OpenRaiser/Intern-Atlas huggingface dataset:
A single hidden layer feedforward network with only one neuron in the hidden layer can approximate any univariate function
Venue: arXiv cs.NE 2016
Year: 2016

The possibility of approximating a continuous function on a compact subset of the real line by a feedforward single hidden layer neural network with a sigmoidal activation function has been studied in many papers. Such networks can approximate an arbitrary continuous function provided that an unlimited number of neurons in a hidden layer is permitted. In this paper, we consider constructive approximation on any finite interval of $\mathbb{R}$ by neural networks with only one neuron in the hidden layer. We construct algorithmically a smooth, sigmoidal, almost monotone activation function $σ$ providing approximation to an arbitrary continuous function within any degree of accuracy. This algorithm is implemented in a computer program, which computes the value of $σ$ at any reasonable point of the real axis.
Neighborhood covering and independence on two superclasses of cographs
Venue: arXiv cs.DM 2016
Year: 2016

Given a simple graph $G$, a set $C \subseteq V(G)$ is a neighborhood cover set if every edge and vertex of $G$ belongs to some $G[v]$ with $v \in C$, where $G[v]$ denotes the subgraph of $G$ induced by the closed neighborhood of the vertex $v$. Two elements of $E(G) \cup V(G)$ are neighborhood-independent if there is no vertex $v\in V(G)$ such that both elements are in $G[v]$. A set $S\subseteq V(G)\cup E(G)$ is neighborhood-independent if every pair of elements of $S$ is neighborhood-independent. Let $ρ_{\mathrm n}(G)$ be the size of a minimum neighborhood cover set and $α_{\mathrm n}(G)$ of a maximum neighborhood-independent set. Lehel and Tuza defined neighborhood-perfect graphs $G$ as those where the equality $ρ_{\mathrm n}(G') = α_{\mathrm n}(G')$ holds for every induced subgraph $G'$ of $G$. In this work we prove forbidden induced subgraph characterizations of the class of neighborhood-perfect graphs, restricted to two superclasses of cographs: $P_4$-tidy graphs and tree-cographs. We give as well linear-time algorithms for solving the recognition problem of neighborhood-perfect graphs and the problem of finding a minimum neighborhood cover set and a maximum neighborhood-independent set in these same classes.
Discriminative Sparsity for Sonar ATR
Venue: arXiv cs.CV 2016
Year: 2016

Advancements in Sonar image capture have enabled researchers to apply sophisticated object identification algorithms in order to locate targets of interest in images such as mines. Despite progress in this field, modern sonar automatic target recognition (ATR) approaches lack robustness to the amount of noise one would expect in real-world scenarios, the capability to handle blurring incurred from the physics of image capture, and the ability to excel with relatively few training samples. We address these challenges by adapting modern sparsity-based techniques with dictionaries comprising of training from each class. We develop new discriminative (as opposed to generative) sparse representations which can help automatically classify targets in Sonar imaging. Using a simulated SAS data set from the Naval Surface Warfare Center (NSWC), we obtained compelling classification rates for multi-class problems even in cases with considerable noise and sparsity in training samples.
Detecting the historical roots of tribology research: a bibliometric analysis
Venue: arXiv cs.DL 2016
Year: 2016

In this study, the historical roots of tribology are investigated using a newly developed scientometric method called Referenced Publication Years Spectroscopy. The study is based on cited references in tribology research publications. The Science Citation Index Expanded is used as data source. The results show that RPYS has the potential to identify the important publications : Most of the publications which have been identified in this study as highly cited (referenced) publications are landmark publications in the field of tribology.
An Improved Intelligent Agent for Mining Real-Time Databases Using Modified Cortical Learning Algorithms
Venue: arXiv cs.NE 2016
Year: 2016

