146,282 results on '"Lee, Sang"'
Search Results
2. Living through Atomization: Runit Dome, Radioactive Matter, and Poetry of Digestion
- Author
-
Lee, Sang Eun Eunice
- Published
- 2023
3. Marshall R. Pihl’s Translation of Ch’ang (Song) in “Sim Ch’ŏng ka”
- Author
-
Lee, Sang-Bin
- Published
- 2021
4. Accelerating High-Fidelity Waveform Generation via Adversarial Flow Matching Optimization
- Author
-
Lee, Sang-Hoon, Choi, Ha-Yeong, and Lee, Seong-Whan
- Subjects
Computer Science - Sound ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Audio and Speech Processing ,Electrical Engineering and Systems Science - Signal Processing - Abstract
This paper introduces PeriodWave-Turbo, a high-fidelity and high-efficient waveform generation model via adversarial flow matching optimization. Recently, conditional flow matching (CFM) generative models have been successfully adopted for waveform generation tasks, leveraging a single vector field estimation objective for training. Although these models can generate high-fidelity waveform signals, they require significantly more ODE steps compared to GAN-based models, which only need a single generation step. Additionally, the generated samples often lack high-frequency information due to noisy vector field estimation, which fails to ensure high-frequency reproduction. To address this limitation, we enhance pre-trained CFM-based generative models by incorporating a fixed-step generator modification. We utilized reconstruction losses and adversarial feedback to accelerate high-fidelity waveform generation. Through adversarial flow matching optimization, it only requires 1,000 steps of fine-tuning to achieve state-of-the-art performance across various objective metrics. Moreover, we significantly reduce inference speed from 16 steps to 2 or 4 steps. Additionally, by scaling up the backbone of PeriodWave from 29M to 70M parameters for improved generalization, PeriodWave-Turbo achieves unprecedented performance, with a perceptual evaluation of speech quality (PESQ) score of 4.454 on the LibriTTS dataset. Audio samples, source code and checkpoints will be available at https://github.com/sh-lee-prml/PeriodWave., Comment: 9 pages, 9 tables, 1 figure
- Published
- 2024
5. PeriodWave: Multi-Period Flow Matching for High-Fidelity Waveform Generation
- Author
-
Lee, Sang-Hoon, Choi, Ha-Yeong, and Lee, Seong-Whan
- Subjects
Computer Science - Sound ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Audio and Speech Processing ,Electrical Engineering and Systems Science - Signal Processing - Abstract
Recently, universal waveform generation tasks have been investigated conditioned on various out-of-distribution scenarios. Although GAN-based methods have shown their strength in fast waveform generation, they are vulnerable to train-inference mismatch scenarios such as two-stage text-to-speech. Meanwhile, diffusion-based models have shown their powerful generative performance in other domains; however, they stay out of the limelight due to slow inference speed in waveform generation tasks. Above all, there is no generator architecture that can explicitly disentangle the natural periodic features of high-resolution waveform signals. In this paper, we propose PeriodWave, a novel universal waveform generation model. First, we introduce a period-aware flow matching estimator that can capture the periodic features of the waveform signal when estimating the vector fields. Additionally, we utilize a multi-period estimator that avoids overlaps to capture different periodic features of waveform signals. Although increasing the number of periods can improve the performance significantly, this requires more computational costs. To reduce this issue, we also propose a single period-conditional universal estimator that can feed-forward parallel by period-wise batch inference. Additionally, we utilize discrete wavelet transform to losslessly disentangle the frequency information of waveform signals for high-frequency modeling, and introduce FreeU to reduce the high-frequency noise for waveform generation. The experimental results demonstrated that our model outperforms the previous models both in Mel-spectrogram reconstruction and text-to-speech tasks. All source code will be available at \url{https://github.com/sh-lee-prml/PeriodWave}., Comment: 24 pages, 16 tables, 4 figures
- Published
- 2024
6. Investigating Characteristics of Media Recommendation Solicitation in r/ifyoulikeblank
- Author
-
Bhuiyan, Md Momen, Hu, Donghan, Jelson, Andrew, Mitra, Tanushree, and Lee, Sang Won
- Subjects
Computer Science - Human-Computer Interaction ,Computer Science - Information Retrieval - Abstract
Despite the existence of search-based recommender systems like Google, Netflix, and Spotify, online users sometimes may turn to crowdsourced recommendations in places like the r/ifyoulikeblank subreddit. In this exploratory study, we probe why users go to r/ifyoulikeblank, how they look for recommendation, and how the subreddit users respond to recommendation requests. To answer, we collected sample posts from r/ifyoulikeblank and analyzed them using a qualitative approach. Our analysis reveals that users come to this subreddit for various reasons, such as exhausting popular search systems, not knowing what or how to search for an item, and thinking crowd have better knowledge than search systems. Examining users query and their description, we found novel information users provide during recommendation seeking using r/ifyoulikeblank. For example, sometimes they ask for artifacts recommendation based on the tools used to create them. Or, sometimes indicating a recommendation seeker's time constraints can help better suit recommendations to their needs. Finally, recommendation responses and interactions revealed patterns of how requesters and responders refine queries and recommendations. Our work informs future intelligent recommender systems design., Comment: page 23
- Published
- 2024
- Full Text
- View/download PDF
7. VizECGNet: Visual ECG Image Network for Cardiovascular Diseases Classification with Multi-Modal Training and Knowledge Distillation
- Author
-
Nam, Ju-Hyeon, Park, Seo-Hyung, Kim, Su Jung, and Lee, Sang-Chul
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
An electrocardiogram (ECG) captures the heart's electrical signal to assess various heart conditions. In practice, ECG data is stored as either digitized signals or printed images. Despite the emergence of numerous deep learning models for digitized signals, many hospitals prefer image storage due to cost considerations. Recognizing the unavailability of raw ECG signals in many clinical settings, we propose VizECGNet, which uses only printed ECG graphics to determine the prognosis of multiple cardiovascular diseases. During training, cross-modal attention modules (CMAM) are used to integrate information from two modalities - image and signal, while self-modality attention modules (SMAM) capture inherent long-range dependencies in ECG data of each modality. Additionally, we utilize knowledge distillation to improve the similarity between two distinct predictions from each modality stream. This innovative multi-modal deep learning architecture enables the utilization of only ECG images during inference. VizECGNet with image input achieves higher performance in precision, recall, and F1-Score compared to signal-based ECG classification models, with improvements of 3.50%, 8.21%, and 7.38%, respectively., Comment: Accepted in International Conference on Image Processing (ICIP) 2024
- Published
- 2024
8. Adaptive Contrastive Decoding in Retrieval-Augmented Generation for Handling Noisy Contexts
- Author
-
Kim, Youna, Kim, Hyuhng Joon, Park, Cheonbok, Park, Choonghyun, Cho, Hyunsoo, Kim, Junyeob, Yoo, Kang Min, Lee, Sang-goo, and Kim, Taeuk
- Subjects
Computer Science - Computation and Language - Abstract
When using large language models (LLMs) in knowledge-intensive tasks, such as open-domain question answering, external context can bridge a gap between external knowledge and LLM's parametric knowledge. Recent research has been developed to amplify contextual knowledge over the parametric knowledge of LLM with contrastive decoding approaches. While these approaches could yield truthful responses when relevant context is provided, they are prone to vulnerabilities when faced with noisy contexts. We extend the scope of previous studies to encompass noisy contexts and propose adaptive contrastive decoding (ACD) to leverage contextual influence effectively. ACD demonstrates improvements in open-domain question answering tasks compared to baselines, especially in robustness by remaining undistracted by noisy contexts in retrieval-augmented generation.
