OpenRaiser/Intern-Atlas huggingface dataset:
Investigation of Parameter Adaptation in RF Power Amplifier Behavioral Models
Venue: arXiv cs.SY 2014
Year: 2014

This paper presents an investigation into parameter adaptation in behavioral model--based digital predistortion for radio frequency power amplifiers. A novel measurement setup framework that emulates real--time adaptation in transmitters is developed that allows evaluation of different parameters, configurations and adaptation algorithms. This setup relieves the need for full feedback loops for parameter adaptation while providing the flexibility needed in the design process of parameter adaptation. Issues such as convergence speed, sensitivity to quantization noise in the feedback loop and predistortion performance are investigated for some different parameter update algorithms using the proposed measurement setup. The approach presented in this paper allows the possibility to analyze different aspects of digital predistortion adaptation algorithms, and is an important enabling step for further research on parameter adaptation before the real--time hardware is implemented for use.
Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling
Venue: arXiv cs.CL 2014
Year: 2014

Dictionaries are often developed using tools that save to Extensible Markup Language (XML)-based standards. These standards often allow high-level repeating elements to represent lexical entries, and utilize descendants of these repeating elements to represent the structure within each lexical entry, in the form of an XML tree. In many cases, dictionaries are published that have errors and inconsistencies that are expensive to find manually. This paper discusses a method for dictionary writers to quickly audit structural regularity across entries in a dictionary by using statistical language modeling. The approach learns the patterns of XML nodes that could occur within an XML tree, and then calculates the probability of each XML tree in the dictionary against these patterns to look for entries that diverge from the norm.
Histogram-Based Flash Channel Estimation
Venue: arXiv cs.IT 2014
Year: 2014

Current generation Flash devices experience significant read-channel degradation from damage to the oxide layer during program and erase operations. Information about the read-channel degradation drives advanced signal processing methods in Flash to mitigate its effect. In this context, channel estimation must be ongoing since channel degradation evolves over time and as a function of the number of program/erase (P/E) cycles. This paper proposes a framework for ongoing model-based channel estimation using limited channel measurements (reads). This paper uses a channel model characterizing degradation resulting from retention time and the amount of charge programmed and erased. For channel histogram measurements, bin selection to achieve approximately equal-probability bins yields a good approximation to the original distribution using only ten bins (i.e. nine reads). With the channel model and binning strategy in place, this paper explores candidate numerical least squares algorithms and ultimately demonstrates the effectiveness of the Levenberg-Marquardt algorithm which provides both speed and accuracy.
UTP2: Higher-Order Equational Reasoning by Pointing
Venue: arXiv cs.LO 2014
Year: 2014

We describe a prototype theorem prover, UTP2, developed to match the style of hand-written proof work in the Unifying Theories of Programming semantical framework. This is based on alphabetised predicates in a 2nd-order logic, with a strong emphasis on equational reasoning. We present here an overview of the user-interface of this prover, which was developed from the outset using a point-and-click approach. We contrast this with the command-line paradigm that continues to dominate the mainstream theorem provers, and raises the question: can we have the best of both worlds?
PIDE for Asynchronous Interaction with Coq
Venue: arXiv cs.HC 2014
Year: 2014

This paper describes the initial progress towards integrating the Coq proof assistant with the PIDE architecture initially developed for Isabelle. The architecture is aimed at asynchronous, parallel interaction with proof assistants, and is tied in heavily with a plugin that allows the jEdit editor to work with Isabelle. We have made some generalizations to the PIDE architecture to accommodate for more provers than just Isabelle, and adapted Coq to understand the core protocol: this delivered a working system in about two man-months.
System description: Isabelle/jEdit in 2014
Venue: arXiv cs.LO 2014
Year: 2014

This is an updated system description for Isabelle/jEdit, according to the official release Isabelle2014 (August 2014). The following new PIDE concepts are explained: asynchronous print functions and document overlays, syntactic and semantic completion, editor navigation, management of auxiliary files within the document-model.
Do Artificial Reinforcement-Learning Agents Matter Morally?
Venue: arXiv cs.AI 2014
Year: 2014

