OpenRaiser/Intern-Atlas huggingface dataset:
Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies
Venue: arXiv cs.CL 2016
Year: 2016

The success of long short-term memory (LSTM) neural networks in language processing is typically attributed to their ability to capture long-distance statistical regularities. Linguistic regularities are often sensitive to syntactic structure; can such dependencies be captured by LSTMs, which do not have explicit structural representations? We begin addressing this question using number agreement in English subject-verb dependencies. We probe the architecture's grammatical competence both using training objectives with an explicit grammatical target (number prediction, grammaticality judgments) and using language models. In the strongly supervised settings, the LSTM achieved very high overall accuracy (less than 1% errors), but errors increased when sequential and structural information conflicted. The frequency of such errors rose sharply in the language-modeling setting. We conclude that LSTMs can capture a non-trivial amount of grammatical structure given targeted supervision, but stronger architectures may be required to further reduce errors; furthermore, the language modeling signal is insufficient for capturing syntax-sensitive dependencies, and should be supplemented with more direct supervision if such dependencies need to be captured.
Stratifying High Dimensional Data Based on Proximity to the Convex Hull Boundary
Venue: arXiv cs.CG 2016
Year: 2016

The convex hull of a set of points, $C$, serves to expose extremal properties of $C$ and can help identify elements in $C$ of high interest. For many problems, particularly in the presence of noise, the true vertex set (and facets) may be difficult to determine. One solution is to expand the list of high interest candidates to points lying near the boundary of the convex hull. We propose a quadratic program for the purpose of stratifying points in a data cloud based on proximity to the boundary of the convex hull. For each data point, a quadratic program is solved to determine an associated weight vector. We show that the weight vector encodes geometric information concerning the point's relationship to the boundary of the convex hull. The computation of the weight vectors can be carried out in parallel, and for a fixed number of points and fixed neighborhood size, the overall computational complexity of the algorithm grows linearly with dimension. As a consequence, meaningful computations can be completed on reasonably large, high dimensional data sets.
Diversity Pulse Shaped Transmission in Ultra-Dense Small Cell Networks
Venue: arXiv cs.IT 2016
Year: 2016

In ultra-dense small cell networks, spatial multiplexing gain is a challenge because of the different propagation conditions. The channels associated with different transmitreceive pairs can be highly correlated due to the i) high probability of line-of-sight (LOS) communication between user equipment (UE) and base station (BS), and ii) insufficient spacing between antenna elements at both UE and BS. In this paper, we propose a novel transmission technique titled Diversity Pulse Shaped Transmission (DPST) to enhance the throughput over the correlated MIMO channels in an ultra-dense small cell network. The fundamental of DPST is to shape transmit signals at adjacent antennas with distinct interpolating filters, introducing pulse shaping diversity. In DPST, each antenna transmits its own data stream with a relative deterministic time offset-which must be a fraction of the symbol period-with respect to the adjacent antenna. The delay is interpolated with the pulse shaped signal generating a virtual MIMO channel that benefits from increased diversity from the receiver perspective. To extract the diversity, the receiver must operate in an over-sampled domain and hence a fractionally spaced equaliser (FSE) is proposed. The joint impact of DPST and FSE helps the receiver to sense a less correlated channel, eventually enhancing the UE's throughput. Moreover, in order to minimise the spatial correlation, we aim to optimise the deterministic fractional delay. Simulation results show that applying DPST to a correlated channel can approximately enhance the UE throughput by 1.93x and 3.76x in 2x2 and 4x4 MIMO systems, respectively.
Scalable Holistic Analysis of Multi-Source, Data-Intensive Problems Using Multilayered Networks
Venue: arXiv cs.DB 2016
Year: 2016

Holistic analysis of many real-world problems are based on data collected from multiple sources contributing to some aspect of that problem. The word fusion has also been used in the literature for such problems involving disparate data types. Holistically understanding traffic patterns, causes of accidents, bombings, terrorist planning and many natural phenomenon such as storms, earthquakes fall into this category. Some may have real-time requirements and some may need to be analyzed after the fact (post-mortem or forensic analysis.) What is common for all these problems is that the amount and types of data associated with the event. Data may also be incomplete and trustworthiness of sources may also vary. Currently, manual and ad-hoc approaches are used in aggregating data in different ways for analyzing and understanding these problems. In this paper, we approach this problem in a novel way using multilayered networks. We identify features of a central event and propose a network layer for each feature. This approach allows us to study the effect of each feature independently and its impact on the event. We also establish that the proposed approach allows us to compose these features in arbitrary ways (without loss of information) to analyze their combined effect. Additionally, formulation of relationships (e.g., distance measure for a single feature instead of several at the same time) is simpler. Further, computations can be done once on each layer in this approach and reused for mixing and matching the features for aggregate impacts and "what if" scenarios to understand the problem holistically. This has been demonstrated by recreating the communities for the AND-Composed network by using the communities of the individual layers. We believe that techniques proposed here make an important contribution to the nascent yet fast growing area of data fusion.
Citation algorithms for identifying research milestones driving biomedical innovation
Venue: arXiv cs.DL 2016
Year: 2016

