OpenRaiser/Intern-Atlas huggingface dataset:
Parameter Estimation of a Nonlinear Burgers Model using Nanoindentation and Finite Element-based Inverse Analysis
Venue: arXiv cs.CE 2016
Year: 2016

Nanoindentation involves probing a hard diamond tip into a material, where the load and the displacement experienced by the tip is recorded continuously. This load-displacement data is a direct function of material's innate stress-strain behavior. Thus, theoretically it is possible to extract mechanical properties of a material through nanoindentation. However, due to various nonlinearities associated with nanoindentation the process of interpreting load-displacement data into material properties is difficult. Although, simple elastic behavior can be characterized easily, a method to characterize complicated material behavior such as nonlinear viscoelasticity is still lacking. In this study, a nanoindentation-based material characterization technique is developed to characterize soft materials exhibiting nonlinear viscoelasticity. Nanoindentation experiment was modeled in finite element analysis software (ABAQUS), where a nonlinear viscoelastic behavior was incorporated using user-defined subroutine (UMAT). The model parameters were calibrated using a process called inverse analysis. In this study, a surrogate model-based approach was used for the inverse analysis. The different factors affecting the surrogate model performance are analyzed in order to optimize the performance with respect to the computational cost.
A Character-Word Compositional Neural Language Model for Finnish
Venue: arXiv cs.CL 2016
Year: 2016

Inspired by recent research, we explore ways to model the highly morphological Finnish language at the level of characters while maintaining the performance of word-level models. We propose a new Character-to-Word-to-Character (C2W2C) compositional language model that uses characters as input and output while still internally processing word level embeddings. Our preliminary experiments, using the Finnish Europarl V7 corpus, indicate that C2W2C can respond well to the challenges of morphologically rich languages such as high out of vocabulary rates, the prediction of novel words, and growing vocabulary size. Notably, the model is able to correctly score inflectional forms that are not present in the training data and sample grammatically and semantically correct Finnish sentences character by character.
Graph based linear error correcting codes
Venue: arXiv cs.IT 2016
Year: 2016

In this article we present a construction of error correcting codes, that have representation as very sparse matrices and belong to the class of Low Density Parity Check Codes. LDPC codes are in the classical Hamming metric. They are very close to well known Shannon bound. The ability to use graphs for code construction was first discussed by Tanner in 1981 and has been used in a number of very effective implementations. We describe how to construct such codes by using special a family of graphs introduced by Ustimenko and Woldar. Graphs that we used are bipartite, bi-regular, very sparse and do not have short cycles C 4 . Due to the very low density of such graphs, the obtained codes are fast decodable. We describe how to choose parameters to obtain a desired code rate. We also show results of computer simulations of BER (bit error rate) of the obtained codes in order to compare them with other known LDPC codes.
GraCT: A Grammar based Compressed representation of Trajectories
Venue: arXiv cs.DS 2016
Year: 2016

We present a compressed data structure to store free trajectories of moving objects (ships over the sea, for example) allowing spatio-temporal queries. Our method, GraCT, uses a $k^2$-tree to store the absolute positions of all objects at regular time intervals (snapshots), whereas the positions between snapshots are represented as logs of relative movements compressed with Re-Pair. Our experimental evaluation shows important savings in space and time with respect to a fair baseline.
Differential flatness for neuroscience population dynamics -- A preliminary study
Venue: arXiv cs.SY 2016
Year: 2016

The present document is devoted to structural properties of neural population dynamics and especially their differential flatness. Several applications of differential flatness in the present context can be envisioned, among which: trajectory tracking, feedforward to feedback switching, cyclic character, positivity and boundedness.
Non-negative Factorization of the Occurrence Tensor from Financial Contracts
Venue: arXiv cs.CE 2016
Year: 2016

We propose an algorithm for the non-negative factorization of an occurrence tensor built from heterogeneous networks. We use l0 norm to model sparse errors over discrete values (occurrences), and use decomposed factors to model the embedded groups of nodes. An efficient splitting method is developed to optimize the nonconvex and nonsmooth objective. We study both synthetic problems and a new dataset built from financial documents, resMBS.
Efficient Methods and Parallel Execution for Algorithm Sensitivity Analysis with Parameter Tuning on Microscopy Imaging Datasets
Venue: arXiv cs.DC 2016
Year: 2016

