pwc-archive/papers-with-abstracts huggingface dataset:
Nowcasting the 2022 mpox outbreak in England
Authors: Christopher E. Overton, Sam Abbott, Rachel Christie, Fergus Cumming, Julie Day, Owen Jones, Rob Paton, Charlie Turner, Thomas Ward
Date: 2023-02-17 00:00:00
Tasks: Additive models

In May 2022, a cluster of mpox cases were detected in the UK that could not be traced to recent travel history from an endemic region. Over the coming months, the outbreak grew, with over 3000 total cases reported in the UK, and similar outbreaks occurring worldwide. These outbreaks appeared linked to sexual contact networks between gay, bisexual and other men who have sex with men. Following the COVID-19 pandemic, local health systems were strained, and therefore effective surveillance for mpox was essential for managing public health policy. However, the mpox outbreak in the UK was characterised by substantial delays in the reporting of the symptom onset date and specimen collection date for confirmed positive cases. These delays led to substantial backfilling in the epidemic curve, making it challenging to interpret the epidemic trajectory in real-time. Many nowcasting models exist to tackle this challenge in epidemiological data, but these lacked sufficient flexibility. We have developed a novel nowcasting model using generalised additive models to correct the mpox epidemic curve in England, and provide real-time characteristics of the state of the epidemic, including the real-time growth rate. This model benefited from close collaboration with individuals involved in collecting and processing the data, enabling temporal changes in the reporting structure to be built into the model, which improved the robustness of the nowcasts generated.
Ergodic characterization of non-ergodic anomalous diffusion processes
Authors: Madhur Mangalam, Ralf Metzler, Damian G. Kelty-Stephen
Date: 2023-02-19 00:00:00
Tasks: Time Series, Time Series Analysis

Canonical characterization techniques that rely upon mean squared displacement ($\mathrm{MSD}$) break down for non-ergodic processes, making it challenging to characterize anomalous diffusion from an individual time-series measurement. Non-ergodicity reigns when the time-averaged mean square displacement $\mathrm{TA}$-$\mathrm{MSD}$ differs from the ensemble-averaged mean squared displacement $\mathrm{EA}$-$\mathrm{MSD}$ even in the limit of long measurement series. In these cases, the typical theoretical results for ensemble averages cannot be used to understand and interpret data acquired from time averages. The difficulty then lies in obtaining statistical descriptors of the measured diffusion process that are not non-ergodic. We show that linear descriptors such as the standard deviation ($SD$), coefficient of variation ($CV$), and root mean square ($RMS$) break ergodicity in proportion to non-ergodicity in the diffusion process. In contrast, time series of descriptors addressing sequential structure and its potential nonlinearity: multifractality change in a time-independent way and fulfill the ergodic assumption, largely independent of the time series' non-ergodicity. We show that these findings follow the multiplicative cascades underlying these diffusion processes. Adding fractal and multifractal descriptors to typical linear descriptors would improve the characterization of anomalous diffusion processes. Two particular points bear emphasis here. First, as an appropriate formalism for encoding the nonlinearity that might generate non-ergodicity, multifractal modeling offers descriptors that can behave ergodically enough to meet the needs of linear modeling. Second, this capacity to describe non-ergodic processes in ergodic terms offers the possibility that multifractal modeling could unify several disparate non-ergodic diffusion processes into a common framework.
Of (Biological) Models and Simulations
Authors: Maurice HT Ling
Date: 2023-02-19 00:00:00

