pwc-archive/papers-with-abstracts huggingface dataset:
A novel nonlocal partial differential equation model of endothelial progenitor cell cluster formation during the early stages of vasculogenesis
Authors: Chiara Villa, Alf Gerisch, Mark A. J. Chaplain
Date: 2021-05-24 00:00:00

Neovascularisation is essential for tissue development and regeneration, in addition to playing a key role in pathological settings such as ischemia and tumour development. Experimental findings in the past two decades have led to the identification of a new mechanism of neovascularisation, cluster-based vasculogenesis, during which endothelial progenitor cells (EPCs) mobilised from the bone marrow are capable of bridging distant vascular beds in a variety of hypoxic settings in vivo. This process is characterised by the formation of EPC clusters during its early stages and, while much progress has been made in identifying various mechanisms underlying cluster formation, we are still far from a comprehensive description of such spatio-temporal dynamics. In order to achieve this, we propose a novel mathematical model of the early stages of cluster-based vasculogenesis, comprising of a system of nonlocal partial differential equations including key mechanisms such as endogenous chemotaxis, matrix degradation, cell proliferation and cell-to-cell adhesion. We conduct a linear stability analysis on the system, solve the equations numerically, conduct a parametric analysis of the numerical solutions of the 1D problem to investigate the role of underlying dynamics on the speed of cluster formation and the size of clusters, and verify the key results of the parametric analysis with simulations of the 2D problem. Our results, which qualitatively compare with data from in vitro experiments, elucidate the complementary role played by endogenous chemotaxis and matrix degradation in the formation of clusters, and they indicate that previous approaches to the nonlocal modelling of cell-to-cell adhesion, while they capture the aggregating effect of cell-to-cell adhesion, are not sufficient to capture its stabilising effect on clusters, and new continuum cell-adhesion modelling strategies are required.
How particular is the physics of the free energy principle?
Authors: Miguel Aguilera, Beren Millidge, Alexander Tschantz, Christopher L. Buckley
Date: 2021-05-24 00:00:00
Tasks: Bayesian Inference, Variational Inference

The free energy principle (FEP) states that any dynamical system can be interpreted as performing Bayesian inference upon its surrounding environment. In this work, we examine in depth the assumptions required to derive the FEP in the simplest possible set of systems -- weakly-coupled non-equilibrium linear stochastic systems. Specifically, we explore (i) how general the requirements imposed on the statistical structure of a system are and (ii) how informative the FEP is about the behaviour of such systems. We discover that two requirements of the FEP -- the Markov blanket condition (i.e. a statistical boundary precluding direct coupling between internal and external states) and stringent restrictions on its solenoidal flows (i.e. tendencies driving a system out of equilibrium) -- are only valid for a very narrow space of parameters. Suitable systems require an absence of perception-action asymmetries that is highly unusual for living systems interacting with an environment. More importantly, we observe that a mathematically central step in the argument, connecting the behaviour of a system to variational inference, relies on an implicit equivalence between the dynamics of the average states of a system with the average of the dynamics of those states. This equivalence does not hold in general even for linear systems, since it requires an effective decoupling from the system's history of interactions. These observations are critical for evaluating the generality and applicability of the FEP and indicate the existence of significant problems of the theory in its current form. These issues make the FEP, as it stands, not straightforwardly applicable to the simple linear systems studied here and suggest that more development is needed before the theory could be applied to the kind of complex systems that describe living and cognitive processes.
Phase Entrainment by Periodic Stimuli In Silico: A Quantitative Study
Authors: Swapna Sasi, Basabdatta Sen Bhattacharya
Date: 2021-05-22 00:00:00
Tasks: SSVEP

We present a quantitative study of phase entrainment by periodic visual stimuli in a biologically inspired neural network. The objective is to understand the neuronal population dynamics that underlie phase entrainment of brain oscillations by external stimuli, which is used for therapeutic treatment in neurological disorders, for example in Parkinsonian tremor. Yet, the neuronal dynamics underpinning such entrainment is not fully understood. Rhythmic sensory stimulation is one way of studying phase synchronization in the brain. A recent experimental study has reported phase entrainment of brain oscillations during steady state visually evoked potentials (SSVEP), which are scalp electroencephalogram corresponding to periodic stimuli. We have simulated SSVEP-like signals corresponding to periodic pulse input to our in silico model. We have used phase locking values, normalised Shannon entropy and conditional probability as synchronisation indices to show phase synchrony in the neuronal populations. Our experiment demonstrates that the phase synchronisation disappears with jitter in the input inter-pulse intervals, and this would not be the case if the output signal were to be the superposition of the responses to the different input signals. Thus, the phase synchronisation implies entrainment of the network response by the periodic input. Overall, our study shows the plausibility of using biologically inspired in silico models, validated by experimental works, to understand and make testable predictions on brain entrainment as a therapeutic treatment in specific neurological disorders.
Individual risk-aversion responses tune epidemics to critical transmissibility ($R=1$)
Authors: Susanna Manrubia, Damián H. Zanette
Date: 2021-05-21 00:00:00

