pwc-archive/papers-with-abstracts huggingface dataset:
ZTBus: A Large Dataset of Time-Resolved City Bus Driving Missions
Authors: Fabio Widmer, Andreas Ritter, Christopher H. Onder
Date: 2023-03-15 00:00:00

This paper presents the Zurich Transit Bus (ZTBus) dataset, which consists of data recorded during driving missions of electric city buses in Zurich, Switzerland. The data was collected over several years on two trolley buses as part of multiple research projects. It involves more than a thousand missions across all seasons, each mission usually covering a full day of operation. The ZTBus dataset contains detailed information on the vehicle's power demand, propulsion system, odometry, global position, ambient temperature, door openings, number of passengers, dispatch patterns within the public transportation network, etc. All signals are synchronized in time and include an absolute timestamp in tabular form. The dataset can be used as a foundation for a variety of studies and analyses. For example, the data can serve as a basis for simulations to estimate the performance of different public transit vehicle types, or to evaluate and optimize control strategies of hybrid electric vehicles. Furthermore, numerous influencing factors on vehicle operation, such as traffic, passenger volume, etc., can be analyzed in detail.
On solving infinite-dimensional Toeplitz Block LMIs
Authors: Flora Vernerey, Pierre Riedinger, Jamal Daafouz
Date: 2023-03-15 00:00:00

This paper focuses on the resolution of infinite-dimensional Toeplitz Block LMIs, which are frequently encountered in the context of stability analysis and control design problems formulated in the harmonic framework. We propose a consistent truncation method that makes this infinite dimensional problem tractable and demonstrate that a solution to the truncated problem can always be found at any order, provided that the original infinite-dimensional Toeplitz Block LMI problem is feasible. Using this approach, we illustrate how the infinite dimensional solution to a Toeplitz Block LMI based convex optimization problem can be recovered up to an arbitrarily small error, by solving a finite dimensional truncated problem. The obtained results are applied to stability analysis and harmonic LQR for linear time periodic (LTP) systems.
Sensor network design for post-combustion CO2 capture plants: economy, complexity and robustness
Authors: Siyu Liu, Xunyuan Yin, Jinfeng Liu
Date: 2023-03-14 00:00:00
Tasks: Sensitivity, State Estimation

State estimation is crucial for the monitoring and control of post-combustion CO2 capture plants (PCCPs). The performance of state estimation is highly reliant on the configuration of sensors. In this work, we consider the problem of sensor selection for PCCPs and propose a computationally efficient method to determine an appropriate number of sensors and the corresponding placement of the sensors. The objective is to find the (near-)optimal set of sensors that provides the maximum degree of observability for state estimation while satisfying the budget constraint. Specifically, we resort to the information contained in the sensitivity matrix calculated around the operating region of a PCCP to quantify the degree of observability of the entire system corresponding to the placed sensors. The sensor selection problem is converted to an optimization problem, and is efficiently solved by a one-by-one removal approach through sensitivity analysis. Next, we extend our approach to study fault tolerance (resilience) of the selected sensors to sensor malfunction. The resilient sensor selection problem is to find a sensor network that gives good estimation performance even when some of the sensors fail, thereby improving the overall system robustness. The resilient sensor selection problem is formulated as a max-min optimization problem. We show how the proposed approach can be adapted to solve the sensor selection max-min optimization problem. By implementing the proposed approaches, the sensor network is configured for the PCCP efficiently.
Adaptive Traffic Light Control for Competing Vehicle and Pedestrian Flows
Authors: Yingqing Chen, Christos G. Cassandras
Date: 2023-03-14 00:00:00

