MOSAIC: A Prune-and-Assemble Approach for Efficient Model Pruning in Privacy-Preserving Deep Learning

To enable common users to capitalize on the power of deep learning, Machine Learning as a Service (MLaaS) has been proposed in the literature, which opens powerful deep learning models of service providers to the public. To protect the data privacy of end users, as well as the model privacy of the server, several state-of-the-art privacy-preserving MLaaS frameworks have also been proposed. Nevertheless, despite the exquisite design of these frameworks to enhance computation efficiency, the computational cost remains expensive for practical applications. To improve the computation efficiency of deep learning (DL) models, model pruning has been adopted as a strategic approach to remarkably compress DL models. However, for practical deep neural networks, a problem called pruning structure inflation significantly limits the pruning efficiency, as it can seriously hurt the model accuracy. In this paper, we propose MOSAIC, a highly flexible pruning framework, to address this critical challenge. By first pruning the network with the carefully selected basic pruning units, then assembling the pruned units into suitable HE Pruning Structures through smart channel transformations, MOSAIC achieves a high pruning ratio while avoiding accuracy reduction, eliminating the problem plagued by the pruning structure inflation. We apply MOSAIC to popular DL models such as VGG and ResNet series on classic datasets such as CIFAR-10 and Tiny ImageNet. Experimental results demonstrate that MOSAIC effectively and flexibly conducts pruning on those models, significantly reducing the Perm, Mult, and Add operations to achieve the global cost reduction without any loss in accuracy. For instance, in VGG-16 on Tiny ImageNet, the total cost is reduced to 21.14% and 29.49% under the MLaaS frameworks GAZELLE and CrypTFlow2, respectively

Learning Disentangled Semantic Representation for Domain Adaptation

Domain adaptation is an important but challenging task. Most of the existing domain adaptation methods struggle to extract the domain-invariant representation on the feature space with entangling domain information and semantic information. Different from previous efforts on the entangled feature space, we aim to extract the domain invariant semantic information in the latent disentangled semantic representation (DSR) of the data. In DSR, we assume the data generation process is controlled by two independent sets of variables, i.e., the semantic latent variables and the domain latent variables. Under the above assumption, we employ a variational auto-encoder to reconstruct the semantic latent variables and domain latent variables behind the data. We further devise a dual adversarial network to disentangle these two sets of reconstructed latent variables. The disentangled semantic latent variables are finally adapted across the domains. Experimental studies testify that our model yields state-of-the-art performance on several domain adaptation benchmark datasets

(E)-N′-(3,4-Dimethoxy­benzyl­idene)-2,4-dihydroxy­benzohydrazide methanol solvate

The title compound, C16H16N2O5·CH3OH, was obtained from a condensation reaction of 3,4-dimethoxy­benzaldehyde and 2,4-dihydroxy­benzohydrazide. The non-H atoms of the Schiff base mol­ecule are approximately coplanar (r.m.s. deviation = 0.043 Å) and the dihedral angle between the two benzene rings is 1.6 (1)°. The mol­ecule adopts an E configuration with respect to the C=N double bond. An intra­molecular O—H⋯O hydrogen bond is observed. The Schiff base and methanol mol­ecules are linked into a two-dimensional network parallel to (10) by inter­molecular N—H⋯O, O—H⋯N and O—H⋯O hydrogen bonds

TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequences

Learning Granger causality from event sequences is a challenging but essential task across various applications. Most existing methods rely on the assumption that event sequences are independent and identically distributed (i.i.d.). However, this i.i.d. assumption is often violated due to the inherent dependencies among the event sequences. Fortunately, in practice, we find these dependencies can be modeled by a topological network, suggesting a potential solution to the non-i.i.d. problem by introducing the prior topological network into Granger causal discovery. This observation prompts us to tackle two ensuing challenges: 1) how to model the event sequences while incorporating both the prior topological network and the latent Granger causal structure, and 2) how to learn the Granger causal structure. To this end, we devise a unified topological neural Poisson auto-regressive model with two processes. In the generation process, we employ a variant of the neural Poisson process to model the event sequences, considering influences from both the topological network and the Granger causal structure. In the inference process, we formulate an amortized inference algorithm to infer the latent Granger causal structure. We encapsulate these two processes within a unified likelihood function, providing an end-to-end framework for this task. Experiments on simulated and real-world data demonstrate the effectiveness of our approach

Threshold quantum cryptograph based on Grover's algorithm

Grover's operator in the two-qubit case can transform a basis into its conjugated basis. A permutation operator can transform a state in the two conjugated bases into its orthogonal state. These properties are included in a threshold quantum protocol. The proposed threshold quantum protocol is secure based the proof that the legitimate participators can only eavesdrop 2 bits of 3 bits operation information on one two-qubit with error probability 3/8. We propose a scheme to detect the Trojan horse attack without destroying the legal qubit.Comment: 7 pages, 1 figure

Mg/ZrO2 metal matrix nanocomposites fabricated by friction stir processing: microstructure, mechanical properties, and corrosion behavior

Magnesium (Mg) and its alloys have attached more and more attention because of their potential as a new type of biodegradable metal materials. In this work, AZ31/ZrO2 nanocomposites with good uniformity were prepared successfully by friction stir processing (FSP). The scanning electron microscope (SEM) and transmission electron microscope (TEM) were used to characterize the microstructure of the composites. The mechanical properties, electrochemical corrosion properties and biological properties were evaluated. In addition, the effect of reinforced particles (ZrO2) on the microstructure and properties of the composite was studied comparing with FSP AZ31 Mg alloy. The results show that compared with the base metal (BM), the AZ31/ZrO2 composite material achieves homogenization, densification, and grain refinement after FSP. The combination of dynamic recrystallization and ZrO2 particles leads to grain refinement of Mg alloy, and the average grain size of AZ31/ZrO2 composites is 3.2 μm. After FSP, the c-axis of grain is deflected under the compression stress of shoulder and the shear stress of pin. The ultimate tensile strength (UTS) and yield strength (YS) of BM were 283 MPa and 137 MPa, respectively, the UTS and YS of AZ31/ZrO2 composites were 427 MPa and 217 MPa, respectively. The grain refinement and Orowan strengthening are the major strengthening mechanisms. Moreover, the corrosion resistance in simulated body fluid of Mg alloy is improved by grain refinement and the barrier effect of ZrO2