OpenRaiser/Intern-Atlas huggingface dataset:
Pangolin: A Fault-Tolerant Persistent Memory Programming Library
Venue: arXiv cs.DC 2019
Year: 2019

Non-volatile main memory (NVMM) allows programmers to build complex, persistent, pointer-based data structures that can offer substantial performance gains over conventional approaches to managing persistent state. This programming model removes the file system from the critical path which improves performance, but it also places these data structures out of reach of file system-based fault tolerance mechanisms (e.g., block-based checksums or erasure coding). Without fault-tolerance, using NVMM to hold critical data will be much less attractive. This paper presents Pangolin, a fault-tolerant persistent object library designed for NVMM. Pangolin uses a combination of checksums, parity, and micro-buffering to protect an application's objects from both media errors and corruption due to software bugs. It provides these protections for objects of any size and supports automatic, online detection of data corruption and recovery. The required storage overhead is small (1% for gigabyte-sized pools of NVMM). Pangolin provides stronger protection, requires orders of magnitude less storage overhead, and achieves comparable performance relative to the current state-of-the-art fault-tolerant persistent object library.
Tertiary Eye Movement Classification by a Hybrid Algorithm
Venue: arXiv cs.CV 2019
Year: 2019

The proper classification of major eye movements, saccades, fixations, and smooth pursuits, remains essential to utilizing eye-tracking data. There is difficulty in separating out smooth pursuits from the other behavior types, particularly from fixations. To this end, we propose a new offline algorithm, I-VDT-HMM, for tertiary classification of eye movements. The algorithm combines the simplicity of two foundational algorithms, I-VT and I-DT, as has been implemented in I-VDT, with the statistical predictive power of the Viterbi algorithm. We evaluate the fitness across a dataset of eight eye movement records at eight sampling rates gathered from previous research, with a comparison to the current state-of-the-art using the proposed quantitative and qualitative behavioral scores. The proposed algorithm achieves promising results in clean high sampling frequency data and with slight modifications could show similar results with lower quality data. Though, the statistical aspect of the algorithm comes at a cost of classification time.
Siamese Attentional Keypoint Network for High Performance Visual Tracking
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we investigate the impacts of three main aspects of visual tracking, i.e., the backbone network, the attentional mechanism, and the detection component, and propose a Siamese Attentional Keypoint Network, dubbed SATIN, for efficient tracking and accurate localization. Firstly, a new Siamese lightweight hourglass network is specially designed for visual tracking. It takes advantage of the benefits of the repeated bottom-up and top-down inference to capture more global and local contextual information at multiple scales. Secondly, a novel cross-attentional module is utilized to leverage both channel-wise and spatial intermediate attentional information, which can enhance both discriminative and localization capabilities of feature maps. Thirdly, a keypoints detection approach is invented to trace any target object by detecting the top-left corner point, the centroid point, and the bottom-right corner point of its bounding box. Therefore, our SATIN tracker not only has a strong capability to learn more effective object representations, but also is computational and memory storage efficiency, either during the training or testing stages. To the best of our knowledge, we are the first to propose this approach. Without bells and whistles, experimental results demonstrate that our approach achieves state-of-the-art performance on several recent benchmark datasets, at a speed far exceeding 27 frames per second.
Analysis and Visualization of Deep Neural Networks in Device-Free Wi-Fi Indoor Localization
Venue: arXiv cs.NI 2019
Year: 2019

Device-free Wi-Fi indoor localization has received significant attention as a key enabling technology for many Internet of Things (IoT) applications. Machine learning-based location estimators, such as the deep neural network (DNN), carry proven potential in achieving high-precision localization performance by automatically learning discriminative features from the noisy wireless signal measurements. However, the inner workings of DNNs are not transparent and not adequately understood especially in the indoor localization application. In this paper, we provide quantitative and visual explanations for the DNN learning process as well as the critical features that DNN has learned during the process. Toward this end, we propose to use several visualization techniques, including: 1) dimensionality reduction visualization, to project the high-dimensional feature space to the 2D space to facilitate visualization and interpretation, and 2) visual analytics and information visualization, to quantify relative contributions of each feature with the proposed feature manipulation procedures. The results provide insightful views and plausible explanations of the DNN in device-free Wi-Fi indoor localization using channel state information (CSI) fingerprints.
Decision Making for Autonomous Vehicles at Unsignalized Intersection in Presence of Malicious Vehicles
Venue: arXiv cs.SY 2019
Year: 2019

