6 research outputs found
Direct measurement of the nonconservative force field generated by optical tweezers
The force field of optical tweezers is commonly assumed to be conservative,
neglecting the complex action of the scattering force. Using a novel method
that extracts local forces from trajectories of an optically trapped particle,
we measure the three dimensional force field experienced by a Rayleigh particle
with 10 nm spatial resolution and femtonewton precision in force. We find that
the force field is nonconservative with the nonconservative component
increasing radially away from the optical axis, in agreement with the Gaussian
beam model of the optical trap. Together with thermal position fluctuations of
the trapped particle, the presence of the nonconservative force can cause a
complex flux of energy into the optical trap depending on the experimental
conditions
Recommended from our members
Measuring the nonconservative force field in an optical trap and imaging biopolymer networks with Brownian motion
textOptical tweezers have been widely used by biophysicists to measure forces in single molecular processes, such as the force of a motor molecule walking and the force of a DNA molecule winding and unwinding. In these and similar force measurements, the usual assumption is that the force applied to a particle inside the tweezers is proportional to the displacement of the particle away from the trap center like Hookean springs, which would imply that the force field is conservative. However, the Gaussian beam model has indicated that the force field generated by optical tweezers is actually nonconservative, yet no experiments have measured or accounted for this effect. We introduce an experimental method -- the local drift method -- that can measure the force field in optical tweezers with high precision without any assumptions about the functional form of the force field. The force field is determined by analyzing the Brownian motion of a trapped particle. We successfully applied this method to different sizes of particles and measured the three dimensional force field with 10 nm spatial resolution and femtonewton precision in force. We find that the force field is indeed nonconservative. The nonconservative contribution increases radially away from the optical axis for both small and large particles. The curl vector field -- a measurement of the nonconservative force field -- reverses direction from counter-clockwise for small particles in the Rayleigh regime to clockwise for large particles in the ray optics regime, consistent with the different scattering force profiles in the two distinct scattering regimes. Together with the thermal fluctuations of the trapped particle, the nonconservative force can cause a complex flux of energy into the system. Optically-confined Brownian motion is further used to probe nanostructures such as a biopolymer network. This technique -- thermal noise imaging -- uses a Brownian particle as a "natural scanner" to explore a biopolymer network by moving the Brownian particle through the network with optical tweezers. The position fluctuations of the probe particle reflect the location of individual filaments as excluded volumes. The resolution of thermal noise imaging is directly coupled to the size of the probe particle. A smaller probe is capable of exploring smaller pore sizes formed by dense network. Previously, a 200 nm polystyrene particle had been used to probe an agar network. In this work, 100 nm gold probe particles are used to enhance the resolution. A 100 nm particle explore a network with mesh 2³ times smaller and therefore enhance the network resolution by 2³ times. A 100 nm particle also improves the imaging speed by a factor of 2 because of its faster diffusion. Three-dimensional thermal noise images of agarose filaments are obtained and a resolution of 10 nm for the position of the filaments is achieved. In addition, a gold particle is trapped with significantly less power than a polystyrene particle of the same size, indicating the possibility for using even smaller gold particles to further improve the resolution.Physic
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Language models demonstrate both quantitative improvement and new qualitative
capabilities with increasing scale. Despite their potentially transformative
impact, these new capabilities are as yet poorly characterized. In order to
inform future research, prepare for disruptive new model capabilities, and
ameliorate socially harmful effects, it is vital that we understand the present
and near-future capabilities and limitations of language models. To address
this challenge, we introduce the Beyond the Imitation Game benchmark
(BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442
authors across 132 institutions. Task topics are diverse, drawing problems from
linguistics, childhood development, math, common-sense reasoning, biology,
physics, social bias, software development, and beyond. BIG-bench focuses on
tasks that are believed to be beyond the capabilities of current language
models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense
transformer architectures, and Switch-style sparse transformers on BIG-bench,
across model sizes spanning millions to hundreds of billions of parameters. In
addition, a team of human expert raters performed all tasks in order to provide
a strong baseline. Findings include: model performance and calibration both
improve with scale, but are poor in absolute terms (and when compared with
rater performance); performance is remarkably similar across model classes,
though with benefits from sparsity; tasks that improve gradually and
predictably commonly involve a large knowledge or memorization component,
whereas tasks that exhibit "breakthrough" behavior at a critical scale often
involve multiple steps or components, or brittle metrics; social bias typically
increases with scale in settings with ambiguous context, but this can be
improved with prompting.Comment: 27 pages, 17 figures + references and appendices, repo:
https://github.com/google/BIG-benc