OpenRaiser/Intern-Atlas huggingface dataset:
Robot Impedance Control and Passivity Analysis with Inner Torque and Velocity Feedback Loops
Venue: arXiv cs.SY 2016
Year: 2016

Impedance control is a well-established technique to control interaction forces in robotics. However, real implementations of impedance control with an inner loop may suffer from several limitations. Although common practice in designing nested control systems is to maximize the bandwidth of the inner loop to improve tracking performance, it may not be the most suitable approach when a certain range of impedance parameters has to be rendered. In particular, it turns out that the viable range of stable stiffness and damping values can be strongly affected by the bandwidth of the inner control loops (e.g. a torque loop) as well as by the filtering and sampling frequency. This paper provides an extensive analysis on how these aspects influence the stability region of impedance parameters as well as the passivity of the system. This will be supported by both simulations and experimental data. Moreover, a methodology for designing joint impedance controllers based on an inner torque loop and a positive velocity feedback loop will be presented. The goal of the velocity feedback is to increase (given the constraints to preserve stability) the bandwidth of the torque loop without the need of a complex controller.
Name Disambiguation from link data in a collaboration graph using temporal and topological features
Venue: arXiv cs.IR 2016
Year: 2016

In a social community, multiple persons may share the same name, phone number or some other identifying attributes. This, along with other phenomena, such as name abbreviation, name misspelling, and human error leads to erroneous aggregation of records of multiple persons under a single reference. Such mistakes affect the performance of document retrieval, web search, database integration, and more importantly, improper attribution of credit (or blame). The task of entity disambiguation partitions the records belonging to multiple persons with the objective that each decomposed partition is composed of records of a unique person. Existing solutions to this task use either biographical attributes, or auxiliary features that are collected from external sources, such as Wikipedia. However, for many scenarios, such auxiliary features are not available, or they are costly to obtain. Besides, the attempt of collecting biographical or external data sustains the risk of privacy violation. In this work, we propose a method for solving entity disambiguation task from link information obtained from a collaboration network. Our method is non-intrusive of privacy as it uses only the time-stamped graph topology of an anonymized network. Experimental results on two real-life academic collaboration networks show that the proposed method has satisfactory performance.
Analysis and Augmented Spatial Processing for Uplink OFDMA MU-MIMO Receiver with Transceiver I/Q Imbalance and External Interference
Venue: arXiv cs.IT 2016
Year: 2016

We address receiver (RX) signal processing in MIMO systems under in-phase/quadrature (I/Q) imbalance, which causes cross-talk of mirror-subcarriers in OFDM systems. We extend the typically reported single-user studies to uplink OFDMA-based multiuser MIMO, with simultaneous user multiplexing in frequency and spatial domains. We also incorporate multiple external interferers, for modeling challenging conditions in heterogeneous networks. In the signal processing developments, we exploit the augmented subcarrier processing, which processes each subcarrier jointly with its counterpart at the image subcarrier, and jointly across RX antennas. Furthermore, we derive an augmented LMMSE RX. The novel approach integrates the I/Q imbalance mitigation, interference suppression and data stream separation into a single processing stage, thus avoiding a separate transceiver calibration. Our numerical results show the signal-to-interference-plus-noise ratio and symbol-error rate of an arbitrary data stream after RX processing as a function of different system parameters. The per-subcarrier processing is shown to suffer heavily under I/Q imbalances, and being particularly sensitive to external interferers, whereas the augmented method provides efficient data stream separation and interference suppression. Finally, we extend the studies to massive MIMO framework and show that the per-subcarrier processing still suffers from performance degradation, whereas the augmented approach can fully exploit the array gain.
Using Optimization to Solve Positive LPs Faster in Parallel
Venue: arXiv cs.DS 2016
Year: 2016

Positive linear programs (LP), also known as packing and covering linear programs, are an important class of problems that bridges computer science, operations research, and optimization. Despite the consistent efforts on this problem, all known nearly-linear-time algorithms require $\tilde{O}(\varepsilon^{-4})$ iterations to converge to $1\pm \varepsilon$ approximate solutions. This $\varepsilon^{-4}$ dependence has not been improved since 1993, and limits the performance of parallel implementations for such algorithms. Moreover, previous algorithms and their analyses rely on update steps and convergence arguments that are combinatorial in nature and do not seem to arise naturally from an optimization viewpoint. In this paper, we leverage new insights from optimization theory to construct a novel algorithm that breaks the longstanding $\varepsilon^{-4}$ barrier. Our algorithm has a simple analysis and a clear motivation. Our work introduces a number of novel techniques, such as the combined application of gradient descent and mirror descent, and a truncated, smoothed version of the standard multiplicative weight update, which may be of independent interest.
Understanding the group dynamics and success of teams
Venue: arXiv cs.SI 2016
Year: 2016

