pwc-archive/papers-with-abstracts huggingface dataset:
Money Creation and Banking: Theory and Evidence
Authors: Heon Lee
Date: 2021-09-30 00:00:00

This paper studies the role of banks' money creation in monetary transmission. I develop a monetary-search model where demand for the monetary base and the money multiplier are endogenously determined through banks' money creation. The model and data show that reserves are not independent of interest rate policy, even with ample reserves. Furthermore, short-term rates and interest on reserves play distinct roles in monetary transmission. I evaluate the theory by matching it with data, and the calibrated model can account for the evolution of reserves, excess reserves, and the money multiplier
The Limits of Personalization in Assortment Optimization
Authors: Guillermo Gallego, Gerardo Berbeglia
Date: 2021-09-30 00:00:00
Tasks: Assortment Optimization, Discrete Choice Models

To study the limits of personalization, we introduce the notion of a clairvoyant firm that can read the mind of consumers and sell them the highest revenue product that they are willing to buy. We show how to compute the expected revenue of the clairvoyant firm for a class of rational discrete choice models, and develop prophet-type inequalities that provide performance guarantees for the expected revenue of the traditional assortment optimization firm (a TAOP firm) relative to the clairvoyant firm, and therefore to any effort to personalize assortments. In particular, we show that the expected revenue of the clairvoyant firm cannot exceed twice the expected revenue of the TAOP for the RCS model, the MNL, the GAM and the Nested Logit Model. On the other hand, there are random utility models for which personalized assortments can earn up to $n$ times more than a TAOP firm, where $n$ is the number of products. Our numerical studies indicate that when the mean utilities of the products are heterogeneous among consumer types, and the variance of the utilities is small, firms can gain substantial benefits from personalized assortments. We support these observations, and others, with theoretical findings. While the consumers surplus can potentially be larger under personalized assortments, clairvoyant firms with pricing power can extract all surplus, and earn arbitrarily more than traditional firms that optimize over prices but do not personalize them. For the price-aware MNL, however, a clairvoyant firm can earn at most $e$ times more than a traditional firm.
Subcarrier Number and Indices-Based KeyGeneration for Future Wireless Networks
Authors: Haji M. Furqan, Jehad M. Hamamreh, Huseyin Arslan
Date: 2021-09-30 00:00:00

Physical layer key generation from the wireless channel is an emerging area of interest to provide confidentiality and authentication. One of the main challenges in this domain is to increase the length of the secret key while maintaining its randomness and uniformity. In this work, new dimensions for wireless channel-based key generation are proposed for orthogonal frequency division multiplexing (OFDM) systems. The novel perspective of the proposed work lies in the generation of key bits not only from the magnitudes of OFDM subchannels as it has conventionally been done but also from the number and positions of those subchannels whose channel gains are above the mean of the respective subblock. The effectiveness of the proposed algorithms is evaluated in terms of key generation rate and key mismatch rate. Additionally, a statistical test suite offered by the National Institute of Standards and Technology is used to evaluate the randomness of the generated key bits. It is shown in the simulation results that the involvement of the proposed dimensions can double the key generation rate compared to conventional algorithms.
DOA Estimation in Nonuniform Sensor Noise
Authors: Majdoddin Esfandiari, Sergiy A. Vorobyov
Date: 2021-09-30 00:00:00

