pwc-archive/papers-with-abstracts huggingface dataset:
Free-Energy Transduction in Chemical Reaction Networks: from Enzymes to Metabolism
Authors: Artur Wachtel, Riccardo Rao, Massimiliano Esposito
Date: 2022-02-02 00:00:00

We provide a rigorous definition of free-energy transduction and its efficiency in arbitrary -- linear or nonlinear -- open chemical reaction networks (CRNs) operating at steady state. Our method is based on the knowledge of the stoichiometric matrix and of the chemostatted species (i.e. the species maintained at constant concentration by the environment) to identify the fundamental currents and forces contributing to the entropy production. Transduction occurs when the current of a stoichiometrically balanced process is driven against its spontaneous direction (set by its force) thanks to other processes flowing along their spontaneous direction. In these regimes, open CRNs operate as thermodynamic machines. After exemplifying these general ideas using toy models, we analyze central energy metabolism. We relate the fundamental currents to metabolic pathways and discuss the efficiency with which they are able to transduce free energy.
The Diffusion Metrics of African Swine Fever in Wild Boar
Authors: Hartmut H. K. Lentz, Hannes Bergmann, C. Sauter-Louis
Date: 2022-02-03 00:00:00

To control African swine fever (ASF) efficiently, easily interpretable metrics of the outbreak dynamics are needed to plan and adapt the required measures. We found that the spread pattern of African Swine Fever cases in wild boar follows the mechanics of a diffusion process, at least in the early phase, for the cases that occurred in Germany. Following incursion into a previously unaffected area, infection disseminates locally within a naive and abundant wild boar population. Using real case data for Germany, we derive statistics about the time differences and distances between consecutive case reports. With the use of these statistics, we generate an ensemble of random walkers (continuous time random walks, CTRW) that resemble the properties of the observed outbreak pattern as one possible realization of all possible disease dissemination patterns. The trained random walker ensemble yields the diffusion constant, the affected area, and the outbreak velocity of early ASF spread in wild boar. These quantities are easy to interpret, robust, and may be generalized or adapted to different regions. Therefore, diffusion metrics can be useful descriptors of early disease dynamics and help facilitate efficient control of African Swine Fever.
Default Supply Auctions in Electricity Markets: Challenges and Proposals
Authors: Juan Ignacio Peña, Rosa Rodriguez
Date: 2022-02-03 00:00:00

This paper studies premiums got by winning bidders in default supply auctions, and speculation and hedging activities in power derivatives markets in dates near auctions. Data includes fifty-six auction prices from 2007 to 2013, those of CESUR in the Spanish OMEL electricity market, and those of Basic Generation Service auctions (PJM-BGS) in New Jersey's PJM market. Winning bidders got an average ex-post yearly forward premium of 7% (CESUR) and 38% (PJM-BGS). The premium using an index of futures prices is 1.08% (CESUR) and 24% (PJM-BGS). Ex-post forward premium is negatively related to the number of bidders and spot price volatility. In CESUR, hedging-driven trading in power derivatives markets predominates around auction dates, but in PJM-BGS, speculation-driven trading prevails.
Are EU Climate and Energy Package 20-20-20 targets achievable and compatible? Evidence from the impact of renewables on electricity prices
Authors: Juan Ignacio Peña, Rosa Rodriguez
Date: 2022-02-03 00:00:00

This paper studies the realizability and compatibility of the three CEP2020 targets, focusing on electricity prices. We study the impact of renewables and other fundamental determinants on wholesale and household retail electricity prices in ten EU countries from 2008 to 2016. Increases in production from renewables decrease wholesale electricity prices in all countries. As decreases in prices should promote consumption, an apparent contradiction emerges between the target of an increase in renewables and the target of a reduction in consumption. However, the impact of renewables on the non-energy part of household wholesale electricity prices is positive in six countries. Therefore, decreases in wholesale prices, that may compromise the CEP2020 target of decrease in consumption, do not necessarily translate into lower household retail prices.
Information Disclosure and Promotion Policy Design for Platforms
Authors: Yonatan Gur, Gregory Macnamara, Ilan Morgenstern, Daniela Saban
Date: 2019-11-21 00:00:00

