45 research outputs found
Spatio-Temporal Dual Graph Neural Networks for Travel Time Estimation
Travel time estimation is one of the core tasks for the development of
intelligent transportation systems. Most previous works model the road segments
or intersections separately by learning their spatio-temporal characteristics
to estimate travel time. However, due to the continuous alternations of the
road segments and intersections in a path, the dynamic features are supposed to
be coupled and interactive. Therefore, modeling one of them limits further
improvement in accuracy of estimating travel time. To address the above
problems, a novel graph-based deep learning framework for travel time
estimation is proposed in this paper, namely Spatio-Temporal Dual Graph Neural
Networks (STDGNN). Specifically, we first establish the node-wise and edge-wise
graphs to respectively characterize the adjacency relations of intersections
and that of road segments. In order to extract the joint spatio-temporal
correlations of the intersections and road segments, we adopt the
spatio-temporal dual graph learning approach that incorporates multiple
spatial-temporal dual graph learning modules with multi-scale network
architectures for capturing multi-level spatial-temporal information from the
dual graph. Finally, we employ the multi-task learning approach to estimate the
travel time of a given whole route, each road segment and intersection
simultaneously. We conduct extensive experiments to evaluate our proposed model
on three real-world trajectory datasets, and the experimental results show that
STDGNN significantly outperforms several state-of-art baselines
Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction
Accurate traffic prediction is a challenging task in intelligent
transportation systems because of the complex spatio-temporal dependencies in
transportation networks. Many existing works utilize sophisticated temporal
modeling approaches to incorporate with graph convolution networks (GCNs) for
capturing short-term and long-term spatio-temporal dependencies. However, these
separated modules with complicated designs could restrict effectiveness and
efficiency of spatio-temporal representation learning. Furthermore, most
previous works adopt the fixed graph construction methods to characterize the
global spatio-temporal relations, which limits the learning capability of the
model for different time periods and even different data scenarios. To overcome
these limitations, we propose an automated dilated spatio-temporal synchronous
graph network, named Auto-DSTSGN for traffic prediction. Specifically, we
design an automated dilated spatio-temporal synchronous graph (Auto-DSTSG)
module to capture the short-term and long-term spatio-temporal correlations by
stacking deeper layers with dilation factors in an increasing order. Further,
we propose a graph structure search approach to automatically construct the
spatio-temporal synchronous graph that can adapt to different data scenarios.
Extensive experiments on four real-world datasets demonstrate that our model
can achieve about 10% improvements compared with the state-of-art methods.
Source codes are available at https://github.com/jinguangyin/Auto-DSTSGN
Discovery and regulation of chiral magnetic solitons: Exact solution from Landau-Lifshitz-Gilbert equation
The Landau-Lifshitz-Gilbert (LLG) equation has emerged as a fundamental and
indispensable framework within the realm of magnetism. However, solving the LLG
equation, encompassing full nonlinearity amidst intricate complexities,
presents formidable challenges. In this context, we develop a precise mapping
through geometric representation, establishing a direct linkage between the LLG
equation and an integrable generalized nonlinear Schr\"odinger equation. This
novel mapping provides accessibility towards acquiring a great number of exact
spatiotemporal solutions. Notably, exact chiral magnetic solitons, critical for
stability and controllability in propagation with and without damping effects
are discovered. Our formulation provides exact solutions for the long-standing
fully nonlinear problem, facilitating practical control through spin current
injection in magnetic memory applications.Comment: main text:5 pages, 4 figures, supplementary materials:5 pages, 2
figure
Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis
Multivariate Time Series (MTS) widely exists in real-word complex systems,
such as traffic and energy systems, making their forecasting crucial for
understanding and influencing these systems. Recently, deep learning-based
approaches have gained much popularity for effectively modeling temporal and
spatial dependencies in MTS, specifically in Long-term Time Series Forecasting
(LTSF) and Spatial-Temporal Forecasting (STF). However, the fair benchmarking
issue and the choice of technical approaches have been hotly debated in related
work. Such controversies significantly hinder our understanding of progress in
this field. Thus, this paper aims to address these controversies to present
insights into advancements achieved. To resolve benchmarking issues, we
introduce BasicTS, a benchmark designed for fair comparisons in MTS
forecasting. BasicTS establishes a unified training pipeline and reasonable
evaluation settings, enabling an unbiased evaluation of over 30 popular MTS
forecasting models on more than 18 datasets. Furthermore, we highlight the
heterogeneity among MTS datasets and classify them based on temporal and
spatial characteristics. We further prove that neglecting heterogeneity is the
primary reason for generating controversies in technical approaches. Moreover,
based on the proposed BasicTS and rich heterogeneous MTS datasets, we conduct
an exhaustive and reproducible performance and efficiency comparison of popular
models, providing insights for researchers in selecting and designing MTS
forecasting models
A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects
Instant delivery services, such as food delivery and package delivery, have
achieved explosive growth in recent years by providing customers with
daily-life convenience. An emerging research area within these services is
service Route\&Time Prediction (RTP), which aims to estimate the future service
route as well as the arrival time of a given worker. As one of the most crucial
tasks in those service platforms, RTP stands central to enhancing user
satisfaction and trimming operational expenditures on these platforms. Despite
a plethora of algorithms developed to date, there is no systematic,
comprehensive survey to guide researchers in this domain. To fill this gap, our
work presents the first comprehensive survey that methodically categorizes
recent advances in service route and time prediction. We start by defining the
RTP challenge and then delve into the metrics that are often employed.
