pwc-archive/papers-with-abstracts huggingface dataset:
An empirical model for feedforward control of laser powder bed fusion
Authors: Aleksandr Shkoruta, Bumsoo Park, Sandipan Mishra
Date: 2022-01-24 00:00:00

While considerable progress has recently been made in real-time melt pool monitoring for laser powder bed fusion (LPBF), results in in-situ melt pool control are relatively sparse, a major reason being lack of suitable control-oriented models. This study demonstrates an empirical control-oriented model of geometry-dependent melt pool behavior, and subsequent melt pool regulation with a model-based feedforward controller for laser power. First, it shows that the melt pool "footprint" exponentially increases when the scan lines become shorter. The empirical model of this behavior is developed and validated on different geometries at different laser power levels. Second, the developed model is used to design a feedforward controller for obtaining optimal laser power profiles. This controller is then validated experimentally and is demonstrated to suppress the in-layer geometry-related melt pool signal deviations, for different part geometries.
Modeling bid and ask price dynamics with an extended Hawkes process and its empirical applications for high-frequency stock market data
Authors: Kyungsub Lee, Byoung Ki Seo
Date: 2022-01-25 00:00:00

This study proposes a versatile model for the dynamics of the best bid and ask prices using an extended Hawkes process. The model incorporates the zero intensities of the spread-narrowing processes at the minimum bid-ask spread, spread-dependent intensities, possible negative excitement, and nonnegative intensities. We apply the model to high-frequency best bid and ask price data from US stock markets. The empirical findings demonstrate a spread-narrowing tendency, excitations of the intensities caused by previous events, the impact of flash crashes, characteristic trends in fast trading over time, and the different features of market participants in the various exchanges.
Contract design in electricity markets with high penetration of renewables: A two-stage approach
Authors: Arega Getaneh Abate, Rossana Riccardi, Carlos Ruiz
Date: 2022-01-24 00:00:00

The interplay between risk aversion and financial derivatives has received increasing attention since the advent of electricity market liberalization. One important challenge in this context is how to develop economically efficient and cost-effective models to integrate renewable energy sources (RES) in the electricity market, which constitutes a relatively new and exciting field of research. This paper proposes a game-theoretical equilibrium model that characterizes the interactions between oligopolistic generators in a two-stage electricity market under the presence of high RES penetration. Given conventional generators with generation cost uncertainty and renewable generators with intermittent and stochastic capacity, we consider a single futures contract market that is cleared prior to a spot market where the energy delivery takes place. We introduce physical and financial contracts to evaluate their performance assess their impact on the electricity market outcomes and examine how these depend on the level of RES penetration. Since market participants are usually risk-averse, a coherent risk measure is introduced to deal with both risk-neutral and risk-averse generators. We derive analytical relationships between contracts, study the implications of uncertainties, test the performance of the proposed equilibrium model and its main properties through numerical examples. Our results show that overall electricity prices, generation costs, profits, and quantities for conventional generators decrease, whereas quantities and profits for RES generators increase with RES penetration. Hence, both physical and financial contracts efficiently mitigate the impact of uncertainties and help the integration of RES into the electricity system.
Zombie-Lending in the United States -- Prevalence versus Relevance
Authors: Maximilian Göbel, Nuno Tavares
Date: 2022-01-23 00:00:00

Extraordinary fiscal and monetary interventions in response to the COVID-19 pandemic have revived concerns about zombie prevalence in advanced economies. Within a sample of publicly listed U.S. companies, we find zombie prevalence and zombie-lending not to be a widespread phenomenon per se. Nevertheless, our results reveal negative spillovers of zombie-lending on productivity, capital-growth, and employment-growth of non-zombies as well as on overall business dynamism. It is predominantly the class of healthy small- and medium-sized companies that is sensitive to zombie-lending activities, with financial constraints further amplifying these effects.
Has EU accession boosted patents performance in the EU-13? -- A critical evaluation using causal impact analysis with Bayesian structural time-series models
Authors: Agnieszka Kleszcz, Krzysztof Rusek
Date: 2022-01-20 00:00:00
Tasks: Time Series, Time Series Analysis

