pwc-archive/papers-with-abstracts huggingface dataset:
The GeoLifeCLEF 2023 Dataset to evaluate plant species distribution models at high spatial resolution across Europe
Authors: Christophe Botella, Benjamin Deneu, Diego Marcos, Maximilien Servajean, Joaquim Estopinan, Théo Larcher, César Leblanc, Pierre Bonnet, Alexis Joly
Date: 2023-08-07 00:00:00

The difficulty to measure or predict species community composition at fine spatio-temporal resolution and over large spatial scales severely hampers our ability to understand species assemblages and take appropriate conservation measures. Despite the progress in species distribution modeling (SDM) over the past decades, SDM have just begun to integrate high resolution remote sensing data and their predictions are still entailed by many biases due to heterogeneity of the available biodiversity observations, most often opportunistic presence only data. We designed a European scale dataset covering around ten thousand plant species to calibrate and evaluate SDM predictions of species composition in space and time at high spatial resolution (~ten meters), and their spatial transferability. For model training, we extracted and harmonized five million heterogeneous presence-only records from selected GBIF datasets and 6 thousand exhaustive presence-absence surveys both sampled during 2017-2021. We associated species observations to diverse environmental rasters classically used in SDMs, as well as to 10 m resolution RGB and Near-Infra-Red satellite images and 20 years-time series of climatic variables and satellite point values. The evaluation dataset is based on 22 thousand standardized presence-absence surveys separated from the training set with a spatial block hold out procedure. The GeoLifeCLEF 2023 dataset is open access and the first benchmark for researchers aiming to improve the prediction of plant species composition at a very fine spatial grain and at continental scale. It is a space to explore new ways of combining massive and diverse species observations and environmental information at various scales. Innovative AI-based approaches, in particular, should be among the most interesting methods to experiment with on the GeoLifeCLEF 2023 dataset.
UQSA -- An R-Package for Uncertainty Quantification and Sensitivity Analysis for Biochemical Reaction Network Models
Authors: Andrei Kramer, Federica Milinanni, Jeanette Hellgren Kotaleski, Pierre Nyquist, Alexandra Jauhiainen, Olivia Eriksson
Date: 2023-08-10 00:00:00
Tasks: Sensitivity, Uncertainty Quantification

Biochemical reaction models describing subcellular processes generally come with a large uncertainty. To be able to account for this during the modeling process, we have developed the R-package UQSA, performing uncertainty quantification and sensitivity analysis in an integrated fashion. UQSA is designed for fast sampling of complicated multi-dimensional parameter distributions, using efficient Markov chain Monte Carlo (MCMC) sampling techniques and Vine-copulas to model complicated joint distributions. We perform MCMC sampling both from stochastic and deterministic models, in either likelihood-free or likelihood-based settings. In the likelihood-free case, we use Approximate Bayesian Computation (ABC), while for likelihood-based sampling we provide different algorithms, including the fast geometry-informed algorithm SMMALA (Simplified Manifold Metropolis-Adjusted Langevin Algorithm). The uncertainty quantification can be followed by a variance decomposition-based global sensitivity analysis. We are aiming for biochemical models, but UQSA can be used for any type of reaction networks. The use of Vine-copulas allows us to describe, evaluate, and sample from complicated parameter distributions, as well as adding new datasets in a sequential manner without redoing the previous parameter fit. The code is written in R, with C as a backend to improve speed. We use the SBtab table format for Systems Biology projects for the model description as well as the experimental data. An event system allows the user to model complicated transient input, common within, e.g., neuroscience. UQSA has an extensive documentation with several examples describing different types of models and data. The code has been tested on up to 2000 cores on several nodes on a computing cluster, but we also include smaller examples that can be run on a laptop. Source code: https://github.com/icpm-kth/uqsa
EasyMergeR: an interactive Shiny application to manipulate multiple XLSX files of multiple sheets
Authors: Ziyu Zhu, Ximing Xu
Date: 2023-08-08 00:00:00

