pwc-archive/papers-with-abstracts huggingface dataset:
Synthesizing study-specific controls using generative models on open access datasets for harmonized multi-study analyses
Authors: Shruti P. Gadewar, Alyssa H. Zhu, Iyad Ba Gari, Sunanda Somu, Sophia I. Thomopoulos, Paul M. Thompson, Talia M. Nir, Neda Jahanshad
Date: 2024-02-29 00:00:00

Neuroimaging consortia can enhance reliability and generalizability of findings by pooling data across studies to achieve larger sample sizes. To adjust for site and MRI protocol effects, imaging datasets are often harmonized based on healthy controls. When data from a control group were not collected, statistical harmonization options are limited as patient characteristics and acquisition-related variables may be confounded. Here, in a multi-study neuroimaging analysis of Alzheimer's patients and controls, we tested whether it is possible to generate synthetic control MRIs. For one case-control study, we used a generative adversarial model for style-based harmonization to generate site-specific controls. Downstream feature extraction, statistical harmonization and group-level multi-study case-control and case-only analyses were performed twice, using either true or synthetic controls. All effect sizes using synthetic controls overlapped with those based on true study controls. This line of work may facilitate wider inclusion of case-only studies in multi-study consortia.
Implementation of an AI-based MRD evaluation and prediction model for multiple myeloma
Authors: Jianfeng Chen, Jize Xiong, Yixu Wang, Qi Xin, Hong Zhou
Date: 2024-02-29 00:00:00
Tasks: Prognosis

With the application of hematopoietic stem cell transplantation and new drugs, the progression-free survival rate and overall survival rate of multiple myeloma have been greatly improved, but it is still considered as a kind of disease that cannot be completely cured. Many patients have disease recurrence after complete remission, which is rooted in the presence of minimal residual disease MRD in patients. Studies have shown that positive MRD is an independent adverse prognostic factor affecting survival, so MRD detection is an important indicator to judge the prognosis of patients and guide clinical treatment. At present, multipa-rameter flow cytometry (MFC), polymerase chain reaction (PCR), positron emission tomography (positron emission) Several techniques, such as PET/computer tomography (CT), have been used for MRD detection of multiple myeloma.However, there is still no cure for the disease. "IFM2013-04" four clinical studies confirmed for the first time that proteasome inhibitors (PIs) and immunomodulatory drugs, The synergism and importance of the combination of IMiDs in the treatment of MM, the large Phase 3 clinical study SWOG SO777 compared the combination of bortezomib plus lenalidomide and dexamethasone. The efficacy of VRD and D established the status of VRD first-line treatment of MM, and due to the good efficacy of CD38 monoclonal antibody in large clinical studies, combination therapy with VRD has been recommended as the first-line treatment of MM. However, to explore the clinical value and problems of applying artificial intelligence bone marrow cell recognition system Morphogo in the detection of multiple myeloma minimal residual disease (MRD)
FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry Benchmarking
Authors: Lorenzo Bini, Fatemeh Nassajian Mojarrad, Margarita Liarou, Thomas Matthes, Stéphane Marchand-Maillet
Date: 2024-02-28 00:00:00
Tasks: Benchmarking, Inductive Learning, Multi-class Classification, Transductive Learning

This paper presents FlowCyt, the first comprehensive benchmark for multi-class single-cell classification in flow cytometry data. The dataset comprises bone marrow samples from 30 patients, with each cell characterized by twelve markers. Ground truth labels identify five hematological cell types: T lymphocytes, B lymphocytes, Monocytes, Mast cells, and Hematopoietic Stem/Progenitor Cells (HSPCs). Experiments utilize supervised inductive learning and semi-supervised transductive learning on up to 1 million cells per patient. Baseline methods include Gaussian Mixture Models, XGBoost, Random Forests, Deep Neural Networks, and Graph Neural Networks (GNNs). GNNs demonstrate superior performance by exploiting spatial relationships in graph-encoded data. The benchmark allows standardized evaluation of clinically relevant classification tasks, along with exploratory analyses to gain insights into hematological cell phenotypes. This represents the first public flow cytometry benchmark with a richly annotated, heterogeneous dataset. It will empower the development and rigorous assessment of novel methodologies for single-cell analysis.
NIR ICG-Enhanced Fluorescence: A Quantitative Evaluation of Bowel Microperfusion and Its Relation to Central Perfusion in Colorectal Surgery
Authors: N. Depalma, S. D Ugo, F. Manoochehri, A. Libia, W. Sergi, T. R. L. Marchese, S. Forciniti, L. L. del Mercato, P. Piscitelli, S. Garritano, F. Castellana, R. Zupo, M. G. Spampinato
Date: 2024-03-04 00:00:00
Tasks: Decision Making, Specificity

