pwc-archive/papers-with-abstracts huggingface dataset:
Ultra-low-energy defibrillation through adjoint optimization
Authors: Alejandro Garzon, Roman O. Grigoriev
Date: 2024-07-06 00:00:00

This study investigates ultra-low-energy defibrillation protocols using a simple two-dimensional model of cardiac tissue. We find that, rather counter-intuitively, a single, properly timed, biphasic pulse can be more effective in defibrillating the tissue than low energy antitachycardia pacing (LEAP) which employs a sequence of such pulses, succeeding where the latter approach fails. Furthermore, we show that, with the help of adjoint optimization, it is possible to reduce the energy required for defibrillation even further, making it three orders of magnitude lower than that required by LEAP. Finally, we establish that this dramatic reduction is achieved through exploiting the sensitivity of the dynamics in vulnerable windows to promote annihilation of pairs of nearby phase singularities.
Stochastic Approaches to Asset Price Analysis
Authors: Michael Sekatchev, Zhengxiang Zhou
Date: 2024-07-09 00:00:00
Tasks: parameter estimation

In this project, we propose to explore the Kalman filter's performance for estimating asset prices. We begin by introducing a stochastic mean-reverting processes, the Ornstein-Uhlenbeck (OU) model. After this we discuss the Kalman filter in detail, and its application with this model. After a demonstration of the Kalman filter on a simulated OU process and a discussion of maximum likelihood estimation (MLE) for estimating model parameters, we apply the Kalman filter with the OU process and trailing parameter estimation to real stock market data. We finish by proposing a simple day-trading algorithm using the Kalman filter with the OU process and backtest its performance using Apple's stock price. We then move to the Heston model, a combination of Geometric Brownian Motion and the OU process. Maximum likelihood estimation is commonly used for Heston model parameter estimation, which results in very complex forms. Here we propose an alternative but easier way of parameter estimation, called the method of moments (MOM). After the derivation of these estimators, we again apply this method to real stock data to assess its performance.
Auction theory and demography
Authors: O. A. Malafeyev, I. E. Khomenko
Date: 2024-07-08 00:00:00

In economics, there are many ways to describe the interaction between a "seller" and a "buyer". The most common one, with which we interact almost every day, is selling for a fixed price. This option is perfect for selling a mass product, when we have a number of sellers and many buyers, and the price for the product varies depending on the conditions of the relationship between supply and demand. Another situation meets us already in markets, where a product can be either mass-produced or more unique, so this option is already closer to the object of our discussion.However, a one-on-one transaction is a much more unstable option, which is why it is also more difficult to model, since it is determined not so much by algorithms as by psychology and the difference in the bargaining ability of the two parties. An even closer example of an auction is price discrimination, when the price for the buyer is determined not only by supply and demand, but also by which group the buyer belongs to. But in this case, the product is not unique, and the final seller is the only one. Thus, we have identified the main auction criteria and their features of the "game".
Unified Approach for Hedging Impermanent Loss of Liquidity Provision
Authors: Alexander Lipton, Vladimir Lucic, Artur Sepp
Date: 2024-07-06 00:00:00

We develop static and dynamic approaches for hedging of the impermanent loss (IL) of liquidity provision (LP) staked at Decentralised Exchanges (DEXes) which employ Uniswap V2 and V3 protocols. We provide detailed definitions and formulas for computing the IL to unify different definitions occurring in the existing literature. We show that the IL can be seen a contingent claim with a non-linear payoff for a fixed maturity date. Thus, we introduce the contingent claim termed as IL protection claim which delivers the negative of IL payoff at the maturity date. We apply arbitrage-based methods for valuation and risk management of this claim. First, we develop the static model-independent replication method for the valuation of IL protection claim using traded European vanilla call and put options. We extend and generalize an existing method to show that the IL protection claim can be hedged perfectly with options if there is a liquid options market. Second, we develop the dynamic model-based approach for the valuation and hedging of IL protection claims under a risk-neutral measure. We derive analytic valuation formulas using a wide class of price dynamics for which the characteristic function is available under the risk-neutral measure. As base cases, we derive analytic valuation formulas for IL protection claim under the Black-Scholes-Merton model and the log-normal stochastic volatility model. We finally discuss estimation of risk-reward of LP staking using our results.
Modelling Uncertain Volatility Using Quantum Stochastic Calculus: Unitary vs Non-Unitary Time Evolution
Authors: Will Hicks
Date: 2024-07-05 00:00:00

