Moses C. Nah, Johannes Lachner, Neville Hogan
et al.
This paper presents a modular framework for motion planning using movement primitives. Central to the approach is Contraction Theory, a modular stability tool for nonlinear dynamical systems. The approach extends prior methods by achieving parallel and sequential combinations of both discrete and rhythmic movements, while enabling independent modulation of each movement. This modular framework enables a divide-and-conquer strategy to simplify the programming of complex robot motion planning. Simulation examples illustrate the flexibility and versatility of the framework, highlighting its potential to address diverse challenges in robot motion planning.
Valerio La Gatta, Marco Postiglione, Jeremy Gilbert
et al.
Numerous social movements (SMs) around the world help support the UN's Sustainable Development Goals (SDGs). Understanding how key events shape SMs is key to the achievement of the SDGs. We have developed SMART (Social Media Analysis & Reasoning Tool) to track social movements related to the SDGs. SMART was designed by a multidisciplinary team of AI researchers, journalists, communications scholars and legal experts. This paper describes SMART's transformer-based multivariate time series Discourse Evolution Engine for Predictions about Social Movements (DEEP) to predict the volume of future articles/posts and the emotions expressed. DEEP outputs probabilistic forecasts with uncertainty estimates, providing critical support for editorial planning and strategic decision-making. We evaluate DEEP with a case study of the #MeToo movement by creating a novel longitudinal dataset (433K Reddit posts and 121K news articles) from September 2024 to June 2025 that will be publicly released for research purposes upon publication of this paper.
Higher-order patterns reveal sequential multistep state transitions, which are usually superior to origin-destination analysis, which depicts only first-order geospatial movement patterns. Conventional methods for higher-order movement modeling first construct a directed acyclic graph (DAG) of movements, then extract higher-order patterns from the DAG. However, DAG-based methods heavily rely on the identification of movement keypoints that are challenging for sparse movements and fail to consider the temporal variants that are critical for movements in urban environments. To overcome the limitations, we propose HoLens, a novel approach for modeling and visualizing higher-order movement patterns in the context of an urban environment. HoLens mainly makes twofold contributions: first, we design an auto-adaptive movement aggregation algorithm that self-organizes movements hierarchically by considering spatial proximity, contextual information, and temporal variability; second, we develop an interactive visual analytics interface consisting of well-established visualization techniques, including the H-Flow for visualizing the higher-order patterns on the map and the higher-order state sequence chart for representing the higher-order state transitions. Two real-world case studies manifest that the method can adaptively aggregate the data and exhibit the process of how to explore the higher-order patterns by HoLens. We also demonstrate our approach's feasibility, usability, and effectiveness through an expert interview with three domain experts.
Understanding how cooperation emerges in public goods games is crucial for addressing societal challenges. While optional participation can establish cooperation without identifying cooperators, it relies on specific assumptions -- that individuals abstain and receive a non-negative payoff, or that non-participants cause damage to public goods -- which limits our understanding of its broader role. We generalize this mechanism by considering non-participants' payoffs and their potential direct influence on public goods, allowing us to examine how various strategic motives for non-participation affect cooperation. Using replicator dynamics, we find that cooperation thrives only when non-participants are motivated by individualistic or prosocial values, with individualistic motivations yielding optimal cooperation. These findings are robust to mutation, which slightly enlarges the region where cooperation can be maintained through cyclic dominance among strategies. Our results suggest that while optional participation can benefit cooperation, its effectiveness is limited and highlights the limitations of bottom-up schemes in supporting public goods.
