pwc-archive/papers-with-abstracts huggingface dataset:
Approximate Stability Radius Analysis and Design in Linear Systems
Authors: Ananta Kant Rai, Vaibhav Katewa
Date: 2024-03-18 00:00:00

The robustness of the stability properties of dynamical systems in the presence of unknown/adversarial perturbations to system parameters is a desirable property. In this paper, we present methods to efficiently compute and improve the approximate stability radius of linear time-invariant systems. We propose two methods to derive closed-form expressions of approximate stability radius, and use these to re-design the system matrix to increase the stability radius. Our numerical studies show that the approximations work well and are able to improve the robustness of the stability of the system.
Kernel Modelling of Fading Memory Systems
Authors: Yongkang Huo, Thomas Chaffey, Rodolphe Sepulchre
Date: 2024-03-18 00:00:00

The paper is a follow-up of the recently introduced kernel-based framework to identify nonlinear input-output systems regularized by desirable input-output incremental properties. Assuming that the system has fading memory, we propose to learn the functional that maps the past input to the present output rather than the operator mapping input trajectories to output trajectories. While retaining the benefits of the previously proposed framework, this modification simplifies the selection of the kernel, enforces causality, and enables temporal simulation.
Data-Enabled Predictive Iterative Control
Authors: Kai Zhang, Riccardo Zuliani, Efe C. Balta, John Lygeros
Date: 2024-03-18 00:00:00

This work introduces the Data-Enabled Predictive iteRative Control (DeePRC) algorithm, a direct data-driven approach for iterative LTI systems. The DeePRC learns from previous iterations to improve its performance and achieves the optimal cost. By utilizing a tube-based variation of the DeePRC scheme, we propose a two-stage approach that enables safe active exploration using a left-kernel-based input disturbance design. This method generates informative trajectories to enrich the historical data, which extends the maximum achievable prediction horizon and leads to faster iteration convergence. In addition, we present an end-to-end formulation of the two-stage approach, integrating the disturbance design procedure into the planning phase. We showcase the effectiveness of the proposed algorithms on a numerical experiment.
Stochastic Mean Field Game for Strategic Bidding of Consumers in Congested Distribution Networks
Authors: Amirreza Silani, Simon H. Tindemans
Date: 2024-03-18 00:00:00

The rapid increase of photovoltaic cells, batteries, and Electric Vehicles (EVs) in electric grids can result in congested distribution networks. An alternative to enhancing network capacity is a redispatch market, allowing Distribution System Operators (DSOs) to alleviate congested networks by asking energy consumers to change their consumption schedules. However, energy consumers can anticipate the redispatch market outcomes and strategically adjust their bids in the day-ahead market. This behaviour, known as increase-decrease gaming, can result in the exacerbation of congestion and enable energy consumers to gain windfall profits from the DSO. In this paper, we consider a two-stage problem consisting of the day-ahead market (first stage) and redispatch market (second stage). Then, we model the increase-decrease game for large populations of energy consumers in power networks using a stochastic mean field game approach. The agents (energy consumers) maximize their individual welfare in the day-ahead market with anticipation of the redispatch market. We show that all the agent strategies are ordered along their utilities and there exists a unique Nash equilibrium for this game.
Symmetry-based Abstraction Algorithm for Accelerating Symbolic Control Synthesis
Authors: Hussein Sibai, Sacha Huriot, Tyler Martin, Murat Arcak
Date: 2024-03-18 00:00:00
Tasks: Position

