pwc-archive/papers-with-abstracts huggingface dataset:
The Short-term Impact of Congestion Taxes on Ridesourcing Demand and Traffic Congestion: Evidence from Chicago
Authors: Yuan Liang, Bingjie Yu, Xiaojian Zhang, Yi Lu, Linchuan Yang
Date: 2022-07-05 00:00:00
Tasks: Causal Inference, regression

Ridesourcing is popular in many cities. Despite its theoretical benefits, a large body of studies have claimed that ridesourcing also brings (negative) externalities (e.g., inducing trips and aggravating traffic congestion). Therefore, many cities are planning to enact or have already enacted policies to regulate its use. However, these policies' effectiveness or impact on ridesourcing demand and traffic congestion is uncertain. To this end, this study applies difference-in-differences (i.e., a regression-based causal inference approach) to empirically evaluate the effects of the congestion tax policy on ridesourcing demand and traffic congestion in Chicago. It shows that this congestion tax policy significantly curtails overall ridesourcing demand but marginally alleviates traffic congestion. The results are robust to the choice of time windows and data sets, additional control variables, alternative model specifications, alternative control groups, and alternative modeling approaches (i.e., regression discontinuity in time). Moreover, considerable heterogeneity exists. For example, the policy notably reduces ridesourcing demand with short travel distances, but such an impact is gradually attenuated as the distance increases.
Balancing India's 2030 Electricity Grid Needs Management of Time Granularity and Uncertainty: Insights from a Parametric Model
Authors: Rahul Tongia
Date: 2022-07-01 00:00:00
Tasks: Management

With some of the world's most ambitious renewable energy (RE) growth targets, especially when normalized for scale, India aims more than quadrupling wind and solar by 2030. Simultaneously, coal dominates the electricity grid, providing roughly three-quarters of electricity today. We present results from the first of a kind model to handle high uncertainty, which uses parametric analysis instead of stochastic analysis for grid balancing based on economic despatch through 2030, covering 30-minute resolution granularity at a national level. The model assumes a range of growing demand, supply options, prices, and other uncertain inputs. It calculates the lowest cost portfolio across a spectrum of parametric uncertainty. We apply simplifications to handle the intersection of capacity planning with optimized despatch. Our results indicate that very high RE scenarios are cost-effective, even if a measurable fraction would be surplus and thus discarded ("curtailed"). We find that high RE without storage as well as existing slack in coal- and gas-powered capacity are insufficient to meet rising demand on a real-time basis, especially adding time-of-day balancing. Storage technologies prove valuable but remain expensive compared to the 2019 portfolio mix, due to issues of duty cycling like seasonal variability, not merely inherent high capital costs. However, examining alternatives to batteries for future growth finds all solutions for peaking power are even more expensive. For balancing at peak times, a smarter grid that applies demand response may be cost-effective. We also find the need for more sophisticated modelling with higher stochasticity across annual timeframes (especially year on year changes in wind output, rainfall, and demand) along with uncertainty on supply and load profiles (shapes).
Relating the fundamental frequency of speech with EEG using a dilated convolutional network
Authors: Corentin Puffay, Jana Van Canneyt, Jonas Vanthornhout, Hugo Van hamme, Tom Francart
Date: 2022-07-05 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

To investigate how speech is processed in the brain, we can model the relation between features of a natural speech signal and the corresponding recorded electroencephalogram (EEG). Usually, linear models are used in regression tasks. Either EEG is predicted, or speech is reconstructed, and the correlation between predicted and actual signal is used to measure the brain's decoding ability. However, given the nonlinear nature of the brain, the modeling ability of linear models is limited. Recent studies introduced nonlinear models to relate the speech envelope to EEG. We set out to include other features of speech that are not coded in the envelope, notably the fundamental frequency of the voice (f0). F0 is a higher-frequency feature primarily coded at the brainstem to midbrain level. We present a dilated-convolutional model to provide evidence of neural tracking of the f0. We show that a combination of f0 and the speech envelope improves the performance of a state-of-the-art envelope-based model. This suggests the dilated-convolutional model can extract non-redundant information from both f0 and the envelope. We also show the ability of the dilated-convolutional model to generalize to subjects not included during training. This latter finding will accelerate f0-based hearing diagnosis.
DEFORMER: Coupling Deformed Localized Patterns with Global Context for Robust End-to-end Speech Recognition
Authors: Jiamin Xie, John H. L. Hansen
Date: 2022-07-04 00:00:00
Tasks: speech-recognition, Speech Recognition

