OpenRaiser/Intern-Atlas huggingface dataset:
Dynamic Task Execution using Active Parameter Identification with the Baxter Research Robot
Venue: arXiv cs.RO 2017
Year: 2017

This paper presents experimental results from real-time parameter estimation of a system model and subsequent trajectory optimization for a dynamic task using the Baxter Research Robot from Rethink Robotics. An active estimator maximizing Fisher information is used in real-time with a closed-loop, non-linear control technique known as Sequential Action Control. Baxter is tasked with estimating the length of a string connected to a load suspended from the gripper with a load cell providing the single source of feedback to the estimator. Following the active estimation, a trajectory is generated using the trep software package that controls Baxter to dynamically swing a suspended load into a box. Several trials are presented with varying initial estimates showing that estimation is required to obtain adequate open-loop trajectories to complete the prescribed task. The result of one trial with and without the active estimation is also shown in the accompanying video.
NiftyNet: a deep-learning platform for medical imaging
Venue: arXiv cs.CV 2017
Year: 2017

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this application requires substantial implementation effort. Thus, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D and 3D images and computational graphs by default. We present 3 illustrative medical image analysis applications built using NiftyNet: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. NiftyNet enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.
Robot Composite Learning and the Nunchaku Flipping Challenge
Venue: arXiv cs.RO 2017
Year: 2017

Advanced motor skills are essential for robots to physically coexist with humans. Much research on robot dynamics and control has achieved success on hyper robot motor capabilities, but mostly through heavily case-specific engineering. Meanwhile, in terms of robot acquiring skills in a ubiquitous manner, robot learning from human demonstration (LfD) has achieved great progress, but still has limitations handling dynamic skills and compound actions. In this paper, we present a composite learning scheme which goes beyond LfD and integrates robot learning from human definition, demonstration, and evaluation. The method tackles advanced motor skills that require dynamic time-critical maneuver, complex contact control, and handling partly soft partly rigid objects. We also introduce the "nunchaku flipping challenge", an extreme test that puts hard requirements to all these three aspects. Continued from our previous presentations, this paper introduces the latest update of the composite learning scheme and the physical success of the nunchaku flipping challenge.
Multi-Level Spherical Locality Sensitive Hashing For Approximate Near Neighbors
Venue: arXiv cs.DS 2017
Year: 2017

This paper introduces "Multi-Level Spherical LSH": parameter-free, a multi-level, data-dependant Locality Sensitive Hashing data structure for solving the Approximate Near Neighbors Problem (ANN). This data structure uses a modified version of a multi-probe adaptive querying algorithm, with the potential of achieving a $O(n^p + t)$ query run time, for all inputs n where $t <= n$.
Recovering Homography from Camera Captured Documents using Convolutional Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

Removing perspective distortion from hand held camera captured document images is one of the primitive tasks in document analysis, but unfortunately, no such method exists that can reliably remove the perspective distortion from document images automatically. In this paper, we propose a convolutional neural network based method for recovering homography from hand-held camera captured documents. Our proposed method works independent of document's underlying content and is trained end-to-end in a fully automatic way. Specifically, this paper makes following three contributions: Firstly, we introduce a large scale synthetic dataset for recovering homography from documents images captured under different geometric and photometric transformations; secondly, we show that a generic convolutional neural network based architecture can be successfully used for regressing the corners positions of documents captured under wild settings; thirdly, we show that L1 loss can be reliably used for corners regression. Our proposed method gives state-of-the-art performance on the tested datasets, and has potential to become an integral part of document analysis pipeline.
Exploring Geometric Property Thresholds For Filtering Non-Text Regions In A Connected Component Based Text Detection Application
Venue: arXiv cs.CV 2017
Year: 2017

Automated text detection is a difficult computer vision task. In order to accurately detect and identity text in an image or video, two major problems must be addressed. The primary problem is implementing a robust and reliable method for distinguishing text vs non-text regions in images and videos. Part of the difficulty stems from the almost unlimited combinations of fonts, lighting conditions, distortions, and other variations that can be found in images and videos. This paper explores key properties of two popular and proven methods for implementing text detection; maximum stable external regions (MSER) and stroke width variation.
Importance Sketching of Influence Dynamics in Billion-scale Networks
Venue: arXiv cs.DS 2017
Year: 2017

