pwc-archive/papers-with-abstracts huggingface dataset:
Multivariate brain-cognition associations in euthymic bipolar disorder
Authors: Bethany Little, Carly Flowers, Andrew Blamire, Peter Thelwall, John-Paul Taylor, Peter Gallagher, David Andrew Cousins, Yujiang Wang
Date: 2023-11-02 00:00:00

Background: People with bipolar disorder (BD) tend to show widespread cognitive impairment compared to healthy controls. Impairments in processing speed (PS), attention, and executive function (EF) may represent 'core' impairments that have a role in wider cognitive dysfunction. Cognitive impairments appear to relate to structural brain abnormalities in BD, but whether core deficits are related to particular brain regions is unclear and much of the research on brain-cognition associations is limited by univariate analysis and small samples. Methods: Euthymic BD patients (n=56) and matched healthy controls (n=26) underwent T1-weighted MRI scans and completed neuropsychological tests of PS, attention, and EF. We utilised public datasets to develop a normative model of cortical thickness (n=5,977) to generate robust estimations of cortical abnormalities in patients. Canonical correlation analysis was used to assess multivariate brain-cognition associations in BD, controlling for age, sex, and premorbid IQ. Results: BD showed impairments on tests of PS, attention, and EF, and abnormal cortical thickness in several brain regions compared to healthy controls. Impairments in tests of PS and EF were most strongly associated with cortical thickness in left inferior temporal, right entorhinal, and right temporal pole areas. Conclusion: Impairments in PS, attention, and EF can be observed in euthymic BD and may be related to abnormal cortical thickness in temporal regions. Future research should continue to leverage multivariate methods to examine complex brain-cognition associations in BD. Future research may benefit from exploring covariance between traditional brain structural morphological metrics such as cortical thickness, cortical volume, and surface area.
Maximizing Portfolio Predictability with Machine Learning
Authors: Michael Pinelis, David Ruppert
Date: 2023-11-03 00:00:00

We construct the maximally predictable portfolio (MPP) of stocks using machine learning. Solving for the optimal constrained weights in the multi-asset MPP gives portfolios with a high monthly coefficient of determination, given the sample covariance matrix of predicted return errors from a machine learning model. Various models for the covariance matrix are tested. The MPPs of S&P 500 index constituents with estimated returns from Elastic Net, Random Forest, and Support Vector Regression models can outperform or underperform the index depending on the time period. Portfolios that take advantage of the high predictability of the MPP's returns and employ a Kelly criterion style strategy consistently outperform the benchmark.
Benchmark Beating with the Increasing Convex Order
Authors: Jianming Xia
Date: 2023-11-03 00:00:00

In this paper we model benchmark beating with the increasing convex order (ICX order). The mean constraint in the mean-variance theory of portfolio selection can be regarded as beating a constant. We then investigate the problem of minimizing the variance of a portfolio with ICX order constraints, based on which we also study the problem of beating-performance-variance efficient portfolios. The optimal and efficient portfolios are all worked out in closed form for complete markets.
Bank Deposits as {\em Money Quanta}
Authors: Fabio Bagarello, Biagio Bossone
Date: 2023-11-02 00:00:00

According to the Accounting View of Money (AVM), the money issued by commercial banks in the form of demand deposits features a hybrid nature, since deposits can be shown to consist of a share of deposits bearing the characteristics of debt (debt-deposits) and a share of deposits bearing the characteristics of equity (equity-deposits), in a mix that depends on factors that relate to the issuing banks and the environment where they operate and interact, which may change over time. Following this important finding of the AVM, it is only consequential to associate the hybrid nature of bank deposits with the dual nature of the objects which is typical in quantum physics, and to investigate whether and how the application of quantum analytical methods and ideas to a form of money showing dualistic features could be used to extract valuable economic information.
Creating Trustworthy LLMs: Dealing with Hallucinations in Healthcare AI
Authors: Muhammad Aurangzeb Ahmad, Ilker Yaramis, Taposh Dutta Roy
Date: 2023-09-26 00:00:00

