OpenRaiser/Intern-Atlas huggingface dataset:
Optimal Representations for Adaptive Streaming in Interactive Multi-View Video Systems
Venue: arXiv cs.MM 2016
Year: 2016

Interactive multi-view video streaming (IMVS) services permit to remotely immerse within a 3D scene. This is possible by transmitting a set of reference camera views (anchor views), which are used by the clients to freely navigate in the scene and possibly synthesize additional viewpoints of interest. From a networking perspective, the big challenge in IMVS systems is to deliver to each client the best set of anchor views that maximizes the navigation quality, minimizes the view-switching delay and yet satisfies the network constraints. Integrating adaptive streaming solutions in free-viewpoint systems offers a promising solution to deploy IMVS in large and heterogeneous scenarios, as long as the multi-view video representations on the server are properly selected. We therefore propose to optimize the multi-view data at the server by minimizing the overall resource requirements, yet offering a good navigation quality to the different users. We propose a video representation set optimization for multiview adaptive streaming systems and we show that it is NP-hard. We therefore introduce the concept of multi-view navigation segment that permits to cast the video representation set selection as an integer linear programming problem with a bounded computational complexity. We then show that the proposed solution reduces the computational complexity while preserving optimality in most of the 3D scenes. We then provide simulation results for different classes of users and show the gain offered by an optimal multi-view video representation selection compared to recommended representation sets (e.g., Netflix and Apple ones) or to a baseline representation selection algorithm where the encoding parameters are decided a priori for all the views.
Waterfiling: Balancing the Tor network with maximum diversity
Venue: arXiv cs.CR 2016
Year: 2016

We present the Waterfilling circuit selection method, which we designed in order to mitigate the risks of a successful end-to-end traffic correlation attack. Waterfilling proceeds by balancing the Tor network load as evenly as possible on endpoints of user paths. We simulate the use of Waterfilling thanks to the TorPS and Shadow tools. Applying several security metrics, we show that the adoption of Waterfilling considerably increases the number of nodes that an adversary needs to control in order to be able to mount a successful attack, while somewhat decreasing the minimum amount of bandwidth required to do so. Moreover, we evaluate Waterfilling into Shadow and show that it does not impact significantly the performance of the network. Furthermore, Waterfilling reduces the benefits that an attacker could obtain by hacking into a top bandwidth Tor relay, hence limiting the risks raised by such relays.
Convolutional Recurrent Neural Networks for Music Classification
Venue: arXiv cs.NE 2016
Year: 2016

We introduce a convolutional recurrent neural network (CRNN) for music tagging. CRNNs take advantage of convolutional neural networks (CNNs) for local feature extraction and recurrent neural networks for temporal summarisation of the extracted features. We compare CRNN with three CNN structures that have been used for music tagging while controlling the number of parameters with respect to their performance and training time per sample. Overall, we found that CRNNs show a strong performance with respect to the number of parameter and training time, indicating the effectiveness of its hybrid structure in music feature extraction and feature summarisation.
Neural Machine Transliteration: Preliminary Results
Venue: arXiv cs.CL 2016
Year: 2016

Machine transliteration is the process of automatically transforming the script of a word from a source language to a target language, while preserving pronunciation. Sequence to sequence learning has recently emerged as a new paradigm in supervised learning. In this paper a character-based encoder-decoder model has been proposed that consists of two Recurrent Neural Networks. The encoder is a Bidirectional recurrent neural network that encodes a sequence of symbols into a fixed-length vector representation, and the decoder generates the target sequence using an attention-based recurrent neural network. The encoder, the decoder and the attention mechanism are jointly trained to maximize the conditional probability of a target sequence given a source sequence. Our experiments on different datasets show that the proposed encoder-decoder model is able to achieve significantly higher transliteration quality over traditional statistical models.
How to Compute Modulo Prime-Power Sums
Venue: arXiv cs.IT 2016
Year: 2016

The problem of computing modulo prime-power sums is investigated in distributed source coding as well as computation over Multiple-Access Channel (MAC). We build upon group codes and present a new class of codes called Quasi Group Codes (QGC). A QGC is a subset of a group code. These codes are not closed under the group addition. We investigate some properties of QGC's, and provide a packing and a covering bound. Next, we use these bounds to derived achievable rates for distributed source coding as well as computation over MAC. We show that strict improvements over the previously known schemes can be obtained using QGC's.
Bayesian Reinforcement Learning: A Survey
Venue: arXiv cs.AI 2016
Year: 2016

Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. In this survey, we provide an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian reasoning in RL are: 1) it provides an elegant approach to action-selection (exploration/exploitation) as a function of the uncertainty in learning; and 2) it provides a machinery to incorporate prior knowledge into the algorithms. We first discuss models and methods for Bayesian inference in the simple single-step Bandit model. We then review the extensive recent literature on Bayesian methods for model-based RL, where prior information can be expressed on the parameters of the Markov model. We also present Bayesian methods for model-free RL, where priors are expressed over the value function or policy class. The objective of the paper is to provide a comprehensive survey on Bayesian RL algorithms and their theoretical and empirical properties.
Distributed power allocation for D2D communications underlaying/overlaying OFDMA cellular networks
Venue: arXiv cs.IT 2016
Year: 2016

The implementation of device-to-device (D2D) underlaying or overlaying pre-existing cellular networks has received much attention due to the potential of enhancing the total cell throughput, reducing power consumption and increasing the instantaneous data rate. In this paper we propose a distributed power allocation scheme for D2D OFDMA communications and, in particular, we consider the two operating modes amenable to a distributed implementation: dedicated and reuse modes. The proposed schemes address the problem of maximizing the users' sum rate subject to power constraints, which is known to be nonconvex and, as such, extremely difficult to be solved exactly. We propose here a fresh approach to this well-known problem, capitalizing on the fact that the power allocation problem can be modeled as a potential game. Exploiting the potential games property of converging under better response dynamics, we propose two fully distributed iterative algorithms, one for each operation mode considered, where each user updates sequentially and autonomously its power allocation. Numerical results, computed for several different user scenarios, show that the proposed methods, which converge to one of the local maxima of the objective function, exhibit performance close to the maximum achievable optimum and outperform other schemes presented in the literature.
The Robotarium: A remotely accessible swarm robotics research testbed
Venue: arXiv cs.RO 2016
Year: 2016

This paper describes the Robotarium -- a remotely accessible, multi-robot research facility. The impetus behind the Robotarium is that multi-robot testbeds constitute an integral and essential part of the multi-robot research cycle, yet they are expensive, complex, and time-consuming to develop, operate, and maintain. These resource constraints, in turn, limit access for large groups of researchers and students, which is what the Robotarium is remedying by providing users with remote access to a state-of-the-art multi-robot test facility. This paper details the design and operation of the Robotarium and discusses the considerations one must take when making complex hardware remotely accessible. In particular, safety must be built into the system already at the design phase without overly constraining what coordinated control programs users can upload and execute, which calls for minimally invasive safety routines with provable performance guarantees.
An object oriented parallel finite element scheme for computations of PDEs: Design and implementation
Venue: arXiv cs.MS 2016
Year: 2016

Parallel finite element algorithms based on object-oriented concepts are presented. Moreover, the design and implementation of a data structure proposed are utilized in realizing a parallel geometric multigrid method. The ParFEMapper and the ParFECommunicator are the key components of the data structure in the proposed parallel scheme. These classes are constructed based on the type of finite elements (continuous or nonconforming or discontinuous) used. The proposed solver is compared with the open source direct solvers, MUMPS and PasTiX. Further, the performance of the parallel multigrid solver is analyzed up to 1080 processors. The solver shows a very good speedup up to 960 processors and the problem size has to be increased in order to maintain the good speedup when the number of processors are increased further. As a result, the parallel solver is able to handle large scale problems on massively parallel supercomputers. The proposed parallel finite element algorithms and multigrid solver are implemented in our in-house package ParMooN.
A Glimpse Far into the Future: Understanding Long-term Crowd Worker Quality
Venue: arXiv cs.HC 2016
Year: 2016

Microtask crowdsourcing is increasingly critical to the creation of extremely large datasets. As a result, crowd workers spend weeks or months repeating the exact same tasks, making it necessary to understand their behavior over these long periods of time. We utilize three large, longitudinal datasets of nine million annotations collected from Amazon Mechanical Turk to examine claims that workers fatigue or satisfice over these long periods, producing lower quality work. We find that, contrary to these claims, workers are extremely stable in their quality over the entire period. To understand whether workers set their quality based on the task's requirements for acceptance, we then perform an experiment where we vary the required quality for a large crowdsourcing task. Workers did not adjust their quality based on the acceptance threshold: workers who were above the threshold continued working at their usual quality level, and workers below the threshold self-selected themselves out of the task. Capitalizing on this consistency, we demonstrate that it is possible to predict workers' long-term quality using just a glimpse of their quality on the first five tasks.
Robot Introspection via Wrench-based Action Grammars
Venue: arXiv cs.RO 2016
Year: 2016

