Miracle-dz/UniFAR-FTUs huggingface dataset:
Topology of leaves for minimal laminations by hyperbolic surfaces

We construct minimal laminations by hyperbolic surfaces whose generic leaf is a disk and contain any prescribed family of surfaces and with a precise control of the topologies of the surfaces that appear. The laminations are constructed via towers of finite coverings of surfaces for which we need to develop a relative version of residual finiteness which may be of independent interest. The main step in establishing this relative version of residual finiteness is to obtain finite covers with control on the second systole of the surface, which is done in the Appendix. In a companion paper, the case of other generic leaves is treated.
Ergodic measures of intermediate entropies for dynamical systems with approximate product property

We study dynamical systems with approximate product property and asymptotic entropy expansiveness. We show that such systems have ergodic measures of arbitrary intermediate entropies and arbitrary intermediate pressures. In fact, we show that ergodic measures of entropy h forms a residual subset in the subspace of all invariant measures whose entropy are no less than h. In particular, the ergodic measures of zero entropy are generic, which extends a result of Sigmund in a much generalized setting where periodic points may be absent.
Brain Network Construction and Classification Toolbox (BrainNetClass)

Brain functional network has become an increasingly used approach in understanding brain functions and diseases. Many network construction methods have been developed, whereas the majority of the studies still used static pairwise Pearson's correlation-based functional connectivity. The goal of this work is to introduce a toolbox namely "Brain Network Construction and Classification" (BrainNetClass) to the field to promote more advanced brain network construction methods. It comprises various brain network construction methods, including some state-of-the-art methods that were recently developed to capture more complex interactions among brain regions along with connectome feature extraction, reduction, parameter optimization towards network-based individualized classification. BrainNetClass is a MATLAB-based, open-source, cross-platform toolbox with graphical user-friendly interfaces for cognitive and clinical neuroscientists to perform rigorous computer-aided diagnosis with interpretable result presentations even though they do not possess neuroimage computing and machine learning knowledge. We demonstrate the implementations of this toolbox on real resting-state functional MRI datasets. BrainNetClass (v1.0) can be downloaded from this https URL.
KSP-SN-2016kf: A Long-rising H-rich Type II Supernova with Unusually High 56Ni Mass Discovered in the KMTNet Supernova Program

We present the discovery and the photometric and spectroscopic study of H-rich Type II supernova (SN) KSP-SN-2016kf (SN2017it) observed in the KMTNet Supernova Program in the outskirts of a small irregular galaxy at z ≃ 0.043 within a day of the explosion. Our high-cadence, multi-color (BVI) light curves of the SN show that it has a very long rise time (trise ≃ 20 days in the V band), a moderately luminous peak (MV ≃ −17.6 mag), a notably luminous and flat plateau (MV ≃ −17.4 mag and decay slope s ≃ 0.53 mag per 100 days), and an exceptionally bright radioactive tail. Using the color-dependent bolometric correction to the light curves, we estimate the 56Ni mass powering the observed radioactive tail to be 0.10 ± 0.01 M☉, making it an H-rich Type II SN with one of the largest 56Ni masses observed to date. The results of our hydrodynamic simulations of the light curves constrain the mass and radius of the progenitor at the explosion to be ∼15 M☉ (evolved from a star with an initial mass of ∼18.8 M☉) and ∼1040 R☉, respectively, with the SN explosion energy of ∼1.3 × 1051 erg. The above-average mass of the KSP-SN-2016kf progenitor, together with its low metallicity of obtained from spectroscopic analysis, is indicative of a link between the explosion of high-mass red supergiants and their low-metallicity environment. The early part of the observed light curves shows the presence of excess emission above what is predicted in model calculations, suggesting there is interaction between the ejecta and circumstellar material. We further discuss the implications of the high initial mass of the progenitor and the low-metallicity environment of KSP-SN-2016kf for our understanding of the origin of Type II SNe.
A Unifying Framework for Variance Reduction Algorithms for Finding Zeroes of Monotone Operators

A wide range of optimization problems can be recast as monotone inclusion problems. We propose a unifying framework for solving the monotone inclusion problem with randomized Forward-Backward algorithms. Our framework covers many existing deterministic and stochastic algorithms. Under various conditions, we can establish both sublinear and linear convergence rates in expectation for the algorithms covered by this framework. In addition, we consider algorithm design as well as asynchronous randomized Forward algorithms. Numerical experiments demonstrate the worth of the new algorithms that emerge from our framework
Learning Grasp Affordance Reasoning Through Semantic Relations

