SelFormaly: Towards Task-Agnostic Unified Anomaly Detection

The core idea of visual anomaly detection is to learn the normality from normal images, but previous works have been developed specifically for certain tasks, leading to fragmentation among various tasks: defect detection, semantic anomaly detection, multi-class anomaly detection, and anomaly clustering. This one-task-one-model approach is resource-intensive and incurs high maintenance costs as the number of tasks increases. This paper presents SelFormaly, a universal and powerful anomaly detection framework. We emphasize the necessity of our off-the-shelf approach by pointing out a suboptimal issue with fluctuating performance in previous online encoder-based methods. In addition, we question the effectiveness of using ConvNets as previously employed in the literature and confirm that self-supervised ViTs are suitable for unified anomaly detection. We introduce back-patch masking and discover the new role of top k-ratio feature matching to achieve unified and powerful anomaly detection. Back-patch masking eliminates irrelevant regions that possibly hinder target-centric detection with representations of the scene layout. The top k-ratio feature matching unifies various anomaly levels and tasks. Finally, SelFormaly achieves state-of-the-art results across various datasets for all the aforementioned tasks.Comment: 11 pages, 7 figure

Carpe Diem: On the Evaluation of World Knowledge in Lifelong Language Models

In an ever-evolving world, the dynamic nature of knowledge presents challenges for language models that are trained on static data, leading to outdated encoded information. However, real-world scenarios require models not only to acquire new knowledge but also to overwrite outdated information into updated ones. To address this under-explored issue, we introduce the temporally evolving question answering benchmark, EvolvingQA - a novel benchmark designed for training and evaluating LMs on an evolving Wikipedia database, where the construction of our benchmark is automated with our pipeline using large language models. Our benchmark incorporates question-answering as a downstream task to emulate real-world applications. Through EvolvingQA, we uncover that existing continual learning baselines have difficulty in updating and forgetting outdated knowledge. Our findings suggest that the models fail to learn updated knowledge due to the small weight gradient. Furthermore, we elucidate that the models struggle mostly on providing numerical or temporal answers to questions asking for updated knowledge. Our work aims to model the dynamic nature of real-world information, offering a robust measure for the evolution-adaptability of language models.Comment: 14 pages, 5 figures, 5 tables; accepted at NeurIPS Syntheticdata4ML workshop, 202

Pulse shape discrimination in an organic scintillation phoswich detector using machine learning techniques

We developed machine learning algorithms for distinguishing scintillation signals from a plastic-liquid coupled detector known as a phoswich. The challenge lies in discriminating signals from organic scintillators with similar shapes and short decay times. Using a single-readout phoswich detector, we successfully identified $\gamma$ radiation signals from two scintillating components. Our Boosted Decision Tree algorithm demonstrated a maximum discrimination power of 3.02 $\pm$ 0.85 standard deviation in the 950 keV region, providing an efficient solution for self-shielding and enhancing radiation detection capabilities.Comment: 11pages, 7 figure

A single gene of a commensal microbe affects host susceptibility to enteric infection

Indigenous microbes inside the host intestine maintain a complex self-regulating community. The mechanisms by which gut microbes interact with intestinal pathogens remain largely unknown. Here we identify a commensal Escherichia coli strain whose expansion predisposes mice to infection by Vibrio cholerae, a human pathogen. We refer to this strain as 'atypical' E. coli (atEc) because of its inability to ferment lactose. The atEc strain is resistant to reactive oxygen species (ROS) and proliferates extensively in antibiotic-treated adult mice. V. cholerae infection is more severe in neonatal mice transplanted with atEc compared with those transplanted with a typical E. coli strain. Intestinal ROS levels are decreased in atEc-transplanted mice, favouring proliferation of ROS-sensitive V. cholerae. An atEc mutant defective in ROS degradation fails to facilitate V. cholerae infection when transplanted, suggesting that host infection susceptibility can be regulated by a single gene product of one particular commensal species.

Why Post-Starburst Galaxies are Now Quiescent

Post-starburst or "E+A" galaxies are rapidly transitioning from star-forming to quiescence. While the current star formation rate of post-starbursts is already at the level of early type galaxies, we recently discovered that many have large CO-traced molecular gas reservoirs consistent with normal star forming galaxies. These observations raise the question of why these galaxies have such low star formation rates. Here we present an ALMA search for the denser gas traced by HCN (1--0) and HCO+ (1--0) in two CO-luminous, quiescent post-starburst galaxies. Intriguingly, we fail to detect either molecule. The upper limits are consistent with the low star formation rates and with early-type galaxies. The HCN/CO luminosity ratio upper limits are low compared to star-forming and even many early type galaxies. This implied low dense gas mass fraction explains the low star formation rates relative to the CO-traced molecular gas and suggests the state of the gas in post-starburst galaxies is unusual, with some mechanism inhibiting its collapse to denser states. We conclude that post-starbursts galaxies are now quiescent because little dense gas is available, in contrast to the significant CO-traced lower density gas reservoirs that still remain.Comment: accepted for publication in Ap

Sequence optimization and multiple gene-targeting improve the inhibitory efficacy of exogenous double-stranded RNA against pepper mottle virus in Nicotiana benthamiana

