pwc-archive/papers-with-abstracts huggingface dataset:
RAN Slicing in Multi-MVNO Environment under Dynamic Channel Conditions
Authors: Darshan A. Ravi, Vijay K. Shah, Chengzhang Li, Tom Hou, Jeffrey H. Reed
Date: 2021-04-11 00:00:00

With the increasing diversity in the requirement of wireless services with guaranteed quality of service(QoS), radio access network(RAN) slicing becomes an important aspect in implementation of next generation wireless systems(5G). RAN slicing involves division of network resources into many logical segments where each segment has specific QoS and can serve users of mobile virtual network operator(MVNO) with these requirements. This allows the Network Operator(NO) to provide service to multiple MVNOs each with different service requirements. Efficient allocation of the available resources to slices becomes vital in determining number of users and therefore, number of MVNOs that a NO can support. In this work, we study the problem of Modulation and Coding Scheme(MCS) aware RAN slicing(MaRS) in the context of a wireless system having MVNOs which have users with minimum data rate requirement. Channel Quality Indicator(CQI) report sent from each user in the network determines the MCS selected, which in turn determines the achievable data rate. But the channel conditions might not remain the same for the entire duration of user being served. For this reason, we consider the channel conditions to be dynamic where the choice of MCS level varies at each time instant. We model the MaRS problem as a Non-Linear Programming problem and show that it is NP-Hard. Next, we propose a solution based on greedy algorithm paradigm. We then develop an upper performance bound for this problem and finally evaluate the performance of proposed solution by comparing against the upper bound under various channel and network configurations.
Millimeter-Wave UAV Coverage in Urban Environments
Authors: Seongjoon Kang, Marco Mezzavilla, Angel Lozano, Giovanni Geraci, William Xia, Sundeep Rangan, Vasilii Semkin, Giuseppe Loianno
Date: 2021-04-05 00:00:00

With growing interest in mmWave connectivity for UAVs, a basic question is whether networks intended for terrestrial users can provide sufficient aerial coverage as well. To assess this possibility, the paper proposes a novel evaluation methodology using generative models trained on detailed ray tracing data. These models capture complex propagation characteristics and can be readily combined with antenna and beamforming assumptions. Extensive simulation using these models indicate that standard (street-level and downtilted) base stations at typical microcellular densities can indeed provide satisfactory UAV coverage. Interestingly, the coverage is possible via a conjunction of antenna sidelobes and strong reflections. With sparser deployments, the coverage is only guaranteed at progressively higher altitudes. Additional dedicated (rooftop-mounted and uptilted) base stations strengthen the coverage provided that their density is comparable to that of the standard deployment, and would be instrumental for sparse deployments of the latter.
Analysis of bank leverage via dynamical systems and deep neural networks
Authors: Fabrizio Lillo, Giulia Livieri, Stefano Marmi, Anton Solomko, Sandro Vaienti
Date: 2021-04-11 00:00:00
Tasks: Time Series, Time Series Analysis

We consider a model of a simple financial system consisting of a leveraged investor that invests in a risky asset and manages risk by using Value-at-Risk (VaR). The VaR is estimated by using past data via an adaptive expectation scheme. We show that the leverage dynamics can be described by a dynamical system of slow-fast type associated with a unimodal map on [0,1] with an additive heteroscedastic noise whose variance is related to the portfolio rebalancing frequency to target leverage. In absence of noise the model is purely deterministic and the parameter space splits in two regions: (i) a region with a globally attracting fixed point or a 2-cycle; (ii) a dynamical core region, where the map could exhibit chaotic behavior. Whenever the model is randomly perturbed, we prove the existence of a unique stationary density with bounded variation, the stochastic stability of the process and the almost certain existence and continuity of the Lyapunov exponent for the stationary measure. We then use deep neural networks to estimate map parameters from a short time series. Using this method, we estimate the model in a large dataset of US commercial banks over the period 2001-2014. We find that the parameters of a substantial fraction of banks lie in the dynamical core, and their leverage time series are consistent with a chaotic behavior. We also present evidence that the time series of the leverage of large banks tend to exhibit chaoticity more frequently than those of small banks.
Practical Resources for Enhancing the Reproducibility of Mechanistic Modeling in Systems Biology
Authors: Michael L. Blinov, John H. Gennari, Jonathan R. Karr, Ion I. Moraru, David P. Nickerson, Herbert M. Sauro
Date: 2021-04-09 00:00:00

