pwc-archive/papers-with-abstracts huggingface dataset:
Hebbian control of fixations in a dyslexic reader
Authors: Albert Le Floch, Guy Ropars
Date: 2023-07-28 00:00:00

During reading, dyslexic readers exhibit more and longer fixations than normal readers. However, there is no significant difference when dyslexic and control readers perform only visual tasks on a string of letters, showing the importance of cognitive processes in reading. This linguistic and cognitive processing demand in reading is often perturbed for dyslexic readers by perceived additional letter and word mirror-images superposed to the primary images on the primary cortex, inducing an internal visual crowding. Here we show that whereas for a normal reader, the number and the duration of fixations remain invariant whatever the nature of the lighting, the excess of fixations and total duration of reading can be controlled for a dyslexic reader using the Hebbian mechanisms to erase the extra images in an optimized pulse-width lighting. The number of fixations can be reduced by a factor of about 1.8, recovering the normal reader records.
Reaction Diffusion TAP
Authors: M. Andrecut
Date: 2023-07-25 00:00:00

The recently introduced Theory of the Adjacent Possible (TAP) is a model of combinatorial innovation aiming to explain the "hockey-stick" upward trend of human technological evolution, where an explosion in the number of produced items with increasing complexity suddenly occurs. In addition, the TAP model was also used to explain the rapidly emerging biological complexity. Inspired by TAP here we propose a reaction-diffusion system aiming to extend the model in both space and time. We show that the new model exhibits similar characteristics to the TAP model, like the sudden increase in the production of items, after a longer period of slow growth. The new model also exhibits wave propagation of "innovation", resulting in self-sustained complex interference patterns.
Group-Heterogeneous Changes-in-Changes and Distributional Synthetic Controls
Authors: Songnian Chen, Junlong Feng
Date: 2023-07-28 00:00:00

We develop new methods for changes-in-changes and distributional synthetic controls when there exists group level heterogeneity. For changes-in-changes, we allow individuals to belong to a large number of heterogeneous groups. The new method extends the changes-in-changes method in Athey and Imbens (2006) by finding appropriate subgroups within the control groups which share similar group level unobserved characteristics to the treatment groups. For distributional synthetic control, we show that the appropriate synthetic control needs to be constructed using units in potentially different time periods in which they have comparable group level heterogeneity to the treatment group, instead of units that are only in the same time period as in Gunsilius (2023). Implementation and data requirements for these new methods are briefly discussed.
On the Efficiency of Finely Stratified Experiments
Authors: Yuehao Bai, Jizhou Liu, Azeem M. Shaikh, Max Tabord-Meehan
Date: 2023-07-27 00:00:00

This paper studies the use of finely stratified designs for the efficient estimation of a large class of treatment effect parameters that arise in the analysis of experiments. By a "finely stratified" design, we mean experiments in which units are divided into groups of a fixed size and a proportion within each group is assigned to a binary treatment uniformly at random. The class of parameters considered are those that can be expressed as the solution to a set of moment conditions constructed using a known function of the observed data. They include, among other things, average treatment effects, quantile treatment effects, and local average treatment effects as well as the counterparts to these quantities in experiments in which the unit is itself a cluster. In this setting, we establish three results. First, we show that under a finely stratified design, the naive method of moments estimator achieves the same asymptotic variance as what could typically be attained under alternative treatment assignment mechanisms only through ex post covariate adjustment. Second, we argue that the naive method of moments estimator under a finely stratified design is asymptotically efficient by deriving a lower bound on the asymptotic variance of regular estimators of the parameter of interest in the form of a convolution theorem. In this sense, finely stratified experiments are attractive because they lead to efficient estimators of treatment effect parameters "by design." Finally, we strengthen this conclusion by establishing conditions under which a "fast-balancing" property of finely stratified designs is in fact necessary for the naive method of moments estimator to attain the efficiency bound.
Predictability Tests Robust against Parameter Instability
Authors: Christis Katsouris
Date: 2023-07-27 00:00:00

