pwc-archive/papers-with-abstracts huggingface dataset:
Detection and Localization of Load Redistribution Attacks on Large Scale Systems
Date: 2020-06-15 00:00:00

A nearest neighbor-based detection scheme against load redistribution attacks
is presented. The detector is designed to scale from small to very large
systems while guaranteeing consistent detection performance. Extensive testing
is performed on a realistic, large scale system to evaluate the performance of
the proposed detector against a wide range of attacks, from simple random noise
attacks to sophisticated load redistribution attacks. The detection capability
is analyzed against different attack parameters to evaluate its sensitivity.
Finally, a statistical test that leverages the proposed detection algorithm is
introduced to identify which loads are likely to have been maliciously
modified, thus, localizing the attack subgraph. This test is based on ascribing
to each load a risk measure (probability of being attacked) and then computing
the best posterior likelihood that minimizes log-loss.
Finite State Markov Modeling of Fading Channels Towards Decoding of LDPC Codes
Date: 2019-10-19 00:00:00

Here we have proposed two decoding strategies of low-density parity-check
(LDPC) codes over Markov noise channels with bit flipping noise. The
sum-product algorithm used for decoding LDPC codes over memoryless channels is
extended to include channel estimation and how much gain we obtain by doing so
is simulated and verified. LDPC codes have been studied for years over
memoryless channels and are known to have excellent performance. However, these
codes over channels with memory is the topic of current research. Here,
channels with memory are characterized by Markov modeling which is a useful
busty channel model. With sufficient no. of states, they are able to model
sufficient noise characteristics. We have gone for a two-state system as it
shows a good compromise between complexity and performance.
Animal behavior facilitates eco-evolutionary dynamics
Date: 2019-12-19 00:00:00

The mechanisms underlying eco-evolutionary dynamics (the feedback between
ecological and evolutionary processes) are often unknown. Here, we propose that
classical theory from behavioral ecology can provide a greater understanding of
the mechanisms underlying eco-evolutionary dynamics, and thus improve
predictions about the outcomes of these dynamics.
Temporal-Difference estimation of dynamic discrete choice models
Authors: Karun Adusumilli, Dita Eckardt
Date: 2019-12-19 00:00:00
Tasks: Discrete Choice Models

We study the use of Temporal-Difference learning for estimating the structural parameters in dynamic discrete choice models. Our algorithms are based on the conditional choice probability approach but use functional approximations to estimate various terms in the pseudo-likelihood function. We suggest two approaches: The first - linear semi-gradient - provides approximations to the recursive terms using basis functions. The second - Approximate Value Iteration - builds a sequence of approximations to the recursive terms by solving non-parametric estimation problems. Our approaches are fast and naturally allow for continuous and/or high-dimensional state spaces. Furthermore, they do not require specification of transition densities. In dynamic games, they avoid integrating over other players' actions, further heightening the computational advantage. Our proposals can be paired with popular existing methods such as pseudo-maximum-likelihood, and we propose locally robust corrections for the latter to achieve parametric rates of convergence. Monte Carlo simulations confirm the properties of our algorithms in practice.
Convergence to periodic regimes in nonlinear feedback systems with a strongly convex backlash
Date: 2019-12-19 00:00:00

This paper considers a class of nonlinear systems consisting of a linear part
with an external input and a nonlinear feedback with a backlash. Assuming that
the latter is specified by a strongly convex set, we establish estimates for
the Lyapunov exponents which quantify the rate of convergence of the system
trajectories to a forced periodic regime when the input is a periodic function
of time. These results employ enhanced dissipation inequalities for
differential inclusions with strongly convex sets, which were used previously
for the Moreau sweeping process.
Argentum: a collaborative saving and investment platform for unstable countries
Date: 2019-11-15 00:00:00

A crypto coin designed to provide a stabilization instrument backed up by
minded like financial investments instruments to maintain the purchase value of
savings across time, in order to construct new tools for unstable economies.
Comparison of various risk measures for an optimal portfolio
Authors: Alev Meral
Date: 2019-12-16 00:00:00

In this paper, we search for optimal portfolio strategies in the presence of various risk measure that are common in financial applications. Particularly, we deal with the static optimization problem with respect to Value at Risk, Expected Loss and Expected Utility Loss measures. To do so, under the Black- Scholes model for the financial market, Martingale method is applied to give closed-form solutions for the optimal terminal wealths; then via representation problem the optimal portfolio strategies are achieved. We compare the performances of these measures on the terminal wealths and optimal strategies of such constrained investors. Finally, we present some numerical results to compare them in several respects to give light to further studies.
A mechanistic-statistical species distribution model to explain and forecast wolf (Canis lupus) colonization in South-Eastern France
Date: 2020-02-17 00:00:00

