Marcin Abram
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View article: Hofstadter Butterflies in Topological Insulators
Hofstadter Butterflies in Topological Insulators Open
In this chapter, we investigate the energy spectra and the bulk and surface states in a two-dimensional system composed of a coupled stack of one-dimensional dimerized chains in the presence of an external magnetic field. Specifically, we …
View article: Why does in-context learning fail sometimes? Evaluating in-context learning on open and closed questions
Why does in-context learning fail sometimes? Evaluating in-context learning on open and closed questions Open
We measure the performance of in-context learning as a function of task novelty and difficulty for open and closed questions. For that purpose, we created a novel benchmark consisting of hard scientific questions, each paired with a contex…
View article: Context Matters: Data-Efficient Augmentation of Large Language Models for Scientific Applications
Context Matters: Data-Efficient Augmentation of Large Language Models for Scientific Applications Open
In this paper, we explore the challenges inherent to Large Language Models (LLMs) like GPT-4, particularly their propensity for hallucinations, logic mistakes, and incorrect conclusions when tasked with answering complex questions. The cap…
View article: Emulating quantum dynamics with neural networks via knowledge distillation
Emulating quantum dynamics with neural networks via knowledge distillation Open
We introduce an efficient training framework for constructing machine learning-based emulators and demonstrate its capability by training an artificial neural network to predict the time evolution of quantum wave packets propagating throug…
View article: Performance Weighting for Robust Federated Learning Against Corrupted Sources
Performance Weighting for Robust Federated Learning Against Corrupted Sources Open
Federated Learning has emerged as a dominant computational paradigm for distributed machine learning. Its unique data privacy properties allow us to collaboratively train models while offering participating clients certain privacy-preservi…
View article: Emulating Quantum Dynamics with Neural Networks via Knowledge Distillation
Emulating Quantum Dynamics with Neural Networks via Knowledge Distillation Open
High-fidelity quantum dynamics emulators can be used to predict the time evolution of complex physical systems. Here, we introduce an efficient training framework for constructing machine learning-based emulators. Our approach is based on …
View article: Inferring topological transitions in pattern-forming processes with self-supervised learning
Inferring topological transitions in pattern-forming processes with self-supervised learning Open
The identification and classification of transitions in topological and microstructural regimes in pattern-forming processes are critical for understanding and fabricating microstructurally precise novel materials in many application domai…
View article: Model-Free Real-Time Autonomous Energy Management for a Residential Multi-Carrier Energy System: A Deep Reinforcement Learning Approach
Model-Free Real-Time Autonomous Energy Management for a Residential Multi-Carrier Energy System: A Deep Reinforcement Learning Approach Open
The problem of real-time autonomous energy management is an application area that is receiving unprecedented attention from consumers, governments, academia, and industry. This paper showcases the first application of deep reinforcement le…
View article: Democratising blockchain: A minimal agency consensus model
Democratising blockchain: A minimal agency consensus model Open
We propose a novel consensus protocol based on a hybrid approach, that combines a directed acyclic graph (DAG) and a classical chain of blocks. This architecture allows us to enforce collective block construction, minimising the monopolist…
View article: Antiferromagnetism, charge density wave, and<i>d</i>-wave superconductivity in the extended<i>t</i>-<i>J</i>-<i>U</i>model: role of intersite Coulomb interaction and a critical overview of renormalized mean field theory
Antiferromagnetism, charge density wave, and<i>d</i>-wave superconductivity in the extended<i>t</i>-<i>J</i>-<i>U</i>model: role of intersite Coulomb interaction and a critical overview of renormalized mean field theory Open
In the first part of the paper, we study the stability of antiferromagnetic\n(AF), charge density wave (CDW), and superconducting (SC) states within the\n$t$-$J$-$U$-$V$ model of strongly correlated electrons by using the\nstatistically co…
View article: Antiferromagnetism, charge density wave and $d$-wave superconductivity in the $t$-$J$-$U$-$V$ model of correlated electrons
Antiferromagnetism, charge density wave and $d$-wave superconductivity in the $t$-$J$-$U$-$V$ model of correlated electrons Open
In the first part of the paper, we study the stability of antiferromagnetic (AF), charge density wave (CDW), and superconducting (SC) states within the $t$-$J$-$U$-$V$ model of strongly correlated electrons by using the statistically consi…
View article: Antiferromagnetism, charge density wave, and d-wave superconductivity in the $t$-$J$-$U$-$V$ model of correlated electrons: Role of direct Coulomb interactions
Antiferromagnetism, charge density wave, and d-wave superconductivity in the $t$-$J$-$U$-$V$ model of correlated electrons: Role of direct Coulomb interactions Open
We study the stability of antiferromagnetic (AF), charge density wave (CDW), and superconducting (SC) states within the $t$-$J$-$U$-$V$ model of strongly correlated electrons using the statistically consistent Gutzwiller approximation (SGA…