In this article we look at stochastic processes with uncertain parameters, and consider different ways in which information is obtained when carrying out observations. For example we focus on the case of a the random evolution of a traded financial asset price with uncertain volatility. The quantum approach presented, allows us to encode different volatility levels in a state acting on a Hilbert space. We consider different means of defining projective measurements in order to track the evolution of a traded market price, and discuss the results of different Monte-Carlo simulations.
Unwinding Toxic Flow with Partial Information
Authors: Alexander Barzykin, Robert Boyce, Eyal Neuman
Date: 2024-07-05 00:00:00

We consider a central trading desk which aggregates the inflow of clients' orders with unobserved toxicity, i.e. persistent adverse directionality. The desk chooses either to internalise the inflow or externalise it to the market in a cost effective manner. In this model, externalising the order flow creates both price impact costs and an additional market feedback reaction for the inflow of trades. The desk's objective is to maximise the daily trading P&L subject to end of the day inventory penalization. We formulate this setting as a partially observable stochastic control problem and solve it in two steps. First, we derive the filtered dynamics of the inventory and toxicity, projected to the observed filtration, which turns the stochastic control problem into a fully observed problem. Then we use a variational approach in order to derive the unique optimal trading strategy. We illustrate our results for various scenarios in which the desk is facing momentum and mean-reverting toxicity. Our implementation shows that the P&L performance gap between the partially observable problem and the full information case are of order $0.01\%$ in all tested scenarios.
Optimal hedging with variational preferences under convex risk measures
Authors: Marcelo Righi
Date: 2024-07-03 00:00:00

We expose a theoretical hedging optimization framework with variational preferences under convex risk measures. We explore a general dual representation for the composition between risk measures and utilities. We study the properties of the optimization problem as a convex and monotone map per se. We also derive results for optimality and indifference pricing conditions. We also explore particular examples inside our setup.
Longitudinal market structure detection using a dynamic modularity-spectral algorithm
Authors: Philipp Wirth, Francesca Medda, Thomas Schröder
Date: 2024-07-05 00:00:00
Tasks: Clustering

In this paper, we introduce the Dynamic Modularity-Spectral Algorithm (DynMSA), a novel approach to identify clusters of stocks with high intra-cluster correlations and low inter-cluster correlations by combining Random Matrix Theory with modularity optimisation and spectral clustering. The primary objective is to uncover hidden market structures and find diversifiers based on return correlations, thereby achieving a more effective risk-reducing portfolio allocation. We applied DynMSA to constituents of the S&P 500 and compared the results to sector- and market-based benchmarks. Besides the conception of this algorithm, our contributions further include implementing a sector-based calibration for modularity optimisation and a correlation-based distance function for spectral clustering. Testing revealed that DynMSA outperforms baseline models in intra- and inter-cluster correlation differences, particularly over medium-term correlation look-backs. It also identifies stable clusters and detects regime changes due to exogenous shocks, such as the COVID-19 pandemic. Portfolios constructed using our clusters showed higher Sortino and Sharpe ratios, lower downside volatility, reduced maximum drawdown and higher annualised returns compared to an equally weighted market benchmark.
Constructing an Investment Fund through Stock Clustering and Integer Programming
Authors: Maysam Khodayari Gharanchaei, Prabhu Prasad Panda
Date: 2024-04-27 00:00:00
Tasks: Clustering, Management

