OpenRaiser/Intern-Atlas huggingface dataset:
Spatial modeling and analysis of cellular networks using the Ginibre point process: A tutorial
Venue: arXiv cs.IT 2016
Year: 2016

Spatial stochastic models have been much used for performance analysis of wireless communication networks. This is due to the fact that the performance of wireless networks depends on the spatial configuration of wireless nodes and the irregularity of node locations in a real wireless network can be captured by a spatial point process. Most works on such spatial stochastic models of wireless networks have adopted homogeneous Poisson point processes as the models of wireless node locations. While this adoption makes the models analytically tractable, it assumes that the wireless nodes are located independently of each other and their spatial correlation is ignored. Recently, the authors have proposed to adopt the Ginibre point process---one of the determinantal point processes---as the deployment models of base stations (BSs) in cellular networks. The determinantal point processes constitute a class of repulsive point processes and have been attracting attention due to their mathematically interesting properties and efficient simulation methods. In this tutorial, we provide a brief guide to the Ginibre point process and its variant, $α$-Ginibre point process, as the models of BS deployments in cellular networks and show some existing results on the performance analysis of cellular network models with $α$-Ginibre deployed BSs. The authors hope the readers to use such point processes as a tool for analyzing various problems arising in future cellular networks.
Edinburgh Neural Machine Translation Systems for WMT 16
Venue: arXiv cs.CL 2016
Year: 2016

We participated in the WMT 2016 shared news translation task by building neural translation systems for four language pairs, each trained in both directions: English<->Czech, English<->German, English<->Romanian and English<->Russian. Our systems are based on an attentional encoder-decoder, using BPE subword segmentation for open-vocabulary translation with a fixed vocabulary. We experimented with using automatic back-translations of the monolingual News corpus as additional training data, pervasive dropout, and target-bidirectional models. All reported methods give substantial improvements, and we see improvements of 4.3--11.2 BLEU over our baseline systems. In the human evaluation, our systems were the (tied) best constrained system for 7 out of 8 translation directions in which we participated.
A Cognitive Architecture for the Implementation of Emotions in Computing Systems
Venue: arXiv cs.AI 2016
Year: 2016

In this paper we present a new neurobiologically-inspired affective cognitive architecture: NEUCOGAR (NEUromodulating COGnitive ARchitecture). The objective of NEUCOGAR is the identification of a mapping from the influence of serotonin, dopamine and noradrenaline to the computing processes based on Von Neuman's architecture, in order to implement affective phenomena which can operate on the Turing's machine model. As basis of the modeling we use and extend the Lövheim Cube of Emotion with parameters of the Von Neumann architecture. Validation is conducted via simulation on a computing system of dopamine neuromodulation and its effects on the Cortex. In the experimental phase of the project, the increase of computing power and storage redistribution due to emotion stimulus modulated by the dopamine system, confirmed the soundness of the model.
Modeling Variability in Template-based Code Generators for Product Line Engineering
Venue: arXiv cs.SE 2016
Year: 2016

Generating software from abstract models is a prime activity in model-drivenengineering. Adaptable and extendable code generators are important to address changing technologies as well as user needs. However, theyare less established, as variability is often designed as configuration options of monolithic systems. Thus, code generation is often tied to a fixed set of features, hardly reusable in different contexts, and without means for configuration of variants. In this paper,we present an approach for developing product lines of template-based code generators. This approach applies concepts from feature-oriented programming to make variability explicit and manageable. Moreover, it relies on explicit variability regions (VR) in a code generators templates, refinements of VRs, and the aggregation of templates and refinements into reusable layers. Aconcrete product is defined by selecting one or multiple layers. If necessary, additional layers required due to VR refinements are automatically selected.
Implicit Tubular Surface Generation Guided by Centerline
Venue: arXiv cs.CV 2016
Year: 2016

Most machine learning-based coronary artery segmentation methods represent the vascular lumen surface in an implicit way by the centerline and the associated lumen radii, which makes the subsequent modeling process to generate a whole piece of watertight coronary artery tree model difficult. To solve this problem, in this paper, we propose a modeling method with the learning-based segmentation results by (1) considering mesh vertices as physical particles and using interaction force model and particle expansion model to generate uniformly distributed point cloud on the implicit lumen surface and; (2) doing incremental Delaunay-based triangulation. Our method has the advantage of being able to consider the complex shape of the coronary artery tree as a whole piece; hence no extra stitching or intersection removal algorithm is needed to generate a watertight model. Experiment results demonstrate that our method is capable of generating high quality mesh model which is highly consistent with the given implicit vascular lumen surface, with an average error of 0.08 mm.
Convolutional Sketch Inversion
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we use deep neural networks for inverting face sketches to synthesize photorealistic face images. We first construct a semi-simulated dataset containing a very large number of computer-generated face sketches with different styles and corresponding face images by expanding existing unconstrained face data sets. We then train models achieving state-of-the-art results on both computer-generated sketches and hand-drawn sketches by leveraging recent advances in deep learning such as batch normalization, deep residual learning, perceptual losses and stochastic optimization in combination with our new dataset. We finally demonstrate potential applications of our models in fine arts and forensic arts. In contrast to existing patch-based approaches, our deep-neural-network-based approach can be used for synthesizing photorealistic face images by inverting face sketches in the wild.
Calculi for Intuitionistic Normal Modal Logic
Venue: arXiv cs.LO 2016
Year: 2016

