ALEXANDER OF APHRODISIAS’ OVERVIEW OF THE METAPHYSICS IN THE PROEM OF AVERROES’ LONG COMMENTARY ON METAPHYSICS LAMBDA

The prediction of expressway vehicle trajectories is a crucial aspect in the development of intelligent expressways. This paper proposes a novel approach, namely the W-GRU-Attention (WGA) model, which utilizes ETC transaction data to predict trajectory selection based on historical traffic paths and previous passed gantry information. In this study, we apply the concept of word embedding models to extract contextual semantics from the historical trajectories on expressways. Additionally, we introduce an average pooling technique for converting the historical vehicle trajectory into a fixed-length Historical Trajectory Vector (HTV), enabling us to capture dependency relationships within experience paths. By combining proximity gantry vectors during transit, we accurately predict the next gantry location. Finally, our proposed method is evaluated using a real-world expressway ETC dataset. It achieves an impressive accuracy rate of 96.14% in capturing the relationship between historical trajectories and adjacent gantries, surpassing other models in path prediction.

In January 2021, Scotland became the first country in the world to make universal access to free period products a legal right, an initiative which attracted extraordinary international attention. This introduction outlines what is indeed new and ground-breaking about this law from the perspective of the history of menstruation, and what merely continues traditional and widespread conceptions, policies and practices surrounding menstruation. On the basis of an analysis of the parliamentary debates of the Act, we show that it gained broad political support by satisfying a combination of ten different political agendas: (1) promoting gender equality for women, while also (2) acknowledging broader gender diversity; (3) taking practical steps to alleviate one high-profile aspect of poverty at a relatively low overall cost to the state, while also (4) stimulating the production of menstrual products; (5) tackling menstrual stigma; (6) improving access to education; (7) working with grassroots campaigners; (8) improving public health; and (9) accommodating sustainability concerns; as well as (10) the desire to pass world-leading legislation in itself. In each case, we explore the extent to which the political aim is typical of, or departs from, wider trajectories in the history and politics of menstruation, and, where pertinent, trajectories in Scottish political history. The ten agendas in their international context provide kaleidoscopic insight into the current state of menstrual politics and history in Scotland and beyond. This introduction also situates this Special Collection as a whole in relation to the field of Critical Menstruation Studies and provides background information about the legislative process and key terminology in Scottish politics and in the history of menstruation.

Destination prediction is very important in location-based services such as recommendation of targeted advertising location. Most current approaches always predict destination according to existing trip based on history trajectories. However, no existing work has considered the difference between the effects of passing-by locations and the destination in history trajectories, which seriously impacts the accuracy of predicted results as the destination can indicate the purpose of traveling. Meanwhile, the temporal information of history trajectories in destination prediction plays an important role. On one hand, the history trajectories in different periods also differ in the influence, e.g., the history trajectories from last week can reflect the status quo more accurately than the history trajectories two years ago. On the other hand, the history trajectories in different time slots reflect different facts of traffic and moving habits of people, e.g., visiting a restaurant in the daytime and visiting a bar at night. Although a huge amount of history trajectories can be achieved in the era of big data, it is still far from covering all the query trajectories since a road network is widely distributed and trajectory data is sparse. The temporal sensitivity of history trajectories highlights the sparsity problem even more. Therefore, we propose a novel model $\text{T-DesP}$ to solve the aforementioned problems. The model is comprised of two modules: trajectory learning and destination prediction. In the module of trajectory learning, a novel method called the mirror absorbing Markov chain model is proposed for modeling the trajectories for isolating the destination. We build a transition tensor to deduce the transition probability between each location pair in a particular time slot. To address the data sparsity problem, we fill the missing values in transition tensor through a context-aware tensor decomposition approach. In the module of destination prediction, an absorbing tensor is derived from the filled transition tensor, and the theoretical model is established for destination prediction. The experiments prove the effectiveness and efficiency of $\text{T-DesP}$ .

The goal of this chapter and the subsequent one is to provide to instructors and students a resource guide to read and teach Edwidge Danticat’s award-winning Memoir, Brother, I’m Dying. The chapter discusses the background and context, and the historical trajectories, in both Haiti and the United States that have shaped the content and message of the Memoir. This chapter suggests multiple approaches to reading, assessing, and teaching the Memoir, and do not give any preference to any reading strategy or technique proposed in my analysis. The primary objective is to supply the information and knowledge and the complementary material an instructor might need to make the book interesting and worth discussing in the classroom, resulting in student enrichment and intellectual growth. At the end of each analysis on the theme covered, the section is closed with recommended readings, whose central objective is to provide to the reader, and particularly both instructor and students, the historical contexts and trajectories, and the necessary background information needed to the subject matter—to enhance pedagogical technique and empower students.

