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Polychlorinated biphenyls (PCBs) are a group of 209 highly stable molecules that were used extensively in industry. Although their commercial use ceased in 1979, they are still present in many aquatic ecosystems due to improper disposal, oceanic currents, atmospheric deposition, and hydrophobic nature. PCBs pose a significant and ongoing threat to the development and sustainability of aquatic organisms. In areas with PCB exposure high mortality rates of organisms inhabiting them are still seen today, posing a significant threat to local species. Zebrafish were exposed to a standard PCB mixture (Aroclor 1254) for the first 5 days post fertilization, as there is a gap in knowledge during this important developmental period for fish (i.e., organization of the body). This PCB mixture was formally available commercially and has a high prevalence in PCB-contaminated sites. We tested for the effects of PCB dosage (control (embryo water only; 0 mg/L), methanol (solvent control; 0 mg/L); PCB 1 (0.125 mg/L), PCB 2 (0.25 mg/L), PCB 3 (0.35 mg/L), and PCB 4 (0.40 mg/L)) on zebrafish survival, rate of metamorphosis, feeding efficiency, and growth. We found significant, dose-dependent effects of PCB exposure on mortality, feeding efficiency, and growth, but no clear effect of PCBs on the rate of zebrafish metamorphosis. We identified a concentration in which there were no observable effects (NOEC), PCB concentration above the NOEC had a significant impact on life-critical processes. This can further inform local management decisions in environments experiencing PCB contamination. 

Abstract With recent advances in multi‐modal foundation models, the previously text‐only large language models (LLM) have evolved to incorporate visual input, opening up unprecedented opportunities for various applications in visualization. Compared to existing work on LLM‐based visualization works that generate and control visualization with textual input and output only, the proposed approach explores the utilization of the visual processing ability of multi‐modal LLMs to develop Autonomous Visualization Agents (AVAs) that can evaluate the generated visualization and iterate on the result to accomplish user‐defined objectives defined through natural language. We propose the first framework for the design of AVAs and present several usage scenarios intended to demonstrate the general applicability of the proposed paradigm. Our preliminary exploration and proof‐of‐concept agents suggest that this approach can be widely applicable whenever the choices of appropriate visualization parameters require the interpretation of previous visual output. Our study indicates that AVAs represent a general paradigm for designing intelligent visualization systems that can achieve high‐level visualization goals, which pave the way for developing expert‐level visualization agents in the future. 

As scaling of conventional memory devices has stalled, many high-end computing systems have begun to incorporate alternative memory technologies to meet performance goals. Since these technologies present distinct advantages and tradeoffs compared to conventional DDR* SDRAM, such as higher bandwidth with lower capacity or vice versa, they are typically packaged alongside conventional SDRAM in a heterogeneous memory architecture. To utilize the different types of memory efficiently, new data management strategies are needed to match application usage to the best available memory technology. However, current proposals for managing heterogeneous memories are limited, because they either (1) do not consider high-level application behavior when assigning data to different types of memory or (2) require separate program execution (with a representative input) to collect information about how the application uses memory resources. This work presents a new data management toolset to address the limitations of existing approaches for managing complex memories. It extends the application runtime layer with automated monitoring and management routines that assign application data to the best tier of memory based on previous usage, without any need for source code modification or a separate profiling run. It evaluates this approach on a state-of-the-art server platform with both conventional DDR4 SDRAM and non-volatile Intel Optane DC memory, using both memory-intensive high-performance computing (HPC) applications as well as standard benchmarks. Overall, the results show that this approach improves program performance significantly compared to a standard unguided approach across a variety of workloads and system configurations. The HPC applications exhibit the largest benefits, with speedups ranging from 1.4× to 7× in the best cases. Additionally, we show that this approach achieves similar performance as a comparable offline profiling-based approach after a short startup period, without requiring separate program execution or offline analysis steps.