pwc-archive/papers-with-abstracts huggingface dataset:
Ecosystem knowledge should replace coexistence and stability assumptions in ecological network modelling
Authors: Sarah A. Vollert, Christopher Drovandi, Matthew P. Adams
Date: 2024-05-01 00:00:00
Tasks: Decision Making, Management

Quantitative population modelling is an invaluable tool for identifying the cascading effects of ecosystem management and interventions. Ecosystem models are often constructed by assuming stability and coexistence in ecological communities as a proxy for abundance data when monitoring programs are not available. However, a growing body of literature suggests that these assumptions are inappropriate for modelling conservation outcomes. In this work, we develop an alternative for dataless population modelling that instead relies on expert-elicited knowledge of species abundances. While time series abundance data is often not available for ecosystems of interest, these systems may still be highly studied or observed in an informal capacity. In particular, limits on population sizes and their capacity to rapidly change during an observation period can be reasonably elicited for many species. We propose a robust framework for generating an ensemble of ecosystem models whose population predictions match the expected population dynamics, as defined by experts. Our new Bayesian algorithm systematically removes model parameters that lead to unreasonable population predictions without incurring excessive computational costs. Our results demonstrate that models constructed using expert-elicited information, rather than stability and coexistence assumptions, can dramatically impact population predictions, expected responses to management, conservation decision-making, and long-term ecosystem behaviour. In the absence of data, we argue that field observations and expert knowledge are preferred for representing ecosystems observed in nature instead of theoretical assumptions of coexistence and stability.
3D MR Fingerprinting for Dynamic Contrast-Enhanced Imaging of Whole Mouse Brain
Authors: Yuran Zhu, Guanhua Wang, Yuning Gu, Walter Zhao, Jiahao Lu, Junqing Zhu, Christina J. MacAskill, Andrew Dupuis, Mark A. Griswold, Dan Ma, Chris A. Flask, Xin Yu
Date: 2024-05-01 00:00:00
Tasks: Quantitative MRI

Quantitative MRI enables direct quantification of contrast agent concentrations in contrast-enhanced scans. However, the lengthy scan times required by conventional methods are inadequate for tracking contrast agent transport dynamically in mouse brain. We developed a 3D MR fingerprinting (MRF) method for simultaneous T1 and T2 mapping across the whole mouse brain with 4.3-min temporal resolution. We designed a 3D MRF sequence with variable acquisition segment lengths and magnetization preparations on a 9.4T preclinical MRI scanner. Model-based reconstruction approaches were employed to improve the accuracy and speed of MRF acquisition. The method's accuracy for T1 and T2 measurements was validated in vitro, while its repeatability of T1 and T2 measurements was evaluated in vivo (n=3). The utility of the 3D MRF sequence for dynamic tracking of intracisternally infused Gd-DTPA in the whole mouse brain was demonstrated (n=5). Phantom studies confirmed accurate T1 and T2 measurements by 3D MRF with an undersampling factor up to 48. Dynamic contrast-enhanced (DCE) MRF scans achieved a spatial resolution of 192 x 192 x 500 um3 and a temporal resolution of 4.3 min, allowing for the analysis and comparison of dynamic changes in concentration and transport kinetics of intracisternally infused Gd-DTPA across brain regions. The sequence also enabled highly repeatable, high-resolution T1 and T2 mapping of the whole mouse brain (192 x 192 x 250 um3) in 30 min. We present the first dynamic and multi-parametric approach for quantitatively tracking contrast agent transport in the mouse brain using 3D MRF.
The Highly Durable Antibacterial Gel-like Coatings for Textiles
Authors: Seyedali Mirmohammadsadeghi, Davis Juhas, Mikhail Parker, Kristina Peranidze, Dwight Austin Van Horn, Aayushi Sharma, Dhruvi Patel, Tatyana A. Sysoeva, Vladislav Klepov, Vladimir Reukov
Date: 2024-05-01 00:00:00

