pwc-archive/papers-with-abstracts huggingface dataset:
Agile UAV landing control on moving ship in adverse conditions
Authors: James Mordaunt, Xinhua Wang
Date: 2024-11-08 00:00:00
Tasks: Point Tracking

This paper presents an agile Unmanned Aerial Vehicle (UAV) landing control by considering the effect of ship's oscillations and moving, and also disturbance (i.e., crosswind) is considered. The presented control system can make the quadrotor UAV autonomously land whilst overcoming these adverse conditions, and the addition of a rudder beneath each propeller is designed to increase the yaw authority which is found to be lacking in heavy-lift quadrotor UAV. The PID flight control system is proposed based on reference-point tracking, allowing the UAV to follow any desired path in 3D space whilst simultaneously yawing to face any desired heading. Realistic saturation limits on actuator outputs to ensure the real-world performance of actuators. Disturbances include randomised gusting wind in 3 axes, and sensor noise on translation and rotation signals to represent noise from the GPS and accelerometer respectively. The results from the simulations demonstrate that the UAV is capable of landing on a ship which is moving with varying heading and oscillating vertically on ocean waves and has the ability to time its descent such that it meets the ship at the peak of a wave to minimise the relative velocity.
Integrated Power and Thermal Management for Enhancing Energy Efficiency and Battery Life in Connected and Automated Electric Vehicles
Authors: Dongjun Li, Qiuhao Hu, Weiran Jiang, Haoxuan Dong, Ziyou Song
Date: 2024-11-08 00:00:00
Tasks: Management, Model Predictive Control

Effective power and thermal management are essential for ensuring battery efficiency, safety, and longevity in Connected and Automated Electric Vehicles (CAEVs). However, real-time implementation is challenging due to the multi-timescale dynamics and complex trade-offs between energy consumption, battery degradation, traffic efficiency, and thermal regulation. This paper proposes a novel integrated power and thermal management strategy based on the Multi-Horizon Model Predictive Control (MH-MPC) framework to enhance energy efficiency, optimize battery temperature, ensure traffic safety and efficiency, and reduce battery degradation for CAEVs. The proposed strategy is formulated with a focus on the aging term, allowing it to more effectively manage the trade-offs between energy consumption, battery degradation, and temperature regulation. Moreover, the proposed strategy leverages multi-horizon optimization to achieve substantial improvements, reducing computation time by 7.18%, cooling energy by 14.22%, traction energy by 8.26%, battery degradation loss by over 22%, and battery degradation inconsistency by 36.57% compared to the benchmark strategy. Furthermore, sensitivity analyses of key parameters, including weighting factors, sampling time, and prediction horizons, demonstrate the robustness of the strategy and underscore its potential for practical applications in extending battery lifespan while ensuring safety and efficiency.
Infection Pressure on Fish in Cages
Authors: William Waites, Philip Gillibrand, Thomas Adams, Rek Bell, Duncan Guthrie, Tróndur Kragesteen, Crawford Revie, Meadhbh Moriarty
Date: 2024-11-08 00:00:00

We address the question of how to connect predictions by hydrodynamic models of how sea lice move in water to observable measures that count the number of lice on each fish in a cage in the water. This question is important for management and regulation of aquacultural practice that tries to maximise food production and minimise risk to the environment. We do this through a simple rule-based model of interaction between sea lice and caged fish. The model is simple: sea lice can attach and detach from a fish. The model has a novel feature, encoding what is known as a master equation producing a time-series of distributions of lice on fish that one might expect to find if a cage full of fish were placed at any given location. To demonstrate how this works, and to arrive at a rough estimate of the interaction rates, we fit a simplified version of the model with three free parameters to publicly available data about an experiment with sentinel cages in Loch Linnhe in Scotland. Our construction, coupled to the hydrodynamic models driven by surveillance data from industrial farms, quantifies the environmental impact as: what would the infection burden look like in a notional cage at any location and how does it change with time?
A chemostat model with variable dilution rate due to biofilm growth
Authors: Xiaochen Duan, Sergei S. Pilyugin
Date: 2024-11-07 00:00:00

