Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling the Enemy: Recursive Cognitive Models in Dynamic Environments

Much research has been conducted on cognitive models for theoretical (memory) and applied (user modelling) research. This paper explores the efficacy of using such models when the subject of the model is actively trying to defy such attempts. Machine learning algorithms were implemented to model human performance in a dynamic computer game environment. Algorithms were chosen so as to vary the degree to which they were based on human cognitive performance, ranging from a simple state machine, to a neural network, and finally a mixture of experts fed by a clustering algorithm. Recursive modelling, also from cognitive theory, was also included in all three algorithms to ascertain any benefit. It was found that models based on human cognitive theory outperformed the others in most tasks, although recursion failed to offer the advantage reported in other studies.
Structural Conflict: The Third World Against Global Liberalism Berkeley: University of California Pr

An upholstered item of furniture in which tufting buttons have both a decorative tufting effect and serve to attach a padding to a frame. The tufting buttons have internally threaded shank portions, and screws are threaded into such shank portions to secure the padding to a frame. A cushion assembly is provided by forming ridges on the shank portions of the tufting buttons and utilizing washer-type fasteners that are attached to the shank portions to hold the tufting buttons in place.
Automatic effects of alcohol cues on sexual attraction.

AIMS The present study tested whether suboptimal priming (which may be defined as 'under viewing conditions rendering conscious identification highly improbable') with alcohol-related stimuli would activate existing expectancies about alcohol's effects on sexual desire. It was predicted that alcohol cues, relative to non-alcohol cues, would activate expectancies of alcohol's aphrodisiac properties. We hypothesized that for men, stronger expectancies in this regard would predict an increased tendency to judge women as sexually attractive following the alcohol primes. DESIGN Two experimental studies manipulated cue (alcohol versus control) and rating dimension (attractiveness versus intelligence). Self-reported alcohol expectancies of sexual desire were assessed approximately 1 month prior to the study. Study 2 assessed additional expectancy content domains. SETTING AND PARTICIPANTS Study 1 comprised 82 undergraduate males and study 2 78 undergraduate males. Studies were conducted at the University of Missouri-Columbia, Columbia, Missouri, USA. INTERVENTIONS In both experiments, male participants were suboptimally primed with either alcohol-related or control words. Following this priming, they were presented with a series of photographs of young women and asked to either rate their attractiveness or their intelligence. FINDINGS AND CONCLUSIONS In both studies, a three-way interaction between cue, rating dimension and alcohol expectancies was found. Within the attractiveness rating condition a two-way interaction was found, indicating that in this condition, stronger expectancies that alcohol increases sexual desire predicted higher attractiveness ratings after suboptimal exposure to alcohol primes. No effects emerged in the intelligence rating condition. Discussion centers on implications for sexual risk-taking as well as a range of other non-consumptive behaviors.
An improved algorithm for reconstruction of small inhomogeneities from acoustic measurements

Reconstruction of unknown medium characteristics such as shape, refractive index by measurements of scattered acoustic wave data has wide application in nondestructive detection for engineering and medical use, which is a typical nonlinear and severely ill-posed problem. Under the setting of a smooth background containing a small number of unknown small inhomogeneous inclusions, we propose in this paper an efficient algorithm to reconstruct these inhomogeneities from scattered wave measurement data incited by a single-frequency acoustic wave. The proposed algorithm includes a modified treatment of quadrature rule for the singular kernel in the fundamental solution of Helmholtz equation, which enables a more accurate evaluation of the simulated total field. Numerical experiments validate the effectiveness and robustness of this algorithm in reconstructing small inhomogeneous medium in two dimensions.
A Combination of Methods for Building Ensembles of Classifiers

In this paper we make an extensive study of different methods for building ensembles of classifiers. We examine variants of ensemble methods that are based on perturbing features. We illustrate the power of using these variants by applying them to a number of different problems. We find that the best performing ensemble is obtained by combining an approach based on random subspace with a cluster-based input decimated ensemble and the principal direction oracle. Compared with other state-of-the-art standalone classifiers and ensembles, this method consistently performed well across twelve diverse benchmark datasets. Another useful finding is that this approach does not require parameters to be carefully tuned for each dataset (in contrast to the fundamental importance of parameters tuning when using SVM and extreme learning machines), making our ensemble method well suited for practitioners since there is less risk of over-training. Another interesting finding is that random subspace can be coupled with several other ensemble methods to improve performance.
Temporal Lobe Hypoperfusion in Isolated Amnesia with Slow Onset: A Single Photon Emission Computer Tomography Study

