DROP: Dimensionality Reduction Optimization for Time Series

Dimensionality reduction is a critical step in scaling machine learning pipelines. Principal component analysis (PCA) is a standard tool for dimensionality reduction, but performing PCA over a full dataset can be prohibitively expensive. As a result, theoretical work has studied the effectiveness of iterative, stochastic PCA methods that operate over data samples. However, termination conditions for stochastic PCA either execute for a predetermined number of iterations, or until convergence of the solution, frequently sampling too many or too few datapoints for end-to-end runtime improvements. We show how accounting for downstream analytics operations during DR via PCA allows stochastic methods to efficiently terminate after operating over small (e.g., 1%) subsamples of input data, reducing whole workload runtime. Leveraging this, we propose DROP, a DR optimizer that enables speedups of up to 5x over Singular-Value-Decomposition-based PCA techniques, and exceeds conventional approaches like FFT and PAA by up to 16x in end-to-end workloads

To Index or Not to Index: Optimizing Exact Maximum Inner Product Search

Exact Maximum Inner Product Search (MIPS) is an important task that is widely pertinent to recommender systems and high-dimensional similarity search. The brute-force approach to solving exact MIPS is computationally expensive, thus spurring recent development of novel indexes and pruning techniques for this task. In this paper, we show that a hardware-efficient brute-force approach, blocked matrix multiply (BMM), can outperform the state-of-the-art MIPS solvers by over an order of magnitude, for some -- but not all -- inputs. In this paper, we also present a novel MIPS solution, MAXIMUS, that takes advantage of hardware efficiency and pruning of the search space. Like BMM, MAXIMUS is faster than other solvers by up to an order of magnitude, but again only for some inputs. Since no single solution offers the best runtime performance for all inputs, we introduce a new data-dependent optimizer, OPTIMUS, that selects online with minimal overhead the best MIPS solver for a given input. Together, OPTIMUS and MAXIMUS outperform state-of-the-art MIPS solvers by 3.2$\times$ on average, and up to 10.9$\times$, on widely studied MIPS datasets.Comment: 12 pages, 8 figures, 2 table

Reducing Fuel Volatility - An Additional Benefit From Blending Bio-fuels?

Oil price volatility harms economic growth. Diversifying into different fuel types can mitigate this effect by reducing volatility in fuel prices. Producing bio-fuels may thus have additional benefits in terms of avoided damage to macro-economic growth. In this study we investigate trends and patterns in the determinants of a volatility gain in order to provide an estimate of the tendency and the size of the volatility gain in the future. The accumulated avoided loss from blending gasoline with 20 percent ethanol-fuel estimated for the US economy amounts to 795 bn. USD between 2010 and 2019 with growing tendency. An amount that should be considered in cost-benefit analysis of bio-fuels.

Non-instructional roles for Notch signaling in T cell development

Mammalian immunity requires the presence of a broad and diverse repertoire of antigen receptors that can recognize the virtually infinite number of pathogenic epitopes encountered over a lifetime of a host, along with the ability to flexibly mount organized and pathogen-specific immune responses, as orchestrated by cytokines and CD4+ helper T (Th) cells. The Notch signaling pathway plays a critical role both in the generation of the T cell repertoire and in Th cell differentiation. However, while Notch is well understood to instruct early T lineage development at the expense of alternate lineages, it is unclear how Notch regulates the differentiation of a common naïve progenitor into one of the many Th cell subsets. To clarify the molecular mechanism used by Notch to influence Th cell differentiation, the dynamics of Notch target binding and gene regulation were analyzed at early time points after T cell activation. Rather than finding that Notch signaling acts via the canonical instructional paradigm, these studies find that Notch acts as an unbiased integrator of environmental differentiation cues, such that it simultaneously promotes the differentiation of multiple inflammatory Th cell populations. These findings are supported by in vivo gain-of-function studies in which Notch signaling is constitutively activated in peripheral T cells. Unlike previous work showing that hyper-activation of the Notch pathway in T lineage progenitors yields aggressive T cell acute lymphoblastic leukemia, these mice go on to develop a lethal autoinflammatory disorder, resulting from the Notch pathway promoting the differentiation of Th1, Th2, and Th17 cells. Additionally, these studies reveal that Notch signaling acts to destabilize regulatory T cell differentiation. Altogether, the work presented in this thesis evinces a novel non-instructional paradigm for Notch signaling, with broad implications for our understanding of Th cell differentiation, hematopoietic development, and cancer biology