HTRC Data API Performance Study

HathiTrust Research Center (HTRC) allows users to access more than 3 million volumes through a service called Data API. Data API plays an important role in HTRC infrastructure. It hides internal complexity from user, protects against malicious or inadvertent damages to data and separates underlying storage solution with interface so that underlying storage may be replaced with better solutions without affecting client code. We carried out extensive evaluations on the HTRC Data API performance over the Spring 2013. Specifically, we evaluated the rate at which data can be retrieved from the Cassandra cluster under different conditions, impact of different compression levels, and HTTP/HTTPS data transfer. The evaluation presents performance aspects of different software pieces in Data API as well as guides us to have optimal settings for Data API

One-Bit Compressive Sensing: Can We Go Deep and Blind?

One-bit compressive sensing is concerned with the accurate recovery of an underlying sparse signal of interest from its one-bit noisy measurements. The conventional signal recovery approaches for this problem are mainly developed based on the assumption that an exact knowledge of the sensing matrix is available. In this work, however, we present a novel data-driven and model-based methodology that achieves blind recovery; i.e., signal recovery without requiring the knowledge of the sensing matrix. To this end, we make use of the deep unfolding technique and develop a model-driven deep neural architecture which is designed for this specific task. The proposed deep architecture is able to learn an alternative sensing matrix by taking advantage of the underlying unfolded algorithm such that the resulting learned recovery algorithm can accurately and quickly (in terms of the number of iterations) recover the underlying compressed signal of interest from its one-bit noisy measurements. In addition, due to the incorporation of the domain knowledge and the mathematical model of the system into the proposed deep architecture, the resulting network benefits from enhanced interpretability, has a very small number of trainable parameters, and requires very small number of training samples, as compared to the commonly used black-box deep neural network alternatives for the problem at hand.Comment: IEEE SIGNAL PROCESSING LETTERS,202

Dominant skyline query processing

Master'sMASTER OF SCIENC

Mechanism and Application of a Heterogeneous Catalytic Hydrogen-deuterium Exchange Reaction for Polyolefins

University of Minnesota Ph.D. dissertation. April 2018. Major: Chemical Engineering. Advisors: Frank Bates, Timothy Lodge. 1 computer file (PDF); xv, 135 pages.The mechanism of a heterogeneous catalytic H/D exchange reaction with polyolefins is investigated in this thesis. The model polymers used in this study were hydrogenated polybutadienes (hPBDs), and a metallocene linear low density polyethylene (LLDPE). When mixed at 170 ºC with isooctane, Pt/Re-SiO2 catalyst, and gaseous deuterium, the polyolefins dissolve and undergo H/D exchange reaction at the surface of the catalyst, producing partially deuterium labelled polyolefins. Polymers with varying molecular weight, varying ethyl branch density and narrow molecular weight distribution were synthesized by anionic polymerization of 1,3-butadiene followed by saturation with gaseous hydrogen. The LLDPE polymer with relatively broader molecular weight distribution is a commercial product and was supplied by ExxonMobil Chemical Company. The extent of deuterium labelling is analyzed with density measurement, proton nuclear magnetic resonance spectroscopy (1H-NMR) and Fourier transform infrared (FTIR) spectroscopy. A size exclusion chromatography (SEC) instrument equipped with an IR detector was used to analyze the deuterium concentration within the LLDPE polymer as a function of molecular weight. Small angle neutron scattering (SANS) was conducted for both the pure labelled polyolefins and their blends. The partially labelled LLDPE sample was fractionated according to the molecular weight. The partially labelled fractions were blended with the normal LLDPE to create samples with different molecular weight portions labelled. These labelled blends were uniaxially stretched at room temperature while simultaneously monitored with SANS, providing a method to characterize the single chain alignment process at different stages of polyethylene deformation, as a function of time. In this thesis, several aspects of the isotope exchange reaction were investigated. We first examined the dependence of the isotope exchange on the molecular weight and branch content of the substrate polyolefins. The extent of isotope exchange was found to strongly favor the high molecular weight molecules. High branch concentration hinders the exchange reaction, but has a less impact at low branch content. These observations are best explained by viewing the exchange reaction as an absorption controlled process. The deuterium distribution was found to be inhomogeneous evidenced by both the SEC-IR and SANS results. From SANS results modeling, it was confirmed that mathematical accommodation of the inhomogeneous deuterium distribution is necessary to extract chain statistics. Finally, the in situ tensile-SANS experiments revealed that the single chains develop a high degree of alignment along the stretching direction during the elastic and plastic deformation processes of the LLDPE, and maintain that alignment during the strain hardening regime. A remarkable higher degree of chain alignment was found for the high molecular weight chains, a result of longer chains being able to form more tie chains between lamellae. The results of this work provided a scheme of analyzing commercial polyolefins on the single molecular scale, without the necessity to access the synthesis route of the materials

