Speculative Buffer Overflows: Attacks and Defenses

Speculative Buffer Overflows: Attacks and Defenses Vladimir Kiriansky vlk@csail.mit.edu Carl Waldspurger carl@waldspurger.org Abstract Practical attacks that exploit speculative execution can leak

  Attacks

Introduction To Machine Learning - people.csail.mit.edu

Introduction To Machine Learning David Sontag New York University Lecture 21, April 14, 2016 David Sontag (NYU) Introduction To Machine Learning Lecture 21, April 14, 2016 1 / 14. Expectation maximization Algorithm is as follows: 1 Write down the complete log-likelihood log p(x;z; ) in such a way

  Introduction, Machine, Learning, Introduction to machine learning

Computational Imaging: The Race Against Time

The Race Against Time Computational Imaging: The Race Against Time Paul Debevec USC Institute for Creative Technologies USC Viterbi School of Engineering 2005 Symposium on Computational Photography and Video

  Computational, Time, Atingsa, Care, Imaging, Computational imaging, The race against time, Race against time computational imaging

Vantage: Scalable and Efficient Fine-Grain Cache Partitioning

Vantage is derived from analytical models, which allow us to provide strong guarantees and bounds on associativity and siz- ing independent of the number of partitions and their behaviors.

  Fine, Grain, Partitioning, Vantage, Scalable, Cache, Efficient, Scalable and efficient fine grain cache partitioning

Object detection and localization using local and global ...

Object detection and localization using local and global features 5 * = P f g Fig.3. Creating a random dictionary entry consisting of a filter f, patch P and Gaussian mask g. Dotted blue is the annotated bounding box, dashed green is the chosen patch.

  Using, Local, Object, Detection, Localization, Object detection and localization using local

Jade: A High-Level, Machine-Independent Language for ...

Jade: A High-Level, Machine-Independent Language for Parallel Programming Martin C. Rinard, Daniel J. Scales and Monica S. Lam Computer Systems Laboratory Stanford University, CA 94305 1 Introduction The past decade has seen tremendous progress in computer architecture and a …

  Programming, Language, Machine, Independent, Parallel, Jade, Machine independent language for parallel programming

A secure processor architecture for encrypted computation ...

Ascend is marginally more complex than a conventional proces- sor, in the sense that Ascend must implement an ISA and also make sure that the work it does is sufficiently obfuscated.

  Processor, Architecture, Secure, Computation, Ascend, Encrypted, Secure processor architecture for encrypted computation

Bluetooth for Programmers

Because Bluetooth programming shares much in common with network programming, there will be frequent references and comparisons to concepts in network programming such as sockets and the TCP/IP transport protocols.

  Programming, Programmer, Bluetooth, Bluetooth for programmers

Jigsaw: Scalable Software-Defined Caches

caching that Jigsaw builds and improves on: techniques to partition a shared cache, and non-uniform cache architectures. Table 1 summarizes the main differences among techniques.

  Software, Scalable, Cache, Jigsaw, Defined, Scalable software defined caches

JIGSAW - Massachusetts Institute of Technology

Jigsaw is the only scheme to simultaneously benefit network and DRAM latency Optimum . Evaluation: Energy 60 ! 16-core multiprogrammed mixes ! McPAT models of full-system energy (chip + DRAM) ! Jigsaw achieves best energy reduction ! Up to 72%, gmean of 11% ! …

  Jigsaw

Spectral and Algebraic Graph Theory

One must convey how the coordinates of eigenvectors correspond to vertices in a graph. This is obvious to those who understand it, but it can take a while for students to grasp. One must introduce necessary linear algebra and show some interesting interpretations of …

  Some, Graph, Algebraic, Eigenvectors, Algebraic graph

Matrix Analysis

1 Eigenvalues, Eigenvectors, and Similarity 43 1.0 Introduction 43 1.1 The eigenvalue–eigenvector equation 44 1.2 The characteristic polynomial and algebraic multiplicity 49 1.3 Similarity 57 1.4 Left and right eigenvectors and geometric multiplicity 75 2 Unitary Similarity and Unitary Equivalence 83 2.0 Introduction 83

  Analysis, Matrix, Eigenvalue, Eigenvectors, Matrix analysis

Linear Algebra with Applications - InvisibleUp

7 Eigenvalues and Eigenvectors 310 7.1 Diagonalization 310 7.2 Finding the Eigenvalues of a Matrix 327 7.3 Finding the Eigenvectors of a Matrix 339 7.4 More on Dynamical Systems 347 7.5 Complex Eigenvalues 360 7.6 Stability 375 8 Symmetric Matrices and Quadratic Forms 385 8.1 Symmetric Matrices 385 8.2 Quadratic Forms 394 8.3 Singular Values 403

  Applications, Eigenvalue, Eigenvalues and eigenvectors, Eigenvectors

Introduction to Ordinary and Partial Differential Equations

1. Introduction 1.1Introduction This set of lecture notes was built from a one semester course on the Introduction to Ordinary and Differential Equations at Penn State University from 2010-2014.

10.3 POWER METHOD FOR APPROXIMATING EIGENVALUES

eigenvectors with corresponding eigenvalues of We assume that these eigenvalues are ordered so that is the dominant eigenvalue (with a cor-responding eigenvector of x1). Because the n eigenvectors are linearly independent, they must form a basis for Rn. For the initial approximation x 0, we choose a nonzero vector such that the linear combination

  Methods, Power, Eigenvalue, Eigenvectors, Power method

1 Singular values

Step 1. We rst need to nd the eigenvalues of ATA. We compute that ATA= 0 @ 80 100 40 100 170 140 40 140 200 1 A: We know that at least one of the eigenvalues is 0, because this matrix can have rank at most 2. In fact, we can compute that the eigenvalues are p 1 = 360, 2 = 90, and 3 = 0. Thus the singular values of Aare ˙ 1 = 360 = 6 p 10, ˙ 2 ...

  Singular, Eigenvalue, Of the eigenvalues

Linear Algebra, Theory And Applications

best for those who have already had some exposure to linear algebra. It is also assumed that the reader has had calculus. Some optional topics require more analysis than this, however. I think that the subject of linear algebra is likely the most significant topic discussed in undergraduate mathematics courses.

  Applications, Some