Artificial reinforcement learning (RL) is a widely used technique in artificial intelligence that provides a general method for training agents to perform a wide variety of behaviours. RL as used in computer science has striking parallels to reward and punishment learning in animal and human brains. I argue that present-day artificial RL agents have a very small but nonzero degree of ethical importance. This is particularly plausible for views according to which sentience comes in degrees based on the abilities and complexities of minds, but even binary views on consciousness should assign nonzero probability to RL programs having morally relevant experiences. While RL programs are not a top ethical priority today, they may become more significant in the coming decades as RL is increasingly applied to industry, robotics, video games, and other areas. I encourage scientists, philosophers, and citizens to begin a conversation about our ethical duties to reduce the harm that we inflict on powerless, voiceless RL agents.
Notes on Noise Contrastive Estimation and Negative Sampling
Venue: arXiv cs.LG 2014
Year: 2014

Estimating the parameters of probabilistic models of language such as maxent models and probabilistic neural models is computationally difficult since it involves evaluating partition functions by summing over an entire vocabulary, which may be millions of word types in size. Two closely related strategies---noise contrastive estimation (Mnih and Teh, 2012; Mnih and Kavukcuoglu, 2013; Vaswani et al., 2013) and negative sampling (Mikolov et al., 2012; Goldberg and Levy, 2014)---have emerged as popular solutions to this computational problem, but some confusion remains as to which is more appropriate and when. This document explicates their relationships to each other and to other estimation techniques. The analysis shows that, although they are superficially similar, NCE is a general parameter estimation technique that is asymptotically unbiased, while negative sampling is best understood as a family of binary classification models that are useful for learning word representations but not as a general-purpose estimator.
A 13k-kernel for Planar Feedback Vertex Set via Region Decomposition
Venue: arXiv cs.DS 2014
Year: 2014

We show a kernel of at most $13k$ vertices for the Planar Feedback Vertex Set problem restricted to planar graphs, i.e., a polynomial-time algorithm that transforms an input instance $(G,k)$ to an equivalent instance with at most $13k$ vertices. To this end we introduce a few new reduction rules. However, our main contribution is an application of the region decomposition technique in the analysis of the kernel size. We show that our analysis is tight, up to a constant additive term.
Tasks that Require, or can Benefit from, Matching Blank Nodes
Venue: arXiv cs.AI 2014
Year: 2014

In various domains and cases, we observe the creation and usage of information elements which are unnamed. Such elements do not have a name, or may have a name that is not externally referable (usually meaningless and not persistent over time). This paper discusses why we will never `escape' from the problem of having to construct mappings between such unnamed elements in information systems. Since unnamed elements nowadays occur very often in the framework of the Semantic Web and Linked Data as blank nodes, the paper describes scenarios that can benefit from methods that compute mappings between the unnamed elements. For each scenario, the corresponding bnode matching problem is formally defined. Based on this analysis, we try to reach to more a general formulation of the problem, which can be useful for guiding the required technological advances. To this end, the paper finally discusses methods to realize blank node matching, the implementations that exist, and identifies open issues and challenges.
DeepSentiBank: Visual Sentiment Concept Classification with Deep Convolutional Neural Networks
Venue: arXiv cs.CV 2014
Year: 2014

This paper introduces a visual sentiment concept classification method based on deep convolutional neural networks (CNNs). The visual sentiment concepts are adjective noun pairs (ANPs) automatically discovered from the tags of web photos, and can be utilized as effective statistical cues for detecting emotions depicted in the images. Nearly one million Flickr images tagged with these ANPs are downloaded to train the classifiers of the concepts. We adopt the popular model of deep convolutional neural networks which recently shows great performance improvement on classifying large-scale web-based image dataset such as ImageNet. Our deep CNNs model is trained based on Caffe, a newly developed deep learning framework. To deal with the biased training data which only contains images with strong sentiment and to prevent overfitting, we initialize the model with the model weights trained from ImageNet. Performance evaluation shows the newly trained deep CNNs model SentiBank 2.0 (or called DeepSentiBank) is significantly improved in both annotation accuracy and retrieval performance, compared to its predecessors which mainly use binary SVM classification models.
Finite element model based on refined plate theories for laminated glass units
Venue: arXiv cs.CE 2014
Year: 2014

