pwc-archive/papers-with-abstracts huggingface dataset:
The Psychology of Mineral Wealth: Empirical Evidence from Kazakhstan
Authors: Elissaios Pappyrakis, Osiris Jorge Parcero
Date: 2022-04-08 00:00:00

Despite rapidly-expanding academic and policy interest in the links between natural resource wealth and development failures (commonly referred to as the resource curse) little attention has been devoted to the psychology behind the phenomenon. Rent-seeking and excessive reliance on mineral revenues can be attributed largely to social psychology. Mineral booms (whether due to the discovery of mineral reserves or to the drastic rise in commodity prices) start as positive income shocks that can subsequently evolve into influential and expectation-changing public and media narratives; these lead consecutively to unrealistic demands that favor immediate consumption of accrued mineral revenues and to the postponement of productive investment. To our knowledge, this paper is the first empirical analysis that tests hypotheses regarding the psychological underpinnings of resource mismanagement in mineral-rich states. Our study relies on an extensive personal survey (of 1977 respondents) carried out in Almaty, Kazakhstan, between May and August 2018. We find empirical support for a positive link between exposure to news and inflated expectations regarding mineral availability, as well as evidence that the latter can generate preferences for excessive consumption, and hence, rent-seeking.
Optimal allocations to heterogeneous agents with an application to stimulus checks
Authors: Vegard M. Nygaard, Bent E. Sørensen, Fan Wang
Date: 2022-04-08 00:00:00

A planner allocates discrete transfers of size $D_g$ to $N$ heterogeneous groups labeled $g$ and has CES preferences over the resulting outcomes, $H_g(D_g)$. We derive a closed-form solution for optimally allocating a fixed budget subject to group-specific inequality constraints under the assumption that increments in the $H_g$ functions are non-increasing. We illustrate our method by studying allocations of "support checks" from the U.S. government to households during both the Great Recession and the COVID-19 pandemic. We compare the actual allocations to optimal ones under alternative constraints, assuming the government focused on stimulating aggregate consumption during the 2008--2009 crisis and focused on welfare during the 2020--2021 crisis. The inputs for this analysis are obtained from versions of a life-cycle model with heterogeneous households, which predicts household-type-specific consumption and welfare responses to tax rebates and cash transfers.
Finding all stable matchings with assignment constraints
Authors: Gregory Gutin, Philip R. Neary, Anders Yeo
Date: 2022-04-08 00:00:00
Tasks: All

In this paper we consider stable matchings subject to assignment constraints. These are matchings that require certain assigned pairs to be included, insist that some other assigned pairs are not, and, importantly, are stable. Our main contribution is an algorithm, based on the iterated deletion of unattractive alternatives, that determines if assignment constraints are compatible with stability. Whenever there is a stable matching that satisfies the assignment constraints, our algorithm outputs all of them (each in polynomial time per solution). This provides market designers with (i) a tool to test the feasibility of stable matchings subject to assignment constraints, and (ii) a tool to implement them when feasible.
Automatic Census of Mussel Platforms Using Sentinel 2 Images
Authors: Fernando Martin-Rodriguez, Fernando Isasi-de-Vicente, Monica Fernández-Barciela
Date: 2022-04-08 00:00:00
Tasks: Earth Observation

Mussel platforms are big floating structures made of wood (size is normally about 20x20 meters or even a bit larger) that are used for aquaculture, id EST: growing mussels in appropriate marine waters. These structures are very typical in Galician estuaries. Being interesting to produce a periodic census of these structures that would allow knowing their number and positions, as well as changes on those parameters; Satellites that obtain periodic images for earth observation are a natural election for this issue. This paper describes a preliminary application able to construct automatically such a census using Sentinel 2 images (Copernicus Project). Copernicus satellites are run by European Space Agency (ESA) and the produced images are freely distributed on the internet. Sentinel 2 images have thirteen frequency bands and are updated each five days. In our application, we use remote sensing normalized (differential) indexes and artificial Neural Networks applied to multiband data. Different methods are described and tested. Finally, results are presented.
Reconstruction of images taken by a pair of non-regular sampling sensors using correlation based matching
Authors: Markus Jonscher, Jürgen Seiler, Thomas Richter, Michel Bätz, André Kaup
Date: 2022-04-07 00:00:00
Tasks: Image Reconstruction

