{"total":28,"items":[{"citing_arxiv_id":"2606.28221","ref_index":105,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Faraday Tomography with the SKA: A New Era of Cosmic Magnetism Studies","primary_cat":"astro-ph.IM","submitted_at":"2026-06-26T16:11:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"A review of Faraday Rotation Measure Synthesis techniques and SKA Array Assembly stages for high-resolution Faraday tomography of cosmic magnetic structures.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.19920","ref_index":8,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Deep-Unfolded Coordination","primary_cat":"cs.RO","submitted_at":"2026-06-18T08:14:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Deep Coordinator uses deep unfolding to adapt ADMM-DDP penalty parameters at runtime, delivering 6.18-9.44x faster comparable-quality trajectories in car and quadrotor fleet simulations while scaling to 8x larger systems.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.17769","ref_index":47,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A Bilevel Optimization Model for Bottom-Up Coordination of Multiple Low-Voltage Energy Communities and the Medium-Voltage Network","primary_cat":"math.OC","submitted_at":"2026-06-16T10:36:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Bilevel Stackelberg model with KKT single-level reformulation and LDD-ADMM distributed algorithm coordinates LV communities and MV network on IEEE 33-bus and 206-bus LV feeders, achieving 1.7e-4 average deviation from exact solution.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.09594","ref_index":33,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Constraint residuals, graph posteriors, and determinant-corrected full-space targets in Bayesian inverse problems","primary_cat":"math.ST","submitted_at":"2026-06-08T15:04:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Uncorrected Gaussian residual penalties in full-space sampling converge after marginalization to the graph-lifted reduced posterior multiplied by the inverse absolute determinant of the state Jacobian, requiring explicit determinant corrections for equivalence.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.08374","ref_index":5,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Predictive Coding with Bayesian Priors via Proximal Gradients","primary_cat":"eess.SY","submitted_at":"2026-06-06T23:41:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Predictive coding equals proximal gradient descent on MAP problems, with priors setting nonlinearities via proximal operators and yielding leaky firing-rate networks plus hierarchical MRFs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.05053","ref_index":4,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Deep Learning Based Multi-Step Channel Prediction for Adaptive Underwater Acoustic OFDM Systems","primary_cat":"eess.SP","submitted_at":"2026-06-03T16:11:48+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"PatchCSI-T Transformer predicts multi-step CSI for adaptive underwater OFDM, improving BER and spectral efficiency on real UWA datasets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.03279","ref_index":54,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A Geometric Lens on Physics-Aligned Data Compression","primary_cat":"cs.LG","submitted_at":"2026-06-02T07:44:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Develops a local tangent-space rate-distortion theory and eigenspace-overlap diagnostic showing when physics-aligned compression necessarily degrades standard fidelity due to misaligned sensitivity directions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.31022","ref_index":6,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Augmented Lagrangian Predictive Coding","primary_cat":"cs.LG","submitted_at":"2026-05-29T08:54:21+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"PC-ALM uses dual ascent on an augmented Lagrangian to achieve exact backpropagation gradients via layer-local updates in linear networks and matching performance in nonlinear networks up to depth 128.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.31005","ref_index":9,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Learning Multi-Agent Coordination via Sheaf-ADMM","primary_cat":"cs.LG","submitted_at":"2026-05-29T08:39:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Sheaf-ADMM trains multi-agent systems by unrolling ADMM with sheaf-specified constraints, yielding improved MNIST robustness to shifts and higher Sudoku solve rates than MPNN baselines.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.20479","ref_index":2,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Oracle Supervision Transfers for Hyperparameter Prediction in Model-Based Image Denoising","primary_cat":"cs.CV","submitted_at":"2026-05-19T20:41:44+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"HyperDn is a configuration-conditioned predictor that transfers oracle supervision across denoising paradigms to achieve near-oracle hyperparameter prediction with few or zero target labels.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12830","ref_index":14,"ref_count":2,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Linking COPD Prevalence with Income Distribution: A Spatial Heterogeneous Compositional Regression via Geographically Weighted Penalized Approach","primary_cat":"stat.ME","submitted_at":"2026-05-12T23:54:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A new geographically weighted penalized compositional regression model with pairwise fusion penalty is proposed to handle spatial heterogeneity and compositional covariates, demonstrated on U.S. income and COPD data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.07254","ref_index":3,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"High-Fidelity Surface Splatting-Based 3D Reconstruction from Multi-View Images","primary_cat":"cs.CV","submitted_at":"2026-05-08T05:23:28+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A polynomial kernel with local support and Laplacian regularization in IMLS yields higher-fidelity meshes and textures from multi-view images than prior exponential-kernel formulations.