Miracle-dz/UniFAR-FTUs huggingface dataset:
Simultaneous search for all modes in multilinear models

Parallel factor (PARAFAC) analysis is an extension of a low rank decomposition to higher way arrays, usually called tensors. Most of existing methods are based on an alternating least square (ALS) algorithm that proceeds iteratively, and minimizes a criterion (that is usually quadratic) of the fit with respect to individual factors one by one. Convergence of this approach is known to be slow, if some of the factor contain nearly co-linear vectors. This problem can be partly alleviated by an enhanced line search (ELS) by Rajih et al. (2008). In this paper we show that the method originally proposed by Paatero (1997), consisting in optimization with respect to all modes simultaneously, can be simplified, and can far outperform the ALS-ELS in ill-conditioned data in all modes.
The Relative Importance of Input Encoding and Learning Methodology on Protein Secondary Structure Prediction

In this thesis the relative importance of input encoding and learning algorithm on protein secondary structure prediction is explored. A novel input encoding, based on multidimensional scaling applied to a recently published amino acid substitution matrix, is developed and shown to be superior to an arbitrary input encoding. Both decimal valued and binary input encodings are compared. Two neural network learning algorithms, Resilient Propagation and Learning Vector Quantization, which have not previously been applied to the problem of protein secondary structure prediction, are examined. Input encoding is shown to have a greater impact on prediction accuracy than learning methodology with a binary input encoding providing the highest training and test set prediction accuracy. INDEX WORDS: Neural Networks, Protein Secondary Structure Prediction, Input Encoding, Resilient Propagation, Learning Vector Quantization THE RELATIVE IMPORTANCE OF INPUT ENCODING AND LEARNING METHODOLOGY ON PROTEIN SECONDARY STRUCTURE PREDICTION
Automatic fish classification for underwater species behavior understanding

The aim of this work is to propose an automatic fish classification system that operates in the natural underwater environment to assist marine biologists in understanding subehavior. Fish classification is performed by combining two types of features: 1) Texture features extracted by using statistical moments of the gray-level histogram, spatial Gabor filtering and properties of the co-occurrence matrix and 2) Shape Features extracted by using the Curvature Scale Space transform and the histogram of Fourier descriptors of boundaries. An affine transformation is also applied to the acquired images to represent fish in 3D by multiple views for the feature extraction. The system was tested on a database containing 360 images of ten different species achieving as average correct rate of about 92%. Then, fish trajectories extracted using the proposed fish classification combined with a tracking system, are analyzed in order to understand anomalous behavior. In detail, the tracking layer computer fish trajectories, the classification layer associates trajectories to fish species and then by clustering these trajectories we are able to detect unusual fish behaviors to be further investigated by marine biologists.
Semi-Rigid Urethane Foams in the Automotive Industry

The application of urethane foams in the automotive industry has been rising from year to year. The reason for this rapid development lies in the possibility of formulating the urethane foams to almost any desired modulus from flexible types via semi-rigid to extremely rigid varieties. This permits the foam’s use for a wide variety of applications, with its low density affording the weight savings required in the automotive industry. A review of all foam applications in the automotive industry would go beyond the scope of this paper. The production processes will not be discussed, except as is necessary for understanding the subject in its entirety.
High Incidence of Invasive Group A Streptococcus Disease Caused by Strains of Uncommon emm Types in Thunder Bay, Ontario, Canada

