OpenRaiser/Intern-Atlas huggingface dataset:
A numerical study on the softening process of iron ore particles in the cohesive zone of an experimental blast furnace using a coupled CFD-DEM method
Venue: arXiv cs.CE 2018
Year: 2018

Reduced iron-bearing materials start softening in the cohesive zone of a blast furnace due to the high temperature and the weight of the burden above. Softening process causes a reduction of void space between particles. As a result, the pressure drop and gas flow change remarkably in this particular zone. As a consequence, it has a significant influence on the performance of a blast furnace and is needed to be fully characterized. For this reason, the gas rheology along with the deformation of the particles and the heat transfer between particle-particle and particle-gas should be adequately described. In this paper, the eXtended Discrete Element Method (XDEM), as a CFD-DEM approach coupled with the heat transfer, is applied to model complex gas- solid flow during the softening process of pre-reduced iron ore pellets in an Experimental Blast Furnace (EBF). The particle deformation, displacement, temperature, and gas pressure drop and flow under conditions relevant to the EBF operations are examined. Moreover, to accurately capture the high gas velocity inlet, a dual-grid multi-scale approach is applied. The approach and findings are helpful to understand the effect of the softening process on the pressure drop and gas flow in the cohesive zone of the blast furnace.
Real root finding for equivariant semi-algebraic systems
Venue: arXiv cs.SC 2018
Year: 2018

Let $R$ be a real closed field. We consider basic semi-algebraic sets defined by $n$-variate equations/inequalities of $s$ symmetric polynomials and an equivariant family of polynomials, all of them of degree bounded by $2d < n$. Such a semi-algebraic set is invariant by the action of the symmetric group. We show that such a set is either empty or it contains a point with at most $2d-1$ distinct coordinates. Combining this geometric result with efficient algorithms for real root finding (based on the critical point method), one can decide the emptiness of basic semi-algebraic sets defined by $s$ polynomials of degree $d$ in time $(sn)^{O(d)}$. This improves the state-of-the-art which is exponential in $n$. When the variables $x_1, \ldots, x_n$ are quantified and the coefficients of the input system depend on parameters $y_1, \ldots, y_t$, one also demonstrates that the corresponding one-block quantifier elimination problem can be solved in time $(sn)^{O(dt)}$.
Symmetry Aware Evaluation of 3D Object Detection and Pose Estimation in Scenes of Many Parts in Bulk
Venue: arXiv cs.CV 2018
Year: 2018

While 3D object detection and pose estimation has been studied for a long time, its evaluation is not yet completely satisfactory. Indeed, existing datasets typically consist in numerous acquisitions of only a few scenes because of the tediousness of pose annotation, and existing evaluation protocols cannot handle properly objects with symmetries. This work aims at addressing those two points. We first present automatic techniques to produce fully annotated RGBD data of many object instances in arbitrary poses, with which we produce a dataset of thousands of independent scenes of bulk parts composed of both real and synthetic images. We then propose a consistent evaluation methodology suitable for any rigid object, regardless of its symmetries. We illustrate it with two reference object detection and pose estimation methods on different objects, and show that incorporating symmetry considerations into pose estimation methods themselves can lead to significant performance gains. The proposed dataset is available at http://rbregier.github.io/dataset2017.
Log Skeletons: A Classification Approach to Process Discovery
Venue: arXiv cs.AI 2018
Year: 2018

To test the effectiveness of process discovery algorithms, a Process Discovery Contest (PDC) has been set up. This PDC uses a classification approach to measure this effectiveness: The better the discovered model can classify whether or not a new trace conforms to the event log, the better the discovery algorithm is supposed to be. Unfortunately, even the state-of-the-art fully-automated discovery algorithms score poorly on this classification. Even the best of these algorithms, the Inductive Miner, scored only 147 correct classified traces out of 200 traces on the PDC of 2017. This paper introduces the rule-based log skeleton model, which is closely related to the Declare constraint model, together with a way to classify traces using this model. This classification using log skeletons is shown to score better on the PDC of 2017 than state-of-the-art discovery algorithms: 194 out of 200. As a result, one can argue that the fully-automated algorithm to construct (or: discover) a log skeleton from an event log outperforms existing state-of-the-art fully-automated discovery algorithms.
How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experiments
Venue: arXiv cs.LG 2018
Year: 2018