Reasoning about object affordances allows an autonomous agent to perform generalised manipulation tasks among object instances. While current approaches to grasp affordance estimation are effective, they are limited to a single hypothesis. We present an approach for detection and extraction of multiple grasp affordances on an object via visual input. We define semantics as a combination of multiple attributes, which yields benefits in terms of generalisation for grasp affordance prediction. We use Markov Logic Networks to build a knowledge base graph representation to obtain a probability distribution of grasp affordances for an object. To harvest the knowledge base, we collect and make available a novel dataset that relates different semantic attributes. We achieve reliable mappings of the predicted grasp affordances on the object by learning prototypical grasping patches from several examples. We show our method's generalisation capabilities on grasp affordance prediction for novel instances and compare with similar methods in the literature. Moreover, using a robotic platform, on simulated and real scenarios, we evaluate the success of the grasping task when conditioned on the grasp affordance prediction.
Computer Vision with a Single (Robust) Classifier

We show that the basic classification framework alone can be used to tackle some of the most challenging computer vision tasks. In contrast to other state-of-the-art approaches, the toolkit we develop is rather minimal: it uses a single, off-the-shelf classifier for all these tasks. The crux of our approach is that we train this classifier to be adversarially robust. It turns out that adversarial robustness is precisely what we need to directly manipulate salient features of the input. Overall, our findings demonstrate the utility of robustness in the broader machine learning context. Code and models for our experiments can be found at https://git.io/robust-apps.
Domain Adaptation with Randomized Multilinear Adversarial Networks

Adversarial learning has been successfully embedded into deep networks to learn transferable features for domain adaptation, which reduce distribution discrepancy between the source and target domains and improve generalization performance. Prior domain adversarial adaptation methods could not align complex multimode distributions since the discriminative structures and inter-layer interactions across multiple domain-specific layers have not been exploited for distribution alignment. In this paper, we present randomized multilinear adversarial networks (RMAN), which exploit multiple feature layers and the classifier layer based on a randomized multilinear adversary to enable both deep and discriminative adversarial adaptation. The learning can be performed by stochastic gradient descent with the gradients computed by back-propagation in linear-time. Experiments demonstrate that our models exceed the state-of-the-art results on standard domain adaptation datasets.
Image Captioning and Visual Question Answering Based on Attributes and Their Related External Knowledge

Much recent progress in Vision-to-Language problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This approach does not explicitly represent high-level semantic concepts, but rather seeks to progress directly from image features to text. In this paper we first propose a method of incorporating high-level concepts into the successful CNN-RNN approach, and show that it achieves a significant improvement on the state-of-the-art in both image captioning and visual question answering. We further show that the same mechanism can be used to incorporate external knowledge, which is critically important for answering high level visual questions. Specifically, we design a visual question answering model that combines an internal representation of the content of an image with information extracted from a general knowledge base to answer a broad range of image-based questions. It particularly allows questions to be asked about the contents of an image, even when the image itself does not contain a complete answer. Our final model achieves the best reported results on both image captioning and visual question answering on several benchmark datasets.
Novel Deep Convolution Neural Network Applied to MRI Cardiac Segmentation

In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN). As opposed to most cardiac segmentation methods which focus on the left ventricle (and especially the left cavity), our method segments both the left ventricular cavity, the left ventricular epicardium, and the right ventricular cavity. The novelty of our network lies in its maximum a posteriori loss function, which is specifically designed for the cardiac anatomy. Our loss function incorporates the cross-entropy of the predicted labels, the predicted contours, a cardiac shape prior, and an a priori term. Our model also includes a cardiac center-of-mass regression module which allows for an automatic shape prior registration. Also, since our method processes raw MR images without any manual preprocessing and/or image cropping, our CNN learns both high-level features (useful to distinguish the heart from other organs with a similar shape) and low-level features (useful to get accurate segmentation results). Those features are learned with a multi-resolution conv-deconv "grid" architecture which can be seen as an extension of the U-Net. We trained and tested our model on the ACDC MICCAI'17 challenge dataset of 150 patients whose diastolic and systolic images were manually outlined by 2 medical experts. Results reveal that our method can segment all three regions of a 3D MRI cardiac volume in $0.4$ second with an average Dice index of $0.90$, which is significantly better than state-of-the-art deep learning methods.
Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models

