Rational's experience using Ada for very large systems

The experience using the Rational Environment has confirmed the advantages forseen when the project was started. Interactive syntatic and semantic information makes a tremendous difference in the ease of constructing programs and making changes to them. The ability to follow semantic references makes it easier to understand exisiting programs and the impact of changes. The integrated debugger makes it much easier to find bugs and test fixes quickly. Taken together, these facilites have helped greatly in reducing the impact of ongoing maintenance of the ability to produce a new code. Similar improvements are anticipated as the same level of integration and interactivity are achieved for configuration management and version control. The environment has also proven useful in introducing personnel to the project and existing personnel to new parts of the system. Personnel benefit from the assistance with syntax and semantics; everyone benefits from the ability to traverse and understand the structure of unfamiliar software. It is often possible for someone completely unfamiliar with a body of code to use these facilities, to understand it well enough to successfully with a body of code to use these facilities to understand it well enough to successfully diagnose and fix bugs in a matter of minutes

Phase separation in La0.5_{0.5}Ca0.5_{0.5}MnO3_3 doped with 1% 119^{119}Sn detected by M\"ossbauer spectroscopy

1% $^{119}$Sn-doped La$_{0.5}$Ca$_{0.5}$MnO$_3$ was studied by M\"ossbauer spectroscopy, magnetic moment and resistivity measurements. The M\"ossbauer spectra below the charge-ordering temperature are explained with ferromagnetic (FM), antiferromagnetic (AF), and ferromagnetic spin cluster (CL) components. The magnetic and thermal hystereses of the relative intensities of the components observed in the M\"ossbauer spectra, and of the bulk properties such as magnetic moment and electrical resistivity, in the temperature range 125-185 K, are characteristic of phase equilibrium in a first-order transition, i.e. of phase separation in the system below the charge-ordering (CO) transition. The cluster component displays a significant hyperfine field up to $\sim 125$ K. Above this temperature it exhibits superparamagnetism, becoming the dominant component above the charge-ordering transition. These results are discussed in the framework of recent investigations of the manganite system with other techniques which also show phase separation.Comment: Accepted in Phys. Rev.

QuesNet: A Unified Representation for Heterogeneous Test Questions

Understanding learning materials (e.g. test questions) is a crucial issue in online learning systems, which can promote many applications in education domain. Unfortunately, many supervised approaches suffer from the problem of scarce human labeled data, whereas abundant unlabeled resources are highly underutilized. To alleviate this problem, an effective solution is to use pre-trained representations for question understanding. However, existing pre-training methods in NLP area are infeasible to learn test question representations due to several domain-specific characteristics in education. First, questions usually comprise of heterogeneous data including content text, images and side information. Second, there exists both basic linguistic information as well as domain logic and knowledge. To this end, in this paper, we propose a novel pre-training method, namely QuesNet, for comprehensively learning question representations. Specifically, we first design a unified framework to aggregate question information with its heterogeneous inputs into a comprehensive vector. Then we propose a two-level hierarchical pre-training algorithm to learn better understanding of test questions in an unsupervised way. Here, a novel holed language model objective is developed to extract low-level linguistic features, and a domain-oriented objective is proposed to learn high-level logic and knowledge. Moreover, we show that QuesNet has good capability of being fine-tuned in many question-based tasks. We conduct extensive experiments on large-scale real-world question data, where the experimental results clearly demonstrate the effectiveness of QuesNet for question understanding as well as its superior applicability

Strong Isospin-Breaking Effects in K-->pi pi at Next-to-Leading Order in the Chiral Expansion

Strong isospin-breaking (IB) contributions to both the octet and 27-plet weak K-->2 pi transitions are evaluated at next-to-leading order (NLO) in the chiral expansion. NLO contributions are shown to significantly reduce the leading order result for the potentially large contribution to the $\Delta I=3/2$ amplitude resulting from strong isospin-breaking modifications to the weak Delta I=1/2 amplitude. The ratio of strong IB 27-plet to strong IB octet contributions is found to be small for all decay amplitudes. Combined with recent results on the corresponding electromagnetic contributions, we find that the ratio of the intrinsic strengths of octet and 27-plet effective weak operators can be taken to be that obtained from experimental data, analyzed ignoring isospin breaking, to an accuracy better than of order 10 percent.Comment: 10 pages, 1 figure. NLO determination of strong IB modification of weak 27-plet transitions now also included; modified phase discussio

Panel III: Implications of the New Telecommunications Legislation

We present a method that employs a tree-based Neural Network (NN) for performing classification. The novel mechanism, apart from incorporating the information provided by unlabeled and labeled instances, re-arranges the nodes of the tree as per the laws of Adaptive Data Structures (ADSs). Particularly, we investigate the Pattern Recognition (PR) capabilities of the Tree-Based Topology-Oriented SOM (TTOSOM) when Conditional Rotations (CONROT) [8] are incorporated into the learning scheme. The learning methodology inherits all the properties of the TTOSOM-based classifier designed in [4]. However, we now augment it with the property that frequently accessed nodes are moved closer to the root of the tree. Our experimental results show that on average, the classification capabilities of our proposed strategy are reasonably comparable to those obtained by some of the state-of-the-art classification schemes that only use labeled instances during the training phase. The experiments also show that improved levels of accuracy can be obtained by imposing trees with a larger number of nodes