pwc-archive/papers-with-abstracts huggingface dataset:
A stochastic partial differential equation model for limit order book dynamics
Authors: Rama Cont, Marvin S. Mueller
Date: 2019-04-05 00:00:00

We propose an analytically tractable class of models for the dynamics of a limit order book, described through a stochastic partial differential equation (SPDE) with multiplicative noise for the order book centered at the mid-price, along with stochastic dynamics for the mid-price which is consistent with the order flow dynamics. We provide conditions under which the model admits a finite dimensional realization driven by a (low-dimensional) Markov process, leading to efficient estimation and computation methods. We study two examples of parsimonious models in this class: a two-factor model and a model with mean-reverting order book depth. For each model we analyze in detail the role of different parameters, the dynamics of the price, order book depth, volume and order imbalance, provide an intuitive financial interpretation of the variables involved and show how the model reproduces statistical properties of price changes, market depth and order flow in limit order markets.
Light Gradient Boosting Machine as a Regression Method for Quantitative Structure-Activity Relationships
Authors: Robert P. Sheridan, Andy Liaw, Matthew Tudor
Date: 2021-04-28 00:00:00
Tasks: Prediction Intervals, regression

In the pharmaceutical industry, where it is common to generate many QSAR models with large numbers of molecules and descriptors, the best QSAR methods are those that can generate the most accurate predictions but that are also insensitive to hyperparameters and are computationally efficient. Here we compare Light Gradient Boosting Machine (LightGBM) to random forest, single-task deep neural nets, and Extreme Gradient Boosting (XGBoost) on 30 in-house data sets. While any boosting algorithm has many adjustable hyperparameters, we can define a set of standard hyperparameters at which LightGBM makes predictions about as accurate as single-task deep neural nets, but is a factor of 1000-fold faster than random forest and ~4-fold faster than XGBoost in terms of total computational time for the largest models. Another very useful feature of LightGBM is that it includes a native method for estimating prediction intervals.
Kuramoto model based analysis reveals oxytocin effects on brain network dynamics
Authors: Shuhan Zheng, Zhichao Liang, Youzhi Qu, Qingyuan Wu, Haiyan Wu, Quanying Liu
Date: 2021-05-18 00:00:00

The oxytocin effects on large-scale brain networks such as Default Mode Network (DMN) and Frontoparietal Network (FPN) have been largely studied using fMRI data. However, these studies are mainly based on the statistical correlation or Bayesian causality inference, lacking interpretability at physical and neuroscience level. Here, we propose a physics-based framework of Kuramoto model to investigate oxytocin effects on the phase dynamic neural coupling in DMN and FPN. Testing on fMRI data of 59 participants administrated with either oxytocin or placebo, we demonstrate that oxytocin changes the topology of brain communities in DMN and FPN, leading to higher synchronization in the FPN and lower synchronization in the DMN, as well as a higher variance of the coupling strength within the DMN and more flexible coupling patterns across time. These results together indicate that oxytocin may increase the ability to overcome the corresponding internal oscillation dispersion and support the flexibility in neural synchrony in various social contexts, providing new evidence for explaining the oxytocin modulated social behaviors. Our proposed Kuramoto model-based framework can be a potential tool in network neuroscience and offers physical and neural insights into phase dynamics of the brain.
Geometry of fitness landscapes: Peaks, shapes and universal positive epistasis
Authors: Kristina Crona, Joachim Krug, Malvika Srivastava
Date: 2021-05-18 00:00:00

