OpenRaiser/Intern-Atlas huggingface dataset:
Robust Ensemble Classifier Combination Based on Noise Removal with One-Class SVM
Venue: arXiv cs.LG 2016
Year: 2016

In machine learning area, as the number of labeled input samples becomes very large, it is very difficult to build a classification model because of input data set is not fit in a memory in training phase of the algorithm, therefore, it is necessary to utilize data partitioning to handle overall data set. Bagging and boosting based data partitioning methods have been broadly used in data mining and pattern recognition area. Both of these methods have shown a great possibility for improving classification model performance. This study is concerned with the analysis of data set partitioning with noise removal and its impact on the performance of multiple classifier models. In this study, we propose noise filtering preprocessing at each data set partition to increment classifier model performance. We applied Gini impurity approach to find the best split percentage of noise filter ratio. The filtered sub data set is then used to train individual ensemble models.
Variable Transmission Voltage for Loss Minimization in Long Offshore Wind Farm AC Export Cables
Venue: arXiv cs.CE 2016
Year: 2016

Connection of offshore wind farms to shore requires the use of submarine cables. In the case of long HVAC connections, the capacitive charging currents limit the transfer capability and lead to high losses. This paper shows that the losses can be substantially reduced by continuously adjusting the cable operating voltage according to the instantaneous wind farm power production.Calculations for a 320 MW windfarm connected to shore via a 200 km cable at 220 kV nominal voltage shows that an annual loss reduction of 9 percent is achievable by simply using a 15 percent tap changer voltage regulation on the two transformers. Allowing a larger voltage regulation range leads to further loss reduction (13 percent for 0.4-1.0 p.u. voltage range). If the windfarm has a low utilization factor, the loss reduction potential is demonstrated to be as high as 21 percent . The methodology can be applied without introducing new technology that needs to be developed or qualified.
FPGA Hardware Acceleration of Monte Carlo Simulations for the Ising Model
Venue: arXiv cs.AR 2016
Year: 2016

A two-dimensional Ising model with nearest-neighbors ferromagnetic interactions is implemented in a Field Programmable Gate Array (FPGA) board.Extensive Monte Carlo simulations were carried out using an efficient hardware representation of individual spins and a combined global-local LFSR random number generator. Consistent results regarding the descriptive properties of magnetic systems, like energy, magnetization and susceptibility are obtained while a speed-up factor of approximately 6 times is achieved in comparison to previous FPGA-based published works and almost $10^4$ times in comparison to a standard CPU simulation. A detailed description of the logic design used is given together with a careful analysis of the quality of the random number generator used. The obtained results confirm the potential of FPGAs for analyzing the statistical mechanics of magnetic systems.
Minimum Conditional Description Length Estimation for Markov Random Fields
Venue: arXiv cs.IT 2016
Year: 2016

In this paper we discuss a method, which we call Minimum Conditional Description Length (MCDL), for estimating the parameters of a subset of sites within a Markov random field. We assume that the edges are known for the entire graph $G=(V,E)$. Then, for a subset $U\subset V$, we estimate the parameters for nodes and edges in $U$ as well as for edges incident to a node in $U$, by finding the exponential parameter for that subset that yields the best compression conditioned on the values on the boundary $\partial U$. Our estimate is derived from a temporally stationary sequence of observations on the set $U$. We discuss how this method can also be applied to estimate a spatially invariant parameter from a single configuration, and in so doing, derive the Maximum Pseudo-Likelihood (MPL) estimate.
A Graph Isomorphism-based Decentralized Algorithm for Modular Robot Configuration Formation
Venue: arXiv cs.RO 2016
Year: 2016

We consider the problem of configuration formation in modular robot systems where a set of modules that are initially in different configurations and located at different locations are required to assume appropriate positions so that they can get into a new, user-specified, target configuration. We propose a novel algorithm based on graph isomorphism, where the modules select locations or spots in the target configuration using a utility-based framework, while retaining their original configuration to the greatest extent possible, to reduce the time and energy required by the modules to assume the target configuration. We have shown analytically that our proposed algorithm is complete and guarantees a Pareto-optimal allocation. Experimental simulations of our algorithm with different number of modules in different initial configurations and located initially at different locations, show that the planning time of our algorithm is nominal (order of msec. for 100 modules). We have also compared our algorithm against a market-based allocation algorithm and shown that our proposed algorithm performs better in terms of time and number of messages exchanged.
Market Model and Optimal Pricing Scheme of Big Data and Internet of Things (IoT)
Venue: arXiv cs.GT 2016
Year: 2016