This paper focuses on the application of quantitative portfolio management by using integer programming and clustering techniques. Investors seek to gain the highest profits and lowest risk in capital markets. A data-oriented analysis of US stock universe is used to provide portfolio managers a device to track different Exchange Traded Funds. As an example, reconstructing of NASDAQ 100 index fund is presented.
CAESar: Conditional Autoregressive Expected Shortfall
Authors: Federico Gatta, Fabrizio Lillo, Piero Mazzarisi
Date: 2024-07-09 00:00:00
Tasks: Management, regression

In financial risk management, Value at Risk (VaR) is widely used to estimate potential portfolio losses. VaR's limitation is its inability to account for the magnitude of losses beyond a certain threshold. Expected Shortfall (ES) addresses this by providing the conditional expectation of such exceedances, offering a more comprehensive measure of tail risk. Despite its benefits, ES is not elicitable on its own, complicating its direct estimation. However, joint elicitability with VaR allows for their combined estimation. Building on this, we propose a new methodology named Conditional Autoregressive Expected Shortfall (CAESar), inspired by the CAViaR model. CAESar handles dynamic patterns flexibly and includes heteroskedastic effects for both VaR and ES, with no distributional assumption on price returns. CAESar involves a three-step process: estimating VaR via CAViaR regression, formulating ES in an autoregressive manner, and jointly estimating VaR and ES while ensuring a monotonicity constraint to avoid crossing quantiles. By employing various backtesting procedures, we show the effectiveness of CAESar through extensive simulations and empirical testing on daily financial data. Our results demonstrate that CAESar outperforms existing regression methods in terms of forecasting performance, making it a robust tool for financial risk management.
Causality-driven Sequence Segmentation for Enhancing Multiphase Industrial Process Data Analysis and Soft Sensing
Authors: Yimeng He, Le Yao, Xinmin Zhang, Xiangyin Kong, Zhihuan Song
Date: 2024-06-30 00:00:00

The dynamic characteristics of multiphase industrial processes present significant challenges in the field of industrial big data modeling. Traditional soft sensing models frequently neglect the process dynamics and have difficulty in capturing transient phenomena like phase transitions. To address this issue, this article introduces a causality-driven sequence segmentation (CDSS) model. This model first identifies the local dynamic properties of the causal relationships between variables, which are also referred to as causal mechanisms. It then segments the sequence into different phases based on the sudden shifts in causal mechanisms that occur during phase transitions. Additionally, a novel metric, similarity distance, is designed to evaluate the temporal consistency of causal mechanisms, which includes both causal similarity distance and stable similarity distance. The discovered causal relationships in each phase are represented as a temporal causal graph (TCG). Furthermore, a soft sensing model called temporal-causal graph convolutional network (TC-GCN) is trained for each phase, by using the time-extended data and the adjacency matrix of TCG. The numerical examples are utilized to validate the proposed CDSS model, and the segmentation results demonstrate that CDSS has excellent performance on segmenting both stable and unstable multiphase series. Especially, it has higher accuracy in separating non-stationary time series compared to other methods. The effectiveness of the proposed CDSS model and the TC-GCN model is also verified through a penicillin fermentation process. Experimental results indicate that the breakpoints discovered by CDSS align well with the reaction mechanisms and TC-GCN significantly has excellent predictive accuracy.
More Distinctively Black and Feminine Faces Lead to Increased Stereotyping in Vision-Language Models
Authors: Messi H. J. Lee, Jacob M. Montgomery, Calvin K. Lai
Date: 2024-05-22 00:00:00

Vision Language Models (VLMs), exemplified by GPT-4V, adeptly integrate text and vision modalities. This integration enhances Large Language Models' ability to mimic human perception, allowing them to process image inputs. Despite VLMs' advanced capabilities, however, there is a concern that VLMs inherit biases of both modalities in ways that make biases more pervasive and difficult to mitigate. Our study explores how VLMs perpetuate homogeneity bias and trait associations with regards to race and gender. When prompted to write stories based on images of human faces, GPT-4V describes subordinate racial and gender groups with greater homogeneity than dominant groups and relies on distinct, yet generally positive, stereotypes. Importantly, VLM stereotyping is driven by visual cues rather than group membership alone such that faces that are rated as more prototypically Black and feminine are subject to greater stereotyping. These findings suggest that VLMs may associate subtle visual cues related to racial and gender groups with stereotypes in ways that could be challenging to mitigate. We explore the underlying reasons behind this behavior and discuss its implications and emphasize the importance of addressing these biases as VLMs come to mirror human perception.
Poetry2Image: An Iterative Correction Framework for Images Generated from Chinese Classical Poetry
Authors: Jing Jiang, Yiran Ling, Binzhu Li, Pengxiang Li, Junming Piao, Yu Zhang
Date: 2024-06-15 00:00:00
Tasks: Image Generation, Text to Image Generation, Text-to-Image Generation

