Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Modeling of a Smart Torque-Adjustable Rotary Electroadhesive Clutch for Application in Human–Robot Interaction

The increasing need for sharing workspace and interactive physical tasks between robots and humans has raised concerns regarding the safety of such operations. In this regard, controllable clutches have shown great potential for addressing important safety concerns at the hardware level by separating the high-impedance actuator from the end-effector by providing the power transfer from electromagnetic source to the human. However, the existing clutches suffer from high power consumption and large weight, which make them undesirable from the design point of view. In this article, for the first time, the design and development of a novel, lightweight, and low-power torque-adjustable rotary clutch using electroadhesive materials are presented. The performance of three different pairs of clutch plates is investigated in the context of the smoothness and quality of output torque. The performance degradation issue due to the polarization of the insulator is addressed through the utilization of an alternating current waveform activation signal. Moreover, the effect of the activation frequency on the output torque and power consumption of the clutch is investigated. Finally, a time-dependent model for the output torque of the clutch is presented, and the performance of the clutch was evaluated through experiments, including physical human–robot interaction. The proposed clutch offers a torque-to-power consumption ratio that is six times better than commercial magnetic particle clutches. The proposed clutch presents great potential for developing safe, lightweight, and low-power physical human–robot interaction systems, such as exoskeletons and robotic walkers.
Construction Repetition Reduces Information Rate in Dialogue

Speakers repeat constructions frequently in dialogue. Due to their peculiar information-theoretic properties, repetitions can be thought of as a strategy for cost-effective communication. In this study, we focus on the repetition of lexicalised constructions—i.e., recurring multi-word units—in English open-domain spoken dialogues. We hypothesise that speakers use construction repetition to mitigate information rate, leading to an overall decrease in utterance information content over the course of a dialogue. We conduct a quantitative analysis, measuring the information content of constructions and that of their containing utterances, estimating information content with an adaptive neural language model. We observe that construction usage lowers the information content of utterances. This facilitating effect (i) increases throughout dialogues, (ii) is boosted by repetition, (iii) grows as a function of repetition frequency and density, and (iv) is stronger for repetitions of referential constructions.
Self-Reconfigurable Soft-Rigid Mobile Agent With Variable Stiffness and Adaptive Morphology

In this letter, we propose a novel design of a hybrid mobile robot with controllable stiffness and deformable shape. Compared to conventional mobile agents, our system can switch between rigid and compliant phases by solidifying or melting Field's metal in its structure and, thus, alter the shape through the motion of its active components. In the soft state, the robot's main body can bend into circular arcs, which enables it to conform to surrounding curved objects. This variable geometry of the robot creates new motion modes which cannot be described by standard (i.e., fixed geometry) models. To this end, we develop a unified mathematical model that captures the differential kinematics of both rigid and soft states. An optimised control strategy is further proposed to select the most appropriate phase states and motion modes needed to reach a target pose-shape configuration. The performance of our new method is validated with numerical simulations and experiments conducted on a prototype system.
On sketch-and-project methods for solving tensor equations

We first propose the regular sketch-and-project method for solving tensor equations with respect to the popular t-product. Then, three adaptive sampling strategies and three corresponding adaptive sketch-and-project methods are derived. We prove that all the proposed methods have linear convergence in expectation. Furthermore, we investigate the Fourier domain versions and some special cases of the new methods, where the latter corresponds to some famous matrix equation methods. Finally, numerical experiments are presented to demonstrate and test the feasibility and effectiveness of the proposed methods for solving tensor equations
ISEE.U: Distributed online active target localization with unpredictable targets

This paper addresses target localization with an online active learning algorithm defined by distributed, simple and fast computations at each node, with no parameters to tune and where the estimate of the target position at each agent is asymptotically equal in expectation to the centralized maximum-likelihood estimator. ISEE.U takes noisy distances at each agent and finds a control that maximizes localization accuracy. We do not assume specific target dynamics and, thus, our method is robust when facing unpredictable targets. Each agent computes the control that maximizes overall target position accuracy via a local estimate of the Fisher Information Matrix. We compared the proposed method with a state of the art algorithm outperforming it when the target movements do not follow a prescribed trajectory, with x100 less computation time, even when our method is running in one central CPU.
Learning to Schedule Multi-Server Jobs With Fluctuated Processing Speeds