Multi-view image acquisition systems with two or more cameras can be rather costly due to the number of high resolution image sensors that are required. Recently, it has been shown that by covering a low resolution sensor with a non-regular sampling mask and by using an efficient algorithm for image reconstruction, a high resolution image can be obtained. In this paper, a stereo image reconstruction setup for multi-view scenarios is proposed. A scene is captured by a pair of non-regular sampling sensors and by incorporating information from the adjacent view, the reconstruction quality can be increased. Compared to a state-of-the-art single-view reconstruction algorithm, this leads to a visually noticeable average gain in PSNR of 0.74 dB.
Reducing Randomness of Non-Regular Sampling Masks for Image Reconstruction
Authors: Markus Jonscher, Jürgen Seiler, Thomas Richter, André Kaup
Date: 2022-04-07 00:00:00
Tasks: Image Reconstruction

Increasing spatial image resolution is an often required, yet challenging task in image acquisition. Recently, it has been shown that it is possible to obtain a high resolution image by covering a low resolution sensor with a non-regular sampling mask. Due to the masking, however, some pixel information in the resulting high resolution image is not available and has to be reconstructed by an efficient image reconstruction algorithm in order to get a fully reconstructed high resolution image. In this paper, the influence of different sampling masks with a reduced randomness of the non-regularity on the image reconstruction process is evaluated. Simulation results show that it is sufficient to use sampling masks that are non-regular only on a smaller scale. These sampling masks lead to a visually noticeable gain in PSNR compared to arbitrary chosen sampling masks which are non-regular over the whole image sensor size. At the same time, they simplify the manufacturing process and allow for efficient storage.
Reconstruction of Videos Taken by a Non-Regular Sampling Sensor
Authors: Markus Jonscher, Jürgen Seiler, Michel Bätz, Thomas Richter, Wolfgang Schnurrer, André Kaup
Date: 2022-04-07 00:00:00

Recently, it has been shown that a high resolution image can be obtained without the usage of a high resolution sensor. The main idea has been that a low resolution sensor is covered with a non-regular sampling mask followed by a reconstruction of the incomplete high resolution image captured this way. In this paper, a multi-frame reconstruction approach is proposed where a video is taken by a non-regular sampling sensor and fully reconstructed afterwards. By utilizing the temporal correlation between neighboring frames, the reconstruction quality can be further enhanced. Compared to a state-of-the-art single-frame reconstruction approach, this leads to a visually noticeable gain in PSNR of up to 1.19 dB on average.
Design of an Optimal Testbed for Tracking of Tagged Marine Megafauna
Authors: Talmon Alexandri, Roee Diamant
Date: 2022-04-08 00:00:00

Underwater acoustic technologies are a key component for exploring the behavior of marine megafauna such as sea turtles, sharks, and seals. The animals are marked with acoustic devices (tags) that periodically emit signals encoding the device's ID along with sensor data such as depth, temperature, or the dominant acceleration axis - data that is collected by a network of deployed receivers. In this work, we aim to optimize the locations of receivers for best tracking of acoustically tagged marine megafauna. The outcomes of such tracking allows the evaluation of the animals' motion patterns, their hours of activity, and their social interactions. In particular, we focus on how to determine the receivers' deployment positions to maximize the coverage area in which the tagged animals can be tracked. For example, an overly-condensed deployment may not allow accurate tracking, whereas a sparse one, may lead to a small coverage area due to too few detections. We formalize the question of where to best deploy the receivers as a non-convex constraint optimization problem that takes into account the local environment and the specifications of the tags, and offer a sub-optimal, low-complexity solution that can be applied to large testbeds. Numerical investigation for three stimulated sea environments shows that our proposed method is able to increase the localization coverage area by 30%, and results from a test case experiment demonstrate similar performance in a real sea environment. We share the implementation of our work to help researchers set up their own acoustic observatory.
Machine Learning aided Precise Indoor Positioning
Authors: Anqi Yin, Zihuai Lin
Date: 2022-04-08 00:00:00
Tasks: BIG-bench Machine Learning, Indoor Localization

