pwc-archive/papers-with-abstracts huggingface dataset:
Deep calibration with random grids
Authors: Fabio Baschetti, Giacomo Bormetti, Pietro Rossi
Date: 2023-06-19 00:00:00

We propose a neural network-based approach to calibrating stochastic volatility models, which combines the pioneering grid approach by Horvath et al. (2021) with the pointwise two-stage calibration of Bayer et al. (2018) and Liu et al. (2019). Our methodology inherits robustness from the former while not suffering from the need for interpolation/extrapolation techniques, a clear advantage ensured by the pointwise approach. The crucial point to the entire procedure is the generation of implied volatility surfaces on random grids, which one dispenses to the network in the training phase. We support the validity of our calibration technique with several empirical and Monte Carlo experiments for the rough Bergomi and Heston models under a simple but effective parametrization of the forward variance curve. The approach paves the way for valuable applications in financial engineering - for instance, pricing under local stochastic volatility models - and extensions to the fast-growing field of path-dependent volatility models.
Detecting Depegs: Towards Safer Passive Liquidity Provision on Curve Finance
Authors: Thomas N. Cintra, Maxwell P. Holloway
Date: 2023-06-18 00:00:00

We consider a liquidity provider's (LP's) exposure to stablecoin and liquid staking derivative (LSD) depegs on Curve's StableSwap pools. We construct a suite of metrics designed to detect potential asset depegs based on price and trading data. Using our metrics, we fine-tune a Bayesian Online Changepoint Detection (BOCD) algorithm to alert LPs of potential depegs before or as they occur. We train and test our changepoint detection algorithm against Curve LP token prices for 13 StableSwap pools throughout 2022 and 2023, focusing on relevant stablecoin and LSD depegs. We show that our model, trained on 2022 UST data, is able to detect the USDC depeg in March of 2023 at 9pm UTC on March 10th, approximately 5 hours before USDC dips below 99 cents, with few false alarms in the 17 months on which it is tested. Finally, we describe how this research may be used by Curve's liquidity providers, and how it may be extended to dynamically de-risk Curve pools by modifying parameters in anticipation of potential depegs. This research underpins an API developed to alert Curve LPs, in real-time, when their positions might be at risk.
Collective Arbitrage and the Value of Cooperation
Authors: Francesca Biagini, Alessandro Doldi, Jean-Pierre Fouque, Marco Frittelli, Thilo Meyer-Brandis
Date: 2023-06-20 00:00:00

We introduce the notions of Collective Arbitrage and of Collective Super-replication in a discrete-time setting where agents are investing in their markets and are allowed to cooperate through exchanges. We accordingly establish versions of the fundamental theorem of asset pricing and of the pricing-hedging duality. A reduction of the price interval of the contingent claims can be obtained by applying the collective super-replication price.
Are Shortfall Systemic Risk Measures One Dimensional?
Authors: Alessandro Doldi, Marco Frittelli, Emanuela Rosazza Gianin
Date: 2023-06-19 00:00:00

Shortfall systemic (multivariate) risk measures $\rho$ defined through an $N$-dimensional multivariate utility function $U$ and random allocations can be represented as classical (one dimensional) shortfall risk measures associated to an explicitly determined $1$-dimensional function constructed from $U$. This finding allows for simplifying the study of several properties of $\rho$, such as dual representations, law invariance and stability.
Option Pricing for the Variance Gamma Model: A New Perspective
Authors: Yuanda Chen, Zailei Cheng, Haixu Wang
Date: 2023-06-19 00:00:00

The variance gamma model is a widely popular model for option pricing in both academia and industry. In this paper, we provide a new perspective for pricing European style options for the variance gamma model by deriving closed-form formulas combining the randomization method and fractional derivatives. We also compare our results with various existing results in the literature by numerical examples.
Mind the Cap! -- Constrained Portfolio Optimisation in Heston's Stochastic Volatility Model
Authors: Marcos Escobar-Anel, Michel Kschonnek, Rudi Zagst
Date: 2023-06-19 00:00:00

