Enhancing Explainability in Mobility Data Science through a combination of methods

In the domain of Mobility Data Science, the intricate task of interpreting models trained on trajectory data, and elucidating the spatio-temporal movement of entities, has persistently posed significant challenges. Conventional XAI techniques, although brimming with potential, frequently overlook the distinct structure and nuances inherent within trajectory data. Observing this deficiency, we introduced a comprehensive framework that harmonizes pivotal XAI techniques: LIME (Local Interpretable Model-agnostic Explanations), SHAP (SHapley Additive exPlanations), Saliency maps, attention mechanisms, direct trajectory visualization, and Permutation Feature Importance (PFI). Unlike conventional strategies that deploy these methods singularly, our unified approach capitalizes on the collective efficacy of these techniques, yielding deeper and more granular insights for models reliant on trajectory data. In crafting this synthesis, we effectively address the multifaceted essence of trajectories, achieving not only amplified interpretability but also a nuanced, contextually rich comprehension of model decisions. To validate and enhance our framework, we undertook a survey to gauge preferences and reception among various user demographics. Our findings underscored a dichotomy: professionals with academic orientations, particularly those in roles like Data Scientist, IT Expert, and ML Engineer, showcased a profound, technical understanding and often exhibited a predilection for amalgamated methods for interpretability. Conversely, end-users or individuals less acquainted with AI and Data Science showcased simpler inclinations, such as bar plots indicating timestep significance or visual depictions pinpointing pivotal segments of a vessel's trajectory

Bulbous bow design optimization for fast ships

Thesis (Nav. E.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 1996.Includes bibliographical references (leaves 119-121).by Georgios Kyriazis.Nav.E

Transforming Sentiment Analysis in the Financial Domain with ChatGPT

Financial sentiment analysis plays a crucial role in decoding market trends and guiding strategic trading decisions. Despite the deployment of advanced deep learning techniques and language models to refine sentiment analysis in finance, this study breaks new ground by investigating the potential of large language models, particularly ChatGPT 3.5, in financial sentiment analysis, with a strong emphasis on the foreign exchange market (forex). Employing a zero-shot prompting approach, we examine multiple ChatGPT prompts on a meticulously curated dataset of forex-related news headlines, measuring performance using metrics such as precision, recall, f1-score, and Mean Absolute Error (MAE) of the sentiment class. Additionally, we probe the correlation between predicted sentiment and market returns as an additional evaluation approach. ChatGPT, compared to FinBERT, a well-established sentiment analysis model for financial texts, exhibited approximately 35\% enhanced performance in sentiment classification and a 36\% higher correlation with market returns. By underlining the significance of prompt engineering, particularly in zero-shot contexts, this study spotlights ChatGPT's potential to substantially boost sentiment analysis in financial applications. By sharing the utilized dataset, our intention is to stimulate further research and advancements in the field of financial services.Comment: 10 pages, 8 figures, Preprint submitted to Machine Learning with Application

XAI for time-series classification leveraging image highlight methods

Although much work has been done on explainability in the computer vision and natural language processing (NLP) fields, there is still much work to be done to explain methods applied to time series as time series by nature can not be understood at first sight. In this paper, we present a Deep Neural Network (DNN) in a teacher-student architecture (distillation model) that offers interpretability in time-series classification tasks. The explainability of our approach is based on transforming the time series to 2D plots and applying image highlight methods (such as LIME and GradCam), making the predictions interpretable. At the same time, the proposed approach offers increased accuracy competing with the baseline model with the trade-off of increasing the training time

XAI for All: Can Large Language Models Simplify Explainable AI?