Large language models have proliferated across multiple domains in as short period of time. There is however hesitation in the medical and healthcare domain towards their adoption because of issues like factuality, coherence, and hallucinations. Give the high stakes nature of healthcare, many researchers have even cautioned against its usage until these issues are resolved. The key to the implementation and deployment of LLMs in healthcare is to make these models trustworthy, transparent (as much possible) and explainable. In this paper we describe the key elements in creating reliable, trustworthy, and unbiased models as a necessary condition for their adoption in healthcare. Specifically we focus on the quantification, validation, and mitigation of hallucinations in the context in healthcare. Lastly, we discuss how the future of LLMs in healthcare may look like.
Relation Extraction from News Articles (RENA): A Tool for Epidemic Surveillance
Authors: Jaeff Hong, Duong Dung, Danielle Hutchinson, Zubair Akhtar, Rosalie Chen, Rebecca Dawson, Aditya Joshi, Samsung Lim, C Raina MacIntyre, Deepti Gurdasani
Date: 2023-10-31 00:00:00
Tasks: Articles, Relation, Relation Extraction

Relation Extraction from News Articles (RENA) is a browser-based tool designed to extract key entities and their semantic relationships in English language news articles related to infectious diseases. Constructed using the React framework, this system presents users with an elegant and user-friendly interface. It enables users to input a news article and select from a choice of two models to generate a comprehensive list of relations within the provided text. As a result, RENA allows real-time parsing of news articles to extract key information for epidemic surveillance, contributing to EPIWATCH, an open-source intelligence-based epidemic warning system.
Remember what you did so you know what to do next
Authors: Manuel R. Ciosici, Alex Hedges, Yash Kankanampati, Justin Martin, Marjorie Freedman, Ralph Weischedel
Date: 2023-10-30 00:00:00
Tasks: Language Modelling, Large Language Model, reinforcement-learning, Reinforcement Learning

We explore using a moderately sized large language model (GPT-J 6B parameters) to create a plan for a simulated robot to achieve 30 classes of goals in ScienceWorld, a text game simulator for elementary science experiments. Previously published empirical work claimed that large language models (LLMs) are a poor fit (Wang et al., 2022) compared to reinforcement learning. Using the Markov assumption (a single previous step), the LLM outperforms the reinforcement learning-based approach by a factor of 1.4. When we fill the LLM's input buffer with as many prior steps as possible, improvement rises to 3.5x. Even when training on only 6.5% of the training data, we observe a 2.2x improvement over the reinforcement-learning-based approach. Our experiments show that performance varies widely across the 30 classes of actions, indicating that averaging over tasks can hide significant performance issues. In work contemporaneous with ours, Lin et al. (2023) demonstrated a two-part approach (SwiftSage) that uses a small LLM (T5-large) complemented by OpenAI's massive LLMs to achieve outstanding results in ScienceWorld. Our 6-B parameter, single-stage GPT-J matches the performance of SwiftSage's two-stage architecture when it incorporates GPT-3.5 turbo which has 29-times more parameters than GPT-J.
Leveraging Language Models to Detect Greenwashing
Authors: Avalon Vinella, Margaret Capetz, Rebecca Pattichis, Christina Chance, Reshmi Ghosh, Kai-Wei Chang
Date: 2023-10-30 00:00:00
Tasks: Language Modeling, Language Modelling

In recent years, climate change repercussions have increasingly captured public interest. Consequently, corporations are emphasizing their environmental efforts in sustainability reports to bolster their public image. Yet, the absence of stringent regulations in review of such reports allows potential greenwashing. In this study, we introduce a novel preliminary methodology to train a language model on generated labels for greenwashing risk. Our primary contributions encompass: developing a preliminary mathematical formulation to quantify greenwashing risk, a fine-tuned ClimateBERT model for this problem, and a comparative analysis of results. On a test set comprising of sustainability reports, our best model achieved an average accuracy score of 86.34% and F1 score of 0.67, demonstrating that our proof-of-concept methodology shows a promising direction of exploration for this task.
Faithful and Robust Local Interpretability for Textual Predictions
Authors: Gianluigi Lopardo, Frederic Precioso, Damien Garreau
Date: 2023-10-30 00:00:00
Tasks: counterfactual, Prediction

