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Best possible self (BPS) is a positive psychological intervention shown to enhance well-being which involves writing a description of an ideal future scenario. This paper presents a comparison of psychophysiological effects of a BPS activity that has been adapted for classroom settings and a time-matched control activity (NA). Thirty-three undergraduate students participated in the study that assessed state anxiety (State-Trait Anxiety Inventory, STAI), affect (Affective Slider, AS), and cardiac vagal activity (heart-rate variability, HRV) as an indicator of self-regulatory resource usage, at three time periods (PRE, DURING, POST). Results show that BPS led to a significantly greater increase in positive valence (DURING) and overall higher levels of cardiac vagal activity (HRV) compared to NA. These findings suggest that BPS has promising characteristics as a self-regulatory technique aimed at fostering positive affect and positively impacting self-regulatory resources. As BPS does not require expert knowledge nor specialized technology to administer, it may be a suitable activity for educators to use when teaching and having students practice self-regulation. This study presents evidence collected in a replicable multimodal approach of the self-regulatory effects of a brief BPS activity on undergraduate students.

Objective: Our objective is to develop and validate TrajVis, an interactive tool that assists clinicians in using artificial intelligence (AI) models to leverage patients' longitudinal electronic medical records (EMR) for personalized precision management of chronic disease progression. Methods: We first perform requirement analysis with clinicians and data scientists to determine the visual analytics tasks of the TrajVis system as well as its design and functionalities. A graph AI model for chronic kidney disease (CKD) trajectory inference named DEPOT is used for system development and demonstration. TrajVis is implemented as a full-stack web application with synthetic EMR data derived from the Atrium Health Wake Forest Baptist Translational Data Warehouse and the Indiana Network for Patient Care research database. A case study with a nephrologist and a user experience survey of clinicians and data scientists are conducted to evaluate the TrajVis system. Results: The TrajVis clinical information system is composed of four panels: the Patient View for demographic and clinical information, the Trajectory View to visualize the DEPOT-derived CKD trajectories in latent space, the Clinical Indicator View to elucidate longitudinal patterns of clinical features and interpret DEPOT predictions, and the Analysis View to demonstrate personal CKD progression trajectories. System evaluations suggest that TrajVis supports clinicians in summarizing clinical data, identifying individualized risk predictors, and visualizing patient disease progression trajectories, overcoming the barriers of AI implementation in healthcare. Conclusion: TrajVis bridges the gap between the fast-growing AI/ML modeling and the clinical use of such models for personalized and precision management of chronic diseases.

While IoT sensors in physical spaces have provided utility and comfort in our lives, their instrumentation in private and personal spaces has led to growing concerns regarding privacy. The existing notion behind IoT privacy is that the sensors whose data can easily be understood and interpreted by humans (such as cameras) are more privacy-invasive than sensors that are not human-understandable, such as RF (radio-frequency) sensors. However, given recent advancements in machine learning, we can not only make sensitive inferences on RF data but also translate between modalities. Thus, the existing notions of privacy for IoT sensors need to be revisited. In this paper, our goal is to understand what factors affect the privacy notions of a non-expert user (someone who is not well-versed in privacy concepts). To this regard, we conduct an online study of 162 participants from the USA to find out what factors affect the privacy perception of a user regarding an RF-based device or a sensor. Our findings show that a user's perception of privacy not only depends upon the data collected by the sensor but also on the inferences that can be made on that data, familiarity with the device and its form factor as well as the control a user has over the device design and its data policies. When the data collected by the sensor is not human-interpretable, it is the inferences that can be made on the data and not the data itself that users care about when making informed decisions regarding device privacy.

There is a lack of point process models on linear networks. For an arbitrary linear network, we consider new models for a Cox process with an isotropic pair correlation function obtained in various ways by transforming an isotropic Gaussian process which is used for driving the random intensity function of the Cox process. In particular we introduce three model classes given by log Gaussian, interrupted, and permanental Cox processes on linear networks, and consider for the first time statistical procedures and applications for parametric families of such models. Moreover, we construct new simulation algorithms for Gaussian processes on linear networks and discuss whether the geodesic metric or the resistance metric should be used for the kind of Cox processes studied in this paper.

It is well-known that a multilinear system with a nonsingular M-tensor and a positive right-hand side has a unique positive solution. Tensor splitting methods generalizing the classical iterative methods for linear systems have been proposed for finding the unique positive solution. The Alternating Anderson-Richardson (AAR) method is an effective method to accelerate the classical iterative methods. In this study, we apply the idea of AAR for finding the unique positive solution quickly. We first present a tensor Richardson method based on tensor regular splittings, then apply Anderson acceleration to the tensor Richardson method and derive a tensor Anderson-Richardson method, finally, we periodically employ the tensor Anderson-Richardson method within the tensor Richardson method and propose a tensor AAR method. Numerical experiments show that the proposed method is effective in accelerating tensor splitting methods.

