OpenRaiser/Intern-Atlas huggingface dataset:
Compression-Based Compressed Sensing
Venue: arXiv cs.IT 2016
Year: 2016

Modern compression algorithms exploit complex structures that are present in signals to describe them very efficiently. On the other hand, the field of compressed sensing is built upon the observation that "structured" signals can be recovered from their under-determined set of linear projections. Currently, there is a large gap between the complexity of the structures studied in the area of compressed sensing and those employed by the state-of-the-art compression codes. Recent results in the literature on deterministic signals aim at bridging this gap through devising compressed sensing decoders that employ compression codes. This paper focuses on structured stochastic processes and studies the application of rate-distortion codes to compressed sensing of such signals. The performance of the formerly-proposed compressible signal pursuit (CSP) algorithm is studied in this stochastic setting. It is proved that in the very low distortion regime, as the blocklength grows to infinity, the CSP algorithm reliably and robustly recovers $n$ instances of a stationary process from random linear projections as long as their count is slightly more than $n$ times the rate-distortion dimension (RDD) of the source. It is also shown that under some regularity conditions, the RDD of a stationary process is equal to its information dimension (ID). This connection establishes the optimality of the CSP algorithm at least for memoryless stationary sources, for which the fundamental limits are known. Finally, it is shown that the CSP algorithm combined by a family of universal variable-length fixed-distortion compression codes yields a family of universal compressed sensing recovery algorithms.
A MapReduce Approach to NoSQL RDF Databases
Venue: arXiv cs.DB 2016
Year: 2016

In recent years, the increased need to house and process large volumes of data has prompted the need for distributed storage and querying systems. The growth of machine-readable RDF triples has prompted both industry and academia to develop new database systems, called NoSQL, with characteristics that differ from classical databases. Many of these systems compromise ACID properties for increased horizontal scalability and data availability. This thesis concerns the development and evaluation of a NoSQL triplestore. Triplestores are database management systems central to emerging technologies such as the Semantic Web and linked data. The evaluation spans several benchmarks, including the two most commonly used in triplestore evaluation, the Berlin SPARQL Benchmark, and the DBpedia benchmark, a query workload that operates an RDF representation of Wikipedia. Results reveal that the join algorithm used by the system plays a critical role in dictating query runtimes. Distributed graph databases must carefully optimize queries before generating MapReduce query plans as network traffic for large datasets can become prohibitive if the query is executed naively.
Design and implementation of a user interface for a multi-device spatially-aware mobile system
Venue: arXiv cs.HC 2016
Year: 2016

The aim of this thesis was the design and development of an interactive system enhancing collaborative sensemaking. The system design was based on related work research and preliminary user study. The system is based on multiple spatially-aware mobile devices. The spatial awareness is established with a motion tracking system. The information about position of tablets is received by a server. The server communicates with the devices and manages the content of each device. The implemented system supports managing the elements of information across the devices basing on their relative spatial arrangement. The evaluation of the system was done by a user study.
About the Suitability of Clouds in High-Performance Computing
Venue: arXiv cs.DC 2016
Year: 2016

Cloud computing has become the ubiquitous computing and storage paradigm. It is also attractive for scientists, because they do not have to care any more for their own IT infrastructure, but can outsource it to a Cloud Service Provider of their choice. However, for the case of High-Performance Computing (HPC) in a cloud, as it is needed in simulations or for Big Data analysis, things are getting more intricate, because HPC codes must stay highly efficient, even when executed by many virtual cores (vCPUs). Older clouds or new standard clouds can fulfil this only under special precautions, which are given in this article. The results can be extrapolated to other cloud OSes than OpenStack and to other codes than OpenFOAM, which were used as examples.
Yet Another Tutorial of Disturbance Observer: Robust Stabilization and Recovery of Nominal Performance
Venue: arXiv cs.SY 2016
Year: 2016

This paper presents a tutorial-style review on the recent results about the disturbance observer (DOB) in view of robust stabilization and recovery of the nominal performance. The analysis is based on the case when the bandwidth of Q-filter is large, and it is explained in a pedagogical manner that, even in the presence of plant uncertainties and disturbances, the behavior of real uncertain plant can be made almost similar to that of disturbance-free nominal system both in the transient and in the steady-state. The conventional DOB is interpreted in a new perspective, and its restrictions and extensions are discussed.
Mixed-ADC Massive MIMO Uplink in Frequency-Selective Channels
Venue: arXiv cs.IT 2016
Year: 2016

