Dynamics and decoherence in the central spin model using exact methods

The dynamics and decoherence of an electronic spin-1/2 qubit coupled to a bath of nuclear spins via hyperfine interactions in a quantum dot is studied. We show how exact results from the integrable solution can be used to understand the dynamic behavior of the qubit. It is possible to predict the main frequency contributions and their broadening for relatively general initial states analytically, leading to an estimate of the corresponding decay times. Furthermore, for a small bath polarization, a new low-frequency time scale is observed.Comment: 4 pages, 2 figures. Published version. See also http://www.physik.uni-kl.de/eggert/papers/index.htm

Skuggor ur det förgångna - Det auktoritära arvet i Uruguay 1985 - 1990

Då en demokratisk transition, där begreppet transition refererar till den situation som uppstår mellan två olika politiska regimer, avslutas tar en konsoliderande process vid. Denna konsoliderande process ämnar till att förankra det demokratiska styrelseskicket i samhället. Begreppet konsoliderad demokrati är i sig omdebatterat. En transition, som kan vara en övergång eller en återgång, till demokrati kan föra med sig någon form av auktoritärt arv. Detta auktoritära arvet kan sedan utkristalliseras genom att de valda, civila politikerna inte kan fatta beslut på vissa områden, främst då det handlar om hanteringen av minnet från en auktoritär period. Detta kan i sin tur innebära att det demokratiska styrelseskicket inte förmår tillfredställa dess medborgare och missnöjet expanderar. Resultatet kan då bli en kvalitativt sämre demokrati eller vid en alltför konfrontationsinriktad politik en återgång till ett auktoritärt styre. I uppsatsen undersöks Uruguay under den första demokratiska regeringen, ledd av Julio Maria Sanguinetti. Fyra personer med insyn i det politiska arbetet då och nu intervjuas med utgångspunkt av teorin om det auktoritära arvet. Slutsatsen är i uppsatsen att det auktoritära arvet i Uruguay verkar ha varit svagt, eller kanske till och med mer eller mindre obefintligt, varpå juridiska processer hade kunnat genomföras under den första demokratiska regeringen. Att så ej genomfördes kan i sin tur härledes till de intressen och ambitioner som den första regeringen hade, oberoende av militären som institution och politisk påtryckare

Patients’ Perspectives on Artificial Intelligence in Dentistry: A Controlled Study

Background: As artificial intelligence (AI) becomes increasingly important in modern dentistry, we aimed to assess patients' perspectives on AI in dentistry specifically for radiographic caries detection and the impact of AI-based diagnosis on patients' trust. Methods: Validated questionnaires with Likert-scale batteries (1: "strongly disagree" to 5: "strongly agree") were used to query participants' experiences with dental radiographs and their knowledge/attitudes towards AI as well as to assess how AI-based communication of a diagnosis impacted their trust, belief, and understanding. Analyses of variance and ordinal logistic regression (OLR) were used (p < 0.05). Results: Patients were convinced that "AI is useful" (mean Likert +/- standard deviation 4.2 +/- 0.8) and did not fear AI in general (2.2 +/- 1.0) nor in dentistry (1.6 +/- 0.8). Age, education, and employment status were significantly associated with patients' attitudes towards AI for dental diagnostics. When shown a radiograph with a caries lesion highlighted by an arrow, patients recognized the lesion significantly less often than when using AI-generated coloured overlays highlighting the lesion (p < 0.0005). AI-based communication did not significantly affect patients' trust in dentists' diagnosis (p = 0.44; OLR). Conclusions: Patients showed a positive attitude towards AI in dentistry. AI-supported diagnostics may assist communicating radiographic findings by increasing patients' ability to recognize caries lesions on dental radiographs

