OpenRaiser/Intern-Atlas huggingface dataset:
Enhancing Quality for HEVC Compressed Videos
Venue: arXiv cs.MM 2018
Year: 2018

The latest High Efficiency Video Coding (HEVC) standard has been increasingly applied to generate video streams over the Internet. However, HEVC compressed videos may incur severe quality degradation, particularly at low bit-rates. Thus, it is necessary to enhance the visual quality of HEVC videos at the decoder side. To this end, this paper proposes a Quality Enhancement Convolutional Neural Network (QE-CNN) method that does not require any modification of the encoder to achieve quality enhancement for HEVC. In particular, our QE-CNN method learns QE-CNN-I and QE-CNN-P models to reduce the distortion of HEVC I and P frames, respectively. The proposed method differs from the existing CNN-based quality enhancement approaches, which only handle intra-coding distortion and are thus not suitable for P frames. Our experimental results validate that our QE-CNN method is effective in enhancing quality for both I and P frames of HEVC videos. To apply our QE-CNN method in time-constrained scenarios, we further propose a Time-constrained Quality Enhancement Optimization (TQEO) scheme. Our TQEO scheme controls the computational time of QE-CNN to meet a target, meanwhile maximizing the quality enhancement. Next, the experimental results demonstrate the effectiveness of our TQEO scheme from the aspects of time control accuracy and quality enhancement under different time constraints. Finally, we design a prototype to implement our TQEO scheme in a real-time scenario.
A shared latent space matrix factorisation method for recommending new trial evidence for systematic review updates
Venue: arXiv cs.IR 2018
Year: 2018

Clinical trial registries can be used to monitor the production of trial evidence and signal when systematic reviews become out of date. However, this use has been limited to date due to the extensive manual review required to search for and screen relevant trial registrations. Our aim was to evaluate a new method that could partially automate the identification of trial registrations that may be relevant for systematic review updates. We identified 179 systematic reviews of drug interventions for type 2 diabetes, which included 537 clinical trials that had registrations in ClinicalTrials.gov. We tested a matrix factorisation approach that uses a shared latent space to learn how to rank relevant trial registrations for each systematic review, comparing the performance to document similarity to rank relevant trial registrations. The two approaches were tested on a holdout set of the newest trials from the set of type 2 diabetes systematic reviews and an unseen set of 141 clinical trial registrations from 17 updated systematic reviews published in the Cochrane Database of Systematic Reviews. The matrix factorisation approach outperformed the document similarity approach with a median rank of 59 and recall@100 of 60.9%, compared to a median rank of 138 and recall@100 of 42.8% in the document similarity baseline. In the second set of systematic reviews and their updates, the highest performing approach used document similarity and gave a median rank of 67 (recall@100 of 62.9%). The proposed method was useful for ranking trial registrations to reduce the manual workload associated with finding relevant trials for systematic review updates. The results suggest that the approach could be used as part of a semi-automated pipeline for monitoring potentially new evidence for inclusion in a review update.
Computing Maximal Expected Termination Time of Probabilistic Timed Automata
Venue: arXiv cs.FL 2018
Year: 2018

The paper addresses the problem of computing maximal expected time to termination of probabilistic timed automata (PTA) models, under the condition that the system will, eventually, terminate. This problem can exhibit high computational complexity, in particular when the automaton under analysis contains cycles that may be repeated very often (due to very high probabilities, e.g. p =0.999). Such cycles can degrade the performance of typical model checking algorithms, as the likelihood of repeating the cycle converges to zero arbitrarily slowly. We introduce an acceleration technique that can be applied to improve the execution of such cycles by collapsing their iterations. The acceleration process of a cyclic PTA consists of several formal steps necessary to handle the cumulative timing and probability information that result from successive executions of a cycle. The advantages of acceleration are twofold. First, it helps to reduce the computational complexity of the problem without adversely affecting the outcome of the analysis. Second, it can bring the "worst case execution time" problem of PTAs within the bounds of feasibility for model checking techniques. To our knowledge, this is the first work that addresses the problem of accelerating execution of cycles that exhibit both timing and probabilistic behavior.
Multi-label Pixelwise Classification for Reconstruction of Large-scale Urban Areas
Venue: arXiv cs.CV 2018
Year: 2018

