Presents a fully photometric framework to measure individual cluster splashback radii and masses from SDSS data and constructs the first observational splashback mass function.
super hub Mixed citations
An introduction to ROC analysis
Mixed citation behavior. Most common role is background (60%).
33
Pith papers citing it
21.3k
external citations · OpenAlex
Background
60% of classified citations
hub tools
citation-role summary
background 3
method 2
citation-polarity summary
representative citing papers
Pandora's Regret is a closed-form pairwise scoring rule derived from expected optimal search costs that elicits true probabilities and outperforms log loss, accuracy, and F1 at predicting diagnostic costs on MedMNIST models.
Gated SAEs decouple which features to use from how large their activations should be, applying the L1 penalty only to selection and thereby eliminating shrinkage while halving the number of firing features needed for good fidelity.
MiqraBERT, a finetuned Sentence-BERT model, achieves 2.7-fold better distributional separation of parallel versus non-parallel Biblical Hebrew verses and reduces ambiguous overlap from 24% to 6%, with strong performance on narrative but weak on poetic parallels.
Introduces predictive prefetching for RAG that anticipates retrieval needs several tokens ahead via three components, reporting up to 43.5% latency reduction and 62.4% TTFT improvement while preserving answer quality.
Pilot study uses pretrained video encoder features from lung ultrasound to predict 30-day CHF readmission, finding lower-lung views and temporal differences most informative with top MLP F1 of 0.80.
A framework for cross-validation optimal feature selection in linear SVM classification is developed by reformulating the bilevel problem into a single-level mixed-integer optimization using LS-SVM, with simulation results indicating competitive performance.
A new MFPCA approach for variable domain data is proposed by running univariate variable-domain FPCA on each variable, stacking the scores, and smoothing the empirical covariance matrix over domain length to recover joint eigenfunctions and scores.
MDE computes three entropy features from flow stats to match conventional ML performance (F1 0.708-0.989) on four IDS benchmarks while exposing aggregate-metric failures and providing stable SHAP attributions.
DeXposure-Claw combines a graph time-series foundation model for forecasting DeFi networks with rule-based monitors and data-health gates to emit regulator-aligned risk tickets, evaluated via a new six-axis benchmark on five years of real weekly data.
Disentangling input ambiguity from uncertainty quantification improves error prediction for LLMs on QA tasks, yielding over 10 PRR point gains across models and datasets.
SBBT separates Brier-score calibration gains from AUROC ranking gains in prefix-conditioned success estimation for LLM math reasoning, with structure-aware signals yielding up to +0.110 AUROC over baselines.
Token-level confidence trajectories in LLMs encode a content-agnostic geometry that separates correct and incorrect reasoning traces and supports a lightweight correctness estimator called NeuralConf.
Empirical study of real NISQ order-finding data identifies dominant verified mass fraction as the strongest predictor of whether standard post-processing recovers the true order.
The paper develops a design science framework for governing AI-assisted operational decision support in security operations centers by specifying a query-broker artifact that separates AI planning from execution through approved templates, policy validation, and engineering review gates.
High AUC does not guarantee a defect prediction model outperforms random guessing on true and false positive rates for all thresholds.
Users' memory of privacy settings drifts over time from exact recall to gist-based impressions that bias toward sharing with larger audiences than originally intended.
Risk-score based fusion of radiomics and deep learning features from CT images improves AUC for overall survival prediction in resectable PDAC by 51% over radiomics alone.
End-Net, a multiscale inception-based CNN, reaches 0.9761 accuracy on a balanced multi-class MRI dataset of Alzheimer, tumors, MS and controls and is deployed as a public web service.
Metagente is an LLM multi-agent system using Teacher-Student collaboration that outperforms baselines on real-world software documentation summarization for requirements analysis and technical docs.
Internal LLM artifacts can be used to build classifiers that identify incorrect predictions on legal classification tasks.
Normalized velocity descriptors from facial keypoints with Random Forest yield 0.826 balanced accuracy and 0.855 AUROC on YouTubePD video classification, stable across 10 seeds with region ablation and permutation importance.
A new dictionary-based text score from 10-K narratives adds incremental power to accounting-based bankruptcy prediction, lifting AUC by 0.07 and top-decile capture from 44% to 65% in holdout evaluation.
SAILRec uses dual-side semantic alignment and hierarchical attention steering to improve how LLMs incorporate collaborative embeddings for recommendations, outperforming baselines on MovieLens-1M and Amazon-Book datasets.
citing papers explorer
-
The Splashback Mass Function of Galaxy Clusters from Photometric Data
Presents a fully photometric framework to measure individual cluster splashback radii and masses from SDSS data and constructs the first observational splashback mass function.