In this paper, we investigate the decision making of autonomous vehicles in an unsignalized intersection in presence of malicious vehicles, which are vehicles that do not respect the law by not using the proper rules of the right of way. Each vehicle computes its control input as a Nash equilibrium of a game determined by the priority order based on its own belief: each of non-malicious vehicle bases its order on the law, while a malicious one considers itself as having priority. To illustrate our method, we provide numerical simulations, with different scenarios given by different cases of malicious vehicles.
Migration patterns under different scenarios of sea level rise
Venue: arXiv cs.CY 2019
Year: 2019

We propose a framework to examine future migration patterns of people under different sea level rise scenarios using models of human migration. Specifically, we couple a sea level rise model with a data-driven model of human migration, creating a generalized joint model of climate driven migration that can be used to simulate population distributions under potential future sea level rise scenarios. We show how this joint model relaxes assumptions in existing efforts to model climate driven human migration, and use it to simulate how migration, driven by sea level rise, differs from baseline migration patterns. Our results show that the effects of sea level rise are pervasive, expanding beyond coastal areas via increased migration, and disproportionately affecting some areas of the United States. The code for reproducing this study is available at https://github.com/calebrob6/migration-slr.
Fully-/Partially-Connected Hybrid Beamforming Architectures for mmWave MU-MIMO
Venue: arXiv cs.IT 2019
Year: 2019

Hybrid digital analog (HDA) beamforming has attracted considerable attention in practical implementation of millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems due to the low power consumption with respect to its fully digital baseband counterpart. The implementation cost, performance, and power efficiency of HDA beamforming depends on the level of connectivity and reconfigurability of the analog beamforming network. In this paper, we investigate the performance of two typical architectures that can be regarded as extreme cases, namely, the fully-connected (FC) and the one-stream-per-subarray (OSPS) architectures. In the FC architecture each RF antenna port is connected to all antenna elements of the array, while in the OSPS architecture the RF antenna ports are connected to disjoint subarrays. We jointly consider the initial beam acquisition and data communication phases, such that the latter takes place by using the beam direction information obtained by the former. We use the state-of-the-art beam alignment (BA) scheme previously proposed by the authors and consider a family of MU-MIMO precoding schemes well adapted to the beam information extracted from the BA phase. We also evaluate the power efficiency of the two HDA architectures taking into account the power dissipation at different hardware components as well as the power backoff under typical power amplifier constraints. Numerical results show that the two architectures achieve similar sum spectral efficiency, while the OSPS architecture is advantageous with respect to the FC case in terms of hardware complexity and power efficiency, at the sole cost of a slightly longer BA time-to-acquisition due to its reduced beam angle resolution.
Wasserstein-Fisher-Rao Document Distance
Venue: arXiv cs.LG 2019
Year: 2019

As a fundamental problem of natural language processing, it is important to measure the distance between different documents. Among the existing methods, the Word Mover's Distance (WMD) has shown remarkable success in document semantic matching for its clear physical insight as a parameter-free model. However, WMD is essentially based on the classical Wasserstein metric, thus it often fails to robustly represent the semantic similarity between texts of different lengths. In this paper, we apply the newly developed Wasserstein-Fisher-Rao (WFR) metric from unbalanced optimal transport theory to measure the distance between different documents. The proposed WFR document distance maintains the great interpretability and simplicity as WMD. We demonstrate that the WFR document distance has significant advantages when comparing the texts of different lengths. In addition, an accelerated Sinkhorn based algorithm with GPU implementation has been developed for the fast computation of WFR distances. The KNN classification results on eight datasets have shown its clear improvement over WMD.
A Neural Influence Diffusion Model for Social Recommendation
Venue: arXiv cs.IR 2019
Year: 2019

Precise user and item embedding learning is the key to building a successful recommender system. Traditionally, Collaborative Filtering(CF) provides a way to learn user and item embeddings from the user-item interaction history. However, the performance is limited due to the sparseness of user behavior data. With the emergence of online social networks, social recommender systems have been proposed to utilize each user's local neighbors' preferences to alleviate the data sparsity for better user embedding modeling. We argue that, for each user of a social platform, her potential embedding is influenced by her trusted users. As social influence recursively propagates and diffuses in the social network, each user's interests change in the recursive process. Nevertheless, the current social recommendation models simply developed static models by leveraging the local neighbors of each user without simulating the recursive diffusion in the global social network, leading to suboptimal recommendation performance. In this paper, we propose a deep influence propagation model to stimulate how users are influenced by the recursive social diffusion process for social recommendation. For each user, the diffusion process starts with an initial embedding that fuses the related features and a free user latent vector that captures the latent behavior preference. The key idea of our proposed model is that we design a layer-wise influence propagation structure to model how users' latent embeddings evolve as the social diffusion process continues. We further show that our proposed model is general and could be applied when the user~(item) attributes or the social network structure is not available. Finally, extensive experimental results on two real-world datasets clearly show the effectiveness of our proposed model, with more than 13% performance improvements over the best baselines.
Identifying cross country skiing techniques using power meters in ski poles
Venue: arXiv cs.LG 2019
Year: 2019