We study the Traffic Light Control (TLC) problem for a single intersection, considering both straight driving vehicle flows and corresponding crossing pedestrian flows with the goal of achieving a fair jointly optimal sharing policy in terms of average waiting times. Using a stochastic hybrid system model, we design a quasi-dynamic policy controlling the traffic light cycles with several threshold parameters applied to the light cycles and the partially observed contents of vehicle and pedestrian queues. Infinitesimal Perturbation Analysis (IPA) is then used to derive a data-driven gradient estimator of a cost metric with respect to the policy parameters and to iteratively adjust these parameters through an online gradient-based algorithm in order to improve overall performance on this intersection and adapt the policy to changing traffic conditions. The controller is applied to a simulated intersection in the town of Veber\"od, Sweden, to illustrate the performance of this approach using real traffic data from this intersection.
Artificial intelligence for artificial materials: moiré atom
Authors: Di Luo, Aidan P. Reddy, Trithep Devakul, Liang Fu
Date: 2023-03-14 00:00:00

Moir\'e engineering in atomically thin van der Waals heterostructures creates artificial quantum materials with designer properties. We solve the many-body problem of interacting electrons confined to a moir\'e superlattice potential minimum (the moir\'e atom) using a 2D fermionic neural network. We show that strong Coulomb interactions in combination with the anisotropic moir\'e potential lead to striking ``Wigner molecule" charge density distributions observable with scanning tunneling microscopy.
Combined effects of STDP and homeostatic structural plasticity on coherence resonance
Authors: Marius E. Yamakou, Christian Kuehn
Date: 2023-03-15 00:00:00

Efficient processing and transfer of information in neurons have been linked to noise-induced resonance phenomena such as coherence resonance (CR), and adaptive rules in neural networks have been mostly linked to two prevalent mechanisms: spike-timing-dependent plasticity (STDP) and homeostatic structural plasticity (HSP). Thus, this paper investigates CR in small-world and random adaptive networks of Hodgkin-Huxley neurons driven by STDP and HSP. Our numerical study indicates that the degree of CR strongly depends, and in different ways, on the adjusting rate parameter $P$, which controls STDP, on the characteristic rewiring frequency parameter $F$, which controls HSP, and on the parameters of the network topology. In particular, we found two robust behaviors: (i) Decreasing $P$ (which enhances the weakening effect of STDP on synaptic weights) and decreasing $F$ (which slows down the swapping rate of synapses between neurons) always leads to higher degrees of CR in small-world and random networks, provided that the synaptic time delay parameter $\tau_c$ has some appropriate values. (ii) Increasing the synaptic time delay $\tau_c$ induces multiple CR (MCR) -- the occurrence of multiple peaks in the degree of coherence as $\tau_c$ changes -- in small-world and random networks, with MCR becoming more pronounced at smaller values of $P$ and $F$. Our results imply that STDP and HSP can jointly play an essential role in enhancing the time precision of firing necessary for optimal information processing and transfer in neural systems and could thus have applications in designing networks of noisy artificial neural circuits engineered to use CR to optimize information processing and transfer.
Eco-evolutionary cyclic dominance among predators, prey, and parasites
Authors: Sayantan Nag Chowdhury, Jeet Banerjee, Matjaž Perc, Dibakar Ghosh
Date: 2023-03-14 00:00:00

Predator prey interactions are one of ecology's central research themes, but with many interdisciplinary implications across the social and natural sciences. Here we consider an often-overlooked species in these interactions, namely parasites. We first show that a simple predator prey parasite model, inspired by the classical Lotka Volterra equations, fails to produce a stable coexistence of all three species, thus failing to provide a biologically realistic outcome. To improve this, we introduce free space as a relevant eco-evolutionary component in a new mathematical model that uses a game-theoretical payoff matrix to describe a more realistic setup. We then show that the consideration of free space stabilizes the dynamics by means of cyclic dominance that emerges between the three species. We determine the parameter regions of coexistence as well as the types of bifurcations leading to it by means of analytical derivations as well as by means of numerical simulations. We conclude that the consideration of free space as a finite resource reveals the limits of biodiversity in predator prey parasite interactions, and it may also help us in the determination of factors that promote a healthy biota.
Digital staining in optical microscopy using deep learning -- a review
Authors: Lucas Kreiss, Shaowei Jiang, Xiang Li, Shiqi Xu, Kevin C. Zhou, Alexander Mühlberg, Kyung Chul Lee, Kanghyun Kim, Amey Chaware, Michael Ando, Laura Barisoni, Seung Ah Lee, Guoan Zheng, Kyle Lafata, Oliver Friedrich, Roarke Horstmeyer
Date: 2023-03-14 00:00:00
Tasks: Deep Learning, Specificity

