Block-wise LoRA: Revisiting Fine-grained LoRA for Effective Personalization and Stylization in Text-to-Image Generation

The objective of personalization and stylization in text-to-image is to instruct a pre-trained diffusion model to analyze new concepts introduced by users and incorporate them into expected styles. Recently, parameter-efficient fine-tuning (PEFT) approaches have been widely adopted to address this task and have greatly propelled the development of this field. Despite their popularity, existing efficient fine-tuning methods still struggle to achieve effective personalization and stylization in T2I generation. To address this issue, we propose block-wise Low-Rank Adaptation (LoRA) to perform fine-grained fine-tuning for different blocks of SD, which can generate images faithful to input prompts and target identity and also with desired style. Extensive experiments demonstrate the effectiveness of the proposed method

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Dynamic Reliability Analysis of Large-Span Structures under Crowd Bouncing Excitation

Bouncing is one of the most common human crowd activities on civil infrastructures such as sports stadiums and concert halls, where the audience tends to make their bodies jump up and down to celebrate or participate in sport and musical events. Dynamic loads are thus generated and exerted on the structures, giving unpleasant structural vibration, which may affect the functionality of the structure or even lead to a panic of the crowd. Although researchers have studied human-induced vibration from many perspectives including load models, calculation methods, criteria for serviceability evaluation, etc., there has been minimal work regarding crowd-induced reliability analysis, mainly because the stochastic feature of the crowd load as well as the mechanism describing the crowd–structure interaction is still not clear. In this paper, a framework to calculate crowd-induced structural vibration that considers the crowd–structure interaction effect is proposed and is validated through an experimental test. The dynamic parameters of the bouncing person in the crowd are adopted from a previous statistical study. The feasibility of a probability density evolution method (PDEM) is proved to be effective to calculate structural stochastic vibration under the bouncing crowd. The dynamic reliability of the structure is thus analyzed based on the stochastic responses. Results show that the consideration of the crowd–structure interaction effect significantly affects the dynamic reliability, which is also dependent on various factors including bouncing frequency, failure criteria, limit threshold, human model parameter distribution, etc. This paper provides a foundation for the performance-based vibration serviceability design of large-span structures

An approach for medical event detection in Chinese clinical notes of electronic health records

Abstract Background Medical event detection in narrative clinical notes of electronic health records (EHRs) is a task designed for reading text and extracting information. Most of the previous work of medical event detection treats the task as extracting concepts at word granularity, which omits the overall structural information of the clinical notes. In this work, we treat each clinical note as a sequence of short sentences and propose an end-to-end deep neural network framework. Methods We redefined the task as a sequence labelling task at short sentence granularity, and proposed a novel tag system correspondingly. The dataset were derived from a third-level grade-A hospital, consisting of 2000 annotated clinical notes according to our proposed tag system. The proposed end-to-end deep neural network framework consists of a feature extractor and a sequence labeller, and we explored different implementations respectively. We additionally proposed a smoothed Viterbi decoder as sequence labeller without additional parameter training, which can be a good alternative to conditional random field (CRF) when computing resources are limited. Results Our sequence labelling models were compared to four baselines which treat the task as text classification of short sentences. Experimental results showed that our approach significantly outperforms the baselines. The best result was obtained by using the convolutional neural networks (CNNs) feature extractor and the sequential CRF sequence labeller, achieving an accuracy of 92.6%. Our proposed smoothed Viterbi decoder achieved a comparable accuracy of 90.07% with reduced training parameters, and brought more balanced performance across all categories, which means better generalization ability. Conclusions Evaluated on our annotated dataset, the comparison results demonstrated the effectiveness of our approach for medical event detection in Chinese clinical notes of EHRs. The best feature extractor is the CNNs feature extractor, and the best sequence labeller is the sequential CRF decoder. And it was empirically verified that our proposed smoothed Viterbi decoder could bring better generalization ability while achieving comparable performance to the sequential CRF decoder

Comparative Transcriptomics Analysis Reveals Rusty Grain Beetle’s Aggregation Pheromone Biosynthesis Mechanism in Response to Starvation

Pheromones are the basis of insect aggregation, mating, and other behaviors. Cucujoid grain beetles produce macrocyclic lactones as aggregation pheromones, yet research on their biosynthesis at the molecular level remains limited. The rusty grain beetle, C. ferrugineus, is an important economic species in China. Although two aggregation pheromone components have been identified, their suspected biosynthesis via the MVA pathway and the FAS pathway lacks molecular elucidation. Previous evidence supports that starvation affects the production of aggregation pheromones. Therefore, we constructed comparative transcriptome libraries of pheromone production sites in C. ferrugineus under starvation stress and identified genes related to pheromone biosynthesis and hormone regulation. A total of 2665 genes were significantly differentially expressed, of which 2029 genes were down-regulated in starved beetles. Putative C. ferrugineus genes directly involved in pheromone biosynthesis were identified, as well as some genes related to the juvenile hormone (JH) pathway and the insulin pathway, both of which were depressed in the starved beetles, suggesting possible functions in pheromone biosynthesis and regulation. The identification of genes involved in macrolide lactone biosynthesis in vivo holds great significance, aiding in the elucidation of the synthesis and regulatory mechanisms of cucujoid grain beetle pheromones