Miracle-dz/UniFAR-FTUs huggingface dataset:
The narrative braid: a model for tackling the narrative paradox in adaptive documentaries

The Narrative Paradox is a theory that describes interaction and narrative cohesion as being in tension, and asserts that the structure of a narrative is disrupted by user adaptivity, leading to possible incoherence as the system accounts for interaction. We propose an approach that may reduce this disruption. Specifically, we propose to model a narrative as a collection of threads, woven together into the final discourse as a narrative braid. By separately maintaining logical coherence within a thread and thematic coherence between threads we believe it is possible to introduce interactivity while maintaining a strong narrative structure. We discuss how this may be applied to adaptive documentaries which, with a wide base of recorded material and diverse plot threads, provide a rich medium for initial experimentation in this area.
Surveys of Galaxy Clusters with the Sunyaev-Zel’dovich Effect

We have created mock Sunyaev-Zel'dovich effect (SZE) surveys of galaxy clusters using high-resolution N-body simulations. To the pure surveys, we add "noise" contributions appropriate to instrument and primary cosmic microwave background anisotropies. Applying various cluster finding strategies to these mock surveys, we generate catalogs that can be compared to the known positions and masses of the clusters in the simulations. We thus show that the completeness and efficiency that can be achieved depend strongly on the frequency coverage, noise, and beam characteristics of the instruments, as well as on the candidate threshold. We study the effects of matched filtering techniques on completeness and bias. We suggest a gentler filtering method than matched filtering in single-frequency analyses. We summarize the complications that arise when analyzing the SZE signal at a single frequency and assess the limitations of such an analysis. Our results suggest that some sophistication is required when searching for "clusters" within an SZE map.
Change management and workflow processing using Dynamics AX objects

M Microsoft Dynamics AX continuously extends functional and technical approaches. Former Axapta and Navision applications are being replaced with the modern and smart AX and NAV solutions, like the other Dynamics modules and components. A brand new MS Dynamics implementation follows standard methodologies of Sure Step. However, companies, where one can find former Microsoft ERP systems, like Axapta or Navision, face to several change management issues and problems. One can figure out that business requirements for Software change management depend on specific business functions, industry, technologies and so on.
Mechanism design with approximate valuations

We study single-good auctions when each player knows his own valuation only within a constant multiplicative factor Δ ε (0, 1), known to the mechanism designer. The classical notions of implementation in dominant strategies and implementation in undominated strategies are naturally extended to this setting, but their power is vastly different. On the negative side, we prove that no dominant-strategy mechanism can guarantee social welfare that is significantly better than that achievable by assigning the good to a random player. On the positive side, we provide tight upper and lower bounds for the fraction of the maximum social welfare achievable in undominated strategies, whether deterministically or probabilistically.
Fast projection plane classifier

A new approach for classification is presented, the "Fast Projection Plane Classifier", abbreviated here to FPPC. The main idea of FPPC is very simple: a classification problem for n-dimensional feature vectors is transformed into several two dimensional (2D) classification problems. Each transformation is a projection of the feature space into a plane. The projection into 2D-planes simplifies the classification task massively and leads to a very fast classification algorithm which uses binary lookup tables as a representation of the distribution of the previously trained feature vectors. The algorithm has some beneficial properties: it can handle high dimensional problems without an explosion of the number of required training samples, and with all possible 2D-projections the algorithm uses a reliable and precise representation of the training samples.
De Novo Sequences of Haloquadratum walsbyi from Lake Tyrrell, Australia, Reveal a Variable Genomic Landscape

Hypersaline systems near salt saturation levels represent an extreme environment, in which organisms grow and survive near the limits of life. One of the abundant members of the microbial communities in hypersaline systems is the square archaeon, Haloquadratum walsbyi. Utilizing a short-read metagenome from Lake Tyrrell, a hypersaline ecosystem in Victoria, Australia, we performed a comparative genomic analysis of H. walsbyi to better understand the extent of variation between strains/subspecies. Results revealed that previously isolated strains/subspecies do not fully describe the complete repertoire of the genomic landscape present in H. walsbyi. Rearrangements, insertions, and deletions were observed for the Lake Tyrrell derived Haloquadratum genomes and were supported by environmental de novo sequences, including shifts in the dominant genomic landscape of the two most abundant strains. Analysis pertaining to halomucins indicated that homologs for this large protein are not a feature common for all species of Haloquadratum. Further, we analyzed ATP-binding cassette transporters (ABC-type transporters) for evidence of niche partitioning between different strains/subspecies. We were able to identify unique and variable transporter subunits from all five genomes analyzed and the de novo environmental sequences, suggesting that differences in nutrient and carbon source acquisition may play a role in maintaining distinct strains/subspecies.
Structural Change and the Kaldor Facts in a Growth Model with Relative Price Effects and Non-Gorman Preferences

