pwc-archive/papers-with-abstracts huggingface dataset:
Tri-criterion model for constructing low-carbon mutual fund portfolios: a preference-based multi-objective genetic algorithm approach
Authors: A. Hilario-Caballero, A. Garcia-Bernabeu, J. V. Salcedo, M. Vercher
Date: 2020-06-21 00:00:00

Sustainable finance, which integrates environmental, social and governance (ESG) criteria on financial decisions rests on the fact that money should be used for good purposes. Thus, the financial sector is also expected to play a more important role to decarbonise the global economy. To align financial flows with a pathway towards a low-carbon economy, investors should be able to integrate in their financial decisions additional criteria beyond return and risk to manage climate risk. We propose a tri-criterion portfolio selection model to extend the classical Markowitz mean-variance approach in order to include investors preferences on the portfolio carbon risk exposure as an additional criterion. To approximate the 3D Pareto front we apply an efficient multi-objective genetic algorithm called ev-MOGA which is based on the concept of e-dominance. Furthermore, we introduce an a posteriori approach to incorporate the investor's preferences into the solution process regarding their sustainability preferences measured by the carbon risk exposure and his/her loss-adverse attitude. We test the performance of the proposed algorithm in a cross section of European SRI open-end funds to assess the extent to which climate related risk could be embedded in the portfolio according to the investor's preferences.
Detection of pure epileptic transitory among gamma oscillatory activities in MEG signals by SVD, embedded on a partial dynamic reconfiguration
Authors: Nawel Jmail, Tarek Frikha, Abir Hadriche, Christian-G Benar
Date: 2020-06-21 00:00:00

In this work we used the SVD to reconstruct pure epileptic transitory activities for the definition of the accurate sources responsible of the excessive discharge implied by the transitory hallmark activities in pharmaco resistant epilepsy. We applied firstly an automatic detection of the local peaks of the transitory activities through a thresholding the energy distrubition, then we performed the SVD on the detectable transitory (300 ms window) simulated events to recover only the non contaminated transitory activities by gamma oscillations, we calculate the precision of the SVD to evaluate the robustness of this technique in separation between transitory and gamma oscillations. In second phase we applied the SVD on small window of transitory activities from real MEG signal, we obtained dipolar topographic map for the pure transitory activities, however the time excitation for the recover of pure epileptic transitory activities among the gamma oscillations were very heavy and hence we propose to integrate the SVD on a dynamic partial reconfiguration. This integration were very useful, in fact we did obtained about 27 Times faster in the SVD execution.
Toward the biological model of the hippocampus as the successor representation agent
Authors: Hyunsu Lee
Date: 2020-06-22 00:00:00
Tasks: Hippocampus

The hippocampus is an essential brain region for spatial memory and learning. Recently, a theoretical model of the hippocampus based on temporal difference (TD) learning has been published. Inspired by the successor representation (SR) learning algorithms, which decompose value function of TD learning into reward and state transition, they argued that the rate of firing of CA1 place cells in the hippocampus represents the probability of state transition. This theory, called predictive map theory, claims that the hippocampus representing space learns the probability of transition from the current state to the future state. The neural correlates of expecting the future state are the firing rates of the CA1 place cells. This explanation is plausible for the results recorded in behavioral experiments, but it is lacking the neurobiological implications. Modifying the SR learning algorithm added biological implications to the predictive map theory. Similar with the simultaneous needs of information of the current and future state in the SR learning algorithm, the CA1 place cells receive two inputs from CA3 and entorhinal cortex. Mathematical transformation showed that the SR learning algorithm is equivalent to the heterosynaptic plasticity rule. The heterosynaptic plasticity phenomena in CA1 were discussed and compared with the modified SR update rule. This study attempted to interpret the TD algorithm as the neurobiological mechanism occurring in place learning, and to integrate the neuroscience and artificial intelligence approaches in the field.
From the Black-Karasinski to the Verhulst model to accommodate the unconventional Fed's policy
Authors: A. Itkin, A. Lipton, D. Muravey
Date: 2020-06-22 00:00:00
Tasks: Epidemiology

