Miracle-dz/UniFAR-FTUs huggingface dataset:
Randomized, oblivious, minimal routing algorithms for multicomputers

E cient data motion has been critical in high performance computing for as long as computers have been in existence. Massively parallel computers use a sparse interconnection network between processing nodes with local memories. Minimizing the potential for high congestion of communication links is an important goal in the design of routing algorithms and interconnection networks in these systems. In these distributed{memory architectures, the communication system represents a signi cant portion of the total system cost, but is nevertheless often a weak link in the system with respect to performance. E cient interprocessor communication is one of the most important and most challenging problems associated with massively parallel computing. Communication delays can easily represent a large fraction of the total running time, inhibiting high performance computing for a wide range of problems. E cient use of the communication system is the focus of this thesis. The design of the interconnection network and the routing algorithms used to transport data between nodes are critical to overall system performance. The constraints imposed by a sparse interconnection network suggest that preserving locality of reference through careful data allocation and minimizing network load by using minimal algorithms are desirable objectives. In this thesis, we present ROMM, a new class of general{purpose message routing algorithms for large{scale, distributed{memory multicomputers. ROMM is a class of Randomized, Oblivious, Multi{phase, Minimal routing algorithms. We will show that ROMM routing o ers the potential for improved performance compared to both fully randomized algorithms and deterministic oblivious algorithms, under both light and heavy loads. ROMM routing also o ers close to best{case performance for many common routing tasks. These claims are supported by extensive analysis and simulation of ROMM routing on several di erent interconnection network architectures, for a set of representative routing tasks. Furthermore, our results show that non{minimality and adaptivity, two common techniques for reducing congestion, are not always required for good routing performance.
Emulating Human Play in a Leading Mobile Card Game

Monte Carlo tree search (MCTS) has become a popular solution for game artificial intelligence (AI), capable of creating strong game playing opponents. However, the emergent playstyle of agents using MCTS is not necessarily human-like, believable or enjoyable. AI Factory Spades, currently the top rated Spades game in the Google Play store, uses a variant of MCTS to control AI allies and opponents. In collaboration with the developers, we showed in a previous study that the playstyle of human players significantly differed from that of the AI players. This paper presents a method for player modeling using gameplay data and neural networks that does not require domain knowledge, and a method of biasing MCTS with such a player model to create Spades playing agents that emulate human play whilst maintaining strong, competitive performance. The methods of player modeling and biasing MCTS presented in this study are applied to the commercial codebase of AI Factory Spades, and are transferable to MCTS implementations for discrete-action games where relevant gameplay data are available.
Visible and infrared hyperspectral visualization of normal and ischemic tissue

The authors have developed a new in-vivo spectroscopic imaging system (IVIS) for real-time biomedical applications. IVIS visualization produces images that are acquired non-invasively during clinical procedures. This paper describes data collected from tissue to determine the efficacy of this type of hyperspectral imaging modality for measuring changes in the spatial distribution of regional tissue oxygenation during vascular occlusion and reperfusion.
Apply Uncertainty in Document-Oriented Database (MongoDB) Using F-XML

As moving to big data world where data is increasing in unstructured way with high velocity, there is a need of data-store to store this bundle amount of data. Traditionally, relational databases are used which are now not compatible to handle this large amount of data, so it is needed to move on to non-relational data-stores. In the current study, we have proposed an extension of the MongoDB architecture to support FMQL by integrating a software layer on MongoDB translating FMQL queries to corresponding F-XML queries. Flexible queries have been done in the oriented-document database (MongoDB) by using fuzzy xml commands in the document-centric (MongoDB) injected uncertainty and expressed this commands based on F-XML and by using uncertainty. We used the fuzzy properties to improve query in the document-oriented database (MongoDB) and we showed the Position of the proposed method by the implementation and evaluation of a case study.
Verb-argument constructions in advanced L2 English learner production: Insights from corpora and verbal fluency tasks

Abstract This paper draws on data from learner and native-speaker corpora as well as psycholinguistic data to gain insights into second language speaker knowledge of English verb-argument constructions (VACs). For each of 34 VACs, L1 German and L1 Spanish advanced English learners’ and English native speakers’ dominant verb–VAC associations are examined based on data retrieved from the International Corpus of Learner English (ICLE), the Louvain International Database of Spoken English Interlanguage (LINDSEI), their respective Native Speaker (NS) reference corpora, and data collected in verbal fluency tasks in which participants complete VAC frames, such as, ‘she _______ with the…’ with verbs that come to mind. We compare findings from the different data sets and consider the strengths and limitations of each in relation to questions in usage-based second language acquisition and Construction Grammar.
Semantic support for a speech understanding system

