pwc-archive/papers-with-abstracts huggingface dataset:
The interplay of inhibitory and electrical synapses results in complex persistent activity
Authors: R. Janaki, A. S. Vytheeswaran
Date: 2021-04-06 00:00:00

Inhibitory neurons play a crucial role in maintaining persistent neuronal activity. Although connected extensively through electrical synapses (gap-junctions), these neurons also exhibit interactions through chemical synapses in certain regions of the brain. When the coupling is sufficiently strong, the effects of these two synaptic modalities combine in a nonlinear way. Hence, in this work, we focus on the strong inhibition regime and identify the parametric conditions that result in the emergence of self-sustained oscillations in systems of coupled excitable neurons, in the presence of a brief sub-threshold stimulus. Our investigation on the dynamics in a minimal network of two neurons reveals a rich set of dynamical behaviors viz., periodic and various complex oscillations including period-n (n=2,4,8...) dynamics and chaos. We further extend our study by considering a system of inhibitory neurons arranged in a one-dimensional ring topology and determine the optimal conditions for sustained activity. Our work highlights the nonlinear dynamical behavior arising due to the combined effects of gap-junctions and strong synaptic inhibition, which can have potential implications in maintaining robust memory patterns.
Min(d)ing the President: A text analytic approach to measuring tax news
Authors: Lenard Lieb, Adam Jassem, Rui Jorge Almeida, Nalan Baştürk, Stephan Smeekes
Date: 2021-04-07 00:00:00
Tasks: Time Series, Time Series Analysis

Economic agents react to signals about future tax policy changes. Consequently, estimating their macroeconomic effects requires identification of such signals. We propose a novel text analytic approach for transforming textual information into an economically meaningful time series. Using this method, we create a tax news measure from all publicly available post-war communications of U.S. presidents. Our measure predicts the direction and size of future tax changes and contains signals not present in previously considered (narrative) measures of tax changes. We investigate the effects of tax news and find that, for long anticipation horizons, pre-implementation effects lead initially to contractions in output.
Bootstrap Inference for Hawkes and General Point Processes
Authors: Giuseppe Cavaliere, Ye Lu, Anders Rahbek, Jacob Stærk-Østergaard
Date: 2021-04-07 00:00:00
Tasks: Point Processes, Time Series Analysis

Inference and testing in general point process models such as the Hawkes model is predominantly based on asymptotic approximations for likelihood-based estimators and tests. As an alternative, and to improve finite sample performance, this paper considers bootstrap-based inference for interval estimation and testing. Specifically, for a wide class of point process models we consider a novel bootstrap scheme labeled 'fixed intensity bootstrap' (FIB), where the conditional intensity is kept fixed across bootstrap repetitions. The FIB, which is very simple to implement and fast in practice, extends previous ideas from the bootstrap literature on time series in discrete time, where the so-called 'fixed design' and 'fixed volatility' bootstrap schemes have shown to be particularly useful and effective. We compare the FIB with the classic recursive bootstrap, which is here labeled 'recursive intensity bootstrap' (RIB). In RIB algorithms, the intensity is stochastic in the bootstrap world and implementation of the bootstrap is more involved, due to its sequential structure. For both bootstrap schemes, we provide new bootstrap (asymptotic) theory which allows to assess bootstrap validity, and propose a 'non-parametric' approach based on resampling time-changed transformations of the original waiting times. We also establish the link between the proposed bootstraps for point process models and the related autoregressive conditional duration (ACD) models. Lastly, we show effectiveness of the different bootstrap schemes in finite samples through a set of detailed Monte Carlo experiments, and provide applications to both financial data and social media data to illustrate the proposed methodology.
Fiscal Stimulus of Last Resort
Authors: Alessandro Piergallini
Date: 2021-04-06 00:00:00

I examine global dynamics in a monetary model with overlapping generations of finite-horizon agents and a binding lower bound on nominal interest rates. Debt targeting rules exacerbate the possibility of self-fulfilling liquidity traps, for agents expect austerity following deflationary slumps. Conversely, activist but sustainable fiscal policy regimes - implementing intertemporally balanced tax cuts and/or transfer increases in response to disinflationary trajectories - are capable of escaping liquidity traps and embarking inflation into a globally stable path that converges to the target. Should fiscal stimulus of last resort be overly aggressive, however, spiral dynamics around the liquidity-trap steady state exist, causing global indeterminacy.
SREDS: A dichromatic separation based measure of skin color
Authors: Keivan Bahmani, Richard Plesh, Chinmay Sahu, Mahesh Banavar, Stephanie Schuckers
Date: 2021-04-07 00:00:00
Tasks: Face Recognition

