Reading Scene Text in Deep Convolutional Sequences

We develop a Deep-Text Recurrent Network (DTRN) that regards scene text reading as a sequence labelling problem. We leverage recent advances of deep convolutional neural networks to generate an ordered high-level sequence from a whole word image, avoiding the difficult character segmentation problem. Then a deep recurrent model, building on long short-term memory (LSTM), is developed to robustly recognize the generated CNN sequences, departing from most existing approaches recognising each character independently. Our model has a number of appealing properties in comparison to existing scene text recognition methods: (i) It can recognise highly ambiguous words by leveraging meaningful context information, allowing it to work reliably without either pre- or post-processing; (ii) the deep CNN feature is robust to various image distortions; (iii) it retains the explicit order information in word image, which is essential to discriminate word strings; (iv) the model does not depend on pre-defined dictionary, and it can process unknown words and arbitrary strings. Codes for the DTRN will be available.Comment: To appear in the 13th AAAI Conference on Artificial Intelligence (AAAI-16), 201

Mutual dependency grid for stakeholder mapping: a component-based approach to supply chain participant analysis

Stakeholder analysis plays a critical role in business analysis. However, the majority of the stakeholder identification and analysis methods focus on the activities and processes and ignore the artefacts being processed by human beings. By focusing on the outputs of the organisation, an artefact-centric view helps create a network of artefacts, and a component-based structure of the organisation and its supply chain participants. Since the relationship is based on the components, i.e. after the stakeholders are identified, the interdependency between stakeholders and the focal organisation can be measured. Each stakeholder is associated with two types of dependency, namely the stakeholder’s dependency on the focal organisation and the focal organisation’s dependency on the stakeholder. We identify three factors for each type of dependency and propose the equations that calculate the dependency indexes. Once both types of the dependency indexes are calculated, each stakeholder can be placed and categorised into one of the four groups, namely critical stakeholder, mutual benefits stakeholder, replaceable stakeholder, and easy care stakeholder. The mutual dependency grid and the dependency gap analysis, which further investigates the priority of each stakeholder by calculating the weighted dependency gap between the focal organisation and the stakeholder, subsequently help the focal organisation to better understand its stakeholders and manage its stakeholder relationships

A component-based method for stakeholder analysis

Stakeholders can facilitate or hinder an organisation’s performance significantly. The identification and management of the stakeholder is one of the key business activities for organisations. Although stakeholder identification is the first step of stakeholder analysis, there is little attention paid to the methodologies for stakeholder identification. This paper uses a system view point and proposes a component-based method for stakeholder identification and analysis, which focuses on the artefacts as linkage between different sub-systems of an organisation. Stakeholders, identified through components, include the processors who produce, use, communicate and control the component making process. The identified stakeholders can then be mapped into a stakeholder relationship map according to the components that are being used to identify the stakeholders. This method provides a novel approach to identify stakeholders through artefacts and define stakeholder relationship, through the a rtefacts they are involved in. Hence, it provides a comprehensive and better understanding of stakeholder management

Mindfulness-Based Intervention For Nurses In AIDS Care In China: A Pilot Study.

Background/purpose:Workplace stress among nurses providing care for people living with human immunodeficiency virus is a serious problem in China that may increase rates of job burnout and affect quality of care. Mindfulness-based intervention has been shown to be effective in relieving stress and burnout in nurses. Therefore, we designed a mixed-method pilot study to evaluate a mindfulness-based intervention for nurses providing care for people living with human immunodeficiency virus. Methods:Twenty nurses caring for people living with human immunodeficiency virus in the First Hospital of Changsha, China participated in a mindfulness-based intervention for 2 hr sessions weekly for 6 weeks. The Perceived Stress Scale, Maslach Burnout Inventory, Five Facets Mindfulness Questionnaire, State-Trait Anxiety Inventory, and the Beck Depression Inventory were used to collect data before and after the mindfulness-based intervention. Participants were invited to attend an in-depth interview 1 week after the end of the mindfulness-based intervention to give feedback. Results:The quantitative analyses revealed a significant change in Five Facets Mindfulness Questionnaire scores. There were no significant differences between pre- and post-intervention measures of any other variables. Qualitative results showed nurses experienced a decrease in work and daily life pressures; improvements in communications with patients, colleagues and families, with better regulation of negative emotions, and acceptance of other people and attention. Conclusion:This study supports the acceptability and potential benefits of the mindfulness-based intervention in helping nurses caring for people living with human immunodeficiency virus to manage stress and emotions, and improve their acceptance of others and attention. A larger study with a randomized controlled trial design is warranted to confirm the effectiveness of this mindfulness-based intervention

