Order-Free RNN with Visual Attention for Multi-Label Classification

In this paper, we propose the joint learning attention and recurrent neural network (RNN) models for multi-label classification. While approaches based on the use of either model exist (e.g., for the task of image captioning), training such existing network architectures typically require pre-defined label sequences. For multi-label classification, it would be desirable to have a robust inference process, so that the prediction error would not propagate and thus affect the performance. Our proposed model uniquely integrates attention and Long Short Term Memory (LSTM) models, which not only addresses the above problem but also allows one to identify visual objects of interests with varying sizes without the prior knowledge of particular label ordering. More importantly, label co-occurrence information can be jointly exploited by our LSTM model. Finally, by advancing the technique of beam search, prediction of multiple labels can be efficiently achieved by our proposed network model.Comment: Accepted at 32nd AAAI Conference on Artificial Intelligence (AAAI-18

Rapid Amygdala Kindling Causes Motor Seizure and Comorbidity of Anxiety- and Depression-Like Behaviors in Rats

Amygdala kindling is a model of temporal lobe epilepsy (TLE) with convulsion. The rapid amygdala kindling has an advantage on quick development of motor seizures and for antiepileptic drugs screening. The rapid amygdala kindling causes epileptogenesis accompanied by an anxiolytic response in early isolation of rat pups or depressive behavior in immature rats. However, the effect of rapid amygdala kindling on comorbidity of anxiety- and depression-like behaviors is unexplored in adult rats with normal breeding. In the present study, 40 amygdala stimulations given within 2 days were applied in adult Wistar rats. Afterdischarge (AD) and seizure stage were recorded throughout the amygdala kindling. Anxiety-like behaviors were evaluated by the elevated plus maze (EPM) test and open field (OF) test, whereas depression-like behaviors were assessed by the forced swim (FS) and sucrose consumption (SC) tests. A tonic-clonic convulsion was provoked in the kindle group. Rapid amygdala kindling resulted in a significantly lower frequency entering an open area of either open arms of the EPM or the central zone of an OF, lower sucrose intake, and longer immobility of the FS test in the kindle group. Our results suggest that rapid amygdala kindling elicited severe motor seizures comorbid with anxiety- and depression-like behaviors

The effect of mechanism design on the performance of a quadruped walking machine

The objective of this paper is to investigate the effect of mechanism design on the performance of a quadruped walking machine. For studying the effect of mechanism design on the performance of a quadruped walking machine, four designs with different crank and leg arrangements are proposed and analyzed. The performance of the walking machine, including the stance leg sequence, foot trajectory, pitch angle, and dynamic response of the quadruped walking machine are investigated and compared with the existing design. The results show that the phrase angle between front and rear legs on the same side should be 0° or 90° and the one between the legs on the different sides should be 180°. And, the design with the front and rear legs bent in the same direction has better performance in dynamic responses. The results of this study can serve as a reference for future design and optimization of quadruped walking machines

Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection

Multi-label image classification is a fundamental but challenging task towards general visual understanding. Existing methods found the region-level cues (e.g., features from RoIs) can facilitate multi-label classification. Nevertheless, such methods usually require laborious object-level annotations (i.e., object labels and bounding boxes) for effective learning of the object-level visual features. In this paper, we propose a novel and efficient deep framework to boost multi-label classification by distilling knowledge from weakly-supervised detection task without bounding box annotations. Specifically, given the image-level annotations, (1) we first develop a weakly-supervised detection (WSD) model, and then (2) construct an end-to-end multi-label image classification framework augmented by a knowledge distillation module that guides the classification model by the WSD model according to the class-level predictions for the whole image and the object-level visual features for object RoIs. The WSD model is the teacher model and the classification model is the student model. After this cross-task knowledge distillation, the performance of the classification model is significantly improved and the efficiency is maintained since the WSD model can be safely discarded in the test phase. Extensive experiments on two large-scale datasets (MS-COCO and NUS-WIDE) show that our framework achieves superior performances over the state-of-the-art methods on both performance and efficiency.Comment: accepted by ACM Multimedia 2018, 9 pages, 4 figures, 5 table

Fluorescent nanoparticles for sensing

Nanoparticle-based fluorescent sensors have emerged as a competitive alternative to small molecule sensors, due to their excellent fluorescence-based sensing capabilities. The tailorability of design, architecture, and photophysical properties has attracted the attention of many research groups, resulting in numerous reports related to novel nanosensors applied in sensing a vast variety of biological analytes. Although semiconducting quantum dots have been the best-known representative of fluorescent nanoparticles for a long time, the increasing popularity of new classes of organic nanoparticle-based sensors, such as carbon dots and polymeric nanoparticles, is due to their biocompatibility, ease of synthesis, and biofunctionalization capabilities. For instance, fluorescent gold and silver nanoclusters have emerged as a less cytotoxic replacement for semiconducting quantum dot sensors. This chapter provides an overview of recent developments in nanoparticle-based sensors for chemical and biological sensing and includes a discussion on unique properties of nanoparticles of different composition, along with their basic mechanism of fluorescence, route of synthesis, and their advantages and limitations

Constraining Intra-cluster Gas Models with AMiBA13

Clusters of galaxies have been used extensively to determine cosmological parameters. A major difficulty in making best use of Sunyaev-Zel'dovich (SZ) and X-ray observations of clusters for cosmology is that using X-ray observations it is difficult to measure the temperature distribution and therefore determine the density distribution in individual clusters of galaxies out to the virial radius. Observations with the new generation of SZ instruments are a promising alternative approach. We use clusters of galaxies drawn from high-resolution adaptive mesh refinement (AMR) cosmological simulations to study how well we should be able to constrain the large-scale distribution of the intra-cluster gas (ICG) in individual massive relaxed clusters using AMiBA in its configuration with 13 1.2-m diameter dishes (AMiBA13) along with X-ray observations. We show that non-isothermal beta models provide a good description of the ICG in our simulated relaxed clusters. We use simulated X-ray observations to estimate the quality of constraints on the distribution of gas density, and simulated SZ visibilities (AMiBA13 observations) for constraints on the large-scale temperature distribution of the ICG. We find that AMiBA13 visibilities should constrain the scale radius of the temperature distribution to about 50% accuracy. We conclude that the upgraded AMiBA, AMiBA13, should be a powerful instrument to constrain the large-scale distribution of the ICG.Comment: Accepted for publication in The Astrophysical Journal, 12 pages, 9 figure

Cooperation in the snowdrift game on directed small-world networks under self-questioning and noisy conditions

Cooperation in the evolutionary snowdrift game with a self-questioning updating mechanism is studied on annealed and quenched small-world networks with directed couplings. Around the payoff parameter value $r=0.5$, we find a size-invariant symmetrical cooperation effect. While generally suppressing cooperation for $r>0.5$ payoffs, rewired networks facilitated cooperative behavior for $r<0.5$. Fair amounts of noise were found to break the observed symmetry and further weaken cooperation at relatively large values of $r$. However, in the absence of noise, the self-questioning mechanism recovers symmetrical behavior and elevates altruism even under large-reward conditions. Our results suggest that an updating mechanism of this type is necessary to stabilize cooperation in a spatially structured environment which is otherwise detrimental to cooperative behavior, especially at high cost-to-benefit ratios. Additionally, we employ component and local stability analyses to better understand the nature of the manifested dynamics.Comment: 7 pages, 6 figures, 1 tabl