- Published
- 2024
9. X-ray and multiwavelength polarization of Mrk 501 from 2022 to 2023
- Author
-
Chen, Chien-Ting J., Liodakis, Ioannis, Middei, Riccardo, Kim, Dawoon E., Di Gesu, Laura, Di Marco, Alessandro, Ehlert, Steven R., Errando, Manel, Negro, Michela, Jorstad, Svetlana G., Marscher, Alan P., Wu, Kinwah, Agudo, Iván, Poutanen, Juri, Mizuno, Tsunefumi, Kouch, Pouya M., Lindfors, Elina, Borman, George A., Grishina, Tatiana S., Kopatskaya, Evgenia N., Larionova, Elena G., Morozova, Daria A., Savchenko, Sergey S., Troitsky, Ivan S., Troitskaya, Yulia V., Vasilyev, Andrey A., Zhovtan, Alexey V., Aceituno, Francisco José, Bonnoli, Giacomo, Casanova, Víctor, Escudero, Juan, Agís-González, Beatriz, Husillos, César, Santos, Jorge Otero, Sota, Alfredo, Piirola, Vilppu, Myserlis, Ioannis, Angelakis, Emmanouil, Kraus, Alexander, Gurwell, Mark, Keating, Garrett, Rao, Ramprasad, Kang, Sincheol, Lee, Sang-Sung, Kim, Sang-Hyun, Cheong, Whee Yeon, Jeong, Hyeon-Woo, Song, Chanwoo, Berdyugin, Andrei V., Kagitani, Masato, Kravtsov, Vadim, Nitindala, Anagha P., Sakanoi, Takeshi, Imazawa, Ryo, Sasada, Mahito, Fukazawa, Yasushi, Kawabata, Koji S., Uemura, Makoto, Nakaoka, Tatsuya, Akitaya, Hiroshi, Casadio, Carolina, Sievers, Albrecht, Antonelli, Lucio Angelo, Bachetti, Matteo, Baldini, Luca, Baumgartner, Wayne H., Bellazzini, Ronaldo, Bianchi, Stefano, Bongiorno, Stephen D., Bonino, Raffaella, Brez, Alessandro, Bucciantini, Niccoló, Capitanio, Fiamma, Castellano, Simone, Cavazzuti, Elisabetta, Ciprini, Stefano, Costa, Enrico, De Rosa, Alessandra, Del Monte, Ettore, Di Lalla, Niccoló, Donnarumma, Immacolata, Doroshenko, Victor, Dovčiak, Michal, Enoto, Teruaki, Evangelista, Yuri, Fabiani, Sergio, Ferrazzoli, Riccardo, Garcia, Javier A., Gunji, Shuichi, Hayashida, Kiyoshi, Heyl, Jeremy, Iwakiri, Wataru, Kaaret, Philip, Karas, Vladimir, Kislat, Fabian, Kitaguchi, Takao, Kolodziejczak, Jeffery J., Krawczynski, Henric, La Monaca, Fabio, Latronico, Luca, Maldera, Simone, Manfreda, Alberto, Marin, Frédéric, Marinucci, Andrea, Marshall, Herman L., Massaro, Francesco, Matt, Giorgio, Mitsuishi, Ikuyuki, Muleri, Fabio, Ng, C. -Y., O'Dell, Stephen L., Omodei, Nicola, Oppedisano, Chiara, Papitto, Alessandro, Pavlov, George G., Peirson, Abel Lawrence, Perri, Matteo, Pesce-Rollins, Melissa, Petrucci, Pierre-Olivier, Pilia, Maura, Possenti, Andrea, Puccetti, Simonetta, Ramsey, Brian D., Rankin, John, Ratheesh, Ajay, Roberts, Oliver J., Romani, Roger W., Sgró, Carmelo, Slane, Patrick, Soffitta, Paolo, Spandre, Gloria, Swartz, Douglas A., Tamagawa, Toru, Tavecchio, Fabrizio, Taverna, Roberto, Tawara, Yuzuru, Tennant, Allyn F., Thomas, Nicholas E., Tombesi, Francesco, Trois, Alessio, Tsygankov, Sergey S., Turolla, Roberto, Vink, Jacco, Weisskopf, Martin C., Xie, Fei, and Zane, Silvia
- Subjects
Astrophysics - High Energy Astrophysical Phenomena ,Astrophysics - Astrophysics of Galaxies - Abstract
We present multiwavelength polarization measurements of the luminous blazar Mrk~501 over a 14-month period. The 2--8 keV X-ray polarization was measured with the Imaging X-ray Polarimetry Explorer (IXPE) with six 100-ks observations spanning from 2022 March to 2023 April. Each IXPE observation was accompanied by simultaneous X-ray data from NuSTAR, Swift/XRT, and/or XMM-Newton. Complementary optical-infrared polarization measurements were also available in the B, V, R, I, and J bands, as were radio polarization measurements from 4.85 GHz to 225.5 GHz. Among the first five IXPE observations, we did not find significant variability in the X-ray polarization degree and angle with IXPE. However, the most recent sixth observation found an elevated polarization degree at $>3\sigma$ above the average of the other five observations. The optical and radio measurements show no apparent correlations with the X-ray polarization properties. Throughout the six IXPE observations, the X-ray polarization degree remained higher than, or similar to, the R-band optical polarization degree, which remained higher than the radio value. This is consistent with the energy-stratified shock scenario proposed to explain the first two IXPE observations, in which the polarized X-ray, optical, and radio emission arises from different regions., Comment: Accepted for publication in ApJ
- Published
- 2024
10. Global decomposition of networks into multiple cores formed by local hubs
- Author
-
Jeong, Wonhee, Yu, Unjong, and Lee, Sang Hoon
- Subjects
Physics - Physics and Society ,Condensed Matter - Statistical Mechanics ,Computer Science - Social and Information Networks - Abstract
Networks are ubiquitous in various fields, representing systems where nodes and their interconnections constitute their intricate structures. We introduce a network decomposition scheme to reveal multiscale core-periphery structures lurking inside, using the concept of locally defined nodal hub centrality and edge-pruning techniques built upon it. We demonstrate that the hub-centrality-based edge pruning reveals a series of breaking points in network decomposition, which effectively separates a network into its backbone and shell structures. Our local-edge decomposition method iteratively identifies and removes locally least important nodes, and uncovers an onion-like hierarchical structure as a result. Compared with the conventional $k$-core decomposition method, our method based on relative information residing in local structures exhibits a clear advantage in terms of discovering locally crucial substructures. Furthermore, we introduce the core-periphery score to properly separate the core and periphery with our decomposition scheme. By extending the method combined with the network community structure, we successfully detect multiple core-periphery structures by decomposition inside each community. Moreover, the application of our decomposition to supernode networks defined from the communities reveals the intricate relation between the two representative mesoscale structures., Comment: 10 pages, 8 figures, 1 table
- Published
- 2024
11. Statistical Analysis on Scale and Regional Distribution of Undergraduate Physics Programs in Korean Universities
- Author
-
Gim, Gahyoun and Lee, Sang Hoon
- Subjects
Physics - Physics Education ,Physics - Physics and Society - Abstract
We report on the temporal changes in undergraduate-level physics programs at Korean universities from 1915 to 2023 by analyzing data on physics-related departments and their students using basic statistics and the scaling theory of statistical physics. Our analysis reveals that the number of departments peaked around the turn of the 21st century, and it has been steadily decreasing ever since, with particularly severe declines in private universities located outside the capital region. Besides the change in the overall numbers, we also show the change in the self-identity of physics-related departments reflected in department names, which reveals a recent trend of emphasizing more application-side such as semiconductors and data. As a sophisticated measure to quantify regional imbalances relative to the population eligible for higher education, we present scaling exponents from the scaling theory, which shows a shift from sublinear to linear for departments and a shift from linear to superlinear for students. The result indicates the exacerbation of the regional imbalance of university-level physics education in Korea., Comment: 11 pages, in Korean language, 8 figures, 1 table, in Korean
- Published
- 2024
- Full Text
- View/download PDF
12. Investigating the Influence of Prompt-Specific Shortcuts in AI Generated Text Detection
- Author
-
Park, Choonghyun, Kim, Hyuhng Joon, Kim, Junyeob, Kim, Youna, Kim, Taeuk, Cho, Hyunsoo, Jo, Hwiyeol, Lee, Sang-goo, and Yoo, Kang Min
- Subjects
Computer Science - Computation and Language - Abstract
AI Generated Text (AIGT) detectors are developed with texts from humans and LLMs of common tasks. Despite the diversity of plausible prompt choices, these datasets are generally constructed with a limited number of prompts. The lack of prompt variation can introduce prompt-specific shortcut features that exist in data collected with the chosen prompt, but do not generalize to others. In this paper, we analyze the impact of such shortcuts in AIGT detection. We propose Feedback-based Adversarial Instruction List Optimization (FAILOpt), an attack that searches for instructions deceptive to AIGT detectors exploiting prompt-specific shortcuts. FAILOpt effectively drops the detection performance of the target detector, comparable to other attacks based on adversarial in-context examples. We also utilize our method to enhance the robustness of the detector by mitigating the shortcuts. Based on the findings, we further train the classifier with the dataset augmented by FAILOpt prompt. The augmented classifier exhibits improvements across generation models, tasks, and attacks. Our code will be available at https://github.com/zxcvvxcz/FAILOpt., Comment: 19 pages, 3 figures, 13 tables, under review
- Published
- 2024
13. Improving Text-To-Audio Models with Synthetic Captions
- Author
-
Kong, Zhifeng, Lee, Sang-gil, Ghosal, Deepanway, Majumder, Navonil, Mehrish, Ambuj, Valle, Rafael, Poria, Soujanya, and Catanzaro, Bryan
- Subjects
Computer Science - Computation and Language ,Computer Science - Machine Learning ,Computer Science - Sound ,Electrical Engineering and Systems Science - Audio and Speech Processing - Abstract
It is an open challenge to obtain high quality training data, especially captions, for text-to-audio models. Although prior methods have leveraged \textit{text-only language models} to augment and improve captions, such methods have limitations related to scale and coherence between audio and captions. In this work, we propose an audio captioning pipeline that uses an \textit{audio language model} to synthesize accurate and diverse captions for audio at scale. We leverage this pipeline to produce a dataset of synthetic captions for AudioSet, named \texttt{AF-AudioSet}, and then evaluate the benefit of pre-training text-to-audio models on these synthetic captions. Through systematic evaluations on AudioCaps and MusicCaps, we find leveraging our pipeline and synthetic captions leads to significant improvements on audio generation quality, achieving a new \textit{state-of-the-art}.
