We record and analyze the movement patterns of the marsupial {\it Didelphis aurita} at different temporal scales. Animals trajectories are collected at a daily scale by using spool-and-line techniques, and with the help of radio-tracking devices animals traveled distances are estimated at intervals of weeks. Small-scale movements are well described by truncated Lévy flight, while large-scale movements produce a distribution of distances which is compatible with a Brownian motion. A model of the movement behavior of these animals, based on a truncated Lévy flight calibrated on the small scale data, converges towards a Brownian behavior after a short time interval of the order of one week. These results show that whether Lévy flight or Brownian motion behaviors apply, will depend on the scale of aggregation of the animals paths. In this specific case, as the effect of the rude truncation present in the daily data generates a fast convergence towards Brownian behaviors, Lévy flights become of scarce interest for describing the local dispersion properties of these animals, which result well approximated by a normal diffusion process and not a fast, anomalous one. Interestingly, we are able to describe two movement phases as the consequence of a statistical effect generated by aggregation, without the necessity of introducing ecological constraints or mechanisms operating at different spatio-temporal scales. This result is of general interest, as it can be a key element for describing movement phenomenology at distinct spatio-temporal scales across different taxa and in a variety of systems.
Jose J. Quintana, Miguel A. Ferrer, Moises Diaz
et al.
Collaborative robots or cobots interact with humans in a common work environment. In cobots, one under investigated but important issue is related to their movement and how it is perceived by humans. This paper tries to analyze whether humans prefer a robot moving in a human or in a robotic fashion. To this end, the present work lays out what differentiates the movement performed by an industrial robotic arm from that performed by a human one. The main difference lies in the fact that the robotic movement has a trapezoidal speed profile, while for the human arm, the speed profile is bell-shaped and during complex movements, it can be considered as a sum of superimposed bell-shaped movements. Based on the lognormality principle, a procedure was developed for a robotic arm to perform human-like movements. Both speed profiles were implemented in two industrial robots, namely, an ABB IRB 120 and a Universal Robot UR3. Three tests were used to study the subjects' preference when seeing both movements and another analyzed the same when interacting with the robot by touching its ends with their fingers.
Eifu Narita, Keigo Ushiyama, Izumi Mizoguchi
et al.
Kinesthetic illusion can present a sense of movement without actual physical movement of the body, but it often lacks a sense of agency over the movement. Therefore, we focused on the sensation of walking induced by the kinesthetic illusion and hypothesized that incorporating coordinated arm swing movements as actual actions could enhance the sense of agency over the kinesthetic illusion. In this study, we implemented a system that switches the vibrations of the thighs and ankles back and forth based on arm swing movements and investigated whether the sense of agency over the walking sensation induced by the kinesthetic illusion changes with or without arm swing movements. The results suggest a tendency for the sense of agency to be enhanced when arm swing movements are combined.
Surface electromyographic (sEMG) signal serve as a signal source commonly used for lower limb movement recognition, reflecting the intent of human movement. However, it has been a challenge to improve the movements recognition rate while using fewer features in this area of research area. In this paper, a method for lower limb movements recognition based on recursive feature elimination and backpropagation neural network of support vector machine is proposed. First, the sEMG signal of five subjects performing eight different lower limb movements was recorded using a BIOPAC collector. The optimal feature subset consists of 25 feature vectors, determined using a Recursive Feature Elimination based on Support Vector Machine (SVM-RFE). Finally, this study used five supervised classification algorithms to recognize these eight different lower limb movements. The results of the experimental study show that the combination of the BPNN classifier and the SVM-RFE feature selection algorithm is able to achieve an excellent action recognition accuracy of 95\%, which provides sufficient support for the feasibility of this approach.
