The work proves that approximating correlation clustering to additive εn² error requires Ω(n/ε²) adjacency-matrix queries, with stronger bounds under memory constraints in random and general query models.
2019 IEEE 60th Annual Symposium on Foundations of Computer Science (FOCS) , pages =
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The first dynamic algorithms for matrix rank and related objects achieve update times scaling with rank r, specifically Õ(r^1.405) per entry update and Õ(r^1.528 + z) per column update, extending to dynamic maximum matching.
An algorithm for online Steiner forest achieves constant competitiveness with amortized O(log n) recourse.
A deterministic semi-streaming algorithm achieves an O(Δ)-coloring in O(√log Δ) passes, the first with linear palette size and sublogarithmic passes.
MultiLogBench shows that LLM performance on automated logging varies substantially across programming languages, demonstrating that single-language evidence is insufficient for general claims about model behavior or tool design.
Unsupervised GNN model learns local updates for approximate MaxIS on dynamic graphs, achieving competitive ratios on 200-1000 node instances and 1.00-1.18x larger solutions than other unsupervised models when generalizing to 100x larger graphs.
Small-scale programs exhibit notable compile-time and run-time configurability that grows over time and correlates with size, supporting the value of reducing variability for simpler software.
UFPR-VeSV is a new real-world dataset for fine-grained vehicle classification and automatic license plate recognition collected from Brazilian police cameras, with benchmarks demonstrating its difficulty and the value of joint task use.
VARENN encodes spatiotemporal climate data as RGB images for CNN-based classification of temperature and precipitation changes.
Multi-telescope spectral modeling of HESS J1825-137 using Naima and MCMC shows leptonic dominance for existing GeV-TeV data but lepto-hadronic preference when adding simulated CTAO or LHAASO UHE points.
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Unsupervised Learning of Local Updates for Maximum Independent Set in Dynamic Graphs
Unsupervised GNN model learns local updates for approximate MaxIS on dynamic graphs, achieving competitive ratios on 200-1000 node instances and 1.00-1.18x larger solutions than other unsupervised models when generalizing to 100x larger graphs.