On Guaranteed Minimum Maturity Benefits and First-to-Default Type Problems

A new class of exponential functionals arises when pricing certain equity-linked insurance products.We study the distribution of these exponential functionals using tools from Probability and Complex Analysis. In the case of the Kou process we obtain an explicit formula for the probability density function of the exponential functional and we apply this result to pricing equity-linked insurance products. As a by-product of this research we have also derived a new class of duality relations for hypergeometric functions. In the second part of the thesis, we study correlation uncertainty in Credit Risk. The goal is to price analogues of first-to-default options under the assumption that the assets follow correlated stochastic processes with known marginal distributions and unknown dependence structure. We solve this problem using tools from Stochastic Analysis and Optimal Control Theory. We provide explicit solutions in some specific examples and numerical approximations in the more general case

CIRNN: An Ultra-Wideband Non-Line-of-Sight Signal Classifier Based on Deep-Learning

Non-line-of-sight (NLOS) error is the main factor that reduces indoor positioning accuracy. Identifying NLOS signals and eliminating NLOS errors are the keys to improving indoor positioning accuracy. To better identify NLOS signals, a multi-stream model channel-impulse-response-neural-network (CIRNN) was proposed. The inputs of CIRNN include the channel impulse response (CIR) and a small number of channel parameters. To make a more obvious comparison between NLOS signals and line-of-sight (LOS) signals, a new energy normalization method is proposed. Fusing multi-dimensional features, the CIRNN network has a good convergence performance and shows stronger sensitivity to NLOS signals. Experimental results show that the CIRNN achieves the best accuracy on the open-source data set, the F1 score is 89.3%. At the same time, the working efficiency of CIRNN meets industry needs, CIRNN can refresh the target position at about 92.6 Hz per second

Application of geostatistical analyst methods in discovering concealed gold and pathfinder elements as geochemical anomalies related to ore mineralisation

The study area in the West Junggar Basin is known to be rich in hydrothermal gold deposits and occurrences, even though there has been minimum exploration in the area. It is here hypothesised that this area could host more gold deposits if mineral exploration methods were to be reinforced. This research is aimed at identifying geochemical anomalies of Au, and determining possible factors and conditions which facilitate the formation of anomalies by referring to As and Hg as gold pathfinders. Geostatistical analyst techniques have been applied to 9,852 stream sediments and bedrock data collected on a total surface of 1,280 km 2 of West Junggar, Xinjiang (northwest China). The kriging interpolation and quantile-quantile plot methods, combined with statistical methods, successfully identified both Au and its pathfinders’ anomalies. In the present study, median was considered as background values (10.2 ppm for As, 9.13 ppb for Hg and 2.5 ppb for Au), whereas the 95 th percentile were threshold values (28.03 ppm for As, 16.71 ppb for Hg and 8.2 ppb for Au) and values greater than thresholds are geochemical anomalies. Moreover, the high concentrations of these three discovered elements are caused primarily by hydrothermal ore mineralisation and are found to be controlled mainly by the Hatu and Sartohay faults of a northeast-southwesterly direction as well as their related secondary faults of variable orientation, which facilitate the easy flow of hydrothermal fluids towards the surface resulting in the formation of geochemical anomalies. Most of anomalies concentration of Au are found near the mining sites, which indicates that the formation of new Au anomalies is influenced by current or previous mining sites through geological or weathering processes. In addition, the low concentration of gold and its pathfinders found far from active gold mine or faults indicates that those anomalies are formed due to primary dispersion of hosting rock

UWB-INS Fusion Positioning Based on a Two-Stage Optimization Algorithm

Ultra-wideband (UWB) is a carrier-less communication technology that transmits data using narrow pulses of non-sine waves on the nanosecond scale. The UWB positioning system uses the multi-lateral positioning algorithm to accurately locate the target, and the positioning accuracy is seriously affected by the non-line-of-sight (NLOS) error. The existing non-line-of-sight error compensation methods lack multidimensional consideration. To combine the advantages of various methods, a two-stage UWB-INS fusion localization algorithm is proposed. In the first stage, an NLOS signal filter is designed based on support vector machines (SVM). In the second stage, the results of UWB and Inertial Navigation System (INS) are fused based on Kalman filter algorithm. The two-stage fusion localization algorithm achieves a great improvement on positioning system, it can improve the localization accuracy by 79.8% in the NLOS environment and by 36% in the (line-of-sight) LOS environment

SimiSketch: Efficiently Estimating Similarity of streaming Multisets

The challenge of estimating similarity between sets has been a significant concern in data science, finding diverse applications across various domains. However, previous approaches, such as MinHash, have predominantly centered around hashing techniques, which are well-suited for sets but less naturally adaptable to multisets, a common occurrence in scenarios like network streams and text data. Moreover, with the increasing prevalence of data arriving in streaming patterns, many existing methods struggle to handle cases where set items are presented in a continuous stream. Consequently, our focus in this paper is on the challenging scenario of multisets with item streams. To address this, we propose SimiSketch, a sketching algorithm designed to tackle this specific problem. The paper begins by presenting two simpler versions that employ intuitive sketches for similarity estimation. Subsequently, we formally introduce SimiSketch and leverage SALSA to enhance accuracy. To validate our algorithms, we conduct extensive testing on synthetic datasets, real-world network traffic, and text articles. Our experiment shows that compared with the state-of-the-art, SimiSketch can improve the accuracy by up to 42 times, and increase the throughput by up to 360 times. The complete source code is open-sourced and available on GitHub for reference

Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms

The main purpose of blasting operation is to produce desired and optimum mean size rock fragments. Smaller or fine fragments cause the loss of ore during loading and transportation, whereas large or coarser fragments need to be further processed, which enhances production cost. Therefore, accurate prediction of rock fragmentation is crucial in blasting operations. Mean fragment size (MFS) is a crucial index that measures the goodness of blasting designs. Over the past decades, various models have been proposed to evaluate and predict blasting fragmentation. Among these models, artificial intelligence (AI)-based models are becoming more popular due to their outstanding prediction results for multi-influential factors. In this study, support vector regression (SVR) techniques are adopted as the basic prediction tools, and five types of optimization algorithms, i.e. grid search (GS), grey wolf optimization (GWO), particle swarm optimization (PSO), genetic algorithm (GA) and salp swarm algorithm (SSA), are implemented to improve the prediction performance and optimize the hyper-parameters. The prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI techniques. Among all the models, the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting operation. Three types of mathematical indices, i.e. mean square error (MSE), coefficient of determination (R2) and variance accounted for (VAF), are utilized for evaluating the performance of different prediction models. The R2, MSE and VAF values for the training set are 0.8355, 0.00138 and 80.98, respectively, whereas 0.8353, 0.00348 and 82.41, respectively for the testing set. Finally, sensitivity analysis is performed to understand the influence of input parameters on MFS. It shows that the most sensitive factor in blasting MFS is the uniaxial compressive strength. © 2021 Institute of Rock and Soil Mechanics, Chinese Academy of Science

Robust mmWave Beamforming by Self-Supervised Hybrid Deep Learning

Beamforming with large-scale antenna arrays has been widely used in recent years, which is acknowledged as an important part in 5G and incoming 6G. Thus, various techniques are leveraged to improve its performance, e.g., deep learning, advanced optimization algorithms, etc. Although its performance in many previous research scenarios with deep learning is quite attractive, usually it drops rapidly when the environment or dataset is changed. Therefore, designing effective beamforming network with strong robustness is an open issue for the intelligent wireless communications. In this paper, we propose a robust beamforming self-supervised network, and verify it in two kinds of different datasets with various scenarios. Simulation results show that the proposed self-supervised network with hybrid learning performs well in both classic DeepMIMO and new WAIR-D dataset with the strong robustness under the various environments. Also, we present the principle to explain the rationality of this kind of hybrid learning, which is instructive to apply with more kinds of datasets

A miR-137-XIAP axis contributes to the sensitivity of TRAIL-induced cell death in glioblastoma

Glioblastoma (GBM) is the most lethal primary brain tumor in the central nervous system with limited therapeutic strategies to prolong the survival rate in clinic. TNF-related apoptosis-inducing ligand (TRAIL)-based strategy has been demonstrated to induce cell death in an extensive spectrum of tumor cells, including GBM, while a considerable proportion of malignant cells are resistant to TRAIL-induced apoptosis. MiR-137 is highly expressed in the brain, but significantly decreases with advanced progression of GBM. However, the functional link between miR-137 and TRAIL-induced apoptosis in GBM cells has not been established. Here, GBM cells were transfected with miR-137, and gene expression levels were examined by qRT-PCR and western blot. Apoptotic cells were measured by Annexin-V staining and TUNEL assay. Our data showed that miR-137 sensitizes GBM cells to the TRAIL-mediated apoptosis. Mechanistically, we identified that XIAP is a bona fide target of miR-137, which is essential for miR-137-regulated sensitivity of TRAIL-induced cell death in GBM cells. Finally, in a xenograft model, combined utilization of miR-137 and TRAIL potently suppresses tumor growth in vivo. Collectively, we demonstrate that a miR-137-XIAP axis is required for the sensitivity of TRAIL-induced cell death and shed a light on the avenue for the treatment of GBM

Identification of a major QTL and candidate genes analysis for branch angle in rapeseed (Brassica napus L.) using QTL-seq and RNA-seq

IntroductionBranching angle is an essential trait in determining the planting density of rapeseed (Brassica napus L.) and hence the yield per unit area. However, the mechanism of branching angle formation in rapeseed is not well understood.MethodsIn this study, two rapeseed germplasm with extreme branching angles were used to construct an F2 segregating population; then bulked segregant analysis sequencing (BSA-seq) and quantitative trait loci (QTL) mapping were utilized to localize branching anglerelated loci and combined with transcriptome sequencing (RNA-seq) and quantitative real-time PCR (qPCR) for candidate gene miningResults and discussionA branching angle-associated quantitative trait loci (QTL) was mapped on chromosome C3 (C3: 1.54-2.65 Mb) by combining BSA-seq as well as traditional QTL mapping. A total of 54 genes had SNP/Indel variants within the QTL interval were identified. Further, RNA-seq of the two parents revealed that 12 of the 54 genes were differentially expressed between the two parents. Finally, we further validated the differentially expressed genes using qPCR and found that six of them presented consistent differential expression in all small branching angle samples and large branching angles, and thus were considered as candidate genes related to branching angles in rapeseed. Our results introduce new candidate genes for the regulation of branching angle formation in rapeseed, and provide an important reference for the subsequent exploration of its formation mechanism