Future Location Research Articles

LSTM-BEND: Predicting the Trajectories of Basketball

The capacity to comprehend and anticipate the movements of dynamic objects deployed in sports contexts is a need for autonomous systems. A basketball game is an example of one such ambiance, with its dynamic and intricate motions fueled by numerous social interactions. One such crucial part of such systems is to forecast future basketball positions to determine whether or not the ball will hit or misses the basket based on those predictions. This is a difficult task as basketball motion depends on several factors, including the stroke applied by the player on the ball, environmental factors like topology and geometry, etc., and the subject of analyzing the trajectory of basketball is one in which particularly interested. In this paper, it was concentrated on predicting basketball motion trajectories in actual basketball tournaments and the objective is to forecast basketball's future locations based on its past positions. Therefore, this paper investigates whether long short-term memory (LSTM) models can accurately anticipate the motion trajectories of basketballs in light of their performance in sequence prediction challenges. In particular, it was suggested a long-short-term memory network-integrated relation network-based bimodal exponential normal distribution (BEND) process.

A Learned Map for Places and Concepts in the Human Medial Temporal Lobe.

Distinct lines of research in both humans and animals point to a specific role of the hippocampus in both spatial and episodic memory function. The discovery of concept cells in the hippocampus and surrounding medial temporal lobe (MTL) regions suggests that the MTL maps physical and semantic spaces with a similar neural architecture. Here, we studied the emergence of such maps using MTL microwire recordings from 20 patients (9 female, 11 male) navigating a virtual environment featuring salient landmarks with established semantic meaning. We present several key findings. The array of local field potentials in the MTL contains sufficient information for above-chance decoding of subjects' instantaneous location in the environment. Closer examination revealed that as subjects gain experience with the environment the field potentials come to represent both the subjects' locations in virtual space and in high-dimensional semantic space. Similarly, we observe a learning effect on temporal sequence coding. Over time, field potentials come to represent future locations, even after controlling for spatial proximity. This predictive coding of future states, more so than the strength of spatial representations per se, is linked to variability in subjects' navigation performance. Our results thus support the conceptualization of the MTL as a memory space, representing both spatial- and nonspatial information to plan future actions and predict their outcomes.SIGNIFICANCE STATEMENT Using rare microwire recordings, we studied the representation of spatial, semantic, and temporal information in the human MTL. Our findings demonstrate that subjects acquire a cognitive map that simultaneously represents the spatial and semantic relations between landmarks. We further show that the same learned representation is used to predict future states, implicating MTL cell assemblies as the building blocks of prospective memory functions.

A probabilistic framework with the gradient-based method for multi-objective land use optimization

Land use planning seeks to outline the future location and type of development activity. The planning process should reconcile development with environmental conservation and other concerns pertaining to sustainability; hence multi-objective spatial optimization is considered an effective tool to serve this purpose. However, as the number of social, economic, and environmental objectives increases, especially when numerous spatial units exist, the curse of dimensionality becomes a serious problem, making previous methods unsuitable. In this paper, we formulate a probabilistic framework based on the gradient descent algorithm (GDA) to search for Pareto optimal solutions more effectively and efficiently. Under this framework, land use as decision parameter(s) in each cell is represented as a probability vector instead of an integer value. Thus, the objectives can be designed as differentiable functions such that the GDA can be used for multi-objective optimization. An initial experiment is conducted using simulation data to compare the GDA with the genetic algorithm, with the results showing that the GDA outperforms the genetic algorithm, especially for large-scale problems. Furthermore, the outcomes in a real-world case study of Shenzhen demonstrate that the proposed framework is capable of generating effective optimal scenarios more efficiently, rendering it a pragmatic tool for planning practices.

Rapid learning of predictive maps with STDP and theta phase precession.

