pwc-archive/papers-with-abstracts huggingface dataset:
Aperiodic Communication for MPC in Autonomous Cooperative Landing
Authors: Dženan Lapandić, Linnea Persson, Dimos V. Dimarogonas, Bo Wahlberg
Date: 2021-02-15 00:00:00
Tasks: Model Predictive Control

This paper investigates the rendezvous problem for the autonomous cooperative landing of an unmanned aerial vehicle (UAV) on an unmanned surface vehicle (USV). Such heterogeneous agents, with nonlinear dynamics, are dynamically decoupled but share a common cooperative rendezvous task. The underlying control scheme is based on distributed Model Predictive Control (MPC). The main contribution is a rendezvous algorithm with an online update rule of the rendezvous location. The algorithm only requires the agents to exchange information when they can not guarantee to rendezvous. Hence, the exchange of information occurs aperiodically, which reduces the necessary communication between the agents. Furthermore, we prove that the algorithm guarantees recursive feasibility. The simulation results illustrate the effectiveness of the proposed algorithm applied to the problem of autonomous cooperative landing.
Identification of cancer-keeping genes as therapeutic targets by finding network control hubs
Authors: Xizhe Zhang, Chunyu Pan, Xinru Wei, Meng Yu, Shuangjie Liu, Jun An, Jieping Yang, Baojun Wei, Wenjun Hao, Yang Yao, Yuyan Zhu, Weixiong Zhang
Date: 2022-06-13 00:00:00

Finding cancer driver genes has been a focal theme of cancer research and clinical studies. One of the recent approaches is based on network structural controllability that focuses on finding a control scheme and driver genes that can steer the cell from an arbitrary state to a designated state. While theoretically sound, this approach is impractical for many reasons, e.g., the control scheme is often not unique and half of the nodes may be driver genes for the cell. We developed a novel approach that transcends structural controllability. Instead of considering driver genes for one control scheme, we considered control hub genes that reside in the middle of a control path of every control scheme. Control hubs are the most vulnerable spots for controlling the cell and exogenous stimuli on them may render the cell uncontrollable. We adopted control hubs as cancer-keep genes (CKGs) and applied them to a gene regulatory network of bladder cancer (BLCA). All the genes on the cell cycle and p53 singling pathways in BLCA are CKGs, confirming the importance of these genes and the two pathways in cancer. A smaller set of 35 sensitive CKGs (sCKGs) for BLCA was identified by removing network links. Six sCKGs (RPS6KA3, FGFR3, N-cadherin (CDH2), EP300, caspase-1, and FN1) were subjected to small-interferencing-RNA knockdown in four cell lines to validate their effects on the proliferation or migration of cancer cells. Knocking down RPS6KA3 in a mouse model of BLCA significantly inhibited the growth of tumor xenografts in the mouse model. Combined, our results demonstrated the value of CKGs as therapeutic targets for cancer therapy and the potential of CKGs as an effective means for studying and characterizing cancer etiology.
Sequential Convex Programming for Optimal Line of Sight Steering in Agile Missions
Authors: Jesús Ramírez, Lukas Hewing
Date: 2022-06-13 00:00:00
Tasks: Management

The trend toward onboard autonomy and spacecraft minimization present significant potential for advances in efficient Line of Sight management by making optimal use of the limited torque resources available. At SENER Aeroespacial, we are implementing AOCS algorithms capable of providing agility in different observation scenarios in which the exploitation of the resources is fundamental for the mission success. In this contribution, we present an in-house optimization toolbox for onboard guidance, the SENER Optimization Toolbox, and we propose its use for online attitude guidance of an agile space-craft using Control Moment Gyroscopes. We propose different optimization schemes based on Sequential Convex Programming aiming for reducing the computational burden for real-time implementations. The results highlight the potential for performance improvements when making use of embedded optimal control for fast slew maneuvers: with proposed schemes, we find solutions that implicitly manage singularities and are up to 12.2% faster than classical bang-bang maneuvers while increasing the smoothness of the trajectories to minimize the excitation of flexible modes.
Reduction and Observer Design for a Grey-Box Model in Continuous Pharmaceutical Manufacturing
Authors: Ahmed Elkhashap, Dirk Abel
Date: 2022-06-13 00:00:00

