54 research outputs found
Barriers to commercial solar power systems: CSP National Internship Report
In the recent years, because of the growing economy and increasing population, the demand for electricity has increased substantially. Renewable technology as a green (sustainable) solution of this dramatic increase has therefore attracted more attentions.
Through the process of designing and installing a commercial solar power system, several market barriers have been discovered, which include:
State government barriers;
Local government barriers;
Western Power (network distributor) barriers;
Energy retailer barriers;
Customer barriers.
This dissertation included the experience of installing a commercial solar PV system, and discusses the barriers identified. To overcome those barriers, more efforts would need to be made at all levels
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
LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images
A few lightweight convolutional neural network (CNN) models have been
recently designed for remote sensing object detection (RSOD). However, most of
them simply replace vanilla convolutions with stacked separable convolutions,
which may not be efficient due to a lot of precision losses and may not be able
to detect oriented bounding boxes (OBB). Also, the existing OBB detection
methods are difficult to constrain the shape of objects predicted by CNNs
accurately. In this paper, we propose an effective lightweight oriented object
detector (LO-Det). Specifically, a channel separation-aggregation (CSA)
structure is designed to simplify the complexity of stacked separable
convolutions, and a dynamic receptive field (DRF) mechanism is developed to
maintain high accuracy by customizing the convolution kernel and its perception
range dynamically when reducing the network complexity. The CSA-DRF component
optimizes efficiency while maintaining high accuracy. Then, a diagonal support
constraint head (DSC-Head) component is designed to detect OBBs and constrain
their shapes more accurately and stably. Extensive experiments on public
datasets demonstrate that the proposed LO-Det can run very fast even on
embedded devices with the competitive accuracy of detecting oriented objects.Comment: 15 page
Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering
Graph collaborative filtering, which learns user and item representations
through message propagation over the user-item interaction graph, has been
shown to effectively enhance recommendation performance. However, most current
graph collaborative filtering models mainly construct the interaction graph on
a single behavior domain (e.g. click), even though users exhibit various types
of behaviors on real-world platforms, including actions like click, cart, and
purchase. Furthermore, due to variations in user engagement, there exists an
imbalance in the scale of different types of behaviors. For instance, users may
click and view multiple items but only make selective purchases from a small
subset of them. How to alleviate the behavior imbalance problem and utilize
information from the multiple behavior graphs concurrently to improve the
target behavior conversion (e.g. purchase) remains underexplored. To this end,
we propose IMGCF, a simple but effective model to alleviate behavior data
imbalance for multi-behavior graph collaborative filtering. Specifically, IMGCF
utilizes a multi-task learning framework for collaborative filtering on
multi-behavior graphs. Then, to mitigate the data imbalance issue, IMGCF
improves representation learning on the sparse behavior by leveraging
representations learned from the behavior domain with abundant data volumes.
Experiments on two widely-used multi-behavior datasets demonstrate the
effectiveness of IMGCF.Comment: accepted by ICDM2023 Worksho
Improving recombinant protein production by yeast through genome-scale modeling using proteome constraints
Eukaryotic cells are used as cell factories to produce and secrete multitudes of recombinant pharmaceutical proteins, including several of the current top-selling drugs. Due to the essential role and complexity of the secretory pathway, improvement for recombinant protein production through metabolic engineering has traditionally been relatively ad-hoc; and a more systematic approach is required to generate novel design principles. Here, we present the proteome-constrained genome-scale protein secretory model of yeast Saccharomyces cerevisiae (pcSecYeast), which enables us to simulate and explain phenotypes caused by limited secretory capacity. We further apply the pcSecYeast model to predict overexpression targets for the production of several recombinant proteins. We experimentally validate many of the predicted targets for alpha-amylase production to demonstrate pcSecYeast application as a computational tool in guiding yeast engineering and improving recombinant protein production. Due to the complexity of the protein secretory pathway, strategy suitable for the production of a certain recombination protein cannot be generalized. Here, the authors construct a proteome-constrained genome-scale protein secretory model for yeast and show its application in the production of different misfolded or recombinant proteins
Phase transitions associated with magnetic-field induced topological orbital momenta in a non-collinear antiferromagnet
Resistivity measurements are widely exploited to uncover electronic
