OpenRaiser/Intern-Atlas huggingface dataset:
Historical Document Image Segmentation with LDA-Initialized Deep Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we present a novel approach to perform deep neural networks layer-wise weight initialization using Linear Discriminant Analysis (LDA). Typically, the weights of a deep neural network are initialized with: random values, greedy layer-wise pre-training (usually as Deep Belief Network or as auto-encoder) or by re-using the layers from another network (transfer learning). Hence, many training epochs are needed before meaningful weights are learned, or a rather similar dataset is required for seeding a fine-tuning of transfer learning. In this paper, we describe how to turn an LDA into either a neural layer or a classification layer. We analyze the initialization technique on historical documents. First, we show that an LDA-based initialization is quick and leads to a very stable initialization. Furthermore, for the task of layout analysis at pixel level, we investigate the effectiveness of LDA-based initialization and show that it outperforms state-of-the-art random weight initialization methods.
Self-adaptive static analysis
Venue: arXiv cs.SE 2017
Year: 2017

Static code analysis is a powerful approach to detect quality deficiencies such as performance bottlenecks, safety violations or security vulnerabilities already during a software system's implementation. Yet, as current software systems continue to grow, current static-analysis systems more frequently face the problem of insufficient scalability. We argue that this is mainly due to the fact that current static analyses are implemented fully manually, often in general-purpose programming languages such as Java or C, or in declarative languages such as Datalog. This design choice predefines the way in which the static analysis evaluates, and limits the optimizations and extensions static-analysis designers can apply. To boost scalability to a new level, we propose to fuse static-analysis with just-in-time-optimization technology, introducing for the first time static analyses that are managed and inherently self-adaptive. Those analyses automatically adapt themselves to yield a performance/precision tradeoff that is optimal with respect to the analyzed software system and to the analysis itself. Self-adaptivity is enabled by the novel idea of designing a dedicated intermediate representation, not for the analyzed program but for the analysis itself. This representation allows for an automatic optimization and adaptation of the analysis code, both ahead-of-time (through static analysis of the static analysis) as well as just-in-time during the analysis' execution, similar to just-in-time compilers.
HDR image reconstruction from a single exposure using deep CNNs
Venue: arXiv cs.CV 2017
Year: 2017

Camera sensors can only capture a limited range of luminance simultaneously, and in order to create high dynamic range (HDR) images a set of different exposures are typically combined. In this paper we address the problem of predicting information that have been lost in saturated image areas, in order to enable HDR reconstruction from a single exposure. We show that this problem is well-suited for deep learning algorithms, and propose a deep convolutional neural network (CNN) that is specifically designed taking into account the challenges in predicting HDR values. To train the CNN we gather a large dataset of HDR images, which we augment by simulating sensor saturation for a range of cameras. To further boost robustness, we pre-train the CNN on a simulated HDR dataset created from a subset of the MIT Places database. We demonstrate that our approach can reconstruct high-resolution visually convincing HDR results in a wide range of situations, and that it generalizes well to reconstruction of images captured with arbitrary and low-end cameras that use unknown camera response functions and post-processing. Furthermore, we compare to existing methods for HDR expansion, and show high quality results also for image based lighting. Finally, we evaluate the results in a subjective experiment performed on an HDR display. This shows that the reconstructed HDR images are visually convincing, with large improvements as compared to existing methods.
Data-Free Knowledge Distillation for Deep Neural Networks
Venue: arXiv cs.LG 2017
Year: 2017

Recent advances in model compression have provided procedures for compressing large neural networks to a fraction of their original size while retaining most if not all of their accuracy. However, all of these approaches rely on access to the original training set, which might not always be possible if the network to be compressed was trained on a very large dataset, or on a dataset whose release poses privacy or safety concerns as may be the case for biometrics tasks. We present a method for data-free knowledge distillation, which is able to compress deep neural networks trained on large-scale datasets to a fraction of their size leveraging only some extra metadata to be provided with a pretrained model release. We also explore different kinds of metadata that can be used with our method, and discuss tradeoffs involved in using each of them.
A Space-Efficient Method for Navigable Ensemble Analysis and Visualization
Venue: arXiv cs.HC 2017
Year: 2017

Scientists increasingly rely on simulation runs of complex models in lieu of cost-prohibitive or infeasible experimentation. The data output of many controlled simulation runs, the ensemble, is used to verify correctness and quantify uncertainty. However, due to their size and complexity, ensembles are difficult to visually analyze because the working set often exceeds strict memory limitations. We present a navigable ensemble analysis tool, NEA, for interactive exploration of ensembles. NEA's pre-processing component takes advantage of the data similarity characteristics of ensembles to represent the data in a new, spatially-efficient data structure which does not require fully reconstructing the original data at visualization time. This data structure allows a fine degree of control in working set management, which enables interactive ensemble exploration while fitting within memory limitations. Scientists can also gain new insights from the data-similarity analysis in the pre-processing component.
Verb Pattern: A Probabilistic Semantic Representation on Verbs
Venue: arXiv cs.CL 2017
Year: 2017