ABSTRACT An outbreak of type emm59 invasive group A Streptococcus (iGAS) disease was declared in 2008 in Thunder Bay District, Northwestern Ontario, 2 years after a countrywide emm59 epidemic was recognized in Canada. Despite a declining number of emm59 infections since 2010, numerous cases of iGAS disease continue to be reported in the area. We collected clinical information on all iGAS cases recorded in Thunder Bay District from 2008 to 2013. We also emm typed and sequenced the genomes of all available strains isolated from 2011 to 2013 from iGAS infections and from severe cases of soft tissue infections. We used whole-genome sequencing data to investigate the population structure of GAS strains of the most frequently isolated emm types. We report an increased incidence of iGAS in Thunder Bay compared to the metropolitan area of Toronto/Peel and the province of Ontario. Illicit drug use, alcohol abuse, homelessness, and hepatitis C infection were underlying diseases or conditions that might have predisposed patients to iGAS disease. Most cases were caused by clonal strains of skin or generalist emm types (i.e., emm82, emm87, emm101, emm4, emm83, and emm114) uncommonly seen in other areas of the province. We observed rapid waxing and waning of emm types causing disease and their replacement by other emm types associated with the same tissue tropisms. Thus, iGAS disease in Thunder Bay District predominantly affects a select population of disadvantaged persons and is caused by clonally related strains of a few skin and generalist emm types less commonly associated with iGAS in other areas of Ontario.
Development in vitro of free-living infective larvae to the parasitic stage of Strongyloides venezuelensis by temperature shift

Free-living infective larvae Strongyloides venezuelensis were cultured in Dulbecco's modified Eagle's medium at 25 and 37 °C, and development to the parasitic stage was evaluated using morphological, protein and antigenicity criteria. Few larvae cultured at 25 °C showed development whereas, in most of the larvae cultured at 37 °C, there appeared characteristic changes such as a bulb-like head and droplets under the cuticle with an increase of body width of the larvae. The results obtained from two-dimensional polyacrylamide gel electrophoresis(2D–PAGE) revealed that the protein spot patterns of the larvae cultured at 25 and 37 °C were differentiated by 17 specific spots. In addition, Western blot analysis combined with 2D–PAGE for reaction with serum obtained from an infected rat revealed that protein spots showing immunodominant antigen at 37 °C were almost the same as those of the larvae recovered from the rats rather than those of the larvae at 25 °C. These results strongly suggested that a temperature shift from 25 to 37 °C has an important role in the development of free-living infective larvae to the parasitic stage of S. venezuelensis. The culture system established in the present study was useful for biological and biochemical studies in the development from/of the free-living to the parasitic stage of Strongyloides species.
Pelvic floor muscle surface electromyography. Reliability and clinical predictive validity.

OBJECTIVE To study the reliability and clinical predictive validity of pelvic floor muscle surface electromyography (sEMG) for use in early detection and prophylaxis of urogynecologic disorders. STUDY DESIGN Fifty-seven women ranging from 19 to 69 years of age completed a written questionnaire and underwent digital pelvic examination followed by pelvic floor muscle sEMG using an intravaginal sensor. Thirty-seven subjects underwent repeat evaluations one week or more later. RESULTS sEMG data demonstrated significant test-retest reliability (P < .001) and significant clinical predictive validity (P < .05) for undifferentiated urinary incontinence, stress incontinence, urge incontinence, menstrual status and parity on both initial and repeat examinations. CONCLUSIONS Pelvic floor muscle sEMG is reliable and consistently predictive of several important clinical status variables, suggesting that it can be a useful tool in early at-risk detection and prophylactic intervention for disorders of pelvic floor muscle laxity. Recent advances in sEMG technology make it cost-effective, convenient, noninvasive and easy to learn and administer by assisting staff. This technology is a powerful complementary tool for digital assessment of pelvic floor muscles and should be considered for use in gynecologic practice.
Automatic Grammar Rule Extraction and Ranking for Definitions

Plain text corpora contain much information which can only be accessed through human annotation and semantic analysis, which is typically very time consuming to perform. Analysis of such texts at a syntactic or grammatical structure level can however extract some of this information in an automated manner, even if identifying effective rules can be extremely difficult. One such type of implicit information present in texts is that of definitional phrases and sentences. In this paper, we investigate the use of evolutionary algorithms to learn classifiers to discriminate between definitional and non-definitional sentences in non-technical texts, and show how effective grammar-based definition discriminators can be automatically learnt with minor human intervention.
Pushing the Limits of 3D Color Printing

