95,095 results on '"Heo BE"'
Search Results
102. Pre-exposure Prophylaxis Provided in the Emergency Department: Physician Perspectives
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Guess, Sarah, Roth, Ava, Gormley, Mirinda, Roth, Prerana, Litwin, Alain H., Hobbs, Jessica, Heo, Moonseong, and Moschella, Phillip
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- 2024
103. Does the use of tranexamic acid intraoperatively reduce postoperative blood loss and complications following biportal endoscopic lumbosacral decompression?
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Upfill-Brown, Alexander, Olson, Thomas, Adejuyigbe, Babapelumi, Shah, Akash, Sheppard, William, Park, Cheol Wung, Heo, Dong Hwa, and Park, Don Young
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Biomedical and Clinical Sciences ,Clinical Sciences ,Clinical Research ,Evaluation of treatments and therapeutic interventions ,6.4 Surgery ,Endoscopic spine surgery ,biportal spinal endoscopy ,lumbosacral decompression ,postoperative drain ,tranexamic acid - Abstract
BackgroundBiportal endoscopic spine surgery is an effective minimally invasive technique for treating common lumbar pathologies. We aim to evaluate the impact of intraoperative tranexamic acid (TXA) use on postoperative blood loss in biportal endoscopic decompression surgery.MethodsPatients undergoing biportal endoscopic lumbar discectomies and decompressions either by same day surgery or overnight stay at a single institution beginning in October 2021 were prospectively enrolled. This study was non-randomized, non-blinded with the first cohort of consecutive patients receiving 1 g of intravenous TXA intra-operatively before closure and the second cohort of consecutive patients receiving no TXA. Exclusion criteria included any revision surgery, any surgery for the diagnosis of spinal instability, infection, tumor, or trauma, any contraindication for TXA.ResultsEighty-four patients were included in the study, with 45 (54%) receiving TXA and 39 (46%) not receiving TXA. Median follow-up was 168 days [interquartile range (IQR), 85-368 days]. There were no differences in patient or surgical characteristics between cohorts. Estimated blood loss (EBL) was similar (P=0.20), while post-operative drain output was significantly lower in the TXA cohort (P=0.0028). Single level discectomies had significantly less drain output as compared to 2 level unilateral laminotomy, bilateral decompression (ULBD) cases (P0.99 for both). Oswestry disability index (ODI), visual analog scale (VAS) back and VAS leg scores decreased significantly; the absolute decrease in scores did not differ between groups (P=0.71, 0.22, 0.86, respectively).ConclusionsSystemic intraoperative TXA administration is associated with a significant decrease in post-operative blood loss in biportal spinal endoscopy, with no impact on the improvement in patient-reported outcomes (PROs) or rate of post-operative complications. Single level biportal discectomies had significantly less postoperative drainage with TXA and may not need drains postoperatively. Larger, randomized studies are necessary to evaluate the cost-effectiveness of TXA use in biportal spinal endoscopy.
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- 2024
104. Changes in the innate immune response to SARS-CoV-2 with advancing age in humans
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Agrawal, Sudhanshu, Tran, Michelle Thu, Jennings, Tara Sinta Kartika, Soliman, Marlaine Maged Hosny, Heo, Sally, Sasson, Bobby, Rahmatpanah, Farah, and Agrawal, Anshu
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Medical Microbiology ,Biomedical and Clinical Sciences ,Immunology ,Vaccine Related ,Emerging Infectious Diseases ,Prevention ,Infectious Diseases ,Biodefense ,Clinical Research ,Aging ,Lung ,2.1 Biological and endogenous factors ,Aetiology ,Infection ,Inflammatory and immune system ,Good Health and Well Being ,CD8 T cells ,DCs ,IL-29 ,Monocytes ,RNA-seq ,SARS-CoV-2 ,innate immunity ,Clinical Sciences ,Clinical sciences - Abstract
BackgroundAdvancing age is a major risk factor for respiratory viral infections. The infections are often prolonged and difficult to resolve resulting hospitalizations and mortality. The recent COVID-19 pandemic has highlighted this as elderly subjects have emerged as vulnerable populations that display increased susceptibility and severity to SARS-CoV-2. There is an urgent need to identify the probable mechanisms underlying this to protect against future outbreaks of such nature. Innate immunity is the first line of defense against viruses and its decline impacts downstream immune responses. This is because dendritic cells (DCs) and macrophages are key cellular elements of the innate immune system that can sense and respond to viruses by producing inflammatory mediators and priming CD4 and CD8 T-cell responses.ResultsWe investigated the changes in innate immune responses to SARS-CoV-2 as a function of age. Our results using human PBMCs from aged, middle-aged, and young subjects indicate that the activation of DCs and monocytes in response to SARS-CoV-2 is compromised with age. The impairment is most apparent in pDCs where both aged and middle-aged display reduced responses. The secretion of IL-29 that confers protection against respiratory viruses is also decreased in both aged and middle-aged subjects. In contrast, inflammatory mediators associated with severe COVID-19 including CXCL-8, TREM-1 are increased with age. This is also apparent in the gene expression data where pathways related host defense display an age dependent decrease with a concomitant increase in inflammatory pathways. Not only are the inflammatory pathways and mediators increased after stimulation with SARS-CoV-2 but also at homeostasis. In keeping with reduced DC activation, the induction of cytotoxic CD8 T cells is also impaired in aged subjects. However, the CD8 T cells from aged subjects display increased baseline activation in accordance with the enhanced baseline inflammation.ConclusionsOur results demonstrate a decline in protective anti-viral immune responses and increase in damaging inflammatory responses with age indicating that dysregulated innate immune responses play a significant role in the increased susceptibility of aged subjects to COVID-19. Furthermore, the dysregulation in immune responses develops early on as middle-aged demonstrate several of these changes.
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- 2024
105. Unraveling the role of the mitochondrial one-carbon pathway in undifferentiated thyroid cancer by multi-omics analyses.
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Lee, Seong, Park, Seongyeol, Yi, Shinae, Choi, Na, Lim, Mi, Chang, Jae, Won, Ho-Ryun, Kim, Je, Ko, Hye, Chung, Eun-Jae, Park, Young, Cho, Sun, Yu, Hyeong, Choi, June, Yeo, Min-Kyung, Yi, Boram, Yi, Kijong, Lim, Joonoh, Koh, Jun-Young, Lee, Min, Heo, Jun, Yoon, Sang, Kwon, Sung, Park, Jong-Lyul, Chu, In, Kim, Jin, Kim, Seon-Young, Shan, Yujuan, Liu, Lihua, Hong, Sung-A, Choi, Dong, Park, Junyoung, Ju, Young, Shong, Minho, Kim, Seon-Kyu, Koo, Bon, and Kang, Yea
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The role of the serine/glycine metabolic pathway (SGP) has recently been demonstrated in tumors; however, the pathological relevance of the SGP in thyroid cancer remains unexplored. Here, we perform metabolomic profiling of 17 tumor-normal pairs; bulk transcriptomics of 263 normal thyroid, 348 papillary, and 21 undifferentiated thyroid cancer samples; and single-cell transcriptomes from 15 cases, showing the impact of mitochondrial one-carbon metabolism in thyroid tumors. High expression of serine hydroxymethyltransferase-2 (SHMT2) and methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) is associated with low thyroid differentiation scores and poor clinical features. A subpopulation of tumor cells with high mitochondrial one-carbon pathway activity is observed in the single-cell dataset. SHMT2 inhibition significantly compromises mitochondrial respiration and decreases cell proliferation and tumor size in vitro and in vivo. Collectively, our results highlight the importance of the mitochondrial one-carbon pathway in undifferentiated thyroid cancer and suggest that SHMT2 is a potent therapeutic target.
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- 2024
106. Compact CRISPR genetic screens enabled by improved guide RNA library cloning.
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Heo, Seok-Jin, Enriquez, Lauren, Federman, Scot, Chang, Amy, Mace, Rachel, Shevade, Kaivalya, Nguyen, Phuong, Litterman, Adam, Shafer, Shawn, Przybyla, Laralynne, and Chow, Eric
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CRISPR screening ,Guide library ,Library cloning ,Primary cell CRISPR screening ,iPSC-derived CRISPR screening ,Humans ,CRISPR-Cas Systems ,RNA ,Guide ,CRISPR-Cas Systems ,Clustered Regularly Interspaced Short Palindromic Repeats ,Gene Library ,Gene Editing ,Cloning ,Molecular - Abstract
CRISPR genome editing approaches theoretically enable researchers to define the function of each human gene in specific cell types, but challenges remain to efficiently perform genetic perturbations in relevant models. In this work, we develop a library cloning protocol that increases sgRNA uniformity and greatly reduces bias in existing genome-wide libraries. We demonstrate that our libraries can achieve equivalent or better statistical power compared to previously reported screens using an order of magnitude fewer cells. This improved cloning protocol enables genome-scale CRISPR screens in technically challenging cell models and screen formats.