Text-to-image generation models often struggle with key element loss or semantic confusion in tasks involving Chinese classical poetry.Addressing this issue through fine-tuning models needs considerable training costs. Additionally, manual prompts for re-diffusion adjustments need professional knowledge. To solve this problem, we propose Poetry2Image, an iterative correction framework for images generated from Chinese classical poetry. Utilizing an external poetry dataset, Poetry2Image establishes an automated feedback and correction loop, which enhances the alignment between poetry and image through image generation models and subsequent re-diffusion modifications suggested by large language models (LLM). Using a test set of 200 sentences of Chinese classical poetry, the proposed method--when integrated with five popular image generation models--achieves an average element completeness of 70.63%, representing an improvement of 25.56% over direct image generation. In tests of semantic correctness, our method attains an average semantic consistency of 80.09%. The study not only promotes the dissemination of ancient poetry culture but also offers a reference for similar non-fine-tuning methods to enhance LLM generation.
A Survey on Mixture of Experts
Authors: Weilin Cai, Juyong Jiang, Fan Wang, Jing Tang, Sunghun Kim, Jiayi Huang
Date: 2024-06-26 00:00:00
Tasks: In-Context Learning, Mixture-of-Experts, Survey

Large language models (LLMs) have garnered unprecedented advancements across diverse fields, ranging from natural language processing to computer vision and beyond. The prowess of LLMs is underpinned by their substantial model size, extensive and diverse datasets, and the vast computational power harnessed during training, all of which contribute to the emergent abilities of LLMs (e.g., in-context learning) that are not present in small models. Within this context, the mixture of experts (MoE) has emerged as an effective method for substantially scaling up model capacity with minimal computation overhead, gaining significant attention from academia and industry. Despite its growing prevalence, there lacks a systematic and comprehensive review of the literature on MoE. This survey seeks to bridge that gap, serving as an essential resource for researchers delving into the intricacies of MoE. We first briefly introduce the structure of the MoE layer, followed by proposing a new taxonomy of MoE. Next, we overview the core designs for various MoE models including both algorithmic and systemic aspects, alongside collections of available open-source implementations, hyperparameter configurations and empirical evaluations. Furthermore, we delineate the multifaceted applications of MoE in practice, and outline some potential directions for future research. To facilitate ongoing updates and the sharing of cutting-edge developments in MoE research, we have established a resource repository accessible at https://github.com/withinmiaov/A-Survey-on-Mixture-of-Experts.
SGOR: Outlier Removal by Leveraging Semantic and Geometric Information for Robust Point Cloud Registration
Authors: Guiyu Zhao, Zhentao Guo, Hongbin Ma
Date: 2024-07-08 00:00:00
Tasks: Point Cloud Registration, Semantic correspondence

In this paper, we introduce a new outlier removal method that fully leverages geometric and semantic information, to achieve robust registration. Current semantic-based registration methods only use semantics for point-to-point or instance semantic correspondence generation, which has two problems. First, these methods are highly dependent on the correctness of semantics. They perform poorly in scenarios with incorrect semantics and sparse semantics. Second, the use of semantics is limited only to the correspondence generation, resulting in bad performance in the weak geometry scene. To solve these problems, on the one hand, we propose secondary ground segmentation and loose semantic consistency based on regional voting. It improves the robustness to semantic correctness by reducing the dependence on single-point semantics. On the other hand, we propose semantic-geometric consistency for outlier removal, which makes full use of semantic information and significantly improves the quality of correspondences. In addition, a two-stage hypothesis verification is proposed, which solves the problem of incorrect transformation selection in the weak geometry scene. In the outdoor dataset, our method demonstrates superior performance, boosting a 22.5 percentage points improvement in registration recall and achieving better robustness under various conditions. Our code is available.
Tile Compression and Embeddings for Multi-Label Classification in GeoLifeCLEF 2024
Authors: Anthony Miyaguchi, Patcharapong Aphiwetsa, Mark McDuffie
Date: 2024-07-08 00:00:00
Tasks: Contrastive Learning, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