The field of Explainable Artificial Intelligence (XAI) often focuses on users with a strong technical background, making it challenging for non-experts to understand XAI methods. This paper presents "x-[plAIn]", a new approach to make XAI more accessible to a wider audience through a custom Large Language Model (LLM), developed using ChatGPT Builder. Our goal was to design a model that can generate clear, concise summaries of various XAI methods, tailored for different audiences, including business professionals and academics. The key feature of our model is its ability to adapt explanations to match each audience group's knowledge level and interests. Our approach still offers timely insights, facilitating the decision-making process by the end users. Results from our use-case studies show that our model is effective in providing easy-to-understand, audience-specific explanations, regardless of the XAI method used. This adaptability improves the accessibility of XAI, bridging the gap between complex AI technologies and their practical applications. Our findings indicate a promising direction for LLMs in making advanced AI concepts more accessible to a diverse range of users

Assessment of visuo-spatial memory in patients with schizophrenia using the Location Learning Test

Recent studies give good evidence that memory impairment is one of the most profound cognitive deficits in schizophrenia. Multiple meta-analytic studies have demonstrated impairments especially in working and episodic memory including both its verbal and visual aspects. The scope of the present study was to examine the short and long term visuo-spatial memory in free immediate and delay recall conditions in patients with schizophrenia. We used Learning Location Test (LLT), a brief test designed initially to measure visuo-spatial recall and learning in older adults with possible dementia, as a new approach to the assessment of visuo-spatial memory and learning impairment in schizophrenia. We studied 30 pa­tients with schizophrenia in comparison with 30 normal subjects matched with socio-demographic parameters. Patients with schizophrenia performed significantly lower in all tasks of LLT compared to the healthy subjects. The comparison between the two diagnostic groups of patients (paranoid and non paranoid patients) did not show any statistically significant difference regarding the three main index of LLT. Furthermore, the results indicate that patients have a tendency to form two separate groups: one achieving “good scores” and one achieving “very bad scores”. In order to enhance the validity of the test and to reveal the characteristics of the two subgroups, further study in this population is needed

Inflammatory Markers in Middle-Aged Obese Subjects: Does Obstructive Sleep Apnea Syndrome Play a Role?

Background. Obstructive Sleep Apnea Syndrome (OSAS) is associated with inflammation, but obesity may be a confounding factor. Thus, the aim of this study was to explore differences in serum levels of inflammation markers between obese individuals with or without OSAS. Methods. Healthy individuals (n = 61) from an outpatient obesity clinic were examined by polysomnography and blood analysis, for measurement of TNF-α, IL-6, CRP, and fibrinogen levels. According to Apnea-Hypopnea Index (AHI), participants were divided into two BMI-matched groups: controls (AHI < 15/h, n = 23) and OSAS patients (AHI ≥ 15/h, n = 38). Results. OSAS patients had significantly higher TNF-α levels (P < .001) while no other difference in the examined inflammation markers was recorded between groups. Overall, TNF-α levels were correlated with neck circumference (P < .001), AHI (P = .002), and Oxygen Desaturation Index (P = .002). Conclusions. Obese OSAS patients have elevated TNF-α levels compared to BMI-matched controls, suggesting a role of OSAS in promoting inflammation, possibly mediated by TNF-a

Measuring and shimming the magnetic field of a 4 Tesla MRI magnet

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references.The Biomedical Magnetic Resonance Laboratory (BMRL) of the University of Illinois at Urbana-Champaign (UIUC) has ordered from the Texas Accelerator Center (TAC) a superconducting, self-shielded, solenoidal magnet with a maximum field of 4 Tesla and a bore diameter of 1.05 in. The magnet is going to be used for research on full-body MRI which is targeted to functional imaging of the human brain. This is a completely new and promising approach to MRI. The critical specification is that the magnetic field must be homogeneous within a 25 cm. radius around the center and within 10 ppm (parts per million). The whole design is very challenging, since not many superconducting magnets of such a big diameter and, at the same time, high field exist. Even more challenging is the fact that this is the first self-shielded magnet working at such a field, which poses many design problems. Additionally, the magnet is expected to operate with the same degree of homogeneity from 1.5 to 4 Tesla. Therefore, a system had to be implemented that would map the magnetic field and calculate the necessary adjustments to be made, so that changes from one field intensity to another would be fast. Needless to say, the field homogeneity of a magnet used for MRI is of crucial importance. In this thesis, first the design of the magnet in question is described, then the theory of measuring the magnetic field of such a magnet is presented. Consequently, the device and the program which were used are explained. Last, the measurement data taken on the MAGNEX magnet are presented