The state-of-the-art researches indicate that analytic algorithms are promising in handling complex floorplanning scenarios. However, it is challenging to generate compact floorplans with excellent wirelength optimization effect due to the local convergence of gradient-based optimization algorithms designed for constructed smooth optimization models. Accordingly, we propose to construct a nonsmooth analytic floorplanning model addressed by the conjugate subgradient algorithm (CSA), which is accelerated by a population-based scheme adaptively regulating the stepsize with the assistance of Q-learning. In this way, the proposed CSA assisted by Q-learning (CSAQ) can strike a good balance on exploration and exploitation. Experimental results on the MCNC and GSRC benchmarks demonstrate that the proposed fixed-outline floorplanning algorithm based on CSAQ (CSF) not only address global floorplanning effectively, but also get legal floorplans more efficiently than the constraint graph-based legalization algorithm as well as its improved variants. It is also demonstrated that the CSF is competitive to the state-of-the-art algorithms on floorplanning scenarios only containing hard modules.

Existing works focus on fixed-size layout pattern generation, while the more practical free-size pattern generation receives limited attention. In this paper, we propose ChatPattern, a novel Large-Language-Model (LLM) powered framework for flexible pattern customization. ChatPattern utilizes a two-part system featuring an expert LLM agent and a highly controllable layout pattern generator. The LLM agent can interpret natural language requirements and operate design tools to meet specified needs, while the generator excels in conditional layout generation, pattern modification, and memory-friendly patterns extension. Experiments on challenging pattern generation setting shows the ability of ChatPattern to synthesize high-quality large-scale patterns.

This study enhances stance detection on social media by incorporating deeper psychological attributes, specifically individuals' moral foundations. These theoretically-derived dimensions aim to provide a comprehensive profile of an individual's moral concerns which, in recent work, has been linked to behaviour in a range of domains, including society, politics, health, and the environment. In this paper, we investigate how moral foundation dimensions can contribute to predicting an individual's stance on a given target. Specifically we incorporate moral foundation features extracted from text, along with message semantic features, to classify stances at both message- and user-levels across a range of targets and models. Our preliminary results suggest that encoding moral foundations can enhance the performance of stance detection tasks and help illuminate the associations between specific moral foundations and online stances on target topics. The results highlight the importance of considering deeper psychological attributes in stance analysis and underscores the role of moral foundations in guiding online social behavior.

This paper presents two self-contained tutorials on stance detection in Twitter data using BERT fine-tuning and prompting large language models (LLMs). The first tutorial explains BERT architecture and tokenization, guiding users through training, tuning, and evaluating standard and domain-specific BERT models with HuggingFace transformers. The second focuses on constructing prompts and few-shot examples to elicit stances from ChatGPT and open-source FLAN-T5 without fine-tuning. Various prompting strategies are implemented and evaluated using confusion matrices and macro F1 scores. The tutorials provide code, visualizations, and insights revealing the strengths of few-shot ChatGPT and FLAN-T5 which outperform fine-tuned BERTs. By covering both model fine-tuning and prompting-based techniques in an accessible, hands-on manner, these tutorials enable learners to gain applied experience with cutting-edge methods for stance detection.

This paper develops a natural-language agent-based model of argumentation (ABMA). Its artificial deliberative agents (ADAs) are constructed with the help of so-called neural language models recently developed in AI and computational linguistics. ADAs are equipped with a minimalist belief system and may generate and submit novel contributions to a conversation. The natural-language ABMA allows us to simulate collective deliberation in English, i.e. with arguments, reasons, and claims themselves -- rather than with their mathematical representations (as in formal models). This paper uses the natural-language ABMA to test the robustness of formal reason-balancing models of argumentation [Maes & Flache 2013, Singer et al. 2019]: First of all, as long as ADAs remain passive, confirmation bias and homophily updating trigger polarization, which is consistent with results from formal models. However, once ADAs start to actively generate new contributions, the evolution of a conservation is dominated by properties of the agents *as authors*. This suggests that the creation of new arguments, reasons, and claims critically affects a conversation and is of pivotal importance for understanding the dynamics of collective deliberation. The paper closes by pointing out further fruitful applications of the model and challenges for future research.

As we near the end of the year, it's fair to say that 2019 has been a massive year, with sweeping new changes across most major industries. With so many emerging markets, we're seeing a substantial evolution in everything from technology (that computer-altered scene from the Shining, where they seamlessly replaced Jack Nicholson with Jim Carrey, blew everyone's minds when it first came out online) to developing sustainable methods of producing meat (if you live in Canada, many fast food restaurant chains from A&W to Tim Hortons now offers motherless meat substitutes, like Beyond Meat, in their sandwiches as an alternative). And then, there are controversial discussions around the legalization, wholesale distribution and non-medical retail presence of cannabis and its related consumer goods, like CBD. Taking the time to look at some of these "hot-trend" initiatives across North America's business world can be crucial to understanding their current (or future) economic impact. Our team at GURUS have put together a list of 10 big market trends that are hot topics for business leaders and investors going into 2020.

The Portuguese private consultancy LTPLabs has developed an algorithm to predict which countries will legalize cannabis recreationally and for medicinal use. Artificial intelligence, masses of data and 98 variables were the magic ingredients used to create this futuristic tool. LTPLabs is a private Portuguese consultancy made up of three professors from the University of Oporto, which was founded in 2014 to help companies make decisions. At the beginning of 2019, they published an algorithm that predicts which countries will legalize cannabis in the near future. The project came to them through a direct request from a client in the pharmaceutical area.

California voters decided last year that the sale of recreational marijuana should be made legal, beginning on Jan. 1, 2018. But Proposition 64 left many of the details to local governments and state regulators. So the last several months have been a race against the calendar, as officials have sought to develop rules governing where, when and how businesses may grow, transport and sell marijuana to adults. Last month, the state unveiled 276 pages of regulations for the new recreational pot marketplace. Among other things, the rules set hefty licensing fees, regulate how much THC will be allowed in edibles and other cannabis products, and require marijuana businesses to track their product from seed to sale.

Imagine if a third of your family died of the plague as religious fanatics preached "The end is nigh!" and threatened that the few pleasures you could squeeze out of life would lead to your eternal damnation. As wars raged around you, surrounded by death, disease, and the lowest grimy depths of poverty, it would be understandable to think the world was in the midst of an apocalyptic collapse. There have been a lot of periods throughout history when people thought they were living during the end times, and for some, this was an understandable attitude. But what about the person who tweets that the world is going to hell while safely sipping a latte on a city avenue? In many ways, things have never been better, yet there is still so much pessimism about where the world is heading. There are those who dedicate their careers to making sure the population stays angry, stressed and scared–we call them the media. I admit to being a part of the problem at times out of my failure to balance alerting readers to real dangers and giving breath to needless anxiety.

The legality of and public's view towards marijuana is rapidly changing as more states decriminalize and legalize the drug. As such, how have the words associated with marijuana in news articles changed over time? I developed a two-step approach to to try to answer this question. First, I wanted to identify distinct eras characterized by the use of key words associated with marijuana. Second, I aimed to assess whether words in each era represented larger themes regarding the public's view toards marijuana and the the drug's legality.