pwc-archive/papers-with-abstracts huggingface dataset:
Unbiased estimation of sampling variance for Simpson's diversity index
Authors: Andreas Tiffeau-Mayer
Date: 2023-10-05 00:00:00
Tasks: Diversity, Specificity

Quantification of measurement uncertainty is crucial for robust scientific inference, yet accurate estimates of this uncertainty remain elusive for ecological measures of diversity. Here, we address this longstanding challenge by deriving a closed-form unbiased estimator for the sampling variance of Simpson's diversity index. In numerical tests the estimator consistently outperforms existing approaches, particularly for applications in which species richness exceeds sample size. We apply the estimator to quantify biodiversity loss in marine ecosystems and to demonstrate ligand-dependent contributions of T cell receptor chains to specificity, illustrating its versatility across fields. The novel estimator provides researchers with a reliable method for comparing diversity between samples, essential for quantifying biodiversity trends and making informed conservation decisions.
Coarse-graining and criticality in the human connectome
Authors: Youssef Kora, Christoph Simon
Date: 2023-09-29 00:00:00

In the face of the stupefying complexity of the human brain, network analysis is a most useful tool that allows one to greatly simplify the problem, typically by approximating the billions of neurons comprising the brain by means of a coarse-grained picture with a practicable number of nodes. But even such relatively small and coarse networks, such as the human connectome with its 100-1000 nodes, may present challenges for some computationally demanding analyses that are incapable of handling networks with more than a handful of nodes. With such applications in mind, we set out to further coarse-grain the human connectome by taking a modularity-based approach, the goal being to produce a network of a relatively small number of modules. We applied this approach to study critical phenomena in the brain; we formulated a hypothesis based on the coarse-grained networks in the context of criticality in the Wilson-Cowan and Ising models, and we verified the hypothesis, which connected a transition value of the former with the critical temperature of the latter, using the original networks. We found that the qualitative behavior of the coarse-grained networks reflected that of the original networks, albeit to a less pronounced extent. This, in principle, allows for the drawing of similar qualitative conclusions by analysing the smaller networks, which opens the door for studying the human connectome in contexts typically regarded as computationally intractable, such Integrated Information Theory and quantum models of the human brain.
Index-Modulated Metasurface Transceiver Design using Reconfigurable Intelligent Surfaces for 6G Wireless Networks
Authors: JohnA. Hodge, Kumar Vijay Mishra, Brian M. Sadler, Amir I. Zaghloul
Date: 2023-10-04 00:00:00

Higher spectral and energy efficiencies are the envisioned defining characteristics of high data-rate sixth-generation (6G) wireless networks. One of the enabling technologies to meet these requirements is index modulation (IM), which transmits information through permutations of indices of spatial, frequency, or temporal media. In this paper, we propose novel electromagnetics-compliant designs of reconfigurable intelligent surface (RIS) apertures for realizing IM in 6G transceivers. We consider RIS modeling and implementation of spatial and subcarrier IMs, including beam steering, spatial multiplexing, and phase modulation capabilities. Numerical experiments for our proposed implementations show that the bit error rates obtained via RIS-aided IM outperform traditional implementations. We further establish the programmability of these transceivers to vary the reflection phase and generate frequency harmonics for IM through full-wave electromagnetic analyses of a specific reflect-array metasurface implementation.
Tradeoffs in concentration sensing in dynamic environments
Authors: Aparajita Kashyap, Wei Wang, Brian A. Camley
Date: 2023-09-29 00:00:00

When cells measure concentrations of chemical signals, they may average multiple measurements over time in order to reduce noise in their measurements. However, when cells are in a environment that changes over time, past measurements may not reflect current conditions - creating a new source of error that trades off against noise in chemical sensing. What statistics in the cell's environment control this tradeoff? What properties of the environment make it variable enough that this tradeoff is relevant? We model a single eukaryotic cell sensing a chemical secreted from bacteria (e.g. folic acid). In this case, the environment changes because the bacteria swim - leading to changes in the true concentration at the cell. We develop analytical calculations and stochastic simulations of sensing in this environment. We find that cells can have a huge variety of optimal sensing strategies, ranging from not time averaging at all, to averaging over an arbitrarily long time, or having a finite optimal averaging time. The factors that primarily control the ideal averaging are the ratio of sensing noise to environmental variation, and the ratio of timescales of sensing to the timescale of environmental variation. Sensing noise depends on the receptor-ligand kinetics, while the environmental variation depends on the density of bacteria and the degradation and diffusion properties of the secreted chemoattractant. Our results suggest that fluctuating environmental concentrations may be a relevant source of noise even in a relatively static environment.
Multiplex ultrasound imaging of perfluorocarbon nanodroplets enabled by decomposition of post-vaporization dynamics
Authors: Austin Van Namen, Sidhartha Jandhyala, Catalina-Paula Spatarelu, Kenneth M. Tichauer, Kimberley S. Samkoe, Geoffrey P. Luke
Date: 2023-09-26 00:00:00

