82 research outputs found
Learning to Detect Noisy Labels Using Model-Based Features
Label noise is ubiquitous in various machine learning scenarios such as
self-labeling with model predictions and erroneous data annotation. Many
existing approaches are based on heuristics such as sample losses, which might
not be flexible enough to achieve optimal solutions. Meta learning based
methods address this issue by learning a data selection function, but can be
hard to optimize. In light of these pros and cons, we propose
Selection-Enhanced Noisy label Training (SENT) that does not rely on meta
learning while having the flexibility of being data-driven. SENT transfers the
noise distribution to a clean set and trains a model to distinguish noisy
labels from clean ones using model-based features. Empirically, on a wide range
of tasks including text classification and speech recognition, SENT improves
performance over strong baselines under the settings of self-training and label
corruption
Visualizing Band Offsets and Edge States in Bilayer-Monolayer Transition Metal Dichalcogenides Lateral Heterojunction
Semiconductor heterostructures are fundamental building blocks for many
important device applications. The emergence of two-dimensional semiconductors
opens up a new realm for creating heterostructures. As the bandgaps of
transition metal dichalcogenides thin films have sensitive layer dependence, it
is natural to create lateral heterojunctions using the same materials with
different thicknesses. Using scanning tunneling microscopy and spectroscopy,
here we show the real space image of electronic structures across the
bilayer-monolayer interface in MoSe2 and WSe2. Most bilayer-monolayer
heterojunctions are found to have a zigzag-orientated interface, and the band
alignment of such atomically sharp heterojunctions is of type-I with a
well-defined interface mode which acts as a narrower-gap quantum wire. The
ability to utilize such commonly existing thickness terrace as lateral
heterojunctions is a crucial addition to the tool set for device applications
based on atomically thin transition metal dichalcogenides, with the advantage
of easy and flexible implementation.Comment: 19 pages in total, 4 figures and 1 tabl
Magnetic-coupled electronic landscape in bilayer-distorted titanium-based kagome metals
Quantum materials whose atoms are arranged on a lattice of corner-sharing
triangles, , the kagome lattice, have recently emerged as a
captivating platform for investigating exotic correlated and topological
electronic phenomena. Here, we combine ultra-low temperature angle-resolved
photoemission spectroscopy (ARPES) with scanning tunneling microscopy and
density functional theory calculations to reveal the fascinating electronic
structure of the bilayer-distorted kagome material
TiBi, where stands for Nd and Yb.
Distinct from other kagome materials, TiBi exhibits
two-fold, rather than six-fold, symmetries, stemming from the distorted kagome
lattice, which leads to a unique electronic structure. Combining experiment and
theory we map out the electronic structure and discover double flat bands as
well as multiple van Hove singularities (VHSs), with one VHS exhibiting
higher-order characteristics near the Fermi level. Notably, in the magnetic
version NdTiBi, the ultra-low base temperature ARPES measurements
unveil an unconventional band splitting in the band dispersions which is
induced by the ferromagnetic ordering. These findings reveal the potential of
bilayer-distorted kagome metals TiBi as a promising
platform for exploring novel emergent phases of matter at the intersection of
strong correlation and magnetism
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