FSNet: Redesign Self-Supervised MonoDepth for Full-Scale Depth Prediction for Autonomous Driving

Predicting accurate depth with monocular images is important for low-cost robotic applications and autonomous driving. This study proposes a comprehensive self-supervised framework for accurate scale-aware depth prediction on autonomous driving scenes utilizing inter-frame poses obtained from inertial measurements. In particular, we introduce a Full-Scale depth prediction network named FSNet. FSNet contains four important improvements over existing self-supervised models: (1) a multichannel output representation for stable training of depth prediction in driving scenarios, (2) an optical-flow-based mask designed for dynamic object removal, (3) a self-distillation training strategy to augment the training process, and (4) an optimization-based post-processing algorithm in test time, fusing the results from visual odometry. With this framework, robots and vehicles with only one well-calibrated camera can collect sequences of training image frames and camera poses, and infer accurate 3D depths of the environment without extra labeling work or 3D data. Extensive experiments on the KITTI dataset, KITTI-360 dataset and the nuScenes dataset demonstrate the potential of FSNet. More visualizations are presented in \url{https://sites.google.com/view/fsnet/home}Comment: 12 pages. conditionally accepted by IEEE T-AS

Structure-based substrate screening for an enzyme

<p>Abstract</p> <p>Background</p> <p>Nowadays, more and more novel enzymes can be easily found in the whole enzyme pool with the rapid development of genetic operation. However, experimental work for substrate screening of a new enzyme is laborious, time consuming and costly. On the other hand, many computational methods have been widely used in lead screening of drug design. Seeing that the ligand-target protein system in drug design and the substrate-enzyme system in enzyme applications share the similar molecular recognition mechanism, we aim to fulfill the goal of substrate screening by in silico means in the present study.</p> <p>Results</p> <p>A computer-aided substrate screening (CASS) system which was based on the enzyme structure was designed and employed successfully to help screen substrates of <it>Candida antarctica </it>lipase B (CALB). In this system, restricted molecular docking which was derived from the mechanism of the enzyme was applied to predict the energetically favorable poses of substrate-enzyme complexes. Thereafter, substrate conformation, distance between the oxygen atom of the alcohol part of the ester (in some compounds, this oxygen atom was replaced by nitrogen atom of the amine part of acid amine or sulfur atom of the thioester) and the hydrogen atom of imidazole of His224, distance between the carbon atom of the carbonyl group of the compound and the oxygen atom of hydroxyl group of Ser105 were used sequentially as the criteria to screen the binding poses. 223 out of 233 compounds were identified correctly for the enzyme by this screening system. Such high accuracy guaranteed the feasibility and reliability of the CASS system.</p> <p>Conclusion</p> <p>The idea of computer-aided substrate screening is a creative combination of computational skills and enzymology. Although the case studied in this paper is tentative, high accuracy of the CASS system sheds light on the field of computer-aided substrate screening.</p

Ti-MAE: Self-Supervised Masked Time Series Autoencoders

Multivariate Time Series forecasting has been an increasingly popular topic in various applications and scenarios. Recently, contrastive learning and Transformer-based models have achieved good performance in many long-term series forecasting tasks. However, there are still several issues in existing methods. First, the training paradigm of contrastive learning and downstream prediction tasks are inconsistent, leading to inaccurate prediction results. Second, existing Transformer-based models which resort to similar patterns in historical time series data for predicting future values generally induce severe distribution shift problems, and do not fully leverage the sequence information compared to self-supervised methods. To address these issues, we propose a novel framework named Ti-MAE, in which the input time series are assumed to follow an integrate distribution. In detail, Ti-MAE randomly masks out embedded time series data and learns an autoencoder to reconstruct them at the point-level. Ti-MAE adopts mask modeling (rather than contrastive learning) as the auxiliary task and bridges the connection between existing representation learning and generative Transformer-based methods, reducing the difference between upstream and downstream forecasting tasks while maintaining the utilization of original time series data. Experiments on several public real-world datasets demonstrate that our framework of masked autoencoding could learn strong representations directly from the raw data, yielding better performance in time series forecasting and classification tasks.Comment: 20 pages, 7 figure

CenterLineDet: Road Lane CenterLine Graph Detection With Vehicle-Mounted Sensors by Transformer for High-definition Map Creation

With the rapid development of autonomous vehicles, there witnesses a booming demand for high-definition maps (HD maps) that provide reliable and robust prior information of static surroundings in autonomous driving scenarios. As one of the main high-level elements in the HD map, the road lane centerline is critical for downstream tasks, such as prediction and planning. Manually annotating lane centerline HD maps by human annotators is labor-intensive, expensive and inefficient, severely restricting the wide application and fast deployment of autonomous driving systems. Previous works seldom explore the centerline HD map mapping problem due to the complicated topology and severe overlapping issues of road centerlines. In this paper, we propose a novel method named CenterLineDet to create the lane centerline HD map automatically. CenterLineDet is trained by imitation learning and can effectively detect the graph of lane centerlines by iterations with vehicle-mounted sensors. Due to the application of the DETR-like transformer network, CenterLineDet can handle complicated graph topology, such as lane intersections. The proposed approach is evaluated on a large publicly available dataset Nuscenes, and the superiority of CenterLineDet is well demonstrated by the comparison results. This paper is accompanied by a demo video and a supplementary document that are available at \url{https://tonyxuqaq.github.io/projects/CenterLineDet/}.Comment: Under revie

CA1-projecting subiculum neurons facilitate object-place learning.

Recent anatomical evidence suggests a functionally significant back-projection pathway from the subiculum to the CA1. Here we show that the afferent circuitry of CA1-projecting subicular neurons is biased by inputs from CA1 inhibitory neurons and the visual cortex, but lacks input from the entorhinal cortex. Efferents of the CA1-projecting subiculum neurons also target the perirhinal cortex, an area strongly implicated in object-place learning. We identify a critical role for CA1-projecting subicular neurons in object-location learning and memory, and show that this projection modulates place-specific activity of CA1 neurons and their responses to displaced objects. Together, these experiments reveal a novel pathway by which cortical inputs, particularly those from the visual cortex, reach the hippocampal output region CA1. Our findings also implicate this circuitry in the formation of complex spatial representations and learning of object-place associations

Imaging of the relative saturation current density and sheet resistance of laser doped regions via photoluminescence

We present an approach to characterize the relative saturation current density (J oe) and sheet resistance (RSH) of laser doped regions on silicon wafers based on rapid photoluminescence (PL) imaging. In the absence of surface passivation layers, the RSH of laser doped regions using a wide range of laser parameters is found to be inversely proportional to the PL intensity (I PL ). We explain the underlying mechanism for this correlation, which reveals that, in principle, I PL is inversely proportional to J oe at any injection level. The validity of this relationship under a wide range of typical experimental conditions is confirmed by numerical simulations. This method allows the optimal laser parameters for achieving low RSH and J oe to be determined from a simple PL image.The authors acknowledge financial support from the Australian Solar Institute (ASI)/Australian Renewable Energy Agency (ARENA) under the ANU PV Core project, Postdoctoral Fellowship and Australia-Germany Collaborative Solar Research and Development projects. The authors also acknowledge support from the Australian Government’s NCRIS/EIF funding programs for access to Heavy Ion Accelerator Facilities at the Australian National University