Miracle-dz/UniFAR-FTUs huggingface dataset:
Retargetable instruction scheduling for pipelined processors

Retargetable code generators for complex instruction set computers (CISCs) have focused on sophisticated pattern matching code selection, because CISCs provide many machine instruction sequence choices. Recent pipelined processors, known as reduced instruction set computers (RISCs), provide fewer instruction sequence choices, but expose pipeline and functional unit costs to the compiler. For RISCs the compiler's emphasis must be shifted from code selection to instruction scheduling, resulting in code generation issues that are different than those for CISCs. In particular, the machine description language for a retargetable RISC compiler must contain scheduling requirements. Also, the interaction between register allocation and instruction scheduling is significant. This dissertation comprises three components. The first component discusses the Marion retargetable code generator system, which includes a machine description language that contains instruction scheduling requirements, along with other code generation information. Using Marion, code generators have been constructed for the MIPS R2000, Motorola 88000, Intel i860. The second component compares three code generation strategies for handling the interaction between instruction scheduling and register allocation, including one that I developed, RASE, that integrates the two phases. On a computation-intensive workload for three RISCs, RASE produces significantly better code than the Postpass strategy, which does not integrate the two phases, and slightly better code than IPS, which integrates the two phases to a lesser degree than RASE. The third component investigates the interaction between code generation strategies and architectural features, including register set size and structure, and operation and load latencies. On a computation-intensive workload, 64 registers yields a significant improvement over 32 registers for Postpass, but little improvement for IPS or RASE. This study also shows that, on architectures with long latencies or small register sets, or on programs with large basic blocks, RASE produces significantly better code than IPS.
Towards an Axiomatization of Dialogue Acts

Conversations involve all sorts of verbal activities beyond those strictly related to the performance of the task at hand. Among other things, the participants in a conversation have to make sure they both understand what's going on, to manage turn taking, and to keep each other informed about their progress in achieving their task. The participants share information about the status of all of these processes; this suggests that the view of the conversational score they share is rather more complex than assumed in previous accounts. We proposed a preliminary formalization of this more complex view of the conversational score in previous work; in this paper we revise that earlier model, and use our theory of the conversational score to give a partial speci(cid:12)cation of the e(cid:11)ect of the dialogue acts included in the DRI classi(cid:12)cation.
Formal Models for Aspect-Oriented Software Development

ASPECT ORIENTED PROGRAMMING (AOP) is a new technology for separation of concerns. AOP techniques make it possible to modularise cross-cutting aspects of a system: those that would otherwise be scattered throughout the code because the concerns they address are not aligned with the conceptual decomposition of the system. Without AOP techniques, such aspects are hard to encapsulate. The essential components of AOP are exemplified by its flagship language ASPECTJ– an aspect oriented extension to JAVA– these are: member introduction: a way for one class to add members to other classes, parent introduction: a way for classes to influence the inheritance of other classes, join points: places where behaviour can be added to existing code, advice: a way to modify behaviour at join points; aspects: encapsulated units combining join point and behaviour specifications together with their associated state; and weaving a method of incorporating these units into programs. This thesis is a study of the semantics of the novel features of ASPECTJ. It presents a formal model of a simple JAVA-like language with a type system and operational semantics, then goes on to extend it with the most paradigmatic features of ASPECTJ: formal definitions of: join points, advice and aspects are presented for the first time, in the context of an operational semantics. A formal definition of weaving is developed. The impact of these features on the static type system is investigated. Examples are used to demonstrate their expressiveness. The increments to the semantics of the base language are relatively small for each feature making the presentation simple to understand. The work is suitable as a base for further investigation of aspect oriented language features using formal techniques.
Opera and Drama

With Richard Wagner, opera reached the apex of German Romanticism. Originally published in 1851, when Wagner was in political exile, "Opera and Drama" outlines a new, revolutionary type of musical stage work, which would finally materialize as "The Ring of the Nibelung". Wagner's music drama, as he called it, aimed at a union of poetry, drama, music, and stagecraft. In a rare book-length study, the composer discusses the enhancement of dramas by operatic treatment and the subjects that make the best dramas. The expected Wagnerian voltage is here: in his thinking about myths such as "Oedipus", his theories about operatic goals and musical possibilities, his contempt for musical politics, his exaltation of feeling and fantasy, his reflections about genius, and his recasting of Schopenhauer. This edition includes the full text of volume 2 of William Ashton Ellis' 1893 translation commissioned by the London Wagner Society.
Influence de l'état hydrique de la vigne sur le style de vin