Object classification is one of the many holy grails in computer vision and as such has resulted in a very large number of algorithms being proposed already. Specifically in recent years there has been considerable progress in this area primarily due to the increased efficiency and accessibility of deep learning techniques. In fact, for single-label object classification [i.e. only one object present in the image] the state-of-the-art techniques employ deep neural networks and are reporting very close to human-like performance. There are specialized applications in which single-label object-level classification will not suffice; for example in cases where the image contains multiple intertwined objects of different labels. In this paper, we address the complex problem of multi-label pixelwise classification. We present our distinct solution based on a convolutional neural network (CNN) for performing multi-label pixelwise classification and its application to large-scale urban reconstruction. A supervised learning approach is followed for training a 13-layer CNN using both LiDAR and satellite images. An empirical study has been conducted to determine the hyperparameters which result in the optimal performance of the CNN. Scale invariance is introduced by training the network on five different scales of the input and labeled data. This results in six pixelwise classifications for each different scale. An SVM is then trained to map the six pixelwise classifications into a single-label. Lastly, we refine boundary pixel labels using graph-cuts for maximum a-posteriori (MAP) estimation with Markov Random Field (MRF) priors. The resulting pixelwise classification is then used to accurately extract and reconstruct the buildings in large-scale urban areas. The proposed approach has been extensively tested and the results are reported.
Coverage-Rate Tradeoff Analysis in mmWave Heterogeneous Cellular Networks
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we first introduce a generalized modeling and analysis framework to explore the fundamental interactions between user association, coverage probability and link rate in a millimeter wave (mmWave) heterogeneous cellular network (HetNet) in which there are multiple tiers of the ultra-high-frequency (UHF) macrocell and small cell base stations (BSs) and a single tier of mmWave small cell BSs. A generalized user association scheme that can cover many path-loss-based user association schemes is proposed and its related probabilistic properties that facilitate the derivations of the coverage probability and link rate are derived. The derived general expressions of the coverage and link rate not only shed light on how to design user association functions in order to maximize the coverage and link rate but also show that it is impossible to devise a user association scheme that maximizes the coverage and link rate at the same time. Namely, there exists a fundamental tradeoff between the coverage and link rate in mmWave HetNets with distinct bandwidths in the UHF and mmWave bands while a user is associating with a BS. We characterize the coverage-optimal and rate-optimal user association schemes and numerically validate their performances and show the coverage-rate tradeoff problem.
Domain Adaptation from Synthesis to Reality in Single-model Detector for Video Smoke Detection
Venue: arXiv cs.CV 2018
Year: 2018

This paper proposes a method for video smoke detection using synthetic smoke samples. The virtual data can automatically offer precise and rich annotated samples. However, the learning of smoke representations will be hurt by the appearance gap between real and synthetic smoke samples. The existed researches mainly work on the adaptation to samples extracted from original annotated samples. These methods take the object detection and domain adaptation as two independent parts. To train a strong detector with rich synthetic samples, we construct the adaptation to the detection layer of state-of-the-art single-model detectors (SSD and MS-CNN). The training procedure is an end-to-end stage. The classification, location and adaptation are combined in the learning. The performance of the proposed model surpasses the original baseline in our experiments. Meanwhile, our results show that the detectors based on the adversarial adaptation are superior to the detectors based on the discrepancy adaptation. Code will be made publicly available on http://smoke.ustc.edu.cn. Moreover, the domain adaptation for two-stage detector is described in Appendix A.
The Efficient Server Audit Problem, Deduplicated Re-execution, and the Web
Venue: arXiv cs.CR 2018
Year: 2018

You put a program on a concurrent server, but you don't trust the server; later, you get a trace of the actual requests that the server received from its clients and the responses that it delivered. You separately get logs from the server; these are untrusted. How can you use the logs to efficiently _verify_ that the responses were derived from running the program on the requests? This is the _Efficient Server Audit Problem_, and it abstracts real-world scenarios, including running a web application on an untrusted provider. We give a solution based on several new techniques, including simultaneous replay and efficient verification of concurrent executions. We implement the solution for PHP web applications. For several applications, our verifier achieves 5.6--10.9x speedup versus simply re-executing, with less than 10 percent overhead for the server.
Dense scale selection over space, time and space-time
Venue: arXiv cs.CV 2018
Year: 2018

Scale selection methods based on local extrema over scale of scale-normalized derivatives have been primarily developed to be applied sparsely --- at image points where the magnitude of a scale-normalized differential expression additionally assumes local extrema over the domain where the data are defined. This paper presents a methodology for performing dense scale selection, so that hypotheses about local characteristic scales in images, temporal signals and video can be computed at every image point and every time moment. A critical problem when designing mechanisms for dense scale selection is that the scale at which scale-normalized differential entities assume local extrema over scale can be strongly dependent on the local order of the locally dominant differential structure. To address this problem, we propose a methodology where local extrema over scale are detected of a quasi quadrature measure involving scale-space derivatives up to order two and propose two independent mechanisms to reduce the phase dependency of the local scale estimates by: (i) introducing a second layer of post-smoothing prior to the detection of local extrema over scale and (ii) performing local phase compensation based on a model of the phase dependency of the local scale estimates depending on the relative strengths between first- vs. second-order differential structure. This general methodology is applied over three types of domains: (i) spatial images, (ii) temporal signals and (iii) spatio-temporal video. Experiments show that the proposed methodology leads to intuitively reasonable results with local scale estimates that reflect variations in the characteristic scales of locally dominant structures over space and time.
A Randomized Greedy Algorithm for Near-Optimal Sensor Scheduling in Large-Scale Sensor Networks
Venue: arXiv cs.SY 2018
Year: 2018

