pwc-archive/papers-with-abstracts huggingface dataset:
A novel perspective on denoising using quantum localization with application to medical imaging
Authors: Amirreza Hashemi, Sayantan Dutta, Bertrand Georgeot, Denis Kouame, Hamid Sabet
Date: 2024-04-22 00:00:00
Tasks: Denoising, Image Denoising, Image Reconstruction

Background noise in many fields such as medical imaging poses significant challenges for accurate diagnosis, prompting the development of denoising algorithms. Traditional methodologies, however, often struggle to address the complexities of noisy environments in high dimensional imaging systems. This paper introduces a novel quantum-inspired approach for image denoising, drawing upon principles of quantum and condensed matter physics. Our approach views medical images as amorphous structures akin to those found in condensed matter physics and we propose an algorithm that incorporates the concept of mode resolved localization directly into the denoising process. Notably, unlike previous studies that considered localization as a hindrance, our approach considers quantum localization as a fundamental component of image reconstruction which is used to differentiate between noisy and non-noisy modes based on diffusivity and localization measurements. This perspective eliminates the need for hyperparameter tuning, making the proposed method a standalone algorithm which can be implemented with minimal manual intervention and can perform automatic filtering of noise regardless of noise level. Through numerical validation, we showcase the effectiveness of our approach in addressing noise-related challenges in imaging and especially medical imaging, underscoring its relevance for possible quantum computing applications.
Statistical Mechanics and Artificial Neural Networks: Principles, Models, and Applications
Authors: Lucas Böttcher, Gregory Wheeler
Date: 2024-04-05 00:00:00
Tasks: Learning Theory

The field of neuroscience and the development of artificial neural networks (ANNs) have mutually influenced each other, drawing from and contributing to many concepts initially developed in statistical mechanics. Notably, Hopfield networks and Boltzmann machines are versions of the Ising model, a model extensively studied in statistical mechanics for over a century. In the first part of this chapter, we provide an overview of the principles, models, and applications of ANNs, highlighting their connections to statistical mechanics and statistical learning theory. Artificial neural networks can be seen as high-dimensional mathematical functions, and understanding the geometric properties of their loss landscapes (i.e., the high-dimensional space on which one wishes to find extrema or saddles) can provide valuable insights into their optimization behavior, generalization abilities, and overall performance. Visualizing these functions can help us design better optimization methods and improve their generalization abilities. Thus, the second part of this chapter focuses on quantifying geometric properties and visualizing loss functions associated with deep ANNs.
From Single Genes to Populations: Quantifying Broken Detailed Balance in Transcription
Authors: James Holehouse
Date: 2024-05-21 00:00:00

Are there thermodynamic or information theoretic constraints governing mRNA expression? Here we use the framework of stochastic thermodynamics to assess detailed balance breaking in transcription. For the canonical two-state model of transcription, we derive exact analytic expressions for the entropy production rate of transcription at steady state, expressions that can be evaluated from knowledge of the kinetic parameters of the two-state model. This allows us to easily evaluate the entropy production rate of thousands of genes across seven datasets of two-state model parameters without needing to evaluate the entropy production rate from trajectory-based computation. A data-driven approach then exposes that most genes avoid parameter regimes associated with large entropy production rates, akin to a mesoscopic version of energy expenditure minimization. Importantly, we show that this is not a thermodynamic phenomenon, since the entropy production rate from the two state gene model provides only a weak bound on the housekeeping energy needed to power transcription. Finally, we show that cell-to-cell variability can make mRNA expression seem more or less irreversible than a ``representative cell'' would imply. Overall, our study presents a theoretical and data-driven approach to uncovering the potential thermodynamic and information theoretic constraints that dictate observed behaviors in transcription.
A minimal scenario for the origin of non-equilibrium order
Authors: Riccardo Ravasio, Kabir Husain, Constantine G. Evans, Rob Phillips, Marco Ribezzi, Jack W. Szostak, Arvind Murugan
Date: 2024-05-17 00:00:00

