pwc-archive/papers-with-abstracts huggingface dataset:
The Cell Physiome: What do we need in a computational physiology framework for predicting single cell biology?
Authors: Vijay Rajagopal, Senthil Arumugam, Peter Hunter, Afshin Khadangi, Joshua Chung, Michael Pan
Date: 2022-02-27 00:00:00

Modern biology and biomedicine are undergoing a big-data explosion needing advanced computational algorithms to extract mechanistic insights on the physiological state of living cells. We present the motivation for the Cell Physiome: a framework and approach for creating, sharing, and using biophysics-based computational models of single cell physiology. Using examples in calcium signaling, bioenergetics, and endosomal trafficking, we highlight the need for spatially detailed, biophysics-based computational models to uncover new mechanisms underlying cell biology. We review progress and challenges to date towards creating cell physiome models. We then introduce bond graphs as an efficient way to create cell physiome models that integrate chemical, mechanical, electromagnetic, and thermal processes while maintaining mass and energy balance. Bond graphs enhance modularization and re-usability of computational models of cells at scale. We conclude with a look forward into steps that will help fully realize this exciting new field of mechanistic biomedical data science.
Forecasting US Inflation Using Bayesian Nonparametric Models
Authors: Todd E. Clark, Florian Huber, Gary Koop, Massimiliano Marcellino
Date: 2022-02-28 00:00:00

The relationship between inflation and predictors such as unemployment is potentially nonlinear with a strength that varies over time, and prediction errors error may be subject to large, asymmetric shocks. Inspired by these concerns, we develop a model for inflation forecasting that is nonparametric both in the conditional mean and in the error using Gaussian and Dirichlet processes, respectively. We discuss how both these features may be important in producing accurate forecasts of inflation. In a forecasting exercise involving CPI inflation, we find that our approach has substantial benefits, both overall and in the left tail, with nonparametric modeling of the conditional mean being of particular importance.
Personalized Subsidy Rules
Authors: Yu-Chang Chen, Haitian Xie
Date: 2022-02-28 00:00:00

Subsidies are commonly used to encourage behaviors that can lead to short- or long-term benefits. Typical examples include subsidized job training programs and provisions of preventive health products, in which both behavioral responses and associated gains can exhibit heterogeneity. This study uses the marginal treatment effect (MTE) framework to study personalized assignments of subsidies based on individual characteristics. First, we derive the optimality condition for a welfare-maximizing subsidy rule by showing that the welfare can be represented as a function of the MTE. Next, we show that subsidies generally result in better welfare than directly mandating the encouraged behavior because subsidy rules implicitly target individuals through unobserved heterogeneity in the behavioral response. When there is positive selection, that is, when individuals with higher returns are more likely to select the encouraged behavior, the optimal subsidy rule achieves the first-best welfare, which is the optimal welfare if a policy-maker can observe individuals' private information. We then provide methods to (partially) identify the optimal subsidy rule when the MTE is identified and unidentified. Particularly, positive selection allows for the point identification of the optimal subsidy rule even when the MTE curve is not. As an empirical application, we study the optimal wage subsidy using the experimental data from the Jordan New Opportunities for Women pilot study.
Taxpayer deductions and the endogenous probability of tax penalisation
Authors: Alex A. T. Rathke
Date: 2022-02-28 00:00:00

We propose a parametric specification of the probability of tax penalisation faced by a taxpayer, based on the amount of deduction chosen by her to reduce total taxation. Comparative analyses lead to a closed-form solution for the optimum tax deduction, and provide the maximising conditions with respect to the probability parameters.
Instant Loans Can Lift Subjective Well-Being: A Randomized Evaluation of Digital Credit in Nigeria
Authors: Daniel Björkegren, Joshua Blumenstock, Omowunmi Folajimi-Senjobi, Jacqueline Mauro, Suraj R. Nair
Date: 2022-02-28 00:00:00
Tasks: Credit score

