OpenRaiser/Intern-Atlas huggingface dataset:
cvpaper.challenge in 2015 - A review of CVPR2015 and DeepSurvey
Venue: arXiv cs.CV 2016
Year: 2016

The "cvpaper.challenge" is a group composed of members from AIST, Tokyo Denki Univ. (TDU), and Univ. of Tsukuba that aims to systematically summarize papers on computer vision, pattern recognition, and related fields. For this particular review, we focused on reading the ALL 602 conference papers presented at the CVPR2015, the premier annual computer vision event held in June 2015, in order to grasp the trends in the field. Further, we are proposing "DeepSurvey" as a mechanism embodying the entire process from the reading through all the papers, the generation of ideas, and to the writing of paper.
Efficient Multiple Line-Based Intra Prediction for HEVC
Venue: arXiv cs.MM 2016
Year: 2016

Traditional intra prediction usually utilizes the nearest reference line to generate the predicted block when considering strong spatial correlation. However, this kind of single line-based method does not always work well due to at least two issues. One is the incoherence caused by the signal noise or the texture of other object, where this texture deviates from the inherent texture of the current block. The other reason is that the nearest reference line usually has worse reconstruction quality in block-based video coding. Due to these two issues, this paper proposes an efficient multiple line-based intra prediction scheme to improve coding efficiency. Besides the nearest reference line, further reference lines are also utilized. The further reference lines with relatively higher quality can provide potential better prediction. At the same time, the residue compensation is introduced to calibrate the prediction of boundary regions in a block when we utilize further reference lines. To speed up the encoding process, this paper designs several fast algorithms. Experimental results show that, compared with HM-16.9, the proposed fast search method achieves 2.0% bit saving on average and up to 3.7%, with increasing the encoding time by 112%.
3D Stochastic Geometry Model for Large-Scale Molecular Communication Systems
Venue: arXiv cs.IT 2016
Year: 2016

Information delivery using chemical molecules is an integral part of biology at multiple distance scales and has attracted recent interest in bioengineering and communication. The collective signal strength at the receiver (i.e., the expected number of observed molecules inside the receiver), resulting from a large number of transmitters at random distances (e.g., due to mobility), can have a major impact on the reliability and efficiency of the molecular communication system. Modeling the collective signal from multiple diffusion sources can be computationally and analytically challenging. In this paper, we present the first tractable analytical model for the collective signal strength due to randomly-placed transmitters, whose positions are modelled as a homogeneous Poisson point process in three-dimensional (3D) space. By applying stochastic geometry, we derive analytical expressions for the expected number of observed molecules at a fully absorbing receiver and a passive receiver. Our results reveal that the collective signal strength at both types of receivers increases proportionally with increasing transmitter density. The proposed framework dramatically simplifies the analysis of large-scale molecular systems in both communication and biological applications.
Near Optimal Channel Assignment for Interference Mitigation in Wireless Mesh Networks
Venue: arXiv cs.NI 2016
Year: 2016

In multi-radio multi-channel (MRMC) WMNs, interference alleviation is affected through several network design techniques e.g., channel assignment (CA), link scheduling, routing etc., intelligent CA schemes being the most effective tool for interference mitigation. CA in WMNs is an NP-Hard problem, and makes optimality a desired yet elusive goal in real-time deployments which are characterized by fast transmission and switching times and minimal end-to-end latency. The trade-off between optimal performance and minimal response times is often achieved through CA schemes that employ heuristics to propose efficient solutions. WMN configuration and physical layout are also crucial factors which decide network performance, and it has been demonstrated in numerous research works that rectangular/square grid WMNs outperform random or unplanned WMN deployments in terms of network capacity, latency, and network resilience. In this work, we propose a smart heuristic approach to devise a near-optimal CA algorithm for grid WMNs (NOCAG). We demonstrate the efficacy of NOCAG by evaluating its performance against the minimal-interference CA generated through a rudimentary brute-force technique (BFCA), for the same WMN configuration. We assess its ability to mitigate interference both, theoretically (through interference estimation metrics) and experimentally (by running rigorous simulations in NS-3). We demonstrate that the performance of NOCAG is almost as good as the BFCA, at a minimal computational overhead of O(n) compared to the exponential of BFCA.
Performance Enhancement of the Golden Code by Utilizing ORIOL Antenna
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, a novel method is exposed to improve the performance of the Golden code, by using octagonal reconfigurable isolated orthogonal element (ORIOL) antennas, instead of a conventional microstrip patch antenna. The aforementioned antenna, should be employed in both the transmitter and the receiver sides, to approach the mentioned improvement. As a matter fact, in this paper, we recommend space-time-polarization diversity instead of space-time singly; therefore it is obvious that by employing the aforementioned technique, the system obtains more strength against destructive fading. The simulations for different rates have confirmed that, utilizing ORIOL antenna outperforms patch microstrip one, which is roughly about $2$ to $3$ dB, according to the rates.
The Implementation of Low-cost Urban Acoustic Monitoring Devices
Venue: arXiv cs.SD 2016
Year: 2016

