OpenRaiser/Intern-Atlas huggingface dataset:
On exact and optimal recovering of missing values for sequences
Venue: arXiv cs.IT 2017
Year: 2017

The paper studies recoverability of missing values for sequences in a pathwise setting without probabilistic assumptions. This setting is oriented on a situation where the underlying sequence is considered as a sole sequence rather than a member of an ensemble with known statistical properties. Sufficient conditions of recoverability are obtained; it is shown that sequences are recoverable if there is a certain degree of degeneracy of the Z-transforms. We found that, in some cases, this degree can be measured as the number of the derivatives of Z-transform vanishing at a point. For processes with non-degenerate Z-transform, an optimal recovering based on the projection on a set of recoverable sequences is suggested. Some robustness of the solution with respect to noise contamination and truncation is established.
Characterization and Lower Bounds for Branching Program Size using Projective Dimension
Venue: arXiv cs.CC 2017
Year: 2017

We study projective dimension, a graph parameter (denoted by pd$(G)$ for a graph $G$), introduced by (Pudlák, Rödl 1992), who showed that proving lower bounds for pd$(G_f)$ for bipartite graphs $G_f$ associated with a Boolean function $f$ imply size lower bounds for branching programs computing $f$. Despite several attempts (Pudlák, Rödl 1992 ; Babai, Rónyai, Ganapathy 2000), proving super-linear lower bounds for projective dimension of explicit families of graphs has remained elusive. We show that there exist a Boolean function $f$ (on $n$ bits) for which the gap between the projective dimension and size of the optimal branching program computing $f$ (denoted by bpsize$(f)$), is $2^{Ω(n)}$. Motivated by the argument in (Pudlák, Rödl 1992), we define two variants of projective dimension - projective dimension with intersection dimension 1 (denoted by upd$(G)$) and bitwise decomposable projective dimension (denoted by bitpdim$(G)$). As our main result, we show that there is an explicit family of graphs on $N = 2^n$ vertices such that the projective dimension is $O(\sqrt{n})$, the projective dimension with intersection dimension $1$ is $Ω(n)$ and the bitwise decomposable projective dimension is $Ω(\frac{n^{1.5}}{\log n})$. We also show that there exist a Boolean function $f$ (on $n$ bits) for which the gap between upd$(G_f)$ and bpsize$(f)$ is $2^{Ω(n)}$. In contrast, we also show that the bitwise decomposable projective dimension characterizes size of the branching program up to a polynomial factor. That is, there exists a constant $c>0$ and for any function $f$, $\textrm{bitpdim}(G_f)/6 \le \textrm{bpsize}(f) \le (\textrm{bitpdim}(G_f))^c$. We also study two other variants of projective dimension and show that they are exactly equal to well-studied graph parameters - bipartite clique cover number and bipartite partition number respectively.
Semantic Change Detection with Hypermaps
Venue: arXiv cs.CV 2017
Year: 2017

Change detection is the study of detecting changes between two different images of a scene taken at different times. By the detected change areas, however, a human cannot understand how different the two images. Therefore, a semantic understanding is required in the change detection research such as disaster investigation. The paper proposes the concept of semantic change detection, which involves intuitively inserting semantic meaning into detected change areas. We mainly focus on the novel semantic segmentation in addition to a conventional change detection approach. In order to solve this problem and obtain a high-level of performance, we propose an improvement to the hypercolumns representation, hereafter known as hypermaps, which effectively uses convolutional maps obtained from convolutional neural networks (CNNs). We also employ multi-scale feature representation captured by different image patches. We applied our method to the TSUNAMI Panoramic Change Detection dataset, and re-annotated the changed areas of the dataset via semantic classes. The results show that our multi-scale hypermaps provided outstanding performance on the re-annotated TSUNAMI dataset.
Divide and Congruence II: From Decomposition of Modal Formulas to Preservation of Delay and Weak Bisimilarity
Venue: arXiv cs.LO 2017
Year: 2017

