Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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An Introduction To The Theory Of Point Processes

Delay aware energy efficient reliable routing for data transmission in heterogeneous mobile sink wireless sensor network

Mobile sinks were introduced in wireless sensor networks (WSNs) to mitigate the infamous hotspot problem. However, routing in mobile-sink-based WSNs requires frequent updation of sink location information to all the sensor nodes; which is an energy-expensive process for resource-constrained WSNs. Therefore, it is required to develop a green routing protocol that can minimize the energy overhead in sink location updation as well as reduce the data delivery delay. This article proposes a virtual-infrastructure-based delay-aware green routing protocol (DGRP) that creates multiple rings in the sensor field and limits the updation of mobile sink location information to the nodes belonging to the rings only. Simulation results show that DGRP outperforms existing routing protocols in terms of energy consumption and throughput. In addition to this, DGRP results in $\approx 26$ %, $\approx 39$ %, and $\approx 35$ % improvement in data delivery delay for a varying number of sensor nodes, sink speeds, and network sizes, respectively, when compared with the state of the art.

Delay-Aware Green Routing for Mobile-Sink-Based Wireless Sensor Networks

In Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs), there are two major factors which degrade the performance of the network. One is the void hole which occurs in a particular region due to unavailability of forwarder nodes. The other is the presence of energy hole which occurs due to imbalanced data traffic load on intermediate nodes. Therefore, an optimum transmission strategy is required to maximize the network lifespan via hole alleviation. In this regard, we propose a heterogeneous network solution that is capable to balance energy dissipation among network nodes. In addition, the divide and conquer approach is exploited to evenly distribute number of transmissions over various network areas. An efficient forwarder node selection is performed to alleviate coverage and energy holes. Linear optimization is performed to validate the effectiveness of our proposed work in term of energy minimization. Furthermore, simulations are conducted to show that our claims are well grounded. Results show the superiority of our work as compared to the baseline scheme in terms of energy consumption and network lifetime.

/pdf/lifetime-maximization-via-hole-alleviation-in-iot-enabling-3bld5jzldz.pdf

Lifetime Maximization via Hole Alleviation in IoT Enabling Heterogeneous Wireless Sensor Networks.

The location check-ins of users through various location-based services such as Foursquare, Twitter and Facebook Places, generate large traces of geo-tagged events. These event-traces often manifest in hidden (possibly overlapping) communities of users with similar interests. Inferring these implicit communities is crucial for forming user profiles for improvements in recommendation and prediction tasks. Given only time-stamped geo-tagged traces of users, can we find out these implicit communities, and characteristics of the underlying influence network? Can we use this network to improve the next location prediction task? In this paper, we focus on the problem of community detection as well as capturing the underlying diffusion process. We propose CoLAB, based on spatio-temporal point processes for information diffusion in continuous time but discrete space of locations. It simultaneously models the implicit communities of users based on their check-in activities, without making use of their social network connections. CoLAB captures the semantic features of the location, user-to-user influence along with spatial and temporal preferences of users. The latent community of users and model parameters are learnt through stochastic variational inference. To the best of our knowledge, this is the first attempt at jointly modeling the diffusion process with activity-driven implicit communities. We demonstrate CoLAB achieves upto 27% improvements in location prediction task over recent deep point-process based methods on geo-tagged event traces collected from Foursquare check-ins.

Modeling Implicit Communities from Geo-Tagged Event Traces Using Spatio-Temporal Point Processes

In wireless sensor networks (WSNs), sink mobility requires frequent advertisement of current location of sink across the network which may introduce an overhead in terms of traffic load, collision, and energy consumption. Hence, an energy efficient routing protocol is required to minimize these overheads for reliable data delivery in the network. This paper proposes a load balanced ring routing (LBRR) protocol for the large scale heterogeneous sensor networks with sink mobility support. The design objective of proposed protocol is to balance the traffic load towards the mobile sink in a large densely deployed network. The simulation results show that the proposed protocol is an energy efficient routing protocol in case of heavy traffic load. The packet drop ratio is significantly reduced as compared to benchmark ring routing protocol by 84.36%, 82.75%, 83.5% and 82.9% when sink mobility is 1, 5, 10 and 15 km#x002F;h respectively.

LBRR: Load Balanced Ring Routing Protocol for Heterogeneous Sensor Networks with Sink Mobility

There exists a high variability in mobility data volumes across different regions, which deteriorates the performance of spatial recommender systems that rely on region-specific data. In this paper, we propose a novel transfer learning framework called Reformd, for continuous-time location prediction for regions with sparse checkin data. Specifically, we model user-specific checkin-sequences in a region using a marked temporal point process (MTPP) with normalizing flows to learn the inter-checkin time and geo-distributions. Later, we transfer the model parameters of spatial and temporal flows trained on a data-rich origin region for the next check-in and time prediction in a target region with scarce checkin data. We capture the evolving region-specific checkin dynamics for MTPP and spatial-temporal flows by maximizing the joint likelihood of next checkin with three channels (1) checkin-category prediction, (2) checkin-time prediction, and (3) travel distance prediction. Extensive experiments on different user mobility datasets across the U.S. and Japan show that our model significantly outperforms state-of-the-art methods for modeling continuous-time sequences. Moreover, we also show that Reformd can be easily adapted for product recommendations i.e., sequences without any spatial component.

https://dl.acm.org/doi/pdf/10.1145/3459637.3482169

Region Invariant Normalizing Flows for Mobility Transfer

Any human activity can be represented as a temporal sequence of actions performed to achieve a certain goal. Unlike machine-made time series, these action sequences are highly disparate as the time taken to finish a similar action might vary between different persons. Therefore, understanding the dynamics of these sequences is essential for many downstream tasks such as activity length prediction, goal prediction, etc. Existing neural approaches that model an activity sequence are either limited to visual data or are task-specific, 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. Moreover, for time-sensitive prediction, we perform an early detection of sequence goal via a constrained margin-based optimization procedure. This in-turn allows ProActive to predict the sequence goal using a limited number of 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.

/pdf/proactive-self-attentive-temporal-point-process-flows-for-n43fm4bm.pdf

ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences

Recent developments in predictive modeling using marked temporal point processes (MTPPs) have enabled an accurate characterization of several real-world applications involving continuous-time event sequences (CTESs). However, the retrieval problem of such sequences remains largely unaddressed in literature. To tackle this, we propose NEUROSEQRET which learns to retrieve and rank a relevant set of continuous-time event sequences for a given query sequence, from a large corpus of sequences. More specifically, NEUROSEQRET first applies a trainable unwarping function on the query sequence, which makes it comparable with corpus sequences, especially when a relevant query-corpus pair has individually different attributes. Next, it feeds the unwarped query sequence and the corpus sequence into MTPP guided neural relevance models. We develop two variants of the relevance model which offer a tradeoff between accuracy and efficiency. We also propose an optimization framework to learn binary sequence embeddings from the relevance scores, suitable for the locality-sensitive hashing leading to a significant speedup in returning top-K results for a given query sequence. Our experiments with several datasets show the significant accuracy boost of NEUROSEQRET beyond several baselines, as well as the efficacy of our hashing mechanism.

Vinayak Gupta

Papers

Modeling Implicit Communities from Geo-Tagged Event Traces Using Spatio-Temporal Point Processes

LBRR: Load Balanced Ring Routing Protocol for Heterogeneous Sensor Networks with Sink Mobility

Region Invariant Normalizing Flows for Mobility Transfer

ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences

Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event Sequences