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1. Next Generation Computers Warrant Next Generation Groundwater Models

   https://doi.org/10.1111/gwat.13325  

   Engdahl, Nicholas B. (May 2023, Groundwater)

   Modern hydrologic models have extraordinary capabilities for representing complex process in surface-subsurface systems. These capabilities have revolutionized the way we conceptualize flow systems, but how to represent uncertainty in simulated flow systems is not as well developed. Currently, characterizing model uncertainty can be computationally expensive, in part, because the techniques are appended to the numerical methods rather than seamlessly integrated. The next generation of computers, however, presents opportunities to reformulate the modeling problem so that the uncertainty components are handled more directly within the flow system simulation. Misconceptions about quantum computing abound and they will not be a “silver bullet” for solving all complex problems, but they might be leveraged for certain kinds of highly uncertain problems, such as groundwater (GW). The point of this issue paper is that the GW community could try to revise the foundations of our models so that the governing equations being solved are tailored specifically for quantum computers. The goal moving forward should not just be to accelerate the models we have, but also to address their deficiencies. Embedding uncertainty into the models by evolving distribution functions will make predictive GW modeling more complicated, but doing so places the problem into a complexity class that is highly efficient on quantum computing hardware. Next generation GW models could put uncertainty into the problem at the very beginning of a simulation and leave it there throughout, providing a completely new way of simulating subsurface flows. 

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2. Validation and inter-comparison of models for landslide tsunami generation

   https://doi.org/10.1016/j.ocemod.2021.101943  

   Kirby, James T.; Grilli, Stephan T.; Horrillo, Juan; Liu, Philip L.-F.; Nicolsky, Dmitry; Abadie, Stephane; Ataie-Ashtiani, Behzad; Castro, Manuel J.; Clous, Lucie; Escalante, Cipriano; et al (February 2022, Ocean Modelling)
3. Measuring And Improving Engagement of Text-to-Image Generation Models

   Khurana, Varun; Singla, Yaman_Kumar; Subramanian, Jayakumar; Chen, Changyou; Shah, Rajiv_Ratn; Xu, zhiqiang; Krishnamurthy, Balaji (April 2025, The Thirteenth International Conference on Learning Representations)
4. PPM: Automated Generation of Diverse Programming Problems for Benchmarking Code Generation Models

   https://doi.org/10.1145/3643780  

   Chen, Simin; Feng, XiaoNing; Han, Xiaohong; Liu, Cong; Yang, Wei (July 2024, Proceedings of the ACM on Software Engineering)

   In recent times, a plethora of Large Code Generation Models (LCGMs) have been proposed, showcasing significant potential in assisting developers with complex programming tasks. Within the surge of LCGM proposals, a critical aspect of code generation research involves effectively benchmarking the programming capabilities of models. Benchmarking LCGMs necessitates the creation of a set of diverse programming problems, and each problem comprises the prompt (including the task description), canonical solution, and test inputs. The existing methods for constructing such a problem set can be categorized into two main types: manual methods and perturbation-based methods. However, %both these methods exhibit major limitations. %Firstly, manually-based methods require substantial human effort and are not easily scalable. Moreover, programming problem sets created manually struggle to maintain long-term data integrity due to the greedy training data collection mechanism in LCGMs. On the other hand, perturbation-based approaches primarily produce semantically homogeneous problems, resulting in generated programming problems with identical Canonical Solutions to the seed problem. These methods also tend to introduce typos to the prompt, easily detectable by IDEs, rendering them unrealistic. manual methods demand high effort and lack scalability, while also risking data integrity due to LCGMs' potentially contaminated data collection, and perturbation-based approaches mainly generate semantically homogeneous problems with the same canonical solutions and introduce typos that can be easily auto-corrected by IDE, making them ineffective and unrealistic. Addressing the aforementioned limitations presents several challenges: (1) How to automatically generate semantically diverse Canonical Solutions to enable comprehensive benchmarking on the models, (2) how to ensure long-term data integrity to prevent data contamination, and (3) how to generate natural and realistic programming problems. To tackle the first challenge, we draw key insights from viewing a program as a series of mappings from the input to the output domain. These mappings can be transformed, split, reordered, or merged to construct new programs. Based on this insight, we propose programming problem merging, where two existing programming problems are combined to create new ones. In addressing the second challenge, we incorporate randomness to our programming problem-generation process. Our tool can probabilistically guarantee no data repetition across two random trials. To tackle the third challenge, we propose the concept of a Lambda Programming Problem, comprising a concise one-sentence task description in natural language accompanied by a corresponding program implementation. Our tool ensures the program prompt is grammatically correct. Additionally, the tool leverages return value type analysis to verify the correctness of newly created Canonical Solutions. In our empirical evaluation, we utilize our tool on two widely-used datasets and compare it against nine baseline methods using eight code generation models. The results demonstrate the effectiveness of our tool in generating more challenging, diverse, and natural programming problems, comparing to the baselines. 

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5. Mirror Diffusion Models for Constrained and Watermarked Generation

   Liu, Guan-Horng; Chen, Tianrong; Theodorou, Evangelos A; Tao, Molei (December 2023, NeurIPS)