Rich Interaction Research Articles

On the geometry and representation theory of isomeric matrices

The space of $n \times m$ complex matrices can be regarded as an algebraic variety on which the group ${\bf GL}_n \times {\bf GL}_m$ acts. There is a rich interaction between geometry and representation theory in this example. In an important paper, de Concini, Eisenbud, and Procesi classified the equivariant ideals in the coordinate ring. More recently, we proved a noetherian result for families of equivariant modules as $n$ and $m$ vary. In this paper, we establish analogs of these results for the space of $(n|n) \times (m|m)$ isomeric matrices with respect to the action of ${\bf Q}_n \times {\bf Q}_m$, where ${\bf Q}_n$ is the automorphism group of the isomeric structure (commonly known as the "queer supergroup"). Our work is motivated by connections to the Brauer category and the theory of twisted commutative algebras.

Early stressful experiences are associated with reduced neural responses to naturalistic emotional and social content in children

How do children’s experiences relate to their naturalistic emotional and social processing? Because children can struggle with tasks in the scanner, we collected fMRI data while 4-to-11-year-olds watched a short film with positive and negative emotional events, and rich parent-child interactions (n = 70). We captured broad, normative stressful experiences by examining socioeconomic status (SES) and stressful life events, as well as children’s more proximal experiences with their parents. For a sub-sample (n = 30), parenting behaviors were measured during a parent-child interaction, consisting of a picture book, a challenging puzzle, and free play with novel toys. We characterized positive parenting behaviors (e.g., warmth, praise) and negative parenting behaviors (e.g., harsh tone, physical control). We found that higher SES was related to greater activity in medial orbitofrontal cortex during parent-child interaction movie events. Negative parenting behaviors were associated with less activation of the ventral tegmental area and cerebellum during positive emotional events. In a region-of-interest analysis, we found that stressful life events and negative parenting behaviors were associated with less activation of the amygdala during positive emotional events. These exploratory results demonstrate the promise of using movie fMRI to study how early experiences may shape emotional, social, and motivational processes.

Influence of Spatial Correlation Function on Characteristics of Wideband Electromagnetic Wave Absorbers with Chaotic Surface

Electromagnetic metasurface with chaos patterned surface could bring rich interaction modes contributing to fully disordered random motions in deterministic systems, which preform uncertainty, irreducibility and unpredictability. We investigate the influence of the correlation function (CF) properties of surface random patterns on the wave absorption performance. The complicated correlation function provides a fully developed random state, broadening the absorption bandwidth significantly and is helpful for reaching higher absorption rate. With the increasing number of peaks in the correlation function, the absorption band at –15 dB reflectivity widens significantly, band at –20 dB reflectivity begins to emerge. As the first peak’s distance from the original point in the CF is enlarged, the absorption trough is gradually formed and deepened to –35 dB level. The results give in-depth understanding of the relation between absorption behavior and controlling parameters including correlation, image information and foam spacer layer thickness. This high absorption absorber has great application potential in customizable radio communication compatibility device and anechoic testing chamber.

S2Q-Net: Mining the High-Pass Filtered Phase Data in Susceptibility Weighted Imaging for Quantitative Susceptibility Mapping.

Susceptibility weighted imaging (SWI) is a routine magnetic resonance imaging (MRI) sequence that combines the magnitude and high-pass filtered phase images to qualitatively enhance the image contrasts related to tissue susceptibility. Tremendous amounts of the high-pass filtered phase data with low signal to noise ratio and incomplete background field removal have thus been collected under default clinical settings. Since SWI cannot quantitatively estimate the susceptibility, it is thus non-trivial to derive quantitative susceptibility mapping (QSM) directly from these redundant phase data, which effectively promotes the mining of the SWI data collected previously. To this end, a novel deep learning based SWI-to-QSM-Net (S2Q-Net) is proposed for QSM reconstruction from SWI high-pass filtered phase data. S2Q-Net firstly estimates the edge maps of QSM to integrate edge prior into features, which benefits the network to reconstruct QSM with realistic and clear tissue boundaries. Furthermore, a novel Second-order Cross Dense Block is proposed in S2Q-Net, which can capture rich inter-region interactions to provide more non-local phase information related to local tissue susceptibility. Experimental results on both simulated and in-vivo data indicate its superiority over all the compared deep learning based QSM reconstruction methods.