In this paper, we argue that some of the most popular short-term interest models have to be revisited and modified to reflect current market conditions better. In particular, we propose a modification of the popular Black-Karasinski model, which is widely used by practitioners for modeling interest rates, credit, and commodities. Our adjustment gives rise to the stochastic Verhulst model, which is well-known in the population dynamics and epidemiology as a logistic model. We demonstrate that the Verhulst model's dynamics are well suited to the current economic environment and the Fed's actions. Besides, we derive new integral equations for the zero-coupon bond prices for both the BK and Verhulst models. For the BK model for small maturities up to 2 years, we solve the corresponding integral equation by using the reduced differential transform method. For the Verhulst integral equation, under some mild assumptions, we find the closed-form solution. Numerical examples show that computationally our approach is significantly more efficient than the standard finite difference method.
Did the lockdown curb the spread of COVID-19 infection rate in India: A data-driven analysis
Date: 2020-06-22 00:00:00

In order to analyze the effectiveness of three successive nationwide lockdown
enforced in India, we present a data-driven analysis of four key parameters,
reducing the transmission rate, restraining the growth rate, flattening the
epidemic curve and improving the health care system. These were quantified by
the consideration of four different metrics, namely, reproduction rate, growth
rate, doubling time and death to recovery ratio. The incidence data of the
COVID-19 (during the period of 2nd March 2020 to 31st May 2020) outbreak in
India was analyzed for the best fit to the epidemic curve, making use of the
exponential growth, the maximum likelihood estimation, sequential Bayesian
method and estimation of time-dependent reproduction. The best fit (based on
the data considered) was for the time-dependent approach. Accordingly, this
approach was used to assess the impact on the effective reproduction rate. The
period of pre-lockdown to the end of lockdown 3, saw a $45\%$ reduction in the
rate of effective reproduction rate. During the same period the growth rate
reduced from $393\%$ during the pre-lockdown to $33\%$ after lockdown 3,
accompanied by the average doubling time increasing form $4$-$6$ days to
$12$-$14$ days. Finally, the death-to-recovery ratio dropped from $0.28$
(pre-lockdown) to $0.08$ after lockdown 3. In conclusion, all the four metrics
considered to assess the effectiveness of the lockdown, exhibited significant
favourable changes, from the pre-lockdown period to the end of lockdown 3.
Analysis of the data in the post-lockdown period with these metrics will
provide greater clarity with regards to the extent of the success of the
lockdown.
Dynamics of transposable elements generates structure and symmetries in genetic sequences
Authors: Giampaolo Cristadoro, Mirko Degli Esposti, Eduardo G. Altmann
Date: 2020-06-22 00:00:00

Genetic sequences are known to possess non-trivial composition together with symmetries in the frequencies of their components. Recently, it has been shown that symmetry and structure are hierarchically intertwined in DNA, suggesting a common origin for both features. However, the mechanism leading to this relationship is unknown. Here we investigate a biologically motivated dynamics for the evolution of genetic sequences. We show that a metastable (long-lived) regime emerges in which sequences have symmetry and structure interlaced in a way that matches that of extant genomes.
Metasurface-Coated Devices: A New Paradigm for Energy-Efficient and Secure 6G Communications
Authors: Theodoros A. Tsiftsis, Constantinos Valagiannopoulos, Hongwu Liu, Alexandros-Apostolos A. Boulogeorgos, Nikolaos I. Miridakis
Date: 2020-06-22 00:00:00

