Miracle-dz/UniFAR-FTUs huggingface dataset:
Spread-spectrum path diversity in a shadowed Rician fading land-mobile satellite channel

The effects of two types of path diversity techniques, namely selection diversity and maximal ratio combining, on the bit error probability are investigated for direct-sequence spread-spectrum (DS/SS) transmission in a land mobile satellite channel using coherent binary phase-shift keying (BPSK) modulation. It is assumed that the channel consists of a log-normally shadowed line-of-sight signal plus Rayleigh distributed multipath signals. The bit error probability is evaluated for light, average, and heavy shadowing. The performance is also measured in terms of the outage probability. >
Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics

Several issues relating to goodness of fit in structural equations are examined. The convergence and differentiation criteria, as applied by Bagozzi, are shown not to stand up under mathematical or statistical analysis. The authors argue that the choice of interpretative statistic must be based on the research objective. They demonstrate that when this is done the Fornell-Larcker testing system is internally consistent and that it conforms to the rules of correspondence for relating data to abstract variables.
Nonlinear Science Today

sults are presented. The contributors to the journal include some wellknown mathematicians and computer scientists. A particularly striking supplement was contributed byJohn Sullivan. Entitled "Generating and Rendering FourDimensional Polytopes", it is an example of the hands-on, open-ended quality of articles that include software supplements: it is easy to reproduce, modify and extend the results given in the article by using Mathematica with Sullivan's software 4-D Polytopes. This article is of general interest (even to non-users of Mathematica) and is full of examples, such as the relationship between polytopes and soap bubbles, which is discussed in some detail. The article continues with a discussion of the geometrical symmetries of the polytopes and ways to use these symmetries to make the computing of the polytope images proceed more efficiently. For Mathematica users, Sullivan includes discussion of alternative tools available for generating the images with Mathematica. This part of the article is a tutorial that aids interested readers in best using Sullivan's methods and the capabilities of his software. The combination of subject matter and the interactive possibilities of the journal contribute to the trend of ineluding examples and working algorithms in papers. This is particularly useful as the volume and sophistication of computer-based art increase. It is helpful to have some familiarity with Mathematica or access to reference material [l]. A l-year subscription to the journal
Sculpture: Science Fiction Machines

On 19 June 1974, Granada's television programme 'Granada Reports' contained a review of the John Moore Exhibition at Liverpool and on the next evening's programme there was an item on one entitled 'The Even Moore', a cocky salon des refuses, organized by Peter O'Halligan (Dream Merchant), that opened in Liverpool on the 11th June. It was in the latter exhibition that my 'Pandora's Box' was first shown. Granada's reporter described it as a sculpture for people who eat chickens. A little girl called Dawn, no more than two years old, visited the exhibiton each day, climbing up the stairs from the shop below where her mother worked, just to watch and listen to my chickens. 'Pandora's Box' (Fig. 1) is a sinister-looking dark wooden chest with a wooden moulding at top and bottom. The construction consists of a strong wooden frame covered with marine plywood sheet in order to give the impression of a strong solid piece of furniture. At the top of the front face is a small door with a brass lock and at the bottom of the rear face is an ominous thick electric cable that curls away to a small black box on a 3 ft. stand. In this box is played a tape recording of the noise made by lots of real chickens. (Some thought that I had recorded the sounds made at a
Dissecting the Genetics of Stroke

textabstractStroke is a leading cause of death and disability in the Western world. It is a complex disease resulting from environmental factors and genetic factors, as well as gene-gene and geneenvironment interactions. Many studies have attempted to unravel the genetic aetiology of stroke, but results have been inconsistent. Most have used the candidate gene approach, but genome-wide linkage analyses have also been performed. Recently, results of genome-wide association studies have been reported, however, this approach has not yet been used to study the genetics of stroke. Stroke is a heterogeneous disease, which can be subtyped into ischaemic stroke (80%) and haemorrhagic stroke (20%), with diff erent underlying pathways. In order to reduce the complexity of stroke, intermediate phenotypes have been studied, such as hypertension, carotid atherosclerosis and cerebral white matter lesions. Another approach has been to study stroke in a genetically isolated population.
Characterization of medical malpractice claims against obstetricians affiliated to FEPASDE in Colombia 1999–2014: historic cohort

