330 research outputs found
Pushing bacterial biohybrids to in vivo applications
Bacterial biohybrids use the energy of bacteria to manipulate synthetic materials with the goal of solving biomedical problems at the micro- and nanoscale. We explore current in vitro studies of bacterial biohybrids, the first attempts at in vivo biohybrid research, and problems to be addressed for the future
Bayesian Optimization with Conformal Prediction Sets
Bayesian optimization is a coherent, ubiquitous approach to decision-making
under uncertainty, with applications including multi-arm bandits, active
learning, and black-box optimization. Bayesian optimization selects decisions
(i.e. objective function queries) with maximal expected utility with respect to
the posterior distribution of a Bayesian model, which quantifies reducible,
epistemic uncertainty about query outcomes. In practice, subjectively
implausible outcomes can occur regularly for two reasons: 1) model
misspecification and 2) covariate shift. Conformal prediction is an uncertainty
quantification method with coverage guarantees even for misspecified models and
a simple mechanism to correct for covariate shift. We propose conformal
Bayesian optimization, which directs queries towards regions of search space
where the model predictions have guaranteed validity, and investigate its
behavior on a suite of black-box optimization tasks and tabular ranking tasks.
In many cases we find that query coverage can be significantly improved without
harming sample-efficiency.Comment: For code, see
https://www.github.com/samuelstanton/conformal-bayesopt.gi
The Electronic Spectrum of Fullerenes from the Dirac Equation
The electronic spectrum of sheets of graphite (plane honeycomb lattice)
folded into regular polihedra is studied. A continuum limit valid for
sufficiently large molecules and based on a tight binding approximation is
derived. It is found that a Dirac equation describes the flat graphite lattice.
Curving the lattice by insertion of odd numbered rings can be mimicked by
coupling effective gauge fields. In particular the and related
molecules are well described by the Dirac equation on the surface of a sphere
coupled to a color monopole sitting at its center.Comment: 29 pages, 7 figures. IASSNS-HEP-92/5
Circumlocution in diagnostic medical queries. In:
ABSTRACT Circumlocution is when many words are used to describe what could be said with fewer, e.g., "a machine that takes moisture out of the air" instead of "dehumidifier". Web search is a perfect backdrop for circumlocution where people struggle to name what they seek. In some domains, not knowing the correct term can have a significant impact on the search results that are retrieved. We study the medical domain, where professional medical terms are not commonly known and where the consequence of not knowing the correct term can impact the accuracy of surfaced information, as well as escalation of anxiety, and ultimately the medical care sought. Given a free-form colloquial health search query, our objective is to find the underlying professional medical term. The problem is complicated by the fact that people issue quite varied queries to describe what they have. Machine-learning algorithms can be brought to bear on the problem, but there are two key complexities: creating highquality training data and identifying predictive features. To our knowledge, no prior work has been able to crack this important problem due to the lack of training data. We give novel solutions and demonstrate their efficacy via extensive experiments, greatly improving over the prior art
Dynamics of novel photoactive AgCl microstars and their environmental applications
In the field of micromotors many efforts have been taken to find a substitute for peroxide as fuel. While most approaches turn towards other toxic high energy chemicals such as hydrazine, we introduce here an energy source that is widely used in nature: light. Light is an ideal source of energy and some materials, such as AgCl, have the inherent property to transform light energy for chemical processes, which can be used to achieve propulsion. In the case of silver chloride, one process observed after light exposure is surface modification, which leads to the release of ions, generating chemo-osmotic gradients. Here we present endeavors to use those processes to propel uniquely shaped micro-objects of microstar morphology with a high surfaceto- volume ratio, study their dynamics and present approaches to go towards real environmental applications
PropertyDAG: Multi-objective Bayesian optimization of partially ordered, mixed-variable properties for biological sequence design
Bayesian optimization offers a sample-efficient framework for navigating the
exploration-exploitation trade-off in the vast design space of biological
sequences. Whereas it is possible to optimize the various properties of
interest jointly using a multi-objective acquisition function, such as the
expected hypervolume improvement (EHVI), this approach does not account for
objectives with a hierarchical dependency structure. We consider a common use
case where some regions of the Pareto frontier are prioritized over others
according to a specified in the objectives. For
instance, when designing antibodies, we would like to maximize the binding
affinity to a target antigen only if it can be expressed in live cell culture
-- modeling the experimental dependency in which affinity can only be measured
for antibodies that can be expressed and thus produced in viable quantities. In
general, we may want to confer a partial ordering to the properties such that
each property is optimized conditioned on its parent properties satisfying some
feasibility condition. To this end, we present PropertyDAG, a framework that
operates on top of the traditional multi-objective BO to impose this desired
ordering on the objectives, e.g. expression affinity. We
demonstrate its performance over multiple simulated active learning iterations
on a penicillin production task, toy numerical problem, and a real-world
antibody design task.Comment: 9 pages, 7 figures. Submitted to NeurIPS 2022 AI4Science Worksho
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