Digital loans have exploded in popularity across low and middle income countries, providing short term, high interest credit via mobile phones. This paper reports the results of a randomized evaluation of a digital loan product in Nigeria. Being randomly approved for digital credit (irrespective of credit score) substantially increases subjective well-being after an average of three months. For those who are approved, being randomly offered larger loans has an insignificant effect. Neither treatment significantly impacts other measures of welfare. We rule out large short-term impacts either positive or negative: on income and expenditures, resilience, and women's economic empowerment.
The attachment of adult women to the Italian labour market in the shadow of COVID-19
Authors: Davide Fiaschi, Cristina Tealdi
Date: 2022-02-27 00:00:00

We investigate the attachment to the labour market of women in their 30s, who are combining career and family choices, through their reactions to an exogenous, and potentially symmetric shock, such as the COVID-19 pandemic. We find that in Italy a large number of females with small children, living in the North, left permanent (and temporary) employment and became inactive in 2020. Despite the short period of observation after the burst of the pandemic, the identified impacts appear large and persistent, particularly with respect to the males of the same age. We argue that this evidence is ascribable to specific regional socio-cultural factors, which foreshadow a potential long-term detrimental impact on female labour force participation.
An analysis of indifference curves and areas from a human nutrition perspective
Authors: Diego Roldán, Angélica Abad Cisneros, Francisco Roldán-Aráuz, Samantha Leta Angamarca, Anahí Ramírez Zambrano
Date: 2022-02-27 00:00:00
Tasks: Nutrition

Through documentary research and interviews with nutrition experts, we found that all nutrients have two thresholds, the Recommended Daily Allowance (RDA) and the Tolerable Upper Intake Level (UL). Intake less than the RDA or more than the UL negatively affects health. Intake quantities of nutrients within these limits covers 100% of the objective physiological needs without negative repercussions. These characteristics, and others, are common knowledge among nutrition experts; however, these are not adequately reflected in the microeconomics models that study these needs. We conclude that the generalized presence of these thresholds determines the existence of significant indifference areas that should be added to the microeconomics models of the indifference curves, thus improving the modelling of reality.
Project selection with partially verifiable information
Authors: Sumit Goel, Wade Hann-Caruthers
Date: 2020-07-02 00:00:00

We consider a principal agent project selection problem with asymmetric information. There are $N$ projects and the principal must select exactly one of them. Each project provides some profit to the principal and some payoff to the agent and these profits and payoffs are the agent's private information. We consider the principal's problem of finding an optimal mechanism for two different objectives: maximizing expected profit and maximizing the probability of choosing the most profitable project. Importantly, we assume partial verifiability so that the agent cannot report a project to be more profitable to the principal than it actually is. Under this no-overselling constraint, we characterize the set of implementable mechanisms. Using this characterization, we find that in the case of two projects, the optimal mechanism under both objectives takes the form of a simple cutoff mechanism. The simple structure of the optimal mechanism also allows us to find evidence in support of the well-known ally-principle which says that principal delegates more authority to an agent who shares their preferences.
Real-Time Frequency Selective Reconstruction through Register-Based Argmax Calculation
Authors: Andy Regensky, Simon Grosche, Jürgen Seiler, André Kaup
Date: 2022-02-28 00:00:00
Tasks: Image Reconstruction

Frequency Selective Reconstruction (FSR) is a state-of-the-art algorithm for solving diverse image reconstruction tasks, where a subset of pixel values in the image is missing. However, it entails a high computational complexity due to its iterative, blockwise procedure to reconstruct the missing pixel values. Although the complexity of FSR can be considerably decreased by performing its computations in the frequency domain, the reconstruction procedure still takes multiple seconds up to multiple minutes depending on the parameterization. However, FSR has the potential for a massive parallelization greatly improving its reconstruction time. In this paper, we introduce a novel highly parallelized formulation of FSR adapted to the capabilities of modern GPUs and propose a considerably accelerated calculation of the inherent argmax calculation. Altogether, we achieve a 100-fold speed-up, which enables the usage of FSR for real-time applications.
Opening the Black Box of Learned Image Coders
Authors: Zhihao Duan, Ming Lu, Zhan Ma, Fengqing Zhu
Date: 2022-02-26 00:00:00

