Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling the Contributions of Basal Ganglia and Hippocampus to Spatial Navigation Using Reinforcement Learning

A computational neural model that describes the competing roles of Basal Ganglia and Hippocampus in spatial navigation is presented. Model performance is evaluated on a simulated Morris water maze explored by a model rat. Cue-based and place-based navigational strategies, thought to be subserved by the Basal ganglia and Hippocampus respectively, are described. In cue-based navigation, the model rat learns to directly head towards a visible target, while in place-based navigation the target position is represented in terms of spatial context provided by an array of poles placed around the pool. Learning is formulated within the framework of Reinforcement Learning, with the nigrostriatal dopamine signal playing the role of Temporal Difference Error. Navigation inherently involves two apparently contradictory movements: goal oriented movements vs. random, wandering movements. The model hypothesizes that while the goal-directedness is determined by the gradient in Value function, randomness is driven by the complex activity of the SubThalamic Nucleus (STN)-Globus Pallidus externa (GPe) system. Each navigational system is associated with a Critic, prescribing actions that maximize value gradients for the corresponding system. In the integrated system, that incorporates both cue-based and place-based forms of navigation, navigation at a given position is determined by the system whose value function is greater at that position. The proposed model describes the experimental results of [1], a lesion-study that investigates the competition between cue-based and place-based navigational systems. The present study also examines impaired navigational performance under Parkinsonian-like conditions. The integrated navigational system, operated under dopamine-deficient conditions, exhibits increased escape latency as was observed in experimental literature describing MPTP model rats navigating a water maze.
Stakeholder cross-impact analysis: a segmentation method

Purpose – The purpose of this paper is to introduce a method that the authors call stakeholder cross-impact analysis (SCIA), which is aimed at analyzing how a given set of stakeholders influence one another and also how such stakeholders relate to a given set of issues. Design/methodology/approach – The authors first identify, in the current literature, a lack of analytical tools for assessing mutual influences among stakeholders. The authors then identify cross-impact analysis, a method that was initially developed in the field of futures research, as a suitable method to be applied in the present research. Its application, which the authors call SCIA, is described in detail through a fictitious case. Findings – SCIA permits to assess the direction and the strength of relationships between stakeholders. Furthermore, it allows for the classification of stakeholders based on their level of dependence and influence on others. Also, it is possible to integrate SCIA with social network analysis in order to un.
Vocabulary Length Experiments for Binary Image Classification Using BOV Approach

Bag-of-Visual-words (BoV) approach to image classification is popular among computer vision scientists. The visual words come from the visual vocabulary which is constructed using the key points extracted from the image database. Unlike the natural language, the length of such vocabulary for image classification is task dependent. The visual words capture the local invariant features of the image. The region of image over which a visual word is constrained forms the spatial content for the visual word. Spatial pyramid representation of images is an approach to handle spatial information. In this paper, we study the role of vocabulary lengths for the levels of a simple two level spatial pyramid to perform binary classifications. Two binary classification problems namely to detect the presence of persons and cars are studied. Relevant images from PASCAL dataset are being used for the learning activities involved in this work
TEX-I-bylon: advanced synchronization concepts for a concurrent hybrid expert system tool

This paper describes how we extended the LISP-based hybrid knowledge representation system Babylon 2.1 [4] in such a way that we can now define communicating knowledge bases running concurrently. While the mapping of knowledge bases to processes in order to allow for concurrency has not been too costly, greater efforts had to be taken to provide proper means for maintaining synchronization and data consistency among knowledge bases. Although every knowledge base can keep its own local view on data there also exist shared global data; for the access of this data we provide low level and higher level synchronization. We have implemented a 2-phase locking transaction concept such that rule sets, individual rules, premises or conclusions can be executed as transactions thus providing advanced synchronization at every desirable level.
The AMAS authoring tool 2.0: a UX evaluation

Adaptive hypermedia has been well documented as being very beneficial in the domain of online education. Authoring adaptive educational hypermedia is however a complex and difficult task. There have been a number of tools developed to address the issue of authoring so as to ease the cognitive load involved in composition. This paper examines two key areas related authoring tool design: hypertext representation and User Experience (UX) design. Both of these are important factors that should be considered when designing hypertext authoring tools. The paper also presents the AMAS Authoring Tool. A new and unique authoring tool that allows non-technical Subject Matter Experts to compose adaptive activity based courses without the needing to write any code or technical languages.
Musical Instrument Recognition and Transcription using Neural Network

