Past Trajectories Research Articles

BT-Tree: A Reinforcement Learning Based Index for Big Trajectory Data

With the increasing availability of trajectory data, it is important to have good indexes to facilitate query processing. In this work, we propose BT-Tree, which is built through a recursive bi-partitioning approach, for the processing of range and KNN queries for past trajectory data. We first propose a cost function based method (CFBM) to build the BT-Tree. Specifically, we design a novel cost function, which incorporates the characteristics of both the data and historical query workload, to decide how to partition a BT-Tree node. Then we propose a reinforcement learning (RL) based method to address CFBM's limitations, such as making locally optimal decisions that may lead to global suboptimality. Experiments on three real datasets with up to 800 million data points show that the CFBM generally outperforms the baselines in terms of query processing time and the RL based method consistently outperforms the baselines and has more significant advantages on larger datasets.

MORE MILK, FEWER FARMS, and REGIONAL CONCENTRATION: MAPPING TRANSFORMATIONS IN CALIFORNIA’S DAIRY INDUSTRY

ABSTRACT The transformation of California’s dairy industry is a prime example of structural change in agriculture. The state has been on the leading edge of changes in the geography of food and agriculture for 150 years, and many characteristics of modern large-scale agriculture emerged—or reached their culmination—in California. We examine the spatial transformation of dairy production in California as a window into the potential futures for the dairy industry both within California and elsewhere. We use data from county agricultural reports and USDA Censuses of Agriculture data to demonstrate, through a series of original maps and animations, the dramatic increase in production of milk, the rapid fall in the number of dairies, and the spatial redistribution of the industry over the past 40 years. These visualizations reveal illustrative spatial patterns and insights into rapid changes in the geography of dairy production—and agriculture more generally—within California. Based on these visualizations and analyses, we propose a framework for understanding the intersecting phenomena of dairy transformations in California: regional concentration, industry consolidation, and farm-level intensification. In explaining how these processes are nested, overlapping, and multiscalar, we offer an account of past and current trajectories, while examining the applicability and implications of these findings for other dairy-producing regions into the future.

Health impact of policies to reduce agriculture-related air pollutants in the UK: The relative contribution of change in PM2.5 exposure and diets to morbidity and mortality

Food systems can negatively impact health outcomes through unhealthy diets and indirectly through ammonia emissions originating from agricultural production, which contribute to air pollution and consequently cardiovascular and respiratory health outcomes. In the UK, ammonia emissions from agriculture have not declined in the same way as other air pollutants in recent years. We applied a novel integrated modelling framework to assess the health impacts from six ammonia reduction scenarios to 2030: two agriculture scenarios – a “Current trends” scenario projecting current mitigation measures to reflect a low ambition future, and “High ambition mitigation” based on measures included in the Climate Change Committee's Balanced Pathway to Net Zero; three dietary scenarios – a “Business as usual” based on past trajectories, “Fiscal” applying 20% tax on meat and dairy and 20% subsidy on fruit and vegetables, and “Innovation” applying a 30% switch to plant-based alternatives; one combination of “High ambition mitigation” and “Innovation”. Compared to “Current trends”, the “High ambition mitigation” scenario would result in a reduction in premature mortality of 13,000, increase life years by 90,000 and reduce incidence of respiratory diseases by 270,000 cases over a 30 year period. Compared to Business as Usual, the dietary scenarios would reduce the number of premature deaths by 65,000 and 550,000–600,000 life years gained over 30 years, with most of the benefits gained by reducing ischemic heart disease (incidence reduction: 190,000). The “High ambition combination” would lead to 67,000 deaths averted, 536,000 incidence reductions and 650,000 life-years gained. For all scenarios, older age groups and those living in lower income households would experience the greatest benefits, because of higher underlying mortality rates or higher levels of risk factors. Our study shows that combining mitigation policies targeting agricultural production systems with diet-related policies would lead to significant reductions in emissions and improvement in health outcomes.

Deep estimation of the intensity and timing of natural selection from ancient genomes.