Interpretability is essential for machine learning models to be trusted and deployed in critical domains. However, existing methods for interpreting text models are often complex, lack mathematical foundations, and their performance is not guaranteed. In this paper, we propose FRED (Faithful and Robust Explainer for textual Documents), a novel method for interpreting predictions over text. FRED offers three key insights to explain a model prediction: (1) it identifies the minimal set of words in a document whose removal has the strongest influence on the prediction, (2) it assigns an importance score to each token, reflecting its influence on the model's output, and (3) it provides counterfactual explanations by generating examples similar to the original document, but leading to a different prediction. We establish the reliability of FRED through formal definitions and theoretical analyses on interpretable classifiers. Additionally, our empirical evaluation against state-of-the-art methods demonstrates the effectiveness of FRED in providing insights into text models.
KG-FRUS: a Novel Graph-based Dataset of 127 Years of US Diplomatic Relations
Authors: Gökberk Özsoy, Luis Salamanca, Matthew Connelly, Raymond Hicks, Fernando Pérez-Cruz
Date: 2023-10-30 00:00:00

In the current paper, we present the KG-FRUS dataset, comprised of more than 300,000 US government diplomatic documents encoded in a Knowledge Graph (KG). We leverage the data of the Foreign Relations of the United States (FRUS) (available as XML files) to extract information about the documents and the individuals and countries mentioned within them. We use the extracted entities, and associated metadata, to create a graph-based dataset. Further, we supplement the created KG with additional entities and relations from Wikidata. The relations in the KG capture the synergies and dynamics required to study and understand the complex fields of diplomacy, foreign relations, and politics. This goes well beyond a simple collection of documents which neglects the relations between entities in the text. We showcase a range of possibilities of the current dataset by illustrating different approaches to probe the KG. In the paper, we exemplify how to use a query language to answer simple research questions and how to use graph algorithms such as Node2Vec and PageRank, that benefit from the complete graph structure. More importantly, the chosen structure provides total flexibility for continuously expanding and enriching the graph. Our solution is general, so the proposed pipeline for building the KG can encode other original corpora of time-dependent and complex phenomena. Overall, we present a mechanism to create KG databases providing a more versatile representation of time-dependent related text data and a particular application to the all-important FRUS database.
Resilient Simultaneous Localization and Mapping Fusing Ultra Wide Band Range Measurements and Visual Odometry
Authors: Fabrizio Romanelli, Francesco Martinelli, Simone Mattogno
Date: 2023-11-04 00:00:00
Proceeding: Journal of Intelligent & Robotic Systems 2023 11
Tasks: Position, Simultaneous Localization and Mapping, Visual Odometry

In this paper we consider a Simultaneous Localization and Mapping (SLAM) problem for a moving agent using Visual Odometry (VO) while measuring the range from a set of Ultra Wide Band (UWB) antennas, deployed in unknown position in the environment. The solution approach is based on a switching observer which, under standard working conditions, for each observed UWB, uses a two dimensional Extended Kalman Filter (EKF) providing an estimate of the range and bearing of the observed UWB with respect to the agent. This information is then used in a Robust EKF algorithm which solves the SLAM problem with performances that, even before closing the loop, are comparable to the ones that a VO algorithm (namely ORB-SLAM2) would obtain only after closing the loop. Moreover, a resilient module is added to the algorithm to evaluate the reliability of the position estimate of each observed UWB. When the Visual Odometry is not available, the switching observer uses an auxiliary EKF to provide an estimate of the agent position. This makes the proposed approach robust with respect to several kinds of unmodeled disturbances, like multipath effects, and automatically adapts to sensor failures with resilience (e.g. when Visual Odometry or UWB measurements are not available).
RSDiff: Remote Sensing Image Generation from Text Using Diffusion Model
Authors: Ahmad Sebaq, Mohamed ElHelw
Date: 2023-09-03 00:00:00
Tasks: Decision Making, Image Captioning, Image Generation, Super-Resolution

