Past Trajectories Research Articles

S5: Probing the Milky Way and Magellanic Clouds potentials with the 6D map of the Orphan–Chenab stream

ABSTRACTWe present a 6D map of the Orphan–Chenab (OC) stream by combining the data from Southern Stellar Stream Spectroscopic Survey (S5) and Gaia. We reconstruct the proper motion, radial velocity, distance, on-sky track, and stellar density along the stream with spline models. The stream has a total luminosity of MV = −8.2 and metallicity of [Fe/H] = −1.9, similar to classical Milky Way (MW) satellites like Draco. The stream shows drastic changes in its physical width varying from 200 pc to 1 kpc, but a constant line-of-sight velocity dispersion of 5 $\mathrm{km\, s^{-1}}$. Despite the large apparent variation in the stellar number density along the stream, the flow rate of stars along the stream is remarkably constant. We model the 6D stream track by a Lagrange-point stripping method with a flexible MW potential in the presence of a moving extended Large Magellanic Cloud (LMC). This allows us to constrain the mass profile of the MW within the distance range 15.6 < r < 55.5 kpc, with the best measured enclosed mass of $(2.85\pm 0.1)\times 10^{11}\, \mathrm{\, M_\odot }$ within 32.4 kpc. Our stream measurements are highly sensitive to the LMC mass profile with the most precise measurement of its enclosed mass made at 32.8 kpc, $(7.02\pm 0.9)\times 10^{10}\, {\rm M}_\odot$. We also detect that the LMC dark matter halo extends to at least 53 kpc. The fitting of the OC stream allows us to constrain the past LMC trajectory and the degree of dynamical friction it experienced. We demonstrate that the stars in the OC stream show large energy and angular momentum spreads caused by LMC perturbation.

Execution of new trajectories toward a stable goal without a functional hippocampus.

The hippocampus is a critical component of a mammalian spatial navigation system, with the firing sequences of hippocampal place cells during sleep or immobility constituting a "replay" of an animal's past trajectories. A novel spatial navigation task recently revealed that such "replay" sequences of place fields can also prospectively map onto imminent new paths to a goal that occupies a stable location during each session. It was hypothesized that such "prospective replay" sequences may play a causal role in goal-directed navigation. In the present study, we query this putative causal role in finding only minimal effects of muscimol-induced inactivation of the dorsal and intermediate hippocampus on the same spatial navigation task. The concentration of muscimol used demonstrably inhibited hippocampal cell firing in vivo and caused a severe deficit in a hippocampal-dependent "episodic-like" spatial memory task in a watermaze. These findings call into question whether "prospective replay" of an imminent and direct path is actually necessary for its execution in certain navigational tasks.

Land Planning, Agro-forest Systems, and Implications for Ecosystem Services: Insights from Northern Italy

Negative environmental changes generally addressed as ‘syndromes’ areevaluated in the context of Soil Degradation (SD) and interpreted by usinga ‘Land-Use/Land Cover Changes’ (LULCCs) framework in order to dis-entangle ‘past trajectories’, ‘present patterns’, and ‘future changes’. Thisapproach allows to discuss the potential impact on SD processes and itrepresents an informed basis for identifying measurable outcomes of SD.This study focuses on the case of Emilia Romagna, a region located inthe North of Italy with high-value added agricultural productions. A multi-temporal analysis of land-use changes between 1954 and 2008 has been proposed, discussing the evolution of associated SD syndromes in EmiliaRomagna. The contributing information have been used as a baseline forSustainable Land Management (SLM) strategies. This framework of analysisprovides useful tools to investigate and to monitor the effects of SD in theMediterranean basin where several regions underwent common developmentpatterns yelding global pathological symptoms of environmental degradation.

