Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Interactions with Printed Data Visualizations in Augmented Reality

This paper presents a design space of interaction techniques to engage with visualizations that are printed on paper and augmented through Augmented Reality. Paper sheets are widely used to deploy visualizations and provide a rich set of tangible affordances for interactions, such as touch, folding, tilting, or stacking. At the same time, augmented reality can dynamically update visualization content to provide <italic>commands</italic> such as pan, zoom, filter, or detail on demand. This paper is the first to provide a structured approach to mapping possible actions with the paper to interaction commands. This design space and the findings of a controlled user study have implications for future designs of augmented reality systems involving paper sheets and visualizations. Through workshops (<inline-formula><tex-math notation="LaTeX">$\mathrm{N}=20$</tex-math><alternatives><graphic position="float" orientation="portrait" xlink:href="tvcg-tong-3209386-eqinline-1-small.tif"/></alternatives></inline-formula>) and ideation, we identified 81 interactions that we classify in three dimensions: 1) <italic>commands</italic> that can be supported by an interaction, 2) the specific <italic>parameters</italic> provided by an (inter)<italic>action</italic> with paper, and 3) the <italic>number</italic> of paper sheets involved in an interaction. We tested user preference and viability of 11 of these interactions with a prototype implementation in a controlled study (<inline-formula><tex-math notation="LaTeX">$\mathrm{N}=12$</tex-math><alternatives><graphic position="float" orientation="portrait" xlink:href="tvcg-tong-3209386-eqinline-2-small.tif"/></alternatives></inline-formula>, HoloLens 2) and found that most of the interactions are intuitive and engaging to use. We summarized interactions (e.g., tilt to pan) that have strong affordance to complement “point” for data exploration, physical limitations and properties of paper as a medium, cases requiring redundancy and shortcuts, and other implications for design.
Quantum information approach to the implementation of a neutron cavity

Using the quantum information model of dynamical diffraction we consider a neutron cavity composed of two perfect crystal silicon blades capable of containing the neutron wavefunction. We show that the internal confinement of the neutrons through Bragg diffraction can be modelled by a quantum random walk. Furthermore, we introduce a toolbox for modelling crystal imperfections such as surface roughness and defects. Good agreement is found between the simulation and the experimental implementation, where leakage beams are present, modelling of which is impractical with the conventional theory of dynamical diffraction. Analysis of the standing neutron waves is presented in regards to the crystal geometry and parameters; and the conditions required for well-defined bounces are derived. The presented results enable new approaches to studying the setups utilizing neutron confinement, such as the experiments to measure neutron magnetic and electric dipole moments.
DIDER: Discovering Interpretable Dynamically Evolving Relations

Effective understanding of dynamically evolving multiagent interactions is crucial to capturing the underlying behavior of agents in social systems. It is usually challenging to observe these interactions directly, and therefore modeling the latent interactions is essential for realizing the complex behaviors. Recent work on Dynamic Neural Relational Inference (DNRI) captures explicit inter-agent interactions at every step. However, prediction at every step results in noisy interactions and lacks intrinsic interpretability without post-hoc inspection. Moreover, it requires access to ground truth annotations to analyze the predicted interactions, which are hard to obtain. This letter introduces DIDER, Discovering Interpretable Dynamically Evolving Relations, a generic end-to-end interaction modeling framework with intrinsic interpretability. DIDER discovers an interpretable sequence of inter-agent interactions by disentangling the task of latent interaction prediction into sub-interaction prediction and duration estimation. By imposing the consistency of a sub-interaction type over an extended time duration, the proposed framework achieves intrinsic interpretability without requiring any post-hoc inspection. We evaluate DIDER on both synthetic and real-world datasets. The experimental results demonstrate that modeling disentangled and interpretable dynamic relations improves performance on trajectory forecasting tasks.
Enhancing level set-based topology optimization with anisotropic graded meshes

We propose a new algorithm for the design of topologically optimized lightweight structures, under a minimum compliance requirement. The new process enhances a standard level set formulation in terms of computational efficiency, thanks to the employment of a strategic computational mesh. We pursue a twofold goal, i.e., to deliver a final layout characterized by a smooth contour and reliable mechanical properties. The smoothness of the optimized structure is ensured by the employment of an anisotropic adapted mesh, which sharply captures the material/void interface. A robust mechanical performance is guaranteed by a uniform tessellation of the internal part of the optimized configuration. A thorough numerical investigation corroborates the effectiveness of the proposed algorithm as a reliable and computationally affordable design tool, both in two- and three-dimensional contexts.
DualVoice: Speech Interaction that Discriminates between Normal and Whispered Voice Input

