OpenRaiser/Intern-Atlas huggingface dataset:
AIDE: An Automated Sample-based Approach for Interactive Data Exploration
Venue: arXiv cs.DB 2015
Year: 2015

In this paper, we argue that database systems be augmented with an automated data exploration service that methodically steers users through the data in a meaningful way. Such an automated system is crucial for deriving insights from complex datasets found in many big data applications such as scientific and healthcare applications as well as for reducing the human effort of data exploration. Towards this end, we present AIDE, an Automatic Interactive Data Exploration framework that assists users in discovering new interesting data patterns and eliminate expensive ad-hoc exploratory queries. AIDE relies on a seamless integration of classification algorithms and data management optimization techniques that collectively strive to accurately learn the user interests based on his relevance feedback on strategically collected samples. We present a number of exploration techniques as well as optimizations that minimize the number of samples presented to the user while offering interactive performance. AIDE can deliver highly accurate query predictions for very common conjunctive queries with small user effort while, given a reasonable number of samples, it can predict with high accuracy complex disjunctive queries. It provides interactive performance as it limits the user wait time per iteration of exploration to less than a few seconds.
Optimal distributed control for platooning via sparse coprime factorizations
Venue: arXiv cs.SY 2015
Year: 2015

We introduce a novel distributed control architecture for heterogeneous platoons of linear time--invariant autonomous vehicles. Our approach is based on a generalization of the concept of {\em leader--follower} controllers for which we provide a Youla--like parameterization while the sparsity constraints are imposed on the controller's left coprime factors, outlying a new concept of structural constraints in distributed control. The proposed scheme is amenable to optimal controller design via norm based costs, it guarantees string stability and eliminates the accordion effect from the behavior of the platoon. We also introduce a synchronization mechanism for the exact compensation of the time delays induced by the wireless broadcasting of information.
Increasing Indoor Spectrum Sharing Capacity using Smart Reflect-Array
Venue: arXiv cs.IT 2015
Year: 2015

The radio frequency (RF) spectrum becomes overly crowded in some indoor environments due to the high density of users and bandwidth demands. To accommodate the tremendous wireless data demands, efficient spectrum-sharing approaches are highly desired. To this end, this paper introduces a new spectrum sharing solution for indoor environments based on the usage of a reconfigurable reflect-array in the middle of the wireless channel. By optimally controlling the phase shift of each element on the reflect-array, the useful signals for each transmission pair can be enhanced while the interferences can be canceled. As a result, multiple wireless users in the same room can access the same spectrum band at the same time without interfering each other. Hence, the network capacity can be dramatically increased. To prove the feasibility of the proposed solution, an experimental testbed is first developed and evaluated. Then, the effects of the reflect-array on transport capacity of the indoor wireless networks are investigated. Through experiments, theoretical deduction, and simulations, this paper demonstrates that significantly higher spectrum-spatial efficiency can be achieved by using the smart reflect-array without any modification of the hardware and software in the users' devices.
CONQUER: Confusion Queried Online Bandit Learning
Venue: arXiv cs.LG 2015
Year: 2015

We present a new recommendation setting for picking out two items from a given set to be highlighted to a user, based on contextual input. These two items are presented to a user who chooses one of them, possibly stochastically, with a bias that favours the item with the higher value. We propose a second-order algorithm framework that members of it use uses relative upper-confidence bounds to trade off exploration and exploitation, and some explore via sampling. We analyze one algorithm in this framework in an adversarial setting with only mild assumption on the data, and prove a regret bound of $O(Q_T + \sqrt{TQ_T\log T} + \sqrt{T}\log T)$, where $T$ is the number of rounds and $Q_T$ is the cumulative approximation error of item values using a linear model. Experiments with product reviews from 33 domains show the advantage of our methods over algorithms designed for related settings, and that UCB based algorithms are inferior to greed or sampling based algorithms.
The Complexity of Equilibria for Risk-Modeling Valuations
Venue: arXiv cs.GT 2015
Year: 2015

