Search arXivSearch

arXiv · 2608.30928

Further Remarks on Separating Words

Abstract

We revisit questions on separating words raised by Demaine, Eisenstat, Shallit, and Wilson, together with Ebrahimnejad's follow-up to their reversal problem. For length-$n$ pairs whose difference word has $d$ runs, we prove an $O(d\log n)$ bound, extending the Hamming-distance theorem of Demaine et al. For conjugate words, we give bounds controlled by the arithmetic of the shift. We resolve Demaine et al.'s Open Problem 2 by showing that the order of two words can change nondeterministic separation by an unbounded factor. Our reversal construction addresses Ebrahimnejad's follow-up to Open Problem 1: forward and reversed deterministic separation can differ by an unbounded factor. Since nondeterministic separation is invariant under reversal, the same construction also improves the lower bound in Open Problem 3.

Explore related subjects

Keep this discovery

BibTeXRIS

John Nicol. 2026-08-31. Further Remarks on Separating Words. https://arxiv.org/abs/2608.30928

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related papers

CARVY-FL: Client Anticlustering for Robust Voting in Provably Secure Federated Learning

Federated learning (FL) enables collaborative training without directly sharing raw data, but remains vulnerable to malicious clients. Voting-based FL improves robustness by partitioning clients into groups, training one model per group, and aggregating predictions by plurality voting. However, under class-disjoint non-IID data, distribution-oblivious grouping can yield highly variable certified accuracy (CA). We propose CARVY-FL, which estimates client distribution types from one-epoch model updates and uses anticlustering to increase within-group distributional diversity. Under a fixed grouping, CARVY-FL retains the voting-based CA guarantee while increasing vote margins. Experiments on MNIST and Fashion-MNIST show higher CA than FLCert. Under BadNets with model replacement, CARVY-FL improves the AUC of 100-ASR by 11.1% and 14.9%, respectively.

cs.CR

DART-FL: Burst-Aware Multitask Federated Learning under Dynamic Inference Demand at the Edge

Edge intelligence systems increasingly require model training and online inference to coexist on resource-constrained devices, while inference demand can vary substantially across tasks over time. This creates two coupled challenges: sufficient computation must be reserved for inference to maintain service-level objectives (SLOs), while the remaining training capacity should adapt to task-specific demand so that frequently requested tasks can improve earlier during training. We propose an SLO-aware, demand-driven multitask federated learning framework (DART-FL) that jointly adapts the inference-training resource split and task-level training emphasis. At each scheduling interval, DART-FL uses the inference backlog and profiled service capacity to determine the minimum resource allocation required for inference. The remaining training capacity is then distributed across tasks using a queue-aware DPP-inspired scheduler, and the resulting task allocations are mapped to dynamic loss weights. This allows tasks experiencing higher inference demand to receive greater training emphasis in earlier communication rounds. Clients train a shared backbone with task-specific heads, and the complete multitask model is aggregated through FedAvg. We evaluate DART-FL using Stanford Cars and Oxford Flowers 102 under both synthetic and real Alibaba trace-derived workloads. Results show that DART-FL dynamically adapts the inference-training resource split to time-varying inference demand and shifts the learning progress of high-demand tasks toward their burst periods, improving model accuracy when those tasks are frequently requested while maintaining comparable long-term multitask performance.

cs.LG

On smallest synchronizing terms over constant alphabets

We show a subexponential lower bound on the reset threshold of synchronizing deterministic finite tree automata (DTA) over alphabets of just two symbols. This significantly improves the previous one, which was quadratic in the number of states. Our result also narrows the gap towards the lower bound for DTA over alphabets that grow linearly with the number of states, as well as the best known upper bound, both of which are currently exponential.

cs.FL