Convolutional neural networks (CNN) have improved speech recognition performance greatly by exploiting localized time-frequency patterns. But these patterns are assumed to appear in symmetric and rigid kernels by the conventional CNN operation. It motivates the question: What about asymmetric kernels? In this study, we illustrate adaptive views can discover local features which couple better with attention than fixed views of the input. We replace depthwise CNNs in the Conformer architecture with a deformable counterpart, dubbed this "Deformer". By analyzing our best-performing model, we visualize both local receptive fields and global attention maps learned by the Deformer and show increased feature associations on the utterance level. The statistical analysis of learned kernel offsets provides an insight into the change of information in features with the network depth. Finally, replacing only half of the layers in the encoder, the Deformer improves +5.6% relative WER without a LM and +6.4% relative WER with a LM over the Conformer baseline on the WSJ eval92 set.
Adversarial Multi-Task Deep Learning for Noise-Robust Voice Activity Detection with Low Algorithmic Delay
Authors: Claus Meyer Larsen, Peter Koch, Zheng-Hua Tan
Date: 2022-07-04 00:00:00
Tasks: Action Detection, Activity Detection, Multi-Task Learning

Voice Activity Detection (VAD) is an important pre-processing step in a wide variety of speech processing systems. VAD should in a practical application be able to detect speech in both noisy and noise-free environments, while not introducing significant latency. In this work we propose using an adversarial multi-task learning method when training a supervised VAD. The method has been applied to the state-of-the-art VAD Waveform-based Voice Activity Detection. Additionally the performance of the VADis investigated under different algorithmic delays, which is an important factor in latency. Introducing adversarial multi-task learning to the model is observed to increase performance in terms of Area Under Curve (AUC), particularly in noisy environments, while the performance is not degraded at higher SNR levels. The adversarial multi-task learning is only applied in the training phase and thus introduces no additional cost in testing. Furthermore the correlation between performance and algorithmic delays is investigated, and it is observed that the VAD performance degradation is only moderate when lowering the algorithmic delay from 398 ms to 23 ms.
Effects of seat back height and posture on 3D vibration transmission to pelvis, trunk and head
Authors: Mojtaba Mirakhorlo, Nick Kluft, Barys Shyrokau, Riender Happee
Date: 2022-07-05 00:00:00

Vibration transmission is essential in the design of comfortable vehicle seats but knowledge is lacking on 3D trunk and head motion and the role of seat back and posture. We hypothesized that head motion is reduced when participants upper back is unsupported, as this stimulates active postural control. We developed an experimental methodology to evaluate 3D vibration transmission from compliant seats to the human body. Wide-band (0.1-12 Hz) motion stimuli were applied in fore-aft, lateral and vertical direction to evaluate the translational and rotational body response in pelvis, trunk and head. A standard car seat was equipped with a configurable and compliant back support to test 3 support heights and 3 sitting postures (erect, slouched, and preferred) where we also tested head down looking at a smartphone. Seat back support height and sitting posture substantially affected vibration transmission and affected low-frequency responses in particular for body segment rotation. According to our hypothesis a low support height proved beneficial in reducing head motion. Relevance to industry: Our methodology effectively evaluates 3D wide-band vibration transmission from compliant seats to the human body. The lowest back support height reduced head motion but was perceived as least comfortable. This calls for seat designs which support but do not so much constrain the upper back. The head down posture enlarged head motion, pleading for computer system integration allowing heads up postures in future automated cars. The biomechanical data will serve to validate human models supporting the design of comfortable (automated) vehicles.
Phase Shift Compression for Control Signaling Reduction in IRS-Aided Wireless Systems: Global Attention and Lightweight Design
Authors: Xianhua Yu, Dong Li
Date: 2022-07-05 00:00:00
Tasks: Decoder