In this paper musical instrument recognition and transcription for piano, guitar, violin is discussed. The system is implementing in two stages; first stage is musical instrument recognised using spectral features after recognising instrument musical note is recognised using different frequency estimation methods. Feed forward Neural Network has been used as classifier. The system is implemented for Single Instrument Single Note (SISN), Single Instrument Multiple Note (SIMN) and Multiple Instrument Multiple Note (MIMN). The average accuracy is achieved for three instruments is recorded 80%.
EXTRACTING WIRE-FRAME MODELS OF STREET FACADES FROM 3D POINT CLOUDS AND THE CORRESPONDING CADASTRAL MAP

Commission III/3 ABSTRACT: This paper presents an operational approach for extracting wire-fra me models of street facades. Georeferenced terrestrial laser rawdata are acquired by a Mobile Mapping System (MMS). The MMS constitutes an adapted tool to massively collect accurate data at street level. After a filtering step of the 3D raw point cloud of street, the street poin t cloud is segmented into a cloud of dominant facade walls. Piecewise quasi-planar facade cloud are then extracted using an adapted Progressive Probabilistic Hough Transform (PPHT). At the planimetric level, a cadastral map issue from an urban database is employed to segment the cloud of planar clusters into residential facade portion. Finally, each portion is vertically delimited using heuristic app roaches. The adapted PPHT allows the automatic modeling of facade outlines with a fine facade line detection and a low computa tion time. The adapted approach has been tested on a set of point clouds acquired in the city of Paris under real conditions. E xamples and experimental results show the efficiency of the proposed approach.
Informed data-hiding with partial side information at the decoder

In this paper, we extend a traditional robust data-hiding set-up with host state at the encoder to a case when a partial side information about host statistics is also available at the decoder. We demonstrate that the knowledge of host statistics at the decoder can relax the critical requirements of random binning-based methods concerning attack channel statistics at the encoder. We also analyze performance improvement of some known data-hiding methods showing that they are particular cases of the generalized set-up. Finally, we consider some related open research issues and future possible extensions.
Development of a learning algorithm for facial recognition under varying illumination

Face recognition under varying illumination and dimensionality reduction has been a key problem in the field of Computer Vision. An extension of Principal Component Analysis (PCA) called Independent Component Analysis (ICA) has been utilised in this paper as a feature extraction technique. In the proposed approach three feature selection techniques have been investigated namely Adaboost, Gentle-Adaboost and Sequential Forward Floating Selection (SFFS). The classifier used in this research is the supervised learning algorithm, Support Vector Machines (SVM). The algorithm was tested on the Extended Yale B face database and a five person database created for the purpose of this research. Face recognition rate was highest when all features extracted were used for recognition and when feature selection techniques Adaboost and Gentle Adaboost were used. Therefore, we conclude that by using these two feature selection techniques only 23% of the total features are required giving the same accuracy as when using all features. This increases dimensionality reduction and performance of the algorithm enabling recognition of frontal face images under varying illumination.
Rasterized Bounding Volume Hierarchies

We present the rasterized bounding volume hierarchy (RBVH), a compact data structure that accelerates approximate ray casting of complex meshes and provides adjustable level of detail. During construction, we identify subtrees of BVHs containing surfaces that can be represented by height fields. For these subtrees the conventional ray‐surface intersection, which possibly involves a large number of triangles, is replaced by a simple ray marching procedure to find the intersection with the surface. We describe GPU algorithms for construction, ray casting, and data querying of the RBVH that achieve comparable or higher performance than state of the art acceleration structures for triangle meshes. Moreover, RBVHs provide an inherent surface parameterization for storing data on the surfaces and natively handle triangle and point‐based surface representations. We also show that RBVHs support adaptive level‐of‐detail and can be combined with traditional BVHs to handle complex scenes.
An experiments based modeling method for Pneumatic Artificial Muscle

The Pneumatic Artificial Muscle (PAM) provides many advantages over conventional actuators, especially pneumatic cylinders. It provides light weight, simple structure, high output force to weight ratio, low cost and so on. Despite of these advantages, there are also challenges in using a PAM actuator, such as the behavior of nonlinearity and high hysteresis. To get optimum design and usage, it is significant to find a good way to model the PAM. In this paper, an experiments based modeling method for pneumatic artificial muscle is proposed. This model is a polynomial model and the form of results is simple. At the same time, the parameters are easy to be obtained. Validation experiments are carried out in this paper. The experimental results show the feasibility and correctness of this method. Meanwhile, its simple structure is convenient for further design of PAM control system.
Complex Adaptations and the Structure of Recombination Spaces

