Determinant-based Fast Greedy Sensor Selection Algorithm. (arXiv:1911.08757v1 [eess.SP])

In this study, the sparse sensor placement problem for the least square estimation is considered. First, the objective function of the problem is redefined to be the maximization of the determinant of the matrix appearing in pseudo inverse matrix operations, leading to the maximization of the corresponding confidence intervals. The procedure for the maximization of…

Reinforcement Learning for a Cellular Internet of UAVs: Protocol Design, Trajectory Control, and Resource Management. (arXiv:1911.08771v1 [eess.SP])

Unmanned aerial vehicles (UAVs) can be powerful Internet-of-Things components to execute sensing tasks over the next-generation cellular networks, which are generally referred to as the cellular Internet of UAVs. However, due to the high mobility of UAVs and the shadowing in the air-to-ground channels, UAVs operate in an environment with dynamics and uncertainties. Therefore, UAVs…

A Framework for End-to-End Deep Learning-Based Anomaly Detection in Transportation Networks. (arXiv:1911.08793v1 [cs.LG])

We develop an end-to-end deep learning-based anomaly detection model for temporal data in transportation networks. The proposed EVT-LSTM model is derived from the popular LSTM (Long Short-Term Memory) network and adopts an objective function that is based on fundamental results from EVT (Extreme Value Theory). We compare the EVT-LSTM model with some established statistical, machine…

Segmentation of Defective Skulls from CT Data for Tissue Modelling. (arXiv:1911.08805v1 [eess.IV])

In this work we present a method of automatic segmentation of defective skulls for custom cranial implant design and 3D printing purposes. Since such tissue models are usually required in patient cases with complex anatomical defects and variety of external objects present in the acquired data, most deep learning-based approaches fall short because it is…

Online Power Allocation at Energy Harvesting Transmitter for Multiple Receivers with and without Individual Rate Constraints for OMA and NOMA Transmissions. (arXiv:1911.08839v1 [cs.IT])

In this paper, we propose an online power allocation scheme to maximize the time averaged sum rate for multiple downlink receivers with energy harvesting transmitter. The transmitter employs non-orthogonal multiple access (NOMA) and/or orthogonal multiple access (OMA) to transmit data to multiple users. Additionally, we consider the scenario where each individual user has a quality…

Model-based optimal AML consolidation treatment. (arXiv:1911.08980v1 [q-bio.QM])

Neutropenia is an adverse event commonly arising during intensive chemotherapy of acute myeloid leukemia (AML). It is often associated with infectious complications. Mathematical modeling, simulation, and optimization of the treatment process would be a valuable tool to support clinical decision making, potentially resulting in less severe side effects and deeper remissions. However, until now, there…

Deep Reinforcement Learning in Cryptocurrency Market Making. (arXiv:1911.08647v1 [q-fin.TR])

This paper sets forth a framework for deep reinforcement learning as applied to market making (DRLMM) for cryptocurrencies. Two advanced policy gradient-based algorithms were selected as agents to interact with an environment that represents the observation space through limit order book data, and order flow arrival statistics. Within the experiment, a forward-feed neural network is…

Can artificial neural networks supplant the polygene risk score for risk prediction of complex disorders given very large sample sizes?. (arXiv:1911.08996v1 [q-bio.GN])

Genome-wide association studies (GWAS) provide a means of examining the common genetic variation underlying a range of traits and disorders. In addition, it is hoped that GWAS may provide a means of differentiating affected from unaffected individuals. This has potential applications in the area of risk prediction. Current attempts to address this problem focus on…

Competition of noise and collectivity in global cryptocurrency trading: route to a self-contained market. (arXiv:1911.08944v1 [q-fin.ST])

Cross-correlations in fluctuations of the daily exchange rates within the basket of the 100 highest-capitalization cryptocurrencies over the period October 1, 2015, through March 31, 2019, are studied. The corresponding dynamics predominantly involve one leading eigenvalue of the correlation matrix, while the others largely coincide with those of Wishart random matrices. However, the magnitude of…

Learning Embeddings from Cancer Mutation Sets for Classification Tasks. (arXiv:1911.09008v1 [eess.IV])

Analysis of somatic mutation profiles from cancer patients is essential in the development of cancer research. However, the low frequency of most mutations and the varying rates of mutations across patients makes the data extremely challenging to statistically analyze as well as difficult to use in classification problems, for clustering, visualization or for learning useful…