Modeling and simulation are recognized as important aspects of the scientific method for more than 70 years but its adoption in biology has been slow. Debates on its representativeness, usefulness, and whether the effort spent on such endeavors is worthwhile, exist to this day. Here, I argue that most of learning is modeling; hence, arriving at a contradiction if models are not useful. Representing biological systems through mathematical models can be difficult but the modeling procedure is a process in itself that follows a semi-formal set of rules. Although seldom reported, failure in modeling is not a rare event but I argue that this is usually a result of erroneous underlying knowledge or misapplication of a model beyond its intended purpose. I argue that in many biological studies, simulation is the only experimental tool. In others, simulation is a means of reducing possible combinations of experimental work; thereby, presenting an economical case for simulation; thus, worthwhile to engage in this endeavor. The representativeness of simulation depends on the validation, verification, assumptions, and limitations of the underlying model. This will be illustrated using the inter-relationship between population, samples, probability theory, and statistics.
Identify local limiting factors of species distribution using min-linear logistic regression
Authors: Hongliang Bu, Yunyi Shen
Date: 2023-02-18 00:00:00
Tasks: regression

Logistic regression is a commonly used building block in ecological modeling, but its additive structure among environmental predictors often assumes compensatory relationships between predictors, which can lead to problematic results. In reality, the distribution of species is often determined by the least-favored factor, according to von Liebig's Law of the Minimum, which is not addressed in modeling. To address this issue, we introduced the min-linear logistic regression model, which has a built-in minimum structure of competing factors. In our empirical analysis of the distribution of Asiatic black bears ($\textit{Ursus thibetanus}$), we found that the min-linear model performs well compared to other methods and has several advantages. By using the model, we were able to identify ecologically meaningful limiting factors on bear distribution across the survey area. The model's inherent simplicity and interpretability make it a promising tool for extending into other widely used ecological models.
The financial health of a company and the risk of its default: Back to the future
Authors: Gianmarco Bet, Francesco Dainelli, Eugenio Fabrizi
Date: 2023-02-20 00:00:00

We theorize the financial health of a company and the risk of its default. A company is financially healthy as long as its equilibrium in the financial system is maintained, which depends on the cost attributable to the probability that equilibrium may decay. The estimate of that probability is based on the credibility and uncertainty of the company's financial forecasts. Accordingly, we develop an equilibrium model establishing ranges of interest rates as a function of predictable corporate performance and of its credit supply conditions. As a result, our model estimates idiosyncratic default risk and provides intrinsically forward-looking PD.
Optimal Mix Among PAYGO, EET and Individual Savings
Authors: Lin He, Zongxia Liang, Zhaojie Ren, Yilun Song
Date: 2023-02-18 00:00:00
Tasks: Decision Making

In order to deal with the aging problem, pension system is actively transformed into the funded scheme. However, the funded scheme does not completely replace PAYGO (Pay as You Go) scheme and there exist heterogeneous mixes among PAYGO, EET (Exempt, Exempt, Taxed) and individual savings in different countries. In this paper, we establish the optimal mix by solving a Nash equilibrium between the pension participants and the government. Given the obligatory PAYGO and EET contribution rates, the participants choose the optimal asset allocation of the individual savings and the consumption policies to achieve the objective. The results extend the ``Samuelson-Aaron" criterion to age-dependent preference orderings. And we identify three critical ages to distinguish the multiple outcomes of preference orderings based on heterogeneous characteristic parameters. The government is fully aware of the optimal feedback of the participants. It chooses the optimal PAYGO and EET contribution rates to maximize the overall utility of the participants weighted by each cohort's population. As such, the negative population growth rate leads to the decline of the PAYGO attractiveness as well as the increase of the older cohorts' weight in the government decision-making. The optimal mix is the comprehensive result of the two effects.
Co-trading networks for modeling dynamic interdependency structures and estimating high-dimensional covariances in US equity markets
Authors: Yutong Lu, Gesine Reinert, Mihai Cucuringu
Date: 2023-02-18 00:00:00

