pwc-archive/papers-with-abstracts huggingface dataset:
Sliding mode control with a neural network compensation scheme for electro-hydraulic systems
Authors: Josiane Maria de Macedo Fernandes, Marcelo Costa Tanaka, Wallace Moreira Bessa
Date: 2022-05-23 00:00:00

Electro-hydraulic servo-systems are widely employed in industrial applications such as robotic manipulators, active suspensions, precision machine tools and aerospace systems. They provide many advantages over electric motors, including high force to weight ratio, fast response time and compact size. However, precise control of electro-hydraulic systems, due to their inherent nonlinear characteristics, cannot be easily obtained with conventional linear controllers. Most flow control valves can also exhibit some hard nonlinearities such as dead-zone due to valve spool overlap. This work describes the development of a sliding mode controller with a neural network compensation scheme for electro-hydraulic systems subject to an unknown dead-zone input. The boundedness and convergence properties of the closed-loop signals are proven using Lyapunov stability theory. Numerical results are presented in order to demonstrate the control system performance.
Unpredictable repeatability in molecular evolution
Authors: Suman G Das, Joachim Krug
Date: 2022-05-26 00:00:00

The extent of parallel evolution at the genotypic level is quantitatively linked to the distribution of beneficial fitness effects (DBFE) of mutations. The standard view, based on light-tailed distributions (i.e. distributions with finite moments), is that the probability of parallel evolution in duplicate populations is inversely proportional to the number of available mutations, and moreover that the DBFE is sufficient to determine the probability when the number of available mutations is large. Here we show that when the DBFE is heavy-tailed, as found in several recent experiments, these expectations are defied. The probability of parallel evolution decays anomalously slowly in the number of mutations or even becomes independent of it, implying higher repeatability of evolution. At the same time, the probability of parallel evolution is non-self-averaging, that is, it does not converge to its mean value even when a large number of mutations are involved. This behavior arises because the evolutionary process is dominated by only a few mutations of high weight. Consequently, the probability varies widely across systems with the same DBFE. Contrary to the standard view, the DBFE is no longer sufficient to determine the extent of parallel evolution, making it much less predictable. We illustrate these ideas theoretically and through analysis of empirical data on antibiotic resistance evolution.
An Algorithmic Approach to Emergence
Authors: Charles Alexandre Bédard, Geoffroy Bergeron
Date: 2022-05-19 00:00:00

We suggest a quantitative and objective notion of emergence. Our proposal uses algorithmic information theory as a basis for an objective framework in which a bit string encodes observational data. A plurality of drops in the Kolmogorov structure function of such a string is seen as the hallmark of emergence. Our definition offers some theoretical results, in addition to extending the notions of coarse-graining and boundary conditions. Finally, we confront our proposal with applications to dynamical systems and thermodynamics.
Transferable Know-How: Evidence from the Upstream Oil & Gas Industry
Authors: Michele Fioretti, Alessandro Iaria, Aljoscha Janssen, Clément Mazet-Sonilhac, Robert K. Perrons
Date: 2022-05-26 00:00:00

We investigate the consequences of the adoption of Enhanced Oil Recovery (EOR) on productivity, competition, and further technological adoption in the upstream oil \& gas industry in the North Sea. Exploiting geological heterogeneity and a Norwegian Supreme Court decision that reduced policy uncertainty, we show that early EOR adopters increased their productivity and gained shares of the North Sea market through three channels. First, direct productivity gains due to EOR adoption. Second, specialization by transferring the acquired know-how to other EOR-eligible fields. Third, diversification through know-how-driven economies of scope that facilitated investments in non-EOR-eligible fields requiring alternative technologies.
Immigrant and native export benefiting from business collaborations: a global study
Authors: Shayegheh Ashourizadeh, Mehrzad Saeedikiya
Date: 2022-05-26 00:00:00

