pwc-archive/papers-with-abstracts huggingface dataset:
Genetic-Algorithm-Based Proportional Integral Controller (GAPI) for ROV Steering Control
Authors: Ahsan Tanveer, Sarvat Mushtaq Ahmad
Date: 2023-04-20 00:00:00
Tasks: Steering Control

This article presents the design and real-time implementation of an optimal controller for precise steering control of a remotely operated underwater vehicle (ROV). A PI controller is investigated to achieve the desired steering performance. The gain parameters of the controller are tuned using the genetic algorithm (GA). The experimental response corresponding to the step waveform for the GA is obtained. A root-locus-tuned PI controller alongside a simulated-annealing-based PI controller (SAPI) is used to benchmark the response characteristics such as overshoot, peak time, and settling time. The experimental findings indicate that GAPI provides considerably better performance than SAPI and the root-locus-tuned controller.
Regularization for distributionally robust state estimation and prediction
Authors: Jean-Sébastien Brouillon, Florian Dörfler, Giancarlo Ferrari-Trecate
Date: 2023-04-19 00:00:00
Tasks: Prediction, State Estimation

The increasing availability of sensing techniques provides a great opportunity for engineers to design state estimation methods, which are optimal for the system under observation and the observed noise patterns. However, these patterns often do not fulfill the assumptions of existing methods. We provide a direct method using samples of the noise to create a moving horizon observer for linear time-varying and nonlinear systems, which is optimal under the empirical noise distribution. Moreover, we show how to enhance the observer with distributional robustness properties in order to handle unmodeled components in the noise profile, as well as different noise realizations. We prove that, even though the design of distributionally robust estimators is a complex minmax problem over an infinite-dimensional space, it can be transformed into a regularized linear program using a system level synthesis approach. Numerical experiments with the Van der Pol oscillator show the benefits of not only using empirical samples of the noise to design the state estimator, but also of adding distributional robustness. We show that our method can significantly outperform state-of-the-art approaches under challenging noise distributions, including multi-modal and deterministic components.
Robust trajectory tracking for underactuated mechanical systems without velocity measurements
Authors: N. Javanmardi, P. Borja, M. J. Yazdanpanah, J. M. A. Scherpen
Date: 2023-04-19 00:00:00

In this paper, the notion of contraction is used to solve the trajectory-tracking problem for a class of mechanical systems. Additionally, we propose a dynamic extension to remove velocity measurements from the controller while rejecting matched disturbances. In particular, we propose three control designs stemming from the Interconnection and Damping Assignment Passivity-Based Control approach. The first controller is a tracker that does not require velocity measurements. The second control design solves the trajectory-tracking problem while guaranteeing robustness with respect to matched disturbances. Then, the third approach is a combination of both mentioned controllers. It is shown that all proposed design methods guarantee exponential convergence of the mechanical system to the desired (feasible) trajectory due to the contraction property of the closed-loop system. The applicability of this method is illustrated via the design of a controller for an underactuated mechanical system.
Universal Slow Dynamics of Chemical Reaction Networks
Authors: Masanari Shimada, Pegah Behrad, Eric De Giuli
Date: 2023-04-20 00:00:00

Understanding the emergent behavior of chemical reaction networks (CRNs) is a fundamental aspect of biology and its origin from inanimate matter. A closed CRN monotonically tends to thermal equilibrium, but when it is opened to external reservoirs, a range of behaviors is possible, including transition to a new equilibrium state, a non-equilibrium state, or indefinite growth. This study shows that slowly driven CRNs are governed by the conserved quantities of the closed system, which are generally far fewer in number than the species. Considering both deterministic and stochastic dynamics, a universal slow dynamics equation is derived with singular perturbation methods, and is shown to be thermodynamically consistent. The slow dynamics is highly robust against microscopic details of the network, which may be unknown in practical situations. In particular, non-equilibrium states of realistic large CRNs can be sought without knowledge of bulk reaction rates. The framework is successfully tested against a suite of networks of increasing complexity and argued to be relevant in the treatment of open CRNs as chemical machines.
Data-driven discovery of stochastic dynamical equations of collective motion
Authors: Arshed Nabeel, Vivek Jadhav, Danny Raj M, Clément Sire, Guy Theraulaz, Ramón Escobedo, Srikanth K. Iyer, Vishwesha Guttal
Date: 2023-04-20 00:00:00
Tasks: Equation Discovery

