Miracle-dz/UniFAR-FTUs huggingface dataset:
Precise critical exponents of the 
O(N)
-symmetric quantum field model using hypergeometric-Meijer resummation

In this work, we show that one can select different types of hypergeometric approximants for the resummation of divergent series with different large-order growth factors. Being of $n! $ growth factor, the divergent series for the $ϵ$ expansion of the critical exponents of the $O(N)$-symmetric model is approximated by the hypergeometric functions ${_{k+1}F}_{k\ensuremath{-}1}$. The divergent ${_{k+1}F}_{k\ensuremath{-}1}$ functions are then resummed using their equivalent Meijer-G function representation. The convergence of the resummation results for the exponents $\ensuremath{\nu}$, $\ensuremath{\eta}$, and $\ensuremath{\omega}$ has been shown to improve systematically in going from low order to the highest known six-loop order. Our six-loop resummation results are very competitive to the recent six-loop Borel with conformal mapping predictions and to recent Monte Carlo simulation results. To show that precise results extend for high $N$ values, we listed the five-loop results for $\ensuremath{\nu}$ which are very accurate as well. The recent seven-loop order ($g$ series) for the renormalization group functions $\ensuremath{\beta},{\ensuremath{\gamma}}_{{\ensuremath{\phi}}^{2}}$, and ${\ensuremath{\gamma}}_{{m}^{2}}$ has been resummed too. Accurate predictions for the critical coupling and the exponents $\ensuremath{\nu}$, $\ensuremath{\eta}$, and $\ensuremath{\omega}$ have been extracted from $\ensuremath{\beta}$, ${\ensuremath{\gamma}}_{{\ensuremath{\phi}}^{2}}$, and ${\ensuremath{\gamma}}_{{m}^{2}}$ approximants.
Are there precursor lesions for ovarian cancer: A review

Precancer is a very early noninvasive lesion that has genetic abnormalities, loss of cellular control functions, some phenotypic characteristics of invasive cancer, and predicts a significant likelihood of developing invasive cancer. Currently, there is no consensus on the existence of ovarian cancer precursor lesions, though many dysplasias in the ovary have been found to date. Our inability to detect precancer in the ovary has hampered early detection efforts. After review of current literature, inclusion cysts, deep surface invaginations and papillomatosis, commonly referred to as “putative ovarian precancer lesions” appear to correlate with known genetic alterations found in the advanced disease in predisposed cohorts. Although we suggest that screening for these lesions would underestimate the ovarian cancer risk in general population, data on chemoprevention studies offers new hope for combating this deadly disease.
An LDA-based Approach for Product Attribute Identification from Online Customer Reviews

This paper proposes an Latent Dirichlet Allocation (LDA)-based approach to identify product attributes from online customer reviews. Identifying product attributes from the customers’ perspectives is essential to analyze satisfaction, importance, and Kano category of each product attribute for product design. The previous works overlooked the importance of keyword extraction and ﬁltering in keyword preprocessing. The proposed approach provides an automated method to select product-feature words by improving manual works in keyword preprocessing of LDA. This research can consider noun phrases as product-feature words and group product-feature words that are frequently mentioned together by customers into the same product attribute better than the previous approach based on the word similarity. The case study of android smartphones shows that the proposed approach can identify product attributes better than the previous approaches
Statistical Modeling of the FSO Fronthaul Channel for UAV-Based Communications

In this paper, we investigate the statistics of the free space optics (FSO) communication channel between a hovering unmanned aerial vehicle (UAV) and a central unit. Two unique characteristics make UAV-based FSO systems significantly different from conventional FSO systems with static transceivers. First, for UAV-based FSO systems, the incident laser beam is not always orthogonal to the receiver lens plane. Second, both position and orientation of the UAV fluctuate over time due to dynamic wind load, inherent random air fluctuations in the atmosphere around the UAV, and internal vibrations of the UAV. On the contrary, for conventional FSO systems, the laser beam is always perpendicular to the receiver lens plane and the relative movement of the transceivers is limited. In this paper, we develop a novel channel model for UAV-based FSO systems by quantifying the corresponding geometric and misalignment losses (GML), while taking into account the non-orthogonality of the laser beam and the random fluctuations of the position and orientation of the UAV. In particular, for diverse weather conditions, we propose different fluctuation models for the position and orientation of the UAV and derive corresponding statistical models for the GML. We further analyze the performance of a UAV-based FSO link in terms of outage probability and ergodic rate and simplify the resulting analytical expressions for the high signal-to-noise ratio (SNR) regime. Finally, simulations validate the accuracy of the presented analysis and provide important insights for system design. For instance, we show that for a given variance of the fluctuations, the beam width should be properly adjusted to minimize the outage probability.
Understanding the User Experience of AI Through the Lens of Game Studies

In recent years, video games have arisen as a frontier for Artificial Intelligence (AI), with game developers making extensive use of AI technologies in their games. However, scholars have called for a more user-focused understanding of how AI affects the gaming experience. In this paper, we present a theoretical framework for how the user experience of AI can be understood and analyzed in terms of the affordances that are provided to players. We also present a typology of affordances relevant to AI in games.
Continual Local Replacement for Few-shot Learning.

