Simplicity of Kmeans versus Deepness of Deep Learning: A Case of Unsupervised Feature Learning with Limited Data

We study a bio-detection application as a case study to demonstrate that Kmeans -- based unsupervised feature learning can be a simple yet effective alternative to deep learning techniques for small data sets with limited intra-as well as inter-class diversity. We investigate the effect on the classifier performance of data augmentation as well as feature extraction with multiple patch sizes and at different image scales. Our data set includes 1833 images from four different classes of bacteria, each bacterial culture captured at three different wavelengths and overall data collected during a three-day period. The limited number and diversity of images present, potential random effects across multiple days, and the multi-mode nature of class distributions pose a challenging setting for representation learning. Using images collected on the first day for training, on the second day for validation, and on the third day for testing Kmeans -- based representation learning achieves 97% classification accuracy on the test data. This compares very favorably to 56% accuracy achieved by deep learning and 74% accuracy achieved by handcrafted features. Our results suggest that data augmentation or dropping connections between units offers little help for deep-learning algorithms, whereas significant boost can be achieved by Kmeans -- based representation learning by augmenting data and by concatenating features obtained at multiple patch sizes or image scales

Understanding ME? Multimodal Evaluation for Fine-grained Visual Commonsense

Visual commonsense understanding requires Vision Language (VL) models to not only understand image and text but also cross-reference in-between to fully integrate and achieve comprehension of the visual scene described. Recently, various approaches have been developed and have achieved high performance on visual commonsense benchmarks. However, it is unclear whether the models really understand the visual scene and underlying commonsense knowledge due to limited evaluation data resources. To provide an in-depth analysis, we present a Multimodal Evaluation (ME) pipeline to automatically generate question-answer pairs to test models' understanding of the visual scene, text, and related knowledge. We then take a step further to show that training with the ME data boosts the model's performance in standard VCR evaluation. Lastly, our in-depth analysis and comparison reveal interesting findings: (1) semantically low-level information can assist the learning of high-level information but not the opposite; (2) visual information is generally under utilization compared with text.Comment: Accepted to EMNLP 2022 Long Pape

Three-dimensional carbon-based anodes promoted the accumulation of exoelectrogens in bioelectrochemical systems.

To achieve deep understandings on the effects of structure and surface properties of anode material on the performance of bioelectrochemical systems, the present research investigated the bacterial community structures of biofilms attached to different three-dimensional anodes including carbon felt and materials derived from pomelo peel, kenaf stem, and cardboard with 454 pyrosequencing analysis based on the bacterial 16S rRNA gene. The results showed that bacterial community structures, especially the relative abundance of exoelectrogens, were significantly related to the types of adopted three-dimensional anode materials. Proteobacteria was the shared predominant phylum, accounting for 55.4%, 52.1%, 66.7%, and 56.1% for carbon felt, cardboard, pomelo peel, and kenaf stem carbon, respectively. The most abundant OTU was phylogenetically related to the well-known exoelectrogen of Geobacter, with a relative abundance of 16.3%, 19.0%, 36.3%, and 28.6% in carbon felt, cardboard, pomelo peel, and kenaf stem, respectively. Moreover, another exoelectrogen of Pseudomonas sp. accounted for 4.9% in kenaf stem and 3.9% in carbonboard, respectively. The results implied the macrostructure and properties of different anode materials might result in different niches such as hydrodynamics and substrate transport dynamics, leading to different bacterial structure, especially different relative abundance of exoelectrogens, which consequently affected the performance of bioelectrochemical systems. PRACTITIONER POINTS: Bioelectrochemical systems (BESs) represent a novel biotechnology platform to simultaneously treat wastewaters and produce electrical power. Three-dimensional materials derived from nature plant as anode to promote electricity output from BESs and reduce the construct cost of BESs. Macrostructure of the three-dimensional anode material affected phylotype richness and phylogenetic diversity of microorganisms in anodic biofilm of BESs. Geobacter as well-known exoelectrogen was the most abundant in biofilm attached to three-dimensional anode