FairAdaBN: Mitigating unfairness with adaptive batch normalization and its application to dermatological disease classification

Deep learning is becoming increasingly ubiquitous in medical research and applications while involving sensitive information and even critical diagnosis decisions. Researchers observe a significant performance disparity among subgroups with different demographic attributes, which is called model unfairness, and put lots of effort into carefully designing elegant architectures to address unfairness, which poses heavy training burden, brings poor generalization, and reveals the trade-off between model performance and fairness. To tackle these issues, we propose FairAdaBN by making batch normalization adaptive to sensitive attribute. This simple but effective design can be adopted to several classification backbones that are originally unaware of fairness. Additionally, we derive a novel loss function that restrains statistical parity between subgroups on mini-batches, encouraging the model to converge with considerable fairness. In order to evaluate the trade-off between model performance and fairness, we propose a new metric, named Fairness-Accuracy Trade-off Efficiency (FATE), to compute normalized fairness improvement over accuracy drop. Experiments on two dermatological datasets show that our proposed method outperforms other methods on fairness criteria and FATE.Comment: Accepted by MICCAI 202

An asymmetric supercapacitor with excellent cycling performance realized by hierarchical porous NiGa2O4 nanosheets

Rational design of composition and electrochemically favorable structure configuration of electrode materials are highly required to develop high-performance supercapacitors. Here, we report our findings on the design of interconnected NiGa2O4 nanosheets as advanced cathode electrodes for supercapacitors. Rietveld refinement analysis demonstrates that the incorporation of Ga in NiO leads to a larger cubic lattice parameter that promotes faster charge-transfer kinetics, enabling significantly improved electrochemical performance. The NiGa2O4 electrode delivers a specific capacitance of 1508 F g−1 at a current density of 1 A g−1 with the capacitance retention of 63.7% at 20 A g−1, together with excellent cycling stability after 10000 charge–discharge cycles (capacitance retention of 102.4%). An asymmetric supercapacitor device was assembled by using NiGa2O4 and Fe2O3 as cathode and anode electrodes, respectively. The ASC delivers a high energy density of 45.2 Wh kg−1 at a power density of 1600 W kg−1 with exceptional cycling stability (94.3% cell capacitance retention after 10000 cycles). These results suggest that NiGa2O4 can serve as a new class cathode material for advanced electrochemical energy storage applications

UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy

In this work, we tackle the problem of learning universal robotic dexterous grasping from a point cloud observation under a table-top setting. The goal is to grasp and lift up objects in high-quality and diverse ways and generalize across hundreds of categories and even the unseen. Inspired by successful pipelines used in parallel gripper grasping, we split the task into two stages: 1) grasp proposal (pose) generation and 2) goal-conditioned grasp execution. For the first stage, we propose a novel probabilistic model of grasp pose conditioned on the point cloud observation that factorizes rotation from translation and articulation. Trained on our synthesized large-scale dexterous grasp dataset, this model enables us to sample diverse and high-quality dexterous grasp poses for the object point cloud.For the second stage, we propose to replace the motion planning used in parallel gripper grasping with a goal-conditioned grasp policy, due to the complexity involved in dexterous grasping execution. Note that it is very challenging to learn this highly generalizable grasp policy that only takes realistic inputs without oracle states. We thus propose several important innovations, including state canonicalization, object curriculum, and teacher-student distillation. Integrating the two stages, our final pipeline becomes the first to achieve universal generalization for dexterous grasping, demonstrating an average success rate of more than 60\% on thousands of object instances, which significantly outperforms all baselines, meanwhile showing only a minimal generalization gap.Comment: Accepted to CVPR 202

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

SAR Ship Detection in Complex Scenes Based on Adaptive Anchor Assignment and IOU Supervise

This study aims to address the unreasonable assignment of positive and negative samples and poor localization quality in ship detection in complex scenes. Therefore, in this study, a Synthetic Aperture Radar (SAR) ship detection network (A3-IOUS-Net) based on adaptive anchor assignment and Intersection over Union (IOU) supervise in complex scenes is proposed. First, an adaptive anchor assignment mechanism is proposed, where a probability distribution model is established to adaptively assign anchors as positive and negative samples to enhance the ship samples’ learning ability in complex scenes. Then, an IOU supervise mechanism is proposed, which adds an IOU prediction branch in the prediction head to supervise the localization quality of detection boxes, allowing the network to accurately locate the SAR ship targets in complex scenes. Furthermore, a coordinate attention module is introduced into the prediction branch to suppress the background clutter interference and improve the SAR ship detection accuracy. The experimental results on the open SAR Ship Detection Dataset (SSDD) show that the Average Precision (AP) of A3-IOUS-Net in complex scenes is 82.04%, superior to the other 15 comparison models

