Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction

Visual media are powerful means of expressing emotions and sentiments. The constant generation of new content in social networks highlights the need of automated visual sentiment analysis tools. While Convolutional Neural Networks (CNNs) have established a new state-of-the-art in several vision problems, their application to the task of sentiment analysis is mostly unexplored and there are few studies regarding how to design CNNs for this purpose. In this work, we study the suitability of fine-tuning a CNN for visual sentiment prediction as well as explore performance boosting techniques within this deep learning setting. Finally, we provide a deep-dive analysis into a benchmark, state-of-the-art network architecture to gain insight about how to design patterns for CNNs on the task of visual sentiment prediction.Comment: Preprint of the paper accepted at the 1st Workshop on Affect and Sentiment in Multimedia (ASM), in ACM MultiMedia 2015. Brisbane, Australi

A Low Latency Adaptive Coding Spiking Framework for Deep Reinforcement Learning

With the help of Deep Neural Networks, Deep Reinforcement Learning (DRL) has achieved great success on many complex tasks during the past few years. Spiking Neural Networks (SNNs) have been used for the implementation of Deep Neural Networks with superb energy efficiency on dedicated neuromorphic hardware, and recent years have witnessed increasing attention on combining SNNs with Reinforcement Learning, whereas most approaches still work with huge energy consumption and high latency. This work proposes the Adaptive Coding Spiking Framework (ACSF) for SNN-based DRL and achieves low latency and great energy efficiency at the same time. Inspired by classical conditioning in biology, we simulate receptors, central interneurons, and effectors with spike encoders, SNNs, and spike decoders, respectively. We use our proposed ACSF to estimate the value function in reinforcement learning and conduct extensive experiments to verify the effectiveness of our proposed framework

Visualizing topological edge states of single and double bilayer Bi supported on multibilayer Bi(111) films

Freestanding single-bilayer Bi(111) is a two-dimensional topological insulator with edge states propagating along its perimeter. Given the interlayer coupling experimentally, the topological nature of Bi(111) thin films and the impact of the supporting substrate on the topmost Bi bilayer are still under debate. Here, combined with scanning tunneling microscopy and first-principles calculations, we systematically study the electronic properties of Bi(111) thin films grown on a NbSe2 substrate. Two types of non-magnetic edge structures, i.e., a conventional zigzag edge and a 2x1 reconstructed edge, coexist alternately at the boundaries of single bilayer islands, the topological edge states of which exhibit remarkably different energy and spatial distributions. Prominent edge states are persistently visualized at the edges of both single and double bilayer Bi islands, regardless of the underlying thickness of Bi(111) thin films. We provide an explanation for the topological origin of the observed edge states that is verified with first-principles calculations. Our paper clarifies the long-standing controversy regarding the topology of Bi(111) thin films and reveals the tunability of topological edge states via edge modifications.Comment: 36 pages, 10 figure

Stable isochronal synchronization of mutually coupled chaotic lasers

The dynamics of two mutually coupled chaotic diode lasers are investigated experimentally and numerically. By adding self feedback to each laser, stable isochronal synchronization is established. This stability, which can be achieved for symmetric operation, is essential for constructing an optical public-channel cryptographic system. The experimental results on diode lasers are well described by rate equations of coupled single mode lasers

Preparation of Kaolin Composites and Its Adsorption for Sb(Ⅲ)

Antimony is an important element in the production of flame retardants and semiconductor materials. In the process of antimony mining, it may cause local environmental pollution, which has adverse effects on human health, and the development of economical and efficient adsorbents to remove antimony from wastewater has become a hot research topic. In this paper, the hydrothermal synthesis method was adopted, and purified Kaolin was selected as the carrier, potassium permanganate, manganese chloride and ferric chloride are the metal sources, urea is the precipitant, and sodium dodecyl benzene sulfonate is the structure guide agent. Under the conditions of 5% mass fraction of dispersant, loading temperature of 140 ℃, reaction time of 8 h, mass ratio of iron to manganese of 1.84:1, and mass of precipitant of 0.9 g, the composites prepared were effective in adsorbing the Sb(Ⅲ) from the wastewater. The optimum adsorption efficiency of the prepared composites on Sb(Ⅲ) is 92.83%, which showed excellent adsorption performance

catena-Poly[[trimeth­yl(4-sulfanylphen­yl)aza­nium] [(chloridocadmate)-di-μ-chlorido]]

The title compound, {(C9H14NS)[CdCl3]}n, consists of a linear [CdCl3]nn − polyanion and a trimeth­yl(4-sulfanylphen­yl)aza­nium cation. The CdII atom is penta­coordinated by four μ2-Cl atoms and one terminal Cl atom in a trigonal–bipyramidal geometry. The trigonal–bipyramidal units are linked by two opposite shared faces, giving rise to infinite [CdCl3]n chains parallel to the a axis. The cations surround the chain and are linked to them by S—H⋯Cl and C—H⋯Cl hydrogen bonds, forming a three-dimensional network