In this contribution, a novel Reduced Order Model (ROM) formulation of the grey-box model proposed in Elkhashap et al. (2020a) for the pharmaceutical continuous vibrated fluid bed dryer (VFBD) is presented. The ROM exploits the $\mathcal{H}_2$-norm projection-based model order reduction method after a special solution formulation of the model's infinite-dimensional part. This is mainly by introducing a vector field mapping between the model parts casting the semi-discretized PDE into a bilinear form. The ROM produced is then integrated into an nonlinear Kalman Filtering-based observer design also handling the estimation of the model's algebraic variables. Evaluations of the FOM, ROM, ROM-based observer variants, and the FOM-based observer are performed using Monte-Carlo simulations as well as simulations based on experimental data of the real system. It is shown that the ROM could reproduce the FOM states accurately with a relative mean square error below $0.3\,\%$ for the experimental data simulation. This is while reaching a computational-time reduction up to a factor of $40$. The ROM-based observer with algebraic states correction is shown (using Monte-Carlo simulations) to be able to converge to the true values for all cases regardless of initialization. Moreover, it is also shown that the performance degradation of the observer due to reduction is practically insignificant. This is while the computational speedup of the observer due to reduction reached a factor of more than third order of magnitude.
Techno Economic Modeling for Agrivoltaics: Can Agrivoltaics be more profitable than Ground mounted PV?
Authors: Habeel Alam, Muhammad Ashraful Alam, Nauman Zafar Butt
Date: 2022-06-13 00:00:00

Agrivoltaics (AV) is a dual land-use approach to collocate solar energy generation with agriculture for preserving the terrestrial ecosystem and enabling food-energy-water synergies. Here, we present a systematic approach to model the economic performance of AV relative to standalone ground-mounted PV (GMPV) and explore how the module design configuration can affect the dual food-energy economic performance. A remarkably simple criterion for economic feasibility is quantified that relates the land preservation cost to dual food-energy profit. We explore case studies including both high and low value crops under fixed tilt bifacial modules oriented either along the conventional North/South (N/S) facings or vertical East/West (E/W) facings. For each module configuration, the array density is varied to explore an economically feasible design space relative to GMPV for a range of module to land cost ratio (M_L) - a location-specific indicator relating the module technology (hardware and installation) costs to the soft (land acquisition, tax, overheads, etc.) costs. To offset a typically higher AV module cost needed to preserve the cropland, both E/W and N/S orientated modules favor high value crops, reduced (<60%) module density, and higher M_L (>25). In contrast, higher module density and an increased feed-in-tariff (FIT) relative to GMPV are desirable at lower M_L. The economic trends vary sharply for M_L< 10 but tend to saturate for M_L> 20. For low value crops, ~15% additional FIT can enable economic equivalence to GMPV at standard module density. The proposed modeling framework can provide a valuable tool for AV stakeholders to assess, predict, and optimize the techno-economic design for AV
Adaptive Multi-robot Implicit Control of Heterogeneous Herds
Authors: Eduardo Sebastián, Eduardo Montijano, Carlos Sagüés
Date: 2022-06-13 00:00:00

This paper presents a novel control strategy to herd groups of non-cooperative evaders by means of a team of robotic herders. In herding problems, the motion of the evaders is typically determined by strongly nonlinear and heterogeneous reactive dynamics, which makes the development of flexible control solutions a challenging problem. In this context, we propose Implicit Control, an approach that leverages numerical analysis theory to find suitable herding inputs even when the nonlinearities in the evaders' dynamics yield to implicit equations. The intuition behind this methodology consists in driving the input, rather than computing it, towards the unknown value that achieves the desired dynamic behavior of the herd. The same idea is exploited to develop an adaptation law, with stability guarantees, that copes with uncertainties in the herd's models. Moreover, our solution is completed with a novel caging technique based on uncertainty models and Control Barrier Functions (CBFs), together with a distributed estimator to overcome the need of complete perfect measurements. Different simulations and experiments validate the generality and flexibility of the proposal.
Concurrent Learning Based Adaptive Control of Euler Lagrange Systems with Guaranteed Parameter Convergence
Authors: Erkan Zergeroglu, Enver Tatlicioglu, Serhat Obuz
Date: 2022-06-12 00:00:00
Tasks: parameter estimation, Position, regression