This paper provides a call-by-name and a call-by-value term calculus, both of which have a Curry-Howard correspondence to the box fragment of the intuitionistic modal logic IK. The strong normalizability and the confluency of the calculi are shown. Moreover, we define a CPS transformation from the call-by-value calculus to the call-by-name calculus, and show its soundness and completeness.
PSDVec: a Toolbox for Incremental and Scalable Word Embedding
Venue: arXiv cs.CL 2016
Year: 2016

PSDVec is a Python/Perl toolbox that learns word embeddings, i.e. the mapping of words in a natural language to continuous vectors which encode the semantic/syntactic regularities between the words. PSDVec implements a word embedding learning method based on a weighted low-rank positive semidefinite approximation. To scale up the learning process, we implement a blockwise online learning algorithm to learn the embeddings incrementally. This strategy greatly reduces the learning time of word embeddings on a large vocabulary, and can learn the embeddings of new words without re-learning the whole vocabulary. On 9 word similarity/analogy benchmark sets and 2 Natural Language Processing (NLP) tasks, PSDVec produces embeddings that has the best average performance among popular word embedding tools. PSDVec provides a new option for NLP practitioners.
Distributed stabilization control of rigid formations with prescribed orientation
Venue: arXiv cs.SY 2016
Year: 2016

Most rigid formation controllers reported in the literature aim to only stabilize a rigid formation shape, while the formation orientation is not controlled. This paper studies the problem of controlling rigid formations with prescribed orientations in both 2-D and 3-D spaces. The proposed controllers involve the commonly-used gradient descent control for shape stabilization, and an additional term to control the directions of certain relative position vectors associated with certain chosen agents. In this control framework, we show the minimal number of agents which should have knowledge of a global coordinate system (2 agents for a 2-D rigid formation and 3 agents for a 3-D rigid formation), while all other agents do not require any global coordinate knowledge or any coordinate frame alignment to implement the proposed control. The exponential convergence to the desired rigid shape and formation orientation is also proved. Typical simulation examples are shown to support the analysis and performance of the proposed formation controllers.
Constraining the Number of Positive Responses in Adaptive, Non-Adaptive, and Two-Stage Group Testing
Venue: arXiv cs.DS 2016
Year: 2016

Group testing is a well known search problem that consists in detecting the defective members of a set of objects O by performing tests on properly chosen subsets (pools) of the given set O. In classical group testing the goal is to find all defectives by using as few tests as possible. We consider a variant of classical group testing in which one is concerned not only with minimizing the total number of tests but aims also at reducing the number of tests involving defective elements. The rationale behind this search model is that in many practical applications the devices used for the tests are subject to deterioration due to exposure to or interaction with the defective elements. In this paper we consider adaptive, non-adaptive and two-stage group testing. For all three considered scenarios, we derive upper and lower bounds on the number of "yes" responses that must be admitted by any strategy performing at most a certain number t of tests. In particular, for the adaptive case we provide an algorithm that uses a number of "yes" responses that exceeds the given lower bound by a small constant. Interestingly, this bound can be asymptotically attained also by our two-stage algorithm, which is a phenomenon analogous to the one occurring in classical group testing. For the non-adaptive scenario we give almost matching upper and lower bounds on the number of "yes" responses. In particular, we give two constructions both achieving the same asymptotic bound. An interesting feature of one of these constructions is that it is an explicit construction. The bounds for the non-adaptive and the two-stage cases follow from the bounds on the optimal sizes of new variants of d-cover free families and (p,d)-cover free families introduced in this paper, which we believe may be of interest also in other contexts.
Parameterized Algorithmics for Graph Modification Problems: On Interactions with Heuristics
Venue: arXiv cs.DS 2016
Year: 2016

In graph modification problems, one is given a graph G and the goal is to apply a minimum number of modification operations (such as edge deletions) to G such that the resulting graph fulfills a certain property. For example, the Cluster Deletion problem asks to delete as few edges as possible such that the resulting graph is a disjoint union of cliques. Graph modification problems appear in numerous applications, including the analysis of biological and social networks. Typically, graph modification problems are NP-hard, making them natural candidates for parameterized complexity studies. We discuss several fruitful interactions between the development of fixed-parameter algorithms and the design of heuristics for graph modification problems, featuring quite different aspects of mutual benefits.
Defining and Conceptualizing Actionable Insight: A Conceptual Framework for Decision-centric Analytics
Venue: arXiv cs.CY 2016
Year: 2016