Darwinian evolution is driven by random mutations, genetic recombination (gene shuffling) and selection that favors genotypes with high fitness. For systems where each genotype can be represented as a bitstring of length $L$, an overview of possible evolutionary trajectories is provided by the $L$-cube graph with nodes labeled by genotypes and edges directed toward the genotype with higher fitness. Peaks (sinks in the graphs) are important since a population can get stranded at a suboptimal peak. The fitness landscape is defined by the fitness values of all genotypes in the system. Some notion of curvature is necessary for a more complete analysis of the landscapes, including the effect of recombination. The shape approach uses triangulations (shapes) induced by fitness landscapes. The main topic for this work is the interplay between peak patterns and shapes. Because of constraints on the shapes for $L=3$ imposed by peaks, there are in total 25 possible combinations of peak patterns and shapes. Similar constraints exist for higher $L$. Specifically, we show that the constraints induced by the staircase triangulation can be formulated as a condition of {\emph{universal positive epistasis}}, an order relation on the fitness effects of arbitrary sets of mutations that respects the inclusion relation between the corresponding genetic backgrounds. We apply the concept to a large protein fitness landscape for an immunoglobulin-binding protein expressed in Streptococcal bacteria.
Molecular approaches reveal weak sibship aggregation and a high dispersal propensity in a non-native fish parasite
Authors: Jérôme Prunier, Keoni Saint-Pé, Simon Blanchet, Géraldine Loot, Olivier Rey
Date: 2021-05-18 00:00:00

Inferring parameters related to the aggregation pattern of parasites and to their dispersal propensity is important for predicting their ecological consequences and evolutionary potential. Nonetheless, it is notoriously difficult to infer these parameters from wildlife parasites given the difficulty in tracking these organisms. Molecular-based inferences constitute a promising approach that has yet rarely been applied in the wild.Here, we combinedseveral population genetic analyses including sibship reconstruction to documentthe genetic structure, patterns of sibship aggregation and the dispersal dynamics of a non-native parasite of fish, the freshwater copepod ectoparasiteTracheliastespolycolpus. We collected parasites according to a hierarchical sampling design,withthe sampling of all parasites from all host individualscapturedineight sites spread along an upstream-downstream river gradient. Individual multilocus genotypes were obtained from 14 microsatellite markers, and used to assign parasites to full-sib families and to investigate the genetic structure of T.polycolpus among both hosts and sampling sites. The distribution of full-sibs obtainedamong the sampling sites was used to estimate individual dispersal distances within families. Our results showed that T. polycolpus sibs tend to be aggregated within sites but not withinhost individuals. We detected important upstream-to-downstream dispersal events of T.polycolpusbetween sites (modal distance: 25.4 km; 95% CI [22.9, 27.7]), becoming scarcer as the geographic distance from their family core location increases. Such a dispersal pattern likely contributes to the strong isolation-by-distance observed at the river scale. We also detected some downstream-to-upstream dispersal events (modal distance: 2.6 km; 95% CI [2.2-23.3]) that likely result from movementsof infected hosts.Within each site, the dispersal of free-living infective larvae among hosts likely contributes to increasing genetic diversity on hosts, possibly fostering the evolutionary potential of T. polycolpus.
Classifying Contaminated Cell Cultures using Time Series Features
Authors: Laura L. Tupper, Charles R. Keese, David S. Matteson
Date: 2021-05-15 00:00:00
Tasks: Classification, Time Series, Time Series Analysis

We examine the use of time series data, derived from Electric Cell-substrate Impedance Sensing (ECIS), to differentiate between standard mammalian cell cultures and those infected with a mycoplasma organism. With the goal of interpretable results, we perform low-dimensional feature-based classification, extracting application-relevant features from the ECIS time courses. We can achieve very high classification accuracy using only two features, which depend on the cell line under examination. Initial results also show the existence of experimental variation between plates and suggest types of features that may prove more robust to such variation. Our paper is the first to perform a broad examination of ECIS time course features in the context of detecting contamination; to combine different types of features to achieve classification accuracy while preserving interpretability; and to describe and suggest possibilities for ameliorating plate-to-plate variation.
Adaptive Complementary Ensemble EMD and Energy-Frequency Spectra of Cryptocurrency Prices
Authors: Tim Leung, Theodore Zhao
Date: 2021-05-17 00:00:00
Tasks: Time Series, Time Series Analysis

