OpenRaiser/Intern-Atlas huggingface dataset:
Towards High Resolution Video Generation with Progressive Growing of Sliced Wasserstein GANs
Venue: arXiv cs.CV 2018
Year: 2018

The extension of image generation to video generation turns out to be a very difficult task, since the temporal dimension of videos introduces an extra challenge during the generation process. Besides, due to the limitation of memory and training stability, the generation becomes increasingly challenging with the increase of the resolution/duration of videos. In this work, we exploit the idea of progressive growing of Generative Adversarial Networks (GANs) for higher resolution video generation. In particular, we begin to produce video samples of low-resolution and short-duration, and then progressively increase both resolution and duration alone (or jointly) by adding new spatiotemporal convolutional layers to the current networks. Starting from the learning on a very raw-level spatial appearance and temporal movement of the video distribution, the proposed progressive method learns spatiotemporal information incrementally to generate higher resolution videos. Furthermore, we introduce a sliced version of Wasserstein GAN (SWGAN) loss to improve the distribution learning on the video data of high-dimension and mixed-spatiotemporal distribution. SWGAN loss replaces the distance between joint distributions by that of one-dimensional marginal distributions, making the loss easier to compute. We evaluate the proposed model on our collected face video dataset of 10,900 videos to generate photorealistic face videos of 256x256x32 resolution. In addition, our model also reaches a record inception score of 14.57 in unsupervised action recognition dataset UCF-101.
AutoLoss: Learning Discrete Schedules for Alternate Optimization
Venue: arXiv cs.LG 2018
Year: 2018

Many machine learning problems involve iteratively and alternately optimizing different task objectives with respect to different sets of parameters. Appropriately scheduling the optimization of a task objective or a set of parameters is usually crucial to the quality of convergence. In this paper, we present AutoLoss, a meta-learning framework that automatically learns and determines the optimization schedule. AutoLoss provides a generic way to represent and learn the discrete optimization schedule from metadata, allows for a dynamic and data-driven schedule in ML problems that involve alternating updates of different parameters or from different loss objectives. We apply AutoLoss on four ML tasks: d-ary quadratic regression, classification using a multi-layer perceptron (MLP), image generation using GANs, and multi-task neural machine translation (NMT). We show that the AutoLoss controller is able to capture the distribution of better optimization schedules that result in higher quality of convergence on all four tasks. The trained AutoLoss controller is generalizable -- it can guide and improve the learning of a new task model with different specifications, or on different datasets.
Characterizing QoS Parameters and Application of Soft-QoS Scheme for 3G Wireless Networks
Venue: arXiv cs.NI 2018
Year: 2018

In wireless communication systems, Quality of Service (QoS) is one of the most important issues from both the users and operators point of view. All the parameters related to QoS are not same important for all users and applications. The satisfaction level of different users also does not depend on same QoS parameters. In this paper, we discuss the QoS parameters and then propose a priority order of QoS parameters based on protocol layers and service applications. We present the relation among the QoS parameters those influence the performance of other QoS parameters and, finally, we demonstrate the numerical analysis results for our proposed soft-QoS scheme to reduce the dropped call rate which is the most important QoS parameter for all types of services
Medical Images Analysis in Cancer Diagnostic
Venue: arXiv cs.CV 2018
Year: 2018

This paper shows results of computer analysis of images in the purpose of finding differences between medical images in order of their classifications in terms of separation malign tissue from a normal and benign tissue. The diagnostics of malign tissue is of the crucial importance in medicine. Therefore, ascertainment of the correlation between multifractals parameters and "chaotic" cells could be of the great appliance. This paper shows the application of multifractal analysis for additional help in cancer diagnosis, as well as diminishing. of the subjective factor and error probability
Multi-Vehicle Trajectory Optimisation On Road Networks
Venue: arXiv cs.RO 2018
Year: 2018

This paper addresses the problem of planning time-optimal trajectories for multiple cooperative agents along specified paths through a static road network. Vehicle interactions at intersections create non-trivial decisions, with complex flow-on effects for subsequent interactions. A globally optimal, minimum time trajectory is found for all vehicles using Mixed Integer Linear Programming (MILP). Computational performance is improved by minimising binary variables using iteratively applied targeted collision constraints, and efficient goal constraints. Simulation results in an open-pit mining scenario compare the proposed method against a fast heuristic method and a reactive approach based on site practices. The heuristic is found to scale better with problem size while the MILP is able to avoid local minima.
Where Did My Optimum Go?: An Empirical Analysis of Gradient Descent Optimization in Policy Gradient Methods
Venue: arXiv cs.LG 2018
Year: 2018

