pwc-archive/papers-with-abstracts huggingface dataset:
Elementary Intracellular Ca Signals are Initiated by a Transition of Release Channel System from a Metastable State
Authors: Guillermo Veron, Victor A. Maltsev, Michael D. Stern, Anna V. Maltsev
Date: 2021-05-04 00:00:00

Cardiac muscle contraction is initiated by an elementary Ca signal (called Ca spark) which is achieved by collective action of Ca release channels in a cluster. The mechanism of this synchronization remains uncertain. This paper approaches Ca spark activation as an emergent phenomenon of an interactive system of release channels. We construct a Markov chain that applies an Ising model formalism to such release channel clusters and realistic open channel configurations to demonstrate that spark activation is described as a system transition from a metastable to an absorbing state, analogous to the pressure required to overcome surface tension in bubble formation. This yields quantitative estimates of the spark generation probability as a function of various system parameters. Our model of the release channel system yields similar results for the sarcoplasmic reticulum Ca concentration threshold for spark activation as previous experimental results, providing a mechanistic explanation of the spark initiation. Additionally, we perform numerical simulations to find spark probabilities as a function of sarcoplasmic reticulum Ca concentration obtaining similar values for spark activation threshold as our analytic model, as well as those reported in experimental studies.
Metastable spiking networks in the replica-mean-field limit
Authors: Luyan Yu, Thibaud Taillefumier
Date: 2021-05-04 00:00:00
Tasks: Point Processes

Characterizing metastable neural dynamics in finite-size spiking networks remains a daunting challenge. We propose to address this challenge in the recently introduced replica-mean-field (RMF) limit. In this limit, networks are made of infinitely many replicas of the finite network of interest, but with randomized interactions across replicas. Such randomization renders certain excitatory networks fully tractable at the cost of neglecting activity correlations, but with explicit dependence on the finite size of the neural constituents. However, metastable dynamics typically unfold in networks with mixed inhibition and excitation. Here, we extend the RMF computational framework to point-process-based neural network models with exponential stochastic intensities, allowing for mixed excitation and inhibition. Within this setting, we show that metastable finite-size networks admit multistable RMF limits, which are fully characterized by stationary firing rates. Technically, these stationary rates are determined as the solutions of a set of delayed differential equations under certain regularity conditions that any physical solutions shall satisfy. We solve this original problem by combining the resolvent formalism and singular-perturbation theory. Importantly, we find that these rates specify probabilistic pseudo-equilibria which accurately capture the neural variability observed in the original finite-size network. We also discuss the emergence of metastability as a stochastic bifurcation, which can be interpreted as a static phase transition in the RMF limits. In turn, we expect to leverage the static picture of RMF limits to infer purely dynamical features of metastable finite-size networks, such as the transition rates between pseudo-equilibria.
Evolution of cooperation with asymmetric social interactions
Authors: Qi Su, Joshua. B Plotkin
Date: 2021-05-03 00:00:00

How cooperation emerges in human societies is both an evolutionary enigma, and a practical problem with tangible implications for societal health. Population structure has long been recognized as a catalyst for cooperation because local interactions enable reciprocity. Analysis of this phenomenon typically assumes bi-directional social interactions, even though real-world interactions are often uni-directional. Uni-directional interactions -- where one individual has the opportunity to contribute altruistically to another, but not conversely -- arise in real-world populations as the result of organizational hierarchies, social stratification, popularity effects, and endogenous mechanisms of network growth. Here we expand the theory of cooperation in structured populations to account for both uni- and bi-directional social interactions. Even though directed interactions remove the opportunity for reciprocity, we find that cooperation can nonetheless be favored in directed social networks and that cooperation is provably maximized for networks with an intermediate proportion of directed interactions, as observed in many empirical settings. We also identify two simple structural motifs that allow efficient modification of interaction directionality to promote cooperation by orders of magnitude. We discuss how our results relate to the concepts of generalized and indirect reciprocity.
Reputational Bargaining with Ultimatum Opportunities
Authors: Mehmet Ekmekci, HanZhe Zhang
Date: 2021-05-04 00:00:00

We study two-sided reputational bargaining with opportunities to issue an ultimatum -- threats to force dispute resolution. Each player is either a justified type, who never concedes and issues an ultimatum whenever an opportunity arrives, or an unjustified type, who can concede, wait, or bluff with an ultimatum. In equilibrium, the presence of ultimatum opportunities can harm or benefit a player by decelerating or accelerating reputation building. When only one player can issue an ultimatum, equilibrium play is unique. The hazard rate of dispute resolution is discontinuous and piecewise monotonic in time. As the probabilities of being justified vanish, agreement is immediate and efficient, and if the set of justifiable demands is rich, payoffs modify Abreu and Gul (2000), with the discount rate replaced by the ultimatum opportunity arrival rate if the former is smaller. When both players' ultimatum opportunities arrive sufficiently fast, there may exist multiple equilibria in which their reputations do not build up and negotiation lasts forever.
Home advantage and crowd attendance: Evidence from rugby during the Covid 19 pandemic
Authors: Federico Fioravanti, Fernando Delbianco, Fernando Tohmé
Date: 2021-05-04 00:00:00
Tasks: Decision Making

