OpenRaiser/Intern-Atlas huggingface dataset:
A Novel BiLevel Paradigm for Image-to-Image Translation
Venue: arXiv cs.CV 2019
Year: 2019

Image-to-image (I2I) translation is a pixel-level mapping that requires a large number of paired training data and often suffers from the problems of high diversity and strong category bias in image scenes. In order to tackle these problems, we propose a novel BiLevel (BiL) learning paradigm that alternates the learning of two models, respectively at an instance-specific (IS) and a general-purpose (GP) level. In each scene, the IS model learns to maintain the specific scene attributes. It is initialized by the GP model that learns from all the scenes to obtain the generalizable translation knowledge. This GP initialization gives the IS model an efficient starting point, thus enabling its fast adaptation to the new scene with scarce training data. We conduct extensive I2I translation experiments on human face and street view datasets. Quantitative results validate that our approach can significantly boost the performance of classical I2I translation models, such as PG2 and Pix2Pix. Our visualization results show both higher image quality and more appropriate instance-specific details, e.g., the translated image of a person looks more like that person in terms of identity.
Deep Learning for Power System Security Assessment
Venue: arXiv cs.LG 2019
Year: 2019

Security assessment is among the most fundamental functions of power system operator. The sheer complexity of power systems exceeding a few buses, however, makes it an extremely computationally demanding task. The emergence of deep learning methods that are able to handle immense amounts of data, and infer valuable information appears as a promising alternative. This paper has two main contributions. First, inspired by the remarkable performance of convolutional neural networks for image processing, we represent for the first time power system snapshots as 2-dimensional images, thus taking advantage of the wide range of deep learning methods available for image processing. Second, we train deep neural networks on a large database for the NESTA 162-bus system to assess both N-1 security and small-signal stability. We find that our approach is over 255 times faster than a standard small-signal stability assessment, and it can correctly determine unsafe points with over 99% accuracy.
Towards Solving the Navier-Stokes Equation on Quantum Computers
Venue: arXiv cs.NA 2019
Year: 2019

In this paper, we explore the suitability of upcoming novel computing technologies, in particular adiabatic annealing based quantum computers, to solve fluid dynamics problems that form a critical component of several science and engineering applications. We start with simple flows with well-studied flow properties, and provide a framework to convert such systems to a form amenable for deployment on such quantum annealers. We analyze the solutions obtained both qualitatively and quantitatively as well as the sensitivities of the various solution selection schemes on the obtained solution.
Suggestion Mining from Online Reviews using ULMFiT
Venue: arXiv cs.CL 2019
Year: 2019

In this paper we present our approach and the system description for Sub Task A of SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums. Given a sentence, the task asks to predict whether the sentence consists of a suggestion or not. Our model is based on Universal Language Model Fine-tuning for Text Classification. We apply various pre-processing techniques before training the language and the classification model. We further provide detailed analysis of the results obtained using the trained model. Our team ranked 10th out of 34 participants, achieving an F1 score of 0.7011. We publicly share our implementation at https://github.com/isarth/SemEval9_MIDAS
Optimal initialization of K-means using Particle Swarm Optimization
Venue: arXiv cs.LG 2019
Year: 2019

This paper proposes the use of an optimization algorithm, namely PSO to decide the initial centroids in K-means, to eventually get better accuracy. The vectorized notation of the optimal centroids can be thought of as entities in an optimization space, where the accuracy of K-means over a random subset of the data could act as a fitness measure. The resultant optimal vector can be used as the initial centroids for K-means.
Detecting driver distraction using stimuli-response EEG analysis
Venue: arXiv cs.HC 2019
Year: 2019

Detecting driver distraction is a significant concern for future intelligent transportation systems. We present a new approach for identifying distracted driving behavior by evaluating a stimulus and response interaction with the brain signals in two ways. First, measuring the driver response through EEG by creating various types of distraction stimuli such as reading, texting, calling and using phone camera (risk odds ratio of these activities determined by NHTSA study). Second, using a survey, comparing driver's order/perception of severity of distraction with the derived distraction index from EEG bands. A 14 electrodes headset was used to record the brain signals while driving in the pilot study with two subjects and a single dry electrode headset with 13 subjects in the main study. We used a naturalistic driving study as opposed to a virtual reality driving simulator to perform the distracted driving maneuvers, consisting of over 100 short duration trials (three to five seconds) for a subject. We overcame a big challenge in EEG analysis - reducing the number of electrodes by isolating one electrode (FC5) from 14 electrode locations to identify certain distractions. Our machine learning methods achieved a mean accuracy (averaged over the subjects and tasks) of 91.54 +/- 5.23% to detect a distracted driving event and 76.99 +/- 8.63% to distinguish between the five distraction cases in our study (read, text, call, and snapshot) using a single electrode. The quantification of distracted driving detailed in this paper is necessary to guide future policies in road safety. Our system addresses the safety concerns resulting from driver distraction and aims to bring about behavioral changes in drivers.
Recognizing the vocabulary of Brazilian popular newspapers with a free-access computational dictionary
Venue: arXiv cs.CL 2019
Year: 2019

