Semantic Parsing in Limited Resource Conditions

This thesis explores challenges in semantic parsing, specifically focusing on scenarios with limited data and computational resources. It offers solutions using techniques like automatic data curation, knowledge transfer, active learning, and continual learning. For tasks with no parallel training data, the thesis proposes generating synthetic training examples from structured database schemas. When there is abundant data in a source domain but limited parallel data in a target domain, knowledge from the source is leveraged to improve parsing in the target domain. For multilingual situations with limited data in the target languages, the thesis introduces a method to adapt parsers using a limited human translation budget. Active learning is applied to select source-language samples for manual translation, maximizing parser performance in the target language. In addition, an alternative method is also proposed to utilize machine translation services, supplemented by human-translated data, to train a more effective parser. When computational resources are limited, a continual learning approach is introduced to minimize training time and computational memory. This maintains the parser's efficiency in previously learned tasks while adapting it to new tasks, mitigating the problem of catastrophic forgetting. Overall, the thesis provides a comprehensive set of methods to improve semantic parsing in resource-constrained conditions.Comment: PhD thesis, year of award 2023, 172 page

Isonicotinium hydrogen sulfate

The crystal structure of the title compound, C6H6NO2 +·HSO4 −, is stabilized by inter­molecular N—H⋯O and O—H⋯O hydrogen bonds

Quininium tetra­chloridozinc(II)

The asymmetric unit of the title compound {systematic name: 2-[hydr­oxy(6-meth­oxy­quinolin-1-ium-4-yl)meth­yl]-8-vinyl­quinuclidin-1-ium tetra­chlorido­zinc(II)}, (C20H26N2O2)[ZnCl4], consists of a double proton­ated quininium cation and a tetra­chloridozinc(II) anion. The ZnII ion is in a slightly distorted tetra­hedral coordination environment. The crystal structure is stabilized by inter­molecular N—H⋯Cl and O—H⋯Cl hydrogen bonds

Ethyl­enediammonium dichloro­iodide chloride

The asymmetric unit of the crystal structure of the title compound, C2H10N2 2+·Cl2I−·Cl−, contains two ethyl­ene­diammonium cations, two [ICl2]− anions and two Cl− anions, of which one cation, one [ICl2]− anion and one Cl− anion have site symmetry 2, with the mid-point of the C—C bond of the cation, the I atom of [ICl2]− anion and the Cl− anion located on the twofold rotation axes. The two independent cations show different conformations, the N—C—C—N torsion angles being 160.1 (2) and −73.1 (4)°. The crystal structure is stabilized by extensive inter­molecular N—H⋯Cl hydrogen bonding

1,4-Diazo­niabicyclo­[2.2.2]octane tetra­chloroiodate(III) chloride

In the title compound, C6H14N2 2+·Cl4I−·Cl−, the dication and the anions lie on special positions. The dication has mm2 symmetry with two bonded C atoms and the two N atoms located on a crystallographic mirror plane parallel to bc, and with a mirror plane parallel to ab passing through the mid points of the three C—C bonds. In the square-planar Cl4I− anion, two Cl atoms and the I atom are located on the mm2 axis; the other two Cl atoms are disordered over two postions of equal occupancy (0.25) across the mirror parallel to the ab plane. The Cl− anion is located on the mm2 axis. The crystal structure is stabilized by inter­molecular N—H⋯Cl hydrogen bonds

Unauthorized Use Change and Control System of China’s Industrial Buildings: Taking S District of Chongqing as an Example

This paper uses both theoretical research and empirical research in analyzing the changing scale and characteristics of spatial-temporal variations of the unauthorized change of the use of the industrial buildings in Chongqing’s S District. Through the in-depth exploration of the driving factors and mechanism of China’s unauthorized change of the use of the industrial buildings, this paper finally builds scientific and reasonable use change control mechanism for industrial buildings. It has been found through the empirical study that unauthorized use change causes great loss of state-owned land resources and serious impact on commercial real estate and leads to very baneful social consequences. Use change of industrial buildings includes five driving factors: economy, structure of land supply, laws, industry development and system. To avoid such use changes, we must improve the existing laws and regulations and vitalize the industrial building resources; optimize both land supply structure and the spatial arrangement of industrial buildings; explore to develop supervisory control system based on the building certification process and principle of rent-to-grant; construct multi-sector linked supervision system for the use change of industrial buildings; effectively use economic levers to squeeze the profit brought about by the use change of industrial buildings; and know clearly about the industry direction and settled businesses

Adversarial Convolutional Networks with Weak Domain-Transfer for Multi-sequence Cardiac MR Images Segmentation

Analysis and modeling of the ventricles and myocardium are important in the diagnostic and treatment of heart diseases. Manual delineation of those tissues in cardiac MR (CMR) scans is laborious and time-consuming. The ambiguity of the boundaries makes the segmentation task rather challenging. Furthermore, the annotations on some modalities such as Late Gadolinium Enhancement (LGE) MRI, are often not available. We propose an end-to-end segmentation framework based on convolutional neural network (CNN) and adversarial learning. A dilated residual U-shape network is used as a segmentor to generate the prediction mask; meanwhile, a CNN is utilized as a discriminator model to judge the segmentation quality. To leverage the available annotations across modalities per patient, a new loss function named weak domain-transfer loss is introduced to the pipeline. The proposed model is evaluated on the public dataset released by the challenge organizer in MICCAI 2019, which consists of 45 sets of multi-sequence CMR images. We demonstrate that the proposed adversarial pipeline outperforms baseline deep-learning methods.Comment: 9 pages, 4 figures, conferenc