A Forest from the Trees: Generation through Neighborhoods

In this work, we propose to learn a generative model using both learned features (through a latent space) and memories (through neighbors). Although human learning makes seamless use of both learned perceptual features and instance recall, current generative learning paradigms only make use of one of these two components. Take, for instance, flow models, which learn a latent space of invertible features that follow a simple distribution. Conversely, kernel density techniques use instances to shift a simple distribution into an aggregate mixture model. Here we propose multiple methods to enhance the latent space of a flow model with neighborhood information. Not only does our proposed framework represent a more human-like approach by leveraging both learned features and memories, but it may also be viewed as a step forward in non-parametric methods. The efficacy of our model is shown empirically with standard image datasets. We observe compelling results and a significant improvement over baselines

Adaptive Ranking-based Sample Selection for Weakly Supervised Class-imbalanced Text Classification

To obtain a large amount of training labels inexpensively, researchers have recently adopted the weak supervision (WS) paradigm, which leverages labeling rules to synthesize training labels rather than using individual annotations to achieve competitive results for natural language processing (NLP) tasks. However, data imbalance is often overlooked in applying the WS paradigm, despite being a common issue in a variety of NLP tasks. To address this challenge, we propose Adaptive Ranking-based Sample Selection (ARS2), a model-agnostic framework to alleviate the data imbalance issue in the WS paradigm. Specifically, it calculates a probabilistic margin score based on the output of the current model to measure and rank the cleanliness of each data point. Then, the ranked data are sampled based on both class-wise and rule-aware ranking. In particular, the two sample strategies corresponds to our motivations: (1) to train the model with balanced data batches to reduce the data imbalance issue and (2) to exploit the expertise of each labeling rule for collecting clean samples. Experiments on four text classification datasets with four different imbalance ratios show that ARS2 outperformed the state-of-the-art imbalanced learning and WS methods, leading to a 2%-57.8% improvement on their F1-score

Predicting trucking accidents with truck drivers 'safety climate perception across companies: A transfer learning approach

There is a rising interest in using artificial intelligence (AI)-powered safety analytics to predict accidents in the trucking industry. Companies may face the practical challenge, however, of not having enough data to develop good safety analytics models. Although pretrained models may offer a solution for such companies, existing safety research using transfer learning has mostly focused on computer vision and natural language processing, rather than accident analytics. To fill the above gap, we propose a pretrain-then-fine-tune transfer learning approach to help any company leverage other companies' data to develop AI models for a more accurate prediction of accident risk. We also develop SafeNet, a deep neural network algorithm for classification tasks suitable for accident prediction. Using the safety climate survey data from seven trucking companies with different data sizes, we show that our proposed approach results in better model performance compared to training the model from scratch using only the target company's data. We also show that for the transfer learning model to be effective, the pretrained model should be developed with larger datasets from diverse sources. The trucking industry may, thus, consider pooling safety analytics data from a wide range of companies to develop pretrained models and share them within the industry for better knowledge and resource transfer. The above contributions point to the promise of advanced safety analytics to make the industry safer and more sustainable.Comment: submitted to journal: accident analysis and preventio

Aesthetic Enhancement via Color Area and Location Awareness

Choosing a suitable color palette can typically improve image aesthetic, where a naive way is choosing harmonious colors from some pre-defined color combinations in color wheels. However, color palettes only consider the usage of color types without specifying their amount in an image. Also, it is still challenging to automatically assign individual palette colors to suitable image regions for maximizing image aesthetic quality. Motivated by these, we propose to construct a contribution-aware color palette from images with high aesthetic quality, enabling color transfer by matching the coloring and regional characteristics of an input image. We hence exploit public image datasets, extracting color composition and embedded color contribution features from aesthetic images to generate our proposed color palettes. We consider both image area ratio and image location as the color contribution features to extract. We have conducted quantitative experiments to demonstrate that our method outperforms existing methods through SSIM (Structural SIMilarity) and PSNR (Peak Signal to Noise Ratio) for objective image quality measurement and no-reference image assessment (NIMA) for image aesthetic scoring