Even todays most advanced machine learning models are easily fooled by almost imperceptible perturbations of their inputs. Foolbox is a new Python package to generate such adversarial perturbations and to quantify and compare the robustness of machine learning models. It is build around the idea that the most comparable robustness measure is the minimum perturbation needed to craft an adversarial example. To this end, Foolbox provides reference implementations of most published adversarial attack methods alongside some new ones, all of which perform internal hyperparameter tuning to find the minimum adversarial perturbation. Additionally, Foolbox interfaces with most popular deep learning frameworks such as PyTorch, Keras, TensorFlow, Theano and MXNet and allows different adversarial criteria such as targeted misclassification and top-k misclassification as well as different distance measures. The code is licensed under the MIT license and is openly available at this https URL . The most up-to-date documentation can be found at this http URL .
Hierarchical Saliency Detection on Extended CSSD

Complex structures commonly exist in natural images. When an image contains small-scale high-contrast patterns either in the background or foreground, saliency detection could be adversely affected, resulting erroneous and non-uniform saliency assignment. The issue forms a fundamental challenge for prior methods. We tackle it from a scale point of view and propose a multi-layer approach to analyze saliency cues. Different from varying patch sizes or downsizing images, we measure region-based scales. The final saliency values are inferred optimally combining all the saliency cues in different scales using hierarchical inference. Through our inference model, single-scale information is selected to obtain a saliency map. Our method improves detection quality on many images that cannot be handled well traditionally. We also construct an extended Complex Scene Saliency Dataset (ECSSD) to include complex but general natural images.
Improvements to Frank-Wolfe optimization for multi-detector multi-object tracking

This paper proposes a novel formulation for the multi-object tracking-by-detection paradigm for two (or more) input detectors. Using full-body and heads detections, the fusion helps to recover heavily occluded persons and to reduce false positives. The assignment of the two input features to a person and the extraction of the trajectories is commonly solved from one binary quadratic program (BQP). Due to the computational complexity of the NP-hard QP, we approximate the solution using the Frank-Wolfe al-gorithm. We propose several improvements to this solver affecting better minimization and shorter computations, compared to off-the-shelf BQP-solvers and the standard Frank-Wolfe algorithm. Evaluation on pedestrian tracking is provided for multiple scenarios, showing improved tracking quality over single input feature trackers and standard QP-solvers. Finally we present the performance of our tracker on the challenging MOT16 benchmark, being comparable to state-of-the-art trackers.
Predicting drug-target interactions via sparse learning

Drug-target interaction (DTI) prediction plays a very important role in drug development. Biochemical experiments or in vitro methods to identify such interactions are very expensive, laborious and time-consuming. Therefore, in silico approaches including docking simulation and machine learning have been proposed to solve this problem. In particular, machine learning approaches have attracted increasing attentions recently. However, in addition to the known drug-target interactions, most of the machine learning methods require extra information such as chemical structures, genome sequences, binding types and so on. Whenever such information is not available, they may perform poor. Very recently, the similarity-based link prediction methods were extended to bipartite networks, which can be applied to solve the DTI prediction problem by using topological information only. In this work, we propose a sparse learning method to solve the DTI prediction problem, which does not require extra information and performs much better than similarity-based methods. We compare the proposed method with similarity-based methods including common neighbor index, Katz index and Jaccard index on the DTI prediction problem over the four renowned and benchmark datasets. The proposed method performs remarkably better. The results suggest that although the proposed method utilizes only the known drug-target interactions, it performs very satisfactorily. The method is very suitable to predict the potential uses of the existing drugs, especially, when extra information about the drugs and targets is not available.
Coupled 3D Convolutional Neural Networks for Audio-Visual Recognition

Audio-visual recognition (AVR) has been considered as a solution for speech recognition tasks when the audio is corrupted, as well as a visual recognition method used for speaker verification in multi-speaker scenarios. The approach of AVR systems is to leverage the extracted information from one modality to improve the recognition ability of the other modality by complementing the missing information. The essential problem is to find the correspondence between the audio and visual streams, which is the goal of this work. We propose the use of a coupled 3D Convolutional Neural Network (CNN) architecture that can map both modalities into a representation space to evaluate the correspondence of audio-visual streams using the learned multimodal features. The proposed architecture will incorporate both spatial and temporal information jointly to effectively find the correlation between temporal information for different modalities. By using a relatively small network architecture and much smaller dataset, our proposed method surpasses the performance of the existing similar methods for audio-visual matching which use CNNs for feature representation. We also demonstrate that effective pair selection method can significantly increase the performance.
A Survey of Random Forest Based Methods for Intrusion Detection Systems