Verbs are important in semantic understanding of natural language. Traditional verb representations, such as FrameNet, PropBank, VerbNet, focus on verbs' roles. These roles are too coarse to represent verbs' semantics. In this paper, we introduce verb patterns to represent verbs' semantics, such that each pattern corresponds to a single semantic of the verb. First we analyze the principles for verb patterns: generality and specificity. Then we propose a nonparametric model based on description length. Experimental results prove the high effectiveness of verb patterns. We further apply verb patterns to context-aware conceptualization, to show that verb patterns are helpful in semantic-related tasks.
Is space a word, too?
Venue: arXiv cs.CL 2017
Year: 2017

For words, rank-frequency distributions have long been heralded for adherence to a potentially-universal phenomenon known as Zipf's law. The hypothetical form of this empirical phenomenon was refined by Benîot Mandelbrot to that which is presently referred to as the Zipf-Mandelbrot law. Parallel to this, Herbet Simon proposed a selection model potentially explaining Zipf's law. However, a significant dispute between Simon and Mandelbrot, notable empirical exceptions, and the lack of a strong empirical connection between Simon's model and the Zipf-Mandelbrot law have left the questions of universality and mechanistic generation open. We offer a resolution to these issues by exhibiting how the dark matter of word segmentation, i.e., space, punctuation, etc., connect the Zipf-Mandelbrot law to Simon's mechanistic process. This explains Mandelbrot's refinement as no more than a fudge factor, accommodating the effects of the exclusion of the rank-frequency dark matter. Thus, integrating these non-word objects resolves a more-generalized rank-frequency law. Since this relies upon the integration of space, etc., we find support for the hypothesis that $all$ are generated by common processes, indicating from a physical perspective that space is a word, too.
Dynamic mode decomposition for compressive system identification
Venue: arXiv cs.SY 2017
Year: 2017

Dynamic mode decomposition has emerged as a leading technique to identify spatiotemporal coherent structures from high-dimensional data, benefiting from a strong connection to nonlinear dynamical systems via the Koopman operator. In this work, we integrate and unify two recent innovations that extend DMD to systems with actuation [Proctor et al., 2016] and systems with heavily subsampled measurements [Brunton et al., 2015]. When combined, these methods yield a novel framework for compressive system identification [code is publicly available at: https://github.com/zhbai/cDMDc]. It is possible to identify a low-order model from limited input-output data and reconstruct the associated full-state dynamic modes with compressed sensing, adding interpretability to the state of the reduced-order model. Moreover, when full-state data is available, it is possible to dramatically accelerate downstream computations by first compressing the data. We demonstrate this unified framework on two model systems, investigating the effects of sensor noise, different types of measurements (e.g., point sensors, Gaussian random projections, etc.), compression ratios, and different choices of actuation (e.g., localized, broadband, etc.). In the first example, we explore this architecture on a test system with known low-rank dynamics and an artificially inflated state dimension. The second example consists of a real-world engineering application given by the fluid flow past a pitching airfoil at low Reynolds number. This example provides a challenging and realistic test-case for the proposed method, and results demonstrate that the dominant coherent structures are well characterized despite actuation and heavily subsampled data.
Leveraging the Flow of Collective Attention for Computational Communication Research
Venue: arXiv cs.CY 2017
Year: 2017

Human attention becomes an increasingly important resource for our understanding or collective human behaviors in the age of information explosion. To better understand the flow of collective attention, we construct the attention flow network using anonymous smartphone data of 100,000 users in a major city of China. In the constructed network, nodes are websites visited by users, and links denote the switch of users between two websites. We quantify the flow of collective attention by computing the flow network statistics, such as flow impact, flow dissipation, and flow distance. The findings reveal a strong concentration and fragmentation of collective attention for smartphone users, while the duplication of attention cross websites proves to be unfounded in mobile using. We further confirmed the law of dissipation and the allowmetric scaling of flow impact. Surprisingly, there is a centralized flow structure, suggesting that the website with large traffic can easily control the circulated collective attention. Additionally, we find that flow network analysis can effectively explain the page views and sale volume of products. Finally, we discuss the benefits and limitations of using the flow network analysis for computational communication research.
Constrained Optimisation of Rational Functions for Accelerating Subspace Iteration
Venue: arXiv cs.NA 2017
Year: 2017

