The flowerlike ZnO nanostructures, which consisted of swordlike ZnO nanorods, have been prepared by a cetyltrimethylammonium bromide (CTAB)-assisted hydrothermal process at low temperature (120 °C). The XRD pattern indicated that the flowerlike ZnO nanostructures were hexagonal. Furthermore, the SAED and HRTEM revealed that the swordlike ZnO nanorods were single crystal in nature and preferentially grew up along [001]. Finally, the mechanism for the CTAB-assisted hydrothermal synthesis of flowerlike ZnO nanostructures has been preliminarily explained by polar crystal growth theory and surfactant action theory.

Flower-like ZnO nanostructures, which consisted of sword-like ZnO nanorods, have been prepared by an organic-free hydrothermal process. The XRD pattern indicated that the flower-like ZnO nanostructures were hexagonal. The SAED and HRTEM experiments implied that the sword-like ZnO nanorods were single crystal in nature and preferentially grew up along the [001] direction. The effects of temperature, pH value and mineralizer on the morphology have been also investigated. It is considered that pH value is the main factor to influence the morphology because of its effect on the initial nuclei and growth environment of ZnO. Finally, the mechanism for organic-free hydrothermal synthesis of the flower-like ZnO nanostructure is discussed.

Many of today's machine learning (ML) systems are built by reusing an array of, often pre-trained, primitive models, each fulfilling distinct functionality (e.g., feature extraction). The increasing use of primitive models significantly simplifies and expedites the development cycles of ML systems. Yet, because most of such models are contributed and maintained by untrusted sources, their lack of standardization or regulation entails profound security implications, about which little is known thus far.In this paper, we demonstrate that malicious primitive models pose immense threats to the security of ML systems. We present a broad class of model-reuse attacks wherein maliciously crafted models trigger host ML systems to misbehave on targeted inputs in a highly predictable manner. By empirically studying four deep learning systems (including both individual and ensemble systems) used in skin cancer screening, speech recognition, face verification, and autonomous steering, we show that such attacks are (i) effective -the host systems misbehave on the targeted inputs as desired by the adversary with high probability, (ii) evasive -the malicious models function indistinguishably from their benign counterparts on non-targeted inputs, (iii) elastic -the malicious models remain effective regardless of various system design choices and tuning strategies, and (iv) easy -the adversary needs little prior knowledge about the data used for system tuning or inference. We provide analytical justification for the effectiveness of model-reuse attacks, which points to the unprecedented complexity of today's primitive models. This issue thus seems fundamental to many ML systems. We further discuss potential countermeasures and their challenges, which lead to several promising research directions.

In this communication, we demonstrate a new approach to well-controlled growth of Se nanowires and nanotubes, which comprises a hydrothermal process and a following sonication. The hydrothermal process was used to derive Se particles of trigonal phase. In the subsequent sonication, if the Se particles were large enough, they were first broken, and then aggregated along the circumferential edge of the gap thus forming Se nanotubes; conversely, the Se particles were not broken and then aligned into nanowires. The high-resolution transmission electron microscopy (HRTEM) proved that both the Se nanotubes and nanowires were single crystalline in nature and 〈100〉 oriented. Based on a series of the TEM observation, a phenomenological mechanism for the elucidation of the controllable growth of Se nanowires and nanotubes is presented.

Long Bi2S3 nanowires have been prepared via the thioglyolic acid (HSCH2COOH, TGA) assisted hydrothermal method. The x-ray diffraction pattern shows that the Bi2S3 nanowires obtained are of orthorhombic phase. High resolution transmission electron microscopy identifies that the Bi2S3 nanowires are single crystalline in nature. Furthermore, we give a preliminary presentation of the mechanism for the TGA-assisted hydrothermal synthesis of Bi2S3 nanowires.