Cortical Learning Algorithms based on the Hierarchical Temporal Memory, HTM have been developed by Numenta Incorporation from which variations and modifications are currently being investigated upon. HTM offers better promises as a future computational model of the neocortex the seat of intelligence in the brain. Currently, intelligent agents are embedded in almost every modern day electronic system found in homes, offices and industries worldwide. In this paper, we present a first step in realising useful HTM like applications specifically for mining a synthetic and real time dataset based on a novel intelligent agent framework, and demonstrate how a modified version of this very important computational technique will lead to improved recognition.
Funding acknowledgment analysis:Queries and Caveats
Venue: arXiv cs.DL 2016
Year: 2016

Thomson Reuters' Web of Science (WoS) began systematically collecting acknowledgment information in August 2008. Since then, bibliometric analysis of funding acknowledgment (FA) has been growing and has aroused intense interest and attention from both academia and policy makers. Examining the distribution of FA by citation index database, by language, and by acknowledgment type, we noted coverage limitations and potential biases in each analysis. We argue that in spite of its great value, bibliometric analysis of FA should be used with caution.
Greedy online colouring with buffering
Venue: arXiv cs.DM 2016
Year: 2016

We consider the problem of online graph colouring. Whenever a node is requested, a colour must be assigned to the node, and this colour must be different from the colours of any of its neighbours. According to the greedy algorithm the node is coloured by the colour with the smallest possible $k$. The goal is to use as few colours as possible. We propose an algorithm, where the node is coloured not immediately, but only after the collection of next requests stored in the buffer of size $j$. In other words, the first node in the buffer is coloured definitively taking into account all possible colourisations of the remaining nodes in the buffer. If there are $r$ possible corrected colourings, then the one with the probability $1/r$ is chosen. The first coloured node is removed from the buffer to enable the entrance of the next request. A number of colours in a two examples of graphs: crown graphs and Kneser graphs have been analysed.
Structure-Preserving Sparsification Methods for Social Networks
Venue: arXiv cs.SI 2016
Year: 2016

Sparsification reduces the size of networks while preserving structural and statistical properties of interest. Various sparsifying algorithms have been proposed in different contexts. We contribute the first systematic conceptual and experimental comparison of \textit{edge sparsification} methods on a diverse set of network properties. It is shown that they can be understood as methods for rating edges by importance and then filtering globally or locally by these scores. We show that applying a local filtering technique improves the preservation of all kinds of properties. In addition, we propose a new sparsification method (\textit{Local Degree}) which preserves edges leading to local hub nodes. All methods are evaluated on a set of social networks from Facebook, Google+, Twitter and LiveJournal with respect to network properties including diameter, connected components, community structure, multiple node centrality measures and the behavior of epidemic simulations. In order to assess the preservation of the community structure, we also include experiments on synthetically generated networks with ground truth communities. Experiments with our implementations of the sparsification methods (included in the open-source network analysis tool suite NetworKit) show that many network properties can be preserved down to about 20\% of the original set of edges for sparse graphs with a reasonable density. The experimental results allow us to differentiate the behavior of different methods and show which method is suitable with respect to which property. While our Local Degree method is best for preserving connectivity and short distances, other newly introduced local variants are best for preserving the community structure.
The Design of a Community Science Cloud: The Open Science Data Cloud Perspective
Venue: arXiv cs.CE 2016
Year: 2016

In this paper we describe the design, and implementation of the Open Science Data Cloud, or OSDC. The goal of the OSDC is to provide petabyte-scale data cloud infrastructure and related services for scientists working with large quantities of data. Currently, the OSDC consists of more than 2000 cores and 2 PB of storage distributed across four data centers connected by 10G networks. We discuss some of the lessons learned during the past three years of operation and describe the software stacks used in the OSDC. We also describe some of the research projects in biology, the earth sciences, and social sciences enabled by the OSDC.
Mutual Information and Diverse Decoding Improve Neural Machine Translation
Venue: arXiv cs.CL 2016
Year: 2016

