Robust Neural Architecture Search

Neural Architectures Search (NAS) becomes more and more popular over these years. However, NAS-generated models tends to suffer greater vulnerability to various malicious attacks. Lots of robust NAS methods leverage adversarial training to enhance the robustness of NAS-generated models, however, they neglected the nature accuracy of NAS-generated models. In our paper, we propose a novel NAS method, Robust Neural Architecture Search (RNAS). To design a regularization term to balance accuracy and robustness, RNAS generates architectures with both high accuracy and good robustness. To reduce search cost, we further propose to use noise examples instead adversarial examples as input to search architectures. Extensive experiments show that RNAS achieves state-of-the-art (SOTA) performance on both image classification and adversarial attacks, which illustrates the proposed RNAS achieves a good tradeoff between robustness and accuracy

Improving Differentiable Architecture Search via Self-Distillation

Differentiable Architecture Search (DARTS) is a simple yet efficient Neural Architecture Search (NAS) method. During the search stage, DARTS trains a supernet by jointly optimizing architecture parameters and network parameters. During the evaluation stage, DARTS discretizes the supernet to derive the optimal architecture based on architecture parameters. However, recent research has shown that during the training process, the supernet tends to converge towards sharp minima rather than flat minima. This is evidenced by the higher sharpness of the loss landscape of the supernet, which ultimately leads to a performance gap between the supernet and the optimal architecture. In this paper, we propose Self-Distillation Differentiable Neural Architecture Search (SD-DARTS) to alleviate the discretization gap. We utilize self-distillation to distill knowledge from previous steps of the supernet to guide its training in the current step, effectively reducing the sharpness of the supernet's loss and bridging the performance gap between the supernet and the optimal architecture. Furthermore, we introduce the concept of voting teachers, where multiple previous supernets are selected as teachers, and their output probabilities are aggregated through voting to obtain the final teacher prediction. Experimental results on real datasets demonstrate the advantages of our novel self-distillation-based NAS method compared to state-of-the-art alternatives.Comment: Accepted by Neural Network

Application of Rough Classification of Multi-objective Extension Group Decision-making under Uncertainty

On account of the problem of incomplete information system in classification of extension group decision-making, this paper studies attribution reduction with decision-making function based on the group interaction and individual preferences assembly for achieving the goal of rough classification of multi-objective extension group decision-making under uncertainty. Then, this paper describes the idea and operating processes of multi-objective extension classification model in order to provide decision-makers with more practical, easy to operate and objective classification. Finally, an example concerning practical problem is given to demonstrate the classification process. Combining by extension association and rough reduction, this method not only takes the advantages of dynamic classification in extension decision-making, but also achieves the elimination of redundant attributes, conducive to the promotion on the accuracy and the reliability of the classification results in multi-objective extension group decision-making. Keywords: extension group decision-making; matter-element analysis; extension association; rough set; attribution reductio

Magnetic flux penetration in polycrystalline SmFeO0.75F0.2As

The recently discovered Fe–As superconducting materials which show high potential ability to carry current due to their low anisotropy have attracted a great number of attentions to understand their superconductivity mechanism and explore their applications. This paper presents a method to synthesis SmFeO0.75F0.20As polycrystalline by hot press in detail. The magnetization at different temperatures and applied fields obtained by a superconducting quantum interference device are also discussed. In addition, the local magnetization process is presented by magneto-optical imaging technique at the conditions of zero-field-cooling and field-cooling. It is found that the collective magnetization process of the newly discovered Fe–As superconductors is very similar to that of high-Tc cuprates. For instance, the Fe–As superconductors and high-Tc cuprates have the same magnetization features due to strong pining and intergrain weak link. The global supercurrent is significantly lower than local grain supercurrent due to the weak line between the grains

Iron(III) bromide catalyzed bromination of 2-tert-butylpyrene and corresponding position-dependent aryl-functionalized pyrene derivatives

The present work probes the bromination mechanism of 2-tert-butylpyrene (1), which regioselectively affords mono-, di-, tri- and tetra-bromopyrenes, by theoretical calculation and detailed experimental methods. The bromine atom may be directed to the K-region (positions 5- and 9-) instead of the more reactive 6- and 8-positions in the presence of iron powder. In this process, FeBr₃ plays a significant role to release steric hindrance or lower the activation energy of the rearrangement. The intermediate bromopyrene derivatives were isolated and confirmed by ¹H NMR spectrometry, mass spectroscopy and elemental analysis. Further evidence on substitution position originated from a series of aryl substituted pyrene derivatives, which were obtained from the corresponding bromopyrenes on reaction with 4-methoxy-phenylboronic acid by a Suzuki–Miyaura cross-coupling reaction. All position-dependent aryl-functionalized pyrene derivatives are characterized by single X-ray diffraction, ¹H/¹³C NMR, FT-IR and MS, and offered straightforward evidence to support our conclusion. Furthermore, the photophysical properties of a series of compounds were confirmed by fluorescence and absorption, as well as by fluorescence lifetime measurements

SLC6A4 Repeat and Single-Nucleotide Polymorphisms Are Associated With Depression and Rest Tremor in Parkinson's Disease: An Exploratory Study

Introduction: Level of serotonin is mainly regulated by the serotonin reuptake transporter encoded by SLC6A4. The promoter region of SLC6A4 bears a repeat polymorphism 5-HTTLPR and a single nucleotide polymorphism rs25531. We have previously studied the association between these two variants and sporadic PD. The objective of the current study was to determine whether the SLC6A4 polymorphisms were associated with key motor and non-motor symptoms of PD.Methods: A total of 370 PD patients of Han Chinese were included. Associations between the SLC6A4 polymorphisms and PD symptoms including depression, intellectual impairment, tremor and rigidity were analyzed.Results: 5-HTTLPR was associated with depression in PD patients and presence of the LL genotype was protective against the depression risk. The rs25531 was associated with rest tremor in PD and the A allele serves as a recessive risk allele. No associations were found in the two polymorphisms with respect to intellectual impairment and rigidity in the cohort.Conclusion: The current study reveals two PD symptoms associated with SLC6A4 polymorphisms, and provides new insight into how serotonergic system genetically participates in the symptomatic progression of PD. Further study is warranted in additional populations