Nowadays innovation is one of the main determinants of economic development. Patents are a key measure of innovation output, as patent indicators reflect the inventive performance of countries, technologies and firms. This paper provides new insights on the causal effects of the enlargement of the European Union (EU) by investigating the patents performance within the new EU member states (EU-13). The empirical results based on data collected from the OECD database from 1985-2017 and causal impact using a Bayesian structural time-series model (proposed by Google) point towards a conclusion that joining the EU has had a significant impact on patents performance in Romania, Estonia, Poland, Czech Republic, Croatia and Lithuania, although in the latter two countries the impact was negative. For the rest of the EU-13 countries there is no significant effect on patent performance. Whether the EU accession effect is significant or not, the EU-13 are far behind the EU-15 (countries which entered the EU before 2004) in terms of patent performance. The majority of patents (98.66\%) are assigned to the EU-15, with just 1.34\% of assignees belonging to the EU-13.
Relaxed Notions of Condorcet-Consistency and Efficiency for Strategyproof Social Decision Schemes
Authors: Felix Brandt, Patrick Lederer, René Romen
Date: 2022-01-25 00:00:00

Social decision schemes (SDSs) map the preferences of a group of voters over some set of $m$ alternatives to a probability distribution over the alternatives. A seminal characterization of strategyproof SDSs by Gibbard implies that there are no strategyproof Condorcet extensions and that only random dictatorships satisfy ex post efficiency and strategyproofness. The latter is known as the random dictatorship theorem. We relax Condorcet-consistency and ex post efficiency by introducing a lower bound on the probability of Condorcet winners and an upper bound on the probability of Pareto-dominated alternatives, respectively. We then show that the SDS that assigns probabilities proportional to Copeland scores is the only anonymous, neutral, and strategyproof SDS that can guarantee the Condorcet winner a probability of at least 2/m. Moreover, no strategyproof SDS can exceed this bound, even when dropping anonymity and neutrality. Secondly, we prove a continuous strengthening of Gibbard's random dictatorship theorem: the less probability we put on Pareto-dominated alternatives, the closer to a random dictatorship is the resulting SDS. Finally, we show that the only anonymous, neutral, and strategyproof SDSs that maximize the probability of Condorcet winners while minimizing the probability of Pareto-dominated alternatives are mixtures of the uniform random dictatorship and the randomized Copeland rule.
The Benefits of Coarse Preferences
Authors: Joseph Y. Halpern, Yuval Heller, Eyal Winter
Date: 2022-01-25 00:00:00
Tasks: Clustering

We study the strategic advantages of coarsening one's utility by clustering nearby payoffs together (i.e., classifying them the same way). Our solution concept, coarse-utility equilibrium (CUE) requires that (1) each player maximizes her coarse utility, given the opponent's strategy, and (2) the classifications form best replies to one another. We characterize CUEs in various games. In particular, we show that there is a qualitative difference between CUEs in which only one of the players clusters payoffs, and those in which all players cluster their payoffs, and that the latter type induce players to treat co-players better than in Nash equilibria in the large class of games with monotone externalities.
Automated brain parcellation rendering and visualization in R with coldcuts
Authors: Giuseppe A. D'Agostino, Sarah R. Langley
Date: 2022-01-25 00:00:00

Parcellations are fundamental tools in neuroanatomy, allowing researchers to place functional imaging and molecular data within a structural context in the brain. Visualizing these parcellations is critical to guide biological understanding of clinical and experimental datasets in humans and model organisms. However, software used to visualize parcellations is different from the one used to analyze these datasets, greatly limiting the visualization of experimental data within parcellations. We present coldcuts, an open source R package that allows to automatically generate, store and visualize any volume-based parcellation easily and with minimal manual curation. coldcuts allows to integrate external datasets and offers rich 2D and 3D visualizations. coldcuts is freely available at http://github.com/langleylab/coldcuts and several curated coldcuts objects are made available for human, mouse, chimpanzee and Drosophila parcellations at https://github.com/langleylab/coldcuts_segmentations.
Motion Estimation and Compensation in Automotive MIMO SAR
Authors: Marco Manzoni, Dario Tagliaferri, Marco Rizzi, Stefano Tebaldini, Andrea Virgilio Monti-Guarnieri, Claudio Maria Prati, Monica Nicoli, Ivan Russo, Sergi Duque, Christian Mazzucco, Umberto Spagnolini
Date: 2022-01-25 00:00:00
Tasks: Autonomous Driving, Motion Estimation