Coarse-grained descriptions of collective motion of flocking systems are often derived for the macroscopic or the thermodynamic limit. However, many real flocks are small sized (10 to 100 individuals), called the mesoscopic scales, where stochasticity arising from the finite flock sizes is important. Developing mesoscopic scale equations, typically in the form of stochastic differential equations, can be challenging even for the simplest of the collective motion models. Here, we take a novel data-driven equation learning approach to construct the stochastic mesoscopic descriptions of a simple self-propelled particle (SPP) model of collective motion. In our SPP model, a focal individual can interact with k randomly chosen neighbours within an interaction radius. We consider k = 1 (called stochastic pairwise interactions), k = 2 (stochastic ternary interactions), and k equalling all available neighbours within the interaction radius (equivalent to Vicsek-like local averaging). The data-driven mesoscopic equations reveal that the stochastic pairwise interaction model produces a novel form of collective motion driven by a multiplicative noise term (hence termed, noise-induced flocking). In contrast, for higher order interactions (k > 1), including Vicsek-like averaging interactions, yield collective motion driven primarily by the deterministic forces. We find that the relation between the parameters of the mesoscopic equations describing the dynamics and the population size are sensitive to the density and to the interaction radius, exhibiting deviations from mean-field theoretical expectations. We provide semi-analytic arguments potentially explaining these observed deviations. In summary, our study emphasizes the importance of mesoscopic descriptions of flocking systems and demonstrates the potential of the data-driven equation discovery methods for complex systems studies.
Dark-field and directional dark-field on low coherence X-ray sources with random mask modulations: validation with SAXS anisotropy measurements
Authors: Clara Magnin, Laurene Quenot, Sylvain Bohic, Dan Mihai Cenda, Manuel Fernández Martínez, Blandine Lantz, Bertrand Faure, Emmanuel Brun
Date: 2023-04-20 00:00:00

Phase Contrast Imaging (PCI), Dark-Field (DF) and Directional Dark-Field (DDF) imaging are recent X-ray imaging modalities that have demonstrated their interest by providing access to information and contrasts different from those provided by conventional absorption X-ray imaging. However, access to these two types of images is currently limited because the acquisitions require the use of coherent sources such as synchrotron radiation or complicated optical setups to exploit the coherence requirements. This work demonstrates the possibility of efficiently performing phase contrast, dark-field and directional dark-field imaging on a low-coherence laboratory system equipped with a conventional X-ray tube, using a simple, fast and robust single-mask technique. The transfer to a low spatial coherence laboratory system was made possible by using random modulation based imaging (MoBI) and extending the low coherence system algorithm to retrieve dark-field and directional dark-field.
Revealing the unseen: Likely half of the Americans relied on others' experience when deciding on taking the COVID-19 vaccine
Authors: Azadeh Aghaeeyan, Pouria Ramazi, Mark A. Lewis
Date: 2023-04-19 00:00:00

Efficient coverage for newly developed vaccines requires knowing which groups of individuals will accept the vaccine immediately and which will take longer to accept or never accept. Of those who may eventually accept the vaccine, there are two main types: success-based learners, basing their decisions on others' satisfaction, and myopic rationalists, attending to their own immediate perceived benefit. We used COVID-19 vaccination data to fit a mechanistic model capturing the distinct effects of the two types on the vaccination progress. We estimated that 47 percent of Americans behaved as myopic rationalist with a high variations across the jurisdictions, from 31 percent in Mississippi to 76 percent in Vermont. The proportion was correlated with the vaccination coverage, proportion of votes in favor of Democrats in 2020 presidential election, and education score.
Robust Market Potential Assessment: Designing optimal policies for low-carbon technology adoption in an increasingly uncertain world
Authors: Tom Savage, Antonio del Rio Chanona, Gbemi Oluleye
Date: 2023-04-20 00:00:00

