pwc-archive/papers-with-abstracts huggingface dataset:
GPS Multipath Detection Based on Carrier-to-Noise-Density Ratio Measurements from a Dual-Polarized Antenna
Authors: Sanghyun Kim, Halim Lee, Kwansik Park
Date: 2021-08-21 00:00:00

In this study, the global positioning system (GPS) multipath detection was performed based on the carrier-to-noise-density ratio, C/N0, measured through a dual-polarized antenna. As the right hand circular polarization (RHCP) antenna is sensitive to the signals directly received from the GPS, and the left hand circular polarization (LHCP) antenna is sensitive to the singly reflected signals, the C/N0 difference between the RHCP and LHCP measurements is used for multipath detection. Once we collected the GPS signals in a low multipath location, we calculated the C/N0 difference to obtain a threshold value that can be used to detect the multipath GPS signal received from another location. The results were validated through a ray-tracing simulation.
Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learning
Authors: Sayan Ghosh, Govinda A. Padmanabha, Cheng Peng, Steven Atkinson, Valeria Andreoli, Piyush Pandita, Thomas Vandeputte, Nicholas Zabaras, Liping Wang
Date: 2021-08-17 00:00:00
Tasks: BIG-bench Machine Learning

One of the critical components in Industrial Gas Turbines (IGT) is the turbine blade. Design of turbine blades needs to consider multiple aspects like aerodynamic efficiency, durability, safety and manufacturing, which make the design process sequential and iterative.The sequential nature of these iterations forces a long design cycle time, ranging from several months to years. Due to the reactionary nature of these iterations, little effort has been made to accumulate data in a manner that allows for deep exploration and understanding of the total design space. This is exemplified in the process of designing the individual components of the IGT resulting in a potential unrealized efficiency. To overcome the aforementioned challenges, we demonstrate a probabilistic inverse design machine learning framework (PMI), to carry out an explicit inverse design. PMI calculates the design explicitly without excessive costly iteration and overcomes the challenges associated with ill-posed inverse problems. In this work, the framework will be demonstrated on inverse aerodynamic design of three-dimensional turbine blades.
Study of Proximal Normalized Subband Adaptive Algorithm for Acoustic Echo Cancellation
Authors: Gang Guo, Yi Yu, Rodrigo C. de Lamare, Zongsheng Zheng, Lu Lu, Qiangming Cai
Date: 2021-08-14 00:00:00
Tasks: Acoustic echo cancellation

In this paper, we propose a novel normalized subband adaptive filter algorithm suited for sparse scenarios, which combines the proportionate and sparsity-aware mechanisms. The proposed algorithm is derived based on the proximal forward-backward splitting and the soft-thresholding methods. We analyze the mean and mean square behaviors of the algorithm, which is supported by simulations. In addition, an adaptive approach for the choice of the thresholding parameter in the proximal step is also proposed based on the minimization of the mean square deviation. Simulations in the contexts of system identification and acoustic echo cancellation verify the superiority of the proposed algorithm over its counterparts.
Ultralow complexity long short-term memory network for fiber nonlinearity mitigation in coherent optical communication systems
Authors: Hao Ming, Xinyu Chen, Xiansong Fang, Lei Zhang, Chenjia Li, Fan Zhang
Date: 2021-08-12 00:00:00