The integration of sequencing data with clinical information is a widely accepted strategy in bioinformatics and health informatics. Despite advanced databases and sophisticated tools for processing omics data, challenges remain in handling the raw clinical data (typically in XLSX format with multiple sheets inside), either exported from health information system (HIS) or manually collected by investigators. This is particularly difficult for time-constrained medical staff with little or no programming background, and it is typically the first bottleneck in many clinical-oriented studies. To fill this gap, we developed EasyMergeR, a simple, user-friendly, code-free R Shiny application that allows interactive manipulation of multiple XLSX files with multiple sheets and provides basic data manipulation capabilities based on the tidyverse and other handy R packages.
Advanced-Glycation Endproducts: How cross-linking properties affect the collagen fibril behavior
Authors: Julia Kamml, Claire Acevedo, David Kammer
Date: 2023-08-10 00:00:00
Tasks: Friction

Advanced-Glycation-Endproducts (AGEs) are known to be a major cause of impaired tissue material properties. In collagen fibrils, the main building component of human tissue, these AGEs appear as fibrillar cross-links. When AGEs accumulate in collagen fibrils, a process often caused by diabetes and aging, the mechanical properties of the collagen fibril are altered. However, current knowledge about the mechanical properties of different types of AGEs, and their quantity in collagen fibrils is limited owing to the scarcity of available experimental data. Consequently, the precise relationship between the nano-scale cross-link properties, their density in collagen fibrils, and the mechanical properties of the collagen fibrils at larger scales remains poorly understood. In our study, we use coarse-grained molecular dynamics simulations and perform destructive tensile tests on collagen fibrils to evaluate the effect of different cross-link densities and their mechanical properties on collagen fibril deformation and fracture behavior. We observe that the collagen fibril stiffens at high strain levels when either the AGEs density or the loading energy capacity of AGEs are increased. We demonstrate that this stiffening is caused by a mechanism that favors energy absorption via stretching rather than inter-molecular sliding. Hence, in cross-linked collagen fibrils, the absorbed energy is stored rather than dissipated through friction, resulting in brittle fracture upon fibrillar failure. Further, by varying multiple AGEs nano-scale parameters, we show that the AGEs loading energy capacity is, aside from their density in the fibril, the unique factor determining the effect of different types of AGEs on the mechanical behavior of collagen fibrils. Our results show that knowing AGEs properties is crucial for understanding of the origin of impaired tissue behavior.
SmartDCA superiority
Authors: Calvet, Emmanuel, Herranz-Celotti, Luca, Valimamode, Karim
Date: 2023-08-09 00:00:00
Tasks: Mathematical Proofs

Dollar-Cost Averaging (DCA) is a widely used technique to mitigate volatility in long-term investments of appreciating assets. However, the inefficiency of DCA arises from fixing the investment amount regardless of market conditions. In this paper, we present a more efficient approach that we name SmartDCA, which consists in adjusting asset purchases based on price levels. The simplicity of SmartDCA allows for rigorous mathematical analysis, enabling us to establish its superiority through the application of Cauchy-Schwartz inequality and Lehmer means. We further extend our analysis to what we refer to as $\rho$-SmartDCA, where the invested amount is raised to the power of $\rho$. We demonstrate that higher values of $\rho$ lead to enhanced performance. However, this approach may result in unbounded investments. To address this concern, we introduce a bounded version of SmartDCA, taking advantage of two novel mean definitions that we name quasi-Lehmer means. The bounded SmartDCA is specifically designed to retain its superiority to DCA. To support our claims, we provide rigorous mathematical proofs and conduct numerical analyses across various scenarios. The performance gain of different SmartDCA alternatives is compared against DCA using data from S\&P500 and Bitcoin. The results consistently demonstrate that all SmartDCA variations yield higher long-term investment returns compared to DCA.
Balancing Accuracy and Training Time in Federated Learning for Violence Detection in Surveillance Videos: A Study of Neural Network Architectures
Authors: Pajon Quentin, Serre Swan, Wissocq Hugo, Rabaud Léo, Haidar Siba, Yaacoub Antoun
Date: 2023-06-29 00:00:00
Tasks: Federated Learning, Transfer Learning

