OpenRaiser/Intern-Atlas huggingface dataset:
LOOC: Localize Overlapping Objects with Count Supervision
Venue: arXiv cs.CV 2020
Year: 2020

Acquiring count annotations generally requires less human effort than point-level and bounding box annotations. Thus, we propose the novel problem setup of localizing objects in dense scenes under this weaker supervision. We propose LOOC, a method to Localize Overlapping Objects with Count supervision. We train LOOC by alternating between two stages. In the first stage, LOOC learns to generate pseudo point-level annotations in a semi-supervised manner. In the second stage, LOOC uses a fully-supervised localization method that trains on these pseudo labels. The localization method is used to progressively improve the quality of the pseudo labels. We conducted experiments on popular counting datasets. For localization, LOOC achieves a strong new baseline in the novel problem setup where only count supervision is available. For counting, LOOC outperforms current state-of-the-art methods that only use count as their supervision. Code is available at: https://github.com/ElementAI/looc.
An Analysis of Data Driven, Decision-Making Capabilities of Managers in Banks
Venue: arXiv cs.HC 2020
Year: 2020

Organizations are adopting data analytics and Business Intelligence (BI) tools to gain insights from the past data, forecast future events, and to get timely and reliable information for decision making. While the tools are becoming mature, affordable, and more comfortable to use, it is also essential to understand whether the contemporary managers and leaders are ready for Data-Driven Decision Making (DDDM). We explore the extent the Decision Makers (DMs) utilize data and tools, as well as their ability to interpret various forms of outputs from tools and to apply those insights to gain competitive advantage. Our methodology was based on a qualitative survey, where we interviewed 12 DMs of six commercial banks in Sri Lanka at the branch, regional, and CTO, CIO, and Head of IT levels. We identified that on many occasions, DMs' intuition overrules the DDDM due to uncertainty, lack of trust, knowledge, and risk-taking. Moreover, it was identified that the quality of visualizations has a significant impact on the use of intuition by overruling DDDM. We further provide a set of recommendations on the adoption of BI tools and how to overcome the struggles faced while performing DDDM.
Examining Redundancy in the Context of Safe Machine Learning
Venue: arXiv cs.LG 2020
Year: 2020

This paper describes a set of experiments with neural network classifiers on the MNIST database of digits. The purpose is to investigate naïve implementations of redundant architectures as a first step towards safe and dependable machine learning. We report on a set of measurements using the MNIST database which ultimately serve to underline the expected difficulties in using NN classifiers in safe and dependable systems.
Multigrid for Bundle Adjustment
Venue: arXiv cs.CV 2020
Year: 2020

Bundle adjustment is an important global optimization step in many structure from motion pipelines. Performance is dependent on the speed of the linear solver used to compute steps towards the optimum. For large problems, the current state of the art scales superlinearly with the number of cameras in the problem. We investigate the conditioning of global bundle adjustment problems as the number of images increases in different regimes and fundamental consequences in terms of superlinear scaling of the current state of the art methods. We present an unsmoothed aggregation multigrid preconditioner that accurately represents the global modes that underlie poor scaling of existing methods and demonstrate solves of up to 13 times faster than the state of the art on large, challenging problem sets.
Building benchmarking frameworks for supporting replicability and reproducibility: spatial and textual analysis as an example
Venue: arXiv cs.IR 2020
Year: 2020

Replicability and reproducibility (R&R) are critical for the long-term prosperity of a scientific discipline. In GIScience, researchers have discussed R&R related to different research topics and problems, such as local spatial statistics, digital earth, and metadata (Fotheringham, 2009; Goodchild, 2012; Anselin et al., 2014). This position paper proposes to further support R&R by building benchmarking frameworks in order to facilitate the replication of previous research for effective and effcient comparisons of methods and software tools developed for addressing the same or similar problems. Particularly, this paper will use geoparsing, an important research problem in spatial and textual analysis, as an example to explain the values of such benchmarking frameworks.
Robust Prediction of Punctuation and Truecasing for Medical ASR
Venue: arXiv cs.CL 2020
Year: 2020

Automatic speech recognition (ASR) systems in the medical domain that focus on transcribing clinical dictations and doctor-patient conversations often pose many challenges due to the complexity of the domain. ASR output typically undergoes automatic punctuation to enable users to speak naturally, without having to vocalise awkward and explicit punctuation commands, such as "period", "add comma" or "exclamation point", while truecasing enhances user readability and improves the performance of downstream NLP tasks. This paper proposes a conditional joint modeling framework for prediction of punctuation and truecasing using pretrained masked language models such as BERT, BioBERT and RoBERTa. We also present techniques for domain and task specific adaptation by fine-tuning masked language models with medical domain data. Finally, we improve the robustness of the model against common errors made in ASR by performing data augmentation. Experiments performed on dictation and conversational style corpora show that our proposed model achieves ~5% absolute improvement on ground truth text and ~10% improvement on ASR outputs over baseline models under F1 metric.
Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains
Venue: arXiv cs.CV 2020
Year: 2020

