Error-resistant Single Qubit Gates with Trapped Ions

Coherent operations constitutive for the implementation of single and multi-qubit quantum gates with trapped ions are demonstrated that are robust against variations in experimental parameters and intrinsically indeterministic system parameters. In particular, pulses developed using optimal control theory are demonstrated for the first time with trapped ions. Their performance as a function of error parameters is systematically investigated and compared to composite pulses.Comment: 5 pages 5 figure

Isotropy of unitary involutions

We prove the so-called Unitary Isotropy Theorem, a result on isotropy of a unitary involution. The analogous previously known results on isotropy of orthogonal and symplectic involutions as well as on hyperbolicity of orthogonal, symplectic, and unitary involutions are formal consequences of this theorem. A component of the proof is a detailed study of the quasi-split unitary grassmannians.Comment: final version, to appear in Acta Mat

Using Regular Languages to Explore the Representational Capacity of Recurrent Neural Architectures

The presence of Long Distance Dependencies (LDDs) in sequential data poses significant challenges for computational models. Various recurrent neural architectures have been designed to mitigate this issue. In order to test these state-of-the-art architectures, there is growing need for rich benchmarking datasets. However, one of the drawbacks of existing datasets is the lack of experimental control with regards to the presence and/or degree of LDDs. This lack of control limits the analysis of model performance in relation to the specific challenge posed by LDDs. One way to address this is to use synthetic data having the properties of subregular languages. The degree of LDDs within the generated data can be controlled through the k parameter, length of the generated strings, and by choosing appropriate forbidden strings. In this paper, we explore the capacity of different RNN extensions to model LDDs, by evaluating these models on a sequence of SPk synthesized datasets, where each subsequent dataset exhibits a longer degree of LDD. Even though SPk are simple languages, the presence of LDDs does have significant impact on the performance of recurrent neural architectures, thus making them prime candidate in benchmarking tasks.Comment: International Conference of Artificial Neural Networks (ICANN) 201

Label-Dependencies Aware Recurrent Neural Networks

In the last few years, Recurrent Neural Networks (RNNs) have proved effective on several NLP tasks. Despite such great success, their ability to model \emph{sequence labeling} is still limited. This lead research toward solutions where RNNs are combined with models which already proved effective in this domain, such as CRFs. In this work we propose a solution far simpler but very effective: an evolution of the simple Jordan RNN, where labels are re-injected as input into the network, and converted into embeddings, in the same way as words. We compare this RNN variant to all the other RNN models, Elman and Jordan RNN, LSTM and GRU, on two well-known tasks of Spoken Language Understanding (SLU). Thanks to label embeddings and their combination at the hidden layer, the proposed variant, which uses more parameters than Elman and Jordan RNNs, but far fewer than LSTM and GRU, is more effective than other RNNs, but also outperforms sophisticated CRF models.Comment: 22 pages, 3 figures. Accepted at CICling 2017 conference. Best Verifiability, Reproducibility, and Working Description awar

Androgens correlate with increased erythropoiesis in women with congenital adrenal hyperplasia.

OBJECTIVE: Hyperandrogenism in congenital adrenal hyperplasia (CAH) provides an in vivo model for exploring the effect of androgens on erythropoiesis in women. We investigated the association of androgens with haemoglobin (Hb) and haematocrit (Hct) in women with CAH. DESIGN: Cross-validation study PATIENTS: Women with CAH from Sheffield Teaching Hospitals, UK (cohort 1, the training set: n=23) and National Institutes of Health, USA (cohort 2, the validation set: n=53). MEASUREMENTS: Androgens, full blood count and basic biochemistry, all measured on the same day. Demographic and anthropometric data. RESULTS: Significant age-adjusted correlations (P<0.001) were observed for Ln testosterone with Hb and Hct in cohorts 1 and 2 (Hb r=0.712 & 0.524 and Hct r=0.705 & 0.466), which remained significant after adjustments for CAH status, glucocorticoid treatment dose and serum creatinine. In the combined cohorts Hb correlated with androstenedione (P=0.002) and 17-hydroxyprogesterone (P=0.008). Hb and Hct were significantly higher in cohort 1 than those in cohort 2, while there were no group differences in androgen levels, glucocorticoid treatment dose or body mass index. In both cohorts, women with Hb and Hct in the highest tertile had significantly higher testosterone levels than women with Hb and Hct in the lowest tertile. CONCLUSIONS: In women with CAH, erythropoiesis may be driven by androgens and could be considered a biomarker for disease control