Scientific activity plays a major role in innovation for biomedicine and healthcare. For instance, fundamental research on disease pathologies and mechanisms can generate potential targets for drug therapy. This co-evolution is punctuated by papers which provide new perspectives and open new domains. Despite the relationship between scientific discovery and biomedical advancement, identifying these research milestones that truly impact biomedical innovation can be difficult and is largely based solely on the opinions of subject matter experts. Here, we consider whether a new class of citation algorithms that identify seminal scientific works in a field, Reference Publication Year Spectroscopy (RPYS) and multi-RPYS, can identify the connections between innovation (e.g. therapeutic treatments) and the foundational research underlying them. Specifically, we assess whether the results of these analytic techniques converge with expert opinions on research milestones driving biomedical innovation in the treatment of Basal Cell Carcinoma. Our results show that these algorithms successfully identify the majority of milestone papers detailed by experts (Wong and Dlugosz 2014) thereby validating the power of these algorithms to converge on independent opinions of seminal scientific works derived by subject matter experts. These advances offer an opportunity to identify scientific activities enabling innovation in biomedicine.
Spanning Trees in Multipartite Geometric Graphs
Venue: arXiv cs.CG 2016
Year: 2016

Let $R$ and $B$ be two disjoint sets of points in the plane where the points of $R$ are colored red and the points of $B$ are colored blue, and let $n=|R\cup B|$. A bichromatic spanning tree is a spanning tree in the complete bipartite geometric graph with bipartition $(R,B)$. The minimum (respectively maximum) bichromatic spanning tree problem is the problem of computing a bichromatic spanning tree of minimum (respectively maximum) total edge length. 1. We present a simple algorithm that solves the minimum bichromatic spanning tree problem in $O(n\log^3 n)$ time. This algorithm can easily be extended to solve the maximum bichromatic spanning tree problem within the same time bound. It also can easily be generalized to multicolored point sets. 2. We present $Θ(n\log n)$-time algorithms that solve the minimum and the maximum bichromatic spanning tree problems. 3. We extend the bichromatic spanning tree algorithms and solve the multicolored version of these problems in $O(n\log n\log k)$ time, where $k$ is the number of different colors (or the size of the multipartition in a complete multipartite geometric graph).
Comparing learning algorithms in neural network for diagnosing cardiovascular disease
Venue: arXiv cs.LG 2016
Year: 2016

Today data mining techniques are exploited in medical science for diagnosing, overcoming and treating diseases. Neural network is one of the techniques which are widely used for diagnosis in medical field. In this article efficiency of nine algorithms, which are basis of neural network learning in diagnosing cardiovascular diseases, will be assessed. Algorithms are assessed in terms of accuracy, sensitivity, transparency, AROC and convergence rate by means of 10 fold cross validation. The results suggest that in training phase, Lonberg-M algorithm has the best efficiency in terms of all metrics, algorithm OSS has maximum accuracy in testing phase, algorithm SCG has the maximum transparency and algorithm CGB has the maximum sensitivity.
A Fault-tolerance Linguistic Structure for Distributed Applications
Venue: arXiv cs.DC 2016
Year: 2016

The structures for the expression of fault-tolerance provisions into the application software are the central topic of this dissertation. Structuring techniques provide means to control complexity, the latter being a relevant factor for the introduction of design faults. This fact and the ever increasing complexity of today's distributed software justify the need for simple, coherent, and effective structures for the expression of fault-tolerance in the application software. A first contribution of this dissertation is the definition of a base of structural attributes with which application-level fault-tolerance structures can be qualitatively assessed and compared with each other and with respect to the above mentioned need. This result is then used to provide an elaborated survey of the state-of-the-art of software fault-tolerance structures. The key contribution of this work is a novel structuring technique for the expression of the fault-tolerance design concerns in the application layer of those distributed software systems that are characterized by soft real-time requirements and with a number of processing nodes known at compile-time. The main thesis of this dissertation is that this new structuring technique is capable of exhibiting satisfactory values of the structural attributes in the domain of soft real-time, distributed and parallel applications. Following this novel approach, beside the conventional programming language addressing the functional design concerns, a special-purpose linguistic structure (the so-called "recovery language") is available to address error recovery and reconfiguration. This recovery language comes into play as soon as an error is detected by an underlying error detection layer, or when some erroneous condition is signaled by the application processes.
High-Fidelity Model Order Reduction for Microgrids Stability Assessment
Venue: arXiv cs.SY 2016
Year: 2016