Understanding and modeling pedestrian movements in the real world is crucial for applications like motion forecasting and scene simulation. Many factors influence pedestrian movements, such as scene context, individual characteristics, and goals, which are often ignored by the existing human generation methods. Web videos contain natural pedestrian behavior and rich motion context, but annotating them with pre-trained predictors leads to noisy labels. In this work, we propose learning diverse pedestrian movements from web videos. We first curate a large-scale dataset called CityWalkers that captures diverse real-world pedestrian movements in urban scenes. Then, based on CityWalkers, we propose a generative model called PedGen for diverse pedestrian movement generation. PedGen introduces automatic label filtering to remove the low-quality labels and a mask embedding to train with partial labels. It also contains a novel context encoder that lifts the 2D scene context to 3D and can incorporate various context factors in generating realistic pedestrian movements in urban scenes. Experiments show that PedGen outperforms existing baseline methods for pedestrian movement generation by learning from noisy labels and incorporating the context factors. In addition, PedGen achieves zero-shot generalization in both real-world and simulated environments. The code, model, and data will be made publicly available at https://genforce.github.io/PedGen/ .
Science is about facts and truth. Yet sometimes the truth and facts are not obvious. For example, in the field of MRI (Magnetic Resonance Imaging), there has been a long-lasting debate about who were the major contributors in its development. Particularly, there was a strong dispute between the followers of two scientists, R. Damadian and P. Lauterbur. In this review, we carefully trace the major developments in applying NMR for cancer detection starting almost 50 years ago. The research records show that the truth was beyond the claims of either research camps. The development of NMR for cancer detection involved multiple research groups, who made critical contributions at different junctures.
Human hand and head movements are the most pervasive input modalities in extended reality (XR) and are significant for a wide range of applications. However, prior works on hand and head modelling in XR only explored a single modality or focused on specific applications. We present HaHeAE - a novel self-supervised method for learning generalisable joint representations of hand and head movements in XR. At the core of our method is an autoencoder (AE) that uses a graph convolutional network-based semantic encoder and a diffusion-based stochastic encoder to learn the joint semantic and stochastic representations of hand-head movements. It also features a diffusion-based decoder to reconstruct the original signals. Through extensive evaluations on three public XR datasets, we show that our method 1) significantly outperforms commonly used self-supervised methods by up to 74.0% in terms of reconstruction quality and is generalisable across users, activities, and XR environments, 2) enables new applications, including interpretable hand-head cluster identification and variable hand-head movement generation, and 3) can serve as an effective feature extractor for downstream tasks. Together, these results demonstrate the effectiveness of our method and underline the potential of self-supervised methods for jointly modelling hand-head behaviours in extended reality.
The Doctrine of Triangles is the second half of Glen Van Brummelen’s history of trigonometry. The first part, The Mathematics of the Heavens and the Earth [Van Brummelen 2009] dealt with the early history of trigonometry, from “precursors” in ancient Egyptian and Babylonian sources (dating from the second millennium BC), through to the trigonometric tables of Georg Rheticus in the 16th century AD. The present volume begins with a brief account of the story so far and then continues the story through to the early 20th century. It will be helpful to review briefly that “story so far”.
En mayo de 1597 el capitán William Parker, junto con medio centenar de hombres, incursionó sobre la villa y puerto de San Francisco de Campeche con el fin de obtener algún tipo de botín. El presente ensayo tiene como propósito analizar este caso para aproximarnos a la configuración y características de esta incursión a partir de las violencias suscitadas en ella. Dar cuenta de este proceso implica abordar los contextos que arroparon las violencias, así como develar a los sujetos involucrados y sus acciones en el marco de este acometimiento. Por tanto, el estudio de este caso es también una propuesta de análisis micro. Nuestra exposición conlleva las siguientes secciones: a) marcos de racionalidad, b) intencionalidades, c) despliegue de las violencias y d) conclusiones. Afirmamos que las incursiones fueron hechos sociales violentos multidimensionales, multifacéticos e históricamente situados.
Kamal Rana, Kushanav Bhuyan, Joaquin Vicente Ferrer
et al.
