Exploring Spatially Varying Relationships between Forest Fire and Environmental Factors in Fujian, China

In recent decades, the occurrence of forest fires has risen in the world and led to significant, long-lasting impacts on ecological, social, and economic systems. Along with the traditional tools for fire prediction, statistical modeling has been playing an important role in understanding the nature of forest fires and providing guidelines for decision making of fire prevention and management. In this dissertation, a large data set was collected from 2001 to 2016 in Fujian province, China, including the occurrence of forest fires and many environmental factors. I developed spatial generalized linear models and spatial quantile models under the framework of geographically weighted regression (GWR) to investigate the relationships between the counts and proportion or rate of forest fires and driving topographical, meteorological, human, vegetation, and land coverage factors. The corresponding global models were used as the benchmarks for model comparisons. These spatial models included: (1) geographically weighted Poisson and geographically weighted negative binomial models designed for the counts of forest fires; (2) geographically weighted quantile models for the counts of forest fires at different quantiles or risk levels; and (3) geographically weighted beta model for the proportion or rate of forest fires. The results indicated that the observed forest fires were highly likely to occur in lower elevation, smaller aspect index (meaning stronger sunlight), heavier precipitation, smaller population density, less vegetation, wider grassland, and/or less cropland, while other environmental factors varied greatly with the forest fire occurrence. This study showed the great superiority of these GWR models to the corresponding global models in terms of characterizing the spatial nonstationary relationships, producing better model fitting and prediction, providing a more complete view on the spatial distribution of forest fires, and highlighting the risky local “hot spots” of forest fires as well as environmental factors across the Fujian province, China. Hopefully, the more detailed and localized information would help and assist the forest and fire managers to better understand the behavior of forest fires and influences of the environmental factors across the study area. Thus, the government agencies can make wiser and better decisions on where and what the fire management and prevention should be focused on with reduced economic expenses and improved the efficiency of forest fire management

From Common to Special: When Multi-Attribute Learning Meets Personalized Opinions

Visual attributes, which refer to human-labeled semantic annotations, have gained increasing popularity in a wide range of real world applications. Generally, the existing attribute learning methods fall into two categories: one focuses on learning user-specific labels separately for different attributes, while the other one focuses on learning crowd-sourced global labels jointly for multiple attributes. However, both categories ignore the joint effect of the two mentioned factors: the personal diversity with respect to the global consensus; and the intrinsic correlation among multiple attributes. To overcome this challenge, we propose a novel model to learn user-specific predictors across multiple attributes. In our proposed model, the diversity of personalized opinions and the intrinsic relationship among multiple attributes are unified in a common-to-special manner. To this end, we adopt a three-component decomposition. Specifically, our model integrates a common cognition factor, an attribute-specific bias factor and a user-specific bias factor. Meanwhile Lasso and group Lasso penalties are adopted to leverage efficient feature selection. Furthermore, theoretical analysis is conducted to show that our proposed method could reach reasonable performance. Eventually, the empirical study carried out in this paper demonstrates the effectiveness of our proposed method

DNA packaging in viral capsids with peptide arms

Strong chain rigidity and electrostatic self-repulsion of packed double-stranded DNA in viruses require a molecular motor to pull the DNA into the capsid. However, what is the role of electrostatic interactions between different charged components in the packaging process? Though various theories and computer simulation models were developed for the understanding of viral assembly and packaging dynamics of the genome, long-range electrostatic interactions and capsid structure have typically been neglected or oversimplified. By means of molecular dynamics simulations, we explore the effects of electrostatic interactions on the packaging dynamics of DNA based on a coarse-grained DNA and capsid model by explicitly including peptide arms (PAs), linked to the inner surface of the capsid, and counterions. Our results indicate that the electrostatic interactions between PAs, DNA, and counterions have a significant influence on the packaging dynamics. We also find that the packed DNA conformations are largely affected by the structure of the PA layer, but the packaging rate is insensitive to the layer structure

