216 research outputs found
Nanoscale magnetometry through quantum control of nitrogen-vacancy centres in rotationally diffusing nanodiamonds
The confluence of quantum physics and biology is driving a new generation of
quantum-based sensing and imaging technology capable of harnessing the power of
quantum effects to provide tools to understand the fundamental processes of
life. One of the most promising systems in this area is the nitrogen-vacancy
centre in diamond - a natural spin qubit which remarkably has all the right
attributes for nanoscale sensing in ambient biological conditions. Typically
the nitrogen-vacancy qubits are fixed in tightly controlled/isolated
experimental conditions. In this work quantum control principles of
nitrogen-vacancy magnetometry are developed for a randomly diffusing diamond
nanocrystal. We find that the accumulation of geometric phases, due to the
rotation of the nanodiamond plays a crucial role in the application of a
diffusing nanodiamond as a bio-label and magnetometer. Specifically, we show
that a freely diffusing nanodiamond can offer real-time information about local
magnetic fields and its own rotational behaviour, beyond continuous optically
detected magnetic resonance monitoring, in parallel with operation as a
fluorescent biomarker.Comment: 9 pages, with 5 figure
Single atom-scale diamond defect allows large Aharonov-Casher phase
We propose an experiment that would produce and measure a large
Aharonov-Casher (A-C) phase in a solid-state system under macroscopic motion. A
diamond crystal is mounted on a spinning disk in the presence of a uniform
electric field. Internal magnetic states of a single NV defect, replacing
interferometer trajectories, are coherently controlled by microwave pulses. The
A-C phase shift is manifested as a relative phase, of up to 17 radians, between
components of a superposition of magnetic substates, which is two orders of
magnitude larger than that measured in any other atom-scale quantum system.Comment: 5 pages, 2 figure
Measurable quantum geometric phase from a rotating single spin
We demonstrate that the internal magnetic states of a single nitrogen-vacancy
defect, within a rotating diamond crystal, acquire geometric phases. The
geometric phase shift is manifest as a relative phase between components of a
superposition of magnetic substates. We demonstrate that under reasonable
experimental conditions a phase shift of up to four radians could be measured.
Such a measurement of the accumulation of a geometric phase, due to macroscopic
rotation, would be the first for a single atom-scale quantum system.Comment: 5 pages, 2 figures: Accepted for publication in Physical Review
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Timekeeping with electron spin states in diamond
Frequency standards based on atomic states, such as Rb or Cs vapors, or single-trapped ions, are the most precise measures of time. Here we propose and analyze a precision oscillator approach based upon spins in a solid-state system, in particular, the nitrogen-vacancy defect in single-crystal diamond. We show that this system can have stability approaching portable atomic standards and is readily incorporable as a chip-scale device. Using a pulsed spin-echo technique, we anticipate an Allan deviation of Ïy=10â7Ïâ1/2 limited by thermally-induced strain variations; in the absence of such thermal fluctuations, the system is limited by spin dephasing and harbors an Allan deviation nearing âŒ10â12Ïâ1/2. Potential improvements based upon advanced diamond material processing, temperature stabilization, and nanophotonic engineering are discussed.Physic
BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees
The rising volume of datasets has made training machine learning (ML) models
a major computational cost in the enterprise. Given the iterative nature of
model and parameter tuning, many analysts use a small sample of their entire
data during their initial stage of analysis to make quick decisions (e.g., what
features or hyperparameters to use) and use the entire dataset only in later
stages (i.e., when they have converged to a specific model). This sampling,
however, is performed in an ad-hoc fashion. Most practitioners cannot precisely
capture the effect of sampling on the quality of their model, and eventually on
their decision-making process during the tuning phase. Moreover, without
systematic support for sampling operators, many optimizations and reuse
opportunities are lost.
