OpenRaiser/Intern-Atlas huggingface dataset:
Multi-Task Deep Networks for Depth-Based 6D Object Pose and Joint Registration in Crowd Scenarios
Venue: arXiv cs.CV 2018
Year: 2018

In bin-picking scenarios, multiple instances of an object of interest are stacked in a pile randomly, and hence, the instances are inherently subjected to the challenges: severe occlusion, clutter, and similar-looking distractors. Most existing methods are, however, for single isolated object instances, while some recent methods tackle crowd scenarios as post-refinement which accounts multiple object relations. In this paper, we address recovering 6D poses of multiple instances in bin-picking scenarios in depth modality by multi-task learning in deep neural networks. Our architecture jointly learns multiple sub-tasks: 2D detection, depth, and 3D pose estimation of individual objects; and joint registration of multiple objects. For training data generation, depth images of physically plausible object pose configurations are generated by a 3D object model in a physics simulation, which yields diverse occlusion patterns to learn. We adopt a state-of-the-art object detector, and 2D offsets are further estimated via a network to refine misaligned 2D detections. The depth and 3D pose estimator is designed to generate multiple hypotheses per detection. This allows the joint registration network to learn occlusion patterns and remove physically implausible pose hypotheses. We apply our architecture on both synthetic (our own and Sileane dataset) and real (a public Bin-Picking dataset) data, showing that it significantly outperforms state-of-the-art methods by 15-31% in average precision.
Retinal Optic Disc Segmentation using Conditional Generative Adversarial Network
Venue: arXiv cs.CV 2018
Year: 2018

This paper proposed a retinal image segmentation method based on conditional Generative Adversarial Network (cGAN) to segment optic disc. The proposed model consists of two successive networks: generator and discriminator. The generator learns to map information from the observing input (i.e., retinal fundus color image), to the output (i.e., binary mask). Then, the discriminator learns as a loss function to train this mapping by comparing the ground-truth and the predicted output with observing the input image as a condition.Experiments were performed on two publicly available dataset; DRISHTI GS1 and RIM-ONE. The proposed model outperformed state-of-the-art-methods by achieving around 0.96% and 0.98% of Jaccard and Dice coefficients, respectively. Moreover, an image segmentation is performed in less than a second on recent GPU.
Symbolic Computation via Program Transformation
Venue: arXiv cs.PL 2018
Year: 2018

Symbolic computation is an important approach in automated program analysis. Most state-of-the-art tools perform symbolic computation as interpreters and directly maintain symbolic data. In this paper, we show that it is feasible, and in fact practical, to use a compiler-based strategy instead. Using compiler tooling, we propose and implement a transformation which takes a standard program and outputs a program that performs semantically equivalent, but partially symbolic, computation. The transformed program maintains symbolic values internally and operates directly on them hence the program can be processed by a tool without support for symbolic manipulation. The main motivation for the transformation is in symbolic verification, but there are many other possible use-cases, including test generation and concolic testing. Moreover using the transformation simplifies tools, since the symbolic computation is handled by the program directly. We have implemented the transformation at the level of LLVM bitcode. The paper includes an experimental evaluation, based on an explicit-state software model checker as a verification backend.
Rethinking Radiology: An Analysis of Different Approaches to BraTS
Venue: arXiv cs.CV 2018
Year: 2018

This paper discusses the deep learning architectures currently used for pixel-wise segmentation of primary and secondary glioblastomas and low-grade gliomas. We implement various models such as the popular UNet architecture and compare the performance of these implementations on the BRATS dataset. This paper will explore the different approaches and combinations, offering an in depth discussion of how they perform and how we may improve upon them using more recent advancements in deep learning architectures.
Baselines and a datasheet for the Cerema AWP dataset
Venue: arXiv cs.LG 2018
Year: 2018

