Dynamic Routing and Knowledge Re-Learning for Data-Free Black-Box Attack.

Deep learning models have emerged as strong and efficient tools that can be applied to a broad spectrum of complex learning problems and many real-world applications. However, more and more works show that deep models are vulnerable to adversarial examples. Compared to vanilla attack settings, this paper advocates a more practical setting of data-free black-box attack, for which the attackers can completely not access the structures and parameters of the target model, as well as the intermediate features and any training data associated with the model.

A colloidal gold immunochromatographic method for rapid screening of imidacloprid residues in Chinese herbal medicines.

An imidacloprid colloidal gold immunochromatographic strip was developed in this work, and systematic analytical conditions were deeply investigated. The test strips were used for rapid screening of imidacloprid residues in Chinese herbal medicines. The performance of the colloidal gold test strips was investigated by using five selected Chinese herbal medicines (malt, Coix seed, lotus seed, dried ginger and honeysuckle).

Brain-machine interactive neuromodulation research tool with edge AI computing.

Closed-loop neuromodulation with intelligence methods has shown great potentials in providing novel neuro-technology for treating neurological and psychiatric diseases. Development of brain-machine interactive neuromodulation strategies could lead to breakthroughs in precision and personalized electronic medicine. The neuromodulation research tool integrating artificial intelligent computing and performing neural sensing and stimulation in real-time could accelerate the development of closed-loop neuromodulation strategies and translational research into clinical application.

Knockoffs-SPR: Clean Sample Selection in Learning With Noisy Labels.

A noisy training set usually leads to the degradation of the generalization and robustness of neural networks. In this article, we propose a novel theoretically guaranteed clean sample selection framework for learning with noisy labels. Specifically, we first present a Scalable Penalized Regression (SPR) method, to model the linear relation between network features and one-hot labels.

H4MER: Human 4D Modeling by Learning Neural Compositional Representation With Transformer.

Despite the impressive results achieved by deep learning based 3D reconstruction, the techniques of directly learning to model 4D human captures with detailed geometry have been less studied. This work presents a novel neural compositional representation for Human 4D Modeling with transformER (H4MER). Specifically, our H4MER is a compact and compositional representation for dynamic human by exploiting the human body prior from the widely used SMPL parametric model.

DeepSFM: Robust Deep Iterative Refinement for Structure From Motion.

Structure from Motion (SfM) is a fundamental computer vision problem which has not been well handled by deep learning. One of the promising solutions is to apply explicit structural constraint, e.g.

A simple and rapid strategy for monitoring vancomycin concentration in serum using a multicolor immunosensor based on the ratio of gold nanobipyramids and a product of cetyltrimethylammonium bromide-blue oxide of 3,3',5,5'-tetramethylbenzidine interaction.

Rapid and simple monitoring of vancomycin (VAN) concentration in blood is a vital strategy for maximizing therapeutic efficacy, minimizing toxicity and developing a personalized treatment plan. In this work, a simple multicolor immunosensor is proposed to enable rapid monitoring of VAN concentration in serum, without using any expensive and bulky instrument. The multicolor immunosensor platform is a system that works based on the principle that the product of cetyltrimethylammonium bromide-blue oxide of 3,3',5,5'-tetramethylbenzidine interaction (CTAB/TMB) and TMB increases simultaneously with the decrease in VAN concentration, whereas AuNBPs are insensitive to VAN.

ZITS++: Image Inpainting by Improving the Incremental Transformer on Structural Priors.

Image inpainting involves filling missing areas of a corrupted image. Despite impressive results have been achieved recently, restoring images with both vivid textures and reasonable structures remains a significant challenge. Previous methods have primarily addressed regular textures while disregarding holistic structures due to the limited receptive fields of Convolutional Neural Networks (CNNs).

Line-scanning technique using a PDMS grating in a microscope configuration.

We report the concept of a 1550 nm laser line scanning microscope based on a polydimethylsiloxane (PDMS) grating with scanning by stretching the PDMS grating to improve the scanning speed and enable low-cost scanning. Zemax is used to verify the possibility of realizing the system by simulating the illumination light path and the emission light path. The scanning field of view is 0.

PatchMix Augmentation to Identify Causal Features in Few-Shot Learning.

The task of Few-shot learning (FSL) aims to transfer the knowledge learned from base categories with sufficient labelled data to novel categories with scarce known information. It is currently an important research question and has great practical values in the real-world applications. Despite extensive previous efforts are made on few-shot learning tasks, we emphasize that most existing methods did not take into account the distributional shift caused by sample selection bias in the FSL scenario.

Generalized Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data.

The vanilla Few-shot Learning (FSL) learns to build a classifier for a new concept from one or very few target examples, with the general assumption that source and target classes are sampled from the same domain. Recently, the task of Cross-Domain Few-Shot Learning (CD-FSL) aims at tackling the FSL where there is a huge domain shift between the source and target datasets. Extensive efforts on CD-FSL have been made via either directly extending the meta-learning paradigm of vanilla FSL methods, or employing massive unlabeled target data to help learn models.

Pixel2Mesh++: 3D Mesh Generation and Refinement From Multi-View Images.

We study the problem of shape generation in 3D mesh representation from a small number of color images with or without camera poses. While many previous works learn to hallucinate the shape directly from priors, we adopt to further improve the shape quality by leveraging cross-view information with a graph convolution network. Instead of building a direct mapping function from images to 3D shape, our model learns to predict series of deformations to improve a coarse shape iteratively.

