Hybrid sketching : a new middle ground between 2- and 3-D

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Architecture, 2005.Includes bibliographical references (leaves 124-133).This thesis investigates the geometric representation of ideas during the early stages of design. When a designer's ideas are still in gestation, the exploration of form is more important than its precise specification. Digital modelers facilitate such exploration, but only for forms built with discrete collections of high-level geometric primitives; we introduce techniques that operate on designers' medium of choice, 2-D sketches. Designers' explorations also shift between 2-D and 3-D, yet 3-D form must also be specified with these high-level primitives, requiring an entirely different mindset from 2-D sketching. We introduce a new approach to transform existing 2-D sketches directly into a new kind of sketch-like 3-D model. Finally, we present a novel sketching technique that removes the distinction between 2-D and 3-D altogether. This thesis makes five contributions: point-dragging and curve-drawing techniques for editing sketches; two techniques to help designers bring 2-D sketches to 3-D; and a sketching interface that dissolves the boundaries between 2-D and 3-D representation. The first two contributions of this thesis introduce smooth exploration techniques that work on sketched form composed of strokes, in 2-D or 3-D. First, we present a technique, inspired by classical painting practices, whereby the designer can explore a range of curves with a single stroke. As the user draws near an existing curve, our technique automatically and interactively replaces sections of the old curve with the new one. Second, we present a method to enable smooth exploration of sketched form by point-dragging. The user constructs a high-level "proxy" description that can be used, somewhat like a skeleton, to deform a sketch independent of(cont.) the internal stroke description. Next, we leverage the proxy deformation capability to help the designer move directly from existing 2-D sketches to 3-D models. Our reconstruction techniques generate a novel kind of 3-D model which maintains the appearance and stroke structure of the original 2-D sketch. One technique transforms a single sketch with help from annotations by the designer; the other combines two sketches. Since these interfaces are user-guided, they can operate on ambiguous sketches, relying on the designer to choose an interpretation. Finally, we present an interface to build an even sparser, more suggestive, type of 3-D model, either from existing sketches or from scratch. "Camera planes" provide a complex 3-D scaffolding on which to hang sketches, which can still be drawn as rapidly and freely as before. A sparse set of 2-D sketches placed on planes provides a novel visualization of 3-D form, with enough information present to suggest 3-D shape, but enough missing that the designer can 'read into' the form, seeing multiple possibilities. This unspecified information--this empty space--can spur the designer on to new ideas.by John Alex.Ph.D

Sparsely Faceted Arrays: A Mechanism Supporting Parallel Allocation, Communication, and Garbage Collection

Conventional parallel computer architectures do not provide support for non-uniformly distributed objects. In this thesis, I introduce sparsely faceted arrays (SFAs), a new low-level mechanism for naming regions of memory, or facets, on different processors in a distributed, shared memory parallel processing system. Sparsely faceted arrays address the disconnect between the global distributed arrays provided by conventional architectures (e.g. the Cray T3 series), and the requirements of high-level parallel programming methods that wish to use objects that are distributed over only a subset of processing elements. A sparsely faceted array names a virtual globally-distributed array, but actual facets are lazily allocated. By providing simple semantics and making efficient use of memory, SFAs enable efficient implementation of a variety of non-uniformly distributed data structures and related algorithms. I present example applications which use SFAs, and describe and evaluate simple hardware mechanisms for implementing SFAs. Keeping track of which nodes have allocated facets for a particular SFA is an important task that suggests the need for automatic memory management, including garbage collection. To address this need, I first argue that conventional tracing techniques such as mark/sweep and copying GC are inherently unscalable in parallel systems. I then present a parallel memory-management strategy, based on reference-counting, that is capable of garbage collecting sparsely faceted arrays. I also discuss opportunities for hardware support of this garbage collection strategy. I have implemented a high-level hardware/OS simulator featuring hardware support for sparsely faceted arrays and automatic garbage collection. I describe the simulator and outline a few of the numerous details associated with a "real" implementation of SFAs and SFA-aware garbage collection. Simulation results are used throughout this thesis in the evaluation of hardware support mechanisms

Proceedings of the 21st Conference on Formal Methods in Computer-Aided Design – FMCAD 2021

The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing

Algorithm Libraries for Multi-Core Processors

By providing parallelized versions of established algorithm libraries, we ease the exploitation of the multiple cores on modern processors for the programmer. The Multi-Core STL provides basic algorithms for internal memory, while the parallelized STXXL enables multi-core acceleration for algorithms on large data sets stored on disk. Some parallelized geometric algorithms are introduced into CGAL. Further, we design and implement sorting algorithms for huge data in distributed external memory