Earlier we presented a method to decompose modal formulas for processes with the internal action $τ$, and congruence formats for branching and $η$-bisimilarity were derived on the basis of this decomposition method. The idea is that a congruence format for a semantics must ensure that the formulas in the modal characterisation of this semantics are always decomposed into formulas that are again in this modal characterisation. In this follow-up paper the decomposition method is enhanced to deal with modal characterisations that contain a modality $\langleε\rangle\langle a\rangleϕ$, to derive congruence formats for delay and weak bisimilarity.
An Efficient Hybrid Route-Path Planning Model For Dynamic Task Allocation and Safe Maneuvering of an Underwater Vehicle in a Realistic Environment
Venue: arXiv cs.RO 2017
Year: 2017

This paper presents a hybrid route-path planning model for an Autonomous Underwater Vehicle's task assignment and management while the AUV is operating through the variable littoral waters. Several prioritized tasks distributed in a large scale terrain is defined first; then, considering the limitations over the mission time, vehicle's battery, uncertainty and variability of the underlying operating field, appropriate mission timing and energy management is undertaken. The proposed objective is fulfilled by incorporating a route-planner that is in charge of prioritizing the list of available tasks according to available battery and a path-planer that acts in a smaller scale to provide vehicle's safe deployment against environmental sudden changes. The synchronous process of the task assign-route and path planning is simulated using a specific composition of Differential Evolution and Firefly Optimization (DEFO) Algorithms. The simulation results indicate that the proposed hybrid model offers efficient performance in terms of completion of maximum number of assigned tasks while perfectly expending the minimum energy, provided by using the favorable current flow, and controlling the associated mission time. The Monte-Carlo test is also performed for further analysis. The corresponding results show the significant robustness of the model against uncertainties of the operating field and variations of mission conditions.
The Power of Arc Consistency for CSPs Defined by Partially-Ordered Forbidden Patterns
Venue: arXiv cs.CC 2017
Year: 2017

Characterising tractable fragments of the constraint satisfaction problem (CSP) is an important challenge in theoretical computer science and artificial intelligence. Forbidding patterns (generic sub-instances) provides a means of defining CSP fragments which are neither exclusively language-based nor exclusively structure-based. It is known that the class of binary CSP instances in which the broken-triangle pattern (BTP) does not occur, a class which includes all tree-structured instances, are decided by arc consistency (AC), a ubiquitous reduction operation in constraint solvers. We provide a characterisation of simple partially-ordered forbidden patterns which have this AC-solvability property. It turns out that BTP is just one of five such AC-solvable patterns. The four other patterns allow us to exhibit new tractable classes.
Hiring Secretaries over Time: The Benefit of Concurrent Employment
Venue: arXiv cs.DS 2017
Year: 2017

We consider a stochastic online problem where $n$ applicants arrive over time, one per time step. Upon arrival of each applicant their cost per time step is revealed, and we have to fix the duration of employment, starting immediately. This decision is irrevocable, i.e., we can neither extend a contract nor dismiss a candidate once hired. In every time step, at least one candidate needs to be under contract, and our goal is to minimize the total hiring cost, which is the sum of the applicants' costs multiplied with their respective employment durations. We provide a competitive online algorithm for the case that the applicants' costs are drawn independently from a known distribution. Specifically, the algorithm achieves a competitive ratio of 2.965 for the case of uniform distributions. For this case, we give an analytical lower bound of 2 and a computational lower bound of 2.148. We then adapt our algorithm to stay competitive even in settings with one or more of the following restrictions: (i) at most two applicants can be hired concurrently; (ii) the distribution of the applicants' costs is unknown; (iii) the total number $n$ of time steps is unknown. On the other hand, we show that concurrent employment is a necessary feature of competitive algorithms by proving that no algorithm has a competitive ratio better than $Ω(\sqrt{n} / \log n)$ if concurrent employment is forbidden.
On Approximating Functions of the Singular Values in a Stream
Venue: arXiv cs.DS 2017
Year: 2017

