OpenRaiser/Intern-Atlas huggingface dataset:
Underwater Doppler Navigation with Self-calibration
Venue: arXiv cs.RO 2015
Year: 2015

Precise autonomous navigation remains a substantial challenge to all underwater platforms. Inertial Measurement Units (IMU) and Doppler Velocity Logs (DVL) have complementary characteristics and are promising sensors that could enable fully autonomous underwater navigation in unexplored areas without relying on additional external Global Positioning System (GPS) or acoustic beacons. This paper addresses the combined IMU/DVL navigation system from the viewpoint of observability. We show by analysis that under moderate conditions the combined system is observable. Specifically, the DVL parameters, including the scale factor and misalignment angles, can be calibrated in-situ without using external GPS or acoustic beacon sensors. Simulation results using a practical estimator validate the analytic conclusions.
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching
Venue: arXiv cs.AI 2015
Year: 2015

Lightweight, source-to-source transformation approaches to implementing MCMC for probabilistic programming languages are popular for their simplicity, support of existing deterministic code, and ability to execute on existing fast runtimes. However, they are also slow, requiring a complete re-execution of the program on every Metropolis Hastings proposal. We present a new extension to the lightweight approach, C3, which enables efficient, incrementalized re-execution of MH proposals. C3 is based on two core ideas: transforming probabilistic programs into continuation passing style (CPS), and caching the results of function calls. We show that on several common models, C3 reduces proposal runtime by 20-100x, in some cases reducing runtime complexity from linear in model size to constant. We also demonstrate nearly an order of magnitude speedup on a complex inverse procedural modeling application.
Self-calibration of a differential wheeled robot using only a gyroscope and a distance sensor
Venue: arXiv cs.RO 2015
Year: 2015

Research in mobile robotics often demands platforms that have an adequate balance between cost and reliability. In the case of terrestrial robots, one of the available options is the GNBot, an open-hardware project intended for the evaluation of swarm search strategies. The lack of basic odometry sensors such as wheel encoders had so far difficulted the implementation of an accurate high-level controller in this platform. Thus, the aim of this thesis is to improve motion control in the GNBot by incorporating a gyroscope whilst maintaining the requisite of no wheel encoders. Among the problems that have been tackled are: accurate in-place rotations, minimal drift during linear motions, and arc-performing functionality. Additionally, the resulting controller is calibrated autonomously by using both the gyroscope module and the infrared rangefinder on board each robot, greatly simplifying the calibration of large swarms. The report first explains the design decisions that were made in order to implement the self-calibration routine, and then evaluates the performance of the new motion controller by means of off-line video tracking. The motion accuracy of the new controller is also compared with the previously existing solution in an odor search experiment.
Extended inequalities for weighted Renyi entropy involving generalized Gaussian densities
Venue: arXiv cs.IT 2015
Year: 2015

In this paper the author analyses the weighted Renyi entropy in order to derive several inequalities in weighted case. Furthermore, using the proposed notions $α$-th generalized derivation and ($α$; p)-th weighted Fisher information, extended versions of the moment-entropy, Fisher information and Cramer-Rao inequalities in terms of generalized Gaussian densities are given.
Personalized Search
Venue: arXiv cs.IR 2015
Year: 2015

As the volume of electronically available information grows, relevant items become harder to find. This work presents an approach to personalizing search results in scientific publication databases. This work focuses on re-ranking search results from existing search engines like Solr or ElasticSearch. This work also includes the development of Obelix, a new recommendation system used to re-rank search results. The project was proposed and performed at CERN, using the scientific publications available on the CERN Document Server (CDS). This work experiments with re-ranking using offline and online evaluation of users and documents in CDS. The experiments conclude that the personalized search result outperform both latest first and word similarity in terms of click position in the search result for global search in CDS.
Object Proposals for Text Extraction in the Wild
Venue: arXiv cs.CV 2015
Year: 2015

Object Proposals is a recent computer vision technique receiving increasing interest from the research community. Its main objective is to generate a relatively small set of bounding box proposals that are most likely to contain objects of interest. The use of Object Proposals techniques in the scene text understanding field is innovative. Motivated by the success of powerful while expensive techniques to recognize words in a holistic way, Object Proposals techniques emerge as an alternative to the traditional text detectors. In this paper we study to what extent the existing generic Object Proposals methods may be useful for scene text understanding. Also, we propose a new Object Proposals algorithm that is specifically designed for text and compare it with other generic methods in the state of the art. Experiments show that our proposal is superior in its ability of producing good quality word proposals in an efficient way. The source code of our method is made publicly available.
Learning Efficient Representations for Reinforcement Learning
Venue: arXiv cs.AI 2015
Year: 2015

