Miracle-dz/UniFAR-FTUs huggingface dataset:
Exponential Family Factors for Bayesian Factor Analysis

Expressing data as linear functions of a small number of unknown variables is a useful approach employed by several classical data analysis methods, e.g., factor analysis, principal component analysis, or latent semantic indexing. These models represent the data using the product of two factors. In practice, one important concern is how to link the learned factors to relevant quantities in the context of the application. To this end, various specialized forms of the factors have been proposed to improve interpretability. Toward developing a unified view and clarifying the statistical significance of the specialized factors, we propose a Bayesian model family. We employ exponential family distributions to specify various types of factors, which provide a unified probabilistic formulation. A Gibbs sampling procedure is constructed as a general computation routine. We verify the model by experiments, in which the proposed model is shown to be effective in both emulating existing models and motivating new model designs for particular problem settings.
A Novel Limited Feedback Approach Based on Prediction for MIMO Beamforming System

This paper proposes a novel limited feedback approach based on prediction for MIMO (multi-input multi-output) beam forming systems over temporally-correlated channels. In the proposed approach, the receiver reports a codeword representing CSI (channel state information) back to the transmitter at a regular interval of several data frames, thereby the feedback load is remarkably reduced. As temporal correlation across adjacent frames, the AR (auto-regressive) prediction model is exploited to compensate for performance impairment due to feedback delay. Selection of the representational beam forming codeword is optimized based on a cost function that involves CSI of all frames. The simulation results show that, the proposed approach can reduce efficiently the feedback rate meanwhile maintaining good BER (bit error ratio) performance.
Characteristics of the Current-Controlled Phase Transition of VO2 Microwires for Hybrid Optoelectronic Devices

The optical and electrical characteristics of the insulator-metal phase transition of vanadium dioxide (VO2) enable the realization of power-efficient, miniaturized hybrid optoelectronic devices. This work studies the current-controlled, two-step insulator-metal phase transition of VO2 in varying microwire geometries. Geometry-dependent scaling trends extracted from current-voltage measurements show that the first step induced by carrier injection is delocalized over the microwire, while the second, thermally-induced step is localized to a filament about 1 to 2 μm wide for 100 nm-thick sputtered VO2 films on SiO2. These effects are confirmed by direct infrared imaging, which also measures the change in optical absorption in the two steps. The difference between the threshold currents of the two steps increases as the microwires are narrowed. Micron- and sub-micron-wide VO2 structures can be used to separate the two phase transition steps in photonic and electronic devices.
Non-destructive evaluation through a sparse reconstruction strategy

In this paper, the retrieval of sparse defects within known two-dimensional structures is addressed. A Bayesian Compressive Sensing technique has been developed to determine the differential induced currents thanks to a proper definition of an inhomogeneous Green's function enabled from the a-priori knowledge of the unperturbed structure. Successively, a contrast source inversion method has been applied in order to reconstruct the dielectric features within the estimated region of the defect.
The Discrete and Continuum Broken Line Process

In this work we introduce the discrete-space broken line process (with discrete and continues parameter values) and derive some of its properties. We explore polygonal Markov fields techniques developed by Arak-Surgailis. The discrete version is presented first and a natural continuum generalization to a continuous object living on the discrete lattice is then proposed and studied. The broken lines also resemble the Young diagram and the Hammersley process and are useful for computing last passage percolation values and finding maximal oriented paths. For a class of passage time distributions there is a family of boundary conditions that make the process stationary and self-dual. For such distributions there is a law of large numbers and the process extends to the infinite lattice. A proof of Burke's theorem emerges from the construction. We present a simple proof of the explicit law of large numbers for last passage percolation as an application. Finally we show that the exponential and geometric distributions are the only non-trivial ones that yield self-duality.
Detecting Entailment Using an Extended Implementation of the Basic Elements Overlap Metric

In this paper we evaluate the utility of the recently proposed Basic Elements (BE) summarisation evaluation metric as a means of detecting entailment in a text/hypothesis sentence pair. Basic Elements are tuples representing important syntactic chunks in the Minipar dependency parse of a sentence. We extend the BE matching function with additional semantic knowledge, and show that both the basic and extended metrics outperform a baseline cosine similarity metric. Our results also show that a simple linear combination of BE–style sentence features can achieve an accuracy of 57.0% on the RTE 2006 test set.
Characterization of a Low Pathogenic Avian Influenza Virus (H6N1) Isolated from Turkeys

