OpenRaiser/Intern-Atlas huggingface dataset:
Humanoid Momentum Estimation Using Sensed Contact Wrenches
Venue: arXiv cs.RO 2015
Year: 2015

This work presents approaches for the estimation of quantities important for the control of the momentum of a humanoid robot. In contrast to previous approaches which use simplified models such as the Linear Inverted Pendulum Model, we present estimators based on the momentum dynamics of the robot. By using this simple yet dynamically-consistent model, we avoid the issues of using simplified models for estimation. We develop an estimator for the center of mass and full momentum which can be reformulated to estimate center of mass offsets as well as external wrenches applied to the robot. The observability of these estimators is investigated and their performance is evaluated in comparison to previous approaches.
Bridge the gap between network-based inference method and global ranking method in personal recommendation
Venue: arXiv cs.SI 2015
Year: 2015

In this paper, we study the relationship between the network-based inference method and global ranking method in personal recommendation. By some theoretical analysis, we prove that the recommendation result under the global ranking method is the limit of applying network-based inference method with infinity times.
Asymmetry in in-degree and out-degree distributions of large-scale industrial networks
Venue: arXiv cs.SI 2015
Year: 2015

Many natural, physical and social networks commonly exhibit power-law degree distributions. In this paper, we discover previously unreported asymmetrical patterns in the degree distributions of incoming and outgoing links in the investigation of large-scale industrial networks, and provide interpretations. In industrial networks, nodes are firms and links are directed supplier-customer relationships. While both in- and out-degree distributions have "power law" regimes, out-degree distribution decays faster than in-degree distribution and crosses it at a consistent nodal degree. It implies that, as link degree increases, the constraints to the capacity for designing, producing and transmitting artifacts out to others grow faster than and surpasses those for acquiring, absorbing and synthesizing artifacts provided from others. We further discover that this asymmetry in decaying rates of in-degree and out-degree distributions is smaller in networks that process and transmit more decomposable artifacts, e.g. informational artifacts in contrast with physical artifacts. This asymmetry in in-degree and out-degree distributions is likely to hold for other directed networks, but to different degrees, depending on the decomposability of the processed and transmitted artifacts.
Diagnosing State-Of-The-Art Object Proposal Methods
Venue: arXiv cs.CV 2015
Year: 2015

Object proposal has become a popular paradigm to replace exhaustive sliding window search in current top-performing methods in PASCAL VOC and ImageNet. Recently, Hosang et al. conduct the first unified study of existing methods' in terms of various image-level degradations. On the other hand, the vital question "what object-level characteristics really affect existing methods' performance?" is not yet answered. Inspired by Hoiem et al.'s work in categorical object detection, this paper conducts the first meta-analysis of various object-level characteristics' impact on state-of-the-art object proposal methods. Specifically, we examine the effects of object size, aspect ratio, iconic view, color contrast, shape regularity and texture. We also analyse existing methods' localization accuracy and latency for various PASCAL VOC object classes. Our study reveals the limitations of existing methods in terms of non-iconic view, small object size, low color contrast, shape regularity etc. Based on our observations, lessons are also learned and shared with respect to the selection of existing object proposal technologies as well as the design of the future ones.
Energy-Efficient Hybrid Analog and Digital Precoding for mmWave MIMO Systems with Large Antenna Arrays
Venue: arXiv cs.IT 2015
Year: 2015

Millimeter wave (mmWave) MIMO will likely use hybrid analog and digital precoding, which uses a small number of RF chains to avoid energy consumption associated with mixed signal components like analog-to-digital components not to mention baseband processing complexity. However, most hybrid precoding techniques consider a fully-connected architecture requiring a large number of phase shifters, which is also energyintensive. In this paper, we focus on the more energy-efficient hybrid precoding with sub-connected architecture, and propose a successive interference cancelation (SIC)-based hybrid precoding with near-optimal performance and low complexity. Inspired by the idea of SIC for multi-user signal detection, we first propose to decompose the total achievable rate optimization problem with non-convex constraints into a series of simple sub-rate optimization problems, each of which only considers one sub-antenna array. Then, we prove that maximizing the achievable sub-rate of each sub-antenna array is equivalent to simply seeking a precoding vector sufficiently close (in terms of Euclidean distance) to the unconstrained optimal solution. Finally, we propose a low-complexity algorithm to realize SICbased hybrid precoding, which can avoid the need for the singular value decomposition (SVD) and matrix inversion. Complexity evaluation shows that the complexity of SIC-based hybrid precoding is only about 10% as complex as that of the recently proposed spatially sparse precoding in typical mmWave MIMO systems. Simulation results verify the near-optimal performance of SIC-based hybrid precoding.
Optimizing the computation of overriding
Venue: arXiv cs.AI 2015
Year: 2015

