Uniformly convex neural networks and non-stationary iterated network Tikhonov (iNETT) method

We propose a non-stationary iterated network Tikhonov (iNETT) method for the solution of ill-posed inverse problems. The iNETT employs deep neural networks to build a data-driven regularizer, and it avoids the difficult task of estimating the optimal regularization parameter. To achieve the theoretical convergence of iNETT, we introduce uniformly convex neural networks to build the data-driven regularizer. Rigorous theories and detailed algorithms are proposed for the construction of convex and uniformly convex neural networks. In particular, given a general neural network architecture, we prescribe sufficient conditions to achieve a trained neural network which is component-wise convex or uniformly convex; moreover, we provide concrete examples of realizing convexity and uniform convexity in the modern U-net architecture. With the tools of convex and uniformly convex neural networks, the iNETT algorithm is developed and a rigorous convergence analysis is provided. Lastly, we show applications of the iNETT algorithm in 2D computerized tomography, where numerical examples illustrate the efficacy of the proposed algorithm

Playing Lottery Tickets in Style Transfer Models

Style transfer has achieved great success and attracted a wide range of attention from both academic and industrial communities due to its flexible application scenarios. However, the dependence on a pretty large VGG-based autoencoder leads to existing style transfer models having high parameter complexities, which limits their applications on resource-constrained devices. Compared with many other tasks, the compression of style transfer models has been less explored. Recently, the lottery ticket hypothesis (LTH) has shown great potential in finding extremely sparse matching subnetworks which can achieve on par or even better performance than the original full networks when trained in isolation. In this work, we for the first time perform an empirical study to verify whether such trainable matching subnetworks also exist in style transfer models. Specifically, we take two most popular style transfer models, i.e., AdaIN and SANet, as the main testbeds, which represent global and local transformation based style transfer methods respectively. We carry out extensive experiments and comprehensive analysis, and draw the following conclusions. (1) Compared with fixing the VGG encoder, style transfer models can benefit more from training the whole network together. (2) Using iterative magnitude pruning, we find the matching subnetworks at 89.2% sparsity in AdaIN and 73.7% sparsity in SANet, which demonstrates that style transfer models can play lottery tickets too. (3) The feature transformation module should also be pruned to obtain a much sparser model without affecting the existence and quality of the matching subnetworks. (4) Besides AdaIN and SANet, other models such as LST, MANet, AdaAttN and MCCNet can also play lottery tickets, which shows that LTH can be generalized to various style transfer models

A broad spectrum, one-step reverse-transcription PCR amplification of the neuraminidase gene from multiple subtypes of influenza A virus

Background: The emergence of high pathogenicity strains of Influenza A virus in a variety of human and animal hosts, with wide geographic distribution, has highlighted the importance of rapid identification and subtyping of the virus for outbreak management and treatment. Type A virus can be classified into subtypes according to the viral envelope glycoproteins, hemagglutinin and neuraminidase. Here we review the existing specificity and amplification of published primers to subtype neuraminidase genes and describe a new broad spectrum primer pair that can detect all 9 neuraminidase subtypes

Playing lottery tickets in style transfer models

Style transfer has achieved great success and attracted a wide range of attention from both academic and industrial communities due to its flexible application scenarios. However, the dependence on a pretty large VGG-based autoencoder leads to existing style transfer models having high parameter complexities, which limits their applications on resource-constrained devices. Compared with many other tasks, the compression of style transfer models has been less explored. Recently, the lottery ticket hypothesis (LTH) has shown great potential in finding extremely sparse matching subnetworks which can achieve on par or even better performance than the original full networks when trained in isolation. In this work, we for the first time perform an empirical study to verify whether such trainable matching subnetworks also exist in style transfer models. Specifically, we take two most popular style transfer models, i.e., AdaIN and SANet, as the main testbeds, which represent global and local transformation based style transfer methods respectively. We carry out extensive experiments and comprehensive analysis, and draw the following conclusions. (1) Compared with fixing the VGG encoder, style transfer models can benefit more from training the whole network together. (2) Using iterative magnitude pruning, we find the matching subnetworks at 89.2% sparsity in AdaIN and 73.7% sparsity in SANet, which demonstrates that Style transfer models can play lottery tickets too. (3) The feature transformation module should also be pruned to obtain a much sparser model without affecting the existence and quality of the matching subnetworks. (4) Besides AdaIN and SANet, other models such as LST, MANet, AdaAttN and MCCNet can also play lottery tickets, which shows that LTH can be generalized to various style transfer models

Simulation-Based Selective Opening Security for Receivers under Chosen-Ciphertext Attacks

Security against selective opening attack (SOA) for receivers requires that in a multi-user setting, even if an adversary has access to all ciphertexts, and adaptively corrupts some fraction of the users to obtain the decryption keys corresponding to some of the ciphertexts, the remaining (potentially related) ciphertexts retain their privacy. In this paper, we study simulation-based selective opening security for receivers of public key encryption (PKE) schemes under chosen-ciphertext attacks (RSIM-SO-CCA). Concretely, we first show that some known PKE schemes meet RSIM-SO-CCA security. Then, we introduce the notion of master-key SOA security for identity-based encryption (IBE), and extend the Canetti-Halevi-Katz (CHK) transformation to show generic PKE constructions achieving RSIM-SO-CCA security. Finally, we show how to construct an IBE scheme achieving master-key SOA security

Hedged Nonce-Based Public-Key Encryption: Adaptive Security under Randomness Failures

Nowadays it is well known that randomness may fail due to bugs or deliberate randomness subversion. As a result, the security of traditional public-key encryption (PKE) cannot be guaranteed any more. Currently there are mainly three approaches dealing with the problem of randomness failures: deterministic PKE, hedged PKE, and nonce-based PKE. However, these three approaches only apply to different application scenarios respectively. Since the situations in practice are dynamic and very complex, it\u27s almost impossible to predict the situation in which a scheme is deployed, and determine which approach should be used beforehand. In this paper, we initiate the study of hedged security for nonce-based PKE, which adaptively applies to the situations whenever randomness fails, and achieves the best-possible security. Specifically, we lift the hedged security to the setting of nonce-based PKE, and formalize the notion of chosen-ciphertext security against chosen-distribution attacks (IND-CDA2) for nonce-based PKE. By presenting two counterexamples, we show a separation between our IND-CDA2 security for nonce-based PKE and the original NBP1/NBP2 security defined by Bellare and Tackmann (EUROCRYPT 2016). We show two nonce-based PKE constructions meeting IND-CDA2, NBP1 and NBP2 security simultaneously. The first one is a concrete construction in the random oracle model, and the second one is a generic construction based on a nonce-based PKE scheme and a deterministic PKE scheme

The experimental research on axial compression performance of concrete-filled steel square tube strengthened by internal transverse stiffened bars

The application of Concrete-filled square steel tubular column in structural engineering is more and more widely. In order to improve the confinement of concrete core, the configuration of concrete-filled square steel tube strengthened by internal transverse stiffened bars is proposed. This paper employs a experimental research on concrete-filled square steel tube strengthened by internal transverse stiffened bars under axial compression and the influencing factors of bearing capacity such as diameter and spacing of steel bars is analyzed. The results shows that its axial load capacity is increased 2.3%~13.1% than that of ordinary concrete-filled square steel tubular column, spacing changes to improve the bearing capacity of concrete filled square steel tubular column strengthened by transverse stiffened bars is more effective than the increase in stiffened bars diameter