The goal of few-shot learning is to learn a model that can recognize novel classes based on one or few training data. It is challenging mainly due to two aspects: (1) it lacks good feature representation of novel classes; (2) a few of labeled data could not accurately represent the true data distribution and thus it's hard to learn a good decision function for classification. In this work, we use a sophisticated network architecture to learn better feature representation and focus on the second issue. A novel continual local replacement strategy is proposed to address the data deficiency problem. It takes advantage of the content in unlabeled images to continually enhance labeled ones. Specifically, a pseudo labeling method is adopted to constantly select semantically similar images on the fly. Original labeled images will be locally replaced by the selected images for the next epoch training. In this way, the model can directly learn new semantic information from unlabeled images and the capacity of supervised signals in the embedding space can be significantly enlarged. This allows the model to improve generalization and learn a better decision boundary for classification. Our method is conceptually simple and easy to implement. Extensive experiments demonstrate that it can achieve state-of-the-art results on various few-shot image recognition benchmarks.
Implementation of an anthropomorphic model observer using convolutional neural network for breast tomosynthesis images

Image quality assessment is important to maintain and improve the imaging system performance, and conducting a human observer study is considered the most desirable approach for the given task because the human makes the diagnostic decision. However, performing a human observer study is time-consuming and expensive. As an alternative method, mathematical model observers to mimic the human observer performance have been proposed. In this work, we proposed convolutional neural network (CNN) based anthropomorphic model observer and compared its performance with human observer and dense difference-of-Gaussian channelized Hotelling observers (D-DOG CHO) for breast tomosynthesis images. The proposed network contained input image, 2D convolution, batch normalization, leaky ReLU, fully connected, and regression layers, and we trained the network using stochastic gradient with momentum (SGDM) optimizer with design parameters, such as filter size and number of filters. For training, validation, and testing data set, anatomical background with 30% volume glandular fraction was generated using the power law spectrum of breast anatomy, and sphere object with a 1 mm diameter was used as a lesion for detection task. In-plane breast tomosynthesis images were obtained using filtered back-projection based tomosynthesis reconstruction. To evaluate detection performance of human observer, D-DOG CHO, and the proposed network, we calculated percent correct (Pc) as a figure of merit. Our results show that the detectability of the proposed network containing 20 number of 11 by 11 convolution filters is most similar to that of human observer.
Characterization of Left Ventricular Activation in Patients With Heart Failure and Left Bundle-Branch Block

Background— Conventional activation mapping in the dilated human left ventricle (LV) with left bundle-branch block (LBBB) morphology is incomplete given the limited number of recording sites that may be collected in a reasonable time and given the lack of precision in marking specific anatomic locations. Methods and Results— We studied LV activation sequences in 24 patients with heart failure and LBBB QRS morphology with simultaneous application of 3D contact and noncontact mapping during intrinsic rhythm and asynchronous pacing. Approximately one third of the patients with typical LBBB QRS morphology had normal transseptal activation time and a slightly prolonged or near-normal LV endocardial activation time. A “U-shaped” activation wave front was present in 23 patients because of a line of block that was located anteriorly (n=12), laterally (n=8), and inferiorly (n=3). Patients with a lateral line of block had significantly shorter QRS (P<0.003) and transseptal durations (P<0.001) and a longer distance .
Vital Signs Before Preemployment and Functional Testing: An Observational Study

Purpose: To test the impact of pretest blood pressure (BP) and heart rate (HR) screening criteria used to determine risk of adverse events (AEs) during preemployment functional testing for jobs in the “medium” to “very heavy” physical demand categories. Methods: Retrospective analysis of functional testing data collected from 2005 to 2012 at 990 regional service clinics for 37 employers on job applicants from 16,511 zip codes across all the US states and the District of Columbia. Results: Data on 269,527 subjects demonstrate that failure rates [99% CIs] for BP screening criteria based on general health risk (GHR) are much higher when compared with screening criteria–based risk of AEs during testing. Using systolic BP and diastolic BP, first trial failure rate (% [99% CI]) increased from 1.74 [1.66, 1.78] when based on AE to 39.31 [39.07, 39.55] when based on GHR. Of subjects that took the functional test but failed GHR screening criteria (MAP ≥ 106 mm Hg), 27.18% [26.73, 27.63] failed functional testing and no subjects experienced an AE during the functional testing. Conclusions: Screening criteria based on GHR intended to be predictive of future disease risk is too stringent as a screening criteria for preemployment functional testing and may unduly penalize job applicants by delaying the opportunity for functional preemployment testing. Further work is needed to identify safe boundaries and risk factors of an AE during functional testing besides use of isolated vital signs such as BP and HR.
Gene Prediction Using Unsupervised Deep Networks

