{"results":[{"record_id":"knows:generated/ai-empowered-low-altitude-economy/1.0.0","profile":"paper@1","title":"AI-Empowered Low-Altitude Economy: Cooperative Sensing With Fixed Wireless Access","summary":"This paper proposes an AI-empowered two-stage cooperative sensing pipeline that leverages fixed wireless access (FWA) customer premises equipment (CPEs) as auxiliary sensing nodes to detect and localize unauthorized UAVs, achieving a missed detection probability of 0.63% and a 95%-confidence positioning error of 6.50 meters satisfying 3GPP requirements.","venue":"arXiv preprint","year":2026,"discipline":null,"keywords":["low-altitude economy","UAV detection and localization","fixed wireless access","cooperative sensing","AI-empowered sensing","CSI-based sensing","integrated sensing and communication"],"coverage_statements":"exhaustive","coverage_evidence":"key_evidence_only","provenance_origin":"machine","provenance_actor_name":"knows-gen","version_record":"1.0.0","lint_passed":true,"download_count":0,"created_at":"2026-05-11T17:58:35.705522+00:00","stats":{"stmt_count":20,"evidence_count":8,"relation_count":30,"artifact_count":2,"claim_count":10,"method_count":6,"limitation_count":1}},{"record_id":"knows:generated/asteroseismic-deep-learning-tess-k2/1.0.0","profile":"paper@1","title":"Inferring Asteroseismic Parameters from Short Observations Using Deep Learning: Application to TESS and K2 Red Giants","summary":"Deep learning models are developed to infer asteroseismic parameters (large frequency separation Δν, frequency at maximum power νmax, and dipolar period spacing ΔΠ₁) from short-duration TESS and K2 observations of red giants, recasting regression as a classification problem and outputting Bayesian posterior approximations via ResNet architectures.","venue":"arXiv preprint","year":2026,"discipline":null,"keywords":[],"coverage_statements":"exhaustive","coverage_evidence":"key_evidence_only","provenance_origin":"machine","provenance_actor_name":"knows-gen","version_record":"1.0.0","lint_passed":true,"download_count":0,"created_at":"2026-05-11T17:58:35.388943+00:00","stats":{"stmt_count":17,"evidence_count":10,"relation_count":30,"artifact_count":6,"claim_count":13,"method_count":1,"limitation_count":2}},{"record_id":"knows:generated/joint-beamforming-antenna-placement/1.0.0","profile":"paper@1","title":"Joint Beamforming and Antenna Placement Optimization in Pinching Antenna Systems with User Mobility: A Deep Reinforcement Learning Approach","summary":"This paper proposes a joint optimization framework for pinching antenna systems that simultaneously determines beamforming vectors and pinching antenna locations to maximize average sum rate for mobile users under quality-of-service constraints, employing a Deep Deterministic Policy Gradient approach.","venue":"arXiv preprint","year":2026,"discipline":null,"keywords":[],"coverage_statements":"exhaustive","coverage_evidence":"key_evidence_only","provenance_origin":"machine","provenance_actor_name":"knows-gen","version_record":"1.0.0","lint_passed":true,"download_count":0,"created_at":"2026-05-11T17:58:02.426530+00:00","stats":{"stmt_count":18,"evidence_count":7,"relation_count":24,"artifact_count":3,"claim_count":5,"method_count":4,"limitation_count":3}},{"record_id":"knows:generated/cellwise-casewise-robust-multivariate-regression/1.0.0","profile":"paper@1","title":"Cellwise and Casewise Robust Multivariate Regression with Inference","summary":"The paper proposes cellMR, a robust multivariate regression method that simultaneously handles cellwise outliers, casewise outliers, missing data, and high-dimensional settings using a cellwise robust covariance estimator with ridge regularization. It also introduces cellBoot, a bootstrap-based inference procedure using indirect inference that provides asymptotically valid confidence intervals robust to contamination.","venue":"arXiv preprint","year":2026,"discipline":null,"keywords":[],"coverage_statements":"exhaustive","coverage_evidence":"key_evidence_only","provenance_origin":"machine","provenance_actor_name":"knows-gen","version_record":"1.0.0","lint_passed":true,"download_count":0,"created_at":"2026-05-11T17:57:56.920827+00:00","stats":{"stmt_count":18,"evidence_count":11,"relation_count":34,"artifact_count":5,"claim_count":9,"method_count":3,"limitation_count":1}},{"record_id":"knows:generated/papal-names-random-copying/1.0.0","profile":"paper@1","title":"Quo nomine vis vocari? 