OpenRaiser/Intern-Atlas huggingface dataset:
Monge-Ampère Flow for Generative Modeling
Venue: arXiv cs.LG 2018
Year: 2018

We present a deep generative model, named Monge-Ampère flow, which builds on continuous-time gradient flow arising from the Monge-Ampère equation in optimal transport theory. The generative map from the latent space to the data space follows a dynamical system, where a learnable potential function guides a compressible fluid to flow towards the target density distribution. Training of the model amounts to solving an optimal control problem. The Monge-Ampère flow has tractable likelihoods and supports efficient sampling and inference. One can easily impose symmetry constraints in the generative model by designing suitable scalar potential functions. We apply the approach to unsupervised density estimation of the MNIST dataset and variational calculation of the two-dimensional Ising model at the critical point. This approach brings insights and techniques from Monge-Ampère equation, optimal transport, and fluid dynamics into reversible flow-based generative models.
Automatic Dataset Annotation to Learn CNN Pore Description for Fingerprint Recognition
Venue: arXiv cs.CV 2018
Year: 2018

High-resolution fingerprint recognition often relies on sophisticated matching algorithms based on hand-crafted keypoint descriptors, with pores being the most common keypoint choice. Our method is the opposite of the prevalent approach: we use instead a simple matching algorithm based on robust local pore descriptors that are learned from the data using a CNN. In order to train this CNN in a fully supervised manner, we describe how the automatic alignment of fingerprint images can be used to obtain the required training annotations, which are otherwise missing in all publicly available datasets. This improves the state-of-the-art recognition results for both partial and full fingerprints in a public benchmark. To confirm that the observed improvement is due to the adoption of learned descriptors, we conduct an ablation study using the most successful pore descriptors previously used in the literature. All our code is available at https://github.com/gdahia/high-res-fingerprint-recognition
Generic Vehicle Tracking Framework Capable of Handling Occlusions Based on Modified Mixture Particle Filter
Venue: arXiv cs.CV 2018
Year: 2018

Accurate and robust tracking of surrounding road participants plays an important role in autonomous driving. However, there is usually no prior knowledge of the number of tracking targets due to object emergence, object disappearance and false alarms. To overcome this challenge, we propose a generic vehicle tracking framework based on modified mixture particle filter, which can make the number of tracking targets adaptive to real-time observations and track all the vehicles within sensor range simultaneously in a uniform architecture without explicit data association. Each object corresponds to a mixture component whose distribution is non-parametric and approximated by particle hypotheses. Most tracking approaches employ vehicle kinematic models as the prediction model. However, it is hard for these models to make proper predictions when sensor measurements are lost or become low quality due to partial or complete occlusions. Moreover, these models are incapable of forecasting sudden maneuvers. To address these problems, we propose to incorporate learning-based behavioral models instead of pure vehicle kinematic models to realize prediction in the prior update of recursive Bayesian state estimation. Two typical driving scenarios including lane keeping and lane change are demonstrated to verify the effectiveness and accuracy of the proposed framework as well as the advantages of employing learning-based models.
Addressing Training Bias via Automated Image Annotation
Venue: arXiv cs.CV 2018
Year: 2018

Build accurate DNN models requires training on large labeled, context specific datasets, especially those matching the target scenario. We believe advances in wireless localization, working in unison with cameras, can produce automated annotation of targets on images and videos captured in the wild. Using pedestrian and vehicle detection as examples, we demonstrate the feasibility, benefits, and challenges of an automatic image annotation system. Our work calls for new technical development on passive localization, mobile data analytics, and error-resilient ML models, as well as design issues in user privacy policies.
Cylindrical Transform: 3D Semantic Segmentation of Kidneys With Limited Annotated Images
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose a novel technique for sampling sequential images using a cylindrical transform in a cylindrical coordinate system for kidney semantic segmentation in abdominal computed tomography (CT). The images generated from a cylindrical transform augment a limited annotated set of images in three dimensions. This approach enables us to train contemporary classification deep convolutional neural networks (DCNNs) instead of fully convolutional networks (FCNs) for semantic segmentation. Typical semantic segmentation models segment a sequential set of images (e.g. CT or video) by segmenting each image independently. However, the proposed method not only considers the spatial dependency in the x-y plane, but also the spatial sequential dependency along the z-axis. The results show that classification DCNNs, trained on cylindrical transformed images, can achieve a higher segmentation performance value than FCNs using a limited number of annotated images.
Semantic Sentence Embeddings for Paraphrasing and Text Summarization
Venue: arXiv cs.CL 2018
Year: 2018

