OpenRaiser/Intern-Atlas huggingface dataset:
Class Correlation affects Single Object Localization using Pre-trained ConvNets
Venue: arXiv cs.CV 2017
Year: 2017

The problem of object localization has become one of the mainstream problems of vision. Most of the algorithms proposed involve the design for the model to be specifically for localizing objects. In this paper, we explore whether a pre-trained canonical ConvNet (without fine-tuning) trained purely for object classification on one dataset with global image level labels can be used to localize objects in images containing a single instance on a separate dataset while generalizing to novel classes. We propose a simple algorithm involving cropping and blackening out regions in the image space called Explicit Image Space based Search (EISS) for locating the most responsive regions in an image in the context of object localization. EISS brings to light the interesting phenomenon of a ConvNets responding more to features within objects as opposed to object level descriptors, as the classes in the training data get more correlated (visually/semantically similar).
The relationship between the number of editorial board members and the scientific output of universities in the chemistry field
Venue: arXiv cs.DL 2017
Year: 2017

Editorial board members, who are considered the gatekeepers of scientific journals, play an important role in academia, and may directly or indirectly affect the scientific output of a university. In this article, we used the quantile regression method among a sample of 1,387 university in chemistry to characterize the correlation between the number of editorial board members and the scientific output of their universities. Furthermore, we used time-series data and the Granger causality test to explore the causal relationship between the number of editorial board members and the number of articles of some top universities. Our results suggest that the number of editorial board members is positively and significantly related to the scientific output (as measured by the number of articles, total number of citations, citations per paper, and h index) of their universities. However, the Granger causality test results suggest that the causal relationship between the number of editorial board members and the number of articles of some top universities is not obvious. Combining these findings with the results of qualitative interviews with editorial board members, we discuss the causal relationship between the number of editorial board members and the scientific output of their universities.
Lip2AudSpec: Speech reconstruction from silent lip movements video
Venue: arXiv cs.CV 2017
Year: 2017

In this study, we propose a deep neural network for reconstructing intelligible speech from silent lip movement videos. We use auditory spectrogram as spectral representation of speech and its corresponding sound generation method resulting in a more natural sounding reconstructed speech. Our proposed network consists of an autoencoder to extract bottleneck features from the auditory spectrogram which is then used as target to our main lip reading network comprising of CNN, LSTM and fully connected layers. Our experiments show that the autoencoder is able to reconstruct the original auditory spectrogram with a 98% correlation and also improves the quality of reconstructed speech from the main lip reading network. Our model, trained jointly on different speakers is able to extract individual speaker characteristics and gives promising results of reconstructing intelligible speech with superior word recognition accuracy.
Critical trees: counterexamples in model checking of CSM systems using CBS algorithm
Venue: arXiv cs.SE 2017
Year: 2017

The important feature of temporal model checking is the generation of counterexamples. In the report, the requirements for generation of counterexample (called critical tree) in model checking of CSM systems are described. The output of TempoRG model checker for QsCTL logic (a version of CTL) is presented. A contradiction between counterexample generation and state space reduction is commented.
Optimal Crowdsourced Classification with a Reject Option in the Presence of Spammers
Venue: arXiv cs.HC 2017
Year: 2017

We explore the design of an effective crowdsourcing system for an $M$-ary classification task. Crowd workers complete simple binary microtasks whose results are aggregated to give the final decision. We consider the scenario where the workers have a reject option so that they are allowed to skip microtasks when they are unable to or choose not to respond to binary microtasks. We present an aggregation approach using a weighted majority voting rule, where each worker's response is assigned an optimized weight to maximize crowd's classification performance.
Data-driven Feature Sampling for Deep Hyperspectral Classification and Segmentation
Venue: arXiv cs.NE 2017
Year: 2017

