pwc-archive/papers-with-abstracts huggingface dataset:
Digital Twin Aided RIS Communication: Robust Beamforming and Interference Management
Authors: Sadjad Alikhani, Ahmed Alkhateeb
Date: 2024-06-06 00:00:00
Tasks: Management

Reconfigurable intelligent surfaces (RISs) are envisioned to play a key role in future wireless communication networks. However, channel estimation in RIS-aided wireless networks is challenging due to their passive nature and the large number of reflective elements, leading to high channel estimation overhead. Additionally, conventional methods like beam sweeping, which do not rely on explicit channel state information, often struggle in managing interference in multi-user networks. In this paper, we propose a novel approach that leverages digital twins (DTs) of the physical environments to approximate channels using electromagnetic 3D models and ray tracing, thus relaxing the need for channel estimation and extensive over-the-air computations in RIS-aided wireless networks. To address the digital twins channel approximation errors, we further refine this approach with a DT-specific robust transmission design that reliably meets minimum desired rates. The results show that our method secures these rates over 90% of the time, significantly outperforming beam sweeping, which achieves these rates less than 8% of the time due to its poor management of transmitting power and interference.
Mini Honor of Kings: A Lightweight Environment for Multi-Agent Reinforcement Learning
Authors: Lin Liu, Jian Zhao, Cheng Hu, Zhengtao Cao, Youpeng Zhao, Zhenbin Ye, Meng Meng, Wenjun Wang, Zhaofeng He, Houqiang Li, Xia Lin, Lanxiao Huang
Date: 2024-06-06 00:00:00
Tasks: Multi-agent Reinforcement Learning

Games are widely used as research environments for multi-agent reinforcement learning (MARL), but they pose three significant challenges: limited customization, high computational demands, and oversimplification. To address these issues, we introduce the first publicly available map editor for the popular mobile game Honor of Kings and design a lightweight environment, Mini Honor of Kings (Mini HoK), for researchers to conduct experiments. Mini HoK is highly efficient, allowing experiments to be run on personal PCs or laptops while still presenting sufficient challenges for existing MARL algorithms. We have tested our environment on common MARL algorithms and demonstrated that these algorithms have yet to find optimal solutions within this environment. This facilitates the dissemination and advancement of MARL methods within the research community. Additionally, we hope that more researchers will leverage the Honor of Kings map editor to develop innovative and scientifically valuable new maps. Our code and user manual are available at: https://github.com/tencent-ailab/mini-hok.
Beyond Performance Plateaus: A Comprehensive Study on Scalability in Speech Enhancement
Authors: Wangyou Zhang, Kohei Saijo, Jee-weon Jung, Chenda Li, Shinji Watanabe, Yanmin Qian
Date: 2024-06-06 00:00:00
Tasks: Diversity, Speech Enhancement, speech-recognition, Speech Recognition

Deep learning-based speech enhancement (SE) models have achieved impressive performance in the past decade. Numerous advanced architectures have been designed to deliver state-of-the-art performance; however, their scalability potential remains unrevealed. Meanwhile, the majority of research focuses on small-sized datasets with restricted diversity, leading to a plateau in performance improvement. In this paper, we aim to provide new insights for addressing the above issues by exploring the scalability of SE models in terms of architectures, model sizes, compute budgets, and dataset sizes. Our investigation involves several popular SE architectures and speech data from different domains. Experiments reveal both similarities and distinctions between the scaling effects in SE and other tasks such as speech recognition. These findings further provide insights into the under-explored SE directions, e.g., larger-scale multi-domain corpora and efficiently scalable architectures.
Sound Event Bounding Boxes
Authors: Janek Ebbers, Francois G. Germain, Gordon Wichern, Jonathan Le Roux
Date: 2024-06-06 00:00:00
Tasks: Change Detection, Event Detection, Prediction, Sound Event Detection