Changes in human behavior are increasingly recognized as a major determinant of epidemic dynamics. Although collective activity can be modified through imposed measures to control epidemic progression, spontaneous changes can also arise as a result of uncoordinated individual responses to the perceived risk of contagion. Here we introduce a stochastic epidemic model that implements population responses driven by individual- and time-dependent risk-taking propensity. The model reveals an emergent mechanism for the generation of multiple infection waves of decreasing amplitude without the need to consider external modulation of parameters. Successive waves tune the effective reproduction number to its critical value $R=1$. This process is a consequence of the interplay of the fractions of susceptible and infected population and the average risk-taking propensity, as shown by a mean-field approach. The proposed mechanism also shows how, under the threat of contagion, the distribution of individual risk propensities evolves towards a well-defined profile. Successive waves trigger selective sweeps of risk-taking propensity at a pace determined by individual risk reaction rates. This kind of collective, self-generated pressure, may therefore shape risk-aversion profiles associated to epidemics in human groups. The final state is self-organized and generic, independent of the parameter values. We conclude that uncoordinated changes in human behavior can, by themselves, explain major qualitative and quantitative features of the epidemic process, as the emergence of multiple waves and the tendency to remain around $R=1$ observed worldwide after the first few waves of COVID-19.
Centralizing data to unlock whole-cell models
Authors: Yin Hoon Chew, Jonathan R. Karr
Date: 2021-05-22 00:00:00

Despite substantial potential to transform bioscience, medicine, and bioengineering, whole-cell models remain elusive. One of the biggest challenges to whole-cell models is assembling the large and diverse array of data needed to model an entire cell. Thanks to rapid advances in experimentation, much of the necessary data is becoming available. Furthermore, investigators are increasingly sharing their data due to increased emphasis on reproducibility. However, the scattered organization of this data continues to hamper modeling. Toward more predictive models, we highlight the challenges to assembling the data needed for whole-cell modeling and outline how we can overcome these challenges by working together to build a central data warehouse.
Financial Time Series Analysis and Forecasting with HHT Feature Generation and Machine Learning
Authors: Tim Leung, Theodore Zhao
Date: 2021-05-23 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis

We present the method of complementary ensemble empirical mode decomposition (CEEMD) and Hilbert-Huang transform (HHT) for analyzing nonstationary financial time series. This noise-assisted approach decomposes any time series into a number of intrinsic mode functions, along with the corresponding instantaneous amplitudes and instantaneous frequencies. 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 associated instantaneous energy-frequency spectrum to illustrate the properties of various timescales embedded in the original time series. Using HHT, we generate a collection of new features and integrate them into machine learning models, such as regression tree ensemble, support vector machine (SVM), and long short-term memory (LSTM) neural network. Using empirical financial data, we compare several HHT-enhanced machine learning models in terms of forecasting performance.
Empirical Analysis of Service Quality, Reliability and End-User Satisfaction on Electronic Banking in Nigeria
Authors: Esther Enoch Yusuf, Abubakar Bala
Date: 2021-05-24 00:00:00

Today, almost all banks have adopted ICT as a means of enhancing their banking service quality. These banks provide ICT based electronic service which is also called electronic banking, internet banking or online banking etc to their customers. Despite the increasing adoption of electronic banking and it relevance towards end users satisfaction, few investigations has been conducted on factors that enhanced end users satisfaction perception. In this research, an empirical analysis has been conducted on factors that influence electronic banking user's satisfaction perception and the relationship between these factors and the customer's satisfaction. The study will help bank industries in improving the level of their customer's satisfaction and increase the bond between a bank and its customer.
The Problems of Personal Income Tax on Revenue Generation in Gombe State
Authors: Abubakar Bala, Esther Yusuf Enoch, Salisu Yakubu
Date: 2021-05-23 00:00:00

This study examined the problems of personal income tax on revenue generation in Gombe state. The methodology used in data collection is survey, which utilized both primary and secondary types of data. Purposive sampling technique was adopted in selecting a sample of 150 respondents from both employees of state board of internal revenue service and tax payers in the state. The chi square statistics test was used in testing the hypotheses. The study found that tax avoidance/evasion and complete absences of information technology are serious problems affecting revenue generation in the state. It recommends that government should device strict measures in dealing and punishing individuals engage in tax avoidance and evasion. It should also employ the use of information technology as it is the only way problems experience in personal income tax collection can be reduced drastically.
Evaluating the Effect of Credit Collection Policy on Portfolio Quality of Micro-Finance Bank
Authors: Esther Yusuf Enoch, Abubakar Mahmud Digil, Usman Abubakar Arabo
Date: 2021-05-23 00:00:00
Tasks: Descriptive, regression