Markov decision processes (MDPs) are a well studied framework for solving sequential decision making problems under uncertainty. Exact methods for solving MDPs based on dynamic programming such as policy iteration and value iteration are effective on small problems. In problems with a large discrete state space or with continuous state spaces, a compact representation is essential for providing an efficient approximation solutions to MDPs. Commonly used approximation algorithms involving constructing basis functions for projecting the value function onto a low dimensional subspace, and building a factored or hierarchical graphical model to decompose the transition and reward functions. However, hand-coding a good compact representation for a given reinforcement learning (RL) task can be quite difficult and time consuming. Recent approaches have attempted to automatically discover efficient representations for RL. In this thesis proposal, we discuss the problems of automatically constructing structured kernel for kernel based RL, a popular approach to learning non-parametric approximations for value function. We explore a space of kernel structures which are built compositionally from base kernels using a context-free grammar. We examine a greedy algorithm for searching over the structure space. To demonstrate how the learned structure can represent and approximate the original RL problem in terms of compactness and efficiency, we plan to evaluate our method on a synthetic problem and compare it to other RL baselines.
Approximate Message Passing in Coded Aperture Snapshot Spectral Imaging
Venue: arXiv cs.IT 2015
Year: 2015

We consider a compressive hyperspectral imaging reconstruction problem, where three-dimensional spatio-spectral information about a scene is sensed by a coded aperture snapshot spectral imager (CASSI). The approximate message passing (AMP) framework is utilized to reconstruct hyperspectral images from CASSI measurements, and an adaptive Wiener filter is employed as a three-dimensional image denoiser within AMP. We call our algorithm "AMP-3D-Wiener." The simulation results show that AMP-3D-Wiener outperforms existing widely-used algorithms such as gradient projection for sparse reconstruction (GPSR) and two-step iterative shrinkage/thresholding (TwIST) given the same amount of runtime. Moreover, in contrast to GPSR and TwIST, AMP-3D-Wiener need not tune any parameters, which simplifies the reconstruction process.
To Love or to Loathe: How is the World Reacting to China's Rise?
Venue: arXiv cs.CY 2015
Year: 2015

China has experienced a spectacular economic growth in recent decades. Its economy grew more than 48 times from 1980 to 2013. How are the other countries reacting to China's rise? Do they see it as an economic opportunity or a security threat? In this paper, we answer this question by analyzing online news reports about China published in Australia, France, Germany, Japan, Russia, South Korea, the UK and the US. More specifically, we first analyze the frequency with which China has appeared in news headlines, which is a measure of China's influence in the world. Second, we build a Naive Bayes classifier to study the evolving nature of the news reports, i.e., whether they are economic or political. We then evaluate the friendliness of the news coverage based on sentiment analysis. Empirical results indicate that there has been increasing news coverage of China in all the countries under study. We also find that the emphasis of the reports is generally shifting towards China's economy. Here Japan and South Korea are exceptions: they are reporting more on Chinese politics. In terms of global sentiment, the picture is quite gloomy. With the exception of Australia and, to some extent, France, all the other countries under examination are becoming less positive towards China.
Uplink Pilot and Data Power Control for Single Cell Massive MIMO Systems with MRC
Venue: arXiv cs.IT 2015
Year: 2015

This paper considers the jointly optimal pilot and data power allocation in single cell uplink massive MIMO systems. A closed form solution for the optimal length of the training interval is derived. Using the spectral efficiency (SE) as performance metric and setting a total energy budget per co- herence interval the power control is formulated as optimization problems for two different objective functions: the minimum SE among the users and the sum SE. The optimal power control policy is found for the case of maximizing the minimum SE by converting it to a geometric program (GP). Since maximizing the sum SE is an NP-hard problem, an efficient algorithm is developed for finding KKT (local maximum) points. Simulation results show the advantage of optimizing the power control over both pilot and data power, as compared to heuristic power control policies.
Proposal-free Network for Instance-level Object Segmentation
Venue: arXiv cs.CV 2015
Year: 2015

Instance-level object segmentation is an important yet under-explored task. The few existing studies are almost all based on region proposal methods to extract candidate segments and then utilize object classification to produce final results. Nonetheless, generating accurate region proposals itself is quite challenging. In this work, we propose a Proposal-Free Network (PFN ) to address the instance-level object segmentation problem, which outputs the instance numbers of different categories and the pixel-level information on 1) the coordinates of the instance bounding box each pixel belongs to, and 2) the confidences of different categories for each pixel, based on pixel-to-pixel deep convolutional neural network. All the outputs together, by using any off-the-shelf clustering method for simple post-processing, can naturally generate the ultimate instance-level object segmentation results. The whole PFN can be easily trained in an end-to-end way without the requirement of a proposal generation stage. Extensive evaluations on the challenging PASCAL VOC 2012 semantic segmentation benchmark demonstrate that the proposed PFN solution well beats the state-of-the-arts for instance-level object segmentation. In particular, the $AP^r$ over 20 classes at 0.5 IoU reaches 58.7% by PFN, significantly higher than 43.8% and 46.3% by the state-of-the-art algorithms, SDS [9] and [16], respectively.
Parameterized Complexity of Graph Constraint Logic
Venue: arXiv cs.CC 2015
Year: 2015