Growth of per-capita income is associated with (i) significant shifts in the sectoral economic structure, (ii) systematic changes in relative prices and (iii) the Kaldor facts. Moreover, (iv) cross-sectional data shows systematic expenditure structure difference between rich and poor households. Ngai and Pissarides (2006) and Acemoglu and Guerrieri (2008) are consistent with observation (i)-(iii) but abstract form non-homotheticities of preferences. However, they cannot replicate the structural change between the U.S. goods and service sector quantitatively. This paper presents a growth model, which reconciles both forces of structural change - relative price and income effects - with balanced growth on the aggregate. The theory is simple and parsimonious and contains an analytical solution. The model can replicate shape and magnitude of the nonbalanced sectoral facts as well as the balanced nature of growth on the aggregate. In a structural estimation, the model’s functional form is exploited to obtain estimates for the relative importance of income and price effects as determinants of the structural change.
Geometric construction of crystal bases

We realize the crystal associated to the quantized enveloping algebras with a symmetric generalized Cartan matrix as a set of Lagrangian subvarieties of the cotangent bundle of the quiver variety. As a by-product, we give a counterexample to the conjecture of Kazhdan--Lusztig on the irreducibility of the characteristic variety of the intersection cohomology sheaves associated with the Schubert cells of type A and also to the similar problem asked by Lusztig on the characteristic variety of the perverse sheaves corresponding to canonical bases.
Control strategies for robotic manipulators

This survey is aimed at presenting the major robust control strategies for rigid robot manipulators. The techniques discussed are feedback linearization/Computed torque control, Variable structure compensator, Passivity based approach and Disturbance observer based control. The first one is based on complete dynamic model of a robot. It results in simple linear control which offers guaranteed stability. Variable structure compensator uses a switching/relay action to overcome dynamic uncertainties and disturbances. Passivity based controller make use of passive structure of a robot. If passivity of a feedback system is proved, nonlinearities and uncertainties will not affect the stability. Disturbance observer based controllers estimate disturbances, which can be cancelled out to achieve a nominal model, for which a simple controller can then be designed. This paper, after explaining each control strategy in detail, finally compares these strategies for their pros and cons. Possible solutions to cope with the drawbacks have also been presented in tabular form.
Constructing a Fast Algorithm for Multi-label Classification with Support Vector Data Description

For multi-label classification, problem transform algorithms have received more attention due to their good performance and low computational complexity. But how to speed up training and test procedures is still a challenging issue. In this paper, one-by-one data decomposition trick is adopted to divide a k-label problem into k sub-problems, where a specific sub-problem only consists of instances with a specific class. We train each sub-classifier using support vector data description that learns a smallest hyper-sphere to capture the majority of training instances of each class, and integrate k sub-classifiers into an entire multi-label classification algorithm using both pseudo posterior probabilities and linear ridge regression. Our new method has the lowest time complexity, compared with existing problem transform support vector machines for multi-label classification. Experimental results on the Yeast dataset illustrate that our algorithm works better than several state-of-the-art ones.
Analytic approaches to twin data using structural equation models

The classical twin study is the most popular design in behavioural genetics. It has strong roots in biometrical genetic theory, which allows predictions to be made about the correlations between observed traits of identical and fraternal twins in terms of underlying genetic and environmental components. One can infer the relative importance of these 'latent' factors (model parameters) by structural equation modelling (SEM) of observed covariances of both twin types. SEM programs estimate model parameters by minimising a goodness-of-fit function between observed and predicted covariance matrices, usually by the maximum-likelihood criterion. Likelihood ratio statistics also allow the comparison of fit of different competing models. The program Mx, specifically developed to model genetically sensitive data, is now widely used in twin analyses. The flexibility of Mx allows the modelling of multivariate data to examine the genetic and environmental relations between two or more phenotypes and the modelling to categorical traits under liability-threshold models.
Study on the Dynamic Compound Structure Composed of Mast Cells, Blood Vessels, and Nerves in Rat Acupoint

Background. Circulation system, immunity system, and nervous system have a close relationship with meridian phenomen. However, there is still lack of the results of dynamic changes of these structures in acupoint. The aim of this study is to explore the interrelationship by composite staining techniques. Methodology/Principal Findings. Twenty rats were separated into electroacupuncture group (EA) and control group (Con) randomly. In EA group, the Zusanli and Weishu were stimulated with the 0.1 mA for 25 min. The tissue of these acupoints was double-stained with acetylcholinesterase and Toluidine blue. The compound structure of mast cells, nervous fibers, and mast cells in the acupoint was observed. Conclusions/Significance. The blood vessels, mast cells and acetylcholinesterase responded nerves were clearly observed in acupoint tissues. EA can result in the mast cell recruitment and migration along the blood vessels and nervous bundle, which conformed the dynamic compound structure and played important roles in acupuncture.
A Sequential Algorithmic Pattern 1 A Sequential Algorithmic Pattern

