Animating Unpredictable Effects

Uncanny computer-generated animations of splashing waves, billowing smoke clouds, and characters’ flowing hair have become a ubiquitous presence on screens of all types since the 1980s. This Open Access book charts the history of these digital moving images and the software tools that make them. Unpredictable Visual Effects uncovers an institutional and industrial history that saw media industries conducting more private R&D as Cold War federal funding began to wane in the late 1980s. In this context studios and media software companies took concepts used for studying and managing unpredictable systems like markets, weather, and fluids and turned them into tools for animation. Unpredictable Visual Effects theorizes how these animations are part of a paradigm of control evident across society, while at the same time exploring what they can teach us about the relationship between making and knowing

ASTROBIOLOGICAL EFFECTS OF GAMMA-RAY BURSTS IN THE MILKY WAY GALAXY

A planet having protective ozone within the collimated beam of a gamma-ray burst (GRB) may suffer ozone depletion, potentially causing a mass extinction event to existing life on a planet's surface and oceans. We model the dangers of long GRBs to planets in the Milky Way and utilize a static statistical model of the Galaxy, which matches major observable properties, such as the inside-out star formation history (SFH), metallicity evolution, and three-dimensional stellar number density distribution. The GRB formation rate is a function of both the SFH and metallicity. However, the extent to which chemical evolution reduces the GRB rate over time in the Milky Way is still an open question. Therefore, we compare the damaging effects of GRBs to biospheres in the Milky Way using two models. One model generates GRBs as a function of the inside-out SFH. The other model follows the SFH, but generates GRB progenitors as a function of metallicity, thereby favoring metal-poor host regions of the Galaxy over time. If the GRB rate only follows the SFH, the majority of the GRBs occur in the inner Galaxy. However, if GRB progenitors are constrained to low-metallicity environments, then GRBs only form in the metal-poor outskirts at recent epochs. Interestingly, over the past 1 Gyr, the surface density of stars (and their corresponding planets), which survive a GRB is still greatest in the inner galaxy in both models. The present-day danger of long GRBs to life at the solar radius (R ⊙ = 8 kpc) is low. We find that at least ~65% of stars survive a GRB over the past 1 Gyr. Furthermore, when the GRB rate was expected to have been enhanced at higher redshifts, such as z ≳ 0.5, our results suggest that a large fraction of planets would have survived these lethal GRB events

Computing Double Precision Euclidean Distances using GPU Tensor Cores

Tensor cores (TCs) are a type of Application-Specific Integrated Circuit (ASIC) and are a recent addition to Graphics Processing Unit (GPU) architectures. As such, TCs are purposefully designed to greatly improve the performance of Matrix Multiply-Accumulate (MMA) operations. While TCs are heavily studied for machine learning and closely related fields, where their high efficiency is undeniable, MMA operations are not unique to these fields. More generally, any computation that can be expressed as MMA operations can leverage TCs, and potentially benefit from their higher computational throughput compared to other general-purpose cores, such as CUDA cores on Nvidia GPUs. In this paper, we propose the first double precision (FP64) Euclidean distance calculation algorithm, which is expressed as MMA operations to leverage TCs on Nvidia GPUs, rather than the more commonly used CUDA cores. To show that the Euclidean distance can be accelerated in a real-world application, we evaluate our proposed TC algorithm on the distance similarity self-join problem, as the most computationally intensive part of the algorithm consists of computing distances in a multi-dimensional space. We find that the performance gain from using the tensor core algorithm over the CUDA core algorithm depends weakly on the dataset size and distribution, but is strongly dependent on data dimensionality. Overall, TCs are a compelling alternative to CUDA cores, particularly when the data dimensionality is low ($\leq{4}$), as we achieve an average speedup of $1.28\times$ and up to $2.23\times$ against a state-of-the-art GPU distance similarity self-join algorithm. Furthermore, because this paper is among the first to explore the use of TCs for FP64 general-purpose computation, future research is promising.Comment: Accepted for publicatio

Assessing Researcher Interdisciplinarity: A Case Study of the University of Hawaii NASA Astrobiology Institute

In this study, we combine bibliometric techniques with a machine learning algorithm, the sequential Information Bottleneck, to assess the interdisciplinarity of research produced by the University of Hawaii NASA Astrobiology Institute (UHNAI). In particular, we cluster abstract data to evaluate ISI Web of Knowledge subject categories as descriptive labels for astrobiology documents, and to assess individual researcher interdisciplinarity to determine where collabo- ration opportunities might occur. We find that the majority of the UHNAI team is engaged in interdisciplinary research, and suggest that our method could be applied to additional NASA Astrobiology Institute teams to identify and facilitate collaboration opportunities