pwc-archive/papers-with-abstracts huggingface dataset:
Bank Business Models, Size, and Profitability
Authors: F. Bolivar, M. A. Duran, A. Lozano-Vivas
Date: 2024-01-22 00:00:00

To examine the relation between profitability and business models (BMs) across bank sizes, the paper proposes a research strategy based on machine learning techniques. This strategy allows for analyzing whether size and profit performance underlie BM heterogeneity, with BM identification being based on how the components of the bank portfolio contribute to profitability. The empirical exercise focuses on the European Union banking system. Our results suggest that banks with analogous levels of performance and different sizes share strategic features. Additionally, high capital ratios seem compatible with high profitability if banks, relative to their size peers, adopt a standard retail BM.
The Risk-Return Relation in the Corporate Loan Market
Authors: Miguel A. Duran
Date: 2024-01-22 00:00:00
Tasks: Relation

This paper analyzes the hypothesis that returns play a risk-compensating role in the market for corporate revolving lines of credit. Specifically, we test whether borrower risk and the expected return on these debt instruments are positively related. Our main findings support this prediction, in contrast to the only previous work that examined this problem two decades ago. Nevertheless, we find evidence of mispricing regarding the risk of deteriorating firms using their facilities more intensively and during the subprime crisis.
Pricing and Usage: An Empirical Analysis of Lines of Credit
Authors: Miguel A. Duran
Date: 2024-01-22 00:00:00

The hypothesis that committed revolving credit lines with fixed spreads can provide firms with interest rate insurance is a standard feature of models on these credit facilities' interest rate structure. Nevertheless, this hypothesis has not been tested. Its empirical examination is the main contribution of this paper. To perform this analysis, and given the unavailability of data, we hand-collect data on usage at the credit line level itself. The resulting dataset enables us also to take into account characteristics of credit lines that have been ignored by previous research. One of them is that credit lines can have simultaneously fixed and performance-based spreads.
Are Charter Value and Supervision Aligned? A Segmentation Analysis
Authors: Juan Aparicio, Miguel A. Duran, Ana Lozano-Vivas, Jesus T. Pastor
Date: 2024-01-22 00:00:00

Previous work suggests that the charter value hypothesis is theoretically grounded and empirically supported, but not universally. Accordingly, this paper aims to perform an analysis of the relations between charter value, risk taking, and supervision, taking into account the relations' complexity. Specifically, using the CAMELS rating system as a general framework for supervision, we study how charter value relates to risk and supervision by means of classification and regression tree analysis. The sample covers the period 2005-2016 and consists of listed banks in countries that were members of the Eurozone when it came into existence, along with Greece. To evaluate the crisis consequences, we also separately analyze four subperiods and countries that required financial aid from third parties and those that did not so, along with large and small banks. Our results reflect the complexity of the relations between charter value, supervision, and risk. Indeed, supervision and charter value seem aligned regarding only some types of risk
Beveridgean Phillips Curve
Authors: Pascal Michaillat, Emmanuel Saez
Date: 2024-01-23 00:00:00

This paper proposes a new, Beveridgean model of the Phillips curve. While the New Keynesian Phillips Curve is based on monopolistic pricing under price-adjustment costs, the Beveridgean Phillips curve is based on directed-search pricing under price-adjustment costs. Under directed-search pricing, prices respond to slack instead of marginal costs. The Beveridgean Phillips curve links the inflation gap to the unemployment gap, with the following properties. First, it produces the divine coincidence: it guarantees that the rate of inflation is on target whenever the rate of unemployment is efficient. Second, whenever the Beveridge curve shifts, the Phillips curve shifts if it is formulated with inflation and unemployment, but it remains unaffected if it is formulated with inflation and labor-market tightness. Third, the Phillips curve displays a kink at the point of divine coincidence if we assume that wage decreases -- which reduce workers' morale -- are more costly to producers than price increases -- which upset customers. These three properties describe recent US data well.
An Efficient Algorithm for Spatial-Spectral Partial Volume Compartment Mapping with Applications to Multicomponent Diffusion and Relaxation MRI
Authors: Yunsong Liu, Debdut Mandal, Congyu Liao, Kawin Setsompop, Justin P. Haldar
Date: 2024-01-23 00:00:00
Tasks: Magnetic Resonance Fingerprinting