We introduce optimization techniques for reasoning in DLN---a recently introduced family of nonmonotonic description logics whose characterizing features appear well-suited to model the applicative examples naturally arising in biomedical domains and semantic web access control policies. Such optimizations are validated experimentally on large KBs with more than 30K axioms. Speedups exceed 1 order of magnitude. For the first time, response times compatible with real-time reasoning are obtained with nonmonotonic KBs of this size.
A Theory for the Operation of the Independent System Operator in a Smart Grid with Stochastic Renewables, Demand Response and Storage
Venue: arXiv cs.SY 2015
Year: 2015

In this paper, we address a key issue of designing architectures and algorithms which generate optimal demand response in a decentralized manner for a smart-grid consisting of several stochastic renewables and dynamic loads. By optimal demand response, we refer to the demand response which maximizes the utility of the agents connected to the smart-grid. By decentralized we refer to the desirable case where neither the independent system operator (ISO) needs to know the dynamics/utilities of the agents, nor do the agents need to have a knowledge of the dynamics/utilities of other agents connected to the grid. The communication between the ISO and agents is restricted to the ISO announcing a pricing policy and the agents responding with their energy generation/consumption bids in response to the pricing policy. We provide a complete solution for both the deterministic and stochastic cases. It features a price iteration scheme that results in optimality of social welfare. We also provide an optimal solution for the case where there is a common randomness affecting and observed by all agents. This solution can be computationally complex, and we pose approximations. For the more general partially observed randomness case, we exhibit a relaxation that significantly reduces complexity. We also provide an approximation strategy that leads to a model predictive control (MPC) approach. Simulation results comparing the resulting optimal demand response with the existing architectures employed by the ISO illustrate the benefit in social welfare utility realized by our scheme. To the best of the authors' knowledge, this is the first work of its kind to explicitly mark out the optimal response of dynamic demand.
Tractable Resource Management in Millimeter-Wave Overlaid Ultra-Dense Cellular Networks
Venue: arXiv cs.NI 2015
Year: 2015

What does millimeter-wave (mmW) seek assistance for from micro-wave (μW) in a mmW overlaid 5G cellular network? This paper raises the question of whether to complement downlink (DL) or uplink (UL) transmissions, and concludes that μW should aid UL more. Such dedication to UL results from the low mmW UL rate due to high peak-to-average power ratio (PAPR) at mobile users. The DL/UL allocations are tractably provided based on a novel closed-form mm-μW spectral efficiency (SE) derivation via stochastic geometry. The findings explicitly indicate: (i) both DL/UL mmW (or μW) SEs coincidentally converge on the same value in an ultra-dense cellular network (UDN) and (ii) such a mmW (or μW) UDN SE is a logarithmic function of BS-to-user density ratio. The corresponding mm-μW resource management is evaluated by utilizing a three dimensional (3D) blockage model with real geography in Seoul, Korea.
Learning Robust Deep Face Representation
Venue: arXiv cs.CV 2015
Year: 2015

With the development of convolution neural network, more and more researchers focus their attention on the advantage of CNN for face recognition task. In this paper, we propose a deep convolution network for learning a robust face representation. The deep convolution net is constructed by 4 convolution layers, 4 max pooling layers and 2 fully connected layers, which totally contains about 4M parameters. The Max-Feature-Map activation function is used instead of ReLU because the ReLU might lead to the loss of information due to the sparsity while the Max-Feature-Map can get the compact and discriminative feature vectors. The model is trained on CASIA-WebFace dataset and evaluated on LFW dataset. The result on LFW achieves 97.77% on unsupervised setting for single net.
Tree-based Visualization and Optimization for Image Collection
Venue: arXiv cs.MM 2015
Year: 2015

The visualization of an image collection is the process of displaying a collection of images on a screen under some specific layout requirements. This paper focuses on an important problem that is not well addressed by the previous methods: visualizing image collections into arbitrary layout shapes while arranging images according to user-defined semantic or visual correlations (e.g., color or object category). To this end, we first propose a property-based tree construction scheme to organize images of a collection into a tree structure according to user-defined properties. In this way, images can be adaptively placed with the desired semantic or visual correlations in the final visualization layout. Then, we design a two-step visualization optimization scheme to further optimize image layouts. As a result, multiple layout effects including layout shape and image overlap ratio can be effectively controlled to guarantee a satisfactory visualization. Finally, we also propose a tree-transfer scheme such that visualization layouts can be adaptively changed when users select different "images of interest". We demonstrate the effectiveness of our proposed approach through the comparisons with state-of-the-art visualization techniques.
FRULER: Fuzzy Rule Learning through Evolution for Regression
Venue: arXiv cs.LG 2015
Year: 2015

