pwc-archive/papers-with-abstracts huggingface dataset:
Molecular Communication for Quorum Sensing Inspired Cooperative Drug Delivery
Authors: Yuting Fang, Stuart T. Johnston, Matt Faria, Xinyu Huang, Andrew W. Eckford, Jamie Evans
Date: 2023-02-15 00:00:00

A cooperative drug delivery system is proposed, where quorum sensing (QS), a density-dependent bacterial behavior coordination mechanism, is employed by synthetic bacterium-based nanomachines (B-NMs) for controllable drug delivery. In our proposed system, drug delivery is only triggered when there are enough QS molecules, which in turn only happens when there are enough B-NMs. This makes the proposed system can be used to achieve a high release rate of drug molecules from a high number of B-NMs when the population density of B-NMs may not be known. Analytical expressions for i) the expected activation probability of the B-NM due to randomly-distributed B-NMs and ii) the expected aggregate absorption rate of drug molecules due to randomly-distributed QS activated B-NMs are derived. Analytical results are verified by particle-based simulations. The derived results can help to predict and control the impact of environmental factors (e.g. diffusion coefficient and degradation rate) on the absorption rate of drug molecules since rigorous diffusion-based molecular channels are considered. Our results show that the activation probability at the B-NM increases as this B-NM is located closer to the center of the B-NM population and the aggregate absorption rate of the drug molecules non-linearly increases as the population density increases.
Ruin probability for the quota share model with~phase-type distributed claims
Authors: Krzysztof Burnecki, Zbigniew Palmowski, Marek Teuerle, Aleksandra Wilkowska
Date: 2023-03-14 00:00:00

In this paper, we generalise the results presented in the literature for the ruin probability for the insurer--reinsurer model under a pro-rata reinsurance contract. We consider claim amounts that are described by a phase-type distribution that includes exponential, mixture of exponential, Erlang, and mixture of Erlang distributions. We derive the ruin probability formulas with the use of change-of-measure technique and present important special cases. We illustrate the usefulness of the introduced model by fitting it to the real-world loss data. With the use of statistical tests and graphical tools, we show that the mixture of Erlangs is well-fitted to the data and is superior to other considered distributions. This justifies the fact that the presented results can be useful in the context of risk assessment of co-operating insurance companies.
Victoria Amazonica Optimization (VAO): An Algorithm Inspired by the Giant Water Lily Plant
Authors: Seyed Muhammad Hossein Mousavi
Date: 2023-01-22 00:00:00
Tasks: feature selection, Image Segmentation, Model Optimization, Quantization, regression, Scheduling, Semantic Segmentation

The Victoria Amazonica plant, often known as the Giant Water Lily, has the largest floating spherical leaf in the world, with a maximum leaf diameter of 3 meters. It spreads its leaves by the force of its spines and creates a large shadow underneath, killing any plants that require sunlight. These water tyrants use their formidable spines to compel each other to the surface and increase their strength to grab more space from the surface. As they spread throughout the pond or basin, with the earliest-growing leaves having more room to grow, each leaf gains a unique size. Its flowers are transsexual and when they bloom, Cyclocephala beetles are responsible for the pollination process, being attracted to the scent of the female flower. After entering the flower, the beetle becomes covered with pollen and transfers it to another flower for fertilization. After the beetle leaves, the flower turns into a male and changes color from white to pink. The male flower dies and sinks into the water, releasing its seed to help create a new generation. In this paper, the mathematical life cycle of this magnificent plant is introduced, and each leaf and blossom are treated as a single entity. The proposed bio-inspired algorithm is tested with 24 benchmark optimization test functions, such as Ackley, and compared to ten other famous algorithms, including the Genetic Algorithm. The proposed algorithm is tested on 10 optimization problems: Minimum Spanning Tree, Hub Location Allocation, Quadratic Assignment, Clustering, Feature Selection, Regression, Economic Dispatching, Parallel Machine Scheduling, Color Quantization, and Image Segmentation and compared to traditional and bio-inspired algorithms. Overall, the performance of the algorithm in all tasks is satisfactory.
Solar Power Prediction Using Machine Learning
Authors: E. Subramanian, M. Mithun Karthik, G Prem Krishna, D. Vaisnav Prasath, V. Sukesh Kumar
Date: 2023-03-11 00:00:00
Tasks: feature selection, Missing Values, Model Selection, Prediction

