Future Movements Research Articles

Goal-Guided Graph Attention Network with Interactive State Refinement for Multi-Agent Trajectory Prediction.

The accurate prediction of the future trajectories of traffic participants is crucial for enhancing the safety and decision-making capabilities of autonomous vehicles. Modeling social interactions among agents and revealing the inherent relationships is crucial for accurate trajectory prediction. In this context, we propose a goal-guided and interaction-aware state refinement graph attention network (SRGAT) for multi-agent trajectory prediction. This model effectively integrates high-precision map data and dynamic traffic states and captures long-term temporal dependencies through the Transformer network. Based on these dependencies, it generates multiple potential goals and Points of Interest (POIs). Through its dual-branch, multimodal prediction approach, the model not only proposes various plausible future trajectories associated with these POIs, but also rigorously assesses the confidence levels of each trajectory. This goal-oriented strategy enables SRGAT to accurately predict the future movement trajectories of other vehicles in complex traffic scenarios. Tested on the Argoverse and nuScenes datasets, SRGAT surpasses existing algorithms in key performance metrics by adeptly integrating past trajectories and current context. This goal-guided approach not only enhances long-term prediction accuracy, but also ensures its reliability, demonstrating a significant advancement in trajectory forecasting.

Prediction of ship future motions at different predictable time scales for shipboard helicopter landing in regular and irregular waves

ABSTRACT Differing from the traditional helicopter landing on land, due to ship motions induced by actual waves, the landing safety of shipboard helicopter on vessels in waves deserves to be studied. To provide a judgment basis for the timing of shipboard helicopter landing, a hydrodynamic analysis method based on the Long-Short-Term Memory (LSTM) neural network is proposed to achieve the real-time prediction of ship motions in this study. Firstly, the hydrodynamic formulations based on potential flow theory for nonlinear ship motions in waves are presented. The implementation details of the LSTM neural networks are described. Secondly, the numerical verification study is carried out in regular and irregular waves, respectively. Furthermore, a destroyer model is employed to illustrate the proposed framework, and the results corresponding to the different time scales in waves are presented. Finally, the work of this paper is summarized and the certain conclusions are drawn.

Utilizing Machine Learning and Deep Learning for Predicting Crypto-currency Trends

In the dynamic and often volatile world of the cryptocurrency market, accurately predicting future market movements is crucial for making informed trading decisions. While manual trading involves traders making subjective judgments based on market observations, the development of algorithmic trading systems, incorporating Machine Learning and Deep Learning, has introduced a more systematic approach to trading. These systems often employ technical analysis and machine learning techniques to analyze historical price data and generate trading signals. This study delves into a comparative analysis of two charting techniques, Heikin-Ashi and alternate candlestick patterns, in the context of forecasting single-step future price movements of cryptocurrency pairs. Utilizing a range of time windows (1 day, 12 hours, 8 hours, ..., 5 minutes) and various regression algorithms (Huber regressor, k-nearest neighbors regressor, Light Gradient Boosting Machine, linear regression, and random forest regressor), the study evaluates the effectiveness of each technique in forecasting future price movements. The primary outcomes of the research indicate that the application of ensemble learning methods to the alternate candlestick patterns consistently surpasses the performance of Heikin-Ashi candlesticks across all examined time windows. This suggests that alternate candlestick patterns provide more reliable information for predicting short-term price movements. Additionally, the study highlights the varying behavior of Heikin-Ashi candlesticks over different time windows

Sequential control barrier functions for mobile robots with dynamic temporal logic specifications

We address a motion planning and control problem for mobile robots to satisfy rich, time-varying tasks expressed as Signal Temporal Logic (STL) specifications. The specifications may include tasks with nested temporal operators or time-conflicting requirements (e.g., achieving periodic tasks or tasks defined within the same time interval). Moreover, the tasks can be defined in locations changing with time (i.e., dynamic targets), and their future motions are not known a priori. This unpredictability requires an online control approach which motivates us to investigate the use of control barrier functions (CBFs). The proposed CBFs take into account the actuation limits of the robots and a feasible sequence of STL tasks. They define time-varying feasible sets of states the system must always stay inside. We show the feasible sequence generation process that even includes the decomposition of periodic tasks and alternative scenarios due to disjunction operators. The sequence is used to define CBFs, ensuring STL satisfaction. We also show some theoretical results on the correctness of the proposed method. We illustrate the benefits of the proposed method and analyze its performance via simulations and experiments with aerial robots.