Although reproducibility is a core tenet of the scientific method, it remains challenging to reproduce many results. Surprisingly, this also holds true for computational results in domains such as systems biology where there have been extensive standardization efforts. For example, Tiwari et al. recently found that they could only repeat 50% of published simulation results in systems biology. Toward improving the reproducibility of computational systems research, we identified several resources that investigators can leverage to make their research more accessible, executable, and comprehensible by others. In particular, we identified several domain standards and curation services, as well as powerful approaches pioneered by the software engineering industry that we believe many investigators could adopt. Together, we believe these approaches could substantially enhance the reproducibility of systems biology research. In turn, we believe enhanced reproducibility would accelerate the development of more sophisticated models that could inform precision medicine and synthetic biology.
Tree species, crown cover, and age as determinants of the vertical distribution of airborne LiDAR returns
Authors: Etienne Racine, Nicholas C. Coops, Jean Bégin, Mari Myllymäki
Date: 2021-04-11 00:00:00

Light detection and ranging (LiDAR) provides information on the vertical structure of forest stands enabling detailed and extensive ecosystem study. The vertical structure is often summarized by scalar features and data-reduction techniques that limit the interpretation of results. Instead, we quantified the influence of three variables, species, crown cover, and age, on the vertical distribution of airborne LiDAR returns from forest stands. We studied 5,428 regular, even-aged stands in Quebec (Canada) with five dominant species: balsam fir (Abies balsamea (L.) Mill.), paper birch (Betula papyrifera Marsh), black spruce (Picea mariana (Mill.) BSP), white spruce (Picea glauca Moench) and aspen (Populus tremuloides Michx.). We modeled the vertical distribution against the three variables using a functional general linear model and a novel nonparametric graphical test of significance. Results indicate that LiDAR returns from aspen stands had the most uniform vertical distribution. Balsam fir and white birch distributions were similar and centered at around 50% of the stand height, and black spruce and white spruce distributions were skewed to below 30% of stand height (p<0.001). Increased crown cover concentrated the distributions around 50% of stand height. Increasing age gradually shifted the distributions higher in the stand for stands younger than 70-years, before plateauing and slowly declining at 90-120 years. Results suggest that the vertical distributions of LiDAR returns depend on the three variables studied.
Light-weight sleep monitoring: electrode distance matters more than placement for automatic scoring
Authors: Kaare B. Mikkelsen, Huy Phan, Mike L. Rank, Martin C. Hemmsen, Maarten De Vos, Preben Kidmose
Date: 2021-04-09 00:00:00
Tasks: Position

Modern sleep monitoring development is shifting towards the use of unobtrusive sensors combined with algorithms for automatic sleep scoring. Many different combinations of wet and dry electrodes, ear-centered, forehead-mounted or headband-inspired designs have been proposed, alongside an ever growing variety of machine learning algorithms for automatic sleep scoring. In this paper, we compare 13 different, realistic sensor setups derived from the same data set and analysed with the same pipeline. We find that all setups which include both a lateral and an EOG derivation show similar, state-of-the-art performance, with average Cohen's kappa values of at least 0.80. This indicates that electrode distance, rather than position, is important for accurate sleep scoring. Finally, based on the results presented, we argue that with the current competitive performance of automated staging approaches, there is an urgent need for establishing an improved benchmark beyond current single human rater scoring.
The Efficient Hedging Frontier with Deep Neural Networks
Authors: Zheng Gong, Carmine Ventre, John O'Hara
Date: 2021-04-12 00:00:00
Tasks: Navigate

The trade off between risks and returns gives rise to multi-criteria optimisation problems that are well understood in finance, efficient frontiers being the tool to navigate their set of optimal solutions. Motivated by the recent advances in the use of deep neural networks in the context of hedging vanilla options when markets have frictions, we introduce the Efficient Hedging Frontier (EHF) by enriching the pipeline with a filtering step that allows to trade off costs and risks. This way, a trader's risk preference is matched with an expected hedging cost on the frontier, and the corresponding hedging strategy can be computed with a deep neural network. We further develop our framework to improve the EHF and find better hedging strategies. By adding a random forest classifier to the pipeline to forecast market movements, we show how the frontier shifts towards lower costs and reduced risks, which indicates that the overall hedging performances have improved. In addition, by designing a new recurrent neural network, we also find strategies on the frontier where hedging costs are even lower.
Oil-US Stock Market Nexus: Some insights about the New Coronavirus Crisis
Authors: Claudiu Albulescu, Michel Mina, Cornel Oros
Date: 2021-04-12 00:00:00

We provide a new investigation of the relationship between oil and stock prices in the context of the outbreak of the new coronavirus crisis. Specifically, we assess to what extent the uncertainty induced by COVID-19 affects the interaction between oil and the United States (US) stock markets. To this end, we use a wavelet approach and daily data from February 18, 2020 to August 15, 2020. We identify the lead-lag relationship between oil and stock prices, and the intensity of this relationship at different frequency cycles and moments in time. Our unique findings show that co-movements between oil and stock prices manifest at 3-5-day cycle and are stronger in the first part of March and the second part of April 2020, when oil prices are leading stock prices. The partial wavelet coherence analysis, controlling for the effect of COVID-19 and US economic policy-induced uncertainty, reveals that the coronavirus crisis amplifies the shock propagation between oil and stock prices.
Modelling uncertainty in financial tail risk: a forecast combination and weighted quantile approach
Authors: Giuseppe Storti, Chao Wang
Date: 2021-04-11 00:00:00