Leveraging past allele frequencies has proven to be key for identifying the impact of natural selection across time. However, this approach suffers from imprecise estimations of the intensity (s) and timing (T) of selection, particularly when ancient samples are scarce in specific epochs. Here, we aimed to bypass the computation of allele frequencies across arbitrarily defined past epochs and refine the estimations of selection parameters by implementing convolutional neural networks (CNNs) algorithms that directly use ancient genotypes sampled across time. Using computer simulations, we first show that genotype-based CNNs consistently outperform an approximate Bayesian computation (ABC) approach based on past allele frequency trajectories, regardless of the selection model assumed and the number of available ancient genotypes. When applying this method to empirical data from modern and ancient Europeans, we replicated the reported increased number of selection events in post-Neolithic Europe, independently of the continental subregion studied. Furthermore, we substantially refined the ABC-based estimations of s and T for a set of positively and negatively selected variants, including iconic cases of positive selection and experimentally validated disease-risk variants. Our CNN predictions support a history of recent positive and negative selection targeting variants associated with host defence against pathogens, aligning with previous work that highlights the significant impact of infectious diseases, such as tuberculosis, in Europe. These findings collectively demonstrate that detecting the footprints of natural selection on ancient genomes is crucial for unravelling the history of severe human diseases.

Noisy Circumnutations Facilitate Self-Organized Shade Avoidance in Sunflowers

Circumnutations are widespread in plants and typically associated with exploratory movements; however, a quantitative understanding of their role remains elusive. In this study we report, for the first time, the role of noisy circumnutations in facilitating an optimal growth pattern within a crowded group of mutually shading plants. We revisit the problem of self-organization observed for sunflowers, mediated by shade response interactions. Our analysis reveals that circumnutation movements conform to a bounded random walk characterized by a remarkably broad distribution of velocities, covering 3 orders of magnitude. In motile animal systems such wide distributions of movement velocities are frequently identified with enhancement of behavioral processes, suggesting that circumnutations may serve as a source of functional noise. To test our hypothesis, we developed a Langevin-type parsimonious model of interacting growing disks, informed by experiments, successfully capturing the characteristic dynamics of individual and multiple interacting plants. Employing our simulation framework we examine the role of circumnutations in the system, and find that the observed breadth of the velocity distribution represents a sharp transition in the force-noise ratio, conferring advantageous effects by facilitating exploration of potential configurations, leading to an optimized arrangement with minimal shading. These findings represent the first report of functional noise in plant movements and establish a theoretical foundation for investigating how plants navigate their environment by employing computational processes such as task-oriented processes, optimization, and active sensing. Since plants move by growing, space and time are coupled, and dynamics of self-organization lead to emergent 3D patterns. As such, this system provides conceptual insight for other interacting growth-driven systems such as fungal hyphae, neurons and self-growing robots, as well as active matter systems where agents interact with past trajectories of their counterparts, such as stigmergy in social insects. This foundational insight has implications in statistical physics, ecological dynamics, agriculture, and even swarm robotics. Published by the American Physical Society 2024

Impact of Perception Errors in Vision-Based Detection and Tracking Pipelines on Pedestrian Trajectory Prediction in Autonomous Driving Systems.

Pedestrian trajectory prediction is crucial for developing collision avoidance algorithms in autonomous driving systems, aiming to predict the future movement of the detected pedestrians based on their past trajectories. The traditional methods for pedestrian trajectory prediction involve a sequence of tasks, including detection and tracking to gather the historical movement of the observed pedestrians. Consequently, the accuracy of trajectory prediction heavily relies on the accuracy of the detection and tracking models, making it susceptible to their performance. The prior research in trajectory prediction has mainly assessed the model performance using public datasets, which often overlook the errors originating from detection and tracking models. This oversight fails to capture the real-world scenario of inevitable detection and tracking inaccuracies. In this study, we investigate the cumulative effect of errors within integrated detection, tracking, and trajectory prediction pipelines. Through empirical analysis, we examine the errors introduced at each stage of the pipeline and assess their collective impact on the trajectory prediction accuracy. We evaluate these models across various custom datasets collected in Taiwan to provide a comprehensive assessment. Our analysis of the results derived from these integrated pipelines illuminates the significant influence of detection and tracking errors on downstream tasks, such as trajectory prediction and distance estimation.

