OpenRaiser/Intern-Atlas huggingface dataset:
Deep Probabilistic Imaging: Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imaging
Venue: arXiv cs.LG 2020
Year: 2020

Computational image reconstruction algorithms generally produce a single image without any measure of uncertainty or confidence. Regularized Maximum Likelihood (RML) and feed-forward deep learning approaches for inverse problems typically focus on recovering a point estimate. This is a serious limitation when working with underdetermined imaging systems, where it is conceivable that multiple image modes would be consistent with the measured data. Characterizing the space of probable images that explain the observational data is therefore crucial. In this paper, we propose a variational deep probabilistic imaging approach to quantify reconstruction uncertainty. Deep Probabilistic Imaging (DPI) employs an untrained deep generative model to estimate a posterior distribution of an unobserved image. This approach does not require any training data; instead, it optimizes the weights of a neural network to generate image samples that fit a particular measurement dataset. Once the network weights have been learned, the posterior distribution can be efficiently sampled. We demonstrate this approach in the context of interferometric radio imaging, which is used for black hole imaging with the Event Horizon Telescope, and compressed sensing Magnetic Resonance Imaging (MRI).
It's All in the Name: A Character Based Approach To Infer Religion
Venue: arXiv cs.CL 2020
Year: 2020

Demographic inference from text has received a surge of attention in the field of natural language processing in the last decade. In this paper, we use personal names to infer religion in South Asia - where religion is a salient social division, and yet, disaggregated data on it remains scarce. Existing work predicts religion using dictionary based method, and therefore, can not classify unseen names. We use character based models which learn character patterns and, therefore, can classify unseen names as well with high accuracy. These models are also much faster and can easily be scaled to large data sets. We improve our classifier by combining the name of an individual with that of their parent/spouse and achieve remarkably high accuracy. Finally, we trace the classification decisions of a convolutional neural network model using layer-wise relevance propagation which can explain the predictions of complex non-linear classifiers and circumvent their purported black box nature. We show how character patterns learned by the classifier are rooted in the linguistic origins of names.
Fast Interleaved Bidirectional Sequence Generation
Venue: arXiv cs.CL 2020
Year: 2020

Independence assumptions during sequence generation can speed up inference, but parallel generation of highly inter-dependent tokens comes at a cost in quality. Instead of assuming independence between neighbouring tokens (semi-autoregressive decoding, SA), we take inspiration from bidirectional sequence generation and introduce a decoder that generates target words from the left-to-right and right-to-left directions simultaneously. We show that we can easily convert a standard architecture for unidirectional decoding into a bidirectional decoder by simply interleaving the two directions and adapting the word positions and self-attention masks. Our interleaved bidirectional decoder (IBDecoder) retains the model simplicity and training efficiency of the standard Transformer, and on five machine translation tasks and two document summarization tasks, achieves a decoding speedup of ~2X compared to autoregressive decoding with comparable quality. Notably, it outperforms left-to-right SA because the independence assumptions in IBDecoder are more felicitous. To achieve even higher speedups, we explore hybrid models where we either simultaneously predict multiple neighbouring tokens per direction, or perform multi-directional decoding by partitioning the target sequence. These methods achieve speedups to 4X-11X across different tasks at the cost of <1 BLEU or <0.5 ROUGE (on average). Source code is released at https://github.com/bzhangGo/zero.
Perception for Autonomous Systems (PAZ)
Venue: arXiv cs.CV 2020
Year: 2020

In this paper we introduce the Perception for Autonomous Systems (PAZ) software library. PAZ is a hierarchical perception library that allow users to manipulate multiple levels of abstraction in accordance to their requirements or skill level. More specifically, PAZ is divided into three hierarchical levels which we refer to as pipelines, processors, and backends. These abstractions allows users to compose functions in a hierarchical modular scheme that can be applied for preprocessing, data-augmentation, prediction and postprocessing of inputs and outputs of machine learning (ML) models. PAZ uses these abstractions to build reusable training and prediction pipelines for multiple robot perception tasks such as: 2D keypoint estimation, 2D object detection, 3D keypoint discovery, 6D pose estimation, emotion classification, face recognition, instance segmentation, and attention mechanisms.
Learning Time Reduction Using Warm Start Methods for a Reinforcement Learning Based Supervisory Control in Hybrid Electric Vehicle Applications
Venue: arXiv cs.RO 2020
Year: 2020