Until recently, conventional biochemical staining had the undisputed status as well-established benchmark for most biomedical problems related to clinical diagnostics, fundamental research and biotechnology. Despite this role as gold-standard, staining protocols face several challenges, such as a need for extensive, manual processing of samples, substantial time delays, altered tissue homeostasis, limited choice of contrast agents for a given sample, 2D imaging instead of 3D tomography and many more. Label-free optical technologies, on the other hand, do not rely on exogenous and artificial markers, by exploiting intrinsic optical contrast mechanisms, where the specificity is typically less obvious to the human observer. Over the past few years, digital staining has emerged as a promising concept to use modern deep learning for the translation from optical contrast to established biochemical contrast of actual stainings. In this review article, we provide an in-depth analysis of the current state-of-the-art in this field, suggest methods of good practice, identify pitfalls and challenges and postulate promising advances towards potential future implementations and applications.
Molecular Identifification, Antioxidant Effifficacy of Phenolic Compounds, and Antimicrobial Activity of Beta-Carotene Isolated from Fruiting Bodies of Suillus sp
Authors: Shimal Yonuis Abdulhadi, Raghad Nawaf Gergees, Ghazwan Qasim Hasan
Date: 2023-03-12 00:00:00

Suillus species, in general, are edible mushrooms, and environmentally important that are associated mostly with pine trees in the tropics regions. These fungi considered a remarkable source of phenolic compounds that play a crucial role as antioxidants which may reduce the risk of most human chronic diseases such as cancer, diabetes, asthma, atherosclerosis, Alzheimer, and others. On the other hand, carotenoids (\b{eta} carotene) are the most popular natural pigments which play an important role to protect the plants from photo-oxidative reactions. In human, these compounds prevent oxidative stress and expects to have antimicrobial activity. Here, the phenolic compounds were extracted with Ethyl acetate from fruiting bodies of Suillus sp and analyzed by HPLC, the antioxidant activity (reducing power%) of phenolic compounds was determined at the concentrations of 1, 2.5, and 5 mg/mL. Antimicrobial activity of \b{eta} carotene pigment was measured at a concentration of 100 mg/mL against some human pathogenic bacteria such as Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumonia, and Staphylococcus aureus. The specific DNA region ITS was amplified and sequenced using ITS1 and ITS4 primers with some bioinformatics analyses. The phenolic extract isolated from fruiting bodies of Suillus sp showed a remarkable antioxidant activity by increasing the reducing power percent (from F+3 ions to F+2 ions) comparing with the industrial antioxidant (Propyl gallate) at all used concentrations. Percent of reducing power of phenolic compounds were 75.5, 84.9 and 95.7% at concentrations of 1, 2.5, and 5 mg/mL respectively; comparing with PG were 65.9, 81.3, and 93.3 at 1, 2.5, and 5 mg/mL respectively. The \b{eta} carotene pigment revealed a significant antimicrobial activity at a concentration of 100 mg/mL against K. pneumonia, E. coli, and S. aureus.
Automated patent extraction powers generative modeling in focused chemical spaces
Authors: Akshay Subramanian, Kevin P. Greenman, Alexis Gervaix, Tzuhsiung Yang, Rafael Gómez-Bombarelli
Date: 2023-03-14 00:00:00

Deep generative models have emerged as an exciting avenue for inverse molecular design, with progress coming from the interplay between training algorithms and molecular representations. One of the key challenges in their applicability to materials science and chemistry has been the lack of access to sizeable training datasets with property labels. Published patents contain the first disclosure of new materials prior to their publication in journals, and are a vast source of scientific knowledge that has remained relatively untapped in the field of data-driven molecular design. Because patents are filed seeking to protect specific uses, molecules in patents can be considered to be weakly labeled into application classes. Furthermore, patents published by the US Patent and Trademark Office (USPTO) are downloadable and have machine-readable text and molecular structures. In this work, we train domain-specific generative models using patent data sources by developing an automated pipeline to go from USPTO patent digital files to the generation of novel candidates with minimal human intervention. We test the approach on two in-class extracted datasets, one in organic electronics and another in tyrosine kinase inhibitors. We then evaluate the ability of generative models trained on these in-class datasets on two categories of tasks (distribution learning and property optimization), identify strengths and limitations, and suggest possible explanations and remedies that could be used to overcome these in practice.
Small time delay approximation in replicator dynamics
Authors: Jacek Miȩkisz, Javad Mohamadichamgavi, Raffi Vardanyan
Date: 2023-03-14 00:00:00

