Optimizing Information Gathering for Environmental Monitoring Applications

The goal of environmental monitoring is to collect information from the environment and to generate an accurate model for a specific phenomena of interest. We can distinguish environmental monitoring applications into two macro areas that have different strategies for acquiring data from the environment. On one hand the use of fixed sensors deployed in the environment allows a constant monitoring and a steady flow of information coming from a predetermined set of locations in space. On the other hand the use of mobile platforms allows to adaptively and rapidly choose the sensing locations based on needs. For some applications (e.g. water monitoring) this can significantly reduce costs associated with monitoring compared with classical analysis made by human operators. However, both cases share a common problem to be solved. The data collection process must consider limited resources and the key problem is to choose where to perform observations (measurements) in order to most effectively acquire information from the environment and decrease the uncertainty about the analyzed phenomena. We can generalize this concept under the name of information gathering. In general, maximizing the information that we can obtain from the environment is an NP-hard problem. Hence, optimizing the selection of the sampling locations is crucial in this context. For example, in case of mobile sensors the problem of reducing uncertainty about a physical process requires to compute sensing trajectories constrained by the limited resources available, such as, the battery lifetime of the platform or the computation power available on board. This problem is usually referred to as Informative Path Planning (IPP). In the other case, observation with a network of fixed sensors requires to decide beforehand the specific locations where the sensors has to be deployed. Usually the process of selecting a limited set of informative locations is performed by solving a combinatorial optimization problem that model the information gathering process. This thesis focuses on the above mentioned scenario. Specifically, we investigate diverse problems and propose innovative algorithms and heuristics related to the optimization of information gathering techniques for environmental monitoring applications, both in case of deployment of mobile and fixed sensors. Moreover, we also investigate the possibility of using a quantum computation approach in the context of information gathering optimization

Risk profiles and one-year outcomes of patients with newly diagnosed atrial fibrillation in India: Insights from the GARFIELD-AF Registry.

BACKGROUND: The Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) is an ongoing prospective noninterventional registry, which is providing important information on the baseline characteristics, treatment patterns, and 1-year outcomes in patients with newly diagnosed non-valvular atrial fibrillation (NVAF). This report describes data from Indian patients recruited in this registry. METHODS AND RESULTS: A total of 52,014 patients with newly diagnosed AF were enrolled globally; of these, 1388 patients were recruited from 26 sites within India (2012-2016). In India, the mean age was 65.8 years at diagnosis of NVAF. Hypertension was the most prevalent risk factor for AF, present in 68.5% of patients from India and in 76.3% of patients globally (P < 0.001). Diabetes and coronary artery disease (CAD) were prevalent in 36.2% and 28.1% of patients as compared with global prevalence of 22.2% and 21.6%, respectively (P < 0.001 for both). Antiplatelet therapy was the most common antithrombotic treatment in India. With increasing stroke risk, however, patients were more likely to receive oral anticoagulant therapy [mainly vitamin K antagonist (VKA)], but average international normalized ratio (INR) was lower among Indian patients [median INR value 1.6 (interquartile range {IQR}: 1.3-2.3) versus 2.3 (IQR 1.8-2.8) (P < 0.001)]. Compared with other countries, patients from India had markedly higher rates of all-cause mortality [7.68 per 100 person-years (95% confidence interval 6.32-9.35) vs 4.34 (4.16-4.53), P < 0.0001], while rates of stroke/systemic embolism and major bleeding were lower after 1 year of follow-up. CONCLUSION: Compared to previously published registries from India, the GARFIELD-AF registry describes clinical profiles and outcomes in Indian patients with AF of a different etiology. The registry data show that compared to the rest of the world, Indian AF patients are younger in age and have more diabetes and CAD. Patients with a higher stroke risk are more likely to receive anticoagulation therapy with VKA but are underdosed compared with the global average in the GARFIELD-AF. CLINICAL TRIAL REGISTRATION-URL: http://www.clinicaltrials.gov. Unique identifier: NCT01090362

Path efficient level set estimation for mobile sensors

The interest in using robotic sensors for monitoring spa- tial phenomena is steadily increasing. In the context of environmental analysis, operators typically focus their at- tention where measurements belong to a region of interest (e.g., when monitoring a body of water we might want to determine where the pH level is above a critical threshold). Most of the previous work in the literature represents the environmental phenomena with a Gaussian Process model, and then uses such a model to determine the best locations for measurements [3, 7]. In this paper we consider a specific scenario where a mobile platform with low computational power can continuously acquire measurements with a negli- gible cost. In this scenario, we seek to reduce the distance traveled by the mobile platform as it gathers information and to reduce the computation required by this path se- lection process. Starting from the LSE algorithm [7], we propose two novel approaches, PULSE and PULSE-batch, that exploit a new fast path selection procedure. We eval- uate the effectiveness of our approaches on two datasets: a dataset of the pH level of the water, acquired with a mobile watercraft, and a publicly available dataset that represents CO2 maps. Results show that our techniques can compute informative paths with a computation time that is an order of magnitude lower than other techniques