A STUDY ON PULMONARY FUNCTION TEST AMONG PETROL-PUMP WORKERS IN TIRUNELVELI TOWN

Objective: In India, automobiles plying on roads are increasing every day. This has led to an increase in petrol-pump stations and an increase in pump workers. Petrol vapors and exhaust from vehicles have ill effects on the respiratory system. Petrol is a complex combination of hydrocarbons. Continuous exposure to petroleum vapours and exhaust from vehicles causes altered respiratory physiology. Methods: This study involved 50 petrol-pump workers (PPWs) with another 50 age-matched healthy adults. Their age, history of past illness, smoking exposure, and working hours were noted. Pulmonary function test was done using a computerized Spirometer to measure various parameters. Results: Parameters of pulmonary function in PPWs have a decline in mean values such as FVC, FEV1, peak expiratory flow rate, FIVC, forced expiratory flow (FEF) (25–75%), peak inspiratory flow, VC, and ERV when compared to the control group. It reflects the development of mixed patterns of lung disease (both obstructive and restrictive). Conclusion: It is suggested that reduction or control measures against air pollution including protective equipment, the future movement toward green fuels, and identifying and protecting susceptible workers occupationally exposed to petrol and diesel fumes would be beneficial to humanity and the ecosystem.

На пути к реформам: правовое регулирование электоральных финансов в США до начала XX века

The subject of the article is the first steps of electoral fundraising in United States since the end of XVIII till 1907. In that period legal regulation of electoral finance is either almost absent or very partial. The key factors that defined the basis for legal regulation of US electoral finance in the 20th century were: the powers of federal government; expanding of rights to vote; new methods of electoral communications; shaping of party politics; determination of the ideological foundations of the future reform movement.

Hierarchical vector transformer vehicle trajectories prediction with diffusion convolutional neural networks

In dynamic and interactive autonomous driving scenarios, accurately predicting the future movements of vehicle agents is crucial. However, current methods often fail to capture trajectory uncertainty, leading to limitations in trajectory prediction performance. To address these limitations, this paper introduces the hierarchical vector transformer diffusion model, a novel trajectory prediction method that prioritizes both speed and accuracy. The proposed model decomposes the traffic scene modeling into local patches and global interactions, allowing for the acquisition of relevant environmental and global information. Moreover, a local diffusion encoder is employed to effectively capture the aleatoric uncertainty. The proposed model utilizes an adaptive graph structure to exploit the spatial and temporal relationships inherent in the trajectory data. By employing a graph diffusion process, the model effectively captures dynamic features from the historical trajectory information. Moreover, the model demonstrates adaptability by dynamically adjusting to diverse trajectory data and scenarios, thereby enabling the generation of predicted trajectories that are uncertainty aware. This approach contributes to more effective and efficient modeling of dynamic autonomous driving scenarios. Experimental results demonstrate the superior speed and accuracy of the proposed method compared to existing approaches for trajectory prediction. The proposed method significantly enhances prediction accuracy, achieving results of ADE 0.68 and FDE 1.02 on the Argoverse dataset. In comparison to the baseline model, there are notable improvements in ADE and FDE by 0.03 and 0.06, respectively. It is noteworthy that this method also reduces the inference time by 7% when compared to the currently fastest method.

Predicting future velocity of mineral flotation froth using STMA-LSTM with sequence images

During the mineral flotation process, the surface froth image contains characteristic information that is closely related to the production index of the process. However, a delay often exists between the state of the flotation tank and the taste calculation results, which hinders real-time process regulation. To address this challenge, a video sequence prediction algorithm is proposed to forecast the future sequence of flotation froth, and a Spatiotemporal Motion-Attentive Long Short-Term Memory (STMA-LSTM) network model is introduced to estimate and predict the future image information of the froth by integrating spatiotemporal information. After obtaining the future froth image, an image velocity measurement algorithm is utilized to achieve the future velocity prediction of the flotation froth. To address the ambiguity of future froth images and the real-time demand of future velocity estimation, a Fast Froth Velocity Estimation (FFVE) algorithm is introduced. This algorithm is suitable for fast estimation of flotation froth velocity due to its low sensitivity to image blurring factors and fast feature matching. The proposed approach implements future motion velocity prediction of froth images, thereby addressing the delay in froth taste prediction and enhancing the overall efficiency of the process control in the flotation field.

