pwc-archive/papers-with-abstracts huggingface dataset:
A control theory approach to optimal pandemic mitigation
Authors: Prakhar Godara, Stephan Herminghaus, Knut M. Heidemann
Date: 2020-09-05 00:00:00

In the framework of homogeneous susceptible-infected-recovered (SIR) models, we use a control theory approach to identify optimal pandemic mitigation strategies. We derive rather general conditions for reaching herd immunity while minimizing the costs incurred by the introduction of societal control measures (such as closing schools, social distancing, lockdowns, etc.), under the constraint that the infected fraction of the population does never exceed a certain maximum corresponding to public health system capacity. Optimality is derived and verified by variational and numerical methods for a number of model cost functions. The effects of immune response decay after recovery are taken into account and discussed in terms of the feasibility of strategies based on herd immunity.
Detectability of the novel coronavirus (SARS-CoV-2) infection and rates of mortality from the novel coronavirus infection in different regions of the Russian Federation
Date: 2020-10-25 00:00:00

Relevance: Laboratory diagnosis of the novel coronavirus (SARS-CoV-2)
infection combined with quarantine for contacts of infected individuals affects
the spread of SARS-CoV-2 infection and levels of related mortality. Practices
for testing for SARS-CoV-2 infection vary geographically in Russia. For
example, in the city of St. Petersburg, where mortality rate for COVID-19 is
the highest in the Russian Federation on Oct. 25, 2020, every death for
COVID-19 corresponds to 15.7 detected cases of COVID-19 in the population,
while the corresponding number for the whole of Russia is 58.1, suggesting
limited detection of mild/moderate cases of COVID-19 in St. Petersburg.
Methods: More active testing for SARS-CoV-2 results in lower case-fatality
ratio (i.e. the proportion of detected COVID-19 cases among all cases of
SARS-CoV-2 infection in the population). We used data on COVID-19 cases and
deaths to examine the correlation between case-fatality ratios and rates of
mortality for COVID-19 in different regions of the Russian Federation. Results:
The correlation between case-fatality ratios and rates of mortality for
COVID-19 in different regions of the Russian Federation on Oct. 25, 2020 is
0.64 (0.50,0.75). For several regions of the Russian Federation, detectability
of SARS-CoV-2 infection is relatively low, while rates of mortality for
COVID-19 are relatively high. Conclusions: Detectability of the SARS-CoV-2
infection is one of the factors that affects the levels of mortality from
COVID-19. To increase detectability, one ought to test all individuals with
respiratory symptoms seeking medical care for SARS-CoV-2 infection, and to
undertake additional measures to increase the volume of testing for SARS-CoV-2.
Such measures, in combination with quarantine for infected cases and their
close contacts help to mitigate the spread of the SARS-CoV-2 infection and
diminish the related mortality.
Cryopreservation of pineapple (Ananas comosus) apices
Authors: M. T. Gonzalez-Arnao, M. M. Ravelo, C. U. Villavicencio, M. E. Martinez-Montero, F. Engelmann
Date: 2020-08-29 00:00:00

The encapsulation-dehydration and vitrification techniques were experimented for freezing apices of pineapple in vitro plantlets. Positive results were achieved using vitrification only. Optimal conditions included a 2-d preculture of apices on medium supplemented with 0.3M sucrose, loading treatment for 25 min in medium with 0.75M sucrose + 1M glycerol and dehydration with PVS2 vitrification solution at 0 degrees C for 7 h before rapid freezing in liquid nitrogen. This method resulted in 65, 35 and 25% survival with apices of varieties Puerto Rico, Perolera and Smooth Cayenne, respectively. Recovery of cryopreserved apices took place directly, without transitory callus formation.
Globalization? Trade War? A Counterbalance Perspective
Authors: Xingwei Hu
Date: 2020-09-04 00:00:00