This paper presents a machine learning-based approach for predicting solar power generation with high accuracy using a 99% AUC (Area Under the Curve) metric. The approach includes data collection, pre-processing, feature selection, model selection, training, evaluation, and deployment. High-quality data from multiple sources, including weather data, solar irradiance data, and historical solar power generation data, are collected and pre-processed to remove outliers, handle missing values, and normalize the data. Relevant features such as temperature, humidity, wind speed, and solar irradiance are selected for model training. Support Vector Machines (SVM), Random Forest, and Gradient Boosting are used as machine learning algorithms to produce accurate predictions. The models are trained on a large dataset of historical solar power generation data and other relevant features. The performance of the models is evaluated using AUC and other metrics such as precision, recall, and F1-score. The trained machine learning models are then deployed in a production environment, where they can be used to make real-time predictions about solar power generation. The results show that the proposed approach achieves a 99% AUC for solar power generation prediction, which can help energy companies better manage their solar power systems, reduce costs, and improve energy efficiency.
Human-Inspired Framework to Accelerate Reinforcement Learning
Authors: Ali Beikmohammadi, Sindri Magnússon
Date: 2023-02-28 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Reinforcement learning (RL) is crucial for data science decision-making but suffers from sample inefficiency, particularly in real-world scenarios with costly physical interactions. This paper introduces a novel human-inspired framework to enhance RL algorithm sample efficiency. It achieves this by initially exposing the learning agent to simpler tasks that progressively increase in complexity, ultimately leading to the main task. This method requires no pre-training and involves learning simpler tasks for just one iteration. The resulting knowledge can facilitate various transfer learning approaches, such as value and policy transfer, without increasing computational complexity. It can be applied across different goals, environments, and RL algorithms, including value-based, policy-based, tabular, and deep RL methods. Experimental evaluations demonstrate the framework's effectiveness in enhancing sample efficiency, especially in challenging main tasks, demonstrated through both a simple Random Walk and more complex optimal control problems with constraints.
Style Feature Extraction Using Contrastive Conditioned Variational Autoencoders with Mutual Information Constraints
Authors: Suguru Yasutomi, Toshihisa Tanaka
Date: 2023-02-03 00:00:00
Tasks: Contrastive Learning, Data Augmentation

Extracting fine-grained features such as styles from unlabeled data is crucial for data analysis. Unsupervised methods such as variational autoencoders (VAEs) can extract styles that are usually mixed with other features. Conditional VAEs (CVAEs) can isolate styles using class labels; however, there are no established methods to extract only styles using unlabeled data. In this paper, we propose a CVAE-based method that extracts style features using only unlabeled data. The proposed model consists of a contrastive learning (CL) part that extracts style-independent features and a CVAE part that extracts style features. The CL model learns representations independent of data augmentation, which can be viewed as a perturbation in styles, in a self-supervised manner. Considering the style-independent features from the pretrained CL model as a condition, the CVAE learns to extract only styles. Additionally, we introduce a constraint based on mutual information between the CL and VAE features to prevent the CVAE from ignoring the condition. Experiments conducted using two simple datasets, MNIST and an original dataset based on Google Fonts, demonstrate that the proposed method can efficiently extract style features. Further experiments using real-world natural image datasets were also conducted to illustrate the method's extendability.
Optimizing Quantum Federated Learning Based on Federated Quantum Natural Gradient Descent
Authors: Jun Qi, Xiao-Lei Zhang, Javier Tejedor
Date: 2023-02-27 00:00:00
Tasks: Federated Learning