The generation and enhancement of satellite imagery are critical in remote sensing, requiring high-quality, detailed images for accurate analysis. This research introduces a two-stage diffusion model methodology for synthesizing high-resolution satellite images from textual prompts. The pipeline comprises a Low-Resolution Diffusion Model (LRDM) that generates initial images based on text inputs and a Super-Resolution Diffusion Model (SRDM) that refines these images into high-resolution outputs. The LRDM merges text and image embeddings within a shared latent space, capturing essential scene content and structure. The SRDM then enhances these images, focusing on spatial features and visual clarity. Experiments conducted using the Remote Sensing Image Captioning Dataset (RSICD) demonstrate that our method outperforms existing models, producing satellite images with accurate geographical details and improved spatial resolution.
Diffusion-based Time Series Data Imputation for Microsoft 365
Authors: Fangkai Yang, Wenjie Yin, Lu Wang, Tianci Li, Pu Zhao, Bo Liu, Paul Wang, Bo Qiao, Yudong Liu, Mårten Björkman, Saravan Rajmohan, QIngwei Lin, Dongmei Zhang
Date: 2023-08-03 00:00:00
Tasks: Imputation, Time Series

Reliability is extremely important for large-scale cloud systems like Microsoft 365. Cloud failures such as disk failure, node failure, etc. threaten service reliability, resulting in online service interruptions and economic loss. Existing works focus on predicting cloud failures and proactively taking action before failures happen. However, they suffer from poor data quality like data missing in model training and prediction, which limits the performance. In this paper, we focus on enhancing data quality through data imputation by the proposed Diffusion+, a sample-efficient diffusion model, to impute the missing data efficiently based on the observed data. Our experiments and application practice show that our model contributes to improving the performance of the downstream failure prediction task.
WANDS: Dataset for Product Search Relevance Assessment
Authors: Yan Chen, Shujian Liu, Zheng Liu, Weiyi Sun, Linas Baltrunas, Benjamin Schroeder
Date: 2022-04-05 00:00:00
Proceeding: European Conference on Information Retrieval 2022 4

Search relevance is an important performance indicator used to evaluate search engines. It measures the relationship between users’ queries and products returned in search results. E-commerce sites use search engines to help customers find relevant products among millions of options. The scale of the data makes it difficult to create relevance-focused evaluation datasets manually. As an alternative, user click logs are often mined to create datasets. However, such logs only capture a slice of user behavior in the production environment, and do not provide a complete set of candidates for annotation. To overcome these challenges, we propose a systematic and effective way to build a discriminative, reusable, and fair human-labeled dataset, Wayfair Annotation DataSet (WANDS), for e-commerce scenarios. Our proposal introduces an important cross-referencing step to the annotation process which significantly increases dataset completeness. Experimental results show that this process is effective in improving the scalability of human annotation efforts. We also show that the dataset is effective in evaluating and discriminating between different search models. As part of this contribution, we also released the dataset. To our knowledge, it is the biggest publicly available search relevance dataset in the e-commerce domain.
Dissipative quadratizations of polynomial ODE systems
Authors: Yubo Cai, Gleb Pogudin
Date: 2023-11-04 00:00:00

Quadratization refers to a transformation of an arbitrary system of polynomial ordinary differential equations to a system with at most quadratic right-hand side. Such a transformation unveils new variables and model structures that facilitate model analysis, simulation, and control and offers a convenient parameterization for data-driven approaches. Quadratization techniques have found applications in diverse fields, including systems theory, fluid mechanics, chemical reaction modeling, and mathematical analysis. In this study, we focus on quadratizations that preserve the stability properties of the original model, specifically dissipativity at given equilibria. This preservation is desirable in many applications of quadratization including reachability analysis and synthetic biology. We establish the existence of dissipativity-preserving quadratizations, develop an algorithm for their computation, and demonstrate it in several case studies.
Complex Organ Mask Guided Radiology Report Generation
Authors: Tiancheng Gu, Dongnan Liu, Zhiyuan Li, Weidong Cai
Date: 2023-11-04 00:00:00
Tasks: Medical Report Generation