We consider a portfolio optimisation problem for a utility-maximising investor who faces convex constraints on his portfolio allocation in Heston's stochastic volatility model. We apply the duality methods developed in previous work to obtain a closed-form expression for the optimal portfolio allocation. In doing so, we observe that allocation constraints impact the optimal constrained portfolio allocation in a fundamentally different way in Heston's stochastic volatility model than in the Black Scholes model. In particular, the optimal constrained portfolio may be different from the naive capped portfolio, which caps off the optimal unconstrained portfolio at the boundaries of the constraints. Despite this difference, we illustrate by way of a numerical analysis that in most realistic scenarios the capped portfolio leads to slim annual wealth equivalent losses compared to the optimal constrained portfolio. During a financial crisis, however, a capped solution might lead to compelling annual wealth equivalent losses.
Testing for intrinsic multifractality in the global grain spot market indices: A multifractal detrended fluctuation analysis
Authors: Li Wang, Xing-Lu Gao, Wei-Xing Zhou
Date: 2023-06-18 00:00:00

Grains account for more than 50% of the calories consumed by people worldwide, and military conflicts, pandemics, climate change, and soaring grain prices all have vital impacts on food security. However, the complex price behavior of the global grain spot markets has not been well understood. A recent study performed multifractal moving average analysis (MF-DMA) of the Grains & Oilseeds Index (GOI) and its sub-indices of wheat, maize, soyabeans, rice, and barley and found that only the maize and barley sub-indices exhibit an intrinsic multifractal nature with convincing evidence. Here, we utilize multifractal fluctuation analysis (MF-DFA) to investigate the same problem. Extensive statistical tests confirm the presence of intrinsic multifractality in the maize and barley sub-indices and the absence of intrinsic multifractality in the wheat and rice sub-indices. Different from the MF-DMA results, the MF-DFA results suggest that there is also intrinsic multifractality in the GOI and soyabeans sub-indices. Our comparative analysis does not provide conclusive information about the GOI and soyabeans and highlights the high complexity of the global grain spot markets.
Convolutional and Deep Learning based techniques for Time Series Ordinal Classification
Authors: Rafael Ayllón-Gavilán, David Guijo-Rubio, Pedro Antonio Gutiérrez, Anthony Bagnall, César Hervás-Martínez
Date: 2023-06-16 00:00:00
Tasks: Benchmarking, Ordinal Classification, Time Series, Time Series Classification

Time Series Classification (TSC) covers the supervised learning problem where input data is provided in the form of series of values observed through repeated measurements over time, and whose objective is to predict the category to which they belong. When the class values are ordinal, classifiers that take this into account can perform better than nominal classifiers. Time Series Ordinal Classification (TSOC) is the field covering this gap, yet unexplored in the literature. There are a wide range of time series problems showing an ordered label structure, and TSC techniques that ignore the order relationship discard useful information. Hence, this paper presents a first benchmarking of TSOC methodologies, exploiting the ordering of the target labels to boost the performance of current TSC state-of-the-art. Both convolutional- and deep learning-based methodologies (among the best performing alternatives for nominal TSC) are adapted for TSOC. For the experiments, a selection of 29 ordinal problems from two well-known archives has been made. In this way, this paper contributes to the establishment of the state-of-the-art in TSOC. The results obtained by ordinal versions are found to be significantly better than current nominal TSC techniques in terms of ordinal performance metrics, outlining the importance of considering the ordering of the labels when dealing with this kind of problems.
Microstructural segmentation using a union of attention guided U-Net models with different color transformed images
Authors: Momojit Biswas, Rishav Pramanik, Shibaprasad Sen, Aleksandr Sinitca, Dmitry Kaplun, Ram Sarkar
Date: 2023-04-07 00:00:00
Proceeding: Scientific Reports 2023 4
Tasks: 2D Semantic Segmentation, Image Segmentation, Segmentation, Semantic Segmentation

