pwc-archive/papers-with-abstracts huggingface dataset:
Aggregation of Models, Choices, Beliefs, and Preferences
Authors: Hamed Hamze Bajgiran, Houman Owhadi
Date: 2021-11-23 00:00:00
Tasks: Decision Making, Uncertainty Quantification

A natural notion of rationality/consistency for aggregating models is that, for all (possibly aggregated) models $A$ and $B$, if the output of model $A$ is $f(A)$ and if the output model $B$ is $f(B)$, then the output of the model obtained by aggregating $A$ and $B$ must be a weighted average of $f(A)$ and $f(B)$. Similarly, a natural notion of rationality for aggregating preferences of ensembles of experts is that, for all (possibly aggregated) experts $A$ and $B$, and all possible choices $x$ and $y$, if both $A$ and $B$ prefer $x$ over $y$, then the expert obtained by aggregating $A$ and $B$ must also prefer $x$ over $y$. Rational aggregation is an important element of uncertainty quantification, and it lies behind many seemingly different results in economic theory: spanning social choice, belief formation, and individual decision making. Three examples of rational aggregation rules are as follows. (1) Give each individual model (expert) a weight (a score) and use weighted averaging to aggregate individual or finite ensembles of models (experts). (2) Order/rank individual model (expert) and let the aggregation of a finite ensemble of individual models (experts) be the highest-ranked individual model (expert) in that ensemble. (3) Give each individual model (expert) a weight, introduce a weak order/ranking over the set of models/experts, aggregate $A$ and $B$ as the weighted average of the highest-ranked models (experts) in $A$ or $B$. Note that (1) and (2) are particular cases of (3). In this paper, we show that all rational aggregation rules are of the form (3). This result unifies aggregation procedures across different economic environments. Following the main representation, we show applications and extensions of our representation in various separated economics topics such as belief formation, choice theory, and social welfare economics.
Symbol-Based Over-the-Air Digital Predistortion Using Reinforcement Learning
Authors: Yibo Wu, Jinxiang Song, Christian Häger, Ulf Gustavsson, Alexandre Graell i Amat, Henk Wymeersch
Date: 2021-11-23 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose an over-the-air digital predistortion optimization algorithm using reinforcement learning. Based on a symbol-based criterion, the algorithm minimizes the errors between downsampled messages at the receiver side. The algorithm does not require any knowledge about the underlying hardware or channel. For a generalized memory polynomial power amplifier and additive white Gaussian noise channel, we show that the proposed algorithm achieves performance improvements in terms of symbol error rate compared with an indirect learning architecture even when the latter is coupled with a full sampling rate ADC in the feedback path. Furthermore, it maintains a satisfactory adjacent channel power ratio.
GEVD-based Low-Rank Channel Covariance Matrix Estimation and MMSE Channel Estimation for Uplink Cellular Massive MIMO Systems
Authors: Robbe Van Rompaey, Marc Moonen
Date: 2021-11-23 00:00:00

Uplink channel estimation is a crucial component for the performance of cellular massive MIMO systems. However, when the number of user equipments (UEs) grows, the sharing of the available resources causes interference between UEs in neighboring cells. Minimum mean squared error (MMSE) channel estimators have been proposed to mitigate this interference, but these require estimates of the channel covariance matrices. Therefore, a new channel covariance matrix estimator for low-rank channel covariance matrices is presented in this paper, using a generalized eigenvalue decomposition (GEVD) of two covariance matrices that can be estimated from the available uplink data. The requirements for the systems are minimal and, except for synchronization, there is no need for communication between the different cells and no prior knowledge on the background noise is required. Approximate MMSE estimators are also derived based on the newly proposed channel covariance matrix estimator. The effectiveness of the proposed methods is demonstrated in numerical simulations.
IF equation: a feature extractor for high-concentration time-frequency representation of mixed signals
Authors: Xiangxiang Zhu, Kunde Yang, Zhuosheng Zhang
Date: 2021-11-23 00:00:00

