Traffic Sign Recognition Using Local Vision Transformer

Recognition of traffic signs is a crucial aspect of self-driving cars and driver assistance systems, and machine vision tasks such as traffic sign recognition have gained significant attention. CNNs have been frequently used in machine vision, but introducing vision transformers has provided an alternative approach to global feature learning. This paper proposes a new novel model that blends the advantages of both convolutional and transformer-based networks for traffic sign recognition. The proposed model includes convolutional blocks for capturing local correlations and transformer-based blocks for learning global dependencies. Additionally, a locality module is incorporated to enhance local perception. The performance of the suggested model is evaluated on the Persian Traffic Sign Dataset and German Traffic Sign Recognition Benchmark and compared with SOTA convolutional and transformer-based models. The experimental evaluations demonstrate that the hybrid network with the locality module outperforms pure transformer-based models and some of the best convolutional networks in accuracy. Specifically, our proposed final model reached 99.66% accuracy in the German traffic sign recognition benchmark and 99.8% in the Persian traffic sign dataset, higher than the best convolutional models. Moreover, it outperforms existing CNNs and ViTs while maintaining fast inference speed. Consequently, the proposed model proves to be significantly faster and more suitable for real-world applications

Pyramid Transformer for Traffic Sign Detection

Traffic sign detection is a vital task in the visual system of self-driving cars and the automated driving system. Recently, novel Transformer-based models have achieved encouraging results for various computer vision tasks. We still observed that vanilla ViT could not yield satisfactory results in traffic sign detection because the overall size of the datasets is very small and the class distribution of traffic signs is extremely unbalanced. To overcome this problem, a novel Pyramid Transformer with locality mechanisms is proposed in this paper. Specifically, Pyramid Transformer has several spatial pyramid reduction layers to shrink and embed the input image into tokens with rich multi-scale context by using atrous convolutions. Moreover, it inherits an intrinsic scale invariance inductive bias and is able to learn local feature representation for objects at various scales, thereby enhancing the network robustness against the size discrepancy of traffic signs. The experiments are conducted on the German Traffic Sign Detection Benchmark (GTSDB). The results demonstrate the superiority of the proposed model in the traffic sign detection tasks. More specifically, Pyramid Transformer achieves 77.8% mAP on GTSDB when applied to the Cascade RCNN as the backbone, which surpasses most well-known and widely-used state-of-the-art models

Validating the Persian Intuitive Eating Scale-2 among breast cancer survivors who are overweight/obese

Women with breast cancer are at risk of being overweight/obese which may consequently increase mortality. Intuitive eating is an adaptive eating behavior which might be beneficial for weight outcomes. The present study validated the Persian Intuitive Eating Scale-2 (IES-2) among overweight/obese Iranian females with breast cancer. Women who were overweight/obese with breast cancer (n = 762; mean ± SD age = 55.1 ± 5.7 years) completed the following questionnaires: IES-2, General Self-Efficacy Scale (GSE-6), Hospital Anxiety and Depression Scale (HADS), Short Form-12 (SF-12), Weight Bias Internalization Scale (WBIS), Body Appreciation Scale-2 (BAS-2), and Eating Attitudes Test (EAT-26). Confirmatory factor analysis (CFA) and Rasch analysis were applied to examine the psychometric properties of the IES-2. Associations between IES-2 score and other scale scores were assessed. CFA and Rasch analysis suggested that the Persian IES-2 had robust psychometric properties and all IES-2 items were meaningful in their embedded domains. The four-factor structure of the Persian IES-2 was confirmed. Concurrent validity was supported by the positive correlations between the IES-2 score and scores on the GSE-6, SF-12 mental component, and BAS-2. Negative correlations were found between the IES-2 score and the HADS (anxiety and depression subscales), WBIS, and EAT-26. The present study demonstrated that the Persian IES-2 is a well-designed instrument and is applicable for women who are overweight/obese with breast cancer

The association of emotional intelligence with sport injuries and receiving penalty cards among Iranian professional soccer players

Background: High emotional intelligence (EI) seems to be preventive for unconventional sports behavior within competitions leading prevention of sport injuries and also minimization of giving penalty cards. Objectives: The present study aimed to examine this relationship among Iranian Premier League footballers. Methods: This study was performed on Iranian professional soccer players participating in Premier League in 2014-2015 season. To assess emotional intelligence among athletes, the Schutte Self-Report Emotional Intelligence test (SSEIT) was employed. Sport-related injuries were recorded by the physician of each team. Also, the reports of the number of yellow and red cards for each athlete as well as for all teams in two phases (middle and end of each season) was recorded by the Football League Organization were reviewed and recorded. The chi-square test and t-test were used for comparing the variables. The Pearson�s correlation test and the multivariable regression model were also used for discovering association and relationship, respectively. P values of 0.05 or less were considered statistically significant. Results: Among different subscales of EI, only regulation of emotions was significantly different between injured and non-injured athletes (P = 0.04). Lower ability to regulate emotions was associated with higher risk for sport injuries (OR = 0.88, 95 CI: 0.79-0.98, P = 0.02). None of the subscales of EI was related to receiving yellow card, but utilizing emotions was adversely related to receiving red card. The association between utilizing emotions and receiving red card changed to insignificant after using the multivariable regression modeling. Conclusions: By regulating emotions, sport-related injuries can be preventable in soccer players. However, EI may not be helpful in reducing sport fines. © 2019, Author(s)