The aim of this paper is to investigate the recently developed mixed-ADC architecture for frequency-selective channels. Multi-carrier techniques such as orthogonal frequency division multiplexing (OFDM) are employed to handle inter-symbol interference (ISI). A frequency-domain equalizer is designed for mitigating the inter-carrier interference (ICI) introduced by the nonlinearity of one-bit quantization. For static single-input-multiple-output (SIMO) channels, a closed-form expression of the generalized mutual information (GMI) is derived, and based on which the linear frequency-domain equalizer is optimized. The analysis is then extended to ergodic time-varying SIMO channels with estimated channel state information (CSI), where numerically tight lower and upper bounds of the GMI are derived. The analytical framework is naturally applicable to the multi-user scenario, for both static and time-varying channels. Extensive numerical studies reveal that the mixed-ADC architecture with a small proportion of high-resolution ADCs does achieve a dominant portion of the achievable rate of ideal conventional architecture, and that it remarkably improves the performance as compared with one-bit massive MIMO.
Invertible binary matrix with maximum number of $2$-by-$2$ invertible submatrices
Venue: arXiv cs.IT 2016
Year: 2016

The problem is related to all-or-nothing transforms (AONT) suggested by Rivest as a preprocessing for encrypting data with a block cipher. Since then there have been various applications of AONTs in cryptography and security. D'Arco, Esfahani and Stinson posed the problem on the constructions of binary matrices for which the desired properties of an AONT hold with the maximum probability. That is, for given integers $t\le s$, what is the maximum number of $t$-by-$t$ invertible submatrices in a binary matrix of order $s$? For the case $t=2$, let $R_2(s)$ denote the maximal proportion of 2-by-2 invertible submatrices. D'Arco, Esfahani and Stinson conjectured that the limit is between 0.492 and 0.625. We completely solve the case $t=2$ by showing that $\lim_{s\rightarrow\infty}R_2(s)=0.5$.
Sliding Mode Control Design: a Sum of Squares Approach
Venue: arXiv cs.SY 2016
Year: 2016

This paper presents an approach to systematically design sliding mode control and manifold to stabilize nonlinear uncertain systems. The objective is also accomplished to enlarge the inner bound of region of attraction for closed-loop dynamics. The method is proposed to design a control that guarantees both asymptotic and finite time stability given helped by (bilinear) sum of squares programming. The approach introduces an iterative algorithm to search over sliding mode manifold and Lyapunov function simultaneity. In the case of local stability it concludes also the subset of estimated region of attraction for reduced order sliding mode dynamics. The sliding mode manifold and the corresponding Lyapunov function are obtained if the iterative SOS optimization program has a solution. Results are demonstrated employing the method for several examples to show potential of the proposed technique.
Stochastic Interchange Scheduling in the Real-Time Electricity Market
Venue: arXiv cs.SY 2016
Year: 2016

The problem of multi-area interchange scheduling in the presence of stochastic generation and load is considered. A new interchange scheduling technique based on a two-stage stochastic minimization of overall expected operating cost is proposed. Because directly solving the stochastic optimization is intractable, an equivalent problem that maximizes the expected social welfare is formulated. The proposed technique leverages the operator's capability of forecasting locational marginal prices (LMPs) and obtains the optimal interchange schedule without iterations among operators.
Identifying Stable Patterns over Time for Emotion Recognition from EEG
Venue: arXiv cs.HC 2016
Year: 2016

In this paper, we investigate stable patterns of electroencephalogram (EEG) over time for emotion recognition using a machine learning approach. Up to now, various findings of activated patterns associated with different emotions have been reported. However, their stability over time has not been fully investigated yet. In this paper, we focus on identifying EEG stability in emotion recognition. To validate the efficiency of the machine learning algorithms used in this study, we systematically evaluate the performance of various popular feature extraction, feature selection, feature smoothing and pattern classification methods with the DEAP dataset and a newly developed dataset for this study. The experimental results indicate that stable patterns exhibit consistency across sessions; the lateral temporal areas activate more for positive emotion than negative one in beta and gamma bands; the neural patterns of neutral emotion have higher alpha responses at parietal and occipital sites; and for negative emotion, the neural patterns have significant higher delta responses at parietal and occipital sites and higher gamma responses at prefrontal sites. The performance of our emotion recognition system shows that the neural patterns are relatively stable within and between sessions.
nodewatcher: A Substrate for Growing Your own Community Network
Venue: arXiv cs.SI 2016
Year: 2016