Euro-denominated high-yield corporate bonds

High-yield bonds are a unique and increasingly important asset class. They are different from investment-grade bonds because they exhibit higher default risk and are less sensitive to changes in interest rates. There is, however, a paucity of literature on the high-yield bond market, regardless of its market size and economic importance. This thesis concentrates on Euro-denominated high-yield corporate bonds from the perspective of the secondary and primary markets. In the first empirical chapter, we critically compare three major databases: Bloomberg, Refinitiv Eikon, and Refinitiv Datastream, which provide data for Euro-denominated high-yield corporate bonds. We find that Bloomberg provides more comprehensive data than Refinitiv Eikon and includes a higher number of bonds with available clean prices than Refinitiv Datastream. In addition, we observe that accrued interest, prices, and price returns differ from an individual bond viewpoint. Therefore, we use Bloomberg as our primary data source for sample size and data consistency purposes. In the second empirical chapter, we investigate the term, default, illiquidity, and downside factors in pricing Euro-denominated high-yield corporate bonds between 2000 and 2021. We find that the term, default, illiquidity, and downside factors are positively related to excess returns. Results of our Markov-switching model suggest that the illiquidity factor plays a vital role in explaining excess returns and fluctuates in different market scenarios, particularly for high-yield bonds with the lowest credit ratings (e.g., CCC and below). The effect of illiquidity on BB-rated bonds is different from the effect on the high-yield bonds with the lowest credit ratings. In the third empirical chapter, we investigate the extent of underpricing in the primary market for Euro-denominated high-yield corporate bonds. Determinants of underpricing are examined with an ordinary least squares (OLS) regression with year, industry, and country fixed effects. Our evidence suggests that high-yield bonds are underpriced. The underpricing is more likely caused by information asymmetry problems and the frequency of trading following issuance in the secondary market. Overall, our findings provide valuable information that may be used for performance analysis and asset allocation in the high-yield bond market

Trip energy consumption estimation for electric buses

This study aims to develop a trip energy consumption (TEC) estimation model for the electric bus (EB) fleet planning, operation, and life-cycle assessment. Leveraging the vast variations of temperature in Jilin Province, China, real-world data of 31 ​EBs operating in 14 months were collected with temperatures fluctuating from −27.0 ​to 35.0 ​\ub0C. TEC of an EB was divided into two parts, which are the energy required by the traction and battery thermal management system, and the energy required by the air conditioner (AC) system operation, respectively. The former was regressed by a logarithmic linear model with ambient temperature, curb weight, travel distance, and trip travel time as contributing factors. The optimum working temperature and regression parameters were obtained by combining Fibonacci and Weighted Least Square. The latter was estimated by the operation time of the AC system in cooling mode or heating mode. Model evaluation and sensitivity analysis were conducted. The results show that: (i) the mean absolute percentage error (MAPE) of the proposed model is 12.108%; (ii) the estimation accuracy of the model has a probability of 99.7814% meeting the requirements of EB fleet scheduling; (iii) the MAPE has a 1.746% reduction if considering passengers’ boarding and alighting

MultiLoRA: Democratizing LoRA for Better Multi-Task Learning

LoRA achieves remarkable resource efficiency and comparable performance when adapting LLMs for specific tasks. Since ChatGPT demonstrated superior performance on various tasks, there has been a growing desire to adapt one model for all tasks. However, the explicit low-rank of LoRA limits the adaptation performance in complex multi-task scenarios. LoRA is dominated by a small number of top singular vectors while fine-tuning decomposes into a set of less important unitary transforms. In this paper, we propose MultiLoRA for better multi-task adaptation by reducing the dominance of top singular vectors observed in LoRA. MultiLoRA scales LoRA modules horizontally and change parameter initialization of adaptation matrices to reduce parameter dependency, thus yields more balanced unitary subspaces. We unprecedentedly construct specialized training data by mixing datasets of instruction follow, natural language understanding, world knowledge, to cover semantically and syntactically different samples. With only 2.5% of additional parameters, MultiLoRA outperforms single LoRA counterparts and fine-tuning on multiple benchmarks and model scales. Further investigation into weight update matrices of MultiLoRA exhibits reduced dependency on top singular vectors and more democratic unitary transform contributions

Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes

Despite the recent successes of vanilla Graph Neural Networks (GNNs) on many tasks, their foundation on pairwise interaction networks inherently limits their capacity to discern latent higher-order interactions in complex systems. To bridge this capability gap, we propose a novel approach exploiting the rich mathematical theory of simplicial complexes (SCs) - a robust tool for modeling higher-order interactions. Current SC-based GNNs are burdened by high complexity and rigidity, and quantifying higher-order interaction strengths remains challenging. Innovatively, we present a higher-order Flower-Petals (FP) model, incorporating FP Laplacians into SCs. Further, we introduce a Higher-order Graph Convolutional Network (HiGCN) grounded in FP Laplacians, capable of discerning intrinsic features across varying topological scales. By employing learnable graph filters, a parameter group within each FP Laplacian domain, we can identify diverse patterns where the filters' weights serve as a quantifiable measure of higher-order interaction strengths. The theoretical underpinnings of HiGCN's advanced expressiveness are rigorously demonstrated. Additionally, our empirical investigations reveal that the proposed model accomplishes state-of-the-art (SOTA) performance on a range of graph tasks and provides a scalable and flexible solution to explore higher-order interactions in graphs