularization radius, which can explain the observed gaps/rings at the outer region of disks. When the disk mass and viscosity become low, we find that an eccentric planet can even open gaps and produce dust rings close to the pericenter and apocenter radii before its circularization. This offers alternative scenarios for explaining the observed dust rings and gaps in protoplanetary disks. A lower disk viscosity is favored to produce brighter rings in observations. An eccentric planet can also potentially slow down the dust radial drift in the outer region of the disk when the disk viscosity is low ($alpha lesssim2times10^{-4}$) and the circularization is faster than the dust radial drift.
Kink instabilities are likely to occur in the current-carrying magnetized plasma jets. Recent observations of the blazar radiation and polarization signatures suggest that the blazar emission region may be considerably magnetized. While the kink inst
ability has been studied with first-principle magnetohydrodynamic (MHD) simulations, the corresponding time-dependent radiation and polarization signatures have not been investigated. In this paper, we perform comprehensive polarization-dependent radiation modeling of the kink instability in the blazar emission region based on relativistic MHD (RMHD) simulations. We find that the kink instability may give rise to strong flares with polarization angle (PA) swings or weak flares with polarization fluctuations, depending on the initial magnetic topology and magnetization. These findings are consistent with observations. Compared with the shock model, the kink model generates polarization signatures that are in better agreement with the general polarization observations. Therefore, we suggest that kink instabilities may widely exist in the jet environment, and provide an efficient way to convert the magnetic energy and produce multiwavelength flares and polarization variations.
Optical Network-on-Chip (ONoC) is an emerging technology considered as one of the key solutions for future generation on-chip interconnects. However, silicon photonic devices in ONoC are highly sensitive to temperature variation, which leads to a low
er efficiency of Vertical-Cavity Surface-Emitting Lasers (VCSELs), a resonant wavelength shift of Microring Resonators (MR), and results in a lower Signal to Noise Ratio (SNR). In this paper, we propose a methodology enabling thermal-aware design for optical interconnects relying on CMOS-compatible VCSEL. Thermal simulations allow designing ONoC interfaces with low gradient temperature and analytical models allow evaluating the SNR.