Hospital-acquired infections are considered a priority for public health systems, which poses a significant burden for society. High-touch surfaces of healthcare centers, including textiles, provide a suitable environment for pathogenic bacteria to grow, necessitating incorporating effective antibacterial agents into textiles. This paper introduces a highly durable antibacterial gel-like solution, Silver Shell finish, which contains chitosan-bound silver chloride microparticles. The study investigates the coating's environmental impact, health risks, and durability during repeated washing. The structure of the Silver Shell finish was studied using Transmission Electron Microscopy (TEM) and Energy-Dispersive X-ray Spectroscopy (EDX). TEM images showed a core-shell structure, with chitosan forming a protective shell around groupings of silver micro-particles. Field Emission Scanning Electron Microscopy (FESEM) demonstrated the uniform deposition of Silver Shell on the surface of fabrics. AATCC Test Method 100 was employed to quantitatively analyze the antibacterial properties of fabrics coated with silver microparticles. Two types of bacteria, Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) were used in this study. The antibacterial results showed that after 75 wash cycles, a 100% reduction for both S. aureus and E. coli in the coated samples using crosslinking agents was observed. The coated samples without a crosslinking agent exhibited a 99.88% and 99.81% reduction for S. aureus and E. coli after 50 washing cycles. AATCC-147 was performed to investigate the coated samples' leaching properties and the crosslinking agent's effect against S. aureus and E. coli. All coated samples demonstrated remarkable antibacterial efficacy even after 75 wash cycles.
Learning parameter dependence for Fourier-based option pricing with tensor trains
Authors: Rihito Sakurai, Haruto Takahashi, Koichi Miyamoto
Date: 2024-04-17 00:00:00
Tasks: Tensor Networks

A long-standing issue in mathematical finance is the speed-up of option pricing, especially for multi-asset options. A recent study has proposed to use tensor train learning algorithms to speed up Fourier transform (FT)-based option pricing, utilizing the ability of tensor trains to compress high-dimensional tensors. Another usage of the tensor train is to compress functions, including their parameter dependence. Here, we propose a pricing method, where, by a tensor train learning algorithm, we build tensor trains that approximate functions appearing in FT-based option pricing with their parameter dependence and efficiently calculate the option price for the varying input parameters. As a benchmark test, we run the proposed method to price a multi-asset option for the various values of volatilities and present asset prices. We show that, in the tested cases involving up to 11 assets, the proposed method outperforms Monte Carlo-based option pricing with $10^6$ paths in terms of computational complexity while keeping better accuracy.
Unveiling Nonlinear Dynamics in Catastrophe Bond Pricing: A Machine Learning Perspective
Authors: Xiaowei Chen, Hong Li, Yufan Lu, Rui Zhou
Date: 2024-04-10 00:00:00
Tasks: Conformal Prediction, Prediction Intervals, regression

This paper explores the implications of using machine learning models in the pricing of catastrophe (CAT) bonds. By integrating advanced machine learning techniques, our approach uncovers nonlinear relationships and complex interactions between key risk factors and CAT bond spreads -- dynamics that are often overlooked by traditional linear regression models. Using primary market CAT bond transaction records between January 1999 and March 2021, our findings demonstrate that machine learning models not only enhance the accuracy of CAT bond pricing but also provide a deeper understanding of how various risk factors interact and influence bond prices in a nonlinear way. These findings suggest that investors and issuers can benefit from incorporating machine learning to better capture the intricate interplay between risk factors when pricing CAT bonds. The results also highlight the potential for machine learning models to refine our understanding of asset pricing in markets characterized by complex risk structures.
Calibration of the rating transition model for high and low default portfolios
Authors: Jian He, Asma Khedher, Peter Spreij
Date: 2024-05-01 00:00:00