This paper introduces a sentence to vector encoding framework suitable for advanced natural language processing. Our latent representation is shown to encode sentences with common semantic information with similar vector representations. The vector representation is extracted from an encoder-decoder model which is trained on sentence paraphrase pairs. We demonstrate the application of the sentence representations for two different tasks -- sentence paraphrasing and paragraph summarization, making it attractive for commonly used recurrent frameworks that process text. Experimental results help gain insight how vector representations are suitable for advanced language embedding.
Adaptive Pruning of Neural Language Models for Mobile Devices
Venue: arXiv cs.CL 2018
Year: 2018

Neural language models (NLMs) exist in an accuracy-efficiency tradeoff space where better perplexity typically comes at the cost of greater computation complexity. In a software keyboard application on mobile devices, this translates into higher power consumption and shorter battery life. This paper represents the first attempt, to our knowledge, in exploring accuracy-efficiency tradeoffs for NLMs. Building on quasi-recurrent neural networks (QRNNs), we apply pruning techniques to provide a "knob" to select different operating points. In addition, we propose a simple technique to recover some perplexity using a negligible amount of memory. Our empirical evaluations consider both perplexity as well as energy consumption on a Raspberry Pi, where we demonstrate which methods provide the best perplexity-power consumption operating point. At one operating point, one of the techniques is able to provide energy savings of 40% over the state of the art with only a 17% relative increase in perplexity.
A Way to Facilitate Decision Making in a Mixed Group of Manned and Unmanned Aerial Vehicles
Venue: arXiv cs.AI 2018
Year: 2018

A mixed group of manned and unmanned aerial vehicles is considered as a distributed system. A lattice of tasks which may be fulfilled by the system matches to it. An external multiplication operation is defined at the lattice, which defines correspondingly linear logic operations. Linear implication and tensor product are used to choose a system reconfiguration variant, i.e., to determine a new task executor choice. The task lattice structure (i.e., the system purpose) and the operation definitions largely define the choice. Thus, the choice is mainly the system purpose consequence. Such a method of the behavior variant choice facilitates the decision making by the pilot controlling the group. The suggested approach is illustrated using an example of a mixed group control at forest fire compression.
A Simple Framework to Leverage State-Of-The-Art Single-Image Super-Resolution Methods to Restore Light Fields
Venue: arXiv cs.CV 2018
Year: 2018

Plenoptic cameras offer a cost effective solution to capture light fields by multiplexing multiple views on a single image sensor. However, the high angular resolution is achieved at the expense of reducing the spatial resolution of each view by orders of magnitude compared to the raw sensor image. While light field super-resolution is still at an early stage, the field of single image super-resolution (SISR) has recently known significant advances with the use of deep learning techniques. This paper describes a simple framework allowing us to leverage state-of-the-art SISR techniques into light fields, while taking into account specific light field geometrical constraints. The idea is to first compute a representation compacting most of the light field energy into as few components as possible. This is achieved by aligning the light field using optical flows and then by decomposing the aligned light field using singular value decomposition (SVD). The principal basis captures the information that is coherent across all the views, while the other basis contain the high angular frequencies. Super-resolving this principal basis using an SISR method allows us to super-resolve all the information that is coherent across the entire light field. This framework allows the proposed light field super-resolution method to inherit the benefits of the SISR method used. Experimental results show that the proposed method is competitive, and most of the time superior, to recent light field super-resolution methods in terms of both PSNR and SSIM quality metrics, with a lower complexity.
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

The U-Net was presented in 2015. With its straight-forward and successful architecture it quickly evolved to a commonly used benchmark in medical image segmentation. The adaptation of the U-Net to novel problems, however, comprises several degrees of freedom regarding the exact architecture, preprocessing, training and inference. These choices are not independent of each other and substantially impact the overall performance. The present paper introduces the nnU-Net ('no-new-Net'), which refers to a robust and self-adapting framework on the basis of 2D and 3D vanilla U-Nets. We argue the strong case for taking away superfluous bells and whistles of many proposed network designs and instead focus on the remaining aspects that make out the performance and generalizability of a method. We evaluate the nnU-Net in the context of the Medical Segmentation Decathlon challenge, which measures segmentation performance in ten disciplines comprising distinct entities, image modalities, image geometries and dataset sizes, with no manual adjustments between datasets allowed. At the time of manuscript submission, nnU-Net achieves the highest mean dice scores across all classes and seven phase 1 tasks (except class 1 in BrainTumour) in the online leaderboard of the challenge.
Physics Informed Topology Learning in Networks of Linear Dynamical Systems
Venue: arXiv cs.SY 2018
Year: 2018