We consider Wald type statistics designed for joint predictability and structural break testing based on the instrumentation method of Phillips and Magdalinos (2009). We show that under the assumption of nonstationary predictors: (i) the tests based on the OLS estimators converge to a nonstandard limiting distribution which depends on the nuisance coefficient of persistence; and (ii) the tests based on the IVX estimators can filter out the persistence under certain parameter restrictions due to the supremum functional. These results contribute to the literature of joint predictability and parameter instability testing by providing analytical tractable asymptotic theory when taking into account nonstationary regressors. We compare the finite-sample size and power performance of the Wald tests under both estimators via extensive Monte Carlo experiments. Critical values are computed using standard bootstrap inference methodologies. We illustrate the usefulness of the proposed framework to test for predictability under the presence of parameter instability by examining the stock market predictability puzzle for the US equity premium.
Global air quality inequality over 2000-2020
Authors: Lutz Sager
Date: 2023-07-28 00:00:00

Air pollution generates substantial health damages and economic costs worldwide. Pollution exposure varies greatly, both between countries and within them. However, the degree of air quality inequality and its' trajectory over time have not been quantified at a global level. Here I use economic inequality indices to measure global inequality in exposure to ambient fine particles with 2.5 microns or less in diameter (PM2.5). I find high and rising levels of global air quality inequality. The global PM2.5 Gini Index increased from 0.32 in 2000 to 0.36 in 2020, exceeding levels of income inequality in many countries. Air quality inequality is mostly driven by differences between countries and less so by variation within them, as decomposition analysis shows. A large share of people facing the highest levels of PM2.5 exposure are concentrated in only a few countries. The findings suggest that research and policy efforts that focus only on differences within countries are overlooking an important global dimension of environmental justice.
Quantifying the Influence of Climate on Human Mind and Culture: Evidence from Visual Art
Authors: Shuhei Kitamura
Date: 2023-07-28 00:00:00

While connections between climate and the human mind and culture are widely acknowledged, they are not thoroughly quantified. Analyzing 100,000 paintings and data on 2,000 artists from the 13th to 21st centuries, the study reveals that the lightness of the paintings exhibited an interesting U-shaped pattern mirroring global temperature trends. There is a significant association between the two, even after controlling for various factors. Event study analysis using the artist-level data further reveals that high-temperature shocks resulted in brighter paintings in later periods for artists who experienced them compared to the control group. The effect is particularly pronounced in art genres that rely on artists' imaginations, indicating a notable influence on artists' minds. These findings underscore the enduring impact of climate on the human mind and culture throughout history and highlight art as a valuable tool for understanding people's minds and cultures.
Only-child matching penalty in the marriage market
Authors: Keisuke Kawata, Mizuki Komura
Date: 2023-07-28 00:00:00

This study explores the marriage matching of only-child individuals and its outcome. Specifically, we analyze two aspects. First, we investigate how marital status (i.e., marriage with an only child, that with a non-only child and remaining single) differs between only children and non-only children. This analysis allows us to know whether people choose mates in a positive or a negative assortative manner regarding only-child status, and to predict whether only-child individuals benefit from marriage matching premiums or are subject to penalties regarding partner attractiveness. Second, we measure the premium/penalty by the size of the gap in partner's socio economic status (SES, here, years of schooling) between only-child and non--only-child individuals. The conventional economic theory and the observed marriage patterns of positive assortative mating on only-child status predict that only-child individuals are subject to a matching penalty in the marriage market, especially when their partner is also an only child. Furthermore, our estimation confirms that among especially women marrying an only-child husband, only children are penalized in terms of 0.57-years-lower educational attainment on the part of the partner.
On the mathematics of the circular flow of economic activity with applications to the topic of caring for the vulnerable during pandemics
Authors: Aziz Guergachi, Javid Hakim
Date: 2023-07-27 00:00:00