This paper presents the recently published Cerema AWP (Adverse Weather Pedestrian) dataset for various machine learning tasks and its exports in machine learning friendly format. We explain why this dataset can be interesting (mainly because it is a greatly controlled and fully annotated image dataset) and present baseline results for various tasks. Moreover, we decided to follow the very recent suggestions of datasheets for dataset, trying to standardize all the available information of the dataset, with a transparency objective.
Sheep identity recognition, age and weight estimation datasets
Venue: arXiv cs.CV 2018
Year: 2018

Increased interest of scientists, producers and consumers in sheep identification has been stimulated by the dramatic increase in population and the urge to increase productivity. The world population is expected to exceed 9.6 million in 2050. For this reason, awareness is raised towards the necessity of effective livestock production. Sheep is considered as one of the main of food resources. Most of the research now is directed towards developing real time applications that facilitate sheep identification for breed management and gathering related information like weight and age. Weight and age are key matrices in assessing the effectiveness of production. For this reason, visual analysis proved recently its significant success over other approaches. Visual analysis techniques need enough images for testing and study completion. For this reason, collecting sheep images database is a vital step to fulfill such objective. We provide here datasets for testing and comparing such algorithms which are under development. Our collected dataset consists of 416 color images for different features of sheep in different postures. Images were collected fifty two sheep at a range of year from three months to six years. For each sheep, two images were captured for both sides of the body, two images for both sides of the face, one image from the top view, one image for the hip and one image for the teeth. The collected images cover different illumination, quality levels and angle of rotation. The allocated data set can be used to test sheep identification, weigh estimation, and age detection algorithms. Such algorithms are crucial for disease management, animal assessment and ownership.
Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences
Venue: arXiv cs.CV 2018
Year: 2018

Cardiac motion estimation and segmentation play important roles in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In this paper, we propose a novel deep learning method for joint estimation of motion and segmentation from cardiac MR image sequences. The proposed network consists of two branches: a cardiac motion estimation branch which is built on a novel unsupervised Siamese style recurrent spatial transformer network, and a cardiac segmentation branch that is based on a fully convolutional network. In particular, a joint multi-scale feature encoder is learned by optimizing the segmentation branch and the motion estimation branch simultaneously. This enables the weakly-supervised segmentation by taking advantage of features that are unsupervisedly learned in the motion estimation branch from a large amount of unannotated data. Experimental results using cardiac MRI images from 220 subjects show that the joint learning of both tasks is complementary and the proposed models outperform the competing methods significantly in terms of accuracy and speed.
Tensor-based Hardness of the Shortest Vector Problem to within Almost Polynomial Factors
Venue: arXiv cs.CC 2018
Year: 2018

$ \newcommand{\SVP}{\mathsf{SVP}} \newcommand{\NP}{\mathsf{NP}} \newcommand{\RTIME}{\mathsf{RTIME}} \newcommand{\RSUBEXP}{\mathsf{RSUBEXP}} \newcommand{\eps}ε \newcommand{\poly}{\mathop{\mathrm{poly}}} $We show that unless $\NP \subseteq \RTIME (2^{\poly(\log{n})})$, there is no polynomial-time algorithm approximating the Shortest Vector Problem ($\SVP$) on $n$-dimensional lattices in the $\ell_p$ norm ($1 \leq p< \infty$) to within a factor of $2^{(\log{n})^{1-\eps}}$ for any $\eps > 0$. This improves the previous best factor of $2^{(\log{n})^{1/2-\eps}}$ under the same complexity assumption due to Khot (J. ACM, 2005). Under the stronger assumption $\NP \nsubseteq \RSUBEXP$, we obtain a hardness factor of $n^{c/\log\log{n}}$ for some $c> 0$. Our proof starts with Khot's $\SVP$ instances that are hard to approximate to within some constant. To boost the hardness factor we simply apply the standard tensor product of lattices. The main novelty is in the analysis, where we show that the lattices of Khot behave nicely under tensorization. At the heart of the analysis is a certain matrix inequality which was first used in the context of lattices by de Shalit and Parzanchevski (2006).
Synthetic Depth-of-Field with a Single-Camera Mobile Phone
Venue: arXiv cs.CV 2018
Year: 2018

