Multi-task super resolution method for vector field critical points enhancement

It is a challenging task to handle the vector field visualization at local critical points. Generally, topological based methods firstly divide critical regions into different categories, and then process the different types of critical regions to improve the effect, which pipeline is complex. In the paper, a learning based multi-task super resolution (SR) method is proposed to improve the refinement of vector field, and enhance the visualization effect, especially at the critical region. In detail, the multi-task model consists of two important designs on task branches: one task is to simulate the interpolation of discrete vector fields based on an improved super-resolution network; and the other is a classification task to identify the types of critical vector fields. It is an efficient end-to-end architecture for both training and inferencing stages, which simplifies the pipeline of critical vector field visualization and improves the visualization effect. In experiment, we compare our method with both traditional interpolation and pure SR network on both simulation data and real data, and the reported results indicate our method lower the error and improve PSNR significantly

Study on Greenway Plant Landscape Based on Bird Habitat Conservation - A Case Study of Wenyu River - North Canal Greenway in Beijing

In recent years, rapid urbanization is leading to a sharp decrease of bird diversity in city. The plant landscape in the greenway plays an important role in habitat conservation. This paper aims to explore the effects of plant landscape planning for the bird habitat conservation in urban greenway, and to study the design methods of greenway plant landscapes based on bird habitats conservation. Wenyu River - North Canal, a river located in the east of Beijing with uninterrupted green spaces along the coast, has the potential to become the migration channel for migratory birds. Dongjiao Wetland Park is an important node. At the macro level, the program investigated the vegetation pattern of Wenyu River-North Canal by using GIS technology and analyzed the distribution and ecological connectivity of different bird habitat types in the greenway. The results show that along the Wenyu River-North Canal, the distribution of habitats for some bird groups is uneven and some habitat types are poorly connected. At the micro level, a field study was conducted in Dongjiao Wetland Park in combination with actual projects, in which the forest form distribution and plant species composition were analyzed and the bird biotope was mapped. The results show that in the Dongjiao Wetland Park, the plant community is dominated by arbor-herb type; evergreen plants, shrubs and food plants are lacking; grasslands habitats and wetlands habitats were small and the area disturbed by human is large. According to the analysis results, aiming at bird habitat conservation, a vegetation landscape optimization plan of Wenyu River-North Canal Greenway and a plant landscape reconstruction design of the Northern Park of Dongjiao Wetland Park were proposed, including protecting important habitat patches, optimizing plant community structure and selecting plant species

Masked Imitation Learning: Discovering Environment-Invariant Modalities in Multimodal Demonstrations

Multimodal demonstrations provide robots with an abundance of information to make sense of the world. However, such abundance may not always lead to good performance when it comes to learning sensorimotor control policies from human demonstrations. Extraneous data modalities can lead to state over-specification, where the state contains modalities that are not only useless for decision-making but also can change data distribution across environments. State over-specification leads to issues such as the learned policy not generalizing outside of the training data distribution. In this work, we propose Masked Imitation Learning (MIL) to address state over-specification by selectively using informative modalities. Specifically, we design a masked policy network with a binary mask to block certain modalities. We develop a bi-level optimization algorithm that learns this mask to accurately filter over-specified modalities. We demonstrate empirically that MIL outperforms baseline algorithms in simulated domains including MuJoCo and a robot arm environment using the Robomimic dataset, and effectively recovers the environment-invariant modalities on a multimodal dataset collected on a real robot. Our project website presents supplemental details and videos of our results at: https://tinyurl.com/masked-ilComment: 13 page

DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network

The rapid advances in Vision Transformer (ViT) refresh the state-of-the-art performances in various vision tasks, overshadowing the conventional CNN-based models. This ignites a few recent striking-back research in the CNN world showing that pure CNN models can achieve as good performance as ViT models when carefully tuned. While encouraging, designing such high-performance CNN models is challenging, requiring non-trivial prior knowledge of network design. To this end, a novel framework termed Mathematical Architecture Design for Deep CNN (DeepMAD) is proposed to design high-performance CNN models in a principled way. In DeepMAD, a CNN network is modeled as an information processing system whose expressiveness and effectiveness can be analytically formulated by their structural parameters. Then a constrained mathematical programming (MP) problem is proposed to optimize these structural parameters. The MP problem can be easily solved by off-the-shelf MP solvers on CPUs with a small memory footprint. In addition, DeepMAD is a pure mathematical framework: no GPU or training data is required during network design. The superiority of DeepMAD is validated on multiple large-scale computer vision benchmark datasets. Notably on ImageNet-1k, only using conventional convolutional layers, DeepMAD achieves 0.7% and 1.5% higher top-1 accuracy than ConvNeXt and Swin on Tiny level, and 0.8% and 0.9% higher on Small level.Comment: Accepted by CVPR 202

Searching for new globular clusters in M 31 with Gaia EDR3

We found 50 new globular cluster (GC) candidates around M\,31 with Gaia Early Data Release 3 (EDR3), with the help from Pan-STARRS1 DR1 magnitudes and Pan-Andromeda Archaeological Survey (PAndAS) images. Based on the latest Revised Bologna Catalog and \textit{simbad}, we trained 2 Random Forest (RF) classifiers, the first one to distinguish extended sources from point sources and the second one to further select GCs from extended sources. From 1.85 million sources of $16^m{<}g{<}19.5^m$ and within a large area of $\sim$392\,deg$^2$ around M\,31, we selected 20,658 extended sources and 1,934 initial GC candidates. After visual inspection of the PAndAS images to eliminate the contamination of non-cluster sources, particularly galaxies, we finally got 50 candidates. These candidates are divided into 3 types (\textbf{a}, \textbf{b}, \textbf{c}) according to their projected distance $D$ to the center of M\,31 and their probability to be a true GC, $P_{GC}$, which is calculated by our second RF classifier. Among these candidates, 14 are found to be associated (in projection) with the large-scale structures within the halo of M\,31. We also provided several simple parameter criteria for selecting extended sources effectively from the Gaia EDR3, which can reach a completeness of 92.1\% with a contamination fraction lower than 10\%