The predictive map hypothesis is a promising candidate principle for hippocampal function. A favoured formalisation of this hypothesis, called the successor representation, proposes that each place cell encodes the expected state occupancy of its target location in the near future. This predictive framework is supported by behavioural as well as electrophysiological evidence and has desirable consequences for both the generalisability and efficiency of reinforcement learning algorithms. However, it is unclear how the successor representation might be learnt in the brain. Error-driven temporal difference learning, commonly used to learn successor representations in artificial agents, is not known to be implemented in hippocampal networks. Instead, we demonstrate that spike-timing dependent plasticity (STDP), a form of Hebbian learning, acting on temporally compressed trajectories known as 'theta sweeps', is sufficient to rapidly learn a close approximation to the successor representation. The model is biologically plausible - it uses spiking neurons modulated by theta-band oscillations, diffuse and overlapping place cell-like state representations, and experimentally matched parameters. We show how this model maps onto known aspects of hippocampal circuitry and explains substantial variance in the temporal difference successor matrix, consequently giving rise to place cells that demonstrate experimentally observed successor representation-related phenomena including backwards expansion on a 1D track and elongation near walls in 2D. Finally, our model provides insight into the observed topographical ordering of place field sizes along the dorsal-ventral axis by showing this is necessary to prevent the detrimental mixing of larger place fields, which encode longer timescale successor representations, with more fine-grained predictions of spatial location.

Non-Autoregressive Sparse Transformer Networks for Pedestrian Trajectory Prediction

Pedestrian trajectory prediction is an important task in practical applications such as automatic driving and surveillance systems. It is challenging to effectively model social interactions among pedestrians and capture temporal dependencies. Previous methods typically emphasized social interactions among pedestrians but ignored the temporal consistency of predictions and suffered from superfluous interactions by dense undirected graphs, resulting in a considerable deviance from reality. In addition, autoregressive approaches predicted future locations conditioning on previous predictions one by one, which would lead to error accumulation and time consuming. To address these issues, we present Non-autoregressive Sparse Transformer (NaST) networks for pedestrian trajectory prediction. Specifically, NaST models sparse spatial interactions and sparse temporal dependency via a sparse spatial transformer and a sparse temporal transformer separately. Different from previous predictions such as RNN-based approaches, the transformer decoder works in non-autoregressive pattern and predicts all the future locations at one time from a query sequence, which could avoid the error accumulation and be less computationally intensive. We evaluate our proposed method on the ETH and UCY datasets, and the experimental results show our method outperforms comparative state-of-the-art methods.

VALIDATION OF EXTENDED ELASTIC IMPEDANCE BASED ON MINIMUM ENERGY ANGLE. A CASE STUDY APPLICATION FOR OPTIMIZED PETROLEUM RESERVOIR CHARACTERIZATION

This work intends to showcase a validation of the applicability of Extended Elastic Impedance (EEI) inversion method in reservoir characterization and modeling. In order to achieve that, deterministic seismic inversion and extended elastic impedance (EEI) analysis were applied to obtain quantitative estimates of reservoir properties over the Pu field of the West African Congo basin. Optimum EEI angles corresponding to the reservoir properties were then analyzed using well logs data, together with a lithology indicator. Pre-stack seismic data were simultaneously inverted into density, acoustic and gradient impedances cubes, through model based inversion algorithm. The last two broadband inverted volumes were then projected to corresponding Chi angles proportionate to petrophysical indicators, thus resulting to two broadband EEI volumes. At well locations, the EEI versus petrophysical parameters linear trends were then applied to convert EEI volumes into porosity and shale volumes based on specified lithology. In order to generate reservoir facies distribution, minimum angle was applied based on background EEI, thus allowing for mapping of reservoir facies. In order to validate the EEI approach, a Geo-statistical model was further developed for the same field. Hence, from porosity, shale content and background EEI cubes, a comparison was made between the properties generated from the EEI and that generated based on geo-statistical method, which shows that EEI is a robust way of reservoir characterization that pinpointing favorable reservoir potentials which shall guide future drilling locations.