L’eau est un facteur important et determinant des terroirs viticoles. La teneur en eau de la vigne est un element fondamental de sa croissance vegetative et fructifere et de son fonctionnement physiologique et biochimique. L’evolution de l’etat hydrique de la vigne au cours de son cycle de developpement est un facteur clef de la comprehension des terroirs viticoles. La qualite de la vendange, c’est a dire la qualite sanitaire du raisin, mais aussi l’evolution biochimique de la baie (sucres, acidite totale, pH, composes phenoliques et aromatiques, potassium, azote,…) vont etre determinants pour le style de vin. Toutefois, au dela des variables climatiques eau et temperature, il faut rappeler que les facteurs suivants sont essentiels pour la maturation du raisin: - le systeme de conduite et partant l’architecture de la vegetation {hauteur de la vegetation et palissage (i.e. nombre de fils releveurs), distribution des rameaux sur le cordon, homogeneite du plan de palissage}, - l’equilibre surface foliaire exposee et production (SFE/P ;Kg/m
The semiology of graphics

A liquid proportioning system includes two groups of positive metering tanks, each group consisting of at least two tanks each containing a supply of liquid to be blended and including outlet control device for selectively regulating the volume of liquid leaving the tank per unit of time. The liquids from each tank are fed together through a single conduit to a mixer which continuously blends the liquid components as they are flowing, and the blended liquids are fed to a reservoir tank, from which they are fed to a point of use in accordance with the variable requirements of the latter. One group of positive metering tanks feeds the liquid components to the mixer in exact proportions until these tanks are depleted, at which time the flow from these tanks is shut off automatically and the second group of tanks begins to feed the portioned liquid components to the mixer, while the first group of tanks are refilled. When the requirements of the point of use are decreased, sensors slow down the feeding of the liquid to the reservoir tank by increasing the time interval between the emptying of one group of metering tanks and the commencement of feeding from the other group of tanks.
Two manipulation planning algorithms

This paper addresses the motion planning problem for a robot in presence of movable objects. Motion planning in this context appears as a constrained instance of the coordinated motion planning problem for multiple movable bodies. Indeed, a solution path in the configuration space of the robot and all movable objects is a sequence of transit paths where the robot moves alone and transfer paths where a movable object follows the robot. A major problem is to find the set of configurations where the robot has to grasp or release objects. The paper gives an overview of a general approach which consists in building a manipulation graph whose connected components characterize the existence of solutions. Two planners developed at LAAS-CNRS illustrate how the general formulation can be instantiated in specific cases.
New reordering and modeling approaches for statistical machine translation

This thesis focuses on the statistical machine translation (SMT) framework and primarly on the definition and experimentation of novel algorithms for building a correct structural reordering for translated words, Moreover, challenging techniques regarding language modeling and system combination are successfully applied to state-of-the-art SMT systems. To begin, a thorough study of the SMT state-of-the-art is performed. Ngram- and phrase-based SMT feature functions are described. The former, which has been developed in our research group, is used as a baseline system and the latter, given its popularity, is used to deepen the new techniques during experimentation. Then, the introduction of continuous space language models is reported and analyzed in an Ngram-based system that uses translation and target language models. The continuous space language modeling technique is based on projecting word indices onto a continuous space.The resulting probability functions are smooth functions of the word representation. Events are better estimated than in standard smoothing methods, which is shown by the significant reduction in perplexity. This better probability estimation allows for an improvement in translation quality. Moreover, this thesis performs a two-system combination considering the phrase and Ngram-based systems. Multiple outputs of both systems with their corresponding score are concatenated, and for each system translation the score given by the opposite system is computed. The final translation is properly chosen by simultaneously considering the scores given by both systems. Finally, this thesis proposes the introduction of novel statistical reordering techniques in an SMT system. The first approach is based on an algorithm that detects, learns and infers pairs of words in the source language that swap in the target language providing accurate local reorderings. The second approach consists of generating weighted reordering hypotheses using the same powerful techniques of SMT systems in order to undo the source language structure and to make it more similar to the target language structure. Therefore, the translation challenge is divided into two steps: predicting the order of the words in the target language and substituting these words in the target language. In order to infer new reorderings that were not learnt during training, the SMR system uses word classes instead of words themselves. In order to correctly integrate the SMR and SMT systems, both are concatenated, by using a word graph. This approach is an elegant and efficient reordering approach that is capable of achieving significantly improved translation in the target language.
Controlled natural language policies

