pwc-archive/papers-with-abstracts huggingface dataset:
Network resilience
Authors: Xueming Liu, Daqing Li, Manqing Ma, Boleslaw K. Szymanski, H. Eugene Stanley, Jianxi Gao
Date: 2020-07-26 00:00:00

Many systems on our planet are known to shift abruptly and irreversibly from one state to another when they are forced across a "tipping point," such as mass extinctions in ecological networks, cascading failures in infrastructure systems, and social convention changes in human and animal networks. Such a regime shift demonstrates a system's resilience that characterizes the ability of a system to adjust its activity to retain its basic functionality in the face of internal disturbances or external environmental changes. In the past 50 years, attention was almost exclusively given to low dimensional systems and calibration of their resilience functions and indicators of early warning signals without considerations for the interactions between the components. Only in recent years, taking advantages of the network theory and lavish real data sets, network scientists have directed their interest to the real-world complex networked multidimensional systems and their resilience function and early warning indicators. This report is devoted to a comprehensive review of resilience function and regime shift of complex systems in different domains, such as ecology, biology, social systems and infrastructure. We cover the related research about empirical observations, experimental studies, mathematical modeling, and theoretical analysis. We also discuss some ambiguous definitions, such as robustness, resilience, and stability.
AutoClip: Adaptive Gradient Clipping for Source Separation Networks
Authors: Prem Seetharaman, Gordon Wichern, Bryan Pardo, Jonathan Le Roux
Date: 2020-07-25 00:00:00
Tasks: Audio Source Separation

Clipping the gradient is a known approach to improving gradient descent, but requires hand selection of a clipping threshold hyperparameter. We present AutoClip, a simple method for automatically and adaptively choosing a gradient clipping threshold, based on the history of gradient norms observed during training. Experimental results show that applying AutoClip results in improved generalization performance for audio source separation networks. Observation of the training dynamics of a separation network trained with and without AutoClip show that AutoClip guides optimization into smoother parts of the loss landscape. AutoClip is very simple to implement and can be integrated readily into a variety of applications across multiple domains.
Few-Shot Keyword Spotting With Prototypical Networks
Authors: Archit Parnami, Minwoo Lee
Date: 2020-07-25 00:00:00
Proceeding: arXiv 2020 6
Tasks: Keyword Spotting, Metric Learning

Recognizing a particular command or a keyword, keyword spotting has been widely used in many voice interfaces such as Amazon's Alexa and Google Home. In order to recognize a set of keywords, most of the recent deep learning based approaches use a neural network trained with a large number of samples to identify certain pre-defined keywords. This restricts the system from recognizing new, user-defined keywords. Therefore, we first formulate this problem as a few-shot keyword spotting and approach it using metric learning. To enable this research, we also synthesize and publish a Few-shot Google Speech Commands dataset. We then propose a solution to the few-shot keyword spotting problem using temporal and dilated convolutions on prototypical networks. Our comparative experimental results demonstrate keyword spotting of new keywords using just a small number of samples.
Towards 3D Visualization of Video from Frames
Authors: Slimane Larabi
Date: 2020-07-25 00:00:00
Tasks: Object

We explain theoretically how to reconstruct the 3D scene from successive frames in order to see the video in 3D. To do this, features, associated to moving rigid objects in 3D, are extracted in frames and matched. The vanishing point computed in frame corresponding to the direction of moving object is used for 3D positioning of the 3D structure of the moving object. First experiments are conducted and the obtained results are shown and publicly available. They demonstrate the feasibility of our method. We conclude this paper by future works in order to improve this method tacking into account non-rigid objects and the case of moving camera.
Learning to predict metal deformations in hot-rolling processes
Authors: R. Omar Chavez-Garcia, Emian Furger, Samuele Kronauer, Christian Brianza, Marco Scarfò, Luca Diviani, Alessandro Giusti
Date: 2020-07-22 00:00:00

