Discriminative Density-ratio Estimation

The covariate shift is a challenging problem in supervised learning that results from the discrepancy between the training and test distributions. An effective approach which recently drew a considerable attention in the research community is to reweight the training samples to minimize that discrepancy. In specific, many methods are based on developing Density-ratio (DR) estimation techniques that apply to both regression and classification problems. Although these methods work well for regression problems, their performance on classification problems is not satisfactory. This is due to a key observation that these methods focus on matching the sample marginal distributions without paying attention to preserving the separation between classes in the reweighted space. In this paper, we propose a novel method for Discriminative Density-ratio (DDR) estimation that addresses the aforementioned problem and aims at estimating the density-ratio of joint distributions in a class-wise manner. The proposed algorithm is an iterative procedure that alternates between estimating the class information for the test data and estimating new density ratio for each class. To incorporate the estimated class information of the test data, a soft matching technique is proposed. In addition, we employ an effective criterion which adopts mutual information as an indicator to stop the iterative procedure while resulting in a decision boundary that lies in a sparse region. Experiments on synthetic and benchmark datasets demonstrate the superiority of the proposed method in terms of both accuracy and robustness

Comparative analysis of binocular summation of pattern visual evoked potential before and after the surgery of concomitant strabismus

AIM: To investigate the opportunity of the concomitant strabismus operation and the function in the treatment of strabismic amblyopia through analyzing the changes of binocular summation of pattern visual evoked potential(P-VEP)before and after the surgery of concomitant strabismus. <p>METHODS: In this retrospective study we investigated 67 cases admitted in our hospital. All patients were less than 18a and the postoperation squint angle was less than ±10^△. Patients were divided into three groups according to the strabismus type, age, and amblyopia degree. P-VEP binocular summation response was recorded in all cases, to observe the changes of the binocular summation response of P-VEP before strabismus surgery and 1mo, 3mo after surgery. The P-VEP response of binocular /monocular(B/M)ratio was taken as an evaluation index. <p>RESULTS: B/M value of three groups all improved obviously 1mo after surgery, which the difference showed statistical significant(<i>P</i><0.01). 1)After 3mo surgery, B/M value in esotropia group was higher than that in exotropia group(<i>P</i><0.05). 2)After 3mo surgery, B/M value in ≤6a group was higher than that in >12a group(<i>P</i><0.05). 3)After 1mo surgery, B/M value in severe amblyopia group was higher than that in mild group(<i>P</i><0.05). After 3mo surgery, B/M value in severe amblyopia group was higher than that in mild group significantly(<i>P</i><0.01). <p>CONCLUSION: Concomitant strabismus surgery is suggested to be performed before 6 years old when the patients are difficult to improve the vision after amblyopia treatment, especially with the severe amblyopia and esotropia(accommodative esotropia must be excluded). The early operation is better to amblyopia treatment and binocular vision recovery

Shallow feature based dense attention network for crowd counting

While the performance of crowd counting via deep learning has been improved dramatically in the recent years, it remains an ingrained problem due to cluttered backgrounds and varying scales of people within an image. In this paper, we propose a Shallow feature based Dense Attention Network (SDANet) for crowd counting from still images, which diminishes the impact of backgrounds via involving a shallow feature based attention model, and meanwhile, captures multi-scale information via densely connecting hierarchical image features. Specifically, inspired by the observation that backgrounds and human crowds generally have noticeably different responses in shallow features, we decide to build our attention model upon shallow-feature maps, which results in accurate background-pixel detection. Moreover, considering that the most representative features of people across different scales can appear in different layers of a feature extraction network, to better keep them all, we propose to densely connect hierarchical image features of different layers and subsequently encode them for estimating crowd density. Experimental results on three benchmark datasets clearly demonstrate the superiority of SDANet when dealing with different scenarios. Particularly, on the challenging UCF CC 50 dataset, our method outperforms other existing methods by a large margin, as is evident from a remarkable 11.9% Mean Absolute Error (MAE) drop of our SDANet

Shock wave propagation in vibrofluidized granular materials

Shock wave formation and propagation in two-dimensional granular materials under vertical vibration are studied by digital high speed photography. The steepen density and temperature wave fronts form near the plate as granular layer collides with vibrating plate and propagate upward through the layer. The temperature front is always in the transition region between the upward and downward granular flows. The effects of driving parameters and particle number on the shock are also explored.Comment: 9 pages, 4 figures, submitted to PR

Selective Jamming of LoRaWAN using Commodity Hardware

Long range, low power networks are rapidly gaining acceptance in the Internet of Things (IoT) due to their ability to economically support long-range sensing and control applications while providing multi-year battery life. LoRa is a key example of this new class of network and is being deployed at large scale in several countries worldwide. As these networks move out of the lab and into the real world, they expose a large cyber-physical attack surface. Securing these networks is therefore both critical and urgent. This paper highlights security issues in LoRa and LoRaWAN that arise due to the choice of a robust but slow modulation type in the protocol. We exploit these issues to develop a suite of practical attacks based around selective jamming. These attacks are conducted and evaluated using commodity hardware. The paper concludes by suggesting a range of countermeasures that can be used to mitigate the attacks.Comment: Mobiquitous 2017, November 7-10, 2017, Melbourne, VIC, Australi

Fucoxanthin Enhances Cisplatin-Induced Cytotoxicity via NFκB-Mediated Pathway and Downregulates DNA Repair Gene Expression in Human Hepatoma HepG2 Cells

Cisplain, a platinum-containing anticancer drug, has been shown to enhance DNA repair and to inhibit cell apoptosis, leading to drug resistance. Thus, the combination of anticancer drugs with nutritional factors is a potential strategy for improving the efficacy of cisplatin chemotherapy. In this study, we investigated the anti-proliferative effects of a combination of fucoxanthin, the major non-provitamin A carotenoid found in Undaria Pinnatifida, and cisplatin in human hepatoma HepG2 cells. We found that fucoxanthin (1–10 μΜ) pretreatment for 24 h followed by cisplatin (10 μΜ) for 24 h significantly decreased cell proliferation, as compared with cisplatin treatment alone. Mechanistically, we showed that fucoxanthin attenuated cisplatin-induced NFκB expression and enhanced the NFκB-regulated Bax/Bcl-2 mRNA ratio. Cisplatin alone induced mRNA expression of excision repair cross complementation 1 (ERCC1) and thymidine phosphorylase (TP) through phosphorylation of ERK, p38 and PI3K/AKT pathways. However, fucoxanthin pretreatment significantly attenuated cisplatin-induced ERCC1 and TP mRNA expression, leading to improvement of chemotherapeutic efficacy of cisplatin. The results suggest that a combined treatment with fucoxanthin and cisplatin could lead to a potentially important new therapeutic strategy against human hepatoma cells