pwc-archive/papers-with-abstracts huggingface dataset:
Learning-based Position and Stiffness Feedforward Control of Antagonistic Soft Pneumatic Actuators using Gaussian Processes
Authors: Tim-Lukas Habich, Sarah Kleinjohann, Moritz Schappler
Date: 2023-03-03 00:00:00
Tasks: Gaussian Processes, Position

Variable stiffness actuator (VSA) designs are manifold. Conventional model-based control of these nonlinear systems is associated with high effort and design-dependent assumptions. In contrast, machine learning offers a promising alternative as models are trained on real measured data and nonlinearities are inherently taken into account. Our work presents a universal, learning-based approach for position and stiffness control of soft actuators. After introducing a soft pneumatic VSA, the model is learned with input-output data. For this purpose, a test bench was set up which enables automated measurement of the variable joint stiffness. During control, Gaussian processes are used to predict pressures for achieving desired position and stiffness. The feedforward error is on average 11.5% of the total pressure range and is compensated by feedback control. Experiments with the soft actuator show that the learning-based approach allows continuous adjustment of position and stiffness without model knowledge.
An investigation into the adaptability of a diffusion-based TTS model
Authors: Haolin Chen, Philip N. Garner
Date: 2023-03-03 00:00:00

Given the recent success of diffusion in producing natural-sounding synthetic speech, we investigate how diffusion can be used in speaker adaptive TTS. Taking cues from more traditional adaptation approaches, we show that adaptation can be included in a diffusion pipeline using conditional layer normalization with a step embedding. However, we show experimentally that, whilst the approach has merit, such adaptation alone cannot approach the performance of Transformer-based techniques. In a second experiment, we show that diffusion can be optimally combined with Transformer, with the latter taking the bulk of the adaptation load and the former contributing to improved naturalness.
Interruptions detection in video conferences
Authors: Shmuel Horowitz, Dima Kagan, Galit Fuhrmann Alpert, Michael Fire
Date: 2023-02-25 00:00:00
Tasks: Anomaly Detection

In recent years, video conferencing (VC) popularity has skyrocketed for a wide range of activities. As a result, the number of VC users surged sharply. The sharp increase in VC usage has been accompanied by various newly emerging privacy and security challenges. VC meetings became a target for various security attacks, such as Zoombombing. Other VC-related challenges also emerged. For example, during COVID lockdowns, educators had to teach in online environments struggling with keeping students engaged for extended periods. In parallel, the amount of available VC videos has grown exponentially. Thus, users and companies are limited in finding abnormal segments in VC meetings within the converging volumes of data. Such abnormal events that affect most meeting participants may be indicators of interesting points in time, including security attacks or other changes in meeting climate, like someone joining a meeting or sharing a dramatic content. Here, we present a novel algorithm for detecting abnormal events in VC data. We curated VC publicly available recordings, including meetings with interruptions. We analyzed the videos using our algorithm, extracting time windows where abnormal occurrences were detected. Our algorithm is a pipeline that combines multiple methods in several steps to detect users' faces in each video frame, track face locations during the meeting and generate vector representations of a facial expression for each face in each frame. Vector representations are used to monitor changes in facial expressions throughout the meeting for each participant. The overall change in meeting climate is quantified using those parameters across all participants, and translating them into event anomaly detection. This is the first open pipeline for automatically detecting anomaly events in VC meetings. Our model detects abnormal events with 92.3% precision over the collected dataset.
On SORA for High-Risk UAV Operations under New EU Regulations: Perspectives for Automated Approach
Authors: Hamed Habibi, D. M. K. K. Venkateswara Rao, Jose Luis Sanchez-Lopez, Holger Voos
Date: 2023-03-03 00:00:00