A genetic algorithm-based strategic planning framework for optimising accessibility and costs of general practices in Northland, New Zealand

ABSTRACT Shortage of general practitioners (GP) is a challenge worldwide, not only in Europe, but also in countries like New Zealand. Providing primary care in rural areas is especially challenging. In order to support decision makers, it is necessary to first assess the current GP coverage and then to determine different scenarios and plans for the future. In this paper, we first present a thorough overview of related literature on locating GP practices. Second, we propose an approach for assessing the GP coverage and determining future GP locations based on a genetic algorithm framework. As a use case, we have chosen the rural New Zealand region of Northland. We also perform a sensitivity analysis for the main input parameters.

Predicting future production system bottlenecks with a graph neural network approach

Predicting throughput bottlenecks has received an increasing research attention in the recent years. Several statistical and machine learning models have been developed, with the objective of identifying future bottleneck locations for more efficient and cost-saving productions. However, bottleneck prediction remains a challenging task due to the complexity of manufacturing systems and the interactive effects from many impacting factors. This paper proposed the BSTAN (bottleneck spatial-temporal attention network) – an interpretable modeling framework to improve system bottlenecks prediction capability for complex manufacturing systems by: (1) characterizing line topological features using graph representations; (2) modeling spatial-temporal relationships and interactions between stations using graph attention network with gated recurrent units. In this proposed framework, the production line is first transformed into the representation of a weighted undirected graph to depict network structural information of a production line. An attention-based graph neural network with gated recurrent units is applied to capture both the temporal trends and station interactions to predict each station’s blockage and starvation. Future bottleneck stations are identified by analyzing the forecasted blockage and starvation distribution. To evaluate the model performance, an industrial case study is performed with data collected from a one-year production at an automotive powertrain assembly line. With the proper model construction, BSTAN has outperformed both statistical and machine learning benchmark models, achieving up to 15% lower root mean square error compared to the best benchmark method in predicting overall system blockage and starvation time.

Anticipatory Saccades Towards the Future Consequences of One's Actions - an Online Eye Tracking Study.

When an action contingently yields a predictable effect, we form bi-directional action-effect associations that allow us to anticipate both the location and timing of our actions' effects. This is evident in anticipatory eye movements towards the future effect's location which are performed earlier when the effect's delay is short rather than long. Such anticipatory eye movements reflect a proactive process of effect monitoring which prepares a comparison of expected and actual effects. Here, in two online eye tracking experiments, we manipulated effect locations (spatially compatible vs. incompatible in one half) and effect delays (short vs. long) to determine whether in-laboratory effects could be reliably replicated online using participants' individual webcams. Extending prior research, we further compared irrelevant (Experiment 1) to relevant effects (response to effect feature; Experiment 2). In contrast to prior in-laboratory studies, participants anticipatorily looked towards future effects above chance only when effects were relevant. Post-experiment questions suggested that online-participants intentionally ignore irrelevant information to optimize task performance. Nevertheless, replicating in-laboratory experiments, both for relevant and irrelevant effects, participants' first saccade towards a future effect occurred earlier for the short rather than the long effect delay. Thus, we demonstrate that anticipatory eye movements reflecting a time-sensitive proactive effect monitoring process can reliably be assessed both in-laboratory as well as online. However, when investigating anticipatory saccade frequencies online, additional aspects like effect relevance have to be considered.

VECMAN: A Framework for Energy-Aware Resource Management in Vehicular Edge Computing Systems

In Vehicular Edge Computing (VEC) systems, the computing resources of connected Electric Vehicles (EV) are used to fulfill the low-latency computation requirements of vehicles. However, local execution of heavy workloads may drain a considerable amount of energy in EVs. One promising way to improve the energy efficiency is to share and coordinate computing resources among connected EVs. However, the uncertainties in the future location of vehicles make it hard to decide which vehicles participate in resource sharing and how long they share their resources so that all participants benefit from resource sharing. In this paper, we propose VECMAN, a framework for energy-aware resource management in VEC systems composed of two algorithms: (i) a resource selector algorithm that determines the participating vehicles and the duration of resource sharing period; and (ii) an energy manager algorithm that manages computing resources of the participating vehicles with the aim of minimizing the computational energy consumption. We evaluate the proposed algorithms and show that they considerably reduce the vehicles’ computational energy consumption compared to the state-of-the-art baselines. Specifically, our algorithms achieve between 7 and 18 percent energy savings compared to a baseline that executes workload locally and an average of 13 percent energy savings compared to a baseline that offloads vehicles’ workloads to RSUs.

