Multimodal Polynomial Fusion for Detecting Driver Distraction

Distracted driving is deadly, claiming 3,477 lives in the U.S. in 2015 alone. Although there has been a considerable amount of research on modeling the distracted behavior of drivers under various conditions, accurate automatic detection using multiple modalities and especially the contribution of using the speech modality to improve accuracy has received little attention. This paper introduces a new multimodal dataset for distracted driving behavior and discusses automatic distraction detection using features from three modalities: facial expression, speech and car signals. Detailed multimodal feature analysis shows that adding more modalities monotonically increases the predictive accuracy of the model. Finally, a simple and effective multimodal fusion technique using a polynomial fusion layer shows superior distraction detection results compared to the baseline SVM and neural network models.Comment: INTERSPEECH 201

STORYWARS: A Dataset and Instruction Tuning Baselines for Collaborative Story Understanding and Generation

Collaborative stories, which are texts created through the collaborative efforts of multiple authors with different writing styles and intentions, pose unique challenges for NLP models. Understanding and generating such stories remains an underexplored area due to the lack of open-domain corpora. To address this, we introduce STORYWARS, a new dataset of over 40,000 collaborative stories written by 9,400 different authors from an online platform. We design 12 task types, comprising 7 understanding and 5 generation task types, on STORYWARS, deriving 101 diverse story-related tasks in total as a multi-task benchmark covering all fully-supervised, few-shot, and zero-shot scenarios. Furthermore, we present our instruction-tuned model, INSTRUCTSTORY, for the story tasks showing that instruction tuning, in addition to achieving superior results in zero-shot and few-shot scenarios, can also obtain the best performance on the fully-supervised tasks in STORYWARS, establishing strong multi-task benchmark performances on STORYWARS.Comment: ACL 202

Online Detection of Surface Defects Based on Improved YOLOV3

Aiming at the problems of low efficiency and poor accuracy in the product surface defect detection. In this paper, an online surface defects detection method based on YOLOV3 is proposed. Firstly, using lightweight network MobileNetV2 to replace the original backbone as the feature extractor to improve network speed. Then, we propose an extended feature pyramid network (EFPN) to extend the detection layer for multi-size object detection and design a novel feature fusing module (FFM) embedded in the extend layer to super-resolve features and capture more regional details. In addition, we add an IoU loss function to solve the mismatch between classification and bounding box regression. The proposed method is used to train and test on the hot rolled steel open dataset NEU-DET, which contains six typical defects of a steel surface, namely rolled-in scale, patches, crazing, pitted surface, inclusion and scratches. The experimental results show that our method achieves a satisfactory balance between performance and consumption and reaches 86.96% mAP with a speed of 80.96 FPS, which is more accurate and faster than many other algorithms and can realize real-time and high-precision inspection of product surface defects

Distribution Matching for Rationalization

The task of rationalization aims to extract pieces of input text as rationales to justify neural network predictions on text classification tasks. By definition, rationales represent key text pieces used for prediction and thus should have similar classification feature distribution compared to the original input text. However, previous methods mainly focused on maximizing the mutual information between rationales and labels while neglecting the relationship between rationales and input text. To address this issue, we propose a novel rationalization method that matches the distributions of rationales and input text in both the feature space and output space. Empirically, the proposed distribution matching approach consistently outperforms previous methods by a large margin. Our data and code are available

EGF/EGFR upregulates and cooperates with Netrin-4 to protect glioblastoma cells from DNA damage-induced senescence

