914 research outputs found
A Topology-aware Graph Coarsening Framework for Continual Graph Learning
Continual learning on graphs tackles the problem of training a graph neural
network (GNN) where graph data arrive in a streaming fashion and the model
tends to forget knowledge from previous tasks when updating with new data.
Traditional continual learning strategies such as Experience Replay can be
adapted to streaming graphs, however, these methods often face challenges such
as inefficiency in preserving graph topology and incapability of capturing the
correlation between old and new tasks. To address these challenges, we propose
TA, a (t)opology-(a)ware graph (co)arsening and (co)ntinual
learning framework that stores information from previous tasks as a reduced
graph. At each time period, this reduced graph expands by combining with a new
graph and aligning shared nodes, and then it undergoes a "zoom out" process by
reduction to maintain a stable size. We design a graph coarsening algorithm
based on node representation proximities to efficiently reduce a graph and
preserve topological information. We empirically demonstrate the learning
process on the reduced graph can approximate that of the original graph. Our
experiments validate the effectiveness of the proposed framework on three
real-world datasets using different backbone GNN models
A New Two-Dimensional Functional Material with Desirable Bandgap and Ultrahigh Carrier Mobility
Two-dimensional (2D) semiconductors with direct and modest bandgap and
ultrahigh carrier mobility are highly desired functional materials for
nanoelectronic applications. Herein, we predict that monolayer CaP3 is a new 2D
functional material that possesses not only a direct bandgap of 1.15 eV (based
on HSE06 computation), and also a very high electron mobility up to 19930 cm2
V-1 s-1, comparable to that of monolayer phosphorene. More remarkably, contrary
to the bilayer phosphorene which possesses dramatically reduced carrier
mobility compared to its monolayer counterpart, CaP3 bilayer possesses even
higher electron mobility (22380 cm2 V-1 s-1) than its monolayer counterpart.
The bandgap of 2D CaP3 can be tuned over a wide range from 1.15 to 0.37 eV
(HSE06 values) through controlling the number of stacked CaP3 layers. Besides
novel electronic properties, 2D CaP3 also exhibits optical absorption over the
entire visible-light range. The combined novel electronic, charge mobility, and
optical properties render 2D CaP3 an exciting functional material for future
nanoelectronic and optoelectronic applications
Learning Interpretable Rules for Scalable Data Representation and Classification
Rule-based models, e.g., decision trees, are widely used in scenarios
demanding high model interpretability for their transparent inner structures
and good model expressivity. However, rule-based models are hard to optimize,
especially on large data sets, due to their discrete parameters and structures.
Ensemble methods and fuzzy/soft rules are commonly used to improve performance,
but they sacrifice the model interpretability. To obtain both good scalability
and interpretability, we propose a new classifier, named Rule-based
Representation Learner (RRL), that automatically learns interpretable non-fuzzy
rules for data representation and classification. To train the
non-differentiable RRL effectively, we project it to a continuous space and
propose a novel training method, called Gradient Grafting, that can directly
optimize the discrete model using gradient descent. A novel design of logical
activation functions is also devised to increase the scalability of RRL and
enable it to discretize the continuous features end-to-end. Exhaustive
experiments on ten small and four large data sets show that RRL outperforms the
competitive interpretable approaches and can be easily adjusted to obtain a
trade-off between classification accuracy and model complexity for different
scenarios. Our code is available at: https://github.com/12wang3/rrl.Comment: Accepted by IEEE TPAMI in October 2023; Interpretable ML;
Neuro-Symbolic AI; Preliminary conference version (NeurIPS 2021) available at
arXiv:2109.1510
Surge-varying LOS based path following of under actuated surface vehicles
1048-1055Subject to harsh ocean environment, a novel path following control scheme with accurate guidance and high anti-disturbance ability for under actuated surface vehicles is proposed. The innovative work is as follow: (1) Based on the traditional line-of-sight (LOS), a surge-varying LOS (SVLOS) guidance law is designed to achieve double guidance of speed and heading, which enhances the flexibility and precision of the previous LOS; (2) Unknown disturbances are exactly estimated by an exact disturbance observer (EDO), wherein the limitations of bounded and asymptotic observations can be avoided; (3) The EDO-based robust tracking controllers enable accurate disturbance compensation and guided signal tracking in harsh ocean environment. Rigorous theoretical analysis and significant simulation comparison have been done to demonstrate superiority of the EDO-SVLOS scheme
Pre-evaluation on surface profile in turning process based on cutting parameters