Sound event detection is the task of recognizing sounds and determining their extent (onset/offset times) within an audio clip. Existing systems commonly predict sound presence confidence in short time frames. Then, thresholding produces binary frame-level presence decisions, with the extent of individual events determined by merging consecutive positive frames. In this paper, we show that frame-level thresholding degrades the prediction of the event extent by coupling it with the system's sound presence confidence. We propose to decouple the prediction of event extent and confidence by introducing SEBBs, which format each sound event prediction as a tuple of a class type, extent, and overall confidence. We also propose a change-detection-based algorithm to convert legacy frame-level outputs into SEBBs. We find the algorithm significantly improves the performance of DCASE 2023 Challenge systems, boosting the state of the art from .644 to .686 PSDS1.
UrBAN: Urban Beehive Acoustics and PheNotyping Dataset
Authors: Mahsa Abdollahi, Yi Zhu, Heitor R. Guimarães, Nico Coallier, Ségolène Maucourt, Pierre Giovenazzo, Tiago H. Falk
Date: 2024-06-05 00:00:00

In this paper, we present a multimodal dataset obtained from a honey bee colony in Montr\'eal, Quebec, Canada, spanning the years of 2021 to 2022. This apiary comprised 10 beehives, with microphones recording more than 2000 hours of high quality raw audio, and also sensors capturing temperature, and humidity. Periodic hive inspections involved monitoring colony honey bee population changes, assessing queen-related conditions, and documenting overall hive health. Additionally, health metrics, such as Varroa mite infestation rates and winter mortality assessments were recorded, offering valuable insights into factors affecting hive health status and resilience. In this study, we first outline the data collection process, sensor data description, and dataset structure. Furthermore, we demonstrate a practical application of this dataset by extracting various features from the raw audio to predict colony population using the number of frames of bees as a proxy.
An overview of systems-theoretic guarantees in data-driven model predictive control
Authors: Julian Berberich, Frank Allgöwer
Date: 2024-06-06 00:00:00
Tasks: LEMMA, Model Predictive Control

The development of control methods based on data has seen a surge of interest in recent years. When applying data-driven controllers in real-world applications, providing theoretical guarantees for the closed-loop system is of crucial importance to ensure reliable operation. In this review, we provide an overview of data-driven model predictive control (MPC) methods for controlling unknown systems with guarantees on systems-theoretic properties such as stability, robustness, and constraint satisfaction. The considered approaches rely on the Fundamental Lemma from behavioral theory in order to predict input-output trajectories directly from data. We cover various setups, ranging from linear systems and noise-free data to more realistic formulations with noise and nonlinearities, and we provide an overview of different techniques to ensure guarantees for the closed-loop system. Moreover, we discuss avenues for future research that may further improve the theoretical understanding and practical applicability of data-driven MPC.
Self-tunable approximated explicit MPC: Heat exchanger implementation and analysis
Authors: Lenka Galčíková, Juraj Oravec
Date: 2024-06-06 00:00:00
Tasks: Model Predictive Control

The tunable approximated explicit model predictive control (MPC) comes with the benefits of real-time tunability without the necessity of solving the optimization problem online. This paper provides a novel self-tunable control policy that does not require any interventions of the control engineer during operation in order to retune the controller subject to the changed working conditions. Based on the current operating conditions, the autonomous tuning parameter scales the control input using linear interpolation between the boundary optimal control actions. The adjustment of the tuning parameter depends on the current reference value, which makes this strategy suitable for reference tracking problems. Furthermore, a novel technique for scaling the tuning parameter is proposed. This extension provides to exploit different ranges of the tuning parameter assigned to specified operating conditions. The self-tunable explicit MPC was implemented on a laboratory heat exchanger with nonlinear and asymmetric behavior. The asymmetric behavior of the plant was compensated by tuning the controller's aggressiveness, as the negative or positive sign of reference change was considered in the tuning procedure. The designed self-tunable controller improved control performance by decreasing sum-of-squared control error, maximal overshoots/ undershoots, and settling time compared to the conventional control strategy based on a single (non-tunable) controller.
Energy-storing analysis and fishtail stiffness optimization for a wire-driven elastic robotic fish
Authors: Xiaocun Liao, Chao Zhou, Junfeng Fan, Zhuoliang Zhang, Zhaoran Yin, Liangwei Deng
Date: 2024-06-06 00:00:00
Tasks: Cantilever Beam

