pwc-archive/papers-with-abstracts huggingface dataset:
Multichannel Voice Trigger Detection Based on Transform-average-concatenate
Authors: Takuya Higuchi, Avamarie Brueggeman, Masood Delfarah, Stephen Shum
Date: 2023-09-27 00:00:00
Tasks: channel selection, Speech Enhancement

Voice triggering (VT) enables users to activate their devices by just speaking a trigger phrase. A front-end system is typically used to perform speech enhancement and/or separation, and produces multiple enhanced and/or separated signals. Since conventional VT systems take only single-channel audio as input, channel selection is performed. A drawback of this approach is that unselected channels are discarded, even if the discarded channels could contain useful information for VT. In this work, we propose multichannel acoustic models for VT, where the multichannel output from the frond-end is fed directly into a VT model. We adopt a transform-average-concatenate (TAC) block and modify the TAC block by incorporating the channel from the conventional channel selection so that the model can attend to a target speaker when multiple speakers are present. The proposed approach achieves up to 30% reduction in the false rejection rate compared to the baseline channel selection approach.
Adaptive Output-Feedback Model Predictive Control of Hammerstein Systems with Unknown Linear Dynamics
Authors: Mohammadreza Kamaldar, Dennis S. Bernstein
Date: 2023-09-28 00:00:00
Tasks: Model Predictive Control

This paper considers model predictive control of Hammerstein systems, where the linear dynamics are a priori unknown and the input nonlinearity is known. Predictive cost adaptive control (PCAC) is applied to this system using recursive least squares for online, closed-loop system identification with optimization over a receding horizon performed by quadratic programming (QP). In order to account for the input nonlinearity, the input matrix is defined to be control dependent, and the optimization is performed iteratively. This technique is applied to output stabilization of a chain of integrators with unknown dynamics under control saturation and deadzone input nonlinearity.
Connecting Space Missions Through NGSO Constellations: Feasibility Study
Authors: Houcine Chougrani, Oltjon Kodheli, Ali Georganaki, Jan Thoemel, Chiara Vittoria Turtoro, Frank Zeppenfeldt, Petros Pissias, Mahulena Hofmann, Symeon Chatzinotas
Date: 2023-09-28 00:00:00

A satellite internet provider (e.g., Starlink, OneWeb, O3b mPOWER), despite possessing the capability to provide internet services to on-ground users in a global scale, can dramatically change the way space missions are designed and operated in the foreseeable future. Assuming a scenario where space mission satellites can access the internet via a space internet system, the satellite can be connected to the network permanently (24x7) and act as mere terminal independently from its location. The ability to communicate with the satellite on-demand has the potential to improve aspects such as real-time tasking, outage minimization, operation cost, and dependency on the ground. This paper performs a feasibility study on the concept of connecting space missions to the network through commercial mega-constellations. This study includes a review of existing and near-future space internet systems, identification of candidate space missions for the aforementioned concept, a necessary adaptation of existing Commercial off-the-shelf (COTS) terminals to be plugged into space mission satellites, assessment of communication performance, and investigation of the legal aspects of the radio frequency (RF) spectrum usage. The paper evidences that the concept is practically possible to implement in the near future. Among the studied space internet systems (i.e. Starlink, OneWeb, O3b mPOWER), O3b mPOWER stands out as the most suitable system allowing permanent coverage of low earth orbit (LEO) space missions with data rates that can reach up to 21 Mbps per satellite. Although the concept is very promising and can be implemented in the near future, our investigations show that some regulatory aspects regarding the RF usage should be solved for future exploitation of connecting space missions through NGSO constellations.
Structural Risk Minimization for Learning Nonlinear Dynamics
Authors: Charis Stamouli, Evangelos Chatzipantazis, George J. Pappas
Date: 2023-09-28 00:00:00
Tasks: Model Selection