Among the various molecular imaging modalities, ultrasound imaging benefits from its real-time, nonionizing, and cost-effective nature. Despite its benefits, there is a dearth of methods to visualize two or more populations of contrast agents simultaneously, a technique known as multiplex imaging. In this paper, we present a new approach to multiplex ultrasound imaging using perfluorocarbon (PFC) nanodroplets. The nanodroplets, which undergo a liquid-to-gas phase transition in response to an acoustic trigger, act as activatable contrast agents. By using two populations of PFC nanodroplets, each with a different core boiling point, their unique temporal responses to an acoustic trigger were leveraged to differentiate their unique contributions to the overall ultrasound signal. This work characterized the dynamic responses of two PFC nanodroplets with boiling points of 28 and 56 {\deg}C. These characteristic responses were then used to demonstrate that the relative concentrations of the two populations of PFC nanodroplets could be accurately measured in the same imaging volume within an average error of 1.1%. Overall, the findings indicate the potential of this approach for multiplex ultrasound imaging, allowing for the visualization of multiple molecular targets simultaneously.
Gramian Angular Fields for leveraging pretrained computer vision models with anomalous diffusion trajectories
Authors: Òscar Garibo-i-Orts, Nicolás Firbas, Laura Sebastiá, J. Alberto Conejero
Date: 2023-09-02 00:00:00

Anomalous diffusion is present at all scales, from atomic to large scales. Some exemplary systems are; ultra-cold atoms, telomeres in the nucleus of cells, moisture transport in cement-based materials, the free movement of arthropods, and the migration patterns of birds. The characterization of the diffusion gives critical information about the dynamics of these systems and provides an interdisciplinary framework with which to study diffusive transport. Thus, the problem of identifying underlying diffusive regimes and inferring the anomalous diffusion exponent {$\alpha$} with high confidence is critical to physics, chemistry, biology, and ecology. Classification and analysis of raw trajectories combining machine learning techniques with statistics extracted from them have widely been studied in the Anomalous Diffusion Challenge ge (Munoz-Gil et al., 2021). Here we present a new data-driven method for working with diffusive trajectories. This method utilizes Gramian Angular Fields (GAF) to encode one-dimensional trajectories as images (Gramian Matrices), while preserving their spatiotemporal structure for input to computer-vision models. This allows us to leverage two well-established pre-trained computer-vision models, ResNet and MobileNet, to characterize the underlying diffusive regime, and infer the anomalous diffusion exponent {$\alpha$}. Short raw trajectories, of lengths between 10 and 50, are commonly encountered in single-particle tracking experiments and are the most difficult to characterize. We show that by using GAF images, we can outperform the current state-of-the-art while increasing accessibility to machine learning methods in an applied setting.
GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction
Authors: Suryanarayanan Balaji, Rishikesh Magar, Yayati Jadhav, Amir Barati Farimani
Date: 2023-09-20 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Molecular Property Prediction, Property Prediction

With the emergence of Transformer architectures and their powerful understanding of textual data, a new horizon has opened up to predict the molecular properties based on text description. While SMILES are the most common form of representation, they are lacking robustness, rich information and canonicity, which limit their effectiveness in becoming generalizable representations. Here, we present GPT-MolBERTa, a self-supervised large language model (LLM) which uses detailed textual descriptions of molecules to predict their properties. A text based description of 326000 molecules were collected using ChatGPT and used to train LLM to learn the representation of molecules. To predict the properties for the downstream tasks, both BERT and RoBERTa models were used in the finetuning stage. Experiments show that GPT-MolBERTa performs well on various molecule property benchmarks, and approaching state of the art performance in regression tasks. Additionally, further analysis of the attention mechanisms show that GPT-MolBERTa is able to pick up important information from the input textual data, displaying the interpretability of the model.
SAF: Smart Aggregation Framework for Revealing Atoms Importance Rank and Improving Prediction Rates in Drug Discovery
Authors: Ronen Taub, Yonatan Savir
Date: 2023-09-12 00:00:00
Tasks: Activity Prediction, Drug Discovery, Graph Neural Network, Representation Learning

