Minimum-action paths for wave-number selection in nonequilibrium systems

The problem of wave-number selections in nonequilibrium pattern-forming systems in the presence of noise is investigated. The minimum-action method is proposed to study the noise-induced transitions between the different spatiotemporal states by generalizing the traditional theory previously applied in low-dimensional dynamical systems. The scheme is shown as an example in the stabilized Kuramoto-Sivashinsky equation. The present method allows us to conveniently find the unique noise selected state, in contrast to previous work using direct simulations of the stochastic partial differential equation, where the constraints of the simulation only allow a narrow band to be determined

eRPCAe^{\text{RPCA}}: Robust Principal Component Analysis for Exponential Family Distributions

Robust Principal Component Analysis (RPCA) is a widely used method for recovering low-rank structure from data matrices corrupted by significant and sparse outliers. These corruptions may arise from occlusions, malicious tampering, or other causes for anomalies, and the joint identification of such corruptions with low-rank background is critical for process monitoring and diagnosis. However, existing RPCA methods and their extensions largely do not account for the underlying probabilistic distribution for the data matrices, which in many applications are known and can be highly non-Gaussian. We thus propose a new method called Robust Principal Component Analysis for Exponential Family distributions ($e^{\text{RPCA}}$), which can perform the desired decomposition into low-rank and sparse matrices when such a distribution falls within the exponential family. We present a novel alternating direction method of multiplier optimization algorithm for efficient $e^{\text{RPCA}}$ decomposition. The effectiveness of $e^{\text{RPCA}}$ is then demonstrated in two applications: the first for steel sheet defect detection, and the second for crime activity monitoring in the Atlanta metropolitan area

Study on the Evolution Mechanism of Lane Change Decision in Urban Expressway Diversion Area

Urban expressway is the artery of modern urban traffic network, and the vehicle operation at off-ramp and diverging area affects the operation efficiency of the whole traffic system. The lane-changing game behavior between off ramp vehicles and going straight vehicles is very important in the whole driving behavior, and the lane-changing behavior between vehicles is easy to cause traffic accidents and stopping phenomena. To improve the driving efficiency in the diversion area and reduce the accident risk, by analyzing the process of the lane-changing behavior at off-ramp, this paper establishes a two-vehicle game model, solves the replicator dynamic equation according to the Dynamic Evolutionary Game Theory, uses MATLAB to calculate the evolution process and evolution speed based on different payoff factors, explores the influence of safety and speed on the stability of turn-out, and judges the evolutionary equilibrium point according to the determinant and trail of the Jacobi matrix. We build a realistic turn-out scenario and simulate it using the micro-traffic simulation software SUMO and it is found that: (1) The speed of different evolutionary equilibrium points based on speed payoff increased by 8.3% and 4.4% respectively compared with the speed of initial point. (2) The number of conflicts at the evolutionary equilibrium point based on the security payoff reduced to 22% of the initial point. (3) Compared with the initial point, the speed of the evolutionary stable point based on comprehensive payoff increased by 10.3%, and the number of conflicts reduced to 11% of the initial point. The simulation results show that the strategy of stable point of the evolutionary game model can effectively reduce the accident rate and improve the road operation efficiency

Timber production assessment of a plantation forest: An integrated framework with field-based inventory, multi-source remote sensing data and forest management history