Shallow depth-of-field is commonly used by photographers to isolate a subject from a distracting background. However, standard cell phone cameras cannot produce such images optically, as their short focal lengths and small apertures capture nearly all-in-focus images. We present a system to computationally synthesize shallow depth-of-field images with a single mobile camera and a single button press. If the image is of a person, we use a person segmentation network to separate the person and their accessories from the background. If available, we also use dense dual-pixel auto-focus hardware, effectively a 2-sample light field with an approximately 1 millimeter baseline, to compute a dense depth map. These two signals are combined and used to render a defocused image. Our system can process a 5.4 megapixel image in 4 seconds on a mobile phone, is fully automatic, and is robust enough to be used by non-experts. The modular nature of our system allows it to degrade naturally in the absence of a dual-pixel sensor or a human subject.
3D Convolutional Neural Networks for Classification of Functional Connectomes
Venue: arXiv cs.CV 2018
Year: 2018

Resting-state functional MRI (rs-fMRI) scans hold the potential to serve as a diagnostic or prognostic tool for a wide variety of conditions, such as autism, Alzheimer's disease, and stroke. While a growing number of studies have demonstrated the promise of machine learning algorithms for rs-fMRI based clinical or behavioral prediction, most prior models have been limited in their capacity to exploit the richness of the data. For example, classification techniques applied to rs-fMRI often rely on region-based summary statistics and/or linear models. In this work, we propose a novel volumetric Convolutional Neural Network (CNN) framework that takes advantage of the full-resolution 3D spatial structure of rs-fMRI data and fits non-linear predictive models. We showcase our approach on a challenging large-scale dataset (ABIDE, with N > 2,000) and report state-of-the-art accuracy results on rs-fMRI-based discrimination of autism patients and healthy controls.
How Curiosity can be modeled for a Clickbait Detector
Venue: arXiv cs.AI 2018
Year: 2018

The impact of continually evolving digital technologies and the proliferation of communications and content has now been widely acknowledged to be central to understanding our world. What is less acknowledged is that this is based on the successful arousing of curiosity both at the collective and individual levels. Advertisers, communication professionals and news editors are in constant competition to capture attention of the digital population perennially shifty and distracted. This paper, tries to understand how curiosity works in the digital world by attempting the first ever work done on quantifying human curiosity, basing itself on various theories drawn from humanities and social sciences. Curious communication pushes people to spot, read and click the message from their social feed or any other form of online presentation. Our approach focuses on measuring the strength of the stimulus to generate reader curiosity by using unsupervised and supervised machine learning algorithms, but is also informed by philosophical, psychological, neural and cognitive studies on this topic. Manually annotated news headlines - clickbaits - have been selected for the study, which are known to have drawn huge reader response. A binary classifier was developed based on human curiosity (unlike the work done so far using words and other linguistic features). Our classifier shows an accuracy of 97% . This work is part of the research in computational humanities on digital politics quantifying the emotions of curiosity and outrage on digital media.
Let's do it "again": A First Computational Approach to Detecting Adverbial Presupposition Triggers
Venue: arXiv cs.CL 2018
Year: 2018

We introduce the task of predicting adverbial presupposition triggers such as also and again. Solving such a task requires detecting recurring or similar events in the discourse context, and has applications in natural language generation tasks such as summarization and dialogue systems. We create two new datasets for the task, derived from the Penn Treebank and the Annotated English Gigaword corpora, as well as a novel attention mechanism tailored to this task. Our attention mechanism augments a baseline recurrent neural network without the need for additional trainable parameters, minimizing the added computational cost of our mechanism. We demonstrate that our model statistically outperforms a number of baselines, including an LSTM-based language model.
The NES Music Database: A multi-instrumental dataset with expressive performance attributes
Venue: arXiv cs.SD 2018
Year: 2018