Laminated glass units exhibit complex response as a result of different mechanical behavior and properties of glass and polymer foil. We aim to develop a finite element model for elastic laminated glass plates based on the refined plate theory by Mau. For a geometrically nonlinear description of the behavior of units, each layer behaves according to the Reissner-Mindlin kinematics, complemented with membrane effects and the von Kármán assumptions. Nodal Lagrange multipliers enforce the compatibility of independent layers in this approach. We have derived the discretized model by the energy-minimization arguments, assuming that the unknown fields are approximated by bi-linear functions at the element level, and solved the resulting system by the Newton method with consistent linearization. We have demonstrated through verification and validation examples that the proposed formulation is reliable and accurately reproduces the behavior of laminated glass units. This study represents a first step to the development of a comprehensive, mechanics-based model for laminated glass systems that is suitable for implementation in common engineering finite element solvers.
CONDENSER: A Graph-Based Approachfor Detecting Botnets
Venue: arXiv cs.CR 2014
Year: 2014

Botnets represent a global problem and are responsible for causing large financial and operational damage to their victims. They are implemented with evasion in mind, and aim at hiding their architecture and authors, making them difficult to detect in general. These kinds of networks are mainly used for identity theft, virtual extortion, spam campaigns and malware dissemination. Botnets have a great potential in warfare and terrorist activities, making it of utmost importance to take action against. We present CONDENSER, a method for identifying data generated by botnet activity. We start by selecting appropriate the features from several data feeds, namely DNS non-existent domain responses and live communication packages directed to command and control servers that we previously sinkholed. By using machine learning algorithms and a graph based representation of data, then allows one to identify botnet activity, helps identifying anomalous traffic, quickly detect new botnets and improve activities of tracking known botnets. Our main contributions are threefold: first, the use of a machine learning classifier for classifying domain names as being generated by domain generation algorithms (DGA); second, a clustering algorithm using the set of selected features that groups network communication with similar patterns; third, a graph based knowledge representation framework where we store processed data, allowing us to perform queries.
Low Rank Matrix Approximation in Linear Time
Venue: arXiv cs.CG 2014
Year: 2014

$\newcommand{\MatA}{\mathcal{M}}$ $\newcommand{\eps}{\varepsilon}$ $\newcommand{\NSize}{\mathsf{N}{}}$ $\newcommand{\MatB}{\mathcal{B}}$ $\newcommand{\Fnorm}[1]{\left\| {#1} \right\|_F}$ $\newcommand{\PrcOpt}[2]{μ_{\mathrm{opt}}\pth{#1, #2}}$ $\newcommand{\pth}[1]{\left(#1\right)}$ Given a matrix $\MatA$ with $n$ rows and $d$ columns, and fixed $k$ and $\eps$, we present an algorithm that in linear time (i.e., $O(\NSize )$) computes a $k$-rank matrix $\MatB$ with approximation error $\Fnorm{\MatA - \MatB}^2 \leq (1+\eps) \PrcOpt{\MatA}{k}$, where $\NSize = n d$ is the input size, and $\PrcOpt{\MatA}{k}$ is the minimum error of a $k$-rank approximation to $\MatA$. This algorithm succeeds with constant probability, and to our knowledge it is the first linear-time algorithm to achieve multiplicative approximation.
A Semantic Web of Know-How: Linked Data for Community-Centric Tasks
Venue: arXiv cs.AI 2014
Year: 2014

