pwc-archive/papers-with-abstracts huggingface dataset:
Understanding Short-Term Implied Volatility Dynamics: A Model-Independent Approach Beyond Stochastic Volatility
Authors: Liexin Cheng, Xue Cheng
Date: 2024-01-08 00:00:00
Tasks: Model Selection

This study investigates the short-term asymptotic behavior of the implied volatility surface (IVS), with a particular focus on the at-the-money (ATM) skew and curvature, which are key determinants of the IVS shape and whose are widely concerned in the option market. Departing from conventional process-based models that rely on explicit stochastic differential equations, our work adopts a model-independent, distribution-based approach by imposing cumulant conditions on the asset's log return distribution. Under these broad assumptions, we derive a quadratic expansion for the implied volatility as a function of moneyness for near-the-money options, along with asymptotic expressions for the ATM skew and curvature as the time to maturity tends to zero. These results not only highlight the differences in the properties of ATM asymptotics among different types of models, but also offer a versatile framework for model calibration, including regular stochastic volatility, rough volatility, and discrete-time distribution-based models, using simple moment information. Overall, our findings provide robust analytical tools for the accurate approximation of short-term option prices and the evaluation of model performance against market stylized features.
Modelling and Predicting the Conditional Variance of Bitcoin Daily Returns: Comparsion of Markov Switching GARCH and SV Models
Authors: Dennis Koch, Vahidin Jeleskovic, Zahid I. Younas
Date: 2024-01-07 00:00:00
Tasks: Management

This paper introduces a unique and valuable research design aimed at analyzing Bitcoin price volatility. To achieve this, a range of models from the Markov Switching-GARCH and Stochastic Autoregressive Volatility (SARV) model classes are considered and their out-of-sample forecasting performance is thoroughly examined. The paper provides insights into the rationale behind the recommendation for a two-stage estimation approach, emphasizing the separate estimation of coefficients in the mean and variance equations. The results presented in this paper indicate that Stochastic Volatility models, particularly SARV models, outperform MS-GARCH models in forecasting Bitcoin price volatility. Moreover, the study suggests that in certain situations, persistent simple GARCH models may even outperform Markov-Switching GARCH models in predicting the variance of Bitcoin log returns. These findings offer valuable guidance for risk management experts, highlighting the potential advantages of SARV models in managing and forecasting Bitcoin price volatility.
Structured factor copulas for modeling the systemic risk of European and United States banks
Authors: Hoang Nguyen, Audronė Virbickaitė, M. Concepción Ausín, Pedro Galeano
Date: 2024-01-07 00:00:00

In this paper, we employ Credit Default Swaps (CDS) to model the joint and conditional distress probabilities of banks in Europe and the U.S. using factor copulas. We propose multi-factor, structured factor, and factor-vine models where the banks in the sample are clustered according to their geographic location. We find that within each region, the co-dependence between banks is best described using both, systematic and idiosyncratic, financial contagion channels. However, if we consider the banking system as a whole, then the systematic contagion channel prevails, meaning that the distress probabilities are driven by a latent global factor and region-specific factors. In all cases, the co-dependence structure of bank CDS spreads is highly correlated in the tail. The out-of-sample forecasts of several measures of systematic risk allow us to identify the periods of distress in the banking sector over the recent years including the COVID-19 pandemic, the interest rate hikes in 2022, and the banking crisis in 2023.
Leveraging IS and TC: Optimal order execution subject to reference strategies
Authors: Xue Cheng, Peng Guo, Tai-Ho Wang
Date: 2024-01-06 00:00:00