Consistently checking the statistical significance of experimental results is one of the mandatory methodological steps to address the so-called "reproducibility crisis" in deep reinforcement learning. In this tutorial paper, we explain how the number of random seeds relates to the probabilities of statistical errors. For both the t-test and the bootstrap confidence interval test, we recall theoretical guidelines to determine the number of random seeds one should use to provide a statistically significant comparison of the performance of two algorithms. Finally, we discuss the influence of deviations from the assumptions usually made by statistical tests. We show that they can lead to inaccurate evaluations of statistical errors and provide guidelines to counter these negative effects. We make our code available to perform the tests.
Learning Graph Weighted Models on Pictures
Venue: arXiv cs.FL 2018
Year: 2018

Graph Weighted Models (GWMs) have recently been proposed as a natural generalization of weighted automata over strings and trees to arbitrary families of labeled graphs (and hypergraphs). A GWM generically associates a labeled graph with a tensor network and computes a value by successive contractions directed by its edges. In this paper, we consider the problem of learning GWMs defined over the graph family of pictures (or 2-dimensional words). As a proof of concept, we consider regression and classification tasks over the simple Bars & Stripes and Shifting Bits picture languages and provide an experimental study investigating whether these languages can be learned in the form of a GWM from positive and negative examples using gradient-based methods. Our results suggest that this is indeed possible and that investigating the use of gradient-based methods to learn picture series and functions computed by GWMs over other families of graphs could be a fruitful direction.
A Credal Extension of Independent Choice Logic
Venue: arXiv cs.LO 2018
Year: 2018

We propose an extension of Poole's independent choice logic based on a relaxation of the underlying independence assumptions. A credal semantics involving multiple joint probability mass functions over the possible worlds is adopted. This represents a conservative approach to probabilistic logic programming achieved by considering all the mass functions consistent with the probabilistic facts. This allows to model tasks for which independence among some probabilistic choices cannot be assumed, and a specific dependence model cannot be assessed. Preliminary tests on an object ranking application show that, despite the loose underlying assumptions, informative inferences can be extracted.
Examining key features and platforms of IoT
Venue: arXiv cs.SE 2018
Year: 2018

To help facilitate expertise in IoT technologies, NLeSC and SURF worked together on a project focusing on IoT applications and platforms. The information included in this case study show the results of NLeSC and SURF's investigation, examining different features offered by cloud and self-maintained IoT platforms with an overall summary of an IoT architecture.
Quantizing deep convolutional networks for efficient inference: A whitepaper
Venue: arXiv cs.LG 2018
Year: 2018

We present an overview of techniques for quantizing convolutional neural networks for inference with integer weights and activations. Per-channel quantization of weights and per-layer quantization of activations to 8-bits of precision post-training produces classification accuracies within 2% of floating point networks for a wide variety of CNN architectures. Model sizes can be reduced by a factor of 4 by quantizing weights to 8-bits, even when 8-bit arithmetic is not supported. This can be achieved with simple, post training quantization of weights.We benchmark latencies of quantized networks on CPUs and DSPs and observe a speedup of 2x-3x for quantized implementations compared to floating point on CPUs. Speedups of up to 10x are observed on specialized processors with fixed point SIMD capabilities, like the Qualcomm QDSPs with HVX. Quantization-aware training can provide further improvements, reducing the gap to floating point to 1% at 8-bit precision. Quantization-aware training also allows for reducing the precision of weights to four bits with accuracy losses ranging from 2% to 10%, with higher accuracy drop for smaller networks.We introduce tools in TensorFlow and TensorFlowLite for quantizing convolutional networks and review best practices for quantization-aware training to obtain high accuracy with quantized weights and activations. We recommend that per-channel quantization of weights and per-layer quantization of activations be the preferred quantization scheme for hardware acceleration and kernel optimization. We also propose that future processors and hardware accelerators for optimized inference support precisions of 4, 8 and 16 bits.
Learning Instance Segmentation by Interaction
Venue: arXiv cs.CV 2018
Year: 2018