Policy languages allow users to define in a flexible way under which conditions their data can be shared and with whom. Nonetheless policy languages are not widely used yet: one reason for this is that they are too complex to be easily exploited by common users in a profitable way. This paper describes an approach that uses (controlled) natural language in order to express policies, therefore allowing non-computer experts to easily specify, refine and understand policies.
Non-Learning Semantic Analysis for Context Discovery and Sentiment Estimation: Transportation Application

Non-Learning Semantic Analysis for Context Discovery and Sentiment Estimation: Transportation Application by Himanshu Verma 〈Dr. Pushkin Kachroo〉, Examination Committee Chair Professor of Electrical and Computer Engineering University of Nevada, Las Vegas With enormous amount of linguistic data present on web, text analysis has become one of the major fields of interest today. This field includes sentiment analysis, information retrieval, text document classification, knowledge based modeling, content similarity measure, data clustering, words prediction/correction, decision making etc. Managing and processing such data has vital importance. The field being quite broad, our focus is mainly on transportation related social media(Twitter) data extraction, text categorization/classification which can be further sub-divided into concept discovery, word sense disambiguation and sentiment analysis to analyze performance of existing transportation system worldwide. Concept discovery is the method of extracting the actual concept/context in which the text is about. This also allows us to filter irrelevant data. Word sense disambiguation is to find the correct sense in which a word is being used in a sentence. It is the basic necessity for concept discovery. iii A lot of research has been done in this field with major improvements. However, when it comes to short texts, the field still seems in nascent stage. Moreover, most of the methods today require huge amount training corpus(database). Arranging such corpus is a cumbersome task and requires a lot of human effort. The other problem with the existing methods are that they require a set of defined concepts from which a concept is chosen and labeled to text. We will consider the case of finding a general context. In this work a novel approach has been proposed for word sense disambiguation which in turn allows us to find general context. For this purpose, I have used the existing knowledge based semantic dictionary called WordNet. This methodology helps in avoiding the use of huge corpus and works for general context recognition. Our focus is on short-texts(Tweets) but the concept is easily applicable to text documents as well. Sentiment measuring technique was applied on extracted data and the scores were mapped to google maps based on the location information present in the tweets. This clearly points out the locations where people are more frustrated with the existing transportation system and need immediate attention for improvements.
Robustness of linear and non-linear reconstructions algorithms for brain EITS. Non-linear—is it worth the effort

When choosing a reconstruction algorithm for clinical use accuracy, speed and robustness, with respect to errors in the measured data, must be considered. Until recently the UCL brain EIT group have used a 3D, Truncated Singular Value Decomposition algorithm (TSVD), and published time-difference images of evoked responses using this. However, the group is extending its interests to stroke, where time difference imaging cannot be used and additional reconstruction algorithms must be implemented. This paper compares two approaches – TSVD with a computer model for the reference image, and Non-linear Conjugate Gradients. These are assessed using simulated and tank data with known sources of measurement error. The solutions are found to be very sensitive to discrepancies between the position of the electrodes used to obtain the boundary voltages, and those used for reconstruction. Position errors of 1% causing significant artefacts. The TSVD algorithm was also unable to return the true conductivity of a homogenous image if the reference voltage was out by more than 10%. The non-linear algorithm could compensate for the reference voltage being out by 30%, but is much more computationally expensive than TSVD, and would require speeding up before routine clinical use.
A Genetic Algorithm (GA) Shell for Iterative Timetabling

We present a new approach to timetabling which is a typical Constraints Satisfaction Problem. The new approach is based on an iterative scheduling model equipped with a generic Genetic Algorithm (GA) search engine. The iterative model is the model that practitioners used to schedule school time tables. Generality of the GA engine is achieved by dynamic chromosome encoding, generic constraint data structure and robust GA operators. The implemented system has been tested on two university timetabling problems. The rst one is a departmental timetable with hypothetical schedule requirements. The second one is also a departmental timetable which is speci ed in a previous research. Timetables generated in the second experiment are comparable to previous work using parallel machines. Performance is satisfactory, although performance data was not available in previous work for further comparison.
A Grammar of (Western) Garrwa

Mushin provides the first full grammatical description of Garrwa, a critically endangered language of the Southwest Gulf of Carpentaria region in Northern Australia. Garrwa is typologically interesting because of its uncertain status in the Australian language family, its pronouns and its word order syntax. This book covers Garrwa phonology, morphology and syntax, with a particular focus on the use of grammar in discourse. The grammatical description is supplemented with a word list and text collection, including transcriptions of ordinary conversation.
Mobile Augmented Reality for Learning: A Case Study

