Electrical Tuning of Single Nitrogen-Vacancy Center Optical Transitions Enhanced by Photoinduced Fields

We demonstrate precise control over the zero-phonon optical transition energies of individual nitrogen-vacancy (NV) centers in diamond by applying multiaxis electric fields, via the dc Stark effect. The Stark shifts display surprising asymmetries that we attribute to an enhancement and rectification of the local electric field by photoionized charge traps in the diamond. Using this effect, we tune the excited-state orbitals of strained NV centers to degeneracy and vary the resulting degenerate optical transition frequency by >10 GHz, a scale comparable to the inhomogeneous frequency distribution. This technique will facilitate the integration of NV-center spins within photonic networks.Comment: 10 pages, 6 figure

Long dephasing time and high temperature ballistic transport in an InGaAs open quantum dot

We report on measurements of the magnetoconductance of an open circular InGaAs quantum dot between 1.3K and 204K. We observe two types of magnetoconductance fluctuations: universal conductance fluctuations (UCFs), and 'focusing' fluctuations related to ballistic trajectories between openings. The electron phase coherence time extracted from UCFs amplitude is larger than in GaAs/AlGaAs quantum dots and follows a similar temperature dependence (between T^-1 and T^-2). Below 150K, the characteristic length associated with 'focusing' fluctuations shows a slightly different temperature dependence from that of the conductivity.Comment: 6 pages, 4 figures, proceedings of ICSNN2002, to appear in Physica

Atomic Layer Deposition Nucleation Dependence on Diamond Surface Termination

Surface termination and interfacial interactions are critical for advanced solid-state quantum applications. In this paper, we demonstrate that atomic layer deposition (ALD) can both provide valuable insight on the chemical environment of the surface, having sufficient sensitivity to distinguish between the common diamond (001) surface termination types and passivate these interfaces as desired. We selected diamond substrates exhibiting both smooth and anomalously rough surfaces to probe the effect of morphology on ALD nucleation. We use high resolution in situ spectroscopic ellipsometry to monitor the surface reaction with sub-angstrom resolution, to evaluate the nucleation of an ALD Al2O3 process as a function of different ex and in situ treatments to the diamond surface. In situ water dosing and high vacuum annealing provided the most favorable environment for nucleation of dimethylaluminum isopropoxide and water ALD. Hydrogen termination passivated both smooth and rough surfaces while triacid cleaning passivated the smooth surface only, with striking effectiveness.Comment: 31 pages, 14 figure

From unsupervised to semi-supervised adversarial domain adaptation in EEG-based sleep staging.

OBJECTIVE: The recent breakthrough of wearable sleep monitoring devices results in large amounts of sleep data. However, as limited labels are available, interpreting these data requires automated sleep stage classification methods with a small need for labeled training data. Transfer learning and domain adaptation offer possible solutions by enabling models to learn on a source dataset and adapt to a target dataset. APPROACH: In this paper, we investigate adversarial domain adaptation applied to real use cases with wearable sleep datasets acquired from diseased patient populations. Different practical aspects of the adversarial domain adaptation framework \hl{are examined}, including the added value of (pseudo-)labels from the target dataset and the influence of domain mismatch between the source and target data. The method is also implemented for personalization to specific patients. MAIN RESULTS: The results show that adversarial domain adaptation is effective in the application of sleep staging on wearable data. When compared to a model applied on a target dataset without any adaptation, the domain adaptation method in its simplest form achieves relative gains of 7%-27% in accuracy. The performance on the target domain is further boosted by adding pseudo-labels and real target domain labels when available, and by choosing an appropriate source dataset. Furthermore, unsupervised adversarial domain adaptation can also personalize a model, improving the performance by 1%-2% compared to a non-personal model. SIGNIFICANCE: In conclusion, adversarial domain adaptation provides a flexible framework for semi-supervised and unsupervised transfer learning. This is particularly useful in sleep staging and other wearable EEG applications

Open Data for Global Multimodal Land Use Classification: Outcome of the 2017 IEEE GRSS Data Fusion Contest

In this paper, we present the scientific outcomes of the 2017 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The 2017 Contest was aimed at addressing the problem of local climate zones classification based on a multitemporal and multimodal dataset, including image (Landsat 8 and Sentinel-2) and vector data (from OpenStreetMap). The competition, based on separate geographical locations for the training and testing of the proposed solution, aimed at models that were accurate (assessed by accuracy metrics on an undisclosed reference for the test cities), general (assessed by spreading the test cities across the globe), and computationally feasible (assessed by having a test phase of limited time). The techniques proposed by the participants to the Contest spanned across a rather broad range of topics, and of mixed ideas and methodologies deriving from computer vision and machine learning but also deeply rooted in the specificities of remote sensing. In particular, rigorous atmospheric correction, the use of multidate images, and the use of ensemble methods fusing results obtained from different data sources/time instants made the difference

Origin of multiple memory states in organic ferroelectric field-effect transistors

In this work, we investigate the ferroelectric polarization state in metal-ferroelectric-semiconductor-metal structures and in ferroelectric field-effect transistors (FeFET). Poly(vinylidene fluoride-trifluoroethylene) and pentacene was used as the ferroelectric and semiconductor, respectively. This material combination in a bottom gate—top contact transistor architecture exhibits three reprogrammable memory states by applying appropriate gate voltages. Scanning Kelvin probe microscopy in conjunction with standard electrical characterization techniques reveals the state of the ferroelectric polarization in the three memory states as well as the device operation of the FeFET