An avian influenza virus (AIV), A/turkey/Israel/09 subtype H6N1, was isolated from turkey poults exhibiting typical pathology associated with AIV infection. The virus was characterized by RT-PCR using AIV subtype-specific primers and by the haemagglutination inhibition test using AIV subtype-specific antisera. The virus has an intravenous pathogenicity index of 0 and possessed a nucleotide sequence at the cleavage site of the hemagglutinin gene, PQIETR*GLF, associated with avian influenza viruses of low pathogenicity. Unlike the two previous H6N2 isolates originating from domestic ducks and mallard, the A/turkey/Israel/09 (H6N1) was isolated from turkeys. The gene sequences of the A/turkey/Israel/09 (H6N1) virus show divergence from the former Israeli H6 isolates.
International Conference on Information Systems ( ICIS ) 2010 GOVERNING THIRD-PARTY DEVELOPMENT THROUGH PLATFORM BOUNDARY RESOURCES

Third-party development is increasingly relevant for software platform owners seeking to generate complementary assets in the form of applications. The governance of such development involves two seemingly conflicting goals: the maintenance of platform control and the transfer of design capability to users. A key element in simultaneously accommodating these goals is platform boundary resources. However, so far, there is a dearth of theoretical accounts of the role of boundary resources and the process by which such resources can be used to govern third-party development. Drawing on case study research of Apple’s iPhone developer program, we synthesize boundary objects theory and innovation networks literature to develop a process perspective of third-party development governance through boundary resources. In doing this, our research extends and complements existing governance literature and contributes new knowledge about an alternative form of system development.
Game-Theoretic Procedures for Learning Structured Strategies from Demonstration

Acquiring new skills is an ability which would be very useful for a robot to possess. One conceivable method whereby a robot could learn new skills is if a teacher with expert knowledge of those skills could demonstrate these skills to the learning robot, in a variety of different settings. What is required would be that the robot does not merely learn to mimic the teacher exactly, as the teacher would be unable to demonstrate the skill in every possible state of the world. Instead the robot should be equipped with some robust method for adapting the learned skill to new situations. The research presented in this document embarks down a path towards solving this problem. Drawing on previous work from the fields of apprenticeship learning and reinforcement learning, an algorithm is presented for teaching an agent a new skill. This algorithm consists of two phases: the first in which the robot learns primitive skills from an expert, and the second which is a mechanism for composing these skills to provide strategies for successfully using the skill in unseen situations. This two phase solution is inspired by curriculum learning, and allows the agent to construct structured strategies in a hierarchical framework. The learning task is posed as a two-player game of the learning agent against disturbance processes generated by the environment or nature. By doing so, an equilibrium can be found using regret minimisation techniques to find an optimal mixture of the basis strategies into a composite strategy for a new scenario. A detailed example is provided, where the learning agent is required to learn how to navigate a busy multi-lane road of various shapes and sizes. The results demonstrate that the agent is able to successfully learn the primitive strategies from the expert. Then, in a variety of experiments, it is shown that the second phase of the algorithm can reconstruct parts of the basis that are present in new examples, as well as adapt the learned behaviours to totally unseen terrains, in a robust and flexible manner. By doing this, the agent shows an ability to synthesise its previously acquired knowledge in novel ways to overcome unpredicted challenges.
UDP – a New Universal Demosaicing Pipeline Algorithm for RGB Color Filter Arrays

Abstract A new universal demosaicing method suitable for imaging pipelines using a RGB color filter array (CFA) is proposed. The proposed algorithm can reconstruct single-sensor images registered by a digital camera equipped with any RGB CFA currently in use. The algorithm includes a spatio-spectral interpolation model, a new edge-detection technique, and a post processing to reduce the color shifts and artifacts of the reconstructed image. Experimental results over various test CFA patterns and real images demonstrate the efficiency and feasibility of a universal demosaicing algorithm proposed in the paper. Two methods - objective metrics (mean squared error - MSE and peak signal to noise ratio - PSNR), and subjective evaluation are used to analyze the results
Low resolution spectroscopy of hot post-AGB candidates ?

Spectral types are determined from low resolution optical spectra of 40 IRAS sources with far-infrared colours similar to planetary nebulae. The presence of circumstellar dust, high galactic latitude, and early supergiant or giant spectral type, sometimes with emission in the Balmer lines, indicate that some of these stars are low mass stars in the post-AGB stage of evolution.
Trace element geochemistry of Manilkara zapota (L.) P. Royen, fruit from winder, Balochistan, Pakistan in perspective of medical geology.