Interactions based on automatic speech recognition (ASR) have become widely used, with speech input being increasingly utilized to create documents. However, as there is no easy way to distinguish between commands being issued and text required to be input in speech, misrecognitions are difficult to identify and correct, meaning that documents need to be manually edited and corrected. The input of symbols and commands is also challenging because these may be misrecognized as text letters. To address these problems, this study proposes a speech interaction method called DualVoice, by which commands can be input in a whispered voice and letters in a normal voice. The proposed method does not require any specialized hardware other than a regular microphone, enabling a complete hands-free interaction. The method can be used in a wide range of situations where speech recognition is already available, ranging from text input to mobile/wearable computing. Two neural networks were designed in this study, one for discriminating normal speech from whispered speech, and the second for recognizing whisper speech. A prototype of a text input system was then developed to show how normal and whispered voice can be used in speech text input. Other potential applications using DualVoice are also discussed.
We Are in This Together: Quantifying Community Subjective Wellbeing and Resilience

The COVID-19 pandemic disrupted everyone's life across the world. In this work, we characterize the subjective wellbeing patterns of 112 cities across the United States during the pandemic prior to vaccine availability, as exhibited in subreddits corresponding to the cities. We quantify subjective wellbeing using positive and negative affect. We then measure the pandemic's impact by comparing a community's observed wellbeing with its expected wellbeing, as forecasted by time series models derived from prior to the pandemic. We show that general community traits reflected in language can be predictive of community resilience. We predict how the pandemic would impact the wellbeing of each community based on linguistic and interaction features from normal times before the pandemic. We find that communities with interaction characteristics corresponding to more closely connected users and higher engagement were less likely to be significantly impacted. Notably, we find that communities that talked more about social ties normally experienced in-person, such as friends, family, and affiliations, were actually more likely to be impacted. Additionally, we use the same features to also predict how quickly each community would recover after the initial onset of the pandemic. We similarly find that communities that talked more about family, affiliations, and identifying as part of a group had a slower recovery.
Probing CP violation in exclusive $b \to s \nu \bar \nu$ transitions

We consider the time-dependent analysis of rare $B_d$ and $B_s$ decays mediated by $b\to s \nu \bar \nu$ transitions. The inclusion of time evolution allows us to construct novel observables with specific sensitivity to CP-odd phases in these processes. The sensitivity to CP violation of corresponding time-integrated measurements in presence of flavor-tagging is also explored. We provide precise predictions for these observables in the SM and explore their sensitivity to new CP-violating NP contributions at present and planned future $B$-physics experiments. As such, these observables provide unique probes of CP violation in $b \to s \nu \bar \nu$ transitions.
AppGNN: Approximation-Aware Functional Reverse Engineering using Graph Neural Networks

The globalization of the Integrated Circuit (IC) market is attracting an ever-growing number of partners, while remarkably lengthening the supply chain. Thereby, security concerns, such as those imposed by functional Reverse Engineering (RE), have become quintessential. RE leads to disclosure of confidential information to competitors, potentially enabling the theft of intellectual property. Traditional functional RE methods analyze a given gate-level netlist through employing pattern matching towards reconstructing the underlying basic blocks, and hence, reverse engineer the circuit’s function.In this work, we are the first to demonstrate that applying Approximate Computing (AxC) principles to circuits significantly improves the resiliency against RE. This is attributed to the increased complexity in the underlying pattern-matching process. The resiliency remains effective even for Graph Neural Networks (GNNs) that are presently one of the most powerful state-of-the-art techniques in functional RE. Using AxC, we demonstrate a substantial reduction in GNN average classification accuracy– from 98% to a mere 53%. To surmount the challenges introduced by AxC in RE, we propose the highly promising AppGNN platform, which enables GNNs (still being trained on exact circuits) to: (i) perform accurate classifications, and (ii) reverse engineer the circuit functionality, notwithstanding the applied approximation technique. AppGNN accomplishes this by implementing a novel graph-based node sampling approach that mimics generic approximation methodologies, requiring zero knowledge of the targeted approximation type.We perform an extensive evaluation targeting wide-ranging adder and multiplier circuits that are approximated using various AxC techniques, including state-of-the-art evolutionary-based approaches. We show that, using our method, we can improve the classification accuracy from 53% to 81% when classifying approximate adder circuits that have been generated using evolutionary algorithms, which our method is oblivious of. Our AppGNN framework is publicly available under https://github.com/ML-CAD/AppGNN
Learning linear modules in a dynamic network with missing node observations