Motion prediction for beating heart surgery with GRU

This work aims to * predict * the * 3D * coordinates of the * point * of * interest * (POI) on * the surface of beating heart in dynamic minimally invasive surgery, which can improve the manoeuvrability of cardiac surgical robots and expand their functions. For accurate and robust POI motion prediction, a deep learning technique, the gated recurrent unit (GRU), is employed to learn the spatio-temporal (ST) correlation of the POI and its auxiliary points (APs) from their past trajectories. For reference, two neural network models that exploit only spatial and temporal correlations, respectively, are also investigated. The prediction accuracy and robustness of the above three models are verified based on the motion datasets of phantom and in vivo hearts collected by da Vinci robots. Source codes and motion data are shared on GitHub (https://github.com/oww-file/ST-GRU). The experimental results show that the proposed ST-GRU model is significantly better than the other two reference models, and it outperforms the state-of-the-art deep learning method on the same datasets.

Position representations of moving objects align with real-time position in the early visual response.

When interacting with the dynamic world, the brain receives outdated sensory information, due to the time required for neural transmission and processing. In motion perception, the brain may overcome these fundamental delays through predictively encoding the position of moving objects using information from their past trajectories. In the present study, we evaluated this proposition using multivariate analysis of high temporal resolution electroencephalographic data. We tracked neural position representations of moving objects at different stages of visual processing, relative to the real-time position of the object. During early stimulus-evoked activity, position representations of moving objects were activated substantially earlier than the equivalent activity evoked by unpredictable flashes, aligning the earliest representations of moving stimuli with their real-time positions. These findings indicate that the predictability of straight trajectories enables full compensation for the neural delays accumulated early in stimulus processing, but that delays still accumulate across later stages of cortical processing.

New remarks on the Kolmogorov entropy of certain coarse-grained deterministic systems

<abstract> <p>Unless an appropriate dissipation mechanism is introduced in its evolution, a deterministic system generally does not tend to equilibrium. However, coarse-graining such a system implies a mesoscopic representation which is no longer deterministic. The mesoscopic system should be addressed by stochastic methods, but they lead to practically infeasible calculations. However, following the pioneering work of Kolmogorov, one finds that such mesoscopic systems can be approximated by Markov processes in relevant conditions, mainly, if the microscopic system is ergodic. So, the mesoscopic system tends to stationarity in specific situations, as expected from thermodynamics. Kolmogorov proved that in the stationary case, the instantaneous entropy of the mesoscopic process, conditioned by its past trajectory, tends to a finite limit at infinite times. Thus, one can define the Kolmogorov entropy. It can be shown that in certain situations, this property remains true even in the nonstationary case. We anticipated this important conclusion in a previous article, giving some elements of a justification, whereas it is precisely derived below in relevant conditions and in the case of a discrete system. It demonstrates that the Kolmogorov entropy is linked to basic aspects of time, such as its irreversibility. This extends the well-known conclusions of Boltzmann and of more recent researchers and gives a general insight to the fascinating relation between time and entropy.</p> </abstract>

Distributed Data-Driven Control of Network Systems

Imperfect models lead to imperfect controllers and deriving accurate models from first principles or system identification is especially challenging in networked systems. Instead, data can be used to directly compute controllers, without requiring any system identification or modeling. In this paper we propose a strategy to directly learn control actions when data from past system trajectories is distributed among multiple agents in a network. The approach we develop provably converges to a suboptimal solution in a finite number of steps, bounded by the diameter of the network, and with a sub-optimality gap that can be characterized as a function of data, and that can be made arbitrarily small. We further characterize the robustness properties of our approach and give provable guarantees on its performance when data are affected by noise or by a class of attacks.

Globally Convergent Policy Gradient Methods for Linear Quadratic Control of Partially Observed Systems

While the optimization landscape of policy gradient methods has been recently investigated for partially observed linear systems in terms of both static output feedback and dynamical controllers, they only provide convergence guarantees to stationary points. In this paper, we propose a new policy parameterization for partially observed linear systems, using a past input-output trajectory of finite length as feedback. We show that the solution set to the parameterized optimization problem is a matrix space, which is invariant to similarity transformation. By proving a gradient dominance property, we show the global convergence of policy gradient methods. Moreover, we observe that the gradient is orthogonal to the solution set, revealing an explicit relation between the resulting solution and the initial policy. Finally, we perform simulations to validate our theoretical results.

