Claims Browser

Adaptive domain augmentation for inverse problems can be given a principled Bayesian justification that ensures robust inference under incomplete prior knowledge

"This provides a principled, Bayesian justification for adaptive domain augmentation and ensures robust inference for inverse problems under incomplete prior knowledge."

The proposed diffusion-based inverse solver is effective for quantum chromodynamics analysis of nucleon structure, specifically parameterizing quantum correlation functions to event observables

"We demonstrate the effectiveness of our inverse solver for a toy inverse problem with infinite solutions, and for the parameterization of the quantum correlation functions to event observables in a Quantum Chromodynamics analysis of nucleon structure."

A retrieval-augmented few-shot prompting pipeline with Qwen2.5-14B-Instruct achieves 0.8692 Semantic Graph Similarity and 0.9200 Term-Typing F1 on the ontology extension task

"The approach achieves Semantic Graph Similarity of 0.8692, Term-Typing F1 of 0.9200, and Taxonomy Discovery F1 of 0.8540 on Task B"

The proposed framework has no completed implementation, experiment, dataset, or measured results yet.

"It reports no completed implementation, experiment, dataset, or measured result."

The autonomous robotic platform enables automated, repeatable, and spatially resolved physiological measurements that go beyond passive imaging

"The platform enables automated, repeatable, and spatially resolved physiological measurements that go beyond passive imaging"

A class-level automated multi-objective search-based system using SPEA-2 achieved higher precision and recall than NSGA-II and MOEA/D for bug localization

"Results indicate that SPEA-2 achieved higher precision and recall than both multi-objective and single-objective baseline methods."

The bug localization recommender system successfully identified buggy classes for 88.5% of bug reports within the top 10 recommendations and 94% within the top 20

"The proposed recommender system successfully identified buggy classes or files for 88.5\% of bug reports within the top 10 recommendations and 94\% within the top 20."

The multi-objective bug localization model demonstrates adaptability across programming languages, validated on both Java and Kotlin projects

"The effectiveness of the model was further validated on an industrial Android project written in Kotlin, demonstrating its adaptability across programming languages."

Deep learning segmentation models for OCT typically fail to generalize to clinical data collected under different acquisition protocols

"Most deep-learning segmentation work for OCT is validated only in-domain, leaving generalization to clinical data collected under different acquisition protocols largely untested."

The developed AMD and DME lesion-segmentation pipelines achieve Dice scores between 0.76 and 0.82 with strong calibration on in-domain validation

"reaching Dice scores of 0.76 to 0.82 with strong volumetric and surface calibration (r vol, r surf greater than or equal to 0.97 across all four pipelines) on an in-domain validation set"

Ensemble composition is the most consistent driver of improvement in OCT lesion segmentation models

"identifies ensemble composition as the most consistent driver of improvement"

Ontology learning from text remains challenging despite significant progress in Large Language Models

"Ontology learning from text remains challenging despite significant progress in Large Language Models (LLMs)"

LLMs favor hierarchical relations over associative relations in ontology learning tasks

"favor hierarchical over associative relations"

LLMs are currently operating in critical domains including hospitals, courtrooms, banks, and public service desks

"Large Language Models (LLMs) operate in hospitals, courtrooms, banks, and public service desks"

A systematic review of 122 primary studies plus 12 regulatory documents reveals four persistent gaps in LLM accountability frameworks

"of 4,512 records identified, 122 primary studies were included, together with 12 regulatory and standards documents analysed as primary sources"

GASHE (Gradient-Aware Selective Homomorphic Encryption) dynamically encrypts only gradient components exceeding a DP-calibrated sensitivity threshold, improving efficiency over uniform encryption schemes

"GASHE (Gradient-Aware Selective Homomorphic Encryption), a novel selective encryption strategy that dynamically identifies and encrypts only the gradient components exceeding a DP-calibrated sensitivity threshold, rather than encrypting all parameters uniformly as in static layer-based or full-parameter CKKS schemes"

SecureDrive-FL creates the first closed-loop DP+HE privacy pipeline by coupling DP-SGD with GASHE, unifying training-time privacy and communication-time confidentiality

"SecureDrive-FL, a federated driver monitoring framework that couples DP-SGD with GASHE to create the first closed-loop DP+HE privacy pipeline: DP-SGD calibration parameters directly derive the GASHE encryption mask, unifying training-time privacy and communication-time confidentiality"

SecureDrive-FL matches DP-SGD's poisoning resistance while additionally protecting against MitM attacks with only 8-10% runtime overhead

"SecureDrive-FL matches DP-SGD alone's poisoning resistance (73.6% vs. 74.0% accuracy, 3.9% Attack Success Rate for both) while additionally withstanding MitM interception, where DP-SGD alone collapses to near-random accuracy (78.2% vs. 10.4%), all under only approx. 8--10% additional runtime overhead relative to DP-SGD alone"