We report an experiment to check the identification of a set of words in popular written Portuguese with two versions of a computational dictionary of Brazilian Portuguese, DELAF PB 2004 and DELAF PB 2015. This dictionary is freely available for use in linguistic analyses of Brazilian Portuguese and other researches, which justifies critical study. The vocabulary comes from the PorPopular corpus, made of popular newspapers Di{á}rio Ga{ú}cho (DG) and Massa! (MA). From DG, we retained a set of texts with 984.465 words (tokens), published in 2008, with the spelling used before the Portuguese Language Orthographic Agreement adopted in 2009. From MA, we examined papers of 2012, 2014 e 2015, with 215.776 words (tokens), all with the new spelling. The checking involved: a) generating lists of words (types) occurring in DG and MA; b) comparing them with the entry lists of both versions of DELAF PB; c) assessing the coverage of this vocabulary; d) proposing ways of incorporating the items not covered. The results of the work show that an average of 19% of the types in DG were not found in DELAF PB 2004 or 2015. In MA, this average is 13%. Switching versions of the dictionary affected slightly the performance in recognizing the words.
Enabling Socially Competent navigation through incorporating HRI
Venue: arXiv cs.RO 2019
Year: 2019

Over the last years, social robots have been deployed in public environments making evident the need of human-aware navigation capabilities. In this regard, the robotics community have made efforts to include proxemics or social conventions within the navigation approaches. Nevertheless, few works have tackled the problem of labelling humans as an interactive agent when blocking the robot motion trajectory. Current state of the art navigation planners will either propose an alternative path or freeze the motion until the path is free. We present the first prototype of a framework designed to enhance social competency of robots while navigating in indoor environments. The implementation is done using Navigation and Object Detection open-source software. Specifically, the Robot Operating System (ROS) navigation stack, and OpenCV with Caffe deep learning models and MobileNet Single Shot Detector (SSD), respectively.
Preservation of normality by transducers
Venue: arXiv cs.FL 2019
Year: 2019

We consider input-deterministic finite state transducers with infinite inputs and infinite outputs, and we consider the property of Borel normality on infinite words. When these transducers are given by a strongly connected set of states, and when the input is a Borel normal sequence, the output is an infinite word such that every word has a frequency given by a weighted automaton over the rationals. We prove that there is an algorithm that decides in cubic time whether an input-deterministic transducer preserves normality.
ERNIE: Enhanced Representation through Knowledge Integration
Venue: arXiv cs.CL 2019
Year: 2019

We present a novel language representation model enhanced by knowledge called ERNIE (Enhanced Representation through kNowledge IntEgration). Inspired by the masking strategy of BERT, ERNIE is designed to learn language representation enhanced by knowledge masking strategies, which includes entity-level masking and phrase-level masking. Entity-level strategy masks entities which are usually composed of multiple words.Phrase-level strategy masks the whole phrase which is composed of several words standing together as a conceptual unit.Experimental results show that ERNIE outperforms other baseline methods, achieving new state-of-the-art results on five Chinese natural language processing tasks including natural language inference, semantic similarity, named entity recognition, sentiment analysis and question answering. We also demonstrate that ERNIE has more powerful knowledge inference capacity on a cloze test.
On the Convergence of Adam and Beyond
Venue: arXiv cs.LG 2019
Year: 2019

Several recently proposed stochastic optimization methods that have been successfully used in training deep networks such as RMSProp, Adam, Adadelta, Nadam are based on using gradient updates scaled by square roots of exponential moving averages of squared past gradients. In many applications, e.g. learning with large output spaces, it has been empirically observed that these algorithms fail to converge to an optimal solution (or a critical point in nonconvex settings). We show that one cause for such failures is the exponential moving average used in the algorithms. We provide an explicit example of a simple convex optimization setting where Adam does not converge to the optimal solution, and describe the precise problems with the previous analysis of Adam algorithm. Our analysis suggests that the convergence issues can be fixed by endowing such algorithms with `long-term memory' of past gradients, and propose new variants of the Adam algorithm which not only fix the convergence issues but often also lead to improved empirical performance.
Contact-Aided Invariant Extended Kalman Filtering for Robot State Estimation
Venue: arXiv cs.RO 2019
Year: 2019