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The main advantage is that e-values are easier to aggregate without sacrificing validity, unlike p-values which suffer from a validity gap when averaged.","venue":"arXiv preprint","year":2026,"discipline":null,"keywords":[],"coverage_statements":"exhaustive","coverage_evidence":"key_evidence_only","provenance_origin":"machine","provenance_actor_name":"knows-gen","version_record":"1.0.0","lint_passed":true,"download_count":0,"created_at":"2026-05-11T17:57:16.349762+00:00","stats":{"stmt_count":23,"evidence_count":11,"relation_count":29,"artifact_count":4,"claim_count":14,"method_count":3,"limitation_count":2}},{"record_id":"knows:generated/CA-DEL/1.0.0","profile":"paper@1","title":"CA-DEL: An Open Multi-Target, Multi-Modal Benchmark for Learning from DNA-Encoded Library Screens","summary":"CA-DEL is a multi-dimensional public benchmark dataset for DNA-Encoded Library analysis featuring screens against three homologous carbonic anhydrase isoforms (CAII, CAIX, CAXII), integrating noisy DEL enrichment data with high-fidelity ChEMBL binding affinities to enable rigorous Sim-to-Real evaluation of machine learning models.","venue":"arXiv preprint","year":2026,"discipline":null,"keywords":[],"coverage_statements":"exhaustive","coverage_evidence":"key_evidence_only","provenance_origin":"machine","provenance_actor_name":"knows-gen","version_record":"1.0.0","lint_passed":true,"download_count":0,"created_at":"2026-05-11T17:57:14.198939+00:00","stats":{"stmt_count":16,"evidence_count":10,"relation_count":24,"artifact_count":6,"claim_count":11,"method_count":2,"limitation_count":2}},{"record_id":"knows:generated/mental-health-human-capital-olg-pensions/1.0.0","profile":"paper@1","title":"Mental Health and Human Capital Composition in a Dynastic OLG Model with PAYG Pensions","summary":"This paper develops a two-period dynastic OLG model where parents choose consumption, savings, fertility, and three dimensions of child quality—education, physical health, and mental health—under a PAYG pension system. The central innovation models mental health as an independent productivity-enhancing input with its own elasticity θ in a Cobb-Douglas human-capital production function, revealing that higher PAYG contribution rates raise fertility but crowd out all quality investments including mental health.","venue":"arXiv preprint","year":2026,"discipline":null,"keywords":["Fertility","Human Capital","Mental Health","OLG Model","PAYG Pension"],"coverage_statements":"exhaustive","coverage_evidence":"key_evidence_only","provenance_origin":"machine","provenance_actor_name":"knows-gen","version_record":"1.0.0","lint_passed":true,"download_count":0,"created_at":"2026-05-11T17:57:00.038034+00:00","stats":{"stmt_count":16,"evidence_count":7,"relation_count":25,"artifact_count":4,"claim_count":6,"method_count":2,"limitation_count":3}},{"record_id":"knows:generated/density-estimation-sinc-kernel/1.0.0","profile":"paper@1","title":"Density Estimation Using the Sinc Kernel","summary":"This paper demonstrates that the sinc kernel density estimator outperforms conventional kernel estimators in accuracy for moderate sample sizes, achieves better asymptotic consistency even when the density has only one derivative, and provides superior solutions for bandwidth selection and derivative estimation.","venue":"arXiv preprint","year":2026,"discipline":null,"keywords":[],"coverage_statements":"exhaustive","coverage_evidence":"key_evidence_only","provenance_origin":"machine","provenance_actor_name":"knows-gen","version_record":"1.0.0","lint_passed":true,"download_count":0,"created_at":"2026-05-11T17:56:59.845690+00:00","stats":{"stmt_count":17,"evidence_count":10,"relation_count":25,"artifact_count":5,"claim_count":5,"method_count":4,"limitation_count":1}},{"record_id":"knows:generated/essential-role-extrinsic-noise-ecoli-division/1.0.0","profile":"paper@1","title":"Essential Role of Extrinsic Noise in Models of E. coli Division Control","summary":"The paper analytically