Orienteering-based informative path planning for environmental monitoring

The use of robotic mobile sensors for environmental monitoring applications has gained increasing attention in recent years. In this context, a common application is to determine the region of space where the analyzed phenomena is above or below a given threshold level this problem is known as level set estimation. One example is the analysis of water in a lake, where the operators might want to determine where the dissolved oxygen level is above a critical threshold value. Recent research proposes to model the spatial phenomena of interest using Gaussian Processes, and then use an informative path planning procedure to determine where to gather data. In this paper, in contrast to previous works, we consider the case where a mobile platform with low computational power can continuously acquire measurements with a negligible energy cost. This scenario imposes a change in the perspective, since now efficiency is achieved by reducing the distance traveled by the mobile platform and the computation required by this path selection process. In this paper we propose two active learning algorithms aimed at facing this issue: specifically, (i) SBOLSE casts informative path planning into an orienteering problem and (ii) PULSE that exploits a less accurate but computationally faster path selection procedure. Evaluation of our algorithms, both on a real world and a synthetic dataset show that our approaches can compute informative paths that achieve a high quality classification, while significantly reducing the travel distance and the computation time

Skeleton-Based Orienteering for Level Set Estimation

In recent years, the use of unmanned vehicles for monitoring spatial environmental phenomena has gained increasing attention. Within this context, an interesting problem is level set estimation, where the goal is to identify regions of space where the analyzed phenomena (for example the PH value in a body of water) is above or below a given threshold level. Typically, in the literature this problem is approached with techniques which search for the most interesting sampling locations to collect the desired information (i.e., locations where we can gain the most information by sampling). However, the common assumption underlying this class of approaches is that acquiring a sample is expensive (e.g., in terms of consumed energy and time). In this paper, we take a different perspective on the same problem by considering the case where a mobile vehicle can continuously acquire measurements with a negligible cost, through high rate sampling sensors. In this scenario, it is crucial to reduce the path length that the mobile platform executes to collect the data. To address this issue, we propose a novel algorithm, called Skeleton-Based Orienteering for Level Set Estimation (SBOLSE). Our approach starts from the LSE formulation introduced in [10] and formulates the level set estimation problem as an orienteering problem. This allows one to determine informative locations while considering the length of the path. To combat the complexity associated with the orienteering approach, we propose a heuristic approach based on the concept of topological skeletonization. We evaluate our algorithm by comparing it with the state of the art approaches (i.e., LSE and LSE-batch) both on a real world dataset extracted from mobile platforms and on a synthetic dataset extracted from CO2 maps. Results show that our approach achieves a near optimal classification accuracy while significantly reducing the travel distance (up to 70% w.r.t LSE and 30% w.r.t. LSE-batch)

Orienteering-based path selection for mobile sensors

In many applications the information gathering process requires to obtain measurements of the phenomena of interest in harsh or dangerous conditions (e.g., environmental monitoring applications of water in a lake or search and rescue operations in disaster response). In recent years, the interest towards robotic sensors such as Unmanned Ground Vehicles (UGVs), Unmanned Aerial Vehicles (UAVs) or Autonomous Surface Vessels (ASVs) for information gathering application is steadily increasing. In general, when using mobile robotic systems, different path selection strategies could be identified. Offline strategies rely on a predefined path for the agent that is independent from the data that the sensors read. Conversely, using online strategies, the path selection procedure is dependent on the data that has been previously collected from the sensor. In this work we show two different applications for online path selection procedures that rely on a common orienteering formulation. Specifically the contribution is to highlight the formulation of the orienteering problem in the context of information gathering through the use of mobile sensors

A Quantum Annealing Approach to Biclustering

Several problem in Artificial Intelligence and Pattern Recognition are computationally intractable due to their inherent complexity and the exponential size of the solution space. One example of such problems is biclustering, a specific clustering problem where rows and columns of a data-matrix must be clustered simultaneously. Quantum information processing could provide a viable alternative to combat such a complexity. A notable work in this direction is the recent development of the D-Wave computer, whose processor is able to exploit quantum mechanical effects in order to perform quantum annealing. The question motivating this work is whether the use of this special hardware is a viable approach to efficiently solve the biclustering problem. As a first step towards the solution of this problem, we show a feasible encoding of biclustering into the D-Wave quantum annealing hardware, and provide a theoretical analysis of its correctness

Biclustering with a quantum annealer

Several problem in Artificial Intelligence and Pattern Recognition are computationally intractable due to their inherent complexity and the exponential size of the solution space. One example of such problems is biclustering, a specific clustering problem where rows and columns of a data-matrix must be clustered simultaneously. Quantum information processing could provide a viable alternative to combat such a complexity. A notable work in this direction is the recent development of the D-Wave computer, whose processor has been designed to the purpose of solving Quadratic Unconstrained Binary Optimization (QUBO) problems. In this paper, we investigate the use of quantum annealing by providing the first QUBO model for biclustering and a theoretical analysis of its properties (correctness and complexity). We empirically evaluated the accuracy of the model on a synthetic data-set and then performed experiments on a D-Wave machine discussing its practical applicability and embedding propertie