- Published
- 2024
14. PITCH: Productivity and Mental Well-being Coaching through Daily Conversational Interaction
- Author
-
Abbas, Adnan and Lee, Sang Won
- Subjects
Computer Science - Human-Computer Interaction - Abstract
Efficient task planning is essential for productivity and mental well-being, yet individuals often struggle to create realistic plans and reflect upon their productivity. Leveraging the advancement in artificial intelligence (AI), conversational agents have emerged as a promising tool for enhancing productivity. Our work focuses on externalizing plans through conversation, aiming to solidify intentions and foster focused action, thereby positively impacting their productivity and mental well-being. We share our plan of designing a conversational agent to offer insightful questions and reflective prompts for increasing plan adherence by leveraging the social interactivity of natural conversations. Previous studies have shown the effectiveness of such agents, but many interventions remain static, leading to decreased user engagement over time. To address this limitation, we propose a novel rotation and context-aware prompting strategy, providing users with varied interventions daily. Our system, PITCH, utilizes large language models (LLMs) to facilitate externalization and reflection on daily plans. Through this study, we investigate the impact of externalizing tasks with conversational agents on productivity and mental well-being, and the effectiveness of a rotation strategy in maintaining user engagement.
- Published
- 2024
15. EmoSphere-TTS: Emotional Style and Intensity Modeling via Spherical Emotion Vector for Controllable Emotional Text-to-Speech
- Author
-
Cho, Deok-Hyeon, Oh, Hyung-Seok, Kim, Seung-Bin, Lee, Sang-Hoon, and Lee, Seong-Whan
- Subjects
Computer Science - Sound ,Computer Science - Artificial Intelligence ,Electrical Engineering and Systems Science - Audio and Speech Processing - Abstract
Despite rapid advances in the field of emotional text-to-speech (TTS), recent studies primarily focus on mimicking the average style of a particular emotion. As a result, the ability to manipulate speech emotion remains constrained to several predefined labels, compromising the ability to reflect the nuanced variations of emotion. In this paper, we propose EmoSphere-TTS, which synthesizes expressive emotional speech by using a spherical emotion vector to control the emotional style and intensity of the synthetic speech. Without any human annotation, we use the arousal, valence, and dominance pseudo-labels to model the complex nature of emotion via a Cartesian-spherical transformation. Furthermore, we propose a dual conditional adversarial network to improve the quality of generated speech by reflecting the multi-aspect characteristics. The experimental results demonstrate the model ability to control emotional style and intensity with high-quality expressive speech., Comment: Accepted at INTERSPEECH 2024
- Published
- 2024
16. Diamond molecular balance: Revolutionizing high-resolution mass spectrometry from MDa to TDa at room temperature
- Author
-
Lee, Donggeun, Jeon, Seung-Woo, Yi, Chang-Hwan, Kim, Yang-Hee, Choi, Yeeun, Lee, Sang-Hun, Cha, Jinwoong, Shim, Seung-Bo, Suh, Junho, Kim, Il-Young, Kang, Dongyeon Daniel, Jung, Hojoong, Jeong, Cherlhyun, Ahn, Jae-pyoung, Park, Hee Chul, Han, Sang-Wook, and Kim, Chulki
- Subjects
Condensed Matter - Mesoscale and Nanoscale Physics ,Physics - Applied Physics - Abstract
The significance of mass spectrometry lies in its unparalleled ability to accurately identify and quantify molecules in complex samples, providing invaluable insights into molecular structures and interactions. Here, we leverage diamond nanostructures as highly sensitive mass sensors by utilizing a self-excitation mechanism under an electron beam in a conventional scanning electron microscope (SEM). The diamond molecular balance (DMB) exhibits an exceptional mass resolution of 0.36 MDa, based on its outstanding mechanical quality factor and frequency stability, along with an extensive dynamic range from MDa to TDa. This positions the DMB at the forefront of molecular balances operating at room temperature. Notably, the DMB demonstrates its ability to measure the mass of a single bacteriophage T4 by precisely locating the analyte on the device. These findings highlight the groundbreaking potential of the DMB as a revolutionary tool for mass spectrometry at room temperature., Comment: 16 pages, 4 figures
- Published
- 2024
17. IXPE observation of PKS 2155-304 reveals the most highly polarized blazar
- Author
-
Kouch, Pouya M., Liodakis, Ioannis, Middei, Riccardo, Kim, Dawoon E., Tavecchio, Fabrizio, Marscher, Alan P., Marshall, Herman L., Ehlert, Steven R., Di Gesu, Laura, Jorstad, Svetlana G., Agudo, Iván, Madejski, Grzegorz M., Romani, Roger W., Errando, Manel, Lindfors, Elina, Nilsson, Kari, Toppari, Ella, Potter, Stephen B., Imazawa, Ryo, Sasada, Mahito, Fukazawa, Yasushi, Kawabata, Koji S., Uemura, Makoto, Mizuno, Tsunefumi, Nakaoka, Tatsuya, Akitaya, Hiroshi, McCall, Callum, Jermak, Helen E., Steele, Iain A., Myserlis, Ioannis, Gurwell, Mark, Keating, Garrett K., Rao, Ramprasad, Kang, Sincheol, Lee, Sang-Sung, Kim, Sang-Hyun, Cheong, Whee Yeon, Jeong, Hyeon-Woo, Angelakis, Emmanouil, Kraus, Alexander, Aceituno, Francisco José, Bonnoli, Giacomo, Casanova, Víctor, Escudero, Juan, Agís-González, Beatriz, Husillos, César, Morcuende, Daniel, Otero-Santos, Jorge, Sota, Alfredo, Bachev, Rumen, Antonelli, Lucio Angelo, Bachetti, Matteo, Baldini, Luca, Baumgartner, Wayne H., Bellazzini, Ronaldo, Bianchi, Stefano, Bongiorno, Stephen D., Bonino, Raffaella, Brez, Alessandro, Bucciantini, Niccolò, Capitanio, Fiamma, Castellano, Simone, Cavazzuti, Elisabetta, Chen, Chien-Ting, Ciprini, Stefano, Costa, Enrico, De Rosa, Alessandra, Del Monte, Ettore, Di Lalla, Niccolò, Di Marco, Alessandro, Donnarumma, Immacolata, Doroshenko, Victor, Dovčiak, Michal, Enoto, Teruaki, Evangelista, Yuri, Fabiani, Sergio, Ferrazzoli, Riccardo, Garcia, Javier A., Gunji, Shuichi, Hayashida, Kiyoshi, Heyl, Jeremy, Iwakiri, Wataru, Kaaret, Philip, Karas, Vladimir, Kislat, Fabian, Kitaguchi, Takao, Kolodziejczak, Jeffery J., Krawczynski, Henric, La Monaca, Fabio, Latronico, Luca, Maldera, Simone, Manfreda, Alberto, Marin, Frédéric, Marinucci, Andrea, Massaro, Francesco, Matt, Giorgio, Mitsuishi, Ikuyuki, Muleri, Fabio, Negro, Michela, Ng, C. -Y., O'Dell, Stephen L., Omodei, Nicola, Oppedisano, Chiara, Papitto, Alessandro, Pavlov, George G., Peirson, Abel Lawrence, Perri, Matteo, Pesce-Rollins, Melissa, Petrucci, Pierre-Olivier, Pilia, Maura, Possenti, Andrea, Poutanen, Juri, Puccetti, Simonetta, Ramsey, Brian D., Rankin, John, Ratheesh, Ajay, Roberts, Oliver J., Sgrò, Carmelo, Slane, Patrick, Soffitta, Paolo, Spandre, Gloria, Swartz, Douglas A., Tamagawa, Toru, Taverna, Roberto, Tawara, Yuzuru, Tennant, Allyn F., Thomas, Nicholas E., Tombesi, Francesco, Trois, Alessio, Tsygankov, Sergey S., Turolla, Roberto, Vink, Jacco, Weisskopf, Martin C., Wu, Kinwah, Xie, Fei, and Zane, Silvia
- Subjects
Astrophysics - High Energy Astrophysical Phenomena - Abstract
We report the X-ray polarization properties of the high-synchrotron-peaked (HSP) blazar PKS 2155$-$304 based on observations with the Imaging X-ray Polarimetry Explorer (IXPE). We observed the source between Oct 27 and Nov 7, 2023. We also conducted an extensive contemporaneous multiwavelength (MW) campaign. We find that during the first half ($T_1$) of the IXPE pointing, the source exhibited the highest X-ray polarization degree detected for an HSP blazar thus far, (30.7$\pm$2.0)%, which dropped to (15.3$\pm$2.1)% during the second half ($T_2$). The X-ray polarization angle remained stable during the IXPE pointing at 129.4$^\circ$$\pm$1.8$^\circ$ and 125.4$^\circ$$\pm$3.9$^\circ$ during $T_1$ and $T_2$, respectively. Meanwhile, the optical polarization degree remained stable during the IXPE pointing, with average host-galaxy-corrected values of (4.3$\pm$0.7)% and (3.8$\pm$0.9)% during the $T_1$ and $T_2$, respectively. During the IXPE pointing, the optical polarization angle changed achromatically from $\sim$140$^\circ$ to $\sim$90$^\circ$ and back to $\sim$130$^\circ$. Despite several attempts, we only detected (99.7% conf.) the radio polarization once (during $T_2$, at 225.5 GHz): with degree (1.7$\pm$0.4)% and angle 112.5$^\circ$$\pm$5.5$^\circ$. The direction of the broad pc-scale jet is rather ambiguous and has been found to point to the east and south at different epochs; however, on larger scales (> 1.5 pc) the jet points toward the southeast ($\sim$135$^\circ$), similar to all of the MW polarization angles. Moreover, the X-ray to optical polarization degree ratios of $\sim$7 and $\sim$4 during $T_1$ and $T_2$, respectively, are similar to previous IXPE results for several HSP blazars. These findings, combined with the lack of correlation of temporal variability between the MW polarization properties, agree with an energy-stratified shock-acceleration scenario in HSP blazars., Comment: 17 pages, 10 figures, 4 tables, Accepted for publication in A&A
