OpenRaiser/Intern-Atlas huggingface dataset:
Clustering-Based Quantisation for PDE-Based Image Compression
Venue: arXiv cs.CV 2017
Year: 2017

Finding optimal data for inpainting is a key problem in the context of partial differential equation based image compression. The data that yields the most accurate reconstruction is real-valued. Thus, quantisation models are mandatory to allow an efficient encoding. These can also be understood as challenging data clustering problems. Although clustering approaches are well suited for this kind of compression codecs, very few works actually consider them. Each pixel has a global impact on the reconstruction and optimal data locations are strongly correlated with their corresponding colour values. These facts make it hard to predict which feature works best. In this paper we discuss quantisation strategies based on popular methods such as k-means. We are lead to the central question which kind of feature vectors are best suited for image compression. To this end we consider choices such as the pixel values, the histogram or the colour map. Our findings show that the number of colours can be reduced significantly without impacting the reconstruction quality. Surprisingly, these benefits do not directly translate to a good image compression performance. The gains in the compression ratio are lost due to increased storage costs. This suggests that it is integral to evaluate the clustering on both, the reconstruction error and the final file size.
A Thorough Formalization of Conceptual Spaces
Venue: arXiv cs.AI 2017
Year: 2017

The highly influential framework of conceptual spaces provides a geometric way of representing knowledge. Instances are represented by points in a high-dimensional space and concepts are represented by convex regions in this space. After pointing out a problem with the convexity requirement, we propose a formalization of conceptual spaces based on fuzzy star-shaped sets. Our formalization uses a parametric definition of concepts and extends the original framework by adding means to represent correlations between different domains in a geometric way. Moreover, we define computationally efficient operations on concepts (intersection, union, and projection onto a subspace) and show that these operations can support both learning and reasoning processes.
Leveraging Adaptive Model Predictive Controller for Active Cell Balancing in Li-ion Battery
Venue: arXiv cs.SY 2017
Year: 2017

Automotive industry is moving toward fully electric and hybrid electric vehicles. Accordingly, energy storage unit is one of the most important blocks in these electric drives. Battery stacks which contain a number of cells are being used for supplying the vehicles' energy. Charge equalization for series connected battery strings has a significant effect on battery life. In this paper, an adaptive model predictive controller (AMPC) is proposed to manage the cell equalizing process. The series connected cells' voltages and currents are collected, then leveraging Recursive Least Square (RLS) method, the future voltage samples for all of the cells are predicted. MPC controller specifies a sequence which results in the optimum balancing performance of the proposed circuit. Simulation results prove that using the suggested algorithm, the voltage set of the series cells has moved more uniformly.
Investigating Exploratory Search Activities based on the Stratagem Level in Digital Libraries
Venue: arXiv cs.DL 2017
Year: 2017

In this paper we present the results of a user study on exploratory search activities in a social science digital library. We conducted a user study with 32 participants with a social sciences background -- 16 postdoctoral researchers and 16 students -- who were asked to solve a task on searching related work to a given topic. The exploratory search task was performed in a 10-minutes time slot. The use of certain search activities is measured and compared to gaze data recorded with an eye tracking device. We use a novel tree graph representation to visualise the users' search patterns and introduce a way to combine multiple search session trees. The tree graph representation is capable to create one single tree for multiple users and to identify common search patterns. In addition, the information behaviour of students and postdoctoral researchers is being compared. The results show that search activities on the stratagem level are frequently utilised by both user groups. The most heavily used search activities were keyword search, followed by browsing through references and citations, and author searching. The eye tracking results showed an intense examination of documents metadata, especially on the level of citations and references. When comparing the group of students and postdoctoral researchers we found significant differences regarding gaze data on the area of the journal name of the seed document. In general, we found a tendency of the postdoctoral researchers to examine the metadata records more intensively with regards to dwell time and the number of fixations.
Control-Data Separation with Decentralized Edge Control in Fog-Assisted Uplink Communications
Venue: arXiv cs.IT 2017
Year: 2017