Sequence-to-sequence neural translation models learn semantic and syntactic relations between sentence pairs by optimizing the likelihood of the target given the source, i.e., $p(y|x)$, an objective that ignores other potentially useful sources of information. We introduce an alternative objective function for neural MT that maximizes the mutual information between the source and target sentences, modeling the bi-directional dependency of sources and targets. We implement the model with a simple re-ranking method, and also introduce a decoding algorithm that increases diversity in the N-best list produced by the first pass. Applied to the WMT German/English and French/English tasks, the proposed models offers a consistent performance boost on both standard LSTM and attention-based neural MT architectures.
On the Reducibility of Submodular Functions
Venue: arXiv cs.LG 2016
Year: 2016

The scalability of submodular optimization methods is critical for their usability in practice. In this paper, we study the reducibility of submodular functions, a property that enables us to reduce the solution space of submodular optimization problems without performance loss. We introduce the concept of reducibility using marginal gains. Then we show that by adding perturbation, we can endow irreducible functions with reducibility, based on which we propose the perturbation-reduction optimization framework. Our theoretical analysis proves that given the perturbation scales, the reducibility gain could be computed, and the performance loss has additive upper bounds. We further conduct empirical studies and the results demonstrate that our proposed framework significantly accelerates existing optimization methods for irreducible submodular functions with a cost of only small performance losses.
Multi-task CNN Model for Attribute Prediction
Venue: arXiv cs.CV 2016
Year: 2016

This paper proposes a joint multi-task learning algorithm to better predict attributes in images using deep convolutional neural networks (CNN). We consider learning binary semantic attributes through a multi-task CNN model, where each CNN will predict one binary attribute. The multi-task learning allows CNN models to simultaneously share visual knowledge among different attribute categories. Each CNN will generate attribute-specific feature representations, and then we apply multi-task learning on the features to predict their attributes. In our multi-task framework, we propose a method to decompose the overall model's parameters into a latent task matrix and combination matrix. Furthermore, under-sampled classifiers can leverage shared statistics from other classifiers to improve their performance. Natural grouping of attributes is applied such that attributes in the same group are encouraged to share more knowledge. Meanwhile, attributes in different groups will generally compete with each other, and consequently share less knowledge. We show the effectiveness of our method on two popular attribute datasets.
Energy Efficient Location and Activity-aware On-Demand Mobile Distributed Sensing Platform for Sensing as a Service in IoT Clouds
Venue: arXiv cs.NI 2016
Year: 2016

The Internet of Things (IoT) envisions billions of sensors deployed around us and connected to the Internet, where the mobile crowd sensing technologies are widely used to collect data in different contexts of the IoT paradigm. Due to the popularity of Big Data technologies, processing and storing large volumes of data has become easier than ever. However, large scale data management tasks still require significant amounts of resources that can be expensive regardless of whether they are purchased or rented (e.g. pay-as-you-go infrastructure). Further, not everyone is interested in such large scale data collection and analysis. More importantly, not everyone has the financial and computational resources to deal with such large volumes of data. Therefore, a timely need exists for a cloud-integrated mobile crowd sensing platform that is capable of capturing sensors data, on-demand, based on conditions enforced by the data consumers. In this paper, we propose a context-aware, specifically, location and activity-aware mobile sensing platform called C-MOSDEN ( Context-aware Mobile Sensor Data ENgine) for the IoT domain. We evaluated the proposed platform using three real-world scenarios that highlight the importance of 'selective sensing'. The computational effectiveness and efficiency of the proposed platform are investigated and is used to highlight the advantages of context-aware selective sensing.
The discretised lognormal and hooked power law distributions for complete citation data: Best options for modelling and regression
Venue: arXiv cs.DL 2016
Year: 2016