The COVID-19 pandemic forced almost all professional and amateur sports to be played without attending crowds. Thus, it induced a large-scale natural experiment on the impact of social pressure on decision making and behavior in sports fields. Using a data set of 1027 rugby union matches from 11 tournaments in 10 countries, we find that home teams have won less matches and their points difference decreased during the pandemics, shedding light on the impact of crowd attendance on the {\em home advantage} of sports teams.
How the 'Auction Cube' Supports the Selection of Auction Designs in Industrial Procurement
Authors: Gregor Berz, Florian Rupp, Brian Sieben
Date: 2021-05-03 00:00:00

It is well known that rightly applied reverse auctions offer big commercial potential to procurement departments. However, the sheer number of auction types often overwhelms users in practice. And since the implications of a wrongly chosen auction type are equally well known, the overall usage of reverse auctions lacks its potential significantly. In this paper, a novel method is being proposed that guides the user in selecting the right combination of basic auction forms for single lot events, considering both market-, as well as supplier-related, bijective criteria.
Relationship among state reopening policies, health outcomes and economic recovery through first wave of the COVID-19 pandemic in the U.S
Authors: Alexandre K. Ligo, Emerson Mahoney, Jeffrey Cegan, Benjamin D. Trump, Andrew S. Jin, Maksim Kitsak, Jesse Keenan, Igor Linkov
Date: 2021-05-03 00:00:00

State governments in the U.S. have been facing difficult decisions involving tradeoffs between economic and health-related outcomes during the COVID-19 pandemic. Despite evidence of the effectiveness of government-mandated restrictions mitigating the spread of contagion, these orders are stigmatized due to undesirable economic consequences. This tradeoff resulted in state governments employing mandates in widely different ways. We compare the different policies states implemented during periods of restriction (lockdown) and reopening with indicators of COVID-19 spread and consumer card spending at each state during the first wave of the pandemic in the U.S. between March and August 2020. We find that while some states enacted reopening decisions when the incidence rate of COVID-19 was minimal or sustained in its relative decline, other states relaxed socioeconomic restrictions near their highest incidence and prevalence rates experienced so far. Nevertheless, all states experienced similar trends in consumer card spending recovery, which was strongly correlated with reopening policies following the lockdowns and relatively independent from COVID-19 incidence rates at the time. Our findings suggest that consumer card spending patterns can be attributed to government mandates rather than COVID-19 incidence in the states. We estimate the recovery in states that reopened in late April was more than the recovery in states that did not reopen in the same period - 15% for consumer card spending and 18% for spending by high income households. This result highlights the important role of state policies in minimizing health impacts while promoting economic recovery and helps planning effective interventions in subsequent waves and immunization efforts.
On Wholesale Electricity Prices and Market Values in a Carbon-Neutral Energy System
Authors: Diana Böttger, Philipp Härtel
Date: 2021-05-03 00:00:00

Climate and energy policy targets of the European Commission aim to make Europe the first climate-neutral continent by 2050. For low-carbon and net-neutral energy systems primarily based on variable renewable power generation, issues related to the market integration, cannibalisation of revenues, and cost recovery of wind and solar photovoltaics have become major concerns. The traditional discussion of the merit-order effect expects wholesale power prices in a system with 100 % renewable energy sources to alternate between very high and very low values. Unlike previous work, we present a structured and technology-specific analysis of the cross-sectoral demand bidding effect for the price formation in low-carbon power markets. Starting from a stylised market arrangement and by successively augmenting it with all relevant technologies, we construct and quantify the cross-sectoral demand bidding effects in future European power markets with the cross-sectoral market modelling framework SCOPE SD. As the main contribution, we explain and substantiate the market clearing effects of new market participants in detail. Hereby, we put a special focus on hybrid heat supply systems consisting of combined heat and power plant, fuel boiler, thermal storage and electrical back up and derive the opportunity costs of these systems. Furthermore, we show the effects of cross-border integration for a large-scale European net-neutral energy scenario. Finally, the detailed information on market clearing effects allows us to evaluate the resulting revenues of all major technology categories on future electricity markets.
Revenue Adequate Prices for Chance-Constrained Electricity Markets with Variable Renewable Energy Sources
Authors: Xin Shi, Alberto J. Lamadrid L., Luis F. Zuluaga
Date: 2021-05-04 00:00:00

