Course evaluation is a critical component in higher education pedagogy. It not only serves to identify limitations in existing course designs and provide a basis for curricular innovation, but also to offer quantitative insights for university administrative decision-making. Traditional evaluation methods, primarily comprising student surveys, instructor self-assessments, and expert reviews, often encounter challenges, including inherent subjectivity, feedback delays, inefficiencies, and limitations in addressing innovative teaching approaches. Recent advancements in large language models (LLMs) within artificial intelligence (AI) present promising new avenues for enhancing course evaluation processes. This study explores the application of LLMs in automated course evaluation from multiple perspectives and conducts rigorous experiments across 100 courses at a major university in China. The findings indicate that: (1) LLMs can be an effective tool for course evaluation; (2) their effectiveness is contingent upon appropriate fine-tuning and prompt engineering; and (3) LLM-generated evaluation results demonstrate a notable level of rationality and interpretability.

Participatory budgeting, as a paradigm for democratic innovations, engages citizens in the distribution of a public budget to projects, which they propose and vote for implementation. So far, voting algorithms have been devised and studied in social choice literature to elect projects that are popular, while others prioritize on a proportional representation of voters' preferences, for instance, equal shares. However, the anticipated impact and novelty in the broader society by the winning projects, as selected by different algorithms, remains totally under-explored, lacking both a universal theory of impact for voting and a rigorous framework for impact and novelty assessments. This papers tackles this grand challenge towards new axiomatic foundations for designing effective and fair voting methods. This is via new and striking insights derived from a large-scale analysis of biases over 345 real-world voting outcomes, characterized for the first time by a novel portfolio of impact and novelty metrics. We find strong causal evidence that equal shares comes with impact loss in several infrastructural projects of different cost levels that have been so far over-represented. However, it also comes with a novel, yet over-represented, impact gain in welfare, education and culture. We discuss broader implications of these results and how impact loss can be mitigated at the stage of campaign design and project ideation.

Dimensionality reduction in vector databases is pivotal for streamlining AI data management, enabling efficient storage, faster computation, and improved model performance. This paper explores the benefits of reducing vector database dimensions, with a focus on computational efficiency and overcoming the curse of dimensionality. We introduce a novel application of Fast Fourier Transform (FFT) to dimensionality reduction, a method previously underexploited in this context. By demonstrating its utility across various AI domains, including Retrieval-Augmented Generation (RAG) models and image processing, this FFT-based approach promises to improve data retrieval processes and enhance the efficiency and scalability of AI solutions. The incorporation of FFT may not only optimize operations in real-time processing and recommendation systems but also extend to advanced image processing techniques, where dimensionality reduction can significantly improve performance and analysis efficiency. This paper advocates for the broader adoption of FFT in vector database management, marking a significant stride towards addressing the challenges of data volume and complexity in AI research and applications. Unlike many existing approaches, we directly handle the embedding vectors produced by the model after processing a test input.

Until recently, allegations of "racism" in the public sphere have operated like first degree murder charges do in courts of law--in order to establish such a charge, mainstream media often demanded proof of the alleged racist's intent. Dictionary definitions have long tracked this blinkered view of'racism.' For decades, Merriam-Webster's entry described racism as a "belief" of racial supremacy, or a program designed to put that belief into action. Because many people--and some judges--treat dictionary definitions as if they were legal prescriptions, accusations of racism have required proof of intent--a purposeful, race-based disparity in conduct or consequence. Thus, the legal framework for considering racial discrimination has largely echoed the dictionary's narrow take on racism.

The recent issues of Australian and Amazon wildfires have raised a burning question – the technology that has been a major facilitator to human evolution and growth, could it not do anything to predict, manage or control such destruction? Its high time that technologies like AI, data science and 5G connectivity should take charge of climatic advancement as well. The latest development in these technologies has shown some significant traits that can work for the betterment of the environment. Let's see how they can serve nature and climate. As noted by a report, the problem with climate change is that time is not on the side of humans -- mankind has to find and implement some solutions relatively fast.

Leveraging AI and IoT for environmental sustainability can help maximize our current efforts for environmental protection. According to a 2018 report by Intel, 74% of 200 business decision-makers in environmental sustainability agreed that AI would help solve environmental problems. Millions of electronic devices are discarded without proper disposal. Billions of dollars are wasted every year for proper disposal or recycling of used parts of discarded devices. To mitigate the issue of improper disposal of redundant electronic devices, companies like Apple use recycled materials or materials which have a low harmful impact on the environment.

By Benjamin Boettner Along developed riverbanks, physical barriers can help contain flooding and combat erosion. In arid regions, check dams can help retain soil after rainfall and restore damaged landscapes. In construction projects, metal plates can provide support for excavations, retaining walls on slopes, or permanent foundations. All of these applications can be addressed with the use of sheet piles, elements folded from flat material and driven vertically into the ground to form walls and stabilize soil. Proper soil stabilization is key to sustainable land management in industries such as construction, mining, and agriculture; and land degradation, the loss of ecosystem services from a given terrain, is a driver of climate change and is estimated to cost up to $10 trillion annually.

Sophisticated tools capable of collecting and analyzing massive data sets and then displaying the results in visual form are no longer an option. They are becoming a necessity. On a daily basis, thousands upon thousands of monitoring stations around the world collect vast quantities of air quality data for use in spotting pollution problems, analyzing air quality trends, and guiding effective responses. To date, these monitoring stations have served as digital eyes and ears trained on the planet's atmosphere. But all of that seems likely to change in the not-too-distant future as evolving networks of air sensors that are just now beginning to be deployed around the globe result in an avalanche of data, all of which has the very real potential to overwhelm those trying to make sense of it.