This paper presents an investigation into machine learning techniques for violence detection in videos and their adaptation to a federated learning context. The study includes experiments with spatio-temporal features extracted from benchmark video datasets, comparison of different methods, and proposal of a modified version of the "Flow-Gated" architecture called "Diff-Gated." Additionally, various machine learning techniques, including super-convergence and transfer learning, are explored, and a method for adapting centralized datasets to a federated learning context is developed. The research achieves better accuracy results compared to state-of-the-art models by training the best violence detection model in a federated learning context.
JutePestDetect: An Intelligent Approach for Jute Pest Identification Using Fine-Tuned Transfer Learning
Authors: Md. Simul Hasan Talukder, Mohammad Raziuddin Chowdhury, Md Sakib Ullah Sourav, Abdullah Al Rakin, Shabbir Ahmed Shuvo, Rejwan Bin Sulaiman, Musarrat Saberin Nipun, Muntarin Islam, Mst Rumpa Islam, Md Aminul Islam, Zubaer Haque
Date: 2023-05-28 00:00:00
Tasks: Transfer Learning

In certain Asian countries, Jute is one of the primary sources of income and Gross Domestic Product (GDP) for the agricultural sector. Like many other crops, Jute is prone to pest infestations, and its identification is typically made visually in countries like Bangladesh, India, Myanmar, and China. In addition, this method is time-consuming, challenging, and somewhat imprecise, which poses a substantial financial risk. To address this issue, the study proposes a high-performing and resilient transfer learning (TL) based JutePestDetect model to identify jute pests at the early stage. Firstly, we prepared jute pest dataset containing 17 classes and around 380 photos per pest class, which were evaluated after manual and automatic pre-processing and cleaning, such as background removal and resizing. Subsequently, five prominent pre-trained models -DenseNet201, InceptionV3, MobileNetV2, VGG19, and ResNet50 were selected from a previous study to design the JutePestDetect model. Each model was revised by replacing the classification layer with a global average pooling layer and incorporating a dropout layer for regularization. To evaluate the models performance, various metrics such as precision, recall, F1 score, ROC curve, and confusion matrix were employed. These analyses provided additional insights for determining the efficacy of the models. Among them, the customized regularized DenseNet201-based proposed JutePestDetect model outperformed the others, achieving an impressive accuracy of 99%. As a result, our proposed method and strategy offer an enhanced approach to pest identification in the case of Jute, which can significantly benefit farmers worldwide.
An Improved Model for Diabetic Retinopathy Detection by using Transfer Learning and Ensemble Learning
Authors: Md. Simul Hasan Talukder, Ajay Kirshno Sarkar, Sharmin Akter, Md. Nuhi-Alamin
Date: 2023-06-03 00:00:00
Tasks: Data Augmentation, Diabetic Retinopathy Detection, Ensemble Learning, Transfer Learning

Diabetic Retinopathy (DR) is an ocular condition caused by a sustained high level of sugar in the blood, which causes the retinal capillaries to block and bleed, causing retinal tissue damage. It usually results in blindness. Early detection can help in lowering the risk of DR and its severity. The robust and accurate prediction and detection of diabetic retinopathy is a challenging task. This paper develops a machine learning model for detecting Diabetic Retinopathy that is entirely accurate. Pre-trained models such as ResNet50, InceptionV3, Xception, DenseNet121, VGG19, NASNetMobile, MobileNetV2, DensNet169, and DenseNet201 with pooling layer, dense layer, and appropriate dropout layer at the bottom of them were carried out in transfer learning (TL) approach. Data augmentation and regularization was performed to reduce overfitting. Transfer Learning model of DenseNet121, Average and weighted ensemble of DenseNet169 and DenseNet201 TL architectures contribute individually the highest accuracy of 100%, the highest precision, recall, F-1 score of 100%, 100%, and 100%, respectively.
Can Attention Be Used to Explain EHR-Based Mortality Prediction Tasks: A Case Study on Hemorrhagic Stroke
Authors: Qizhang Feng, Jiayi Yuan, Forhan Bin Emdad, Karim Hanna, Xia Hu, Zhe He
Date: 2023-08-04 00:00:00
Tasks: Feature Importance, Mortality Prediction

