pwc-archive/papers-with-abstracts huggingface dataset:
On the design of fixed-gain tracking filters by pole placement: Or an introduction to applied signals-and-systems theory for engineers
Authors: Hugh Lachlan Kennedy
Date: 2021-10-01 00:00:00

The Kalman filter computes the optimal variable-gain using prior knowledge of the initial state and random (process and measurement) noise distributions, which are assumed to be Gaussian with known variance. However, when these distributions are unknown, the Kalman filter is not necessarily optimal and other simpler state-estimators, such as fixed-gain ({\alpha}, {\alpha}-\b{eta} or {\alpha}-\b{eta}-{\gamma} etc.) filters may be sufficient. When such filters are used as low-complexity state-estimators in embedded tracking systems, the fixed gain parameters are usually set equal to the steady-state gains of the corresponding Kalman filter. An alternative procedure, that does not rely prior distributions, based on Luenberger observers, is presented here. It is suggested that the arbitrary placement of closed-loop state-observer poles is a simple and intuitive way of tuning the transient and steady-state response of a fixed-gain tracking filter when prior distributions are unknown. All poles are placed inside the unit circle on the positive real axis of the complex z-plane at p for a well damped response and a configurable bandwidth. Transient bias errors, e.g. due to target manoeuvres or process modelling errors, decrease as p=0 is approached for a wider bandwidth. Steady-state random errors, e.g. due to sensor noise, decrease as p=1 is approached for a narrower bandwidth. Thus the p parameter (with 0<p<1) may be interpreted as a dimensionless smoothing factor. This tutorial-style report examines state-observer design by pole placement, which is a standard procedure for feedback controls but unusual for tracking filters, due to the success and popularity of the Kalman filter. As Bayesian trackers are designed via statistical modelling, not by pole-zero placement in the complex plane, the underlying principles of linear time-invariant signals and systems are also reviewed.
Information Design for a Non-atomic Service Scheduling Game
Authors: Nasimeh Heydaribeni, Ketan Savla
Date: 2021-09-30 00:00:00
Tasks: Scheduling

We study an information design problem for a non-atomic service scheduling game. The service starts at a random time and there is a continuum of agent population who have a prior belief about the service start time but do not observe the actual realization of it. The agents want to make decisions of when to join the queue in order to avoid long waits in the queue or not to arrive earlier than the service has started. There is a planner who knows when the service starts and makes suggestions to the agents about when to join the queue through an obedient direct signaling strategy, in order to minimize the average social cost. We characterize the full information and the no information equilibria and we show in what conditions it is optimal for the planner to reveal the full information to the agents. Further, by imposing appropriate assumptions on the model, we formulate the information design problem as a generalized problem of moments (GPM) and use computational tools developed for such problems to solve the problem numerically.
Trajectory Planning with Deep Reinforcement Learning in High-Level Action Spaces
Authors: Kyle R. Williams, Rachel Schlossman, Daniel Whitten, Joe Ingram, Srideep Musuvathy, Anirudh Patel, James Pagan, Kyle A. Williams, Sam Green, Anirban Mazumdar, Julie Parish
Date: 2021-09-30 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL), Trajectory Planning, Vocal Bursts Intensity Prediction

This paper presents a technique for trajectory planning based on continuously parameterized high-level actions (motion primitives) of variable duration. This technique leverages deep reinforcement learning (Deep RL) to formulate a policy which is suitable for real-time implementation. There is no separation of motion primitive generation and trajectory planning: each individual short-horizon motion is formed during the Deep RL training to achieve the full-horizon objective. Effectiveness of the technique is demonstrated numerically on a well-studied trajectory generation problem and a planning problem on a known obstacle-rich map. This paper also develops a new loss function term for policy-gradient-based Deep RL, which is analogous to an anti-windup mechanism in feedback control. We demonstrate the inclusion of this new term in the underlying optimization increases the average policy return in our numerical example.
Theoretical Analysis for Extended Target Recovery in Randomized Stepped Frequency Radars
Authors: Lei Wang, Tianyao Huang, Yimin Liu
Date: 2019-08-08 00:00:00

