OpenRaiser/Intern-Atlas huggingface dataset:
The Area Under a Receiver Operating Characteristic Curve Over Enriched Multipath Fading Conditions
Venue: arXiv cs.IT 2016
Year: 2016

This work is devoted to the analysis of the performance of energy detection based spectrum sensing in the presence of enriched fading conditions which are distinct for the large number of multipath components and the lack of a dominant components. This type of fading conditions are characterized efficiently by the well known Nakagami${-}q$ or Hoyt distribution and the proposed analysis is carried out in the context of the area under the receiver operating characteristics (ROC) curve (AUC). Unlike the widely used probability of detection metric, the AUC is a single metric and has been shown to be rather capable of evaluating the performance of a detector in applications relating to cognitive radio, radar systems and biomedical engineering, among others. Based on this, novel analytic expressions are derived for the average AUC and its complementary metric, average CAUC, for both integer and fractional values of the involved time-bandwidth product. The derived expressions have a tractable algebraic representation which renders them convenient to handle both analytically and numerically. Based on this, they are employed in analyzing the behavior of energy detection based spectrum sensing over enriched fading conditions for different severity scenarios, which demonstrates that the performance of energy detectors is, as expected, closely related to the value of the fading parameter $q$.
COROLA: A Sequential Solution to Moving Object Detection Using Low-rank Approximation
Venue: arXiv cs.CV 2016
Year: 2016

Extracting moving objects from a video sequence and estimating the background of each individual image are fundamental issues in many practical applications such as visual surveillance, intelligent vehicle navigation, and traffic monitoring. Recently, some methods have been proposed to detect moving objects in a video via low-rank approximation and sparse outliers where the background is modeled with the computed low-rank component of the video and the foreground objects are detected as the sparse outliers in the low-rank approximation. All of these existing methods work in a batch manner, preventing them from being applied in real time and long duration tasks. In this paper, we present an online sequential framework, namely contiguous outliers representation via online low-rank approximation (COROLA), to detect moving objects and learn the background model at the same time. We also show that our model can detect moving objects with a moving camera. Our experimental evaluation uses simulated data and real public datasets and demonstrates the superior performance of COROLA in terms of both accuracy and execution time.
A Theory of Formal Synthesis via Inductive Learning
Venue: arXiv cs.AI 2016
Year: 2016

Formal synthesis is the process of generating a program satisfying a high-level formal specification. In recent times, effective formal synthesis methods have been proposed based on the use of inductive learning. We refer to this class of methods that learn programs from examples as formal inductive synthesis. In this paper, we present a theoretical framework for formal inductive synthesis. We discuss how formal inductive synthesis differs from traditional machine learning. We then describe oracle-guided inductive synthesis (OGIS), a framework that captures a family of synthesizers that operate by iteratively querying an oracle. An instance of OGIS that has had much practical impact is counterexample-guided inductive synthesis (CEGIS). We present a theoretical characterization of CEGIS for learning any program that computes a recursive language. In particular, we analyze the relative power of CEGIS variants where the types of counterexamples generated by the oracle varies. We also consider the impact of bounded versus unbounded memory available to the learning algorithm. In the special case where the universe of candidate programs is finite, we relate the speed of convergence to the notion of teaching dimension studied in machine learning theory. Altogether, the results of the paper take a first step towards a theoretical foundation for the emerging field of formal inductive synthesis.
Towards a Systematic View on Cybersecurity Ecology
Venue: arXiv cs.CR 2016
Year: 2016

Current network security systems are progressively showing their limitations. One credible estimate is that only about 45% of new threats are detected. Therefore it is vital to find a new direction that cybersecurity development should follow. We argue that the next generation of cybersecurity systems should seek inspiration in nature. This approach has been used before in the first generation of cybersecurity systems; however, since then cyber threats and environment have evolved significantly, and accordingly the first-generation systems have lost their effectiveness. A next generation of bio-inspired cybersecurity research is emerging, but progress is hindered by the lack of a framework for mapping biological security systems to their cyber analogies. In this paper, using terminology and concepts from biology, we describe a cybersecurity ecology and a framework that may be used to systematically research and develop bio-inspired cybersecurity.
On the maximum quartet distance between phylogenetic trees
Venue: arXiv cs.DM 2016
Year: 2016

A conjecture of Bandelt and Dress states that the maximum quartet distance between any two phylogenetic trees on $n$ leaves is at most $(\frac 23 +o(1))\binom{n}{4}$. Using the machinery of flag algebras we improve the currently known bounds regarding this conjecture, in particular we show that the maximum is at most $(0.69 +o(1))\binom{n}{4}$. We also give further evidence that the conjecture is true by proving that the maximum distance between caterpillar trees is at most $(\frac 23 +o(1))\binom{n}{4}$.
Spatial database implementation of fuzzy region connection calculus for analysing the relationship of diseases
Venue: arXiv cs.DB 2016
Year: 2016

