OpenRaiser/Intern-Atlas huggingface dataset:
Locally Adaptive Translation for Knowledge Graph Embedding
Venue: arXiv cs.AI 2015
Year: 2015

Knowledge graph embedding aims to represent entities and relations in a large-scale knowledge graph as elements in a continuous vector space. Existing methods, e.g., TransE and TransH, learn embedding representation by defining a global margin-based loss function over the data. However, the optimal loss function is determined during experiments whose parameters are examined among a closed set of candidates. Moreover, embeddings over two knowledge graphs with different entities and relations share the same set of candidate loss functions, ignoring the locality of both graphs. This leads to the limited performance of embedding related applications. In this paper, we propose a locally adaptive translation method for knowledge graph embedding, called TransA, to find the optimal loss function by adaptively determining its margin over different knowledge graphs. Experiments on two benchmark data sets demonstrate the superiority of the proposed method, as compared to the-state-of-the-art ones.
Topic segmentation via community detection in complex networks
Venue: arXiv cs.CL 2015
Year: 2015

Many real systems have been modelled in terms of network concepts, and written texts are a particular example of information networks. In recent years, the use of network methods to analyze language has allowed the discovery of several interesting findings, including the proposition of novel models to explain the emergence of fundamental universal patterns. While syntactical networks, one of the most prevalent networked models of written texts, display both scale-free and small-world properties, such representation fails in capturing other textual features, such as the organization in topics or subjects. In this context, we propose a novel network representation whose main purpose is to capture the semantical relationships of words in a simple way. To do so, we link all words co-occurring in the same semantic context, which is defined in a threefold way. We show that the proposed representations favours the emergence of communities of semantically related words, and this feature may be used to identify relevant topics. The proposed methodology to detect topics was applied to segment selected Wikipedia articles. We have found that, in general, our methods outperform traditional bag-of-words representations, which suggests that a high-level textual representation may be useful to study semantical features of texts.
Identifying potential breakthrough publications using refined citation analyses: Three related explorative approaches
Venue: arXiv cs.DL 2015
Year: 2015

The article presents three advanced citation-based methods used to detect potential breakthrough papers among very highly cited papers. We approach the detection of such papers from three different perspectives in order to provide different typologies of breakthrough papers. In all three cases we use the classification of scientific publications developed at CWTS based on direct citation relationships. This classification establishes clusters of papers at three levels of aggregation. Papers are clustered based on their similar citation orientations and it is assumed that they are focused on similar research interests. We use the clustering as the context for detecting potential breakthrough papers. We utilize the Characteristics Scores and Scales (CSS) approach to partition citation distributions and implement a specific filtering algorithm to sort out potential highly-cited followers, papers not considered breakthroughs in themselves. After invoking thresholds and filtering, three methods are explored: A very exclusive one where only the highest cited paper in a micro-cluster is considered as a potential breakthrough paper (M1); as well as two conceptually different methods, one that detects potential breakthrough papers among the two percent highest cited papers according to CSS (M2a), and finally a more restrictive version where, in addition to the CSS two percent filter, knowledge diffusion is also taken in as an extra parameter (M2b). The advance citation-based methods are explored and evaluated using specifically validated publication sets linked to different Danish funding instruments including centres of excellence.
What Makes it Difficult to Understand a Scientific Literature?
Venue: arXiv cs.CL 2015
Year: 2015

In the artificial intelligence area, one of the ultimate goals is to make computers understand human language and offer assistance. In order to achieve this ideal, researchers of computer science have put forward a lot of models and algorithms attempting at enabling the machine to analyze and process human natural language on different levels of semantics. Although recent progress in this field offers much hope, we still have to ask whether current research can provide assistance that people really desire in reading and comprehension. To this end, we conducted a reading comprehension test on two scientific papers which are written in different styles. We use the semantic link models to analyze the understanding obstacles that people will face in the process of reading and figure out what makes it difficult for human to understand a scientific literature. Through such analysis, we summarized some characteristics and problems which are reflected by people with different levels of knowledge on the comprehension of difficult science and technology literature, which can be modeled in semantic link network. We believe that these characteristics and problems will help us re-examine the existing machine models and are helpful in the designing of new one.
Reuse of Neural Modules for General Video Game Playing
Venue: arXiv cs.NE 2015
Year: 2015

