Multiscale Partial Correlation Clustering of Stock Market Returns

This study proposes a wavelet procedure for estimating partial correlation coefficients between stock market returns over different time scales. The estimated partial correlations are subsequently used in a cluster analysis to identify, for each time scale, groups of stocks that exhibit distinct market movement characteristics and are therefore useful for portfolio diversification. The proposed procedure is demonstrated using all the major S&P 500 sector indices as well as precious metals and energy sector futures returns during the last decade. The results suggest cluster formations that vary by time scale, which entails different stock selection strategies for investors differing in terms of their investment horizon orientation.

Approximation Algorithms for the Capacitated Min–Max Correlation Clustering Problem

In this paper, we propose a so-called capacitated min–max correlation clustering model, a natural variant of the min–max correlation clustering problem. As our main contribution, we present an integer programming and its integrality gap analysis for the proposed model. Furthermore, we provide two approximation algorithms for the model, one of which is a bi-criteria approximation algorithm and the other is based on LP-rounding technique.

Three Layered Architecture for Driver Behavior Analysis and Personalized Assistance with Alert Message Dissemination in 5G Envisioned Fog-IoCV

The Internet of connected vehicles (IoCV) has made people more comfortable and safer while driving vehicles. This technology has made it possible to reduce road casualties; however, increased traffic and uncertainties in environments seem to be limitations to improving the safety of environments. In this paper, driver behavior is analyzed to provide personalized assistance and to alert surrounding vehicles in case of emergencies. The processes involved in this research are as follows. (i) Initially, the vehicles in an environment are clustered to reduce the complexity in analyzing a large number of vehicles. Multi-criterion-based hierarchical correlation clustering (MCB-HCC) is performed to dynamically cluster vehicles. Vehicular motion is detected by edge-assisted road side units (E-RSUs) by using an attention-based residual neural network (AttResNet). (ii) Driver behavior is analyzed based on the physiological parameters of drivers, vehicle on-board parameters, and environmental parameters, and driver behavior is classified into different classes by implementing a refined asynchronous advantage actor critic (RA3C) algorithm for assistance generation. (iii) If the driver’s current state is found to be an emergency state, an alert message is disseminated to the surrounding vehicles in that area and to the neighboring areas based on traffic flow by using jelly fish search optimization (JSO). If a neighboring area does not have a fog node, a virtual fog node is deployed by executing a constraint-based quantum entropy function to disseminate alert messages at ultra-low latency. (iv) Personalized assistance is provided to the driver based on behavior analysis to assist the driver by using a multi-attribute utility model, thereby preventing road accidents. The proposed driver behavior analysis and personalized assistance model are experimented on with the Network Simulator 3.26 tool, and performance was evaluated in terms of prediction error, number of alerts, number of risk maneuvers, accuracy, latency, energy consumption, false alarm rate, safety score, and alert-message dissemination efficiency.

Big Data Analysis and Research on Fracturing Construction Parameters of Shale Gas Horizontal Wells—A Case Study of Horizontal Wells in Fuling Demonstration Area, China

With the rapid development of computer science and technology, the Chinese petroleum industry has ushered in the era of big data. In this study, by collecting fracturing data from 303 horizontal wells in the Fuling Shale Gas Demonstration Area in China, a series of big data analysis studies was conducted using Pearson’s correlation coefficient, the unweighted pair group with arithmetic means method, and the graphical plate method to determine which is best. The fracturing parameters were determined through a series of big data analysis studies. The big data analysis process is divided into three main steps. The first is data preprocessing to screen out eligible, high-yielding wells. The second is a fracturing parameter correlation clustering analysis to determine the reasonableness of the parameters. The third is a big data panel method analysis of specific fracturing construction parameters to determine the optimal parameter range. The analyses revealed that the current amount of 100 mesh sand in the Fuling area is unreasonable; further, there are different preferred areas for different fracturing construction parameters. We have combined different fracturing parameter schemes by preferring areas. This analysis process is expected to provide new ideas regarding fracturing scheme design for engineers working on the frontline.

