Image Classification with CNN-based Fisher Vector Coding

Fisher vector coding methods have been demonstrated to be effective for image classification. With the help of convolutional neural networks (CNN), several Fisher vector coding methods have shown state-of-the-art performance by adopting the activations of a single fully-connected layer as region features. These methods generally exploit a diagonal Gaussian mixture model (GMM) to describe the generative process of region features. However, it is difficult to model the complex distribution of high-dimensional feature space with a limited number of Gaussians obtained by unsupervised learning. Simply increasing the number of Gaussians turns out to be inefficient and computationally impractical. To address this issue, we re-interpret a pre-trained CNN as the probabilistic discriminative model, and present a CNN based Fisher vector coding method, termed CNN-FVC. Specifically, activations of the intermediate fully-connected and output soft-max layers are exploited to derive the posteriors, mean and covariance parameters for Fisher vector coding implicitly. To further improve the efficiency, we convert the pre-trained CNN to a fully convolutional one to extract the region features. Extensive experiments have been conducted on two standard scene benchmarks (i.e. SUN397 and MIT67) to evaluate the effectiveness of the proposed method. Classification accuracies of 60.7% and 82.1% are achieved on the SUN397 and MIT67 benchmarks respectively, outperforming previous state-of-the-art approaches. Furthermore, the method is complementary to GMM-FVC methods, allowing a simple fusion scheme to further improve performance to 61.1% and 83.1% respectively

Combining hydrogen peroxide addition with sunlight regulation to control algal blooms

The concentration, light conditions during treatment, and the number of hydrogen peroxide (H2O2) additions as well as the H2O2 treatment combined with subsequent shading to control algal blooms were studied in the field (Lake Dianchi, China). The cyanobacterial stress and injury due to H2O2 were dose dependent, and the control effectiveness and degradation of H2O2 were better and faster under full light than under shading. However, H2O2 was only able to control a bloom for a short time, so it may have promoted the recovery of algae and allowed the biomass to rebound due to the growth of eukaryotic algae. A second addition of H2O2 at the same dose had no obvious effect on algal control in the short term, suggesting that a higher concentration or a delayed addition should be considered, but these alternative strategies are not recommended so that the integrity of the aquatic ecosystem is maintained and algal growth is not promoted. Moreover, shading (85%) after H2O2 addition significantly reduced the algal biomass during the enclosure test, no restoration was observed for nearly a month, and the proportion of eukaryotic algae declined. It can be inferred that algal blooms can be controlled by applying a high degree of shading after treatment with H2O2.</p

Experimental investigation of dielectric barrier impact on breakdown voltage enhancement of copper wire-plane electrode systems

Non-pressurized air is extensively used as basic insulation media in medium / high voltage equipments. An inherent property of air-insulated designs is that the systems tend to become physically large. Application of Dielectric barrier can increase the breakdown voltage and therefore decrease the size of the equipments. In this paper, the impact of dielectric barrier on breakdown voltage enhancement of a copper wire-plane system is investigated. For this purpose, the copper wire is covered with different dielectric materials. Depending on the air gap and dielectric strength of the barrier the breakdown can be initiated in the solid or gas dielectric. Theoretically, free charges are affected by the electric field between the electrodes and accumulated at the dielectric surface, this leads to the reduction of electric field in air gap and enhancement of the ifield in the dielectric layer. Therefore, with appropriate selection of the barrier thickness and material, it is possible to increase the breakdown voltage of the insulation system. The influence of different parameters like inter-electrode spacing, and dielectric material on the break-down voltage is investigated for applied 50 Hz AC and DC voltages. The results indicate that up to 240% increase of the breakdown voltage can be achieved

Dynamics of macrozoobenthos assemblages in the Fubao Bay of Lake Dianchi and their relation to organic pollutants

A two-year-long investigation on the dynamics of the structure and biodiversity of macrozoobenthos was conducted in the Fubao Bay of Dianchi Lake, Southwest China. A high level of organic pollution has been detected in this Bay for the last 10 years. In all, 31 benthic taxa belonging to eight families and 20 genera were identified. Oligochaeta dominated this ecosystem, comprising 53 to 99% of the total abundance and 75 to 99% of the total wet biomass. The standing crop of the solely dominant species Limnodrilus hoffmeisteri rose sharply in the two-year period: It increased from 46% of the total abundance to 73% and from 73% of the wet biomass to 99% in second year. One-way analysis of variance (ANOVA) revealed that there was no significant difference (p &gt; 0.05) in the richness value for all and the most predominant groups between the two years. However, significant differences were detected between the total and oligochaete abundances (p &lt; 0.05). The standing crop was the lowest (188.72 ind/m2 and 0.20 g/m2) in October, 2006 and the highest (14931.7 ind/m2 and 39.33 g/m2) in January, 2008. The annual mean standing crop increased nearly 10 times in density and eight times in wet biomass between the two years, and this increase was mainly contributed by oligochaetes. Analyses of three diversity indices and the K-dominance curve revealed that there was a significant difference between the two years. Multiple regression analysis indicated that the dynamics of the biomass of macrozoobenthos could be largely attributed to nitrate nitrogen.Key words: Macrozoobenthos, structure, biodiversity, Dianchi Lake, organic pollution

Improved language identification using deep bottleneck network

Effective representation plays an important role in automatic spoken language identification (LID). Recently, several representations that employ a pre-trained deep neural network (DNN) as the front-end feature extractor, have achieved state-of-the-art performance. However the performance is still far from satisfactory for dialect and short-duration utterance identification tasks, due to the deficiency of existing representations. To address this issue, this paper proposes the improved representations to exploit the information extracted from different layers of the DNN structure. This is conceptually motivated by regarding the DNN as a bridge between low-level acoustic input and high-level phonetic output features. Specifically, we employ deep bottleneck network (DBN), a DNN with an internal bottleneck layer acting as a feature extractor. We extract representations from two layers of this single network, i.e. DBN-TopLayer and DBN-MidLayer. Evaluations on the NIST LRE2009 dataset, as well as the more specific dialect recognition task, show that each representation can achieve an incremental performance gain. Furthermore, a simple fusion of the representations is shown to exceed current state-of-the-art performance

Application of Bayesian network including Microcystis morphospecies for microcystin risk assessment in three cyanobacterial bloom-plagued lakes, China

Microcystis spp., which occur as colonies of different sizes under natural conditions, have expanded in temperate and tropical freshwater ecosystems and caused seriously environmental and ecological problems. In the current study, a Bayesian network (BN) framework was developed to access the probability of microcystins (MCs) risk in large shallow eutrophic lakes in China, namely, Taihu Lake, Chaohu Lake, and Dianchi Lake. By means of a knowledge-supported way, physicochemical factors, Microcystis morphospecies, and MCs were integrated into different network structures. The sensitive analysis illustrated that Microcystis aeruginosa biomass was overall the best predictor of MCs risk, and its high biomass relied on the combined condition that water temperature exceeded 24 °C and total phosphorus was above 0.2 mg/L. Simulated scenarios suggested that the probability of hazardous MCs (≥1.0 μg/L) was higher under interactive effect of temperature increase and nutrients (nitrogen and phosphorus) imbalance than that of warming alone. Likewise, data-driven model development using a naïve Bayes classifier and equal frequency discretization resulted in a substantial technical performance (CCI = 0.83, K = 0.60), but the performance significantly decreased when model excluded species-specific biomasses from input variables (CCI = 0.76, K = 0.40). The BN framework provided a useful screening tool to evaluate cyanotoxin in three studied lakes in China, and it can also be used in other lakes suffering from cyanobacterial blooms dominated by Microcystis