Earlier this decade, the so-called FEAST algorithm was released for computing the eigenvalues of a matrix in a given interval. Previously, rational filter functions have been examined as a parameter of FEAST. In this thesis, we expand on existing work with the following contributions: (i) Obtaining well-performing rational filter functions via standard minimisation algorithms, (ii) Obtaining constrained rational filter functions efficiently, and (iii) Improving existing rational filter functions algorithmically. Using our new rational filter functions, FEAST requires up to one quarter fewer iterations on average compared to state-of-art rational filter functions.
Cell-Free and User-Centric Massive MIMO at Millimeter Wave Frequencies
Venue: arXiv cs.IT 2017
Year: 2017

In a cell-free (CF) massive MIMO architecture a very large number of distributed access points (APs) simultaneously and jointly serves a much smaller number of mobile stations (MSs); a variant of the cell-free technique is the user-centric (UC) approach, wherein each AP just decodes a reduced set of MSs, practically the ones that are received best. This paper introduces and analyzes the CF and UC architectures at millimeter wave (mmWave) frequencies. First of all, a multiuser clustered channel model is introduced in order to account for the correlation among the channels of nearby users; then, an uplink multiuser channel estimation scheme is described along with low-complexity hybrid analog/digital beamforming architectures. Interestingly, in the proposed scheme no channel estimation is needed at the MSs, and the beamforming schemes used at the MSs are channel-independent and have a very simple structure. Numerical results show that the considered architectures provide good performance, especially in lightly loaded systems, with the UC approach outperforming the CF one.
A Generative Restricted Boltzmann Machine Based Method for High-Dimensional Motion Data Modeling
Venue: arXiv cs.CV 2017
Year: 2017

Many computer vision applications involve modeling complex spatio-temporal patterns in high-dimensional motion data. Recently, restricted Boltzmann machines (RBMs) have been widely used to capture and represent spatial patterns in a single image or temporal patterns in several time slices. To model global dynamics and local spatial interactions, we propose to theoretically extend the conventional RBMs by introducing another term in the energy function to explicitly model the local spatial interactions in the input data. A learning method is then proposed to perform efficient learning for the proposed model. We further introduce a new method for multi-class classification that can effectively estimate the infeasible partition functions of different RBMs such that RBM is treated as a generative model for classification purpose. The improved RBM model is evaluated on two computer vision applications: facial expression recognition and human action recognition. Experimental results on benchmark databases demonstrate the effectiveness of the proposed algorithm.
CLOT Norm Minimization for Continuous Hands-off Control
Venue: arXiv cs.SY 2017
Year: 2017

In this paper, we consider hands-off control via minimization of the CLOT (Combined $L$-One and Two) norm. The maximum hands-off control is the $L^0$-optimal (or the sparsest) control among all feasible controls that are bounded by a specified value and transfer the state from a given initial state to the origin within a fixed time duration. In general, the maximum hands-off control is a bang-off-bang control taking values of $\pm 1$ and $0$. For many real applications, such discontinuity in the control is not desirable. To obtain a continuous but still relatively sparse control, we propose to use the CLOT norm, a convex combination of $L^1$ and $L^2$ norms. We show by numerical simulations that the CLOT control is continuous and much sparser (i.e. has longer time duration on which the control takes 0) than the conventional EN (elastic net) control, which is a convex combination of $L^1$ and squared $L^2$ norms. We also prove that the CLOT control is continuous in the sense that, if $O(h)$ denotes the sampling period, then the difference between successive values of the CLOT-optimal control is $O(\sqrt{h})$, which is a form of continuity. Also, the CLOT formulation is extended to encompass constraints on the state variable.
Hierarchical State Abstractions for Decision-Making Problems with Computational Constraints
Venue: arXiv cs.AI 2017
Year: 2017

In this semi-tutorial paper, we first review the information-theoretic approach to account for the computational costs incurred during the search for optimal actions in a sequential decision-making problem. The traditional (MDP) framework ignores computational limitations while searching for optimal policies, essentially assuming that the acting agent is perfectly rational and aims for exact optimality. Using the free-energy, a variational principle is introduced that accounts not only for the value of a policy alone, but also considers the cost of finding this optimal policy. The solution of the variational equations arising from this formulation can be obtained using familiar Bellman-like value iterations from dynamic programming (DP) and the Blahut-Arimoto (BA) algorithm from rate distortion theory. Finally, we demonstrate the utility of the approach for generating hierarchies of state abstractions that can be used to best exploit the available computational resources. A numerical example showcases these concepts for a path-planning problem in a grid world environment.
Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100$\times$ speed-up
Venue: arXiv cs.CV 2017
Year: 2017