Quantum federated learning (QFL) is a quantum extension of the classical federated learning model across multiple local quantum devices. An efficient optimization algorithm is always expected to minimize the communication overhead among different quantum participants. In this work, we propose an efficient optimization algorithm, namely federated quantum natural gradient descent (FQNGD), and further, apply it to a QFL framework that is composed of a variational quantum circuit (VQC)-based quantum neural networks (QNN). Compared with stochastic gradient descent methods like Adam and Adagrad, the FQNGD algorithm admits much fewer training iterations for the QFL to get converged. Moreover, it can significantly reduce the total communication overhead among local quantum devices. Our experiments on a handwritten digit classification dataset justify the effectiveness of the FQNGD for the QFL framework in terms of a faster convergence rate on the training set and higher accuracy on the test set.
Image Guidance for Robot-Assisted Ankle Fracture Repair
Authors: Asef Islam, Anthony Wu, Jay Mandavilli, Wojtek Zbijewski, Jeff Siewerdsen
Date: 2023-01-31 00:00:00

This project concerns developing and validating an image guidance framework for application to a robotic-assisted fibular reduction in ankle fracture surgery. The aim is to produce and demonstrate proper functioning of software for automatic determination of directions for fibular repositioning with the ultimate goal of application to a robotic reduction procedure that can reduce the time and complexity of the procedure as well as provide the benefits of reduced error in ideal final fibular position, improved syndesmosis restoration and reduced incidence of post-traumatic osteoarthritis. The focus of this product will be developing and testing the image guidance software, from the input of preoperative images through the steps of automated segmentation and registration until the output of a final transformation that can be used as instructions to a robot on how to reposition the fibula, but will not involve developing or implementing the hardware of the robot itself.
DeSmoke-LAP: improved unpaired image-to-image translation for desmoking in laparoscopic surgery
Authors: Yirou Pan, Sophia Bano, Francisco Vasconcelos, Hyun Park, Taikyeong Ted. Jeong, Danail Stoyanov
Date: 2022-03-30 00:00:00
Proceeding: International Journal of Computer Assisted Radiology and Surgery 2022 3
Tasks: Generative Adversarial Network, Image-to-Image Translation

Robotic-assisted laparoscopic surgery has become the trend in medicine thanks to its convenience and lower risk of infection against traditional open surgery. However, the visibility during these procedures may severely deteriorate due to electrocauterisation which generates smoke in the operating cavity. This decreased visibility hinders the procedural time and surgical performance. Recent deep learning-based techniques have shown the potential for smoke and glare removal, but few targets laparoscopic videos. We propose DeSmoke-LAP, a new method for removing smoke from real robotic laparoscopic hysterectomy videos. The proposed method is based on the unpaired image-to-image cycle-consistent generative adversarial network in which two novel loss functions, namely, inter-channel discrepancies and dark channel prior, are integrated to facilitate smoke removal while maintaining the true semantics and illumination of the scene. DeSmoke-LAP is compared with several state-of-the-art desmoking methods qualitatively and quantitatively using referenceless image quality metrics on 10 laparoscopic hysterectomy videos through 5-fold cross-validation. DeSmoke-LAP outperformed existing methods and generated smoke-free images without applying ground truths (paired images) and atmospheric scattering model. This shows distinctive achievement in dehazing in surgery, even in scenarios with partial inhomogenenous smoke. Our code and hysterectomy dataset will be made publicly available at https://www.ucl.ac.uk/interventional-surgical-sciences/weiss-open-research/weiss-open-data-server/desmoke-lap.
3D-SiamMask: Vision-Based Multi-Rotor Aerial-Vehicle Tracking for a Moving Object
Authors: Mohamad Al Mdfaa, Geesara Kulathunga, Alexandr Klimchik
Date: 2022-11-14 00:00:00
Proceeding: Remote Sensing 2022 11
Tasks: 3D Single Object Tracking, Depth Estimation, GPU, Object, Object Tracking, Visual Object Tracking