High-concentration time-frequency (TF) representation provides a valuable tool for characterizing multi-component non-stationary signals. In our previous work, we proposed using an instantaneous frequency (IF) equation to sharpen the TF distribution, and experiments verified its effectiveness. In this paper, we systematically discuss why the IF equation-based TF analysis methods work and how to use the IF equation to improve TF sharpness. By the analysis of the properties of the IF equation, we prove that a good IF equation can unify the well-known IF and group delay estimators and provides an effective way to characterize the mixture of time-varying and frequency-varying signals. By discussing the post-processing techniques based on the IF equation, we can prove that many popular TF post-processing methods, such as the synchroextracting transform, the multi-synchrosqueezing transform, and the time extracting transform, fall into the IF equation-based category. We also propose a novel approach to combine different IF equations to minimize energy spreading based on local sparsity. Numerical simulations and practical experiments are presented to illustrate the performance of the proposed IF equation-based TF analysis method.
An Optimal Weighting Function for the Savitzky-Golay Filter
Authors: Paul W. Oxby
Date: 2021-11-23 00:00:00

The Savitzky-Golay FIR digital filter is based on a least-squares polynomial fit to a hypothetical sample of equally spaced data. This gives the filter the ability to preserve moments of features like peaks in the input. Descriptions of the filter typically consider the case where equal weights are implicitly applied to the residuals of the fit. In a largely overlooked paper Turton showed that weighting the residuals with a triangular function significantly improves the frequency response of the filter in the stopband. The Savitzky-Golay filter is commonly referred to as a smoothing filter. This paper uses a particular measure of smoothness to show that a quadratic residual weighting function optimizes the smoothness of the filter output for a given sample size and degree of the fitting polynomial. This weighting function can provide substantially better smoothness than that with a constant weighting function.
A stochastic model of cell proliferation and death across a sequence of compartments
Authors: Hanan Dreiwi, Flavia Feliciangeli, Mario Castro, Grant Lythe, Carmen Molina-París, Martín López-García
Date: 2021-11-23 00:00:00

Cells of the human body have nearly identical genome but exhibit very different phenotypes that allow them to carry out specific functions and react to changes in their surrounding environment. This division of labour is achieved by cellular division and cellular differentiation, events which lead to a population of cells with unique characteristics. In this paper, we model the dynamics of cells over time across a sequence of compartments. Cells within a compartment may represent being at the same spatial location or sharing the same phenotype. In this sequence of compartments, cells can either die, divide or enter an adjacent compartment. We analyse a set of ordinary differential equations to describe the evolution of the average number of cells in each compartment over time. We also focus on the progeny of a founder cell in terms of a stochastic process and analyse several summary statistics to bring insights into the lifespan of a single cell, the number of divisions during its lifespan, and the probability to die in each compartment. Numerical results inspired by cellular immune processes allow us to illustrate the applicability of our techniques.
CRISPR SWAPnDROP -- A multifunctional system for genome editing and large-scale interspecies gene transfer
Authors: Marc Teufel, Carlo A. Klein, Maurice Mager, Patrick Sobetzko
Date: 2021-11-23 00:00:00

The need for diverse chromosomal modifications in biotechnology, synthetic biology and basic research requires the development of new technologies. With CRISPR SWAPnDROP, we extend the limits of genome editing to large-scale in-vivo DNA transfer between bacterial species. Its modular platform approach facilitates species specific adaptation to confer genome editing in various species. In this study, we show the implementation of the CRISPR SWAPnDROP concept for the model organism Escherichia coli and the currently fastest growing and biotechnologically relevant organism Vibrio natriegens. We demonstrate the excision, transfer and integration of 151kb chromosomal DNA between E. coli strains and from E. coli to V. natriegens without size-limiting intermediate DNA extraction. With the transfer of the E. coli MG1655 wild type lac operon, we establish a functional lactose and galactose degradation pathway in V. natriegens to extend its biotechnological spectrum. We also transfer the E. coli DH5alpha lac operon and make V. natriegens capable of alpha-complementation - a step towards an ultra-fast cloning strain. Furthermore, CRISPR SWAPnDROP is designed to be the swiss army knife of genome engineering. Its spectrum of application comprises scarless, marker-free, iterative and parallel insertions and deletions, genome rearrangements, as well as gene transfer between strains and across species. The modular character facilitates DNA library applications and the recycling of standardized parts. Its novel multi-color scarless co-selection system significantly improves editing efficiency to 92% for single edits and 83% for quadruple edits and provides visual quality controls throughout the assembly and editing process.
Bio-informatics: a working concept. A translation of "Bio-informatica: een werkconcept" by B. Hesper and P. Hogeweg
Authors: Ben Hesper, Paulien Hogeweg
Date: 2021-11-23 00:00:00
Tasks: Translation