Randomized Stepped Frequency Radar (RSFR) is very attractive for tasks under complex electromagnetic environment. Due to the synthetic high range resolution in RSRFs, a target usually occupies a series of range cells and is called an extended target. To reconstruct the range-Doppler information in a RSFR, previous studies based on sparse recovery mainly exploit the sparsity of the target scene but do not adequately address the extended-target characteristics, which exist in many practical applications. Block sparsity, which combines the sparsity and the target extension, better characterizes a priori knowledge of the target scene in a wideband RSFR. This paper studies the RSFR range-Doppler reconstruction problem using block sparse recovery. Particularly, we theoretically analyze the block coherence and spectral norm of the observation matrix in RSFR and build a bound on the parameters of the radar, under which the exact recovery of the range-Doppler information is guaranteed. Both simulation and field experiment results demonstrate the superiority of the block sparse recovery over conventional sparse recovery in RSFRs.
Collective Cell Movement in Cell-Scale Tension Gradient on Tissue Interface
Authors: Katsuyoshi Matsushita, Hidenori Hashimura, Hidekazu Kuwayama, Koichi Fujimoto
Date: 2021-10-01 00:00:00

In this paper, we examine the emergence of cell flow induced by a tension gradient on a tissue interface as in the case of the Marangoni flow on liquid interface. We consider the molecule density polarity of the heterophilic adhesion between tissues as the origin of the tension gradient. By applying the cellular Potts model, we demonstrate that polarization in concentration (i.e., intracellular localization) of heterophilic adhesion molecules can induce a cell flow similar to the Marangoni flow. In contrast to the ordinary Marangoni flow, this flow is oriented in the opposite direction to that of the tension gradient. The optimal range of adhesion strength is also identified for the existence of this flow.
Kinetic modelling of epidemic dynamics: social contacts, control with uncertain data, and multiscale spatial dynamics
Authors: Giacomo Albi, Giulia Bertaglia, Walter Boscheri, Giacomo Dimarco, Lorenzo Pareschi, Giuseppe Toscani, Mattia Zanella
Date: 2021-10-01 00:00:00

In this survey we report some recent results in the mathematical modeling of epidemic phenomena through the use of kinetic equations. We initially consider models of interaction between agents in which social characteristics play a key role in the spread of an epidemic, such as the age of individuals, the number of social contacts, and their economic wealth. Subsequently, for such models, we discuss the possibility of containing the epidemic through an appropriate optimal control formulation based on the policy maker's perception of the progress of the epidemic. The role of uncertainty in the data is also discussed and addressed. Finally, the kinetic modeling is extended to spatially dependent settings using multiscale transport models that can characterize the impact of movement dynamics on epidemic advancement on both one-dimensional networks and realistic two-dimensional geographic settings.
Memory-Efficient Centroid Decomposition for Long Time Series
Authors: Mourad Khayati, Michael Böhlen, Johann Gamper
Date: 2017-04-01 00:00:00
Proceeding: IEEE 30th International Conference on Data Engineering (ICDE) 2017 4
Tasks: Time Series, Time Series Analysis

Real-world applications that deal with time series data often rely on matrix decomposition techniques, such as the Singular Value Decomposition (SVD). The Centroid Decomposition (CD) approximates the Singular Value Decomposition, but does not scale to long time series because of the quadratic space complexity of the sign vector computation.