One function of the Semantics component of SPEECH-LIS, the Bolt Beranek and Newman (BBN) Speech Understanding System, is to gather evidence for hypotheses it has made regarding the content of an utterance, as well as to evaluate the hypotheses made by other components. Another is to produce a representation of the utterance's meaning. Specifically, this involves forming consistent, meaningful collections of words which match regions of the speech waveform, and evaluating and interpreting the possible syntactic structures built of them. This paper discusses the data structures and organization of SPEECHLIS semantics and how they are directed to the above two tasks.
Statistics in Research, Basic Concepts and Techniques for Research Workers

The goals of this book are to (a) indicate statistical methods that have been found useful by workers in many areas of scientific research, (b) present these methods as integral parts of a complete discipline, and (c) provide a text that will facilitate teaching the science of statistics. These goals are essentially the same as for the previous editions. The volume is very large in size (over 660 pages). This is reasonable given its broad goals. The authors state that the book could be used as a text for a course in statistical methods, an integrated course in probability and statistics for students in science and engineering, and a reference book for research workers. However, it is unlikely that any one-semester class (even at the advanced graduate level) would be able to complete the entire volume. They suggest that with judicious selection of topics, it could also function as a text for a course in elementary statistics. The book consists of 18 chapters, covering topics from basic statistical concepts and descriptive statistics through regression, complex ANOVA designs, and nonparametric techniques. It is not, however, a survey of these techniques. Instead, a fairly comprehensive presentation of the topics, including distributional assumptions and statistical tests, is presented. The first five chapters lay the foundation for hypothesis testing by covering probability, descriptive statistics, sampling distributions, and general concepts of statistical inference. The chapter on probability is particularly strong, covering mathematical representations of complex specific probability distributions. The reader is expected to have an intermediate level of knowledge of calculus, including integration of complicated mathematical functions and partial differentiation. In addition, the reader is expected to be familiar with the gamma and beta functions. The text simply uses the gamma and beta mathematical symbols without direction or explanation. The mathematical functions for these two distributions are shown in Appendix 1: Mathematical Concepts (which covers set theory, permutations and combinations, identities and series, some mathematical functions, matrix algebra, and linear equations). It seems unlikely that a student, even with a strong mathematics background, would have been exposed to the gamma and beta functions. Therefore, I feel that the reference to these functions in the text of the book without the necessary explanation is a shortcoming of this section on probability theory. Overall, however, these early chapters are well done and comprehensive. The chapter on probability includes presentations on expected value and
Optimizing Item Exposure Control Algorithms for Polytomous Computerized Adaptive Tests With Restricted Item Banks

In the face of modern-day developments in translating assessment instruments into computer-based adaptive measures, more and more concerns regarding test security and test validity have been raised. Further, as non-cognitive measures are being transformed into functioning CATs, item exposure control has become an important component of managing item banks. With the inception of evaluating exposure control mechanisms conditionally, many of the non-high stakes assessments are now seeking solutions to issues associated with constrained item banks and limited item availability. This simulation study compared three item exposure control mechanisms (Sympson-Hetter conditional, progressive restrictive maximum information, and total rate simplified exposure) against two unmanaged CAT administrations (simple maximum information and random item selection) for two restrictive item bank conditions. The competing methods were evaluated in their ability to provide conditional exposure control along with adequate bias, root mean square errors, and deviations of asymptotic valid standard errors from observed conditional standard deviations. Benefits and shortcomings are discussed.
Object recognition and pose estimation with a fast and versatile 3D robot sensor

Presents a new approach to object recognition and pose estimation based on a 3D robot sensor, which produces range images of a scene along problem specific lines of sight. Recognition is realized as a hypothesis generation/hypothesis verification process. Hypothesis generation is based on a minimal number of predominant and connected object parts in one or more range images determining all 6 degrees of freedom of an objects pose. This set of object parts is transformed into a hypotheses set by simple look-up operations in precalculated hash tables. In the subsequent verification step the authors determine an inspection list for the best recognizable, most distinctive and best visible object parts in this hypotheses set and verify the hypotheses by searching these object parts in existing or new range images. <<ETX>>
A predictive approach to discrete-time time-optimal servomechanism

Presents an approach to a discrete-time time-optimal servomechanism. Differently from existing approaches to discrete-time time-optimal control, we attempt to find the control law minimizing point-wise the distance between the one-step-ahead prediction of the system state and a given reference velocity trajectory. The performance of the proposed controller is shown to be mathematically well supported and, moreover, its practical significance is demonstrated through realistic simulation based on the head-positioning servo in a hard disk drive.
Object-Oriented Class Library for Resource Allocation Problems

