OpenRaiser/Intern-Atlas huggingface dataset:
Visuospatial Skill Learning for Robots
Venue: arXiv cs.RO 2017
Year: 2017

A novel skill learning approach is proposed that allows a robot to acquire human-like visuospatial skills for object manipulation tasks. Visuospatial skills are attained by observing spatial relationships among objects through demonstrations. The proposed Visuospatial Skill Learning (VSL) is a goal-based approach that focuses on achieving a desired goal configuration of objects relative to one another while maintaining the sequence of operations. VSL is capable of learning and generalizing multi-operation skills from a single demonstration, while requiring minimum prior knowledge about the objects and the environment. In contrast to many existing approaches, VSL offers simplicity, efficiency and user-friendly human-robot interaction. We also show that VSL can be easily extended towards 3D object manipulation tasks, simply by employing point cloud processing techniques. In addition, a robot learning framework, VSL-SP, is proposed by integrating VSL, Imitation Learning, and a conventional planning method. In VSL-SP, the sequence of performed actions are learned using VSL, while the sensorimotor skills are learned using a conventional trajectory-based learning approach. such integration easily extends robot capabilities to novel situations, even by users without programming ability. In VSL-SP the internal planner of VSL is integrated with an existing action-level symbolic planner. Using the underlying constraints of the task and extracted symbolic predicates, identified by VSL, symbolic representation of the task is updated. Therefore the planner maintains a generalized representation of each skill as a reusable action, which can be used in planning and performed independently during the learning phase. The proposed approach is validated through several real-world experiments.
Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration
Venue: arXiv cs.LG 2017
Year: 2017

We study the combinatorial pure exploration problem Best-Set in stochastic multi-armed bandits. In a Best-Set instance, we are given $n$ arms with unknown reward distributions, as well as a family $\mathcal{F}$ of feasible subsets over the arms. Our goal is to identify the feasible subset in $\mathcal{F}$ with the maximum total mean using as few samples as possible. The problem generalizes the classical best arm identification problem and the top-$k$ arm identification problem, both of which have attracted significant attention in recent years. We provide a novel instance-wise lower bound for the sample complexity of the problem, as well as a nontrivial sampling algorithm, matching the lower bound up to a factor of $\ln|\mathcal{F}|$. For an important class of combinatorial families, we also provide polynomial time implementation of the sampling algorithm, using the equivalence of separation and optimization for convex program, and approximate Pareto curves in multi-objective optimization. We also show that the $\ln|\mathcal{F}|$ factor is inevitable in general through a nontrivial lower bound construction. Our results significantly improve several previous results for several important combinatorial constraints, and provide a tighter understanding of the general Best-Set problem. We further introduce an even more general problem, formulated in geometric terms. We are given $n$ Gaussian arms with unknown means and unit variance. Consider the $n$-dimensional Euclidean space $\mathbb{R}^n$, and a collection $\mathcal{O}$ of disjoint subsets. Our goal is to determine the subset in $\mathcal{O}$ that contains the $n$-dimensional vector of the means. The problem generalizes most pure exploration bandit problems studied in the literature. We provide the first nearly optimal sample complexity upper and lower bounds for the problem.
Virtual Constraints and Hybrid Zero Dynamics for Realizing Underactuated Bipedal Locomotion
Venue: arXiv cs.RO 2017
Year: 2017

Underactuation is ubiquitous in human locomotion and should be ubiquitous in bipedal robotic locomotion as well. This chapter presents a coherent theory for the design of feedback controllers that achieve stable walking gaits in underactuated bipedal robots. Two fundamental tools are introduced, virtual constraints and hybrid zero dynamics. Virtual constraints are relations on the state variables of a mechanical model that are imposed through a time-invariant feedback controller. One of their roles is to synchronize the robot's joints to an internal gait phasing variable. A second role is to induce a low dimensional system, the zero dynamics, that captures the underactuated aspects of a robot's model, without any approximations. To enhance intuition, the relation between physical constraints and virtual constraints is first established. From here, the hybrid zero dynamics of an underactuated bipedal model is developed, and its fundamental role in the design of asymptotically stable walking motions is established. The chapter includes numerous references to robots on which the highlighted techniques have been implemented.
DORE: An Experimental Framework to Enable Outband D2D Relay in Cellular Networks
Venue: arXiv cs.NI 2017
Year: 2017