We investigate, at the fundamental level, the questions of `why', `when' and `how' one could or should reach out to poor and vulnerable people to support them in the absence of governmental institutions. We provide a simple and new approach that is rooted in linear algebra and basic graph theory to capture the dynamics of income circulation among economic agents. A new linear algebraic model for income circulation is introduced, based on which we are able to categorize societies as fragmented or cohesive. We show that, in the case of fragmented societies, convincing wealthy agents at the top of the social hierarchy to support the poor and vulnerable will be very difficult. We also highlight how linear-algebraic and simple graph-theoretic methods help explain, from a fundamental point of view, some of the mechanics of class struggle in fragmented societies. Then, we explain intuitively and prove mathematically why, in cohesive societies, wealthy agents at the top of the social hierarchy tend to benefit by supporting the vulnerable in their society. A number of new concepts emerge naturally from our mathematical analysis to describe the level of cohesiveness of the society, the number of degrees of separation in business (as opposed to social) networks, and the level of generosity of the overall economy, which all tend to affect the rate at which the top wealthy class recovers its support money back. In the discussion on future perspectives, the connections between the proposed matrix model and statistical physics concepts are highlighted.
Efficient Acoustic Echo Suppression with Condition-Aware Training
Authors: Ernst Seidel, Pejman Mowlaee, Tim Fingscheidt
Date: 2023-07-28 00:00:00
Tasks: Decoder

The topic of deep acoustic echo control (DAEC) has seen many approaches with various model topologies in recent years. Convolutional recurrent networks (CRNs), consisting of a convolutional encoder and decoder encompassing a recurrent bottleneck, are repeatedly employed due to their ability to preserve nearend speech even in double-talk (DT) condition. However, past architectures are either computationally complex or trade off smaller model sizes with a decrease in performance. We propose an improved CRN topology which, compared to other realizations of this class of architectures, not only saves parameters and computational complexity, but also shows improved performance in DT, outperforming both baseline architectures FCRN and CRUSE. Striving for a condition-aware training, we also demonstrate the importance of a high proportion of double-talk and the missing value of nearend-only speech in DAEC training data. Finally, we show how to control the trade-off between aggressive echo suppression and near-end speech preservation by fine-tuning with condition-aware component loss functions.
A Time-Frequency Generative Adversarial based method for Audio Packet Loss Concealment
Authors: Carlo Aironi, Samuele Cornell, Luca Serafini, Stefano Squartini
Date: 2023-07-28 00:00:00
Tasks: Image-to-Image Translation, Packet Loss Concealment, Translation

Packet loss is a major cause of voice quality degradation in VoIP transmissions with serious impact on intelligibility and user experience. This paper describes a system based on a generative adversarial approach, which aims to repair the lost fragments during the transmission of audio streams. Inspired by the powerful image-to-image translation capability of Generative Adversarial Networks (GANs), we propose bin2bin, an improved pix2pix framework to achieve the translation task from magnitude spectrograms of audio frames with lost packets, to noncorrupted speech spectrograms. In order to better maintain the structural information after spectrogram translation, this paper introduces the combination of two STFT-based loss functions, mixed with the traditional GAN objective. Furthermore, we employ a modified PatchGAN structure as discriminator and we lower the concealment time by a proper initialization of the phase reconstruction algorithm. Experimental results show that the proposed method has obvious advantages when compared with the current state-of-the-art methods, as it can better handle both high packet loss rates and large gaps.
RAWIW: RAW Image Watermarking Robust to ISP Pipeline
Authors: Kang Fu, Xiaohong Liu, Jun Jia, ZiCheng Zhang, Yicong Peng, Jia Wang, Guangtao Zhai
Date: 2023-07-28 00:00:00