The object-oriented class library designed for solving various optimization problems of resource allocation, including problems of cutting materials and any dimensional packing problems, is described in this paper. The class library enables obtaining of suboptimal solutions of NP-completed resource allocation problems using standard evolutionary and modified heuristic optimization algorithms. The developed class library can be used in creation of an applied software for a wide class of optimization problems, including problems of resource allocation in storage systems and logistics, problems of cutting materials on machine tools with numerical control, scheduling problems and a large set of other practical problems.
Equalization of the network sensitivity enhances predictive power at criticality in a whole-brain model

Understanding the relationship between structural and functional connectivity remains a crucial issue in modern neuroscience. Here we investigate such relationship through a stochastic neural whole-brain model, using as input the structural connectivity matrix defined by the human connectome. Importantly, we address the structure-function relationship both at the group and individual-based levels. In this work, we show that equalization of the nodes excitatory input improves the correlation between simulated neural patterns of the model and various brain functional data. We find that the best model performance is achieved when the model control parameter is in its critical value and that equalization minimizes both the variability of the critical points and neuronal activity patterns among subjects. Our results open new perspectives in personalized brain modelling with potential applications to investigate the deviation from criticality due to structural lesions (e.g. stroke) or brain disorders.
A New Conceptual Map of English

The Longman Language Activator (published 1993) is a conceptually-organized dictionary that was designed and written, from scratch, specifically to meet the encoding needs of learners of English. The book's macrostructure represents a completely new conceptual map of English. This paper describes how the conceptual framework was developed and how it was influenced by earlier models; how it works in practice to supply users' languageproduction needs; and what steps were taken to test and validate the system while it was under development.
Metareasoning and Mental Simulation

Author(s): Hamrick, Jessica B. | Advisor(s): Griffiths, Thomas L. | Abstract: At any given moment, the mind needs to decide how to think about what, and for how long. The mind's ability to manage itself is one of the hallmarks of human cognition, and these meta-level questions are crucially important to understanding how cognition is so fluid and flexible across so many situations. In this thesis, I investigate the problem of cognitive resource management by focusing in particular on the domain of mental simulation. Mental simulation is a phenomenon in which people can perceive and manipulate objects and scenes in their imagination in order to make decisions, predictions, and inferences about the world. Importantly, if thinking is computation, then mental simulation is one particular type of computation analogous to having a rich, forward model of the world.Given access to such a model as rich and flexible as mental simulation, how should the mind use it? How does the mind infer anything from the outcomes of its simulations? How many resources should be allocated to running which simulations? When should such a rich forward model be used in the first place, in contrast to other types of computation such as heuristics or rules? Understanding the answers to these questions provides broad insight into people's meta-level reasoning because mental simulation is involved in almost every aspect of cognition, including perception, memory, planning, physical reasoning, language, social cognition, problem solving, scientific reasoning, and even creativity. Through a series of behavioral experiments combined with machine learning models, I show how people adaptively use their mental simulations to learn new things about the world; that they choose which simulations to run based on which they think will be more informative; and that they allocate their cognitive resources to spend less time on easy problems and more time on hard problems.
Acquiring sounds and meaning jointly in early word learning

To acquire their native language, babies have to learn both the forms of words (e.g., “dog” in English, “chien” in French) and their meanings (the category of dogs). These two aspects of language learning have typically been studied independently. However, recent findings from developmental psychology and machine learning have pointed out that this assumption is problematic, and have suggested that form and meaning may interact with one another throughout development. This dissertation explores this hypothesis through an interdisciplinary investigation that combines tools from speech recognition and experimental psychology. First, I developed a computational model of joint form and meaning acquisition, capable of learning from a corpus of natural speech. Second, I tested the cognitive plausibility of this model with adult subjects.
Iniciación al álgebra en Educación Infantil a través del pensamiento computacional: una experiencia sobre patrones con robots educativos programables