solves a stochastic threshold-accumulation model for E. coli division control, showing that extrinsic noise (threshold variability with autocorrelation) is essential to explain the observed 10–20% birth-size fluctuations, and that the adder division strategy emerges when threshold memory matches the protein reset fraction, revising the view that full protein reset is necessary for adder control.","venue":"arXiv preprint","year":2026,"discipline":null,"keywords":[],"coverage_statements":"exhaustive","coverage_evidence":"key_evidence_only","provenance_origin":"machine","provenance_actor_name":"knows-gen","version_record":"1.0.0","lint_passed":true,"download_count":0,"created_at":"2026-05-11T17:56:42.144150+00:00","stats":{"stmt_count":13,"evidence_count":8,"relation_count":24,"artifact_count":1,"claim_count":7,"method_count":1,"limitation_count":1}},{"record_id":"knows:generated/breakdown-adiabatic-scaling-noise-induced/1.0.0","profile":"paper@1","title":"Breakdown of Adiabatic Scaling and Noise-Induced Functional Synchronization in Deeply Quiescent Excitable Systems","summary":"This paper investigates stochastic dynamics in a 3D Sherman-Rinzel-Keizer model driven by multiplicative Feller noise, introducing a logarithmic centroid extraction method to recover Kramers adiabatic scaling (R² > 0.95) despite a 'bathtub effect' that flattens coherence resonance valleys, and demonstrating noise-induced transition from sub-threshold shivering to macroscopic functional synchronization in gap-junction coupled networks.","venue":"arXiv preprint","year":2026,"discipline":null,"keywords":[],"coverage_statements":"exhaustive","coverage_evidence":"key_evidence_only","provenance_origin":"machine","provenance_actor_name":"knows-gen","version_record":"1.0.0","lint_passed":true,"download_count":0,"created_at":"2026-05-11T17:56:32.022744+00:00","stats":{"stmt_count":15,"evidence_count":10,"relation_count":19,"artifact_count":1,"claim_count":7,"method_count":2,"limitation_count":1}},{"record_id":"knows:generated/american-options-heston-curriculum-pinns/1.0.0","profile":"paper@1","title":"American Options Pricing under Heston Model via Curriculum Learning in Coupled PINNs","summary":"This paper proposes a coupled physics-informed neural network (PINN) framework for pricing American put options under the Heston stochastic volatility model, employing a three-phase curriculum learning strategy and adaptive resampling to train two networks simultaneously—one for the option price and one for the free exercise boundary.","venue":"arXiv preprint","year":2026,"discipline":null,"keywords":["Heston model","American options","Physics-Informed Neural Networks","Curriculum learning","Free boundary problem","Stochastic volatility"],"coverage_statements":"exhaustive","coverage_evidence":"key_evidence_only","provenance_origin":"machine","provenance_actor_name":"knows-gen","version_record":"1.0.0","lint_passed":true,"download_count":0,"created_at":"2026-05-11T17:56:31.642957+00:00","stats":{"stmt_count":17,"evidence_count":12,"relation_count":23,"artifact_count":2,"claim_count":4,"method_count":5,"limitation_count":2}},{"record_id":"knows:generated/BAMIFun/1.0.0","profile":"paper@1","title":"BAMIFun: Bayesian Multiple Imputation for Functional Data","summary":"BAMIFun is a novel Bayesian multiple imputation framework for functional data that uses low-rank models with penalized splines to enforce eigenfunction smoothness, provide uncertainty quantification, and extend to multiway functional data via FTSVD.","venue":"arXiv preprint","year":2026,"discipline":null,"keywords":[],"coverage_statements":"exhaustive","coverage_evidence":"key_evidence_only","provenance_origin":"machine","provenance_actor_name":"knows-gen","version_record":"1.0.0","lint_passed":true,"download_count":0,"created_at":"2026-05-11T17:56:30.808371+00:00","stats":{"stmt_count":15,"evidence_count":7,"relation_count":20,"artifact_count":6,"claim_count":7,"method_count":4,"limitation_count":2}},{"record_id":"knows:generated/extrema-barrier-options-leverage-corrections/1.0.0","profile":"paper@1","title":"Extrema, Barrier Options, and Semi-Analytic Leverage Corrections in Stochastic-Clock Volatility Models","summary":"The paper develops transform-only pricing