pwc-archive/papers-with-abstracts huggingface dataset:
Structural maturation of myofilaments in engineered 3D cardiac microtissues characterized using small angle X-ray scattering
Authors: Geoffrey van Dover, Josh Javor, Jourdan Ewoldt, Ha Eun Lee, Mikhail Zhernenkov, Guillaume Freychet, Patryk Wasik, Dana Brown, David Bishop, Christopher Chen
Date: 2023-12-19 00:00:00

Understanding the structural and functional development of human-induced pluripotent stem-cell-derived cardiomyocytes is essential to engineering cardiac tissue that enables pharmaceutical testing, modeling diseases, and designing therapies. Here we use a method not commonly applied to biological materials, small angle X-ray scattering, to characterize the structural development of human-induced pluripotent stem-cell-derived cardiomyocytes within 3D engineered tissues during their preliminary stages of maturation. An X-ray scattering experimental method enables the reliable characterization of the cardiomyocyte myofilament spacing with maturation time. The myofilament lattice spacing monotonically decreases as the tissue matures from its initial post-seeding state over the span of ten days. Visualization of the spacing at a grid of positions in the tissue provides an approach to characterizing the maturation and organization of cardiomyocyte myofilaments and has the potential to help elucidate mechanisms of pathophysiology, and disease progression, thereby stimulating new biological hypotheses in stem cell engineering.
Cardiopulmonary responses and muscle strength influence running performance parameters differently at different slopes
Authors: Corentin Hingrand, Adrien Combes, Nicolas Olivier, Samir Bensaid, Frédéric Daussin
Date: 2023-12-19 00:00:00

The analysis of trail-running performance appears to be complex and cardio-respiratory and muscular factors could have a variable importance depending on the inclination. Our study aims to determine the role of these parameters in performance. 13 subjects with heterogeneous levels participated in the study. They carried out 7 visits including 3 maximal aerobic speed (MAS) test at 1, 10 and 25% slope on treadmill, 3 endurance tests at 100% of the MAS reached at 1, 10 and 25% and an evaluation on isokinetic ergometer at different speeds (60-180-240 {\textdegree}/s). Gas exchange measured during the incremental tests. We were able to identify 2 groups, a performance and a recreational group. We observe a difference in VO2max, MAS at 1 and 10%, and maximal aerobic ascensional speed (MAaS) at 25%, between the 2 groups but no difference in VO2max and exhaustion time at 100% MAS between the different conditions (1-10-25%). Interestingly, at ventilatory thresholds the metabolic parameters, expressed as absolute or relative values, are similar between conditions (10-25%) while the ascensional speed are different. This study suggests that the measurement of ascensional speed is not as relevant as heart rate for controlling intensity given the variety of slope gradients in trail-running races.
Leveraging Sample Entropy for Enhanced Volatility Measurement and Prediction in International Oil Price Returns
Authors: Radhika Prosad Datta
Date: 2023-12-20 00:00:00
Tasks: Decision Making, regression, Time Series, Time Series Regression

This paper explores the application of Sample Entropy (SampEn) as a sophisticated tool for quantifying and predicting volatility in international oil price returns. SampEn, known for its ability to capture underlying patterns and predict periods of heightened volatility, is compared with traditional measures like standard deviation. The study utilizes a comprehensive dataset spanning 27 years (1986-2023) and employs both time series regression and machine learning methods. Results indicate SampEn's efficacy in predicting traditional volatility measures, with machine learning algorithms outperforming standard regression techniques during financial crises. The findings underscore SampEn's potential as a valuable tool for risk assessment and decision-making in the realm of oil price investments.
Cross-Currency Heath-Jarrow-Morton Framework in the Multiple-Curve Setting
Authors: Alessandro Gnoatto, Silvia Lavagnini
Date: 2023-12-20 00:00:00

We provide a general HJM framework for forward contracts written on abstract market indices with arbitrary fixing and payment adjustments, and featuring collateralization in any currency denominations. In view of this, we first provide a thorough study of cross-currency markets in the presence of collateral and incompleteness. Then we give a general treatment of collateral dislocations by describing the instantaneous cross-currency basis spreads by means of HJM models, for which we derive appropriate drift conditions. The framework obtained allows us to simultaneously cover forward-looking risky IBOR rates, such as EURIBOR, and backward-looking rates based on overnight rates, such as SOFR. Due to the discrepancies in market conventions of different currency areas created by the benchmark transition, this is pivotal for describing portfolios of interest-rate products that are denominated in multiple currencies. As an example of contract simultaneously depending on all the risk factors that we describe within our framework, we treat cross-currency swaps using our proposed abstract indices.
Asset and Factor Risk Budgeting: A Balanced Approach
Authors: Adil Rengim Cetingoz, Olivier Guéant
Date: 2023-12-18 00:00:00
Tasks: Management, Portfolio Optimization