We introduce a new algorithm to solve a regularized spatial-spectral image estimation problem. Our approach is based on the linearized alternating directions method of multipliers (LADMM), which is a variation of the popular ADMM algorithm. Although LADMM has existed for some time, it has not been very widely used in the computational imaging literature. This is in part because there are many possible ways of mapping LADMM to a specific optimization problem, and it is nontrivial to find a computationally efficient implementation out of the many competing alternatives. We believe that our proposed implementation represents the first application of LADMM to the type of optimization problem considered in this work (involving a linear-mixture forward model, spatial regularization, and nonnegativity constraints). We evaluate our algorithm in a variety of multiparametric MRI partial volume mapping scenarios (diffusion-relaxation, relaxation-relaxation, relaxometry, and fingerprinting), where we consistently observe substantial ($\sim$3$\times$-50$\times$) speed improvements. We expect this to reduce barriers to using spatially-regularized partial volume compartment mapping methods. Further, the considerable improvements we observed also suggest the potential value of considering LADMM for a broader set of computational imaging problems.
A database of physical therapy exercises with variability of execution collected by wearable sensors
Authors: Sara García-de-Villa, Ana Jiménez-Martín, Juan Jesús García-Domínguez
Date: 2024-01-23 00:00:00

This document introduces the PHYTMO database, which contains data from physical therapies recorded with inertial sensors, including information from an optical reference system. PHYTMO includes the recording of 30 volunteers, aged between 20 and 70 years old. A total amount of 6 exercises and 3 gait variations were recorded. The volunteers performed two series with a minimum of 8 repetitions in each one. PHYTMO includes magneto-inertial data, together with a highly accurate location and orientation in the 3D space provided by the optical system. The files were stored in CSV format to ensure its usability. The aim of this dataset is the availability of data for two main purposes: the analysis of techniques for the identification and evaluation of exercises using inertial sensors and the validation of inertial sensor-based algorithms for human motion monitoring. Furthermore, the database stores enough data to apply Machine Learning-based algorithms. The participants' age range is large enough to establish age-based metrics for the exercises evaluation or the study of differences in motions between different groups.
Nonlinear Distortion Radiated from Large Arrays and Active Reconfigurable Intelligent Surfaces
Authors: Nikolaos Kolomvakis, Alva Kosasih, Emil Björnson
Date: 2024-01-23 00:00:00
Tasks: Scheduling

Extremely large aperture arrays (ELAAs) and reconfigurable intelligent surfaces (RISs) are candidate enablers to realize connectivity goals for the sixth-generation (6G) wireless networks. For instance, ELAAs can provide orders-of-magnitude higher area throughput compared to what massive multiple-input multiple-output (MIMO) can deliver through spatial multiplexing, while RISs can improve the propagation conditions over wireless channels but a passively reflecting RIS must be large to be effective. Active RIS with amplifiers can deal with this issue. In this paper, we analyze the distortion generated by nonlinear amplifiers in both ELAAs and active RIS. We derive analytical expressions for the angular directions and depth of nonlinear distortion in both near-field and far-field channels. These insights are then used in a distortion-aware scheduling scheme that predicts the beamforming directions of the distortion and strategically allocates users in frequency to minimize its impact. Numerical results validate our theoretical analysis and compare distortion-aware and distortion-unaware scheduling methods, highlighting the benefits of accounting for nonlinearities. We conclude that nonlinearities can both create in-band and out-of-band distortion that is beamformed in entirely new directions and distances from the transmitter.
Towards Adaptive Subspace Detection in Heterogeneous Environment
Authors: Aref Miri Rekavandi
Date: 2024-01-23 00:00:00

In this paper, we aim to take one step forward to the scenario where an adaptive subspace detection framework is required to detect subspace signals in non-stationary environments. Despite the fact that this scenario is more realistic, the existing studies in detection theory mostly rely on homogeneous, or partially homogeneous assumptions in the environments for their design process meaning that the covariance matrices of primary and secondary datasets are exactly the same or different up to a scale factor. In this study, we allow some partial information of the train covariance matrix to be shared with the primary dataset, but the covariance matrix in the primary set can be entirely different in the structure. This is particularly true in radar systems where the secondary set is collected in distinct spatial and time zones. We design a Generalized Likelihood Ratio Test (GLRT) based detector where the noise is multivariate Gaussian and the subspace interference is assumed to be known. The simulation results reveal the superiority of the proposed approach in comparison with conventional detectors for such a realistic and general scenario.
Distributionally Robust Receive Combining
Authors: Shixiong Wang, Wei Dai, Geoffrey Ye Li
Date: 2024-01-22 00:00:00
Tasks: Integrated sensing and communication

