pwc-archive/papers-with-abstracts huggingface dataset:
Theory of cellular homochirality and trait evolution in flocking systems
Authors: Ludwig A. Hoffmann, Luca Giomi
Date: 2023-07-24 00:00:00

Chirality is a feature of many biological systems and much research has been focused on understanding the origin and implications of this property. Famously, sugars and amino acids found in nature are homochiral, i.e., chiral symmetry is broken and only one of the two possible chiral states is ever observed. Certain types of cells show chiral behavior, too. Understanding the origin of cellular chirality and its effect on tissues and cellular dynamics is still an open problem and subject to much (recent) research, e.g., in the context of drosophila morphogenesis. Here, we develop a simple model to describe the possible origin of homochirality in cells. Combining the Vicsek model for collective behavior with the model of Jafarpour et al., developed to describe the emergence of molecular homochirality, we investigate how a homochiral state might have evolved in cells from an initially symmetric state without any mechanisms that explicitly break chiral symmetry. We investigate the transition to homochirality and show how the "openness" of the system as well as noise determine if and when a globally homochiral state is reached. We discuss how our model can be applied to the evolution of traits in flocking systems in general, or to study systems consisting of multiple interacting species.
One-step smoothing splines instrumental regression
Authors: Jad Beyhum, Elia Lapenta, Pascal Lavergne
Date: 2023-07-27 00:00:00
Tasks: regression

We extend nonparametric regression smoothing splines to a context where there is endogeneity and instrumental variables are available. Unlike popular existing estimators, the resulting estimator is one-step and relies on a unique regularization parameter. We derive rates of the convergence for the estimator and its first derivative, which are uniform in the support of the endogenous variable. We also address the issue of imposing monotonicity in estimation and extend the approach to a partly linear model. Simulations confirm the good performances of our estimator compared to two-step procedures. Our method yields economically sensible results when used to estimate Engel curves.
Weak (Proxy) Factors Robust Hansen-Jagannathan Distance For Linear Asset Pricing Models
Authors: Lingwei Kong
Date: 2023-07-26 00:00:00

The Hansen-Jagannathan (HJ) distance statistic is one of the most dominant measures of model misspecification. However, the conventional HJ specification test procedure has poor finite sample performance, and we show that it can be size distorted even in large samples when (proxy) factors exhibit small correlations with asset returns. In other words, applied researchers are likely to falsely reject a model even when it is correctly specified. We provide two alternatives for the HJ statistic and two corresponding novel procedures for model specification tests, which are robust against the presence of weak (proxy) factors, and we also offer a novel robust risk premia estimator. Simulation exercises support our theory. Our empirical application documents the non-reliability of the traditional HJ test since it may produce counter-intuitive results when comparing nested models by rejecting a four-factor model but not the reduced three-factor model. At the same time, our proposed methods are practically more appealing and show support for a four-factor model for Fama French portfolios.
Bootstrapping Nonstationary Autoregressive Processes with Predictive Regression Models
Authors: Christis Katsouris
Date: 2023-07-26 00:00:00
Tasks: regression, Unity

We establish the asymptotic validity of the bootstrap-based IVX estimator proposed by Phillips and Magdalinos (2009) for the predictive regression model parameter based on a local-to-unity specification of the autoregressive coefficient which covers both nearly nonstationary and nearly stationary processes. A mixed Gaussian limit distribution is obtained for the bootstrap-based IVX estimator. The statistical validity of the theoretical results are illustrated by Monte Carlo experiments for various statistical inference problems.
Smoothing of numerical series by the triangle method on the example of hungarian gdp data 1992-2022 based on approximation by series of exponents
Authors: Yekimov Sergey
Date: 2023-07-25 00:00:00

