A Comparative Study of the Application of Different Learning Techniques to Natural Language Interfaces

In this paper we present first results from a comparative study. Its aim is to test the feasibility of different inductive learning techniques to perform the automatic acquisition of linguistic knowledge within a natural language database interface. In our interface architecture the machine learning module replaces an elaborate semantic analysis component. The learning module learns the correct mapping of a user's input to the corresponding database command based on a collection of past input data. We use an existing interface to a production planning and control system as evaluation and compare the results achieved by different instance-based and model-based learning algorithms.Comment: 10 pages, to appear CoNLL9

Characterization of the T cell receptor repertoire causing collagen arthritis in mice

Collagen type II-induced arthritis (CIA) is generated in susceptible rodent strains by intradermal injections of homologous or heterologous native type II collagen in complete Freund's adjuvant. Symptoms of CIA are analogous to those of the human autoimmune disease, rheumatoid arthritis. CIA is a model system for T cell-mediated autoimmune disease. To study the T cell receptor (TCR) repertoire of bovine type II-specific T cells that may be involved in the pathogenesis of CIA in DBA/1Lac.J (H-2q) mice, 13 clonally distinct T cell hybridomas specific for bovine type II collagen have been established and the alpha and beta chains of their TCRs have been analyzed. These T cell hybridomas recognize epitopes that are shared by type II collagens from distinct species and not by type I collagens, and exhibit a highly restricted TCR-alpha/beta repertoire. The alpha chains of the TCRs employ three V alpha gene subfamilies (V alpha 11, V alpha 8, and V alpha 22) and four J alpha gene segments (J alpha 42, J alpha 24, J alpha 37, and J alpha 32). The V alpha 22 is a newly identified subfamily consisting of approximately four to six members, and exhibits a high degree of polymorphism among four mouse strains of distinct V alpha haplotypes. In addition, the beta chains of the TCRs employ three V beta gene subfamilies (V beta 8, V beta 1, and V beta 6), however the V beta 8.2 gene segment is preferentially utilized (58.3%). In contrast, the J beta gene segment usage is more heterogeneous. On the basis of the highly limited TCR-alpha/beta repertoire of the TCRs of the panel of bovine type II-specific T cell hybrid clones, a significant reduction (60%) of the incidence of arthritis in DBA/1Lac.J mice is accomplished by the use of anti-V beta 8.2 antibody therapy

Wireless sensor systems in indoor situation modeling II (WISM II)

fi=vertaisarvioimaton|en=nonPeerReviewed

A SDN-based On-Demand Path Provisioning Approach across Multi-domain Optical Networks

The interconnection of remote datacentres with optical networks are emerging use cases and such orchestration of multi-domains require the design of new network control, management, and orchestration architectures. Such heterogeneity needs to adopt end-to-end services like on-demand path provisioning. It is acknowledged that such scenarios are more complexed and have fundamental limitations in terms of high performance and delay. To address these issues, and as a means to cope with the complexity growth, research in this area is considering the concept of Software-Defined Network (SDN) orchestration for multi-domain optical networks to coordinated the control of heterogeneous systems. This paper presents a SDN path provisioning approach across Multi-Domain Optical Networks. The aim is to develop an efficient on-demand path provisioning platform in a software defined optical network at the control plane to dynamically manage the network's load, especially in emergency scenarios. The proposed distributed system architecture will help to solve the longstanding problem of inter-domain path provisioning. Our proposed architecture is implemented and validated in a control plane testbed to validate the approach. The paper also evaluated the factors such Quality of Service (QoS) of the network deployment associated with delay or control overhead. Our results show that the method will reduce additional delays in a multi-domain optical network, where high capacity and low latency are requirements for data-intensive applications and cloud services. The proposed method also maintains the total number of flows as low as possible to make the algorithm fast and reduce overheads

Intermediate mass fragment emission in Fe + Au collisions

Experimental results are presented on the charge, velocity, and angular distributions of intermediate mass fragments (IMFs) for the reaction Fe+Au at bombarding energies of 50 and 100 MeV/nucleon. Results are compared to the quantum molecular dynamics (QMD) model and a modified QMD which includes a Pauli potential and follows the subsequent statistical decay of excited reaction products. The more complete model gives a good representation of the data and suggests that the major source of IMFs at large angles is due to multifragmentation of the target residue

Word-Level vs Sentence-Level Language Identification: Application to Algerian and Arabic Dialects.

Abstract In this paper, we investigate a set of methods for textual Arabic Dialect Identification, where we considered word-level and sentence-level approaches. We used three classifiers, namely: Linear Support Vector Machine L-SVM, Bernoulli Naive Bayes BNB and Multinomial Naive Bayes MNB. Then we combined them by using a voting procedure. We carried out experiments on two sets of dialects: the first one, PADIC, which consists of parallel sentences in Maghrebi and Middle Eastern dialects; and the second, a set of Algerian dialects only, that we built manually. For the Arabic dialects, we obtained an average accuracy of 92%. For Algerian dialects, our approach yielded an average accuracy of about 76%

Semi-analytical solutions for solute transport and exchange in fractured porous media

International audienceFracture-matrix interactions can significantly affect solute transport in fractured porous media and rocks, even when fractures are major (or sole) conduits of flow. We develop a semi-analytical solution for transport of conservative solutes in a single fracture. Our solution accounts for two-dimensional dispersion in the fracture, two-dimensional diffusion in the ambient matrix, and fully coupled fracture-matrix exchange, without resorting to simplifying assumptions regarding any of these transport mechanisms. It also enables one to deal with arbitrary initial and boundary conditions, as well as with distributed and point sources. We investigate the impact of transverse dispersion in a fracture and longitudinal diffusion in the ambient matrix on the fracture-matrix exchange, both of which are neglected in standard models of transport in fractured media

DFL: Dynamic Federated Split Learning in Heterogeneous IoT

Federated Learning (FL) in edge Internet of Things (IoT) environments is challenging due to the heterogeneous nature of the learning environment, mainly embodied in two aspects. Firstly, the statistically heterogeneous data, usually non-independent identically distributed (non-IID), from geographically distributed clients can deteriorate the FL training accuracy. Secondly, the heterogeneous computing and communication resources in IoT devices often result in unstable training processes that slow down the training of a global model and affect energy consumption. Most existing solutions address only the unilateral side of the heterogeneity issue but neglect the joint problem of resources and data heterogeneity for the resource-constrained IoT. In this article, we propose Dynamic Federated split Learning (DFL) to address the joint problem of data and resource heterogeneity for distributed training in IoT. DFL enhances training efficiency in heterogeneous dynamic IoT through resource-aware split computing of deep neural networks and dynamic clustering of training participants based on the similarity of their sub-model layers. We evaluate DFL on a real testbed comprising heterogeneous IoT devices using two widely-adopted datasets, in various non-IID settings. Results show that DFL improves training performance in terms of training time by up to 48%, accuracy by up to 32%, and energy consumption by up to 62.8% compared to classic FL and Federated Split Learning in scenarios with both data and resource heterogeneity