pwc-archive/papers-with-abstracts huggingface dataset:
Classification algorithms applied to structure formation simulations
Authors: Jazhiel Chacón, J. Alberto Vázquez, Erick Almaraz
Date: 2021-06-11 00:00:00
Tasks: Binary Classification, Classification

Throughout cosmological simulations, the properties of the matter density field in the initial conditions have a decisive impact on the features of the structures formed today. In this paper we use a random-forest classification algorithm to infer whether or not dark matter particles, traced back to the initial conditions, would end up in dark matter halos whose masses are above some threshold. This problem might be posed as a binary classification task, where the initial conditions of the matter density field are mapped into classification labels provided by a halo finder program. Our results show that random forests are effective tools to predict the output of cosmological simulations without running the full process. These techniques might be used in the future to decrease the computational time and to explore more efficiently the effect of different dark matter/dark energy candidates on the formation of cosmological structures.
On the Enabling of Multi-receiver Communications with Reconfigurable Intelligent Surfaces
Authors: Hamidreza Taghvaee, Akshay Jain, Sergi Abadal, Gabriele Gradoni, Eduard Alarcón, Albert Cabellos-Aparicio
Date: 2021-06-12 00:00:00

The reconfigurable intelligent surface is a promising technology for the manipulation and control of wireless electromagnetic signals. In particular, it has the potential to provide significant performance improvements for wireless networks. However, to do so, a proper reconfiguration of the reflection coefficients of unit cells is required, which often leads to complex and expensive devices. To amortize the cost, one may share the system resources among multiple transmitters and receivers. In this paper, we propose an efficient reconfiguration technique providing control over multiple beams independently. Compared to time-consuming optimization techniques, the proposed strategy utilizes an analytical method to configure the surface for multi-beam radiation. This method is easy to implement, effective and efficient since it only requires phase reconfiguration. We analyze the performance for indoor and outdoor scenarios, given the broadcast mode of operation. The aforesaid scenarios encompass some of the most challenging scenarios that wireless networks encounter. We show that our proposed technique provisions sufficient improvements in the observed channel capacity when the receivers are close to the surface in the indoor office environment scenario. Further, we report a considerable increase in the system throughput given the outdoor environment.
Measuring the repertoire of age-related behavioral changes in Drosophila melanogaster
Authors: Katherine E. Overman, Daniel M. Choi, Kawai Leung, Joshua W. Shaevitz, Gordon J. Berman
Date: 2021-06-14 00:00:00

Aging affects almost all aspects of an organism -- its morphology, its physiology, its behavior. Isolating which biological mechanisms are regulating these changes, however, has proven difficult, potentially due to our inability to characterize the full repertoire of an animal's behavior across the lifespan. Using data from fruit flies (D. melanogaster) we measure the full repertoire of behaviors as a function of age. We observe a sexually dimorphic pattern of changes in the behavioral repertoire during aging. Although the stereotypy of the behaviors and the complexity of the repertoire overall remains relatively unchanged, we find evidence that the observed alterations in behavior can be explained by changing the fly's overall energy budget, suggesting potential connections between metabolism, aging, and behavior.
Nonlinear signal transduction network with multistate
Authors: Han-Yu Jiang, Jun He
Date: 2021-06-14 00:00:00

