An Algorithmic Framework for MINLP with Separable Non-Convexity

An Algorithmic Framework for Performing Collaborative Filtering

ACM SIGIR Forum ◽

10.1145/3130348.3130372 ◽

2017 ◽

Vol 51 (2) ◽

pp. 227-234 ◽

Cited By ~ 24

Author(s):

Jonathan L. Herlocker ◽

Joseph A. Konstan ◽

Al Borchers ◽

John Riedl

Keyword(s):

Collaborative Filtering ◽

Algorithmic Framework

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Prioritising surgical cases deferred by the COVID-19 pandemic: an ethics-inspired algorithmic framework for health leaders

BMJ Leader ◽

10.1136/leader-2020-000343 ◽

2021 ◽

pp. leader-2020-000343

Author(s):

Amit Jain ◽

Tinglong Dai ◽

Christopher G Myers ◽

Punya Jain ◽

Shruti Aggarwal

Keyword(s):

Decision Analysis ◽

Health System ◽

System Capacity ◽

Surgical Case ◽

The World ◽

The Face ◽

Algorithmic Framework ◽

Limited Health ◽

Healthcare Leaders

Elective surgical suspension during the COVID-19 pandemic resulted in a sizeable surgical case backlog throughout the world. As we ramp back up, how do we decide which cases take priority? Potential future waves (or a future pandemic) may lead to additional surgical shutdown and subsequent reopening. Deciding which cases to prioritise in the face of limited health system capacity has emerged as a new challenge for healthcare leaders. Here we present an ethically grounded and operationally efficient surgical prioritisation framework for healthcare leaders and practitioners, drawing insights from decision analysis and organisational sciences.

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Minimum-Time Link Scheduling for Emptying Wireless Systems: Solution Characterization and Algorithmic Framework

IEEE Transactions on Information Theory ◽

10.1109/tit.2013.2292065 ◽

2014 ◽

Vol 60 (2) ◽

pp. 1083-1100 ◽

Cited By ~ 32

Author(s):

Vangelis Angelakis ◽

Anthony Ephremides ◽

Qing He ◽

Di Yuan

Keyword(s):

Wireless Systems ◽

Minimum Time ◽

Link Scheduling ◽

Algorithmic Framework

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Two-step machine learning enables optimized nanoparticle synthesis

npj Computational Materials ◽

10.1038/s41524-021-00520-w ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Flore Mekki-Berrada ◽

Zekun Ren ◽

Tan Huang ◽

Wai Kuan Wong ◽

Fang Zheng ◽

...

Keyword(s):

Machine Learning ◽

Optical Properties ◽

Materials Science ◽

Nanoparticle Synthesis ◽

Bayesian Optimization ◽

Absorbance Spectrum ◽

Nanomaterial Synthesis ◽

Target Spectrum ◽

Colour Palette ◽

Algorithmic Framework

AbstractIn materials science, the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming. In this study, we present a two-step framework for a machine learning-driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with the desired absorbance spectrum. Combining a Gaussian process-based Bayesian optimization (BO) with a deep neural network (DNN), the algorithmic framework is able to converge towards the target spectrum after sampling 120 conditions. Once the dataset is large enough to train the DNN with sufficient accuracy in the region of the target spectrum, the DNN is used to predict the colour palette accessible with the reaction synthesis. While remaining interpretable by humans, the proposed framework efficiently optimizes the nanomaterial synthesis and can extract fundamental knowledge of the relationship between chemical composition and optical properties, such as the role of each reactant on the shape and amplitude of the absorbance spectrum.

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A reconfiguration strategy for modular robots using origami folding

The International Journal of Robotics Research ◽

10.1177/0278364918815757 ◽

2018 ◽

Vol 38 (1) ◽

pp. 73-89 ◽

Cited By ~ 8

Author(s):

Meibao Yao ◽

Christoph H. Belke ◽

Hutao Cui ◽

Jamie Paik

Keyword(s):

Heuristic Algorithm ◽

Minimum Energy ◽

Computational Time ◽

Modular Robots ◽

Automatic Modeling ◽

Minimum Energy Consumption ◽

Modular Units ◽

Optimal Reconfiguration ◽

Algorithmic Framework ◽

Modeling Algorithm

Reconfigurability in versatile systems of modular robots is achieved by changing the morphology of the overall structure as well as by connecting and disconnecting modules. Recurrent connectivity changes can cause misalignment that leads to mechanical failure of the system. This paper presents a new approach to reconfiguration, inspired by the art of origami, that eliminates connectivity changes during transformation. Our method consists of an energy-optimal reconfiguration planner that generates an initial 2D assembly pattern and an actuation sequence of the modular units, both resulting in minimum energy consumption. The algorithmic framework includes two approaches, an automatic modeling algorithm as well as a heuristic algorithm. We further demonstrate the effectiveness of our method by applying the algorithms to Mori, a modular origami robot, in simulation. Our results show that the heuristic algorithm yields reconfiguration schemes with high quality, compared with the automatic modeling algorithm, simultaneously saving a considerable amount of computational time and effort.

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