Model generalization capacity at domain shift (e.g., various imaging protocols and scanners) is crucial for deep learning methods in real-world clinical deployment. This paper tackles the challenging problem of domain generalization, i.e., learning a model from multi-domain source data such that it can directly generalize to an unseen target domain. We present a novel shape-aware meta-learning scheme to improve the model generalization in prostate MRI segmentation. Our learning scheme roots in the gradient-based meta-learning, by explicitly simulating domain shift with virtual meta-train and meta-test during training. Importantly, considering the deficiencies encountered when applying a segmentation model to unseen domains (i.e., incomplete shape and ambiguous boundary of the prediction masks), we further introduce two complementary loss objectives to enhance the meta-optimization, by particularly encouraging the shape compactness and shape smoothness of the segmentations under simulated domain shift. We evaluate our method on prostate MRI data from six different institutions with distribution shifts acquired from public datasets. Experimental results show that our approach outperforms many state-of-the-art generalization methods consistently across all six settings of unseen domains.
Relationship between manifold smoothness and adversarial vulnerability in deep learning with local errors
Venue: arXiv cs.LG 2020
Year: 2020

Artificial neural networks can achieve impressive performances, and even outperform humans in some specific tasks. Nevertheless, unlike biological brains, the artificial neural networks suffer from tiny perturbations in sensory input, under various kinds of adversarial attacks. It is therefore necessary to study the origin of the adversarial vulnerability. Here, we establish a fundamental relationship between geometry of hidden representations (manifold perspective) and the generalization capability of the deep networks. For this purpose, we choose a deep neural network trained by local errors, and then analyze emergent properties of trained networks through the manifold dimensionality, manifold smoothness, and the generalization capability. To explore effects of adversarial examples, we consider independent Gaussian noise attacks and fast-gradient-sign-method (FGSM) attacks. Our study reveals that a high generalization accuracy requires a relatively fast power-law decay of the eigen-spectrum of hidden representations. Under Gaussian attacks, the relationship between generalization accuracy and power-law exponent is monotonic, while a non-monotonic behavior is observed for FGSM attacks. Our empirical study provides a route towards a final mechanistic interpretation of adversarial vulnerability under adversarial attacks.
RDP-GAN: A Rényi-Differential Privacy based Generative Adversarial Network
Venue: arXiv cs.LG 2020
Year: 2020

Generative adversarial network (GAN) has attracted increasing attention recently owing to its impressive ability to generate realistic samples with high privacy protection. Without directly interactive with training examples, the generative model can be fully used to estimate the underlying distribution of an original dataset while the discriminative model can examine the quality of the generated samples by comparing the label values with the training examples. However, when GANs are applied on sensitive or private training examples, such as medical or financial records, it is still probable to divulge individuals' sensitive and private information. To mitigate this information leakage and construct a private GAN, in this work we propose a Rényi-differentially private-GAN (RDP-GAN), which achieves differential privacy (DP) in a GAN by carefully adding random noises on the value of the loss function during training. Moreover, we derive the analytical results of the total privacy loss under the subsampling method and cumulated iterations, which show its effectiveness on the privacy budget allocation. In addition, in order to mitigate the negative impact brought by the injecting noise, we enhance the proposed algorithm by adding an adaptive noise tuning step, which will change the volume of added noise according to the testing accuracy. Through extensive experimental results, we verify that the proposed algorithm can achieve a better privacy level while producing high-quality samples compared with a benchmark DP-GAN scheme based on noise perturbation on training gradients.
Immersed boundary finite element method for blood flow simulation
Venue: arXiv cs.CE 2020
Year: 2020

We present an efficient and accurate immersed boundary (IB) finite element (FE) solver for numerically solving incompressible Navier--Stokes equations. Particular emphasis is given to internal flows with complex geometries (blood flow in the vasculature system). IB methods are computationally costly for internal flows, mainly due to the large percentage of grid points that lie outside the flow domain. In this study, we apply a local refinement strategy, along with a domain reduction approach in order to reduce the grid that covers the flow domain and increase the percentage of the grid nodes that fall inside the flow domain. The proposed method utilizes an efficient and accurate FE solver with the incremental pressure correction scheme (IPCS), along with the boundary condition enforced IB method to numerically solve the transient, incompressible Navier--Stokes flow equations. We verify the accuracy of the numerical method using the analytical solution for Poiseuille flow in a cylinder. We further examine the accuracy and applicability of the proposed method by considering flow within complex geometries, such as blood flow in aneurysmal vessels and the aorta, flow configurations which would otherwise be extremely difficult to solve by most IB methods. Our method offers high accuracy, as demonstrated by the verification examples, and high efficiency, as demonstrated through the solution of blood flow within complex geometry on an off-the-shelf laptop computer.
Pynsett: A programmable relation extractor
Venue: arXiv cs.CL 2020
Year: 2020