Invisible image watermarking is essential for image copyright protection. Compared to RGB images, RAW format images use a higher dynamic range to capture the radiometric characteristics of the camera sensor, providing greater flexibility in post-processing and retouching. Similar to the master recording in the music industry, RAW images are considered the original format for distribution and image production, thus requiring copyright protection. Existing watermarking methods typically target RGB images, leaving a gap for RAW images. To address this issue, we propose the first deep learning-based RAW Image Watermarking (RAWIW) framework for copyright protection. Unlike RGB image watermarking, our method achieves cross-domain copyright protection. We directly embed copyright information into RAW images, which can be later extracted from the corresponding RGB images generated by different post-processing methods. To achieve end-to-end training of the framework, we integrate a neural network that simulates the ISP pipeline to handle the RAW-to-RGB conversion process. To further validate the generalization of our framework to traditional ISP pipelines and its robustness to transmission distortion, we adopt a distortion network. This network simulates various types of noises introduced during the traditional ISP pipeline and transmission. Furthermore, we employ a three-stage training strategy to strike a balance between robustness and concealment of watermarking. Our extensive experiments demonstrate that RAWIW successfully achieves cross-domain copyright protection for RAW images while maintaining their visual quality and robustness to ISP pipeline distortions.
An approach to Physics Based Single Photon Vision System Design
Authors: Yizhou Lu, Trevor Seets, Felipe Gutierrez-Barragan, Ehsan Ahmadi, Andreas Velten
Date: 2023-07-27 00:00:00
Tasks: compressed sensing

The design of the camera and optical measurement is a crucial part of optimizing machine vision systems. However, camera designs are usually optimized to produce human-interpretable images. Moreover, camera optimization typically makes the assumption of additive noise, while modern optical imaging systems are mainly affected by photon noise which is not additive. Previous studies have highlighted the fundamental effect of the noise model on the outcome of the design process in the context of coded single-pixel and compressed sensing cameras. In addition to the exact noise model, the nature of the data encountered and the vision task fundamentally affect the optimal hardware design. This means that a camera or compressed sensing code that is optimal for capturing images is almost certainly not optimal for any downstream vision task. In this work, we used a simple end-to-end model on a single-pixel camera to study the effects of the noise model for different vision tasks. We demonstrate the importance of incorporating non-linear noise features in optimizing even the simplest vision systems.
Empirical Validation of a Class of Ray-Based Fading Models
Authors: Juan E. Galeote-Cazorla, Alejandro Ramírez-Arroyo, F. Javier Lopez-Martinez, Juan F. Valenzuela-Valdés
Date: 2023-07-28 00:00:00

As new wireless standards are developed, the use of higher operation frequencies comes in hand with new use cases and propagation effects that differ from the well-established state of the art. Numerous stochastic fading models have recently emerged under the umbrella of generalized fading conditions to provide a fine-grain characterization of propagation channels in the mmWave and sub-THz bands. For the first time in literature, this work carries out an experimental validation of a class of such ray-based models in a wide range of propagation conditions (anechoic, reverberation and indoor scenarios) at mmWave bands. These models allow to characterize the communication channel with a reduced number of physically interpretable parameters. In specific, we show that the independent fluctuating two-ray (IFTR) model has good capabilities to recreate rather dissimilar environments with high accuracy and only four parameters. We also put forth that the key limitations of the IFTR model arise in the presence of reduced diffuse propagation, and also due to a limited phase variability for the dominant specular components.
A Realistic Radar Ray Tracing Simulator for Hand Pose Imaging
Authors: Johanna Bräunig, Christian Schüßler, Vanessa Wirth, Marc Stamminger, Ingrid Ullmann, Martin Vossiek
Date: 2023-07-28 00:00:00

With the increasing popularity of human-computer interaction applications, there is also growing interest in generating sufficiently large and diverse data sets for automatic radar-based recognition of hand poses and gestures. Radar simulations are a vital approach to generating training data (e.g., for machine learning). Therefore, this work applies a ray tracing method to radar imaging of the hand. The performance of the proposed simulation approach is verified by a comparison of simulation and measurement data based on an imaging radar with a high lateral resolution. In addition, the surface material model incorporated into the ray tracer is highlighted in more detail and parameterized for radar hand imaging. Measurements and simulations show a very high similarity between synthetic and real radar image captures. The presented results demonstrate that it is possible to generate very realistic simulations of radar measurement data even for complex radar hand pose imaging systems.
Direct Power Flow Controller with Continuous Full Regulation Range
Authors: Chong Yao, Youjun Zhang
Date: 2023-07-28 00:00:00

