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1. An investigation of the very rare $$ {K}^{+}\to {\pi}^{+}\nu \overline{\nu} $$ decay

   https://doi.org/10.1007/JHEP11(2020)042  

   Cortina Gil, E. ; Kleimenova, A. ; Minucci, E. ; Padolski, S. ; Petrov, P. ; Shaikhiev, A. ; Volpe, R. ; Numao, T. ; Velghe, B. ; Bryman, D. ; et al ( November 2020 , Journal of High Energy Physics)

   null (Ed.)

   A bstract The NA62 experiment reports an investigation of the $$ {K}^{+}\to {\pi}^{+}\nu \overline{\nu} $$ K + → π + ν ν ¯ mode from a sample of K + decays collected in 2017 at the CERN SPS. The experiment has achieved a single event sensitivity of (0 . 389 ± 0 . 024) × 10 − 10 , corresponding to 2.2 events assuming the Standard Model branching ratio of (8 . 4 ± 1 . 0) × 10 − 11 . Two signal candidates are observed with an expected background of 1.5 events. Combined with the result of a similar analysis conducted by NA62 on a smaller data set recorded in 2016, the collaboration now reports an upper limit of 1 . 78 × 10 − 10 for the $$ {K}^{+}\to {\pi}^{+}\nu \overline{\nu} $$ K + → π + ν ν ¯ branching ratio at 90% CL. This, together with the corresponding 68% CL measurement of ( $$ {0.48}_{-0.48}^{+0.72} $$ 0.48 − 0.48 + 0.72 ) × 10 − 10 , are currently the most precise results worldwide, and are able to constrain some New Physics models that predict large enhancements still allowed by previous measurements. 

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2. CrowdGAIL: A spatiotemporal aware method for agent navigation

   https://doi.org/10.3934/era.2023057  

   Da, Longchao ; Wei, Hua ( January 2022 , Electronic Research Archive)

   Agent navigation has been a crucial task in today's service and automated factories. Many efforts are to set specific rules for agents in a certain scenario to regulate the agent's behaviors. However, not all situations could be in advance considered, which might lead to terrible performance in a real-world application. In this paper, we propose CrowdGAIL, a method to learn from expert behaviors as an instructing policy, can train most 'human-like' agents in navigation problems without manually setting any reward function or beforehand regulations. First, the proposed model structure is based on generative adversarial imitation learning (GAIL), which imitates how humans take actions and move toward the target to a maximum extent, and by comparison, we prove the advantage of proximal policy optimization (PPO) to trust region policy optimization, thus, GAIL-PPO is what we base. Second, we design a special Sequential DemoBuffer compatible with the inner long short-term memory structure to apply spatiotemporal instruction on the agent's next step. Third, the paper demonstrates the potential of the model with an integrated social manner in a multi-agent scenario by considering human collision avoidance as well as social comfort distance. At last, experiments on the generated dataset from CrowdNav verify how close our model would act like a human being in the trajectory aspect and also how it could guide the multi-agents by avoiding any collision. Under the same evaluation metrics, CrowdGAIL shows better results compared with classic Social-GAN.

    

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3. Computing optimal factories in metabolic networks with negative regulation

   https://doi.org/10.1093/bioinformatics/btac231  

   Krieger, Spencer ; Kececioglu, John ( June 2022 , Bioinformatics)

   A factory in a metabolic network specifies how to produce target molecules from source compounds through biochemical reactions, properly accounting for reaction stoichiometry to conserve or not deplete intermediate metabolites. While finding factories is a fundamental problem in systems biology, available methods do not consider the number of reactions used, nor address negative regulation.

   We introduce the new problem of finding optimal factories that use the fewest reactions, for the first time incorporating both first- and second-order negative regulation. We model this problem with directed hypergraphs, prove it is NP-complete, solve it via mixed-integer linear programming, and accommodate second-order negative regulation by an iterative approach that generates next-best factories.

   This optimization-based approach is remarkably fast in practice, typically finding optimal factories in a few seconds, even for metabolic networks involving tens of thousands of reactions and metabolites, as demonstrated through comprehensive experiments across all instances from standard reaction databases.

   Source code for an implementation of our new method for optimal factories with negative regulation in a new tool called Odinn, together with all datasets, is available free for non-commercial use at http://odinn.cs.arizona.edu.

    

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4. The SAMI Galaxy Survey: first detection of a transition in spin orientation with respect to cosmic filaments in the stellar kinematics of galaxies

   https://doi.org/10.1093/mnras/stz2860  

   Welker, C. ; Bland-Hawthorn, J. ; Van de Sande, J. ; Lagos, C. ; Elahi, P. ; Obreschkow, D. ; Bryant, J. ; Pichon, C. ; Cortese, L. ; Richards, S. N. ; et al ( October 2019 , Monthly Notices of the Royal Astronomical Society)

   We present the first detection of mass-dependent galactic spin alignments with local cosmic filaments with >2σ confidence using IFS kinematics. The 3D network of cosmic filaments is reconstructed on Mpc scales across GAlaxy and Mass Assembly fields using the cosmic web extractor DisPerSe. We assign field galaxies from the SAMI survey to their nearest filament segment in 3D and estimate the degree of alignment between SAMI galaxies’ kinematic spin axis and their nearest filament in projection. Low-mass galaxies align their spin with their nearest filament while higher mass counterparts are more likely to display an orthogonal orientation. The stellar transition mass from the first trend to the second is bracketed between $10^{10.4}$ and $10^{10.9}\, \mathrm{ M}_{\odot }$, with hints of an increase with filament scale. Consistent signals are found in the Horizon-AGN cosmological hydrodynamic simulation. This supports a scenario of early angular momentum build-up in vorticity rich quadrants around filaments at low stellar mass followed by progressive flip of spins orthogonal to the cosmic filaments through mergers at high stellar mass. Conversely, we show that dark matter only simulations post-processed with a semi-analytical model treatment of galaxy formation struggles to reproduce this alignment signal. This suggests that gas physics is key in enhancing the galaxy-filament alignment.

    

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5. Polynomials from Combinatorial -theory

   https://doi.org/10.4153/S0008414X19000464  

   Monical, Cara ; Pechenik, Oliver ; Searles, Dominic ( February 2021 , Canadian Journal of Mathematics)

   null (Ed.)

   Abstract We introduce two new bases of the ring of polynomials and study their relations to known bases. The first basis is the quasi-Lascoux basis, which is simultaneously both a $K$ -theoretic deformation of the quasi-key basis and also a lift of the $K$ -analogue of the quasi-Schur basis from quasi-symmetric polynomials to general polynomials. We give positive expansions of this quasi-Lascoux basis into the glide and Lascoux atom bases, as well as a positive expansion of the Lascoux basis into the quasi-Lascoux basis. As a special case, these expansions give the first proof that the $K$ -analogues of quasi-Schur polynomials expand positively in multifundamental quasi-symmetric polynomials of T. Lam and P. Pylyavskyy. The second new basis is the kaon basis, a $K$ -theoretic deformation of the fundamental particle basis. We give positive expansions of the glide and Lascoux atom bases into this kaon basis. Throughout, we explore how the relationships among these $K$ -analogues mirror the relationships among their cohomological counterparts. We make several “alternating sum” conjectures that are suggestive of Euler characteristic calculations. 

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