In this paper, we investigate requirements to prepare an application for Specific Operations Risk Assessment (SORA), regulated by European Union Aviation Safety Agency (EASA) to obtain flight authorization for Unmanned Aerial Vehicles (UAVs) operations and propose some perspectives to automate the approach based on our successful application. Preparation of SORA requires expert knowledge as it contains technicalities. Also, the whole process is an iterative and time-consuming one. It is even more challenging for higher-risk operations, such as those in urban environments, near airports, and multi- and customized models for research activities. SORA process limits the potential socio-economic impacts of innovative UAV capabilities. Therefore, in this paper, we present a SORA example, review the steps and highlight challenges. Accordingly, we propose an alternative workflow, considering the same steps, while addressing the challenges and pitfalls, to shorten the whole process. Furthermore, we present a comprehensive list of preliminary technical procedures, including the pre/during/post-flight checklists, design and installation appraisal, flight logbook, operational manual, training manual, and General Data Protection Regulation (GDPR), which are not explicitly instructed in SORA manual. Moreover, we propose the initial idea to create an automated SORA workflow to facilitate obtaining authorization, which is significantly helpful for operators, especially the scientific community, to conduct experimental operations.
Voltage Estimation for Diode-Clamped MMC Using Compensated State-Space Model
Authors: N. Tashakor, Y. Zhang, S. Banana, F. Blaabjerg, S. Goetz
Date: 2023-03-03 00:00:00

Modular multilevel converters are wellknown in the energy sector and have significant potential in others, such as electromobility. Generally, their stable operation is at the expense of numerous sensors, communication burdens, and complicated balancing strategies that challenge their expansion to cost driven applications. Hence, the introduction of a sensorless voltage balancing strategy with a simple controller is an attractive objective. Diode clamped MMCs offer a simple yet effective solution by providing a unidirectional balancing path between two modules through a diode, and ideally, the modulation technique should compensate for the lack of bidirectional energy transfer. Hence open loop operation is possible. Although the open loop operation is desirable to reduce costs, good knowledge of the modules voltages for system monitoring and protection functions still appears mandatory. This paper develops a compensated state space model for diode clamped MMCs and integrates it with an optimal estimator to reliably estimate the voltage of all modules without any direct measurement at module terminals. Additionally, the model considers the effect of the diode clamped branches and their balancing effect, resulting in a 30 percent to 50 percent reduction in estimation error compared to the conventional model.
Stabilization of cyber-physical systems: a foundational theory of computer-mediated control systems
Authors: Lirong Huang
Date: 2023-03-03 00:00:00

This paper presents the cyber-physcial model of a computer-mediated control system that is a seamless, fully synergistic integration of the physical system and the cyber system, which provides a systematic framework for synthesis of cyber-physical systems (CPSs). In our proposed framework, we establish a Lyapunov stabilty theory for synthesis of CPSs and apply it to sampled-data control systems, which are typically synonymous with computer-mediated control systems. By our CPS approach, we not only develop stability criteria for sampled-data control systems but also reveal the equivalence and inherent relationship between the two main design methods (viz. controller emulation and discrete-time approximation) in the literature. As application of our established theory, we study feedback stabilization of linear sampled-data stochastic systems and propose a control design method. Illustrative examples show that our proposed method has improved the existing results. Our established theory of synthetic CPSs lays a theoretic foundation for computer-mediated control systems and provokes many open and interesting problems for future work.
Improved Inner Approximation for Aggregating Power Flexibility in Active Distribution Networks and its Applications
Authors: Yilin Wen, Zechun Hu, Jinhua He, Yi Guo
Date: 2023-03-03 00:00:00
Tasks: Decision Making