Differentially Flat Learning-Based Model Predictive Control Using a Stability, State, and Input Constraining Safety Filter

Learning-based optimal control algorithms control unknown systems using past trajectory data and a learned model of the system dynamics. These controllers use either a linear approximation of the learned dynamics, trading performance for faster computation, or nonlinear optimization methods, which typically perform better but can limit real-time applicability. In this work, we present a novel nonlinear controller that exploits differential flatness to achieve similar performance to state-of-the-art learning-based controllers but with significantly less computational effort. Differential flatness is a property of dynamical systems whereby nonlinear systems can be exactly linearized through a nonlinear input mapping. Here, the nonlinear transformation is learned as a Gaussian process and is used in a safety filter that guarantees, with high probability, stability as well as input and flat state constraint satisfaction. This safety filter is then used to refine inputs from a flat model predictive controller to perform constrained nonlinear learning-based optimal control through two successive convex optimizations. We compare our method to state-of-the-art learning-based control strategies and achieve similar performance, but with significantly better computational efficiency, while also respecting flat state and input constraints, and guaranteeing stability.

Why do (some) ordinary Americans support tax cuts for the rich? Evidence from a randomised survey experiment

Why do (some) ordinary citizens support tax cuts for the rich? We test four prominent explanations – unenlightened self-interest, fairness considerations, prospect of upward mobility, and trickle-down beliefs – using a randomised, online information provision experiment, embedded in a representative survey of around 3000 US Americans. The results show that preferences for taxing the rich are fundamentally affected by information that shifts citizens’ core fairness beliefs, as well as information on the past trajectory of top tax rates. In contrast, we find no evidence in support of the unenlightened self-interest or prospect of upward mobility explanations. Overall, our results align with theories of tax policy preferences that emphasise the importance of fairness perceptions and reference points.

Implementing circular business models in the textile and clothing industry

Textile companies face high pressure to move towards circular economy. This study investigates capabilities that enable clothing brands to overcome their past in linear economy, and eventually, to shift to CBMs. Previous research has pinpointed that company-level implementation of circular economy is still poorly understood and leaves companies without guidelines about how to make the change happen. To address this gap, two research questions are placed: how past trajectories affect the position of clothing brands in the move towards circular business models, and what capabilities are needed in the transformation process to overcome transformation-related challenges. A multiple case study was conducted in seven fashion brand companies in one country. Product-orientation and dependency on the current product portfolio affect the way the companies can transform the business models. Companies need to develop capabilities to sense opportunities, to break away from a linear business culture and restructure the firm resources accordingly. The position in the global value chain affects the way companies can adopt circularity. Strong position that allows control over the circularity of the value chain can be achieved through ownership, or by allying in business ecosystems. The value of the study is in identifying factors that affect the business model transformation process of incumbent textile companies, and capabilities needed to overcome the inertia by these factors.

Egocentric Human Trajectory Forecasting With a Wearable Camera and Multi-Modal Fusion

In this paper, we address the problem of forecasting the trajectory of an egocentric camera wearer (ego-person) in crowded spaces. The trajectory forecasting ability learned from the data of different camera wearers walking around in the real world can be transferred to assist visually impaired people in navigation, as well as to instill human navigation behaviours in mobile robots, enabling better human-robot interactions. To this end, a novel egocentric human trajectory forecasting dataset was constructed, containing real trajectories of people navigating in crowded spaces wearing a camera, as well as extracted rich contextual data. We extract and utilize three different modalities to forecast the trajectory of the camera wearer, i.e., his/her past trajectory, the past trajectories of nearby people, and the environment such as the scene semantics or the depth of the scene. A Transformer-based encoder-decoder neural network model, integrated with a novel cascaded cross-attention mechanism that fuses multiple modalities, has been designed to predict the future trajectory of the camera wearer. Extensive experiments have been conducted, with results showing that our model outperforms the state-of-the-art methods in egocentric human trajectory forecasting.