Single photon emission computer tomography (SPECT) applied early in the course of Alzheimer’s disease (AD) may identify regions with impaired brain function. Moreover, it may be relevant to characterize SPECT perfusion patterns in patients with mild cognitive impairment (MCI), in particular the subgroup of MCI patients with isolated amnesia, as these patients have been demonstrated to convert to AD in more than half of the cases within 3 years. The primary aim of the present study was to characterize the regional cerebral blood flow (CBF) in patients with neuropsychologically verified isolated amnesia. We examined 32 patients (11 men/21 women) with isolated amnesia according to strict neuropsychological criteria and 15 healthy volunteers (11 men/4 women). All subjects had an SPECT-99mTc-d,l-HMPAO perfusion study and neuropsychological assessments. Cranial MRI or CT was performed in all subjects. Semiquantitative (cerebellar relative) flow values were calculated and statistically compared. Patients with isolated amnesia had significant hypoperfusion in several cortical regions of interest compared to control subjects, most prominently in the left temporal cortex. Additionally, there was a trend towards globally reduced CBF in the patients, although this was not significant. These findings may indicate the presence of a progressive degenerative illness affecting multiple brain regions at its early or preclinical stage.
Coupling the Superstring to a D-Brane Ramond-Ramond Background

We propose a new approach for coupling the type II superstring to the Ramond-Ramond background of D-branes in the RNS formalism, alternative to introducing RR vertex operators. It is based on the mixing between Ramond-Ramond p-form excitations in the closed string spectrum and transversally polarized excitations in the open string spectrum.
Sufficient conditions for strong structural controllability of uncertain linear time-varying systems

In this paper, we extend the notion of strong structural controllability of linear time-invariant systems, a property that requires the controllability of each system in a specific class given by the zero-nonzero pattern of the system matrices, to the linear time-varying case ẋ (t) = A(t) · x(t) + B(t) · u(t), where A and B are matrices of analytic functions. It is demonstrated that the requirements for strong structural controllability of linear time-invariant systems are not sufficient for strong structural controllability of linear time-varying systems. Moreover, in the main result of this paper, sufficient conditions for strong structural controllability of linear time-varying systems are given and an algorithm for verifying this property is provided. Since time-invariant systems are included in the class of time-varying system, these conditions are also new sufficient conditions for strong structural controllability of time-invariant systems. Finally, the results are illustrated by means of an example.
Learning from search engine and human supervision for web image search

Visual reranking aims at improving the precision of text-based Web image search. In this paper we propose to combine two learning strategies for deriving the reranking model: learning from search engine and learning from human supervision. The first strategy learns the reranking model in a pseudo-supervised fashion by interpreting parts of the initial text-based search result as pseudo-relevant. The second strategy involves manual relevance labeling of the text-based search results obtained for a limited number of representative queries. While learning from search engine is query dependent and can therefore adapt better to individual queries, it is essentially unsupervised and noisy. While human supervision can better relate the search results to true relevance criteria, it needs to be deployed in a way to keep the reranking scalable. A combination of the two is expected to benefit from their respective advantages and reduce the impact of their individual deficiencies. We propose a two-stage learning approach to visual reranking, where in the online stage multiple query-relative meta rerankers are learned in a pseudo-supervised fashion from the search results and in the offline stage human supervision is used to derive the final reranking function based on these meta rerankers. The experimental results demonstrate that the proposed method significantly outperforms the existing reranking approaches.
Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates

Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM’s shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data.
Application of Neural Networks for the Classification of Diffuse Liver Disease by Quantitative Echography

Three different methods were investigated to determine their ability to detect and classify various categories of diffuse liver disease. A statistical method, i.e., discriminant analysis, a supervised neural network called backpropagation and a nonsupervised, self-organizing feature map were examined. The investigation was performed on the basis of a previously selected set of acoustic and image texture parameters. The limited number of patients was successfully extended by generating additional but independent data with identical statistical properties. The generated data were used for training and test sets. The final test was made with the original patient data as a validation set. It is concluded that neural networks are an attractive alternative to traditional statistical techniques when dealing with medical detection and classification tasks. Moreover, the use of generated data for training the networks and the discriminant classifier has been shown to be justified and profitable.
Visual Evoked Potentials ( VEP ) Help Separate Psychiatric Patients with Biologically-Based Explosive Behaviors