Accurate color reproduction is important in many applications of 3D printing, from design prototypes to 3D color copies or portraits. Although full color is available via other technologies, multi-jet printers have greater potential for graphical 3D printing, in terms of reproducing complex appearance properties. However, to date these printers cannot produce full color, and doing so poses substantial technical challenges, from the shear amount of data to the translucency of the available color materials. In this article, we propose an error diffusion halftoning approach to achieve full color with multi-jet printers, which operates on multiple isosurfaces or layers within the object. We propose a novel traversal algorithm for voxel surfaces, which allows the transfer of existing error diffusion algorithms from 2D printing. The resulting prints faithfully reproduce colors, color gradients and fine-scale details.
Improving the Performance of Genetic Algorithm in Capacitated Vehicle Routing Problem using Self Imposed Constraints

The capacitated vehicle routing problem (CVRP) is a well known member of the family of NP hard problems. In the past few decades, a number of heuristics was introduced to solve this problem but no heuristic can claim to work well in all possible scenarios. In the literature, genetic algorithm (GA) even lags behind the other heuristics. In this paper, we reveal some of the reasons for the inferior performance of GA, and propose a number of mechanisms to improve its performance. A number of test problems are solved to demonstrate the usefulness of the algorithm.
Liquid State Machine Optimization

In this thesis several possibilities are investigated for improving the performance of Liquid State Machines. A Liquid State Machine is a relatively new system that is a Machine Learning system, which is capable of coping with temporal dependencies. Basic Recurrent Neural Networks often have problems with this. One reason for this is that it takes a long time to train the Recurrent Neural Network. Liquid State Machines train much faster by using a temporal reservoir to map temporal input into a static output pattern. These output patterns can be learned by a statistical learning method. In this thesis, two different subjects are addressed. The first subject is about reducing computation time on calculating the performance. Optimization algorithms for neural networks are often computationally heavy. This is because the performance of the system, here the Liquid State Machine, needs to be evaluated. The computation time can be decreased by using other methods to evaluate the performance. The other subject that is addressed here, is in the optimization of the temporal reservoir in the Liquid State Machine. Two different algorithms are used here, namely Reinforcement Learning and a Genetic Algorithm. The goal is to find out if the algorithms can improve the performance by improving the temporal reservoir and if so, if the performance is increased that much so that it is computationally beneficial to use. The experiments using a different performance measure showed that it will probably not help in improving the performance on classification of the Liquid State Machine. The other experiment using the two different algorithms showed that Reinforcement Learning can not find a better setting for the temporal reservoir to improve the performance given the settings of the experiments. But the Genetic Algorithm is able to improve the temporal reservoir and thus improve the performance, this was tested on two different datasets. The first dataset used was a movement classification task. The results showed an improvement, but comparing it to another system, namely Evolino, the Liquid State Machine is outperformed on both classification and computation time. The second dataset used is a music classification task. The results here where more in favor of the Liquid State Machine, although it is unclear if the parameter setting for Evolino is optimal.
Real-time Moving Object Detection using SURF

Tracking and traffic monitoring are main application of moving Object detection in video. This paper presents SURF (Speed-Up Robust Features) algorithm and real-time detection of objects using frame difference. The main purpose of this proposed work is to solve the difficulty of modeling background and its update rate in background subtraction method. SURF is having fast processing technique to find features of an object image in real-time and then by using this algorithm we will compare object image features with our real-time object image features. After that match object image features and real-time features. Keywords: Background subtraction, Feature Extraction, SURF algorithm
Using IDDs for Packet Filtering