With the advent of self-driving vehicles, autonomous driving systems will have to rely on a vast number of heterogeneous sensors to perform dynamic perception of the surrounding environment. Synthetic Aperture Radar (SAR) systems increase the resolution of conventional mass-market radars by exploiting the vehicle's ego-motion, requiring a very accurate knowledge of the trajectory, usually not compatible with automotive-grade navigation systems. In this regard, this paper deals with the analysis, estimation and compensation of trajectory estimation errors in automotive SAR systems, proposing a complete residual motion estimation and compensation workflow. We start by defining the geometry of the acquisition and the basic processing steps of Multiple-Input Multiple-Output (MIMO) SAR systems. Then, we analytically derive the effects of typical motion errors in automotive SAR imaging. Based on the derived models, the procedure is detailed, outlining the guidelines for its practical implementation. We show the effectiveness of the proposed technique by means of experimental data gathered by a 77 GHz radar mounted in a forward looking configuration.
A Wearable ECG Monitor for Deep Learning Based Real-Time Cardiovascular Disease Detection
Authors: Peng Wang, Zihuai Lin, Xucun Yan, Zijiao Chen, Ming Ding, Yang song, Lu Meng
Date: 2022-01-25 00:00:00
Tasks: ECG Classification

Cardiovascular disease has become one of the most significant threats endangering human life and health. Recently, Electrocardiogram (ECG) monitoring has been transformed into remote cardiac monitoring by Holter surveillance. However, the widely used Holter can bring a great deal of discomfort and inconvenience to the individuals who carry them. We developed a new wireless ECG patch in this work and applied a deep learning framework based on the Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) models. However, we find that the models using the existing techniques are not able to differentiate two main heartbeat types (Supraventricular premature beat and Atrial fibrillation) in our newly obtained dataset, resulting in low accuracy of 58.0 %. We proposed a semi-supervised method to process the badly labelled data samples with using the confidence-level-based training. The experiment results conclude that the proposed method can approach an average accuracy of 90.2 %, i.e., 5.4 % higher than the accuracy of conventional ECG classification methods.
Negative-ResNet: Noisy Ambulatory Electrocardiogram Signal Classification Scheme
Authors: Zijiao Chen, Zihuai Lin, Peng Wang, Ming Ding
Date: 2022-01-25 00:00:00
Tasks: Classification

With recently successful applications of deep learning in computer vision and general signal processing, deep learning has shown many unique advantages in medical signal processing. However, data labelling quality has become one of the most significant issues for AI applications, especially when it requires domain knowledge (e.g. medical image labelling). In addition, there might be noisy labels in practical datasets, which might impair the training process of neural networks. In this work, we propose a semi-supervised algorithm for training data samples with noisy labels by performing selected Positive Learning (PL) and Negative Learning (NL). To verify the effectiveness of the proposed scheme, we designed a portable ECG patch -- iRealCare -- and applied the algorithm on a real-life dataset. Our experimental results show that we can achieve an accuracy of 91.0 %, which is 6.2 % higher than a normal training process with ResNet. There are 65 patients in our dataset and we randomly picked 2 patients to perform validation.
Underwater Acoustic Communication Channel Modeling using Deep Learning
Authors: Oluwaseyi Onasami, Damilola Adesina, Lijun Qian
Date: 2022-01-25 00:00:00
Tasks: Deep Learning

