OpenRaiser/Intern-Atlas huggingface dataset:
Variational models for joint subsampling and reconstruction of turbulence-degraded images
Venue: arXiv cs.CV 2017
Year: 2017

Turbulence-degraded image frames are distorted by both turbulent deformations and space-time-varying blurs. To suppress these effects, we propose a multi-frame reconstruction scheme to recover a latent image from the observed image sequence. Recent approaches are commonly based on registering each frame to a reference image, by which geometric turbulent deformations can be estimated and a sharp image can be restored. A major challenge is that a fine reference image is usually unavailable, as every turbulence-degraded frame is distorted. A high-quality reference image is crucial for the accurate estimation of geometric deformations and fusion of frames. Besides, it is unlikely that all frames from the image sequence are useful, and thus frame selection is necessary and highly beneficial. In this work, we propose a variational model for joint subsampling of frames and extraction of a clear image. A fine image and a suitable choice of subsample are simultaneously obtained by iteratively reducing an energy functional. The energy consists of a fidelity term measuring the discrepancy between the extracted image and the subsampled frames, as well as regularization terms on the extracted image and the subsample. Different choices of fidelity and regularization terms are explored. By carefully selecting suitable frames and extracting the image, the quality of the reconstructed image can be significantly improved. Extensive experiments have been carried out, which demonstrate the efficacy of our proposed model. In addition, the extracted subsamples and images can be put in existing algorithms to produce improved results.
Learning Modality-Invariant Representations for Speech and Images
Venue: arXiv cs.LG 2017
Year: 2017

In this paper, we explore the unsupervised learning of a semantic embedding space for co-occurring sensory inputs. Specifically, we focus on the task of learning a semantic vector space for both spoken and handwritten digits using the TIDIGITs and MNIST datasets. Current techniques encode image and audio/textual inputs directly to semantic embeddings. In contrast, our technique maps an input to the mean and log variance vectors of a diagonal Gaussian from which sample semantic embeddings are drawn. In addition to encouraging semantic similarity between co-occurring inputs,our loss function includes a regularization term borrowed from variational autoencoders (VAEs) which drives the posterior distributions over embeddings to be unit Gaussian. We can use this regularization term to filter out modality information while preserving semantic information. We speculate this technique may be more broadly applicable to other areas of cross-modality/domain information retrieval and transfer learning.
MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments
Venue: arXiv cs.LG 2017
Year: 2017

We present MINOS, a simulator designed to support the development of multisensory models for goal-directed navigation in complex indoor environments. The simulator leverages large datasets of complex 3D environments and supports flexible configuration of multimodal sensor suites. We use MINOS to benchmark deep-learning-based navigation methods, to analyze the influence of environmental complexity on navigation performance, and to carry out a controlled study of multimodality in sensorimotor learning. The experiments show that current deep reinforcement learning approaches fail in large realistic environments. The experiments also indicate that multimodality is beneficial in learning to navigate cluttered scenes. MINOS is released open-source to the research community at http://minosworld.org . A video that shows MINOS can be found at https://youtu.be/c0mL9K64q84
Cross-Layer Optimization for Power-Efficient and Robust Digital Circuits and Systems
Venue: arXiv cs.AR 2017
Year: 2017

With the increasing digital services demand, performance and power-efficiency become vital requirements for digital circuits and systems. However, the enabling CMOS technology scaling has been facing significant challenges of device uncertainties, such as process, voltage, and temperature variations. To ensure system reliability, worst-case corner assumptions are usually made in each design level. However, the over-pessimistic worst-case margin leads to unnecessary power waste and performance loss as high as 2.2x. Since optimizations are traditionally confined to each specific level, those safe margins can hardly be properly exploited. To tackle the challenge, it is therefore advised in this Ph.D. thesis to perform a cross-layer optimization for digital signal processing circuits and systems, to achieve a global balance of power consumption and output quality. To conclude, the traditional over-pessimistic worst-case approach leads to huge power waste. In contrast, the adaptive voltage scaling approach saves power (25% for the CORDIC application) by providing a just-needed supply voltage. The power saving is maximized (46% for CORDIC) when a more aggressive voltage over-scaling scheme is applied. These sparsely occurred circuit errors produced by aggressive voltage over-scaling are mitigated by higher level error resilient designs. For functions like FFT and CORDIC, smart error mitigation schemes were proposed to enhance reliability (soft-errors and timing-errors, respectively). Applications like Massive MIMO systems are robust against lower level errors, thanks to the intrinsically redundant antennas. This property makes it applicable to embrace digital hardware that trades quality for power savings.
Eye In-Painting with Exemplar Generative Adversarial Networks
Venue: arXiv cs.CV 2017
Year: 2017

