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Communication between humans and robots (or virtual agents) is essential for interaction and often inspired by human communication, which uses gestures, facial expressions, gaze direction, and other explicit and implicit means. This work presents an interaction experiment where humans and virtual agents interact through explicit (gestures, manual entries using mouse and keyboard, voice, sound, and information on screen) and implicit (gaze direction, location, facial expressions, and raise of eyebrows) communication to evaluate the effect of mixed explicit-implicit communication against purely explicit communication. Results obtained using Bayesian parameter estimation show that the number of errors and task execution time did not significantly change when mixed explicit and implicit communications were used, and neither the perceived efficiency of the interaction. In contrast, acceptance, sociability, and transparency of the virtual agent increased when using mixed communication modalities (88.3%, 92%, and 92.9% of the effect size posterior distribution of each variable, respectively, were above the upper limit of the region of practical equivalence). This suggests that task-related measures, such as time, number of errors, and perceived efficiency of the interaction, have not been influenced by the communication type in our particular experiment. However, the improvement of subjective measures related to the virtual agent, such as acceptance, sociability, and transparency, suggests that humans are more receptive to mixed explicit and implicit communications., Comment: Main paper with 22 pages, 12 figures, 4 tables. Added supplementary material with 17 pages, 16 figures. Submitted to International Journal of Social Robotics

We present HyperLoader, a simple approach that combines different parameter-efficient fine-tuning methods in a multi-task setting. To achieve this goal, our model uses a hypernetwork to generate the weights of these modules based on the task, the transformer layer, and its position within this layer. Our method combines the benefits of multi-task learning by capturing the structure of all tasks while reducing the task interference problem by encapsulating the task-specific knowledge in the generated weights and the benefits of combining different parameter-efficient methods to outperform full-fine tuning. We provide empirical evidence that HyperLoader outperforms previous approaches in most datasets and obtains the best average performance across tasks in high-resource and low-resource scenarios.

Designers often create visualizations to achieve specific high-level analytical or communication goals. These goals require people to naturally extract complex, contextualized, and interconnected patterns in data. While limited prior work has studied general high-level interpretation, prevailing perceptual studies of visualization effectiveness primarily focus on isolated, predefined, low-level tasks, such as estimating statistical quantities. This study more holistically explores visualization interpretation to examine the alignment between designers' communicative goals and what their audience sees in a visualization, which we refer to as their comprehension. We found that statistics people effectively estimate from visualizations in classical graphical perception studies may differ from the patterns people intuitively comprehend in a visualization. We conducted a qualitative study on three types of visualizations -- line graphs, bar graphs, and scatterplots -- to investigate the high-level patterns people naturally draw from a visualization. Participants described a series of graphs using natural language and think-aloud protocols. We found that comprehension varies with a range of factors, including graph complexity and data distribution. Specifically, 1) a visualization's stated objective often does not align with people's comprehension, 2) results from traditional experiments may not predict the knowledge people build with a graph, and 3) chart type alone is insufficient to predict the information people extract from a graph. Our study confirms the importance of defining visualization effectiveness from multiple perspectives to assess and inform visualization practices., Comment: Accepted for publication in ACM CHI 2024

Australian agriculture supplies many horticultural commodities to domestic and international markets; however, food composition data for many commodities are outdated or unavailable. We produced an up-to-date, nationally representative dataset of up to 148 nutrients and related components in 92 Australian-grown fruit (fresh n=39, dried n=6), vegetables (n=43) and nuts (n=4) by replacing outdated data (pre-2000), confirming concentrations of important nutrients and retaining relevant existing data. Primary samples (n = 902) were purchased during peak growing season in Sydney, Melbourne and Perth between June 2021 and May 2022. While new data reflect current growing practices, varieties, climate and analytical methods, few notable differences were found between old and new data where methods of analysis are comparable. The new data will be incorporated into the Australian Food Composition Database, allowing free online access to stakeholders. The approach used could serve as a model for cost-effective updates of national food composition databases worldwide., Comment: 34 pages, 4 tables

Vitamin K is emerging as a multi-function vitamin that plays a role in bone, brain and vascular health. Vitamin K composition data remain limited globally and Australia has lacked nationally representative data for vitamin K1 (phylloquinone, PK) in horticultural commodities. Primary samples (n = 927) of 90 different Australian-grown fruit, vegetable and nut commodities were purchased in three Australian cities. We measured PK in duplicate in 95 composite samples using liquid chromatography with electrospray ionisation-tandem mass spectrometry. The greatest mean concentrations of PK were found in kale (565 ug/100 g), baby spinach (255 ug/100 g) and Brussels sprouts (195 ug/100 g). The data contribute to the global collection of vitamin K food composition data. They add to the evidence that PK concentrations vary markedly between geographic regions, supporting development of region-specific datasets for national food composition databases that do not yet contain data for vitamin K., Comment: 22 pages, 2 tables