Despite actionable insight is widely recognized as the outcome of data analytics, there is a lack of a systematic and commonly-shared definition for the term. More importantly, existing definitions are generally too abstract for informing the design of data analytics systems. This study proposes a definition for actionable insight in data analytics. For this purpose, this study conceptualizes actionable insight as a multi-component concept. The components, namely analytics insight, synergic insight, and prognostic insights are grounded in theories from multiple disciplines. Collectively, the components provide a holistic definition for actionable insight. Based on the multi-component view, a conceptual framework is introduced to actionable insight in detail, at both the concept-level and component-level. Each component's analytical, cognitive, and computational requirements are explained and relevant design considerations are suggested. We hope this study could be a rudimentary step toward the realization of data-centric data analytics that can deliver the promised actionable insight.
Combining The Fogg Behavioural Model And Hook Model To Design Features In A Persuasive App To Improve Study Habits
Venue: arXiv cs.CY 2016
Year: 2016

Using technology to persuade people to behave in a certain way is an ever-increasing field of study. The ability to persuade individuals is quite clear in e-commerce, where individuals are persuaded to make purchasing decisions. However, it can also be applied to other disciplines, such as education where improving the study behaviour of students would be particularly useful. Forming good study habits can be a challenge for university students who have not done so in the earlier years of their education, or where the pressures of external commitments have eroded previously good habits. We use a combination of the Fogg Behavioural Model and the Hook model to design features for an app as a component of a larger persuasive system to help improve three key areas of study habits: study scheduling, class preparation and group study. The app will be built and tested in a university setting targeting undergraduate students.
Improving pathology reports using business intelligence techniques: An experimental study
Venue: arXiv cs.CY 2016
Year: 2016

Health professionals use pathology reports to monitor and manage a patient's health. Typically, pathologists diagnose patients' conditions and produce these reports which are then used as reference by clinicians and eventually shared with the patient. Pathology reports are difficult to interpret as the reports are written using complex medical terminology. As patients only see their doctors for a limited time, the complexity of report content and the manner in which the information is presented in the reports may hinder patients' understanding of their medical condition and prognosis. The objective of this study was to compare patient comprehension of results from two pathology-reporting styles: the traditional format in current widespread use and new style developed using techniques common in business intelligence system (BI) development. The study found that the reports prepared using a "BI style" improve experimental subject's understanding and satisfaction with the reports.
Interdisciplinary Translation of Comparative Visualization
Venue: arXiv cs.CY 2016
Year: 2016

Spatial visualisation skills and interpretations are critical in the design professions, but traditionally difficult to effectively teach. Visualization and multimedia presentation studies show positive improvements in learner outcomes for specific learning domains. But the development and translation of a comparative visualization pedagogy between disciplines is poorly understood. This research seeks to identify an approach to developing comparable multimodal and interactive visualizations and attendant student reflections for curriculum designers in courses that can utilize visualizations and manipulations. Results from previous use of comparative multimodal visualization pedagogy in a multimedia 3D modelling class are used as a guide to translation of pedagogy to architecture design. The focus is how to guide the use of comparative multimodal visualizations through media properties, lesson sequencing, and reflection to inform effective instruction and learning.
Universal Correspondence Network
Venue: arXiv cs.CV 2016
Year: 2016

We present a deep learning framework for accurate visual correspondences and demonstrate its effectiveness for both geometric and semantic matching, spanning across rigid motions to intra-class shape or appearance variations. In contrast to previous CNN-based approaches that optimize a surrogate patch similarity objective, we use deep metric learning to directly learn a feature space that preserves either geometric or semantic similarity. Our fully convolutional architecture, along with a novel correspondence contrastive loss allows faster training by effective reuse of computations, accurate gradient computation through the use of thousands of examples per image pair and faster testing with $O(n)$ feed forward passes for $n$ keypoints, instead of $O(n^2)$ for typical patch similarity methods. We propose a convolutional spatial transformer to mimic patch normalization in traditional features like SIFT, which is shown to dramatically boost accuracy for semantic correspondences across intra-class shape variations. Extensive experiments on KITTI, PASCAL, and CUB-2011 datasets demonstrate the significant advantages of our features over prior works that use either hand-constructed or learned features.
Sub-Story Detection in Twitter with Hierarchical Dirichlet Processes
Venue: arXiv cs.IR 2016
Year: 2016