This paper introduces a novel approach to in-painting where the identity of the object to remove or change is preserved and accounted for at inference time: Exemplar GANs (ExGANs). ExGANs are a type of conditional GAN that utilize exemplar information to produce high-quality, personalized in painting results. We propose using exemplar information in the form of a reference image of the region to in-paint, or a perceptual code describing that object. Unlike previous conditional GAN formulations, this extra information can be inserted at multiple points within the adversarial network, thus increasing its descriptive power. We show that ExGANs can produce photo-realistic personalized in-painting results that are both perceptually and semantically plausible by applying them to the task of closed to-open eye in-painting in natural pictures. A new benchmark dataset is also introduced for the task of eye in-painting for future comparisons.
The Minimum Edge Compact Spanner Network Design Problem
Venue: arXiv cs.DM 2017
Year: 2017

In this paper we introduce and study the Minimum Edge Compact Spanner~(MECS) problem. We prove hardness results related to the problem, design exact and greedy algorithms for solving the problem, and show related experimental results. The MECS problem looks for sparse subgraphs of an input graph, such that the average shortest path distance is preserved to a constant factor. Average distance is a measure of the ease of communication over the network. As a result such problems have applications in areas where one wants to substitute a dense graph with a sparse subgraph while maintaining a low cost of communication.
Incremental View Maintenance for Property Graph Queries
Venue: arXiv cs.DB 2017
Year: 2017

This paper discusses the challenges of incremental view maintenance for property graph queries. We select a subset of property graph queries and present an approach that uses nested relational algebra to allow incremental evaluation.
Ergodic Capacity of Composite Fading Channels in Cognitive Radios with the Product of $κ$-$μ$ and $α$-$μ$ Variates
Venue: arXiv cs.IT 2017
Year: 2017

In this study, the product of two independent and non-identically distributed (i.n.i.d.) random variables (RVs) for \k{appa}-μ fading distribution and α-μ fading distribution is considered. The method of the product model of RVs has been widely applied in numerous of communications fields, such as cascaded fading channels, multiple input multiple output (MIMO) systems, radar communications and cognitive radio networks (CRs). The exact series expressions of the product of two i.n.i.d. RVs X for \k{appa}-μ variates and Y for α-μ variates are derived instead of Fox H-function to solve the problem that Fox H-function in the RVs product could not be implemented in popular mathematical software packages as Mathematica and Maple. Novel Exact close-form expressions of probability density function (PDF) and cumulative distribution function (CDF) of proposed models are deduced to present the series expressions of product and generalized composite multipath shadowing models. Furthermore, novel exact expressions of the ergodic channel capacity (ECC) are obtained under optimal rate adaptation with constant transmit power (ORA). At last, these analytical results are confirmed with monte-carlo simulations to evaluate spectrum efficiency over generalized composite shadowing fading scenarios in CRs.
Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systems
Venue: arXiv cs.IT 2017
Year: 2017

Intelligent transportation systems (ITSs) will be a major component of tomorrow's smart cities. However, realizing the true potential of ITSs requires ultra-low latency and reliable data analytics solutions that can combine, in real-time, a heterogeneous mix of data stemming from the ITS network and its environment. Such data analytics capabilities cannot be provided by conventional cloud-centric data processing techniques whose communication and computing latency can be high. Instead, edge-centric solutions that are tailored to the unique ITS environment must be developed. In this paper, an edge analytics architecture for ITSs is introduced in which data is processed at the vehicle or roadside smart sensor level in order to overcome the ITS latency and reliability challenges. With a higher capability of passengers' mobile devices and intra-vehicle processors, such a distributed edge computing architecture can leverage deep learning techniques for reliable mobile sensing in ITSs. In this context, the ITS mobile edge analytics challenges pertaining to heterogeneous data, autonomous control, vehicular platoon control, and cyber-physical security are investigated. Then, different deep learning solutions for such challenges are proposed. The proposed deep learning solutions will enable ITS edge analytics by endowing the ITS devices with powerful computer vision and signal processing functions. Preliminary results show that the proposed edge analytics architecture, coupled with the power of deep learning algorithms, can provide a reliable, secure, and truly smart transportation environment.
A vision based system for underwater docking
Venue: arXiv cs.CV 2017
Year: 2017