The blooming availability of traces for social, biological, and communication networks opens up unprecedented opportunities in analyzing diffusion processes in networks. However, the sheer sizes of the nowadays networks raise serious challenges in computational efficiency and scalability. In this paper, we propose a new hyper-graph sketching framework for inflence dynamics in networks. The central of our sketching framework, called SKIS, is an efficient importance sampling algorithm that returns only non-singular reverse cascades in the network. Comparing to previously developed sketches like RIS and SKIM, our sketch significantly enhances estimation quality while substantially reducing processing time and memory-footprint. Further, we present general strategies of using SKIS to enhance existing algorithms for influence estimation and influence maximization which are motivated by practical applications like viral marketing. Using SKIS, we design high-quality influence oracle for seed sets with average estimation error up to 10x times smaller than those using RIS and 6x times smaller than SKIM. In addition, our influence maximization using SKIS substantially improves the quality of solutions for greedy algorithms. It achieves up to 10x times speed-up and 4x memory reduction for the fastest RIS-based DSSA algorithm, while maintaining the same theoretical guarantees.
Anomaly Detection in Hierarchical Data Streams under Unknown Models
Venue: arXiv cs.LG 2017
Year: 2017

We consider the problem of detecting a few targets among a large number of hierarchical data streams. The data streams are modeled as random processes with unknown and potentially heavy-tailed distributions. The objective is an active inference strategy that determines, sequentially, which data stream to collect samples from in order to minimize the sample complexity under a reliability constraint. We propose an active inference strategy that induces a biased random walk on the tree-structured hierarchy based on confidence bounds of sample statistics. We then establish its order optimality in terms of both the size of the search space (i.e., the number of data streams) and the reliability requirement. The results find applications in hierarchical heavy hitter detection, noisy group testing, and adaptive sampling for active learning, classification, and stochastic root finding.
Capturing the contributions of the semantic web to the IoT: a unifying vision
Venue: arXiv cs.CY 2017
Year: 2017

The Internet of Things (IoT) is a technological topic with a very important societal impact. IoT application domains are various and include: smart cities, precision farming, smart factories, and smart buildings. The diversity of these application domains is the source of the very high technological heterogeneity in the IoT, leading to interoperability issues. The semantic web principles and technologies are more and more adopted as a solution to these interoperability issues, leading to the emergence of a new domain, the Semantic Web Of Things (SWoT). Scientific contributions to the SWoT are many, and the diversity of architectures in which they are expressed complicates comparison. To unify the presented architectures, we propose an architectural pattern, LMU-N. LMU-N provides a reading grid used to classify processes to which the SWoT community contributes, and to describe how the semantic web impacts the IoT. Then, the evolutions of the semantic web to adapt to the IoT constraints are described as well, in order to give a twofold view of the convergence between the IoT and the semantic web toward the SWoT.
Macro-molecular data storage with petabyte/cm^3 density, highly parallel read/write operations, and genuine 3D storage capability
Venue: arXiv cs.ET 2017
Year: 2017

Digital information can be encoded in the building-block sequence of macro-molecules, such as RNA and single-stranded DNA. Methods of "writing" and "reading" macromolecular strands are currently available, but they are slow and expensive. In an ideal molecular data storage system, routine operations such as write, read, erase, store, and transfer must be done reliably and at high speed within an integrated chip. As a first step toward demonstrating the feasibility of this concept, we report preliminary results of DNA readout experiments conducted in miniaturized chambers that are scalable to even smaller dimensions. We show that translocation of a single-stranded DNA molecule (consisting of 50 adenosine bases followed by 100 cytosine bases) through an ion-channel yields a characteristic signal that is attributable to the 2-segment structure of the molecule. We also examine the dependence of the rate and speed of molecular translocation on the adjustable parameters of the experiment.
Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification
Venue: arXiv cs.CV 2017
Year: 2017

Makeup is widely used to improve facial attractiveness and is well accepted by the public. However, different makeup styles will result in significant facial appearance changes. It remains a challenging problem to match makeup and non-makeup face images. This paper proposes a learning from generation approach for makeup-invariant face verification by introducing a bi-level adversarial network (BLAN). To alleviate the negative effects from makeup, we first generate non-makeup images from makeup ones, and then use the synthesized non-makeup images for further verification. Two adversarial networks in BLAN are integrated in an end-to-end deep network, with the one on pixel level for reconstructing appealing facial images and the other on feature level for preserving identity information. These two networks jointly reduce the sensing gap between makeup and non-makeup images. Moreover, we make the generator well constrained by incorporating multiple perceptual losses. Experimental results on three benchmark makeup face datasets demonstrate that our method achieves state-of-the-art verification accuracy across makeup status and can produce photo-realistic non-makeup face images.
Adversarial Discriminative Heterogeneous Face Recognition
Venue: arXiv cs.CV 2017
Year: 2017

