OpenRaiser/Intern-Atlas huggingface dataset:
Structure Aware SLAM using Quadrics and Planes
Venue: arXiv cs.RO 2018
Year: 2018

Simultaneous Localization And Mapping (SLAM) is a fundamental problem in mobile robotics. While point-based SLAM methods provide accurate camera localization, the generated maps lack semantic information. On the other hand, state of the art object detection methods provide rich information about entities present in the scene from a single image. This work marries the two and proposes a method for representing generic objects as quadrics which allows object detections to be seamlessly integrated in a SLAM framework. For scene coverage, additional dominant planar structures are modeled as infinite planes. Experiments show that the proposed points-planes-quadrics representation can easily incorporate Manhattan and object affordance constraints, greatly improving camera localization and leading to semantically meaningful maps. The performance of our SLAM system is demonstrated in https://youtu.be/dR-rB9keF8M .
Layered Fields for Natural Tessellations on Surfaces
Venue: arXiv cs.GR 2018
Year: 2018

Mimicking natural tessellation patterns is a fascinating multi-disciplinary problem. Geometric methods aiming at reproducing such partitions on surface meshes are commonly based on the Voronoi model and its variants, and are often faced with challenging issues such as metric estimation, geometric, topological complications, and most critically parallelization. In this paper, we introduce an alternate model which may be of value for resolving these issues. We drop the assumption that regions need to be separated by lines. Instead, we regard region boundaries as narrow bands and we model the partition as a set of smooth functions layered over the surface. Given an initial set of seeds or regions, the partition emerges as the solution of a time dependent set of partial differential equations describing concurrently evolving fronts on the surface. Our solution does not require geodesic estimation, elaborate numerical solvers, or complicated bookkeeping data structures. The cost per time-iteration is dominated by the multiplication and addition of two sparse matrices. Extension of our approach in a Lloyd's algorithm fashion can be easily achieved and the extraction of the dual mesh can be conveniently preformed in parallel through matrix algebra. As our approach relies mainly on basic linear algebra kernels, it lends itself to efficient implementation on modern graphics hardware.
Reconfiguration of graph minors
Venue: arXiv cs.DS 2018
Year: 2018

Under the reconfiguration framework, we consider the various ways that a target graph $H$ is a {\em minor} of a host graph $G$, where a subgraph of $G$ can be transformed into $H$ by means of {\em edge contraction} (replacement of both endpoints of an edge by a new vertex adjacent to any vertex adjacent to either endpoint). Equivalently, an {\em $H$-model} of $G$ is a labeling of the vertices of $G$ with the vertices of $H$, where the contraction of all edges between identically-labeled vertices results in a graph containing representations of all edges in $H$. We explore the properties of $G$ and $H$ that result in a connected {\em reconfiguration graph}, in which nodes represent $H$-models and two nodes are adjacent if their corresponding $H$-models differ by the label of a single vertex of $G$. Various operations on $G$ or $H$ are shown to preserve connectivity. In addition, we demonstrate properties of graphs $G$ that result in connectivity for the target graphs $K_2$, $K_3$, and $K_4$, including a full characterization of graphs $G$ that result in connectivity for $K_2$-models, as well as the relationship between connectivity of $G$ and other $H$-models.
DIDO Hammerstein Identification of Mild Steel Welding Pool in Pulsed GTAW Dynamic Process with Wire Filler
Venue: arXiv cs.SY 2018
Year: 2018

This paper analyzed the nonlinearity of welding dynamic process, and then adopted MIMO Hammerstein model to describe approximately the process. An identification algorithm was developed and pseudo random signals were adopted as model input. Through a welding experiment, input-output data were obtained and the Hammerstein model of welding pool was identified
A Closer Look at Weak Label Learning for Audio Events
Venue: arXiv cs.SD 2018
Year: 2018

Audio content analysis in terms of sound events is an important research problem for a variety of applications. Recently, the development of weak labeling approaches for audio or sound event detection (AED) and availability of large scale weakly labeled dataset have finally opened up the possibility of large scale AED. However, a deeper understanding of how weak labels affect the learning for sound events is still missing from literature. In this work, we first describe a CNN based approach for weakly supervised training of audio events. The approach follows some basic design principle desirable in a learning method relying on weakly labeled audio. We then describe important characteristics, which naturally arise in weakly supervised learning of sound events. We show how these aspects of weak labels affect the generalization of models. More specifically, we study how characteristics such as label density and corruption of labels affects weakly supervised training for audio events. We also study the feasibility of directly obtaining weak labeled data from the web without any manual label and compare it with a dataset which has been manually labeled. The analysis and understanding of these factors should be taken into picture in the development of future weak label learning methods. Audioset, a large scale weakly labeled dataset for sound events is used in our experiments.
Taichi: An Open-Source Computer Graphics Library
Venue: arXiv cs.GR 2018
Year: 2018