Driver Intent Prediction and Collision Avoidance With Barrier Functions

For autonomous vehicles and driver assist systems, path planning and collision avoidance algorithms benefit from accurate predictions of future location of other vehicles and intent of their drivers. In the literature, the algorithms that provide driver intent belong to two categories: those that use physics based models with some type of filtering, and machine learning based approaches. In this paper we employ barrier functions (BF) to decide driver intent. BFs are typically used to prove safety by establishing forward invariance of an admissible set. Here, we decide if the “target” vehicle is violating one or more possibly fictitious (i.e., non-physical) barrier constraints determined based on the context provided by the road geometry. The algorithm has a very small computational footprint and better false positive and negative rates than some of the alternatives. The predicted intent is then used by a control barrier function (CBF) based collision avoidance system to prevent unnecessary interventions, for either an autonomous or human-driven vehicle.

Monocular Quasi-Dense 3D Object Tracking.

A reliable and accurate 3D tracking framework is essential for predicting future locations of surrounding objects and planning the observer's actions in numerous applications such as autonomous driving. We propose a framework that can effectively associate moving objects over time and estimate their full 3D bounding box information from a sequence of 2D images captured on a moving platform. The object association leverages quasi-dense similarity learning to identify objects in various poses and viewpoints with appearance cues only. After initial 2D association, we further utilize 3D bounding boxes depth-ordering heuristics for robust instance association and motion-based 3D trajectory prediction for re-identification of occluded vehicles. In the end, an LSTM-based object velocity learning module aggregates the long-term trajectory information for more accurate motion extrapolation. Experiments on our proposed simulation data and real-world benchmarks, including KITTI, nuScenes, and Waymo datasets, show that our tracking framework offers robust object association and tracking on urban-driving scenarios. On the Waymo Open benchmark, we establish the first camera-only baseline in the 3D tracking and 3D detection challenges. Our quasi-dense 3D tracking pipeline achieves impressive improvements on the nuScenes 3D tracking benchmark with near five times tracking accuracy of the best vision-only submission among all published methods.

Cache-Based Privacy Protection Scheme for Continuous Location Query

Users who initiate continuous location queries are prone to trajectory information leakage, and the obtained query information is not effectively utilized. To address these problems, we propose a continuous location query protection scheme based on caching and an adaptive variable-order Markov model. When a user initiates a query request, we first query the cache information to obtain the required data. When the local cache cannot satisfy the user's demand, we use a variable-order Markov model to predict the user's future query location and generate a k-anonymous set based on the predicted location and cache contribution. We perturb the location set using differential privacy, then send the perturbed location set to the location service provider to obtain the service. We cache the query results returned by the service provider to the local device and update the local cache results according to time. By comparing the experiment with other schemes, the proposed scheme in this paper reduces the number of interactions with location providers, improves the local cache hit rate, and effectively ensures the security of the users' location privacy.

School Dental Services Theoretical Model-Based on Geographic Information System in Al-Madinah, Saudi Arabia.

The study aimed to design a geographic theoretical model for school dental services (SDS) in Al-Madinah, Saudi Arabia (SA), using a geographic information system (GIS). The location of all primary public schools and the student population at each school were obtained from the General Administration of Education in Al-Madinah Al-Munawwarah Region website. The geographic modeling for SDS was analyzed using GIS according to two models. A scenario was developed to simulate the demand for dental care for the two models based on schoolchildren's estimated oral health profile. The areas with the higher number of schools; higher number of students; and dense child population as presented in the map suggest the future location of SDS. The total number of dentists required to work in SDS settings was 415 for the first model, and 277 for the second model. The suggested average number of dentists per district in the highest child population density districts is 18 dentists in the first model, compared to 14 in the second model. Establishing SDS is suggested as a solution to the persistently high prevalence of dental caries among schoolchildren in Al-Madinah and SA in general. A model was suggested for SDS with a guide of the proposed SDS locations and the number of dentists to hire for the services to meet the child population's oral health needs.