Robotic failure is all too common in unstructured robot tasks. Despite well designed controllers, robots often fail due to unexpected events. How do robots measure unexpected events? Many do not. Most robots are driven by the senseplan- act paradigm, however more recently robots are working with a sense-plan-act-verify paradigm. In this work we present a principled methodology to bootstrap robot introspection for contact tasks. In effect, we are trying to answer the question, what did the robot do? To this end, we hypothesize that all noisy wrench data inherently contains patterns that can be effectively represented by a vocabulary. The vocabulary is generated by meaningfully segmenting the data and then encoding it. When the wrench information represents a sequence of sub-tasks, we can think of the vocabulary forming sets of words or sentences, such that each subtask is uniquely represented by a word set. Such sets can be classified using statistical or machine learning techniques. We use SVMs and Mondrian Forests to classify contacts tasks both in simulation and in real robots for one and dual arm scenarios showing the general robustness of the approach. The contribution of our work is the presentation of a simple but generalizable semantic scheme that enables a robot to understand its high level state. This verification mechanism can provide feedback for high-level planners or reasoning systems that use semantic descriptors as well. The code, data, and other supporting documentation can be found at: http://www.juanrojas.net/2017icra_wrench_introspection.
What You Will Gain By Rounding: Theory and Algorithms for Rounding Rank
Venue: arXiv cs.DM 2016
Year: 2016

When factorizing binary matrices, we often have to make a choice between using expensive combinatorial methods that retain the discrete nature of the data and using continuous methods that can be more efficient but destroy the discrete structure. Alternatively, we can first compute a continuous factorization and subsequently apply a rounding procedure to obtain a discrete representation. But what will we gain by rounding? Will this yield lower reconstruction errors? Is it easy to find a low-rank matrix that rounds to a given binary matrix? Does it matter which threshold we use for rounding? Does it matter if we allow for only non-negative factorizations? In this paper, we approach these and further questions by presenting and studying the concept of rounding rank. We show that rounding rank is related to linear classification, dimensionality reduction, and nested matrices. We also report on an extensive experimental study that compares different algorithms for finding good factorizations under the rounding rank model.
Bleach: A Distributed Stream Data Cleaning System
Venue: arXiv cs.DB 2016
Year: 2016

In this paper we address the problem of rule-based stream data cleaning, which sets stringent requirements on latency, rule dynamics and ability to cope with the unbounded nature of data streams. We design a system, called Bleach, which achieves real-time violation detection and data repair on a dirty data stream. Bleach relies on efficient, compact and distributed data structures to maintain the necessary state to repair data, using an incremental version of the equivalence class algorithm. Additionally, it supports rule dynamics and uses a "cumulative" sliding window operation to improve cleaning accuracy. We evaluate a prototype of Bleach using a TPC-DS derived dirty data stream and observe its high throughput, low latency and high cleaning accuracy, even with rule dynamics. Experimental results indicate superior performance of Bleach compared to a baseline system built on the micro-batch streaming paradigm.
Adaptive Windowing for ICI Mitigation in Doubly Selective Channels with Unknown Statistics
Venue: arXiv cs.IT 2016
Year: 2016

In doubly selective channels, receiver windowing constitutes an effective technique for enhancing the banded structure of the frequency-domain channel matrix, and thus improving the effectiveness of a banded equalizer for intercarrier interference (ICI) mitigation. A common window design technique, which performs close to optimal, is based on the criterion of maximum average signal-to-interference-plus-noise ratio (SINR). The optimality of this technique has been verified for stationary channels with perfectly known statistics. However, in cases where this assumption does not hold, a near optimal performance can be achieved at the expense of high complexity cost. To overcome these limitations, an adaptive windowing technique is proposed that is able to track the optimal receiver window offering low-complexity requirements. Through simulation experiments it has been verified that the proposed technique is able to adapt to the varying channel statistics with increased robustness to channel modeling errors.
Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition
Venue: arXiv cs.CV 2016
Year: 2016

Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition. The network is trained end-to-end for predicting the Big Five personality traits of people from their videos. That is, the network does not require any feature engineering or visual analysis such as face detection, face landmark alignment or facial expression recognition. Recently, the network won the third place in the ChaLearn First Impressions Challenge with a test accuracy of 0.9109.
SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networks
Venue: arXiv cs.CV 2016
Year: 2016

Ever more robust, accurate and detailed mapping using visual sensing has proven to be an enabling factor for mobile robots across a wide variety of applications. For the next level of robot intelligence and intuitive user interaction, maps need extend beyond geometry and appearence - they need to contain semantics. We address this challenge by combining Convolutional Neural Networks (CNNs) and a state of the art dense Simultaneous Localisation and Mapping (SLAM) system, ElasticFusion, which provides long-term dense correspondence between frames of indoor RGB-D video even during loopy scanning trajectories. These correspondences allow the CNN's semantic predictions from multiple view points to be probabilistically fused into a map. This not only produces a useful semantic 3D map, but we also show on the NYUv2 dataset that fusing multiple predictions leads to an improvement even in the 2D semantic labelling over baseline single frame predictions. We also show that for a smaller reconstruction dataset with larger variation in prediction viewpoint, the improvement over single frame segmentation increases. Our system is efficient enough to allow real-time interactive use at frame-rates of approximately 25Hz.
Ultrafast photo-magnetic recording in transparent medium
Venue: arXiv cs.ET 2016
Year: 2016