Graph constraint logic is a framework introduced by Hearn and Demaine, which provides several problems that are often a convenient starting point for reductions. We study the parameterized complexity of Constraint Graph Satisfiability and both bounded and unbounded versions of Nondeterministic Constraint Logic (NCL) with respect to solution length, treewidth and maximum degree of the underlying constraint graph as parameters. As a main result we show that restricted NCL remains PSPACE-complete on graphs of bounded bandwidth, strengthening Hearn and Demaine's framework. This allows us to improve upon existing results obtained by reduction from NCL. We show that reconfiguration versions of several classical graph problems (including independent set, feedback vertex set and dominating set) are PSPACE-complete on planar graphs of bounded bandwidth and that Rush Hour, generalized to $k\times n$ boards, is PSPACE-complete even when $k$ is at most a constant.
Finite Dictionary Variants of the Diffusion KLMS Algorithm
Venue: arXiv cs.SY 2015
Year: 2015

The diffusion based distributed learning approaches have been found to be a viable solution for learning over linearly separable datasets over a network. However, approaches till date are suitable for linearly separable datasets and need to be extended to scenarios in which we need to learn a non-linearity. In such scenarios, the recently proposed diffusion kernel least mean squares (KLMS) has been found to be performing better than diffusion least mean squares (LMS). The drawback of diffusion KLMS is that it requires infinite storage for observations (also called dictionary). This paper formulates the diffusion KLMS in a fixed budget setting such that the storage requirement is curtailed while maintaining appreciable performance in terms of convergence. Simulations have been carried out to validate the two newly proposed algorithms named as quantised diffusion KLMS (QDKLMS) and fixed budget diffusion KLMS (FBDKLMS) against KLMS, which indicate that both the proposed algorithms deliver better performance as compared to the KLMS while reducing the dictionary size storage requirement.
LearnWeb-OER: Improving Accessibility of Open Educational Resources
Venue: arXiv cs.HC 2015
Year: 2015

In addition to user-generated content, Open Educational Resources are increasingly made available on the Web by several institutions and organizations with the aim of being re-used. Nevertheless, it is still difficult for users to find appropriate resources for specific learning scenarios among the vast amount offered on the Web. Our goal is to give users the opportunity to search for authentic resources from the Web and reuse them in a learning context. The LearnWeb-OER platform enhances collaborative searching and sharing of educational resources providing specific means and facilities for education. In the following, we provide a description of the functionalities that support users in collaboratively collecting, selecting, annotating and discussing search results and learning resources.
Explicit model predictive control accuracy analysis
Venue: arXiv cs.SY 2015
Year: 2015

Model Predictive Control (MPC) can efficiently control constrained systems in real-time applications. MPC feedback law for a linear system with linear inequality constraints can be explicitly computed off-line, which results in an off-line partition of the state space into non-overlapped convex regions, with affine control laws associated to each region of the partition. An actual implementation of this explicit MPC in low cost micro-controllers requires the data to be "quantized", i.e. represented with a small number of memory bits. An aggressive quantization decreases the number of bits and the controller manufacturing costs, and may increase the speed of the controller, but reduces accuracy of the control input computation. We derive upper bounds for the absolute error in the control depending on the number of quantization bits and system parameters. The bounds can be used to determine how many quantization bits are needed in order to guarantee a specific level of accuracy in the control input.
Three-Dimensional Stateful Material Implication Logic
Venue: arXiv cs.ET 2015
Year: 2015

Monolithic three-dimensional integration of memory and logic circuits could dramatically improve performance and energy efficiency of computing systems. Some conventional and emerging memories are suitable for vertical integration, including highly scalable metal-oxide resistive switching devices (memristors), yet integration of logic circuits proves to be much more challenging. Here we demonstrate memory and logic functionality in a monolithic three-dimensional circuit by adapting recently proposed memristor-based stateful material implication logic. Though such logic has been already implemented with a variety of memory devices, prohibitively large device variability in the most prospective memristor-based circuits has limited experimental demonstrations to simple gates and just a few cycles of operations. By developing a low-temperature, low-variability fabrication process, and modifying the original circuit to increase its robustness to device imperfections, we experimentally show, for the first time, reliable multi-cycle multi-gate material implication logic operation within a three-dimensional stack of monolithically integrated memristors. The direct data manipulation in three dimensions enables extremely compact and high-throughput logic-in-memory computing and, remarkably, presents a viable solution for the Feynman grand challenge of implementing an 8-bit adder at the nanoscale.
Merge Frame Design for Video Stream Switching using Piecewise Constant Functions
Venue: arXiv cs.MM 2015
Year: 2015