A translation of the Dutch article from 1970 in which the term "bioinformatics" was first introduced by Ben Hesper and Paulien Hogeweg.
A new logistic growth model applied to COVID-19 fatality data
Authors: S. Triambak, D. P. Mahapatra, N. Mallick, R. Sahoo
Date: 2021-11-20 00:00:00

Background: Recent work showed that the temporal growth of the novel coronavirus disease (COVID-19) follows a sub-exponential power-law scaling whenever effective control interventions are in place. Taking this into consideration, we present a new phenomenological logistic model that is well-suited for such power-law epidemic growth. Methods: We empirically develop the logistic growth model using simple scaling arguments, known boundary conditions and a comparison with available data from four countries, Belgium, China, Denmark and Germany, where (arguably) effective containment measures were put in place during the first wave of the pandemic. A non-linear least-squares minimization algorithm is used to map the parameter space and make optimal predictions. Results: Unlike other logistic growth models, our presented model is shown to consistently make accurate predictions of peak heights, peak locations and cumulative saturation values for incomplete epidemic growth curves. We further show that the power-law growth model also works reasonably well when containment and lock down strategies are not as stringent as they were during the first wave of infections in 2020. On the basis of this agreement, the model was used to forecast COVID-19 fatalities for the third wave in South Africa, which is currently in progress. Conclusions: We anticipate that our presented model will be useful for a similar forecasting of COVID-19 induced infections/deaths in other regions as well as other cases of infectious disease outbreaks, particularly when power-law scaling is observed.
Development and Validation of MicrobEx: an Open-Source Package for Microbiology Culture Concept Extraction
Authors: Garrett Eickelberg, Yuan Luo, L. Nelson Sanchez-Pinto
Date: 2021-11-22 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Microbiology culture reports contain critical information for important clinical and public health applications. However, microbiology reports often have complex, semi-structured, free-text data that present a barrier for secondary use. Here we present the development and validation of an open-source package designed to ingest free-text microbiology reports, determine whether the culture is positive, and return a list of SNOMED-CT mapped bacteria. Our rule-based natural language processing algorithm was developed using microbiology reports from two different electronic health record systems in a large healthcare organization, and then externally validated on the reports of two other institutions with manually-extracted results as a benchmark. Our algorithm achieved F-1 scores >0.95 on all classification tasks across both validation sets. Our concept extraction Python package, MicrobEx, is designed to be reused and adapted to individual institutions as an upstream process for other clinical applications, such as machine learning studies, clinical decision support, and disease surveillance systems.
Pricing cryptocurrencies : Modelling the ETHBTC spot-quotient variation as a diffusion process
Authors: Sidharth Mallik
Date: 2021-11-23 00:00:00

This research proposes a model for the intraday variation between the ETHBTC spot and the quotient of ETHUSDT and BTCUSDT traded on Binance. Under conditions of no-arbitrage, perfect accuracy and no microstructure effects, the variation must be equal to its theoretically computed value of 0. We conduct our research on 4 years of data. We find that the variation is not constantly 0. The variation shows a fluctuating behaviour on either side of 0. Furthermore, the deviations tend to be larger in the first year than the rest of the years. We test the sample for the nature of diffusion where we find evidence of mean-reversion. We model the variation using an Ornstein-Uhlenbeck process. A maximum likelihood estimation procedure is used. From the accuracy of the sampling distribution of the parameters obtained, we conclude that the variation may be accurately modelled as an Ornstein-Uhlenbeck process. From the parameters obtained, the long-term mean is shown to have a negative sign and differs from the theoretical value of 0 at 1e-05 precision. We take note of the results in light of efficiency of the markets to price publicly known information.
What are the critical barriers to the development of hydrogen refueling stations in China? A modified fuzzy DEMATEL approach
Authors: Author links open overlay panelChuanboXuabYunnaWuabShuyuDaic
Date: 2020-07-01 00:00:00
Proceeding: Energy Policy Volume 142, July 2020, 111495 2020 7
Tasks: Decision Making

