Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast and Effective Approximations for Summarization and Categorization of Very Large Text Corpora

Author(s): Godbehere, Andrew B. | Advisor(s): El Ghaoui, Laurent | Abstract: Given the overwhelming quantities of data generated every day, there is a pressing need for tools that can extract valuable and timely information. Vast reams of text data are now published daily, containing information of interest to those in social science, marketing, finance, and public policy, to name a few. Consider the case of the micro-blogging website Twitter, which in May 2013 was estimated to contain 58 million messages per day: in a single day, Twitter generates a greater volume of words than the Encyclopedia Brittanica. The magnitude of the data being analyzed, even over short time-spans, is out of reach of unassisted human comprehension. This thesis explores scalable computational methodologies that can assist human analysts and researchers in understanding very large text corpora. Existing methods for sparse and interpretable text classification, regression, and topic modeling, such as the Lasso, Sparse PCA, and probabilistic Latent Semantic Indexing, provide the foundation for this work. While these methods are either linear algebraic or probabilistic in nature, this thesis contributes a hybrid approach wherein simple probability models provide dramatic dimensionality reduction to linear algebraic problems, resulting in computationally efficient solutions suitable for real-time human interaction. Specifically, minimizing the probability of large deviations of a linear regression model while assuming a $k$-class probabilistic text model yields a $k$-dimensional optimization problem, where $k$ can be much smaller than either the number of documents or features. Further, a simple non-negativity constraint on the problem yields a sparse result without the need of an $\ell_1$ regularization. The problem is also considered and analyzed in the case of uncertainty in the model parameters. Towards the problem of estimating such probabilistic text models, a fast implementation of Sparse Principal Component Analysis is investigated and compared with Latent Dirichlet Allocation. Methods of fitting topic models to a dataset are discussed. Specific examples on a variety of text datasets are provided to demonstrate the efficacy of the proposed methods.
Is a Finance-led growth regime a viable alternative to Fordism? A preliminary analysis

The viability and desirability of a finance-led growth regime is first assessed against the historical evidence about the many alternative regimes that have been proposed as successors to Fordism. A purely hypothetical model is then built by assembling various hypotheses derived from the observation of current American trends. The imposition of financial norms, such as shareholder value, requires a new and coherent architecture for the mode of governance of firms, the form of competition, the wage labour nexus and the objectives of monetary policy, public budget and tax system. According to the model, any requirement for increased profit has a variable macro-economic impact on wages and economic activity according to the size of accelerator effects and the relative importance of wage and profit in income formation. The stability of an equity-based regime depends on monetary policy which controls financial bubbles and thus the diffusion of finance may push the economy into a zone of structural instability. The next major financial crisis may originate in the USA whose economy approximates most closely to the model. But, the so-called American 'new economy' combines diverse but interdependent structural transformations: diffusion of Information and Communication Technologies, search for new rules for competition, increased flexibility in wages and employment, shift from manufacturing to services. Finance is an element in, but not the whole of, this complex emerging regime.
Molecular Detection of Torque Teno Sus Virus and Coinfection with African Swine Fever Virus in Blood Samples of Pigs from Some Slaughterhouses in Nigeria

Torque teno sus virus 1 (TTSuV1a/TTSuV1b) infection is present in pig herds worldwide. This study investigated the prevalence of TTSuV1a/TTSuV1b infections in domestic pigs from some slaughterhouses in Nigeria as well as coinfection with African swine fever virus (ASFV) and described the phylogeny in relation to global strains. One hundred and eighty-one (181) blood samples from four slaughterhouses were used for the study and viral nucleic acid detection was carried out by PCR. Comparative sequence analysis was carried out to infer phylogeny. The overall prevalence of TTSuV1a/b was 17.7%. Prevalence of individual genotypes was 10.5% and 7.2% for TTSuV1a and TTSuV1b, respectively. Coinfection of ASFV/TTSuV1a/b was 7.7% while that of TTSuV1a and TTSuV1b was 1.7%. ASFV alone was detected in 11.91% of the total samples. The Nigerian TTSuV1a and TTSuV1b shared a sequence identity of 91–100% and 95–100%, respectively, among each other. The ASFV sequences were 100% identical to members of genotype 1. This is the first report on the presence of TTSuV1a/b in domestic pigs in Nigeria and coinfection with ASFV. Although the prevalence of TTSuV1a/b in Nigeria was low, we recommend further studies to establish the trend and possible role in the pathogenesis of ASFV.
Blind Separation of Time/Position Varying Mixtures