A general approach to knowledge transfer is introduced in which an agent controlled by a neural network adapts how it reuses existing networks as it learns in a new domain. Networks trained for a new domain can improve their performance by routing activation selectively through previously learned neural structure, regardless of how or for what it was learned. A neuroevolution implementation of this approach is presented with application to high-dimensional sequential decision-making domains. This approach is more general than previous approaches to neural transfer for reinforcement learning. It is domain-agnostic and requires no prior assumptions about the nature of task relatedness or mappings. The method is analyzed in a stochastic version of the Arcade Learning Environment, demonstrating that it improves performance in some of the more complex Atari 2600 games, and that the success of transfer can be predicted based on a high-level characterization of game dynamics.
Hybrid Approach for Inductive Semi Supervised Learning using Label Propagation and Support Vector Machine
Venue: arXiv cs.LG 2015
Year: 2015

Semi supervised learning methods have gained importance in today's world because of large expenses and time involved in labeling the unlabeled data by human experts. The proposed hybrid approach uses SVM and Label Propagation to label the unlabeled data. In the process, at each step SVM is trained to minimize the error and thus improve the prediction quality. Experiments are conducted by using SVM and logistic regression(Logreg). Results prove that SVM performs tremendously better than Logreg. The approach is tested using 12 datasets of different sizes ranging from the order of 1000s to the order of 10000s. Results show that the proposed approach outperforms Label Propagation by a large margin with F-measure of almost twice on average. The parallel version of the proposed approach is also designed and implemented, the analysis shows that the training time decreases significantly when parallel version is used.
Study of Efficient Robust Adaptive Beamforming Algorithms Based on Shrinkage Techniques
Venue: arXiv cs.IT 2015
Year: 2015

This paper proposes low-complexity robust adaptive beamforming (RAB) techniques based on shrinkage methods. We firstly briefly review a Low-Complexity Shrinkage-Based Mismatch Estimation (LOCSME) batch algorithm to estimate the desired signal steering vector mismatch, in which the interference-plus-noise covariance (INC) matrix is also estimated with a recursive matrix shrinkage method. Then we develop low complexity adaptive robust version of the conjugate gradient (CG) algorithm to both estimate the steering vector mismatch and update the beamforming weights. A computational complexity study of the proposed and existing algorithms is carried out. Simulations are conducted in local scattering scenarios and comparisons to existing RAB techniques are provided.
A Novel Paradigm for Calculating Ramsey Number via Artificial Bee Colony Algorithm
Venue: arXiv cs.AI 2015
Year: 2015

The Ramsey number is of vital importance in Ramsey's theorem. This paper proposed a novel methodology for constructing Ramsey graphs about R(3,10), which uses Artificial Bee Colony optimization(ABC) to raise the lower bound of Ramsey number R(3,10). The r(3,10)-graph contains two limitations, that is, neither complete graphs of order 3 nor independent sets of order 10. To resolve these limitations, a special mathematical model is put in the paradigm to convert the problems into discrete optimization whose smaller minimizers are correspondent to bigger lower bound as approximation of inf R(3,10). To demonstrate the potential of the proposed method, simulations are done to to minimize the amount of these two types of graphs. For the first time, four r(3,9,39) graphs with best approximation for inf R(3,10) are reported in simulations to support the current lower bound for R(3,10). The experiments' results show that the proposed paradigm for Ramsey number's calculation driven by ABC is a successful method with the advantages of high precision and robustness.
A Deep Structured Model with Radius-Margin Bound for 3D Human Activity Recognition
Venue: arXiv cs.CV 2015
Year: 2015

