99 research outputs found
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Soft phototactic swimmer based on self-sustained hydrogel oscillator.
Oscillations are widely found in living organisms to generate propulsion-based locomotion often driven by constant ambient conditions, such as phototactic movements. Such environment-powered and environment-directed locomotions may advance fully autonomous remotely steered robots. However, most man-made oscillations require nonconstant energy input and cannot perform environment-dictated movement. Here, we report a self-sustained soft oscillator that exhibits perpetual and untethered locomotion as a phototactic soft swimming robot, remotely fueled and steered by constant visible light. This particular out-of-equilibrium actuation arises from a self-shadowing-enabled negative feedback loop inherent in the dynamic light-material interactions, promoted by the fast and substantial volume change of the photoresponsive hydrogel. Our analytical model and governing equation unveil the oscillation mechanism and design principle with key parameters identified to tune the dynamics. On this autonomous oscillator platform, we establish a broadly applicable principle for converting a continuous input into a discontinuous output. The modular design can be customized to accommodate various forms of input energy and to generate diverse oscillatory behaviors. The hydrogel oscillator showcases agile life-like omnidirectional motion in the entire three-dimensional space with near-infinite degrees of freedom. The large force generated by the powerful and long-lasting oscillation can sufficiently overcome water damping and effectively self-propel away from a light source. Such a hydrogel oscillator-based all-soft swimming robot, named OsciBot, demonstrated high-speed and controllable phototactic locomotion. This autonomous robot is battery free, deployable, scalable, and integratable. Artificial phototaxis opens broad opportunities in maneuverable marine automated systems, miniaturized transportation, and solar sails
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Flexible and Transparent High-Dielectric-Constant Polymer Films Based on Molecular Ferroelectric-Modified Poly(Vinyl Alcohol)
ZerNet: Convolutional Neural Networks on Arbitrary Surfaces via Zernike Local Tangent Space Estimation
In this paper, we propose a novel formulation to extend CNNs to
two-dimensional (2D) manifolds using orthogonal basis functions, called Zernike
polynomials. In many areas, geometric features play a key role in understanding
scientific phenomena. Thus, an ability to codify geometric features into a
mathematical quantity can be critical. Recently, convolutional neural networks
(CNNs) have demonstrated the promising capability of extracting and codifying
features from visual information. However, the progress has been concentrated
in computer vision applications where there exists an inherent grid-like
structure. In contrast, many geometry processing problems are defined on curved
surfaces, and the generalization of CNNs is not quite trivial. The difficulties
are rooted in the lack of key ingredients such as the canonical grid-like
representation, the notion of consistent orientation, and a compatible local
topology across the domain. In this paper, we prove that the convolution of two
functions can be represented as a simple dot product between Zernike polynomial
coefficients; and the rotation of a convolution kernel is essentially a set of
2-by-2 rotation matrices applied to the coefficients. As such, the key
contribution of this work resides in a concise but rigorous mathematical
generalization of the CNN building blocks
Tensor-network-assisted variational quantum algorithm
Near-term quantum devices generally suffer from shallow circuit depth and
hence limited expressivity due to noise and decoherence. To address this, we
propose tensor-network-assisted parametrized quantum circuits, which
concatenate a classical tensor-network operator with a quantum circuit to
effectively increase the circuit's expressivity without requiring a physically
deeper circuit. We present a framework for tensor-network-assisted variational
quantum algorithms that can solve quantum many-body problems using shallower
quantum circuits. We demonstrate the efficiency of this approach by considering
two examples of unitary matrix-product operators and unitary tree tensor
networks, showing that they can both be implemented efficiently. Through
numerical simulations, we show that the expressivity of these circuits is
greatly enhanced with the assistance of tensor networks. We apply our method to
two-dimensional Ising models and one-dimensional time-crystal Hamiltonian
models with up to 16 qubits and demonstrate that our approach consistently
outperforms conventional methods using shallow quantum circuits.Comment: 12 pages, 8 figures, 37 reference
Mutual Information Learned Regressor: an Information-theoretic Viewpoint of Training Regression Systems
As one of the central tasks in machine learning, regression finds lots of
applications in different fields. An existing common practice for solving
regression problems is the mean square error (MSE) minimization approach or its
regularized variants which require prior knowledge about the models. Recently,
Yi et al., proposed a mutual information based supervised learning framework
where they introduced a label entropy regularization which does not require any
prior knowledge. When applied to classification tasks and solved via a
stochastic gradient descent (SGD) optimization algorithm, their approach
achieved significant improvement over the commonly used cross entropy loss and
its variants. However, they did not provide a theoretical convergence analysis
of the SGD algorithm for the proposed formulation. Besides, applying the
framework to regression tasks is nontrivial due to the potentially infinite
support set of the label. In this paper, we investigate the regression under
the mutual information based supervised learning framework. We first argue that
the MSE minimization approach is equivalent to a conditional entropy learning
problem, and then propose a mutual information learning formulation for solving
regression problems by using a reparameterization technique. For the proposed
formulation, we give the convergence analysis of the SGD algorithm for solving
it in practice. Finally, we consider a multi-output regression data model where
we derive the generalization performance lower bound in terms of the mutual
information associated with the underlying data distribution. The result shows
that the high dimensionality can be a bless instead of a curse, which is
controlled by a threshold. We hope our work will serve as a good starting point
for further research on the mutual information based regression.Comment: 28 pages, 2 figures, presubmitted to AISTATS2023 for reviewin
Bilateral striatal necrosis due to homoplasmic mitochondrial 3697G\u3eA mutation presents with incomplete penetrance and sex bias
© 2019 The Authors. Molecular Genetics & Genomic Medicine published by Wiley Periodicals, Inc. Background: Heteroplasmic mitochondrial 3697G\u3eA mutation has been associated with leber hereditary optic neuropathy (LHON), mitochondrial encephalopathy, lactic acidosis and stroke-like episodes (MELAS), and LHON/MELAS overlap syndrome. However, homoplasmic m.3697G\u3eA mutation was only found in a family with Leigh syndrome, and the phenotype and pathogenicity of this homoplasmic mutation still need to be investigated in new patients. Methods: The clinical interviews were conducted in 12 individuals from a multiple-generation inherited family. Mutations were screened through exome next-generation sequencing and subsequently confirmed by PCR-restriction fragment length polymorphism. Mitochondrial complex activities and ATP production rate were measured by biochemical analysis. Results: The male offspring with bilateral striatal necrosis (BSN) were characterized by severe spastic dystonia and complete penetrance, while the female offspring presented with mild symptom and low penetrance. All offspring carried homoplasmic mutation of NC_012920.1: m.3697G\u3eA, p.(Gly131Ser). Biochemical analysis revealed an isolated defect of complex I, but the magnitude of the defect was higher in the male patients than that in the female ones. The ATP production rate also exhibited a similar pattern. However, no possible modifier genes on the X chromosome were identified. Conclusion: Homoplasmic m.3697G\u3eA mutation could be associated with BSN, which expanded the clinical spectrum of m.3697G\u3eA. Our preliminary investigations had not found the underlying modifiers to support the double hit hypothesis, while the high level of estrogens in the female patients might exert a potential compensatory effect on mutant cell metabolism
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