Fog-aided network architectures for 5G systems encompass wireless edge nodes, referred to as remote radio systems (RRSs), as well as remote cloud center (RCC) processors, which are connected to the RRSs via a fronthaul access network. RRSs and RCC are operated via Network Functions Virtualization (NFV), enabling a flexible split of network functionalities that adapts to network parameters such as fronthaul latency and capacity. This work focuses on uplink communications and investigates the cloud-edge allocation of two important network functions, namely the control functionality of rate selection and the data-plane function of decoding. Three functional splits are considered: (i) Distributed Radio Access Network (D-RAN), in which both functions are implemented in a decentralized way at the RRSs, (ii) Cloud RAN (C-RAN), in which instead both functions are carried out centrally at the RCC, and (iii) a new functional split, referred to as Fog RAN (F-RAN), with separate decentralized edge control and centralized cloud data processing. The model under study consists of a time-varying uplink channel in which the RCC has global but delayed channel state information (CSI) due to fronthaul latency, while the RRSs have local but more timely CSI. Using the adaptive sum-rate as the performance criterion, it is concluded that the F-RAN architecture can provide significant gains in the presence of user mobility.
On Pairwise Clustering with Side Information
Venue: arXiv cs.LG 2017
Year: 2017

Pairwise clustering, in general, partitions a set of items via a known similarity function. In our treatment, clustering is modeled as a transductive prediction problem. Thus rather than beginning with a known similarity function, the function instead is hidden and the learner only receives a random sample consisting of a subset of the pairwise similarities. An additional set of pairwise side-information may be given to the learner, which then determines the inductive bias of our algorithms. We measure performance not based on the recovery of the hidden similarity function, but instead on how well we classify each item. We give tight bounds on the number of misclassifications. We provide two algorithms. The first algorithm SACA is a simple agglomerative clustering algorithm which runs in near linear time, and which serves as a baseline for our analyses. Whereas the second algorithm, RGCA, enables the incorporation of side-information which may lead to improved bounds at the cost of a longer running time.
Mean-Payoff Optimization in Continuous-Time Markov Chains with Parametric Alarms
Venue: arXiv cs.PF 2017
Year: 2017

Continuous-time Markov chains with alarms (ACTMCs) allow for alarm events that can be non-exponentially distributed. Within parametric ACTMCs, the parameters of alarm-event distributions are not given explicitly and can be subject of parameter synthesis. An algorithm solving the $\varepsilon$-optimal parameter synthesis problem for parametric ACTMCs with long-run average optimization objectives is presented. Our approach is based on reduction of the problem to finding long-run average optimal strategies in semi-Markov decision processes (semi-MDPs) and sufficient discretization of parameter (i.e., action) space. Since the set of actions in the discretized semi-MDP can be very large, a straightforward approach based on explicit action-space construction fails to solve even simple instances of the problem. The presented algorithm uses an enhanced policy iteration on symbolic representations of the action space. The soundness of the algorithm is established for parametric ACTMCs with alarm-event distributions satisfying four mild assumptions that are shown to hold for uniform, Dirac and Weibull distributions in particular, but are satisfied for many other distributions as well. An experimental implementation shows that the symbolic technique substantially improves the efficiency of the synthesis algorithm and allows to solve instances of realistic size.
Orthogonal Compaction Using Additional Bends
Venue: arXiv cs.DS 2017
Year: 2017

Compacting orthogonal drawings is a challenging task. Usually algorithms try to compute drawings with small area or edge length while preserving the underlying orthogonal shape. We present a one-dimensional compaction algorithm that alters the orthogonal shape of edges for better geometric results. An experimental evaluation shows that we were able to reduce the total edge length and the drawing area, but at the expense of additional bends.
Inference in Deep Networks in High Dimensions
Venue: arXiv cs.LG 2017
Year: 2017

Deep generative networks provide a powerful tool for modeling complex data in a wide range of applications. In inverse problems that use these networks as generative priors on data, one must often perform inference of the inputs of the networks from the outputs. Inference is also required for sampling during stochastic training on these generative models. This paper considers inference in a deep stochastic neural network where the parameters (e.g., weights, biases and activation functions) are known and the problem is to estimate the values of the input and hidden units from the output. While several approximate algorithms have been proposed for this task, there are few analytic tools that can provide rigorous guarantees in the reconstruction error. This work presents a novel and computationally tractable output-to-input inference method called Multi-Layer Vector Approximate Message Passing (ML-VAMP). The proposed algorithm, derived from expectation propagation, extends earlier AMP methods that are known to achieve the replica predictions for optimality in simple linear inverse problems. Our main contribution shows that the mean-squared error (MSE) of ML-VAMP can be exactly predicted in a certain large system limit (LSL) where the numbers of layers is fixed and weight matrices are random and orthogonally-invariant with dimensions that grow to infinity. ML-VAMP is thus a principled method for output-to-input inference in deep networks with a rigorous and precise performance achievability result in high dimensions.
A Unified Approach to Adaptive Regularization in Online and Stochastic Optimization
Venue: arXiv cs.LG 2017
Year: 2017