In many real life applications, a continuous culture bioreactor may cease to function properly due to bioclogging which is typically caused by the microbial overgrowth. This is a problem that has been largely overlooked in the chemostat modeling literature, despite the fact that a number of models explicitly accounted for biofilm development inside the bioreactor. In a typical chemostat model, the physical volume of the biofilm is considered negligible when compared to the volume of the fluid. In this paper, we investigate the theoretical consequences of removing such assumption. Specifically, we formulate a novel mathematical model of a chemostat where the increase of the biofilm volume occurs at the expense of the fluid volume of the bioreactor, and as a result the corresponding dilution rate increases reciprocally. We show that our model is well-posed and describes the bioreactor that can operate in three distinct types of dynamic regimes: the washout equilibrium, the coexistence equilibrium, or a transient towards the clogged state which is reached in finite time. We analyze the multiplicity and the stability of the corresponding equilibria. In particular, we delineate the parameter combinations for which the chemostat never clogs up and those for which it clogs up in finite time. We also derive criteria for microbial persistence and extinction. Finally, we present a numerical evidence that a multistable coexistence in the chemostat with variable dilution rate is feasible.
Optimal reinsurance and investment via stochastic projected gradient method based on Malliavin calculus
Authors: Yuta Otsuki, Shotaro Yagishita
Date: 2024-11-08 00:00:00

This paper proposes a new approach using the stochastic projected gradient method and Malliavin calculus for optimal reinsurance and investment strategies. Unlike traditional methodologies, we aim to optimize static investment and reinsurance strategies by directly minimizing the ruin probability. Furthermore, we provide a convergence analysis of the stochastic projected gradient method for general constrained optimization problems whose objective function has H\"older continuous gradient. Numerical experiments show the effectiveness of our proposed method.
Use of 3D chaos game representation to quantify DNA sequence similarity with applications for hierarchical clustering
Authors: Stephanie Young, Jerome Gilles
Date: 2024-11-08 00:00:00

A 3D chaos game is shown to be a useful way for encoding DNA sequences. Since matching subsequences in DNA converge in space in 3D chaos game encoding, a DNA sequence's 3D chaos game representation can be used to compare DNA sequences without prior alignment and without truncating or padding any of the sequences. Two proposed methods inspired by shape-similarity comparison techniques show that this form of encoding can perform as well as alignment-based techniques for building phylogenetic trees. The first method uses the volume overlap of intersecting spheres and the second uses shape signatures by summarizing the coordinates, oriented angles, and oriented distances of the 3D chaos game trajectory. The methods are tested using: (1) the first exon of the beta-globin gene for 11 species, (2) mitochondrial DNA from four groups of primates, and (3) a set of synthetic DNA sequences. Simulations show that the proposed methods produce distances that reflect the number of mutation events; additionally, on average, distances resulting from deletion mutations are comparable to those produced by substitution mutations.
Relationships between the degrees of freedom in the affine Gaussian derivative model for visual receptive fields and 2-D affine image transformations, with application to covariance properties of simple cells in the primary visual cortex
Authors: Tony Lindeberg
Date: 2024-11-08 00:00:00