The death toll and monetary damages from landslides continue to rise despite advancements in predictive modeling. The predictive capability of these models is limited as landslide databases used in training and assessing the models often have crucial information missing, such as underlying failure types. Here, we present an approach for identifying failure types based on their movements, e.g., slides and flows by leveraging 3D landslide topology. We observe topological proxies reveal prevalent signatures of mass movement mechanics embedded in the landslide's morphology or shape, such as detecting coupled movement styles within complex landslides. We find identical failure types exhibit similar topological properties, and by using them as predictors, we can identify failure types in historic and event-specific landslide databases (including multi-temporal) from various geomorphological and climatic contexts such as Italy, the US Pacific Northwest region, Denmark, Turkey, and China with 80 to 94 % accuracy. To demonstrate the real-world application of the method, we implement it in two undocumented datasets from China and publicly release the datasets. These new insights can considerably improve the performance of landslide predictive models and impact assessments. Moreover, our work introduces a new paradigm for studying landslide shapes to understand underlying processes through the lens of landslide topology.
Origami structures have been widely explored in robotics due to their many potential advantages. Origami robots can be very compact, as well as cheap and efficient to produce. In particular, they can be constructed in a flat format using modern manufacturing techniques. Rotational motion is essential for robotics, and a variety of origami rotational joints have been proposed in the literature. However, few of these are even approximately flat-foldable. One potential enabler of flat origami rotational joints is the inclusion of lightweight pneumatic pouches which actuate the origami's folds; however, pouch actuators only enable a relatively small amount of rotational displacement. The previously proposed Four-Vertex Origami is a flat-foldable structure which provides an angular multiplier for a pouch actuator, but suffers from a degenerate state. This paper presents a novel rigid origami, the Self-Lock Origami, which eliminates this degeneracy by slightly relaxing the assumption of flat-foldability. This joint is analysed in terms of a trade-off between the angular multiplier and the mechanical advantage. Furthermore, the Self-Lock Origami is a modular joint which can be connected to similar or different joints to produce complex movements for various applications; three different manipulator designs are introduced as a proof of concept.
En esta reseña se explora la contribución del libro de Ileana Rodríguez a la historia del pasado reciente nicaragüense. Se reflexiona sobre sus referentes analíticos y, en particular sobre los nuevos focos de acercamiento que este ofrece, entre los cuales destaca una narrativa que se teje vinculando las dimensiones políticas y afectivas. Se indaga en su construcción discursiva en aras de aprehender cómo la autora desde la historia del proceso de la revolución sandinista ofrece nuevas rutas para aproximarnos a la construcción del poder que durante décadas fue cimentando las bases del estado autoritario contemporáneo.
Non-invasive brain-computer interfaces help the subjects to control external devices by brain intentions. The multi-class classification of upper limb movements can provide external devices with more control commands. The onsets of the upper limb movements are located by the external limb trajectory to eliminate the delay and bias among the trials. However, the trajectories are not recorded due to the limitation of experiments. The delay cannot be avoided in the analysis of signals. The delay negatively influences the classification performance, which limits the further application of upper limb movements in the brain-computer interface. This work focuses on multi-channel brain signals analysis in the temporal-frequency approach. It proposes the two-stage-training temporal-spectral neural network (TTSNet) to decode patterns from brain signals. The TTSNet first divides the signals into various filter banks. In each filter bank, task-related component analysis is used to reduce the dimension and reject the noise of the brain. A convolutional neural network (CNN) is then used to optimize the temporal characteristic of signals and extract class-related features. Finally, these class-related features from all filter banks are fused by concatenation and classified by the fully connected layer of the CNN. The proposed method is evaluated in two public datasets. The results show that TTSNet has an improved accuracy of 0.7456$\pm$0.1205 compared to the EEGNet of 0.6506$\pm$0.1275 ($p<0.05$) and FBTRCA of 0.6787$\pm$0.1260 ($p<0.1$) in the movement detection task, which classifies the movement state and the resting state. The proposed method is expected to help detect limb movements and assist in the rehabilitation of stroke patients.