- Published
- 2024
18. Harmful Suicide Content Detection
- Author
-
Park, Kyumin, Baik, Myung Jae, Hwang, YeongJun, Shin, Yen, Lee, HoJae, Lee, Ruda, Lee, Sang Min, Sun, Je Young Hannah, Lee, Ah Rah, Yoon, Si Yeun, Lee, Dong-ho, Moon, Jihyung, Bak, JinYeong, Cho, Kyunghyun, Paik, Jong-Woo, and Park, Sungjoon
- Subjects
Computer Science - Computers and Society ,Computer Science - Artificial Intelligence ,Computer Science - Computation and Language ,Computer Science - Social and Information Networks - Abstract
Harmful suicide content on the Internet is a significant risk factor inducing suicidal thoughts and behaviors among vulnerable populations. Despite global efforts, existing resources are insufficient, specifically in high-risk regions like the Republic of Korea. Current research mainly focuses on understanding negative effects of such content or suicide risk in individuals, rather than on automatically detecting the harmfulness of content. To fill this gap, we introduce a harmful suicide content detection task for classifying online suicide content into five harmfulness levels. We develop a multi-modal benchmark and a task description document in collaboration with medical professionals, and leverage large language models (LLMs) to explore efficient methods for moderating such content. Our contributions include proposing a novel detection task, a multi-modal Korean benchmark with expert annotations, and suggesting strategies using LLMs to detect illegal and harmful content. Owing to the potential harm involved, we publicize our implementations and benchmark, incorporating an ethical verification process., Comment: 30 pages, 7 figures
- Published
- 2024
19. Marshall R. Pihl and His Views on How to Enrich Korean Literature in Translation
- Author
-
LEE, Sang-Bin
- Published
- 2019
20. Effects of Planck Exercise on the Cranio-vertebral Angle, Round shoulder Postureand Forward Head Posture
- Author
-
Lee, Sang-Ho, Yu, Won-Jong, and ee, Dong-Yeop L
- Published
- 2019
- Full Text
- View/download PDF
21. Effect of choline chloride with propylene glycol on growth performance and meat quality in finishing pigs
- Author
-
Jiao, Yang, Lee, Sang In, and Kim, In Ho
- Published
- 2019
- Full Text
- View/download PDF
22. Circulating biomarkers of kidney angiomyolipoma and cysts in tuberous sclerosis complex patients
- Author
-
Rubtsova, Varvara I, Chun, Yujin, Kim, Joohwan, Ramirez, Cuauhtemoc B, Jung, Sunhee, Choi, Wonsuk, Kelly, Miranda E, Lopez, Miranda L, Cassidy, Elizabeth, Rushing, Gabrielle, Aguiar, Dean J, Lau, Wei Ling, Ahdoot, Rebecca S, Smith, Moyra, Edinger, Aimee L, Lee, Sang-Guk, Jang, Cholsoon, and Lee, Gina
- Subjects
Medical Biochemistry and Metabolomics ,Analytical Chemistry ,Biomedical and Clinical Sciences ,Chemical Sciences ,Minority Health ,Clinical Research ,Cancer ,Pediatric Cancer ,Tuberous Sclerosis ,Brain Disorders ,Rare Diseases ,Prevention ,Pediatric ,Kidney Disease ,Health Disparities ,4.1 Discovery and preclinical testing of markers and technologies ,2.1 Biological and endogenous factors ,Renal and urogenital ,Good Health and Well Being ,Clinical genetics ,Endocrinology ,Pathophysiology - Abstract
Patients with tuberous sclerosis complex (TSC) develop multi-organ disease manifestations, with kidney angiomyolipomas (AML) and cysts being one of the most common and deadly. Early and regular AML/cyst detection and monitoring are vital to lower TSC patient morbidity and mortality. However, the current standard of care involves imaging-based methods that are not designed for rapid screening, posing challenges for early detection. To identify potential diagnostic screening biomarkers of AML/cysts, we performed global untargeted metabolomics in blood samples from 283 kidney AML/cyst-positive or -negative TSC patients using mass spectrometry. We identified 7 highly sensitive chemical features, including octanoic acid, that predict kidney AML/cysts in TSC patients. Patients with elevated octanoic acid have lower levels of very long-chain fatty acids (VLCFAs), suggesting that dysregulated peroxisome activity leads to overproduction of octanoic acid via VLCFA oxidation. These data highlight AML/cysts blood biomarkers for TSC patients and offers valuable metabolic insights into the disease.
- Published
- 2024
23. Potential Urban Heat Island Countermeasures and Building Energy Efficiency Improvements in Los Angeles County
- Author
-
Robinson, Alastair, Fernandes, Samuel, Hong, Tianzhen, Lee, Sang Hoon, Levinson, Ronnen, and Piette, Mary Ann
- Abstract
Los Angeles County has been experiencing increasing numbers of very hot days and heatwaves later into the summer. Scientists predict a significant increase in days with temperatures over 95 °F [35 °C] over the next 15-20 years. As the climate warms, deploying energy efficiency technologies at scale can be an effective method to address the associated increase in urban temperatures, particularly during periodic heatwaves.Utilizing existing information and data, the research project provided the Los Angeles County Internal Services Department with supplementary datasets and tools that support the identification and prioritization of neighborhoods/districts, building clusters, and specific buildings for retrofits, according to criteria identified and developed during the analysis process. There was a focus on the County’s multi-family residential building stock, to enable low-cost energy retrofits that also support improvement of thermal resilience and occupant health and comfort.Urban fabric analysis indicated that there are substantial opportunities to reduce unwanted solar heating of buildings and pavements and cool the urban environment with reflective roofs, walls, and pavements. On average, albedo (solar reflectance) values for roofs (0.19) and pavements (0.15) are currently quite low. The potential increases to roof and pavement albedos are 0.33, and 0.25 respectively, indicating substantial opportunities for brightening. Building stock analysis determined that there are over 43,000 multi-family residential buildings, holding over 150,000 housing units in Los Angeles County, that represent priority opportunities for large-scale energy efficiency policies and activities to ameliorate the impacts of the urban heat island effect.Building energy simulations were conducted for 13 multifamily residential buildings, to evaluate a low-cost energy efficiency retrofit package. Implementation of these measures in the baseline housing units are expected to reduce annual electricity use by 17%, lower peak electricity demand by 19%, and provide net annual energy cost savings of $183 per housing unit. Importantly, annual hours of heat stress for building occupants are expected to be dramatically reduced by implementation of the energy efficiency retrofit—and for buildings with existing air-conditioning, eliminated entirely.Unmanned Aerial Vehicle (UAV) flights were conducted as a remote-sensing proof-of-concept for building energy performance evaluation. This approach represents an opportunity for inspection and analysis of existing building stocks, where energy performance can be documented using infrared imaging and 3D photogrammetry. Machine-learning algorithms developed by Berkeley Lab researchers were employed to detect thermal anomalies on building facades, and to indicate issues potentially related to moisture, infiltration, and/or exfiltration. The potential benefits can be seen via implementation of this method at the building test site, which highlighted areas around windows and facade surface corners as locations for further inspection.