For any real number $p > 0$, we nearly completely characterize the space complexity of estimating $\|A\|_p^p = \sum_{i=1}^n σ_i^p$ for $n \times n$ matrices $A$ in which each row and each column has $O(1)$ non-zero entries and whose entries are presented one at a time in a data stream model. Here the $σ_i$ are the singular values of $A$, and when $p \geq 1$, $\|A\|_p^p$ is the $p$-th power of the Schatten $p$-norm. We show that when $p$ is not an even integer, to obtain a $(1+ε)$-approximation to $\|A\|_p^p$ with constant probability, any $1$-pass algorithm requires $n^{1-g(ε)}$ bits of space, where $g(ε) \rightarrow 0$ as $ε\rightarrow 0$ and $ε> 0$ is a constant independent of $n$. However, when $p$ is an even integer, we give an upper bound of $n^{1-2/p} \textrm{poly}(ε^{-1}\log n)$ bits of space, which holds even in the turnstile data stream model. The latter is optimal up to $\textrm{poly}(ε^{-1} \log n)$ factors. Our results considerably strengthen lower bounds in previous work for arbitrary (not necessarily sparse) matrices $A$: the previous best lower bound was $Ω(\log n)$ for $p\in (0,1)$, $Ω(n^{1/p-1/2}/\log n)$ for $p\in [1,2)$ and $Ω(n^{1-2/p})$ for $p\in (2,\infty)$. We note for $p \in (2, \infty)$, while our lower bound for even integers is the same, for other $p$ in this range our lower bound is $n^{1-g(ε)}$, which is considerably stronger than the previous $n^{1-2/p}$ for small enough constant $ε> 0$. We obtain similar near-linear lower bounds for Ky-Fan norms, SVD entropy, eigenvalue shrinkers, and M-estimators, many of which could have been solvable in logarithmic space prior to our work.
I/O-Efficient Generation of Massive Graphs Following the LFR Benchmark
Venue: arXiv cs.DS 2017
Year: 2017

LFR is a popular benchmark graph generator used to evaluate community detection algorithms. We present EM-LFR, the first external memory algorithm able to generate massive complex networks following the LFR benchmark. Its most expensive component is the generation of random graphs with prescribed degree sequences which can be divided into two steps: the graphs are first materialized deterministically using the Havel-Hakimi algorithm, and then randomized. Our main contributions are EM-HH and EM-ES, two I/O-efficient external memory algorithms for these two steps. We also propose EM-CM/ES, an alternative sampling scheme using the Configuration Model and rewiring steps to obtain a random simple graph. In an experimental evaluation we demonstrate their performance; our implementation is able to handle graphs with more than 37 billion edges on a single machine, is competitive with a massive parallel distributed algorithm, and is faster than a state-of-the-art internal memory implementation even on instances fitting in main memory. EM-LFR's implementation is capable of generating large graph instances orders of magnitude faster than the original implementation. We give evidence that both implementations yield graphs with matching properties by applying clustering algorithms to generated instances. Similarly, we analyse the evolution of graph properties as EM-ES is executed on networks obtained with EM-CM/ES and find that the alternative approach can accelerate the sampling process.
Community Evolution
Venue: arXiv cs.SI 2017
Year: 2017

The continuous interest in the social network area contributes to the fast development of this field. The new possibilities of obtaining and storing data facilitate deeper analysis of the entire social network, extracted social groups and single individuals as well. One of the most interesting research topic is the network dynamics and dynamics of social groups in particular, it means analysis of group evolution over time. It is the natural step forward after social community extraction. Having communities extracted, appropriate knowledge and methods for dynamic analysis may be applied in order to identify changes as well as to predict the future of all or some selected groups. Furthermore, knowing the most probably change of a given group some additional steps may be performed in order to change this predicted future according to specific needs. Such ability would be a powerful tool in the hands of human resource managers, personnel recruitment, marketing, telecommunication companies, etc.
Algorithms for the Pagination Problem, a Bin Packing with Overlapping Items
Venue: arXiv cs.DS 2017
Year: 2017

We introduce the strongly NP-complete pagination problem, an extension of BIN PACKING where packing together two items may make them occupy less volume than the sum of their individual sizes. To achieve this property, an item is defined as a finite set of symbols from a given alphabet: while, in BIN PACKING, any two such sets would be disjoint, in PAGINATION, they can share zero, one or more symbols. After formulating the problem as an integer linear program, we try to approximate its solutions with several families of algorithms: from straightforward adaptations of classical BIN PACKING heuristics, to dedicated algorithms (greedy and non-greedy), to standard and grouping genetic algorithms. All of them are studied first theoretically, then experimentally on an extensive random test set. Based upon these data, we propose a predictive measure of the statistical difficulty of a given instance, and finally recommend which algorithm should be used in which case, depending on either time constraints or quality requirements.
Inexact Alternating Optimization for Phase Retrieval In the Presence of Outliers
Venue: arXiv cs.IT 2017
Year: 2017

