523 research outputs found
Global-Scale Resource Survey and Performance Monitoring of Public OGC Web Map Services
One of the most widely-implemented service standards provided by the Open
Geospatial Consortium (OGC) to the user community is the Web Map Service (WMS).
WMS is widely employed globally, but there is limited knowledge of the global
distribution, adoption status or the service quality of these online WMS
resources. To fill this void, we investigated global WMSs resources and
performed distributed performance monitoring of these services. This paper
explicates a distributed monitoring framework that was used to monitor 46,296
WMSs continuously for over one year and a crawling method to discover these
WMSs. We analyzed server locations, provider types, themes, the spatiotemporal
coverage of map layers and the service versions for 41,703 valid WMSs.
Furthermore, we appraised the stability and performance of basic operations for
1210 selected WMSs (i.e., GetCapabilities and GetMap). We discuss the major
reasons for request errors and performance issues, as well as the relationship
between service response times and the spatiotemporal distribution of client
monitoring sites. This paper will help service providers, end users and
developers of standards to grasp the status of global WMS resources, as well as
to understand the adoption status of OGC standards. The conclusions drawn in
this paper can benefit geospatial resource discovery, service performance
evaluation and guide service performance improvements.Comment: 24 pages; 15 figure
Coupling vibration model for hot rolling mills and its application
In this paper, we propose an effective mechanical-electrical-hydraulic-interfacial coupling vibration model for hot rolling mills and obtain a practical measure to relieve mill vibration. First, an experiment related to mill modulus control gain in automatic gauge control (AGC) is carried out during manufacturing. Rolling mill vibration is observed to gradually be enhanced with increasing mill modulus control gain. Then, to explain this phenomenon, the mechanical-electrical-hydraulic-interface coupling dynamic model is modeled based on Sims’ rolling force method. Finally, we analyze the influence of mill modulus control gain on the vibration numerically on the basis of the coupling dynamic model. Moreover, the agreement between the experiment result and the simulation result is confirmed and the measure reducing the mill modulus control gain is obtained to relieve mill vibration
Learning Segmentation Masks with the Independence Prior
An instance with a bad mask might make a composite image that uses it look
fake. This encourages us to learn segmentation by generating realistic
composite images. To achieve this, we propose a novel framework that exploits a
new proposed prior called the independence prior based on Generative
Adversarial Networks (GANs). The generator produces an image with multiple
category-specific instance providers, a layout module and a composition module.
Firstly, each provider independently outputs a category-specific instance image
with a soft mask. Then the provided instances' poses are corrected by the
layout module. Lastly, the composition module combines these instances into a
final image. Training with adversarial loss and penalty for mask area, each
provider learns a mask that is as small as possible but enough to cover a
complete category-specific instance. Weakly supervised semantic segmentation
methods widely use grouping cues modeling the association between image parts,
which are either artificially designed or learned with costly segmentation
labels or only modeled on local pairs. Unlike them, our method automatically
models the dependence between any parts and learns instance segmentation. We
apply our framework in two cases: (1) Foreground segmentation on
category-specific images with box-level annotation. (2) Unsupervised learning
of instance appearances and masks with only one image of homogeneous object
cluster (HOC). We get appealing results in both tasks, which shows the
independence prior is useful for instance segmentation and it is possible to
unsupervisedly learn instance masks with only one image.Comment: 7+5 pages, 13 figures, Accepted to AAAI 201
StegNet: Mega Image Steganography Capacity with Deep Convolutional Network
Traditional image steganography often leans interests towards safely
embedding hidden information into cover images with payload capacity almost
neglected. This paper combines recent deep convolutional neural network methods
with image-into-image steganography. It successfully hides the same size images
with a decoding rate of 98.2% or bpp (bits per pixel) of 23.57 by changing only
0.76% of the cover image on average. Our method directly learns end-to-end
mappings between the cover image and the embedded image and between the hidden
image and the decoded image. We~further show that our embedded image, while
with mega payload capacity, is still robust to statistical analysis.Comment: https://github.com/adamcavendish/StegNet-Mega-Image-Steganography-Capacity-with-Deep-Convolutional-Networ
FACE: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition
Large language models (LLMs) are primarily evaluated by overall performance
on various text understanding and generation tasks. However, such a paradigm
fails to comprehensively differentiate the fine-grained language and cognitive
skills, rendering the lack of sufficient interpretation to LLMs' capabilities.
In this paper, we present FACE, a framework for Fine-grAined and
Cognition-grounded LLMs' Capability Evaluation. Specifically, we formulate
LLMs' evaluation in a multi-dimensional and explainable manner by dissociating
the language-related capabilities and the cognition-related ones. Besides,
through extracting the intermediate reasoning from LLMs, we further break down
the process of applying a specific capability into three sub-steps: recalling
relevant knowledge, utilizing knowledge, and solving problems. Finally,
FACE evaluates each sub-step of each fine-grained capability, providing a
two-faceted diagnosis for LLMs. Utilizing FACE, we identify a common
shortfall in knowledge utilization among models and propose a straightforward,
knowledge-enhanced method to mitigate this issue. Our results not only showcase
promising performance enhancements but also highlight a direction for future
LLM advancements.Comment: Work in Progres
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