Device-to-Device communications represent a paradigm shift in cellular networks. In particular, analytical results on D2D performance for offloading and relay are very promising, but no experimental evidence validates these results to date. This paper is the first to provide an experimental analysis of outband D2D relay schemes. Moreover, we design DORE, a complete framework for handling channel opportunities offered by outband D2D relay nodes. DORE consists of resource allocation optimization tools and protocols suitable to integrate QoS-aware opportunistic D2D communications within the architecture of 3GPP Proximity-based Services. We implement DORE using an SDR framework to profile cellular network dynamics in the presence of opportunistic outband D2D communication schemes. Our experiments reveal that outband D2D communications are suitable for relaying in a large variety of delay-sensitive cellular applications, and that DORE enables notable gains even with a few active D2D relay nodes.
The Capacity of Private Information Retrieval from Byzantine and Colluding Databases
Venue: arXiv cs.IT 2017
Year: 2017

We consider the problem of single-round private information retrieval (PIR) from $N$ replicated databases. We consider the case when $B$ databases are outdated (unsynchronized), or even worse, adversarial (Byzantine), and therefore, can return incorrect answers. In the PIR problem with Byzantine databases (BPIR), a user wishes to retrieve a specific message from a set of $M$ messages with zero-error, irrespective of the actions performed by the Byzantine databases. We consider the $T$-privacy constraint in this paper, where any $T$ databases can collude, and exchange the queries submitted by the user. We derive the information-theoretic capacity of this problem, which is the maximum number of \emph{correct symbols} that can be retrieved privately (under the $T$-privacy constraint) for every symbol of the downloaded data. We determine the exact BPIR capacity to be $C=\frac{N-2B}{N}\cdot\frac{1-\frac{T}{N-2B}}{1-(\frac{T}{N-2B})^M}$, if $2B+T < N$. This capacity expression shows that the effect of Byzantine databases on the retrieval rate is equivalent to removing $2B$ databases from the system, with a penalty factor of $\frac{N-2B}{N}$, which signifies that even though the number of databases needed for PIR is effectively $N-2B$, the user still needs to access the entire $N$ databases. The result shows that for the unsynchronized PIR problem, if the user does not have any knowledge about the fraction of the messages that are mis-synchronized, the single-round capacity is the same as the BPIR capacity. Our achievable scheme extends the optimal achievable scheme for the robust PIR (RPIR) problem to correct the \emph{errors} introduced by the Byzantine databases as opposed to \emph{erasures} in the RPIR problem. Our converse proof uses the idea of the cut-set bound in the network coding problem against adversarial nodes.
Time-dependent shortest paths in bounded treewidth graphs
Venue: arXiv cs.DS 2017
Year: 2017

We present a proof that the number of breakpoints in the arrival function between two terminals in graphs of treewidth $w$ is $n^{O(\log^2 w)}$ when the edge arrival functions are piecewise linear. This is an improvement on the bound of $n^{Θ(\log n)}$ by Foschini, Hershberger, and Suri for graphs without any bound on treewidth. We provide an algorithm for calculating this arrival function using star-mesh transformations, a generalization of the wye-delta-wye transformations.
Will Break for Productivity: Generalized Symptoms of Cognitive Depletion
Venue: arXiv cs.HC 2017
Year: 2017

In this work, we address the symptoms of cognitive depletion as they relate to generalized knowledge workers. We unify previous findings within a single analytical model of cognitive depletion. Our purpose is to develop a model that will help us predict when a person has reached a sufficient state of cognitive depletion such that taking a break or some other restorative action will benefit both his or her own wellbeing and the quality of his or her performance. We provide a definition of each symptom in our model as well as the effect it would have on a knowledge worker's ability to work productively. We discuss methods to detect each symptom that do not require self assessment. Understanding symptoms of cognitive depletion provides the ability to support human knowledge workers by reducing the stress involved with cognitive and work overload while maintaining or improving the quality of their performance.
Progressive Boosting for Class Imbalance
Venue: arXiv cs.LG 2017
Year: 2017

Pattern recognition applications often suffer from skewed data distributions between classes, which may vary during operations w.r.t. the design data. Two-class classification systems designed using skewed data tend to recognize the majority class better than the minority class of interest. Several data-level techniques have been proposed to alleviate this issue by up-sampling minority samples or under-sampling majority samples. However, some informative samples may be neglected by random under-sampling and adding synthetic positive samples through up-sampling adds to training complexity. In this paper, a new ensemble learning algorithm called Progressive Boosting (PBoost) is proposed that progressively inserts uncorrelated groups of samples into a Boosting procedure to avoid loss of information while generating a diverse pool of classifiers. Base classifiers in this ensemble are generated from one iteration to the next, using subsets from a validation set that grows gradually in size and imbalance. Consequently, PBoost is more robust to unknown and variable levels of skew in operational data, and has lower computation complexity than Boosting ensembles in literature. In PBoost, a new loss factor is proposed to avoid bias of performance towards the negative class. Using this loss factor, the weight update of samples and classifier contribution in final predictions are set based on the ability to recognize both classes. Using the proposed loss factor instead of standard accuracy can avoid biasing performance in any Boosting ensemble. The proposed approach was validated and compared using synthetic data, videos from the FIA dataset that emulates face re-identification applications, and KEEL collection of datasets. Results show that PBoost can outperform state of the art techniques in terms of both accuracy and complexity over different levels of imbalance and overlap between classes.
Controller-jammer game models of Denial of Service in control systems operating over packet-dropping links
Venue: arXiv cs.SY 2017
Year: 2017