A potential 6G technology known as intelligent reflecting surface (IRS) has recently gained much attention from academia and industry. However, acquiring the optimized quantized phase shift (QPS) presents challenges for the IRS due to the phenomenon of signaling storms. In this paper, we attempt to solve the above problem by proposing two deep learning models, the global attention phase shift compression network (GAPSCN) and the simplified GAPSCN (S-GAPSCN). In GAPSCN, we propose a novel attention mechanism that emphasizes a greater number of meaningful features than previous attention-related works. Additionally, S-GAPSCN is built with an asymmetric architecture to meet the practical constraints on computation resources of the IRS controller. Moreover, in S-GAPSCN, to compensate for the performance degradation caused by simplifying the model, we design a low-computation complexity joint attention-assisted multi-scale network (JAAMSN) module in the decoder of S-GAPSCN. Simulation results demonstrate that the proposed global attention mechanism achieves prominent performance compared with the existing attention mechanisms and the proposed GAPSCN can achieve reliable reconstruction performance compared with existing state-of-the-art models. Furthermore, the proposed S-GAPSCN can approach the performance of the GAPSCN at a much lower computational cost.
The End-to-End Molecular Communication Model of Extracellular Vesicle-based Drug Delivery
Authors: Hamid Khoshfekr Rudsari, Mohammad Zoofaghari, Mladen Veletic, Jacob Bergsland, Ilangko Balasingham
Date: 2022-07-05 00:00:00
Tasks: Navigate

A closer look at nature has recently brought more interest in exploring and utilizing intra-body communication networks composed of cells as intrinsic, perfectly biocompatible infrastructures to deliver therapeutics. Naturally occurring cell-to-cell communication systems are being manipulated to release, navigate, and take up soluble cell-derived messengers that are either therapeutic by nature or carry therapeutic molecular cargo in their structures. One example of such structures is extracellular vesicles (EVs) which have been recently proven to have favorable pharmacokinetic properties, opening new avenues for developing the next generation biotherapeutics. In this paper, we study theoretical aspects of the EV transfer within heart tissue as a case study by utilizing an information and communication technology-like approach in analyzing molecular communication systems. Our modeling implies the abstraction of the EV releasing cells as transmitters, the extracellular matrix as the channel, and the EV receiving cells as receivers. Our results, derived from the developed analytical models, indicate that the release can be modulated using external forces such as electrical signals, and the transfer and reception can be affected by the extracellular matrix and plasma membrane properties, respectively. The results can predict the EV biodistributions and contribute to avoiding unplanned administration, often resulting in side- and adverse effects.
Event-based sampled ECG morphology reconstruction through self-similarity
Authors: Silvio Zanoli, Tomas Teijeiro, Giovanni Ansaloni, David Atienza
Date: 2022-07-05 00:00:00
Tasks: Dynamic Time Warping, Rhythm