This study describes a UWB and Machine Learning (ML)-based indoor positioning system. We propose a simple mathematical strategy to create data to reduce the job of measurements for fingerprint-based indoor localization systems. A considerable number of measurements can be avoided this way. The paper compares and contrasts the performance of four distinct models. Most test locations' average error may be reduced to less than 150 mm using the best model.
Mitigating Mismatch Compression in Differential Local Field Potentials
Authors: Vineet Tiruvadi, Sam James, Bryan Howell, Mosadoluwa Obatusin, Andrea Crowell, Patricio Riva-Posse, Ki Sueng Choi, Allison Waters, Robert E. Gross, Cameron C. McIntyre, Helen S. Mayberg, Robert Butera
Date: 2022-04-07 00:00:00

Bidirectional deep brain stimulation (bdDBS) devices capable of recording differential local field potentials (dLFP) enable neural recordings alongside clinical therapy. Efforts to identify objective signals of various brain disorders, or disease readouts, are challenging in dLFP, especially during active DBS. In this report we identified, characterized, and mitigated a major source of distortion in dLFP that we introduce as mismatch compression (MC). MC occurs secondary to impedance mismatches across the dLFP channel resulting in incomplete rejection of artifacts and downstream amplifier gain compression. Using in silico and in vitro models we demonstrate that MC accounts for impedance-related distortions sensitive to DBS amplitude. We then use these models to develop and validate a mitigation strategy for MC that is provided as an opensource library for more reliable oscillatory disease readouts.
Deconvolution of the Functional Ultrasound Response in the Mouse Visual Pathway Using Block-Term Decomposition
Authors: Aybuke Erol, Chagajeg Soloukey, Bastian Generowicz, Nikki Van Dorp, Sebastiaan Koekkoek, Pieter Kruizinga, Borbala Hunyadi
Date: 2022-04-07 00:00:00
Tasks: Time Series, Time Series Analysis

Functional ultrasound (fUS) indirectly measures brain activity by recording changes in cerebral blood volume and flow in response to neural activation. Conventional approaches model such functional neuroimaging data as the convolution between an impulse response, known as the hemodynamic response function (HRF), and a binarized representation of the input (i.e., source) signal based on the stimulus onsets, the so-called experimental paradigm (EP). However, the EP may not be enough to characterize the whole complexity of the underlying source signals that evoke the hemodynamic changes, such as in the case of spontaneous resting state activity. Furthermore, the HRF varies across brain areas and stimuli. To achieve an adaptable framework that can capture such dynamics and unknowns of the brain function, we propose a deconvolution method for multivariate fUS time-series that reveals both the region-specific HRFs, and the source signals that induce the hemodynamic responses in the studied regions. We start by modeling the fUS time-series as convolutive mixtures and use a tensor-based approach for deconvolution based on two assumptions: (1) HRFs are parametrizable, and (2) source signals are uncorrelated. We test our approach on fUS data acquired during a visual experiment on a mouse subject, focusing on three regions within the mouse brain's colliculo-cortical, image-forming pathway: the lateral geniculate nucleus, superior colliculus and visual cortex. The estimated HRFs in each region are in agreement with prior works, whereas the estimated source signal is observed to closely follow the EP. Yet, we note a few deviations from the EP in the estimated source signal that most likely arise due to the trial-by-trial variability of the neural response across different repetitions of the stimulus observed in the selected regions.
A Message Passing Based Average Consensus Algorithm for Decentralized Frequency and Phase Synchronization in Distributed Phased Arrays
Authors: Mohammed Rashid, Jeffrey A. Nanzer
Date: 2022-04-07 00:00:00

