OpenRaiser/Intern-Atlas huggingface dataset:
A Human Mixed Strategy Approach to Deep Reinforcement Learning
Venue: arXiv cs.LG 2018
Year: 2018

In 2015, Google's DeepMind announced an advancement in creating an autonomous agent based on deep reinforcement learning (DRL) that could beat a professional player in a series of 49 Atari games. However, the current manifestation of DRL is still immature, and has significant drawbacks. One of DRL's imperfections is its lack of "exploration" during the training process, especially when working with high-dimensional problems. In this paper, we propose a mixed strategy approach that mimics behaviors of human when interacting with environment, and create a "thinking" agent that allows for more efficient exploration in the DRL training process. The simulation results based on the Breakout game show that our scheme achieves a higher probability of obtaining a maximum score than does the baseline DRL algorithm, i.e., the asynchronous advantage actor-critic method. The proposed scheme therefore can be applied effectively to solving a complicated task in a real-world application.
A Fast Fragmentation Algorithm For Data Protection In a Multi-Cloud Environment
Venue: arXiv cs.CR 2018
Year: 2018

Data fragmentation and dispersal over multiple clouds is a way of data protection against honest-but-curious storage or service providers. In this paper, we introduce a novel algorithm for data fragmentation that is particularly well adapted to be used in a multi-cloud environment. An empirical security analysis was performed on data sets provided by a large enterprise and shows that the scheme achieves good data protection. A performance comparison with published related works demonstrates it can be more than twice faster than the fastest of the relevant fragmentation techniques, while producing reasonable storage overhead.
Early Experience on Using Knights Landing Processors for Lattice Boltzmann Applications
Venue: arXiv cs.DC 2018
Year: 2018

The Knights Landing (KNL) is the codename for the latest generation of Intel processors based on Intel Many Integrated Core (MIC) architecture. It relies on massive thread and data parallelism, and fast on-chip memory. This processor operates in standalone mode, booting an off-the-shelf Linux operating system. The KNL peak performance is very high - approximately 3 Tflops in double precision and 6 Tflops in single precision - but sustained performance depends critically on how well all parallel features of the processor are exploited by real-life applications. We assess the performance of this processor for Lattice Boltzmann codes, widely used in computational fluid-dynamics. In our OpenMP code we consider several memory data-layouts that meet the conflicting computing requirements of distinct parts of the application, and sustain a large fraction of peak performance. We make some performance comparisons with other processors and accelerators, and also discuss the impact of the various memory layouts on energy efficiency.
Synchronous Adversarial Feature Learning for LiDAR based Loop Closure Detection
Venue: arXiv cs.RO 2018
Year: 2018

Loop Closure Detection (LCD) is the essential module in the simultaneous localization and mapping (SLAM) task. In the current appearance-based SLAM methods, the visual inputs are usually affected by illumination, appearance and viewpoints changes. Comparing to the visual inputs, with the active property, light detection and ranging (LiDAR) based point-cloud inputs are invariant to the illumination and appearance changes. In this paper, we extract 3D voxel maps and 2D top view maps from LiDAR inputs, and the former could capture the local geometry into a simplified 3D voxel format, the later could capture the local road structure into a 2D image format. However, the most challenge problem is to obtain efficient features from 3D and 2D maps to against the viewpoints difference. In this paper, we proposed a synchronous adversarial feature learning method for the LCD task, which could learn the higher level abstract features from different domains without any label data. To the best of our knowledge, this work is the first to extract multi-domain adversarial features for the LCD task in real time. To investigate the performance, we test the proposed method on the KITTI odometry dataset. The extensive experiments results show that, the proposed method could largely improve LCD accuracy even under huge viewpoints differences.
An End-to-end Argument in Mechanism Design (Prior-independent Auctions for Budgeted Agents)
Venue: arXiv cs.GT 2018
Year: 2018

This paper considers prior-independent mechanism design, namely identifying a single mechanism that has near optimal performance on every prior distribution. We show that mechanisms with truthtelling equilibria, a.k.a., revelation mechanisms, do not always give optimal prior-independent mechanisms and we define the revelation gap to quantify the non-optimality of revelation mechanisms. This study suggests that it is important to develop a theory for the design of non-revelation mechanisms. Our analysis focuses on welfare maximization in single-item auctions for agents with budgets and a natural regularity assumption on their distribution of values. The all-pay auction (a non-revelation mechanism) is the Bayesian optimal mechanism; as it is prior-independent it is also the prior-independent optimal mechanism (a 1-approximation). We prove a lower bound on the prior-independent approximation of revelation mechanisms of 1.013 and that the clinching auction (a revelation mechanism) is a prior-independent $e \approx 2.714$ approximation. Thus the revelation gap for single-item welfare maximization with public budget agents is in $[1.013, e]$. Some of our analyses extend to the revenue objective, position environments, and irregular distributions.
High-dimension Tensor Completion via Gradient-based Optimization Under Tensor-train Format
Venue: arXiv cs.NA 2018
Year: 2018

