SAVE: Protagonist Diversification with Structure Agnostic Video Editing

Driven by the upsurge progress in text-to-image (T2I) generation models, text-to-video (T2V) generation has experienced a significant advance as well. Accordingly, tasks such as modifying the object or changing the style in a video have been possible. However, previous works usually work well on trivial and consistent shapes, and easily collapse on a difficult target that has a largely different body shape from the original one. In this paper, we spot the bias problem in the existing video editing method that restricts the range of choices for the new protagonist and attempt to address this issue using the conventional image-level personalization method. We adopt motion personalization that isolates the motion from a single source video and then modifies the protagonist accordingly. To deal with the natural discrepancy between image and video, we propose a motion word with an inflated textual embedding to properly represent the motion in a source video. We also regulate the motion word to attend to proper motion-related areas by introducing a novel pseudo optical flow, efficiently computed from the pre-calculated attention maps. Finally, we decouple the motion from the appearance of the source video with an additional pseudo word. Extensive experiments demonstrate the editing capability of our method, taking a step toward more diverse and extensive video editing.Comment: Project website: https://ldynx.github.io/SAVE

Traveltime calculations from frequency-domain downward-continuation algorithms

We present a new, fast 3D traveltime calculation algorithm that employs existing frequency-domain waveequation downward-continuation software. By modifying such software to solve for a few complex (rather than real) frequencies, we are able to calculate not only the first arrival and the approximately most energetic traveltimes at each depth point but also their corresponding amplitudes.We compute traveltimes by either taking the logarithm of displacements obtained by the oneway wave equation at a frequency or calculating derivatives of displacements numerically. Amplitudes are estimated from absolute value of the displacement at a frequency. By using the one-way downgoing wave equation, we also circumvent generating traveltimes corresponding to near-surface upcoming head waves not often needed in migration.We compare the traveltimes computed by our algorithm with those obtained by picking the most energetic arrivals from finite-difference solutions of the one-way wave equation, and show that our traveltime calculation method yields traveltimes comparable to solutions of the one-way wave equation. We illustrate the accuracy of our traveltime algorithm by migrating the 2D IFP Marmousi and the 3D SEG/EAGE salt models.This work was financially supported by National Laboratory Project of Ministry of Science and Technology, Brain Korea 21 project of theKorea Ministry of Education, and grant No. R03- 2000-000-00003-0 from the Basic Research Program of the Korea Science & Engineering Foundation

Traveltime and amplitude calculation using a perturbation approach

Accurate amplitudes and correct traveltimes are critical factors that govern the quality of prestack migration images. Because we never know the correct velocity initially, recomputing traveltimes and amplitudes of updated velocity models can dominate the iterative prestack migration procedure. Most tomographic velocity updating techniques require the calculation of the change of traveltime due to local changes in velocity. For such locally updated velocity models, perturbation techniques can be a significantly more economic way of calculating traveltimes and amplitudes than recalculating the entire solutions from scratch. In this paper, we implement an iterative Born perturbation theory applied to the damped wave equation algorithm. Our iterative Born perturbation algorithm yields stable solutions for models having velocity contrasts of 30% about the initial velocity estimate, which is significantly more economic than recalculating the entire solution.This work was financially supported by National Research Laboratory Project of the Korea Ministry of Science and Technology, Brain Korea 21 project of the Korea Ministry of Education, grant No. R05-2000-00003 from the Basic Research Program of the Korea Science&Engineering Foundation, and grant No. PM10300 from Korea Ocean Research & Development Institute

NICE 2023 Zero-shot Image Captioning Challenge

In this report, we introduce NICE project\footnote{\url{https://nice.lgresearch.ai/}} and share the results and outcomes of NICE challenge 2023. This project is designed to challenge the computer vision community to develop robust image captioning models that advance the state-of-the-art both in terms of accuracy and fairness. Through the challenge, the image captioning models were tested using a new evaluation dataset that includes a large variety of visual concepts from many domains. There was no specific training data provided for the challenge, and therefore the challenge entries were required to adapt to new types of image descriptions that had not been seen during training. This report includes information on the newly proposed NICE dataset, evaluation methods, challenge results, and technical details of top-ranking entries. We expect that the outcomes of the challenge will contribute to the improvement of AI models on various vision-language tasks.Comment: Tech report, project page https://nice.lgresearch.ai

Optimization of an Empirical Model for Microorganism-Immobilized Media to Predict Nitrogen Removal Efficiency

The purpose of this study was to develop a model to predict the total nitrogen (T-N) concentration in treated wastewater effluent when microorganism-immobilized media are applied. The operational data for this study were obtained using synthetic wastewater and actual wastewater within a lab-scale reactor. The organic matter removal, nitrification, and denitrification rates were 81.8, 87, and 82.9%, respectively. These rates adequately satisfied the effluent water quality standard. The observed parameters from the lab-scale reactor operation were applied to develop the optimization model, and the model showed correlation coefficients as 0.9785 and 0.9811 for nitrification and denitrification efficiencies, respectively. The model predicted that T-N concentration could be reduced to <10 mg/L with the injection of the external carbon source. The predicted value for the T-N concentration was higher than the observed value from the lab-scale reactor, which operated under the same conditions. The model showed comparable values to the observed data, and the model seems to be useful for predicting related parameters in effluent water quality, with further development of the specifications required in the treatment facilities under various operating conditions