Gene ﬁnding is a well studied topic in bioinformatics. Supervised methods have shown great success in accurately annotating DNA sequences, of which Hidden Markov Models have been the most widely used approach, both in supervised and unsupervised settings. In this thesis we explore the possibilities of using unsupervised neural networks for identifying signals in DNA sequences. First, it is shown that Convolutional Neural Networks are capable of classifying DNA sequences with high speciﬁcity and reasonably high sensitivity. Furthermore, we show that features extracted using Convolutional AutoEncoders do not separate the data enough for either unsupervised or supervised learning. Self-Orginizing Maps, though, are able to create some signiﬁcant visual separation. To the extend of our knowledge this is the ﬁrst proposal for an unsupervised signal sensor for gene prediction, and the ﬁrst time Convolutional AutoEncoders have been used for features extraction on DNA sequences.
Emotion and Themes Recognition in Music Utilising Convolutional and Recurrent Neural Networks

Emotion is an inherent aspect of music, and associations to music can be made via both life experience and specific musical techniques applied by the composer. Computational approaches for music recognition have been well-established in the research community; however, deep approaches have been limited and not yet comparable to conventional approaches. In this study, we present our fusion system of end-to-end convolutional recurrent neural networks (CRNN) and pre-trained convolutional feature extractors for music emotion and theme recognition. We train 9 models and conduct various late fusion experiments. Our best performing model achieves 74.2 % ROC-AUC on the test partition which is 1.6 percentage points over the baseline system of the MediaEval 2019 Emotion & Themes in Music task.
Evaluating Security Aspects for Building a Secure Virtual Machine

One of the essential characteristics of cloud computing which revolutionized the IT business is sharing of computing resources. Despite all the benefits, security is a major concern in cloud virtualization environment. Among those security issues is securely managing the Virtual Machine (VM) images that contain operating systems, configured platforms, and data. Confidentiality, availability and integrity of such images pose major concerns as it determines the overall security of the virtual machines. This paper identified and discussed the attributes that define the degree of security in VM images. It will address this problem by explaining the different methods and frameworks developed in the past to address implementing secure VM images. Finally, this paper analyses the security issues and attributes and proposes a framework that will include an approach that helps to develop secure VM images. This work aims to enhance the security of cloud environments.
Dynamic analysis of synchronous machine using neural network based characterization clustering and pattern recognition

Synchronous generators form the principal source of electric energy in power systems. Dynamic analysis for transient condition of a synchronous machine is done under different fault conditions. Synchronous machine models are simulated numerically based on mathematical models where saturation on main flux was ignored in one model and taken into account in another. The developed models were compared and scrutinized for transient conditions under different kind of faults loss of field (LOF), disturbance in torque (DIT) & short circuit (SC). The simulation was done for LOF and DIT for different levels of fault and time durations, whereas, for SC simulation was done for different time durations. The model is also scrutinized for stability stipulations. Based on the synchronous machine model, a neural network model of synchronous machine is developed using neural network based characterization. The model is trained to approximate different transient conditions; such as loss of field, disturbance in torque and short circuit conditions. In the case of multiple or mixture of different kinds of faults, neural network based clustering is used to distinguish and identify specific fault conditions by looking at the behaviour of the load angle. By observing the weight distribution pattern of the Self Organizing Map (SOM) space, specific kinds of faults is recognized. Neural network patter identification is used to identify and specify unknown fault patterns. Once the faults are identified neural network pattern identification is used to recognize and indicate the level or time duration of the fault.
A Genetic Algorithm with Neighborhood Search to Solve Integer and Linear Programming Problems

In this paper, a metaheuristic algorithm that combines genetic and neighbor search algorithms is proposed to solve integer linear programming problems. The individuals of the population are binary coded into a sequence of chromosomes (variables). Initially, chromosome length is five bits (genes) but if required they grow, up to 21 genes per chromosome, when looking for optima. The algorithm includes a test based on systematic neighborhood search to decide if it continues or stops. The algorithm is able to solve maximal or minimal integer linear programming problems in standard or non-standard form and linear programming problems with a simple adaptation. A comparative study was conducted with three algorithms; LINGO, Simplex LP and Evolutionary. These last two algorithms are from commercial solver in Excel spreadsheet software. The results show that the algorithm was able to find similar solution with LINGO and Simplex LP but better than the Evolutionary. A time study using problems from literature with two, three, four, eight and twelve variables is included.
The MagicBook - Moving Seamlessly between Reality and Virtuality