Legged robots require knowledge of pose and velocity in order to maintain stability and execute walking paths. Current solutions either rely on vision data, which is susceptible to environmental and lighting conditions, or fusion of kinematic and contact data with measurements from an inertial measurement unit (IMU). In this work, we develop a contact-aided invariant extended Kalman filter (InEKF) using the theory of Lie groups and invariant observer design. This filter combines contact-inertial dynamics with forward kinematic corrections to estimate pose and velocity along with all current contact points. We show that the error dynamics follows a log-linear autonomous differential equation with several important consequences: (a) the observable state variables can be rendered convergent with a domain of attraction that is independent of the system's trajectory; (b) unlike the standard EKF, neither the linearized error dynamics nor the linearized observation model depend on the current state estimate, which (c) leads to improved convergence properties and (d) a local observability matrix that is consistent with the underlying nonlinear system. Furthermore, we demonstrate how to include IMU biases, add/remove contacts, and formulate both world-centric and robo-centric versions. We compare the convergence of the proposed InEKF with the commonly used quaternion-based EKF though both simulations and experiments on a Cassie-series bipedal robot. Filter accuracy is analyzed using motion capture, while a LiDAR mapping experiment provides a practical use case. Overall, the developed contact-aided InEKF provides better performance in comparison with the quaternion-based EKF as a result of exploiting symmetries present in system.
"Why did you do that?": Explaining black box models with Inductive Synthesis
Venue: arXiv cs.AI 2019
Year: 2019

By their nature, the composition of black box models is opaque. This makes the ability to generate explanations for the response to stimuli challenging. The importance of explaining black box models has become increasingly important given the prevalence of AI and ML systems and the need to build legal and regulatory frameworks around them. Such explanations can also increase trust in these uncertain systems. In our paper we present RICE, a method for generating explanations of the behaviour of black box models by (1) probing a model to extract model output examples using sensitivity analysis; (2) applying CNPInduce, a method for inductive logic program synthesis, to generate logic programs based on critical input-output pairs; and (3) interpreting the target program as a human-readable explanation. We demonstrate the application of our method by generating explanations of an artificial neural network trained to follow simple traffic rules in a hypothetical self-driving car simulation. We conclude with a discussion on the scalability and usability of our approach and its potential applications to explanation-critical scenarios.
Reconstruction and Error-Correction Codes for Polymer-Based Data Storage
Venue: arXiv cs.IT 2019
Year: 2019

Motivated by polymer-based data-storage platforms that use chains of binary synthetic polymers as the recording media and read the content via tandem mass spectrometers, we propose a new family of codes that allows for unique string reconstruction and correction of one mass error. Our approach is based on introducing redundancy that scales logarithmically with the length of the string and allows for the string to be uniquely reconstructed based only on its erroneous substring composition multiset. The key idea behind our unique reconstruction approach is to interleave Catalan-type paths with arbitrary binary strings and "reflect" them so as to allow prefixes and suffixes of the same length to have different weights. For error correction, we add a constant number of bits that provides information about the weights of reflected pairs of bits and hence enable recovery from a single mass error. The asymptotic code rate of the scheme is one, and decoding is accomplished via a simplified version of the backtracking algorithm used for the Turnpike problem.
Active Adversarial Evader Tracking with a Probabilistic Pursuer under the Pursuit-Evasion Game Framework
Venue: arXiv cs.RO 2019
Year: 2019

Given a mapped environment, we formulate the problem of visually tracking and following an evader using a probabilistic framework. In this work, we consider a non-holonomic robot with a limited visibility depth sensor in an indoor environment with obstacles. The mobile robot that follows the target is considered a pursuer and the agent being followed is considered an evader. We propose a probabilistic framework for both the pursuer and evader to achieve their conflicting goals. We introduce a smart evader that has information about the location of the pursuer. The goal of this variant of the evader is to avoid being tracked by the pursuer by using the visibility region information obtained from the pursuer, to further challenge the proposed smart pursuer. To validate the efficiency of the framework, we conduct several experiments in simulation by using Gazebo and evaluate the success rate of tracking an evader in various environments with different pursuer to evader speed ratios. Through our experiments we validate our hypothesis that a smart pursuer tracks an evader more effectively than a pursuer that just navigates in the environment randomly. We also validate that an evader that is aware of the actions of the pursuer is more successful at avoiding getting tracked by a smart pursuer than a random evader. Finally, we empirically show that while a smart pursuer does increase it's average success rate of tracking compared to a random pursuer, there is an increased variance in its success rate distribution when the evader becomes aware of its actions.
Context-Aware Zero-Shot Recognition
Venue: arXiv cs.CV 2019
Year: 2019