Reinforcement Learning (RL) is widely utilized in the field of robotics, and as such, it is gradually being implemented in the Hybrid Electric Vehicle (HEV) supervisory control. Even though RL exhibits excellent performance in terms of fuel consumption minimization in simulation, the large learning iteration number needs a long learning time, making it hardly applicable in real-world vehicles. In addition, the fuel consumption of initial learning phases is much worse than baseline controls. This study aims to reduce the learning iterations of Q-learning in HEV application and improve fuel consumption in initial learning phases utilizing warm start methods. Different from previous studies, which initiated Q-learning with zero or random Q values, this study initiates the Q-learning with different supervisory controls (i.e., Equivalent Consumption Minimization Strategy control and heuristic control), and detailed analysis is given. The results show that the proposed warm start Q-learning requires 68.8% fewer iterations than cold start Q-learning. The trained Q-learning is validated in two different driving cycles, and the results show 10-16% MPG improvement when compared to Equivalent Consumption Minimization Strategy control. Furthermore, real-time feasibility is analyzed, and the guidance of vehicle implementation is provided. The results of this study can be used to facilitate the deployment of RL in vehicle supervisory control applications.
Stereo Frustums: A Siamese Pipeline for 3D Object Detection
Venue: arXiv cs.CV 2020
Year: 2020

The paper proposes a light-weighted stereo frustums matching module for 3D objection detection. The proposed framework takes advantage of a high-performance 2D detector and a point cloud segmentation network to regress 3D bounding boxes for autonomous driving vehicles. Instead of performing traditional stereo matching to compute disparities, the module directly takes the 2D proposals from both the left and the right views as input. Based on the epipolar constraints recovered from the well-calibrated stereo cameras, we propose four matching algorithms to search for the best match for each proposal between the stereo image pairs. Each matching pair proposes a segmentation of the scene which is then fed into a 3D bounding box regression network. Results of extensive experiments on KITTI dataset demonstrate that the proposed Siamese pipeline outperforms the state-of-the-art stereo-based 3D bounding box regression methods.
Modeling social media contagion using Hawkes processes
Venue: arXiv cs.SI 2020
Year: 2020

The contagion dynamics can emerge in social networks when repeated activation is allowed. An interesting example of this phenomenon is retweet cascades where users allow to re-share content posted by other people with public accounts. To model this type of behaviour we use a Hawkes self-exciting process. To do it properly though one needs to calibrate model under consideration. The main goal of this paper is to construct moments method of estimation of this model. The key step is based on identifying of a generator of a Hawkes process. We perform numerical analysis on real data as well.
COVID-19 in Spain and India: Comparing Policy Implications by Analyzing Epidemiological and Social Media Data
Venue: arXiv cs.SI 2020
Year: 2020

The COVID-19 pandemic has forced public health experts to develop contingent policies to stem the spread of infection, including measures such as partial/complete lockdowns. The effectiveness of these policies has varied with geography, population distribution, and effectiveness in implementation. Consequently, some nations (e.g., Taiwan, Haiti) have been more successful than others (e.g., United States) in curbing the outbreak. A data-driven investigation into effective public health policies of a country would allow public health experts in other nations to decide future courses of action to control the outbreaks of disease and epidemics. We chose Spain and India to present our analysis on regions that were similar in terms of certain factors: (1) population density, (2) unemployment rate, (3) tourism, and (4) quality of living. We posit that citizen ideology obtainable from twitter conversations can provide insights into conformity to policy and suitably reflect on future case predictions. A milestone when the curves show the number of new cases diverging from each other is used to define a time period to extract policy-related tweets while the concepts from a causality network of policy-dependent sub-events are used to generate concept clouds. The number of new cases is predicted using sentiment scores in a regression model. We see that the new case predictions reflects twitter sentiment, meaningfully tied to a trigger sub-event that enables policy-related findings for Spain and India to be effectively compared.
Substream-Centric Maximum Matchings on FPGA
Venue: arXiv cs.DC 2020
Year: 2020

Developing high-performance and energy-efficient algorithms for maximum matchings is becoming increasingly important in social network analysis, computational sciences, scheduling, and others. In this work, we propose the first maximum matching algorithm designed for FPGAs; it is energy-efficient and has provable guarantees on accuracy, performance, and storage utilization. To achieve this, we forego popular graph processing paradigms, such as vertex-centric programming, that often entail large communication costs. Instead, we propose a substream-centric approach, in which the input stream of data is divided into substreams processed independently to enable more parallelism while lowering communication costs. We base our work on the theory of streaming graph algorithms and analyze 14 models and 28 algorithms. We use this analysis to provide theoretical underpinning that matches the physical constraints of FPGA platforms. Our algorithm delivers high performance (more than 4x speedup over tuned parallel CPU variants), low memory, high accuracy, and effective usage of FPGA resources. The substream-centric approach could easily be extended to other algorithms to offer low-power and high-performance graph processing on FPGAs.
Optimal Textures: Fast and Robust Texture Synthesis and Style Transfer through Optimal Transport
Venue: arXiv cs.GR 2020
Year: 2020

