arXiv subjects

Mehdi Rezaee

Publications and source records attributed to Mehdi Rezaee.

10 recordsLinked to original sources

arXiv2026

A scalable platform for nanometer-scale quantum confinement

Overcoming the limitations of current nanofabrication techniques to achieve nanoscale feature sizes is essential for achieving new regimes of light-matter interactions at extreme frequencies and length scales. Here, we demonstrate a scalable nanofabrication platform capable of producing in-plane feature sizes down to 1.75 nm, pushing the boundaries of current top-down nanofabrication techniques. Using precise thickness control of atomic layer deposition (ALD) and employing widely spaced oxide nanofins, we transform conventional ALD into a surface structuring method that produces nanolaminates with sub-10 nm periodicities over large areas. The resulting nanostructures can be used as a one-dimensional gate array to control charge carriers in two-dimensional materials. As an initial demonstration, we integrate the platform with graphene and perform electron transport measurements. In the presence of the gate array enabled by the nanolaminate, we observe satellite Dirac peaks consistent with band-structure modulation, suggestive of quantum-confinement effects. Our platform paves the way for exploring previously inaccessible regimes of nanoscale light-matter interactions, holding significant promise for applications in short wavelength optics, electronics, and polaritonics.

physics.optics↗

arXiv2022

Semantically-informed Hierarchical Event Modeling

Prior work has shown that coupling sequential latent variable models with semantic ontological knowledge can improve the representational capabilities of event modeling approaches. In this work, we present a novel, doubly hierarchical, semi-supervised event modeling framework that provides structural hierarchy while also accounting for ontological hierarchy. Our approach consists of multiple layers of structured latent variables, where each successive layer compresses and abstracts the previous layers. We guide this compression through the injection of structured ontological knowledge that is defined at the type level of events: importantly, our model allows for partial injection of semantic knowledge and it does not depend on observing instances at any particular level of the semantic ontology. Across two different datasets and four different evaluation metrics, we demonstrate that our approach is able to out-perform the previous state-of-the-art approaches by up to 8.5%, demonstrating the benefits of structured and semantic hierarchical knowledge for event modeling.

cs.CL↗

arXiv2022

RevUp: Revise and Update Information Bottleneck for Event Representation

The existence of external (``side'') semantic knowledge has been shown to result in more expressive computational event models. To enable the use of side information that may be noisy or missing, we propose a semi-supervised information bottleneck-based discrete latent variable model. We reparameterize the model's discrete variables with auxiliary continuous latent variables and a light-weight hierarchical structure. Our model is learned to minimize the mutual information between the observed data and optional side knowledge that is not already captured by the new, auxiliary variables. We theoretically show that our approach generalizes past approaches, and perform an empirical case study of our approach on event modeling. We corroborate our theoretical results with strong empirical experiments, showing that the proposed method outperforms previous proposed approaches on multiple datasets.

cs.LG↗

arXiv2021

Discriminative and Generative Transformer-based Models For Situation Entity Classification

We re-examine the situation entity (SE) classification task with varying amounts of available training data. We exploit a Transformer-based variational autoencoder to encode sentences into a lower dimensional latent space, which is used to generate the text and learn a SE classifier. Test set and cross-genre evaluations show that when training data is plentiful, the proposed model can improve over the previous discriminative state-of-the-art models. Our approach performs disproportionately better with smaller amounts of training data, but when faced with extremely small sets (4 instances per label), generative RNN methods outperform transformers. Our work provides guidance for future efforts on SE and semantic prediction tasks, and low-label training regimes.

cs.CL↗

arXiv2021

Electronic Thermal Transport Measurement in Low-Dimensional Materials with Graphene Nonlocal Noise Thermometry

In low-dimensional systems, the combination of reduced dimensionality, strong interactions, and topology has led to a growing number of many-body quantum phenomena. Thermal transport, which is sensitive to all energy-carrying degrees of freedom, provides a discriminating probe of emergent excitations in quantum materials and devices. However, thermal transport measurements in low dimensions are dominated by the phonon contribution of the lattice, requiring an experimental approach to isolate the electronic thermal conductance. Here, we show how the measurement of nonlocal voltage fluctuations in a multiterminal device can reveal the electronic heat transported across a mesoscopic bridge made of low-dimensional materials. By using 2-dimensional graphene as a noise thermometer, we demonstrate quantitative electronic thermal conductance measurements of graphene and carbon nanotubes up to 70 K, achieving a precision of ~1% of the thermal conductance quantum at 5 K. Employing linear and nonlinear thermal transport, we observe signatures of long-range interaction-mediated energy transport in 1-dimensional electron systems, in agreement with a theoretical model. Our versatile nonlocal noise thermometry allows new experiments probing energy transport in emergent states of matter and devices in low dimensions.