Community networks differ from regular networks by their organic growth patterns -- there is no central planning body that would decide how the network is built. Instead, the network grows in a bottom-up fashion as more people express interest in participating in the community and connect with their neighbours. People who participate in community networks are usually volunteers with limited free time. Due to these factors, making the management of community networks simpler and easier for all participants is the key component in boosting their growth. Specifics of individual networks often force communities to develop their own sets of tools and best practices which are hard to share and do not interoperate well with others. We propose a new general community network management platform nodewatcher that is built around the core principle of modularity and extensibility, making it suitable for reuse by different community networks. Devices are configured using platform-independent configuration which nodewatcher can transform into deployable firmware images, eliminating any manual device configuration, reducing errors, and enabling participation of novice maintainers. An embedded monitoring system enables live overview and validation of the whole community network. We show how the system successfully operates in an actual community wireless network, wlan Slovenija.
Fundamental Bounds and Approaches to Sequence Reconstruction from Nanopore Sequencers
Venue: arXiv cs.IT 2016
Year: 2016

Nanopore sequencers are emerging as promising new platforms for high-throughput sequencing. As with other technologies, sequencer errors pose a major challenge for their effective use. In this paper, we present a novel information theoretic analysis of the impact of insertion-deletion (indel) errors in nanopore sequencers. In particular, we consider the following problems: (i) for given indel error characteristics and rate, what is the probability of accurate reconstruction as a function of sequence length; (ii) what is the number of `typical' sequences within the distortion bound induced by indel errors; (iii) using replicated extrusion (the process of passing a DNA strand through the nanopore), what is the number of replicas needed to reduce the distortion bound so that only one typical sequence exists within the distortion bound. Our results provide a number of important insights: (i) the maximum length of a sequence that can be accurately reconstructed in the presence of indel and substitution errors is relatively small; (ii) the number of typical sequences within the distortion bound is large; and (iii) replicated extrusion is an effective technique for unique reconstruction. In particular, we show that the number of replicas is a slow function (logarithmic) of sequence length -- implying that through replicated extrusion, we can sequence large reads using nanopore sequencers. Our model considers indel and substitution errors separately. In this sense, it can be viewed as providing (tight) bounds on reconstruction lengths and repetitions for accurate reconstruction when the two error modes are considered in a single model.
The Hayastan Shakarian cut: measuring the impact of network disconnections
Venue: arXiv cs.SI 2016
Year: 2016

In this article we present the Hayastan Shakarian (HS), a robustness index for complex networks. HS measures the impact of a network disconnection (edge) while comparing the sizes of the remaining connected components. Strictly speaking, the Hayastan Shakarian index is defined as edge removal that produces the maximal inverse of the size of the largest connected component divided by the sum of the sizes of the remaining ones. We tested our index in attack strategies where the nodes are disconnected in decreasing order of a specified metric. We considered using the Hayastan Shakarian cut (disconnecting the edge with max HS) and other well-known strategies as the higher betweenness centrality disconnection. All strategies were compared regarding the behavior of the robustness (R-index) during the attacks. In an attempt to simulate the internet backbone, the attacks were performed in complex networks with power-law degree distributions (scale-free networks). Preliminary results show that attacks based on disconnecting using the Hayastan Shakarian cut are more dangerous (decreasing the robustness) than the same attacks based on other centrality measures. We believe that the Hayastan Shakarian cut, as well as other measures based on the size of the largest connected component, provides a good addition to other robustness metrics for complex networks.
An Application-Level Dependable Technique for Farmer-Worker Parallel Programs
Venue: arXiv cs.DC 2016
Year: 2016

An application-level technique is described for farmer-worker parallel applications which allows a worker to be added or removed from the computing farm at any moment of the run time without affecting the overall outcome of the computation. The technique is based on uncoupling the farmer from the workers by means of a separate module which asynchronously feeds these latter with new "units of work" on an on-demand basis, and on a special feeding strategy based on bookkeeping the status of each work-unit. An augmentation of the LINDA model is finally proposed to exploit the bookkeeping algorithm for tuple management.
Categorization of Tablas by Wavelet Analysis
Venue: arXiv cs.SD 2016
Year: 2016

Tabla, a percussion instrument, mainly used to accompany vocalists, instrumentalists and dancers in every style of music from classical to light in India, mainly used for keeping rhythm. This percussion instrument consists of two drums played by two hands, structurally different and produces different harmonic sounds. Earlier work has done labeling tabla strokes from real time performances by testing neural networks and tree based classification methods. The current work extends previous work by C. V. Raman and S. Kumar in 1920 on spectrum modeling of tabla strokes. In this paper we have studied spectral characteristics (by wavelet analysis by sub band coding method and using torrence wavelet tool) of nine strokes from each of five tablas using Wavelet transform. Wavelet analysis is now a common tool for analyzing localized variations of power within a time series and to find the frequency distribution in time frequency space. Statistically, we will look into the patterns depicted by harmonics of different sub bands and the tablas. Distribution of dominant frequencies at different sub-band of stroke signals, distribution of power and behavior of harmonics are the important features, leads to categorization of tabla.
Software for enumerative and analytic combinatorics
Venue: arXiv cs.MS 2016
Year: 2016