Power meters are becoming a widely used tool for measuring training and racing effort in cycling, and are now spreading also to other sports. This means that increasing volumes of data can be collected from athletes, with the aim of helping coaches and athletes analyse and understanding training load, racing efforts, technique etc. In this project, we have collaborated with Skisens AB, a company producing handles for cross country ski poles equipped with power meters. We have conducted a pilot study in the use of machine learning techniques on data from Skisens poles to identify which "gear" a skier is using (double poling or gears 2-4 in skating), based only on the sensor data from the ski poles. The dataset for this pilot study contained labelled time-series data from three individual skiers using four different gears recorded in varied locations and varied terrain. We systematically evaluated a number of machine learning techniques based on neural networks with best results obtained by a LSTM network (accuracy of 95% correctly classified strokes), when a subset of data from all three skiers was used for training. As expected, accuracy dropped to 78% when the model was trained on data from only two skiers and tested on the third. To achieve better generalisation to individuals not appearing in the training set more data is required, which is ongoing work.
Multi-modal 3D Shape Reconstruction Under Calibration Uncertainty using Parametric Level Set Methods
Venue: arXiv cs.GR 2019
Year: 2019

We consider the problem of 3D shape reconstruction from multi-modal data, given uncertain calibration parameters. Typically, 3D data modalities can be in diverse forms such as sparse point sets, volumetric slices, 2D photos and so on. To jointly process these data modalities, we exploit a parametric level set method that utilizes ellipsoidal radial basis functions. This method not only allows us to analytically and compactly represent the object, it also confers on us the ability to overcome calibration related noise that originates from inaccurate acquisition parameters. This essentially implicit regularization leads to a highly robust and scalable reconstruction, surpassing other traditional methods. In our results we first demonstrate the ability of the method to compactly represent complex objects. We then show that our reconstruction method is robust both to a small number of measurements and to noise in the acquisition parameters. Finally, we demonstrate our reconstruction abilities from diverse modalities such as volume slices obtained from liquid displacement (similar to CTscans and XRays), and visual measurements obtained from shape silhouettes.
Big Math and the One-Brain Barrier A Position Paper and Architecture Proposal
Venue: arXiv cs.MS 2019
Year: 2019

Over the last decades, a class of important mathematical results have required an ever increasing amount of human effort to carry out. For some, the help of computers is now indispensable. We analyze the implications of this trend towards "big mathematics", its relation to human cognition, and how machine support for big math can be organized. The central contribution of this position paper is an information model for "doing mathematics", which posits that humans very efficiently integrate four aspects: inference, computation, tabulation, and narration around a well-organized core of mathematical knowledge. The challenge for mathematical software systems is that these four aspects need to be integrated as well. We briefly survey the state of the art.
BIT: Biologically Inspired Tracker
Venue: arXiv cs.CV 2019
Year: 2019

Visual tracking is challenging due to image variations caused by various factors, such as object deformation, scale change, illumination change and occlusion. Given the superior tracking performance of human visual system (HVS), an ideal design of biologically inspired model is expected to improve computer visual tracking. This is however a difficult task due to the incomplete understanding of neurons' working mechanism in HVS. This paper aims to address this challenge based on the analysis of visual cognitive mechanism of the ventral stream in the visual cortex, which simulates shallow neurons (S1 units and C1 units) to extract low-level biologically inspired features for the target appearance and imitates an advanced learning mechanism (S2 units and C2 units) to combine generative and discriminative models for target location. In addition, fast Gabor approximation (FGA) and fast Fourier transform (FFT) are adopted for real-time learning and detection in this framework. Extensive experiments on large-scale benchmark datasets show that the proposed biologically inspired tracker performs favorably against state-of-the-art methods in terms of efficiency, accuracy, and robustness. The acceleration technique in particular ensures that BIT maintains a speed of approximately 45 frames per second.
GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection
Venue: arXiv cs.CL 2019
Year: 2019

In this paper we present GumDrop, Georgetown University's entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection. Our approach relies on model stacking, creating a heterogeneous ensemble of classifiers, which feed into a metalearner for each final task. The system encompasses three trainable component stacks: one for sentence splitting, one for discourse unit segmentation and one for connective detection. The flexibility of each ensemble allows the system to generalize well to datasets of different sizes and with varying levels of homogeneity.
Counting Induced Subgraphs: An Algebraic Approach to #W[1]-hardness
Venue: arXiv cs.CC 2019
Year: 2019