Recent advances in learning or identification of nonlinear dynamics focus on learning a suitable model within a pre-specified model class. However, a key difficulty that remains is the choice of the model class from which the dynamics will be learned. The fundamental challenge is trading the richness of the model class with the learnability within the model class. Toward addressing the so-called model selection problem, we introduce a novel notion of Structural Risk Minimization (SRM) for learning nonlinear dynamics. Inspired by classical SRM for classification, we minimize a bound on the true prediction error over hierarchies of model classes. The class selected by our SRM scheme is shown to achieve a nearly optimal learning guarantee among all model classes contained in the hierarchy. Employing the proposed scheme along with computable model class complexity bounds, we derive explicit SRM schemes for learning nonlinear dynamics under hierarchies of: i) norm-constrained Reproducing Kernel Hilbert Spaces, and ii) norm-constrained Neural Network classes. We empirically show that even though too loose to be used as absolute estimates, our SRM bounds on the true prediction error are able to track its relative behavior across different model classes of the hierarchy.
A harmonic framework for the identification of linear time-periodic systems
Authors: Flora Vernerey, Pierre Riedinger, Andrea Iannelli, Jamal Daafouz
Date: 2023-09-28 00:00:00

This paper presents a novel approach for the identification of linear time-periodic (LTP) systems in continuous time. This method is based on harmonic modeling and consists in converting any LTP system into an equivalent LTI system with infinite dimension. Leveraging specific harmonic properties, we demonstrate that solving this infinite-dimensional identification problem can be reduced to solving a finitedimensional linear least-squares problem. The result is an approximation of the original solution with an arbitrarily small error. Our approach offers several significant advantages. The first one is closely tied to the harmonic system's inherent LTI characteristic, along with the Toeplitz structure exhibited by its elements. The second advantage is related to the regularization property achieved through the integral action when computing the phasors from input and state trajectories. Finally, our method avoids the computation of signals' derivative. This sets our approach apart from existing methods that rely on such computations, which can be a notable drawback, especially in continuous-time settings. We provide numerical simulations that convincingly demonstrate the effectiveness of the proposed method, even in scenarios where signals are corrupted by noise.
Adaptive Real-Time Numerical Differentiation with Variable-Rate Forgetting and Exponential Resetting
Authors: Shashank Verma, Brian Lai, Dennis S. Bernstein
Date: 2023-09-28 00:00:00
Tasks: State Estimation

Digital PID control requires a differencing operation to implement the D gain. In order to suppress the effects of noisy data, the traditional approach is to filter the data, where the frequency response of the filter is adjusted manually based on the characteristics of the sensor noise. The present paper considers the case where the characteristics of the sensor noise change over time in an unknown way. This problem is addressed by applying adaptive real-time numerical differentiation based on adaptive input and state estimation (AISE). The contribution of this paper is to extend AISE to include variable-rate forgetting with exponential resetting, which allows AISE to more rapidly respond to changing noise characteristics while enforcing the boundedness of the covariance matrix used in recursive least squares.
Scalable Exact Output Synchronization of Discrete-Time Multi-Agent Systems in the Presence of Disturbances and Measurement Noise With Known Frequencies
Authors: Zhenwei Liu, Meirong Zhang, Ali Saberi, Anton A. Stoorvogel
Date: 2023-09-28 00:00:00

This paper aims to achieve scalable exact output and regulated output synchronization for discrete-time multi-agent systems in presence of disturbances and measurement noise with known frequencies. Both homogeneous and heterogeneous multi-agent systems are considered, with parts of agents' states accessible in the latter case. The key contribution of this paper is on the distributed protocol that only uses the information of agent models, rather than the communication network information and the agent number, so as to achieve the scalable exact synchronization under disturbances and measurement noise. The validity of the protocol is verified by numerical simulations with arbitrarily chosen number of agents.
A review of variable-pitch propellers and their control strategies in aerospace systems
Authors: Hanjie Jiang, Ye Zhou, Hann Woei Ho
Date: 2023-09-28 00:00:00
Tasks: Pitch control

