A surrogate accelerated multicanonical Monte Carlo method for uncertainty quantification

In this work we consider a class of uncertainty quantification problems where the system performance or reliability is characterized by a scalar parameter $y$. The performance parameter $y$ is random due to the presence of various sources of uncertainty in the system, and our goal is to estimate the probability density function (PDF) of $y$. We propose to use the multicanonical Monte Carlo (MMC) method, a special type of adaptive importance sampling algorithm, to compute the PDF of interest. Moreover, we develop an adaptive algorithm to construct local Gaussian process surrogates to further accelerate the MMC iterations. With numerical examples we demonstrate that the proposed method can achieve several orders of magnitudes of speedup over the standard Monte Carlo method

Automated Feature Engineering for Time Series Data

Feature engineering for time series data, a critical task in data science, involves the transformation or encoding of raw data to create more predictive input features.This paper introduces a novel web framework designed to automate the labor-intensive and expertise-demanding process of time series feature engineering. The framework comprises advanced methods for automated feature extraction and selection, providing a wide range of application possibilities. A Bayesian Optimization strategy is also integrated to identify optimal features and model parameters for specific datasets, thereby enhancing prediction performance. The paper thoroughly explores the framework\u27s design principles and operational procedures, along with validation of its effectiveness across different domains using real-world datasets

The Impact of Corporate Social Responsibility on the Trust Repair of Brand with Negative Publicity: Mental Account as a Mediator

With the development of internet and popularity of mobile terminals, negative publicity of brand has become more and more widespread. This paper aims to study the impact of corporate social responsibility（CSR） on the trust repair of brand with negative publicity. From Chinese cultural aspect of the differential mode of association, CSR is divided into public morality behavior and private one. The concept of mental account is introduced as a mediating variable and CSR history as a moderate one. By a 2 (CSR type: public VS. private morality behavior) ×2(CSR history: long VS. short) between group experiment, it is found that public morality is more likely to be classified into charity account by consumers, thereby promoting integrity-based trust repair; private morality is more likely to be classified into remedy account, thereby promoting ability-based trust repair. Public morality behavior with long history is more tend to be attributed to charity account by consumers; and CSR including public and private one with short history are more tend to be attributed to remedy account by consumers

Risk governance in Banks-Based on the Study of Joint-stock Commercial Bank in China

The paper will divide into six parts. In chapter 2 and 3, this paper will review and summarize the general theories of corporate governance, commercial bank’s corporate governance, risk management and total risk management. Chapter 4 will introduce the main methodology in studying the current situation of China’s and foreign commercial bank’s corporate governance, especially the risk governance, and clarify the importance of consummating risk governance mechanism to China’s commercial banks, joint-stock commercial bank’s risk governance framework will be studied and designed. Then the methodology of an evaluation system will also be introduced to service the following research for a typical bank in China. In addition, the Mental Carlo method will be introduced to evaluate commercial banks market risk. In chapter 5, the results of the comparation between national and foreign commercial banks governance situations, the assessment of the typical bank in China and the evaluation for its risk governance mechanism will be given. In chapter 6, a brief conclusion will be made

The Impact of Digital Health Interventions for the Management of Type 2 Diabetes on Health and Social Care Utilisation and Costs: A Systematic Review

BACKGROUND: Digital health interventions such as smartphone applications (mHealth) or Internet resources (eHealth) are increasingly used to improve the management of chronic conditions, such as type 2 diabetes mellitus. These digital health interventions can augment or replace traditional health services and may be paid for using healthcare budgets. While the impact of digital health interventions for the management of type 2 diabetes on health outcomes has been reviewed extensively, less attention has been paid to their economic impact. OBJECTIVE: This study aims to critically review existing literature on the impact of digital health interventions for the management of type 2 diabetes on health and social care utilisation and costs. METHODS: Studies that assessed the impact on health and social care utilisation of digital health interventions for type 2 diabetes were included in the study. We restricted the digital health interventions to information provision, self-management and behaviour management. Four databases were searched (MEDLINE, EMBASE, PsycINFO and EconLit) for articles published between January 2010 and March 2021. The studies were analysed using a narrative synthesis approach. The risk of bias and reporting quality were appraised using the ROBINS-I checklist. RESULTS: The review included 22 studies. Overall, studies reported mixed evidence on the impact of digital health interventions on health and social care utilisation and costs, and suggested this impact differs according to the healthcare utilisation component. For example, digital health intervention use was associated with lower medication use and fewer outpatient appointments, whereas evidence on general practitioner visits and inpatient admissions was mixed. Most reviewed studies focus on a single component of healthcare utilisation. CONCLUSIONS: The review shows no clear evidence of an impact of digital health interventions on health and social care utilisation or costs. Further work is needed to assess the impact of digital health interventions across a broader range of care utilisation components and settings, including social and mental healthcare services. CLINICAL TRIAL REGISTRATION: The study protocol was registered on PROSPERO before searches began in April 2021 (registration number: CRD42020172621)