Background: To date, no standardized protocols nor a quantitative assessment of the near-infrared fluorescence angiography with indocyanine green (NIR-ICG) are available. The aim of this study was to evaluate the timing of fluorescence as a reproducible parameter and its efficacy in predicting anastomotic leakage (AL) in colorectal surgery. Methods: A consecutive cohort of 108 patients undergoing minimally invasive elective procedures for colorectal cancer was prospectively enrolled. The difference between macro and microperfusion DeltaT was obtained by calculating the timing of fluorescence at the level of iliac artery division and colonic wall, respectively. Results: Subjects with a DeltaT higher 15.5 s had a higher tendency to develop an AL (p lower 0.01). The DeltaT/heart rate interaction was found to predict AL with an odds ratio of 1.02 (p lower 0.01); a cut-off threshold of 832 was identified (sensitivity 0.86, specificity 0.77). Perfusion parameters were also associated with a faster bowel motility resumption and a reduced length of hospital stay. Conclusions: The analysis of the timing of fluorescence provides a quantitative, easy evaluation of tissue perfusion. A DeltaT/HR interaction higher 832 may be used as a real-time parameter to guide surgical decision making in colorectal surgery.
Volatility-based strategy on Chinese equity index ETF options
Authors: Peng Yifeng
Date: 2024-03-01 00:00:00

This study examines the performance of a volatility-based strategy using Chinese equity index ETF options. Initially successful, the strategy's effectiveness waned post-2018. By integrating GARCH models for volatility forecasting, the strategy's positions and exposures are dynamically adjusted. The results indicate that such an approach can enhance returns in volatile markets, suggesting potential for refined trading strategies in China's evolving derivatives landscape. The research underscores the importance of adaptive strategies in capturing market opportunities amidst changing trading dynamics.
Justifying the Volatility of S&P 500 Daily Returns
Authors: Hayden Brown
Date: 2024-03-02 00:00:00

Over the past 60 years, there has been a gradual increase in the volatility of daily returns for the S&P 500 Index. Hypothetically, suppose that market forces determine daily volatility such that a daily leveraged S&P 500 fund cannot outperform a standard S&P 500 fund in the long run. Then this hypothetical volatility happens to support the increase in volatility seen in the S&P 500 index. On this basis, it appears that the classic argument of the market portfolio being unbeatable in the long run is determining the volatility of S&P 500 daily returns. Moreover, it follows that the long-term volatility of the daily returns for the S&P 500 Index should continue to increase until passing a particular threshold. If, on the other hand, this hypothesis about market forces increasing volatility is invalid, then there is room for daily leveraged S&P 500 funds to outperform their unleveraged counterparts in the long run.
Optimal positioning in derivative securities in incomplete markets
Authors: Tim Leung, Matthew Lorig, Yoshihiro Shirai
Date: 2024-02-29 00:00:00

This paper analyzes a problem of optimal static hedging using derivatives in incomplete markets. The investor is assumed to have a risk exposure to two underlying assets. The hedging instruments are vanilla options written on a single underlying asset. The hedging problem is formulated as a utility maximization problem whereby the form of the optimal static hedge is determined. Among our results, a semi-analytical solution for the optimizer is found through variational methods for exponential, power/logarithmic, and quadratic utility. When vanilla options are available for each underlying asset, the optimal solution is related to the fixed points of a Lipschitz map. In the case of exponential utility, there is only one such fixed point, and subsequent iterations of the map converge to it.
Local sensitivity analysis of heating degree day and cooling degree day temperature derivatives prices
Authors: Sara Ana Solanilla Blanco
Date: 2024-01-24 00:00:00
Tasks: Sensitivity

