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Considerable efforts are currently underway to mitigate the negative impacts of echo chambers, such as increased susceptibility to fake news and resistance towards accepting scientific evidence. Prior research has presented the development of computer systems that support the consumption of news information from diverse political perspectives to mitigate the echo chamber effect. However, existing studies still lack the ability to effectively support the key processes of news information consumption and quantitatively identify a political stance towards the information. In this paper, we present HearHere, an AI-based web system designed to help users accommodate information and opinions from diverse perspectives. HearHere facilitates the key processes of news information consumption through two visualizations. Visualization 1 provides political news with quantitative political stance information, derived from our graph-based political classification model, and users can experience diverse perspectives (Hear). Visualization 2 allows users to express their opinions on specific political issues in a comment form and observe the position of their own opinions relative to pro-liberal and pro-conservative comments presented on a map interface (Here). Through a user study with 94 participants, we demonstrate the feasibility of HearHere in supporting the consumption of information from various perspectives. Our findings highlight the importance of providing political stance information and quantifying users' political status as a means to mitigate political polarization. In addition, we propose design implications for system development, including the consideration of demographics such as political interest and providing users with initiatives., Comment: 34 pages, 6 figures, 6 tables, CSCW 2024

The Fair Graph Anomaly Detection (FairGAD) problem aims to accurately detect anomalous nodes in an input graph while avoiding biased predictions against individuals from sensitive subgroups. However, the current literature does not comprehensively discuss this problem, nor does it provide realistic datasets that encompass actual graph structures, anomaly labels, and sensitive attributes. To bridge this gap, we introduce a formal definition of the FairGAD problem and present two novel datasets constructed from the social media platforms Reddit and Twitter. These datasets comprise 1.2 million and 400,000 edges associated with 9,000 and 47,000 nodes, respectively, and leverage political leanings as sensitive attributes and misinformation spreaders as anomaly labels. We demonstrate that our FairGAD datasets significantly differ from the synthetic datasets used by the research community. Using our datasets, we investigate the performance-fairness trade-off in nine existing GAD and non-graph AD methods on five state-of-the-art fairness methods. Our code and datasets are available at https://github.com/nigelnnk/FairGAD, Comment: Accepted by ACM CIKM 2024

We present the results of a spectral analysis using simultaneous multifrequency (22, 43, 86, and 129 GHz) very long baseline interferometry (VLBI) observations of the Korean VLBI Network (KVN) on BL Lac object, Markarian 421 (Mrk 421). The data we used was obtained from January 2013 to June 2018. The light curves showed several flux enhancements with global decreases. To separate the variable and quiescent components in the multifrequency light curves for milliarcsecond-scale emission regions, we assumed that the quiescent radiation comes from the emission regions radiating constant optically-thin synchrotron emissions (i.e., a minimum flux density with an optically thin spectral index). The quiescent spectrum determined from the multifrequency light curves was subtracted from the total CLEAN flux density, yielding a variable component in the flux that produces the time-dependent spectrum. We found that the observed spectra were flat at 22-43 GHz, and relatively steep at 43-86 GHz, whereas the quiescent-corrected spectra are sometimes quite different from the observed spectra (e.g., sometimes inverted at 22-43 GHz ). The quiescent-corrected spectral indices were much more variable than the observed spectral indices. This spectral investigation implies that the quiescent-spectrum correction can significantly affect the multifrequency spectral index of variable compact radio sources such as blazars. Therefore, the synchrotron self-absorption B-field strength (B_SSA) can be significantly affected because B_SSA is proportional to the fifth power of turnover frequency., Comment: 13 pages, 9 figures, Accepted for publication in ApJ

We present the discovery of a peculiar dwarf nova KSP-OT-201712a using high-cadence, multi-color observations made with the Korea Microlensing Telescope Network. KSP-OT-201712a exhibits a rare presence of outbursts during standstills as well as strong H${\alpha}$ emission for a dwarf nova below the period minimum with an orbital period of 58.75 $\pm$ 0.02 minutes. The outburst cycles are ~ 6.6 days within standstills but increase to ~ 15 days outside of them. Both B-V and V-I colors become bluer and redder as the outburst luminosities increase and decrease, respectively, for the outburst within standstill, while they evolve in the opposite directions outside of the standstills. The presence of strong double-peaked H${\alpha}$ and weak He I emission lines with He/H flux ratio of 0.27, together with absorption lines of Mg b and Na D in the source, leads to the estimation Teff ~ 4570 $\pm$ 40 K, [Fe/H] ~ 0.06 $\pm$ 0.15 dex, and log g ~ 4.5 $\pm$ 0.1 for its secondary. KSP-OT-201712a is the second He-deficient dwarf nova below the period minimum, while the temperature of the secondary is measured for the first time in such objects. We identify it to be an ER UMa type dwarf nova suggesting that the evolution of dwarf novae across the period minimum is accompanied by large mass transfers. The high temperature of the secondary indicates that the system started its mass transfer when the secondary was about 93$\%$ of its main sequence age. The system will evolve to a helium cataclysmic variable or to AM CVn once its hydrogen envelope is exhausted before it explodes as a Type Ia supernova., Comment: 12 pages, 5 figures, accepted for publication in ApJ