Big data has been emerging as a new approach in utilizing large datasets to optimize complex system operations. Big data is fueled with Internet-of-Things (IoT) services that generate immense sensory data from numerous sensors and devices. While most current research focus of big data is on machine learning and resource management design, the economic modeling and analysis have been largely overlooked. This paper thus investigates the big data market model and optimal pricing scheme. We first study the utility of data from the data science perspective, i.e., using the machine learning methods. We then introduce the market model and develop an optimal pricing scheme afterward. The case study shows clearly the suitability of the proposed data utility functions. The numerical examples demonstrate that big data and IoT service provider can achieve the maximum profit through the proposed market model.
Gabor Wavelets in Image Processing
Venue: arXiv cs.CV 2016
Year: 2016

This work shows the use of a two-dimensional Gabor wavelets in image processing. Convolution with such a two-dimensional wavelet can be separated into two series of one-dimensional ones. The key idea of this work is to utilize a Gabor wavelet as a multiscale partial differential operator of a given order. Gabor wavelets are used here to detect edges, corners and blobs. A performance of such an interest point detector is compared to detectors utilizing a Haar wavelet and a derivative of a Gaussian function. The proposed approach may be useful when a fast implementation of the Gabor transform is available or when the transform is already precomputed.
A Remark on Channels with Transceiver Distortion
Venue: arXiv cs.IT 2016
Year: 2016

Information transmission over channels with transceiver distortion is investigated via generalized mutual information (GMI) under Gaussian input distribution and nearest-neighbor decoding. A canonical transceiver structure in which the channel output is processed by a minimum mean-squared error estimator before decoding is established to maximize the GMI, and the well-known Bussgang's decomposition is shown to be a heuristic that is consistent with the GMI under linear output processing.
Graph Wavelets via Sparse Cuts: Extended Version
Venue: arXiv cs.DS 2016
Year: 2016

Modeling information that resides on vertices of large graphs is a key problem in several real-life applications, ranging from social networks to the Internet-of-things. Signal Processing on Graphs and, in particular, graph wavelets can exploit the intrinsic smoothness of these datasets in order to represent them in a both compact and accurate manner. However, how to discover wavelet bases that capture the geometry of the data with respect to the signal as well as the graph structure remains an open question. In this paper, we study the problem of computing graph wavelet bases via sparse cuts in order to produce low-dimensional encodings of data-driven bases. This problem is connected to known hard problems in graph theory (e.g. multiway cuts) and thus requires an efficient heuristic. We formulate the basis discovery task as a relaxation of a vector optimization problem, which leads to an elegant solution as a regularized eigenvalue computation. Moreover, we propose several strategies in order to scale our algorithm to large graphs. Experimental results show that the proposed algorithm can effectively encode both the graph structure and signal, producing compressed and accurate representations for vertex values in a wide range of datasets (e.g. sensor and gene networks) and significantly outperforming the best baseline.
Enhancing Patient Appointments Scheduling that Uses Mobile Technology
Venue: arXiv cs.CY 2016
Year: 2016

Appointment scheduling systems are utilized mainly by specialty care clinics to manage access to service providers as well as by hospitals to schedule patient appointments. When attending hospitals in Tanzania, patients experience challenges to see an appropriate specialist doctor because of service interval inconsistency. Timely availability of doctors is critical whenever a patient needs to see a specialist doctor for treatment and a serious bottleneck lies in the application of appropriate technology techniques to enhance appointment scheduling. In this paper, we present a mobile based application scheduling system for managing patient appointments. Furthermore, forthcoming opportunities for the innovative use of the mobile based application scheduling system are identified.
Iterative Hierarchical Optimization for Misspecified Problems (IHOMP)
Venue: arXiv cs.LG 2016
Year: 2016

For complex, high-dimensional Markov Decision Processes (MDPs), it may be necessary to represent the policy with function approximation. A problem is misspecified whenever, the representation cannot express any policy with acceptable performance. We introduce IHOMP : an approach for solving misspecified problems. IHOMP iteratively learns a set of context specialized options and combines these options to solve an otherwise misspecified problem. Our main contribution is proving that IHOMP enjoys theoretical convergence guarantees. In addition, we extend IHOMP to exploit Option Interruption (OI) enabling it to decide where the learned options can be reused. Our experiments demonstrate that IHOMP can find near-optimal solutions to otherwise misspecified problems and that OI can further improve the solutions.
Relations on words
Venue: arXiv cs.FL 2016
Year: 2016

In the first part of this survey, we present classical notions arising in combinatorics on words: growth function of a language, complexity function of an infinite word, pattern avoidance, periodicity and uniform recurrence. Our presentation tries to set up a unified framework with respect to a given binary relation. In the second part, we mainly focus on abelian equivalence, $k$-abelian equivalence, combinatorial coefficients and associated relations, Parikh matrices and $M$-equivalence. In particular, some new refinements of abelian equivalence are introduced.
Triplet Similarity Embedding for Face Verification
Venue: arXiv cs.CV 2016
Year: 2016