The problem of direction-of-arrival (DOA) estimation in the presence of nonuniform sensor noise is considered and a novel algorithm is developed. The algorithm consists of three phases. First, the diagonal nonuniform sensor noise covariance matrix is estimated using an iterative procedure that requires only few iterations to obtain an accurate estimate. The asymptotic variance of one iteration is derived for the proposed noise covariance estimator. Second, a forward-only rooting-based DOA estimator as well as its forward-backward averaging extension are developed for DOA estimation. The DOA estimators take advantage of using second-order statistics of signal subspace perturbation in constructing a weight matrix of a properly designed generalized least squares minimization problem. Despite the fact that these DOA estimators are iterative, only a few iterations are sufficient to reach accurate results. The asymptotic performance of these DOA estimators is also investigated. Third, a newly designed DOA selection strategy with reasonable computational cost is developed to select L actual sources out of 2L candidates generated at the second phase. Numerical simulations are conducted in order to establish the considerable superiority of the proposed algorithm compared to the the existing state-of-the-art methods in challenging scenarios in both cases of uniform and nonuniform sensor noise.
Neural Networks-based Equalizers for Coherent Optical Transmission: Caveats and Pitfalls
Authors: Pedro J. Freire, Antonio Napoli, Bernhard Spinnler, Nelson Costa, Sergei K. Turitsyn, Jaroslaw E. Prilepsky
Date: 2021-09-30 00:00:00

This paper performs a detailed, multi-faceted analysis of key challenges and common design caveats related to the development of efficient neural networks (NN) nonlinear channel equalizers in coherent optical communication systems. Our study aims to guide researchers and engineers working in this field. We start by clarifying the metrics used to evaluate the equalizers' performance, relating them to the loss functions employed in the training of the NN equalizers. The relationships between the channel propagation model's accuracy and the performance of the equalizers are addressed and quantified. Next, we assess the impact of the order of the pseudo-random bit sequence used to generate the -- numerical and experimental -- data as well as of the DAC memory limitations on the operation of the NN equalizers both during the training and validation phases. Finally, we examine the critical issues of overfitting limitations, the difference between using classification instead of regression, and batch-size-related peculiarities. We conclude by providing analytical expressions for the equalizers' complexity evaluation in the digital signal processing (DSP) terms and relate the metrics to the processing latency.
Sensing Integrated DFT-Spread OFDM Waveform and Deep Learning-powered Receiver Design for Terahertz Integrated Sensing and Communication Systems
Authors: Yongzhi Wu, Filip Lemic, Chong Han, Zhi Chen
Date: 2021-09-30 00:00:00
Tasks: Integrated sensing and communication, ISAC

Terahertz (THz) communications are envisioned as a key technology of next-generation wireless systems due to its ultra-broad bandwidth. One step forward, THz integrated sensing and communication (ISAC) system can realize both unprecedented data rates and millimeter-level accurate sensing. However, THz ISAC meets stringent challenges on waveform and receiver design to fully exploit the peculiarities of THz channel and transceivers. In this work, a sensing integrated discrete Fourier transform spread orthogonal frequency division multiplexing (SI-DFT-s-OFDM) system is proposed for THz ISAC, which can provide lower peak-to-average power ratio than OFDM and is adaptive to flexible delay spread of the THz channel. Without compromising communication capabilities, the proposed SI-DFT-s-OFDM realizes millimeter-level range estimation and decimeter-per-second-level velocity estimation accuracy. In addition, the bit error rate (BER) performance is improved by 5 dB gain at the $10^{-3}$ BER level compared with OFDM. At the receiver, a deep learning based ISAC receiver with two neural networks is developed to recover transmitted data and estimate target range and velocity, while mitigating the imperfections and non-linearities of THz systems. Extensive simulation results demonstrate that the proposed deep learning methods can realize mutually enhanced performance for communication and sensing, and is robust against Doppler effects, phase noise, and multi-target estimation.
Machine Learning Assisted Phase-less Millimeter-Wave Beam Alignment in Multipath Channels
Authors: Benjamin W. Domae, Ruifu Li, Danijela Cabric
Date: 2021-09-29 00:00:00
Tasks: BIG-bench Machine Learning, Compressive Sensing

