OpenRaiser/Intern-Atlas huggingface dataset:
Fast Hermite interpolation and evaluation over finite fields of characteristic two
Venue: arXiv cs.SC 2018
Year: 2018

This paper presents new fast algorithms for Hermite interpolation and evaluation over finite fields of characteristic two. The algorithms reduce the Hermite problems to instances of the standard multipoint interpolation and evaluation problems, which are then solved by existing fast algorithms. The reductions are simple to implement and free of multiplications, allowing low overall multiplicative complexities to be obtained. The algorithms are suitable for use in encoding and decoding algorithms for multiplicity codes.
Flexibility potentials of a combined use of heat storages and batteries in PV-CHP hybrid systems
Venue: arXiv cs.SY 2018
Year: 2018

Due to the 2012 change in the renewable energy act the feed-in tariffs and as result the number of newly installed photovoltaic systems decreased dramatically in Germany. Therefore, there was the need, particularly, in the residential sector to develop new business ideas for photovoltaic systems. In context of this development, combined photovoltaic and heat-power systems were analyzed, which provide not only electricity but also heat throughout one entire year. Flexibilities are provided in form of thermal and electrical storage systems leading to many possible setting options requiring an elaborated control management. In this paper, a new optimized control algorithm is proposed that in contrast to standard strategies can operate optimal even under incorrect weather and load forecasts. A predictive controller based on a mixed integer optimization problem and an additional so called secondary, rule-based controller is combined. The controller is based on several input data. Depending on the choice of these data the PV-CHP hybrid system can be used for different control strategies, like maximum self-consumption, minimum CO2 emission or minimum operational costs. The main focus in this paper is to study if the flexibility potentials of the combined thermal and electrical storage systems can ensure a market oriented or a grid-friendly behavior. This is studied in five control options. As a result we found that the control algorithm is stable and able to adapt to the different conditions. In conclusion, it was discovered that the storage systems play a crucial role in terms of forecast differences and parameter changes. Storage systems are as expected the key-element for the flexibility of PV-CHP hybrid systems.
Impact of UAV Antenna Configuration on Wireless Connectivity in Urban Environments
Venue: arXiv cs.NI 2018
Year: 2018

The growing presence of UAVs has led operators to explore the issue of how to provide wireless service to UAVs. The achieveable service quality will be affected by a number of factors, including the type of antenna that the UAVs are equipped with and whether to use existing cellular infrastructure or deploy dedicated ground stations to service these new user devices. In this paper we employ stochastic geometry to carry out an analysis of the UAV coverage probability that can be achieved with a network of dedicated ground stations using a sub-6GHz technology such as LTE under different deployment conditions, given different types of UAV antennas. We provide analytical expressions for the coverage probability; we also evaluate the performance of the GS network against the case where the UAVs are served by a typical terrestrial BS network.
Global optimization of spin Hamiltonians with gain-dissipative systems
Venue: arXiv cs.ET 2018
Year: 2018

Recently, several platforms were proposed and demonstrated a proof-of-principle for finding the global minimum of the spin Hamiltonians such as the Ising and XY models using gain-dissipative quantum and classical systems. The implementation of dynamical adjustment of the gain and coupling strengths has been established as a vital feedback mechanism for analog Hamiltonian physical systems that aim to simulate spin Hamiltonians. Based on the principle of operation of such simulators we develop a novel class of gain-dissipative algorithms for global optimisation of NP-hard problems and show its performance in comparison with the classical global optimisation algorithms. These systems can be used to study the ground state and statistical properties of spin systems and as a direct benchmark for the performance testing of the gain-dissipative physical simulators. The estimates of the time operation of the physical implementation of the gain-dissipative simulators for large matrices show a possible speed-up of the several orders of magnitude in comparison with classical computations.
Introducing the Simulated Flying Shapes and Simulated Planar Manipulator Datasets
Venue: arXiv cs.CV 2018
Year: 2018

We release two artificial datasets, Simulated Flying Shapes and Simulated Planar Manipulator that allow to test the learning ability of video processing systems. In particular, the dataset is meant as a tool which allows to easily assess the sanity of deep neural network models that aim to encode, reconstruct or predict video frame sequences. The datasets each consist of 90000 videos. The Simulated Flying Shapes dataset comprises scenes showing two objects of equal shape (rectangle, triangle and circle) and size in which one object approaches its counterpart. The Simulated Planar Manipulator shows a 3-DOF planar manipulator that executes a pick-and-place task in which it has to place a size-varying circle on a squared platform. Different from other widely used datasets such as moving MNIST [1], [2], the two presented datasets involve goal-oriented tasks (e.g. the manipulator grasping an object and placing it on a platform), rather than showing random movements. This makes our datasets more suitable for testing prediction capabilities and the learning of sophisticated motions by a machine learning model. This technical document aims at providing an introduction into the usage of both datasets.
Understanding the Effectiveness of Lipschitz-Continuity in Generative Adversarial Nets
Venue: arXiv cs.LG 2018
Year: 2018