For enhancing power flow control in power transmission, a simplified new structure of direct power flow controller with continuous full regulation range (F-DPFC) was proposed. It has only one-stage power conversion and comprises of a three-phase transformer in parallel and a three-phase trans-former in series with grid, three single-phase full-bridge ac units, and a three-phase filter. Compared with previous DPFC, the proposed one dispenses with two complex three-phase se-lection switches which connect with high-voltage grid directly, and has a continuous 360{\deg} adjustment range of compensation voltage by taking place of buck-type ac unit with full-bridge type ac unit, and then expanding the limit of its duty cycle from [0,1] to [-1,1]. Within a large smooth zone replacing six separate zones, the proposed F-DPFC can regulate the ampli-tude and phase angle of grid node voltage respectively and simultaneously, and then the active and reactive power flow in grid can be controlled smoothly and effectively. The new structure is easy to achieve modular expansion and enables it to operate under high voltage and power conditions. Its struc-ture and operational principle were analyzed in detail, and a prototype was developed. The experimental results verified the feasibility and the correctness of the theoretical analysis.
Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions
Authors: Leo Klarner, Tim G. J. Rudner, Michael Reutlinger, Torsten Schindler, Garrett M. Morris, Charlotte Deane, Yee Whye Teh
Date: 2023-07-14 00:00:00
Tasks: Domain Adaptation, Drug Discovery

Accelerating the discovery of novel and more effective therapeutics is an important pharmaceutical problem in which deep learning is playing an increasingly significant role. However, real-world drug discovery tasks are often characterized by a scarcity of labeled data and significant covariate shift$\unicode{x2013}\unicode{x2013}$a setting that poses a challenge to standard deep learning methods. In this paper, we present Q-SAVI, a probabilistic model able to address these challenges by encoding explicit prior knowledge of the data-generating process into a prior distribution over functions, presenting researchers with a transparent and probabilistically principled way to encode data-driven modeling preferences. Building on a novel, gold-standard bioactivity dataset that facilitates a meaningful comparison of models in an extrapolative regime, we explore different approaches to induce data shift and construct a challenging evaluation setup. We then demonstrate that using Q-SAVI to integrate contextualized prior knowledge of drug-like chemical space into the modeling process affords substantial gains in predictive accuracy and calibration, outperforming a broad range of state-of-the-art self-supervised pre-training and domain adaptation techniques.
Somatic mutations in human ageing: New insights from DNA sequencing and inherited mutations
Authors: Kasit Chatsirisupachai, João Pedro de Magalhães
Date: 2023-07-28 00:00:00

The accumulation of somatic mutations is a driver of cancer and has long been associated with ageing. Due to limitations in quantifying mutation burden with age in non-cancerous tissues, the impact of somatic mutations in other ageing phenotypes is unclear. Recent advances in DNA sequencing technologies have allowed the large-scale quantification of somatic mutations in ageing. These studies have revealed a gradual accumulation of mutations in most normal tissues with age as well as a substantial clonal expansion driven mostly by cancer-related mutations. Nevertheless, because of the relatively modest burden of age-related somatic mutations identified so far and their stochastic nature, it is difficult to envision how somatic mutation accumulation alone can explain most ageing phenotypes that develop gradually. Studies across species have also found that longer-lived species have lower somatic mutation rates, though these could be explained by selective pressures to reduce or postpone cancer as longevity increases. Overall, with a few exceptions like cancer, results from recent DNA sequencing studies do not add weight to the idea that somatic mutations with age drive ageing phenotypes and the phenotypic role, if any, of somatic mutations in ageing remains unclear. Recent studies in patients with somatic mutation burden and no signs of accelerated ageing further question the role of somatic mutations in ageing.