pwc-archive/papers-with-abstracts huggingface dataset:
Asynchronous Event-Triggered Control for Non-Linear Systems
Authors: Daniel A. Williams, Airlie Chapman, Chris Manzie
Date: 2022-11-25 00:00:00

With the increasing ubiquity of networked control systems, various strategies for sampling constituent subsystems' outputs have emerged. In contrast with periodic sampling, event-triggered control provides a way to efficiently sample a subsystem and conserve network resource usage, by triggering an update only when a state-dependent error threshold is satisfied. Herein we describe a novel scheme for asynchronous event-triggered measurement and control (ETC) of a nonlinear plant using sampler subsystems with hybrid dynamics. We extend existing ETC literature by adopting a more general representation of the sampler subsystem dynamics that do not require trigger periodicity or simultaneity, thus accommodating different sampling schemes for both synchronous and asynchronous ETC applications. We ensure that the plant and controller trigger rules are not susceptible to Zeno behavior by employing auxiliary timer variables in conjunction with state-dependent error thresholds. We conclude with a numerical example in order to illustrate important practical considerations when applying such schemes.
Asynchronous Computation of Tube-based Model Predictive Control
Authors: Jerome Sieber, Andrea Zanelli, Antoine P. Leeman, Samir Bennani, Melanie N. Zeilinger
Date: 2022-11-24 00:00:00
Tasks: Model Predictive Control

Tube-based model predictive control (MPC) methods bound deviations from a nominal trajectory due to uncertainties in order to ensure constraint satisfaction. While techniques that compute the tubes online reduce conservativeness and increase performance, they suffer from high and potentially prohibitive computational complexity. This paper presents an asynchronous computation mechanism for system level tube-MPC (SLTMPC), a recently proposed tube-based MPC method which optimizes over both the nominal trajectory and the tubes. Computations are split into a primary and a secondary process, computing the nominal trajectory and the tubes, respectively. This enables running the primary process at a high frequency and moving the computationally complex tube computations to the secondary process. We show that the secondary process can continuously update the tubes, while retaining recursive feasibility of the primary process.
Bidirectional optogenetic control of inhibitory neurons in freely-moving mice
Authors: Ori Noked, Amir Levi, Shirly Someck, Ortal Amber-Vitos, Eran Stark
Date: 2022-11-24 00:00:00
Tasks: Hippocampus

Objective: Optogenetic manipulations of excitable cells enable activating or silencing specific types of neurons. By expressing two types of exogenous proteins, a single neuron can be depolarized using light of one wavelength and hyperpolarized with another. However, routing two distinct wavelengths into the same brain locality typically requires bulky optics that cannot be implanted on the head of a freely-moving animal. Methods: We developed a lens-free approach for constructing dual-color head-mounted, fiber-based optical units: any two wavelengths can be combined. Results: Here, each unit was comprised of one 450 nm and one 638 nm laser diode, yielding light power of 0.4 mW and 8 mW at the end of a 50 micrometer multimode fiber. To create a multi-color/multi-site optoelectronic device, a four-shank silicon probe mounted on a microdrive was equipped with two dual-color and two single-color units, for a total weight under 3 g. Devices were implanted in mice expressing the blue-light sensitive cation channel ChR2 and the red-light sensitive chloride pump Jaws in parvalbumin-immunoreactive (PV) inhibitory neurons. The combination of dual-color units with recording electrodes was free from electromagnetic interference, and device heating was under 7{\deg}C even after prolonged operation. Conclusion: Using these devices, the same cortical PV cell could be activated and silenced. This was achieved for multiple cells both in neocortex and hippocampus of freely-moving mice. Significance: This technology can be used for controlling spatially intermingled neurons that have distinct genetic profiles, and for controlling spike timing of cortical neurons during cognitive tasks.
Outan: An On-Head System for Driving micro-LED Arrays Implanted in Freely Moving Mice
Authors: Alexander Tarnavsky Eitan, Shirly Someck, Mario Zajac, Eran Socher, Eran Stark
Date: 2022-11-24 00:00:00

In the intact brain, neural activity can be recorded using sensing electrodes and manipulated using light stimulation. Silicon probes with integrated electrodes and micro-LEDs enable the detection and control of neural activity using a single implanted device. Miniaturized solutions for recordings from small freely moving animals are commercially available, but stimulation is driven by large, stationary current sources. We designed and fabricated a current source chip and integrated it into a headstage PCB that weighs 1.37 g. The proposed system provides 10-bit resolution current control for 32 channels, driving micro-LEDs with up to 4.6 V and sourcing up to 0.9 mA at a refresh rate of 5 kHz per channel. When calibrated against a micro-LED probe, the system allows linear control of light output power, up to 10 micro-W per micro-LED. To demonstrate the capabilities of the system, synthetic sequences of neural spiking activity were produced by driving multiple micro-LEDs implanted in the hippocampal CA1 area of a freely moving mouse. The high spatial, temporal, and amplitude resolution of the system provides a rich variety of stimulation patterns. Combined with commercially available sampling headstages, the system provides an easy to use back-end, fully utilizing the bi-directional potential of integrated opto-electronic arrays.
Link Count Data-driven Static Traffic Assignment Models Through Network Modularity Partitioning
Authors: Alexander Roocroft, Giuliano Punzo, Muhamad Azfar Ramli
Date: 2022-11-24 00:00:00