Introduction: Medical malpractice claims have been increasing at a constant rate worldwide, resulting in a burden for practitioners as well as for the health system. In obstetrics, the problem is even greater considering that it is one of the medical specialties with the largest number of medical malpractice suits. Objective: To characterize medical malpractice claims in the area of obstetrics in Colombia from the perspectives of the physician, the patient, the institution, the medical care provided, and the legal proceeding. Materials and methods: Historical descriptive cohort of closed medical malpractice cases between 1999 and 2014 filed against obstetricians affiliated to a special solidarity fund for support in lawsuit cases. Simple random sampling (n = 279) in a universe of 982 proceedings. Variables related to the proceeding, the obstetrician, the institution, medical care, and the patient were measured. Results: The most frequent lawsuits were related to ethics (44.4%). The proportion of unfavorable rulings was 7.7%, more frequently in civil cases (31.8%). The prevalence of lawsuits was higher in private institutions (60%). The majority of the cases were related to patients in the second half of the gestation period (86%). In 74.7% of the cases, legal action was initiated as a result of events occurring during childbirth. The most frequent cause was neonatal compromise (38.9%), followed by fetal compromise (24.7%). Conclusion: Care during childbirth, fetal, and neonatal demise are critical sources of medical malpractice claims.
An Empirical Study of Consumer's E-commerce Initial Trust Antecedents

Technology Acceptance Model (TAM) is a universal model to explain people’s behavior in acceptance of new technology. B2C E-Commerce is outcome of information technology, and it brings a convenient shopping way for consumers. Meanwhile, online shopping puts consumers into a more complicated shopping environment. Research has shown that consumer’s trust distinctly affects their acceptance of online shopping due to virtual environment. Moreover, consumer’s perception of online shopping risk, convenience and usefulness distinctly affects consumer’s trust as well as consumer’s acceptance behavior of online shopping. Therefore, this paper puts forward a consumer’s trust antecedents model based on
Attitudes and knowledge of alcohol related harm minimization initiatives in a rural Australian city: A baseline survey

Aims: This study investigates the knowledge and attitudes towards alcohol related harm minimization initiatives in Kalgoorlie-Boulder, Western Australia, as part of a community project aimed at reducing harms associated with excessive drinking. Methods: Baseline data were collected from a stratified, community sample, using a purpose designed instrument. The community's readiness was determined by interviewing key stakeholders and community leaders, using the ‘Readiness for Change’ interview instrument. Findings: There were substantial levels of binge drinking accompanied by low awareness of alcohol as a local problem and lack of knowledge about interventions that are currently operating in the community. Support for measures designed to reduce problems associated with excessive use of alcohol was polarized. Community readiness to tackle alcohol related issues was low indicating a need for awareness raising. Conclusions: These findings are critical to informing the development of interventions to ensure they are tailored to this community's needs and abilities. Since knowledge and awareness of alcohol related issues are low in Kalgoorlie-Boulder, the first step in changing community attitudes is to create greater awareness of these issues and their consequences, and provide information on potential interventions to address the current problems.
Deep Item-based Collaborative Filtering for Sparse Implicit Feedback

Recommender systems are ubiquitous in the domain of e-commerce, used to improve the user experience and to market inventory, thereby increasing revenue for the site. Techniques such as item-based collaborative filtering are used to model users' behavioral interactions with items and make recommendations from items that have similar behavioral patterns. However, there are challenges when applying these techniques on extremely sparse and volatile datasets. On some e-commerce sites, such as eBay, the volatile inventory and minimal structured information about items make it very difficult to aggregate user interactions with an item. In this work, we describe a novel deep learning-based method to address the challenges. We propose an objective function that optimizes a similarity measure between binary implicit feedback vectors between two items. We demonstrate formally and empirically that a model trained to optimize this function estimates the log of the cosine similarity between the feedback vectors. We also propose a neural network architecture optimized on this objective. We present the results of experiments comparing the output of the neural network with traditional item-based collaborative filtering models on an implicit-feedback dataset, as well as results of experiments comparing different neural network architectures on user purchase behavior on eBay. Finally, we discuss the results of an A/B test that show marked improvement of the proposed technique over eBay's existing collaborative filtering recommender system.
A Greedy Approach to ℓ0, ∞-Based Convolutional Sparse Coding

Sparse coding techniques for image processing traditionally rely on a processing of small overlapping patches separately followed by averaging. This has the disadvantage that the reconstructed image no longer obeys the sparsity prior used in the processing. For this purpose convolutional sparse coding has been introduced, where a shift-invariant dictionary is used and the sparsity of the recovered image is maintained. Most such strategies target the $\ell_0$ "norm" or the $\ell_1$ norm of the whole image, which may create an imbalanced sparsity across various regions in the image. In order to face this challenge, the $\ell_{0,\infty}$ "norm" has been proposed as an alternative that "operates locally while thinking globally". The approaches taken for tackling the non-convexity of these optimization problems have been either using a convex relaxation or local pursuit algorithms. In this paper, we present an efficient greedy method for sparse coding and dictionary learning, which is specifically tailored to $\ell_{0,\infty}$, and is based on matching pursuit. We demonstrate the usage of our approach in salt-and-pepper noise removal and image inpainting. A code package which reproduces the experiments presented in this work is available at this https URL
Vilin: Unconstrained Numerical Optimization Application