Species distribution models (SDMs) are important statistical tools for
ecologists to understand and predict species range. However, standard SDMs do
not explicitly incorporate dynamic processes like dispersal. This limitation
may lead to bias in inference about species distribution. Here, we adopt the
theory of ecological diffusion that has recently been introduced in statistical
ecology to incorporate spatio-temporal processes in ecological models. As a
case study, we considered the wolf (Canis lupus) that has been recolonizing
Eastern France naturally through dispersal from the Apennines since the early
90's. Using partial differential equations for modelling species diffusion and
growth in a fragmented landscape, we develop a mechanistic-statistical
spatio-temporal model accounting for ecological diffusion, logistic growth and
imperfect species detection. We conduct a simulation study and show the ability
of our model to i) estimate ecological parameters in various situations with
contrasted species detection probability and number of surveyed sites and ii)
forecast the distribution into the future. We found that the growth rate of the
wolf population in France was explained by the proportion of forest cover, that
diffusion was influenced by human density and that species detectability
increased with increasing survey effort. Using the parameters estimated from
the 2007-2015 period, we then forecasted wolf distribution in 2016 and found
good agreement with the actual detections made that year. Our approach may be
useful for managing species that interact with human activities to anticipate
potential conflicts.
From Disequilibrium Markets to Equilibrium
Authors: Christian Lax, Torsten Trimborn
Date: 2019-12-20 00:00:00

The modeling of financial markets as disequilibrium models by ordinary differential equations has become a popular modeling tool. One famous example of such a model is the Beja-Goldman model(The Journal of Finance, 1980) which we consider in this paper. We study the passage from disequilibrium dynamics to equilibrium. Mathematically, this limit corresponds to an asymptotic limit also known as a Tikhonov-Fenichel reduction. Furthermore, we analyze the stability of the reduced equilibrium model and discuss the economic implications. We conduct several numerical examples to visualize and support our analysis.
Monetary Policy and Wealth Inequalities in Great Britain: Assessing the role of unconventional policies for a decade of household data
Date: 2019-12-20 00:00:00
Tasks: Time Series, Time Series Analysis

This paper explores whether unconventional monetary policy operations have
redistributive effects on household wealth. Drawing on household balance sheet
data from the Wealth and Asset Survey, we construct monthly time series
indicators on the distribution of different asset types held by British
households for the period that the monetary policy switched as the policy rate
reached the zero lower bound (2006-2016). Using this series, we estimate the
response of wealth inequalities on monetary policy, taking into account the
effect of unconventional policies conducted by the Bank of England in response
to the Global Financial Crisis. Our evidence reveals that unconventional
monetary policy shocks have significant long-lasting effects on wealth
inequality: an expansionary monetary policy in the form of asset purchases
raises wealth inequality across households, as measured by their Gini
coefficients of net wealth, housing wealth, and financial wealth. The evidence
of our analysis helps to raise awareness of central bankers about the
redistributive effects of their monetary policy decisions.
Grouping of Contracts in Insurance using Neural Networks
Authors: Mark Kiermayer, Christian Weiß
Date: 2019-12-20 00:00:00
Tasks: Clustering

Despite the high importance of grouping in practice, there exists little research on the respective topic. The present work presents a complete framework for grouping and a novel method to optimize model points. Model points are used to substitute clusters of contracts in an insurance portfolio and thus yield a smaller, computationally less burdensome portfolio. This grouped portfolio is controlled to have similar characteristics as the original portfolio. We provide numerical results for term life insurance and defined contribution plans, which indicate the superiority of our approach compared to K-means clustering, a common baseline algorithm for grouping. Lastly, we show that the presented concept can optimize a fixed number of model points for the entire portfolio simultaneously. This eliminates the need for any pre-clustering of the portfolio, e.g. by K-means clustering, and therefore presents our method as an entirely new and independent methodology.
Data-driven distributionally robust LQR with multiplicative noise
Date: 2020-05-26 00:00:00
Proceeding: L4DC 2020 6

We present a data-driven method for solving the linear quadratic regulator
problem for systems with multiplicative disturbances, the distribution of which
is only known through sample estimates. We adopt a distributionally robust
approach to cast the controller synthesis problem as semidefinite programs.
Using results from high dimensional statistics, the proposed methodology
ensures that their solution provides mean-square stabilizing controllers with
high probability even for low sample sizes. As sample size increases the
closed-loop cost approaches that of the optimal controller produced when the
distribution is known. We demonstrate the practical applicability and
performance of the method through a numerical experiment.
Optimal Dynamic Treatment Regimes and Partial Welfare Ordering
Authors: Sukjin Han
Date: 2019-12-20 00:00:00
Tasks: counterfactual

