Determination of complex absorbing potentials from the electron self-energy

The electronic conductance of a molecule making contact to electrodes is determined by the coupling of discrete molecular states to the continuum electrode density of states. Interactions between bound states and continua can be modeled exactly by using the (energy-dependent) self-energy, or approximately by using a complex potential. We discuss the relation between the two approaches and give a prescription for using the self-energy to construct an energy-independent, non-local, complex potential. We apply our scheme to studying single-electron transmission in an atomic chain, obtaining excellent agreement with the exact result. Our approach allows us to treat electron-reservoir couplings independent of single electron energies, allowing for the definition of a one-body operator suitable for inclusion into correlated electron transport calculations.Comment: 11 pages, 8 figures; to be published in the J. Chem. Phy

A protocol for a qualitative synthesis of practitioner research of language learner autonomy in Japan

Postprin

Online Extraction and Single Trial Analysis of Regions Contributing to Erroneous Feedback Detection

International audienceUnderstanding how the brain processes errors is an essential and active field of neuroscience. Real time extraction and analysis of error signals provide an innovative method of assessing how individuals perceive ongoing interactions without recourse to overt behaviour. This area of research is critical in modern Brain–Computer Interface (BCI) design, but may also open fruitful perspectives in cognitive neuroscience research. In this context, we sought to determine whether we can extract discriminatory error-related activity in the source space, online, and on a trial by trial basis from electroencephalography data recorded during motor imagery. Using a data driven approach, based on interpretable inverse solution algorithms, we assessed the extent to which automatically extracted error-related activity was physiologically and functionally interpretable according to performance monitoring literature. The applicability of inverse solution based methods for automatically extracting error signals, in the presence of noise generated by motor imagery, was validated by simulation. Representative regions of interest, outlining the primary generators contributing to classification, were found to correspond closely to networks involved in error detection and performance monitoring. We observed discriminative activity in non-frontal areas, demonstrating that areas outside of the medial frontal cortex can contribute to the classification of error feedback activity

Scaling of critical wave functions at topological Anderson transitions in one dimension

Topological Anderson transitions, which are direct phase transitions between topologically distinct Anderson localized phases, allow for criticality in one-dimensional disordered systems. We analyze the statistical properties of an ensemble of critical wave functions at such transitions. We find that the local moments are strongly inhomogeneous, with significant amplification towards the edges of the system. In particular, we obtain an analytic expression for the spatial profile of the local moments, which is valid at all topological Anderson transitions in one dimension, as we verify by direct comparison with numerical simulations of various lattice models

Impact of alloy disorder on the band structure of compressively strained GaBiAs

The incorporation of bismuth (Bi) in GaAs results in a large reduction of the band gap energy (E$_g$) accompanied with a large increase in the spin-orbit splitting energy ($\bigtriangleup_{SO}$), leading to the condition that $\bigtriangleup_{SO} > E_g$ which is anticipated to reduce so-called CHSH Auger recombination losses whereby the energy and momentum of a recombining electron-hole pair is given to a second hole which is excited into the spin-orbit band. We theoretically investigate the electronic structure of experimentally grown GaBi$_x$As$_{1-x}$ samples on (100) GaAs substrates by directly comparing our data with room temperature photo-modulated reflectance (PR) measurements. Our atomistic theoretical calculations, in agreement with the PR measurements, confirm that E$_g$ is equal to $\bigtriangleup_{SO}$ for $\textit{x} \approx$ 9$$. We then theoretically probe the inhomogeneous broadening of the interband transition energies as a function of the alloy disorder. The broadening associated with spin-split-off transitions arises from conventional alloy effects, while the behaviour of the heavy-hole transitions can be well described using a valence band-anticrossing model. We show that for the samples containing 8.5% and 10.4% Bi the difficulty in identifying a clear light-hole-related transition energy from the measured PR data is due to the significant broadening of the host matrix light-hole states as a result of the presence of a large number of Bi resonant states in the same energy range and disorder in the alloy. We further provide quantitative estimates of the impact of supercell size and the assumed random distribution of Bi atoms on the interband transition energies in GaBi$_{x}$As$_{1-x}$. Our calculations support a type-I band alignment at the GaBi$_x$As$_{1-x}$/GaAs interface, consistent with recent experimental findings

A multi-wavelength discriminating sensor with a wireless mote interface for aquatic pollution monitoring

peer-reviewedThe system presented in this paper demonstrates how a novel fibre optic based sensing platform, capable of detecting minute changes in the level of impurity in a liquid, can be incorporated onto a Mote based platform enabling real time monitoring of a body of water. How these features can be used to detect a representative sample of chlorophyll within a aquatic environment, will be demonstrated. Systems currently deployed worldwide include satellite mapping technology and high cost water monitoring platforms. Growing international emphasis on the management of water quality is giving rise to an expansion of the international market for novel robust, miniaturized, intelligent water monitoring systems capable of measuring local environmentally detrimental events such as localised small scale chemical pollution.PUBLISHEDpeer-reviewe

Local Model Reconstruction Attacks in Federated Learning and their Uses

In this paper, we initiate the study of local model reconstruction attacks for federated learning, where a honest-but-curious adversary eavesdrops the messages exchanged between a targeted client and the server, and then reconstructs the local/personalized model of the victim. The local model reconstruction attack allows the adversary to trigger other classical attacks in a more effective way, since the local model only depends on the client's data and can leak more private information than the global model learned by the server. Additionally, we propose a novel model-based attribute inference attack in federated learning leveraging the local model reconstruction attack. We provide an analytical lower-bound for this attribute inference attack. Empirical results using real world datasets confirm that our local reconstruction attack works well for both regression and classification tasks. Moreover, we benchmark our novel attribute inference attack against the state-of-the-art attacks in federated learning. Our attack results in higher reconstruction accuracy especially when the clients' datasets are heterogeneous. Our work provides a new angle for designing powerful and explainable attacks to effectively quantify the privacy risk in FL

Optimizing P300-speller sequences by RIP-ping groups apart

International audienceSo far P300-speller design has put very little emphasis on the design of optimized flash patterns, a surprising fact given the importance of the sequence of flashes on the selection outcome. Previous work in this domain has consisted in studying consecutive flashes, to prevent the same letter or its neighbors from flashing consecutively. To this effect, the flashing letters form more random groups than the original row-column sequences for the P300 paradigm, but the groups remain fixed across repetitions. This has several important consequences, among which a lack of discrepancy between the scores of the different letters. The new approach proposed in this paper accumulates evidence for individual elements, and optimizes the sequences by relaxing the constraint that letters should belong to fixed groups across repetitions. The method is inspired by the theory of Restricted Isometry Property matrices in Compressed Sensing, and it can be applied to any display grid size, and for any target flash frequency. This leads to P300 sequences which are shown here to perform significantly better than the state of the art, in simulations and online tests

Automated on-disc total RNA extraction from whole blood towards point-of-care for early-stage diagnostics

We present a novel integrated, centrifugo-pneumatic micro-homogenizer (“μHomogenizer”) for automated sample preparation and total RNA extraction from whole blood. Using a Trizol based protocol, this novel μHomogenizer efficiently lyses whole blood spiked with E. coli, retains the organic-mixed fraction and yields the aqueous phase with the total RNA content. By the interplay of microfluidic design and a protocol of rotational frequencies, we concatenate (and parallelize) a sequence of five subsequent liquid handling operations that complete in less than 10 minutes. A comparison of the total nucleotide content yields similar performance as conventional, essentially manual off-disc sample preparation methods