Efficient calculation of electronic structure using O(N) density functional theory

We propose an efficient way to calculate the electronic structure of large systems by combining a large-scale first-principles density functional theory code, Conquest, and an efficient interior eigenproblem solver, the Sakurai-Sugiura method. The electronic Hamiltonian and charge density of large systems are obtained by \conquest and the eigenstates of the Hamiltonians are then obtained by the Sakurai-Sugiura method. Applications to a hydrated DNA system, and adsorbed P2 molecules and Ge hut clusters on large Si substrates demonstrate the applicability of this combination on systems with 10,000+ atoms with high accuracy and efficiency.Comment: Submitted to J. Chem. Theor. Compu

Genetic algorithms with DNN-based trainable crossover as an example of partial specialization of general search

Universal induction relies on some general search procedure that is doomed to be inefficient. One possibility to achieve both generality and efficiency is to specialize this procedure w.r.t. any given narrow task. However, complete specialization that implies direct mapping from the task parameters to solutions (discriminative models) without search is not always possible. In this paper, partial specialization of general search is considered in the form of genetic algorithms (GAs) with a specialized crossover operator. We perform a feasibility study of this idea implementing such an operator in the form of a deep feedforward neural network. GAs with trainable crossover operators are compared with the result of complete specialization, which is also represented as a deep neural network. Experimental results show that specialized GAs can be more efficient than both general GAs and discriminative models.Comment: AGI 2017 procedding, The final publication is available at link.springer.co

Verification of Java Bytecode using Analysis and Transformation of Logic Programs

State of the art analyzers in the Logic Programming (LP) paradigm are nowadays mature and sophisticated. They allow inferring a wide variety of global properties including termination, bounds on resource consumption, etc. The aim of this work is to automatically transfer the power of such analysis tools for LP to the analysis and verification of Java bytecode (JVML). In order to achieve our goal, we rely on well-known techniques for meta-programming and program specialization. More precisely, we propose to partially evaluate a JVML interpreter implemented in LP together with (an LP representation of) a JVML program and then analyze the residual program. Interestingly, at least for the examples we have studied, our approach produces very simple LP representations of the original JVML programs. This can be seen as a decompilation from JVML to high-level LP source. By reasoning about such residual programs, we can automatically prove in the CiaoPP system some non-trivial properties of JVML programs such as termination, run-time error freeness and infer bounds on its resource consumption. We are not aware of any other system which is able to verify such advanced properties of Java bytecode

Clinicopathologic significance of sialyl Le xexpression in advanced gastric carcinoma

Sialyl Lewis xantigen (SLX) is a carbohydrate antigen that serves as a ligand for selectin, an adhesion molecule expressed on vascular endothelial cells. The expression of SLX in 245 patients with advanced gastric carcinoma was examined immunohistochemically, and its clinicopathologic significance was analysed. We classified the patients with advanced gastric carcinoma into 91 with differentiated type and 154 with undifferentiated type. SLX expressed in 135 of 245 patients (55%), comprising 68 (75%) patients with differentiated carcinoma and 67 (44%) with undifferentiated carcinoma. The positive rate for SLX expression was significantly higher among patients with differentiated carcinoma than among those in undifferentiated carcinoma (P < 0.0001). With differentiated carcinoma, the incidence of lymph node metastasis, advanced tumour stage (stage III and IV) and liver recurrence was significantly higher in SLX-positive patients than in SLX-negative ones (P  < .0001, P = 0.0065 and P = 0.028, respectively). Moreover, the prognoses were better in patients with SLX-negative tumours than in those with SLX-positive tumours (P = 0.019). With undifferentiated carcinoma, there were no significant correlations between SLX expression and any clinicopathological features or prognoses. The clinicopathologic significance of SLX expression in gastric carcinoma patients depends on histologic type. SLX expression may be of great relevance in predicting liver metastases in patients with differentiated carcinoma. © 2000 Cancer Research Campaign http://www.bjcancer.co

Programming languages and artificial general intelligence

Despite the fact that there are thousands of programming languages existing there is a huge controversy about what language is better to solve a particular problem. In this paper we discuss requirements for programming language with respect to AGI research. In this article new language will be presented. Unconventional features (e.g. probabilistic programming and partial evaluation) are discussed as important parts of language design and implementation. Besides, we consider possible applications to particular problems related to AGI. Language interpreter for Lisp-like probabilistic mixed paradigm programming language is implemented in Haskell

Specializing Interpreters using Offline Partial Deduction

We present the latest version of the Logen partial evaluation system for logic programs. In particular we present new binding-types, and show how they can be used to effectively specialise a wide variety of interpreters.We show how to achieve Jones-optimality in a systematic way for several interpreters. Finally, we present and specialise a non-trivial interpreter for a small functional programming language. Experimental results are also presented, highlighting that the Logen system can be a good basis for generating compilers for high-level languages