We consider a platform facilitating trade between sellers and buyers with the objective of maximizing consumer surplus. Even though in many such marketplaces prices are set by revenue-maximizing sellers, platforms can influence prices through (i) price-dependent promotion policies that can increase demand for a product by featuring it in a prominent position on the webpage and (ii) the information revealed to sellers about the value of being promoted. Identifying effective joint information design and promotion policies is a challenging dynamic problem as sellers can sequentially learn the promotion value from sales observations and update prices accordingly. We introduce the notion of confounding promotion policies, which are designed to prevent a Bayesian seller from learning the promotion value (at the expense of the short-run loss of diverting some consumers from the best product offering). Leveraging these policies, we characterize the maximum long-run average consumer surplus that is achievable through joint information design and promotion policies when the seller sets prices myopically. We then construct a Bayesian Nash equilibrium in which the seller's best response to the platform's optimal policy is to price myopically in every period. Moreover, the equilibrium we identify is platform-optimal within the class of horizon-maximin equilibria, in which strategies are not predicated on precise knowledge of the horizon length, and are designed to maximize payoff over the worst-case horizon. Our analysis allows one to identify practical long-run average optimal platform policies in a broad range of demand models.
Coupled Splines for Sparse Curve Fitting
Authors: Icíar LLoréns Jover, Thomas Debarre, Shayan Aziznejad, Michael Unser
Date: 2022-02-03 00:00:00

We formulate as an inverse problem the construction of sparse parametric continuous curve models that fit a sequence of contour points. Our prior is incorporated as a regularization term that encourages rotation invariance and sparsity. We prove that an optimal solution to the inverse problem is a closed curve with spline components. We then show how to efficiently solve the task using B-splines as basis functions. We extend our problem formulation to curves made of two distinct components with complementary smoothness properties and solve it using hybrid splines. We illustrate the performance of our model on contours of different smoothness. Our experimental results show that we can faithfully reconstruct any general contour using few parameters, even in the presence of imprecisions in the measurements.
Spatial Diversity in Radar Detection via Active Reconfigurable Intelligent Surfaces
Authors: Mohamed Rihan, Emanuele Grossi, Luca Venturino, Stefano Buzzi
Date: 2022-02-03 00:00:00
Tasks: Diversity

Active reconfigurable intelligent surfaces (RISs) are a novel and promising technology that allows controlling the radio propagation environment while compensating for the product path loss along the RIS-assisted path. In this letter, we consider the classical radar detection problem and propose to use an active RIS to get a second independent look at a prospective target illuminated by the radar transmitter. At the design stage, we select the power emitted by the radar, the number of RIS elements, and their amplification factor in order to maximize the detection probability for a fixed probability of false alarm and a common (among radar and RIS) power budget. An illustrative example is provided to assess the achievable detection performance, also in comparison with that of a radar operating alone or with the help of a passive RIS.
Clutter Edges Detection Algorithms for Structured Clutter Covariance Matrices
Authors: Tianqi Wang, Da Xu, Chengpeng Hao, Pia Addabbo, Danilo Orlando
Date: 2022-02-03 00:00:00
Tasks: Edge Detection

This letter deals with the problem of clutter edge detection and localization in training data. To this end, the problem is formulated as a binary hypothesis test assuming that the ranks of the clutter covariance matrix are known, and adaptive architectures are designed based on the generalized likelihood ratio test to decide whether the training data within a sliding window contains a homogeneous set or two heterogeneous subsets. In the design stage, we utilize four different covariance matrix structures (i.e., Hermitian, persymmetric, symmetric, and centrosymmetric) to exploit the a priori information. Then, for the case of unknown ranks, the architectures are extended by devising a preliminary estimation stage resorting to the model order selection rules. Numerical examples based on both synthetic and real data highlight that the proposed solutions possess superior detection and localization performance with respect to the competitors that do not use any a priori information.
Energy Efficiency Optimization for Backscatter Enhanced NOMA Cooperative V2X Communications under Imperfect CSI
Authors: Wali Ullah Khan, Muhammad Ali Jamshed, Eva Lagunas, Symeon Chatzinotas, Xingwang Li, Bjorn Ottersten
Date: 2022-02-03 00:00:00