Panoramic video is considered to be an attractive video format, since it provides the viewers with an immersive experience, such as virtual reality (VR) gaming. However, the viewers only focus on part of panoramic video, which is referred to as viewport. Hence, the resources consumed for distributing the remaining part of the panoramic video are wasted. It is intuitive to only deliver the video data within this viewport for reducing the distribution cost. Empirically, viewports within a time interval are highly correlated, hence the historical trajectory may be used for predicting the future viewports. On the other hand, a viewer tends to sustain attention on a specific object in a panoramic video. Motivated by these findings, we propose a deep learning-based viewport Prediction scheme, namely HOP, where the Historical viewport trajectory of viewers and Object tracking are jointly exploited by the long short-term memory (LSTM) networks. Additionally, our solution is capable of predicting multiple future viewports, while a single viewport prediction was supported by the state-of-the-art contributions. Simulation results show that our proposed HOP scheme outperforms the benchmarkers by up to 33.5% in terms of the prediction error.

Taking into account the availability of the historical GPS trajectories of drivers, given a new GPS trajectory, Driver mobility fingerprint (DMF) identification aims at (i) determining whether a generated trajectory belongs to a potential driver, and (ii) detecting if a trajectory is likely anomalous based on a driver's historical data. Prior studies often consider hand-crafted feature engineering techniques to extract DMFs while contextual factors like weather and points-of-interest (POIs) are hardly accounted for, which might not achieve satisfactory identification results. To address above, we propose RM-Drive, a novel framework based on reinforced feature extraction and multi-resolution learning. Specifically, we first employ spatio-temporal inverse reinforcement learning (ST-IRL) to extract DMFs from historical trajectories. Then, we generate trajectory embeddings by fusing the extracted DMFs and the contextual factors using the multi-resolution trajectory embedding network (MTE-Net). Our proposed MTE-Net consists of multi-resolution convolutional neural network (MR-CNN), which enables the model to learn the multi-resolution features of the DMFs. Finally, we leverage the trajectory embeddings for the driver classification and anomaly detection. We have conducted extensive evaluation studies upon RM-Drive with two real-world datasets, and our results demonstrate the performance improvements from the state-of-the-art of driver classification and anomaly detection respectively by 21% and 11% on average based on several evaluation metrics, including accuracy, precision, and recall, etc.

We present a description of an ASM-network, a new habit-based robot controller model consisting of a network of adaptive sensorimotor maps. This model draws upon recent theoretical developments in enactive cognition concerning habit and agency at the sensorimotor level. It aims to provide a platform for experimental investigation into the relationship between networked organizations of habits and cognitive behavior. It does this by combining (1) a basic mechanism of generating continuous motor activity as a function of historical sensorimotor trajectories with (2) an evaluative mechanism which reinforces or weakens those historical trajectories as a function of their support of a higher-order structure of higher-order sensorimotor coordinations. After describing the model, we then present the results of applying this model in the context of a well-known minimal cognition task involving object discrimination. In our version of this experiment, an individual robot is able to learn the task through a combination of exploration through random movements and repetition of historic trajectories which support the structure of a pre-given network of sensorimotor coordinations. The experimental results illustrate how, utilizing enactive principles, a robot can display recognizable learning behavior without explicit representational mechanisms or extraneous fitness variables. Instead, our model's behavior adapts according to the internal requirements of the action-generating mechanism itself.

Guided by a self-categorisation and social-identity framework of identity entrepreneurship (Reicher & Hopkins, 2001), and social representations theory of history (Liu & Hilton, 2005), this paper examines how the Hindu nationalist movement of India defines Hindu nationhood by embedding it in an essentialising historical narrative. The heart of the paper consists of a thematic analysis (Braun & Clarke, 2006) of the ideological manifestos of the Hindu nationalist movement in India, “Hindutva: Who is a Hindu?” (1928) and “We, or Our Nationhood Defined” (1939), written by two of its founding leaders – Vinayak Damodar Savarkar and Madhav Sadashiv Golwalkar, respectively. The texts constitute authoritative attempts to define Hindu nationhood that continue to guide the Hindu nationalist movement today. The derived themes and sub-themes indicate that the definition of Hindu nationhood largely was embedded in a narrative about its historical origins and trajectory, but also its future. More specifically, a ‘golden age’ was invoked to define the origins of Hindu nationhood, whereas a dark age in its historical trajectory was invoked to identify peoples considered to be enemies of Hindu nationhood, and thereby to legitimise their exclusion. Through its selective account of past events and its efforts to utilise this as a cohesive mobilising factor, the emergence and rise of the Hindu nationalist movement elucidate lessons that further our understanding of the rise of right-wing movements around the world today.

The spatial and material dimensions of communication have changed dramatically over the past three millennia in South India. The historical and contemporary trajectories of these changes are revealed, explored, documented, critiqued and examined in this work. This book is comprehensive in its engagements with three locations—spatiality, materiality and communication, in the contexts of Tamil Nadu, South India. The book takes a multidisciplinary approach to communication and media studies. It leverages the multifaceted knowledge seeking spirit of the ancient philosophers of Tamil Nadu for understanding the contexts of spatialities, materialities and communication. Across four sections on historical trajectories, everyday lives, public communication and media materialities, its 20 chapters on diverse topics offer unique engagements of the spatial journeys of people, rulers, philosophers, men, women, as well as their material objects, occupations and media during the past three millennia in South India, with a focus on Tamil Nadu.