This article investigates signal estimation in wireless transmission (i.e., receive combining) from the perspective of statistical machine learning, where the transmit signals may be from an integrated sensing and communication system; that is, 1) signals may be not only discrete constellation points but also arbitrary complex values; 2) signals may be spatially correlated. Particular attention is paid to handling various uncertainties such as the uncertainty of the transmit signal covariance, the uncertainty of the channel matrix, the uncertainty of the channel noise covariance, the existence of channel impulse noises, the non-ideality of the power amplifiers, and the limited sample size of pilots. To proceed, a distributionally robust receive combining framework that is insensitive to the above uncertainties is proposed, which reveals that channel estimation is not a necessary operation. For optimal linear estimation, the proposed framework includes several existing combiners as special cases such as diagonal loading and eigenvalue thresholding. For optimal nonlinear estimation, estimators are limited in reproducing kernel Hilbert spaces and neural network function spaces, and corresponding uncertainty-aware solutions (e.g., kernelized diagonal loading) are derived. In addition, we prove that the ridge and kernel ridge regression methods in machine learning are distributionally robust against diagonal perturbation in feature covariance.
A distribution-guided Mapper algorithm
Authors: Yuyang Tao, Shufei Ge
Date: 2024-01-19 00:00:00

Motivation: The Mapper algorithm is an essential tool to explore shape of data in topology data analysis. With a dataset as an input, the Mapper algorithm outputs a graph representing the topological features of the whole dataset. This graph is often regarded as an approximation of a reeb graph of data. The classic Mapper algorithm uses fixed interval lengths and overlapping ratios, which might fail to reveal subtle features of data, especially when the underlying structure is complex. Results: In this work, we introduce a distribution guided Mapper algorithm named D-Mapper, that utilizes the property of the probability model and data intrinsic characteristics to generate density guided covers and provides enhanced topological features. Our proposed algorithm is a probabilistic model-based approach, which could serve as an alternative to non-prababilistic ones. Moreover, we introduce a metric accounting for both the quality of overlap clustering and extended persistence homology to measure the performance of Mapper type algorithm. Our numerical experiments indicate that the D-Mapper outperforms the classical Mapper algorithm in various scenarios. We also apply the D-Mapper to a SARS-COV-2 coronavirus RNA sequences dataset to explore the topological structure of different virus variants. The results indicate that the D-Mapper algorithm can reveal both vertical and horizontal evolution processes of the viruses. Availability: Our package is available at https://github.com/ShufeiGe/D-Mapper.
A dynamic model to study the potential TB infections and assessment of control strategies in China
Authors: Chuanqing Xu, Kedeng Cheng, Songbai Guo, Dehui Yuan, Xiaoyu Zhao
Date: 2024-01-23 00:00:00

China is one of the countries with a high burden of tuberculosis, and although the number of new cases of tuberculosis has been decreasing year by year, the number of new infections per year has remained high and the diagnosis rate of tuberculosis-infected patients has remained low. Based on the analysis of TB infection data, we develop a model of TB transmission dynamics that include potentially infected individuals and BCG vaccination, fit the model parameters to the data on new TB cases, calculate the basic reproduction number \mathcal{R}_v= 0.4442. A parametric sensitivity analysis of \mathcal{R}_v is performed, and we obtained the correlation coefficients of BCG vaccination rate and effectiveness rate with \mathcal{R}_v as -0.810, -0.825. According to the model, we estimate that there are 614,186 (95% CI [562,631,665,741]) potentially infected TB cases in China, accounting for about 39.5% of the total number of TB cases. We assess the feasibility of achieving the goals of the WHO strategy to end tuberculosis in China and find that reducing the number of new cases by 90 per cent by 2035 is very difficult with the current tuberculosis control measures. However, with an effective combination of control measures such as increased detection of potentially infected persons, improved drug treatment, and reduction of overall exposure to tuberculosis patients, it is feasible to reach the WHO strategic goal of ending tuberculosis by 2035.
Machine Learning Modeling Of SiRNA Structure-Potency Relationship With Applications Against Sars-Cov-2 Spike Gene
Authors: Damilola Oshunyinka
Date: 2024-01-18 00:00:00
Tasks: Drug Discovery

The pharmaceutical Research and development (R&D) process is lengthy and costly, taking nearly a decade to bring a new drug to the market. However, advancements in biotechnology, computational methods, and machine learning algorithms have the potential to revolutionize drug discovery, speeding up the process and improving patient outcomes. The COVID-19 pandemic has further accelerated and deepened the recognition of the potential of these techniques, especially in the areas of drug repurposing and efficacy predictions. Meanwhile, non-small molecule therapeutic modalities such as cell therapies, monoclonal antibodies, and RNA interference (RNAi) technology have gained importance due to their ability to target specific disease pathways and/or patient populations. In the field of RNAi, many experiments have been carried out to design and select highly efficient siRNAs. However, the established patterns for efficient siRNAs are sometimes contradictory and unable to consistently determine the most potent siRNA molecules against a target mRNA. Thus, this paper focuses on developing machine learning models based on the cheminformatics representation of the nucleotide composition (i.e. AUTGC) of siRNA to predict their potency and aid the selection of the most efficient siRNAs for further development. The PLS (Partial Least Square) and SVR (Support Vector Regression) machine learning models built in this work outperformed previously published models. These models can help in predicting siRNA potency and aid in selecting the best siRNA molecules for experimental validation and further clinical development. The study has demonstrated the potential of AI/machine learning models to help expedite siRNA-based drug discovery including the discovery of potent siRNAs against SARS-CoV-2.
Analysis of a detailed multi-stage model of stochastic gene expression using queueing theory and model reduction
Authors: Muhan Ma, Juraj Szavits-Nossan, Abhyudai Singh, Ramon Grima
Date: 2024-01-23 00:00:00
Tasks: model