Accurate static traffic assignment models are important tools for the assessment of strategic transportation policies. In this article we present a novel approach to partition road networks through network modularity to produce data-driven static traffic assignment models from loop detector data on large road systems. The use of partitioning allows the estimation of the key model input of Origin-Destination demand matrices from flow counts alone. Previous network tomography-based demand estimation techniques have been limited by the network size. The amount of partitioning changes the Origin-Destination estimation optimisation problems to different levels of computational difficulty. Different approaches to utilising the partitioning were tested, one which degenerated the road network to the scale of the partitions and others which left the network intact. Applied to a subnetwork of England's Strategic Road Network and other test networks, our results for the degenerate case showed flow and travel time errors are reasonable with a small amount of degeneration. The results for the non-degenerate cases showed that similar errors in model prediction with lower computation requirements can be obtained when using large partitions compared with the non-partitioned case. This work could be used to improve the effectiveness of national road systems planning and infrastructure models.
Identifiability of nonlinear ODE Models with Time-Varying Parameters: the General Analytical Solution and Applications in Viral Dynamics
Authors: Agostino Martinelli
Date: 2022-11-24 00:00:00

Identifiability is a structural property of any ODE model characterized by a set of unknown parameters. It describes the possibility of determining the values of these parameters from fusing the observations of the system inputs and outputs. This paper finds the general analytical solution of this fundamental problem and, based on this, provides a general and automated analytical method to determine the identifiability of the unknown parameters. In particular, the method can handle any model, regardless of its complexity and type of non-linearity, and provides the identifiability of the parameters even when they are time-varying. In addition, it is automatic as it simply needs to follow the steps of a systematic procedure that only requires to perform the calculation of derivatives and matrix ranks. Time-varying parameters are treated as unknown inputs and their identification is based on the very recent analytical solution of the unknown input observability problem [1, 2]. The method is used to determine the identifiability of the unknown time-varying parameters that characterize two non-linear models in the field of viral dynamics (HIV and Covid-19) and a non-linear model that characterizes the genetic toggle switch. New fundamental properties that characterize these models are determined and discussed in detail through a comparison with the state-of-the-art results. In particular, regarding the very popular HIV ODE model and the genetic toggle switch model, the method automatically finds new important results that are in contrast with the results in the current literature.
Remote State Estimation with Privacy Against Eavesdroppers
Authors: Matthew Crimson, Justin M. Kennedy, Daniel E. Quevedo
Date: 2022-11-24 00:00:00
Tasks: State Estimation

We study the problem of remote state estimation in the presence of a passive eavesdropper, under the challenging network environment of no packet receipt acknowledgments. A remote legitimate user estimates the state of a linear plant from the state information received from a sensor via an insecure and unreliable network. The transmission from the sensor may be intercepted by the eavesdropper. To maintain state confidentiality, we propose an encoding scheme. Our scheme transmits noise based on a pseudo-random indicator, pre-arranged at the legitimate user and sensor. The transmission of noise harms the eavesdropper's performance, more than that of the legitimate user. Using the proposed encoding scheme, we impair the eavesdropper's expected estimation performance, whilst minimising expected performance degradation at the legitimate user. We explore the trade-off between state confidentiality and legitimate user performance degradation.
Communications for the Planet Mars: Past, Present, and Future
Authors: Enes Koktas, Ertugrul Basar
Date: 2022-11-25 00:00:00

