pwc-archive/papers-with-abstracts huggingface dataset:
Discrete Mechanics and Optimal Control for Passive Walking with Foot Slippage
Authors: Alexandre Anahory Simoes, Asier López-Gordón, Anthony Bloch, Leonardo Colombo
Date: 2022-09-28 00:00:00

Forced variational integrators are given by the discretization of the Lagrange-d'Alembert principle for systems subject to external forces, and have proved useful for numerical simulation studies of complex dynamical systems. In this paper we model a passive walker with foot slip by using techniques of geometric mechanics, and we construct forced variational integrators for the system. Moreover, we present a methodology for generating (locally) optimal control policies for simple hybrid holonomically constrained forced Lagrangian systems, based on discrete mechanics, applied to a controlled walker with foot slip in a trajectory tracking problem.
A Fully Homomorphic Encryption Scheme for Real-Time Safe Control
Authors: Pieter Stobbe, Twan Keijzer, Riccardo M. G. Ferrari
Date: 2022-09-28 00:00:00

Fully Homomorphic Encryption (FHE) has made it possible to perform addition and multiplication operations on encrypted data. Using FHE in control thus has the advantage that control effort for a plant can be calculated remotely without ever decrypting the exchanged information. FHE in its current form is however not practically applicable for real-time control as its computational load is very high compared to traditional encryption methods. In this paper a reformulation of the Gentry FHE scheme is proposed and applied on an FPGA to solve this problem. It is shown that the resulting FHE scheme can be implemented for real-time stabilization of an inverted double pendulum using discrete time control.
Methodical Approach for Centralization Evaluation of Modern Automotive E/E Architectures
Authors: Lucas Mauser, Stefan Wagner, Peter Ziegler
Date: 2022-09-28 00:00:00
Tasks: CPU

Centralization is considered as a key enabler to master the CPU-intensive features of the modern car. The development and architecture change towards the next generation car is influenced by ADAS, connectivity, infotainment and the consequential need for cyber-security. There is already a high number of papers describing future centralized E/E architectures and technical instruments for centralization. What is missing is a methodical approach to analyze an existing system and find its potential for centralization on the function level. This paper introduces an approach, which serves a system designer or engineer to abstract functions and thus enables to shape a more centralized system architecture. The commonly known E/E architecture designs and the named instruments of current research are the basis for this abstraction. Based on the approach, new system architecture proposals can be set up to discuss and outweigh advantages and disadvantages of those. The approach is validated by applying it step by step to the inlet's derating function of a modern electric vehicle. A following discussion points out that many different factors affect the potential for centralization and centralization may not be the future of every function and system in general.
Improved event-based particle filtering in resource-constrained remote state estimation
Authors: Johan Ruuskanen, Anton Cervin
Date: 2022-09-28 00:00:00
Tasks: State Estimation

Event-based sampling has been proposed as a general technique for lowering the average communication rate in remote state estimation, which can be important in scenarios with constraints on resources such as network bandwidth or sensor energy. Recently, the interest of applying particle filters to event-based state estimation has seen an upswing, partly to tackle nonlinear and non-Gaussian problems, but also since event-based sampling does not allow an analytic solution for linear--Gaussian systems. Thus far, very little has been mentioned regarding the practical issues that arise when applying particle filtering to event-based state estimation. In this paper, we provide such a discussion by (i) demonstrating that there exists a high risk of sample degeneracy at new events, for which the auxiliary particle filter provides an intuitive solution, (ii) introducing a new alternative to the local predictor approach based on precomputing state estimates which is better suited to solve the issue of observer-to-sensor communication for closed-loop triggering in difficult systems, and (iii) exploring the difficulties surrounding the increased computational load when implementing the particle filter under event-based sampling.
Noise in Biomolecular Systems: Modeling, Analysis, and Control Implications
Authors: Corentin Briat, Mustafa Khammash
Date: 2022-09-28 00:00:00

While noise is generally associated with uncertainties and often has a negative connotation in engineering, living organisms have evolved to adapt to (and even exploit) such uncertainty to ensure the survival of a species or implement certain functions that would have been difficult or even impossible otherwise. In this article, we review the role and impact of noise in systems and synthetic biology, with a particular emphasis on its role in the genetic control of biological systems, an area we refer to as Cybergenetics. The main modeling paradigm is that of stochastic reaction networks, whose applicability goes beyond biology, as these networks can represent any population dynamics system, including ecological, epidemiological, and opinion dynamics networks. We review different ways to mathematically represent these systems, and we notably argue that the concept of ergodicity presents a particularly suitable way to characterize their stability. We then discuss noise-induced properties and show that noise can be both an asset and a nuisance in this setting. Finally, we discuss recent results on (stochastic) Cybergenetics and explore their relationships to noise. Along the way, we detail the different technical and biological constraints that need to be respected when designing synthetic biological circuits. Finally, we discuss the concepts, problems, and solutions exposed in the article; raise criticisms and concerns about current ideas and approaches; suggest current (open) problems with potential solutions; and provide some ideas for future research directions.
Hierarchical Cyber-Attack Detection in Large-Scale Interconnected Systems
Authors: Twan Keijzer, Alexander J. Gallo, Riccardo M. G. Ferrari
Date: 2022-09-28 00:00:00
Tasks: Cyber Attack Detection