Learning influence pathways of a network of dynamically related processes from observations is of considerable importance in many disciplines. In this article, influence networks of agents which interact dynamically via linear dependencies are considered. An algorithm for the reconstruction of the topology of interaction based on multivariate Wiener filtering is analyzed. It is shown that for a vast and important class of interactions, that respect flow conservation, the topology of the interactions can be exactly recovered. The class of problems where reconstruction is guaranteed to be exact includes power distribution networks, dynamic thermal networks and consensus networks. The efficacy of the approach is illustrated through simulation and experiments on consensus networks, IEEE power distribution networks and thermal dynamics of buildings.
Cross-Layer Effects on Training Neural Algorithms for Video Streaming
Venue: arXiv cs.NI 2018
Year: 2018

Nowadays Dynamic Adaptive Streaming over HTTP (DASH) is the most prevalent solution on the Internet for multimedia streaming and responsible for the majority of global traffic. DASH uses adaptive bit rate (ABR) algorithms, which select the video quality considering performance metrics such as throughput and playout buffer level. Pensieve is a system that allows to train ABR algorithms using reinforcement learning within a simulated network environment and is outperforming existing approaches in terms of achieved performance. In this paper, we demonstrate that the performance of the trained ABR algorithms depends on the implementation of the simulated environment used to train the neural network. We also show that the used congestion control algorithm impacts the algorithms' performance due to cross-layer effects.
Optimal Multicast of Tiled 360 VR Video in OFDMA Systems
Venue: arXiv cs.IT 2018
Year: 2018

In this letter, we study optimal multicast of tiled 360 virtual reality (VR) video from one server (base station or access point) to multiple users in an orthogonal frequency division multiple access (OFDMA) system. For given video quality, we optimize the subcarrier, transmission power and transmission rate allocation to minimize the total transmission power. For given transmission power budget, we optimize the subcarrier, transmission power and transmission rate allocation to maximize the received video quality. These two optimization problems are non-convex problems. We obtain a globally optimal closed-form solution and a near optimal solution of the two problems, separately, both revealing important design insights for multicast of tiled 360 VR video in OFDMA systems.
Child-sized 3D Printed igus Humanoid Open Platform
Venue: arXiv cs.RO 2018
Year: 2018

The use of standard platforms in the field of humanoid robotics can accelerate research, and lower the entry barrier for new research groups. While many affordable humanoid standard platforms exist in the lower size ranges of up to 60cm, beyond this the few available standard platforms quickly become significantly more expensive, and difficult to operate and maintain. In this paper, the igus Humanoid Open Platform is presented---a new, affordable, versatile and easily customisable standard platform for humanoid robots in the child-sized range. At 90cm, the robot is large enough to interact with a human-scale environment in a meaningful way, and is equipped with enough torque and computing power to foster research in many possible directions. The structure of the robot is entirely 3D printed, allowing for a lightweight and appealing design. The electrical and mechanical designs of the robot are presented, and the main features of the corresponding open-source ROS software are discussed. The 3D CAD files for all of the robot parts have been released open-source in conjunction with this paper.
Adaptive Tensegrity Locomotion on Rough Terrain via Reinforcement Learning
Venue: arXiv cs.RO 2018
Year: 2018

The dynamical properties of tensegrity robots give them appealing ruggedness and adaptability, but present major challenges with respect to locomotion control. Due to high-dimensionality and complex contact responses, data-driven approaches are apt for producing viable feedback policies. Guided Policy Search (GPS), a sample-efficient and model-free hybrid framework for optimization and reinforcement learning, has recently been used to produce periodic locomotion for a spherical 6-bar tensegrity robot on flat or slightly varied surfaces. This work provides an extension to non-periodic locomotion and achieves rough terrain traversal, which requires more broadly varied, adaptive, and non-periodic rover behavior. The contribution alters the control optimization step of GPS, which locally fits and exploits surrogate models of the dynamics, and employs the existing supervised learning step. The proposed solution incorporates new processes to ensure effective local modeling despite the disorganized nature of sample data in rough terrain locomotion. Demonstrations in simulation reveal that the resulting controller sustains the highly adaptive behavior necessary to reliably traverse rough terrain.
SAIL: Machine Learning Guided Structural Analysis Attack on Hardware Obfuscation
Venue: arXiv cs.CR 2018
Year: 2018

Obfuscation is a technique for protecting hardware intellectual property (IP) blocks against reverse engineering, piracy, and malicious modifications. Current obfuscation efforts mainly focus on functional locking of a design to prevent black-box usage. They do not directly address hiding design intent through structural transformations, which is an important objective of obfuscation. We note that current obfuscation techniques incorporate only: (1) local, and (2) predictable changes in circuit topology. In this paper, we present SAIL, a structural attack on obfuscation using machine learning (ML) models that exposes a critical vulnerability of these methods. Through this attack, we demonstrate that the gate-level structure of an obfuscated design can be retrieved in most parts through a systematic set of steps. The proposed attack is applicable to all forms of logic obfuscation, and significantly more powerful than existing attacks, e.g., SAT-based attacks, since it does not require the availability of golden functional responses (e.g. an unlocked IC). Evaluation on benchmark circuits show that we can recover an average of around 84% (up to 95%) transformations introduced by obfuscation. We also show that this attack is scalable, flexible, and versatile.
Performance of MPI sends of non-contiguous data
Venue: arXiv cs.DC 2018
Year: 2018

