Three new species of the Fannia serena species subgroup from China (Diptera: Fanniidae)

The Fannia serena species group (Diptera: Fanniidae) ismainly distributed in the Holarctic region and comprises four subgroups with a total of 32 species. Three new species of the Fannia serena-subgroup, Fannia aureomarginata Wang et Cheng, sp. n., F. suberemna Wang, sp. n. and F. wui Wang, sp. n., are described from China. An identification key to all known species of the Fannia serena-subgroup is also provided

Helina subpyriforma sp. n., a newmuscid fly (Diptera: Muscidae) from Yunnan, China

Helina subpyriforma Wang sp. n., a species from Yunnan, China, is described and illustrated as new to science. The new species can be assigned to the Helina quadrum-group, based on male morphological and genitalic structures. The species is also incorporated into the existing key of H. quadrum-group (males) from China

Helina fratercula (Zetterstedt, 1845) (Diptera: Muscidae) newly recorded from China, with a redescription of male

Helina fratercula (Zetterstedt, 1845), so far known only from Central Europe, is newly recorded from China. The species is redescribed in detail morphological characters. The characteristic photos and the illustrations of male terminalia based on the specimens from Xinjiang are provided, and also incorporated into the existing key of Helina males of China

Intellectual Property Protection for Deep Learning Models: Taxonomy, Methods, Attacks, and Evaluations

The training and creation of deep learning model is usually costly, thus it can be regarded as an intellectual property (IP) of the model creator. However, malicious users who obtain high-performance models may illegally copy, redistribute, or abuse the models without permission. To deal with such security threats, a few deep neural networks (DNN) IP protection methods have been proposed in recent years. This paper attempts to provide a review of the existing DNN IP protection works and also an outlook. First, we propose the first taxonomy for DNN IP protection methods in terms of six attributes: scenario, mechanism, capacity, type, function, and target models. Then, we present a survey on existing DNN IP protection works in terms of the above six attributes, especially focusing on the challenges these methods face, whether these methods can provide proactive protection, and their resistances to different levels of attacks. After that, we analyze the potential attacks on DNN IP protection methods from the aspects of model modifications, evasion attacks, and active attacks. Besides, a systematic evaluation method for DNN IP protection methods with respect to basic functional metrics, attack-resistance metrics, and customized metrics for different application scenarios is given. Lastly, future research opportunities and challenges on DNN IP protection are presented

Detect and remove watermark in deep neural networks via generative adversarial networks

Deep neural networks (DNN) have achieved remarkable performance in various fields. However, training a DNN model from scratch requires a lot of computing resources and training data. It is difficult for most individual users to obtain such computing resources and training data. Model copyright infringement is an emerging problem in recent years. For instance, pre-trained models may be stolen or abuse by illegal users without the authorization of the model owner. Recently, many works on protecting the intellectual property of DNN models have been proposed. In these works, embedding watermarks into DNN based on backdoor is one of the widely used methods. However, when the DNN model is stolen, the backdoor-based watermark may face the risk of being detected and removed by an adversary. In this paper, we propose a scheme to detect and remove watermark in deep neural networks via generative adversarial networks (GAN). We demonstrate that the backdoor-based DNN watermarks are vulnerable to the proposed GAN-based watermark removal attack. The proposed attack method includes two phases. In the first phase, we use the GAN and few clean images to detect and reverse the watermark in the DNN model. In the second phase, we fine-tune the watermarked DNN based on the reversed backdoor images. Experimental evaluations on the MNIST and CIFAR10 datasets demonstrate that, the proposed method can effectively remove about 98% of the watermark in DNN models, as the watermark retention rate reduces from 100% to less than 2% after applying the proposed attack. In the meantime, the proposed attack hardly affects the model's performance. The test accuracy of the watermarked DNN on the MNIST and the CIFAR10 datasets drops by less than 1% and 3%, respectively

