Claims Browser

A new sepsis index using mortality as a treatment-level ranking signal rather than per-state target allows credit redistribution non-uniformly across timesteps, improving on previous approaches

"Unlike previous studies, we use mortality as a treatment-level ranking signal rather than a per-state target, allowing credit to be redistributed non-uniformly across timesteps."

The new sepsis index separates non-survivors from survivors by 1.19-1.64 points on a 0-10 scale across all baseline SOFA-2 strata

"Under this ranking scheme, non-survivors scored 1.19-1.64 points higher than survivors on a 0-10 scale within all strata of baseline SOFA-2, with similar results stratifying within lactate, mean arterial pressure (MAP), and creatinine."

Cross-institutional agreement for the sepsis index models trained on different sites achieved 70-77% of same-site correlation, demonstrating reasonable generalization

"On a cohort level, cross-institutional agreement measured by Spearman correlation between models trained on different sites, were 70-77% of same-site correlation."

JEPA world model encoders with fixed-size Vision Transformer encoders are over-provisioned for simple tasks and under-provisioned for complex ones, with significant redundancy across attention heads

"Joint-Embedding Predictive Architectures (JEPAs) for world modeling typically employ fixed-size Vision Transformer encoders that are over-provisioned for simple tasks and under-provisioned for complex ones, with significant redundancy across attention heads"

Successive Capacity Growth (SCG) achieves 20.3% improvement in prediction loss over fixed small baseline with 56 times greater parameter efficiency on multi-object dynamics tasks

"On a 60-dimensional multi-object dynamics task, SCG naturally triggers depth expansion, improving prediction loss by 20.3% over the fixed small baseline with 56 times greater parameter efficiency than scaling to the fixed large model"

SCG achieves 23% improvement over fixed large models on 2D navigation tasks with a single width expansion

"on a 2D navigation task, a single width expansion yields even an 23% improvement over the fixed large model"

The Sketched Isotropic Gaussian Regularizer (SIGReg) prevents representation collapse by ensuring all learned semantic dimensions remain statistically independent and aligned with the predictive objective

"The Sketched Isotropic Gaussian Regularizer (SIGReg) ensures that all learned semantic dimensions remain statistically independent and aligned with the predictive objective, preventing collapse even as the architecture grows"

SCG's adaptive encoder matches or exceeds fixed small baseline across all tested environments with zero false-positive expansions and bit-exact function preservation

"Across all three tested environments of increasing complexity, the adaptive encoder matches or exceeds the fixed small baseline, with zero false-positive expansions and bit-exact function preservation (ratio = 1.0, absolute difference = 0.0)"

Generative AI is transforming how people access information and challenging traditional advertising mechanisms

"Generative AI is transforming how people access information, challenging traditional advertising mechanisms built around predefined slots."

LAMA improves platform welfare and revenue while maintaining user-facing response quality in commercial search applications

"Proof-of-concept experiments on real-world commercial-search query splits show that LAMA improves platform welfare and revenue while maintaining user-facing response quality"

QuantumBoostNet, a hybrid classical-quantum architecture with a 10-qubit quantum circuit, consistently outperforms state-of-the-art classical and hybrid classical-quantum models in cardiac ultrasound view identification

"Extensive experiments indicate that, despite the limited number of qubits that can be simulated, QuantumBoostNet consistently outperforms state-of-the-art classical and hybrid classical-quantum models in cardiac ultrasound view identification, achieving a relative improvement over the best competitor."

QuantumBoostNet demonstrates superior performance on established image classification benchmarks and exhibits robustness to noise

"QuantumBoostNet also demonstrates superior performance on established image classification benchmarks and exhibits robustness to noise."

A smartphone-based safety-check system using image capture and drug reference matching can provide allergy alerts in resource-constrained hospital settings

"This paper proposes and outlines the evaluation of a lightweight, smartphone-based safety-check system for this setting. At registration a soft identifier (a phone number) is recorded; after the physician writes a prescription, its image is captured, the brand names are resolved to active ingredients using national drug references, and the ingredients are matched against the patient's recorded severe reaction history."

The low base rate of severe adverse drug events places clinical-outcome effects beyond the scope of a single-site feasibility study

"We explicitly do not claim a clinical-outcome effect, which the low base rate of severe events places beyond a single-site feasibility study."

Neural operators have demonstrated broad empirical success at approximating solution operators of PDEs from data

"Neural operators have demonstrated broad empirical success at approximating such maps from data."

The proposed architecture satisfies homogeneous Dirichlet boundary conditions independently of training whilst retaining expressivity of existing kernel-integral neural operators

"we propose an architecture which, independently of training, satisfies homogeneous Dirichlet boundary conditions, whilst simultaneously retaining the expressivity of existing kernel-integral neural operator architectures"

The proposed method is applicable to arbitrary mesh data and general geometries by requiring only that the output domain be bounded with Lipschitz boundary

"The method requires only that the output domain be bounded with Lipschitz boundary and places no restriction on the choice of discretization, making it applicable to arbitrary mesh data and general geometries."

Universal approximation has been proven for the proposed architecture and the analysis proves universality for a broad class of kernel-integral neural operators

"We prove universal approximation for the resulting architecture; furthermore the approach we adopt in the analysis proves universality for a broad class of kernel-integral neural operators thereby uniting existing theory for a variety of operator learning methods."