Optimal privacy protection of mobility data: a predictive approach

Location data are extensively used to provide geo-personalized contents to mobile devices users. Sharing such personal data is a major threat to privacy, with risks of re-identification or inference of sensitive information. Location data broadcasted to services can be sanitized, i.e., by adding noise to spatial coordinates. Such protection mechanisms from the literature are widely generic, e.g., not specific to a user and mobility properties. In this work, we advocate that taking into account the specificities of location data (temporal correlation, human mobility patterns, etc.) enables to gain in the privacy-utility trade-off. specifically, using future mobility prediction greatly improves privacy. We present a novel protection mechanism, based on model predictive control (MPC). The sanitized location is optimally computed so that it maximizes privacy while guaranteeing a utility loss constraint, for present and future locations. Our formulation explicitly takes into account non-constant sampling time, due to moments when no location data is broadcasted. We evaluate experimentally our control on real mobility dataset and compare to the state of the art. Results show that with knowledge of user's future mobility over a few of minutes, we can gain up to 10% of privacy compared to state of the art, while preserving data utility.

The value of extended short-term medical training placements in smaller rural and remote locations on future work location: a cohort study

ObjectivesTo investigate the effects of extended short-term medical training placements in small rural and remote communities on postgraduate work location.Design and settingCohort study of medical graduates of The University of...

Optimization of water filter station locations on polluted rivers of Jakarta

Flooding has always been one of the challenging problems Jakarta faces for decades, damaging not only the infrastructure but also the economic aspects of the city. Almost every year, significant losses are recorded in both parts, including for the residents who depend on it. One of the repressive steps for flood prevention is the placement of waste filtering stations on the rivers in multiple locations. According to the Ministry of Environment and Forestry representative for Jakarta, there are currently 28 waste filtering stations installed within Jakarta's 661.5 km2 region and 13 rivers. Continuously challenged with the increasing amount of waste, flood prevention efforts must include installing more filtering stations. Therefore, studying the optimum future station location to minimize the flooding hazard effectively becomes vital. This paper utilized the Hot Spot Spatial Analysis method to estimate several optimal areas that fit the criterion of previously built filtering stations. We obtained the local hotspot statistics for each parameter from the Getis-Ord local statistics equation. In addition, many necessary layers are also utilized, such as volume of waste filter points, population density, and flood-affected areas datasets to roughly approximate the real-world condition. This study aims to provide recommendations that can determine the optimal waste filtering points and provide a better solution for Jakarta from the problem of the harmful amount of waste in the river that could induce flooding hazards. The result shows that the waste filtering stations in the 13 major rivers in Jakarta are still not effective. This research helps to show the optimal locations suitable for constructing new waste filters.

DoTA: Unsupervised Detection of Traffic Anomaly in Driving Videos.

Video anomaly detection (VAD) has been extensively studied for static cameras but is much more challenging in egocentric driving videos where the scenes are extremely dynamic. This paper proposes an unsupervised method for traffic VAD based on future object localization. The idea is to predict future locations of traffic participants over a short horizon, and then monitor the accuracy and consistency of these predictions as evidence of an anomaly. Inconsistent predictions tend to indicate an anomaly has occurred or is about to occur. To evaluate our method, we introduce a new large-scale benchmark dataset called Detection of Traffic Anomaly (DoTA)containing 4,677 videos with temporal, spatial, and categorical annotations. We also propose a new VAD evaluation metric, called spatial-temporal area under curve (STAUC), and show that it captures how well a model detects both temporal and spatial locations of anomalies unlike existing metrics that focus only on temporal localization. Experimental results show our method outperforms state-of-the-art methods on DoTA in terms of both metrics. We offer rich categorical annotations in DoTA to benchmark video action detection and online action detection methods. The DoTA dataset has been made available at: https://github.com/MoonBlvd/Detection-of-Traffic-Anomaly.