Concrete words are more quickly recognized than abstract words (Bransford & McCarrell, 1974; Paivio, 1986). Different theories of semantic processing argue that either there are more brain areas that process concrete than abstract words (bilateral versus unilateral; Paivio, 1986), or that the same brain areas process both kinds but that concrete words have more context associated with them (Bransford & McCarrell, 1974). Deep acquired dyslexia is a syndrome caused by extensive lesions to the left hemisphere, and is characterized by more semantic paralexias (e.g., read "dog" for "cat") for abstract than for concrete words (Coltheart, 1980). Some developmental disabilities also exhibit handicaps with abstract words. Second-grade poor readers recall disproportionately fewer abstract than concrete words than normally reading second-graders (Vellutino & Scanlon, 1985). Similar findings are found with pervasively hyperactive children (Sharma & Sinha, 2001).
Optimizing OpenCL applications on Xilinx FPGA

In this presentation we focus on current Xilinx FPGA (Field-Programmable Gate Array) platforms with the SDAccel OpenCL environment. FPGA have the unique feature of a reconfigurable architecture by opposition to CPU, GPU or DSP which have a fixed architecture and are only programmable. For example the elementary functions in an FPGA can be configured according to an addressable memory, as such the interconnection among them, the internal memory organization, but also even the ultra high-speed input/output of the chip to interface with the outside world. This fine grain configurability allows high performance and power efficiency. We introduce the architecture of modern FPGA with their main building blocks and how functional operations can be expressed. The translation of imperative languages down to the hardware level is done through High-Level Synthesis. It can be done in several ways with different time/surface trade-off, for example by playing on parallelism and pipelining.
Explorations on the commodity futures pricing with unknown parameters: An expectation oriented approach

We proposed an expectation-oriented approach to deals with the futures pricing in the presence of incomplete information. The expectation model can be considered as a kind of consistent expectation based on widely accepted futures pricing model. Furthermore, we show that the expected pricing function can be verified directly from the observed data. The proposed approach can be considered as an extension of those existing no-arbitrage approaches. We take it as a first step in pricing commodity futures under unknown parameters.
CLRMA: Compact Low Rank Matrix Approximation for Data Compression

Low rank matrix approximation (LRMA) is a powerful technique for signal processing and pattern analysis. However, the performance of existing LRMA-based compression methods are still limited. In this paper, we propose compact low rank matrix approximation (CLRMA), a very effective tool for data compression, which extends the LRMA by exploring both the intra- and inter-coherence of data samples simultaneously. Technically, under the assistance of prescribed orthogonal transforms (such as such as discrete cosine/wavelet transform and graph transform), the CLRMA decomposes a matrix into a product of two smaller matrices so that one consists of extremely sparse and orthogonal column vectors, and the other is a transformed coefficient matrix for reconstruction. Numerically, we formulate the CLRMA problem by minimizing the $\ell_0$-norm and orthogonality regularized approximation (or reconstruction) error, and solve it by the inexact augmented Lagrangian multiplier method. We demonstrate the efficacy of CLMRA on various types of real-world data, including 3D meshes, image datasets, videos as well as human motion capture data, that is, the proposed CLRMA, in a much more compact form, can produce comparable approximation (or reconstruction) error as LRMA. Moreover, we present a CLRMA-based compression scheme for 3D dynamic meshes, and experimental results show that it outperforms the state-of-the-art scheme to a large extend in terms of compression performance.
Skew category, Galois covering and smash product of a k-category

In this paper we consider categories over a commutative ring provided either with a free action or with a grading of a not necessarily finite group. We define the smash product category and the skew category and we show that these constructions agree with the usual ones for algebras. In the case of the smash product for an infinite group our construction specialized for a ring agrees with M. Beattie's construction of a ring with local units. We recover in a categorical generalized setting the Duality Theorems of M. Cohen and S. Montgomery (1984), and we provide a unification with the results on coverings of quivers and relations by E. Green (1983). We obtain a confirmation in a quiver and relations-free categorical setting that both constructions are mutual inverses, namely the quotient of a free action category and the smash product of a graded category. Finally we describe functorial relations between the representation theories of a category and of a Galois cover of it.
In silico representation and discovery of transcription factor binding sites

