pwc-archive/papers-with-abstracts huggingface dataset:
Optimizing illumination patterns for classical ghost imaging
Authors: Andrew M. Kingston, Lindon Roberts, Alaleh Aminzadeh, Daniele Pelliccia, Imants D. Svalbe, David M. Paganin
Date: 2022-11-07 00:00:00
Tasks: Object

Classical ghost imaging is a new paradigm in imaging where the image of an object is not measured directly with a pixelated detector. Rather, the object is subject to a set of illumination patterns and the total interaction of the object, e.g., reflected or transmitted photons or particles, is measured for each pattern with a single-pixel or bucket detector. An image of the object is then computed through the correlation of each pattern and the corresponding bucket value. Assuming no prior knowledge of the object, the set of patterns used to compute the ghost image dictates the image quality. In the visible-light regime, programmable spatial light modulators can generate the illumination patterns. In many other regimes -- such as x rays, electrons, and neutrons -- no such dynamically configurable modulators exist, and patterns are commonly produced by employing a transversely-translated mask. In this paper we explore some of the properties of masks or speckle that should be considered to maximize ghost-image quality, given a certain experimental classical ghost-imaging setup employing a transversely-displaced but otherwise non-configurable mask.
Modified SIS model applied to a Zombie apocalypse with terminators
Authors: Gabriel Vilela de Sousa, Ulisses Ferreira Kaneko, Paulo Freitas Gomes
Date: 2022-11-08 00:00:00

In this work we study the dynamics in an apocalypse where the individuals can temporarily become zombies, returning to the living state again or die. We describe this dynamics using a modified version of the epidemic SIS model. The zombies can die when in contact with a terminator, which is part of the living people. To define the possible interactions we use an Erd\"{o}s-R\`{e}nyi network and we calculate how the absorbing and active phase of the original SIS model is influenced by the average network degree. We also use the component and domain distribution of the network to understand how this influence occurs.
On the Past, Present, and Future of the Diebold-Yilmaz Approach to Dynamic Network Connectedness
Authors: Francis X. Diebold, Kamil Yilmaz
Date: 2022-11-08 00:00:00

We offer retrospective and prospective assessments of the Diebold-Yilmaz connectedness research program, combined with personal recollections of its development. Its centerpiece in many respects is Diebold and Yilmaz (2014), around which our discussion is organized.
Bootstraps for Dynamic Panel Threshold Models
Authors: Woosik Gong, Myung Hwan Seo
Date: 2022-11-08 00:00:00
Tasks: valid

This paper develops valid bootstrap inference methods for the dynamic short panel threshold regression. We demonstrate that the standard nonparametric bootstrap is inconsistent for the first-differenced generalized method of moments (GMM) estimator. The inconsistency is due to an $n^{1/4}$-consistent non-normal asymptotic distribution for the threshold estimate when the parameter resides within the continuity region of the parameter space. It stems from the rank deficiency of the approximate Jacobian of the sample moment conditions on the continuity region. To address this, we propose a grid bootstrap to construct confidence intervals of the threshold, a residual bootstrap to construct confidence intervals of the coefficients, and a bootstrap for testing continuity. They are shown to be valid under uncertain continuity, while the grid bootstrap is additionally shown to be uniformly valid. A set of Monte Carlo experiments demonstrate that the proposed bootstraps perform well in the finite samples and improve upon the standard nonparametric bootstrap.
Profit Shifting and International Tax Reforms
Authors: Alessandro Ferrari, Sébastien Laffitte, Mathieu Parenti, Farid Toubal
Date: 2022-11-08 00:00:00

International taxation rules are outdated, allowing multinationals to shift profits to tax havens. This paper examines how tax reforms affect profit shifting and cross-country welfare. We propose a model that separates real economic profits from paper profits, introducing 'triangle identities' to estimate bilateral profit-shifting flows. Using macro- and firm-level data, paper profits' elasticity is three times that of the tax base. Global minimum tax reforms improve welfare by increasing public goods funding and reducing tax competition. We also identify optimal minimum rates under various taxing-right scenarios and demonstrate that unilateral destination-based-cash-flow-tax reforms' welfare effects depend highly on trade imbalances.
Digital Transformation of Nature Tourism
Authors: Raul Enrique Rodriguez Luna, Jose Luis Rosenstiehl Martinez
Date: 2022-11-08 00:00:00
Tasks: Decision Making, Marketing