CLRM-13. INTRAOPERATIVE MICRODIALYSIS: GLIOMA INTELLIGENCE FROM BEHIND ENEMY LINES

INTRODUCTION Gliomas present a formidable challenge for translational progress. Heterogeneity within and between tumors may demand empirically individualized and adaptive paradigms requiring rapid mechanistic feedback. We asked if tumor-associated metabolic biomarkers from glioma extracellular fluid could impart mechanistic “intelligence” reflecting intra- and inter-tumoral heterogeneity. METHODS Five live human gliomas (2 oligos; 2 IDH-WT GBMs; 1 IDH-mutant GBM), were evaluated in situ with high molecular weight (100kDA) intraoperative microdialysis using 3 disparately placed catheters. Isotonic 3% dextran perfusate was collected in 20 min (40mL) aliquots. CSF samples (n=21) were additionally evaluated from these and other patients with diverse brain tumors. The IDH-mutant glioma-associated oncometabolite D2-hydroxyglutarate (D2-HG) was quantified with targeted Liquid Chromotography-Mass Spectrometry (LC-MS). Over 200 metabolites were further evaluated via untargeted LC-MS using the Metabolon platform. Correlation, clustering, ROC and enrichment analyses were employed to identify correlations within and between patient samples. RESULTS CSF samples from patients with IDH-mutant gliomas contained over twenty-fold higher levels of D2-HG (median 4.1 mM, range 1.6-13.2, n=7) compared to those from IDH-wild type tumors (median 0.19 mM; range 0.89-0.35, n=14). Microdialysate from IDH-mutant gliomas contained 10-953mM D2-HG, 9-63x higher than paired CSF samples. Interestingly, IDH status failed to predict the global metabolic signature of microdialysate. Microdialysate samples clustered into 2 major metabolic phenotype clusters with IDH-WT and IDH-mutant gliomas in each cluster. A superimposed metabolic signature distinguishing enhancing from non-enhancing tumor, was conserved in both patient clusters. Amino acid and carnitine metabolism predominated in microdialysate signatures. TCA cycle and Warburg-associated metabolites were differentially enriched in CSF samples after prior therapy independent of tumor burden. CONCLUSIONS Intra-operative micro-dialysis may complement currently available “intelligence” regarding the phenotype, burden, and metabolism of live human gliomas and is feasible within standard-of-care surgical procedures. Future work will evaluate utility for pharmacodynamic feedback following novel early phase candidate therapies.

Individualized diversity in the extracellular metabolome of live human gliomas

Gliomas present a formidable challenge for translational progress. Heterogeneity within and between tumors may demand empirically individualized insights, though relatively little is known about the biochemical milieu within which malignant cells thrive in the in vivo human glioma. We performed a pilot study of intraoperative high molecular weight microdialysis to sample the extracellular tumor environment within three locations in each of five molecularly diverse human gliomas spanning WHO grade 2 oligodendroglioma to WHO grade 4 glioblastoma (GBM). Microdialysates were subjected to targeted (D/L-2-hydroxyglutarate (2-HG)) and untargeted metabolomic analyses, enabling correlation, clustering, fold change, and enrichment analyses. IDH-mutant tumor microdialysate contained markedly higher levels of D2-HG than IDH-wild type tumors. However, IDH status was not predictive of the global metabolomic signature. Rather, two distinct metabolic phenotypes (α and β) emerged, with IDH-WT and IDH-mutant patient samples in each group. Individualized metabolic signatures of enhancing tumor versus adjacent brain were conserved across patients with glioblastoma regardless of metabolic phenotype. Untargeted metabolomic analysis additionally enabled correlative quantification of multiple peri-operatively administered drugs, illustrating regional heterogeneity of blood-brain barrier permeability. As such, acute intraoperative microdialysis affords a previously unharnessed window into individualized heterogeneous microenvironments within and between live human gliomas. Such access to the interstitial milieu of live human gliomas may provide a complementary tool for the development of individualized glioma therapies.

Productivity Enhancement of Cucumber (Cucumis sativus L.) through Optimized Use of Poultry Manure and Mineral Fertilizers under Greenhouse Cultivation

Cucumber, a widely cultivated vegetable, is mostly grown under greenhouse conditions. In recent years, the overuse of inorganic fertilizers for higher yield attainment adversely has affected human health and the environment. Therefore, a greenhouse experiment was designed to evaluate the effects of different nutrient sources (poultry manure (PM) and mineral fertilizer (MF)) on productivity-enhancing parameters of cucumber via univariate and multivariate analyses. Amounts of PM and MF (NPK15:15:15) were added to coco-peat per cubic meter by weight/volume (w/v) ratios as follows: T1 (control), 60 kg PM; T2, 30 kg PM + 3 kg MF; T3, 30 kg PM + 5 kg MF, and T4, 30 kg PM + 7 kg MF. The univariate analysis performed on the collected data illustrated the significant enhancement in growth and productivity for the integrated use of PM and MF. Multivariate analyses (correlation, clustering, and Principal Component Analysis) validated the results of univariate analysis by differentiating treatments into two groups. The three treatments obtained a distinguished group from T1 (Control) and did not show significant differences among each other, with a maximum yield increase by T2 (74.6%). According to these results, T2 could improve cucumber productivity under greenhouse conditions. It can be taken as recommendations for better quality and yield enhancement in future improvement programs and cucumber-related farming communities.

Scalable community detection via parallel correlation clustering

Graph clustering and community detection are central problems in modern data mining. The increasing need for analyzing billion-scale data calls for faster and more scalable algorithms for these problems. There are certain trade-offs between the quality and speed of such clustering algorithms. In this paper, we design scalable algorithms that achieve high quality when evaluated based on ground truth. We develop a generalized sequential and shared-memory parallel framework based on the LAMBDACC objective (introduced by Veldt et al.), which encompasses modularity and correlation clustering. Our framework consists of highly-optimized implementations that scale to large data sets of billions of edges and that obtain high-quality clusters compared to ground-truth data, on both unweighted and weighted graphs. Our empirical evaluation shows that this framework improves the state-of-the-art trade-offs between speed and quality of scalable community detection. For example, on a 30-core machine with two-way hyper-threading, our implementations achieve orders of magnitude speedups over other correlation clustering baselines, and up to 28.44× speedups over our own sequential baselines while maintaining or improving quality.