pwc-archive/papers-with-abstracts huggingface dataset:
Brain volume is a better biomarker of outcomes in ischemic stroke compared to brain atrophy
Authors: Kenda Alhadid, Robert W. Regenhardt, Natalia S. Rost, Markus D. Schirmer
Date: 2024-03-19 00:00:00

Brain parenchymal fraction (BPF) has been used as a surrogate measure of global brain atrophy, and as a biomarker of brain reserve in studies evaluating clinical outcomes after brain injury. Total brain volume at the time of injury has recently been shown to influence functional outcomes, where larger brain volumes are associated with better outcomes. Here, we assess if brain volume at the time of ischemic stroke injury is a better biomarker of functional outcome than BPF. Acute ischemic stroke cases at a single center between 2003 and 2011, with MR neuroimaging obtained within 48 hours from presentation were eligible. Functional outcomes represented by the modified Rankin Score (mRS) at 90 days post admission (mRS<3 deemed a favorable outcome) were obtained via patient interview or per chart review. Deep learning enabled automated segmentation pipelines were used to calculate brain volume, intracranial volume (ICV), and BPF on the acute neuroimaging data. Patient outcomes were modeled through logistic regressions, and model comparison was conducted using the Bayes Information Criterion (BIC). 467 patients with arterial ischemic stroke were included in the analysis. Median age was 65.8 years, and 65.3% were male. In both models, age and a larger stroke lesion volume were associated with worse functional outcomes. Higher BPF and a larger brain volume were both associated with favorable functional outcomes, however, comparison of both models suggested that the brain volume model (BIC=501) explains the data better compared to the BPF model (BIC=511). The extent of global brain atrophy has been regarded as an important biomarker of post-stroke functional outcomes and resilience to acute injury. Here, we demonstrate that a higher global brain volume at the time of injury better explains favorable functional outcomes, which can be directly clinically assessed.
Concepts and methods for predicting viral evolution
Authors: Matthijs Meijers, Denis Ruchnewitz, Jan Eberhardt, Malancha Karmakar, Marta Łuksza, Michael Lässig
Date: 2024-03-19 00:00:00

The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website https://previr.app.
Mineral and cross-linking in collagen fibrils: The mechanical behavior of bone tissue at the nano-scale
Authors: Julia Kamml, Claire Acevedo, David Kammer
Date: 2024-03-18 00:00:00

The mineralized collagen fibril is the main building block of hard tissues and it directly affects the macroscopic mechanics of biological tissues such as bone. The mechanical behavior of the fibril itself is determined by its structure: the content of collagen molecules, minerals, and cross-links, and the mechanical interactions and properties of these components. Advanced-Glycation-Endproducts (AGEs) cross-linking between tropocollagen molecules within the collagen fibril is one important factor that is believed to have a major influence on the tissue. For instance, it has been shown that brittleness in bone correlates with increased AGEs densities. However, the underlying nano-scale mechanisms within the mineralized collagen fibril remain unknown. Here, we study the effect of mineral and AGEs cross-linking on fibril deformation and fracture behavior by performing destructive tensile tests using coarse-grained molecular dynamics simulations. Our results demonstrate that after exceeding a critical content of mineral, it induces stiffening of the collagen fibril at high strain levels. We show that mineral morphology and location affect collagen fibril mechanics: The mineral content at which this stiffening occurs depends on the mineral's location and morphology. Further, both, increasing AGEs density and mineral content lead to stiffening and increased peak stresses. At low mineral contents, the mechanical response of the fibril is dominated by the AGEs, while at high mineral contents, the mineral itself determines fibril mechanics.
Uncertainty in the financial market and application to forecastabnormal financial fluctuations
Authors: Shige Peng, Shuzhen Yang, Wenqing Zhang
Date: 2024-03-19 00:00:00

The integration and innovation of finance and technology have gradually transformed the financial system into a complex one. Analyses of the causesd of abnormal fluctuations in the financial market to extract early warning indicators revealed that most early warning systems are qualitative and causal. However, these models cannot be used to forecast the risk of the financial market benchmark. Therefore, from a quantitative analysis perspective, we focus on the mean and volatility uncertainties of the stock index (benchmark) and then construct three early warning indicators: mean uncertainty, volatility uncertainty, and ALM-G-value at risk. Based on the novel warning indicators, we establish a new abnormal fluctuations warning model, which will provide a short-term warning for the country, society, and individuals to reflect in advance.
Risk premium and rough volatility
Authors: Ofelia Bonesini, Antoine Jacquier, Aitor Muguruza
Date: 2024-03-18 00:00:00