Existing research on music generation focuses on composition, but often ignores the expressive performance characteristics required for plausible renditions of resultant pieces. In this paper, we introduce the Nintendo Entertainment System Music Database (NES-MDB), a large corpus allowing for separate examination of the tasks of composition and performance. NES-MDB contains thousands of multi-instrumental songs composed for playback by the compositionally-constrained NES audio synthesizer. For each song, the dataset contains a musical score for four instrument voices as well as expressive attributes for the dynamics and timbre of each voice. Unlike datasets comprised of General MIDI files, NES-MDB includes all of the information needed to render exact acoustic performances of the original compositions. Alongside the dataset, we provide a tool that renders generated compositions as NES-style audio by emulating the device's audio processor. Additionally, we establish baselines for the tasks of composition, which consists of learning the semantics of composing for the NES synthesizer, and performance, which involves finding a mapping between a composition and realistic expressive attributes.
Collective Story Writing through Linking Images
Venue: arXiv cs.HC 2018
Year: 2018

Collaborative creativity is the approach of employing crowd to accomplish creative tasks. In this paper, we present a collaborative crowdsourcing platform for writing stories by means of connecting a series of `images'. These connected images are termed as Image Chains, reflecting successive scenarios. Users can either start or extend an Image Chain by uploading their own image or choosing from the available ones. These users are allowed to pen their stories from the Image Chains. Finally, stories get published based on the number of votes obtained. This provides an organized framework of story writing unlike most of the state-of-the-art collaborative editing platforms. Our experiments on 25 contributors highlight their interest in growing shorter Image Chains but voting longer Image Chains.
Embedding Text in Hyperbolic Spaces
Venue: arXiv cs.CL 2018
Year: 2018

Natural language text exhibits hierarchical structure in a variety of respects. Ideally, we could incorporate our prior knowledge of this hierarchical structure into unsupervised learning algorithms that work on text data. Recent work by Nickel & Kiela (2017) proposed using hyperbolic instead of Euclidean embedding spaces to represent hierarchical data and demonstrated encouraging results when embedding graphs. In this work, we extend their method with a re-parameterization technique that allows us to learn hyperbolic embeddings of arbitrarily parameterized objects. We apply this framework to learn word and sentence embeddings in hyperbolic space in an unsupervised manner from text corpora. The resulting embeddings seem to encode certain intuitive notions of hierarchy, such as word-context frequency and phrase constituency. However, the implicit continuous hierarchy in the learned hyperbolic space makes interrogating the model's learned hierarchies more difficult than for models that learn explicit edges between items. The learned hyperbolic embeddings show improvements over Euclidean embeddings in some -- but not all -- downstream tasks, suggesting that hierarchical organization is more useful for some tasks than others.
Codes and Stability
Venue: arXiv cs.IT 2018
Year: 2018

We introduce new yet easily accessible codes for elements of $GL_r(A)$ with $A$ the adelic ring of a (dimension one) function field over a finite field. They are linear codes, and coincide with classical algebraic geometry codes when $r=1$. Basic properties of these codes are presented. In particular, when offering better bounds for the associated dimensions, naturally introduced is the well-known stability condition. This condition is further used to determine the minimal distances of these codes. To end this paper, for reader's convenience, we add two appendices on some details of the adelic theory of curves and classical AG codes, respectively.
Adaptive MPC for Autonomous Lane Keeping
Venue: arXiv cs.SY 2018
Year: 2018

This paper proposes an Adaptive Robust Model Predictive Control strategy for lateral control in lane keeping problems, where we continuously learn an unknown, but constant steering angle offset present in the steering system. Longitudinal velocity is assumed constant. The goal is to minimize the outputs, which are distance from lane center line and the steady state heading angle error, while satisfying respective safety constraints. We do not assume perfect knowledge of the vehicle lateral dynamics model and estimate and adapt in real-time the maximum possible bound of the steering angle offset from data using a robust Set Membership Method based approach. Our approach is even well-suited for scenarios with sharp curvatures on high speed, where obtaining a precise model bias for constrained control is difficult, but learning from data can be helpful. We ensure persistent feasibility using a switching strategy during change of lane curvature. The proposed methodology is general and can be applied to more complex vehicle dynamics problems.
When Will Gradient Methods Converge to Max-margin Classifier under ReLU Models?
Venue: arXiv cs.LG 2018
Year: 2018