Communication systems at millimeter-wave (mmW) and sub-terahertz frequencies are of increasing interest for future high-data rate networks. One critical challenge faced by phased array systems at these high frequencies is the efficiency of the initial beam alignment, typically using only phase-less power measurements due to high frequency oscillator phase noise. Traditional methods for beam alignment require exhaustive sweeps of all possible beam directions, thus scale communications overhead linearly with antenna array size. For better scaling with the large arrays required at high mmW bands, compressive sensing methods have been proposed as their overhead scales logarithmically with the array size. However, algorithms utilizing machine learning have shown more efficient and more accurate alignment when using real hardware due to array impairments. Additionally, few existing phase-less beam alignment algorithms have been tested over varied secondary path strength in multipath channels. In this work, we introduce a novel, machine learning based algorithm for beam alignment in multipath environments using only phase-less received power measurements. We consider the impacts of phased array sounding beam design and machine learning architectures on beam alignment performance and validate our findings experimentally using 60 GHz radios with 36-element phased arrays. Using experimental data in multipath channels, our proposed algorithm demonstrates an 88\% reduction in beam alignment overhead compared to an exhaustive search and at least a 62\% reduction in overhead compared to existing compressive methods.
Visualisation to Explain Personal Health Trends in Smart Homes
Authors: Glenn Forbes, Stewart Massie, Susan Craw
Date: 2021-09-28 00:00:00
Tasks: Decision Making, Explainable Artificial Intelligence (XAI)

An ambient sensor network is installed in Smart Homes to identify low-level events taking place by residents, which are then analysed to generate a profile of activities of daily living. These profiles are compared to both the resident's typical profile and to known "risky" profiles to support recommendation of evidence-based interventions. Maintaining trust presents an XAI challenge because the recommendations are not easily interpretable. Trust in the system can be improved by making the decision-making process more transparent. We propose a visualisation workflow which presents the data in clear, colour-coded graphs.
Analysis of a functional response with prey-density dependent handling time from an evolutionary perspective
Authors: Cecilia Berardo, Stefan Geritz
Date: 2021-09-30 00:00:00

Theoretical models show that in a non-constant environment two predator species feeding on one and the same prey may coexist because the two species occupy different temporal niches: the one with the longer handling time has the advantage when prey is rare so that holding on to the same catch is the better option, while the species with the shorter handling time has the advantage when prey is common and easy to catch. In this paper we address the question whether a predator species with a handling time that is not fixed but decreases with prey density could be selective superior regardless of whether the prey is rare or common, as such predator would be able to occupy both temporal niches all by itself. To that end we study the Rosenzweig-MacArthur model with a modified Holling type II functional response with a density dependent handling time and a handling time dependent conversion factor. We find that the population dynamics tend to be richer than that of the standard model with fixed handling times because of the possibility of multiple positive equilibria and positive attractors. Increasing the strength of the density dependence eventually stabilises the population. Using the framework of adaptive dynamics, we study the evolution of the strength of the density dependence. We find that a predator with even a weakly density dependent handling time can invade both monomorphic and dimorphic populations of predators with fixed handling times. Eventually the strength of the density dependence of the handling time evolves to a level where population cycles are lost, with that the possibility of predator coexistence as well.
Probing conformational dynamics of antibodies with geometric simulations
Authors: Andrejs Tucs, Koji Tsuda, Adnan Sljoka
Date: 2021-09-29 00:00:00

This chapter describes the application of constrained geometric simulations for prediction of antibody structural dynamics. We utilize constrained geometric simulations method FRODAN, which is a low computational complexity alternative to Molecular Dynamics (MD) simulations that can rapidly explore flexible motions in protein structures. FRODAN is highly suited for conformational dynamics analysis of large proteins, complexes, intrinsically disordered proteins and dynamics that occurs on longer biologically relevant time scales which are normally inaccessible to classical MD simulations. This approach predicts protein dynamics at an all-atom scale while retaining realistic covalent bonding, maintaining dihedral angles in energetically good conformations while avoiding steric clashes in addition to performing other geometric and stereochemical criteria checks. In this chapter, we apply FRODAN to showcase its applicability for probing functionally relevant dynamics of IgG2a, including large amplitude domain-domain motions and motions of complementarity determining region (CDR) loops. As was suggested in previous experimental studies, our simulations show that antibodies can explore a large range of conformational space.
Tumor Spheroid Chemotaxis in Epidermal Growth Factor Gradients Revealed by a 3D Microfluidic Device
Authors: Young Joon Suh, Mrinal Pandey, Jeffrey E Segall, Mingming Wu
Date: 2021-09-29 00:00:00

