pwc-archive/papers-with-abstracts huggingface dataset:
Nowcasting with signature methods
Authors: Samuel N. Cohen, Silvia Lui, Will Malpass, Giulia Mantoan, Lars Nesheim, Áureo de Paula, Andrew Reeves, Craig Scott, Emma Small, Lingyi Yang
Date: 2023-05-17 00:00:00
Tasks: regression

Key economic variables are often published with a significant delay of over a month. The nowcasting literature has arisen to provide fast, reliable estimates of delayed economic indicators and is closely related to filtering methods in signal processing. The path signature is a mathematical object which captures geometric properties of sequential data; it naturally handles missing data from mixed frequency and/or irregular sampling -- issues often encountered when merging multiple data sources -- by embedding the observed data in continuous time. Calculating path signatures and using them as features in models has achieved state-of-the-art results in fields such as finance, medicine, and cyber security. We look at the nowcasting problem by applying regression on signatures, a simple linear model on these nonlinear objects that we show subsumes the popular Kalman filter. We quantify the performance via a simulation exercise, and through application to nowcasting US GDP growth, where we see a lower error than a dynamic factor model based on the New York Fed staff nowcasting model. Finally we demonstrate the flexibility of this method by applying regression on signatures to nowcast weekly fuel prices using daily data. Regression on signatures is an easy-to-apply approach that allows great flexibility for data with complex sampling patterns.
Rational Dialogues
Authors: John Geanakoplos, Herakles Polemarchakis
Date: 2023-05-15 00:00:00

Any finite conversation can be rationalized.
Affective interdependence and welfare
Authors: Aviad Heifetz, Enrico Minelli, Herakles Polemarchakis
Date: 2023-05-12 00:00:00

Purely affective interaction allows the welfare of an individual to depend on her own actions and on the profile of welfare levels of others. Under an assumption on the structure of mutual affection that we interpret as "non-explosive mutual affection," we show that equilibria of simultaneous-move affective interaction are Pareto optimal independently of whether or not an induced standard game exists. Moreover, if purely affective interaction induces a standard game, then an equilibrium profile of actions is a Nash equilibrium of the game, and this Nash equilibrium and Pareto optimal profile of strategies is locally dominant.
Heterogeneous Noise and Stable Miscoordination
Authors: Srinivas Arigapudi, Yuval Heller, Amnon Schreiber
Date: 2023-05-17 00:00:00

Coordination games admit two types of equilibria: pure equilibria, where all players successfully coordinate their actions, and mixed equilibria, where players frequently experience miscoordination. The existing literature shows that under many evolutionary dynamics, populations converge to a pure equilibrium from almost any initial distribution of actions. By contrast, we show that under plausible learning dynamics, where agents observe the actions of a random sample of their opponents and adjust their strategies accordingly, stable miscoordination can arise when there is heterogeneity in the sample sizes. This occurs when some agents make decisions based on small samples (anecdotal evidence) while others rely on large samples. Finally, we demonstrate the empirical relevance of our results in a bargaining application. Final pre-print of a manuscript accepted for publication in American Economic Journal: Microeconomics.
On-board Change Detection for Resource-efficient Earth Observation with LEO Satellites
Authors: Van-Phuc Bui, Thinh Q. Dinh, Israel Leyva-Mayorga, Shashi Raj Pandey, Eva Lagunas, Petar Popovski
Date: 2023-05-17 00:00:00
Tasks: Change Detection, Cloud Removal, Earth Observation, Semantic Communication

The amount of data generated by Earth observation satellites can be enormous, which poses a great challenge to the satellite-to-ground connections with limited rate. This paper considers problem of efficient downlink communication of multi-spectral satellite images for Earth observation using change detection. The proposed method for image processing consists of the joint design of cloud removal and change encoding, which can be seen as an instance of semantic communication, as it encodes important information, such as changed multi-spectral pixels (MPs), while aiming to minimize energy consumption. It comprises a three-stage end-to-end scoring mechanism that determines the importance of each MP before deciding its transmission. Specifically, the sensing image is (1) standardized, (2) passed through a high-performance cloud filtering via the Cloud-Net model, and (3) passed to the proposed scoring algorithm that uses Change-Net to identify MPs that have a high likelihood of being changed, compress them and forward the result to the ground station. The experimental results indicate that the proposed framework is effective in optimizing energy usage while preserving high-quality data transmission in satellite-based Earth observation applications.
Wavefield Networked Sensing: Principles, Algorithms and Applications
Authors: Marco Manzoni, Dario Tagliaferri, Stefano Tebaldini, Marouan Mizmizi, Andrea Virgilio Monti-Guarnieri, Claudio Maria Prati, Umberto Spagnolini
Date: 2023-05-17 00:00:00
Tasks: valid