This paper proposes a novel framework for representing community know-how on the Semantic Web. Procedural knowledge generated by web communities typically takes the form of natural language instructions or videos and is largely unstructured. The absence of semantic structure impedes the deployment of many useful applications, in particular the ability to discover and integrate know-how automatically. We discuss the characteristics of community know-how and argue that existing knowledge representation frameworks fail to represent it adequately. We present a novel framework for representing the semantic structure of community know-how and demonstrate the feasibility of our approach by providing a concrete implementation which includes a method for automatically acquiring procedural knowledge for real-world tasks.
Resilience of Social Networks Under Different Attack Strategies
Venue: arXiv cs.SI 2014
Year: 2014

Recent years have seen the world become a closely connected society with the emergence of different types of social networks. Online social networks have provided a way to bridge long distances and establish numerous communication channels which were not possible earlier. These networks exhibit interesting behavior under intentional attacks and random failures where different structural properties influence the resilience in different ways. In this paper, we perform two sets of experiments and draw conclusions from the results pertaining to the resilience of social networks. The first experiment performs a comparative analysis of four different classes of networks namely small world networks, scale free networks, small world-scale free networks and random networks with four semantically different social networks under different attack strategies. The second experiment compares the resilience of these semantically different social networks under different attack strategies. Empirical analysis reveals interesting behavior of different classes of networks with different attack strategies.
Rapid Adaptation of POS Tagging for Domain Specific Uses
Venue: arXiv cs.CL 2014
Year: 2014

Part-of-speech (POS) tagging is a fundamental component for performing natural language tasks such as parsing, information extraction, and question answering. When POS taggers are trained in one domain and applied in significantly different domains, their performance can degrade dramatically. We present a methodology for rapid adaptation of POS taggers to new domains. Our technique is unsupervised in that a manually annotated corpus for the new domain is not necessary. We use suffix information gathered from large amounts of raw text as well as orthographic information to increase the lexical coverage. We present an experiment in the Biological domain where our POS tagger achieves results comparable to POS taggers specifically trained to this domain.
Secrecy in Cascade Networks
Venue: arXiv cs.IT 2014
Year: 2014

We consider a cascade network where a sequence of nodes each send a message to their downstream neighbor to enable coordination, the first node having access to an information signal. An adversary also receives all of the communication as well as additional side-information. The performance of the system is measured by a payoff function evaluated on actions produced at each of the nodes, including the adversary. The challenge is to effectively use a secret key to infuse some level of privacy into the encoding, in order thwart the adversary's attempt to reduce the payoff. We obtain information-theoretic inner and outer bounds on performance, and give examples where they are tight. From these bounds, we also derive the optimal equivocation for this setting as a special case.
Emergent Behaviors over Signed Random Dynamical Networks: State-Flipping Model
Venue: arXiv cs.SI 2014
Year: 2014

Recent studies from social, biological, and engineering network systems have drawn attention to the dynamics over signed networks, where each link is associated with a positive/negative sign indicating trustful/mistrustful, activator/inhibitor, or secure/malicious interactions. We study asymptotic dynamical patterns that emerge among a set of nodes that interact in a dynamically evolving signed random network. Node interactions take place at random on a sequence of deterministic signed graphs. Each node receives positive or negative recommendations from its neighbors depending on the sign of the interaction arcs, and updates its state accordingly. Recommendations along a positive arc follow the standard consensus update. As in the work by Altafini, negative recommendations use an update where the sign of the neighbor state is flipped. Nodes may weight positive and negative recommendations differently, and random processes are introduced to model the time-varying attention that nodes pay to these recommendations. Conditions for almost sure convergence and divergence of the node states are established. We show that under this so-called state-flipping model, all links contribute to a consensus of the absolute values of the nodes, even under switching sign patterns and dynamically changing environment. A no-survivor property is established, indicating that every node state diverges almost surely if the maximum network state diverges.
Secrecy Outage Probability Analysis of Multi-User Multi-Eavesdropper Wireless Systems
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we explore the physical-layer security of a multi-user wireless system that consists of multiple users intending to transmit to a base station (BS), while multiple eavesdroppers attempt to tap the user transmissions. We examine the employment of multi-user scheduling for improving the transmission security against eavesdropping and propose a multi-user scheduling scheme, which only requires the channel state information (CSI) of BS without the need of the passive eavesdroppers' CSI. We also consider the round-robin scheduling for comparison purposes. The closed-form secrecy outage probability expressions of the round-robin scheduling and proposed multi-user scheduling are derived over Rayleigh fading channels. Numerical results demonstrate that the proposed multi-user scheduling outperforms the round-robin scheduling in terms of the secrecy outage probability. As the number of users increases, the secrecy outage probability of round-robin scheduling keeps unchanged. By contrast, the secrecy outage performance of the proposed multi-user scheduling improves significantly with an increasing number of users.
Capacity Region Continuity of the Compound Broadcast Channel with Confidential Messages
Venue: arXiv cs.IT 2014
Year: 2014