A Color-Pair Based Approach for Accurate Color Harmony Estimation

Harmonious color combinations can stimulate positive user emotional responses. However, a widely open research question is: how can we establish a robust and accurate color harmony measure for the public and professional designers to identify the harmony level of a color theme or color set. Building upon the key discovery that color pairs play an important role in harmony estimation, in this paper we present a novel color-pair based estimation model to accurately measure the color harmony. It first takes a two-layer maximum likelihood estimation (MLE) based method to compute an initial prediction of color harmony by statistically modeling the pair-wise color preferences from existing datasets. Then, the initial scores are refined through a back-propagation neural network (BPNN) with a variety of color features extracted in different color spaces, so that an accurate harmony estimation can be obtained at the end. Our extensive experiments, including performance comparisons of harmony estimation applications, show the advantages of our method in comparison with the state of the art methods

A review of enhancement of biohydrogen productions by chemical addition using a supervised machine learning method

In this work, the impact of chemical additions, especially nano‐particles (NPs), was quan-titatively analyzed using our constructed artificial neural networks (ANNs)‐response surface methodology (RSM) algorithm. Fe‐based and Ni‐based NPs and ions, including Mg2+, Cu2+, Na+, NH4+, and K+, behave differently towards the response of hydrogen yield (HY) and hydrogen evolution rate (HER). Manipulating the size and concentration of NPs was found to be effective in enhancing the HY for Fe‐based NPs and ions, but not for Ni‐based NPs and ions. An optimal range of particle size (86–120 nm) and Ni‐ion/NP concentration (81–120 mg L−1) existed for HER. Meanwhile, the manipulation of the size and concentration of NPs was found to be ineffective for both iron and nickel for the improvement of HER. In fact, the variation in size of NPs for the enhancement of HY and HER demonstrated an appreciable difference. The smaller (less than 42 nm) NPs were found to definitely improve the HY, whereas for the HER, the relatively bigger size of NPs (40–50 nm) seemed to significantly increase the H2 evolution rate. It was also found that the variations in the concentration of the investigated ions only statistically influenced the HER, not the HY. The level of response (the enhanced HER) towards inputs was underpinned and the order of significance towards HER was identified as the following: Na+ \u3e Mg2+ \u3e Cu2+ \u3e NH4+ \u3e K+

Example-based Image Recoloring in Indoor Environment

Color structure of a home scene image closely relates to the material properties of its local regions. Existing color migration methods typically fail to fully infer the correlation between the coloring of local home scene regions, leading to a local blur problem. In this paper, we propose a color migration framework for home scene images. It picks the coloring from a template image and transforms such coloring to a home scene image through a simple interaction. Our framework comprises three main parts. First, we carry out an interactive segmentation to divide an image into local regions and extract their corresponding colors. Second, we generate a matching color table by sampling the template image according to the color structure of the original home scene image. Finally, we transform colors from the matching color table to the target home scene image with the boundary transition maintained. Experimental results show that our method can effectively transform the coloring of a scene matching with the color composition of a given natural or interior scenery

Population genetics and molecular phylogeography of Thamnaconus modestus (Tetraodontiformes, Monachanthidae) in Northwestern Pacific inferred from variation of the mtDNA control region

In order to study the genetic diversity of Thamnaconus modestus, a species of great commercial importance in Southeast Asia, the 5′-end hypervariable regions (423 bp) of the mitochondrial control region of T. modestus in nine geographical populations (248 individuals) were sequenced and analysed in this study. The target sequence fragment contained large numbers of polymorphic sites (87) involved in high levels of haplotype diversity (h = 0.97 ± 0.01) and nucleotide diversity (π = 0.0285 ± 0.0143). The genetic variations within populations (92.71%) were significantly larger than those among populations (7.29%). No significant genetic divergences were detected among the wild populations owing to their gregarious habits, strong moving ability, r-selection strategy. Significant genetic divergences were found between the cultured and wild populations, probably resulting from kin selection and aquacultural environment. Three significant phylogenetic lineages were identified, and the variation among lineages (56.90%) was greater than that among individuals within the lineages (43.10%), with the significant ΦST value (ΦST = 0.57, P = 0.0000). The results showed great and significant genetic differentiations among these three lineages, indicating that they may have independent phylogenetic dynamics. Dominant shared haplotypes that included individuals from each population and the median-joining network of haplotypes presented a star-like structure. Historic demographic analysis of each lineage showed that population expansion occurred after the Pleistocene glacial period. At the last glacial maximum, T. modestus in China seas was scattered across variable refuges, including Central South China Sea and Okinawa Trough