Hydrogen fuel cell vehicle is one of the important modalities of the next generation of new energy vehicles. As the basic infrastructure of hydrogen fuel cell vehicles, hydrogen refueling stations (HRSs) have drawn attention from all social circles. However, hindered by various barriers, the development of HRSs is much slower than the Chinese government's expectation. Hence, this study aims to explore critical barriers of the HRS development in China based on a modified fuzzy decision-making trial and evaluation laboratory (DEMATEL) method. Firstly, a barrier list is identified through a literature survey and expert consultation, which consists of four categories (economic, technological, social and political-regulatory aspect) and 12 potential barriers. Later, considering the subjectivity of threshold setting in the traditional fuzzy DEMATEL method, the K-means clustering algorithm is utilized to optimize the threshold value, and thus a modified fuzzy DEMATEL approach is proposed to investigate the prominence of these barriers and the cause-effect relationships among them. The results show that six barriers are regarded as key barriers to China's HRS development, and corresponding policy measures are formulated to mitigate or eliminate these critical barriers.
MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation
Authors: Wenhao Li, Hong Liu, Hao Tang, Pichao Wang, Luc van Gool
Date: 2021-11-24 00:00:00
Proceeding: CVPR 2022 1
Tasks: 3D Human Pose Estimation, Pose Estimation

Estimating 3D human poses from monocular videos is a challenging task due to depth ambiguity and self-occlusion. Most existing works attempt to solve both issues by exploiting spatial and temporal relationships. However, those works ignore the fact that it is an inverse problem where multiple feasible solutions (i.e., hypotheses) exist. To relieve this limitation, we propose a Multi-Hypothesis Transformer (MHFormer) that learns spatio-temporal representations of multiple plausible pose hypotheses. In order to effectively model multi-hypothesis dependencies and build strong relationships across hypothesis features, the task is decomposed into three stages: (i) Generate multiple initial hypothesis representations; (ii) Model self-hypothesis communication, merge multiple hypotheses into a single converged representation and then partition it into several diverged hypotheses; (iii) Learn cross-hypothesis communication and aggregate the multi-hypothesis features to synthesize the final 3D pose. Through the above processes, the final representation is enhanced and the synthesized pose is much more accurate. Extensive experiments show that MHFormer achieves state-of-the-art results on two challenging datasets: Human3.6M and MPI-INF-3DHP. Without bells and whistles, its performance surpasses the previous best result by a large margin of 3% on Human3.6M. Code and models are available at \url{https://github.com/Vegetebird/MHFormer}.
MixSyn: Learning Composition and Style for Multi-Source Image Synthesis
Authors: Ilke Demir, Umur A. Ciftci
Date: 2021-11-24 00:00:00
Tasks: Diversity, Image Generation, Semantic Composition

Synthetic images created by generative models increase in quality and expressiveness as newer models utilize larger datasets and novel architectures. Although this photorealism is a positive side-effect from a creative standpoint, it becomes problematic when such generative models are used for impersonation without consent. Most of these approaches are built on the partial transfer between source and target pairs, or they generate completely new samples based on an ideal distribution, still resembling the closest real sample in the dataset. We propose MixSyn (read as " mixin' ") for learning novel fuzzy compositions from multiple sources and creating novel images as a mix of image regions corresponding to the compositions. MixSyn not only combines uncorrelated regions from multiple source masks into a coherent semantic composition, but also generates mask-aware high quality reconstructions of non-existing images. We compare MixSyn to state-of-the-art single-source sequential generation and collage generation approaches in terms of quality, diversity, realism, and expressive power; while also showcasing interactive synthesis, mix & match, and edit propagation tasks, with no mask dependency.
A Lightweight Graph Transformer Network for Human Mesh Reconstruction from 2D Human Pose
Authors: Ce Zheng, Matias Mendieta, Pu Wang, Aidong Lu, Chen Chen
Date: 2021-11-24 00:00:00
Tasks: 3D Human Pose Estimation, 3D Human Shape Estimation, Human Mesh Recovery, Virtual Try-on