Social Distancing and Social Biosensing: Intersubjectivity from Afar

The shelter-in-place orders across the U.S. in response to the COVID-19 pandemic forced many relationships once sustained by in-person interaction into remote states through computer-mediated communication (CMC). Work, school, holidays, social engagements, and everyday conversations formerly experienced through rich and contextual in-person interactions instead have taken place on messaging, voice, and video chatting platforms that diminish or altogether lack many social cues and other qualities critical to social interaction. The difficulties feeling connected to one another observed during this period have stressed the need for novel forms of communication that enable deeper interactions. Social biosensing, the interpersonal sharing of physiological information, has shown promise facilitating social connection at a distance. In the present research we document the experiences of nine pairs of friends (N = 18) who navigated living through a shelter-in-place order, reporting on their experiences sharing their electrodermal activity (EDA) in response to short videos. Participants described the artificial and unnatural nature of communicating using typical forms of CMC and a range of interpretations of EDA as both emotional response and as representative of personal characteristics. We implemented a phased approach to study the temporal nature of forming an understanding of unfamiliar yet intimate data like EDA. Our results indicate typologies of meaning-making processes: “stablers”, “broadeners”, and “puzzlers”. We also interpreted our findings through the lens of intersubjectivity, analyzing how analogical apperception and dialogical interaction both play a role in participants’ meaning-making about their own and their partner’s biosensory information. We conclude with implications from this work pertinent to intersubjectivity theorists, social biosensing researchers, and CMC system designers and developers.

Multi-scale dilated convolutional network for knowledge graph embedding

It aims to learn how to represent the low-dimensional vectors of entities and relations using observed triplets. KGE can benefit a variety of downstream tasks, such as KG completion and triplet classification. Deep models achieve state-of-the-art performance by leveraging the powerful nonlinear fitting ability of neural networks. However, most existing methods ignore multi-scale interaction features between entities and relations except InceptionE, which is hard to train because of high computation costs. In this paper, we propose a new KGE model called MDCE, that uses multi-scale dilated convolution to capture rich interaction features at different scales. Meanwhile, MDCE has lower computation costs than InceptionE. We perform extensive experiments on multiple benchmark datasets; results on the link prediction task show that the proposed model MDCE not only significantly outperforms existing state-of-the-art models but is also efficient and robust.

Smart Factory Using Virtual Reality and Online Multi-User: Towards a Metaverse for Experimental Frameworks

Virtual reality (VR) has been brought closer to the general public over the past decade as it has become increasingly available for desktop and mobile platforms. As a result, consumer-grade VR may redefine how people learn by creating an engaging “hands-on” training experience. Today, VR applications leverage rich interactivity in a virtual environment without real-world consequences to optimize training programs in companies and educational institutions. Therefore, the main objective of this article was to improve the collaboration and communication practices in 3D virtual worlds with VR and metaverse focused on the educational and productive sector in smart factory. A key premise of our work is that the characteristics of the real environment can be replicated in a virtual world through digital twins, wherein new, configurable, innovative, and valuable ways of working and learning collaboratively can be created using avatar models. To do so, we present a proposal for the development of an experimental framework that constitutes a crucial first step in the process of formalizing collaboration in virtual environments through VR-powered metaverses. The VR system includes functional components, object-oriented configurations, advanced core, interfaces, and an online multi-user system. We present the study of the first application case of the framework with VR in a metaverse, focused on the smart factory, that shows the most relevant technologies of Industry 4.0. Functionality tests were carried out and evaluated with users through usability metrics that showed the satisfactory results of its potential educational and commercial use. Finally, the experimental results show that a commercial software framework for VR games can accelerate the development of experiments in the metaverse to connect users from different parts of the world in real time.

A Novel Attention-based Global and Local Information Fusion Neural Network for Group Recommendation

Due to the popularity of group activities in social media, group recommendation becomes increasingly significant. It aims to pursue a list of preferred items for a target group. Most deep learning-based methods on group recommendation have focused on learning group representations from single interaction between groups and users. However, these methods may suffer from data sparsity problem. Except for the interaction between groups and users, there also exist other interactions that may enrich group representation, such as the interaction between groups and items. Such interactions, which take place in the range of a group, form a local view of a certain group. In addition to local information, groups with common interests may also show similar tastes on items. Therefore, group representation can be conducted according to the similarity among groups, which forms a global view of a certain group. In this paper, we propose a novel global and local information fusion neural network (GLIF) model for group recommendation. In GLIF, an attentive neural network (ANN) activates rich interactions among groups, users and items with respect to forming a group′s local representation. Moreover, our model also leverages ANN to obtain a group′s global representation based on the similarity among different groups. Then, it fuses global and local representations based on attention mechanism to form a group′s comprehensive representation. Finally, group recommendation is conducted under neural collaborative filtering (NCF) framework. Extensive experiments on three public datasets demonstrate its superiority over the state-of-the-art methods for group recommendation.