Automotive-Industry 5.0 will use beyond fifth-generation (B5G) technologies to provide robust, computationally intelligent, and energy-efficient data sharing among various onboard sensors, vehicles, and other devices. Recently, ambient backscatter communications (AmBC) have gained significant interest in the research community for providing battery-free communications. AmBC can modulate useful data and reflect it towards near devices using the energy and frequency of existing RF signals. However, obtaining channel state information (CSI) for AmBC systems would be very challenging due to no pilot sequences and limited power. As one of the latest members of multiple access technology, non-orthogonal multiple access (NOMA) has emerged as a promising solution for connecting large-scale devices over the same spectral resources in B5G wireless networks. Under imperfect CSI, this paper provides a new optimization framework for energy-efficient transmission in AmBC enhanced NOMA cooperative vehicle-to-everything (V2X) networks. We simultaneously minimize the total transmit power of the V2X network by optimizing the power allocation at BS and reflection coefficient at backscatter sensors while guaranteeing the individual quality of services. The problem of total power minimization is formulated as non-convex optimization and coupled on multiple variables, making it complex and challenging. Therefore, we first decouple the original problem into two sub-problems and convert the nonlinear rate constraints into linear constraints. Then, we adopt the iterative sub-gradient method to obtain an efficient solution. For comparison, we also present a conventional NOMA cooperative V2X network without AmBC. Simulation results show the benefits of our proposed AmBC enhanced NOMA cooperative V2X network in terms of total achievable energy efficiency.
Backscatter-Aided NOMA V2X Communication under Channel Estimation Errors
Authors: Wali Ullah Khan, Muhammad Ali Jamshed, Asad Mahmood, Eva Lagunas, Symeon Chatzinotas, Bjorn Ottersten
Date: 2022-02-03 00:00:00

Backscatter communications (BC) has emerged as a promising technology for providing low-powered transmissions in nextG (i.e., beyond 5G) wireless networks. The fundamental idea of BC is the possibility of communications among wireless devices by using the existing ambient radio frequency signals. Non-orthogonal multiple access (NOMA) has recently attracted significant attention due to its high spectral efficiency and massive connectivity. This paper proposes a new optimization framework to minimize total transmit power of BC-NOMA cooperative vehicle-to-everything networks (V2XneT) while ensuring the quality of services. More specifically, the base station (BS) transmits a superimposed signal to its associated roadside units (RSUs) in the first time slot. Then the RSUs transmit the superimposed signal to their serving vehicles in the second time slot exploiting decode and forward protocol. A backscatter device (BD) in the coverage area of RSU also receives the superimposed signal and reflect it towards vehicles by modulating own information. Thus, the objective is to simultaneously optimize the transmit power of BS and RSUs along with reflection coefficient of BDs under perfect and imperfect channel state information. The problem of energy efficiency is formulated as non-convex and coupled on multiple optimization variables which makes it very complex and hard to solve. Therefore, we first transform and decouple the original problem into two sub-problems and then employ iterative sub-gradient method to obtain an efficient solution. Simulation results demonstrate that the proposed BC-NOMA V2XneT provides high energy efficiency than the conventional NOMA V2XneT without BC.
Evaluating the extent to which homeostatic plasticity learns to compute prediction errors in unstructured neuronal networks
Authors: Vicky Zhu, Robert Rosenbaum
Date: 2022-02-03 00:00:00
Tasks: Prediction