Signal transduction is an important and basic mechanism to cell life activities. The stochastic state transition of receptor induces the release of signaling molecular, which triggers the state transition of other receptors. It constructs a nonlinear sigaling network, and leads to robust switchlike properties which are critical to biological function. Network architectures and state transitions of receptor affect the performance of this biological network. In this work, we perform a study of nonlinear signaling on biological polymorphic network by analyzing network dynamics of the Ca$^{2+}$ induced Ca$^{2+}$ release mechanism, where fast and slow processes are involved and the receptor has four conformational states. Three types of networks, Erd\"os-R\'enyi network, Watts-Strogatz network and BaraB\'asi-Albert network, are considered with different parameters. The dynamics of the biological networks exhibit different patterns at different time scales. At short time scale, the second open state is essential to reproduce the quasi-bistable regime, which emerges at a critical strength of connection for all three states involved in the fast processes and disappears at another critical point. The pattern at short time scale is not sensitive to the network architecture. At long time scale, only monostable regime is observed, and difference of network architectures affects the results more seriously. Our finding identifies features of nonlinear signaling networks with multistate that may underlie their biological function.
The Auditory Tuning of a Keyboard
Authors: Johan M. Broekaert
Date: 2021-06-11 00:00:00

An optimal auditory tunable well (circular) temperament is determined. A temperament that is applicable in practice is derived from this optimum. No other historical temperament fits as well, with this optimum. A brief comparison of temperaments is worked out.
Geodesic Density Regression for Correcting 4DCT Pulmonary Respiratory Motion Artifacts
Authors: Wei Shao, Yue Pan, Oguz C. Durumeric, Joseph M. Reinhardt, John E. Bayouth, Mirabela Rusu, Gary E. Christensen
Date: 2021-06-12 00:00:00
Tasks: regression, Time Series Analysis

Pulmonary respiratory motion artifacts are common in four-dimensional computed tomography (4DCT) of lungs and are caused by missing, duplicated, and misaligned image data. This paper presents a geodesic density regression (GDR) algorithm to correct motion artifacts in 4DCT by correcting artifacts in one breathing phase with artifact-free data from corresponding regions of other breathing phases. The GDR algorithm estimates an artifact-free lung template image and a smooth, dense, 4D (space plus time) vector field that deforms the template image to each breathing phase to produce an artifact-free 4DCT scan. Correspondences are estimated by accounting for the local tissue density change associated with air entering and leaving the lungs, and using binary artifact masks to exclude regions with artifacts from image regression. The artifact-free lung template image is generated by mapping the artifact-free regions of each phase volume to a common reference coordinate system using the estimated correspondences and then averaging. This procedure generates a fixed view of the lung with an improved signal-to-noise ratio. The GDR algorithm was evaluated and compared to a state-of-the-art geodesic intensity regression (GIR) algorithm using simulated CT time-series and 4DCT scans with clinically observed motion artifacts. The simulation shows that the GDR algorithm has achieved significantly more accurate Jacobian images and sharper template images, and is less sensitive to data dropout than the GIR algorithm. We also demonstrate that the GDR algorithm is more effective than the GIR algorithm for removing clinically observed motion artifacts in treatment planning 4DCT scans. Our code is freely available at https://github.com/Wei-Shao-Reg/GDR.
Least Squares Optimal Density Compensation for the Gridding Non-uniform Discrete Fourier Transform
Authors: Nicholas Dwork, Daniel O'Connor, Ethan M. I. Johnson, Corey A. Baron, Jeremy W. Gordon, John M. Pauly, Peder E. Z. Larson
Date: 2021-06-12 00:00:00

The Gridding algorithm has shown great utility for reconstructing images from non-uniformly spaced samples in the Fourier domain in several imaging modalities. Due to the non-uniform spacing, some correction for the variable density of the samples must be made. Existing methods for generating density compensation values are either sub-optimal or only consider a finite set of points (a set of measure 0) in the optimization. This manuscript presents the first density compensation algorithm for a general trajectory that takes into account the point spread function over a set of non-zero measure. We show that the images reconstructed with Gridding using the density compensation values of this method are of superior quality when compared to density compensation weights determined in other ways. Results are shown with a numerical phantom and with magnetic resonance images of the abdomen and the knee.
Stability and selective extinction in complex mutualistic networks
Authors: Hyun Woo Lee, Jae Woo Lee, Deok-Sun Lee
Date: 2021-06-14 00:00:00