The urban sound environment of New York City (NYC) can be, amongst other things: loud, intrusive, exciting and dynamic. As indicated by the large majority of noise complaints registered with the NYC 311 information/complaints line, the urban sound environment has a profound effect on the quality of life of the city's inhabitants. To monitor and ultimately understand these sonic environments, a process of long-term acoustic measurement and analysis is required. The traditional method of environmental acoustic monitoring utilizes short term measurement periods using expensive equipment, setup and operated by experienced and costly personnel. In this paper a different approach is proposed to this application which implements a smart, low-cost, static, acoustic sensing device based around consumer hardware. These devices can be deployed in numerous and varied urban locations for long periods of time, allowing for the collection of longitudinal urban acoustic data. The varied environmental conditions of urban settings make for a challenge in gathering calibrated sound pressure level data for prospective stakeholders. This paper details the sensors' design, development and potential future applications, with a focus on the calibration of the devices' Microelectromechanical systems (MEMS) microphone in order to generate reliable decibel levels at the type/class 2 level.
Tracing the Attention of Moving Citizens
Venue: arXiv cs.SI 2016
Year: 2016

With the widespread use of mobile computing devices in contemporary society, our trajectories in the physical space and virtual world are increasingly closely connected. Using the anonymous smartphone data of $1 \times 10^5$ users in 30 days, we constructed the mobility network and the attention network to study the correlations between online and offline human behaviours. In the mobility network, nodes are physical locations and edges represent the movements between locations, and in the attention network, nodes are websites and edges represent the switch of users between websites. We apply the box-covering method to renormalise the networks. The investigated network properties include the size of box $l_B$ and the number of boxes $N(l_B)$. We find two universal classes of behaviours: the mobility network is featured by a small-world property, $N(l_B) \simeq e^{-l_B}$, whereas the attention network is characterised by a self-similar property $N(l_B) \simeq l_B^{-γ}$. In particular, with the increasing of the length of box $l_B$, the degree correlation of the network changes from positive to negative which indicates that there are two layers of structure in the mobility network. We use the results of network renormalisation to detect the community and map the structure of the mobility network. Further, we located the most relevant websites visited in these communities, and identified three typical location-based behaviours, including the shopping, dating, and taxi-calling. Finally, we offered a revised geometric network model to explain our findings in the perspective of spatial-constrained attachment.
Maximum-expectation matching under recourse
Venue: arXiv cs.DS 2016
Year: 2016

This paper addresses the problem of maximizing the expected size of a matching in the case of unreliable vertices and/or edges. The assumption is that upon failure, remaining vertices that have not been matched may be subject to a new assignment. This process may be repeated a given number of times, and the objective is to end with the overall maximum number of matched vertices. The origin of this problem is in kidney exchange programs, going on in several countries, where a vertex is an incompatible patient-donor pair; the objective is to match these pairs so as to maximize the number of served patients. A new scheme is proposed for matching rearrangement in case of failure, along with a prototype algorithm for computing the optimal expectation for the number of matched vertices. Computational experiments reveal the relevance and limitations of the algorithm, in general terms and for the kidney exchange application.
Achieving stable subspace clustering by post-processing generic clustering results
Venue: arXiv cs.CV 2016
Year: 2016

We propose an effective subspace selection scheme as a post-processing step to improve results obtained by sparse subspace clustering (SSC). Our method starts by the computation of stable subspaces using a novel random sampling scheme. Thus constructed preliminary subspaces are used to identify the initially incorrectly clustered data points and then to reassign them to more suitable clusters based on their goodness-of-fit to the preliminary model. To improve the robustness of the algorithm, we use a dominant nearest subspace classification scheme that controls the level of sensitivity against reassignment. We demonstrate that our algorithm is convergent and superior to the direct application of a generic alternative such as principal component analysis. On several popular datasets for motion segmentation and face clustering pervasively used in the sparse subspace clustering literature the proposed method is shown to reduce greatly the incidence of clustering errors while introducing negligible disturbance to the data points already correctly clustered.
Visual Model Validation via Inline Replication
Venue: arXiv cs.HC 2016
Year: 2016

