796 research outputs found
Számelmélet és kombinatorikus vonatkozásai = Number Theory and its Interactions with Combinatorics
A kutatĂłk számos Ă©rdekes eredmĂ©nyt Ă©rtek el a kombinatorikus számelmĂ©let Ă©s geometria, gráfelmĂ©let, diofantikus approximáciĂł terĂĽletĂ©n, itt csak nĂ©hányat emlĂtĂĽnk. Elekes Ă©s Ruzsa a Freiman, Balog-SzemerĂ©di Ă©s Laczkovich-Ruzsa tĂ©telek közös általánosĂtását adják, ezzel a tĂ©makört egysĂ©gesĂtik, Ă©s számos kombinatorikus geometriai tĂ©telt fejlesztenek tovább. Elekes SzabĂł E.-vel áttörĂ©st Ă©rt el a sok szabályosságot tartalmazĂł konfiguráciĂłk karakterizáciĂłjának általános problĂ©májában, nĂ©hány korábbi eredmĂ©nyt jelentĹ‘sen továbbfejlesztve. SzemerĂ©di A. Khalfalah-val igazolja Sárközy, Roth Ă©s T. SĂłs azon sejtĂ©sĂ©t, hogy: ha beosztjuk az egĂ©sz számokat vĂ©ges sok osztályba, akkor valamely osztályban van kĂ©t olyan szám, amelyek összege nĂ©gyzetszám, V. Vu-val közösen pedig Folkman egy sejtĂ©sĂ©t bizonyĂtja. BirĂł javĂtja Ruzsa Ă©s Kolountzakis egĂ©sz számok parkettázására vonatkozĂł eredmĂ©nyĂ©t. ErĹ‘sĂti Ă©s általánosĂtja a "karakterizálĂł sorozatok" tĂ©makör korábbi eredmĂ©nyeit. Ruzsa Ă©s B. Green meghatározzák tetszĹ‘leges vĂ©ges kommutatĂv csoportban a legnagyobb összegmentes halmaz elemszámát. T. SĂłs Lovász L.-val megmutatja, hogy ha gráfok egy sorozatában a kis rĂ©szgráfoknak ugyanaz az eloszlása, mint egy általánosĂtott G vĂ©letlen gráfban, akkor ezen gráfoknak aszimptotikusan olyan struktĂşrája van, mint G-nek. T. SĂłs társszerzĹ‘kkel azt az alapkĂ©rdĂ©st vizsgálja, mikor van közel egymáshoz kĂ©t gráf. | The participants obtaind several interesting results in combinatorial number theory and geometry, graph theory, diophantine approximation, we list just a few of these results.. Elekes and Ruzsa give a common generalization of the Freiman, Balog-SzemerĂ©di and Laczkovich-Ruzsa theorems, unifying in this way the subject and improving a lot of earlier results. Elekes with E. SzabĂł achieved a breakthrough in the general problem of characterizing configurations having a lot of reguarity, improving some earlier results. SzemerĂ©di with A. Khalfalah proves the follwing conjecture of Sárközy, Roth and T. SĂłs: if we divide the set of integers into finitely many classes, then in one of the classes we can find two numbers such that their sum is a square, and with V. Vu he proves a conjecture of Folkman. BirĂł improves a result of Ruzsa and Kolountzakis on tilings of the integers, and, he proves generalizations and strengthenings of some results in the subject 'characterizing sequences'. Ruzsa and B. Green determine the size of the largest sumfree set in an arbitrary finite Abelian group. L. Lovász and T. SĂłs showed that generalized quasirandom sequences (whose subgraph densities match those of a fixed finite weighted graph) have a finite structure. T. SĂłs with co-authors defines the distance of two graphs that reflects the similarity , the closeness of both local and global properties
Self-Evaluation Applied Mathematics 2003-2008 University of Twente
This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008
How you move reveals who you are: understanding human behavior by analyzing trajectory data
The widespread use of mobile devices is producing a huge amount of trajectory data, making the discovery of movement patterns possible, which are crucial for understanding human behavior. Significant advances have been made with regard to knowledge discovery, but the process now needs to be extended bearing in mind the emerging field of behavior informatics. This paper describes the formalization of a semantic-enriched KDD process for supporting meaningful pattern interpretations of human behavior. Our approach is based on the integration of inductive reasoning (movement pattern discovery) and deductive reasoning (human behavior inference). We describe the implemented Athena system, which supports such a process, along with the experimental results on two different application domains related to traffic and recreation management
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