We present an approach for building an active agent that learns to segment its visual observations into individual objects by interacting with its environment in a completely self-supervised manner. The agent uses its current segmentation model to infer pixels that constitute objects and refines the segmentation model by interacting with these pixels. The model learned from over 50K interactions generalizes to novel objects and backgrounds. To deal with noisy training signal for segmenting objects obtained by self-supervised interactions, we propose robust set loss. A dataset of robot's interactions along-with a few human labeled examples is provided as a benchmark for future research. We test the utility of the learned segmentation model by providing results on a downstream vision-based control task of rearranging multiple objects into target configurations from visual inputs alone. Videos, code, and robotic interaction dataset are available at https://pathak22.github.io/seg-by-interaction/
On the weight distribution of random binary linear codes
Venue: arXiv cs.IT 2018
Year: 2018

We investigate the weight distribution of random binary linear codes. For $0<λ<1$ and $n\to\infty$ pick uniformly at random $λn$ vectors in $\mathbb{F}_2^n$ and let $C \le \mathbb{F}_2^n$ be the orthogonal complement of their span. Given $0<γ<1/2$ with $0< λ< h(γ)$ let $X$ be the random variable that counts the number of words in $C$ of Hamming weight $γn$. In this paper we determine the asymptotics of the moments of $X$ of all orders $o(\frac{n}{\log n})$.
Visual Content Privacy Leaks on Social Media Networks
Venue: arXiv cs.CY 2018
Year: 2018

With the growth and accessibility of mobile devices and internet, the ease of posting and sharing content on social media networks (SMNs) has increased exponentially. Many users post images that contain "privacy leaks" regarding themselves or someone else. Privacy leaks include any instance in which a transfer of personal identifying visual content is shared on SMNs. Private visual content (images and videos) exposes intimate information that can be detrimental to your finances, personal life, and reputation. Private visual content can include baby faces, credit cards, social security cards, house keys and others. The Hawaii Emergency Agency example provides evidence that visual content privacy leaks can happen on an individual or organization level. We find that monitoring techniques are essential for the improvement of private life and the development of future techniques. More extensive and enduring techniques will allow typical users, organizations, and the government to have a positive social media footprint.
Efficient Semantic Segmentation using Gradual Grouping
Venue: arXiv cs.CV 2018
Year: 2018

Deep CNNs for semantic segmentation have high memory and run time requirements. Various approaches have been proposed to make CNNs efficient like grouped, shuffled, depth-wise separable convolutions. We study the effectiveness of these techniques on a real-time semantic segmentation architecture like ERFNet for improving run time by over 5X. We apply these techniques to CNN layers partially or fully and evaluate the testing accuracies on Cityscapes dataset. We obtain accuracy vs parameters/FLOPs trade offs, giving accuracy scores for models that can run under specified runtime budgets. We further propose a novel training procedure which starts out with a dense convolution but gradually evolves towards a grouped convolution. We show that our proposed training method and efficient architecture design can improve accuracies by over 8% with depth wise separable convolutions applied on the encoder of ERFNet and attaching a light weight decoder. This results in a model which has a 5X improvement in FLOPs while only suffering a 4% degradation in accuracy with respect to ERFNet.
Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions
Venue: arXiv cs.CV 2018
Year: 2018