Recent analyses of certain gradient descent optimization methods have shown that performance can degrade in some settings - such as with stochasticity or implicit momentum. In deep reinforcement learning (Deep RL), such optimization methods are often used for training neural networks via the temporal difference error or policy gradient. As an agent improves over time, the optimization target changes and thus the loss landscape (and local optima) change. Due to the failure modes of those methods, the ideal choice of optimizer for Deep RL remains unclear. As such, we provide an empirical analysis of the effects that a wide range of gradient descent optimizers and their hyperparameters have on policy gradient methods, a subset of Deep RL algorithms, for benchmark continuous control tasks. We find that adaptive optimizers have a narrow window of effective learning rates, diverging in other cases, and that the effectiveness of momentum varies depending on the properties of the environment. Our analysis suggests that there is significant interplay between the dynamics of the environment and Deep RL algorithm properties which aren't necessarily accounted for by traditional adaptive gradient methods. We provide suggestions for optimal settings of current methods and further lines of research based on our findings.
Class-Based Interference Management in Wireless Networks
Venue: arXiv cs.NI 2018
Year: 2018

Technological advancement has brought revolutionary change in the converged wireless networks. Due to the existence of different types of traffic, provisioning of Quality of Service (QoS) becomes a challenge in the wireless networks. In case of a congested network, resource allocation has emerged as an effective way to provide the excessive users with desirable QoS. Since QoS for non-real-time traffic are not as strict as for real-time traffic, the unoccupied channels of the adjacent cells can be assigned to the non-real-time traffic to retain QoS for real-time traffic. This results in the intensified bandwidth utilization as well as less interference for the real-time traffic. In this paper, we propose an effective radio resource management scheme that relies on the dynamically assigned bandwidth allocation process. In case of interference management, we classify the traffic into real-time traffic and non-real-time traffic and give priority to the real-time traffic. According to our scheme, the real-time traffic among the excessive number of users are reassigned to the original channels which have been occupied by non-real-time traffic and the non-real-time traffic are allocated to the assigned channels of those real-time traffic. The architecture allows improved signal to interference plus noise ratio (SINR) for real-time traffic along with intensification in the bandwidth utilization of the network. Besides, the increased system capacity and lower outage probability of the network bear the significance of the proposed scheme.
Weakly Supervised Object Detection in Artworks
Venue: arXiv cs.CV 2018
Year: 2018

We propose a method for the weakly supervised detection of objects in paintings. At training time, only image-level annotations are needed. This, combined with the efficiency of our multiple-instance learning method, enables one to learn new classes on-the-fly from globally annotated databases, avoiding the tedious task of manually marking objects. We show on several databases that dropping the instance-level annotations only yields mild performance losses. We also introduce a new database, IconArt, on which we perform detection experiments on classes that could not be learned on photographs, such as Jesus Child or Saint Sebastian. To the best of our knowledge, these are the first experiments dealing with the automatic (and in our case weakly supervised) detection of iconographic elements in paintings. We believe that such a method is of great benefit for helping art historians to explore large digital databases.
C-DLSI: An Extended LSI Tailored for Federated Text Retrieval
Venue: arXiv cs.IR 2018
Year: 2018

As the web expands in data volume and in geographical distribution, centralized search methods become inefficient, leading to increasing interest in cooperative information retrieval, e.g., federated text retrieval (FTR). Different from existing centralized information retrieval (IR) methods, in which search is done on a logically centralized document collection, FTR is composed of a number of peers, each of which is a complete search engine by itself. To process a query, FTR requires firstly the identification of promising peers that host the relevant documents and secondly the retrieval of the most relevant documents from the selected peers. Most of the existing methods only apply traditional IR techniques that treat each text collection as a single large document and utilize term matching to rank the collections. In this paper, we formalize the problem and identify the properties of FTR, and analyze the feasibility of extending LSI with clustering to adapt to FTR, based on which a novel approach called Cluster-based Distributed Latent Semantic Indexing (C-DLSI) is proposed. C-DLSI distinguishes the topics of a peer with clustering, captures the local LSI spaces within the clusters, and consider the relations among these LSI spaces, thus providing more precise characterization of the peer. Accordingly, novel descriptors of the peers and a compatible local text retrieval are proposed. The experimental results show that C-DLSI outperforms existing methods.
Game-Based Approach for QoS Provisioning and Interference Management in Heterogeneous Networks
Venue: arXiv cs.NI 2018
Year: 2018