Concise and reliable modeling for aggregating power flexibility of distributed energy resources in active distribution networks (ADNs) is a crucial technique for coordinating transmission and distribution networks. Our recent research has successfully derived an explicit expression for the exact aggregation model (EAM) of power flexibility at the substation level under linearized distribution network constraints. The EAM, however, is impractical for decision-making purposes due to its exponential complexity. In this paper, we propose an inner approximation method for aggregating flexibility in ADNs that utilizes the properties of the EAM to improve performance. Specifically, the geometric prototype of the inner approximation model is defined according to a subset of the coefficient vector set of the EAM, which enhances the accuracy. On the other hand, the computation efficiency of the inner approximation is also significantly improved by exploiting the regularity of coefficient vectors in the EAM in the parameter calculation process. The inner approximated flexibility model of ADNs is further incorporated into the security-constrained unit commitment problem as an application. Numerical simulations verify the effectiveness of the proposed method.
Insights from number theory into the critical Kauffman model with connectivity one
Authors: F. C. Sheldon, T. M. A. Fink
Date: 2023-03-03 00:00:00

The Kauffman model of genetic computation highlights the importance of criticality at the border of order and chaos. The model with connectivity one is of special interest because it is exactly solvable. But our understanding of its behavior is incomplete, and much of what we do know relies on heuristic arguments. Here, we show that the key quantities in the model are intimately related to aspects of number theory. Using these links, we derive improved bounds for the number of attractors as well as the mean attractor length, which is harder to compute. Our work suggests that number theory is the natural language for deducing many properties of the critical Kauffman model with connectivity one, and opens the door to further insight into this deceptively simple model.
Assisted percolation of slow-spreading mutants in heterogeneous environments
Authors: Thomas Tunstall, Tim Rogers, Wolfram Möbius
Date: 2023-03-02 00:00:00

Environmental heterogeneity can drive genetic heterogeneity in expanding populations; mutant strains may emerge that trade overall growth rate for an improved ability to survive in patches that are hostile to the wild type. This evolutionary dynamic is of practical importance when seeking to prevent the emergence of damaging traits. We show that a sub-critical slow-spreading mutant can attain dominance even when the density of patches is below their percolation threshold and predict this transition using geometrical arguments. This work demonstrates a phenomenon of ''assisted percolation'', where one sub-critical process assists another to achieve super-criticality.
Upper Bounds on Overshoot in SIR Models with Nonlinear Incidence
Authors: Maximilian Nguyen
Date: 2023-03-03 00:00:00

We expand the calculation of the upper bound on epidemic overshoot in SIR models to account for nonlinear incidence. We lay out the general procedure and restrictions to perform the calculation analytically for nonlinear functions in the number of susceptibles. We demonstrate the procedure by working through several examples and also numerically study what happens to the upper bound on overshoot when nonlinear incidence manifests in the form of epidemic dynamics over a contact network. We find that both steeper incidence terms and larger contact heterogeneity can increase the range of communicable diseases at which the overshoot remains a relatively large public health hazard.
Fundamental Bound on Epidemic Overshoot in the SIR Model
Authors: Maximilian Nguyen, Ari Freedman, Sinan Ozbay, Simon Levin
Date: 2023-03-03 00:00:00

We derive an exact upper bound on the epidemic overshoot for the Kermack-McKendrick SIR model. This maximal overshoot value of 0.2984... occurs at $R_0^*$ = 2.151... . In considering the utility of the notion of overshoot, a rudimentary analysis of data from the first wave of the COVID-19 pandemic in Manaus, Brazil highlights the public health hazard posed by overshoot for epidemics with $R_0$ near 2. Using the general analysis framework presented within, we then consider more complex SIR models that incorporate vaccination.
Control flow in active inference systems
Authors: Chris Fields, Filippo Fabrocini, Karl Friston, James F. Glazebrook, Hananel Hazan, Michael Levin, Antonino Marciano
Date: 2023-02-25 00:00:00
Tasks: Tensor Networks

Living systems face both environmental complexity and limited access to free-energy resources. Survival under these conditions requires a control system that can activate, or deploy, available perception and action resources in a context specific way. We show here that when systems are described as executing active inference driven by the free-energy principle (and hence can be considered Bayesian prediction-error minimizers), their control flow systems can always be represented as tensor networks (TNs). We show how TNs as control systems can be implmented within the general framework of quantum topological neural networks, and discuss the implications of these results for modeling biological systems at multiple scales.
Finding the Optimal Currency Composition of Foreign Exchange Reserves with a Quantum Computer
Authors: Martin Vesely
Date: 2023-03-03 00:00:00
Tasks: Portfolio Optimization