This paper aims to develop a multi-rotor-based visual tracker for a specified moving object. Visual object-tracking algorithms for multi-rotors are challenging due to multiple issues such as occlusion, quick camera motion, and out-of-view scenarios. Hence, algorithmic changes are required for dealing with images or video sequences obtained by multi-rotors. Therefore, we propose two approaches: a generic object tracker and a class-specific tracker. Both tracking settings require the object bounding box to be selected in the first frame. As part of the later steps, the object tracker uses the updated template set and the calibrated RGBD sensor data as inputs to track the target object using a Siamese network and a machine-learning model for depth estimation. The class-specific tracker is quite similar to the generic object tracker but has an additional auxiliary object classifier. The experimental study and validation were carried out in a robot simulation environment. The simulation environment was designed to serve multiple case scenarios using Gazebo. According to the experiment results, the class-specific object tracker performed better than the generic object tracker in terms of stability and accuracy. Experiments show that the proposed generic tracker achieves promising results on three challenging datasets. Our tracker runs at approximately 36 fps on GPU.
Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques
Authors: Monika Simjanoska, Martin Gjoreski, Matjaž Gams, Ana Madevska Bogdanova
Date: 2018-04-18 00:00:00
Proceeding: Sensors 2018 4
Tasks: Blood pressure estimation

Background: Blood pressure (BP) measurements have been used widely in clinical and private environments. Recently, the use of ECG monitors has proliferated; however, they are not enabled with BP estimation. We have developed a method for BP estimation using only electrocardiogram (ECG) signals. Methods: Raw ECG data are filtered and segmented, and, following this, a complexity analysis is performed for feature extraction. Then, a machine-learning method is applied, combining a stacking-based classification module and a regression module for building systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP) predictive models. In addition, the method allows a probability distribution-based calibration to adapt the models to a particular user. Results: Using ECG recordings from 51 different subjects, 3129 30-s ECG segments are constructed, and seven features are extracted. Using a train-validation-test evaluation, the method achieves a mean absolute error (MAE) of 8.64 mmHg for SBP, 18.20 mmHg for DBP, and 13.52 mmHg for the MAP prediction. When models are calibrated, the MAE decreases to 7.72 mmHg for SBP, 9.45 mmHg for DBP and 8.13 mmHg for MAP. Conclusion: The experimental results indicate that, when a probability distribution-based calibration is used, the proposed method can achieve results close to those of a certified medical device for BP estimation.
A Multifactor Analysis Model for Stock Market Prediction
Authors: Akash Deep
Date: 2023-03-05 00:00:00
Proceeding: International Journal of Computer Science and Telecommunications 2023 3
Tasks: model, Prediction, Sentiment Analysis, Stock Market Prediction, Stock Price Prediction

Stock Market predictions have historically been a problem tackled by different singular approaches even though markets are influenced by many different factors. This paper presents a novel multi-factor analysis model for stock price prediction that combines Technical analysis, Fundamental analysis, Machine learning, and, Sentiment Analysis (TFMS Analysis). The proposed model leverages Random Forest Regressor (RFR) to predict a stock price and long short-term memory(LSTM) approach to predict a multiplier. Sentiment analysis is then used to capture the impact of various factors on stock prices, including market trends, economic indicators, and public opinion. The results of the model are compared to traditional prediction models using historical stock data, and it is shown that the proposed model provides improved accuracy in predicting future stock prices. The proposed model represents a significant step forward in stock price prediction, providing a more comprehensive and effective approach to predicting stock prices based on multiple factors.
MRGAN360: Multi-stage Recurrent Generative Adversarial Network for 360 Degree Image Saliency Prediction
Authors: Pan Gao, Xinlang Chen, Rong Quan, Wei Xiang
Date: 2023-03-15 00:00:00
Tasks: Generative Adversarial Network, Prediction, Saliency Prediction