A Blockchain-Based Data Sharing System with Enhanced Auditability

Cloud platforms provide a low-cost and convenient way for users to share data. One important issue of cloud-based data sharing systems is how to prevent the sensitive information contained in users’ data from being disclosed. Existing studies often utilize cryptographic primitives, such as attribute-based encryption and proxy re-encryption, to protect data privacy. These approaches generally rely on a centralized server which may cause a single point of failure problem. Blockchain is known for its ability to solve such a problem. Some blockchain-based approaches have been proposed to realize privacy-preserving data sharing. However, these approaches did not fully explore the auditability provided by the blockchain. The dishonest cloud server can share data with a requester without notifying the data owner or being logged by the blockchain. In this paper, we propose a blockchain-based privacy-preserving data sharing system with enhanced auditability. The proposed system follows the idea of hybrid encryption to protect data privacy. The data to be shared are encrypted with a symmetric key, and the symmetric key is encrypted with a joint public key which is the sum of multiple blockchain nodes’ public keys. Only if a data requester is authorized, the blockchain nodes will be triggered to execute a verifiable key switch protocol. By using the output of the protocol, the data requester can get the plaintext of the symmetric key. The blockchain nodes participate in both the authorization process and the key switch process, which means the behavior of the data requester is witnessed by multi-parties and is auditable. We implement the proposed system on Hyperledger Fabric. The simulation results show that the performance overhead is acceptable

RBFA-Net: A Rotated Balanced Feature-Aligned Network for Rotated SAR Ship Detection and Classification

Ship detection with rotated bounding boxes in synthetic aperture radar (SAR) images is now a hot spot. However, there are still some obstacles, such as multi-scale ships, misalignment between rotated anchors and features, and the opposite requirements for spatial sensitivity of regression tasks and classification tasks. In order to solve these problems, we propose a rotated balanced feature-aligned network (RBFA-Net) where three targeted networks are designed. They are, respectively, a balanced attention feature pyramid network (BAFPN), an anchor-guided feature alignment network (AFAN) and a rotational detection network (RDN). BAFPN is an improved FPN, with attention module for fusing and enhancing multi-level features, by which we can decrease the negative impact of multi-scale ship feature differences. In AFAN, we adopt an alignment convolution layer to adaptively align the convolution features according to rotated anchor boxes for solving the misalignment problem. In RDN, we propose a task decoupling module (TDM) to adjust the feature maps, respectively, for solving the conflict between the regression task and classification task. In addition, we adopt a balanced L1 loss to balance the classification loss and regression loss. Based on the SAR rotation ship detection dataset, we conduct extensive ablation experiments and compare our RBFA-Net with eight other state-of-the-art rotated detection networks. The experiment results show that among the eight state-of-the-art rotated detection networks, RBFA-Net makes a 7.19% improvement with mean average precision compared to the second-best network

Trans-level multi-scale simulation of porous catalytic systems: Bridging reaction kinetics and reactor performance

Multi-scale porous structures inside and/or between the catalyst pellets or particles are found in many chemical processes, where strong coupling of reaction and transport results in complex apparent reaction kinetics influ-ential to the reactor performance. Traditional continuum-based porous media models and simulation methods can hardly describe such structures and their scale effects faithfully. A trans-level multi-scale discrete compu-tational framework is hence proposed to address this complexity and implemented for an olefin catalytic cracking (OCC) process. The apparent reaction kinetics at the REV (representative elementary volume) scale is obtained by hard-sphere pseudo-particle modeling (HS-PPM), and coupled with computational fluid dynamics / discrete element method (CFD-DEM) for the reactor-level hydrodynamics via a one-dimensional (1D) finite difference scheme for particle-level diffusion. The mesoscales of the REVs and the flow networks between the particles are thus covered by the framework, which are previously described by simple average quantities in the continuum methods. The reactant conversion rate and target product selectivity obtained agree well with experimental results, while a continuum approach may give significantly different and unreasonable results. The multi-scale method is, therefore, demonstrated to be necessary and effective for bridging the intrinsic reaction kinetics with the performance of porous catalytic reactors

Morphology and Optical Property of ZnO Nanostructures Grown by Solvothermal Method: Effect of the Solution Pretreatment

Zinc oxide (ZnO) nanostructures with different morphologies such as nanopyramids, nanosheets, and nanoparticles have been grown by a simple solvothermal method. The influence of solution pretreatmentt on the morphology and optical properties of ZnO nanostructures has been studied. The experimental results revealed the morphology of ZnO transformed from nanopyramids or nanosheets to nanoparticles after solution pretreatment. Raman and photoluminescence spectra are recorded to examine the crystallinity and optical property of the samples