Understanding human activity is very challenging even with the recently developed 3D/depth sensors. To solve this problem, this work investigates a novel deep structured model, which adaptively decomposes an activity instance into temporal parts using the convolutional neural networks (CNNs). Our model advances the traditional deep learning approaches in two aspects. First, { we incorporate latent temporal structure into the deep model, accounting for large temporal variations of diverse human activities. In particular, we utilize the latent variables to decompose the input activity into a number of temporally segmented sub-activities, and accordingly feed them into the parts (i.e. sub-networks) of the deep architecture}. Second, we incorporate a radius-margin bound as a regularization term into our deep model, which effectively improves the generalization performance for classification. For model training, we propose a principled learning algorithm that iteratively (i) discovers the optimal latent variables (i.e. the ways of activity decomposition) for all training instances, (ii) { updates the classifiers} based on the generated features, and (iii) updates the parameters of multi-layer neural networks. In the experiments, our approach is validated on several complex scenarios for human activity recognition and demonstrates superior performances over other state-of-the-art approaches.
The precision of the arithmetic mean, geometric mean and percentiles for citation data: An experimental simulation modelling approach
Venue: arXiv cs.DL 2015
Year: 2015

When comparing the citation impact of nations, departments or other groups of researchers within individual fields, three approaches have been proposed: arithmetic means, geometric means, and percentage in the top X%. This article compares the precision of these statistics using 97 trillion experimentally simulated citation counts from 6875 sets of different parameters (although all having the same scale parameter) based upon the discretised lognormal distribution with limits from 1000 repetitions for each parameter set. The results show that the geometric mean is the most precise, closely followed by the percentage of a country's articles in the top 50% most cited articles for a field, year and document type. Thus the geometric mean citation count is recommended for future citation-based comparisons between nations. The percentage of a country's articles in the top 1% most cited is a particularly imprecise indicator and is not recommended for international comparisons based on individual fields. Moreover, whereas standard confidence interval formulae for the geometric mean appear to be accurate, confidence interval formulae are less accurate and consistent for percentile indicators. These recommendations assume that the scale parameters of the samples are the same but the choice of indicator is complex and partly conceptual if they are not.
Maximum Entropy Binary Encoding for Face Template Protection
Venue: arXiv cs.CV 2015
Year: 2015

In this paper we present a framework for secure identification using deep neural networks, and apply it to the task of template protection for face authentication. We use deep convolutional neural networks (CNNs) to learn a mapping from face images to maximum entropy binary (MEB) codes. The mapping is robust enough to tackle the problem of exact matching, yielding the same code for new samples of a user as the code assigned during training. These codes are then hashed using any hash function that follows the random oracle model (like SHA-512) to generate protected face templates (similar to text based password protection). The algorithm makes no unrealistic assumptions and offers high template security, cancelability, and state-of-the-art matching performance. The efficacy of the approach is shown on CMU-PIE, Extended Yale B, and Multi-PIE face databases. We achieve high (~95%) genuine accept rates (GAR) at zero false accept rate (FAR) with up to 1024 bits of template security.
Generating News Headlines with Recurrent Neural Networks
Venue: arXiv cs.CL 2015
Year: 2015

We describe an application of an encoder-decoder recurrent neural network with LSTM units and attention to generating headlines from the text of news articles. We find that the model is quite effective at concisely paraphrasing news articles. Furthermore, we study how the neural network decides which input words to pay attention to, and specifically we identify the function of the different neurons in a simplified attention mechanism. Interestingly, our simplified attention mechanism performs better that the more complex attention mechanism on a held out set of articles.
Driverseat: Crowdstrapping Learning Tasks for Autonomous Driving
Venue: arXiv cs.HC 2015
Year: 2015