We address the challenging open problem of blindly separating time/position varying mixtures, and attempt to separate the sources from such mixtures without having prior information about the sources or the mixing system. Unlike studies concerning instantaneous or convolutive mixtures, we assume that the mixing system (medium) is varying in time/position. Attempts to solve this problem have mostly utilized, so far, online algorithms based on tracking the mixing system by methods previously developed for the instantaneous or convolutive mixtures. In contrast with these attempts, we develop a unified approach in the form of staged sparse component analysis (SSCA). Accordingly, we assume that the sources are either sparse or can be “sparsified. ” In the first stage, we estimate the filters of the mixing system, based on the scatter plot of the sparse mixtures' data, using a proper clustering and curve/surface fitting. In the second stage, the mixing system is inverted, yielding the estimated sources. We use the SSCA approach for solving three types of mixtures: time/position varying instantaneous mixtures, single-path mixtures, and multipath mixtures. Real-life scenarios and simulated mixtures are used to demonstrate the performance of our approach.
GFDM Interference Cancellation for Flexible Cognitive Radio PHY Design

Generalized frequency division multiplexing (GFDM) is a new digital multicarrier concept. The GFDM modulation technique is extremely attractive for applications in a fragmented spectrum, as it provides the flexibility to choose a pulse shape and thus allows reduction of the out-of-band leakage of opportunistic cognitive radio signals into incumbent frequency space. However, this degree of freedom is obtained at the cost of loss of subcarrier orthogonality, which leads to self-inter-carrier-interference. This paper will explain how self-interference can be reduced by a basic and a double-sided serial interference cancellation technique and show that these interference cancellation techniques improve the GFDM bit error rate to match the theoretical performance of the well studied orthogonal frequency division multiplexing (OFDM).
Distinguishing defined concepts from prerequisite concepts in learning resources

The objective of any tutoring system is to provide meaningful learning to the learner, thence it is important to know whether a concept mentioned in a document is a prerequisite for studying that document, or it can be learned from it. In this paper, we study the problem of identifying defined concepts and prerequisite concepts from learning resources available on the web. Statistics and machine learning tools are exploited in order to predict the class of each concept. Two groups of features are constructed to categorise the concepts: contextual features and local features. The contextual features enclose linguistic information and the local features contain the concept properties such as font size and font weigh. An aggregation method is proposed as a solution to the problem of the multiple occurrences of a defined concept in a document. This paper shows that best results are obtained with the SVM classifier than with other classifiers.
Adaptive Fuzzy Control Approach for Enhancement of Electron Magnetic Resonance Tomograms

Electron magnetic resonance imaging (EMRI), a technique akin to MRI, is fast emerging as a functional imaging modality. But, the poor SNR, originating from the low frequency operation of the EMRI scanner as well as the toxicity-limited dose of the exogenously administrated imaging agents, has hindered the development of EMRI to a viable biomedical technology. Hence novel image enhancement techniques are in need to enhance the potential of EMRI. In this paper, we propose one such system based on a fuzzy control filtering approach, using adaptively varying membership functions and incorporating fuzzy associative memory (FAM) with conventional multilevel median filter (MLMF). The performance of the system is tested using in vivo EMR images, acquired from a continuous wave (CW) radio frequency (RF) EMR imager and is evaluated visually as well as by computing quantitative metrics such as root mean square error (RMSE) and peak signal to noise ratio (PSNR). Visual and quantitative evaluation show fuzzy filters, especially the one with a new parabolic membership function, to outperform the conventional MLMF method.
Design and Simulation of a Modified Minkowski Fractal Antenna for Tri-Band Application

This work presents a novel tri-band Minkowski planar antenna, using a modified planar monopole-shaped ground plane. The fractal structure is advantageous in generating multiple resonances, while the modified ground plane was found to be capable of enhancing each bandwidth. Application of the Minkowski fractal structure was also successful in reducing the arm length, and consequently, sideways size of the designed antenna. The proposed antenna was designed to effectively support WiMAX, WLAN and HiperLAN applications, at frequencies of 2.3 GHz, 2.45 GHz and 5.2 GHz, respectively. The performance properties of the antenna such as return losses, radiation patterns and gain were determined by simulation. A conventional Minkowski antenna with full ground plane was also designed to provide proper benchmarking of the improvement level provided by the modified ground plane.
Model predictive control approach to global air collision avoidance

Purpose – Most of the existing approaches for flight collision avoidance are concerned with local traffic alone for which the separation is based on the pairwise analysis of aircraft trajectory trends, which is not efficient with regard to some flight path requirements along waypoints. The purpose of this paper is to deal with the global collision avoidance problem which aims at separating aircraft taking into consideration the global traffic in a given area instead of considering them pairwise. It aims to model the concept of global collision avoidance and propose a validated algorithm for the purpose in the framework of free‐flight.Design/methodology/approach – The collision avoidance procedure computes online the appropriate speed and heading for each aircraft, at each sampling time‐instant, to generate a collision‐free flight trajectory along scheduled waypoints. The method accounts for automatic assignment of priority indexes that are updated from one control time horizon to the next. The paper consi.
ASR for electro-laryngeal speech