The robotic fish with high propulsion efficiency and good maneuverability achieves underwater fishlike propulsion by commonly adopting the motor to drive the fishtail, causing the significant fluctuations of the motor power due to the uneven swing speed of the fishtail in one swing cycle. Hence, we propose a wire-driven robotic fish with a spring-steel-based active-segment elastic spine. This bionic spine can produce elastic deformation to store energy under the action of the wire driving and motor for responding to the fluctuations of the motor power. Further, we analyze the effects of the energy-storing of the active-segment elastic spine on the smoothness of motor power. Based on the developed Lagrangian dynamic model and cantilever beam model, the power-variance-based nonlinear optimization model for the stiffness of the active-segment elastic spine is established to respond to the sharp fluctuations of motor power during each fishtail swing cycle. Results validate that the energy-storing of the active-segment elastic spine plays a vital role in improving the power fluctuations and maximum frequency of the motor by adjusting its stiffness reasonably, which is beneficial to achieving high propulsion and high speed for robotic fish. Compared with the active-segment rigid spine that is incapable of storing energy, the energy-storing of the active-segment elastic spine is beneficial to increase the maximum frequency of the motor and the average thrust of the fishtail by 0.41 Hz, and 0.06 N, respectively.
Maximum Likelihood Identification of Linear Models with Integrating Disturbances for Offset-Free Control
Authors: Steven J. Kuntz, James B. Rawlings
Date: 2024-06-06 00:00:00
Tasks: Model Predictive Control

This paper addresses the identification of models for offset-free model predictive control (MPC), where LTI models are augmented with (fictitious) uncontrollable integrating modes, called integrating disturbances. The states and disturbances are typically estimated with a Kalman filter. The disturbance estimates effectively provide integral control, so the quality of the disturbance model (and resulting filter) directly influences the control performance. We implement eigenvalue constraints to protect against undesirable filter behavior (unstable or marginally stable modes, high-frequency oscillations). Specifically, we consider the class of linear matrix inequality (LMI) regions for eigenvalue constraints. These LMI regions are open sets by default, so we introduce a barrier function method to create tightened, but closed, eigenvalue constraints. To solve the resulting nonlinear semidefinite program, we approximate it as a nonlinear program using a Cholesky factorization method that exploits known sparsity structures of semidefinite optimization variables and matrix inequalities. The algorithm is applied to real-world data taken from two physical systems: first, a low-cost benchmark temperature microcontroller suitable for classroom laboratories, and second, an industrial-scale chemical reactor at Eastman Chemical's plant in Kingsport, TN.
Model fusion for efficient learning of nonlinear dynamical systems
Authors: Vatsal Kedia, Vivek S. Pinnamaraju, Dinesh Patil
Date: 2024-06-06 00:00:00
Tasks: Scheduling

In the context of model-based control of industrial processes, it is a common practice to develop a data-driven linear dynamical model around a specified operating point. However, in applications involving wider operating conditions, representation of the dynamics using a single linear dynamic model is often inadequate, requiring either a nonlinear model or multiple linear models to accommodate the nonlinear behaviour. While the development of the former suffers from the requirements of extensive experiments spanning multiple levels, significant compromise in the nominal product quality and dealing with unmeasured disturbances over wider operating conditions, the latter faces the challenge of model switch scheduling and inadequate description of dynamics for the operating regions in-between. To overcome these challenges, we propose an efficient approach to obtain a parsimonious nonlinear dynamic model by developing multiple linear models from data at multiple operating points, lifting the data features obtained from individual model simulations to adequately accommodate the underlying nonlinear behaviour and finally, sparse optimization techniques to obtain a parsimonious model. The performance and effectiveness of the proposed algorithm is demonstrated through simulation case studies.
Stochastic Dynamic Network Utility Maximization with Application to Disaster Response
Authors: Anna Scaglione, Nurullah Karakoc
Date: 2024-06-06 00:00:00
Tasks: Deep Reinforcement Learning, Disaster Response