End-to-end learned lossy image coders (LICs), as opposed to hand-crafted image codecs, have shown increasing superiority in terms of the rate-distortion performance. However, they are mainly treated as black-box systems and their interpretability is not well studied. In this paper, we show that LICs learn a set of basis functions to transform input image for its compact representation in the latent space, as analogous to the orthogonal transforms used in image coding standards. Our analysis provides insights to help understand how learned image coders work and could benefit future design and development.
Low SNR Multiframe Registration for Cubesats
Authors: Evan Widloski, Farzad Kamalabadi
Date: 2022-02-26 00:00:00
Tasks: Translation

We present a registration algorithm which jointly estimates motion and the ground truth image from a set of noisy frames under rigid, constant translation. The algorithm is non-iterative and needs no hyperparameter tuning. It requires a fixed number of FFT, multiplication, and downsampling operations for a given input size, enabling fast implementation on embedded platforms like cubesats where on-board image fusion can greatly save on limited downlink bandwidth. The algorithm is optimal in the maximum likelihood sense for additive white Gaussian noise and non-stationary Gaussian approximations of Poisson noise. Accurate registration is achieved for very low SNR, even when visible features are below the noise floor.
Resource Allocation for Single Carrier Massive MIMO Systems
Authors: Brent A. Kenney, Arslan J. Majid, Hussein Moradi, Behrouz Farhang-Boroujeny
Date: 2022-02-28 00:00:00

Resource allocation in orthogonal frequency division multiplexing (OFDM) systems is performed through allocating blocks of subcarriers to each user. Even though OFDM is the primary waveform for 5G NR systems, research reports have noted that single carrier modulation (SCM) offers several advantages over OFDM in massive multiple input multiple output (MIMO) systems, making it a preferred candidate for some future applications such as massive machine type communications (mMTC). This paper presents a method for SCM resource allocation and the relevant information recovery algorithms at the receiver. Our emphasis is on cyclic prefixed SCM, where highly flexible and efficient frequency domain detection algorithms enable the operation of many simultaneous users in a massive MIMO uplink scenario. The proposed resource allocation method allows the number of users to exceed the number of antennas at the base station (BS). Each single carrier transmission is partitioned into $L$ interleaved streams, and each user is allocated a number of such streams. One major benefit of SCM is that each data symbol is spread over the entire bandwidth. As such, the receiver performance is dictated by the average channel gain across the transmission band rather than the channel gain at a given frequency bin or a small group of frequencies. In the proposed setup, each stream may be thought of as a resource block in SCM, analogous to resource blocks in OFDM. Hence, in the context of this paper, the terms resource blocks and streams may be used interchangeably.
A Note on "Optimum Sets of Interference-Free Sequences With Zero Autocorrelation Zone"
Authors: Qiping Fang, Zilong Wang
Date: 2022-02-28 00:00:00

In this paper, a simple construction of interference-free zero correlation zone (IF-ZCZ) sequence sets is proposed by well designed finite Zak transform lattice tessellation. Each set is characterized by the period of sequences $KM$, the set size $K$ and the length of zero correlation zone $M-1$, which is optimal with respect to the Tang-Fan-Matsufuji bound. Secondly, the transformations that keep the properties of the optimal IF-ZCZ sequence set unchanged are given, and the equivalent relation of the optimal IF-ZCZ sequence set is defined based on these transformations. Then, it is proved that the general construction of the optimal IF-ZCZ sequence set proposed by Popovic is equivalent to the simple construction of the optimal IF-ZCZ sequence set, which indicates that the generation of the optimal IF-ZCZ sequence set can be simplified. Moreover, it is pointed out that the alphabet size for the special case of the simple construction of the optimal IF-ZCZ sequence set can be a factor of the period. Finally, both the simple construction of the optimal IF-ZCZ sequence set and its special case have sparse and highly structured Zak spectra, which can greatly reduce the computational complexity of implementing matched filter banks.
Fast off-the-grid sparse recovery with over-parametrized projected gradient descent
Authors: Pierre-Jean Bénard, Yann Traonmilin, Jean-François Aujol
Date: 2022-02-28 00:00:00