An ideal software system in computer graphics should be a combination of innovative ideas, solid software engineering and rapid development. However, in reality these requirements are seldom met simultaneously. In this paper, we present early results on an open-source library named Taichi (http://taichi.graphics), which alleviates this practical issue by providing an accessible, portable, extensible, and high-performance infrastructure that is reusable and tailored for computer graphics. As a case study, we share our experience in building a novel physical simulation system using Taichi.
Seq2Seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models
Venue: arXiv cs.CL 2018
Year: 2018

Neural Sequence-to-Sequence models have proven to be accurate and robust for many sequence prediction tasks, and have become the standard approach for automatic translation of text. The models work in a five stage blackbox process that involves encoding a source sequence to a vector space and then decoding out to a new target sequence. This process is now standard, but like many deep learning methods remains quite difficult to understand or debug. In this work, we present a visual analysis tool that allows interaction with a trained sequence-to-sequence model through each stage of the translation process. The aim is to identify which patterns have been learned and to detect model errors. We demonstrate the utility of our tool through several real-world large-scale sequence-to-sequence use cases.
Real-Time Inference of User Types to Assist with More Inclusive Social Media Activism Campaigns
Venue: arXiv cs.SI 2018
Year: 2018

Social media provides a mechanism for people to engage with social causes across a range of issues. It also provides a strategic tool to those looking to advance a cause to exchange, promote or publicize their ideas. In such instances, AI can be either an asset if used appropriately or a barrier. One of the key issues for a workforce diversity campaign is to understand in real-time who is participating - specifically, whether the participants are individuals or organizations, and in case of individuals, whether they are male or female. In this paper, we present a study to demonstrate a case for AI for social good that develops a model to infer in real-time the different user types participating in a cause-driven hashtag campaign on Twitter, ILookLikeAnEngineer (ILLAE). A generic framework is devised to classify a Twitter user into three classes: organization, male and female in a real-time manner. The framework is tested against two datasets (ILLAE and a general dataset) and outperforms the baseline binary classifiers for categorizing organization/individual and male/female. The proposed model can be applied to future social cause-driven campaigns to get real-time insights on the macro-level social behavior of participants.
Vulnerability Analysis of Smart Grids to GPS Spoofing
Venue: arXiv cs.SY 2018
Year: 2018

Sensors such as phasor measurement units (PMUs) endowed with GPS receivers are ubiquitously installed providing real-time grid visibility. A number of PMUs can cooperatively enable state estimation routines. However, GPS spoofing attacks can notably alter the PMU measurements, mislead the network operator, and drastically impact subsequent corrective control actions. Leveraging a novel measurement model that explicitly accounts for the GPS spoofing attacks, this paper formulates an optimization problem to identify the most vulnerable PMUs in the network. A greedy algorithm is developed to solve the aforementioned problem. Furthermore, the paper develops a computationally efficient alternating minimization algorithm for joint state estimation and attack reconstruction. Numerical tests on IEEE benchmark networks validate the developed methods.
Wireless Quantization Index Modulation: Enabling Communication Through Existing Signals
Venue: arXiv cs.NI 2018
Year: 2018

As the number of IoT devices continue to exponentially increase and saturate the wireless spectrum, there is a dire need for additional spectrum to support large networks of wireless devices. Over the past years, many promising solutions have been proposed but they all suffer from the drawback of new infrastructure costs, setup and maintenance, or are difficult to implement due to FCC regulations. In this paper, we propose a novel Wireless Quantization Index Modulation (QIM) technique which uses existing infrastructure to embed information into existing wireless signals to communicate with IoT devices with negligible impact on the original signal and zero spectrum overhead. We explore the design space for wireless QIM and evaluate the performance of embedding information in TV, FM and AM radio broadcast signals under different conditions. We demonstrate that we can embed messages at up to 8-200~kbps with negligible impact on the audio and video quality of the original FM, AM and TV signals respectively.
Driving Policy Transfer via Modularity and Abstraction
Venue: arXiv cs.RO 2018
Year: 2018

End-to-end approaches to autonomous driving have high sample complexity and are difficult to scale to realistic urban driving. Simulation can help end-to-end driving systems by providing a cheap, safe, and diverse training environment. Yet training driving policies in simulation brings up the problem of transferring such policies to the real world. We present an approach to transferring driving policies from simulation to reality via modularity and abstraction. Our approach is inspired by classic driving systems and aims to combine the benefits of modular architectures and end-to-end deep learning approaches. The key idea is to encapsulate the driving policy such that it is not directly exposed to raw perceptual input or low-level vehicle dynamics. We evaluate the presented approach in simulated urban environments and in the real world. In particular, we transfer a driving policy trained in simulation to a 1/5-scale robotic truck that is deployed in a variety of conditions, with no finetuning, on two continents. The supplementary video can be viewed at https://youtu.be/BrMDJqI6H5U
RIFT: Multi-modal Image Matching Based on Radiation-invariant Feature Transform
Venue: arXiv cs.CV 2018
Year: 2018