Underdiagnosis of mild cognitive impairment: A consequence of ignoring practice effects

INTRODUCTION: Longitudinal testing is necessary to accurately measure cognitive change. However, repeated testing is susceptible to practice effects, which may obscure true cognitive decline and delay detection of mild cognitive impairment (MCI). METHODS: We retested 995 late-middle-aged men in a ∼6-year follow-up of the Vietnam Era Twin Study of Aging. In addition, 170 age-matched replacements were tested for the first time at study wave 2. Group differences were used to calculate practice effects after controlling for attrition effects. MCI diagnoses were generated from practice-adjusted scores. RESULTS: There were significant practice effects on most cognitive domains. Conversion to MCI doubled after correcting for practice effects, from 4.5% to 9%. Importantly, practice effects were present although there were declines in uncorrected scores. DISCUSSION: Accounting for practice effects is critical to early detection of MCI. Declines, when lower than expected, can still indicate practice effects. Replacement participants are needed for accurately assessing disease progression.Published versio

Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using discrete and fuzzy dynamical system representations within the XCSF learning classifier system. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules in the discrete case and asynchronous fuzzy logic networks in the continuous-valued case. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such dynamical systems within XCSF to solve a number of well-known test problems

Participation in physical activity in patients 1–4 years post total joint replacement in the Dominican Republic

Background: To address both the growing burden of joint disease and the gaps in medical access in developing nations, medical relief organizations have begun to launch programs to perform total joint replacement (TJR) on resident populations in developing countries. One outcome of TJR of particular interest is physical activity (PA) since it is strongly linked to general health. This study evaluates the amount of postoperative participation in PA in low-income patients who received total joint replacement in the Dominican Republic and identifies preoperative predictors of postoperative PA level. Methods: We used the Yale Physical Activity Survey (YPAS) to assess participation in postoperative PA 1–4 years following total knee or hip replacement. We compared the amount of aerobic PA reported by postoperative TJR patients with the levels of PA recommended by the CDC and WHO. We also analyzed preoperative determinants of postoperative participation in aerobic PA in bivariate and multivariate analyses. Results: 64 patients out of 170 eligible subjects (52/128 TKR and 14/42 THR) who received TJR between 2009–2012 returned for an annual follow-up visit in 2013, with a mean treatment-to-follow-up time of 2.1 years. 43.3% of respondents met CDC/WHO criteria for sufficient participation in aerobic PA. Multivariate analyses including data from 56 individuals identified that patients who were both younger than 65 and at least two years postoperative had an adjusted mean activity dimensions summary index (ADSI) 22.9 points higher than patients who were 65 or older and one year postoperative. Patients who lived with friends or family had adjusted mean ADSI 17.2 points higher than patients living alone. Patients who had the most optimistic preoperative expectations of outcome had adjusted mean ADSI scores that were 19.8 points higher than those who were less optimistic. Conclusion: The TJR patients in the Dominican cohort participate in less PA than recommended by the CDC/WHO. Additionally, several associations were identified that potentially affect PA in this population; specifically, participants who are older than 65, recently postoperative, less optimistic about postoperative outcomes and who live alone participate in less PA

Words, rules, and mechanisms of language acquisition

We review recent artificial language learning studies, especially those following Endress and Bonatti (2007), suggesting that humans can deploy a variety of learning mechanisms to acquire artificial languages. Several experiments provide evidence for multiple learning mechanisms that can be deployed in fluent speech: one mechanism encodes the positions of syllables within words and can be used to extract generalization, while the other registers co-occurrence statistics of syllables and can be used to break a continuum into its components. We review dissociations between these mechanisms and their potential role in language acquisition. We then turn to recent criticisms of the multiple mechanisms hypothesis and show that they are inconsistent with the available data. Our results suggest that artificial and natural language learning is best understood by dissecting the underlying specialized learning abilities, and that these data provide a rare opportunity to link important language phenomena to basic psychological mechanisms