An integrated study of rocks, soils and fruits of Manilkara zapota (L.) (Sapotaceae) of Winder area have been carried out to elaborate trace elements relationship between them. The igneous rocks of the study area have elevated amount of certain trace elements, upon weathering these elements are concentrated in the soil of the area. The trace elements concentration in the soil were found in the range of 0.8-197 for Fe, 1.23-140 for Mn, 0.03-16.7 for Zn, 0.07-9.8 for Cr, 0.05-2.0 for Co, 0.52-13.3 for Ni, 0.03-8.8 for Cu, 0.08-10.55 for Pb and 0.13-1. 8μg/g for Cd. The distribution pattern of elements in the rocks and soils reflected genetic affiliation. Promising elements of edible part of the fruit were Fe (14.17), Mn (1.49), Cr (2.96), Ni (1.13), Co (0.92), Cu (1.70) and Zn (1.02μg/g). The concentration of these elements in the fruits is above the optimum level of recommended dietary intake, probably due to this, disorder in the human health is suspected in the inhabitants of the area.
Inverted-U-shaped correlation between dopamine receptor availability in striatum and sensation seeking

Sensation seeking is a core personality trait that declines with age in both men and women, as do also both density and availability of the dopamine D2/3 receptors in striatum and cortical regions. In contrast, novelty seeking at a given age relates inversely to dopamine receptor availability. The simplest explanation of these findings is an inverted-U-shaped correlation between ratings of sensation seeking on the Zuckerman scale and dopamine D2/3 receptor availability. To test the claim of an inverted-U-shaped relation between ratings of the sensation-seeking personality and measures of dopamine receptor availability, we used PET to record [11C]raclopride binding in striatum of 18 healthy men. Here we report that an inverted-U shape significantly matched the receptor availability as a function of the Zuckerman score, with maximum binding potentials observed in the midrange of the scale. The inverted-U shape is consistent with a negative correlation between sensation seeking and the reactivity (“gain”) of dopaminergic neurotransmission to dopamine. The correlation reflects Zuckerman scores that are linearly linked to dopamine receptor densities in the striatum but nonlinearly linked to dopamine concentrations. Higher dopamine occupancy and dopamine concentrations explain the motivation that drives afflicted individuals to seek sensations, in agreement with reduced protection against addictive behavior that is characteristic of individuals with low binding potentials.
A New Algorithm for Robust Pedestrian Tracking Based on Manifold Learning and Feature Selection

Manifold learning has been a popular method in many areas such as classification and recognition. In this paper, we propose a novel algorithm for pedestrian tracking based on our previous work on manifold learning. A new kind of manifold subspace is introduced, in which the intrinsic features of the target's motion can be best preserved, and the dimensionality of feature is very low. In the proposed subspace, variations of continuous pedestrian postures can be represented well by these intrinsic features. This also validates our conjecture that the movement of pedestrians can be described by some intrinsic and low-dimensional features, which are significant for tracking. Although intrinsic features are useful for tracking, algorithms that directly apply intrinsic features could not guarantee stable performance due to the influence from a complicated background. To address this issue, a foreground extraction method is introduced to enhance tracking stability by selecting the most discriminative color features to automatically distinguish the foreground from the candidate image. This preprocessing stage is proven to promote the accuracy of low-dimensional feature representation in pedestrian tracking. The whole tracking procedure, particularly dimensionality reduction, is linear and fast without complicated calculations. The experimental results validate the effectiveness of our algorithm under challenging conditions, such as a complex background, various pedestrian postures, and even occlusion.
Towards time-critical collision detection for deformable objects based on reduced models

Topics of physically based modeling of deformable objects and collision detection have been extensively researched. Please refer to [1, 2, 3] for recent surveys. Nowadays, the combination of GPU techniques and multiresolution physical models [4] allows interactive simulations of complex deformable objects with a large number of polygons. To achieve this, the geometry of the object is separated from the deformable model in order to represent the latter at different levels of resolutions (reduced models). Recently, James and Pai [5] extended this idea to collision detection. They used the coordinates of the reduced model to update a sphere-tree hierarchy during object deformations. Moreover, many interactive simulations require a constant frame rate which can be obtained by using a time-critical approach. This approach allocates a given time-slice to the collision detection process, so it can be interrupted if the assigned time is expired. To the best of our knowledge, timecritical approaches have been only applied to rigid body collision detection [6, 7]. In this work, we propose an algorithm to carry out collision detection for deformable objects using an interruptible mechanism.
Application of stochastic differential equations and real option theory in investment decision problems

