Framework for implementing file systems in Windows NT

Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references (p. 38-39).by Danilo Almedia.S.B.and M.Eng

Towards Adversarial Robustness of Deep Vision Algorithms

Deep learning methods have achieved great success in solving computer vision tasks, and they have been widely utilized in artificially intelligent systems for image processing, analysis, and understanding. However, deep neural networks have been shown to be vulnerable to adversarial perturbations in input data. The security issues of deep neural networks have thus come to the fore. It is imperative to study the adversarial robustness of deep vision algorithms comprehensively. This talk focuses on the adversarial robustness of image classification models and image denoisers. We will discuss the robustness of deep vision algorithms from three perspectives: 1) robustness evaluation (we propose the ObsAtk to evaluate the robustness of denoisers), 2) robustness improvement (HAT, TisODE, and CIFS are developed to robustify vision models), and 3) the connection between adversarial robustness and generalization capability to new domains (we find that adversarially robust denoisers can deal with unseen types of real-world noise).Comment: PhD thesi

Political Economy of International Climate Finance: Navigating Decisions in PPCR and SREP

This working paper explores how countries can build their own 'climate finance readiness' by understanding their internal political economy and use that understanding to steer consensus-based decisions on climate finance investments. For climate finance to be effective, national leaders must build shared commitments. This involves considering the arguments, incentives and power dynamics at play to ensure priorities are more equitable and representative of a broader group of stakeholders. Doing so will also help to reduce the risk of implementation delays. This paper uses case studies from Bangladesh, Ethiopia and Nepal to explore how narratives and incentives within the political economy drive climate investment outcomes under the Pilot Programme for Climate Resilience (PPCR) and the Scaling up Renewable Energy Programme (SREP). It draws from broader analysis of the discourses around these investments, including 80 interviews with government; multilateral development banks (MDBs) and other stakeholders

CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning

Advances in graph machine learning (ML) have been driven by applications in chemistry as graphs have remained the most expressive representations of molecules. While early graph ML methods focused primarily on small organic molecules, recently, the scope of graph ML has expanded to include inorganic materials. Modelling the periodicity and symmetry of inorganic crystalline materials poses unique challenges, which existing graph ML methods are unable to address. Moving to inorganic nanomaterials increases complexity as the scale of number of nodes within each graph can be broad ($10$ to $10^5$). The bulk of existing graph ML focuses on characterising molecules and materials by predicting target properties with graphs as input. However, the most exciting applications of graph ML will be in their generative capabilities, which is currently not at par with other domains such as images or text. We invite the graph ML community to address these open challenges by presenting two new chemically-informed large-scale inorganic (CHILI) nanomaterials datasets: A medium-scale dataset (with overall >6M nodes, >49M edges) of mono-metallic oxide nanomaterials generated from 12 selected crystal types (CHILI-3K) and a large-scale dataset (with overall >183M nodes, >1.2B edges) of nanomaterials generated from experimentally determined crystal structures (CHILI-100K). We define 11 property prediction tasks and 6 structure prediction tasks, which are of special interest for nanomaterial research. We benchmark the performance of a wide array of baseline methods and use these benchmarking results to highlight areas which need future work. To the best of our knowledge, CHILI-3K and CHILI-100K are the first open-source nanomaterial datasets of this scale -- both on the individual graph level and of the dataset as a whole -- and the only nanomaterials datasets with high structural and elemental diversity.Comment: 16 pages, 15 figures, 8 tables. Dataset is available at https://github.com/UlrikFriisJensen/CHIL

World Bank Energy Sector Lending: Encouraging the World's Addiction to Fossil Fuels

Examines World Bank financing of developing countries' fossil fuel projects and its effect on greenhouse gas emissions. Recommends reassessing lending policies to emphasize renewable energy and energy efficiency projects as well as improving transparency

Samba Openldap Performance in a Simulated Environment

The Information Technology world is developing so fast and it is been reported that Open Source tools will eventually take over proprietary tools in no to distant future. The Open Source Community is integrating its products with that of the proprietary ones and the integration of Windows machines into Linux network is evident of such practices. The purpose of this project is to implement Samba with OpenLDAP in a simulated environment. This implementation is conducted within a virtual environment by simulating the setup of Linux and Windows Operating systems by reducing physical setup of machines. Samba will act as an interface between Linux and Windows, files will be accessible to both server and client. OpenLDAP stores the user accounts and configuration files. A performance test carried out on Samba determining effect on CPU power and Memory usage shows a decrease in the CPU power and an increase in Memory usage

TransCom: a virtual disk-based cloud computing platform for heterogeneous services

PublishedJournal ArticleThis paper presents the design, implementation, and evaluation of TransCom, a virtual disk (Vdisk) based cloud computing platform that supports heterogeneous services of operating systems (OSes) and their applications in enterprise environments. In TransCom, clients store all data and software, including OS and application software, on Vdisks that correspond to disk images located on centralized servers, while computing tasks are carried out by the clients. Users can choose to boot any client for using the desired OS, including Windows, and access software and data services from Vdisks as usual without consideration of any other tasks, such as installation, maintenance, and management. By centralizing storage yet distributing computing tasks, TransCom can greatly reduce the potential system maintenance and management costs. We have implemented a multi-platform TransCom prototype that supports both Windows and Linux services. The extensive evaluation based on both test-bed experiments and real-usage experiments has demonstrated that TransCom is a feasible, scalable, and efficient solution for successful real-world use. © 2004-2012 IEEE