We describe a framework for deriving and analyzing online optimization algorithms that incorporate adaptive, data-dependent regularization, also termed preconditioning. Such algorithms have been proven useful in stochastic optimization by reshaping the gradients according to the geometry of the data. Our framework captures and unifies much of the existing literature on adaptive online methods, including the AdaGrad and Online Newton Step algorithms as well as their diagonal versions. As a result, we obtain new convergence proofs for these algorithms that are substantially simpler than previous analyses. Our framework also exposes the rationale for the different preconditioned updates used in common stochastic optimization methods.
Observational Learning by Reinforcement Learning
Venue: arXiv cs.LG 2017
Year: 2017

Observational learning is a type of learning that occurs as a function of observing, retaining and possibly replicating or imitating the behaviour of another agent. It is a core mechanism appearing in various instances of social learning and has been found to be employed in several intelligent species, including humans. In this paper, we investigate to what extent the explicit modelling of other agents is necessary to achieve observational learning through machine learning. Especially, we argue that observational learning can emerge from pure Reinforcement Learning (RL), potentially coupled with memory. Through simple scenarios, we demonstrate that an RL agent can leverage the information provided by the observations of an other agent performing a task in a shared environment. The other agent is only observed through the effect of its actions on the environment and never explicitly modeled. Two key aspects are borrowed from observational learning: i) the observer behaviour needs to change as a result of viewing a 'teacher' (another agent) and ii) the observer needs to be motivated somehow to engage in making use of the other agent's behaviour. The later is naturally modeled by RL, by correlating the learning agent's reward with the teacher agent's behaviour.
Approximation of Bandwidth for the Interactive Operation in Video on Demand System
Venue: arXiv cs.NI 2017
Year: 2017

An interactive session of video-on-demand (VOD) streaming procedure deserves smooth data transportation for the viewer, irrespective of their geographic location. To access the required video, bandwidth management during the video objects transportation at any interactive session is a mandatory prerequisite. It has been observed in the domain likes movie on demand, electronic encyclopedia, interactive games, and educational resources. The required data is imported from the distributed storage servers through the high speed backbone network. This paper presents the viewer driven session based multi-user model with respect to the overlay mesh network. In virtue of reality, the direct implication of this work elaborately shows the required bandwidth is a causal part in the video on demand system. The analytic model of session based single viewer bandwidth requirement model presents the bandwidth requirement for any interactive session like, pause, move slow, rewind, skip some number of frames, or move fast with some constant number of frames. This work presents the bandwidth requirement model for any interactive session that brings the trade-off in data-transportation and storage costs for different system resources and also for the various system configurations.
Agreement Protocols on an Arbitrary Network in the Presence of a Mobile Adversary
Venue: arXiv cs.DC 2017
Year: 2017

We investigate the problem of obtaining agreement protocols in the presence of a mobile adversary, who can control an ever-changing selection of processors. We make improvements to previous results for the case when the communications network forms a complete graph, and also adapt these to the general case when the network is not complete.
Effective Spoken Language Labeling with Deep Recurrent Neural Networks
Venue: arXiv cs.CL 2017
Year: 2017

Understanding spoken language is a highly complex problem, which can be decomposed into several simpler tasks. In this paper, we focus on Spoken Language Understanding (SLU), the module of spoken dialog systems responsible for extracting a semantic interpretation from the user utterance. The task is treated as a labeling problem. In the past, SLU has been performed with a wide variety of probabilistic models. The rise of neural networks, in the last couple of years, has opened new interesting research directions in this domain. Recurrent Neural Networks (RNNs) in particular are able not only to represent several pieces of information as embeddings but also, thanks to their recurrent architecture, to encode as embeddings relatively long contexts. Such long contexts are in general out of reach for models previously used for SLU. In this paper we propose novel RNNs architectures for SLU which outperform previous ones. Starting from a published idea as base block, we design new deep RNNs achieving state-of-the-art results on two widely used corpora for SLU: ATIS (Air Traveling Information System), in English, and MEDIA (Hotel information and reservation in France), in French.
RSU Cloud and its Resource Management in support of Enhanced Vehicular Applications
Venue: arXiv cs.NI 2017
Year: 2017

We propose Roadside Unit (RSU) Clouds as a novel way to offer non-safety application with QoS for VANETs. The architecture of RSU Clouds is delineated, and consists of traditional RSUs and specialized micro-datacenters and virtual machines (VMs) using Software Defined Networking (SDN). SDN offers the flexibility to migrate or replicate virtual services and reconfigure the data forwarding rules dynamically. However, frequent changes to service hosts and data flows not only result in degradation of services, but are also costly for service providers. In this paper, we use Mininet to analyze and formally quantify the reconfiguration overhead. Our unique RSU Cloud Resource Management (CRM) model jointly minimizes reconfiguration overhead, cost of service deployment and infrastructure routing delay. To the best of our knowledge, we are the first to utilize this approach. We compare the performance of purist approach to our Integer Linear Programming (ILP) model and our innovative heuristic for the CRM technique and discuss the results. We will show the benefits of a holistic approach in Cloud Resource Management with SDN.
The Theory is Predictive, but is it Complete? An Application to Human Perception of Randomness
Venue: arXiv cs.LG 2017
Year: 2017