A novel forecast combination and weighted quantile based tail-risk forecasting framework is proposed, aiming to reduce the impact of modelling uncertainty in tail-risk forecasting. The proposed approach is based on a two-step estimation procedure. The first step involves the combination of Value-at-Risk (VaR) forecasts at a grid of quantile levels. A range of parametric and semi-parametric models is selected as the model universe in the forecast combination procedure. The quantile forecast combination weights are estimated by optimizing the quantile loss. In the second step, the Expected Shortfall (ES) is computed as a weighted average of combined quantiles. The quantiles weighting structure for ES forecasting is determined by minimizing a strictly consistent joint VaR and ES loss function of the Fissler-Ziegel class. The proposed framework is applied to six stock market indices and its forecasting performance is compared to each individual model in the universe, a simple average approach and a weighted quantile approach. The forecasting results support the proposed framework.
Financial Markets Prediction with Deep Learning
Authors: Jia Wang, Tong Sun, Benyuan Liu, Yu Cao, Degang Wang
Date: 2021-04-05 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Prediction

Financial markets are difficult to predict due to its complex systems dynamics. Although there have been some recent studies that use machine learning techniques for financial markets prediction, they do not offer satisfactory performance on financial returns. We propose a novel one-dimensional convolutional neural networks (CNN) model to predict financial market movement. The customized one-dimensional convolutional layers scan financial trading data through time, while different types of data, such as prices and volume, share parameters (kernels) with each other. Our model automatically extracts features instead of using traditional technical indicators and thus can avoid biases caused by selection of technical indicators and pre-defined coefficients in technical indicators. We evaluate the performance of our prediction model with strictly backtesting on historical trading data of six futures from January 2010 to October 2017. The experiment results show that our CNN model can effectively extract more generalized and informative features than traditional technical indicators, and achieves more robust and profitable financial performance than previous machine learning approaches.
Implementing an expert system to evaluate technical solutions innovativeness
Authors: V. K. Ivanov, I. V. Obraztsov, B. V. Palyukh
Date: 2021-03-26 00:00:00

The paper presents a possible solution to the problem of algorithmization for quantifying inno-vativeness indicators of technical products, inventions and technologies. The concepts of technological nov-elty, relevance and implementability as components of product innovation criterion are introduced. Authors propose a model and algorithm to calculate every of these indicators of innovativeness under conditions of incompleteness and inaccuracy, and sometimes inconsistency of the initial information. The paper describes the developed specialized software that is a promising methodological tool for using interval estimations in accordance with the theory of evidence. These estimations are used in the analysis of complex multicomponent systems, aggregations of large volumes of fuzzy and incomplete data of various structures. Composition and structure of a multi-agent expert system are presented. The purpose of such system is to process groups of measurement results and to estimate indicators values of objects innovativeness. The paper defines active elements of the system, their functionality, roles, interaction order, input and output inter-faces, as well as the general software functioning algorithm. It describes implementation of software modules and gives an example of solving a specific problem to determine the level of technical products innovation.
Age Estimation Using Expectation of Label Distribution Learning
Authors: Bin-Bin Gao, Hong-Yu Zhou, Jianxin Wu, Xin Geng
Date: 2018-07-13 00:00:00
Tasks: Age Estimation, Face Recognition, image-classification, Image Classification

Age estimation performance has been greatly improved by using convolutional neural network. However, existing methods have an inconsistency between the training objectives and evaluation metric, so they may be suboptimal. In addition, these methods always adopt image classification or face recognition models with a large amount of parameters, which bring expensive computation cost and
storage overhead. To alleviate these issues, we design a lightweight network architecture and propose a unified framework which can jointly learn age distribution and regress age. The effectiveness of our approach has been demonstrated on apparent and real age estimation tasks. Our method achieves new state-of-the-art results using the single model with 36× fewer parameters and 2.6× reduction in inference time. Moreover, our method can achieve comparable results as the state-of-the-art even though model parameters are further reduced to 0.9M (3.8MB disk storage). We also analyze that Ranking methods are implicitly learning label
distributions.
Analysis of Hand-Crafted and Automatic-Learned Features for Glaucoma Detection Through Raw Circmpapillary OCT Images
Authors: Gabriel García, Adrián Colomer, Valery Naranjo
Date: 2020-09-09 00:00:00