Tensor train (TT) decomposition has drawn people's attention due to its powerful representation ability and performance stability in high-order tensors. In this paper, we propose a novel approach to recover the missing entries of incomplete data represented by higher-order tensors. We attempt to find the low-rank TT decomposition of the incomplete data which captures the latent features of the whole data and then reconstruct the missing entries. By applying gradient descent algorithms, tensor completion problem is efficiently solved by optimization models. We propose two TT-based algorithms: Tensor Train Weighted Optimization (TT-WOPT) and Tensor Train Stochastic Gradient Descent (TT-SGD) to optimize TT decomposition factors. In addition, a method named Visual Data Tensorization (VDT) is proposed to transform visual data into higher-order tensors, resulting in the performance improvement of our algorithms. The experiments in synthetic data and visual data show high efficiency and performance of our algorithms compared to the state-of-the-art completion algorithms, especially in high-order, high missing rate, and large-scale tensor completion situations.
Look into Person: Joint Body Parsing & Pose Estimation Network and A New Benchmark
Venue: arXiv cs.CV 2018
Year: 2018

Human parsing and pose estimation have recently received considerable interest due to their substantial application potentials. However, the existing datasets have limited numbers of images and annotations and lack a variety of human appearances and coverage of challenging cases in unconstrained environments. In this paper, we introduce a new benchmark named "Look into Person (LIP)" that provides a significant advancement in terms of scalability, diversity, and difficulty, which are crucial for future developments in human-centric analysis. This comprehensive dataset contains over 50,000 elaborately annotated images with 19 semantic part labels and 16 body joints, which are captured from a broad range of viewpoints, occlusions, and background complexities. Using these rich annotations, we perform detailed analyses of the leading human parsing and pose estimation approaches, thereby obtaining insights into the successes and failures of these methods. To further explore and take advantage of the semantic correlation of these two tasks, we propose a novel joint human parsing and pose estimation network to explore efficient context modeling, which can simultaneously predict parsing and pose with extremely high quality. Furthermore, we simplify the network to solve human parsing by exploring a novel self-supervised structure-sensitive learning approach, which imposes human pose structures into the parsing results without resorting to extra supervision. The dataset, code and models are available at http://www.sysu-hcp.net/lip/.
Metrics Dashboard: A Hosted Platform for Software Quality Metrics
Venue: arXiv cs.SE 2018
Year: 2018

There is an emerging consensus in the scientific software community that progress in scientific research is dependent on the "quality and accessibility of software at all levels" (wssspe.researchcomputing.org.uk/). This progress depends on embracing the best traditional---and emergent---practices in software engineering, especially agile practices that intersect with the more formal tradition of software engineering. As a first step in our larger exploratory project to study in-process quality metrics for software development projects in Computational Science and Engineering (CSE), we have developed the Metrics Dashboard, a platform for producing and observing metrics by mining open-source software repositories on GitHub. Unlike GitHub and similar systems that provide individual performance metrics (e.g. commits), the Metrics Dashboard focuses on metrics indicative of team progress and project health. The Metrics Dashboard allows the user to submit the URL of a hosted repository for batch analysis, whose results are then cached. Upon completion, the user can interactively study various metrics over time (at varying granularity), numerically and visually. The initial version of the system is up and running as a public cloud service (SaaS) and supports project size (KLOC), defect density, defect spoilage, and productivity. While our system is by no means the first to support software metrics, we believe it may be one of the first community-focused extensible resources that can be used by any hosted project.
Performance Evaluation of 3D Correspondence Grouping Algorithms
Venue: arXiv cs.CV 2018
Year: 2018

This paper presents a thorough evaluation of several widely-used 3D correspondence grouping algorithms, motived by their significance in vision tasks relying on correct feature correspondences. A good correspondence grouping algorithm is desired to retrieve as many as inliers from initial feature matches, giving a rise in both precision and recall. Towards this rule, we deploy the experiments on three benchmarks respectively addressing shape retrieval, 3D object recognition and point cloud registration scenarios. The variety in application context brings a rich category of nuisances including noise, varying point densities, clutter, occlusion and partial overlaps. It also results to different ratios of inliers and correspondence distributions for comprehensive evaluation. Based on the quantitative outcomes, we give a summarization of the merits/demerits of the evaluated algorithms from both performance and efficiency perspectives.
Impact of ultrasound image reconstruction method on breast lesion classification with neural transfer learning
Venue: arXiv cs.CV 2018
Year: 2018