Verse: A Python library for reasoning about multi-agent hybrid system scenarios

We present the Verse library with the aim of making hybrid system verification more usable for multi-agent scenarios. In Verse, decision making agents move in a map and interact with each other through sensors. The decision logic for each agent is written in a subset of Python and the continuous dynamics is given by a black-box simulator. Multiple agents can be instantiated and they can be ported to different maps for creating scenarios. Verse provides functions for simulating and verifying such scenarios using existing reachability analysis algorithms. We illustrate several capabilities and use cases of the library with heterogeneous agents, incremental verification, different sensor models, and the flexibility of plugging in different subroutines for post computations.Comment: 26 pages, 16 figure

Towards Improving Document Understanding: An Exploration on Text-Grounding via MLLMs

In the field of document understanding, significant advances have been made in the fine-tuning of Multimodal Large Language Models (MLLMs) with instruction-following data. Nevertheless, the potential of text-grounding capability within text-rich scenarios remains underexplored. In this paper, we present a text-grounding document understanding model, termed TGDoc, which addresses this deficiency by enhancing MLLMs with the ability to discern the spatial positioning of text within images. Empirical evidence suggests that text-grounding improves the model's interpretation of textual content, thereby elevating its proficiency in comprehending text-rich images. Specifically, we compile a dataset containing 99K PowerPoint presentations sourced from the internet. We formulate instruction tuning tasks including text detection, recognition, and spotting to facilitate the cohesive alignment between the visual encoder and large language model. Moreover, we curate a collection of text-rich images and prompt the text-only GPT-4 to generate 12K high-quality conversations, featuring textual locations within text-rich scenarios. By integrating text location data into the instructions, TGDoc is adept at discerning text locations during the visual question process. Extensive experiments demonstrate that our method achieves state-of-the-art performance across multiple text-rich benchmarks, validating the effectiveness of our method

Does a working day keep the doctor away? A critical review of the impact of unemployment and job insecurity on health and social care utilisation

While the negative impact of unemployment on health is relatively well established, the extent to which that impact reflects on changes in health and social care utilisation is not well understood. This paper critically reviews the direction, magnitude and drivers of the impact of unemployment and job insecurity on health and social care utilisation across different care settings. We identified 28 relevant studies, which included 79 estimates of association between unemployment/job insecurity and healthcare utilisation. Positive associations dominated mental health services (N = 8 out of 11), but not necessarily primary care (N = 25 out of 43) or hospital care (N = 5 out of 22). We conducted a meta-analysis to summarise the magnitude of the impact and found that unemployed individuals were about 30% more likely to use health services compared to those employed, although this was largely driven by mental health service use. Key driving factors included financial pressure, health insurance, social network, disposable time and depression/anxiety. This review suggests that unemployment is likely to be associated with increased mental health service use, but there is considerable uncertainty around primary and hospital care utilisation. Future work to examine the impact across other settings, including community and social care, and further explore non-health determinants of utilisation is needed. The protocol was registered in PROSPERO (CRD42020177668)

ABatRe-Sim: A Comprehensive Framework for Automated Battery Recycling Simulation

With the rapid surge in the number of on-road Electric Vehicles (EVs), the amount of spent lithium-ion (Li-ion) batteries is also expected to explosively grow. The spent battery packs contain valuable metal and materials that should be recovered, recycled, and reused. However, only less than 5% of the Li-ion batteries are currently recycled, due to a multitude of challenges in technology, logistics and regulation. Existing battery recycling is performed manually, which can pose a series of risks to the human operator as a consequence of remaining high voltage and chemical hazards. Therefore, there is a critical need to develop an automated battery recycling system. In this paper, we present ABatRe-sim, an open-source robotic battery recycling simulator, to facilitate the research and development in efficient and effective battery recycling au-omation. Specifically, we develop a detailed CAD model of the battery pack (with screws, wires, and battery modules), which is imported into Gazebo to enable robot-object interaction in the robot operating system (ROS) environment. It also allows the simulation of battery packs of various aging conditions. Furthermore, perception, planning, and control algorithms are developed to establish the benchmark to demonstrate the interface and realize the basic functionalities for further user customization. Discussions on the utilization and future extensions of the simulator are also presented