Automated medical image segmentation, specifically using deep learning, has shown outstanding performance in semantic segmentation tasks. However, these methods rarely quantify their uncertainty, which may lead to errors in downstream analysis. In this work we propose to use Bayesian neural networks to quantify uncertainty within the domain of semantic segmentation. We also propose a method to convert voxel-wise segmentation uncertainty into volumetric uncertainty, and calibrate the accuracy and reliability of confidence intervals of derived measurements. When applied to a tumour volume estimation application, we demonstrate that by using such modelling of uncertainty, deep learning systems can be made to report volume estimates with well-calibrated error-bars, making them safer for clinical use. We also show that the uncertainty estimates extrapolate to unseen data, and that the confidence intervals are robust in the presence of artificial noise. This could be used to provide a form of quality control and quality assurance, and may permit further adoption of deep learning tools in the clinic.
Evaluating language models of tonal harmony
Venue: arXiv cs.SD 2018
Year: 2018

This study borrows and extends probabilistic language models from natural language processing to discover the syntactic properties of tonal harmony. Language models come in many shapes and sizes, but their central purpose is always the same: to predict the next event in a sequence of letters, words, notes, or chords. However, few studies employing such models have evaluated the most state-of-the-art architectures using a large-scale corpus of Western tonal music, instead preferring to use relatively small datasets containing chord annotations from contemporary genres like jazz, pop, and rock. Using symbolic representations of prominent instrumental genres from the common-practice period, this study applies a flexible, data-driven encoding scheme to (1) evaluate Finite Context (or n-gram) models and Recurrent Neural Networks (RNNs) in a chord prediction task; (2) compare predictive accuracy from the best-performing models for chord onsets from each of the selected datasets; and (3) explain differences between the two model architectures in a regression analysis. We find that Finite Context models using the Prediction by Partial Match (PPM) algorithm outperform RNNs, particularly for the piano datasets, with the regression model suggesting that RNNs struggle with particularly rare chord types.
Scalable Simple Linear Iterative Clustering (SSLIC) Using a Generic and Parallel Approach
Venue: arXiv cs.CV 2018
Year: 2018

Superpixel algorithms have proven to be a useful initial step for segmentation and subsequent processing of images, reducing computational complexity by replacing the use of expensive per-pixel primitives with a higher-level abstraction, superpixels. They have been successfully applied both in the context of traditional image analysis and deep learning based approaches. In this work, we present a generalized implementation of the simple linear iterative clustering (SLIC) superpixel algorithm that has been generalized for n-dimensional scalar and multi-channel images. Additionally, the standard iterative implementation is replaced by a parallel, multi-threaded one. We describe the implementation details and analyze its scalability using a strong scaling formulation. Quantitative evaluation is performed using a 3D image, the Visible Human cryosection dataset, and a 2D image from the same dataset. Results show good scalability with runtime gains even when using a large number of threads that exceeds the physical number of available cores (hyperthreading).
Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation
Venue: arXiv cs.RO 2018
Year: 2018

What is the right object representation for manipulation? We would like robots to visually perceive scenes and learn an understanding of the objects in them that (i) is task-agnostic and can be used as a building block for a variety of manipulation tasks, (ii) is generally applicable to both rigid and non-rigid objects, (iii) takes advantage of the strong priors provided by 3D vision, and (iv) is entirely learned from self-supervision. This is hard to achieve with previous methods: much recent work in grasping does not extend to grasping specific objects or other tasks, whereas task-specific learning may require many trials to generalize well across object configurations or other tasks. In this paper we present Dense Object Nets, which build on recent developments in self-supervised dense descriptor learning, as a consistent object representation for visual understanding and manipulation. We demonstrate they can be trained quickly (approximately 20 minutes) for a wide variety of previously unseen and potentially non-rigid objects. We additionally present novel contributions to enable multi-object descriptor learning, and show that by modifying our training procedure, we can either acquire descriptors which generalize across classes of objects, or descriptors that are distinct for each object instance. Finally, we demonstrate the novel application of learned dense descriptors to robotic manipulation. We demonstrate grasping of specific points on an object across potentially deformed object configurations, and demonstrate using class general descriptors to transfer specific grasps across objects in a class.
What Does This Notation Mean Anyway?
Venue: arXiv cs.LO 2018
Year: 2018