In this paper we present a hierarchical scheme to detect cyber-attacks in a hierarchical control architecture for large-scale interconnected systems (LSS). We consider the LSS as a network of physically coupled subsystems, equipped with a two-layer controller: on the local level, decentralized controllers guarantee overall stability and reference tracking; on the supervisory level, a centralized coordinator sets references for the local regulators. We present a scheme to detect attacks that occur at the local level, with malicious agents capable of affecting the local control. The detection scheme is computed at the supervisory level, requiring only limited exchange of data and model knowledge. We offer detailed theoretical analysis of the proposed scheme, highlighting its detection properties in terms of robustness, detectability and stealthiness conditions.
Spectral clustering and model reduction for weakly-connected coherent network systems
Authors: Hancheng Min, Enrique Mallada
Date: 2022-09-27 00:00:00
Tasks: Clustering

We propose a novel model-reduction methodology for large-scale dynamic networks with tightly-connected components. First, the coherent groups are identified by a spectral clustering algorithm on the graph Laplacian matrix that models the network feedback. Then, a reduced network is built, where each node represents the aggregate dynamics of each coherent group, and the reduced network captures the dynamic coupling between the groups. Our approach is theoretically justified under a random graph setting. Finally, numerical experiments align with and validate our theoretical findings.
Switched-Actuator Systems with Setup Times: Efficient Modeling, MPC, and Application to Hyperthermia Therapy
Authors: D. A. Deenen, E. Maljaars, L. C. Sebeke, B. de Jager, E. Heijman, H. Grüll, W. P. M. H. Heemels
Date: 2022-09-27 00:00:00
Tasks: continuous-control, Continuous Control, Model Predictive Control

Switched-actuator systems with setup times (SAcSSs) are systems in which the actuator configuration has to be switched during operation, and where the switching induces non-negligible actuator downtime. Optimally controlling SAcSSs requires the online solving of both a discrete actuator allocation problem, in which the switch-induced actuator downtime is taken into account, as well as an optimization problem for the (typically continuous) control inputs. Mixed-integer model predictive control (MI-MPC) offers a powerful framework for tackling such problems. However, the efficient modeling of SAcSSs for MI-MPC is not straightforward, and real-time feasibility is often a major hurdle in practice. It is the objective of this paper to provide an intuitive and systematic modeling procedure tailored to SAcSSs, which is specifically designed to allow for user-friendly controller synthesis, and to yield efficient MI-MPCs. We apply these new results in a case study of large-volume magnetic-resonance-guided high-intensity focused ultrasound hyperthermia, which involves the heating of tumors (using real-valued local heating controls, as well as discrete range-extending actuator relocation during which no heating is allowed) to enhance the efficacy of radio- and chemotherapy.
On the Adversarial Convex Body Chasing Problem
Authors: Yue Guan, Longxu Pan, Daigo Shishika, Panagiotis Tsiotras
Date: 2022-09-27 00:00:00

In this work, we extend the convex bodies chasing problem (CBC) to an adversarial setting, where an agent (the Player) is tasked with chasing a sequence of convex bodies generated adversarially by another agent (the Opponent). The Player aims to minimize the total cost associated with its own movements, while the Opponent tries to maximize the same cost. The set of feasible convex bodies is finite and known to both agents, which allows us to provide performance guarantees with max-min optimality. Under certain assumptions, we show the continuity of the optimal value function, and propose an algorithm to numerically approximate the optimal policies for both the Player and the Opponent within a guaranteed tolerance. Finally, the theoretical results are verified through numerical examples.
Immune cells use active tugging forces to distinguish affinity and accelerate evolution
Authors: Hongda Jiang, Shenshen Wang
Date: 2022-09-28 00:00:00

