Recommendation systems are widely used in e-commerce websites and online platforms to address information overload. However, existing systems primarily rely on historical data and user feedback, making it difficult to capture user intent transitions. Recently, Knowledge Base (KB)-based models are proposed to incorporate expert knowledge, but it struggle to adapt to new items and the evolving e-commerce environment. To address these challenges, we propose a novel Large Language Model based Complementary Knowledge Enhanced Recommendation System (LLM-KERec). It introduces an entity extractor that extracts unified concept terms from item and user information. To provide cost-effective and reliable prior knowledge, entity pairs are generated based on entity popularity and specific strategies. The large language model determines complementary relationships in each entity pair, constructing a complementary knowledge graph. Furthermore, a new complementary recall module and an Entity-Entity-Item (E-E-I) weight decision model refine the scoring of the ranking model using real complementary exposure-click samples. Extensive experiments conducted on three industry datasets demonstrate the significant performance improvement of our model compared to existing approaches. Additionally, detailed analysis shows that LLM-KERec enhances users' enthusiasm for consumption by recommending complementary items. In summary, LLM-KERec addresses the limitations of traditional recommendation systems by incorporating complementary knowledge and utilizing a large language model to capture user intent transitions, adapt to new items, and enhance recommendation efficiency in the evolving e-commerce landscape.

Artificial Intelligence (AI) plays a critical role in the advancement of autonomous driving. It is likely the main facilitator of high levels of automation, as there are certain technical issues that only seem to be resolvable through advanced AI systems, particularly those based on machine learning. However, the introduction of AI systems in the realm of driver assistance systems and automated driving systems creates new uncertainties due to specific characteristics of AI that make it a distinct technology from traditional systems developed in the field of motor vehicles. Some of these characteristics include unpredictability, opacity, self and continuous learning and lack of causality [1], among other horizontal features such as autonomy, complexity, overfitting and bias. As an example of the specificity that the introduction of AI systems in vehicles entails, the UNECE's Working Party on Automated/Autonomous and Connected Vehicles (GRVA) has been specifically discussing the impact of AI on vehicle regulations since 2020 [2].

Addressing heart failure (HF) as a prevalent global health concern poses difficulties in implementing innovative approaches for enhanced patient care. Predicting mortality rates in HF patients, in particular, is difficult yet critical, necessitating individualized care, proactive management, and enabling educated decision-making to enhance outcomes. Recently, the significance of voice biomarkers coupled with Machine Learning (ML) has surged, demonstrating remarkable efficacy, particularly in predicting heart failure. The synergy of voice analysis and ML algorithms provides a non-invasive and easily accessible means to evaluate patients' health. However, there is a lack of voice biomarkers for predicting mortality rates among heart failure patients with standardized speech protocols. Here, we demonstrate a powerful and effective ML model for predicting mortality rates in hospitalized HF patients through the utilization of voice biomarkers. By seamlessly integrating voice biomarkers into routine patient monitoring, this strategy has the potential to improve patient outcomes, optimize resource allocation, and advance patient-centered HF management. In this study, a Machine Learning system, specifically a logistic regression model, is trained to predict patients' 5-year mortality rates using their speech as input. The model performs admirably and consistently, as demonstrated by cross-validation and statistical approaches (p-value < 0.001). Furthermore, integrating NT-proBNP, a diagnostic biomarker in HF, improves the model's predictive accuracy substantially.

Ensembles are important tools for improving the performance of machine learning models. In cases related to natural language processing, ensembles boost the performance of a method due to multiple large models available in open source. However, existing approaches mostly rely on simple averaging of predictions by ensembles with equal weights for each model, ignoring differences in the quality and conformity of models. We propose to estimate weights for ensembles of NLP models using not only knowledge of their individual performance but also their similarity to each other. By adopting distance measures based on Topological Data Analysis (TDA), we improve our ensemble. The quality improves for both text classification accuracy and relevant uncertainty estimation.

On January 28, 2022, at 6.39 a.m. EST, the Fern Hollow Bridge in Pittsburgh, Pennsylvania collapsed. Due to the timing of the failure, thankfully, fewer vehicles were on the bridge and only ten people were injured with no fatalities. Pittsburgh, also known as the City of Bridges, was getting ready for a visit from President Biden that day. Biden visited the collapse site and assured federal assistance to rebuild the bridge on the spot. This infrastructural failure, coinciding with a high-profile political visit and a push towards passing the Build Back Better infrastructure bill, attracted considerable media attention to the flailing infrastructural health in the US. As we were sifting through the social web discussions surrounding this issue, broad themes such as words of compassion for the victims and typical responses in social web political discourse such as political name-calling, conspiracy theories, and partisan mud-slinging emerged. However, apart from these expected social web reactions, we noticed a small minority of interactions that talked about anticipatory failures of other bridges in the US.