In regression problems, the use of TSK fuzzy systems is widely extended due to the precision of the obtained models. Moreover, the use of simple linear TSK models is a good choice in many real problems due to the easy understanding of the relationship between the output and input variables. In this paper we present FRULER, a new genetic fuzzy system for automatically learning accurate and simple linguistic TSK fuzzy rule bases for regression problems. In order to reduce the complexity of the learned models while keeping a high accuracy, the algorithm consists of three stages: instance selection, multi-granularity fuzzy discretization of the input variables, and the evolutionary learning of the rule base that uses the Elastic Net regularization to obtain the consequents of the rules. Each stage was validated using 28 real-world datasets and FRULER was compared with three state of the art enetic fuzzy systems. Experimental results show that FRULER achieves the most accurate and simple models compared even with approximative approaches.
A Polynomial Time Bounded-error Quantum Algorithm for Boolean Satisfiability
Venue: arXiv cs.CC 2015
Year: 2015

The aim of the paper is to answer a long-standing open problem on the relationship between NP and BQP. The paper shows that BQP contains NP by proposing a BQP quantum algorithm for the MAX-E3-SAT problem which is a fundamental NP-hard problem. Given an E3-CNF Boolean formula, the aim of the MAX-E3-SAT problem is to find the variable assignment that maximizes the number of satisfied clauses. The proposed algorithm runs in $O(m^2)$ for an E3-CNF Boolean formula with $m$ clauses and in the worst case runs in $O(n^6)$ for an E3-CNF Boolean formula with $n$ inputs. The proposed algorithm maximizes the set of satisfied clauses using a novel iterative partial negation and partial measurement technique. The algorithm is shown to achieve an arbitrary high probability of success of $1-ε$ for small $ε>0$ using a polynomial resources. In addition to solving the MAX-E3-SAT problem, the proposed algorithm can also be used to decide if an E3-CNF Boolean formula is satisfiable or not, which is an NP-complete problem, based on the maximum number of satisfied clauses.
Towards Understanding User Preferences from User Tagging Behavior for Personalization
Venue: arXiv cs.MM 2015
Year: 2015

Personalizing image tags is a relatively new and growing area of research, and in order to advance this research community, we must review and challenge the de-facto standard of defining tag importance. We believe that for greater progress to be made, we must go beyond tags that merely describe objects that are visually represented in the image, towards more user-centric and subjective notions such as emotion, sentiment, and preferences. We focus on the notion of user preferences and show that the order that users list tags on images is correlated to the order of preference over the tags that they provided for the image. While this observation is not completely surprising, to our knowledge, we are the first to explore this aspect of user tagging behavior systematically and report empirical results to support this observation. We argue that this observation can be exploited to help advance the image tagging (and related) communities. Our contributions include: 1.) conducting a user study demonstrating this observation, 2.) collecting a dataset with user tag preferences explicitly collected.
Diffusion LMS Strategies in Sensor Networks with Noisy Input Data
Venue: arXiv cs.SY 2015
Year: 2015

We investigate the performance of distributed least-mean square (LMS) algorithms for parameter estimation over sensor networks where the regression data of each node are corrupted by white measurement noise. Under this condition, we show that the estimates produced by distributed LMS algorithms will be biased if the regression noise is excluded from consideration. We propose a bias-elimination technique and develop a novel class of diffusion LMS algorithms that can mitigate the effect of regression noise and obtain an unbiased estimate of the unknown parameter vector over the network. In our development, we first assume that the variances of the regression noises are known a-priori. Later, we relax this assumption by estimating these variances in real-time. We analyze the stability and convergence of the proposed algorithms and derive closed-form expressions to characterize their mean-square error performance in transient and steady-state regimes. We further provide computer experiment results that illustrate the efficiency of the proposed algorithms and support the analytical findings.
Throughput-Optimal Multihop Broadcast on Directed Acyclic Wireless Networks
Venue: arXiv cs.IT 2015
Year: 2015

We study the problem of efficiently broadcasting packets in multi-hop wireless networks. At each time slot the network controller activates a set of non-interfering links and forwards selected copies of packets on each activated link. A packet is considered jointly received only when all nodes in the network have obtained a copy of it. The maximum rate of jointly received packets is referred to as the broadcast capacity of the network. Existing policies achieve the broadcast capacity by balancing traffic over a set of spanning trees, which are difficult to maintain in a large and time-varying wireless network. We propose a new dynamic algorithm that achieves the broadcast capacity when the underlying network topology is a directed acyclic graph (DAG). This algorithm is decentralized, utilizes local queue-length information only and does not require the use of global topological structures such as spanning trees. The principal technical challenge inherent in the problem is the absence of work-conservation principle due to the duplication of packets, which renders traditional queuing modelling inapplicable. We overcome this difficulty by studying relative packet deficits and imposing in-order delivery constraints to every node in the network. Although in-order packet delivery, in general, leads to degraded throughput in graphs with cycles, we show that it is throughput optimal in DAGs and can be exploited to simplify the design and analysis of optimal algorithms. Our characterization leads to a polynomial time algorithm for computing the broadcast capacity of any wireless DAG under the primary interference constraints. Additionally, we propose an extension of our algorithm which can be effectively used for broadcasting in any network with arbitrary topology.
Handwriting Recognition
Venue: arXiv cs.CV 2015
Year: 2015