In this paper, we investigate the underlying factor that leads to failure and success in the training of GANs. We study the property of the optimal discriminative function and show that in many GANs, the gradient from the optimal discriminative function is not reliable, which turns out to be the fundamental cause of failure in training of GANs. We further demonstrate that a well-defined distance metric does not necessarily guarantee the convergence of GANs. Finally, we prove in this paper that Lipschitz-continuity condition is a general solution to make the gradient of the optimal discriminative function reliable, and characterized the necessary condition where Lipschitz-continuity ensures the convergence, which leads to a broad family of valid GAN objectives under Lipschitz-continuity condition, where Wasserstein distance is one special case. We experiment with several new objectives, which are sound according to our theorems, and we found that, compared with Wasserstein distance, the outputs of the discriminator with new objectives are more stable and the final qualities of generated samples are also consistently higher than those produced by Wasserstein distance.
Monitoring Hyperproperties
Venue: arXiv cs.LO 2018
Year: 2018

Hyperproperties, such as non-interference and observational determinism, relate multiple system executions to each other. They are not expressible in standard temporal logics, like LTL, CTL, and CTL*, and thus cannot be monitored with standard runtime verification techniques. HyperLTL extends linear-time temporal logic (LTL) with explicit quantification over traces in order to express Hyperproperties. We investigate the runtime verification problem of HyperLTL formulas for three different input models: (1) The parallel model, where a fixed number of system executions is processed in parallel. (2) The unbounded sequential model, where system executions are processed sequentially, one execution at a time. In this model, the number of incoming executions may grow forever. (3) The bounded sequential model where the traces are processed sequentially and the number of incoming executions is bounded. We show that deciding monitorability of HyperLTL formulas is PSPACE-complete for input models (1) and (3). Deciding monitorability is PSPACE-complete for alternation-free HyperLTL formulas in input model (2). For every input model, we provide practical monitoring algorithms. We also present various optimization techniques. By recognizing properties of specifications such as reflexivity, symmetry, and transitivity, we reduce the number of comparisons between traces. For the sequential models, we present a technique that minimized the number of traces that need to be stored. Finally, we provide an optimization that succinctly represents the stored traces by sharing common prefixes. We evaluate our optimizations, showing that this leads to much more scalable monitoring, in particular, significantly lower memory consumption.
Make (Nearly) Every Neural Network Better: Generating Neural Network Ensembles by Weight Parameter Resampling
Venue: arXiv cs.LG 2018
Year: 2018

Deep Neural Networks (DNNs) have become increasingly popular in computer vision, natural language processing, and other areas. However, training and fine-tuning a deep learning model is computationally intensive and time-consuming. We propose a new method to improve the performance of nearly every model including pre-trained models. The proposed method uses an ensemble approach where the networks in the ensemble are constructed by reassigning model parameter values based on the probabilistic distribution of these parameters, calculated towards the end of the training process. For pre-trained models, this approach results in an additional training step (usually less than one epoch). We perform a variety of analysis using the MNIST dataset and validate the approach with a number of DNN models using pre-trained models on the ImageNet dataset.
Hypertree Decompositions Revisited for PGMs
Venue: arXiv cs.AI 2018
Year: 2018

We revisit the classical problem of exact inference on probabilistic graphical models (PGMs). Our algorithm is based on recent \emph{worst-case optimal database join} algorithms, which can be asymptotically faster than traditional data processing methods. We present the first empirical evaluation of these algorithms via JoinInfer -- a new exact inference engine. We empirically explore the properties of the data for which our engine can be expected to outperform traditional inference engines, refining current theoretical notions. Further, JoinInfer outperforms existing state-of-the-art inference engines (ACE, IJGP and libDAI) on some standard benchmark datasets by up to a factor of 630x. Finally, we propose a promising data-driven heuristic that extends JoinInfer to automatically tailor its parameters and/or switch to the traditional inference algorithms.
Long Activity Video Understanding using Functional Object-Oriented Network
Venue: arXiv cs.CV 2018
Year: 2018