Past, Present, and Future Movement in Early Recollection Work

ABSTRACT: This article offers a view of working with early recollections (ERs) in therapy while incorporating encouragement, reconstruction, and movement between the past, present, and future. Adler saw ERs as stories representing individuals’ “life stories,” meaning their lifestyles (personalities). As metaphoric narratives, ERs also reflect current experiences and emotions. Working with ERs in therapy provides the therapist and client with opportunities for encouragement, growth, and awareness. Engaging with ERs invites varied, creative paths for development and insight. They may be used as an ongoing and interactive tool for assessment and therapeutic interventions. This article presents a process of moving from the past to the present and back to the past by reconstructing an ER. Using the client’s wisdom from the present in the reconstruction opens a significant window to future change and allows the client to expand their perceptions.

Survey on Stock Price Forecasting

This research project delves into the challenging domain of stock price prediction by harnessing the capabilities of neural networks. In an ever-evolving financial landscape, the project seeks to develop a robust model that can analyze historical stock data, identifying intricate patterns and trends to inform future price movements. Through the convergence of data science and financial analysis, this endeavor aims to provide investors with valuable insights, aiding them in navigating the complexities of the stock market. Employing machine learning algorithms, particularly neural networks, the model scrutinizes extensive sets of historical stock data, considering factors such as past price trends, trading volumes, and relevant financial indicators. Techniques such as regression, time-series analysis, and neural networks are leveraged to unveil complex relationships within the data, enhancing the model's predictive capabilities. The model undergoes meticulous training on historical data to glean insights from past market behaviors. Subsequently, it undergoes rigorous testing on unseen data to evaluate its predictive accuracy. Continuous refinement and optimization strategies are implemented to ensure the model's adaptability to the ever-changing dynamics of the market. Ultimately, this project aspires to furnish investors with a powerful tool for making informed decisions in the face of the unpredictable nature of financial markets. By integrating the precision of neural networks with the intricacies of stock market analysis, the research aims to contribute to the advancement of stock price forecasting methodologies.

Using Artificial Intelligence for Stock Price Prediction and Profitable Trading

Investing in the stock market is generally considered a safe and reliable way to protect and grow your savings over the long term. However, stock investments are risky and investing in the wrong companies can lead to financial loss. This research paper aims to see how the emerging field of Artificial Intelligence (AI) can be used to predict a stock’s future movement and capitalize on it by anticipating the movement and automatically buying or selling the stock, in an attempt to safely generate profit while minimizing risk. In this paper, a Multi-Layer Perceptron regressor model was trained to predict a stock’s future price using the stock’s previous prices, two industry competitors’ previous stock prices, and previous daily crude oil prices. The model was evaluated based on its average accuracy to the actual price in percent, and it was found that the model had an average 1.7% error on AAPL (Apple Inc) stock. An automated trading bot was then created and used the model’s daily output to perform a simulated backtest, where it was given a simulated $100,000 portfolio. Its performance was measured in multiple different trading scenarios. It was found that the model can reliably outperform stocks in fairly flat or downward market conditions and still profit on upward-moving stocks, but can still end up failing to profit if the underlying stock it was given drops in price significantly over the testing period.

Estimasi Arah Robot Pembersih Panel surya Menggunakan Algoritma Line Detection Klasik

Currently, solar panels are widely used in homes, offices, and buildings. Solar energy is converted into electrical energy by solar panels. The surface of solar panels determines how efficient they are; if the surface is dirty, the efficiency of solar panels will decrease. Therefore, they must be cleaned to maintain their effectiveness. Cleaning solar panels is one of the tasks that can be performed by an autonomous robot, which can move without the need for human control or remote control usage. To make the robot move autonomously, a camera is used to capture images of solar panels so that the robot can move on its own. The images captured by the camera will then be processed using a classic line detection algorithm (Hough line transform) to estimate the direction, in the form of the angle (theta), of the robot relative to the solar panel based on the lines detected by the algorithm. The direction of the robot relative to the solar panel determines the robot's future movement. The algorithm used (Hough line transform) has been able to estimate the direction of the robot quite well with a relatively fast processing time. Thus, it can be concluded that this research successfully determines the direction based on the available images