The paper introduces a class of zero-sum games between the adversary and controller as a scenario for a `denial of service' in a networked control system. The communication link is modeled as a set of transmission regimes controlled by a strategic jammer whose intention is to wage an attack on the plant by choosing a most damaging regime-switching strategy. We demonstrate that even in the one-step case, the introduced games admit a saddle-point equilibrium, at which the jammer's optimal policy is to randomize in a region of the plant's state space, thus requiring the controller to undertake a nontrivial response which is different from what one would expect in a standard stochastic control problem over a packet dropping link. The paper derives conditions for the introduced games to have such a saddle-point equilibrium. Furthermore, we show that in more general multi-stage games, these conditions provide `greedy' jamming strategies for the adversary.
Dynamic Bayesian Multitaper Spectral Analysis
Venue: arXiv cs.IT 2017
Year: 2017

Spectral analysis using overlapping sliding windows is among the most widely used techniques in analyzing non-stationary time series. Although sliding window analysis is convenient to implement, the resulting estimates are sensitive to the window length and overlap size. In addition, it undermines the dynamics of the time series as the estimate associated to each window uses only the data within. Finally, the overlap between consecutive windows hinders a precise statistical assessment. In this paper, we address these shortcomings by explicitly modeling the spectral dynamics through integrating the multitaper method with state-space models in a Bayesian estimation framework. The underlying states pertaining to the eigen-spectral quantities arising in multitaper analysis are estimated using instances of the Expectation-Maximization algorithm, and are used to construct spectrograms and their respective confidence intervals. We propose two spectral estimators that are robust to noise and are able to capture spectral dynamics at high spectrotemporal resolution. We provide theoretical analysis of the bias-variance trade-off, which establishes performance gains over the standard overlapping multitaper method. We apply our algorithms to synthetic data as well as real data from human EEG and electric network frequency recordings, the results of which validate our theoretical analysis.
Embedding Feature Selection for Large-scale Hierarchical Classification
Venue: arXiv cs.LG 2017
Year: 2017

Large-scale Hierarchical Classification (HC) involves datasets consisting of thousands of classes and millions of training instances with high-dimensional features posing several big data challenges. Feature selection that aims to select the subset of discriminant features is an effective strategy to deal with large-scale HC problem. It speeds up the training process, reduces the prediction time and minimizes the memory requirements by compressing the total size of learned model weight vectors. Majority of the studies have also shown feature selection to be competent and successful in improving the classification accuracy by removing irrelevant features. In this work, we investigate various filter-based feature selection methods for dimensionality reduction to solve the large-scale HC problem. Our experimental evaluation on text and image datasets with varying distribution of features, classes and instances shows upto 3x order of speed-up on massive datasets and upto 45% less memory requirements for storing the weight vectors of learned model without any significant loss (improvement for some datasets) in the classification accuracy. Source Code: https://cs.gmu.edu/~mlbio/featureselection.
Tailoring Architecture Centric Design Method with Rapid Prototyping
Venue: arXiv cs.SE 2017
Year: 2017

Many engineering processes exist in the industry, text books and international standards. However, in practice rarely any of the processes are followed consistently and literally. It is observed across industries the processes are altered based on the requirements of the projects. Two features commonly lacking from many engineering processes are, 1) the formal capacity to rapidly develop prototypes in the rudimentary stage of the project, 2) transitioning of requirements into architectural designs, when and how to evaluate designs and how to use the throw away prototypes throughout the system lifecycle. Prototypes are useful for eliciting requirements, generating customer feedback and identifying, examining or mitigating risks in a project where the product concept is at a cutting edge or not fully perceived. Apart from the work that the product is intended to do, systemic properties like availability, performance and modifiability matter as much as functionality. Architects must even these concerns with the method they select to promote these systemic properties and at the same time equip the stakeholders with the desired functionality. Architectural design and prototyping is one of the key ways to build the right product embedded with the desired systemic properties. Once the product is built it can be almost impossible to retrofit the system with the desired attributes. This paper customizes the architecture centric development method with rapid prototyping to achieve the above-mentioned goals and reducing the number of iterations across the stages of ACDM.
Compression Fractures Detection on CT
Venue: arXiv cs.CV 2017
Year: 2017