- Published
- 2024
24. Pretraining with Random Noise for Fast and Robust Learning without Weight Transport
- Author
-
Cheon, Jeonghwan, Lee, Sang Wan, and Paik, Se-Bum
- Subjects
Computer Science - Machine Learning ,Computer Science - Neural and Evolutionary Computing - Abstract
The brain prepares for learning even before interacting with the environment, by refining and optimizing its structures through spontaneous neural activity that resembles random noise. However, the mechanism of such a process has yet to be thoroughly understood, and it is unclear whether this process can benefit the algorithm of machine learning. Here, we study this issue using a neural network with a feedback alignment algorithm, demonstrating that pretraining neural networks with random noise increases the learning efficiency as well as generalization abilities without weight transport. First, we found that random noise training modifies forward weights to match backward synaptic feedback, which is necessary for teaching errors by feedback alignment. As a result, a network with pre-aligned weights learns notably faster than a network without random noise training, even reaching a convergence speed comparable to that of a backpropagation algorithm. Sequential training with both random noise and data brings weights closer to synaptic feedback than training solely with data, enabling more precise credit assignment and faster learning. We also found that each readout probability approaches the chance level and that the effective dimensionality of weights decreases in a network pretrained with random noise. This pre-regularization allows the network to learn simple solutions of a low rank, reducing the generalization loss during subsequent training. This also enables the network robustly to generalize a novel, out-of-distribution dataset. Lastly, we confirmed that random noise pretraining reduces the amount of meta-loss, enhancing the network ability to adapt to various tasks. Overall, our results suggest that random noise training with feedback alignment offers a straightforward yet effective method of pretraining that facilitates quick and reliable learning without weight transport.
- Published
- 2024
25. TaleMate: Exploring the use of Voice Agents for Parent-Child Joint Reading Experiences
- Author
-
Vargas-Diaz, Daniel, Kim, Jisun, Karunaratna, Sulakna, Reinhardt, Maegan, Hornburg, Caroline, Choi, Koeun, and Lee, Sang Won
- Subjects
Computer Science - Human-Computer Interaction - Abstract
Joint reading is a key activity for early learners, with caregiver-child interactions such as questioning and feedback playing an essential role in children's cognitive and linguistic development. However, for some parents, actively engaging children in storytelling can be challenging. To address this, we introduce TaleMate a platform designed to enhance shared reading by leveraging conversational agents that have been shown to support children's engagement and learning. TaleMate enables a dynamic, participatory reading experience where parents and children can choose which characters they wish to embody. Moreover, the system navigates the challenges posed by digital reading tools, such as decreased parent-child interaction, and builds upon the benefits of traditional and digital reading techniques. TaleMate offers an innovative approach to fostering early reading habits, bridging the gap between traditional joint reading practices and the digital reading landscape., Comment: 4 pages, 2 figures, CHI 2024 Workshop on Child-centred AI Design
- Published
- 2024
26. An empirical study to understand how students use ChatGPT for writing essays and how it affects their ownership
- Author
-
Jelson, Andrew and Lee, Sang Won
- Subjects
Computer Science - Human-Computer Interaction - Abstract
As large language models (LLMs) become more powerful and ubiquitous, systems like ChatGPT are increasingly used by students to help them with writing tasks. To better understand how these tools are used, we investigate how students might use an LLM for essay writing, for example, to study the queries asked to ChatGPT and the responses that ChatGPT gives. To that end, we plan to conduct a user study that will record the user writing process and present them with the opportunity to use ChatGPT as an AI assistant. This study's findings will help us understand how these tools are used and how practitioners -- such as educators and essay readers -- should consider writing education and evaluation based on essay writing., Comment: 5 pages, 2 figures, submitted and accepted to ACM CHI Workshop In2Writing in 2024
- Published
- 2024
27. Autonomous Algorithm for Training Autonomous Vehicles with Minimal Human Intervention
- Author
-
Lee, Sang-Hyun, Kwon, Daehyeok, and Seo, Seung-Woo
- Subjects
Computer Science - Robotics ,Computer Science - Machine Learning - Abstract
Reinforcement learning (RL) provides a compelling framework for enabling autonomous vehicles to continue to learn and improve diverse driving behaviors on their own. However, training real-world autonomous vehicles with current RL algorithms presents several challenges. One critical challenge, often overlooked in these algorithms, is the need to reset a driving environment between every episode. While resetting an environment after each episode is trivial in simulated settings, it demands significant human intervention in the real world. In this paper, we introduce a novel autonomous algorithm that allows off-the-shelf RL algorithms to train an autonomous vehicle with minimal human intervention. Our algorithm takes into account the learning progress of the autonomous vehicle to determine when to abort episodes before it enters unsafe states and where to reset it for subsequent episodes in order to gather informative transitions. The learning progress is estimated based on the novelty of both current and future states. We also take advantage of rule-based autonomous driving algorithms to safely reset an autonomous vehicle to an initial state. We evaluate our algorithm against baselines on diverse urban driving tasks. The experimental results show that our algorithm is task-agnostic and achieves better driving performance with fewer manual resets than baselines., Comment: 8 pages, 6 figures, 2 tables, conference
- Published
- 2024
28. Generating A Crowdsourced Conversation Dataset to Combat Cybergrooming
- Author
-
Zhang, Xinyi, Wisniewski, Pamela J., Cho, Jin-hee, Huang, Lifu, and Lee, Sang Won
- Subjects
Computer Science - Human-Computer Interaction - Abstract
Cybergrooming emerges as a growing threat to adolescent safety and mental health. One way to combat cybergrooming is to leverage predictive artificial intelligence (AI) to detect predatory behaviors in social media. However, these methods can encounter challenges like false positives and negative implications such as privacy concerns. Another complementary strategy involves using generative artificial intelligence to empower adolescents by educating them about predatory behaviors. To this end, we envision developing state-of-the-art conversational agents to simulate the conversations between adolescents and predators for educational purposes. Yet, one key challenge is the lack of a dataset to train such conversational agents. In this position paper, we present our motivation for empowering adolescents to cope with cybergrooming. We propose to develop large-scale, authentic datasets through an online survey targeting adolescents and parents. We discuss some initial background behind our motivation and proposed design of the survey, such as situating the participants in artificial cybergrooming scenarios, then allowing participants to respond to the survey to obtain their authentic responses. We also present several open questions related to our proposed approach and hope to discuss them with the workshop attendees.
- Published
- 2024
29. Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attention
- Author
-
Nam, Ju-Hyeon, Syazwany, Nur Suriza, Kim, Su Jung, and Lee, Sang-Chul
- Subjects
Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
Generalizability in deep neural networks plays a pivotal role in medical image segmentation. However, deep learning-based medical image analyses tend to overlook the importance of frequency variance, which is critical element for achieving a model that is both modality-agnostic and domain-generalizable. Additionally, various models fail to account for the potential information loss that can arise from multi-task learning under deep supervision, a factor that can impair the model representation ability. To address these challenges, we propose a Modality-agnostic Domain Generalizable Network (MADGNet) for medical image segmentation, which comprises two key components: a Multi-Frequency in Multi-Scale Attention (MFMSA) block and Ensemble Sub-Decoding Module (E-SDM). The MFMSA block refines the process of spatial feature extraction, particularly in capturing boundary features, by incorporating multi-frequency and multi-scale features, thereby offering informative cues for tissue outline and anatomical structures. Moreover, we propose E-SDM to mitigate information loss in multi-task learning with deep supervision, especially during substantial upsampling from low resolution. We evaluate the segmentation performance of MADGNet across six modalities and fifteen datasets. Through extensive experiments, we demonstrate that MADGNet consistently outperforms state-of-the-art models across various modalities, showcasing superior segmentation performance. This affirms MADGNet as a robust solution for medical image segmentation that excels in diverse imaging scenarios. Our MADGNet code is available in GitHub Link., Comment: Accepted in Computer Vision and Pattern Recognition (CVPR) 2024
- Published
- 2024
30. Model Predictive Guidance for Fuel-Optimal Landing of Reusable Launch Vehicles
- Author
-
Jung, Ki-Wook, Lee, Sang-Don, Jung, Cheol-Goo, and Lee, Chang-Hun
- Subjects
Electrical Engineering and Systems Science - Systems and Control - Abstract
This paper introduces a landing guidance strategy for reusable launch vehicles (RLVs) using a model predictive approach based on sequential convex programming (SCP). The proposed approach devises two distinct optimal control problems (OCPs): planning a fuel-optimal landing trajectory that accommodates practical path constraints specific to RLVs, and determining real-time optimal tracking commands. This dual optimization strategy allows for reduced computational load through adjustable prediction horizon lengths in the tracking task, achieving near closed-loop performance. Enhancements in model fidelity for the tracking task are achieved through an alternative rotational dynamics representation, enabling a more stable numerical solution of the OCP and accounting for vehicle transient dynamics. Furthermore, modifications of aerodynamic force in both planning and tracking phases are proposed, tailored for thrust-vector-controlled RLVs, to reduce the fidelity gap without adding computational complexity. Extensive 6-DOF simulation experiments validate the effectiveness and improved guidance performance of the proposed algorithm.
- Published
- 2024
31. Controlling 4f antiferromagnetic dynamics via itinerant electronic susceptibility
- Author
-
Lee, Sang-Eun, Windsor, Yoav William, Zahn, Daniela, Kraiker, Alexej, Kummer, Kurt, Kliemt, Kristin, Krellner, Cornelius, Schüßler-Langeheine, Christian, Pontius, Niko, Staub, Urs, Vyalikh, Denis V., Ernst, Arthur, and Rettig, Laurenz
- Subjects
Condensed Matter - Materials Science ,Condensed Matter - Strongly Correlated Electrons - Abstract
Optical manipulation of magnetism holds promise for future ultrafast spintronics, especially with lanthanides and their huge, localized 4f magnetic moments. These moments interact indirectly via the conduction electrons (RKKY exchange), influenced by interatomic orbital overlap, and the conduction electron susceptibility. Here, we study this influence in a series of 4f antiferromagnets, GdT2Si2 (T=Co, Rh, Ir), using ultrafast resonant X-ray diffraction. We observe a twofold increase in ultrafast angular momentum transfer between the materials, originating from modifications in the conduction electron susceptibility, as confirmed by first-principles calculations.