A Longitudinal Tree-Based Framework for Lapse Management in Life Insurance

Developing an informed lapse management strategy (LMS) is critical for life insurers to improve profitability and gain insight into the risk of their global portfolio. Prior research in actuarial science has shown that targeting policyholders by maximising their individual customer lifetime value is more advantageous than targeting all those likely to lapse. However, most existing lapse analyses do not leverage the variability of features and targets over time. We propose a longitudinal LMS framework, utilising tree-based models for longitudinal data, such as left-truncated and right-censored (LTRC) trees and forests, as well as mixed-effect tree-based models. Our methodology provides time-informed insights, leading to increased precision in targeting. Our findings indicate that the use of longitudinally structured data significantly enhances the precision of models in predicting lapse behaviour, estimating customer lifetime value, and evaluating individual retention gains. The implementation of mixed-effect random forests enables the production of time-varying predictions that are highly relevant for decision-making. This paper contributes to the field of lapse analysis for life insurers by demonstrating the importance of exploiting the complete past trajectory of policyholders, which is often available in insurers’ information systems but has yet to be fully utilised.

Reconstruction of single-cell lineage trajectories and identification of diversity in fates during the epithelial-to-mesenchymal transition

Exploring the complexity of the epithelial-to-mesenchymal transition (EMT) unveils a diversity of potential cell fates; however, the exact timing and mechanisms by which early cell states diverge into distinct EMT trajectories remain unclear. Studying these EMT trajectories through single-cell RNA sequencing is challenging due to the necessity of sacrificing cells for each measurement. In this study, we employed optimal-transport analysis to reconstruct the past trajectories of different cell fates during TGF-beta-induced EMT in the MCF10A cell line. Our analysis revealed three distinct trajectories leading to low EMT, partial EMT, and high EMT states. Cells along the partial EMT trajectory showed substantial variations in the EMT signature and exhibited pronounced stemness. Throughout this EMT trajectory, we observed a consistent downregulation of the EED and EZH2 genes. This finding was validated by recent inhibitor screens of EMT regulators and CRISPR screen studies. Moreover, we applied our analysis of early-phase differential gene expression to gene sets associated with stemness and proliferation, pinpointing ITGB4, LAMA3, and LAMB3 as genes differentially expressed in the initial stages of the partial versus high EMT trajectories. We also found that CENPF, CKS1B, and MKI67 showed significant upregulation in the high EMT trajectory. While the first group of genes aligns with findings from previous studies, our work uniquely pinpoints the precise timing of these upregulations. Finally, the identification of the latter group of genes sheds light on potential cell cycle targets for modulating EMT trajectories.

Self-avoidance dominates the selection of hippocampal replay.

Spontaneous neural activity sequences are generated by the brain in the absence of external input 1-12 , yet how they are produced remains unknown. During immobility, hippocampal replay sequences depict spatial paths related to the animal's past experience or predicted future 13 . By recording from large ensembles of hippocampal place cells 14 in combination with optogenetic manipulation of cortical input in freely behaving rats, we show here that the selection of hippocampal replay is governed by a novel self-avoidance principle. Following movement cessation, replay of the animal's past path is strongly avoided, while replay of the future path predominates. Moreover, when the past and future paths overlap, early replays avoid both and depict entirely different trajectories. Further, replays avoid self-repetition, on a shorter timescale compared to the avoidance of previous behavioral trajectories. Eventually, several seconds into the stopping period, replay of the past trajectory dominates. This temporal organization contrasts with established and recent predictions 9,10,15,16 but is well-recapitulated by a symmetry-breaking attractor model of sequence generation in which individual neurons adapt their firing rates over time 26-35 . However, while the model is sufficient to produce avoidance of recently traversed or reactivated paths, it requires an additional excitatory input into recently activated cells to produce the later window of past-dominance. We performed optogenetic perturbations to demonstrate that this input is provided by medial entorhinal cortex, revealing its role in maintaining a memory of past experience that biases hippocampal replay. Together, these data provide specific evidence for how hippocampal replays are generated.