In this paper, we propose a greedy algorithm, termed Scalable Sign Vector (SSV), to efficiently determine sign vectors for CD applications with long time series, i.e., where the number of rows (observations) is much larger than the number of columns (time series). The SSV algorithm starts with a sign vector consisting of only 1s and iteratively changes the sign of the element that maximizes the benefit. The space complexity of the SSV algorithm is linear in the length of the time series. We provide proofs for the scalability, the termination, and the correctness of the SSV algorithm. Experiments with real-world hydrological time series and data sets from the UCR repository validate the analytical results and show the scalability of SSV.
Relative Contagiousness of Emerging Virus Variants: An Analysis of the Alpha, Delta, and Omicron SARS-CoV-2 Variants
Authors: Peter Reinhard Hansen
Date: 2021-10-01 00:00:00

We propose a simple dynamic model for estimating the relative contagiousness of two virus variants. Maximum likelihood estimation and inference is conveniently invariant to variation in the total number of cases over the sample period and can be expressed as a logistic regression. We apply the model to Danish SARS-CoV-2 variant data. We estimate the reproduction numbers of Alpha and Delta to be larger than that of the ancestral variant by a factor of 1.51 [CI 95%: 1.50, 1.53] and 3.28 [CI 95%: 3.01, 3.58], respectively. In a predominately vaccinated population, we estimate Omicron to be 3.15 [CI 95%: 2.83, 3.50] times more infectious than Delta. Forecasting the proportion of an emerging virus variant is straight forward and we proceed to show how the effective reproduction number for a new variant can be estimated without contemporary sequencing results. This is useful for assessing the state of the pandemic in real time as we illustrate empirically with the inferred effective reproduction number for the Alpha variant.
Universal Database for Economic Complexity
Authors: Aurelio Patelli, Andrea Zaccaria, Luciano Pietronero
Date: 2021-10-01 00:00:00

We present an integrated database suitable for the investigations of the Economic development of countries by using the Economic Fitness and Complexity framework. Firstly, we implement machine learning techniques to reconstruct the database of Trade of Services and we integrate it with the database of the Trade of the physical Goods, generating a complete view of the International Trade and denoted the Universal database. Using this data, we derive a statistically significant network of interaction of the Economic activities, where preferred paths of development and clusters of High-Tech industries naturally emerge. Finally, we compute the Economic Fitness, an algorithmic assessment of the competitiveness of countries, removing the unexpected misbehaviour of Economies under-represented by the sole consideration of the Trade of the physical Goods.
Economic valuation of tourism of the Sundarban Mangroves, Bangladesh
Authors: Mohammad Nur Nobi, A. H. M. Raihan Sarker, Biswajit Nath, Eivin Røskaft, Ma Suza, Paul Kvinta
Date: 2021-10-01 00:00:00

The Sundarban Reserve Forest (SRF) of Bangladesh provides tourism services to local and international visitors. Indeed, tourism is one of the major ecosystem services that this biodiversity-rich mangrove forest provides. Through a convenient sampling technique, 421 tourist respondents were interviewed to assess their willingness to pay for the tourism services of the Sundarban, using the Zonal Travel Cost Method (ZTCM). The estimated annual economic contribution of tourism in the Sundarban mangroves to the Bangladesh economy is USD 53 million. The findings of this study showed that facilities for watching wildlife and walking inside the forest can increase the number of tourists in the SRF. The findings also show that the availability of information like forest maps, wildlife precautionary signs, and danger zones would increase the number of tourists as well. Thus, the government of Bangladesh should consider increasing visitor entry fees to fund improvements and to enhance the ecotourism potential of the Sundarban mangroves.
Capital Demand Driven Business Cycles: Mechanism and Effects
Authors: Karl Naumann-Woleske, Michael Benzaquen, Maxim Gusev, Dimitri Kroujiline
Date: 2021-09-30 00:00:00

