Search

Data physicalization is gaining popularity in public and educational contexts due to its potential to make abstract data more tangible and understandable. Despite its growing use, there remains a significant gap in our understanding of how large-size physical visualizations compare to their digital counterparts in terms of user comprehension and memory retention. This study aims to bridge this knowledge gap by comparing the effectiveness of visualizing school building history data on large digital screens versus large physical models. Our experimental approach involved 32 participants who were exposed to one of the visualization mediums. We assessed their user experience and immediate understanding of the content, measured through tests after exposure, and evaluated memory retention with follow-up tests seven days later. The results revealed notable differences between the two forms of visualization: physicalization not only facilitated better initial comprehension but also significantly enhanced long-term memory retention. Furthermore, user feedback on usability was also higher on physicalization. These findings underscore the substantial impact of physicalization in improving information comprehension and retention. This study contributes crucial insights into future visualization media selection in educational and public settings., Comment: 15 pages, 7 figures, The 11th China Visualization and Visual Analytics Conference

Over the past ten years, several breakthroughs have been made in multi-messenger astronomy. Thanks to the IceCube Neutrino Observatory, the detection of astrophysical neutrinos was proved to be practical. However, no source has been significantly identified due to the lack of statistics and uncovered field of view. The next generation of high-energy neutrino telescope is in high demand. We propose the NEutrino Observatory in the Nanhai (NEON), located in the South China Sea to be complementary for the global neutrino detectors. This proposal describes the design and layout of the array and reports on comprehensive simulations conducted to assess its performance. The NEON project, with a volume of 10 km$^3$, achieves an angular resolution of 0.1$^\circ$ at 100 TeV. With 10 years of operation, the project's 5$\sigma$ sensitivity is estimated as $E^2\Phi \sim 2 \times 10^{-10}$ GeV cm$^{-2}$ s$^{-1}$ for a source spectrum index of -2. We found that the variation in depth from 1700 to 3500 meters does not significantly influence the sensitivity to steady sources., Comment: 10 pages, 5 figures

The first LHAASO catalog presents six mysterious Ultra-High-Energy (UHE) $\gamma$-ray sources -- 1LHAASO J0007$+$5659u, 1LHAASO J0206$+$4302u, 1LHAASO J0212$+$4254u, 1LHAASO J0216$+$4237u, 1LHAASO J1740$+$0948u and 1LHAASO J1959$+$1129u, which have emission only detected by LHAASO-KM2A (energy $>$ 25 TeV). No significant counterparts of these six sources are observed, except the two pulsars PSR J0218$+$4232 and PSR J1740$+$1000. All the six sources are out of Galactic plane with galactic latitudes (absolute value) over 5$^{\circ}$. Three of them -- 1LHAASO J0206$+$4302u, 1LHAASO J0212$+$4254u and 1LHAASO J0216$+$4237u are connected on the significance map and forming a dumbbell-like structure. They are close in position and show a similar spectral shape, suggesting a physical association among them. In this work, we first present a multi-wavelength and multi-messenger study of these six sources based on the Fermi-LAT, Swift-XRT, Planck and LAMBDA datasets as well as the data of IceCube. The corresponding leptonic and hadronic modeling results are also provided. To explain the interesting dumbbell-like structure, we established a traveling Pulsar Wind Nebula (PWN) model. Our model has the ability to explain both the morphology and spectrum of KM2A. However, both a high proper-motion speed $>$ 1000 km s$^{-1}$ and a short distance $<$ 1 kpc are required in our model, which contradict the parameters of the only known nearby pulsar - PSR J0218+4232. For more detailed researches on this structure, we intend to utilize XMM-Newton to acquire X-ray data with higher sensitivity., Comment: 16 pages, 8 Figures. Submitted to AAS Journals