Cell responses and hemocompatibility of g-HA/PLA composites

The objective of this study was to investigate the hemocompatibility and cell responses to some novel poly(L-lactide) (PLA) composites containing surface modified hydroxyapatite particles for potential applications as a bone substitute material. The surface of hydroxyapatite (HA) particles was first grafted with L-lactic acid oligomers to form grafted HA (g-HA) particles. The g-HA particles were further blended with PLA to prepare g-HA/PLA composites. Our previous study has shown significant improvement in tensile properties of these materials due to the enhanced interfacial adhesion between the polymer matrix and HA particles. To further investigate the potential applications of these composites in bone repair and other orthopedic surgeries, a series of in vitro and in vivo experiments were conducted to examine the cell responses and hemocompatibility of the materials. In vitro experiments showed that the g-HA/PLA composites were well tolerated by the L-929 cells. Hemolysis of the composites was lower than that of pure PLA. Subcutaneous implantation demonstrated that the g-HA/PLA composites were more favorable than the control materials for soft tissue responses. The results suggested that the g-HA/PLA composites are promising and safe materials with potential applications in tissue engineering

International genomic evaluation methods for dairy cattle

<p>Abstract</p> <p>Background</p> <p>Genomic evaluations are rapidly replacing traditional evaluation systems used for dairy cattle selection. Higher reliabilities from larger genotype files promote cooperation across country borders. Genomic information can be exchanged across countries using simple conversion equations, by modifying multi-trait across-country evaluation (MACE) to account for correlated residuals originating from the use of foreign evaluations, or by multi-trait analysis of genotypes for countries that use the same reference animals.</p> <p>Methods</p> <p>Traditional MACE assumes independent residuals because each daughter is measured in only one country. Genomic MACE could account for residual correlations using daughter equivalents from genomic data as a fraction of the total in each country and proportions of bulls shared. MACE methods developed to combine separate within-country genomic evaluations were compared to direct, multi-country analysis of combined genotypes using simulated genomic and phenotypic data for 8,193 bulls in nine countries.</p> <p>Results</p> <p>Reliabilities for young bulls were much higher for across-country than within-country genomic evaluations as measured by squared correlations of estimated with true breeding values. Gains in reliability from genomic MACE were similar to those of multi-trait evaluation of genotypes but required less computation. Sharing of reference genotypes among countries created large residual correlations, especially for young bulls, that are accounted for in genomic MACE.</p> <p>Conclusions</p> <p>International genomic evaluations can be computed either by modifying MACE to account for residual correlations across countries or by multi-trait evaluation of combined genotype files. The gains in reliability justify the increased computation but require more cooperation than in previous breeding programs.</p

Diagnosing and measuring incompatibilities between pairs of services

International audienceThis text presents a tool, from its design to its implementation, which detects all behavioural incompatibilities between two service interfaces. Unlike prior work, the proposed solution does not simply check whether two services are incompatible or not, it rather provides detailed diagnosis, including the incompatibilities and for each one the location in the service interfaces where these incompatibilities occur. A measure of similarity between interfaces which considers outputs from the detection algorithm is proposed too. A visual report of the comparison analysis is also provided which pinpoints a set of incompatibilities that cause a behavioural interface not to simulate another one

Critical success factors for embedding carbon management in organizations: lessons from the UK higher education sector

Organizations are under increasing pressure from governments and stakeholders to reduce carbon emissions from their business operations for climate change mitigation. Universities are not exempt from this challenge and are operating in a complex external environment, not least responding to the UK government's Climate Change Act 2008 (80% carbon reductions by 2050 as per 1990 baseline). In 2012–2013, the UK Higher Education (HE) sector consumed 7.9 billion kWh of energy and produced 2.3 million tonnes of carbon emissions. This indicates the scale of the challenge and carbon management is central to reduce carbon emissions. However, effective processes for implementing and embedding carbon management in organizations in general, and universities in particular, have yet to be realized. This paper explores the critical success factors (CSFs) for embedding carbon management in universities and, more widely, in organizations. This exploratory study adopted a mixed-methods approach including the content analysis of universities' carbon management plans alongside semi-structured interviews in the UK HE sector. The paper identifies six key factors for successfully embedding carbon management that are pertinent not just for the HE sector, but to organizations broadly: senior management leadership; funding and resources; stakeholder engagement; planning; governance and management; and evaluation and reporting

A thorough psychometric comparison between Athens Insomnia Scale and Insomnia Severity Index among patients with advanced cancer

For patients with cancer, sleep disturbance is commonplace. Using classical test theory and Rasch analyses, the present study compared two commonly used psychometric instruments for insomnia – Athens Insomnia Scale (AIS) and Insomnia Severity Index (ISI) – among patients with advanced cancer. Through convenience sampling, patients with cancer at stage III or IV (n=573; 326 males; mean age=61.3 years; SD=10.7) from eight oncology units of university hospitals in Iran participated in the study. All the participants completed the AIS, ISI, Edmonton Symptom Assessment Scale (ESAS), Hospital Anxiety and Depression Scale (HADS), General Health Questionnaire-12 (GHQ-12), Epworth Sleepiness Scale (ESS), and Pittsburgh Sleep Quality Index (PSQI). Additionally, 433 participants wore an Actigraph device for two continuous weekdays. Classical test theory and Rasch analysis both supported the construct validity for AIS (factor loadings from confirmatory factor analysis [CFA] = 0.61 to 0.87; test-retest reliability = 0.72 to 0.82; infit mean square [MnSq] = 0.81 to 1.17; outfit MnSq = 0.79 to 1.14) and for ISI (factor loadings from CFA = 0.61 to 0.81; test-retest reliability = 0.72 to 0.82; infit MnSq = 0.72 to 1.14; outfit MnSq = 0.76 to 1.11). Both AIS and ISI had significant associations with ESAS, HADS, GHQ-12, ESS, and PSQI, as well as having good sensitivity and specificity. Significant differences in the actigraphy measure were found between insomniacs and non-insomniacs based on AIS or ISI score. With promising results, healthcare providers can use either AIS or ISI to understand the insomnia of patients with advanced cancer