We study the complexity of deciding the existence of mixed equilibria for minimization games where players use valuations other than expectation to evaluate their costs. We consider risk-averse players seeking to minimize the sum ${\mathsf{V}} = {\mathsf{E}} + {\mathsf{R}}$ of expectation ${\mathsf{E}}$ and a risk valuation ${\mathsf{R}}$ of their costs; ${\mathsf{R}}$ is non-negative and vanishes exactly when the cost incurred to a player is constant over all choices of strategies by the other players. In a ${\mathsf{V}}$-equilibrium, no player could unilaterally reduce her cost. Say that ${\mathsf{V}}$ has the Weak-Equilibrium-for-Expectation property if all strategies supported in a player's best-response mixed strategy incur the same conditional expectation of her cost. We introduce ${\mathsf{E}}$-strict concavity and observe that every ${\mathsf{E}}$-strictly concave valuation has the Weak-Equilibrium-for-Expectation property. We focus on a broad class of valuations shown to have the Weak-Equilibrium-for-Expectation property, which we exploit to prove two main complexity results, the first of their kind, for the two simplest cases of the problem: games with two strategies, or games with two players. For each case, we show that deciding the existence of a ${\mathsf{V}}$-equilibrium is strongly ${\mathcal{NP}}$-hard for certain choices of significant valuations (including variance and standard deviation).
Formalization of context-free language theory
Venue: arXiv cs.FL 2015
Year: 2015

Context-free language theory is a subject of high importance in computer language processing technology as well as in formal language theory. This paper presents a formalization, using the Coq proof assistant, of fundamental results related to context-free grammars and languages. These include closure properties (union, concatenation and Kleene star), grammar simplification (elimination of useless symbols inaccessible symbols, empty rules and unit rules) and the existence of a Chomsky Normal Form for context-free grammars.
A weakly universal cellular automaton on the pentagrid with three states
Venue: arXiv cs.DM 2015
Year: 2015

In this paper, we prove that there is a weakly universal cellular automaton on the pentagrid with three states which is rotation invariant and which uses à la Moore neighbourhood. Moreover, at each step of the computation, the set of non quiescent states has always infinitely many cycles.
Bioinspired Visual Motion Estimation
Venue: arXiv cs.CV 2015
Year: 2015

Visual motion estimation is a computationally intensive, but important task for sighted animals. Replicating the robustness and efficiency of biological visual motion estimation in artificial systems would significantly enhance the capabilities of future robotic agents. 25 years ago, in this very journal, Carver Mead outlined his argument for replicating biological processing in silicon circuits. His vision served as the foundation for the field of neuromorphic engineering, which has experienced a rapid growth in interest over recent years as the ideas and technologies mature. Replicating biological visual sensing was one of the first tasks attempted in the neuromorphic field. In this paper we focus specifically on the task of visual motion estimation. We describe the task itself, present the progression of works from the early first attempts through to the modern day state-of-the-art, and provide an outlook for future directions in the field.
Semantic Cross-View Matching
Venue: arXiv cs.CV 2015
Year: 2015

Matching cross-view images is challenging because the appearance and viewpoints are significantly different. While low-level features based on gradient orientations or filter responses can drastically vary with such changes in viewpoint, semantic information of images however shows an invariant characteristic in this respect. Consequently, semantically labeled regions can be used for performing cross-view matching. In this paper, we therefore explore this idea and propose an automatic method for detecting and representing the semantic information of an RGB image with the goal of performing cross-view matching with a (non-RGB) geographic information system (GIS). A segmented image forms the input to our system with segments assigned to semantic concepts such as traffic signs, lakes, roads, foliage, etc. We design a descriptor to robustly capture both, the presence of semantic concepts and the spatial layout of those segments. Pairwise distances between the descriptors extracted from the GIS map and the query image are then used to generate a shortlist of the most promising locations with similar semantic concepts in a consistent spatial layout. An experimental evaluation with challenging query images and a large urban area shows promising results.
A Contraction Theory Approach for Analysis of Performance Recovery in Dynamic Surface Control
Venue: arXiv cs.SY 2015
Year: 2015