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- 2024
107. Expansion of Pathogenic Cardiac Macrophages in Immune Checkpoint Inhibitor Myocarditis.
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Ma, Pan, Liu, Jing, Qin, Juan, Lai, Lulu, Heo, Gyu, Luehmann, Hannah, Sultan, Deborah, Bredemeyer, Andrea, Bajapa, Geetika, Feng, Guoshuai, Jimenez, Jesus, He, Ruijun, Parks, Antanisha, Amrute, Junedh, Villanueva, Ana, Liu, Yongjian, Lin, Chieh-Yu, Mack, Matthias, Amancherla, Kaushik, Moslehi, Javid, and Lavine, Kory
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CXCL9 chemokine ,IFN-gamma ,T-cells ,cytotoxic T lymphocyte-associated antigen 4-immunoglobulin ,macrophages ,myocarditis ,programmed cell death protein 1 ,Humans ,Mice ,Animals ,Immune Checkpoint Inhibitors ,CD8-Positive T-Lymphocytes ,Myocarditis ,Programmed Cell Death 1 Receptor ,CTLA-4 Antigen ,Ligands ,Chemokines ,Macrophages ,RNA - Abstract
BACKGROUND: Immune checkpoint inhibitors (ICIs), antibodies targeting PD-1 (programmed cell death protein 1)/PD-L1 (programmed death-ligand 1) or CTLA4 (cytotoxic T-lymphocyte-associated protein 4), have revolutionized cancer management but are associated with devastating immune-related adverse events including myocarditis. The main risk factor for ICI myocarditis is the use of combination PD-1 and CTLA4 inhibition. ICI myocarditis is often fulminant and is pathologically characterized by myocardial infiltration of T lymphocytes and macrophages. Although much has been learned about the role of T-cells in ICI myocarditis, little is understood about the identity, transcriptional diversity, and functions of infiltrating macrophages. METHODS: We used an established murine ICI myocarditis model (Ctla4+/-Pdcd1-/- mice) to explore the cardiac immune landscape using single-cell RNA-sequencing, immunostaining, flow cytometry, in situ RNA hybridization, molecular imaging, and antibody neutralization studies. RESULTS: We observed marked increases in CCR2 (C-C chemokine receptor type 2)+ monocyte-derived macrophages and CD8+ T-cells in this model. The macrophage compartment was heterogeneous and displayed marked enrichment in an inflammatory CCR2+ subpopulation highly expressing Cxcl9 (chemokine [C-X-C motif] ligand 9), Cxcl10 (chemokine [C-X-C motif] ligand 10), Gbp2b (interferon-induced guanylate-binding protein 2b), and Fcgr4 (Fc receptor, IgG, low affinity IV) that originated from CCR2+ monocytes. It is important that a similar macrophage population expressing CXCL9, CXCL10, and CD16α (human homologue of mouse FcgR4) was expanded in patients with ICI myocarditis. In silico prediction of cell-cell communication suggested interactions between T-cells and Cxcl9+Cxcl10+ macrophages via IFN-γ (interferon gamma) and CXCR3 (CXC chemokine receptor 3) signaling pathways. Depleting CD8+ T-cells or macrophages and blockade of IFN-γ signaling blunted the expansion of Cxcl9+Cxcl10+ macrophages in the heart and attenuated myocarditis, suggesting that this interaction was necessary for disease pathogenesis. CONCLUSIONS: These data demonstrate that ICI myocarditis is associated with the expansion of a specific population of IFN-γ-induced inflammatory macrophages and suggest the possibility that IFN-γ blockade may be considered as a treatment option for this devastating condition.
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- 2024
108. CogME: A Cognition-Inspired Multi-Dimensional Evaluation Metric for Story Understanding
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Shin, Minjung, Choi, Seongho, Heo, Yu-Jung, Lee, Minsu, Zhang, Byoung-Tak, and Ryu, Jeh-Kwang
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Computer Science ,Psychology ,Human Factors ,Language understanding ,Computer-based experiment - Abstract
We introduce CogME, a cognition-inspired, multi-dimensional evaluation metric for AI models focusing on story understanding. CogME is a framework grounded in human thinking strategies and story elements that involve story understanding. With a specific breakdown of the questions, this approach provides a nuanced assessment revealing not only AI models' particular strengths and weaknesses but also the characteristics of the benchmark dataset. Our case study with the DramaQA dataset demonstrates a refined analysis of the model and the benchmark dataset. It is imperative that metrics align closely with human cognitive processes by comprehending the tasks' nature. This approach provides insights beyond traditional overall scores and paves the way for more sophisticated AI development targeting higher cognitive functions.
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- 2024
109. Small, solubilized platinum nanocrystals consist of an ordered core surrounded by mobile surface atoms
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Wietfeldt, Henry, Meana-Pañeda, Rubén, Machello, Chiara, Reboul, Cyril F, Van, Cong TS, Kim, Sungin, Heo, Junyoung, Kim, Byung Hyo, Kang, Sungsu, Ercius, Peter, Park, Jungwon, and Elmlund, Hans
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Chemical Sciences ,Chemical sciences - Abstract
In situ structures of Platinum (Pt) nanoparticles (NPs) can be determined with graphene liquid cell transmission electron microscopy. Atomic-scale three-dimensional structural information about their physiochemical properties in solution is critical for understanding their chemical function. We here analyze eight atomic-resolution maps of small (
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- 2024
110. Morphing Tokens Draw Strong Masked Image Models
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Kim, Taekyung, Heo, Byeongho, and Han, Dongyoon
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Masked image modeling (MIM) is a promising option for training Vision Transformers among various self-supervised learning (SSL) methods. The essence of MIM lies in token-wise masked token predictions, with targets tokenized from images or generated by pre-trained models such as vision-language models. While tokenizers or pre-trained models are plausible MIM targets, they often offer spatially inconsistent targets even for neighboring tokens, complicating models to learn unified discriminative representations. Our pilot study confirms that addressing spatial inconsistencies has the potential to enhance representation quality. Motivated by the findings, we introduce a novel self-supervision signal called Dynamic Token Morphing (DTM), which dynamically aggregates contextually related tokens to yield contextualized targets. DTM is compatible with various SSL frameworks; we showcase an improved MIM by employing DTM, barely introducing extra training costs. Our experiments on ImageNet-1K and ADE20K demonstrate the superiority of our methods compared with state-of-the-art, complex MIM methods. Furthermore, the comparative evaluation of the iNaturalists and fine-grained visual classification datasets further validates the transferability of our method on various downstream tasks. Code is available at https://github.com/naver-ai/dtm, Comment: 27 pages, 17 tables, 6 figures
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- 2023
111. Noise-free Optimization in Early Training Steps for Image Super-Resolution
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Lee, MinKyu and Heo, Jae-Pil
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Recent deep-learning-based single image super-resolution (SISR) methods have shown impressive performance whereas typical methods train their networks by minimizing the pixel-wise distance with respect to a given high-resolution (HR) image. However, despite the basic training scheme being the predominant choice, its use in the context of ill-posed inverse problems has not been thoroughly investigated. In this work, we aim to provide a better comprehension of the underlying constituent by decomposing target HR images into two subcomponents: (1) the optimal centroid which is the expectation over multiple potential HR images, and (2) the inherent noise defined as the residual between the HR image and the centroid. Our findings show that the current training scheme cannot capture the ill-posed nature of SISR and becomes vulnerable to the inherent noise term, especially during early training steps. To tackle this issue, we propose a novel optimization method that can effectively remove the inherent noise term in the early steps of vanilla training by estimating the optimal centroid and directly optimizing toward the estimation. Experimental results show that the proposed method can effectively enhance the stability of vanilla training, leading to overall performance gain. Codes are available at github.com/2minkyulee/ECO., Comment: Accepted to AAAI 2024. Codes are available at github.com/2minkyulee/ECO
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- 2023
112. VLCounter: Text-aware Visual Representation for Zero-Shot Object Counting
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Kang, Seunggu, Moon, WonJun, Kim, Euiyeon, and Heo, Jae-Pil
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Zero-Shot Object Counting (ZSOC) aims to count referred instances of arbitrary classes in a query image without human-annotated exemplars. To deal with ZSOC, preceding studies proposed a two-stage pipeline: discovering exemplars and counting. However, there remains a challenge of vulnerability to error propagation of the sequentially designed two-stage process. In this work, an one-stage baseline, Visual-Language Baseline (VLBase), exploring the implicit association of the semantic-patch embeddings of CLIP is proposed. Subsequently, the extension of VLBase to Visual-language Counter (VLCounter) is achieved by incorporating three modules devised to tailor VLBase for object counting. First, Semantic-conditioned Prompt Tuning (SPT) is introduced within the image encoder to acquire target-highlighted representations. Second, Learnable Affine Transformation (LAT) is employed to translate the semantic-patch similarity map to be appropriate for the counting task. Lastly, the layer-wisely encoded features are transferred to the decoder through Segment-aware Skip Connection (SaSC) to keep the generalization capability for unseen classes. Through extensive experiments on FSC147, CARPK, and PUCPR+, the benefits of the end-to-end framework, VLCounter, are demonstrated., Comment: Accepted to AAAI 2024. Code is available at https://github.com/Seunggu0305/VLCounter
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- 2023
113. Task-Disruptive Background Suppression for Few-Shot Segmentation
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Park, Suho, Lee, SuBeen, Hyun, Sangeek, Seong, Hyun Seok, and Heo, Jae-Pil
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Few-shot segmentation aims to accurately segment novel target objects within query images using only a limited number of annotated support images. The recent works exploit support background as well as its foreground to precisely compute the dense correlations between query and support. However, they overlook the characteristics of the background that generally contains various types of objects. In this paper, we highlight this characteristic of background which can bring problematic cases as follows: (1) when the query and support backgrounds are dissimilar and (2) when objects in the support background are similar to the target object in the query. Without any consideration of the above cases, adopting the entire support background leads to a misprediction of the query foreground as background. To address this issue, we propose Task-disruptive Background Suppression (TBS), a module to suppress those disruptive support background features based on two spatial-wise scores: query-relevant and target-relevant scores. The former aims to mitigate the impact of unshared features solely existing in the support background, while the latter aims to reduce the influence of target-similar support background features. Based on these two scores, we define a query background relevant score that captures the similarity between the backgrounds of the query and the support, and utilize it to scale support background features to adaptively restrict the impact of disruptive support backgrounds. Our proposed method achieves state-of-the-art performance on PASCAL-5 and COCO-20 datasets on 1-shot segmentation. Our official code is available at github.com/SuhoPark0706/TBSNet.