Phase retrieval has been mainly considered in the presence of Gaussian noise. However, the performance of the algorithms proposed under the Gaussian noise model severely degrades when grossly corrupted data, i.e., outliers, exist. This paper investigates techniques for phase retrieval in the presence of heavy-tailed noise -- which is considered a better model for situations where outliers exist. An $\ell_p$-norm ($0<p<2$) based estimator is proposed for fending against such noise, and two-block inexact alternating optimization is proposed as the algorithmic framework to tackle the resulting optimization problem. Two specific algorithms are devised by exploring different local approximations within this framework. Interestingly, the core conditional minimization steps can be interpreted as iteratively reweighted least squares and gradient descent. Convergence properties of the algorithms are discussed, and the Cramér-Rao bound (CRB) is derived. Simulations demonstrate that the proposed algorithms approach the CRB and outperform state-of-the-art algorithms in heavy-tailed noise.
The Completeness Problem for Modal Logic
Venue: arXiv cs.LO 2017
Year: 2017

We introduce the completeness problem for Modal Logic and examine its complexity. For a definition of completeness for formulas, given a formula of a modal logic, the completeness problem asks whether the formula is complete for that logic. We discover that completeness and validity have the same complexity --- with certain exceptions for which there are, in general, no complete formulas. To prove upper bounds, we present a non-deterministic polynomial-time procedure with an oracle from PSPACE that combines tableaux and a test for bisimulation, and determines whether a formula is complete.
The Optimal Pessimistic Transactional Memory Algorithm
Venue: arXiv cs.DC 2017
Year: 2017

Transactional Memory (TM) is an approach aiming to simplify concurrent programming by automating synchronization while maintaining efficiency. TM usually employs the optimistic concurrency control approach, which relies on transactions aborting and restarting if conflicts occur. However, an aborted transaction can still leave some effects in the system that cannot be cleaned up, if irrevocable operations are present within its code. The pessimistic approach eliminates that problem, since it relies on deferring operations in case of conflict rather than aborting, but hitherto pessimistic TMs suffered from low parallelism due to the need of serializing transactions. In this paper, we aim to introduce OptSVA, a pessimistic TM concurrency control algorithm that ensures a high level of parallelism through a battery of far-reaching optimizations including early release, asynchronous execution, and the extensive use of buffering.
Sampling Requirements for Stable Autoregressive Estimation
Venue: arXiv cs.IT 2017
Year: 2017

We consider the problem of estimating the parameters of a linear univariate autoregressive model with sub-Gaussian innovations from a limited sequence of consecutive observations. Assuming that the parameters are compressible, we analyze the performance of the $\ell_1$-regularized least squares as well as a greedy estimator of the parameters and characterize the sampling trade-offs required for stable recovery in the non-asymptotic regime. In particular, we show that for a fixed sparsity level, stable recovery of AR parameters is possible when the number of samples scale sub-linearly with the AR order. Our results improve over existing sampling complexity requirements in AR estimation using the LASSO, when the sparsity level scales faster than the square root of the model order. We further derive sufficient conditions on the sparsity level that guarantee the minimax optimality of the $\ell_1$-regularized least squares estimate. Applying these techniques to simulated data as well as real-world datasets from crude oil prices and traffic speed data confirm our predicted theoretical performance gains in terms of estimation accuracy and model selection.
Towards Understanding Generics in Mainstream OOP
Venue: arXiv cs.PL 2017
Year: 2017

This article reports on steps towards building a simple and accurate domain-theoretic model of generic nominally-typed OOP.
Not Just a Black Box: Learning Important Features Through Propagating Activation Differences
Venue: arXiv cs.LG 2017
Year: 2017

