PPM level gaseous ammonia detection using laser Induced fluorescence on vapochromic coordination polymers

The detection of ammonia in parts per millions range has been challenging in sensors research and is of great importance for industrial applications. This thesis document efforts to develop and test a low-cost optical detection system for ppm level ammonia measurements utilizing a Vapochromic Coordination Polymer (VCP) Zn[Au(CN)2]2 as the sensing material. Upon high concentration ammonia exposure, the polymer’s fluorescent peak under near-UV stimulation undergoes a spectral shift from 470nm to 530nm, while the intensity increases by 3~4X. At ammonia concentrations < 1000ppm, the spectral shift becomes hidden within the overall changing fluorescent spectrum shape so simple detection methods do not work. The key point in this analysis is to note the way the spectrum changes in each wavelength bins varies in different ammonia concentration exposures. We then developed two customized spectral processing techniques named Spectral Region Subtraction (SRS) method and Sum of Integrated Emissions (SIE) method to characterize hidden changes in spectral shape for concentrations < 1000ppm. Both methods give excellent sensitivity between 0 – 50 ppm and > 300 ppm. For wide-range concentration detection, a combination of two metrics have to be used together

Whole-Chain Recommendations

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems. In practical recommendation sessions, users will sequentially access multiple scenarios, such as the entrance pages and the item detail pages, and each scenario has its specific characteristics. However, the majority of existing RL-based recommender systems focus on optimizing one strategy for all scenarios or separately optimizing each strategy, which could lead to sub-optimal overall performance. In this paper, we study the recommendation problem with multiple (consecutive) scenarios, i.e., whole-chain recommendations. We propose a multi-agent RL-based approach (DeepChain), which can capture the sequential correlation among different scenarios and jointly optimize multiple recommendation strategies. To be specific, all recommender agents (RAs) share the same memory of users' historical behaviors, and they work collaboratively to maximize the overall reward of a session. Note that optimizing multiple recommendation strategies jointly faces two challenges in the existing model-free RL model - (i) it requires huge amounts of user behavior data, and (ii) the distribution of reward (users' feedback) are extremely unbalanced. In this paper, we introduce model-based RL techniques to reduce the training data requirement and execute more accurate strategy updates. The experimental results based on a real e-commerce platform demonstrate the effectiveness of the proposed framework.Comment: 29th ACM International Conference on Information and Knowledge Managemen

Bridge Diffusion Model: bridge non-English language-native text-to-image diffusion model with English communities

Text-to-Image generation (TTI) technologies are advancing rapidly, especially in the English language communities. However, English-native TTI models inherently carry biases from English world centric training data, which creates a dilemma for development of other language-native TTI models. One common choice is fine-tuning the English-native TTI model with translated samples from non-English communities. It falls short of fully addressing the model bias problem. Alternatively, training non-English language native models from scratch can effectively resolve the English world bias, but diverges from the English TTI communities, thus not able to utilize the strides continuously gaining in the English TTI communities any more. To build non-English language native TTI model meanwhile keep compatability with the English TTI communities, we propose a novel model structure referred as "Bridge Diffusion Model" (BDM). The proposed BDM employs a backbone-branch network structure to learn the non-English language semantics while keep the latent space compatible with the English-native TTI backbone, in an end-to-end manner. The unique advantages of the proposed BDM are that it's not only adept at generating images that precisely depict non-English language semantics, but also compatible with various English-native TTI plugins, such as different checkpoints, LoRA, ControlNet, Dreambooth, and Textual Inversion, etc. Moreover, BDM can concurrently generate content seamlessly combining both non-English native and English-native semantics within a single image, fostering cultural interaction. We verify our method by applying BDM to build a Chinese-native TTI model, whereas the method is generic and applicable to any other language

Visualizing the impact of Covid-19 vaccine passports on pedestrian access to metro stations in Hong Kong

Pedestrian infrastructures in Hong Kong enable multilevel city life in a vertical metropolis plagued by land scarcity. Public spaces integrated into pedestrian networks play an indispensable role in neighbourhood accessibility. We visualize the impact of the Covid-19 vaccine passport (VP) restrictions on the use of public space on pedestrian accessibility to all 97 metro stations in Hong Kong. Pedestrians without a vaccine passport (PwoVP) need to walk significantly longer alternative routes. Specifically, VP-related access restrictions to indoor walkways have doubled the shortest travel time for PwoVP and a 50% reduction in accessibility of two-thirds of stations