The electro-larynx device (EL) offers the possibility to re-obtain speech when the larynx is removed after a total laryngectomy. Speech produced with an EL suffers from inadequate speech sound quality, therefore there is a strong need to enhance EL speech. When disordered speech is applied to Automatic Speech Recognition (ASR) systems, the performance will significantly decrease. ASR systems are increasingly part of daily life and therefore, the word accuracy rate of disordered speech should be reasonably high in order to be able to make ASR technologies accessible for patients suffering from speech disorders. Moreover, ASR is a method to get an objective rating for the intelligibility of disordered speech. In this paper we apply disordered speech, namely speech produced by an EL, on an ASR system which was designed for normal, healthy speech and evaluate its performance with different types of adaptation. Furthermore, we show that two approaches to reduce the directly radiated EL (DREL) noise from the device itself are able to increase the word accuracy rate compared to the unprocessed EL speech.
Examination of the Mental Health and Family Dynamics in Caribbean Immigrants using the National Survey of American Life

Previous studies have documented an association between mental illness rates and US-nativity, younger age at immigration, and recent immigration status among Caribbean immigrants to the US. This analysis examines these associations with the addition of important demographic controls and two indices of family support and conflict in Caribbean immigrants represented in the National Survey of American Life (n = 1623). The results indicate that previous correlates disappear when the index of family conflict is taken into account. Future efforts should focus on culturally appropriate identification and treatment methods addressing family dynamics in Caribbean immigrants. INDEX WORDS: immigrant mental health, family dynamics, National Survey of American Life. EXAMINATION OF THE MENTAL HEALTH AND FAMILY DYNAMICS IN CARIBBEAN IMMIGRANTS USING THE NATIONAL SURVEY OF AMERICAN LIFE
Geometry-based PSF estimation and deblurring of defocused images with depth information

We propose in this paper an algorithm to recover the blurred details of an image caused by defocusing during the photo-taking. Our algorithm takes one RGB image as well as its depth map as the input. We build up a model in which each captured image pixel is regarded as a light-emitting source that goes through a synthetic camera system. Thanks to the depth map, we have the geometrical information of the scene so that the point spread function (PSF) can be derived for each pixel more accurately as compared to conventional approaches where only RGB images are involved. Then, we make use of the derived PSFs to solve an optimization so as to reconstruct an all-in-focus image. The reconstructed results are evaluated by comparison with the original all-in-focus images. Compared to other methods for the deblurring of defocused images, our method shows a better recovery of image details.
SIMPLE/LITAF Expression Induces the Translocation of the Ubiquitin Ligase Itch towards the Lysosomal Compartments

LITAF is a small cellular protein with an unknown function. The C-terminus of LITAF contains a highly conserved domain termed the SIMPLE-like domain (SLD), while the N-terminus contains two PPXY motifs that mediate protein-protein interactions with WW-domain containing proteins. LITAF also harbors two endosome/lysosome targeting sequences at its C-terminus, but there has been conflicting reports regarding its intracellular localization. Here, we demonstrate that LITAF is localized to the late endosome/lysosomal compartment in a variety of cell lines. We also show that Itch, a WW-domain containing protein, and LITAF strongly interact and that this interaction depends on the two PPXY motifs in the N-terminus of LITAF. Interestingly, co-expression of LITAF with Itch induces major changes in Itch intracellular localization, bringing Itch from the trans-Golgi network to lysosomes. We show that this re-localization is dependent upon the interaction with the PPXY sequences of LITAF, since disruption of these binding motifs completely abrogates Itch re-localization.
What is this thing called confidence? A comparative analysis of consumer confidence indices in eight major countries

The paper examines the evolution of consumer confidence indices in Australia, Canada, France, Germany, Italy, Japan, the United Kingdom and the United States of America since the 1970s, by modelling them in a multivariate framework of common macroeconomic variables for each country. Results suggest that: (a) the main economic determinants of consumer confidence cannot be summarized only on the basis of some macroeconomic variables; (b) consumer confidence indices have some ability to forecast economic activity, provided that both their coincident nature is taken into account and that a number of data-coherent parameter restrictions are imposed. A number of analyses (both insample and out-of-sample) are devoted to assessing the robustness of previous findings.
Perceptually motivated linear prediction cepstral features for network speech recognition

