Trusting Beliefs and Bases in the Adoption of Autonomous Last Mile Services (ALMS)

Last mile connectivity is crucial in transporting people from a transportation hub to a final destination. Autonomous last-mile service (ALMS) is one of the latest solutions for this problem, offering on-demand transportation connecting to the primary transportation method and operated automatically. However, the implementation of ALMS poses several challenges. Trust is an essential factor in enabling users to overcome their concerns about risk and uncertainty. Although trust can be developed towards various entities, existing studies have only explored trust in autonomous vehicles, without addressing overall trust in the ALMS. Additionally, ALMS is a sophisticated social-technological service, consisting of multiple components that could lead to different trust bases. Our research aims to identify the factors influencing trust in ALMS and identify ways to promote trust and overcome potential obstacles to adoption

Characterisation of dormancy cycling responses to environmental signals in contrasting Arabidopsis thaliana ecotypes

Seed dormancy is an important trait refined by evolution, to aid survival in adverse environments and to time germination and thereby select the correct habitat and climate space for subsequent plant growth and reproduction. Depth of dormancy changes continuously in response to the environment surrounding the seed and is therefore a relative rather than an absolute condition. In nature, these changes are triggered by seasonally characteristic environmental signals that are integrated by the seed over time to select the optimum conditions for germination. The mechanisms by which environmental signals influence this dormancy cycling have been studied in the present work using a combination of eco-physiology and molecular biology. Two contrasting Arabidopsis thaliana ecotypes Cape Verdi Isle (Cvi) and Burren (Bur) have been compared. They are adapted to a hot dry (Cvi) and a cool damp (Bur) climate and exhibit winter and summer annual phenotypes respectively. Experimental work in the laboratory, controlled environment and field has focussed on the effect of temperature, light and nitrate during seed maturation and subsequent imbibition. The work was also extended to studying other life cycle events such as the transition from vegetative growth to reproductive growth, flowering and seed maturity. This work has extended our understanding of the responses of life cycle traits to environmental signals. However, climates are changing and further data was collected in a series of experiments in a unique thermal gradient tunnel to provide insight into the impact of predicted global warming scenarios on these traits. The results presented indicate the plasticity of the plant life cycle and the extent to which global warming might affect this in Arabidopsis, and how increased temperature is likely to affect different annual phenotypes

Characterisation of dormancy cycling responses to environmental signals in contrasting Arabidopsis thaliana ecotypes

Seed dormancy is an important trait refined by evolution, to aid survival in adverse environments and to time germination and thereby select the correct habitat and climate space for subsequent plant growth and reproduction. Depth of dormancy changes continuously in response to the environment surrounding the seed and is therefore a relative rather than an absolute condition. In nature, these changes are triggered by seasonally characteristic environmental signals that are integrated by the seed over time to select the optimum conditions for germination. The mechanisms by which environmental signals influence this dormancy cycling have been studied in the present work using a combination of eco-physiology and molecular biology. Two contrasting Arabidopsis thaliana ecotypes Cape Verdi Isle (Cvi) and Burren (Bur) have been compared. They are adapted to a hot dry (Cvi) and a cool damp (Bur) climate and exhibit winter and summer annual phenotypes respectively. Experimental work in the laboratory, controlled environment and field has focussed on the effect of temperature, light and nitrate during seed maturation and subsequent imbibition. The work was also extended to studying other life cycle events such as the transition from vegetative growth to reproductive growth, flowering and seed maturity. This work has extended our understanding of the responses of life cycle traits to environmental signals. However, climates are changing and further data was collected in a series of experiments in a unique thermal gradient tunnel to provide insight into the impact of predicted global warming scenarios on these traits. The results presented indicate the plasticity of the plant life cycle and the extent to which global warming might affect this in Arabidopsis, and how increased temperature is likely to affect different annual phenotypes

Approximate Range Counting Under Differential Privacy

Range counting under differential privacy has been studied extensively. Unfortunately, lower bounds based on discrepancy theory suggest that large errors have to be introduced in order to preserve privacy: Essentially for any range space (except axis-parallel rectangles), the error has to be polynomial. In this paper, we show that by allowing a standard notion of geometric approximation where points near the boundary of the range may or may not be counted, the error can be reduced to logarithmic. Furthermore, our approximate range counting data structure can be used to solve the approximate nearest neighbor (ANN) problem and k-NN classification, leading to the first differentially private algorithms for these two problems with provable guarantees on the utility

Temperature, light and nitrate sensing coordinate Arabidopsis seed dormancy cycling resulting in winter and summer annual phenotypes