With the recent increase in the number of underwater activities, having effective underwater communication systems has become increasingly important. Underwater acoustic communication has been widely used but greatly impaired due to the complicated nature of the underwater environment. In a bid to better understand the underwater acoustic channel so as to help in the design and improvement of underwater communication systems, attempts have been made to model the underwater acoustic channel using mathematical equations and approximations under some assumptions. In this paper, we explore the capability of machine learning and deep learning methods to learn and accurately model the underwater acoustic channel using real underwater data collected from a water tank with disturbance and from lake Tahoe. Specifically, Deep Neural Network (DNN) and Long Short Term Memory (LSTM) are applied to model the underwater acoustic channel. Experimental results show that these models are able to model the underwater acoustic communication channel well and that deep learning models, especially LSTM are better models in terms of mean absolute percentage error.
AI-Driven Demodulators for Nonlinear Receivers in Shared Spectrum with High-Power Blockers
Authors: Hossein Mohammadi, Walaa AlQwider, Talha Faizur Rahman, Vuk Marojevic
Date: 2022-01-24 00:00:00

Research has shown that communications systems and receivers suffer from high power adjacent channel signals, called blockers, that drive the radio frequency (RF) front end into nonlinear operation. Since simple systems, such as the Internet of Things (IoT), will coexist with sophisticated communications transceivers, radars and other spectrum consumers, these need to be protected employing a simple, yet adaptive solution to RF nonlinearity. This paper therefore proposes a flexible data driven approach that uses a simple artificial neural network (ANN) to aid in the removal of the third order intermodulation distortion (IMD) as part of the demodulation process. We introduce and numerically evaluate two artificial intelligence (AI)-enhanced receivers-ANN as the IMD canceler and ANN as the demodulator. Our results show that a simple ANN structure can significantly improve the bit error rate (BER) performance of nonlinear receivers with strong blockers and that the ANN architecture and configuration depends mainly on the RF front end characteristics, such as the third order intercept point (IP3). We therefore recommend that receivers have hardware tags and ways to monitor those over time so that the AI and software radio processing stack can be effectively customized and automatically updated to deal with changing operating conditions.
Proteome-scale Deployment of Protein Structure Prediction Workflows on the Summit Supercomputer
Authors: Mu Gao, Mark Coletti, Russell B. Davidson, Ryan Prout, Subil Abraham, Benjamin Hernandez, Ada Sedova
Date: 2022-01-25 00:00:00
Tasks: Prediction, Protein Structure Prediction

Deep learning has contributed to major advances in the prediction of protein structure from sequence, a fundamental problem in structural bioinformatics. With predictions now approaching the accuracy of crystallographic resolution in some cases, and with accelerators like GPUs and TPUs making inference using large models rapid, fast genome-level structure prediction becomes an obvious aim. Leadership-class computing resources can be used to perform genome-scale protein structure prediction using state-of-the-art deep learning models, providing a wealth of new data for systems biology applications. Here we describe our efforts to efficiently deploy the AlphaFold2 program, for full-proteome structure prediction, at scale on the Oak Ridge Leadership Computing Facility's resources, including the Summit supercomputer. We performed inference to produce the predicted structures for 35,634 protein sequences, corresponding to three prokaryotic proteomes and one plant proteome, using under 4,000 total Summit node hours, equivalent to using the majority of the supercomputer for one hour. We also designed an optimized structure refinement that reduced the time for the relaxation stage of the AlphaFold pipeline by over 10X for longer sequences. We demonstrate the types of analyses that can be performed on proteome-scale collections of sequences, including a search for novel quaternary structures and implications for functional annotation.
Ab Initio Nucleic Acid Folding Simulations Using a Physics-Based Atomistic Free Energy Model
Authors: Chi H. Mak
Date: 2022-01-24 00:00:00
Tasks: Computational Efficiency