Existing deep learning-based human mesh reconstruction approaches have a tendency to build larger networks in order to achieve higher accuracy. Computational complexity and model size are often neglected, despite being key characteristics for practical use of human mesh reconstruction models (e.g. virtual try-on systems). In this paper, we present GTRS, a lightweight pose-based method that can reconstruct human mesh from 2D human pose. We propose a pose analysis module that uses graph transformers to exploit structured and implicit joint correlations, and a mesh regression module that combines the extracted pose feature with the mesh template to reconstruct the final human mesh. We demonstrate the efficiency and generalization of GTRS by extensive evaluations on the Human3.6M and 3DPW datasets. In particular, GTRS achieves better accuracy than the SOTA pose-based method Pose2Mesh while only using 10.2% of the parameters (Params) and 2.5% of the FLOPs on the challenging in-the-wild 3DPW dataset. Code will be publicly available.
A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engine
Authors: David Solis-Martin, Juan Galan-Paez, Joaquin Borrego-Diaz
Date: 2021-11-24 00:00:00
Tasks: Bayesian Optimization, Model Selection, Remaining Useful Lifetime Estimation

This paper presents the data-driven techniques and methodologies used to predict the remaining useful life (RUL) of a fleet of aircraft engines that can suffer failures of diverse nature. The solution presented is based on two Deep Convolutional Neural Networks (DCNN) stacked in two levels. The first DCNN is used to extract a low-dimensional feature vector using the normalized raw data as input. The second DCNN ingests a list of vectors taken from the former DCNN and estimates the RUL. Model selection was carried out by means of Bayesian optimization using a repeated random subsampling validation approach. The proposed methodology was ranked in the third place of the 2021 PHM Conference Data Challenge.
An XGBoost-Based Forecasting Framework for Product Cannibalization
Authors: Gautham Bekal, Mohammad Bari
Date: 2021-11-24 00:00:00
Tasks: Demand Forecasting

Two major challenges in demand forecasting are product cannibalization and long term forecasting. Product cannibalization is a phenomenon in which high demand of some products leads to reduction in sales of other products. Long term forecasting involves forecasting the sales over longer time frame that is critical for strategic business purposes. Also, conventional methods, for instance, recurrent neural networks may be ineffective where train data size is small as in the case in this study. This work presents XGBoost-based three-stage framework that addresses product cannibalization and associated long term error propagation problems. The performance of the proposed three-stage XGBoost-based framework is compared to and is found superior than that of regular XGBoost algorithm.
On the (In)Tractability of Reinforcement Learning for LTL Objectives
Authors: Cambridge Yang, Michael Littman, Michael Carbin
Date: 2021-11-24 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In recent years, researchers have made significant progress in devising reinforcement-learning algorithms for optimizing linear temporal logic (LTL) objectives and LTL-like objectives. Despite these advancements, there are fundamental limitations to how well this problem can be solved. Previous studies have alluded to this fact but have not examined it in depth. In this paper, we address the tractability of reinforcement learning for general LTL objectives from a theoretical perspective. We formalize the problem under the probably approximately correct learning in Markov decision processes (PAC-MDP) framework, a standard framework for measuring sample complexity in reinforcement learning. In this formalization, we prove that the optimal policy for any LTL formula is PAC-MDP-learnable if and only if the formula is in the most limited class in the LTL hierarchy, consisting of formulas that are decidable within a finite horizon. Practically, our result implies that it is impossible for a reinforcement-learning algorithm to obtain a PAC-MDP guarantee on the performance of its learned policy after finitely many interactions with an unconstrained environment for LTL objectives that are not decidable within a finite horizon.