We study species abundance in the empirical plant-pollinator mutualistic networks exhibiting broad degree distributions, with uniform intra-group competition assumed, by the Lotka-Volterra equation. The stability of a fixed point is found to be identified by the signs of its non-zero components and those of its neighboring fixed points. Taking the annealed approximation, we derive the non-zero components to be formulated in terms of degrees and the rescaled interaction strengths, which lead us to find different stable fixed points depending on parameters, and we obtain the phase diagram. The selective extinction phase finds small-degree species extinct and effective interaction reduced, maintaining stability and hindering the onset of instability. The non-zero minimum species abundances from different empirical networks show data collapse when rescaled as predicted theoretically.
Exogenous and Endogenous Price Jumps Belong to Different Dynamical Classes
Authors: Riccardo Marcaccioli, Jean-Philippe Bouchaud, Michael Benzaquen
Date: 2021-06-13 00:00:00

Synchronising a database of stock specific news with 5 years worth of order book data on 300 stocks, we show that abnormal price movements following news releases (exogenous) exhibit markedly different dynamical features from those arising spontaneously (endogenous). On average, large volatility fluctuations induced by exogenous events occur abruptly and are followed by a decaying power-law relaxation, while endogenous price jumps are characterized by progressively accelerating growth of volatility, also followed by a power-law relaxation, but slower than for exogenous jumps. Remarkably, our results are reminiscent of what is observed in different contexts, namely Amazon book sales and YouTube views. Finally, we show that fitting power-laws to {\it individual} volatility profiles allows one to classify large events into endogenous and exogenous dynamical classes, without relying on the news feed.
The Data Forecast in COVID-19 Model with Applications to US, South Korea, Brazil, India, Russia and Italy
Authors: Bo-Cyuan Lin, Yen-Jia Chen, Yi-Cheng Hung, Chun-Sheng Chen, Han-Chun Wang, Jann-Long Chern
Date: 2020-11-05 00:00:00

In this paper, we firstly propose SQIARD and SIARD models to investigate the transmission of COVID-19 with quarantine, infected and asymptomatic infected, and discuss the relation between the respective basic reproduction number $R_0, R_Q$ and the stability of the equilibrium points of model. Secondly, after training the related data parameters, in our numerical simulations, we respectively conduct the forecast of the data of US, South Korea, Brazil, India, Russia and Italy, and the effect of prediction of the epidemic situation in each country. Furthermore, we apply US data to compare SQIARD with SIARD, and display the effects of predictions.
Quantum Portfolio Optimization with Investment Bands and Target Volatility
Authors: Samuel Palmer, Serkan Sahin, Rodrigo Hernandez, Samuel Mugel, Roman Orus
Date: 2021-06-12 00:00:00
Tasks: Portfolio Optimization

In this paper we show how to implement in a simple way some complex real-life constraints on the portfolio optimization problem, so that it becomes amenable to quantum optimization algorithms. Specifically, first we explain how to obtain the best investment portfolio with a given target risk. This is important in order to produce portfolios with different risk profiles, as typically offered by financial institutions. Second, we show how to implement individual investment bands, i.e., minimum and maximum possible investments for each asset. This is also important in order to impose diversification and avoid corner solutions. Quite remarkably, we show how to build the constrained cost function as a quadratic binary optimization (QUBO) problem, this being the natural input of quantum annealers. The validity of our implementation is proven by finding the optimal portfolios, using D-Wave Hybrid and its Advantage quantum processor, on portfolios built with all the assets from S&P100 and S&P500. Our results show how practical daily constraints found in quantitative finance can be implemented in a simple way in current NISQ quantum processors, with real data, and under realistic market conditions. In combination with clustering algorithms, our methods would allow to replicate the behaviour of more complex indexes, such as Nasdaq Composite or others, in turn being particularly useful to build and replicate Exchange Traded Funds (ETF).
Dynamic Asymmetric Causality Tests with an Application
Authors: Abdulnasser Hatemi-J
Date: 2021-06-14 00:00:00
Tasks: Time Series, Time Series Analysis

