Modeling Time-Series and Spatial Data for Recommendations and Other Applications

With the research directions described in this thesis, we seek to address the critical challenges in designing recommender systems that can understand the dynamics of continuous-time event sequences. We follow a ground-up approach, i.e., first, we address the problems that may arise due to the poor quality of CTES data being fed into a recommender system. Later, we handle the task of designing accurate recommender systems. To improve the quality of the CTES data, we address a fundamental problem of overcoming missing events in temporal sequences. Moreover, to provide accurate sequence modeling frameworks, we design solutions for points-of-interest recommendation, i.e., models that can handle spatial mobility data of users to various POI check-ins and recommend candidate locations for the next check-in. Lastly, we highlight that the capabilities of the proposed models can have applications beyond recommender systems, and we extend their abilities to design solutions for large-scale CTES retrieval and human activity prediction. A significant part of this thesis uses the idea of modeling the underlying distribution of CTES via neural marked temporal point processes (MTPP). Traditional MTPP models are stochastic processes that utilize a fixed formulation to capture the generative mechanism of a sequence of discrete events localized in continuous time. In contrast, neural MTPP combine the underlying ideas from the point process literature with modern deep learning architectures. The ability of deep-learning models as accurate function approximators has led to a significant gain in the predictive prowess of neural MTPP models. In this thesis, we utilize and present several neural network-based enhancements for the current MTPP frameworks for the aforementioned real-world applications.Comment: Ph.D. Thesis (2022

Tapestry of Time and Actions: Modeling Human Activity Sequences using Temporal Point Process Flows

Human beings always engage in a vast range of activities and tasks that demonstrate their ability to adapt to different scenarios. Any human activity can be represented as a temporal sequence of actions performed to achieve a certain goal. Unlike the time series datasets extracted from electronics or machines, these action sequences are highly disparate in their nature -- the time to finish a sequence of actions can vary between different persons. Therefore, understanding the dynamics of these sequences is essential for many downstream tasks such as activity length prediction, goal prediction, next action recommendation, etc. Existing neural network-based approaches that learn a continuous-time activity sequence (or CTAS) are limited to the presence of only visual data or are designed specifically for a particular task, i.e., limited to next action or goal prediction. In this paper, we present ProActive, a neural marked temporal point process (MTPP) framework for modeling the continuous-time distribution of actions in an activity sequence while simultaneously addressing three high-impact problems -- next action prediction, sequence-goal prediction, and end-to-end sequence generation. Specifically, we utilize a self-attention module with temporal normalizing flows to model the influence and the inter-arrival times between actions in a sequence. In addition, we propose a novel addition over the ProActive model that can handle variations in the order of actions, i.e., different methods of achieving a given goal. We demonstrate that this variant can learn the order in which the person or actor prefers to do their actions. Extensive experiments on sequences derived from three activity recognition datasets show the significant accuracy boost of ProActive over the state-of-the-art in terms of action and goal prediction, and the first-ever application of end-to-end action sequence generation.Comment: Extended version of Gupta and Bedathur [arXiv:2206.05291] (SIGKDD 2022). Under review in a journa

Modeling Spatial Trajectories using Coarse-Grained Smartphone Logs

Current approaches for points-of-interest (POI) recommendation learn the preferences of a user via the standard spatial features such as the POI coordinates, the social network, etc. These models ignore a crucial aspect of spatial mobility -- every user carries their smartphones wherever they go. In addition, with growing privacy concerns, users refrain from sharing their exact geographical coordinates and their social media activity. In this paper, we present REVAMP, a sequential POI recommendation approach that utilizes the user activity on smartphone applications (or apps) to identify their mobility preferences. This work aligns with the recent psychological studies of online urban users, which show that their spatial mobility behavior is largely influenced by the activity of their smartphone apps. In addition, our proposal of coarse-grained smartphone data refers to data logs collected in a privacy-conscious manner, i.e., consisting only of (a) category of the smartphone app and (b) category of check-in location. Thus, REVAMP is not privy to precise geo-coordinates, social networks, or the specific application being accessed. Buoyed by the efficacy of self-attention models, we learn the POI preferences of a user using two forms of positional encodings -- absolute and relative -- with each extracted from the inter-check-in dynamics in the check-in sequence of a user. Extensive experiments across two large-scale datasets from China show the predictive prowess of REVAMP and its ability to predict app- and POI categories.Comment: IEEE Transactions on Big Dat

Effect of Nicotine on Planktonic and Biofilm Growth Phases of an Experiment

poster abstractTobacco and cigarette smoke increase the risk of periodontal disease, one of the most widespread human diseases. It has been established that Porphormonas gingivalis, a gram negative anaerobic bacterium, is one of the main causative agents of periodontal disease. Prior research indicates that P. gingivalis binds to Fusobacterium nucleatum in oral biofilms. It is not yet understood if nicotine, a major component of cigarette smoke, affects the growth of bacteria differently if added in the planktonic phase, defined as the primary subculture from agar to broth before the start of a biofilm formation experiment, or the biofilm phase, defined as the secondary subculture from broth culture to a microtiter plate. Therefore, the main objective of this study is to understand this methodological difference. F. nucleatum and P. gingivalis were both grown in anaerobic GasPak containers on blood agar plates. The media for primary subculture consisted of a Brain Heart Infusion (BBL) broth supplemented with 5 g/L yeast extract and 5% vitamin K & hemin serum at 37oC. F. nucleatum was subcultured in the absence of nicotine and plated on a 96 well plate to establish biofilm. P. gingivalis was subcultured in varying concentrations of nicotine and subcultured on top of the F. nucleate biofilm. Biofilm mass was analyzed using the crystal violet technique and samples were measured in a spectrophotometer at 490 nm. The results demonstrated a statistically significant increase in biofilm formation when P. gingivalis was subjected to a higher nicotine concentration in the planktonic phase in comparison to a lower nicotine concentration in the biofilm phase. This data suggests a nicotine assisted activation of receptors on the surface of P. gingivalis specific for binding to F. nucleatum. Further testing on the receptors through a biotinylation assay will confirm the results

