pwc-archive/papers-with-abstracts huggingface dataset:
Optical Diffraction Tomography Meets Fluorescence Localization Microscopy
Authors: Thanh-an Pham, Emmanuel Soubies, Ferréol Soulez, Michael Unser
Date: 2023-07-18 00:00:00
Tasks: Position

We show that structural information can be extracted from single molecule localization microscopy (SMLM) data. More precisely, we reinterpret SMLM data as the measures of a phaseless optical diffraction tomography system for which the illumination sources are fluorophores within the sample. Building upon this model, we propose a joint optimization framework to estimate both the refractive index map and the position of fluorescent molecules from the sole SMLM frames.
Graph Representation of the Magnetic Field Topology in High-Fidelity Plasma Simulations for Machine Learning Applications
Authors: Ioanna Bouri, Fanni Franssila, Markku Alho, Giulia Cozzani, Ivan Zaitsev, Minna Palmroth, Teemu Roos
Date: 2023-07-10 00:00:00
Tasks: Topological Data Analysis

Topological analysis of the magnetic field in simulated plasmas allows the study of various physical phenomena in a wide range of settings. One such application is magnetic reconnection, a phenomenon related to the dynamics of the magnetic field topology, which is difficult to detect and characterize in three dimensions. We propose a scalable pipeline for topological data analysis and spatiotemporal graph representation of three-dimensional magnetic vector fields. We demonstrate our methods on simulations of the Earth's magnetosphere produced by Vlasiator, a supercomputer-scale Vlasov theory-based simulation for near-Earth space. The purpose of this work is to challenge the machine learning community to explore graph-based machine learning approaches to address a largely open scientific problem with wide-ranging potential impact.
Asymptotic equivalence of Principal Components and Quasi Maximum Likelihood estimators in Large Approximate Factor Models
Authors: Matteo Barigozzi
Date: 2023-07-19 00:00:00
Tasks: regression, Time Series, Time Series Regression

This paper investigates the properties of Quasi Maximum Likelihood estimation of an approximate factor model for an $n$-dimensional vector of stationary time series. We prove that the factor loadings estimated by Quasi Maximum Likelihood are asymptotically equivalent, as $n\to\infty$, to those estimated via Principal Components. Both estimators are, in turn, also asymptotically equivalent, as $n\to\infty$, to the unfeasible Ordinary Least Squares estimator we would have if the factors were observed. We also show that the usual sandwich form of the asymptotic covariance matrix of the Quasi Maximum Likelihood estimator is asymptotically equivalent to the simpler asymptotic covariance matrix of the unfeasible Ordinary Least Squares. All these results hold in the general case in which the idiosyncratic components are cross-sectionally heteroskedastic, as well as serially and cross-sectionally weakly correlated. The intuition behind these results is that as $n\to\infty$ the factors can be considered as observed, thus showing that factor models enjoy a blessing of dimensionality.
Risk Preference Types, Limited Consideration, and Welfare
Authors: Levon Barseghyan, Francesca Molinari
Date: 2023-07-18 00:00:00
Tasks: Decision Making

We provide sufficient conditions for semi-nonparametric point identification of a mixture model of decision making under risk, when agents make choices in multiple lines of insurance coverage (contexts) by purchasing a bundle. As a first departure from the related literature, the model allows for two preference types. In the first one, agents behave according to standard expected utility theory with CARA Bernoulli utility function, with an agent-specific coefficient of absolute risk aversion whose distribution is left completely unspecified. In the other, agents behave according to the dual theory of choice under risk(Yaari, 1987) combined with a one-parameter family distortion function, where the parameter is agent-specific and is drawn from a distribution that is left completely unspecified. Within each preference type, the model allows for unobserved heterogeneity in consideration sets, where the latter form at the bundle level -- a second departure from the related literature. Our point identification result rests on observing sufficient variation in covariates across contexts, without requiring any independent variation across alternatives within a single context. We estimate the model on data on households' deductible choices in two lines of property insurance, and use the results to assess the welfare implications of a hypothetical market intervention where the two lines of insurance are combined into a single one. We study the role of limited consideration in mediating the welfare effects of such intervention.
Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models Evidence from European Financial Markets and Bitcoins
Authors: Apostolos Ampountolas
Date: 2023-07-17 00:00:00
Tasks: Time Series Forecasting