We develop a tractable macroeconomic model that captures dynamic behaviors across multiple timescales, including business cycles. The model is anchored in a dynamic capital demand framework reflecting an interactions-based process whereby firms determine capital needs and make investment decisions at the micro level. We derive equations for aggregate demand from this micro setting and embed them in the Solow growth economy. As a result, we obtain a closed-form dynamical system with which we study economic fluctuations and their impact on long-term growth. For realistic parameters, the model has two attracting equilibria: one at which the economy contracts and one at which it expands. This bi-stable configuration gives rise to quasiperiodic fluctuations, characterized by the economy's prolonged entrapment in either a contraction or expansion mode punctuated by rapid alternations between them. We identify the underlying endogenous mechanism as a coherence resonance phenomenon. In addition, the model admits a stochastic limit cycle likewise capable of generating quasiperiodic fluctuations; however, we show that these fluctuations cannot be realized as they induce unrealistic growth dynamics. We further find that while the fluctuations powered by coherence resonance can cause substantial excursions from the equilibrium growth path, such deviations vanish in the long run as supply and demand converge.
The emergence of cooperation from shared goals in the Systemic Sustainability Game of common pool resources
Authors: Chengyi Tu, Paolo DOdorico, Zhe Li, Samir Suweis
Date: 2021-10-01 00:00:00

The sustainable use of common-pool resources (CPRs) is a major environmental governance challenge because of their possible over-exploitation. Research in this field has overlooked the feedback between user decisions and resource dynamics. Here we develop an online game to perform a set of experiments in which users of the same CPR decide on their individual harvesting rates, which in turn depend on the resource dynamics. We show that, if users share common goals, a high level of self-organized cooperation emerges, leading to long-term resource sustainability. Otherwise, selfish/individualistic behaviors lead to resource depletion ("Tragedy of the Commons"). To explain these results, we develop an analytical model of coupled resource-decision dynamics based on optimal control theory and show how this framework reproduces the empirical results.
Truly Costly Search and Word-of-Mouth Communication
Authors: Atabek Atayev
Date: 2021-09-30 00:00:00

In markets with search frictions, consumers can acquire information about goods either through costly search or from friends via word-of-mouth (WOM) communication. How do sellers' market power react to a very large increase in the number of consumers' friends with whom they engage in WOM? The answer to the question depends on whether consumers are freely endowed with price information. If acquiring price quotes is costly, equilibrium prices are dispersed and the expected price is higher than the marginal cost of production. This implies that firms retain market power even if price information is disseminated among a very large number of consumers due to technological progress, such as social networking websites.
A Wideband Signal Recognition Dataset
Authors: Nathan West, Timothy O'Shea, Tamoghna Roy
Date: 2021-10-01 00:00:00
Tasks: Classification

Signal recognition is a spectrum sensing problem that jointly requires detection, localization in time and frequency, and classification. This is a step beyond most spectrum sensing work which involves signal detection to estimate "present" or "not present" detections for either a single channel or fixed sized channels or classification which assumes a signal is present. We define the signal recognition task, present the metrics of precision and recall to the RF domain, and review recent machine-learning based approaches to this problem. We introduce a new dataset that is useful for training neural networks to perform these tasks and show a training framework to train wideband signal recognizers.
A Bayesian approach to location estimation of mobile devices from mobile network operator data
Authors: Martijn Tennekes, Yvonne A. P. M. Gootzen
Date: 2021-10-01 00:00:00

Mobile network operator (MNO) data are a rich data source for official statistics, such as present population, mobility, migration, and tourism. Estimating the geographic location of mobile devices is an essential step for statistical inference. Most studies use the Voronoi tessellation for this, which is based on the assumption that mobile devices are always connected to the nearest radio cell. This paper uses a modular Bayesian approach, allowing for different modules of prior knowledge about where devices are expected to be, and different modules for the likelihood of connection given a geographic location. We discuss and compare the use of several prior modules, including one that is based on land use. We show that the Voronoi tessellation can be used as a likelihood module. Alternatively, we propose a signal strength model using radio cell properties such as antenna height, propagation direction, and power. Using Bayes' rule, we derive a posterior probability distribution that is an estimate for the geographic location, which can be used for further statistical inference. We describe the method and provide illustrations of a fictional example that resembles a real-world situation. The method has been implemented in the R packages mobloc and mobvis, which are briefly described.
LiDAR Aided Human Blockage Prediction for 6G
Authors: Dileepa Marasinghe, Nandana Rajatheva, Matti Latva-aho
Date: 2021-10-01 00:00:00
Tasks: Prediction

