3,168 research outputs found
Combining decision procedures for the reals
We address the general problem of determining the validity of boolean
combinations of equalities and inequalities between real-valued expressions. In
particular, we consider methods of establishing such assertions using only
restricted forms of distributivity. At the same time, we explore ways in which
"local" decision or heuristic procedures for fragments of the theory of the
reals can be amalgamated into global ones. Let Tadd[Q] be the
first-order theory of the real numbers in the language of ordered groups, with
negation, a constant 1, and function symbols for multiplication by
rational constants. Let Tmult[Q] be the analogous theory for the
multiplicative structure, and let T[Q] be the union of the two. We
show that although T[Q] is undecidable, the universal fragment of
T[Q] is decidable. We also show that terms of T[Q]can
fruitfully be put in a normal form. We prove analogous results for theories in
which Q is replaced, more generally, by suitable subfields F
of the reals. Finally, we consider practical methods of establishing
quantifier-free validities that approximate our (impractical) decidability
results.Comment: Will appear in Logical Methods in Computer Scienc
User Centered, Application Independent Visualization of National Airspace Data
This paper describes an application independent software tool, IV4D, built to visualize animated and still 3D National Airspace System (NAS) data specifically for aeronautics engineers who research aggregate, as well as single, flight efficiencies and behavior. IV4D was origin ally developed in a joint effort between the National Aeronautics and Space Administration (NASA) and the Air Force Research Laboratory (A FRL) to support the visualization of air traffic data from the Airspa ce Concept Evaluation System (ACES) simulation program. The three mai n challenges tackled by IV4D developers were: 1) determining how to d istill multiple NASA data formats into a few minimal dataset types; 2 ) creating an environment, consisting of a user interface, heuristic algorithms, and retained metadata, that facilitates easy setup and fa st visualization; and 3) maximizing the user?s ability to utilize the extended range of visualization available with AFRL?s existing 3D te chnologies. IV4D is currently being used by air traffic management re searchers at NASA?s Ames and Langley Research Centers to support data visualizations
Hierarchical relational models for document networks
We develop the relational topic model (RTM), a hierarchical model of both
network structure and node attributes. We focus on document networks, where the
attributes of each document are its words, that is, discrete observations taken
from a fixed vocabulary. For each pair of documents, the RTM models their link
as a binary random variable that is conditioned on their contents. The model
can be used to summarize a network of documents, predict links between them,
and predict words within them. We derive efficient inference and estimation
algorithms based on variational methods that take advantage of sparsity and
scale with the number of links. We evaluate the predictive performance of the
RTM for large networks of scientific abstracts, web documents, and
geographically tagged news.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS309 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Augmenting Assessment with Learning Analytics
Learning analytics as currently deployed has tended to consist of large-scale analyses of available learning process data to provide descriptive or predictive insight into behaviours. What is sometimes missing in this analysis is a connection to human-interpretable, actionable, diagnostic information. To gain traction, learning analytics researchers should work within existing good practice particularly in assessment, where high quality assessments are designed to provide both student and educator with diagnostic or formative feedback. Such a model keeps the human in the analytics design and implementation loop, by supporting student, peer, tutor, and instructor sense-making of assessment data, while adding value from computational analyses
Sequential decision modeling in uncertain conditions
Cette thĂšse consiste en une sĂ©rie dâapproches pour la modĂ©lisation de dĂ©cision structurĂ©e - câest-Ă -dire quâelle propose des solutions utilisant des modĂšles gĂ©nĂ©ratifs pour des tĂąches intĂ©grant plusieurs entrĂ©es et sorties, ces entrĂ©es et sorties Ă©tant dictĂ©es par des interactions complexes entre leurs Ă©lĂ©ments. Un aspect crucial de ces problĂšmes est la prĂ©sence en plus dâun rĂ©sultat correct, des rĂ©sultats structurellement diffĂ©rents mais considĂ©rĂ©s tout aussi corrects, rĂ©sultant dâune grande mais nĂ©cessaire incertitude sur les sorties du systĂšme. Cette thĂšse prĂ©sente quatre articles sur ce sujet, se concentrent en particulier sur le domaine de la synthĂšse vocale Ă partir de texte, gĂ©nĂ©ration symbolique de musique, traitement de texte, reconnaissance automatique de la parole, et apprentissage de reprĂ©sentations pour la parole et le texte. Chaque article prĂ©sente une approche particuliĂšre Ă un problĂšme dans ces domaines respectifs, en proposant et Ă©tudiant des architectures profondes pour ces domaines. Bien que ces techniques dâapprentissage profond utilisĂ©es dans ces articles sont suffisamment versatiles et expressives pour ĂȘtre utilisĂ©es dans dâautres domaines, nous resterons concentrĂ©s sur les applications dĂ©crites dans chaque article.