In this paper, we introduce BlinkML, a system for fast, quality-guaranteed ML
training. BlinkML allows users to make error-computation tradeoffs: instead of
training a model on their full data (i.e., full model), BlinkML can quickly
train an approximate model with quality guarantees using a sample. The quality
guarantees ensure that, with high probability, the approximate model makes the
same predictions as the full model. BlinkML currently supports any ML model
that relies on maximum likelihood estimation (MLE), which includes Generalized
Linear Models (e.g., linear regression, logistic regression, max entropy
classifier, Poisson regression) as well as PPCA (Probabilistic Principal
Component Analysis). Our experiments show that BlinkML can speed up the
training of large-scale ML tasks by 6.26x-629x while guaranteeing the same
predictions, with 95% probability, as the full model.Comment: 22 pages, SIGMOD 201
Placement decisions and disparities among Aboriginal groups: An application of the Decision Making Ecology through multi-level analysis
a b s t r a c t Objective: This paper examined the relative influence of clinical and organizational characteristics on the decision to place a child in out-of-home care at the conclusion of a child maltreatment investigation. It tested the hypothesis that extraneous factors, specifically, organizational characteristics, impact the decision to place a child in out-of-home care. A secondary aim was to identify possible decision making influences related to disparities in placement decisions tied to Aboriginal children. Research suggests that the Aboriginal status of the child and structural risk factors affecting the family, such as poverty and poor housing, substantially account for this overrepresentation. Methods: The decision to place a child in out-of-home care was examined using data from the Canadian Incidence Study of Reported Child Abuse and Neglect. This child welfare dataset collected information about the results of nearly 5,000 child maltreatment investigations as well as a description of the characteristics of the workers and organization responsible for conducting those investigations. Multi-level statistical models were developed using MPlus software, which can accommodate dichotomous outcome variables, which are more reflective of decision making in child welfare. Mplus allows the specific case of the logistic link function for binary outcome variables under maximum likelihood estimation. Results: Final models revealed the importance of the number of Aboriginal reports to an organization as a key second level predictor of the placement decision. It is the only second level factor that remains in the final model. This finding was very stable when tested over several different levels of proportionate caseload representation ranging from greater than 50% to 20% of the caseload. Conclusions: Disparities among Aboriginal children in child welfare decision making were identified at the agency level. Practice implications: The study provides additional evidence supporting the possibility that one source of overrepresentation of Aboriginal children in the Canadian foster care system is a lack of appropriate resources at the agency or community level
On the alleged simplicity of impure proof
Roughly, a proof of a theorem, is âpureâ if it draws only on what is âcloseâ or âintrinsicâ to that theorem. Mathematicians employ a variety of terms to identify pure proofs, saying that a pure proof is one that avoids what is âextrinsic,â âextraneous,â âdistant,â âremote,â âalien,â or âforeignâ to the problem or theorem under investigation. In the background of these attributions is the view that there is a distance measure (or a variety of such measures) between mathematical statements and proofs. Mathematicians have paid little attention to specifying such distance measures precisely because in practice certain methods of proof have seemed self- evidently impure by design: think for instance of analytic geometry and analytic number theory. By contrast, mathematicians have paid considerable attention to whether such impurities are a good thing or to be avoided, and some have claimed that they are valuable because generally impure proofs are simpler than pure proofs. This article is an investigation of this claim, formulated more precisely by proof- theoretic means. After assembling evidence from proof theory that may be thought to support this claim, we will argue that on the contrary this evidence does not support the claim
Convolutional Networks on Graphs for Learning Molecular Fingerprints.
We introduce a convolutional neural network that operates directly on graphs. These networks allow end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape. The architecture we present generalizes standard molecular feature extraction methods based on circular fingerprints. We show that these data-driven features are more interpretable, and have better predictive performance on a variety of tasks.Chemistry and Chemical Biolog
All-optical electrophysiology in mammalian neurons using engineered microbial rhodopsins
All-optical electrophysiologyâspatially resolved simultaneous optical perturbation and measurement of membrane voltageâwould open new vistas in neuroscience research. We evolved two archaerhodopsin-based voltage indicators, QuasAr1 and QuasAr2, which show improved brightness and voltage sensitivity, have microsecond response times and produce no photocurrent. We engineered a channelrhodopsin actuator, CheRiff, which shows high light sensitivity and rapid kinetics and is spectrally orthogonal to the QuasArs. A coexpression vector, Optopatch, enabled cross-talkâfree genetically targeted all-optical electrophysiology. In cultured rat neurons, we combined Optopatch with patterned optical excitation to probe back-propagating action potentials (APs) in dendritic spines, synaptic transmission, subcellular microsecond-timescale details of AP propagation, and simultaneous firing of many neurons in a network. Optopatch measurements revealed homeostatic tuning of intrinsic excitability in human stem cellâderived neurons. In rat brain slices, Optopatch induced and reported APs and subthreshold events with high signal-to-noise ratios. The Optopatch platform enables high-throughput, spatially resolved electrophysiology without the use of conventional electrodes
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