Portfolio optimization methods have evolved significantly since Markowitz introduced the mean-variance framework in 1952. While the theoretical appeal of this approach is undeniable, its practical implementation poses important challenges, primarily revolving around the intricate task of estimating expected returns. As a result, practitioners and scholars have explored alternative methods that prioritize risk management and diversification. One such approach is Risk Budgeting, where portfolio risk is allocated among assets according to predefined risk budgets. The effectiveness of Risk Budgeting in achieving true diversification can, however, be questioned, given that asset returns are often influenced by a small number of risk factors. From this perspective, one question arises: is it possible to allocate risk at the factor level using the Risk Budgeting approach? First, we introduce a comprehensive framework to address this question by introducing risk measures directly associated with risk factor exposures and demonstrating the desirable mathematical properties of these risk measures, making them suitable for optimization. Then, we propose a novel framework to find portfolios that effectively balance the risk contributions from both assets and factors. Leveraging standard stochastic algorithms, our framework enables the use of a wide range of risk measures to construct diversified portfolios.
A Hedonic Index for Collectables Arising from Modelling Diamond Prices
Authors: Nicholas I Fisher, Alan J Lee
Date: 2023-12-05 00:00:00

This article describes a case study concerned with modelling the price of wholesale diamonds, as part of a project to develop an online diamond auction platform. The work was extended to exploring how to develop an index that could be used to track market trends of wholesale diamond prices. The approach we used is readily generalised to defining market indices for so-called Collectables, and can provide the basis for construction of derivatives. With the burgeoning interest in new markets of collectables such as those generated by the concept of a Non-Fungible Token, it is reasonable to suppose that there will be concomitant increasing interest in developing derivatives for these markets.
Single-pixel 3D imaging based on fusion temporal data of single photon detector and millimeter-wave radar
Authors: Tingqin Lai, Xiaolin Liang, Yi Zhu, Xinyi Wu, Lianye Liao, Xuelin Yuan, Ping Su, Shihai Sun
Date: 2023-10-20 00:00:00

Recently, there has been increased attention towards 3D imaging using single-pixel single-photon detection (also known as temporal data) due to its potential advantages in terms of cost and power efficiency. However, to eliminate the symmetry blur in the reconstructed images, a fixed background is required. This paper proposes a fusion-data-based 3D imaging method that utilizes a single-pixel single-photon detector and a millimeter-wave radar to capture temporal histograms of a scene from multiple perspectives. Subsequently, the 3D information can be reconstructed from the one-dimensional fusion temporal data by using Artificial Neural Network (ANN). Both the simulation and experimental results demonstrate that our fusion method effectively eliminates symmetry blur and improves the quality of the reconstructed images.
DiffSpectralNet : Unveiling the Potential of Diffusion Models for Hyperspectral Image Classification
Authors: Neetu Sigger, Tuan Thanh Nguyen, Gianluca Tozzi, Quoc-Tuan Vien, Sinh Van Nguyen
Date: 2023-10-29 00:00:00
Tasks: Classification, Denoising, Hyperspectral Image Classification, image-classification, Image Classification

Hyperspectral images (HSI) have become popular for analysing remotely sensed images in multiple domain like agriculture, medical. However, existing models struggle with complex relationships and characteristics of spectral-spatial data due to the multi-band nature and data redundancy of hyperspectral data. To address this limitation, we propose a new network called DiffSpectralNet, which combines diffusion and transformer techniques. Our approach involves a two-step process. First, we use an unsupervised learning framework based on the diffusion model to extract both high-level and low-level spectral-spatial features. The diffusion method is capable of extracting diverse and meaningful spectral-spatial features, leading to improvement in HSI classification. Then, we employ a pretrained denoising U-Net to extract intermediate hierarchical features for classification. Finally, we use a supervised transformer-based classifier to perform the HSI classification. Through comprehensive experiments on HSI datasets, we evaluate the classification performance of DiffSpectralNet. The results demonstrate that our framework significantly outperforms existing approaches, achieving state-of-the-art performance.
Hierarchical Classification System for Breast Cancer Specimen Report (HCSBC) -- an end-to-end model for characterizing severity and diagnosis
Authors: Thiago Santos, Harish Kamath, Christopher R. McAdams, Mary S. Newell, Marina Mosunjac, Gabriela Oprea-Ilies, Geoffrey Smith, Constance Lehman, Judy Gichoya, Imon Banerjee, Hari Trivedi
Date: 2023-11-02 00:00:00