The purpose of this article is to explore the digital behavior of nature tourism SMEs in the department of Magdalena-Colombia, hereinafter referred to as the region. In this sense, the concept of endogenization as an evolutionary mechanism refers to the application of the discrete choice model as an engine of analysis for the variables to be studied within the model. The type of study was quantitative, of correlational level, the sample was 386 agents of the tourism chain; a survey-type instrument with five factors and a Likert- type scale was used for data collection. For the extraction of the factors, a confirmatory factor analysis was used, using structural equations, followed by a discrete choice model and then the analysis of the results. Among the main findings are that the SMEs in the tourism chain that tried to incorporate Big Data activities in the decision-making processes, have greater chances of success in the digital transformation, in addition, statistical evidence was found that the training of staff in Data Science, contributes significantly to the marketing and commercialization processes within the SME in this region.
The Optimal Size and Progressivity of Old-Age Social Security
Authors: Francisco Cabezon
Date: 2022-11-07 00:00:00

Almost every public pension system shares two attributes: earning deductions to finance benefits, and benefits that depend on earnings. This paper analyzes theoretically and empirically the trade-off between social insurance and incentive provision faced by reforms to these two attributes. First, I combine the social insurance and the optimal linear-income literature to build a model with a flexible pension contribution rate and benefits' progressivity that incorporates inter-temporal and inter-worker types of redistribution and incentive distortion. The model is general, allowing workers to be heterogeneous on productivity and retirement preparedness, and they exhibit present-focused bias. I then estimate the model by leveraging three quasi-experimental variations on the design of the Chilean pension system and administrative data merged with a panel survey. I find that taxable earnings respond to changes in the benefit-earnings link, future pension payments, and net-of-tax rate, which increases the costs of reforms. I also find that lifetime payroll earnings have a strong positive relationship with productivity and retirement preparedness, and that pension transfers are effective in increasing retirement consumption. Therefore, there is a large inter-worker redistribution value through the pension system. Overall, there are significant social gains from marginal reforms: a 1% increase in the contribution rate and in the benefit progressivity generates social gains of 0.08% and 0.29% of the GDP, respectively. The optimal design has a pension contribution rate of 17% and focuses 42% of pension public spending on workers below the median of lifetime earnings.
HDRfeat: A Feature-Rich Network for High Dynamic Range Image Reconstruction
Authors: Lingkai Zhu, Fei Zhou, Bozhi Liu, Orcun Göksel
Date: 2022-11-08 00:00:00
Tasks: HDR Reconstruction, Image Reconstruction, Vocal Bursts Intensity Prediction

A major challenge for high dynamic range (HDR) image reconstruction from multi-exposed low dynamic range (LDR) images, especially with dynamic scenes, is the extraction and merging of relevant contextual features in order to suppress any ghosting and blurring artifacts from moving objects. To tackle this, in this work we propose a novel network for HDR reconstruction with deep and rich feature extraction layers, including residual attention blocks with sequential channel and spatial attention. For the compression of the rich-features to the HDR domain, a residual feature distillation block (RFDB) based architecture is adopted. In contrast to earlier deep-learning methods for HDR, the above contributions shift focus from merging/compression to feature extraction, the added value of which we demonstrate with ablation experiments. We present qualitative and quantitative comparisons on a public benchmark dataset, showing that our proposed method outperforms the state-of-the-art.
Single-anchor UWB Localization using Channel Impulse Response Distributions
Authors: Sitian Li, Alexios Balatsoukas-Stimming, Andreas Burg
Date: 2022-11-08 00:00:00
Tasks: Indoor Localization

Ultra-wideband (UWB) devices are widely used in indoor localization scenarios. Single-anchor UWB localization shows advantages because of its simple system setup compared to conventional two-way ranging (TWR) and trilateration localization methods. In this work, we focus on single-anchor UWB localization methods that learn statistical features of the channel impulse response (CIR) in different location areas using a Gaussian mixture model (GMM). We show that by learning the joint distributions of the amplitudes of different delay components, we achieve a more accurate location estimate compared to considering each delay bin independently. Moreover, we develop a similarity metric between sets of CIRs. With this set-based similarity metric, we can further improve the estimation performance, compared to treating each snapshot separately. We showcase the advantages of the proposed methods in multiple application scenarios.
Optimal Discrete Beamforming of RIS-Aided Wireless Communications: An Inner Product Maximization Approach
Authors: Rujing Xiong, Xuihui Dong, Tiebin Mi, Kai Wan, Robert Caiming Qiu
Date: 2022-11-08 00:00:00
Tasks: Combinatorial Optimization, Quantization

