OpenRaiser/Intern-Atlas huggingface dataset:
On String Contact Representations in 3D
Venue: arXiv cs.CG 2017
Year: 2017

An axis-aligned string is a simple polygonal path, where each line segment is parallel to an axis in $\mathbb{R}^3$. Given a graph $G$, a string contact representation $Ψ$ of $G$ maps the vertices of $G$ to interior disjoint axis-aligned strings, where no three strings meet at a point, and two strings share a common point if and only if their corresponding vertices are adjacent in $G$. The complexity of $Ψ$ is the minimum integer $r$ such that every string in $Ψ$ is a $B_r$-string, i.e., a string with at most $r$ bends. While a result of Duncan et al. implies that every graph $G$ with maximum degree 4 has a string contact representation using $B_4$-strings, we examine constraints on $G$ that allow string contact representations with complexity 3, 2 or 1. We prove that if $G$ is Hamiltonian and triangle-free, then $G$ admits a contact representation where all the strings but one are $B_3$-strings. If $G$ is 3-regular and bipartite, then $G$ admits a contact representation with string complexity 2, and if we further restrict $G$ to be Hamiltonian, then $G$ has a contact representation, where all the strings but one are $B_1$-strings (i.e., $L$-shapes). Finally, we prove some complementary lower bounds on the complexity of string contact representations.
TAPAS: Two-pass Approximate Adaptive Sampling for Softmax
Venue: arXiv cs.LG 2017
Year: 2017

TAPAS is a novel adaptive sampling method for the softmax model. It uses a two pass sampling strategy where the examples used to approximate the gradient of the partition function are first sampled according to a squashed population distribution and then resampled adaptively using the context and current model. We describe an efficient distributed implementation of TAPAS. We show, on both synthetic data and a large real dataset, that TAPAS has low computational overhead and works well for minimizing the rank loss for multi-class classification problems with a very large label space.
Robot Autonomy for Surgery
Venue: arXiv cs.RO 2017
Year: 2017

Autonomous surgery involves having surgical tasks performed by a robot operating under its own will, with partial or no human involvement. There are several important advantages of automation in surgery, which include increasing precision of care due to sub-millimeter robot control, real-time utilization of biosignals for interventional care, improvements to surgical efficiency and execution, and computer-aided guidance under various medical imaging and sensing modalities. While these methods may displace some tasks of surgical teams and individual surgeons, they also present new capabilities in interventions that are too difficult or go beyond the skills of a human. In this chapter, we provide an overview of robot autonomy in commercial use and in research, and present some of the challenges faced in developing autonomous surgical robots.
Look Who's Talking: Bipartite Networks as Representations of a Topic Model of New Zealand Parliamentary Speeches
Venue: arXiv cs.CL 2017
Year: 2017

Quantitative methods to measure the participation to parliamentary debate and discourse of elected Members of Parliament (MPs) and the parties they belong to are lacking. This is an exploratory study in which we propose the development of a new approach for a quantitative analysis of such participation. We utilize the New Zealand government's digital Hansard database to construct a topic model of parliamentary speeches consisting of nearly 40 million words in the period 2003-2016. A Latent Dirichlet Allocation topic model is implemented in order to reveal the thematic structure of our set of documents. This generative statistical model enables the detection of major themes or topics that are publicly discussed in the New Zealand parliament, as well as permitting their classification by MP. Information on topic proportions is subsequently analyzed using a combination of statistical methods. We observe patterns arising from time-series analysis of topic frequencies which can be related to specific social, economic and legislative events. We then construct a bipartite network representation, linking MPs to topics, for each of four parliamentary terms in this time frame. We build projected networks (onto the set of nodes represented by MPs) and proceed to the study of the dynamical changes of their topology, including community structure. By performing this longitudinal network analysis, we can observe the evolution of the New Zealand parliamentary topic network and its main parties in the period studied.
An Opportunistic AODV Routing Scheme : A Cognitive Mobile Agents Approach
Venue: arXiv cs.NI 2017
Year: 2017