Hot-rolling is a metal forming process that produces a workpiece with a desired target cross-section from an input workpiece through a sequence of plastic deformations; each deformation is generated by a stand composed of opposing rolls with a specific geometry. In current practice, the rolling sequence (i.e., the sequence of stands and the geometry of their rolls) needed to achieve a given final cross-section is designed by experts based on previous experience, and iteratively refined in a costly trial-and-error process. Finite Element Method simulations are increasingly adopted to make this process more efficient and to test potential rolling sequences, achieving good accuracy at the cost of long simulation times, limiting the practical use of the approach. We propose a supervised learning approach to predict the deformation of a given workpiece by a set of rolls with a given geometry; the model is trained on a large dataset of procedurally-generated FEM simulations, which we publish as supplementary material. The resulting predictor is four orders of magnitude faster than simulations, and yields an average Jaccard Similarity Index of 0.972 (against ground truth from simulations) and 0.925 (against real-world measured deformations); we additionally report preliminary results on using the predictor for automatic planning of rolling sequences.
Anomaly Awareness
Authors: Charanjit K. Khosa, Veronica Sanz
Date: 2020-07-20 00:00:00
Tasks: Anomaly Detection, BIG-bench Machine Learning

We present a new algorithm for anomaly detection called Anomaly Awareness. The algorithm learns about normal events while being made aware of the anomalies through a modification of the cost function. We show how this method works in different Particle Physics situations and in standard Computer Vision tasks. For example, we apply the method to images from a Fat Jet topology generated by Standard Model Top and QCD events, and test it against an array of new physics scenarios, including Higgs production with EFT effects and resonances decaying into two, three or four subjets. We find that the algorithm is effective identifying anomalies not seen before, and becomes robust as we make it aware of a varied-enough set of anomalies.
Automated Intracranial Artery Labeling using a Graph Neural Network and Hierarchical Refinement
Authors: Li Chen, Thomas Hatsukami, Jenq-Neng Hwang, Chun Yuan
Date: 2020-07-11 00:00:00
Tasks: Graph Neural Network

Automatically labeling intracranial arteries (ICA) with their anatomical names is beneficial for feature extraction and detailed analysis of intracranial vascular structures. There are significant variations in the ICA due to natural and pathological causes, making it challenging for automated labeling. However, the existing public dataset for evaluation of anatomical labeling is limited. We construct a comprehensive dataset with 729 Magnetic Resonance Angiography scans and propose a Graph Neural Network (GNN) method to label arteries by classifying types of nodes and edges in an attributed relational graph. In addition, a hierarchical refinement framework is developed for further improving the GNN outputs to incorporate structural and relational knowledge about the ICA. Our method achieved a node labeling accuracy of 97.5%, and 63.8% of scans were correctly labeled for all Circle of Willis nodes, on a testing set of 105 scans with both healthy and diseased subjects. This is a significant improvement over available state-of-the-art methods. Automatic artery labeling is promising to minimize manual effort in characterizing the complicated ICA networks and provides valuable information for the identification of geometric risk factors of vascular disease. Our code and dataset are available at https://github.com/clatfd/GNN-ARTLABEL.
COVID-19 Data Analysis and Forecasting: Algeria and the World
Authors: Sami Belkacem
Date: 2020-07-19 00:00:00
Tasks: Time Series, Time Series Analysis

