pwc-archive/papers-with-abstracts huggingface dataset:
Reconstructing Cardiac Electrical Excitations from Optical Mapping Recordings
Authors: Christopher D. Marcotte, Matthew J. Hoffman, Flavio H. Fenton, Elizabeth M. Cherry
Date: 2023-04-28 00:00:00

The reconstruction of electrical excitation patterns through the unobserved depth of the tissue is essential to realizing the potential of computational models in cardiac medicine. We have utilized experimental optical-mapping recordings of cardiac electrical excitation on the epicardial and endocardial surfaces of a canine ventricle as observations directing a local ensemble transform Kalman Filter (LETKF) data assimilation scheme. We demonstrate that the inclusion of explicit information about the stimulation protocol can marginally improve the confidence of the ensemble reconstruction and the reliability of the assimilation over time. Likewise, we consider the efficacy of stochastic modeling additions to the assimilation scheme in the context of experimentally derived observation sets. Approximation error is addressed at both the observation and modeling stages, through the uncertainty of observations and the specification of the model used in the assimilation ensemble. We find that perturbative modifications to the observations have marginal to deleterious effects on the accuracy and robustness of the state reconstruction. Further, we find that incorporating additional information from the observations into the model itself (in the case of stimulus and stochastic currents) has a marginal improvement on the reconstruction accuracy over a fully autonomous model, while complicating the model itself and thus introducing potential for new types of model error. That the inclusion of explicit modeling information has negligible to negative effects on the reconstruction implies the need for new avenues for optimization of data assimilation schemes applied to cardiac electrical excitation.
Neural Network Accelerated Process Design of Polycrystalline Microstructures
Authors: Junrong Lin, Mahmudul Hasan, Pinar Acar, Jose Blanchet, Vahid Tarokh
Date: 2023-04-11 00:00:00

Computational experiments are exploited in finding a well-designed processing path to optimize material structures for desired properties. This requires understanding the interplay between the processing-(micro)structure-property linkages using a multi-scale approach that connects the macro-scale (process parameters) to meso (homogenized properties) and micro (crystallographic texture) scales. Due to the nature of the problem's multi-scale modeling setup, possible processing path choices could grow exponentially as the decision tree becomes deeper, and the traditional simulators' speed reaches a critical computational threshold. To lessen the computational burden for predicting microstructural evolution under given loading conditions, we develop a neural network (NN)-based method with physics-infused constraints. The NN aims to learn the evolution of microstructures under each elementary process. Our method is effective and robust in finding optimal processing paths. In this study, our NN-based method is applied to maximize the homogenized stiffness of a Copper microstructure, and it is found to be 686 times faster while achieving 0.053% error in the resulting homogenized stiffness compared to the traditional finite element simulator on a 10-process experiment.
Data navigation on the ENCODE portal
Authors: Meenakshi S. Kagda, Bonita Lam, Casey Litton, Corinn Small, Cricket A. Sloan, Emma Spragins, Forrest Tanaka, Ian Whaling, Idan Gabdank, Ingrid Youngworth, J. Seth Strattan, Jason Hilton, Jennifer Jou, Jessica Au, Jin-Wook Lee, Kalina Andreeva, Khine Lin, Matt Simison, Otto Jolanki, Philip Adenekan, Eric Douglas, Mingjie Li, Pedro Assis, Keenan Graham, Paul Sud, Stuart Miyasato, Weiwei Zhong, Yunhai Luo, Zachary Myers, J. Michael Cherry, Benjamin C. Hitz
Date: 2023-04-27 00:00:00