This paper studies the beamforming optimization challenge in reconfigurable intelligent surface (RIS)-aided multiple-input single-output (MISO) systems, where the RIS phase configuration is discrete. Conventional optimization methods for this discrete optimization problem necessitate resource-intensive exponential search and thus fall within the universal (NP-hard) category. We formally define this task as a discrete inner product maximization problem. Leveraging the inherent structure of this problem, we propose an efficient divide-and-sort (DaS) search algorithm to reach the global optimality for the maximization problem. The complexity of the proposed algorithm can be minimized to $\mathcal{O}(2^BN)$, a linear correlation with the count of phase discrete levels $2^B$ and reflecting units $N$. This is notably lower than the exhaustive search complexity of $\mathcal{O}(2^{BN})$. Numerical evaluations and experiments over real prototype also demonstrate the efficiency of the proposed DaS algorithm. Finally, by using the proposed algorithm, we show that over some resolution quantization level on each RIS unit (4-bit and above), there is no noticeable difference in power gains between continuous and discrete phase configurations.
Integrated Sensing and Communication Beamforming Design Based on Mutual Information
Authors: Jin Li, Nan Liu
Date: 2022-11-08 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) unifies radar sensing and communication, and improves the efficiency of the spectrum, energy and hardware. In this paper, we investigate the ISAC beamforming design in a downlink system with a communication user and a point radar target to be perceived. The design criterion for radar sensing is the mutual information between the target response matrix and the echo signals, while the single user transmission rate is used as the performance metric of the communication. Two scenarios without and with interference from the communication user are investigated. A closed-form solution with low complexity and a solution based on the semidefinite relaxation (SDR) method are provided to solve these two problems, respectively. Numerical results demonstrate that, compared to the results obtained by SDR based on the CVX toolbox, the closed-form solution is tight in the feasible region. In addition, we show that the SDR method obtains an optimal solution.
Performance Analysis of LEO Satellite-Based IoT Networks in the Presence of Interference
Authors: Ayush Kumar Dwivedi, Sachin Chaudhari, Neeraj Varshney, Pramod K. Varshney
Date: 2022-11-08 00:00:00

This paper presents a star-of-star topology for internet-of-things (IoT) networks using mega low-Earth-orbit constellations. The proposed topology enables IoT users to broadcast their sensed data to multiple satellites simultaneously over a shared channel, which is then relayed to the ground station (GS) using amplify-and-forward relaying. The GS coherently combines the signals from multiple satellites using maximal ratio combining. To analyze the performance of the proposed topology in the presence of interference, a comprehensive outage probability (OP) analysis is performed, assuming imperfect channel state information at the GS. The paper employs stochastic geometry to model the random locations of satellites, making the analysis general and independent of any specific constellation. Furthermore, the paper examines successive interference cancellation (SIC) and capture model (CM)-based decoding schemes at the GS to mitigate interference. The average OP for the CM-based scheme and the OP of the best user for the SIC scheme are derived analytically. The paper also presents simplified expressions for the OP under a high signal-to-noise ratio (SNR) assumption, which are utilized to optimize the system parameters for achieving a target OP. The simulation results are consistent with the analytical expressions and provide insights into the impact of various system parameters, such as mask angle, altitude, number of satellites, and decoding order. The findings of this study demonstrate that the proposed topology can effectively leverage the benefits of multiple satellites to achieve the desired OP and enable burst transmissions without coordination among IoT users, making it an attractive choice for satellite-based IoT networks.
Dynamic Sensor Placement Based on Sampling Theory for Graph Signals
Authors: Saki Nomura, Junya Hara, Hiroshi Higashi, Yuichi Tanaka
Date: 2022-11-08 00:00:00
Tasks: Dictionary Learning, Graph Sampling