Identifying the statistical distribution that best fits citation data is important to allow robust and powerful quantitative analyses. Whilst previous studies have suggested that both the hooked power law and discretised lognormal distributions fit better than the power law and negative binomial distributions, no comparisons so far have covered all articles within a discipline, including those that are uncited. Based on an analysis of 26 different Scopus subject areas in seven different years, this article reports comparisons of the discretised lognormal and the hooked power law with citation data, adding 1 to citation counts in order to include zeros. The hooked power law fits better in two thirds of the subject/year combinations tested for journal articles that are at least three years old, including most medical, life and natural sciences, and for virtually all subject areas for younger articles. Conversely, the discretised lognormal tends to fit best for arts, humanities, social science and engineering fields. The difference between the fits of the distributions is mostly small, however, and so either could reasonably be used for modelling citation data. For regression analyses, however, the best option is to use ordinary least squares regression applied to the natural logarithm of citation counts plus one, especially for sets of younger articles, because of the increased precision of the parameters.
Motivating Time-Inconsistent Agents: A Computational Approach
Venue: arXiv cs.CC 2016
Year: 2016

In this paper we investigate the computational complexity of motivating time-inconsistent agents to complete long term projects. We resort to an elegant graph-theoretic model, introduced by Kleinberg and Oren, which consists of a task graph $G$ with $n$ vertices, including a source $s$ and target $t$, and an agent that incrementally constructs a path from $s$ to $t$ in order to collect rewards. The twist is that the agent is present-biased and discounts future costs and rewards by a factor $β\in [0,1]$. Our design objective is to ensure that the agent reaches $t$ i.e.\ completes the project, for as little reward as possible. Such graphs are called motivating. We consider two strategies. First, we place a single reward $r$ at $t$ and try to guide the agent by removing edges from $G$. We prove that deciding the existence of such motivating subgraphs is NP-complete if $r$ is fixed. More importantly, we generalize our reduction to a hardness of approximation result for computing the minimum $r$ that admits a motivating subgraph. In particular, we show that no polynomial-time approximation to within a ratio of $\sqrt{n}/4$ or less is possible, unless ${\rm P}={\rm NP}$. Furthermore, we develop a $(1+\sqrt{n})$-approximation algorithm and thus settle the approximability of computing motivating subgraphs. Secondly, we study motivating reward configurations, where non-negative rewards $r(v)$ may be placed on arbitrary vertices $v$ of $G$. The agent only receives the rewards of visited vertices. Again we give an NP-completeness result for deciding the existence of a motivating reward configuration within a fixed budget $b$. This result even holds if $b=0$, which in turn implies that no efficient approximation of a minimum $b$ within a ration grater or equal to $1$ is possible, unless ${\rm P}={\rm NP}$.
Why Just Boogie? Translating Between Intermediate Verification Languages
Venue: arXiv cs.LO 2016
Year: 2016

The verification systems Boogie and Why3 use their respective intermediate languages to generate verification conditions from high-level programs. Since the two systems support different back-end provers (such as Z3 and Alt-Ergo) and are used to encode different high-level languages (such as C# and Java), being able to translate between their intermediate languages would provide a way to reuse one system's features to verify programs meant for the other. This paper describes a translation of Boogie into WhyML (Why3's intermediate language) that preserves semantics, verifiability, and program structure to a large degree. We implemented the translation as a tool and applied it to 194 Boogie-verified programs of various sources and sizes; Why3 verified 83% of the translated programs with the same outcome as Boogie. These results indicate that the translation is often effective and practically applicable.
Scheduling and Power Allocation to Optimize Service and Queue-Waiting Times in Cognitive Radio Uplinks
Venue: arXiv cs.IT 2016
Year: 2016