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- 2023
114. Towards Squeezing-Averse Virtual Try-On via Sequential Deformation
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Shim, Sang-Heon, Chung, Jiwoo, and Heo, Jae-Pil
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Computer Science - Computer Vision and Pattern Recognition - Abstract
In this paper, we first investigate a visual quality degradation problem observed in recent high-resolution virtual try-on approach. The tendency is empirically found that the textures of clothes are squeezed at the sleeve, as visualized in the upper row of Fig.1(a). A main reason for the issue arises from a gradient conflict between two popular losses, the Total Variation (TV) and adversarial losses. Specifically, the TV loss aims to disconnect boundaries between the sleeve and torso in a warped clothing mask, whereas the adversarial loss aims to combine between them. Such contrary objectives feedback the misaligned gradients to a cascaded appearance flow estimation, resulting in undesirable squeezing artifacts. To reduce this, we propose a Sequential Deformation (SD-VITON) that disentangles the appearance flow prediction layers into TV objective-dominant (TVOB) layers and a task-coexistence (TACO) layer. Specifically, we coarsely fit the clothes onto a human body via the TVOB layers, and then keep on refining via the TACO layer. In addition, the bottom row of Fig.1(a) shows a different type of squeezing artifacts around the waist. To address it, we further propose that we first warp the clothes into a tucked-out shirts style, and then partially erase the texture from the warped clothes without hurting the smoothness of the appearance flows. Experimental results show that our SD-VITON successfully resolves both types of artifacts and outperforms the baseline methods. Source code will be available at https://github.com/SHShim0513/SD-VITON., Comment: Accepted to AAAI 2024
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- 2023
115. VITA: 'Carefully Chosen and Weighted Less' Is Better in Medication Recommendation
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Kim, Taeri, Heo, Jiho, Kim, Hongil, Shin, Kijung, and Kim, Sang-Wook
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Computer Science - Information Retrieval ,Computer Science - Artificial Intelligence - Abstract
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
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- 2023
116. SeiT++: Masked Token Modeling Improves Storage-efficient Training
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Lee, Minhyun, Park, Song, Heo, Byeongho, Han, Dongyoon, and Shim, Hyunjung
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Recent advancements in Deep Neural Network (DNN) models have significantly improved performance across computer vision tasks. However, achieving highly generalizable and high-performing vision models requires expansive datasets, resulting in significant storage requirements. This storage challenge is a critical bottleneck for scaling up models. A recent breakthrough by SeiT proposed the use of Vector-Quantized (VQ) feature vectors (i.e., tokens) as network inputs for vision classification. This approach achieved 90% of the performance of a model trained on full-pixel images with only 1% of the storage. While SeiT needs labeled data, its potential in scenarios beyond fully supervised learning remains largely untapped. In this paper, we extend SeiT by integrating Masked Token Modeling (MTM) for self-supervised pre-training. Recognizing that self-supervised approaches often demand more data due to the lack of labels, we introduce TokenAdapt and ColorAdapt. These methods facilitate comprehensive token-friendly data augmentation, effectively addressing the increased data requirements of self-supervised learning. We evaluate our approach across various scenarios, including storage-efficient ImageNet-1k classification, fine-grained classification, ADE-20k semantic segmentation, and robustness benchmarks. Experimental results demonstrate consistent performance improvement in diverse experiments, validating the effectiveness of our method. Code is available at https://github.com/naver-ai/seit., Comment: Accepted to ECCV 2024. First two authors contributed equally
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- 2023
117. NeXt-TDNN: Modernizing Multi-Scale Temporal Convolution Backbone for Speaker Verification
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Heo, Hyun-Jun, Shin, Ui-Hyeop, Lee, Ran, Cheon, YoungJu, and Park, Hyung-Min
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Electrical Engineering and Systems Science - Audio and Speech Processing ,Computer Science - Sound - Abstract
In speaker verification, ECAPA-TDNN has shown remarkable improvement by utilizing one-dimensional(1D) Res2Net block and squeeze-and-excitation(SE) module, along with multi-layer feature aggregation (MFA). Meanwhile, in vision tasks, ConvNet structures have been modernized by referring to Transformer, resulting in improved performance. In this paper, we present an improved block design for TDNN in speaker verification. Inspired by recent ConvNet structures, we replace the SE-Res2Net block in ECAPA-TDNN with a novel 1D two-step multi-scale ConvNeXt block, which we call TS-ConvNeXt. The TS-ConvNeXt block is constructed using two separated sub-modules: a temporal multi-scale convolution (MSC) and a frame-wise feed-forward network (FFN). This two-step design allows for flexible capturing of inter-frame and intra-frame contexts. Additionally, we introduce global response normalization (GRN) for the FFN modules to enable more selective feature propagation, similar to the SE module in ECAPA-TDNN. Experimental results demonstrate that NeXt-TDNN, with a modernized backbone block, significantly improved performance in speaker verification tasks while reducing parameter size and inference time. We have released our code for future studies., Comment: Accepted by ICASSP 2024
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- 2023
118. Estimation of Concept Explanations Should be Uncertainty Aware
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Piratla, Vihari, Heo, Juyeon, Collins, Katherine M., Singh, Sukriti, and Weller, Adrian
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Computation and Language - Abstract
Model explanations can be valuable for interpreting and debugging predictive models. We study a specific kind called Concept Explanations, where the goal is to interpret a model using human-understandable concepts. Although popular for their easy interpretation, concept explanations are known to be noisy. We begin our work by identifying various sources of uncertainty in the estimation pipeline that lead to such noise. We then propose an uncertainty-aware Bayesian estimation method to address these issues, which readily improved the quality of explanations. We demonstrate with theoretical analysis and empirical evaluation that explanations computed by our method are robust to train-time choices while also being label-efficient. Further, our method proved capable of recovering relevant concepts amongst a bank of thousands, in an evaluation with real-datasets and off-the-shelf models, demonstrating its scalability. We believe the improved quality of uncertainty-aware concept explanations make them a strong candidate for more reliable model interpretation. We release our code at https://github.com/vps-anonconfs/uace.
