Zobrazeno 1 - 10
of 2 931
pro vyhledávání: '"P, BRAZIL"'
Autor:
Priyadarsini, Indra, Takeda, Seiji, Hamada, Lisa, Brazil, Emilio Vital, Soares, Eduardo, Shinohara, Hajime
Large-scale molecular representation methods have revolutionized applications in material science, such as drug discovery, chemical modeling, and material design. With the rise of transformers, models now learn representations directly from molecular
Externí odkaz:
http://arxiv.org/abs/2410.12348
Autor:
Soares, Eduardo, Shirasuna, Victor, Brazil, Emilio Vital, Cerqueira, Renato, Zubarev, Dmitry, Schmidt, Kristin
Large-scale pre-training methodologies for chemical language models represent a breakthrough in cheminformatics. These methods excel in tasks such as property prediction and molecule generation by learning contextualized representations of input toke
Externí odkaz:
http://arxiv.org/abs/2407.20267
Autor:
Shuaib, Haris, Barker, Gareth J, Sasieni, Peter, De Vita, Enrico, Chelliah, Alysha, Andrei, Roman, Ashkan, Keyoumars, Beaumont, Erica, Brazil, Lucy, Rowland-Hill, Chris, Lau, Yue Hui, Luis, Aysha, Powell, James, Swampillai, Angela, Tenant, Sean, Thust, Stefanie C, Wastling, Stephen, Young, Tom, Booth, Thomas C
Objective: To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrating deep-learning models in glioblastoma care pathways. Additionally, to understand the most common i
Externí odkaz:
http://arxiv.org/abs/2405.05980
Autor:
Pozdniakov, Stanislav, Brazil, Jonathan, Abdi, Solmaz, Bakharia, Aneesha, Sadiq, Shazia, Gasevic, Dragan, Denny, Paul, Khosravi, Hassan
Incorporating Generative AI (GenAI) and Large Language Models (LLMs) in education can enhance teaching efficiency and enrich student learning. Current LLM usage involves conversational user interfaces (CUIs) for tasks like generating materials or pro
Externí odkaz:
http://arxiv.org/abs/2404.11072
Autor:
Fox, Geoffrey, Thomas, Mary P, Bhatia, Sajal, Brazil, Marisa, Gasparini, Nicole M, Merwade, Venkatesh Mohan, Neeman, Henry J., Carver, Jeff, Casanova, Henri, Chaudhary, Vipin, Colbry, Dirk, Crosby, Lonnie, Dewan, Prasun, Eisma, Jessica, Irfan, Ahmed, Kaehey, Kate, Liu, Qianqian, Ni, Zhen, Prasad, Sushil, Qasem, Apan, Saule, Erik, Sundaravadivel, Prabha, Tomko, Karen
This document describes a two-day meeting held for the Principal Investigators (PIs) of NSF CyberTraining grants. The report covers invited talks, panels, and six breakout sessions. The meeting involved over 80 PIs and NSF program managers (PMs). The
Externí odkaz:
http://arxiv.org/abs/2312.14199
Autor:
Soares, Eduardo, Kishimoto, Akihiro, Brazil, Emilio Vital, Takeda, Seiji, Kajino, Hiroshi, Cerqueira, Renato
Pre-trained Language Models have emerged as promising tools for predicting molecular properties, yet their development is in its early stages, necessitating further research to enhance their efficacy and address challenges such as generalization and
Externí odkaz:
http://arxiv.org/abs/2310.13802
Publikováno v:
BMC Psychology, Vol 12, Iss 1, Pp 1-15 (2024)
Abstract Background The academic development and widespread adoption of meditation practices for well-being and therapy have predominantly focused on secularised adaptations of Buddhist and Hindu techniques. This study aims to expand the field by inv
Externí odkaz:
https://doaj.org/article/78f033454dad47b4bd1a68f368719bb1
Autor:
Dimana V. Atanassova, Christoph Mathys, Andreea O. Diaconescu, Victor I. Madariaga, Joukje M. Oosterman, Inti A. Brazil
Publikováno v:
Communications Psychology, Vol 2, Iss 1, Pp 1-12 (2024)
Abstract Individuals with elevated psychopathic traits exhibit decision-making deficits linked to a failure to learn from negative outcomes. We investigated how reduced pain sensitivity affects reinforcement-based decision-making in individuals with
Externí odkaz:
https://doaj.org/article/be006fd1ba1849e2920834384b7440c4
The construction of 3D geological models is an essential task in oil/gas exploration, development and production. However, it is a cumbersome, time-consuming and error-prone task mainly because of the model's geometric and topological complexity. The
Externí odkaz:
http://arxiv.org/abs/2308.12152
Autor:
Soares, Eduardo, Brazil, Emilio Vital, Gutierrez, Karen Fiorela Aquino, Cerqueira, Renato, Sanders, Dan, Schmidt, Kristin, Zubarev, Dmitry
We present a novel multimodal language model approach for predicting molecular properties by combining chemical language representation with physicochemical features. Our approach, MULTIMODAL-MOLFORMER, utilizes a causal multistage feature selection
Externí odkaz:
http://arxiv.org/abs/2306.14919