Networked sensing refers to the capability of properly orchestrating multiple sensing terminals to enhance specific figures of merit, e.g., positioning accuracy or imaging resolution. Regarding radio-based sensing, it is essential to understand \textit{when} and \textit{how} sensing terminals should be orchestrated, namely the best cooperation that trades between performance and cost (e.g., energy consumption, communication overhead, and complexity). This paper addresses networked sensing from a physics-driven perspective, aiming to provide a general theoretical benchmark to evaluate its \textit{imaging} performance bounds and to guide the sensing orchestration accordingly. Diffraction tomography theory (DTT) is the method to quantify the imaging resolution of any radio sensing experiment from inspection of its spectral (or wavenumber) content. In networked sensing, the image formation is based on the back-projection integral, valid for any network topology and physical configuration of the terminals. The \textit{wavefield networked sensing} is a framework in which multiple sensing terminals are orchestrated during the acquisition process to maximize the imaging quality (resolution and grating lobes suppression) by pursuing the deceptively simple \textit{wavenumber tessellation principle}. We discuss all the cooperation possibilities between sensing terminals and possible killer applications. Remarkably, we show that the proposed method allows obtaining high-quality images of the environment in limited bandwidth conditions, leveraging the coherent combination of multiple multi-static low-resolution images.
Novel Electromagnetism-Based Radar Propagation Model for 5G and Beyond
Authors: François De Saint Moulin, Christophe Craeye, Luc Vandendorpe, Claude Oestges
Date: 2023-05-17 00:00:00

In order to evaluate the performance of radar and communication systems in future wireless networks, accurate propagation models are needed to predict efficiently the received powers at each node, and draw correct conclusions. In this paper, we present new radar propagation models based on the electromagnetism theory. The target is modelled as a flat or curved square plate to compute the scattered field and derive accurate radar cross section modellings. With a flat square plate, the model makes the link between the radar equation and the geometrical optics propagation model used in ray-tracing applications. It is then applied to popular automotive scenarios within the stochastic geometry framework to observe the impact of such modelling.
Subject-based Non-contrastive Self-Supervised Learning for ECG Signal Processing
Authors: Adrian Atienza, Jakob Bardram, Sadasivan Puthusserypady
Date: 2023-05-12 00:00:00
Tasks: Data Augmentation, Electrocardiography (ECG), Rhythm, Self-Supervised Learning

Extracting information from the electrocardiography (ECG) signal is an essential step in the design of digital health technologies in cardiology. In recent years, several machine learning (ML) algorithms for automatic extraction of information in ECG have been proposed. Supervised learning methods have successfully been used to identify specific aspects in the signal, like detection of rhythm disorders (arrhythmias). Self-supervised learning (SSL) methods, on the other hand, can be used to extract all the features contained in the data. The model is optimized without any specific goal and learns from the data itself. By adapting state-of-the-art computer vision methodologies to the signal processing domain, a few SSL approaches have been reported recently for ECG processing. However, such SSL methods require either data augmentation or negative pairs, which limits the method to only look for similarities between two ECG inputs, either two versions of the same signal or two signals from the same subject. This leads to models that are very effective at extracting characteristics that are stable in a subject, such as gender or age. But they are not successful at capturing changes within the ECG recording that can explain dynamic aspects, like different arrhythmias or different sleep stages. In this work, we introduce the first SSL method that uses neither data augmentation nor negative pairs for understanding ECG signals, and still, achieves comparable quality representations. As a result, it is possible to design a SSL method that not only captures similarities between two inputs, but also captures dissimilarities for a complete understanding of the data. In addition, a model based on transformer blocks is presented, which produces better results than a model based on convolutional layers (XResNet50) with almost the same number of parameters.
BIOT: Cross-data Biosignal Learning in the Wild
Authors: Chaoqi Yang, M. Brandon Westover, Jimeng Sun
Date: 2023-05-10 00:00:00
Tasks: EEG, Missing Values, Seizure Detection

Biological signals, such as electroencephalograms (EEG), play a crucial role in numerous clinical applications, exhibiting diverse data formats and quality profiles. Current deep learning models for biosignals are typically specialized for specific datasets and clinical settings, limiting their broader applicability. Motivated by the success of large language models in text processing, we explore the development of foundational models that are trained from multiple data sources and can be fine-tuned on different downstream biosignal tasks. To overcome the unique challenges associated with biosignals of various formats, such as mismatched channels, variable sample lengths, and prevalent missing values, we propose a Biosignal Transformer (\method). The proposed \method model can enable cross-data learning with mismatched channels, variable lengths, and missing values by tokenizing diverse biosignals into unified "biosignal sentences". Specifically, we tokenize each channel into fixed-length segments containing local signal features, flattening them to form consistent "sentences". Channel embeddings and {\em relative} position embeddings are added to preserve spatio-temporal features. The \method model is versatile and applicable to various biosignal learning settings across different datasets, including joint pre-training for larger models. Comprehensive evaluations on EEG, electrocardiogram (ECG), and human activity sensory signals demonstrate that \method outperforms robust baselines in common settings and facilitates learning across multiple datasets with different formats. Use CHB-MIT seizure detection task as an example, our vanilla \method model shows 3\% improvement over baselines in balanced accuracy, and the pre-trained \method models (optimized from other data sources) can further bring up to 4\% improvements.
GPS Alignment from Multiple Sources to Extract Aircraft Bearing in Aerial Surveys
Authors: Joshua Power, Derek Jacoby, Marc-Antoine Drouin, Guillaume Durand, Yvonne Coady, Julian Meng
Date: 2023-05-10 00:00:00