Automated classification of cancer pathology reports can extract information from unstructured reports and categorize each report into structured diagnosis and severity categories. Thus, such system can reduce the burden for populating tumor registries, help registration for clinical trial as well as developing large dataset for deep learning model development using true pathologic ground truth. However, the content of breast pathology reports can be difficult for categorize due to the high linguistic variability in content and wide variety of potential diagnoses >50. Existing NLP models are primarily focused on developing classifier for primary breast cancer types (e.g. IDC, DCIS, ILC) and tumor characteristics, and ignore the rare diagnosis of cancer subtypes. We then developed a hierarchical hybrid transformer-based pipeline (59 labels) - Hierarchical Classification System for Breast Cancer Specimen Report (HCSBC), which utilizes the potential of the transformer context-preserving NLP technique and compared our model to several state of the art ML and DL models. We trained the model on the EUH data and evaluated our model's performance on two external datasets - MGH and Mayo Clinic. We publicly release the code and a live application under Huggingface spaces repository
What Makes Pre-Trained Visual Representations Successful for Robust Manipulation?
Authors: Kaylee Burns, Zach Witzel, Jubayer Ibn Hamid, Tianhe Yu, Chelsea Finn, Karol Hausman
Date: 2023-11-03 00:00:00
Tasks: Out-of-Distribution Generalization, Transfer Learning

Inspired by the success of transfer learning in computer vision, roboticists have investigated visual pre-training as a means to improve the learning efficiency and generalization ability of policies learned from pixels. To that end, past work has favored large object interaction datasets, such as first-person videos of humans completing diverse tasks, in pursuit of manipulation-relevant features. Although this approach improves the efficiency of policy learning, it remains unclear how reliable these representations are in the presence of distribution shifts that arise commonly in robotic applications. Surprisingly, we find that visual representations designed for manipulation and control tasks do not necessarily generalize under subtle changes in lighting and scene texture or the introduction of distractor objects. To understand what properties do lead to robust representations, we compare the performance of 15 pre-trained vision models under different visual appearances. We find that emergent segmentation ability is a strong predictor of out-of-distribution generalization among ViT models. The rank order induced by this metric is more predictive than metrics that have previously guided generalization research within computer vision and machine learning, such as downstream ImageNet accuracy, in-domain accuracy, or shape-bias as evaluated by cue-conflict performance. We test this finding extensively on a suite of distribution shifts in ten tasks across two simulated manipulation environments. On the ALOHA setup, segmentation score predicts real-world performance after offline training with 50 demonstrations.
Prediction of Crash Injury Severity in Florida's Interstate-95
Authors: B M Tazbiul Hassan Anik, Md Mobasshir Rashid, Md Jamil Ahsan
Date: 2023-12-16 00:00:00
Tasks: Crash injury severity, feature selection, Prediction

Drivers can sustain serious injuries in traffic accidents. In this study, traffic crashes on Florida's Interstate-95 from 2016 to 2021 were gathered, and several classification methods were used to estimate the severity of driver injuries. In the feature selection method, logistic regression was applied. To compare model performances, various model assessment matrices such as accuracy, recall, and area under curve (AUC) were developed. The Adaboost algorithm outperformed the others in terms of recall and AUC. SHAP values were also generated to explain the classification model's results. This analytical study can be used to examine factors that contribute to the severity of driver injuries in crashes.
Bird Movement Prediction Using Long Short-Term Memory Networks to Prevent Bird Strikes with Low Altitude Aircraft
Authors: Elaheh Sabziyan Varnousfaderani, Syed A. M. Shihab
Date: 2023-12-17 00:00:00

The number of collisions between aircraft and birds in the airspace has been increasing at an alarming rate over the past decade due to increasing bird population, air traffic and usage of quieter aircraft. Bird strikes with aircraft are anticipated to increase dramatically when emerging Advanced Air Mobility aircraft start operating in the low altitude airspace where probability of bird strikes is the highest. Not only do such bird strikes can result in human and bird fatalities, but they also cost the aviation industry millions of dollars in damages to aircraft annually. To better understand the causes and effects of bird strikes, research to date has mainly focused on analyzing factors which increase the probability of bird strikes, identifying high risk birds in different locations, predicting the future number of bird strike incidents, and estimating cost of bird strike damages. However, research on bird movement prediction for use in flight planning algorithms to minimize the probability of bird strikes is very limited. To address this gap in research, we implement four different types of Long Short-Term Memory (LSTM) models to predict bird movement latitudes and longitudes. A publicly available data set on the movement of pigeons is utilized to train the models and evaluate their performances. Using the bird flight track predictions, aircraft departures from Cleveland Hopkins airport are simulated to be delayed by varying amounts to avoid potential bird strikes with aircraft during takeoff. Results demonstrate that the LSTM models can predict bird movement with high accuracy, achieving a Mean Absolute Error of less than 100 meters, outperforming linear and nonlinear regression models. Our findings indicate that incorporating bird movement prediction into flight planning can be highly beneficial.
Software Effort Estimation with Ridge Regression and Evolutionary Attribute Selection
Authors: Efi Papatheocharous, Harris Papadopoulos, Andreas S. Andreou
Date: 2010-12-28 00:00:00
Tasks: Attribute, regression