In practice , quite often there is a need to describe the values set by means of a table in the form of some functional dependence . The observed values , due to certain circumstances , have an error . For approximation, it is advisable to use a functional dependence that would allow smoothing out the errors of the observation results. Approximation allows you to determine intermediate values of functions that are not listed among the data in the observation table. The use of exponential series for data approximation allows you to get a result no worse than from approximation by polynomials In the economic scientific literature, approximation in the form of power functions, for example, the Cobb-Douglas function, has become widespread. The advantage of this type of approximation can be called a simple type of approximating function , and the disadvantage is that in nature not all processes can be described by power functions with a given accuracy. An example is the GDP indicator for several decades . For this case , it is difficult to find a power function approximating a numerical series . But in this case, as shown in this article, you can use exponential series to approximate the data. In this paper, the time series of Hungary's GDP in the period from 1992 to 2022 was approximated by a series of thirty exponents of a complex variable. The use of data smoothing by the method of triangles allows you to average the data and increase the accuracy of approximation . This is of practical importance if the observed random variable contains outliers that need to be smoothed out.
Exploration of legal implications of air and space travel for international and domestic travel and the Environment
Authors: Jayanthi Vajiram, Negha Senthil, Nean Adhith. P, Ritikaa. VN
Date: 2023-07-27 00:00:00
Tasks: International Law

The rapid growth of air and space travel in recent years has resulted in an increased demand for legal regulation in the aviation and aerospace fields. This paper provides an overview of air and space law, including the topics of aircraft accident investigations, air traffic control, international borders and law, and the regulation of space activities. With the increasing complexity of air and space travel, it is important to understand the legal implications of these activities. This paper examines the various legal aspects of air and space law, including the roles of national governments, international organizations, and private entities. It also provides an overview of the legal frameworks that govern these activities and the implications of international law. Finally, it considers the potential for future developments in the field of air and space law. This paper provides a comprehensive overview of the legal aspects of air and space travel and their implications for international and domestic travel, as well as for international business and other activities in the air and space domains.
The misuse of law by Women in India -Constitutionality of Gender Bias
Authors: Negha Senthil, Jayanthi Vajiram, Nirmala. V
Date: 2023-07-27 00:00:00

The misuse of law by women in India is a serious issue that has been receiving increased attention in recent years. In India, women are often discriminated against and are not provided with equal rights and opportunities, leading to a gender bias in many aspects of life. This gender bias is further exacerbated by the misuse of law by women. There are numerous instances of women using the law to their advantage, often at the expense of men. This practice is not only unethical but also unconstitutional. The Indian Constitution does not explicitly guarantee gender equality. However, several amendments have been made to the Constitution to ensure that women are treated equally in accordance with the law. The protection of women from all forms of discrimination is considered a fundamental right. Despite this, women continue to be discriminated against in various spheres of life, including marriage, education, employment and other areas. The misuse of law by women in India is primarily seen in cases of domestic violence and dowry-related issues and are punishable by law. However, women often file false dowry harassment cases against their husbands or in-laws in order to gain an advantage in a divorce or property dispute.
Long Tails, Automation and Labor
Authors: B. N. Kausik
Date: 2023-07-26 00:00:00

A central question in economics is whether automation will displace human labor and diminish standards of living. Whilst prior works typically frame this question as a competition between human labor and machines, we frame it as a competition between human consumers and human suppliers. Specifically, we observe that human needs favor long tail distributions, i.e., a long list of niche items that are substantial in aggregate demand. In turn, the long tails are reflected in the goods and services that fulfill those needs. With this background, we propose a theoretical model of economic activity on a long tail distribution, where innovation in demand for new niche outputs competes with innovation in supply automation for mature outputs. Our model yields analytic expressions and asymptotes for the shares of automation and labor in terms of just four parameters: the rates of innovation in supply and demand, the exponent of the long tail distribution and an initial value. We validate the model via non-linear stochastic regression on historical US economic data with surprising accuracy.
Audio Inputs for Active Speaker Detection and Localization via Microphone Array
Authors: Davide Berghi, Philip J. B. Jackson
Date: 2023-07-27 00:00:00
Tasks: Active Speaker Detection

