ὅδε οἶκος, ὦ ἑταῖρε, μνημεῖον ἐστιν ζῴων τῶν σοφῶν ἀνδρῶν, καὶ τῶν ἔργων αὐτῶν

Seminar on Computer Science and Applied Mathematics

 

PROGRAM


Matematički Institut SANU, Beograd
Knez Mihajlova 36
Fakultet organizacionih nauka, Univerzitet u Beogradu,
Jove Ilica 154
IEEE Chapter Computer Science (CO-16) Belgrade, Republic of Serbia

SEMINAR ZA RAČUNARSTVO I PRIMENJENU MATEMATIKU

MI SANU, Knez Mihailova 36, sala 301f

Upravni odbor Matematickog instituta SANU je na nedavnoj sednici doneo odluku da se dosadasnji Seminar za primenjenu matematiku, sada nazove Seminar za racunarstvo i primenjenu matematiku, a u cilju potenciranja znacaja racunarstva kao jedne od oblasti delatnosti Instituta. Istovremeno, Upravni odbor doneo je odluku o osnivanju Odeljenja za racunarstvo i primenjenu matematiku i vezao rad novog odeljenja za rad Seminara za racunarstvo i primenjenu matematiku.

PLAN RADA SEMINARA ZA JUL 2013. GODINE

Ponedeljak, 22.07.2013. u 12:00, Sala 301f, MI SANU:

prof. Zoran Obradovic, Director, Data Analytics and Biomedical Informatics Center,
Professor, Computer and Information Sciences Department,
Professor, Statistics Department, Fox School of Business (secondary appointment),
Temple University

CONTINUOUS CONDITIONAL RANDOM FIELDS FOR EFFICIENT REGRESSION IN LARGE FULLY CONNECTED GRAPHS

Abstract: When used for structured regression, powerful Conditional Random Fields (CRFs) are typically restricted to modeling effects of interactions among examples in local neighborhoods. Using more expressive representation would result in dense graphs, making these methods impractical for large-scale applications. To address this issue, we propose an effective CRF model with linear scale-up properties regarding approximate learning and inference for structured regression on large, fully connected graphs. The proposed method is validated on real-world large-scale problems of image denoising and remote sensing. In conducted experiments, we demonstrated that dense connectivity provides an improvement in prediction accuracy. Inference time of less than ten seconds on graphs with millions of nodes and trillions of edges makes the proposed model an attractive tool for large-scale, structured regression problems.
This is joint research with Vladan Radosavljevic, Kosta Ristovski, and Slobodan Vucetic and the results will be published at Proc. Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI-13), Bellevue, Washington, July 2013.

Utorak, 23.07.2013. u 14:15h, Sala 301f, MI SANU:

Snezana Minic, Simon Fraser University, Vancouver, Canada
IMAGE ACQUISITION SCHEDULING AND CLOUD AVOIDANCE FOR AGILE HI-RESOLUTION EARTH OBSERVING OPTICAL SATELLITE

Abstract: An agile satellite that can rapidly slew its imager boresight in both roll and pitch angles can be used for obtaining cloud-free optical images. After detecting the cloud coverage for the area over which the satellite will pass in the next few minutes, the image acquisition scheduling problem is solved on-board the satellite. We present a mathematical programming formulation of the problem and an experimental study comparing heuristic solution approaches. The problem instances were generated with varying cloud coverage scenarios simulating the weather conditions over a real geographic area.

Joint work with Joe Steyn, MDA Systems.

RUKOVODIOCI SEMINARA

MI SANU
Vera Kovačević-Vujčić
Milan Dražić

FON
Zorica Bogdanovic
Marijana Despotovic-Zrakic

IEEE
Bozidar Radenkovic