Probing Single Vacancies in Black Phosphorus at the Atomic Level

Utilizing a combination of low-temperature scanning tunneling microscopy/spectroscopy (STM/STS) and electronic structure calculations, we characterize the structural and electronic properties of single atomic vacancies within several monolayers of the surface of black phosphorus. We illustrate, with experimental analysis and tight-binding calculations, that we can depth profile these vacancies and assign them to specific sublattices within the unit cell. Measurements reveal that the single vacancies exhibit strongly anisotropic and highly delocalized charge density, laterally extended up to 20 atomic unit cells. The vacancies are then studied with STS, which reveals in-gap resonance states near the valence band edge and a strong p-doping of the bulk black phosphorus crystal. Finally, quasiparticle interference generated near these vacancies enables the direct visualization of the anisotropic band structure of black phosphorus.Comment: Nano Letters (2017

Supporting High-Uncertainty Decisions through AI and Logic-Style Explanations

A common criteria for Explainable AI (XAI) is to support users in establishing appropriate trust in the AI - rejecting advice when it is incorrect, and accepting advice when it is correct. Previous findings suggest that explanations can cause an over-reliance on AI (overly accepting advice). Explanations that evoke appropriate trust are even more challenging for decision-making tasks that are difficult for humans and AI. For this reason, we study decision-making by non-experts in the high-uncertainty domain of stock trading. We compare the effectiveness of three different explanation styles (influenced by inductive, abductive, and deductive reasoning) and the role of AI confidence in terms of a) the users' reliance on the XAI interface elements (charts with indicators, AI prediction, explanation), b) the correctness of the decision (task performance), and c) the agreement with the AI's prediction. In contrast to previous work, we look at interactions between different aspects of decision-making, including AI correctness, and the combined effects of AI confidence and explanations styles. Our results show that specific explanation styles (abductive and deductive) improve the user's task performance in the case of high AI confidence compared to inductive explanations. In other words, these styles of explanations were able to invoke correct decisions (for both positive and negative decisions) when the system was certain. In such a condition, the agreement between the user's decision and the AI prediction confirms this finding, highlighting a significant agreement increase when the AI is correct. This suggests that both explanation styles are suitable for evoking appropriate trust in a confident AI. Our findings further indicate a need to consider AI confidence as a criterion for including or excluding explanations from AI interfaces. In addition, this paper highlights the importance of carefully selecting an explanation style according to the characteristics of the task and data

Convolutional Neural Network for Material Decomposition in Spectral CT Scans

Spectral computed tomography acquires energy-resolved data that allows recovery of densities of constituents of an object. This can be achieved by decomposing the measured spectral projection into material projections, and passing these decomposed projections through a tomographic reconstruction algorithm, to get the volumetric mass density of each material. Material decomposition is a nonlinear inverse problem that has been traditionally solved using model-based material decomposition algorithms. However, the forward model is difficult to estimate in real prototypes. Moreover, the traditional regularizers used to stabilized inversions are not fully relevant in the projection domain.In this study, we propose a deep-learning method for material decomposition in the projection domain. We validate our methodology with numerical phantoms of human knees that are created from synchrotron CT scans. We consider four different scans for training, and one for validation. The measurements are corrupted by Poisson noise, assuming that at most 10 5 photons hit the detector. Compared to a regularized Gauss-Newton algorithm, the proposed deep-learning approach provides a compromise between noise and resolution, which reduces the computation time by a factor of 100

Using Key Performance Indicators for traffic management and Intelligent Transport Systems as a prediction tool

In recent research work (FP7 CONDUITS) a performance evaluation framework for traffic management and Intelligent Transport Systems was developed. The new framework consists of a set of Key Performance Indicators (KPIs) for the strategic themes of traffic efficiency, safety, pollution reduction and social inclusion, and the last stages of the project saw its validation through its application to four case studies. Following up from this work, this paper presents the extension of the framework for use as a prediction tool enabling urban transport authorities to assess the impacts of relevant policies and technologies before implementing them. Focussing on pollution reduction, a tool (CONDUITS-DST) integrating the respective KPIs with microsimulation modelling is developed. The paper describes the integration process, including the model chosen for calculating the emissions levels of a number of scenarios, presents the results of the application to a case study in the city of Brussels, and outlines future developments targeted at broadening the integration of the KPIs into decision-makin

Development and testing of a predictive traffic safety evaluation tool for road traffic management and ITS impact assessment

In recent research the CONDUITS performance evaluation framework for traffic management and Intelligent Transport Systems (ITS) was developed, consisting of a set of Key Performance Indicators (KPIs) for the strategic themes of traffic efficiency, safety, pollution reduction and social inclusion. Follow-up work has concentrated on integrating the developed CONDUITS KPIs with microscopic traffic simulation. The outcome has been a predictive evaluation tool for traffic management and ITS, called CONDUITS_DST, in which two of the four KPI categories have been integrated to date: pollution and traffic efficiency. The objective of the present study is to further extend the predictive evaluation framework to include the theme of traffic safety. Contributing to the development of the CONDUITS_DST traffic safety module, the paper identifies and proposes relevant models and metrics linking traffic characteristics with road safety impacts. In doing so, it enables the extraction of the necessary input data for each of the three CONDUITS KPIs for traffic safety (accidents, direct impacts, and indirect impacts) directly from microscopic traffic simulation models. The proposed models and metrics are tested in conjunction with the relevant CONDUITS KPIs for safety using data from simulation models before and after the implementation of a bus priority signalling system in Brussels. Testing takes place both at the network level, but also at the level of individual links, and the results show that the framework is able to capture the expected safety impacts adequately well, paving the way towards its implementation is the traffic safety module of CONDUITS_DST