We introduce an application for executing and testing different unconstrained optimization algorithms. The application contains a library of various test functions with pre-defined starting points. A several known classes of methods as well as different classes of line search procedures are covered. Each method can be tested on various test function with a chosen number of parameters. Solvers come with optimal pre-defined parameter values which simplifies the usage. Additionally, user friendly interface gives an opportunity for advanced users to use their expertise and also easily fine-tune a large number of hyper parameters for obtaining even more optimal solution. This application can be used as a tool for developing new optimization algorithms (by using simple API), as well as for testing and comparing existing ones, by using given standard library of test functions. Special care has been given in order to achieve good numerical stability of all vital parts of the application. The application is implemented in programming language Matlab with very helpful gui support.
Bridge Damage Detection using Single-Stage Detector and Field Inspection Images

Detecting concrete surface damages is a vital task for maintaining the structural health and reliability of highway bridges. Currently, most of these tasks are conducted manually which could be cumbersome and time-consuming. Recent rapid advancement in convolution neural network has enabled the development of deep learning-based visual inspection techniques for detecting multiple structural damages. However, most deep learning-based techniques are built on two-stage, proposal-driven detectors and using less complex image data, which is not promising to promote practical applications and integration within intelligent autonomous inspection systems. In this study, a faster, simpler single-stage detector is proposed based on YOLOv3 for detecting multiple concrete damages of highway bridges. To realize this, a bridge damage dataset consisting of field inspection images is prepared. These images have large variations in damage appearance and monitoring scene and are labeled with four types of concrete damages: crack, pop-out, spalling and exposed rebar. The original YOLOv3 is further improved by introducing a novel transfer learning method, Batch Renormalization and Focal Loss. The improved YOLOv3 is evaluated in terms of average precision and speed. The results show that the improved YOLOv3 has a detection accuracy of up to 80%. Its performance is about 13% better than the original YOLOv3.
Salient Object Detection via High-to-Low Hierarchical Context Aggregation

Recent progress on salient object detection mainly aims at exploiting how to effectively integrate convolutional side-output features in convolutional neural networks (CNN). Based on this, most of the existing state-of-the-art saliency detectors design complex network structures to fuse the side-output features of the backbone feature extraction networks. However, should the fusion strategies be more and more complex for accurate salient object detection? In this paper, we observe that the contexts of a natural image can be well expressed by a high-to-low self-learning of side-output convolutional features. As we know, the contexts of an image usually refer to the global structures, and the top layers of CNN usually learn to convey global information. On the other hand, it is difficult for the intermediate side-output features to express contextual information. Here, we design an hourglass network with intermediate supervision to learn contextual features in a high-to-low manner. The learned hierarchical contexts are aggregated to generate the hybrid contextual expression for an input image. At last, the hybrid contextual features can be used for accurate saliency estimation. We extensively evaluate our method on six challenging saliency datasets, and our simple method achieves state-of-the-art performance under various evaluation metrics. Code will be released upon paper acceptance.
Learning How to Mutate Source Code from Bug-Fixes

Mutation testing has been widely accepted as an approach to guide test case generation or to assess the effectiveness of test suites. Empirical studies have shown that mutants are representative of real faults; yet they also indicated a clear need for better, possibly customized, mutation operators and strategies. While methods to devise domain-specific or general-purpose mutation operators from real faults exist, they are effort-and error-prone, and do not help the tester to decide whether and how to mutate a given source code element. We propose a novel approach to automatically learn mutants from faults in real programs. First, our approach processes bug fixing changes using fine-grained differencing, code abstraction, and change clustering. Then, it learns mutation models using a deep learning strategy. We have trained and evaluated our technique on a set of ~787k bug fixes mined from GitHub. Our empirical evaluation showed that our models are able to predict mutants that resemble the actual fixed bugs in between 9% and 45% of the cases, and over 98% of the automatically generated mutants are lexically and syntactically correct.
Signal Classification under structure sparsity constraints