excitations and phase transitions in metallic solids. While single crystals are
preferably studied to explore crystalline anisotropies, these usually cancel
out in polycrystalline materials. Here we show that in polycrystalline
Mn3Zn0.5Ge0.5N with non-collinear antiferromagnetic order, changes in the
diagonal and, rather unexpected, off-diagonal components of the resistivity
tensor occur at low temperatures indicating subtle transitions between magnetic
phases of different symmetry. This is supported by neutron scattering and
explained within a phenomenological model which suggests that the phase
transitions in magnetic field are associated with field induced topological
orbital momenta. The fact that we observe transitions between spin phases in a
polycrystal, where effects of crystalline anisotropy are cancelled suggests
that they are only controlled by exchange interactions. The observation of an
off-diagonal resistivity extends the possibilities for realising
antiferromagnetic spintronics with polycrystalline materials.Comment: 4 figures, 1 tabl
RGB2LIDAR: Towards Solving Large-Scale Cross-Modal Visual Localization
We study an important, yet largely unexplored problem of large-scale
cross-modal visual localization by matching ground RGB images to a
geo-referenced aerial LIDAR 3D point cloud (rendered as depth images). Prior
works were demonstrated on small datasets and did not lend themselves to
scaling up for large-scale applications. To enable large-scale evaluation, we
introduce a new dataset containing over 550K pairs (covering 143 km^2 area) of
RGB and aerial LIDAR depth images. We propose a novel joint embedding based
method that effectively combines the appearance and semantic cues from both
modalities to handle drastic cross-modal variations. Experiments on the
proposed dataset show that our model achieves a strong result of a median rank
of 5 in matching across a large test set of 50K location pairs collected from a
14km^2 area. This represents a significant advancement over prior works in
performance and scale. We conclude with qualitative results to highlight the
challenging nature of this task and the benefits of the proposed model. Our
work provides a foundation for further research in cross-modal visual
localization.Comment: ACM Multimedia 202
Pathway-Consensus Approach to Metabolic Network Reconstruction for Pseudomonas putida KT2440 by Systematic Comparison of Published Models
Over 100 genome-scale metabolic networks (GSMNs) have been published in recent years and widely used for phenotype prediction and pathway design. However, GSMNs for a specific organism reconstructed by different research groups usually produce inconsistent simulation results, which makes it difficult to use the GSMNs for precise optimal pathway design. Therefore, it is necessary to compare and identify the discrepancies among networks and build a consensus metabolic network for an organism. Here we proposed a process for systematic comparison of metabolic networks at pathway level. We compared four published GSMNs of Pseudomonas putida KT2440 and identified the discrepancies leading to inconsistent pathway calculation results. The mistakes in the models were corrected based on information from literature so that all the calculated synthesis and uptake pathways were the same. Subsequently we built a pathway-consensus model and then further updated it with the latest genome annotation information to obtain modelPpuQY1140 for P. putida KT2440, which includes 1140 genes, 1171 reactions and 1104 metabolites. We found that even small errors in a GSMN could have great impacts on the calculated optimal pathways and thus may lead to incorrect pathway design strategies. Careful investigation of the calculated pathways during the metabolic network reconstruction process is essential for building proper GSMNs for pathway design
What motivates and restricts chinese Wikipedians to contribute to english Wikipedia?
Wikipedia, worldâs biggest and most popular online encyclopedia, contains more than 26 million articles in over 280 languages, behind which are contributors voluntarily dedicating their time and effort. Hence, the Wikipedia contributorsâ motivations have been a popular topic in academic researches. According to the prior studies, people contribute to Wikipedia entries are motivated by altruism, reputation and enjoyment. However, researches on the motivation and restrictions of Chinese Wikipedia users contributing to Wikipedia articles in English still remains blank. To bridge this gap, this study aims to explore and address the motivations and restrictions of Chinese Wikipedians contributing to the English version of Wikipedia articles. This study was an explorative case study with the data and interviews contributed by four Chinese Wikipedians. The main findings were divided into two domains: motivations and restrictions. To be more detailed, Chinese Wikipedians are motivated by altruism, reputation, self-development and improvement of content quality. Meanwhile, they are driven by restrictions such as the blockage of access to Wikipedia in Chinese language from the mainland of China, and the limited source of articles in Chinese. The findings of this study contribute to the research on cross-linguistic participation: people contributing to Wikipedia in a language other than their mother language. In addition, the findings could be helpful for future researches on the Internet blockage in China
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