116 research outputs found
Explainable Spatio-Temporal Graph Neural Networks
Spatio-temporal graph neural networks (STGNNs) have gained popularity as a
powerful tool for effectively modeling spatio-temporal dependencies in diverse
real-world urban applications, including intelligent transportation and public
safety. However, the black-box nature of STGNNs limits their interpretability,
hindering their application in scenarios related to urban resource allocation
and policy formulation. To bridge this gap, we propose an Explainable
Spatio-Temporal Graph Neural Networks (STExplainer) framework that enhances
STGNNs with inherent explainability, enabling them to provide accurate
predictions and faithful explanations simultaneously. Our framework integrates
a unified spatio-temporal graph attention network with a positional information
fusion layer as the STG encoder and decoder, respectively. Furthermore, we
propose a structure distillation approach based on the Graph Information
Bottleneck (GIB) principle with an explainable objective, which is instantiated
by the STG encoder and decoder. Through extensive experiments, we demonstrate
that our STExplainer outperforms state-of-the-art baselines in terms of
predictive accuracy and explainability metrics (i.e., sparsity and fidelity) on
traffic and crime prediction tasks. Furthermore, our model exhibits superior
representation ability in alleviating data missing and sparsity issues. The
implementation code is available at: https://github.com/HKUDS/STExplainer.Comment: 32nd ACM International Conference on Information and Knowledge
Management (CIKM' 23
Spatio-Temporal Meta Contrastive Learning
Spatio-temporal prediction is crucial in numerous real-world applications,
including traffic forecasting and crime prediction, which aim to improve public
transportation and safety management. Many state-of-the-art models demonstrate
the strong capability of spatio-temporal graph neural networks (STGNN) to
capture complex spatio-temporal correlations. However, despite their
effectiveness, existing approaches do not adequately address several key
challenges. Data quality issues, such as data scarcity and sparsity, lead to
data noise and a lack of supervised signals, which significantly limit the
performance of STGNN. Although recent STGNN models with contrastive learning
aim to address these challenges, most of them use pre-defined augmentation
strategies that heavily depend on manual design and cannot be customized for
different Spatio-Temporal Graph (STG) scenarios. To tackle these challenges, we
propose a new spatio-temporal contrastive learning (CL4ST) framework to encode
robust and generalizable STG representations via the STG augmentation paradigm.
Specifically, we design the meta view generator to automatically construct node
and edge augmentation views for each disentangled spatial and temporal graph in
a data-driven manner. The meta view generator employs meta networks with
parameterized generative model to customize the augmentations for each input.
This personalizes the augmentation strategies for every STG and endows the
learning framework with spatio-temporal-aware information. Additionally, we
integrate a unified spatio-temporal graph attention network with the proposed
meta view generator and two-branch graph contrastive learning paradigms.