This work presents a solution to the adaptive tracking control of Euler Lagrange systems with guaranteed tracking and parameter estimation error convergence. Specifically a concurrent learning based update rule fused by the filtered version of the desired system dynamics in conjunction with a desired state based regression matrix has been utilized to ensure that both the position tracking error and parameter estimation error terms converge to origin exponentially. As the regression matrix used in proposed controller makes use of the desired versions of the system states, an initial, sufficiently exciting memory stack can be formed from the knowledge of the desired system trajectory a priori, thus removing the initial excitation condition required for the previously proposed concurrent learning based controllers in the literature. The output feedback versions of the proposed method where only the position measurements are available for the controller design, (for both gradient and composite type adaptions) are also presented in order to illustrate the modularity of the proposed method. The stability and boundedness of the closed loop signals for all the proposed controllers are ensured via Lyapunov based analysis. %Trajectory tracking control of a class of fully actuated Euler Lagrange systems is considered in this work. System dynamics is considered to be subject to parametric uncertainties and on--line identification uncertain model parameters is also aimed. When compared with the relevant past research, via a novel approach, desired states are proposed to be used in forming the regression matrix and a desired compensation based concurrent learning type adaptive update rule is designed. Via utilizing novel Lyapunov analysis, semi--global exponential convergence of both tracking and parameter identification error to the origin is ensured.
Scheduling Delays and Curtailment for Household Appliances with Deterministic Load Profiles using MPC
Authors: Yingzhao Lian, Yuning Jiang, Colin N. Jones, Daniel F. Opila
Date: 2022-06-12 00:00:00
Tasks: Management, Model Predictive Control, Scheduling

Smart home appliances can time-shift and curtail their power demand to assist demand side management or allow operation with limited power, as in an off-grid application. This paper proposes a scheduling process to start appliances with time-varying deterministic load profiles. Self-triggered model predictive control is used to limit the household net power demand below a given threshold. Meanwhile, deterministic load profiles are more difficult to schedule compared to variable charging or thermal loads because system failure will occur once power demand is not satisfied. The proposed scheme formulates the decision of the load shifting time as a continuous optimization problem, and an inhomogeneous time grid system is introduced to handle the optimization of different appliances and their consensus at this resolution. The efficacy of the proposed scheme is studied by numerical comparison with a mixed-integer MPC controller and by a case study of three home appliances and an interruptible washing machine.
Resilience for Distributed Consensus with Constraints
Authors: Xuan Wang, Shaoshuai Mou, Shreyas Sundaram
Date: 2022-06-12 00:00:00

This paper proposes a new approach that enables multi-agent systems to achieve resilient \textit{constrained} consensus in the presence of Byzantine attacks, in contrast to existing literature that is only applicable to \textit{unconstrained} resilient consensus problems. The key enabler for our approach is a new device called a \textit{$(\gamma_i,\alpha_i)$-resilient convex combination}, which allows normal agents in the network to utilize their locally available information to automatically isolate the impact of the Byzantine agents. Such a resilient convex combination is computable through linear programming, whose complexity scales well with the size of the overall system. By applying this new device to multi-agent systems, we introduce network and constraint redundancy conditions under which resilient constrained consensus can be achieved with an exponential convergence rate. We also provide insights on the design of a network such that the redundancy conditions are satisfied. Finally, numerical simulations and an example of safe multi-agent learning are provided to demonstrate the effectiveness of the proposed results.
Neural Network-based Flight Control Systems: Present and Future
Authors: Seyyed Ali Emami, Paolo Castaldi, Afshin Banazadeh
Date: 2022-06-11 00:00:00
Tasks: Management