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- 2023
119. Style Injection in Diffusion: A Training-free Approach for Adapting Large-scale Diffusion Models for Style Transfer
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Chung, Jiwoo, Hyun, Sangeek, and Heo, Jae-Pil
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Despite the impressive generative capabilities of diffusion models, existing diffusion model-based style transfer methods require inference-stage optimization (e.g. fine-tuning or textual inversion of style) which is time-consuming, or fails to leverage the generative ability of large-scale diffusion models. To address these issues, we introduce a novel artistic style transfer method based on a pre-trained large-scale diffusion model without any optimization. Specifically, we manipulate the features of self-attention layers as the way the cross-attention mechanism works; in the generation process, substituting the key and value of content with those of style image. This approach provides several desirable characteristics for style transfer including 1) preservation of content by transferring similar styles into similar image patches and 2) transfer of style based on similarity of local texture (e.g. edge) between content and style images. Furthermore, we introduce query preservation and attention temperature scaling to mitigate the issue of disruption of original content, and initial latent Adaptive Instance Normalization (AdaIN) to deal with the disharmonious color (failure to transfer the colors of style). Our experimental results demonstrate that our proposed method surpasses state-of-the-art methods in both conventional and diffusion-based style transfer baselines., Comment: Accepted to CVPR 2024. Project page: https://jiwoogit.github.io/StyleID_site
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- 2023
120. VISAGE: Video Instance Segmentation with Appearance-Guided Enhancement
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Kim, Hanjung, Kang, Jaehyun, Heo, Miran, Hwang, Sukjun, Oh, Seoung Wug, and Kim, Seon Joo
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Computer Science - Computer Vision and Pattern Recognition - Abstract
In recent years, online Video Instance Segmentation (VIS) methods have shown remarkable advancement with their powerful query-based detectors. Utilizing the output queries of the detector at the frame-level, these methods achieve high accuracy on challenging benchmarks. However, our observations demonstrate that these methods heavily rely on location information, which often causes incorrect associations between objects. This paper presents that a key axis of object matching in trackers is appearance information, which becomes greatly instructive under conditions where positional cues are insufficient for distinguishing their identities. Therefore, we suggest a simple yet powerful extension to object decoders that explicitly extract embeddings from backbone features and drive queries to capture the appearances of objects, which greatly enhances instance association accuracy. Furthermore, recognizing the limitations of existing benchmarks in fully evaluating appearance awareness, we have constructed a synthetic dataset to rigorously validate our method. By effectively resolving the over-reliance on location information, we achieve state-of-the-art results on YouTube-VIS 2019/2021 and Occluded VIS (OVIS). Code is available at https://github.com/KimHanjung/VISAGE., Comment: Technical report
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- 2023
121. True image construction in quantum-secured single-pixel imaging under spoofing attack
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Heo, Jaesung, Jeong, Taek, Park, Nam Hun, and Jo, Yonggi
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Quantum Physics - Abstract
In this paper, we introduce a quantum-secured single-pixel imaging (QS-SPI) technique designed to withstand spoofing attacks, wherein adversaries attempt to deceive imaging systems with fake signals. Unlike previous quantum-secured protocols that impose a threshold error rate limiting their operation, even with the existence of true signals, our approach not only identifies spoofing attacks but also facilitates the reconstruction of a true image. Our method involves the analysis of a specific mode correlation of a photon-pair, which is independent of the mode used for image construction, to check security. Through this analysis, we can identify both the targeted image region by the attack and the type of spoofing attack, enabling reconstruction of the true image. A proof-of-principle demonstration employing polarization-correlation of a photon-pair is provided, showcasing successful image reconstruction even under the condition of spoofing signals 2000 times stronger than the true signals. We expect our approach to be applied to quantum-secured signal processing such as quantum target detection or ranging., Comment: 10 pages, 6 figures
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- 2023
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122. Generalized hypergeometric functions with several variables
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Matsubara-Heo, Saiei-Jaeyeong and Oshima, Toshio
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Mathematics - Classical Analysis and ODEs - Abstract
We introduce a hypergoemetirc series with two complex variables, which generalizes Appell's, Lauricella's and Kemp\'e de F\'eriet's hypergeometric series, and study the system of differential equations that it satisfies. We determine the singularities, the rank and the condition for the reducibility of the system. We give complete local solutions of the system at many singular points of the system and solve the connection problem among these local solutions. Under some assumptions, the system is written as a KZ equation. We determine its spectral type in the direction of coordinates as well as simultaneous eigenspace decompositions of residue matrices. The system may or may not be rigid in the sense of N.~Katz viewed as an ordinary differential equation in some direction. We also show that the system is a special case of Gel'fand-Kapranov-Zelevinsky system. From this point of view, we discuss multivariate generalizations., Comment: 69 pages, revised version
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- 2023
123. HD Maps are Lane Detection Generalizers: A Novel Generative Framework for Single-Source Domain Generalization
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Lee, Daeun, Heo, Minhyeok, and Kim, Jiwon
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning ,Computer Science - Robotics - Abstract
Lane detection is a vital task for vehicles to navigate and localize their position on the road. To ensure reliable driving, lane detection models must have robust generalization performance in various road environments. However, despite the advanced performance in the trained domain, their generalization performance still falls short of expectations due to the domain discrepancy. To bridge this gap, we propose a novel generative framework using HD Maps for Single-Source Domain Generalization (SSDG) in lane detection. We first generate numerous front-view images from lane markings of HD Maps. Next, we strategically select a core subset among the generated images using (i) lane structure and (ii) road surrounding criteria to maximize their diversity. In the end, utilizing this core set, we train lane detection models to boost their generalization performance. We validate that our generative framework from HD Maps outperforms the Domain Adaptation model MLDA with +3.01%p accuracy improvement, even though we do not access the target domain images., Comment: Accepted by CVPR Data-Driven Autonomous Driving Simulation Workshop, 2024
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- 2023
124. On long-term fatigue damage estimation for a floating offshore wind turbine using a surrogate model
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Liu, Ding Peng, Ferri, Giulio, Heo, Taemin, Marino, Enzo, and Manuel, Lance
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Statistics - Applications - Abstract
This study is concerned with the estimation of long-term fatigue damage for a floating offshore wind turbine. With the ultimate goal of efficient evaluation of fatigue limit states for floating offshore wind turbine systems, a detailed computational framework is introduced and used to develop a surrogate model using Gaussian process regression. The surrogate model, at first, relies only on a small subset of representative sea states and, then, is supplemented by the evaluation of additional sea states that leads to efficient convergence and accurate prediction of fatigue damage. A 5-MW offshore wind turbine supported by a semi-submersible floating platform is selected to demonstrate the proposed framework. The fore-aft bending moment at the turbine tower base and the fairlead tension in the windward mooring line are used for evaluation. Metocean data provide information on joint statistics of the wind and wave along with their relative likelihoods for the installation site in the Mediterranean Sea, near the coast of Sicily. \textcolor{black}{A coupled frequency-domain model} provides needed power spectra for the desired response processes. The proposed approach offers an efficient and accurate alternative to the exhaustive evaluation of a larger number of sea states and, as such, avoids excessive response simulations.
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- 2023
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125. Retention in STEM: Factors Influencing Student Persistence and Employment
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Zhou, Linli, Stratton, Damji Heo, and Li, Xin
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Statistics - Applications - Abstract
This study utilizes data from the Baccalaureate and Beyond Longitudinal Study to explore factors associated with the likelihood of students' employment in STEM fields one year after graduation. We examined various factors related to students' individual characteristics (e.g., gender, race, and financial situation), institutional experiences (e.g., major, academic standing, research involvement, internships, extracurricular activities, and undergraduate practicum), and institutional and national trends. The results indicate lower STEM employment likelihood for minority groups and students with academic probation. The findings also highlight the positive impact of undergraduate practicum and job relevance to major on STEM employment likelihood. On the contrary, career services were negatively associated with the likelihood of students' STEM occupation choice, suggesting potential shortcomings in STEM job preparation within these services. The study provides valuable insights and actionable recommendations for policymakers and educators seeking to increase diversity and inclusion in STEM fields, suggesting the need for more efficient and tailored educational interventions and curriculum development.
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- 2023
126. Enhancing Data Efficiency and Feature Identification for Lithium-Ion Battery Lifespan Prediction by Deciphering Interpretation of Temporal Patterns and Cyclic Variability Using Attention-Based Models
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Lee, Jaewook, Heo, Seongmin, and Lee, Jay H.