Effects of ion specificity and interfacial water structure

We study the electrokinetic transport behavior of water molecules and ions in hydrophobic graphene nanochannels with variable surface charge densities as well as the interfacial water structure based on detailed molecular dynamics simulations. The interfacial water structure, described by the water density, hydrogen bonding, diffusion, distribution of the OH bond and dipole orientations, is strikingly influenced by the surface charge sign and density. We find anomalous electrokinetic effects which are related to the distribution of counterions close to the surface, ion-specific effects and the interfacial water structure. On a negatively charged graphene layer, the attraction of Na+ ions towards the surface enhances the interfacial friction. In contrast, if the surface is positively charged, high surface charge density triggers an anomalous enhancement of electroosmotic flow, accompanied by an abrupt change of the interfacial water structure. At high surface charge densities, the mobility of the interfacial water at the positively charged surfaces is suppressed more strongly compared to the negatively charged surface. Our results reveal new electrokinetic phenomena by the comparison of negatively and positively charged surfaces

Robust Ordinal Embedding from Contaminated Relative Comparisons

Existing ordinal embedding methods usually follow a two-stage routine: outlier detection is first employed to pick out the inconsistent comparisons; then an embedding is learned from the clean data. However, learning in a multi-stage manner is well-known to suffer from sub-optimal solutions. In this paper, we propose a unified framework to jointly identify the contaminated comparisons and derive reliable embeddings. The merits of our method are three-fold: (1) By virtue of the proposed unified framework, the sub-optimality of traditional methods is largely alleviated; (2) The proposed method is aware of global inconsistency by minimizing a corresponding cost, while traditional methods only involve local inconsistency; (3) Instead of considering the nuclear norm heuristics, we adopt an exact solution for rank equality constraint. Our studies are supported by experiments with both simulated examples and real-world data. The proposed framework provides us a promising tool for robust ordinal embedding from the contaminated comparisons.Comment: Accepted by AAAI 201

A Large-Scale Car Parts (LSCP) Dataset for Lightweight Fine-Grained Detection

Automotive related datasets have previously been used for training autonomous driving systems or vehicle classification tasks. However, there is a lack of datasets in the field of automotive AI for car parts detection, and most available datasets are limited in size and scope, struggling to cover diverse scenarios. To address this gap, this paper presents a large-scale and fine-grained automotive dataset consisting of 84,162 images for detecting 12 different types of car parts. This dataset was collected from natural cameras and online websites which covers various car brands, scenarios, and shooting angles. To alleviate the burden of manual annotation, we propose a novel semi-supervised auto-labeling method that leverages state-of-the-art pre-trained detectors. Moreover, we study the limitations of the Grounding DINO approach for zero-shot labeling. Finally, we evaluate the effectiveness of our proposed dataset through fine-grained car parts detection by training several lightweight YOLO-series detectors

Stochastic Non-convex Ordinal Embedding with Stabilized Barzilai-Borwein Step Size

Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are batch methods designed mainly based on the convex optimization, say, the projected gradient descent method. However, they are generally time-consuming due to that the singular value decomposition (SVD) is commonly adopted during the update, especially when the data size is very large. To overcome this challenge, we propose a stochastic algorithm called SVRG-SBB, which has the following features: (a) SVD-free via dropping convexity, with good scalability by the use of stochastic algorithm, i.e., stochastic variance reduced gradient (SVRG), and (b) adaptive step size choice via introducing a new stabilized Barzilai-Borwein (SBB) method as the original version for convex problems might fail for the considered stochastic \textit{non-convex} optimization problem. Moreover, we show that the proposed algorithm converges to a stationary point at a rate $\mathcal{O}(\frac{1}{T})$ in our setting, where $T$ is the number of total iterations. Numerous simulations and real-world data experiments are conducted to show the effectiveness of the proposed algorithm via comparing with the state-of-the-art methods, particularly, much lower computational cost with good prediction performance.Comment: 11 pages, 3 figures, 2 tables, accepted by AAAI201