Fiber Kerr nonlinearity is a fundamental limitation to the achievable capacity of long-distance optical fiber communication. Digital back-propagation (DBP) is a primary methodology to mitigate both linear and nonlinear impairments by solving the inverse-propagating nonlinear Schr\"odinger equation (NLSE), which requires detailed link information. Recently, the paradigms based on neural network (NN) were proposed to mitigate nonlinear transmission impairments in optical communication systems. However, almost all neural network-based equalization schemes yield high computation complexity, which prevents the practical implementation in commercial transmission systems. In this paper, we propose a center-oriented long short-term memory network (Co-LSTM) incorporating a simplified mode with a recycling mechanism in the equalization operation, which can mitigate fiber nonlinearity in coherent optical communication systems with ultralow complexity. To validate the proposed methodology, we carry out an experiment of ten-channel wavelength division multiplexing (WDM) transmission with 64 Gbaud polarization-division-multiplexed 16-ary quadrature amplitude modulation (16-QAM) signals. Co-LSTM and DBP achieve a comparable performance of nonlinear mitigation. However, the complexity of Co-LSTM with a simplified mode is almost independent of the transmission distance, which is much lower than that of the DBP. The proposed Co-LSTM methodology presents an attractive approach for low complexity nonlinearity mitigation with neural networks.
Performance Analysis of Mixed RF/FSO Relaying under HPA Nonlinearity and IQ Imbalance
Authors: Elyes Balti
Date: 2021-08-11 00:00:00

In this paper, we present the performance analysis of asymmetric dual-hop RF/FSO system with multiple relays. The RF channels follow the correlated Rayleigh fading while the optical links are subject to the Gamma-Gamma fading. To select the candidate relay to forward the communication, we assume Partial Relay Selection (PRS) with outdated Channel State Information (CSI). Unlike the vast majority of work in this area, we introduce the impairments to the relays and the destination. We will propose three impairment models called Soft Envelope Limiter (SEL), Traveling Wave Tube Amplifier (TWTA) and IQ Imbalance in order to compare the resilience of our system with the RF one against the hardware impairments. Closed-from of the outage probability (OP) is derived in terms of Meijer's G function as well as the upper bound of the ergodic capacity (EC). The Bit Error Rate (BER) and the exact EC are evaluated numerically. Finally, analytical and numerical results are presented and validated by Monte Carlo simulation.
Anomaly Detection Based on Generalized Gaussian Distribution approach for Ultra-Wideband (UWB) Indoor Positioning System
Authors: Fuhu Che, Qasim Zeeshan Ahmed, Faheem A. Khan, Pavlos I. Lazaridis
Date: 2021-08-09 00:00:00
Tasks: Anomaly Detection

With the rapid development of the Internet of Things (IoT), Indoor Positioning System (IPS) has attracted significant interest in academic research. Ultra-Wideband (UWB) is an emerging technology that can be employed for IPS as it offers centimetre-level accuracy. However, the UWB system still faces several technical challenges in practice, one of which is Non-Line-of-Sight (NLoS) signal propagation. Several machine learning approaches have been applied for the NLoS component identification. However, when the data contains a very small amount of NLoS components it becomes very difficult for existing algorithms to classify them. This paper focuses on employing an anomaly detection approach based on Gaussian Distribution (GD) and Generalized Gaussian Distribution (GGD) algorithms to detect and identify the NLoS components. The simulation results indicate that the proposed approach can provide a robust NLoS component identification which improves the NLoS signal classification accuracy which results in significant improvement in the UWB positioning system.
Power transformer faults diagnosis using undestructive methods (Roger and IEC) and artificial neural network for dissolved gas analysis applied on the functional transformer in the Algerian north-eastern: a comparative study
Authors: Bouchaoui Lahcene, Kamel Eddine Hemsas, Hacene Mellah, saad eddine benlahneche
Date: 2021-08-02 00:00:00
Tasks: Asset Management, Management

Nowadays, power transformer aging and failures are viewed with great attention in power transmission industry. Dissolved gas analysis (DGA) is classified among the biggest widely used methods used within the context of asset management policy to detect the incipient faults in their earlier stage in power transformers. Up to now, several procedures have been employed for the lecture of DGA results. Among these useful means, we find Key Gases, Rogers Ratios, IEC Ratios, the historical technique less used today Doernenburg Ratios, the two types of Duval Pentagons methods, several versions of the Duval Triangles method and Logarithmic Nomograph. Problem. DGA data extracted from different units in service served to verify the ability and reliability of these methods in assessing the state of health of the power transformer. Aim. An improving the quality of diagnostics of electrical power transformer by artificial neural network tools based on two conventional methods in the case of a functional power transformer at S\'etif province in East North of Algeria. Methodology. Design an inelegant tool for power transformer diagnosis using neural networks based on traditional methods IEC and Rogers, which allows to early detection faults, to increase the reliability, of the entire electrical energy system from transport to consumers and improve a continuity and quality of service. Results. The solution of the problem was carried out by using feed-forward back-propagation neural networks implemented in MATLAB-Simulink environment. Four real power transformers working under different environment and climate conditions such as: desert, humid, cold were taken into account. The practical results of the diagnosis of these power transformers by the DGA are presented. Practical value.....
A Simple Probabilistic Model With Extended Kalman Filter To Predict Multi-leak In Pipelines
Authors: Radhika P, Anu Mol Joy
Date: 2021-07-31 00:00:00