The MagicBook project is an early attempt to explore how we can use a physical object to smoothly transport users between reality and virtuality. Young children often fantasize about flying into the pages of a fairy tale and becoming part of the story. The MagicBook project makes this fantasy a reality using a normal book as the main interface object. People can turn the pages of the book, look at the pictures, and read the text without any additional technology. However, if a person looks at the pages through an augmented reality display, they see 3D virtual models appearing out of the pages. The models appear attached to the real page so users can see the augmented reality scene from any perspective by moving themselves or the book. The virtual content can be any size and is animated, so the augmented reality view is an enhanced version of a traditional 3D pop-up book.
Development of a general monitoring program for bridge stays and hangers in Wallonia

Recently, dramatic accidents drew attention to the monitoring of bridges. In this context and as Walloon bridges are aging, the Wallonia Public Service department has decided to launch a research project which aims at remotely keeping track of the time evolution of tension in cables. The present paper reports on the design, implementation and preliminary testing of an accurate and non-intrusive axial force monitoring system. Wireless sensors are used to measure accelerations at selected points along the cables. Vibration measurements are acquired on a daily basis and processed to get estimates of the natural frequencies of vibration of the cable that feed up a numerical algorithm for the axial force identification. All cables studied in this project are highly tensioned, but their flexural rigidities are not always negligible, although small. Preliminary tests have also revealed that the flexibility of end conditions is actually far from the ideal cases of perfectly hinged or clamped. Based on these observations, the cables are deemed to be accurately described as prestressed Euler-Bernoulli beam anchored to flexible supports. A computationally efficient semi-analytical approach has been derived to calculate the natural frequencies of the structural model. A custom implementation of the well-known Differential Evolution optimization algorithm (Storn and Price, 1995) is then adopted to adjust the model parameters in order to minimize the difference between calculated and measured natural frequencies. Special attention is devoted, within this framework, to the definitions of conditions leading to an accurate identification of the parameters representing the flexibility of the anchorages whenever in presence of noisy experimental frequencies. The delivered talk will cover the mathematical details of the model as well as the practical challenges of its deployment to the whole stock of bridges in Wallonia.
Signal Processing on Textured Meshes

In this thesis we extend signal processing techniques originally formulated in the context of image processing to techniques that can be applied to signals on arbitrary triangles meshes. We develop methods for the two most common representations of signals on triangle meshes: signals sampled at the vertices of a finely tessellated mesh, and signals mapped to a coarsely tessellated mesh through texture maps. Our first contribution is the combination of Lagrangian Integration and the Finite Elements Method in the formulation of two signal processing tasks: Shock Filters for texture and geometry sharpening, and Optical Flow for texture registration. Our second contribution is the formulation of Gradient-Domain processing within the texture atlas. We define a function space that handles chart discontinuities, and linear operators that capture the metric distortion introduced by the parameterization. Our third contribution is the construction of a spatiotemporal atlas parameterization for evolving meshes. Our method introduces localized remeshing operations and a compact parameterization that improves geometry and texture video compression. We show temporally coherent signal processing using partial correspondences. Primary reader and advisor: Michael Kazhdan. Secondary reader: Hugues Hoppe. Auxiliary reader: Laurent Younes. ii
MOEA/D com Busca Local para o Flow Shop Multiobjetivo

Este artigo aborda o Flow Shop de Permutação, um problema de sequenciamento presente em muitos mecanismos de gerenciamento de processos de produção industrial. A abordagem multiobjetivo considerada neste trabalho envolve a minimização do tempo máximo para completar um trabalho (makespan) e do tempo total de atraso (total tardiness). Para isso é utilizada uma plataforma multiobjetivo denominada MOEA/D-DRA (do inglês Multi-objective Evolutionary Algorithm based on Decomposition with Dynamic Resource Allocation). O foco do trabalho reside na utilização de um mecanismo muito conhecido por seus bons resultados nas versões mono-objetivo do problema. Este mecanismo, denominado NEH, é adaptado para ser utilizado na busca local incluída no MOEA/D-DRA, aplicado na solução de 11 instâncias do Flow Shop de permutação com tamanhos variando de 20 a 200 tarefas e 5 a 20 máquinas. A abordagem proposta é comparada com o MOEA/D-DRA utilizando NEH apenas na inicialização da população. Os resultados mostram que, em apenas 2 instâncias, a versão do MOEA/D-DRA sem busca local supera a abordagem proposta.