This paper describes the method to recognize offline handwritten characters. A robust algorithm for handwriting segmentation is described here with the help of which individual characters can be segmented from a selected word from a paragraph of handwritten text image which is given as input.
A Unifying Framework for Adaptive Radar Detection in Homogeneous plus Structured Interference-Part II: Detectors Design
Venue: arXiv cs.IT 2015
Year: 2015

This paper deals with the problem of adaptive multidimensional/multichannel signal detection in homogeneous Gaussian disturbance with unknown covariance matrix and structured (unknown) deterministic interference. The aforementioned problem extends the well-known Generalized Multivariate Analysis of Variance (GMANOVA) tackled in the open literature. In a companion paper, we have obtained the Maximal Invariant Statistic (MIS) for the problem under consideration, as an enabling tool for the design of suitable detectors which possess the Constant False-Alarm Rate (CFAR) property. Herein, we focus on the development of several theoretically-founded detectors for the problem under consideration. First, all the considered detectors are shown to be function of the MIS, thus proving their CFARness property. Secondly, coincidence or statistical equivalence among some of them in such a general signal model is proved. Thirdly, strong connections to well-known simpler scenarios found in adaptive detection literature are established. Finally, simulation results are provided for a comparison of the proposed receivers.
Deterministic parallel communicating Watson-Crick automata systems
Venue: arXiv cs.FL 2015
Year: 2015

In this paper, we have introduced the deterministic variant of parallel communicating Watson-Crick automata systems. We show that similar to the non-deterministic version, the deterministic version can also recognise some non-regular uniletter languages. We further establish that strongly deterministic Watson-Crick automata systems and deterministic Watson-Crick automata system are incomparable in terms of their computational ability. We have also compared the computational ability of our system with multihead finite automata and parallel communicating finite automata systems.
Properties and constructions of coincident functions
Venue: arXiv cs.CR 2015
Year: 2015

Extensive studies of Boolean functions are carried in many fields. The Mobius transform is often involved for these studies. In particular, it plays a central role in coincident functions, the class of Boolean functions invariant by this transformation. This class -- which has been recently introduced -- has interesting properties, in particular if we want to control both the Hamming weight and the degree. We propose an innovative way to handle the Mobius transform which allows the composition between several Boolean functions and the use of Shannon or Reed-Muller decompositions. Thus we benefit from a better knowledge of coin-cident functions and introduce new properties. We show experimentally that for many features, coincident functions look like any Boolean functions.
Opportunistic Beamforming with Wireless Powered 1-bit Feedback Through Rectenna Array
Venue: arXiv cs.IT 2015
Year: 2015

This letter deals with the opportunistic beamforming (OBF) scheme for multi-antenna downlink with spatial randomness. In contrast to conventional OBF, the terminals return only 1-bit feedback, which is powered by wireless power transfer through a rectenna array. We study two fundamental topologies for the combination of the rectenna elements; the direct-current combiner and the radio-frequency combiner. The beam outage probability is derived in closed form for both combination schemes, by using high order statistics and stochastic geometry.
Data Acquisition and Control System for High-Performance Large-Area CCD Systems
Venue: arXiv cs.CE 2015
Year: 2015

Astronomical CCD systems based on second-generation DINACON controllers were developed at the SAO RAS Advanced Design Laboratory more than seven years ago and since then have been in constant operation at the 6-meter and Zeiss-1000 telescopes. Such systems use monolithic large-area CCDs. We describe the software developed for the control of a family of large-area CCD systems equipped with a DINACON-II controller. The software suite serves for acquisition, primary reduction, visualization, and storage of video data, and also for the control, setup, and diagnostics of the CCD system.
RAPS: A Recommender Algorithm Based on Pattern Structures
Venue: arXiv cs.IR 2015
Year: 2015

We propose a new algorithm for recommender systems with numeric ratings which is based on Pattern Structures (RAPS). As the input the algorithm takes rating matrix, e.g., such that it contains movies rated by users. For a target user, the algorithm returns a rated list of items (movies) based on its previous ratings and ratings of other users. We compare the results of the proposed algorithm in terms of precision and recall measures with Slope One, one of the state-of-the-art item-based algorithms, on Movie Lens dataset and RAPS demonstrates the best or comparable quality.