Pipelines for water supply are susceptible to burst-leakage due to fluid pressures of various nature. High pressure heads resulting in circumferential and (or) axial stresses larger than the material yield stress could cause pipe failure. Of equal concern is the local boiling or cavitation effect in regions of fluid pressure dropping below its vapor pressure, which in turn develop air bubbles that get transported through the pipeline, bursting later at remote locations. We initially developed a simple probabilistic model based on Method of Characteristics (MOC) to simulate burst leakage in pipelines, and compared with a pure deterministic hydraulic model. We had not considered cavitation effects for simplicity. The results indicated that the simple probabilistic model was only marginally different in its prediction of the transients on comparison with the latter. In order to determine the position and amount of leakage in the distribution system, the detection method based on simulating hydraulic transients was further evaluated using Extended Kalman Filter (EKF). We found that this non-linear filtering approach on the fluid transient model considerably reduced the number of input parameters required, and it was able to predict leakage rate and burst positions even in a highly noisy environment.
Neuromodulators in food ingredients: insights from network pharmacological evaluation of Ayurvedic herbs
Authors: Neha Choudhary, Vikram Singh
Date: 2021-08-22 00:00:00
Tasks: Drug Discovery, Management

The global burden of neurological diseases, the second leading cause of death after heart dis-eases constitutes one of the major challenges of modern medicine. Ayurveda, the traditional Indian medicinal systemenrooted in the Vedic literature and considered as a schema for the holistic management of health, characterizes various neurological diseases disorders (NDDs) and prescribes several herbs, formulations, and bio-cleansing regimes for their care and cure. In this work, we examined neuro-phytoregulatory potential of 34,472 phytochemicals among 3,038 herbs (including their varieties) mentioned in Ayurveda using network pharmacology approach and found that 45% of these Ayurvedic phytochemicals (APCs) have regulatory associations with 1,643 approved protein targets. Metabolite interconversion enzymes and protein modifying enzymes were found to be the major target classes of APCs against NDDs. The study further suggests that the actions of Ayurvedic herbs in managing NDDs were majorly via regulating signalling processes, like, G-protein signaling, acetylcholine signaling, chemokine signaling pathway and GnRH signaling. A high confidence network specific to 219 pharmaceutically relevant neuro-phytoregulators (NPRs) from 1,197 Ayurvedic herbs against 102 approved protein-targets involved in NDDs was developed and analyzed for gaining mechanistic insights. The key protein targets of NPRs to elicit their neuro-regulatory effect were highlighted as CYP and TRPA, while estradiol and melatonin were identified as the NPRs with high multi-targeting ability. 32 herbs enriched in NPRs were identified that include some of the well-known Ayurvedic neurological recommendations, like, Papaver somniferum, Glycyrrhiza glabra, Citrus aurantium, Cannabis sativa etc. Herbs enriched in NPRs may be used as a chemical source library for drug-discovery against NDDs from systems medicine perspectives.
A roadmap to reverse engineering real-world generalization by combining naturalistic paradigms, deep sampling, and predictive computational models
Authors: Peer Herholz, Eddy Fortier, Mariya Toneva, Nicolas Farrugia, Leila Wehbe, Valentina Borghesani
Date: 2021-08-23 00:00:00

