pwc-archive/papers-with-abstracts huggingface dataset:
Plug-in for visualizing 3D tool tracking from videos of Minimally Invasive Surgeries
Authors: Shubhangi Nema, Abhishek Mathur, Leena Vachhani
Date: 2024-01-12 00:00:00
Tasks: Computational Efficiency, Segmentation

This paper tackles instrument tracking and 3D visualization challenges in minimally invasive surgery (MIS), crucial for computer-assisted interventions. Conventional and robot-assisted MIS encounter issues with limited 2D camera projections and minimal hardware integration. The objective is to track and visualize the entire surgical instrument, including shaft and metallic clasper, enabling safe navigation within the surgical environment. The proposed method involves 2D tracking based on segmentation maps, facilitating creation of labeled dataset without extensive ground-truth knowledge. Geometric changes in 2D intervals express motion, and kinematics based algorithms process results into 3D tracking information. Synthesized and experimental results in 2D and 3D motion estimates demonstrate negligible errors, validating the method for labeling and motion tracking of instruments in MIS videos. The conclusion underscores the proposed 2D segmentation technique's simplicity and computational efficiency, emphasizing its potential as direct plug-in for 3D visualization in instrument tracking and MIS practices.
Self Supervised Vision for Climate Downscaling
Authors: Karandeep Singh, Chaeyoon Jeong, Naufal Shidqi, Sungwon Park, Arjun Nellikkattil, Elke Zeller, Meeyoung Cha
Date: 2024-01-09 00:00:00
Tasks: Model Optimization

Climate change is one of the most critical challenges that our planet is facing today. Rising global temperatures are already bringing noticeable changes to Earth's weather and climate patterns with an increased frequency of unpredictable and extreme weather events. Future projections for climate change research are based on Earth System Models (ESMs), the computer models that simulate the Earth's climate system. ESMs provide a framework to integrate various physical systems, but their output is bound by the enormous computational resources required for running and archiving higher-resolution simulations. For a given resource budget, the ESMs are generally run on a coarser grid, followed by a computationally lighter $downscaling$ process to obtain a finer-resolution output. In this work, we present a deep-learning model for downscaling ESM simulation data that does not require high-resolution ground truth data for model optimization. This is realized by leveraging salient data distribution patterns and the hidden dependencies between weather variables for an $\textit{individual}$ data point at $\textit{runtime}$. Extensive evaluation with $2$x, $3$x, and $4$x scaling factors demonstrates that the proposed model consistently obtains superior performance over that of various baselines. The improved downscaling performance and no dependence on high-resolution ground truth data make the proposed method a valuable tool for climate research and mark it as a promising direction for future research.
Precipitation Prediction Using an Ensemble of Lightweight Learners
Authors: Xinzhe Li, Sun Rui, Yiming Niu, Yao Liu
Date: 2023-11-30 00:00:00
Tasks: Ensemble Learning, Prediction

Precipitation prediction plays a crucial role in modern agriculture and industry. However, it poses significant challenges due to the diverse patterns and dynamics in time and space, as well as the scarcity of high precipitation events. To address this challenge, we propose an ensemble learning framework that leverages multiple learners to capture the diverse patterns of precipitation distribution. Specifically, the framework consists of a precipitation predictor with multiple lightweight heads (learners) and a controller that combines the outputs from these heads. The learners and the controller are separately optimized with a proposed 3-stage training scheme. By utilizing provided satellite images, the proposed approach can effectively model the intricate rainfall patterns, especially for high precipitation events. It achieved 1st place on the core test as well as the nowcasting leaderboards of the Weather4Cast 2023 competition. For detailed implementation, please refer to our GitHub repository at: https://github.com/lxz1217/weather4cast-2023-lxz.
Is the Emergence of Life an Expected Phase Transition in the Evolving Universe?
Authors: Stuart Kauffman, Andrea Roli
Date: 2024-01-17 00:00:00