In order to identify a system (module) embedded in a dynamic network, one has to formulate a multiple-input estimation problem that necessitates certain nodes to be measured and included as predictor inputs. However, some of these nodes may not be measurable in many practical cases due to sensor selection and placement issues. This may result in biased estimates of the target module. Furthermore, the identification problem associated with the multiple-input structure may require determining a large number of parameters that are not of particular interest to the experimenter, with increased computational complexity in large-sized networks. In this paper, we tackle these problems by using a data augmentation strategy that allows us to reconstruct the missing node measurements and increase the accuracy of the estimated target module. To this end, we develop a system identification method using regularized kernel-based methods coupled with approximate inference methods. Keeping a parametric model for the module of interest, we model the other modules as Gaussian Processes (GP) with a kernel given by the so-called stable spline kernel. An Empirical Bayes (EB) approach is used to estimate the parameters of the target module. The related optimization problem is solved using an Expectation-Maximization (EM) method, where we employ a Markov-chain Monte Carlo (MCMC) technique to reconstruct the unknown missing node information and the network dynamics. Numerical simulations on dynamic network examples illustrate the potentials of the developed method.
A First Look at Dataset Bias in License Plate Recognition

Public datasets have played a key role in advancing the state of the art in License Plate Recognition (LPR). Although dataset bias has been recognized as a severe problem in the computer vision community, it has been largely overlooked in the LPR literature. LPR models are usually trained and evaluated separately on each dataset. In this scenario, they have often proven robust in the dataset they were trained in but showed limited performance in unseen ones. Therefore, this work investigates the dataset bias problem in the LPR context. We performed experiments on eight datasets, four collected in Brazil and four in mainland China, and observed that each dataset has a unique, identifiable “signature” since a lightweight classification model predicts the source dataset of a license plate (LP) image with more than 95 % accuracy. In our discussion, we draw attention to the fact that most LPR models are probably exploiting such signatures to improve the results achieved in each dataset at the cost of losing generalization capability. These results emphasize the importance of evaluating LPR models in cross-dataset setups, as they provide a better indication of generalization (hence real-world performance) than within-dataset ones.
2022 Galois hulls of linear codes and new EAQECCs of arbitrary lengths

Let q = p h be a prime power and e be an integer with 0 ≤ e ≤ h − 1. The e -Galois hull of a linear code C is the intersection of C and its e -Galois dual code C ⊥ e , which is of great signiﬁcance in both theory and practice. In this paper, we ﬁrst study the dimensions of Galois hulls of linear codes and ﬁnd that the dimensions have symmetry. We further derive some new suﬃcient and necessary conditions for a linear code being an e -Galois self-orthogonal or linear complementary dual code based on this property. Then, we generalize the previous conclusions and propose the explicit constructions of e -Galois self-orthogonal codes of lengths n + 2 i ( i ≥ 0) and n + 2 i + 1 ( i ≥ 1) from e -Galois self-orthogonal codes of length n . It means that linear codes of lengths n + 2 i and n + 2 i + 1 with Galois hulls of arbitrary dimensions exist if an e -Galois self-orthogonal code of length n exists. Finally, we construct two new classes of Hermitian self-orthogonal GRS codes and apply them together with all the results to the constructions of entanglement-assisted quantum error-correcting codes (EAQECCs for short). Some new EAQECCs and MDS EAQECCs of lengths n + 2 i and n + 2 i + 1 are obtained.
Learning Car Speed Using Inertial Sensors for Dead Reckoning Navigation

A deep neural network (DNN) is trained to estimate the speed of a car driving in an urban area using as input a stream of measurements from a low-cost six-axis inertial measurement unit (IMU). Three hours of data were collected by driving through the city of Ashdod, Israel, in a car equipped with a global navigation satellite system (GNSS) real-time kinematic positioning device and a synchronized IMU. Ground truth labels for the car speed were calculated using the position measurements obtained at the high rate of 50 Hz. A DNN architecture with long short-term memory layers is proposed to enable high-frequency speed estimation that accounts for previous input history and the nonlinear relation between speed, acceleration, and angular velocity. A simplified aided dead reckoning localization scheme is formulated to assess the trained model, which provides the speed pseudomeasurement. The trained model is shown to substantially improve the position accuracy during a 4-min drive without the use of GNSS position updates.
Rapid Telehealth-Centered Response to COVID-19 Outbreaks in Postacute and Long-Term Care Facilities

