Weakly Supervised Learning Method for Semantic Segmentation of Large-Scale 3D Point Cloud Based on Transformers

Nowadays, semantic segmentation results of 3D point cloud have been widely applied in the fields of robotics, autonomous driving, and augmented reality etc. Thanks to the development of relevant deep learning models (such as PointNet), supervised training methods have become hotspot, in which two common limitations exists: inferior feature representation of 3D points and massive annotations. To improve 3D point feature, inspired by the idea of transformer, we employ a so-call LCP network that extracts better feature by investigating attentions between target 3D points and its corresponding local neighbors via local context propagation. Training transformer-based network needs amount of training samples, which itself is a labor-intensive, costly and error-prone work, therefore, this work proposes a weakly supervised framework, in particular, pseudo-labels are estimated based on the feature distances between unlabeled points and prototypes, which are calculated based on labeled data. The extensive experimental results show that, the proposed PL-LCP can yield considerable results (67.6% mIOU for indoor and 67.3% for outdoor) even if only using 1% real labels, and comparing to several state-of-the-art method using all labels, we achieve superior results in mIOU, OA for indoor (65.9%, 89.2%)

Modulation of Negative Index Metamaterials in the Near-IR Range

Optical modulation of the effective refractive properties of a "fishnet" metamaterial with a Ag/Si/Ag heterostructure is demonstrated in the near-IR range and the associated fast dynamics of negative refractive index is studied by pump-probe method. Photo excitation of the amorphous Si layer at visible wavelength and corresponding modification of its optical parameters is found to be responsible for the observed modulation of negative refractive index in near-IR.Comment: 11 figures, 4 figure

Zero-shot Model Diagnosis

When it comes to deploying deep vision models, the behavior of these systems must be explicable to ensure confidence in their reliability and fairness. A common approach to evaluate deep learning models is to build a labeled test set with attributes of interest and assess how well it performs. However, creating a balanced test set (i.e., one that is uniformly sampled over all the important traits) is often time-consuming, expensive, and prone to mistakes. The question we try to address is: can we evaluate the sensitivity of deep learning models to arbitrary visual attributes without an annotated test set? This paper argues the case that Zero-shot Model Diagnosis (ZOOM) is possible without the need for a test set nor labeling. To avoid the need for test sets, our system relies on a generative model and CLIP. The key idea is enabling the user to select a set of prompts (relevant to the problem) and our system will automatically search for semantic counterfactual images (i.e., synthesized images that flip the prediction in the case of a binary classifier) using the generative model. We evaluate several visual tasks (classification, key-point detection, and segmentation) in multiple visual domains to demonstrate the viability of our methodology. Extensive experiments demonstrate that our method is capable of producing counterfactual images and offering sensitivity analysis for model diagnosis without the need for a test set.Comment: Accepted in CVPR 202

Characterizing and correcting camera noise in back-illuminated sCMOS cameras

With promising properties of fast imaging speed, large field-of-view, relative low cost and many others, back-illuminated sCMOS cameras have been receiving intensive attention for low light level imaging in the past several years. However, due to the pixel-to-pixel difference of camera noise (called noise non-uniformity) in sCMOS cameras, researchers may hesitate to use them in some application fields, and sometimes wonder whether they should optimize the noise non-uniformity of their sCMOS cameras before using them in a specific application scenario. In this paper, we systematically characterize the impact of different types of sCMOS noise on image quality and perform corrections to these types of sCMOS noise using three representative algorithms (PURE, NCS and MLEsCMOS). We verify that it is possible to use appropriate correction methods to push the non-uniformity of major types of camera noise, including readout noise, offset, and photon response, to a satisfactory level for conventional microscopy and single molecule localization microscopy. We further find out that, after these corrections, global read noise becomes a major concern that limits the imaging performance of back-illuminated sCMOS cameras. We believe this study provides new insights into the understanding of camera noise in back-illuminated sCMOS cameras, and also provides useful information for future development of this promising camera technology. &nbsp;</p

Statics and dynamics of single DNA molecules confined in nanochannels

The successful design of nanofluidic devices for the manipulation of biopolymers requires an understanding of how the predictions of soft condensed matter physics scale with device dimensions. Here we present measurements of DNA extended in nanochannels and show that below a critical width roughly twice the persistence length there is a crossover in the polymer physics

Genome-wide identification of the soybean cytokinin oxidase/dehydrogenase gene family and its diverse roles in response to multiple abiotic stress

Cytokinin oxidase/dehydrogenase (CKX) irreversibly degrades cytokinin, regulates growth and development, and helps plants to respond to environmental stress. Although the CKX gene has been well characterized in various plants, its role in soybean remains elusive. Therefore, in this study, the evolutionary relationship, chromosomal location, gene structure, motifs, cis-regulatory elements, collinearity, and gene expression patterns of GmCKXs were analyzed using RNA-seq, quantitative real-time PCR (qRT-PCR), and bioinformatics. We identified 18 GmCKX genes from the soybean genome and grouped them into five clades, each comprising members with similar gene structures and motifs. Cis-acting elements involved in hormones, resistance, and physiological metabolism were detected in the promoter regions of GmCKXs. Synteny analysis indicated that segmental duplication events contributed to the expansion of the soybean CKX family. The expression profiling of the GmCKXs genes using qRT-PCR showed tissue-specific expression patterns. The RNA-seq analysis also indicated that GmCKXs play an important role in response to salt and drought stresses at the seedling stage. The responses of the genes to salt, drought, synthetic cytokinin 6-benzyl aminopurine (6-BA), and the auxin indole-3-acetic acid (IAA) at the germination stage were further evaluated by qRT-PCR. Specifically, the GmCKX14 gene was downregulated in the roots and the radicles at the germination stage. The hormones 6-BA and IAA repressed the expression levels of GmCKX1, GmCKX6, and GmCKX9 genes but upregulated the expression levels of GmCKX10 and GmCKX18 genes. The three abiotic stresses also decreased the zeatin content in soybean radicle but enhanced the activity of the CKX enzymes. Conversely, the 6-BA and IAA treatments enhanced the CKX enzymes’ activity but reduced the zeatin content in the radicles. This study, therefore, provides a reference for the functional analysis of GmCKXs in soybean in response to abiotic stresses

A calcineurin–Hoxb13 axis regulates growth mode of mammalian cardiomyocytes

10.1038/s41586-020-2228-6Nature5827811271-27

Roadmap on commercialization of metal halide perovskite photovoltaics

Perovskite solar cells (PSCs) represent one of the most promising emerging photovoltaic technologies due to their high power conversion efficiency. However, despite the huge progress made not only in terms of the efficiency achieved, but also fundamental understanding of the relevant physics of the devices and issues which affect their efficiency and stability, there are still unresolved problems and obstacles on the path toward commercialization of this promising technology. In this roadmap, we aim to provide a concise and up to date summary of outstanding issues and challenges, and the progress made toward addressing these issues. While the format of this article is not meant to be a comprehensive review of the topic, it provides a collection of the viewpoints of the experts in the field, which covers a broad range of topics related to PSC commercialization, including those relevant for manufacturing (scaling up, different types of devices), operation and stability (various factors), and environmental issues (in particular the use of lead). We hope that the article will provide a useful resource for researchers in the field and that it will facilitate discussions and move forward toward addressing the outstanding challenges in this fast-developing field