This study analyzes the transmission of market uncertainty on key European financial markets and the cryptocurrency market over an extended period, encompassing the pre, during, and post-pandemic periods. Daily financial market indices and price observations are used to assess the forecasting models. We compare statistical, machine learning, and deep learning forecasting models to evaluate the financial markets, such as the ARIMA, hybrid ETS-ANN, and kNN predictive models. The study results indicate that predicting financial market fluctuations is challenging, and the accuracy levels are generally low in several instances. ARIMA and hybrid ETS-ANN models perform better over extended periods compared to the kNN model, with ARIMA being the best-performing model in 2018-2021 and the hybrid ETS-ANN model being the best-performing model in most of the other subperiods. Still, the kNN model outperforms the others in several periods, depending on the observed accuracy measure. Researchers have advocated using parametric and non-parametric modeling combinations to generate better results. In this study, the results suggest that the hybrid ETS-ANN model is the best-performing model despite its moderate level of accuracy. Thus, the hybrid ETS-ANN model is a promising financial time series forecasting approach. The findings offer financial analysts an additional source that can provide valuable insights for investment decisions.
The Canonical Decomposition of Factor Models: Weak Factors are Everywhere
Authors: Philipp Gersing, Matteo Barigozzi, Christoph Rust, Manfred Deistler
Date: 2023-07-13 00:00:00

There are two approaches to time series approximate factor models: the static factor model, where the factors are loaded contemporaneously by the common component, and the Generalised Dynamic Factor Model, where the factors are loaded with lags. In this paper we derive a canonical decomposition which nests both models by introducing the weak common component which is the difference between the dynamic- and the static common component. Such component is driven by potentially infinitely many non-pervasive weak factors which live in the dynamically common space (not to be confused with rate-weak factors, being pervasive but associated with a slower rate). Our result shows that the relation between the two approaches is far more rich and complex than what usually assumed. We exemplify why the weak common component shall not be neglected by means of theoretical and empirical examples. Furthermore, we propose a simple estimation procedure for the canonical decomposition. Our empirical estimates on US macroeconomic data reveal that the weak common component can account for a large part of the variation of individual variables. Furthermore in a pseudo real-time forecasting evaluation for industrial production and inflation, we show that gains can be obtained from considering the dynamic approach over the static approach.
Socio-spatial Inequalities in a Context of "Great Economic Wealth". Case study of neighbourhoods of Luxembourg City
Authors: Natalia Zdanowska
Date: 2023-07-18 00:00:00

In spite of being one of the smallest and wealthiest countries in the European Union in terms of GDP per capita, Luxembourg is facing socio-economic challenges due to recent rapid urban transformations. This article contributes by approaching this phenomenon at the most granular and rarely analysed geographical level - the neighbourhoods of the capital, Luxembourg City. Based on collected empirical data covering various socio-demographic dimensions for 2020-2021, an ascending hierarchical classification on principal components is set out to establish neighbourhoods' socio-spatial patterns. In addition, Chi2 tests are carried out to examine residents' socio-demographic characteristics and determine income inequalities in neighbourhoods. The results reveal a clear socio-spatial divide along a north-west south-east axis. Moreover, classical factors such as gender or citizenship differences are revealed to be poorly determinant of income inequalities compared with the proportion of social benefits recipients and single residents.
COVID-19 Demand Shocks Revisited: Did Advertising Technology Help Mitigate Adverse Consequences for Small and Midsize Businesses?
Authors: Shun-Yang Lee, Julian Runge, Daniel Yoo, Yakov Bart, Anett Gyurak, J. W. Schneider
Date: 2023-07-18 00:00:00