Purpose: The vulnerability of postacute and long-term care (PA/LTC) facility residents to COVID-19 has manifested across the world with increasing facility outbreaks associated with high hospitalization and mortality rates. Systematic protocols to guide telehealth-centered interventions in response to COVID-19 outbreaks have yet to be delineated. This article is intended to inform PA/LTC facilities and neighboring health care partners how to collaboratively utilize telehealth-centered strategies to improve outcomes in facility outbreaks. Methods : The University of Virginia rapidly developed a multidisciplinary telehealth-centered COVID-19 facility outbreak strategy in response to a LTC facility outbreak in which 41 (out of 48) facility residents and 7 staff members tested positive. This strategy focused on supporting the facility team remotely using rapidly deployed technologic solutions. Goals included (1) early identification of patients who need their care escalated, (2) monitoring and treating patients deemed safe to remain in the facility, (3) care coordination to facilitate bidirectional transfers between the skilled nursing facility (SNF) and hospital, and (4) daily facility needs assessment related to technology, infection control, and staff well-being. To achieve these goals, a standardized approach centered on daily multidisciplinary virtual rounds and telemedicine consultation was provided. Results: Over a month since the outbreak began, 18 out of 48 (38%) facility residents required hospitalization and 6 (12.5%) died. Eleven facility residents have since returned back to the SNF after recovering from their hospitalization. No staff required hospitalization. Conclusions: Interventions that reduce hospitalizations and mortality are a critical need during the COVID-19 pandemic. The mortality and hospitalization rates seen in this PA/LTC facility outbreak are significantly lower than has been documented in other facility outbreaks. Our multidisciplinary approach centered on telemedicine should be considered as other PA/LTC facilities partner with neighboring health care systems in responding to COVID-19 outbreaks. We have begun replicating these services to additional PA/LTC facilities facing COVID-19 outbreaks.
Complementary and alternative medicine use in infertility: A review of infertile women's needs

The use of complementary and alternative medicine (CAM) is common in infertile women in different countries. The purpose of the current study was to review the infertile women's needs in relation to CAM use. This narrative review was conducted through searching English databases including Scopus, PubMed, Embase, Web of Science, Cochrane library as well as Persian databases consisted of SID, and Magiran. The used keywords included “CAM/therapy, needs, and Infertility. ” All studies published in English peer-reviewed journals from conception to October 2020, which examined the infertile women's needs in the field of CAM use were included in the review. In the process of data extraction, two researchers screened the title, abstract, and full text of the articles. Out of the 2166 articles reviewed, 29 articles including six qualitative and mixed methods studies, four review, and 19 quantitative studies met the inclusion criteria. The results showed that infertile women have different needs in six domains consisted of educational and informational needs, the need for psychological counseling, supportive needs, the need for CAM use counseling, the need to treatment consistent with women's culture and demands, and the need to the integration of CAM with conventional medicine. Awareness of infertile women's needs toward CAM use can help health policymakers and planners in designing and implementing counseling services in accordance with the demands and culture of infertile couples. It also helps to develop a coherent program to integrate the use of CAM in the classical infertility treatment.
Phase Advancing Is a Common Property of Multiple Neuron Classes in the Mouse Retina

Abstract Behavioral interactions with moving objects are challenged by response latencies within the sensory and motor nervous systems. In vision, the combined latency from phototransduction and synaptic transmission from the retina to central visual areas amounts to 50–100 ms, depending on stimulus conditions. Time required for generating appropriate motor output adds to this latency and further compounds the behavioral delay. Neuronal adaptations that help counter sensory latency within the retina have been demonstrated in some species, but how general these specializations are, and where in the circuitry they originate, remains unclear. To address this, we studied the timing of object motion-evoked responses at multiple signaling stages within the mouse retina using two-photon fluorescence calcium and glutamate imaging, targeted whole-cell electrophysiology, and computational modeling. We found that both ON-type and OFF-type ganglion cells, as well as the bipolar cells that innervate them, temporally advance the position encoding of a moving object and so help counter the inherent signaling delay in the retina. Model simulations show that this predictive capability is a direct consequence of the spatial extent of the cells’ linear visual receptive field, with no apparent specialized circuits that help predict beyond it.
Tri‐level transmission Expansion planning under intentional attacks: virtual attacker approach‐part II: case studies

In the second part of this two-part study, the authors numerically validate and analyse the tri-level transmission expansion planning (TTEP) which is formulated and explained in the first part. TTEP is performed aim to minimise the destructive impacts of physical intentional attacks. At first, an illustrative example (a three-bus system) is provided to show the capability of the proposed TTEP model to find the Nash equilibria and identify the Pareto equilibria of the game among virtual attackers. Then, the proposed TTEP model is applied on the Garver network and the modified IEEE 30-bus network and numerical results for several case studies are provided. The numerical results confirm the proposed model and show its significant capability in reducing the vulnerability of the power networks.