Life uses non-equilibrium mechanisms to create ordered structures not attainable at equilibrium; the resulting order is assumed to provide functional benefits that outweigh costs of time and energy needed by these mechanisms. Here, we show that models of DNA replication and self-assembly, when expanded to include known stalling effects, can evolve error correcting mechanisms like kinetic proofreading and dynamic instability through selection for fast replication alone. We abstract these results into a general framework that predicts a counterintuitive ''order through speed'' effect if the distribution of replication times is wide enough. We test our results against recent mutational screens of proofreading polymerases. Our work suggests the intriguing possibility that non-equilibrium order can evolve even before that order is directly functional, with consequences for the evolution of mutation rates, viral capsid assembly, and the origin of life.
Metacognitive particles, mental action and the sense of agency
Authors: Lars Sandved-Smith, Lancelot Da Costa
Date: 2024-05-21 00:00:00

This paper articulates metacognition using the language of statistical physics and Bayesian mechanics. Metacognitive beliefs, defined as beliefs about beliefs, find a natural description within this formalism, which allows us to define the dynamics of 'metacognitive particles', i.e., systems possessing metacognitive beliefs. We further unpack this typology of metacognitive systems by distinguishing passive and active metacognitive particles, where active particles are endowed with the capacity for mental actions that update the parameters of other beliefs. We provide arguments for the necessity of this architecture in the emergence of a subjective sense of agency and the experience of being separate from the environment. The motivation is to pave the way towards a mathematical and physical understanding of cognition -- and higher forms thereof -- furthering the study and formalization of cognitive science in the language of mathematical physics.
Integer spin particles necessarily produce half-integer angular momentum in a simple complex and periodic Hamiltonian
Authors: Troy Shinbrot
Date: 1995-08-10 00:00:00

Exact wave functions are is derived from an azimuthally periodic a self-consistent quantum Hamiltonian in 2+1 dimensions using both the Klein-Gordon and the Schroedinger equations. It isWe shown that, curiously, for both relativistic and non-relativistic equations, integer spin wave equations necessarily produce half-integer angular momentum in this potential. We find additionally that the higher energy, relativistic, solutions require an asymptotically free potential, while the lower energy, Schroedinger, solutions can exist in a potential that grows linearly with r. These are purely mathematical results, however we speculate on possible physical interpretations.
Application of Tensorized Neural Networks for Cloud Classification
Authors: Alifu Xiafukaiti, Devanshu Garg, Aruto Hosaka, Koichi Yanagisawa, Yuichiro Minato, Tsuyoshi Yoshida
Date: 2024-03-21 00:00:00
Tasks: Autonomous Driving, Classification, Data Compression, Medical Image Analysis, Self-Supervised Learning, Weather Forecasting

Convolutional neural networks (CNNs) have gained widespread usage across various fields such as weather forecasting, computer vision, autonomous driving, and medical image analysis due to its exceptional ability to extract spatial information, share parameters, and learn local features. However, the practical implementation and commercialization of CNNs in these domains are hindered by challenges related to model sizes, overfitting, and computational time. To address these limitations, our study proposes a groundbreaking approach that involves tensorizing the dense layers in the CNN to reduce model size and computational time. Additionally, we incorporate attention layers into the CNN and train it using Contrastive self-supervised learning to effectively classify cloud information, which is crucial for accurate weather forecasting. We elucidate the key characteristics of tensorized neural network (TNN), including the data compression rate, accuracy, and computational speed. The results indicate how TNN change their properties under the batch size setting.
Lattice physics approaches for neural networks
Authors: Giampiero Bardella, Simone Franchini, Pierpaolo Pani, Stefano Ferraina
Date: 2024-05-20 00:00:00