Testing for causation, defined as the preceding impact of the past values of one variable on the current value of another one when all other pertinent information is accounted for, is increasingly utilized in empirical research of the time-series data in different scientific disciplines. A relatively recent extension of this approach has been allowing for potential asymmetric impacts since it is harmonious with the way reality operates in many cases according to Hatemi-J (2012). The current paper maintains that it is also important to account for the potential change in the parameters when asymmetric causation tests are conducted, as there exists a number of reasons for changing the potential causal connection between variables across time. The current paper extends therefore the static asymmetric causality tests by making them dynamic via the usage of subsamples. An application is also provided consistent with measurable definitions of economic or financial bad as well as good news and their potential interaction across time.
An Integration and Operation Framework of Geothermal Heat Pumps in Distribution Networks
Authors: Lei Liang, Xuan Zhang, Hongbin Sun
Date: 2021-06-12 00:00:00
Tasks: energy management, Management

The application of the energy-efficient thermal and energy management mechanism in Geothermal Heat Pumps (GHPs) is indispensable to reduce the overall energy consumption and carbon emission across the building sector. Besides, in the Virtual Power Plant (VPP) system, the demand response of clustered GHP systems can improve the operating flexibility of the power grid. This paper presents an integration and operation framework of GHPs in the distribution network, applying a layered communication and optimization method to coordinate multiple clustered GHPs in a community. In the proposed hierarchical operation scheme, the operator of regional GHPs collects the thermal zone information and the disturbance prediction of buildings in a short time granularity, predicts the energy demand, and transmits the information to an aggregator. Using a novel linearized optimal power flow model, the aggregator coordinates and aggregates load resources of GHP systems in the distribution network. In this way, GHP systems with thermal and energy management mechanisms can be applied to achieve the demand response in the VPP and offer more energy flexibility to the community.
The value of travel speed
Authors: Cornelis Dirk van Goeverden
Date: 2021-06-11 00:00:00

Travel speed is an intrinsic feature of transport, and enlarging the speed is considered as beneficial. The benefit of a speed increase is generally assessed as the value of the saved travel time. However, this approach conflicts with the observation that time spent on travelling is rather constant and might not be affected by speed changes. The paper aims to define the benefits of a speed increase and addresses two research questions. First, how will a speed increase in person transport work out, which factors are affected? Second, is the value of time a good proxy for the value of speed? Based on studies on time spending and research on the association between speed and land use, we argue that human wealth could be the main affected factor by speed changes, rather than time or access. Then the value of time is not a good proxy for the value of speed: the benefits of a wealth increase are negatively correlated with prosperity following the law of diminishing marginal utility, while the calculated benefits of saved travel time prove to be positively correlated. The inadequacy of the value of time is explained by some shortcomings with respect to the willingness to pay that is generally used for assessing the value of time: people do not predict correctly the personal benefits that will be gained from a decision, and they neglect the social impacts.
A new measure between sets of probability distributions with applications to erratic financial behavior
Authors: Nick James, Max Menzies
Date: 2021-06-10 00:00:00
Tasks: Change Point Detection, Portfolio Optimization, Time Series, Time Series Analysis

This paper introduces a new framework to quantify distance between finite sets with uncertainty present, where probability distributions determine the locations of individual elements. Combining this with a Bayesian change point detection algorithm, we produce a new measure of similarity between time series with respect to their structural breaks. First, we demonstrate the algorithm's effectiveness on a collection of piecewise autoregressive processes. Next, we apply this to financial data to study the erratic behavior profiles of 19 countries and 11 sectors over the past 20 years. Our measure provides quantitative evidence that there is greater collective similarity among sectors' erratic behavior profiles than those of countries, which we observe upon individual inspection of these time series. Our measure could be used as a new framework or complementary tool for investors seeking to make asset allocation decisions for financial portfolios.
The relationship between the US broad money supply and US GDP for the time period 2001 to 2019 with that of the corresponding time series for US national property and stock market indices, using an information entropy methodology
Authors: Laurence Lacey
Date: 2021-06-03 00:00:00
Tasks: Time Series Analysis