We consider the problem of recovering off-the-grid spikes from Fourier measurements. Successful methods such as sliding Frank-Wolfe and continuous orthogonal matching pursuit (OMP) iteratively add spikes to the solution then perform a costly (when the number of spikes is large) descent on all parameters at each iteration. In 2D, it was shown that performing a projected gradient descent (PGD) from a gridded over-parametrized initialization was faster than continuous orthogonal matching pursuit. In this paper, we propose an off-the-grid over-parametrized initialization of the PGD based on OMP that permits to fully avoid grids and gives faster results in 3D.
Signal-to-noise ratio is more important than sampling rate in beat-to-beat interval estimation from optical sensors
Authors: Sebastian Zaunseder, Antti Vehkaoja, Vincent Fleischhauer, Christoph Hoog Antink
Date: 2022-02-28 00:00:00

Photoplethysmographic Imaging (PPGI) allows the determination of pulse rate variability from sequential beat-to-beat intervals (BBI) and pulse wave velocity from spatially resolved recorded pulse waves. In either case, sufficient temporal accuracy is essential. The presented work investigates the temporal accuracy of BBI estimation from photoplethysmographic signals. Within comprehensive numerical simulation, we systematically assess the impact of sampling rate, signal-to-noise ratio (SNR), and beat-to-beat shape variations on the root mean square error (RMSE) between real and estimated BBI. Our results show that at sampling rates beyond 14 Hz only small errors exist when interpolation is used. For example, the average RMSE is 3 ms for a sampling rate of 14 Hz and an SNR of 18 dB. Further increasing the sampling rate only results in marginal improvements, e.g. more than tripling the sampling rate to 50 Hz reduces the error by approx. 14%. The most important finding relates to the SNR, which is shown to have a much stronger influence on the error than the sampling rate. For example, increasing the SNR from 18 dB to 24 dB at 14 Hz sampling rate reduced the error by almost 50% to 1.5 ms. Subtle beat-to-beat shape variations, moreover, increase the error decisively by up to 800%. Our results are highly relevant in three regards: first, they partially explain different results in the literature on minimum sampling rates. Second, they emphasize the importance to consider SNR and possibly shape variation in investigations on the minimal sampling rate. Third, they underline the importance of appropriate processing techniques to increase SNR. Importantly, though our motivation is PPGI, the presented work immediately applies to contact PPG and PPG in other settings such as wearables. To enable further investigations, we make the scripts used in modelling and simulation freely available.
Improved Sensing and Positioning via 5G and mmWave radar for Airport Surveillance
Authors: Bo Tan, Elena Simona Lohan, Bo Sun, Wenbo Wang, Taylan Yesilyurt, Christophe Morlaas, Carlos David Morales Pena, Kanaan Abdo, Fathia Ben Slama, Alexandre Simonin, Mohamed Ellejmi
Date: 2022-02-28 00:00:00
Tasks: Management

This paper explores an integrated approach for improved sensing and positioning with applications in air traffic management (ATM) and in the Advanced Surface Movement Guidance and Control System (A-SMGCS). The integrated approach includes the synergy of 3D Vector Antenna with the novel time-of-arrival and angle-of-arrival estimate methods for accurate positioning, combining the sensing on the sub-6GHz and mmWave spectrum for the enhanced non-cooperative surveillance. For the positioning scope, both uplink and downlink 5G reference signals are investigated and their performance is evaluated. For the non-cooperative sensing scope, a novel 5G-signal-based imaging function is proposed and verified with realistic airport radio-propagation modelling and the AI-based targets tracking-and-motion recognition are investigated. The 5G-based imaging and mmWave radar based detection can be potentially fused to enhance surveillance in the airport. The work is being done within the European-funded project NewSense and it delves into the 5G, Vector Antennas, and mmWave capabilities for future ATM solutions.
Weight Selection for Pattern Control of Paraboloidal Reflector Antennas with Reconfigurable Rim Scattering
Authors: R. Michael Buehrer, Steve W. Ellingson
Date: 2022-02-26 00:00:00

