pwc-archive/papers-with-abstracts huggingface dataset:
The evolution of cooperation: an evolutionary advantage of individuals impedes the evolution of the population
Authors: Xiaoliang Wang, Andrew Harrison
Date: 2022-01-14 00:00:00

Range expansion is a universal process in biological systems, and therefore plays a part in biological evolution. Using a quantitative individual-based method based on the stochastic process, we identify that enhancing the inherent self-proliferation advantage of cooperators relative to defectors is a more effective channel to promote the evolution of cooperation in range expansion than weakening the benefit acquisition of defectors from cooperators. With this self-proliferation advantage, cooperators can rapidly colonize virgin space and establish spatial segregation more readily, which acts like a protective shield to further promote the evolution of cooperation in return. We also show that lower cell density and migration rate have a positive effect on the competition of cooperators with defectors. Biological evolution is based on competition between individuals and should therefore favor selfish behaviors. However, we observe a counterintuitive phenomenon that the evolution of a population is impeded by the fitness-enhancing chemotactic movement of individuals. This highlights a conflict between the interests of the individual and the population. The short-sighted selfish behavior of individuals may not be that favored in the competition between populations. Such information provides important implications for the handling of cooperation.
Monitoring the Economy in Real Time: Trends and Gaps in Real Activity and Prices
Authors: Thomas Hasenzagl, Filippo Pellegrino, Lucrezia Reichlin, Giovanni Ricco
Date: 2022-01-14 00:00:00
Tasks: Time Series

We propose two specifications of a real-time mixed-frequency semi-structural time series model for evaluating the output potential, output gap, Phillips curve, and Okun's law for the US. The baseline model uses minimal theory-based multivariate identification restrictions to inform trend-cycle decomposition, while the alternative model adds the CBO's output gap measure as an observed variable. The latter model results in a smoother output potential and lower cyclical correlation between inflation and real variables but performs worse in forecasting beyond the short term. This methodology allows for the assessment and real-time monitoring of official trend and gap estimates.
Detecting Multiple Structural Breaks in Systems of Linear Regression Equations with Integrated and Stationary Regressors
Authors: Karsten Schweikert
Date: 2022-01-14 00:00:00
Tasks: regression

In this paper, we propose a two-step procedure based on the group LASSO estimator in combination with a backward elimination algorithm to detect multiple structural breaks in linear regressions with multivariate responses. Applying the two-step estimator, we jointly detect the number and location of structural breaks, and provide consistent estimates of the coefficients. Our framework is flexible enough to allow for a mix of integrated and stationary regressors, as well as deterministic terms. Using simulation experiments, we show that the proposed two-step estimator performs competitively against the likelihood-based approach (Qu and Perron, 2007; Li and Perron, 2017; Oka and Perron, 2018) in finite samples. However, the two-step estimator is computationally much more efficient. An economic application to the identification of structural breaks in the term structure of interest rates illustrates this methodology.
Utilizing geospatial data for assessing energy security: Mapping small solar home systems using unmanned aerial vehicles and deep learning
Authors: Simiao Ren, Jordan Malof, T. Robert Fetter, Robert Beach, Jay Rineer, Kyle Bradbury
Date: 2022-01-14 00:00:00

Solar home systems (SHS), a cost-effective solution for rural communities far from the grid in developing countries, are small solar panels and associated equipment that provides power to a single household. A crucial resource for targeting further investment of public and private resources, as well as tracking the progress of universal electrification goals, is shared access to high-quality data on individual SHS installations including information such as location and power capacity. Though recent studies utilizing satellite imagery and machine learning to detect solar panels have emerged, they struggle to accurately locate many SHS due to limited image resolution (some small solar panels only occupy several pixels in satellite imagery). In this work, we explore the viability and cost-performance tradeoff of using automatic SHS detection on unmanned aerial vehicle (UAV) imagery as an alternative to satellite imagery. More specifically, we explore three questions: (i) what is the detection performance of SHS using drone imagery; (ii) how expensive is the drone data collection, compared to satellite imagery; and (iii) how well does drone-based SHS detection perform in real-world scenarios. We collect and publicly-release a dataset of high-resolution drone imagery encompassing SHS imaged under real-world conditions and use this dataset and a dataset from Rwanda to evaluate the capabilities of deep learning models to recognize SHS, including those that are too small to be reliably recognized in satellite imagery. The results suggest that UAV imagery may be a viable alternative to identify very small SHS from perspectives of both detection accuracy and financial costs of data collection. UAV-based data collection may be a practical option for supporting electricity access planning strategies for achieving sustainable development goals and for monitoring the progress towards those goals.
Adaptive Deep PnP Algorithm for Video Snapshot Compressive Imaging
Authors: Zongliang Wu, Chengshuai Yang, Xiongfei Su, Xin Yuan
Date: 2022-01-14 00:00:00
Tasks: Demosaicking, Denoising