We survey some general-purpose symbolic software packages that implement algorithms from enumerative and analytic combinatorics. Software for the following areas is covered: basic combinatorial objects, symbolic combinatorics, Pólya theory, combinatorial species, and asymptotics. We describe the capabilities that the packages offer as well as some of the algorithms used, and provide links to original documentation. Most of the packages are freely downloadable from the web.
Polynomial-Sized Topological Approximations Using The Permutahedron
Venue: arXiv cs.CG 2016
Year: 2016

Classical methods to model topological properties of point clouds, such as the Vietoris-Rips complex, suffer from the combinatorial explosion of complex sizes. We propose a novel technique to approximate a multi-scale filtration of the Rips complex with improved bounds for size: precisely, for $n$ points in $\mathbb{R}^d$, we obtain a $O(d)$-approximation with at most $n2^{O(d \log k)}$ simplices of dimension $k$ or lower. In conjunction with dimension reduction techniques, our approach yields a $O(\mathrm{polylog} (n))$-approximation of size $n^{O(1)}$ for Rips filtrations on arbitrary metric spaces. This result stems from high-dimensional lattice geometry and exploits properties of the permutahedral lattice, a well-studied structure in discrete geometry. Building on the same geometric concept, we also present a lower bound result on the size of an approximate filtration: we construct a point set for which every $(1+ε)$-approximation of the Čech filtration has to contain $n^{Ω(\log\log n)}$ features, provided that $ε<\frac{1}{\log^{1+c} n}$ for $c\in(0,1)$.
Deep Learning of Part-based Representation of Data Using Sparse Autoencoders with Nonnegativity Constraints
Venue: arXiv cs.LG 2016
Year: 2016

We demonstrate a new deep learning autoencoder network, trained by a nonnegativity constraint algorithm (NCAE), that learns features which show part-based representation of data. The learning algorithm is based on constraining negative weights. The performance of the algorithm is assessed based on decomposing data into parts and its prediction performance is tested on three standard image data sets and one text dataset. The results indicate that the nonnegativity constraint forces the autoencoder to learn features that amount to a part-based representation of data, while improving sparsity and reconstruction quality in comparison with the traditional sparse autoencoder and Nonnegative Matrix Factorization. It is also shown that this newly acquired representation improves the prediction performance of a deep neural network.
Property Checking By Logic Relaxation
Venue: arXiv cs.LO 2016
Year: 2016

We introduce a new framework for Property Checking (PC) of sequential circuits. It is based on a method called Lo-gic Relaxation (LoR). Given a safety property, the LoR method relaxes the transition system at hand, which leads to expanding the set of reachable states. For j-th time frame, the LoR method computes a superset A_j of the set of bad states reachable in j transitions only by the relaxed system. Set A_j is constructed by a technique called partial quantifier elimination. If A_j does not contain a bad state and this state is reachable in j transitions in the relaxed system, it is also reachable in the original system. Hence the property in question does not hold. The appeal of PC by LoR is as follows. An inductive invariant (or a counterexample) generated by LoR is a result of computing the states reachable only in the relaxed system. So, the complexity of PC can be drastically reduced by finding a "faulty" relaxation that is close to the original system. This is analogous to equivalence checking whose complexity strongly depends on how similar the designs to be compared are.
Factorization of C-finite Sequences
Venue: arXiv cs.SC 2016
Year: 2016

We discuss how to decide whether a given C-finite sequence can be written nontrivially as a product of two other C-finite sequences.
Essence' Description
Venue: arXiv cs.AI 2016
Year: 2016

A description of the Essence' language as used by the tool Savile Row.
Learning Subclass Representations for Visually-varied Image Classification
Venue: arXiv cs.MM 2016
Year: 2016

In this paper, we present a subclass-representation approach that predicts the probability of a social image belonging to one particular class. We explore the co-occurrence of user-contributed tags to find subclasses with a strong connection to the top level class. We then project each image on to the resulting subclass space to generate a subclass representation for the image. The novelty of the approach is that subclass representations make use of not only the content of the photos themselves, but also information on the co-occurrence of their tags, which determines membership in both subclasses and top-level classes. The novelty is also that the images are classified into smaller classes, which have a chance of being more visually stable and easier to model. These subclasses are used as a latent space and images are represented in this space by their probability of relatedness to all of the subclasses. In contrast to approaches directly modeling each top-level class based on the image content, the proposed method can exploit more information for visually diverse classes. The approach is evaluated on a set of $2$ million photos with 10 classes, released by the Multimedia 2013 Yahoo! Large-scale Flickr-tag Image Classification Grand Challenge. Experiments show that the proposed system delivers sound performance for visually diverse classes compared with methods that directly model top classes.