{"total":13,"items":[{"citing_arxiv_id":"2606.17745","ref_index":18,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Separating wiring-specific from statistical control of dynamics in a complete connectome","primary_cat":"q-bio.NC","submitted_at":"2026-06-16T10:05:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Coarse wiring statistics set the dynamical regime while precise connections set activity geometry in a parameter-free model of the complete larval Drosophila connectome.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.10257","ref_index":11,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Downward conditional monotonicity gives survival and extinction for contact processes in random environments","primary_cat":"math.PR","submitted_at":"2026-06-08T23:56:24+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Introduces downward conditional monotonicity for MMPP to obtain stochastic domination bounds that determine survival and extinction regimes for contact processes in finite-state random environments via QBD eigenvalue comparison.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00360","ref_index":48,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Ambiguity Analysis and Design of Sparse Arrays via Generalized Vandermonde Rank Conditions","primary_cat":"eess.SP","submitted_at":"2026-05-29T21:02:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Introduces a scalable algebraic framework relating rank deficiency of generalized Vandermonde matrices for sparse steering vectors to thinned Toeplitz matrices and augmented full-ULA matrices to characterize and avoid multi-source ambiguities in thinned uniform linear arrays.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.27837","ref_index":13,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Optimal Spectral Design with Prior Information","primary_cat":"math.OC","submitted_at":"2026-05-27T01:50:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Convex reformulation and polynomial-time algorithm for spectral design problems that update a prior information matrix by rank-one updates under Euclidean-norm bounds on the design vectors.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.23708","ref_index":285,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Learning Dynamic Stability Landscapes in Synchronization Networks","primary_cat":"cs.LG","submitted_at":"2026-05-22T14:55:09+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Introduces graph-to-image prediction of per-node dynamic stability landscapes in oscillator networks from topology, releases two 10k-graph datasets, and shows GNN-CNN models achieve good accuracy with cross-size generalization.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.21324","ref_index":20,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Stimulus symmetries can confound representational similarity analyses","primary_cat":"q-bio.NC","submitted_at":"2026-05-20T15:51:21+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Stimulus symmetries render many neural representations functionally equivalent yet produce qualitatively different RSMs, including drifting ones from SGD or regularization in image-encoding networks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16189","ref_index":74,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Quantum Solvers for Nonlinear Matrix Equations in Quantum Chemistry","primary_cat":"quant-ph","submitted_at":"2026-05-15T17:07:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Quantum algorithm block-encodes Riccati solutions for m-particle m-hole RPA using Riesz projectors and QSVT, claiming linear system-size scaling under sparsity and polynomial cost in excitation rank m.","context_count":1,"top_context_role":"background","top_context_polarity":"unclear","context_text":"Higham,Functions of Matrices(Society for Industrial and Applied Mathematics, 2008). [72] I. Gohberg, S. Goldberg, and M. A. Kaashoek, Riesz projections and functional calculus, inClasses of Linear Operators Vol. I (Birkh¨auser Basel, 1990) pp. 4-24. [73] A. P. Campbell and D. Daners, Linear algebra via complex analysis, The American Mathematical Monthly120, 877 (2013). [74] T. Kato,Perturbation Theory for Linear Operators(Springer Berlin Heidelberg, 1995). [75] R. A. Horn and C. R. Johnson,Matrix Analysis, 2nd ed. (Cam- bridge University Press, 2012). [76] H. Xu, Transformations between discrete-time and continuous- time algebraic Riccati equations, Linear Algebra and its Appli- cations425, 77 (2007). [77] J. B. Conway,Functions of one complex variable I(Springer,"},{"citing_arxiv_id":"2605.09891","ref_index":44,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Harnessing Floating Car Data, Traffic Camera Observations, and Network Flow Analysis for Traffic Volume Estimation","primary_cat":"eess.SY","submitted_at":"2026-05-11T02:31:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A CTM-GNN model with EnSRF assimilation and flow-weighted transition matrix fuses floating car data and camera observations to deliver physically consistent, network-wide traffic volume estimates and forecasts, demonstrated with improved accuracy in Manhattan.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"(i) denotes the set of downstream road neighbors of segmenti. Calibration biases can correlate both along downstream flow (vehicles leavingienterj) and along upstream influence (shared demand patterns). A bidirectional propagation kernel is therefore formed by symmetrizing the transition matrix and applying a propagation decay factorγ PD ∈(0,1): W =γ PD · 1 2 \u0010 P + P⊤ \u0011 .(44) To capture influence beyond immediate neighbors, multi-hop kernels are constructed iteratively as: W(2) =γ PD W2,W (3) =γ PD W(2)W.(45) For each camera observed segmenti, a weighted combination of hop estimates is computed using a flow- localization vectorρ (i) ∈[0,1] N, constructed using a geometric hop decay influence: ˜ρ(i) = W:,i + 1 2 W(2) :,i + 1"},{"citing_arxiv_id":"2605.04893","ref_index":16,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics","primary_cat":"cs.LG","submitted_at":"2026-05-06T13:25:13+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2502.12984","ref_index":23,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"On Erlang mixture approximations for differential equations with distributed time delays","primary_cat":"math.DS","submitted_at":"2025-02-18T16:06:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Erlang mixture approximations with the linear chain trick convert distributed delay DDEs into ODEs, with convergence proofs for bounded kernels and applications to stability analysis.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2502.09165","ref_index":21,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Generalizing Reduced Rank Extrapolation to Low-Rank Matrix Sequences","primary_cat":"math.NA","submitted_at":"2025-02-13T10:48:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Two generalizations of reduced rank extrapolation are derived for low-rank matrix sequences and iteration-dependent mapping functions, with numerical tests on Lyapunov and Riccati equations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2502.04006","ref_index":22,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Facial structure of copositive and completely positive cones over a second-order cone","primary_cat":"math.OC","submitted_at":"2025-02-06T12:06:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Classifies faces of copositive and completely positive cones over the second-order cone, examines dimension and exposedness, and computes two chain-related parameters.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2408.04894","ref_index":33,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"On generalization of Williamson's theorem to real symmetric matrices","primary_cat":"math.FA","submitted_at":"2024-08-09T06:42:12+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}