Stroke is a significant cause of mortality and morbidity, necessitating early predictive strategies to minimize risks. Traditional methods for evaluating patients, such as Acute Physiology and Chronic Health Evaluation (APACHE II, IV) and Simplified Acute Physiology Score III (SAPS III), have limited accuracy and interpretability. This paper proposes a novel approach: an interpretable, attention-based transformer model for early stroke mortality prediction. This model seeks to address the limitations of previous predictive models, providing both interpretability (providing clear, understandable explanations of the model) and fidelity (giving a truthful explanation of the model's dynamics from input to output). Furthermore, the study explores and compares fidelity and interpretability scores using Shapley values and attention-based scores to improve model explainability. The research objectives include designing an interpretable attention-based transformer model, evaluating its performance compared to existing models, and providing feature importance derived from the model.
DeepPatent: Large scale patent drawing recognition and retrieval
Authors: Michal Kucer, Diane Oyen, Juan Castorena
Date: 2022-01-01 00:00:00
Proceeding: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022 1
Tasks: Image Retrieval, Retrieval

We tackle the problem of analyzing and retrieving technical drawings. First, we introduce DeepPatent, a new large-scale dataset for recognition and retrieval of design patent drawings. The dataset provides more than 350,000 design patent drawings for the purpose of image retrieval. Unlike existing datasets, DeepPatent provides fine-grained image retrieval associations within the collection of drawings and does not rely on cross-domain associations for supervision. We develop a baseline deep learning model, named Patent-Net, based on best practices for training retrieval models for static images. We demonstrate the superior performance of PatentNet when trained on our fine-grained associations of DeepPatent against other deep learning approaches and classic computer vision descriptors. With the introduction of this new dataset, and benchmark algorithms, we demonstrate that the analysis and retrieval of technical drawings remains an open challenge in computer vision; and that patent drawing retrieval provides a real-world testbench to spur research.
Patent Image Retrieval Using Cross-entropy-based Metric Learning
Authors: Kotaro Higuchi, Yuma Honbu, and Keiji Yanai
Date: 2023-02-20 00:00:00
Proceeding: International Workshop on Frontiers of Computer Vision 2023 2
Tasks: Image Retrieval, Metric Learning, Retrieval, Triplet

Intellectual property work covers a wide range of areas. In particular, prior art literature searching in the patent field requires finding documents that can be used to determine novelty and inventive steps from a vast amount of past literature. 

Concerning this search practice, research and development of a drawing search technology that directly searches drawings, and essential information about inventions, has long been desired. However, patent drawings are described as black-and-white abstract drawings, and their modal characteristics are very different from those of natural images, so they have yet to be explored. 

This study achieved higher accuracy than the previous one by introducing InfoNCE and ArcFace in the DeepPatent dataset instead of the conventional Triplet. In addition, we developed an application that enables users to search for patent drawings using any images. Our architecture can be applied to patent drawings and many other modal-like drawings, such as mechanical drawings, design patents, trademarks, diagrams, and sketches.
Patent image retrieval using transformer-based deep metric learning
Authors: Kotaro Higuchi, Keiji Yanai
Date: 2023-09-01 00:00:00
Proceeding: World Patent Information 2023 9
Tasks: Image Retrieval, Metric Learning, Retrieval, Triplet