Cells are known to exert forces to sense their physical surroundings for guidance of motion and fate decisions. Here, we propose that cells might do mechanical work to drive their own evolution, taking inspiration from the adaptive immune system. Growing evidence indicates that immune B cells - capable of rapid Darwinian evolution - use cytoskeletal forces to actively extract antigen from other cells' surface. To elucidate the evolutionary significance of force usage, we develop a theory of tug-of-war antigen extraction that maps receptor binding characteristics to clonal reproductive fitness, revealing physical determinants of selection strength. This framework unifies mechanosensing and affinity-discrimination capabilities of evolving cells: pulling against stiff antigen tethers enhances discrimination stringency at the expense of absolute extraction. As a consequence, active force usage can accelerate adaptation but may also cause extinction of cell populations, resulting in an optimal range of pulling strength that matches molecular rupture forces observed in cells. Our work suggests that nonequilibrium, physical extraction of environmental signals can make biological systems more evolvable at a moderate energy cost.
Data-driven soliton mappings for integrable fractional nonlinear wave equations via deep learning with Fourier neural operator
Authors: Ming Zhong, Zhenya Yan
Date: 2022-08-29 00:00:00

In this paper, we firstly extend the Fourier neural operator (FNO) to discovery the soliton mapping between two function spaces, where one is the fractional-order index space $\{\epsilon|\epsilon\in (0, 1)\}$ in the fractional integrable nonlinear wave equations while another denotes the solitonic solution function space. To be specific, the fractional nonlinear Schr\"{o}dinger (fNLS), fractional Korteweg-de Vries (fKdV), fractional modified Korteweg-de Vries (fmKdV) and fractional sine-Gordon (fsineG) equations proposed recently are studied in this paper. We present the train and evaluate progress by recording the train and test loss. To illustrate the accuracies, the data-driven solitons are also compared to the exact solutions. Moreover, we consider the influences of several critical factors (e.g., activation functions containing Relu$(x)$, Sigmoid$(x)$, Swish$(x)$ and $x\tanh(x)$, depths of fully connected layer) on the performance of the FNO algorithm. We also use a new activation function, namely, $x\tanh(x)$, which is not used in the field of deep learning. The results obtained in this paper may be useful to further understand the neural networks in the fractional integrable nonlinear wave systems and the mappings between two spaces.
The Neoplasia as embryological phenomenon and its implication in the animal evolution and the origin of cancer. II. The neoplastic process as an evolutionary engine
Authors: Jaime Cofre
Date: 2022-09-27 00:00:00

In this article, I put forward the idea that the neoplastic process (NP) has deep evolutionary roots and make specific predictions about the connection between cancer and the formation of the first embryo, which allowed for the evolutionary radiation of metazoans. My main hypothesis is that the NP is at the heart of cellular mechanisms responsible for animal morphogenesis and, given its embryological basis, also at the center of animal evolution. It is thus understood that NP-associated mechanisms are deeply rooted in evolutionary history and tied to the formation of the first animal embryo. In my consideration of these arguments, I expound on how cancer biology is perfectly intertwined with evolutionary biology. I describe essential cellular components of unicellular holozoans that served as a basis for the formation of the neoplastic functional module (NFM) and its subsequent exaptation, which brought forth two great biophysical revolutions within the first embryo. Finally, I examine the role of Physics in the modeling of the NFM and its contribution to morphogenesis to reveal the totipotency of the zygote.
Linear estimation of global average treatment effects
Authors: Stefan Faridani, Paul Niehaus
Date: 2022-09-28 00:00:00
Tasks: Experimental Design

We study the problem of estimating the average causal effect of treating every member of a population, as opposed to none, using an experiment that treats only some. We consider settings where spillovers have global support and decay slowly with (a generalized notion of) distance. We derive the minimax rate over both estimators and designs, and show that it increases with the spatial rate of spillover decay. Estimators based on OLS regressions like those used to analyze recent large-scale experiments are consistent (though only after de-weighting), achieve the minimax rate when the DGP is linear, and converge faster than IPW-based alternatives when treatment clusters are small, providing one justification for OLS's ubiquity. When the DGP is nonlinear they remain consistent but converge slowly. We further address inference and bandwidth selection. Applied to the cash transfer experiment studied by Egger et al. (2022) these methods yield a 20% larger estimated effect on consumption.
The Impact of Perceptions of Social Media Advertisements on Advertising Value, Brand Awareness and Brand Associations: Research on Generation Y Instagram Users
Authors: Ibrahim Halil Efendioglu, Yakup Durmaz
Date: 2022-09-27 00:00:00
Tasks: Decision Making

The aim of this paper is to examine how consumer perceptions of social media advertisements affect advertising value and brand awareness. With a rapid increase in the number of social media users over the last ten years, a new advertising domain has become available for companies. Brands that manage social media well in their advertising strategies can quickly influence consumer decision-making and create awareness. However, in social media advertising, which is different from traditional advertising, creating content should be produced and this content should be perceived in a short time by consumers. To achieve this, it is necessary to build rapport with consumers and to present correctly what they wish to see in advertisements by creating awareness. In view of the increasing importance of social media advertising, the study examines how consumer perceptions of Instagram advertisements affect advertising value and brand awareness. This study was conducted with Generation Y consumers on the basis of their Instagram habits, a popular social media app. For this purpose, surveys were held with 665 participants who use Instagram. The collected data were analyzed using structural equation modeling. According to the analysis results, Y generations perceptions of Instagram advertisements have both a positive and negative impact on advertising value and brand awareness and brand associations.
Optimally Biased Expertise
Authors: Pavel Ilinov, Andrei Matveenko, Maxim Senkov, Egor Starkov
Date: 2022-09-27 00:00:00