This paper explores the exciting dynamics of international trade, focusing on the strategic balance between competition and cooperation in the global trade network. It highlights how competitive advantages, rather than comparative advantages, drive trade conflicts and deglobalization. By using the balance of power, the paper introduces a quantitative measure of competitiveness that serves as a common goal for all countries, alongside trade balance. It then examines how nations can boost their competitive strength and trade balance through globalization, protectionism, trade collaboration, or conflict. The study provides practical insights for policymakers on managing trade relationships, resolving conflicts, and determining the right level of globalization by analyzing trade data. The analysis is supported by a comparison between theory-driven quantitative evidence and historical events, using real-world trade data from 2000 to 2019.
An IMM-based Decentralized Cooperative Localization with LoS and NLoS UWB Inter-agent Ranging
Authors: Jianan Zhu, Solmaz S. Kia
Date: 2020-09-08 00:00:00

This paper investigates an infra-structure free global localization of a group of communicating mobile agents (e.g., first responders or exploring robots) via an ultra-wideband (UWB) inter-agent ranging aided dead-reckoning. We propose a loosely coupled cooperative localization algorithm that acts as an augmentation atop the local dead-reckoning system of each mobile agent. This augmentation becomes active only when an agent wants to process a relative measurement it has taken. The main contribution of this paper is addressing the challenges in the proper processing of the UWB range measurements in the framework of a loosely coupled cooperative localization. Even though UWB offers a decimeter level accuracy in line-of-sight (LoS) ranging, its accuracy degrades significantly in non-line-of-sight (NLoS) due to the significant unknown positive bias in the measurements. Thus, the measurement models for the UWB LoS and NLoS ranging conditions are different, and proper processing of NLoS measurements requires a bias compensation measure. We also show that, in practice, the measurement modal discriminators determine the type of UWB range measurements should be probabilistic. To take into account the probabilistic nature of the NLoS identifiers when processing UWB inter-agent ranging feedback, we employ an interacting multiple model (IMM) estimator in our localization filter. We also propose a bias compensation method for NLoS UWB measurements. The effectiveness of our cooperative localization is demonstrated via an experiment for a group of pedestrians who use UWB relative range measurements among themselves to improve their shoe-mounted INS geolocation.
Payoff distribution in robust coalitional games on time-varying networks
Date: 2020-10-16 00:00:00

In this paper, we consider a sequence of transferable utility (TU)
coalitional games where the coalitional values are unknown but vary within
certain bounds. As a solution to the resulting family of games, we formalise
the notion of "robust CORE". Our main contribution is to design two distributed
algorithms, namely, distributed payoff allocation and distributed bargaining,
that converge to a consensual payoff distribution in the robust CORE. We adopt
an operator-theoretic perspective to show convergence of both algorithms
executed on time-varying communication networks. An energy storage optimization
application motivates our framework for "robust coalitional games".
Edge Selection in Bilinear Dynamical Networks
Authors: Arthur Castello B. de Oliveira, Milad Siami, Eduardo D. Sontag
Date: 2020-09-08 00:00:00

We develop some basic principles for the design and robustness analysis of a continuous-time bilinear dynamical network, where an attacker can manipulate the strength of the interconnections/edges between some of the agents/nodes. We formulate the edge protection optimization problem of picking a limited number of attack-free edges and minimizing the impact of the attack over the bilinear dynamical network.In particular, the $\mathcal{H}_2$-norm of bilinear systems is known to capture robustness and performance properties analogous to its linear counterpart and provides valuable insights for identifying which edges are most sensitive to attacks. The exact optimization problem is combinatorial in the number of edges, and brute-force approaches show poor scalability. However, we show that the $\mathcal{H}_2$-norm as a cost function is supermodular and, therefore, allows for efficient greedy approximations of the optimal solution. We illustrate and compare the effectiveness of our theoretical findings via numerical simulations.
Beyond the brain: towards a mathematical modeling of emotions
Date: 2020-09-14 00:00:00

