[PRL] Torture chamber tomorrow 1:30–3:00

Daniel Brown dbrown at ccs.neu.edu
Wed Apr 1 08:48:54 EDT 2009


I've reserved 366 WVH.

Recap: I'm giving an MFPS practice talk at 10:30–11:45 this morning in
366. I'd appreciate feedback from whoever can spare the time to go a
third talk today. :)

Thanks!

 Dan

On Tue, Mar 31, 2009 at 15:19, Daniel Brown <dbrown at ccs.neu.edu> wrote:
> There's also a hiring talk tomorrow at 1:30, so I'll give my practice
> talk 10:30–11:45 right before seminar.
>
> Room TBA.
>
>  Dan
>
> On Tue, Mar 31, 2009 at 12:13, Daniel Brown <dbrown at ccs.neu.edu> wrote:
>> I need to bump this back to tomorrow; sorry that this puts two talks
>> on the same day. I'll follow up with the new time after I check
>> Riccardo's schedule and reserve a room.
>>
>>  Dan
>>
>> On Mon, Mar 30, 2009 at 15:28, Daniel Brown <dbrown at ccs.neu.edu> wrote:
>>> I'd like to run through my MFPS talk tomorrow (Tue) from 1:30–3:00pm
>>> in 366. It's a 40-minute talk, so I expect feedback will last past
>>> 2:30 but hopefully not up until 3:00. Thanks in advance to anyone that
>>> can make it!
>>>
>>>  Dan
>>>
>>> ——
>>>
>>> Categories of Timed Stochastic Relations
>>>
>>> Daniel Brown and Riccardo Pucella
>>>
>>> Stochastic behavior—the probabilistic evolution of a system in time—is
>>> essential to modeling the complexity of real-world systems. It enables
>>> realistic performance modeling, quality-of-service guarantees, and
>>> especially simulations for biological systems. Languages like the
>>> stochastic pi calculus have emerged as effective tools to describe and
>>> reason about systems exhibiting stochastic behavior. These languages
>>> essentially denote continuous-time stochastic processes, obtained
>>> through an operational semantics in a probabilistic transition system.
>>> We seek a more descriptive foundation for the semantics of stochastic
>>> behavior using categories and monads. We model a first-order
>>> imperative language with stochastic delay by identifying probabilistic
>>> choice and delay as separate effects, modeling each with a monad, and
>>> combining the monads to build a model for the stochastic language.
>>
>



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