Following the introduction of BNF notation by Backus for the Algol 60 report and subsequent notational variants, a metalanguage involving formal "grammars" has developed for discussing structured objects in Computer Science and Mathematical Logic. We refer to this offspring of BNF as Math-BNF or MBNF, to the original BNF and its notational variants just as BNF, and to aspects common to both as BNF-style. What all BNF-style notations share is the use of production rules roughly of this form: $$\bullet \mathrel{::=} \circ_1 \mid \cdots \mid \circ_n $$ Normally, such a rule says "every instance of $\circ_i$ for $i \in \{1, \ldots, n\}$ is also an in stance of $\bullet$". MBNF is distinct from BNF in the entities and operations it allows. Instead of strings, MBNF builds arrangements of symbols that we call math-text. Sometimes "syntax" is defined by interleaving MBNF production rules and other mathematical definitions that can contain chunks of math-text. There is no clear definition of MBNF. Readers do not have a document which tells them how MBNF is to be read and must learn MBNF through a process of cultural initiation. To the extent that MBNF is defined, it is largely through examples scattered throughout the literature and which require readers to guess the mathematical structures underpinning them. This paper gives MBNF examples illustrating some of the differences between MBNF and BNF. We propose a definition of syntactic math text (SMT) which handles many (but far from all) uses of math-text and MBNF in the wild. We aim to balance the goal of being accessible and not requiring too much prerequisite knowledge with the conflicting goal of providing a rich mathematical structure that already supports many uses and has possibilities to be extended to support more challenging cases.
Diffusion Scattering Transforms on Graphs
Venue: arXiv cs.LG 2018
Year: 2018

Stability is a key aspect of data analysis. In many applications, the natural notion of stability is geometric, as illustrated for example in computer vision. Scattering transforms construct deep convolutional representations which are certified stable to input deformations. This stability to deformations can be interpreted as stability with respect to changes in the metric structure of the domain. In this work, we show that scattering transforms can be generalized to non-Euclidean domains using diffusion wavelets, while preserving a notion of stability with respect to metric changes in the domain, measured with diffusion maps. The resulting representation is stable to metric perturbations of the domain while being able to capture "high-frequency" information, akin to the Euclidean Scattering.
Multi-Task Handwritten Document Layout Analysis
Venue: arXiv cs.CV 2018
Year: 2018

Document Layout Analysis is a fundamental step in Handwritten Text Processing systems, from the extraction of the text lines to the type of zone it belongs to. We present a system based on artificial neural networks which is able to determine not only the baselines of text lines present in the document, but also performs geometric and logic layout analysis of the document. Experiments in three different datasets demonstrate the potential of the method and show competitive results with respect to state-of-the-art methods.
A deep learning framework for segmentation of retinal layers from OCT images
Venue: arXiv cs.CV 2018
Year: 2018

Segmentation of retinal layers from Optical Coherence Tomography (OCT) volumes is a fundamental problem for any computer aided diagnostic algorithm development. This requires preprocessing steps such as denoising, region of interest extraction, flattening and edge detection all of which involve separate parameter tuning. In this paper, we explore deep learning techniques to automate all these steps and handle the presence/absence of pathologies. A model is proposed consisting of a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The CNN is used to extract layers of interest image and extract the edges, while the LSTM is used to trace the layer boundary. This model is trained on a mixture of normal and AMD cases using minimal data. Validation results on three public datasets show that the pixel-wise mean absolute error obtained with our system is 1.30 plus or minus 0.48 which is lower than the inter-marker error of 1.79 plus or minus 0.76. Our model's performance is also on par with the existing methods.