pwc-archive/papers-with-abstracts huggingface dataset:
Distinguishable Cash, Bosonic Bitcoin, and Fermionic Non-fungible Token
Authors: Zae Young Kim, Jeong-Hyuck Park
Date: 2022-11-01 00:00:00

Modern technology has brought novel types of wealth. In contrast to hard cashes, digital currencies do not have a physical form. They exist in electronic forms only. Yet, it has not been clear what impacts their ongoing growth will make, if any, on wealth distribution. Here we propose to identify all forms of contemporary wealth into two classes: 'distinguishable' or 'identical'. Traditional tangible moneys are all distinguishable. Financial assets and cryptocurrencies, such as bank deposits and Bitcoin, are boson-like, while non-fungible tokens are fermion-like. We derive their ownership-based distributions in a unified manner. Each class follows essentially the Poisson or the geometric distribution. We contrast their distinct features such as Gini coefficients. Further, aggregating different kinds of wealth corresponds to a weighted convolution where the number of banks matters and Bitcoin follows Bose-Einstein distribution. Our proposal opens a new avenue to understand the deepened inequality in modern economy, which is based on the statistical physics property of wealth rather than the individual ability of owners. We call for verifications with real data.
Pairing cellular and synaptic dynamics into building blocks of rhythmic neural circuits
Authors: James Scully, Jassem Bourahmah, David Bloom, Andrey L Shilnikov
Date: 2022-11-01 00:00:00
Tasks: Rhythm

The purpose of this paper is trifold -- to serve as an instructive resource and a reference catalog for biologically plausible modeling with i) conductance-based models, coupled with ii) strength-varying slow synapse models, culminating in iii) two canonical pair-wise rhythm-generating networks. We document the properties of basic network components: cell models and synaptic models, which are prerequisites for proper network assembly. Using the slow-fast decomposition we present a detailed analysis of the cellular dynamics including a discussion of the most relevant bifurcations. Several approaches to model synaptic coupling are also discussed, and a new logistic model of slow synapses is introduced. Finally, we describe and examine two types of bicellular rhythm-generating networks: i) half-center oscillators ii) excitatory-inhibitory pairs and elucidate a key principle -- the network hysteresis underlying the stable onset of emergent slow bursting in these neural building blocks. These two cell networks are a basis for more complicated neural circuits of rhythmogensis and feature in our models of swim central pattern generators.
Evolutionary Innovation by Polyploidy
Authors: Tetsuhiro S. Hatakeyama, Ryudo Ohbayashi
Date: 2022-11-01 00:00:00

The preferred conditions for evolutionary innovation is a fundamental question, but little is known, in part because the question involves rare events. We focused on the potential role of polyploidy in the evolution of novel traits. There are two hypotheses regarding the effects of polyploidy on evolution: Polyploidy reduces the effect of a single mutation and slows evolution. In contrast, the gene redundancy introduced by polyploidy will promote neofunctionalization and accelerate evolution. Does polyploidy speed up or slow down evolution? In this study, we proposed a simple model of polyploid cells and showed that the evolutionary rate of polyploids is similar to or much slower than that of haploids under neutral selection or during gradual evolution. However, on a fitness landscape where cells should jump over a lethal valley to increase their fitness, the probability of evolution in polyploidy could be drastically increased, and the optimal number of chromosomes was identified. We theoretically discussed the existence of this optimal chromosome number from the large deviation theory. Furthermore, we proposed that the optimization for achieving evolutionary innovation could determine the range of chromosome numbers in polyploid bacteria.
Population and Technological Growth: Evidence from Roe v. Wade
Authors: John T. H. Wong, Matthias Hei Man, Alex Li Cheuk Hung
Date: 2022-11-01 00:00:00