Over the past decades, researchers have been proposing different Intrusion Detection approaches to deal with the increasing number and complexity of threats for computer systems. In this context, Random Forest models have been providing a notable performance on their applications in the realm of the behaviour-based Intrusion Detection Systems. Specificities of the Random Forest model are used to provide classification, feature selection, and proximity metrics. This work provides a comprehensive review of the general basic concepts related to Intrusion Detection Systems, including taxonomies, attacks, data collection, modelling, evaluation metrics, and commonly used methods. It also provides a survey of Random Forest based methods applied in this context, considering the particularities involved in these models. Finally, some open questions and challenges are posed combined with possible directions to deal with them, which may guide future works on the area.
Massive-scale Online Feature Selection for Sparse Ultra-high Dimensional Data

Feature Selection is an important technique in machine learning and pattern classification, especially for handling high-dimensional data. Most existing studies have been restricted to batch learning, which is often inefficient and poorly scalable when handling big data in real world, especially when data arrives sequentially. Recent years have witnessed some emerging feature selection techniques using online learning. Despite enjoying significant advantages in efficiency and scalability, the existing online feature selection methods are not always accurate enough, and still not sufficiently fast when handling massive-scale data with ultra-high dimensionality. To address the limitations, we propose a novel online feature selection method by exploiting second-order information with optimized implementations, which not only improves the learning efficacy, but also significantly enhances computational efficiency. We conduct extensive experiments for evaluating both learning accuracy and time cost of different algorithms on massive-scale synthetic and real-world datasets, including a dataset with billion-scale features. Our results show that our technique achieves highly competitive accuracy as compared with state-of-the-art batch feature selection methods, but consumes significantly low computational cost that is orders of magnitude lower than both state-of-the-art batch and online feature selection methods. On a billion-scale synthetic dataset (1-billion dimensions, 1-billion nonzero features, and 1-million samples), our algorithm took only eight minutes with a normal single machine.
Statistical and practical significance of empirical software engineering research: A maturity model

Software engineering research is maturing and papers increasingly support their arguments with empirical data from a multitude of sources, using statistical tests to judge if and to what degree empirical evidence supports their hypotheses. This paper presents trends and current state of art regarding statistical maturity in empirical software engineering research; the objective being the development of a statistical maturity model. First, we manually reviewed papers from four well-known and top ranked journals producing a review protocol along with the view of current (2015) state of art concerning statistical maturity, practical significance and reproducibility of empirical software engineering research. Our protocol was then used as ground truth, i.e., training set, for a semi-automatic classification of studies for the years 2001--2015 using a total of 3,011 papers. We used the extracted data to develop a statistical maturity model which also includes a model for how to argue for practical significance. The statistical maturity of empirical software engineering research has an upward trend in certain areas (e.g., use of nonparametric statistics, but also more generally in the usage of quantitative analysis). However, we also see how our research area currently often fails to connect the statistical analysis to practical significance. For instance, we argue that conclusions should explicitly state contributions to software engineering practice, e.g., the return on investment for practitioners. We argue that the statistical maturity model can be used by researchers and practitioners to build a coherent statistical analysis and guide them in the choice of statistical approaches of its steps. The final goal for a researcher would be to, in a clearer way, present and argue for the practical significance of their findings. Bayesian analysis, we believe, has a role to play in this.
Multifeature Selection for 3D Human Action Recognition

In mainstream approaches for 3D human action recognition, depth and skeleton features are combined to improve recognition accuracy. However, this strategy results in high feature dimensions and low discrimination due to redundant feature vectors. To solve this drawback, a multi-feature selection approach for 3D human action recognition is proposed in this paper. First, three novel single-modal features are proposed to describe depth appearance, depth motion, and skeleton motion. Second, a classification entropy of random forest is used to evaluate the discrimination of the depth appearance based features. Finally, one of the three features is selected to recognize the sample according to the discrimination evaluation. Experimental results show that the proposed multi-feature selection approach significantly outperforms other approaches based on single-modal feature and feature fusion.
A survey of cross-lingual embedding models

Cross-lingual embedding models allow us to project words from different languages into a shared embedding space. This allows us to apply models trained on languages with a lot of data, e.g. English to low-resource languages. In the following, we will survey models that seek to learn cross-lingual embeddings. We will discuss them based on the type of approach and the nature of parallel data that they employ. Finally, we will present challenges and summarize how to evaluate cross-lingual embedding models.