Using Key Performance Indicators for multi-criteria traffic management strategic decisions

In recent research work (FP7 CONDUITS) a performance evaluation framework for traffic management and Intelligent Transport Systems was developed. The new framework consists of a set of Key Performance Indicators (KPIs) for the strategic themes of traffic efficiency, safety, pollution reduction and social inclusion, and the last stages of the project saw its validation through its application to four case studies. Following up from this work, this paper presents the extension of the framework for use as a prediction tool enabling urban transport authorities to assess the impacts of relevant policies and technologies before implementing them. The first stage of the extension focused on pollution reduction, and a novel decision support tool (CONDUITS_DST) integrating the respective KPI with micro-simulation modelling was developed. Case studies executed in Brussels and Zurich demonstrated the usability and viability of the tool. This paper takes the development one step further and reports on the extension of the approach, which moves from single-criterion to a multi-criteria decision support tool through the inclusion of the KPI on traffic efficiency, again based onmicro-simulation modelling outputs

Interference of a first-order transition with the formation of a spin-Peierls state in alpha'-NaV2O5?

We present results of high-resolution thermal-expansion and specific-heat measurements on single crystalline alpha'-NaV2O5. We find clear evidence for two almost degenerate phase transitions associated with the formation of the dimerized state around 33K: A sharp first-order transition at T1=(33+-0.1)K slightly below the onset of a second-order transition at T2onset around (34+-0.1)K. The latter is accompanied by pronounced spontaneous strains. Our results are consistent with a structural transformation at T1 induced by the incipient spin-Peierls (SP) order parameter above T2=TSP.Comment: 5 pages, 7 figure

Magnetic Resonance in the Spin-Peierls compound α′−NaV2O5\alpha'-NaV_2O_5

We present results from magnetic resonance measurements for 75-350 GHz in $\alpha$'-NaV$_{2}$O$_{5}$. The temperature dependence of the integrated intensity indicates that we observe transitions in the excited state. A quantitative description gives resonances in the triplet state at high symmetry points of the excitation spectrum of this Spin-Peierls compound. This energy has the same temperature dependence as the Spin-Peierls gap. Similarities and differences with the other inorganic compound CuGeO$_{3}$ are discussed.Comment: 2 pages, REVTEX, 3 figures. to be published in Phys.Rev.

Magnetic bound states in the quarter-filled ladder system α′−NaV2O5\alpha'-NaV_{2}O_{5}}

Raman scattering in the quarter-filled spin ladder system alpha'-NaV_2O_5 shows in the dimerized singlet ground state ($T \leq T_{SP}=35K$) an unexpected sequence of three magnetic bound states. Our results suggest that the recently proposed mapping onto an effective spin chain for $T > T_{SP}$ has to be given up in favor of the full topology and exchange paths of a ladder in the dimerized phase for $T < T_{SP}$. As the new ground state we propose a dynamic superposition of energetically nearly degenerate dimer configurations on the ladder.Comment: 5 pages, 4 figures, to be published in PRB, brief reports, Dec. 199

Use of gene expression profiling to identify candidate genes for pretherapeutic patient classification in acute appendicitis

Background: Phlegmonous and gangrenous appendicitis represent independent pathophysiological entities with different clinical courses ranging from spontaneous resolution to septic disease. However, reliable predictive methods for these clinical phenotypes have not yet been established. In an attempt to provide pathophysiological insights into the matter, a genomewide gene expression analysis was undertaken in patients with acute appendicitis. Methods: Peripheral blood mononuclear cells were isolated and, after histological confirmation of PA or GA, analysed for genomewide gene expression profiling using RNA microarray technology and subsequent pathway analysis. Results: Samples from 29 patients aged 7–17 years were included. Genomewide gene expression analysis was performed on 13 samples of phlegmonous and 16 of gangrenous appendicitis. From a total of 56 666 genes, 3594 were significantly differently expressed. Distinct interaction between T and B cells in the phlegmonous appendicitis group was suggested by overexpression of T cell receptor α and β subunits, CD2, CD3, MHC II, CD40L, and the B cell markers CD72 and CD79, indicating an antiviral mechanism. In the gangrenous appendicitis group, expression of genes delineating antibacterial mechanisms was found. Conclusion: These results provide evidence for different and independent gene expression in phlegmonous and gangrenous appendicitis in general, but also suggest distinct immunological patterns for the respective entities. In particular, the findings are compatible with previous evidence of spontaneous resolution in phlegmonous and progressive disease in gangrenous appendicitis