We propose an efficient symbolic control synthesis algorithm for equivariant continuous-time dynamical systems to satisfy reach-avoid specifications. The algorithm exploits dynamical symmetries to construct lean abstractions to avoid redundant computations during synthesis. Our proposed algorithm adds another layer of abstraction over the common grid-based discrete abstraction before solving the synthesis problem. It combines each set of grid cells that are at a similar relative position from the targets and nearby obstacles, defined by the symmetries, into a single abstract state. It uses this layer of abstraction to guide the order by which actions are explored during synthesis over the grid-based abstraction. We demonstrate the potential of our algorithm by synthesizing a reach-avoid controller for a 3-dimensional ship model with translation and rotation symmetries in the special Euclidean group SE(2).
Enlightening the blind spot of the Michaelis-Menten rate law: The role of relaxation dynamics in molecular complex formation
Authors: Junghun Chae, Roktaek Lim, Thomas L. P. Martin, Cheol-Min Ghim, Pan-Jun Kim
Date: 2024-03-18 00:00:00

The century-long Michaelis-Menten rate law and its modifications in the modeling of biochemical rate processes stand on the assumption that the concentration of the complex of interacting molecules, at each moment, rapidly approaches an equilibrium (quasi-steady state) compared to the pace of molecular concentration changes. Yet, in the case of actively time-varying molecular concentrations with transient or oscillatory dynamics, the deviation of the complex profile from the quasi-steady state becomes relevant. A recent theoretical approach, known as the effective time-delay scheme (ETS), suggests that the delay by the relaxation time of molecular complex formation contributes to the substantial breakdown of the quasi-steady state assumption. Here, we systematically expand this ETS and inquire into the comprehensive roles of relaxation dynamics in complex formation. Through the modeling of rhythmic protein-protein and protein-DNA interactions and the mammalian circadian clock, our analysis reveals the effect of the relaxation dynamics beyond the time delay, which extends to the dampening of changes in the complex concentration with a reduction in the oscillation amplitude compared to the quasi-steady state. Interestingly, the combined effect of the time delay and amplitude reduction shapes both qualitative and quantitative oscillatory patterns such as the emergence and variability of the mammalian circadian rhythms. These findings highlight the downside of the routine assumption of quasi-steady states and enhance the mechanistic understanding of rich time-varying biomolecular processes.
Evaluation cartographique du niveau de potentialit{é}s {é}cologiques des sites des partenaires (CARPO). Cadre m{é}thodologique V0
Authors: Santiago Forero
Date: 2024-03-18 00:00:00

The Cartographic Assessment of Ecological Potential (CARPO) allows users to characterize the ecological context of a group of sites at several scales (its perimeter and surrounding area) and on the basis of 4 themes: biodiversity zonings, land use (major types of environment, natural character and degree of permeability of territories), ecological connectivity (biodiversity reservoirs and corridors, non-fragmentation of the sector studied), as well as the richness of the territory in terms of heritage species. It is based on a battery of 8 indicators linked to these themes and a system of evaluation thresholds, used to define 3 levels of ecological potential (average, strong and very strong) for each indicator and at each scale of study. The level of potentiality reflects the importance that an ecological element takes on in a study area, and therefore the degree of contribution and responsibility of a site in maintaining this element favorable to biodiversity. It also translates into an alert level for biodiversity that needs to be taken into account at a site.The ecological diagnosis is materialized by means of a cartographic atlas containing a set of commented maps and figures, as well as evaluation grids and radars for the various indicators.CARPO is a decision-making tool, as it enables users to compare the ecological potential within a site with that of its surroundings, in order to define preservation, management or restoration actions; and to compare the extent of potential between the different sites in the group, with a goal of prioritizing action.This guide presents the methodological framework for applying CARPO to a group of sites.
Stochastic compartment model with mortality and its application to epidemic spreading in complex networks
Authors: Teo Granger, Thomas M. Michelitsch, Michael Bestehorn, Alejandro P. Riascos, Bernard A. Collet
Date: 2024-03-18 00:00:00