The relentless pursuit of aircraft flight efficiency has thrust variable-pitch propeller technology into the forefront of aviation innovation. This technology, rooted in the ancient power unit of propellers, has found renewed significance, particularly in the realms of unmanned aerial vehicles and urban air mobility. This underscores the profound interplay between visionary aviation concepts and the enduring utility of propellers. Variable-pitch propellers are poised to be pivotal in shaping the future of human aviation, offering benefits such as extended endurance, enhanced maneuverability, improved fuel economy, and prolonged engine life. However, with additional capabilities come new technical challenges. The development of an online adaptive control of variable-pitch propellers that does not depend on an accurate dynamic model stands as a critical imperative. Therefore, a comprehensive review and forward-looking analysis of this technology is warranted. This paper introduces the development background of variable-pitch aviation propeller technology, encompassing diverse pitch angle adjustment schemes and their integration with various engine types. It places a central focus on the latest research frontiers and emerging directions in pitch control strategies. Lastly, it delves into the research domain of constant speed pitch control, articulating the three main challenges confronting this technology: inadequacies in system modeling, the intricacies of propeller-engine compatibility, and the impact of external, time-varying factors. By shedding light on these multifaceted aspects of variable-pitch propeller technology, this paper serves as a resource for aviation professionals and researchers navigating the intricate landscape of future aircraft development.
Coordinates in low-dimensional cell shape-space discriminate migration dynamics from single static cell images
Authors: Xiuxiu He, Kuangcai Chen, Ning Fang, Yi Jiang
Date: 2023-09-28 00:00:00

Cell shape has long been used to discern cell phenotypes and states, but the underlying premise has not been quantitatively tested. Here, we show that a single cell image can be used to discriminate its migration behavior by analyzing a large number of cell migration data in vitro. We analyzed a large number of two-dimensional cell migration images over time and found that the cell shape variation space has only six dimensions, and migration behavior can be determined by the coordinates of a single cell image in this 6-dimensional shape-space. We further show that this is possible because persistent cell migration is characterized by spatial-temporally coordinated protrusion and contraction, and a distribution signature in the shape-space. Our findings provide a quantitative underpinning for using cell morphology to differentiate cell dynamical behavior.
Impossible ecologies: Interaction networks and stability of coexistence in ecological communities
Authors: Yu Meng, Szabolcs Horvát, Carl D. Modes, Pierre A. Haas
Date: 2023-09-28 00:00:00

Does an ecological community allow stable coexistence? Identifying the general principles that determine the answer to this question is a central problem of theoretical ecology. Random matrix theory approaches have uncovered the general trends of the effect of competitive, mutualistic, and predator-prey interactions between species on stability of coexistence. However, an ecological community is determined not only by the counts of these different interaction types, but also by their network arrangement. This cannot be accounted for in a direct statistical description that would enable random matrix theory approaches. Here, we therefore develop a different approach, of exhaustive analysis of small ecological communities, to show that this arrangement of interactions can influence stability of coexistence more than these general trends. We analyse all interaction networks of $N\leqslant 5$ species with Lotka-Volterra dynamics by combining exact results for $N\leqslant 3$ species and numerical exploration. Surprisingly, we find that a very small subset of these networks are "impossible ecologies", in which stable coexistence is non-trivially impossible. We prove that the possibility of stable coexistence in general ecologies is determined by similarly rare "irreducible ecologies". By random sampling of interaction strengths, we then show that the probability of stable coexistence varies over many orders of magnitude even in ecologies that differ only in the network arrangement of identical ecological interactions. Finally, we demonstrate that our approach can reveal the effect of evolutionary or environmental perturbations of the interaction network. Overall, this work reveals the importance of the full structure of the network of interactions for stability of coexistence in ecological communities.
Smoothing the Nonsmoothness
Authors: Chaohua Dong, Jiti Gao, Bin Peng, Yundong Tu
Date: 2023-09-28 00:00:00