The sixth-generation (6G) era comes with the challenge of offering highly energy-efficient and autonomous communications securely. In this direction, we report energy efficiency (EE), energy harvesting (EH), and secure performance by employing power-collecting metasurface-coated devices capable of supporting ultra-low-power (ULP) transmissions. Contrary to reconfigurable intelligent surfaces (RIS), where the reflected signal can be combined at the receiver by being treated as transmitted from a relay, the proposed metasurface claddings can be deployed at either or both the transmitter and receiver. The passive metasurface-coated devices can achieve ultra-high EE and EH besides the signal detection, combined with an enhanced secrecy rate at the legitimate user and/or improved spying capabilities of the eavesdroppers under ULP transmission. To quantify their efficiency, we provide a holistic model for the utilization of the metasurface shells. Building upon the aforementioned model, preliminary results are presented that reveal the unprecedented superiority of the proposed concept compared to the RIS paradigm. Additionally, we enumerate the main advantages of the new concept and define its role in the 6G era. Finally, possible research directions are discussed.
CSI-based versus RSS-based Secret-Key Generation under Correlated Eavesdropping
Authors: François Rottenberg, Trung-Hien Nguyen, Jean-Michel Dricot, François Horlin, Jérôme Louveaux
Date: 2020-06-22 00:00:00

Physical-layer security (PLS) has the potential to strongly enhance the overall system security as an alternative to or in combination with conventional cryptographic primitives usually implemented at higher network layers. Secret-key generation relying on wireless channel reciprocity is an interesting solution as it can be efficiently implemented at the physical layer of emerging wireless communication networks, while providing information-theoretic security guarantees. In this paper, we investigate and compare the secret-key capacity based on the sampling of the entire complex channel state information (CSI) or only its envelope, the received signal strength (RSS). Moreover, as opposed to previous works, we take into account the fact that the eavesdropper's observations might be correlated and we consider the high signal-to-noise ratio (SNR) regime where we can find simple analytical expressions for the secret-key capacity. As already found in previous works, we find that RSS-based secret-key generation is heavily penalized as compared to CSI-based systems. At high SNR, we are able to precisely and simply quantify this penalty: a halved pre-log factor and a constant penalty of about 0.69 bit, which disappears as Eve's channel gets highly correlated.
Rule-based epidemic models
Authors: William Waites, Matteo Cavaliere, David Manheim, Jasmina Panovska-Griffiths, Vincent Danos
Date: 2020-06-22 00:00:00
Tasks: Decision Making, Epidemiology

This paper gives an introduction to rule-based modelling applied to topics in infectious diseases. Rule-based models generalise reaction-based models with reagents that have internal state and may be bound together to form complexes, as in chemistry. Rule-based modelling is directly transferable from molecular biology to epidemiology and allows us to express a broad class of models for processes of interest in epidemiology that would not otherwise be feasible in compartmental models. This includes dynamics commonly found in compartmental models such as the spread of a virus from an infectious to a susceptible population, and more complex dynamics outside the typical scope of such models such as social behaviours and decision-making, testing capacity constraints, and tracing of people exposed to a virus but not yet symptomatic. We propose that such dynamics are well-captured with rule-based models, and that doing so combines intuitiveness and transparency of representation with scalability and compositionality. We demonstrate this feasibility of our approach using a suite of seven models to describe a spread of infectious diseases under different scenarios: wearing masks, infection via fomites and prevention by hand-washing, the concept of vector-borne diseases, testing and contact tracing interventions, disease propagation within motif-structured populations with shared environments such as schools, and superspreading events. The machine-readable description of these models corresponds closely to the mathematical description and also functions as a human-readable format so that one knows readily "what is in the model".
Optimal Control of SOAs with Artificial Intelligence for Sub-Nanosecond Optical Switching
Authors: Christopher W. F. Parsonson, Zacharaya Shabka, W. Konrad Chlupka, Bawang Goh, Georgios Zervas
Date: 2020-06-22 00:00:00

Novel approaches to switching ultra-fast semiconductor optical amplifiers using artificial intelligence algorithms (particle swarm optimisation, ant colony optimisation, and a genetic algorithm) are developed and applied both in simulation and experiment. Effective off-on switching (settling) times of 542 ps are demonstrated with just 4.8% overshoot, achieving an order of magnitude improvement over previous attempts described in the literature and standard dampening techniques from control theory.
A novel direct structured-light inspection technique for contaminant and defect detection
Date: 2020-05-19 00:00:00
Tasks: Defect Detection, Transparent objects