Background and Objective: Event-based analog-to-digital converters allow for sparse bio-signal acquisition, enabling local sub-Nyquist sampling frequency. However, aggressive event selection can cause the loss of important bio-markers, not recoverable with standard interpolation techniques. In this work, we leverage the self-similarity of the electrocardiogram (ECG) signal to recover missing features in event-based sampled ECG signals, dynamically selecting patient-representative templates together with a novel dynamic time warping algorithm to infer the morphology of event-based sampled heartbeats. Methods: We acquire a set of uniformly sampled heartbeats and use a graph-based clustering algorithm to define representative templates for the patient. Then, for each event-based sampled heartbeat, we select the morphologically nearest template, and we then reconstruct the heartbeat with piece-wise linear deformations of the selected template, according to a novel dynamic time warping algorithm that matches events to template segments. Results: Synthetic tests on a standard normal sinus rhythm dataset, composed of approximately 1.8 million normal heartbeats, show a big leap in performance with respect to standard resampling techniques. In particular (when compared to classic linear resampling), we show an improvement in P-wave detection of up to 10 times, an improvement in T-wave detection of up to three times, and a 30\% improvement in the dynamic time warping morphological distance. Conclusion: In this work, we have developed an event-based processing pipeline that leverages signal self-similarity to reconstruct event-based sampled ECG signals. Synthetic tests show clear advantages over classical resampling techniques.
Identification of Distorted RF Components via Deep Multi-Task Learning
Authors: Mehmet Ali Aygul, Ebubekir Memisoglu, Hakan Ali Cirpan, Huseyin Arslan
Date: 2022-07-04 00:00:00
Tasks: Multi-Task Learning

High-quality radio frequency (RF) components are imperative for efficient wireless communication. However, these components can degrade over time and need to be identified so that either they can be replaced or their effects can be compensated. The identification of these components can be done through observation and analysis of constellation diagrams. However, in the presence of multiple distortions, it is very challenging to isolate and identify the RF components responsible for the degradation. This paper highlights the difficulties of distorted RF components' identification and their importance. Furthermore, a deep multi-task learning algorithm is proposed to identify the distorted components in the challenging scenario. Extensive simulations show that the proposed algorithm can automatically detect multiple distorted RF components with high accuracy in different scenarios.
Extending the Monod Model of Microbial Growth with Memory
Authors: Mohammad M. Amirian, Andrew J. Irwin, Zoe V. Finkel
Date: 2022-07-05 00:00:00

Monod's model describes the growth of microorganisms using a hyperbolic function of extracellular resource concentration. Under fluctuating or limited resource concentrations this model performs poorly against experimental data, motivating the more complex Droop model with a time-varying internal storage pool. We extend the Monod model to incorporate memory of past conditions, adding a single parameter motivated by a fractional calculus analysis. We show how to interpret the memory element in a biological context and describe its connection to a resource storage pool. Under nitrogen starvation at non-equilibrium conditions, we validate the model with simulations and empirical data obtained from lab cultures of diatoms (T. pseudonana and T. weissflogii) and prasinophytes (Micromonas sp. and O. tauri), globally influential phytoplankton taxa. Using statistical analysis, we show that our Monod-memory model estimates the growth rate, cell density, and resource concentration as well as the Droop model while requiring one less state variable. Our simple model may improve descriptions of phytoplankton dynamics in complex earth system models at a lower computational cost than is presently achievable.
A non-local kinetic model for cell migration: a study of the interplay between contact guidance and steric hindrance
Authors: Martina Conte, Nadia Loy
Date: 2022-07-05 00:00:00

We propose a non-local model for contact guidance and steric hindrance depending on a single external cue, namely the extracellular matrix, that affects in a twofold way the polarization and speed of motion of the cells. We start from a microscopic description of the stochastic processes underlying the cell re-orientation mechanism related to the change of cell speed and direction. Then, we formally derive the corresponding kinetic model that implements exactly the prescribed microscopic dynamics and, from it, it is possible to deduce the macroscopic limit in the appropriate regime. Moreover, we test our model in several scenarios. In particular, we numerically investigate the minimal microscopic mechanisms that are necessary to reproduce cell dynamics by comparing the outcomes of our model with some experimental results related to breast cancer cell migration. This allows us to validate the proposed modeling approach and, also, to highlight its capability of predicting the qualitative cell behaviors in diverse heterogeneous microenvironments.
Population genetics: coalescence rate and demographic parameters inference
Authors: Olivier Mazet, Camille Noûs
Date: 2022-07-05 00:00:00
Tasks: Articles