The paper addresses the problem of meta order execution from a broker-dealer's point of view in Almgren-Chriss model under execution risk. A broker-dealer agency is authorized to execute an order of trading on some client's behalf. The strategies that the agent is allowed to deploy is subject to a benchmark, referred to as the reference strategy, regulated by the client. We formulate the broker's problem as a utility maximization problem in which the broker seeks to maximize his utility of excess profit-and-loss at the execution horizon, of which optimal feedback strategies are obtained in closed form. In the absence of execution risk, the optimal strategies subject to reference strategies are deterministic. We establish an affine structure among the trading trajectories under optimal strategies subject to general reference strategies using implementation shortfall (IS) and target close (TC) orders as basis. Furthermore, an approximation theorem is proposed to show that with small error, general reference strategies can be approximated by piece-wise constant ones, of which the optimal strategy is piece-wise linear combination between IS and TC orders. We conclude the paper with numerical experiments illustrating the trading trajectories as well as histograms of terminal wealth and utility at investment horizon under optimal strategies versus those under TWAP strategies.
Walnut Detection Through Deep Learning Enhanced by Multispectral Synthetic Images
Authors: Kaiming Fu, Tong Lei, Maryia Halubok, Brian N. Bailey
Date: 2023-11-01 00:00:00
Tasks: Deep Learning, Management

The accurate identification of walnuts within orchards brings forth a plethora of advantages, profoundly amplifying the efficiency and productivity of walnut orchard management. Nevertheless, the unique characteristics of walnut trees, characterized by their closely resembling shapes, colors, and textures between the walnuts and leaves, present a formidable challenge in precisely distinguishing between them during the annotation process. In this study, we present a novel approach to improve walnut detection efficiency, utilizing YOLOv5 trained on an enriched image set that incorporates both real and synthetic RGB and NIR images. Our analysis comparing results from our original and augmented datasets shows clear improvements in detection when using the synthetic images.
Classifying cow stall numbers using YOLO
Authors: Dheeraj Vajjarapu
Date: 2023-11-23 00:00:00
Tasks: Data Augmentation

This paper introduces the CowStallNumbers dataset, a collection of images extracted from videos focusing on cow teats, designed to advance the field of cow stall number detection. The dataset comprises 1042 training images and 261 test images, featuring stall numbers ranging from 0 to 60. To enhance the dataset, we performed fine-tuning on a YOLO model and applied data augmentation techniques, including random crop, center crop, and random rotation. The experimental outcomes demonstrate a notable 95.4\% accuracy in recognizing stall numbers.
Image Inpainting via Tractable Steering of Diffusion Models
Authors: Anji Liu, Mathias Niepert, Guy Van Den Broeck
Date: 2023-11-28 00:00:00
Tasks: Decoder, Denoising, Image Generation, Image Inpainting

Diffusion models are the current state of the art for generating photorealistic images. Controlling the sampling process for constrained image generation tasks such as inpainting, however, remains challenging since exact conditioning on such constraints is intractable. While existing methods use various techniques to approximate the constrained posterior, this paper proposes to exploit the ability of Tractable Probabilistic Models (TPMs) to exactly and efficiently compute the constrained posterior, and to leverage this signal to steer the denoising process of diffusion models. Specifically, this paper adopts a class of expressive TPMs termed Probabilistic Circuits (PCs). Building upon prior advances, we further scale up PCs and make them capable of guiding the image generation process of diffusion models. Empirical results suggest that our approach can consistently improve the overall quality and semantic coherence of inpainted images across three natural image datasets (i.e., CelebA-HQ, ImageNet, and LSUN) with only $\sim\! 10 \%$ additional computational overhead brought by the TPM. Further, with the help of an image encoder and decoder, our method can readily accept semantic constraints on specific regions of the image, which opens up the potential for more controlled image generation tasks. In addition to proposing a new framework for constrained image generation, this paper highlights the benefit of more tractable models and motivates the development of expressive TPMs.
Forensic Video Analytic Software
Authors: Anton Jeran Ratnarajah, Sahani Goonetilleke, Dumindu Tissera, Kapilan Balagopalan, Ranga Rodrigo
Date: 2023-09-17 00:00:00
Tasks: Activity Recognition, Anomaly Detection, GPU, Image Enhancement, Multiple Object Tracking, object-detection, Object Detection, Object Tracking, Video Synopsis