Tensor Graph Attention Network for Knowledge Reasoning in Internet of Things

Knowledge graph builds the bridge from massive data generated by the interaction and communication between various objects to intelligent applications and services in Internet of Things. The graph representation learning technology represented by graph neural networks plays an essential role in the understanding and reasoning of the knowledge graph with complicated internal structure. Although they are capable of assigning different attention weights to neighbors, the graph attention network (GAT) and its variants are inherently flawed and inadequate in modeling high-order knowledge graphs with high heterogeneity. Therefore, we propose a novel multirelational GAT framework in this article for knowledge reasoning over heterogeneous graphs by employing tensor and tensor operations. Specifically, we formulate the general high-order heterogeneous knowledge graph first. Then, the tensor GAT (TGAT), composed of three components: 1) heterogeneous information propagation; 2) multimodal semantic-aware attention; and 3) knowledge aggregation, is developed to simulate rich interactions between mixed triples, entities, and relationships when aggregating local information. What is more, we utilize the Tucker model to compress the parameters of TGAT and further reduce the storage and calculation consumption of the intermediate calculation process on the premise of maintaining the expressive power. We conduct extensive experiments to solve the link prediction task on four real-world heterogeneous graphs, and the results demonstrate that the TGAT model proposed in this article remarkably outperforms state-of-the-art competitors and improves the hits@1 accuracy by up to 7.6%.

3D Printed Acoustically Programmable Soft Microactuators.

The concept of creating all-mechanical soft microrobotic systems has great potential to address outstanding challenges in biomedical applications, and introduce more sustainable and multifunctional products. To this end, magnetic fields and light have been extensively studied as potential energy sources. On the other hand, coupling the response of materials to pressure waves has been overlooked despite the abundant use of acoustics in nature and engineering solutions. In this study, we show that programmed commands can be contained on 3D nanoprinted polymer systems with the introduction of selectively excited air bubbles and rationally designed compliant mechanisms. A repertoire of micromechanical systems is engineered using experimentally validated computational models that consider the effects of primary and secondary pressure fields on entrapped air bubbles and the surrounding fluid. Coupling the dynamics of bubble oscillators reveals rich acoustofluidic interactions that can be programmed in space and time. We prescribe kinematics by harnessing the forces generated through these interactions to deform structural elements, which can be remotely reconfigured on demand with the incorporation of mechanical switches. These basic actuation and analog control modules will serve as the building blocks for the development of a novel class of micromechanical systems powered and programmed by acoustic signals.

ModelWise: Interactive Model Comparison for Model Diagnosis, Improvement and Selection

AbstractModel comparison is an important process to facilitate model diagnosis, improvement, and selection when multiple models are developed for a classification task. It involves careful comparison concerning model performance and interpretation. Current visual analytics solutions often ignore the feature selection process. They either do not support detailed analysis of multiple multi‐class classifiers or rely on feature analysis alone to interpret model results. Understanding how different models make classification decisions, especially classification disagreements of the same instances, requires a deeper model understanding. We present ModelWise, a visual analytics method to compare multiple multi‐class classifiers in terms of model performance, feature space, and model explanation. ModelWise adapts visualizations with rich interactions to support multiple workflows to achieve model diagnosis, improvement, and selection. It considers feature subspaces generated for use in different models and improves model understanding by model explanation. We demonstrate the usability of ModelWise with two case studies, one with a small exemplar dataset and another developed with a machine learning expert with real‐world perioperative data.

Online Social Support via Avatar Communication Buffers Harmful Effects of Offline Bullying Victimization

Online social support via avatar communication is a powerful tool for bullying victims because they often lack offline social resources. Additionally, avatar communication allows users rich nonverbal interactions (e.g., emotional expressions) while maintaining online anonymity. This study investigates the role of online social support via avatars for victims and how to facilitate such support. Accordingly, we conducted an online questionnaire survey twice on an avatar communication application, Pigg Party, regarding mental health, offline and online social support, and offline bullying victimization (participants: 3,288 (1st wave) and 758 (2nd wave)). We found that online social support via avatars supplemented insufficient offline social resources, particularly when there was a high risk of offline bullying victimization. Furthermore, we investigated how online social support is improved by ego networks using social network data from Pigg Party. We demonstrated that belonging to large and closely connected communities can enhance online social support. Our findings suggest that avatar communication applications can improve players' mental health through online social support, reinforced by facilitating ego networks.

