Field Induced Jet Micro-EDM

Electrical discharge machining (EDM) is of the potential of micro/nano meter scale machining capability. However, electrode wear in micro-EDM significantly deteriorates the machining accuracy, thus, it needs to be compensated in process. To solve this problem, a novel micromachining method, namely field induced jet micro-EDM, is proposed in this paper, in which the electrical field induced jet is used as the micro tool electrode. A series of experiments were carried out to investigate the feasibility of proposed method. Due to the electrolyte can be supplied automatically by the capillary effect and the electrostatic field, it is not necessary to use pump or valves. The problem of electrode wear does not exist at all in the machining process because of the field induced jet will be generated periodically. It is also found that the workpiece material can be effectively removed with a crater size of about 2 micrometer in diameter. The preliminary experimental results verified that the field induced jet micro-EDM is an effective micromachining method

Culturability, Temporal Change, Phylogenetic Analysis, And Yield Of Bacterial Communities In A Subarctic Lake: Harding Lake

Thesis (Ph.D.) University of Alaska Fairbanks, 2005Heterotrophic bacteria, adapted to small concentrations of substrate, are a main component of the microbial flywheel. However, understandings of their activity, isolation, genetics, and nutrition are restricted to the large, easily isolated and culturable bacteria. By using the dilution culture method, apparent culturabilities could approach 10% in unamended lake water and were inversely proportional to the number of cells inoculated from mixed species in a natural environment from Harding Lake. Substrate additions could not improve bacterial culturability in the dilution cultures. Comparative sequence analyses of 16S rDNA genes showed that all bacterial species have similar lengths in the phylogenetic tree, suggesting similar evolution rates. These indicated close relationships among the six bacterial divisions: alpha-proteobacteria, beta-proteobacteria, gamma-proteobacteria, cytophaga/flexibacter/bacteriodes, acidobacteria, and cyanobacteria. Possible reasons include that metabolic enzymes of these bacteria were modified to adapt to low temperatures from tropical temperatures in arctic areas at the same time. These findings may provide insight into the recent evolution of the bacteria in near polar freshwater. Moreover, a high abundance of alpha-proteobacteria and gamma-proteobacteria was found in Harding Lake, suggesting high growth rates of these bacteria in the freshwater region. This is consistent with a rapid continuous shift in the distribution of dominant species observed in Harding Lake, according to the TRFLP, DGGE, and flow cytometry data. Our results also suggested that input of dissolved organic matter derived from terrestrial plants and soils, introduction of terrestrial bacteria, and bacteria themselves led to the bacterial species shifts associated with the seasonal change. Bacterial growth yield is used to measure this carbon conversion efficiency. However, bacterial growth yields have been seriously underestimated in previous studies. Our in situ values for bacterial growth yield from an amino acid mix were actually closer to 50% and 70% in active systems by using a modified, sensitive and accurate method and increased with the increase of temperature between 1�C and 6�C

Learning Social Image Embedding with Deep Multimodal Attention Networks

Learning social media data embedding by deep models has attracted extensive research interest as well as boomed a lot of applications, such as link prediction, classification, and cross-modal search. However, for social images which contain both link information and multimodal contents (e.g., text description, and visual content), simply employing the embedding learnt from network structure or data content results in sub-optimal social image representation. In this paper, we propose a novel social image embedding approach called Deep Multimodal Attention Networks (DMAN), which employs a deep model to jointly embed multimodal contents and link information. Specifically, to effectively capture the correlations between multimodal contents, we propose a multimodal attention network to encode the fine-granularity relation between image regions and textual words. To leverage the network structure for embedding learning, a novel Siamese-Triplet neural network is proposed to model the links among images. With the joint deep model, the learnt embedding can capture both the multimodal contents and the nonlinear network information. Extensive experiments are conducted to investigate the effectiveness of our approach in the applications of multi-label classification and cross-modal search. Compared to state-of-the-art image embeddings, our proposed DMAN achieves significant improvement in the tasks of multi-label classification and cross-modal search

Context Modeling for Ranking and Tagging Bursty Features in Text Streams

Bursty features in text streams are very useful in many text mining applications. Most existing studies detect bursty features based purely on term frequency changes without taking into account the semantic contexts of terms, and as a result the detected bursty features may not always be interesting or easy to interpret. In this paper we propose to model the contexts of bursty features using a language modeling approach. We then propose a novel topic diversity-based metric using the context models to find newsworthy bursty features. We also propose to use the context models to automatically assign meaningful tags to bursty features. Using a large corpus of a stream of news articles, we quantitatively show that the proposed context language models for bursty features can effectively help rank bursty features based on their newsworthiness and to assign meaningful tags to annotate bursty features. ? 2010 ACM.EI

Imaging Neural Activity in the Primary Somatosensory Cortex Using Thy1-GCaMP6s Transgenic Mice

The mammalian brain exhibits marked symmetry across the sagittal plane. However, detailed description of neural dynamics in symmetric brain regions in adult mammalian animals remains elusive. In this study, we describe an experimental procedure for measuring calcium dynamics through dual optical windows above bilateral primary somatosensory corticies (S1) in Thy1-GCaMP6s transgenic mice using 2-photon (2P) microscopy. This method enables recordings and quantifications of neural activity in bilateral mouse brain regions one at a time in the same experiment for a prolonged period in vivo. Key aspects of this method, which can be completed within an hour, include minimally invasive surgery procedures for creating dual optical windows, and the use of 2P imaging. Although we only demonstrate the technique in the S1 area, the method can be applied to other regions of the living brain facilitating the elucidation of structural and functional complexities of brain neural networks