The high dimensionality of hyperspectral imaging forces unique challenges in scope, size and processing requirements. Motivated by the potential for an in-the-field cell sorting detector, we examine a $\textit{Synechocystis sp.}$ PCC 6803 dataset wherein cells are grown alternatively in nitrogen rich or deplete cultures. We use deep learning techniques to both successfully classify cells and generate a mask segmenting the cells/condition from the background. Further, we use the classification accuracy to guide a data-driven, iterative feature selection method, allowing the design neural networks requiring 90% fewer input features with little accuracy degradation.
Espresso: Brewing Java For More Non-Volatility with Non-volatile Memory
Venue: arXiv cs.PL 2017
Year: 2017

Fast, byte-addressable non-volatile memory (NVM) embraces both near-DRAM latency and disk-like persistence, which has generated considerable interests to revolutionize system software stack and programming models. However, it is less understood how NVM can be combined with managed runtime like Java virtual machine (JVM) to ease persistence management. This paper proposes Espresso, a holistic extension to Java and its runtime, to enable Java programmers to exploit NVM for persistence management with high performance. Espresso first provides a general persistent heap design called Persistent Java Heap (PJH) to manage persistent data as normal Java objects. The heap is then strengthened with a recoverable mechanism to provide crash consistency for heap metadata. It then provides a new abstraction called Persistent Java Object (PJO) to provide an easy-to-use but safe persistent programming model for programmers to persist application data. The evaluation confirms that Espresso significantly outperforms state-of-art NVM support for Java (i.e., JPA and PCJ) while being compatible to existing data structures in Java programs.
Watching Videos with Certain and Constant Quality: PID-based Quality Control Method
Venue: arXiv cs.MM 2017
Year: 2017

In video coding, compressed videos with certain and constant quality can ensure quality of experience (QoE). To this end, we propose in this paper a novel PID-based quality control (PQC) method for video coding. Specifically, a formulation is modelled to control quality of video coding with two objectives: minimizing control error and quality fluctuation. Then, we apply the Laplace domain analysis to model the relationship between quantization parameter (QP) and control error in this formulation. Given the relationship between QP and control error, we propose a solution to the PQC formulation, such that videos can be compressed at certain and constant quality. Finally, experimental results show that our PQC method is effective in both control accuracy and quality fluctuation.
Performance optimizations for scalable CFD applications on hybrid CPU+MIC heterogeneous computing system with millions of cores
Venue: arXiv cs.PF 2017
Year: 2017

For computational fluid dynamics (CFD) applications with a large number of grid points/cells, parallel computing is a common efficient strategy to reduce the computational time. How to achieve the best performance in the modern supercomputer system, especially with heterogeneous computing resources such as hybrid CPU+GPU, or a CPU + Intel Xeon Phi (MIC) co-processors, is still a great challenge. An in-house parallel CFD code capable of simulating three dimensional structured grid applications is developed and tested in this study. Several methods of parallelization, performance optimization and code tuning both in the CPU-only homogeneous system and in the heterogeneous system are proposed based on identifying potential parallelism of applications, balancing the work load among all kinds of computing devices, tuning the multi-thread code toward better performance in intra-machine node with hundreds of CPU/MIC cores, and optimizing the communication among inter-nodes, inter-cores, and between CPUs and MICs. Some benchmark cases from model and/or industrial CFD applications are tested on the Tianhe-1A and Tianhe-2 supercomputer to evaluate the performance. Among these CFD cases, the maximum number of grid cells reached 780 billion. The tuned solver successfully scales up to half of the entire Tianhe-2 supercomputer system with over 1.376 million of heterogeneous cores. The test results and performance analysis are discussed in detail.
Declarative vs Rule-based Control for Flocking Dynamics
Venue: arXiv cs.MA 2017
Year: 2017

