Inferring constraints from multiple snapshots

Inferring Centrality from Network Snapshots

The topology and dynamics of a complex network shape its functionality. However, the topologies of many large-scale networks are either unavailable or incomplete. Without the explicit knowledge of network topology, we show how the data generated from the network dynamics can be utilised to infer the tempo centrality, which is proposed to quantify the influence of nodes in a consensus network. W...

Inference Bear: Inferring Behavior from before and after Snapshots

We present Inference Bear (Inference Based On Before And After Snapshots) which lets users build functional graphical user interfaces by demonstration. Inference Bear is the first Programming By Demonstration system based on the abstract inference engine described in [5]. Among other things, Inference Bear lets you align, center, move, resize, create and delete user interface elements by demons...

Inferring feasible assemblies from spatial constraints

Inferring Ordered Trees from Local Constraints

We consider a problem of inferring an ordered tree from a set of local constraints on its leaves which we term as the ordered local consensus tree problem. Using our eecient decremental interval union algorithm, we show that the ordered local consensus tree problem for m constraints on n leaves can be deterministically solved in time O((m+n) log n): We also show that the related optimization pr...

Inferring clonal composition from multiple tumor biopsies

Motivation. Knowledge about the clonal evolution of each tumor can inform driver-alteration discovery by pointing out initiating genetic events as well as events that contribute to the selective advantage of proliferative, and potentially drug-resistant tumor subclones. A necessary building block to the reconstruction of clonal evolution from tumor profiles is the estimation of the cellular com...