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Statistics - Applications - Abstract
Accurately predicting the lifespan of lithium-ion batteries is crucial for optimizing operational strategies and mitigating risks. While numerous studies have aimed at predicting battery lifespan, few have examined the interpretability of their models or how such insights could improve predictions. Addressing this gap, we introduce three innovative models that integrate shallow attention layers into a foundational model from our previous work, which combined elements of recurrent and convolutional neural networks. Utilizing a well-known public dataset, we showcase our methodology's effectiveness. Temporal attention is applied to identify critical timesteps and highlight differences among test cell batches, particularly underscoring the significance of the "rest" phase. Furthermore, by applying cyclic attention via self-attention to context vectors, our approach effectively identifies key cycles, enabling us to strategically decrease the input size for quicker predictions. Employing both single- and multi-head attention mechanisms, we have systematically minimized the required input from 100 to 50 and then to 30 cycles, refining this process based on cyclic attention scores. Our refined model exhibits strong regression capabilities, accurately forecasting the initiation of rapid capacity fade with an average deviation of only 58 cycles by analyzing just the initial 30 cycles of easily accessible input data.
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- 2023
127. Correlation-Guided Query-Dependency Calibration for Video Temporal Grounding
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Moon, WonJun, Hyun, Sangeek, Lee, SuBeen, and Heo, Jae-Pil
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Temporal Grounding is to identify specific moments or highlights from a video corresponding to textual descriptions. Typical approaches in temporal grounding treat all video clips equally during the encoding process regardless of their semantic relevance with the text query. Therefore, we propose Correlation-Guided DEtection TRansformer (CG-DETR), exploring to provide clues for query-associated video clips within the cross-modal attention. First, we design an adaptive cross-attention with dummy tokens. Dummy tokens conditioned by text query take portions of the attention weights, preventing irrelevant video clips from being represented by the text query. Yet, not all words equally inherit the text query's correlation to video clips. Thus, we further guide the cross-attention map by inferring the fine-grained correlation between video clips and words. We enable this by learning a joint embedding space for high-level concepts, i.e., moment and sentence level, and inferring the clip-word correlation. Lastly, we exploit the moment-specific characteristics and combine them with the context of each video to form a moment-adaptive saliency detector. By exploiting the degrees of text engagement in each video clip, it precisely measures the highlightness of each clip. CG-DETR achieves state-of-the-art results on various benchmarks for temporal grounding. Codes are available at https://github.com/wjun0830/CGDETR., Comment: 29 pages, 15 figures, 14 tables, Code is available at https://github.com/wjun0830/CGDETR
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- 2023
128. Coupled resonator acoustic waveguides-based acoustic interferometers designed within two-dimensional phononic crystals: experiment and theory
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Martínez-Esquivel, David, Méndez-Sánchez, Rafael Alberto, Heo, Hyeonu, Martínez-Argüello, Angel Marbel, Mayorga-Rojas, Miguel, Neogi, Arup, and Reyes-Contreras, Delfino
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Physics - Applied Physics - Abstract
The acoustic response of defect-based acoustic interferometer-like designs, known as Coupled Resonator Acoustic Waveguides (CRAWs), in two-dimensional phononic crystals (PnCs) is reported. The PnC is composed of steel cylinders arranged in a square lattice within a water matrix with defects induced by selectively removing cylinders to create Mach-Zehnder-like (MZ) defect-based interferometers. Two defect-based acoustic interferometers of MZ-type are fabricated, one with arms oriented horizontally and another one with arms oriented diagonally, and their transmission features are experimentally characterized using ultrasonic spectroscopy. The experimental data are compared with finite element method (FEM) simulations and with tight-binding (TB) calculations in which each defect is treated as a resonator coupled to its neighboring ones. Significantly, the results exhibit excellent agreement indicating the reliability of the proposed approach. This comprehensive match is of paramount importance for accurately predicting and optimizing resonant modes supported by defect arrays, thus enabling the tailoring of phononic structures and defect-based waveguides to meet specific requirements. This successful implementation of FEM and TB calculations in investigating CRAWs systems within phononic crystals paves the way for designing advanced acoustic devices with desired functionalities for various practical applications, demonstrating the application of solid-state electronics principles to underwater acoustic devices description.
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- 2023
129. Enhancing Lightweight Neural Networks for Small Object Detection in IoT Applications
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Boyle, Liam, Baumann, Nicolas, Heo, Seonyeong, and Magno, Michele
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
Advances in lightweight neural networks have revolutionized computer vision in a broad range of IoT applications, encompassing remote monitoring and process automation. However, the detection of small objects, which is crucial for many of these applications, remains an underexplored area in current computer vision research, particularly for embedded devices. To address this gap, the paper proposes a novel adaptive tiling method that can be used on top of any existing object detector including the popular FOMO network for object detection on microcontrollers. Our experimental results show that the proposed tiling method can boost the F1-score by up to 225% while reducing the average object count error by up to 76%. Furthermore, the findings of this work suggest that using a soft F1 loss over the popular binary cross-entropy loss can significantly reduce the negative impact of imbalanced data. Finally, we validate our approach by conducting experiments on the Sony Spresense microcontroller, showcasing the proposed method's ability to strike a balance between detection performance, low latency, and minimal memory consumption.
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- 2023
130. Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization
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Liu, Weiyang, Qiu, Zeju, Feng, Yao, Xiu, Yuliang, Xue, Yuxuan, Yu, Longhui, Feng, Haiwen, Liu, Zhen, Heo, Juyeon, Peng, Songyou, Wen, Yandong, Black, Michael J., Weller, Adrian, and Schölkopf, Bernhard
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Computation and Language ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Large foundation models are becoming ubiquitous, but training them from scratch is prohibitively expensive. Thus, efficiently adapting these powerful models to downstream tasks is increasingly important. In this paper, we study a principled finetuning paradigm -- Orthogonal Finetuning (OFT) -- for downstream task adaptation. Despite demonstrating good generalizability, OFT still uses a fairly large number of trainable parameters due to the high dimensionality of orthogonal matrices. To address this, we start by examining OFT from an information transmission perspective, and then identify a few key desiderata that enable better parameter-efficiency. Inspired by how the Cooley-Tukey fast Fourier transform algorithm enables efficient information transmission, we propose an efficient orthogonal parameterization using butterfly structures. We apply this parameterization to OFT, creating a novel parameter-efficient finetuning method, called Orthogonal Butterfly (BOFT). By subsuming OFT as a special case, BOFT introduces a generalized orthogonal finetuning framework. Finally, we conduct an extensive empirical study of adapting large vision transformers, large language models, and text-to-image diffusion models to various downstream tasks in vision and language., Comment: ICLR 2024 (v2: 34 pages, 19 figures)
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- 2023
131. SPLUS J142445.34-254247.1: An R-Process Enhanced, Actinide-Boost, Extremely Metal-Poor star observed with GHOST
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Placco, Vinicius M., Almeida-Fernandes, Felipe, Holmbeck, Erika M., Roederer, Ian U., Mardini, Mohammad K., Hayes, Christian R., Venn, Kim, Chiboucas, Kristin, Deibert, Emily, Gamen, Roberto, Heo, Jeong-Eun, Jeong, Miji, Kalari, Venu, Martioli, Eder, Xu, Siyi, Diaz, Ruben, Gomez-Jimenez, Manuel, Henderson, David, Prado, Pablo, Quiroz, Carlos, Ruiz-Carmona, Roque, Simpson, Chris, Urrutia, Cristian, McConnachie, Alan W., Pazder, John, Burley, Gregory, Ireland, Michael, Waller, Fletcher, Berg, Trystyn A. M., Robertson, J. Gordon, Hartman, Zachary, Jones, David O., Labrie, Kathleen, Perez, Gabriel, Ridgway, Susan, and Thomas-Osip, Joanna
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Astrophysics - Solar and Stellar Astrophysics ,Astrophysics - Astrophysics of Galaxies - Abstract
We report on the chemo-dynamical analysis of SPLUS J142445.34-254247.1, an extremely metal-poor halo star enhanced in elements formed by the rapid neutron-capture process. This star was first selected as a metal-poor candidate from its narrow-band S-PLUS photometry and followed up spectroscopically in medium-resolution with Gemini South/GMOS, which confirmed its low-metallicity status. High-resolution spectroscopy was gathered with GHOST at Gemini South, allowing for the determination of chemical abundances for 36 elements, from carbon to thorium. At [Fe/H]=-3.39, SPLUS J1424-2542 is one of the lowest metallicity stars with measured Th and has the highest logeps(Th/Eu) observed to date, making it part of the "actinide-boost" category of r-process enhanced stars. The analysis presented here suggests that the gas cloud from which SPLUS J1424-2542 was formed must have been enriched by at least two progenitor populations. The light-element (Z<=30) abundance pattern is consistent with the yields from a supernova explosion of metal-free stars with 11.3-13.4 Msun, and the heavy-element (Z>=38) abundance pattern can be reproduced by the yields from a neutron star merger (1.66Msun and 1.27Msun) event. A kinematical analysis also reveals that SPLUS J1424-2542 is a low-mass, old halo star with a likely in-situ origin, not associated with any known early merger events in the Milky Way., Comment: 26 pages, 11 figures, accepted for publication on ApJ
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- 2023
132. Coyote C++: An Industrial-Strength Fully Automated Unit Testing Tool
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Rho, Sanghoon, Martens, Philipp, Shin, Seungcheol, Kim, Yeoneo, Heo, Hoon, and Oh, SeungHyun
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Computer Science - Programming Languages ,Computer Science - Software Engineering - Abstract
Coyote C++ is an automated testing tool that uses a sophisticated concolic-execution-based approach to realize fully automated unit testing for C and C++. While concolic testing has proven effective for languages such as C and Java, tools have struggled to achieve a practical level of automation for C++ due to its many syntactical intricacies and overall complexity. Coyote C++ is the first automated testing tool to breach the barrier and bring automated unit testing for C++ to a practical level suitable for industrial adoption, consistently reaching around 90% code coverage. Notably, this testing process requires no user involvement and performs test harness generation, test case generation and test execution with "one-click" automation. In this paper, we introduce Coyote C++ by outlining its high-level structure and discussing the core design decisions that shaped the implementation of its concolic execution engine. Finally, we demonstrate that Coyote C++ is capable of achieving high coverage results within a reasonable timespan by presenting the results from experiments on both open-source and industrial software.
