OpenRaiser/Intern-Atlas huggingface dataset:
Attitude Control of Spacecraft Formations Subject To Distributed Communication Delays
Venue: arXiv cs.SY 2017
Year: 2017

This paper considers the problem of achieving attitude consensus in spacecraft formations with bounded, time-varying communication delays between spacecraft connected as specified by a strongly connected topology. A state feedback con- troller is proposed and investigated using a time domain approach (via LMIs) and a frequency domain approach (via the small-gain theorem) to obtain delay depen- dent stability criteria to achieve the desired consensus. Simulations are presented to demonstrate the application of the strategy in a specific scenario.
Copy-move Forgery Detection based on Convolutional Kernel Network
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, a copy-move forgery detection method based on Convolutional Kernel Network is proposed. Different from methods based on conventional hand-crafted features, Convolutional Kernel Network is a kind of data-driven local descriptor with the deep convolutional structure. Thanks to the development of deep learning theories and widely available datasets, the data-driven methods can achieve competitive performance on different conditions for its excellent discriminative capability. Besides, our Convolutional Kernel Network is reformulated as a series of matrix computations and convolutional operations which are easy to parallelize and accelerate by GPU, leading to high efficiency. Then, appropriate preprocessing and postprocessing for Convolutional Kernel Network are adopted to achieve copy-move forgery detection. Particularly, a segmentation-based keypoints distribution strategy is proposed and a GPU-based adaptive oversegmentation method is adopted. Numerous experiments are conducted to demonstrate the effectiveness and robustness of the GPU version of Convolutional Kernel Network, and the state-of-the-art performance of the proposed copy-move forgery detection method based on Convolutional Kernel Network.
Learning Geometric Concepts with Nasty Noise
Venue: arXiv cs.LG 2017
Year: 2017

We study the efficient learnability of geometric concept classes - specifically, low-degree polynomial threshold functions (PTFs) and intersections of halfspaces - when a fraction of the data is adversarially corrupted. We give the first polynomial-time PAC learning algorithms for these concept classes with dimension-independent error guarantees in the presence of nasty noise under the Gaussian distribution. In the nasty noise model, an omniscient adversary can arbitrarily corrupt a small fraction of both the unlabeled data points and their labels. This model generalizes well-studied noise models, including the malicious noise model and the agnostic (adversarial label noise) model. Prior to our work, the only concept class for which efficient malicious learning algorithms were known was the class of origin-centered halfspaces. Specifically, our robust learning algorithm for low-degree PTFs succeeds under a number of tame distributions -- including the Gaussian distribution and, more generally, any log-concave distribution with (approximately) known low-degree moments. For LTFs under the Gaussian distribution, we give a polynomial-time algorithm that achieves error $O(ε)$, where $ε$ is the noise rate. At the core of our PAC learning results is an efficient algorithm to approximate the low-degree Chow-parameters of any bounded function in the presence of nasty noise. To achieve this, we employ an iterative spectral method for outlier detection and removal, inspired by recent work in robust unsupervised learning. Our aforementioned algorithm succeeds for a range of distributions satisfying mild concentration bounds and moment assumptions. The correctness of our robust learning algorithm for intersections of halfspaces makes essential use of a novel robust inverse independence lemma that may be of broader interest.
Multiple Range-Restricted Bidirectional Gated Recurrent Units with Attention for Relation Classification
Venue: arXiv cs.CL 2017
Year: 2017

Most of neural approaches to relation classification have focused on finding short patterns that represent the semantic relation using Convolutional Neural Networks (CNNs) and those approaches have generally achieved better performances than using Recurrent Neural Networks (RNNs). In a similar intuition to the CNN models, we propose a novel RNN-based model that strongly focuses on only important parts of a sentence using multiple range-restricted bidirectional layers and attention for relation classification. Experimental results on the SemEval-2010 relation classification task show that our model is comparable to the state-of-the-art CNN-based and RNN-based models that use additional linguistic information.
Generative diffeomorphic atlas construction from brain and spinal cord MRI data
Venue: arXiv cs.CV 2017
Year: 2017

In this paper we will focus on the potential and on the challenges associated with the development of an integrated brain and spinal cord modelling framework for processing MR neuroimaging data. The aim of the work is to explore how a hierarchical generative model of imaging data, which captures simultaneously the distribution of signal intensities and the variability of anatomical shapes across a large population of subjects, can serve to quantitatively investigate, in vivo, the morphology of the central nervous system (CNS). In fact, the generality of the proposed Bayesian approach, which extends the hierarchical structure of the segmentation method implemented in the SPM software, allows processing simultaneously information relative to different compartments of the CNS, namely the brain and the spinal cord, without having to resort to organ specific solutions (e.g. tools optimised only for the brain, or only for the spinal cord), which are inevitably harder to integrate and generalise.
Compositions of Functions and Permutations Specified by Minimal Reaction Systems
Venue: arXiv cs.FL 2017
Year: 2017