In vitro cell biology experiments are routinely used to characterize cell migration properties under various experimental conditions. These experiments can be interpreted using lattice-based random walk models to provide insight into underlying biological mechanisms, and continuum limit partial differential equation (PDE) descriptions of the stochastic models can be used to efficiently explore model properties instead of relying on repeated stochastic simulations. Working with efficient PDE models is of high interest for parameter estimation algorithms that typically require a large number of forward model simulations. Quantitative data from cell biology experiments usually involves non-negative cell counts in different regions of the experimental images, and it is not obvious how to relate finite, noisy count data to the solutions of continuous PDE models that correspond to noise-free density profiles. In this work we illustrate how to develop and implement likelihood-based methods for parameter estimation, parameter identifiability and model prediction for lattice-based models describing collective migration with an arbitrary number of interacting subpopulations. We implement a standard additive Gaussian measurement error model as well as a new physically-motivated multinomial measurement error model that relates noisy count data with the solution of continuous PDE models. Both measurement error models lead to similar outcomes for parameter estimation and parameter identifiability, whereas the standard additive Gaussian measurement error model leads to non-physical prediction outcomes. In contrast, the new multinomial measurement error model involves a lower computational overhead for parameter estimation and identifiability analysis, as well as leading to physically meaningful model predictions., Comment: 34 pages, 7 figures

Symmetry lies at the heart of 2D bioelectronics, determining material properties at the fundamental level. Breaking the symmetry allows emergent functionalities and effects. However, symmetry modulation in 2D bioelectronics and the resultant applications have been largely overlooked. Here we devise an oxidized architectural MXene, referred as OXene, that couples orbit symmetric breaking with inverse symmetric breaking to entitle the optimized interfacial impedance and Schottky-induced piezoelectric effects. The resulting OXene validates applications ranging from microelectrode arrays, gait analysis, active transistor matrix, and wireless signaling transmission, which enables highly-fidelity signal transmission and reconfigurable logic gates. Further OXene interfaces are investigated in both rodent and porcine myocardium, featuring high-quality and spatiotemporally resolved physiological recordings, while accurate differentiated predictions, enabled via various machine learning pipelines.

Bioelectronic implants with soft mechanics, biocompatibility, and excellent electrical performance enable biomedical implants to record electrophysiological signals and execute interventions within internal organs, promising to revolutionize the diagnosing, monitoring, and treatment of various pathological conditions. However, challenges remain in improving excessive impedance at the bioelectronic-tissue interface and thus the efficacy of electrophysiological signaling and intervention. Here, we devise orbit symmetry breaking in MXene (a low-cost scalability, biocompatible, and conductive 2D layered material, that we refer to as OBXene), that exhibits low bioelectronic-tissue impedance, originating from the out-of-plane charge transfer. Furthermore, the Schottky-induced piezoelectricity stemming from the asymmetric orbital configuration of OBXene facilitates interlayered charge transport in the device. In this study, we report an OBXene-based cardiac patch applied on the left ventricular epicardium of both rodent and porcine models to enable spatiotemporal epicardium mapping and pacing, while coupling the wireless and battery-free operation for long-term real-time recording and closed-loop stimulation.

The proliferation of large language models (LLMs) has revolutionized the capabilities of natural language interfaces (NLIs) for data analysis. LLMs can perform multi-step and complex reasoning to generate data insights based on users' analytic intents. However, these insights often entangle with an abundance of contexts in analytic conversations such as code, visualizations, and natural language explanations. This hinders efficient identification, verification, and interpretation of insights within the current chat-based interfaces of LLMs. In this paper, we first conduct a formative study with eight experienced data analysts to understand their general workflow and pain points during LLM-powered data analysis. Then, we propose an LLM-based multi-agent framework to automatically extract, associate, and organize insights along with the analysis process. Based on this, we introduce InsightLens, an interactive system that visualizes the intricate conversational contexts from multiple aspects to facilitate insight discovery and exploration. A user study with twelve data analysts demonstrates the effectiveness of InsightLens, showing that it significantly reduces users' manual and cognitive effort without disrupting their conversational data analysis workflow, leading to a more efficient analysis experience.