Dynamic treatment regimes are treatment allocations tailored to heterogeneous individuals. The optimal dynamic treatment regime is a regime that maximizes counterfactual welfare. We introduce a framework in which we can partially learn the optimal dynamic regime from observational data, relaxing the sequential randomization assumption commonly employed in the literature but instead using (binary) instrumental variables. We propose the notion of sharp partial ordering of counterfactual welfares with respect to dynamic regimes and establish mapping from data to partial ordering via a set of linear programs. We then characterize the identified set of the optimal regime as the set of maximal elements associated with the partial ordering. We relate the notion of partial ordering with a more conventional notion of partial identification using topological sorts. Practically, topological sorts can be served as a policy benchmark for a policymaker. We apply our method to understand returns to schooling and post-school training as a sequence of treatments by combining data from multiple sources. The framework of this paper can be used beyond the current context, e.g., in establishing rankings of multiple treatments or policies across different counterfactual scenarios.
Delay-Constrained Joint Power Control, User Detection and Passive Beamforming in Intelligent Reflecting Surface Assisted Uplink mmWave System
Authors: Yashuai Cao, Tiejun Lv, Zhipeng Lin, Wei Ni
Date: 2019-12-20 00:00:00

Millimeter-wave (mmWave) communications provide access to spectra with bandwidths and in abundance. However, the high susceptibility of mmWave to blockage imposes crucial challenges, especially to low-latency services. In this paper, a novel intelligent reflecting surface (IRS) assisted mmWave scheme is proposed to overcome the impact of blockage. New methods are developed to minimize the user power for a multi-user mmWave system by jointly optimizing individual device power, multi-user detection matrix and passive beamforming, subject to delay requirements. An alternating optimization framework is delivered so that the joint optimization problem can be decomposed into three subproblems iteratively optimized till convergence. In particular, closed-form expressions are devised for the update of the powers and multi-user detection vectors. The configuration of the IRS is formulated as a sum-of-inverse minimization (SIMin) fractional programming problem and solved by developing a new solution based on the alternating direction method of multipliers (ADMM). The configuration is also interpreted as a latency residual maximization problem, and solved efficiently by designing a new complex circle manifold optimization (CCMO) method. Numerical results corroborate the feasibility and effectiveness of our algorithms in terms of power saving, as compared with an existing semidefinite relaxation technique.
Reconfigurable Intelligent Surface Aided NOMA Networks
Date: 2019-12-20 00:00:00
Tasks: Diversity

Reconfigurable intelligent surfaces (RISs) constitute a promising performance
enhancement for next-generation (NG) wireless networks in terms of enhancing
both their spectrum efficiency (SE) and energy efficiency (EE). We conceive a
system for serving paired power-domain non-orthogonal multiple access (NOMA)
users by designing the passive beamforming weights at the RISs. In an effort to
evaluate the network performance, we first derive the best-case and worst-case
of new channel statistics for characterizing the effective channel gains. Then,
we derive the best-case and worst-case of our closed-form expressions derived
both for the outage probability and for the ergodic rate of the prioritized
user. For gleaning further insights, we investigate both the diversity orders
of the outage probability and the high-signal-to-noise (SNR) slopes of the
ergodic rate. We also derive both the SE and EE of the proposed network. Our
analytical results demonstrate that the base station (BS)-user links have
almost no impact on the diversity orders attained when the number of RISs is
high enough. Numerical results are provided for confirming that: i) the
high-SNR slope of the RIS-aided network is one; ii) the proposed RIS-aided NOMA
network has superior network performance compared to its orthogonal
counterpart.
ResLogit: A residual neural network logit model for data-driven choice modelling
Authors: Melvin Wong, Bilal Farooq
Date: 2019-12-20 00:00:00

This paper presents a novel deep learning-based travel behaviour choice model.Our proposed Residual Logit (ResLogit) model formulation seamlessly integrates a Deep Neural Network (DNN) architecture into a multinomial logit model. Recently, DNN models such as the Multi-layer Perceptron (MLP) and the Recurrent Neural Network (RNN) have shown remarkable success in modelling complex and noisy behavioural data. However, econometric studies have argued that machine learning techniques are a `black-box' and difficult to interpret for use in the choice analysis.We develop a data-driven choice model that extends the systematic utility function to incorporate non-linear cross-effects using a series of residual layers and using skipped connections to handle model identifiability in estimating a large number of parameters.The model structure accounts for cross-effects and choice heterogeneity arising from substitution, interactions with non-chosen alternatives and other effects in a non-linear manner.We describe the formulation, model estimation, interpretability and examine the relative performance and econometric implications of our proposed model.We present an illustrative example of the model on a classic red/blue bus choice scenario example. For a real-world application, we use a travel mode choice dataset to analyze the model characteristics compared to traditional neural networks and Logit formulations.Our findings show that our ResLogit approach significantly outperforms MLP models while providing similar interpretability as a Multinomial Logit model.
AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information
Date: 2019-12-20 00:00:00