We study the implicit bias of gradient descent methods in solving a binary classification problem over a linearly separable dataset. The classifier is described by a nonlinear ReLU model and the objective function adopts the exponential loss function. We first characterize the landscape of the loss function and show that there can exist spurious asymptotic local minima besides asymptotic global minima. We then show that gradient descent (GD) can converge to either a global or a local max-margin direction, or may diverge from the desired max-margin direction in a general context. For stochastic gradient descent (SGD), we show that it converges in expectation to either the global or the local max-margin direction if SGD converges. We further explore the implicit bias of these algorithms in learning a multi-neuron network under certain stationary conditions, and show that the learned classifier maximizes the margins of each sample pattern partition under the ReLU activation.
SoK: Sanitizing for Security
Venue: arXiv cs.CR 2018
Year: 2018

The C and C++ programming languages are notoriously insecure yet remain indispensable. Developers therefore resort to a multi-pronged approach to find security issues before adversaries. These include manual, static, and dynamic program analysis. Dynamic bug finding tools --- henceforth "sanitizers" --- can find bugs that elude other types of analysis because they observe the actual execution of a program, and can therefore directly observe incorrect program behavior as it happens. A vast number of sanitizers have been prototyped by academics and refined by practitioners. We provide a systematic overview of sanitizers with an emphasis on their role in finding security issues. Specifically, we taxonomize the available tools and the security vulnerabilities they cover, describe their performance and compatibility properties, and highlight various trade-offs.
A Directed Acyclic Graph Approach to Online Log Parsing
Venue: arXiv cs.SE 2018
Year: 2018

Logs are widely used in modern software system management because they are often the only data accessible that record system events at runtime. In recent years, because of the ever-increasing log size, data mining techniques are often utilized to help developers and operators conduct system reliability management. A typical log-based system reliability management procedure is to first parse log messages because of their unstructured format; and apply data mining techniques on the parsed logs to obtain critical system behavior information. Most of existing research studies focus on offline log parsing, which need to parse logs in batch mode. However, software systems, especially distributed systems, require online monitoring and maintenance. Thus, a log parser that can parse log messages in a streaming manner is highly in demand. To address this problem, we propose an online log parsing method, namely Drain, based on directed acyclic graph, which encodes specially designed rules for parsing. Drain can automatically generate a directed acyclic graph for a new system and update the graph according to the incoming log messages. Besides, Drain frees developers from the burden of parameter tuning by allowing them use Drain with no pre-defined parameters. To evaluate the performance of Drain, we collect 11 log datasets generated by real-world systems, ranging from distributed systems, Web applications, supercomputers, operating systems, to standalone software. The experimental results show that Drain has the highest accuracy on all 11 datasets. Moreover, Drain obtains 37.15\%$\sim$ 97.14\% improvement in the running time over the state-of-the-art online parsers. We also conduct a case study on a log-based anomaly detection task using Drain in the parsing step, which determines its effectiveness in system reliability management.
Bipartite graph analysis as an alternative to reveal clusterization in complex systems
Venue: arXiv cs.DL 2018
Year: 2018

We demonstrate how analysis of co-clustering in bipartite networks may be used as a bridge to connect, compare and complement clustering results about community structure in two different spaces: single-mode bipartite network projections. As a case study we consider scientific knowledge, which is represented as a complex bipartite network of articles and related concepts. Connecting clusters of articles and clusters of concepts via article-to-concept bipartite co-clustering, we demonstrate how concept features (e.g. subject classes) may be inferred from the article ones.