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- 2023
133. Electrical conductivity enhancement of epitaxially grown TiN thin films
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Khim, Yeong Gwang, Park, Beomjin, Heo, Jin Eun, Khim, Young Hun, Khim, Young Rok, Gu, Minsun, Rhee, Tae Gyu, Chang, Seo Hyoung, Han, Moonsup, and Chang, Young Jun
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Condensed Matter - Materials Science - Abstract
Titanium nitride (TiN) presents superior electrical conductivity with mechanical and chemical stability and compatibility with the semiconductor fabrication process. Here, we fabricated epitaxial and polycrystalline TiN (111) thin films on MgO (111), sapphire (001), and mica substrates at 640oC and room temperature by using a DC sputtering, respectively. The epitaxial films show less amount of surface oxidation than the polycrystalline ones grown at room temperature. The epitaxial films show drastically reduced resistivity (~30 micro-ohm-cm), much smaller than the polycrystalline films. Temperature-dependent resistivity measurements show a nearly monotonic temperature slope down to low temperature. These results demonstrate that high temperature growth of TiN thin films leads to significant enhancement of electrical conductivity, promising for durable and scalable electrode applications., Comment: 14 pages, 3 figures
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- 2023
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134. Learning with Unmasked Tokens Drives Stronger Vision Learners
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Kim, Taekyung, Chun, Sanghyuk, Heo, Byeongho, and Han, Dongyoon
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Masked image modeling (MIM) has become a leading self-supervised learning strategy. MIMs such as Masked Autoencoder (MAE) learn strong representations by randomly masking input tokens for the encoder to process, with the decoder reconstructing the masked tokens to the input. However, MIM pre-trained encoders often exhibit a limited attention span, attributed to MIM's sole focus on regressing masked tokens only, which may impede the encoder's broader context learning. To tackle the limitation, we improve MIM by explicitly incorporating unmasked tokens into the training process. Specifically, our method enables the encoder to learn from broader context supervision, allowing unmasked tokens to experience broader contexts while the decoder reconstructs masked tokens. Thus, the encoded unmasked tokens are equipped with extensive contextual information, empowering masked tokens to leverage the enhanced unmasked tokens for MIM. As a result, our simple remedy trains more discriminative representations revealed by achieving 84.2% top-1 accuracy with ViT-B on ImageNet-1K with 0.6%p gain. We attribute the success to the enhanced pre-training method, as evidenced by the singular value spectrum and attention analyses. Finally, our models achieve significant performance gains at the downstream semantic segmentation and fine-grained visual classification tasks; and on diverse robust evaluation metrics. Code is available at https://github.com/naver-ai/lut, Comment: 24 pages, 6 figures, 10 tables. To be presented at ECCV'24
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- 2023
135. Open X-Embodiment: Robotic Learning Datasets and RT-X Models
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Collaboration, Open X-Embodiment, O'Neill, Abby, Rehman, Abdul, Gupta, Abhinav, Maddukuri, Abhiram, Gupta, Abhishek, Padalkar, Abhishek, Lee, Abraham, Pooley, Acorn, Gupta, Agrim, Mandlekar, Ajay, Jain, Ajinkya, Tung, Albert, Bewley, Alex, Herzog, Alex, Irpan, Alex, Khazatsky, Alexander, Rai, Anant, Gupta, Anchit, Wang, Andrew, Kolobov, Andrey, Singh, Anikait, Garg, Animesh, Kembhavi, Aniruddha, Xie, Annie, Brohan, Anthony, Raffin, Antonin, Sharma, Archit, Yavary, Arefeh, Jain, Arhan, Balakrishna, Ashwin, Wahid, Ayzaan, Burgess-Limerick, Ben, Kim, Beomjoon, Schölkopf, Bernhard, Wulfe, Blake, Ichter, Brian, Lu, Cewu, Xu, Charles, Le, Charlotte, Finn, Chelsea, Wang, Chen, Xu, Chenfeng, Chi, Cheng, Huang, Chenguang, Chan, Christine, Agia, Christopher, Pan, Chuer, Fu, Chuyuan, Devin, Coline, Xu, Danfei, Morton, Daniel, Driess, Danny, Chen, Daphne, Pathak, Deepak, Shah, Dhruv, Büchler, Dieter, Jayaraman, Dinesh, Kalashnikov, Dmitry, Sadigh, Dorsa, Johns, Edward, Foster, Ethan, Liu, Fangchen, Ceola, Federico, Xia, Fei, Zhao, Feiyu, Frujeri, Felipe Vieira, Stulp, Freek, Zhou, Gaoyue, Sukhatme, Gaurav S., Salhotra, Gautam, Yan, Ge, Feng, Gilbert, Schiavi, Giulio, Berseth, Glen, Kahn, Gregory, Yang, Guangwen, Wang, Guanzhi, Su, Hao, Fang, Hao-Shu, Shi, Haochen, Bao, Henghui, Amor, Heni Ben, Christensen, Henrik I, Furuta, Hiroki, Bharadhwaj, Homanga, Walke, Homer, Fang, Hongjie, Ha, Huy, Mordatch, Igor, Radosavovic, Ilija, Leal, Isabel, Liang, Jacky, Abou-Chakra, Jad, Kim, Jaehyung, Drake, Jaimyn, Peters, Jan, Schneider, Jan, Hsu, Jasmine, Vakil, Jay, Bohg, Jeannette, Bingham, Jeffrey, Wu, Jeffrey, Gao, Jensen, Hu, Jiaheng, Wu, Jiajun, Wu, Jialin, Sun, Jiankai, Luo, Jianlan, Gu, Jiayuan, Tan, Jie, Oh, Jihoon, Wu, Jimmy, Lu, Jingpei, Yang, Jingyun, Malik, Jitendra, Silvério, João, Hejna, Joey, Booher, Jonathan, Tompson, Jonathan, Yang, Jonathan, Salvador, Jordi, Lim, Joseph J., Han, Junhyek, Wang, Kaiyuan, Rao, Kanishka, Pertsch, Karl, Hausman, Karol, Go, Keegan, Gopalakrishnan, Keerthana, Goldberg, Ken, Byrne, Kendra, Oslund, Kenneth, Kawaharazuka, Kento, Black, Kevin, Lin, Kevin, Zhang, Kevin, Ehsani, Kiana, Lekkala, Kiran, Ellis, Kirsty, Rana, Krishan, Srinivasan, Krishnan, Fang, Kuan, Singh, Kunal Pratap, Zeng, Kuo-Hao, Hatch, Kyle, Hsu, Kyle, Itti, Laurent, Chen, Lawrence Yunliang, Pinto, Lerrel, Fei-Fei, Li, Tan, Liam, Fan, Linxi "Jim", Ott, Lionel, Lee, Lisa, Weihs, Luca, Chen, Magnum, Lepert, Marion, Memmel, Marius, Tomizuka, Masayoshi, Itkina, Masha, Castro, Mateo Guaman, Spero, Max, Du, Maximilian, Ahn, Michael, Yip, Michael C., Zhang, Mingtong, Ding, Mingyu, Heo, Minho, Srirama, Mohan Kumar, Sharma, Mohit, Kim, Moo Jin, Kanazawa, Naoaki, Hansen, Nicklas, Heess, Nicolas, Joshi, Nikhil J, Suenderhauf, Niko, Liu, Ning, Di Palo, Norman, Shafiullah, Nur Muhammad Mahi, Mees, Oier, Kroemer, Oliver, Bastani, Osbert, Sanketi, Pannag R, Miller, Patrick "Tree", Yin, Patrick, Wohlhart, Paul, Xu, Peng, Fagan, Peter David, Mitrano, Peter, Sermanet, Pierre, Abbeel, Pieter, Sundaresan, Priya, Chen, Qiuyu, Vuong, Quan, Rafailov, Rafael, Tian, Ran, Doshi, Ria, Mart'in-Mart'in, Roberto, Baijal, Rohan, Scalise, Rosario, Hendrix, Rose, Lin, Roy, Qian, Runjia, Zhang, Ruohan, Mendonca, Russell, Shah, Rutav, Hoque, Ryan, Julian, Ryan, Bustamante, Samuel, Kirmani, Sean, Levine, Sergey, Lin, Shan, Moore, Sherry, Bahl, Shikhar, Dass, Shivin, Sonawani, Shubham, Tulsiani, Shubham, Song, Shuran, Xu, Sichun, Haldar, Siddhant, Karamcheti, Siddharth, Adebola, Simeon, Guist, Simon, Nasiriany, Soroush, Schaal, Stefan, Welker, Stefan, Tian, Stephen, Ramamoorthy, Subramanian, Dasari, Sudeep, Belkhale, Suneel, Park, Sungjae, Nair, Suraj, Mirchandani, Suvir, Osa, Takayuki, Gupta, Tanmay, Harada, Tatsuya, Matsushima, Tatsuya, Xiao, Ted, Kollar, Thomas, Yu, Tianhe, Ding, Tianli, Davchev, Todor, Zhao, Tony Z., Armstrong, Travis, Darrell, Trevor, Chung, Trinity, Jain, Vidhi, Kumar, Vikash, Vanhoucke, Vincent, Zhan, Wei, Zhou, Wenxuan, Burgard, Wolfram, Chen, Xi, Chen, Xiangyu, Wang, Xiaolong, Zhu, Xinghao, Geng, Xinyang, Liu, Xiyuan, Liangwei, Xu, Li, Xuanlin, Pang, Yansong, Lu, Yao, Ma, Yecheng Jason, Kim, Yejin, Chebotar, Yevgen, Zhou, Yifan, Zhu, Yifeng, Wu, Yilin, Xu, Ying, Wang, Yixuan, Bisk, Yonatan, Dou, Yongqiang, Cho, Yoonyoung, Lee, Youngwoon, Cui, Yuchen, Cao, Yue, Wu, Yueh-Hua, Tang, Yujin, Zhu, Yuke, Zhang, Yunchu, Jiang, Yunfan, Li, Yunshuang, Li, Yunzhu, Iwasawa, Yusuke, Matsuo, Yutaka, Ma, Zehan, Xu, Zhuo, Cui, Zichen Jeff, Zhang, Zichen, Fu, Zipeng, and Lin, Zipeng