Machine learning, and representation learning in particular, has the potential to facilitate drug discovery by screening a large chemical space in silico. A successful approach for representing molecules is to treat them as a graph and utilize graph neural networks. One of the key limitations of such methods is the necessity to represent compounds with different numbers of atoms, which requires aggregating the atom's information. Common aggregation operators, such as averaging, result in loss of information at the atom level. In this work, we propose a novel aggregating approach where each atom is weighted non-linearly using the Boltzmann distribution with a hyperparameter analogous to temperature. We show that using this weighted aggregation improves the ability of the gold standard message-passing neural network to predict antibiotic activity. Moreover, by changing the temperature hyperparameter, our approach can reveal the atoms that are important for activity prediction in a smooth and consistent way, thus providing a novel, regulated attention mechanism for graph neural networks. We further validate our method by showing that it recapitulates the functional group in beta-Lactam antibiotics. The ability of our approach to rank the atoms' importance for a desired function can be used within any graph neural network to provide interpretability of the results and predictions at the node level.
Multispectral Imaging with Fresnel Lens
Authors: Khen Cohen, Tuval Kay
Date: 2023-10-05 00:00:00
Tasks: Deep Learning, Spectral Reconstruction

This paper presents a Multispectral imaging (MSI) approach that combines the use of a diffractive optical element, and a deep learning algorithm for spectral reconstruction. Traditional MSI techniques often face challenges such as high costs, compromised spatial or spectral resolution, or prolonged acquisition times. In contrast, our methodology uses a single diffractive lens, a grayscale sensor, and an optical motor to capture the Multispectral image without sacrificing spatial resolution, however with some temporal domain redundancy. Through an experimental demonstration, we show how we can reconstruct up to 50 spectral channel images using diffraction physical theory and a UNet-based deep learning algorithm. This approach holds promise for a cost-effective, compact MSI camera that could be feasibly integrated into mobile devices.
Moran's I Lasso for models with spatially correlated data
Authors: Sylvain Barde, Rowan Cherodian, Guy Tchuente
Date: 2023-10-04 00:00:00

This paper proposes a Lasso-based estimator which uses information embedded in the Moran statistic to develop a selection procedure called Moran's I Lasso (Mi-Lasso) to solve the Eigenvector Spatial Filtering (ESF) eigenvector selection problem. ESF uses a subset of eigenvectors from a spatial weights matrix to efficiently account for any omitted cross-sectional correlation terms in a classical linear regression framework, thus does not require the researcher to explicitly specify the spatial part of the underlying structural model. We derive performance bounds and show the necessary conditions for consistent eigenvector selection. The key advantages of the proposed estimator are that it is intuitive, theoretically grounded, and substantially faster than Lasso based on cross-validation or any proposed forward stepwise procedure. Our main simulation results show the proposed selection procedure performs well in finite samples. Compared to existing selection procedures, we find Mi-Lasso has one of the smallest biases and mean squared errors across a range of sample sizes and levels of spatial correlation. An application on house prices further demonstrates Mi-Lasso performs well compared to existing procedures.
Sharp and Robust Estimation of Partially Identified Discrete Response Models
Authors: Shakeeb Khan, Tatiana Komarova, Denis Nekipelov
Date: 2023-10-03 00:00:00
Tasks: Discrete Choice Models

Semiparametric discrete choice models are widely used in a variety of practical applications. While these models are point identified in the presence of continuous covariates, they can become partially identified when covariates are discrete. In this paper we find that classical estimators, including the maximum score estimator, (Manski (1975)), loose their attractive statistical properties without point identification. First of all, they are not sharp with the estimator converging to an outer region of the identified set, (Komarova (2013)), and in many discrete designs it weakly converges to a random set. Second, they are not robust, with their distribution limit discontinuously changing with respect to the parameters of the model. We propose a novel class of estimators based on the concept of a quantile of a random set, which we show to be both sharp and robust. We demonstrate that our approach extends from cross-sectional settings to classical static and dynamic discrete panel data models.
Impact of Economic Uncertainty, Geopolitical Risk, Pandemic, Financial & Macroeconomic Factors on Crude Oil Returns -- An Empirical Investigation
Authors: Sarit Maitra
Date: 2023-10-02 00:00:00
Tasks: quantile regression

This study aims to use simultaneous quantile regression (SQR) to examine the impact of macroeconomic and financial uncertainty including global pandemic, geopolitical risk on the futures returns of crude oil (ROC). The data for this study is sourced from the FRED (Federal Reserve Economic Database) economic dataset; the importance of the factors have been validated by using variation inflation factor (VIF) and principal component analysis (PCA). To fully understand the combined effect of these factors on WTI, study includes interaction terms in the multi-factor model. Empirical results suggest that changes in ROC can have varying impacts depending on the specific period and market conditions. The results can be used for informed investment decisions and to construct portfolios that are well-balanced in terms of risk and return. Structural breaks, such as changes in global economic conditions or shifts in demand for crude oil, can cause return on crude oil to be sensitive to changes in different time periods. The unique aspect ness of this study also lies in its inclusion of explanatory factors related to the pandemic, geopolitical risk, and inflation.
Specification testing with grouped fixed effects
Authors: Claudia Pigini, Alessandro Pionati, Francesco Valentini
Date: 2023-10-03 00:00:00