Extensive experiments demonstrate that our CL4ST significantly improves
performance over various state-of-the-art baselines in traffic and crime
prediction.Comment: 32nd ACM International Conference on Information and Knowledge
Management (CIKM' 23
Void Fraction Measurement of Gas-Liquid Two-Phase Flow from Differential Pressure
Void fraction is an important process variable for the volume and mass computation required for transportation of gasâliquid mixture in pipelines, storage in tanks, metering and custody transfer. Inaccurate measurement would introduce errors in product measurement with potentials for loss of revenue. Accurate measurement is often constrained by invasive and expensive online measurement techniques. This work focuses on the use of cost effective and non-invasive pressure sensors to calculate the gas void fraction of gasâliquid flow. The differential pressure readings from the vertical upward bubbly and slug airâwater flow are substituted into classical mathematical models based on energy conservation to derive the void fraction. Electrical Resistance Tomography (ERT) and Wire-mesh Sensor (WMS) are used as benchmark to validate the void fraction obtained from the differential pressure. Consequently the model is able to produce reasonable agreement with ERT and WMS on the void fraction measurement. The effect of the friction loss on the mathematical models is also investigated and discussed. It is concluded the friction loss cannot be neglected, particularly when gas void fraction is less than 0.2
Reducing the gap between streaming and non-streaming Transducer-based ASR by adaptive two-stage knowledge distillation
Transducer is one of the mainstream frameworks for streaming speech
recognition. There is a performance gap between the streaming and non-streaming
transducer models due to limited context. To reduce this gap, an effective way
is to ensure that their hidden and output distributions are consistent, which
can be achieved by hierarchical knowledge distillation. However, it is
difficult to ensure the distribution consistency simultaneously because the
learning of the output distribution depends on the hidden one. In this paper,
we propose an adaptive two-stage knowledge distillation method consisting of
hidden layer learning and output layer learning. In the former stage, we learn
hidden representation with full context by applying mean square error loss
function. In the latter stage, we design a power transformation based adaptive
smoothness method to learn stable output distribution. It achieved 19\%
relative reduction in word error rate, and a faster response for the first
token compared with the original streaming model in LibriSpeech corpus
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Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning
Abstract: Forecasting the state of health and remaining useful life of Li-ion batteries is an unsolved challenge that limits technologies such as consumer electronics and electric vehicles. Here, we build an accurate battery forecasting system by combining electrochemical impedance spectroscopy (EIS)âa real-time, non-invasive and information-rich measurement that is hitherto underused in battery diagnosisâwith Gaussian process machine learning. Over 20,000 EIS spectra of commercial Li-ion batteries are collected at different states of health, states of charge and temperaturesâthe largest dataset to our knowledge of its kind. Our Gaussian process model takes the entire spectrum as input, without further feature engineering, and automatically determines which spectral features predict degradation. Our model accurately predicts the remaining useful life, even without complete knowledge of past operating conditions of the battery. Our results demonstrate the value of EIS signals in battery management systems
Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning
Abstract: Forecasting the state of health and remaining useful life of Li-ion batteries is an unsolved challenge that limits technologies such as consumer electronics and electric vehicles. Here, we build an accurate battery forecasting system by combining electrochemical impedance spectroscopy (EIS)âa real-time, non-invasive and information-rich measurement that is hitherto underused in battery diagnosisâwith Gaussian process machine learning. Over 20,000 EIS spectra of commercial Li-ion batteries are collected at different states of health, states of charge and temperaturesâthe largest dataset to our knowledge of its kind. Our Gaussian process model takes the entire spectrum as input, without further feature engineering, and automatically determines which spectral features predict degradation. Our model accurately predicts the remaining useful life, even without complete knowledge of past operating conditions of the battery. Our results demonstrate the value of EIS signals in battery management systems
Association of Intraoperative Hypotension with Acute Kidney Injury after Noncardiac Surgery in Patients Younger than 60 Years Old