For the current 5G era, the deployment of small cells in residential and commercial areas plays an imperious preamble in improving network coverage and the quality of service (QoS). Major technical problems associated with the mass deployment of small cells, such as femtocells are interference management and QoS provisioning. These are important for service-providing operators because the system capacity and achievable data rates mainly depend on interference. Future generation wireless networks will use autonomous and distributed architecture for ameliorating the efficacy and flexibility of communication systems. In this paper, we propose a game theory-based model along with dynamic channel allocation and self-optimizing power control scheme for resolving priority-based access exposure by applying the concept of primary and secondary users. It is expected that the consumers will experience better QoS with reduced interference levels, and the service-providing operators will be able to increase their revenue, while ensuring optimal price for the consumers. We assimilated extensive numerical results to demonstrate the efficacy of our proposed model.
Integrated RF/Optical Wireless Networks for Improving QoS in Indoor and Transportation Applications
Venue: arXiv cs.NI 2018
Year: 2018

Communications based solely on radio frequency (RF) networks cannot provide adequate quality of service for the rapidly growing demands of wireless connectivity. Since devices operating in the optical spectrum do not interfere with those using the RF spectrum, wireless networks based on the optical spectrum can be added to existing RF networks to fulfill this demand. Hence, optical wireless communication (OWC) technology can be an excellent complement to RF-based technology to provide improved service. Promising OWC systems include light fidelity (LiFi), visible light communication, optical camera communication (OCC), and free-space optical communication (FSOC). OWC and RF systems have differing limitations, and the integration of RF and optical wireless networks can overcome the limitations of both systems. This paper describes an LiFi/femtocell hybrid network system for indoor environments. Low signal-to-interference-plus-noise ratios and the shortage bandwidth problems of existing RF femtocell networks can be overcome with the proposed hybrid model. Moreover, we describe an integrated RF/optical wireless system that can be employed for users inside a vehicle, remote monitoring of ambulance patients, vehicle tracking, and vehicle-to-vehicle communications. We consider LiFi, OCC, and FSOC as the optical wireless technologies to be used for communication support in transportation, and assume macrocells, femtocells, and wireless fidelity to be the corresponding RF technologies. We describe handover management-including detailed call flow, interference management, link reliability improvement, and group handover provisioning-for integrated networks. Performance analyses demonstrate the significance of the proposed integrated RF/optical wireless systems.
Distributed optimization in wireless sensor networks: an island-model framework
Venue: arXiv cs.NE 2018
Year: 2018

Wireless Sensor Networks (WSNs) is an emerging technology in several application domains, ranging from urban surveillance to environmental and structural monitoring. Computational Intelligence (CI) techniques are particularly suitable for enhancing these systems. However, when embedding CI into wireless sensors, severe hardware limitations must be taken into account. In this paper we investigate the possibility to perform an online, distributed optimization process within a WSN. Such a system might be used, for example, to implement advanced network features like distributed modelling, self-optimizing protocols, and anomaly detection, to name a few. The proposed approach, called DOWSN (Distributed Optimization for WSN) is an island-model infrastructure in which each node executes a simple, computationally cheap (both in terms of CPU and memory) optimization algorithm, and shares promising solutions with its neighbors. We perform extensive tests of different DOWSN configurations on a benchmark made up of continuous optimization problems; we analyze the influence of the network parameters (number of nodes, inter-node communication period and probability of accepting incoming solutions) on the optimization performance. Finally, we profile energy and memory consumption of DOWSN to show the efficient usage of the limited hardware resources available on the sensor nodes.
Clustering-based Anomaly Detection for microservices
Venue: arXiv cs.DC 2018
Year: 2018

Anomaly detection is an important step in the management and monitoring of data centers and cloud computing platforms. The ability to detect anomalous virtual machines before real failures occur results in reduced downtime while operations engineers urgently recover malfunctioning virtual machines, efficient root cause analysis, and improved customer optics in the event said malfunction lead to an outage. Virtual machines could fail at any time, whether in a lab or production system. If there is no anomaly detection system, and a virtual machine in a lab environment fails, the QA and DEV team will have to switch to another environment while the OPS team fixes the failure. The potential impact of failing to detect anomalous virtual machines can result in financial ramifications, both when developing new features and servicing existing ones. This paper presents a model that can efficiently detect anomalous virtual machines both in production and testing environments.
A Comparison between Background Modelling Methods for Vehicle Segmentation in Highway Traffic Videos
Venue: arXiv cs.CV 2018
Year: 2018

The objective of this paper is to compare the performance of three background-modeling algorithms in segmenting and detecting vehicles in highway traffic videos. All algorithms are available in OpenCV and were all coded in Python. We analyzed seven videos, totaling 2 hours of recording. To compare the algorithms, we created 35 ground-truth images, five from each video, and we used three different metrics: accuracy rate, precision rate, and processing time. By using accuracy and precision, we aim to identify how well the algorithms perform in detection and segmentation, while using the processing time to evaluate the impact on the computational system. Results indicate that all three algorithms had more than 90% of precision rate, while obtaining an average of 80% on accuracy. The algorithm with the lowest impact on processing time allowed the computation of 60 frames per second.
Stochastic Chemical Reaction Networks for Robustly Approximating Arbitrary Probability Distributions
Venue: arXiv cs.CC 2018
Year: 2018