The popularity of rule-based flocking models, such as Reynolds' classic flocking model, raises the question of whether more declarative flocking models are possible. This question is motivated by the observation that declarative models are generally simpler and easier to design, understand, and analyze than operational models. We introduce a very simple control law for flocking based on a cost function capturing cohesion (agents want to stay together) and separation (agents do not want to get too close). We refer to it as {\textit declarative flocking} (DF). We use model-predictive control (MPC) to define controllers for DF in centralized and distributed settings. A thorough performance comparison of our declarative flocking with Reynolds' model, and with more recent flocking models that use MPC with a cost function based on lattice structures, demonstrate that DF-MPC yields the best cohesion and least fragmentation, and maintains a surprisingly good level of geometric regularity while still producing natural flock shapes similar to those produced by Reynolds' model. We also show that DF-MPC has high resilience to sensor noise.
A note on faithful coupling of Markov chains
Venue: arXiv cs.DS 2017
Year: 2017

One often needs to turn a coupling $(X_i, Y_i)_{i\geq 0}$ of a Markov chain into a sticky coupling where once $X_T = Y_T$ at some $T$, then from then on, at each subsequent time step $T'\geq T$, we shall have $X_{T'} = Y_{T'}$. However, not all of what are considered couplings in literature, even Markovian couplings, can be turned into sticky couplings, as proved by Rosenthal through a counter example. Rosenthal then proposed a strengthening of the Markovian coupling notion, termed as faithful coupling, from which a sticky coupling can indeed be obtained. We identify the reason why a sticky coupling could not be obtained in the counter example of Rosenthal, which motivates us to define a type of coupling which can obviously be turned into a sticky coupling. We show then that the new type of coupling that we define, and the faithful coupling as defined by Rosenthal, are actually identical. Our note may be seen as a demonstration of the naturalness of the notion of faithful coupling.
SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data
Venue: arXiv cs.LG 2017
Year: 2017

Neural networks exhibit good generalization behavior in the over-parameterized regime, where the number of network parameters exceeds the number of observations. Nonetheless, current generalization bounds for neural networks fail to explain this phenomenon. In an attempt to bridge this gap, we study the problem of learning a two-layer over-parameterized neural network, when the data is generated by a linearly separable function. In the case where the network has Leaky ReLU activations, we provide both optimization and generalization guarantees for over-parameterized networks. Specifically, we prove convergence rates of SGD to a global minimum and provide generalization guarantees for this global minimum that are independent of the network size. Therefore, our result clearly shows that the use of SGD for optimization both finds a global minimum, and avoids overfitting despite the high capacity of the model. This is the first theoretical demonstration that SGD can avoid overfitting, when learning over-specified neural network classifiers.
The James construction and $π_4(\mathbb{S}^3)$ in homotopy type theory
Venue: arXiv cs.LO 2017
Year: 2017

In the first part of this paper we present a formalization in Agda of the James construction in homotopy type theory. We include several fragments of code to show what the Agda code looks like, and we explain several techniques that we used in the formalization. In the second part, we use the James construction to give a constructive proof that $π_4(\mathbb{S}^3)$ is of the form $\mathbb{Z}/n\mathbb{Z}$ (but we do not compute the $n$ here).
Optimal Control of Wireless Computing Networks
Venue: arXiv cs.NI 2017
Year: 2017

Augmented information (AgI) services allow users to consume information that results from the execution of a chain of service functions that process source information to create real-time augmented value. Applications include real-time analysis of remote sensing data, real-time computer vision, personalized video streaming, and augmented reality, among others. We consider the problem of optimal distribution of AgI services over a wireless computing network, in which nodes are equipped with both communication and computing resources. We characterize the wireless computing network capacity region and design a joint flow scheduling and resource allocation algorithm that stabilizes the underlying queuing system while achieving a network cost arbitrarily close to the minimum, with a tradeoff in network delay. Our solution captures the unique chaining and flow scaling aspects of AgI services, while exploiting the use of the broadcast approach coding scheme over the wireless channel.
Lower Bounds for Two-Sample Structural Change Detection in Ising and Gaussian Models
Venue: arXiv cs.IT 2017
Year: 2017

