Home Knowledge Base MMS

MMS

No mentions found

This entity hasn't been tracked yet, or Iris is still building its knowledge base.

Related Articles from SNS

Simultaneous EF1 and approximate MMS allocations for submodular valuations

Announce Type: new Abstract: There are two common classes of fairness notions that are considered when allocating $m$ indivisible items to $n$ agents of equal entitlements. One is that of share-based fairness notions, with the maximin share (MMS) and its relaxations to $\rho$-MMS being prominent representatives of this class. The other is that of comparison-based fairness notions, with envy-freeness (EF) and its relaxations such as EF1 being prominent representatives of this class.

arXiv CS 5d ago

Best-of-Both-Worlds Fairness of the Envy-Cycle-Elimination Algorithm

arXiv:2410.08986v2 Announce Type: replace Abstract: We consider the problem of fairly dividing indivisible goods among agents with additive valuations. It is known that an Epistemic EFX and $2/3$-MMS allocation can be obtained using the Envy-Cycle-Elimination (ECE) algorithm. In this work, we explore whether this algorithm can be randomized to also ensure ex-ante proportionality.

arXiv CS 5d ago

Mosque, mazar, 2 temples: Jaipur sees internet curbs before anti-encroachment drive

The city administration has ordered the temporary suspension of mobile internet services in Jaipur North and Jaipur East police districts for 24 hours starting Sunday midnight, ahead of a JDA anti-encroachment drive scheduled for Monday morning. The order, issued by Jaipur divisional commissioner V Saravana Kumar, says 2G, 3G, 4G and 5G mobile internet services, bulk SMS, MMS and social media services, including WhatsApp, Facebook and X, will remain suspended. Voice calls, however, will...

Times of India 2d ago

Physics-Guided Geometric Diffusion for Macro Placement Generation

arXiv:2605.16451v2 Announce Type: replace Abstract: Macro placement is a pivotal stage in VLSI physical design, fundamentally determining the overall chip performance. Recent data-driven placement methods have demonstrated significant potential, yet they often struggle to handle sequential dependencies and to balance topological connectivity with physical constraints. To bridge this gap, we propose MacroDiff+, a physics-guided geometric diffusion framework.

arXiv CS 8d ago