Autonomous underwater vehicles (AUVs) have been deployed for underwater exploration. However, its potential is confined by its limited on-board battery energy and data storage capacity. This problem has been addressed using docking systems by underwater recharging and data transfer for AUVs. In this work, we propose a vision based framework for underwater docking following these systems. The proposed framework comprises two modules; (i) a detection module which provides location information on underwater docking stations in 2D images captured by an on-board camera, and (ii) a pose estimation module which recovers the relative 3D position and orientation between docking stations and AUVs from the 2D images. For robust and credible detection of docking stations, we propose a convolutional neural network called Docking Neural Network (DoNN). For accurate pose estimation, a perspective-n-point algorithm is integrated into our framework. In order to examine our framework in underwater docking tasks, we collected a dataset of 2D images, named Underwater Docking Images Dataset (UDID), in an experimental water pool. To the best of our knowledge, UDID is the first publicly available underwater docking dataset. In the experiments, we first evaluate performance of the proposed detection module on UDID and its deformed variations. Next, we assess the accuracy of the pose estimation module by ground experiments, since it is not feasible to obtain true relative position and orientation between docking stations and AUVs under water. Then, we examine the pose estimation module by underwater experiments in our experimental water pool. Experimental results show that the proposed framework can be used to detect docking stations and estimate their relative pose efficiently and successfully, compared to the state-of-the-art baseline systems.
Backpropagation generalized for output derivatives
Venue: arXiv cs.NE 2017
Year: 2017

Backpropagation algorithm is the cornerstone for neural network analysis. Paper extends it for training any derivatives of neural network's output with respect to its input. By the dint of it feedforward networks can be used to solve or verify solutions of partial or simple, linear or nonlinear differential equations. This method vastly differs from traditional ones like finite differences on a mesh. It contains no approximations, but rather an exact form of differential operators. Algorithm is built to train a feed forward network with any number of hidden layers and any kind of sufficiently smooth activation functions. It's presented in a form of matrix-vector products so highly parallel implementation is readily possible. First part derives the method for 2D case with first and second order derivatives, second part extends it to N-dimensional case with any derivatives. All necessary expressions for using this method to solve most applied PDE can be found in Appendix D.
Congestion Control Approach based on Effective Random Early Detection and Fuzzy Logic
Venue: arXiv cs.NI 2017
Year: 2017

Congestion in router buffer increases the delay and packet loss. Active Queue Management (AQM) methods are able to detect congestion in early stage and control it by packet dropping. Effective Random Early Detection (ERED) method, among many other AQM methods, gives a good performance in detect and control congestion and preserve packet loss. However, the ERED neglect the delay factor, which is effect the performance of the network. Moreover, ERED has a real parameterization problem. Several parameters have to be initialized to optimal values to obtain satisfactory results. This paper proposed an extended ERED method that considers the delay in its process and combines the extended ERED method with a Fuzzy Inference Process that eases the problem of parameter initialization. The results show that the parametric-based form of the proposed work gives a better performance results, according to the performance measures, delay, dropping and packet loss. The loss has been enhanced by 10-100%. Delay has been enhanced by 30-60%. The performance of the fuzzy-based form of the proposed method is better than the parametric-based form and ERED in terms of delay and packet loss.
Layer-Adaptive Communication and Collaborative Transformed-Domain Representations for Performance Optimization in WSNs
Venue: arXiv cs.NI 2017
Year: 2017

In this paper, we combat the problem of performance optimization in wireless sensor networks. Specifically, a novel framework is proposed to handle two major research issues. Firstly, we optimize the utilization of resources available to various nodes at hand. This is achieved via proposed optimal network clustering enriched with layer-adaptive 3-tier communication mechanism to diminish energy holes. We also introduce a mathematical coverage model that helps us minimize the number of coverage holes. Secondly, we present a novel approach to recover the corrupted version of the data received over noisy wireless channels. A robust sparse-domain based recovery method equipped with specially developed averaging filter is used to take care of the unwanted noisy components added to the data samples. Our proposed framework provides a handy routing protocol that enjoys improved computation complexity and elongated network lifetime as demonstrated with the help of extensive simulation results.
The Zero Resource Speech Challenge 2017
Venue: arXiv cs.CL 2017
Year: 2017

We describe a new challenge aimed at discovering subword and word units from raw speech. This challenge is the followup to the Zero Resource Speech Challenge 2015. It aims at constructing systems that generalize across languages and adapt to new speakers. The design features and evaluation metrics of the challenge are presented and the results of seventeen models are discussed.
Logo Synthesis and Manipulation with Clustered Generative Adversarial Networks
Venue: arXiv cs.CV 2017
Year: 2017