Dynamic surface control (DSC) method uses high gain filters to avoid the "explosion of complexity" issue inherent in backstepping based controller designs. As a result, the closed loop system and filter dynamics possess time scale separation between them. This paper attempts to design a novel disturbance observer based dynamic surface controller using contraction framework. In doing so the steady state error bounds are obtained in terms of design parameters which are exploited to tune the closed loop system performance. The results not only show that DSC technique recover the performance of a backstepping controller for a small range of filter parameter but also derive the maximum bound for it. Furthermore the stability bounds are also derived in the presence of disturbances and convergence of trajectories to a small penultimate bound is proved. The convergence results are shown to hold for less conservative choice of filter parameter and observer gain. The effectiveness of the proposed controller is verified through simulation example.
Group Isomorphism with Fixed Subnormal Chains
Venue: arXiv cs.CC 2015
Year: 2015

In recent work, Rosenbaum and Wagner showed that isomorphism of explicitly listed $p$-groups of order $n$ could be tested in $n^{\frac{1}{2}\log_p n + O(p)}$ time, roughly a square root of the classical bound. The $O(p)$ term is entirely due to an $n^{O(p)}$ cost of testing for isomorphisms that match fixed composition series in the two groups. We focus here on the fixed-composition-series subproblem and exhibit a polynomial-time algorithm that is valid for general groups. A subsequent paper will construct canonical forms within the same time bound.
Automatic Prosody Prediction for Chinese Speech Synthesis using BLSTM-RNN and Embedding Features
Venue: arXiv cs.CL 2015
Year: 2015

Prosody affects the naturalness and intelligibility of speech. However, automatic prosody prediction from text for Chinese speech synthesis is still a great challenge and the traditional conditional random fields (CRF) based method always heavily relies on feature engineering. In this paper, we propose to use neural networks to predict prosodic boundary labels directly from Chinese characters without any feature engineering. Experimental results show that stacking feed-forward and bidirectional long short-term memory (BLSTM) recurrent network layers achieves superior performance over the CRF-based method. The embedding features learned from raw text further enhance the performance.
Upstream Polling Protocols for Flow Control in PON/xDSL Hybrid Access Networks
Venue: arXiv cs.NI 2015
Year: 2015

In a hybrid PON/xDSL access network, multiple Customer Premise Equipment (CPE) nodes connect over individual Digital Subscriber Lines (DSLs) to a drop-point device. The drop-point device, which is typically reverse powered from the customer, is co-located with an Optical Network Unit (ONU) of the Passive Optical Network (PON). We demonstrate that the drop-point experiences very high buffer occupancies when no flow control or standard Ethernet PAUSE frame flow control is employed. In order to reduce the buffer occupancies in the drop-point, we introduce two gated flow control protocols that extend the polling-based PON medium access control to the DSL segments between the CPEs and the ONUs. We analyze the timing of the gated flow control mechanisms to specify the latest possible time instant when CPEs can start the DSL upstream transmissions so that the ONU can forward the upstream transmissions at the full PON upstream transmission bit rate. Through extensive simulations for a wide range of bursty traffic models, we find that the gated flow control mechanisms, specifically, the ONU and CPE grant sizing policies, enable effective control of the maximum drop-point buffer occupancies.
Ball*-tree: Efficient spatial indexing for constrained nearest-neighbor search in metric spaces
Venue: arXiv cs.DB 2015
Year: 2015

Emerging location-based systems and data analysis frameworks requires efficient management of spatial data for approximate and exact search. Exact similarity search can be done using space partitioning data structures, such as Kd-tree, R*-tree, and Ball-tree. In this paper, we focus on Ball-tree, an efficient search tree that is specific for spatial queries which use euclidean distance. Each node of a Ball-tree defines a ball, i.e. a hypersphere that contains a subset of the points to be searched. In this paper, we propose Ball*-tree, an improved Ball-tree that is more efficient for spatial queries. Ball*-tree enjoys a modified space partitioning algorithm that considers the distribution of the data points in order to find an efficient splitting hyperplane. Also, we propose a new algorithm for KNN queries with restricted range using Ball*-tree, which performs better than both KNN and range search for such queries. Results show that Ball*-tree performs 39%-57% faster than the original Ball-tree algorithm.
Strategy Synthesis for Stochastic Rabin Games with Discounted Reward
Venue: arXiv cs.SY 2015
Year: 2015