LogPrompt: Prompt Engineering Towards Zero-Shot and Interpretable Log Analysis

Automated log analysis is crucial in modern software-intensive systems for ensuring reliability and resilience throughout software maintenance and engineering life cycles. Existing methods perform tasks such as log parsing and log anomaly detection by providing a single prediction value without interpretation. However, given the increasing volume of system events, the limited interpretability of analysis results hinders analysts' trust and their ability to take appropriate actions. Moreover, these methods require substantial in-domain training data, and their performance declines sharply (by up to 62.5%) in online scenarios involving unseen logs from new domains, a common occurrence due to rapid software updates. In this paper, we propose LogPrompt, a novel zero-shot and interpretable log analysis approach. LogPrompt employs large language models (LLMs) to perform zero-shot log analysis tasks via a suite of advanced prompt strategies tailored for log tasks, which enhances LLMs' performance by up to 107.5% compared with simple prompts. Experiments on nine publicly available evaluation datasets across two tasks demonstrate that LogPrompt, despite using no training data, outperforms existing approaches trained on thousands of logs by up to around 50%. We also conduct a human evaluation of LogPrompt's interpretability, with six practitioners possessing over 10 years of experience, who highly rated the generated content in terms of usefulness and readability (averagely 4.42/5). LogPrompt also exhibits remarkable compatibility with open-source and smaller-scale LLMs, making it flexible for practical deployment

NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities

We present Neural Signal Operated Intelligent Robots (NOIR), a general-purpose, intelligent brain-robot interface system that enables humans to command robots to perform everyday activities through brain signals. Through this interface, humans communicate their intended objects of interest and actions to the robots using electroencephalography (EEG). Our novel system demonstrates success in an expansive array of 20 challenging, everyday household activities, including cooking, cleaning, personal care, and entertainment. The effectiveness of the system is improved by its synergistic integration of robot learning algorithms, allowing for NOIR to adapt to individual users and predict their intentions. Our work enhances the way humans interact with robots, replacing traditional channels of interaction with direct, neural communication. Project website: https://noir-corl.github.io/

Community-based study on CKD subjects and the associated risk factors

Background. The study was performed to investigate the prevalence, awareness and the risk factors of chronic kidney disease (CKD) in the community population in Shanghai, China

An investigation into remanufactured toner cartridges vs. OEM cartridges

Across the University of British Columbia (UBC) toner cartridges are utilized by over 16,000 faculty and staff in over 400 departments. The sheer amount of toner cartridges on campus plays a major role on the economy and has a huge impact on the environment. As a leader in sustainability, UBC wants to perform a detailed analysis on the type of toner cartridge that would contribute the most to a sustainable future. The two main choices for toner cartridges are Original Equipment Manufacturer (OEM) cartridges and remanufactured cartridges. Throughout the analysis, each cartridge was compared based on its economic, environmental, and social impacts. This paper utilizes a wide range of primary and secondary sources. The primary sources came in the form of a departmental survey and a discussion with a representative from Digitech (a local cartridge remanufacturer currently partnered with UBC). The secondary sources came through online databases such as Google Scholar and the UBC library databases, including both peer-reviewed articles and online websites. The triple bottom line analysis was utilized in this report to determine the recommended cartridge. In addition to the sources, there were several constraints and assumptions made regarding the usage of toner cartridges on campus. One of these assumptions was made while analyzing the survey. The survey yielded only 11 out of the 400 university departments responses as many departments were either unwilling or too preoccupied to respond. However, the departments surveyed were arbitrarily chosen, allowing for an assumption to be made that the responses were an accurate representation of the entire campus. Another assumption made was during the research and discussion of secondary sources. Peer-reviewed sources were assumed to be unbiased, while non-peer-reviewed sources (e.g. an article from HP - an OEM company) were assumed to be biased. To help evaluate the more sustainable cartridge, this paper uses various indicators for economic, environmental, social impacts. For economic comparisons, the total cost of remanufactured cartridges was found to be cheaper than the total cost of OEM cartridges. The environmental comparison showed that remanufactured cartridges have a lower toll on the environment than OEM cartridges. And in the social comparison, it was found that there was no noticeable difference in quality between remanufactured and OEM cartridges. However, the use of remanufactured cartridges can potentially increase sustainability awareness and the possibility of new job opportunities. Based on the findings in this report, it is recommended that remanufactured cartridges be implemented. In addition, it was found that many departments across campus are in a contract with Xerox, a cartridge remanufacturing firm. Through the course of this analysis, details about the contract with Xerox were not found, thus placing restrictions on the types of recommendations made. With the existence of this contract, several criteria were developed for future and existing contracts to ensure the sustainable usage of toner cartridges on campus. These criteria are the following: the company must supply remanufactured cartridges, the company must be local to the Lower Mainland, and the contract should involve the majority of the university departments. By following these criteria, UBC can be sure that the sustainable usage of toner cartridges on campus. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”Applied Science, Faculty ofUnreviewedUndergraduat