Portfolio optimization is an inseparable part of strategic asset allocation at the Czech National Bank. Quantum computing is a new technology offering algorithms for that problem. The capabilities and limitations of quantum computers with regard to portfolio optimization should therefore be investigated. In this paper, we focus on applications of quantum algorithms to dynamic portfolio optimization based on the Markowitz model. In particular, we compare algorithms for universal gate-based quantum computers (the QAOA, the VQE and Grover adaptive search), single-purpose quantum annealers, the classical exact branch and bound solver and classical heuristic algorithms (simulated annealing and genetic optimization). To run the quantum algorithms we use the IBM Quantum\textsuperscript{TM} gate-based quantum computer. We also employ the quantum annealer offered by D-Wave. We demonstrate portfolio optimization on finding the optimal currency composition of the CNB's FX reserves. A secondary goal of the paper is to provide staff of central banks and other financial market regulators with literature on quantum optimization algorithms, because financial firms are active in finding possible applications of quantum computing.
Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms
Authors: Yu Jeffrey Hu, Jeroen Rombouts, Ines Wilms
Date: 2023-03-03 00:00:00

On-demand service platforms face a challenging problem of forecasting a large collection of high-frequency regional demand data streams that exhibit instabilities. This paper develops a novel forecast framework that is fast and scalable, and automatically assesses changing environments without human intervention. We empirically test our framework on a large-scale demand data set from a leading on-demand delivery platform in Europe, and find strong performance gains from using our framework against several industry benchmarks, across all geographical regions, loss functions, and both pre- and post-Covid periods. We translate forecast gains to economic impacts for this on-demand service platform by computing financial gains and reductions in computing costs.
Constructing High Frequency Economic Indicators by Imputation
Authors: Serena Ng, Susannah Scanlan
Date: 2023-03-03 00:00:00
Tasks: counterfactual, Imputation, Matrix Completion, Missing Values, Vocal Bursts Intensity Prediction

Monthly and weekly economic indicators are often taken to be the largest common factor estimated from high and low frequency data, either separately or jointly. To incorporate mixed frequency information without directly modeling them, we target a low frequency diffusion index that is already available, and treat high frequency values as missing. We impute these values using multiple factors estimated from the high frequency data. In the empirical examples considered, static matrix completion that does not account for serial correlation in the idiosyncratic errors yields imprecise estimates of the missing values irrespective of how the factors are estimated. Single equation and systems-based dynamic procedures that account for serial correlation yield imputed values that are closer to the observed low frequency ones. This is the case in the counterfactual exercise that imputes the monthly values of consumer sentiment series before 1978 when the data was released only on a quarterly basis. This is also the case for a weekly version of the CFNAI index of economic activity that is imputed using seasonally unadjusted data. The imputed series reveals episodes of increased variability of weekly economic information that are masked by the monthly data, notably around the 2014-15 collapse in oil prices.
The barriers to sustainable risk transfer in the cyber-insurance market
Authors: Henry Skeoch, Christos Ioannidis
Date: 2023-03-03 00:00:00

Efficient risk transfer is an important condition for ensuring the sustainability of a market according to the established economics literature. In an inefficient market, significant financial imbalances may develop and potentially jeopardise the solvency of some market participants. The constantly evolving nature of cyber-threats and lack of public data sharing mean that the economic conditions required for quoted cyber-insurance premiums to be considered efficient are highly unlikely to be met. This paper develops Monte Carlo simulations of an artificial cyber-insurance market and compares the efficient and inefficient outcomes based on the informational setup between the market participants. The existence of diverse loss distributions is justified by the dynamic nature of cyber-threats and the absence of any reliable and centralised incident reporting. It is shown that the limited involvement of reinsurers when loss expectations are not shared leads to increased premiums and lower overall capacity. This suggests that the sustainability of the cyber-insurance market requires both better data sharing and external sources of risk tolerant capital.
Revisiting the effect of search frictions on market concentration
Authors: Jules Depersin, Bérengère Patault
Date: 2023-03-03 00:00:00
Tasks: Friction