To tackle difficulties for theoretical studies in situations involving nonsmooth functions, we propose a sequence of infinitely differentiable functions to approximate the nonsmooth function under consideration. A rate of approximation is established and an illustration of its application is then provided.
The effect of COVID restriction levels on shared micromobility travel patterns: A comparison between dockless bike sharing and e-scooter services
Authors: Marco Diana, Andrea Chicco
Date: 2023-09-28 00:00:00

The spread of the coronavirus pandemic had negative repercussions on the majority of transport systems in virtually all countries. After the lockdown period, travel restriction policies are now frequently adapted almost real-time according to observed trends in the spread of the disease, resulting in a rapidly changing transport market situation. Shared micromobility operators, whose revenues entirely come from their customers, need to understand how the demand is affected to adapt their operations. Within this framework, the present paper investigates how different COVID-19 restriction levels have affected the usage patterns of shared micromobility. Usage data of two dockless micromobility services (bike and e-scooters) operating in Turin (Italy) are analyzed between October 2020 and March 2021, a period characterized by different travel restriction levels. The average number of daily trips, trip distances and trip duration are retrieved for both services, and then compared to identify significant differences in trends as restriction levels change. Additionally, related impacts on the spatial dimension of the services are studied through hotspot maps. Results show that both services decreased during restrictions, however e-scooters experienced a larger variability in their demand and they had a quicker recovery when travel restrictions were loosened. Shared bikes, in general, suffered less from travel restriction levels, suggesting their larger usage for work and study-related trip purposes, which is confirmed also by the analysis of hotspots. E-scooters are both substituting and complementing public transport according to restriction levels, while usage patterns of shared bikes are more independent.
Intergenerational Equity in Models of Climate Change Mitigation: Stochastic Interest Rates introduce Adverse Effects, but (Non-linear) Funding Costs can Improve Intergenerational Equity
Authors: Christian Fries, Lennart Quante
Date: 2023-09-28 00:00:00

Assessing the costs of climate change is essential to finding efficient pathways for the transition to a net-zero emissions economy, which is necessary to stabilise global temperatures at any level. In evaluating the benefits and costs of climate change mitigation, the discount rate converting future damages and costs into net-present values influences the timing of mitigation. Here, we amend the DICE model with a stochastic interest rate model to consider the uncertainty of discount rates in the future. Since abatement reduces future damages, changing interest rates renders abatement investments more or less beneficial. Stochastic interest rates will hence lead to a stochastic abatement strategy. We introduce a simple stochastic abatement model and show that this can increase intergenerational inequality concerning cost and risk. Analysing the sensitivities of the model calibration analytically and numerically exhibits that intergenerational inequality is a consequence of the DICE model calibration (and maybe that of IAMs in general). We then show that introducing funding of abatement costs reduces the variation of future cash-flows, which occur at different times but are off-setting in their net-present value. This effect can be interpreted as improving intergenerational effort sharing, which might be neglected in classical optimisation. This mechanism is amplified, including dependence of the interest rate risk on the amount of debt to be financed, i.e. considering the limited capacity of funding sources. As an alternative policy optimisation method, we propose limiting the total cost of damages and abatement below a fixed level relative to GDP - this modification induces equality between generations compared to their respective economic welfare, inducing early and fast mitigation of climate change to keep the total cost of climate change below 3% of global GDP.
Adaptive Priority Mechanisms
Authors: Oguzhan Celebi, Joel Flynn
Date: 2023-09-27 00:00:00
Tasks: Diversity