According to the Darwinian theory of evolution, adaptation results from spontaneously generated genetic variation and natural selection. Mathematical models of this process can be seen as describing a dynamics on an algebraic structure which in turn is defined by the processes which generate geneetic variation (mutation and/or recombination). The theory of comoplex adaptive system has shown that the properties of the algebraic structure induced by mutation and recombination is more important for understanding the dynamics than the differential equations themselves. This has motivated new directions in the mathematical analysis of evolutionary models in which the algebraic properties induced by mutation and recombination are at the center of interest. In this paper we summarize some new results on the algebraic properties of recombination spaces. It is shown that the algebraic structure induced by recombination can be represented by a map from the pairs of types to the power set of the types. This construct is called P-structure. Utilizing this approach deep commonalities between the recombination spaces defined by string recombination models and the corresponding point mutation is also elementary for string recombination. This is an unexpected result because of the fundamentally different nature of mutation and recombination processes.
The Effects of Knowledge Interaction for Business Innovation

The objective of this paper is to examine the effects of knowledge interaction on different types of business innovation. We first identified three indicators that reflect on the quality of the interaction between customers and technological knowledge, and then classified business innovations as product innovation, problem-solving innovation, or general innovation capability. Hypotheses about the impact of different qualities of knowledge interaction on business innovations were tested by collecting data from 178 high-technology firms in Taiwan. The results revealed that product innovation requires both wide-ranging and deep interaction between customers and technological knowledge, that problem-solving innovation requires either wide-ranging or deeper interaction between customers and technological knowledge, and that wide-ranging knowledge interaction is the most important driver for building general innovation capability. The research results enhance our understanding of knowledge interaction, with a special focus on the content and quality of the knowledge interactions within an enterprise. It also helps business managers in allocating resources and facilitating interorganizational communications for different situations related to innovation.
Thickness stretch vibrations of piezoelectric ceramic plates for resonator applications

The thickness stretch vibrations of piezoelectric ceramic plates are analyzed by solving the first-order Mindlin plate equations with finite element method in the two-dimensional domain. The precise resonance frequency and distribution of displacements are obtained from the analysis in detail. The results based on the two-dimensional solutions are more important particularly in the evaluation of the energy-trapping feature of ceramic resonators because the accurate mapping of the displacements including the vital thickness stretch mode is of great practical interests. We start from the first-order Mindlin plate theory for PZT type ceramic plates for resonator applications. The cut-off frequency of the thickness stretch vibrations is obtained from the coupled equations. Then these equations are reformatted with the known fundamental resonance frequency and related elastic constants for finite element solutions. With given geometry of a resonator model, the numerical solutions include the resonance frequencies and associated mode shapes are calculated. The thickness stretch vibrations and the associated frequency are of great importance because these results can be directly used for the determination of the frequency of a resonator and the optimal layout of the electrodes for best performance. All these analyses are intended for the direct applications in the design of ceramic resonators. Further considerations of the effects of electrodes, support structures, and other complications can be readily included in current finite element analysis.
LoTi Turns Up the Heat

W hen LoTi was first introduced in 1994 as the Levels of Technology Implementation framework, the intent was to create a tool to help district leadership quantify how teachers were using technology in the classroom. After serving the past 15 years as a research framework, self-reporting technology integration survey, and school improvement model, LoTi has changed focus. The original LoTi framework provided an empirically validated model for school systems to gauge the effectiveness of technology implementation. However, with the emergence of new standards from the Partnership for 21st Century Skills and ISTE’s NETS•T, it was clear the framework needed to be refreshed. The Levels of Technology Implementation framework thus became the Levels of Teaching Innovation framework. It includes the same stages contained in the original framework, but the newer model emphasizes powerful learning and teaching as well as the use of digital tools and resources in the classroom. Each level or stage of the new LoTi framework addresses unique attributes of the pedagogical continuum as teachers gravitate from:
Latino immigration, globalization and health: striving to find equality in the United States

iii ACKNOWLEDGEMENTS . v TABLE OF CONTENTS . vii LIST OF TABLES . x LIST OF FIGURES . xi CHAPTER 1: INTRODUCTION . 1 CHAPTER 2: LITERATURE REVIEW . 5 United States History with Immigrant Labor . 6 Economic Globalization and Migration . 9 The North American Free Trade Agreement and Latino Immigration . 11 Racial Prejudice . 14 Undocumented Immigrants as Criminals . 16 Immigration Status . 17 Racism and Policy . 18 Immigration and Health. 19 Barriers to Health Services . 20 Resource barriers in Rural Environments . 23 CHAPTER 3: CONCEPTUAL FRAMEWORK . 25 Explanation of Concepts . 29 Globalized System . 29 viii Immigration Status . 30 Social Inequality . 32 Racism . 34 Health. 35 Flow of Conceptual Framework . 36 CHAPTER 4: METHODS . 41 Survey Data . 42 Sample Design . 44 Data Analysis . 44 Limitations of the LatinoNet Surveys . 45 Newspaper Content Analysis . 45 Newspaper Sample . 46 Data Collection . 49 Data Analysis . 50 Engagement with the Latino Population on the North Coast . 53 CHAPTER 5: RESULTS . 55 LatinoNet Health Fair Survey Data . 56 Demographics of Health Fair Participants . 56 Health . 61 Barriers to Healthcare . 63 Immigration Patterns . 68 Newspaper Content Analysis . 70 Statewide Content Analysis Themes . 74
Performance evaluation of DECK combining multithreading and communication on Myrinet and SCI clusters