Complex problems often require coordinated group effort and can consume significant resources, yet our understanding of how teams form and succeed has been limited by a lack of large-scale, quantitative data. We analyze activity traces and success levels for ~150,000 self-organized, online team projects. While larger teams tend to be more successful, workload is highly focused across the team, with only a few members performing most work. We find that highly successful teams are significantly more focused than average teams of the same size, that their members have worked on more diverse sets of projects, and the members of highly successful teams are more likely to be core members or 'leads' of other teams. The relations between team success and size, focus and especially team experience cannot be explained by confounding factors such as team age, external contributions from non-team members, nor by group mechanisms such as social loafing. Taken together, these features point to organizational principles that may maximize the success of collaborative endeavors.
Compressed representation of Learning Spaces
Venue: arXiv cs.DS 2016
Year: 2016

Learning Spaces are certain set systems that are applied in the mathematical modeling of education. We propose a suitable compression (without loss of information) of such set systems to facilitate their logical and statistical analysis. Under certain circumstances compression is the prerequisite to calculate the Learning Space in the first place. There are connections to the dual framework of Formal Concept Analysis and in particular to so called attribute exploration.
Equilibria on a Game with Discrete Variables
Venue: arXiv cs.GT 2016
Year: 2016

Equilibrium in Economics has been seldom addressed in a situation where some variables are discrete. This work introduces a problem related to lot-sizing with several players, and analyses some strategies which are likely to be found in real world games. An illustration with a simple example is presented, with concerns about the difficulty of the problem and computation possibilities.
Scalable Greedy Algorithms for Transfer Learning
Venue: arXiv cs.CV 2016
Year: 2016

In this paper we consider the binary transfer learning problem, focusing on how to select and combine sources from a large pool to yield a good performance on a target task. Constraining our scenario to real world, we do not assume the direct access to the source data, but rather we employ the source hypotheses trained from them. We propose an efficient algorithm that selects relevant source hypotheses and feature dimensions simultaneously, building on the literature on the best subset selection problem. Our algorithm achieves state-of-the-art results on three computer vision datasets, substantially outperforming both transfer learning and popular feature selection baselines in a small-sample setting. We also present a randomized variant that achieves the same results with the computational cost independent from the number of source hypotheses and feature dimensions. Also, we theoretically prove that, under reasonable assumptions on the source hypotheses, our algorithm can learn effectively from few examples.
Bags of Affine Subspaces for Robust Object Tracking
Venue: arXiv cs.CV 2016
Year: 2016

We propose an adaptive tracking algorithm where the object is modelled as a continuously updated bag of affine subspaces, with each subspace constructed from the object's appearance over several consecutive frames. In contrast to linear subspaces, affine subspaces explicitly model the origin of subspaces. Furthermore, instead of using a brittle point-to-subspace distance during the search for the object in a new frame, we propose to use a subspace-to-subspace distance by representing candidate image areas also as affine subspaces. Distances between subspaces are then obtained by exploiting the non-Euclidean geometry of Grassmann manifolds. Experiments on challenging videos (containing object occlusions, deformations, as well as variations in pose and illumination) indicate that the proposed method achieves higher tracking accuracy than several recent discriminative trackers.
Tractable Pathfinding for the Stochastic On-Time Arrival Problem
Venue: arXiv cs.DS 2016
Year: 2016

We present a new and more efficient technique for computing the route that maximizes the probability of on-time arrival in stochastic networks, also known as the path-based stochastic on-time arrival (SOTA) problem. Our primary contribution is a pathfinding algorithm that uses the solution to the policy-based SOTA problem---which is of pseudo-polynomial-time complexity in the time budget of the journey---as a search heuristic for the optimal path. In particular, we show that this heuristic can be exceptionally efficient in practice, effectively making it possible to solve the path-based SOTA problem as quickly as the policy-based SOTA problem. Our secondary contribution is the extension of policy-based preprocessing to path-based preprocessing for the SOTA problem. In the process, we also introduce Arc-Potentials, a more efficient generalization of Stochastic Arc-Flags that can be used for both policy- and path-based SOTA. After developing the pathfinding and preprocessing algorithms, we evaluate their performance on two different real-world networks. To the best of our knowledge, these techniques provide the most efficient computation strategy for the path-based SOTA problem for general probability distributions, both with and without preprocessing.
Label Distribution Learning
Venue: arXiv cs.LG 2016
Year: 2016