formulas for single and double barrier options under independent stochastic-clock volatility models, and introduces a systematic small-ρ expansion that incorporates leverage effects through semi-analytic forced problems, stabilized by Padé resummation for equity-like correlations.","venue":"arXiv preprint","year":2026,"discipline":null,"keywords":[],"coverage_statements":"exhaustive","coverage_evidence":"key_evidence_only","provenance_origin":"machine","provenance_actor_name":"knows-gen","version_record":"1.0.0","lint_passed":true,"download_count":0,"created_at":"2026-05-11T17:56:25.007327+00:00","stats":{"stmt_count":17,"evidence_count":11,"relation_count":28,"artifact_count":1,"claim_count":3,"method_count":4,"limitation_count":4}},{"record_id":"knows:generated/introducing-feedback-thinking-system-dynamics-economics-education/1.0.0","profile":"paper@1","title":"Introducing Feedback Thinking and System Dynamics Modeling in Economics Education","summary":"This paper discusses opportunities and barriers for introducing feedback thinking and system dynamics models in the economics curriculum, presenting a pricing feedback model, summarizing authors' teaching experiences, and developing a four-level course hierarchy for economics education with system dynamics.","venue":"arxiv","year":2026,"discipline":null,"keywords":[],"coverage_statements":"exhaustive","coverage_evidence":"key_evidence_only","provenance_origin":"machine","provenance_actor_name":"knows-gen","version_record":"1.0.0","lint_passed":true,"download_count":0,"created_at":"2026-05-11T17:56:23.350475+00:00","stats":{"stmt_count":15,"evidence_count":8,"relation_count":25,"artifact_count":4,"claim_count":5,"method_count":4,"limitation_count":2}},{"record_id":"knows:generated/finite-horizon-mixture-cure-model/1.0.0","profile":"paper@1","title":"A Finite-Horizon Mixture Cure Model with Application to Online Flea Market Data","summary":"This study proposes a finite-horizon mixture cure model that classifies a population based on event occurrence within a prespecified time horizon [0, c), replacing the infinite-horizon assumption of conventional models. 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Simulation studies validate the estimator's statistical properties, and application to Mercari transaction data reveals seasonally-adjusted patterns and different significant covariates compared to conventional infinite-horizon models.","venue":"arXiv preprint","year":2026,"discipline":null,"keywords":[],"coverage_statements":"exhaustive","coverage_evidence":"key_evidence_only","provenance_origin":"machine","provenance_actor_name":"knows-gen","version_record":"1.0.0","lint_passed":true,"download_count":0,"created_at":"2026-05-11T17:56:22.316476+00:00","stats":{"stmt_count":16,"evidence_count":9,"relation_count":23,"artifact_count":4,"claim_count":7,"method_count":3,"limitation_count":2}},{"record_id":"knows:generated/partitioning-neural-co-variability/1.0.0","profile":"paper@1","title":"Partitioning Neural Co-Variability","summary":"This paper introduces the Poisson matrix-normal latent variable (PMNLV) model, which extends single-neuron overdispersion to neural populations by placing a matrix-normal prior over latent gain with Kronecker-factored covariance, enabling study of network-level gain covariance previously invisible to scalar summaries.","venue":"arxiv","year":2026,"discipline":null,"keywords":[],"coverage_statements":"exhaustive","coverage_evidence":"key_evidence_only","provenance_origin":"machine","provenance_actor_name":"knows-gen","version_record":"1.0.0","lint_passed":true,"download_count":0,"created_at":"2026-05-11T17:56:08.583318+00:00","stats":{"stmt_count":16,"evidence_count":8,"relation_count":20,"artifact_count":3,"claim_count":8,"method_count":3,"limitation_count":1}},{"record_id":"knows:generated/transporting-treatment-effects-calibrating-observational-outcomes/1.0.0","profile":"paper@1","title":"Transporting treatment effects by calibrating large-scale observational outcomes","summary":"The paper proposes a method for transporting treatment effects from a small experimental dataset to a large observational dataset with imperfect outcome measurements. 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