Domain transfer for deep natural language generation from abstract meaning representations

Stochastic natural language generation systems that are trained from labelled datasets are often domainspecific in their annotation and in their mapping from semantic input representations to lexical-syntactic outputs. As a result, learnt models fail to generalize across domains, heavily restricting their usability beyond single applications. In this article, we focus on the problem of domain adaptation for natural language generation. We show how linguistic knowledge from a source domain, for which labelled data is available, can be adapted to a target domain by reusing training data across domains. As a key to this, we propose to employ abstract meaning representations as a common semantic representation across domains. We model natural language generation as a long short-term memory recurrent neural network encoderdecoder, in which one recurrent neural network learns a latent representation of a semantic input, and a second recurrent neural network learns to decode it to a sequence of words. We show that the learnt representations can be transferred across domains and can be leveraged effectively to improve training on new unseen domains. Experiments in three different domains and with six datasets demonstrate that the lexical-syntactic constructions learnt in one domain can be transferred to new domains and achieve up to 75-100% of the performance of in-domain training. This is based on objective metrics such as BLEU and semantic error rate and a subjective human rating study. Training a policy from prior knowledge from a different domain is consistently better than pure in-domain training by up to 10%

Discrete Element modelling of drag finishing

Drag finishing is a machining process that is used to improve the surface topology of workpieces. Workpieces are moved through a bulk of differently shaped abrasives, the so called media. Material removal is caused by the relative motion between workpiece and media. The material removal rate is mainly depending on the contact intensity between workpiece and media. Up to now there is no viable way to determine the intensity of single contacts empirically. However, a sound understanding of single contacts with respect to impact forces and velocities could greatly improve process comprehension and reduce trial and error process design efforts. For that reason the movement of media and workpiece is modelled using the Discrete Element Method (DEM). In this paper a comprehensive approach is presented covering formulation, calibration, validation and utilization of the DEM. Media is considered as an aggregation of elastic particles that are subject to contact, damping and gravitational forces causing particle movement. Geometric boundary conditions, i.e. workpiece and drag finishing bowl, are implemented as elastic facets. Contact forces are calculated according to a non-linear, simplified Hertz-Mindlin contact force model. Energy is dissipated by viscous damping and friction at contacts. Necessary parameters of the model are determined experimentally. The validation of the model's behaviour shows good agreement with experimental data. Finally the model is used to determine local contact intensities on the workpiece surface and between particles. By analysing simulated contact forces, the formation of dominant contact chains between particles is observed and investigated

Nonstrict hierarchical reinforcement learning for interactive systems and robots

Conversational systems and robots that use reinforcement learning for policy optimization in large domains often face the problem of limited scalability. This problem has been addressed either by using function approximation techniques that estimate the approximate true value function of a policy or by using a hierarchical decomposition of a learning task into subtasks. We present a novel approach for dialogue policy optimization that combines the benefits of both hierarchical control and function approximation and that allows flexible transitions between dialogue subtasks to give human users more control over the dialogue. To this end, each reinforcement learning agent in the hierarchy is extended with a subtask transition function and a dynamic state space to allow flexible switching between subdialogues. In addition, the subtask policies are represented with linear function approximation in order to generalize the decision making to situations unseen in training. Our proposed approach is evaluated in an interactive conversational robot that learns to play quiz games. Experimental results, using simulation and real users, provide evidence that our proposed approach can lead to more flexible (natural) interactions than strict hierarchical control and that it is preferred by human users

Breathing Signature as Vitality Score Index Created by Exercises of Qigong: Implications of Artificial Intelligence Tools Used in Traditional Chinese Medicine.