The authors hypothesised that export develops in the network of business collaborations that are embedded in migration status. In that, collaborative networking positively affects export performance and immigrant entrepreneurs enjoy higher collaborative networking than native entrepreneurs due to their advantage of being embedded in the home and the host country. Moreover, the advantage of being an immigrant promotes the benefits of collaborative networking for export compared to those of native entrepreneurs. A total of 47,200 entrepreneurs starting, running and owning firms in 71 countries were surveyed by Global Entrepreneurship Monitor and analysed through the hierarchical linear modelling technique. Collaborative networking facilitated export and migration status influenced entrepreneur networking, in that, immigrant entrepreneurs had a higher level of collaborative networking than native entrepreneurs. Consequently, immigrant entrepreneurs seemed to have benefited from their network collaborations more than their native counterparts did. This study sheds light on how immigrant entrepreneur network collaborations can be effective for their exporting.
Railroad Bailouts in the Great Depression
Authors: Lyndon Moore, Gertjan Verdickt
Date: 2022-05-25 00:00:00

The Reconstruction Finance Corporation and Public Works Administration loaned 50 U.S. railroads over $1.1 billion between 1932 and 1939. The government goal was to decrease the likelihood of bond defaults and increase employment. Bailouts had little effect on employment, instead they increased the average wage of their employees. Bailouts reduced leverage, but did not significantly impact bond default. Overall, bailing out railroads had little effect on their stock prices, but resulted in an increase in their bond prices and reduced the likelihood of ratings downgrades. We find some evidence that manufacturing firms located close to railroads benefited from bailout spillovers.
The effect of the brand in the decision to purchase the mobile phone a research on Y generation consumers
Authors: İbrahim Halil Efendioğlu, Adnan Talha Mutlu, Yakup Durmaz
Date: 2022-05-25 00:00:00

The aim of the study is to determine the effect of the brand on purchasing decision on generation Y. For this purpose, a face-to-face survey was conducted with 231 people in the Y age range who have purchased mobile phones in the last year. The study was conducted with young academicians and university students working in Harran University Vocational High Schools. The collected data were analysed with AMOS and SPSS statistical package programs using structural equation modelling. According to the results of the research, the prestige, name and reliability of the brand have a significant impact on the purchase decision of the Y generation. Price, advertising campaigns and technical services of the brand also affect this decision less. On the other hand, the place where mobile phone brands are produced and the importance they attach to social responsibility projects do not affect the purchasing decision.
Statistical inference in social networks: how sampling bias and uncertainty shape decisions
Authors: Andreas Bjerre-Nielsen, Martin Benedikt Busch
Date: 2022-05-25 00:00:00

We investigate how individuals form expectations about population behavior using statistical inference based on observations of their social relations. Misperceptions about others' connectedness and behavior arise from sampling bias stemming from the friendship paradox and uncertainty from small samples. In a game where actions are strategic complements, we characterize the equilibrium and analyze equilibrium behavior. We allow for agent sophistication to account for the sampling bias and demonstrate how sophistication affects the equilibrium. We show how population behavior depends on both sources of misperceptions and illustrate when sampling uncertainty plays a critical role compared to sampling bias.
Audio Data Augmentation for Acoustic-to-articulatory Speech Inversion using Bidirectional Gated RNNs
Authors: Yashish M. Siriwardena, Ahmed Adel Attia, Ganesh Sivaraman, Carol Espy-Wilson
Date: 2022-05-25 00:00:00
Tasks: Data Augmentation

Data augmentation has proven to be a promising prospect in improving the performance of deep learning models by adding variability to training data. In previous work with developing a noise robust acoustic-to-articulatory speech inversion system, we have shown the importance of noise augmentation to improve the performance of speech inversion in noisy speech. In this work, we compare and contrast different ways of doing data augmentation and show how this technique improves the performance of articulatory speech inversion not only on noisy speech, but also on clean speech data. We also propose a Bidirectional Gated Recurrent Neural Network as the speech inversion system instead of the previously used feed forward neural network. The inversion system uses mel-frequency cepstral coefficients (MFCCs) as the input acoustic features and six vocal tract-variables (TVs) as the output articulatory features. The Performance of the system was measured by computing the correlation between estimated and actual TVs on the U. Wisc. X-ray Microbeam database. The proposed speech inversion system shows a 5% relative improvement in correlation over the baseline noise robust system for clean speech data. The pre-trained model, when adapted to each unseen speaker in the test set, improves the average correlation by another 6%.
New methods of removing debris and high-throughput counting of cyst nematode eggs extracted from field soil
Authors: Upender Kalwa, Christopher Legner, Elizabeth Wlezien, Gregory Tylka, Santosh Pandey
Date: 2022-05-25 00:00:00