We exploit the heterogeneous impact of the Roe v. Wade ruling by the US Supreme Court, which ruled most abortion restrictions unconstitutional. Our identifying assumption is that states which had not liberalized their abortion laws prior to Roe would experience a negative birth shock of greater proportion than states which had undergone pre-Roe reforms. We estimate the difference-in-difference in births and use estimated births as an exogenous treatment variable to predict patents per capita. Our results show that one standard deviation increase in cohort starting population increases per capita patents by 0.24 standard deviation. These results suggest that at the margins, increasing fertility can increase patent production. Insofar as patent production is a sufficient proxy for technological growth, increasing births has a positive impact on technological growth. This paper and its results do not pertain to the issue of abortion itself.
Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data
Authors: Giovanni Ballarin, Petros Dellaportas, Lyudmila Grigoryeva, Marcel Hirt, Sophie van Huellen, Juan-Pablo Ortega
Date: 2022-11-01 00:00:00

Macroeconomic forecasting has recently started embracing techniques that can deal with large-scale datasets and series with unequal release periods. MIxed-DAta Sampling (MIDAS) and Dynamic Factor Models (DFM) are the two main state-of-the-art approaches that allow modeling series with non-homogeneous frequencies. We introduce a new framework called the Multi-Frequency Echo State Network (MFESN) based on a relatively novel machine learning paradigm called reservoir computing. Echo State Networks (ESN) are recurrent neural networks formulated as nonlinear state-space systems with random state coefficients where only the observation map is subject to estimation. MFESNs are considerably more efficient than DFMs and allow for incorporating many series, as opposed to MIDAS models, which are prone to the curse of dimensionality. All methods are compared in extensive multistep forecasting exercises targeting US GDP growth. We find that our MFESN models achieve superior or comparable performance over MIDAS and DFMs at a much lower computational cost.
Weak Identification in Low-Dimensional Factor Models with One or Two Factors
Authors: Gregory Cox
Date: 2022-11-01 00:00:00

This paper describes how to reparameterize low-dimensional factor models with one or two factors to fit weak identification theory developed for generalized method of moments models. Some identification-robust tests, here called "plug-in" tests, require a reparameterization to distinguish weakly identified parameters from strongly identified parameters. The reparameterizations in this paper make plug-in tests available for subvector hypotheses in low-dimensional factor models with one or two factors. Simulations show that the plug-in tests are less conservative than identification-robust tests that use the original parameterization. An empirical application to a factor model of parental investments in children is included.
New Concept for the Value Function of Prospect Theory
Authors: Kazuo Sano
Date: 2022-10-31 00:00:00
Tasks: Position

In the prospect theory, value function is typically concave for gains, commonly convex for losses, with losses usually having a steeper slope than gains. The neural system largely differs from the loss and gains sides. Five new studies on neurons related to this issue have examined neuronal responses to losses, gains, and reference points. This study investigates a new concept of the value function. A value function with a neuronal cusp may show variations and behavior cusps with catastrophe where a trader closes one's position.
TITAN: Bringing The Deep Image Prior to Implicit Representations
Authors: Lorenzo Luzi, Daniel LeJeune, Ali Siahkoohi, Sina AlEMohammad, Vishwanath Saragadam, Hossein Babaei, Naiming Liu, Zichao Wang, Richard G. Baraniuk
Date: 2022-11-01 00:00:00
Tasks: Decoder, Super-Resolution

We study the interpolation capabilities of implicit neural representations (INRs) of images. In principle, INRs promise a number of advantages, such as continuous derivatives and arbitrary sampling, being freed from the restrictions of a raster grid. However, empirically, INRs have been observed to poorly interpolate between the pixels of the fit image; in other words, they do not inherently possess a suitable prior for natural images. In this paper, we propose to address and improve INRs' interpolation capabilities by explicitly integrating image prior information into the INR architecture via deep decoder, a specific implementation of the deep image prior (DIP). Our method, which we call TITAN, leverages a residual connection from the input which enables integrating the principles of the grid-based DIP into the grid-free INR. Through super-resolution and computed tomography experiments, we demonstrate that our method significantly improves upon classic INRs, thanks to the induced natural image bias. We also find that by constraining the weights to be sparse, image quality and sharpness are enhanced, increasing the Lipschitz constant.
Accelerated Motion Correction with Deep Generative Diffusion Models
Authors: Brett Levac, Sidharth Kumar, Ajil Jalal, Jonathan I. Tamir
Date: 2022-11-01 00:00:00
Tasks: Image Reconstruction