BackgroundGlioblastoma multiforme (GBM) is the most malignant central nervous system tumor. Alkylating agent, temozolomide (TMZ), is currently the first-line chemotherapeutic agent for GBM. However, the sensitivity of GBM cells to TMZ is affected by many factors. And, several clinic trials, including co-administration of TMZ with other drugs, have failed in successful treatment of GBM. We have previously reported that Netrin-4 (NTN4), a laminin-like axon guidance protein, plays a protective role in GBM cell senescence upon TMZ-triggered DNA damage. However, the master regulator of NTN4 needs further elucidation. Epidermal growth factor/Epidermal growth factor receptor (EGF/EGFR) can modulate the expression of various extracellular matrix related molecules, and prevent DNA damage in GBM cells. In this study, we investigated the relationship between EGF/EGFR signaling and NTN4, and explored their effect on therapeutic efficacy in GBM cells upon TMZ treatment.MethodsCo-expression analysis were performed by using the RNA sequencing data from NIH 934 cell lines and from single cell RNA sequencing data of GBM tumor. The co-expressing genes were used for GO enrichment and signaling pathway enrichment. mRNA expression of the target genes were quantified by qPCR, and cell senescence were investigated by Senescence-Associated Beta-Galactosidase Staining. Protein phosphorylation were observed and analyzed by immunoblotting. The RNA sequencing data and clinical information of TMZ treated patients were extracted from TCGA-glioblastoma project, and then used for Kaplan-Meier survival analysis.ResultsAnalysis of RNA sequencing data revealed a potential co-expression relationship between NTN4 and EGFR. GO enrichment of EGFR-correlated genes indicated that EGFR regulates GBM cells in a manner similar to that in central nervous system development and neural cell differentiation. Pathway analysis suggested that EGFR and its related genes contribute to cell adhesion, extracellular matrix (ECM) organization and caspase related signaling. We also show that EGF stimulates NTN4 expression in GBM cells and cooperates with NTN4 to attenuate GBM cell senescence induced by DNA damage, possibly via AKT and ERK. Clinical analysis showed that co-expression of EGFR and NTN4 significantly predicts poor survival in TMZ-treated GBM patients.ConclusionsThis study indicates that EGF/EGFR regulates and cooperates with NTN4 in DNA damage resistance in GBM. Therefore, our findings provide a potential therapeutic target for GBM.Peer reviewe

The Zfx gene is expressed in human gliomas and is important in the proliferation and apoptosis of the human malignant glioma cell line U251

Abstract Background Zfx is a zinc finger protein of the Zfy family, whose members are highly conserved in vertebrates. Zfx is a shared transcriptional regulator of both embryonic stem cells (ESC) and hematopoietic stem cells (HSC), which suggests a common genetic basis of self-renewal in embryonic and adult stem cells. The level of Zfx expression correlates with aggressiveness and severity in many cancer types, including prostate cancer, breast cancer, and leukemia. However, the importance of Zfx in human glioma is largely unknown. In the present study, we examined the role of Zfx in human glioma. Methods We detected expression levels of Zfx mRNA in U251 cells, U87 cells, U373 cells, and A172 cells by semi-quantitative RT-PCR. To analyze the expression of Zfx mRNA in glioma tissues, we performed real-time quantitative PCR on 35 pathologically confirmed glioma samples (Grade I-4cases, Grade II-13cases, Grade III-11cases, and Grade IV-7cases) and on 5 noncancerous brain tissue samples. We used lentivirus-mediated small interfering RNAs (siRNAs) to knock down Zfx expression in the human malignant glioma cell line U251. Changes in Zfx target gene expression were determined by real-time RT-PCR. Cell proliferation was examined by a High Content Screening assay. DNA synthesis in proliferating cells was determined by BrdU incorporation. Cell cycle distribution and apoptosis were detected by flowcytometric analysis. Results We discovered that Zfx mRNA was expressed in U251 cells, U87 cells, U373 cells, and A172 cells. The expression level of Zfx is significantly higher in gliomas compared to noncancerous brain tissue. Using a lentivirus-based RNAi approach, Zfx expression was significantly inhibited in human glioblastoma U251 cells. The effects of Zfx knockdown on cell proliferation, cell cycle distribution, and apoptosis were assessed. Inhibition of Zfx expression in U251 cells by RNAi significantly impaired cell proliferation, increased apoptosis, and arrested cells in S phase. Conclusions The results of our study demonstrate that the Zfx gene is highly expressed in glioma tissue and in glioma cell lines. Furthermore, Zfx may play a critical role in cell proliferation, cell cycle distribution, and apoptosis of human malignant glioma cells.</p