This paper proposes a programmable relation extraction method for the English language by parsing texts into semantic graphs. A person can define rules in plain English that act as matching patterns onto the graph representation. These rules are designed to capture the semantic content of the documents, allowing for flexibility and ad-hoc entities. Relation extraction is a complex task that typically requires sizable training corpora. The method proposed here is ideal for extracting specialized ontologies in a limited collection of documents.
Blockchain-Based Trusted Achievement Record System Design
Venue: arXiv cs.CY 2020
Year: 2020

The primary purpose of this paper is to provide a design of a blockchain-based system, which produces a verifiable record of achievements. Such a system has a wide range of potential benefits for students, employers and higher education institutions. A verifiable record of achievements enables students to present academic accomplishments to employers, within a trusted framework. Furthermore, the availability of such a record system would enable students to review their learning throughout their career, giving them a platform on which to plan for their future accomplishments, both individually and with support from other parties (for example, academic advisors, supervisors, or potential employers). The proposed system will help students in universities to increase their extra-curricular activities and improve non-academic skills. Moreover, the system will facilitate communication between industry, students, and universities for employment purposes and simplify the search for the most appropriate potential employees for the job.
A Ring Router Microarchitecture for NoCs
Venue: arXiv cs.AR 2020
Year: 2020

Network-on-Chip (NoC) has become a popular choice for connecting a large number of processing cores in chip multiprocessor design. In a conventional NoC design, most of the area in the router is occupied by the buffers and the crossbar switch. These two components also consume the majority of the router's power. Much of the research in NoC has been based on the conventional router microarchitecture. We propose a novel router microarchitecture that treats the router itself as a small network of the ring topology. It eliminates the large crossbar switch in the conventional design. In addition, network latency is much reduced. Simulation and circuit synthesis show that the proposed microarchitecture can reduce the latency, area and power by 53%, 34% and 27%, respectively, compared to the conventional design.
Stereo Visual Inertial Pose Estimation Based on Feedforward-Feedback Loops
Venue: arXiv cs.RO 2020
Year: 2020

In this paper, we present a novel stereo visual inertial pose estimation method. Compared to the widely used filter-based or optimization-based approaches, the pose estimation process is modeled as a control system. Designed feedback or feedforward loops are introduced to achieve the stable control of the system, which include a gradient decreased feedback loop, a roll-pitch feed forward loop and a bias estimation feedback loop. This system, named FLVIS (Feedforward-feedback Loop-based Visual Inertial System), is evaluated on the popular EuRoc MAV dataset. FLVIS achieves high accuracy and robustness with respect to other state-of-the-art visual SLAM approaches. The system has also been implemented and tested on a UAV platform. The source code of this research is public to the research community.
Weakly Supervised Domain Adaptation for Built-up Region Segmentation in Aerial and Satellite Imagery
Venue: arXiv cs.CV 2020
Year: 2020

This paper proposes a novel domain adaptation algorithm to handle the challenges posed by the satellite and aerial imagery, and demonstrates its effectiveness on the built-up region segmentation problem. Built-up area estimation is an important component in understanding the human impact on the environment, the effect of public policy, and general urban population analysis. The diverse nature of aerial and satellite imagery and lack of labeled data covering this diversity makes machine learning algorithms difficult to generalize for such tasks, especially across multiple domains. On the other hand, due to the lack of strong spatial context and structure, in comparison to the ground imagery, the application of existing unsupervised domain adaptation methods results in the sub-optimal adaptation. We thoroughly study the limitations of existing domain adaptation methods and propose a weakly-supervised adaptation strategy where we assume image-level labels are available for the target domain. More specifically, we design a built-up area segmentation network (as encoder-decoder), with an image classification head added to guide the adaptation. The devised system is able to address the problem of visual differences in multiple satellite and aerial imagery datasets, ranging from high resolution (HR) to very high resolution (VHR). A realistic and challenging HR dataset is created by hand-tagging the 73.4 sq-km of Rwanda, capturing a variety of build-up structures over different terrain. The developed dataset is spatially rich compared to existing datasets and covers diverse built-up scenarios including built-up areas in forests and deserts, mud houses, tin, and colored rooftops. Extensive experiments are performed by adapting from the single-source domain, to segment out the target domain. We achieve high gains ranging 11.6%-52% in IoU over the existing state-of-the-art methods.
Multi view stereo with semantic priors
Venue: arXiv cs.CV 2020
Year: 2020