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"To obtain a three-dimensional SDF function F(q) representing the surface S, the point functions Ω(q)are combined using a kernel,γ(q). F(q) = P pi∈P γ(q)·Ω(q) P pi∈P γ(q) (2) The 0-level set of F obtains the reconstructed surface S as presented in the works by Kolluri [2008]. In the IMLS Splatting algorithm, the authors used an exponential function for the kernel defined as γimls(q) = exp \b −∥q−p i∥2/r2 i (3) where ri is the influence radius. It determines the contribution of a specific sample point pi to the reconstructed surface at any given pointq. Because the weight decays exponentially, the reconstructed surface S is primarily determined by samples within a small distance, specifically within a ball of radius2r i of the evaluation pointq. However, the exponential kernel used in Yang et al."},{"citing_arxiv_id":"2605.04746","ref_index":17,"ref_count":2,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Distributed Energy System Design including Unbalanced AC Power Flow for Large LV Networks with ADMM","primary_cat":"math.OC","submitted_at":"2026-05-06T10:52:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A hybrid MILP-NLP-complementarity decomposition solved via spatial/temporal ADMM yields up to 13x speedup on unbalanced AC power flow-constrained DES design for networks with 55 loads, with maximum 0.61% optimality gap.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"When considering large-scale optimisation problems in power systems, the use of distributed optimisa- tion can ensure that the computation of the solution remains tractable by taking the main problem and decomposing it into several smaller subproblems [16]. These subproblems are then solved separately, whilst the distributed optimisation algorithm must also ensure that their solutions remain feasible with respect to the main problem [17]. The OPF problem has been tackled by a number of distributed optimisation approaches [16, 18, 19, 20], from which the alternating direction method of multipliers (ADMM) [17] has proven to be particularly popular [16]. In general, ADMM requires convex formu- lations for proof of convergence [17], such as SDP/SOCP formulations or linearisations such as direct"},{"citing_arxiv_id":"2605.01860","ref_index":58,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Optimizing Trajectory-Trees in Belief Space: An Application from Model Predictive Control to Task and Motion Planning","primary_cat":"cs.RO","submitted_at":"2026-05-03T13:06:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Optimizing trajectory-trees in belief space improves performance in partially observable robotic planning by capturing observation-dependent contingencies, shown via PO-MPC with D-AuLa optimization and PO-LGP extending LGP.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.19084","ref_index":33,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"DUSG-Tomo-Net: A Deep Unfolded Neural Network for Super-Resolving Gridless Spaceborne SAR Tomography via Learned Toeplitz-Structured Covariance Representation","primary_cat":"eess.SP","submitted_at":"2026-04-21T04:54:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"DUSG-Tomo-Net performs super-resolved gridless TomoSAR inversion by learning a Toeplitz-structured covariance representation from single-look nonuniform-baseline data via deep unfolding and projection enforcement.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"λi uiuH i , λ1≥λ2≥···≥λNv≥0,(31) whereλi andu i denote thei-th eigenvalue and eigenvector, respectively. The noise- subspace matrix is then formed by collecting theNv−Keigenvectors associated with the smallest eigenvalues, ˆUN = [ uK+1,u K+2, ...,uNv ] ∈CNv×(Nv−K).(32) 11 The Root-MUSIC polynomial is then constructed as q(z) =a T (z−1)ˆUNˆUH N a(z),(33) wherez∈Cis a complex variable and a(z) = [1, z, ..., zNv−1]⊤ (34) is the polynomial-domain steering vector on the virtual lag grid. Note thata(z)here is defined in theNv-dimensional virtual lag domain and should not be confused with the physical-domain steering atoma(s)in Eq. (8). Multiplyingq(z)byz Nv−1yields an ordinary polynomial of degree2(Nv−1)whose"},{"citing_arxiv_id":"2604.18894","ref_index":8,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Kazhdan-Lusztig Basis and Optimization","primary_cat":"math.RT","submitted_at":"2026-04-20T22:33:24+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"Maximizing a quadratic objective over unitriangular bases with non-negative 1+s action recovers the Kazhdan-Lusztig basis for all partitions of n≤7 and is conjectured to do so more generally, while minimization recovers Young's seminormal basis.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"example, the task of finding structure constants leads to an optimization problem where there is one coefficient for each pair of permutations ofn, leading to a naive parameter count of(n!)2. Whilst linear solvers can handle such high-dimensional problems with millions of variables, they struggle with the quadratic optimization problems that arise here. It would be very interesting to attempt gradient-based methods [3] or ADMM [8]. Another promising approach is to use semidefinite programming (SDP) to obtain relaxations of the quadratic optimization problem, see [49]. We hope to return to this in future work. 1.2.3.Reinforcement learning.Reinforcement learning is the field of AI used to solve problems like Go and Chess [53]. In this setting, one has a set of possible moves (in a known or unknown environment) and the goal is to"},{"citing_arxiv_id":"2604.02676","ref_index":2,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Low-Complexity Algorithm for Stackelberg Prediction Games with Global Optimality","primary_cat":"eess.SP","submitted_at":"2026-04-03T03:13:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"An ADMM algorithm with consensus splitting solves the SCLS problem for Stackelberg prediction games using closed-form linear-system and sphere-projection steps.