We study the local sensitivity of heating degree day (HDD) and cooling degree day (CDD) temperature futures and option prices with respect to perturbations in the deseasonalized temperature or in one of its derivatives up to a certain order determined by the continuous-time autoregressive process modelling the deseasonalized temperature in the HDD and CDD indexes. We also consider an empirical case where a CAR process of autoregressive order 3 is fitted to New York temperatures and we perform a study of the local sensitivity of these financial contracts and a posterior analysis of the results.
Dimensionality reduction techniques to support insider trading detection
Authors: Adele Ravagnani, Fabrizio Lillo, Paola Deriu, Piero Mazzarisi, Francesca Medda, Antonio Russo
Date: 2024-03-01 00:00:00
Tasks: Anomaly Detection, Contextual Anomaly Detection, Dimensionality Reduction, Position

Identification of market abuse is an extremely complicated activity that requires the analysis of large and complex datasets. We propose an unsupervised machine learning method for contextual anomaly detection, which allows to support market surveillance aimed at identifying potential insider trading activities. This method lies in the reconstruction-based paradigm and employs principal component analysis and autoencoders as dimensionality reduction techniques. The only input of this method is the trading position of each investor active on the asset for which we have a price sensitive event (PSE). After determining reconstruction errors related to the trading profiles, several conditions are imposed in order to identify investors whose behavior could be suspicious of insider trading related to the PSE. As a case study, we apply our method to investor resolved data of Italian stocks around takeover bids.
Applying News and Media Sentiment Analysis for Generating Forex Trading Signals
Authors: Oluwafemi F Olaiyapo
Date: 2024-02-19 00:00:00
Tasks: Articles, Decision Making, Sentiment Analysis

The objective of this research is to examine how sentiment analysis can be employed to generate trading signals for the Foreign Exchange (Forex) market. The author assessed sentiment in social media posts and news articles pertaining to the United States Dollar (USD) using a combination of methods: lexicon-based analysis and the Naive Bayes machine learning algorithm. The findings indicate that sentiment analysis proves valuable in forecasting market movements and devising trading signals. Notably, its effectiveness is consistent across different market conditions. The author concludes that by analyzing sentiment expressed in news and social media, traders can glean insights into prevailing market sentiments towards the USD and other pertinent countries, thereby aiding trading decision-making. This study underscores the importance of weaving sentiment analysis into trading strategies as a pivotal tool for predicting market dynamics.
Ploutos: Towards interpretable stock movement prediction with financial large language model
Authors: Hanshuang Tong, Jun Li, Ning Wu, Ming Gong, Dongmei Zhang, Qi Zhang
Date: 2024-02-18 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Recent advancements in large language models (LLMs) have opened new pathways for many domains. However, the full potential of LLMs in financial investments remains largely untapped. There are two main challenges for typical deep learning-based methods for quantitative finance. First, they struggle to fuse textual and numerical information flexibly for stock movement prediction. Second, traditional methods lack clarity and interpretability, which impedes their application in scenarios where the justification for predictions is essential. To solve the above challenges, we propose Ploutos, a novel financial LLM framework that consists of PloutosGen and PloutosGPT. The PloutosGen contains multiple primary experts that can analyze different modal data, such as text and numbers, and provide quantitative strategies from different perspectives. Then PloutosGPT combines their insights and predictions and generates interpretable rationales. To generate accurate and faithful rationales, the training strategy of PloutosGPT leverage rearview-mirror prompting mechanism to guide GPT-4 to generate rationales, and a dynamic token weighting mechanism to finetune LLM by increasing key tokens weight. Extensive experiments show our framework outperforms the state-of-the-art methods on both prediction accuracy and interpretability.
Combating Financial Crimes with Unsupervised Learning Techniques: Clustering and Dimensionality Reduction for Anti-Money Laundering
Authors: Ahmed N. Bakry, Almohammady S. Alsharkawy, Mohamed S. Farag, Kamal R. Raslan
Date: 2024-02-14 00:00:00
Tasks: Clustering, Dimensionality Reduction