Law enforcement officials heavily depend on Forensic Video Analytic (FVA) Software in their evidence extraction process. However present-day FVA software are complex, time consuming, equipment dependent and expensive. Developing countries struggle to gain access to this gateway to a secure haven. The term forensic pertains the application of scientific methods to the investigation of crime through post-processing, whereas surveillance is the close monitoring of real-time feeds. The principle objective of this Final Year Project was to develop an efficient and effective FVA Software, addressing the shortcomings through a stringent and systematic review of scholarly research papers, online databases and legal documentation. The scope spans multiple object detection, multiple object tracking, anomaly detection, activity recognition, tampering detection, general and specific image enhancement and video synopsis. Methods employed include many machine learning techniques, GPU acceleration and efficient, integrated architecture development both for real-time and postprocessing. For this CNN, GMM, multithreading and OpenCV C++ coding were used. The implications of the proposed methodology would rapidly speed up the FVA process especially through the novel video synopsis research arena. This project has resulted in three research outcomes Moving Object Based Collision Free Video Synopsis, Forensic and Surveillance Analytic Tool Architecture and Tampering Detection Inter-Frame Forgery. The results include forensic and surveillance panel outcomes with emphasis on video synopsis and Sri Lankan context. Principal conclusions include the optimization and efficient algorithm integration to overcome limitations in processing power, memory and compromise between real-time performance and accuracy.
A Surrogate-Assisted Extended Generative Adversarial Network for Parameter Optimization in Free-Form Metasurface Design
Authors: Manna Dai, Yang Jiang, Feng Yang, Joyjit Chattoraj, Yingzhi Xia, Xinxing Xu, Weijiang Zhao, My Ha Dao, Yong liu
Date: 2023-10-18 00:00:00
Tasks: Drug Discovery, Form, Generative Adversarial Network

Metasurfaces have widespread applications in fifth-generation (5G) microwave communication. Among the metasurface family, free-form metasurfaces excel in achieving intricate spectral responses compared to regular-shape counterparts. However, conventional numerical methods for free-form metasurfaces are time-consuming and demand specialized expertise. Alternatively, recent studies demonstrate that deep learning has great potential to accelerate and refine metasurface designs. Here, we present XGAN, an extended generative adversarial network (GAN) with a surrogate for high-quality free-form metasurface designs. The proposed surrogate provides a physical constraint to XGAN so that XGAN can accurately generate metasurfaces monolithically from input spectral responses. In comparative experiments involving 20000 free-form metasurface designs, XGAN achieves 0.9734 average accuracy and is 500 times faster than the conventional methodology. This method facilitates the metasurface library building for specific spectral responses and can be extended to various inverse design problems, including optical metamaterials, nanophotonic devices, and drug discovery.
Automated Localization of Blood Vessels in Retinal Images
Authors: Vahid Mohammadi Safarzadeh
Date: 2023-10-22 00:00:00
Tasks: Medical Image Analysis

Vessel structure is one of the most important parts of the retina which physicians can detect many diseases by analysing its features. Localization of blood vessels in retina images is an important process in medical image analysis. This process is also more challenging with the presence of bright and dark lesions. In this thesis, two automated vessel localization methods to handle both healthy and unhealthy (pathological) retina images are analyzed. Each method consists of two major steps and the second step is the same in the two methods. In the first step, an algorithm is used to decrease the effect of bright lesions. In Method 1, this algorithm is based on K- Means segmentation, and in Method 2, it is based on a regularization procedure. In the second step of both methods, a multi-scale line operator is used to localize the line-shaped vascular structures and ignore the dark lesions which are generally assumed to have irregular patterns. After the introduction of the methods, a detailed quantitative and qualitative comparison of the methods with one another as well as the state-of-the-art solutions in the literature based on the segmentation results on the images of the two publicly available datasets, DRIVE and STARE, is reported. The results demonstrate that the methods are highly comparable with other solutions.
Evolution of urban areas and land surface temperature
Authors: Sudipan Saha, Tushar Verma, Dario Augusto Borges Oliveira
Date: 2024-01-05 00:00:00
Tasks: Time Series