Methodical aerial population surveys monitoring critically endangered species in Canadian North Atlantic waters are instrumental in influencing government policies both in economic and conservational efforts. The primary factor hindering the success of these missions is poor visibility caused by glare. This paper builds off our foundational paper [1] and pushes the envelope toward a data-driven glare modelling system. Said data-driven system makes use of meteorological and astronomical data to assist aircraft in navigating in order to mitigate acquisition errors and optimize the quality of acquired data. It is found that reliably extracting aircraft orientation is critical to our approach, to that end, we present a GPS alignment methodology which makes use of the fusion of two GPS signals. Using the complementary strengths and weaknesses of these two signals a synthetic interpolation of fused data is used to generate more reliable flight tracks, substantially improving glare modelling. This methodology could be applied to any other applications with similar signal restrictions.
Appliance Detection Using Very Low-Frequency Smart Meter Time Series
Authors: Adrien Petralia, Philippe Charpentier, Paul Boniol, Themis Palpanas
Date: 2023-05-10 00:00:00
Tasks: Meter Reading, Time Series, Time Series Classification

In recent years, smart meters have been widely adopted by electricity suppliers to improve the management of the smart grid system. These meters usually collect energy consumption data at a very low frequency (every 30min), enabling utilities to bill customers more accurately. To provide more personalized recommendations, the next step is to detect the appliances owned by customers, which is a challenging problem, due to the very-low meter reading frequency. Even though the appliance detection problem can be cast as a time series classification problem, with many such classifiers having been proposed in the literature, no study has applied and compared them on this specific problem. This paper presents an in-depth evaluation and comparison of state-of-the-art time series classifiers applied to detecting the presence/absence of diverse appliances in very low-frequency smart meter data. We report results with five real datasets. We first study the impact of the detection quality of 13 different appliances using 30min sampled data, and we subsequently propose an analysis of the possible detection performance gain by using a higher meter reading frequency. The results indicate that the performance of current time series classifiers varies significantly. Some of them, namely deep learning-based classifiers, provide promising results in terms of accuracy (especially for certain appliances), even using 30min sampled data, and are scalable to the large smart meter time series collections of energy consumption data currently available to electricity suppliers. Nevertheless, our study shows that more work is needed in this area to further improve the accuracy of the proposed solutions. This paper appeared in ACM e-Energy 2023.
Upper bounds for integrated information
Authors: Alireza Zaeemzadeh, Giulio Tononi
Date: 2023-05-16 00:00:00

Originally developed as a theory of consciousness, integrated information theory provides a mathematical framework to quantify the causal irreducibility of systems and subsets of units in the system. Specifically, mechanism integrated information quantifies how much of the causal powers of a subset of units in a state, also referred to as a mechanism, cannot be accounted for by its parts. If the causal powers of the mechanism can be fully explained by its parts, it is reducible and its integrated information is zero. Here, we study the upper bound of this measure and how it is achieved. We study mechanisms in isolation, groups of mechanisms, and groups of causal relations among mechanisms. We put forward new theoretical results that show mechanisms that share parts with each other cannot all achieve their maximum. We also introduce techniques to design systems that can maximize the integrated information of a subset of their mechanisms or relations. Our results can potentially be used to exploit the symmetries and constraints to reduce the computations significantly and to compare different connectivity profiles in terms of their maximal achievable integrated information.
Cells Solved the Gibbs Paradox by Learning to Contain Entropic Forces
Authors: Josh E. Baker
Date: 2023-05-17 00:00:00

As Nature's version of machine learning, evolution has solved many extraordinarily complex problems, none perhaps more remarkable than learning to harness an increase in chemical entropy (disorder) to generate directed chemical forces (order). Using muscle as a model system, here I unpack the basic mechanism by which life creates order from disorder. In short, evolution tuned the physical properties of certain proteins to contain changes in chemical entropy. As it happens, these are the "sensible" properties Gibbs postulated were needed to solve his paradox.
Molecule-Morphology Contrastive Pretraining for Transferable Molecular Representation
Authors: Cuong Q. Nguyen, Dante Pertusi, Kim M. Branson
Date: 2023-04-27 00:00:00
Tasks: Molecular Property Prediction, molecular representation, Property Prediction