Emotions are a central key for understanding human beings and of fundamental
importance regarding their impact in human and animal behaviors. They have been
for a long time a subject of study for various scholars including in particular
philosophers and mystics. In modern science, the emotional phenomenon has
attracted for a few decades an increasing number of studies, notably in the
fields of Psychology, Psychiatry, Neuroscience and Biochemistry. However, since
our perception of emotions is not, so far, directly detectable nor recordable
by our measure instruments, Physics and Mathematics have not been so far used
academically to provide a precise description of the phenomenon of feeling an
emotion. Relying upon the works of O. Elahi and on the hypothesis that the
human soul and its psyche may manifest in ourselves (in both conscious and
unconscious manner) in an analog way as electromagnetic waves, we propose here
a few mathematical descriptions consistent with the human personal experience,
of the feeling and cognition of emotions. As far as we know, such a
mathematical description has never been provided before. It allows a
quantitative (intensity) and qualitative (nature of feelings/frequency) of the
emotional phenomenon which provides a novel scientific approach of the nature
of the mind, complementary to the on going research of physiological
manifestation of emotions. We anticipate such an approach and the associated
mathematical modeling to become an important tool to describe emotions and
their subsequent behavior. In complement of the modeling of oscillations and
brain dynamics, it provides a fruitful direction of research with potentially
broad and deep impacts in both applied mathematics, physics, cognitive and
behavioral sciences.
Probabilistic Dipole Inversion for Adaptive Quantitative Susceptibility Mapping
Authors: Jinwei Zhang, Hang Zhang, Mert Sabuncu, Pascal Spincemaille, Thanh Nguyen, Yi Wang
Date: 2020-09-07 00:00:00
Tasks: Density Estimation

A learning-based posterior distribution estimation method, Probabilistic Dipole Inversion (PDI), is proposed to solve the quantitative susceptibility mapping (QSM) inverse problem in MRI with uncertainty estimation. In PDI, a deep convolutional neural network (CNN) is used to represent the multivariate Gaussian distribution as the approximate posterior distribution of susceptibility given the input measured field. Such CNN is first trained on healthy subjects via posterior density estimation, where the training dataset contains samples from the true posterior distribution. Domain adaptations are then deployed on patient datasets with new pathologies not included in pre-training, where PDI updates the pre-trained CNN's weights in an unsupervised fashion by minimizing the Kullback-Leibler divergence between the approximate posterior distribution represented by CNN and the true posterior distribution from the likelihood distribution of a known physical model and pre-defined prior distribution. Based on our experiments, PDI provides additional uncertainty estimation compared to the conventional MAP approach, while addressing the potential issue of the pre-trained CNN when test data deviates from training.
Surveying the side-chain network approach to protein structure and dynamics: The SARS-CoV-2 spike protein as an illustrative case
Authors: Anushka Halder, Arinnia Anto, Varsha Subramanyan, Moitrayee Bhattacharyya, Smitha Vishveshwara, Saraswathi Vishveshwara
Date: 2020-09-09 00:00:00
Tasks: Drug Design

Network theory-based approaches provide valuable insights into the variations in global structural connectivity between differing dynamical states of proteins. Our objective is to review network-based analyses to elucidate such variations, especially in the context of subtle conformational changes. We present technical details of the construction and analyses of protein structure networks, encompassing both the non-covalent connectivity and dynamics. We examine the selection of optimal criteria for connectivity based on the physical concept of percolation. We highlight the advantages of using side-chain based network metrics in contrast to backbone measurements. As an illustrative example, we apply the described network approach to investigate the global conformational change between the closed and partially open states of the SARS-CoV-2 spike protein. This conformational change in the spike protein is crucial for coronavirus entry and fusion into human cells. Our analysis reveals global structural reorientations between the two states of the spike protein despite small changes between the two states at the backbone level. We also observe some differences at strategic locations in the structures, correlating with their functions, asserting the advantages of the side-chain network analysis. Finally we present a view of allostery as a subtle synergistic-global change between the ligand and the receptor, the incorporation of which would enhance the drug design strategies.
Investing with Cryptocurrencies -- evaluating their potential for portfolio allocation strategies
Authors: Alla Petukhina, Simon Trimborn, Wolfgang Karl Härdle, Hermann Elendner
Date: 2020-09-09 00:00:00
Tasks: Management

