Cilj ovog projekta je objasniti hijerarhiju masa elementarnih čestica pomoću aproksimativnih simetrija okusa. Konkretno, istraživati ćemo varijante U(2) simetrije koje mogu da izraze redove veličina masa svih čestica kao funkciju dva parametra slamanja simetrije.

This project idea revolves around creating a sophisticated and intuitive interface engineered to deliver multimodal explanations suitable for various data types including tabular, textual, and visual data. The interface should be designed to incorporate a wide array of explanation methods, tailored to meet the specific interpretability requirements of different users. By integrating diverse interpretable models and techniques, the interface facilitates a comprehensive understanding of the decision-making processes underpinning machine learning models employed across multiple domains.

Local explanation methods are essential in explainable artificial intelligence (XAI), providing insights into the decision-making processes of machine learning models at the instance level. These methods offer transparent, understandable explanations for specific predictions made by complex models, such as deep neural networks or ensemble methods. This comparative analysis evaluates and contrasts various local explanation techniques to assess their effectiveness, interpretability, and applicability across different domains and model architectures.

This project aims to conduct a comprehensive comparative analysis of various state-of-the-art path planning algorithms, including A*, Theta*, TO-AA (TO-AA-SIPP), Anya, Polyanya, Informed RRT*, BIT*, RRT, and RRT*. By evaluating the behavior and performance of these diverse navigation approaches, the project seeks to identify their strengths, weaknesses, and optimal application scenarios in dynamic and static environments. The ultimate goal is to provide insights that could guide the selection and implementation of path-planning algorithms in robotics and autonomous systems across various domains.

Kandidati/Kandidatkinje će zajedno sa mentorom analizirati druge kanale produkcije i raspada ovog leptokvarka. Tačnije, analizira ćemo druge moguće kombinacije produkcije i raspada koje uključuju kvarkove i leptone prve i druge generacije (odnosno kombinacije down-quarka/strangequarka sa elektronima ili mionima). Konačni rezultati bi trebali biti granice na parametarski prostor leptokvarka, odnosno na njegovu masu i na njegovu konstantu vezivanja). Naposljetku ćemo uporediti izvedene rezultate sa granicama koje proizilaze iz eksperimenata na niskim energijama.

This project aims to study the rotation and total magnetization (as a function of time) of spherical, cubic, and disk-shaped, multidomain magnetic colloids in oscillating magnetic fields. To simulate the internal magnetic structure and dynamics of the colloids, we will take a multiscale approach consisting of a single-spin Landau-Lifshitz-Gilbert equation model [3], and a raspberry colloid model where the domains are thermal Stoner-Wohlfarth particles [4]. The knowledge gained in contrasting these approaches to “ground-truth” micromagnetics, using varying levels of complexity, will circumvent current limitations and help expand the potential applications of magnetic fluids in cancer treatment.

In this project, we are looking to compare this new implementation with the reference NumPy implementation, as well as the other performant NumPy implementations (e.g., Cython, Numba, Pythran, etc.) through the standardized benchmark sets such as NPBench.

In this project, we are looking to apply Codon and its NumPy libraries to implement various LLMs, such as Gemini, Llama, Mixtral, and to compare the new implementations with the current state-of-the-art implementations in other languages.

Ciljani projekat ucenja analiziranja relevantnih clanaka iz oblasti sumarstva, s pracenjem ostalih izvora informacija o izvrsenju kriminaliteta unutar sumskih gazdinstava, lovista, kao i izvora informacija o kradji lovackih pasa, ilegalnoj distribuciji lovnih dobara, lovnih pasa i organizaciji drugih ilegalnih aktivnosti u sumama u BiH.

Cilj ovog projekta je unapređenje postoječih algoritama planiranja sa metodama mašinskog učenja da bi brže pronalazili validne planove u sličnim situacijama korištenjem prethodnog iskustva.

Health data are characterised by the large numbers of variables categorical in their nature. One-hot encoding is commonly used in health data analysis when dealing with categorical variables. It is a popular technique used in machine learning to represent categorical variables numerically. It converts categorical variables into binary vectors, where each category is represented by a binary value (0 or 1) in a separate feature. While one-hot encoding can be useful in certain scenarios, it can also have an impact on model performance e.g., exacerbate the curse of dimensionality problem and model complexity and interpretability. This project aims to develop novel encoding method using real health data and compare it against the conventional such as one-hot encoding and embedding-based.

Rješavanje Poissonove jednačine i njene modifikacije u galaktičkim okolnostima Duraković Amel i Mistele Tobias Observatoire astronomique de Strasbourg, Institute of Physics of the Czech Academy of Sciences Case Western Reserve University

Brzine kretanja u krajevima bliskih galaksija kao moguć problem za teorije tamne materije Duraković Amel i Mistele Tobias Observatoire astronomique de Strasbourg, Institute of Physics of the Czech Academy of Sciences Case Western Reserve University