Increasing the adoption of alternative technologies is vital to ensure a successful transition to net-zero emissions in the manufacturing sector. Yet there is no model to analyse technology adoption and the impact of policy interventions in generating sufficient demand to reduce cost. Such a model is vital for assessing policy-instruments for the implementation of future energy scenarios. The design of successful policies for technology uptake becomes increasingly difficult when associated market forces/factors are uncertain, such as energy prices or technology efficiencies. In this paper we formulate a novel robust market potential assessment problem under uncertainty, resulting in policies that are immune to uncertain factors. We demonstrate two case studies: the potential use of carbon capture and storage for iron and steel production across the EU, and the transition to hydrogen from natural gas in steam boilers across the chemicals industry in the UK. Each robust optimisation problem is solved using an iterative cutting planes algorithm which enables existing models to be solved under uncertainty. By taking advantage of parallelisation we are able to solve the nonlinear robust market assessment problem for technology adoption in times within the same order of magnitude as the nominal problem. Policy makers often wish to trade-off certainty with effectiveness of a solution. Therefore, we apply an approximation to chance constraints, varying the amount of uncertainty to locate less certain but more effective solutions. Our results demonstrate the possibility of locating robust policies for the implementation of low-carbon technologies, as well as providing direct insights for policy-makers into the decrease in policy effectiveness resulting from increasing robustness. The approach we present is extensible to a large number of policy design and alternative technology adoption problems.
Agricultural Roots of Social Conflict in Southeast Asia
Authors: Justin Hastings, David Ubilava
Date: 2023-04-20 00:00:00

We examine whether harvest-time transitory shifts in employment and income lead to changes in political violence and social unrest in rice-producing croplands of Southeast Asia. Using monthly data from 2010 to 2023 on over 86,000 incidents covering 376 one-degree cells across eight Southeast Asian countries, we estimate a general increase in political violence and a decrease in social unrest in croplands with rice production during the harvest season relative to the rest of the crop year. In a finding that is least sensitive to alternative model specifications and data subsetting, we estimate a nine percent increase in violence against civilians in locations with considerable rice production compared to other parts of the region during the harvest season, relative to the rest of the year. We show that the harvest-time changes in conflict are most evident in rural cells with rainfed agriculture. Using location-specific annual variation in growing season rainfall, we then show that the harvest-time increase in violence against civilians occurs in presumably good harvest years, whereas increase in battles between actors of political violence follows growing seasons with scarce rainfall. The harvest-time decrease in social unrest, protests in particular, occurs after presumably bad harvest years. These findings contribute to research on the agroclimatic and economic roots of conflict and offer insights to policymakers by suggesting the spatiotemporal concentration of conflict as well as diverging effects by forms of conflict at harvest time in the rice-producing regions of Southeast Asia.
Utilitarian Theorems and Equivalence of Utility Theories
Authors: Yuhki Hosoya
Date: 2023-04-19 00:00:00

In this paper, we consider an environment in which the utilitarian theorem for the NM utility function derived by Harsanyi and the utilitarian theorem for Alt's utility function derived by Harvey hold simultaneously, and prove that the NM utility function coincides with Alt's utility function under this setup. This result is so paradoxical that we must presume that at least one of the utilitarian theorems contains a strong assumption. We examine the assumptions one by one and conclude that one of Harsanyi's axioms is strong.
Privatization of Probability Distributions by the Wavelet Integral approach
Authors: Helio M. de Oliveira, Raydonal Ospina, Victor Leiva, Carlos Martin-Barreiro, Christophe Chesneau
Date: 2023-01-13 00:00:00