In this work, we present an unconstrained face verification algorithm and evaluate it on the recently released IJB-A dataset that aims to push the boundaries of face verification methods. The proposed algorithm couples a deep CNN-based approach with a low-dimensional discriminative embedding learnt using triplet similarity constraints in a large margin fashion. Aside from yielding performance improvement, this embedding provides significant advantages in terms of memory and post-processing operations like hashing and visualization. Experiments on the IJB-A dataset show that the proposed algorithm outperforms state of the art methods in verification and identification metrics, while requiring less training time.
Improved group testing rates with constant column weight designs
Venue: arXiv cs.IT 2016
Year: 2016

We consider nonadaptive group testing where each item is placed in a constant number of tests. The tests are chosen uniformly at random with replacement, so the testing matrix has (almost) constant column weights. We show that performance is improved compared to Bernoulli designs, where each item is placed in each test independently with a fixed probability. In particular, we show that the rate of the practical COMP detection algorithm is increased by 31% in all sparsity regimes. In dense cases, this beats the best possible algorithm with Bernoulli tests, and in sparse cases is the best proven performance of any practical algorithm. We also give an algorithm-independent upper bound for the constant column weight case; for dense cases this is again a 31% increase over the analogous Bernoulli result.
Analog Coding of a Source with Erasures
Venue: arXiv cs.IT 2016
Year: 2016

Analog coding decouples the tasks of protecting against erasures and noise. For erasure correction, it creates an "analog redundancy" by means of band-limited discrete Fourier transform (DFT) interpolation, or more generally, by an over-complete expansion based on a frame. We examine the analog coding paradigm for the dual setup of a source with "erasure" side-information (SI) at the encoder. The excess rate of analog coding above the rate-distortion function (RDF) is associated with the energy of the inverse of submatrices of the frame, where each submatrix corresponds to a possible erasure pattern. We give a partial theoretical as well as numerical evidence that a variety of structured frames, in particular DFT frames with difference-set spectrum and more general equiangular tight frames (ETFs), with a common MANOVA limiting spectrum, minimize the excess rate over all possible frames. However, they do not achieve the RDF even in the limit as the dimension goes to infinity.
Retractable Contracts
Venue: arXiv cs.LO 2016
Year: 2016

In calculi for modelling communication protocols, internal and external choices play dual roles. Two external choices can be viewed naturally as dual too, as they represent an agreement between the communicating parties. If the interaction fails, the past agreements are good candidates as points where to roll back, in order to take a different agreement. We propose a variant of contracts with synchronous rollbacks to agreement points in case of deadlock. The new calculus is equipped with a compliance relation which is shown to be decidable.
Behavioural types for non-uniform memory accesses
Venue: arXiv cs.PL 2016
Year: 2016

Concurrent programs executing on NUMA architectures consist of concurrent entities (e.g. threads, actors) and data placed on different nodes. Execution of these concurrent entities often reads or updates states from remote nodes. The performance of such systems depends on the extent to which the concurrent entities can be executing in parallel, and on the amount of the remote reads and writes. We consider an actor-based object oriented language, and propose a type system which expresses the topology of the program (the placement of the actors and data on the nodes), and an effect system which characterises remote reads and writes (in terms of which node reads/writes from which other nodes). We use a variant of ownership types for the topology, and a combination of behavioural and ownership types for the effect system.
Attentive Pooling Networks
Venue: arXiv cs.CL 2016
Year: 2016

In this work, we propose Attentive Pooling (AP), a two-way attention mechanism for discriminative model training. In the context of pair-wise ranking or classification with neural networks, AP enables the pooling layer to be aware of the current input pair, in a way that information from the two input items can directly influence the computation of each other's representations. Along with such representations of the paired inputs, AP jointly learns a similarity measure over projected segments (e.g. trigrams) of the pair, and subsequently, derives the corresponding attention vector for each input to guide the pooling. Our two-way attention mechanism is a general framework independent of the underlying representation learning, and it has been applied to both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in our studies. The empirical results, from three very different benchmark tasks of question answering/answer selection, demonstrate that our proposed models outperform a variety of strong baselines and achieve state-of-the-art performance in all the benchmarks.
Dynamic Group Behaviors for Interactive Crowd Simulation
Venue: arXiv cs.MA 2016
Year: 2016