Data visualizations typically show retrospective views of an existing dataset with little or no focus on repeatability. However, consumers of these tools often use insights gleaned from retrospective visualizations as the basis for decisions about future events. In this way, visualizations often serve as visual predictive models despite the fact that they are typically designed to present historical views of the data. This "visual predictive model" approach, however, can lead to invalid inferences. In this paper, we describe an approach to visual model validation called Inline Replication (IR) which, similar to the cross-validation technique used widely in machine learning, provides a nonparametric and broadly applicable technique for visual model assessment and repeatability. This paper describes the overall IR process and outlines how it can be integrated into both traditional and emerging "big data" visualization pipelines. Examples are provided showing IR integrated within common visualization techniques (such as bar charts and linear regression lines) as well as a more fully-featured visualization system designed for complex exploratory analysis tasks.
Smartphone Fingerprinting Via Motion Sensors: Analyzing Feasibility at Large-Scale and Studying Real Usage Patterns
Venue: arXiv cs.CY 2016
Year: 2016

Advertisers are increasingly turning to fingerprinting techniques to track users across the web. As web browsing activity shifts to mobile platforms, traditional browser fingerprinting techniques become less effective; however, device fingerprinting using built-in sensors offers a new avenue for attack. We study the feasibility of using motion sensors to perform device fingerprinting at scale, and explore countermeasures that can be used to protect privacy. We perform a large-scale user study to demonstrate that motion sensor fingerprinting is effective with even 500 users. We also develop a model to estimate prediction accuracy for larger user populations; our model provides a conservative estimate of at least 12% classification accuracy with 100000 users. We then investigate the use of motion sensors on the web and find, distressingly, that many sites send motion sensor data to servers for storage and analysis, paving the way to potential fingerprinting. Finally, we consider the problem of developing fingerprinting countermeasures; we evaluate a previously proposed obfuscation technique and a newly developed quantization technique via a user study. We find that both techniques are able to drastically reduce fingerprinting accuracy without significantly impacting the utility of the sensors in web applications.
Weighted Residuals for Very Deep Networks
Venue: arXiv cs.CV 2016
Year: 2016

Deep residual networks have recently shown appealing performance on many challenging computer vision tasks. However, the original residual structure still has some defects making it difficult to converge on very deep networks. In this paper, we introduce a weighted residual network to address the incompatibility between \texttt{ReLU} and element-wise addition and the deep network initialization problem. The weighted residual network is able to learn to combine residuals from different layers effectively and efficiently. The proposed models enjoy a consistent improvement over accuracy and convergence with increasing depths from 100+ layers to 1000+ layers. Besides, the weighted residual networks have little more computation and GPU memory burden than the original residual networks. The networks are optimized by projected stochastic gradient descent. Experiments on CIFAR-10 have shown that our algorithm has a \emph{faster convergence speed} than the original residual networks and reaches a \emph{high accuracy} at 95.3\% with a 1192-layer model.
Optimal dynamic mechanisms with ex-post IR via bank accounts
Venue: arXiv cs.GT 2016
Year: 2016

Lately, the problem of designing multi-stage dynamic mechanisms has been shown to be both theoretically challenging and practically important. In this paper, we consider the problem of designing revenue optimal dynamic mechanism for a setting where an auctioneer sells a set of items to a buyer in multiple stages. At each stage, there could be multiple items for sale but each item can only appear in one stage. The type of the buyer at each stage is thus a multi-dimensional vector characterizing the buyer's valuations of the items at that stage and is assumed to be stage-wise independent. In particular, we propose a novel class of mechanisms called bank account mechanisms. Roughly, a bank account mechanism is no different from any stage-wise individual mechanism except for an augmented structure called bank account, a real number for each node that summarizes the history so far. We first establish that the optimal revenue from any dynamic mechanism in this setting can be achieved by a bank account mechanism, and we provide a simple characterization of the set of incentive compatible and ex-post individually rational bank account mechanisms. Based on these characterizations, we then investigate the problem of finding the (approximately) optimal bank account mechanisms. We prove that there exists a simple, randomized bank account mechanism that approximates optimal revenue up to a constant factor. Our result is general and can accommodate previous approximation results in single-shot multi-dimensional mechanism design. Based on the previous mechanism, we further show that there exists a deterministic bank account mechanism that achieves constant-factor approximation as well. Finally, we consider the problem of computing optimal mechanisms when the type space is discrete and provide an FPTAS via linear and dynamic programming.
Surviving an "Eternal September" - How an Online Community Managed a Surge of Newcomers
Venue: arXiv cs.HC 2016
Year: 2016

We present a qualitative analysis of interviews with participants in the NoSleep community within Reddit where millions of fans and writers of horror fiction congregate. We explore how the community handled a massive, sudden, and sustained increase in new members. Although existing theory and stories like Usenet's infamous "Eternal September" suggest that large influxes of newcomers can hurt online communities, our interviews suggest that NoSleep survived without major incident. We propose that three features of NoSleep allowed it to manage the rapid influx of newcomers gracefully: (1) an active and well-coordinated group of administrators, (2) a shared sense of community which facilitated community moderation, and (3) technological systems that mitigated norm violations. We also point to several important trade-offs and limitations.
k-neighborhood for Cellular Automata
Venue: arXiv cs.DM 2016
Year: 2016