In this paper we develop Maximum likelihood (ML) based algorithms to calibrate the model parameters in credit rating transition models. Since the credit rating transition models are not Gaussian linear models, the celebrated Kalman filter is not suitable to compute the likelihood of observed migrations. Therefore, we develop a Laplace approximation of the likelihood function and as a result the Kalman filter can be used in the end to compute the likelihood function. This approach is applied to so-called high-default portfolios, in which the number of migrations (defaults) is large enough to obtain high accuracy of the Laplace approximation. By contrast, low-default portfolios have a limited number of observed migrations (defaults). Therefore, in order to calibrate low-default portfolios, we develop a ML algorithm using a particle filter (PF) and Gaussian process regression. Experiments show that both algorithms are efficient and produce accurate approximations of the likelihood function and the ML estimates of the model parameters.
Quantifying Price Improvement in Order Flow Auctions
Authors: Brad Bachu, Xin Wan, Ciamac C. Moallemi
Date: 2024-05-01 00:00:00

This work introduces a framework for evaluating onchain order flow auctions (OFAs), emphasizing the metric of price improvement. Utilizing a set of open-source tools, our methodology systematically attributes price improvements to specific modifiable inputs of the system such as routing efficiency, gas optimization, and priority fee settings. When applied to leading Ethereum-based trading interfaces such as 1Inch and Uniswap, the results reveal that auction-enhanced interfaces can provide statistically significant improvements in trading outcomes, averaging 4-5 basis points in our sample. We further identify the sources of such price improvements to be added liquidity for large swaps. This research lays a foundation for future innovations in blockchain based trading platforms.
NuTrea: Neural Tree Search for Context-guided Multi-hop KGQA
Authors: Hyeong Kyu Choi, Seunghun Lee, Jaewon Chu, Hyunwoo J. Kim
Date: 2023-10-24 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Graph Question Answering, Proper Noun, Question Answering

Multi-hop Knowledge Graph Question Answering (KGQA) is a task that involves retrieving nodes from a knowledge graph (KG) to answer natural language questions. Recent GNN-based approaches formulate this task as a KG path searching problem, where messages are sequentially propagated from the seed node towards the answer nodes. However, these messages are past-oriented, and they do not consider the full KG context. To make matters worse, KG nodes often represent proper noun entities and are sometimes encrypted, being uninformative in selecting between paths. To address these problems, we propose Neural Tree Search (NuTrea), a tree search-based GNN model that incorporates the broader KG context. Our model adopts a message-passing scheme that probes the unreached subtree regions to boost the past-oriented embeddings. In addition, we introduce the Relation Frequency-Inverse Entity Frequency (RF-IEF) node embedding that considers the global KG context to better characterize ambiguous KG nodes. The general effectiveness of our approach is demonstrated through experiments on three major multi-hop KGQA benchmark datasets, and our extensive analyses further validate its expressiveness and robustness. Overall, NuTrea provides a powerful means to query the KG with complex natural language questions. Code is available at https://github.com/mlvlab/NuTrea.
Pairwise GUI Dataset Construction Between Android Phones and Tablets
Authors: Han Hu, Haolan Zhan, Yujin Huang, Di Liu
Date: 2023-10-07 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Deep Learning

In the current landscape of pervasive smartphones and tablets, apps frequently exist across both platforms. Although apps share most graphic user interfaces (GUIs) and functionalities across phones and tablets, developers often rebuild from scratch for tablet versions, escalating costs and squandering existing design resources. Researchers are attempting to collect data and employ deep learning in automated GUIs development to enhance developers' productivity. There are currently several publicly accessible GUI page datasets for phones, but none for pairwise GUIs between phones and tablets. This poses a significant barrier to the employment of deep learning in automated GUI development. In this paper, we introduce the Papt dataset, a pioneering pairwise GUI dataset tailored for Android phones and tablets, encompassing 10,035 phone-tablet GUI page pairs sourced from 5,593 unique app pairs. We propose novel pairwise GUI collection approaches for constructing this dataset and delineate its advantages over currently prevailing datasets in the field. Through preliminary experiments on this dataset, we analyze the present challenges of utilizing deep learning in automated GUI development.
SIMPLOT: Enhancing Chart Question Answering by Distilling Essentials
Authors: Wonjoong Kim, Sangwu Park, Yeonjun In, Seokwon Han, Chanyoung Park
Date: 2024-02-22 00:00:00
Tasks: Chart Question Answering, Language Modeling, Language Modelling, Large Language Model, Logical Reasoning, Question Answering, Table Extraction

