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1. Well-Posedness for Ohkitani Model and Long-Time Existence for Surface Quasi-geostrophic Equations

   https://doi.org/10.1007/s00220-025-05257-x  

   Chae, Dongho; Jeong, In-Jee; Na, Jungkyoung; Oh, Sung-Jin (April 2025, Communications in Mathematical Physics)

   Abstract We consider the Cauchy problem for the logarithmically singular surface quasi-geostrophic (SQG) equation, introduced by Ohkitani,$$\begin{aligned} \begin{aligned} \partial _t \theta - \nabla ^\perp \log (10+(-\Delta )^{\frac{1}{2}})\theta \cdot \nabla \theta = 0, \end{aligned} \end{aligned}$$ $\begin{array}{c}\begin{array}{c}{\partial }_t\theta -{\nabla }^{\perp }log(10+{(-\Delta )}^{\frac{1}{2}})\theta ·\nabla \theta =0,\end{array}\end{array}$and establish local existence and uniqueness of smooth solutions in the scale of Sobolev spaces with exponent decreasing with time. Such a decrease of the Sobolev exponent is necessary, as we have shown in the companion paper (Chae et al. in Illposedness via degenerate dispersion for generalized surface quasi-geostrophic equations with singular velocities,arXiv:2308.02120) that the problem is strongly ill-posed in any fixed Sobolev spaces. The time dependence of the Sobolev exponent can be removed when there is a dissipation term strictly stronger than log. These results improve wellposedness statements by Chae et al. (Comm Pure Appl Math 65(8):1037–1066, 2012). This well-posedness result can be applied to describe the long-time dynamics of the$$\delta $$ $\delta$-SQG equations, defined by$$\begin{aligned} \begin{aligned} \partial _t \theta + \nabla ^\perp (10+(-\Delta )^{\frac{1}{2}})^{-\delta }\theta \cdot \nabla \theta = 0, \end{aligned} \end{aligned}$$ $\begin{array}{c}\begin{array}{c}{\partial }_t\theta +{\nabla }^{\perp }{(10+{(-\Delta )}^{\frac{1}{2}})}^{-\delta }\theta ·\nabla \theta =0,\end{array}\end{array}$for all sufficiently small$$\delta >0$$ $\delta >0$depending on the size of the initial data. For the same range of$$\delta $$ $\delta$, we establish global well-posedness of smooth solutions to the dissipative SQG equations. 

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2. Measurements of the branching fractions of $$ {\Xi}_c^0\to {\Xi}^0{\pi}^0 $$, $$ {\Xi}_c^0\to {\Xi}^0\eta $$, and $$ {\Xi}_c^0\to {\Xi}^0{\eta}^{\prime } $$ and asymmetry parameter of $$ {\Xi}_c^0\to {\Xi}^0{\pi}^0 $$

   https://doi.org/10.1007/JHEP10(2024)045  

   Adachi, I; Aggarwal, L; Aihara, H; Akopov, N; Aloisio, A; Althubiti, N; Anh_Ky, N; Asner, D M; Atmacan, H; Aushev, T; et al (October 2024, Journal of High Energy Physics)