Face recognition (FR) systems are fast becoming ubiquitous. However, differential performance among certain demographics was identified in several widely used FR models. The skin tone of the subject is an important factor in addressing the differential performance. Previous work has used modeling methods to propose skin tone measures of subjects across different illuminations or utilized subjective labels of skin color and demographic information. However, such models heavily rely on consistent background and lighting for calibration, or utilize labeled datasets, which are time-consuming to generate or are unavailable. In this work, we have developed a novel and data-driven skin color measure capable of accurately representing subjects' skin tone from a single image, without requiring a consistent background or illumination. Our measure leverages the dichromatic reflection model in RGB space to decompose skin patches into diffuse and specular bases.
A General Destriping Framework for Remote Sensing Images Using Flatness Constraint
Authors: Kazuki Naganuma, Shunsuke Ono
Date: 2021-04-07 00:00:00

Removing stripe noise, i.e., destriping, from remote sensing images is an essential task in terms of visual quality and subsequent processing. Most existing destriping methods are designed by combining a particular image regularization with a stripe noise characterization that cooperates with the regularization, which precludes us to examine and activate different regularizations to adapt to various target images. To resolve this, two requirements need to be considered: a general framework that can handle a variety of image regularizations in destriping, and a strong stripe noise characterization that can consistently capture the nature of stripe noise, regardless of the choice of image regularization. To this end, this paper proposes a general destriping framework using a newly-introduced stripe noise characterization, named flatness constraint, where we can handle various regularization functions in a unified manner. Specifically, we formulate the destriping problem as a nonsmooth convex optimization problem involving a general form of image regularization and the flatness constraint. The constraint mathematically models that the intensity of each stripe is constant along one direction, resulting in a strong characterization of stripe noise. For solving the optimization problem, we also develop an efficient algorithm based on a diagonally preconditioned primal-dual splitting algorithm (DP-PDS), which can automatically adjust the stepsizes. The effectiveness of our framework is demonstrated through destriping experiments, where we comprehensively compare combinations of a variety of image regularizations and stripe noise characterizations using hyperspectral images (HSI) and infrared (IR) videos.
Fast Unmixing and Change Detection in Multitemporal Hyperspectral Data
Authors: Ricardo Augusto Borsoi, Tales Imbiriba, José Carlos Moreira Bermudez, Cédric Richard
Date: 2021-04-07 00:00:00
Tasks: Change Detection

Multitemporal spectral unmixing (SU) is a powerful tool to process hyperspectral image (HI) sequences due to its ability to reveal the evolution of materials over time and space in a scene. However, significant spectral variability is often observed between collection of images due to variations in acquisition or seasonal conditions. This characteristic has to be considered in the design of SU algorithms. Because of its good performance, the multiple endmember spectral mixture analysis algorithm (MESMA) has been recently used to perform SU in multitemporal scenarios arising in several practical applications. However, MESMA does not consider the relationship between the different HIs, and its computational complexity is extremely high for large spectral libraries. In this work, we propose an efficient multitemporal SU method that exploits the high temporal correlation between the abundances to provide more accurate results at a lower computational complexity. We propose to solve the complex general multitemporal SU problem by separately addressing the endmember selection and the abundance estimation problems. This leads to a simpler solution without sacrificing the accuracy of the results. We also propose a strategy to detect and address abrupt abundance variations in time. Theoretical results demonstrate how the proposed method compares to MESMA in terms of quality, and how effective it is in detecting abundance changes. This analysis provides valuable insight into the conditions under which the algorithm succeeds. Simulation results show that the proposed method achieves state-of-the-art performance at a smaller computational cost.
Large-scale phase retrieval
Authors: Xuyang Chang, Liheng Bian, Jun Zhang
Date: 2021-04-06 00:00:00
Tasks: 8k, Retrieval

High-throughput computational imaging requires efficient processing algorithms to retrieve multi-dimensional and multi-scale information. In computational phase imaging, phase retrieval (PR) is required to reconstruct both amplitude and phase in complex space from intensity-only measurements. The existing PR algorithms suffer from the tradeoff among low computational complexity, robustness to measurement noise and strong generalization on different modalities. In this work, we report an efficient large-scale phase retrieval technique termed as LPR. It extends the plug-and-play generalized-alternating-projection framework from real space to nonlinear complex space. The alternating projection solver and enhancing neural network are respectively derived to tackle the measurement formation and statistical prior regularization. This framework compensates the shortcomings of each operator, so as to realize high-fidelity phase retrieval with low computational complexity and strong generalization. We applied the technique for a series of computational phase imaging modalities including coherent diffraction imaging, coded diffraction pattern imaging, and Fourier ptychographic microscopy. Extensive simulations and experiments validate that the technique outperforms the existing PR algorithms with as much as 17dB enhancement on signal-to-noise ratio, and more than one order-of-magnitude increased running efficiency. Besides, we for the first time demonstrate ultra-large-scale phase retrieval at the 8K level (7680$\times$4320 pixels) in minute-level time.
Design of MIMO Radar Waveforms based on lp-Norm Criteria
Authors: Ehsan Raei, Mohammad Alaee-Kerahroodi, Prabhu Babu, M. R. Bhavani Shankar
Date: 2021-04-07 00:00:00

