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30 May

BioBERT-based Deep Learning and Merged ChemProt-DrugProt for Enhanced Biomedical Relation Extraction

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arXiv:2405.18605v1 Announce Type: new Abstract: This paper presents a methodology for enhancing relation extraction from biomedical texts, focusing specifically on chemical-gene interactions. Leveraging the BioBERT model and a multi-layer fully connected network architecture, our approach integrates the ChemProt and DrugProt datasets using a novel merging strategy. Through extensive experimentation, we demonstrate significant performance improvements, particularly in CPR groups shared between the datasets. The findings underscore the importance of dataset merging in augmenting sample counts and improving model accuracy. Moreover, the study highlights the potential of automated information extraction in biomedical research and clinical practice. Source link lol

30 May

IBM Unveils watsonx BI assistant to Simplify Business Decision-Making

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via Shutterstock As the business world eagerly embraces the potential of GenAI, many companies are discovering that there are still lots of challenges along the way. One of the key challenges is complexity. Companies are struggling to use decision-making tools because they are too complex. This is evident in the lack of growth in the adoption of business intelligence (BI) and data analytics tools. The percentage of employees actively using BI and analytics tools currently stands at only 25% on average. (Laborant/Shutterstock) Despite advances in technology and investment in literacy programs to educate employees on the use of data, the…

30 May

Introducing Salesforce BYOM for Databricks

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Salesforce and Databricks are excited to announce an expanded strategic partnership that delivers a powerful new integration - Salesforce Bring Your Own Model (BYOM) for Databricks. This collaboration enables data scientists and machine learning engineers to seamlessly leverage the best of both worlds: the robust customer data and business capabilities in Salesforce and the advanced analytics and AI capabilities of Databricks. With this integration, you can now build, train, and deploy custom AI models in Databricks and effortlessly integrate them into Salesforce to deliver intelligent and personalized customer experiences. Get ready to unlock the full potential of your data and…

30 May

Injecting Hierarchical Biological Priors into Graph Neural Networks for Flow Cytometry Prediction

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arXiv:2405.18507v1 Announce Type: new Abstract: In the complex landscape of hematologic samples such as peripheral blood or bone marrow derived from flow cytometry (FC) data, cell-level prediction presents profound challenges. This work explores injecting hierarchical prior knowledge into graph neural networks (GNNs) for single-cell multi-class classification of tabular cellular data. By representing the data as graphs and encoding hierarchical relationships between classes, we propose our hierarchical plug-in method to be applied to several GNN models, namely, FCHC-GNN, and effectively designed to capture neighborhood information crucial for single-cell FC domain. Extensive experiments on our cohort of 19 distinct patients, demonstrate that…

30 May

Anomaly detection for the identification of volcanic unrest in satellite imagery

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[Submitted on 28 May 2024] View a PDF of the paper titled Anomaly detection for the identification of volcanic unrest in satellite imagery, by Robert Gabriel Popescu and 2 other authors View PDF HTML (experimental) Abstract:Satellite images have the potential to detect volcanic deformation prior to eruptions, but while a vast number of images are routinely acquired, only a small percentage contain volcanic deformation events. Manual inspection could miss these anomalies, and an automatic system modelled with supervised learning requires suitably labelled datasets. To tackle these issues, this paper explores the use of unsupervised deep learning on satellite data for…

30 May

Learning diverse attacks on large language models for robust red-teaming and safety tuning

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arXiv:2405.18540v1 Announce Type: new Abstract: Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe and responsible deployment of large language models (LLMs). Developing effective protection against many modes of attack prompts requires discovering diverse attacks. Automated red-teaming typically uses reinforcement learning to fine-tune an attacker language model to generate prompts that elicit undesirable responses from a target LLM, as measured, for example, by an auxiliary toxicity classifier. We show that even with explicit regularization to favor novelty and diversity, existing approaches suffer from mode collapse or fail to generate effective attacks. As a…

30 May

The Unified Balance Theory of Second-Moment Exponential Scaling Optimizers in Visual Tasks

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arXiv:2405.18498v1 Announce Type: new Abstract: We have identified a potential method for unifying first-order optimizers through the use of variable Second-Moment Exponential Scaling(SMES). We begin with back propagation, addressing classic phenomena such as gradient vanishing and explosion, as well as issues related to dataset sparsity, and introduce the theory of balance in optimization. Through this theory, we suggest that SGD and adaptive optimizers can be unified under a broader inference, employing variable moving exponential scaling to achieve a balanced approach within a generalized formula for first-order optimizers. We conducted tests on some classic datasets and networks to confirm the impact…

30 May

Towards Open Domain Text-Driven Synthesis of Multi-Person Motions

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arXiv:2405.18483v1 Announce Type: new Abstract: This work aims to generate natural and diverse group motions of multiple humans from textual descriptions. While single-person text-to-motion generation is extensively studied, it remains challenging to synthesize motions for more than one or two subjects from in-the-wild prompts, mainly due to the lack of available datasets. In this work, we curate human pose and motion datasets by estimating pose information from large-scale image and video datasets. Our models use a transformer-based diffusion framework that accommodates multiple datasets with any number of subjects or frames. Experiments explore both generation of multi-person static poses and generation…

30 May

LLMs and Memorization: On Quality and Specificity of Copyright Compliance

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arXiv:2405.18492v1 Announce Type: new Abstract: Memorization in large language models (LLMs) is a growing concern. LLMs have been shown to easily reproduce parts of their training data, including copyrighted work. This is an important problem to solve, as it may violate existing copyright laws as well as the European AI Act. In this work, we propose a systematic analysis to quantify the extent of potential copyright infringements in LLMs using European law as an example. Unlike previous work, we evaluate instruction-finetuned models in a realistic end-user scenario. Our analysis builds on a proposed threshold of 160 characters, which we borrow…

30 May

A Comparison of Past, Present, and Future Payroll Systems

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Payroll systems have made remarkable progress from their inception to the digital era. In this article, we explore the evolution and revolutionary impact of technology on payroll management, helping readers better understand this critical business operation's future. Over the years, payroll systems have evolved tremendously. Payroll management has evolved into a sophisticated digital system that automates what used to be a manual process. To understand how emerging trends will shape the future of payroll systems, we'll look at their past, current state in the digital era, and potential. Taking a look back Starting at the beginning, let's look back at…