Note: This paper describes an older version of DeepLIFT. See https://arxiv.org/abs/1704.02685 for the newer version. Original abstract follows: The purported "black box" nature of neural networks is a barrier to adoption in applications where interpretability is essential. Here we present DeepLIFT (Learning Important FeaTures), an efficient and effective method for computing importance scores in a neural network. DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. We apply DeepLIFT to models trained on natural images and genomic data, and show significant advantages over gradient-based methods.
Lower Bound on the Redundancy of PIR Codes
Venue: arXiv cs.IT 2017
Year: 2017

We prove that the redundancy of a $k$-server PIR code of dimension $s$ is $Ω(\sqrt{s})$ for all $k \ge 3$. This coincides with a known upper bound of $O(\sqrt{s})$ on the redundancy of PIR codes. Moreover, for $k=3$ and $k = 4$, we determine the lowest possible redundancy of $k$-server PIR codes exactly. Similar results were proved independently by Mary Wootters using a different method.
LSTM with Working Memory
Venue: arXiv cs.NE 2017
Year: 2017

Previous RNN architectures have largely been superseded by LSTM, or "Long Short-Term Memory". Since its introduction, there have been many variations on this simple design. However, it is still widely used and we are not aware of a gated-RNN architecture that outperforms LSTM in a broad sense while still being as simple and efficient. In this paper we propose a modified LSTM-like architecture. Our architecture is still simple and achieves better performance on the tasks that we tested on. We also introduce a new RNN performance benchmark that uses the handwritten digits and stresses several important network capabilities.
Frequency-Selective Vandermonde Decomposition of Toeplitz Matrices with Applications
Venue: arXiv cs.IT 2017
Year: 2017

The classical result of Vandermonde decomposition of positive semidefinite Toeplitz matrices, which dates back to the early twentieth century, forms the basis of modern subspace and recent atomic norm methods for frequency estimation. In this paper, we study the Vandermonde decomposition in which the frequencies are restricted to lie in a given interval, referred to as frequency-selective Vandermonde decomposition. The existence and uniqueness of the decomposition are studied under explicit conditions on the Toeplitz matrix. The new result is connected by duality to the positive real lemma for trigonometric polynomials nonnegative on the same frequency interval. Its applications in the theory of moments and line spectral estimation are illustrated. In particular, it provides a solution to the truncated trigonometric $K$-moment problem. It is used to derive a primal semidefinite program formulation of the frequency-selective atomic norm in which the frequencies are known {\em a priori} to lie in certain frequency bands. Numerical examples are also provided.
ZeroBlock: Timestamp-Free Prevention of Block-Withholding Attack in Bitcoin
Venue: arXiv cs.CR 2017
Year: 2017

Bitcoin was recently introduced as a peer-to-peer electronic currency in order to facilitate transactions outside the traditional financial system. The core of Bitcoin, the Blockchain, is the history of the transactions in the system maintained by all miners as a distributed shared register. New blocks in the Blockchain contain the last transactions in the system and are added by miners after a block mining process that consists in solving a resource consuming proof-of-work (cryptographic puzzle). The reward is a motivation for mining process but also could be an incentive for attacks such as selfish mining. In this paper we propose a solution for one of the major problems in Bitcoin : selfish mining or block-withholding attack. This attack is conducted by adversarial or selfish miners in order to either earn undue rewards or waste the computational power of honest miners. Contrary to recent solutions, our solution, ZeroBlock, prevents block-withholding using a technique free of timestamp that can be forged. Moreover, we show that our solution is compliant with nodes churn.
A Vizing-like theorem for union vertex-distinguishing edge coloring
Venue: arXiv cs.DM 2017
Year: 2017

We introduce a variant of the vertex-distinguishing edge coloring problem, where each edge is assigned a subset of colors. The label of a vertex is the union of the sets of colors on edges incident to it. In this paper we investigate the problem of finding a coloring with the minimum number of colors where every vertex receives a distinct label. Finding such a coloring generalizes several other well-known problems of vertex-distinguishing colorings in graphs.We show that for any graph (without connected component reduced to an edge or a single vertex), the minimum number of colors for which such a coloring exists can only take 3possible values depending on the order of the graph. Moreover, we provide the exact value for paths, cycles and complete binary trees.