As the first review in this field, this paper presents an in-depth mathematical view of Intelligent Flight Control Systems (IFCSs), particularly those based on artificial neural networks. The rapid evolution of IFCSs in the last two decades in both the methodological and technical aspects necessitates a comprehensive view of them to better demonstrate the current stage and the crucial remaining steps towards developing a truly intelligent flight management unit. To this end, in this paper, we will provide a detailed mathematical view of Neural Network (NN)-based flight control systems and the challenging problems that still remain. The paper will cover both the model-based and model-free IFCSs. The model-based methods consist of the basic feedback error learning scheme, the pseudocontrol strategy, and the neural backstepping method. Besides, different approaches to analyze the closed-loop stability in IFCSs, their requirements, and their limitations will be discussed in detail. Various supplementary features, which can be integrated with a basic IFCS such as the fault-tolerance capability, the consideration of system constraints, and the combination of NNs with other robust and adaptive elements like disturbance observers, would be covered, as well. On the other hand, concerning model-free flight controllers, both the indirect and direct adaptive control systems including indirect adaptive control using NN-based system identification, the approximate dynamic programming using NN, and the reinforcement learning-based adaptive optimal control will be carefully addressed. Finally, by demonstrating a well-organized view of the current stage in the development of IFCSs, the challenging issues, which are critical to be addressed in the future, are thoroughly identified.
Optogenetic switching of migration of contractile cells
Authors: Oliver M. Drozdowski, Falko Ziebert, Ulrich S. Schwarz
Date: 2022-06-13 00:00:00

Cell crawling on flat substrates is based on intracellular flows of the actin cytoskeleton that are driven by both actin polymerization at the front and myosin contractility at the back. The new experimental tool of optogenetics makes it possible to spatially control contraction and thereby possibly also cell migration. Here we analyze this situation theoretically using a one-dimensional active gel model in which the excluded volume interactions of myosin and their aggregation into minifilaments is modeled by a supercritical van der Waals fluid. This physically simple and transparent, but nonlinear and thermodynamically rigorous model predicts bistability between sessile and motile solutions. We then show that one can switch between these two states at realistic parameter ranges via optogenetic activation or inhibition of contractility, in agreement with recent experiments. We also predict the required activation strengths and initiation times.
Analytical cell size distribution: lineage-population bias and parameter inference
Authors: Arthur Genthon
Date: 2022-06-13 00:00:00

Solving population balance equations, we derive analytical steady-state cell size distributions for single-lineage experiments, such as the mother machine. These experiments are fundamentally different from batch cultures where populations of cells grow freely, and the statistical bias between them is obtained by comparing our results to cell size distributions measured in population. For exponential single-cell growth, characterizing most bacteria, the lineage-population bias is obtained explicitly. In addition, if volume is evenly split between the daughter cells at division, we show that cells are on average smaller in populations. For more general power-law growth rates and deterministic volume partitioning, both symmetric and asymmetric, we derive the exact lineage distribution. This solution is in good agreement with E. Coli mother machine data, and can be used to infer cell cycle parameters, such as the strength of the size control and the asymmetry of the division. When introducing stochastic volume partitioning, we derive the large-size and small-size tails of the lineage size distributions, and show that they respectively do not depend on the partitioning of volume and on the size control strength. Finally, we show that introducing noise, either on the volume partitioning or on the single cell growth rate, can cancel the lineage-population bias.
Clustering coefficients as measures of the complex interactions in a directed weighted multilayer network
Authors: Paolo Bartesaghi, Gian Paolo Clemente, Rosanna Grassi
Date: 2022-06-13 00:00:00
Tasks: Clustering