Video understanding is one of the most challenging topics in computer vision. In this paper, a four-stage video understanding pipeline is presented to simultaneously recognize all atomic actions and the single on-going activity in a video. This pipeline uses objects and motions from the video and a graph-based knowledge representation network as prior reference. Two deep networks are trained to identify objects and motions in each video sequence associated with an action. Low Level image features are then used to identify objects of interest in that video sequence. Confidence scores are assigned to objects of interest based on their involvement in the action and to motion classes based on results from a deep neural network that classifies the on-going action in video into motion classes. Confidence scores are computed for each candidate functional unit associated with an action using a knowledge representation network, object confidences, and motion confidences. Each action is therefore associated with a functional unit and the sequence of actions is further evaluated to identify the single on-going activity in the video. The knowledge representation used in the pipeline is called the functional object-oriented network which is a graph-based network useful for encoding knowledge about manipulation tasks. Experiments are performed on a dataset of cooking videos to test the proposed algorithm with action inference and activity classification. Experiments show that using functional object oriented network improves video understanding significantly.
Is Neuromorphic MNIST neuromorphic? Analyzing the discriminative power of neuromorphic datasets in the time domain
Venue: arXiv cs.NE 2018
Year: 2018

The advantage of spiking neural networks (SNNs) over their predecessors is their ability to spike, enabling them to use spike timing for coding and efficient computing. A neuromorphic dataset should allow a neuromorphic algorithm to clearly show that a SNN is able to perform better on the dataset than an ANN. We have analyzed both N-MNIST and N-Caltech101 along these lines, but focus our study on N-MNIST. First we evaluate if additional information is encoded in the time domain in a neuromoprhic dataset. We show that an ANN trained with backpropagation on frame based versions of N-MNIST and N-Caltech101 images achieve 99.23% and 78.01% accuracy. These are the best classification accuracies obtained on these datasets to date. Second we present the first unsupervised SNN to be trained on N-MNIST and demonstrate results of 91.78%. We also use this SNN for further experiments on N-MNIST to show that rate based SNNs perform better, and precise spike timings are not important in N-MNIST. N-MNIST does not, therefore, highlight the unique ability of SNNs. The conclusion of this study opens an important question in neuromorphic engineering - what, then, constitutes a good neuromorphic dataset?
Rethinking Misalignment to Raise the Bar for Heap Pointer Corruption
Venue: arXiv cs.CR 2018
Year: 2018

Heap layout randomization renders a good portion of heap vulnerabilities unexploitable. However, some remnants of the vulnerabilities are still exploitable even under the randomized layout. According to our analysis, such heap exploits often abuse pointer-width allocation granularity to spray crafted pointers. To address this problem, we explore the efficacy of byte-granularity (the most fine-grained) heap randomization. Heap randomization, in general, has been a well-trodden area; however, the efficacy of byte-granularity randomization has never been fully explored as \emph{misalignment} raises various concerns. This paper unravels the pros and cons of byte-granularity heap randomization by conducting comprehensive analysis in three folds: (i) security effectiveness, (ii) performance impact, and (iii) compatibility analysis to measure deployment cost. Security discussion based on 20 CVE case studies suggests that byte-granularity heap randomization raises the bar against heap exploits more than we initially expected; as pointer spraying approach is becoming prevalent in modern heap exploits. Afterward, to demystify the skeptical concerns regarding misalignment, we conduct cycle-level microbenchmarks and report that the performance cost is highly concentrated to edge cases depending on L1-cache line. Based on such observations, we design and implement an allocator suited to optimize the performance cost of byte-granularity heap randomization; then evaluate the performance with the memory-intensive benchmark (SPEC2006). Finally, we discuss compatibility issues using Coreutils, Nginx, and ChakraCore.
The Learning Technique of the SageMathCloud Use for Students Collaboration Support
Venue: arXiv cs.CY 2018
Year: 2018

The article describes the advisable ways of the cloud-based systems use to support students collaboration in the process of math disciplines learning. The SageMathCloud-based component that aggregates electronic resources for several math disciplines training is introduced. The learning technique of the SageMathCloud use in the process of educational staff training is proposed. The expediency of this technique implementation for more active take up of innovative approaches, forms and methods of math training with the use of the cloud-based tools is substantiated. The experimental results of the SageMathCloud learning component introduction research along with the methods of its use that were elaborated in the course of the study are presented. The use of the evidence-based technique as improving the educational environment of the univer-sity, empowering access to electronic learning resources in the course of math training and engaging with this the educational community and also rising their ICT competence is grounded.
Getting the subtext without the text: Scalable multimodal sentiment classification from visual and acoustic modalities
Venue: arXiv cs.CV 2018
Year: 2018

