We report a broadband electro-optical (EO) modulator based on tunable plasmonic metamaterial. Transparent conducting oxides provide an excellent active plasmonic material for optoelectronic applications. By utilizing our indium-tin-oxide- (ITO) based multilayer structure, light absorption of the active ITO layer can be electrically modulated over a large spectrum range. Based on the attenuated total reflectance configuration, bias polarity-dependent modulation up to 37% has been experimentally demonstrated. This EO modulator has advantages of simple design, easy fabrication, compact size, broadband performance, large modulation depth, as well as compatibility with existing silicon photonics platforms.

Poisoning attacks are emerging threats to deep neural networks where the adversaries attempt to compromise the models by injecting malicious data points in the clean training data. Poisoning attacks target either the availability or integrity of a model. The availability attack aims to degrade the overall accuracy while the integrity attack causes misclassification only for specific instances without affecting the accuracy of clean data. Although clean-label integrity attacks are proven to be effective in recent studies, the feasibility of clean-label availability attacks remains unclear. This paper, for the first time, proposes a clean-label approach, CLPA, for the poisoning availability attack. We reveal that due to the intrinsic imperfection of classifiers, naturally misclassified inputs can be considered as a special type of poisoned data, which we refer to as "natural poisoned data''. We then propose a two-phase generative adversarial net (GAN) based poisoned data generation framework along with a triplet loss function for synthesizing clean-label poisoned samples that locate in a similar distribution as natural poisoned data. The generated poisoned data are plausible to human perception and can also bypass the singular vector decomposition (SVD) based defense. We demonstrate the effectiveness of our approach on CIFAR-10 and ImageNet dataset over a variety type of models. Codes are available at: https://github.com/bxz9200/CLPA.

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Polycarboxylate superplasticizer (PCE) is widely used in concrete, but the clay content in the aggregate is increasing at present, which seriously weakens the dispersing function of PCE. Therefore, in order to lower the sensitivity of PCE to the clay, β‐cyclodextrin (β‐CD) was used for synthesizing anti‐clay polycarboxylate superplasticizers (AC‐PCE). Firstly, the clay tolerance property of AC‐PCE was explored by the fluidity and rheological test; then, the PCE was mixed with montmorillonite (MMT), and interaction mechanism was studied. The results showed that compared with the conventional polycarboxylate superplasticizer (CO‐PCE), an increase of 19.5% and 14.9% was observed in the fluidity of cement paste and mortar for AC‐PCE, indicating that the sensitivity of AC‐PCE to the clay was lower than that of CO‐PCE. Both CO‐PCE and AC‐PCE were adsorpted on MMT. XRD results showed that intercalation adsorption had occurred for both CO‐PCE and AC‐PCE. N2 adsorption results showed that although AC‐PCE was also inserted into the interlayer of MMT, its adsorption amount was lower than that of CO‐PCE due to steric hindrance from β‐CD, which increased the sorption level of the AC‐PCE on the cement clinkers particles. Therefore, the clay tolerance of PCE was enhanced significantly by β‐CD.