We study epidemic spreading in complex networks by a multiple random walker approach. Each walker performs an independent simple Markovian random walk on a complex undirected (ergodic) random graph where we focus on Barab\'asi-Albert (BA), Erd\"os-R\'enyi (ER) and Watts-Strogatz (WS) types. Both, walkers and nodes can be either susceptible (S) or infected and infectious (I) representing their states of health. Susceptible nodes may be infected by visits of infected walkers, and susceptible walkers may be infected by visiting infected nodes. No direct transmission of the disease among walkers (or among nodes) is possible. This model mimics a large class of diseases such as Dengue and Malaria with transmission of the disease via vectors (mosquitos). Infected walkers may die during the time span of their infection introducing an additional compartment D of dead walkers. Infected nodes never die and always recover from their infection after a random finite time. We derive stochastic evolution equations for the mean-field compartmental populations with mortality of walkers and delayed transitions among the compartments. From linear stability analysis, we derive the basic reproduction numbers R M , R 0 with and without mortality, respectively, and prove that R M < R 0 . For R M , R 0 > 1 the healthy state is unstable whereas for zero mortality a stable endemic equilibrium exists (independent of the initial conditions) which we obtained explicitly. We observe that the solutions of the random walk simulations in the considered networks agree well with the mean-field solutions for strongly connected graph topologies, whereas less well for weakly connected structures and for diseases with high mortality.
Composite likelihood estimation of stationary Gaussian processes with a view toward stochastic volatility
Authors: Mikkel Bennedsen, Kim Christensen, Peter Christensen
Date: 2024-03-19 00:00:00
Tasks: Gaussian Processes

We develop a framework for composite likelihood inference of parametric continuous-time stationary Gaussian processes. We derive the asymptotic theory of the associated maximum composite likelihood estimator. We implement our approach on a pair of models that has been proposed to describe the random log-spot variance of financial asset returns. A simulation study shows that it delivers good performance in these settings and improves upon a method-of-moments estimation. In an application, we inspect the dynamic of an intraday measure of spot variance computed with high-frequency data from the cryptocurrency market. The empirical evidence supports a mechanism, where the short- and long-term correlation structure of stochastic volatility are decoupled in order to capture its properties at different time scales.
Inflation Target at Risk: A Time-varying Parameter Distributional Regression
Authors: Yunyun Wang, Tatsushi Oka, Dan Zhu
Date: 2024-03-19 00:00:00
Tasks: regression

Macro variables frequently display time-varying distributions, driven by the dynamic and evolving characteristics of economic, social, and environmental factors that consistently reshape the fundamental patterns and relationships governing these variables. To better understand the distributional dynamics beyond the central tendency, this paper introduces a novel semi-parametric approach for constructing time-varying conditional distributions, relying on the recent advances in distributional regression. We present an efficient precision-based Markov Chain Monte Carlo algorithm that simultaneously estimates all model parameters while explicitly enforcing the monotonicity condition on the conditional distribution function. Our model is applied to construct the forecasting distribution of inflation for the U.S., conditional on a set of macroeconomic and financial indicators. The risks of future inflation deviating excessively high or low from the desired range are carefully evaluated. Moreover, we provide a thorough discussion about the interplay between inflation and unemployment rates during the Global Financial Crisis, COVID, and the third quarter of 2023.
Performance, Knowledge Acquisition and Satisfaction in Self-selected Groups: Evidence from a Classroom Field Experiment
Authors: Julius Düker, Alexander Rieber
Date: 2024-03-19 00:00:00

We investigate how to efficiently set up work groups to boost group productivity, individual satisfaction, and learning. Therefore, we conduct a natural field experiment in a compulsory undergraduate course and study differences between self-selected and randomly assigned groups. We find that self-selected groups perform significantly worse on group assignments. Yet, students in self-selected groups learn more and are more satisfied than those in randomly assigned groups. The effect of allowing students to pick group members dominates the effect of different group compositions in self-selected groups: When controlling for the skill, gender, and home region composition of groups, the differences between self-selected and randomly formed groups persist almost unaltered. The distribution of GitHub commits per group reveals that the better average performance of randomly assigned groups is mainly driven by highly skilled individuals distributed over more groups due to the assignment mechanism. Moreover, these highly skilled individuals contribute more to the group in randomly formed groups. We argue that this mechanism explains why self-selected groups perform worse on the projects but acquire more knowledge than randomly formed groups. These findings are relevant for setting up workgroups in academic, business, and governmental organizations when tasks are not constrained to the skill set of specific individuals.
When is Trust Robust?
Authors: Luca Anderlini, Larry Samuelson, Daniele Terlizzese
Date: 2024-03-19 00:00:00