This study considers the problem of detecting and locating an active talker's horizontal position from multichannel audio captured by a microphone array. We refer to this as active speaker detection and localization (ASDL). Our goal was to investigate the performance of spatial acoustic features extracted from the multichannel audio as the input of a convolutional recurrent neural network (CRNN), in relation to the number of channels employed and additive noise. To this end, experiments were conducted to compare the generalized cross-correlation with phase transform (GCC-PHAT), the spatial cue-augmented log-spectrogram (SALSA) features, and a recently-proposed beamforming method, evaluating their robustness to various noise intensities. The array aperture and sampling density were tested by taking subsets from the 16-microphone array. Results and tests of statistical significance demonstrate the microphones' contribution to performance on the TragicTalkers dataset, which offers opportunities to investigate audio-visual approaches in the future.
Spatial Upsampling of Head-Related Transfer Functions Using a Physics-Informed Neural Network
Authors: Fei Ma, Thushara D. Abhayapala, Prasanga N. Samarasinghe, Xingyu Chen
Date: 2023-07-27 00:00:00
Tasks: valid

Head-related transfer function (HRTF) capture the information that a person uses to localize sound sources in space, and thus is crucial for creating personalized virtual acoustic experiences. However, practical HRTF measurement systems may only measure a person's HRTFs sparsely, and this necessitates HRTF upsampling. This paper proposes a physics-informed neural network (PINN) method for HRTF upsampling. The PINN exploits the Helmholtz equation, the governing equation of acoustic wave propagation, for regularizing the upsampling process. This helps the generation of physically valid upsamplings which generalize beyond the measured HRTF. Furthermore, the size (width and depth) of the PINN is set according to the Helmholtz equation and its solutions, the spherical harmonics (SHs). This makes the PINN have an appropriate level of expressive power and thus does not suffer from the over-fitting problem. Since the PINN is designed independent of any specific HRTF dataset, it offers more generalizability compared to pure data-driven methods. Numerical experiments confirm the better performance of the PINN method for HRTF upsampling in both interpolation and extrapolation scenarios in comparison with the SH method and the HRTF field method.
Symbol-Level Precoding for MU-MIMO System with RIRC Receiver
Authors: Xiao Tong, Ang Li, Lei Lei, Fan Liu, Fuwang Dong
Date: 2023-07-27 00:00:00

Consider a multiuser multiple-input multiple-output (MU-MIMO) downlink system in which the base station (BS) sends multiple data streams to multi-antenna users via symbol-level precoding (SLP), where the optimization of receive combining matrix becomes crucial, unlike in the single-antenna user scenario. We begin by introducing a joint optimization problem on the symbol-level transmit precoder and receive combiner. The problem is solved using the alternating optimization (AO) method, and the optimal solution structures for transmit precoding and receive combining matrices are derived by using Lagrangian and Karush-Kuhn-Tucker (KKT) conditions, based on which, the original problem is transformed into an equivalent quadratic programming problem, enabling more efficient solutions. To address the challenge that the above joint design is difficult to implement, we propose a more practical scheme where the receive combining optimization is replaced by the interference rejection combiner (IRC), which is however difficult to directly use because of the rank-one transmit precoding matrix. Therefore, we introduce a new regularized IRC (RIRC) receiver to circumvent the above issue. Numerical results demonstrate that the practical SLP-RIRC method enjoys only a slight communication performance loss compared to the joint transmit precoding and receive combining design, both offering substantial performance gains over the conventional BD-based approaches.
Bayesian Algorithms for Kronecker-structured Sparse Vector Recovery With Application to IRS-MIMO Channel Estimation
Authors: Yanbin He, Geethu Joseph
Date: 2023-07-27 00:00:00

