Environmental Justice of Repetitive Flood Loss and Freeboard Mitigation in New Orleans

After the 2005 Hurricane Katrina disaster, discussions of social and racial inequity in environmental justice became more significant. Flooding is the most destructive type of natural hazard in terms of property damage and economic loss throughout the United States. However, the National Flood Insurance Program (NFIP) has continuously faced difficulty in financing itself during climate change (GAO, 2019). Many New Orleans residents are seasonal victims of both hurricane-level disaster flooding and frequent repetitive flood loss due to heavy everyday stormwater runoff. The Federal Emergency Management Agency (FEMA; 2020) recommends freeboard elevation as the best form of hazard mitigation to reduce the cost of repetitive flood loss. However, only about 10% of New Orleans structures are adequately mitigated to freeboard standards (CHART, 2021). Underlying socioeconomic and geographical vulnerabilities have amplified the flood risk for the most disadvantaged communities. Accordingly, this study examined the city\u27s latent historical inequality that has shaped its inequitable distribution of flood risk and mitigation. The study’s explanatory, sequential mixed methods identified the socioeconomic and geographical inequities that have hindered freeboard mitigation to reduce repetitive flood loss. As CHART lead graduate researcher, the research team analyzed more than 40 years of New Orleans’ NFIP flood claim data and over 140,000 properties data. Furthermore, we collected qualitative data from more than 130 stakeholders. The disparity in Katrina recovery funding between socioeconomic groups disproportionately offered economic support for freeboard mitigation to reduce repetitive flood loss. Environmental racism has created a geographical uneven resiliency between New Orleans’ affluent White and low-income Black neighborhoods. The dissertation\u27s findings identify an environmental justice approach to equitable emergency management for shared resilience in New Orleans

Recurrent Neural Networks For Accurate RSSI Indoor Localization

This paper proposes recurrent neuron networks (RNNs) for a fingerprinting indoor localization using WiFi. Instead of locating user's position one at a time as in the cases of conventional algorithms, our RNN solution aims at trajectory positioning and takes into account the relation among the received signal strength indicator (RSSI) measurements in a trajectory. Furthermore, a weighted average filter is proposed for both input RSSI data and sequential output locations to enhance the accuracy among the temporal fluctuations of RSSI. The results using different types of RNN including vanilla RNN, long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are presented. On-site experiments demonstrate that the proposed structure achieves an average localization error of $0.75$ m with $80\%$ of the errors under $1$ m, which outperforms the conventional KNN algorithms and probabilistic algorithms by approximately $30\%$ under the same test environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization, recurrent neuron network (RNN), long shortterm memory (LSTM), fingerprint-based localizatio

Enhancing Automatic Chinese Essay Scoring System from Figures-of-Speech

PACLIC 20 / Wuhan, China / 1-3 November, 200

Universal Activation Function For Machine Learning

This article proposes a Universal Activation Function (UAF) that achieves near optimal performance in quantification, classification, and reinforcement learning (RL) problems. For any given problem, the optimization algorithms are able to evolve the UAF to a suitable activation function by tuning the UAF's parameters. For the CIFAR-10 classification and VGG-8, the UAF converges to the Mish like activation function, which has near optimal performance $F_{1} = 0.9017\pm0.0040$ when compared to other activation functions. For the quantification of simulated 9-gas mixtures in 30 dB signal-to-noise ratio (SNR) environments, the UAF converges to the identity function, which has near optimal root mean square error of $0.4888 \pm 0.0032$ $\mu M$. In the BipedalWalker-v2 RL dataset, the UAF achieves the 250 reward in $961 \pm 193$ epochs, which proves that the UAF converges in the lowest number of epochs. Furthermore, the UAF converges to a new activation function in the BipedalWalker-v2 RL dataset

Analysis of Spirit of Charity Innovation District: Community Engagement, Development & Planning

The purpose of this project is to give an overview of history, background, planning process of the Spirit of Charity Innovation District and the upcoming development of the former Charity Hospital Building. Purpose of content analysis is to evaluate all forms of documents and articles available to see what major themes are discussed and carried out to get a sense of what qualities are the most important in the SCID

Enhancing Cross-lingual Transfer via Phonemic Transcription Integration

Previous cross-lingual transfer methods are restricted to orthographic representation learning via textual scripts. This limitation hampers cross-lingual transfer and is biased towards languages sharing similar well-known scripts. To alleviate the gap between languages from different writing scripts, we propose PhoneXL, a framework incorporating phonemic transcriptions as an additional linguistic modality beyond the traditional orthographic transcriptions for cross-lingual transfer. Particularly, we propose unsupervised alignment objectives to capture (1) local one-to-one alignment between the two different modalities, (2) alignment via multi-modality contexts to leverage information from additional modalities, and (3) alignment via multilingual contexts where additional bilingual dictionaries are incorporated. We also release the first phonemic-orthographic alignment dataset on two token-level tasks (Named Entity Recognition and Part-of-Speech Tagging) among the understudied but interconnected Chinese-Japanese-Korean-Vietnamese (CJKV) languages. Our pilot study reveals phonemic transcription provides essential information beyond the orthography to enhance cross-lingual transfer and bridge the gap among CJKV languages, leading to consistent improvements on cross-lingual token-level tasks over orthographic-based multilingual PLMs.Comment: 11 pages,1 figure, 7 tables. To appear in Findings of ACL 202

CoF-CoT: Enhancing Large Language Models with Coarse-to-Fine Chain-of-Thought Prompting for Multi-domain NLU Tasks

While Chain-of-Thought prompting is popular in reasoning tasks, its application to Large Language Models (LLMs) in Natural Language Understanding (NLU) is under-explored. Motivated by multi-step reasoning of LLMs, we propose Coarse-to-Fine Chain-of-Thought (CoF-CoT) approach that breaks down NLU tasks into multiple reasoning steps where LLMs can learn to acquire and leverage essential concepts to solve tasks from different granularities. Moreover, we propose leveraging semantic-based Abstract Meaning Representation (AMR) structured knowledge as an intermediate step to capture the nuances and diverse structures of utterances, and to understand connections between their varying levels of granularity. Our proposed approach is demonstrated effective in assisting the LLMs adapt to the multi-grained NLU tasks under both zero-shot and few-shot multi-domain settings.Comment: Accepted at EMNLP 2023 (Main Conference

Semi-Sequential Probabilistic Model For Indoor Localization Enhancement

This paper proposes a semi-sequential probabilistic model (SSP) that applies an additional short term memory to enhance the performance of the probabilistic indoor localization. The conventional probabilistic methods normally treat the locations in the database indiscriminately. In contrast, SSP leverages the information of the previous position to determine the probable location since the user's speed in an indoor environment is bounded and locations near the previous one have higher probability than the other locations. Although the SSP utilizes the previous location information, it does not require the exact moving speed and direction of the user. On-site experiments using the received signal strength indicator (RSSI) and channel state information (CSI) fingerprints for localization demonstrate that SSP reduces the maximum error and boosts the performance of existing probabilistic approaches by 25% - 30%