In this paper, we are interested in solving Network Utility Maximization (NUM) problems whose underlying local utilities and constraints depend on a complex stochastic dynamic environment. While the general model applies broadly, this work is motivated by resource sharing during disasters concurrently occurring in multiple areas. In such situations, hierarchical layers of Incident Command Systems (ICS) are engaged; specifically, a central entity (e.g., the federal government) typically coordinates the incident response allocating resources to different sites, which then get distributed to the affected by local entities. The benefits of an allocation decision to the different sites are generally not expressed explicitly as a closed-form utility function because of the complexity of the response and the random nature of the underlying phenomenon we try to contain. We use the classic approach of decomposing the NUM formulation and applying a primal-dual algorithm to achieve optimal higher-level decisions under coupled constraints while modeling the optimized response to the local dynamics with deep reinforcement learning algorithms. The decomposition we propose has several benefits: 1) the entities respond to their local utilities based on a congestion signal conveyed by the ICS upper layers; 2) the complexity of capturing the utility of local responses and their diversity is addressed effectively without sharing local parameters and priorities with the ICS layers above; 3) utilities, known as explicit functions, are approximated as convex functions of the resources allocated; 4) decisions rely on up-to-date data from the ground along with future forecasts.
Monte-Carlo Integration Based Multiple-Scattering Channel Modeling for Ultraviolet Communications in Turbulent Atmosphere
Authors: Renzhi Yuan, Xinyi Chu, Tao Shan, Mugen Peng
Date: 2024-06-06 00:00:00

Modeling of multiple-scattering channels in atmospheric turbulence is essential for the performance analysis of long-distance non-line-of-sight (NLOS) ultraviolet (UV) communications. Existing works on the turbulent channel modeling for NLOS UV communications either ignored the turbulence-induced scattering effect or erroneously estimated the turbulent fluctuation effect, resulting in a contradiction with reported experiments. In this paper, we establish a comprehensive multiple-scattering turbulent channel model for NLOS UV communications considering both the turbulence-induced scattering effect and the turbulent fluctuation effect. We first derive the turbulent scattering coefficient and turbulent phase scattering function based on the Booker-Gordon turbulent power spectral density model. Then an improved estimation method is proposed for both the turbulent fluctuation and the turbulent fading coefficient based on the Monte-Carlo integration approach. Numerical results demonstrate that the turbulence-induced scattering effect can always be ignored for typical UV communication scenarios. Besides, the turbulent fluctuation will increase as either the communication distance, the elevation angle, or the divergence angle increases, which is compatible with existing experimental results. Moreover, we find that the probability density of the equivalent turbulent fading for multiple-scattering turbulent channels can be approximated as a Gaussian distribution.
Generalized two-point visual control model of human steering for accurate state estimation
Authors: Rene Mai, Katherine Sears, Grace Roessling, Agung Julius, Sandipan Mishra
Date: 2024-06-05 00:00:00
Tasks: State Estimation

We derive and validate a generalization of the two-point visual control model, an accepted cognitive science model for human steering behavior. The generalized model is needed as current steering models are either insufficiently accurate or too complex for online state estimation. We demonstrate that the generalized model replicates specific human steering behavior with high precision (85\% reduction in modeling error) and integrate this model into a human-as-advisor framework where human steering inputs are used for state estimation. As a benchmark study, we use this framework to decipher ambiguous lane markings represented by biased lateral position measurements. We demonstrate that, with the generalized model, the state estimator can accurately estimate the true vehicle state, providing lateral state estimates with under 0.25 m error on average across participants. However, without the generalized model, the estimator cannot accurately estimate the vehicle's lateral state.
Data-driven discovery of self-similarity using neural networks
Authors: Ryota Watanabe, Takanori Ishii, Yuji Hirono, Hirokazu Maruoka
Date: 2024-06-06 00:00:00