Performing full-resolution atomistic simulations of nucleic acid folding has remained a challenge for biomolecular modeling. Understanding how nucleic acids fold and how they transition between different folded structures as they unfold and refold has important implications for biology. This paper reports a theoretical model and computer simulation of the ab initio folding of DNA inverted repeat sequences. The formulation is based on an all-atom conformational model of the sugar-phosphate backbone via chain closure, and it incorporates three major molecular-level driving forces - base stacking, counterion-induced backbone self-interactions and base pairing - via separate analytical theories designed to capture and reproduce the effects of the solvent without requiring explicit water and ions in the simulation. To accelerate computational throughput, a mixed numerical/analytical algorithm for the calculation of the backbone conformational volume is incorporated into the Monte Carlo simulation, and special stochastic sampling techniques were employed to achieve the computational efficiency needed to fold nucleic acids from scratch. This paper describes implementation details, benchmark results and the advantages and technical challenges with this approach.
Using unsupervised machine learning to quantify physical activity from accelerometry in a diverse and rapidly changing population
Authors: Christopher B Thornton, Niina Kolehmainen, Kianoush Nazarpour
Date: 2022-01-25 00:00:00

Accelerometers are widely used to measure physical activity behaviour, including in children. The traditional method for processing acceleration data uses cut points to define physical activity intensity, relying on calibration studies that relate the magnitude of acceleration to energy expenditure. However, these relationships do not generalise across diverse populations and hence they must be parametrised for each subpopulation (e.g., age groups) which is costly and makes studies across diverse populations and over time difficult. A data driven approach that allows physical activity intensity states to emerge from the data, without relying on parameters derived from external populations, and offers a new perspective on this problem and potentially improved results. We applied an unsupervised machine learning approach, namely a hidden semi Markov model, to segment and cluster the accelerometer data recorded from 279 children (9 to 38 months old) with a diverse range of physical and social-cognitive abilities (measured using the Paediatric Evaluation of Disability Inventory). We benchmarked this analysis with the cut points approach calculated using the best available thresholds for the population. Time spent active as measured by this unsupervised approach correlated more strongly with measures of the childs mobility, social-cognitive capacity, independence, daily activity, and age than that measured using the cut points approach. Unsupervised machine learning offers the potential to provide a more sensitive, appropriate, and cost-effective approach to quantifying physical activity behaviour in diverse populations, compared to the current cut points approach. This, in turn, supports research that is more inclusive of diverse or rapidly changing populations.
Symbiotic bacterial network structure involved in carbon and nitrogen metabolism of wood-utilizing insect larvae
Authors: Hirokuni Miyamoto, Futo Asano, Koutarou Ishizawa, Wataru Suda, Hisashi Miyamoto, Naoko Tsuji, Makiko Matsuura, Arisa Tsuboi, Chitose Ishii, Teruno Nakaguma, Chie Shindo, Tamotsu Kato, Atsushi Kurotani, Hideaki Shima, Shigeharu Moriya, Masahira Hattori, Hiroaki Kodama, Hiroshi Ohno, Jun Kikuchi
Date: 2022-01-24 00:00:00
Tasks: Causal Inference

Effective biological utilization of wood biomass is necessary worldwide. Since several insect larvae can use wood biomass as a nutrient source, studies on their digestive mechanism are expected to speculate a novel rule in wood biomass processing. Here, the relationships of inhabitant bacteria involved in carbon and nitrogen metabolism in the intestine of beetle larvae, an insect model, are investigated. Bacterial analysis of larval feces showed enrichment of members of which could include candidates for plant growth promotion, nitrogen cycle modulation, and/or environmental protection. The abundances of these bacteria were not necessarily positively correlated with the abundance in the habitat, suggesting that they might be selectively enriched in the intestines of larvae. Further association analysis predicted that carbon and nitrogen metabolism in the intestine was affected by the presence of the other common bacteria, the populations of which were not remarkably altered in the habitat and feces. Based on hypotheses targeting these selected bacterial groups, structural estimation modeling analyses statistically suggested that their metabolism of carbon and nitrogen and their stable isotopes, {\delta}13C and {\delta}15N, may be associated with fecal enriched bacteria and other common bacteria. In addition, other causal inference analyses, such as causal mediation analysis, linear non-Gaussian acyclic model (LiNGAM), and BayesLiNGAM, did not necessarily affirm the existence of prominent bacteria involved in metabolism, implying its importance as the bacterial groups for metabolism rather than a remarkable bacterium. Thus, these observations highlight a multifaceted view of symbiotic bacterial groups utilizing carbon and nitrogen from wood biomass in insect larvae as a cultivator of potentially environmentally beneficial bacteria.