This study evaluates the effect of collection policy on portfolio quality of microfinance banks in Adamawa State, Nigeria. Real data were collected from 51 credit officers, then a multi-stage sampling method was used to select a sample of 21 respondents from the population (i.e., 51 credit officers). In addition, we used regression analysis and descriptive statistics to analyze the data collected and to also test our proposed hypothesis. Based on the evaluation performed, the results showed that collection policy has a higher effect on portfolio quality. Hence, the study showed that microfinance banks should adhere to strict or stiff debt collection policy as strictness in collection policy help the banks to recover their loans, thereby improving the portfolio quality of the bank.
Pricing multivariate european equity option using gaussian mixture distributions and evt-based copulas
Authors: Hassane Abba Mallam, Diakarya Barro, Yameogo WendKouni, Bisso Saley
Date: 2021-05-21 00:00:00

In this article, we present an approach which allows to take into account the effect of extreme values in the modeling of financial asset returns and in the valorisation of associeted options. Specifically, the marginal distribution of assets returns is modeled by a mixture of two gaussiens distributions. Moreover, we model the joint dependence structure of the returns using an extremal copula which is suitable for our financial data. Applications are made on the Atos and Dassault Systems actions of the CAC40 index. Monte-Carlo method is used to compute the values of some equity options: the call on maximum, the call on minimum, the digital option and the spreads option with the basket (Atos, Dassault systems).
A General Theory for the Evolution of Application Models -- Full version
Authors: H. A. Proper, Th. P. van der Weide
Date: 2021-05-18 00:00:00
Tasks: Object

In this article we focus on evolving information systems. First a delimitation of the concept of evolution is provided, resulting in a first attempt to a general theory for such evolutions. The theory makes a distinction between the underlying information structure at the conceptual level, its evolution on the one hand, and the description and semantics of operations on the information structure and its population on the other hand. Main issues within this theory are object typing, type relatedness and identification of objects. In terms of these concepts, we propose some axioms on the well-formedness of evolution. In this general theory, the underlying data model is a parameter, making the theory applicable for a wide range of modelling techniques, including object-role modelling and object oriented techniques.
Domain Consensus Clustering for Universal Domain Adaptation
Authors: Guangrui Li, Guoliang Kang, Yi Zhu, Yunchao Wei, Yi Yang
Date: 2021-06-05 00:00:00
Proceeding: CVPR 2021 1
Tasks: Clustering, Domain Adaptation, domain classification, Open Set Learning, Partial Domain Adaptation, Universal Domain Adaptation

In this paper, we investigate Universal Domain Adaptation (UniDA) problem, which aims to transfer the knowledge from source to target under unaligned label space. The main challenge of UniDA lies in how to separate common classes (i.e., classes shared across domains), from private classes (i.e., classes only exist in one domain). Previous works treat the private samples in the target as one generic class but ignore their intrinsic structure. Consequently, the resulting representations are not compact enough in the latent space and can be easily confused with common samples. To better exploit the intrinsic structure of the target domain, we propose Domain Consensus Clustering (DCC), which exploits the domain consensus knowledge to discover discriminative clusters on both common samples and private ones. Specifically, we draw the domain consensus knowledge from two aspects to facilitate the clustering and the private class discovery, i.e., the semantic-level consensus, which identifies the cycle-consistent clusters as the common classes, and the sample-level consensus, which utilizes the cross-domain classification agreement to determine the number of clusters and discover the private classes. Based on DCC, we are able to separate the private classes from the common ones, and differentiate the private classes themselves. Finally, we apply a class-aware alignment technique on identified common samples to minimize the distribution shift, and a prototypical regularizer to inspire discriminative target clusters. 
Experiments on four benchmarks demonstrate DCC significantly outperforms previous state-of-the-arts.
Unsupervised Offline Changepoint Detection Ensembles
Authors: Katser I, Kozitsin V, Lobachev V, Maksimov I.
Date: 2021-05-09 00:00:00
Tasks: Anomaly Detection, Change Point Detection, Model Selection, Outlier Detection

