18 research outputs found

    Data-driven Parametric Insurance Framework Using Bayesian Neural Networks

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    As climate change poses new and more unpredictable challenges to society, insurance is an essential avenue to protect against loss caused by extreme events. Traditional insurance risk models employ statistical analyses that are inaccurate and are becoming increasingly flawed as climate change renders weather more erratic and extreme. Data-driven parametric insurance could provide necessary protection to supplement traditional insurance. We use a technique referred to as the deep sigma point process, which is one of the Bayesian neural network approaches, for the data analysis portion of parametric insurance using residential internet connectivity dropout in US as a case study. We show that our model has significantly improved accuracy compared to traditional statistical models. We further demonstrate that each state in US has a unique weather factor that primarily influences dropout rates and that by combining multiple weather factors we can build highly accurate risk models for parametric insurance. We expect that our method can be applied to many types of risk to build parametric insurance options, particularly as climate change makes risk modeling more challenging

    Enhanced interlayer neutral excitons and trions in trilayer van der Waals heterostructures

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    Vertically stacked van der Waals heterostructures constitute a promising platform for providing tailored band alignment with enhanced excitonic systems. Here we report observations of neutral and charged interlayer excitons in trilayer WSe2-MoSe2-WSe2 van der Waals heterostructures and their dynamics. The addition of a WSe2 layer in the trilayer leads to significantly higher photoluminescence quantum yields and tunable spectral resonance compared to its bilayer heterostructures at cryogenic temperatures. The observed enhancement in the photoluminescence quantum yield is due to significantly larger electron-hole overlap and higher light absorbance in the trilayer heterostructure, supported via first-principle pseudopotential calculations based on spin-polarized density functional theory. We further uncover the temperature- and power-dependence, as well as time-resolved photoluminescence of the trilayer heterostructure interlayer neutral excitons and trions. Our study elucidates the prospects of manipulating light emission from interlayer excitons and designing atomic heterostructures from first-principles for optoelectronics.Comment: 25 pages, 5 figures(Maintext). 9 pages, 7 figures(Supplementary Information). - Accepted for publication in npg: 2D materials and applications and reformatted to its standard. - Updated co-authors and references. - Title and abstract are modified for clarity. - Errors have been corrected, npg: 2D materials and applications (2018

    Hybrid integrated photomedical devices for wearable vital sign tracking

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    In light of the importance of and challenges inherent in realizing a wearable healthcare platform for simultaneously recognizing, preventing, and treating diseases while tracking vital signs, the development of simple and customized functional devices has been required. Here, we suggest a new approach to make a stretchable light waveguide which can be combined with integrated functional devices, such as organic photodetectors and nanowire-based heaters, for multifunctional healthcare monitoring. Controlling the reflection condition of the medium gave us a solid design rule for strong light emission in our stretchable waveguides. Based on this rule, the stretchable light waveguide (up to 50% strain) made of polydimethylsiloxane was successfully demonstrated with strong emissions. We also incorporated highly sensitive organic photodetectors and silver nanowire-based heaters with the stretchable waveguide for the detection of vital signs, including heart rate, deep breathing, coughs, and blood oxygen saturation. Through these multifunctional performances, we have successfully demonstrated that our stretchable light waveguide has a strong potential for multifunctional healthcare monitoring

    Large-scale neuromorphic computing hardware for analog AI enabled by epitaxial random access memory

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    This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages [47]-50).A neuromorphic computing on memristor-based crossbars is one of promising next generation analog computing methods since it features fast switching speed, extremely small cell footprint, low energy consumption for matrix-vector multiplication, capability of both storage and computing, three-dimensionality, and many analog weight steps. Although there have been intensive studies on the development of an analog memristive device and its large-scale crossbar to implement neuromorphic hardware systems for deep neural networks, only limited approaches, such as inference task, were suggested due to spatial/temporal variations and nonlinear/step-limited weight update properties. In order to address those issues, this thesis presents epitaxial random access memory and relevant techniques at material-, device-, array-, architecture-, algorithm-level. The proposed methods have great potential to improve device performance and relax the large-scale system-level requirements for analog AI computing.by Chanyeol Choi.S.M.S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienc

    Memristor-based AI Hardware for Reliable and Reconfigurable Neuromorphic Computing

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    In the field of artificial intelligence hardware, a memristor has been proposed as an artificial synapse for creating neuromorphic computer applications. Changes in weight values in the form of conductance must be identifiable and uniform to train a neural network in memristor arrays. Because of the high mobility of metal ions in the Si switching medium, an electrochemical metallization (ECM) memory has shown a high analogue switching capacity. However, switching unpredictability is caused by the extreme stochasticity of ion transport. I demonstrated a Si memristor with alloyed conduction channels that works dependably and enables large-scale crossbar array deployment. In addition, heterogeneously integrated neuromorphic chips have been developed to allow physically reconfigurable neuromorphic computing. This thesis examines alloyed metal-based silicon memristors and stackable neuromorphic chips with heterogeneous integration for reliable and reconfigurable neuromorphic computing.Ph.D

    Group-based Dynamic Computational Replication Mechanism

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    A peer-to-peer grid computing is complicated by heterogeneous capabilities, failures, volatility, and lack of trust because it is based on desktop computers at the edge of the Internet. In order to improve the reliability of computation and gain better performance, a replication mechanism must adapt to these distinct features. In other words, it is required to classify volunteers into groups that have similar properties and then dynamically apply different replication algorithms to each group. However, existing mechanisms do not provide such a replication mechanism on a per group basis. As a result, they cause a high overhead and poor performance. To solve the problems, we propose a new group-based computational replication mechanism to adapt to a unstable, untrusted, dynamic peer-to-peer grid computing environment. Our mechanism can reduce the number of redundancy and therefore complete many tasks by adaptively replicating computations on the basis of the properties of volunteer group such as availability, credibility, and volunteering service time. I

    ListDB: Union of Write-Ahead Logs and SkipLists for Incremental Checkpointing on Persistent Memory

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    Due to the latency difference between DRAM and non-volatile main memory (NVMM) and the limited capacity of DRAM, incoming writes are often stalled in LSM tree-based key-value stores. This paper presents ListDB, a write-optimized key-value store for NVMM to overcome the gap between DRAM and NVMM write latencies and thereby, resolve the write stall problem. The contribution of ListDB consists of three novel techniques: (i) byte-addressable Index-Unified Logging, which incrementally converts write-ahead logs into SkipLists, (ii) Braided SkipList, a simple NUMA-aware SkipList that effectively reduces the NUMA effects of NVMM, and (iii) Zipper Compaction, which moves down the LSM-tree levels without copying key-value objects, but by merging SkipLists in place without blocking concurrent reads. Using the three techniques, ListDB makes background compaction fast enough to resolve the infamous write stall proble
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