We present an experimental investigation of the performance of MPI derived datatypes. For messages up to the megabyte range most schemes perform comparably to each other and to manual copying into a regular send buffer. However, for large messages the internal buffering of MPI causes differences in efficiency. The optimal scheme is a combination of packing and derived types.
Patient Risk Assessment and Warning Symptom Detection Using Deep Attention-Based Neural Networks
Venue: arXiv cs.CL 2018
Year: 2018

We present an operational component of a real-world patient triage system. Given a specific patient presentation, the system is able to assess the level of medical urgency and issue the most appropriate recommendation in terms of best point of care and time to treat. We use an attention-based convolutional neural network architecture trained on 600,000 doctor notes in German. We compare two approaches, one that uses the full text of the medical notes and one that uses only a selected list of medical entities extracted from the text. These approaches achieve 79% and 66% precision, respectively, but on a confidence threshold of 0.6, precision increases to 85% and 75%, respectively. In addition, a method to detect warning symptoms is implemented to render the classification task transparent from a medical perspective. The method is based on the learning of attention scores and a method of automatic validation using the same data.
Answering Analytical Queries on Text Data with Temporal Term Histograms
Venue: arXiv cs.DB 2018
Year: 2018

Temporal text, i.e., time-stamped text data are found abundantly in a variety of data sources like newspapers, blogs and social media posts. While today's data management systems provide facilities for searching full-text data, they do not provide any simple primitives for performing analytical operations with text. This paper proposes the temporal term histograms (TTH) as an intermediate primitive that can be used for analytical tasks. We propose an algebra, with operators and equivalence rules for TTH and present a reference implementation on a relational database system.
Semantic Segmentation for Urban Planning Maps based on U-Net
Venue: arXiv cs.LG 2018
Year: 2018

The automatic digitizing of paper maps is a significant and challenging task for both academia and industry. As an important procedure of map digitizing, the semantic segmentation section mainly relies on manual visual interpretation with low efficiency. In this study, we select urban planning maps as a representative sample and investigate the feasibility of utilizing U-shape fully convolutional based architecture to perform end-to-end map semantic segmentation. The experimental results obtained from the test area in Shibuya district, Tokyo, demonstrate that our proposed method could achieve a very high Jaccard similarity coefficient of 93.63% and an overall accuracy of 99.36%. For implementation on GPGPU and cuDNN, the required processing time for the whole Shibuya district can be less than three minutes. The results indicate the proposed method can serve as a viable tool for urban planning map semantic segmentation task with high accuracy and efficiency.
HyperST-Net: Hypernetworks for Spatio-Temporal Forecasting
Venue: arXiv cs.LG 2018
Year: 2018

Spatio-temporal (ST) data, which represent multiple time series data corresponding to different spatial locations, are ubiquitous in real-world dynamic systems, such as air quality readings. Forecasting over ST data is of great importance but challenging as it is affected by many complex factors, including spatial characteristics, temporal characteristics and the intrinsic causality between them. In this paper, we propose a general framework (HyperST-Net) based on hypernetworks for deep ST models. More specifically, it consists of three major modules: a spatial module, a temporal module and a deduction module. Among them, the deduction module derives the parameter weights of the temporal module from the spatial characteristics, which are extracted by the spatial module. Then, we design a general form of HyperST layer as well as different forms for several basic layers in neural networks, including the dense layer (HyperST-Dense) and the convolutional layer (HyperST-Conv). Experiments on three types of real-world tasks demonstrate that the predictive models integrated with our framework achieve significant improvements, and outperform the state-of-the-art baselines as well.
cISP: A Speed-of-Light Internet Service Provider
Venue: arXiv cs.NI 2018
Year: 2018

Low latency is a requirement for a variety of interactive network applications. The Internet, however, is not optimized for latency. We thus explore the design of cost-effective wide-area networks that move data over paths very close to great-circle paths, at speeds very close to the speed of light in vacuum. Our cISP design augments the Internet's fiber with free-space wireless connectivity. cISP addresses the fundamental challenge of simultaneously providing low latency and scalable bandwidth, while accounting for numerous practical factors ranging from transmission tower availability to packet queuing. We show that instantiations of cISP across the contiguous United States and Europe would achieve mean latencies within 5% of that achievable using great-circle paths at the speed of light, over medium and long distances. Further, we estimate that the economic value from such networks would substantially exceed their expense.