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CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation

Autor: Krause, Claudius, Giannelli, Michele Faucci, Kasieczka, Gregor, Nachman, Benjamin, Salamani, Dalila, Shih, David, Zaborowska, Anna, Amram, Oz, Borras, Kerstin, Buckley, Matthew R., Buhmann, Erik, Buss, Thorsten, Cardoso, Renato Paulo Da Costa, Caterini, Anthony L., Chernyavskaya, Nadezda, Corchia, Federico A. G., Cresswell, Jesse C., Diefenbacher, Sascha, Dreyer, Etienne, Ekambaram, Vijay, Eren, Engin, Ernst, Florian, Favaro, Luigi, Franchini, Matteo, Gaede, Frank, Gross, Eilam, Hsu, Shih-Chieh, Jaruskova, Kristina, Käch, Benno, Kalagnanam, Jayant, Kansal, Raghav, Kim, Taewoo, Kobylianskii, Dmitrii, Korol, Anatolii, Korcari, William, Krücker, Dirk, Krüger, Katja, Letizia, Marco, Li, Shu, Liu, Qibin, Liu, Xiulong, Loaiza-Ganem, Gabriel, Madula, Thandikire, McKeown, Peter, Melzer-Pellmann, Isabell-A., Mikuni, Vinicius, Nguyen, Nam, Ore, Ayodele, Schweitzer, Sofia Palacios, Pang, Ian, Pedro, Kevin, Plehn, Tilman, Pokorski, Witold, Qu, Huilin, Raikwar, Piyush, Raine, John A., Reyes-Gonzalez, Humberto, Rinaldi, Lorenzo, Ross, Brendan Leigh, Scham, Moritz A. W., Schnake, Simon, Shimmin, Chase, Shlizerman, Eli, Soybelman, Nathalie, Srivatsa, Mudhakar, Tsolaki, Kalliopi, Vallecorsa, Sofia, Yeo, Kyongmin, Zhang, Rui

We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few t

Externí odkaz: http://arxiv.org/abs/2410.21611

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FL-DABE-BC: A Privacy-Enhanced, Decentralized Authentication, and Secure Communication for Federated Learning Framework with Decentralized Attribute-Based Encryption and Blockchain for IoT Scenarios

Autor: Narkedimilli, Sathwik, Sriram, Amballa Venkata, Raghav, Satvik

This study proposes an advanced Federated Learning (FL) framework designed to enhance data privacy and security in IoT environments by integrating Decentralized Attribute-Based Encryption (DABE), Homomorphic Encryption (HE), Secure Multi-Party Comput

Externí odkaz: http://arxiv.org/abs/2410.20259

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Information-Theoretic Minimax Regret Bounds for Reinforcement Learning based on Duality

Autor: Bongole, Raghav, Gouverneur, Amaury, Rodríguez-Gálvez, Borja, Oechtering, Tobias J., Skoglund, Mikael

We study agents acting in an unknown environment where the agent's goal is to find a robust policy. We consider robust policies as policies that achieve high cumulative rewards for all possible environments. To this end, we consider agents minimizing

Externí odkaz: http://arxiv.org/abs/2410.16013

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Quantum computation of SU(2) lattice gauge theory with continuous variables

Autor: Ale, Victor, Bauer, Nora M., Jha, Raghav G., Ringer, Felix, Siopsis, George

We present a quantum computational framework for SU(2) lattice gauge theory, leveraging continuous variables instead of discrete qubits to represent the infinite-dimensional Hilbert space of the gauge fields. We consider a ladder as well as a two-dim

Externí odkaz: http://arxiv.org/abs/2410.14580

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Non-Hermitian topology in the quantum Hall effect of graphene

Autor: Özer, Burak, Ochkan, Kyrylo, Chaturvedi, Raghav, Maltsev, Evgenii, Könye, Viktor, Giraud, Romain, Veyrat, Arthur, Hankiewicz, Ewelina M., Watanabe, Kenji, Taniguchi, Takashi, Büchner, Bernd, Brink, Jeroen van den, Fulga, Ion Cosma, Dufouleur, Joseph, Veyrat, Louis

Quantum Hall phases have recently emerged as a platform to investigate non-Hermitian topology in condensed-matter systems. This platform is particularly interesting due to its tunability, which allows to modify the properties and topology of the inve

Externí odkaz: http://arxiv.org/abs/2410.14329

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Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation Models

Autor: Singhal, Raghav, Ponkshe, Kaustubh, Vepakomma, Praneeth

Low-Rank Adaptation (LoRA) is a popular technique for efficient fine-tuning of foundation models. However, applying LoRA in federated learning environments, where data is distributed across multiple clients, presents unique challenges. Existing metho

Externí odkaz: http://arxiv.org/abs/2410.09432

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Photometric Analysis for Predicting Star Formation Rates in Large Galaxies Using Machine Learning and Deep Learning Techniques

Autor: Raghav, Satvik, Ayitapu, Prasanth, Narkedimilli, Sathwik, Makam, Sujith, H, Aswath Babu

Star formation rates (SFRs) are a crucial observational tracer of galaxy formation and evolution. Spectroscopy, which is expensive, is traditionally used to estimate SFRs. This study tests the possibility of inferring SFRs of large samples of galaxie

Externí odkaz: http://arxiv.org/abs/2410.06736

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Predicting Stellar Metallicity: A Comparative Analysis of Regression Models for Solar Twin Stars

Autor: Narkedimilli, Sathwik, Raghav, Satvik, Makam, Sujith, Ayitapu, Prasanth, H, Aswath Babu

The research focuses on determining the metallicity ([Fe/H]) predicted in the solar twin stars by using various regression modeling techniques which are, Random Forest, Linear Regression, Decision Tree, Support Vector, and Gradient Boosting. The data

Externí odkaz: http://arxiv.org/abs/2410.06709

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