Le premier article prĂ©sente une approche permettant le contrĂŽle dĂ©taillĂ©, au niveau phonĂ©tique et symbolique, dâun systĂšme de synthĂšse vocale, en utilisant une mĂ©thode dâĂ©change efficace permettant de combiner des reprĂ©sentations Ă un niveau lexical. Puisque cette combinaison permet un contrĂŽle proportionnĂ© sur les conditions dâentrĂ©e, et amĂ©liore les prononciations faisant uniquement usage de caractĂšres, ce systĂšme de combinaison pour la synthĂšse vocale a Ă©tĂ© prĂ©fĂ©rĂ© durant des tests A/B par rapport Ă des modĂšles de rĂ©fĂ©rence Ă©quivalents utilisant les mĂȘmes modalitĂ©s. Le deuxiĂšme article se concentre sur un autre systĂšme de synthĂšse vocale, cette fois-ci centrĂ© sur la construction dâune reprĂ©sentation multi-Ă©chelle de la parole Ă travers une dĂ©composition structurĂ©e des descripteurs audio. En particulier, lâintĂ©rĂȘt de ce travail est dans sa mĂ©thodologie Ă©conome en calcul malgrĂ© avoir Ă©tĂ© bĂąti Ă partir de travaux antĂ©rieurs beaucoup plus demandant en ressources de calcul. Afin de bien pouvoir faire de la synthĂšse vocale sous ces contraintes computationelles, plusieurs nouvelles composantes ont Ă©tĂ© conçues et intĂ©grĂ©es Ă ce qui devient un modĂšle efficace de synthĂšse vocale. Le troisiĂšme article un nouveau modĂšle auto-rĂ©gressif pour modĂ©liser des chaĂźnes de symboles. Ce modĂšle fait usage de prĂ©dictions et dâestimations itĂ©rative et rĂ©pĂ©tĂ©es afin de construire une sortie structurĂ©e respectant plusieurs contraintes correspondant au domaine sous-jacent. Ce modĂšle est testĂ© dans le cadre de la gĂ©nĂ©ration symbolique de musique et la modĂ©lisation de texte, faisant preuve dâexcellentes performances en particulier quand la quantitĂ© de donnĂ©es sâavĂšre limitĂ©e. Le dernier article de la thĂšse se concentre sur lâĂ©tude des reprĂ©sentations pour la parole et le texte apprise Ă partir dâun systĂšme de reconnaissance vocale dâun travail antĂ©rieur. Ă travers une sĂ©rie dâĂ©tudes systĂ©matiques utilisant des modĂšles prĂ©-entraĂźnĂ©s de texte et de durĂ©e, relations qualitatives entre les donnĂ©es de texte et de parole, et Ă©tudes de performance sur la rĂ©cupĂ©ration transmodal âfew shotâ, nous exposons plusieurs propriĂ©tĂ©s essentielles sous-jacent Ă la performance du systĂšme, ouvrant la voie pour des dĂ©veloppements algorithmiques futurs. De plus, les diffĂ©rents modĂšles rĂ©sultants de cette Ă©tude obtiennent des rĂ©sultats impressionnants sur un nombre de tĂąches de rĂ©fĂ©rence utilisant des modĂšles prĂ©-entraĂźnĂ© transfĂ©rĂ© sans modification.This thesis presents a sequence of approaches to structured decision modeling - that is, proposing generative solutions to tasks with multiple inputs and outputs, featuring complicated interactions between input elements and output elements. Crucially, these problems also include a high amount of uncertainty about the correct outcome and many largely equivalent but structurally different outcomes can be considered equally correct. This thesis presents four articles about these topics, particularly focusing on the domains of text-to-speech synthesis, symbolic music generation, text processing, automatic speech recognition, and speech-text representation learning. Each article presents a particular approach to solving problems in these respective domains, focused on proposing and understanding deep learning architectures for these domains. The deep learning techniques used in these articles are broadly applicable, flexible, and powerful enough that these general approaches may find application to other areas however we remain focused on the domains discussed in each respective article.