The soybean cyst nematode (SCN), Heterodera glycines, is the most damaging pathogen of soybeans in the United States. To assess the severity of nematode infestations in the field, SCN egg population densities are determined. Cysts (dead females) of the nematode must be extracted from soil samples and then ground to extract the eggs within. Sucrose centrifugation commonly is used to separate debris from suspensions of extracted nematode eggs. We present a method using OptiPrep as a density gradient medium with improved separation and recovery of extracted eggs compared to the sucrose centrifugation technique. Also, computerized methods were developed to automate the identification and counting of nematode eggs from the processed samples. In one approach, a high-resolution scanner was used to take static images of extracted eggs and debris on filter papers, and a deep learning network was trained to identify and count the eggs among the debris. In the second approach, a lensless imaging setup was developed using off-the-shelf components, and the processed egg samples were passed through a microfluidic flow chip made from double-sided adhesive tape. Holographic videos were recorded of the passing eggs and debris, and the videos were reconstructed and processed by custom software program to obtain egg counts. The performance of the software programs for egg counting was characterized with SCN-infested soil collected from two farms, and the results using these methods were compared with those obtained through manual counting.
An Event-Driven Compressive Neuromorphic System for Cardiac Arrhythmia Detection
Authors: Jinbo Chen, Fengshi Tian, Jie Yang, Mohamad Sawan
Date: 2022-05-26 00:00:00
Tasks: Arrhythmia Detection

Wearable electrocardiograph (ECG) recording and processing systems have been developed to detect cardiac arrhythmia to help prevent heart attacks. Conventional wearable systems, however, suffer from high energy consumption at both circuit and system levels. To overcome the design challenges, this paper proposes an event-driven compressive ECG recording and neuromorphic processing system for cardiac arrhythmia detection. The proposed system achieves low power consumption and high arrhythmia detection accuracy via system level co-design with spike-based information representation. Event-driven level-crossing ADC (LC-ADC) is exploited in the recording system, which utilizes the sparsity of ECG signal to enable compressive recording and save ADC energy during the silent signal period. Meanwhile, the proposed spiking convolutional neural network (SCNN) based neuromorphic arrhythmia detection method is inherently compatible with the spike-based output of LC-ADC, hence realizing accurate detection and low energy consumption at system level. Simulation results show that the proposed system with 5-bit LC-ADC achieves 88.6\% reduction of sampled data points compared with Nyquist sampling in the MIT-BIH dataset, and 93.59\% arrhythmia detection accuracy with SCNN, demonstrating the compression ability of LC-ADC and the effectiveness of system level co-design with SCNN.
Cellular gradient flow structure connects single-cell-level rules and population-level dynamics
Authors: Shuhei A. Horiguchi, Tetsuya J. Kobayashi
Date: 2022-05-26 00:00:00

In multicellular systems, the single-cell behaviors should be coordinated consistently with the overall population dynamics and functions. However, the interrelation between single-cell rules and the population-level goal is still elusive. In this work, we reveal that these two levels are naturally connected via a gradient flow structure of the heterogeneous cellular population and that biologically prevalent single-cell rules such as unidirectional type-switching and hierarchical order in types emerge from this structure. We also demonstrate the gradient flow structure in a standard model of the T-cell immune response. This theoretical framework works as a basis for understanding multicellular dynamics and functions.
Towards future directions in data-integrative supervised prediction of human aging-related genes
Authors: Qi Li, Khalique Newaz, Tijana Milenković
Date: 2022-05-26 00:00:00
Tasks: Data Integration, Human Aging, Prediction