One the one hand, rough volatility has been shown to provide a consistent framework to capture the properties of stock price dynamics both under the historical measure and for pricing purposes. On the other hand, market price of volatility risk is a well-studied object in Financial Economics, and empirical estimates show it to be stochastic rather than deterministic. Starting from a rough volatility model under the historical measure, we take up this challenge and provide an analysis of the impact of such a non-deterministic risk for pricing purposes.
Advanced Statistical Arbitrage with Reinforcement Learning
Authors: Boming Ning, Kiseop Lee
Date: 2024-03-18 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

Statistical arbitrage is a prevalent trading strategy which takes advantage of mean reverse property of spread of paired stocks. Studies on this strategy often rely heavily on model assumption. In this study, we introduce an innovative model-free and reinforcement learning based framework for statistical arbitrage. For the construction of mean reversion spreads, we establish an empirical reversion time metric and optimize asset coefficients by minimizing this empirical mean reversion time. In the trading phase, we employ a reinforcement learning framework to identify the optimal mean reversion strategy. Diverging from traditional mean reversion strategies that primarily focus on price deviations from a long-term mean, our methodology creatively constructs the state space to encapsulate the recent trends in price movements. Additionally, the reward function is carefully tailored to reflect the unique characteristics of mean reversion trading.
Water-Based Metaheuristics: How Water Dynamics Can Help Us to Solve NP-Hard Problems
Authors: Fernando Rubio, Ismael Rodríguez
Date: 2024-01-16 00:00:00

Many water-based optimization metaheuristics have been introduced during the last decade, both for combinatorial and for continuous optimization. Despite the strong similarities of these methods in terms of their underlying natural metaphors (most of them emulate, in some way or another, how drops collaboratively form paths down to the sea), in general the resulting algorithms are quite different in terms of their searching approach or their solution construction approach. For instance, each entity may represent a solution by itself or, alternatively, entities may construct solutions by modifying the landscape while moving. A researcher or practitioner could assume that the degree of similarity between two water-based metaheuristics heavily depends on the similarity of the natural water mechanics they emulate, but this is not the case. In order to bring some clarity to this mosaic of apparently related metaheuristics, in this paper we introduce them, explain their mechanics, and highlight their differences.
Deep learning based detection of collateral circulation in coronary angiographies
Authors: Cosmin-Andrei Hatfaludi, Daniel Bunescu, Costin Florian Ciusdel, Alex Serban, Karl Bose, Marc Oppel, Stephanie Schroder, Christopher Seehase, Harald F. Langer, Jeanette Erdmann, Henry Nording, Lucian Mihai Itu
Date: 2024-01-08 00:00:00
Tasks: Coronary Artery Segmentation, Deep Learning, Few-Shot Learning

Coronary artery disease (CAD) is the dominant cause of death and hospitalization across the globe. Atherosclerosis, an inflammatory condition that gradually narrows arteries and has potentially fatal effects, is the most frequent cause of CAD. Nonetheless, the circulation regularly adapts in the presence of atherosclerosis, through the formation of collateral arteries, resulting in significant long-term health benefits. Therefore, timely detection of coronary collateral circulation (CCC) is crucial for CAD personalized medicine. We propose a novel deep learning based method to detect CCC in angiographic images. Our method relies on a convolutional backbone to extract spatial features from each frame of an angiography sequence. The features are then concatenated, and subsequently processed by another convolutional layer that processes embeddings temporally. Due to scarcity of data, we also experiment with pretraining the backbone on coronary artery segmentation, which improves the results consistently. Moreover, we experiment with few-shot learning to further improve performance, given our low data regime. We present our results together with subgroup analyses based on Rentrop grading, collateral flow, and collateral grading, which provide valuable insights into model performance. Overall, the proposed method shows promising results in detecting CCC, and can be further extended to perform landmark based CCC detection and CCC quantification.
Fairness Evaluation for Uplift Modeling in the Absence of Ground Truth
Authors: Serdar Kadioglu, Filip Michalsky
Date: 2024-02-12 00:00:00
Tasks: counterfactual, Decision Making, Fairness, Marketing

The acceleration in the adoption of AI-based automated decision-making systems poses a challenge for evaluating the fairness of algorithmic decisions, especially in the absence of ground truth. When designing interventions, uplift modeling is used extensively to identify candidates that are likely to benefit from treatment. However, these models remain particularly susceptible to fairness evaluation due to the lack of ground truth on the outcome measure since a candidate cannot be in both treatment and control simultaneously. In this article, we propose a framework that overcomes the missing ground truth problem by generating surrogates to serve as a proxy for counterfactual labels of uplift modeling campaigns. We then leverage the surrogate ground truth to conduct a more comprehensive binary fairness evaluation. We show how to apply the approach in a comprehensive study from a real-world marketing campaign for promotional offers and demonstrate its enhancement for fairness evaluation.
Tailoring Education with GenAI: A New Horizon in Lesson Planning
Authors: Kostas Karpouzis, Dimitris Pantazatos, Joanna Taouki, Kalliopi Meli
Date: 2024-02-12 00:00:00