Space exploration has been on the rise since the 1960s. Along with the other planets such as Mercury, Venus, Saturn, and Jupiter, Mars certainly plays a significant role in the history of space exploration and has the potential to be the first extraterrestrial planet to host human life. In this context, tremendous effort has been put into developing new technologies to photograph, measure, and analyze the red planet. As the amount of data collected from science instruments around and on Mars increased, the need for fast and reliable communication between Earth and space probes has emerged. However, communicating over deep space has always been a big challenge due to the propagation characteristics of radio waves. Nowadays, the collaboration of private companies like SpaceX with space agencies to make Mars colonization a reality, introduces even more challenges, such as providing high data rate, low latency, energy-efficient, reliable, and mobility-resistant communication infrastructures in the Martian environment. Propagation medium and wireless channel characteristics of Mars should be extensively studied to achieve these goals. This survey article presents a comprehensive overview of the Mars missions and channel modeling studies of the near-Earth, interstellar, and near-planet links. Studies featuring three-dimensional (3D) channel modeling simulations on the Martian surface are also reviewed. We have also presented our own computer simulations considering various scenarios based on realistic 3D Martian terrains using the Wireless Insite software. Path loss exponent, power delay profile, and root-mean-square delay spread for these scenarios are calculated and tabularized in this study. Furthermore, future insights on emerging communication technologies for Mars are given.
A small-correlation expansion to quantify information in noisy sensory systems
Authors: Gabriel Mahuas, Olivier Marre, Thierry Mora, Ulisse Ferrari
Date: 2022-11-24 00:00:00

Neural networks encode information through their collective spiking activity in response to external stimuli. This population response is noisy and strongly correlated, with complex interplay between correlations induced by the stimulus, and correlations caused by shared noise. Understanding how these correlations affect information transmission has so far been limited to pairs or small groups of neurons, because the curse of dimensionality impedes the evaluation of mutual information in larger populations. Here we develop a small-correlation expansion to compute the stimulus information carried by a large population of neurons, yielding interpretable analytical expressions in terms of the neurons' firing rates and pairwise correlations. We validate the approximation on synthetic data and demonstrate its applicability to electrophysiological recordings in the vertebrate retina, allowing us to quantify the effects of noise correlations between neurons and of memory in single neurons.
Molecular dynamics simulations of chemically modified ribonucleotides
Authors: Valerio Piomponi, Mattia Bernetti, Giovanni Bussi
Date: 2022-11-24 00:00:00
Tasks: Articles

Post-transcriptional modifications are crucial for RNA function, with roles ranging from the stabilization of functional RNA structures to modulation of RNA--protein interactions. Additionally, artificially modified RNAs have been suggested as optimal oligonucleotides for therapeutic purposes. The impact of chemical modifications on secondary structure has been rationalized for some of the most common modifications. However, the characterization of how the modifications affect the three-dimensional RNA structure and dynamics and its capability to bind proteins is still highly challenging. Molecular dynamics simulations, coupled with enhanced sampling methods and integration of experimental data, provide a direct access to RNA structural dynamics. In the context of RNA chemical modifications, alchemical simulations where a wild type nucleotide is converted to a modified one are particularly common. In this Chapter, we review recent molecular dynamics studies of modified ribonucleotides. We discuss the technical aspects of the reviewed works, including the employed force fields, enhanced sampling methods, and alchemical methods, in a way that is accessible to experimentalists. Finally, we provide our perspective on this quickly growing field of research. The goal of this Chapter is to provide a guide for experimentalists to understand molecular dynamics works and, at the same time, give to molecular dynamics experts a solid review of published articles that will be a useful starting point for new research.
Temporal Super-Resolution using Multi-Channel Illumination Source
Authors: Khen Cohen, Dan Raviv, David Mendlovic
Date: 2022-11-25 00:00:00
Tasks: Motion Estimation, Object, Super-Resolution

While sensing in high temporal resolution is necessary for wide range of application, it is still limited nowadays due to cameras sampling rate. In this work we try to increase the temporal resolution beyond the Nyquist frequency, which is limited by the sampling rate of the sensor. This work establishes a novel approach for Temporal-Super-Resolution that uses the object reflecting properties from an active illumination source to go beyond this limit. Following theoretical derivation, we demonstrate how we can increase the temporal spectral detected range by a factor of 6 and possibly even more. Our method is supported by simulations and experiments and we demonstrate as an application, how we use our method to improve in about factor two the accuracy of object motion estimation.
Modelling plausible scenarios for the Omicron SARS-CoV-2 variant from early-stage surveillance
Authors: Christopher J. Banks, Ewan Colman, Anthony Wood, Thomas Doherty, Rowland R. Kao
Date: 2022-11-24 00:00:00

We used a spatially explicit agent-based model of SARS-CoV-2 transmission combined with spatially fine-grained COVID-19 observation data from Public Health Scotland to investigate the initial rise of the Omicron (BA.1) variant of concern. We evaluated plausible scenarios for transmission rate advantage and vaccine immune escape relative to the Delta variant based on the data that would have been available at that time. We also explored possible outcomes of different levels of imposed non-pharmaceutical intervention. The initial results of these scenarios were used to inform the Scottish Government in the early outbreak stages of the Omicron variant. Using the model with parameters fit over the Delta variant epidemic, some initial assumptions about Omicron transmission rate advantage and vaccine escape, and a simple growth rate fitting procedure, we were able to capture the initial outbreak dynamics for Omicron. We found that the modelled dynamics hold up to retrospective scrutiny. The modelled imposition of extra non-pharmaceutical interventions planned by the Scottish Government at the time would likely have little effect in light of the transmission rate advantage held by the Omicron variant and the fact that the planned interventions would have occurred too late in the outbreak's trajectory. Finally, we found that any assumptions made about the projected distribution of vaccines in the model population had little bearing on the outcome, in terms of outbreak size and timing. Instead, it was the landscape of prior immunity that was most important.
Spectral estimation for mixed causal-noncausal autoregressive models
Authors: Alain Hecq, Daniel Velasquez-Gaviria
Date: 2022-11-24 00:00:00