We study the price dynamics of cryptocurrencies using adaptive complementary ensemble empirical mode decomposition (ACE-EMD) and Hilbert spectral analysis. This is a multiscale noise-assisted approach that decomposes any time series into a number of intrinsic mode functions, along with the corresponding instantaneous amplitudes and instantaneous frequencies. The decomposition is adaptive to the time-varying volatility of each cryptocurrency price evolution. Different combinations of modes allow us to reconstruct the time series using components of different timescales. We then apply Hilbert spectral analysis to define and compute the instantaneous energy-frequency spectrum of each cryptocurrency to illustrate the properties of various timescales embedded in the original time series.
Liquidity Stress Testing in Asset Management -- Part 2. Modeling the Asset Liquidity Risk
Authors: Thierry Roncalli, Amina Cherief, Fatma Karray-Meziou, Margaux Regnault
Date: 2021-05-18 00:00:00
Tasks: Asset Management, Management

This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers liability liquidity risk (or funding liquidity) modeling, the second dimension focuses on asset liquidity risk (or market liquidity) modeling, and the third dimension considers the asset-liability management of the liquidity gap risk (or asset-liability matching). The purpose of this research is to propose a methodological and practical framework in order to perform liquidity stress testing programs, which comply with regulatory guidelines (ESMA, 2019, 2020) and are useful for fund managers. The review of the academic literature and professional research studies shows that there is a lack of standardized and analytical models. The aim of this research project is then to fill the gap with the goal of developing mathematical and statistical approaches, and providing appropriate answers. In this second article focused on asset liquidity risk modeling, we propose a market impact model to estimate transaction costs. After presenting a toy model that helps to understand the main concepts of asset liquidity, we consider a two-regime model, which is based on the power-law property of price impact. Then, we define several asset liquidity measures such as liquidity cost, liquidation ratio and shortfall or time to liquidation in order to assess the different dimensions of asset liquidity. Finally, we apply this asset liquidity framework to stocks and bonds and discuss the issues of calibrating the transaction cost model.
Partitioned Active Learning for Heterogeneous Systems
Authors: Cheolhei Lee, Kaiwen Wang, Jianguo Wu, Wenjun Cai, Xiaowei Yue
Date: 2021-05-14 00:00:00
Tasks: Active Learning, Computational Efficiency

Active learning is a subfield of machine learning that focuses on improving the data collection efficiency of expensive-to-evaluate systems. Especially, active learning integrated surrogate modeling has shown remarkable performance in computationally demanding engineering systems. However, the existence of heterogeneity in underlying systems may adversely affect the performance of active learning. In order to improve the learning efficiency under this regime, we propose the partitioned active learning that seeks the most informative design points for partitioned Gaussian process modeling of heterogeneous systems. The proposed active learning consists of two systematic subsequent steps: the global searching scheme accelerates the exploration of active learning by investigating the most uncertain design space, and the local searching exploits the circumscribed information induced by the local GP. We also propose Cholesky update driven numerical remedies for our active learning to address the computational complexity challenge. The proposed method is applied to numerical simulations and two real-world case studies about (i) the cost-efficient automatic fuselage shape control in aerospace manufacturing; and (ii) the optimal design of tribocorrosion-resistant alloys in materials science. The results show that our approach outperforms benchmark methods with respect to prediction accuracy and computational efficiency.
A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study
Authors: Giovanni Cicceri, Giuseppe Inserra ，Michele Limosani
Date: 2020-08-02 00:00:00
Tasks: BIG-bench Machine Learning

