Supervised Machine Learning Under Test-Time Resource Constraints: A Trade-off Between Accuracy and Cost

The past decade has witnessed how the field of machine learning has established itself as a necessary component in several multi-billion-dollar industries. The real-world industrial setting introduces an interesting new problem to machine learning research: computational resources must be budgeted and cost must be strictly accounted for during test-time. A typical problem is that if an application consumes x additional units of cost during test-time, but will improve accuracy by y percent, should the additional x resources be allocated? The core of this problem is a trade-off between accuracy and cost. In this thesis, we examine components of test-time cost, and develop different strategies to manage this trade-off. We first investigate test-time cost and discover that it typically consists of two parts: feature extraction cost and classifier evaluation cost. The former reflects the computational efforts of transforming data instances to feature vectors, and could be highly variable when features are heterogeneous. The latter reflects the effort of evaluating a classifier, which could be substantial, in particular nonparametric algorithms. We then propose three strategies to explicitly trade-off accuracy and the two components of test-time cost during classifier training. To budget the feature extraction cost, we first introduce two algorithms: GreedyMiser and Anytime Representation Learning (AFR). GreedyMiser employs a strategy that incorporates the extraction cost information during classifier training to explicitly minimize the test-time cost. AFR extends GreedyMiser to learn a cost-sensitive feature representation rather than a classifier, and turns traditional Support Vector Machines (SVM) into test- time cost-sensitive anytime classifiers. GreedyMiser and AFR are evaluated on two real-world data sets from two different application domains, and both achieve record performance. We then introduce Cost Sensitive Tree of Classifiers (CSTC) and Cost Sensitive Cascade of Classifiers (CSCC), which share a common strategy that trades-off the accuracy and the amortized test-time cost. CSTC introduces a tree structure and directs test inputs along different tree traversal paths, each is optimized for a specific sub-partition of the input space, extracting different, specialized subsets of features. CSCC extends CSTC and builds a linear cascade, instead of a tree, to cope with class-imbalanced binary classification tasks. Since both CSTC and CSCC extract different features for different inputs, the amortized test-time cost is greatly reduced while maintaining high accuracy. Both approaches out-perform the current state-of-the-art on real-world data sets. To trade-off accuracy and high classifier evaluation cost of nonparametric classifiers, we propose a model compression strategy and develop Compressed Vector Machines (CVM). CVM focuses on the nonparametric kernel Support Vector Machines (SVM), whose test-time evaluation cost is typically substantial when learned from large training sets. CVM is a post-processing algorithm which compresses the learned SVM model by reducing and optimizing support vectors. On several benchmark data sets, CVM maintains high test accuracy while reducing the test-time evaluation cost by several orders of magnitude

Insecticidal Activity of the Whole Grass Extract of Typha angustifolia and its Active Component against Solenopsis invicta

In this study, the toxicity of whole grass Typha angustifolia L. extract was determined in vitro by a “water tube” method to investigate the bioactivity of T. angustifolia L. against micrergates of red imported fire ants. Results indicated that the ethanol extract exhibited toxicity against the micrergates of red imported fire ants. Mortality was 100% after the micrergates were treated with 2000 mg/mL of ethanol extract for 72 h. After 48 h of treatment, LC50 values of ethanol extract and petroleum ether fraction were 956.85 and 398.73 mg/mL, respectively. After 120 h, LC50 values of the same substances were 271.23 and 152.86 mg/mL, respectively. A bioactivity-guided fractionation and chemical investigation of petroleum ether fraction yielded an active component (compound 1). NMR spectra revealed that the structure of compound 1 corresponded to 3β-hydroxy-25-methylenecycloartan-24-ol. Compound 1 also exhibited strong toxicity against the micrergates of red imported fire ants, thereby eradicating all of the tested ants treated with 240 mg/mL for 120 h. LC50 values of compound 1 at 48 and 120 h were 316.50 and 28.52 mg/mL, respectively

Unabridged phase diagram for single-phased FeSexTe1-x thin films

A complete phase diagram and its corresponding physical properties are essential prerequisites to understand the underlying mechanism of iron based superconductivity. For the structurally simplest 11 (FeSeTe) system, earlier attempts using bulk samples have not been able to do so due to the fabrication difficulties. Here, thin FeSexTe1-x films with the Se content covering the full range were fabricated by using pulsed laser deposition method. Crystal structure analysis shows that all films retain the tetragonal structure in room temperature. Significantly, the highest superconducting transition temperature (TC = 20 K) occurs in the newly discovered domain, 0.6 - 0.8. The single-phased superconducting dome for the full Se doping range is the first of its kind in iron chalcogenide superconductors. Our results present a new avenue to explore novel physics as well as to optimize superconductors

Effects on the Physicochemical Properties of Hydrochar Originating from Deep Eutectic Solvent (Urea and ZnCl2)-Assisted Hydrothermal Carbonization of Sewage Sludge

Deep eutectic solvents (DESs) (ZnCl2 and urea) have been used to solubilize organic matter from sewage sludge (SS), followed by subsequent hydrothermal carbonization (HTC) to obtain low-nitrogen-content hydrochar. The nitrogen content in hydrochar obtained after DES addition decreased to 1.93 from 3.15% (no DES) at 210 °C. DES can notably dissolve proteins and lipids during HTC of SS. HTC of polysaccharides was enhanced, increasing the degree of carbonization. The key role of DES in SS during HTC was the dissolution of proteins, promoting carbonization of polysaccharides, Maillard reactions, deamination, and decarboxylation of proteins. ZnCl2 was probably converted into β-Zn(OH)C1 and ZnO during HTC. Results pointed to relevant enhancements when DES was added, useful for organic waste valorization such as SS, food waste, poultry manure, and related waste feedstock