This paper investigates new ways of estimating and identifying causal, noncausal, and mixed causal-noncausal autoregressive models driven by a non-Gaussian error sequence. We do not assume any parametric distribution function for the innovations. Instead, we use the information of higher-order cumulants, combining the spectrum and the bispectrum in a minimum distance estimation. We show how to circumvent the nonlinearity of the parameters and the multimodality in the noncausal and mixed models by selecting the appropriate initial values in the estimation. In addition, we propose a method of identification using a simple comparison criterion based on the global minimum of the estimation function. By means of a Monte Carlo study, we find unbiased estimated parameters and a correct identification as the data depart from normality. We propose an empirical application on eight monthly commodity prices, finding noncausal and mixed causal-noncausal dynamics.
Cross-Sectional Dynamics Under Network Structure: Theory and Macroeconomic Applications
Authors: Marko Mlikota
Date: 2022-11-24 00:00:00
Tasks: Dimensionality Reduction

Many environments in economics involve units linked by bilateral ties. I develop an econometric framework that rationalizes the dynamics of cross-sectional variables as the innovation transmission along fixed bilateral links and that can accommodate rich patterns of how network effects of higher order accumulate over time. The proposed Network-VAR (NVAR) can be used to estimate dynamic network effects, with the network given or inferred from dynamic cross-correlations in the data. In the latter case, it also offers a dimensionality-reduction technique for modeling high-dimensional (cross-sectional) processes, owing to networks' ability to summarize complex relations among variables (units) by relatively few bilateral links. In a first application, I show that sectoral output growth in an RBC economy with lagged input-output conversion follows an NVAR. I characterize impulse-responses to TFP shocks in this environment, and I estimate that the lagged transmission of productivity shocks along supply chains can account for a third of the persistence in aggregate output growth. The remainder is due to persistence in the aggregate TFP process, leaving a negligible role for persistence in sectoral TFP. In a second application, I forecast macroeconomic aggregates across OECD countries by assuming and estimating a network that underlies the dynamics. In line with an equivalence result I provide, this reduces out-of-sample mean squared errors relative to a dynamic factor model. The reductions range from -12% for quarterly real GDP growth to -68% for monthly CPI inflation.
The Correlation: minimum wage - unemployment in the conditions of transition to digital economy
Authors: Shteryo Nozharov, Petya Koralova-Nozharova
Date: 2022-11-23 00:00:00

The research is done in the context of the upcoming introduction of new European legislation for the first time for regulation of minimum wage at European level. Its purpose is to identify the direction and strength of the correlation amongst changes of minimum wage and unemployment rate in the context of conflicting findings of the scientific literature. There will be used statistical instruments for the purposes of the analysis as it incorporates data for Bulgaria for the period 1991-2021. The significance of the research is related to the transition to digital economy and the necessity for complex transformation of the minimum wage functions in the context of the new socio-economic reality.
A Rigorous Proof of the Index Theorem for Economists
Authors: Yuhki Hosoya
Date: 2022-11-25 00:00:00

This paper provides a rigorous and gap-free proof of the index theorem used in the theory of regular economy. In the index theorem that is the subject of this paper, the assumptions for the excess demand function are only several usual assumptions and continuous differentiability around any equilibrium price, and thus it has a form that is applicable to many economies. However, the textbooks on this theme contain only abbreviated proofs and there is no known monograph that contains a rigorous proof of this theorem. Hence, the purpose of this paper is to make this theorem available to more economists by constructing a readable proof.
Efficient Communication in Organizations
Authors: Federico Vaccari
Date: 2022-11-24 00:00:00
Tasks: Decision Making

This paper studies the organization of communication between biased senders and a receiver. Senders can misreport their private information at a cost. Efficiency is achieved by clearing information asymmetries without incurring costs. Results show that only one communication protocol is efficient, robust to collusion, and free from unnecessary complexities. This protocol has a simple, adversarial, and public structure. It always induces efficient equilibria, for which a closed-form characterization is provided. The findings are relevant for the design of organizations that seek to improve decision-making while limiting wasteful influence activities.