OpenRaiser/Intern-Atlas huggingface dataset:
Nine Recommendations for Decision Aid Implementation from the Clinician Perspective
Venue: arXiv cs.CY 2020
Year: 2020

Background: Shared decision-making (SDM) aims to empower patients to take an active role in their treatment choices, supported by clinicians and patient decision aids (PDAs). The purpose of this study is to explore barriers and possible facilitators to SDM and a PDA in the prostate cancer trajectory. In the process we identify possible actions that organizations and individuals can take to support implementation in practice. Methods: We use the Ottawa Model of Research Use as a framework to determine the barriers and facilitators to SDM and PDAs from the perspective of clinicians. Semi-structured interviews were conducted with urologists (n=4), radiation oncologists (n=3), and oncology nurses (n=2), focusing on the current decision-making process experienced by these stakeholders. Questions included their attitudes towards SDM and PDAs, barriers to implementation and possible strategies to overcome them. Results: Time pressure and patient characteristics were cited as major barriers by 55% of the clinicians we interviewed. Structural factors such as external quotas for certain treatment procedures were also considered as barriers by 44% of the clinicians. Facilitating factors involved organizational changes to em-bed PDAs in the treatment trajectory, training in using PDAs as a tool for SDM, and clinician motivation by disseminating positive clinical outcomes. Our findings also suggest a role for external stakeholders such as healthcare insurers in creating economic incentives to facilitate implementation. Conclusion: Our findings highlight the importance of a multi-faceted implementation strategy to support SDM. While clinician motivation and patient activation are essential, structural/economic barriers may hamper implementation. Action must also be taken at the administrative and policy levels to foster a collaborative environment for SDM and, in the process, for PDAs.
CS-NET at SemEval-2020 Task 4: Siamese BERT for ComVE
Venue: arXiv cs.CL 2020
Year: 2020

In this paper, we describe our system for Task 4 of SemEval 2020, which involves differentiating between natural language statements that confirm to common sense and those that do not. The organizers propose three subtasks - first, selecting between two sentences, the one which is against common sense. Second, identifying the most crucial reason why a statement does not make sense. Third, generating novel reasons for explaining the against common sense statement. Out of the three subtasks, this paper reports the system description of subtask A and subtask B. This paper proposes a model based on transformer neural network architecture for addressing the subtasks. The novelty in work lies in the architecture design, which handles the logical implication of contradicting statements and simultaneous information extraction from both sentences. We use a parallel instance of transformers, which is responsible for a boost in the performance. We achieved an accuracy of 94.8% in subtask A and 89% in subtask B on the test set.
Reasoning with failures
Venue: arXiv cs.SE 2020
Year: 2020

Safety Instrumented Systems (SIS) protect major hazard facilities, e.g. power plants, against catastrophic accidents. An SIS consists of hardware components and a controller software -- the ``program''. Current safety analyses of SIS' include the construction of a fault tree, summarising potential faults of the components and how they can arise within an SIS. The exercise of identifying faults typically relies on the experience of the safety engineer. Unfortunately the program part is often too complicated to be analysed in such a ``by hand" manner and so the impact it has on the resulting safety analysis is not accurately captured. In this paper we demonstrate how a formal model for faults and failure modes can be used to analyse the impact of an SIS program. We outline the underlying concepts of \emph{Failure Mode Reasoning} and its application in safety analysis, and we illustrate the ideas on a practical example.
Who Verifies the Verifiers? A Computer-Checked Implementation of the DPLL Algorithm in Dafny
Venue: arXiv cs.LO 2020
Year: 2020

We build a SAT solver implementing the DPLL algorithm in the verification-enabled programming language Dafny. The resulting solver is fully verified (soundness, completeness and termination are computer checked). We benchmark our Dafny solver and we show that it is just as efficient as an equivalent DPLL solver implemented in C# and roughly two times less efficient than an equivalent solver written in C++. We conclude that auto-active verification is a promising approach to increasing trust in SAT solvers, as it combines a good trade-off between execution speed and degree of trustworthiness of the final product.
DecoSurf: Recursive Geodesic Patterns on Triangle Meshes
Venue: arXiv cs.GR 2020
Year: 2020

In this paper, we show that many complex patterns, which characterize the decorative style of many artisanal objects, can be generated by the recursive application of only four operators. Each operator is derived from tracing the isolines or the integral curves of geodesics fields generated from selected seeds on the surface. Based on this formulation, we present an interactive application that lets designers model complex recursive patterns directly on the object surface, without relying on parametrization. We support interaction on commodity hardware on meshes of a few million triangles, by combining light data structures together with an efficient approximate graph-based geodesic solver. We validate our approach by matching decoration styles from real-world photos, by analyzing the speed and accuracy of our geodesic solver, and by validating the interface with a user study.
Adaptive Traffic Control with Deep Reinforcement Learning: Towards State-of-the-art and Beyond
Venue: arXiv cs.LG 2020
Year: 2020