Research has investigated the impact of the COVID-19 pandemic on business performance and survival, indicating particularly adverse effects for small and midsize businesses (SMBs). Yet only limited work has examined whether and how online advertising technology may have helped shape these outcomes, particularly for SMBs. The aim of this study is to address this gap. By constructing and analyzing a novel data set of more than 60,000 businesses in 49 countries, we examine the impact of government lockdowns on business survival. Using discrete-time survival models with instrumental variables and staggered difference-in-differences estimators, we find that government lockdowns increased the likelihood of SMB closure around the world but that use of online advertising technology attenuates this adverse effect. The findings show heterogeneity in country, industry, and business size, which we discuss and is consistent with theoretical expectations.
The Beginning of the Trend: Interest Rates, Profits, and Markups
Authors: Anton Bobrov, James Traina
Date: 2023-07-18 00:00:00
Tasks: Sensitivity, Time Series

Recent highly cited research uses time-series evidence to argue the decline in interest rates led to a large rise in economic profits and markups. We show the size of these estimates is sensitive to the sample start date: The rise in markups from 1984 to 2019 is 14% larger than from 1980 to 2019, a difference amounting to a $3000 change in income per worker in 2019. The sensitivity comes from a peak in interest rates in 1984, during a period of heightened volatility. Our results imply researchers should justify their time-series selection and incorporate sensitivity checks in their analysis.
Culture, Gender, and Labor Force Participation: Evidence from Colombia
Authors: Hector Galindo-Silva, Paula Herrera-Idárraga
Date: 2023-07-17 00:00:00

This study investigates the impact of integrating gender equality into the Colombian constitution of 1991 on attitudes towards gender equality, experiences of gender-based discrimination, and labor market participation. Using a difference-in-discontinuities framework, we compare individuals exposed to mandatory high school courses on the Constitution with those who were not exposed. Our findings show a significant increase in labor market participation, primarily driven by women. Exposure to these courses also shapes attitudes towards gender equality, with men demonstrating greater support. Women report experiencing less gender-based discrimination. Importantly, our results suggest that women's increased labor market participation is unlikely due to reduced barriers from male partners. A disparity in opinions regarding traditional gender norms concerning household domains is observed between men and women, highlighting an ongoing power struggle within the home. However, the presence of a younger woman in the household appears to influence men's more positive view of gender equality, potentially indicating a desire to empower younger women in their future lives. These findings highlight the crucial role of cultural shocks and the constitutional inclusion of women's rights in shaping labor market dynamics.
An effective interest rate cap: a clarification
Authors: Mikhail V. Sokolov
Date: 2023-07-17 00:00:00

The national legislation of many countries imposes restrictions on lending rates known as interest rate caps. In most cases, the effective (rather than nominal) interest rate is restricted, which includes all commissions and fees associated with a loan. Typically, the generic wording of this restriction is ambiguous in three respects. First, the literature provides several nonequivalent concepts of internal rate of return (IRR). Since the effective interest rate is the IRR of the cash flow stream of a loan, the wording should specify which concept of IRR is used. Second, most definitions of IRR are partial in the sense that there are cash flow streams that have no IRR. Thus, the wording is vague for loans that have no IRR. Third, when loan advances and repayments alternate in time, the respective roles of the borrower and lender can be blurred. In this case, it is unclear to which of the parties of the loan contract the regulatory measure should be addressed and, if it treats the contract as illegal, which party should be brought to justice. This paper aims to resolve these ambiguities. We start by clarifying the concept of IRR. We axiomatize the conventional definition of IRR (as a unique root of the IRR polynomial) and show that any extension to a larger domain necessarily violates a natural axiom. Given this result, we show how to derive an effective interest rate cap. We prove that there is a unique solution consistent with a set of reasonable conditions. Finally, we suggest a way to avoid the problem of identification of the roles of the loan contract parties by imposing both a floor and a ceiling on lending rates.
Power to the teens? A model of parents' and teens' collective labor supply
Authors: José Alfonso Muñoz-Alvarado
Date: 2023-06-30 00:00:00
Tasks: Decision Making