Analyzing huge amounts of spatial data plays an important role in many emerging analysis and decision-making domains such as healthcare, urban planning, agriculture and so on. For extracting meaningful knowledge from geographical data, the relationships between spatial data objects need to be analyzed. An important class of such relationships are topological relations like the connectedness or overlap between regions. While real-world geographical regions such as lakes or forests do not have exact boundaries and are fuzzy, most of the existing analysis methods neglect this inherent feature of topological relations. In this paper, we propose a method for handling the topological relations in spatial databases based on fuzzy region connection calculus (RCC). The proposed method is implemented in PostGIS spatial database and evaluated in analyzing the relationship of diseases as an important application domain. We also used our fuzzy RCC implementation for fuzzification of the skyline operator in spatial databases. The results of the evaluation show that our method provides a more realistic view of spatial relationships and gives more flexibility to the data analyst to extract meaningful and accurate results in comparison with the existing methods.
SAF: Stochastic Adaptive Forwarding in Named Data Networking
Venue: arXiv cs.NI 2016
Year: 2016

Forwarding decisions in classical IP-based networks are predetermined by routing. This is necessary to avoid loops, inhibiting opportunities to implement an adaptive and intelligent forwarding plane. Consequently, content distribution efficiency is reduced due to a lack of inherent multi-path transmission. In Named Data Networking (NDN) instead, routing shall hold a supporting role to forwarding, providing sufficient potential to enhance content dissemination at the forwarding plane. In this paper we design, implement, and evaluate a novel probability-based forwarding strategy, called Stochastic Adaptive Forwarding (SAF) for NDN. SAF imitates a self-adjusting water pipe system, intelligently guiding and distributing Interests through network crossings circumventing link failures and bottlenecks. Just as real pipe systems, SAF employs overpressure valves enabling congested nodes to lower pressure autonomously. Through an implicit feedback mechanism it is ensured that the fraction of the traffic forwarded via congested nodes decreases. By conducting simulations we show that our approach outperforms existing forwarding strategies in terms of the Interest satisfaction ratio in the majority of the evaluated scenarios. This is achieved by extensive utilization of NDN's multipath and content-lookup capabilities without relying on the routing plane. SAF explores the local environment by redirecting requests that are likely to be dropped anyway. This enables SAF to identify new paths to the content origin or to cached replicas, circumventing link failures and resource shortages without relying on routing updates.
Experiences with efficient methodologies for teaching computer programming to geoscientists
Venue: arXiv cs.CY 2016
Year: 2016

Computer programming was once thought of as a skill required only by professional software developers. But today, given the ubiquitous nature of computation and data science it is quickly becoming necessary for all scientists and engineers to have at least a basic knowledge of how to program. Teaching how to program, particularly to those students with little or no computing background, is well-known to be a difficult task. However, there is also a wealth of evidence-based teaching practices for teaching programming skills which can be applied to greatly improve learning outcomes and the student experience. Adopting these practices naturally gives rise to greater learning efficiency - this is critical if programming is to be integrated into an already busy geoscience curriculum. This paper considers an undergraduate computer programming course, run during the last 5 years in the Department of Earth Science and Engineering at Imperial College London. The teaching methodologies that were used each year are discussed alongside the challenges that were encountered, and how the methodologies affected student performance. Anonymised student marks and feedback are used to highlight this, and also how the adjustments made to the course eventually resulted in a highly effective learning environment.
Complexity Theoretic Limitations on Learning Halfspaces
Venue: arXiv cs.CC 2016
Year: 2016