Recently, interpreting complex charts with logical reasoning has emerged as challenges due to the development of vision-language models. A prior state-of-the-art (SOTA) model has presented an end-to-end method that leverages the vision-language model to convert charts into table format utilizing Large Language Model (LLM) for reasoning. However, unlike natural images, charts contain a mix of essential and irrelevant information required for chart reasoning, and we discover that this characteristic can lower the performance of chart-to-table extraction. In this paper, we introduce SIMPLOT, a method designed to extract only the elements necessary for chart reasoning. The proposed method involves two steps: 1) training to mimic a simple plot that contains only the essential information from a complex chart for table extraction, followed by 2) performing reasoning based on the table. Our model enables accurate chart reasoning without the need for additional annotations or datasets, and its effectiveness is demonstrated through various experiments. Furthermore, we propose a novel prompt mimicking how human interpret charts for more accurate reasoning. Our source code is available at https://github.com/sangwu99/Simplot.
Oxygen vacancies modulated VO2 for neurons and Spiking Neural Network construction
Authors: Liang Li, Ting Zhou, Tong Liu, Zhiwei Liu, Yaping Li, Shuo Wu, Shanguang Zhao, Jinglin Zhu, Meiling Liu, Zhihan Lin, Bowen Sun, Jianjun Li, Fangwen Sun, Chongwen Zou
Date: 2024-04-16 00:00:00

Artificial neuronal devices are the basic building blocks for neuromorphic computing systems, which have been motivated by realistic brain emulation. Aiming for these applications, various device concepts have been proposed to mimic the neuronal dynamics and functions. While till now, the artificial neuron devices with high efficiency, high stability and low power consumption are still far from practical application. Due to the special insulator-metal phase transition, Vanadium Dioxide (VO2) has been considered as an idea candidate for neuronal device fabrication. However, its intrinsic insulating state requires the VO2 neuronal device to be driven under large bias voltage, resulting in high power consumption and low frequency. Thus in the current study, we have addressed this challenge by preparing oxygen vacancies modulated VO2 film(VO2-x) and fabricating the VO2-x neuronal devices for Spiking Neural Networks (SNNs) construction. Results indicate the neuron devices can be operated under lower voltage with improved processing speed. The proposed VO2-x based back-propagation SNNs (BP-SNNs) system, trained with the MNIST dataset, demonstrates excellent accuracy in image recognition. Our study not only demonstrates the VO2-x based neurons and SNN system for practical application, but also offers an effective way to optimize the future neuromorphic computing systems by defect engineering strategy.
Direct Training Needs Regularisation: Anytime Optimal Inference Spiking Neural Network
Authors: Dengyu Wu, Yi Qi, Kaiwen Cai, Gaojie Jin, Xinping Yi, Xiaowei Huang
Date: 2024-04-15 00:00:00

Spiking Neural Network (SNN) is acknowledged as the next generation of Artificial Neural Network (ANN) and hold great promise in effectively processing spatial-temporal information. However, the choice of timestep becomes crucial as it significantly impacts the accuracy of the neural network training. Specifically, a smaller timestep indicates better performance in efficient computing, resulting in reduced latency and operations. While, using a small timestep may lead to low accuracy due to insufficient information presentation with few spikes. This observation motivates us to develop an SNN that is more reliable for adaptive timestep by introducing a novel regularisation technique, namely Spatial-Temporal Regulariser (STR). Our approach regulates the ratio between the strength of spikes and membrane potential at each timestep. This effectively balances spatial and temporal performance during training, ultimately resulting in an Anytime Optimal Inference (AOI) SNN. Through extensive experiments on frame-based and event-based datasets, our method, in combination with cutoff based on softmax output, achieves state-of-the-art performance in terms of both latency and accuracy. Notably, with STR and cutoff, SNN achieves 2.14 to 2.89 faster in inference compared to the pre-configured timestep with near-zero accuracy drop of 0.50% to 0.64% over the event-based datasets. Code available: https://github.com/Dengyu-Wu/AOI-SNN-Regularisation
CUDA-Accelerated Soft Robot Neural Evolution with Large Language Model Supervision
Authors: Lechen Zhang
Date: 2024-04-12 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