GI/Geom/1/N/MWV queue with changeover time and searching for the optimum service rate in working vacation period

AbstractIn this paper, we consider a finite buffer size discrete-time multiple working vacation queue with changeover time. Employing the supplementary variable and embedded Markov chain techniques, we derive the steady state system length distributions at different time epochs. Based on the various system length distributions, the blocking probability, probability mass function of sojourn time and other performance measures along with some numerical examples have been discussed. Then, we use the parabolic method to search the optimum value of the service rate in working vacation period under a given cost structure

Optimal Batched Best Arm Identification

We study the batched best arm identification (BBAI) problem, where the learner's goal is to identify the best arm while switching the policy as less as possible. In particular, we aim to find the best arm with probability $1-\delta$ for some small constant $\delta>0$ while minimizing both the sample complexity (total number of arm pulls) and the batch complexity (total number of batches). We propose the three-batch best arm identification (Tri-BBAI) algorithm, which is the first batched algorithm that achieves the optimal sample complexity in the asymptotic setting (i.e., $\delta\rightarrow 0$) and runs only in at most $3$ batches. Based on Tri-BBAI, we further propose the almost optimal batched best arm identification (Opt-BBAI) algorithm, which is the first algorithm that achieves the near-optimal sample and batch complexity in the non-asymptotic setting (i.e., $\delta>0$ is arbitrarily fixed), while enjoying the same batch and sample complexity as Tri-BBAI when $\delta$ tends to zero. Moreover, in the non-asymptotic setting, the complexity of previous batch algorithms is usually conditioned on the event that the best arm is returned (with a probability of at least $1-\delta$), which is potentially unbounded in cases where a sub-optimal arm is returned. In contrast, the complexity of Opt-BBAI does not rely on such an event. This is achieved through a novel procedure that we design for checking whether the best arm is eliminated, which is of independent interest.Comment: 32 pages, 1 figure, 3 table

Enhanced Federated Optimization: Adaptive Unbiased Sampling with Reduced Variance

Federated Learning (FL) is a distributed learning paradigm to train a global model across multiple devices without collecting local data. In FL, a server typically selects a subset of clients for each training round to optimize resource usage. Central to this process is the technique of unbiased client sampling, which ensures a representative selection of clients. Current methods primarily utilize a random sampling procedure which, despite its effectiveness, achieves suboptimal efficiency owing to the loose upper bound caused by the sampling variance. In this work, by adopting an independent sampling procedure, we propose a federated optimization framework focused on adaptive unbiased client sampling, improving the convergence rate via an online variance reduction strategy. In particular, we present the first adaptive client sampler, K-Vib, employing an independent sampling procedure. K-Vib achieves a linear speed-up on the regret bound $\tilde{\mathcal{O}}\big(N^{\frac{1}{3}}T^{\frac{2}{3}}/K^{\frac{4}{3}}\big)$ within a set communication budget $K$. Empirical studies indicate that K-Vib doubles the speed compared to baseline algorithms, demonstrating significant potential in federated optimization.Comment: Under revie

A New ZrCuSiAs-Type Superconductor: ThFeAsN

We report the first nitrogen-containing iron-pnictide superconductor ThFeAsN, which is synthesized by a solid-state reaction in an evacuated container. The compound crystallizes in a ZrCuSiAs-type structure with the space group P4/nmm and lattice parameters a=4.0367(1) {\AA} and c=8.5262(2) {\AA} at 300 K. The electrical resistivity and dc magnetic susceptibility measurements indicate superconductivity at 30 K for the nominally undoped ThFeAsN.Comment: 6 pages, 4 figures, 1 tabl