3,270 research outputs found

    Avoiding entanglement loss when two-qubit quantum gates are controlled by electronic excitation

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    A solid-state two-qubit quantum gate was recently proposed that might be made in a silicon fabrication plant in the near future. In this class of device, entanglement between two quantum bits is controlled by a change from a largely unentangled ground electronic state to an excited state in which useful entanglement can be produced. Such gates have potential advantages, both because they exploit known solid-state behaviour and they separate the storage and manipulation of quantum information. It is important that the excitation step does not create decoherence. We analyse a type of gate proposed before, in which the excitation involves a control electron that interacts with the qubit spins in the excited state. The dynamics of an idealized (but fairly general) gate of this type show that it can be operated to produce a standard two-qubit entangling state

    Effects of promotion and compunction interventions on real intergroup interactions: promotion helps but high compunction hurts

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    We show the promotion intervention has positive effects during intergroup contact, but that high levels of compunction can have negative effects. Intergroup contact is probably the longest standing and most comprehensively researched intervention to reduce discrimination. It is also part of ordinary social experience, and a key context in which discrimination is played out. In this paper, we explore two additional interventions which are also designed to reduce discrimination, but which have not yet been applied to real intergroup interactions. The promotion intervention encourages participants to relax and enjoy an interaction, while the compunction intervention motivates participants to avoid discrimination. Across two studies, we tested the separate effects of promotion (Study 1) and then compunction (Study 2) on participants' interactions with a confederate whom they believed to have a history of schizophrenia. In Study 1, participants received either a promotion intervention to “relax and have an enjoyable dialogue” or no intervention (control; n = 67). In Study 2, participants completed a Single-Category Implicit Attitude Test before being told that they were high in prejudice (high compunction condition) or low in prejudice (low compunction condition; n = 62). Results indicated that promotion was associated with broadly positive effects: participants reported more positive experience of the interaction (enjoyment and interest in a future interaction), and more positive evaluations of their contact partner (increased friendliness and reduced stereotyping). There were no effects on participants' reported intergroup anxiety. In contrast, high compunction had broadly negative effects: participants reported more negative experiences of the interaction and more negative evaluations of their contact partner (using the same dependent measures outlined above). In addition, participants in the high compunction condition reported increased intergroup anxiety and increased self-anxiety (anxiety around thinking or doing something that is prejudiced). Participants in the high compunction condition also reported reduced expectancies of self-efficacy (i.e., they were less confident that they would be able to make a good impression)

    Causal inference based on counterfactuals

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    BACKGROUND: The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. DISCUSSION: This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, time-varying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures. SUMMARY: Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept

    Nonexponential decay of an unstable quantum system: Small-QQ-value s-wave decay

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    We study the decay process of an unstable quantum system, especially the deviation from the exponential decay law. We show that the exponential period no longer exists in the case of the s-wave decay with small QQ value, where the QQ value is the difference between the energy of the initially prepared state and the minimum energy of the continuous eigenstates in the system. We also derive the quantitative condition that this kind of decay process takes place and discuss what kind of system is suitable to observe the decay.Comment: 17 pages, 6 figure

    The various power decays of the survival probability at long times for free quantum particle

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    The long time behaviour of the survival probability of initial state and its dependence on the initial states are considered, for the one dimensional free quantum particle. We derive the asymptotic expansion of the time evolution operator at long times, in terms of the integral operators. This enables us to obtain the asymptotic formula for the survival probability of the initial state ψ(x)\psi (x), which is assumed to decrease sufficiently rapidly at large x|x|. We then show that the behaviour of the survival probability at long times is determined by that of the initial state ψ\psi at zero momentum k=0k=0. Indeed, it is proved that the survival probability can exhibit the various power-decays like t2m1t^{-2m-1} for an arbitrary non-negative integers mm as tt \to \infty , corresponding to the initial states with the condition ψ^(k)=O(km)\hat{\psi} (k) = O(k^m) as k0k\to 0.Comment: 15 pages, to appear in J. Phys.

    Re-interpreting conventional interval estimates taking into account bias and extra-variation

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    BACKGROUND: The study design with the smallest bias for causal inference is a perfect randomized clinical trial. Since this design is often not feasible in epidemiologic studies, an important challenge is to model bias properly and take random and systematic variation properly into account. A value for a target parameter might be said to be "incompatible" with the data (under the model used) if the parameter's confidence interval excludes it. However, this "incompatibility" may be due to bias and/or extra-variation. DISCUSSION: We propose the following way of re-interpreting conventional results. Given a specified focal value for a target parameter (typically the null value, but possibly a non-null value like that representing a twofold risk), the difference between the focal value and the nearest boundary of the confidence interval for the parameter is calculated. This represents the maximum correction of the interval boundary, for bias and extra-variation, that would still leave the focal value outside the interval, so that the focal value remained "incompatible" with the data. We describe a short example application concerning a meta analysis of air versus pure oxygen resuscitation treatment in newborn infants. Some general guidelines are provided for how to assess the probability that the appropriate correction for a particular study would be greater than this maximum (e.g. using knowledge of the general effects of bias and extra-variation from published bias-adjusted results). SUMMARY: Although this approach does not yet provide a method, because the latter probability can not be objectively assessed, this paper aims to stimulate the re-interpretation of conventional confidence intervals, and more and better studies of the effects of different biases

    Vasectomy, cigarette smoking, and age at first sexual intercourse as risk factors for prostate cancer in middle-aged men.