Spanning two decades, the Encyclopaedia of DNA Elements (ENCODE) is a collaborative research project that aims to identify all the functional elements in the human and mouse genomes. To best serve the scientific community, all data generated by the consortium is shared through a web-portal (https://www.encodeproject.org/) with no access restrictions. The fourth and final phase of the project added a diverse set of new samples (including those associated with human disease), and a wide range of new assays aimed at detection, characterization and validation of functional genomic elements. The ENCODE data portal hosts results from over 23,000 functional genomics experiments, over 800 functional elements characterization experiments (including in vivo transgenic enhancer assays, reporter assays and CRISPR screens) along with over 60,000 results of computational and integrative analyses (including imputations, predictions and genome annotations). The ENCODE Data Coordination Center (DCC) is responsible for development and maintenance of the data portal, along with the implementation and utilisation of the ENCODE uniform processing pipelines to generate uniformly processed data. Here we report recent updates to the data portal. Specifically, we have completely redesigned the home page, improved search interface, added several new pages to highlight collections of biologically related data (deeply profiled cell lines, immune cells, Alzheimer's Disease, RNA-Protein interactions, degron matrix and a matrix of experiments organised by human donors), added single-cell experiments, and enhanced the cart interface for visualisation and download of user-selected datasets.
3D Patient-specific Modelling and Characterisation of Muscle-Skeletal Districts
Authors: Martina Paccini, Giuseppe Patanè, Michela Spagnuolo
Date: 2023-04-18 00:00:00
Tasks: Diagnostic

This work addresses the patient-specific characterisation of the morphology and pathologies of muscle-skeletal districts (e.g., wrist, spine) to support diagnostic activities and follow-up exams through the integration of morphological and tissue information. We propose different methods for the integration of morphological information, retrieved from the geometrical analysis of 3D surface models, with tissue information extracted from volume images. For the qualitative and quantitative validation, we will discuss the localisation of bone erosion sites on the wrists to monitor rheumatic diseases and the characterisation of the three functional regions of the spinal vertebrae to study the presence of osteoporotic fractures. The proposed approach supports the quantitative and visual evaluation of possible damages, surgery planning, and early diagnosis or follow-up studies. Finally, our analysis is general enough to be applied to different districts.
Non-Orthogonal Multiple Access Assisted by Reconfigurable Intelligent Surface Using Unsupervised Machine Learning
Authors: Finn Siegismund-Poschmann, Bile Peng, Eduard A. Jorswieck
Date: 2023-05-01 00:00:00
Tasks: Fairness

Nonorthogonal multiple access (NOMA) with multi-antenna base station (BS) is a promising technology for next-generation wireless communication, which has high potential in performance and user fairness. Since the performance of NOMA depends on the channel conditions, we can combine NOMA and reconfigurable intelligent surface (RIS), which is a large and passive antenna array and can optimize the wireless channel. However, the high dimensionality makes the RIS optimization a complicated problem. In this work, we propose a machine learning approach to solve the problem of joint optimization of precoding and RIS configuration. We apply the RIS to realize the quasi-degradation of the channel, which allows for optimal precoding in closed form. The neural network architecture RISnet is used, which is designed dedicatedly for RIS optimization. The proposed solution is superior to the works in the literature in terms of performance and computation time.
RISnet: A Scalable Approach for Reconfigurable Intelligent Surface Optimization with Partial CSI
Authors: Bile Peng, Karl-Ludwig Besser, Ramprasad Raghunath, Vahid Jamali, Eduard A. Jorswieck
Date: 2023-05-01 00:00:00
Tasks: Hybrid Machine Learning

The reconfigurable intelligent surface (RIS) is a promising technology that enables wireless communication systems to achieve improved performance by intelligently manipulating wireless channels. In this paper, we consider the sum-rate maximization problem in a downlink multi-user multi-input-single-output (MISO) channel via space-division multiple access (SDMA). Two major challenges of this problem are the high dimensionality due to the large number of RIS elements and the difficulty to obtain the full channel state information (CSI), which is assumed known in many algorithms proposed in the literature. Instead, we propose a hybrid machine learning approach using the weighted minimum mean squared error (WMMSE) precoder at the base station (BS) and a dedicated neural network (NN) architecture, RISnet, for RIS configuration. The RISnet has a good scalability to optimize 1296 RIS elements and requires partial CSI of only 16 RIS elements as input. We show it achieves a high performance with low requirement for channel estimation for geometric channel models obtained with ray-tracing simulation. The unsupervised learning lets the RISnet find an optimized RIS configuration by itself. Numerical results show that a trained model configures the RIS with low computational effort, considerably outperforms the baselines, and can work with discrete phase shifts.
BiSPARCs for Unsourced Random Access in Massive MIMO
Authors: Patrick Agostini, Zoran Utkovski, Slawomir Stanczak
Date: 2023-04-27 00:00:00