The gap between sensing patterns of different face modalities remains a challenging problem in heterogeneous face recognition (HFR). This paper proposes an adversarial discriminative feature learning framework to close the sensing gap via adversarial learning on both raw-pixel space and compact feature space. This framework integrates cross-spectral face hallucination and discriminative feature learning into an end-to-end adversarial network. In the pixel space, we make use of generative adversarial networks to perform cross-spectral face hallucination. An elaborate two-path model is introduced to alleviate the lack of paired images, which gives consideration to both global structures and local textures. In the feature space, an adversarial loss and a high-order variance discrepancy loss are employed to measure the global and local discrepancy between two heterogeneous distributions respectively. These two losses enhance domain-invariant feature learning and modality independent noise removing. Experimental results on three NIR-VIS databases show that our proposed approach outperforms state-of-the-art HFR methods, without requiring of complex network or large-scale training dataset.
Joint Dictionaries for Zero-Shot Learning
Venue: arXiv cs.CV 2017
Year: 2017

A classic approach toward zero-shot learning (ZSL) is to map the input domain to a set of semantically meaningful attributes that could be used later on to classify unseen classes of data (e.g. visual data). In this paper, we propose to learn a visual feature dictionary that has semantically meaningful atoms. Such dictionary is learned via joint dictionary learning for the visual domain and the attribute domain, while enforcing the same sparse coding for both dictionaries. Our novel attribute aware formulation provides an algorithmic solution to the domain shift/hubness problem in ZSL. Upon learning the joint dictionaries, images from unseen classes can be mapped into the attribute space by finding the attribute aware joint sparse representation using solely the visual data. We demonstrate that our approach provides superior or comparable performance to that of the state of the art on benchmark datasets.
Amplifying Inter-message Distance: On Information Divergence Measures in Big Data
Venue: arXiv cs.IT 2017
Year: 2017

Message identification (M-I) divergence is an important measure of the information distance between probability distributions, similar to Kullback-Leibler (K-L) and Renyi divergence. In fact, M-I divergence with a variable parameter can make an effect on characterization of distinction between two distributions. Furthermore, by choosing an appropriate parameter of M-I divergence, it is possible to amplify the information distance between adjacent distributions while maintaining enough gap between two nonadjacent ones. Therefore, M-I divergence can play a vital role in distinguishing distributions more clearly. In this paper, we first define a parametric M-I divergence in the view of information theory and then present its major properties. In addition, we design a M-I divergence estimation algorithm by means of the ensemble estimator of the proposed weight kernel estimators, which can improve the convergence of mean squared error from ${O(\varGamma^{-j/d})}$ to ${O(\varGamma^{-1})}$ $({j\in (0,d]})$. We also discuss the decision with M-I divergence for clustering or classification, and investigate its performance in a statistical sequence model of big data for the outlier detection problem.
DEARF: Delay and Energy Aware RAW Formation Scheme to Support Delay Sensitive M2M Traffic in IEEE 802.11ah Networks
Venue: arXiv cs.NI 2017
Year: 2017

The IEEE 802.11ah amendment is designed to support upto 8K M2M devices over the Sub-GHz channel. To achieve this, it introduces new modifications to the PHY and MAC layers. A dynamic Restricted Access Window~(RAW) mechanism is introduced at the MAC layer. RAW splits the access for different devices into small chunks of time. Using the RAW mechanism, we propose a novel Delay and Energy Aware RAW Formation (DEARF) scheme to support delay sensitive devices along with other delay tolerant devices. We exploit this to introduce four new RAWs. These new RAWs help split the access into contention free for delay sensitive devices and contention based for other devices. For scheduling resources between these devices, we give the DEARF resource allocation and evaluate the scheme with help of simulations. Devices suffer a higher delay but, at high loads, devices using the proposed scheme are able to save 30% energy per packet and upto 330% energy per device. Packet delivery ratio is 100% at low loads and 90% at high loads as compared to 95% and 27% given by the traditional access. Other delay tolerant devices save upto 16% on energy per packet transmitted and 23% on delay at high loads.
Learning Graph-Level Representation for Drug Discovery
Venue: arXiv cs.LG 2017
Year: 2017