Understanding the complex mechanisms governing basic biological processes requires the characterisation of regulatory motifs modulating gene expression at transcriptional and post-transcriptional level. In particular, extent, chronology and cell-specificity of transcription are modulated by the interaction of transcription factors with their corresponding binding sites, mostly located near (or sometimes quite far away from) the transcription start site of the gene. The constantly growing amount of genomic data, complemented by other sources of information such as expression data derived from microarray experiments, has opened new opportunities to researchers in this field. Many different methods have been proposed for the identification of transcription factor binding sites in the regulatory regions of co-expressed genes: unfortunately this is a very challenging problem both from the computational and the biological viewpoint. This paper provides a survey of existing methods proposed for the problem, focusing both on the ideas underlying them and their availability to the scientific community.
MAP Symbol Detection of CPM Bursts

Author(s): Murphy, P. A. ; Ford, Gary; Golanbari, Michael | Abstract: A receiver is developed for MAP symbol detection of a burst (i.e., finite-length)CPM signal received in additve white Gaussian noise. This minimum probability of symbol error receiver requires the entire burst of data and involves the use of both forward and backward recursions. Performance results are provided comparing the MAP symbol detector to the more commonly used ML sequence detector (i.e., the Viterbi detector) applied to GMSK signals with the smae burst structure as those employed in the GSM system. It is also shown that using only the forward recursions, the transmitted symbols can be detected in real-time (i.e., without the delay required by the ML sequence detector or the MAP symbol detector) with only slight degradation in performance.
On Using Materialized Views for Query Execution in Distributed Real-Time Database Management Systems

This paper describes how materialized views could be used in distributed Real-Time Database Management System. We provide algorithm for dynamic materialized views building and cost evaluation. The main difference of provided method is that we take into account temporal properties of base relations and data manipulation operations. Experimental part provides algorithm which builds materialized view for given subset of physical query execution plans.
Turning around Failing Schools: An Analysis

High-stakes testing and accountability implicate failing schools more frequently and more precisely than ever before. Consequently efforts to turn around these schools have become paramount for educators, policy actors, and community members. Through a synthesis of research on failing schools, this article unpacks the constructs of school failure and turnaround. It also details causes of decline and crisis. Finally, it analyzes current educational strategies intended to turn failing schools into successful ones.
Facial Expression Recognition Based on Selective Feature Extraction

As one of the key techniques for futuristic human computer interaction, facial expression recognition has received much attention in recent years. A method of facial expression recognition based on selective feature extraction is presented in this paper. In this method we classify expressions roughly into three kinds according to the deformation of mouth firstly. Then we select some expression feature areas which contribute much to each kind expression according to the rough classification results and extract features for them. Lastly we classify expressions finely using method based on rule. Experiments show that facial expression recognition based on selective feature extraction can get high recognition rate and has strong robustness
Rényi Divergence Based Generalization for Learning of Classification Restricted Boltzmann Machines

As a derivative of Restricted Boltzmann Machine (RBM), classification RBM (Class RBM) is proved to be an effective classifier with a probabilistic interpretation. Several elegant learning methods/models related to Class RBM have been proposed. This paper proposes and analyzes a Rényi divergence based generalization for discriminative learning objective of Class RBM. Specifically, we extend the Conditional Log Likelihood (CLL) objective to a general learning criterion. We prove that, some existing popular training methods can be derived from this generalization, via adjusting the parameters to specific values. Intuitively, the regularization with different settings of parameters constrain the learned RBM distribution in different ways, and the parameter setting that provide a suitable distribution constraints for a particular sample set leads to the optimal performance. Moreover, we show that this generalized criterion actually extends the CLL objective with a Rényi divergence-based regularization. The uniform distribution used in this divergence-based regularization can be replaced by some sample-based distribution. This modification is applicable to any specific case of the general objective, and we call the appended loss as general margin. The proposed generalization enables an effective model selection procedure and experiments on human face recognition and document classification achieved significant performance improvement over the existing learning methods. It is also shown empirically that general margin loss is able to stabilize the parameter sensitivity and further improve the performance of the classifiers.