When observing the surface patterns of objects delimited by smooth surfaces, the projections of the surface patterns to the image domain will be subject to substantial variabilities, as induced by variabilities in the geometric viewing conditions, and as generated by either monocular or binocular imaging conditions, or by relative motions between the object and the observer over time. To first order of approximation, the image deformations of such projected surface patterns can be modelled as local linearizations in terms of local 2-D spatial affine transformations. This paper presents a theoretical analysis of relationships between the degrees of freedom in 2-D spatial affine image transformations and the degrees of freedom in the affine Gaussian derivative model for visual receptive fields. For this purpose, we first describe a canonical decomposition of 2-D affine transformations on a product form, closely related to a singular value decomposition, while in closed form, and which reveals the degrees of freedom in terms of (i) uniform scaling transformations, (ii) an overall amount of global rotation, (iii) a complementary non-uniform scaling transformation and (iv) a relative normalization to a preferred symmetry orientation in the image domain. Then, we show how these degrees of freedom relate to the degrees of freedom in the affine Gaussian derivative model. Finally, we use these theoretical results to consider whether we could regard the biological receptive fields in the primary visual cortex of higher mammals as being able to span the degrees of freedom of 2-D spatial affine transformations, based on interpretations of existing neurophysiological experimental results.
Electro-diffusive modeling and the role of spine geometry on action potential propagation in neurons
Authors: Rahul Gulati, Shiva Rudraraju
Date: 2024-11-08 00:00:00
Tasks: Super-Resolution

Electrical signaling in the brain plays a vital role to our existence but at the same time, the fundamental mechanism of this propagation is undeciphered. Notable advancements have been made in the numerical modeling supplementing the related experimental findings. Cable theory based models provided a significant breakthrough in understanding the mechanism of electrical propagation in the neuronal axons. Cable theory, however, fails for thin geometries such as a spine or a dendrite of a neuron, amongst its other limitations. Recently, the spatiotemporal propagation has been precisely modeled using the Poisson-Nernst-Planck (PNP) electro-diffusive theory in the neuronal axons as well as the dendritic spines respectively. Patch clamp and voltage imaging experiments have extensively aided the study of action potential propagation exclusively for the neuronal axons but not the dendritic spines because of the challenges linked with their thin geometry. Assisted by the super-resolution microscopes and the voltage dyeing experiments, it has become possible to precisely measure the voltage in the dendritic spines. This has facilitated the requirement of a high fidelity numerical frame that is capable of acting as a digital twin. Here, using the PNP theory, we integrate the dendritic spine, soma and the axon region to numerically model the propagation of excitatory synaptic potential in a complete neuronal geometry with the synaptic input at the spines, potential initiating at the axon hillock and propagating through the neuronal axon. The model outputs the forward propagation of the action potential along the neuronal axons as well as the back propagation into the spines. We point out the significance of the intricate geometry of the dendritic spines, namely the spine neck length and radius, and the ion channel density in the axon hillock to the action potential initiation and propagation.
BayesianFitForecast: A User-Friendly R Toolbox for Parameter Estimation and Forecasting with Ordinary Differential Equations
Authors: Hamed Karami, Amanda Bleichrodt, Ruiyan Luo, Gerardo Chowell
Date: 2024-11-08 00:00:00
Tasks: Bayesian Inference, Decision Making, parameter estimation

Background: Mathematical models based on ordinary differential equations (ODEs) are essential tools across various scientific disciplines, including biology, ecology, and healthcare informatics. They are used to simulate complex dynamic systems and inform decision-making. In this paper, we introduce BayesianFitForecast, an R toolbox specifically developed to streamline Bayesian parameter estimation and forecasting in ODE models, making it particularly relevant to health informatics and public health decision-making. The toolbox is available at https://github.com/gchowell/BayesianFitForecast/. Results: This toolbox enables automatic generation of Stan files, allowing users to configure models, define priors, and analyze results with minimal programming expertise. To demonstrate the versatility and robustness of BayesianFitForecast, we apply it to the analysis of the 1918 influenza pandemic in San Francisco, comparing Poisson and negative binomial error structures within the SEIR model. We also test it by fitting multiple time series of state variables using simulated data. BayesianFitForecast provides robust tools for evaluating model performance, including convergence diagnostics, posterior distributions, credible intervals, and performance metrics. Conclusion: By improving the accessibility of advanced Bayesian methods, this toolbox significantly broadens the application of Bayesian inference methods to dynamical systems critical for healthcare and epidemiological forecasting. A tutorial video demonstrating the toolbox's functionality is available at https://youtu.be/jnxMjz3V3n8.
Nonperfused Retinal Capillaries -- A New Method Developed on OCT and OCTA
Authors: Min Gao, Yukun Guo, Tristan T. Hormel, Jie Wang, Elizabeth White, Dong-Wouk Park, Thomas S. Hwang, Steven T. Bailey, Yali Jia
Date: 2024-11-07 00:00:00
Tasks: Denoising