Object Classification is a key direction of research in signal and image processing, computer vision and artificial intelligence. The goal is to come up with algorithms that automatically analyze images and put them in predefined categories. This dissertation focuses on the theory and application of sparse signal processing and learning algorithms for image processing and computer vision, especially object classification problems. A key emphasis of this work is to formulate novel optimization problems for learning dictionary and structured sparse representations. Tractable solutions are proposed subsequently for the corresponding optimization problems. An important goal of this dissertation is to demonstrate the wide applications of these algorithmic tools for real-world applications. To that end, we explored important problems in the areas of: 1. Medical imaging: histopathological images acquired from mammalian tissues, human breast tissues, and human brain tissues. 2. Low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture radar: detecting bombs and mines buried under rough surfaces. 3. General object classification: face, flowers, objects, dogs, indoor scenes, etc.
Chart-Text: A Fully Automated Chart Image Descriptor

Images greatly help in understanding, interpreting and visualizing data. Adding textual description to images is the first and foremost principle of web accessibility. Visually impaired users using screen readers will use these textual descriptions to get better understanding of images present in digital contents. In this paper, we propose Chart-Text a novel fully automated system that creates textual description of chart images. Given a PNG image of a chart, our Chart-Text system creates a complete textual description of it. First, the system classifies the type of chart and then it detects and classifies the labels and texts in the charts. Finally, it uses specific image processing algorithms to extract relevant information from the chart images. Our proposed system achieves an accuracy of 99.72% in classifying the charts and an accuracy of 78.9% in extracting the data and creating the corresponding textual description.
Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon

We show that minimum-norm interpolation in the Reproducing Kernel Hilbert Space corresponding to the Laplace kernel is not consistent if input dimension is constant. The lower bound holds for any choice of kernel bandwidth, even if selected based on data. The result supports the empirical observation that minimum-norm interpolation (that is, exact fit to training data) in RKHS generalizes well for some high-dimensional datasets, but not for low-dimensional ones.
S4-Net: Geometry-Consistent Semi-Supervised Semantic Segmentation

We show that it is possible to learn semantic segmentation from very limited amounts of manual annotations, by enforcing geometric 3D constraints between multiple views. More exactly, image locations corresponding to the same physical 3D point should all have the same label. We show that introducing such constraints during learning is very effective, even when no manual label is available for a 3D point, and can be done simply by employing techniques from 'general' semi-supervised learning to the context of semantic segmentation. To demonstrate this idea, we use RGB-D image sequences of rigid scenes, for a 4-class segmentation problem derived from the ScanNet dataset. Starting from RGB-D sequences with a few annotated frames, we show that we can incorporate RGB-D sequences without any manual annotations to improve the performance, which makes our approach very convenient. Furthermore, we demonstrate our approach for semantic segmentation of objects on the LabelFusion dataset, where we show that one manually labeled image in a scene is sufficient for high performance on the whole scene.
Deflecting 3D Adversarial Point Clouds Through Outlier-Guided Removal

Neural networks are vulnerable to adversarial examples, which poses a threat to their application in security sensitive systems. We propose simple random sampling (SRS) and statistical outlier removal (SOR) as defenses for 3D point cloud classification, where both methods remove points by estimating probability of points serving as adversarial points. Compared with ensemble adversarial training which is the state-of-the-art defending method, SOR has several advantages: better defense performance, randomization makes the network more robust to adversarial point clouds, no additional training or fine-tuning required, and few computations are needed by adding the points-removal layer. In particular, our experiments on ModelNet40 show that SOR is very effective as defense in practice. The strength of those defenses lies in their non-differentiable nature and inherent randomness, which makes it difficult for an adversary to circumvent the defenses. Our best defense eliminates 81.4% of strong white-box attacks by C&W and l2 loss based attack methods.
On Evaluating and Comparing Open Domain Dialog Systems

Conversational agents are exploding in popularity. However, much work remains in the area of non goal-oriented conversations, despite significant growth in research interest over recent years. To advance the state of the art in conversational AI, Amazon launched the Alexa Prize, a 2.5-million dollar university competition where sixteen selected university teams built conversational agents to deliver the best social conversational experience. Alexa Prize provided the academic community with the unique opportunity to perform research with a live system used by millions of users. The subjectivity associated with evaluating conversations is key element underlying the challenge of building non-goal oriented dialogue systems. In this paper, we propose a comprehensive evaluation strategy with multiple metrics designed to reduce subjectivity by selecting metrics which correlate well with human judgement. The proposed metrics provide granular analysis of the conversational agents, which is not captured in human ratings. We show that these metrics can be used as a reasonable proxy for human judgment. We provide a mechanism to unify the metrics for selecting the top performing agents, which has also been applied throughout the Alexa Prize competition. To our knowledge, to date it is the largest setting for evaluating agents with millions of conversations and hundreds of thousands of ratings from users. We believe that this work is a step towards an automatic evaluation process for conversational AIs.