Learning Diffusions without Timestamps

Learning from Contagion (Without Timestamps)

We introduce and study new models for learning from contagion processes in a network. A learning algorithm is allowed to either choose or passively observe an initial set of seed infections. This seed set then induces a final set of infections resulting from the underlying stochastic contagion dynamics. Our models differ from prior work in that detailed vertex-byvertex timestamps for the spread...

Skewed Timestamps

In the well known timestamp algorithm each writing process reads all the registers of the other processes, and then sets a higher timestamp (natural number) to itself. In thèskewed algorithm' only one writing process in each pair reads the other. 1 Preface Messages are often timestamped. In a fax, the timestamp includes the date and exact time of the day, and in a book only the publication year...

Flow Timestamps

We consider the problem of a packet multiplexor that receives packets from multiple input flows, then forwards them to a single output channel. The multiplexor ensures a predefined forwarding rate for each flow. This is achieved as follows. The multiplexor assigns a timestamp to each received packet. When the channel becomes idle, the multiplexor forwards the packet with the smallest timestamp....

Labeling Documents with Timestamps: Learning from their Time Expressions

Temporal reasoners for document understanding typically assume that a document’s creation date is known. Algorithms to ground relative time expressions and order events often rely on this timestamp to assist the learner. Unfortunately, the timestamp is not always known, particularly on the Web. This paper addresses the task of automatic document timestamping, presenting two new models that inco...

Self-stabilizing timestamps