Abstract:In economic activity , recessions represent a period of failure in Gross Domestic Product
(GDP) and usually are presented as episodic and non-linear. For this reason, they are difficultto
predictandappearasoneofthemainproblemsinmacroeconomicsforecasts.Aclassicexample
turnsouttobethegreatrecessionthatoccurredbetween2008and2009thatwasnotpredicted.
Inthispaper,thegoalistogiveadifferent,althoughcomplementary,approachconcerningthe
classical econometric techniques, and to show how Machine Learning (ML) techniques may improve
short-term forecasting accuracy . As a case study , we use Italian data on GDP and a few related
variables. In particular, we evaluate the goodness of fitoftheforecastingproposedmodelina
case study of the Italian GDP . The algorithm is trained on Italian macroeconomic variables over the
period 1995:Q1-2019:Q2. We also compare the results using the same dataset through Classic Linear
Regression Model. As a result, both statistical and ML approaches are able to predict economic
downturns but higher accuracy is obtained using Nonlinear Autoregressive with exogenous variables
(NARX) model.
Representation Iterative Fusion based on Heterogeneous Graph Neural Network for Joint Entity and Relation Extraction
Authors: Kang Zhao, Hua Xu, Yue Cheng, Xiaoteng Li, Kai Gao
Date: 2021-05-08 00:00:00
Tasks: Graph Neural Network, Joint Entity and Relation Extraction, Relation, Relation Classification, Relation Extraction, valid

Joint entity and relation extraction is an essential task in information extraction, which aims to extract all relational triples from unstructured text. However, few existing works consider possible relations information between entities before extracting them, which may lead to the fact that most of the extracted entities cannot constitute valid triples. In this paper, we propose a representation iterative fusion based on heterogeneous graph neural networks for relation extraction (RIFRE). We model relations and words as nodes on the graph and fuse the two types of semantic nodes by the message passing mechanism iteratively to obtain nodes representation that is more suitable for relation extraction tasks. The model performs relation extraction after nodes representation is updated. We evaluate RIFRE on two public relation extraction datasets: NYT and WebNLG. The results show that RIFRE can effectively extract triples and achieve state-of-the-art performance. Moreover, RIFRE is also suitable for the relation classification task, and significantly outperforms the previous methods on SemEval 2010 Task 8 datasets.
Exploring the Impact of Noise on Hybrid Inversion of PROSAIL RTM on Sentinel-2 Data
Authors: Nuno Cesar de Sa, Mitra Baratchi, Leon T. Hauser, Peter van Bodegom
Date: 2021-02-11 00:00:00
Tasks: Gaussian Processes, GPR, Retrieval

Remote sensing (RS) of biophysical variables plays a vital role in providing the information necessary for understanding spatio-temporal dynamics in ecosystems. The hybrid approach to retrieve biophysical variables from RS by combining Machine Learning (ML) algorithms with surrogate data generated by Radiative Transfer Models (RTM). The susceptibility of the ill-posed solutions to noise currently constrains further application of hybrid approaches. Here, we explored how noise affects the performance of ML algorithms for biophysical trait retrieval. We focused on synthetic Sentinel-2 (S2) data generated using the PROSAIL RTM and four commonly applied ML algorithms: Gaussian Processes (GPR), Random Forests (RFR), and Artificial Neural Networks (ANN) and Multi-task Neural Networks (MTN). After identifying which biophysical variables can be retrieved from S2 using a Global Sensitivity Analysis, we evaluated the performance loss of each algorithm using the Mean Absolute Percentage Error (MAPE) with increasing noise levels. We found that, for S2 data, Carotenoid concentrations are uniquely dependent on band 2, Chlorophyll is almost exclusively dependent on the visible ranges, and Leaf Area Index, water, and dry matter contents are mostly dependent on infrared bands. Without added noise, GPR was the best algorithm (<0.05%), followed by the MTN (<3%) and ANN (<5%), with the RFR performing very poorly (<50%). The addition of noise critically affected the performance of all algorithms (>20%) even at low levels of added noise (≈5%). Overall, both neural networks performed significantly better than GPR and RFR when noise was added with the MTN being slightly better when compared to the ANN. Our results imply that the performance of the commonly used algorithms in hybrid-RTM inversion are pervasively sensitive to noise. The implication is that more advanced models or approaches are necessary to minimize the impact of noise to improve near real-time and accurate RS monitoring of biophysical trait retrieval. View Full-Text
Theoretical tool bridging cell polarities with development of robust morphologies
Authors: Silas Boye Nissen, Steven Rønhild, Ala Trusina, Kim Sneppen
Date: 2017-11-27 00:00:00
Tasks: Diversity