With the global population on the rise, our cities have been expanding to accommodate the growing number of people. The expansion of cities generally leads to the engulfment of peripheral areas. However, such expansion of urban areas is likely to cause increment in areas with increased land surface temperature (LST). By considering each summer as a data point, we form LST multi-year time-series and cluster it to obtain spatio-temporal pattern. We observe several interesting phenomena from these patterns, e.g., some clusters show reasonable similarity to the built-up area, whereas the locations with high temporal variation are seen more in the peripheral areas. Furthermore, the LST center of mass shifts over the years for cities with development activities tilted towards a direction. We conduct the above-mentioned studies for three different cities in three different continents.
Rule-Guided Joint Embedding Learning over Knowledge Graphs
Authors: Qisong Li, Ji Lin, Sijia Wei, Neng Liu
Date: 2023-12-01 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs

Recent studies focus on embedding learning over knowledge graphs, which map entities and relations in knowledge graphs into low-dimensional vector spaces. While existing models mainly consider the aspect of graph structure, there exists a wealth of contextual and literal information that can be utilized for more effective embedding learning. This paper introduces a novel model that incorporates both contextual and literal information into entity and relation embeddings by utilizing graph convolutional networks. Specifically, for contextual information, we assess its significance through confidence and relatedness metrics. In addition, a unique rule-based method is developed to calculate the confidence metric, and the relatedness metric is derived from the literal information's representations. We validate our model performance with thorough experiments on two established benchmark datasets.
Deep Anomaly Detection in Text
Authors: Andrei Manolache
Date: 2023-12-14 00:00:00
Tasks: Anomaly Detection, Representation Learning, Self-Supervised Learning, Unsupervised Anomaly Detection

Deep anomaly detection methods have become increasingly popular in recent years, with methods like Stacked Autoencoders, Variational Autoencoders, and Generative Adversarial Networks greatly improving the state-of-the-art. Other methods rely on augmenting classical models (such as the One-Class Support Vector Machine), by learning an appropriate kernel function using Neural Networks. Recent developments in representation learning by self-supervision are proving to be very beneficial in the context of anomaly detection. Inspired by the advancements in anomaly detection using self-supervised learning in the field of computer vision, this thesis aims to develop a method for detecting anomalies by exploiting pretext tasks tailored for text corpora. This approach greatly improves the state-of-the-art on two datasets, 20Newsgroups, and AG News, for both semi-supervised and unsupervised anomaly detection, thus proving the potential for self-supervised anomaly detectors in the field of natural language processing.
REE-HDSC: Recognizing Extracted Entities for the Historical Database Suriname Curacao
Authors: Erik Tjong Kim Sang
Date: 2023-12-19 00:00:00
Tasks: HTR

We describe the project REE-HDSC and outline our efforts to improve the quality of named entities extracted automatically from texts generated by hand-written text recognition (HTR) software. We describe a six-step processing pipeline and test it by processing 19th and 20th century death certificates from the civil registry of Curacao. We find that the pipeline extracts dates with high precision but that the precision of person name extraction is low. Next we show how name precision extraction can be improved by retraining HTR models with names, post-processing and by identifying and removing incorrect names.
Comparison of Microservice Call Rate Predictions for Replication in the Cloud
Authors: Narges Mehran, Arman Haghighi, Pedram Aminharati, Nikolay Nikolov, Ahmet Soylu, Dumitru Roman, Radu Prodan
Date: 2023-10-29 00:00:00
Tasks: Prediction, regression