In Manets Dynamics and Robustness are the key features of the nodes and are governed by several routing protocols such as AODV, DSR and so on. However in the network the growing resource demand leads to resource scarcity. The Node Mobility often leads to the link breakages and high routing overhead decreasing the stability and reliability of the network connectivity. In this context, the paper proposes a novel opportunistic AODV routing scheme which implements a cognitive agent based intelligent technique to set up a stable connectivity over the Manet. The Scheme computes the routing metric (rf) based on the collaboration sensitivity levels of the nodes obtained based through the knowledge based decision. This Routing Metric is subsequently used to set up the stable path for network connectivity. Thus minimizes the route overhead and increases the stability of the path. The Performance evaluation is conducted in comparison with the AODV and sleep AODV routing protocol and validated.
RegNet: Multimodal Sensor Registration Using Deep Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we present RegNet, the first deep convolutional neural network (CNN) to infer a 6 degrees of freedom (DOF) extrinsic calibration between multimodal sensors, exemplified using a scanning LiDAR and a monocular camera. Compared to existing approaches, RegNet casts all three conventional calibration steps (feature extraction, feature matching and global regression) into a single real-time capable CNN. Our method does not require any human interaction and bridges the gap between classical offline and target-less online calibration approaches as it provides both a stable initial estimation as well as a continuous online correction of the extrinsic parameters. During training we randomly decalibrate our system in order to train RegNet to infer the correspondence between projected depth measurements and RGB image and finally regress the extrinsic calibration. Additionally, with an iterative execution of multiple CNNs, that are trained on different magnitudes of decalibration, our approach compares favorably to state-of-the-art methods in terms of a mean calibration error of 0.28 degrees for the rotational and 6 cm for the translation components even for large decalibrations up to 1.5 m and 20 degrees.
Dataset for a Neural Natural Language Interface for Databases (NNLIDB)
Venue: arXiv cs.CL 2017
Year: 2017

Progress in natural language interfaces to databases (NLIDB) has been slow mainly due to linguistic issues (such as language ambiguity) and domain portability. Moreover, the lack of a large corpus to be used as a standard benchmark has made data-driven approaches difficult to develop and compare. In this paper, we revisit the problem of NLIDBs and recast it as a sequence translation problem. To this end, we introduce a large dataset extracted from the Stack Exchange Data Explorer website, which can be used for training neural natural language interfaces for databases. We also report encouraging baseline results on a smaller manually annotated test corpus, obtained using an attention-based sequence-to-sequence neural network.
Binary Images of Z2Z4-Additive Cyclic Codes
Venue: arXiv cs.IT 2017
Year: 2017

A Z2Z4-additive code C is called cyclic if the set of coordinates can be partitioned into two subsets, the set of Z_2 and the set of Z_4 coordinates, such that any cyclic shift of the coordinates of both subsets leaves the code invariant. We study the binary images of Z2Z4-additive cyclic codes. We determine all Z2Z4-additive cyclic codes with odd beta whose Gray images are linear binary codes.
Synthesis of Optimal Resilient Control Strategies
Venue: arXiv cs.SY 2017
Year: 2017

Repair mechanisms are important within resilient systems to maintain the system in an operational state after an error occurred. Usually, constraints on the repair mechanisms are imposed, e.g., concerning the time or resources required (such as energy consumption or other kinds of costs). For systems modeled by Markov decision processes (MDPs), we introduce the concept of resilient schedulers, which represent control strategies guaranteeing that these constraints are always met within some given probability. Assigning rewards to the operational states of the system, we then aim towards resilient schedulers which maximize the long-run average reward, i.e., the expected mean payoff. We present a pseudo-polynomial algorithm that decides whether a resilient scheduler exists and if so, yields an optimal resilient scheduler. We show also that already the decision problem asking whether there exists a resilient scheduler is PSPACE-hard.
Hierarchical Deep Recurrent Architecture for Video Understanding
Venue: arXiv cs.CV 2017
Year: 2017

This paper introduces the system we developed for the Youtube-8M Video Understanding Challenge, in which a large-scale benchmark dataset was used for multi-label video classification. The proposed framework contains hierarchical deep architecture, including the frame-level sequence modeling part and the video-level classification part. In the frame-level sequence modelling part, we explore a set of methods including Pooling-LSTM (PLSTM), Hierarchical-LSTM (HLSTM), Random-LSTM (RLSTM) in order to address the problem of large amount of frames in a video. We also introduce two attention pooling methods, single attention pooling (ATT) and multiply attention pooling (Multi-ATT) so that we can pay more attention to the informative frames in a video and ignore the useless frames. In the video-level classification part, two methods are proposed to increase the classification performance, i.e. Hierarchical-Mixture-of-Experts (HMoE) and Classifier Chains (CC). Our final submission is an ensemble consisting of 18 sub-models. In terms of the official evaluation metric Global Average Precision (GAP) at 20, our best submission achieves 0.84346 on the public 50% of test dataset and 0.84333 on the private 50% of test data.
Recommendation with k-anonymized Ratings
Venue: arXiv cs.IR 2017
Year: 2017