This paperpresentsa performanceevaluation of DECK (Distributed Executionand Communication Kernel), a multithreadedparallel programmingenvironmentfor clustersof SMPs,with theparallel implementationof theclassicalMandelbrot fractal generation and Laplace’s Equation algorithms. The applications have been run on Myrinet and SCI clusters and the results arecomparedto correspondingMPI implementations. Thecomparisonshowsthat DECK is able to achieve very good performancewhen multithreadingis combinedwith communication, without theneedof multipleprocessesona singlemachine.
Research of Intrusion Detection Based on Support Vector Machine

For the network data sets too large, too slow learning speed problem, in this paper, a SVM algorithm based on space block and sample density is proposed and applied into intrusion and detection. According to the local density the algorithm selects training samples and reduces the number of training sample to enhance learning speed. The algorithm can guarantee the accuracy of detection and at the same time the learning speed of it is faster than the traditional SVM intrusion detection method.
Ultracold atomic collisions in tight harmonic traps: perturbation theory, ionization losses and application to metastable helium atoms

Collisions between tightly confined atoms can lead to ionization and hence to loss of atoms from the trap. We develop second-order perturbation theory for a tensorial perturbation of a spherically symmetric system and the theory is then applied to processes mediated by the spin–dipole interaction. Redistribution and loss mechanisms are studied for the case of spin-polarized metastable helium atoms and results obtained for the five lowest s states in the trap and trapping frequencies ranging from 1 kHz to 10 MHz.
Learning from positive and negative rewards in a spiking neural network model of basal ganglia

Despite the vast amount of experimental findings on the role of the basal ganglia in reinforcement learning, there is still general lack of network models that use spiking neurons and plausible plasticity mechanisms to demonstrate network-level reward-based learning. In this work we extend a recent spiking actor-critic network model of the basal ganglia, aiming to create a minimal realistic model of learning from both positive and negative rewards. We hypothesize and implement in the model segregation of not only the dorsal striatum, but also of the ventral striatum into populations of medium spiny neurons (MSNs) that carry either D1 or D2 dopamine (DA) receptor type. This segregation allows explicit representation of both positive and negative expected reward within respective population. In line with recent experiments, we further assume that D1 and D2 MSN populations have distinct, opposing DA-modulated bidirectional synaptic plasticity. We implement the spiking network model in the simulator NEST and conduct experiments involving application of delayed rewards in a grid world setting, where a moving agent has to reach a goal state while maximizing the total obtained reward. We demonstrate that the network can learn not only to approach the positive rewards, but also to consequently avoid punishments as opposed to the original model. The spiking network model highlights thus functional role of D1-D2 MSN segregation within striatum and explains necessity for reversed direction of DA-dependent plasticity found at synapses converging on different types of striatal MSNs.
Governments, globalization, and international business

It is a cliche to say that we live in a globalized world in which investment flows, communications and the operations of multinationals from all parts of the world have changed the character of the international business environment. But the easy concept of globalization poses as many questions as it answers and it is the purpose of this book to address these challenges. In Governments, Globalization, and International Business a prestigious group of international scholars explore in detail the consequences of globalization defined as the deepening structural interdependence of the world economy. In Part 1 John Dunning, Richard Lipsey, Susan Strange and Stephen Kobrin analyse these changes from different disciplinary perspectives and intellectual backgrounds. The basic question they address is what are the consequences of globalization on the nature, form, and level of domestic economic activity? In doing so, they also consider the increasing mobility of knowledge and information, the role of international corporations, and the sovereignty of the nation state in the modern borderless world. In Part 2 the different experiences and policies of a number of economies are assessed in a series of country studies, These include the G7 countries as well as the developing East Asian economies, Latin America and smaller developed countries. In the final part John Stopford and Edward Graham stand back and look at the changing role of National and Supranational governance. In doing so they underscore a fundamental tenet of the volume, that globalization requires national governments to re-evaluate various factors of their systemic governance. Yet despite apparently convergent trends they argue there are no universal prescriptions for the way governments should respond to globalization, and the policy challenge is a more complex one than merely more or less government.