Developing strain-hardening ultra-rapid-hardening mortar containing high-volume supplementary cementitious materials and polyethylene fibers

This study aims to develop a robust strain-hardening ultra-rapid-hardening mortar (URHM) with high-volume cementitious materials and polyethylene (PE) fibers. To achieve this, the combined effect of cement kiln dust (CKD) and silica fume (SF) on the initial hydration process of ultra-rapid-hardening cement and the tensile performance of URHM was analyzed. Optimum amounts of CKD and SF of 0.15 and 0.2, respectively, by weight ratios to cement, were determined to develop the strain-hardening URHM containing 2% PE fibers. As a result, the tensile strength of 7.3 MPa, strain capacity of 5.12%, and energy absorption capacity prior to tension softening of 297.5 kJ/m3, respectively, were achieved at a very early age (4 h) of air-drying curing. The tensile performance of URHM deteriorated when the CKD content was 0.4 or greater, regardless of the SF content. A lower SF content of 0.2 was effective in terms of the tensile performance enhancement compared with the higher content of 0.4 up to the CKD content of 0.2, but they became similarly lower at higher CKD contents due to insufficient initial hydration

Harmonizing Visual and Textual Embeddings for Zero-Shot Text-to-Image Customization

In a surge of text-to-image (T2I) models and their customization methods that generate new images of a user-provided subject, current works focus on alleviating the costs incurred by a lengthy per-subject optimization. These zero-shot customization methods encode the image of a specified subject into a visual embedding which is then utilized alongside the textual embedding for diffusion guidance. The visual embedding incorporates intrinsic information about the subject, while the textual embedding provides a new, transient context. However, the existing methods often 1) are significantly affected by the input images, eg., generating images with the same pose, and 2) exhibit deterioration in the subject's identity. We first pin down the problem and show that redundant pose information in the visual embedding interferes with the textual embedding containing the desired pose information. To address this issue, we propose orthogonal visual embedding which effectively harmonizes with the given textual embedding. We also adopt the visual-only embedding and inject the subject's clear features utilizing a self-attention swap. Our results demonstrate the effectiveness and robustness of our method, which offers highly flexible zero-shot generation while effectively maintaining the subject's identity.Comment: Project page: https://ldynx.github.io/harmony-zero-t2i

Vaccinia-related kinase 2 modulates role of dysbindin by regulating protein stability

Vaccinia-related kinase 2 (VRK2) is a serine/threonine kinase that belongs to the casein kinase 1 family. VRK2 has long been known for its relationship with to neurodegenerative disorders such as schizophrenia. However, the role of VRK2 and the substrates associated with it are unknown. Dysbindin is known as one of the strong risk factors for schizophrenia. The expression of dysbindin is indeed significantly reduced in schizophrenia patients. Moreover, dysbindin is involved in neurite outgrowth and regulation of N-methyl-D-aspartate receptor (NMDAR) signaling. Here, we first identified dysbindin as a novel interacting protein of VRK2 through immunoprecipitation. We hypothesized that dysbindin is phosphorylated by VRK2 and further that this phosphorylation plays an important role in the function of dysbindin. We show that VRK2 phosphorylates Ser 297 and Ser 299 of dysbindin using in vitro kinase assay. In addition, We found that VRK2-mediated phosphorylation of dysbindin enhanced ubiquitination of dysbindin and consequently resulted in the decrease of its protein stability through Western blotting. Overexpression of VRK2 in human neuroblastoma (SH-SY5Y) cells reduced neurite outgrowth induced by retinoic acid. Furthermore, a phosphomimetic mutant of dysbindin alleviated neurite outgrowth and affected surface expression of N-methyl D-aspartate 2A (NR2A), a subunit of NMDAR in mouse hippocampal neurons. Together, our work reveals the regulation of dysbindin by VRK2, providing the association of between these two proteins, which are commonly implicated in schizophrenia.1

Sedimentation and Rheological Study of Microalgal Cell (Chlorella sp. HS2) Suspension

Microalgae (Chlorella sp. HS2) have a high potential as a new biomass filler resource. Microalgae suspension is investigated depending on pH condition, focusing on microscopic sedimentation and a rheological behavior in order to understand in-depth the behavior of Chlorella sp. HS2 for harvesting process design. In terms of sedimentation analysis, it is found that Chlorella sp. HS2 cells settle down due to high density of 1.56 gcm(-3). Meanwhile due to its small size and dilute concentration, the settling velocity is too slow for harvesting by natural sedimentation. Chlorella sp. HS2 cells undergo weak aggregation in the medium depending on pH condition. When the Chlorella sp. HS2 suspension (pH 5.4) is adjusted at pH 2.5, the surfaces of the microalgal cells turn neutral and cells are aggregated by van der Waals force between cells, leading to relatively faster sedimentation compared to Chlorella sp. HS2 cells without pH adjustment. The aggregation of Chlorella sp. HS2 cells depending on pH condition is reflected in rheological properties of the suspension. At pH 2.5, shear viscosity of the Chlorella sp. HS2 suspension increases and the suspension shows shear thinning behavior, meaning that the neutralized surface of Chlorella sp. HS2 makes cells aggregation. However, the aggregation of microalgal HS2 cells is easily dissociated and aligned along shear flow. Therefore, for the successful harvesting of biomass Chlorella sp. HS2, the flow and colloidal condition must be considered along with coagulation for rapid harvesting of cells.N