Prior Knowledge Guided Unsupervised Domain Adaptation

The waive of labels in the target domain makes Unsupervised Domain Adaptation (UDA) an attractive technique in many real-world applications, though it also brings great challenges as model adaptation becomes harder without labeled target data. In this paper, we address this issue by seeking compensation from target domain prior knowledge, which is often (partially) available in practice, e.g., from human expertise. This leads to a novel yet practical setting where in addition to the training data, some prior knowledge about the target class distribution are available. We term the setting as Knowledge-guided Unsupervised Domain Adaptation (KUDA). In particular, we consider two specific types of prior knowledge about the class distribution in the target domain: Unary Bound that describes the lower and upper bounds of individual class probabilities, and Binary Relationship that describes the relations between two class probabilities. We propose a general rectification module that uses such prior knowledge to refine model generated pseudo labels. The module is formulated as a Zero-One Programming problem derived from the prior knowledge and a smooth regularizer. It can be easily plugged into self-training based UDA methods, and we combine it with two state-of-the-art methods, SHOT and DINE. Empirical results on four benchmarks confirm that the rectification module clearly improves the quality of pseudo labels, which in turn benefits the self-training stage. With the guidance from prior knowledge, the performances of both methods are substantially boosted. We expect our work to inspire further investigations in integrating prior knowledge in UDA. Code is available at https://github.com/tsun/KUDA.Comment: To appear in ECCV 202

Mask and Restore: Blind Backdoor Defense at Test Time with Masked Autoencoder

Deep neural networks are vulnerable to backdoor attacks, where an adversary maliciously manipulates the model behavior through overlaying images with special triggers. Existing backdoor defense methods often require accessing a few validation data and model parameters, which are impractical in many real-world applications, e.g., when the model is provided as a cloud service. In this paper, we address the practical task of blind backdoor defense at test time, in particular for black-box models. The true label of every test image needs to be recovered on the fly from the hard label predictions of a suspicious model. The heuristic trigger search in image space, however, is not scalable to complex triggers or high image resolution. We circumvent such barrier by leveraging generic image generation models, and propose a framework of Blind Defense with Masked AutoEncoder (BDMAE). It uses the image structural similarity and label consistency between the test image and MAE restorations to detect possible triggers. The detection result is refined by considering the topology of triggers. We obtain a purified test image from restorations for making prediction. Our approach is blind to the model architectures, trigger patterns or image benignity. Extensive experiments on multiple datasets with different backdoor attacks validate its effectiveness and generalizability. Code is available at https://github.com/tsun/BDMAE

Backdoor Cleansing with Unlabeled Data

Due to the increasing computational demand of Deep Neural Networks (DNNs), companies and organizations have begun to outsource the training process. However, the externally trained DNNs can potentially be backdoor attacked. It is crucial to defend against such attacks, i.e., to postprocess a suspicious model so that its backdoor behavior is mitigated while its normal prediction power on clean inputs remain uncompromised. To remove the abnormal backdoor behavior, existing methods mostly rely on additional labeled clean samples. However, such requirement may be unrealistic as the training data are often unavailable to end users. In this paper, we investigate the possibility of circumventing such barrier. We propose a novel defense method that does not require training labels. Through a carefully designed layer-wise weight re-initialization and knowledge distillation, our method can effectively cleanse backdoor behaviors of a suspicious network with negligible compromise in its normal behavior. In experiments, we show that our method, trained without labels, is on-par with state-of-the-art defense methods trained using labels. We also observe promising defense results even on out-of-distribution data. This makes our method very practical

In vitro culture method of powdery mildew (Oidium heveae Steinmann) of Hevea brasiliensis

A method for culturing powdery mildew (Oidium heveae) from isolated leaves of Hevea brasiliensis was evaluated, which included three steps: Leaves and fungi selection, nutrient solution and culture dish  preparation, fungi inoculation and culture. The culture time and produced conidia number were considered as decision index. We tested the influence of micro components of nutrient solution including 6-benzylaminopurine (6-BA), salicylic acid (SA) and vitamin C (VC) and evaluated the culture difference of various leaf phenological phases and rubber tree clones. The results show that the longest culture time of isolated leaves emerged on modified Murashige and Skoog (MS) macro elements with 4 mg/L 6-BA, 20 mg/L SA, 1 mg/L VC. The colour phase leaf was the preferable choice for culturing average 15 to 16 days and producing 3.2222 × 106 mL-1 conidia. The culture effects of using various rubber clones were different and higher resistance clones cultured less conidia. The method leading to mass production of powdery mildew was simple using a climate incubator to resolve problems linked to season and space limitation and preservation of powdery mildew. This method could improve rubber resistance breeding process.Key words: Hevea brasiliensis, Oidium heveae, in vitro culture, nutrient solution, phenological phase

Gas pressure sintering of BN/Si3N4 wave-transparent material with Y2O3–MgO nanopowders addition

AbstractBN/Si3N4 ceramics performed as wave-transparent material in spacecraft were fabricated with boron nitride powders, silicon nitride powders and Y2O3–MgO nanopowders by gas pressure sintering at 1700°C under 6MPa in N2 atmosphere. The effects of Y2O3–MgO nanopowders on densification, phase evolution, microstructure and mechanical properties of BN/Si3N4 material were investigated. The addition of Y2O3–MgO nanopowders was found beneficial to the mechanical properties of BN/Si3N4 composites. The BN/Si3N4 ceramics with 8wt% Y2O3–MgO nanopowders showed a relative density of 80.2%, combining a fracture toughness of 4.6MPam1/2 with an acceptable flexural strength of 396.5MPa