Positive Selection of B Cells Expressing Low Densities of Self-reactive BCRs

B cell tolerance or autoimmunity is determined by selective events. Negative selection of self-reactive B cells is well documented and proven. In contrast, positive selection of conventional B cells is yet to be firmly established. Here, we demonstrate that developing self-reactive B cells are not always highly sensitive to the deletion mechanisms imposed by membrane-bound self-antigens. At low amounts, membrane-bound antigens allow survival of B cells bearing a single high affinity self-reactive B cell receptor (BCR). More importantly, we show that forced allelic inclusion modifies B cell fate; low quantities of self-antigen induce the selection and accumulation of increased numbers of self-reactive B cells with decreased expression of antigen-specific BCRs. By directly measuring antigen binding by intact B cells, we show that the low amounts of self-antigen select self-reactive B cells with a lower association constant. A fraction of these B cells is activated and secretes autoantibodies that form circulating immune complexes with self-antigen. These findings demonstrate that conventional B cells can undergo positive selection and that the fate of a self-reactive B cell depends on the quantity of self-antigen, the number of BCRs engaged, and on its overall antigen-binding avidity, rather than on the affinity of individual BCRs

Generalizability of deep learning models for dental image analysis

We assessed the generalizability of deep learning models and how to improve it. Our exemplary use-case was the detection of apical lesions on panoramic radiographs. We employed two datasets of panoramic radiographs from two centers, one in Germany (Charite, Berlin, n=650) and one in India (KGMU, Lucknow, n=650): First, U-Net type models were trained on images from Charite (n=500) and assessed on test sets from Charite and KGMU (each n=150). Second, the relevance of image characteristics was explored using pixel-value transformations, aligning the image characteristics in the datasets. Third, cross-center training effects on generalizability were evaluated by stepwise replacing Charite with KGMU images. Last, we assessed the impact of the dental status (presence of root-canal fillings or restorations). Models trained only on Charite images showed a (mean +/- SD) F1-score of 54.1 +/- 0.8% on Charite and 32.7 +/- 0.8% on KGMU data (p<0.001/t-test). Alignment of image data characteristics between the centers did not improve generalizability. However, by gradually increasing the fraction of KGMU images in the training set (from 0 to 100%) the F1-score on KGMU images improved (46.1 +/- 0.9%) at a moderate decrease on Charite images (50.9 +/- 0.9%, p<0.01). Model performance was good on KGMU images showing root-canal fillings and/or restorations, but much lower on KGMU images without root-canal fillings and/or restorations. Our deep learning models were not generalizable across centers. Cross-center training improved generalizability. Noteworthy, the dental status, but not image characteristics were relevant. Understanding the reasons behind limits in generalizability helps to mitigate generalizability problems

Deep learning for accurately recognizing common causes of shoulder pain on radiographs

Objective: Training a convolutional neural network (CNN) to detect the most common causes of shoulder pain on plain radiographs and to assess its potential value in serving as an assistive device to physicians. Materials and methods: We used a CNN of the ResNet-50 architecture which was trained on 2700 shoulder radiographs from clinical practice of multiple institutions. All radiographs were reviewed and labeled for six findings: proximal humeral fractures, joint dislocation, periarticular calcification, osteoarthritis, osteosynthesis, and joint endoprosthesis. The trained model was then evaluated on a separate test dataset, which was previously annotated by three independent expert radiologists. Both the training and the test datasets included radiographs of highly variable image quality to reflect the clinical situation and to foster robustness of the CNN. Performance of the model was evaluated using receiver operating characteristic (ROC) curves, the thereof derived AUC as well as sensitivity and specificity. Results: The developed CNN demonstrated a high accuracy with an area under the curve (AUC) of 0.871 for detecting fractures, 0.896 for joint dislocation, 0.945 for osteoarthritis, and 0.800 for periarticular calcifications. It also detected osteosynthesis and endoprosthesis with near perfect accuracy (AUC 0.998 and 1.0, respectively). Sensitivity and specificity were 0.75 and 0.86 for fractures, 0.95 and 0.65 for joint dislocation, 0.90 and 0.86 for osteoarthrosis, and 0.60 and 0.89 for calcification. Conclusion: CNNs have the potential to serve as an assistive device by providing clinicians a means to prioritize worklists or providing additional safety in situations of increased workload

Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs

Periapical radiolucencies, which can be detected on panoramic radiographs, are one of the most common radiographic findings in dentistry and have a differential diagnosis including infections, granuloma, cysts and tumors. In this study, we seek to investigate the ability with which 24 oral and maxillofacial (OMF) surgeons assess the presence of periapical lucencies on panoramic radiographs, and we compare these findings to the performance of a predictive deep learning algorithm that we have developed using a curated data set of 2902 de-identified panoramic radiographs. The mean diagnostic positive predictive value (PPV) of OMF surgeons based on their assessment of panoramic radiographic images was 0.69(± 0.13), indicating that dentists on average falsely diagnose 31% of cases as radiolucencies. However, the mean diagnostic true positive rate (TPR) was 0.51(± 0.14), indicating that on average 49% of all radiolucencies were missed. We demonstrate that the deep learning algorithm achieves a better performance than 14 of 24 OMF surgeons within the cohort, exhibiting an average precision of 0.60(± 0.04), and an F1 score of 0.58(± 0.04) corresponding to a PPV of 0.67(± 0.05) and TPR of 0.51(± 0.05). The algorithm, trained on limited data and evaluated on clinically validated ground truth, has potential to assist OMF surgeons in detecting periapical lucencies on panoramic radiographs

Diamond photodetectors for next generation 157-nm deep-UV photolithography tools

Abstract Next generation photolithography stepper tools will operate at 157 nm and require robust solid state photodetectors to ensure efficient operation and facilitate direct beam monitoring for photoresist exposure dosimetry. There is currently no commercial detector system able to fully meet all the demanding requirements of this application. Diamond, which is intrinsically visible blind and radiation hard, is an obvious candidate for consideration. In this paper we report the results of the first study to assess the viability of thin film polycrystalline diamond photodetectors for use in 157 nm F2-He based laser lithography tools. Co-planar inter-digitated gold photoconductor structures were fabricated on free standing thin film diamond and exposed to pulses from an industrial F2-He laser in the fluence range 0-1.4 mJ cm-2. The electrical and optical characteristics of the devices have been measured and are compared to the response of a standard vacuum photodiode. The suitability of the diamond devices for use at 157 nm is discussed

StrategyNZ: mapping our future strategy maps - from Te Papa to the Legislative Council Chamber

This report explains the inputs, processes and outputs of the StrategyNZ workshop held in March 2011. The aim was to encourage a conversation about our long-term future. Consensus emerged that New Zealand should work to ‘create a place where talent wants to live’. See Report 12 and the workshop booklet

Quantitative X-ray diffraction phase analysis of poorly ordered nontronite clay in nickel laterites

Studies of the extraction of nickel from low-grade laterite ores require a much better quantitative understanding of the poorly ordered mineral phases present, including turbostratically disordered nontronite. Whole pattern refinements with nontronite X-ray diffraction data from a Western Australian nickel deposit (Bulong) using a nontronite lattice model (Pawley phase) with two space groups(P3 and C2/m) and a peaks phase group model were performed to improve the accuracy of quantitative X-ray diffraction of nickel laterite ore samples. Modifications were applied when building the new models to accommodate asymmetric peak shape and anisotropic peak broadening due to the turbostraticdisorder. Spherical harmonics were used as convolution factors to represent anisotropic crystal size and strain and asymmetric peak shape when using the lattice model. A peaks phase group model was also developed to fit the anisotropic peak broadening in the nontronite pattern. The quantitative resultsof the new Pawley phase and peaks phase group models were compared and verified with synthetic mixtures of nontronite, quartz and goethite simulating various West Australian laterite ore compositions. The models developed in this paper demonstrate adequate accuracy for quantification of nontronite in the synthesized reference materials and should be generally applicable toquantitative phase analysis of nontronite in nickel laterite ore samples