Metallographic images or often called the microstructures contain important information about metals, such as strength, toughness, ductility, corrosion resistance, which are used to choose the proper materials for various engineering applications. Thus by understanding the microstructures, one can determine the behaviour of a component made of a particular metal, and can predict the failure of that component in certain conditions. Image segmentation is a powerful technique for determination of morphological features of the microstructure like volume fraction, inclusion morphology, void, and crystal orientations. These are some key factors for determining the physical properties of metal. Therefore, automatic micro-structure characterization using image processing is useful for industrial applications which currently adopts deep learning-based segmentation models. In this paper, we propose a metallographic image segmentation method using an ensemble of modified U-Nets. Three U-Net models having the same architecture are separately fed with color transformed imaged (RGB, HSV and YUV). We improvise the U-Net with dilated convolutions and attention mechanisms to get finer grained features. Then we apply the sum-rule-based ensemble method on the outcomes of U-Net models to get the final prediction mask. We achieve the mean intersection over union (IoU) score of 0.677 on a publicly available standard dataset, namely MetalDAM. We also show that the proposed method obtains results comparable to state-of-the-art methods with fewer number of model parameters. The source code of the proposed work can be found at https://github.com/mb16biswas/attention-unet.
DACCORD : un jeu de données pour la Détection Automatique d'énonCés COntRaDictoires en français
Authors: Maximos Skandalis, Richard Moot, Simon Robillard
Date: 2023-06-08 00:00:00
Proceeding: Actes de 18e Conférence en Recherche d'Information et Applications (CORIA) - 30e Conférence sur le Traitement Automatique des Langues Naturelles (TALN), Paris, France 2023 6
Tasks: Binary Classification, Binary text classification, Sentence, Sentence-Pair Classification, Text Pair Classification

La tâche de détection automatique de contradictions logiques entre énoncés en TALN est une tâche de classification binaire, où chaque paire de phrases reçoit une étiquette selon que les deux phrases se contredisent ou non. Elle peut être utilisée afin de lutter contre la désinformation. Dans cet article, nous présentons DACCORD, un jeu de données dédié à la tâche de détection automatique de contradictions entre phrases en français. Le jeu de données élaboré est actuellement composé de 1034 paires de phrases. Il couvre les thématiques de l'invasion de la Russie en Ukraine en 2022, de la pandémie de Covid-19 et de la crise climatique. Pour mettre en avant les possibilités de notre jeu de données, nous évaluons les performances de certains modèles de transformeurs sur lui. Nous constatons qu'il constitue pour eux un défi plus élevé que les jeux de données existants pour le français, qui sont déjà peu nombreux.

In NLP, the automatic detection of logical contradictions between statements is a binary classification task, in which a pair of sentences receives a label according to whether or not the two sentences contradict each other. This task has many potential applications, including combating disinformation. In this article, we present DACCORD, a new dataset dedicated to the task of automatically detecting contradictions between sentences in French. The dataset is currently composed of 1034 sentence pairs. It covers the themes of Russia's invasion of Ukraine in 2022, the Covid-19 pandemic, and the climate crisis. To highlight the possibilities of our dataset, we evaluate the performance of some recent Transformer models on it. We conclude that our dataset is considerably more challenging than the few existing datasets for French.
Visualizing Relation Between (De)Motivating Topics and Public Stance toward COVID-19 Vaccine
Authors: Ashiqur Rahman, Hamed Alhoori
Date: 2023-06-21 00:00:00
Tasks: Misinformation, Relation

