In the present paper, we consider elliptic equations with nonlinear and nonlao mogeneous Robin boundary conditions of the type {-div（B（x,u）△u） = f in Ω,u=0 on Гo, B（x,u）Vu·n^-＋γ（x）h（u） = 9 on Г1,w...In the present paper, we consider elliptic equations with nonlinear and nonlao mogeneous Robin boundary conditions of the type {-div（B（x,u）△u） = f in Ω,u=0 on Гo, B（x,u）Vu·n^-＋γ（x）h（u） = 9 on Г1,where f and g are the element of L^1（Ω） and L^1（Г1）, respectively. We define a notion of renormalized solution and we prove the existence of a solution. Under additionM assumptions on the matrix field B we show that the renormalized solution is unique.展开更多
In this paper, we consider the regularized learning schemes based on l1-regularizer and pinball loss in a data dependent hypothesis space. The target is the error analysis for the quantile regression learning. There i...In this paper, we consider the regularized learning schemes based on l1-regularizer and pinball loss in a data dependent hypothesis space. The target is the error analysis for the quantile regression learning. There is no regularized condition with the kernel function, excepting continuity and boundness. The graph-based semi-supervised algorithm leads to an extra error term called manifold error. Part of new error bounds and convergence rates are exactly derived with the techniques consisting of l1-empirical covering number and boundness decomposition.展开更多
【目的】分离和克隆黄瓜谷氨酰胺合成酶GS1基因,分析其序列特征,了解其在低氮条件下的表达情况。【方法】依据黄瓜基因组数据库中Csa015274基因编码区全序列,应用引物设计软件Primer Premier 5.0设计引物,从黄瓜叶片cDNA中克隆该基因,...【目的】分离和克隆黄瓜谷氨酰胺合成酶GS1基因,分析其序列特征,了解其在低氮条件下的表达情况。【方法】依据黄瓜基因组数据库中Csa015274基因编码区全序列,应用引物设计软件Primer Premier 5.0设计引物,从黄瓜叶片cDNA中克隆该基因,用生物信息学方法对获得的cDNA序列及推定氨基酸序列进行分析鉴定,并用实时荧光定量PCR法研究GS1基因在不同氮素浓度下的表达变化。【结果】分离到GS1基因,GenBank登录号为JQ277263。该基因长1 071 bp,编码356个氨基酸,与甜瓜(Cucumis melo L.)GS1基因同源性高达97%。该基因编码的蛋白是1个不稳定的疏水蛋白,无跨膜结构,无信号肽,存在蛋白激酶C磷酸化位点,酪蛋白激酶Ⅱ磷酸化位点,N-十四酰化位点,酪氨酸激酶磷酸化位点等活性位点。GS1基因表达模式分析显示,在低氮条件下,该基因下调表达,随着氮素浓度的增高GS1基因的表达量增加。在高浓度的氮素水平下,该基因的表达同样受到抑制。【结论】成功从黄瓜叶片中分离克隆到GS1基因,该基因具有已知物种GS1基因的特征,可用于该基因的功能研究。展开更多
The behavior on the space L^∞（R^n） for the multilinear singular integral operator defined by TAf（x）=∫RnΩ（x-y）/｜x-y｜^n＋1（A（x）-A（y）△A（y）（x-y））f（y）dy is considered, where 12 is homogeneous of deg...The behavior on the space L^∞（R^n） for the multilinear singular integral operator defined by TAf（x）=∫RnΩ（x-y）/｜x-y｜^n＋1（A（x）-A（y）△A（y）（x-y））f（y）dy is considered, where 12 is homogeneous of degree zero, integrable on the unit sphere and has vanishing is considered, where Ω is homogeneous of degree zero, integrable on the unit sphere and has vanishingmoment of order one, A has derivatives of order one in BMO（R^n）. It is proved that if Ω satisfies some minimum size condition and an L1-Dini type regularity condition, then for f ∈ L^∞（R^n）, TAf is either infinite almost everywhere or finite almost everywhere, and in the latter case, TAf ∈ BMO（R^n）.展开更多
The objective of this paper is to propose an exact l1 penalty method for constrained interval-valued programming problems which transform the constrained problem into an unconstrained interval-valued penalized optimiz...The objective of this paper is to propose an exact l1 penalty method for constrained interval-valued programming problems which transform the constrained problem into an unconstrained interval-valued penalized optimization problem.Under some suitable conditions,we establish the equivalence between an optimal solution of interval-valued primal and penalized optimization problem.Moreover,saddle-point type optimality conditions are also established in order to find the relation between an optimal solution of penalized optimization problem and saddle-point of Lagrangian function.Numerical examples are given to illustrate the derived results.展开更多
基金University of the Philippines Diliman for their support
文摘In the present paper, we consider elliptic equations with nonlinear and nonlao mogeneous Robin boundary conditions of the type {-div（B（x,u）△u） = f in Ω,u=0 on Гo, B（x,u）Vu·n^-＋γ（x）h（u） = 9 on Г1,where f and g are the element of L^1（Ω） and L^1（Г1）, respectively. We define a notion of renormalized solution and we prove the existence of a solution. Under additionM assumptions on the matrix field B we show that the renormalized solution is unique.
文摘In this paper, we consider the regularized learning schemes based on l1-regularizer and pinball loss in a data dependent hypothesis space. The target is the error analysis for the quantile regression learning. There is no regularized condition with the kernel function, excepting continuity and boundness. The graph-based semi-supervised algorithm leads to an extra error term called manifold error. Part of new error bounds and convergence rates are exactly derived with the techniques consisting of l1-empirical covering number and boundness decomposition.
文摘The behavior on the space L^∞（R^n） for the multilinear singular integral operator defined by TAf（x）=∫RnΩ（x-y）/｜x-y｜^n＋1（A（x）-A（y）△A（y）（x-y））f（y）dy is considered, where 12 is homogeneous of degree zero, integrable on the unit sphere and has vanishing is considered, where Ω is homogeneous of degree zero, integrable on the unit sphere and has vanishingmoment of order one, A has derivatives of order one in BMO（R^n）. It is proved that if Ω satisfies some minimum size condition and an L1-Dini type regularity condition, then for f ∈ L^∞（R^n）, TAf is either infinite almost everywhere or finite almost everywhere, and in the latter case, TAf ∈ BMO（R^n）.
基金the Department of Science and Technology,New Delhi,India(No.SR/FTP/MS-007/2011).
文摘The objective of this paper is to propose an exact l1 penalty method for constrained interval-valued programming problems which transform the constrained problem into an unconstrained interval-valued penalized optimization problem.Under some suitable conditions,we establish the equivalence between an optimal solution of interval-valued primal and penalized optimization problem.Moreover,saddle-point type optimality conditions are also established in order to find the relation between an optimal solution of penalized optimization problem and saddle-point of Lagrangian function.Numerical examples are given to illustrate the derived results.