Today, many users deploy their microservice-based applications with various interconnections on a cluster of Cloud machines, subject to stochastic changes due to dynamic user requirements. To address this problem, we compare three machine learning (ML) models for predicting the microservice call rates based on the microservice times and aiming at estimating the scalability requirements. We apply the linear regression (LR), multilayer perception (MLP), and gradient boosting regression (GBR) models on the Alibaba microservice traces. The prediction results reveal that the LR model reaches a lower training time than the GBR and MLP models. However, the GBR reduces the mean absolute error and the mean absolute percentage error compared to LR and MLP models. Moreover, the prediction results show that the required number of replicas for each microservice by the gradient boosting model is close to the actual test data without any prediction.
MTAC: Hierarchical Reinforcement Learning-based Multi-gait Terrain-adaptive Quadruped Controller
Authors: Nishaant Shah, Kshitij Tiwari, Aniket Bera
Date: 2023-11-01 00:00:00
Tasks: Hierarchical Reinforcement Learning, Humanitarian, reinforcement-learning

Urban search and rescue missions require rapid first response to minimize loss of life and damage. Often, such efforts are assisted by humanitarian robots which need to handle dynamic operational conditions such as uneven and rough terrains, especially during mass casualty incidents like an earthquake. Quadruped robots, owing to their versatile design, have the potential to assist in such scenarios. However, control of quadruped robots in dynamic and rough terrain environments is a challenging problem due to the many degrees of freedom of these robots. Current locomotion controllers for quadrupeds are limited in their ability to produce multiple adaptive gaits, solve tasks in a time and resource-efficient manner, and require tedious training and manual tuning procedures. To address these challenges, we propose MTAC: a multi-gait terrain-adaptive controller, which utilizes a Hierarchical reinforcement learning (HRL) approach while being time and memory-efficient. We show that our proposed method scales well to a diverse range of environments with similar compute times as state-of-the-art methods. Our method showed greater than 75% on most tasks, outperforming previous work on the majority of test cases.
The HAPPY HEDGEHOG Project
Authors: Oliver Bendel, Emanuel Graf, Kevin Bollier
Date: 2023-08-30 00:00:00
Tasks: Ethics

Semi-autonomous machines, autonomous machines and robots inhabit closed, semi-closed and open environments, more structured environments like the household or more unstructured environments like cultural landscapes or the wilderness. There they encounter domestic animals, farm animals, working animals, and wild animals. These creatures could be disturbed, displaced, injured, or killed by the machines. Within the context of machine ethics and social robotics, the School of Business FHNW developed several design studies and prototypes for animal-friendly machines, which can be understood as moral and social machines in the spirit of these disciplines. In 2019-20, a team led by the main author developed a prototype robot lawnmower that can recognize hedgehogs, interrupt its work for them and thus protect them. Every year many of these animals die worldwide because of traditional service robots. HAPPY HEDGEHOG (HHH), as the invention is called, could be a solution to this problem. This article begins by providing an introduction to the background. Then it focuses on navigation (where the machine comes across certain objects that need to be recognized) and thermal and image recognition (with the help of machine learning) of the machine. It also presents obvious weaknesses and possible improvements. The results could be relevant for an industry that wants to market their products as animal-friendly machines.
Addressing The Knapsack Challenge Through Cultural Algorithm Optimization
Authors: Mohammad Saleh Vahdatpour
Date: 2023-10-30 00:00:00
Tasks: Combinatorial Optimization

The "0-1 knapsack problem" stands as a classical combinatorial optimization conundrum, necessitating the selection of a subset of items from a given set. Each item possesses inherent values and weights, and the primary objective is to formulate a selection strategy that maximizes the total value while adhering to a predefined capacity constraint. In this research paper, we introduce a novel variant of Cultural Algorithms tailored specifically for solving 0-1 knapsack problems, a well-known combinatorial optimization challenge. Our proposed algorithm incorporates a belief space to refine the population and introduces two vital functions for dynamically adjusting the crossover and mutation rates during the evolutionary process. Through extensive experimentation, we provide compelling evidence of the algorithm's remarkable efficiency in consistently locating the global optimum, even in knapsack problems characterized by high dimensions and intricate constraints.