Predicating macroscopic influences of drugs on human body, like efficacy and toxicity, is a central problem of small-molecule based drug discovery. Molecules can be represented as an undirected graph, and we can utilize graph convolution networks to predication molecular properties. However, graph convolutional networks and other graph neural networks all focus on learning node-level representation rather than graph-level representation. Previous works simply sum all feature vectors for all nodes in the graph to obtain the graph feature vector for drug predication. In this paper, we introduce a dummy super node that is connected with all nodes in the graph by a directed edge as the representation of the graph and modify the graph operation to help the dummy super node learn graph-level feature. Thus, we can handle graph-level classification and regression in the same way as node-level classification and regression. In addition, we apply focal loss to address class imbalance in drug datasets. The experiments on MoleculeNet show that our method can effectively improve the performance of molecular properties predication.
Language Models of Spoken Dutch
Venue: arXiv cs.CL 2017
Year: 2017

In Flanders, all TV shows are subtitled. However, the process of subtitling is a very time-consuming one and can be sped up by providing the output of a speech recognizer run on the audio of the TV show, prior to the subtitling. Naturally, this speech recognition will perform much better if the employed language model is adapted to the register and the topic of the program. We present several language models trained on subtitles of television shows provided by the Flemish public-service broadcaster VRT. This data was gathered in the context of the project STON which has as purpose to facilitate the process of subtitling TV shows. One model is trained on all available data (46M word tokens), but we also trained models on a specific type of TV show or domain/topic. Language models of spoken language are quite rare due to the lack of training data. The size of this corpus is relatively large for a corpus of spoken language (compare with e.g. CGN which has 9M words), but still rather small for a language model. Thus, in practice it is advised to interpolate these models with a large background language model trained on written language. The models can be freely downloaded on http://www.esat.kuleuven.be/psi/spraak/downloads/.
Characterizations of o-polynomials by the Walsh transform
Venue: arXiv cs.IT 2017
Year: 2017

The notion of o-polynomial comes from finite projective geometry. In 2011 and later, it has been shown that those objects play an important role in symmetric cryptography and coding theory to design bent Boolean functions, bent vectorial Boolean functions, semi-bent functions and to construct good linear codes. In this note, we characterize o-polynomials by the Walsh transform of the associated vectorial functions.
ExprGAN: Facial Expression Editing with Controllable Expression Intensity
Venue: arXiv cs.CV 2017
Year: 2017

Facial expression editing is a challenging task as it needs a high-level semantic understanding of the input face image. In conventional methods, either paired training data is required or the synthetic face resolution is low. Moreover, only the categories of facial expression can be changed. To address these limitations, we propose an Expression Generative Adversarial Network (ExprGAN) for photo-realistic facial expression editing with controllable expression intensity. An expression controller module is specially designed to learn an expressive and compact expression code in addition to the encoder-decoder network. This novel architecture enables the expression intensity to be continuously adjusted from low to high. We further show that our ExprGAN can be applied for other tasks, such as expression transfer, image retrieval, and data augmentation for training improved face expression recognition models. To tackle the small size of the training database, an effective incremental learning scheme is proposed. Quantitative and qualitative evaluations on the widely used Oulu-CASIA dataset demonstrate the effectiveness of ExprGAN.
Adaptive Modulation and Coding and Cooperative ARQ in a Cognitive Radio System
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, a joint cross-layer design of adaptive modulation and coding (AMC) and cooperative automatic repeat request (C-ARQ) scheme is proposed for a secondary user in a shared-spectrum environment. First, based on the statistical descriptions of the channel, closed-form expressions of the average spectral efficiency (SE) and the average packet loss rate (PLR) are presented. Then, the cross-layer scheme is designed, with the aim of maximizing the average SE while maintaining the average PLR under a prescribed level. An optimization problem is formed, and a sub-optimal solution is found: the target packet error rates (PER) for the secondary system channels are obtained and the corresponding sub-optimal AMC rate adaptation policy is derived based on the target PERs. Finally, the average SE and the average PLR performance of the proposed scheme are presented.