We consider the problem of decentralized frequency and phase synchronization in distributed phased arrays via local broadcast of the node electrical states. Frequency and phase synchronization between nodes in a distributed array is necessary to support beamforming, but due to the operational dynamics of the local oscillators of the nodes, the frequencies and phases of their output signals undergo the random drift and jitter in between the update intervals. Furthermore, frequency and phase estimation errors contribute to the total phase errors, leading to a residual phase error in the array that degrades coherent operation. Recently, a classical decentralized frequency and phase synchronization algorithm based on consensus averaging was proposed with which the standard deviation of the residual phase errors upon convergence was reduced to $10^{-4}$ degrees for internode update intervals of $0.1$ ms, however this was obtained for arrays with at least $400$ nodes and a high connectivity ratio of $0.9$. In this paper, we propose a message passing based average consensus (MPAC) algorithm to improve the synchronization of the electrical states of the nodes in distributed arrays. Simulation results show that the proposed MPAC algorithm significantly reduces the residual phase errors to about $10^{-11}$ degrees, requiring only $20$ moderately connected nodes in an array. Furthermore, MPAC converges faster than the DFPC-based algorithms, particularly for the larger arrays with a moderate connectivity.
Permanence via invasion graphs: Incorporating community assembly into Modern Coexistence Theory
Authors: Josef Hofbauer, Sebastian J. Schreiber
Date: 2022-04-07 00:00:00

To understand the mechanisms underlying species coexistence, ecologists often study invasion growth rates of theoretical and data-driven models. These growth rates correspond to average per-capita growth rates of one species with respect to an ergodic measure supporting other species. In the ecological literature, coexistence often is equated with the invasion growth rates being positive. Intuitively, positive invasion growth rates ensure that species recover from being rare. To provide a mathematically rigorous framework for this approach, we prove theorems that answer two questions: (i) When do the signs of the invasion growth rates determine coexistence? (ii) When signs are sufficient, which invasion growth rates need to be positive? We focus on deterministic models and equate coexistence with permanence, i.e., a global attractor bounded away from extinction. For models satisfying certain technical assumptions, we introduce invasion graphs where vertices correspond to proper subsets of species (communities) supporting an ergodic measure and directed edges correspond to potential transitions between communities due to invasions by missing species. These directed edges are determined by the signs of invasion growth rates. When the invasion graph is acyclic (i.e. there is no sequence of invasions starting and ending at the same community), we show that permanence is determined by the signs of the invasion growth rates. In this case, permanence is characterized by the invasibility of all -i communities, i.e., communities without species i where all other missing species having negative invasion growth rates. We show that dissipative Lotka-Volterra models satisfy our technical assumptions and computing their invasion graphs reduces to solving systems of linear equations. We provide additional applications of the results and discuss open problems.
An optional decomposition of $\mathscr{Y}^{g,ξ}-submartingales$ and applications to the hedging of American options in incomplete markets
Authors: Roxana Dumitrescu
Date: 2019-01-08 00:00:00

In the recent paper \cite{DESZ}, the notion of $\mathscr{Y}^{g,\xi}$-submartingale processes has been introduced. Within a jump-diffusion model, we prove here that a process $X$ which satisfies the simultaneous $\mathscr{Y}^{\mathbb{Q},g,\xi}$ -submartingale property under a suitable family of equivalent probability measures $\mathbb{Q}$, admits a \textit{nonlinear optional decomposition}. This is an analogous result to the well known optional decomposition of simultaneous (classical and $\mathscr{E}^g$-)supermartingales. We then apply this decomposition to the super-hedging problem of an American option in a jump-diffusion model, from the buyer's point of view. We obtain an \textit{infinitesimal characterization} of the buyer's superhedging price, this result being completely new in the literature. Indeed, it is well known that the seller's superheding price of an American option admits an infinitesimal representation in terms of the \textit{minimal supersolution of a constrained reflected BSDE}. To the best of our knowledge, no analogous result has been established for the buyer of the American option in an incomplete market. Our results fill this gap, and show that the buyer's super-hedging price admits an infinitesimal charcaterization in terms of the \textit{maximal subsolution of a constrained reflected BSDE}.
On the robustness of the in vivo cyanobacterial circadian clock
Authors: Dorota Youmbi Fouego, Sophie de Buyl
Date: 2022-04-08 00:00:00