Rising concerns about the short- and long-term detrimental consequences of administration of conventional pharmacopeia are fueling the search for alternative, complementary, personalized, and comprehensive approaches to human healthcare. Qigong, a form of Traditional Chinese Medicine, represents a viable alternative approach. Here, we started with the practical, philosophical, and psychological background of Ki (in Japanese) or Qi (in Chinese) and their relationship to Qigong theory and clinical application. Noting the drawbacks of the current state of Qigong clinic, herein we propose that to manage the unique aspects of the Eastern 'non-linearity' and 'holistic' approach, it needs to be integrated with the Western "linearity" "one-direction" approach. This is done through developing the concepts of "Qigong breathing signatures," which can define our life breathing patterns associated with diseases using machine learning technology. We predict that this can be achieved by establishing an artificial intelligence (AI)-Medicine training camp of databases, which will integrate Qigong-like breathing patterns with different pathologies unique to individuals. Such an integrated connection will allow the AI-Medicine algorithm to identify breathing patterns and guide medical intervention. This unique view of potentially connecting Eastern Medicine and Western Technology can further add a novel insight to our current understanding of both Western and Eastern medicine, thereby establishing a vitality score index (VSI) that can predict the outcomes of lifestyle behaviors and medical conditions

Introduction to the special issue on Machine learning for multiple modalities in interactive systems and robots

This special issue highlights research articles that apply machine learning to robots and other systems that interact with users through more than one modality, such as speech, gestures, and vision. For example, a robot may coordinate its speech with its actions, taking into account (audio-)visual feedback during their execution. Machine learning provides interactive systems with opportunities to improve performance not only of individual components but also of the system as a whole. However, machine learning methods that encompass multiple modalities of an interactive system are still relatively hard to find. The articles in this special issue represent examples that contribute to filling this gap

Smart packaging for laser modules

Treball desenvolupat dins el marc del programa 'European Project Semester'.Most current day packages consist of only a cardboard box and stickers with a postal address. In the case of shipment of very sensitive items, a "FRAGILE" sticker is added. In order to change this state, The Smart Packaging group under the supervision of the Monocrom company and UPC University has been established. The objective is to record several conditions which are present during shipment and to pass history of the events to the customer before opening the package. What is more, the content of the package has to be secured against shocks, ESD and humidity. The following paper shows results done in various realms such as: electronics, sensors, inner foams, outer box, materials and describes the first steps of designing process

Mass analyzed threshold ionization spectra of phenol⋯Ar2: ionization energy and cation intermolecular vibrational frequencies

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The phenol+⋯Ar2 complex has been characterized in a supersonic jet by mass analyzed threshold ionization (MATI) spectroscopyvia different intermediate intermolecular vibrational states of the first electronically excited state (S1). From the spectra recorded via the S100 origin and the S1βx intermolecular vibrational state, the ionization energy (IE) has been determined as 68 288 ± 5 cm−1, displaying a red shift of 340 cm−1 from the IE of the phenol+ monomer. Well-resolved, nearly harmonic vibrational progressions with a fundamental frequency of 10 cm−1 have been observed in the ion ground state (D0) and assigned to the symmetric van der Waals (vdW) bending mode, βx, along the x axis containing the C–O bond. MATI spectra recorded via the S1 state involving other higher-lying intermolecular vibrational states (σ1s, β3x, σ1sβ1x, σ1sβ2x) are characterized by unresolved broad structures

Optical studies of soot formation and the addition of organic peroxides to flames

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Information Sharing and Interoperability in Law Enforcement: An Investigation of Federal Criminal Justice Information Systems Use by State/Local Law Enforcement Organizations

This thesis investigates the frequency of use and perceptions of usefulness of federal criminal justice information systems among state and local law enforcement personnel and certain IS environmental factors that affect usage. The study is predicated by a demonstrated need for increased information sharing, interoperability, and collaboration among the three tiers of law enforcement as public safety threats within U.S. borders increase in complexity; e.g., the Murrah Federal Building bombing, Columbine High School shooting, 9/11 terrorist attacks, and D.C. sniper case. The results of this research indicate high usage and perceived usefulness of the National Crime Information Center Network (NCIC Net), National Law Enforcement Telecommunications System (NLETS), Uniform Crime Reporting/National Incident Based Reporting System (UCR/NIBRS), National Instant Criminal Background Check System (NICS), and federal LE websites. The results also indicated that the IS environmental factors information quality and trust influenced the usage and perceived usefulness of federal criminal justice information systems