Large-Scale Growth of Ultrathin Low-Dimensional Perovskite Nanosheets for High-Detectivity Photodetectors

2019 ◽  
Vol 12 (2) ◽  
pp. 2884-2891 ◽  
Author(s):  
Xianwei Fu ◽  
Shilong Jiao ◽  
Ying Jiang ◽  
Lihui Li ◽  
Xiaoxia Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Scale Growth ◽  
Low Dimensional

BIOTECHNOLOGY: Large-Scale Growth

Science ◽  
2001 ◽  
Vol 294 (5545) ◽  
pp. 1243d-1243
Author(s):  
B. Purnell
Keyword(s):  
Large Scale ◽  
Scale Growth

Large scale growth of vertically standing MoS 2 flakes on 2D nanosheet using organic promoter

2D Materials ◽  
2017 ◽  
Vol 4 (2) ◽  
pp. 025042 ◽  
Author(s):  
Min-A Kang ◽  
Seong K Kim ◽  
Jin Kyu Han ◽  
Seong Jun Kim ◽  
Sung-Jin Chang ◽  
...  
Keyword(s):  
Large Scale ◽  
Scale Growth

scholarly journals Small-variance asymptotics for non-parametric online robot learning

2018 ◽  
Vol 38 (1) ◽  
pp. 3-22 ◽  
Author(s):  
Ajay Kumar Tanwani ◽  
Sylvain Calinon
Keyword(s):  
Mixture Models ◽  
Dirichlet Process ◽  
Large Scale ◽  
Principal Component ◽  
Small Variance ◽  
Remote Manipulation ◽  
State Duration ◽  
Duration Information ◽  
Low Dimensional ◽  
Non Parametric

Small-variance asymptotics is emerging as a useful technique for inference in large-scale Bayesian non-parametric mixture models. This paper analyzes the online learning of robot manipulation tasks with Bayesian non-parametric mixture models under small-variance asymptotics. The analysis yields a scalable online sequence clustering (SOSC) algorithm that is non-parametric in the number of clusters and the subspace dimension of each cluster. SOSC groups the new datapoint in low-dimensional subspaces by online inference in a non-parametric mixture of probabilistic principal component analyzers (MPPCA) based on a Dirichlet process, and captures the state transition and state duration information online in a hidden semi-Markov model (HSMM) based on a hierarchical Dirichlet process. A task-parameterized formulation of our approach autonomously adapts the model to changing environmental situations during manipulation. We apply the algorithm in a teleoperation setting to recognize the intention of the operator and remotely adjust the movement of the robot using the learned model. The generative model is used to synthesize both time-independent and time-dependent behaviors by relying on the principles of shared and autonomous control. Experiments with the Baxter robot yield parsimonious clusters that adapt online with new demonstrations and assist the operator in performing remote manipulation tasks.

scholarly journals Neural Excursions from Low-Dimensional Manifold Structure Explain Intersubject Variation in Human Motor Learning

2021 ◽  
Author(s):  
Corson N Areshenkoff ◽  
Daniel J Gale ◽  
Joe Y Nashed ◽  
Dominic Standage ◽  
John Randall Flanagan ◽  
...  
Keyword(s):  
Motor Learning ◽  
Large Scale ◽  
Brain Activity ◽  
Dimensional Subspace ◽  
Learning Performance ◽  
Dimensional Manifold ◽  
Learning Abilities ◽  
Manifold Structure ◽  
Electrophysiological Studies ◽  
Low Dimensional

Humans vary greatly in their motor learning abilities, yet little is known about the neural mechanisms that underlie this variability. Recent neuroimaging and electrophysiological studies demonstrate that large-scale neural dynamics inhabit a low-dimensional subspace or manifold, and that learning is constrained by this intrinsic manifold architecture. Here we asked, using functional MRI, whether subject-level differences in neural excursion from manifold structure can explain differences in learning across participants. We had subjects perform a sensorimotor adaptation task in the MRI scanner on two consecutive days, allowing us to assess their learning performance across days, as well as continuously measure brain activity. We find that the overall neural excursion from manifold activity in both cognitive and sensorimotor brain networks is associated with differences in subjects' patterns of learning and relearning across days. These findings suggest that off-manifold activity provides an index of the relative engagement of different neural systems during learning, and that intersubject differences in patterns of learning and relearning across days are related to reconfiguration processes in cognitive and sensorimotor networks during learning.

scholarly journals Deep Learning Triplet Ordinal Relation Preserving Binary Code for Remote Sensing Image Retrieval Task

2021 ◽  
Vol 13 (23) ◽  
pp. 4786
Author(s):  
Zhen Wang ◽  
Nannan Wu ◽  
Xiaohan Yang ◽  
Bingqi Yan ◽  
Pingping Liu
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Binary Code ◽  
Satellite Observation ◽  
Cross Entropy ◽  
Retrieval Task ◽  
Search Tasks ◽  
Low Dimensional