-
Pandora's Regret: A Proper Scoring Rule for Evaluating Sequential Search
Pandora's Regret is a closed-form pairwise scoring rule derived from expected optimal search costs that elicits true probabilities and outperforms log loss, accuracy, and F1 at predicting diagnostic costs on MedMNIST models.
-
Improving Dictionary Learning with Gated Sparse Autoencoders
Gated SAEs decouple which features to use from how large their activations should be, applying the L1 penalty only to selection and thereby eliminating shrinkage while halving the number of firing features needed for good fidelity.
-
MiqraBERT: Regression-Based Sentence-BERT Finetuning for Biblical Hebrew Parallel Detection
MiqraBERT, a finetuned Sentence-BERT model, achieves 2.7-fold better distributional separation of parallel versus non-parallel Biblical Hebrew verses and reduces ambiguous overlap from 24% to 6%, with strong performance on narrative but weak on poetic parallels.
-
Predictive Prefetching for Retrieval-Augmented Generation
Introduces predictive prefetching for RAG that anticipates retrieval needs several tokens ahead via three components, reporting up to 43.5% latency reduction and 62.4% TTFT improvement while preserving answer quality.
-
Prognostic Value of Lung Ultrasound Biomarkers for Readmission Risk in Congestive Heart Failure: A Pilot Data-Driven Analysis
Pilot study uses pretrained video encoder features from lung ultrasound to predict 30-day CHF readmission, finding lower-lung views and temporal differences most informative with top MLP F1 of 0.80.
-
Cross-validation-based optimal feature selection for linear SVM classification
A framework for cross-validation optimal feature selection in linear SVM classification is developed by reformulating the bilevel problem into a single-level mixed-integer optimization using LS-SVM, with simulation results indicating competitive performance.
-
Variable Domain Multivariate Functional Principal Component Analysis
A new MFPCA approach for variable domain data is proposed by running univariate variable-domain FPCA on each variable, stacking the scores, and smoothing the empirical covariance matrix over domain length to recover joint eigenfunctions and scores.
-
Multi-Level Distributional Entropy for Explainable Network Intrusion Detection
MDE computes three entropy features from flow stats to match conventional ML performance (F1 0.708-0.989) on four IDS benchmarks while exposing aggregate-metric failures and providing stable SHAP attributions.
-
DeXposure-Claw: An Agentic System for DeFi Risk Supervision
DeXposure-Claw combines a graph time-series foundation model for forecasting DeFi networks with rule-based monitors and data-health gates to emit regulator-aligned risk tickets, evaluated via a new six-axis benchmark on five years of real weekly data.
-
The Role of Ambiguity in Error Prediction via Uncertainty Quantification
Disentangling input ambiguity from uncertainty quantification improves error prediction for LLMs on QA tasks, yielding over 10 PRR point gains across models and datasets.
-
Prefix-Safe Bayesian Belief Tracking for LLM Reasoning Reliability:Separating Calibration from Ranking
SBBT separates Brier-score calibration gains from AUROC ranking gains in prefix-conditioned success estimation for LLM math reasoning, with structure-aware signals yielding up to +0.110 AUROC over baselines.
-
Confidence Geometry Reveals Trace-Level Correctness in Large Language Model Reasoning
Token-level confidence trajectories in LLMs encode a content-agnostic geometry that separates correct and incorrect reasoning traces and supports a lightweight correctness estimator called NeuralConf.
-
When Noisy Quantum Order Finding Remains Recoverable for Shor's Algorithm
Empirical study of real NISQ order-finding data identifies dominant verified mass fraction as the strongest predictor of whether standard post-processing recovers the true order.
-
Governing AI-Assisted Security Operations: A Design Science Framework for Operational Decision Support
The paper develops a design science framework for governing AI-assisted operational decision support in security operations centers by specifying a query-broker artifact that separates AI planning from execution through approved templates, policy validation, and engineering review gates.
-
Evaluating Software Defect Prediction Models via the Area Under the ROC Curve Can Be Misleading
High AUC does not guarantee a defect prediction model outperforms random guessing on true and false positive rates for all thresholds.
-
Temporal Drift in Privacy Recall: Users Misremember From Verbatim Loss to Gist-Based Overexposure
Users' memory of privacy settings drifts over time from exact recall to gist-based impressions that bias toward sharing with larger audiences than originally intended.