In this work, we study adaptive data-guided traffic planning and control using Reinforcement Learning (RL). We shift from the plain use of classic methods towards state-of-the-art in deep RL community. We embed several recent techniques in our algorithm that improve the original Deep Q-Networks (DQN) for discrete control and discuss the traffic-related interpretations that follow. We propose a novel DQN-based algorithm for Traffic Control (called TC-DQN+) as a tool for fast and more reliable traffic decision-making. We introduce a new form of reward function which is further discussed using illustrative examples with comparisons to traditional traffic control methods.
Recommendations for Planning Inclusive Astronomy Conferences
Venue: arXiv cs.CY 2020
Year: 2020

The Inclusive Astronomy (IA) conference series aims to create a safe space where community members can listen to the experiences of marginalized individuals in astronomy, discuss actions being taken to address inequities, and give recommendations to the community for how to improve diversity, equity, and inclusion in astronomy. The first IA was held in Nashville, TN, USA, 17-19 June, 2015. The Inclusive Astronomy 2 (IA2) conference was held in Baltimore, MD, USA, 14-15 October, 2019. The Inclusive Astronomy 2 (IA2) Local Organizing Committee (LOC) has put together a comprehensive document of recommendations for planning future Inclusive Astronomy conferences based on feedback received and lessons learned. While these are specific to the IA series, many parts will be applicable to other conferences as well. Please find the recommendations and accompanying letter to the community here: https://outerspace.stsci.edu/display/IA2/LOC+Recommendations.
A Gradient-based Bilevel Optimization Approach for Tuning Hyperparameters in Machine Learning
Venue: arXiv cs.LG 2020
Year: 2020

Hyperparameter tuning is an active area of research in machine learning, where the aim is to identify the optimal hyperparameters that provide the best performance on the validation set. Hyperparameter tuning is often achieved using naive techniques, such as random search and grid search. However, most of these methods seldom lead to an optimal set of hyperparameters and often get very expensive. In this paper, we propose a bilevel solution method for solving the hyperparameter optimization problem that does not suffer from the drawbacks of the earlier studies. The proposed method is general and can be easily applied to any class of machine learning algorithms. The idea is based on the approximation of the lower level optimal value function mapping, which is an important mapping in bilevel optimization and helps in reducing the bilevel problem to a single level constrained optimization task. The single-level constrained optimization problem is solved using the augmented Lagrangian method. We discuss the theory behind the proposed algorithm and perform extensive computational study on two datasets that confirm the efficiency of the proposed method. We perform a comparative study against grid search, random search and Bayesian optimization techniques that shows that the proposed algorithm is multiple times faster on problems with one or two hyperparameters. The computational gain is expected to be significantly higher as the number of hyperparameters increase. Corresponding to a given hyperparameter most of the techniques in the literature often assume a unique optimal parameter set that minimizes loss on the training set. Such an assumption is often violated by deep learning architectures and the proposed method does not require any such assumption.
On the Rademacher Complexity of Linear Hypothesis Sets
Venue: arXiv cs.LG 2020
Year: 2020

Linear predictors form a rich class of hypotheses used in a variety of learning algorithms. We present a tight analysis of the empirical Rademacher complexity of the family of linear hypothesis classes with weight vectors bounded in $\ell_p$-norm for any $p \geq 1$. This provides a tight analysis of generalization using these hypothesis sets and helps derive sharp data-dependent learning guarantees. We give both upper and lower bounds on the Rademacher complexity of these families and show that our bounds improve upon or match existing bounds, which are known only for $1 \leq p \leq 2$.
An Image Analogies Approach for Multi-Scale Contour Detection
Venue: arXiv cs.CV 2020
Year: 2020

In this paper we deal with contour detection based on the recent image analogy principle which has been successfully used for super-resolution, texture and curves synthesis and interactive editing. Hand-drawn outlines are initially as benchmarks. Given such a reference image, we present a new method based on this expertise to locate contours of a query image in the same way that it is done for the reference (i.e by analogy). Applying a image analogies for contour detection using hand drawn images as leaning images cannot gives good result for any query image. The contour detection may be improved if we increase the number of learning images such that there will be exist similarity between query image and some reference images. In addition of the hardness of contours drawing task, this will increase considerably the time computation. We investigated in this work, how can we avoid this constraint in order to guaranty that all contour pixels will be located for any query image. Fourteen derived stereo patches, derived from a mathematical study, are the knowledge used in order to locate contours at different scales independently of the light conditions. Comprehensive experiments are conducted on different data sets (BSD 500, Horses of Weizmann). The obtained results show superior performance via precision and recall vs. hand-drawn contours at multiple resolutions to the reported state of the art.
Accelerating Deep Learning Applications in Space
Venue: arXiv cs.CV 2020
Year: 2020