   A<sc>bstract</sc> We present a study of$$ {\Xi}_c^0\to {\Xi}^0{\pi}^0 $$ ${\Xi }_c^0\to {\Xi }^0{\pi }^0$,$$ {\Xi}_c^0\to {\Xi}^0\eta $$ ${\Xi }_c^0\to {\Xi }^0\eta$, and$$ {\Xi}_c^0\to {\Xi}^0{\eta}^{\prime } $$ ${\Xi }_c^0\to {\Xi }^0{\eta }^{\prime }$decays using the Belle and Belle II data samples, which have integrated luminosities of 980 fb⁻¹and 426 fb⁻¹, respectively. We measure the following relative branching fractions$$ {\displaystyle \begin{array}{c}\mathcal{B}\left({\Xi}_c^0\to {\Xi}^0{\pi}^0\right)/\mathcal{B}\left({\Xi}_c^0\to {\Xi}^{-}{\pi}^{+}\right)=0.48\pm 0.02\left(\textrm{stat}\right)\pm 0.03\left(\textrm{syst}\right),\\ {}\mathcal{B}\left({\Xi}_c^0\to {\Xi}^0\eta \right)/\mathcal{B}\left({\Xi}_c^0\to {\Xi}^{-}{\pi}^{+}\right)=0.11\pm 0.01\left(\textrm{stat}\right)\pm 0.01\left(\textrm{syst}\right),\\ {}\mathcal{B}\left({\Xi}_c^0\to {\Xi}^0{\eta}^{\prime}\right)/\mathcal{B}\left({\Xi}_c^0\to {\Xi}^{-}{\pi}^{+}\right)=0.08\pm 0.02\left(\textrm{stat}\right)\pm 0.01\left(\textrm{syst}\right)\end{array}} $$ $\begin{array}{c}B\left({\Xi }_c^0\to {\Xi }^0{\pi }^0\right)/B\left({\Xi }_c^0\to {\Xi }^{-}{\pi }^{+}\right)=0.48\pm 0.02\left(\text{stat}\right)\pm 0.03\left(\text{syst}\right),\\ B\left({\Xi }_c^0\to {\Xi }^0\eta \right)/B\left({\Xi }_c^0\to {\Xi }^{-}{\pi }^{+}\right)=0.11\pm 0.01\left(\text{stat}\right)\pm 0.01\left(\text{syst}\right),\\ B\left({\Xi }_c^0\to {\Xi }^0{\eta }^{\prime }\right)/B\left({\Xi }_c^0\to {\Xi }^{-}{\pi }^{+}\right)=0.08\pm 0.02\left(\text{stat}\right)\pm 0.01\left(\text{syst}\right)\end{array}$ for the first time, where the uncertainties are statistical (stat) and systematic (syst). By multiplying by the branching fraction of the normalization mode,$$ \mathcal{B}\left({\Xi}_c^0\to {\Xi}^{-}{\pi}^{+}\right) $$ $B\left({\Xi }_c^0\to {\Xi }^{-}{\pi }^{+}\right)$, we obtain the following absolute branching fraction results$$ {\displaystyle \begin{array}{c}\mathcal{B}\left({\Xi}_c^0\to {\Xi}^0{\pi}^0\right)=\left(6.9\pm 0.3\left(\textrm{stat}\right)\pm 0.5\left(\textrm{syst}\right)\pm 1.3\left(\operatorname{norm}\right)\right)\times {10}^{-3},\\ {}\mathcal{B}\left({\Xi}_c^0\to {\Xi}^0\eta \right)=\left(1.6\pm 0.2\left(\textrm{stat}\right)\pm 0.2\left(\textrm{syst}\right)\pm 0.3\left(\operatorname{norm}\right)\right)\times {10}^{-3},\\ {}\mathcal{B}\left({\varXi}_c^0\to {\Xi}^0{\eta}^{\prime}\right)=\left(1.2\pm 0.3\left(\textrm{stat}\right)\pm 0.1\left(\textrm{syst}\right)\pm 0.2\left(\operatorname{norm}\right)\right)\times {10}^{-3},\end{array}} $$ $\begin{array}{c}B\left({\Xi }_c^0\to {\Xi }^0{\pi }^0\right)=\left(6.9\pm 0.3\left(\text{stat}\right)\pm 0.5\left(\text{syst}\right)\pm 1.3\left(norm\right)\right)\times {10}^{-3},\\ B\left({\Xi }_c^0\to {\Xi }^0\eta \right)=\left(1.6\pm 0.2\left(\text{stat}\right)\pm 0.2\left(\text{syst}\right)\pm 0.3\left(norm\right)\right)\times {10}^{-3},\\ B\left({\Xi }_c^0\to {\Xi }^0{\eta }^{\prime }\right)=\left(1.2\pm 0.3\left(\text{stat}\right)\pm 0.1\left(\text{syst}\right)\pm 0.2\left(norm\right)\right)\times {10}^{-3},\end{array}$ where the third uncertainties are from$$ \mathcal{B}\left({\Xi}_c^0\to {\Xi}^{-}{\pi}^{+}\right) $$ $B\left({\Xi }_c^0\to {\Xi }^{-}{\pi }^{+}\right)$. The asymmetry parameter for$$ {\Xi}_c^0\to {\Xi}^0{\pi}^0 $$ ${\Xi }_c^0\to {\Xi }^0{\pi }^0$is measured to be$$ \alpha \left({\Xi}_c^0\to {\Xi}^0{\pi}^0\right)=-0.90\pm 0.15\left(\textrm{stat}\right)\pm 0.23\left(\textrm{syst}\right) $$ $\alpha \left({\Xi }_c^0\to {\Xi }^0{\pi }^0\right)=-0.90\pm 0.15\left(\text{stat}\right)\pm 0.23\left(\text{syst}\right)$. 