Finding self-similarity is a key step for understanding the governing law behind complex physical phenomena. Traditional methods for identifying self-similarity often rely on specific models, which can introduce significant bias. In this paper, we present a novel neural network-based approach that discovers self-similarity directly from observed data, without presupposing any models. The presence of self-similar solutions in a physical problem signals that the governing law contains a function whose arguments are given by power-law monomials of physical parameters, which are characterized by power-law exponents. The basic idea is to enforce such particular forms structurally in a neural network in a parametrized way. We train the neural network model using the observed data, and when the training is successful, we can extract the power exponents that characterize scale-transformation symmetries of the physical problem. We demonstrate the effectiveness of our method with both synthetic and experimental data, validating its potential as a robust, model-independent tool for exploring self-similarity in complex systems.
Towards Physically Consistent Deep Learning For Climate Model Parameterizations
Authors: Birgit Kühbacher, Fernando Iglesias-Suarez, Niki Kilbertus, Veronika Eyring
Date: 2024-06-06 00:00:00
Tasks: Deep Learning

Climate models play a critical role in understanding and projecting climate change. Due to their complexity, their horizontal resolution of about 40-100 km remains too coarse to resolve processes such as clouds and convection, which need to be approximated via parameterizations. These parameterizations are a major source of systematic errors and large uncertainties in climate projections. Deep learning (DL)-based parameterizations, trained on data from computationally expensive short, high-resolution simulations, have shown great promise for improving climate models in that regard. However, their lack of interpretability and tendency to learn spurious non-physical correlations result in reduced trust in the climate simulation. We propose an efficient supervised learning framework for DL-based parameterizations that leads to physically consistent models with improved interpretability and negligible computational overhead compared to standard supervised training. First, key features determining the target physical processes are uncovered. Subsequently, the neural network is fine-tuned using only those relevant features. We show empirically that our method robustly identifies a small subset of the inputs as actual physical drivers, therefore removing spurious non-physical relationships. This results in by design physically consistent and interpretable neural networks while maintaining the predictive performance of unconstrained black-box DL-based parameterizations.
GLOBUS: Global building renovation potential by 2070
Authors: Shufan Zhang, Minda Ma, Nan Zhou, Jinyue Yan
Date: 2024-06-06 00:00:00

Surpassing the two large emission sectors of transportation and industry, the building sector accounted for 34% and 37% of global energy consumption and carbon emissions in 2021, respectively. The building sector, the final piece to be addressed in the transition to net-zero carbon emissions, requires a comprehensive, multisectoral strategy for reducing emissions. Until now, the absence of data on global building floorspace has impeded the measurement of building carbon intensity (carbon emissions per floorspace) and the identification of ways to achieve carbon neutrality for buildings. For this study, we develop a global building stock model (GLOBUS) to fill that data gap. Our study's primary contribution lies in providing a dataset of global building stock turnover using scenarios that incorporate various levels of building renovation. By unifying the evaluation indicators, the dataset empowers building science researchers to perform comparative analyses based on floorspace. Specifically, the building stock dataset establishes a reference for measuring carbon emission intensity and decarbonization intensity of buildings within different countries. Further, we emphasize the sufficiency of existing buildings by incorporating building renovation into the model. Renovation can minimize the need to expand the building stock, thereby bolstering decarbonization of the building sector.
Comments on B. Hansen's Reply to "A Comment on: `A Modern Gauss-Markov Theorem'", and Some Related Discussion
Authors: Benedikt M. Pötscher
Date: 2024-06-06 00:00:00
Tasks: Econometrics

In P\"otscher and Preinerstorfer (2022) and in the abridged version P\"otscher and Preinerstorfer (2024, published in Econometrica) we have tried to clear up the confusion introduced in Hansen (2022a) and in the earlier versions Hansen (2021a,b). Unfortunatelly, Hansen's (2024) reply to P\"otscher and Preinerstorfer (2024) further adds to the confusion. While we are already somewhat tired of the matter, for the sake of the econometrics community we feel compelled to provide clarification. We also add a comment on Portnoy (2023), a "correction" to Portnoy (2022), as well as on Lei and Wooldridge (2022).