A complete accounting of nearby objects -- from the highest-mass white dwarf progenitors down to low-mass brown dwarfs -- is now possible, thanks to an almost complete set of trigonometric parallax determinations from Gaia, ground-based surveys, and Spitzer follow-up. We create a census of objects within a Sun-centered sphere of 20-pc radius and check published literature to decompose each binary or higher-order system into its separate components. The result is a volume-limited census of $\sim$3,600 individual star formation products useful in measuring the initial mass function across the stellar ($<8 M_\odot$) and substellar ($\gtrsim 5 M_{Jup}$) regimes. Comparing our resulting initial mass function to previous measurements shows good agreement above 0.8$M_\odot$ and a divergence at lower masses. Our 20-pc space densities are best fit with a quadripartite power law, $\xi(M) = dN/dM \propto M^{-\alpha}$ with long-established values of $\alpha = 2.3$ at high masses ($0.55 < M < 8.00 M_\odot$) and $\alpha = 1.3$ at intermediate masses ($0.22 < M < 0.55 M_\odot$), but at lower masses we find $\alpha = 0.25$ for $0.05 < M <0.22 M_\odot$ and $\alpha = 0.6$ for $0.01 < M < 0.05 M_\odot$. This implies that the rate of production as a function of decreasing mass diminishes in the low-mass star/high-mass brown dwarf regime before increasing again in the low-mass brown dwarf regime. Correcting for completeness, we find a star to brown dwarf number ratio of, currently, 4:1, and an average mass per object of 0.41 $M_\odot$., Comment: Accepted for publication in The Astrophysical Journal Supplement Series. 123 pages with four ancillary files

Effective field theory of low-energy exitations-magnons that describes antiferromagnets is mapped into scalar electrodynamics of a charged scalar field interacting with an external electromagnetic potential. In the presence of a constant inhomogeneous external magnetic field the latter problem is technically reduced to the problem of charged-particle creation from the vacuum by an electric potential step (x step). Magnetic moment plays here the role of the electric charge, and magnons and antimagnons differ from each other in the sign of the magnetic moment. In the framework of such a consideration, it is important to take into account the vacuum instability (the Schwinger effect) under the magnon-antimagnon production on magnetic field inhomogeneities (an analog of pair creation from the vacuum by electric-like fields). We demonstrate how to use the strong field QED with x steps developed by the authors (SPG and DMG) to study the magnon-antimagnon pair production on magnetic field inhomogeneities. Characteristics of the vacuum instability obtained for some magnetic steps that allows exact solving the Klein-Gordon equation are presented. In particular, we consider examples of magnetic steps with very sharp field derivatives that correspond to a regularization of the Klein step. In the case of smooth-gradient steps, we describe an universal behavior of the flux density of created magnon pairs. We also note that since the low-energy magnons are bosons with small effective mass, then for the first time maybe the opportunity will arise to observe the Schwinger effect in the case of the Bose statistics, in particular, the bosonic Klein effect in laboratory conditions. Moreover, it turns out that in the case of the Bose statistics appears a new mechanism for amplifying the effect of the pair creation, which we call statistically assisted Schwinger effect., Comment: 27 pages, misprints corrected, version accepted for publication in Physical Review B

We address the problem of the electromagnetic radiation produced by charge distributions in the framework of a semiclassical approach proposed in the work by Bagrov, Gitman, Shishmarev and Farias [J. Synchrotron Rad. (2020). 27, 902-911]. In this approach, currents, generating the radiation are considered classically, while the quantum nature of the radiation is kept exactly. Quantum states of the electromagnetic field are solutions of Schr\"odinger's equation and relevant quantities to the problem are evaluated with the aid of transition probabilities. This construction allows us to introduce the quantum transition time in physical quantities and assess its role in radiation problems by classical currents. We study radiated electromagnetic energies in detail and present a definition for the rate at which radiation is emitted from sources. In calculating the total energy and rate radiated by a pointlike charged particle accelerated by a constant and uniform electric field, we discover that our results are compatible with results obtained by other authors in the framework of the classical radiation theory under an appropriate limit. We also perform numerical and asymptotic analysis of the results., Comment: 22 pages, 3 figures

Extracting relevant urban patterns from multiple data sources can be difficult using classical clustering algorithms since we have to make a suitable setup of the hyperparameters of the algorithms and deal with outliers. It should be addressed correctly to help urban planners in the decision-making process for the further development of a big city. For instance, experts' main interest in criminology is comprehending the relationship between crimes and the socio-economic characteristics at specific georeferenced locations. In addition, the classical clustering algorithms take little notice of the intricate spatial correlations in georeferenced data sources. This paper presents a new approach to detecting the most relevant urban patterns from multiple data sources based on tensor decomposition. Compared to classical methods, the proposed approach's performance is attested to validate the identified patterns' quality. The result indicates that the approach can effectively identify functional patterns to characterize the data set for further analysis in achieving good clustering quality. Furthermore, we developed a generic framework named TensorAnalyzer, where the effectiveness and usefulness of the proposed methodology are tested by a set of experiments and a real-world case study showing the relationship between the crime events around schools and students performance and other variables involved in the analysis.