Despite continual renewal and damages, a multicellular organism is able to maintain its complex morphology. How is this stability compatible with the complexity and diversity of living forms? Looking for answers at protein level may be limiting as diverging protein sequences can result in similar morphologies. Inspired by the progressive role of apical-basal and planar cell polarity in development, we propose that stability, complexity, and diversity are emergent properties in populations of proliferating polarized cells. We support our hypothesis by a theoretical approach, developed to effectively capture both types of polar cell adhesions. When applied to specific cases of development – gastrulation and the origins of folds and tubes – our theoretical tool suggests experimentally testable predictions pointing to the strength of polar adhesion, restricted directions of cell polarities, and the rate of cell proliferation to be major determinants of morphological diversity and stability.
Aircraft engines Remaining Useful Life prediction with an adaptive denoising online sequential Extreme Learning Machine
Authors: Tarek Berghout, Leïla-Haye Mouss, Ouahab Kadri, Lotfi Saïdi, Mohamed Benbouzid
Date: 2020-09-08 00:00:00
Tasks: Denoising, Exponential degradation, Management

Remaining Useful Life (RUL) prediction for aircraft engines based on the available run-to-failure measurements of similar systems becomes more prevalent in Prognostic Health Management (PHM) thanks to the new advanced methods of estimation. However, feature extraction and RUL prediction are challenging tasks, especially for data-driven prognostics. The key issue is how to design a suitable feature extractor that is able to give a raw of time-varying sensors measurements more meaningful representation to enhance prediction accuracy with low computational costs. In this paper, a new Denoising Online Sequential Extreme Learning Machine (DOS-ELM) with double dynamic forgetting factors (DDFF) and Updated Selection Strategy (USS) is proposed. First, depending on the characteristics of the training data that comes from aircraft sensors, robust feature extraction using a modified Denoising Autoencoder (DAE) is introduced to learn important patterns from data. Then, USS is integrated to ensure that only the useful data sequences pass through the training process. Finally, OS-ELM is used to fit the non-accumulative linear degradation function of the engine and to address dynamic programming by trucking the new coming data and forgetting gradually the old ones based on the proposed DDFF. The proposed DOS-ELM is tested on the public dataset of commercial modular aeropropulsion system simulation (C-MAPSS) of a turbofan engine and compared with OS-ELM trained with ordinary Autoencoder (AE), basic OS-ELM and previous works from the literature. Comparison results prove the effectiveness of the new integrated robust feature extraction scheme by showing more stability of the network responses even under random solutions.
Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine
Authors: Tarek Berghout, Leïla-Hayet Mouss, Ouahab Kadri, Lotfi Saïdi, Mohamed Benbouzid
Date: 2020-02-01 00:00:00

The efficient data investigation for fast and accurate remaining useful life prediction of aircraft engines can be considered as a very important task for maintenance operations. In this context, the key issue is how an appropriate investigation can be conducted for the extraction of important information from data-driven sequences in high dimensional space in order to guarantee a reliable conclusion. In this paper, a new data-driven learning scheme based on an online sequential extreme learning machine algorithm is proposed for remaining useful life prediction. Firstly, a new feature mapping technique based on stacked autoencoders is proposed to enhance features representations through an accurate reconstruction. In addition, to attempt into addressing dynamic programming based on environmental feedback, a new dynamic forgetting function based on the temporal difference of recursive learning is introduced to enhance dynamic tracking ability of newly coming data. Moreover, a new updated selection strategy was developed in order to discard the unwanted data sequences and to ensure the convergence of the training model parameters to their appropriate values. The proposed approach is validated on the C-MAPSS dataset where experimental results confirm that it yields satisfactory accuracy and efficiency of the prediction model compared to other existing methods.