The goal of automatic report generation is to generate a clinically accurate and coherent phrase from a single given X-ray image, which could alleviate the workload of traditional radiology reporting. However, in a real-world scenario, radiologists frequently face the challenge of producing extensive reports derived from numerous medical images, thereby medical report generation from multi-image perspective is needed. In this paper, we propose the Complex Organ Mask Guided (termed as COMG) report generation model, which incorporates masks from multiple organs (e.g., bones, lungs, heart, and mediastinum), to provide more detailed information and guide the model's attention to these crucial body regions. Specifically, we leverage prior knowledge of the disease corresponding to each organ in the fusion process to enhance the disease identification phase during the report generation process. Additionally, cosine similarity loss is introduced as target function to ensure the convergence of cross-modal consistency and facilitate model optimization.Experimental results on two public datasets show that COMG achieves a 11.4% and 9.7% improvement in terms of BLEU@4 scores over the SOTA model KiUT on IU-Xray and MIMIC, respectively. The code is publicly available at https://github.com/GaryGuTC/COMG_model.
Modeling Cancer Progression: An Integrated Workflow Extending Data-Driven Kinetic Models to Bio-Mechanical PDE Models
Authors: Navid Mohammad Mirzaei, Leili Shahriyari
Date: 2023-11-03 00:00:00
Tasks: Diagnostic, parameter estimation

Computational modeling of cancer can help unveil dynamics and interactions that are hard to replicate experimentally. Thanks to the advancement in cancer databases and data analysis technologies, these models have become more robust than ever. There are many mathematical models which investigate cancer through different approaches, from sub-cellular to tissue scale, and from treatment to diagnostic points of view. In this study, we lay out a step-by-step methodology for a data-driven mechanistic model of the tumor microenvironment. We discuss data acquisition strategies, data preparation, parameter estimation, and sensitivity analysis techniques. Furthermore, we propose a possible approach to extend mechanistic ODE models to PDE models coupled with mechanical growth. The workflow discussed in this article can help understand the complex temporal and spatial interactions between cells and cytokines in the tumor microenvironment and their effect on tumor growth.
Holistic Analysis of Hallucination in GPT-4V(ision): Bias and Interference Challenges
Authors: Chenhang Cui, Yiyang Zhou, Xinyu Yang, Shirley Wu, Linjun Zhang, James Zou, Huaxiu Yao
Date: 2023-11-06 00:00:00
Tasks: Hallucination

While GPT-4V(ision) impressively models both visual and textual information simultaneously, it's hallucination behavior has not been systematically assessed. To bridge this gap, we introduce a new benchmark, namely, the Bias and Interference Challenges in Visual Language Models (Bingo). This benchmark is designed to evaluate and shed light on the two common types of hallucinations in visual language models: bias and interference. Here, bias refers to the model's tendency to hallucinate certain types of responses, possibly due to imbalance in its training data. Interference pertains to scenarios where the judgment of GPT-4V(ision) can be disrupted due to how the text prompt is phrased or how the input image is presented. We identify a notable regional bias, whereby GPT-4V(ision) is better at interpreting Western images or images with English writing compared to images from other countries or containing text in other languages. Moreover, GPT-4V(ision) is vulnerable to leading questions and is often confused when interpreting multiple images together. Popular mitigation approaches, such as self-correction and chain-of-thought reasoning, are not effective in resolving these challenges. We also identified similar biases and interference vulnerabilities with LLaVA and Bard. Our results characterize the hallucination challenges in GPT-4V(ision) and state-of-the-art visual-language models, and highlight the need for new solutions. The Bingo benchmark is available at https://github.com/gzcch/Bingo.