Teens make life-changing decisions while constrained by the needs and resources of the households they grow up in. Household behavior models frequently delegate decision-making to the teen or their parents, ignoring joint decision-making in the household. I show that teens and parents allocate time and income jointly by using data from the Costa Rican Encuesta Nacional de Hogares from 2011 to 2019 and a conditional cash transfer program. First, I present gender differences in household responses to the transfer using a marginal treatment effect framework. Second, I explain how the gender gap from the results is due to the bargaining process between parents and teens. I propose a collective household model and show that sons bargain cooperatively with their parents while daughters do not. This result implies that sons have a higher opportunity cost of attending school than daughters. Public policy targeting teens must account for this gender disparity to be effective.
Self-Supervised Acoustic Word Embedding Learning via Correspondence Transformer Encoder
Authors: Jingru Lin, Xianghu Yue, Junyi Ao, Haizhou Li
Date: 2023-07-19 00:00:00
Tasks: Word Embeddings

Acoustic word embeddings (AWEs) aims to map a variable-length speech segment into a fixed-dimensional representation. High-quality AWEs should be invariant to variations, such as duration, pitch and speaker. In this paper, we introduce a novel self-supervised method to learn robust AWEs from a large-scale unlabelled speech corpus. Our model, named Correspondence Transformer Encoder (CTE), employs a teacher-student learning framework. We train the model based on the idea that different realisations of the same word should be close in the underlying embedding space. Specifically, we feed the teacher and student encoder with different acoustic instances of the same word and pre-train the model with a word-level loss. Our experiments show that the embeddings extracted from the proposed CTE model are robust to speech variations, e.g. speakers and domains. Additionally, when evaluated on Xitsonga, a low-resource cross-lingual setting, the CTE model achieves new state-of-the-art performance.
Low bit rate binaural link for improved ultra low-latency low-complexity multichannel speech enhancement in Hearing Aids
Authors: Nils L. Westhausen, Bernd T. Meyer
Date: 2023-07-17 00:00:00
Tasks: Quantization, Speech Enhancement

Speech enhancement in hearing aids is a challenging task since the hardware limits the number of possible operations and the latency needs to be in the range of only a few milliseconds. We propose a deep-learning model compatible with these limitations, which we refer to as Group-Communication Filter-and-Sum Network (GCFSnet). GCFSnet is a causal multiple-input single output enhancement model using filter-and-sum processing in the time-frequency domain and a multi-frame deep post filter. All filters are complex-valued and are estimated by a deep-learning model using weight-sharing through Group Communication and quantization-aware training for reducing model size and computational footprint. For a further increase in performance, a low bit rate binaural link for delayed binaural features is proposed to use binaural information while retaining a latency of 2ms. The performance of an oracle binaural LCMV beamformer in non-low-latency configuration can be matched even by a unilateral configuration of the GCFSnet in terms of objective metrics.
Image Processing Methods Applied to Motion Tracking of Nanomechanical Buckling on SEM Recordings
Authors: Ege Erdem, Berke Demiralp, Hadi S Pisheh, Peyman Firoozy, Ahmet Hakan Karakurt, M. Selim Hanay
Date: 2023-07-17 00:00:00

The scanning electron microscope (SEM) recordings of dynamic nano-electromechanical systems (NEMS) are difficult to analyze due to the noise caused by low frame rate, insufficient resolution and blurriness induced by applied electric potentials. Here, we develop an image processing algorithm enhanced by the physics of the underlying system to track the motion of buckling NEMS structures in the presence of high noise levels. The algorithm is composed of an image filter, two data filters, and a nonlinear regression model, which utilizes the expected form of the physical solution. The method was applied to the recordings of a NEMS beam about 150 nm wide, undergoing intra-and inter-well post-buckling states with a transition rate of approximately 0.5 Hz. The algorithm can track the dynamical motion of the NEMS and capture the dependency of deflection amplitude on the compressive force on the beam. With the help of the proposed algorithm, the transition from inter-well to intra-well motion is clearly resolved for buckling NEMS imaged under SEM.
Transient Neural Radiance Fields for Lidar View Synthesis and 3D Reconstruction
Authors: Anagh Malik, Parsa Mirdehghan, Sotiris Nousias, Kiriakos N. Kutulakos, David B. Lindell
Date: 2023-07-14 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: 3D Reconstruction, Autonomous Driving, NeRF