This paper shows that the principal can strictly benefit from delegating a decision to an agent whose opinion differs from that of the principal. We consider a "delegated expertise" problem, in which the agent has an advantage in information acquisition relative to the principal, as opposed to having preexisting private information. When the principal is ex ante predisposed towards some action, it is optimal for her to hire an agent who is predisposed towards the same action, but to a smaller extent, since such an agent would acquire more information, which outweighs the bias stemming from misalignment. We show that belief misalignment between an agent and a principal is a viable instrument in delegation, performing on par with contracting and communication in a class of problems.
Quality Assurance of Weld Seams Using Laser Triangulation Imaging and Deep Neural Networks
Authors: Andreas Spruck, Jürgen Seiler, Michael Roll, Thomas Dudziak, Jürgen Eckstein, André Kaup
Date: 2022-09-27 00:00:00

In this paper, a novel optical inspection system is presented that is directly suitable for Industry 4.0 and the implementation on IoT-devices controlling the manufacturing process. The proposed system is capable of distinguishing between erroneous and faultless weld seams, without explicitly defining measurement criteria . The developed system uses a deep neural network based classifier for the class prediction. A weld seam dataset was acquired and labelled by an expert committee. Thereby, the visual impression and assessment of the experts is learnt accurately. In the scope of this paper laser triangulation images are used. Due to their special characteristics, the images must be pre-processed to enable the use of a deep neural network. Furthermore, two different approaches are investigated to enable an inspection of differently sized weld seams. Both approaches yield very high classification accuracies of up to 96.88\%, which is competitive to current state of the art optical inspection systems. Moreover, the proposed system enables a higher flexibility and an increased robustness towards systematic errors and environmental conditions due to its ability to generalize. A further benefit of the proposed system is the fast decision process enabling the usage directly within the manufacturing line. Furthermore, standard hardware is used throughout the whole presented work, keeping the roll-out costs for the proposed system as low as possible.
Joint Optimization of Resource Allocation and Trajectory Control for Mobile Group Users in Fixed-Wing UAV-Enabled Wireless Network
Authors: Xuezhen Yan, Xuming Fang, Cailian Deng, Xianbin Wang
Date: 2022-09-28 00:00:00
Tasks: Fairness, Scheduling

Owing to the controlling flexibility and cost-effectiveness, fixed-wing unmanned aerial vehicles (UAVs) are expected to serve as flying base stations (BSs) in the air-ground integrated network. By exploiting the mobility of UAVs, controllable coverage can be provided for mobile group users (MGUs) under challenging scenarios or even somewhere without communication infrastructure. However, in such dual mobility scenario where the UAV and MGUs are all moving, both the non-hovering feature of the fixed-wing UAV and the movement of MGUs will exacerbate the dynamic changes of user scheduling, which eventually leads to the degradation of MGUs' quality-of-service (QoS). In this paper, we propose a fixed-wing UAV-enabled wireless network architecture to provide moving coverage for MGUs. In order to achieve fairness among MGUs, we maximize the minimum average throughput between all users by jointly optimizing the user scheduling, resource allocation, and UAV trajectory control under the constraints on users' QoS requirements, communication resources, and UAV trajectory switching. Considering the optimization problem is mixed-integer non-convex, we decompose it into three optimization subproblems. An efficient algorithm is proposed to solve these three subproblems alternately till the convergence is realized. Simulation results demonstrate that the proposed algorithm can significantly improve the minimum average throughput of MGUs.
On semi shift invariant graph filters
Authors: Feng Ji, See Hian Lee, Wee Peng Tay
Date: 2022-09-28 00:00:00

In graph signal processing, one of the most important subjects is the study of filters, i.e., linear transformations that capture relations between graph signals. One of the most important families of filters is the space of shift invariant filters, defined as transformations commute with a preferred graph shift operator. Shift invariant filters have a wide range of applications in graph signal processing and graph neural networks. A shift invariant filter can be interpreted geometrically as an information aggregation procedure (from local neighborhood), and can be computed easily using matrix multiplication. However, there are still drawbacks to using solely shift invariant filters in applications, such as being restrictively homogeneous. In this paper, we generalize shift invariant filters by introducing and studying semi shift invariant filters. We give an application of semi shift invariant filters with a new signal processing framework, the subgraph signal processing. Moreover, we also demonstrate how semi shift invariant filters can be used in graph neural networks.