- Subjects
Computer Science - Robotics - Abstract
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train generalist X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. More details can be found on the project website https://robotics-transformer-x.github.io., Comment: Project website: https://robotics-transformer-x.github.io
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- 2023
136. A Learnable Counter-condition Analysis Framework for Functional Connectivity-based Neurological Disorder Diagnosis
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Kang, Eunsong, Heo, Da-woon, Lee, Jiwon, and Suk, Heung-Il
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Quantitative Biology - Neurons and Cognition - Abstract
To understand the biological characteristics of neurological disorders with functional connectivity (FC), recent studies have widely utilized deep learning-based models to identify the disease and conducted post-hoc analyses via explainable models to discover disease-related biomarkers. Most existing frameworks consist of three stages, namely, feature selection, feature extraction for classification, and analysis, where each stage is implemented separately. However, if the results at each stage lack reliability, it can cause misdiagnosis and incorrect analysis in afterward stages. In this study, we propose a novel unified framework that systemically integrates diagnoses (i.e., feature selection and feature extraction) and explanations. Notably, we devised an adaptive attention network as a feature selection approach to identify individual-specific disease-related connections. We also propose a functional network relational encoder that summarizes the global topological properties of FC by learning the inter-network relations without pre-defined edges between functional networks. Last but not least, our framework provides a novel explanatory power for neuroscientific interpretation, also termed counter-condition analysis. We simulated the FC that reverses the diagnostic information (i.e., counter-condition FC): converting a normal brain to be abnormal and vice versa. We validated the effectiveness of our framework by using two large resting-state functional magnetic resonance imaging (fMRI) datasets, Autism Brain Imaging Data Exchange (ABIDE) and REST-meta-MDD, and demonstrated that our framework outperforms other competing methods for disease identification. Furthermore, we analyzed the disease-related neurological patterns based on counter-condition analysis.
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- 2023
137. A Quantitatively Interpretable Model for Alzheimer's Disease Prediction Using Deep Counterfactuals
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Oh, Kwanseok, Heo, Da-Woon, Mulyadi, Ahmad Wisnu, Jung, Wonsik, Kang, Eunsong, Lee, Kun Ho, and Suk, Heung-Il
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Computer Science - Artificial Intelligence ,Electrical Engineering and Systems Science - Image and Video Processing - Abstract
Deep learning (DL) for predicting Alzheimer's disease (AD) has provided timely intervention in disease progression yet still demands attentive interpretability to explain how their DL models make definitive decisions. Recently, counterfactual reasoning has gained increasing attention in medical research because of its ability to provide a refined visual explanatory map. However, such visual explanatory maps based on visual inspection alone are insufficient unless we intuitively demonstrate their medical or neuroscientific validity via quantitative features. In this study, we synthesize the counterfactual-labeled structural MRIs using our proposed framework and transform it into a gray matter density map to measure its volumetric changes over the parcellated region of interest (ROI). We also devised a lightweight linear classifier to boost the effectiveness of constructed ROIs, promoted quantitative interpretation, and achieved comparable predictive performance to DL methods. Throughout this, our framework produces an ``AD-relatedness index'' for each ROI and offers an intuitive understanding of brain status for an individual patient and across patient groups with respect to AD progression., Comment: 15 pages, 5 figures, 4 tables
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- 2023
138. Probing the early Milky Way with GHOST spectra of an extremely metal-poor star in the Galactic disk
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Dovgal, Anya, Venn, Kim A., Sestito, Federico, Hayes, Christian R., McConnachie, Alan W., Navarro, Julio F., Placco, Vinicius M., Starkenburg, Else, Martin, Nicolas F., Pazder, John S., Chiboucas, Kristin, Deibert, Emily, Gamen, Roberto, Heo, Jeong-Eun, Kalari, Venu M., Martioli, Eder, Xu, Siyi, Diaz, Ruben, Gomez-Jiminez, Manuel, Henderson, David, Prado, Pablo, Quiroz, Carlos, Robertson, J. Gordon, Ruiz-Carmona, Roque, Simpson, Chris, Urrutia, Cristian, Waller, Fletcher, Berg, Trsytyn, Burley, Gregory, Hartman, Zachary, Ireland, Michael, Margheim, Steve, Perez, Gabriel, and Thomas-Osip, Joanna
- Subjects
Astrophysics - Astrophysics of Galaxies ,Astrophysics - Solar and Stellar Astrophysics - Abstract
Pristine_183.6849+04.8619 (P1836849) is an extremely metal-poor ([Fe/H]$=-3.3\pm0.1$) star on a prograde orbit confined to the Galactic disk. Such stars are rare and may have their origins in protogalactic fragments that formed the early Milky Way, in low mass satellites accreted later, or forming in situ in the Galactic plane. Here we present a chemo-dynamical analysis of the spectral features between $3700-11000$\r{A} from a high-resolution spectrum taken during Science Verification of the new Gemini High-resolution Optical SpecTrograph (GHOST). Spectral features for many chemical elements are analysed (Mg, Al, Si, Ca, Sc, Ti, Cr, Mn, Fe, Ni), and valuable upper limits are determined for others (C, Na, Sr, Ba). This main sequence star exhibits several rare chemical signatures, including (i) extremely low metallicity for a star in the Galactic disk, (ii) very low abundances of the light $\alpha$-elements (Na, Mg, Si) compared to other metal-poor stars, and (iii) unusually large abundances of Cr and Mn, where [Cr, Mn/Fe]$_{\rm NLTE}>+0.5$. A comparison to theoretical yields from supernova models suggests that two low mass Population III objects (one 10 M$_\odot$ supernova and one 17 M$_\odot$ hypernova) can reproduce the abundance pattern well (reduced $\chi^2<1$). When this star is compared to other extremely metal-poor stars on quasi-circular, prograde planar orbits, differences in both chemistry and kinematics imply there is little evidence for a common origin. The unique chemistry of P1836849 is discussed in terms of the earliest stages in the formation of the Milky Way., Comment: 16 pages, 10 figures, 6 tables. Accepted by MNRAS November 22; Revisions include comparisons to more EMP stars, results unchanged
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- 2023
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139. Quantum spin nematic phase in a square-lattice iridate
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Kim, Hoon, Kim, Jin-Kwang, Kim, Jimin, Kim, Hyun-Woo J., Ha, Seunghyeok, Kim, Kwangrae, Lee, Wonjun, Kim, Jonghwan, Cho, Gil Young, Heo, Hyeokjun, Jang, Joonho, Strempfer, J., Fabbris, G., Choi, Y., Haskel, D., Kim, Jungho, Kim, J. -W., and Kim, B. J.