Multiple-input multiple-output (MIMO) radars transmit a set of sequences that exhibit small cross-correlation sidelobes, to enhance sensing performance by separating them at the matched filter outputs. The waveforms also require small auto-correlation sidelobes to avoid masking of weak targets by the range sidelobes of strong targets and to mitigate deleterious effects of distributed clutter. In light of these requirements, in this paper, we design a set of phase-only (constant modulus) sequences that exhibit near-optimal properties in terms of Peak Sidelobe Level (PSL) and Integrated Sidelobe Level (ISL). At the design stage, we adopt weighted lp-norm of auto- and cross-correlation sidelobes as the objective function and minimize it for a general p value, using block successive upper bound minimization (BSUM). Considering the limitation of radar amplifiers, we design unimodular sequences which make the design problem non-convex and NP-hard. To tackle the problem, in every iteration of the BSUM algorithm, we introduce different local approximation functions and optimize them concerning a block, containing a code entry or a code vector. The numerical results show that the performance of the optimized set of sequences outperforms the state-of-the-art counterparts, in both terms of PSL values and computational time.
Gradient-free training of autoencoders for non-differentiable communication channels
Authors: Ognjen Jovanovic, Metodi Plamenov Yankov, Francesco Da Ros, Darko Zibar
Date: 2020-12-21 00:00:00

Training of autoencoders using the back-propagation algorithm is challenging for non-differential channel models or in an experimental environment where gradients cannot be computed. In this paper, we study a gradient-free training method based on the cubature Kalman filter. To numerically validate the method, the autoencoder is employed to perform geometric constellation shaping on differentiable communication channels, showing the same performance as the back-propagation algorithm. Further investigation is done on a non-differentiable communication channel that includes: laser phase noise, additive white Gaussian noise and blind phase search-based phase noise compensation. Our results indicate that the autoencoder can be successfully optimized using the proposed training method to achieve better robustness to residual phase noise with respect to standard constellation schemes such as Quadrature Amplitude Modulation and Iterative Polar Modulation for the considered conditions.
On the stochastic engine of transmittable diseases in exponentially growing populations
Authors: Torsten Lindström
Date: 2021-04-07 00:00:00

The purpose of this paper is to analyze the mechanism for the interplay of deterministic and stochastic models for contagious diseases. Deterministic models for contagious diseases are prone to predict global stability. Small natural birth and death rates in comparison to disease parameters like the contact rate and the removal rate ensures that the globally stable endemic equilibrium corresponds to a tiny average proportion of infected individuals. Asymptotic equilibrium levels corresponding to low numbers of individuals invalidate the deterministic results. Diffusion effects force frequency functions of the stochastic model to possess similar stability properties as the deterministic model. Particular simulations of the stochastic model predict, however, oscillatory patterns. Small and isolated populations show longer periods, more violent oscillations, and larger probabilities of extinction. We prove that evolution maximizes the infectiousness of the disease as measured by the ability to increase the proportion of infected individuals. This holds provided the stochastic oscillations are moderate enough to keep the proportion of susceptible individuals near a deterministic equilibrium. We close our paper with a discussion of the herd-immunity concept and stress its close relation to vaccination-programs.
Functional annotation of creeping bentgrass protein sequences based on convolutional neural network
Authors: Han-Yu Jiang, Jun He
Date: 2021-04-07 00:00:00

Background: Creeping bentgrass (Agrostis soionifera) is a perennial grass of Gramineae, belonging to cold season turfgrass, but has poor disease resistance. Up to now, little is known about the induced systemic resistance (ISR) mechanism, especially the relevant functional proteins, which is important to disease resistance of turfgrass. Achieving more information of proteins of infected creeping bentgrass is helpful to understand the ISR mechanism. Results: With BDO treatment, creeping bentgrass seedlings were grown, and the ISR response was induced by infecting Rhizoctonia solani. High-quality protein sequences of creeping bentgrass seedlings were obtained. Some of protein sequences were functionally annotated according to the database alignment while a large part of the obtained protein sequences was left non-annotated. To treat the non-annotated sequences, a prediction model based on convolutional neural network was established with the dataset from Uniport database in three domains to acquire good performance, especially the higher false positive control rate. With established model, the non-annotated protein sequences of creeping bentgrass were analyzed to annotate proteins relevant to disease-resistance response and signal transduction. Conclusions: The prediction model based on convolutional neural network was successfully applied to select good candidates of the proteins with functions relevant to the ISR mechanism from the protein sequences which cannot be annotated by database alignment. The waste of sequence data can be avoided, and research time and labor will be saved in further research of protein of creeping bentgrass by molecular biology technology. It also provides reference for other sequence analysis of turfgrass disease-resistance research.
Local stability of cooperation in a continuous model of indirect reciprocity
Authors: SangHun Lee, Yohsuke Murase, Seung Ki Baek
Date: 2021-04-07 00:00:00
Tasks: Sensitivity