Deep learning algorithms, especially convolutional neural networks, have become a methodology of choice in medical image analysis. However, recent studies in computer vision show that even a small modification of input image intensities may cause a deep learning model to classify the image differently. In medical imaging, the distribution of image intensities is related to applied image reconstruction algorithm. In this paper we investigate the impact of ultrasound image reconstruction method on breast lesion classification with neural transfer learning. Due to high dynamic range raw ultrasonic signals are commonly compressed in order to reconstruct B-mode images. Based on raw data acquired from breast lesions, we reconstruct B-mode images using different compression levels. Next, transfer learning is applied for classification. Differently reconstructed images are employed for training and evaluation. We show that the modification of the reconstruction algorithm leads to decrease of classification performance. As a remedy, we propose a method of data augmentation. We show that the augmentation of the training set with differently reconstructed B-mode images leads to a more robust and efficient classification. Our study suggests that it is important to take into account image reconstruction algorithms implemented in medical scanners during development of computer aided diagnosis systems.
Context-aware Data Aggregation with Localized Information Privacy
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, localized information privacy (LIP) is proposed, as a new privacy definition, which allows statistical aggregation while protecting users' privacy without relying on a trusted third party. The notion of context-awareness is incorporated in LIP by the introduction of priors, which enables the design of privacy-preserving data aggregation with knowledge of priors. We show that LIP relaxes the Localized Differential Privacy (LDP) notion by explicitly modeling the adversary's knowledge. However, it is stricter than $2ε$-LDP and $ε$-mutual information privacy. The incorporation of local priors allows LIP to achieve higher utility compared to other approaches. We then present an optimization framework for privacy-preserving data aggregation, with the goal of minimizing the expected squared error while satisfying the LIP privacy constraints. Utility-privacy tradeoffs are obtained under several models in closed-form. We then validate our analysis by {numerical analysis} using both synthetic and real-world data. Results show that our LIP mechanism provides better utility-privacy tradeoffs than LDP and when the prior is not uniformly distributed, the advantage of LIP is even more significant.
Enumerating Graph Partitions Without Too Small Connected Components Using Zero-suppressed Binary and Ternary Decision Diagrams
Venue: arXiv cs.DS 2018
Year: 2018

Partitioning a graph into balanced components is important for several applications. For multi-objective problems, it is useful not only to find one solution but also to enumerate all the solutions with good values of objectives. However, there are a vast number of graph partitions in a graph, and thus it is difficult to enumerate desired graph partitions efficiently. In this paper, an algorithm to enumerate all the graph partitions such that all the weights of the connected components are at least a specified value is proposed. To deal with a large search space, we use zero-suppressed binary decision diagrams (ZDDs) to represent sets of graph partitions and we design a new algorithm based on frontier-based search, which is a framework to directly construct a ZDD. Our algorithm utilizes not only ZDDs but also ternary decision diagrams (TDDs) and realizes an operation which seems difficult to be designed only by ZDDs. Experimental results show that the proposed algorithm runs up to tens of times faster than an existing state-of-the-art algorithm.
Ensemble Manifold Segmentation for Model Distillation and Semi-supervised Learning
Venue: arXiv cs.CV 2018
Year: 2018

Manifold theory has been the central concept of many learning methods. However, learning modern CNNs with manifold structures has not raised due attention, mainly because of the inconvenience of imposing manifold structures onto the architecture of the CNNs. In this paper we present ManifoldNet, a novel method to encourage learning of manifold-aware representations. Our approach segments the input manifold into a set of fragments. By assigning the corresponding segmentation id as a pseudo label to every sample, we convert the problem of preserving the local manifold structure into a point-wise classification task. Due to its unsupervised nature, the segmentation tends to be noisy. We mitigate this by introducing ensemble manifold segmentation (EMS). EMS accounts for the manifold structure by dividing the training data into an ensemble of classification training sets that contain samples of local proximity. CNNs are trained on these ensembles under a multi-task learning framework to conform to the manifold. ManifoldNet can be trained with only the pseudo labels or together with task-specific labels. We evaluate ManifoldNet on two different tasks: network imitation (distillation) and semi-supervised learning. Our experiments show that the manifold structures are effectively utilized for both unsupervised and semi-supervised learning.
A Wikipedia-based approach to profiling activities on social media
Venue: arXiv cs.IR 2018
Year: 2018