Neural implicit surface representations are currently receiving a lot of interest as a means to achieve high-fidelity surface reconstruction at a low memory cost, compared to traditional explicit representations.However, state-of-the-art methods still struggle with excessive memory usage and non-smooth surfaces. This is particularly problematic in large-scale applications with sparse inputs, as is common in robotics use cases. To address these issues, we first introduce a sparse structure, \emph{tri-quadtrees}, which represents the environment using learnable features stored in three planar quadtree projections. Secondly, we concatenate the learnable features with a Fourier feature positional encoding. The combined features are then decoded into signed distance values through a small multi-layer perceptron. We demonstrate that this approach facilitates smoother reconstruction with a higher completion ratio with fewer holes. Compared to two recent baselines, one implicit and one explicit, our approach requires only 10\%--50\% as much memory, while achieving competitive quality.

Identifying reaction coordinates(RCs) is an active area of research, given the crucial role RCs play in determining the progress of a chemical reaction. The choice of the reaction coordinate is often based on heuristic knowledge. However, an essential criterion for the choice is that the coordinate should capture both the reactant and product states unequivocally. Also, the coordinate should be the slowest one so that all the other degrees of freedom can easily equilibrate along the reaction coordinate. Also, the coordinate should be the slowest one so that all the other degrees of freedom can easily equilibrate along the reaction coordinate. We used a regularised sparse autoencoder, an energy-based model, to discover a crucial set of reaction coordinates. Along with discovering reaction coordinates, our model also predicts the evolution of a molecular dynamics(MD) trajectory. We showcased that including sparsity enforcing regularisation helps in choosing a small but important set of reaction coordinates. We used two model systems to demonstrate our approach: alanine dipeptide system and proflavine and DNA system, which exhibited intercalation of proflavine into DNA minor groove in an aqueous environment. We model MD trajectory as a multivariate time series, and our latent variable model performs the task of multi-step time series prediction. This idea is inspired by the popular sparse coding approach - to represent each input sample as a linear combination of few elements taken from a set of representative patterns.

This paper proposes a comprehensive hierarchical control framework for autonomous decision-making arising in robotics and autonomous systems. In a typical hierarchical control architecture, high-level decision making is often characterised by discrete state and decision/control sets. However, a rational decision is usually affected by not only the discrete states of the autonomous system, but also the underlying continuous dynamics even the evolution of its operational environment. This paper proposes a holistic and comprehensive design process and framework for this type of challenging problems, from new modelling and design problem formulation to control design and stability analysis. It addresses the intricate interplay between traditional continuous systems dynamics utilized at the low levels for control design and discrete Markov decision processes (MDP) for facilitating high-level decision making. We model the decision making system in complex environments as a hybrid system consisting of a controlled MDP and autonomous (i.e. uncontrolled) continuous dynamics. Consequently, the new formulation is called as hybrid Markov decision process (HMDP). The design problem is formulated with a focus on ensuring both safety and optimality while taking into account the influence of both the discrete and continuous state variables of different levels. With the help of the model predictive control (MPC) concept, a decision maker design scheme is proposed for the proposed hybrid decision making model. By carefully designing key ingredients involved in this scheme, it is shown that the recursive feasibility and stability of the proposed autonomous decision making scheme are guaranteed. The proposed framework is applied to develop an autonomous lane changing system for intelligent vehicles.

Generating high-quality and person-generic visual dubbing remains a challenge. Recent innovation has seen the advent of a two-stage paradigm, decoupling the rendering and lip synchronization process facilitated by intermediate representation as a conduit. Still, previous methodologies rely on rough landmarks or are confined to a single speaker, thus limiting their performance. In this paper, we propose DiffDub: Diffusion-based dubbing. We first craft the Diffusion auto-encoder by an inpainting renderer incorporating a mask to delineate editable zones and unaltered regions. This allows for seamless filling of the lower-face region while preserving the remaining parts. Throughout our experiments, we encountered several challenges. Primarily, the semantic encoder lacks robustness, constricting its ability to capture high-level features. Besides, the modeling ignored facial positioning, causing mouth or nose jitters across frames. To tackle these issues, we employ versatile strategies, including data augmentation and supplementary eye guidance. Moreover, we encapsulated a conformer-based reference encoder and motion generator fortified by a cross-attention mechanism. This enables our model to learn person-specific textures with varying references and reduces reliance on paired audio-visual data. Our rigorous experiments comprehensively highlight that our ground-breaking approach outpaces existing methods with considerable margins and delivers seamless, intelligible videos in person-generic and multilingual scenarios.

Insights: - The human-centered AI (HCAI) approach and the sociotechnical systems (STS) theory share the same goal: ensuring that new technologies such as AI best serve humans in a sociotechnical environment. - HCAI practice needs to fully embrace sociotechnical systems thinking, while traditional STS needs to evolve to address the emerging characteristics of AI technology. - We propose a conceptual framework for intelligent sociotechnical systems (iSTS) to enhance traditional STS theory in the AI era. - Based on iSTS, we further propose a sociotechnical-based hierarchical HCAI approach as a paradigmatic extension to existing HCAI practice, further advancing HCAI practice.