Software cost estimation is one of the prerequisite managerial activities carried out at the software development initiation stages and also repeated throughout the whole software life-cycle so that amendments to the total cost are made. In software cost estimation typically, a selection of project attributes is employed to produce effort estimations of the expected human resources to deliver a software product. However, choosing the appropriate project cost drivers in each case requires a lot of experience and knowledge on behalf of the project manager which can only be obtained through years of software engineering practice. A number of studies indicate that popular methods applied in the literature for software cost estimation, such as linear regression, are not robust enough and do not yield accurate predictions. Recently the dual variables Ridge Regression (RR) technique has been used for effort estimation yielding promising results. In this work we show that results may be further improved if an AI method is used to automatically select appropriate project cost drivers (inputs) for the technique. We propose a hybrid approach combining RR with a Genetic Algorithm, the latter evolving the subset of attributes for approximating effort more accurately. The proposed hybrid cost model has been applied on a widely known high-dimensional dataset of software project samples and the results obtained show that accuracy may be increased if redundant attributes are eliminated.
Feature Subset Selection for Software Cost Modelling and Estimation
Authors: Efi Papatheocharous, Harris Papadopoulos, Andreas S. Andreou
Date: 2012-10-03 00:00:00
Tasks: feature selection

Feature selection has been recently used in the area of software engineering for improving the accuracy and robustness of software cost models. The idea behind selecting the most informative subset of features from a pool of available cost drivers stems from the hypothesis that reducing the dimensionality of datasets will significantly minimise the complexity and time required to reach to an estimation using a particular modelling technique. This work investigates the appropriateness of attributes, obtained from empirical project databases and aims to reduce the cost drivers used while preserving performance. Finding suitable subset selections that may cater improved predictions may be considered as a pre-processing step of a particular technique employed for cost estimation (filter or wrapper) or an internal (embedded) step to minimise the fitting error. This paper compares nine relatively popular feature selection methods and uses the empirical values of selected attributes recorded in the ISBSG and Desharnais datasets to estimate software development effort.
LayerDoc: Layer-wise Extraction of Spatial Hierarchical Structure in Visually-Rich Documents
Authors: Puneet Mathur, Rajiv Jain, Ashutosh Mehra, Jiuxiang Gu, Franck Dernoncourt, Anandhavelu N, Quan Tran, Verena Kaynig-Fittkau, Ani Nenkova, Dinesh Manocha, Vlad I. Morariu
Date: 2023-01-07 00:00:00
Proceeding: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023 1
Tasks: Reading Order Detection

Digital documents often contain images and scanned text. Parsing such visually-rich documents is a core task for work-flow automation, but it remains challenging since most documents do not encode explicit layout information, e.g., how characters and words are grouped into boxes and ordered into larger semantic entities. Current state-of-the-art layout extraction methods are challenged by such documents as they rely on word sequences to have correct reading order and do not exploit their hierarchical structure. We propose LayerDoc, an approach that uses visual features, textual semantics, and spatial coordinates along with constraint inference to extract the hierarchical layout structure of documents in a bottom-up layer-wise fashion. LayerDoc recursively groups smaller regions into larger semantic elements in 2D to infer complex nested hierarchies. Experiments show that our approach outperforms competitive baselines by 10-15% on three diverse datasets of forms and mobile app screen layouts for the tasks of spatial region classification, higher-order group identification, layout hierarchy extraction, reading order detection, and word grouping.
Deep transfer learning for visual analysis and attribution of paintings by Raphael
Authors: Hassan Ugail, David G. Stork, Howell Edwards, Steven C. Seward, Christopher Brooke
Date: 2023-12-20 00:00:00
Proceeding: Heritage Science 2023 12
Tasks: Edge Detection, Transfer Learning

Visual analysis and authentication of artworks are challenging tasks central to art history and criticism. This preliminary study presents a computational tool for scholars examining and authenticating a restricted class of paintings, with a specific focus on the paintings of Raffaello Sanzio da Urbino, more popularly known as Raphael. We applied transfer learning to the ResNet50 deep neural network for feature extraction and used a support vector machine (SVM) binary classifier in support of authentication. Edge detection and analysis algorithms, considered to be crucial for capturing the essence of Raphael’s artistic style, including the brushwork signatures, were also integrated and are used as an authentication tool. The machine learning approach we have developed demonstrates an accuracy of 98% in image-based classification tasks during validation using a test set of well known and authentic paintings by Raphael. Of course, a full authentication protocol relies on provenance, history, material studies, iconography, studies of a work’s condition, and more. Our work, then, contributes to just a portion of a full authentication protocol. Our findings suggest that machine learning methods, properly employed by experts aware of context, may enhance and expand traditional visual analysis for problems in art authentication.