We study the problem of agnostically learning halfspaces which is defined by a fixed but unknown distribution $\mathcal{D}$ on $\mathbb{Q}^n\times \{\pm 1\}$. We define $\mathrm{Err}_{\mathrm{HALF}}(\mathcal{D})$ as the least error of a halfspace classifier for $\mathcal{D}$. A learner who can access $\mathcal{D}$ has to return a hypothesis whose error is small compared to $\mathrm{Err}_{\mathrm{HALF}}(\mathcal{D})$. Using the recently developed method of the author, Linial and Shalev-Shwartz we prove hardness of learning results under a natural assumption on the complexity of refuting random $K$-$\mathrm{XOR}$ formulas. We show that no efficient learning algorithm has non-trivial worst-case performance even under the guarantees that $\mathrm{Err}_{\mathrm{HALF}}(\mathcal{D}) \le η$ for arbitrarily small constant $η>0$, and that $\mathcal{D}$ is supported in $\{\pm 1\}^n\times \{\pm 1\}$. Namely, even under these favorable conditions its error must be $\ge \frac{1}{2}-\frac{1}{n^c}$ for every $c>0$. In particular, no efficient algorithm can achieve a constant approximation ratio. Under a stronger version of the assumption (where $K$ can be poly-logarithmic in $n$), we can take $η= 2^{-\log^{1-ν}(n)}$ for arbitrarily small $ν>0$. Interestingly, this is even stronger than the best known lower bounds (Arora et. al. 1993, Feldamn et. al. 2006, Guruswami and Raghavendra 2006) for the case that the learner is restricted to return a halfspace classifier (i.e. proper learning).
Line of Sight 2 x nr MIMO with Random Antenna Orientations
Venue: arXiv cs.IT 2016
Year: 2016

Line-of-sight (LoS) multiple-input multiple-output (MIMO) gives full spatial-multiplexing gain when the antenna array geometry and orientation are designed based on the inter-terminal distance. These known design methodologies, that hold for antenna arrays with fixed orientation, do not provide full MIMO gains for arbitrary array orientations. In this paper, we study LoS MIMO channels with random array orientations when the number of transmit antennas used for signalling is 2. We study the impact of common array geometries on error probability, and identify the code design parameter that describes the high signal-to-noise ratio (SNR) error performance of an arbitrary coding scheme. For planar receive arrays, the error rate is shown to decay only as fast as that of a rank 1 channel, and no better than 1/SNR^3 for a class of coding schemes that includes spatial multiplexing. We then show that for the tetrahedral receive array, which uses the smallest number of antennas among non-planar arrays, the error rate decays faster than that of rank 1 channels and is exponential in SNR for every coding scheme. Finally, we design a LoS MIMO system that guarantees a good error performance for all transmit/receive array orientations and over a range of inter-terminal distances.
Towards Optimal Adaptive Wireless Communications in Unknown Environments
Venue: arXiv cs.NI 2016
Year: 2016

Designing efficient channel access schemes for wireless communications without any prior knowledge about the nature of environments has been a very challenging issue, especially when the channel states distribution of all spectrum resources could be entirely or partially stochastic and/or adversarial at different time and locations. In this paper, we propose an adaptive channel access algorithm for wireless communications in unknown environments based on the theory of multi-armed bandits (MAB) problems. By automatically tuning two control parameters, i.e., learning rate and exploration probability, our algorithms are capable of finding the optimal channel access strategies and achieving the almost optimal learning performance over time under our defined four typical regimes for general unknown environments, e.g., the stochastic regime where channels follow some unknown i.i.d process, the adversarial regime where all channels are suffered by adversarial jamming attack, the mixed stochastic and adversarial regime where a subset of channels are attacked and the contaminated stochastic regime where occasionally adversarial events contaminate the stochastic channel process, etc. To reduce the implementation time and space complexity, we further develop an enhanced algorithm by exploiting the internal structure of the selection of channel access strategy. We conduct extensive simulations in all these regimes to validate our theoretical analysis. The quantitative performance studies indicate the superior throughput gain and the flexibility of our algorithm in practice, which is resilient to both oblivious and adaptive jamming attacks with different intelligence and any attacking strength that ranges from no-attack to the full-attack of all spectrum resources.
Resilient and Decentralized Control of Multi-level Cooperative Mobile Networks to Maintain Connectivity under Adversarial Environment
Venue: arXiv cs.SY 2016
Year: 2016

Network connectivity plays an important role in the information exchange between different agents in the multi-level networks. In this paper, we establish a game-theoretic framework to capture the uncoordinated nature of the decision-making at different layers of the multi-level networks. Specifically, we design a decentralized algorithm that aims to maximize the algebraic connectivity of the global network iteratively. In addition, we show that the designed algorithm converges to a Nash equilibrium asymptotically and yields an equilibrium network. To study the network resiliency, we introduce three adversarial attack models and characterize their worst-case impacts on the network performance. Case studies based on a two-layer mobile robotic network are used to corroborate the effectiveness and resiliency of the proposed algorithm and show the interdependency between different layers of the network during the recovery processes.
Computational lower limits on small Ramsey numbers
Venue: arXiv cs.DM 2016
Year: 2016

Computer-based attempts to construct lower bounds for small Ramsey numbers are discussed. A systematic review of cyclic Ramsey graphs is attempted. Many known lower bounds are reproduced. Several new bounds are reported.
Exploring multimodal data fusion through joint decompositions with flexible couplings
Venue: arXiv cs.IT 2016
Year: 2016