We propose in this article a brief description of the work, over almost a decade, resulting from a collaboration between mathematicians and biologists from four different research laboratories, identifiable as the co-authors of the articles whose results are described here, and implicitely co-authors of this article, under the signature of Camille No\^us. This modeling work is part of population genetics, of which we give a definition below. It is therefore essentially at the interface between mathematical tools, more particularly probabilistic ones, and biological data, more specifically genetic ones.
Eco-evolutionary tradeoffs in the dynamics of prion strain competition
Authors: Saul Acevedo, Alexander J. Stewart
Date: 2022-07-05 00:00:00
Tasks: Epidemiology

Prion and prion-like molecules are a type of self replicating aggregate protein that have been implicated in a variety of neurodegenerative diseases. Over recent decades the molecular dynamics of prions have been characterized both empirically and through mathematical models, providing insights into the epidemiology of prion diseases, and the impact of prions on the evolution of cellular processes. At the same time, a variety of evidence indicates that prions are themselves capable of a form of evolution, in which changes to their structure that impact their rate of growth or fragmentation are replicated, making such changes subject to natural selection. Here we study the role of such selection in shaping the characteristics of prions under the nucleated polymerization model (NPM). We show that fragmentation rates evolve to an evolutionary stable value which balances rapid reproduction of $PrP^{Sc}$ aggregates with the need to produce stable polymers. We further show that this evolved fragmentation rate differs in general from the rate that optimizes transmission between cells. We find that under the NPM, prions that are both evolutionary stable and optimized for transmission have a characteristic length of $3n$, i.e three times the critical length below which they become unstable. Finally we study the dynamics of inter-cellular competition between strains, and show that the eco-evolutionary tradeoff between intra- and inter-cellular competition favors coexistence.
Stochastic Variational Methods in Generalized Hidden Semi-Markov Models to Characterize Functionality in Random Heteropolymers
Authors: Yun Zhou, Boying Gong, Tao Jiang, Ting Xu, Haiyan Huang
Date: 2022-07-05 00:00:00
Tasks: parameter estimation

Recent years have seen substantial advances in the development of biofunctional materials using synthetic polymers. The growing problem of elusive sequence-functionality relations for most biomaterials has driven researchers to seek more effective tools and analysis methods. In this study, statistical models are used to study sequence features of the recently reported random heteropolymers (RHP), which transport protons across lipid bilayers selectively and rapidly like natural proton channels. We utilized the probabilistic graphical model framework and developed a generalized hidden semi-Markov model (GHSMM-RHP) to extract the function-determining sequence features, including the transmembrane segments within a chain and the sequence heterogeneity among different chains. We developed stochastic variational methods for efficient inference on parameter estimation and predictions, and empirically studied their computational performance from a comparative perspective on Bayesian (i.e., stochastic variational Bayes) versus frequentist (i.e., stochastic variational expectation-maximization) frameworks that have been studied separately before. The real data results agree well with the laboratory experiments, and suggest GHSMM-RHP's potential in predicting protein-like behavior at the polymer-chain level.
Balancing Profit, Risk, and Sustainability for Portfolio Management
Authors: Charl Maree, Christian W. Omlin
Date: 2022-06-06 00:00:00
Tasks: Management, Portfolio Optimization, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Stock portfolio optimization is the process of continuous reallocation of funds to a selection of stocks. This is a particularly well-suited problem for reinforcement learning, as daily rewards are compounding and objective functions may include more than just profit, e.g., risk and sustainability. We developed a novel utility function with the Sharpe ratio representing risk and the environmental, social, and governance score (ESG) representing sustainability. We show that a state-of-the-art policy gradient method - multi-agent deep deterministic policy gradients (MADDPG) - fails to find the optimum policy due to flat policy gradients and we therefore replaced gradient descent with a genetic algorithm for parameter optimization. We show that our system outperforms MADDPG while improving on deep Q-learning approaches by allowing for continuous action spaces. Crucially, by incorporating risk and sustainability criteria in the utility function, we improve on the state-of-the-art in reinforcement learning for portfolio optimization; risk and sustainability are essential in any modern trading strategy and we propose a system that does not merely report these metrics, but that actively optimizes the portfolio to improve on them.