This article discusses technological developments and applications of mobile augmented reality (AR) and their application in learning. Augmented reality interaction design patterns are introduced and educational patterns for supporting certain learning objectives with AR approaches are discussed. The article then identifies several dimensions of a user context identified with sensors contained in mobile devices and used for the contextualization of learning experiences. Finally, an AR game concept, “Locatory”, is presented that combines a game logic with collaborative game play and personalized mobile augmented reality visualization.
Automatic generation of synchronization instructions for parallel processors

The development of high speed parallel multi-processors, capable of parallel execution of doacross and forall loops, has stimulated the development of compilers to transform serial FORTRAN programs to parallel forms. One of the duties of such a compiler must be to place synchronization instructions in the parallel version of the program to insure the legal execution order of doacross and forall loops. This thesis gives strategies usable by a compiler to generate these synchronization instructions. It presents algorithms for reducing the parallelism in FORTRAN programs to match a target architecture, recovering some of the parallelism so discarded, and reducing the number of synchronization instructions that must be added to a FORTRAN program, as well as basic strategies for placing synchronization instructions. These algorithms are developed for two synchronization instruction sets. 20 refs., 56 figs.
Error Classification for MT Evaluation

A classification system for errors in machine translation (MT) output is presented. Translation errors are assigned to categories to provide a systematic basis for comparing the translations produced by competing MT systems. The classification system is designed for use by potential MT users, rather than MT developers. The error categories can be ranked according to criteria which are important to the user, such as improvability and intelligibility. The results of CompuServe's use of the error classification methodology are presented as a case study.
Resources for linguistically motivated Multilingual Anaphora Resolution

An actual trend in the computational linguistics and natural language processing is the implementation of multilingual utilities for different tasks, like information retrival, summarization of documents in different languages or machine translation, tasks in which the resolution of anaphoric references plays a crucial role. This dissertation presents a proposal of annotation scheme for the creation of corpus resources for linguistic based multilingual anaphora resolution. This scheme has been implemented for the annotation of English and Italian data. Inter-annotator agreement studies show that the annotation scheme is relaiable. The annotated corpora have been used for the anaphora resolution task, and the results have been compared with well known corpora. Finally hand annotated linguistic features have been used to help in the anaphora resolution process. The results show that our multilingual annotation scheme proposal has been utilized to produce data useful to build anaphora resolution systems for languages with different grammatical and typological features, like English and Italian.
Stochastic Scheduling and Planning Using Reinforcement Learning

Abstract : This project investigated the extension of reinforcement learning (RL) methods to large-scale optimization problems relevant to Air Force operations planning, scheduling, and maintenance. The objectives of this project were to: (1) investigate the utility of RL on large-scale logistics problems; (2) extend existing RL theory and practice to enhance the ease of application and the performance of RL on these problems; and (3) explore new problem formulations in order to take maximal advantage of RL methods. A method using RL to modify local search cost functions was developed and shown to yield significant improvement over a traditional local search method on a core vehicle routing problem. A new method for stochastic dynamic optimization was studied, a theoretical result proven, and utility demonstrated using a simulated aerial mission planning task. A learning-based method for optimizing subproblem selection in divide-and-conquer approaches was developed and demonstrated on graph-coloring and on a multiple-vehicle mission planning task.
A Draft Manual for the Validation of Lexica Final Report

Acknowledgements: We would like to thank Bente Maegaard, Anna Braasch and Henning Spang-Hanssen for fruitful and interesting discussions during the development of this manual, and for their comments on the text. In addition we would like to thank the experts from ELRA Panel for Validation of Written Language Resources (EPV-WLR) who gave valuable feedback on earlier versions of this document.
Interpreting Symptoms of Cognitive Load and Time Pressure in Manual Input

Users of computing devices are increasingly likely to be subject to situationally determined distractions that produce exceptionally high cognitive load and/or time pressure. The question arises of how a system can automatically interpret symptoms of such cognitive load and time pressure in the user’s manual input behavior while taking the user’s knowledge of the system into account. An approach to this problem is presented which the results of relevant previous research are used as the basis for the construction and manipulation of a dynamic Bayesian network.
The Complexity of Learning Separable ceteris paribus Preferences

We address the problem of learning preference relations on multi-attribute (or combinatorial) domains. We do so by making a very simple hypothesis about the dependence structure between attributes that the preference relation enjoys, namely separability (no preferential dependencies between attributes). Given a set of examples consisting of comparisons between alternatives, we want to output a separable CP-net, consisting of local preferences on each of the attributes, that fits the examples. We consider three forms of compatibility between a CP-net and a set of examples, and for each of them we give useful characterizations as well as complexity results.