Comparative Efficacy of Oral Triclofos in Pediatric Neuroimaging and Other Procedural Sedation: A Systematic Review and Meta-analysis

Sedation is a mandatory part of successfully performing diagnostic and therapeutic  procedures in struggling children or when lying still is a necessity. Many drugs are advocated but there is no consensus on choice of drug , route , dose etc. Practice varies by country, area,  procedure and clinician widely. Oral route is always preferred, for its low cost , safety and non requirement of expert handling by anesthetists. Triclofos is active metabolite of Chloral hydrate sedative anxiolytic. Both lost their ground with inclusion in WHO list of restricted pharmaceuticals in 2010. Unmonitored misuse of drug  was the reasons behind. However Triclofos remained in use in India, and no serious side effects were noted during one time use under medical supervision for procedural sedation in last 20 years. This Systematic review ascertains its safety and efficacy.  Protocol was registered at PROSPERO vide no CRD42021237574. Twenty four  studies with 2337 subjects were included, 18 clinical trials for safety  and efficacy both while 6 observational studies for safety only. Triclofos and oral Midazolam both appeared as preferred drugs for procedural sedation without any statistically significant difference. Triclofos was used in much higher doses in all studies varying  from 2.5 to 5 times higher than recommended dose of 20 mg/kg in Indian books. Preservative free IV midazolam preparation mixed with fruit juice was commonly used orally. No serious side effects were noted for triclofos in any study. We concluded that one time use of triclofos under medical supervision is safe and effective. &nbsp

EFFECTS OF PORPHYROMONAS GINGIVALIS TREATED WITH VARIOUS CIGARETTE CONSTITUENTS ON HUMAN UMBILICAL VEIN ENDOTHELIAL CELLS

poster abstractTobacco use affects the cardiovascular system and increases the rate of cardiovascular disease among smokers. However, the effects of tobacco on the endothelial cells that line blood vessels are not yet fully understood. Thus, the objective of this study was to examine some of the effects that a periodontal pathogen such as Porphyromonas gingivalis (P. gingivalis) treated with cigarette smoke condensate (CSC), nicotine, and dissolvable tobacco strips (DST) have on human umbilical vein endothelial cells (HUVECs). P.gingivalis was grown in an anaerobic environment at 37oC with and without CSC, DST, and nicotine. The cells and supernatants were harvested 96 hours later. A Bradford protein assay was conducted to determine the protein amounts of the cells and in the supernatant. The HUVEC will be cultured in Endothelial Basal Medium-2 and plated in 6 well plates and exposed to the P. gingivalis cells and supernatants and after 72 hours, lactate dehydrogenase (LDH) assays will be used to cytotoxicity. Non-toxic amounts of the cells and supernatants will then be used to treat HUVEC cells for 72 hours before the media is collected and analyzed for cytokine/growth factor expression by protein arrays. It is believed that the treated bacteria will increase the levels of the pro-inflammatory cytokines and growth factors expressed by the HUVECs, which could play roles in vascular diseases. The protein assays showed that only the protein amount in the supernatant from the CSC treated bacteria was decreased

INTERACTIONS OF HUMAN UMBILICAL VEIN ENDOTHELIAL CELLS WITH TOBACCO TREATED STREPTOCOCCUS MUTANS

poster abstractStreptococcus mutans (S. mutans) is a major contributor to dental caries. Previous research has shown that there is a positive relationship between smoking and dental carries, however the pathway of S. mutans growth is not yet understood. Tobacco use affects the cardiovascular system and increases the rate of cardiovascular disease among smokers. However, the effects of tobacco on the endothelial cells that line blood vessels are not yet fully understood. Thus, the objective of this study was to examine some of the effects that a periodontal pathogen such as S. mutans treated with cigarette smoke condensate (CSC) and nicotine have on human umbilical vein endothelial cells (HUVEC’s). The S. mutans was grown at 37°C and then the planktonic cells were harvested, washed with saline, and then killed with formaldehyde. To standardize the samples, they were diluted to the same OD at 600nm wavelength using a spectroscope. The HUVEC were cultured in Endothelial Basal Medium-2 and plated in 12 well plates and exposed to the P. gingivalis cells and supernatants and after 72 hours, lactate dehydrogenase (LDH) assays will be used to cytotoxicity. Non-toxic amounts of the cells and supernatants will then be used to treat HUVEC cells for 72 hours before the media is collected and analyzed for cytokine/growth factor expression by protein arrays. Second messenger signaling pathways will be analyzed with ERK and JNK antagonists and agonists to determine the pathway of up regulation of S. mutans. A better understanding of the detrimental effects that tobacco has on the underlining causes of periodontal disease can advance the quest of controlling the disease