The 1D Heisenberg antiferromagnet model by the variation after projection method

The 4 sites and 8 sites 1D anti-ferromagnetic Heisenberg chains in the Jordan-Wigner representation are investigated within the standard Hartree-Fock and RPA approaches, both in the symmetry unbroken and in the symmetry broken phases. A translation invariant groundstate, obtained by the projection method as a linear combination of a symmetry-broken HF state and its image under reflection, is also considered, for each chain type. It is found that the projection method considerably improves the HF treatment for instance as far as the groundstate energy is concerned, but also with respect to the RPA energies. The results are furthermore confronted with the ones obtained within so-called SCRPA scheme.Comment: 15 pages, 8 figures, 1 table, accepted for publication in Int. J. Mod. Phys.

Stable Model Counting and Its Application in Probabilistic Logic Programming

Model counting is the problem of computing the number of models that satisfy a given propositional theory. It has recently been applied to solving inference tasks in probabilistic logic programming, where the goal is to compute the probability of given queries being true provided a set of mutually independent random variables, a model (a logic program) and some evidence. The core of solving this inference task involves translating the logic program to a propositional theory and using a model counter. In this paper, we show that for some problems that involve inductive definitions like reachability in a graph, the translation of logic programs to SAT can be expensive for the purpose of solving inference tasks. For such problems, direct implementation of stable model semantics allows for more efficient solving. We present two implementation techniques, based on unfounded set detection, that extend a propositional model counter to a stable model counter. Our experiments show that for particular problems, our approach can outperform a state-of-the-art probabilistic logic programming solver by several orders of magnitude in terms of running time and space requirements, and can solve instances of significantly larger sizes on which the current solver runs out of time or memory.Comment: Accepted in AAAI, 201

Differential medial temporal lobe morphometric predictors of item- and relational-encoded memories in healthy individuals and in individuals with mild cognitive impairment and Alzheimer's disease.

INTRODUCTION:Episodic memory processes are supported by different subregions of the medial temporal lobe (MTL). In contrast to a unitary model of memory recognition supported solely by the hippocampus, a current model suggests that item encoding engages perirhinal cortex, whereas relational encoding engages parahippocampal cortex and the hippocampus. However, this model has not been examined in the context of aging, neurodegeneration, and MTL morphometrics. METHODS:Forty-four healthy subjects (HSs) and 18 cognitively impaired subjects (nine mild cognitive impairment [MCI] and nine Alzheimer's disease [AD] patients) were assessed with the relational and item-specific encoding task (RISE) and underwent 3T magnetic resonance imaging. The RISE assessed the differential contribution of relational and item-specific memory. FreeSurfer was used to obtain measures of cortical thickness of MTL regions and hippocampus volume. RESULTS:Memory accuracies for both item and relational memory were significantly better in the HS group than in the MCI/AD group. In MCI/AD group, relational memory was disproportionately impaired. In HSs, hierarchical regressions demonstrated that memory was predicted by perirhinal thickness after item encoding, and by hippocampus volume after relational encoding (both at trend level) and significantly by parahippocampal thickness at associative recognition. The same brain morphometry profiles predicted memory accuracy in MCI/AD, although more robustly perirhinal thickness for item encoding (R2 = 0.31) and hippocampal volume and parahippocampal thickness for relational encoding (R2 = 0.31). DISCUSSION:Our results supported a model of episodic memory in which item-specific encoding was associated with greater perirhinal cortical thickness, while relational encoding was associated with parahippocampal thickness and hippocampus volume. We identified these relationships not only in HSs but also in individuals with MCI and AD. In the subjects with cognitive impairment, reductions in hippocampal volume and impairments in relational memory were especially prominent

Advances in the knowledge of quinoa pests

A wide range of quinoa pests are known throughout the world. The most serious of the Andean pests are Eurysacca melanocampta (Meyrick) and E. quinoae Povolny´ (Lepidoptera: Gelechiidae), found mainly in Peru and Bolivia, which cause considerable yield losses. Insects found elsewhere in the world are polyphagous pests constituting a wide range of potential pests if quinoa is implemented as a crop in those regions. Other major pests include a group of cutworms (Noctuidae). Apart from insects birds cause a major loss through foraging, damaging cotyledonous plants and inflorescences, with yield losses of up to 60%. Cultural practices and host plant resistances will be important components of integrated pest management (IPM). Biological control of the main pests is good; for example, up to 45% of Eurysacca melanocampta in the field are usually controlled by a range of parasitoid species as well as predators in the field. Future research should focus on the main pests in order to reveal basic information on interactions with the host plant. Population carryover from one growth season to another and the role of environmental factors on insect development and population size should also be studied