It has been recently demonstrated that modifying the rim scattering of a paraboloidal reflector antenna through the use of reconfigurable elements along the rim facilitates sidelobe modification including cancelling sidelobes. In this work we investigate techniques for determining the unit-magnitude weights (i.e., weights which modify the phase of the scattered signals) to accomplish sidelobe cancellation at arbitrary angles from the reflector axis. Specifically, it is shown that despite the large search space and the non-convexity of the cost function, weights can be found with reasonable complexity which provide significant cancellation capability. First, the optimal weights without any magnitude constraints are found. Afterwards, algorithms are developed for determining the unit-modulus weights with both quantized and unquantized phases. Further, it is shown that weights can be obtained that both cancel sidelobes while providing a constant main lobe gain. A primary finding is that sufficiently deep nulls are possible with essentially no change in the main lobe with practical (binary or quaternary) phase-only weights.
Random Access with Massive MIMO-OTFS in LEO Satellite Communications
Authors: Boxiao Shen, Yongpeng Wu, Jianping An, Chengwen Xing, Lian Zhao, Wenjun Zhang
Date: 2022-02-26 00:00:00
Tasks: Action Detection, Activity Detection

This paper considers the joint channel estimation and device activity detection in the grant-free random access systems, where a large number of Internet-of-Things devices intend to communicate with a low-earth orbit satellite in a sporadic way. In addition, the massive multiple-input multiple-output (MIMO) with orthogonal time-frequency space (OTFS) modulation is adopted to combat the dynamics of the terrestrial-satellite link. We first analyze the input-output relationship of the single-input single-output OTFS when the large delay and Doppler shift both exist, and then extend it to the grant-free random access with massive MIMO-OTFS. Next, by exploring the sparsity of channel in the delay-Doppler-angle domain, a two-dimensional pattern coupled hierarchical prior with the sparse Bayesian learning and covariance-free method (TDSBL-CF) is developed for the channel estimation. Then, the active devices are detected by computing the energy of the estimated channel. Finally, the generalized approximate message passing algorithm combined with the sparse Bayesian learning and two-dimensional convolution (ConvSBL-GAMP) is proposed to decrease the computations of the TDSBL-CF algorithm. Simulation results demonstrate that the proposed algorithms outperform conventional methods.
Bayesian Ridge Regression Based Model to Predict Fault Location in HVdc Network
Authors: Timothy Flavin, Thomas Steiner, Bhaskar Mitra, Vidhyashree Nagaraju
Date: 2022-02-26 00:00:00
Tasks: regression, Sensitivity

This paper discusses a method for accurately estimating the fault location in multi-terminal High Voltage direct current (HVdc) transmission network using single ended current and voltage measurements. The post-fault voltage and current signatures are a function of multiple factors and thus accurately locating faults on a multi-terminal network is challenging. We discuss a novel data-driven Bayes Regression based method for accurately predicting fault locations. The sensitivity of the proposed algorithm to measurement noise, fault location, resistance and current limiting inductance are performed on a radial three-terminal MTdc network. The test system is designed in Power System Computer Aided Design (PSCAD)/Electromagnetic Transients including dc (EMTdc).
Quickest Change Detection in Anonymous Heterogeneous Sensor Networks
Authors: Zhongchang Sun, Shaofeng Zou, Ruizhi Zhang, Qunwei Li
Date: 2022-02-26 00:00:00
Tasks: Change Detection

The problem of quickest change detection (QCD) in anonymous heterogeneous sensor networks is studied. There are $n$ heterogeneous sensors and a fusion center. The sensors are clustered into $K$ groups, and different groups follow different data-generating distributions. At some unknown time, an event occurs in the network and changes the data-generating distribution of the sensors. The goal is to detect the change as quickly as possible, subject to false alarm constraints. The anonymous setting is studied, where at each time step, the fusion center receives $n$ unordered samples, and the fusion center does not know which sensor each sample comes from, and thus does not know its exact distribution. A simple optimality proof is first derived for the mixture likelihood ratio test, which was constructed and proved to be optimal for the non-sequential anonymous setting in (Chen and Wang, 2019). For the QCD problem, a mixture CuSum algorithm is further constructed, and is further shown to be optimal under Lorden's criterion. For large networks, a computationally efficient test is proposed and a novel theoretical characterization of its false alarm rate is developed. Numerical results are provided to validate the theoretical results.