Identification of human genes involved in the aging process is critical due to the incidence of many diseases with age. A state-of-the-art approach for this purpose infers a weighted dynamic aging-specific subnetwork by mapping gene expression (GE) levels at different ages onto the protein-protein interaction network (PPIN). Then, it analyzes this subnetwork in a supervised manner by training a predictive model to learn how network topologies of known aging- vs. non-aging-related genes change across ages. Finally, it uses the trained model to predict novel aging-related genes. However, the best current subnetwork resulting from this approach still yields suboptimal prediction accuracy. This could be because it was inferred using outdated GE and PPIN data. Here, we evaluate whether analyzing a weighted dynamic aging-specific subnetwork inferred from newer GE and PPIN data improves prediction accuracy upon analyzing the best current subnetwork inferred from outdated data. Unexpectedly, we find that not to be the case. To understand this, we perform aging-related pathway and Gene Ontology (GO) term enrichment analyses. We find that the suboptimal prediction accuracy, regardless of which GE or PPIN data is used, may be caused by the current knowledge about which genes are aging-related being incomplete, or by the current methods for inferring or analyzing an aging-specific subnetwork being unable to capture all of the aging-related knowledge. These findings can potentially guide future directions towards improving supervised prediction of aging-related genes via -omics data integration.
The promise of behavioral tracking systems for advancing primate animal welfare
Authors: Brenna Knaebe, Claudia C. Weiss, Jan Zimmermann, Benjamin Y. Hayden
Date: 2022-05-26 00:00:00

Recent years have witnessed major advances in the ability of computerized systems to track the positions of animals as they move through large and unconstrained environments. These systems have so far been a great boon in the fields of primatology, psychology, neuroscience, and biomedicine. Here, we discuss the promise of these technologies for animal welfare. Their potential benefits include identifying and reducing pain, suffering, and distress in captive populations, improving laboratory animal welfare within the context of the three Rs of animal research (reduction, refinement, and replacement), and applying our understanding of animal behavior to increase the natural behaviors in captive and wild populations facing human impact challenges. We note that these benefits are often incidental to the designed purpose of these tracking systems, a reflection of the fact that animal welfare is not inimical to research progress, but instead, that the aligned interests between basic research and welfare hold great promise for improvements to animal well-being.
More than energy cost: Multiple benefits of the long Achilles tendon in human walking and running
Authors: Anthony J Blazevich, Jared R Fletcher
Date: 2022-05-26 00:00:00

Elastic strain energy is stored and released from long, distal tendons such as the Achilles during locomotion, reducing locomotor energy cost by minimising muscle shortening distance and speed, and thus activation. However, numerous additional, often unrecognised, advantages of long tendons may speculatively be of greater evolutionary advantage, including the reduced limb inertia afforded by shorter and lighter muscles (reducing proximal muscle force requirement); reduced energy dissipation during the foot-ground collision; capacity to store and reuse the muscle work done to dampen the vibrations triggered by foot-ground collisions; and attenuation of work-induced muscle damage. Cumulatively, these effects should reduce both neuromotor fatigue and sense of locomotor effort, allowing humans to choose to move at faster speeds for longer. As these benefits are greater at faster locomotor speeds, they are consistent with the hypothesis that running gaits used by our ancestors exerted substantial evolutionary pressure on Achilles tendon length.
The evidence contained in the P-value is context dependent
Authors: Florian Hartig, Frédéric Barraquand
Date: 2022-05-26 00:00:00

In a recent opinion article, Muff et al. recapitulate well-known objections to the Neyman-Pearson Null-Hypothesis Significance Testing (NHST) framework and call for reforming our practices in statistical reporting. We agree with them on several important points: the significance threshold P<0.05 is only a convention, chosen as a compromise between type I and II error rates; transforming the p-value into a dichotomous statement leads to a loss of information; and p-values should be interpreted together with other statistical indicators, in particular effect sizes and their uncertainty. In our view, a lot of progress in reporting results can already be achieved by keeping these three points in mind. We were surprised and worried, however, by Muff et al.'s suggestion to interpret the p-value as a "gradual notion of evidence". Muff et al. recommend, for example, that a P-value > 0.1 should be reported as "little or no evidence" and a P-value of 0.001 as "strong evidence" in favor of the alternative hypothesis H1.
A new self-exciting jump-diffusion process for option pricing
Authors: Luis A. Souto Arias, Pasquale Cirillo, Cornelis W. Oosterlee
Date: 2022-05-26 00:00:00