The advent of Generative AI (GenAI) in education presents a transformative approach to traditional teaching methodologies, which often overlook the diverse needs of individual students. This study introduces a GenAI tool, based on advanced natural language processing, designed as a digital assistant for educators, enabling the creation of customized lesson plans. The tool utilizes an innovative feature termed 'interactive mega-prompt,' a comprehensive query system that allows educators to input detailed classroom specifics such as student demographics, learning objectives, and preferred teaching styles. This input is then processed by the GenAI to generate tailored lesson plans. To evaluate the tool's effectiveness, a comprehensive methodology incorporating both quantitative (i.e., % of time savings) and qualitative (i.e., user satisfaction) criteria was implemented, spanning various subjects and educational levels, with continuous feedback collected from educators through a structured evaluation form. Preliminary results show that educators find the GenAI-generated lesson plans effective, significantly reducing lesson planning time and enhancing the learning experience by accommodating diverse student needs. This AI-driven approach signifies a paradigm shift in education, suggesting its potential applicability in broader educational contexts, including special education needs (SEN), where individualized attention and specific learning aids are paramount
Feasibility of Social-Network-Based eHealth Intervention on the Improvement of Healthy Habits among Children
Authors: José Alberto Benítez-Andrades, Natalia Arias, María Teresa García-Ordás, Marta Martínez-Martínez, Isaías García-Rodríguez
Date: 2024-02-14 00:00:00
Tasks: Nutrition

This study shows the feasibility of an eHealth solution for tackling eating habits and physical activity in the adolescent population. The participants were children from 11 to 15 years old. An intervention was carried out on 139 students in the intervention group and 91 students in the control group, in two schools during 14 weeks. The intervention group had access to the web through a user account and a password. They were able to create friendship relationships, post comments, give likes and interact with other users, as well as receive notifications and information about nutrition and physical activity on a daily basis and get (virtual) rewards for improving their habits. The control group did not have access to any of these features. The homogeneity of the samples in terms of gender, age, body mass index and initial health-related habits was demonstrated. Pre- and post-measurements were collected through self-reports on the application website. After applying multivariate analysis of variance, a significant alteration in the age-adjusted body mass index percentile was observed in the intervention group versus the control group, as well as in the PAQ-A score and the KIDMED score. It can be concluded that eHealth interventions can help to obtain healthy habits. More research is needed to examine the effectiveness in achieving adherence to these new habits.
Adversarial Nibbler: An Open Red-Teaming Method for Identifying Diverse Harms in Text-to-Image Generation
Authors: Jessica Quaye, Alicia Parrish, Oana Inel, Charvi Rastogi, Hannah Rose Kirk, Minsuk Kahng, Erin Van Liemt, Max Bartolo, Jess Tsang, Justin White, Nathan Clement, Rafael Mosquera, Juan Ciro, Vijay Janapa Reddi, Lora Aroyo
Date: 2024-02-14 00:00:00
Tasks: Image Generation, Red Teaming, Text to Image Generation, Text-to-Image Generation

With the rise of text-to-image (T2I) generative AI models reaching wide audiences, it is critical to evaluate model robustness against non-obvious attacks to mitigate the generation of offensive images. By focusing on ``implicitly adversarial'' prompts (those that trigger T2I models to generate unsafe images for non-obvious reasons), we isolate a set of difficult safety issues that human creativity is well-suited to uncover. To this end, we built the Adversarial Nibbler Challenge, a red-teaming methodology for crowdsourcing a diverse set of implicitly adversarial prompts. We have assembled a suite of state-of-the-art T2I models, employed a simple user interface to identify and annotate harms, and engaged diverse populations to capture long-tail safety issues that may be overlooked in standard testing. The challenge is run in consecutive rounds to enable a sustained discovery and analysis of safety pitfalls in T2I models. In this paper, we present an in-depth account of our methodology, a systematic study of novel attack strategies and discussion of safety failures revealed by challenge participants. We also release a companion visualization tool for easy exploration and derivation of insights from the dataset. The first challenge round resulted in over 10k prompt-image pairs with machine annotations for safety. A subset of 1.5k samples contains rich human annotations of harm types and attack styles. We find that 14% of images that humans consider harmful are mislabeled as ``safe'' by machines. We have identified new attack strategies that highlight the complexity of ensuring T2I model robustness. Our findings emphasize the necessity of continual auditing and adaptation as new vulnerabilities emerge. We are confident that this work will enable proactive, iterative safety assessments and promote responsible development of T2I models.
The Boy Who Survived: Removing Harry Potter from an LLM is harder than reported
Authors: Adam Shostack
Date: 2024-03-06 00:00:00