Video Snapshot compressive imaging (SCI) is a promising technique to capture high-speed videos, which transforms the imaging speed from the detector to mask modulating and only needs a single measurement to capture multiple frames. The algorithm to reconstruct high-speed frames from the measurement plays a vital role in SCI. In this paper, we consider the promising reconstruction algorithm framework, namely plug-and-play (PnP), which is flexible to the encoding process comparing with other deep learning networks. One drawback of existing PnP algorithms is that they use a pre-trained denoising network as a plugged prior while the training data of the network might be different from the task in real applications. Towards this end, in this work, we propose the online PnP algorithm which can adaptively update the network's parameters within the PnP iteration; this makes the denoising network more applicable to the desired data in the SCI reconstruction. Furthermore, for color video imaging, RGB frames need to be recovered from Bayer pattern or named demosaicing in the camera pipeline. To address this challenge, we design a two-stage reconstruction framework to optimize these two coupled ill-posed problems and introduce a deep demosaicing prior specifically for video demosaicing which does not have much past works instead of using single image demosaicing networks. Extensive results on both simulation and real datasets verify the superiority of our adaptive deep PnP algorithm.
Unsupervised Sparse Unmixing of Atmospheric Trace Gases from Hyperspectral Satellite Data
Authors: Nicomino Fiscante, Pia Addabbo, Filippo Biondi, Gaetano Giunta, Danilo Orlando
Date: 2022-01-14 00:00:00
Tasks: blind source separation, Compressive Sensing, Retrieval

In this letter, a new approach for the retrieval of the vertical column concentrations of trace gases from hyperspectral satellite observations, is proposed. The main idea is to perform a linear spectral unmixing by estimating the abundances of trace gases spectral signatures in each mixed pixel collected by an imaging spectrometer in the ultraviolet region. To this aim, the sparse nature of the measurements is brought to light and the compressive sensing paradigm is applied to estimate the concentrations of the gases' endemembers given by an a priori wide spectral library, including reference cross sections measured at different temperatures and pressures at the same time. The proposed approach has been experimentally assessed using both simulated and real hyperspectral dataset. Specifically, the experimental analysis relies on the retrieval of sulfur dioxide during volcanic emissions using data collected by the TROPOspheric Monitoring Instrument. To validate the procedure, we also compare the obtained results with the sulfur dioxide total column product based on the differential optical absorption spectroscopy technique and the retrieved concentrations estimated using the blind source separation.
On the influence of several factors on pathway enrichment analysis
Authors: Sarah Mubeen, Alpha Tom Kodamullil, Martin Hofmann-Apitius, Daniel Domingo-Fernández
Date: 2022-01-14 00:00:00