Image restoration methods aim to recover the underlying clean image from corrupted observations. The Expected Patch Log-likelihood (EPLL) algorithm is a powerful image restoration method that uses a Gaussian mixture model (GMM) prior on the patches of natural images. Although it is very effective for restoring images, its high runtime complexity makes EPLL ill-suited for most practical applications. In this paper, we propose three approximations to the original EPLL algorithm. The resulting algorithm, which we call the fast-EPLL (FEPLL), attains a dramatic speed-up of two orders of magnitude over EPLL while incurring a negligible drop in the restored image quality (less than 0.5 dB). We demonstrate the efficacy and versatility of our algorithm on a number of inverse problems such as denoising, deblurring, super-resolution, inpainting and devignetting. To the best of our knowledge, FEPLL is the first algorithm that can competitively restore a 512x512 pixel image in under 0.5s for all the degradations mentioned above without specialized code optimizations such as CPU parallelization or GPU implementation.
An iterative closest point method for measuring the level of similarity of 3d log scans in wood industry
Venue: arXiv cs.CV 2017
Year: 2017

In the Canadian's lumber industry, simulators are used to predict the lumbers resulting from the sawing of a log at a given sawmill. Giving a log or several logs' 3D scans as input, simulators perform a real-time job to predict the lumbers. These simulators, however, tend to be slow at processing large volume of wood. We thus explore an alternative approximation techniques based on the Iterative Closest Point (ICP) algorithm to identify the already processed log to which an unseen log resembles the most. The main benefit of the ICP approach is that it can easily handle 3D scans with a variable number of points. We compare this ICP-based nearest neighbor predictor, to predictors built using machine learning algorithms such as the K-nearest-neighbor (kNN) and Random Forest (RF). The implemented ICP-based predictor enabled us to identify key points in using the 3D scans directly for distance calculation. The long-term goal of this ongoing research is to integrated ICP distance calculations and machine learning.
POD-based reduced-order model of an eddy-current levitation problem
Venue: arXiv cs.CE 2017
Year: 2017

The accurate and efficient treatment of eddy-current problems with movement is still a challenge. Very few works applying reduced-order models are available in the literature. In this paper, we propose a proper-orthogonal-decomposition reduced-order model to handle these kind of motional problems. A classical magnetodynamic finite element formulation based on the magnetic vector potential is used as reference and to build up the reduced models. Two approaches are proposed. The TEAM workshop problem 28 is chosen as a test case for validation. Results are compared in terms of accuracy and computational cost.
A Framework for Efficient Adaptively Secure Composable Oblivious Transfer in the ROM
Venue: arXiv cs.CR 2017
Year: 2017

Oblivious Transfer (OT) is a fundamental cryptographic protocol that finds a number of applications, in particular, as an essential building block for two-party and multi-party computation. We construct a round-optimal (2 rounds) universally composable (UC) protocol for oblivious transfer secure against active adaptive adversaries from any OW-CPA secure public-key encryption scheme with certain properties in the random oracle model (ROM). In terms of computation, our protocol only requires the generation of a public/secret-key pair, two encryption operations and one decryption operation, apart from a few calls to the random oracle. In~terms of communication, our protocol only requires the transfer of one public-key, two ciphertexts, and three binary strings of roughly the same size as the message. Next, we show how to instantiate our construction under the low noise LPN, McEliece, QC-MDPC, LWE, and CDH assumptions. Our instantiations based on the low noise LPN, McEliece, and QC-MDPC assumptions are the first UC-secure OT protocols based on coding assumptions to achieve: 1) adaptive security, 2) optimal round complexity, 3) low communication and computational complexities. Previous results in this setting only achieved static security and used costly cut-and-choose techniques.Our instantiation based on CDH achieves adaptive security at the small cost of communicating only two more group elements as compared to the gap-DH based Simplest OT protocol of Chou and Orlandi (Latincrypt 15), which only achieves static security in the ROM.
An In-field Automatic Wheat Disease Diagnosis System
Venue: arXiv cs.CV 2017
Year: 2017

Crop diseases are responsible for the major production reduction and economic losses in agricultural industry world- wide. Monitoring for health status of crops is critical to control the spread of diseases and implement effective management. This paper presents an in-field automatic wheat disease diagnosis system based on a weakly super- vised deep learning framework, i.e. deep multiple instance learning, which achieves an integration of identification for wheat diseases and localization for disease areas with only image-level annotation for training images in wild conditions. Furthermore, a new in-field image dataset for wheat disease, Wheat Disease Database 2017 (WDD2017), is collected to verify the effectiveness of our system. Under two different architectures, i.e. VGG-FCN-VD16 and VGG-FCN-S, our system achieves the mean recognition accuracies of 97.95% and 95.12% respectively over 5-fold cross-validation on WDD2017, exceeding the results of 93.27% and 73.00% by two conventional CNN frameworks, i.e. VGG-CNN-VD16 and VGG-CNN-S. Experimental results demonstrate that the proposed system outperforms conventional CNN architectures on recognition accuracy under the same amount of parameters, meanwhile main- taining accurate localization for corresponding disease areas. Moreover, the proposed system has been packed into a real-time mobile app to provide support for agricultural disease diagnosis.