Reputation is a powerful mechanism to enforce cooperation among unrelated individuals through indirect reciprocity, but it suffers from disagreement originating from private assessment, noise, and incomplete information. In this work, we investigate stability of cooperation in the donation game by regarding each player's reputation and behaviour as continuous variables. Through perturbative calculation, we derive a condition that a social norm should satisfy to give penalties to its close variants, provided that everyone initially cooperates with a good reputation, and this result is supported by numerical simulation. A crucial factor of the condition is whether a well-reputed player's donation to an ill-reputed co-player is appreciated by other members of the society, and the condition can be reduced to a threshold for the benefit-cost ratio of cooperation which depends on the reputational sensitivity to a donor's behaviour as well as on the behavioural sensitivity to a recipient's reputation. Our continuum formulation suggests how indirect reciprocity can work beyond the dichotomy between good and bad even in the presence of inhomogeneity, noise, and incomplete information.
An SEIR epidemic model of fractional order to analyze the evolution of the COVID-19 epidemic in Argentina
Authors: Juan Santos, José Carcione, Gabriela Savioli, Patricia Gauzellino
Date: 2021-04-07 00:00:00

A pandemic caused by a new coronavirus (COVID-19) has spread worldwide, inducing an epidemic still active in Argentina. In this chapter, we present a case study using an SEIR (Susceptible-Exposed-Infected-Recovered) diffusion model of fractional order in time to analyze the evolution of the epidemic in Buenos Aires and neighboring areas (Regi\'on Metropolitana de Buenos Aires, (RMBA)) comprising about 15 million inhabitants. In the SEIR model, individuals are divided into four classes, namely, susceptible (S), exposed (E), infected (I) and recovered (R). The SEIR model of fractional order allows for the incorporation of memory, with hereditary properties of the system, being a generalization of the classic SEIR first-order system, where such effects are ignored. Furthermore, the fractional model provides one additional parameter to obtain a better fit of the data. The parameters of the model are calibrated by using as data the number of casualties officially reported. Since infinite solutions honour the data, we show a set of cases with different values of the lockdown parameters, fatality rate, and incubation and infectious periods. The different reproduction ratios R0 and infection fatality rates (IFR) so obtained indicate the results may differ from recent reported values, constituting possible alternative solutions. A comparison with results obtained with the classic SEIR model is also included. The analysis allows us to study how isolation and social distancing measures affect the time evolution of the epidemic.
Conductance-based Dynamic Causal Modeling: A mathematical review of its application to cross-power spectral densities
Authors: Inês Pereira, Stefan Frässle, Jakob Heinzle, Dario Schöbi, Cao Tri Do, Moritz Gruber, Klaas E. Stephan
Date: 2021-04-07 00:00:00

Dynamic Causal Modeling (DCM) is a Bayesian framework for inferring on hidden (latent) neuronal states, based on measurements of brain activity. Since its introduction in 2003 for functional magnetic resonance imaging data, DCM has been extended to electrophysiological data, and several variants have been developed. Their biophysically motivated formulations make these models promising candidates for providing a mechanistic understanding of human brain dynamics, both in health and disease. However, due to their complexity and reliance on concepts from several fields, fully understanding the mathematical and conceptual basis behind certain variants of DCM can be challenging. At the same time, a solid theoretical knowledge of the models is crucial to avoid pitfalls in the application of these models and interpretation of their results. In this paper, we focus on one of the most advanced formulations of DCM, i.e. conductance-based DCM for cross-spectral densities, whose components are described across multiple technical papers. The aim of the present article is to provide an accessible exposition of the mathematical background, together with an illustration of the model's behavior. To this end, we include step-by-step derivations of the model equations, point to important aspects in the software implementation of those models, and use simulations to provide an intuitive understanding of the type of responses that can be generated and the role that specific parameters play in the model. Furthermore, all code utilized for our simulations is made publicly available alongside the manuscript to allow readers an easy hands-on experience with conductance-based DCM.