Timber production is the purpose for managing plantation forests, and its spatial and quantitative information is critical for advising management strategies. Previous studies have focused on growing stock volume (GSV), which represents the current potential of timber production, yet few studies have investigated historical process-harvested timber. This resulted in a gap in a synthetical ecosystem service assessment of timber production. In this paper, we established a Management Process-based Timber production (MPT) framework to integrate the current GSV and the harvested timber derived from historical logging regimes, trying to synthetically assess timber production for a historical period. In the MPT framework, age-class and current GSV determine the times of historical thinning and the corresponding harvested timber, by using a "space-for-time" substitution. The total timber production can be estimated by the historical harvested timber in each thinning and the current GSV. To test this MPT framework, an empirical study on a larch plantation (LP) with area of 43,946 ha was conducted in North China for a period from 1962 to 2010. Field-based inventory data was integrated with ALOS PALSAR (Advanced Land-Observing Satellite Phased Array L-band Synthetic Aperture Radar) and Landsat-8 OLI (Operational Land Imager) data for estimating the age-class and current GSV of LP. The random forest model with PALSAR backscatter intensity channels and OLI bands as input predictive variables yielded an accuracy of 67.9% with a Kappa coefficient of 0.59 for age-class classification. The regression model using PALSAR data produced a root mean square error (RMSE) of 36.5 m(3) ha(-1). The total timber production of LP was estimated to be 7.27 x 10(6) m(3), with 4.87 x 10(6) m(3) in current GSV and 2.40 x 10(6) m(3) in harvested timber through historical thinning. The historical process-harvested timber accounts to 33.0% of the total timber production, which component has been neglected in the assessments for current status of plantation forests. Synthetically considering the RMSE for predictive GSV and misclassification of age-class, the error in timber production were supposed to range from -55.2 to 56.3 m(3) ha(-1). The MPT framework can be used to assess timber production of other tree species at a larger spatial scale, providing crucial information for a better understanding of forest ecosystem service. (C) 2016 Elsevier B.V. All rights reserved.ArticleINTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION.52:155-165(2016)journal articl

Age-related network topological difference based on the sleep ECG signal

Age has been shown to be a crucial factor for the EEG and fMRI small-world networks during sleep. However, the characteristics of the age-related network based on sleep ECG signal and how the network changes during different sleep stages are poorly understood. This study focuses on to explore the age-related scale-free and small-world network properties of the ECG signal from male subjects during distinct sleep stages, including the wakeful(W), light sleep (LS), deep sleep (DS) and rapid eye movement (REM) stages. The subjects are divided into two age groups: younger (age<=40, n=11) group and older group (age>40, n=25). For the scale-free network analysis, our results reveal a distinctive pattern of the scale free network topologies between two age groups, including the mean degree ( ), the clustering coefficient ( ), and the path length ( )features, such as the slope distribution of in younger group increased from 1.99 during W to above 2.05 during DS. In addition, the results indicate that the small-world properties can be found across all sleep stages in both age groups. But the small-world index in the LS and REM stages significantly decreased with age (p=0.0006 and p=0.05 respectively). The comparison analysis result indicates that the network topology variations of the sleep ECG signals prone to show age-relevant differences which could be used for sleep stage classification and sleep disorder diagnosis

Serum Procalcitonin Correlates with Renal Function in Hepatitis B Virus-Related Acute-on-Chronic Liver Failure

Background/Aims: To investigate the relationship between elevated serum procalcitonin (PCT) and renal function in hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). Methods: HBV-ACLF patients (n = 201) presenting to the State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University, from January 2013 to November 2016 were categorized into three groups according to serum PCT levels: (i) normal group (n = 74) had PCT of ≤ 0.5 ng/mL; (ii) elevated group (n = 85) had PCT in the range 0.5–1.0 ng/mL; and (iii) highly elevated group (n = 42) had PCT of > 1.0 ng/mL. Thirty-five cases received standard care after admission. Serum PCT levels and renal function were determined during a two-week follow-up. Results: Significant increases in serum creatinine (Cr) were recorded in male and female patients in the elevated group and highly elevated group compared with the normal group (P < 0.05). In addition, serum Cr levels in male and female patients were significantly higher in the highly elevated group than in the elevated group (P < 0.05). The glomerular filtration rate (GFR) was significantly lower in the highly elevated group (P < 0.05) and this group had the highest risk of altered Cr (45.9% in males; 80% in females) and abnormal GFR (37.5%). Serum PCT levels correlated significantly with all renal function parameters including homocysteine (Hcy), GFR, Cr, blood urea nitrogen, uric acid, and cystatin C at baseline and during treatment. Univariate and multivariate analyses indicated that serum PCT was a strong predictor of renal dysfunction. Conclusion: Serum PCT is closely related to renal dysfunction in HBV-ACLF

PowerFusion: A Tensor Compiler with Explicit Data Movement Description and Instruction-level Graph IR