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3. Instantaneous everywhere-blowup of parabolic SPDEs

   https://doi.org/10.1007/s00440-024-01263-7  

   Foondun, Mohammud; Khoshnevisan, Davar; Nualart, Eulalia (October 2024, Probability Theory and Related Fields)

   Abstract We consider the following stochastic heat equation$$\begin{aligned} \partial _t u(t,x) = \tfrac{1}{2} \partial ^2_x u(t,x) + b(u(t,x)) + \sigma (u(t,x)) {\dot{W}}(t,x), \end{aligned}$$ $\begin{array}{c}{\partial }_{t}u(t,x)=\frac{1}{2}{\partial }_x^{2}u(t,x)+b\left(u(t,x)\right)+\sigma \left(u(t,x)\right)\dot{W}(t,x),\end{array}$defined for$$(t,x)\in (0,\infty )\times {\mathbb {R}}$$ $(t,x)\in (0,\infty )\times R$, where$${\dot{W}}$$ $\dot{W}$denotes space-time white noise. The function$$\sigma $$ $\sigma$is assumed to be positive, bounded, globally Lipschitz, and bounded uniformly away from the origin, and the functionbis assumed to be positive, locally Lipschitz and nondecreasing. We prove that the Osgood condition$$\begin{aligned} \int _1^\infty \frac{\textrm{d}y}{b(y)}<\infty \end{aligned}$$ $\begin{array}{c}{\int }_1^{\infty }\frac{\text{d}y}{b\left(y\right)}<\infty \end{array}$implies that the solution almost surely blows up everywhere and instantaneously, In other words, the Osgood condition ensures that$$\textrm{P}\{ u(t,x)=\infty \quad \hbox { for all } t>0 \hbox { and } x\in {\mathbb {R}}\}=1.$$ $\text{P}\left\{u\right(t,x)=\infty \text{for all}t>0\text{and}x\in R\}=1.$The main ingredients of the proof involve a hitting-time bound for a class of differential inequalities (Remark 3.3), and the study of the spatial growth of stochastic convolutions using techniques from the Malliavin calculus and the Poincaré inequalities that were developed in Chen et al. (Electron J Probab 26:1–37, 2021, J Funct Anal 282(2):109290, 2022). 

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4. Softmax policy gradient methods can take exponential time to converge

   https://doi.org/10.1007/s10107-022-01920-6  

   Li, Gen; Wei, Yuting; Chi, Yuejie; Chen, Yuxin (January 2023, Mathematical Programming)