This paper considers the massive MIMO unsourced random access problem in a quasi-static Rayleigh fading setting. The proposed coding scheme is based on a concatenation of a "conventional" channel code (such as, e.g., LDPC) serving as an outer code, and a sparse regression code (SPARC) serving as an inner code. The scheme combines channel estimation, single-user decoding, and successive interference cancellation in a novel way. The receiver performs joint channel estimation and SPARC decoding via an instance of a bilinear generalized approximate message passing (BiGAMP) based algorithm, which leverages the intrinsic bilinear structure that arises in the considered communication regime. The detection step is followed by a per-user soft-input-soft-output (SISO) decoding of the outer channel code in combination with a successive interference cancellation (SIC) step. We show via numerical simulation that the resulting scheme achieves stat-of-the-art performance in the massive connectivity setting, while attaining comparatively low implementation complexity.
Logic-based similarity
Authors: Christian Antić
Date: 2023-03-08 00:00:00

This paper develops a {\em qualitative} and logic-based notion of similarity from the ground up using only elementary concepts of first-order logic centered around the fundamental model-theoretic notion of type.
Input-to-State Stability in Probability
Authors: Preston Culbertson, Ryan K. Cosner, Maegan Tucker, Aaron D. Ames
Date: 2023-04-28 00:00:00

Input-to-State Stability (ISS) is fundamental in mathematically quantifying how stability degrades in the presence of bounded disturbances. If a system is ISS, its trajectories will remain bounded, and will converge to a neighborhood of an equilibrium of the undisturbed system. This graceful degradation of stability in the presence of disturbances describes a variety of real-world control implementations. Despite its utility, this property requires the disturbance to be bounded and provides invariance and stability guarantees only with respect to this worst-case bound. In this work, we introduce the concept of ``ISS in probability (ISSp)'' which generalizes ISS to discrete-time systems subject to unbounded stochastic disturbances. Using tools from martingale theory, we provide Lyapunov conditions for a system to be exponentially ISSp, and connect ISSp to stochastic stability conditions found in literature. We exemplify the utility of this method through its application to a bipedal robot confronted with step heights sampled from a truncated Gaussian distribution.
Deep sound-field denoiser: optically-measured sound-field denoising using deep neural network
Authors: Kenji Ishikawa, Daiki Takeuchi, Noboru Harada, Takehiro Moriya
Date: 2023-04-27 00:00:00
Tasks: Denoising

This paper proposes a deep sound-field denoiser, a deep neural network (DNN) based denoising of optically measured sound-field images. Sound-field imaging using optical methods has gained considerable attention due to its ability to achieve high-spatial-resolution imaging of acoustic phenomena that conventional acoustic sensors cannot accomplish. However, the optically measured sound-field images are often heavily contaminated by noise because of the low sensitivity of optical interferometric measurements to airborne sound. Here, we propose a DNN-based sound-field denoising method. Time-varying sound-field image sequences are decomposed into harmonic complex-amplitude images by using a time-directional Fourier transform. The complex images are converted into two-channel images consisting of real and imaginary parts and denoised by a nonlinear-activation-free network. The network is trained on a sound-field dataset obtained from numerical acoustic simulations with randomized parameters. We compared the method with conventional ones, such as image filters, a spatiotemporal filter, and other DNN architectures, on numerical and experimental data. The experimental data were measured by parallel phase-shifting interferometry and holographic speckle interferometry. The proposed deep sound-field denoiser significantly outperformed the conventional methods on both the numerical and experimental data. Code is available on GitHub: https://github.com/nttcslab/deep-sound-field-denoiser.
Prospects of BRICS currency dominance in international trade
Authors: Célestin Coquidé, José Lages, Dima L. Shepelyansky
Date: 2023-04-30 00:00:00