We present a microscopic model of replicator dynamics with strategy-dependent time delays. In such a model, new players are born from parents who interacted and received payoffs in the past. In the case of small delays, we use Taylor expansion to get ordinary differential equations for frequencies of strategies with time delays as parameters. We apply our technique to get analytic expressions for interior stationary states in two games: Snowdrift and Stag-hunt. We show that interior stationary states depend continuously upon time delays. Our analytic formulas for stationary states approximate well exact numerical results for small time delays.
Hall effect thruster design via deep neural network for additive manufacturing
Authors: Konstantin Korolev
Date: 2023-03-14 00:00:00

Hall effect thrusters are one of the most versatile and popular electric propulsion systems for space use. Industry trends towards interplanetary missions arise advances in design development of such propulsion systems. It is understood that correct sizing of discharge channel in Hall effect thruster impact performance greatly. Since the complete physics model of such propulsion system is not yet optimized for fast computations and design iterations, most thrusters are being designed using so-called scaling laws. But this work focuses on rather novel approach, which is outlined less frequently than ordinary scaling design approach in literature. Using deep machine learning it is possible to create predictive performance model, which can be used to effortlessly get design of required hall thruster with required characteristics using way less computational power than design from scratch and way more flexible than usual scaling approach.
Identifying an Earnings Process With Dependent Contemporaneous Income Shocks
Authors: Dan Ben-Moshe
Date: 2023-03-15 00:00:00

This paper proposes a novel approach for identifying coefficients in an earnings dynamics model with arbitrarily dependent contemporaneous income shocks. Traditional methods relying on second moments fail to identify these coefficients, emphasizing the need for nongaussianity assumptions that capture information from higher moments. Our results contribute to the literature on earnings dynamics by allowing models of earnings to have, for example, the permanent income shock of a job change to be linked to the contemporaneous transitory income shock of a relocation bonus.
The Effects of the Pandemic on Market Power and Profitability
Authors: Juan Andres Espinosa-Torres, Jaime Ramirez-Cuellar
Date: 2023-03-15 00:00:00

We explore firm-level markup and profit rates during the COVID-19 pandemic for a panel of 3,611 publicly traded firms in Compustat and find increases for the average firm. We offer conditions to give markups and profit rate forecasts a causal interpretation of what would have happened had the pandemic not happened. Our estimations suggest that had the pandemic not happened, markups would have been 4% and 7% higher than observed in 2020 and 2021, respectively, and profit rates would have been 2.1 and 6.4 percentage points lower. We perform a battery of tests to assess the robustness of our approach. We further show significant heterogeneity in the impact of the pandemic on firms by key firm characteristics and industry. We find that firms with lower than forecasted markups tend to have lower stock-exchange tenure and fewer employees.
Are high school degrees and university diplomas equally heritable in the US? A new measure of relative intergenerational mobility
Authors: Anna Naszodi, Liliana Cuccu
Date: 2023-03-15 00:00:00

This paper proposes a new measure of relative intergenerational mobility along the educational trait as a proxy of inequality of opportunity. The new measure is more suitable for controlling for the variations in the trait distributions of individuals and their parents than the commonly used intergenerational persistence coefficient. This point is illustrated by our empirical analysis of US census data from the period between 1960 and 2015: we show that controlling for the variations in the trait distributions adequately is vital in assessing the part of intergenerational mobility which is not caused by the educational expansion. Failing to do so can potentially reverse the relative priority of various policies aiming at reducing the "heritability" of high school degrees and tertiary education diplomas.
Measuring Stochastic Rationality
Authors: Efe A. Ok, Gerelt Tserenjigmid
Date: 2023-03-14 00:00:00

