Public Participation in Municipal Budget Decision Process: City of Toronto’s 2011 Core Service Review

This paper examines the impact of public participation on decision-making, with a specific focus on the City of Toronto’s 2011 Core Service Review. It uses both qualitative and quantitative data gathered from the case. Quantitative data is collected through the feedback forms the City used for public consultation during the Core Service Review, while qualitative data is collected from both the feedback forms and the roundtable discussions held across Toronto in 2011. The findings reveal that, from a process perspective, government efforts were directed at enlarging participation through a well-designed mechanism and project promotion, while the impact analysis found that public input indicated a strong willingness to pay more in order to maintain or increase a service

Scaled frequency-dependent transport in the mesoscopically phase-separated colossal magnetoresistive manganite La_{0.625-y}Pr_yCa_{0.375}MnO_3

We address the issue of massive phase separation (PS) in manganite family of doped Mott insulators through ac conductivity measurements on La$_{0.625-y}$Pr$_{y}$Ca$_{0.375}$MnO$_{3}$ (0.375 $\leq$ y $\leq$ 0.275), and establish applicability of the scaling theory of percolation in the critical regime of the PS. Measurements of dc resistivity, magnetization (M(T)) and electron diffraction show incomplete growth of a ferromagnetic (FM) metallic component on cooling the high temperature charge ordered (CO) phase well below Curie temperature. The impedance $\mid$Z(T,f)$\mid$ measured over a frequency (f) range of 10 Hz to 10 MHz in the critical regime follows a universal scaling of the form $\approx$ R(T,0)g(f$\xi^{2+\theta}$) with $\theta$ $\approx$ 0.86 and the normalized correlation length varying from 1 to 4, suggesting anomalous diffusion of holes in percolating FM clusters.Comment: 12 pages and 5 figure

Safety Study Related to Hydrogen Leakage from Fuel Cell Systems

The main challenge for the wide spread use of hydrogen in fuel cell systems is the safety concerns due to its ease of leaking, low-energy ignition, large flammability range, high buoyancy and diffusion rate in air. To alleviate concern of explosion during experiments, scientists are using helium as a stimulant for hydrogen safety studies. However, the equivalent behavior between the two gases only relies on numerical or experimental results, and the similarity is not connected by a theoretical correlation. This thesis assesses similarity relations using helium for hydrogen studies and develops a theoretical helium plume model. Meanwhile, a case study of leakage in fuel cell vehicles is simulated by Computational Fluid Dynamics (CFD). The accuracy of three different correlations, i.e., equal volumetric flow rate, equal buoyancy and equal concentration between helium and hydrogen was compared by CFD simulations validated by helium experiment in a 1/4 sub-scale residential garage model. The accuracy of these different methods at different leakage rate, stage of release, ventilation method and location was discussed. An updated theoretical helium plume model was validated by PIV (Particle Image Velocimetry) experiment and CFD. It is found that the new model could be used in estimating the plume size and velocity. In the case study of hydrogen leakage inside a FCV (Fuel Cell Vehicle), ventilation and sunroof show critical effect to reduce the level of hydrogen concentration accumulation

MAPS-KB: A Million-scale Probabilistic Simile Knowledge Base

The ability to understand and generate similes is an imperative step to realize human-level AI. However, there is still a considerable gap between machine intelligence and human cognition in similes, since deep models based on statistical distribution tend to favour high-frequency similes. Hence, a large-scale symbolic knowledge base of similes is required, as it contributes to the modeling of diverse yet unpopular similes while facilitating additional evaluation and reasoning. To bridge the gap, we propose a novel framework for large-scale simile knowledge base construction, as well as two probabilistic metrics which enable an improved understanding of simile phenomena in natural language. Overall, we construct MAPS-KB, a million-scale probabilistic simile knowledge base, covering 4.3 million triplets over 0.4 million terms from 70 GB corpora. We conduct sufficient experiments to justify the effectiveness and necessity of the methods of our framework. We also apply MAPS-KB on three downstream tasks to achieve state-of-the-art performance, further demonstrating the value of MAPS-KB.Comment: Accepted to AAAI 202

Language Models as Knowledge Embeddings

Knowledge embeddings (KE) represent a knowledge graph (KG) by embedding entities and relations into continuous vector spaces. Existing methods are mainly structure-based or description-based. Structure-based methods learn representations that preserve the inherent structure of KGs. They cannot well represent abundant long-tail entities in real-world KGs with limited structural information. Description-based methods leverage textual information and language models. Prior approaches in this direction barely outperform structure-based ones, and suffer from problems like expensive negative sampling and restrictive description demand. In this paper, we propose LMKE, which adopts Language Models to derive Knowledge Embeddings, aiming at both enriching representations of long-tail entities and solving problems of prior description-based methods. We formulate description-based KE learning with a contrastive learning framework to improve efficiency in training and evaluation. Experimental results show that LMKE achieves state-of-the-art performance on KE benchmarks of link prediction and triple classification, especially for long-tail entities.Comment: This revision corrects some texts after fixing a data leakage issu

Activation-Induced T Helper Cell Death Contributes to Th1/Th2 Polarization following Murine Schistosoma japonicum Infection

In chronic infectious diseases, such as schistosomiasis, pathogen growth and immunopathology are affected by the induction of a proper balanced Th1/Th2 response to the pathogen and by antigen-triggered activation-induced T cell death. Here, by using S. japonicum infection or schistosome antigens-immunized mouse model, or antigens in vitro stimulation, we report that during the early stage of S. japonicum infection, nonegg antigens trigger Th2 cell apoptosis via the granzyme B signal pathway, contributing to Th1 polarization, which is thought to be associated with worm clearance and severe schistosomiasis. Meanwhile, after the adult worms lay their eggs, the egg antigens trigger Th1 cell apoptosis via the caspase pathway, contributing to Th2 polarization, which is associated with mild pathology and enhanced survival of both worms and their hosts. Thus, our study suggests that S. japonicum antigen-induced Th1 and Th2 cell apoptosis involves the Th1/Th2 shift and favorites both hosts and parasites