Learning byassociating ambiguously labeled images.

The present note studies \emph{surjective rational endomorphisms} $f: \mathbb{P}^2 \dashrightarrow \mathbb{P}^2$ with \emph{cubic} terms and the indeterminacy locus $I_f \ne \emptyset$. We develop an experimental approach, based on some Python programming and Machine Learning, towards the classification of such maps; a couple of new explicit $f$ is constructed in this way. We also prove (via pure projective geometry) that a general non-regular cubic endomorphism $f$ of $\mathbb{P}^2$ is surjective if and only if the set $I_f$ has cardinality at least $3$.

This paper presents linear DML models for causal inference using the simplest Python code on a Jupyter notebook based on an Anaconda platform and compares the performance of different DML models. The results show that current Library API technology is not yet sufficient to enable novice Python users to build qualified and high-quality DML models with the simplest coding approach. Novice users attempting to perform DML causal inference using Python still have to improve their mathematical and computer knowledge to adapt to more flexible DML programming. Additionally, the issue of mismatched outcome variable dimensions is also widespread when building linear DML models in Jupyter notebook.

Accurate solar flare prediction is crucial due to the significant risks that intense solar flares pose to astronauts, space equipment, and satellite communication systems. Our research enhances solar flare prediction by utilizing advanced data preprocessing and classification methods on a multivariate time series-based dataset of photospheric magnetic field parameters. First, our study employs a novel preprocessing pipeline that includes missing value imputation, normalization, balanced sampling, near decision boundary sample removal, and feature selection to significantly boost prediction accuracy. Second, we integrate contrastive learning with a GRU regression model to develop a novel classifier, termed ContReg, which employs dual learning methodologies, thereby further enhancing prediction performance. To validate the effectiveness of our preprocessing pipeline, we compare and demonstrate the performance gain of each step, and to demonstrate the efficacy of the ContReg classifier, we compare its performance to that of sequence-based deep learning architectures, machine learning models, and findings from previous studies. Our results illustrate exceptional True Skill Statistic (TSS) scores, surpassing previous methods and highlighting the critical role of precise data preprocessing and classifier development in time series-based solar flare prediction.

In recent years, urban safety has become a paramount concern for city planners and law enforcement agencies. Accurate prediction of likely crime occurrences can significantly enhance preventive measures and resource allocation. However, many law enforcement departments lack the tools to analyze and apply advanced AI and ML techniques that can support city planners, watch programs, and safety leaders to take proactive steps towards overall community safety. This paper explores the effectiveness of ML techniques to predict spatial and temporal patterns of crimes in urban areas. Leveraging police dispatch call data from San Jose, CA, the research goal is to achieve a high degree of accuracy in categorizing calls into priority levels particularly for more dangerous situations that require an immediate law enforcement response. This categorization is informed by the time, place, and nature of the call. The research steps include data extraction, preprocessing, feature engineering, exploratory data analysis, implementation, optimization and tuning of different supervised machine learning models and neural networks. The accuracy and precision are examined for different models and features at varying granularity of crime categories and location precision. The results demonstrate that when compared to a variety of other models, Random Forest classification models are most effective in identifying dangerous situations and their corresponding priority levels with high accuracy (Accuracy = 85%, AUC = 0.92) at a local level while ensuring a minimum amount of false negatives. While further research and data gathering is needed to include other social and economic factors, these results provide valuable insights for law enforcement agencies to optimize resources, develop proactive deployment approaches, and adjust response patterns to enhance overall public safety outcomes in an unbiased way.