We study the sparse recovery problem with an underdetermined linear system characterized by a Kronecker-structured dictionary and a Kronecker-supported sparse vector. We cast this problem into the sparse Bayesian learning (SBL) framework and rely on the expectation-maximization method for a solution. To this end, we model the Kronecker-structured support with a hierarchical Gaussian prior distribution parameterized by a Kronecker-structured hyperparameter, leading to a non-convex optimization problem. The optimization problem is solved using the alternating minimization (AM) method and a singular value decomposition (SVD)-based method, resulting in two algorithms. Further, we analytically guarantee that the AM-based method converges to the stationary point of the SBL cost function. The SVD-based method, though it adopts approximations, is empirically shown to be more efficient and accurate. We then apply our algorithm to estimate the uplink wireless channel in an intelligent reflecting surface-aided MIMO system and extend the AM-based algorithm to address block sparsity in the channel. We also study the SBL cost to show that the minima of the cost function are achieved at sparse solutions and that incorporating the Kronecker structure reduces the number of local minima of the SBL cost function. Our numerical results demonstrate the effectiveness of our algorithms compared to the state-of-the-art.
Signal processing after quadratic random sketching with optical units
Authors: Rémi Delogne, Vincent Schellekens, Laurent Daudet, Laurent Jacques
Date: 2023-07-27 00:00:00
Tasks: Compressive Sensing

Random data sketching (or projection) is now a classical technique enabling, for instance, approximate numerical linear algebra and machine learning algorithms with reduced computational complexity and memory. In this context, the possibility of performing data processing (such as pattern detection or classification) directly in the sketched domain without accessing the original data was previously achieved for linear random sketching methods and compressive sensing. In this work, we show how to estimate simple signal processing tasks (such as deducing local variations in a image) directly using random quadratic projections achieved by an optical processing unit. The same approach allows for naive data classification methods directly operated in the sketched domain. We report several experiments confirming the power of our approach.
A GPS Alternative using Electrical Transmission Grids as Precision Timing Networks
Authors: Stephen Robson
Date: 2023-07-26 00:00:00

It is widely recognised that over-reliance on GNSS (e.g GPS) for time synchronisation represents an acute threat to modern society, and a diversity of alternatives are required to mitigate the threat of an outage. This paper proposes a GNSS alternative using time dissemination over national scale transmission or distribution networks. The method utilises the same frequency bandwidth and coupling technology as established power line carrier technology in conjunction with modern chirp Spread Spectrum modulation. The basis of the method is the transmission of a time synchronised chirp from a central substation. During GNSS operation, all substations can estimate the time of flight by correlating the received chirp with a time-synchronised local copy. During GNSS outage, time sychronisation to the central substation is maintained by correcting for the precalculated time of flight. It is shown that recent advances in chirp spread spectrum allow for a computationally efficient algorithm with the capacity to compute hundreds of thousand of chirp correlations every second, improving the resolution of the system. ATP-EMTP simulations of the method on large transmission networks demonstrate sub-$\mu$s timing accuracy even in the presence of low SNR and impulsive noise. An FPGA based prototype is developed and experimental testing also demonstrates sub-$\mu$s accuracy for time dissemination over a distance of 700 m. Averaging over time allows operation down to -20 dB, which could extend the range of the system to a national scale.
Surface Deformation of New York City from Multitemporal Interferometric Analysis of Sentinel-1 SAR Datasets
Authors: Manoochehr Shirzaei
Date: 2023-07-26 00:00:00
Tasks: Time Series

Sentinel-1, the Synthetic Aperture Radar (SAR) mission operated by the European Space Agency (ESA) as part of the Copernicus program, provides Free and Open Data with global coverage, transforming our ability to monitor subtle changes in land surface elevation due to natural and anthropogenic process. Here, we use SAR datasets over New York spanning 2015/03/12 to 2022/08/26 and apply an advanced multitemporal SAR interferometric algorithm to measure rates and time series of surface deformation at an unprecedented resolution and accuracy. We found Line-Of-Sight (LOS) subsidence and uplift rates of up to 25.7 -+ 0.2 mm/yr and 8.7 -+ 0.3 mm/yr affecting parts of the city. The LOS velocities, standard deviations, and local incidence angles for ~1,792,000 pixels at ~25m -+ 25 m resolution are provided in an online repository.
Meeting IMT 2030 Performance Targets: The Potential of OTFDM Waveform and Structural MIMO Technologies
Authors: Spandan Bisoyi, Murali Mohan Pasupuleti, Kiran Kuchi, Koteswara Rao, Harish Kumar, Pavan Reddy M., Smriti Kumari, Sai Dhiraj Amuru
Date: 2023-07-26 00:00:00