Anti-Money Laundering (AML) is a crucial task in ensuring the integrity of financial systems. One keychallenge in AML is identifying high-risk groups based on their behavior. Unsupervised learning, particularly clustering, is a promising solution for this task. However, the use of hundreds of features todescribe behavior results in a highdimensional dataset that negatively impacts clustering performance.In this paper, we investigate the effectiveness of combining clustering method agglomerative hierarchicalclustering with four dimensionality reduction techniques -Independent Component Analysis (ICA), andKernel Principal Component Analysis (KPCA), Singular Value Decomposition (SVD), Locality Preserving Projections (LPP)- to overcome the issue of high-dimensionality in AML data and improve clusteringresults. This study aims to provide insights into the most effective way of reducing the dimensionality ofAML data and enhance the accuracy of clustering-based AML systems. The experimental results demonstrate that KPCA outperforms other dimension reduction techniques when combined with agglomerativehierarchical clustering. This superiority is observed in the majority of situations, as confirmed by threedistinct validation indices.
Blockchain Metrics and Indicators in Cryptocurrency Trading
Authors: Juan C. King, Roberto Dale, José M. Amigó
Date: 2024-02-11 00:00:00
Tasks: Algorithmic Trading

The objective of this paper is the construction of new indicators that can be useful to operate in the cryptocurrency market. These indicators are based on public data obtained from the blockchain network, specifically from the nodes that make up Bitcoin mining. Therefore, our analysis is unique to that network. The results obtained with numerical simulations of algorithmic trading and prediction via statistical models and Machine Learning demonstrate the importance of variables such as the hash rate, the difficulty of mining or the cost per transaction when it comes to trade Bitcoin assets or predict the direction of price. Variables obtained from the blockchain network will be called here blockchain metrics. The corresponding indicators (inspired by the "Hash Ribbon") perform well in locating buy signals. From our results, we conclude that such blockchain indicators allow obtaining information with a statistical advantage in the highly volatile cryptocurrency market.
Knowledge-guided EEG Representation Learning
Authors: Aditya Kommineni, Kleanthis Avramidis, Richard Leahy, Shrikanth Narayanan
Date: 2024-02-15 00:00:00
Tasks: EEG, Representation Learning, Self-Supervised Learning

Self-supervised learning has produced impressive results in multimedia domains of audio, vision and speech. This paradigm is equally, if not more, relevant for the domain of biosignals, owing to the scarcity of labelled data in such scenarios. The ability to leverage large-scale unlabelled data to learn robust representations could help improve the performance of numerous inference tasks on biosignals. Given the inherent domain differences between multimedia modalities and biosignals, the established objectives for self-supervised learning may not translate well to this domain. Hence, there is an unmet need to adapt these methods to biosignal analysis. In this work we propose a self-supervised model for EEG, which provides robust performance and remarkable parameter efficiency by using state space-based deep learning architecture. We also propose a novel knowledge-guided pre-training objective that accounts for the idiosyncrasies of the EEG signal. The results indicate improved embedding representation learning and downstream performance compared to prior works on exemplary tasks. Also, the proposed objective significantly reduces the amount of pre-training data required to obtain performance equivalent to prior works.
A personalized benchmark for face anti-spoofing
Authors: Davide Belli, Debasmit Das, Bence Major, Fatih Porikli
Date: 2022-01-05 00:00:00
Proceeding: WACV 2022 1
Tasks: Face Anti-Spoofing, User Identification

Thanks to their ease-of-use and effectiveness, face authentication systems are nowadays ubiquitous in electronic devices to control access to protected data. However, the widespread adoption of such systems comes with security and reliability issues. This is because spoofs of face images can be easily fabricated to deceive the recognition systems. Hence, there is a need to integrate the user identification system with a robust face anti-spoofing element, which has the goal to detect whether a queried face image is a spoof or live. Most contemporary face anti-spoofing systems only rely on the query image to accept or reject tentative access. In real-world scenarios, however, face authentication systems often have an initial enrollment step where a few live images of the user are recorded and stored for identification purposes. In this paper, we present a complementary approach to augment existing face anti-spoofing benchmarks to account for enrollment images associated with each query image. We apply this strategy on two recently introduced datasets: CelebA-Spoof and SiW. We showcase how existing anti-spoofing models can be easily personalized using the subject's enrollment data, and we evaluate the effectiveness of the enhanced methods on the newly proposed datasets splits CelebA-Spoof-Enroll and SiW-Enroll.
Implicit Convolutional Kernels for Steerable CNNs
Authors: Maksim Zhdanov, Nico Hoffmann, Gabriele Cesa
Date: 2022-12-12 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Molecular Property Prediction, Point Cloud Classification, Property Prediction