Building Management System (BMS) through a data-driven method always faces data and model scalability issues. We propose a methodology to tackle the scalability challenges associated with the development of data-driven models for BMS by using Large Language Models (LLMs). LLMs' code generation adaptability can enable broader adoption of BMS by "automating the automation," particularly the data handling and data-driven modeling processes. In this paper, we use LLMs to generate code that processes structured data from BMS and build data-driven models for BMS's specific requirements. This eliminates the need for manual data and model development, reducing the time, effort, and cost associated with this process. Our hypothesis is that LLMs can incorporate domain knowledge about data science and BMS into data processing and modeling, ensuring that the data-driven modeling is automated for specific requirements of different building types and control objectives, which also improves accuracy and scalability. We generate a prompt template following the framework of Machine Learning Operations so that the prompts are designed to systematically generate Python code for data-driven modeling. Our case study indicates that bi-sequential prompting under the prompt template can achieve a high success rate of code generation and code accuracy, and significantly reduce human labor costs.

Understanding and mitigating political bias in online social media platforms are crucial tasks to combat misinformation and echo chamber effects. However, characterizing political bias temporally using computational methods presents challenges due to the high frequency of noise in social media datasets. While existing research has explored various approaches to political bias characterization, the ability to forecast political bias and anticipate how political conversations might evolve in the near future has not been extensively studied. In this paper, we propose a heuristic approach to classify social media posts into five distinct political leaning categories. Since there is a lack of prior work on forecasting political bias, we conduct an in-depth analysis of existing baseline models to identify which model best fits to forecast political leaning time series. Our approach involves utilizing existing time series forecasting models on two social media datasets with different political ideologies, specifically Twitter and Gab. Through our experiments and analyses, we seek to shed light on the challenges and opportunities in forecasting political bias in social media platforms. Ultimately, our work aims to pave the way for developing more effective strategies to mitigate the negative impact of political bias in the digital realm.

The future of AI robotics never seems to have a silver lining, and here's a new nightmarish vision of what could come to pass from Jung_E, the latest film from Yeon Sang-ho (Train to Busan). The Netflix release is slated to hit the streaming service exclusively on January 20, 2023. The official synopsis from Netflix reveals a tease of the plot: "In a post-apocalyptic 22nd century, a researcher at an AI lab leads the effort to end a civil war by cloning the brain of a heroic soldier--her mother." The sleek, dystopian shine of Kronoid Lab's "most advanced A.I. combat warrior" invokes a feeling of dread when the very human-looking creation is built. Even the full-on wired brain that looks like one of those steel wool sponges gave me the icks.

Photo editing app Lensa grew massively popular over the last week as social media has become flooded with people posting AI-generated selfies from the app's latest feature. For $3.99, Lensa users can upload 10-20 images of themselves and then receive 50 selfies generated by the app's artificial intelligence in a variety of art styles. But, before you slam the purchase button, a word of warning: Lensa's privacy policy and terms of use stipulate that the images users submit to generate their selfies, or rather the "Face Data," can be used by Prisma AI, the company behind Lensa, to further train the AI's neural network. An artificial neural network like the one used by Lensa, or the popular text-to-image generator Dall-E 2, studies vast quantitites of data to learn how to create better and better results. To be able to convert simple sentences into surprisingly well-crafted images, Dall-E 2 was trained on hundreds of millions of images to learn the association between different words and different visual characteristics. Similarly, Lensa's neural network is continuously learning how to more accurately portray faces.

Bayesian additive regression tree (BART) models have seen increased attention in recent years as a general-purpose nonparametric modeling technique. BART combines the flexibility of modern machine learning techniques with the principled uncertainty quantification of Bayesian inference, and it has been shown to be uniquely appropriate for addressing the high-noise problems that occur commonly in many areas of science, including medicine and the social sciences. This paper introduces the SoftBart package for fitting the Soft BART algorithm of Linero and Yang (2018). In addition to improving upon the predictive performance of other BART packages, a major goal of this package has been to facilitate the inclusion of BART in larger models, making it ideal for researchers in Bayesian statistics. I show both how to use this package for standard prediction tasks and how to embed BART models in larger models; I illustrate by using SoftBart to implement a nonparametric probit regression model, a semiparametric varying coefficient model, and a partial linear model.