Background: We describe an informatics framework for researchers and clinical investigators to efficiently perform parameter sensitivity analysis and auto-tuning for algorithms that segment and classify image features in a large dataset of high-resolution images. The computational cost of the sensitivity analysis process can be very high, because the process requires processing the input dataset several times to systematically evaluate how output varies when input parameters are varied. Thus, high performance computing techniques are required to quickly execute the sensitivity analysis process. Results: We carried out an empirical evaluation of the proposed method on high performance computing clusters with multi-core CPUs and co-processors (GPUs and Intel Xeon Phis). Our results show that (1) the framework achieves excellent scalability and efficiency on a high performance computing cluster -- execution efficiency remained above 85% in all experiments; (2) the parameter auto-tuning methods are able to converge by visiting only a small fraction (0.0009%) of the search space with limited impact to the algorithm output (0.56% on average). Conclusions: The sensitivity analysis framework provides a range of strategies for the efficient exploration of the parameter space, as well as multiple indexes to evaluate the effect of parameter modification to outputs or even correlation between parameters. Our work demonstrates the feasibility of performing sensitivity analyses, parameter studies, and auto-tuning with large datasets with the use of high-performance systems and techniques. The proposed technologies will enable the quantification of error estimations and output variations in these pipelines, which may be used in application specific ways to assess uncertainty of conclusions extracted from data generated by these image analysis pipelines.
Energy Allocation Policy for Cellular Networks Powered by Renewable Energy
Venue: arXiv cs.NI 2016
Year: 2016

The explosive wireless data service requirement accompanied with carbon dioxide emission and consumption of traditional energy has put pressure on both industry and academia. Wireless networks powered with the uneven and intermittent generated renewable energy have been widely researched and lead to a new research paradigm called green communication. In this paper, we comprehensively consider the total generated renewable energy, QoS requirement and channel quality, then propose a utility based renewable energy allocation policy. The utility here means the satisfaction degree of users with a certain amount allocated renewable energy. The energy allocation problem is formulated as a constraint optimization problem and a heuristic algorithm with low complexity is derived to solve the raised problem. Numerical results show that the renewable energy allocation policy is applicable for any situation. When the renewable energy is very scarce, only users with good channel quality can achieve allocated energy.
Bias reduction of peer influence effects with latent coordinates and community membership
Venue: arXiv cs.SI 2016
Year: 2016

The importance of peer influence on consumer actions plays a vital role in marketing efforts. However, peer influence effects are often confounded with latent homophily, which are unobserved commonalities that drive friendship. Understanding causality has become one of the pressing issues of current research. We present an approach to explicitly account for various causal influences. We implement a simulation framework to show the effectiveness of two latent homophily proxies, latent coordinates and community membership, in improving peer influence effect estimates on game downloads in a Japanese social network website. We demonstrate that latent homophily proxies have no significant improvement in peer influence effect bias in the available website data.
Flu Detector: Estimating influenza-like illness rates from online user-generated content
Venue: arXiv cs.AI 2016
Year: 2016

We provide a brief technical description of an online platform for disease monitoring, titled as the Flu Detector (fludetector.cs.ucl.ac.uk). Flu Detector, in its current version (v.0.5), uses either Twitter or Google search data in conjunction with statistical Natural Language Processing models to estimate the rate of influenza-like illness in the population of England. Its back-end is a live service that collects online data, utilises modern technologies for large-scale text processing, and finally applies statistical inference models that are trained offline. The front-end visualises the various disease rate estimates. Notably, the models based on Google data achieve a high level of accuracy with respect to the most recent four flu seasons in England (2012/13 to 2015/16). This highlighted Flu Detector as having a great potential of becoming a complementary source to the domestic traditional flu surveillance schemes.
Developing Security Reputation Metrics for Hosting Providers
Venue: arXiv cs.CR 2016
Year: 2016

Research into cybercrime often points to concentrations of abuse at certain hosting providers. The implication is that these providers are worse in terms of security; some are considered `bad' or even `bullet proof'. Remarkably little work exists on systematically comparing the security performance of providers. Existing metrics typically count instances of abuse and sometimes normalize these counts by taking into account the advertised address space of the provider. None of these attempts have worked through the serious methodological challenges that plague metric design. In this paper we present a systematic approach for metrics development and identify the main challenges: (i) identification of providers, (ii) abuse data coverage and quality, (iii) normalization, (iv) aggregation and (v) metric interpretation. We describe a pragmatic approach to deal with these challenges. In the process, we answer an urgent question posed to us by the Dutch police: `which are the worst providers in our jurisdiction?'. Notwithstanding their limitations, there is a clear need for security metrics for hosting providers in the fight against cybercrime.
Analysis and Optimization of Loss Functions for Multiclass, Top-k, and Multilabel Classification
Venue: arXiv cs.CV 2016
Year: 2016