Recommender systems are widely used to predict personalized preferences of goods or services using users' past activities, such as item ratings or purchase histories. If collections of such personal activities were made publicly available, they could be used to personalize a diverse range of services, including targeted advertisement or recommendations. However, there would be an accompanying risk of privacy violations. The pioneering work of Narayanan et al.\ demonstrated that even if the identifiers are eliminated, the public release of user ratings can allow for the identification of users by those who have only a small amount of data on the users' past ratings. In this paper, we assume the following setting. A collector collects user ratings, then anonymizes and distributes them. A recommender constructs a recommender system based on the anonymized ratings provided by the collector. Based on this setting, we exhaustively list the models of recommender systems that use anonymized ratings. For each model, we then present an item-based collaborative filtering algorithm for making recommendations based on anonymized ratings. Our experimental results show that an item-based collaborative filtering based on anonymized ratings can perform better than collaborative filterings based on 5--10 non-anonymized ratings. This surprising result indicates that, in some settings, privacy protection does not necessarily reduce the usefulness of recommendations. From the experimental analysis of this counterintuitive result, we observed that the sparsity of the ratings can be reduced by anonymization and the variance of the prediction can be reduced if $k$, the anonymization parameter, is appropriately tuned. In this way, the predictive performance of recommendations based on anonymized ratings can be improved in some settings.
Observing IoT Resources over ICN
Venue: arXiv cs.NI 2017
Year: 2017

The Constrained Application Protocol (CoAP) is an HTTP-like protocol for RESTful applications intended to run on constrained devices, typically part of the Internet of Things. CoAP observe is an extension to the CoAP specification that allows CoAP clients to observe a resource through a simple publish/subscribe mechanism. In this paper we leverage Information-Centric Networking (ICN), transparently deployed within the domain of a network provider, to provide enhanced CoAP services. We present the design and the implementation of CoAP observe over ICN and we discuss how ICN can provide benefits to both network providers and CoAP applications, even though the latter are not aware of the existence of ICN. In particular, the use of ICN results in smaller state management and simpler implementation at CoAP endpoints, and less communication overhead in the network.
Achieving Near MAP Performance with an Excited Markov Chain Monte Carlo MIMO Detector
Venue: arXiv cs.IT 2017
Year: 2017

We introduce a revised derivation of the bitwise Markov Chain Monte Carlo (MCMC) multiple-input multiple-output (MIMO) detector. The new approach resolves the previously reported high SNR stalling problem of MCMC without the need for hybridization with another detector method or adding heuristic temperature scaling factors. Another common problem with MCMC algorithms is the unknown convergence time making predictable fixed-length implementations problematic. When an insufficient number of iterations is used on a slowly converging example, the output LLRs can be unstable and overconfident. Therefore, we develop a method to identify rare slowly converging runs and mitigate their degrading effects on the soft-output information. This improves forward-error-correcting code performance and removes a symptomatic error floor in BER plots. Next, pseudo-convergence is identified with a novel way to visualize the internal behavior of the Gibbs sampler. An effective and efficient pseudo-convergence detection and escape strategy is suggested. Finally, the new excited MCMC (X-MCMC) detector is shown to have near maximum-a-posteriori (MAP) performance even with challenging, realistic, highly-correlated channels at the maximum MIMO sizes and modulation rates supported by the 802.11ac WiFi specification, 8x8 MIMO 256 QAM.
Multi-Task Learning Using Neighborhood Kernels
Venue: arXiv cs.LG 2017
Year: 2017

This paper introduces a new and effective algorithm for learning kernels in a Multi-Task Learning (MTL) setting. Although, we consider a MTL scenario here, our approach can be easily applied to standard single task learning, as well. As shown by our empirical results, our algorithm consistently outperforms the traditional kernel learning algorithms such as uniform combination solution, convex combinations of base kernels as well as some kernel alignment-based models, which have been proven to give promising results in the past. We present a Rademacher complexity bound based on which a new Multi-Task Multiple Kernel Learning (MT-MKL) model is derived. In particular, we propose a Support Vector Machine-regularized model in which, for each task, an optimal kernel is learned based on a neighborhood-defining kernel that is not restricted to be positive semi-definite. Comparative experimental results are showcased that underline the merits of our neighborhood-defining framework in both classification and regression problems.
Machine Learning in Appearance-based Robot Self-localization
Venue: arXiv cs.CV 2017
Year: 2017

An appearance-based robot self-localization problem is considered in the machine learning framework. The appearance space is composed of all possible images, which can be captured by a robot's visual system under all robot localizations. Using recent manifold learning and deep learning techniques, we propose a new geometrically motivated solution based on training data consisting of a finite set of images captured in known locations of the robot. The solution includes estimation of the robot localization mapping from the appearance space to the robot localization space, as well as estimation of the inverse mapping for modeling visual image features. The latter allows solving the robot localization problem as the Kalman filtering problem.
Value Prediction Network
Venue: arXiv cs.AI 2017
Year: 2017

