Disorganized Inhibitory Dynamics in Hippocampal area CA1 of 22q11.2 Deletion Mutant Mice

Posted by dbos Columbia

Kavli Affiliate: Attila Losonczy | Authors: Stephanie A Herrlinger, Bovey Rao, Margaret E Conde Paredes, Anna L Tuttman, Haroon Arain, Erdem Varol, Joseph A Gogos and Attila Losonczy | Summary: Individuals with the 22q11.2 deletion syndrome, one of the strongest genetic risk factors for schizophrenia, demonstrate cognitive impairments, including episodic memory dysfunction. Place cell activity […]


Continue.. Disorganized Inhibitory Dynamics in Hippocampal area CA1 of 22q11.2 Deletion Mutant Mice

Revealing Short- and Long-range Li-ion diffusion in Li$_2$MnO$_3$ from finite-temperature dynamical mean field theory

Posted by klaurent Berkeley

Kavli Affiliate: Kristin Persson | Summary:Li$_2$MnO$_3$ is a key component of Li-excess layered cathodes of the form $(1-x),mathrmLiMO_2 + x,mathrmLi_2MnO_3$ ($M$ = Mn, Ni, Co, dots), yet its role in setting Li-ion transport limitations remains under debate. Here we combine DFT+$U$, finite-temperature DFT+DMFT with a continuous-time quantum Monte Carlo impurity solver, and nudged-elastic-band (NEB) calculations […]


Continue.. Revealing Short- and Long-range Li-ion diffusion in Li$_2$MnO$_3$ from finite-temperature dynamical mean field theory

Revealing Short- and Long-range Li-ion diffusion in Li$_2$MnO$_3$ from finite-temperature dynamical mean field theory

Posted by klaurent Berkeley

Kavli Affiliate: Kristin Persson | Summary:Li$_2$MnO$_3$ remains a crucial component of the Li-excess layered cathode family, $(1-x),mathrmLiMO_2 + x,mathrmLi_2MnO_3$ ($M$ = Mn, Ni, Co, dots), but its role in limiting Li-ion mobility remains under debate. Here we combine DFT+$U$, finite-temperature DMFT with a continuous-time quantum Monte Carlo impurity solver, and nudged-elastic-band (NEB) calculations to investigate […]


Continue.. Revealing Short- and Long-range Li-ion diffusion in Li$_2$MnO$_3$ from finite-temperature dynamical mean field theory

Alteration of Water Exchange Rates Following Focused Ultrasound-Mediated BBB Opening in the Dorsal Striatum of Non-Human Primates: A Diffusion-Prepared pCASL Study

Posted by dbos Columbia

Kavli Affiliate: Vincent Ferrera | Authors: Dong Liu, Xingfeng Shao, Fabian A Munoz Silva, Soroosh Sanatkhani, Ray Lee, Elisa Konofagou, Danny JJ Wang and Vincent P Ferrera | Summary: This study applied diffusion-prepared pseudo-continuous arterial spin labeling (DP-pCASL) to quantify cerebral blood flow (CBF), arterial transit time (ATT), and blood-brain barrier (BBB) water exchange rate […]


Continue.. Alteration of Water Exchange Rates Following Focused Ultrasound-Mediated BBB Opening in the Dorsal Striatum of Non-Human Primates: A Diffusion-Prepared pCASL Study

Lightcurve Modelling of 2,205 ZTF DR2 Type~Ia Supernovae: Implications for SN Ia Physics and Cosmology

Posted by klaurent Cambridge

Kavli Affiliate: Kaisey Mandel | Summary:We fit the multi-band light curves of 2,205 Type Ia supernovae (SNe~Ia) from the Zwicky Transient Facility DR2 with a one-zone radioactive decay model with a phenomenological addition to include Fe recombination physics. We find a strong correlation between inferred nickel mass and SALT2 stretch, which within our simplified modelling […]


Continue.. Lightcurve Modelling of 2,205 ZTF DR2 Type~Ia Supernovae: Implications for SN Ia Physics and Cosmology

Visualizing the Odd-parity Superconducting Order Parameter and its Quasiparticle Surface Band in UTe2

Posted by klaurent Cornell

Kavli Affiliate: J. C. Seamus Davis | Summary:A distinctive identifier of nodal intrinsic topological superconductivity (ITS) would the appearance of an Andreev bound state on crystal surfaces parallel to the nodal axis, in the form of a topological quasiparticle surface band (QSB) appearing only for $T < T_C$. Moreover, theory shows that specific QSB characteristics […]


Continue.. Visualizing the Odd-parity Superconducting Order Parameter and its Quasiparticle Surface Band in UTe2

Preserve-Then-Quantize: Balancing Rank Budgets for Quantization Error Reconstruction in LLMs

Posted by klaurent Stanford

Kavli Affiliate: Hsiao-Mei (Sherry) Cho| First 5 Authors: [#item_custom_name[1, [#item_custom_name[2, [#item_custom_name[3, [#item_custom_name[4, [#item_custom_name[5| Summary:Quantization Error Reconstruction (QER) reduces accuracy loss in Post-Training Quantization (PTQ) by approximating weights as $mathbfW approx mathbfQ + mathbfLmathbfR$, using a rank-$r$ correction to reconstruct quantization error. Prior methods devote the full rank budget to error reconstruction, which is suboptimal when […]


Continue.. Preserve-Then-Quantize: Balancing Rank Budgets for Quantization Error Reconstruction in LLMs

Enhancing Multi-Image Understanding through Delimiter Token Scaling

Posted by klaurent Stanford

Kavli Affiliate: Hsiao-Mei (Sherry) Cho| First 5 Authors: [#item_custom_name[1, [#item_custom_name[2, [#item_custom_name[3, [#item_custom_name[4, [#item_custom_name[5| Summary:Large Vision-Language Models (LVLMs) achieve strong performance on single-image tasks, but their performance declines when multiple images are provided as input. One major reason is the cross-image information leakage, where the model struggles to distinguish information across different images. Existing LVLMs already […]


Continue.. Enhancing Multi-Image Understanding through Delimiter Token Scaling

Physics-Informed Neural Networks for Modeling Galactic Gravitational Potentials

Posted by klaurent MIT

Kavli Affiliate: Lina Necib | Summary:We introduce a physics-informed neural framework for modeling static and time-dependent galactic gravitational potentials. The method combines data-driven learning with embedded physical constraints to capture complex, small-scale features while preserving global physical consistency. We quantify predictive uncertainty through a Bayesian framework, and model time evolution using a neural ODE approach. […]


Continue.. Physics-Informed Neural Networks for Modeling Galactic Gravitational Potentials

Physics-Informed Neural Networks for Modeling Galactic Gravitational Potentials

Posted by klaurent MIT

Kavli Affiliate: Lina Necib | Summary:We introduce a physics-informed neural framework for modeling static and time-dependent galactic gravitational potentials. The method combines data-driven learning with embedded physical constraints to capture complex, small-scale features while preserving global physical consistency. We quantify predictive uncertainty through a Bayesian framework, and model time evolution using a neural ODE approach. […]


Continue.. Physics-Informed Neural Networks for Modeling Galactic Gravitational Potentials