The change detection problem is to determine if the Markov network structures of two Markov random fields differ from one another given two sets of samples drawn from the respective underlying distributions. We study the trade-off between the sample sizes and the reliability of change detection, measured as a minimax risk, for the important cases of the Ising models and the Gaussian Markov random fields restricted to the models which have network structures with $p$ nodes and degree at most $d$, and obtain information-theoretic lower bounds for reliable change detection over these models. We show that for the Ising model, $Ω\left(\frac{d^2}{(\log d)^2}\log p\right)$ samples are required from each dataset to detect even the sparsest possible changes, and that for the Gaussian, $Ω\left( γ^{-2} \log(p)\right)$ samples are required from each dataset to detect change, where $γ$ is the smallest ratio of off-diagonal to diagonal terms in the precision matrices of the distributions. These bounds are compared to the corresponding results in structure learning, and closely match them under mild conditions on the model parameters. Thus, our change detection bounds inherit partial tightness from the structure learning schemes in previous literature, demonstrating that in certain parameter regimes, the naive structure learning based approach to change detection is minimax optimal up to constant factors.
M-RWTL: Learning Signal-Matched Rational Wavelet Transform in Lifting Framework
Venue: arXiv cs.SY 2017
Year: 2017

Transform learning is being extensively applied in several applications because of its ability to adapt to a class of signals of interest. Often, a transform is learned using a large amount of training data, while only limited data may be available in many applications. Motivated with this, we propose wavelet transform learning in the lifting framework for a given signal. Significant contributions of this work are: 1) the existing theory of lifting framework of the dyadic wavelet is extended to more generic rational wavelet design, where dyadic is a special case and 2) the proposed work allows to learn rational wavelet transform from a given signal and does not require large training data. Since it is a signal-matched design, the proposed methodology is called Signal-Matched Rational Wavelet Transform Learning in the Lifting Framework (M-RWTL). The proposed M-RWTL method inherits all the advantages of lifting, i.e., the learned rational wavelet transform is always invertible, method is modular, and the corresponding M-RWTL system can also incorporate nonlinear filters, if required. This may enhance the use of RWT in applications which is so far restricted. M-RWTL is observed to perform better compared to standard wavelet transforms in the applications of compressed sensing based signal reconstruction.
Channel Coherence Classification with Frame-Shifting in Massive MIMO Systems
Venue: arXiv cs.IT 2017
Year: 2017

This paper considers the uplink pilot overhead in a time division duplexing (TDD) massive Multiple Input Multiple Output (MIMO) mobile systems. A common scenario of conventional massive MIMO systems is a Base Station (BS) serving all user terminals (UTs) in the cell with the same TDD frame format that fits the coherence interval of the worst-case scenario of user mobility (e.g. a moving train with velocity 300 Km/s). Furthermore, the BS has to estimate all the channels each time-slot for all users even for those with long coherence intervals. In fact, within the same cell, sensors or pedestrian with low mobility UTs (e.g. moving 1.38 m/s) share the same short TDD frame and thus are obliged to upload their pilots each time-slot. The channel coherence interval of the pedestrian UTs with a carrier frequency of 1.9 GHz can be as long as 60 times that of the train passenger users. In other words, conventional techniques waste 59-uploaded pilot sequences for channel estimation. In this paper, we are aware of the resources waste due to various coherence intervals among different user mobility. We classify users based on their coherence interval length, and we propose to skip uploading pilots of UTs with large coherence intervals. Then, we shift frames with the same pilot reused sequence toward an empty pilot time-slot. Simulation results had proved that the proposed technique overcome the performance of conventional massive MIMO systems in both energy and spectral efficiency.
Topic-based Integrator Matching for Pull Request
Venue: arXiv cs.SE 2017
Year: 2017