In a commodity market, revenue adequate prices refer to compensations that ensure that a market participant has a non-negative profit. In this article, we study the problem of deriving revenue adequate prices for an electricity market-clearing model with uncertainties resulting from the use of variable renewable energy sources (VRES). To handle the uncertain nature of the problem, we use a chance-constrained optimization (CCO) approach, which has recently become very popular choice when constructing dispatch electricity models with penetration of VRES (or other sources of uncertainty). Then, we show how prices that satisfy revenue adequacy in expectation for the market administrator, and cost recovery in expectation for all conventional and VRES generators, can be obtained from the optimal dual variables associated with the deterministic equivalent of the CCO market-clearing model. These results constitute a novel contribution to the research of research on revenue adequate, equilibrium, and other types of pricing schemes that have been derived in the literature when the market uncertainties are modeled using stochastic or robust optimization approaches. Unlike in the stochastic approach, the CCO market-clearing model studied here produces uncertainty uniform real-time prices that do not depend on the real-time realization of the VRES generation outcomes. To illustrate our results, we consider a case study electricity market, and contrast the market prices obtained using a revenue adequate stochastic approach and the proposed revenue adequate CCO approach.
Efficient Data Optimisation for Harmonic Inpainting with Finite Elements
Authors: Vassillen Chizhov, Joachim Weickert
Date: 2021-05-04 00:00:00
Tasks: Image Compression

Harmonic inpainting with optimised data is very popular for inpainting-based image compression. We improve this approach in three important aspects. Firstly, we replace the standard finite differences discretisation by a finite element method with triangle elements. This does not only speed up inpainting and data selection, but even improves the reconstruction quality. Secondly, we propose highly efficient algorithms for spatial and tonal data optimisation that are several orders of magnitude faster than state-of-the-art methods. Last but not least, we show that our algorithms also allow working with very large images. This has previously been impractical due to the memory and runtime requirements of prior algorithms.
JPEG Meets PDE-based Image Compression
Authors: Sarah Andris, Joachim Weickert, Tobias Alt, Pascal Peter
Date: 2021-02-01 00:00:00
Tasks: Image Compression

Inpainting-based image compression is emerging as a promising competitor to transform-based compression techniques. Its key idea is to reconstruct image information from only few known regions through inpainting. Specific partial differential equations (PDEs) such as edge-enhancing diffusion (EED) give high quality reconstructions of image structures with low or medium texture. Even though the strengths of PDE- and transform-based compression are complementary, they have rarely been combined within a hybrid codec. We propose to sparsify blocks of a JPEG compressed image and reconstruct them with EED inpainting. Our codec consistently outperforms JPEG and gives useful indications for successfully developing hybrid codecs further. Furthermore, our method is the first to choose regions rather than pixels as known data for PDE-based compression. It also gives novel insights into the importance of corner regions for EED-based codecs.
Some open questions on morphological operators and representations in the deep learning era
Authors: Jesus Angulo
Date: 2021-05-04 00:00:00
Tasks: Deep Learning

During recent years, the renaissance of neural networks as the major machine learning paradigm and more specifically, the confirmation that deep learning techniques provide state-of-the-art results for most of computer vision tasks has been shaking up traditional research in image processing. The same can be said for research in communities working on applied harmonic analysis, information geometry, variational methods, etc. For many researchers, this is viewed as an existential threat. On the one hand, research funding agencies privilege mainstream approaches especially when these are unquestionably suitable for solving real problems and for making progress on artificial intelligence. On the other hand, successful publishing of research in our communities is becoming almost exclusively based on a quantitative improvement of the accuracy of any benchmark task. As most of my colleagues sharing this research field, I am confronted with the dilemma of continuing to invest my time and intellectual effort on mathematical morphology as my driving force for research, or simply focussing on how to use deep learning and contributing to it. The solution is not obvious to any of us since our research is not fundamental, it is just oriented to solve challenging problems, which can be more or less theoretical. Certainly, it would be foolish for anyone to claim that deep learning is insignificant or to think that one's favourite image processing domain is productive enough to ignore the state-of-the-art. I fully understand that the labs and leading people in image processing communities have been shifting their research to almost exclusively focus on deep learning techniques. My own position is different: I do think there is room for progress on mathematically grounded image processing branches, under the condition that these are rethought in a broader sense from the deep learning paradigm. Indeed, I firmly believe that the convergence between mathematical morphology and the computation methods which gravitate around deep learning (fully connected networks, convolutional neural networks, residual neural networks, recurrent neural networks, etc.) is worthwhile. The goal of this talk is to discuss my personal vision regarding these potential interactions. Without any pretension of being exhaustive, I want to address it with a series of open questions, covering a wide range of specificities of morphological operators and representations, which could be tackled and revisited under the paradigm of deep learning. An expected benefit of such convergence between morphology and deep learning is a cross-fertilization of concepts and techniques between both fields. In addition, I think the future answer to some of these questions can provide some insight on understanding, interpreting and simplifying deep learning networks.
ReBeatICG: Real-time Low-Complexity Beat-to-beat Impedance Cardiogram Delineation Algorithm
Authors: Una Pale, Nathan Müller, Adriana Arza, David Atienza
Date: 2021-05-04 00:00:00