Epidermal growth factor (EGF), a potent cytokine, is known to promote tumor invasion both in vivo and in vitro. Previously, we observed that single breast tumor cells (MDA-MB-231 cell line) embedded within a 3D collagen matrix displayed enhanced motility but no discernible chemotaxis in the presence of linear EGF gradients using a microfluidic platform. Inspired by a recent theoretical development that clustered mammalian cells respond differently to chemical gradients than single cells, we studied tumor spheroid invasion within a 3D extracellular matrix (ECM) in the presence of EGF gradients. We found that EGF gradients promoted tumor cell detachment from the spheroid core, and the position of the tumor spheroid core showed a mild chemotactic response towards the EGF gradients. For those tumor cells detached from the spheroids, they showed an enhanced chemokinesis response in contrast to previous experimental results using single cells embedded within an ECM. No discernible chemotactic response towards the EGF gradients was found for the cells outside the spheroid core. This work demonstrates that a cluster of tumor cells responds differently than single tumor cells towards EGF gradients and highlights the importance of a tumor spheroid platform for chemotaxis studies.
Spatial and temporal dynamics of RhoA activities of single breast tumor cells in a 3D environment revealed by a machine learning-assisted FRET technique
Authors: Brian CH Cheung, Louis Hodgson, Jeffrey E Segall, Mingming Wu
Date: 2021-09-29 00:00:00

One of the hallmarks of cancer cells is their exceptional ability to migrate within the extracellular matrix (ECM) for gaining access to the circulatory system, a critical step of cancer metastasis. RhoA, a small GTPase, is known to be a key molecular switch that toggles between actomyosin contractility and lamellipodial protrusion during cell migration. Current understanding of RhoA activity in cell migration has been largely derived from studies of cells plated on a two-dimensional (2D) substrate using a FRET biosensor. There has been increasing evidence that cells behave differently in a more physiologically relevant three-dimensional (3D) environment, however, studies of RhoA activities in 3D have been hindered by low signal-to-noise ratio in fluorescence imaging. In this paper, we present a machine learning-assisted FRET technique to follow the spatiotemporal dynamics of RhoA activities of single breast tumor cells (MDA-MB-231) migrating in a 3D as well as a 2D environment using a RhoA biosensor. We found that RhoA activity is more polarized along the long axis of the cell for single cells migrating on 2D fibronectin-coated glass versus those embedded in 3D collagen matrices. In particular, RhoA activities of cells in 2D exhibit a distinct front-to-back and back-to-front movement during migration in contrast to those in 3D. Finally, regardless of dimensionality, RhoA polarization is found to be correlated with cell shape.
Genomic prediction: progress and perspectives for rice improvement
Authors: Jérôme Bartholomé, Parthiban Thathapalli Prakash, Joshua N. Cobb
Date: 2021-09-30 00:00:00
Tasks: Prediction

Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (Oryza sativa) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage: "To someone with a hammer, everything looks like a nail" describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context.
Gene regulatory network inference from single-cell data using a self-consistent proteomic field
Authors: Ulysse Herbach
Date: 2021-09-30 00:00:00