Intellectual property work covers a wide range of areas. In particular, prior art literature searching in the patent field requires finding documents that can be used to determine novelty and inventive steps from a vast amount of past literature. Concerning this search practice, research and development of a drawing search technology that directly searches drawings, and essential information about inventions, has long been desired. However, patent drawings are usually described as black-and-white abstract drawings, except in some countries, and their modal characteristics are very different from those of natural images, so they have yet to be explored. This study achieved higher accuracy than the previous ones by introducing InfoNCE and ArcFace in the DeepPatent (Kucer et al., 2022) dataset instead of the conventional Triplet. In addition, we developed an application that enables users to search for patent drawings using any images. Our architecture can be applied to patent drawings and many other modal-like drawings, such as mechanical drawings, design patents, trademarks, diagrams, and sketches.
airline itinerary modeling using machine learning
Authors: Alix Lhéritier, Michael Bocamazo, Thierry Delahaye, Rodrigo Acuna-Agos
Date: 2018-02-16 00:00:00
Proceeding: Journal of Choice Modelling 2018 2

Understanding how customers choose between different itineraries when searching for flights is very important for the travel industry. This knowledge can help travel providers, either airlines or travel agents, to better adapt their offer to market conditions and customer needs. This has a particular importance for pricing and ranking suggestions to travelers when searching for flights. This problem has been historically handled using Multinomial Logit (MNL) models. While MNL models offer the dual advantage of simplicity and readability, they lack flexibility to handle collinear attributes and correlations between alternatives. Additionally, they require expert knowledge to introduce non-linearity in the effect of alternatives’ attributes and to model individual heterogeneity. In this work, we present an alternative modeling approach based on non-parametric machine learning (ML) that is able to automatically segment the travelers and to take into account non-linear relationships within attributes of alternatives and characteristics of the decision maker. We test the models on a dataset consisting of flight searches and bookings on European markets. The experiments show our approach outperforming the standard and the latent class Multinomial Logit model in terms of accuracy and computation time, with less modeling effort.
Comparison of single image HDR reconstruction methods — the caveats of quality assessment
Authors: Param Hanji, Rafał K. Mantiuk, Gabriel Eilertsen, Saghi Hajisharif, Jonas Unger
Date: 2022-07-24 00:00:00
Proceeding: ACM SIGGRAPH Conference Proceedings 2022 7
Tasks: HDR Reconstruction

As the problem of reconstructing high dynamic range (HDR) images from a single exposure has attracted much research effort, it is essential to provide a robust protocol and clear guidelines on how to evaluate and compare new methods. In this work, we compared six recent single image HDR reconstruction (SI-HDR) methods in a subjective image quality experiment on an HDR display. We found that only two methods produced results that are, on average, more preferred than the unprocessed single exposure images. When the same methods are evaluated using image quality metrics, as typically done in papers, the metric predictions correlate poorly with subjective quality scores. The main reason is a significant tone and color difference between the reference and reconstructed HDR images. To improve the predictions of image quality metrics, we propose correcting for the inaccuracies of the estimated camera response curve before computing quality values. We further analyze, the sources of prediction noise when evaluating SI-HDR methods and demonstrate that existing metrics can reliably predict only large quality differences.
DemogPairs: Quantifying the Impact of Demographic Imbalance in Deep Face Recognition
Authors: Isabelle HUPONT; Carles FERNÁNDEZ
Date: 2019-07-11 00:00:00
Proceeding: IEEE International Conference on Automatic Face & Gesture Recognition 2019 7
Tasks: Face Recognition

Although deep face recognition has achieved impressive results in recent years, controversy has arisen regarding racial and gender bias of the models, questioning their deployment into sensitive scenarios. This work quantifies for the first time the demographic imbalance of popular public face datasets in terms of identity, gender and ethnicity. We also publicly release DemogPairs, a new validation set with 10.8K facial images and 58.3M identity verification pairs, distributed in demographically-balanced folds of Asian, Black and White females and males. A benchmark of experiments is carried out using DemogPairs over state-of-the-art deep face recognition models (SphereFace, FaceNet and ResNet50), in order to analyze their cross-demographic behavior. Experimental results demonstrate that studied models suffer from a very structured and damaging demographic bias. Our experiments shine a light on novel testing protocols to appropriately validate the generalization capabilities of face recognition models.