Pathway enrichment analysis has become a widely used knowledge-based approach for the interpretation of biomedical data. Its popularity has led to an explosion of both enrichment methods and pathway databases. While the elegance of pathway enrichment lies in its simplicity, multiple factors can impact the results of such an analysis which may not be accounted for. Researchers may fail to give influential aspects their due, resorting instead to popular methods and gene set collections, or default settings. Despite ongoing efforts to establish set guidelines, meaningful results are still hampered by a lack of consensus or gold standards around how enrichment analysis should be conducted. Nonetheless, such concerns have prompted a series of benchmark studies specifically focused on evaluating the influence of various factors on pathway enrichment results. In this review, we organize and summarize the findings of these benchmarks to provide a comprehensive overview on the influence of these factors. Our work covers a broad spectrum of factors, spanning from methodological assumptions to those related to prior biological knowledge, such as pathway definitions and database choice. In doing so, we aim to shed light on how these aspects can lead to insignificant, uninteresting, or even contradictory results. Finally, we conclude the review by proposing future benchmarks as well as solutions to overcome some of the challenges which originate from the outlined factors.
Consciousness beyond neural fields: expanding the possibilities of what has not yet happened
Authors: Birgitta Dresp-Langley
Date: 2022-01-14 00:00:00

In the field theories in physics, any particular region of the presumed space-time continuum and all interactions between elementary objects therein can be objectively measured and/or accounted for mathematically. Since this does not apply to any of the field theories, or any other neural theory, of consciousness, their explanatory power is limited. As discussed in detail herein, the matter is complicated further by the facts than any scientifically operational definition of consciousness is inevitably partial, and that the phenomenon has no spatial dimensionality. Under the light of insights from research on meditation and expanded consciousness, chronic pain syndrome, healthy ageing, and eudaimonic well-being, we may conceive consciousness as a source of potential energy that has no clearly defined spatial dimensionality, but can produce significant changes in others and in the world, observable in terms of changes in time. It is argued that consciousness may have evolved to enable the human species to generate such changes in order to cope with unprecedented and/or unpredictable adversity. Such coping could, ultimately, include the conscious planning of our own extinction when survival on the planet is no longer an acceptable option.
Genome-wide networks reveal emergence of epidemic strains of Salmonella Enteritidis
Authors: Adam J. Svahn, Sheryl L. Chang, Rebecca J. Rockett, Oliver M. Cliff, Qinning Wang, Alicia Arnott, Marc Ramsperger, Tania C. Sorrell, Vitali Sintchenko, Mikhail Prokopenko
Date: 2022-01-14 00:00:00

Objectives: To enhance monitoring of high-burden foodborne pathogens, there is opportunity to combine pangenome data with network analysis. Methods: Salmonella enterica subspecies Enterica serovar Enteritidis isolates were referred to the New South Wales (NSW) Enteric Reference Laboratory between August 2015 and December 2019 (1033 isolates in total), inclusive of a confirmed outbreak. All isolates underwent whole genome sequencing. Distances between genomes were quantified by in silico MLVA as well as core SNPs, which informed construction of undirected networks. Prevalence-centrality spaces were generated from the undirected networks. Components on the undirected SNP network were considered alongside a phylogenetic tree representation. Results: Outbreak isolates were identifiable as distinct components on the MLVA and SNP networks. The MLVA network based centrality/prevalence space did not delineate the outbreak, whereas the outbreak was clearly delineated in the SNP network based centrality/prevalence space. Components on the undirected SNP network showed a high concordance to the SNP clusters based on phylogenetic analysis. Conclusions: Bacterial whole genome data in network based analysis can improve the resolution of population analysis. High concordance of network components and SNP clusters is promising for rapid population analyses of foodborne Salmonella spp. due to the low overhead of network analysis.
Systematic analysis reveals key microRNAs as diagnostic and prognostic factors in progressive stages of lung cancer
Authors: Dietrich Kong, Ke Wang, Qiu-Ning Zhang, Zhi-Tong Bing
Date: 2022-01-14 00:00:00
Tasks: Diagnostic, Dimensionality Reduction

