While Large Language Models (LLMs) become ever more dominant, classic pre-trained word embeddings sustain their relevance through computational efficiency and nuanced linguistic interpretation. Drawing from recent studies demonstrating that the convergence of GloVe and word2vec optimizations all tend towards log-co-occurrence matrix variants, we construct a novel word representation system called Bit-cipher that eliminates the need of backpropagation while leveraging contextual information and hyper-efficient dimensionality reduction techniques based on unigram frequency, providing strong interpretability, alongside efficiency. We use the bit-cipher algorithm to train word vectors via a two-step process that critically relies on a hyperparameter -- bits -- that controls the vector dimension. While the first step trains the bit-cipher, the second utilizes it under two different aggregation modes -- summation or concatenation -- to produce contextually rich representations from word co-occurrences. We extend our investigation into bit-cipher's efficacy, performing probing experiments on part-of-speech (POS) tagging and named entity recognition (NER) to assess its competitiveness with classic embeddings like word2vec and GloVe. Additionally, we explore its applicability in LM training and fine-tuning. By replacing embedding layers with cipher embeddings, our experiments illustrate the notable efficiency of cipher in accelerating the training process and attaining better optima compared to conventional training paradigms. Experiments on the integration of bit-cipher embedding layers with Roberta, T5, and OPT, prior to or as a substitute for fine-tuning, showcase a promising enhancement to transfer learning, allowing rapid model convergence while preserving competitive performance.

Global floods, exacerbated by climate change, pose severe threats to human life, infrastructure, and the environment. This urgency is highlighted by recent catastrophic events in Pakistan and New Zealand, underlining the critical need for precise flood mapping for guiding restoration efforts, understanding vulnerabilities, and preparing for future events. While Synthetic Aperture Radar (SAR) offers day-and-night, all-weather imaging capabilities, harnessing it for deep learning is hindered by the absence of a large annotated dataset. To bridge this gap, we introduce Kuro Siwo, a meticulously curated multi-temporal dataset, spanning 32 flood events globally. Our dataset maps more than 63 billion m2 of land, with 12.1 billion of them being either a flooded area or a permanent water body. Kuro Siwo stands out for its unparalleled annotation quality to facilitate rapid flood mapping in a supervised setting. We also augment learning by including a large unlabeled set of SAR samples, aimed at self-supervised pretraining. We provide an extensive benchmark and strong baselines for a diverse set of flood events from Europe, America, Africa and Australia. Our benchmark demonstrates the quality of Kuro Siwo annotations, training models that can achieve $\approx$ 85% and $\approx$ 87% in F1-score for flooded areas and general water detection respectively. This work calls on the deep learning community to develop solution-driven algorithms for rapid flood mapping, with the potential to aid civil protection and humanitarian agencies amid climate change challenges. Our code and data will be made available at https://github.com/Orion-AI-Lab/KuroSiwo

The rise of advanced technology in project management (PM) highlights a crucial need for inclusiveness. This work examines the enhancement of both inclusivity and efficiency in PM through technological integration, focusing on defining and measuring inclusiveness. This approach illuminates how inclusivity-centered technology can significantly elevate project outcomes. The research navigates through the challenges of achieving inclusivity, mainly biases in learning databases and the design process of these technologies, assessment of transformative potential of these technologies, particularly in automating tasks like data collection and analysis, thus enabling managers to prioritize human-centric aspects of projects. However, the integration of such technology transcends efficiency, indicating a paradigm shift in understanding their societal roles. This shift necessitates a new approach in the development of these systems to prevent perpetuating social inequalities. We proposed a methodology involving criteria development for evaluating the inclusiveness and effectiveness of these technologies. This methodical approach is vital to comprehensively address the challenges and limitations inherent in these systems. Emphasizing the importance of inclusivity, the study advocates for a balance between technological advancement and ethical considerations, calling for a holistic understanding and regulation. In conclusion, the paper underscores that while these technologies can significantly improve outcomes, their mindful integration, ensuring inclusivity, is paramount. This exploration into the ethical and practical aspects of technology in PM contributes to a more informed and balanced approach within the field.

Single cell analysis of skeletal muscle (SM) tissue is a fundamental tool for understanding many neuromuscular disorders. For this analysis to be reliable and reproducible, identification of individual fibres within microscopy images (segmentation) of SM tissue should be precise. There is currently no tool or pipeline that makes automatic and precise segmentation and curation of images of SM tissue cross-sections possible. Biomedical scientists in this field rely on custom tools and general machine learning (ML) models, both followed by labour intensive and subjective manual interventions to get the segmentation right. We believe that automated, precise, reproducible segmentation is possible by training ML models. However, there are currently no good quality, publicly available annotated imaging datasets available for ML model training. In this paper we release NCL-SM: a high quality bioimaging dataset of 46 human tissue sections from healthy control subjects and from patients with genetically diagnosed muscle pathology. These images include $>$ 50k manually segmented muscle fibres (myofibres). In addition we also curated high quality myofibres and annotated reasons for rejecting low quality myofibres and regions in SM tissue images, making this data completely ready for downstream analysis. This, we believe, will pave the way for development of a fully automatic pipeline that identifies individual myofibres within images of tissue sections and, in particular, also classifies individual myofibres that are fit for further analysis.