Although multi-label learning can deal with many problems with label ambiguity, it does not fit some real applications well where the overall distribution of the importance of the labels matters. This paper proposes a novel learning paradigm named \emph{label distribution learning} (LDL) for such kind of applications. The label distribution covers a certain number of labels, representing the degree to which each label describes the instance. LDL is a more general learning framework which includes both single-label and multi-label learning as its special cases. This paper proposes six working LDL algorithms in three ways: problem transformation, algorithm adaptation, and specialized algorithm design. In order to compare the performance of the LDL algorithms, six representative and diverse evaluation measures are selected via a clustering analysis, and the first batch of label distribution datasets are collected and made publicly available. Experimental results on one artificial and fifteen real-world datasets show clear advantages of the specialized algorithms, which indicates the importance of special design for the characteristics of the LDL problem.
Self-taught Object Localization with Deep Networks
Venue: arXiv cs.CV 2016
Year: 2016

This paper introduces self-taught object localization, a novel approach that leverages deep convolutional networks trained for whole-image recognition to localize objects in images without additional human supervision, i.e., without using any ground-truth bounding boxes for training. The key idea is to analyze the change in the recognition scores when artificially masking out different regions of the image. The masking out of a region that includes the object typically causes a significant drop in recognition score. This idea is embedded into an agglomerative clustering technique that generates self-taught localization hypotheses. Our object localization scheme outperforms existing proposal methods in both precision and recall for small number of subwindow proposals (e.g., on ILSVRC-2012 it produces a relative gain of 23.4% over the state-of-the-art for top-1 hypothesis). Furthermore, our experiments show that the annotations automatically-generated by our method can be used to train object detectors yielding recognition results remarkably close to those obtained by training on manually-annotated bounding boxes.
Approximability of Guarding Weak Visibility Polygons
Venue: arXiv cs.CG 2016
Year: 2016

The art gallery problem enquires about the least number of guards that are sufficient to ensure that an art gallery, represented by a polygon $P$, is fully guarded. In 1998, the problems of finding the minimum number of point guards, vertex guards, and edge guards required to guard $P$ were shown to be APX-hard by Eidenbenz, Widmayer and Stamm. In 1987, Ghosh presented approximation algorithms for vertex guards and edge guards that achieved a ratio of $\mathcal{O}(\log n)$, which was improved upto $\mathcal{O}(\log\log OPT)$ by King and Kirkpatrick in 2011. It has been conjectured that constant-factor approximation algorithms exist for these problems. We settle the conjecture for the special class of polygons that are weakly visible from an edge and contain no holes by presenting a 6-approximation algorithm for finding the minimum number of vertex guards that runs in $\mathcal{O}(n^2)$ time. On the other hand, for weak visibility polygons with holes, we present a reduction from the Set Cover problem to show that there cannot exist a polynomial time algorithm for the vertex guard problem with an approximation ratio better than $((1 - ε)/12)\ln n$ for any $ε>0$, unless NP=P. We also show that, for the special class of polygons without holes that are orthogonal as well as weakly visible from an edge, the approximation ratio can be improved to 3. Finally, we consider the Point Guard problem and show that it is NP-hard in the case of polygons weakly visible from an edge.
Key Capacity for Product Sources with Application to Stationary Gaussian Processes
Venue: arXiv cs.IT 2016
Year: 2016

We show that for product sources, rate splitting is optimal for secret key agreement using limited one-way communication between two terminals. This yields an alternative information-theoretic-converse-style proof of the tensorization property of a strong data processing inequality originally studied by Erkip and Cover and amended recently by Anantharam et al. We derive a water-filling solution of the communication-rate--key-rate tradeoff for a wide class of discrete memoryless vector Gaussian sources which subsumes the case without an eavesdropper. Moreover, we derive an explicit formula for the maximum secret key per bit of communication for all discrete memoryless vector Gaussian sources using a tensorization property and a variation on the enhanced channel technique of Weingarten et al. Finally, a one-shot information spectrum achievability bound for key generation is proved from which we characterize the communication-rate--key-rate tradeoff for stationary Gaussian processes.
FLiER: Practical Topology Update Detection Using Sparse PMUs
Venue: arXiv cs.SY 2016
Year: 2016

