Quickest Sequence Phase Detection

A phase detection sequence is a length-$n$ cyclic sequence, such that the location of any length-$k$ contiguous subsequence can be determined from a noisy observation of that subsequence. In this paper, we derive bounds on the minimal possible $k$ in the limit of $n\to\infty$, and describe some sequence constructions. We further consider multiple phase detection sequences, where the location of any length-$k$ contiguous subsequence of each sequence can be determined simultaneously from a noisy mixture of those subsequences. We study the optimal trade-offs between the lengths of the sequences, and describe some sequence constructions. We compare these phase detection problems to their natural channel coding counterparts, and show a strict separation between the fundamental limits in the multiple sequence case. Both adversarial and probabilistic noise models are addressed.Comment: To appear in the IEEE Transactions on Information Theor

Community-Aware Efficient Graph Contrastive Learning via Personalized Self-Training

In recent years, graph contrastive learning (GCL) has emerged as one of the optimal solutions for various supervised tasks at the node level. However, for unsupervised and structure-related tasks such as community detection, current GCL algorithms face difficulties in acquiring the necessary community-level information, resulting in poor performance. In addition, general contrastive learning algorithms improve the performance of downstream tasks by increasing the number of negative samples, which leads to severe class collision and unfairness of community detection. To address above issues, we propose a novel Community-aware Efficient Graph Contrastive Learning Framework (CEGCL) to jointly learn community partition and node representations in an end-to-end manner. Specifically, we first design a personalized self-training (PeST) strategy for unsupervised scenarios, which enables our model to capture precise community-level personalized information in a graph. With the benefit of the PeST, we alleviate class collision and unfairness without sacrificing the overall model performance. Furthermore, the aligned graph clustering (AlGC) is employed to obtain the community partition. In this module, we align the clustering space of our downstream task with that in PeST to achieve more consistent node embeddings. Finally, we demonstrate the effectiveness of our model for community detection both theoretically and experimentally. Extensive experimental results also show that our CEGCL exhibits state-of-the-art performance on three benchmark datasets with different scales.Comment: 12 pages, 7 figure

Calculation of Carbon Sink of Bamboo Forest in Zhejiang Province and Its Value Realization Path

The biomass method was applied to measure the carbon sequestration status of bamboo forests in Zhejiang province, and the carbon emissions of Zhejiang province from 1989 to 2018 were estimated by using the energy activity CO2 emission measurement method, and the carbon sink contribution of bamboo forests was analyzed by comparison. The results show that the total carbon sequestration in bamboo forests increased from 18,206,400 t to 34,604,000 t during the ninth national forest inventory, with a net increase of 16,397,600 t and a growth rate of 90.07%, showing an overall increasing trend, among which moso bamboo forests are the main carbon sequestration species; the carbon emissions in Zhejiang province showed a stable growth trend, but the growth rate has decreased in recent periods; the amount of carbon sequestered by bamboo forests and carbon emissions show a convergent growth trend, but the amount of carbon sequestered by bamboo forests is relatively small for the overall carbon emissions, and the contribution of carbon sequestration is small. In order to effectively contribute to the process of carbon peaking and carbon neutrality in Zhejiang province, corresponding measures should be taken to effectively play the function of bamboo forest carbon sink and realize its value

Comparison of outflow boundary conditions for subsonic aeroacoustic simulations

Aeroacoustics simulations require much more precise boundary conditions than classical aerodynamics. Two classes of non-reflecting boundary conditions for aeroacoustics are compared in the present work: characteristic analysis based methods and Tam and Dong approach. In characteristic methods, waves are identified and manipulated at the boundaries while Tam and Dong use modified linearized Euler equations in a buffer zone near outlets to mimic a non-reflecting boundary. The principles of both approaches are recalled and recent characteristic methods incorporating the treatment of transverse terms are discussed. Three characteristic techniques (the original NSCBC formulation of Poinsot and Lele and two versions of the modified method of Yoo and Im) are compared to the Tam and Dong method for four typical aeroacoustics problems: vortex convection on a uniform flow, vortex convection on a shear flow, acoustic propagation from a monopole and from a dipole. Results demonstrate that the Tam and Dong method generally provides the best results and is a serious alternative solution to characteristic methods even though its implementation might require more care than usual NSCBC approaches

An absorbing boundary formulation for the stratified, linearized, ideal MHD equations based on an unsplit, convolutional perfectly matched layer

Perfectly matched layers are a very efficient and accurate way to absorb waves in media. We present a stable convolutional unsplit perfectly matched formulation designed for the linearized stratified Euler equations. However, the technique as applied to the Magneto-hydrodynamic (MHD) equations requires the use of a sponge, which, despite placing the perfectly matched status in question, is still highly efficient at absorbing outgoing waves. We study solutions of the equations in the backdrop of models of linearized wave propagation in the Sun. We test the numerical stability of the schemes by integrating the equations over a large number of wave periods.Comment: 8 pages, 7 figures, accepted, A &

Prediction of Pharmacological and Xenobiotic Responses to Drugs Based on Time Course Gene Expression Profiles

