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1. Progress on the University of Hawaii 2.2-meter adaptive secondary mirror

   https://doi.org/10.1117/12.2629046  

   Chun, Mark R.; Ryan, Alan; Zhang, Ruihan; Kuiper, Stefan; Ackaert, Gilles; Baranec, Christoph; Baeten, M. J.; Bos, Arjo; Bowens-Rubin, Rachel; Dekker, Bert; et al (August 2022, Proc. SPIE 12185, Adaptive Optics Systems VIII)

   Schmidt, Dirk; Schreiber, Laura; Vernet, Elise (Ed.)

   We report on progress at the University of Hawaii on the integration and testing setups for the adaptive secondary mirror (ASM) for the University of Hawaii 2.2-meter telescope on Maunakea, Hawaii. We report on the development of the handling fixtures and alignment tools we will use along with progress on the optical metrology tools we will use for the lab and on-sky testing of the system. 

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2. Embeddings Among Quantum Affine sl_n

   https://doi.org/10.1007/s10114-023-2073-2  

   Li, Yi Qiang (June 2023, Acta Mathematica Sinica, English Series)

   We establish an explicit embedding of a quantum affine sl_n into a quantum affine sl_{n+1} . This embedding serves as a common generalization of two natural, but seemingly unrelated embeddings, one on the quantum affine Schur algebra level and the other on the non-quantum level. The embedding on the quantum affine Schur algebras is used extensively in the analysis of canonical bases of quantum affine sl_n and gl_n. The embedding on the non-quantum level is used crucially in a work of Riche and Williamson on the study of modular representation theory of general linear groups over a finite field. The same embedding is also used in a work of Maksimau on the categorical representations of affine general linear algebras. We further provide a more natural compatibility statement of the em- bedding on the idempotent version with that on the quantum affine Schur algebra level. A gl_n-variant of the embedding is also established. 

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3. Sulfur Modification in Natural RNA and Therapeutic Oligonucleotides

   https://doi.org/10.1039/D1CB00038A  

   Zheng, Ya Ying; Wu, Ying; Begley, Thomas; Sheng, Jia (January 2021, RSC Chemical Biology)

   null (Ed.)

   Sulfur modifications have been discovered on both DNA and RNA. Sulfur substitution of oxygen atoms at nucleobase or backbone locations in the nucleic acid framework led to a wide variety of sulfur-modified nucleosides and nucleotides. While the discovery, regulation and functions of DNA phosphorothioate (PS) modification, where one of the non-bridging oxygen atoms is replaced by sulfur on the DNA backbone, are important topics, this review focuses on the sulfur modification in natural cellular RNAs and therapeutic nucleic acids. The sulfur modifications on RNAs exhibit diversity in terms of modification locations and cellular functions, but the various sulfur modifications share common biosynthetic strategies across RNA species, cell types and all domains of life. The first section reviews the post-transcriptional sulfur modifications on nucleobase with emphasis on thiouridine on tRNA and phosphorothioate modification on RNA backbones, as well as the functions of the sulfur modifications on different species of cellular RNAs. The second section reviews the biosynthesis of different types of sulfur modifications and summarizes the general strategy for the biosynthesis of sulfur-containing RNA residues. One of the main goals of investigating the sulfur modifications is to enrich the genomic drug development pipeline and enhance our understandings of the rapidly growing nucleic acid-based gene therapy. The last section of the review focuses on the current drug development strategies employing sulfur substitution of oxygen atoms in therapeutic RNAs. 

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4. Antimatter Gravity: Second Quantization and Lagrangian Formalism

   https://doi.org/10.3390/physics2030022  

   Jentschura, Ulrich D. (September 2020, Physics)

   null (Ed.)

   The application of the CPT (charge-conjugation, parity, and time reversal) theorem to an apple falling on Earth leads to the description of an anti-apple falling on anti–Earth (not on Earth). On the microscopic level, the Dirac equation in curved space-time simultaneously describes spin-1/2 particles and their antiparticles coupled to the same curved space-time metric (e.g., the metric describing the gravitational field of the Earth). On the macroscopic level, the electromagnetically and gravitationally coupled Dirac equation therefore describes apples and anti-apples, falling on Earth, simultaneously. A particle-to-antiparticle transformation of the gravitationally coupled Dirac equation therefore yields information on the behavior of “anti-apples on Earth”. However, the problem is exacerbated by the fact that the operation of charge conjugation is much more complicated in curved, as opposed to flat, space-time. Our treatment is based on second-quantized field operators and uses the Lagrangian formalism. As an additional helpful result, prerequisite to our calculations, we establish the general form of the Dirac adjoint in curved space-time. On the basis of a theorem, we refute the existence of tiny, but potentially important, particle-antiparticle symmetry breaking terms in which possible existence has been investigated in the literature. Consequences for antimatter gravity experiments are discussed. 

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5. MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge

   Yuan, Geng; Ma, Xiaolong; Niu, Wei; Li, Zhengang; Kong, Zhenglun; Liu, Ning; Gong, Yifan; Zhan, Zheng; He, Chaoyang; Jin, Qing; et al (January 2021, Advances in Neural Information Processing Systems 34 (NeurIPS 2021))

   Recently, a new trend of exploring sparsity for accelerating neural network training has emerged, embracing the paradigm of training on the edge. This paper proposes a novel Memory-Economic Sparse Training (MEST) framework targeting for accurate and fast execution on edge devices. The proposed MEST framework consists of enhancements by Elastic Mutation (EM) and Soft Memory Bound (&S) that ensure superior accuracy at high sparsity ratios. Different from the existing works for sparse training, this current work reveals the importance of sparsity schemes on the performance of sparse training in terms of accuracy as well as training speed on real edge devices. On top of that, the paper proposes to employ data efficiency for further acceleration of sparse training. Our results suggest that unforgettable examples can be identified in-situ even during the dynamic exploration of sparsity masks in the sparse training process, and therefore can be removed for further training speedup on edge devices. Comparing with state-of-the-art (SOTA) works on accuracy, our MEST increases Top-1 accuracy significantly on ImageNet when using the same unstructured sparsity scheme. Systematical evaluation on accuracy, training speed, and memory footprint are conducted, where the proposed MEST framework consistently outperforms representative SOTA works. A reviewer strongly against our work based on his false assumptions and misunderstandings. On top of the previous submission, we employ data efficiency for further acceleration of sparse training. And we explore the impact of model sparsity, sparsity schemes, and sparse training algorithms on the number of removable training examples. Our codes are publicly available at: https://github.com/boone891214/MEST. 

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