This paper discuss the general class of algorithms that can b e implemented using sequential constructions. Common characteristics of these algorithms are also de cribed in order to provide a generic representation for sequential algorithms. In addition it descri bes the sequential pattern in terms of pixel scans and its relationship within mathematical morphology. Such a pattern is essential for the development of morphological operators and operations. Examples of pat tern usage are the distance tranform, reconstruction, extrema detection, minima imposition and so on.
A stochastic approach for dialog management based on neural networks

In this article, we present an approach for the construction of a stochastic dialog manager, in which the system answer is selected by means of a classification procedure. In particular, we use neural networks for the implementation of this classification process, which takes into account the data supplied by the user and the last system turn. The stochastic model is automatically learnt from training data which are labeled in terms of dialog acts. An important characteristic of this approach is the introduction of a partition in the space of sequences of dialog acts in order to deal with the scarcity of available training data. This system has been developed in the DIHANA project, whose goal is the design and development of a dialog system to access a railway information system using spontaneous speech in Spanish. An evaluation of this approach is also presented. Index Terms: Spoken dialog systems, dialog management, stochastic models, unseen situations, MLP.
Organizational creative climate and learning organization factors: Their contributions towards innovation within the private sectorMalaysia

Among the many streams of research regarding influencing factors on innovation, the idea of having a creative working climate (or environment) within an organization which relates to a suitable working culture to facilitate an environment that will enhance the organizational power is very much often being mentioned. This idea has been put forward during the middle 1980’s and late 1990’s by several scholars among others include Ekvall, Arvonen & Waldenstrom-Lindblad (1983), Ekvall & TangebergAnderson (1986), Zain Mohamed (1995), Zain Mohamed and Rickards (1996) and Amabile & Conti (1999). Zain Mohamed’s (1995) study identifies fifteen factors deem favorable for innovation implementations in private organizations both large and small of which five of them are similar to Amabile and Conti’s (1999) eight organizational creative climate factors likely to foster innovation. Organizational climate is regarded as an attribute of the organization, a conglomerate of attitudes, feelings and behaviors which characterize life in organizations and exists independently of the perceptions and understandings of the members of the organizations (Ekvall, 1996: 105). It is conceived as an organizational reality in ‘objective’ sense. Creativity on the other hand, is a thinking process which helps generate ideas (Majaro, 1992).
A New Anytime Dynamic Navigation Algorithm

—Dynamic navigation algorithm has been an important component in planning. Navigation algorithm is required to ﬁnd out an optimal solution to its goal. However, under some environments where time is more critical than optimality, a sub-optimal solution is acceptable. Therefore for practical applications it is useful to ﬁnd a high sub-optimal solution in limited time. The dynamic algorithm Anytime D*(AD*) is currently the best anytime algorithm which aims to return a high sub-optimal solution with short corresponding time and control of sub-optimality. In this paper, a new algorithm named Improved Anytime D*(IAD*) is introduced. Experiment comparison is made to show IAD* better outperforms Anytime D* in various random benchmarks.
HTILDE: scaling up relational decision trees for very large databases

Nowadays, many organizations have relational databases with millions of records and an important question is how to extract information from them. This work proposes HTILDE (Hoeffding TILDE) to handle very large relational databases, based on the Inductive Logic Programming (ILP) system TILDE (Top-down Induction of Logical Decision Trees) and the propositional Very Fast Decision Tree (VFDT) learner. It is an incremental and anytime algorithm that uses the Hoeffding bound to find out the amount of examples that must be considered for choosing the best test for a node. The results show that, compared to TILDE, HTILDE generates theories from very large relational datasets more efficiently without harming their quality measures (F-measure, precision, recall and accuracy). Also, HTILDE learns less complex theories than TILDE.
Supporting Complex Tasks in Visual Sensor Networks