The Direct Structured-Light Inspection Technique (DSIT) proposed in this
paper is a novel method that can be implemented under two types of binary
structured light illumination to detect contaminants and defects on specular
surfaces and transparent objects, in which light reflection system is used to
detect specular surfaces, while light transmission system is applied for
transparent object inspection. Based on this technique, contaminant and defect
distribution can be directly obtained without any calculation process. Relevant
simulations and experiments are performed to prove the effectiveness of DSIT.
Deforestation and world population sustainability: a quantitative analysis
Date: 2020-06-22 00:00:00

In this paper we afford a quantitative analysis of the sustainability of
current world population growth in relation to the parallel deforestation
process adopting a statistical point of view. We consider a simplified model
based on a stochastic growth process driven by a continuous time random walk,
which depicts the technological evolution of human kind, in conjunction with a
deterministic generalised logistic model for humans-forest interaction and we
evaluate the probability of avoiding the self-destruction of our civilisation.
Based on the current resource consumption rates and best estimate of
technological rate growth our study shows that we have very low probability,
less than 10% in most optimistic estimate, to survive without facing a
catastrophic collapse.
A random walk Monte Carlo simulation study of COVID-19-like infection spread
Authors: S. Triambak, D. P. Mahapatra
Date: 2020-06-17 00:00:00

Recent analysis of early COVID-19 data from China showed that the number of confirmed cases followed a subexponential power-law increase, with a growth exponent of around 2.2 [B.\,F.~Maier, D.~Brockmann, {\it Science} {\bf 368}, 742 (2020)]. The power-law behavior was attributed to a combination of effective containment and mitigation measures employed as well as behavioral changes by the population. In this work, we report a random walk Monte Carlo simulation study of proximity-based infection spread. Control interventions such as lockdown measures and mobility restrictions are incorporated in the simulations through a single parameter, the size of each step in the random walk process. The step size $l$ is taken to be a multiple of $\langle r \rangle$, which is the average separation between individuals. Three temporal growth regimes (quadratic, intermediate power-law and exponential) are shown to emerge naturally from our simulations. For $l = \langle r \rangle$, we get intermediate power-law growth exponents that are in general agreement with available data from China. On the other hand, we obtain a quadratic growth for smaller step sizes $l \lesssim \langle r \rangle/2 $, while for large $l$ the growth is found to be exponential. %Together with available data, these results suggest that the early containment of the disease within China was close to optimal. We further performed a comparative case study of early fatality data (under varying levels of lockdown conditions) from three other countries, India, Brazil and South Africa. We show that reasonable agreement with these data can be obtained by incorporating small-world-like connections in our simulations.
Decentralized Beamforming Design for Intelligent Reflecting Surface-enhanced Cell-free Networks
Authors: Shaocheng Huang, Yu Ye, Ming Xiao, H. Vincent Poor, Mikael Skoglund
Date: 2020-06-22 00:00:00

Cell-free networks are considered as a promising distributed network architecture to satisfy the increasing number of users and high rate expectations in beyond-5G systems. However, to further enhance network capacity, an increasing number of high-cost base stations (BSs) are required. To address this problem and inspired by the cost-effective intelligent reflecting surface (IRS) technique, we propose a fully decentralized design framework for cooperative beamforming in IRS-aided cell-free networks. We first transform the centralized weighted sum-rate maximization problem into a tractable consensus optimization problem, and then an incremental alternating direction method of multipliers (ADMM) algorithm is proposed to locally update the beamformer. The complexity and convergence of the proposed method are analyzed, and these results show that the performance of the new scheme can asymptotically approach that of the centralized one as the number of iterations increases. Results also show that IRSs can significantly increase the system sum-rate of cell-free networks and the proposed method outperforms existing decentralized methods.
Cost Restrained Hybrid Attacks in Power Grids
Date: 2020-06-22 00:00:00

