Abundances and Physical Conditions in the Warm Neutral Medium Towards mu Columbae

We present ultraviolet interstellar absorption line measurements for the sightline towards the O9.5 V star mu Columbae obtained with the Goddard High Resolution Spectrograph (GHRS) on board the Hubble Space Telescope. These archival data represent the most complete GHRS interstellar absorption line measurements for any line of sight towards an early-type star. The 3.5 km/s resolution of the instrument allow us to accurately derive the gas-phase column densities of many important ionic species in the diffuse warm neutral medium using a combination of apparent column density and component fitting techniques, and we study in detail the contamination from ionized gas along this sightline. The low-velocity material shows gas-phase abundance patterns similar to the warm cloud (cloud A) towards the disk star zeta Oph, while the component at v = +20.1 km/s shows gas-phase abundances similar to those found in warm halo clouds. We find the velocity-integrated gas-phase abundances of Zn, P, and S relative to H along this sightline are indistinguishable from solar system abundances. We discuss the implications of our gas-phase abundance measurements for the composition of interstellar dust. The relative ionic column density ratios of the intermediate velocity components show the imprint both of elemental incorporation into grains and (photo)ionization. The components at v = -30 and -48 km/s along this sightline likely trace shocked gas with very low hydrogen column densities. Appendices include a new derivation of the GHRS instrumental line spread function, and a new very accurate determination of the total H I column along this sightline. (Abridged)Comment: Accepted for publication in the Astrophysical Journal. 80 pages including 19 embedded figures and 12 embedded tables. Version with higher resolution figures can be downloaded from http://fuse.pha.jhu.edu/~howk/Papers/papers.htm

Optimal Big Data Aggregation Systems - From Theory to Practical Application

The integration of computers into many facets of our lives has made the collection and storage of staggering amounts of data feasible. However, the data on its own is not so useful to us as the analysis and manipulation which allows manageable descriptive information to be extracted. New tools to extract this information from ever growing repositories of data are required. Some of these analyses can take the form of a two phase problem which is easily distributed to take advantage of available computing power. The first phase involves computing some descriptive partial result from some subset of the original data, and the second phase involves aggregating all the partial results to create a combined output. We formalize this compute-aggregate model for a rigorous performance analysis in an effort to minimize the latency of the aggregation phase with minimal intrusive analysis or modification. Based on our model we find an aggregation overlay attribute which highly affects aggregation latency and its dependence on an easily findable trait of aggregation. We rigorously prove the dependence and find optimal overlays for aggregation. We use the proven optima to create simple heuristics and build a system, NOAH, to take advantage of the findings. NOAH can be used by big data analysis systems. We also study an individual problem, top-k matching, to explore the effects of optimizing the computation phase separately from aggregation and create a complete distributed system to fulfill an economically relevant task

Compiling Irregular Software to Specialized Hardware

High-level synthesis (HLS) has simplified the design process for energy-efficient hardware accelerators: a designer specifies an accelerator’s behavior in a “high-level” language, and a toolchain synthesizes register-transfer level (RTL) code from this specification. Many HLS systems produce efficient hardware designs for regular algorithms (i.e., those with limited conditionals or regular memory access patterns), but most struggle with irregular algorithms that rely on dynamic, data-dependent memory access patterns (e.g., traversing pointer-based structures like lists, trees, or graphs). HLS tools typically provide imperative, side-effectful languages to the designer, which makes it difficult to correctly specify and optimize complex, memory-bound applications. In this dissertation, I present an alternative HLS methodology that leverages properties of functional languages to synthesize hardware for irregular algorithms. The main contribution is an optimizing compiler that translates pure functional programs into modular, parallel dataflow networks in hardware. I give an overview of this compiler, explain how its source and target together enable parallelism in the face of irregularity, and present two specific optimizations that further exploit this parallelism. Taken together, this dissertation verifies my thesis that pure functional programs exhibiting irregular memory access patterns can be compiled into specialized hardware and optimized for parallelism. This work extends the scope of modern HLS toolchains. By relying on properties of pure functional languages, our compiler can synthesize hardware from programs containing constructs that commercial HLS tools prohibit, e.g., recursive functions and dynamic memory allocation. Hardware designers may thus use our compiler in conjunction with existing HLS systems to accelerate a wider class of algorithms than before

A compiler level intermediate representation based binary analysis system and its applications