More and more people are concerned by the risk of unexpected side effects observed in the later steps of the development of new drugs, either in late clinical development or after marketing approval. In order to reduce the risk of the side effects, it is important to look out for the possible xenobiotic responses at an early stage. We attempt such an effort through a prediction by assuming that similarities in microarray profiles indicate shared mechanisms of action and/or toxicological responses among the chemicals being compared. A large time course microarray database derived from livers of compound-treated rats with thirty-four distinct pharmacological and toxicological responses were studied. The mRMR (Minimum-Redundancy-Maximum-Relevance) method and IFS (Incremental Feature Selection) were used to select a compact feature set (141 features) for the reduction of feature dimension and improvement of prediction performance. With these 141 features, the Leave-one-out cross-validation prediction accuracy of first order response using NNA (Nearest Neighbor Algorithm) was 63.9%. Our method can be used for pharmacological and xenobiotic responses prediction of new compounds and accelerate drug development

Maintaining the stability of a leapfrog scheme in the presence of source terms

The instability encountered by applying the upwind leapfrog method to the advection equation having a source term is resolved. The source term is eliminated by transforming the governing equation. Two types of transformation are examined and the method of the space transformation leads to a stable and accurate scheme for the one-dimensional advection equation. The method is also extended to the two-dimensional acoustic equations in polar co-ordinates. Copyright © 2003 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/34813/1/549_ftp.pd

Altered dynamic functional network connectivity in drug-naïve Parkinson’s disease patients with excessive daytime sleepiness

BackgroundExcessive daytime sleepiness (EDS) is a frequent nonmotor symptoms of Parkinson’s disease (PD), which seriously affects the quality of life of PD patients and exacerbates other nonmotor symptoms. Previous studies have used static analyses of these resting-state functional magnetic resonance imaging (rs-fMRI) data were measured under the assumption that the intrinsic fluctuations during MRI scans are stationary. However, dynamic functional network connectivity (dFNC) analysis captures time-varying connectivity over short time scales and may reveal complex functional tissues in the brain.PurposeTo identify dynamic functional connectivity characteristics in PD-EDS patients in order to explain the underlying neuropathological mechanisms.MethodsBased on rs-fMRI data from 16 PD patients with EDS and 41 PD patients without EDS, we applied the sliding window approach, k-means clustering and independent component analysis to estimate the inherent dynamic connectivity states associated with EDS in PD patients and investigated the differences between groups. Furthermore, to assess the correlations between the altered temporal properties and the Epworth sleepiness scale (ESS) scores.ResultsWe found four distinct functional connectivity states in PD patients. The patients in the PD-EDS group showed increased fractional time and mean dwell time in state IV, which was characterized by strong connectivity in the sensorimotor (SMN) and visual (VIS) networks, and reduced fractional time in state I, which was characterized by strong positive connectivity intranetwork of the default mode network (DMN) and VIS, while negative connectivity internetwork between the DMN and VIS. Moreover, the ESS scores were positively correlated with fraction time in state IV.ConclusionOur results indicated that the strong connectivity within and between the SMN and VIS was characteristic of EDS in PD patients, which may be a potential marker of pathophysiological features related to EDS in PD patients

LINC01614 is a promising diagnostic and prognostic marker in HNSC linked to the tumor microenvironment and oncogenic function

BackgroundTumor initiation and metastasis influence tumor immune exclusion and immunosuppression. Long non-coding RNA (lncRNA) LINC01614 is associated with the prognosis and metastasis of several cancers. However, the relationship between LINC01614 and cancer immune infiltration and the biofunction of LINC01614 in head and neck squamous cell carcinoma (HNSC) remain unclear.MethodsThe Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) datasets were used to analyze the expression difference and diagnostic value of LINC01614 in normal and tumor tissues. The correlation of pan-cancer prognosis and tumor stage of LINC01614 was analyzed based on the TCGA database. The pan-cancer association of LINC01614 expression with the tumor microenvironment (TME) including immune infiltration, expression of immune-related genes, and genomic instability parameters, was analyzed using the Spearman correlation method. The correlation between LINC01614 and tumor stemness evaluation indicators, RNA methylation-related genes, and drug resistance was also analyzed. The functional analysis of LINC01614 was performed using the clusterProfiler R package. The protein–protein interaction (PPI) network and ceRNA network of LINC01614 co-expressed genes and miRNA were constructed and visualized by STRING and Cytoscape, respectively. Finally, the cell location and influence of LINC01614 on cell proliferation and metastasis of HNSC cell lines were evaluated using FISH, CCK-8, wound-healing assay, and transwell assay.ResultsLINC01614 was found to be overexpressed in 23 cancers and showed a highly sensitive prediction value in nine cancers (AUC >0.85). LINC01614 dysregulation was associated with tumor stage in 12 cancers and significantly influenced the survival outcomes of 26 cancer types, with only Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (DLBC), uterine corpus endometrial carcinoma (UCEC), and bladder urothelial carcinoma (BLCA) showing a benign influence. LINC01614 was also associated with immune cell infiltration, tumor heterogeneity, cancer stemness, RNA methylation modification, and drug resistance. The potential biological function of LINC01614 was verified in HNSC, and it was found to play important roles in proliferation, immune infiltration, immunotherapy response, and metastasis of HNSC.ConclusionLINC01614 may serve as a cancer diagnosis and prognosis biomarker and an immunotherapy target for specific cancers