Leveraging higher frequencies up to THz band paves the way towards a faster network in the next generation of wireless communications. However, such shorter wavelengths are susceptible to higher scattering and path loss forcing the link to depend predominantly on the line-of-sight (LOS) path. Dynamic movement of humans has been identified as a major source of blockages to such LOS links. In this work, we aim to overcome this challenge by predicting human blockages to the LOS link enabling the transmitter to anticipate the blockage and act intelligently. We propose an end-to-end system of infrastructure-mounted LiDAR sensors to capture the dynamics of the communication environment visually, process the data with deep learning and ray casting techniques to predict future blockages. Experiments indicate that the system achieves an accuracy of 87% predicting the upcoming blockages while maintaining a precision of 78% and a recall of 79% for a window of 300 ms.
Reformulating the SIR model in terms of the number of COVID-19 detected cases: well-posedness of the observational model
Authors: Eduard Campillo-Funollet, Hayley Wragg, James Van Yperen, Duc-Lam Duong, Anotida Madzvamuse
Date: 2021-10-01 00:00:00
Tasks: Epidemiology

Compartmental models are popular in the mathematics of epidemiology for their simplicity and wide range of applications. Although they are typically solved as initial value problems for a system of ordinary differential equations, the observed data is typically akin of a boundary value type problem: we observe some of the dependent variables at given times, but we do not know the initial conditions. In this paper, we reformulate the classical Susceptible-Infectious-Recovered system in terms of the number of detected positive infected cases at different times, we then prove the existence and uniqueness of a solution to the derived boundary value problem and then present a numerical algorithm to approximate the solution.
Effects of stage structure on coexistence: mixed benefits
Authors: Gaël Bardon, Frédéric Barraquand
Date: 2021-10-01 00:00:00

The properties of competition models where all individuals are identical are relatively well-understood; however, juveniles and adults can experience or generate competition differently. We study here less well-known structured competition models in discrete time that allow multiple life history parameters to depend on adult or juvenile population densities. A numerical study with Ricker density-dependence suggested that when competition coefficients acting on juvenile survival and fertility reflect opposite competitive hierarchies, stage structure could foster coexistence. We revisit and expand those results. First, through a Beverton-Holt two-species juvenile-adult model, we confirm that these findings do not depend on the specifics of density-dependence or life cycles, and obtain analytical expressions explaining how this coexistence emerging from stage structure can occur. Second, we show using a community-level sensitivity analysis that such emergent coexistence is robust to perturbations of parameter values. Finally, we ask whether these results extend from two to many species, using simulations. We show that they do not, as coexistence emerging from stage structure is only seen for very similar life-history parameters. Such emergent coexistence is therefore not likely to be a key mechanism of coexistence in very diverse ecosystems, although it may contribute to explaining coexistence of certain pairs of intensely competing species.
Surveillance Testing for Rapid Detection of Outbreaks in Facilities
Authors: Yanyue Ding, Sudesh K. Agrawal, Jincheng Cao, Lauren Meyers, John J. Hasenbein
Date: 2021-10-01 00:00:00

This paper develops an agent-based disease spread model on a contact network in an effort to guide efforts at surveillance testing in small to moderate facilities such as nursing homes and meat-packing plants. The model employs Monte Carlo simulations of viral spread sample paths in the contact network. The original motivation was to detect COVID-19 outbreaks quickly in such facilities, but the model can be applied to any communicable disease. In particular, the model provides guidance on how many test to administer each day and on the importance of the testing order among staff or workers.