Deep neural networks (DNNs) are of critical use in different domains. To accelerate DNN computation, tensor compilers are proposed to generate efficient code on different domain-specific accelerators. Existing tensor compilers mainly focus on optimizing computation efficiency. However, memory access is becoming a key performance bottleneck because the computational performance of accelerators is increasing much faster than memory performance. The lack of direct description of memory access and data dependence in current tensor compilers' intermediate representation (IR) brings significant challenges to generate memory-efficient code. In this paper, we propose IntelliGen, a tensor compiler that can generate high-performance code for memory-intensive operators by considering both computation and data movement optimizations. IntelliGen represent a DNN program using GIR, which includes primitives indicating its computation, data movement, and parallel strategies. This information will be further composed as an instruction-level dataflow graph to perform holistic optimizations by searching different memory access patterns and computation operations, and generating memory-efficient code on different hardware. We evaluate IntelliGen on NVIDIA GPU, AMD GPU, and Cambricon MLU, showing speedup up to 1.97x, 2.93x, and 16.91x(1.28x, 1.23x, and 2.31x on average), respectively, compared to current most performant frameworks.Comment: 12 pages, 14 figure

OLLIE: Derivation-based Tensor Program Optimizer

Boosting the runtime performance of deep neural networks (DNNs) is critical due to their wide adoption in real-world tasks. Existing approaches to optimizing the tensor algebra expression of a DNN only consider expressions representable by a fixed set of predefined operators, missing possible optimization opportunities between general expressions. We propose OLLIE, the first derivation-based tensor program optimizer. OLLIE optimizes tensor programs by leveraging transformations between general tensor algebra expressions, enabling a significantly larger expression search space that includes those supported by prior work as special cases. OLLIE uses a hybrid derivation-based optimizer that effectively combines explorative and guided derivations to quickly discover highly optimized expressions. Evaluation on seven DNNs shows that OLLIE can outperform existing optimizers by up to 2.73$\times$ (1.46$\times$ on average) on an A100 GPU and up to 2.68$\times$ (1.51$\times$) on a V100 GPU, respectively

Construction and comprehensive analysis of a curoptosis-related lncRNA signature for predicting prognosis and immune response in cervical cancer

Cuproptosis (copper-ion-dependent cell death) is an unprogrammed cell death, and intracellular copper accumulation, causing copper homeostasis imbalance and then leading to increased intracellular toxicity, which can affect the rate of cancer cell growth and proliferation. This study aimed to create a newly cuproptosis-related lncRNA signature that can be used to predict survival and immunotherapy in patients with cervical cancer, but also to predict prognosis in patients treated with radiotherapy and may play a role in predicting radiosensitivity. First of all, we found lncRNAs associated with cuproptosis between cervical cancer tumor tissues and normal tissues. By LASSO-Cox analysis, overlapping lncRNAs were then used to construct lncRNA signatures associated with cuproptosis, which can be used to predict the prognosis of patients, especially the prognosis of radiotherapy patients, ROC curves and PCA analysis based on cuprotosis-related lncRNA signature and clinical signatures were developed and demonstrated to have good predictive potential. In addition, differences in immune cell subset infiltration and differences in immune checkpoint expression between high-risk and low-risk score groups were analyzed, and we investigated the relationship between this signature and tumor mutation burden. In summary, we constructed a lncRNA prediction signature associated with cuproptosis. This has important clinical implications, including improving the predictive value of cervical cancer patients and providing a biomarker for cervical cancer

Synthesis and biological activities of novel danshensu amide derivatives as anti-myocardial ischemia agents

A series of novel danshensu amide derivatives were synthesized, and the protective effects of all the compounds on rat myocardial cell lines H9C2 by hypoxia were investigated. The results showed that all the seven compounds could significantly increased cell viability compared with hypoxia group. Among these compounds, 3-(3,4-dimethoxyphenyl)-2-hydroxy-N-propylpropanamide (6) exhibited good activities, with cell viability reached 94.2 % compared to the normal. The novel danshensu amide derivatives, possessing an additional lipophilic alkyl chain showed a good lipophilicity.Colegio de Farmacéuticos de la Provincia de Buenos Aire