Future land use and land cover simulations with cellular automata-based artificial neural network: A case study over Delhi megacity (India)

According to United Nations projections, future global urban growth will mostly occur in Asian megacities. In this study, a Cellular Automata based Artificial Neural Network (CA-ANN) model is used to simulate the future land use and land cover (LULC) over Delhi megacity (India). Delhi, projected to become the world's most populated city by 2030, is an example of a data poor city in Asia, having millions of climate vulnerable people. The CA-ANN model of Modules for Land Change Simulation (MOLUSCE), an open-source plugin, is first tested to simulate the LULC for 2009. Based on good validation results-structural similarity (SSIM; 0.8288), overall accuracy (79.78 %), kappa index of agreement (KIA; 77.25 %), and minimum validation overall error (0.0379), the same model set-up is used to carry out LULC simulation for 2030. This model is found to be simple, efficient, and computationally less expensive tool, and can be used to model future LULCs with a minimal set of inputs, a constraint often found in data poor cities. Results show continued increase in built-up area from 38.3 % (2014) to 53.8 % (2030), at the expense of cultivable areas, forests, and wastelands. The study incorporates past and future LULC change trajectories to highlight the changing LULC dynamics of the megacity from 1977 to 2030. Rate of urban sprawl, calculated using compound annual growth rate (CAGR) is projected to be 2.51 % for 2014–2030, substantially higher than the estimates for 2006–2014 (0.62 %). Further, the past and future urban growth patterns for Delhi are found to mimic other big Asian cities. The database generated from the present study has wide applicability for scientific research community, governmental bodies, profit and non-profit organizations for topics concerning-future urban climate research, climate risk and adaption policy frameworks, climate finance budgeting, future town planning, etc.

A bibliometric review of sustainability and firm performance

What is the past, present and future trajectories of sustainability and firm performance? The study identified seminal works, current and future trends relevant to sustainability and firm performance. The research employed bibliometric analysis tools such as co-citation, bibliographic coupling, and co-occurrence analysis combined with enrichment tools. Utilizing a dataset that spanned from 1998 to 2023, it used Biblioshiny and analyzed 735 articles from Scopus. The research results showed a growth rate of 23 percent and an average of 37 citations per article. The research established stakeholder theory and theory of the firm as foundational pillar and latest developments focused on finance, corporate social responsibility, environmental assessment, and sustainability. Emerging trends involved sustainability performance, disclosures, internal factors, sustainability and firm performance, corporate social responsibility and governance, sustainability reporting and stakeholder engagement, and costs. Researchers and practitioners can utilize these findings to direct their work and contribute to the ongoing discourse in sustainability and firm performance research.

Structural Inequalities in Namibia and South Africa: A Critical Social Work Perspective

Namibia and South Africa share land and maritime borders and the interrelated structural challenges of poverty and socio-economic inequalities amidst progressive economic growth that are rooted in their apartheid past and contemporary economic development trajectories. Such inequalities are defined along racial, class, gender and other social locations that either grant privilege, power and access to socio-economic opportunities or result in marginalisation, oppression and resource deprivation. The rationale for this article is linked to the historic call for social work to intensify efforts in promoting social and economic equality. Despite Namibia and South Africa’s geographical proximity and their intertwined histories, there is a dearth of social work studies that offer a comparative critical social work perspective on structural inequalities in these former apartheid strongholds. While social work should actively engage in contesting the structural contradictions of poverty and inequalities amidst abundant resources, the reality is often that of the uncritical acceptance of existing socio-political inequalities, such that the profession’s enunciated commitment to social justice becomes perimetric. Thus, the article argues for the inclusion of critical social work approaches in social work education and practice against a backdrop of ideological divides, political trends and contextual factors that limit social workers’ critical and structural level engagement.