- Published
- 2024
32. Aligning Language Models to Explicitly Handle Ambiguity
- Author
-
Kim, Hyuhng Joon, Kim, Youna, Park, Cheonbok, Kim, Junyeob, Park, Choonghyun, Yoo, Kang Min, Lee, Sang-goo, and Kim, Taeuk
- Subjects
Computer Science - Computation and Language - Abstract
In interactions between users and language model agents, user utterances frequently exhibit ellipsis (omission of words or phrases) or imprecision (lack of exactness) to prioritize efficiency. This can lead to varying interpretations of the same input based on different assumptions or background knowledge. It is thus crucial for agents to adeptly handle the inherent ambiguity in queries to ensure reliability. However, even state-of-the-art large language models (LLMs) still face challenges in such scenarios, primarily due to the following hurdles: (1) LLMs are not explicitly trained to deal with ambiguous utterances; (2) the degree of ambiguity perceived by the LLMs may vary depending on the possessed knowledge. To address these issues, we propose Alignment with Perceived Ambiguity (APA), a novel pipeline that aligns LLMs to manage ambiguous queries by leveraging their own assessment of ambiguity (i.e., perceived ambiguity). Experimental results on question-answering datasets demonstrate that APA empowers LLMs to explicitly detect and manage ambiguous queries while retaining the ability to answer clear questions. Furthermore, our finding proves that APA excels beyond training with gold-standard labels, especially in out-of-distribution scenarios.
- Published
- 2024
33. Decomposition of Longitudinal Disparities: an Application to the Fetal Growth-Singletons Study
- Author
-
Lee, Sang Kyu, Kim, Seonjin, Kim, Mi-Ok, Grantz, Katherine L., and Hong, Hyokyoung G.
- Subjects
Statistics - Applications ,Statistics - Methodology ,62 - Abstract
Addressing health disparities among different demographic groups is a key challenge in public health. Despite many efforts, there is still a gap in understanding how these disparities unfold over time. Our paper focuses on this overlooked longitudinal aspect, which is crucial in both clinical and public health settings. In this paper, we introduce a longitudinal disparity decomposition method that decomposes disparities into three components: the explained disparity linked to differences in the exploratory variables' conditional distribution when the modifier distribution is identical between majority and minority groups, the explained disparity that emerges specifically from the unequal distribution of the modifier and its interaction with covariates, and the unexplained disparity. The proposed method offers a dynamic alternative to the traditional Peters-Belson decomposition approach, tackling both the potential reduction in disparity if the covariate distributions of minority groups matched those of the majority group and the evolving nature of disparity over time. We apply the proposed approach to a fetal growth study to gain insights into disparities between different race/ethnicity groups in fetal developmental progress throughout the course of pregnancy., Comment: 21 pages
- Published
- 2024
34. SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMs
- Author
-
Kim, Jaehyung, Nam, Jaehyun, Mo, Sangwoo, Park, Jongjin, Lee, Sang-Woo, Seo, Minjoon, Ha, Jung-Woo, and Shin, Jinwoo
- Subjects
Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
Large language models (LLMs) have made significant advancements in various natural language processing tasks, including question answering (QA) tasks. While incorporating new information with the retrieval of relevant passages is a promising way to improve QA with LLMs, the existing methods often require additional fine-tuning which becomes infeasible with recent LLMs. Augmenting retrieved passages via prompting has the potential to address this limitation, but this direction has been limitedly explored. To this end, we design a simple yet effective framework to enhance open-domain QA (ODQA) with LLMs, based on the summarized retrieval (SuRe). SuRe helps LLMs predict more accurate answers for a given question, which are well-supported by the summarized retrieval that could be viewed as an explicit rationale extracted from the retrieved passages. Specifically, SuRe first constructs summaries of the retrieved passages for each of the multiple answer candidates. Then, SuRe confirms the most plausible answer from the candidate set by evaluating the validity and ranking of the generated summaries. Experimental results on diverse ODQA benchmarks demonstrate the superiority of SuRe, with improvements of up to 4.6% in exact match (EM) and 4.0% in F1 score over standard prompting approaches. SuRe also can be integrated with a broad range of retrieval methods and LLMs. Finally, the generated summaries from SuRe show additional advantages to measure the importance of retrieved passages and serve as more preferred rationales by models and humans., Comment: Accepted at ICLR 2024
- Published
- 2024
35. A Multi-wavelength Study on A Gamma-ray Bright AGN 1308+326 Using KVN at 22 and 43 GHz
- Author
-
Li, Shan, Lee, Sang-Sung, and Cheong, Whee Yeon
- Subjects
Astrophysics - High Energy Astrophysical Phenomena ,Astrophysics - Astrophysics of Galaxies - Abstract
In this paper, we conduct a multi-frequency analysis of the gamma-ray bright blazar 1308+326 from February 2013 to March 2020, using the Korean VLBI Network at 22 and 43 GHz and gamma-ray data from the Fermi Large Area Telescope (LAT). Our findings reveal spectral variations around the 2014 gamma-ray flare, aligning with the shock-in-jet model. A strong correlation is observed between gamma-ray and 43 GHz emissions, with a 27-day lag in the VLBI core light curve, indicating a 50-day delay from the beginning of a specific radio flare to the gamma-ray peak. This radio flare correlates with a new jet component, suggesting the 2014 gamma-ray flare resulted from its interaction with a stationary component. Our analysis indicates the 2014 gamma-ray flare originated 40-63 parsecs from the central engine, with seed photons for the gamma-ray emission unlikely from the broad-line region., Comment: 16 pages, 7 figures, Accepted for publication in JKAS
- Published
- 2024
36. Machine Learning-Aided Cooperative Localization under Dense Urban Environment
- Author
-
Lee, Hoon, Kim, Hong Ki, Oh, Seung Hyun, and Lee, Sang Hyun
- Subjects
Computer Science - Information Theory ,Electrical Engineering and Systems Science - Signal Processing - Abstract
Future wireless network technology provides automobiles with the connectivity feature to consolidate the concept of vehicular networks that collaborate on conducting cooperative driving tasks. The full potential of connected vehicles, which promises road safety and quality driving experience, can be leveraged if machine learning models guarantee the robustness in performing core functions including localization and controls. Location awareness, in particular, lends itself to the deployment of location-specific services and the improvement of the operation performance. The localization entails direct communication to the network infrastructure, and the resulting centralized positioning solutions readily become intractable as the network scales up. As an alternative to the centralized solutions, this article addresses decentralized principle of vehicular localization reinforced by machine learning techniques in dense urban environments with frequent inaccessibility to reliable measurement. As such, the collaboration of multiple vehicles enhances the positioning performance of machine learning approaches. A virtual testbed is developed to validate this machine learning model for real-map vehicular networks. Numerical results demonstrate universal feasibility of cooperative localization, in particular, for dense urban area configurations.