The presence of a vertebral compression fracture is highly indicative of osteoporosis and represents the single most robust predictor for development of a second osteoporotic fracture in the spine or elsewhere. Less than one third of vertebral compression fractures are diagnosed clinically. We present an automated method for detecting spine compression fractures in Computed Tomography (CT) scans. The algorithm is composed of three processes. First, the spinal column is segmented and sagittal patches are extracted. The patches are then binary classified using a Convolutional Neural Network (CNN). Finally a Recurrent Neural Network (RNN) is utilized to predict whether a vertebral fracture is present in the series of patches.
Reduction of Second-Order Network Systems with Structure Preservation
Venue: arXiv cs.SY 2017
Year: 2017

This paper proposes a general framework for structure-preserving model reduction of a secondorder network system based on graph clustering. In this approach, vertex dynamics are captured by the transfer functions from inputs to individual states, and the dissimilarities of vertices are quantified by the H2-norms of the transfer function discrepancies. A greedy hierarchical clustering algorithm is proposed to place those vertices with similar dynamics into same clusters. Then, the reduced-order model is generated by the Petrov-Galerkin method, where the projection is formed by the characteristic matrix of the resulting network clustering. It is shown that the simplified system preserves an interconnection structure, i.e., it can be again interpreted as a second-order system evolving over a reduced graph. Furthermore, this paper generalizes the definition of network controllability Gramian to second-order network systems. Based on it, we develop an efficient method to compute H2-norms and derive the approximation error between the full-order and reduced-order models. Finally, the approach is illustrated by the example of a small-world network.
Sharing Data Homomorphically Encrypted with Different Encryption Keys
Venue: arXiv cs.CR 2017
Year: 2017

In this paper, we propose the first homomorphic based proxy re-encryption (HPRE) solution that allows different users to share data they outsourced homomorphically encrypted using their respective public keys with the possibility to process such data remotely. More clearly, this scheme makes possible to switch the public encryption key to another one without the help of a trusted third party. Its originality stands on a method we propose so as to compute the difference between two encrypted data without decrypting them and with no extra communications. Basically, in our HPRE scheme, the two users, the delegator and the delegate, ask the cloud server to generate an encrypted noise based on a secret key, both users previously agreed on. Based on our solution for comparing encrypted data, the cloud computes in clear the differences in-between the encrypted noise and the encrypted data of the delegator, obtaining thus blinded data. By next the cloud encrypts these differences with the public key of the delegate. As the noise is also known of the delegate, this one just has to remove it to get access to the data encrypted with his public key. This solution has been experimented in the case of the sharing of images outsourced into a semihonest cloud server.
A WL-SPPIM Semantic Model for Document Classification
Venue: arXiv cs.CL 2017
Year: 2017

In this paper, we explore SPPIM-based text classification method, and the experiment reveals that the SPPIM method is equal to or even superior than SGNS method in text classification task on three international and standard text datasets, namely 20newsgroups, Reuters52 and WebKB. Comparing to SGNS, although SPPMI provides a better solution, it is not necessarily better than SGNS in text classification tasks. Based on our analysis, SGNS takes into the consideration of weight calculation during decomposition process, so it has better performance than SPPIM in some standard datasets. Inspired by this, we propose a WL-SPPIM semantic model based on SPPIM model, and experiment shows that WL-SPPIM approach has better classification and higher scalability in the text classification task compared with LDA, SGNS and SPPIM approaches.
5G Radio Access above 6 GHz
Venue: arXiv cs.IT 2017
Year: 2017

Designing and developing a millimetre-wave(mmWave) based mobile Radio Access Technology (RAT) in the 6-100 GHz frequency range is a fundamental component in the standardization of the new 5G radio interface, recently kicked off by 3GPP. Such component, herein called the new mmWave RAT, will not only enable extreme mobile broadband (eMBB) services,but also support UHD/3D streaming, offer immersive applications and ultra-responsive cloud services to provide an outstanding Quality of Experience (QoE) to the mobile users. The main objective of this paper is to develop the network architectural elements and functions that will enable tight integration of mmWave technology into the overall 5G radio access network (RAN). A broad range of topics addressing mobile architecture and network functionalities will be covered-starting with the architectural facets of network slicing, multiconnectivity and cells clustering, to more functional elements of initial access, mobility, radio resource management (RRM) and self-backhauling. The intention of the concepts presented here is to lay foundation for future studies towards the first commercial implementation of the mmWave RAT above 6 GHz.