While emerging deep-learning systems have outclassed knowledge-based approaches in many tasks, their application to detection tasks for autonomous technologies remains an open field for scientific exploration. Broadly, there are two major developmental bottlenecks: the unavailability of comprehensively labeled datasets and of expressive evaluation strategies. Approaches for labeling datasets have relied on intensive hand-engineering, and strategies for evaluating learning systems have been unable to identify failure-case scenarios. Human intelligence offers an untapped approach for breaking through these bottlenecks. This paper introduces Driverseat, a technology for embedding crowds around learning systems for autonomous driving. Driverseat utilizes crowd contributions for (a) collecting complex 3D labels and (b) tagging diverse scenarios for ready evaluation of learning systems. We demonstrate how Driverseat can crowdstrap a convolutional neural network on the lane-detection task. More generally, crowdstrapping introduces a valuable paradigm for any technology that can benefit from leveraging the powerful combination of human and computer intelligence.
On exploration of geometrically constrained space by medicinal leeches Hirudo verbana
Venue: arXiv cs.ET 2015
Year: 2015

Leeches are fascinating creatures: they have simple modular nervous circuitry m yet exhibit a rich spectrum of behavioural modes. Leeches could be ideal blue-prints for designing flexible soft robots which are modular, multi-functional, fault-tolerant, easy to control, capable for navigating using optical, mechanical and chemical sensorial inputs, have autonomous inter-segmental coordination and adaptive decision-making. With future designs of leech-robots in mind we study how leeches behave in geometrically constrained spaces. Core results of the paper deal with leeches exploring a row of rooms arranged along a narrow corridor. In laboratory experiments we find that rooms closer to ends of the corridor are explored by leeches more often than rooms in the middle of the corridor. Also, in series of scoping experiments, we evaluate leeches capabilities to navigating in mazes towards sources of vibration and chemo-attraction. We believe our results lay foundation for future developments of robots mimicking behaviour of leeches.
Sparsified Cholesky and Multigrid Solvers for Connection Laplacians
Venue: arXiv cs.DS 2015
Year: 2015

We introduce the sparsified Cholesky and sparsified multigrid algorithms for solving systems of linear equations. These algorithms accelerate Gaussian elimination by sparsifying the nonzero matrix entries created by the elimination process. We use these new algorithms to derive the first nearly linear time algorithms for solving systems of equations in connection Laplacians, a generalization of Laplacian matrices that arise in many problems in image and signal processing. We also prove that every connection Laplacian has a linear sized approximate inverse. This is an LU factorization with a linear number of nonzero entries that is a strong approximation of the original matrix. Using such a factorization one can solve systems of equations in a connection Laplacian in linear time. Such a factorization was unknown even for ordinary graph Laplacians.
Thinking Required
Venue: arXiv cs.LG 2015
Year: 2015

There exists a theory of a single general-purpose learning algorithm which could explain the principles its operation. It assumes the initial rough architecture, a small library of simple innate circuits which are prewired at birth. and proposes that all significant mental algorithms are learned. Given current understanding and observations, this paper reviews and lists the ingredients of such an algorithm from architectural and functional perspectives.
Living Wires - Effects of Size and Coating of Gold Nanoparticles in Altering the Electrical Properties of Physarum polycephalum and Lettuce Seedlings
Venue: arXiv cs.ET 2015
Year: 2015

The manipulation of biological substrates is becoming more popular route towards generating novel computing devices. Physarum polycephalum is used as a model organism in biocomputing because it can create `wires' for use in hybrid circuits; programmable growth by manipulation through external stimuli and the ability withstanding a current and its tolerance to hybridisation with a variety of nano/microparticles. Lettuce seedlings have also had previous interest invested in them for generating plant wires, although currently there is little information as to their suitability for such applications. In this study both P. polycephalum and Lettuce seedlings were hybridised with gold nanoparticles - functionalised and unfunctionalised - to explore their uptake, toxicological effects and, crucially, any alterations in electrical properties they bestow upon the organisms. Using various microscopy techniques it was shown that P. polycephalum and lettuce seedlings are able to internalize nanoparticles and assemble them in vivo, however some toxicological effects were observed. The electrical resistance of both lettuce seedlings and P. polycephalum was found to decrease, the most significant reduction being with lettuce seedlings whose resistance reduced from 3MOhms to 0.5MOhms. We conclude that gold is a suitable nanomaterial for biohybridisation specifically in creating conductive pathways for more efficient biological wires in self-growing hybrid circuitry.
A bloated FM-index reducing the number of cache misses during the search
Venue: arXiv cs.DS 2015
Year: 2015