MicroRNAs play an indispensable role in numerous biological processes ranging from organismic development to tumor progression.In oncology,these microRNAs constitute a fundamental regulation role in the pathology of cancer that provides the basis for probing into the influences on clinical features through transcriptome data. Previous work focused on machine learning (ML) for searching biomarkers in different cancer databases, but the functions of these biomarkers are fully not clear. Taking lung cancer as a prototype case of study. Through integrating clinical information into the transcripts expression data, we systematically analyzed the effect of microRNA on diagnostic and prognostic factors at deteriorative lung adenocarcinoma (LUAD). After dimension reduction, unsupervised hierarchical clustering was used to find the diagnostic factors which represent the unique expression patterns of microRNA at various patient's stages. In addition, we developed a classification framework, Light Gradient Boosting Machine (LightGBM) and SHAPley Additive explanation (SHAP) algorithm, to screen out the prognostic factors. Enrichment analyses show that the diagnostic and prognostic factors are not only enriched in cancer-related athways, but also involved in many vital cellular signaling transduction and immune responses. These key microRNAs also impact the survival risk of LUAD patients at all (or a specific) stage(s) and some of them target some important Transcription Factors (TF).The key finding is that five microRNAs (hsa-mir-196b, hsa-mir-31, hsa-mir-891a, hsa-mir-34c, and hsa-mir-653) can then serve as not only potential diagnostic factors but also prognostic tools in the monitoring of lung cancer.
A simple, sensitive and quantitative FACS-based test for SARS-CoV-2 serology in humans and animals
Authors: Agnès Maurel Ribes, Pierre Bessière, Jean Charles Guéry, Eloïse Joly Featherstone, Timothée Bruel, Remy Robinot, Olivier Schwartz, Romain Volmer, Florence Abravanel, Jacques Izopet, Etienne Joly
Date: 2022-01-14 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Specificity

Serological tests are important for understanding the physiopathology and following the evolution of the Covid-19 pandemic. Assays based on flow cytometry (FACS) of tissue culture cells expressing the spike (S) protein of SARS-CoV-2 have repeatedly proven to perform slightly better than the plate-based assays ELISA and CLIA (chemiluminescent immuno-assay), and markedly better than lateral flow immuno-assays (LFIA). Here, we describe an optimized and very simple FACS assay based on staining a mix of two Jurkat cell lines, expressing either high levels of the S protein (Jurkat-S) or a fluorescent protein (Jurkat-R expressing m-Cherry, or Jurkat-G, expressing GFP, which serve as an internal negative control). We show that the Jurkat-S\&R-flow test has a much broader dynamic range than a commercial ELISA test and performs at least as well in terms of sensitivity and specificity. Also, it is more sensitive and quantitative than the hemagglutination-based test HAT, which we described recently. The Jurkat-flow test requires only a few microliters of blood; thus, it can be used to quantify various Ig isotypes in capillary blood collected from a finger prick. It can be used also to evaluate serological responses in mice, hamsters, cats and dogs. FACS tests offer a very attractive solution for laboratories with access to tissue culture and flow cytometry who want to monitor serological responses in humans or in animals, and how these relate to susceptibility to infection, or re-infection, by the virus, and to protection against Covid-19.
Quantifying yeast colony morphologies with feature engineering from time-lapse photography
Authors: Andy Goldschmidt, James Kunert-Graf, Adrian C. Scott, Zhihao Tan, Aimée M. Dudley, J. Nathan Kutz
Date: 2022-01-14 00:00:00
Tasks: Clustering, Feature Engineering

Baker's yeast (Saccharomyces cerevisiae) is a model organism for studying the morphology that emerges at the scale of multi-cell colonies. To look at how morphology develops, we collect a dataset of time-lapse photographs of the growth of different strains of S. cerevisiae. We discuss the general statistical challenges that arise when using time-lapse photographs to extract time-dependent features. In particular, we show how texture-based feature engineering and representative clustering can be successfully applied to categorize the development of yeast colony morphology using our dataset. The local binary pattern (LBP) from image processing is used to score the surface texture of colonies. This texture score develops along a smooth trajectory during growth. The path taken depends on how the morphology emerges. A hierarchical clustering of the colonies is performed according to their texture development trajectories. The clustering method is designed for practical interpretability; it obtains the best representative colony image for any hierarchical sub-cluster.
Tropical Group Testing
Authors: Hsin-Po Wang, Ryan Gabrys, Alexander Vardy
Date: 2022-01-12 00:00:00
Tasks: compressed sensing

