arXiv subjects

Mohammad Amini

Publications and source records attributed to Mohammad Amini.

23 records · Page 2Linked to original sources

arXiv2023

Visualization of moiré magnons in monolayer ferromagnet

Two-dimensional magnetic materials provide an ideal platform to explore collective many-body excitations associated with spin fluctuations. In particular, it should be feasible to explore, manipulate and ultimately design magnonic excitations in two-dimensional van der Waals magnets in a controllable way. Here we demonstrate the emergence of moiré magnon excitations, stemming from the interplay of spin-excitations in monolayer CrBr$_3$ and the moiré pattern stemming from the lattice mismatch with the underlying substrate. The existence of moiré magnons is further confirmed via inelastic quasiparticle interference, showing the appearance of a dispersion pattern correlated with the moiré length scale. Our results provide a direct visualization in real-space of the dispersion of moiré magnons, demonstrating the versatility of moiré patterns in creating emerging many-body excitations.

cond-mat.mes-hall↗

arXiv2023

PatchUp: A Feature-Space Block-Level Regularization Technique for Convolutional Neural Networks

Large capacity deep learning models are often prone to a high generalization gap when trained with a limited amount of labeled training data. A recent class of methods to address this problem uses various ways to construct a new training sample by mixing a pair (or more) of training samples. We propose PatchUp, a hidden state block-level regularization technique for Convolutional Neural Networks (CNNs), that is applied on selected contiguous blocks of feature maps from a random pair of samples. Our approach improves the robustness of CNN models against the manifold intrusion problem that may occur in other state-of-the-art mixing approaches. Moreover, since we are mixing the contiguous block of features in the hidden space, which has more dimensions than the input space, we obtain more diverse samples for training towards different dimensions. Our experiments on CIFAR10/100, SVHN, Tiny-ImageNet, and ImageNet using ResNet architectures including PreActResnet18/34, WRN-28-10, ResNet101/152 models show that PatchUp improves upon, or equals, the performance of current state-of-the-art regularizers for CNNs. We also show that PatchUp can provide a better generalization to deformed samples and is more robust against adversarial attacks.

cs.LG↗

arXiv2022

Improving Sample Efficiency of Value Based Models Using Attention and Vision Transformers

Much of recent Deep Reinforcement Learning success is owed to the neural architecture's potential to learn and use effective internal representations of the world. While many current algorithms access a simulator to train with a large amount of data, in realistic settings, including while playing games that may be played against people, collecting experience can be quite costly. In this paper, we introduce a deep reinforcement learning architecture whose purpose is to increase sample efficiency without sacrificing performance. We design this architecture by incorporating advances achieved in recent years in the field of Natural Language Processing and Computer Vision. Specifically, we propose a visually attentive model that uses transformers to learn a self-attention mechanism on the feature maps of the state representation, while simultaneously optimizing return. We demonstrate empirically that this architecture improves sample complexity for several Atari environments, while also achieving better performance in some of the games.

cs.AI↗

arXiv2021

Artificial heavy fermions in a van der Waals heterostructure

Heavy fermion systems represent one of the paradigmatic strongly correlated states of matter. They have been used as a platform for investigating exotic behavior ranging from quantum criticality and non-Fermi liquid behavior to unconventional topological superconductivity. Heavy fermions arise from the exchange interaction between localized magnetic moments and conduction electrons that leads to the well-known Kondo effect. In a Kondo lattice, the interaction between the localized moments gives rise to a band with heavy effective mass. This intriguing phenomenology has so far only been realized in compounds containing rare-earth elements with 4f or 5f electrons. Here, we realize a designer van der Waals heterostructure where artificial heavy fermions emerge from the Kondo coupling between a lattice of localized magnetic moments and itinerant electrons in a 1T/1H-TaS$_2$ heterostructure. We study the heterostructure using scanning tunneling microscopy (STM) and spectroscopy (STS) and show that depending on the stacking order of the monolayers, we can either reveal the localized magnetic moments and the associated Kondo effect, or the conduction electrons with a heavy-fermion hybridization gap. Our experiments realize an ultimately tuneable platform for future experiments probing enhanced many-body correlations, dimensional tuning of quantum criticality, and unconventional superconductivity in two-dimensional artificial heavy-fermion systems.

cond-mat.mes-hall↗

arXiv2020

A simulation study of semiparametric estimation in copula models based on minimum Alpha-Divergence

The purpose of this paper is to introduce two semiparametric methods for the estimation of copula parameter. These methods are based on minimum Alpha-Divergence between a non-parametric estimation of copula density using local likelihood probit transformation method and a true copula density function. A Monte Carlo study is performed to measure the performance of these methods based on Hellinger distance and Neyman divergence as special cases of Alpha-Divergence. Simulation results are compared to the Maximum Pseudo-Likelihood (MPL) estimation as a conventional estimation method in well-known bivariate copula models. These results show that the proposed method based on Minimum Pseudo Hellinger Distance estimation has a good performance in small sample size and weak dependency situations. The parameter estimation methods are applied to a real data set in Hydrology.

stat.ME↗