The time proximity of trades across stocks reveals interesting topological structures of the equity market in the United States. In this article, we investigate how such concurrent cross-stock trading behaviors, which we denote as co-trading, shape the market structures and affect stock price co-movements. By leveraging a co-trading-based pairwise similarity measure, we propose a novel method to construct dynamic networks of stocks. Our empirical studies employ high-frequency limit order book data from 2017-01-03 to 2019-12-09. By applying spectral clustering on co-trading networks, we uncover economically meaningful clusters of stocks. Beyond the static Global Industry Classification Standard (GICS) sectors, our data-driven clusters capture the time evolution of the dependency among stocks. Furthermore, we demonstrate statistically significant positive relations between low-latency co-trading and return covariance. With the aid of co-trading networks, we develop a robust estimator for high-dimensional covariance matrix, which yields superior economic value on portfolio allocation. The mean-variance portfolios based on our covariance estimates achieve both lower volatility and higher Sharpe ratios than standard benchmarks.
Revealing production networks from firm growth dynamics
Authors: Luca Mungo, José Moran
Date: 2023-02-20 00:00:00

We study the correlation structure of firm growth rates. We show that most firms are correlated because of their exposure to a common factor but that firms linked through the supply chain exhibit a stronger correlation on average than firms that are not. Removing this common factor significantly reduces the average correlation between two firms with no relationship in the supply chain while maintaining a significant correlation between two firms that are linked. We then investigate if this observation can be used to reconstruct the topology of a supply chain network using Gaussian Markov Models.
Stress Test for BERT and Deep Models: Predicting Words from Italian Poetry
Authors: Rodolfo Delmonte, Nicolò Busetto
Date: 2023-01-21 00:00:00
Tasks: Sentence

In this paper we present a set of experiments carried out with BERT on a number of Italian sentences taken from poetry domain. The experiments are organized on the hypothesis of a very high level of difficulty in predictability at the three levels of linguistic complexity that we intend to monitor: lexical, syntactic and semantic level. To test this hypothesis we ran the Italian version of BERT with 80 sentences for a total of 900 tokens mostly extracted from Italian poetry of the first half of last century. Then we alternated canonical and noncanonical versions of the same sentence before processing them with the same DL model. We used then sentences from the newswire domain containing similar syntactic structures. The results show that the DL model is highly sensitive to presence of noncanonical structures. However, DLs are also very sensitive to word frequency and to local non literal meaning compositional effect. This is also apparent by the preference for predicting function vs content words, collocates vs infrequent word phrases. In the paper, we focused our attention on the use of subword units done by BERT for out of vocabulary words.
Interpretability in Activation Space Analysis of Transformers: A Focused Survey
Authors: Soniya Vijayakumar
Date: 2023-01-22 00:00:00
Tasks: Survey

The field of natural language processing has reached breakthroughs with the advent of transformers. They have remained state-of-the-art since then, and there also has been much research in analyzing, interpreting, and evaluating the attention layers and the underlying embedding space. In addition to the self-attention layers, the feed-forward layers in the transformer are a prominent architectural component. From extensive research, we observe that its role is under-explored. We focus on the latent space, known as the Activation Space, that consists of the neuron activations from these feed-forward layers. In this survey paper, we review interpretability methods that examine the learnings that occurred in this activation space. Since there exists only limited research in this direction, we conduct a detailed examination of each work and point out potential future directions of research. We hope our work provides a step towards strengthening activation space analysis.
Causal Confirmation Measures: From Simpson's Paradox to COVID-19
Authors: Chenguang Lu
Date: 2023-02-03 00:00:00
Tasks: Causal Inference

When we compare the influences of two causes on an outcome, if the conclusion from every group is against that from the conflation, we think there is Simpson's Paradox. The Existing Causal Inference Theory (ECIT) can make the overall conclusion consistent with the grouping conclusion by removing the confounder's influence to eliminate the paradox. The ECIT uses relative risk difference Pd = max(0, (R - 1)/R) (R denotes the risk ratio) as the probability of causation. In contrast, Philosopher Fitelson uses confirmation measure D (posterior probability minus prior probability) to measure the strength of causation. Fitelson concludes that from the perspective of Bayesian confirmation, we should directly accept the overall conclusion without considering the paradox. The author proposed a Bayesian confirmation measure b* similar to Pd before. To overcome the contradiction between the ECIT and Bayesian confirmation, the author uses the semantic information method with the minimum cross-entropy criterion to deduce causal confirmation measure Cc = (R -1)/max(R, 1). Cc is like Pd but has normalizing property (between -1 and 1) and cause symmetry. It especially fits cases where a cause restrains an outcome, such as the COVID-19 vaccine controlling the infection. Some examples (about kidney stone treatments and COVID-19) reveal that Pd and Cc are more reasonable than D; Cc is more useful than Pd.
Advances in Automatically Rating the Trustworthiness of Text Processing Services
Authors: Biplav Srivastava, Kausik Lakkaraju, Mariana Bernagozzi, Marco Valtorta
Date: 2023-02-04 00:00:00
Tasks: Food recommendation