Modern neuroscience has evolved into a frontier field that draws on numerous disciplines, resulting in the flourishing of novel conceptual frames primarily inspired by physics and complex systems science. Contributing in this direction, we recently introduced a mathematical framework to describe the spatiotemporal interactions of systems of neurons using lattice field theory, the reference paradigm for theoretical particle physics. In this note, we provide a concise summary of the basics of the theory, aiming to be intuitive to the interdisciplinary neuroscience community. We contextualize our methods, illustrating how to readily connect the parameters of our formulation to experimental variables using well-known renormalization procedures. This synopsis yields the key concepts needed to describe neural networks using lattice physics. Such classes of methods are attention-worthy in an era of blistering improvements in numerical computations, as they can facilitate relating the observation of neural activity to generative models underpinned by physical principles.
The Best Radar Ranging Pulse to Resolve Two Reflectors
Authors: Andrew N. Jordan, John C. Howell, Achim Kempf, Shunxing Zhang, Derek White
Date: 2024-05-11 00:00:00

Previous work established fundamental bounds on subwavelength resolution for the radar range resolution problem, called superradar [Phys. Rev. Appl. 20, 064046 (2023)]. In this work, we identify the optimal waveforms for distinguishing the range resolution between two reflectors of identical strength. We discuss both the unnormalized optimal waveform as well as the best square-integrable pulse, and their variants. Using orthogonal function theory, we give an explicit algorithm to optimize the wave pulse in finite time to have the best performance. We also explore range resolution estimation with unnormalized waveforms with multi-parameter methods to also independently estimate loss and time of arrival. These results are consistent with the earlier single parameter approach of range resolution only and give deeper insight into the ranging estimation problem. Experimental results are presented using radio pulse reflections inside coaxial cables, showing robust range resolution smaller than a tenth of the inverse bandedge, with uncertainties close to the derived Cram\'er-Rao bound.
Optimizing Coded-Apertures for Depth-Resolved Diffraction
Authors: Doğa Gürsoy, Dina Sheyfer, Michael Wojcik, Wenjun Liu, Jonathan Tischler
Date: 2024-05-21 00:00:00
Tasks: Astronomy

Coded apertures, traditionally employed in x-ray astronomy for imaging celestial objects, are now being adapted for micro-scale applications, particularly in studying microscopic specimens with synchrotron light diffraction. In this paper, we focus on micro-coded aperture imaging and its capacity to accomplish depth-resolved micro-diffraction analysis within crystalline specimens. We study aperture specifications and scanning parameters by assessing characteristics like size, thickness, and patterns. Numerical experiments assist in assessing their impact on reconstruction quality. Empirical data from a Laue diffraction microscope at a synchrotron undulator beamline supports our findings. Overall, our results offer key insights for optimizing aperture design in advancing micro-scale diffraction imaging at synchrotrons. This study contributes insights to this expanding field and suggests significant advancements, especially when coupled with the enhanced flux anticipated from the global upgrades of synchrotron sources.
Ptychographic non-line-of-sight imaging for depth-resolved visualization of hidden objects
Authors: Pengming Song, Qianhao Zhao, Ruihai Wang, Ninghe Liu, Yingqi Qiang, Tianbo Wang, Xincheng Zhang, Yi Zhang, Guoan Zheng
Date: 2024-05-17 00:00:00

Non-line-of-sight (NLOS) imaging enables the visualization of objects hidden from direct view, with applications in surveillance, remote sensing, and light detection and ranging. Here, we introduce a NLOS imaging technique termed ptychographic NLOS (pNLOS), which leverages coded ptychography for depth-resolved imaging of obscured objects. Our approach involves scanning a laser spot on a wall to illuminate the hidden objects in an obscured region. The reflected wavefields from these objects then travel back to the wall, get modulated by the wall's complex-valued profile, and the resulting diffraction patterns are captured by a camera. By modulating the object wavefields, the wall surface serves the role of the coded layer as in coded ptychography. As we scan the laser spot to different positions, the reflected object wavefields on the wall translate accordingly, with the shifts varying for objects at different depths. This translational diversity enables the acquisition of a set of modulated diffraction patterns referred to as a ptychogram. By processing the ptychogram, we recover both the objects at different depths and the modulation profile of the wall surface. Experimental results demonstrate high-resolution, high-fidelity imaging of hidden objects, showcasing the potential of pNLOS for depth-aware vision beyond the direct line of sight.
Estimating the Impact of Social Distance Policy in Mitigating COVID-19 Spread with Factor-Based Imputation Approach
Authors: Difang Huang, Ying Liang, Boyao Wu, Yanyi Ye
Date: 2024-05-20 00:00:00
Tasks: Imputation

