Understanding Calibration for Multilingual Question Answering Models

Multilingual pre-trained language models are incredibly effective at Question Answering (QA), a core task in Natural Language Understanding, achieving high accuracies on several multilingual benchmarks. However, little is known about how well they are calibrated. In this paper, we study the calibration properties of several pre-trained multilingual large language models (LLMs) on a variety of question-answering tasks. We perform extensive experiments, spanning both extractive and generative QA model designs and diverse languages, spanning both high-resource and low-resource ones. We study different dimensions of calibration in in-distribution, out-of-distribution, and cross-lingual transfer settings, and investigate strategies to improve it, including post-hoc methods and regularized fine-tuning. We demonstrate automatically translated data augmentation as a highly effective technique to improve model calibration. We also conduct a number of ablation experiments to study the effect of model size on calibration and how multilingual models compare with their monolingual counterparts for diverse tasks and languages.Comment: Preprint. Under Submissio

Stroke Extraction of Chinese Character Based on Deep Structure Deformable Image Registration

Stroke extraction of Chinese characters plays an important role in the field of character recognition and generation. The most existing character stroke extraction methods focus on image morphological features. These methods usually lead to errors of cross strokes extraction and stroke matching due to rarely using stroke semantics and prior information. In this paper, we propose a deep learning-based character stroke extraction method that takes semantic features and prior information of strokes into consideration. This method consists of three parts: image registration-based stroke registration that establishes the rough registration of the reference strokes and the target as prior information; image semantic segmentation-based stroke segmentation that preliminarily separates target strokes into seven categories; and high-precision extraction of single strokes. In the stroke registration, we propose a structure deformable image registration network to achieve structure-deformable transformation while maintaining the stable morphology of single strokes for character images with complex structures. In order to verify the effectiveness of the method, we construct two datasets respectively for calligraphy characters and regular handwriting characters. The experimental results show that our method strongly outperforms the baselines. Code is available at https://github.com/MengLi-l1/StrokeExtraction.Comment: 10 pages, 8 figures, published to AAAI-23 (oral

Real-Time Detectors for Digital and Physical Adversarial Inputs to Perception Systems

Deep neural network (DNN) models have proven to be vulnerable to adversarial digital and physical attacks. In this paper, we propose a novel attack- and dataset-agnostic and real-time detector for both types of adversarial inputs to DNN-based perception systems. In particular, the proposed detector relies on the observation that adversarial images are sensitive to certain label-invariant transformations. Specifically, to determine if an image has been adversarially manipulated, the proposed detector checks if the output of the target classifier on a given input image changes significantly after feeding it a transformed version of the image under investigation. Moreover, we show that the proposed detector is computationally-light both at runtime and design-time which makes it suitable for real-time applications that may also involve large-scale image domains. To highlight this, we demonstrate the efficiency of the proposed detector on ImageNet, a task that is computationally challenging for the majority of relevant defenses, and on physically attacked traffic signs that may be encountered in real-time autonomy applications. Finally, we propose the first adversarial dataset, called AdvNet that includes both clean and physical traffic sign images. Our extensive comparative experiments on the MNIST, CIFAR10, ImageNet, and AdvNet datasets show that VisionGuard outperforms existing defenses in terms of scalability and detection performance. We have also evaluated the proposed detector on field test data obtained on a moving vehicle equipped with a perception-based DNN being under attack

The iNOS/Src/FAK axis is critical in Toll-like receptor-mediated cell motility in macrophages

AbstractThe Toll-like receptors (TLRs) play a pivotal role in innate immunity for the detection of highly conserved, pathogen-expressed molecules. Previously, we demonstrated that lipopolysaccharide (LPS, TLR4 ligand)-increased macrophage motility required the participation of Src and FAK, which was inducible nitric oxide synthase (iNOS)-dependent. To investigate whether this iNOS/Src/FAK pathway is a general mechanism for macrophages to mobilize in response to engagement of TLRs other than TLR4, peptidoglycan (PGN, TLR2 ligand), polyinosinic–polycytidylic acid (polyI:C, TLR3 ligand) and CpG-oligodeoxynucleotides (CpG, TLR9 ligand) were used to treat macrophages in this study. Like LPS stimulation, simultaneous increase of cell motility and Src (but not Fgr, Hck, and Lyn) was detected in RAW264.7, peritoneal macrophages, and bone marrow-derived macrophages exposed to PGN, polyI:C and CpG. Attenuation of Src suppressed PGN-, polyI:C-, and CpG-elicited movement and the level of FAK Pi-Tyr861, which could be reversed by the reintroduction of siRNA-resistant Src. Besides, knockdown of FAK reduced the mobility of macrophages stimulated with anyone of these TLR ligands. Remarkably, PGN-, polyI:C-, and CpG-induced Src expression, FAK Pi-Tyr861, and cell mobility were inhibited in macrophages devoid of iNOS, indicating the importance of iNOS. These findings corroborate that iNOS/Src/FAK axis occupies a central role in macrophage locomotion in response to engagement of TLRs

Behavior of Solutions of Model Equations for Incompressible Fluid Flow

AbstractWe study the behavior of solutions of model equations of inviscid incompressible fluid flow proposed by Constantin, Lax and Majda together with a viscous version studied by Schochet. A condition is found on initial data to guarantee that solution of viscous equation remains smooth when the inviscid solution blows up. We prove that global smooth solution of viscous model equation exists for a class of initial data, for which the explicit solution is not known. Singularities of solutions previously obtained are characterized here as distributions

The Effects of Ontario Menu Labelling Regulations on Nutritional Quality of Chain Restaurant Menu Items—Cross-Sectional Examination