The ability to efficiently switch from one pre-encoded video stream to another (e.g., for bitrate adaptation or view switching) is important for many interactive streaming applications. Recently, stream-switching mechanisms based on distributed source coding (DSC) have been proposed. In order to reduce the overall transmission rate, these approaches provide a "merge" mechanism, where information is sent to the decoder such that the exact same frame can be reconstructed given that any one of a known set of side information (SI) frames is available at the decoder (e.g., each SI frame may correspond to a different stream from which we are switching). However, the use of bit-plane coding and channel coding in many DSC approaches leads to complex coding and decoding. In this paper, we propose an alternative approach for merging multiple SI frames, using a piecewise constant (PWC) function as the merge operator. In our approach, for each block to be reconstructed, a series of parameters of these PWC merge functions are transmitted in order to guarantee identical reconstruction given the known side information blocks. We consider two different scenarios. In the first case, a target frame is first given, and then merge parameters are chosen so that this frame can be reconstructed exactly at the decoder. In contrast, in the second scenario, the reconstructed frame and merge parameters are jointly optimized to meet a rate-distortion criteria. Experiments show that for both scenarios, our proposed merge techniques can outperform both a recent approach based on DSC and the SP-frame approach in H.264, in terms of compression efficiency and decoder complexity.
Weak Completeness of Coalgebraic Dynamic Logics
Venue: arXiv cs.LO 2015
Year: 2015

We present a coalgebraic generalisation of Fischer and Ladner's Propositional Dynamic Logic (PDL) and Parikh's Game Logic (GL). In earlier work, we proved a generic strong completeness result for coalgebraic dynamic logics without iteration. The coalgebraic semantics of such programs is given by a monad T, and modalities are interpreted via a predicate lifting Î whose transpose is a monad morphism from T to the neighbourhood monad. In this paper, we show that if the monad T carries a complete semilattice structure, then we can define an iteration construct, and suitable notions of diamond-likeness and box-likeness of predicate-liftings which allows for the definition of an axiomatisation parametric in T, Î and a chosen set of pointwise program operations. As our main result, we show that if the pointwise operations are "negation-free" and Kleisli composition left-distributes over the induced join on Kleisli arrows, then this axiomatisation is weakly complete with respect to the class of standard models. As special instances, we recover the weak completeness of PDL and of dual-free Game Logic. As a modest new result we obtain completeness for dual-free GL extended with intersection (demonic choice) of games.
A Type-Directed Negation Elimination
Venue: arXiv cs.LO 2015
Year: 2015

In the modal mu-calculus, a formula is well-formed if each recursive variable occurs underneath an even number of negations. By means of De Morgan's laws, it is easy to transform any well-formed formula into an equivalent formula without negations -- its negation normal form. Moreover, if the formula is of size n, its negation normal form of is of the same size O(n). The full modal mu-calculus and the negation normal form fragment are thus equally expressive and concise. In this paper we extend this result to the higher-order modal fixed point logic (HFL), an extension of the modal mu-calculus with higher-order recursive predicate transformers. We present a procedure that converts a formula into an equivalent formula without negations of quadratic size in the worst case and of linear size when the number of variables of the formula is fixed.
Improvements in OMNeT++/INET Real-Time Scheduler for Emulation Mode
Venue: arXiv cs.NI 2015
Year: 2015

In this paper the performance of INET's emulation mode is evaluated. In particular, the focus of the study is on the precision of the delay emulation. It is shown, that this precision is low in INET 2.6 (respectively a later version provided in the integration branch that fixed the crashes of 2.6 in emulation mode). Two errors in the implementation are identified and an alternative configuration for packet capturing is proposed. The performance tests are re-run with such a modified version of the real-time scheduler (which is now included in the recent INET 3.0 release) and it is shown that the responsiveness of the emulation mode and the precision of delay emulation are improved significantly. Finally, the negative impact of the modified capturing configuration is briefly analyzed. Packet loss in the capturing process has deteriorated but in fact is has already plagued the emulation mode of previous implementations and this topic clearly demands for further studies.
Integration of RTMFP in the OMNeT++ Simulation Environment
Venue: arXiv cs.NI 2015
Year: 2015

This paper introduces the new Real-Time Media Flow Protocol (RTMFP) simulation model for the INET framework for the OMNeT++ simulation environment. RTMFP is a message orientated protocol with a focus on real time peer-to-peer communication. After Adobe Inc. released the specifications, we were able to implement the protocol in INET and compare its performance to the similar Stream Control Transmission Protocol (SCTP) with a focus on congestion control and flow control mechanisms.