Real-world generalization, e.g., deciding to approach a never-seen-before animal, relies on contextual information as well as previous experiences. Such a seemingly easy behavioral choice requires the interplay of multiple neural mechanisms, from integrative encoding to category-based inference, weighted differently according to the circumstances. Here, we argue that a comprehensive theory of the neuro-cognitive substrates of real-world generalization will greatly benefit from empirical research with three key elements. First, the ecological validity provided by multimodal, naturalistic paradigms. Second, the model stability afforded by deep sampling. Finally, the statistical rigor granted by predictive modeling and computational controls.
Community Detection in Cryptocurrencies with Potential Applications to Portfolio Diversification
Authors: J. Gavin, M. Crane
Date: 2021-08-22 00:00:00
Tasks: Community Detection

In this paper, the cross-correlations of cryptocurrency returns are analysed. The paper examines one years worth of data for 146 cryptocurrencies from the period January 1 2019 to December 31 2019. The cross-correlations of these returns are firstly analysed by comparing eigenvalues and eigenvector components of the cross-correlation matrix C with Random Matrix Theory (RMT) assumptions. Results show that C deviates from these assumptions indicating that C contains genuine information about the correlations between the different cryptocurrencies. From here, Louvain community detection method is applied as a clustering mechanism and 15 community groupings are detected. Finally, PCA is completed on the standardised returns of each of these clusters to create a portfolio of cryptocurrencies for investment. This method selects a portfolio which contains a number of high value coins when compared back against their market ranking in the same year. In the interest of assessing continuity of the initial results, the method is also applied to a smaller dataset of the top 50 cryptocurrencies across three time periods of T = 125 days, which produces similar results. The results obtained in this paper show that these methods could be useful for constructing a portfolio of optimally performing cryptocurrencies.
Effect of Share Capital on Financial Growth of Non-Financial Firms Listed at the Nairobi Securities Exchange
Authors: David Haritone Shikumo
Date: 2021-08-23 00:00:00
Tasks: Descriptive

Purpose: A significant number of the non-financial firms listed at the Nairobi Securities Exchange (NSE) have been experiencing declining financial performance which deters investors from investing in such firms. The lenders are also not willing to lend to such firms. As such, the firms struggle to raise funds for their operations. Prudent financing decisions can lead to financial growth of the firm. The purpose of this study is to assess the effect of Share capital on financial growth of Non-financial firms listed at the Nairobi Securities Exchange. Financial firms were excluded because of their specific sector characteristics and stringent regulatory framework. The study is guided by Market Timing Theory and Theory of Growth of the Firm. Methodology: Explanatory research design was adopted. The target population of the study comprised of 45 non-financial firms listed at NSE for a period of ten years from 2008 to 2017. The study conducted both descriptive statistics analysis and panel data analysis. Findings: The result indicates that, share capital explains 32.73% and 11.62% of variations in financial growth as measure by growth in earnings per share and growth in market capitalization respectively. Share capital positively and significantly influences financial growth as measured by both growth in earnings per share and growth in market capitalization. Implications: The study recommends for the Non-financial firms to utilize equity financing as a way of raising capital for major expansions, asset growth or acquisitions which may require heavy funding. In this way, firms will be assured of improved performance as well as high financial growth. The study also recommends for substantial firm financing through equity. Value: Equity financing is important to any firm, if the proceeds are used to invest in projects which eventually bring growth to the firm.
Fragmentation, Price Formation, and Cross-Impact in Bitcoin Markets
Authors: Jakob Albers, Mihai Cucuringu, Sam Howison, Alexander Y. Shestopaloff
Date: 2021-08-22 00:00:00