Graph-Based Spatial-Temporal Convolutional Network for Vehicle Trajectory Prediction in Autonomous Driving

Forecasting the trajectories of neighbor vehicles is a crucial step for decision making and motion planning of autonomous vehicles. This paper proposes a graph-based spatial-temporal convolutional network (GSTCN) to predict future trajectory distributions of all neighbor vehicles using past trajectories. This network tackles the spatial interactions using a graph convolutional network (GCN), and captures the temporal features with a convolutional neural network (CNN). The spatial-temporal features are encoded and decoded by a gated recurrent unit (GRU) network to generate future trajectory distributions. Besides, we propose a weighted adjacency matrix to describe the intensities of mutual influence between vehicles, and the ablation study demonstrates the effectiveness of our proposed scheme. Our network is evaluated on two real-world freeway trajectory datasets: I-80 and US-101 in the Next Generation Simulation (NGSIM).Comparisons in three aspects, including prediction errors, model sizes, and inference speeds, show that our network can achieve state-of-the-art performance.

Entrenching Authoritarian Rule and Thailand's Foreign Policy Dilemma as a Middle Power

executive summary: This essay explores the strategies that Thailand has used to navigate the great-power competition between the U.S. and China as well as the factors that are driving contemporary Thailand's foreign policy orientation and dilemmas. main argument Recent Thai governments, whether authoritarian or democratically elected, have maintained amicable relations with Beijing while retaining close military cooperation with Washington. However, domestic political dynamics within Thailand are critical to the past and future trajectory of Thailand's bilateral relations with the U.S. and China. Bangkok's reversion toward authoritarian military rule over the past few years has created a different logic in its foreign relations. For the U.S., Thailand's authoritarian turn, although not to Washington's liking, can be tolerated as long as Thailand continues to serve U.S. strategic interests in the region. For China, cordial relations with the incumbent military government of Thailand pave the way for closer economic and political relations, yet anti-authoritarian sentiment within Thailand holds China liable and condemns China's complicity in Thailand's turn to military rule. Thus, balancing Thailand's domestic political dynamic with the foreign policy interests of the U.S. and China is the defining feature of Bangkok's maneuvering act between the two great powers. policy implications • As U.S.-China relations grow more tense, countries in Southeast Asia are feeling the squeeze from both sides. These states sense they need to prepare for choosing between the two powers while trying to delay or prevent such a choice from being necessary. • Thailand's turn toward domestic authoritarian rule creates a dilemma for both the U.S. and China. For the U.S., there is a need to tolerate an authoritarian government in Bangkok to maintain stable military and security cooperation. For China, Thailand's domestic political situation creates a backlash against Chinese interests in the country. • The current military government in Thailand is likely to prioritize its own domestic political legitimacy over relations with either power and will take advantage of the Sino-U.S. competition however best suits its own regime survival. • If Thailand reverts to a democratically elected government with popular support, healthy relationships with both China and the U.S. are more likely to result and endure.