Traditional feature matching methods such as scale-invariant feature transform (SIFT) usually use image intensity or gradient information to detect and describe feature points; however, both intensity and gradient are sensitive to nonlinear radiation distortions (NRD). To solve the problem, this paper proposes a novel feature matching algorithm that is robust to large NRD. The proposed method is called radiation-invariant feature transform (RIFT). There are three main contributions in RIFT: first, RIFT uses phase congruency (PC) instead of image intensity for feature point detection. RIFT considers both the number and repeatability of feature points, and detects both corner points and edge points on the PC map. Second, RIFT originally proposes a maximum index map (MIM) for feature description. MIM is constructed from the log-Gabor convolution sequence and is much more robust to NRD than traditional gradient map. Thus, RIFT not only largely improves the stability of feature detection, but also overcomes the limitation of gradient information for feature description. Third, RIFT analyzes the inherent influence of rotations on the values of MIM, and realizes rotation invariance. We use six different types of multi-model image datasets to evaluate RIFT, including optical-optical, infrared-optical, synthetic aperture radar (SAR)-optical, depth-optical, map-optical, and day-night datasets. Experimental results show that RIFT is much more superior to SIFT and SAR-SIFT. To the best of our knowledge, RIFT is the first feature matching algorithm that can achieve good performance on all the above-mentioned types of multi-model images. The source code of RIFT and multi-modal remote sensing image datasets are made public .
From Real to Complex: Enhancing Radio-based Activity Recognition Using Complex-Valued CSI
Venue: arXiv cs.NI 2018
Year: 2018

Activity recognition is an important component of many pervasive computing applications. Radio-based activity recognition has the advantage that it does not have the privacy concern and the subjects do not have to carry a device on them. Recently, it has been shown channel state information (CSI) can be used for activity recognition in a device-free setting. With the proliferation of wireless devices, it is important to understand how radio frequency interference (RFI) can impact on pervasive computing applications. In this paper, we investigate the impact of RFI on device-free CSI-based location-oriented activity recognition. We present data to show that RFI can have a significant impact on the CSI vectors. In the absence of RFI, different activities give rise to different CSI vectors that can be differentiated visually. However, in the presence of RFI, the CSI vectors become much noisier and activity recognition also becomes harder. Our extensive experiments show that the performance of state-of-the-art classification methods may degrade significantly with RFI. We then propose a number of counter measures to mitigate the impact of RFI and improve the location-oriented activity recognition performance. We are also the first to use complex-valued CSI to improve the performance in the environment with RFI.
Joint Energy and SINR Coverage in Spatially Clustered RF-powered IoT Network
Venue: arXiv cs.IT 2018
Year: 2018

Owing to the ubiquitous availability of radio-frequency (RF) signals, RF energy harvesting is emerging as an appealing solution for powering IoT devices. In this paper, we model and analyze an IoT network which harvests RF energy and receives information from the same wireless network. In order to enable this operation, each time slot is partitioned into charging and information reception phases. For this setup, we characterize two performance metrics: (i) energy coverage and (ii) joint signal-to-interference-plus-noise (SINR) and energy coverage. The analysis is performed using a realistic spatial model that captures the spatial coupling between the locations of the IoT devices and the nodes of the wireless network (referred henceforth as the IoT gateways), which is often ignored in the literature. In particular, we model the locations of the IoT devices using a Poisson cluster process (PCP) and assume that some of the clusters have IoT gateways (GWs) deployed at their centers while the other GWs are deployed independently of the IoT devices. The level of coupling can be controlled by tuning the fraction of total GWs that are deployed at the cluster centers. Due to the inherent intractability of computing the distribution of shot noise process for this setup, we propose two accurate approximations, using which the aforementioned metrics are characterized. Multiple system design insights are drawn from our results. For instance, we demonstrate the existence of optimal slot partitioning that maximizes the system throughput. In addition, we explore the effect of the level of coupling between the locations of the IoT devices and the GWs on this optimal slot partitioning. Particularly, our results reveal that the optimal value of time duration for the charging phase increases as the level of coupling decreases.
High-Performance Massive Subgraph Counting using Pipelined Adaptive-Group Communication
Venue: arXiv cs.DC 2018
Year: 2018