Top-k error is currently a popular performance measure on large scale image classification benchmarks such as ImageNet and Places. Despite its wide acceptance, our understanding of this metric is limited as most of the previous research is focused on its special case, the top-1 error. In this work, we explore two directions that shed more light on the top-k error. First, we provide an in-depth analysis of established and recently proposed single-label multiclass methods along with a detailed account of efficient optimization algorithms for them. Our results indicate that the softmax loss and the smooth multiclass SVM are surprisingly competitive in top-k error uniformly across all k, which can be explained by our analysis of multiclass top-k calibration. Further improvements for a specific k are possible with a number of proposed top-k loss functions. Second, we use the top-k methods to explore the transition from multiclass to multilabel learning. In particular, we find that it is possible to obtain effective multilabel classifiers on Pascal VOC using a single label per image for training, while the gap between multiclass and multilabel methods on MS COCO is more significant. Finally, our contribution of efficient algorithms for training with the considered top-k and multilabel loss functions is of independent interest.
Distributed Computation of the Cech Complex and Applications in Wireless Networks
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, we introduce a distributed algorithm to compute thě Cech complex. This algorithm is aimed at solving coverage problems in self organized wireless networks. Two applications based on the distributed computation of thě Cech complex are proposed. The first application detects coverage holes while the later one optimizes coverage of wireless networks.
Geographical Load Balancing across Green Datacenters
Venue: arXiv cs.PF 2016
Year: 2016

"Geographic Load Balancing" is a strategy for reducing the energy cost of data centers spreading across different terrestrial locations. In this paper, we focus on load balancing among micro-datacenters powered by renewable energy sources. We model via a Markov Chain the problem of scheduling jobs by prioritizing datacenters where renewable energy is currently available. Not finding a convenient closed form solution for the resulting chain, we use mean field techniques to derive an asymptotic approximate model which instead is shown to have an extremely simple and intuitive steady state solution. After proving, using both theoretical and discrete event simulation results, that the system performance converges to the asymptotic model for an increasing number of datacenters, we exploit the simple closed form model's solution to investigate relationships and trade-offs among the various system parameters.
From narrative descriptions to MedDRA: automagically encoding adverse drug reactions
Venue: arXiv cs.CL 2016
Year: 2016

The collection of narrative spontaneous reports is an irreplaceable source for the prompt detection of suspected adverse drug reactions (ADRs): qualified domain experts manually revise a huge amount of narrative descriptions and then encode texts according to MedDRA standard terminology. The manual annotation of narrative documents with medical terminology is a subtle and expensive task, since the number of reports is growing up day-by-day. MagiCoder, a Natural Language Processing algorithm, is proposed for the automatic encoding of free-text descriptions into MedDRA terms. MagiCoder procedure is efficient in terms of computational complexity (in particular, it is linear in the size of the narrative input and the terminology). We tested it on a large dataset of about 4500 manually revised reports, by performing an automated comparison between human and MagiCoder revisions. For the current base version of MagiCoder, we measured: on short descriptions, an average recall of $86\%$ and an average precision of $88\%$; on medium-long descriptions (up to 255 characters), an average recall of $64\%$ and an average precision of $63\%$. From a practical point of view, MagiCoder reduces the time required for encoding ADR reports. Pharmacologists have simply to review and validate the MagiCoder terms proposed by the application, instead of choosing the right terms among the 70K low level terms of MedDRA. Such improvement in the efficiency of pharmacologists' work has a relevant impact also on the quality of the subsequent data analysis. We developed MagiCoder for the Italian pharmacovigilance language. However, our proposal is based on a general approach, not depending on the considered language nor the term dictionary.
FaaS: Federation-as-a-Service
Venue: arXiv cs.DC 2016
Year: 2016

This document is the main high-level architecture specification of the SUNFISH cloud federation solution. Its main objective is to introduce the concept of Federation-as-a-Service (FaaS) and the SUNFISH platform. FaaS is the new and innovative cloud federation service proposed by the SUNFISH project. The document defines the functionalities of FaaS, its governance and precise objectives. With respect to these objectives, the document proposes the high-level architecture of the SUNFISH platform: the software architecture that permits realising a FaaS federation. More specifically, the document describes all the components forming the platform, the offered functionalities and their high-level interactions underlying the main FaaS functionalities. The document concludes by outlining the main implementation strategies towards the actual implementation of the proposed cloud federation solution.
Understanding the Impact of Precision Quantization on the Accuracy and Energy of Neural Networks
Venue: arXiv cs.NE 2016
Year: 2016