A naive theory of additive perturbations on a continuous probability distribution is presented. We propose a new privatization mechanism based on a naive theory of a perturbation on a probability using wavelets, such as a noise perturbs the signal of a digital image sensor. The cumulative wavelet integral function is defined and builds up the perturbations with the help of this function. We show that an arbitrary distribution function additively perturbed is still a distribution function, which can be seen as a privatized distribution, with the privatization mechanism being a wavelet function. It is shown that an arbitrary cumulative distribution function added to such an additive perturbation is still a cumulative distribution function. Thus, we offer a mathematical method for choosing a suitable probability distribution to data by starting from some guessed initial distribution. The areas of artificial intelligence and machine learning are constantly in need of data fitting techniques, closely related to sensors. The proposed privatization mechanism is therefore a contribution to increasing the scope of existing techniques.
IOT based Smart Helmet for Hazard Detection in mining industry
Authors: B Kartik, Dr. MANIMARAN P
Date: 2023-04-20 00:00:00

One of the most important parts of business, especially in the coal mining sector, is industrial safety. Suffocation, gas poisoning, object falls, roof collapses, and gas explosions are among the risks associated with underground mining. Therefore, air quality and the detection of hazardous events are crucial in the mining business. This technology offers a wireless sensor network so that base stations can keep an eye on the situation in underground mines in real time. It offers temperature and dangerous gases including CO, CH4, and LPG real-time monitoring. The main cause of mining deaths is that when they fall and lose consciousness for whatever reason, medical attention is not given to them in a timely manner. In order to solve this issue, the system sends an emergency notice to the supervisor in the event that a person falls down for any cause. Some employees are negligent when it comes to safety and don't wear helmets. Then, a miner's helmet removal status was successfully determined using a Limit switch.
Prospects and Applications of Incoherent Light in Non-contact Wireless Sensing Systems
Authors: Md Zobaer Islam, Sabit Ekin, John F. O'Hara
Date: 2023-04-19 00:00:00
Tasks: Gesture Recognition, Structural Health Monitoring

The increasing demand for wireless sensing systems has led to the exploration of alternative technologies to overcome the spectrum scarcity of traditional approaches based on radio frequency (RF) waves or microwaves. Incoherent light sources such as light-emitting diodes (LED), paired with light sensors, have the potential to become an attractive option for wireless sensing due to their energy efficiency, longer lifespan, and lower cost. Although coherent light or laser may present safety risks to human eyes and skin, incoherent visible and infrared light has low intensity, and does not harm the human body. Incoherent light has the potential to supersede other wireless sensing technologies, namely RF, laser and camera, by providing many additional benefits including easy implementation, wide bandwidth, reusable frequency, minimum interference, enhanced privacy and simpler data processing. However, the application of incoherent light in the wireless sensing domain is still in its infancy and is an emerging research topic. This study explores the enormous potential and benefits of incoherent visible and infrared light in wireless sensing through various indoor and outdoor applications including speed estimation of vehicles, human vitals monitoring, blood glucose sensing, gesture recognition, occupancy estimation and structural health monitoring.
Infinite Physical Monkey: Do Deep Learning Methods Really Perform Better in Conformation Generation?
Authors: Haotian Zhang, Jintu Zhang, Huifeng Zhao, Dejun Jiang, Yafeng Deng
Date: 2023-03-08 00:00:00
Tasks: Drug Design, Drug Discovery, Molecular Docking, Pose Prediction