Designing a logo for a new brand is a lengthy and tedious back-and-forth process between a designer and a client. In this paper we explore to what extent machine learning can solve the creative task of the designer. For this, we build a dataset -- LLD -- of 600k+ logos crawled from the world wide web. Training Generative Adversarial Networks (GANs) for logo synthesis on such multi-modal data is not straightforward and results in mode collapse for some state-of-the-art methods. We propose the use of synthetic labels obtained through clustering to disentangle and stabilize GAN training. We are able to generate a high diversity of plausible logos and we demonstrate latent space exploration techniques to ease the logo design task in an interactive manner. Moreover, we validate the proposed clustered GAN training on CIFAR 10, achieving state-of-the-art Inception scores when using synthetic labels obtained via clustering the features of an ImageNet classifier. GANs can cope with multi-modal data by means of synthetic labels achieved through clustering, and our results show the creative potential of such techniques for logo synthesis and manipulation. Our dataset and models will be made publicly available at https://data.vision.ee.ethz.ch/cvl/lld/.
Fingerprint Spoof Buster
Venue: arXiv cs.CV 2017
Year: 2017

The primary purpose of a fingerprint recognition system is to ensure a reliable and accurate user authentication, but the security of the recognition system itself can be jeopardized by spoof attacks. This study addresses the problem of developing accurate, generalizable, and efficient algorithms for detecting fingerprint spoof attacks. Specifically, we propose a deep convolutional neural network based approach utilizing local patches centered and aligned using fingerprint minutiae. Experimental results on three public-domain LivDet datasets (2011, 2013, and 2015) show that the proposed approach provides state-of-the-art accuracies in fingerprint spoof detection for intra-sensor, cross-material, cross-sensor, as well as cross-dataset testing scenarios. For example, in LivDet 2015, the proposed approach achieves 99.03% average accuracy over all sensors compared to 95.51% achieved by the LivDet 2015 competition winners. Additionally, two new fingerprint presentation attack datasets containing more than 20,000 images, using two different fingerprint readers, and over 12 different spoof fabrication materials are collected. We also present a graphical user interface, called Fingerprint Spoof Buster, that allows the operator to visually examine the local regions of the fingerprint highlighted as live or spoof, instead of relying on only a single score as output by the traditional approaches.
Over the Air Deep Learning Based Radio Signal Classification
Venue: arXiv cs.LG 2017
Year: 2017

We conduct an in depth study on the performance of deep learning based radio signal classification for radio communications signals. We consider a rigorous baseline method using higher order moments and strong boosted gradient tree classification and compare performance between the two approaches across a range of configurations and channel impairments. We consider the effects of carrier frequency offset, symbol rate, and multi-path fading in simulation and conduct over-the-air measurement of radio classification performance in the lab using software radios and compare performance and training strategies for both. Finally we conclude with a discussion of remaining problems, and design considerations for using such techniques.
Learning Disentangling and Fusing Networks for Face Completion Under Structured Occlusions
Venue: arXiv cs.CV 2017
Year: 2017

Face completion aims to generate semantically new pixels for missing facial components. It is a challenging generative task due to large variations of face appearance. This paper studies generative face completion under structured occlusions. We treat the face completion and corruption as disentangling and fusing processes of clean faces and occlusions, and propose a jointly disentangling and fusing Generative Adversarial Network (DF-GAN). First, three domains are constructed, corresponding to the distributions of occluded faces, clean faces and structured occlusions. The disentangling and fusing processes are formulated as the transformations between the three domains. Then the disentangling and fusing networks are built to learn the transformations from unpaired data, where the encoder-decoder structure is adopted and allows DF-GAN to simulate structure occlusions by modifying the latent representations. Finally, the disentangling and fusing processes are unified into a dual learning framework along with an adversarial strategy. The proposed method is evaluated on Meshface verification problem. Experimental results on four Meshface databases demonstrate the effectiveness of our proposed method for the face completion under structured occlusions.
The Blockchain Revolution: Insights from Top-Management
Venue: arXiv cs.CY 2017
Year: 2017

This is an exploration of Blockchain technology that is growing in popularity and it seems to be able to disrupt a plethora of industries. A research is being conducted to examine Blockchain potential to be adopted by enterprises from different sectors as well as the parameters that could affect its adoption. Mostly known as the technology that underpins Bitcoin, this concept raised a significant interest within various markets. Blockchain offers a new approach to valued information management and sharing and it is introduced as a solution against the inefficiencies that affect the industry. Experts, infrastructure providers and banks can now work on this technology and explore its uses. This is a new technology journey with obstacles that will need to be overcome and it can not be clear yet what will eventually arise. Professionals from around the world express their views on the adoption of Blockchain by organisations and how these plan to support its deployment. Thoughts are shared in terms of the required budget and the parameters that can impact its adoption. There is a great interest in Blockchain technology and its revolutionary potential to modernize the world economy and this is only the beginning.