While social media plays a vital role in communication nowadays, misinformation and trolls can easily take over the conversation and steer public opinion on these platforms. We saw the effect of misinformation during the COVID-19 pandemic when public health officials faced significant push-back while trying to motivate the public to vaccinate. To tackle the current and any future threats in emergencies and motivate the public towards a common goal, it is essential to understand how public motivation shifts and which topics resonate among the general population. In this study, we proposed an interactive visualization tool to inspect and analyze the topics that resonated among Twitter-sphere during the COVID-19 pandemic and understand the key factors that shifted public stance for vaccination. This tool can easily be generalized for any scenario for visual analysis and to increase the transparency of social media data for researchers and the general population alike.
BMAD: Benchmarks for Medical Anomaly Detection
Authors: Jinan Bao, Hanshi Sun, Hanqiu Deng, Yinsheng He, Zhaoxiang Zhang, Xingyu Li
Date: 2023-06-20 00:00:00
Tasks: Anomaly Detection, Medical Diagnosis

Anomaly detection (AD) is a fundamental research problem in machine learning and computer vision, with practical applications in industrial inspection, video surveillance, and medical diagnosis. In medical imaging, AD is especially vital for detecting and diagnosing anomalies that may indicate rare diseases or conditions. However, there is a lack of a universal and fair benchmark for evaluating AD methods on medical images, which hinders the development of more generalized and robust AD methods in this specific domain. To bridge this gap, we introduce a comprehensive evaluation benchmark for assessing anomaly detection methods on medical images. This benchmark encompasses six reorganized datasets from five medical domains (i.e. brain MRI, liver CT, retinal OCT, chest X-ray, and digital histopathology) and three key evaluation metrics, and includes a total of fourteen state-of-the-art AD algorithms. This standardized and well-curated medical benchmark with the well-structured codebase enables comprehensive comparisons among recently proposed anomaly detection methods. It will facilitate the community to conduct a fair comparison and advance the field of AD on medical imaging. More information on BMAD is available in our GitHub repository: https://github.com/DorisBao/BMAD
Learning Hierarchical Sparse Representations using Iterative Dictionary Learning and Dimension Reduction
Authors: Mohamad Tarifi, Meera Sitharam, Jeffery Ho
Date: 2011-06-02 00:00:00
Tasks: Dictionary Learning, Dimensionality Reduction, Object Recognition

This paper introduces an elemental building block which combines Dictionary Learning and Dimension Reduction (DRDL). We show how this foundational element can be used to iteratively construct a Hierarchical Sparse Representation (HSR) of a sensory stream. We compare our approach to existing models showing the generality of our simple prescription. We then perform preliminary experiments using this framework, illustrating with the example of an object recognition task using standard datasets. This work introduces the very first steps towards an integrated framework for designing and analyzing various computational tasks from learning to attention to action. The ultimate goal is building a mathematically rigorous, integrated theory of intelligence.
OphGLM: Training an Ophthalmology Large Language-and-Vision Assistant based on Instructions and Dialogue
Authors: Weihao Gao, Zhuo Deng, Zhiyuan Niu, Fuju Rong, Chucheng Chen, Zheng Gong, Wenze Zhang, Daimin Xiao, Fang Li, Zhenjie Cao, Zhaoyi Ma, Wenbin Wei, Lan Ma
Date: 2023-06-21 00:00:00
Tasks: Instruction Following, Language Modeling, Language Modelling, Large Language Model, Lesion Segmentation

Large multimodal language models (LMMs) have achieved significant success in general domains. However, due to the significant differences between medical images and text and general web content, the performance of LMMs in medical scenarios is limited. In ophthalmology, clinical diagnosis relies on multiple modalities of medical images, but unfortunately, multimodal ophthalmic large language models have not been explored to date. In this paper, we study and construct an ophthalmic large multimodal model. Firstly, we use fundus images as an entry point to build a disease assessment and diagnosis pipeline to achieve common ophthalmic disease diagnosis and lesion segmentation. Then, we establish a new ophthalmic multimodal instruction-following and dialogue fine-tuning dataset based on disease-related knowledge data and publicly available real-world medical dialogue. We introduce visual ability into the large language model to complete the ophthalmic large language and vision assistant (OphGLM). Our experimental results demonstrate that the OphGLM model performs exceptionally well, and it has the potential to revolutionize clinical applications in ophthalmology. The dataset, code, and models will be made publicly available at https://github.com/ML-AILab/OphGLM.
LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models
Authors: Shizhe Diao, Rui Pan, Hanze Dong, Ka Shun Shum, Jipeng Zhang, Wei Xiong, Tong Zhang
Date: 2023-06-21 00:00:00