In this report, we study the packet delay as a QoS metric in CR systems. The packet delay includes the queue waiting time and the service time. In this work, we study the effect of both the scheduling and the power allocation algorithms on the delay performance of the SUs. To study the delay due to the service time we study a multichannel system where the channels are sensed sequentially, we study the tradeoff between throughput and delay. The problem is formulated as an optimal stopping rule problem where there is a tradeoff between the service time and the throughput. This tradeoff results from skipping low-quality channels to seek the possibility of finding high-quality ones in the future at the expense of a higher probability of being blocked from transmission since these future channels might be busy. On the other hand, the queue waiting time is studied by considering a multi-user single channel system. Specifically, we study the effect of scheduling and power allocation on the delay performance of all SUs in the system. We propose a delay optimal algorithm that protects the PUs from harmful interference and provides the required delay guarantees to users. Conventional scheduling algorithms do not provide such guarantees if the interference channels are heterogeneous. This is because they are developed for conventional non-CR wireless systems that neglect interference since channels are orthogonal. Finally, we present two potential extensions to these studied problems.
Coresets and Sketches
Venue: arXiv cs.CG 2016
Year: 2016

Geometric data summarization has become an essential tool in both geometric approximation algorithms and where geometry intersects with big data problems. In linear or near-linear time large data sets can be compressed into a summary, and then more intricate algorithms can be run on the summaries whose results approximate those of the full data set. Coresets and sketches are the two most important classes of these summaries. We survey five types of coresets and sketches: shape-fitting, density estimation, high-dimensional vectors, high-dimensional point sets / matrices, and clustering.
Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis
Venue: arXiv cs.LG 2016
Year: 2016

An important problem for both graphics and vision is to synthesize novel views of a 3D object from a single image. This is particularly challenging due to the partial observability inherent in projecting a 3D object onto the image space, and the ill-posedness of inferring object shape and pose. However, we can train a neural network to address the problem if we restrict our attention to specific object categories (in our case faces and chairs) for which we can gather ample training data. In this paper, we propose a novel recurrent convolutional encoder-decoder network that is trained end-to-end on the task of rendering rotated objects starting from a single image. The recurrent structure allows our model to capture long-term dependencies along a sequence of transformations. We demonstrate the quality of its predictions for human faces on the Multi-PIE dataset and for a dataset of 3D chair models, and also show its ability to disentangle latent factors of variation (e.g., identity and pose) without using full supervision.
Matrix Variate RBM and Its Applications
Venue: arXiv cs.CV 2016
Year: 2016

Restricted Boltzmann Machine (RBM) is an importan- t generative model modeling vectorial data. While applying an RBM in practice to images, the data have to be vec- torized. This results in high-dimensional data and valu- able spatial information has got lost in vectorization. In this paper, a Matrix-Variate Restricted Boltzmann Machine (MVRBM) model is proposed by generalizing the classic RBM to explicitly model matrix data. In the new RBM model, both input and hidden variables are in matrix forms which are connected by bilinear transforms. The MVRBM has much less model parameters, resulting in a faster train- ing algorithm while retaining comparable performance as the classic RBM. The advantages of the MVRBM have been demonstrated on two real-world applications: Image super- resolution and handwritten digit recognition.
Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture
Venue: arXiv cs.RO 2016
Year: 2016

Advanced Driver Assistance Systems (ADAS) have made driving safer over the last decade. They prepare vehicles for unsafe road conditions and alert drivers if they perform a dangerous maneuver. However, many accidents are unavoidable because by the time drivers are alerted, it is already too late. Anticipating maneuvers beforehand can alert drivers before they perform the maneuver and also give ADAS more time to avoid or prepare for the danger. In this work we propose a vehicular sensor-rich platform and learning algorithms for maneuver anticipation. For this purpose we equip a car with cameras, Global Positioning System (GPS), and a computing device to capture the driving context from both inside and outside of the car. In order to anticipate maneuvers, we propose a sensory-fusion deep learning architecture which jointly learns to anticipate and fuse multiple sensory streams. Our architecture consists of Recurrent Neural Networks (RNNs) that use Long Short-Term Memory (LSTM) units to capture long temporal dependencies. We propose a novel training procedure which allows the network to predict the future given only a partial temporal context. We introduce a diverse data set with 1180 miles of natural freeway and city driving, and show that we can anticipate maneuvers 3.5 seconds before they occur in real-time with a precision and recall of 90.5\% and 87.4\% respectively.