Self-Evaluation of Trajectory Predictors for Autonomous Driving

Driving experience and anticipatory driving are essential skills for humans to operate vehicles in complex environments. In the context of autonomous vehicles, the software must offer the related features of scenario understanding and motion prediction. The latter feature of motion prediction is extensively researched with several competing large datasets, and established methods provide promising results. However, the incorporation of scenario understanding has been sparsely investigated. It comprises two aspects. First, by means of scenario understanding, individual assumptions of an object’s behavior can be derived to adaptively predict its future motion. Second, scenario understanding enables the detection of challenging scenarios for autonomous vehicle software to prevent safety-critical situations. Therefore, we propose a method incorporating scenario understanding into the motion prediction task to improve adaptivity and avoid prediction failures. This is realized by an a priori evaluation of the scenario based on semantic information. The evaluation adaptively selects the most accurate prediction model but also recognizes if no model is capable of accurately predicting this scenario and high prediction errors are expected. The results on the comprehensive scenario library CommonRoad reveal a decrease in the Euclidean prediction error by 81.0% and a 90.8% reduction in mispredictions of our method compared to the benchmark model.

Two Crises, One Strategy: A Comparative Analysis of Polio and COVID-19 Response Strategies

Polio and COVID-19 are contagious diseases that affect the world to varying degrees. In America, both viruses overwhelmed communities and terrorized the public. Polio’s peak epidemics in America during the early to mid-1950s and the COVID-19 pandemic, starting in 2020 but slowly ending in 2023, were shortened through similar restrictions and fundraising efforts by the American public and government. There are many similarities between the government’s handling of these two viruses regarding campaigns and quarantines. Public health fundraisers for funding and vaccination for polio were related to the future movements for COVID-19. In addition, lessons learned from polio’s time about social restrictions helped the government enforce successful and effective constraints for COVID-19. The resemblance of the management between the poliovirus and the coronavirus highlights the importance of understanding the similarities between different viral infections and how this can help with future outbreaks.

Fluctuation and forecasting of gold prices in Saudi Arabia’s market

PurposeThe purpose of this study is to analyze the fluctuations in gold prices within the Saudi Arabian market and to develop a reliable forecasting model to aid market participants and policymakers in making informed decisions.Design/methodology/approachIn this study, we employ a rigorous time series analysis methodology, including the ARIMA (Auto Regressive Integrated Moving Average) model, to analyze historical gold price data in the Saudi Arabian market. The approach involves identifying optimal model parameters and assessing forecast accuracy to provide actionable insights for market participants.FindingsThe study showcases that the autoregressive properties of past gold prices play a pivotal role in capturing the inherent serial correlation within the market, enabling the ARIMA model to effectively forecast future gold price movements with accuracy.Research limitations/implicationsOur study primarily focuses on quantitative analysis, whereas few qualitative parameters are not included. Future studies may benefit from incorporating qualitative factors and expert opinions to enhance the robustness of gold price predictions and capture the full spectrum of market dynamics.Social implicationsParticipants and policymakers may find this study helpful in navigating the complicated Saudi Arabian gold market. By understanding financial stability and investment decisions more thoroughly, individuals and institutions may be able to manage their portfolios more effectively.Originality/valueBy combining historical insights with advanced ARIMA modeling techniques, this research provides valuable insight into gold price dynamics in the Saudi Arabian market.

MTAN: Multi-degree Tail-aware Attention Network for human motion prediction

Human motion prediction (HMP) aims to predict future human motions from historical pose sequences. Extensive efforts have adopted the Transformers or Graph Neural Networks (GNNs) to capture the spatio-temporal relationships between poses and thus incorporate the contextual information and complex behavior relationships for motion inference. However, most existing approaches treat the HMP task as a deterministic problem, thus resulting in poor diversity and long tail problems. This study attributes such issues to positional bias within the Transformers and the lack of degrees of freedom within the predictive model. Hence, we propose a novel Multi-degree Tail-aware Attention Network (MTAN) involving a tail-aware attention mechanism and a multi-degree feature representation strategy. Specifically, we introduce a tail-aware attention mechanism to adeptly capture spatio-temporal dependencies that accommodate both head and tail actions. Based on CVAE, the multi-degree feature representation strategy learns to capture temporal diversity by learning the joint distribution of observed and future sequences. Ultimately, we leverage GCN to model spatial dependencies effectively, culminating in a comprehensive spatiotemporal prediction model. We evaluate the effectiveness of our approach using three benchmark datasets, including Human3.6M, AMASS, and 3DPW. The results demonstrate that our approach surpasses state-of-the-art transformer methods, establishing its superiority in HMP.