The financial industry poses great challenges with risk modeling and profit generation. These entities are intricately tied to the sophisticated prediction of stock movements. A stock forecaster must untangle the randomness and ever-changing behaviors of the stock market. Stock movements are influenced by a myriad of factors, including company history, performance, and economic-industry connections. However, there are other factors that aren't traditionally included, such as social media and correlations between stocks. Social platforms such as Reddit, Facebook, and X (Twitter) create opportunities for niche communities to share their sentiment on financial assets. By aggregating these opinions from social media in various mediums such as posts, interviews, and news updates, we propose a more holistic approach to include these "media moments" within stock market movement prediction. We introduce a method that combines financial data, social media, and correlated stock relationships via a graph neural network in a hierarchical temporal fashion. Through numerous trials on current S&P 500 index data, with results showing an improvement in cumulative returns by 28%, we provide empirical evidence of our tool's applicability for use in investment decisions.
Rémi Grisot, Pierre Laurent, Claire Migliaccio
et al.
Monitoring the activity of the heart is important for diagnosing and preventing cardiovascular diseases. The electrocardiogram (ECG) is the gold standard for diagnosing such diseases. It monitors the heart's electrical activity, and while this is highly correlated with the cardiac mechanical activity, it does not provide all the information. Other sensors such as the echocardiograph are able to monitor the heart's movements, but such tools are expensive and hard to operate. Therefore, contactless monitoring of the heart using RF sensing has gained interest in recent years. In this paper, we describe a process to extract the movements of the heart from millimeter wave radar with high accuracy, and thus we provide a noninvasive and affordable way to monitor cardiac movements. We then demonstrate the correlation between the observed movements and the ECG. Furthermore, we propose an algorithm to synchronize the ECG signal and the processed signal from the radar sensor. The results we obtained provide insights on the mechanical activity of the heart, which could assist cardiologists in their diagnoses.
In this paper we describe the process of collection, transcription, and annotation of recordings of spontaneous speech samples from Turkish-German bilinguals, and the compilation of a corpus called TuGeBiC. Participants in the study were adult Turkish-German bilinguals living in Germany or Turkey at the time of recording in the first half of the 1990s. The data were manually tokenised and normalised, and all proper names (names of participants and places mentioned in the conversations) were replaced with pseudonyms. Token-level automatic language identification was performed, which made it possible to establish the proportions of words from each language. The corpus is roughly balanced between both languages. We also present quantitative information about the number of code-switches, and give examples of different types of code-switching found in the data. The resulting corpus has been made freely available to the research community.
We present a method to simulate movement in interaction with computers, using Model Predictive Control (MPC). The method starts from understanding interaction from an Optimal Feedback Control (OFC) perspective. We assume that users aim to minimize an internalized cost function, subject to the constraints imposed by the human body and the interactive system. In contrast to previous linear approaches used in HCI, MPC can compute optimal controls for nonlinear systems. This allows us to use state-of-the-art biomechanical models and handle nonlinearities that occur in almost any interactive system. Instead of torque actuation, our model employs second-order muscles acting directly at the joints. We compare three different cost functions and evaluate the simulated trajectories against user movements in a Fitts' Law type pointing study with four different interaction techniques. Our results show that the combination of distance, control, and joint acceleration cost matches individual users' movements best, and predicts movements with an accuracy that is within the between-user variance. To aid HCI researchers and designers, we introduce CFAT, a novel method to identify maximum voluntary torques in joint-actuated models based on experimental data, and give practical advice on how to simulate human movement for different users, interaction techniques, and tasks.
Este artículo consiste en una aproximación a la forma en que el Estado costarricense buscaba aún, en la segunda del siglo XX, incorporar la región Norte a los proyectos emanados desde el centro del país. Se toman los proyectos viales como un hilo conductor, los cuales revelan las dimensiones de vínculo territorial intra e inter regional, teniendo presente el contexto de Guerra Fría y la inestabilidad política en Centroamérica, que hacía, de estas zonas fronterizas, espacios de importante dinamismo y progresivo cambio. Las principales fuentes utilizadas fueron de carácter cartográfico, correspondientes a los censos de 1973 y 1984, para reconstruir el entramado de comunicaciones durante esas décadas.