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.04516","ref_index":39,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"TACO: Temporal Consensus Optimization for Continual Neural Mapping","primary_cat":"cs.RO","submitted_at":"2026-02-04T13:07:08+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":"2512.14213","ref_index":16,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Graph Signal Denoising Using Regularization by Denoising and Its Parameter Estimation","primary_cat":"eess.SP","submitted_at":"2025-12-16T09:10:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"RED is adapted to graph signals with deep unrolling for parameter estimation, yielding lower MSE than prior graph denoising methods on synthetic and real data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2509.07404","ref_index":8,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Reinforcement learning for adaptive interior point methods in convex quadratic programming","primary_cat":"math.OC","submitted_at":"2025-09-09T05:33:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Reinforcement learning learns a policy that adapts control parameters of a regularized interior-point method, accelerating high-accuracy solutions for convex quadratic programs and generalizing across problem classes after lightweight training.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2507.08299","ref_index":24,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Two-Level Distributed Interference Management for Large-Scale HAPS-Empowered vHetNets","primary_cat":"eess.SY","submitted_at":"2025-07-11T04:09:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Develops a distributed proportional fairness beamforming algorithm using ALM and three-block ADMM for interference management in HAPS-empowered vertical heterogeneous networks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2502.21202","ref_index":4,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"An Adaptive Multiparameter Penalty Selection Method for Multiconstraint and Multiblock ADMM","primary_cat":"eess.IV","submitted_at":"2025-02-28T16:21:19+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A new adaptive multiparameter penalty selection method for multiconstraint and multiblock ADMM provides robustness to scale differences and initial parameter choices.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2411.09764","ref_index":5,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"ModelPredictiveControl.jl: advanced process control made easy in Julia","primary_cat":"eess.SY","submitted_at":"2024-11-14T19:21:24+00:00","verdict":"ACCEPT","verdict_confidence":"MODERATE","novelty_score":7.0,"formal_verification":"none","one_line_summary":"The paper presents ModelPredictiveControl.jl, an open-source Julia toolkit for model predictive control including nonlinear, economic, and successive linearization variants, illustrated with CSTR and inverted pendulum simulations and benchmarked against MATLAB.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2409.13477","ref_index":6,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A Plug-and-Play Method for Guided Multi-contrast MRI Reconstruction based on Content/Style Modeling","primary_cat":"eess.IV","submitted_at":"2024-09-20T13:08:51+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":"2305.14209","ref_index":11,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-resolving SAR Tomography","primary_cat":"eess.SP","submitted_at":"2023-05-23T16:28:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"An RNN with novel sparse minimal gated units solves BPDN for TomoSAR super-resolution and achieves 10-20% higher double-scatterer detection rates than prior deep unrolling methods.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2006.05332","ref_index":60,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Advance Warning Methodologies for COVID-19 using Chest X-Ray Images","primary_cat":"eess.IV","submitted_at":"2020-06-07T20:42:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Introduces the Early-QaTa-COV19 dataset and reports that CSEN reaches over 97% sensitivity and over 95.5% specificity for early COVID-19 detection from X-rays.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2005.06674","ref_index":11,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"On the Convergence of Overlapping Schwarz Decomposition for Nonlinear Optimal Control","primary_cat":"math.OC","submitted_at":"2020-05-14T00:19:28+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Overlapping Schwarz decomposition for nonlinear OCPs achieves local linear convergence with rate improving exponentially with overlap size, based on exponential decay of sensitivity for primal and dual solutions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"1907.00920","ref_index":3,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Exact Augmented Lagrangian Duality for Mixed Integer Quadratic Programming","primary_cat":"math.OC","submitted_at":"2019-07-01T16:47:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Augmented Lagrangian duality for MIQP is exact with finite norm penalties and polynomially bounded weights.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}