Data-driven Parametric Insurance Framework Using Bayesian Neural Networks

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

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

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

Group-based Dynamic Computational Replication Mechanism

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

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

Controlled crack propagation for atomic precision handling of wafer-scale two-dimensional materials

International audienceAlthough flakes of two-dimensional (2D) heterostructures at the micrometer scale can be formed with adhesive-tape exfoliation methods, isolation of 2D flakes into monolayers is extremely time consuming because it is a trial-and-error process. Controlling the number of 2D layers through direct growth also presents difficulty because of the high nucleation barrier on 2D materials. We demonstrate a layer-resolved 2D material splitting technique that permits high-throughput production of multiple monolayers of wafer-scale (5-centimeter diameter) 2D materials by splitting single stacks of thick 2D materials grown on a single wafer. Wafer-scale uniformity of hexagonal boron nitride, tungsten disulfide, tungsten diselenide, molybdenum disulfide, and molybdenum diselenide monolayers was verified by photoluminescence response and by substantial retention of electronic conductivity. We fabricated wafer-scale van der Waals heterostructures, including field-effect transistors, with single-atom thickness resolution

Impact of 2D–3D Heterointerface on Remote Epitaxial Interaction through Graphene

Remote epitaxy has drawn attention as it offers epitaxy of functional materials that can be released from the substrates with atomic precision, thus enabling production and heterointegration of flexible, transferrable, and stackable freestanding single-crystalline membranes. In addition, the remote interaction of atoms and adatoms through two-dimensional (2D) materials in remote epitaxy allows investigation and utilization of electrical/chemical/physical coupling of bulk (3D) materials via 2D materials (3D-2D-3D coupling). Here, we unveil the respective roles and impacts of the substrate material, graphene, substrate-graphene interface, and epitaxial material for electrostatic coupling of these materials, which governs cohesive ordering and can lead to single-crystal epitaxy in the overlying film. We show that simply coating a graphene layer on wafers does not guarantee successful implementation of remote epitaxy, since atomically precise control of the graphene-coated interface is required, and provides key considerations for maximizing the remote electrostatic interaction between the substrate and adatoms. This was enabled by exploring various material systems and processing conditions, and we demonstrate that the rules of remote epitaxy vary significantly depending on the ionicity of material systems as well as the graphene-substrate interface and the epitaxy environment. The general rule of thumb discovered here enables expanding 3D material libraries that can be stacked in freestanding form