Double-stranded RNA (dsRNA)-induced RNA interference is a promising agricultural technology for crop protection against various pathogens. Recent advances in this field have enhanced the overall efficiency with which this approach inhibits pathogenic viruses. Our previous study verified that treatment of Nicotiana benthamiana plants with dsRNAs targeting helper component-proteinase (HC-Pro) and nuclear inclusion b (NIb) genes protected the plant from pepper mottle virus (PepMoV) infection. The aim of this study was to improve the inhibitory efficacy of dsRNAs by optimizing the target sequences and their length and by targeting multiple genes via co-treatment of dsRNAs. Each of the two targeting dsRNAs were divided into three shorter compartments and we found that HC-Pro:mid-1st and NIb:mid-3rd showed significantly superior antiviral potency than the other fragments, including the parent dsRNA. In addition, we confirmed that the co-treatment of two dsRNAs targeting HC-Pro and NIb produced a greater inhibition of PepMoV replication than that obtained from individual dsRNA treatment. Complementing our previous study, this study will provide future directions for designing dsRNAs and enhancing their efficiency in dsRNA-mediated RNA interference technologies.This study was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A5A1032428 and No. 2022R1A2C1011032). This was also supported by a grant from the New breeding technologies development Program (Project No. PJ01652102), Rural Development Administration, Republic of Korea

The dynamic transcriptional and translational landscape of the model antibiotic producer Streptomyces coelicolor A3(2)

Individual Streptomyces species have the genetic potential to produce a diverse array of natural products of commercial, medical and veterinary interest. However, these products are often not detectable under laboratory culture conditions. To harness their full biosynthetic potential, it is important to develop a detailed understanding of the regulatory networks that orchestrate their metabolism. Here we integrate nucleotide resolution genome-scale measurements of the transcriptome and translatome of Streptomyces coelicolor, the model antibiotic-producing actinomycete. Our systematic study determines 3,570 transcription start sites and identifies 230 small RNAs and a considerable proportion (∼21%) of leaderless mRNAs; this enables deduction of genome-wide promoter architecture. Ribosome profiling reveals that the translation efficiency of secondary metabolic genes is negatively correlated with transcription and that several key antibiotic regulatory genes are translationally induced at transition growth phase. These findings might facilitate the design of new approaches to antibiotic discovery and development

Evaluating myelophil, a 30% ethanol extract of Astragalus membranaceus and Salvia miltiorrhiza, for alleviating fatigue in long COVID: a real-world observational study

BackgroundPersistent post-infectious symptoms, predominantly fatigue, characterize Long COVID. This study investigated the efficacy of Myelophil (MYP), which contains metabolites extracted from Astragalus membranaceus and Salvia miltiorrhiza using 30% ethanol, in alleviating fatigue among subjects with Long COVID.MethodsIn this prospective observational study, we enrolled subjects with significant fatigue related to Long COVID, using criteria of scores of 60 or higher on the modified Korean Chalder Fatigue scale (mKCFQ11), or five or higher on the Visual Analog Scale (VAS) for brain fog. Utilizing a single-arm design, participants were orally administered MYP (2,000 mg daily) for 4 weeks. Changes in fatigue severity were assessed using mKCFQ11, Multidimensional Fatigue Inventory (MFI-20), and VAS for fatigue and brain fog. In addition, changes in quality of life using the short form 12 (SF-12) were also assessed along with plasma cortisol levels.ResultsA total of 50 participants (18 males, 32 females) were enrolled; 49 were included in the intention-to-treat analysis with scores of 66.9 ± 11.7 on mKCFQ11 and 6.3 ± 1.5 on the brain fog VAS. After 4 weeks of MYP administration, there were statistically significant improvements in fatigue levels: mKCFQ11 was measured at 34.8 ± 17.1 and brain fog VAS at 3.0 ± 1.9. Additionally, MFI-20 decreased from 64.8 ± 9.8 to 49.3 ± 10.8, fatigue VAS dropped from 7.4 ± 1.0 to 3.4 ± 1.7, SF-12 scores rose from 53.3 ± 14.9 to 78.6 ± 14.3, and plasma cortisol levels also elevated from 138.8 ± 50.1 to 176.9 ± 62.0 /mL. No safety concerns emerged during the trial.ConclusionCurrent findings underline MYP’s potential in managing Long COVID-induced fatigue. However, comprehensive studies remain imperative.Clinical Trial Registrationhttps://cris.nih.go.kr, identifier KCT0008948

An Adaptive UI Based on User-Satisfaction Prediction in Mixed Reality

As people begin to notice mixed reality, various studies on user satisfaction in mixed reality (MR) have been conducted. User interface (UI) is one of the representative factors that affect interaction satisfaction in MR. In conventional platforms such as mobile devices and personal computers, various studies have been conducted on providing adaptive UI, and recently, such studies have also been conducted in MR environments. However, there have been few studies on providing an adaptive UI based on interaction satisfaction. Therefore, in this paper, we propose a method based on interaction-satisfaction prediction to provide an adaptive UI in MR. The proposed method predicts interaction satisfaction based on interaction information (gaze, hand, head, object) and provides an adaptive UI based on predicted interaction satisfaction. To develop the proposed method, an experiment to measure data was performed, and a user-satisfaction-prediction model was developed based on the data collected through the experiment. Next, to evaluate the proposed method, an adaptive UI providing an application using the developed user-satisfaction-prediction model was implemented. From the experimental results using the implemented application, it was confirmed that the proposed method could improve user satisfaction compared to the conventional method