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Register a new userA simple, reliable, and no-destructive method for the measurement of vacuum pressure
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We present a simple, reliable, and no-destructive method for the measurement of vacuum pressure in a magneto-optical trap. The vacuum pressure is verified to be proportional to collision rate constant between cold atoms and background gas with a coefficient k, which can be calculated by simple ideal gas law. The rate constant for loss due to collisions with all background gases can be derived from the total collision loss rate by a series of loading curve of cold atoms under different trapping laser intensities. The presented method is also applicable for other cold atom systems and meets the miniaturization requirement of commercial applications.
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The loading dynamics of an alkali-atom magneto-optical trap can be used as a reliable measure of vacuum pressure, with loading time T indicating a pressure less than or equal to [2x10^(-8) Torr s]/T. This relation is accurate to approximately a factor of two over wide variations in trap parameters, background gas composition, or trapped alkali species. The low-pressure limit of the method does depend on the trap parameters, but typically extends to the 10^(-10) Torr range.
It is often observed that the probabilistic predictions given by a machine learning model can disagree with averaged actual outcomes on specific subsets of data, which is also known as the issue of miscalibration. It is responsible for the unreliability of practical machine learning systems. For example, in online advertising, an ad can receive a click-through rate prediction of 0.1 over some population of users where its actual click rate is 0.15. In such cases, the probabilistic predictions have to be fixed before the system can be deployed. In this paper, we first introduce a new evaluation metric named field-level calibration error that measures the bias in predictions over the sensitive input field that the decision-maker concerns. We show that existing post-hoc calibration methods have limited improvements in the new field-level metric and other non-calibration metrics such as the AUC score. To this end, we propose Neural Calibration, a simple yet powerful post-hoc calibration method that learns to calibrate by making full use of the field-aware information over the validation set. We present extensive experiments on five large-scale datasets. The results showed that Neural Calibration significantly improves against uncalibrated predictions in common metrics such as the negative log-likelihood, Brier score and AUC, as well as the proposed field-level calibration error.
Weak interactions within a nucleus generate a nuclear spin dependent parity violating electromagnetic moment; the anapole moment. In heavy nuclei, the anapole moment is the dominant contribution to spin-dependent atomic parity violation. We analyze a method to measure the nuclear anapole moment through the electric dipole transition it induces between hyperfine states of the ground level. The method requires tight confinement of the atoms to position them at the anti-node of a standing wave driving the anapole-induced E1 transiton. We explore the necessary limits in the number of atoms, excitation fields, trap type, interrogation method, and systematic tests necessary for such measurements in francium, the heaviest alkali.
Pulse transit time (PTT) has been widely used for cuffless blood pressure (BP) measurement. But, it requires more than one cardiovascular signals involving more than one sensing device. In this paper, we propose a method for continuous cuffless blood pressure measurement with the help of left ventricular ejection time (LVET). The LVET is estimated using a signal obtained through a micro-electromechanical system (MEMS)-based accelerometric sensor. The sensor acquires a seismocardiogram (SCG) signal at the chest surface, and the LVET information is extracted. Both systolic blood pressure (SBP) and diastolic blood pressure (DBP) are estimated by calibrating the system with the original arterial blood pressure values of the subjects. The proposed method is evaluated using different quantitative measures on the signals collected from ten subjects under the supine position. The performance of the proposed method is also compared with two earlier approaches, where PTT intervals are estimated from electrocardiogram (ECG)-photoplethysmogram (PPG) and SCG-PPG, respectively. The performance results clearly show that the proposed method is comparable with the state-of-the-art methods. Also, the computed blood pressure is compared with the original one, measured through a CNAP system. It gives the mean errors of the estimated systolic BP and diastolic BP within the range of -0.19 +/- 3.3 mmHg and -1.29 +/- 2.6 mmHg, respectively. The mean absolute errors for systolic BP and diastolic BP are 3.2 mmHg and 2.6 mmHg, respectively. The accuracy of BPs estimated from the proposed method satisfies the requirements of the IEEE standard of 5 +/- 8 mmHg deviation, and thus, it may be used for ubiquitous long term blood pressure monitoring.
An imaging system is presented that is capable of far-detuned non-destructive imaging of a Bose-Einstein condensate with the signal proportional to the second spatial derivative of the density. Whilst demonstrated with application to $^{85}text{Rb}$, the technique generalizes to other atomic species and is shown to be capable of a signal to noise of ${sim}25$ at $1$GHz detuning with $100$ in-trap images showing no observable heating or atom loss. The technique is also applied to the observation of individual trajectories of stochastic dynamics inaccessible to single shot imaging. Coupled with a fast optical phase lock loop, the system is capable of dynamically switching to resonant absorption imaging during the experiment.
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