Following that, we scrutinize the existing RTP methodologies, presenting a
novel taxonomy of them. We categorize these methods based on three criteria:
(i) type of task, subdivided into only-route prediction, only-time prediction,
and joint route\&time prediction; (ii) model architecture, which encompasses
sequence-based and graph-based models; and (iii) learning paradigm, including
Supervised Learning (SL) and Deep Reinforcement Learning (DRL). Conclusively,
we highlight the limitations of current research and suggest prospective
avenues. We believe that the taxonomy, progress, and prospects introduced in
this paper can significantly promote the development of this field
Label-free visualization of carbapenemase activity in living bacteria
Evaluating enzyme activity intracellularly on natural substrates is a significant experimental challenge in biomedical research. We report a labelâfree method for realâtime monitoring of the catalytic behavior of classâ
A, B, and D carbapenemases in live bacteria based on measurement of heat changes. By this means, novel biphasic kinetics for classâ
D OXAâ48 with imipenem as substrate is revealed, providing a new approach to detect OXAâ48âlike producers. This inâcell calorimetry approach offers major advantages in the rapid screening (10â
min) of carbapenemaseâproducing Enterobacteriaceae from 142 clinical bacterial isolates, with superior sensitivity (97â%) and excellent specificity (100â%) compared to conventional methods. As a general, labelâfree method for the study of living cells, this protocol has potential for application to a wider range and variety of cellular components and physiological processes
Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction
Traffic congestion event prediction is an important yet challenging task in intelligent transportation systems. Many existing works about traffic prediction integrate various temporal encoders and graph convolution networks (GCNs), called spatio-temporal graph-based neural networks, which focus on predicting dense variables such as flow, speed and demand in time snapshots, but they can hardly forecast the traffic congestion events that are sparsely distributed on the continuous time axis. In recent years, neural point process (NPP) has emerged as an appropriate framework for event prediction in continuous time scenarios. However, most conventional works about NPP cannot model the complex spatio-temporal dependencies and congestion evolution patterns. To address these limitations, we propose a spatio-temporal graph neural point process framework, named STGNPP for traffic congestion event prediction. Specifically, we first design the spatio-temporal graph learning module to fully capture the long-range spatio-temporal dependencies from the historical traffic state data along with the road network. The extracted spatio-temporal hidden representation and congestion event information are then fed into a continuous gated recurrent unit to model the congestion evolution patterns. In particular, to fully exploit the periodic information, we also improve the intensity function calculation of the point process with a periodic gated mechanism. Finally, our model simultaneously predicts the occurrence time and duration of the next congestion. Extensive experiments on two real-world datasets demonstrate that our method achieves superior performance in comparison to existing state-of-the-art approaches
Modeling and optimization of urban rail transit scheduling with adaptive fruit fly optimization algorithm
Despite the rapid development of urban rail transit in China, there are still some problems in train operation, such as low efficiency and poor punctuality. To realize a proper allocation of passenger flows and increase train frequency, this paper has proposed an improved urban rail transit scheduling model and solved the model with an adaptive fruit fly optimization algorithm (AFOA). For the benefits of both passengers and operators, the shortest average waiting time of passengers and the least train frequency are chosen as the optimization objective, and train headway is taken as the decision variable in the proposed model. To obtain higher computational efficiency and accuracy, an adaptive dynamic step size is built in the conventional FOA. Moreover, the data of urban rail transit in Zhengzhou was simulated for case study. The comparison results reveal that the proposed AFOA exhibits faster convergence speed and preferable accuracy than the conventional FOA, particle swarm optimization, and bacterial foraging optimization algorithms. Due to these superiorities, the proposed AFOA is feasible and effective for optimizing the scheduling of urban rail transit
A D-band SPST switch using parallel-stripline swap with defected ground structure
In this paper, we propose a D-band switch design using a parallel-stripline swap hybrid coupler with defected ground structure (DGS). Compared to the conventional transmission line (TL) with high insertion loss at high frequency, the equivalent capacitance introduced by the coupler gap leads to series resonance condition which results in lower insertion loss at the higher frequency. The hybrid coupler enables two additional degrees of design freedom since the capacitance and inductance can be tuned by the structural dimension of the coupler. The fabricated switch with core area of 0.0036 mm2 shows measured insertion loss of 1.6â3 dB from 110â134 GHz, return loss of higher than 10 dB and isolation level around 16 dB.Published versio