This paper addresses the challenge of co-designing morphology and control in soft robots via a novel neural network evolution approach. We propose an innovative method to implicitly dual-encode soft robots, thus facilitating the simultaneous design of morphology and control. Additionally, we introduce the large language model to serve as the control center during the evolutionary process. This advancement considerably optimizes the evolution speed compared to traditional soft-bodied robot co-design methods. Further complementing our work is the implementation of Gaussian positional encoding - an approach that augments the neural network's comprehension of robot morphology. Our paper offers a new perspective on soft robot design, illustrating substantial improvements in efficiency and comprehension during the design and evolutionary process.
SOAR: Advancements in Small Body Object Detection for Aerial Imagery Using State Space Models and Programmable Gradients
Authors: Tushar Verma, Jyotsna Singh, Yash Bhartari, Rishi Jarwal, Suraj Singh, Shubhkarman Singh
Date: 2024-05-02 00:00:00
Tasks: image-classification, Image Classification, Mamba, object-detection, Object Detection, Object Recognition, Remote Sensing Image Classification, Small Object Detection, State Space Models

Small object detection in aerial imagery presents significant challenges in computer vision due to the minimal data inherent in small-sized objects and their propensity to be obscured by larger objects and background noise. Traditional methods using transformer-based models often face limitations stemming from the lack of specialized databases, which adversely affect their performance with objects of varying orientations and scales. This underscores the need for more adaptable, lightweight models. In response, this paper introduces two innovative approaches that significantly enhance detection and segmentation capabilities for small aerial objects. Firstly, we explore the use of the SAHI framework on the newly introduced lightweight YOLO v9 architecture, which utilizes Programmable Gradient Information (PGI) to reduce the substantial information loss typically encountered in sequential feature extraction processes. The paper employs the Vision Mamba model, which incorporates position embeddings to facilitate precise location-aware visual understanding, combined with a novel bidirectional State Space Model (SSM) for effective visual context modeling. This State Space Model adeptly harnesses the linear complexity of CNNs and the global receptive field of Transformers, making it particularly effective in remote sensing image classification. Our experimental results demonstrate substantial improvements in detection accuracy and processing efficiency, validating the applicability of these approaches for real-time small object detection across diverse aerial scenarios. This paper also discusses how these methodologies could serve as foundational models for future advancements in aerial object recognition technologies. The source code will be made accessible here.
Physics-Informed Neural Networks: Minimizing Residual Loss with Wide Networks and Effective Activations
Authors: Nima Hosseini Dashtbayaz, Ghazal Farhani, Boyu Wang, Charles X. Ling
Date: 2024-05-02 00:00:00

The residual loss in Physics-Informed Neural Networks (PINNs) alters the simple recursive relation of layers in a feed-forward neural network by applying a differential operator, resulting in a loss landscape that is inherently different from those of common supervised problems. Therefore, relying on the existing theory leads to unjustified design choices and suboptimal performance. In this work, we analyze the residual loss by studying its characteristics at critical points to find the conditions that result in effective training of PINNs. Specifically, we first show that under certain conditions, the residual loss of PINNs can be globally minimized by a wide neural network. Furthermore, our analysis also reveals that an activation function with well-behaved high-order derivatives plays a crucial role in minimizing the residual loss. In particular, to solve a $k$-th order PDE, the $k$-th derivative of the activation function should be bijective. The established theory paves the way for designing and choosing effective activation functions for PINNs and explains why periodic activations have shown promising performance in certain cases. Finally, we verify our findings by conducting a set of experiments on several PDEs. Our code is publicly available at https://github.com/nimahsn/pinns_tf2.