Cell Migration in the Immune System: the Evolving Inter-Related Roles of Adhesion Molecules and Proteinases

Leukocyte extravasation into perivascular tissue during inflammation and lymphocyte homing to lymphoid organs involve transient adhesion to the vessel endothelium, followed by transmigration through the endothelial cell (EC) layer and establishment of residency at the tissue site for a period of time. In these processes, leukocytes undergo multiple attachments to, and detachments from, the vessel-lining endothelial cells, prior to transendothelial cell migration. Transmigrating leukocytes must traverse a subendothelial basement membrane en route to perivascular tissues and utilize enzymes known as matrix metalloproteinases to make selective clips in the extracellular matrix components of the basement membrane. This review will focus on the evidence for a link between adhesion of leukocytes to endothelial cells, the induction of matrix metalloproteinases mediated by engagement of adhesion receptors on leukocytes, and the ability to utilize these matrix metalloproteinases to facilitate leukocyte invasion of tissues. Leukocytes with invasive phenotypes express high levels of MMPs, and expression of MMPs enhances the migratory and invasive properties of these cells. Furthermore, MMPs may be used by lymphocytes to proteolytically cleave molecules such as adhesion receptors and membrane bound cytokines, increasing their efficiency in the immune response. Engagement of leukocyte adhesion receptors may modulate adhesive (modulation of integrin affinities and expression), synthetic (proteinase induction and activation), and surface organization (clustering of proteolyric complexes) behaviors of invasive leukocytes. Elucidation of these pathways will lead to better understanding of controlling mechanisms in order to develop rational therapeutic approaches in the areas of inflammation and autoimmunity

Cannabidivarin is anticonvulsant in mouse and rat in vitro and in seizure models

Summary Background and purpose: Phytocannabinoids in Cannabis sativa have diverse pharmacological targets extending beyond cannabinoid receptors and several exert notable anticonvulsant effects. For the first time, we investigated the anticonvulsant profile of the phytocannabinoid cannabidivarin (CBDV) in vitro and in in vivo seizure models. Experimental approach: The effect of CBDV (1-100μM) on epileptiform local field potentials (LFPs) induced in rat hippocampal brain slices by 4-AP application or Mg2+-free conditions was assessed by in vitro multi-electrode array recordings. Additionally, the anticonvulsant profile of CBDV (50-200 mg kg-1) in vivo was investigated in four rodent seizure models: maximal electroshock (mES) and audiogenic seizures in mice, and pentylenetetrazole (PTZ) and pilocarpine-induced seizures in rat. CBDV effects in combination with commonly-used antiepileptic drugs were investigated in rat seizures. Finally, the motor side effect profile of CBDV was investigated using static beam and gripstrength assays. Key results: CDBV significantly attenuated status epilepticus-like epileptiform LFPs induced by 4-AP and Mg2+-free conditions. CBDV had significant anticonvulsant effects in mES (≥100 mg kg-1), audiogenic (≥50 mg kg-1) and PTZ-induced seizures (≥100 mg kg-1). CBDV alone had no effect against pilocarpine-induced seizures, but significantly attenuated these seizures when administered with valproate or phenobarbital at 200 mg kg-1 CBDV. CBDV had no effect on motor function. Conclusions and Implications: These results indicate that CBDV is an effective anticonvulsant across a broad range of seizure models, does not significantly affect normal motor function and therefore merits further investigation in chronic epilepsy models to justify human trials

Just-In-Time GPU Compilation for Interpreted Languages with Partial Evaluation

Computer systems are increasingly featuring powerful parallel devices with the advent of many-core CPUs and GPUs. This offers the opportunity to solve computationally-intensive problems at a fraction of the time traditional CPUs need. However, exploiting heterogeneous hardware requires the use of low-level programming language approaches such as OpenCL, which is incredibly challenging, even for advanced programmers. On the application side, interpreted dynamic languages are increasingly becoming popular in many domains due to their simplicity, expressiveness and flexibility. However, this creates a wide gap between the high-level abstractions offered to programmers and the low-level hardware-specific interface. Currently, programmers must rely on high performance libraries or they are forced to write parts of their application in a low-level language like OpenCL. Ideally, non-expert programmers should be able to exploit heterogeneous hardware directly from their interpreted dynamic languages. In this paper, we present a technique to transparently and automatically offload computations from interpreted dynamic languages to heterogeneous devices. Using just-in-time compilation, we automatically generate OpenCL code at runtime which is specialized to the actual observed data types using profiling information. We demonstrate our technique using R, which is a popular interpreted dynamic language predominately used in big data analytic. Our experimental results show the execution on a GPU yields speedups of over 150x compared to the sequential FastR implementation and the obtained performance is competitive with manually written GPU code. We also show that when taking into account start-up time, large speedups are achievable, even when the applications run for as little as a few seconds