In the last decade, video blogs (vlogs) have become an extremely popular method through which people express sentiment. The ubiquitousness of these videos has increased the importance of multimodal fusion models, which incorporate video and audio features with traditional text features for automatic sentiment detection. Multimodal fusion offers a unique opportunity to build models that learn from the full depth of expression available to human viewers. In the detection of sentiment in these videos, acoustic and video features provide clarity to otherwise ambiguous transcripts. In this paper, we present a multimodal fusion model that exclusively uses high-level video and audio features to analyze spoken sentences for sentiment. We discard traditional transcription features in order to minimize human intervention and to maximize the deployability of our model on at-scale real-world data. We select high-level features for our model that have been successful in nonaffect domains in order to test their generalizability in the sentiment detection domain. We train and test our model on the newly released CMU Multimodal Opinion Sentiment and Emotion Intensity (CMUMOSEI) dataset, obtaining an F1 score of 0.8049 on the validation set and an F1 score of 0.6325 on the held-out challenge test set.
FastTrack: Minimizing Stalls for CDN-based Over-the-top Video Streaming Systems
Venue: arXiv cs.NI 2018
Year: 2018

Traffic for internet video streaming has been rapidly increasing and is further expected to increase with the higher definition videos and IoT applications, such as 360 degree videos and augmented virtual reality applications. While efficient management of heterogeneous cloud resources to optimize the quality of experience is important, existing work in this problem space often left out important factors. In this paper, we present a model for describing a today's representative system architecture for video streaming applications, typically composed of a centralized origin server and several CDN sites. Our model comprehensively considers the following factors: limited caching spaces at the CDN sites, allocation of CDN for a video request, choice of different ports from the CDN, and the central storage and bandwidth allocation. With the model, we focus on minimizing a performance metric, stall duration tail probability (SDTP), and present a novel, yet efficient, algorithm to solve the formulated optimization problem. The theoretical bounds with respect to the SDTP metric are also analyzed and presented. Our extensive simulation results demonstrate that the proposed algorithms can significantly improve the SDTP metric, compared to the baseline strategies. Small-scale video streaming system implementation in a real cloud environment further validates our results.
Variable neighborhood search for partitioning sparse biological networks into the maximum edge-weighted $k$-plexes
Venue: arXiv cs.DS 2018
Year: 2018

In a network, a $k$-plex represents a subset of $n$ vertices where the degree of each vertex in the subnetwork induced by this subset is at least $n-k$. The maximum edge-weight $k$-plex partitioning problem (Max-EkPP) is to find the $k$-plex partitioning in edge-weighted network, such that the sum of edge weights is maximal. The Max-EkPP has an important role in discovering new information in large sparse biological networks. We propose a variable neighborhood search (VNS) algorithm for solving Max-EkPP. The VNS implements a local search based on the 1-swap first improvement strategy and the objective function that takes into account the degree of every vertex in each partition. The objective function favors feasible solutions, also enabling a gradual increase in terms of objective function value when moving from slightly infeasible to barely feasible solutions. A comprehensive experimental computation is performed on real metabolic networks and other benchmark instances from literature. Comparing to the integer linear programming method from literature, our approach succeeds to find all known optimal solutions. For all other instances, the VNS either reaches previous best known solution or improves it. The proposed VNS is also tested on a large-scaled dataset which was not previously considered in literature.
Styling with Attention to Details
Venue: arXiv cs.IR 2018
Year: 2018

Fashion as characterized by its nature, is driven by style. In this paper, we propose a method that takes into account the style information to complete a given set of selected fashion items with a complementary fashion item. Complementary items are those items that can be worn along with the selected items according to the style. Addressing this problem facilitates in automatically generating stylish fashion ensembles leading to a richer shopping experience for users. Recently, there has been a surge of online social websites where fashion enthusiasts post the outfit of the day and other users can like and comment on them. These posts contain a gold-mine of information about style. In this paper, we exploit these posts to train a deep neural network which captures style in an automated manner. We pose the problem of predicting complementary fashion items as a sequence to sequence problem where the input is the selected set of fashion items and the output is a complementary fashion item based on the style information learned by the model. We use the encoder decoder architecture to solve this problem of completing the set of fashion items. We evaluate the goodness of the proposed model through a variety of experiments. We empirically observe that our proposed model outperforms competitive baseline like apriori algorithm by ~28 in terms of accuracy for top-1 recommendation to complete the fashion ensemble. We also perform retrieval based experiments to understand the ability of the model to learn style and rank the complementary fashion items and find that using attention in our encoder decoder model helps in improving the mean reciprocal rank by ~24. Qualitatively we find the complementary fashion items generated by our proposed model are richer than the apriori algorithm.