We propose a novel definition of life in terms of which its emergence in the universe is expected, and its ever-creative open-ended evolution is entailed by no law. Living organisms are Kantian Wholes that achieve Catalytic Closure, Constraint Closure, and Spatial Closure. We here unite for the first time two established mathematical theories, namely Collectively Autocatalytic Sets and the Theory of the Adjacent Possible. The former establishes that a first-order phase transition to molecular reproduction is expected in the chemical evolution of the universe where the diversity and complexity of molecules increases; the latter posits that, under loose hypotheses, if the system starts with a small number of beginning molecules, each of which can combine with copies of itself or other molecules to make new molecules, over time the number of kinds of molecules increases slowly but then explodes upward hyperbolically. Together these theories imply that life is expected as a phase transition in the evolving universe. The familiar distinction between software and hardware loses its meaning in living cells. We propose new ways to study the phylogeny of metabolisms, new astronomical ways to search for life on exoplanets, new experiments to seek the emergence of the most rudimentary life, and the hint of a coherent testable pathway to prokaryotes with template replication and coding.
Diffusion-Driven Generative Framework for Molecular Conformation Prediction
Authors: Bobin Yang, Jie Deng, Zhenghan Chen, Ruoxue Wu
Date: 2023-12-22 00:00:00
Tasks: Bilevel Optimization, Computational chemistry, Prediction

The task of deducing three-dimensional molecular configurations from their two-dimensional graph representations holds paramount importance in the fields of computational chemistry and pharmaceutical development. The rapid advancement of machine learning, particularly within the domain of deep generative networks, has revolutionized the precision of predictive modeling in this context. Traditional approaches often adopt a two-step strategy: initially estimating interatomic distances and subsequently refining the spatial molecular structure by solving a distance geometry problem. However, this sequential approach occasionally falls short in accurately capturing the intricacies of local atomic arrangements, thereby compromising the fidelity of the resulting structural models. Addressing these limitations, this research introduces a cutting-edge generative framework named \method{}. This framework is grounded in the principles of diffusion observed in classical non-equilibrium thermodynamics. \method{} views atoms as discrete entities and excels in guiding the reversal of diffusion, transforming a distribution of stochastic noise back into coherent molecular structures through a process akin to a Markov chain. This transformation commences with the initial representation of a molecular graph in an abstract latent space, culminating in the realization of three-dimensional structures via a sophisticated bilevel optimization scheme meticulously tailored to meet the specific requirements of the task. One of the formidable challenges in this modeling endeavor involves preserving roto-translational invariance to ensure that the generated molecular conformations adhere to the laws of physics. Extensive experimental evaluations confirm the efficacy of the proposed \method{} in comparison to state-of-the-art methods.
4D-ONIX: A deep learning approach for reconstructing 3D movies from sparse X-ray projections
Authors: Yuhe Zhang, Zisheng Yao, Robert Klöfkorn, Tobias Ritschel, Pablo Villanueva-Perez
Date: 2024-01-17 00:00:00

The X-ray flux provided by X-ray free-electron lasers and storage rings offers new spatiotemporal possibilities to study in-situ and operando dynamics, even using single pulses of such facilities. X-ray Multi-Projection Imaging (XMPI) is a novel technique that enables volumetric information using single pulses of such facilities and avoids centrifugal forces induced by state-of-the-art time-resolved 3D methods such as time-resolved tomography. As a result, XMPI offers the potential to acquire 3D movies (4D) at least three orders of magnitude faster than current methods. However, it is exceptionally challenging to reconstruct 4D from highly sparse projections as acquired by XMPI with current algorithms. Here, we present 4D-ONIX, a Deep Learning (DL)-based approach that learns to reconstruct 3D movies (4D) from an extremely limited number of projections. It combines the computational physical model of X-ray interaction with matter and state-of-the-art DL methods. We demonstrate the potential of 4D-ONIX to generate high-quality 4D by generalizing over multiple experiments with only two to three projections per timestamp for binary droplet collisions and additive manufacturing. We envision that 4D-ONIX will become an enabling tool for 4D analysis, offering new spatiotemporal resolutions to study processes not possible before.
The impact of Covid-19 vaccination in Aotearoa New Zealand: a modelling study
Authors: Samik Datta, Giorgia Vattiato, Oliver J Maclaren, Ning Hua, Andrew Sporle, Michael J Plank
Date: 2024-01-18 00:00:00
Tasks: counterfactual

