Magnetic-field-induced charge-stripe order in the high-temperature superconductor YBa2Cu3Oy

Electronic charges introduced in copper-oxide (CuO(2)) planes generate high-transition-temperature (T(c)) superconductivity but, under special circumstances, they can also order into filaments called stripes. Whether an underlying tendency towards charge order is present in all copper oxides and whether this has any relationship with superconductivity are, however, two highly controversial issues. To uncover underlying electronic order, magnetic fields strong enough to destabilize superconductivity can be used. Such experiments, including quantum oscillations in YBa(2)Cu(3)O(y) (an extremely clean copper oxide in which charge order has not until now been observed) have suggested that superconductivity competes with spin, rather than charge, order. Here we report nuclear magnetic resonance measurements showing that high magnetic fields actually induce charge order, without spin order, in the CuO(2) planes of YBa(2)Cu(3)O(y). The observed static, unidirectional, modulation of the charge density breaks translational symmetry, thus explaining quantum oscillation results, and we argue that it is most probably the same 4a-periodic modulation as in stripe-ordered copper oxides. That it develops only when superconductivity fades away and near the same 1/8 hole doping as in La(2-x)Ba(x)CuO(4) (ref.?1) suggests that charge order, although visibly pinned by CuO chains in YBa(2)Cu(3)O(y), is an intrinsic propensity of the superconducting planes of high-T(c) copper oxides.     

A new simple 2-D piecewise linear map

<正> A new simple piecewise linear map of the plane is presented and analyzed,then a detailedstudy of its dynamical behaviour is described,along with some other dynamical phenomena,especiallyfixed points and their stability,observation of a new chaotic attractors obtained via border collisionbifurcation.An important result about coexisting chaotic attractors is also numerically studied anddiscussed.     

Insect behaviour: arboreal ants build traps to capture prey

To meet their need for nitrogen in the restricted foraging environment provided by their host plants, some arboreal ants deploy group ambush tactics in order to capture flying and jumping prey that might otherwise escape. Here we show that the ant Allomerus decemarticulatus uses hair from the host plant's stem, which it cuts and binds together with a purpose-grown fungal mycelium, to build a spongy 'galleried' platform for trapping much larger insects. Ants beneath the platform reach through the holes and immobilize the prey, which is then stretched, transported and carved up by a swarm of nestmates. To our knowledge, the collective creation of a trap as a predatory strategy has not been described before in ants.     

一种新的语义相似度计算方法

在信息检索领域,基于不精确的语义信息进行查询,需要用户多次进行筛选,降低了查询效率,因此,语义相似度计算的精确性至关重要.目前,人们主要利用概念词的距离、内容、属性等信息进行语义相似度计算,其中综合距离,信息内容和概念词属性等因素的混合式语义相似度计算方法是比较热门的方法,但该方法进行语义相似度计算时,权值的确定是根据专家的经验,人为的进行确定,具有一定的主观性,影响了语义相似度计算的准确性和客观性.因此,本文提出了一种新的混合式语义相似度计算方法,采用模糊优化的思想确定混合式语义相似度计算方法中的权值,避免了主观性,使语义相似度的计算更准确,查询结果更符合人们的需求.     

Forecasting carbon price using a multi-objective least squares support vector machine with mixture kernels

For improving forecasting accuracy and trading performance, this paper proposes a new multi-objective least squares support vector machine with mixture kernels to forecast asset prices. First, a mixture kernel function is introduced into taking full use of global and local kernel functions, which is adaptively determined following a data-driven procedure. Second, a multi-objective fitness function is proposed by incorporating level forecasting and trading performance, and particle swarm optimization is used to synchronously search the optimal model selections of least squares support vector machine with mixture kernels. Taking CO₂ assets as examples, the results obtained show that compared with the popular models, the proposed model can achieve higher forecasting accuracy and higher trading performance. The advantages of the mixture kernel function and the multi-objective fitness function can improve the forecasting ability of the asset price. The findings also show that the models with a high-level forecasting accuracy cannot always have a high trading performance of asset price forecasting. In contrast, high directional forecasting usually means a high trading performance.