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    A population-based case-control study was conducted in men aged 60 or less to assess the risk of prostate cancer associated with vasectomy and other factors. Data were obtained from 216 case-control pairs by telephone interviews; this number represented 55% of all eligible cases. The matched pairs relative risk (RR) for vasectomy in ever married men was 1.4 with a 95% confidence interval (CI) of 0.9-2.3. There was a positive association between the number of years since vasectomy and prostate cancer risk (1-sided P = 0.01). Early age at first sexual intercourse was associated with increased prostate cancer risk (age less than 17 vs. 21+, RR = 2.3, 95% CI = 1.3, 4.0) but there were no consistent associations with number of sexual partners or frequency of sexual intercourse. Cigarette smoking was also associated with increased risk of prostate cancer (RR = 1.9, 95% CI = 1.2, 3.0) and there was a positive dose-response relationship with years of smoking (1-sided P = 0.001). We discuss the possible implication of the low response rate on each of these findings. To determine whether the association with vasectomy might have a hormonal basis, we compared levels of testosterone (T) and testosterone binding globulin-binding capacity (TeBG-bc) in 33 of the vasectomized control men with levels in 33 non-vasectomized controls of the same age, weight and height. T levels were higher in vasectomized than in non-vasectomized controls (1-sided P = 0.06). The ratio of T to TeBG-bc (an index of bioavailable T) was 13.5% higher in vasectomized men (1-sided P = 0.03)

    Association of 6-Minute Walk Performance and Physical Activity With Incident Ischemic Heart Disease Events and Stroke in Peripheral Artery Disease.

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    BackgroundWe determined whether poorer 6-minute walk performance and lower physical activity levels are associated with higher rates of ischemic heart disease (IHD) events in people with lower extremity peripheral artery disease (PAD).Methods and resultsFive hundred ten PAD participants were identified from Chicago-area medical centers and followed prospectively for 19.0±9.5 months. At baseline, participants completed the 6-minute walk and reported number of blocks walked during the past week (physical activity). IHD events were systematically adjudicated and consisted of new myocardial infarction, unstable angina, and cardiac death. For 6-minute walk, IHD event rates were 25/170 (14.7%) for the third (poorest) tertile, 10/171 (5.8%%) for the second tertile, and 6/169 (3.5%) for the first (best) tertile (P=0.003). For physical activity, IHD event rates were 21/154 (13.6%) for the third (poorest) tertile, 15/174 (8.6%) for the second tertile, and 5/182 (2.7%) for the first (best) tertile (P=0.001). Adjusting for age, sex, race, smoking, body mass index, comorbidities, and physical activity, participants in the poorest 6-minute walk tertile had a 3.28-fold (95% CI 1.17 to 9.17, P=0.024) higher hazard for IHD events, compared with those in the best tertile. Adjusting for confounders including 6-minute walk, participants in the poorest physical activity tertile had a 3.72-fold (95% CI 1.24 to 11.19, P=0.019) higher hazard for IHD events, compared with the highest tertile.ConclusionsSix-minute walk and physical activity predict IHD event rates in PAD. Further study is needed to determine whether interventions that improve 6-minute walk, physical activity, or both can reduce IHD events in PAD

    Maximum Likelihood, Profile Likelihood, and Penalized Likelihood: A Primer

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    The method of maximum likelihood is widely used in epidemiology, yet many epidemiologists receive little or no education in the conceptual underpinnings of the approach. Here we provide a primer on maximum likelihood and some important extensions which have proven useful in epidemiologic research, and which reveal connections between maximum likelihood and Bayesian methods. For a given data set and probability model, maximum likelihood finds values of the model parameters that give the observed data the highest probability. As with all inferential statistical methods, maximum likelihood is based on an assumed model and cannot account for bias sources that are not controlled by the model or the study design. Maximum likelihood is nonetheless popular, because it is computationally straightforward and intuitive and because maximum likelihood estimators have desirable large-sample properties in the (largely fictitious) case in which the model has been correctly specified. Here, we work through an example to illustrate the mechanics of maximum likelihood estimation and indicate how improvements can be made easily with commercial software. We then describe recent extensions and generalizations which are better suited to observational health research and which should arguably replace standard maximum likelihood as the default method
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