Stochastic games are often used to model reactive processes. We consider the problem of synthesizing an optimal almost-sure winning strategy in a two-player (namely a system and its environment) turn-based stochastic game with both a qualitative objective as a Rabin winning condition, and a quantitative objective as a discounted reward. Optimality is considered only over the almost-sure winning strategies, i.e., system strategies that guarantee the satisfaction of the Rabin condition with probability 1 regardless of the environment's strategy. We show that optimal almost-sure winning strategies may need infinite memory, but epsilon-optimal almost-sure winning strategies can always be finite-memory or even memoryless. We identify a sufficient and necessary condition of the existence of memoryless epsilon-optimal almost-sure winning strategies and propose an algorithm to compute one when this condition is satisfied.
PAC Learning-Based Verification and Model Synthesis
Venue: arXiv cs.SE 2015
Year: 2015

We introduce a novel technique for verification and model synthesis of sequential programs. Our technique is based on learning a regular model of the set of feasible paths in a program, and testing whether this model contains an incorrect behavior. Exact learning algorithms require checking equivalence between the model and the program, which is a difficult problem, in general undecidable. Our learning procedure is therefore based on the framework of probably approximately correct (PAC) learning, which uses sampling instead and provides correctness guarantees expressed using the terms error probability and confidence. Besides the verification result, our procedure also outputs the model with the said correctness guarantees. Obtained preliminary experiments show encouraging results, in some cases even outperforming mature software verifiers.
Designing dedicated data compression for physics experiments within FPGA already used for data acquisition
Venue: arXiv cs.IT 2015
Year: 2015

Physics experiments produce enormous amount of raw data, counted in petabytes per day. Hence, there is large effort to reduce this amount, mainly by using some filters. The situation can be improved by additionally applying some data compression techniques: removing redundancy and optimally encoding the actual information. Preferably, both filtering and data compression should fit in FPGA already used for data acquisition - reducing requirements of both data storage and networking architecture. We will briefly explain and discuss some basic techniques, for a better focus applied to design a dedicated data compression system basing on a sample data from a prototype of a tracking detector: 10000 events for 48 channels. We will focus on the time data here, which after neglecting the headers and applying data filtering, requires on average 1170 bits/event using the current coding. Encoding relative times (differences) and grouping data by channels, reduces this number to 798 bits/channel, still using fixed length coding: a fixed number of bits used for a given value. Using variable length Huffman coding to encode numbers of digital pulses for a channel and the most significant bits of values (simple binning) reduces further this number to 552 bits/event. Using adaptive binning: denser for frequent values, and an accurate entropy coder we get further down to 455 bits/event - this option can easily fit unused resources of FPGA currently used for data acquisition. Finally, using separate probability distributions for different channels, what could be done by a software compressor, leads to 437bits/event, what is 2.67 times less than the original 1170 bits/event.
Reinventing the Triangles: Rule of Thumb for Assessing Detectability
Venue: arXiv cs.SI 2015
Year: 2015

Statistical significance of network clustering has been an unresolved problem since it was observed that community detection algorithms produce false positives even in random graphs. After a phase transition between undetectable and detectable cluster structures was discovered, the connection between spectra of adjacency matrices and detectability limits were shown, and both were calculated for a wide range of networks with arbitrary degree distributions and community structure. In practice the full eigenspectrum is not known, and whether a given network has any communities within detectability regime cannot be easily established. Based on the global clustering coefficient we construct a criterion telling whether in an undirected, unweighted network there is some/no detectable community structure, or if the network is in a transient regime. The method is simple and faster than methods involving bootstrapping.
Lowering the learning curve for declarative programming: a Python API for the IDP system
Venue: arXiv cs.PL 2015
Year: 2015