In light of micro-scale inefficiencies induced by the high degree of fragmentation of the Bitcoin trading landscape, we utilize a granular data set comprised of orderbook and trades data from the most liquid Bitcoin markets, in order to understand the price formation process at sub-1 second time scales. To achieve this goal, we construct a set of features that encapsulate relevant microstructural information over short lookback windows. These features are subsequently leveraged first to generate a leader-lagger network that quantifies how markets impact one another, and then to train linear models capable of explaining between 10% and 37% of total variation in $500$ms future returns (depending on which market is the prediction target). The results are then compared with those of various PnL calculations that take trading realities, such as transaction costs, into account. The PnL calculations are based on natural $\textit{taker}$ strategies (meaning they employ market orders) that we associate to each model. Our findings emphasize the role of a market's fee regime in determining its propensity to being a leader or a lagger, as well as the profitability of our taker strategy. Taking our analysis further, we also derive a natural $\textit{maker}$ strategy (i.e., one that uses only passive limit orders), which, due to the difficulties associated with backtesting maker strategies, we test in a real-world live trading experiment, in which we turned over 1.5 million USD in notional volume. Lending additional confidence to our models, and by extension to the features they are based on, the results indicate a significant improvement over a naive benchmark strategy, which we also deploy in a live trading environment with real capital, for the sake of comparison.
Continuous-time Portfolio Optimization for Absolute Return Funds
Authors: Masashi Ieda
Date: 2021-08-23 00:00:00
Tasks: Portfolio Optimization

This paper investigates a continuous-time portfolio optimization problem with the following features: (i) a no-short selling constraint; (ii) a leverage constraint, that is, an upper limit for the sum of portfolio weights; and (iii) a performance criterion based on the lower mean square error between the investor's wealth and a predetermined target wealth level. Since the target level is defined by a deterministic function independent of market indices, it corresponds to the criterion of absolute return funds. The model is formulated using the stochastic control framework with explicit boundary conditions. The corresponding Hamilton-Jacobi-Bellman equation is solved numerically using the kernel-based collocation method. However, a straightforward implementation does not offer a stable and acceptable investment strategy; thus, some techniques to address this shortcoming are proposed. By applying the proposed methodology, two numerical results are obtained: one uses artificial data, and the other uses empirical data from Japanese organizations. There are two implications from the first result: how to stabilize the numerical solution, and a technique to circumvent the plummeting achievement rate close to the terminal time. The second result implies that leverage is inevitable to achieve the target level in the setting discussed in this paper.
Minimizing ruin probability under dependencies for insurance pricing
Authors: Ragnar Levy Gudmundarson, Manuel Guerra, Alexandra Bugalho de Moura
Date: 2021-08-03 00:00:00

In this work the ruin probability of the Lundberg risk process is used as a criterion for determining the optimal security loading of premia in the presence of price-sensitive demand for insurance. Both single and aggregated claim processes are considered and the independent and the dependent cases are analyzed. For the single-risk case, we show that the optimal loading does not depend on the initial reserve. In the multiple risk case we account for arbitrary dependency structures between different risks and for dependencies between the probabilities of a client acquiring policies for different risks. In this case, the optimal loadings depend on the initial reserve. In all cases the loadings minimizing the ruin probability do not coincide with the loadings maximizing the expected profit.
Previsão dos preços de abertura, mínima e máxima de índices de mercados financeiros usando a associação de redes neurais LSTM
Authors: Gabriel de Oliveira Guedes Nogueira, Marcel Otoboni de Lima
Date: 2021-07-29 00:00:00
Tasks: Time Series, Time Series Analysis

In order to make good investment decisions, it is vitally important for an investor to know how to make good analysis of financial time series. Within this context, studies on the forecast of the values and trends of stock prices have become more relevant. Currently, there are different approaches to dealing with the task. The two main ones are the historical analysis of stock prices and technical indicators and the analysis of sentiments in news, blogs and tweets about the market. Some of the most used statistical and artificial intelligence techniques are genetic algorithms, Support Vector Machines (SVM) and different architectures of artificial neural networks. This work proposes the improvement of a model based on the association of three distinct LSTM neural networks, each acting in parallel to predict the opening, minimum and maximum prices of stock exchange indices on the day following the analysis. The dataset is composed of historical data from more than 10 indices from the world's largest stock exchanges. The results demonstrate that the model is able to predict trends and stock prices with reasonable accuracy.