Proper modeling of inverter-based microgrids is crucial for accurate assessment of stability boundaries. It has been recently realized that the stability conditions for such microgrids are significantly different from those known for large- scale power systems. While detailed models are available, they are both computationally expensive and can not provide the insight into the instability mechanisms and factors. In this paper, a computationally efficient and accurate reduced-order model is proposed for modeling the inverter-based microgrids. The main factors affecting microgrid stability are analyzed using the developed reduced-order model and are shown to be unique for the microgrid-based network, which has no direct analogy to large-scale power systems. Particularly, it has been discovered that the stability limits for the conventional droop-based system (omega - P/V - Q) are determined by the ratio of inverter rating to network capacity, leading to a smaller stability region for microgrids with shorter lines. The theoretical derivation has been provided to verify the above investigation based on both the simplified and generalized network configurations. More impor- tantly, the proposed reduced-order model not only maintains the modeling accuracy but also enhances the computation efficiency. Finally, the results are verified with the detailed model via both frequency and time domain analyses.
A Semi-Automatic Approach for Detecting Dataset References in Social Science Texts
Venue: arXiv cs.DL 2016
Year: 2016

Today, full-texts of scientific articles are often stored in different locations than the used datasets. Dataset registries aim at a closer integration by making datasets citable but authors typically refer to datasets using inconsistent abbreviations and heterogeneous metadata (e.g. title, publication year). It is thus hard to reproduce research results, to access datasets for further analysis, and to determine the impact of a dataset. Manually detecting references to datasets in scientific articles is time-consuming and requires expert knowledge in the underlying research domain.We propose and evaluate a semi-automatic three-step approach for finding explicit references to datasets in social sciences articles.We first extract pre-defined special features from dataset titles in the da|ra registry, then detect references to datasets using the extracted features, and finally match the references found with corresponding dataset titles. The approach does not require a corpus of articles (avoiding the cold start problem) and performs well on a test corpus. We achieved an F-measure of 0.84 for detecting references in full-texts and an F-measure of 0.83 for finding correct matches of detected references in the da|ra dataset registry.
On the Approximate Analysis of Energy Detection over n*Rayleigh Fading Channels through Cooperative Spectrum Sensing
Venue: arXiv cs.IT 2016
Year: 2016

In this letter, we consider the problem of energy detection of unknown signals in an intervehicular communication (IVC) system over n*Rayleigh fading channels (also known as cascaded Rayleigh). Novel tight approximations for the probability of detection are derived for the no-diversity and the maximum ratio combining (MRC)) diversity schemes. Moreover, we investigate the system performance when cooperative spectrum sensing (CSS) is considered with and without imperfect reporting channels. The analytical results show that the detection reliability is decreased as the fading severity parameter n increases but reliability is substantially improved when CSS employs MRC schemes.
Pilot Precoding and Combining in Multiuser MIMO Networks
Venue: arXiv cs.IT 2016
Year: 2016

Although the benefits of precoding and combining of data signals are widely recognized, the potential of these techniques for pilot transmission is not fully understood. This is particularly relevant for multiuser multiple-input multiple-output (MU-MIMO) cellular systems using millimeter-wave (mmWave) communications, where multiple antennas will have to be used both at the transmitter and the receiver to overcome the severe path loss. In this paper, we characterize the gains of pilot precoding and combining in terms of channel estimation quality and achievable data rate. Specifically, we consider three uplink pilot transmission scenarios in a mmWave MU-MIMO cellular system: 1) non-precoded and uncombined, 2) precoded but uncombined, and 3) precoded and combined. We show that a simple precoder that utilizes only the second-order statistics of the channel reduces the variance of the channel estimation error by a factor that is proportional to the number of user equipment (UE) antennas. We also show that using a linear combiner designed based on the second-order statistics of the channel significantly reduces multiuser interference and provides the possibility of reusing some pilots. Specifically, in the large antenna regime, pilot precoding and combining help to accommodate a large number of UEs in one cell, significantly improve channel estimation quality, boost the signal-to-noise ratio of the UEs located close to the cell edges, alleviate pilot contamination, and address the imbalanced coverage of pilot and data signals.
Reinforcement-based Simultaneous Algorithm and its Hyperparameters Selection
Venue: arXiv cs.LG 2016
Year: 2016