The compound broadcast channel with confidential messages (BCC) generalizes the BCC by modeling the uncertainty of the channel. For the compound BCC, it is only known that the actual channel realization belongs to a pre-specified uncertainty set of channels and that it is constant during the whole transmission. For reliable and secure communication is necessary to operate at a rate pair within the compound BCC capacity region. Therefore, the question whether small variations of the uncertainty set lead to large losses of the compound BCC capacity region is studied. It is shown that the compound BCC model is robust, i.e., the capacity region depends continuously on the uncertainty set.
Planting colourings silently
Venue: arXiv cs.DM 2014
Year: 2014

Let $k\geq3$ be a fixed integer and let $Z_k(G)$ be the number of $k$-colourings of the graph $G$. For certain values of the average degree, the random variable $Z_k(G(n,m))$ is known to be concentrated in the sense that $\frac1n(\ln Z_k(G(n,m))-\ln E[Z_k(G(n,m))])$ converges to $0$ in probability [Achlioptas and Coja-Oghlan: FOCS 2008]. In the present paper we prove a significantly stronger concentration result. Namely, we show that for a wide range of average degrees, $\frac1ω(\ln Z_k(G(n,m))-\ln E[Z_k(G(n,m))])$ converges to $0$ in probability for any diverging function $ω=ω(n)\to\infty$. For $k$ exceeding a certain constant $k_0$ this result covers all average degrees up to the so-called condensation phase transition, and this is best possible. As an application, we show that the experiment of choosing a $k$-colouring of the random graph $G(n,m)$ uniformly at random is contiguous with respect to the so-called "planted model".
State-of-the-Art in Retinal Optical Coherence Tomography Image Analysis
Venue: arXiv cs.CV 2014
Year: 2014

Optical Coherence Tomography (OCT) is one of the most emerging imaging modalities that has been used widely in the field of biomedical imaging. From its emergence in 1990's, plenty of hardware and software improvements have been made. Its applications range from ophthalmology to dermatology to coronary imaging etc. Here, the focus is on applications of OCT in ophthalmology and retinal imaging. OCT is able to non-invasively produce cross-sectional volume images of the tissues which are further used for analysis of the tissue structure and its properties. Due to the underlying physics, OCT images usually suffer from a granular pattern, called speckle noise, which restricts the process of interpretation, hence requiring specialized noise reduction techniques to remove the noise while preserving image details. Also, given the fact that OCT images are in the $μm$ -level, further analysis in needed to distinguish between the different structures in the imaged volume. Therefore the use of different segmentation techniques are of high importance. The movement of the tissue under imaging or the progression of disease in the tissue also imposes further implications both on the quality and the proper interpretation of the acquired images. Thus, use of image registration techniques can be very helpful. In this work, an overview of such image analysis techniques will be given.
Greatest solutions of equations in $\text{CLL}_R$ and its application
Venue: arXiv cs.LO 2014
Year: 2014

This paper explores the process calculus $\text{CLL}_R$ furtherly. First, we prove that for any equation $X=_{RS} t_X$ such that $X$ is strongly guarded in $t_X$, $\langle X|X=t_X \rangle$ is the largest solution w.r.t $\sqsubseteq_{RS}$. Second, we encode a fragment of action-based CTL in $\text{CLL}_R$.