Multi-server jobs are imperative in modern cloud computing systems. A noteworthy feature of multi-server jobs is that, they usually request multiple computing devices simultaneously for their execution. How to schedule multi-server jobs online with a high system efficiency is a topic of great concern. First, the scheduling decisions have to satisfy the service locality constraints. Second, the scheduling decisions needs to be made online without the knowledge of future job arrivals. Third, and most importantly, the actual service rate experienced by a job is usually in fluctuation because of the dynamic voltage and frequency scaling (DVFS) and power oversubscription techniques when multiple types of jobs co-locate. A majority of online algorithms with theoretical performance guarantees are proposed. However, most of them require the processing speeds to be knowable, thereby the job completion times can be exactly calculated. To present a theoretically guaranteed online scheduling algorithm for multi-server jobs without knowing actual processing speeds apriori, in this article, we propose Esdp (Efficient Sampling-based Dynamic Programming), which learns the distribution of the fluctuated processing speeds over time and simultaneously seeks to maximize the cumulative overall utility. The cumulative overall utility is formulated as the sum of the utilities of successfully serving each multi-server job minus the penalty on the operating, maintaining, and energy cost. Esdp is proved to have a polynomial complexity and a logarithmic regret, which is a State-of-the-Art result. We also validate it with extensive simulations and the results show that the proposed algorithm outperforms several benchmark policies with improvements by up to 73%, 36%, and 28%, respectively.
Analysis of Microstate Organization During Emotional Events

Understanding the dynamics of emotional experience is an old problem. However, a clear understanding of the mechanism of emotional experience is still far away. In the presented work, we tried to address this problem using a well-established method called microstate analysis using multichannel electroencephalography (EEG). We recorded the brain activity of spontaneous emotional experiences while participants were watching multimedia emotional stimuli. The time duration where the participants spontaneously felt an emotion, we termed it an emotional event. Microstate segmentation is performed for all emotional events to calculate the set of microstates (MS). Followed by a comparison of calculated statistical parameters and transition probabilities for the emotional and non-emotional conditions. We found a set of MS (four MS) for emotional and non-emotional conditions that differ from each other. We observed that MS1 has a higher value of occurrence, duration and coverage for emotional conditions. In addition, the transition to MS1 for the emotional condition was higher. On the other hand, for non-emotion (or neutral) condition, transition to MS3 was higher. A set of MS related to neutral condition was source localized to brain regions involved in higher-level sensory feature processing. On the other hand, for emotional conditions, MS1 \&MS2 are localized to sensory feature processing regions, and MS3 \&MS4 are additionally localized to regions related to socio-emotional processing. Our results hint toward the constructionist mechanism, which has an asymmetric contribution from bottom-up and top-down processing during an emotional experience.
SPEEDING UP LOW-MASS PLANETARY MICROLENSING SIMULATIONS AND MODELING: THE CAUSTIC REGION OF INFLUENCE

Extensive simulations of planetary microlensing are necessary both before and after a survey is conducted: before to design and optimize the survey and after to understand its detection efficiency. The major bottleneck in such computations is the computation of light curves. However, for low-mass planets, most of these computations are wasteful, as most light curves do not contain detectable planetary signatures. In this paper, I develop a parameterization of the binary microlens that is conducive to avoiding light curve computations. I empirically find analytic expressions describing the limits of the parameter space that contain the vast majority of low-mass planet detections. Through a large-scale simulation, I measure the (in)completeness of the parameterization and the speed-up it is possible to achieve. For Earth-mass planets in a wide range of orbits, it is possible to speed up simulations by a factor of ∼30–125 (depending on the survey's annual duty-cycle) at the cost of missing ∼1% of detections (which is actually a smaller loss than for the arbitrary parameter limits typically applied in microlensing simulations). The benefits of the parameterization probably outweigh the costs for planets below 100 M⊕. For planets at the sensitivity limit of AFTA-WFIRST, simulation speed-ups of a factor ∼1000 or more are possible.
SPECULARITY IN ALGEBRA