Magnetic Resonance Imaging (MRI) is a powerful medical imaging modality, but unfortunately suffers from long scan times which, aside from increasing operational costs, can lead to image artifacts due to patient motion. Motion during the acquisition leads to inconsistencies in measured data that manifest as blurring and ghosting if unaccounted for in the image reconstruction process. Various deep learning based reconstruction techniques have been proposed which decrease scan time by reducing the number of measurements needed for a high fidelity reconstructed image. Additionally, deep learning has been used to correct motion using end-to-end techniques. This, however, increases susceptibility to distribution shifts at test time (sampling pattern, motion level). In this work we propose a framework for jointly reconstructing highly sub-sampled MRI data while estimating patient motion using diffusion based generative models. Our method does not make specific assumptions on the sampling trajectory or motion pattern at training time and thus can be flexibly applied to various types of measurement models and patient motion. We demonstrate our framework on retrospectively accelerated 2D brain MRI corrupted by rigid motion.
On the Performance Gain of Integrated Sensing and Communications: A Subspace Correlation Perspective
Authors: Shihang Lu, Xiao Meng, Zhen Du, Yifeng Xiong, Fan Liu
Date: 2022-11-01 00:00:00
Tasks: ISAC

In this paper, we shed light on the performance gain of integrated sensing and communications (ISAC) from the perspective of channel correlations between radar sensing and communication (S&C), namely ISAC subspace correlation. To begin with, we consider a multi-input multi-output (MIMO) ISAC system and reveal that the optimal ISAC signal is in the subspace spanned by the transmitted steering vectors of the sensing channel and the right singular matrix of the communication channel. By leveraging this result, we study a basic ISAC scenario with a single target and a single-antenna communication user, and derive the optimal waveform covariance matrix for minimizing the estimation error under a given communication rate constraint. To quantify the integration gain of ISAC systems, we define the subspace "correlation coefficient" to characterize the coupling effect between S&C channels. Finally, numerical results are provided to validate the effectiveness of the proposed approaches.
Binary sequence set optimization for CDMA applications via mixed-integer quadratic programming
Authors: Alan Yang, Tara Mina, Grace Gao
Date: 2022-11-01 00:00:00
Tasks: Combinatorial Optimization

Finding sets of binary sequences with low auto- and cross-correlation properties is a hard combinatorial optimization problem with numerous applications, including multiple-input-multiple-output (MIMO) radar and global navigation satellite systems (GNSS). The sum of squared correlations, sometimes referred to as the integrated sidelobe level (ISL), is a quartic function in the variables and is a commonly-used metric of sequence set quality. In this paper, we show that the ISL minimization problem may be formulated as a mixed-integer quadratic program (MIQP). We then present a block coordinate descent (BCD) algorithm that iteratively optimizes over subsets of variables. The subset optimization subproblems are also MIQPs which may be handled more efficiently using specialized solvers than using exhaustive search; this allows us to perform BCD over larger variable subsets than previously possible. Our approach was used to find sets of four binary sequences of lengths up to 1023 with better ISL performance than Gold codes and sequence sets found using existing BCD methods.
RF-CHORD: Towards Deployable RFID Localization System for Logistics Network
Authors: Bo Liang, Purui Wang, Renjie Zhao, Heyu Guo, Pengyu Zhang, Junchen Guo, Shunmin Zhu, Hongqiang Harry Liu, Xinyu Zhang, Chenren Xu
Date: 2022-11-01 00:00:00
Tasks: Management