We propose a bootstrap generalized Hausman test for the correct specification of unobserved heterogeneity in both linear and nonlinear fixed-effects panel data models. We consider as null hypotheses two scenarios in which the unobserved heterogeneity is either time-invariant or specified as additive individual and time effects. We contrast the standard fixed-effects estimators with the recently developed two-way grouped fixed-effects estimator, that is consistent in the presence of time-varying heterogeneity under minimal specification and distributional assumptions for the unobserved effects. The Hausman test exploits the general formulation for the variance of the vector of contrasts and critical values are computed via parametric percentile bootstrap, so as to account for the non-centrality of the asymptotic chi-square distribution arising from the incidental parameters and approximation biases. Monte Carlo evidence shows that the test has correct size and good power properties. We provide two empirical applications to illustrate the proposed test: the first one is based on a linear model for the determinants of the wage of working women and the second analyzes the trade extensive margin.
Multi-period static hedging of European options
Authors: Purba Banerjee, Srikanth Iyer, Shashi Jain
Date: 2023-10-02 00:00:00

We consider the hedging of European options when the price of the underlying asset follows a single-factor Markovian framework. By working in such a setting, Carr and Wu \cite{carr2014static} derived a spanning relation between a given option and a continuum of shorter-term options written on the same asset. In this paper, we have extended their approach to simultaneously include options over multiple short maturities. We then show a practical implementation of this with a finite set of shorter-term options to determine the hedging error using a Gaussian Quadrature method. We perform a wide range of experiments for both the \textit{Black-Scholes} and \textit{Merton Jump Diffusion} models, illustrating the comparative performance of the two methods.
Semidiscrete optimal transport with unknown costs
Authors: Yinchu Zhu, Ilya O. Ryzhov
Date: 2023-10-01 00:00:00

Semidiscrete optimal transport is a challenging generalization of the classical transportation problem in linear programming. The goal is to design a joint distribution for two random variables (one continuous, one discrete) with fixed marginals, in a way that minimizes expected cost. We formulate a novel variant of this problem in which the cost functions are unknown, but can be learned through noisy observations; however, only one function can be sampled at a time. We develop a semi-myopic algorithm that couples online learning with stochastic approximation, and prove that it achieves optimal convergence rates, despite the non-smoothness of the stochastic gradient and the lack of strong concavity in the objective function.
What would it cost to connect the unconnected? Estimating global universal broadband infrastructure investment
Authors: Edward J. Oughton, David Amaglobeli, Marian Moszoro
Date: 2023-10-05 00:00:00

Roughly 3 billion citizens remain offline, equating to approximately 40 percent of the global population. Therefore, providing Internet connectivity is an essential part of the Sustainable Development Goals (SDGs) (Goal 9). In this paper a high-resolution global model is developed to evaluate the necessary investment requirements to achieve affordable universal broadband. The results indicate that approximately $418 billion needs to be mobilized to connect all unconnected citizens globally (targeting 40-50 GB/Month per user with 95 percent reliability). The bulk of additional investment is for emerging market economies (73 percent) and low-income developing countries (24 percent). To our knowledge, the paper contributes the first high-resolution global assessment which quantifies universal broadband investment at the sub-national level to achieve SDG Goal 9.
Initiatives Based on the Psychology of Scarcity Can Increase Covid-19 Vaccinations
Authors: Alessandro Del Ponte, Audrey De Dominicis, Paolo Canofari
Date: 2023-10-05 00:00:00
Tasks: Marketing

Background: Here we investigate whether releasing COVID-19 vaccines in limited quantities and at limited times boosted Italy's vaccination campaign in 2021. This strategy exploits insights from psychology and consumer marketing. Methods: We built an original dataset covering 200 days of vaccination data in Italy, including 'open day' events. Open-day events (in short: open days) are instances where COVID-19 vaccines were released in limited quantities and only for a specific day at a specified location (usually, a large pavilion or a public building). Our dependent variables are the number of total and first doses administered in proportion to the eligible population. Our key independent variable is the presence of open-day events in a given region on a specific day. We analyzed the data using regression with fixed effects for time and region. The analysis was robust to alternative model specifications. Findings: We find that when an open day event was organized, in proportion to the eligible population, there was an average 0.39-0.44 percentage point increase in total doses administered and a 0.30-0.33 percentage point increase in first doses administered. These figures correspond to an average increase of 10,455-11,796 in total doses administered and 8,043-8,847 in the first doses administered. Interpretation: Releasing vaccines in limited quantities and at limited times by organizing open-day events was associated with an increase in COVID-19 vaccinations in most Italian regions. These results call for wider adoption of vaccination strategies based on the limited release of vaccines for other infectious diseases or future pandemics.