Co-Salient Object Detection with Co-Representation Purification

Co-salient object detection (Co-SOD) aims at discovering the common objects in a group of relevant images. Mining a co-representation is essential for locating co-salient objects. Unfortunately, the current Co-SOD method does not pay enough attention that the information not related to the co-salient object is included in the co-representation. Such irrelevant information in the co-representation interferes with its locating of co-salient objects. In this paper, we propose a Co-Representation Purification (CoRP) method aiming at searching noise-free co-representation. We search a few pixel-wise embeddings probably belonging to co-salient regions. These embeddings constitute our co-representation and guide our prediction. For obtaining purer co-representation, we use the prediction to iteratively reduce irrelevant embeddings in our co-representation. Experiments on three datasets demonstrate that our CoRP achieves state-of-the-art performances on the benchmark datasets. Our source code is available at https://github.com/ZZY816/CoRP.Comment: Accepted by TPAMI 202

Droplets as Carriers for Flexible Electronic Devices

Coupling soft bodies and dynamic motions with multifunctional flexible electronics is challenging, but is essential in satisfying the urgent and soaring demands of fully soft and comprehensive robotic systems that can perform tasks in spite of rigorous spatial constraints. Here, the mobility and adaptability of liquid droplets with the functionality of flexible electronics, and techniques to use droplets as carriers for flexible devices are combined. The resulting active droplets (ADs) with volumes ranging from 150 to 600 µL can conduct programmable functions, such as sensing, actuation, and energy harvesting defined by the carried flexible devices and move under the excitation of gravitational force or magnetic force. They work in both dry and wet environments, and adapt to the surrounding environment through reversible shape shifting. These ADs can achieve controllable motions at a maximum velocity of 226 cm min−1 on a dry surface and 32 cm min-1 in a liquid environment. The conceptual system may eventually lead to individually addressable ADs that offer sophisticated functions for high-throughput molecule analysis, drug assessment, chemical synthesis, and information collection

Extraction of power lines and pylons from LiDAR point clouds using a GCN-based method

The routine power line inspection is critical to maintain the reliability, availability, and sustainability of electricity supply. As a key part of inspection, power lines and pylons extraction is essential for resource management and power corridor safety, especially in the mountain regions. In this paper, we proposed a deep learning based method to extract power lines and pylons using ALS point clouds. First, a structure information preserved module is designed to mine the relationship of local neighborhood points. Then, a graph convolutional network (GCN) is used as basic module to extract point features. Finally, three categories, power lines, pylons and other objects are segmented from input point clouds. In addition, we provide an effective data enhancement strategy to generate enough samples to train the proposed model. We evaluated our method using a dataset acquired by our ALS scanning system. Experimental results demonstrate that our method is superior to the state-of-the-art methods on descriptiveness and efficiency. The overall accuracy and mean time are 99.1% and 9.3 seconds, respectively

NVIDIA FLARE: Federated Learning from Simulation to Real-World

Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data. We created NVIDIA FLARE as an open-source software development kit (SDK) to make it easier for data scientists to use FL in their research and real-world applications. The SDK includes solutions for state-of-the-art FL algorithms and federated machine learning approaches, which facilitate building workflows for distributed learning across enterprises and enable platform developers to create a secure, privacy-preserving offering for multiparty collaboration utilizing homomorphic encryption or differential privacy. The SDK is a lightweight, flexible, and scalable Python package. It allows researchers to apply their data science workflows in any training libraries (PyTorch, TensorFlow, XGBoost, or even NumPy) in real-world FL settings. This paper introduces the key design principles of NVFlare and illustrates some use cases (e.g., COVID analysis) with customizable FL workflows that implement different privacy-preserving algorithms. Code is available at https://github.com/NVIDIA/NVFlare.Comment: Accepted at the International Workshop on Federated Learning, NeurIPS 2022, New Orleans, USA (https://federated-learning.org/fl-neurips-2022); Revised version v2: added Key Components list, system metrics for homomorphic encryption experiment; Extended v3 for journal submissio

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

PMCHWT Solver Accelerated by Adaptive Cross Approximation for Efficient Computation of Scattering from Metal Nanoparticles

An accelerated algorithm that can efficiently calculate the light scattering of a single metal nanoparticle was proposed. According to the equivalent principle, the method of moment (MoM) transforms the Poggio–Miller–Chang–Harrington–Wu–Tsai (PMCHWT) integral equations into linear algebraic equations, which are solved by the flexible generalized minimal residual solver (FGMRES). Each element of near field MoM impedance matrix was described by Rao–Wilton–Glisson (RWG) basis functions and calculated by double surface integrals. Due to the low-rank property, the adaptive cross approximation (ACA) algorithm based on the octree data structure was applied to compress the MoM impedance matrix of far field action leading to the significant reduction of solution time and memory. Numerical results demonstrated that the proposed method is both accurate and efficient. Compared with the traditional MoM, the ACA algorithm can significantly reduce the impedance matrix filling time and accelerate the scattering field’s computation from actual metal nanoparticles using PMCHWT integral equations

Designing and Modeling of a Dual-Band High-Efficiency Rectenna Using Dielectric Resonant Antenna Array

In this paper, we proposed a high-efficiency 2.45 GHz and 5.8 GHz dual-band rectenna for wireless energy harvesting (WEH). First, a dual-band dielectric resonant antenna (DRA) was designed. A 4-element DRA array was further developed using a compact feeding network. The measured gains of the DRA array were 5.5 dBi at 2.45 GHz and 12.9 dBi at 5.8 GHz. Then, a new type of impedance regulation stub (IRS) was introduced that significantly improved the conversion efficiency of the rectenna. The result showed that the conversion efficiency of the rectifier was 66% and 62% at 2.45 GHz and 5.8 GHz, respectively, when the input power level was 10 dBm. Furthermore, the proposed rectenna was validated to activate a standard BQ25504 DC-to-DC boost converter and charge a supercapacitor. The results demonstrated that the proposed rectenna could be an appropriate solution for WEH applications