Offline changepoint detection (CPD) algorithms are used for signal segmentation in an optimal way. Generally, these algorithms are based on the assumption that signal’s changed statistical properties are known, and the appropriate models (metrics, cost functions) for changepoint detection are used. Otherwise, the process of proper model selection can become laborious and time-consuming with uncertain results. Although an ensemble approach is well known for increasing the robustness of the individual algorithms and dealing with mentioned challenges, it is weakly formalized and much less highlighted for CPD problems than for outlier detection or classification problems. This paper proposes an unsupervised CPD ensemble (CPDE) procedure with the pseudocode of the particular proposed ensemble algorithms and the link to their Python realization. The approach’s novelty is in aggregating several cost functions before the changepoint search procedure running during the offline analysis. The numerical experiment showed that the proposed CPDE outperforms non-ensemble CPD procedures. Additionally, we focused on analyzing common CPD algorithms, scaling, and aggregation functions, comparing them during the numerical experiment. The results were obtained on the two anomaly benchmarks that contain industrial faults and failures—Tennessee Eastman Process (TEP) and Skoltech Anomaly Benchmark (SKAB). One of the possible applications of our research is the estimation of the failure time for fault identification and isolation problems of the technical diagnostics.
Online Forecasting and Anomaly Detection Based on the ARIMA Model
Authors: Kozitsin V, Katser I, Lakontsev D.
Date: 2021-04-02 00:00:00
Tasks: Anomaly Detection, Change Point Detection, Diagnostic, Fault Detection

Real-time diagnostics of complex technical systems such as power plants are critical to keep the system in its working state. An ideal diagnostic system must detect any fault in advance and predict the future state of the technical system, so predictive algorithms are used in the diagnostics. This paper proposes a novel, computationally simple algorithm based on the Auto-Regressive Integrated Moving Average model to solve anomaly detection and forecasting problems. The good performance of the proposed algorithm was confirmed in numerous numerical experiments for both anomaly detection and forecasting problems. Moreover, a description of the Autoregressive Integrated Moving Average Fault Detection (ARIMAFD) library, which includes the proposed algorithms, is provided in this paper. The developed algorithm proves to be an efficient algorithm and can be applied to problems related to anomaly detection and technological parameter forecasting in real diagnostic systems.
Physiological Parameter Monitoring from Optical Recordings with a Mobile Phone
Authors: Christopher G. Scully, Student Member, Jinseok Lee, Joseph Meyer, Alexander M. Gorbach, Domhnull Granquist-Fraser, Yitzhak Mendelson, Member, and Ki H. Chon, Senior Member, IEEE
Date: 2011-07-29 00:00:00
Tasks: Heart rate estimation, Heart Rate Variability, SpO2 estimation

We show that a mobile phone can serve as an accurate monitor for several physiological variables, based on its ability to record and analyze the varying color signals of a fingertip placed in contact with its optical sensor. We confirm the accuracy of measurements of breathing rate, cardiac R-R intervals, and blood oxygen saturation, by comparisons to standard methods for making such measurements (respiration belts, ECGs, and pulse-oximeters, respectively). Measurement of respiratory rate uses a previously reported algorithm developed for use with a pulse-oximeter, based on amplitude and frequency modulation sequences within the light signal. We note that this technology can also be used with recently developed algorithms for detection of atrial fibrillation or blood loss.

Index Terms: non-invasive monitoring, telemonitoring, heart rate variability (HRV), breathing rate, oxygen saturation
Unsupervised Scale-consistent Depth Learning from Video
Authors: Jia-Wang Bian, Huangying Zhan, Naiyan Wang, Zhichao Li, Le Zhang, Chunhua Shen, Ming-Ming Cheng, Ian Reid
Date: 2021-05-25 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation, Monocular Visual Odometry, Simultaneous Localization and Mapping

We propose a monocular depth estimator SC-Depth, which requires only unlabelled videos for training and enables the scale-consistent prediction at inference time. Our contributions include: (i) we propose a geometry consistency loss, which penalizes the inconsistency of predicted depths between adjacent views; (ii) we propose a self-discovered mask to automatically localize moving objects that violate the underlying static scene assumption and cause noisy signals during training; (iii) we demonstrate the efficacy of each component with a detailed ablation study and show high-quality depth estimation results in both KITTI and NYUv2 datasets. Moreover, thanks to the capability of scale-consistent prediction, we show that our monocular-trained deep networks are readily integrated into the ORB-SLAM2 system for more robust and accurate tracking. The proposed hybrid Pseudo-RGBD SLAM shows compelling results in KITTI, and it generalizes well to the KAIST dataset without additional training. Finally, we provide several demos for qualitative evaluation.
Matching Targets Across Domains with RADON, the Re-Identification Across Domain Network
Authors: Cassandra Burgess, Cordelia Neisinger, Rafael Dinner
Date: 2021-05-25 00:00:00
Tasks: Person Identification

We present a novel convolutional neural network that learns to match images of an object taken from different viewpoints or by different optical sensors. Our Re-Identification Across Domain Network (RADON) scores pairs of input images from different domains on similarity. Our approach extends previous work on Siamese networks and modifies them to more challenging use cases, including low- and no-shot learning, in which few images of a specific target are available for training. RADON shows strong performance on cross-view vehicle matching and cross-domain person identification in a no-shot learning environment.