Este artículo presenta un análisis de la composición del estudiantado de la Sede Regional Brunca de la Universidad Nacional, con el fin de determinar la trayectoria histórica seguida por esta población beneficiada gracias al proceso de regionalización ejecutado por este centro de estudios. Dicho análisis procura vislumbrar, en alguna medida, la implementación de la óptica democratizante de esta universidad estatal. El argumento central de la investigación es el vínculo establecido entre el fortalecimiento institucional de la sede y la expansión de los servicios educativos que ofrece. Así, el crecimiento de la Sede Regional Brunca ha significado un mayor acceso a educación superior de grupos sociales vulnerables. Las fuentes utilizadas son las Estadísticas de Educación Superior del Consejo Nacional de Rectores y datos estadísticos de la Oficina de Registro de la Universidad Nacional.
El presente artículo parte del concepto de «memoria-patrimonio», el cual se construye a través de tres canales básicos: la lengua, la educación y las instituciones. En la ciudad de Puebla, la Iglesia y el Ayuntamiento se basaron en dichos canales para formar al ciudadano socialmente útil y moralmente religioso. Mediante estos discursos, se generó una memoria colectiva que comenzó a apoyarse en imágenes que defendían su patriotismo; así, aparecieron personajes históricamente sobresalientes como Iturbide o la Virgen de Guadalupe, imágenes que formaron parte del panteón de celebraciones nacionales y locales, y que se convirtieron en referentes de identidad y educativos de la población angelopolitana.
El antropólogo e historiador de origen holandés Ralph Sprenkels murió inesperadamente en septiembre de 2019. Sprenkels dedicó la mayor parte de su investigación a entender y explicar el conflicto armado y la posguerra en El Salvador. El presente artículo es un recorrido sobre cómo Sprenkels se vinculó a dicho país y el impacto que tuvo en su vida. Sprenkels decidió involucrarse en el trabajo a favor de los derechos humanos y, posteriormente, se dedicó a la investigación de la polarización política en la posguerra, al estudio de la memoria y la justicia transicional, así como a comprender la dinámica de la posguerra de uno de los movimientos revolucionarios más importantes de Latinoamérica: el FMLN. En este sentido, el artículo realiza un recuento general de su obra, a partir de lo cual concluye que el trabajo académico de Sprenkels constituye un aporte significativo para la comprensión de la realidad salvadoreña, y que realiza contribuciones teóricas relevantes para nuevas investigaciones.
Esta entrevista constituye un material inédito que remite al trabajo del historiador Juan José Marín Hernández (Q.E.D.P), a propósito de su trayectoria en la investigación sobre la historia de la prostitución en Costa Rica. La entrevista fue realizada en febrero del año 2015 y constituyó también una mirada de Marín en torno a la historia reciente de Costa Rica, a través de la cual él analizó problemáticas como el neoliberalismo, el auge del turismo, la exclusión, las desigualdades, la moral pública, entre otras. Su trabajo se vincula con fuentes y autores, algunos de los cuales fueron retomados en el contenido de este diálogo.
We investigate critical phenomena of the Yang-Mills (YM) type one-dimensional matrix model that is a large-$N$ reduction (or dimensional reduction) of the $D+1$ dimensional $U(N)$ pure YM theory (bosonic BFSS model). This model shows a large-$N$ phase transition at finite temperature, which is analogous to the confinement/deconfinement transition of the original YM theory. We study the matrix model at a three-loop calculation via the "principle of minimum sensitivity" and find that there is a critical dimension $D=35.5$: At $D \le 35$, the transition is of first order, while it is of second order at $D\ge 36$. Furthermore, we evaluate several observables in our method, and they nicely reproduce the existing Monte Carlo results. Through the gauge/gravity correspondence, the transition is expected to be related to a Gregory-Laflamme transition in gravity, and we argue that the existence of the critical dimension is qualitatively consistent with it. Besides, in the first order transition case, a stable phase having negative specific heat appears in the microcanonical ensemble, which is similar to Schwarzschild black holes. We study some properties of this phase.