The first article presents an approach allowing for flexible phonetic and character control of a text-to-speech system, utilizing an efficient "swap-out" method for blending representations at the word level. This blending allows for smooth control over input conditions, and also strengthens character only pronunciations, resulting in a preference for a blended text-to-speech system in A/B testing, compared to an equivalent baselines even when using the same input information modalities. The second article focuses on another text-to-speech system, this time centered on building multi-scale representations of speech audio using a structured decomposition of audio features. Particularly this work focuses on a compute efficient methodology, while building on prior work which requires a much greater computational budget than the proposed system. In order to effectively perform text-to-speech synthesis under these computational constraints, a number of new components are constructed and integrated, resulting in an efficient model for text-to-speech synthesis. The third article presents a new non-autoregressive model for modeling symbolic sequences. This model uses iterative prediction and re-estimation in order to build structured outputs, which respect numerous constraints in the underlying sequence domain. This model is applied to symbolic music modeling and text modeling, showing excellent performance particularly in limited data generative settings. The final article in this thesis focuses on understanding the speech-text representations learned by a text-injected speech recognition system from prior literature. Through a systematic series of studies utilizing pre-trained text and duration models, qualitative relations between text and speech sequences, and performance studies in few-shot cross-modal retrieval, we reveal a number of crucial properties underlying the performance of this system, paving the way for future algorithmic development. In addition, model variants built during this study achieve impressive performance results on a number of benchmark tasks using partially frozen and transferred parameters
The Role of Leadersâ Regulatory Focus Towards Creativity and Safety Ambidextrous Behavior. A Conceptual View
Most research has explored ambidexterity at the organisational level and very limited research is available on individual ambidextrous behaviours. This research paper reviews the role of leadersâ regulatory focus in promoting individual ambidexterity in the form of creativity and safety. The main aim is to contribute to ambidexterity and self-regulatory literature by examining the role of leadersâ regulatory focus in managing ambidextrous behaviours. Ambidexterity is the ability to manage conflicting task demands, which poses a fundamental self-regulatory and motivational challenge in the process of pursuing different goals
Machine learning with screens for detecting bid-rigging cartels
We combine machine learning techniques with statistical screens computed from the distribution of bids in tenders within the Swiss construction sector to predict collusion through bid-rigging cartels. We assess the out of sample performance of this approach and find it to correctly classify more than 80% of the total of bidding processes as collusive or non-collusive. As the correct classification rate, however, differs across truly non-collusive and collusive processes, we also investigate tradeoffs in reducing false positive vs. false negative predictions. Finally, we discuss policy implications of our method for competition agencies aiming at detecting bid- rigging cartel
Residual Reinforcement Learning from Demonstrations
Residual reinforcement learning (RL) has been proposed as a way to solve
challenging robotic tasks by adapting control actions from a conventional
feedback controller to maximize a reward signal. We extend the residual
formulation to learn from visual inputs and sparse rewards using
demonstrations. Learning from images, proprioceptive inputs and a sparse
task-completion reward relaxes the requirement of accessing full state
features, such as object and target positions. In addition, replacing the base
controller with a policy learned from demonstrations removes the dependency on
a hand-engineered controller in favour of a dataset of demonstrations, which
can be provided by non-experts. Our experimental evaluation on simulated
manipulation tasks on a 6-DoF UR5 arm and a 28-DoF dexterous hand demonstrates
that residual RL from demonstrations is able to generalize to unseen
environment conditions more flexibly than either behavioral cloning or RL
fine-tuning, and is capable of solving high-dimensional, sparse-reward tasks
out of reach for RL from scratch
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