In this paper, we provide novel definitions of clustering coefficient for weighted and directed multilayer networks. We extend in the multilayer theoretical context the clustering coefficients proposed in the literature for weighted directed monoplex networks. We quantify how deeply a node is involved in a choesive structure focusing on a single node, on a single layer or on the entire system. The coefficients convey several characteristics inherent to the complex topology of the multilayer network. We test their effectiveness applying them to a particularly complex structure such as the international trade network. The trade data integrate different aspects and they can be described by a directed and weighted multilayer network, where each layer represents import and export relationships between countries for a given sector. The proposed coefficients find successful application in describing the interrelations of the trade network, allowing to disentangle the effects of countries and sectors and jointly consider the interactions between them.
Robust Knockoffs for Controlling False Discoveries With an Application to Bond Recovery Rates
Authors: Konstantin Görgen, Abdolreza Nazemi, Melanie Schienle
Date: 2022-06-13 00:00:00
Tasks: Variable Selection

We address challenges in variable selection with highly correlated data that are frequently present in finance, economics, but also in complex natural systems as e.g. weather. We develop a robustified version of the knockoff framework, which addresses challenges with high dependence among possibly many influencing factors and strong time correlation. In particular, the repeated subsampling strategy tackles the variability of the knockoffs and the dependency of factors. Simultaneously, we also control the proportion of false discoveries over a grid of all possible values, which mitigates variability of selected factors from ad-hoc choices of a specific false discovery level. In the application for corporate bond recovery rates, we identify new important groups of relevant factors on top of the known standard drivers. But we also show that out-of-sample, the resulting sparse model has similar predictive power to state-of-the-art machine learning models that use the entire set of predictors.
The distribution of loss to future USS pensions due to the UUK cuts of April 2022
Authors: Jackie Grant, Mark Hindmarsh, Sergey E. Koposov
Date: 2022-06-13 00:00:00
Tasks: Management

We present the first global analysis of the impact of the April 2022 cuts to the future pensions of members of the Universities Superannuation Scheme. For the 196,000 active members, if Consumer Price Inflation (CPI) remains at its historic average of 2.5%, the distribution of the range of cuts peaks between 30%-35%. This peak increases to 40%-45% cuts if CPI averages 3.0%. The global loss across current USS scheme members, in today's money, is calculated to be 16-18 billion GBP, with most of the 71,000 staff under the age of 40 losing between 100k-200k GBP each, for CPI averaging 2.5%-3.0%. A repeated claim made during the formal consultation by the body representing university management (Universities UK) that those earning under 40k GBP would receive a "headline" cut of 12% to their future pension is shown to be a serious underestimate for realistic CPI projections.
Realistic Gramophone Noise Synthesis using a Diffusion Model
Authors: Eloi Moliner, Vesa Välimäki
Date: 2022-06-13 00:00:00
Tasks: Audio Synthesis

This paper introduces a novel data-driven strategy for synthesizing gramophone noise audio textures. A diffusion probabilistic model is applied to generate highly realistic quasiperiodic noises. The proposed model is designed to generate samples of length equal to one disk revolution, but a method to generate plausible periodic variations between revolutions is also proposed. A guided approach is also applied as a conditioning method, where an audio signal generated with manually-tuned signal processing is refined via reverse diffusion to improve realism. The method has been evaluated in a subjective listening test, in which the participants were often unable to recognize the synthesized signals from the real ones. The synthetic noises produced with the best proposed unconditional method are statistically indistinguishable from real noise recordings. This work shows the potential of diffusion models for highly realistic audio synthesis tasks.
AmbiSep: Ambisonic-to-Ambisonic Reverberant Speech Separation Using Transformer Networks
Authors: Adrian Herzog, Srikanth Raj Chetupalli, Emanuël A. P. Habets
Date: 2022-06-13 00:00:00
Tasks: Speech Separation

Consider a multichannel Ambisonic recording containing a mixture of several reverberant speech signals. Retreiving the reverberant Ambisonic signals corresponding to the individual speech sources blindly from the mixture is a challenging task as it requires to estimate multiple signal channels for each source. In this work, we propose AmbiSep, a deep neural network-based plane-wave domain masking approach to solve this task. The masking network uses learned feature representations and transformers in a triple-path processing configuration. We train and evaluate the proposed network architecture on a spatialized WSJ0-2mix dataset, and show that the method achieves a multichannel scale-invariant signal-to-distortion ratio improvement of 17.7 dB on the blind test set, while preserving the spatial characteristics of the separated sounds.