The primary objective of this paper was to investigate whether the growth in the major US asset indices could be a function of the US broad money supply and/or US GDP, over the time period 2001 to 2019, using an information entropy methodology. The four US asset indices investigated were: (1) US National Property index; (2) Russell 2000 index; (3) S&P 500 index; and (4) NASDAQ index. Notwithstanding the financial crisis of 2007-2008, US real GDP increased exponentially over the period 2001 to 2019, with an average annual growth rate of approximately 2%. However, over this time period, the average annual rate of growth of US GDP was considerably lower than the average annual rate of growth of the US broad money supply (5.7%). The main determinant of the average growth rate for all four US asset indices studied would appear to be the growth rate in the US broad money supply. In addition, the growth rate in the US Russell 2000 stock index and the NASDAQ index would appear to be a function of the combined positive effects of both the growth rate in the US Broad Money Supply and the growth rate of US GDP.
Mechanism Design meets Priority Design: Redesigning the US Army's Branching Process
Authors: Kyle Greenberg, Parag A. Pathak, Tayfun Sonmez
Date: 2021-06-11 00:00:00

Army cadets obtain occupations through a centralized process. Three objectives -- increasing retention, aligning talent, and enhancing trust -- have guided reforms to this process since 2006. West Point's mechanism for the Class of 2020 exacerbated challenges implementing Army policy aims. We formulate these desiderata as axioms and study their implications theoretically and with administrative data. We show that the Army's objectives not only determine an allocation mechanism, but also a specific priority policy, a uniqueness result that integrates mechanism and priority design. These results led to a re-design of the mechanism, now adopted at both West Point and ROTC.
Dual-Path Filter Network: Speaker-Aware Modeling for Speech Separation
Authors: Fan-Lin Wang, Yu-Huai Peng, Hung-Shin Lee, Hsin-Min Wang
Date: 2021-06-14 00:00:00
Tasks: Speech Separation

Speech separation has been extensively studied to deal with the cocktail party problem in recent years. All related approaches can be divided into two categories: time-frequency domain methods and time domain methods. In addition, some methods try to generate speaker vectors to support source separation. In this study, we propose a new model called dual-path filter network (DPFN). Our model focuses on the post-processing of speech separation to improve speech separation performance. DPFN is composed of two parts: the speaker module and the separation module. First, the speaker module infers the identities of the speakers. Then, the separation module uses the speakers' information to extract the voices of individual speakers from the mixture. DPFN constructed based on DPRNN-TasNet is not only superior to DPRNN-TasNet, but also avoids the problem of permutation-invariant training (PIT).
Speech Disorder Classification Using Extended Factorized Hierarchical Variational Auto-encoders
Authors: Jinzi Qi, Hugo Van hamme
Date: 2021-06-14 00:00:00
Tasks: Classification, Disentanglement, Representation Learning, Sentence

Objective speech disorder classification for speakers with communication difficulty is desirable for diagnosis and administering therapy. With the current state of speech technology, it is evident to propose neural networks for this application. But neural network model training is hampered by a lack of labeled disordered speech data. In this research, we apply an extended version of Factorized Hierarchical Variational Auto-encoders (FHVAE) for representation learning on disordered speech. The FHVAE model extracts both content-related and sequence-related latent variables from speech data, and we utilize the extracted variables to explore how disorder type information is represented in the latent variables. For better classification performance, the latent variables are aggregated at the word and sentence level. We show that an extension of the FHVAE model succeeds in the better disentanglement of the content-related and sequence-related related representations, but both representations are still required for best results on disorder type classification.