Steerable convolutional neural networks (CNNs) provide a general framework for building neural networks equivariant to translations and transformations of an origin-preserving group $G$, such as reflections and rotations. They rely on standard convolutions with $G$-steerable kernels obtained by analytically solving the group-specific equivariance constraint imposed onto the kernel space. As the solution is tailored to a particular group $G$, implementing a kernel basis does not generalize to other symmetry transformations, complicating the development of general group equivariant models. We propose using implicit neural representation via multi-layer perceptrons (MLPs) to parameterize $G$-steerable kernels. The resulting framework offers a simple and flexible way to implement Steerable CNNs and generalizes to any group $G$ for which a $G$-equivariant MLP can be built. We prove the effectiveness of our method on multiple tasks, including N-body simulations, point cloud classification and molecular property prediction.
GLOBER: Coherent Non-autoregressive Video Generation via GLOBal Guided Video DecodER
Authors: Mingzhen Sun, Weining Wang, Zihan Qin, Jiahui Sun, Sihan Chen, Jing Liu
Date: 2023-09-23 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Decoder, Video Generation

Video generation necessitates both global coherence and local realism. This work presents a novel non-autoregressive method GLOBER, which first generates global features to obtain comprehensive global guidance and then synthesizes video frames based on the global features to generate coherent videos. Specifically, we propose a video auto-encoder, where a video encoder encodes videos into global features, and a video decoder, built on a diffusion model, decodes the global features and synthesizes video frames in a non-autoregressive manner. To achieve maximum flexibility, our video decoder perceives temporal information through normalized frame indexes, which enables it to synthesize arbitrary sub video clips with predetermined starting and ending frame indexes. Moreover, a novel adversarial loss is introduced to improve the global coherence and local realism between the synthesized video frames. Finally, we employ a diffusion-based video generator to fit the global features outputted by the video encoder for video generation. Extensive experimental results demonstrate the effectiveness and efficiency of our proposed method, and new state-of-the-art results have been achieved on multiple benchmarks.
UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction
Authors: Yansong Ning, Hao liu, Hao Wang, Zhenyu Zeng, Hui Xiong
Date: 2023-06-20 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Knowledge Graphs

Accurate Urban SpatioTemporal Prediction (USTP) is of great importance to the development and operation of the smart city. As an emerging building block, multi-sourced urban data are usually integrated as urban knowledge graphs (UrbanKGs) to provide critical knowledge for urban spatiotemporal prediction models. However, existing UrbanKGs are often tailored for specific downstream prediction tasks and are not publicly available, which limits the potential advancement. This paper presents UUKG, the unified urban knowledge graph dataset for knowledge-enhanced urban spatiotemporal predictions. Specifically, we first construct UrbanKGs consisting of millions of triplets for two metropolises by connecting heterogeneous urban entities such as administrative boroughs, POIs, and road segments. Moreover, we conduct qualitative and quantitative analysis on constructed UrbanKGs and uncover diverse high-order structural patterns, such as hierarchies and cycles, that can be leveraged to benefit downstream USTP tasks. To validate and facilitate the use of UrbanKGs, we implement and evaluate 15 KG embedding methods on the KG completion task and integrate the learned KG embeddings into 9 spatiotemporal models for five different USTP tasks. The extensive experimental results not only provide benchmarks of knowledge-enhanced USTP models under different task settings but also highlight the potential of state-of-the-art high-order structure-aware UrbanKG embedding methods. We hope the proposed UUKG fosters research on urban knowledge graphs and broad smart city applications. The dataset and source code are available at https://github.com/usail-hkust/UUKG/.