Programmers may be hesitant to use declarative systems, because of the associated learning curve. In this paper, we present an API that integrates the IDP Knowledge Base system into the Python programming language. IDP is a state-of-the-art logical system, which uses SAT, SMT, Logic Programming and Answer Set Programming technology. Python is currently one of the most widely used (teaching) languages for programming. The first goal of our API is to allow a Python programmer to use the declarative power of IDP, without needing to learn any new syntax or semantics. The second goal is allow IDP to be added to/removed from an existing code base with minimal changes.
Data Stream Classification using Random Feature Functions and Novel Method Combinations
Venue: arXiv cs.LG 2015
Year: 2015

Big Data streams are being generated in a faster, bigger, and more commonplace. In this scenario, Hoeffding Trees are an established method for classification. Several extensions exist, including high-performing ensemble setups such as online and leveraging bagging. Also, $k$-nearest neighbors is a popular choice, with most extensions dealing with the inherent performance limitations over a potentially-infinite stream. At the same time, gradient descent methods are becoming increasingly popular, owing in part to the successes of deep learning. Although deep neural networks can learn incrementally, they have so far proved too sensitive to hyper-parameter options and initial conditions to be considered an effective `off-the-shelf' data-streams solution. In this work, we look at combinations of Hoeffding-trees, nearest neighbour, and gradient descent methods with a streaming preprocessing approach in the form of a random feature functions filter for additional predictive power. We further extend the investigation to implementing methods on GPUs, which we test on some large real-world datasets, and show the benefits of using GPUs for data-stream learning due to their high scalability. Our empirical evaluation yields positive results for the novel approaches that we experiment with, highlighting important issues, and shed light on promising future directions in approaches to data-stream classification.
A 7/3-Approximation for Feedback Vertex Sets in Tournaments
Venue: arXiv cs.DS 2015
Year: 2015

We consider the minimum-weight feedback vertex set problem in tournaments: given a tournament with non-negative vertex weights, remove a minimum-weight set of vertices that intersects all cycles. This problem is $\mathsf{NP}$-hard to solve exactly, and Unique Games-hard to approximate by a factor better than 2. We present the first $7/3$ approximation algorithm for this problem, improving on the previously best known ratio $5/2$ given by Cai et al. [FOCS 1998, SICOMP 2001].
Base collapse of holographic algorithms
Venue: arXiv cs.CC 2015
Year: 2015

A holographic algorithm solves a problem in domain of size $n$, by reducing it to counting perfect matchings in planar graphs. It may simulate a $n$-value variable by a bunch of $t$ matchgate bits, which has $2^t$ values. The transformation in the simulation can be expressed as a $n \times 2^t$ matrix $M$, called the base of the holographic algorithm. We wonder whether more matchgate bits bring us more powerful holographic algorithms. In another word, whether we can solve the same original problem, with a collapsed base of size $n \times 2^{r}$, where $r<t$. Base collapse was discovered for small domain $n=2,3,4$. For $n=3, 4$, the base collapse was proved under the condition that there is a full rank generator. We prove for any $n$, the base collapse to a $r\leq \lfloor \log n \rfloor$, with some similar conditions. One of them is that the original problem is defined by one symmetric function. In the proof, we utilize elementary matchgate transformations instead of matchgate identities.
Generalized bent functions and their Gray images
Venue: arXiv cs.IT 2015
Year: 2015

In this paper we prove that generalized bent (gbent) functions defined on $\mathbb{Z}_2^n$ with values in $\mathbb{Z}_{2^k}$ are regular, and find connections between the (generalized) Walsh spectrum of these functions and their components. We comprehensively characterize generalized bent and semibent functions with values in $\mathbb{Z}_{16}$, which extends earlier results on gbent functions with values in $\mathbb{Z}_4$ and $\mathbb{Z}_8$. We also show that the Gray images of gbent functions with values in $\mathbb{Z}_{2^k}$ are semibent/plateaued when $k=3,4$.