Three waves of critical terrorism studies: agenda-setting, elaboration, problematisation

ABSTRACT The past twenty years have witnessed critical terrorism studies (CTS) become an increasingly influential body of work. In this article, I offer the first sustained effort to chart CTS’ development and transformation by positing three successive waves in its evolution. CTS’ first wave, I argue, was characterised by agenda-setting efforts to map the prospects of a distinctively critical approach to terrorism research. Second wave literature elaborated on this nascent project’s scope, strengthening and broadening its empirical, conceptual, and methodological ambitions. More recent third wave work seeks to problematise the underpinning assumptions and commitments of earlier CTS literature, especially in relation to its engagement with race and colonialism. Mapping CTS’ development in this way, I argue, offers three contributions to knowledge. First, it helps historicise CTS research by detailing the academic and wider contexts of its development. Second, it enables engagement with CTS’ pluralism, foregrounding the importance of internal disagreements for its past and future trajectories. And, third, it enables new reflection on the political and normative stakes of differing approaches to, and aspirations for, critical terrorism research.

Pedestrian Trajectory Prediction Based on an Intention Randomness Influence Strategy

Pedestrian trajectory prediction is a key technical prerequisite for autonomous vehicle trajectory planning. However, a pedestrian is a changeable individual, and their intentions exhibit certain degrees of randomness and uncertainty, which leads to the issue that modeling only past trajectories does not enable the effective description of the random intentions and future trajectory directions of the pedestrian. Therefore, this paper proposes a flexible and embeddable stochastic intention vector construction strategy for modeling sudden pedestrian intention changes in real scenes and for better fitting the stochastic properties of pedestrian behaviors. First, we dynamically fuse historical trajectory information with random factors and construct an intention change probability based on the historical trajectory fitting errors of pedestrians, aiming to explicitly model the associated direction and velocity changes caused by random pedestrian intentions. Second, a new intention loss function is designed to guide the model to adaptively learn the probability of intention changes, which is used to dynamically describe pedestrian intention changes. Our proposed method is generalizable and can be applied as an embeddable module to any baseline pedestrian trajectory prediction method. The experimental results obtained on multiple large-scale public pedestrian trajectory prediction datasets demonstrate that our strategy achieves consistent performance improvements over different baselines.

Channel changes over the last 200 years: A meta data analysis on European rivers

AbstractThe combined analysis of past evolutionary trajectories of channel morphology and temporal patterns of driving factors is fundamental to understanding present river conditions, supporting river management and evaluating future changes. Rivers in Europe underwent important channel changes during the Anthropocene in response to changing natural drivers and anthropogenic pressures. A considerable number of papers have been published on this topic, in the last decades. In this study, a comprehensive meta‐analysis on channel changes during the last 200 years in Europe was performed, aiming to provide quantitative information on the intensity of changes, to highlight regional scale similarities and dissimilarities in evolutionary morphological trajectories and to discuss the main causes of such changes. Based on a review, 102 papers were selected, addressing 145 channel reaches flowing through five main mountain ranges (Iberians, Alps, Apennines, Balkans and Carpathians) in the southern and eastern parts of Europe. The results show that active channel narrowing (between 26% and 36% on average) and incision (between 1 and 2 m) prevailed in most rivers between the 1800s and the 1950s, although widening was documented in some rivers of the Alps and the Apennines. Most multi‐thread reaches maintained their pattern until the mid‐20th century. Active channel changes accelerated during the 1950s–1990s (or 2000s) period, with channel narrowing up to 60% and channel incision up to 14 m. Multi‐thread patterns strongly decreased in frequency, with anabranching channels disappearing and single‐thread patterns becoming predominant. The cumulative effect of multiple and concomitant human pressures (gravel mining, channelisation and damming) was identified as the main driving factor for these accelerated changes. These findings must feed the public debate about preventing alterations of river ecosystems—exerted by anthropic disturbances—in a context of rapid economic development, especially in river systems still poorly altered and thus preserving wide, active and heterogeneous fluvial corridors.

