\textbf{Objectives}: We aimed to investigate how errors from automatic speech recognition (ASR) systems affect dementia classification accuracy, specifically in the ``Cookie Theft'' picture description task. We aimed to assess whether imperfect ASR-generated transcripts could provide valuable information for distinguishing between language samples from cognitively healthy individuals and those with Alzheimer's disease (AD). \textbf{Methods}: We conducted experiments using various ASR models, refining their transcripts with post-editing techniques. Both these imperfect ASR transcripts and manually transcribed ones were used as inputs for the downstream dementia classification. We conducted comprehensive error analysis to compare model performance and assess ASR-generated transcript effectiveness in dementia classification. \textbf{Results}: Imperfect ASR-generated transcripts surprisingly outperformed manual transcription for distinguishing between individuals with AD and those without in the ``Cookie Theft'' task. These ASR-based models surpassed the previous state-of-the-art approach, indicating that ASR errors may contain valuable cues related to dementia. The synergy between ASR and classification models improved overall accuracy in dementia classification. \textbf{Conclusion}: Imperfect ASR transcripts effectively capture linguistic anomalies linked to dementia, improving accuracy in classification tasks. This synergy between ASR and classification models underscores ASR's potential as a valuable tool in assessing cognitive impairment and related clinical applications.

Machine learning models typically focus on specific targets like creating classifiers, often based on known population feature distributions in a business context. However, models calculating individual features adapt over time to improve precision, introducing the concept of decoupling: shifting from point evaluation to data distribution. We use calibration strategies as strategy for decoupling machine learning (ML) classifiers from score-based actions within business logic frameworks. To evaluate these strategies, we perform a comparative analysis using a real-world business scenario and multiple ML models. Our findings highlight the trade-offs and performance implications of the approach, offering valuable insights for practitioners seeking to optimize their decoupling efforts. In particular, the Isotonic and Beta calibration methods stand out for scenarios in which there is shift between training and testing data.

We consider the task of identifying the causal parents of a target variable among a set of candidate variables from observational data. Our main assumption is that the candidate variables are observed in different environments which may, for example, correspond to different settings of a machine or different time intervals in a dynamical process. Under certain assumptions different environments can be regarded as interventions on the observed system. We assume a linear relationship between target and covariates, which can be different in each environment with the only restriction that the causal structure is invariant across environments. This is an extension of the ICP ($\textbf{I}$nvariant $\textbf{C}$ausal $\textbf{P}$rediction) principle by Peters et al. [2016], who assumed a fixed linear relationship across all environments. Within our proposed setting we provide sufficient conditions for identifiability of the causal parents and introduce a practical method called LoLICaP ($\textbf{Lo}$cally $\textbf{L}$inear $\textbf{I}$nvariant $\textbf{Ca}$usal $\textbf{P}$rediction), which is based on a hypothesis test for parent identification using a ratio of minimum and maximum statistics. We then show in a simplified setting that the statistical power of LoLICaP converges exponentially fast in the sample size, and finally we analyze the behavior of LoLICaP experimentally in more general settings.

Projected to impact 1.6 million people in the UK by 2040 and costing {\pounds}25 billion annually, dementia presents a growing challenge to society. This study, a pioneering effort to predict the translational potential of dementia research using machine learning, hopes to address the slow translation of fundamental discoveries into practical applications despite dementia's significant societal and economic impact. We used the Dimensions database to extract data from 43,091 UK dementia research publications between the years 1990-2023, specifically metadata (authors, publication year etc.), concepts mentioned in the paper, and the paper abstract. To prepare the data for machine learning we applied methods such as one hot encoding and/or word embeddings. We trained a CatBoost Classifier to predict if a publication will be cited in a future patent or clinical trial. We trained several model variations. The model combining metadata, concept, and abstract embeddings yielded the highest performance: for patent predictions, an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.84 and 77.17% accuracy; for clinical trial predictions, an AUROC of 0.81 and 75.11% accuracy. The results demonstrate that integrating machine learning within current research methodologies can uncover overlooked publications, expediting the identification of promising research and potentially transforming dementia research by predicting real-world impact and guiding translational strategies.

Research in toxicity detection in natural language processing for the speech modality (audio-based) is quite limited, particularly for languages other than English. To address these limitations and lay the groundwork for truly multilingual audio-based toxicity detection, we introduce MuTox, the first highly multilingual audio-based dataset with toxicity labels. The dataset comprises 20,000 audio utterances for English and Spanish, and 4,000 for the other 19 languages. To demonstrate the quality of this dataset, we trained the MuTox audio-based toxicity classifier, which enables zero-shot toxicity detection across a wide range of languages. This classifier outperforms existing text-based trainable classifiers by more than 1% AUC, while expanding the language coverage more than tenfold. When compared to a wordlist-based classifier that covers a similar number of languages, MuTox improves precision and recall by approximately 2.5 times. This significant improvement underscores the potential of MuTox in advancing the field of audio-based toxicity detection.