We introduce a biologically detailed, stochastic model of gene expression describing the multiple rate-limiting steps of transcription, nuclear pre-mRNA processing, nuclear mRNA export, cytoplasmic mRNA degradation and translation of mRNA into protein. The processes in sub-cellular compartments are described by an arbitrary number of processing stages, thus accounting for a significantly finer molecular description of gene expression than conventional models such as the telegraph, two-stage and three-stage models of gene expression. We use two distinct tools, queueing theory and model reduction using the slow-scale linear-noise approximation, to derive exact or approximate analytic expressions for the moments or distributions of nuclear mRNA, cytoplasmic mRNA and protein fluctuations, as well as lower bounds for their Fano factors in steady-state conditions. We use these to study the phase diagram of the stochastic model; in particular we derive parametric conditions determining three types of transitions in the properties of mRNA fluctuations: from sub-Poissonian to super-Poissonian noise, from high noise in the nucleus to high noise in the cytoplasm, and from a monotonic increase to a monotonic decrease of the Fano factor with the number of processing stages. In contrast, protein fluctuations are always super-Poissonian and show weak dependence on the number of mRNA processing stages. Our results delineate the region of parameter space where conventional models give qualitatively incorrect results and provide insight into how the number of processing stages, e.g. the number of rate-limiting steps in initiation, splicing and mRNA degradation, shape stochastic gene expression by modulation of molecular memory.
Understanding Cellular Noise with Optical Perturbation and Deep Learning
Authors: Chuanbo Liu, Yu Fu, Lu Lin, Elliot L. Elson, Jin Wang
Date: 2024-01-23 00:00:00
Tasks: Deep Learning

Noise plays a crucial role in the regulation of cellular and organismal function and behavior. Exploring noise's impact is key to understanding fundamental biological processes, such as gene expression, signal transduction, and the mechanisms of development and evolution. Currently, a comprehensive method to quantify dynamical behavior of cellular noise within these biochemical systems is lacking. In this study, we introduce an optically-controlled perturbation system utilizing the light-sensitive Phytochrome B (PhyB) from \textit{Arabidopsis thaliana}, which enables precise noise modulation with high spatial-temporal resolution. Our system exhibits exceptional sensitivity to light, reacting consistently to pulsed light signals, distinguishing it from other photoreceptor-based promoter systems that respond to a single light wavelength. To characterize our system, we developed a stochastic model for phytochromes that accounts for photoactivation/deactivation, thermal reversion, and the dynamics of the light-activated gene promoter system. To precisely control our system, we determined the rate constants for this model using an omniscient deep neural network that can directly map rate constant combinations to time-dependent state joint distributions. By adjusting the activation rates through light intensity and degradation rates via N-terminal mutagenesis, we illustrate that out optical-controlled perturbation can effectively modulate molecular expression level as well as noise. Our results highlight the potential of employing an optically-controlled gene perturbation system as a noise-controlled stimulus source. This approach, when combined with the analytical capabilities of a sophisticated deep neural network, enables the accurate estimation of rate constants from observational data in a broad range of biochemical reaction networks.
Modular control of Boolean network models
Authors: David Murrugarra, Alan Veliz-Cuba, Elena Dimitrova, Claus Kadelka, Matthew Wheeler, Reinhard Laubenbacher
Date: 2024-01-23 00:00:00

The concept of control is crucial for effectively understanding and applying biological network models. Key structural features relate to control functions through gene regulation, signaling, or metabolic mechanisms, and computational models need to encode these. Applications often focus on model-based control, such as in biomedicine or metabolic engineering. In a recent paper, the authors developed a theoretical framework of modularity in Boolean networks, which lead to a canonical semidirect product decomposition of these systems. In this paper, we present an approach to model-based control that exploits this modular structure, as well as the canalizing features of the regulatory mechanisms. We show how to identify control strategies from the individual modules, and we present a criterion based on canalizing features of the regulatory rules to identify modules that do not contribute to network control and can be excluded. For even moderately sized networks, finding global control inputs is computationally challenging. Our modular approach leads to an efficient approach to solving this problem. We apply it to a published Boolean network model of blood cancer large granular lymphocyte (T-LGL) leukemia to identify a minimal control set that achieves a desired control objective.