Computing at the edge offers intriguing possibilities for the development of autonomy and artificial intelligence. The advancements in autonomous technologies and the resurgence of computer vision have led to a rise in demand for fast and reliable deep learning applications. In recent years, the industry has introduced devices with impressive processing power to perform various object detection tasks. However, with real-time detection, devices are constrained in memory, computational capacity, and power, which may compromise the overall performance. This could be solved either by optimizing the object detector or modifying the images. In this paper, we investigate the performance of CNN-based object detectors on constrained devices when applying different image compression techniques. We examine the capabilities of a NVIDIA Jetson Nano; a low-power, high-performance computer, with an integrated GPU, small enough to fit on-board a CubeSat. We take a closer look at the Single Shot MultiBox Detector (SSD) and Region-based Fully Convolutional Network (R-FCN) that are pre-trained on DOTA - a Large Scale Dataset for Object Detection in Aerial Images. The performance is measured in terms of inference time, memory consumption, and accuracy. By applying image compression techniques, we are able to optimize performance. The two techniques applied, lossless compression and image scaling, improves speed and memory consumption with no or little change in accuracy. The image scaling technique achieves a 100% runnable dataset and we suggest combining both techniques in order to optimize the speed/memory/accuracy trade-off.
Improved lower and upper bounds on the tile complexity of uniquely self-assembling a thin rectangle non-cooperatively in 3D
Venue: arXiv cs.DS 2020
Year: 2020

We investigate a fundamental question regarding a benchmark class of shapes in one of the simplest, yet most widely utilized abstract models of algorithmic tile self-assembly. Specifically, we study the directed tile complexity of a $k \times N$ thin rectangle in Winfree's abstract Tile Assembly Model, assuming that cooperative binding cannot be enforced (temperature-1 self-assembly) and that tiles are allowed to be placed at most one step into the third dimension (just-barely 3D). While the directed tile complexities of a square and a scaled-up version of any algorithmically specified shape at temperature 1 in just-barely 3D are both asymptotically the same as they are (respectively) at temperature 2 in 2D, the bounds on the directed tile complexity of a thin rectangle at temperature 2 in 2D are not known to hold at temperature 1 in just-barely 3D. Motivated by this discrepancy, we establish new lower and upper bounds on the directed tile complexity of a thin rectangle at temperature 1 in just-barely 3D. We develop a new, more powerful type of Window Movie Lemma that lets us upper bound the number of "sufficiently similar" ways to assign glues to a set of fixed locations. Consequently, our lower bound, $Ω\left(N^{\frac{1}{k}}\right)$, is an asymptotic improvement over the previous best lower bound and is more aesthetically pleasing since it eliminates the $k$ that used to divide $N^{\frac{1}{k}}$. The proof of our upper bound is based on a just-barely 3D, temperature-1 counter, organized according to "digit regions", which affords it roughly fifty percent more digits for the same target rectangle compared to the previous best counter. This increase in digit density results in an upper bound of $O\left(N^{\frac{1}{\left\lfloor\frac{k}{2}\right\rfloor}}+\log N\right)$, that is an asymptotic improvement over the previous best upper bound and roughly the square of our lower bound.
New Data Structures for Orthogonal Range Reporting and Range Minima Queries
Venue: arXiv cs.DS 2020
Year: 2020

In this paper we present new data structures for two extensively studied variants of the orthogonal range searching problem. First, we describe a data structure that supports two-dimensional orthogonal range minima queries in $O(n)$ space and $O(\log^{\varepsilon} n)$ time, where $n$ is the number of points in the data structure and $\varepsilon$ is an arbitrarily small positive constant. Previously known linear-space solutions for this problem require $O(\log^{1+\varepsilon} n)$ (Chazelle, 1988) or $O(\log n\log \log n)$ time (Farzan et al., 2012). A modification of our data structure uses space $O(n\log \log n)$ and supports range minima queries in time $O(\log \log n)$. Both results can be extended to support three-dimensional five-sided reporting queries. Next, we turn to the four-dimensional orthogonal range reporting problem and present a data structure that answers queries in optimal $O(\log n/\log \log n + k)$ time, where $k$ is the number of points in the answer. This is the first data structure that achieves the optimal query time for this problem. Our results are obtained by exploiting the properties of three-dimensional shallow cuttings.
A New Combinatorial Coded Design for Heterogeneous Distributed Computing
Venue: arXiv cs.IT 2020
Year: 2020