Cryptocurrencies (CCs) have risen rapidly in market capitalization over the last years. Despite striking price volatility, their high average returns have drawn attention to CCs as alternative investment assets for portfolio and risk management. We investigate the utility gains for different types of investors when they consider cryptocurrencies as an addition to their portfolio of traditional assets. We consider risk-averse, return-seeking as well as diversificationpreferring investors who trade along different allocation frequencies, namely daily, weekly or monthly. Out-of-sample performance and diversification benefits are studied for the most popular portfolio-construction rules, including mean-variance optimization, risk-parity, and maximum-diversification strategies, as well as combined strategies. To account for low liquidity in CC markets, we incorporate liquidity constraints via the LIBRO method. Our results show that CCs can improve the risk-return profile of portfolios. In particular, a maximum-diversification strategy (maximizing the Portfolio Diversification Index, PDI) draws appreciably on CCs, and spanning tests clearly indicate that CC returns are non-redundant additions to the investment universe. Though our analysis also shows that illiquidity of CCs potentially reverses the results.
A Minimum Energy Filter for Localisation of an Unmanned Aerial Vehicle
Date: 2020-09-10 00:00:00
Tasks: Pose Estimation

Accurate localisation of unmanned aerial vehicles is vital for the next
generation of automation tasks. This paper proposes a minimum energy filter for
velocity-aided pose estimation on the extended special Euclidean group. The
approach taken exploits the Lie-group symmetry of the problem to combine
Inertial Measurement Unit (IMU) sensor output with landmark measurements into a
robust and high performance state estimate. We propose an asynchronous
discrete-time implementation to fuse high bandwidth IMU with low bandwidth
discrete-time landmark measurements typical of real-world scenarios. The
filter's performance is demonstrated by simulation.
Towards Reliable UAV-Enabled Positioning in Mountainous Environments: System Design and Preliminary Results
Authors: Zijie Wang, Rongke Liu, Qirui Liu, Lincong Han
Date: 2020-09-10 00:00:00

Reliable positioning services are extremely important for users and devices in mountainous environments as it enables a variety of location-based applications. However, in such environments, the service reliability of conventional wireless positioning technologies is often disappointing. Frequent non-line-of-sight (NLoS) propagation and poor geometry of available anchor nodes are two significant challenges. Due to the high maneuverability and flexible deployment of unmanned aerial vehicles (UAVs), UAV-enabled positioning could be a promising solution to these challenges. Compared with satellites and terrestrial base stations, UAVs are capable of flying to places where both the propagation conditions and geometry are favorable for positioning. The eventual aim of this research project is to design a novel UAV-enabled positioning system that uses a low-altitude UAV platform to provide highly reliable services for ground users in mountainous environments. In this article, we introduce the recent progress made in the first phase of our project, including the following. First, the structure of the proposed system and the positioning method used are determined after comprehensive consideration of various factors. Utilizing the digital elevation model of the realistic terrain, we then establish a geometry-based NLoS probability model so that the NLoS propagation can be treated as a type of fault during the reliability analysis. Most importantly, a reliability prediction method and the corresponding metric are developed to evaluate the system's ability to provide reliable positioning services. At the end of this article, we also propose a voting-based method for improving the service reliability. Numerical results demonstrate the tremendous potential of the proposed system in reliable positioning.
Price formation and optimal trading in intraday electricity markets
Authors: Olivier Féron, Peter Tankov, Laura Tinsi
Date: 2020-09-10 00:00:00

