A Security Assessment of Trusted Platform Modules

Trusted Platform Modules (TPMs) are becoming ubiquitous devices included in newly released personal computers. Broadly speaking, the aim of this technology is to provide a facility for authenticating the platform on which they are running: they are able to measure attest to the authenticity of a hardware and software configuration. Designed to be cheap, commodity devices which motherboard and processor vendors can include in their products with minimal marginal cost, these devices have a good theoretical design. Unfortunately, there exist several practical constraints on the effectiveness of TPMs and the architectures which employ them which leave them open to attack. We demonstrate some hardware and software attacks against these devices and architectures. These attacks include Time of Check/Time of Use attacks on the Integrity Measurment Architecture, and a bus attack against the Low Pin Count bus. Further, we explore the possibility of side-channel attacks against TPMs

The Regulation of Facial Recognition Technology and Potential First Amendment Concerns

This thesis is designed to explore the patchwork regulatory structure that governs the useof facial recognition technology by government and private actors. With minimal federalregulation, state and local regulations are an important bulwark against the unregulated use offacial recognition technology. The author examines the few state and local regulations that doexist, analyzing statutes using a coding protocol created by the author. He then discusses anypotential First Amendment interests present in the use of facial recognition technology,ultimately concluding that facial recognition technology will likely receive some level of FirstAmendment protection under the rule of “information as speech” first stated in Sorrell v. IMSHealth. Lastly, the author evaluates how a First Amendment interest may hinder state and localattempts to regulate the use of facial recognition technology.Master of Art

Scientific Computing Meets Big Data Technology: An Astronomy Use Case

Scientific analyses commonly compose multiple single-process programs into a dataflow. An end-to-end dataflow of single-process programs is known as a many-task application. Typically, tools from the HPC software stack are used to parallelize these analyses. In this work, we investigate an alternate approach that uses Apache Spark -- a modern big data platform -- to parallelize many-task applications. We present Kira, a flexible and distributed astronomy image processing toolkit using Apache Spark. We then use the Kira toolkit to implement a Source Extractor application for astronomy images, called Kira SE. With Kira SE as the use case, we study the programming flexibility, dataflow richness, scheduling capacity and performance of Apache Spark running on the EC2 cloud. By exploiting data locality, Kira SE achieves a 2.5x speedup over an equivalent C program when analyzing a 1TB dataset using 512 cores on the Amazon EC2 cloud. Furthermore, we show that by leveraging software originally designed for big data infrastructure, Kira SE achieves competitive performance to the C implementation running on the NERSC Edison supercomputer. Our experience with Kira indicates that emerging Big Data platforms such as Apache Spark are a performant alternative for many-task scientific applications

MLI: An API for Distributed Machine Learning

MLI is an Application Programming Interface designed to address the challenges of building Machine Learn- ing algorithms in a distributed setting based on data-centric computing. Its primary goal is to simplify the development of high-performance, scalable, distributed algorithms. Our initial results show that, relative to existing systems, this interface can be used to build distributed implementations of a wide variety of common Machine Learning algorithms with minimal complexity and highly competitive performance and scalability

Patch-Burn Grazing Impacts Forage Resources in Subtropical Humid Grazing Lands

Subtropical humid grazing lands represent a large global land use and are important for livestock production, as well as supplying multiple ecosystem services. Patch-burn grazing (PBG) management is applied in temperate grazing lands to enhance environmental and economic sustainability; however, this management system has not been widely tested in subtropical humid grazing lands. The objective of this study was to determine how PBG affected forage resources, in comparison with the business-as-usual full-burn (FB) management in both intensively managed pastures (IMP) and seminative (SN) pastures in subtropical humid grazinglands. We hypothesized that PBG management would create patch contrasts in forage quantity and nutritive value in both IMP and SN pastures, with a greater effect in SN pastures. A randomized block design experiment was established in 2017 with 16 pastures (16 ha each), 8 each in IMP and SN at Archbold Biological Station\u27s Buck Island Ranch in Florida. PBG management employed on IMP and SN resulted in creation of patch contrast in forage nutritive value and biomass metrics, and recent fire increased forage nutritive value. Residual standing biomass was significantly lower in burned patches of each year, creating heterogeneity within both pasture types under PBG. PBG increased digestible forage production in SN but not IMP pastures. These results suggest that PBG may be a useful management tool for enhancing forage nutritive value and creating patch contrast in both SN and IMP, but PBG does not necessarily increase production relative to FB management. The annual increase in tissue quality and digestible forage production in a PBG system as opposed to once every 3 yr in an FB system is an important consideration for ranchers. Economic impacts of PBG and FB management in the two different pasture types are discussed, and we compare and contrast results from subtropical humid grazing lands with continental temperate grazing lands

MLlib: Machine learning in Apache Spark

Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks. In this paper we present MLLIB, Spark's open-source distributed machine learning library. MLLIB provides efficient functionality for a wide range of learning settings and includes several underlying statistical, optimization, and linear algebra primitives. Shipped with Spark, MLLIB supports several languages and provides a high-level API that leverages Spark's rich ecosystem to simplify the development of end-to-end machine learning pipelines. MLLIB has experienced a rapid growth due to its vibrant open-source community of over 140 contributors, and includes extensive documentation to support further growth and to let users quickly get up to speed

Evidence for a direct effect of the NAD+ precursor acipimox on muscle mitochondrial function in humans.

Recent preclinical studies showed the potential of nicotinamide adenine dinucleotide (NAD(+)) precursors to increase oxidative phosphorylation and improve metabolic health, but human data are lacking. We hypothesize that the nicotinic acid derivative acipimox, an NAD(+) precursor, would directly affect mitochondrial function independent of reductions in nonesterified fatty acid (NEFA) concentrations. In a multicenter randomized crossover trial, 21 patients with type 2 diabetes (age 57.7 +/- 1.1 years, BMI 33.4 +/- 0.8 kg/m(2)) received either placebo or acipimox 250 mg three times daily dosage for 2 weeks. Acipimox treatment increased plasma NEFA levels (759 +/- 44 vs. 1,135 +/- 97 mumol/L for placebo vs. acipimox, P < 0.01) owing to a previously described rebound effect. As a result, skeletal muscle lipid content increased and insulin sensitivity decreased. Despite the elevated plasma NEFA levels, ex vivo mitochondrial respiration in skeletal muscle increased. Subsequently, we showed that acipimox treatment resulted in a robust elevation in expression of nuclear-encoded mitochondrial gene sets and a mitonuclear protein imbalance, which may indicate activation of the mitochondrial unfolded protein response. Further studies in C2C12 myotubes confirmed a direct effect of acipimox on NAD(+) levels, mitonuclear protein imbalance, and mitochondrial oxidative capacity. To the best of our knowledge, this study is the first to demonstrate that NAD(+) boosters can also directly affect skeletal muscle mitochondrial function in humans