Tibor Sekelj, explorador, antropólogo, periodista y escritor, de padres húngaros, fue un ciudadano del mundo. Su vida aventurera inició en 1939, en América Latina, donde pasó quince años realizando expediciones andinistas y viajes de investigación a territorios habitados por comunidades indígenas, sobre todo en Brasil. Sus primeros libros publicados trataron sobre sus expediciones y fueron seguidos por publicaciones relacionadas con sus viajes posteriores en Europa, Asia y África. En América Latina, Sekelj visitó una serie de países, entre ellos Costa Rica en 1953. Después de su llegada a San José, el Diario de Costa Rica adquirió los derechos exclusivos para publicar veinte de sus artículos escritos sobre sus experiencias y viajes en América. El objetivo del presente ensayo es, además de describir brevemente la trayectoria de Sekelj en el subcontinente, presentar el proyecto costarricense mencionado, utilizando como fuente sus artículos, que se conservan en una colección privada.
A comienzos de las décadas de 1940 y 1950, el indigenismo latinoamericano desempeñó un papel central en el pensamiento de las agencias internacionales sobre la elaboración de políticas diferenciales destinadas a las poblaciones indígenas. Varios autores —Luis Rodríguez-Piñero, Todd Shepard— han destacado la influencia de México en la OIT y la UNESCO, en particular en torno al concepto de “integración”. Sin cuestionar tal continuidad, este artículo también insiste en las discrepancias, reinterpretaciones, malentendidos entre el Instituto Indigenista Interamericano, la OIT y la UNESCO. Al apropiarse de las propuestas del Instituto Indigenista Interamericano, las agencias internacionales transforman los dos sujetos figuras centrales del indigenismo —el «indígena» y el «antropólogo»— en actores globalizados y reubicables en otros contextos —el ‹‹subdesarrollado›› y el ‹‹experto››—. La cuestión de la relación entre derecho y diferencia, entre desarrollo y discriminación, en el corazón del indigenismo, sigue sin resolverse.
The ultimate goal of video prediction is not forecasting future pixel-values given some previous frames. Rather, the end goal of video prediction is to discover valuable internal representations from the vast amount of available unlabeled video data in a self-supervised fashion for downstream tasks. One of the primary downstream tasks is interpreting the scene's semantic composition and using it for decision-making. For example, by predicting human movements, an observer can anticipate human activities and collaborate in a shared workspace. There are two main ways to achieve the same outcome, given a pre-trained video prediction and pre-trained semantic extraction model; one can first apply predictions and then extract semantics or first extract semantics and then predict. We investigate these configurations using the Local Frequency Domain Transformer Network (LFDTN) as the video prediction model and U-Net as the semantic extraction model on synthetic and real datasets.
With Cyrano, Voltaire, and Verne, France provided important milestones in the history of early science fiction. However, even if the genre was not very common a few centuries ago, there were numerous additional contributions by French-speaking writers. In this paper, we review two cases of interplanetary novels written in the second half of the eighteenth century and sharing a rare particularity: their authors were female. Voyages de Milord Ceton was imagined by Marie-Anne de Roumier-Robert whereas Cornelie Wouters de Wasse conceived Le Char Volant. While their personal lives were very different, and their writing style too, both authors share in these novels a common philosophy in which equality -- between ranks but also between genders -- takes an important place. Their works thus clearly fit into the context of the Enlightenment.
Amanda Fernández-Fontelo, Pascal J. Kieslich, Felix Henninger
et al.
A central goal of survey research is to collect robust and reliable data from respondents. However, despite researchers' best efforts in designing questionnaires, respondents may experience difficulty understanding questions' intent and therefore may struggle to respond appropriately. If it were possible to detect such difficulty, this knowledge could be used to inform real-time interventions through responsive questionnaire design, or to indicate and correct measurement error after the fact. Previous research in the context of web surveys has used paradata, specifically response times, to detect difficulties and to help improve user experience and data quality. However, richer data sources are now available, in the form of the movements respondents make with the mouse, as an additional and far more detailed indicator for the respondent-survey interaction. This paper uses machine learning techniques to explore the predictive value of mouse-tracking data with regard to respondents' difficulty. We use data from a survey on respondents' employment history and demographic information, in which we experimentally manipulate the difficulty of several questions. Using features derived from the cursor movements, we predict whether respondents answered the easy or difficult version of a question, using and comparing several state-of-the-art supervised learning methods. In addition, we develop a personalization method that adjusts for respondents' baseline mouse behavior and evaluate its performance. For all three manipulated survey questions, we find that including the full set of mouse movement features improved prediction performance over response-time-only models in nested cross-validation. Accounting for individual differences in mouse movements led to further improvements.