of “Supporting Complex Tasks in Visual Sensor Networks” by Jie Mao, Ph.D., Brown University, May 2012. Visual sensor networks (VSN) are networks of smart cameras capable of local image processing and data communication. Unlike traditional camera-based surveillance network in which cameras stream all image data to a centralized server for processing, cameras in VSNs form a distributed system, performing information extraction and collaborating on application-specific tasks. This thesis studies how complex vision tasks can be integrated with system resource constraints such as computation capacity, battery power and bandwidth, in two different VSN contexts. The first context is large-scale ad-hoc wireless smart cameras working on battery power which resembles the architecture of general wireless sensor networks. In this context we build geographic hash table based network protocols that are adapted to the nature of image sensors. These protocols decouple the event sensing from the camera location. Simulation results show that these protocols allow efficient distributed camera calibration and eventbased constraint processing. The second context is smaller-scale static wired smart cameras with constant power supplies which can be found in public spaces that need surveillance such as airports and casinos. We study the performance advantages of applying probabilistic fusion methods in cross-camera object tracking. Object tracking based on a single feature type can produce high error rates due to environmental and view changes. We present a probabilistic object matching framework which employs multiple object features and a decision mechanism to combine results from multiple features. The framework builds matching probability distributions for each feature algorithm based on empirical data and combines these historical results into an aggregated result. Our experimental studies on realistic data show that while there is no single feature algorithm works best all the time, our probabilistic integration method on multiple features can almost always achieve better object matching accuracy than the best individual feature algorithm. Supporting Complex Tasks in Visual
Integrated RF Interference Suppression Filter Design Using Bond-Wire Inductors

Design techniques are presented for the realization of high-performance integrated interference suppression filters using bond-wire inductors. A new configuration is proposed for mitigating the impact of mutual coupling between the bond wires. A differential low-noise amplifier with an integrated on-chip passive interference suppression filter is designed at 2.1 GHz in a 0.18-mum CMOS process, and achieves a transmit leakage suppression of 10 dB at 190-MHz offset. The differential filter uses metal-insulator-metal capacitors and bond-wire inductors and occupies only 0.22 mm2. The cascaded system achieves a measured gain of 9.5 dB with a 1.6-dB noise figure and -5 dBm out-of-band IIP3 and consumes 11 mA from a 2-V supply.
Gaussian Process Covariance Kernels for Pattern Discovery and Extrapolation

Gaussian processes are rich distributions over functions, which provide a Bayesian nonparametric approach to smoothing and interpolation. We introduce simple closed form kernels that can be used with Gaussian processes to discover patterns and enable extrapolation. These kernels are derived by modelling a spectral density – the Fourier transform of a kernel – with a Gaussian mixture. The proposed kernels support a broad class of stationary covariances, but Gaussian process inference remains simple and analytic. We demonstrate the proposed kernels by discovering patterns and performing long range extrapolation on synthetic examples, as well as atmospheric CO2 trends and airline passenger data. We also show that we can reconstruct standard covariances within our framework.
New Rationales for Innovation Policy? A Comparison of the Systems of Innovation Policy Approach and the Neoclassical Perspective

Ever since its introduction in the 1990s, the systems of innovations (SI) concept has received a great deal of attention from researchers and politicians. The systems of innovation policy (SIP) approach, which is based on the SI concept, is considered an alternative to neoclassical theory. Its goal is to provide new rationales and criteria for innovation policy, as well as concrete implications and guidelines for policymakers, that are more appropriate for innovation processes in comparison to the rationales and criteria of standard economic theory. The aim of this paper is to critically investigate to what extent the SIP approach provides additional rationales for public intervention in innovation processes compared to neoclassical theory.
Analytical Study of Different Approaches to Determine Optimal Cutting Force Model

Determination of optimal machining parameters is an engineering task with aim to reduce the production cost and achieve desired product quality. Such exercise can be tackled on many different ways. The goal of this work is to present some of the possible approaches and to benchmark them among each other. These principles are analyzed: response surface methodology (RSM), evolutionary algorithms (GA & GP), support vector regression (SVR) and artificial neural networks (ANN). All methods implement completely different data handling philosophies with the same goal, to build the model which is able to predict cutting force in satisfying manner. Those aspects are chosen to be evaluated and compared: average percentage deviation of all data, ability to find generalized model and minimize the risk of over fitting and at least the runtime of each single model determination. Average percentage deviation is one of the best indicators of the quality of model. The ability to find generalized model is good indicator of the flexibility of model, and shows how model deals with unknown data. The runtime is important in a real time environment or in scenarios where conditions change frequently. Cutting force data used in this benchmark comes from experimental research of longitudinal turning process.
A Performance Evaluation of an Alpha EV7 Processing Node

In this paper we detail the performance of a new Alpha-Server node containing 16 Alpha EV7 CPUs. The EV7 processor is based on the EV68 processor core that is used in terascale systems at Los Alamos National Laboratory and the Pittsburgh Supercomputing Center. The EV68 processor core is supplemented with six-way router circuitry that forms connections from the processor internals to four neighboring CPUs in a two-dimensional torus, to a I/O controller and to local memory. The performance evaluation presented in this paper considers memory hierarchy, intra-node MPI communication, and also the performance of a number of complete applications. The measurements are compared with those taken on existing AlphaServer machines. It is clear from our analysis that the superior application performance of the EV7 relative to a similarspeed EV68 is attributable to its excellent main memory bandwidth – over 4 GB/s.