We study the problem of scheduling sensors in a resource-constrained linear dynamical system, where the objective is to select a small subset of sensors from a large network to perform the state estimation task. We formulate this problem as the maximization of a monotone set function under a matroid constraint. We propose a randomized greedy algorithm that is significantly faster than state-of-the-art methods. By introducing the notion of curvature which quantifies how close a function is to being submodular, we analyze the performance of the proposed algorithm and find a bound on the expected mean square error (MSE) of the estimator that uses the selected sensors in terms of the optimal MSE. Moreover, we derive a probabilistic bound on the curvature for the scenario where{\color{black}{ the measurements are i.i.d. random vectors with bounded $\ell_2$ norm.}} Simulation results demonstrate efficacy of the randomized greedy algorithm in a comparison with greedy and semidefinite programming relaxation methods.
Pseudo-labels for Supervised Learning on Dynamic Vision Sensor Data, Applied to Object Detection under Ego-motion
Venue: arXiv cs.CV 2018
Year: 2018

In recent years, dynamic vision sensors (DVS), also known as event-based cameras or neuromorphic sensors, have seen increased use due to various advantages over conventional frame-based cameras. Using principles inspired by the retina, its high temporal resolution overcomes motion blurring, its high dynamic range overcomes extreme illumination conditions and its low power consumption makes it ideal for embedded systems on platforms such as drones and self-driving cars. However, event-based data sets are scarce and labels are even rarer for tasks such as object detection. We transferred discriminative knowledge from a state-of-the-art frame-based convolutional neural network (CNN) to the event-based modality via intermediate pseudo-labels, which are used as targets for supervised learning. We show, for the first time, event-based car detection under ego-motion in a real environment at 100 frames per second with a test average precision of 40.3% relative to our annotated ground truth. The event-based car detector handles motion blur and poor illumination conditions despite not explicitly trained to do so, and even complements frame-based CNN detectors, suggesting that it has learnt generalized visual representations.
Fast Convolutional Sparse Coding in the Dual Domain
Venue: arXiv cs.CV 2018
Year: 2018

Convolutional sparse coding (CSC) is an important building block of many computer vision applications ranging from image and video compression to deep learning. We present two contributions to the state of the art in CSC. First, we significantly speed up the computation by proposing a new optimization framework that tackles the problem in the dual domain. Second, we extend the original formulation to higher dimensions in order to process a wider range of inputs, such as RGB images and videos. Our results show up to 20 times speedup compared to current state-of-the-art CSC solvers.
An attentive neural architecture for joint segmentation and parsing and its application to real estate ads
Venue: arXiv cs.CL 2018
Year: 2018

In processing human produced text using natural language processing (NLP) techniques, two fundamental subtasks that arise are (i) segmentation of the plain text into meaningful subunits (e.g., entities), and (ii) dependency parsing, to establish relations between subunits. In this paper, we develop a relatively simple and effective neural joint model that performs both segmentation and dependency parsing together, instead of one after the other as in most state-of-the-art works. We will focus in particular on the real estate ad setting, aiming to convert an ad to a structured description, which we name property tree, comprising the tasks of (1) identifying important entities of a property (e.g., rooms) from classifieds and (2) structuring them into a tree format. In this work, we propose a new joint model that is able to tackle the two tasks simultaneously and construct the property tree by (i) avoiding the error propagation that would arise from the subtasks one after the other in a pipelined fashion, and (ii) exploiting the interactions between the subtasks. For this purpose, we perform an extensive comparative study of the pipeline methods and the new proposed joint model, reporting an improvement of over three percentage points in the overall edge F1 score of the property tree. Also, we propose attention methods, to encourage our model to focus on salient tokens during the construction of the property tree. Thus we experimentally demonstrate the usefulness of attentive neural architectures for the proposed joint model, showcasing a further improvement of two percentage points in edge F1 score for our application.
Privacy-Preserving Platform for Transactive Energy Systems
Venue: arXiv cs.DC 2018
Year: 2018

Transactive energy systems (TES) are emerging as a transformative solution for the problems faced by distribution system operators due to an increase in the use of distributed energy resources and a rapid acceleration in renewable energy generation. These, on one hand, pose a decentralized power system controls problem, requiring strategic microgrid control to maintain stability for the community and for the utility. On the other hand, they require robust financial markets operating on distributed software platforms that preserve privacy. In this paper, we describe the implementation of a novel, blockchain-based transactive energy system. We outline the key requirements and motivation of this platform, describe the lessons learned, and provide a description of key architectural components of this system.
Asymptotically Good Additive Cyclic Codes Exist
Venue: arXiv cs.IT 2018
Year: 2018