We develop a tractable equilibrium model for price formation in intraday electricity markets in the presence of intermittent renewable generation. Using stochastic control theory, we identify the optimal strategies of agents with market impact and exhibit the Nash equilibrium in closed form for a finite number of agents as well as in the asymptotic framework of mean field games. Our model reproduces the empirical features of intraday market prices, such as increasing price volatility at the approach of the delivery date and the correlation between price and renewable infeed forecasts, and relates these features with market characteristics like liquidity, number of agents, and imbalance penalty.
All-Weather sub-50-cm Radar-Inertial Positioning
Authors: Lakshay Narula, Peter A. Iannucci, Todd E. Humphreys
Date: 2020-09-09 00:00:00
Tasks: All

Deployment of automated ground vehicles beyond the confines of sunny and dry climes will require sub-lane-level positioning techniques based on radio waves rather than near-visible-light radiation. Like human sight, lidar and cameras perform poorly in low-visibility conditions. This paper develops and demonstrates a novel technique for robust sub-50-cm-accurate urban ground vehicle positioning based on all-weather sensors. The technique incorporates a computationally-efficient globally-optimal radar scan batch registration algorithm into a larger estimation pipeline that fuses data from commercially-available low-cost automotive radars, low-cost inertial sensors, vehicle motion constraints, and, when available, precise GNSS measurements. Performance is evaluated on an extensive and realistic urban data set. Comparison against ground truth shows that during 60 minutes of GNSS-denied driving in the urban center of Austin, TX, the technique maintains 95th-percentile errors below 50 cm in horizontal position and 0.5 degrees in heading.
Is Factor Momentum More than Stock Momentum?
Authors: Antoine Falck, Adam Rej, David Thesmar
Date: 2020-09-10 00:00:00

Yes, but only at short lags. In this paper we investigate the relationship between factor momentum and stock momentum. Using a sample of 72 factors documented in the literature, we first replicate earlier findings that factor momentum exists and works both directionally and cross-sectionally. We then ask if factor momentum is spanned by stock momentum. A simple spanning test reveals that after controlling for stock momentum and factor exposure, statistically significant Sharpe ratios only belong to implementations which include the last month of returns. We conclude this study with a simple theoretical model that captures these forces: (1) there is stock-level mean reversion at short lags and momentum at longer lags, (2) there is stock and factor momentum at all lags and (3) there is natural comovement between the PNLs of stock and factor momentums at all horizons.
Artificial Intelligence for 5G Wireless Systems: Opportunities, Challenges, and Future Research Directions
Authors: Youness Arjoune, Saleh Faruque
Date: 2020-09-10 00:00:00
Tasks: Management

The advent of the wireless communications systems augurs new cutting-edge technologies, including self-driving vehicles, unmanned aerial systems, autonomous robots, Internet-of-Things, and virtual reality. These technologies require high data rates, ultra-low latency, and high reliability, all of which are promised by the fifth generation of wireless communication systems (5G). Many research groups state that 5G cannot meet its demands without artificial intelligence (AI) integration as 5G wireless networks are expected to generate unprecedented traffic giving wireless research designers access to big data that can help in predicting the demands and adjust cell designs to meet the users requirements. Subsequently, many researchers applied AI in many aspects of 5G wireless communication design including radio resource allocation, network management, and cyber-security. In this paper, we provide an in-depth review of AI for 5G wireless communication systems. In this respect, the aim of this paper is to survey AI in 5G wireless communication systems by discussing many case studies and the associated challenges, and shedding new light on future research directions for leveraging AI in 5G wireless communications.
A Configuration Based Pumped Storage Hydro Model in MISO Day-Ahead Market
Date: 2020-09-10 00:00:00

Pumped storage hydro units (PSHU) can provide flexibility to power systems.
This becomes particularly valuable in recent years with the increasing shares
of intermittent renewable resources. However, due to emphasis on thermal
generation in the current market practices, the flexibility from PSHUs have not
been fully explored and utilized. This paper proposes a configuration based
pumped storage hydro (PSH) model for the day-ahead market, in order to enhance
the use of PSH resources in the system. A strategic design of incorporating and
fully optimizing PSHUs in the day-ahead market is presented. We show the
compactness of the proposed model. Numerical studies are presented in an
illustrative test system and the Midcontinent Independent System Operator
(MISO) system.