Interaction effects of pseudospin-based magnetic monopoles and kinks in a doped dipolar superlattice gas

Magnetic monopoles and kinks are topological excitations that have been extensively investigated in quantum spin systems, but usually, they are studied in different setups. We explore the conditions for the coexistence and interaction effects of these quasiparticles in the pseudospin chain of an atomic dipolar superlattice gas. In this chain, the magnetic kink is the intrinsic quasiparticle, and the particle (hole) defect takes over the role of the north (south) magnetic monopole, exerting monopolar magnetic fields on neighboring spins. A binding effect between the monopole and kink is revealed, which renormalizes the dispersion of the kink. The corresponding dynamical antibinding process is observed and arises due to the kink-antikink annihilation. The rich interaction effects of the two quasiparticles could stimulate corresponding investigations in bulk spin systems.

High-density grafting of carbon nanotube/carbon nanofiber hybrid on carbon fiber surface by vacuum filtration for effective interfacial reinforcement of its epoxy composites

Attempts to incorporate nanofillers onto the circumferential surface of traditional high-performance fibers via vacuum filtration toward gaining improved interfacial adhesion of corresponding composites are still in its infancy. Herein, we first achieve high-density grafting of carbon nanotube/carbon nanofiber (CNT/CNF) successfully on carbon fiber surface. The treated fibers with depositing positive and negative 5 ml CNT/CNF nanohybrid suspension shows 40.7% (85.4 MPa) gain in interfacial shear strength over that of untreated fiber (60.7 MPa). The reinforcement effect is mainly ascribed to the markedly improved wettability, which provides sufficient hydrogen bonding and pinning effect at interphase zone. Moreover, appropriate amount of CNT/CNF hybrid endows the interphase with gradual modulus transition, minimizing the stress concentration. Our research offers a promising, efficient and green approach for fully utilization of diverse nanofillers and rich interfacial interactions to realize substantial enhancement in interfacial adhesion in the future.

Painlevé analysis, integrability property and multiwave interaction solutions for a new (4+1)-dimensional KdV–Calogero–Bogoyavlenkskii–Schiff equation

In this letter a novel (4+1)-dimensional KdV–Calogero–Bogoyavlenkskii–Schiff (KdV-CBS) equation is investigated. The Painlevé analysis shows that this higher dimensional nonlinear equation passes the integrability test. Its integrable properties, including bilinear Bäcklund transformation, Lax pair as well as N-soliton solution, are systematically derived using the binary Bell polynomial method. Furthermore, incorporating the inheritance solving technique into direct algebraic method, we construct the multiwave solutions of 1-lump and (N−2)-soliton (N>2). Multiple choices of parameters in the obtained solutions lead to rich interactions among lump waves, kink waves and breather waves. Several interesting interactions of the multiwave solutions are shown by graphs.

Optimizing interneuron circuits for compartment-specific feedback inhibition

Cortical circuits process information by rich recurrent interactions between excitatory neurons and inhibitory interneurons. One of the prime functions of interneurons is to stabilize the circuit by feedback inhibition, but the level of specificity on which inhibitory feedback operates is not fully resolved. We hypothesized that inhibitory circuits could enable separate feedback control loops for different synaptic input streams, by means of specific feedback inhibition to different neuronal compartments. To investigate this hypothesis, we adopted an optimization approach. Leveraging recent advances in training spiking network models, we optimized the connectivity and short-term plasticity of interneuron circuits for compartment-specific feedback inhibition onto pyramidal neurons. Over the course of the optimization, the interneurons diversified into two classes that resembled parvalbumin (PV) and somatostatin (SST) expressing interneurons. Using simulations and mathematical analyses, we show that the resulting circuit can be understood as a neural decoder that inverts the nonlinear biophysical computations performed within the pyramidal cells. Our model provides a proof of concept for studying structure-function relations in cortical circuits by a combination of gradient-based optimization and biologically plausible phenomenological models.