Firewalls are one of the key technologies used to control the traffic going in and out of a network. A central feature of the firewall is the packet filter . In this paper, we propose a complete framework for packet classification. Through two applications we demonstrate that both performance and security can be improved. We show that a traditional ordered rule set can always be expressed as a first-order logic formula on integer variables. Moreover, we emphasize that, with such specification, the packet filtering problem is known to be constant time. We propose to represent the first-order logic formula as Interval Decision Diagrams . This structure has several advantages. First, the algorithm for removing redundancy and unnecessary tests is very simple. Secondly, it allows us to handle integer variables which makes it efficient on a generic CPUs. And, finally, we introduce an extension of IDDs called Multi-Terminal Interval Decision Diagrams in order to deal with any number of policies. In matter of efficiency, we evaluate the performance our framework through a prototype toolkit composed by a compiler and a packet filter . The results of the experiments shows that this method is efficient in terms of CPU usage and has a low storage requirements. Finally, we outline a tool, called Network Access Verifier . This tool demonstrates how the IDD representation can be used for verifying access properties of a network. In total, potentially improving the security of a network.
Essential Indexicals and Quasi‐Indicators

In this paper I shall focus on Casta˜ neda’s notion of quasi-indicators and I shall defend the following theses: • Essential indexicals (‘I’, ‘here’ and ‘now’) are intrinsically perspectival mechanisms of reference and, as such, they are not reducible to any other mechanism of reference. • Quasi-indicators (e.g. expressions like ‘she (herself )’ exist in natural language and cannot be explained away as merely reflexive pronouns. • Quasi-indicators are the only mechanism that allows the attribution of an indexical reference. As such they must appear in oratio obliqua constructions. • When linked to a referring NP, quasi-indicators, like anaphors, inherit their reference from the antecedent to which they are linked (and thus coindexed with). When linked to a quantified expression, quasi-indicators work like (quasiindexical) bound variables. • Quasi-indicators must be understood along the lines of logophoric pronouns (from ‘logos’ meaning discourse and ‘phoros’ meaning bearing or transporting). As such, they are best explained using such notions as perspective and point of view.
GPi vs STN deep brain stimulation for Parkinson disease

Objective: To compare motor symptoms, cognition, mood, and behavior 3 years after deep brain stimulation (DBS) of the globus pallidus pars interna (GPi) and subthalamic nucleus (STN) in advanced Parkinson disease (PD). Methods: Patients with PD eligible for DBS were randomized to bilateral GPi DBS and bilateral STN DBS (1:1). The primary outcome measures were (1) improvement in motor symptoms in off-drug phase measured with the Unified Parkinson Disease Rating Scale (UPDRS) and (2) a composite score for cognitive, mood, and behavioral effects, and inability to complete follow-up at 36 months after surgery. Results: Of the 128 patients enrolled, 90 were able to complete the 3-year follow-up. We found significantly more improvement of motor symptoms after STN DBS (median [interquartile range (IQR)] at 3 years, GPi 33 [23–41], STN 28 [20–36], p = 0.04). No between-group differences were observed on the composite score (GPi 83%, STN 86%). Secondary outcomes showed larger improvement in off-drug functioning in the AMC Linear Disability Scale score after STN DBS (mean ± SD, GPi 65.2 ± 20.1, STN 72.6 ± 18.0, p = 0.05). Medication was reduced more after STN DBS (median levodopa equivalent dose [IQR] at 3 years, GPi 1,060 [657–1,860], STN 605 [411–875], p < 0.001). No differences in adverse effects were recorded, apart from more reoperations to a different target after GPi DBS (GPi n = 8, STN n = 1). Conclusions: Off-drug phase motor symptoms and functioning improve more after STN DBS than after GPi DBS. No between-group differences were observed on a composite score for cognition, mood, and behavior, and the inability to participate in follow-up. Classification of evidence: This study provides Class II evidence that STN DBS provides more off-phase motor improvement than GPi DBS, but with a similar risk for cognitive, mood, and behavioral complications.
Semi-supervised Segmentation Fusion of Multi-spectral and Aerial Images

