Quantum coherence, correlated noise and Parrondo games

We discuss the effect of correlated noise on the robustness of quantum coherent phenomena. First we consider a simple, toy model to illustrate the effect of such correlations on the decoherence process. Then we show how decoherence rates can be suppressed using a Parrondo-like effect. Finally, we report the results of many-body calculations in which an experimentally-measurable quantum coherence phenomenon is significantly enhanced by non-Markovian dynamics arising from the noise source.Comment: 8 page

Electromagnetic Stirring in a Microbioreactor with Non-conventional Chamber Morphology and Implementation of Multiplexed Mixing

© 2015 The Authors. Journal of Chemical Technology & Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.Background: Microbioreactors have recently emerged as novel tools for early bioprocess development. Mixing lies at the heart of bioreactor operation (at all scales), and the successful implementation of micro-stirring methods is thus central to the further advancement of microbioreactor technology. The aim of this study was to develop a micro-stirring method that aids robust microbioreactor operation and facilitates cost effective parallelization. Results: We developed a microbioreactor with a novel micro-stirring method involving the movement of a magnetic bead by sequenced activation of a ring of electromagnets. The micro-stirring method offers flexibility in chamber designs and we demonstrate mixing in cylindrical, diamond and triangular shaped reactor chambers. Mixing times between the cylindrical and diamond-shaped chamber compared well, with the shortest mixing times in both being 3.4 s. Ease of micro-bubble free priming, a typical challenge of cylindrical microbioreactor chambers, was obtained with diamond shaped chamber. Consistent mixing behaviour was observed between the constituent reactors in a duplex system, and batch and continuous culture fermentation of Staphylococcus carnosus successfully performed. Conclusion: A novel stirring method using electromagnetic actuation offering rapid mixing and easy integration with microbioreactors was characterized. The gained design flexibility enables fabrication of chambers suitable for microfluidic operation, and a duplex demonstrator highlights potential for cost-effective parallelization. Combined with a previously published cassette-like fabrication of microbioreactors, these advances will facilitate the development of robust parallelized systems for both batch and chemostat bioreactor operation.Peer reviewe

TossingBot: Learning to Throw Arbitrary Objects with Residual Physics

We investigate whether a robot arm can learn to pick and throw arbitrary objects into selected boxes quickly and accurately. Throwing has the potential to increase the physical reachability and picking speed of a robot arm. However, precisely throwing arbitrary objects in unstructured settings presents many challenges: from acquiring reliable pre-throw conditions (e.g. initial pose of object in manipulator) to handling varying object-centric properties (e.g. mass distribution, friction, shape) and dynamics (e.g. aerodynamics). In this work, we propose an end-to-end formulation that jointly learns to infer control parameters for grasping and throwing motion primitives from visual observations (images of arbitrary objects in a bin) through trial and error. Within this formulation, we investigate the synergies between grasping and throwing (i.e., learning grasps that enable more accurate throws) and between simulation and deep learning (i.e., using deep networks to predict residuals on top of control parameters predicted by a physics simulator). The resulting system, TossingBot, is able to grasp and throw arbitrary objects into boxes located outside its maximum reach range at 500+ mean picks per hour (600+ grasps per hour with 85% throwing accuracy); and generalizes to new objects and target locations. Videos are available at https://tossingbot.cs.princeton.eduComment: Summary Video: https://youtu.be/f5Zn2Up2RjQ Project webpage: https://tossingbot.cs.princeton.ed

Altitude calibration of an F100, S/N P680063, turbofan engine

An airflow and thrust calibration of an F100 engine was conducted in coordination with a flight test program to study airframe-propulsion system integration characteristics of turbofan-powered high-performance aircraft. The tests were conducted with and without augmentation for a variety of simulated flight conditions with emphasis on the transonic regime. Test results for all conditions are presented in terms of corrected airflow and corrected gross thrust as functions of corrected fan speed for nonaugmented power and an augmented thrust ratio as a function of fuel-air ratio for augmented power. Comparisons of measured and predicted data are presented along with the results of an uncertainty analysis for both corrected airflow and gross thrust

Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning

Skilled robotic manipulation benefits from complex synergies between non-prehensile (e.g. pushing) and prehensile (e.g. grasping) actions: pushing can help rearrange cluttered objects to make space for arms and fingers; likewise, grasping can help displace objects to make pushing movements more precise and collision-free. In this work, we demonstrate that it is possible to discover and learn these synergies from scratch through model-free deep reinforcement learning. Our method involves training two fully convolutional networks that map from visual observations to actions: one infers the utility of pushes for a dense pixel-wise sampling of end effector orientations and locations, while the other does the same for grasping. Both networks are trained jointly in a Q-learning framework and are entirely self-supervised by trial and error, where rewards are provided from successful grasps. In this way, our policy learns pushing motions that enable future grasps, while learning grasps that can leverage past pushes. During picking experiments in both simulation and real-world scenarios, we find that our system quickly learns complex behaviors amid challenging cases of clutter, and achieves better grasping success rates and picking efficiencies than baseline alternatives after only a few hours of training. We further demonstrate that our method is capable of generalizing to novel objects. Qualitative results (videos), code, pre-trained models, and simulation environments are available at http://vpg.cs.princeton.eduComment: To appear at the International Conference On Intelligent Robots and Systems (IROS) 2018. Project webpage: http://vpg.cs.princeton.edu Summary video: https://youtu.be/-OkyX7Zlhi

Understanding the Impact of Physical Functioning on the Experience, Desire, and Satisfaction of Physical, Emotional and Social Intimacies in Persons with Amyotrophic Lateral Sclerosis (ALS)

The understanding of intimate relationships in persons with amyotrophic lateral sclerosis (ALS) is not yet understood. A review of the current literature, including an overview of ALS and the development and maintenance of intimate relationships using psychological theory, is included. Comparisons to multiple sclerosis, acquired physical disability, and older adults are included to gain a greater understanding of how changes in physical functioning may impact an intimate relationship. This study used archival data, in which participants completed the following measures: ALS Functional Rating Scale–Revised, Personal Assessment of Intimacy in Relationships, and ALS Specific Quality of Life Measurement–Revised. Results suggest high levels of intimacy and that gender, age, time since symptom onset, and physical and bulbar functional ability are not predictors for experience of, desire for, and satisfaction with intimate relationships. Couples appeared to be resilient, and intimacy was maintained regardless of physical functioning. Potential explanations, limitations of the study, and implications of the research are also explored

A New SX Phe Star in the Globular Cluster M15

A new SX Phe star (labelled SXP1) found from $BV$ CCD photometry is the first to be discovered in the globular cluster M15. It is a blue straggler and is located 102\arcsec.8 north and 285\arcsec.6 west of the center of M15 \citep{har96}. Mean magnitudes of SXP1 are = 18$\fm$671 and = 18\fm445. The amplitude of variability of SXP1 is measured to be $\Delta V \approx 0.15$. From multiple-frequency analysis based on the Fourier decomposition method, we detect two very closely separated pulsating frequencies: the primary frequency at $f_1=24.630$ c/d for both $B$- and $V$-bands, and the secondary frequency at $f_2=24.338$ c/d for the $B$-band and 24.343 c/d for the $V$-band. This star is the second among known SX Phe stars found to pulsate with very closely separated frequencies ($f_2/f_1\ge0.95$). These frequencies may be explained by excitation of nonradial modes; however, we have an incomplete understanding of this phenomenon in the case of SX Phe stars with relatively high amplitudes. The metallicity-period and the variability amplitude-period relations for SXP1 in M15 are found to be consistent with those for SX Phe stars in other globular clusters.Comment: 15 pages with 6 figures, accepted by the Astronomical Journal (scheduled May 2001