Patch-based stereo is nowadays a commonly used image-based technique for dense 3D reconstruction in large scale multi-view applications. The typical steps of such a pipeline can be summarized in stereo pair selection, depth map computation, depth map refinement and, finally, fusion in order to generate a complete and accurate representation of the scene in 3D. In this study, we aim to support the standard dense 3D reconstruction of scenes as implemented in the open source library OpenMVS by using semantic priors. To this end, during the depth map fusion step, along with the depth consistency check between depth maps of neighbouring views referring to the same part of the 3D scene, we impose extra semantic constraints in order to remove possible errors and selectively obtain segmented point clouds per label, boosting automation towards this direction. I n order to reassure semantic coherence between neighbouring views, additional semantic criterions can be considered, aiming to elim inate mismatches of pixels belonging in different classes.
Radial Intersection Count Image: a Clutter Resistant 3D Shape Descriptor
Venue: arXiv cs.CV 2020
Year: 2020

A novel shape descriptor for cluttered scenes is presented, the Radial Intersection Count Image (RICI), and is shown to significantly outperform the classic Spin Image (SI) and 3D Shape Context (3DSC) in both uncluttered and, more significantly, cluttered scenes. It is also faster to compute and compare. The clutter resistance of the RICI is mainly due to the design of a novel distance function, capable of disregarding clutter to a great extent. As opposed to the SI and 3DSC, which both count point samples, the RICI uses intersection counts with the mesh surface, and is therefore noise-free. For efficient RICI construction, novel algorithms of general interest were developed. These include an efficient circle-triangle intersection algorithm and an algorithm for projecting a point into SI-like ($α$, $β$) coordinates. The 'clutterbox experiment' is also introduced as a better way of evaluating descriptors' response to clutter. The SI, 3DSC, and RICI are evaluated in this framework and the advantage of the RICI is clearly demonstrated.
The influence of sponsors on organizational structure of free software communities
Venue: arXiv cs.SE 2020
Year: 2020

Initially, free software communities are characterized by selfmanagement, however, they were also influenced by public and private organizations that identified potential gains in the use of the geographically distributed production model. In this context, this research aims to answer the following questions: Do sponsors influence the organizational structures of free software communities by promoting differences between sponsored and non-sponsored communities? What strategies are adopted by the sponsor to influence the organizational structure of free software communities? Two constructs are central to the study: organizational structure and sponsorship. For this research, we adopted case study methodology and three free software communities were studied. In the analysis of the results it was evidenced that sponsors influence decision making, definition of community key roles, and a formalization of norms. In turn, nonsponsored communities were characterized by the centralization and informality of the norms. We conclude that differences were identified in the organizational structure of sponsored and nonsponsored free software communities, and this differentiation was influenced by sponsors. In addition, it was possible to describe strategies and mechanisms used by sponsors to influence the community organizational structure.
Anatomical Data Augmentation via Fluid-based Image Registration
Venue: arXiv cs.CV 2020
Year: 2020

We introduce a fluid-based image augmentation method for medical image analysis. In contrast to existing methods, our framework generates anatomically meaningful images via interpolation from the geodesic subspace underlying given samples. Our approach consists of three steps: 1) given a source image and a set of target images, we construct a geodesic subspace using the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model; 2) we sample transformations from the resulting geodesic subspace; 3) we obtain deformed images and segmentations via interpolation. Experiments on brain (LPBA) and knee (OAI) data illustrate the performance of our approach on two tasks: 1) data augmentation during training and testing for image segmentation; 2) one-shot learning for single atlas image segmentation. We demonstrate that our approach generates anatomically meaningful data and improves performance on these tasks over competing approaches. Code is available at https://github.com/uncbiag/easyreg.
CORD19STS: COVID-19 Semantic Textual Similarity Dataset
Venue: arXiv cs.CL 2020
Year: 2020

In order to combat the COVID-19 pandemic, society can benefit from various natural language processing applications, such as dialog medical diagnosis systems and information retrieval engines calibrated specifically for COVID-19. These applications rely on the ability to measure semantic textual similarity (STS), making STS a fundamental task that can benefit several downstream applications. However, existing STS datasets and models fail to translate their performance to a domain-specific environment such as COVID-19. To overcome this gap, we introduce CORD19STS dataset which includes 13,710 annotated sentence pairs collected from COVID-19 open research dataset (CORD-19) challenge. To be specific, we generated one million sentence pairs using different sampling strategies. We then used a finetuned BERT-like language model, which we call Sen-SCI-CORD19-BERT, to calculate the similarity scores between sentence pairs to provide a balanced dataset with respect to the different semantic similarity levels, which gives us a total of 32K sentence pairs. Each sentence pair was annotated by five Amazon Mechanical Turk (AMT) crowd workers, where the labels represent different semantic similarity levels between the sentence pairs (i.e. related, somewhat-related, and not-related). After employing a rigorous qualification tasks to verify collected annotations, our final CORD19STS dataset includes 13,710 sentence pairs.