Thanks to the ability of providing an immersive and interactive experience, the uptake of 360 degree image content has been rapidly growing in consumer and industrial applications. Compared to planar 2D images, saliency prediction for 360 degree images is more challenging due to their high resolutions and spherical viewing ranges. Currently, most high-performance saliency prediction models for omnidirectional images (ODIs) rely on deeper or broader convolutional neural networks (CNNs), which benefit from CNNs' superior feature representation capabilities while suffering from their high computational costs. In this paper, inspired by the human visual cognitive process, i.e., human being's perception of a visual scene is always accomplished by multiple stages of analysis, we propose a novel multi-stage recurrent generative adversarial networks for ODIs dubbed MRGAN360, to predict the saliency maps stage by stage. At each stage, the prediction model takes as input the original image and the output of the previous stage and outputs a more accurate saliency map. We employ a recurrent neural network among adjacent prediction stages to model their correlations, and exploit a discriminator at the end of each stage to supervise the output saliency map. In addition, we share the weights among all the stages to obtain a lightweight architecture that is computationally cheap. Extensive experiments are conducted to demonstrate that our proposed model outperforms the state-of-the-art model in terms of both prediction accuracy and model size.
Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph Construction
Authors: Bencheng Liao, Shaoyu Chen, Bo Jiang, Tianheng Cheng, Qian Zhang, Wenyu Liu, Chang Huang, Xinggang Wang
Date: 2023-03-15 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, graph construction, Trajectory Planning

Online lane graph construction is a promising but challenging task in autonomous driving. Previous methods usually model the lane graph at the pixel or piece level, and recover the lane graph by pixel-wise or piece-wise connection, which breaks down the continuity of the lane and results in suboptimal performance. Human drivers focus on and drive along the continuous and complete paths instead of considering lane pieces. Autonomous vehicles also require path-specific guidance from lane graph for trajectory planning. We argue that the path, which indicates the traffic flow, is the primitive of the lane graph. Motivated by this, we propose to model the lane graph in a novel path-wise manner, which well preserves the continuity of the lane and encodes traffic information for planning. We present a path-based online lane graph construction method, termed LaneGAP, which end-to-end learns the path and recovers the lane graph via a Path2Graph algorithm. We qualitatively and quantitatively demonstrate the superior accuracy and efficiency of LaneGAP over conventional pixel-based and piece-based methods on the challenging nuScenes and Argoverse2 datasets under controllable and fair conditions. Compared to the recent state-of-the-art piece-wise method TopoNet on the OpenLane-V2 dataset, LaneGAP still outperforms by 1.6 mIoU, further validating the effectiveness of path-wise modeling. Abundant visualizations in the supplementary material show LaneGAP can cope with diverse traffic conditions. Code is released at \url{https://github.com/hustvl/LaneGAP}.
Understanding Post-hoc Explainers: The Case of Anchors
Authors: Gianluigi Lopardo, Frederic Precioso, Damien Garreau
Date: 2023-03-15 00:00:00

In many scenarios, the interpretability of machine learning models is a highly required but difficult task. To explain the individual predictions of such models, local model-agnostic approaches have been proposed. However, the process generating the explanations can be, for a user, as mysterious as the prediction to be explained. Furthermore, interpretability methods frequently lack theoretical guarantees, and their behavior on simple models is frequently unknown. While it is difficult, if not impossible, to ensure that an explainer behaves as expected on a cutting-edge model, we can at least ensure that everything works on simple, already interpretable models. In this paper, we present a theoretical analysis of Anchors (Ribeiro et al., 2018): a popular rule-based interpretability method that highlights a small set of words to explain a text classifier's decision. After formalizing its algorithm and providing useful insights, we demonstrate mathematically that Anchors produces meaningful results when used with linear text classifiers on top of a TF-IDF vectorization. We believe that our analysis framework can aid in the development of new explainability methods based on solid theoretical foundations.