ZOOpt: Toolbox for Derivative-Free Optimization

Recent advances of derivative-free optimization allow efficient approximating the global optimal solutions of sophisticated functions, such as functions with many local optima, non-differentiable and non-continuous functions. This article describes the ZOOpt (https://github.com/eyounx/ZOOpt) toolbox that provides efficient derivative-free solvers and are designed easy to use. ZOOpt provides a Python package for single-thread optimization, and a light-weighted distributed version with the help of the Julia language for Python described functions. ZOOpt toolbox particularly focuses on optimization problems in machine learning, addressing high-dimensional, noisy, and large-scale problems. The toolbox is being maintained toward ready-to-use tool in real-world machine learning tasks

Look, Listen and Learn - A Multimodal LSTM for Speaker Identification

Speaker identification refers to the task of localizing the face of a person who has the same identity as the ongoing voice in a video. This task not only requires collective perception over both visual and auditory signals, the robustness to handle severe quality degradations and unconstrained content variations are also indispensable. In this paper, we describe a novel multimodal Long Short-Term Memory (LSTM) architecture which seamlessly unifies both visual and auditory modalities from the beginning of each sequence input. The key idea is to extend the conventional LSTM by not only sharing weights across time steps, but also sharing weights across modalities. We show that modeling the temporal dependency across face and voice can significantly improve the robustness to content quality degradations and variations. We also found that our multimodal LSTM is robustness to distractors, namely the non-speaking identities. We applied our multimodal LSTM to The Big Bang Theory dataset and showed that our system outperforms the state-of-the-art systems in speaker identification with lower false alarm rate and higher recognition accuracy.Comment: The 30th AAAI Conference on Artificial Intelligence (AAAI-16

Dynamics of delay induced composite multi-scroll attractor and its application in encryption

This work was supported in part by NSFC (60804040, 61172070), Key Program of Nature Science Foundation of Shaanxi Province (2016ZDJC-01), Innovative Research Team of Shaanxi Province(2013KCT-04), Fok Ying Tong Education Foundation Young Teacher Foundation(111065), Chao Bai was supported by Excellent Ph.D. research fund (310-252071603) at XAUT.Peer reviewedPostprin

Pseudozyma spp. and Barnettozyma spp. effectively kill cancer cells in vitro

AbstractCancer is the overall leading cause of death in developed countries and also worldwide, and being able to exploit an effective anticancer drug is the aim of all cancer scientists. However, many of the synthetic drugs produced so far usually cause serious side effects, which reduces their therapeutic efficacy. Discovering new drugs or auxiliary therapies derived from natural products might thus provide a novel opportunity for cancer therapy. A recent study reported that some lethal toxins can maintain their activity after being injected into mice. We therefore used two Pseudozyma spp. and three Barnettozyma spp. to examine whether these killer yeasts can preserve their lethal effect on cancer cells under the physical environment (optimum pH, temperature and osmolality, supporting a living cell accomplishes to proliferate, metabolize, differentiate and survive). Our preliminary results showed that both Barnettozyma spp. and Pseudozyma spp. have stronger cytotoxicity against HepG2 than Chang’s liver cells. According to the results of two-dimensional difference gel electrophoresis (2D-DIGE), a total of 115 and 27 proteins differentially expressed by 1.5-fold or more were observed for HepG2 and Chang’s liver cells, respectively. Furthermore, we explored the mechanism involved in the effect of the lethal yeast filtrates on liver cancer cells using 2D-DIGE and mass spectrometry

Intrinsically stretchable and transparent thin-film transistors based on printable silver nanowires, carbon nanotubes and an elastomeric dielectric.

