127 research outputs found
Dirac dispersion and non-trivial Berry's phase in three-dimensional semimetal RhSb3
We report observations of magnetoresistance, quantum oscillations and
angle-resolved photoemission in RhSb, a unfilled skutterudite semimetal
with low carrier density. The calculated electronic band structure of RhSb
entails a quantum number in analogy to
strong topological insulators, and inverted linear valence/conduction bands
that touch at discrete points close to the Fermi level, in agreement with
angle-resolved photoemission results. Transport experiments reveal an
unsaturated linear magnetoresistance that approaches a factor of 200 at 60 T
magnetic fields, and quantum oscillations observable up to 150~K that are
consistent with a large Fermi velocity ( ms), high
carrier mobility ( /Vs), and small three dimensional hole pockets
with nontrivial Berry phase. A very small, sample-dependent effective mass that
falls as low as bare masses scales with Fermi velocity, suggesting
RhSb is a new class of zero-gap three-dimensional Dirac semimetal.Comment: 9 pages, 4 figure
Intra- and Interband Electron Scattering in the Complex Hybrid Topological Insulator Bismuth Bilayer on BiSe
The band structure, intra- and interband scattering processes of the
electrons at the surface of a bismuth-bilayer on BiSe have been
experimentally investigated by low-temperature Fourier-transform scanning
tunneling spectroscopy. The observed complex quasiparticle interference
patterns are compared to a simulation based on the spin-dependent joint density
of states approach using the surface-localized spectral function calculated
from first principles as the only input. Thereby, the origin of the
quasiparticle interferences can be traced back to intraband scattering in the
bismuth bilayer valence band and BiSe conduction band, and to interband
scattering between the two-dimensional topological state and the
bismuth-bilayer valence band. The investigation reveals that the bilayer band
gap, which is predicted to host one-dimensional topological states at the edges
of the bilayer, is pushed several hundred milli-electronvolts above the Fermi
level. This result is rationalized by an electron transfer from the bilayer to
BiSe which also leads to a two-dimensional electron state in the
BiSe conduction band with a strong Rashba spin-splitting, coexisting
with the topological state and bilayer valence band.Comment: 11 pages, 5 figure
Band gap engineering by Bi intercalation of graphene on Ir(111)
We report on the structural and electronic properties of a single bismuth
layer intercalated underneath a graphene layer grown on an Ir(111) single
crystal. Scanning tunneling microscopy (STM) reveals a hexagonal surface
structure and a dislocation network upon Bi intercalation, which we attribute
to a Bi structure on the underlying Ir(111)
surface. Ab-initio calculations show that this Bi structure is the most
energetically favorable, and also illustrate that STM measurements are most
sensitive to C atoms in close proximity to intercalated Bi atoms. Additionally,
Bi intercalation induces a band gap (eV) at the Dirac point of
graphene and an overall n-doping (eV), as seen in angular-resolved
photoemission spectroscopy. We attribute the emergence of the band gap to the
dislocation network which forms favorably along certain parts of the moir\'e
structure induced by the graphene/Ir(111) interface.Comment: 5 figure
Improved optimization strategies for deep Multi-Task Networks
In Multi-Task Learning (MTL), it is a common practice to train multi-task
networks by optimizing an objective function, which is a weighted average of
the task-specific objective functions. Although the computational advantages of
this strategy are clear, the complexity of the resulting loss landscape has not
been studied in the literature. Arguably, its optimization may be more
difficult than a separate optimization of the constituting task-specific
objectives. In this work, we investigate the benefits of such an alternative,
by alternating independent gradient descent steps on the different
task-specific objective functions and we formulate a novel way to combine this
approach with state-of-the-art optimizers. As the separation of task-specific
objectives comes at the cost of increased computational time, we propose a
random task grouping as a trade-off between better optimization and
computational efficiency. Experimental results over three well-known visual MTL
datasets show better overall absolute performance on losses and standard
metrics compared to an averaged objective function and other state-of-the-art
MTL methods. In particular, our method shows the most benefits when dealing
with tasks of different nature and it enables a wider exploration of the shared
parameter space. We also show that our random grouping strategy allows to
trade-off between these benefits and computational efficiency
Maximum Roaming Multi-Task Learning
Multi-task learning has gained popularity due to the advantages it provides
with respect to resource usage and performance. Nonetheless, the joint
optimization of parameters with respect to multiple tasks remains an active
research topic. Sub-partitioning the parameters between different tasks has
proven to be an efficient way to relax the optimization constraints over the
shared weights, may the partitions be disjoint or overlapping. However, one
drawback of this approach is that it can weaken the inductive bias generally
set up by the joint task optimization. In this work, we present a novel way to
partition the parameter space without weakening the inductive bias.
Specifically, we propose Maximum Roaming, a method inspired by dropout that
randomly varies the parameter partitioning, while forcing them to visit as many
tasks as possible at a regulated frequency, so that the network fully adapts to
each update. We study the properties of our method through experiments on a
variety of visual multi-task data sets. Experimental results suggest that the
regularization brought by roaming has more impact on performance than usual
partitioning optimization strategies. The overall method is flexible, easily
applicable, provides superior regularization and consistently achieves improved
performances compared to recent multi-task learning formulations.Comment: Accepted at the 35th AAAI Conference on Artificial Intelligence (AAAI
2021
Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?
Anomaly detection in time series is a complex task that has been widely
studied. In recent years, the ability of unsupervised anomaly detection
algorithms has received much attention. This trend has led researchers to
compare only learning-based methods in their articles, abandoning some more
conventional approaches. As a result, the community in this field has been
encouraged to propose increasingly complex learning-based models mainly based
on deep neural networks. To our knowledge, there are no comparative studies
between conventional, machine learning-based and, deep neural network methods
for the detection of anomalies in multivariate time series. In this work, we
study the anomaly detection performance of sixteen conventional, machine
learning-based and, deep neural network approaches on five real-world open
datasets. By analyzing and comparing the performance of each of the sixteen
methods, we show that no family of methods outperforms the others. Therefore,
we encourage the community to reincorporate the three categories of methods in
the anomaly detection in multivariate time series benchmarks
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