Social media has now become the de facto information source on real world events. The challenge, however, due to the high volume and velocity nature of social media streams, is in how to follow all posts pertaining to a given event over time, a task referred to as story detection. Moreover, there are often several different stories pertaining to a given event, which we refer to as sub-stories and the corresponding task of their automatic detection as sub-story detection. This paper proposes hierarchical Dirichlet processes (HDP), a probabilistic topic model, as an effective method for automatic sub-story detection. HDP can learn sub-topics associated with sub-stories which enables it to handle subtle variations in sub-stories. It is compared with state- of-the-art story detection approaches based on locality sensitive hashing and spectral clustering. We demonstrate the superior performance of HDP for sub-story detection on real world Twitter data sets using various evaluation measures. The ability of HDP to learn sub-topics helps it to recall the sub- stories with high precision. Another contribution of this paper is in demonstrating that the conversational structures within the Twitter stream can be used to improve sub-story detection performance significantly.
BigEAR: Inferring the Ambient and Emotional Correlates from Smartphone-based Acoustic Big Data
Venue: arXiv cs.SD 2016
Year: 2016

This paper presents a novel BigEAR big data framework that employs psychological audio processing chain (PAPC) to process smartphone-based acoustic big data collected when the user performs social conversations in naturalistic scenarios. The overarching goal of BigEAR is to identify moods of the wearer from various activities such as laughing, singing, crying, arguing, and sighing. These annotations are based on ground truth relevant for psychologists who intend to monitor/infer the social context of individuals coping with breast cancer. We pursued a case study on couples coping with breast cancer to know how the conversations affect emotional and social well being. In the state-of-the-art methods, psychologists and their team have to hear the audio recordings for making these inferences by subjective evaluations that not only are time-consuming and costly, but also demand manual data coding for thousands of audio files. The BigEAR framework automates the audio analysis. We computed the accuracy of BigEAR with respect to the ground truth obtained from a human rater. Our approach yielded overall average accuracy of 88.76% on real-world data from couples coping with breast cancer.
Weakly Supervised Scalable Audio Content Analysis
Venue: arXiv cs.SD 2016
Year: 2016

Audio Event Detection is an important task for content analysis of multimedia data. Most of the current works on detection of audio events is driven through supervised learning approaches. We propose a weakly supervised learning framework which can make use of the tremendous amount of web multimedia data with significantly reduced annotation effort and expense. Specifically, we use several multiple instance learning algorithms to show that audio event detection through weak labels is feasible. We also propose a novel scalable multiple instance learning algorithm and show that its competitive with other multiple instance learning algorithms for audio event detection tasks.
Comparison of Several Sparse Recovery Methods for Low Rank Matrices with Random Samples
Venue: arXiv cs.LG 2016
Year: 2016

In this paper, we will investigate the efficacy of IMAT (Iterative Method of Adaptive Thresholding) in recovering the sparse signal (parameters) for linear models with missing data. Sparse recovery rises in compressed sensing and machine learning problems and has various applications necessitating viable reconstruction methods specifically when we work with big data. This paper will focus on comparing the power of IMAT in reconstruction of the desired sparse signal with LASSO. Additionally, we will assume the model has random missing information. Missing data has been recently of interest in big data and machine learning problems since they appear in many cases including but not limited to medical imaging datasets, hospital datasets, and massive MIMO. The dominance of IMAT over the well-known LASSO will be taken into account in different scenarios. Simulations and numerical results are also provided to verify the arguments.
Variable Neighborhood Search Based Algorithm for University Course Timetabling Problem
Venue: arXiv cs.DS 2016
Year: 2016

In this paper a variable neighborhood search approach as a method for solving combinatoric optimization problems is presented. A variable neighborhood search based algorithm for solving the problem concerning the university course timetable design has been developed. This algorithm is used to solve the real problem regarding the university course timetable design. It is compared with other algorithms that are tested on the same sets of input data. The object and the methodology of study are presented. The main objectives of the experiment are formulated. The conditions for conducting the experiment are specified. The results are analyzed and appropriate conclusions are made. The future trends of work in this field are presented.
Efficient KLMS and KRLS Algorithms: A Random Fourier Feature Perspective
Venue: arXiv cs.LG 2016
Year: 2016

We present a new framework for online Least Squares algorithms for nonlinear modeling in RKH spaces (RKHS). Instead of implicitly mapping the data to a RKHS (e.g., kernel trick), we map the data to a finite dimensional Euclidean space, using random features of the kernel's Fourier transform. The advantage is that, the inner product of the mapped data approximates the kernel function. The resulting "linear" algorithm does not require any form of sparsification, since, in contrast to all existing algorithms, the solution's size remains fixed and does not increase with the iteration steps. As a result, the obtained algorithms are computationally significantly more efficient compared to previously derived variants, while, at the same time, they converge at similar speeds and to similar error floors.