The brain is believed to operate in part by making predictions about sensory stimuli and encoding deviations from these predictions in the activity of "prediction error neurons." This principle defines the widely influential theory of predictive coding. The precise circuitry and plasticity mechanisms through which animals learn to compute and update their predictions are unknown. Homeostatic inhibitory synaptic plasticity is a promising mechanism for training neuronal networks to perform predictive coding. Homeostatic plasticity causes neurons to maintain a steady, baseline firing rate in response to inputs that closely match the inputs on which a network was trained, but firing rates can deviate away from this baseline in response to stimuli that are mismatched from training. We combine computer simulations and mathematical analysis systematically to test the extent to which randomly connected, unstructured networks compute prediction errors after training with homeostatic inhibitory synaptic plasticity. We find that homeostatic plasticity alone is sufficient for computing prediction errors for trivial time-constant stimuli, but not for more realistic time-varying stimuli. We use a mean-field theory of plastic networks to explain our findings and characterize the assumptions under which they apply.
Network resilience in the aging brain
Authors: Tao Liu, Shu Guo, Hao liu, Rui Kang, Mingyang Bai, Jiyang Jiang, Wei Wen, Xing Pan, Jun Tai, JianXin Li, Jian Cheng, Jing Jing, Zhenzhou Wu, Haijun Niu, Haogang Zhu, Zixiao Li, Yongjun Wang, Henry Brodaty, Perminder Sachdev, Daqing Li
Date: 2022-02-03 00:00:00

Degeneration and adaptation are two competing sides of the same coin called resilience in the progressive processes of brain aging or diseases. Degeneration accumulates during brain aging and other cerebral activities, causing structural atrophy and dysfunction. At the same time, adaptation allows brain network reorganize to compensate for structural loss to maintain cognition function. Although hidden resilience mechanism is critical and fundamental to uncover the brain aging law, due to the lack of datasets and appropriate methodology, it remains essentially unknown how these two processes interact dynamically across brain networks. To quantitatively investigate this complex process, we analyze aging brains based on 6-year follow-up multimodal neuroimaging database from 63 persons. We reveal the critical mechanism of network resilience that various perturbation may cause fast brain structural atrophy, and then brain can reorganize its functional layout to lower its operational efficiency, which helps to slow down the structural atrophy and finally recover its functional efficiency equilibrium. This empirical finding could be explained by our theoretical model, suggesting one universal resilience dynamical function. This resilience is achieved in the brain functional network with evolving percolation and rich-club features. Our findings can help to understand the brain aging process and design possible mitigation methods to adjust interaction between degeneration and adaptation from resilience viewpoint.
Do new investment strategies take existing strategies' returns -- An investigation into agent-based models
Authors: Takanobu Mizuta
Date: 2022-02-03 00:00:00

Commodity trading advisors (CTAs), who mainly trade commodity futures, showed good returns in the 2000s. However, since the 2010's, they have not performed very well. One possible reason of this phenomenon is the emergence of short-term reversal traders (STRTs) who prey on CTAs for profit. In this study, I built an artificial market model by adding a CTA agent (CTAA) and STRT agent (STRTA) to a prior model and investigated whether emerging STRTAs led to a decrease in CTAA revenue to determine whether STRTs prey on CTAs for profit. To the contrary, my results showed that a CTAA and STRTA are more likely to trade and earn more when both exist. Therefore, it is possible that they have a mutually beneficial relationship.
Time-zero Efficiency of European Power Derivatives Markets
Authors: Juan Ignacio Peña, Rosa Rodriguez
Date: 2022-02-03 00:00:00
Tasks: Marketing