The frequent occurrences of cascading failures in power grids have been
receiving continuous attention in recent years. An urgent task for us is to
understand the cascading failure vulnerability of power grids against various
kinds of attacks. We consider a cost restrained hybrid attack problem in power
grids, in which both nodes and links are targeted with a limited total attack
cost. We propose an attack centrality metric for a component (node or link)
based on the consequence and cost of the removal of the component. Depending on
the width of cascading failures considered, the attack centrality can be a
local or global attack centrality. With the attack centrality, we further
provide a greedy hybrid attack, and an optimal hybrid attack with the Particle
Swarm Optimization (PSO) framework. Simulation results on IEEE bus test data
show that the optimal hybrid attack is more efficient than the greedy hybrid
attack. Furthermore, we find counterintuitively that the local centrality based
algorithms are better than the global centrality based ones when the cost
constraint is considered in the attack problem.
A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics
Date: 2020-06-23 00:00:00
Tasks: Variable Selection

We introduce tools for controlled variable selection to economists. In
particular, we apply a recently introduced aggregation scheme for false
discovery rate (FDR) control to German administrative data to determine the
parts of the individual employment histories that are relevant for the career
outcomes of women. Our results suggest that career outcomes can be predicted
based on a small set of variables, such as daily earnings, wage increases in
combination with a high level of education, employment status, and working
experience.
Modeling glioma invasion with anisotropy- and hypoxia-triggered motility enhancement: from subcellular dynamics to macroscopic PDEs with multiple taxis
Authors: G. Corbin, C. Engwer, A. Klar, J. Nieto, J. Soler, C. Surulescu, M. Wenske
Date: 2020-06-22 00:00:00

We deduce a model for glioma invasion making use of DTI data and accounting for the dynamics of brain tissue being actively degraded by tumor cells via excessive acidity production, but also according to the local orientation of tissue fibers. Our approach has a multiscale character: we start with a microscopic description of single cell dynamics including biochemical and/or biophysical effects of the tumor microenvironment, translated on the one hand into cell stress and corresponding forces and on the other hand into receptor binding dynamics; these lead on the mesoscopic level to kinetic equations involving transport terms w.r.t. all kinetic variables and eventually, by appropriate upscaling, to a macroscopic reaction-diffusion equation for glioma density with multiple taxis, coupled to (integro-)differential equations characterizing the evolution of acidity and macro- and mesoscopic tissue. Our approach also allows for a switch between fast and slower moving regimes, %diffusion- and drift-dominated regimes, according to the local tissue anisotropy. We perform numerical simulations to investigate the behavior of solutions w.r.t. various scenarios of tissue dynamics and the dominance of each of the tactic terms, also suggesting how the model can be used to perform a numerical necrosis-based tumor grading or support radiotherapy planning by dose painting. We also provide a discussion about alternative ways of including cell level environmental influences in such multiscale modeling approach, ultimately leading in the macroscopic limit to (multiple) taxis.
Locked in Syndrome Machine Learning Classification using Sentence Comprehension EEG Data
Authors: Daniël van den Corput
Date: 2020-06-17 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic, EEG, Electroencephalogram (EEG), ERP, General Classification, Prognosis, Sentence

Locked-in Syndrome patients are often misdiagnosed and face pessimistic prognosis because of similarities with disorders of consciousness, a lack of objective biomarkers and a difficult-to-recognize pathogenesis. Biomarkers show promise in identifying similar conditions, utilizing electroencephalography (EEG) data. This data, particularly in the form of event-related potentials (ERPs), while successful in varying applications, suffers from methodological constraints and interpretation obstacles. The study documented in this body of work explores a machine learning paradigm with regards to N400 ERP data retrieved from a sentence comprehension task to tackle these hindrances and proposes a new auxiliary diagnostic tool for LIS and possibly disorders of consciousness. A support vector machine (SVC) and a random forest classifier (RF) were able to classify conscious individuals from unconscious ones with optimistic performance metrics. Based on these results, the proposed models and continuations thereof present valuable opportunities for the development of an auxiliary diagnostic tool for the classification of LIS patients, aiding diagnosis, improving prognosis, stimulating recovery and reducing mortality rates.