We propose a revisited version of the in vivo model of the cyanobacterial circadian clock. Our aim is to address the lack of robustness predicted for the mutant cyanobacteria without transcriptional regulation of the original model. For this, we rely on an in vitro model of the clock describing explicitly the hexameric structure of the core protein of the clock. Our model is able to reproduce oscillatory behavior for the mutant, as observed experimentally, without finely tuned parameters.
Evaluacion de un producto a base de aceite esencial de oregano sobre la integridad intestinal, la capacidad de absorcion de nutrientes y el comportamiento productivo de pollos de carne
Authors: Diego Martinez
Date: 2022-04-07 00:00:00

Eight experiments were conducted to test an oregano-essential-oil-based product (PRO) on performance, gut health, and nutrient absorption capacity in broilers. The first three, up to 14d and without specific challenges, tested the effects on intestinal histology, skeletal integrity, and growth. Other two experiments, up to 28d birds were challenged to induce feed passage syndrome (STR) and test the effect on the antioxidant status and performance, contrasting later the results under a natural challenge in commercial conditions. Finally, in two other experiments, up to 21 d, birds challenged with coccidia were tested to determine the effects on coccidiosis, nutrient digestibility, and feed efficiency (FCR). Corn-soybean-fish meal diets were used. Under healthy conditions, PRO effects were observed on intestinal villus height and surface area, mucosa density and structure, villus to crypt ratio, goblet cells density, mucus production, body weight gain, and FCR (P<0.04), and carcass yield and bone density (P<0.08). Birds affected by STR showed effects (P<0.05) on superoxide dismutase activity, bodyweight gain, FCRE, non-normal feces frequency, bursal diameter, and dysbacteriosis. Birds affected by coccidia showed effects on cocci-positive ratio, cocci microscopic score, oocysts count, bursal diameter, body weight gain, FCR, apparent digestibility of dry matter and crude protein, protein and energy efficiencies (P<0.01), and nitrogen apparent retention (P<0.07). Results showed PRO added value to the diet favoring feed efficiency and performance.
Embedding of Functional Human Brain Networks on a Sphere
Authors: Moo K. Chung, Zijian Chen
Date: 2022-04-07 00:00:00

Human brain activity is often measured using the blood-oxygen-level dependent (BOLD) signals obtained through functional magnetic resonance imaging (fMRI). The strength of connectivity between brain regions is then measured as a Pearson correlation matrix. As the number of brain regions increases, the dimension of matrix increases. It becomes extremely cumbersome to even visualize and quantify such weighted complete networks. To remedy the problem, we propose to embed brain networks onto a sphere, which is a Riemannian manifold with constant positive curvature. The Matlab code for the spherical embedding is given in https://github.com/laplcebeltrami/sphericalMDS.
Log-optimal portfolio after a random time: Existence, description and sensitivity analysis
Authors: Ferdoos Alharbi, Tahir Choulli
Date: 2022-04-08 00:00:00
Tasks: Sensitivity

In this paper, we consider an informational market model with two flows of informations. The smallest flow F, which is available to all agents, is the filtration of the initial market model(S,F,P), where S is the assets' prices and P is a probability measure. The largest flow G contains additional information about the occurrence of a random time T. This setting covers credit risk theory where T models the default time of a firm, and life insurance where T represents the death time of an insured. For the model (S-S^T,G,P), we address the log-optimal portfolio problem in many aspects. In particular, we answer the following questions and beyond: 1) What are the necessary and sufficient conditions for the existence of log-optimal portfolio of the model under consideration? 2) what are the various type of risks induced by T that affect this portfolio and how? 3) What are the factors that completely describe the sensitivity of the log-portfolio to the parameters of T? The answers to these questions and other related discussions definitely complement the work of Choulli and Yansori [12] which deals with the stopped model (S^T,G).
The market drives ETFs or ETFs the market: causality without Granger
Authors: Peter Lerner
Date: 2022-04-07 00:00:00
Tasks: Time Series, Time Series Analysis

This paper develops a deep learning-based econometric methodology to determine the causality of the financial time series. This method is applied to the imbalances in daily transactions in individual stocks, as well as the ETFs reported to SEC with a nanosecond time stamp. Based on our method, we conclude that transaction imbalances of ETFs alone are more informative than the transaction imbalances in the entire market. Characteristically, a sheer number of imbalance messages related to the individual stocks dominates the imbalance messages due to the ETF in the proportion of 8:1.