With expeditious development of wireless communications, location
fingerprinting (LF) has nurtured considerable indoor location based services
(ILBSs) in the field of Internet of Things (IoT). For most pattern-matching
based LF solutions, previous works either appeal to the simple received signal
strength (RSS), which suffers from dramatic performance degradation due to
sophisticated environmental dynamics, or rely on the fine-grained physical
layer channel state information (CSI), whose intricate structure leads to an
increased computational complexity. Meanwhile, the harsh indoor environment can
also breed similar radio signatures among certain predefined reference points
(RPs), which may be randomly distributed in the area of interest, thus mightily
tampering the location mapping accuracy. To work out these dilemmas, during the
offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI
amplitude as location fingerprint, which shares the structural simplicity of
RSS while reserving the most location-specific statistical channel information.
Moreover, an additional angle of arrival (AoA) fingerprint can be accurately
retrieved from CSI phase through an enhanced subspace based algorithm, which
serves to further eliminate the error-prone RP candidates. In the online phase,
by exploiting both CSI amplitude and phase information, a novel bivariate
kernel regression scheme is proposed to precisely infer the target's location.
Results from extensive indoor experiments validate the superior localization
performance of our proposed system over previous approaches.
Dual Stochastic MPC for Systems with Parametric and Structural Uncertainty
Date: 2019-12-26 00:00:00
Proceeding: L4DC 2020 6
Tasks: Model Predictive Control

Designing controllers for systems affected by model uncertainty can prove to
be a challenge, especially when seeking the optimal compromise between the
conflicting goals of identification and control. This trade-off is explicitly
taken into account in the dual control problem, for which the exact solution is
provided by stochastic dynamic programming. Due to its computational
intractability, we propose a sampling-based approximation for systems affected
by both parametric and structural model uncertainty. The approach proposed in
this paper separates the prediction horizon in a dual and an exploitation part.
The dual part is formulated as a scenario tree that actively discriminates
among a set of potential models while learning unknown parameters. In the
exploitation part, achieved information is fixed for each scenario, and
open-loop control sequences are computed for the remainder of the horizon. As a
result, we solve one optimization problem over a collection of control
sequences for the entire horizon, explicitly considering the knowledge gained
in each scenario, leading to a dual model predictive control formulation.
Comparative Study of Two Extensions of Heston Stochastic Volatility Model
Date: 2019-12-21 00:00:00
Tasks: Vocal Bursts Valence Prediction

In the option valuation literature, the shortcomings of one factor stochastic
volatility models have traditionally been addressed by adding jumps to the
stock price process. An alternate approach in the context of option pricing and
calibration of implied volatility is the addition of a few other factors to the
volatility process. This paper contemplates two extensions of the Heston
stochastic volatility model. Out of which, one considers the addition of jumps
to the stock price process (a stochastic volatility jump diffusion model) and
another considers an additional stochastic volatility factor varying at a
different time scale (a multiscale stochastic volatility model). An empirical
analysis is carried out on the market data of options with different strike
prices and maturities, to compare the pricing performance of these models and
to capture their implied volatility fit. The unknown parameters of these models
are calibrated using the non-linear least square optimization. It has been
found that the multiscale stochastic volatility model performs better than the
Heston stochastic volatility model and the stochastic volatility jump diffusion
model for the data set under consideration.
Direction Finding for a Mixture of Single-Transmission and Dual-Transmission Signals
Date: 2019-12-21 00:00:00

Currently, most of existing research in direction of arrival (DOA) estimation
is focused on single signal transmission (SST) based signal. However, to make
full use of the degree of freedom provided by the system in the polarisation
domain, the dual signal transmission (DST) model has been adopted more and more
widely in wireless communications. In this work, a DOA estimation method for a
mixture of SST and DST signals (referred to as the mixed signal transmission
(MST) model) is proposed. To our best knowledge, this is the first time to
study the DOA estimation problem for such an MST model. There are two steps in
the proposed method, which deals with the two kinds of signals separately. The
performance of the proposed method is compared with the Cram\'{e}r-Rao Bound
(CRB) based on computer simulations.
Weakly reversible mass-action systems with infinitely many positive steady states
Authors: Balázs Boros, Gheorghe Craciun, Polly Y. Yu
Date: 2019-12-21 00:00:00

We show that weakly reversible mass-action systems can have a continuum of positive steady states, coming from the zeroes of a multivariate polynomial. Moreover, the same is true of systems whose underlying reaction network is reversible and has a single connected component. In our construction, we relate operations on the reaction network to the multivariate polynomial occurring as a common factor in the system of differential equations.