Scientific facts are often spun in the popular press with the intent to influence public opinion and action, as was evidenced during the COVID-19 pandemic. Automatic detection of misinformation in the scientific domain is challenging because of the distinct styles of writing in these two media types and is still in its nascence. Most research on the validity of scientific reporting treats this problem as a claim verification challenge. In doing so, significant expert human effort is required to generate appropriate claims. Our solution bypasses this step and addresses a more real-world scenario where such explicit, labeled claims may not be available. The central research question of this paper is whether it is possible to use large language models (LLMs) to detect misinformation in scientific reporting. To this end, we first present a new labeled dataset SciNews, containing 2.4k scientific news stories drawn from trusted and untrustworthy sources, paired with related abstracts from the CORD-19 database. Our dataset includes both human-written and LLM-generated news articles, making it more comprehensive in terms of capturing the growing trend of using LLMs to generate popular press articles. Then, we identify dimensions of scientific validity in science news articles and explore how this can be integrated into the automated detection of scientific misinformation. We propose several baseline architectures using LLMs to automatically detect false representations of scientific findings in the popular press. For each of these architectures, we use several prompt engineering strategies including zero-shot, few-shot, and chain-of-thought prompting. We also test these architectures and prompting strategies on GPT-3.5, GPT-4, and Llama2-7B, Llama2-13B.

Since the seminal paper by Tversky and Kahneman, the conjunction fallacy has been the subject of multiple debates and become a fundamental challenge for cognitive theories in decision-making. In this article, we take a rather uncommon perspective on this phenomenon. Instead of trying to explain the nature or causes of the conjunction fallacy (intensional definition), we analyze its range of factual possibilities (extensional definition). We show that the majority of research on the conjunction fallacy, according to our sample of experiments reviewed which covers literature between 1983 and 2016, has focused on a narrow part of the a priori factual possibilities, implying that explanations of the conjunction fallacy are fundamentally biased by the short scope of possibilities explored. The latter is a rather curious aspect of the research evolution in the conjunction fallacy considering that the very nature of it is motivated by extensional considerations.

In recent years, self-supervised learning has emerged as a powerful tool to harness abundant unlabelled data for representation learning and has been broadly adopted in diverse areas. However, when applied to molecular representation learning (MRL), prevailing techniques such as masked sub-unit reconstruction often fall short, due to the high degree of freedom in the possible combinations of atoms within molecules, which brings insurmountable complexity to the masking-reconstruction paradigm. To tackle this challenge, we introduce REMO, a self-supervised learning framework that takes advantage of well-defined atom-combination rules in common chemistry. Specifically, REMO pre-trains graph/Transformer encoders on 1.7 million known chemical reactions in the literature. We propose two pre-training objectives: Masked Reaction Centre Reconstruction (MRCR) and Reaction Centre Identification (RCI). REMO offers a novel solution to MRL by exploiting the underlying shared patterns in chemical reactions as \textit{context} for pre-training, which effectively infers meaningful representations of common chemistry knowledge. Such contextual representations can then be utilized to support diverse downstream molecular tasks with minimum finetuning, such as affinity prediction and drug-drug interaction prediction. Extensive experimental results on MoleculeACE, ACNet, drug-drug interaction (DDI), and reaction type classification show that across all tested downstream tasks, REMO outperforms the standard baseline of single-molecule masked modeling used in current MRL. Remarkably, REMO is the pioneering deep learning model surpassing fingerprint-based methods in activity cliff benchmarks.

Neural Machine Translation (NMT) continues to improve in quality and adoption, yet the inadvertent perpetuation of gender bias remains a significant concern. Despite numerous studies on gender bias in translations into English from weakly gendered-languages, there are no benchmarks for evaluating this phenomenon or for assessing mitigation strategies. To address this gap, we introduce GATE X-E, an extension to the GATE (Rarrick et al., 2023) corpus, that consists of human translations from Turkish, Hungarian, Finnish, and Persian into English. Each translation is accompanied by feminine, masculine, and neutral variants. The dataset, which contains between 1250 and 1850 instances for each of the four language pairs, features natural sentences with a wide range of sentence lengths and domains, challenging translation rewriters on various linguistic phenomena. Additionally, we present a translation gender rewriting solution built with GPT-4 and use GATE X-E to evaluate it. We open source our contributions to encourage further research on gender debiasing.

The online diffusion of information related to Europe and migration has been little investigated from an external point of view. However, this is a very relevant topic, especially if users have had no direct contact with Europe and its perception depends solely on information retrieved online. In this work we analyse the information circulating online about Europe and migration after retrieving a large amount of data from social media (Twitter), to gain new insights into topics, magnitude, and dynamics of their diffusion. We combine retweets and hashtags network analysis with geolocation of users, linking thus data to geography and allowing analysis from an "outside Europe" perspective, with a special focus on Africa. We also introduce a novel approach based on cross-lingual quotes, i.e. when content in a language is commented and retweeted in another language, assuming these interactions are a proxy for connections between very distant communities. Results show how the majority of online discussions occurs at a national level, especially when discussing migration. Language (English) is pivotal for information to become transnational and reach far. Transnational information flow is strongly unbalanced, with content mainly produced in Europe and amplified outside. Conversely Europe-based accounts tend to be self-referential when they discuss migration-related topics. Football is the most exported topic from Europe worldwide. Moreover, important nodes in the communities discussing migration-related topics include accounts of official institutions and international agencies, together with journalists, news, commentators and activists.