Online user profiling is a very active research field, catalyzing great interest by both scientists and practitioners. In this paper, in particular, we look at approaches able to mine social media activities of users to create a rich user profile. We look at the case in which the profiling is meant to characterize the user's interests along a set of predefined dimensions (that we refer to as categories). A conventional way to do so is to use semantic analysis techniques to (i) extract relevant entities from the online conversations of users (ii) mapping said entities to the predefined categories of interest. While entity extraction is a well-understood topic, the mapping part lacks a reference standardized approach. In this paper we propose using graph navigation techniques on the Wikipedia tree to achieve such a mapping. A prototypical implementation is presented and some preliminary results are reported.
Adaptive Cost-sensitive Online Classification
Venue: arXiv cs.LG 2018
Year: 2018

Cost-Sensitive Online Classification has drawn extensive attention in recent years, where the main approach is to directly online optimize two well-known cost-sensitive metrics: (i) weighted sum of sensitivity and specificity; (ii) weighted misclassification cost. However, previous existing methods only considered first-order information of data stream. It is insufficient in practice, since many recent studies have proved that incorporating second-order information enhances the prediction performance of classification models. Thus, we propose a family of cost-sensitive online classification algorithms with adaptive regularization in this paper. We theoretically analyze the proposed algorithms and empirically validate their effectiveness and properties in extensive experiments. Then, for better trade off between the performance and efficiency, we further introduce the sketching technique into our algorithms, which significantly accelerates the computational speed with quite slight performance loss. Finally, we apply our algorithms to tackle several online anomaly detection tasks from real world. Promising results prove that the proposed algorithms are effective and efficient in solving cost-sensitive online classification problems in various real-world domains.
Social Choice with Non Quasi-linear Utilities
Venue: arXiv cs.GT 2018
Year: 2018

Without monetary payments, the Gibbard-Satterthwaite theorem proves that under mild requirements all truthful social choice mechanisms must be dictatorships. When payments are allowed, the Vickrey-Clarke-Groves (VCG) mechanism implements the value-maximizing choice, and has many other good properties: it is strategy-proof, onto, deterministic, individually rational, and does not make positive transfers to the agents. By Roberts' theorem, with three or more alternatives, the weighted VCG mechanisms are essentially unique for domains with quasi-linear utilities. The goal of this paper is to characterize domains of non-quasi-linear utilities where "reasonable" mechanisms (with VCG-like properties) exist. Our main result is a tight characterization of the maximal non quasi-linear utility domain, which we call the largest parallel domain. We extend Roberts' theorem to parallel domains, and use the generalized theorem to prove two impossibility results. First, any reasonable mechanism must be dictatorial when the utility domain is quasi-linear together with any single non-parallel type. Second, for richer utility domains that still differ very slightly from quasi-linearity, every strategy-proof, onto and deterministic mechanism must be a dictatorship.
Chart Parsing Multimodal Grammars
Venue: arXiv cs.CL 2018
Year: 2018

The short note describes the chart parser for multimodal type-logical grammars which has been developed in conjunction with the type-logical treebank for French. The chart parser presents an incomplete but fast implementation of proof search for multimodal type-logical grammars using the "deductive parsing" framework. Proofs found can be transformed to natural deduction proofs.
Image Segmentation Using Subspace Representation and Sparse Decomposition
Venue: arXiv cs.CV 2018
Year: 2018

Image foreground extraction is a classical problem in image processing and vision, with a large range of applications. In this dissertation, we focus on the extraction of text and graphics in mixed-content images, and design novel approaches for various aspects of this problem. We first propose a sparse decomposition framework, which models the background by a subspace containing smooth basis vectors, and foreground as a sparse and connected component. We then formulate an optimization framework to solve this problem, by adding suitable regularizations to the cost function to promote the desired characteristics of each component. We present two techniques to solve the proposed optimization problem, one based on alternating direction method of multipliers (ADMM), and the other one based on robust regression. Promising results are obtained for screen content image segmentation using the proposed algorithm. We then propose a robust subspace learning algorithm for the representation of the background component using training images that could contain both background and foreground components, as well as noise. With the learnt subspace for the background, we can further improve the segmentation results, compared to using a fixed subspace. Lastly, we investigate a different class of signal/image decomposition problem, where only one signal component is active at each signal element. In this case, besides estimating each component, we need to find their supports, which can be specified by a binary mask. We propose a mixed-integer programming problem, that jointly estimates the two components and their supports through an alternating optimization scheme. We show the application of this algorithm on various problems, including image segmentation, video motion segmentation, and also separation of text from textured images.