This paper mainly deals with the Sturm-Liouville operator \begin{equation*} \mathbf{H}=\frac{1}{w(x)}\left( -\frac{\mathrm{d}}{\mathrm{d}x}p(x)\frac{ \mathrm{d}}{\mathrm{d}x}+q(x)\right) ,\text{ }x\in \Gamma \end{equation*} acting in $L_{w}^{2}\left( \Gamma \right) ,$ where $\Gamma $ is a metric graph. We establish a relationship between the bottom of the spectrum and the positive solutions of quantum graphs, which is a generalization of the classical Allegretto-Piepenbrink theorem. Moreover, we prove the Persson-type theorem, which characterizes the infimum of the essential spectrum.

We study the Floating Offshore Wind Turbine (FOWT) dynamic response to high wind velocity scenarios utilizing an extensive Markov Chain Monte Carlo simulation involving 10,000 trials of a reduced model from \cite{betti_development_2014} with a blade-pitch PID controller. The research emphasizes analysis of extreme events in surge, pitch and heave of FOWT, identifying and categorizing them based on their statistics, correlations and causal relations to wind and waves. A significant discovery is the differentiation of anomalies into short-correlated and long-correlated types, with the latter primarily influenced by wind conditions. For example, one of our findings is that while specific wave conditions may amplify anomalies, wind remains the predominant factor in long-term anomalous pitch behaviors. We anticipate that further development of this analysis of the operation's critical extreme events will be pivotal for advancing control strategies and design considerations in FOWTs, accommodating the turbulent environment they encounter., Comment: 20 pages, 43 figures

The rapid development of large language models (LLMs), such as ChatGPT, has revolutionized the efficiency of creating programming tutorials. LLMs can be instructed with text prompts to generate comprehensive text descriptions of code snippets. However, the lack of transparency in the end-to-end generation process has hindered the understanding of model behavior and limited user control over the generated results. To tackle this challenge, we introduce a novel approach that breaks down the programming tutorial creation task into actionable steps. By employing the tree-of-thought method, LLMs engage in an exploratory process to generate diverse and faithful programming tutorials. We then present SPROUT, an authoring tool equipped with a series of interactive visualizations that empower users to have greater control and understanding of the programming tutorial creation process. A formal user study demonstrated the effectiveness of SPROUT, showing that our tool assists users to actively participate in the programming tutorial creation process, leading to more reliable and customizable results. By providing users with greater control and understanding, SPROUT enhances the user experience and improves the overall quality of programming tutorial. A free copy of this paper and all supplemental materials are available at https://osf.io/uez2t/?view_only=5102e958802341daa414707646428f86.

Biomarker detection is an indispensable part in the diagnosis and treatment of low-grade glioma (LGG). However, current LGG biomarker detection methods rely on expensive and complex molecular genetic testing, for which professionals are required to analyze the results, and intra-rater variability is often reported. To overcome these challenges, we propose an interpretable deep learning pipeline, a Multi-Biomarker Histomorphology Discoverer (Multi-Beholder) model based on the multiple instance learning (MIL) framework, to predict the status of five biomarkers in LGG using only hematoxylin and eosin-stained whole slide images and slide-level biomarker status labels. Specifically, by incorporating the one-class classification into the MIL framework, accurate instance pseudo-labeling is realized for instance-level supervision, which greatly complements the slide-level labels and improves the biomarker prediction performance. Multi-Beholder demonstrates superior prediction performance and generalizability for five LGG biomarkers (AUROC=0.6469-0.9735) in two cohorts (n=607) with diverse races and scanning protocols. Moreover, the excellent interpretability of Multi-Beholder allows for discovering the quantitative and qualitative correlations between biomarker status and histomorphology characteristics. Our pipeline not only provides a novel approach for biomarker prediction, enhancing the applicability of molecular treatments for LGG patients but also facilitates the discovery of new mechanisms in molecular functionality and LGG progression., Comment: 47 pages, 6 figures