These two questions are pertinent to all areas of mathemat ics. For example, all school teaching of arithmetic processes (e.g., how to add two digit numbers) is done through partic ular examples intended to be general. In this article, however, I will confine my discussion to the realm of the learning and teaching of algebra, where the act of "seeing the general in the particular" is practiced extensively and explicitly. Indeed, this often quoted phrase underlies the proposition that "much of algebra builds on students' exten sive experiences with number" (NCTM, 2000). However, the route to algebra is not easy. Downgrading students' propensity to calculate (Kuchemann & Hoyles, 2009), over coming the reliance on empirical arguments (Healy & Hoyles, 2000), getting accustomed to seeing the structural features (Radford, 2010a) and perceiving algebraically use ful patterns (Lee, 1996) are just a few challenges in students' passage from number to algebra. And it is a more demanding task for teachers, researchers or curriculum developers to foster it. Lee (1996, p. 95) points to this challenge when she shows how, in work conducted with David Wheeler, the interviewer's perception of pattern can interfere with the stu dents' progress in pattern generalizing activities. This observation also shows how difficult it is, even in a sup portive experimental setting, to find a general answer to Mason and Pimm's second question. However, as philoso phers say, one way to answer a question is to replace it with a new (and hopefully better) question. This is what I attempt to do in this article. First, I introduce two kinds of generalization, as distin guished by Polya (1945). Second, I exemplify a well known algebraic generalizing task in the light of the two types of generalizations. Third, I explain a number of possible actions in response to students' difficulties with the task. The next step is "to withdraw from the immediate action" and discern what is worth noticing, "labeling it in some way so that it has a chance of coming-to-mind in the future" (Mason, 2010, p. 3). The approach used is the most mundane one: stressing and ignoring (Mason & Pimm, 1984) while exemplifying certain algebraic situations in which one of Polya's kinds of generalization is involved. Finally, the original questions are replaced by more subtle (and in a sense, more practical) ones, the aim of which is to direct attention to "noticing more sen sitively and/or having alternative actions come-to-mind in the midst of preparing or conducting lessons or some other relevant setting" (Mason, 2010, p. 9).
Organic electronic ratchets

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication
Quantum Apices: Identifying Limits of Entanglement, Nonlocality, & Contextuality

This work develops analytic methods to quantitatively demarcate quantum reality from its subset of classical phenomenon, as well as from the superset of general probabilistic theories. Regarding quantum nonlocality, we discuss how to determine the quantum limit of Bell-type linear inequalities. In contrast to semidefinite programming approaches, our method allows for the consideration of inequalities with abstract weights, by means of leveraging the Hermiticity of quantum states. Recognizing that classical correlations correspond to measurements made on separable states, we also introduce a practical method for obtaining sufficient separability criteria. We specifically vet the candidacy of driven and undriven superradiance as schema for entanglement generation. We conclude by reviewing current approaches to quantum contextuality, emphasizing the operational distinction between nonlocal and contextual quantum statistics. We utilize our abstractly-weighted linear quantum bounds to explicitly demonstrate a set of conditional probability distributions which are simultaneously compatible with quantum contextuality while being incompatible with quantum nonlocality. It is noted that this novel statistical regime implies an experimentally-testable target for the Consistent Histories theory of quantum gravity.
Building a Community-Based FAIR Metadata Schema for Brazilian Agriculture and Livestock Trading Data

In this paper, we discuss how we are using metadata schemas and controlled vocabularies to improve interoperability between Brazilian agriculture and livestock trading data providers. A new metadata schema is being created based on a community-based approach. This method relies on knowledge from specialists to define a list of relevant metadata properties for a given domain. In the first step of the research, we extracted metadata from three datasets maintained by three Brazilian public institutions: the Center for Advanced Studies in Applied Economics (Cepea), the Institute of Applied Economic Research (Ipea), and The National Supply Company (Conab). The extracted metadata were the input to the definition of a list of 15 potential metadata properties that specialists are validating.
How are learning analytics considering the societal values of fairness, accounta-bility, transparency and human well-being? - A literature review

The scientific community is currently engaged in global efforts towards a movement that promotes positive human values in the ways we formulate and apply Artificial Intelligence (AI) solutions. As the use of intelligent algorithms and analytics are becoming more involved in how decisions are made in public and private life, the societal values of Fairness, Accountability and Transparency (FAT) and the multidimensional value of human Well-being are being discussed in the context of addressing potential negative and positive impacts of AI. This research paper reviews these four values and their implications in algorithms and investigates their empirical existence in the interdisciplinary field of Learning Analytics (LA). We present and highlight results of a literature review that was conducted across all the editions of the Learning Analytics & Knowledge (LAK) ACM conference proceedings. The findings provide different insights on how these societal and human values are being considered in LA research, tools, applications and ethical frameworks.
Towards a Standardized Description of SemanticWeb Machine Learning Systems

In this paper, we report on our proposed approach towards a standardized description for systems combining machine learning (ML) components with techniques developed by the Semantic Web (SW) community (SWeMLS), which is one of lessons learned from our large-scale survey (476 papers) on the topic. We elaborate the key information that should be described of SWeMLS and selected methods to support its documentation.
Commit Classification Into Maintenance Activities Using Aggregated Semantic Word Embeddings of Software Change Messages