During his state visit to China in April 2023, Brazilian President Lula proposed the creation of a trade currency supported by the BRICS countries. Using the United Nations Comtrade database, providing the frame of the world trade network associated to 194 UN countries during the decade 2010 - 2020, we study a mathematical model of influence battle of three currencies, namely, the US dollar, the euro, and such a hypothetical BRICS currency. In this model, a country trade preference for one of the three currencies is determined by a multiplicative factor based on trade flows between countries and their relative weights in the global international trade. The three currency seed groups are formed by 9 eurozone countries for the euro, 5 Anglo-Saxon countries for the US dollar and the 5 BRICS countries for the new proposed currency. The countries belonging to these 3 currency seed groups trade only with their own associated currency whereas the other countries choose their preferred trade currency as a function of the trade relations with their commercial partners. The trade currency preferences of countries are determined on the basis of a Monte Carlo modeling of Ising type interactions in magnetic spin systems commonly used to model opinion formation in social networks. We adapt here these models to the world trade network analysis. The results obtained from our mathematical modeling of the structure of the global trade network show that as early as 2012 about 58 percent of countries would have preferred to trade with the BRICS currency, 23 percent with the euro and 19 percent with the US dollar. Our results announce favorable prospects for a dominance of the BRICS currency in international trade, if only trade relations are taken into account, whereas political and other aspects are neglected.
Adaptive Estimation-Based Safety-Critical Cruise Control of Vehicular Platoons
Authors: Vishrut Bohara, Siavash Farzan
Date: 2023-05-01 00:00:00

Optimal cruise control design can increase highway throughput and vehicle safety in traffic flow. In most heterogeneous platoons, the absence of vehicle-to-vehicle (V2V) communication poses challenges in maintaining system stability and ensuring a safe inter-vehicle distance. This paper presents an adaptive estimation-based control design for adaptive cruise control (ACC) that reliably estimates the states of the preceding vehicle while ensuring the autonomous vehicle operates within a safe region. Lyapunov functions and Control Barrier Functions (CBFs) are employed to design a safety-critical controller that guarantees safety despite potential estimation errors. The proposed unified control formulation addresses limitations in the existing cruise control solutions by simultaneously ensuring safety, stability, and optimal performance. The estimator-controller framework is implemented in scenarios with and without vehicle-to-vehicle communication, demonstrating successful performance in maintaining platoon safety and stability. Additionally, physics engine-based simulations reinforce both the practical viability of the proposed control framework in real-world situations and the controller's adeptness at maintaining safety amidst realistic operating conditions.
Inflation Reduction Act impacts on the economics of clean hydrogen and liquid fuels
Authors: Fangwei Cheng, Hongxi Luo, Jesse D. Jenkins, Eric D. Larson
Date: 2023-05-01 00:00:00