- Published
- 2024
37. HyperCLOVA X Technical Report
- Author
-
Yoo, Kang Min, Han, Jaegeun, In, Sookyo, Jeon, Heewon, Jeong, Jisu, Kang, Jaewook, Kim, Hyunwook, Kim, Kyung-Min, Kim, Munhyong, Kim, Sungju, Kwak, Donghyun, Kwak, Hanock, Kwon, Se Jung, Lee, Bado, Lee, Dongsoo, Lee, Gichang, Lee, Jooho, Park, Baeseong, Shin, Seongjin, Yu, Joonsang, Baek, Seolki, Byeon, Sumin, Cho, Eungsup, Choe, Dooseok, Han, Jeesung, Jin, Youngkyun, Jun, Hyein, Jung, Jaeseung, Kim, Chanwoong, Kim, Jinhong, Kim, Jinuk, Lee, Dokyeong, Park, Dongwook, Sohn, Jeong Min, Han, Sujung, Heo, Jiae, Hong, Sungju, Jeon, Mina, Jung, Hyunhoon, Jung, Jungeun, Jung, Wangkyo, Kim, Chungjoon, Kim, Hyeri, Kim, Jonghyun, Kim, Min Young, Lee, Soeun, Park, Joonhee, Shin, Jieun, Yang, Sojin, Yoon, Jungsoon, Lee, Hwaran, Bae, Sanghwan, Cha, Jeehwan, Gylleus, Karl, Ham, Donghoon, Hong, Mihak, Hong, Youngki, Hong, Yunki, Jang, Dahyun, Jeon, Hyojun, Jeon, Yujin, Jeong, Yeji, Ji, Myunggeun, Jin, Yeguk, Jo, Chansong, Joo, Shinyoung, Jung, Seunghwan, Kim, Adrian Jungmyung, Kim, Byoung Hoon, Kim, Hyomin, Kim, Jungwhan, Kim, Minkyoung, Kim, Minseung, Kim, Sungdong, Kim, Yonghee, Kim, Youngjun, Kim, Youngkwan, Ko, Donghyeon, Lee, Dughyun, Lee, Ha Young, Lee, Jaehong, Lee, Jieun, Lee, Jonghyun, Lee, Jongjin, Lee, Min Young, Lee, Yehbin, Min, Taehong, Min, Yuri, Moon, Kiyoon, Oh, Hyangnam, Park, Jaesun, Park, Kyuyon, Park, Younghun, Seo, Hanbae, Seo, Seunghyun, Sim, Mihyun, Son, Gyubin, Yeo, Matt, Yeom, Kyung Hoon, Yoo, Wonjoon, You, Myungin, Ahn, Doheon, Ahn, Homin, Ahn, Joohee, Ahn, Seongmin, An, Chanwoo, An, Hyeryun, An, Junho, An, Sang-Min, Byun, Boram, Byun, Eunbin, Cha, Jongho, Chang, Minji, Chang, Seunggyu, Cho, Haesong, Cho, Youngdo, Choi, Dalnim, Choi, Daseul, Choi, Hyoseok, Choi, Minseong, Choi, Sangho, Choi, Seongjae, Choi, Wooyong, Chun, Sewhan, Go, Dong Young, Ham, Chiheon, Han, Danbi, Han, Jaemin, Hong, Moonyoung, Hong, Sung Bum, Hwang, Dong-Hyun, Hwang, Seongchan, Im, Jinbae, Jang, Hyuk Jin, Jang, Jaehyung, Jang, Jaeni, Jang, Sihyeon, Jang, Sungwon, Jeon, Joonha, Jeong, Daun, Jeong, Joonhyun, Jeong, Kyeongseok, Jeong, Mini, Jin, Sol, Jo, Hanbyeol, Jo, Hanju, Jo, Minjung, Jung, Chaeyoon, Jung, Hyungsik, Jung, Jaeuk, Jung, Ju Hwan, Jung, Kwangsun, Jung, Seungjae, Ka, Soonwon, Kang, Donghan, Kang, Soyoung, Kil, Taeho, Kim, Areum, Kim, Beomyoung, Kim, Byeongwook, Kim, Daehee, Kim, Dong-Gyun, Kim, Donggook, Kim, Donghyun, Kim, Euna, Kim, Eunchul, Kim, Geewook, Kim, Gyu Ri, Kim, Hanbyul, Kim, Heesu, Kim, Isaac, Kim, Jeonghoon, Kim, Jihye, Kim, Joonghoon, Kim, Minjae, Kim, Minsub, Kim, Pil Hwan, Kim, Sammy, Kim, Seokhun, Kim, Seonghyeon, Kim, Soojin, Kim, Soong, Kim, Soyoon, Kim, Sunyoung, Kim, Taeho, Kim, Wonho, Kim, Yoonsik, Kim, You Jin, Kim, Yuri, Kwon, Beomseok, Kwon, Ohsung, Kwon, Yoo-Hwan, Lee, Anna, Lee, Byungwook, Lee, Changho, Lee, Daun, Lee, Dongjae, Lee, Ha-Ram, Lee, Hodong, Lee, Hwiyeong, Lee, Hyunmi, Lee, Injae, Lee, Jaeung, Lee, Jeongsang, Lee, Jisoo, Lee, Jongsoo, Lee, Joongjae, Lee, Juhan, Lee, Jung Hyun, Lee, Junghoon, Lee, Junwoo, Lee, Se Yun, Lee, Sujin, Lee, Sungjae, Lee, Sungwoo, Lee, Wonjae, Lee, Zoo Hyun, Lim, Jong Kun, Lim, Kun, Lim, Taemin, Na, Nuri, Nam, Jeongyeon, Nam, Kyeong-Min, Noh, Yeonseog, Oh, Biro, Oh, Jung-Sik, Oh, Solgil, Oh, Yeontaek, Park, Boyoun, Park, Cheonbok, Park, Dongju, Park, Hyeonjin, Park, Hyun Tae, Park, Hyunjung, Park, Jihye, Park, Jooseok, Park, Junghwan, Park, Jungsoo, Park, Miru, Park, Sang Hee, Park, Seunghyun, Park, Soyoung, Park, Taerim, Park, Wonkyeong, Ryu, Hyunjoon, Ryu, Jeonghun, Ryu, Nahyeon, Seo, Soonshin, Seo, Suk Min, Shim, Yoonjeong, Shin, Kyuyong, Shin, Wonkwang, Sim, Hyun, Sim, Woongseob, Soh, Hyejin, Son, Bokyong, Son, Hyunjun, Son, Seulah, Song, Chi-Yun, Song, Chiyoung, Song, Ka Yeon, Song, Minchul, Song, Seungmin, Wang, Jisung, Yeo, Yonggoo, Yi, Myeong Yeon, Yim, Moon Bin, Yoo, Taehwan, Yoo, Youngjoon, Yoon, Sungmin, Yoon, Young Jin, Yu, Hangyeol, Yu, Ui Seon, Zuo, Xingdong, Bae, Jeongin, Bae, Joungeun, Cho, Hyunsoo, Cho, Seonghyun, Cho, Yongjin, Choi, Taekyoon, Choi, Yera, Chung, Jiwan, Han, Zhenghui, Heo, Byeongho, Hong, Euisuk, Hwang, Taebaek, Im, Seonyeol, Jegal, Sumin, Jeon, Sumin, Jeong, Yelim, Jeong, Yonghyun, Jiang, Can, Jiang, Juyong, Jin, Jiho, Jo, Ara, Jo, Younghyun, Jung, Hoyoun, Jung, Juyoung, Kang, Seunghyeong, Kim, Dae Hee, Kim, Ginam, Kim, Hangyeol, Kim, Heeseung, Kim, Hyojin, Kim, Hyojun, Kim, Hyun-Ah, Kim, Jeehye, Kim, Jin-Hwa, Kim, Jiseon, Kim, Jonghak, Kim, Jung Yoon, Kim, Rak Yeong, Kim, Seongjin, Kim, Seoyoon, Kim, Sewon, Kim, Sooyoung, Kim, Sukyoung, Kim, Taeyong, Ko, Naeun, Koo, Bonseung, Kwak, Heeyoung, Kwon, Haena, Kwon, Youngjin, Lee, Boram, Lee, Bruce W., Lee, Dagyeong, Lee, Erin, Lee, Euijin, Lee, Ha Gyeong, Lee, Hyojin, Lee, Hyunjeong, Lee, Jeeyoon, Lee, Jeonghyun, Lee, Jongheok, Lee, Joonhyung, Lee, Junhyuk, Lee, Mingu, Lee, Nayeon, Lee, Sangkyu, Lee, Se Young, Lee, Seulgi, Lee, Seung Jin, Lee, Suhyeon, Lee, Yeonjae, Lee, Yesol, Lee, Youngbeom, Lee, Yujin, Li, Shaodong, Liu, Tianyu, Moon, Seong-Eun, Moon, Taehong, Nihlenramstroem, Max-Lasse, Oh, Wonseok, Oh, Yuri, Park, Hongbeen, Park, Hyekyung, Park, Jaeho, Park, Nohil, Park, Sangjin, Ryu, Jiwon, Ryu, Miru, Ryu, Simo, Seo, Ahreum, Seo, Hee, Seo, Kangdeok, Shin, Jamin, Shin, Seungyoun, Sin, Heetae, Wang, Jiangping, Wang, Lei, Xiang, Ning, Xiao, Longxiang, Xu, Jing, Yi, Seonyeong, Yoo, Haanju, Yoo, Haneul, Yoo, Hwanhee, Yu, Liang, Yu, Youngjae, Yuan, Weijie, Zeng, Bo, Zhou, Qian, Cho, Kyunghyun, Ha, Jung-Woo, Park, Joonsuk, Hwang, Jihyun, Kwon, Hyoung Jo, Kwon, Soonyong, Lee, Jungyeon, Lee, Seungho, Lim, Seonghyeon, Noh, Hyunkyung, Choi, Seungho, Lee, Sang-Woo, Lim, Jung Hwa, and Sung, Nako
- Subjects
Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. HyperCLOVA X was trained on a balanced mix of Korean, English, and code data, followed by instruction-tuning with high-quality human-annotated datasets while abiding by strict safety guidelines reflecting our commitment to responsible AI. The model is evaluated across various benchmarks, including comprehensive reasoning, knowledge, commonsense, factuality, coding, math, chatting, instruction-following, and harmlessness, in both Korean and English. HyperCLOVA X exhibits strong reasoning capabilities in Korean backed by a deep understanding of the language and cultural nuances. Further analysis of the inherent bilingual nature and its extension to multilingualism highlights the model's cross-lingual proficiency and strong generalization ability to untargeted languages, including machine translation between several language pairs and cross-lingual inference tasks. We believe that HyperCLOVA X can provide helpful guidance for regions or countries in developing their sovereign LLMs., Comment: 44 pages; updated authors list and fixed author names
- Published
- 2024
38. SentiCSE: A Sentiment-aware Contrastive Sentence Embedding Framework with Sentiment-guided Textual Similarity
- Author
-
Kim, Jaemin, Na, Yohan, Kim, Kangmin, Lee, Sang Rak, and Chae, Dong-Kyu
- Subjects
Computer Science - Computation and Language ,68T50 ,I.2.7 - Abstract
Recently, sentiment-aware pre-trained language models (PLMs) demonstrate impressive results in downstream sentiment analysis tasks. However, they neglect to evaluate the quality of their constructed sentiment representations; they just focus on improving the fine-tuning performance, which overshadows the representation quality. We argue that without guaranteeing the representation quality, their downstream performance can be highly dependent on the supervision of the fine-tuning data rather than representation quality. This problem would make them difficult to foray into other sentiment-related domains, especially where labeled data is scarce. We first propose Sentiment-guided Textual Similarity (SgTS), a novel metric for evaluating the quality of sentiment representations, which is designed based on the degree of equivalence in sentiment polarity between two sentences. We then propose SentiCSE, a novel Sentiment-aware Contrastive Sentence Embedding framework for constructing sentiment representations via combined word-level and sentence-level objectives, whose quality is guaranteed by SgTS. Qualitative and quantitative comparison with the previous sentiment-aware PLMs shows the superiority of our work. Our code is available at: https://github.com/nayohan/SentiCSE, Comment: 14 pages, 8 figures
- Published
- 2024
39. Incorporating Heterogeneous Interactions for Ecological Biodiversity
- Author
-
Park, Jong Il, Lee, Deok-Sun, Lee, Sang Hoon, and Park, Hye Jin
- Subjects
Physics - Biological Physics ,Condensed Matter - Statistical Mechanics - Abstract
Understanding the behaviors of ecological systems is challenging given their multi-faceted complexity. To proceed, theoretical models such as Lotka-Volterra dynamics with random interactions have been investigated by the dynamical mean-field theory to provide insights into underlying principles such as how biodiversity and stability depend on the randomness in interaction strength. Yet the fully-connected structure assumed in these previous studies is not realistic as revealed by a vast amount of empirical data. We derive a generic formula for the abundance distribution under an arbitrary distribution of degree, the number of interacting neighbors, which leads to degree-dependent abundance patterns of species. Notably, in contrast to the well-mixed system, the number of surviving species can be reduced as the community becomes cooperative in heterogeneous interaction structures. Our study, therefore, demonstrates that properly taking into account heterogeneity in the interspecific interaction structure is indispensable to understanding the diversity in large ecosystems, and our general theoretical framework can apply to a much wider range of interacting many-body systems.