Corporate social responsibility knowledge base: A bibliometric analysis

This research aims to analyze the current landscape of Corporate social responsibility (CSR) research. Using the Scopus database, the study employed the PRISMA sampling technique and analyzed 3679 articles using the VOSviewer program. The results show the emergence of CSR research in 1989, with a notable focus on topics such as corporate performance, corporate governance, family business, and socially responsible investment. We also find no evidence of a relationship between the number of an author's articles and the number of citations. Moreover, CSR research is largely monopolized by developed countries, leaving developing economies in the dark. Our study provides valuable insights into the past, present, and future trajectories of CSR research.

The Geography of EU Discontent and the Regional Development Trap

While in recent times many regions have flourished, many others are stuck—or are at risk of becoming stuck—in a development trap. Such regions experience decline in economic growth, employment, and productivity relative to their neighbors and to their own past trajectories. Prolonged periods in development traps are leading to political dissatisfaction and unrest. Such discontent is often translated into support for antisystem parties at the ballot box. In this article we study the link between the risk, intensity, and duration of regional development traps and the rise of discontent in the European Union (EU)—proxied by the support for Eurosceptic parties in national elections between 2013 and 2022—using an econometric analysis at a regional level. The results highlight the strong connection between being stuck in a development trap, often in middle- or high-income regions, and support for Eurosceptic parties. They also suggest that the longer the period of stagnation, the stronger the support for parties opposed to European integration. This relationship remains robust whether considering only the most extreme Eurosceptic parties or including parties with more moderate levels of Euroscepticism.

Billiards with Spatial Memory.

Many classes of active matter develop spatial memory by encoding information in space. We present a framework based on mathematical billiards, wherein particles remember their past trajectories. Despite its deterministic rules, such a system is strongly nonergodic and exhibits intermittent statistics and complex pattern formation. We show how these features emerge from the dynamic change of topology. Our work illustrates how the dynamics of a single-body system can dramatically change with spatial memory, laying the groundwork to further explore systems with complex memory kernels.

SIF-TF: A Scene-Interaction fusion Transformer for trajectory prediction

Accurate pedestrian trajectory prediction is essential for the advancement of intelligent robot or autonomous vehicle, which is a challenging and interesting task. In this paper, a Scene-Interaction fusion Transformer (SIF-TF) for trajectory prediction is proposed, which takes into account three fundamental factors, i.e. social interaction, past trajectory, and semantic scene. A scene-social modeling method is added to the model to integrate social interaction and semantic scene. The proposed SIF-TF contains two critical components: the scene-social transformer and the temporal transformer. The scene-social transformer is tasked with capturing social interaction and semantic scene information, while the temporal transformer focuses on extracting temporal correlation information. Furthermore, the SIF-TF employs a two-stage trajectory prediction approach to jointly generate future trajectories. To evaluate the effectiveness, the comparative experiments were conducted on five widely-used public datasets. The experiments results, with an average evaluation metric of ADE/FDE of 0.23/0.47, significantly outperforms other state-of-the-art methods. These findings demonstrate that the proposed SIF-TF is capable of delivering more precise pedestrian trajectory predictions across diverse scene backgrounds.

Introduction to Special Issue: Activism as the Asian Feminist Critical Race (AsianFemCrit) Praxis

The introduction article of the special issue draws from a rich history of Asian American feminist activism, highlights the evolution of this movement across various domains, and emphasizes the role of intersectionality in combating racism, xenophobia, and misogyny. We propose an Asian Feminist Critical Race (AsianFemCrit) framework with activism as its praxis. Asian American feminist activism is a form of resistance and a transformative practice aimed at dismantling systematic inequalities and advocating for solidarity and justice. Through AsianFemCrit praxis, we offer (a) listening and storytelling and (b) community care and collective intergenerational healing as new ways of understanding our relationships with ourselves, our community, and our integrity. Articles in this special issue present a blend of theoretical insights, empirical studies, and practical interventions, offering a diverse view of Asian American feminist activism’s past, present, and future trajectories. Through this lens, the special issue aims to advance understanding, inspire solidarity, and foster collective healing and liberation, underscoring the strength found in unity and celebrating the persistent spirit of Asian American feminist activism.