In this paper, we consider a sensor placement problem where sensors can move within a network over time. Sensor placement problem aims to select K sensor positions from N candidates where K < N. Most existing methods assume that sensor positions are static, i.e., they do not move, however, many mobile sensors like drones, robots, and vehicles can change their positions over time. Moreover, underlying measurement conditions could also be changed, which are difficult to cover with statically placed sensors. We tackle the problem by allowing the sensors to change their positions in their neighbors on the network. We dynamically determine the sensor positions based on graph signal sampling theory such that the non-observed signals on the network can be best recovered from the observations. For signal recovery, the dictionary is learned from a pool of observed signals. It is also used for the sensor position selection. In experiments, we validate the effectiveness of the proposed method via the mean squared error of the reconstructed signals. The proposed dynamic sensor placement outperforms the existing static ones for both synthetic and real data.
Minimax Concave Penalty Regularized Adaptive System Identification
Authors: Bowen Li, Suya Wu, Erin E. Tripp, Ali Pezeshki, Vahid Tarokh
Date: 2022-11-07 00:00:00
Tasks: Time Series, Time Series Analysis

We develop a recursive least square (RLS) type algorithm with a minimax concave penalty (MCP) for adaptive identification of a sparse tap-weight vector that represents a communication channel. The proposed algorithm recursively yields its estimate of the tap-vector, from noisy streaming observations of a received signal, using expectation-maximization (EM) update. We prove the convergence of our algorithm to a local optimum and provide bounds for the steady state error. Using simulation studies of Rayleigh fading channel, Volterra system and multivariate time series model, we demonstrate that our algorithm outperforms, in the mean-squared error (MSE) sense, the standard RLS and the $\ell_1$-regularized RLS.
LIT01-196, a Metabolically Stable Apelin-17 Analog, Normalizes Blood Pressure in Hypertensive DOCA-Salt Rats via a NO Synthase-dependent Mechanism
Authors: Adrien Flahault, Mathilde Keck, Pierre-Emmanuel Girault-Sotias, Lucie Esteoulle, Nadia de Mota, Dominique Bonnet, Catherine Llorens-Cortes
Date: 2022-11-08 00:00:00
Tasks: Kidney Function

Apelin is a neuro-vasoactive peptide that plays a major role in the control of cardiovascular functions and water balance, but has an in-vivo half-life in the minute range, limiting its therapeutic use. We previously developed LIT01-196, a systemically active metabolically stable apelin-17 analog, produced by chemical addition of a fluorocarbon chain to the N-terminal part of apelin-17. LIT01-196 behaves as a potent full agonist for the apelin receptor and has an in vivo half-life in the bloodstream of 28 min after intravenous (i.v.) and 156 min after subcutaneous (s.c.) administrations in conscious normotensive rats. We aimed to investigate the effects of LIT01-196 following systemic administrations on arterial blood pressure, heart rate, fluid balance and electrolytes in conscious normotensive and hypertensive deoxycorticosterone acetate (DOCA)-salt rats. Acute i.v. LIT01-196 administration, in increasing doses, dose-dependently decreases arterial blood pressure with ED 50 values of 9.8 and 3.1 nmol/kg in normotensive and hypertensive rats, respectively. This effect occurs for both via a nitric oxide-dependent mechanism. Moreover, acute s.c. LIT01-196 administration (90 nmol/kg) normalizes arterial blood pressure in conscious hypertensive DOCA-salt rats for more than 7 h. The LIT01-196-induced blood pressure decrease remains unchanged after 4 consecutive daily s.c. administrations of 90 nmol/kg, and does not induce any alteration of plasma sodium and potassium levels and kidney function as shown by the lack of change in plasma creatinine and urea nitrogen levels. Activating the apelin receptor with LIT01-196 may constitute a novel approach for the treatment of hypertension.
Robust Functional Magnetoencephalographic Brain Measures with 1.0 Millimeter Spatial Separation
Authors: Don Krieger, Paul Shepard, David O. Okonkwo
Date: 2022-11-08 00:00:00