Seeds use environmental cues to sense the seasons and their surroundings to initiate the plants life cycle. Dormancy cycling underlying this process is extensively described, but the molecular mechanism is largely unknown. To address this we selected a range of representative genes from published array experiments in the laboratory and investigated their expression patterns in seeds of Arabidopsis ecotypes, having contrasting life cycles, over an annual dormancy cycle in the field. We show how mechanisms identified in the laboratory are coordinated in response to the soil environment to determine dormancy cycles that result in winter and summer annual phenotypes. Our results are consistent with a seed specific response to seasonal temperature patterns (temporal sensing) involving the gene DELAY OF GERMINATION1 (DOG1) that indicates the correct season; and concurrent temporally driven co-opted mechanisms that sense spatial signals i.e. nitrate via CBL-INTERACTING PROTEIN KINASE 23 (CIPK23) phosphorylation of the NITRATE TRANSPORTER 1 (NRT1.1) and light via PHYTOCHROME A (PHYA). In both ecotypes studied, when all three genes have low expression there is enhanced GIBBERELLIN 3 BETA-HYDROXYLASE 1 (GA3ox1) expression, exhumed seeds have the potential to germinate in the laboratory, and the initiation of seedling emergence occurs following soil disturbance (exposure to light) in the field. Unlike DOG1, expression of MOTHER of FLOWERING TIME (MFT) has an opposite thermal response in seeds of the two ecotypes indicating a role in determining their different dormancy cycling phenotypes

Insights on the Role of Social Media Tools for B2B SMEs: Case of UK Firms during and after COVID-19

In 2020, the outbreak of COVID-19 has brought about national closure, production stagnation and other serious consequences. In this case, enterprises must ensure the safety of workers and stakeholders while maintaining their business vitality. At the same time, customer behavior and consumption habits are undergoing great changes. Even if the economy recovers from the pandemic in the future, some of these habits will continue to exist. Therefore, this is an opportunity that cannot be missed. COVID-19 has accelerated the electronic process of enterprises and forces them to re-consider the existing business model. Among many kinds of enterprises, small and medium-sized enterprises (SMEs) are insufficient in terms of capital, human resources, material resources, information and technology, and thus are especially affected by COVID-19. The business-to-business sector is also negatively influenced as the upstream of the product or service supply chain. Due to the dynamic delay, it will take longer for B2B companies to recover demand after entering the “new normal”. Under this circumstance, adopting using social media tools can effectively mitigate the impact of the epidemic on B2B SMEs. Therefore, this paper selects B2B SMEs in the UK as the research object, because they are more vulnerable to the negative impact of COVID-19, and the disruptive development of e-commerce technology here is faster. This paper uses the method of qualitative research, conducting semi-structured in-depth interview, to collect data from five executives and key employees of British B2B SMEs to explore how they adjust needs and preferences for using social media tools during and after COVID-19, and understand their attitude towards these tools. By analyzing the information obtained from interviews, the study found that LinkedIn became the most popular tool for B2B SMEs in the UK after the outbreak because it could show the business status and positive attitude of the company to stakeholders and potential customers during COVID-19. Secondly, the response of B2B SMEs in the UK varied in different industries. Among them, companies in the manufacturing industry had more changes in the use of social media tools, including more types of tools and higher frequency of use. But on the whole, there were also ways of behaving that are obviously different from those of large companies. They intended to have longer event-oriented online meetings. Most B2B SMEs used a mixture of social media tools and traditional communication tools to complete the company's projects. The B2B SMEs in the UK showed their affirmation of the role of social media tools during the epidemic. However, some companies in the information industry were not sure whether they would continue their current usage habits after entering the "new normal". Besides the problem that social media tools can not meet all the work needs in one stop, most of the reasons were attributed to the attitudes of companies. They believed that B2B companies should pay more attention to sales, and thought that the large-scale use of social media tools in the normal period can not significantly improve their business performance. Therefore, the integrated use of social media tools requires the joint efforts of tool upgrading and companies' re-recognition of industry trends. Finally, this paper describes the theoretical contribution of the research, business implication for B2B SMEs and the shortcomings of the research, hoping to help enterprises plan ahead of time, predict economic recovery, and prepare for the “new normal”

Incremental Object Detection with CLIP

In the incremental detection task, unlike the incremental classification task, data ambiguity exists due to the possibility of an image having different labeled bounding boxes in multiple continuous learning stages. This phenomenon often impairs the model's ability to learn new classes. However, the forward compatibility of the model is less considered in existing work, which hinders the model's suitability for incremental learning. To overcome this obstacle, we propose to use a language-visual model such as CLIP to generate text feature embeddings for different class sets, which enhances the feature space globally. We then employ the broad classes to replace the unavailable novel classes in the early learning stage to simulate the actual incremental scenario. Finally, we use the CLIP image encoder to identify potential objects in the proposals, which are classified into the background by the model. We modify the background labels of those proposals to known classes and add the boxes to the training set to alleviate the problem of data ambiguity. We evaluate our approach on various incremental learning settings on the PASCAL VOC 2007 dataset, and our approach outperforms state-of-the-art methods, particularly for the new classes.Comment: 10 pages, 2 figure