This paper studies mathematical properties of reaction systems that was introduced by Enrenfeucht and Rozenberg as computational models inspired by biochemical reaction in the living cells. In particular, we continue the study on the generative power of functions specified by minimal reaction systems under composition initiated by Salomaa. Allowing degenerate reaction systems, functions specified by minimal reaction systems over a quarternary alphabet that are permutations generate the alternating group on the power set of the background set.
Efficient Approximate Query Answering over Sensor Data with Deterministic Error Guarantees
Venue: arXiv cs.DB 2017
Year: 2017

With the recent proliferation of sensor data, there is an increasing need for the efficient evaluation of analytical queries over multiple sensor datasets. The magnitude of such datasets makes exact query answering infeasible, leading researchers into the development of approximate query answering approaches. However, existing approximate query answering algorithms are not suited for the efficient processing of queries over sensor data, as they exhibit at least one of the following shortcomings: (a) They do not provide deterministic error guarantees, resorting to weaker probabilistic error guarantees that are in many cases not acceptable, (b) they allow queries only over a single dataset, thus not supporting the multitude of queries over multiple datasets that appear in practice, such as correlation or cross-correlation and (c) they support relational data in general and thus miss speedup opportunities created by the special nature of sensor data, which are not random but follow a typically smooth underlying phenomenon. To address these problems, we propose PlatoDB; a system that exploits the nature of sensor data to compress them and provide efficient processing of queries over multiple sensor datasets, while providing deterministic error guarantees. PlatoDB achieves the above through a novel architecture that (a) at data import time pre-processes each dataset, creating for it an intermediate hierarchical data structure that provides a hierarchy of summarizations of the dataset together with appropriate error measures and (b) at query processing time leverages the pre-computed data structures to compute an approximate answer and deterministic error guarantees for ad hoc queries even when these combine multiple datasets.
Spectral Modes of Network Dynamics Reveal Increased Informational Complexity Near Criticality
Venue: arXiv cs.NE 2017
Year: 2017

What does the informational complexity of dynamical networked systems tell us about intrinsic mechanisms and functions of these complex systems? Recent complexity measures such as integrated information have sought to operationalize this problem taking a whole-versus-parts perspective, wherein one explicitly computes the amount of information generated by a network as a whole over and above that generated by the sum of its parts during state transitions. While several numerical schemes for estimating network integrated information exist, it is instructive to pursue an analytic approach that computes integrated information as a function of network weights. Our formulation of integrated information uses a Kullback-Leibler divergence between the multi-variate distribution on the set of network states versus the corresponding factorized distribution over its parts. Implementing stochastic Gaussian dynamics, we perform computations for several prototypical network topologies. Our findings show increased informational complexity near criticality, which remains consistent across network topologies. Spectral decomposition of the system's dynamics reveals how informational complexity is governed by eigenmodes of both, the network's covariance and adjacency matrices. We find that as the dynamics of the system approach criticality, high integrated information is exclusively driven by the eigenmode corresponding to the leading eigenvalue of the covariance matrix, while sub-leading modes get suppressed. The implication of this result is that it might be favorable for complex dynamical networked systems such as the human brain or communication systems to operate near criticality so that efficient information integration might be achieved.
On Directed Feedback Vertex Set parameterized by treewidth
Venue: arXiv cs.DS 2017
Year: 2017

We study the Directed Feedback Vertex Set problem parameterized by the treewidth of the input graph. We prove that unless the Exponential Time Hypothesis fails, the problem cannot be solved in time $2^{o(t\log t)}\cdot n^{\mathcal{O}(1)}$ on general directed graphs, where $t$ is the treewidth of the underlying undirected graph. This is matched by a dynamic programming algorithm with running time $2^{\mathcal{O}(t\log t)}\cdot n^{\mathcal{O}(1)}$. On the other hand, we show that if the input digraph is planar, then the running time can be improved to $2^{\mathcal{O}(t)}\cdot n^{\mathcal{O}(1)}$.
Creative Robot Dance with Variational Encoder
Venue: arXiv cs.AI 2017
Year: 2017

What we appreciate in dance is the ability of people to sponta- neously improvise new movements and choreographies, sur- rendering to the music rhythm, being inspired by the cur- rent perceptions and sensations and by previous experiences, deeply stored in their memory. Like other human abilities, this, of course, is challenging to reproduce in an artificial entity such as a robot. Recent generations of anthropomor- phic robots, the so-called humanoids, however, exhibit more and more sophisticated skills and raised the interest in robotic communities to design and experiment systems devoted to automatic dance generation. In this work, we highlight the importance to model a computational creativity behavior in dancing robots to avoid a mere execution of preprogrammed dances. In particular, we exploit a deep learning approach that allows a robot to generate in real time new dancing move- ments according to to the listened music.
Context Aware Document Embedding
Venue: arXiv cs.CL 2017
Year: 2017