Onfocus detection: identifying individual-camera eye contact from unconstrained images

Onfocus detection aims at identifying whether the focus of the individual captured by a camera is on the camera or not. Based on the behavioral research, the focus of an individual during face-to-camera communication leads to a special type of eye contact, i.e., the individual-camera eye contact, which is a powerful signal in social communication and plays a crucial role in recognizing irregular individual status (e.g., lying or suffering mental disease) and special purposes (e.g., seeking help or attracting fans). Thus, developing effective onfocus detection algorithms is of significance for assisting the criminal investigation, disease discovery, and social behavior analysis. However, the review of the literature shows that very few efforts have been made toward the development of onfocus detector owing to the lack of large-scale public available datasets as well as the challenging nature of this task. To this end, this paper engages in the onfocus detection research by addressing the above two issues. Firstly, we build a large-scale onfocus detection dataset, named as the onfocus detection in the wild (OFDIW). It consists of 20623 images in unconstrained capture conditions (thus called “in the wild”) and contains individuals with diverse emotions, ages, facial characteristics, and rich interactions with surrounding objects and background scenes. On top of that, we propose a novel end-to-end deep model, i.e., the eye-context interaction inferring network (ECIIN), for onfocus detection, which explores eye-context interaction via dynamic capsule routing. Finally, comprehensive experiments are conducted on the proposed OFDIW dataset to benchmark the existing learning models and demonstrate the effectiveness of the proposed ECIIN.

Formation and destruction of striation plasmas in helium glow discharge at medium pressures

The striation plasmas are usually generated within a positive column of glow discharge, where rich and complex physical interactions are involved, especially, in the medium or high pressures. Along these lines, our work aims to thoroughly investigate the formation and destruction of helium striation plasmas at kPa level pressures. The characteristics of the helium striation plasmas, and especially the optical emission properties are explored. The emission lines of 706.52 nm and 391.44 nm related to the energetic electrons and the high-energy metastable helium atoms respectively, were focused on in this work. The formation of striation plasmas in a helium glow discharge, is mainly associated with the instability originating from the stepwise ionization of high-energy metastable state atoms, Maxwellization of the electron distribution functions and gas heating. Additionally, the destruction effect of helium striation plasmas is of great significance when a small amount of nitrogen or oxygen is mixed into the discharge plasmas. The reduction of the mean electron energy and the consumption of the high-energy metastable helium atoms are considered as the underlying reasons for the destruction of striation plasmas.

Structure and DNA binding analysis of AT-rich interaction domain present in human BAF-B specific subunit BAF250b.

BAF250b and its paralog BAF250a are the DNA-binding central hub proteins present in BAF-B and BAF-A classes of SWI/SNF chromatin-remodeling complexes. BAF250b contains an AT-rich interaction domain (ARID) and C-terminal BAF250_C domain, and it is found mutated in several cancers. ARID is a conserved helix-turn-helix motif-containing DNA-binding domain present in several eukaryotic proteins. The ARID of BAF250b has been proposed to play roles in recruiting SWI/SNF to the target gene promoters for their activation. BAF250b ARID structures had been deposited in the protein data bank by a structural genomics consortium. However, it is not well-studied for its DNA-binding and solution dynamic properties. Here, we report complete backbone NMR resonance assignments of human BAF250b ARID. NMR chemical shifts and the backbone dynamics showed that the solution structure of the protein matched the reported crystal structures. The structure and chemical shift indexing revealed the presence of a short β-sheet in the DNA-binding region of BAF250b ARID that was absent in the structure of its paralog BAF250a ARID. NMR chemical shift perturbations identified DNA-binding residues and revealed the DNA-binding interface on BAF250b ARID. NMR data-driven HADDOCK models of BAF250b ARID - DNA complexes revealed its plausible mode of DNA-binding. Isothermal titration calorimetry experiments showed that BAF250b ARID interacts with DNA sequences with moderate affinities like BAF250a ARID. However, distinct thermodynamic signatures were observed for binding of BAF250a ARID and BAF250b ARID to AT-rich DNA sequence, suggesting that subtle sequence and structural differences in these two proteins influence their DNA-binding.

Topology optimization using the discrete element method. Part 2: Material nonlinearity

Structural Topology Optimization typically features continuum-based descriptions of the investigated systems. In Part 1 we have proposed a Topology Optimization method for discrete systems and tested it on quasi-static 2D problems of stiffness maximization, assuming linear elastic material. However, discrete descriptions become particularly convenient in the failure and post-failure regimes, where discontinuous processes take place, such as fracture, fragmentation, and collapse. Here we take a first step towards failure problems, testing Discrete Element Topology Optimization for systems with nonlinear material responses. The incorporation of material nonlinearity does not require any change to the optimization method, only using appropriately rich interaction potentials between the discrete elements. Three simple problems are analysed, to show how various combinations of material nonlinearity in tension and compression can impact the optimum geometries. We also quantify the strength loss when a structure is optimized assuming a certain material behavior, but then the material behaves differently in the actual structure. For the systems considered here, assuming weakest material during optimization produces the most robust structures against incorrect assumptions on material behavior. Such incorrect assumptions, instead, are shown to have minor impact on the serviceability of the optimized structures.