Many algorithms for data analysis exist, especially for classification problems. To solve a data analysis problem, a proper algorithm should be chosen, and also its hyperparameters should be selected. In this paper, we present a new method for the simultaneous selection of an algorithm and its hyperparameters. In order to do so, we reduced this problem to the multi-armed bandit problem. We consider an algorithm as an arm and algorithm hyperparameters search during a fixed time as the corresponding arm play. We also suggest a problem-specific reward function. We performed the experiments on 10 real datasets and compare the suggested method with the existing one implemented in Auto-WEKA. The results show that our method is significantly better in most of the cases and never worse than the Auto-WEKA.
Continuous Hands-off Control by CLOT Norm Minimization
Venue: arXiv cs.SY 2016
Year: 2016

In this paper, we consider hands-off control via minimization of the CLOT (Combined L-One and Two) norm. The maximum hands-off control is the L0-optimal (or the sparsest) control among all feasible controls that are bounded by a specified value and transfer the state from a given initial state to the origin within a fixed time duration. In general, the maximum hands-off control is a bang-off-bang control taking values of +1, -1, and 0. For many real applications, such discontinuity in the control is not desirable. To obtain a continuous but still relatively sparse control, we propose to use the CLOT norm, a convex combination of L1 and L2 norms. We show by numerical simulation that the CLOT control is continuous and much sparser (i.e. has longer time duration on which the control takes 0) than the conventional EN (elastic net) control, which is a convex combination of L1 and squared L2 norms.
Building a comprehensive syntactic and semantic corpus of Chinese clinical texts
Venue: arXiv cs.CL 2016
Year: 2016

Objective: To build a comprehensive corpus covering syntactic and semantic annotations of Chinese clinical texts with corresponding annotation guidelines and methods as well as to develop tools trained on the annotated corpus, which supplies baselines for research on Chinese texts in the clinical domain. Materials and methods: An iterative annotation method was proposed to train annotators and to develop annotation guidelines. Then, by using annotation quality assurance measures, a comprehensive corpus was built, containing annotations of part-of-speech (POS) tags, syntactic tags, entities, assertions, and relations. Inter-annotator agreement (IAA) was calculated to evaluate the annotation quality and a Chinese clinical text processing and information extraction system (CCTPIES) was developed based on our annotated corpus. Results: The syntactic corpus consists of 138 Chinese clinical documents with 47,424 tokens and 2553 full parsing trees, while the semantic corpus includes 992 documents that annotated 39,511 entities with their assertions and 7695 relations. IAA evaluation shows that this comprehensive corpus is of good quality, and the system modules are effective. Discussion: The annotated corpus makes a considerable contribution to natural language processing (NLP) research into Chinese texts in the clinical domain. However, this corpus has a number of limitations. Some additional types of clinical text should be introduced to improve corpus coverage and active learning methods should be utilized to promote annotation efficiency. Conclusions: In this study, several annotation guidelines and an annotation method for Chinese clinical texts were proposed, and a comprehensive corpus with its NLP modules were constructed, providing a foundation for further study of applying NLP techniques to Chinese texts in the clinical domain.
OpenTED Browser: Insights into European Public Spendings
Venue: arXiv cs.CY 2016
Year: 2016

We present the OpenTED browser, a Web application allowing to interactively browse public spending data related to public procurements in the European Union. The application relies on Open Data recently published by the European Commission and the Publications Office of the European Union, from which we imported a curated dataset of 4.2 million contract award notices spanning the period 2006-2015. The application is designed to easily filter notices and visualise relationships between public contracting authorities and private contractors. The simple design allows for example to quickly find information about who the biggest suppliers of local governments are, and the nature of the contracted goods and services. We believe the tool, which we make Open Source, is a valuable source of information for journalists, NGOs, analysts and citizens for getting information on public procurement data, from large scale trends to local municipal developments.
Crowdsourcing in Computer Vision
Venue: arXiv cs.CV 2016
Year: 2016

Computer vision systems require large amounts of manually annotated data to properly learn challenging visual concepts. Crowdsourcing platforms offer an inexpensive method to capture human knowledge and understanding, for a vast number of visual perception tasks. In this survey, we describe the types of annotations computer vision researchers have collected using crowdsourcing, and how they have ensured that this data is of high quality while annotation effort is minimized. We begin by discussing data collection on both classic (e.g., object recognition) and recent (e.g., visual story-telling) vision tasks. We then summarize key design decisions for creating effective data collection interfaces and workflows, and present strategies for intelligently selecting the most important data instances to annotate. Finally, we conclude with some thoughts on the future of crowdsourcing in computer vision.