Deep neural networks are gaining in popularity as they are used to generate state-of-the-art results for a variety of computer vision and machine learning applications. At the same time, these networks have grown in depth and complexity in order to solve harder problems. Given the limitations in power budgets dedicated to these networks, the importance of low-power, low-memory solutions has been stressed in recent years. While a large number of dedicated hardware using different precisions has recently been proposed, there exists no comprehensive study of different bit precisions and arithmetic in both inputs and network parameters. In this work, we address this issue and perform a study of different bit-precisions in neural networks (from floating-point to fixed-point, powers of two, and binary). In our evaluation, we consider and analyze the effect of precision scaling on both network accuracy and hardware metrics including memory footprint, power and energy consumption, and design area. We also investigate training-time methodologies to compensate for the reduction in accuracy due to limited bit precision and demonstrate that in most cases, precision scaling can deliver significant benefits in design metrics at the cost of very modest decreases in network accuracy. In addition, we propose that a small portion of the benefits achieved when using lower precisions can be forfeited to increase the network size and therefore the accuracy. We evaluate our experiments, using three well-recognized networks and datasets to show its generality. We investigate the trade-offs and highlight the benefits of using lower precisions in terms of energy and memory footprint.
On the Identification of SM and Alamouti Coded SC-FDMA Signals: A Statistical-Based Approach
Venue: arXiv cs.IT 2016
Year: 2016

Signal identification represents the task of a receiver to identify the signal type and its parameters, with applications to both military and commercial communications. In this paper, we investigate the identification of spatial multiplexing (SM) and Alamouti (AL) space-time block code (STBC) with single carrier frequency division multiple access (SC-FDMA) signals, when the receiver is equipped with a single antenna. We develop a discriminating feature based on a fourth-order statistic of the received signal, as well as a constant false alarm rate decision criterion which relies on the statistical properties of the feature estimate. Furthermore, we present the theoretical performance analysis of the proposed identification algorithm. The algorithm does not require channel or noise power estimation, modulation classification, and block synchronization. Simulation results show the validity of the proposed algorithm, as well as a very good agreement with the theoretical analysis.
On the Formal Development of Behavioral Reactive Agents: A Systematic Braitenberg-Vehicle Approach
Venue: arXiv cs.RO 2016
Year: 2016

In this paper, a novel process has been developed to realize high-level complex cognitive behaviors into reactive agents, efficiently. This method paves the way for deducting high-level reactive behaviors from low-level perceptive information by autonomous robots. The aforementioned process lets us actualize different generations of Braitenberg vehicles, are which able to mimic desired behaviors to survive in complex environments with high degrees of flexibility in perception and emergence of high-level cognitive actions. The approach has been used to engineer a Braitenberg vehicle with a wide range of perception-action capabilities. Verification would be realized within this framework, due to the efficient traceability between each sequential pair of process phases. The applied simulations demonstrate the efficiency of the established development process, based on the Braitenberg vehicle's behavior.
Lévy robotics
Venue: arXiv cs.RO 2016
Year: 2016

Two the most common tasks for autonomous mobile robots is to explore the environment and locate a target. %In the last case, the objective is either to find a target in the shortest time possible or, alternatively, to find %as many targets as possible for a given amount of time. Targets could range from sources of chemical contamination to people needing assistance in a disaster area. From the very beginning, the quest for most efficient search algorithms was strongly influenced by behavioral science and ecology, where researchers try to figure out the strategies used by leaving beings, from bacteria to mammals. Since then, bio-inspired random search algorithms remain one the most important directions in autonomous robotics. Recently a new wave arrived bringing a specific type of random walks as a universal search strategy exploited by immune cells, insects, mussels, albatrosses, sharks, deers, and a dozen of other animals including humans. These \textit{Lévy} walks combine two key features, the ability of walkers to spread anomalously fast while moving with a finite velocity. The latter is especially valuable in the context of robotics because it respects the reality autonomous robots live in. There is already an impressive body of publications on Lévy robotics; yet research in this field is unfolding further, constantly bringing new results, ideas, hypothesis and speculations. In this mini-review we survey the current state of the field, list latest advances, discuss the prevailing trends, and outline further perspectives.
Spatial Pyramid Convolutional Neural Network for Social Event Detection in Static Image
Venue: arXiv cs.CV 2016
Year: 2016

Social event detection in a static image is a very challenging problem and it's very useful for internet of things applications including automatic photo organization, ads recommender system, or image captioning. Several publications show that variety of objects, scene, and people can be very ambiguous for the system to decide the event that occurs in the image. We proposed the spatial pyramid configuration of convolutional neural network (CNN) classifier for social event detection in a static image. By applying the spatial pyramid configuration to the CNN classifier, the detail that occurs in the image can observe more accurately by the classifier. USED dataset provided by Ahmad et al. is used to evaluate our proposed method, which consists of two different image sets, EiMM, and SED dataset. As a result, the average accuracy of our system outperforms the baseline method by 15% and 2% respectively.
The Journal Impact Factor Should Not Be Discarded
Venue: arXiv cs.DL 2016
Year: 2016

The Journal Impact Factor (JIF) has been heavily criticized over decades. This opinion piece argues that the JIF should not be demonized. It still can be employed for research evaluation purposes by carefully considering the context and academic environment.