{"total":16,"items":[{"citing_arxiv_id":"2606.29797","ref_index":34,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Multi-Level Distributional Entropy for Explainable Network Intrusion Detection","primary_cat":"cs.CR","submitted_at":"2026-06-29T05:19:21+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.25254","ref_index":18,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Dual Agreement Consistency Learning for Semi-Supervised Fetal Ultrasound 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problem to finding the maximum reward-to-cost ratio in a weighted finite graph built from the automaton.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.05032","ref_index":29,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Gaussian mean width strong converse bound on the classical identification capacity of quantum channels","primary_cat":"quant-ph","submitted_at":"2026-06-03T16:00:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A Gaussian mean width bound in weighted geometry yields a single-letter strong converse for the classical identification capacity of quantum channels, improving known results for depolarizing, Pauli, erasure, and amplitude damping 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constrained entropy maximization and equates to prediction error under CLT and LDT.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.27104","ref_index":16,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Low-Complexity Run-Length-Limited ISI-Mitigation (RLIM) Codes for Molecular Communication","primary_cat":"cs.IT","submitted_at":"2026-04-29T18:48:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Enumerative realization of RLIM codes achieves exponential-to-polynomial storage reduction while maintaining code properties and enabling better bit-error-rate performance in larger dimensions for molecular communication.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.24102","ref_index":23,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"SemML 2.0: Synthesizing Controllers for LTL","primary_cat":"cs.AI","submitted_at":"2026-04-27T06:50:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"SemML 2.0 outperforms prior LTL synthesis tools on SYNTCOMP by solving more instances faster with comparable solution quality.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Hence, many of the participating tools only focus on this specific application. On the other hand, providing a reason why a given instance is not satisfiable (i.e. a counterexample) often is equally interesting, e.g. for the counter- example guided refinement loops in [33,32,2]. Further, constructing the system (or counterexample) in the form of an explicit finite state machine (Mealy machine [23]) is often desired or even required for several applications. In particular, this new functionality of our tool has already been successfully applied to several of the mentioned refinement approaches (e.g.sweap4 [2]). Altogether, aside from providing competitive circuits for realizable solutions, there is a need for more general forms of solution extraction."},{"citing_arxiv_id":"2604.11481","ref_index":54,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Emergence of Complex Web Structures","primary_cat":"astro-ph.CO","submitted_at":"2026-04-13T13:47:35+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"1103/PhysRev.108.171 [51] Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, ??? (2012). https://books.google.es/books?id=VWq5GG6ycxMC [52] Kardar, M.: Statistical Physics of Particles. Cambridge University Press, Cam- bridge (2007) [53] Chandler, D.: Introduction to Modern Statistical Mechanics. Oxford University 35 Press, Oxford (1987) [54] Shannon, C.E.: A mathematical theory of communication. Bell System Tech- nical Journal27(3), 379-423 (1948) https://doi.org/10.1002/j.1538-7305. 1948.tb01338.x https://onlinelibrary.wiley.com/doi/pdf/10.1002/j.1538- 7305.1948.tb01338.x [55] Landau, L.D.: Collected Papers of LD Landau. Pergamon, Oxford (1965) [56] Wagner, D., Wohlfarth, E.P.: Theory and applications of the landau-ginzburg"},{"citing_arxiv_id":"2604.14206","ref_index":31,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Portfolio Optimization Proxies under Label Scarcity and Regime Shifts via Bayesian and Deterministic Students under Semi-Supervised Sandwich Training","primary_cat":"cs.LG","submitted_at":"2026-04-04T06:42:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A semi-supervised teacher-student framework enables neural networks to proxy CVaR portfolio optimization using synthetic data augmentation for scarce labels and regime shifts.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"dates provide return scenario windowsR(t)∈RS×N without teacher labels. Scenario portfolio losses are: ℓ(t) =−R(t) ˆwt∈RS.(15) Empirical CVaR at levelα= 0.95is computed by averaging the worstK=⌈(1−α)S⌉losses [2]: \\CVaR0.95(ℓ(t)) = 1 K K∑ k=1 ℓ(t) (S−k+1),(16) where ℓ(t) (1)≤···≤ℓ(t) (S) are sorted losses. An entropy- based diversification regularizer penalizes concentra- tion [31]: Ldiv =−H(ˆwt) = N∑ i=1 ˆwt,i log( ˆwt,i).(17) The combined unsupervised objective is: Lunsup =λcvar·\\CVaR0.95(ℓ(t)) +λdiv·Ldiv,(18) with KL added for Bayesian variants analogously to Eq. (13). 5.4. Semi-Supervised Sandwich Training Semi-supervised variants (DNN-S, BNN-S) are trained using a three-stage sandwich schedule that alternates supervised anchoring with unsupervised"},{"citing_arxiv_id":"2603.23298","ref_index":24,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Kruskal-style algorithm for cubic Schr\\\"odinger equation molecule reduction","primary_cat":"math.AP","submitted_at":"2026-03-24T14:58:50+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"The Deng-Hani molecule reduction algorithm constructs a Kruskal spanning tree of the input molecule.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"1907.02053","ref_index":23,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Evaluation of a Flow-Based Hypergraph Bipartitioning Algorithm","primary_cat":"cs.DS","submitted_at":"2019-07-03T17:44:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ReBaHFC refines PaToH outputs with the new HyperFlowCutter flow algorithm to deliver hypergraph bipartition quality close to KaHyPar and hMETIS while running an order of magnitude faster.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}