Background/Aims: Intraoperative hypotension (IOH) may be associated with surgery-related acute kidney injury (AKI). However, the duration of hypotension that triggers AKI is poorly understood. The incidence of AKI with various durations of IOH and mean arterial pressures (MAPs) was investigated. Materials: A retrospective cohort study of 4,952 patients undergoing noncardiac surgery (2011 to 2016) with MAP monitoring and a length of stay of one or more days was performed. The exclusion criteria were a preoperative estimated glomerular filtration (eGFR) â€60 mL minâ1 1.73 m2â1, a preoperative MAP less than 65 mm Hg, dialysis dependence, urologic surgery, age older than 60 years, and a surgical duration of less than 60 min. The primary exposure was IOH, and the primary outcome was AKI (50% or 0.3 mg dLâ1 increase in creatinine) during the first 7 postoperative days. Multivariable logistic regression was used to model the exposure-outcome relationship. Results: AKI occurred in 186 (3.76%) noncardiac surgery patients. The adjusted odds ratio for surgery-related AKI for a MAP of less than 55 mm Hg was 14.11 (95% confidence interval: 5.02â39.69) for an exposure of more than 20 min. Age was not an interaction factor between AKI and IOH. Conclusion: There was a considerably increased risk of postoperative AKI when intraoperative MAP was less than 55 mm Hg for more than 10 min. Strict blood pressure management is recommended even for patients younger than 60 years old
Translocator protein is a marker of activated microglia in rodent models but not human neurodegenerative diseases
Microglial activation plays central roles in neuroinflammatory and neurodegenerative diseases. Positron emission tomography (PET) targeting 18âkDa Translocator Protein (TSPO) is widely used for localising inflammation in vivo, but its quantitative interpretation remains uncertain. We show that TSPO expression increases in activated microglia in mouse brain disease models but does not change in a non-human primate disease model or in common neurodegenerative and neuroinflammatory human diseases. We describe genetic divergence in the TSPO gene promoter, consistent with the hypothesis that the increase in TSPO expression in activated myeloid cells depends on the transcription factor AP1 and is unique to a subset of rodent species within the Muroidea superfamily. Finally, we identify LCP2 and TFEC as potential markers of microglial activation in humans. These data emphasise that TSPO expression in human myeloid cells is related to different phenomena than in mice, and that TSPO-PET signals in humans reflect the density of inflammatory cells rather than activation state.Published versionThe authors thank the UK MS Society for financial support (grant number: C008-16.1). DRO was funded by an MRC Clinician Scientist Award (MR/N008219/1). P.M.M. acknowledges generous support from Edmond J Safra Foundation and Lily Safra, the NIHR Senior Investigator programme and the UK Dementia Research Institute which receives its funding from DRI Ltd., funded by the UK Medical Research Council, Alzheimerâs Society, and Alzheimerâs Research UK. P.M.M. and D.R.O. thank the Imperial College Healthcare Trust-NIHR Biomedical Research Centre for infrastructure support and the Medical Research Council for support of TSPO studies (MR/N016343/1). E.A. was supported by the ALS Stichting (grant âThe Dutch ALS Tissue Bankâ). P.M. and B.B.T. are funded by the Swiss National Science Foundation (projects 320030_184713 and 310030_212322, respectively). S.T. was supported by an âEarly Postdoc.Mobilityâ scholarship (P2GEP3_191446) from the Swiss National Science Foundation, a âClinical Medicine Plusâ scholarship from the Prof Dr. Max CloĂ«tta Foundation (Zurich, Switzerland), from the Jean et Madeleine Vachoux Foundation (Geneva, Switzerland) and from the University Hospitals of Geneva. This work was funded by NIH grants U01AG061356 (De Jager/Bennett), RF1AG057473 (De Jager/Bennett), and U01AG046152 (De Jager/Bennett) as part of the AMP-AD consortium, as well as NIH grants R01AG066831 (Menon) and U01AG072572 (De Jager/St George-Hyslop)
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Comprehensive molecular characterization of gastric adenocarcinoma
Gastric cancer is a leading cause of cancer deaths, but analysis of its molecular and clinical characteristics has been complicated by histological and aetiological heterogeneity. Here we describe a comprehensive molecular evaluation of 295 primary gastric adenocarcinomas as part of The Cancer Genome Atlas (TCGA) project. We propose a molecular classification dividing gastric cancer into four subtypes: tumours positive for EpsteinâBarr virus, which display recurrent PIK3CA mutations, extreme DNA hypermethylation, and amplification of JAK2, CD274 (also known as PD-L1) and PDCD1LG2 (also knownasPD-L2); microsatellite unstable tumours, which show elevated mutation rates, including mutations of genes encoding targetable oncogenic signalling proteins; genomically stable tumours, which are enriched for the diffuse histological variant and mutations of RHOA or fusions involving RHO-family GTPase-activating proteins; and tumours with chromosomal instability, which show marked aneuploidy and focal amplification of receptor tyrosine kinases. Identification of these subtypes provides a roadmap for patient stratification and trials of targeted therapies
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