Conformation Generation is a fundamental problem in drug discovery and cheminformatics. And organic molecule conformation generation, particularly in vacuum and protein pocket environments, is most relevant to drug design. Recently, with the development of geometric neural networks, the data-driven schemes have been successfully applied in this field, both for molecular conformation generation (in vacuum) and binding pose generation (in protein pocket). The former beats the traditional ETKDG method, while the latter achieves similar accuracy compared with the widely used molecular docking software. Although these methods have shown promising results, some researchers have recently questioned whether deep learning (DL) methods perform better in molecular conformation generation via a parameter-free method. To our surprise, what they have designed is some kind analogous to the famous infinite monkey theorem, the monkeys that are even equipped with physics education. To discuss the feasibility of their proving, we constructed a real infinite stochastic monkey for molecular conformation generation, showing that even with a more stochastic sampler for geometry generation, the coverage of the benchmark QM-computed conformations are higher than those of most DL-based methods. By extending their physical monkey algorithm for binding pose prediction, we also discover that the successful docking rate also achieves near-best performance among existing DL-based docking models. Thus, though their conclusions are right, their proof process needs more concern.
A discrete model for the growth and spread of the Scottish populations of red squirrels (Sciurus vulgaris) and grey squirrels (Sciurus carolinensis)
Authors: Jean-Baptiste Gramain
Date: 2023-04-20 00:00:00

In this article, a model, discrete in space and time, is developed to describe the growth and spread of the Scottish populations of red squirrels (Sciurus vulgaris) and grey squirrel (Sciurus carolinensis). The initial state for the model is designed using a large dataset of records of sightings of individuals of both species reported by members of the public. Choices of parameters involved in the model and their values are informed by the analysis of this dataset for the period 2011-2016, and model predictions are compared to records for the years 2006-2019.
LLMs can generate robotic scripts from goal-oriented instructions in biological laboratory automation
Authors: Takashi Inagaki, Akari Kato, Koichi Takahashi, Haruka Ozaki, Genki N. Kanda
Date: 2023-04-18 00:00:00

The use of laboratory automation by all researchers may substantially accelerate scientific activities by humans, including those in the life sciences. However, computer programs to operate robots should be written to implement laboratory automation, which requires technical knowledge and skills that may not be part of a researcher's training or expertise. In the last few years, there has been remarkable development in large language models (LLMs) such as GPT-4, which can generate computer codes based on natural language instructions. In this study, we used LLMs, including GPT-4, to generate scripts for robot operations in biological experiments based on ambiguous instructions. GPT-4 successfully generates scripts for OT-2, an automated liquid-handling robot, from simple instructions in natural language without specifying the robotic actions. Conventionally, translating the nuances of biological experiments into low-level robot actions requires researchers to understand both biology and robotics, imagine robot actions, and write robotic scripts. Our results showed that GPT-4 can connect the context of biological experiments with robot operation through simple prompts with expert-level contextual understanding and inherent knowledge. Replacing robot script programming, which is a tedious task for biological researchers, with natural-language LLM instructions that do not consider robot behavior significantly increases the number of researchers who can benefit from automating biological experiments.
Patents and intellectual property assets as non-fungible tokens: key technologies and challenges
Authors: Seyed Mojtaba Hosseini Bamakan, Nasim Nezhadsistani, Omid Bodaghi, Qiang Qu
Date: 2023-03-02 00:00:00

With the explosive development of decentralized finance, we witness a phenomenal growth in tokenization of all kinds of assets, including equity, funds, debt, and real estate. By taking advantage of blockchain technology, digital assets are broadly grouped into fungible and non-fungible tokens (NFT). Here non-fungible tokens refer to those with unique and non-substitutable properties. NFT has widely attracted attention, and its protocols, standards, and applications are developing exponentially. It has been successfully applied to digital fantasy artwork, games, collectibles, etc. However, there is a lack of research in utilizing NFT in issues such as Intellectual Property. Applying for a patent and trademark is not only a time-consuming and lengthy process but also costly. NFT has considerable potential in the intellectual property domain. It can promote transparency and liquidity and open the market to innovators who aim to commercialize their inventions efficiently. The main objective of this paper is to examine the requirements of presenting intellectual property assets, specifically patents, as NFTs. Hence, we offer a layered conceptual NFT-based patent framework. Furthermore, a series of open challenges about NFT-based patents and the possible future directions are highlighted. The proposed framework provides fundamental elements and guidance for businesses in taking advantage of NFTs in real-world problems such as grant patents, funding, biotechnology, and so forth.