We explore methods to solve the multi-label classification task posed by the GeoLifeCLEF 2024 competition with the DS@GT team, which aims to predict the presence and absence of plant species at specific locations using spatial and temporal remote sensing data. Our approach uses frequency-domain coefficients via the Discrete Cosine Transform (DCT) to compress and pre-compute the raw input data for convolutional neural networks. We also investigate nearest neighborhood models via locality-sensitive hashing (LSH) for prediction and to aid in the self-supervised contrastive learning of embeddings through tile2vec. Our best competition model utilized geolocation features with a leaderboard score of 0.152 and a best post-competition score of 0.161. Source code and models are available at https://github.com/dsgt-kaggle-clef/geolifeclef-2024.
GeoWATCH for Detecting Heavy Construction in Heterogeneous Time Series of Satellite Images
Authors: Jon Crall, Connor Greenwell, David Joy, Matthew Leotta, Aashish Chaudhary, Anthony Hoogs
Date: 2024-07-08 00:00:00
Tasks: Activity Recognition, image-classification, Image Classification, Object, object-detection, Object Detection, Object Tracking, Time Series

Learning from multiple sensors is challenging due to spatio-temporal misalignment and differences in resolution and captured spectra. To that end, we introduce GeoWATCH, a flexible framework for training models on long sequences of satellite images sourced from multiple sensor platforms, which is designed to handle image classification, activity recognition, object detection, or object tracking tasks. Our system includes a novel partial weight loading mechanism based on sub-graph isomorphism which allows for continually training and modifying a network over many training cycles. This has allowed us to train a lineage of models over a long period of time, which we have observed has improved performance as we adjust configurations while maintaining a core backbone.
High-Throughput Phenotyping using Computer Vision and Machine Learning
Authors: Vivaan Singhvi, Langalibalele Lunga, Pragya Nidhi, Chris Keum, Varrun Prakash
Date: 2024-07-08 00:00:00
Tasks: Image Segmentation, Optical Character Recognition, Optical Character Recognition (OCR), Semantic Segmentation

High-throughput phenotyping refers to the non-destructive and efficient evaluation of plant phenotypes. In recent years, it has been coupled with machine learning in order to improve the process of phenotyping plants by increasing efficiency in handling large datasets and developing methods for the extraction of specific traits. Previous studies have developed methods to advance these challenges through the application of deep neural networks in tandem with automated cameras; however, the datasets being studied often excluded physical labels. In this study, we used a dataset provided by Oak Ridge National Laboratory with 1,672 images of Populus Trichocarpa with white labels displaying treatment (control or drought), block, row, position, and genotype. Optical character recognition (OCR) was used to read these labels on the plants, image segmentation techniques in conjunction with machine learning algorithms were used for morphological classifications, machine learning models were used to predict treatment based on those classifications, and analyzed encoded EXIF tags were used for the purpose of finding leaf size and correlations between phenotypes. We found that our OCR model had an accuracy of 94.31% for non-null text extractions, allowing for the information to be accurately placed in a spreadsheet. Our classification models identified leaf shape, color, and level of brown splotches with an average accuracy of 62.82%, and plant treatment with an accuracy of 60.08%. Finally, we identified a few crucial pieces of information absent from the EXIF tags that prevented the assessment of the leaf size. There was also missing information that prevented the assessment of correlations between phenotypes and conditions. However, future studies could improve upon this to allow for the assessment of these features.