Voucher abuse detection is an important anomaly detection problem in E-commerce. While many GNN-based solutions have emerged, the supervised paradigm depends on a large quantity of labeled data. A popular alternative is to adopt self-supervised pre-training using label-free data, and further fine-tune on a downstream task with limited labels. Nevertheless, the "pre-train, fine-tune" paradigm is often plagued by the objective gap between pre-training and downstream tasks. Hence, we propose VPGNN, a prompt-based fine-tuning framework on GNNs for voucher abuse detection. We design a novel graph prompting function to reformulate the downstream task into a similar template as the pretext task in pre-training, thereby narrowing the objective gap. Extensive experiments on both proprietary and public datasets demonstrate the strength of VPGNN in both few-shot and semi-supervised scenarios. Moreover, an online deployment of VPGNN in a production environment shows a 23.4% improvement over two existing deployed models., Comment: 7 pages, Accepted by CIKM23 Applied Research Track

Quantum computing is a rapidly evolving field that enables exponential speed-up over classical algorithms. At the heart of this revolutionary technology are quantum circuits, which serve as vital tools for implementing, analyzing, and optimizing quantum algorithms. Recent advancements in quantum computing and the increasing capability of quantum devices have led to the development of more complex quantum circuits. However, traditional quantum circuit diagrams suffer from scalability and readability issues, which limit the efficiency of analysis and optimization processes. In this research, we propose a novel visualization approach for large-scale quantum circuits by adopting semantic analysis to facilitate the comprehension of quantum circuits. We first exploit meta-data and semantic information extracted from the underlying code of quantum circuits to create component segmentations and pattern abstractions, allowing for easier wrangling of massive circuit diagrams. We then develop Quantivine, an interactive system for exploring and understanding quantum circuits. A series of novel circuit visualizations are designed to uncover contextual details such as qubit provenance, parallelism, and entanglement. The effectiveness of Quantivine is demonstrated through two usage scenarios of quantum circuits with up to 100 qubits and a formal user evaluation with quantum experts. A free copy of this paper and all supplemental materials are available at https://osf.io/2m9yh/?view_only=0aa1618c97244f5093cd7ce15f1431f9., Comment: Accepted by IEEE VIS 2023

The predominant centralized paradigm in educational data management currently suffers from several critical issues such as vulnerability to malicious tampering, a high prevalence of diploma counterfeiting, and the onerous cost of certificate authentication. Decentralized blockchain technology, with its cutting-edge capabilities, presents a viable solution to these pervasive problems. In this paper, we illuminate the inherent limitations of existing centralized systems and introduce EduChain, a novel heterogeneous blockchain-based system for managing educational data. EduChain uniquely harnesses the strengths of both private and consortium blockchains, offering an unprecedented level of security and efficiency. In addition, we propose a robust mechanism for performing database consistency checks and error tracing. This is achieved through the implementation of a secondary consensus, employing the pt-table-checksum tool. This approach effectively addresses the prevalent issue of database mismatches. Our system demonstrates superior performance in key areas such as information verification, error traceback, and data security, thereby significantly improving the integrity and trustworthiness of educational data management. Through EduChain, we offer a powerful solution for future advancements in secure and efficient educational data management.