Aotearoa New Zealand implemented a Covid-19 elimination strategy in 2020 and 2021, which enabled a large majority of the population to be vaccinated before being exposed to the virus. This strategy delivered one of the lowest pandemic mortality rates in the world. However, quantitative estimates of the population-level health benefits of vaccination are lacking. Here, we use a validated mathematical model to investigate counterfactual scenarios with differing levels of vaccine coverage in different age and ethnicity groups. The model builds on earlier research by adding age- and time-dependent case ascertainment, the effect of antiviral medications, improved hospitalisation rate estimates, and the impact of relaxing control measures. The model was used for scenario analysis and policy advice for the New Zealand Government in 2022 and 2023. We compare the number of Covid-19 hospitalisations, deaths, and years of life lost in each counterfactual scenario to a baseline scenario that is fitted to epidemiological data between January 2022 and June 2023. Our results estimate that vaccines saved 6650 (95% credible interval [4424, 10180]) lives, and prevented 74500 [51000, 115400] years of life lost and 45100 [34400, 55600] hospitalisations during this 18-month period. Making the same comparison before the benefit of antiviral medications is accounted for, the estimated number of lives saved by vaccines increases to 7604 [5080, 11942]. Due to inequities in the vaccine rollout, vaccination rates among M\=aori were lower than in people of European ethnicity. Our results show that, if vaccination rates had been equitable, an estimated 11-26% of the 292 M\=aori Covid-19 deaths that were recorded in this time period could have been prevented. We conclude that Covid-19 vaccination greatly reduced health burden in New Zealand and that equity needs to be a key focus of future vaccination programmes.
Nowcasting economic activity in European regions using a mixed-frequency dynamic factor model
Authors: Luca Barbaglia, Lorenzo Frattarolo, Niko Hauzenberger, Dominik Hirschbuehl, Florian Huber, Luca Onorante, Michael Pfarrhofer, Luca Tiozzo Pezzoli
Date: 2024-01-18 00:00:00

Timely information about the state of regional economies can be essential for planning, implementing and evaluating locally targeted economic policies. However, European regional accounts for output are published at an annual frequency and with a two-year delay. To obtain robust and more timely measures in a computationally efficient manner, we propose a mixed-frequency dynamic factor model that accounts for national information to produce high-frequency estimates of the regional gross value added (GVA). We show that our model produces reliable nowcasts of GVA in 162 regions across 12 European countries.
Equilibrium Multiplicity: A Systematic Approach using Homotopies, with an Application to Chicago
Authors: Amine C-L. Ouazad
Date: 2024-01-18 00:00:00
Tasks: counterfactual, Discrete Choice Models

Discrete choice models with social interactions or spillovers may exhibit multiple equilibria. This paper provides a systematic approach to enumerating them for a quantitative spatial model with discrete locations, social interactions, and elastic housing supply. The approach relies on two homotopies. A homotopy is a smooth function that transforms the solutions of a simpler city where solutions are known, to a city with heterogeneous locations and finite supply elasticity. The first homotopy is that, in the set of cities with perfectly elastic floor surface supply, an economy with heterogeneous locations is homotopic to an economy with homogeneous locations, whose solutions can be comprehensively enumerated. Such an economy is epsilon close to an economy whose equilibria are the zeros of a system of polynomials. This is a well-studied area of mathematics where the enumeration of equilibria can be guaranteed. The second homotopy is that a city with perfectly elastic housing supply is homotopic to a city with an arbitrary supply elasticity. In a small number of cases, the path may bifurcate and a single path yields two or more equilibria. By running the method on thousands of cities, we obtain a large number of equilibria. Each equilibrium has different population distributions. We provide a method that is computationally feasible for economies with a large number of locations choices, with an empirical application to the City of Chicago. There exist multiple ``counterfactual Chicagos'' consistent with the estimated parameters. Population distribution, prices, and welfare are not uniquely pinned down by amenities. The paper's method can be applied to models in trade and IO. Further applications of algebraic geometry are suggested.
A Framework for Digital Currencies for Financial Inclusion in Latin America and the Caribbean
Authors: Gabriel Bizama, Alexander Wu, Bernardo Paniagua, Max Mitre
Date: 2024-01-18 00:00:00