This paper derives the analytical solution of a novel distributed node-specific block-diagonal linearly constrained minimum variance beamformer from the centralized linearly constrained minimum variance (LCMV) beamformer when considering that the noise covariance matrix is block-diagonal. To further reduce the computational complexity of the proposed beamformer, the ShermanMorrison-Woodbury formula is introduced to compute the inversion of noise sample covariance matrix. By doing so, the exchanged signals can be computed with lower dimensions between nodes, where the optimal LCMV beamformer is still available at each node as if each node is to transmit its all raw sensor signal observations. The proposed beamformer is fully distributable without imposing restrictions on the underlying network topology or scaling computational complexity, i.e., there is no increase in the per-node complexity when new nodes are added to the networks. Compared with state-of-the-art distributed node-specific algorithms that are often time-recursive, the proposed beamformer exactly solves the LCMV beamformer optimally frame by frame, which has much lower computational complexity and is more robust to acoustic transfer function estimation error and voice activity detector error. Numerous experimental results are presented to validate the effectiveness of the proposed beamformer.
Retomando un viejo ejemplo de Lacan, Žižek recordaba que, aún cuando la esposa de un hombre patológicamente celoso, efectivamente, se acostara con muchas otras personas a sus espaldas, los celos de ese marido responden a una fantasía obsesiva y paranoide. Del mismo modo, históricamente el anticomunismo, independientemente de lo que los movimientos y países del socialismo histórico hicieran o dejaran de hacer, ha funcionado como una fantasía paranoide que, a través de diversas prácticas sociales e instituciones, ha pesado decisivamente en la política costarricense al menos desde los años treinta del siglo pasado.
In image-guided surgical tasks, the precision and timing of hand movements depend on the effectiveness of visual cues relative to specific target areas in the surgeons peri-personal space. Two-dimensional (2D) image views of real-world movements are known to negatively affect both constrained (with tool) and unconstrained(no tool) hand movements compared with direct action viewing. Task conditions where virtual 3D would generate and advantage for surgical eye-hand coordination are unclear. Here, we compared effects of 2D and 3D image views on the precision and timing of surgical hand movement trajectories in a simulator environment. Eight novices had to pick and place a small cube on target areas across different trajectory segments in the surgeons peri-personal space, with the dominant hand, with and without a tool, under conditions of: (1) direct (2) 2D fisheye camera and (3) virtual 3D viewing (headmounted). Significant effects of the location of trajectories in the surgeons peri-personal space on movement times and precision were found. Subjects were faster and more precise across specific target locations, depending on the viewing modality.
The doctrinal paradox is analysed from a probabilistic point of view assuming a simple parametric model for the committee's behaviour. The well known issue-by-issue and case-by-case majority rules are compared in this model, by means of the concepts of false positive rate (FPR), false negative rate (FNR) and Receiver Operating Characteristics (ROC) space. We introduce also a new rule that we call path-by-path, which is somehow halfway between the other two. Under our model assumptions, the issue-by-issue rule is shown to be the best of the three according to an optimality criterion based in ROC maps, for all values of the model parameters (committee size and competence of its members), when equal weight is given to FPR an FNR. For unequal weights, the relative goodness of the rules depends on the values of the competence and the weights, in a way which is precisely described. The results are illustrated with some numerical examples.
We address jointly two important tasks for Question Answering in community forums: given a new question, (i) find related existing questions, and (ii) find relevant answers to this new question. We further use an auxiliary task to complement the previous two, i.e., (iii) find good answers with respect to the thread question in a question-comment thread. We use deep neural networks (DNNs) to learn meaningful task-specific embeddings, which we then incorporate into a conditional random field (CRF) model for the multitask setting, performing joint learning over a complex graph structure. While DNNs alone achieve competitive results when trained to produce the embeddings, the CRF, which makes use of the embeddings and the dependencies between the tasks, improves the results significantly and consistently across a variety of evaluation metrics, thus showing the complementarity of DNNs and structured learning.