A neighborhood for d-dimensional cellular automata is introduced that spans the range from von Neumann to Moore neighborhood using a parameter which represents the dimension of hypercubes connecting neighboring cells. The neighborhood is extended to include a concept of radius. The number of neighbors is calculated. For diamond-shaped neighborhoods, a sequence is obtained whose partial sums equal Delannoy numbers.
Models Coupling Urban Growth and Transportation Network Growth : An Algorithmic Systematic Review Approach
Venue: arXiv cs.DL 2016
Year: 2016

A broad bibliographical study suggests a scarcity of quantitative models of simulation integrating both network and urban growth. This absence may be due to diverging interests of concerned disciplines, resulting in a lack of communication. We propose to proceed to an algorithmic systematic review to give quantitative elements of answer to this question. A formal iterative algorithm to retrieve corpuses of references from initial keywords, based on text-mining, is developed and implemented. We study its convergence properties and do a sensitivity analysis. We then apply it on queries representative of the specific question, for which results tend to confirm the assumption of disciplines compartmentalisation.
Coding Schemes for Securing Cyber-Physical Systems Against Stealthy Data Injection Attacks
Venue: arXiv cs.CR 2016
Year: 2016

This paper considers a method of coding the sensor outputs in order to detect stealthy false data injection attacks. An intelligent attacker can design a sequence of data injection to sensors and actuators that pass the state estimator and statistical fault detector, based on knowledge of the system parameters. To stay undetected, the injected data should increase the state estimation errors while keep the estimation residues small. We employ a coding matrix to change the original sensor outputs to increase the estimation residues under intelligent data injection attacks. This is a low cost method compared with encryption schemes over all sensor measurements in communication networks. We show the conditions of a feasible coding matrix under the assumption that the attacker does not have knowledge of the exact coding matrix. An algorithm is developed to compute a feasible coding matrix, and, we show that in general, multiple feasible coding matrices exist. To defend against attackers who estimates the coding matrix via sensor and actuator measurements, time-varying coding matrices are designed according to the detection requirements. A heuristic algorithm to decide the time length of updating a coding matrix is then proposed.
TripleSpin - a generic compact paradigm for fast machine learning computations
Venue: arXiv cs.LG 2016
Year: 2016

We present a generic compact computational framework relying on structured random matrices that can be applied to speed up several machine learning algorithms with almost no loss of accuracy. The applications include new fast LSH-based algorithms, efficient kernel computations via random feature maps, convex optimization algorithms, quantization techniques and many more. Certain models of the presented paradigm are even more compressible since they apply only bit matrices. This makes them suitable for deploying on mobile devices. All our findings come with strong theoretical guarantees. In particular, as a byproduct of the presented techniques and by using relatively new Berry-Esseen-type CLT for random vectors, we give the first theoretical guarantees for one of the most efficient existing LSH algorithms based on the $\textbf{HD}_{3}\textbf{HD}_{2}\textbf{HD}_{1}$ structured matrix ("Practical and Optimal LSH for Angular Distance"). These guarantees as well as theoretical results for other aforementioned applications follow from the same general theoretical principle that we present in the paper. Our structured family contains as special cases all previously considered structured schemes, including the recently introduced $P$-model. Experimental evaluation confirms the accuracy and efficiency of TripleSpin matrices.
Spectral Methods for Correlated Topic Models
Venue: arXiv cs.LG 2016
Year: 2016

In this paper, we propose guaranteed spectral methods for learning a broad range of topic models, which generalize the popular Latent Dirichlet Allocation (LDA). We overcome the limitation of LDA to incorporate arbitrary topic correlations, by assuming that the hidden topic proportions are drawn from a flexible class of Normalized Infinitely Divisible (NID) distributions. NID distributions are generated through the process of normalizing a family of independent Infinitely Divisible (ID) random variables. The Dirichlet distribution is a special case obtained by normalizing a set of Gamma random variables. We prove that this flexible topic model class can be learned via spectral methods using only moments up to the third order, with (low order) polynomial sample and computational complexity. The proof is based on a key new technique derived here that allows us to diagonalize the moments of the NID distribution through an efficient procedure that requires evaluating only univariate integrals, despite the fact that we are handling high dimensional multivariate moments. In order to assess the performance of our proposed Latent NID topic model, we use two real datasets of articles collected from New York Times and Pubmed. Our experiments yield improved perplexity on both datasets compared with the baseline.