Confidence-Based Feature Imputation for Graphs with Partially Known Features

This paper investigates a missing feature imputation problem for graph learning tasks. Several methods have previously addressed learning tasks on graphs with missing features. However, in cases of high rates of missing features, they were unable to avoid significant performance degradation. To overcome this limitation, we introduce a novel concept of channel-wise confidence in a node feature, which is assigned to each imputed channel feature of a node for reflecting certainty of the imputation. We then design pseudo-confidence using the channel-wise shortest path distance between a missing-feature node and its nearest known-feature node to replace unavailable true confidence in an actual learning process. Based on the pseudo-confidence, we propose a novel feature imputation scheme that performs channel-wise inter-node diffusion and node-wise inter-channel propagation. The scheme can endure even at an exceedingly high missing rate (e.g., 99.5\%) and it achieves state-of-the-art accuracy for both semi-supervised node classification and link prediction on various datasets containing a high rate of missing features. Codes are available at https://github.com/daehoum1/pcfi.Comment: Accepted to ICLR 2023. 28 page

Breaking of valley degeneracy by magnetic field in monolayer MoSe2

Using polarization-resolved photoluminescence spectroscopy, we investigate valley degeneracy breaking by out-of-plane magnetic field in back-gated monolayer MoSe$_2$ devices. We observe a linear splitting of $-0.22 \frac{\text{meV}}{\text{T}}$ between luminescence peak energies in $\sigma_{+}$ and $\sigma_{-}$ emission for both neutral and charged excitons. The optical selection rules of monolayer MoSe$_2$ couple photon handedness to the exciton valley degree of freedom, so this splitting demonstrates valley degeneracy breaking. In addition, we find that the luminescence handedness can be controlled with magnetic field, to a degree that depends on the back-gate voltage. An applied magnetic field therefore provides effective strategies for control over the valley degree of freedom.Comment: expanded discussion section, corrected typo in eq.

Spatiotemporal Mapping of Photocurrent in a Monolayer Semiconductor Using a Diamond Quantum Sensor

The detection of photocurrents is central to understanding and harnessing the interaction of light with matter. Although widely used, transport-based detection averages over spatial distributions and can suffer from low photocarrier collection efficiency. Here, we introduce a contact-free method to spatially resolve local photocurrent densities using a proximal quantum magnetometer. We interface monolayer MoS2 with a near-surface ensemble of nitrogen-vacancy centers in diamond and map the generated photothermal current distribution through its magnetic field profile. By synchronizing the photoexcitation with dynamical decoupling of the sensor spin, we extend the sensor's quantum coherence and achieve sensitivities to alternating current densities as small as 20 nA per micron. Our spatiotemporal measurements reveal that the photocurrent circulates as vortices, manifesting the Nernst effect, and rises with a timescale indicative of the system's thermal properties. Our method establishes an unprecedented probe for optoelectronic phenomena, ideally suited to the emerging class of two-dimensional materials, and stimulates applications towards large-area photodetectors and stick-on sources of magnetic fields for quantum control.Comment: 19 pages, 4 figure

Uniform Peak Optical Conductivity in Single-Walled Carbon Nanotubes

Recent measurements in single-walled carbon nanotubes show that, on resonance, all nanotubes display the same peak optical conductivity of approximately 8 $e^2/h$, independent of radius or chirality [Joh \emph{et al.}, \emph{Nature Nanotechnology} \textbf{6}, 51 (2011)]. We show that this uniform peak conductivity is a consequence of the relativistic band structure and strength of the Coulomb interaction in carbon nanotubes. We further construct a minimalist model of exciton dynamics that describes the general phenomenology and provides an accurate prediction of the numerical value of the peak optical conductivity. The work illustrates the need for careful treatment of relaxation mechanisms in modeling the optoelectronic properties of carbon nanotubes.Comment: 4 pages, 1 figur

Unsupervised Hyperbolic Representation Learning via Message Passing Auto-Encoders

Most of the existing literature regarding hyperbolic embedding concentrate upon supervised learning, whereas the use of unsupervised hyperbolic embedding is less well explored. In this paper, we analyze how unsupervised tasks can benefit from learned representations in hyperbolic space. To explore how well the hierarchical structure of unlabeled data can be represented in hyperbolic spaces, we design a novel hyperbolic message passing auto-encoder whose overall auto-encoding is performed in hyperbolic space. The proposed model conducts auto-encoding the networks via fully utilizing hyperbolic geometry in message passing. Through extensive quantitative and qualitative analyses, we validate the properties and benefits of the unsupervised hyperbolic representations. Codes are available at https://github.com/junhocho/HGCAE