Revolutionizing educational landscapes: A systematic review of Metaverse applications, paradigms and emerging technologies by Yihe Qian, Jinpeng Wang, Yixin Cai

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Generative Spoken Dialogue Language Modeling by Tu Anh Nguyen, Eugene Kharitonov, Jade Copet, Yossi Adi, Wei-Ning Hsu, Ali Elkahky, Paden Tomasello, Robin Algayres, Benoît Sagot, Abdelrahman Mohamed, Emmanuel Dupoux

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AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentials by Janice Lan, Aini Palizhati, Muhammed Shuaibi, Brandon M. Wood, Brook Wander, Abhishek Das, Matt Uyttendaele, C. Lawrence Zitnick, Zachary W. Ulissi

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DP-Parse: Finding Word Boundaries from Raw Speech with an Instance Lexicon by Robin Algayres, Tristan Ricoul, Julien Karadayi, Hugo Laurençon, Salah Zaiem, Abdelrahman Mohamed, Benoît Sagot, Emmanuel Dupoux

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Tracking the neural codes for words and phrases during semantic composition, working-memory storage, and retrieval by Théo Desbordes, Jean-Rémi King, Stanislas Dehaene

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Questions Are All You Need to Train a Dense Passage Retriever by Devendra Singh Sachan, Mike Lewis, Dani Yogatama, Luke Zettlemoyer, Joelle Pineau, Manzil Zaheer

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Secure human action recognition by encrypted neural network inference by Miran Kim, Xiaoqian Jiang, Kristin Lauter, Elkhan Ismayilzada, Shayan Shams

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Predicting cellular responses to complex perturbations in high‐throughput screens by Mohammad Lotfollahi, Anna Klimovskaia Susmelj, Carlo De Donno, Leon Hetzel, Yuge Ji, Ignacio L Ibarra, Sanjay R Srivatsan, Mohsen Naghipourfar, Riza M Daza, Beth Martin, Jay Shendure, Jose L McFaline‐Figueroa, Pierre Boyeau, F Alexander Wolf, Nafissa Yakubova, Stephan Günnemann, Cole Trapnell, David Lopez‐Paz, Fabian J Theis

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The Segment Anything Model (SAM) for accelerating the smart farming revolution by Alberto Carraro, Marco Sozzi, Francesco Marinello

“…The so-called foundation models in Artificial Intelligence (AI) are becoming increasingly significant. Meta AI Research is working on a project called Segment Anything to provide a base model for image segmentation. …”
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ProoFVer: Natural Logic Theorem Proving for Fact Verification by Amrith Krishna, Sebastian Riedel, Andreas Vlachos

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Artificial intelligence: reflecting on the past and looking towards the next paradigm shift by Radanliev, P

“…This paper surveys the historical progress of AI from its origins in logic-based systems like the Logic Theorist to recent deep learning breakthroughs like Bidirectional Encoder Representations from Transformers (BERT), Generative Pretrained Transformer 3 (GPT-3) and Large Language Model Meta AI (LLaMA). The early rule-based systems using handcrafted expertise gave way to statistical learning techniques and neural networks trained on large datasets. …”

Interactive active learning for fairness with partial group label by Zeyu Yang, Jizhi Zhang, Fuli Feng, Chongming Gao, Qifan Wang, Xiangnan He

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Semantic segmentation of glaciological features across multiple remote sensing platforms with the Segment Anything Model (SAM) by Siddharth Shankar, Leigh A. Stearns, C. J. van der Veen

“…In this study, we assess the utility of the Segment Anything Model (SAM), released by Meta AI Research, for cryosphere research. SAM is a foundational AI model that generates segmentation masks without additional training data. …”
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Accuracy of generative artificial intelligence models in differential diagnoses of familial Mediterranean fever and deficiency of Interleukin-1 receptor antagonist by Joshua Pillai, Kathryn Pillai

“…In this work, the diagnostic accuracy of the Generative Pre-Trained Transformer Model-4 (GPT-4), GPT-3.5, and Large Language Model Meta AI (LLaMa) were evaluated in clinical vignettes of Deficiency of Interleukin-1 Receptor Antagonist (DIRA) and Familial Mediterranean Fever (FMF). …”
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Long-Term Visitation Value for Deep Exploration in Sparse-Reward Reinforcement Learning by Simone Parisi, Davide Tateo, Maximilian Hensel, Carlo D’Eramo, Jan Peters, Joni Pajarinen

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Computers’ Interpretations of Knowledge Representation Using Pre-Conceptual Schemas: An Approach Based on the BERT and Llama 2-Chat Models by Jesus Insuasti, Felipe Roa, Carlos Mario Zapata-Jaramillo

“…The linguistic corpus was mainly fed using Master’s and doctoral theses from the digital repository of the University of Nariño to produce a training dataset for re-training the BERT model; in addition, we complement this by explaining the elicited sentences in triads from the pre-conceptual schemas using one of the cutting-edge large language models in natural language processing: Llama 2-Chat by Meta AI. The diverse topics covered in these theses allowed us to expand the spectrum of linguistic use in the BERT model and empower the generative capabilities using the fine-tuned Llama 2-Chat model and the proposed solution. …”
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A newcomer’s guide to deep learning for inverse design in nano-photonics by Khaireh-Walieh Abdourahman, Langevin Denis, Bennet Pauline, Teytaud Olivier, Moreau Antoine, Wiecha Peter R.

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Analyzing Data Modalities for Cattle Weight Estimation Using Deep Learning Models by Hina Afridi, Mohib Ullah, Øyvind Nordbø, Solvei Cottis Hoff, Siri Furre, Anne Guro Larsgard, Faouzi Alaya Cheikh

“…We explore a recent vision-transformer-based zero-shot model proposed by Meta AI Research for producing the segmentation data modality and for extracting the cattle-only region from the images. …”
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The Segment Anything Model (SAM) for remote sensing applications: From zero to one shot by Lucas Prado Osco, Qiusheng Wu, Eduardo Lopes de Lemos, Wesley Nunes Gonçalves, Ana Paula Marques Ramos, Jonathan Li, José Marcato, Junior

“…This study aims to advance the application of the Segment Anything Model (SAM), an innovative image segmentation model by Meta AI, in the field of remote sensing image analysis. …”
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Evaluating the Efficacy of Segment Anything Model for Delineating Agriculture and Urban Green Spaces in Multiresolution Aerial and Spaceborne Remote Sensing Images by Baoling Gui, Anshuman Bhardwaj, Lydia Sam

“…Notably, the newly released Segment Anything Model (SAM) by META AI is gaining significant recognition in various domains for segmenting conventional images, yielding commendable results. …”
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