RFID localization is considered the key enabler of automating the process of inventory tracking and management for high-performance logistic network. A practical and deployable RFID localization system needs to meet reliability, throughput, and range requirements. This paper presents RF-Chord, the first RFID localization system that simultaneously meets all three requirements. RF-Chord features a one-shot multisine-constructed wideband design that can process RF signal with a 200 MHz bandwidth in real-time to facilitate one-shot localization at scale. In addition, multiple SINR enhancement techniques are designed for range extension. On top of that, a kernel-layer-based near-field localization framework and a multipath-suppression algorithm are proposed to reduce the 99% long-tail errors. Our empirical results show that RF-Chord can localize more than 180 tags 6 m away from a reader within 1 second and with 99% long-tail error of 0.786 m, achieving a 0% miss reading rate and ~0.01% cross-reading rate in the warehouse and fresh food delivery store deployment.
Robust Classification of Digitally Modulated Signals Using Capsule Networks and Cyclic Cumulant Features
Authors: John A. Snoap, James A. Latshaw, Dimitrie C. Popescu, Chad M. Spooner
Date: 2022-11-01 00:00:00
Tasks: Classification, Robust classification

The paper studies the problem of robust classification of digitally modulated signals using capsule networks and cyclic cumulant (CC) features extracted by cyclostationary signal processing (CSP). Two distinct datasets that contain similar classes of digitally modulated signals but that have been generated independently are used in the study, which reveals that capsule networks trained using CCs achieve high classification accuracy while also outperforming other deep learning-based approaches in terms of classification accuracy as well as generalization abilities.
Quickest Change Detection with Leave-one-out Density Estimation
Authors: Yuchen Liang, Venugopal V. Veeravalli
Date: 2022-11-01 00:00:00
Tasks: Change Detection, Density Estimation

The problem of quickest change detection in a sequence of independent observations is considered. The pre-change distribution is assumed to be known, while the post-change distribution is completely unknown. A window-limited leave-one-out (LOO) CuSum test is developed, which does not assume any knowledge of the post-change distribution, and does not require any post-change training samples. It is shown that, with certain convergence conditions on the density estimator, the LOO-CuSum test is first-order asymptotically optimal, as the false alarm rate goes to zero. The analysis is validated through numerical results, where the LOO-CuSum test is compared with baseline tests that have distributional knowledge.
Integrating multiple sources of ordinal information in portfolio optimization
Authors: Eranda Çela, Stephan Hafner, Roland Mestel, Ulrich Pferschy
Date: 2022-11-01 00:00:00
Tasks: Management, Portfolio Optimization

Active portfolio management tries to incorporate any source of meaningful information into the asset selection process. In this contribution we consider qualitative views specified as total orders of the expected asset returns and discuss two different approaches for incorporating this input in a mean-variance portfolio optimization model. In the robust optimization approach we first compute a posterior expectation of asset returns for every given total order by an extension of the Black-Litterman (BL) framework. Then these expected asset returns are considered as possible input scenarios for robust optimization variants of the mean-variance portfolio model (max-min robustness, min regret robustness and soft robustness). In the order aggregation approach rules from social choice theory (Borda, Footrule, Copeland, Best-of-k and MC4) are used to aggregate the total order in a single ``consensus total order''. Then expected asset returns are computed for this ``consensus total order'' by the extended BL framework mentioned above. Finally, these expectations are used as an input of the classical mean-variance optimization. Using data from EUROSTOXX 50 and S&P 100 we empirically compare the success of the two approaches in the context of portfolio performance analysis and observe that in general aggregating orders by social choice methods outperforms robust optimization based methods for both data sets.
Optimal Liquidation with Signals: the General Propagator Case
Authors: Eduardo Abi Jaber, Eyal Neuman
Date: 2022-11-01 00:00:00