We introduce Adapters, an open-source library that unifies parameter-efficient and modular transfer learning in large language models. By integrating 10 diverse adapter methods into a unified interface, Adapters offers ease of use and flexible configuration. Our library allows researchers and practitioners to leverage adapter modularity through composition blocks, enabling the design of complex adapter setups. We demonstrate the library's efficacy by evaluating its performance against full fine-tuning on various NLP tasks. Adapters provides a powerful tool for addressing the challenges of conventional fine-tuning paradigms and promoting more efficient and modular transfer learning. The library is available via https://adapterhub.ml/adapters.

AI-based medical technologies, including wearables, telemedicine, LLMs, and digital care twins, significantly impact healthcare. Ensuring AI results are accurate and interpretable is crucial, especially for clinicians. This paper reviews processes and challenges of interpretable ML (IML) and explainable AI (XAI) in healthcare. Objectives include reviewing XAI processes, methods, applications, and challenges, with a focus on quality control. The IML process is classified into data pre-processing interpretability, interpretable modeling, and post-processing interpretability. The paper aims to establish the importance of robust interpretability in healthcare through experimental results, providing insights for creating communicable clinician-AI tools. Research questions, eligibility criteria, and goals were identified following PRISMA and PICO methods. PubMed, Scopus, and Web of Science were systematically searched using specific strings. The survey introduces a step-by-step roadmap for implementing XAI in clinical applications, addressing existing gaps and acknowledging XAI model limitations.

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible.

Distribution shifts are characterized by differences between the training and test data distributions. They can significantly reduce the accuracy of machine learning models deployed in real-world scenarios. This paper explores the distribution shift problem when classifying pollen grains from microscopic images collected in the wild with a low-cost camera sensor. We leverage the domain knowledge that geometric features are highly important for accurate pollen identification and introduce two novel geometric image augmentation techniques to significantly narrow the accuracy gap between the model performance on the train and test datasets. In particular, we show that Tenengrad and ImageToSketch filters are highly effective to balance the shape and texture information while leaving out unimportant details that may confuse the model. Extensive evaluations on various model architectures demonstrate a consistent improvement of the model generalization to field data of up to 14% achieved by the geometric augmentation techniques when compared to a wide range of standard image augmentations. The approach is validated through an ablation study using pollen hydration tests to recover the shape of dry pollen grains. The proposed geometric augmentations also receive the highest scores according to the affinity and diversity measures from the literature.

Efficient and accurate product relevance assessment is critical for user experiences and business success. Training a proficient relevance assessment model requires high-quality query-product pairs, often obtained through negative sampling strategies. Unfortunately, current methods introduce pooling bias by mistakenly sampling false negatives, diminishing performance and business impact. To address this, we present Bias-mitigating Hard Negative Sampling (BHNS), a novel negative sampling strategy tailored to identify and adjust for false negatives, building upon our original False Negative Estimation algorithm. Our experiments in the Instacart search setting confirm BHNS as effective for practical e-commerce use. Furthermore, comparative analyses on public dataset showcase its domain-agnostic potential for diverse applications.

Various adaptation methods, such as LoRA, prompts, and adapters, have been proposed to enhance the performance of pre-trained vision-language models in specific domains. The robustness of these adaptation methods against distribution shifts have not been studied. In this study, we assess the robustness of 11 widely-used adaptation methods across 4 vision-language datasets under multimodal corruptions. Concretely, we introduce 7 benchmark datasets, including 96 visual and 87 textual corruptions, to investigate the robustness of different adaptation methods, the impact of available adaptation examples, and the influence of trainable parameter size during adaptation. Our analysis reveals that: 1) Adaptation methods are more sensitive to text corruptions than visual corruptions. 2) Full fine-tuning does not consistently provide the highest robustness; instead, adapters can achieve better robustness with comparable clean performance. 3) Contrary to expectations, our findings indicate that increasing the number of adaptation data and parameters does not guarantee enhanced robustness; instead it results in even lower robustness. We hope this study could benefit future research in the development of robust multimodal adaptation methods. The benchmark, code, and dataset used in this study can be accessed at https://adarobustness.github.io .