A Bayesian framework is proposed to define flexible coupling models for joint tensor decompositions of multiple data sets. Under this framework, a natural formulation of the data fusion problem is to cast it in terms of a joint maximum a posteriori (MAP) estimator. Data driven scenarios of joint posterior distributions are provided, including general Gaussian priors and non Gaussian coupling priors. We present and discuss implementation issues of algorithms used to obtain the joint MAP estimator. We also show how this framework can be adapted to tackle the problem of joint decompositions of large datasets. In the case of a conditional Gaussian coupling with a linear transformation, we give theoretical bounds on the data fusion performance using the Bayesian Cramer-Rao bound. Simulations are reported for hybrid coupling models ranging from simple additive Gaussian models, to Gamma-type models with positive variables and to the coupling of data sets which are inherently of different size due to different resolution of the measurement devices.
Saddle-free Hessian-free Optimization
Venue: arXiv cs.NA 2016
Year: 2016

Nonconvex optimization problems such as the ones in training deep neural networks suffer from a phenomenon called saddle point proliferation. This means that there are a vast number of high error saddle points present in the loss function. Second order methods have been tremendously successful and widely adopted in the convex optimization community, while their usefulness in deep learning remains limited. This is due to two problems: computational complexity and the methods being driven towards the high error saddle points. We introduce a novel algorithm specially designed to solve these two issues, providing a crucial first step to take the widely known advantages of Newton's method to the nonconvex optimization community, especially in high dimensional settings.
On the Skolem Problem for Continuous Linear Dynamical Systems
Venue: arXiv cs.SY 2016
Year: 2016

The Continuous Skolem Problem asks whether a real-valued function satisfying a linear differential equation has a zero in a given interval of real numbers. This is a fundamental reachability problem for continuous linear dynamical systems, such as linear hybrid automata and continuous-time Markov chains. Decidability of the problem is currently open---indeed decidability is open even for the sub-problem in which a zero is sought in a bounded interval. In this paper we show decidability of the bounded problem subject to Schanuel's Conjecture, a unifying conjecture in transcendental number theory. We furthermore analyse the unbounded problem in terms of the frequencies of the differential equation, that is, the imaginary parts of the characteristic roots. We show that the unbounded problem can be reduced to the bounded problem if there is at most one rationally linearly independent frequency, or if there are two rationally linearly independent frequencies and all characteristic roots are simple. We complete the picture by showing that decidability of the unbounded problem in the case of two (or more) rationally linearly independent frequencies would entail a major new effectiveness result in Diophantine approximation, namely computability of the Diophantine-approximation types of all real algebraic numbers.
A few reflections on the quality of emergence in complex collective systems
Venue: arXiv cs.OH 2016
Year: 2016

A number of elements towards a classification of the quality of emergence in emergent collective systems are provided. By using those elements, several classes of emergent systems are exemplified, ranging from simple aggregations of simple parts up to complex organizations of complex collective systems. In so doing, the factors likely to play a a significant role in the persistence of emergence and its opposite are highlighted. From this, new elements for discussion are identified also considering elements from the System of Leibniz.
Capturing Hands in Action using Discriminative Salient Points and Physics Simulation
Venue: arXiv cs.CV 2016
Year: 2016

Hand motion capture is a popular research field, recently gaining more attention due to the ubiquity of RGB-D sensors. However, even most recent approaches focus on the case of a single isolated hand. In this work, we focus on hands that interact with other hands or objects and present a framework that successfully captures motion in such interaction scenarios for both rigid and articulated objects. Our framework combines a generative model with discriminatively trained salient points to achieve a low tracking error and with collision detection and physics simulation to achieve physically plausible estimates even in case of occlusions and missing visual data. Since all components are unified in a single objective function which is almost everywhere differentiable, it can be optimized with standard optimization techniques. Our approach works for monocular RGB-D sequences as well as setups with multiple synchronized RGB cameras. For a qualitative and quantitative evaluation, we captured 29 sequences with a large variety of interactions and up to 150 degrees of freedom.
Well-posedness of a nonlinear integro-differential problem and its rearranged formulation
Venue: arXiv cs.CV 2016
Year: 2016

We study the existence and uniqueness of solutions of a nonlinear integro-differential problem which we reformulate introducing the notion of the decreasing rearrangement of the solution. A dimensional reduction of the problem is obtained and a detailed analysis of the properties of the solutions of the model is provided. Finally, a fast numerical method is devised and implemented to show the performance of the model when typical image processing tasks such as filtering and segmentation are performed.