Coded Distributed Computing (CDC) introduced by Li et al. in 2015 offers an efficient approach to trade computing power to reduce the communication load in general distributed computing frameworks such as MapReduce and Spark. In particular, increasing the computation load in the Map phase by a factor of r can create coded multicasting opportunities to reduce the communication load in the Shuffle phase by the same factor. However, the CDC scheme is designed for the homogeneous settings, where the storage, computation load and communication load on the computing nodes are the same. In addition, it requires an exponentially large number of input files (data batches), reduce functions and multicasting groups relative to the number of nodes to achieve the promised gain. We address the CDC limitations by proposing a novel CDC approach based on a combinatorial design, which accommodates heterogeneous networks where nodes have varying storage and computing capabilities. In addition, the proposed approach requires an exponentially less number of input files compared to the original CDC scheme proposed by Li et al. Meanwhile, the resulting computation-communication trade-off maintains the multiplicative gain compared to conventional uncoded unicast and asymptotically achieves the optimal performance proposed by Li et al.
Creating a Large-scale Synthetic Dataset for Human Activity Recognition
Venue: arXiv cs.CV 2020
Year: 2020

Creating and labelling datasets of videos for use in training Human Activity Recognition models is an arduous task. In this paper, we approach this by using 3D rendering tools to generate a synthetic dataset of videos, and show that a classifier trained on these videos can generalise to real videos. We use five different augmentation techniques to generate the videos, leading to a wide variety of accurately labelled unique videos. We fine tune a pre-trained I3D model on our videos, and find that the model is able to achieve a high accuracy of 73% on the HMDB51 dataset over three classes. We also find that augmenting the HMDB training set with our dataset provides a 2% improvement in the performance of the classifier. Finally, we discuss possible extensions to the dataset, including virtual try on and modeling motion of the people.
Unsupervised Learning of Solutions to Differential Equations with Generative Adversarial Networks
Venue: arXiv cs.LG 2020
Year: 2020

Solutions to differential equations are of significant scientific and engineering relevance. Recently, there has been a growing interest in solving differential equations with neural networks. This work develops a novel method for solving differential equations with unsupervised neural networks that applies Generative Adversarial Networks (GANs) to \emph{learn the loss function} for optimizing the neural network. We present empirical results showing that our method, which we call Differential Equation GAN (DEQGAN), can obtain multiple orders of magnitude lower mean squared errors than an alternative unsupervised neural network method based on (squared) $L_2$, $L_1$, and Huber loss functions. Moreover, we show that DEQGAN achieves solution accuracy that is competitive with traditional numerical methods. Finally, we analyze the stability of our approach and find it to be sensitive to the selection of hyperparameters, which we provide in the appendix. Code available at https://github.com/dylanrandle/denn. Please address any electronic correspondence to dylanrandle@alumni.harvard.edu.
Adversarial Training Reduces Information and Improves Transferability
Venue: arXiv cs.LG 2020
Year: 2020

Recent results show that features of adversarially trained networks for classification, in addition to being robust, enable desirable properties such as invertibility. The latter property may seem counter-intuitive as it is widely accepted by the community that classification models should only capture the minimal information (features) required for the task. Motivated by this discrepancy, we investigate the dual relationship between Adversarial Training and Information Theory. We show that the Adversarial Training can improve linear transferability to new tasks, from which arises a new trade-off between transferability of representations and accuracy on the source task. We validate our results employing robust networks trained on CIFAR-10, CIFAR-100 and ImageNet on several datasets. Moreover, we show that Adversarial Training reduces Fisher information of representations about the input and of the weights about the task, and we provide a theoretical argument which explains the invertibility of deterministic networks without violating the principle of minimality. Finally, we leverage our theoretical insights to remarkably improve the quality of reconstructed images through inversion.
An Intelligent QoS Algorithm for Home Networks
Venue: arXiv cs.NI 2020
Year: 2020

A novel quality of service (QoS) management algorithm for home networks is presented in this letter. The algorithm is based on service prediction for intelligent QoS management. The service prediction is carried out by a general regression neural network with a profile containing the past records of the service. A novel profile updating technique is proposed so that the profile size can remain small for fast bandwidth allocation. The analytical study and experiments over real LAN reveal that the proposed algorithm provides reliable QoS management for home networks with low computational overhead.
The mergegram of a dendrogram and its stability
Venue: arXiv cs.CG 2020
Year: 2020

This paper extends the key concept of persistence within Topological Data Analysis (TDA) in a new direction. TDA quantifies topological shapes hidden in unorganized data such as clouds of unordered points. In the 0-dimensional case the distance-based persistence is determined by a single-linkage (SL) clustering of a finite set in a metric space. Equivalently, the 0D persistence captures only edge-lengths of a Minimum Spanning Tree (MST). Both SL dendrogram and MST are unstable under perturbations of points. We define the new stable-under-noise mergegram, which outperforms previous isometry invariants on a classification of point clouds by PersLay.