This work presents ReBeatICG, a real-time, low-complexity beat-to-beat impedance cardiography (ICG) delineation algorithm that allows hemodynamic parameters monitoring. The proposed procedure relies only on the ICG signal compared to most algorithms found in the literature that rely on synchronous electrocardiogram signal (ECG) recordings. ReBeatICG was designed with implementation on an ultra-low-power microcontroller (MCU) in mind. The detection accuracy of the developed algorithm is tested against points manually labeled by cardiologists. It achieves a detection Gmean accuracy of 94.9%, 98.6%, 90.3%, and 84.3% for the B, C, X, and O points, respectively. Furthermore, several hemodynamic parameters were calculated based on annotated characteristic points and compared with values generated from the cardiologists' annotations. ReBeatICG achieved mean error rates of 0.11ms, 9.72ms, 8.32ms, and 3.97% for HR, LVET, IVRT, and relative C-point amplitude, respectively.
Scale Equivariant Neural Networks with Morphological Scale-Spaces
Authors: Mateus Sangalli, Samy Blusseau, Santiago Velasco-Forero, Jesus Angulo
Date: 2021-05-04 00:00:00
Tasks: Segmentation, Semantic Segmentation, Translation

The translation equivariance of convolutions can make convolutional neural networks translation equivariant or invariant. Equivariance to other transformations (e.g. rotations, affine transformations, scalings) may also be desirable as soon as we know a priori that transformed versions of the same objects appear in the data. The semigroup cross-correlation, which is a linear operator equivariant to semigroup actions, was recently proposed and applied in conjunction with the Gaussian scale-space to create architectures which are equivariant to discrete scalings. In this paper, a generalization using a broad class of liftings, including morphological scale-spaces, is proposed. The architectures obtained from different scale-spaces are tested and compared in supervised classification and semantic segmentation tasks where objects in test images appear at different scales compared to training images. In both classification and segmentation tasks, the scale-equivariant architectures improve dramatically the generalization to unseen scales compared to a convolutional baseline. Besides, in our experiments morphological scale-spaces outperformed the Gaussian scale-space in geometrical tasks.
Quantum information in neural systems
Authors: Danko D. Georgiev
Date: 2021-05-04 00:00:00

Identifying the physiological processes in the central nervous system that underlie our conscious experiences has been at the forefront of cognitive neuroscience. While the principles of classical physics were long found to be unaccommodating for a causally effective consciousness, the inherent indeterminism of quantum physics, together with its characteristic dichotomy between quantum states and quantum observables, provides a fertile ground for the physical modeling of consciousness. Here, we utilize the Schr\"odinger equation, together with the Planck--Einstein relation between energy and frequency, in order to determine the appropriate quantum dynamical timescale of conscious processes. Furthermore, with the help of a simple two-qubit toy model we illustrate the importance of non-zero interaction Hamiltonian for the generation of quantum entanglement and manifestation of observable correlations between different measurement outcomes. Employing a quantitative measure of entanglement based on Schmidt decomposition, we show that quantum evolution governed only by internal Hamiltonians for the individual quantum subsystems preserves quantum coherence of separable initial quantum states, but eliminates the possibility of any interaction and quantum entanglement. The presence of non-zero interaction Hamiltonian, however, allows for decoherence of the individual quantum subsystems along with their mutual interaction and quantum entanglement. The presented results show that quantum coherence of individual subsystems cannot be used for cognitive binding because it is a physical mechanism that leads to separability and non-interaction. In contrast, quantum interactions with their associated decoherence of individual subsystems are instrumental for dynamical changes in the quantum entanglement of the composite quantum state vector and manifested correlations of different observable outcomes.
Neural Optimization: Understanding Trade-offs with Pareto Theory
Authors: Fabian Pallasdies, Philipp Norton, Jan-Hendrik Schleimer, Susanne Schreiber
Date: 2021-05-04 00:00:00

Nervous systems, like any organismal structure, have been shaped by evolutionary processes to increase fitness. The resulting neural 'bauplan' has to account for multiple objectives simultaneously, including computational function as well as additional factors like robustness to environmental changes and energetic limitations. Oftentimes these objectives compete and a quantification of the relative impact of individual optimization targets is non-trivial. Pareto optimality offers a theoretical framework to decipher objectives and trade-offs between them. We, therefore, highlight Pareto theory as a useful tool for the analysis of neurobiological systems, from biophysically-detailed cells to large-scale network structures and behavior. The Pareto approach can help to assess optimality, identify relevant objectives and their respective impact, and formulate testable hypotheses.