Search frictions can impede the formation of optimal matches between consumer and supplier, or employee and employer, and lead to inefficiencies. This paper revisits the effect of search frictions on the firm size distribution when challenging two common but strong assumptions: that all agents share the same ranking of firms, and that agents meet all firms, whether small or large, at the same rate. We build a random search model in which we relax those two assumptions and show that the intensity of search frictions has a non monotonic effect on market concentration. An increase in friction intensity increases market concentration up to a certain threshold of frictions, that depends on the slope of the meeting rate with respect to firm size. We leverage unique French customs data to estimate this slope. First, we find that in a range of plausible scenarios, search frictions intensity increases market concentration. Second, we show that slopes have increased over time, which unambiguously increases market concentration in our model. Overall, we shed light on the importance of the structure of frictions, rather than their intensity, to understand market concentration.
Time-inconsistent contract theory
Authors: Camilo Hernández, Dylan Possamaï
Date: 2023-03-02 00:00:00

This paper investigates the moral hazard problem in finite horizon with both continuous and lump-sum payments, involving a time-inconsistent sophisticated agent and a standard utility maximiser principal. Building upon the so-called dynamic programming approach in Cvitani\'c, Possama\"i, and Touzi [18] and the recently available results in Hern\'andez and Possama\"i [43], we present a methodology that covers the previous contracting problem. Our main contribution consists in a characterisation of the moral hazard problem faced by the principal. In particular, it shows that under relatively mild technical conditions on the data of the problem, the supremum of the principal's expected utility over a smaller restricted family of contracts is equal to the supremum over all feasible contracts. Nevertheless, this characterisation yields, as far as we know, a novel class of control problems that involve the control of a forward Volterra equation via Volterra-type controls, and infinite-dimensional stochastic target constraints. Despite the inherent challenges associated to such a problem, we study the solution under three different specifications of utility functions for both the agent and the principal, and draw qualitative implications from the form of the optimal contract. The general case remains the subject of future research.
Efficient Aberration Correction via Optimal Bulk Speed of Sound Compensation
Authors: Scott Schoen Jr, Viksit Kumar, Yuyang Gu, Sunethra Dayavansha, Rimon Tadross, Mike Washburn, Kai Thomenius, Anthony E. Samir
Date: 2023-03-03 00:00:00
Tasks: Diagnostic

Diagnostic ultrasound is a versatile and practical tool in the abdomen, and is particularly vital toward the detection and mitigation of early-stage non-alcoholic fatty liver disease (NAFLD). However, its performance in those with obesity -- who are at increased risk for NAFLD -- is degraded due to distortions of the ultrasound as it traverses thicker, acoustically heterogeneous body walls (aberration). Many aberration correction methods for ultrasound require measures of channel data relationships. Simpler, bulk speed of sound optimizations based on the image itself have demonstrated empirical efficacy, but their analytical limitations have not been evaluated. Herein, we assess analytically the bounds of a single, optimal speed of sound correction in receive beamforming to correct aberration, and improve the resulting images. Additionally, we propose an objective metric on the post-sum B-mode image to identify this speed of sound, and validate this technique through in virto phantom experiments and in vivo abdominal ultrasound data collection with physical aberrating layers. We find that a bulk correction may approximate the aberration profile for layers of relevant thicknesses (1 to 3 cm) and speeds of sound (1400 to 1500 m/s). Additionally, through in vitro experiments, we show significant improvement in resolution (average point target width reduced by 60 %) and improved boundary delineation in vivo with bulk speed of sound correction determined automatically from the beamformed images. Together, our results demonstrate the utility of simple, efficient bulk speed of sound correction to improve the quality of diagnostic liver images.