Polymerase chain reaction (PCR) testing is the gold standard for diagnosing COVID-19. PCR amplifies the virus DNA 40 times to produce measurements of viral loads that span seven orders of magnitude. Unfortunately, the outputs of these tests are imprecise and therefore quantitative group testing methods, which rely on precise measurements, are not applicable. Motivated by the ever-increasing demand to identify individuals infected with SARS-CoV-19, we propose a new model that leverages tropical arithmetic to characterize the PCR testing process. Our proposed framework, termed tropical group testing, overcomes existing limitations of quantitative group testing by allowing for imprecise test measurements. In many cases, some of which are highlighted in this work, tropical group testing is provably more powerful than traditional binary group testing in that it require fewer tests than classical approaches, while additionally providing a mechanism to identify the viral load of each infected individual. It is also empirically stronger than related works that have attempted to combine PCR, quantitative group testing, and compressed sensing.
Design and Development of PCN-Miner: A tool for the Analysis of Protein Contact Networks
Authors: Pietro Hiram Guzzi, Luisa Di Paola, Alessandro Giuliani, Pierangelo Veltri
Date: 2022-01-12 00:00:00
Tasks: Clustering

Protein Contact Network (PCN) is a powerful tool for analysing the structure and function of proteins. In particular, PCN has been used for disclosing the molecular features of allosteric regulation through PCN clustering. Such analysis is relevant in many applications, such as the recent study of SARS-CoV-2 Spike Protein. Despite its relevance, methods for the analysis of PCN are spread into a set of different libraries and tools. Therefore, the introduction of a tool that incorporates all the function may help researchers. We present PCN-Miner a software tool implemented in the Python programming language able to import protein in the Protein Data Bank format and generate the corresponding protein contact network. Then it offers a set of algorithms for the analysis of PCS that cover a large set of applications: from clustering to embedding and subsequent analysis. Software is available at \url{https://github.com/hguzzi/ProteinContactNetworks}
Strategic mean-variance investing under mean-reverting stock returns
Authors: Søren Fiig Jarner
Date: 2022-01-14 00:00:00

In this report we derive the strategic (deterministic) allocation to bonds and stocks resulting in the optimal mean-variance trade-off on a given investment horizon. The underlying capital market features a mean-reverting process for equity returns, and the primary question of interest is how mean-reversion effects the optimal strategy and the resulting portfolio value at the horizon. In particular, we are interested in knowing under which assumptions and on which horizons, the risk-reward trade-off is so favourable that the value of the portfolio is effectively bounded from below on the horizon. In this case, we might think of the portfolio as providing a stochastic excess return on top of a "guarantee" (the lower bound). Deriving optimal strategies is a well-known discipline in mathematical finance. The modern approach is to derive and solve the Hamilton-Jacobi-Bellman (HJB) differential equation characterizing the strategy leading to highest expected utility, for given utility function. However, for two reasons we approach the problem differently in this work. First, we wish to find the optimal strategy depending on time only, i.e., we do not allow for dependencies on capital market state variables, nor the value of the portfolio itself. This constraint characterizes the strategic allocation of long-term investors. Second, to gain insights on the role of mean-reversion, we wish to identify the entire family of extremal strategies, not only the optimal strategies. To derive the strategies we employ methods from calculus of variations, rather than the usual HJB approach.
Pricing principle via Tsallis relative entropy in incomplete market
Authors: Dejian Tian
Date: 2022-01-14 00:00:00
Tasks: Translation

A pricing principle is introduced for non-attainable $q$-exponential bounded contingent claims in an incomplete Brownian motion market setting. The buyer evaluates the contingent claim under the ``distorted Radon-Nikodym derivative'' and adjustment by Tsallis relative entropy over a family of equivalent martingale measures. The pricing principle is proved to be a time consistent and arbitrage-free pricing rule. More importantly, this pricing principle is found to be closely related to backward stochastic differential equations with generators $f(y)|z|^2$ type. The pricing functional is compatible with prices for attainable claims. Except translation invariance, the pricing principle processes lots of elegant properties such as monotonicity and concavity etc. The pricing functional is showed between minimal martingale measure pricing and conditional certainty equivalent pricing under $q$-exponential utility. The asymptotic behavior of the pricing principle for ambiguity aversion coefficient is also investigated.