Pseudorepresentations not arising from genuine representations

We show that a pseudorepresentation $\textbf{D}\colon A[G] \to A$ of a (finite) group $G$ need not arise from a genuine representation, even if one is allowed to extend the ring $A$. This shows that a theorem of the "embedding problem" for residually multiplicity free pseudorepresentations can not be extended to the general setting

Incorporating Discriminator in Sentence Generation: a Gibbs Sampling Method

Generating plausible and fluent sentence with desired properties has long been a challenge. Most of the recent works use recurrent neural networks (RNNs) and their variants to predict following words given previous sequence and target label. In this paper, we propose a novel framework to generate constrained sentences via Gibbs Sampling. The candidate sentences are revised and updated iteratively, with sampled new words replacing old ones. Our experiments show the effectiveness of the proposed method to generate plausible and diverse sentences.Comment: published in The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), 201

The Development of Big Data & Artificial Intelligence in the Field of Healthcare——The Case of Ping An Health (Ping An Good Doctor)

This report explores the utilization of big data and artifcial intelligence (AI) in the healthcare sector, focusing on the case of Ping An Health (formerly Ping An Good Doctor) in China. The rapid advancement of Internet technology has propelled the widespread adoption of these technologies across various industries. PingAn Health leverages its platform’s advantages to continuously innovate and enhance user experience, positioning itself at the forefront of the industry. The report delves into Ping An Health’s AI and big data technologies, ofering critical analyses of the ethical, political, and social implications surrounding the company

UAV based GNSS reflectometry

This project combines GNSS-R technology and UAVs in the context of the aerial remote sensing field, from the design of the assembly of the UAVs and GNSS-R to the execution of the different tests, as well as the data collection and analysis. As it will be seen in the document, this analysis has allowed to improve the original design of the system. The remote sensing technology based on GNSS reflected signals is simply called GNSS-R, and the greatest advantage of UAV as a mounting platform is the low cost, high system availability, and fast installation. Thus the main task of this project is to experimentally verify the possibility of CTTC's own developed GNSS-R working with UAVs. In this report, first I explain some basic principles of GNSS and GNSS-R, as they are important technical backgrounds for the whole project. Secondly, I describe the application of the RTKPOST software, which is an important processing and analyzing tool in the entire project, mainly describing the positioning methods used in the different processing modes in RTKPOST, also the parameter needed for analysis. Then, the design of the UAV-GNSS-R assembly is discussed, with different options based on the characteristics of the UAV, such as weight, payload space, and so on. The whole experimental process is also explained, and all the test data are analyzed and processed by using the Static mode or Kinematic mode in RTKPOST, and the data results are analyzed and compared in terms of satellite visibility, signal-to-noise ratio, elevation angle, Standard Deviation and Root Mean Square. The experimental process is roughly divided into three parts. The initial verification test is used to collect signal data as a basis, through which it is found that drone interferes with GNSS-R to a greater extent, and that the reflector antenna cannot receive the signal if the drone is placed too close to the ground. The second part of the experiment is a stationary test, where the UAV is placed on a tripod, and it's found that there is a difference in accuracy when the UAV is working on different land surfaces, which a higher accuracy can be obtained on rock than on grassy field. It is also found that the interference problem can be mitigated when the receiver is placed farther away from the UAV body. In the last part of the flight test, the optimization of the interference problem is further confirmed by the comparison of the two flight tests, and the positioning error is reduced from about 50m to 6m. At the end of the report, suggestions are given for the future development of this project, as it is clear that the meter-level accuracy does not meet the needs of many applications, and that the integration of the UAS and GNSS-R needs to be further optimized to reduce interference problems and thus achieve higher accuracy positioning.Objectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenible

Automatically Generating a Video Based on User Provided Text Input

Visual stories are a popular format for online storytelling in many contexts. Visualizing text often helps a reader understand the story. There are tools that currently exist which can generate multimedia based on user input text. However, the generated media may not always match the text input and may include images that are diverse in style. This disclosure describes techniques that use generative artificial intelligence to automatically generate images, animation, and audio based on user input text and preferences. The generated assets are combined into a visual story that has a coherent visual theme and that can help viewers understand text-based content better

Property Impacts on Performance of CO2 Pipeline Transport

AbstractCarbon Capture and Storage (CCS) is one of the most potential technologies to mitigate climate change. Usingpipelinesto transport CO2 from emission sources to storage sitesis one of common and mature technologies. The design and operation of pipeline transport process requires careful considerations of thermo-physical properties.This paper studied the impact of properties, including density, viscosity, thermal conductivity and heat capacity, onthe performance of CO2 pipeline transport. The pressure loss and temperature dropin steady state were calculated by using homogenous friction model and Sukhof temperature drop theory, respectively. The results of sensitivity study show thatover-estimating density and viscosity increases the pressure loss while under-estimating of density and viscosity decreases it. Over-estimating density and heat capacity leads to lower temperature drop while under-estimating of density and heat capacity result in higher temperature drop.This study suggests that the accuracy of property models for example, more accurate density model, should be developed for the CO2 transport design

Adversarial Robustness in Unsupervised Machine Learning: A Systematic Review

As the adoption of machine learning models increases, ensuring robust models against adversarial attacks is increasingly important. With unsupervised machine learning gaining more attention, ensuring it is robust against attacks is vital. This paper conducts a systematic literature review on the robustness of unsupervised learning, collecting 86 papers. Our results show that most research focuses on privacy attacks, which have effective defenses; however, many attacks lack effective and general defensive measures. Based on the results, we formulate a model on the properties of an attack on unsupervised learning, contributing to future research by providing a model to use.Comment: 38 pages, 11 figure