This research aims to provide a framework to assess the contribution of digital currencies to promote financial inclusion, based on a diagnosis of the landscape of financial inclusion and domestic and cross-border payments in Latin America and the Caribbean. It also provides insights from central banks in the region on key aspects regarding a possible implementation of central bank digital currencies. Findings show that although digital currencies development is at an early stage, a well-designed system could reduce the cost of domestic and cross-border payments, improve the settlement of transactions to achieve real-time payments, expand the accessibility of central bank money, incorporate programmable payments and achieve system performance demands.
AI and the Opportunity for Shared Prosperity: Lessons from the History of Technology and the Economy
Authors: Guy Ben-Ishai, Jeff Dean, James Manyika, Ruth Porat, Hal Varian, Kent Walker
Date: 2024-01-18 00:00:00

Recent progress in artificial intelligence (AI) marks a pivotal moment in human history. It presents the opportunity for machines to learn, adapt, and perform tasks that have the potential to assist people, from everyday activities to their most creative and ambitious projects. It also has the potential to help businesses and organizations harness knowledge, increase productivity, innovate, transform, and power shared prosperity. This tremendous potential raises two fundamental questions: (1) Will AI actually advance national and global economic transformation to benefit society at large? and (2) What issues must we get right to fully realize AI's economic value, expand prosperity and improve lives everywhere? We explore these questions by considering the recent history of technology and innovation as a guide for the likely impact of AI and what we must do to realize its economic potential to benefit society. While we do not presume the future will be entirely like that past, for reasons we will discuss, we do believe prior experience with technological change offers many useful lessons. We conclude that while progress in AI presents a historic opportunity to advance our economic prosperity and future wellbeing, its economic benefits will not come automatically and that AI risks exacerbating existing economic challenges unless we collectively and purposefully act to enable its potential and address its challenges. We suggest a collective policy agenda - involving developers, deployers and users of AI, infrastructure providers, policymakers, and those involved in workforce training - that may help both realize and harness AI's economic potential and address its risks to our shared prosperity.
Wealth dynamics in a multi-aggregate closed monetary system
Authors: Andrea Monaco, Matteo Ghio, Adamaria Perrotta
Date: 2024-01-18 00:00:00

We examine the statistical properties of a closed monetary economy with multi-aggregates interactions. Building upon Yakovenko's single-agent monetary model (Dragulescu and Yakovenko, 2000), we investigate the joint equilibrium distribution of aggregate size and wealth. By comparing theoretical and simulated data, we validate our findings and investigate the influence of both micro dynamics and macro characteristics of the system on the distribution. Additionally, we analyze the system's convergence towards equilibrium under various conditions. Our laboratory model may offer valuable insights into macroeconomic phenomena allowing to reproduce typical wealth distribution features observed in real economy.
Game Representations and Extensions of the Shapley Value
Authors: Pradeep Dubey
Date: 2024-01-18 00:00:00

We show that any cooperative game can be represented by an assignment of costly facilities to players, in which it is intuitively obvious how to allocate the total cost in an equitable manner. This equitable solution turns out to be the Shapley value of the game, and thus provides as an alternative justification of the value. Game representations also open the door for extending the Shapley value to situations where not all coalitions can form, provided those that can constitute a "semi-algebra"; or, more generally, a "hierarchy"; or, still more generally, have "full span".
Automatic Tuning of Denoising Algorithms Parameters Without Ground Truth
Authors: Arthur Floquet, Sayantan Dutta, Emmanuel Soubies, Duong Hung Pham, Denis Kouame
Date: 2024-01-18 00:00:00
Tasks: Denoising