We consider a class of optimal liquidation problems where the agent's transactions create transient price impact driven by a Volterra-type propagator along with temporary price impact. We formulate these problems as minimization of a revenue-risk functionals, where the agent also exploits available information on a progressively measurable price predicting signal. By using an infinite dimensional stochastic control approach, we characterize the value function in terms of a solution to a free-boundary $L^2$-valued backward stochastic differential equation and an operator-valued Riccati equation. We then derive analytic solutions to these equations which yields an explicit expression for the optimal trading strategy. We show that our formulas can be implemented in a straightforward and efficient way for a large class of price impact kernels with possible singularities such as the power-law kernel.
Focal and Connectomic Mapping of Transiently Disrupted Brain Function
Authors: Michael S. Elmalem, Hanna Moody, James K. Ruffle, Michel Thiebaut de Schotten, Patrick Haggard, Beate Diehl, Parashkev Nachev, Ashwani Jha
Date: 2022-11-01 00:00:00

The distributed nature of the neural substrate, and the difficulty of establishing necessity from correlative data, combine to render the mapping of brain function a far harder task than it seems. Methods capable of combining connective anatomical information with focal disruption of function are needed to disambiguate local from global neural dependence, and critical from merely coincidental activity. Here we present a comprehensive framework for focal and connective spatial inference based on sparse disruptive data, and demonstrate its application in the context of transient direct electrical stimulation of the human medial frontal wall during the pre-surgical evaluation of patients with focal epilepsy. Our framework formalizes voxel-wise mass-univariate inference on sparsely sampled data within the statistical parametric mapping framework, encompassing the analysis of distributed maps defined by any criterion of connectivity. Applied to the medial frontal wall, this transient dysconnectome approach reveals marked discrepancies between local and distributed associations of major categories of motor and sensory behaviour, revealing differentiation by remote connectivity to which purely local analysis is blind. Our framework enables disruptive mapping of the human brain based on sparsely sampled data with minimal spatial assumptions, good statistical efficiency, flexible model formulation, and explicit comparison of local and distributed effects.
A novel approach to quantify volatility prediction
Authors: Suchetana Sadhukhan, Shiv Manjaree Gopaliya, Pushpdant Jain
Date: 2022-11-01 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis

Volatility prediction in the financial market helps to understand the profit and involved risks in investment. However, due to irregularities, high fluctuations, and noise in the time series, predicting volatility poses a challenging task. In the recent Covid-19 pandemic situation, volatility prediction using complex intelligence techniques has attracted enormous attention from researchers worldwide. In this paper, a novel and simple approach based on the robust least squares method in two approaches a) with least absolute residuals (LAR) and b) without LAR, have been applied to the Chicago Board Options Exchange (CBOE) Volatility Index (VIX) for a period of ten years. For a deeper analysis, the volatility time series has been decomposed into long-term trends, and seasonal, and random fluctuations. The data sets have been divided into parts viz. training data set and testing data set. The validation results have been achieved using root mean square error (RMSE) values. It has been found that robust least squares method with LAR approach gives better results for volatility (RMSE = 0.01366) and its components viz. long term trend (RMSE = 0.10087), seasonal (RMSE = 0.010343) and remainder fluctuations (RMSE = 0.014783), respectively. For the first time, generalized prediction equations for volatility and its three components have been presented. Young researchers working in this domain can directly use the presented prediction equations to understand their data sets.
Robust utility maximisation under proportional transaction costs for càdlàg price processes
Authors: Christoph Czichowsky, Raphael Huwyler
Date: 2022-11-01 00:00:00
Tasks: Open-Ended Question Answering

We consider robust utility maximisation in continuous-time financial markets with proportional transaction costs under model uncertainty. For this purpose, we work in the framework of Chau and R\'asonyi (2019), where robustness is achieved by maximising the worst-case expected utility over a possibly uncountable class of models that are all given on the same underlying filtered probability space with incomplete filtration. In this setting, we give sufficient conditions for the existence of an optimal trading strategy extending the result for utility functions on the positive half-line of Chau and R\'asonyi (2019) from continuous to general strictly positive c\`adl\`ag price processes. This result allows us to provide a positive answer to an open question pointed out in Chau and R\'asonyi (2019), and shows that the embedding into a countable product space is not essential.