We examine an economy in which interactions are more productive if agents can trust others to refrain from cheating. Some agents are scoundrels, who cheat at every opportunity, while others cheat only if the cost of cheating, a decreasing function of the proportion of cheaters, is sufficiently low. The economy exhibits multiple equilibria. As the proportion of scoundrels in the economy declines, the high-trust equilibrium can be disrupted by arbitrarily small perturbations or by arbitrarily small infusions of low-trust agents, while the low-trust equilibrium becomes impervious to perturbations and infusions of high-trust agents. Scoundrels may thus have the effect of making trust more robust.
Learning Macroeconomic Policies through Dynamic Stackelberg Mean-Field Games
Authors: Qirui Mi, Zhiyu Zhao, Chengdong Ma, Siyu Xia, Yan Song, Mengyue Yang, Jun Wang, Haifeng Zhang
Date: 2024-03-14 00:00:00
Tasks: Decision Making

Macroeconomic outcomes emerge from individuals' decisions, making it essential to model how agents interact with macro policy via consumption, investment, and labor choices. We formulate this as a dynamic Stackelberg game: the government (leader) sets policies, and agents (followers) respond by optimizing their behavior over time. Unlike static models, this dynamic formulation captures temporal dependencies and strategic feedback critical to policy design. However, as the number of agents increases, explicitly simulating all agent-agent and agent-government interactions becomes computationally infeasible. To address this, we propose the Dynamic Stackelberg Mean Field Game (DSMFG) framework, which approximates these complex interactions via agent-population and government-population couplings. This approximation preserves individual-level feedback while ensuring scalability, enabling DSMFG to jointly model three core features of real-world policymaking: dynamic feedback, asymmetry, and large scale. We further introduce Stackelberg Mean Field Reinforcement Learning (SMFRL), a data-driven algorithm that learns the leader's optimal policies while maintaining personalized responses for individual agents. Empirically, we validate our approach in a large-scale simulated economy, where it scales to 1,000 agents (vs. 100 in prior work) and achieves a fourfold increase in GDP over classical economic methods and a nineteenfold improvement over the static 2022 U.S. federal income tax policy.
Latent CLAP Loss for Better Foley Sound Synthesis
Authors: Tornike Karchkhadze, Hassan Salami Kavaki, Mohammad Rasool Izadi, Bryce Irvin, Mikolaj Kegler, Ari Hertz, Shuo Zhang, Marko Stamenovic
Date: 2024-03-18 00:00:00
Tasks: FAD

Foley sound generation, the art of creating audio for multimedia, has recently seen notable advancements through text-conditioned latent diffusion models. These systems use multimodal text-audio representation models, such as Contrastive Language-Audio Pretraining (CLAP), whose objective is to map corresponding audio and text prompts into a joint embedding space. AudioLDM, a text-to-audio model, was the winner of the DCASE2023 task 7 Foley sound synthesis challenge. The winning system fine-tuned the model for specific audio classes and applied a post-filtering method using CLAP similarity scores between output audio and input text at inference time, requiring the generation of extra samples, thus reducing data generation efficiency. We introduce a new loss term to enhance Foley sound generation in AudioLDM without post-filtering. This loss term uses a new module based on the CLAP mode-Latent CLAP encode-to align the latent diffusion output with real audio in a shared CLAP embedding space. Our experiments demonstrate that our method effectively reduces the Frechet Audio Distance (FAD) score of the generated audio and eliminates the need for post-filtering, thus enhancing generation efficiency.
Ziv-Zakai-Optimal OFDM Resource Allocation for Time-of-Arrival Estimation
Authors: Andrew M. Graff, Todd E. Humphreys
Date: 2024-03-19 00:00:00