As satellite observation technology rapidly develops, the number of remote sensing (RS) images dramatically increases, and this leads RS image retrieval tasks to be more challenging in terms of speed and accuracy. Recently, an increasing number of researchers have turned their attention to this issue, as well as hashing algorithms, which map real-valued data onto a low-dimensional Hamming space and have been widely utilized to respond quickly to large-scale RS image search tasks. However, most existing hashing algorithms only emphasize preserving point-wise or pair-wise similarity, which may lead to an inferior approximate nearest neighbor (ANN) search result. To fix this problem, we propose a novel triplet ordinal cross entropy hashing (TOCEH). In TOCEH, to enhance the ability of preserving the ranking orders in different spaces, we establish a tensor graph representing the Euclidean triplet ordinal relationship among RS images and minimize the cross entropy between the probability distribution of the established Euclidean similarity graph and that of the Hamming triplet ordinal relation with the given binary code. During the training process, to avoid the non-deterministic polynomial (NP) hard problem, we utilize a continuous function instead of the discrete encoding process. Furthermore, we design a quantization objective function based on the principle of preserving triplet ordinal relation to minimize the loss caused by the continuous relaxation procedure. The comparative RS image retrieval experiments are conducted on three publicly available datasets, including UC Merced Land Use Dataset (UCMD), SAT-4 and SAT-6. The experimental results show that the proposed TOCEH algorithm outperforms many existing hashing algorithms in RS image retrieval tasks.

The extended nonsymmetric block Lanczos methods for solving large-scale differential Lyapunov equations

2021 ◽  
Vol 8 (3) ◽  
pp. 526-536
Author(s):  
L. Sadek ◽  
◽  
H. Talibi Alaoui ◽  
Keyword(s):  
Matrix Equation ◽  
Large Scale ◽  
Krylov Subspace ◽  
Low Rank ◽  
Lyapunov Equations ◽  
Lanczos Algorithm ◽  
Lyapunov Matrix ◽  
Block Lanczos ◽  
Low Dimensional ◽  
Differential Lyapunov Equations

In this paper, we present a new approach for solving large-scale differential Lyapunov equations. The proposed approach is based on projection of the initial problem onto an extended block Krylov subspace by using extended nonsymmetric block Lanczos algorithm then, we get a low-dimensional differential Lyapunov matrix equation. The latter differential matrix equation is solved by the Backward Differentiation Formula method (BDF) or Rosenbrock method (ROS), the obtained solution allows to build a low-rank approximate solution of the original problem. Moreover, we also give some theoretical results. The numerical results demonstrate the performance of our approach.

A versatile taxonomy of low-dimensional vortex models for unsteady aerodynamics

2018 ◽  
Vol 858 ◽  
pp. 917-948 ◽  
Author(s):  
Darwin Darakananda ◽  
Jeff D. Eldredge
Keyword(s):  
Large Scale ◽  
Bluff Body ◽  
Point Vortex ◽  
Unsteady Aerodynamics ◽  
Shear Layers ◽  
Point Vortices ◽  
The Body ◽  
Vortex Sheets ◽  
Proposed Model ◽  
Low Dimensional

Inviscid vortex models have been demonstrated to capture the essential physics of massively separated flows past aerodynamic surfaces, but they become computationally expensive as coherent vortex structures are formed and the wake is developed. In this work, we present a two-dimensional vortex model in which vortex sheets represent shear layers that separate from sharp edges of the body and point vortices represent the rolled-up cores of these shear layers and the other coherent vortices in the wake. We develop a circulation transfer procedure that enables each vortex sheet to feed its circulation into a point vortex instead of rolling up. This procedure reduces the number of computational elements required to capture the dynamics of vortex formation while eliminating the spurious force that manifests when transferring circulation between vortex elements. By tuning the rate at which the vortex sheets are siphoned into the point vortices, we can adjust the balance between the model’s dimensionality and dynamical richness, enabling it to span the entire taxonomy of inviscid vortex models. This hybrid model can capture the development and subsequent shedding of the starting vortices with insignificant wall-clock time and remain sufficiently low-dimensional to simulate long-time-horizon events such as periodic bluff-body shedding. We demonstrate the viability of the method by modelling the impulsive translation of a wing at various fixed angles of attack, pitch-up manoeuvres that linearly increase the angle of attack from $0^{\circ }$ to $90^{\circ }$, and oscillatory pitching and heaving. We show that the proposed model correctly predicts the dynamics of large-scale vortical structures in the flow by comparing the distributions of vorticity and force responses from results of the proposed model with a model using only vortex sheets and, in some cases, high-fidelity viscous simulation.

scholarly journals Microalgae for High-Value Products Towards Human Health and Nutrition