To develop a new method to quantify nonperfused retinal capillaries (NPCs) by using co-registered optical coherence tomography (OCT) and OCT angiography (OCTA), and to evaluate NPCs in eyes with age-related macular degeneration (AMD) and diabetic retinopathy (DR). Multiple consecutive 3x3-mm OCT/OCTA scans were obtained using a commercial device (Solix; Visionix/Optovue, Inc., California, USA). We averaged multiple registered OCT/OCTA scans to create high-definition volumes. The deep capillary plexus slab was defined and segmented. A novel deep learning denoising algorithm removed tissue background noise from capillaries in the en face OCT/OCTA. The algorithm segmented NPCs by identifying capillaries from OCT without corresponding flow signals in the OCTA. We then investigated the relationships between NPCs and known features in AMD and DR. The denoised en face OCT/OCTA revealed the structure and flow of the capillaries. The automatically segmented NPC achieved an accuracy of 88.2% compared to manual grading of DR. Compared to healthy controls, both the mean number and total length (mm) of NPCs were significantly increased in eyes with AMD and eyes with DR (P < 0.001). Compared to early and intermediate AMD, the number and total length of NPCs were significantly higher in advanced AMD (number: P<0.001, P<0.001; total length: P = 0.002, P =0.003). Geography atrophy, macular neovascularization, drusen volume, and extrafoveal avascular area (EAA) significantly correlated with increased NPCs (P<0.05). In eyes with DR, NPCs correlated with the number of microaneurysms and EAA (P<0.05). The presence of fluid did not significantly correlate with NPCs in AMD and DR. Conclusions A deep learning-based algorithm can segment and quantify retinal capillaries that lack flow using colocalized OCT/OCTA. This novel biomarker may be useful in AMD and DR.
Leveraging Transfer Learning and Multiple Instance Learning for HER2 Automatic Scoring of H\&E Whole Slide Images
Authors: Rawan S. Abdulsadig, Bryan M. Williams, Nikolay Burlutskiy
Date: 2024-11-05 00:00:00
Tasks: Multiple Instance Learning, Transfer Learning, whole slide images

Expression of human epidermal growth factor receptor 2 (HER2) is an important biomarker in breast cancer patients who can benefit from cost-effective automatic Hematoxylin and Eosin (H\&E) HER2 scoring. However, developing such scoring models requires large pixel-level annotated datasets. Transfer learning allows prior knowledge from different datasets to be reused while multiple-instance learning (MIL) allows the lack of detailed annotations to be mitigated. The aim of this work is to examine the potential of transfer learning on the performance of deep learning models pre-trained on (i) Immunohistochemistry (IHC) images, (ii) H\&E images and (iii) non-medical images. A MIL framework with an attention mechanism is developed using pre-trained models as patch-embedding models. It was found that embedding models pre-trained on H\&E images consistently outperformed the others, resulting in an average AUC-ROC value of $0.622$ across the 4 HER2 scores ($0.59-0.80$ per HER2 score). Furthermore, it was found that using multiple-instance learning with an attention layer not only allows for good classification results to be achieved, but it can also help with producing visual indication of HER2-positive areas in the H\&E slide image by utilising the patch-wise attention weights.
Multi-asset and generalised Local Volatility. An efficient implementation
Authors: Olivier Deloire, Louis Roth
Date: 2024-11-08 00:00:00

This article presents a generic hybrid numerical method to price a wide range of options on one or several assets, as well as assets with stochastic drift or volatility. In particular for equity and interest rate hybrid with local volatility.
Model-free portfolio allocation in continuous-time
Authors: Henry Chiu
Date: 2024-11-08 00:00:00
Tasks: model