Traditional online or in-process surface profile (quality) evaluation (prediction) needs to integrate cutting parameters and several in-process factors (vibration, machine dynamics, tool wear, etc) for high accuracy. However it might result in high measuring cost and complexity, and moreover, the surface profile (quality) evaluation result can only be obtained after machining process. In this paper an approach for surface profile pre-evaluation in turning process using cutting parameters and radial basis function (RBF) neural networks is presented. The aim was to only use three cutting parameters to predict surface profile before machining process for a fast pre-evaluation on surface quality under different cutting parameters. The input parameters of RBF networks are cutting speed, depth of cut, and free rate. The output parameters are FFT vector of surface profile as prediction (pre-evaluation) result. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. It was found that a very good performance of surface profile prediction, in terms of agreement with experimental data, can be achieved before machining process with high accuracy, low cost, and high speed. Furthermore, a new group of training and testing data was also used to analyze the influence of tool wear on prediction accuracy
Identification and characterization of bovine regulator of telomere length elongation helicase gene (RTEL): molecular cloning, expression distribution, splice variants and DNA methylation profile
BACKGROUND: The genetic basis of telomere length heterogeneity among mammalian species is still not well understood. Recently, a gene named regulator of telomere length elongation helicase (RTEL) was identified and predicted to be an essential participant in species-specific telomere length regulation in two murine species. To obtain broader insights into its structure and biological functions and to ascertain whether RTEL is also a candidate gene in the regulation of telomere length diversity in other mammalian species, data from other mammals may be helpful. RESULTS: Here we report the cDNA cloning, genomic structure, chromosomal location, alternative splicing pattern, expression distribution and DNA methylation profile of the bovine homolog of RTEL. The longest transcript of bovine RTEL is 4440 nt, encompassing 24.8 kb of genomic sequence that was mapped to chromosome 13q2.2. It encodes a conserved helicase-like protein containing seven characterized helicase motifs in the first 750 aa and a PIP box in the C-terminus. Four splice variants were identified within the transcripts in both the coding and 5'-untranslated regions; Western blot revealed that the most abundant splice variant SV-1 was translated to a truncated isoform of RTEL. The different 5'UTRs imply alternative transcription start sites in the promoter; Bovine RTEL was transcribed at the blastocyst stage, and expression levels were highest in adult testis, liver and ovary. DNA methylation analysis of tissues that differed significantly in expression level indicated that relatively low DNA methylation is associated with higher expression. CONCLUSION: In this study, we have identified and characterized a bovine RTEL homolog and obtained basic information about it, including gene structure, expression distribution, splice variants and profile of DNA methylation around two putative transcription start sites. These data may be helpful for further comparative and functional analysis of RTEL in mammals
Role of media coverage in mitigating COVID-19 transmission: evidence from China
This paper evaluates the impact of media coverage in mitigating the spread of COVID-19 in China during the early phase of the pandemic. We construct provincial-level data on media coverage and link with COVID-19 indicators and population mobility data, among other control variables. We estimate how media coverage mitigates the temporal and spatial spread of COVID-19. Seemingly unrelated regressions are used to examine the simultaneous impact of media coverage on the number of new cases and close contacts. The results show that the effect of media coverage on COVID-19 transmission in China has an inverse-U curvature and was mediated by within- and across-province population mobility. Based on our simulation results, media coverage in China is associated with a potential reduction of 394,000 COVID-19 cases and 1.4 million close contacts during January 19 and February 29. Our results also support the important role of contact tracing in mitigating the transmission of COVID-19
Origin of the GeV Emission During the X-ray Flaring Activity in GRB 100728A
Recently, Fermi-LAT detected GeV emission during the X-ray flaring activity
in GRB 100728A. We study various scenarios for its origin. The hard spectrum of
the GeV emission favors the external inverse-Compton origin in which X-ray
flare photons are up-scattered by relativistic electrons in the external
forward shock. This external IC scenario, with anisotropic scattering effect
taken into account, can reproduce the temporal and spectral properties of the
GeV emission in GRB 100728A.Comment: Minor revisions, 6 pages, 2 figures, accepted for publication in Ap
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