When modeling complex robot systems such as branched robots, whose kinematic structures are a tree, current techniques often require modeling the whole structure from scratch, even when partial models for the branches are available. This paper proposes a systematic modular procedure for the dynamic modeling of branched robots comprising several subsystems, each composed of an arbitrary number of rigid bodies, providing the final dynamic model by reusing previous models of each branch. Unlike previous approaches, the proposed strategy is applicable even if some subsystems are regarded as black boxes, requiring only twists and their time derivatives, and wrenches at the connection points between those subsystems. To help in the model composition, we also propose a weighted directed graph representation where the weights encode the propagation of twists and their time derivatives, and wrenches between the subsystems. A simple linear operation on the graph interconnection matrix provides the dynamics of the whole system. Numerical results using a 24-DoF fixed-base branched robot composed of eight subsystems show that the proposed formalism is as accurate as a state-of-the-art library for robotic dynamic modeling. Additional results using a 30-DoF holonomic branched mobile manipulator composed of three subsystems demonstrate the fidelity of our model to a modern robotics simulator and its capability of dealing with black box subsystems. To further illustrate how the derived dynamic model can be used in closed-loop control, we also present a simple formulation of a model-based wrench-driven pose control for branched robots., Comment: 17 pages, 9 figures, 4 tables. Under Review for the IEEE Transactions on Robotics (T-RO)

Scatterplots are among the most widely used visualization techniques. Compelling scatterplot visualizations improve understanding of data by leveraging visual perception to boost awareness when performing specific visual analytic tasks. Design choices in scatterplots, such as graphical encodings or data aspects, can directly impact decision-making quality for low-level tasks like clustering. Hence, constructing frameworks that consider both the perceptions of the visual encodings and the task being performed enables optimizing visualizations to maximize efficacy. In this paper, we propose an automatic tool to optimize the design factors of scatterplots to reveal the most salient cluster structure. Our approach leverages the merge tree data structure to identify the clusters and optimize the choice of subsampling algorithm, sampling rate, marker size, and marker opacity used to generate a scatterplot image. We validate our approach with user and case studies that show it efficiently provides high-quality scatterplot designs from a large parameter space.

Every year we grow more dependent on wearable devices to gather personalized data, such as our movements, heart rate, respiration, etc. To capture this data, devices contain sensors, such as accelerometers and gyroscopes, that are able to measure changes in their surroundings and pass along the information for better informed decisions. Although these sensors should behave similarly in different devices, that is not always the case. In this case study, we analyze accelerometers from three different devices recording the same actions with an aim to determine whether the discrepancies are due to variability within or between devices. We found the most significant variation between devices with different specifications, such as sensitivity and sampling frequency. Nevertheless, variance in the data should be assumed, even if data is gathered from the same person, activity, and type of sensor.

Both policy and research benefit from a better understanding of individuals' jobs. However, as large-scale administrative records are increasingly employed to represent labor market activity, new automatic methods to classify jobs will become necessary. We developed an automatic job offers classifier using a dataset collected from the largest job bank of Mexico known as Bumeran https://www.bumeran.com.mx/ Last visited: 19-01-2022.. We applied machine learning algorithms such as Support Vector Machines, Naive-Bayes, Logistic Regression, Random Forest, and deep learning Long-Short Term Memory (LSTM). Using these algorithms, we trained multi-class models to classify job offers in one of the 23 classes (not uniformly distributed): Sales, Administration, Call Center, Technology, Trades, Human Resources, Logistics, Marketing, Health, Gastronomy, Financing, Secretary, Production, Engineering, Education, Design, Legal, Construction, Insurance, Communication, Management, Foreign Trade, and Mining. We used the SMOTE, Geometric-SMOTE, and ADASYN synthetic oversampling algorithms to handle imbalanced classes. The proposed convolutional neural network architecture achieved the best results when applied the Geometric-SMOTE algorithm., Comment: 13 pages, 2 figures, 8th World Conference On Soft Computing

Given the current social distance restrictions across the world, most individuals now use social media as their major medium of communication. Millions of people suffering from mental diseases have been isolated due to this, and they are unable to get help in person. They have become more reliant on online venues to express themselves and seek advice on dealing with their mental disorders. According to the World health organization (WHO), approximately 450 million people are affected. Mental illnesses, such as depression, anxiety, etc., are immensely common and have affected an individuals' physical health. Recently Artificial Intelligence (AI) methods have been presented to help mental health providers, including psychiatrists and psychologists, in decision making based on patients' authentic information (e.g., medical records, behavioral data, social media utilization, etc.). AI innovations have demonstrated predominant execution in numerous real-world applications broadening from computer vision to healthcare. This study analyzes unstructured user data on the Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD. We trained traditional machine learning, deep learning, and transfer learning multi-class models to detect mental disorders of individuals. This effort will benefit the public health system by automating the detection process and informing appropriate authorities about people who require emergency assistance., Comment: 11 pages, 2 figures, 8th World Conference On Soft Computing