We show that discrete distributions on the $d$-dimensional non-negative integer lattice can be approximated arbitrarily well via the marginals of stationary distributions for various classes of stochastic chemical reaction networks. We begin by providing a class of detailed balanced networks and prove that they can approximate any discrete distribution to any desired accuracy. However, these detailed balanced constructions rely on the ability to initialize a system precisely, and are therefore susceptible to perturbations in the initial conditions. We therefore provide another construction based on the ability to approximate point mass distributions and prove that this construction is capable of approximating arbitrary discrete distributions for any choice of initial condition. In particular, the developed models are ergodic, so their limit distributions are robust to a finite number of perturbations over time in the counts of molecules.
Physics Guided Recurrent Neural Networks For Modeling Dynamical Systems: Application to Monitoring Water Temperature And Quality In Lakes
Venue: arXiv cs.LG 2018
Year: 2018

In this paper, we introduce a novel framework for combining scientific knowledge within physics-based models and recurrent neural networks to advance scientific discovery in many dynamical systems. We will first describe the use of outputs from physics-based models in learning a hybrid-physics-data model. Then, we further incorporate physical knowledge in real-world dynamical systems as additional constraints for training recurrent neural networks. We will apply this approach on modeling lake temperature and quality where we take into account the physical constraints along both the depth dimension and time dimension. By using scientific knowledge to guide the construction and learning the data-driven model, we demonstrate that this method can achieve better prediction accuracy as well as scientific consistency of results.
System Consequence
Venue: arXiv cs.LO 2018
Year: 2018

This paper discusses system consequence, a central idea in the project to lift the theory of information flow to the abstract level of universal logic and the theory of institutions. The theory of information flow is a theory of distributed logic. The theory of institutions is abstract model theory. A system is a collection of interconnected parts, where the whole may have properties that cannot be known from an analysis of the constituent parts in isolation. In an information system, the parts represent information resources and the interconnections represent constraints between the parts. System consequence, which is the extension of the consequence operator from theories to systems, models the available regularities represented by an information system as a whole. System consequence (without part-to-part constraints) is defined for a specific logical system (institution) in the theory of information flow. This paper generalizes the idea of system consequence to arbitrary logical systems.
Team NimbRo at MBZIRC 2017: Autonomous Valve Stem Turning using a Wrench
Venue: arXiv cs.RO 2018
Year: 2018

The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 has defined ambitious new benchmarks to advance the state-of-the-art in autonomous operation of ground-based and flying robots. In this article, we describe our winning entry to MBZIRC Challenge 2: the mobile manipulation robot Mario. It is capable of autonomously solving a valve manipulation task using a wrench tool detected, grasped, and finally employed to turn a valve stem. Mario's omnidirectional base allows both fast locomotion and precise close approach to the manipulation panel. We describe an efficient detector for medium-sized objects in 3D laser scans and apply it to detect the manipulation panel. An object detection architecture based on deep neural networks is used to find and select the correct tool from grayscale images. Parametrized motion primitives are adapted online to percepts of the tool and valve stem in order to turn the stem. We report in detail on our winning performance at the challenge and discuss lessons learned.
Gendered behavior as a disadvantage in open source software development
Venue: arXiv cs.CY 2018
Year: 2018

Women are severely marginalized in software development, especially in open source. In this article we argue that disadvantage is more due to gendered behavior than to categorical discrimination: women are at a disadvantage because of what they do, rather than because of who they are. Using data on entire careers of users from GitHub.com, we develop a measure to capture the gendered pattern of behavior: We use a random forest prediction of being female (as opposed to being male) by behavioral choices in the level of activity, specialization in programming languages, and choice of partners. We test differences in success and survival along both categorical gender and the gendered pattern of behavior. We find that 84.5% of women's disadvantage (compared to men) in success and 34.8% of their disadvantage in survival are due to the female pattern of their behavior. Men are also disadvantaged along their interquartile range of the female pattern of their behavior, and users who don't reveal their gender suffer an even more drastic disadvantage in survival probability. Moreover, we do not see evidence for any reduction of these inequalities in time. Our findings are robust to noise in gender recognition, and to taking into account particular programming languages, or decision tree classes of gendered behavior. Our results suggest that fighting categorical gender discrimination will have a limited impact on gender inequalities in open source software development, and that gender hiding is not a viable strategy for women.