We identify the effectiveness of social distancing policies in reducing the transmission of the COVID-19 spread. We build a model that measures the relative frequency and geographic distribution of the virus growth rate and provides hypothetical infection distribution in the states that enacted the social distancing policies, where we control time-varying, observed and unobserved, state-level heterogeneities. Using panel data on infection and deaths in all US states from February 20 to April 20, 2020, we find that stay-at-home orders and other types of social distancing policies significantly reduced the growth rate of infection and deaths. We show that the effects are time-varying and range from the weakest at the beginning of policy intervention to the strongest by the end of our sample period. We also found that social distancing policies were more effective in states with higher income, better education, more white people, more democratic voters, and higher CNN viewership.
Real-Time Go-Around Prediction: A case study of JFK airport
Authors: Ke Liu, Kaijing Ding, Lu Dai, Mark Hansen, Kennis Chan, John Schade
Date: 2024-05-18 00:00:00

In this paper, we employ the long-short-term memory model (LSTM) to predict the real-time go-around probability as an arrival flight is approaching JFK airport and within 10 nm of the landing runway threshold. We further develop methods to examine the causes to go-around occurrences both from a global view and an individual flight perspective. According to our results, in-trail spacing, and simultaneous runway operation appear to be the top factors that contribute to overall go-around occurrences. We then integrate these pre-trained models and analyses with real-time data streaming, and finally develop a demo web-based user interface that integrates the different components designed previously into a real-time tool that can eventually be used by flight crews and other line personnel to identify situations in which there is a high risk of a go-around.
Conditional Choice Probability Estimation of Dynamic Discrete Choice Models with 2-period Finite Dependence
Authors: Yu Hao, Hiroyuki Kasahara
Date: 2024-05-21 00:00:00
Tasks: Discrete Choice Models

This paper extends the work of Arcidiacono and Miller (2011, 2019) by introducing a novel characterization of finite dependence within dynamic discrete choice models, demonstrating that numerous models display 2-period finite dependence. We recast finite dependence as a problem of sequentially searching for weights and introduce a computationally efficient method for determining these weights by utilizing the Kronecker product structure embedded in state transitions. With the estimated weights, we develop a computationally attractive Conditional Choice Probability estimator with 2-period finite dependence. The computational efficacy of our proposed estimator is demonstrated through Monte Carlo simulations.
Instrumented Difference-in-Differences with Heterogeneous Treatment Effects
Authors: Sho Miyaji
Date: 2024-05-20 00:00:00

Many studies exploit variation in policy adoption timing across units as an instrument for treatment. This paper formalizes the underlying identification strategy as an instrumented difference-in-differences (DID-IV). In this design, a Wald-DID estimand, which scales the DID estimand of the outcome by the DID estimand of the treatment, captures the local average treatment effect on the treated (LATET). We extend the canonical DID-IV design to multiple period settings with the staggered adoption of the instrument across units. Moreover, we propose a credible estimation method in this design that is robust to treatment effect heterogeneity. We illustrate the empirical relevance of our findings, estimating returns to schooling in the United Kingdom. In this application, the two-way fixed effects instrumental variable regression, the conventional approach to implement DID-IV designs, yields a negative estimate. By contrast, our estimation method indicates a substantial gain from schooling.
Comparing predictive ability in presence of instability over a very short time
Authors: Fabrizio Iacone, Luca Rossini, Andrea Viselli
Date: 2024-05-20 00:00:00