This thesis contains a discussion of four problems arising from the application of stochastic differential equations and real option theory to investment decision problems in a continuous-time framework. It is based on four papers written jointly with the author’s supervisor. In the first problem, we study an evolutionary stock market model in a continuoustime framework where uncertainty in dividends is produced by a single Wiener process. The model is an adaptation to a continuous-time framework of a discrete evolutionary stock market model developed by Evstigneev, Hens and Schenk-Hoppé (2006). We consider the case of fix-mix strategies and derive the stochastic differential equations which determine the evolution of the wealth processes of the various market players. The wealth dynamics for various initial setups of the market are simulated. In the second problem, we apply an entry-exit model in real option theory to study concessionary agreements between a private company and a state government to run a privatised business or project. The private company can choose the time to enter into the agreement and can also choose the time to exit the agreement if the project becomes unprofitable. An early termination of the agreement by the company might mean that it has to pay a penalty fee to the government. Optimal times for the company to enter and exit the agreement are calculated. The dynamics of the project are assumed to follow either a geometric mean reversion process or geometric Brownian motion. A comparative analysis is provided. Particular emphasis is given to the role of uncertainty and how uncertainty affects the average time that the concessionary agreement is active. The effect of uncertainty is studied by using Monte Carlo simulation.
The role of motor experience in cognitive development: Challenges and insights from atypical development

The most well-known form of learning and knowledge acquisition is motor experience [1]. Although models and theories have found it difficult to account for the wide range of empirical evidence in developmental studies [2], motor experience appears to be critical for cognitive development of typically developing children [3-6]. Through physical manipulation, exploration and interaction with the environment a child develops perceptual and social skills that will allow him/her to learn and act on the world [7-9]. The role of motor experience is highlighted in some of the most influential accounts of cognitive development. Piaget considered cognitive development to be a result of the construction of networked concepts and schemes derived from action on the environment [4]. For Piaget, the incorporation of new categories of knowledge encountered through physical experience determines adaptation and learning [10]. According to Piaget the sensorimotor stage of development during the first two years of life, is critical for the achievement of cognitive milestones such as object permanence and means end analysis [8]. In this period, the child actively manipulates objects, explores them individually and sequentially and finally realizes that one object can be used as a means to reach the other [11]. For example, a child can use a stick [8] or a string [12] to retrieve an object out of reach. The classic Piagetian A-not-B task has been widely used and studied in light of its correlation with motor development [13-15]. Different approaches to the study of development yield different explanations of perseverative errors in the A-not-B task and the role of independent motor experience [16-18]. Piaget's explanation for perseveration was based on the child's lack of cognitive understanding of object permanence of children less than 12 months [4]. A dynamic systems theory explanation of perseverative errors is that the child establishes a motor pattern of reaching to one target (e.g., A) that interferes with the child's correct retrieval when the target is
Prevention of Problem Gambling: Lessons Learned from Two Alberta Programs

The development of effective problem gambling prevention programs is in its infancy. The present paper discusses results of randomized control trials of two programs that have been implemented in Alberta, Canada. The first is a 10 session program delivered to several classes of university students taking Introductory Statistics. This program focused primarily on teaching the probabilities associated with gambling and included several hands-on demonstrations of typical casino table games. The second is a 5 session program delivered to high school students at several sites in southern Alberta. This program was more comprehensive, containing information and exercises on the nature of gambling and problem gambling, gambling fallacies, gambling odds, decisionmaking, coping skills, and social problem-solving skills. Data concerning gambling attitudes, gambling fallacies and gambling behaviour at 3 and 6-months postintervention are presented. The findings of these studies are somewhat counter-intuitive and have important implications for the design of effective prevention programs.
An Automatic Alert Unification Method for Heterogeneous Alert Signatures

Several monitoring systems are usually composed by heterogeneous monitoring sensors. Each sensor raises thousands of alerts to be saved and analyzed in a centralized station. Most of alerts raised by diﬀerent sensors are almost the same but have various formats and various descriptions. The system administrator must identify manually similar alerts in order to decrease the number of generated alerts and to improve the data quality. This paper proposes an alert uniﬁcation method that automatically creates meta-alerts from a set of heterogeneous alert sets coming from diﬀerent security monitoring sensors. Instead of dealing with several sets of alerts, this method allows the administrator to use a unique set of meta-alerts.
A citation analysis of the ICER 2005--07 proceedings

This paper identifies the most commonly cited conferences, journals and books of the 43 papers within the first three ICER proceedings. A large array of conferences, journals, and books were cited. However, only a small set of journals and conferences were cited frequently, and the majority were only cited within a single paper, which is consistent with a power law distribution, as predicted by Zipf's Law. The most commonly cited books are concerned with education in general (29%) or psychology (20%), while 17% of books are concerned with computer science education and 12% with computing content. The citation results for ICER are contrasted with earlier published citation analyses of SIGCSE 2007 and ACE2005--07.