Pull Request (PR) is the main method for code contributions from the external contributors in GitHub. PR review is an essential part of open source software developments to maintain the quality of software. Matching a new PR for an appropriate integrator will make the PR reviewing more effective. However, PR and integrator matching are now organized manually in GitHub. To make this process more efficient, we propose a Topic-based Integrator Matching Algorithm (TIMA) to predict highly relevant collaborators(the core developers) as the integrator to incoming PRs . TIMA takes full advantage of the textual semantics of PRs. To define the relationships between topics and collaborators, TIMA builds a relation matrix about topic and collaborators. According to the relevance between topics and collaborators, TIMA matches the suitable collaborators as the PR integrator.
DevRank: Mining Influential Developers In Github
Venue: arXiv cs.SI 2017
Year: 2017

As the social coding is becoming increasingly popular, understanding the influence of developers can benefit various applications, such as advertisement for new projects and innovations. However, most existing works have focused only on ranking influential nodes in non-weighted and homogeneous networks, which are not able to transfer proper importance scores to the real important node. To rank developers in Github, we define developer's influence on the capacity of attracting attention which can be measured by the number of followers obtained in the future. We further defined a new method, DevRank, which ranks the developers by influence propagation through heterogeneous network constructed according to user behaviors, including "commit" and "follow". Our experiment compares the performance between DevRank and some other link analysis algorithms, the results have shown that DevRank can improve the ranking accuracy.
Reverse Engineering Camouflaged Sequential Integrated Circuits Without Scan Access
Venue: arXiv cs.CR 2017
Year: 2017

Integrated circuit (IC) camouflaging is a promising technique to protect the design of a chip from reverse engineering. However, recent work has shown that even camouflaged ICs can be reverse engineered from the observed input/output behaviour of a chip using SAT solvers. However, these so-called SAT attacks have so far targeted only camouflaged combinational circuits. For camouflaged sequential circuits, the SAT attack requires that the internal state of the circuit is controllable and observable via the scan chain. It has been implicitly assumed that restricting scan chain access increases the security of camouflaged ICs from reverse engineering attacks. In this paper, we develop a new attack methodology to decamouflage sequential circuits without scan access. Our attack uses a model checker (a more powerful reasoning tool than a SAT solver) to find a discriminating set of input sequences, i.e., one that is sufficient to determine the functionality of camouflaged gates. We propose several refinements, including the use of a bounded model checker, and sufficient conditions for determining when a set of input sequences is discriminating to improve the run-time and scalabilty of our attack. Our attack is able to decamouflage a large sequential benchmark circuit that implements a subset of the VIPER processor.
Geographic Differential Privacy for Mobile Crowd Coverage Maximization
Venue: arXiv cs.CR 2017
Year: 2017

For real-world mobile applications such as location-based advertising and spatial crowdsourcing, a key to success is targeting mobile users that can maximally cover certain locations in a future period. To find an optimal group of users, existing methods often require information about users' mobility history, which may cause privacy breaches. In this paper, we propose a method to maximize mobile crowd's future location coverage under a guaranteed location privacy protection scheme. In our approach, users only need to upload one of their frequently visited locations, and more importantly, the uploaded location is obfuscated using a geographic differential privacy policy. We propose both analytic and practical solutions to this problem. Experiments on real user mobility datasets show that our method significantly outperforms the state-of-the-art geographic differential privacy methods by achieving a higher coverage under the same level of privacy protection.
Topic Based Sentiment Analysis Using Deep Learning
Venue: arXiv cs.CL 2017
Year: 2017

In this paper , we tackle Sentiment Analysis conditioned on a Topic in Twitter data using Deep Learning . We propose a 2-tier approach : In the first phase we create our own Word Embeddings and see that they do perform better than state-of-the-art embeddings when used with standard classifiers. We then perform inference on these embeddings to learn more about a word with respect to all the topics being considered, and also the top n-influencing words for each topic. In the second phase we use these embeddings to predict the sentiment of the tweet with respect to a given topic, and all other topics under discussion.