- Published
- 2024
40. Spectral Motion Alignment for Video Motion Transfer using Diffusion Models
- Author
-
Park, Geon Yeong, Jeong, Hyeonho, Lee, Sang Wan, and Ye, Jong Chul
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
The evolution of diffusion models has greatly impacted video generation and understanding. Particularly, text-to-video diffusion models (VDMs) have significantly facilitated the customization of input video with target appearance, motion, etc. Despite these advances, challenges persist in accurately distilling motion information from video frames. While existing works leverage the consecutive frame residual as the target motion vector, they inherently lack global motion context and are vulnerable to frame-wise distortions. To address this, we present Spectral Motion Alignment (SMA), a novel framework that refines and aligns motion vectors using Fourier and wavelet transforms. SMA learns motion patterns by incorporating frequency-domain regularization, facilitating the learning of whole-frame global motion dynamics, and mitigating spatial artifacts. Extensive experiments demonstrate SMA's efficacy in improving motion transfer while maintaining computational efficiency and compatibility across various video customization frameworks., Comment: Project page: https://geonyeong-park.github.io/spectral-motion-alignment/
- Published
- 2024
41. Renormalization of Complex Networks with Partition Functions
- Author
-
Jung, Sungwon, Lee, Sang Hoon, and Cho, Jaeyoon
- Subjects
Condensed Matter - Statistical Mechanics ,Physics - Physics and Society - Abstract
While renormalization groups are fundamental in physics, renormalization of complex networks remains vague in its conceptual definition and methodology. Here, we propose a novel strategy to renormalize complex networks. Rather than resorting to handling the bare structure of a network, we overlay it with a readily renormalizable physical model, which reflects real-world scenarios with a broad generality. From the renormalization of the overlying system, we extract a rigorous and simple renormalization group transformation of arbitrary networks. In this way, we obtain a transparent, model-dependent physical meaning of the network renormalization, which in our case is a scale transformation preserving the transition dynamics of low-density particles. We define the strength of a node in accordance with the physical model and trace the change of its distribution under our renormalization process. This analysis demonstrates that the strength distributions of scale-free networks remain scale-invariant, whereas those of homogeneous random networks do not., Comment: 5 pages, 4 figures
- Published
- 2024
42. False Positive Sampling-based Data Augmentation for Enhanced 3D Object Detection Accuracy
- Author
-
Oh, Jiyong, Lee, Junhaeng, Byun, Woongchan, Kong, Minsang, and Lee, Sang Hun
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
Recent studies have focused on enhancing the performance of 3D object detection models. Among various approaches, ground-truth sampling has been proposed as an augmentation technique to address the challenges posed by limited ground-truth data. However, an inherent issue with ground-truth sampling is its tendency to increase false positives. Therefore, this study aims to overcome the limitations of ground-truth sampling and improve the performance of 3D object detection models by developing a new augmentation technique called false-positive sampling. False-positive sampling involves retraining the model using point clouds that are identified as false positives in the model's predictions. We propose an algorithm that utilizes both ground-truth and false-positive sampling and an algorithm for building the false-positive sample database. Additionally, we analyze the principles behind the performance enhancement due to false-positive sampling. Our experiments demonstrate that models utilizing false-positive sampling show a reduction in false positives and exhibit improved object detection performance. On the KITTI and Waymo Open datasets, models with false-positive sampling surpass the baseline models by a large margin.
- Published
- 2024
43. Results and Lessons Learned from Autonomous Driving Transportation Services in Airfield, Crowded Indoor, and Urban Environments
- Author
-
Baek, Doosan, Kim, Sanghyun, Seo, Seung-Woo, and Lee, Sang-Hyun
- Subjects
Computer Science - Robotics - Abstract
Autonomous vehicles have been actively investigated over the past few decades. Several recent works show the potential of autonomous vehicles in urban environments with impressive experimental results. However, these works note that autonomous vehicles are still occasionally inferior to expert drivers in complex scenarios. Furthermore, they do not focus on the possibilities of autonomous driving transportation services in other areas beyond urban environments. This paper presents the research results and lessons learned from autonomous driving transportation services in airfield, crowded indoor, and urban environments. We discuss how we address several unique challenges in these diverse environments. We also offer an overview of remaining challenges that have not received much attention but must be addressed. This paper aims to share our unique experience to support researchers who are interested in exploring autonomous driving transportation services in various real-world environments., Comment: 8 pages, 7 figures, 4 tables
- Published
- 2024
44. Allergy Wheal and Erythema Segmentation Using Attention U-Net
- Author
-
Lee, Yul Hee, Shim, Ji-Su, Kim, Young Jae, Jeon, Ji Soo, Kang, Sung-Yoon, Lee, Sang Pyo, Lee, Sang Min, and Kim, Kwang Gi
- Published
- 2024
- Full Text
- View/download PDF
45. Rural-urban differences of sarcopenia and spinal health in the older women: A comparative observation study
- Author
-
Kim, Dong-Hyun, Park, Jinhee, Lim, Jae-Young, Chung, Sun Gun, and Lee, Sang Yoon
- Published
- 2023
46. Effect of expanded diets on growth performance, meat quality and carcass characteristics in finishing pigs
- Author
-
Lei, Xin Jian, Bae, Jun Eok, Lee, Ju Seong, Lee, Sang In, and Kim, In Ho
- Published
- 2019
- Full Text
- View/download PDF
47. The Effect of Awareness Toward Dementia on Dementia Education Program of Adolescents
- Author
-
Song, Bo-Kyoung, Kim, Ha-Na, and Lee, Sang-Hwa
- Published
- 2019
- Full Text
- View/download PDF
48. The Effect of Bilateral Upper Limb Training on Recovery of Upper Limb Function in Patients with Acute Stroke
- Author
-
Lee, Sang-Hwa, Song, Bo-Kyoung, and Kim, Ha-Na
- Published
- 2019
- Full Text
- View/download PDF
49. A Study on the Relationship between Social Support and Life Satisfaction of College Students
- Author
-
Kim, Hana, Lee, Sang Hwa, and Song, Bo-Kyoung
- Published
- 2019
- Full Text
- View/download PDF
50. Impacts of rapeseed meal, canola meal and their mixture substitute for soybean meal on performance of lactating sows and their offspring
- Author
-
Liu, Wen Chao, Zhou, Shi Hui, Kim, Yong Min, Lee, Sang In, Pang, Huan Ying, and Kim, In Ho
- Published
- 2019
- Full Text
- View/download PDF
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.