We present a new algorithm to simulate dynamic group behaviors for interactive multi-agent crowd simulation. Our approach is general and makes no assumption about the environment, shape, or size of the groups. We use the least effort principle to perform coherent group navigation and present efficient inter-group and intra-group maintenance techniques. We extend the reciprocal collision avoidance scheme to perform agent-group and group-group collision avoidance that can generate collision-free as well as coherent and trajectories. The additional overhead of dynamic group simulation is relatively small. We highlight its interactive performance on complex scenarios with hundreds of agents and compare the trajectory behaviors with real-world videos.
High performance Python for direct numerical simulations of turbulent flows
Venue: arXiv cs.MS 2016
Year: 2016

Direct Numerical Simulations (DNS) of the Navier Stokes equations is an invaluable research tool in fluid dynamics. Still, there are few publicly available research codes and, due to the heavy number crunching implied, available codes are usually written in low-level languages such as C/C++ or Fortran. In this paper we describe a pure scientific Python pseudo-spectral DNS code that nearly matches the performance of C++ for thousands of processors and billions of unknowns. We also describe a version optimized through Cython, that is found to match the speed of C++. The solvers are written from scratch in Python, both the mesh, the MPI domain decomposition, and the temporal integrators. The solvers have been verified and benchmarked on the Shaheen supercomputer at the KAUST supercomputing laboratory, and we are able to show very good scaling up to several thousand cores. A very important part of the implementation is the mesh decomposition (we implement both slab and pencil decompositions) and 3D parallel Fast Fourier Transforms (FFT). The mesh decomposition and FFT routines have been implemented in Python using serial FFT routines (either NumPy, pyFFTW or any other serial FFT module), NumPy array manipulations and with MPI communications handled by MPI for Python (mpi4py). We show how we are able to execute a 3D parallel FFT in Python for a slab mesh decomposition using 4 lines of compact Python code, for which the parallel performance on Shaheen is found to be slightly better than similar routines provided through the FFTW library. For a pencil mesh decomposition 7 lines of code is required to execute a transform.
Package equivalence in complex software network
Venue: arXiv cs.SI 2016
Year: 2016

The public package registry npm is one of the biggest software registry. With its 216 911 software packages, it forms a big network of software dependencies. In this paper we evaluate various methods for finding similar packages in the npm network, using only the structure of the graph. Namely, we want to find a way of categorizing similar packages, which would be useful for recommendation systems. This size enables us to compute meaningful results, as it softened the particularities of the graph. Npm is also quite famous as it is the default package repository of Node.js. We believe that it will make our results interesting for more people than a less used package repository. This makes it a good subject of analysis of software networks.
A Distributed $(2+ε)$-Approximation for Vertex Cover in $O(\logΔ/ε\log\logΔ)$ Rounds
Venue: arXiv cs.DC 2016
Year: 2016

We present a simple deterministic distributed $(2+ε)$-approximation algorithm for minimum weight vertex cover, which completes in $O(\logΔ/ε\log\logΔ)$ rounds, where $Δ$ is the maximum degree in the graph, for any $ε>0$ which is at most $O(1)$. For a constant $ε$, this implies a constant approximation in $O(\logΔ/\log\logΔ)$ rounds, which contradicts the lower bound of [KMW10].
Fast Distributed Algorithms for Testing Graph Properties
Venue: arXiv cs.DC 2016
Year: 2016

We initiate a thorough study of \emph{distributed property testing} -- producing algorithms for the approximation problems of property testing in the CONGEST model. In particular, for the so-called \emph{dense} testing model we emulate sequential tests for nearly all graph properties having $1$-sided tests, while in the \emph{sparse} and \emph{general} models we obtain faster tests for triangle-freeness and bipartiteness respectively. In most cases, aided by parallelism, the distributed algorithms have a much shorter running time as compared to their counterparts from the sequential querying model of traditional property testing. The simplest property testing algorithms allow a relatively smooth transitioning to the distributed model. For the more complex tasks we develop new machinery that is of independent interest. This includes a method for distributed maintenance of multiple random walks.
Discovering novel ingredient pairings in molecular gastronomy using network analysis
Venue: arXiv cs.SI 2016
Year: 2016

Molecular gastronomy is a distinct sub-discipline of food science that takes an active role in examining chemical and physical properties of ingredients and as such lends itself to more scientific approaches to finding novel ingredient pairings. With thousands of ingredients and molecules, which participate in the creation of each ingredient's flavour, it can be difficult to find compatible flavours in an efficient manner. Existing literature is focused mainly on analysis of already established cuisine based on the flavour profile of its ingredients, but fails to consider the potential in finding flavour compatibility for use in creation of completely new recipes. Expressing relationships between ingredients and their molecular structure as a bipartite network opens up this problem to effective analysis with methods from network science. We describe a series of experiments on a database of food using network analysis, which produce a set of compatible ingredients that can be used in creation of new recipes. We expect this approach and its results to dramatically simplify the creation of new recipes with previously unseen and fresh combinations of ingredients.