Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models

AI Safety is a major concern in many deep learning applications such as autonomous driving. Given a trained deep learning model, an important natural problem is how to reliably verify the model's prediction. In this paper, we propose a novel framework -- deep verifier networks (DVN) to verify the inputs and outputs of deep discriminative models with deep generative models. Our proposed model is based on conditional variational auto-encoders with disentanglement constraints. We give both intuitive and theoretical justifications of the model. Our verifier network is trained independently with the prediction model, which eliminates the need of retraining the verifier network for a new model. We test the verifier network on out-of-distribution detection and adversarial example detection problems, as well as anomaly detection problems in structured prediction tasks such as image caption generation. We achieve state-of-the-art results in all of these problems.Comment: Accepted to AAAI 202

A recombinant avian antibody against VP2 of infectious bursal disease virus protects chicken from viral infection

【Abstract】A stable cell-line was established that expressed the recombinant avian antibody (rAb) against the infectious bursal disease virus (IBDV). rAb exhibited neutralization activity to IBDV-B87 strain in DF1 cells. The minimum rAb concentration required for inhibition of the cytopathic effect (CPE) was 1.563 μg/mL. To test the efficacy of rAb, a 168-h cohabitation challenge experiment was performed to transmit the disease from the chickens challenged with vvIBDV (HLJ0504 strain) to three test groups of chickens, i.e. (1) chickens treated with rAb, (2) chickens treated with yolk antibody, and (3) non-treatment chickens. The survival rates of chickens treated with rAb, yolk antibody and without treatment were 73%, 67% and 20%, respectively. Another batch of chickens was challenged with IBDV (BC6/85 strain) and then injected with rAb (1.0 mg/kg) 6, 24 and 36 h post-challenge. Non-treatment chickens had 100% morbidity, whereas those administered with rAb exhibited only 20% morbidity. Morbidity was evaluated using clinical indicators and bursal histopathological section. This study provides a new approach to treating IBDV and the rAb represents a promising candidate for this IBDV therapy.This research was supported by Heilongjiang province project of applied technology research and development (2013GC13C105) and The National Natural Science Fund biologic science base improve program of research training and capacity (J1210069/J0124)

Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification

Graph classification aims to extract accurate information from graph-structured data for classification and is becoming more and more important in graph learning community. Although Graph Neural Networks (GNNs) have been successfully applied to graph classification tasks, most of them overlook the scarcity of labeled graph data in many applications. For example, in bioinformatics, obtaining protein graph labels usually needs laborious experiments. Recently, few-shot learning has been explored to alleviate this problem with only given a few labeled graph samples of test classes. The shared sub-structures between training classes and test classes are essential in few-shot graph classification. Exiting methods assume that the test classes belong to the same set of super-classes clustered from training classes. However, according to our observations, the label spaces of training classes and test classes usually do not overlap in real-world scenario. As a result, the existing methods don't well capture the local structures of unseen test classes. To overcome the limitation, in this paper, we propose a direct method to capture the sub-structures with well initialized meta-learner within a few adaptation steps. More specifically, (1) we propose a novel framework consisting of a graph meta-learner, which uses GNNs based modules for fast adaptation on graph data, and a step controller for the robustness and generalization of meta-learner; (2) we provide quantitative analysis for the framework and give a graph-dependent upper bound of the generalization error based on our framework; (3) the extensive experiments on real-world datasets demonstrate that our framework gets state-of-the-art results on several few-shot graph classification tasks compared to baselines

MetaGAN: An Adversarial Approach to Few-Shot Learning

In this paper, we propose a conceptually simple and general framework called MetaGAN for few-shot learning problems. Most state-of-the-art few-shot classification models can be integrated with MetaGAN in a principled and straightforward way. By introducing an adversarial generator conditioned on tasks, we augment vanilla few-shot classification models with the ability to discriminate between real and fake data. We argue that this GAN-based approach can help few-shot classifiers to learn sharper decision boundary, which could generalize better. We show that with our MetaGAN framework, we can extend supervised few-shot learning models to naturally cope with unlabeled data. Different from previous work in semi-supervised few-shot learning, our algorithms can deal with semi-supervision at both sample-level and task-level. We give theoretical justifications of the strength of MetaGAN, and validate the effectiveness of MetaGAN on challenging few-shot image classification benchmarks

Molecular Mechanism of Staphylococcus Xylosus Resistance Against Tylosin and Florfenicol

Abstract As a result of evolution, certain microbes develop resistance against antimicrobial treatment. The antimicrobial resistance (AMR) threatens the effective prevention and treatment of an ever-increasing range of severe infections caused by microbes such as bacteria, viruses, fungi and other parasites. The resistance of Staphylococcus xylosus (S. xylosus) against antibiotic treatment is one of the major causes of the world-wide antibiotic crisis and has remained to be well understood at the molecular level. In order to fill this gap, we investigated various mutations in the sequence of ribosomal proteins involved cross resistance. We discovered that for the mutant containing the insertion L22 97KRTSAIN98 the minimum inhibitory concentration against both tylosin and florfenicol changed dramatically. To understand this effect on a molecular basis and to further elucidate the role of cross resistance, we computationally constructed the 3D model of the large ribosomal subunit from S. xylosus as well as its complexes with both tylosin and florfenicol. Using all-atom molecular dynamics simulations, we found that unique structural changes in the β hairpin of L22 played a central role of this variant in the development of antibiotic resistance in S. xylosus. In addition, the regulation of protein network also played an essential role in the development of cross resistance in S. xylosus. Our work provides insightful views into the mechanism of S. xylosus resistance which could be useful for the development of the next generation of antibiotics.</jats:p

Advancements and Prospects of Near-infrared-light Driven Co₂ Reduction Reaction

In the realm of photoconversion of CO2 into high-value chemicals, the importance of near-infrared (NIR) light is gradually gaining recognition. Relative to ultraviolet (UV) and visible light, NIR light (700-2500 nm), accounting for ca. 50% of solar energy, offers unique advantages such as deeper penetration depth and stronger photothermal effects. Thus, utilizing NIR light can not only compensate for the inherent limitations of UV/visible light-based CO2 reduction systems, but also maximize the use of solar energy. However, efficiently harnessing NIR light remains challenging because of its low photon energy, making it difficult to drive CO2 reduction. Additionally, the limited knowledge of the reduction mechanism driven by low-energy photons further hinders progress in this field. In this review, we systematically introduce the motivation and fundamental principles of NIR-light-driven CO2 reduction, the design strategies for NIR-light-activated photocatalysts (including the energy band structure regulation strategy, the energy transfer strategy, and the photothermal utilization strategy), NIR-light absorption mechanisms of these catalysts, and representative applications of these strategies. Finally, we present our perspectives on the challenges facing NIR-light-driven CO2 reduction and provide suggestions for improving current photocatalysts, characterization techniques, evaluation procedures, and potential large-scale applications in future research. With further advancements in NIR-light-driven CO2 reduction, it holds great promise to maximize the exploitation of solar energy, ultimately achieving efficient CO2 photoconversion for industrial applications.LP