cond-mat.mes-hall↗

arXiv2020

A Discrete Variational Recurrent Topic Model without the Reparametrization Trick

We show how to learn a neural topic model with discrete random variables---one that explicitly models each word's assigned topic---using neural variational inference that does not rely on stochastic backpropagation to handle the discrete variables. The model we utilize combines the expressive power of neural methods for representing sequences of text with the topic model's ability to capture global, thematic coherence. Using neural variational inference, we show improved perplexity and document understanding across multiple corpora. We examine the effect of prior parameters both on the model and variational parameters and demonstrate how our approach can compete and surpass a popular topic model implementation on an automatic measure of topic quality.

cs.LG↗

arXiv2020

Event Representation with Sequential, Semi-Supervised Discrete Variables

Within the context of event modeling and understanding, we propose a new method for neural sequence modeling that takes partially-observed sequences of discrete, external knowledge into account. We construct a sequential neural variational autoencoder, which uses Gumbel-Softmax reparametrization within a carefully defined encoder, to allow for successful backpropagation during training. The core idea is to allow semi-supervised external discrete knowledge to guide, but not restrict, the variational latent parameters during training. Our experiments indicate that our approach not only outperforms multiple baselines and the state-of-the-art in narrative script induction, but also converges more quickly.

cs.LG↗

arXiv2020

A Survey on Compressive Sensing: Classical Results and Recent Advancements

Recovering sparse signals from linear measurements has demonstrated outstanding utility in a vast variety of real-world applications. Compressive sensing is the topic that studies the associated raised questions for the possibility of a successful recovery. This topic is well-nourished and numerous results are available in the literature. However, their dispersity makes it challenging and time-consuming for readers and practitioners to quickly grasp its main ideas and classical algorithms, and further touch upon the recent advancements in this surging field. Besides, the sparsity notion has already demonstrated its effectiveness in many contemporary fields. Thus, these results are useful and inspiring for further investigation of related questions in these emerging fields from new perspectives. In this survey, we gather and overview vital classical tools and algorithms in compressive sensing and describe significant recent advancements. We conclude this survey by a numerical comparison of the performance of described approaches on an interesting application.

math.OC↗

arXiv2018

Engineering Phonon Polaritons in van der Waals Heterostructures to Enhance In-Plane Optical Anisotropy

Van der Waals heterostructures assembled from layers of 2D materials have attracted considerable interest due to their novel optical and electrical properties. Here we report a scattering-type scanning near field optical microscopy study of hexagonal boron nitride on black phosphorous (h-BN/BP) heterostructures, demonstrating the first direct observation of in-plane anisotropic phonon polariton modes in vdW heterostructures. Strikingly, the measured in-plane optical anisotropy along armchair and zigzag crystal axes exceeds the ratio of refractive indices of BP in the x-y plane. We explain that this enhancement is due to the high confinement of the phonon polaritons in h-BN. We observe a maximum in-plane optical anisotropy of α_max=1.25 in the 1405-1440 cm-1 frequency spectrum. These results provide new insights on the behavior of polaritons in vdW heterostructures, and the observed anisotropy enhancement paves the way to novel nanophotonic devices and to a new way to characterize optical anisotropy in thin films.

physics.optics↗

arXiv2018

Heterointerface effects in the electro-intercalation of van der Waals heterostructures

Molecular-scale manipulation of electronic/ionic charge accumulation in materials is a preeminent challenge, particularly in electrochemical energy storage. Layered van der Waals (vdW) crystals exemplify a diverse family of materials that permit ions to reversibly associate with a host atomic lattice by intercalation into interlamellar gaps. Motivated principally by the search for high-capacity battery anodes, ion intercalation in composite materials is a subject of intense study. Yet the precise role and ability of heterolayers to modify intercalation reactions remains elusive. Previous studies of vdW hybrids represented ensemble measurements at macroscopic films/powders, which do not permit the isolation and investigation of the chemistry at individual 2-dimensional (2D) interfaces. Here, we demonstrate the intercalation of lithium at the level of individual atomic interfaces of dissimilar vdW layers. Electrochemical devices based on vdW heterostructures comprised of deterministically stacked hexagonal boron nitride, graphene (G) and molybdenum dichalcogenide (MoX2; X = S, Se) layers are fabricated, enabling the direct resolution of intermediate stages in the intercalation of discrete heterointerfaces and the extent of charge transfer to individual layers. Operando magnetoresistance and optical spectroscopy coupled with low-temperature quantum magneto-oscillation measurements show that the creation of intimate vdW heterointerfaces between G and MoX2 engenders over 10-fold accumulation of charge in MoX2 compared to MoX2/MoX2 homointerfaces, while enforcing a more negative intercalation potential than that of bulk MoX2 by at least 0.5 V. Beyond energy storage, our new combined experimental and computational methodology to manipulate and characterize the electrochemical behavior of layered systems opens up new pathways to control the charge density in 2D (opto)electronic devices.

cond-mat.mtrl-sci↗