We present a novel problem setting in zero-shot learning, zero-shot object recognition and detection in the context. Contrary to the traditional zero-shot learning methods, which simply infers unseen categories by transferring knowledge from the objects belonging to semantically similar seen categories, we aim to understand the identity of the novel objects in an image surrounded by the known objects using the inter-object relation prior. Specifically, we leverage the visual context and the geometric relationships between all pairs of objects in a single image, and capture the information useful to infer unseen categories. We integrate our context-aware zero-shot learning framework into the traditional zero-shot learning techniques seamlessly using a Conditional Random Field (CRF). The proposed algorithm is evaluated on both zero-shot region classification and zero-shot detection tasks. The results on Visual Genome (VG) dataset show that our model significantly boosts performance with the additional visual context compared to traditional methods.
Steepest Gradient-Based Orthogonal Precoder For Integer-Forcing MIMO
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we develop an orthogonal precoding scheme for integer-forcing (IF) linear receivers using the steepest gradient algorithm. Although this scheme can be viewed as a special case of the unitary precoded integer-forcing (UPIF), it has two major advantages. First, the orthogonal precoding outperforms its unitary counterpart in terms of achievable rate, outage probability, and error rate. We verify this advantage via theoretical and numerical analyses. Second, it exhibits lower complexity as the dimension of orthogonal matrices is half that of unitary matrices in the real-valued domain. For finding ``good'' orthogonal precoder matrices, we propose an efficient algorithm based on the steepest gradient algorithm that exploits the geometrical properties of orthogonal matrices as a Lie group. The proposed algorithm has low complexity and can be easily applied to an arbitrary MIMO configuration. We also confirm numerically that the proposed orthogonal precoding outperforms UPIF type II in some scenarios and the X-precoder in high-order QAM schemes, e.g., $64$- and $256$-QAM.
Economic Analyses of Security Investments on Cryptocurrency Exchanges
Venue: arXiv cs.CR 2019
Year: 2019

Cryptocurrency exchanges are frequently targeted and compromised by cyber-attacks, which may lead to significant losses for the depositors and closure of the affected exchanges. These risks threaten the viability of the entire public blockchain ecosystem since exchanges serve as major gateways for participation in public blockchain technologies. In this paper, we develop an economic model to capture the short-term incentives of cryptocurrency exchanges with respect to making security investments and establishing transaction fees. Using the model, we derive conclusions regarding an exchange's optimal economic decisions, and illustrate key features of these conclusions using graphs based on real-world data. Our security investment model exhibits horizontal scaling properties with respect to reducing exposure to losses, and may be of special interest to exchanges operating in markets with high price volatility.
Improved Upper Bounds on the Hermite and KZ Constants
Venue: arXiv cs.IT 2019
Year: 2019

The Korkine-Zolotareff (KZ) reduction is a widely used lattice reduction strategy in communications and cryptography. The Hermite constant, which is a vital constant of lattice, has many applications, such as bounding the length of the shortest nonzero lattice vector and orthogonality defect of lattices. The KZ constant can be used in quantifying some useful properties of KZ reduced matrices. In this paper, we first develop a linear upper bound on the Hermite constant and then use the bound to develop an upper bound on the KZ constant. These upper bounds are sharper than those obtained recently by the first two authors. Some examples on the applications of the improved upper bounds are also presented.
Waterfall Bandits: Learning to Sell Ads Online
Venue: arXiv cs.LG 2019
Year: 2019

A popular approach to selling online advertising is by a waterfall, where a publisher makes sequential price offers to ad networks for an inventory, and chooses the winner in that order. The publisher picks the order and prices to maximize her revenue. A traditional solution is to learn the demand model and then subsequently solve the optimization problem for the given demand model. This will incur a linear regret. We design an online learning algorithm for solving this problem, which interleaves learning and optimization, and prove that this algorithm has sublinear regret. We evaluate the algorithm on both synthetic and real-world data, and show that it quickly learns high quality pricing strategies. This is the first principled study of learning a waterfall design online by sequential experimentation.
Multi-modal gated recurrent units for image description
Venue: arXiv cs.CV 2019
Year: 2019

Using a natural language sentence to describe the content of an image is a challenging but very important task. It is challenging because a description must not only capture objects contained in the image and the relationships among them, but also be relevant and grammatically correct. In this paper a multi-modal embedding model based on gated recurrent units (GRU) which can generate variable-length description for a given image. In the training step, we apply the convolutional neural network (CNN) to extract the image feature. Then the feature is imported into the multi-modal GRU as well as the corresponding sentence representations. The multi-modal GRU learns the inter-modal relations between image and sentence. And in the testing step, when an image is imported to our multi-modal GRU model, a sentence which describes the image content is generated. The experimental results demonstrate that our multi-modal GRU model obtains the state-of-the-art performance on Flickr8K, Flickr30K and MS COCO datasets.