Subgraph counting aims to count the number of occurrences of a subgraph T (aka as a template) in a given graph G. The basic problem has found applications in diverse domains. The problem is known to be computationally challenging - the complexity grows both as a function of T and G. Recent applications have motivated solving such problems on massive networks with billions of vertices. In this chapter, we study the subgraph counting problem from a parallel computing perspective. We discuss efficient parallel algorithms for approximately resolving subgraph counting problems by using the color-coding technique. We then present several system-level strategies to substantially improve the overall performance of the algorithm in massive subgraph counting problems. We propose: 1) a novel pipelined Adaptive-Group communication pattern to improve inter-node scalability, 2) a fine-grained pipeline design to effectively reduce the memory space of intermediate results, 3) partitioning neighbor lists of subgraph vertices to achieve better thread concurrency and workload balance. Experimentation on an Intel Xeon E5 cluster shows that our implementation achieves 5x speedup of performance compared to the state-of-the-art work while reduces the peak memory utilization by a factor of 2 on large templates of 12 to 15 vertices and input graphs of 2 to 5 billions of edges.
Optimal measurement selection algorithm and estimator for ultra-wideband symmetric ranging localization
Venue: arXiv cs.RO 2018
Year: 2018

A state estimator is derived for an agent with the ability to measure single ranges to fixed points in its environment, and equipped with an accelerometer and a rate gyroscope. The state estimator makes no agent-specific assumptions, and can be immediately applied to any rigid body with these sensors. Also, the state estimator doesn't use any trilateration-based method to calculate position from range measurements. As the considered system can only make a single range measurement at a time, we present a greedy optimization algorithm for selecting the best measurement. Experiments in an indoor testbed using an externally controlled multicopter demonstrate the efficacy of the algorithm, specifically showing an improvement over a naïve strategy of a fixed sequence of measurements. In separate experiments, the algorithm is also used in feedback control, to control the position of the multicopter.
Fundamental Limits of Coded Linear Transform
Venue: arXiv cs.IT 2018
Year: 2018

In large scale distributed linear transform problems, coded computation plays an important role to effectively deal with "stragglers" (distributed computations that may get delayed due to few slow or faulty processors). We propose a coded computation strategy, referred to as diagonal code, that achieves the optimum recovery threshold and the optimum computation load. This is the first code that simultaneously achieves two-fold optimality in coded distributed linear transforms. Furthermore, by leveraging the idea from random proposal graph theory, we design two random codes that can guarantee optimum recovery threshold with high probability but with much less computation load. These codes provide order-wise improvement over the state-of-the-art. Moreover, the experimental results show significant improvement compared to both uncoded and existing coding schemes.
Sparse Wide-Area Control of Power Systems using Data-driven Reinforcement Learning
Venue: arXiv cs.SY 2018
Year: 2018

In this paper we present an online wide-area oscillation damping control (WAC) design for uncertain models of power systems using ideas from reinforcement learning. We assume that the exact small-signal model of the power system at the onset of a contingency is not known to the operator and use the nominal model and online measurements of the generator states and control inputs to rapidly converge to a state-feedback controller that minimizes a given quadratic energy cost. However, unlike conventional linear quadratic regulators (LQR), we intend our controller to be sparse, so its implementation reduces the communication costs. We, therefore, employ the gradient support pursuit (GraSP) optimization algorithm to impose sparsity constraints on the control gain matrix during learning. The sparse controller is thereafter implemented using distributed communication. Using the IEEE 39-bus power system model with 1149 unknown parameters, it is demonstrated that the proposed learning method provides reliable LQR performance while the controller matched to the nominal model becomes unstable for severely uncertain systems.
Rate-Utility Optimized Streaming of Volumetric Media for Augmented Reality
Venue: arXiv cs.MM 2018
Year: 2018

Volumetric media, popularly known as holograms, need to be delivered to users using both on-demand and live streaming, for new augmented reality (AR) and virtual reality (VR) experiences. As in video streaming, hologram streaming must support network adaptivity and fast startup, but must also moderate large bandwidths, multiple simultaneously streaming objects, and frequent user interaction, which requires low delay. In this paper, we introduce the first system to our knowledge designed specifically for streaming volumetric media. The system reduces bandwidth by introducing 3D tiles, and culling them or reducing their level of detail depending on their relation to the user's view frustum and distance to the user. Our system reduces latency by introducing a window-based buffer, which in contrast to a queue-based buffer allows insertions near the head of the buffer rather than only at the tail of the buffer, to respond quickly to user interaction. To allocate bits between different tiles across multiple objects, we introduce a simple greedy yet provably optimal algorithm for rate-utility optimization. We introduce utility measures based not only on the underlying quality of the representation, but on the level of detail relative to the user's viewpoint and device resolution. Simulation results show that the proposed algorithm provides superior quality compared to existing video-streaming approaches adapted to hologram streaming, in terms of utility and user experience over variable, throughput-constrained networks.