AI services are known to have unstable behavior when subjected to changes in data, models or users. Such behaviors, whether triggered by omission or commission, lead to trust issues when AI works with humans. The current approach of assessing AI services in a black box setting, where the consumer does not have access to the AI's source code or training data, is limited. The consumer has to rely on the AI developer's documentation and trust that the system has been built as stated. Further, if the AI consumer reuses the service to build other services which they sell to their customers, the consumer is at the risk of the service providers (both data and model providers). Our approach, in this context, is inspired by the success of nutritional labeling in food industry to promote health and seeks to assess and rate AI services for trust from the perspective of an independent stakeholder. The ratings become a means to communicate the behavior of AI systems so that the consumer is informed about the risks and can make an informed decision. In this paper, we will first describe recent progress in developing rating methods for text-based machine translator AI services that have been found promising with user studies. Then, we will outline challenges and vision for a principled, multi-modal, causality-based rating methodologies and its implication for decision-support in real-world scenarios like health and food recommendation.
Simulation toolkit for digital material characterization of large image-based microstructures
Authors: Pedro C.F. Lopes, Rafael S. Vianna, Victor W. Sapucaia, Federico Semeraro, Ricardo Leiderman, André M.B. Pereira
Date: 2023-01-18 00:00:00
Proceeding: Computational Materials Science 2023 1
Tasks: Computed Tomography (CT), Physical Simulations

In this paper, an efficient image-based simulation toolkit for material characterization is presented, which is scalable to work from personal computers to workstations. The effective thermal conductivity, elasticity, and permeability are evaluated employing a computational homogenization framework based on the Finite Element Method (FEM). Two complementary open-source packages are presented: one developed in Python, which can convert digital images into voxel meshes (pyTomoviewer); the other developed in Julia, that can run numerical simulations to compute effective material properties (chpack). Also, a CUDA C version of chpack is provided (chfem_gpu). They were designed to deal with large multi-phase models, so strategies were devised to minimize their memory footprint, while avoiding a high toll on execution time. The voxel-based approach significantly simplifies the FEM meshes and allows efficient matrix-free implementations. In that sense, to handle large linear systems of equations, the element-by-element (EBE) technique is adopted, in conjunction with a low-memory implementation of the Preconditioned Conjugate Gradient (PCG) method. The code was thoroughly tested on an artificial geometry made of a square array of cylinders, for which analytical solutions exist, as well as on a real micro-tomographic reconstruction of FiberFormTM, a carbon preform commonly used in thermal protection systems.
Update 3.0 to “PuMA: The Porous Microstructure Analysis software”
Authors: Joseph C. Ferguson, Federico Semeraro, John M. Thornton, Francesco Panerai, Arnaud Borner, Nagi N. Mansour
Date: 2021-07-19 00:00:00
Proceeding: SoftwareX 2021 7
Tasks: Computed Tomography (CT), Physical Simulations

A major update of the Porous Microstructure Analysis (PuMA) software is presented. PuMA is a framework for computing effective material properties and response based on material microstructures. Version 3.0 of the software extends the PuMA capabilities to include computation of anisotropic conductivity, elasticity, permeability, per-voxel material or fiber orientation estimation, as well as expanded computational material generation capabilities. Version 3.0 also introduces pumapy, a Python version of the PuMA software.
N-WL: A New Hierarchy of Expressivity for Graph Neural Networks
Authors: Qing Wang, Dillon Chen, Asiri Wijesinghe, Shouheng Li, Muhammad Farhan
Date: 2023-02-02 00:00:00
Proceeding: The Eleventh International Conference on Learning Representations 2023 2