How should authorities that care about match quality and diversity allocate resources when they are uncertain about the market? We introduce adaptive priority mechanisms (APM) that prioritize agents based on both their scores and characteristics. We derive an APM that is optimal and show that the ubiquitous priority and quota mechanisms are optimal if and only if the authority is risk-neutral or extremely risk-averse over diversity, respectively. With many authorities, each authority using the optimal APM is dominant and implements the unique stable matching. Using Chicago Public Schools data, we find that the gains from adopting APM may be considerable.
Neuromorphic Imaging with Joint Image Deblurring and Event Denoising
Authors: Pei Zhang, Haosen Liu, Zhou Ge, Chutian Wang, Edmund Y. Lam
Date: 2023-09-28 00:00:00
Tasks: Deblurring, Denoising, Image Deblurring

Neuromorphic imaging reacts to per-pixel brightness changes of a dynamic scene with high temporal precision and responds with asynchronous streaming events as a result. It also often supports a simultaneous output of an intensity image. Nevertheless, the raw events typically involve a large amount of noise due to the high sensitivity of the sensor, while capturing fast-moving objects at low frame rates results in blurry images. These deficiencies significantly degrade human observation and machine processing. Fortunately, the two information sources are inherently complementary -- events with microsecond-level temporal resolution, which are triggered by the edges of objects recorded in a latent sharp image, can supply rich motion details missing from the blurry one. In this work, we bring the two types of data together and introduce a simple yet effective unifying algorithm to jointly reconstruct blur-free images and noise-robust events in an iterative coarse-to-fine fashion. Specifically, an event-regularized prior offers precise high-frequency structures and dynamic features for blind deblurring, while image gradients serve as a kind of faithful supervision in regulating neuromorphic noise removal. Comprehensively evaluated on real and synthetic samples, such a synergy delivers superior reconstruction quality for both images with severe motion blur and raw event streams with a storm of noise, and also exhibits greater robustness to challenging realistic scenarios such as varying levels of illumination, contrast and motion magnitude. Meanwhile, it can be driven by much fewer events and holds a competitive edge at computational time overhead, rendering itself preferable as available computing resources are limited. Our solution gives impetus to the improvement of both sensing data and paves the way for highly accurate neuromorphic reasoning and analysis.
HyperLISTA-ABT: An Ultra-light Unfolded Network for Accurate Multi-component Differential Tomographic SAR Inversion
Authors: Kun Qian, Yuanyuan Wang, Peter Jung, Yilei Shi, Xiao Xiang Zhu
Date: 2023-09-28 00:00:00
Tasks: 3D Reconstruction, Computational Efficiency

Deep neural networks based on unrolled iterative algorithms have achieved remarkable success in sparse reconstruction applications, such as synthetic aperture radar (SAR) tomographic inversion (TomoSAR). However, the currently available deep learning-based TomoSAR algorithms are limited to three-dimensional (3D) reconstruction. The extension of deep learning-based algorithms to four-dimensional (4D) imaging, i.e., differential TomoSAR (D-TomoSAR) applications, is impeded mainly due to the high-dimensional weight matrices required by the network designed for D-TomoSAR inversion, which typically contain millions of freely trainable parameters. Learning such huge number of weights requires an enormous number of training samples, resulting in a large memory burden and excessive time consumption. To tackle this issue, we propose an efficient and accurate algorithm called HyperLISTA-ABT. The weights in HyperLISTA-ABT are determined in an analytical way according to a minimum coherence criterion, trimming the model down to an ultra-light one with only three hyperparameters. Additionally, HyperLISTA-ABT improves the global thresholding by utilizing an adaptive blockwise thresholding scheme, which applies block-coordinate techniques and conducts thresholding in local blocks, so that weak expressions and local features can be retained in the shrinkage step layer by layer. Simulations were performed and demonstrated the effectiveness of our approach, showing that HyperLISTA-ABT achieves superior computational efficiency and with no significant performance degradation compared to state-of-the-art methods. Real data experiments showed that a high-quality 4D point cloud could be reconstructed over a large area by the proposed HyperLISTA-ABT with affordable computational resources and in a fast time.