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1. Measurement of the Positive Muon Anomalous Magnetic Moment to 0.46 ppm

   https://doi.org/10.1103/PhysRevLett.126.141801  

   Abi, B.; Albahri, T.; Al-Kilani, S.; Allspach, D.; Alonzi, L. P.; Anastasi, A.; Anisenkov, A.; Azfar, F.; Badgley, K.; Baeßler, S.; et al (April 2021, Physical Review Letters)

   null (Ed.)
2. Photocatalytic ATRP Depolymerization: Temporal Control at Low ppm of Catalyst Concentration

   https://doi.org/10.1021/jacs.3c05632  

   Parkatzidis, Kostas; Truong, Nghia P.; Matyjaszewski, Krzysztof; Anastasaki, Athina (September 2023, Journal of the American Chemical Society)
3. Measurement of the Positive Muon Anomalous Magnetic Moment to 0.20 ppm

   https://doi.org/10.1103/PhysRevLett.131.161802  

   Aguillard, D P; Albahri, T; Allspach, D; Anisenkov, A; Badgley, K; Baeßler, S; Bailey, I; Bailey, L; Baranov, V A; Barlas-Yucel, E; et al (October 2023, Physical Review Letters)

   We present a new measurement of the positive muon magnetic anomaly, 𝑎𝜇≡(𝑔𝜇−2)/2, from the Fermilab Muon 𝑔−2 Experiment using data collected in 2019 and 2020. We have analyzed more than 4 times the number of positrons from muon decay than in our previous result from 2018 data. The systematic error is reduced by more than a factor of 2 due to better running conditions, a more stable beam, and improved knowledge of the magnetic field weighted by the muon distribution, 𝜔𝑝, and of the anomalous precession frequency corrected for beam dynamics effects, 𝜔𝑎. From the ratio 𝜔𝑎/𝜔𝑝, together with precisely determined external parameters, we determine 𝑎𝜇=116 592 057⁢(25)×10−11 (0.21 ppm). Combining this result with our previous result from the 2018 data, we obtain 𝑎𝜇⁡(FNAL)=116 592 055⁢(24)×10−11 (0.20 ppm). The new experimental world average is 𝑎𝜇⁡(exp)=116 592 059⁢(22)×10−11 (0.19 ppm), which represents a factor of 2 improvement in precision. 

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4. Atom transfer radical polymerization in dispersed media with low-ppm catalyst loading

   https://doi.org/10.1016/j.polymer.2023.125913  

   Wang, Yi; Lorandi, Francesca; Fantin, Marco; Matyjaszewski, Krzysztof (May 2023, Polymer)
5. 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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