We study the problem #IndSub(P) of counting all induced subgraphs of size k in a graph G that satisfy the property P. This problem was introduced by Jerrum and Meeks and shown to be #W[1]-hard when parameterized by k for some families of properties P including, among others, connectivity [JCSS 15] and even- or oddness of the number of edges [Combinatorica 17]. Very recently [IPEC 18], two of the authors introduced a novel technique for the complexity analysis of #IndSub(P), inspired by the "topological approach to evasiveness" of Kahn, Saks and Sturtevant [FOCS 83] and the framework of graph motif parameters due to Curticapean, Dell and Marx [STOC 17], allowing them to prove hardness of a wide range of properties P. In this work, we refine this technique for graph properties that are non-trivial on edge-transitive graphs with a prime power number of edges. In particular, we fully classify the case of monotone bipartite graph properties: It is shown that, given any graph property P that is closed under the removal of vertices and edges, and that is non-trivial for bipartite graphs, the problem #IndSub(P) is #W[1]-hard and cannot be solved in time f(k)*n^{o(k)} for any computable function f, unless the Exponential Time Hypothesis fails. This holds true even if the input graph is restricted to be bipartite and counting is done modulo a fixed prime. A similar result is shown for properties that are closed under the removal of edges only.
IRC: Cross-layer design exploration of Intermittent Robust Computation units for IoTs
Venue: arXiv cs.ET 2019
Year: 2019

Energy-harvesting-powered computing offers intriguing and vast opportunities to dramatically transform the landscape of the Internet of Things (IoT) devices by utilizing ambient sources of energy to achieve battery-free computing. In order to operate within the restricted energy capacity and intermittency profile, it is proposed to innovate Intermittent Robust Computation (IRC) Unit as a new duty-cycle-variable computing approach leveraging the non-volatility inherent in spin-based switching devices. The foundations of IRC will be advanced from the device-level upwards, by extending a Spin Hall Effect Magnetic Tunnel Junction (SHE-MTJ) device. The device will then be used to realize SHE-MTJ Majority/Polymorphic Gate (MG/PG) logic approaches and libraries. Then a Logic-Embedded Flip-Flop (LE-FF) is developed to realize rudimentary Boolean logic functions along with an inherent state-holding capability within a compact footprint. Finally, the NV-Clustering synthesis procedure and corresponding tool module are proposed to instantiate the LE-FF library cells within conventional Register Transfer Language (RTL) specifications. This selectively clusters together logic and NV state-holding functionality, based on energy and area minimization criteria. It also realizes middleware-coherent, intermittent computation without checkpointing, micro-tasking, or software bloat and energy overheads vital to IoT. Simulation results for various benchmark circuits including ISCAS-89 validate functionality and power dissipation, area, and delay benefits.
Integer Programming for Learning Directed Acyclic Graphs from Continuous Data
Venue: arXiv cs.LG 2019
Year: 2019

Learning directed acyclic graphs (DAGs) from data is a challenging task both in theory and in practice, because the number of possible DAGs scales superexponentially with the number of nodes. In this paper, we study the problem of learning an optimal DAG from continuous observational data. We cast this problem in the form of a mathematical programming model which can naturally incorporate a super-structure in order to reduce the set of possible candidate DAGs. We use the penalized negative log-likelihood score function with both $\ell_0$ and $\ell_1$ regularizations and propose a new mixed-integer quadratic optimization (MIQO) model, referred to as a layered network (LN) formulation. The LN formulation is a compact model, which enjoys as tight an optimal continuous relaxation value as the stronger but larger formulations under a mild condition. Computational results indicate that the proposed formulation outperforms existing mathematical formulations and scales better than available algorithms that can solve the same problem with only $\ell_1$ regularization. In particular, the LN formulation clearly outperforms existing methods in terms of computational time needed to find an optimal DAG in the presence of a sparse super-structure.
Realizing Petabyte Scale Acoustic Modeling
Venue: arXiv cs.SD 2019
Year: 2019

Large scale machine learning (ML) systems such as the Alexa automatic speech recognition (ASR) system continue to improve with increasing amounts of manually transcribed training data. Instead of scaling manual transcription to impractical levels, we utilize semi-supervised learning (SSL) to learn acoustic models (AM) from the vast firehose of untranscribed audio data. Learning an AM from 1 Million hours of audio presents unique ML and system design challenges. We present the design and evaluation of a highly scalable and resource efficient SSL system for AM. Employing the student/teacher learning paradigm, we focus on the student learning subsystem: a scalable and robust data pipeline that generates features and targets from raw audio, and an efficient model pipeline, including the distributed trainer, that builds a student model. Our evaluations show that, even without extensive hyper-parameter tuning, we obtain relative accuracy improvements in the 10 to 20$\%$ range, with higher gains in noisier conditions. The end-to-end processing time of this SSL system was 12 days, and several components in this system can trivially scale linearly with more compute resources.