Exploring Structural Sparsity of Deep Networks Via Inverse Scale Spaces.

The great success of deep neural networks is built upon their over-parameterization, which smooths the optimization landscape without degrading the generalization ability. Despite the benefits of over-parameterization, a huge amount of parameters makes deep networks cumbersome in daily life applications. On the other hand, training neural networks without over-parameterization faces many practical problems, e.

Reinforcing Generated Images via Meta-Learning for One-Shot Fine-Grained Visual Recognition.

One-shot fine-grained visual recognition often suffers from the problem of having few training examples for new fine-grained classes. To alleviate this problem, off-the-shelf image generation techniques based on Generative Adversarial Networks (GANs) can potentially create additional training images. However, these GAN-generated images are often not helpful for actually improving the accuracy of one-shot fine-grained recognition.

How to Trust Unlabeled Data? Instance Credibility Inference for Few-Shot Learning.

Deep learning based models have excelled in many computer vision tasks and appear to surpass humans' performance. However, these models require an avalanche of expensive human labeled training data and many iterations to train their large number of parameters. This severely limits their scalability to the real-world long-tail distributed categories, some of which are with a large number of instances, but with only a few manually annotated.

M Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening From CT Imaging.

To counter the outbreak of COVID-19, the accurate diagnosis of suspected cases plays a crucial role in timely quarantine, medical treatment, and preventing the spread of the pandemic. Considering the limited training cases and resources (e.g, time and budget), we propose a Multi-task Multi-slice Deep Learning System (M Lung-Sys) for multi-class lung pneumonia screening from CT imaging, which only consists of two 2D CNN networks, i.

Pose-Guided Person Image Synthesis in the Non-iconic Views.

Generating realistic images with the guidance of reference images and human poses is challenging. Despite the success of previous works on synthesizing person images in the iconic views, no efforts are made towards the task of poseguided image synthesis in the non-iconic views. Particularly, we find that previous models cannot handle such a complex task, where the person images are captured in the non-iconic views by commercially-available digital cameras.

[Preparation of highly sensitive monoclonal antibody against aflatoxin B_1 and its application in rapid detection of contamination in Ziziphi Spinosae Semen].

A highly sensitive monoclonal antibody against aflatoxin B_1(AFB_1) was prepared and an indirect competition enzyme-linked immunosorbent assay(ic-ELISA) was established based on the antibody which was used for high-throughput and rapid screening of AFB_1 contamination in Chinese herbal medicines to ensure the safety of medication. In this study, the structure of AFB_1 was modified by improved oxime method, and the carrier protein was coupled by EDC-NHS method to obtain the complete antigen of AFB_1, which was more convenient and environmental friendly. The Balb/c female mice were immunized using increasing the immunization dose and various ways of injection, and finally the AFB_1 monoclonal antibody was prepared.

Learning Layer-Skippable Inference Network.

The process of learning good representations for machine learning tasks can be very computationally expensive. Typically, we facilitate the same backbones learned on the training set to infer the labels of testing data. Interestingly, This learning and inference paradigm, however, is quite different from the typical inference scheme of human biological visual systems.

Deep Ranking for Image Zero-Shot Multi-Label Classification.

During the past decade, both multi-label learning and zero-shot learning have attracted huge research attention, and significant progress has been made. Multi-label learning algorithms aim to predict multiple labels given one instance, while most existing zero-shot learning approaches target at predicting a single testing label for each unseen class via transferring knowledge from auxiliary seen classes to target unseen classes. However, relatively less effort has been made on predicting multiple labels in the zero-shot setting, which is nevertheless a quite challenging task.

Pixel2Mesh: 3D Mesh Model Generation via Image Guided Deformation.

In this paper, we propose an end-to-end deep learning architecture that generates 3D triangular meshes from single color images. Restricted by the nature of prevalent deep learning techniques, the majority of previous works represent 3D shapes in volumes or point clouds. However, it is non-trivial to convert these representations to compact and ready-to-use mesh models.

[QuEchERS pretreatment coupled to gas chromatography and tandem mass spectrometry to fast determination of 34 pesticide residues in Glycyrrhizae Radix et Rhizoma].

This study proposed a quantitative method for 34 pesticides including organochlorine,organophosphorus and pyrethroids in Glycyrrhizae Radix et Rhizoma herbs and medicinal slices,and analyzed the pesticide residues of collected Glycyrrhizae Radix et Rhizoma samples from different regions. With acetonitrile extraction and optimized Qu Ech ERS purification,the 32 batches of Glycyrrhizae Radix et Rhizoma herbs and medicinal slices were analyzed by matrix matching standard curve quantitative analysis under GC-MS/MS multi-response monitoring( MRM) mode. This study investigated the pretreatment of Glycyrrhizae Radix et Rhizoma samples based on the Qu Ech ERS method of Chinese Pharmacopoeia( 2015 edition,4),and the result showed that the recoveries of some pesticide was low and pigment has a strong interference in analysis,which result in worse purification effect.

Comprehensive assessment for the residual characteristics and degradation kinetics of pesticides in Panax notoginseng and planting soil.

Incorrect and excess usage of pesticides during crop cultivation poses a serious threat to human health and ecosystems. In this study, we tested for the presence of 201 pesticide residues in 90 batches of Panax notoginseng (P. notoginseng) and 10 batches of planting soil.