249 research outputs found
A Decentralized Processing Schema for Efficient and Robust Real-time Multi-GNSS Satellite Clock Estimation
Real-time multi-GNSS precise point positioning (PPP) requires the support of high-rate satellite clock corrections. Due to the large number of ambiguity parameters, it is difficult to update clocks at high frequency in real-time for a large reference network. With the increasing number of satellites of multi-GNSS constellations and the number of stations, real-time high-rate clock estimation becomes a big challenge. In this contribution, we propose a decentralized clock estimation (DECE) strategy, in which both undifferenced (UD) and epoch-differenced (ED) mode are implemented but run separately in different computers, and their output clocks are combined in another process to generate a unique product. While redundant UD and/or ED processing lines can be run in offsite computers to improve the robustness, processing lines for different networks can also be included to improve the clock quality. The new strategy is realized based on the Position and Navigation Data Analyst (PANDA) software package and is experimentally validated with about 110 real-time stations for clock estimation by comparison of the estimated clocks and the PPP performance applying estimated clocks. The results of the real-time PPP experiment using 12 global stations show that with the greatly improved computational efficiency, 3.14 cm in horizontal and 5.51 cm in vertical can be achieved using the estimated DECE clock
Adaptive Computation of an Elliptic Eigenvalue Optimization Problem with a Phase-Field Approach
In this paper, we discuss adaptive approximations of an elliptic eigenvalue
optimization problem in a phase-field setting by a conforming finite element
method. An adaptive algorithm is proposed and implemented in several two
dimensional numerical examples for illustration of efficiency and accuracy.
Theoretical findings consist in the vanishing limit of a subsequence of
estimators and the convergence of the relevant subsequence of
adaptively-generated solutions to a solution to the continuous optimality
system.Comment: 36 pages, 24 figures, 2 table
An Adaptive Phase-Field Method for Structural Topology Optimization
In this work, we develop an adaptive algorithm for the efficient numerical
solution of the minimum compliance problem in topology optimization. The
algorithm employs the phase field approximation and continuous density field.
The adaptive procedure is driven by two residual type a posteriori error
estimators, one for the state variable and the other for the objective
functional. The adaptive algorithm is provably convergent in the sense that the
sequence of numerical approximations generated by the adaptive algorithm
contains a subsequence convergent to a solution of the continuous first-order
optimality system. We provide several numerical simulations to show the
distinct features of the algorithm.Comment: 30 pages, 10 figure
Supervised, semi-supervised, and unsupervised learning of the Domany-Kinzel model
The Domany Kinzel (DK) model encompasses several types of non-equilibrium
phase transitions, depending on the selected parameters. We apply supervised,
semi-supervised, and unsupervised learning methods to studying the phase
transitions and critical behaviors of the (1 + 1)-dimensional DK model. The
supervised and the semi-supervised learning methods permit the estimations of
the critical points, the spatial and temporal correlation exponents, concerning
labelled and unlabelled DK configurations, respectively. Furthermore, we also
predict the critical points by employing principal component analysis (PCA) and
autoencoder. The PCA and autoencoder can produce results in good agreement with
simulated particle number density
The effects of banking market structure on corporate cash holdings and the value of cash
We investigate the impact of the local banking market structure on the level of corporate cash holdings and the value of cash. We find that, in more concentrated banking markets, firms increase their cash holdings by issuing more equity. The marginal value of $1 cash increases by 10 cents with a one-standard-deviation increase in bank concentration. The positive relationship between bank concentration and value of cash is robust to a rich set of tests such as for firms having access to bond markets or firms using syndicated loans and is more prominent for more financially constrained firms. We also explore the mechanism, and our results suggest that in more concentrated banking markets firms demand more cash to shield against default risk
Delving into Multimodal Prompting for Fine-grained Visual Classification
Fine-grained visual classification (FGVC) involves categorizing fine
subdivisions within a broader category, which poses challenges due to subtle
inter-class discrepancies and large intra-class variations. However, prevailing
approaches primarily focus on uni-modal visual concepts. Recent advancements in
pre-trained vision-language models have demonstrated remarkable performance in
various high-level vision tasks, yet the applicability of such models to FGVC
tasks remains uncertain. In this paper, we aim to fully exploit the
capabilities of cross-modal description to tackle FGVC tasks and propose a
novel multimodal prompting solution, denoted as MP-FGVC, based on the
contrastive language-image pertaining (CLIP) model. Our MP-FGVC comprises a
multimodal prompts scheme and a multimodal adaptation scheme. The former
includes Subcategory-specific Vision Prompt (SsVP) and Discrepancy-aware Text
Prompt (DaTP), which explicitly highlights the subcategory-specific
discrepancies from the perspectives of both vision and language. The latter
aligns the vision and text prompting elements in a common semantic space,
facilitating cross-modal collaborative reasoning through a Vision-Language
Fusion Module (VLFM) for further improvement on FGVC. Moreover, we tailor a
two-stage optimization strategy for MP-FGVC to fully leverage the pre-trained
CLIP model and expedite efficient adaptation for FGVC. Extensive experiments
conducted on four FGVC datasets demonstrate the effectiveness of our MP-FGVC.Comment: The first two authors contributed equally to this wor
Socio-demographic association of multiple modifiable lifestyle risk factors and their clustering in a representative urban population of adults: a cross-sectional study in Hangzhou, China
<p>Abstract</p> <p>Background</p> <p>To plan long-term prevention strategies and develop tailored intervention activities, it is important to understand the socio-demographic characteristics of the subpopulations at high risk of developing chronic diseases. This study aimed to examine the socio-demographic characteristics associated with multiple lifestyle risk factors and their clustering.</p> <p>Methods</p> <p>We conducted a simple random sampling survey to assess lifestyle risk factors in three districts of Hangzhou, China between 2008 and 2009. A two-step cluster analysis was used to identify different health-related lifestyle clusters based on tobacco use, physical activity, fruit and vegetable consumption, and out-of-home eating. Multinomial logistic regression was used to model the association between socio-demographic factors and lifestyle clusters.</p> <p>Results</p> <p>A total of 2016 eligible people (977 men and 1039 women, ages 18-64 years) completed the survey. Three distinct clusters were identified from the cluster analysis: an unhealthy (UH) group (25.7%), moderately healthy (MH) group (31.1%), and healthy (H) group (43.1%). UH group was characterised by a high prevalence of current daily smoking, a moderate or low level of PA, low FV consumption with regard to the frequency or servings, and more occurrences of eating out. H group was characterised by no current daily smoking, a moderate level of PA, high FV consumption, and the fewest times of eating out. MH group was characterised by no current daily smoking, a low or high level of PA, and an intermediate level of FV consumption and frequency of eating out. Men were more likely than women to have unhealthy lifestyles. Adults aged 50-64 years were more likely to live healthy lifestyles. Adults aged 40-49 years were more likely to be in the UH group. Adults whose highest level of education was junior high school or below were more likely to be in the UH group. Adults with a high asset index were more likely to be in the MH group.</p> <p>Conclusions</p> <p>This study suggests that Chinese urban people who are middle-aged, men, and less educated are most likely to be part of the cluster with a high-risk profile. Those groups will contribute the most to the future burden of major chronic disease and should be targeted for early prevention programs.</p
- ā¦