On the long-run properties of income and stock prices: the stability of the “golden ratios”

PurposeThe challenge of predicting changes in aggregate income and stock prices is one that has occupied the research agendas of economists. This paper aims to use the consumption–income ratio and the dividend–price ratio to predict future income and stock prices.Design/methodology/approachTo examine the stability of the consumption–income ratio and the dividend–price ratio, the authors run a two-variable, two-lag reduced-form VAR in the vein of Cochrane (1994), using a lag of each respective ratio as exogenous to the VAR. Additionally, the authors estimate an AR(4) model for income and prices.FindingsThe consumption–income ratio and the dividend–price ratio remain key to understanding future movements in income and stock prices. The consumption–income ratio significantly predicts future income in the USA, and aggregate income is easier to predict than consumption in the VAR model. The dividend–price ratio does not significantly predict future price growth. Consumption and dividend shocks have lasting impacts on income and prices.Originality/valueThe consumption–income ratio and the dividend–price ratio are still key to understanding future movements in income and stock prices. The consumption–income ratio significantly predicts future income in the USA, and aggregate income is easier to predict than consumption in the VAR model. However, the dividend–price ratio does not significantly predict future price growth, a change from previous research from the 1990s, despite the increasing complexity of stock markets. Consumption and dividend shocks have lasting impacts on income and prices and appear to be significant drivers in both the short- and long-run variance in income and prices.

Self-Supervised Pretraining Based on Noise-Free Motion Reconstruction and Semantic-Aware Contrastive Learning for Human Motion Prediction

Human motion prediction is to forecast future human motions based on the observed ones. Currently, a prediction model always includes an encoder to extract the features of the observed motions and a decoder for motion prediction with the extracted features. However, existing encoders have limited abilities of anti-interference and semantic attribute learning; thus, the prediction results are not fully satisfactory. To solve this problem, we propose two novel pretext tasks, i.e., noise-free motion reconstruction and semantic-aware contrastive learning, to implement self-supervised pretraining on the encoder. The former extracts the features of a noise-added motion to reconstruct a noise-free motion. This pretext task can improve the ability of anti-interference of the encoder. The latter shortens the distance between two motions with the same motion category while widening the distance between two motions from different motion categories. This pretext task enables the encoder to capture the semantic commonality of the motions with the same motion category, thereby enhancing the encoder's ability of semantic attribute learning. After implementing the self-supervised pretraining, the pretrained encoder is connected with a decoder to build a prediction model called ReLe-GCN, which is fine-tuned for human motion prediction. The results on public datasets show the high accuracies of ReLe-GCN in both short-term and long-term motion predictions.

Real-walk modelling: deep learning model for user mobility in virtual reality

This paper presents a study on modelling user free walk mobility in virtual reality (VR) art exhibition. The main objective is to investigate and model users’ mobility sequences during interactions with artwork in VR. We employ a range of machine learning (ML) techniques to define scenes of interest in VR, capturing user mobility patterns. Our approach utilises a long short-term memory (LSTM) model to effectively model and predict users’ future movements in VR environments, particularly in scenarios where clear walking paths and directions are not provided to participants. The DL model demonstrates high accuracy in predicting user movements, enabling a better understanding of audience interactions with the artwork. It opens avenues for developing new VR applications, such as community-based navigation, virtual art guides, and enhanced virtual audience engagement. The results highlight the potential for improved user engagement and effective navigation within virtual environments.

Mapping the neural dynamics of locomotion across the Drosophila brain

Locomotion engages widely distributed networks of neurons. However, our understanding of the spatial architecture and temporal dynamics of the networks that underpin walking remains incomplete. We use volumetric two-photon imaging to map neural activity associated with walking across the entire brain of Drosophila. We define spatially clustered neural signals selectively associated with changes in either forward or angular velocity, demonstrating that neurons with similar behavioral selectivity are clustered. These signals reveal distinct topographic maps in diverse brain regions involved in navigation, memory, sensory processing, and motor control, as well as regions not previously linked to locomotion. We identify temporal trajectories of neural activity that sweep across these maps, including signals that anticipate future movement, representing the sequential engagement of clusters with different behavioral specificities. Finally, we register these maps to a connectome and identify neural networks that we propose underlie the observed signals, setting a foundation for subsequent circuit dissection. Overall, our work suggests a spatiotemporal framework for the emergence and execution of complex walking maneuvers and links this brain-wide neural activity to single neurons and local circuits.