espanolEl objetivo de este articulo es presentar las primeras orientaciones didacticas para desarrollar el razonamiento algebraico en Educacion Infantil a traves del pensamiento computacional, usando la robotica como recurso. A partir de los vinculos entre estos aspectos y el analisis de una experiencia con robots educativos programables para trabajar los patrones en 3-4 anos, se establecen cinco recomendaciones iniciales en el marco de la educacion STEAM: 1) plantear fenomenos relevantes, basados en la resolucion de problemas; 2) fomentar procesos de razonamiento mediante buenas preguntas; 3) impulsar la interaccion, la negociacion y el dialogo; 4) vincular conocimientos de distinta naturaleza; 5) plantear la representacion como medio para comprender, estructurar, capturar y transferir conceptos. EnglishThe aim of this article is to present some first didactic orientations to develop the algebraic reasoning in Pre-school Education through computational thinking, using robotics as a resource. From the links between these aspects and the analysis of a experience with programmable floor robots to work the patterns in 3-4 years, five initial recommendations are established in the framework of STEAM education: 1) raise relevant phenomena, based on problem solving; 2) encourage reasoning processes through good questions; 3) boost interaction, negotiation and dialogue; 4) link knowledge of a different nature; 5) raise representation as a means to understand, structure, capture and transfer concepts. portuguesO objetivo deste artigo e apresentar algumas primeiras orientacoes didaticas para desenvolver o raciocinio algebrico na Educacao Infantil atraves do pensamento computacional, utilizando a robotica como recurso. A partir dos vinculos entre esses aspectos e a analise de uma experiencia com robos educacionais programaveis para trabalhar os padroes em 3-4 anos, cinco recomendacoes iniciais sao estabelecidas no âmbito da educacao STEAM: 1) criar fenomenos relevantes, com base na resolucao de problemas; 2) incentivar os processos de raciocinio atraves de boas questoes; 3) impulsionar interacao, negociacao e dialogo; 4) relacionar o conhecimento de uma natureza diferente; 5) elevar a representacao como um meio para compreender, estruturar, capturar e transferir conceitos.
Learning Algorithms Using Chance-Constrained Programs

This thesis explores Chance-Constrained Programming (CCP) in the context of learning. It is shown that chance-constraint approaches lead to improved algorithms for three important learning problems — classification with specified error rates, large dataset classification and Ordinal Regression (OR). Using moments of training data, the CCPs are posed as Second Order Cone Programs (SOCPs). Novel iterative algorithms for solving the resulting SOCPs are also derived. Borrowing ideas from robust optimization theory, the proposed formulations are made robust to moment estimation errors. A maximum margin classifier with specified false positive and false negative rates is derived. The key idea is to employ chance-constraints for each class which imply that the actual misclassification rates do not exceed the specified. The formulation is applied to the case of biased classification. The problems of large dataset classification and ordinal regression are addressed by deriving formulations which employ chance-constraints for clusters in training data rather than constraints for each data point. Since the number of clusters can be substantially smaller than the number of data points, the resulting formulation size and number of inequalities are very small. Hence the formulations scale well to large datasets. The scalable classification and OR formulations are extended to feature spaces and the kernelized duals turn out to be instances of SOCPs with a single cone constraint. Exploiting this specialty, fast iterative solvers which outperform generic SOCP solvers, are proposed. Compared to state-of-the-art learners, the proposed algorithms achieve a speed up as high as 10000 times, when the specialized SOCP solvers are employed. The proposed formulations involve second order moments of data and hence are
"Flyable” Guidance and Control Algorithms for Orbital Rendezvous Maneuver

Rendezvous orbital maneuvers are planned operations which intend to make two spacecraft meet, while avoiding collisions. Aspects such as trajectory safety and robustness, as well as obstacle avoidance, are fundamental for the mission success. The aim of this paper is to provide an overview of some recent algorithms for guidance and control systems, and also their combined exploitation, which can provide significant enhancements. This overview focuses on those algorithms that are characterized by key selected features: the discussed schemes guarantee low computational effort, low fuel consumption, and high safety and robustness. Different experimental setups, which can be used to evaluate the performance of the considered algorithms, are also taken into account and presented.
A practical example using virtual reality in the assessment of brain injury

Virtual Reality (VR) as a complementary tool for medical practitioners in the assessment and rehabilitation of people who have suffered a brain injury is discussed. A pilot-study has been undertaken on a prototype VR assessment tool. The design involved nine occupational therapists with expertise in the care of traumatic brain-injured patients and one (computer experienced) patient. The aim was to begin a dialogue and to ascertain the potential of a VR system. A common method for occupational therapists to assess function and ability is to ask a patient to brew coffee. From the performance of such a task, an individual’s “functional signature” can be determined. The prototype was built using Superscape ® , a personal computer based VR system, to be close to the real coffee making task, including effects of making mistakes, realistic graphics and sound effects. The world was designed to be as easy to use and intuitive as possible, though problems of mental abstraction level, transfer of training and realistic interaction have yet to be resolved. The comments from the test participants have highlighted problem areas, given positive insight and pointed out other scenarios where VR may be of use in the rehabilitation of people with a traumatic brain injury.