The expressive power of Graph Neural Networks (GNNs) is fundamental for understanding their capabilities and  limitations, i.e., what graph properties can or cannot be learnt by a GNN. Since standard GNNs have been characterised to be upper-bounded by the Weisfeiler-Lehman (1-WL) algorithm, recent attempts concentrated on developing more expressive GNNs in terms of the k-WL hierarchy, a well-established framework for graph isormorphism tests. In this work we show that, contrary to the widely accepted view, the k-WL hierarchy is not well-suited for measuring expressive GNNs. This is due to limitations that are inherent to high-dimensional WL algorithms such as the lack of a natural interpretation and high computational costs, which makes it difficult to draw any firm conclusions about the expressive power of GNNs beyond 1-WL. Thus, we propose a novel hierarchy of graph isomorphism tests, namely Neighbourhood WL (N-WL), and also establish a new theorem on the equivalence of expressivity between induced connected subgraphs and induced subgraphs within this hierarchy. Further, we design a GNN model upon N-WL, Graph Neighbourhood Neural Network (G3N), and empirically verify its expressive power on synthetic and real-world benchmarks.
Epistemic Prediction and Planning with Implicit Coordination for Multi-Robot Teams in Communication Restricted Environments
Authors: Lauren Bramblett, Shijie Gao, Nicola Bezzo
Date: 2023-02-21 00:00:00

In communication restricted environments, a multi-robot system can be deployed to either: i) maintain constant communication but potentially sacrifice operational efficiency due to proximity constraints or ii) allow disconnections to increase environmental coverage efficiency, challenges on how, when, and where to reconnect (rendezvous problem). In this work we tackle the latter problem and notice that most state-of-the-art methods assume that robots will be able to execute a predetermined plan; however system failures and changes in environmental conditions can cause the robots to deviate from the plan with cascading effects across the multi-robot system. This paper proposes a coordinated epistemic prediction and planning framework to achieve consensus without communicating for exploration and coverage, task discovery and completion, and rendezvous applications. Dynamic epistemic logic is the principal component implemented to allow robots to propagate belief states and empathize with other agents. Propagation of belief states and subsequent coverage of the environment is achieved via a frontier-based method within an artificial physics-based framework. The proposed framework is validated with both simulations and experiments with unmanned ground vehicles in various cluttered environments.
Interpreting wealth distribution via poverty map inference using multimodal data
Authors: Lisette Espín-Noboa, János Kertész, Márton Karsai
Date: 2023-02-17 00:00:00

Poverty maps are essential tools for governments and NGOs to track socioeconomic changes and adequately allocate infrastructure and services in places in need. Sensor and online crowd-sourced data combined with machine learning methods have provided a recent breakthrough in poverty map inference. However, these methods do not capture local wealth fluctuations, and are not optimized to produce accountable results that guarantee accurate predictions to all sub-populations. Here, we propose a pipeline of machine learning models to infer the mean and standard deviation of wealth across multiple geographically clustered populated places, and illustrate their performance in Sierra Leone and Uganda. These models leverage seven independent and freely available feature sources based on satellite images, and metadata collected via online crowd-sourcing and social media. Our models show that combined metadata features are the best predictors of wealth in rural areas, outperforming image-based models, which are the best for predicting the highest wealth quintiles. Our results recover the local mean and variation of wealth, and correctly capture the positive yet non-monotonous correlation between them. We further demonstrate the capabilities and limitations of model transfer across countries and the effects of data recency and other biases. Our methodology provides open tools to build towards more transparent and interpretable models to help governments and NGOs to make informed decisions based on data availability, urbanization level, and poverty thresholds.