The Inflation Reduction Act (IRA) in the United States provides unprecedented incentives for deploying low-carbon hydrogen and liquid fuels, among other low greenhouse gas (GHG) emissions technologies. To better understand the prospective competitiveness of low-carbon or negative-carbon hydrogen and liquid fuels under the IRA in the early 2030s, we examine the impacts of IRA provisions on costs of producing hydrogen and synthetic liquid fuel made from natural gas, electricity, short-cycle biomass (agricultural residues), and corn-ethanol. With IRA credits (45V or 45Q), but excluding incentives provided by other national or state policies, hydrogen produced by electrolysis using carbon-free electricity (green H2) and natural gas reforming with carbon capture and storage (CCS) (blue H2) are cost-competitive with the carbon-intensive benchmark gray H2 from steam methane reforming. Biomass-derived H2 with or without CCS is not cost-completive under current IRA provisions. However, if IRA allowed biomass gasification with CCS to claim a 45V credit for carbon-neutral H2 and a 45Q credit for negative biogenic-CO2 emissions, this pathway would be less costly than gray H2. The IRA credit for clean fuels (45Z), currently stipulated to end in 2027, would need to be extended, or similar policy support provided by other national or state policies, for clean synthetic liquid fuel to be cost-competitive with petroleum-derived liquid fuels. Levelized IRA subsidies per unit of CO2 mitigated for all hydrogen and synthetic liquid fuel production pathways, except electricity-derived synthetic liquid fuel, range from 65 to 384 $/t CO2, which is within or below the range in U.S. federal government estimates of the Social Cost of Carbon (SCC) in the 2030 to 2040 timeframe.
A comparison of methods to eliminate regularization weight tuning from data-enabled predictive control
Authors: Manuel Koch, Colin N. Jones
Date: 2023-05-01 00:00:00
Tasks: Model Predictive Control

Data-enabled predictive control (DeePC) is a recently established form of Model Predictive Control (MPC), based on behavioral systems theory. While eliminating the need to explicitly identify a model, it requires an additional regularization with a corresponding weight to function well with noisy data. The tuning of this weight is non-trivial and has a significant impact on performance. In this paper, we compare three reformulations of DeePC that either eliminate the regularization, or simplify the tuning to a trivial point. A building simulation study shows a comparable performance for all three reformulations of DeePC. However, a conventional MPC with a black-box model slightly outperforms them, while solving much faster, and yielding smoother optimal trajectories. Two of the DeePC variants also show sensitivity to an unobserved biased input noise, which is not present in the conventional MPC.
Developing Optimization-Based Inverter Models for Short Circuit Studies
Authors: Thabiso R. Mabote, Jose E. Tabarez, Arthur K. Barnes, Adam Mate, Russell W. Bent, Eduardo Cotilla-Sanchez
Date: 2023-05-01 00:00:00

As inverter-based generation becomes more common in distribution networks, it is important to create models for use in optimization-based problems that accurately represent their non-linear behavior when saturated. This work presents models for grid-following and grid-forming inverters, and demonstrates their use in optimization-based fault studies. The developed models are shown to provide results in line with experimental tests from literature; however, grid-forming inverter models fail to provide feasible solutions for certain fault types even when currents, voltages, and powers from the models seem reasonable. This work lays down the foundation for the development of relaxed models for both grid-following and grid-forming inverter models for use in optimization problems.
Adaptive Sampling of Algal Blooms Using Autonomous Underwater Vehicle and Satellite Imagery: Experimental Validation in the Baltic Sea
Authors: Joana Fonseca, Sriharsha Bhat, Matthew Lock, Ivan Stenius, Karl H. Johansson
Date: 2023-05-01 00:00:00

This paper investigates using satellite data to improve adaptive sampling missions, particularly for front tracking scenarios such as with algal blooms. Our proposed solution to find and track algal bloom fronts uses an Autonomous Underwater Vehicle (AUV) equipped with a sensor that measures the concentration of chlorophyll a and satellite data. The proposed method learns the kernel parameters for a Gaussian process model using satellite images of chlorophyll a from the previous days. Then, using the data collected by the AUV, it models chlorophyll a concentration online. We take the gradient of this model to obtain the direction of the algal bloom front and feed it to our control algorithm. The performance of this method is evaluated through realistic simulations for an algal bloom front in the Baltic sea, using the models of the AUV and the chlorophyll a sensor. We compare the performance of different estimation methods, from GP to curve interpolation using least squares. Sensitivity analysis is performed to evaluate the impact of sensor noise on the methods performance. We implement our method on an AUV and run experiments in the Stockholm archipelago in the summer of 2022.