Neuroelectric currents were extracted from free-running magnetoencephalographic (MEG) rest and task recordings from 617 normative subjects (ages: 18-87). State-dependent neuroelectric differential activation (DA) with spatial resolution comparable to that of local field potentials was detected in the majority of this cohort. Rest-high (rest greater than task) or task-high DA was found in the majority of individual subjects in more than 13,000 1 mm^3 voxels per subject. On average, 6% of the DA voxels bordered a second voxel whose DA was opposite, i.e., one was rest-high and the other was task-high. 516 subjects showed more than 100 such opposite voxel pairs 1 mm apart; 226 subjects showed more than 1000. The number of bordering voxel pairs with the same DA was consistently higher for almost all subjects and averaged 20%, ruling out the possibility that opposite bordering voxels occur simply by chance. For 65 brain regions, more than 10% of the cohort showed significantly more same than opposite pairs. These findings taken together support the conclusion that neuroelectric DA is consistently distinguishable at single 1 mm^3 brain voxels with 1-mm spatial separation. When restricted to voxels with near-zero rest or task counts, significantly more rest-high than task-high voxels were found in 35 regions for at least 10 percent of the subjects. This inequality was not found when all DA-voxels were included. This supports the conclusion that the DA found in many rest-high voxels with near-zero task counts is due in part to task-dependent inhibition.
Splitting expands the application range of Vision Transformer -- variable Vision Transformer (vViT)
Authors: Takuma Usuzaki
Date: 2022-11-08 00:00:00
Tasks: Specificity

Vision Transformer (ViT) has achieved outstanding results in computer vision. Although there are many Transformer-based architectures derived from the original ViT, the dimension of patches are often the same with each other. This disadvantage leads to a limited application range in the medical field because in the medical field, datasets whose dimension is different from each other; e.g. medical image, patients' personal information, laboratory test and so on. To overcome this limitation, we develop a new derived type of ViT termed variable Vision Transformer (vViT). The aim of this study is to introduce vViT and to apply vViT to radiomics using T1 weighted magnetic resonance image (MRI) of glioma. In the prediction of 365 days of survival among glioma patients using radiomics,vViT achieved 0.83, 0.82, 0.81, and 0.76 in sensitivity, specificity, accuracy, and AUC-ROC, respectively. vViT has the potential to handle different types of medical information at once.
Mod-Poisson approximation schemes: Applications to credit risk
Authors: Pierre-Loïc Méliot, Ashkan Nikeghbali, Gabriele Visentin
Date: 2022-10-26 00:00:00

We introduce a new numerical approximation method for functionals of factor credit portfolio models based on the theory of mod-$\phi$ convergence and mod-$\phi$ approximation schemes. The method can be understood as providing correction terms to the classic Poisson approximation, where higher order corrections lead to asymptotically better approximations as the number of obligors increases. We test the model empirically on two tasks: the estimation of risk measures ($\mathrm{VaR}$ and $\mathrm{ES}$) and the computation of CDO tranche prices. We compare it to other commonly used methods -- such as the recursive method, the large deviations approximation, the Chen--Stein method and the Monte Carlo simulation technique (with and without importance sampling) -- and we show that it leads to more accurate estimates while requiring less computational time.
Heterogeneous Recurrent Spiking Neural Network for Spatio-Temporal Classification
Authors: Biswadeep Chakraborty, Saibal Mukhopadhyay
Date: 2022-09-22 00:00:00
Tasks: Activity Recognition, Classification

Spiking Neural Networks are often touted as brain-inspired learning models for the third wave of Artificial Intelligence. Although recent SNNs trained with supervised backpropagation show classification accuracy comparable to deep networks, the performance of unsupervised learning-based SNNs remains much lower. This paper presents a heterogeneous recurrent spiking neural network (HRSNN) with unsupervised learning for spatio-temporal classification of video activity recognition tasks on RGB (KTH, UCF11, UCF101) and event-based datasets (DVS128 Gesture). The key novelty of the HRSNN is that the recurrent layer in HRSNN consists of heterogeneous neurons with varying firing/relaxation dynamics, and they are trained via heterogeneous spike-time-dependent-plasticity (STDP) with varying learning dynamics for each synapse. We show that this novel combination of heterogeneity in architecture and learning method outperforms current homogeneous spiking neural networks. We further show that HRSNN can achieve similar performance to state-of-the-art backpropagation trained supervised SNN, but with less computation (fewer neurons and sparse connection) and less training data.