In this paper, we propose a new method for modifying the power spectrum of input speech to obtain a set of perceptually motivated Linear Prediction (LP) parameters that provide noise-robustness to Automatic Speech Recognition (ASR) features. Experiments were performed to compare the recognition accuracy obtained from Perceptual Linear Prediction-Cepstral Coefficients (PLP-LPCCs) and cepstral features derived from the conventional Linear Prediction Coding (LPC) parameters with that obtained from the proposed method. The results show that, using the proposed approach, the speech recognition performance was on average 4.93% to 7.09% and 3% to 5.71% better than the conventional method and the PLP-LPCCs, respectively, depending on the recognition task.
MULTI-MICROPHONE NOISE REDUCTION TECHNIQUES FOR HANDS-FR EE SPEECH RECOGNITION -A COMPARATIVE STUDY-

In this paper we will describe different multi-microphone noise reduction techniques as for example front-end system s for a command speaker-independent word recognizer in an office environment. Our focus lies on examining the recognition rate if the noise source is not gaussian and stationar y, but a second speaker in the same room. In this case standard noise reduction techniques like spectral subtraction will fail, and only multi-microphone techniques can raise the recognition rate by using the spatial information of the speakers . We will compare the delay-and-sum-beamformer, different superdirective solutions and the post-filter approach. Our results show that these techniques are the right choice for hands–free speech recognition systems. Furthermore, we will give a deeper insight into the superdirective design fo r microphone arrays.
Jinni: Intelligent Mobile Agent Programming at the Intersection of Java and Prolog

Jinni (Java INference engine and Networked Interactor), is a lightweight, multi-threaded, logic programming language, intended to be used as a exible scripting tool for gluing together knowledge processing components and Java objects in distributed applications. Jinni threads are coordinated through blackboards, local to each process. Associative search based on term uniication (a variant of Linda) is used as the basic synchronization mechanism. Threads are controlled with tiny interpreters following a scripting language based on a subset of Prolog. Mobile threads, implemented by capturing rst order continuations in a compact data structure sent over the network, allow Jinni to interoper-ate with remote high performance BinProlog servers for CPU-intensive knowledge processing and with other Jinni components over the Internet. The synergy of these features makes Jinni a convenient development platform for distributed AI, and in particular, for building intelligent autonomous agent applications. The latest version of Jinni is available from
Surveillance, Governance and Professional Sport

The surveillance capacities of professional sports clubs and Leagues are directly related to their modes of governance. This paper identifies how private sports clubs enact surveillance through processes of inclusion and exclusion. Using three examples to demonstrate these processes, we argue that the surveillance mechanisms associated with sports governance at times replicate, at other times contradict, and at other times influence those associated with broader law enforcement and security developments. These examples also suggest potential increases in surveillance activities that emerge in club governance often flow from external concerns regarding allegations of crime, national security breaches and corruption. These context-specific case studies (Flyvbjerg 2001) demonstrate how surveillance and identity authentication are closely tied to the complex, multi-tiered governance structures and practices in three distinct sports. We then explore how these patterns can be interpreted as either connected to or distinct from equivalent developments involving the surveillance surge (Murakami Wood 2009) and concepts of inclusion and exclusion under the criminal law. We conclude by discussing how both internal and external regulatory forces can shape interrelated facets of surveillance, governance and exclusion in elite sports.
Exploiting Heterogeneity for Aging-Aware Load Balancing in Mobile Platforms

The pervasiveness of heterogeneous multiprocessors (HMP) in the mobile domain enables more energy efficient systems. However, current approaches to exploit the energy efficiency of HMPs results in unbalanced usage of resources, which leads to higher aging rates and delay degradation when compared to homogeneous architectures. In this paper, we propose <sc>ADAMANt</sc>, an aging-aware task mapping algorithm for HMPs. <sc>ADAMANt</sc> exploits on-chip sensing of aging, performance, and power in order to enable on-line workload characterization to select task-to-core mappings that yield both increased system lifetime and energy efficiency. Experimental evaluation using a typical mobile workload demonstrates an improvement in chip lifetime by up to 2<inline-formula><tex-math notation="LaTeX">$\times$ </tex-math><alternatives><inline-graphic xlink:href="muck-ieq1-2627541.gif"/></alternatives></inline-formula> on a big.LITTLE architecture.
A View of Neural Networks as Dynamical Systems

We present some recent investigations resulting from the modeling of neural networks as dynamical systems, and deal with the following questions, adressed in the context of specific models. (i) Characterizing the collective dynamics; (ii) Statistical analysis of spike trains; (iii) Interplay between dynamics and network structure; (iv) Effects of synaptic plasticity.