The FM-index is a well-known compressed full-text index, based on the Burrows-Wheeler transform (BWT). During a pattern search, the BWT sequence is accessed at "random" locations, which is cache-unfriendly. In this paper, we are interested in speeding up the FM-index by working on $q$-grams rather than individual characters, at the cost of using more space. The first presented variant is related to an inverted index on $q$-grams, yet the occurrence lists in our solution are in the sorted suffix order rather than text order in a traditional inverted index. This variant obtains $O(m/|CL| + \log n \log m)$ cache misses in the worst case, where $n$ and $m$ are the text and pattern lengths, respectively, and $|CL|$ is the CPU cache line size, in symbols (typically 64 in modern hardware). This index is often several times faster than the fastest known FM-indexes (especially for long patterns), yet the space requirements are enormous, $O(n\log^2 n)$ bits in theory and about $80n$-$95n$ bytes in practice. For this reason, we dub our approach FM-bloated. The second presented variant requires $O(n\log n)$ bits of space.
In-situ multi-scattering tomography
Venue: arXiv cs.CV 2015
Year: 2015

To recover the three dimensional (3D) volumetric distribution of matter in an object, images of the object are captured from multiple directions and locations. Using these images tomographic computations extract the distribution. In highly scattering media and constrained, natural irradiance, tomography must explicitly account for off-axis scattering. Furthermore, the tomographic model and recovery must function when imaging is done in-situ, as occurs in medical imaging and ground-based atmospheric sensing. We formulate tomography that handles arbitrary orders of scattering, using a monte-carlo model. Moreover, the model is highly parallelizable in our formulation. This enables large scale rendering and recovery of volumetric scenes having a large number of variables. We solve stability and conditioning problems that stem from radiative transfer (RT) modeling in-situ.
Money as Minimal Complexity
Venue: arXiv cs.GT 2015
Year: 2015

We consider mechanisms that provide traders the opportunity to exchange commodity $i$ for commodity $j$, for certain ordered pairs $ij$. Given any connected graph $G$ of opportunities, we show that there is a unique mechanism $M_{G}$ that satisfies some natural conditions of "fairness" and "convenience". Let $\mathfrak{M}(m)$ denote the class of mechanisms $M_{G}$ obtained by varying $G$ on the commodity set $\left\{1,\ldots,m\right\} $. We define the complexity of a mechanism $M$ in $\mathfrak{M(m)}$ to be a certain pair of integers $τ(M),π(M)$ which represent the time required to exchange $i$ for $j$ and the information needed to determine the exchange ratio (each in the worst case scenario, across all $i\neq j$). This induces a quasiorder $\preceq$ on $\mathfrak{M}(m)$ by the rule \[ M\preceq M^{\prime}\text{if}τ(M)\leqτ(M^{\prime})\text{and}π(M)\leqπ(M^{\prime}). \] We show that, for $m>3$, there are precisely three $\preceq$-minimal mechanisms $M_{G}$ in $\mathfrak{M}(m)$, where $G$ corresponds to the star, cycle and complete graphs. The star mechanism has a distinguished commodity -- the money -- that serves as the sole medium of exchange and mediates trade between decentralized markets for the other commodities. Our main result is that, for any weights $λ,μ>0,$ the star mechanism is the unique minimizer of $λτ(M)+μπ(M)$ on $\mathfrak{M}(m)$ for large enough $m$.