JWST-TST High Contrast: Achieving direct spectroscopy of faint substellar companions next to bright stars with the NIRSpec IFU

Posted by dbos MIT

Kavli Affiliate: Sara Seager | First 5 Authors: Jean-Baptiste Ruffio, Marshall D. Perrin, Kielan K. W. Hoch, Jens Kammerer, Quinn M. Konopacky | Summary: The JWST NIRSpec integral field unit (IFU) presents a unique opportunity to observe directly imaged exoplanets from 3-5 um at moderate spectral resolution (R~2,700) and thereby better constrain the composition, disequilibrium […]


Continue.. JWST-TST High Contrast: Achieving direct spectroscopy of faint substellar companions next to bright stars with the NIRSpec IFU

JWST-TST High Contrast: Achieving direct spectroscopy of faint substellar companions next to bright stars with the NIRSpec IFU

Posted by dbos MIT

Kavli Affiliate: Sara Seager | First 5 Authors: Jean-Baptiste Ruffio, Marshall D. Perrin, Kielan K. W. Hoch, Jens Kammerer, Quinn M. Konopacky | Summary: The JWST NIRSpec integral field unit (IFU) presents a unique opportunity to observe directly imaged exoplanets from 3-5um at moderate spectral resolution (R~2,700) and thereby better constrain the composition, disequilibrium chemistry, […]


Continue.. JWST-TST High Contrast: Achieving direct spectroscopy of faint substellar companions next to bright stars with the NIRSpec IFU

Federated Multi-Objective Learning

Posted by dbos Tokyo

Kavli Affiliate: Jia Liu | First 5 Authors: Haibo Yang, Zhuqing Liu, Jia Liu, Chaosheng Dong, Michinari Momma | Summary: In recent years, multi-objective optimization (MOO) emerges as a foundational problem underpinning many multi-agent multi-task learning applications. However, existing algorithms in MOO literature remain limited to centralized learning settings, which do not satisfy the distributed […]


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Federated Multi-Objective Learning

Posted by dbos Tokyo

Kavli Affiliate: Jia Liu | First 5 Authors: Haibo Yang, Zhuqing Liu, Jia Liu, Chaosheng Dong, Michinari Momma | Summary: In recent years, multi-objective optimization (MOO) emerges as a foundational problem underpinning many multi-agent multi-task learning applications. However, existing algorithms in MOO literature remain limited to centralized learning settings, which do not satisfy the distributed […]


Continue.. Federated Multi-Objective Learning

Federated Multi-Objective Learning

Posted by dbos Tokyo

Kavli Affiliate: Jia Liu | First 5 Authors: Haibo Yang, Zhuqing Liu, Jia Liu, Chaosheng Dong, Michinari Momma | Summary: In recent years, multi-objective optimization (MOO) emerges as a foundational problem underpinning many multi-agent multi-task learning applications. However, existing algorithms in MOO literature remain limited to centralized learning settings, which do not satisfy the distributed […]


Continue.. Federated Multi-Objective Learning

Enhancing Stance Classification on Social Media Using Quantified Moral Foundations

Posted by Caltech

Kavli Affiliate: Wei Gao | First 5 Authors: Hong Zhang, Quoc-Nam Nguyen, Prasanta Bhattacharya, Wei Gao, Liang Ze Wong | Summary: This study enhances stance detection on social media by incorporating deeper psychological attributes, specifically individuals’ moral foundations. These theoretically-derived dimensions aim to provide a comprehensive profile of an individual’s moral concerns which, in recent […]


Continue.. Enhancing Stance Classification on Social Media Using Quantified Moral Foundations

Enhancing Stance Classification on Social Media Using Quantified Moral Foundations

Posted by Caltech

Kavli Affiliate: Wei Gao | First 5 Authors: Hong Zhang, Prasanta Bhattacharya, Wei Gao, Liang Ze Wong, Brandon Siyuan Loh | Summary: This study enhances stance detection on social media by incorporating deeper psychological attributes, specifically individuals’ moral foundations. These theoretically-derived dimensions aim to provide a comprehensive profile of an individual’s moral concerns which, in […]


Continue.. Enhancing Stance Classification on Social Media Using Quantified Moral Foundations

Enhancing Stance Classification with Quantified Moral Foundations

Posted by Caltech

Kavli Affiliate: Wei Gao | First 5 Authors: Hong Zhang, Prasanta Bhattacharya, Wei Gao, Liang Ze Wong, Brandon Siyuan Loh | Summary: This study enhances stance detection on social media by incorporating deeper psychological attributes, specifically individuals’ moral foundations. These theoretically-derived dimensions aim to provide a comprehensive profile of an individual’s moral concerns which, in […]


Continue.. Enhancing Stance Classification with Quantified Moral Foundations

Diversity of visual inputs to Kenyon cells of the Drosophila mushroom body

Posted by dbos Columbia

Kavli Affiliate: Rudy Behnia | Authors: Ishani Ganguly, Emily L. Heckman, Ashok Litwin-Kumar, E. Josephine Clowney and Rudy Behnia | Summary: The arthropod mushroom body is well-studied as an expansion layer that represents olfactory stimuli and links them to contingent events. However, 8% of mushroom body Kenyon cells in Drosophila melanogaster receive predominantly visual input, […]


Continue.. Diversity of visual inputs to Kenyon cells of the Drosophila mushroom body

Representations of information value in mouse orbitofrontal cortex during information seeking

Posted by dbos Columbia

Kavli Affiliate: Richard Axel | Authors: Jennifer J Bussell, Ryan P Badman, Christian David Márton, Ethan S Bromberg-Martin, LF Abbott, Kanaka Rajan and Richard Axel | Summary: HAnimals are motivated to acquire knowledge of their world. They seek information that does not influence reward outcomes suggesting that information has intrinsic value. We have asked whether […]


Continue.. Representations of information value in mouse orbitofrontal cortex during information seeking