Marine Drugs ◽  
2019 ◽  
Vol 17 (5) ◽  
pp. 304 ◽  
Author(s):  
Ines Barkia ◽  
Nazamid Saari ◽  
Schonna R. Manning
Keyword(s):  
Bioactive Compounds ◽  
Large Scale ◽  
Commercial Production ◽  
Health Conditions ◽  
Omega 3 Fatty Acids ◽  
Omega 3 ◽  
Scale Growth ◽  
Health And Nutrition ◽  
Potential Source ◽  
Common Problems

Microalgae represent a potential source of renewable nutrition and there is growing interest in algae-based dietary supplements in the form of whole biomass, e.g., Chlorella and Arthrospira, or purified extracts containing omega-3 fatty acids and carotenoids. The commercial production of bioactive compounds from microalgae is currently challenged by the biorefinery process. This review focuses on the biochemical composition of microalgae, the complexities of mass cultivation, as well as potential therapeutic applications. The advantages of open and closed growth systems are discussed, including common problems encountered with large-scale growth systems. Several methods are used for the purification and isolation of bioactive compounds, and many products from microalgae have shown potential as antioxidants and treatments for hypertension, among other health conditions. However, there are many unknown algal metabolites and potential impurities that could cause harm, so more research is needed to characterize strains of interest, improve overall operation, and generate safe, functional products.

scholarly journals Thermoelectric Properties of InA Nanowires from Full-Band Atomistic Simulations

Molecules ◽  
2020 ◽  
Vol 25 (22) ◽  
pp. 5350
Author(s):  
Damiano Archetti ◽  
Neophytos Neophytou
Keyword(s):  
Surface Roughness ◽  
Thermoelectric Power ◽  
Power Factor ◽  
Large Scale ◽  
Large Diameter ◽  
Computational Method ◽  
Thermoelectric Power Factor ◽  
Full Band ◽  
Low Dimensional ◽  
Power Factor Improvement

In this work we theoretically explore the effect of dimensionality on the thermoelectric power factor of indium arsenide (InA) nanowires by coupling atomistic tight-binding calculations to the Linearized Boltzmann transport formalism. We consider nanowires with diameters from 40 nm (bulk-like) down to 3 nm close to one-dimensional (1D), which allows for the proper exploration of the power factor within a unified large-scale atomistic description across a large diameter range. We find that as the diameter of the nanowires is reduced below d < 10 nm, the Seebeck coefficient increases substantially, as a consequence of strong subband quantization. Under phonon-limited scattering conditions, a considerable improvement of ~6× in the power factor is observed around d = 10 nm. The introduction of surface roughness scattering in the calculation reduces this power factor improvement to ~2×. As the diameter is decreased to d = 3 nm, the power factor is diminished. Our results show that, although low effective mass materials such as InAs can reach low-dimensional behavior at larger diameters and demonstrate significant thermoelectric power factor improvements, surface roughness is also stronger at larger diameters, which takes most of the anticipated power factor advantages away. However, the power factor improvement that can be observed around d = 10 nm could prove to be beneficial as both the Lorenz number and the phonon thermal conductivity are reduced at that diameter. Thus, this work, by using large-scale full-band simulations that span the corresponding length scales, clarifies properly the reasons behind power factor improvements (or degradations) in low-dimensional materials. The elaborate computational method presented can serve as a platform to develop similar schemes for two-dimensional (2D) and three-dimensional (3D) material electronic structures.

A Meta-Algorithm for Improving Top-N Prediction Efficiency of Matrix Factorization Models in Collaborative Filtering

2019 ◽  
Vol 34 (03) ◽  
pp. 2059007
Author(s):  
A. Murat Yagci ◽  
Tevfik Aytekin ◽  
Fikret S. Gurgen
Keyword(s):  
Collaborative Filtering ◽  
Matrix Factorization ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Space Efficiency ◽  
Neighbor Search ◽  
Prediction Time ◽  
Low Dimensional ◽  
Prediction Efficiency

Matrix factorization models often reveal the low-dimensional latent structure in high-dimensional spaces while bringing space efficiency to large-scale collaborative filtering problems. Improving training and prediction time efficiencies of these models are also important since an accurate model may raise practical concerns if it is slow to capture the changing dynamics of the system. For the training task, powerful improvements have been proposed especially using SGD, ALS, and their parallel versions. In this paper, we focus on the prediction task and combine matrix factorization with approximate nearest neighbor search methods to improve the efficiency of top-N prediction queries. Our efforts result in a meta-algorithm, MMFNN, which can employ various common matrix factorization models, drastically improve their prediction efficiency, and still perform comparably to standard prediction approaches or sometimes even better in terms of predictive power. Using various batch, online, and incremental matrix factorization models, we present detailed empirical analysis results on many large implicit feedback datasets from different application domains.

Sign in / Sign up

Export Citation Format

Share Document