In this paper, we present a Fingerprint Linear Estimation Routine (FLiER) to identify topology changes in power networks using readings from sparsely-deployed phasor measurement units (PMUs). When a power line, load, or generator trips in a network, or when a substation is reconfigured, the event leaves a unique "voltage fingerprint" of bus voltage changes that we can identify using only the portion of the network directly observed by the PMUs. The naive brute-force approach to identify a failed line from such voltage fingerprints, though simple and accurate, is slow. We derive an approximate algorithm based on a local linearization and a novel filtering approach that is faster and only slightly less accurate. We present experimental results using the IEEE 57-bus, IEEE 118-bus, and Polish 1999-2000 winter peak networks.
Wireless Power Meets Energy Harvesting: A Joint Energy Allocation Approach in OFDM-based System
Venue: arXiv cs.IT 2016
Year: 2016

This paper investigates an orthogonal frequency division multiplexing (OFDM)-based wireless powered communication system, where one user harvests energy from an energy access point (EAP) to power its information transmission to a data access point (DAP). The channels from the EAP to the user, i.e., the wireless energy transfer (WET) link, and from the user to the DAP, i.e., the wireless information transfer (WIT) link, vary over both time slots and sub-channels (SCs) in general. To avoid interference at DAP, WET and WIT are scheduled over orthogonal SCs at any slot. Our objective is to maximize the achievable rate at the DAP by jointly optimizing the SC allocation over time and the power allocation over time and SCs for both WET and WIT links. Assuming availability of full channel state information (CSI), the structural results for the optimal SC/power allocation are obtained and an offline algorithm is proposed to solve the problem. Furthermore, we propose a low-complexity online algorithm when causal CSI is available.
A New Gate for Optimal Fault Tolerant & Testable Reversible Sequential Circuit Design
Venue: arXiv cs.ET 2016
Year: 2016

With phenomenal growth of high speed and complex computing applications, the design of low power and high speed logic circuits have created tremendous interest. Conventional computing devices are based on irreversible logic and further reduction in power consumption and/or increase in speed appears non-promising. Reversible computing has emerged as a solution looking to the power and speed requirements of future computing devices. In reversible computing logic gates used are such that input can be generated by reversing the operation from output. A number of reversible combinational circuits have been developed but the growth of sequential circuits was not significant due to feedback and fanout was not allowed. However, allowing feedback in space, a very few sequential logic blocks i.e. flip-flops have been reported in literature. In order to develop sequential circuits, flip-flops are used in conventional circuits. Also good circuit design methods, optimized and fault tolerant designs are also needed to build large, complex and reliable circuits in conventional computing. Reversible flip-flops are the basic memory elements that will be the building block of memory for reversible computing and quantum computing devices. In this dissertation we plan to address above issues. First we have proposed a Pareek gate suitable for low-cost flip-flops design and then design methodology to develop flip-flops are illustrated. Further almost all flip-flops and some example circuit have been developed and finally these circuits have been converted into fault tolerant circuits by preserving their parity and designs of offline as well as online testable circuits have been proposed. In this dissertation work, we have also compared quantum cost as well as other parameters with existing circuits and shown a significant improvement in almost all parameters.
Achieving Secrecy Capacity of the Wiretap Channel and Broadcast Channel with a Confidential Component
Venue: arXiv cs.IT 2016
Year: 2016

The wiretap channel model of Wyner is one of the first communication models with both reliability and security constraints. Capacity-achieving schemes for various models of the wiretap channel have received considerable attention in recent literature. In this paper, we show that capacity of the general (not necessarily degraded or symmetric) wiretap channel under a "strong secrecy constraint" can be achieved using a transmission scheme based on polar codes. We also extend our construction to the case of broadcast channels with confidential messages defined by Csisz{á}r and K{örner}, achieving the entire capacity region of this communication model.
Optimal Scheduling of Electric Vehicles Charging in low-Voltage Distribution Systems
Venue: arXiv cs.SY 2016
Year: 2016

Uncoordinated charging of large-scale electric vehicles (EVs) will have a negative impact on the secure and economic operation of the power system, especially at the distribution level. Given that the charging load of EVs can be controlled to some extent, research on the optimal charging control of EVs has been extensively carried out. In this paper, two possible smart charging scenarios in China are studied: centralized optimal charging operated by an aggregator and decentralized optimal charging managed by individual users. Under the assumption that the aggregators and individual users only concern the economic benefits, new load peaks will arise under time of use (TOU) pricing which is extensively employed in China. To solve this problem, a simple incentive mechanism is proposed for centralized optimal charging while a rolling-update pricing scheme is devised for decentralized optimal charging. The original optimal charging models are modified to account for the developed schemes. Simulated tests corroborate the efficacy of optimal scheduling for charging EVs in various scenarios.