   Abstract The softmax policy gradient (PG) method, which performs gradient ascent under softmax policy parameterization, is arguably one of the de facto implementations of policy optimization in modern reinforcement learning. For$$\gamma $$ $\gamma$-discounted infinite-horizon tabular Markov decision processes (MDPs), remarkable progress has recently been achieved towards establishing global convergence of softmax PG methods in finding a near-optimal policy. However, prior results fall short of delineating clear dependencies of convergence rates on salient parameters such as the cardinality of the state space$${\mathcal {S}}$$ $S$and the effective horizon$$\frac{1}{1-\gamma }$$ $\frac{1}{1-\gamma }$, both of which could be excessively large. In this paper, we deliver a pessimistic message regarding the iteration complexity of softmax PG methods, despite assuming access to exact gradient computation. Specifically, we demonstrate that the softmax PG method with stepsize$$\eta $$ $\eta$can take$$\begin{aligned} \frac{1}{\eta } |{\mathcal {S}}|^{2^{\Omega \big (\frac{1}{1-\gamma }\big )}} ~\text {iterations} \end{aligned}$$ $\begin{array}{c}\frac{1}{\eta }{\left|S\right|}^{2^{\Omega (\frac{1}{1-\gamma })}}\text{iterations}\end{array}$to converge, even in the presence of a benign policy initialization and an initial state distribution amenable to exploration (so that the distribution mismatch coefficient is not exceedingly large). This is accomplished by characterizing the algorithmic dynamics over a carefully-constructed MDP containing only three actions. Our exponential lower bound hints at the necessity of carefully adjusting update rules or enforcing proper regularization in accelerating PG methods. 

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5. Search for $$B_{(s)}^{*0}\rightarrow \mu ^+\mu ^-$$ in $$B_c^+\rightarrow \pi ^+\mu ^+\mu ^-$$ decays

   https://doi.org/10.1140/epjc/s10052-024-13573-0  

   Aaij, R; Abdelmotteleb, A_S W; Beteta, C Abellan; Abudinén, F; Ackernley, T; Adefisoye, A A; Adeva, B; Adinolfi, M; Adlarson, P; Agapopoulou, C; et al (January 2025, The European Physical Journal C)

   Abstract A search for the very rare$$B^{*0}\rightarrow \mu ^+\mu ^-$$ $B_{}^{\ast 0}\to {\mu }^{+}{\mu }^{-}$and$$B_{s}^{*0}\rightarrow \mu ^+\mu ^-$$ $B_s^{\ast 0}\to {\mu }^{+}{\mu }^{-}$decays is conducted by analysing the$$B_c^+\rightarrow \pi ^+\mu ^+\mu ^-$$ $B_c^{+}\to {\pi }^{+}{\mu }^{+}{\mu }^{-}$process. The analysis uses proton-proton collision data collected with the LHCb detector between 2011 and 2018, corresponding to an integrated luminosity of 9$$\text {\,fb}^{-1}$$ ${\text{,fb}}^{-1}$. The signal signatures correspond to simultaneous peaks in the$$\mu ^+\mu ^-$$ ${\mu }^{+}{\mu }^{-}$and$$\pi ^+\mu ^+\mu ^-$$ ${\pi }^{+}{\mu }^{+}{\mu }^{-}$invariant masses. No evidence for an excess of events over background is observed for either signal decay mode. Upper limits at the$$90\%$$ $90\%$confidence level are set on the branching fractions relative to that for$$B_c^+\rightarrow J\hspace{-1.66656pt}/\hspace{-1.111pt}\psi \pi ^+$$ $B_c^{+}\to J/\psi {\pi }^{+}$decays,$$\begin{aligned} \mathcal{R}_{B^{*0}(\mu ^+\mu ^-)\pi ^+/J\hspace{-1.66656pt}/\hspace{-1.111pt}\psi \pi ^+}&< 3.8\times 10^{-5}\ \text { and }\\ \mathcal{R}_{B_{s}^{*0}(\mu ^+\mu ^-)\pi ^+/J\hspace{-1.66656pt}/\hspace{-1.111pt}\psi \pi ^+}&< 5.0\times 10^{-5}. \end{aligned}$$ $\begin{array}{cc}R_{B_{}^{\ast 0}\left({\mu }^{+}{\mu }^{-}\right){\pi }^{+}/J/\psi {\pi }^{+}}^{}& <3.8\times {10}^{-5}\text{and}\\ R_{B_s^{\ast 0}\left({\mu }^{+}{\mu }^{-}\right){\pi }^{+}/J/\psi {\pi }^{+}}^{}& <5.0\times {10}^{-5}.\end{array}$ 

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