Taking into account that glaucoma is the leading cause of blindness worldwide, we propose in this paper three different learning methodologies for glaucoma detection in order to elucidate that traditional machine-learning techniques could outperform deep-learning algorithms, especially when the image data set is small. The experiments were performed on a private database composed of 194 glaucomatous and 198 normal B-scans diagnosed by expert ophthalmologists. As a novelty, we only considered raw circumpapillary OCT images to build the predictive models, without using other expensive tests such as visual field and intraocular pressure measures. The results ratify that the proposed hand-driven learning model, based on novel descriptors, outperforms the automatic learning. Additionally, the hybrid approach consisting of a combination of both strategies reports the best performance, with an area under the ROC curve of 0.85 and an accuracy of 0.82 during the prediction stage.
Screening COVID-19 cases using Deep Neural Networks with X-ray images
Authors: Tarit Sengupta
Date: 2020-11-24 00:00:00
Tasks: Binary Classification, Diagnostic, object-detection, Object Detection, Real-Time Object Detection

descriptionThe novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients. The need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. The application of advanced artificial intelligence (AI) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease, and can also be assistive to overcome the problem of a lack of specialized physicians in remote villages. In this study, a new model for automatic COVID-19 detection using raw chest X-ray images is presented. The proposed model is developed to provide accurate diagnostics for binary classification (COVID vs. No-Findings) and multiclass classification (COVID vs. No-Findings vs. Pneumonia). My model produced a classification accuracy of 98.08% for binary classes and 87.02% for multi-class cases. The DarkNet model was used in my study as a classifier for you only look once (YOLO) real-time object detection system. I implemented 17 convolutional layers and introduced different filtering on each layer. My model can be employed to assist radiologists in invalidating their initial screening, and can also be employed via the cloud to immediately screen patients. With the ever-increasing demand for screening millions of prospective “novel coronavirus” or COVID-19 cases, and due to the emergence of high false negatives in the commonly used PCR tests, the necessity for probing an alternative simple screening mechanism of COVID-19 using radiological images (like chest X-Rays) assumes importance.
Prediction of Apophis Asteroid Flyby Optimal Trajectories and Data Fusion of Earth-Apophis Mission Launch Windows using Deep Neural Networks
Authors: Manuel Ntumba, Saurabh Gore, Jean-Baptiste Awanyo
Date: 2021-04-11 00:00:00

In recent years, understanding asteroids has shifted from light worlds to geological worlds by exploring modern spacecraft and advanced radar and telescopic surveys. However, flyby in 2029 will be an opportunity to conduct an internal geophysical study and test the current hypothesis on the effects of tidal forces on asteroids. The Earth-Apophis mission is driven by additional factors and scientific goals beyond the unique opportunity for natural experimentation. However, the internal geophysical structures remain largely unknown. Understanding the strength and internal integrity of asteroids is not just a matter of scientific curiosity. It is a practical imperative to advance knowledge for planetary defense against the possibility of an asteroid impact. This paper presents a conceptual robotics system required for efficiency at every stage from entry to post-landing and for asteroid monitoring. In short, asteroid surveillance missions are futuristic frontiers, with the potential for technological growth that could revolutionize space exploration. Advanced space technologies and robotic systems are needed to minimize risk and prepare these technologies for future missions. A neural network model is implemented to track and predict asteroids' orbits. Advanced algorithms are also needed to numerically predict orbital events to minimize error
Understanding Fission Gas Bubble Distribution, Lanthanide Transportation, and Thermal Conductivity Degradation in Neutron-irradiated α-U Using Machine Learning
Authors: Lu Cai, Fei Xu, Fidelma Dilemma, Daniel J. Murray, Cynthia A. Adkins, Larry K Aagesen Jr, Min Xian, Luca Caprriot, Tiankai Yao
Date: 2021-04-12 00:00:00

UZr based metallic nuclear fuel is the leading candidate for next-generation sodium-cooled fast reactors in the United States. US research reactors have been using and testing this fuel type since the 1960s and accumulated considerable experience and knowledge about the fuel performance. However, most of knowledge remains empirical. The lack of mechanistic understanding of fuel performance is preventing the qualification of UZr fuel for commercial use. This paper proposes a data-driven approach, coupled with advanced post irradiation examination, powered by machine learning algorithms, to facilitate the development of such understandings by providing unpreceded quantified new insights into fission gas bubbles. Specifically, based on the advanced postirradiation examination data collected on a neutron-irradiated U-10Zr annular fuel, we developed a method to automatically detect, classify ~19,000 fission gas bubbles into different categories, and quantitatively link the data to lanthanide transpiration along the radial temperature gradient. The approach is versatile and can be modified to study different coupled irradiation effects, such as secondary phase redistribution and degradation of thermal conductivity, in irradiated nuclear fuel.