This paper presents a light-weight, high-quality texture synthesis algorithm that easily generalizes to other applications such as style transfer and texture mixing. We represent texture features through the deep neural activation vectors within the bottleneck layer of an auto-encoder and frame the texture synthesis problem as optimal transport between the activation values of the image being synthesized and those of an exemplar texture. To find this optimal transport mapping, we utilize an N-dimensional probability density function (PDF) transfer process that iterates over multiple random rotations of the PDF basis and matches the 1D marginal distributions across each dimension. This achieves quality and flexibility on par with expensive back-propagation based neural texture synthesis methods, but with the potential of achieving interactive rates. We demonstrate that first order statistics offer a more robust representation for texture than the second order statistics that are used today. We propose an extension of this algorithm that reduces the dimensionality of the neural feature space. We utilize a multi-scale coarse-to-fine synthesis pyramid to capture and preserve larger image features; unify color and style transfer under one framework; and further augment this system with a novel masking scheme that re-samples and re-weights the feature distribution for user-guided texture painting and targeted style transfer.
Competitive Analysis of Move-to-Front-or-Middle (MFM) Online List Update Algorithm
Venue: arXiv cs.DS 2020
Year: 2020

The design and analysis of efficient algorithms with the knowledge of current and past inputs is a non-trivial and challenging research area in computer science. In many practical applications the future inputs are not available to the algorithm at any instance of time. So the algorithm has to make decisions based on a sequence of inputs that are in order and on the fly. Such algorithms are known as online algorithms. For measuring the performance of online algorithms, a standard measure, known as competitive analysis, has been extensively used in the literature. List update problem is a well studied research problem in the area of online algorithms since last few decades. One of the widely used deterministic online list update algorithm is the Move-To-Front (MTF) algorithm, which has been shown to be 2-competitive with best performance in practical real life inputs. In this paper we analyse the Move-to-Front-or-Middle (MFM) algorithm using competitive analysis by addressing one of an open question raised by Albers that whether dynamic offline algorithm can be used in finding the competitiveness of an online algorithm? Move-To-Front-or-Middle (MFM) was experimentally studied and observed to be performing better than MTF algorithm by using the Calgary Corpus and Canterbury Corpus data set. However, it is interesting and challenging to find the lower bound and upper bound on the competitive ratio of MFM algorithm. We make a first attempt to find the competitiveness of MFM algorithm. Our new results show that MFM is not 2-competitive with respect to static optimum offline algorithm, whereas it is 2-competitive with respect to dynamic optimum offline algorithm. Our new theoretical results may open up a new direction of research in the online list update problem by characterising the structure of competitive and non competitive deterministic online algorithms.
Fine-grained Information Status Classification Using Discourse Context-Aware BERT
Venue: arXiv cs.CL 2020
Year: 2020

Previous work on bridging anaphora recognition (Hou et al., 2013a) casts the problem as a subtask of learning fine-grained information status (IS). However, these systems heavily depend on many hand-crafted linguistic features. In this paper, we propose a simple discourse context-aware BERT model for fine-grained IS classification. On the ISNotes corpus (Markert et al., 2012), our model achieves new state-of-the-art performance on fine-grained IS classification, obtaining a 4.8 absolute overall accuracy improvement compared to Hou et al. (2013a). More importantly, we also show an improvement of 10.5 F1 points for bridging anaphora recognition without using any complex hand-crafted semantic features designed for capturing the bridging phenomenon. We further analyze the trained model and find that the most attended signals for each IS category correspond well to linguistic notions of information status.
Decoupling Pronunciation and Language for End-to-end Code-switching Automatic Speech Recognition
Venue: arXiv cs.SD 2020
Year: 2020

Despite the recent significant advances witnessed in end-to-end (E2E) ASR system for code-switching, hunger for audio-text paired data limits the further improvement of the models' performance. In this paper, we propose a decoupled transformer model to use monolingual paired data and unpaired text data to alleviate the problem of code-switching data shortage. The model is decoupled into two parts: audio-to-phoneme (A2P) network and phoneme-to-text (P2T) network. The A2P network can learn acoustic pattern scenarios using large-scale monolingual paired data. Meanwhile, it generates multiple phoneme sequence candidates for single audio data in real-time during the training process. Then the generated phoneme-text paired data is used to train the P2T network. This network can be pre-trained with large amounts of external unpaired text data. By using monolingual data and unpaired text data, the decoupled transformer model reduces the high dependency on code-switching paired training data of E2E model to a certain extent. Finally, the two networks are optimized jointly through attention fusion. We evaluate the proposed method on the public Mandarin-English code-switching dataset. Compared with our transformer baseline, the proposed method achieves 18.14% relative mix error rate reduction.
A general method for estimating the prevalence of Influenza-Like-Symptoms with Wikipedia data
Venue: arXiv cs.CY 2020
Year: 2020