Image-based profiling techniques have become increasingly popular over the past decade for their applications in target identification, mechanism-of-action inference, and assay development. These techniques have generated large datasets of cellular morphologies, which are typically used to investigate the effects of small molecule perturbagens. In this work, we extend the impact of such dataset to improving quantitative structure-activity relationship (QSAR) models by introducing Molecule-Morphology Contrastive Pretraining (MoCoP), a framework for learning multi-modal representation of molecular graphs and cellular morphologies. We scale MoCoP to approximately 100K molecules and 600K morphological profiles using data from the JUMP-CP Consortium and show that MoCoP consistently improves performances of graph neural networks (GNNs) on molecular property prediction tasks in ChEMBL20 across all dataset sizes. The pretrained GNNs are also evaluated on internal GSK pharmacokinetic data and show an average improvement of 2.6% and 6.3% in AUPRC for full and low data regimes, respectively. Our findings suggest that integrating cellular morphologies with molecular graphs using MoCoP can significantly improve the performance of QSAR models, ultimately expanding the deep learning toolbox available for QSAR applications.
Stochastic Molecular Reaction Queueing Network Modeling for In Vitro Transcription Process
Authors: Keqi Wang, Wei Xie, Hua Zheng
Date: 2023-05-17 00:00:00

To facilitate a rapid response to pandemic threats, this paper focuses on developing a mechanistic simulation model for in vitro transcription (IVT) process, a crucial step in mRNA vaccine manufacturing. To enhance production and support industry 4.0, this model is proposed to improve the prediction and analysis of IVT enzymatic reaction network. It incorporates a novel stochastic molecular reaction queueing network with a regulatory kinetic model characterizing the effect of bioprocess state variables on reaction rates. The empirical study demonstrates that the proposed model has a promising performance under different production conditions and it could offer potential improvements in mRNA product quality and yield.
Co-mapping Cellular Content and Extracellular Matrix with Hemodynamics in Intact Arterial Tissues Using Scanning Immunofluorescent Multiphoton Microscopy
Authors: Yasutaka Tobe, Anne Robertson, Mehdi Ramezanpour, Juan Cebral, Simon Watkins, Fady Charbel, Sepideh Amin-Hanjani, Alexander Yu, Boyle Cheng, Henry Woo
Date: 2023-05-17 00:00:00

Deviation of blood flow from an optimal range is known to be associated with the initiation and progression of vascular pathologies. Important open questions remain about how the abnormal flow drives specific wall changes in pathologies such as cerebral aneurysms where the flow is highly heterogeneous and complex. This knowledge gap precludes the clinical use of readily available flow data to predict outcomes and improve treatment of these diseases. As both flow and the pathological wall changes are spatially heterogeneous, a crucial requirement for progress in this area is a methodology for co-mapping local data from vascular wall biology with local hemodynamic data. In this study, we developed an imaging pipeline to address this pressing need. A protocol that employs scanning multiphoton microscopy was designed to obtain 3D data sets for smooth muscle actin, collagen and elastin in intact vascular specimens. A cluster analysis was developed to objectively categorize the smooth muscle cells (SMC) across the vascular specimen based on SMC density. In the final step in this pipeline, the location specific categorization of SMC, along with wall thickness was co-mapped with patient specific hemodynamic results, enabling direct quantitative comparison of local flow and wall biology in 3D intact specimens.
A Novel Procrustes Analysis Method to Quantify Multi-Joint Coordination of the Upper Extremity after Stroke
Authors: Khadija F. Zaidi, Michelle Harris-Love
Date: 2023-05-17 00:00:00

Upper extremity motor impairment affects about 80\% of persons after strokes. For stroke rehabilitation, upper limb kinematic assessments have increasingly been used as primary or secondary outcome measures. Studying the upper extremity provides a valuable tool for assessing limb coordination, mal-adaptations, and recovery. There is currently no universal standardized scale for categorizing multi-joint upper extremity movement. We propose a modified Procrustes statistical shape method as a quantitative analysis that can recognize segments of movement where multiple limb segments are coordinating movement. Generalized Procrustes methods allow data points to be compared across an array simultaneously rather than comparing them in pairs. Rather than rely solely on discrete kinematic values to contrast movement, this method allows evaluation of how movement progresses. The Procrustes analysis of able-bodied movement showed that the hand and forearm segments moved in a more coordinated manner during initiation. The shoulder and elbow become more coordinated during movement completion. In impaired movement, this coordination between the hand and forearm is disrupted. Potentially mal-adaptive compensation occurs between the upper arm and forearm after movement enters the deceleration phase. The utilization of Procrustes analysis may be a step towards developing a comprehensive and universal quantitative tool that does not require changes to existing treatments or increase patient burden. Copyright 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.