Long quasi-cyclic codes of any fixed index $>1$ have been shown to be asymptotically good, depending on Artin primitive root conjecture in (A. Alahmadi, C. Güneri, H. Shoaib, P. Solé, 2017). We use this recent result to construct good long additive cyclic codes on any extension of fixed degree of the base field. Similarly self-dual double circulant codes, and self-dual four circulant codes, have been shown to be good, also depending on Artin primitive root conjecture in (A. Alahmadi, F. Özdemir, P. Solé, 2017) and ( M. Shi, H. Zhu, P. Solé, 2017) respectively. Building on these recent results, we can show that long cyclic codes are good over $\F_q,$ for many classes of $q$'s. This is a partial solution to a fifty year old open problem.
Learning to Roam Free from Small-Space Autonomous Driving with A Path Planner
Venue: arXiv cs.RO 2018
Year: 2018

Modern autonomous driving algorithms often rely on learning the mapping from visual inputs to steering actions from human driving data in a variety of scenarios and visual scenes. The required data collection is not only labor intensive, but such data are often noisy, inconsistent, and inflexible, as there is no differentiation between good and bad drivers, or between different driving intentions. We propose a new autonomous driving approach that learns roaming skills from an optimal path planner. Our model car practices reaching random target locations in a small room with obstacles, by following the optimal trajectory and executing the steering actions decided by a planner. We learn the associations of driving behaviours with depth images, instead of raw color images of the visual scene. This more universal spatial representation allows the learned driving skills to transfer immediately to novel environments with different visual appearances. Our model car trained in a simple room, void of many visual features, demonstrates surprisingly good driving performance in a cluttered office environment, avoiding collisions with novel obstacles and unseen layouts of drive-able space. Its performance on outdoor curbside driving is also on par with human driving.
Learning a Structured Neural Network Policy for a Hopping Task
Venue: arXiv cs.RO 2018
Year: 2018

In this work we present a method for learning a reactive policy for a simple dynamic locomotion task involving hard impact and switching contacts where we assume the contact location and contact timing to be unknown. To learn such a policy, we use optimal control to optimize a local controller for a fixed environment and contacts. We learn the contact-rich dynamics for our underactuated systems along these trajectories in a sample efficient manner. We use the optimized policies to learn the reactive policy in form of a neural network. Using a new neural network architecture, we are able to preserve more information from the local policy and make its output interpretable in the sense that its output in terms of desired trajectories, feedforward commands and gains can be interpreted. Extensive simulations demonstrate the robustness of the approach to changing environments, outperforming a model-free gradient policy based methods on the same tasks in simulation. Finally, we show that the learned policy can be robustly transferred on a real robot.
Channel Hardening and Favorable Propagation in Cell-Free Massive MIMO with Stochastic Geometry
Venue: arXiv cs.IT 2018
Year: 2018

Cell-Free (CF) Massive MIMO is an alternative topology for future wireless networks, where a large number of single-antenna access points (APs) are distributed over the coverage area. There are no cells but all users are jointly served by the APs using network MIMO methods. Prior works have claimed that CF Massive MIMO inherits the basic properties of cellular Massive MIMO, namely channel hardening and favorable propagation. In this paper, we evaluate if one can rely on these properties when having a realistic stochastic AP deployment. Our results show that channel hardening only appears in special cases, for example, when the pathloss exponent is small. However, by using 5--10 antennas per AP, instead of one, we can substantially improve the hardening. Only spatially well-separated users will exhibit favorable propagation, but when adding more antennas and/or reducing the pathloss exponent, it becomes more likely for favorable propagation to occur. The conclusion is that we cannot rely on channel hardening and favorable propagation when analyzing and designing CF Massive MIMO networks, but we need to use achievable rate expressions and resource allocation schemes that work well also in the absence of these properties. Some options are reviewed in this paper.
Finding the optimal nets for self-folding Kirigami
Venue: arXiv cs.DS 2018
Year: 2018

Three-dimensional shells can be synthesized from the spontaneous self-folding of two-dimensional templates of interconnected panels, called nets. However, some nets are more likely to self-fold into the desired shell under random movements. The optimal nets are the ones that maximize the number of vertex connections, i.e., vertices that have only two of its faces cut away from each other in the net. Previous methods for finding such nets are based on random search and thus do not guarantee the optimal solution. Here, we propose a deterministic procedure. We map the connectivity of the shell into a shell graph, where the nodes and links of the graph represent the vertices and edges of the shell, respectively. Identifying the nets that maximize the number of vertex connections corresponds to finding the set of maximum leaf spanning trees of the shell graph. This method allows not only to design the self-assembly of much larger shell structures but also to apply additional design criteria, as a complete catalog of the maximum leaf spanning trees is obtained.