We consider forecast comparison in the presence of instability when this affects only a short period of time. We demonstrate that global tests do not perform well in this case, as they were not designed to capture very short-lived instabilities, and their power vanishes altogether when the magnitude of the shock is very large. We then discuss and propose approaches that are more suitable to detect such situations, such as nonparametric methods (S test or MAX procedure). We illustrate these results in different Monte Carlo exercises and in evaluating the nowcast of the quarterly US nominal GDP from the Survey of Professional Forecasters (SPF) against a naive benchmark of no growth, over the period that includes the GDP instability brought by the Covid-19 crisis. We recommend that the forecaster should not pool the sample, but exclude the short periods of high local instability from the evaluation exercise.
Testing Sign Congruence Between Two Parameters
Authors: Douglas L. Miller, Francesca Molinari, Jörg Stoye
Date: 2024-05-20 00:00:00

We test the null hypothesis that two parameters $(\mu_1,\mu_2)$ have the same sign, assuming that (asymptotically) normal estimators $(\hat{\mu}_1,\hat{\mu}_2)$ are available. Examples of this problem include the analysis of heterogeneous treatment effects, causal interpretation of reduced-form estimands, meta-studies, and mediation analysis. A number of tests were recently proposed. We recommend a test that is simple and rejects more often than many of these recent proposals. Like all other tests in the literature, it is conservative if the truth is near $(0,0)$ and therefore also biased. To clarify whether these features are avoidable, we also provide a test that is unbiased and has exact size control on the boundary of the null hypothesis, but which has counterintuitive properties and hence we do not recommend. We use the test to improve p-values in Kowlaksi (2022) from information contained in that paper's main text and to establish statistical significance of some key estimates in Dippel et al. (2021).
Macroeconomic Factors, Industrial Indexes and Bank Spread in Brazil
Authors: Carlos Alberto Durigan Junior, André Taue Saito, Daniel Reed Bergmann, Nuno Manoel Martins Dias Fouto
Date: 2024-05-17 00:00:00
Tasks: Time Series

The main objective of this paper is to Identify which macroe conomic factors and industrial indexes influenced the total Brazilian banking spread between March 2011 and March 2015. This paper considers subclassification of industrial activities in Brazil. Monthly time series data were used in multivariate linear regression models using Eviews (7.0). Eighteen variables were considered as candidates to be determinants. Variables which positively influenced bank spread are; Default, IPIs (Industrial Production Indexes) for capital goods, intermediate goods, du rable consumer goods, semi-durable and non-durable goods, the Selic, GDP, unemployment rate and EMBI +. Variables which influence negatively are; Consumer and general consumer goods IPIs, IPCA, the balance of the loan portfolio and the retail sales index. A p-value of 05% was considered. The main conclusion of this work is that the progress of industry, job creation and consumption can reduce bank spread. Keywords: Credit. Bank spread. Macroeconomics. Industrial Production Indexes. Finance.
Ponzi Funds
Authors: Philippe van der Beck, Jean-Philippe Bouchaud, Dario Villamaina
Date: 2024-05-21 00:00:00

Many active funds hold concentrated portfolios. Flow-driven trading in these securities causes price pressure, which pushes up the funds' existing positions resulting in realized returns. We decompose fund returns into a price pressure (self-inflated) and a fundamental component and show that when allocating capital across funds, investors are unable to identify whether realized returns are self-inflated or fundamental. Because investors chase self-inflated fund returns at a high frequency, even short-lived impact meaningfully affects fund flows at longer time scales. The combination of price impact and return chasing causes an endogenous feedback loop and a reallocation of wealth to early fund investors, which unravels once the price pressure reverts. We find that flows chasing self-inflated returns predict bubbles in ETFs and their subsequent crashes, and lead to a daily wealth reallocation of 500 Million from ETFs alone. We provide a simple regulatory reporting measure -- fund illiquidity -- which captures a fund's potential for self-inflated returns.