Literal researches have proved that most of the algorithms are not capable to solve the problems whose solutions are not locating at the Origin. Due to the large ratio for individuals to maintain their historical trajectories in swarms of the slimed mould (SM), the SM algorithms would perform even worse. Therefore, in this paper, the historical best trajectories were introduced to take part in the updating procedure for positions of individuals in swarms. Simulation experiments were carried out and the final results proved that the improved algorithm could increase capabilities of optimization for those non-symmetric problems.

To improve the accuracy and stability of flight trajectory prediction, a novel four-dimensional (4-D) trajectory management approach is proposed in this paper, which consists of the estimation and updating procedure. Historical flight trajectories are proved to be safe and feasible based on the real-time traffic situation, and serve as the data foundation of 4-D trajectory management in this paper. To achieve the goal of 4-D trajectory management, we firstly apply probabilistic statistical models and machine learning approach to predict the fly-over time and altitude of waypoints along the planning route before the flight takes off. Hidden Markov Models (HMMs) are regarded as the probabilistic model to represent the position and altitude transition patterns of the aircraft during the flight operation. The EM algorithm is applied to optimize model parameters of HMMs to fit the training data (historical trajectory set). Then the models with optimized parameters are used to predict the pre-takeoff 4-D trajectory by inferring an optimal hidden state sequence. Finally, after the flight takes off, we propose an algorithm to correct the pre-takeoff prediction results by considering the trajectory similarity between collected path of current execution and its historical trajectories. Simulations with real data show that the prediction results (fly-over time and altitude) of our proposed algorithm are more accurate than that of other existing methods, and would tend to be more credible after correcting with the proposed algorithm. Moreover, the prediction errors of our approach are stable during the whole flight, which is the bottleneck of existing d\eterministic models.

Preventing and countering violent extremism programmes (P/CVE) programmes in western and postcolonial countries present several differences as well as similarities. The differences are a consequence of specific coordinates of time and space in which security programmes are situated. This article builds on a growing scholarship focusing on the dimension of time, highlighting how the investigation of global and local historical trajectories brings to light postcolonial processes of security policymaking and their role in shaping local security measures. Through interviews carried out in Tunisia with local and international NGOs working on P/CVE, this research examines the agency of international and local actors, and their role in implementing or challenging global and local security narratives. The objective of this research is to show the connection between global and local security narratives and practices, and the role of colonial historical trajectories in the evolution of security.

Travel time estimation is a critical task, useful to many urban applications at the individual citizen and the stakeholder level. This paper presents a novel hybrid algorithm for travel time estimation that leverages historical and sparse real-time trajectory data. Given a path and a departure time we estimate the travel time taking into account the historical information, the real-time trajectory data and the correlations among different road segments. We detect similar road segments using historical trajectories, and use a latent representation to model the similarities. Our experimental evaluation demonstrates the effectiveness of our approach.

Duty-hours policies continue to be debated. Most know the pro and con arguments, but many may not be aware of background information preceding and intertwining the development and implementation of these policies. Interestingly, several aspects of law were involved or potentially correlated with policies enacted. This review updates new generations of physicians and scholars on the historical trajectory of duty-hour policies and highlights policy implications and the current state of evidence. In reviewing the historical and legal trajectory of duty-hours, many updates seemed to be a reaction to potential federal entanglement. Additionally, the review of the postimplementation literature revealed minimal empirical evidence. Instead, the majority of the positive findings were perception based. These summaries demonstrate a need for further outcomes evidence to validate policies.

As an indispensable personalized service within Location-Based Social Networks (LBSNs), the Point-of-Interest (POI) recommendation aims to assist individuals in discovering attractive and engaging places. However, the accurate recommendation capability relies on the powerful server collecting a vast amount of users' historical check-in data, posing significant risks of privacy breaches. Although several collaborative learning (CL) frameworks for POI recommendation enhance recommendation resilience and allow users to keep personal data on-device, they still share personal knowledge to improve recommendation performance, thus leaving vulnerabilities for potential attackers. Given this, we design a new Physical Trajectory Inference Attack (PTIA) to expose users' historical trajectories. Specifically, for each user, we identify the set of interacted POIs by analyzing the aggregated information from the target POIs and their correlated POIs. We evaluate the effectiveness of PTIA on two real-world datasets across two types of decentralized CL frameworks for POI recommendation. Empirical results demonstrate that PTIA poses a significant threat to users' historical trajectories. Furthermore, Local Differential Privacy (LDP), the traditional privacy-preserving method for CL frameworks, has also been proven ineffective against PTIA. In light of this, we propose a novel defense mechanism (AGD) against PTIA based on an adversarial game to eliminate sensitive POIs and their information in correlated POIs. After conducting intensive experiments, AGD has been proven precise and practical, with minimal impact on recommendation performance.