Higher Education in Russia by Yaroslav Kuzminov and Maria Yudkevich

Reviewed by: Higher Education in Russia by Yaroslav Kuzminov and Maria Yudkevich Stephen Webber Kuzminov, Yaroslav and Yudkevich, Maria. Higher Education in Russia. Johns Hopkins University Press, Baltimore, MD, 2022. xxxvi + 389 pp. Tables. Notes. Bibliography. Index. $59.95: £44.50 (paperback & e-book). The societal, political and economic changes introduced in the Soviet Union under Mikhail Gorbachev opened the way to attempts to reform the Soviet education system, both from above, and also through bottom-up efforts of innovators working at the classroom level. Changes were also seen, in the late Soviet period and into the post-Soviet era, at institutional level, in the higher education (HE) sphere (in what had been highly-centralized system to that point), including the introduction of new, independent higher education institutions (HEIs). The motives for change were seemingly clear — a pressing need to modernize the higher education system, release it from ideological control and constraints, reinvigorate disciplines (particularly the social sciences) and introduce new fields of study necessary for the transition to the market economy, instil a spirit of academic freedom in line with the democratic transformations to which the governing elites had apparently committed the country's path forward. One such new HEI was the Higher School of Economics (HSE), established in 1992, with Evgenii Yasin (Minister for the Economy 1994–97) and Yaroslav Kuzminov (rector of HSE from 1992 to 2021) playing the key roles in setting up the new university — which has since gone on to be a key institution in the Russian system, with a headquarters in Moscow and three additional campuses in Russia. Kuzminov, co-author of the volume under review along with Maria Yudkevich (a vice rector at HSE and author of numerous works on HE in Russia) has been at the core of higher education sector reforms — including the transition to the Unified State Examination taken by school-leavers as the entry mechanism to higher education (away from institution-based entry procedures that were rife with corrupt practices), and the entry of the Russian Federation into the Bologna Process. The authors of the volume were therefore able to draw on long and close experience of developments in Russian higher education when preparing this volume. [End Page 787] The book begins with two chapters covering the historical background to the contemporary system, focusing on the development of Russian higher education from the establishment of the Academy of Sciences in 1725 and subsequent expansion of the system in the nineteenth century (with reforms mirroring the swings between liberalization, conservatism and repression seen in Russia as a whole during this time), and under Soviet rule in the period 1917–91. The authors provide an effective overview of the key features that shaped the system in its formative years, and which have continued to exert a strong influence ever since. These include the role of the State in influencing (controlling) the organization, resourcing and content of higher education; the centralized features of the system (including the commonality of curricula across the country, the requirements for HEIs to train students in line with the needs of the State and key sectors of the economy, the disbalanced focus on the first mission of universities in teaching and learning, coupled with separation of the second mission of research and neglect of the third mission of societal engagement). The remaining seven chapters of the book focus on the contemporary Russian higher education system, tracing the developments seen since 1991 in the spheres of Governance and Resources, school-to-higher education transition of students and their subsequent experience of HE studies, the academic profession, the research role of universities, the organization of university activities and the transition to a 'project approach' and the internationalization of Russian higher education. This wide-ranging review, supported by extensive referencing to statistical data, allows the reader to gain an informed perspective on the extent to which such initiatives as the 5-100 programme (aimed at establishing a constant presence of at least five Russian HEIs in the global top 100 rankings by 2020) have been effective with regard to the institutions involved. As the authors note across the review, the legacy of the past trajectories in Russian and...

Everlasting impact of initial perturbations on first-passage times of non-Markovian random walks

Persistence, defined as the probability that a signal has not reached a threshold up to a given observation time, plays a crucial role in the theory of random processes. Often, persistence decays algebraically with time with non trivial exponents. However, general analytical methods to calculate persistence exponents cannot be applied to the ubiquitous case of non-Markovian systems relaxing transiently after an imposed initial perturbation. Here, we introduce a theoretical framework that enables the non-perturbative determination of persistence exponents of Gaussian non-Markovian processes with non stationary dynamics relaxing to a steady state after an initial perturbation. Two situations are analyzed: either the system is subjected to a temperature quench at initial time, or its past trajectory is assumed to have been observed and thus known. Our theory covers the case of spatial dimension higher than one, opening the way to characterize non-trivial reaction kinetics for complex systems with non-equilibrium initial conditions.

CSR: Cascade Conditional Variational Auto Encoder with Socially-aware Regression for Pedestrian Trajectory Prediction

Pedestrian trajectory prediction is a key technology in many real applications such as video surveillance, social robot navigation, and autonomous driving, and significant progress has been made in this research topic. However, there remain two limitations of previous studies. First, the losses of the last time steps are heavier weighted than that of the beginning time steps in the objective function at the learning stage, causing the prediction errors generated at the beginning to accumulate to large errors at the last time steps at the inference stage. Second, the prediction results of multiple pedestrians in the prediction horizon might be socially incompatible with the interactions modeled by past trajectories. To overcome these limitations, this work proposes a novel trajectory prediction method called CSR, which consists of a cascaded conditional variational autoencoder (CVAE) module and a socially-aware regression module. The CVAE module estimates the future trajectories in a cascaded sequential manner. Specifically, each CVAE concatenates the past trajectories and the predicted location points so far as the input and predicts the adjacent location at the following time step. The socially-aware regression module generates offsets from the estimated future trajectories to produce the corrected predictions, which are more reasonable and accurate than the estimated trajectories. Experiments results demonstrate that the proposed method exhibits significant improvements over state-of-the-art methods on the Stanford Drone Dataset (SDD) and the ETH/UCY dataset of approximately 38.0% and 22.2%, respectively. The code is available at https://github.com/zhouhao94/CSR.