Our goal is to develop a partial ordering method for comparing stochastic choice functions on the basis of their individual rationality. To this end, we assign to any stochastic choice function a one-parameter class of deterministic choice correspondences, and then check for the rationality (in the sense of revealed preference) of these correspondences for each parameter value. The resulting ordering detects violations of (stochastic) transitivity as well as inconsistencies between choices from nested menus. We obtain a parameter-free characterization and apply it to some popular stochastic choice models. We also provide empirical applications in terms of two well-known choice experiments.
Expected Utility from a Constructive Viewpoint
Authors: Kislaya Prasad
Date: 2023-03-13 00:00:00

This paper introduces a space of variable lotteries and proves a constructive version of the expected utility theorem. The word ``constructive'' is used here in two senses. First, as in constructive mathematics, the logic underlying proofs is intuitionistic. In a second sense of the word, ``constructive'' is taken to mean ``built up from smaller components.'' Lotteries as well as preferences vary continuously over some topological space. The topology encodes observability or verifiability restrictions -- the open sets of the topology serve as the possible truth values of assertions about preference and reflect constraints on the ability to measure, deduce, or observe. Replacing an open set by a covering of smaller open sets serves as a notion of refinement of information. Within this framework, inability to compare arises as a phenomenon distinct from indifference and this gives rise to the constructive failure of the classical expected utility theorem. A constructive version of the theorem is then proved, and accomplishes several things. First, the representation theorem uses continuous real-valued functions as indicators of preference for variable lotteries and these functions reflect the inability to compare phenomenon. Second, conditions are provided whereby local representations of preference over open sets can be collated to provide a global representation. Third, the proofs are constructive and do not use the law of the excluded middle, which may not hold for variable lotteries. Fourth, a version of the classical theorem is obtained by imposing a condition on the collection of open sets of the topology which has the effect of making the logic classical.
A practical distributed active noise control algorithm overcoming communication restrictions
Authors: Junwei Ji, Dongyuan Shi, Zhengding Luo, Xiaoyi Shen, Woon-Seng Gan
Date: 2023-03-15 00:00:00

By assigning the massive computing tasks of the traditional multichannel active noise control (MCANC) system to several distributed control nodes, distributed multichannel active noise control (DMCANC) techniques have become effective global noise reduction solutions with low computational costs. However, existing DMCANC algorithms simply complete the distribution of traditional centralized algorithms by combining neighbour nodes' information but rarely consider the degraded control performance and system stability of distributed units caused by delays and interruptions in communication. Hence, this paper develops a novel DMCANC algorithm that utilizes the compensation filters and neighbour nodes' information to counterbalance the cross-talk effect between channels while maintaining independent weight updating. Since the neighbours' information required barely affects the local control filter updating in each node, this approach can tolerate communication delay and interruption to some extent. Numerical simulations demonstrate that the proposed algorithm can achieve satisfactory noise reduction performance and high robustness to real-world communication challenges.
A Momentum Two-gradient Direction Algorithm with Variable Step Size Applied to Solve Practical Output Constraint Issue for Active Noise Control
Authors: Xiaoyi Shen, Dongyuan Shi, Zhengding Luo, Junwei Ji, Woon-Seng Gan
Date: 2023-03-15 00:00:00

Active noise control (ANC) has been widely utilized to reduce unwanted environmental noise. The primary objective of ANC is to generate an anti-noise with the same amplitude but the opposite phase of the primary noise using the secondary source. However, the effectiveness of the ANC application is impacted by the speaker's output saturation. This paper proposes a two-gradient direction ANC algorithm with a momentum factor to solve the saturation with faster convergence. In order to make it implemented in real-time, a computation-effective variable step size approach is applied to further reduce the steady-state error brought on by the changing gradient directions. The time constant and step size bound for the momentum two-gradient direction algorithm is analyzed. Simulation results show that the proposed algorithm performs effectively in the time-unvaried and time-varied environment.