Denoising is omnipresent in image processing. It is usually addressed with algorithms relying on a set of hyperparameters that control the quality of the recovered image. Manual tuning of those parameters can be a daunting task, which calls for the development of automatic tuning methods. Given a denoising algorithm, the best set of parameters is the one that minimizes the error between denoised and ground-truth images. Clearly, this ideal approach is unrealistic, as the ground-truth images are unknown in practice. In this work, we propose unsupervised cost functions -- i.e., that only require the noisy image -- that allow us to reach this ideal gold standard performance. Specifically, the proposed approach makes it possible to obtain an average PSNR output within less than 1% of the best achievable PSNR.
Memory Efficient Corner Detection for Event-driven Dynamic Vision Sensors
Authors: Pao-Sheng Vincent Sun, Arren Glover, Chiara Bartolozzi, Arindam Basu
Date: 2024-01-18 00:00:00
Tasks: Data Compression, Edge-computing

Event cameras offer low-latency and data compression for visual applications, through event-driven operation, that can be exploited for edge processing in tiny autonomous agents. Robust, accurate and low latency extraction of highly informative features such as corners is key for most visual processing. While several corner detection algorithms have been proposed, state-of-the-art performance is achieved by luvHarris. However, this algorithm requires a high number of memory accesses per event, making it less-than ideal for low-latency, low-energy implementation in tiny edge processors. In this paper, we propose a new event-driven corner detection implementation tailored for edge computing devices, which requires much lower memory access than luvHarris while also improving accuracy. Our method trades computation for memory access, which is more expensive for large memories. For a DAVIS346 camera, our method requires ~3.8X less memory, ~36.6X less memory accesses with only ~2.3X more computes.
3DMASC: Accessible, explainable 3D point clouds classification. Application to Bi-spectral Topo-bathymetric lidar data
Authors: Mathilde Letard, Dimitri Lague, Arthur Le Guennec, Sébastien Lefèvre, Baptiste Feldmann, Paul Leroy, Daniel Girardeau-Montaut, Thomas Corpetti
Date: 2024-01-15 00:00:00
Tasks: Classification, Descriptive, Earth Observation, feature selection, Multi-class Classification, Point Cloud Classification

Three-dimensional data have become increasingly present in earth observation over the last decades. However, many 3D surveys are still underexploited due to the lack of accessible and explainable automatic classification methods, for example, new topo-bathymetric lidar data. In this work, we introduce explainable machine learning for 3D data classification using Multiple Attributes, Scales, and Clouds under 3DMASC, a new workflow. This workflow introduces multi-cloud classification through dual-cloud features, encrypting local spectral and geometrical ratios and differences. 3DMASC uses classical multi-scale descriptors adapted to all types of 3D point clouds and new ones based on their spatial variations. In this paper, we present the performances of 3DMASC for multi-class classification of topo-bathymetric lidar data in coastal and fluvial environments. We show how multivariate and embedded feature selection allows the building of optimized predictor sets of reduced complexity, and we identify features particularly relevant for coastal and riverine scene descriptions. Our results show the importance of dual-cloud features, lidar return-based attributes averaged over specific scales, and of statistics of dimensionality-based and spectral features. Additionally, they indicate that small to medium spherical neighbourhood diameters (<7 m) are sufficient to build effective classifiers, namely when combined with distance-to-ground or distance-to-water-surface features. Without using optional RGB information, and with a maximum of 37 descriptors, we obtain classification accuracies between 91 % for complex multi-class tasks and 98 % for lower-level processing using models trained on less than 2000 samples per class. Comparisons with classical point cloud classification methods show that 3DMASC features have a significantly improved descriptive power. Our contributions are made available through a plugin in the CloudCompare software, allowing non-specialist users to create classifiers for any type of 3D data characterized by 1 or 2 point clouds (airborne or terrestrial lidar, structure from motion), and two labelled topo-bathymetric lidar datasets, available on https://opentopography.org/.