Deep neural networks have exhibited remarkable performance in a variety of computer vision fields, especially in semantic segmentation tasks. Their success is often attributed to multi-level feature fusion, which enables them to understand both global and local information from an image. However, we found that multi-level features from parallel branches are on different scales. The scale disequilibrium is a universal and unwanted flaw that leads to detrimental gradient descent, thereby degrading performance in semantic segmentation. We discover that scale disequilibrium is caused by bilinear upsampling, which is supported by both theoretical and empirical evidence. Based on this observation, we propose injecting scale equalizers to achieve scale equilibrium across multi-level features after bilinear upsampling. Our proposed scale equalizers are easy to implement, applicable to any architecture, hyperparameter-free, implementable without requiring extra computational cost, and guarantee scale equilibrium for any dataset. Experiments showed that adopting scale equalizers consistently improved the mIoU index across various target datasets, including ADE20K, PASCAL VOC 2012, and Cityscapes, as well as various decoder choices, including UPerHead, PSPHead, ASPPHead, SepASPPHead, and FCNHead., Comment: 10 pages, 3 figures

In this paper, we introduce a Key-point-guided Diffusion probabilistic Model (KDM) that gains precise control over images by manipulating the object's key-point. We propose a two-stage generative model incorporating an optical flow map as an intermediate output. By doing so, a dense pixel-wise understanding of the semantic relation between the image and sparse key point is configured, leading to more realistic image generation. Additionally, the integration of optical flow helps regulate the inter-frame variance of sequential images, demonstrating an authentic sequential image generation. The KDM is evaluated with diverse key-point conditioned image synthesis tasks, including facial image generation, human pose synthesis, and echocardiography video prediction, demonstrating the KDM is proving consistency enhanced and photo-realistic images compared with state-of-the-art models., Comment: 24 pages

Highly magnetized neutron stars exhibit the vacuum non-linear electrodynamics effects, which can be well described using the one-loop effective action for quantum electrodynamics. In this context, we study the propagation and polarization of pulsar radio emission, based on the post-Maxwellian Lagrangian from the Heisenberg-Euler-Schwinger action. Given the refractive index obtained from this Lagrangian, we determine the leading-order corrections to both the propagation and polarization vectors due to quantum refraction via perturbation analysis. In addition, the effects on the orthogonality between the propagation and polarization vectors and the Faraday rotation angle, all due to quantum refraction are investigated. Furthermore, from the dual refractive index and the associated polarization modes, we discuss quantum birefringence, with the optical phenomenology analogous to its classical counterpart., Comment: 14 pages, 5 figures

We address the medication recommendation problem, which aims to recommend effective medications for a patient's current visit by utilizing information (e.g., diagnoses and procedures) given at the patient's current and past visits. While there exist a number of recommender systems designed for this problem, we point out that they are challenged in accurately capturing the relation (spec., the degree of relevance) between the current and each of the past visits for the patient when obtaining her current health status, which is the basis for recommending medications. To address this limitation, we propose a novel medication recommendation framework, named VITA, based on the following two novel ideas: (1) relevant-Visit selectIon; (2) Target-aware Attention. Through extensive experiments using real-world datasets, we demonstrate the superiority of VITA (spec., up to 5.56% higher accuracy, in terms of Jaccard, than the best competitor) and the effectiveness of its two core ideas. The code is available at https://github.com/jhheo0123/VITA., Comment: Accepted by AAAI 2024

In this paper, we focus on multimedia recommender systems using graph convolutional networks (GCNs) where the multimodal features as well as user-item interactions are employed together. Our study aims to exploit multimodal features more effectively in order to accurately capture users' preferences for items. To this end, we point out following two limitations of existing GCN-based multimedia recommender systems: (L1) although multimodal features of interacted items by a user can reveal her preferences on items, existing methods utilize GCN designed to focus only on capturing collaborative signals, resulting in insufficient reflection of the multimodal features in the final user/item embeddings; (L2) although a user decides whether to prefer the target item by considering its multimodal features, existing methods represent her as only a single embedding regardless of the target item's multimodal features and then utilize her embedding to predict her preference for the target item. To address the above issues, we propose a novel multimedia recommender system, named MONET, composed of following two core ideas: modality-embracing GCN (MeGCN) and target-aware attention. Through extensive experiments using four real-world datasets, we demonstrate i) the significant superiority of MONET over seven state-of-the-art competitors (up to 30.32% higher accuracy in terms of recall@20, compared to the best competitor) and ii) the effectiveness of the two core ideas in MONET. All MONET codes are available at https://github.com/Kimyungi/MONET., Comment: Accepted by WSDM 2024

The demand for the three-dimensional (3D) integration of electronic components is on a steady rise. The through-silicon-via (TSV) technique emerges as the only viable method for integrating single-crystalline device components in a 3D format, despite encountering significant processing challenges. While monolithic 3D (M3D) integration schemes show promise, the seamless connection of single-crystalline semiconductors without intervening wafers has yet to be demonstrated. This challenge arises from the inherent difficulty of growing single crystals on amorphous or polycrystalline surfaces post the back-end-of-the-line process at low temperatures to preserve the underlying circuitry. Consequently, a practical growth-based solution for M3D of single crystals remains elusive. Here, we present a method for growing single-crystalline channel materials, specifically composed of transition metal dichalcogenides, on amorphous and polycrystalline surfaces at temperatures lower than 400 {\deg}C. Building on this developed technique, we demonstrate the seamless monolithic integration of vertical single-crystalline logic transistor arrays. This accomplishment leads to the development of unprecedented vertical CMOS arrays, thereby constructing vertical inverters. Ultimately, this achievement sets the stage to pave the way for M3D integration of various electronic and optoelectronic hardware in the form of single crystals.