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Condensed Matter - Strongly Correlated Electrons - Abstract
Spin nematic (SN) is a magnetic analog of classical liquid crystals, a fourth state of matter exhibiting characteristics of both liquid and solid. Particularly intriguing is a valence-bond SN, in which spins are quantum entangled to form a multi-polar order without breaking time-reversal symmetry, but its unambiguous experimental realization remains elusive. Here, we establish a SN phase in the square-lattice iridate Sr$_2$IrO$_4$, which approximately realizes a pseudospin one-half Heisenberg antiferromagnet (AF) in the strong spin-orbit coupling limit. Upon cooling, the transition into the SN phase at T$_C$ $\approx$ 263 K is marked by a divergence in the static spin quadrupole susceptibility extracted from our Raman spectra, and concomitant emergence of a collective mode associated with the spontaneous breaking of rotational symmetries. The quadrupolar order persists in the antiferromagnetic (AF) phase below T$_N$ $\approx$ 230 K, and becomes directly observable through its interference with the AF order in resonant x-ray diffraction, which allows us to uniquely determine its spatial structure. Further, we find using resonant inelastic x-ray scattering a complete breakdown of coherent magnon excitations at short-wavelength scales, suggesting a resonating-valence-bond-like quantum entanglement in the AF state. Taken together, our results reveal a quantum order underlying the N\'eel AF that is widely believed to be intimately connected to the mechanism of high temperature superconductivity (HTSC)., Comment: Published in https://www.nature.com/articles/s41586-023-06829-4
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- 2023
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140. Multimedia Resource Use Behaviour and Learning Outcomes
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Natalie Toomey and Misook Heo
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This research examined how spatial ability, sex, and cognitive styles associate with self-directed multimedia resource use (study 1) and learning outcomes (study 2). In study 1, three learning resource options were offered: two unimodal (text-only and labelled-picture) and one multimodal (picture-with-narration). Findings revealed that lower spatial ability associated with multimodal resource use and that verbalizers also used more picture-containing resources. In study 2, learning outcomes with multimodal resources were associated most significantly with spatial ability followed by sex. These studies offer unique empirical evidence that while spatial ability and cognitive style associate with self-directed resource use, spatial ability and sex associate with multimedia learning outcomes.
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- 2024
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141. Performance of Model Fit and Selection Indices for Bayesian Piecewise Growth Modeling with Missing Data
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Ihnwhi Heo, Fan Jia, and Sarah Depaoli
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The Bayesian piecewise growth model (PGM) is a useful class of models for analyzing nonlinear change processes that consist of distinct growth phases. In applications of Bayesian PGMs, it is important to accurately capture growth trajectories and carefully consider knot placements. The presence of missing data is another challenge researchers commonly encounter. To address these issues, one could use model fit and selection indices to detect misspecified Bayesian PGMs, and should give care to the potential impact of missing data on model evaluation. Here we conducted a simulation study to examine the impact of model misspecification and missing data on the performance of Bayesian model fit and selection indices (PPP-value, BCFI, BTLI, BRMSEA, BIC, and DIC), with an additional focus on prior sensitivity. Results indicated that (a) increasing the degree of model misspecification and amount of missing data aggravated the performance of indices in detecting misfit, and (b) different prior specifications had negligible impact on model assessment. We provide practical guidelines for researchers to facilitate effective implementation of Bayesian PGMs.
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- 2024
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142. Prediction of hemorrhagic transformation in acute ischemic stroke: a never-ending endeavor
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Heo, JoonNyung and Sohn, Beomseok
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- 2024
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143. Data and early results from temporary seismic arrays for monitoring and investigating magmatic processes beneath Mt. Halla and Ulleung Island volcanoes, South Korea
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Han, Jaeseoung, Han, Jongwon, Heo, Dabeen, Kim, Seongryong, Lee, Sujin, Koh, Min Hyug, Kim, Jaeyeon, Kwon, Ki Baek, Ahn, Byeong Seok, Jeon, Youngjun, Jo, Kyeongjun, Lim, Yeonjoo, Lee, Sang-Jun, Kang, Tae-Seob, Rhie, Junkee, and Ahn, Ungsan
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- 2024
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144. Comparative analysis of clinical image evaluation charts for panoramic radiography
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Kim, Yeonhee, Lee, Samsun, Jo, Gyudong, Kwon, Ahyoung, Kang, Juhee, Kim, Joeun, Huh, Kyunghoe, Yi, Wonjin, Heo, Minsuk, and Choi, Soonchul
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- 2024
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145. Developing evidence-based clinical imaging guidelines for the diagnosis of vertically fractured teeth
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Kim, Ki-Hong, Kim, Jo-Eun, Lee, Sam-Sun, Lee, Chena, Choi, Miyoung, Yong, Hwan Seok, Jung, Seung Eun, Heo, Min-Suk, and Huh, Kyung-Hoe
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- 2024
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146. Concurrent Infection with Clade 2.3.4.4b Highly Pathogenic Avian Influenza H5N6 and H5N1 Viruses, South Korea, 2023
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Heo, Gyeong-Beom, Kang, Yong-Myung, An, Se-Hee, Kim, Yeongbu, Cha, Ra Mi, Jang, Yunyueng, Lee, Eun-Kyoung, Lee, Youn-Jeong, and Lee, Kwang-Nyeong
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Avian influenza -- Distribution ,Sentinel health events -- Observations ,Virus research ,Health ,Company distribution practices ,Observations ,Distribution - Abstract
Since clade 2.3.4.4 H5Nx highly pathogenic avian influenza (HPAI) viruses first emerged in East Asia in 2013-14, clade 2.3.4.4b has spread throughout Europe, Africa, and Middle East in 2016-17, causing [...]
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- 2024
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147. Microscopic Study on the Blister Formation Mechanism in Electrogalvanized Steel
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Shin, Sang-Hoon, Trang, T. T. T., Chung, Bong-Hoon, Jeong, Yong-Gyun, Lee, Jae-Sang, and Heo, Yoon-Uk
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- 2024
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148. 2D MoS2 Helical Liquid Crystalline Fibers for Multifunctional Wearable Sensors
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Park, Jun Hyun, Kim, Jang Hwan, Lee, Su Eon, Kim, Hyokyeong, Lim, Heo Yeon, Park, Ji Sung, Yun, Taeyeong, Lee, Jinyong, Kim, Simon, Jin, Ho Jun, Park, Kyeong Jun, Kang, Heemin, Kim, Hoe Joon, Jin, Hyeong Min, Kim, Jiwoong, Kim, Sang Ouk, and Kim, Bong Hoon
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- 2024
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149. Magmatic to aqueous phase transition in Li-pegmatite: microtextural and geochemical study of muscovite–lepidolite from Boam mine area, Uljin, South Korea
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Choi, Woohyun, Park, Changyun, Heo, Chul-Ho, Yang, Seok-Jun, Oh, Il-Hwan, Park, Kyung Su, and Choi, Sung Hwa
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- 2024
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150. Hand Tracking: Survey
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Heo, Jinuk, Choi, Hyelim, Lee, Yongseok, Kim, Hyunsu, Ji, Harim, Park, Hyunreal, Lee, Youngseon, Jung, Cheongkee, Nguyen, Hai-Nguyen, and Lee, Dongjun
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- 2024
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