This paper presents methods of optimizing the placement and power allocations of pilots in an orthogonal frequency-division multiplexing (OFDM) signal to minimize time-of-arrival (TOA) estimation errors under power and resource allocation constraints. TOA errors in this optimization are quantified through the Ziv-Zakai bound (ZZB), which captures error thresholding effects caused by sidelobes in the signal's autocorrelation function (ACF) which are not captured by the Cramer-Rao lower bound. This paper is the first to solve for these ZZB-optimal allocations in the context of OFDM signals, under integer resource allocation constraints, and under both coherent and noncoherent reception. Under convex constraints, the optimization of the ZZB is proven to be convex; under integer constraints, the optimization is lower bounded by a convex relaxation and a branch-and-bound algorithm is proposed for efficiently allocating pilot resources. These allocations are evaluated by their ZZBs and ACFs, compared against a typical uniform allocation, and deployed on a software-defined radio TOA measurement platform to demonstrate their applicability in real-world systems.
Large-Scale RIS Enabled Air-Ground Channels: Near-Field Modeling and Analysis
Authors: Hao Jiang, Wangqi Shi, Zaichen Zhang, Cunhua Pan, Qingqing Wu, Feng Shu, Ruiqi Liu, Jiangzhou Wang
Date: 2024-03-19 00:00:00

Existing works mainly rely on the far-field planar-wave-based channel model to assess the performance of reconfigurable intelligent surface (RIS)-enabled wireless communication systems. However, when the transmitter and receiver are in near-field ranges, this will result in relatively low computing accuracy. To tackle this challenge, we initially develop an analytical framework for sub-array partitioning. This framework divides the large-scale RIS array into multiple sub-arrays, effectively reducing modeling complexity while maintaining acceptable accuracy. Then, we develop a beam domain channel model based on the proposed sub-array partition framework for large-scale RIS-enabled UAV-to-vehicle communication systems, which can be used to efficiently capture the sparse features in RIS-enabled UAV-to-vehicle channels in both near-field and far-field ranges. Furthermore, some important propagation characteristics of the proposed channel model, including the spatial cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), frequency correlation functions (CFs), and channel capacities with respect to the different physical features of the RIS and non-stationary properties of the channel model are derived and analyzed. Finally, simulation results are provided to demonstrate that the proposed framework is helpful to achieve a good tradeoff between model complexity and accuracy for investigating the channel propagation characteristics, and therefore providing highly-efficient communications in RIS-enabled UAV-to-vehicle wireless networks.
Small Distance Increment Method for Measuring Complex Permittivity With mmWave Radar
Authors: Hang Song, Hyun Joon Kim, Mingxia Wan, Bo Wei, Takamaro Kikkawa, Jun-ichi Takada
Date: 2024-03-19 00:00:00

Measuring the complex permittivity of material is essential in many scenarios such as quality check and component analysis. Generally, measurement methods for characterizing the material are based on the usage of vector network analyzer, which is large and not easy for on-site measurement, especially in high frequency range such as millimeter wave (mmWave). In addition, some measurement methods require the destruction of samples, which is not suitable for non-destructive inspection. In this work, a small distance increment (SDI) method is proposed to non-destructively measure the complex permittivity of material. In SDI, the transmitter and receiver are formed as the monostatic radar, which is facing towards the material under test (MUT). During the measurement, the distance between radar and MUT changes with small increments and the signals are recorded at each position. A mathematical model is formulated to depict the relationship among the complex permittivity, distance increment, and measured signals. By fitting the model, the complex permittivity of MUT is estimated. To implement and evaluate the proposed SDI method, a commercial off-the-shelf mmWave radar is utilized and the measurement system is developed. Then, the evaluation was carried out on the acrylic plate. With the proposed method, the estimated complex permittivity of acrylic plate shows good agreement with the literature values, demonstrating the efficacy of SDI method for characterizing the complex permittivity of material.