Large language models (LLMs) are powerful dialogue agents, but specializing them towards fulfilling a specific function can be challenging. Instructing tuning, i.e. tuning models on instruction and sample responses generated by humans (Ouyang et al., 2022), has proven as an effective method to do so, yet requires a number of data samples that a) might not be available or b) costly to generate. Furthermore, this cost increases when the goal is to make the LLM follow a specific workflow within a dialogue instead of single instructions. Inspired by the self-play technique in reinforcement learning and the use of LLMs to simulate human agents, we propose a more effective method for data collection through LLMs engaging in a conversation in various roles. This approach generates a training data via "self-talk" of LLMs that can be refined and utilized for supervised fine-tuning. We introduce an automated way to measure the (partial) success of a dialogue. This metric is used to filter the generated conversational data that is fed back in LLM for training. Based on our automated and human evaluations of conversation quality, we demonstrate that such self-talk data improves results. In addition, we examine the various characteristics that showcase the quality of generated dialogues and how they can be connected to their potential utility as training data.

he evaluation of the impact of actions undertaken is essential in management. This paper assesses the impact of efforts considered to mitigate risk and create safe environments on a global scale. We measure this impact by looking at the probability of improvement over a specific short period of time. Using the World Risk Index, we conduct a temporal analysis of global disaster risk dynamics from 2011 to 2021. This temporal exploration through the lens of the World Risk Index provides insights into the complex dynamics of disaster risk. We found that, despite sustained efforts, the global landscape remains divided into two main clusters: high susceptibility and moderate susceptibility, regardless of geographical location. This clustering was achieved using a semi-supervised approach through the Label Spreading algorithm, with 98% accuracy. We also found that the prediction of clusters achieved through supervised learning on the period considered in this study (one, three, and five years) showed that the Logistic regression (almost 99% at each stage) performed better than other classifiers. This suggests that the current policies and mechanisms are not effective in helping countries move from a hazardous position to a safer one during the period considered. In fact, statistical projections using a scenario analysis indicate that there is only a 1% chance of such a shift occurring within a five-year timeframe. This sobering reality highlights the need for a paradigm shift. Traditional long-term disaster management strategies are not effective for countries that are highly vulnerable. Our findings indicate the need for an innovative approach that is tailored to the specific vulnerabilities of these nations. As the threat of vulnerability persists, our research calls for the development of new strategies that can effectively address the ongoing challenges of disaster risk management

Alzheimer's disease (AD), the predominant form of dementia, poses a growing global challenge and underscores the urgency of accurate and early diagnosis. The clinical technique radiologists adopt for distinguishing between mild cognitive impairment (MCI) and AD using Machine Resonance Imaging (MRI) encounter hurdles because they are not consistent and reliable. Machine learning has been shown to offer promise for early AD diagnosis. However, existing models focused on focal fine-grain features without considerations to focal structural features that give off information on neurodegeneration of the brain cerebral cortex. Therefore, this paper proposes a machine learning (ML) framework that integrates Gamma correction, an image enhancement technique, and includes a structure-focused neurodegeneration convolutional neural network (CNN) architecture called SNeurodCNN for discriminating between AD and MCI. The ML framework leverages the mid-sagittal and para-sagittal brain image viewpoints of the structure-focused Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Through experiments, our proposed machine learning framework shows exceptional performance. The parasagittal viewpoint set achieves 97.8% accuracy, with 97.0% specificity and 98.5% sensitivity. The midsagittal viewpoint is shown to present deeper insights into the structural brain changes given the increase in accuracy, specificity, and sensitivity, which are 98.1% 97.2%, and 99.0%, respectively. Using GradCAM technique, we show that our proposed model is capable of capturing the structural dynamics of MCI and AD which exist about the frontal lobe, occipital lobe, cerebellum, and parietal lobe. Therefore, our model itself as a potential brain structural change Digi-Biomarker for early diagnosis of AD.

The development of facial biometric systems has contributed greatly to the development of the computer vision field. Nowadays, there's always a need to develop a multimodal system that combines multiple biometric traits in an efficient, meaningful way. In this paper, we introduce "IdentiFace" which is a multimodal facial biometric system that combines the core of facial recognition with some of the most important soft biometric traits such as gender, face shape, and emotion. We also focused on developing the system using only VGG-16 inspired architecture with minor changes across different subsystems. This unification allows for simpler integration across modalities. It makes it easier to interpret the learned features between the tasks which gives a good indication about the decision-making process across the facial modalities and potential connection. For the recognition problem, we acquired a 99.2% test accuracy for five classes with high intra-class variations using data collected from the FERET database[1]. We achieved 99.4% on our dataset and 95.15% on the public dataset[2] in the gender recognition problem. We were also able to achieve a testing accuracy of 88.03% in the face-shape problem using the celebrity face-shape dataset[3]. Finally, we achieved a decent testing accuracy of 66.13% in the emotion task which is considered a very acceptable accuracy compared to related work on the FER2013 dataset[4].

The prevalence of dementia has increased over time as global life expectancy improves and populations age. An individual's risk of developing dementia is influenced by various genetic, lifestyle, and environmental factors, among others. Predicting dementia risk may enable individuals to employ mitigation strategies or lifestyle changes to delay dementia onset. Current computational approaches to dementia prediction only return risk upon narrow categories of variables and do not account for interactions between different risk variables. The proposed framework utilizes a novel holistic approach to dementia risk prediction and is the first to incorporate various sources of tabular environmental pollution and lifestyle factor data with network systems biology-based genetic data. LightGBM gradient boosting was employed to ensure validity of included factors. This approach successfully models interactions between variables through an original weighted integration method coined Sysable. Multiple machine learning models trained the algorithm to reduce reliance on a single model. The developed approach surpassed all existing dementia risk prediction approaches, with a sensitivity of 85%, specificity of 99%, geometric accuracy of 92%, and AUROC of 91.7%. A transfer learning model was implemented as well. De-biasing algorithms were run on the model via the AI Fairness 360 Library. Effects of demographic disparities on dementia prevalence were analyzed to potentially highlight areas in need and promote equitable and accessible care. The resulting model was additionally integrated into a user-friendly app providing holistic predictions and personalized risk mitigation strategies. The developed model successfully employs holistic computational dementia risk prediction for clinical use.