Robust Backdoor Attacks against Deep Neural Networks in Real Physical World

Deep neural networks (DNN) have been widely deployed in various applications. However, many researches indicated that DNN is vulnerable to backdoor attacks. The attacker can create a hidden backdoor in target DNN model, and trigger the malicious behaviors by submitting specific backdoor instance. However, almost all the existing backdoor works focused on the digital domain, while few studies investigate the backdoor attacks in real physical world. Restricted to a variety of physical constraints, the performance of backdoor attacks in the real physical world will be severely degraded. In this paper, we propose a robust physical backdoor attack method, PTB (physical transformations for backdoors), to implement the backdoor attacks against deep learning models in the real physical world. Specifically, in the training phase, we perform a series of physical transformations on these injected backdoor instances at each round of model training, so as to simulate various transformations that a backdoor may experience in real world, thus improves its physical robustness. Experimental results on the state-of-the-art face recognition model show that, compared with the backdoor methods that without PTB, the proposed attack method can significantly improve the performance of backdoor attacks in real physical world. Under various complex physical conditions, by injecting only a very small ratio (0.5%) of backdoor instances, the attack success rate of physical backdoor attacks with the PTB method on VGGFace is 82%, while the attack success rate of backdoor attacks without the proposed PTB method is lower than 11%. Meanwhile, the normal performance of the target DNN model has not been affected

Heparin-binding epidermal growth factor inhibits apoptosis in cisplatin-resistant pancreatic cancer cells via upregulation of EGFR and ERCC 1 expressions

Purpose: To investigate the influence of heparin-binding epidermal growth factor (HB-EGF) on apoptosis in cisplatin-resistant pancreatic cancer cells, as well as its mechanism of action. Methods: Pancreatic cancer cisplatin-resistant cells (BXPC-3/CDDP) were transfected with HB-EGF small interfering RNA (siRNA). The cells were randomly assigned to four groups, namely, BXPC-3 group (group A), BXPC-3/CDDP group (group B), transfected group A (group Asi) and transfected group B (group Bsi). Cell proliferation was determined using MTT assay, and the levels of expression of HBEGF, epidermal growth factor receptor (EGFR) and excision repair cross-complementation group 1 (ERCC 1) were determined using Western blotting. The extent of apoptosis was determined by flow cytometry. Results: Cell proliferation was increased in group B, relative to group A, but was significantly decreased after transfection with HB-EGF siRNA (p < 0.05). The half-maximal inhibitory concentration (IC50) of group Bsi was reduced, relative to group Asi (p < 0.05). The expression of HB-EGF was significantly upregulated in group B, relative to group A (p < 0.05). In contrast, HB-EGF siRNA transfection of the cells significantly down-regulated HB-EGF expression (p < 0.05). Early apoptosis was significantly higher in group A than in groups B and Bsi. Higher levels of apoptosis were seen in group Bsi, relative to group B after inhibition of HB-EGF expression (p < 0.05). Conclusion: These results indicate that HB-EGF is resistant to cisplatin, and it inhibits apoptosis in pancreatic cancer cells via the upregulation of EGFR and ERCC 1 expressions

Incorporating Ab Initio energy into threading approaches for protein structure prediction

<p>Abstract</p> <p>Background</p> <p>Native structures of proteins are formed essentially due to the combining effects of local and distant (in the sense of sequence) interactions among residues. These interaction information are, explicitly or implicitly, encoded into the scoring function in protein structure prediction approaches—threading approaches usually measure an alignment in the sense that how well a sequence adopts an existing structure; while the energy functions in <it>Ab Initio</it> methods are designed to measure how likely a conformation is near-native. Encouraging progress has been observed in structure refinement where knowledge-based or physics-based potentials are designed to capture distant interactions. Thus, it is interesting to investigate whether distant interaction information captured by the <it>Ab Initio</it> energy function can be used to improve threading, especially for the weakly/distant homologous templates.</p> <p>Results</p> <p>In this paper, we investigate the possibility to improve alignment-generating through incorporating distant interaction information into the alignment scoring function in a nontrivial approach. Specifically, the distant interaction information is introduced through employing an <it>Ab Initio</it> energy function to evaluate the “partial” decoy built from an alignment. Subsequently, a local search algorithm is utilized to optimize the scoring function.</p> <p>Experimental results demonstrate that with distant interaction items, the quality of generated alignments are improved on 68 out of 127 query-template pairs in Prosup benchmark. In addition, compared with state-to-art threading methods, our method performs better on alignment accuracy comparison.</p> <p>Conclusions</p> <p>Incorporating <it>Ab Initio</it> energy functions into threading can greatly improve alignment accuracy.</p