Mobility-Aware Computation Offloading in Edge Computing Using Machine Learning

Cloudlets are resource-rich computing infrastructures of edge computing that are located at physical proximity of users to provide one-hop, high-bandwidth wireless access to additional computational resources. They enable computation offloading for user applications, which compensates for the resource limitation of user devices by providing ultra-low latency processing for their applications. Although the computation capability of user devices is dramatically augmented by offloading, spatio-temporal uncertainties due to user mobility and changes in application specifications bring the most challenging obstacles in deciding where to offload to provide minimum latency. In this paper, we focus on these challenges by designing efficient offloading approaches that take into account these uncertainties and dynamics in order to minimize the turnaround time of the applications, which is constituted by offloading latency, migration delay, and execution time. We first formulate this NP-hard problem as an integer programming model to obtain optimal offloading decisions. We tackle its intractability by designing two novel offloading approaches, called S-OAMC and G-OAMC, that fully assign applications to cloudlets by considering their expected future locations and specifications predicted by Matrix Completion, a machine learning method. S-OAMC is a sampling-based approximation dynamic programming approach that enhances scalability and obtains near-optimal solutions. G-OAMC is a fast greedy-based approach for finding low-turnaround time offloading decisions. We conduct extensive experiments to assess the performance of our proposed approaches. The results show that S-OAMC and G-OAMC lead to near-optimal turnaround time in a reasonable time, and they both obtain low migration rates.

Universal Health Coverage in Morocco: The Way to Reduce Inequalities: A Cross-sectional Study

Background: Morocco launched an appeal in 2002 to develop a fundamental law on Basic Medical Coverage. Two systems have been put in place: Compulsory Health Insurance (AMO) based on solidarity and social security contributions; and a Medical Assistance Scheme based on the principle of social protection. The objective of these systems is to achieve Universal Health Coverage (UHC) to attain equity and equality in access to health care. In the international trend, access to economic and social rights has become a significant concern in public policies. This concept, based on the value of “equity” is now essential in evaluating equal opportunities in social and health systems. More importantly, there is a need to clarify the difference between the terms equality and equity in health. In most cases, reference is made to the definition used in Anglo-Saxon literature (equity, equality, fairness). Therefore, not all inequalities are inequities. The difference between equality and equity lies in that the first term gives the result of comparison without value judgment, while the second makes a judgment that qualifies the result as fair or unfair. Our study aims to analyze the specific problems related to the healthcare policy focused on allocating the supply of care, and efforts to improve financial accessibility specifically by developing medical coverage. We will present results that reflect the availability and quality of care in Morocco and shed light on the problem of not seeking healthcare for financial, geographical, and other reasons. In addition, we will discuss the difficulties related to the use of care by Moroccan citizens. The findings of this paper can potentially inform national healthcare policy and add to the small but growing literature on this subject in Morocco. Methodology: The methodology is based on research & data taken from official institutional publications from Morocco & United Nations organizations (gray literature) and data derived from articles published in scientific journals. Results: Morocco continues to suffer from disparities in the distribution of health practitioners due to an imbalanced distribution of health infrastructure and human resources between rural and urban areas. The Health Map developed by the Ministry of Health and Social Protection in 2016 is a very good tool to monitor the distribution of public health needs in each region, plan the delivery of care, regulate spending, and consolidate regionalization policy to ensure equity in supply and access to health care. At the end of 2021, the national average ratios were 7.3 physicians and 10 beds per 10000 inhabitants. In 2018, more than 60% of the Moroccan population enjoyed basic medical coverage and the Moroccan Government is committed to reaching 100% of Universal Health Coverage (UHC) by 2030. Conclusion: The health map will make it possible to control health expenditure by allocating human and financial resources according to needs and will determine the future location of health facilities to establish equity in the offer and access to health care.