Influenza is an acute respiratory seasonal disease that affects millions of people worldwide and causes thousands of deaths in Europe alone. Being able to estimate in a fast and reliable way the impact of an illness on a given country is essential to plan and organize effective countermeasures, which is now possible by leveraging unconventional data sources like web searches and visits. In this study, we show the feasibility of exploiting information about Wikipedia's page views of a selected group of articles and machine learning models to obtain accurate estimates of influenza-like illnesses incidence in four European countries: Italy, Germany, Belgium, and the Netherlands. We propose a novel language-agnostic method, based on two algorithms, Personalized PageRank and CycleRank, to automatically select the most relevant Wikipedia pages to be monitored without the need for expert supervision. We then show how our model is able to reach state-of-the-art results by comparing it with previous solutions.
Transferable Universal Adversarial Perturbations Using Generative Models
Venue: arXiv cs.CV 2020
Year: 2020

Deep neural networks tend to be vulnerable to adversarial perturbations, which by adding to a natural image can fool a respective model with high confidence. Recently, the existence of image-agnostic perturbations, also known as universal adversarial perturbations (UAPs), were discovered. However, existing UAPs still lack a sufficiently high fooling rate, when being applied to an unknown target model. In this paper, we propose a novel deep learning technique for generating more transferable UAPs. We utilize a perturbation generator and some given pretrained networks so-called source models to generate UAPs using the ImageNet dataset. Due to the similar feature representation of various model architectures in the first layer, we propose a loss formulation that focuses on the adversarial energy only in the respective first layer of the source models. This supports the transferability of our generated UAPs to any other target model. We further empirically analyze our generated UAPs and demonstrate that these perturbations generalize very well towards different target models. Surpassing the current state of the art in both, fooling rate and model-transferability, we can show the superiority of our proposed approach. Using our generated non-targeted UAPs, we obtain an average fooling rate of 93.36% on the source models (state of the art: 82.16%). Generating our UAPs on the deep ResNet-152, we obtain about a 12% absolute fooling rate advantage vs. cutting-edge methods on VGG-16 and VGG-19 target models.
Analysis of Energy Consumption in a Precision Beekeeping System
Venue: arXiv cs.AR 2020
Year: 2020

Honey bees have been domesticated by humans for several thousand years and mainly provide honey and pollination, which is fundamental for plant reproduction. Nowadays, the work of beekeepers is constrained by external factors that stress their production (parasites and pesticides among others). Taking care of large numbers of beehives is time-consuming, so integrating sensors to track their status can drastically simplify the work of beekeepers. Precision bee-keeping complements beekeepers' work thanks to the In-ternet of Things (IoT) technology. If used correctly, data can help to make the right diagnosis for honey bees colony, increase honey production and decrease bee mortality. Providing enough energy for on-hive and in-hive sensors is a challenge. Some solutions rely on energy harvesting, others target usage of large batteries. Either way, it is mandatory to analyze the energy usage of embedded equipment in order to design an energy efficient and autonomous bee monitoring system. This paper relies on a fully autonomous IoT framework that collects environmental and image data of a beehive. It consists of a data collecting node (environmental data sensors, camera, Raspberry Pi and Arduino) and a solar energy supplying node. Supported services are analyzed task by task from an energy profiling and efficiency standpoint , in order to identify the highly pressured areas of the framework. This first step will guide our goal of designing a sustainable precision beekeeping system, both technically and energy-wise.
Smart Anomaly Detection in Sensor Systems: A Multi-Perspective Review
Venue: arXiv cs.LG 2020
Year: 2020

Anomaly detection is concerned with identifying data patterns that deviate remarkably from the expected behaviour. This is an important research problem, due to its broad set of application domains, from data analysis to e-health, cybersecurity, predictive maintenance, fault prevention, and industrial automation. Herein, we review state-of-the-art methods that may be employed to detect anomalies in the specific area of sensor systems, which poses hard challenges in terms of information fusion, data volumes, data speed, and network/energy efficiency, to mention but the most pressing ones. In this context, anomaly detection is a particularly hard problem, given the need to find computing-energy accuracy trade-offs in a constrained environment. We taxonomize methods ranging from conventional techniques (statistical methods, time-series analysis, signal processing, etc.) to data-driven techniques (supervised learning, reinforcement learning, deep learning, etc.). We also look at the impact that different architectural environments (Cloud, Fog, Edge) can have on the sensors ecosystem. The review points to the most promising intelligent-sensing methods, and pinpoints a set of interesting open issues and challenges.