We present a non-probabilistic, path-by-path framework for studying path-dependent (i.e., where weight is a functional of time and historical time-series), long-only portfolio allocation in continuous-time based on [Chiu & Cont '23], where the fundamental concept of self-financing was introduced, independent of any integration theory. In this article, we extend this concept to a portfolio allocation strategy and characterize it by a path-dependent partial differential equation. We derive the general explicit solution that describes the evolution of wealth in generic markets, including price paths that may not evolve continuously or exhibit variation of any order. Explicit solution examples are provided. As an application of our continuous-time, path-dependent framework, we extend an aggregating algorithm of [Vovk '90] and the universal algorithm of [Cover '91] to continuous-time algorithms that combine multiple strategies into a single strategy. These continuous-time (meta) algorithms take multiple strategies as input (which may themselves be generated by other algorithms) and track the wealth generated by the best individual strategy and the best convex combination of strategies, with tracking error bounds in log wealth of order O(1) and O(ln t), respectively. This work extends Cover's theorem [Cover '91, Thm 6.1] to a continuous-time, model-free setting.
Enhancing literature review with LLM and NLP methods. Algorithmic trading case
Authors: Stanisław Łaniewski, Robert Ślepaczuk
Date: 2024-10-23 00:00:00
Tasks: Algorithmic Trading, Articles, Dimensionality Reduction

This study utilizes machine learning algorithms to analyze and organize knowledge in the field of algorithmic trading. By filtering a dataset of 136 million research papers, we identified 14,342 relevant articles published between 1956 and Q1 2020. We compare traditional practices-such as keyword-based algorithms and embedding techniques-with state-of-the-art topic modeling methods that employ dimensionality reduction and clustering. This comparison allows us to assess the popularity and evolution of different approaches and themes within algorithmic trading. We demonstrate the usefulness of Natural Language Processing (NLP) in the automatic extraction of knowledge, highlighting the new possibilities created by the latest iterations of Large Language Models (LLMs) like ChatGPT. The rationale for focusing on this topic stems from our analysis, which reveals that research articles on algorithmic trading are increasing at a faster rate than the overall number of publications. While stocks and main indices comprise more than half of all assets considered, certain asset classes, such as cryptocurrencies, exhibit a much stronger growth trend. Machine learning models have become the most popular methods in recent years. The study demonstrates the efficacy of LLMs in refining datasets and addressing intricate questions about the analyzed articles, such as comparing the efficiency of different models. Our research shows that by decomposing tasks into smaller components and incorporating reasoning steps, we can effectively tackle complex questions supported by case analyses. This approach contributes to a deeper understanding of algorithmic trading methodologies and underscores the potential of advanced NLP techniques in literature reviews.
Fast and interpretable electricity consumption scenario generation for individual consumers
Authors: J. Soenen, A. Yurtman, T. Becker, K. Vanthournout, H. Blockeel
Date: 2024-10-23 00:00:00
Tasks: Time Series

To enable the transition from fossil fuels towards renewable energy, the low-voltage grid needs to be reinforced at a faster pace and on a larger scale than was historically the case. To efficiently plan reinforcements, one needs to estimate the currents and voltages throughout the grid, which are unknown but can be calculated from the grid layout and the electricity consumption time series of each consumer. However, for many consumers, these time series are unknown and have to be estimated from the available consumer information. We refer to this task as scenario generation. The state-of-the-art approach that generates electricity consumption scenarios is complex, resulting in a computationally expensive procedure with only limited interpretability. To alleviate these drawbacks, we propose a fast and interpretable scenario generation technique based on predictive clustering trees (PCTs) that does not compromise accuracy. In our experiments on three datasets from different locations, we found that our proposed approach generates time series that are at least as accurate as the state-of-the-art while being at least 7 times faster in training and prediction. Moreover, the interpretability of the PCT allows domain experts to gain insight into their data while simultaneously building trust in the predictions of the model.