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DTAI-seminar: Exploration and Generation of Secure Hardware Using Functional Languages (Kris Aerts)

Tuesday November 8 2011, at 16h30 in Celestijnenlaan 200A (Auditorium 00.225)

Exploration and Generation of Secure Hardware Using Functional Languages
By Kris Aerts (docent KH Limburg)

At KHLim, a university college partner in the association K.U.Leuven, the research group Embedded Systems & Security (ES&S) is working on generating cryptographic hardware, under the lead of Nele Mentens and Kris Aerts.  The industry standard to describe hardware is VHDL (which is based on Ada), but this language is not that expressive. Therefore hardware description languages have been built on top of, and embedded in the functional language Haskell, giving rich features to the language. The hardware blocks can be considered as functions receiving input and returning output signals, and are composed in a functional way.

ES&S is currently using Chalmers Lava to perform design exploration for modular arithmetic on big numbers, which is typical for cryptographic algorithms. The design exploration is necessary because there can be big differences in the underlying hardware of the FPGA (e.g. data path width, available combinatorial block or look up tables, ...) and the designer of the hardware wants trade offs between performance and the surface occupied on the FPGA. Moreover, the security of a system depends not only on the safety of the algorithms but also on non-functional features such as power consumption because side channel attacks can exploit this, e.g. by deriving the 0 and 1's of a key from the fact that a multiplication consumes more power than an addition.

This talks gives an introduction in how to describe hardware in a declarative language (Lava on top of Haskell), shows the progress in exploring different architectures and describing a formal finite state machine. It also says something about how we plan to address the side channel attacks already during design, and not only after the generation of VHDL.

 

DTAI-seminar: Inprocessing SAT Solvers: Adding More Reasoning to Search (Matti Järvisalo)

Thursday October 27 2011, at 14h in Celestijnenlaan 200A (room 05.001)

Inprocessing SAT Solvers: Adding More Reasoning to Search
By Matti Järvisalo

Boolean satisfiability (SAT) has become an attractive approach to solving hard decision and optimization problems arising from artificial intelligence, knowledge representation, and various industrially relevant domains. The success of the SAT-based approach relies heavily on the development of increasingly robust and efficient SAT solvers. Effective preprocessing techniques have become increasingly important for enabling fast SAT solving by applying more extensive reasoning techniques on input instances before search. In addition to integrating preprocessing techniques to SAT solvers, a recent trend is that of inprocessing. In the inprocessing SAT solving paradigm, more extensive reasoning is interleaved with the core satisfiability search, not only before search. In this talk I will review some of the most successful SAT preprocessing techniques, and give an overview of our recent work on developing new reasoning techniques for pre- and inprocessing.

Joint work with Armin Biere (JKU Linz) and Marijn Heule (TU Delft).

 

DTAI-seminar: Declarative Modeling for Machine Learning and Data Mining (Luc De Raedt)

Tuesday October 18 2011, at 16h30 in Celestijnenlaan 200A (Auditorium 00.225)

Declarative Modeling for Machine Learning and Data Mining --- A Research Roadmap for DTAI
By Luc De Raedt

The focus of DTAI research for the next 5 year is to be on the development of a declarative modeling paradigm and its application to the areas of machine learning, data mining and experimentation. The declarative modeling paradigm that is pursued consists of three key components:

  1. A modeling language which is a high level declarative language for specifying the relevant domain knowledge, independent of a particular task (M component).

  2. A solver which accepts input in a more low level task oriented language and performs a particular computational task (S component).

  3. A programming platform in which a user employs a general purpose language to solve a specific computational task. (P component).

Last Updated on Wednesday, 12 October 2011 21:20
 

DTAI-seminar: Knowledge compilation and its application to probabilistic inference

Tuesday October 4 2011, at 16h30 in Celestijnenlaan 200A (Auditorium 00.225)

Knowledge compilation and its application to probabilistic inference
By Guy Van den Broeck (PhD student DTAI)

Knowledge compilation is a technique to deal with the computational intractability of reasoning in propositional knowledge bases. Given an inference task, it compiles a propositional logic theory into a representation where the inference task is supported in polytime. Popular compiled representations include Boolean circuits such as binary decision diagrams (BDD) or d-DNNFs. This talk will give an overview of inference tasks and representations used in knowledge compilation. Knowledge compilation has many applications in planning, verification, etc. I will focus on the application to exact inference in probabilistic models, where compilation to d-DNNF is state of the art. Finally, I will talk about our recent work on using first-order knowledge compilation for lifted probabilistic inference.

 

DTAI-seminar: Peptide Identification using Mass Spectrometry Data (Eduardo De Paula Costa)

Monday 23 May 2011, at 16h30 in Celestijnenlaan 200A Auditorium 00.225

Peptide Identification using Mass Spectrometry Data
By Eduardo De Paula Costa (PhD student DTAI)

Peptides are short proteins that play a key role in many physiological processes such as blood pressure regulation, water balance and glucose metabolism. Hence, peptide identification is an important step in understanding how these processes work. Currently, one of the most important techniques for peptide identification is Mass spectrometry (MS), an analytical technique used for measuring the molecular mass of a biological sample. MS produces various types of data. The most common data representation is the mass spectrum. This spectrum is a plot containing the chemical analysis of a molecule (a peptide, for example) and it is used for the molecule identification. As spectrum identification can be a complex and time consuming task, many computational methods and tools have been proposed to assist researches in this task. In this presentation, I will introduce the main aspects involving peptide identification using mass spectrometry data and I will discuss the main computational approaches currently used to interpret mass spectra. More specifically, I will present a method that we are currently developing, which extends the data base search approach for peptide identification by considering additional genomic information during the spectrum identification task.

 


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