UR-94 EmoHydra: Multimodal Emotion Classification using Heterogenous Modality Fusion

Affective computing is a field of growing importance, as human society becomes more integrated with machines. Human feelings are both complex and multi-modal, expressed through various methods and nuances in behavior. In this work we introduce EmoHydra, a multi-modal model created through the fusion of three top-level models fine-tuned on text, vision, and speech respectively. Despite heterogenous heads performing well on the unseen data, as well as generalizing well to other benchmarks, logit concatenation proves to be ineffective at predicting Multimodal data, therefore we implement Multi-Head Attention as our fusion mechanism

UR-78 Transforming Game Play: A Comparative Study of CNN and Transformer based Q-Networks in Reinforcement Learning

In this study we investigate the performance of Deep Q-Networks utilizing Convolutional Neural Networks (CNNs) and Transformer architectures across 3 different Atari Games. The advent of DQNs have significantly advanced Reinforcement Learning, enabling agents to directly learn optimal policy from high dimensional sensory inputs from pixel or RAM data. While CNN based DQNs have been extensively studied and deployed in various domains Transformer based DQNs are relatively unexplored. Our research aims to fill this gap by benchmarking the performance of both DCQNs and DTQNs across the Atari games\u27 Asteroids, Space Invaders and Centipede. Our research finds that our Transformer Agent learned slower than the CNN-based agent, and was slower to learn game-extending policies

UC-85 HELPR: Helping Extrapolate Labels for Police Reports using Large Language Models

Police officers spend many hours a week documenting their findings when reporting to a 911 call. There is so much detail in these reports that they remain an untapped resource for future data analytics by the police department. The reports are currently being analyzed by human experts and categorized into the following categories: “Substance Abuse”, “Mental Health”, “Domestic/Social”, “Nondomestic/Social”, and “Other”. To assist the experts and reduce the amount of time that is spent on reading and analyzing, we are proposing the use of large language models (LLMs) to tag police reports based on their content. Two models, Mistral-7B and TinyLlama, have been trained and fine-tuned to reduce the time needed to complete police report documentation. Both models output both the tag and the reason for the chosen tag, so one of the potential uses is for it to be used to train human analyzers in the future. For the finetuned Mistral-7B model we observed a 84% and 88% agreement with both human annotators and a 96% and 92% agreement with at least one human annotator on sample tagging and sample reasoning respectively

Framing the future for taxonomic monography: Improving recognition, support, and access

No abstract available

Framing the future for taxonomic monography: Improving recognition, support, and access

Taxonomic monographs synthesize biodiversity knowledge and document biodiversity change through recent and geological time for a particular organismal group, sometimes also incorporating cultural and place-based knowledge. They are a vehicle through which broader questions about ecological and evolutionary patterns and processes can be generated and answered (e.g., Muñoz Rodríguez et al., 2019). Chiefly, monography represents the foundational research upon which all biological work is based (Hamilton et al., 2021). Moreover, monography can be a pathway to developing inclusive scientific practices, engaging diverse audiences in expanding and disseminating indigenous and local knowledge and significance of place. Apart from the scientific importance of monography, these comprehensive biodiversity treatments are also crucial for policy, conservation, human wellbeing, and the sustainable use of natural resources. Taxonomic, cultural and biodiversity data within monographs aid in the implementation of law and policy, such as the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), the Nagoya Protocol of the Convention on Biological Diversity (Buck & Hamilton, 2011), and the International Union for Conservation of Nature (IUCN) Red List (e.g., Neo et al., 2017). While vital as a knowledge resource and tool for conservation and research, monographs are not available for many groups of organisms. This is of particular concern for organisms that are threatened with extinction, of medical or economic importance, and those organisms that have the potential to provide insight into biodiversity change over time because they are most susceptible to global change. In discussing the future of collections-based systematics, researchers have highlighted the importance of updated monographic workflows, collaborative teams, and effective ways to educate and disseminate the results of monographs to the public and scientific community (e.g., Wen et al., 2015; Grace et al., 2021). Here, we discuss how improving recognition, support, and access can lead to greater inclusivity while promoting a more active, sustainable, and collaborative outlook for monographic research. </p

Following the Money: Lessons from the Panama Papers, Part 1: Tip of the Iceberg

Widely known as the Panama Papers, the world\u27s largest whistleblower case to date consists of 11.5 million documents and involves a year-long effort by the International Consortium of Investigative Journalists to expose a global pattern of crime and corruption where millions of documents capture heads of state, criminals, and celebrities using secret hideaways in tax havens. Involving the scrutiny of over 400 journalists worldwide, these documents reveal the offshore holdings of at least hundreds of politicians and public officials in over 200 countries. Since these disclosures became public, national security implications already include abrupt regime change and probable future political instability. It appears likely that important revelations obtained from these data will continue to be forthcoming for years to come. Presented here is Part 1 of what may ultimately constitute numerous-installment coverage of this important inquiry into the illicit wealth derived from bribery, corruption, and tax evasion. This article proceeds as follows. First, disclosures regarding the treasure trove of documents from the Panama-based law firm Mossack Fonseca are reviewed. Second is a discussion of the impact and cost of bribery and corruption to the global community. Third, I define and briefly explore issues surrounding tax evasion. Fourth, the impact of social media and technological change on transparency is discussed. Next, a few thoughts about implications for future research are offered