Finding a conceptually new way to control the magnetic state of media with the lowest possible production of heat and simultaneously at the fastest possible time-scale is a new challenge in fundamental magnetism [1-4] as well as an increasingly important issue in modern information technology [5]. Recent results demonstrate that exclusively in metals it is possible to switch magnetization between metastable states by femtosecond circularly polarized laser pulses [6-8]. However, despite the record breaking speed of the switching, the mechanisms in these materials are directly related to strong optical absorption and laser-induced heating close to the Curie temperature [9-12]. Here we report about ultrafast all-optical photo-magnetic recording in transparent dielectrics. In ferrimagnetic garnet film a single linearly polarized femtosecond laser pulse breaks the degeneracy between metastable magnetic states and promotes switching of spins between them. Changing the polarization of the laser pulse we deterministically steer the net magnetization in the garnet, write "0" and "1" magnetic bits at will. This mechanism operates at room temperature and allows ever fastest write-read magnetic recording event (< 20 ps) accompanied by unprecedentedly low heat load (< 6 J/cm3).
Improved Control Strategies for Intermittent Contact Mode Atomic Force Microscopes
Venue: arXiv cs.SY 2016
Year: 2016

Atomic force microscopes have proved to be fundamental research tools in many situations where a gentle imaging process is required, and in a variety of environmental conditions, such as the study of biological samples. Among the possible modes of operation, intermittent contact mode is one that causes less wear to both the sample and the instrument; therefore, it is ideal when imaging soft samples. However, intermittent contact mode is not particularly fast when compared to other imaging strategies. In this paper, we introduce three enhanced control approaches, applied at both the dither and z-axis piezos, to address the limitations of existing control schemes. Our proposed strategies are able to eliminate different image artefacts, automatically adapt scan speed to the sample being scanned and predict its features in real time. The result is that both the image quality and the scan time are improved.
A convolutional approach to reflection symmetry
Venue: arXiv cs.CV 2016
Year: 2016

We present a convolutional approach to reflection symmetry detection in 2D. Our model, built on the products of complex-valued wavelet convolutions, simplifies previous edge-based pairwise methods. Being parameter-centered, as opposed to feature-centered, it has certain computational advantages when the object sizes are known a priori, as demonstrated in an ellipse detection application. The method outperforms the best-performing algorithm on the CVPR 2013 Symmetry Detection Competition Database in the single-symmetry case. Code and a new database for 2D symmetry detection is available.
The ACRV Picking Benchmark (APB): A Robotic Shelf Picking Benchmark to Foster Reproducible Research
Venue: arXiv cs.RO 2016
Year: 2016

Robotic challenges like the Amazon Picking Challenge (APC) or the DARPA Challenges are an established and important way to drive scientific progress. They make research comparable on a well-defined benchmark with equal test conditions for all participants. However, such challenge events occur only occasionally, are limited to a small number of contestants, and the test conditions are very difficult to replicate after the main event. We present a new physical benchmark challenge for robotic picking: the ACRV Picking Benchmark (APB). Designed to be reproducible, it consists of a set of 42 common objects, a widely available shelf, and exact guidelines for object arrangement using stencils. A well-defined evaluation protocol enables the comparison of \emph{complete} robotic systems -- including perception and manipulation -- instead of sub-systems only. Our paper also describes and reports results achieved by an open baseline system based on a Baxter robot.
Deep Kinematic Pose Regression
Venue: arXiv cs.CV 2016
Year: 2016

Learning articulated object pose is inherently difficult because the pose is high dimensional but has many structural constraints. Most existing work do not model such constraints and does not guarantee the geometric validity of their pose estimation, therefore requiring a post-processing to recover the correct geometry if desired, which is cumbersome and sub-optimal. In this work, we propose to directly embed a kinematic object model into the deep neutral network learning for general articulated object pose estimation. The kinematic function is defined on the appropriately parameterized object motion variables. It is differentiable and can be used in the gradient descent based optimization in network training. The prior knowledge on the object geometric model is fully exploited and the structure is guaranteed to be valid. We show convincing experiment results on a toy example and the 3D human pose estimation problem. For the latter we achieve state-of-the-art result on Human3.6M dataset.