Recently, doc2vec has achieved excellent results in different tasks. In this paper, we present a context aware variant of doc2vec. We introduce a novel weight estimating mechanism that generates weights for each word occurrence according to its contribution in the context, using deep neural networks. Our context aware model can achieve similar results compared to doc2vec initialized byWikipedia trained vectors, while being much more efficient and free from heavy external corpus. Analysis of context aware weights shows they are a kind of enhanced IDF weights that capture sub-topic level keywords in documents. They might result from deep neural networks that learn hidden representations with the least entropy.
Information-gain computation
Venue: arXiv cs.PL 2017
Year: 2017

Despite large incentives, ecorrectness in software remains an elusive goal. Declarative programming techniques, where algorithms are derived from a specification of the desired behavior, offer hope to address this problem, since there is a combinatorial reduction in complexity in programming in terms of specifications instead of algorithms, and arbitrary desired properties can be expressed and enforced in specifications directly. However, limitations on performance have prevented programming with declarative specifications from becoming a mainstream technique for general-purpose programming. To address the performance bottleneck in deriving an algorithm from a specification, I propose information-gain computation, a framework where an adaptive evaluation strategy is used to efficiently perform a search which derives algorithms that provide information about a query most directly. Within this framework, opportunities to compress the search space present themselves, which suggest that information-theoretic bounds on the performance of such a system might be articulated and a system designed to achieve them. In a preliminary empirical study of adaptive evaluation for a simple test program, the evaluation strategy adapts successfully to evaluate a query efficiently.
A Coherent vorticity preserving eddy viscosity correction for Large-Eddy Simulation
Venue: arXiv cs.CE 2017
Year: 2017

This paper introduces a new approach to Large-Eddy Simulation (LES) where subgrid-scale (SGS) dissipation is applied proportionally to the degree of local spectral broadening, hence mitigated or deactivated in regions dominated by large-scale and/or laminar vortical motion. The proposed Coherent vorticity preserving (CvP) LES methodology is based on the evaluation of the ratio of the test-filtered to resolved (or grid-filtered) enstrophy $σ$. Values of $σ$ close to 1 indicate low sub-test-filter turbulent activity, justifying local deactivation of the SGS dissipation. The intensity of the SGS dissipation is progressively increased for $σ< 1$ which corresponds to a small-scale spectral broadening. The SGS dissipation is then fully activated in developed turbulence characterized by $σ\le σ_{eq}$, where the value $σ_{eq}$ is derived assuming a Kolmogorov spectrum. The proposed approach can be applied to any eddy-viscosity model, is algorithmically simple and computationally inexpensive. LES of Taylor-Green vortex breakdown demonstrates that the CvP methodology improves the performance of traditional, non-dynamic dissipative SGS models, capturing the peak of total turbulent kinetic energy dissipation during transition. Similar accuracy is obtained by adopting Germano's dynamic procedure albeit at more than twice the computational overhead. A CvP-LES of a pair of unstable periodic helical vortices is shown to predict accurately the experimentally observed growth rate using coarse resolutions. The ability of the CvP methodology to dynamically sort the coherent, large-scale motion from the smaller, broadband scales during transition is demonstrated via flow visualizations. LES of compressible channel are carried out and show a good match with a reference DNS.
Adaptive Estimation for Nonlinear Systems using Reproducing Kernel Hilbert Spaces
Venue: arXiv cs.SY 2017
Year: 2017

This paper extends a conventional, general framework for online adaptive estimation problems for systems governed by unknown nonlinear ordinary differential equations. The central feature of the theory introduced in this paper represents the unknown function as a member of a reproducing kernel Hilbert space (RKHS) and defines a distributed parameter system (DPS) that governs state estimates and estimates of the unknown function. This paper 1) derives sufficient conditions for the existence and stability of the infinite dimensional online estimation problem, 2) derives existence and stability of finite dimensional approximations of the infinite dimensional approximations, and 3) determines sufficient conditions for the convergence of finite dimensional approximations to the infinite dimensional online estimates. A new condition for persistency of excitation in a RKHS in terms of its evaluation functionals is introduced in the paper that enables proof of convergence of the finite dimensional approximations of the unknown function in the RKHS. This paper studies two particular choices of the RKHS, those that are generated by exponential functions and those that are generated by multiscale kernels defined from a multiresolution analysis.
Dual Path Networks
Venue: arXiv cs.CV 2017
Year: 2017