The well-known issue of reconstructing regulatory networks from gene expression measurements has been somewhat disrupted by the emergence and rapid development of single-cell data. Indeed, the traditional way of seeing a gene regulatory network as a deterministic system affected by small noise is being challenged by the highly stochastic, bursty nature of gene expression revealed at single-cell level. In previous work, we described a promising strategy in which network inference is seen as a calibration procedure for a mechanistic model driven by transcriptional bursting: this model inherently captures the typical variability of single-cell data without requiring ad hoc external noise, unlike ordinary or even stochastic differential equations often used in this context. The resulting algorithm, based on approximate resolution of the related master equation using a self-consistent field, was derived in detail but only applied as a proof of concept to simulated two-gene networks. Here we derive a simplified version of the algorithm and apply it, in more relevant situations, to both simulated and real single-cell RNA-Seq data. We point out three interesting features of this approach: it is computationally tractable with realistic numbers of cells and genes, it provides inferred networks with biological interpretability, and the underlying mechanistic model allows testable predictions to be made. A practical implementation of the inference procedure, together with an efficient stochastic simulation algorithm for the model, is available as a Python package.
Unveiling Trail Making Test: Visual and manual trajectories indexing multiple executive processes
Authors: Ignacio Linari, Gustavo Juantorena, Agustin Ibañez, Agustin Petroni, Juan E. Kamienkowski
Date: 2021-09-30 00:00:00
Tasks: Specificity

The Trail Making Test (TMT) is one of the most popular neuropsychological tests in the clinical assessment of executive functions (EF) and research in a wide range of clinical conditions. In addition to its sensitivity to executive dysfunction, the TMT presents several strengths: it is simple and intuitive, it is easy to understand for patients, and has a short administration. However, it has important limitations. First, the underlying EFs articulated during the task are not well discriminated, which makes it a test with low specificity. Second, the traditional pen-and-paper version presents one trial per condition which introduces high variability. Third, only the total time is quantified, which does not allow for a detailed analysis. Fourth, it has a fixed spatial configuration per condition. In the present study we designed a computerized version of the TMT (cTMT) to overcome its main limitations. Eye and hand positions are simultaneously measured with high resolution, several trials are acquired, and spatial configuration of the targets is controlled. Our results showed a very similar performance profile compared to the traditional TMT. Moreover, it revealed similarities and differences in eye movements between the two parts of the task. Most importantly, we found an internal working memory measure of the cTMT based on hand and eye movements that showed an association to a validated working memory task. Additionally, we found another internal measure of the TMT, also based on hand and eye movements, that we propose as a potential marker of inhibitory control. Our results showed that executive functions can be studied in more detail using traditional tests combined with powerful digital setups. Finally, our study paved the way for a detailed analysis of other complex tasks used for clinical evaluation, providing a deeper understanding of the processes underlying its resolution.
Estimation of Ebola's spillover infection exposure in Sierra Leone based on sociodemographic and economic factors
Authors: Sena Mursel, Nathaniel Alter, Lindsay Slavit, Anna Smith, Paolo Bocchini, Javier Buceta
Date: 2021-09-30 00:00:00

Zoonotic diseases spread through pathogens-infected animal carriers. In the case of Ebola Virus Disease (EVD), evidence supports that the main carriers are fruit bats and non-human primates. Further, EVD spread is a multi-factorial problem that depends on sociodemographic and economic (SDE) factors. Here we inquire into this phenomenon and aim at determining, quantitatively, the Ebola spillover infection exposure map and try to link it to SDE factors. To that end, we designed and conducted a survey in Sierra Leone and implement a pipeline to analyze data using regression and machine learning techniques. Our methodology is able (1) to identify the features that are best predictors of an individual's tendency to partake in behaviors that can expose them to Ebola infection, (2) to develop a predictive model about the spillover risk statistics that can be calibrated for different regions and future times, and (3) to compute a spillover exposure map for Sierra Leone. Our results and conclusions are relevant to identify the regions in Sierra Leone at risk of EVD spillover and, consequently, to design and implement policies for an effective deployment of resources (e.g., drug supplies) and other preventative measures (e.g., educational campaigns).