-
Improving Prognostic Performance in Resectable Pancreatic Ductal Adenocarcinoma using Radiomics and Deep Learning Features Fusion in CT Images
Risk-score based fusion of radiomics and deep learning features from CT images improves AUC for overall survival prediction in resectable PDAC by 51% over radiomics alone.
-
A Deep Multiscale Neural Network for Accurate Neurological Disorder Detection from MRI Scans and Real-Time Web Deployment
End-Net, a multiscale inception-based CNN, reaches 0.9761 accuracy on a balanced multi-class MRI dataset of Alzheimer, tumors, MS and controls and is deployed as a public web service.
-
Automated Summarization of Software Documents: An LLM-based Multi-Agent Approach
Metagente is an LLM multi-agent system using Teacher-Student collaboration that outperforms baselines on real-world software documentation summarization for requirements analysis and technical docs.
-
Peeking Inside LLMs: Leveraging Internal Artifacts of LLMs for Enhancing Reliability in Legal Classification
Internal LLM artifacts can be used to build classifiers that identify incorrect predictions on legal classification tasks.
-
Interpretable Temporal Facial-Region Motion Analysis for In-the-Wild Parkinson's Disease Video Classification
Normalized velocity descriptors from facial keypoints with Random Forest yield 0.826 balanced accuracy and 0.855 AUROC on YouTubePD video classification, stable across 10 seeds with region ablation and permutation importance.
-
Bankruptcy Prediction from 10-K Narratives: Evidence from Interpretable Text Scores and Accounting Baselines
A new dictionary-based text score from 10-K narratives adds incremental power to accounting-based bankruptcy prediction, lifting AUC by 0.07 and top-decile capture from 44% to 65% in holdout evaluation.
-
SAILRec: Steering LLM Attention to Dual-Side Semantically Aligned Collaborative Embeddings for Recommendation
SAILRec uses dual-side semantic alignment and hierarchical attention steering to improve how LLMs incorporate collaborative embeddings for recommendations, outperforming baselines on MovieLens-1M and Amazon-Book datasets.
-
Benchmarking Convolutional, Transformer, Hybrid, and Vision Language Models for Multi Disease Retinal Screening
Empirical benchmark finds attention-based models (SwinTiny, CoAtNet0, MaxViTTiny) achieve highest AUC above 84% on RFMiD binary screening and best F1 scores on multi-label task, with VLMs competitive but not superior and external Messidor-2 AUC 66.8-84.7%.
-
Transformer-Based Wildlife Species Classification from Daily Movement Trajectories
Transformer models classify seven wildlife species from daily GPS trajectories, outperforming LSTM, CNN, and TCN baselines by 8-22 percentage points in balanced accuracy under region-holdout evaluation.
-
BARFI-Q: Quantum-Enhanced Block Attention Residual Fusion Framework for Multivariate Time-Series Forecasting in Atom Interferometry
BARFI-Q integrates patch-based embedding, dual-branch temporal modeling, hierarchical fusion, adaptive block-attention residuals, and quantum feature mapping to forecast atom interferometry time-series, outperforming baselines while representing targets in circular sine-cosine space.
-
ClinQueryAgent: A Conversational Agent for Population Health Management
The paper introduces ClinQueryAgent, a conversational agent that converts natural language queries into database queries for population health management while keeping patient data secure, and reports its use by 128 staff across 15 NHS practices covering 148,319 patients.
-
Impact of Validation Strategy on Machine Learning Performance in EEG-Based Alcoholism Classification
Nested cross-validation reveals optimistic bias in standard validation for EEG alcoholism classification, with AdaBoost reaching 78.3% accuracy and most model differences not statistically significant per McNemar's test.
-
A machine-learning-assisted progressive digit-randomness screening framework for detecting non-random patterns in raw numerical research data
FDRS combines digit frequency tests, association metrics, entropy, KL divergence, and ML models to assign risk grades to numerical datasets, showing separation between normal and irregular simulated data with high AUC.
-
ROC Analysis for Evaluating Translation Quality Estimation Systems
ROC analysis is proposed for evaluating translation quality estimation systems, claimed to match existing methods while providing actionable business insights.
-
Modelling the term-structure of default risk under IFRS 9 within a multistate regression framework
Compares Markov chain, beta regression and multinomial logistic regression for loan default term-structures on mortgage data and reports successive outperformance plus new diagnostics.
- Anomaly-Informed Confidence Calibration for Vision-Based Safety Prediction