Analyzing and optimizing programs from their executables has received a lot of attention recently in the research community. There has been a tremendous amount of activity in executable-level research targeting varied applications such as security vulnerability analysis, untrusted code analysis, malware analysis, program testing, and binary optimizations. The vision of this dissertation is to advance the field of static analysis of executables and bridge the gap between source-level analysis and executable analysis. The main thesis of this work is scalable static binary rewriting and analysis using compiler-level intermediate representation without relying on the presence of metadata information such as debug or symbolic information. In spite of a significant overlap in the overall goals of several source-code methods and executables-level techniques, several sophisticated transformations that are well-understood and implemented in source-level infrastructures have yet to become available in executable frameworks. It is a well known fact that a standalone executable without any meta data is less amenable to analysis than the source code. Nonetheless, we believe that one of the prime reasons behind the limitations of existing executable frameworks is that current executable frameworks define their own intermediate representations (IR) which are significantly more constrained than an IR used in a compiler. Intermediate representations used in existing binary frameworks lack high level features like abstract stack, variables, and symbols and are even machine dependent in some cases. This severely limits the application of well-understood compiler transformations to executables and necessitates new research to make them applicable. In the first part of this dissertation, we present techniques to convert the binaries to the same high-level intermediate representation that compilers use. We propose methods to segment the flat address space in an executable containing undifferentiated blocks of memory. We demonstrate the inadequacy of existing variable identification methods for their promotion to symbols and present our methods for symbol promotion. We also present methods to convert the physically addressed stack in an executable to an abstract stack. The proposed methods are practical since they do not employ symbolic, relocation, or debug information which are usually absent in deployed executables. We have integrated our techniques with a prototype x86 binary framework called \emph{SecondWrite} that uses LLVM as the IR. The robustness of the framework is demonstrated by handling executables totaling more than a million lines of source-code, including several real world programs. In the next part of this work, we demonstrate that several well-known source-level analysis frameworks such as symbolic analysis have limited effectiveness in the executable domain since executables typically lack higher-level semantics such as program variables. The IR should have a precise memory abstraction for an analysis to effectively reason about memory operations. Our first work of recovering a compiler-level representation addresses this limitation by recovering several higher-level semantics information from executables. In the next part of this work, we propose methods to handle the scenarios when such semantics cannot be recovered. First, we propose a hybrid static-dynamic mechanism for recovering a precise and correct memory model in executables in presence of executable-specific artifacts such as indirect control transfers. Next, the enhanced memory model is employed to define a novel symbolic analysis framework for executables that can perform the same types of program analysis as source-level tools. Frameworks hitherto fail to simultaneously maintain the properties of correct representation and precise memory model and ignore memory-allocated variables while defining symbolic analysis mechanisms. We exemplify that our framework is robust, efficient and it significantly improves the performance of various traditional analyses like global value numbering, alias analysis and dependence analysis for executables. Finally, the underlying representation and analysis framework is employed for two separate applications. First, the framework is extended to define a novel static analysis framework, \emph{DemandFlow}, for identifying information flow security violations in program executables. Unlike existing static vulnerability detection methods for executables, DemandFlow analyzes memory locations in addition to symbols, thus improving the precision of the analysis. DemandFlow proposes a novel demand-driven mechanism to identify and precisely analyze only those program locations and memory accesses which are relevant to a vulnerability, thus enhancing scalability. DemandFlow uncovers six previously undiscovered format string and directory traversal vulnerabilities in popular ftp and internet relay chat clients. Next, the framework is extended to implement a platform-specific optimization for embedded processors. Several embedded systems provide the facility of locking one or more lines in the cache. We devise the first method in literature that employs instruction cache locking as a mechanism for improving the average-case run-time of general embedded applications. We demonstrate that the optimal solution for instruction cache locking can be obtained in polynomial time. Since our scheme is implemented inside a binary framework, it successfully addresses the portability concern by enabling the implementation of cache locking at the time of deployment when all the details of the memory hierarchy are available

Process Modeling in Pyrometallurgical Engineering

The Special Issue presents almost 40 papers on recent research in modeling of pyrometallurgical systems, including physical models, first-principles models, detailed CFD and DEM models as well as statistical models or models based on machine learning. The models cover the whole production chain from raw materials processing through the reduction and conversion unit processes to ladle treatment, casting, and rolling. The papers illustrate how models can be used for shedding light on complex and inaccessible processes characterized by high temperatures and hostile environment, in order to improve process performance, product quality, or yield and to reduce the requirements of virgin raw materials and to suppress harmful emissions

1971-04-15 Morehead News

Morehead News published on April 15, 1971

Prompt book of the Trojan women, with explanatory essays and illustrations

Thesis (M.A.)--Boston University, 1936. This item was digitized by the Internet Archive

Algorithms Seminar, 2002-2004

These seminar notes constitute the proceedings of a seminar devoted to the analysis of algorithms and related topics. The subjects covered include combinatorics, symbolic computation, and the asymptotic analysis of algorithms, data structures, and network protocols

New Mexico State Record, 09-27-1918

https://digitalrepository.unm.edu/nm_state_record_news/1115/thumbnail.jp