Thin-film field-effect transistor is a fundamental component behind various mordern electronics. The development of stretchable electronics poses fundamental challenges in developing new electronic materials for stretchable thin-film transistors that are mechanically compliant and solution processable. Here we report the fabrication of transparent thin-film transistors that behave like an elastomer film. The entire fabrication is carried out by solution-based techniques, and the resulting devices exhibit a mobility of ∼30 cm(2) V(-1) s(-1), on/off ratio of 10(3)-10(4), switching current >100 μA, transconductance >50 μS and relative low operating voltages. The devices can be stretched by up to 50% strain and subjected to 500 cycles of repeated stretching to 20% strain without significant loss in electrical property. The thin-film transistors are also used to drive organic light-emitting diodes. The approach and results represent an important progress toward the development of stretchable active-matrix displays

LAUN Improved StarGAN for Facial Emotion Recognition

In the field of facial expression recognition, deep learning is extensively used. However, insufficient and unbalanced facial training data in available public databases is a major challenge for improving the expression recognition rate. Generative Adversarial Networks (GANs) can produce more one-to-one faces with different expressions, which can be used to enhance databases. StarGAN can perform one-to-many translations for multiple expressions. Compared with original GANs, StarGAN can increase the efficiency of sample generation. Nevertheless, there are some defects in essential areas of the generated face, such as the mouth and the fuzzy side face image generation. To address these limitations, we improved StarGAN to alleviate the defects of images generation by modifying the reconstruction loss and adding the Contextual loss. Meanwhile, we added the Attention U-Net to StarGAN's generator, replacing StarGAN's original generator. Therefore, we proposed the Contextual loss and Attention U-Net (LAUN) improved StarGAN. The U-shape structure and skip connection in Attention U-Net can effectively integrate the details and semantic features of images. The network's attention structure can pay attention to the essential areas of the human face. The experimental results demonstrate that the improved model can alleviate some flaws in the face generated by the original StarGAN. Therefore, it can generate person images with better quality with different poses and expressions. The experiments were conducted on the Karolinska Directed Emotional Faces database, and the accuracy of facial expression recognition is 95.97%, 2.19% higher than that by using StarGAN. Meanwhile, the experiments were carried out on the MMI Facial Expression Database, and the accuracy of expression is 98.30%, 1.21% higher than that by using StarGAN. Moreover, experiment results have better performance based on the LAUN improved StarGAN enhanced databases than those without enhancement

Cluster observations of the midaltitude cusp under strong northward interplanetary magnetic field

We report on a multispacecraft cusp observation lasting more than 100 min. We determine the cusp boundary motion and reveal the effect on the cusp size of the interplanetary magnetic field (IMF) changing from southward to northward. The cusp shrinks at the beginning of the IMF rotation and it reexpands at the rate of 0.40° invariant latitude per hour under stable northward IMF. On the basis of plasma signatures inside the cusp, such as counterstreaming electrons with balanced fluxes, we propose that pulsed dual lobe reconnection operates during the time of interest. SC1 and SC4 observations suggest a long-term regular periodicity of the pulsed dual reconnection, which we estimate to be ~1–5 min. Further, the distances from the spacecraft to the reconnection site are estimated on the basis of observations from three satellites. The distance determined using SC1 and SC4 observations is ~15 RE and that determined from SC3 data is ~8 RE. The large-scale speed of the reconnection site sunward motion is ~16 km s-1. We observe also a fast motion of the reconnection site by SC1, which provides new information about the transitional phase after the IMF rotation. Finally, a statistical study of the dependency of plasma convection inside the cusp on the IMF clock angle is performed. The relationship between the cusp stagnation, the dual lobe reconnection process, and the IMF clock angle is discussed

The Effect of Superparamagnetic Iron Oxide Nanoparticle Surface Charge on Antigen Cross-Presentation.

Magnetic nanoparticles (NPs) of superparamagnetic iron oxide (SPIO) have been explored for different kinds of applications in biomedicine, mechanics, and information. Here, we explored the synthetic SPIO NPs as an adjuvant on antigen cross-presentation ability by enhancing the intracellular delivery of antigens into antigen presenting cells (APCs). Particles with different chemical modifications and surface charges were used to study the mechanism of action of antigen delivery. Specifically, two types of magnetic NPs, γF