Every change to a software repository (i.e., commit) is described by a software developer committing the change with a message written in natural language, indicating the purpose of the change. Automatically inferring the change intents of commits helps understand and manage software projects and their development. This paper presents and evaluates an approach leveraging the semantic characteristics of textual descriptions in software change messages to classify commits into adaptive, corrective, and perfective maintenance activities. The approach represents a commit message as a set of vectors, using a word2vec model trained on commits of the top starred GitHub repositories. Each vector corresponds to a semantic embedding of a word in the message. The resulting word embeddings are aggregated to a single vector representation for a commit, using the average, maximum, and term frequency-inverse document frequency weighted mapping. The experimental results revealed that the models based on the proposed features and simple classifiers are promising, outperforming the baseline ZeroR algorithm. Compared to the traditional approach that uses keywords as features, the models of the proposed approach performed better overall, and provided a more accurate prediction of adaptive and corrective maintenance activities. The best-performing model with a mean weighted F1-score of 75.9% used the maximum aggregation method, and was based on the Random Forest classifier.
Asymptotic symmetries of Rindler space at the horizon and null infinity

We investigate the asymptotic symmetries of Rindler space at null infinity and at the event horizon using both systematic and ad hoc methods. We find that the approaches that yield infinite-dimensional asymptotic symmetry algebras in the case of anti-de Sitter and flat spaces only give a finite-dimensional algebra for Rindler space at null infinity. We calculate the charges corresponding to these symmetries and confirm that they are finite, conserved, and integrable, and that the algebra of charges gives a representation of the asymptotic symmetry algebra. We also use relaxed boundary conditions to find infinite-dimensional asymptotic symmetry algebras for Rindler space at null infinity and at the event horizon. We compute the charges corresponding to these symmetries and confirm that they are finite and integrable. We also determine sufficient conditions for the charges to be conserved on-shell, and for the charge algebra to give a representation of the asymptotic symmetry algebra. In all cases, we find that the central extension of the charge algebra is trivial.
Applying a Mapping Quality Framework in Cloud Native Monitoring

1 The linked data generation process is a complex process which involves multiple stakeholders in the definition of mapping artefacts which transform source data into linked data representation. The creation of these mapping artefacts is error prone, and the quality of the artefacts should be assessed prior to the generation of the linked data. Producing high quality mappings will result in high quality linked data and provide higher confidence to linked data consumers. Furthermore, the source data of these mappings should be regularly monitored to detect data changes which could impact the quality of the resulting linked data dataset. A process designed to offer fresh and high-quality data will benefit both data consumers and producers. Applying the process to a real-world use case demonstrates the feasibility and effectiveness of the process. In this paper we describe a process designed to improve quality within the linked data generation process. Furthermore, we describe the application of the process to a real-world cloud monitoring use case
Sloan Great Wall as a complex of superclusters with collapsing cores

In the cosmic web, galaxy superclusters or their high-density cores are the largest objects that may collapse at present or during the future evolution. We study the dynamical state and possible future evolution of galaxy superclusters from the Sloan Great Wall (SGW), the richest galaxy system in the nearby Universe. We calculated supercluster masses using dynamical masses of galaxy groups and stellar masses of galaxies. We employed normal mixture modelling to study the structure of rich SGW superclusters and search for components (cores) in superclusters. We analysed the radial mass distribution in the high-density cores of superclusters centred approximately at rich clusters and used the spherical collapse model to study their dynamical state. We found that the lower limit of the total mass of the SGW is approximately $M = 2.5\times~10^{16}h^{-1}M_\odot$. Different mass estimators of superclusters agree well, the main uncertainties in masses of superclusters come from missing groups and clusters. We detected three high-density cores in the richest SGW supercluster (SCl~027) and two in the second richest supercluster (SCl~019). They have masses of $1.2 - 5.9 \times~10^{15}h^{-1}M_\odot$ and sizes of up to $\approx 60 h^{-1}$ Mpc. The high-density cores of superclusters are very elongated, flattened perpendicularly to the line of sight. The comparison of the radial mass distribution in the high-density cores with the predictions of spherical collapse model suggests that their central regions with radii smaller than $8 h^{-1}$Mpc and masses of up to $M = 2\times~10^{15}h^{-1}M_\odot$ may be collapsing. The rich SGW superclusters with their high-density cores represent dynamically evolving environments for studies of the properties of galaxies and galaxy systems.
MINIMAL LENGTH ELEMENTS IN CONJUGACY CLASSES OF EXTENDED AFFINE WEYL GROUP

We study the minimal length elements in an integral conjugacy class of a classical extended affine Weyl group and we show that these elements are quite "special" in the sense of Geck and Pfeiffer (GP1). We also discuss some application on extended affine Hecke algebras and loop groups.