Foundation models have demonstrated a great ability to achieve general human-level intelligence far beyond traditional approaches. As the technique keeps attracting attention from the AI community, an increasing number of foundation models are becoming publicly accessible. However, a significant shortcoming of most of these models lies in their performance in specialized-domain and task-specific applications, necessitating domain- and task-aware fine-tuning to develop effective scientific language models. As the number of available foundation models and specialized tasks keeps growing, the job of training scientific language models becomes highly nontrivial. In this paper, we initiate steps to tackle this issue. We introduce an extensible and lightweight toolkit, LMFlow, which aims to simplify the domain- and task-aware finetuning of general foundation models. LMFlow offers a complete finetuning workflow for a foundation model to support specialized training with limited computing resources. Furthermore, it supports continuous pretraining, instruction tuning, parameter-efficient finetuning, alignment tuning, inference acceleration, long context generalization, model customization, and even multimodal finetuning, along with carefully designed and extensible APIs. This toolkit has been thoroughly tested and is available at https://github.com/OptimalScale/LMFlow.
Improving Software Requirements Prioritization through the Lens of Constraint Solving
Authors: Jonathan Winton, Francis Palma
Date: 2023-06-21 00:00:00

Requirements prioritization is a critical activity during the early software development process, which produces a set of key requirements to implement. The prioritization process offers a parity among the requirements based on multiple characteristics, including end-users' preferences, cost to implement, and technical dependencies. This paper presents an interactive method to requirements prioritization that leverages the pairwise comparisons and a constraint solver. Our method employs an interactive accumulation of knowledge from the requirements analyst when the relative priority among the requirements cannot be determined based on the existing knowledge from the requirements documents. The final ranking of the requirements is produced via the constraint solver and interactive pairwise comparisons. We evaluate the proposed method using the requirements from a real healthcare project. The proposed prioritization method relying on a constraint solver outperforms state-of-the-art interactive prioritization methods in terms of effectiveness and robustness to analyst's errors.
Probing the limit of hydrologic predictability with the Transformer network
Authors: Jiangtao Liu, Yuchen Bian, Chaopeng Shen
Date: 2023-06-21 00:00:00

For a number of years since its introduction to hydrology, recurrent neural networks like long short-term memory (LSTM) have proven remarkably difficult to surpass in terms of daily hydrograph metrics on known, comparable benchmarks. Outside of hydrology, Transformers have now become the model of choice for sequential prediction tasks, making it a curious architecture to investigate. Here, we first show that a vanilla Transformer architecture is not competitive against LSTM on the widely benchmarked CAMELS dataset, and lagged especially for the high-flow metrics due to short-term processes. However, a recurrence-free variant of Transformer can obtain mixed comparisons with LSTM, producing the same Kling-Gupta efficiency coefficient (KGE), along with other metrics. The lack of advantages for the Transformer is linked to the Markovian nature of the hydrologic prediction problem. Similar to LSTM, the Transformer can also merge multiple forcing dataset to improve model performance. While the Transformer results are not higher than current state-of-the-art, we still learned some valuable lessons: (1) the vanilla Transformer architecture is not suitable for hydrologic modeling; (2) the proposed recurrence-free modification can improve Transformer performance so future work can continue to test more of such modifications; and (3) the prediction limits on the dataset should be close to the current state-of-the-art model. As a non-recurrent model, the Transformer may bear scale advantages for learning from bigger datasets and storing knowledge. This work serves as a reference point for future modifications of the model.