This paper proposes a novel deep reinforcement learning (RL) architecture, called Value Prediction Network (VPN), which integrates model-free and model-based RL methods into a single neural network. In contrast to typical model-based RL methods, VPN learns a dynamics model whose abstract states are trained to make option-conditional predictions of future values (discounted sum of rewards) rather than of future observations. Our experimental results show that VPN has several advantages over both model-free and model-based baselines in a stochastic environment where careful planning is required but building an accurate observation-prediction model is difficult. Furthermore, VPN outperforms Deep Q-Network (DQN) on several Atari games even with short-lookahead planning, demonstrating its potential as a new way of learning a good state representation.
Benchmarking Data Analysis and Machine Learning Applications on the Intel KNL Many-Core Processor
Venue: arXiv cs.PF 2017
Year: 2017

Knights Landing (KNL) is the code name for the second-generation Intel Xeon Phi product family. KNL has generated significant interest in the data analysis and machine learning communities because its new many-core architecture targets both of these workloads. The KNL many-core vector processor design enables it to exploit much higher levels of parallelism. At the Lincoln Laboratory Supercomputing Center (LLSC), the majority of users are running data analysis applications such as MATLAB and Octave. More recently, machine learning applications, such as the UC Berkeley Caffe deep learning framework, have become increasingly important to LLSC users. Thus, the performance of these applications on KNL systems is of high interest to LLSC users and the broader data analysis and machine learning communities. Our data analysis benchmarks of these application on the Intel KNL processor indicate that single-core double-precision generalized matrix multiply (DGEMM) performance on KNL systems has improved by ~3.5x compared to prior Intel Xeon technologies. Our data analysis applications also achieved ~60% of the theoretical peak performance. Also a performance comparison of a machine learning application, Caffe, between the two different Intel CPUs, Xeon E5 v3 and Xeon Phi 7210, demonstrated a 2.7x improvement on a KNL node.
Oseba: Optimization for Selective Bulk Analysis in Big Data Processing
Venue: arXiv cs.DC 2017
Year: 2017

Selective bulk analyses, such as statistical learning on temporal/spatial data, are fundamental to a wide range of contemporary data analysis. However, with the increasingly larger data-sets, such as weather data and marketing transactions, the data organization/access becomes more challenging in selective bulk data processing with the use of current big data processing frameworks such as Spark or keyvalue stores. In this paper, we propose a method to optimize selective bulk analysis in big data processing and referred to as Oseba. Oseba maintains a super index for the data organization in memory to support fast lookup through targeting the data involved with each selective analysis program. Oseba is able to save memory as well as computation in comparison to the default data processing frameworks.
Audio to score matching by combining phonetic and duration information
Venue: arXiv cs.SD 2017
Year: 2017

We approach the singing phrase audio to score matching problem by using phonetic and duration information - with a focus on studying the jingju a cappella singing case. We argue that, due to the existence of a basic melodic contour for each mode in jingju music, only using melodic information (such as pitch contour) will result in an ambiguous matching. This leads us to propose a matching approach based on the use of phonetic and duration information. Phonetic information is extracted with an acoustic model shaped with our data, and duration information is considered with the Hidden Markov Models (HMMs) variants we investigate. We build a model for each lyric path in our scores and we achieve the matching by ranking the posterior probabilities of the decoded most likely state sequences. Three acoustic models are investigated: (i) convolutional neural networks (CNNs), (ii) deep neural networks (DNNs) and (iii) Gaussian mixture models (GMMs). Also, two duration models are compared: (i) hidden semi-Markov model (HSMM) and (ii) post-processor duration model. Results show that CNNs perform better in our (small) audio dataset and also that HSMM outperforms the post-processor duration model.
Discriminative Block-Diagonal Representation Learning for Image Recognition
Venue: arXiv cs.CV 2017
Year: 2017

Existing block-diagonal representation researches mainly focuses on casting block-diagonal regularization on training data, while only little attention is dedicated to concurrently learning both block-diagonal representations of training and test data. In this paper, we propose a discriminative block-diagonal low-rank representation (BDLRR) method for recognition. In particular, the elaborate BDLRR is formulated as a joint optimization problem of shrinking the unfavorable representation from off-block-diagonal elements and strengthening the compact block-diagonal representation under the semi-supervised framework of low-rank representation. To this end, we first impose penalty constraints on the negative representation to eliminate the correlation between different classes such that the incoherence criterion of the extra-class representation is boosted. Moreover, a constructed subspace model is developed to enhance the self-expressive power of training samples and further build the representation bridge between the training and test samples, such that the coherence of the learned intra-class representation is consistently heightened. Finally, the resulting optimization problem is solved elegantly by employing an alternative optimization strategy, and a simple recognition algorithm on the learned representation is utilized for final prediction. Extensive experimental results demonstrate that the proposed method achieves superb recognition results on four face image datasets, three character datasets, and the fifteen scene multi-categories dataset. It not only shows superior potential on image recognition but also outperforms state-of-the-art methods.