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$_3$, a unfilled skutterudite semimetal with low carrier density. The calculated electronic band structure of RhSb$_3$ entails a $Z_2$ quantum number $\nu_0=0,\nu_1=\nu_2=\nu_3=1$ 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 ($\sim 1.3\times 10^6$ ms$^{-1}$), high carrier mobility ($\sim 14$ $m^2$/Vs), and small three dimensional hole pockets with nontrivial Berry phase. A very small, sample-dependent effective mass that falls as low as $0.015(7)$ bare masses scales with Fermi velocity, suggesting RhSb$_3$ 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 Bi2_2Se3_3

The band structure, intra- and interband scattering processes of the electrons at the surface of a bismuth-bilayer on Bi$_2$Se$_3$ 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 Bi$_2$Se$_3$ 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 Bi$_2$Se$_3$ which also leads to a two-dimensional electron state in the Bi$_2$Se$_3$ 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 $\sqrt{3}\times\sqrt{3}R30{\deg}$ 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 ($E_g=0.42\,$eV) at the Dirac point of graphene and an overall n-doping ($\sim 0.39\,$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