We propose a new jump-diffusion process, the Heston-Queue-Hawkes (HQH) model, combining the well-known Heston model and the recently introduced Queue-Hawkes (Q-Hawkes) jump process. Like the Hawkes process, the HQH model can capture the effects of self-excitation and contagion. However, since the characteristic function of the HQH process is known in closed-form, Fourier-based fast pricing algorithms, like the COS method, can be fully exploited with this model. Furthermore, we show that by using partial integrals of the characteristic function, which are also explicitly known for the HQH process, we can reduce the dimensionality of the COS method, and so its numerical complexity. Numerical results for European and Bermudan options show that the HQH model offers a wider range of volatility smiles compared to the Bates model, while its computational burden is considerably smaller than that of the Heston-Hawkes (HH) process.
Do price trajectory data increase the efficiency of market impact estimation?
Authors: Fengpei Li, Vitalii Ihnatiuk, Ryan Kinnear, Anderson Schneider, Yuriy Nevmyvaka
Date: 2022-05-26 00:00:00

Market impact is an important problem faced by large institutional investor and active market participant. In this paper, we rigorously investigate whether price trajectory data from the metaorder increases the efficiency of estimation, from an asymptotic view of statistical estimation. We show that, for popular market impact models, estimation methods based on partial price trajectory data, especially those containing early trade prices, can outperform established estimation methods (e.g., VWAP-based) asymptotically. We discuss theoretical and empirical implications of such phenomenon, and how they could be readily incorporated into practice.
Principled Knowledge Extrapolation with GANs
Authors: Ruili Feng, Jie Xiao, Kecheng Zheng, Deli Zhao, Jingren Zhou, Qibin Sun, Zheng-Jun Zha
Date: 2022-05-21 00:00:00
Tasks: counterfactual

Human can extrapolate well, generalize daily knowledge into unseen scenarios, raise and answer counterfactual questions. To imitate this ability via generative models, previous works have extensively studied explicitly encoding Structural Causal Models (SCMs) into architectures of generator networks. This methodology, however, limits the flexibility of the generator as they must be carefully crafted to follow the causal graph, and demands a ground truth SCM with strong ignorability assumption as prior, which is a nontrivial assumption in many real scenarios. Thus, many current causal GAN methods fail to generate high fidelity counterfactual results as they cannot easily leverage state-of-the-art generative models. In this paper, we propose to study counterfactual synthesis from a new perspective of knowledge extrapolation, where a given knowledge dimension of the data distribution is extrapolated, but the remaining knowledge is kept indistinguishable from the original distribution. We show that an adversarial game with a closed-form discriminator can be used to address the knowledge extrapolation problem, and a novel principal knowledge descent method can efficiently estimate the extrapolated distribution through the adversarial game. Our method enjoys both elegant theoretical guarantees and superior performance in many scenarios.
Coalgebraic Fuzzy geometric logic
Authors: Litan Kumar Das, Kumar Sankar Ray, Prakash Chandra Mali
Date: 2022-05-02 00:00:00

The paper aims to develop a framework for coalgebraic fuzzy geometric logic by adding modalities to the language of fuzzy geometric logic. Using the methods of coalgebra, the modal operators are introduced in the language of fuzzy geometric logic. To define the modal operators, we introduce a notion of fuzzy-open predicate lifting. Based on coalgebras for an endofunctor $T$ on the category $\textbf{Fuzzy-Top}$ of fuzzy topological spaces and fuzzy continuous maps, we build models for the coalgebraic fuzzy geometric logic. Bisimulations for the defined models are discussed in this work.