No Spaces without History

Abstract Research on urban spaces in the Gulf region has increased substantially over the last two decades, particularly with a strong focus on contemporary phenomena. However, this focus often overlooks entangled histories and past trajectories that are formative for the present. Moreover, it perpetuates the notion of the region’s ahistoricity. To challenge the Gulf cities’ presumed lack of history, we have used a media-historical approach engaging with the history of a medium (e.g., architecture, film, magazine, photography, social media) in relation to a specific city. The article first provides an overview of recent research on the Gulf’s urban cultures in various disciplines. After introducing our approach, the article then considers temporality and spatiality as research perspectives in media studies and subsequently shifts to established media-historical approaches within Middle Eastern and South Asian area studies. It evaluates the complexities of writing on the art and architectural histories of the Gulf as specific forms of media. Finally, it addresses the potential of transdisciplinarity and collaboration as methods resituating the Gulf within the Arab region, the Persianate world and the Indian Ocean, respectively.

Long-term growth decline precedes sudden crown dieback of European beech

European beech (Fagus sylvatica L.) has strongly suffered from the exceptional 2018 drought and subsequent dry years that hit Central Europe. While many trees showed severe signs of crown dieback or died following the 2018 extreme drought, other co-occurring and neighboring trees showed no sign of dieback or only minor damage. The reasons why some trees were more severely impacted than others and which predisposing factors make some trees more vulnerable than others are still poorly understood. Here, we analyzed differences in long-term growth trends, neighborhood composition (competition and species diversity), early-warning signals, and growth responses to past severe droughts of co-occurring vital and severely declining beech trees at six sites in Switzerland. We aimed to connect tree vitality after 2018 with past long-term growth trajectories and investigated whether declining trees had already been more susceptible to drought than vital trees before dieback occurred.Overall, trees that showed severe crown dieback had a stronger growth decline than vital trees in the last 50 years. Declining trees exhibited stagnating and then decreasing growth trajectories even before signs of crown dieback occurred. Interestingly, we did not find significant differences in growth response to past severe droughts between the vitality classes, with the exception that vital trees recovered faster from past more severe droughts. Further, we could neither detect any difference in the effect of competition and neighborhood species composition on growth response, nor predict crown dieback based on early-warning signals which try to predict regime shifts by sudden changes in the autoregressive coefficient with lag 1, standard deviation and skewness. Our results indicate that unlike vital trees, declining beech trees showed predisposing signs for crown dieback by having lower growth rates during the last 50 years.

Ancient Yersinia pestis and Salmonella enterica genomes from Bronze Age Crete

During the late 3rd millennium BCE, the Eastern Mediterranean and Near East witnessed societal changes in many regions, which are usually explained with a combination of social and climatic factors.1-4 However, recent archaeogenetic research forces us to rethink models regarding the role of infectious diseases in past societal trajectories.5 The plague bacterium Yersinia pestis, which was involved in some of the most destructive historical pandemics,5-8 circulated across Eurasia at least from the onset of the 3rd millennium BCE,9-13 but the challenging preservation of ancient DNA in warmer climates has restricted the identification of Y.pestis from this period to temperate climatic regions. As such, evidence from culturally prominent regions such as the Eastern Mediterranean is currently lacking. Here, we present genetic evidence for the presence of Y.pestis and Salmonella enterica, the causative agent of typhoid/enteric fever, from this period of transformation in Crete, detected at the cave site Hagios Charalambos. We reconstructed one Y.pestis genome that forms part of a now-extinct lineage of Y.pestis strains from the Late Neolithic and Bronze Age that were likely not yet adapted for transmission via fleas. Furthermore, we reconstructed two ancient S.enterica genomes from the Para C lineage, which cluster with contemporary strains that were likely not yet fully host adapted to humans. The occurrence of these two virulent pathogens at the end of the Early Minoan period in Crete emphasizes the necessity to re-introduce infectious diseases as an additional factor possibly contributing to the transformation of early complex societies in the Aegean and beyond.