In this work, we present a simple, highly efficient and modularized Dual Path Network (DPN) for image classification which presents a new topology of connection paths internally. By revealing the equivalence of the state-of-the-art Residual Network (ResNet) and Densely Convolutional Network (DenseNet) within the HORNN framework, we find that ResNet enables feature re-usage while DenseNet enables new features exploration which are both important for learning good representations. To enjoy the benefits from both path topologies, our proposed Dual Path Network shares common features while maintaining the flexibility to explore new features through dual path architectures. Extensive experiments on three benchmark datasets, ImagNet-1k, Places365 and PASCAL VOC, clearly demonstrate superior performance of the proposed DPN over state-of-the-arts. In particular, on the ImagNet-1k dataset, a shallow DPN surpasses the best ResNeXt-101(64x4d) with 26% smaller model size, 25% less computational cost and 8% lower memory consumption, and a deeper DPN (DPN-131) further pushes the state-of-the-art single model performance with about 2 times faster training speed. Experiments on the Places365 large-scale scene dataset, PASCAL VOC detection dataset, and PASCAL VOC segmentation dataset also demonstrate its consistently better performance than DenseNet, ResNet and the latest ResNeXt model over various applications.
pch2csd: an application for converting Nord Modular G2 patches into Csound code
Venue: arXiv cs.SD 2017
Year: 2017

The paper presents the pch2csd project, focused on converting patches of popular Clavia Nord Modular G2 synthesizer into code of Csound language. Now discontinued, Nord Modular G2 left a lot of interesting patches for sound synthesis and algorithmic composition. To give this heritage a new life, we created our project with the hope for being able to simulate the original sound and behavior of Nord Modular.
Extracting analytic proofs from numerically solved Shannon-type Inequalities
Venue: arXiv cs.IT 2017
Year: 2017

A class of information inequalities, called Shannon-type inequalities (STIs), can be proven via a computer software called ITIP. In previous work, we have shown how this technique can be utilized to Fourier-Motzkin elimination algorithm for Information Theoretic Inequalities. Here, we provide an algorithm for extracting \emph{analytic} proofs of information inequalities. Shannon-type inequalities are proven by solving an optimization problem. We will show how to extract a \emph{formal} proof of numerically solved information inequality. Such proof may become useful when an inequality is implied by several constraints due to the PMF, and the proof is not apparent easily. More complicated are cases where an inequality holds due to both constraints from the PMF and due to other constraints that arise from the statistical model. Such cases include information theoretic capacity regions, rate-distortion functions and lossless compression rates. We begin with formal definition of Shannon-type information inequalities. We then review the optimal solution of the optimization problem and how to extract a proof that is readable to the user.
Securing Information-Centric Networking without negating Middleboxes
Venue: arXiv cs.NI 2017
Year: 2017

Information-Centric Networking is a promising networking paradigm that overcomes many of the limitations of current networking architectures. Various research efforts investigate solutions for securing ICN. Nevertheless, most of these solutions relax security requirements in favor of network performance. In particular, they weaken end-user privacy and the architecture's tolerance to security breaches in order to support middleboxes that offer services such as caching and content replication. In this paper, we adapt TLS, a widely used security standard, to an ICN context. We design solutions that allow session reuse and migration among multiple stakeholders and we propose an extension that allows authorized middleboxes to lawfully and transparently intercept secured communications.
When the Optimum is also Blind: a New Perspective on Universal Optimization
Venue: arXiv cs.CC 2017
Year: 2017

Consider the following variant of the set cover problem. We are given a universe $U=\{1,...,n\}$ and a collection of subsets $\mathcal{C} = \{S_1,...,S_m\}$ where $S_i \subseteq U$. For every element $u \in U$ we need to find a set $ϕ(u) \in \mathcal C$ such that $u\in ϕ(u)$. Once we construct and fix the mapping $ϕ:U \rightarrow \mathcal{C}$ a subset $X \subseteq U$ of the universe is revealed, and we need to cover all elements from $X$ with exactly $ϕ(X):=\cup_{u\in X} ϕ(u)$. The goal is to find a mapping such that the cover $ϕ(X)$ is as cheap as possible. This is an example of a universal problem where the solution has to be created before the actual instance to deal with is revealed. Such problems appear naturally in some settings when we need to optimize under uncertainty and it may be actually too expensive to begin finding a good solution once the input starts being revealed. A rich body of work was devoted to investigate the approximability of such problems under the regime of worst case analysis or when the input instance is drawn randomly from some probability distribution. Here one typically compares the quality of the produced solution with the optimal offline solution. In this paper we consider a different viewpoint: What if we would compare our approximate universal solution against an optimal universal solution that obeys the same rules as we do? We show that under this viewpoint it is possible to achieve improved approximation algorithms for the stochastic version of universal set cover. Our result is based on rounding a proper configuration IP that captures the optimal universal solution, and using tools from submodular optimization. The same basic approach leads to improved approximation algorithms also for other related problems.