Cross-Modal Interaction Networks for Query-Based Moment Retrieval in Videos

Query-based moment retrieval aims to localize the most relevant moment in an untrimmed video according to the given natural language query. Existing works often only focus on one aspect of this emerging task, such as the query representation learning, video context modeling or multi-modal fusion, thus fail to develop a comprehensive system for further performance improvement. In this paper, we introduce a novel Cross-Modal Interaction Network (CMIN) to consider multiple crucial factors for this challenging task, including (1) the syntactic structure of natural language queries; (2) long-range semantic dependencies in video context and (3) the sufficient cross-modal interaction. Specifically, we devise a syntactic GCN to leverage the syntactic structure of queries for fine-grained representation learning, propose a multi-head self-attention to capture long-range semantic dependencies from video context, and next employ a multi-stage cross-modal interaction to explore the potential relations of video and query contents. The extensive experiments demonstrate the effectiveness of our proposed method.Comment: Accepted by SIGIR 2019 as a full pape

ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning

Cross-domain few-shot learning (CD-FSL), where there are few target samples under extreme differences between source and target domains, has recently attracted huge attention. Recent studies on CD-FSL generally focus on transfer learning based approaches, where a neural network is pre-trained on popular labeled source domain datasets and then transferred to target domain data. Although the labeled datasets may provide suitable initial parameters for the target data, the domain difference between the source and target might hinder fine-tuning on the target domain. This paper proposes a simple yet powerful method that re-randomizes the parameters fitted on the source domain before adapting to the target data. The re-randomization resets source-specific parameters of the source pre-trained model and thus facilitates fine-tuning on the target domain, improving few-shot performance.Comment: CIKM 2022 Short; 5 pages, 3 figures, 4 table

Interactions between Food Additive Silica Nanoparticles and Food Matrices

Nanoparticles (NPs) have been widely utilized in the food industry as additives with their beneficial characteristics, such as improving sensory property and processing suitability, enhancing functional and nutritional values, and extending shelf-life of foods. Silica is used as an anti-caking agent to improve flow property of powered ingredients and as a carrier for flavors or active compounds in food. Along with the rapid development of nanotechnology, the sizes of silica fall into nanoscale, thereby raising concerns about the potential toxicity of nano-sized silica materials. There have been a number of studies carried out to investigate possible adverse effects of NPs on the gastrointestinal tract. The interactions between NPs and surrounding food matrices should be also taken into account since the interactions can affect their bioavailability, efficacy, and toxicity. In the present study, we investigated the interactions between food additive silica NPs and food matrices, such as saccharides, proteins, lipids, and minerals. Quantitative analysis was performed to determine food component-NP corona using HPLC, fluorescence quenching, GC-MS, and ICP-AES. The results demonstrate that zeta potential and hydrodynamic radius of silica NPs changed in the presence of all food matrices, but their solubility was not affected. However, quantitative analysis on the interactions revealed that a small portion of food matrices interacted with silica NPs and the interactions were highly dependent on the type of food component. Moreover, minor nutrients could also affect the interactions, as evidenced by higher NP interaction with honey rather than with a simple sugar mixture containing an equivalent amount of fructose, glucose, sucrose, and maltose. These findings provide fundamental information to extend our understanding about the interactions between silica NPs and food components and to predict the interaction effect on the safety aspects of food-grade NPs

Query-Efficient Black-Box Red Teaming via Bayesian Optimization

The deployment of large-scale generative models is often restricted by their potential risk of causing harm to users in unpredictable ways. We focus on the problem of black-box red teaming, where a red team generates test cases and interacts with the victim model to discover a diverse set of failures with limited query access. Existing red teaming methods construct test cases based on human supervision or language model (LM) and query all test cases in a brute-force manner without incorporating any information from past evaluations, resulting in a prohibitively large number of queries. To this end, we propose Bayesian red teaming (BRT), novel query-efficient black-box red teaming methods based on Bayesian optimization, which iteratively identify diverse positive test cases leading to model failures by utilizing the pre-defined user input pool and the past evaluations. Experimental results on various user input pools demonstrate that our method consistently finds a significantly larger number of diverse positive test cases under the limited query budget than the baseline methods. The source code is available at https://github.com/snu-mllab/Bayesian-Red-Teaming.Comment: ACL 2023 Long Paper - Main Conferenc

Differential effect of NF-κB activity on β-catenin/Tcf pathway in various cancer cells