A Semi-supervised Segmentation Fusion algorithm is proposed using consensus and distributed learning. The aim of Unsupervised Segmentation Fusion (USF) is to achieve a consensus among different segmentation outputs obtained from different segmentation algorithms by computing an approximate solution to the NP problem with less computational complexity. Semi-supervision is incorporated in USF using a new algorithm called Semi-supervised Segmentation Fusion (SSSF). In SSSF, side information about the co-occurrence of pixels in the same or different segments is formulated as the constraints of a convex optimization problem. The results of the experiments employed on artificial and real-world benchmark multi-spectral and aerial images show that the proposed algorithms perform better than the individual state-of-the art segmentation algorithms.
State and Trait Cheerfulness and the Induction of Exhilaration: A FACS Study

The present paper examines whether state and trait cheerfulness represent actual and habitual dispositions for the emotion of exhilaration. In Experiment I, 60 research participants were involved in a 10-minute interaction with a clowning vs. neutral experimenter. Individuals high in trait cheerfulness, as assessed by the State-Trait-Cheerfulness Inventory (STCI), displayed facial signs of exhilaration with higher frequency, intensity, and duration than did low cheerful individuals. In Experiment II (N = 60), the experimenter was instructed to laugh or not laugh at certain preselected scenes while watching a movie. The experimenter's laughter facilitated enjoyment displays among individuals high in state cheerfulness, but not among those low in state cheerfulness. In both experiments, smiling and laughter predicted the subsequent mood level.
Physical exercise speeds up motor timing

This study aimed to examine effect of physical exercise on motor timing: personal, maximum and “once per second” tapping. The acute effect was examined by comparing the baseline tapping with that after acute exercise in 9 amateur athletes, 8 elite synchronous swimmers and 9 elite biathletes. Then the baseline tapping was compared among athletes of different sports and professional levels (15 elite biathletes, 27 elite cross-country skiers, 15 elite synchronous swimmers and 9 amateur wrestlers) with a control group (44 non-athletes) not involved in regular exercise to examine the sport-specific or long-term effects. Maximum and “once per second” tapping speed increased after acute physical exercise and were also faster in elite athletes compared to controls during the baseline condition. However, personal tapping tempo was not affected by exercise. In addition, physical exercise had no effects on the variability of the intertap interval. The accuracy of “once per second” tapping differentiates controls and amateur wrestlers from elite synchronous swimmers and skiers suggesting sport-specific adaptations to play a role. It is concluded that acute physical exercise selectively speeds up motor timing but does not affect its variability and accuracy, and this speeding-up is suggested to transfer into a long-term effect in elite athletes.
Experimenting with Drugs (and Topic Models): Multi-Dimensional Exploration of Recreational Drug Discussions

Clinical research of new recreational drugs and trends requires mining current information from non-traditional text sources. In this work we support such research through the use of multi-dimensional latent text models, such as factorial LDA, that capture orthogonal factors of corpora, creating structured output for researchers to better understand the contents of a corpus. Since a purely unsupervised model is unlikely to discover specific factors of interests to clinical researchers, we modify the structure of factorial LDA to incorporate prior knowledge, including the use of of observed variables, informative priors and background components. The resulting model learns factors that correspond to drug type, delivery method (smoking, injection, etc.), and aspect (chemistry, culture, effects, health, usage). We demonstrate that the improved model yields better quantitative and more interpretable results.
Laser Surface Annealing of Plasma Sprayed Coatings

Laser surface annealing provides a rapid and efficient means for surface alloying and modification of ceramic materials. In this study, Alumina-13% Titania coatings were sprayed with a water-stabilized plasma spray gun. The coated surface was treated by Excimer laser having a wavelength of 248 nm and pulse duration of 24 ns. The surface structure of the treated coating was examined by field emission scanning electron microscope and X-ray diffraction (XRD). A detailed analysis of the effects of various laser parameters including laser energy density (fluence), pulse repetition rate (PRR), and number of pulses on the morphology and the microstructure of the coatings are presented.