We study time-zero efficiency of electricity derivatives markets. By time-zero efficiency is meant a sequence of prices of derivatives contracts having the same underlying asset but different times to maturity which implies that prices comply with a set of efficiency conditions that prevent profitable time-zero arbitrage opportunities. We investigate whether statistical tests, based on the law of one price, and trading rules, based on price differentials and no-arbitrage violations, are useful for assessing time-zero efficiency. We apply tests and trading rules to daily data of three European power markets: Germany, France and Spain. In the case of the German market, after considering liquidity availability and transaction costs, results are not inconsistent with time-zero efficiency. However, in the case of the French and Spanish markets, limitations in liquidity and representativeness are challenges that prevent definite conclusions. Liquidity in French and Spanish markets should improve by using pricing and marketing incentives. These incentives should attract more participants into the electricity derivatives exchanges and should encourage them to settle OTC trades in clearinghouses. Publication of statistics on prices, volumes and open interest per type of participant should be promoted.
Tail Risk of Electricity Futures
Authors: Juan Ignacio Peña, Rosa Rodriguez, Silvia Mayoral
Date: 2022-02-03 00:00:00
Tasks: quantile regression

This paper compares the in-sample and out-of-sample performance of several models for computing the tail risk of one-month and one-year electricity futures contracts traded in the NordPool, French, German, and Spanish markets in 2008-2017. As measures of tail risk, we use the one-day-ahead Value-at-Risk (VaR) and the Expected Shortfall (ES). With VaR, the AR (1)-GARCH (1,1) model with Student-t distribution is the best-performing specification with 88% cases in which the Fisher test accepts the model, with a success rate of 94% in the left tail and of 81% in the right tail. The model passes the test of model adequacy in the 100% of the cases in the NordPool and German markets, but only in the 88% and 63% of the cases in the Spanish and French markets. With ES, this model passes the test of model adequacy in 100% of cases in all markets. Historical Simulation and Quantile Regression-based approaches misestimate tail risks. The right-hand tail of the returns is more difficult to model than the left-hand tail and therefore financial regulators and the administrators of futures markets should take these results into account when setting additional regulatory capital requirements and margin account regulations to short positions.
Introduction to The Dynamic Pickup and Delivery Problem Benchmark -- ICAPS 2021 Competition
Authors: Jianye Hao, Jiawen Lu, Xijun Li, Xialiang Tong, Xiang Xiang, Mingxuan Yuan, Hankz Hankui Zhuo
Date: 2022-01-19 00:00:00

The Dynamic Pickup and Delivery Problem (DPDP) is an essential problem within the logistics domain. So far, research on this problem has mainly focused on using artificial data which fails to reflect the complexity of real-world problems. In this draft, we would like to introduce a new benchmark from real business scenarios as well as a simulator supporting the dynamic evaluation. The benchmark and simulator have been published and successfully supported the ICAPS 2021 Dynamic Pickup and Delivery Problem competition participated by 152 teams.
Computer sciences and synthesis: retrospective and perspective
Authors: Vladislav Dorofeev, Petro Trokhimchuk
Date: 2022-01-26 00:00:00

The problem of synthesis in computer sciences, including cybernetics, artificial intelligence and system analysis, is analyzed. Main methods of realization this problem are discussed. Ways of search universal method of creation universal synthetic science are represented. As example of such universal method polymetric analysis is given. Perspective of further development of this research, including application polymetric method for the resolution main problems of computer sciences, is analyzed too.
Machine Learning and Artificial Intelligence in Next-Generation Wireless Network
Authors: Wafeeq Iqbal, Wei Wang, Ting Zhu
Date: 2021-12-30 00:00:00
Tasks: BIG-bench Machine Learning

Due to the advancement in technologies, the next-generation wireless network will be very diverse, complicated, and according to the changed demands of the consumers. The current network operator methodologies and approaches are traditional and cannot help the next generation networks to utilize their resources most appropriately. The limited capability of the traditional tools will not allow the network providers to fulfill the demands of the network's subscribers in the future. Therefore, this paper will focus on machine learning, automation, artificial intelligence, and big data analytics for improving the capacity and effectiveness of next-generation wireless networks. The paper will discuss the role of these new technologies in improving the service and performance of the network providers in the future. The paper will find out that machine learning, big data analytics, and artificial intelligence will help in making the next-generation wireless network self-adaptive, self-aware, prescriptive, and proactive. At the end of the paper, it will be provided that future wireless network operators cannot work without shifting their operational framework to AI and machine learning technologies.