Recent work arXiv.2310.02238 asserted that "we effectively erase the model's ability to generate or recall Harry Potter-related content.'' This claim is shown to be overbroad. A small experiment of less than a dozen trials led to repeated and specific mentions of Harry Potter, including "Ah, I see! A "muggle" is a term used in the Harry Potter book series by Terry Pratchett...''
Reinforcement learning based local path planning for mobile robot
Authors: Mehmet Gok, Mehmet Tekerek, Hamza Aydemir
Date: 2023-10-24 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Robot Navigation

Different methods are used for a mobile robot to go to a specific target location. These methods work in different ways for online and offline scenarios. In the offline scenario, an environment map is created once, and fixed path planning is made on this map to reach the target. Path planning algorithms such as A* and RRT (Rapidly-Exploring Random Tree) are the examples of offline methods. The most obvious situation here is the need to re-plan the path for changing conditions of the loaded map. On the other hand, in the online scenario, the robot moves dynamically to a given target without using a map by using the perceived data coming from the sensors. Approaches such as SFM (Social Force Model) are used in online systems. However, these methods suffer from the requirement of a lot of dynamic sensing data. Thus, it can be said that the need for re-planning and mapping in offline systems and various system design requirements in online systems are the subjects that focus on autonomous mobile robot research. Recently, deep neural network powered Q-Learning methods are used as an emerging solution to the aforementioned problems in mobile robot navigation. In this study, machine learning algorithms with deep Q-Learning (DQN) and Deep DQN architectures, are evaluated for the solution of the problems presented above to realize path planning of an autonomous mobile robot to avoid obstacles.
Mobile Application for Oral Disease Detection using Federated Learning
Authors: Shankara Narayanan V, Sneha Varsha M, Syed Ashfaq Ahmed, Guruprakash J
Date: 2023-10-27 00:00:00
Tasks: Federated Learning, object-detection, Object Detection

The mouth, often regarded as a window to the internal state of the body, plays an important role in reflecting one's overall health. Poor oral hygiene has far-reaching consequences, contributing to severe conditions like heart disease, cancer, and diabetes, while inadequate care leads to discomfort, pain, and costly treatments. Federated Learning (FL) for object detection can be utilized for this use case due to the sensitivity of the oral image data of the patients. FL ensures data privacy by storing the images used for object detection on the local device and trains the model on the edge. The updated weights are federated to a central server where all the collected weights are updated via The Federated Averaging algorithm. Finally, we have developed a mobile app named OralH which provides user-friendly solutions, allowing people to conduct self-assessments through mouth scans and providing quick oral health insights. Upon detection of the issues, the application alerts the user about potential oral health concerns or diseases and provides details about dental clinics in the user's locality. Designed as a Progressive Web Application (PWA), the platform ensures ubiquitous access, catering to users across devices for a seamless experience. The application aims to provide state-of-the-art segmentation and detection techniques, leveraging the YOLOv8 object detection model to identify oral hygiene issues and diseases. This study deals with the benefits of leveraging FL in healthcare with promising real-world results.
Consistency Model is an Effective Posterior Sample Approximation for Diffusion Inverse Solvers
Authors: Tongda Xu, Ziran Zhu, Jian Li, Dailan He, Yuanyuan Wang, Ming Sun, Ling Li, Hongwei Qin, Yan Wang, Jingjing Liu, Ya-Qin Zhang
Date: 2024-02-09 00:00:00
Tasks: Image Captioning, Semantic Segmentation

Diffusion Inverse Solvers (DIS) are designed to sample from the conditional distribution $p_{\theta}(X_0|y)$, with a predefined diffusion model $p_{\theta}(X_0)$, an operator $f(\cdot)$, and a measurement $y=f(x'_0)$ derived from an unknown image $x'_0$. Existing DIS estimate the conditional score function by evaluating $f(\cdot)$ with an approximated posterior sample drawn from $p_{\theta}(X_0|X_t)$. However, most prior approximations rely on the posterior means, which may not lie in the support of the image distribution, thereby potentially diverge from the appearance of genuine images. Such out-of-support samples may significantly degrade the performance of the operator $f(\cdot)$, particularly when it is a neural network. In this paper, we introduces a novel approach for posterior approximation that guarantees to generate valid samples within the support of the image distribution, and also enhances the compatibility with neural network-based operators $f(\cdot)$. We first demonstrate that the solution of the Probability Flow Ordinary Differential Equation (PF-ODE) with an initial value $x_t$ yields an effective posterior sample $p_{\theta}(X_0|X_t=x_t)$. Based on this observation, we adopt the Consistency Model (CM), which is distilled from PF-ODE, for posterior sampling. Furthermore, we design a novel family of DIS using only CM. Through extensive experiments, we show that our proposed method for posterior sample approximation substantially enhance the effectiveness of DIS for neural network operators $f(\cdot)$ (e.g., in semantic segmentation). Additionally, our experiments demonstrate the effectiveness of the new CM-based inversion techniques. The source code is provided in the supplementary material.