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

After heat distribution of a mobile nuclear power plant

A computer program was developed to analyze the transient afterheat temperature and pressure response of a mobile gas-cooled reactor power plant following impact. The program considers (in addition to the standard modes of heat transfer) fission product decay and transport, metal-water reactions, core and shield melting and displacement, and pressure and containment vessel stress response. Analyses were performed for eight cases (both deformed and undeformed models) to verify operability of the program options. The results indicated that for a 350 psi (241 n/sq cm) initial internal pressure, the containment vessel can survive over 100,000 seconds following impact before creep rupture occurs. Recommendations were developed as to directions for redesign to extend containment vessel life

Bridging Atomistic/Continuum Scales in Solids with Moving Dislocations

We propose a multiscale method for simulating solids with moving dislocations. Away from atomistic subdomains where the atomistic dynamics are fully resolved, a dislocation is represented by a localized jump profile, superposed on a defect-free field. We assign a thin relay zone around an atomistic subdomain to detect the dislocation profile and its propagation speed at a selected relay time. The detection technique utilizes a lattice time history integral treatment. After the relay, an atomistic computation is performed only for the defect-free field. The method allows one to effectively absorb the fine scale fluctuations and the dynamic dislocations at the interface between the atomistic and continuum domains. In the surrounding region, a coarse grid computation is adequate

Thermal optical non-linearity of nematic mesophase enhanced by gold nanoparticles – an experimental and numerical investigation

In this work the mechanisms leading to the enhancement of optical nonlinearity of nematic liquid crystalline material through localized heating by doping the liquid crystals (LCs) with gold nanoparticles (GNPs) are investigated. We present some experimental and theoretical results on the effect of voltage and nanoparticle concentration on the nonlinear response of GNP-LC suspensions. The optical nonlinearity of these systems is characterized by diffraction measurements and the second order nonlinear refractive index, n 2 , is used to compare systems with different configurations and operating conditions. A theoretical model based on heat diffusion that takes into account the intensity and finite size of the incident beam, the nanoparticle concentration dependent absorbance of GNP doped LC systems and the presence of bounding substrates is developed and validated. We use the model to discuss the possibilities of further enhancing the optical nonlinearity