Restaurant foods are associated with excessive energy intake and poor nutritional quality. In 2017, the Healthy Menu Choices Act mandated food service establishments with ≥20 outlets in Ontario to display the energy content on menus. To examine the potential impact of menu labelling, nutrition information for 18,760 menu items were collected from 88 regulated and 53 unregulated restaurants. Descriptive statistics were calculated for serving size, energy, saturated fat, sodium and total sugars. Quantile regression was used to determine the differences between regulated and unregulated restaurants. The energy content of menu items from regulated restaurants (median (95% CI): 320 kcal (310, 320)) was significantly lower than those from unregulated restaurants (470 kcal (460, 486), p p < 0.001). This study showed that menu items from regulated restaurants had smaller serving size, lower levels of energy and nutrients of public health concern compared to those from the unregulated restaurants, suggesting potential downstream beneficial effects of menu labelling in lowering caloric content and nutrients of public health concern in foods

Robust collaborative passenger flow control on a congested metro line: A joint optimization with train timetabling

With the rapid increase in residents in megacities, the passenger demand of metro systems is rising sharply and steadily, bringing immense pressure to train operations. To improve the service quality, this paper discusses systematically investigating a joint optimization of the robust passenger flow control strategy and train timetable on a congested metro line. A deterministic model for train timetabling and passenger flow control at each station is first developed to make a trade-off between operation efficiency and service fairness. Then, the uncertain passenger demand is further considered at each station, and three integer linear programming models are formulated to derive the robust passenger flow control strategies. The first two models are related to the technique of Light Robustness, in which the uncertainty is handled by inserting expected protection levels at stations or on trains. In addition, with a stochastic scenario set that characterizes the uncertain passenger information, the last model aims to find a solution that is feasible for all involved scenarios, and thus, reduces the impact of the uncertainty in metro systems. To improve the computational efficiency of large-scale instances, a customized decomposition-based algorithm is developed. Finally, some real-world case studies based on the operation data of the Beijing metro Batong line are conducted to verify the performance and effectiveness of the proposed approaches

Diversity of formyltetrahydrofolate synthetase genes in the rumens of roe deer (Capreolus pygargus) and sika deer (Cervus nippon) fed different diets

Reductive acetogenesis by homoacetogens represents an alternative pathway to methanogenesis to remove metabolic hydrogen during rumen fermentation. In this study, we investigated the occurrence of homoacetogen in the rumens of pasture-fed roe deer (Capreolus pygargus) and sika deer (Cervus nippon) fed either oak leaf (tannin-rich, 100 mg/kg dried matter), corn stover, or corn silage-based diets, by using formyltetrahydrofolate synthetase (FTHFS) gene sequences as a marker. The diversity and richness of FTHFS sequences was lowest in animals fed oak leaf, indicating that tannin-containing plants may affect rumen homoacetogen diversity. FTHFS amino acid sequences in the rumen of roe deer significantly differed from those of sika deer. The phylogenetic analyses showed that 44.8% of sequences in pasture-fed roe deer, and 72.1%, 81.1%, and 37.5% of sequences in sika deer fed oak leaf, corn stover, and corn silage based diets, respectively, may represent novel bacteria that have not yet been cultured. These results demonstrate that the rumens of roe deer and sika deer harbor potentially novel homoacetogens and that diet may influence homoacetogen community structure.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Associations between socioeconomic status and screen time among children and adolescents in China: A cross-sectional study.

BackgroundSocioeconomic status (SES) is an important determinant of screen time (ST) in children and adolescents, however, the association between SES and ST is not fully understood in China. This study aimed to investigate the association between SES and ST (operationalized as meeting the ST guidelines; no more than 2 hours per day) in Chinese children and adolescents.MethodsCross-sectional data of 2,955 Chinese children and adolescents aged 8 to 17(53.4% girls) were used. SES was measured using indicators of parental education and perceived family wealth. ST was assessed with detailed items from the Health Behaviour School-aged Children survey questionnaires. Descriptive statistics and a Chi-square test were used to report the sample characteristics and analyse ST differences across different sociodemographic groups. A binary logistic regression was then applied to analyse the association of SES indicators with ST in children and adolescents.ResultsOverall, 25.3% of children and adolescents met the ST guidelines. Children and adolescents with higher parental education levels were 1.84 [95% CI 1.31-2.57; father] and 1.42 [95% CI 1.02-1.98; mother] times more likely to meet the ST guidelines than those with lower parental education levels. Associations between SES and ST varied across sex and grade groups. Moreover, the associations of SES with ST on weekdays and weekends were different.ConclusionsThis study demonstrated the association between SES and ST in children and adolescents, highlighting the importance of targeting children and adolescents with low SES levels as an intervention priority. Based on our findings, specific interventions can be tailored to effectively reduce ST. Future studies are encouraged to use longitudinal or interventional designs to further determine the association between SES and ST

Memory Classifiers: Two-stage Classification for Robustness in Machine Learning

The performance of machine learning models can significantly degrade under distribution shifts of the data. We propose a new method for classification which can improve robustness to distribution shifts, by combining expert knowledge about the ``high-level" structure of the data with standard classifiers. Specifically, we introduce two-stage classifiers called \textit{memory classifiers}. First, these identify prototypical data points -- \textit{memories} -- to cluster the training data. This step is based on features designed with expert guidance; for instance, for image data they can be extracted using digital image processing algorithms. Then, within each cluster, we learn local classifiers based on finer discriminating features, via standard models like deep neural networks. We establish generalization bounds for memory classifiers. We illustrate in experiments that they can improve generalization and robustness to distribution shifts on image datasets. We show improvements which push beyond standard data augmentation techniques