Context. Solar extreme ultraviolet (EUV) waves are propagating disturbances in the corona, and they usually accompany with various solar eruptions, from large-scale coronal mass ejections to small-scale coronal jets. Aims. Generally, it is believed that EUV waves are driven by the rapid expansion of coronal loops overlying the erupting cores. In this Letter, we present an exception of EUV wave that was not triggered by the expansion of coronal loops overlying the erupting core. Methods. Combining the multiwavelength observations from multiple instruments, we studied the event in detail. Results. The eruption was restricted in the active region (AR) and disturbed the nearby sheared arcades (SAs) connecting the source AR to a remote AR. Interestingly, following the disturbance, an EUV wave formed close to the SAs, but far away from the eruption source. Conclusions. All the results showed that the EUV wave had a closer temporal and spatial relationship with the disappearing part of SAs than the confined eruption. Hence, we suggest that the EUV wave was likely triggered by the expansion of some strands of SAs, rather than the expansion of erupting loops. It can be a possible complement for the driving mechanisms of EUV waves., Comment: 9 pages, 6 figures

Solar coronal waves frequently appear as bright disturbances that propagate globally from the eruption center in the solar atmosphere, just like the tsunamis in the ocean on Earth. Theoretically, coronal waves can sweep over the underlying chromosphere and leave an imprint in the form of Moreton wave, due to the enhanced pressure beneath their coronal wavefront. Despite the frequent observations of coronal waves, their counterparts in the chromosphere are rarely detected. Why the chromosphere rarely bears the imprints of solar tsunamis remained a mystery since their discovery three decades ago. To resolve this question, all coronal waves and associated Moreton waves in the last decade have been initially surveyed, though the detection of Moreton waves could be hampered by utilising the low-quality H$\alpha$ data from Global Oscillations Network Group. Here, we present 8 cases (including 5 in Appendix) of the coexistence of coronal and Moreton waves in inclined eruptions where it is argued that the extreme inclination is key to providing an answer to address the question. For all these events, the lowest part of the coronal wavefront near the solar surface appears very bright, and the simultaneous disturbances in the solar transition region and the chromosphere predominantly occur beneath the bright segment. Therefore, evidenced by observations, we propose a scenario for the excitation mechanism of the coronal-Moreton waves in highly inclined eruptions, in which the lowest part of a coronal wave can effectively disturb the chromosphere even for a weak (e.g., B-class) solar flare., Comment: 24 pages, 14 figures

Magnetic flux ropes (MFRs), sets of coherently twisted magnetic field lines, are believed as core structures of various solar eruptions. Their evolution plays an important role to understand the physical mechanisms of solar eruptions, and can shed light on adverse space weather near the Earth. However, the erupting MFRs are occasionally prevented by strong overlying magnetic fields, and the MFR evolution during the descending phase in the confined cases is lack of attention. Here, we present the deformation of an erupting MFR accompanied by a confined double-peaked solar flare. The first peak corresponded to the MFR eruption in a standard flare model, and the second peak was closely associated with the flashings of an underlying sheared arcade (SA), the reversal slipping motion of the L-shaped flare ribbon, the falling of the MFR, and the shifting of top of filament threads. All results suggest that the confined MFR eruption involved in two-step magnetic reconnection presenting two distinct episodes of energy release in the flare impulsive phase, and the latter magnetic reconnection between the confined MFR and the underlying SA caused the deformation of MFR.

Heavy metal-based two-dimensional van der Waals materials have a large, coupled spin and valley Hall effect (SVHE) that has potential use in spintronics and valleytronics. Optical measurements of the SVHE have largely been performed below 30 K and understanding of the SVHE-induced spin/valley polarizations that can be electrically generated is limited. Here, we study the SVHE in monolayer p-type tungsten diselenide (WSe2). Kerr rotation (KR) measurements show the spatial distribution of the SVHE at different temperatures, its persistence up to 160 K, and that it can be electrically modulated via gate and drain bias. A spin/valley drift and diffusion model together with reflection spectra data is used to interpret the KR data and predict a lower-bound spin/valley lifetime of 4.1 ns below 90 K and 0.26 ns at 160 K. The excess spin and valley per unit length along the edge is calculated to be 109 per micron at 45 K, which corresponds to a spin/valley polarization on the edge of 6%. These results are important steps towards practical use of the SVHE.