CDCL(Crypto) and Machine Learning based SAT Solvers for Cryptanalysis

Over the last two decades, we have seen a dramatic improvement in the efficiency of conflict-driven clause-learning Boolean satisfiability (CDCL SAT) solvers over industrial problems from a variety of applications such as verification, testing, security, and AI. The availability of such powerful general-purpose search tools as the SAT solver has led many researchers to propose SAT-based methods for cryptanalysis, including techniques for finding collisions in hash functions and breaking symmetric encryption schemes. A feature of all of the previously proposed SAT-based cryptanalysis work is that they are \textit{blackbox}, in the sense that the cryptanalysis problem is encoded as a SAT instance and then a CDCL SAT solver is invoked to solve said instance. A weakness of this approach is that the encoding thus generated may be too large for any modern solver to solve it efficiently. Perhaps a more important weakness of this approach is that the solver is in no way specialized or tuned to solve the given instance. Finally, very little work has been done to leverage parallelism in the context of SAT-based cryptanalysis. To address these issues, we developed a set of methods that improve on the state-of-the-art SAT-based cryptanalysis along three fronts. First, we describe an approach called \cdcl (inspired by the CDCL($T$) paradigm) to tailor the internal subroutines of the CDCL SAT solver with domain-specific knowledge about cryptographic primitives. Specifically, we extend the propagation and conflict analysis subroutines of CDCL solvers with specialized codes that have knowledge about the cryptographic primitive being analyzed by the solver. We demonstrate the power of this framework in two cryptanalysis tasks of algebraic fault attack and differential cryptanalysis of SHA-1 and SHA-256 cryptographic hash functions. Second, we propose a machine-learning based parallel SAT solver that performs well on cryptographic problems relative to many state-of-the-art parallel SAT solvers. Finally, we use a formulation of SAT into Bayesian moment matching to address heuristic initialization problem in SAT solvers

Discriminative, generative, and imitative learning

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2002.Includes bibliographical references (leaves 201-212).I propose a common framework that combines three different paradigms in machine learning: generative, discriminative and imitative learning. A generative probabilistic distribution is a principled way to model many machine learning and machine perception problems. Therein, one provides domain specific knowledge in terms of structure and parameter priors over the joint space of variables. Bayesian networks and Bayesian statistics provide a rich and flexible language for specifying this knowledge and subsequently refining it with data and observations. The final result is a distribution that is a good generator of novel exemplars. Conversely, discriminative algorithms adjust a possibly non-distributional model to data optimizing for a specific task, such as classification or prediction. This typically leads to superior performance yet compromises the flexibility of generative modeling. I present Maximum Entropy Discrimination (MED) as a framework to combine both discriminative estimation and generative probability densities. Calculations involve distributions over parameters, margins, and priors and are provably and uniquely solvable for the exponential family. Extensions include regression, feature selection, and transduction. SVMs are also naturally subsumed and can be augmented with, for example, feature selection, to obtain substantial improvements. To extend to mixtures of exponential families, I derive a discriminative variant of the Expectation-Maximization (EM) algorithm for latent discriminative learning (or latent MED).(cont.) While EM and Jensen lower bound log-likelihood, a dual upper bound is made possible via a novel reverse-Jensen inequality. The variational upper bound on latent log-likelihood has the same form as EM bounds, is computable efficiently and is globally guaranteed. It permits powerful discriminative learning with the wide range of contemporary probabilistic mixture models (mixtures of Gaussians, mixtures of multinomials and hidden Markov models). We provide empirical results on standardized data sets that demonstrate the viability of the hybrid discriminative-generative approaches of MED and reverse-Jensen bounds over state of the art discriminative techniques or generative approaches. Subsequently, imitative learning is presented as another variation on generative modeling which also learns from exemplars from an observed data source. However, the distinction is that the generative model is an agent that is interacting in a much more complex surrounding external world. It is not efficient to model the aggregate space in a generative setting. I demonstrate that imitative learning (under appropriate conditions) can be adequately addressed as a discriminative prediction task which outperforms the usual generative approach. This discriminative-imitative learning approach is applied with a generative perceptual system to synthesize a real-time agent that learns to engage in social interactive behavior.by Tony Jebara.Ph.D

Evaluation Functions in General Game Playing

While in traditional computer game playing agents were designed solely for the purpose of playing one single game, General Game Playing is concerned with agents capable of playing classes of games. Given the game's rules and a few minutes time, the agent is supposed to play any game of the class and eventually win it. Since the game is unknown beforehand, previously optimized data structures or human-provided features are not applicable. Instead, the agent must derive a strategy on its own. One approach to obtain such a strategy is to analyze the game rules and create a state evaluation function that can be subsequently used to direct the agent to promising states in the match. In this thesis we will discuss existing methods and present a general approach on how to construct such an evaluation function. Each topic is discussed in a modular fashion and evaluated along the lines of quality and efficiency, resulting in a strong agent.:Introduction Game Playing Evaluation Functions I - Aggregation Evaluation Functions II - Features General Evaluation Related Work Discussio

Computer Aided Verification

This open access two-volume set LNCS 13371 and 13372 constitutes the refereed proceedings of the 34rd International Conference on Computer Aided Verification, CAV 2022, which was held in Haifa, Israel, in August 2022. The 40 full papers presented together with 9 tool papers and 2 case studies were carefully reviewed and selected from 209 submissions. The papers were organized in the following topical sections: Part I: Invited papers; formal methods for probabilistic programs; formal methods for neural networks; software Verification and model checking; hyperproperties and security; formal methods for hardware, cyber-physical, and hybrid systems. Part II: Probabilistic techniques; automata and logic; deductive verification and decision procedures; machine learning; synthesis and concurrency. This is an open access book