Abstractβ-Catenin/Tcf and NF-κB pathways play an important role in biological functions. We determined the underlying mechanisms of differential interaction between two pathways in various human cancer cell lines. NF-κB positively regulated β-catenin/Tcf pathways in human glioblastoma, whereas it has an opposite effect on β-catenin/Tcf pathways in colon, liver, and breast cancer cells. Expression of lucine zipper tumor suppressor 2 (lzts2) was positively regulated by NF-κB activity in colon, liver, and breast cancer cells, whereas negatively regulated in glioma cells. Downregulation of lzts2 increased the β-catenin/Tcf promoter activity and inhibited NF-κB-induced modulation of the nuclear translocation of β-catenin. These data indicate that the differential crosstalk between β-catenin/Tcf and NF-κB pathway in various cancer cells is resulted from the differences in the regulation of NF-κB-induced lzts2 expression

JNK pathway is involved in the inhibition of inflammatory target gene expression and NF-kappaB activation by melittin

<p>Abstract</p> <p>Background</p> <p>Bee venom therapy has been used to treat inflammatory diseases including rheumatoid arthritis in humans and in experimental animals. We previously found that bee venom and melittin (a major component of bee venom) have anti-inflammatory effect by reacting with the sulfhydryl group of p50 of nuclear factor-kappa B (NF-κB) and IκB kinases (IKKs). Since mitogen activated protein (MAP) kinase family is implicated in the NF-κB activation and inflammatory reaction, we further investigated whether activation of MAP kinase may be also involved in the anti-inflammatory effect of melittin and bee venom.</p> <p>Methods</p> <p>The anti-inflammatory effects of melittin and bee venom were investigated in cultured Raw 264.7 cells, THP-1 human monocytic cells and Synoviocytes. The activation of NF-κB was investigated by electrophoretic mobility shift assay. Nitric oxide (NO) and prostaglandin E₂(PGE₂) were determined either by Enzyme Linked Immuno Sorbent Assay or by biochemical assay. Expression of IκB, p50, p65, inducible nitric oxide synthetase (iNOS), cyclooxygenase-2 (COX-2) as well as phosphorylation of MAP kinase family was determined by Western blot.</p> <p>Results</p> <p>Melittin (0.5–5 μg/ml) and bee venom (5 and 10 μg/ml) inhibited lipopolysaccharide (LPS, 1 μg/ml) and sodium nitroprusside (SNP, 200 μM)-induced activation of c-Jun NH2-terminal kinase (JNK) in RAW 264.7 cells in a dose dependent manner. However, JNK inhibitor, anthra [1,9-cd]pyrazole-6 (2H)-one (SP600215, 10–50 μM) dose dependently suppressed the inhibitory effects of melittin and bee venom on NF-κB dependent luciferase and DNA binding activity via suppression of the inhibitory effect of melittin and bee venom on the LPS and SNP-induced translocation of p65 and p50 into nucleus as well as cytosolic release of IκB. Moreover, JNK inhibitor suppressed the inhibitory effects of melittin and bee venom on iNOS and COX-2 expression, and on NO and PGE₂generation.</p> <p>Conclusion</p> <p>These data show that melittin and bee venom prevent LPS and SNP-induced NO and PGE₂production via JNK pathway dependent inactivation of NF-κB, and suggest that inactivation of JNK pathways may also contribute to the anti-inflammatory and anti-arthritis effects of melittin and bee venom.</p

Clinical and laboratory profiles of hospitalized children with acute respiratory virus infection

PurposeDespite the availability of molecular methods, identification of the causative virus in children with acute respiratory infections (ARIs) has proven difficult as the same viruses are often detected in asymptomatic children.MethodsMultiplex reverse transcription polymerase chain reaction assays were performed to detect 15 common respiratory viruses in children under 15 years of age who were hospitalized with ARI between January 2013 and December 2015. Viral epidemiology and clinical profiles of single virus infections were evaluated.ResultsOf 3,505 patients, viruses were identified in 2,424 (69.1%), with the assay revealing a single virus in 1,747 cases (49.8%). While major pathogens in single virus-positive cases differed according to age, human rhinovirus (hRV) was common in patients of all ages. Respiratory syncytial virus (RSV), influenza virus (IF), and human metapneumovirus (hMPV) were found to be seasonal pathogens, appearing from fall through winter and spring, whereas hRV and adenovirus (AdV) were detected in every season. Patients with ARIs caused by RSV and hRV were frequently afebrile and more commonly had wheezing compared with patients with other viral ARIs. Neutrophil-dominant inflammation was observed in ARIs caused by IF, AdV, and hRV, whereas lymphocyte-dominant inflammation was observed with RSV A, parainfluenza virus, and hMPV. Monocytosis was common with RSV and AdV, whereas eosinophilia was observed with hRV.ConclusionIn combination with viral identification, recognition of virus-specific clinical and laboratory patterns will expand our understanding of the epidemiology of viral ARIs and help us to establish more efficient therapeutic and preventive strategies