Neural radiance fields (NeRFs) have become a ubiquitous tool for modeling scene appearance and geometry from multiview imagery. Recent work has also begun to explore how to use additional supervision from lidar or depth sensor measurements in the NeRF framework. However, previous lidar-supervised NeRFs focus on rendering conventional camera imagery and use lidar-derived point cloud data as auxiliary supervision; thus, they fail to incorporate the underlying image formation model of the lidar. Here, we propose a novel method for rendering transient NeRFs that take as input the raw, time-resolved photon count histograms measured by a single-photon lidar system, and we seek to render such histograms from novel views. Different from conventional NeRFs, the approach relies on a time-resolved version of the volume rendering equation to render the lidar measurements and capture transient light transport phenomena at picosecond timescales. We evaluate our method on a first-of-its-kind dataset of simulated and captured transient multiview scans from a prototype single-photon lidar. Overall, our work brings NeRFs to a new dimension of imaging at transient timescales, newly enabling rendering of transient imagery from novel views. Additionally, we show that our approach recovers improved geometry and conventional appearance compared to point cloud-based supervision when training on few input viewpoints. Transient NeRFs may be especially useful for applications which seek to simulate raw lidar measurements for downstream tasks in autonomous driving, robotics, and remote sensing.
SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology
Authors: Jingwei Zhang, Ke Ma, Saarthak Kapse, Joel Saltz, Maria Vakalopoulou, Prateek Prasanna, Dimitris Samaras
Date: 2023-07-12 00:00:00
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

Semantic segmentations of pathological entities have crucial clinical value in computational pathology workflows. Foundation models, such as the Segment Anything Model (SAM), have been recently proposed for universal use in segmentation tasks. SAM shows remarkable promise in instance segmentation on natural images. However, the applicability of SAM to computational pathology tasks is limited due to the following factors: (1) lack of comprehensive pathology datasets used in SAM training and (2) the design of SAM is not inherently optimized for semantic segmentation tasks. In this work, we adapt SAM for semantic segmentation by introducing trainable class prompts, followed by further enhancements through the incorporation of a pathology encoder, specifically a pathology foundation model. Our framework, SAM-Path enhances SAM's ability to conduct semantic segmentation in digital pathology without human input prompts. Through experiments on two public pathology datasets, the BCSS and the CRAG datasets, we demonstrate that the fine-tuning with trainable class prompts outperforms vanilla SAM with manual prompts and post-processing by 27.52% in Dice score and 71.63% in IOU. On these two datasets, the proposed additional pathology foundation model further achieves a relative improvement of 5.07% to 5.12% in Dice score and 4.50% to 8.48% in IOU.
Learned Proximal Operator for Solving Seismic Deconvolution Problem
Authors: Peimeng Guan, Naveed Iqbal, Mark A. Davenport, Mudassir Masood
Date: 2023-07-19 00:00:00

Seismic deconvolution is an essential step in seismic data processing that aims to extract layer information from noisy observed traces. In general, this is an ill-posed problem with non-unique solutions. Due to the sparse nature of the reflectivity sequence, spike-promoting regularizers such as the $\ell_1$-norm are frequently used. They either require rigorous coefficient tuning or strong assumptions about reflectivity, such as assuming reflectivity as sparse signals with known sparsity levels and zero-mean Gaussian noise with known noise levels. To overcome the limitations of traditional regularizers, learning-based regularizers are proposed in the recent past. This paper proposes a Learned Proximal operator for Seismic Deconvolution (LP4SD), which leverages a neural network to learn the proximal operator of a regularizer. LP4SD is trained in a loop unrolled manner and is capable of learning complicated structures from the training data. It is worth mentioning that the network is trained with synthetic data and evaluated on both synthetic and real data. LP4SD is shown to generate better reconstruction results in terms of three different metrics as compared to learning a direct inverse.