We study the Ginzburg dg algebra Γ_T associated with the quiver with potential arising from a triangulation T of a decorated marked surface S_△⁠, in the sense of [22]. We show that there is a canonical way to identify all finite-dimensional derived categories D_{fd}(Γ_T)⁠, denoted by D_{fd}(S_△)⁠. As an application, we show that the spherical twist group ST(S_△) associated with D_{fd}(S_△) acts faithfully on its space of stability conditions.

We provide a uniform construction of “mixed versions” or “graded lifts” in the sense of Beilinson-Ginzburg–Soergel which works for arbitrary Artin stacks. In particular, we obtain a general construction of graded lifts of many categories arising in geometric representation theory and categorified knot invariants. Our new theory associates to each Artin stack of finite type Y over F_q a symmetric monoidal DG-category Shv_{gr,c}(Y) of constructible graded sheaves on Y along with the six-functor formalism, a perverse t-structure, and a weight (or co-t-)structure in the sense of Bondarko and Pauksztello. This category sits in between the category Shv_{m,c}(Y_n) of constructible mixed ℓ-adic sheaves in the sense of Beilinson–Bernstein–Deligne--Gabber for any F_{q^n} -form Y_n of Y and the category Shv_c(Y) of constructible ℓ-adic sheaves on Y, compatible with the six-functor formalism, perverse t-structures, and Frobenius weights. Classically, mixed versions were only constructed in very special cases. However, the category Shv_{gr,c}(Y) agrees with those previously constructed when they are available. For example, for any reductive group G with a fixed pair T ⊂ B of a maximal torus and a Borel subgroup, we have an equivalence of monoidal DG weight categories Shv_{gr,c}(B\G/B) ≃ Ch^b(SBim_W), where Ch^b(SBim_W) is the monoidal DG-category of bounded chain complexes of Soergel bimodules and W is the Weyl group of G.

The log-optimal strategy in gambling and asset allocation problem attempts to maximize the expectation of the logarithmic rate of return but not the gross wealth itself. This strategy has been shown to have long termsuperiority overother strategies in theory. Another remarkable virtue is it allows updating the real-time market information quickly which could increase of the logarithmic rate of return. We will prove these properties ourselves in a simple way. We further consider how to do asset allocation by applying the log-optimal strategy in practice. The distribution function of the returns is usually unknown and need to be estimated from the history in a real world. The estimation error will affect the asset allocation directly, but such effect mayresult in a "butterfly effect" which could bring an investment disaster. Therefore, it is an important issue to answer whether there is logoptimal strategy resisted "butterfly effect". We develop a new log-optimal strategy based on the allocation utility which is a quadratic approximation to the expectation of the logarithmic rate of return. We show that the upper bound of the allocation utility deviation can be controlled by the l_1-norm of portfolio and l_∞-norm of the bias of variance matrix estimator. This turns out that our proposed strategy is robust. We apply our strategy for NYSE data. The results showed that our strategy has high performance in return.

The Orlicz–Legendre ellipsoids, which are in the framework of emerging dual Orlicz Brunn–Minkowski theory, are introduced for the first time. They are in some sense dual to the recently found Orlicz–John ellipsoids, and have largely generalized the classical Legendre ellipsoid of inertia. Several new affine isoperimetric inequalities are established. The connection between the characterization of Orlicz– Legendre ellipsoids and isotropy of measures is demonstrated.

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