When we test a theory using data, it is common to focus on correctness: do the predictions of the theory match what we see in the data? But we also care about completeness: how much of the predictable variation in the data is captured by the theory? This question is difficult to answer, because in general we do not know how much "predictable variation" there is in the problem. In this paper, we consider approaches motivated by machine learning algorithms as a means of constructing a benchmark for the best attainable level of prediction. We illustrate our methods on the task of predicting human-generated random sequences. Relative to an atheoretical machine learning algorithm benchmark, we find that existing behavioral models explain roughly 15 percent of the predictable variation in this problem. This fraction is robust across several variations on the problem. We also consider a version of this approach for analyzing field data from domains in which human perception and generation of randomness has been used as a conceptual framework; these include sequential decision-making and repeated zero-sum games. In these domains, our framework for testing the completeness of theories provides a way of assessing their effectiveness over different contexts; we find that despite some differences, the existing theories are fairly stable across our field domains in their performance relative to the benchmark. Overall, our results indicate that (i) there is a significant amount of structure in this problem that existing models have yet to capture and (ii) there are rich domains in which machine learning may provide a viable approach to testing completeness.
Constant Composition Codes as Subcodes of Linear Codes
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, on one hand, a class of linear codes with one or two weights is obtained. Based on these linear codes, we construct two classes of constant composition codes, which includes optimal constant composition codes depending on LVFC bound. On the other hand, a class of constant composition codes is derived from known linear codes.
Distributed Least-Squares Iterative Methods in Networks: A Survey
Venue: arXiv cs.DC 2017
Year: 2017

Many science and engineering applications involve solving a linear least-squares system formed from some field measurements. In the distributed cyber-physical systems (CPS), often each sensor node used for measurement only knows partial independent rows of the least-squares system. To compute the least-squares solution they need to gather all these measurement at a centralized location and then compute the solution. These data collection and computation are inefficient because of bandwidth and time constraints and sometimes are infeasible because of data privacy concerns. Thus distributed computations are strongly preferred or demanded in many of the real world applications e.g.: smart-grid, target tracking etc. To compute least squares for the large sparse system of linear equation iterative methods are natural candidates and there are a lot of studies regarding this, however, most of them are related to the efficiency of centralized/parallel computations while and only a few are explicitly about distributed computation or have the potential to apply in distributed networks. This paper surveys the representative iterative methods from several research communities. Some of them were not originally designed for this need, so we slightly modified them to suit our requirement and maintain the consistency. In this survey, we sketch the skeleton of the algorithm first and then analyze its time-to-completion and communication cost. To our best knowledge, this is the first survey of distributed least-squares in distributed networks.
A Useful Motif for Flexible Task Learning in an Embodied Two-Dimensional Visual Environment
Venue: arXiv cs.LG 2017
Year: 2017

Animals (especially humans) have an amazing ability to learn new tasks quickly, and switch between them flexibly. How brains support this ability is largely unknown, both neuroscientifically and algorithmically. One reasonable supposition is that modules drawing on an underlying general-purpose sensory representation are dynamically allocated on a per-task basis. Recent results from neuroscience and artificial intelligence suggest the role of the general purpose visual representation may be played by a deep convolutional neural network, and give some clues how task modules based on such a representation might be discovered and constructed. In this work, we investigate module architectures in an embodied two-dimensional touchscreen environment, in which an agent's learning must occur via interactions with an environment that emits images and rewards, and accepts touches as input. This environment is designed to capture the physical structure of the task environments that are commonly deployed in visual neuroscience and psychophysics. We show that in this context, very simple changes in the nonlinear activations used by such a module can significantly influence how fast it is at learning visual tasks and how suitable it is for switching to new tasks.
Content-Centric Networking - Architectural Overview and Protocol Description
Venue: arXiv cs.NI 2017
Year: 2017

This document describes the core concepts of the CCNx architecture and presents a minimum network protocol based on two messages: Interests and Content Objects. It specifies the set of mandatory and optional fields within those messages and describes their behavior and interpretation. This architecture and protocol specification is independent of a specific wire encoding.