The white paper focuses on several candidate technologies that could play a crucial role in the development of 6G systems. Two of the key technologies explored in detail are Orthogonal Time Frequency Division Multiplexing (OTFDM) waveform and Structural MIMO (S-MIMO).
Measuring 3D tree imbalance of plant models using graph-theoretical approaches
Authors: Sophie J. Kersting, A. Luise Kühn, Mareike Fischer
Date: 2023-07-26 00:00:00

Imbalance in the 3D structure of plants can be an important indicator of insufficient light or nutrient supply, as well as excessive wind, (formerly present) physical barriers, neighbor or storm damage. It can also be a simple means to detect certain illnesses, since some diseases like the apple proliferation disease, an infection with the barley yellow dwarf virus or plant canker can cause abnormal growth, like \enquote{witches' brooms} or burls, resulting in a deviating 3D plant architecture. However, quantifying imbalance of plant growth is not an easy task, and it requires a mathematically sound 3D model of plants to which imbalance indices can be applied. Current models of plants are often based on stacked cylinders or voxel matrices and do not allow for measuring the degree of 3D imbalance in the branching structure of the whole plant. On the other hand, various imbalance indices are readily available for so-called graph-theoretical trees and are frequently used in areas like phylogenetics and computer science. While only some basic ideas of these indices can be transferred to the 3D setting, graph-theoretical trees are a logical foundation for 3D plant models that allow for elegant and natural imbalance measures. In this manuscript, our aim is thus threefold: We first present a new graph-theoretical 3D model of plants and discuss desirable properties of imbalance measures in the 3D setting. We then introduce and analyze eight different 3D imbalance indices and their properties. Thirdly, we illustrate all our findings using a data set of 63 bush beans. Moreover, we implemented all our indices in the publicly available \textsf{R}-software package \textsf{treeDbalance} accompanying this manuscript.
Neural Schrödinger Bridge with Sinkhorn Losses: Application to Data-driven Minimum Effort Control of Colloidal Self-assembly
Authors: Iman Nodozi, Charlie Yan, Mira Khare, Abhishek Halder, Ali Mesbah
Date: 2023-07-26 00:00:00

We show that the minimum effort control of colloidal self-assembly can be naturally formulated in the order-parameter space as a generalized Schr\"{o}dinger bridge problem -- a class of fixed-horizon stochastic optimal control problems that originated in the works of Erwin Schr\"{o}dinger in the early 1930s. In recent years, this class of problems has seen a resurgence of research activities in the control and machine learning communities. Different from the existing literature on the theory and computation for such problems, the controlled drift and diffusion coefficients for colloidal self-assembly are typically nonaffine in control, and are difficult to obtain from physics-based modeling. We deduce the conditions of optimality for such generalized problems, and show that the resulting system of equations is structurally very different from the existing results in a way that standard computational approaches no longer apply. Thus motivated, we propose a data-driven learning and control framework, named `neural Schr\"{o}dinger bridge', to solve such generalized Schr\"{o}dinger bridge problems by innovating on recent advances in neural networks. We illustrate the effectiveness of the proposed framework using a numerical case study of colloidal self-assembly. We learn the controlled drift and diffusion coefficients as two neural networks using molecular dynamics simulation data, and then use these two to train a third network with Sinkhorn losses designed for distributional endpoint constraints, specific for this class of control problems.