The novel coronavirus disease 2019 COVID-19 has been leading the world into a prominent crisis. As of May 19, 2020, the virus had spread to 215 countries with more than 4,622,001 confirmed cases and 311,916 reported deaths worldwide, including Algeria with 7201 cases and 555 deaths. Analyze and forecast COVID-19 cases and deaths growth could be useful in many ways, governments could estimate medical equipment and take appropriate policy responses, and experts could approximate the peak and the end of the disease. In this work, we first train a time series Prophet model to analyze and forecast the number of COVID-19 cases and deaths in Algeria based on the previously reported numbers. Then, to better understand the spread and the properties of the COVID-19, we include external factors that may contribute to accelerate/slow the spread of the virus, construct a dataset from reliable sources, and conduct a large-scale data analysis considering 82 countries worldwide. The evaluation results show that the time series Prophet model accurately predicts the number of cases and deaths in Algeria with low RMSE scores of 218.87 and 4.79 respectively, while the forecast suggests that the total number of cases and deaths are expected to increase in the coming weeks. Moreover, the worldwide data-driven analysis reveals several correlations between the increase/decrease in the number of cases and deaths and external factors that may contribute to accelerate/slow the spread of the virus such as geographic, climatic, health, economic, and demographic factors.
Intelligent Exploration for User Interface Modules of Mobile App with Collective Learning
Authors: Jingbo Zhou, Zhenwei Tang, Min Zhao, Xiang Ge, Fuzhen Zhuang, Meng Zhou, Liming Zou, Chenglei Yang, Hui Xiong
Date: 2020-07-21 00:00:00

A mobile app interface usually consists of a set of user interface modules. How to properly design these user interface modules is vital to achieving user satisfaction for a mobile app. However, there are few methods to determine design variables for user interface modules except for relying on the judgment of designers. Usually, a laborious post-processing step is necessary to verify the key change of each design variable. Therefore, there is a only very limited amount of design solutions that can be tested. It is timeconsuming and almost impossible to figure out the best design solutions as there are many modules. To this end, we introduce FEELER, a framework to fast and intelligently explore design solutions of user interface modules with a collective machine learning approach. FEELER can help designers quantitatively measure the preference score of different design solutions, aiming to facilitate the designers to conveniently and quickly adjust user interface module. We conducted extensive experimental evaluations on two real-life datasets to demonstrate its applicability in real-life cases of user interface module design in the Baidu App, which is one of the most popular mobile apps in China.
Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario
Authors: Federico Bianchi, Jacopo Tagliabue, Bingqing Yu, Luca Bigon, Ciro Greco
Date: 2020-07-20 00:00:00

This paper addresses the challenge of leveraging multiple embedding spaces for multi-shop personalization, proving that zero-shot inference is possible by transferring shopping intent from one website to another without manual intervention. We detail a machine learning pipeline to train and optimize embeddings within shops first, and support the quantitative findings with additional qualitative insights. We then turn to the harder task of using learned embeddings across shops: if products from different shops live in the same vector space, user intent - as represented by regions in this space - can then be transferred in a zero-shot fashion across websites. We propose and benchmark unsupervised and supervised methods to "travel" between embedding spaces, each with its own assumptions on data quantity and quality. We show that zero-shot personalization is indeed possible at scale by testing the shared embedding space with two downstream tasks, event prediction and type-ahead suggestions. Finally, we curate a cross-shop anonymized embeddings dataset to foster an inclusive discussion of this important business scenario.
Contrastive Learning for Unpaired Image-to-Image Translation
Authors: Taesung Park, Alexei A. Efros, Richard Zhang, Jun-Yan Zhu
Date: 2020-07-30 00:00:00
Tasks: Contrastive Learning, Image Generation, Image-to-Image Translation, Translation

In image-to-image translation, each patch in the output should reflect the content of the corresponding patch in the input, independent of domain. We propose a straightforward method for doing so -- maximizing mutual information between the two, using a framework based on contrastive learning. The method encourages two elements (corresponding patches) to map to a similar point in a learned feature space, relative to other elements (other patches) in the dataset, referred to as negatives. We explore several critical design choices for making contrastive learning effective in the image synthesis setting. Notably, we use a multilayer, patch-based approach, rather than operate on entire images. Furthermore, we draw negatives from within the input image itself, rather than from the rest of the dataset. We demonstrate that our framework enables one-sided translation in the unpaired image-to-image translation setting, while improving quality and reducing training time. In addition, our method can even be extended to the training setting where each "domain" is only a single image.
Deriving Differential Target Propagation from Iterating Approximate Inverses
Authors: Yoshua Bengio
Date: 2020-07-29 00:00:00

We show that a particular form of target propagation, i.e., relying on learned inverses of each layer, which is differential, i.e., where the target is a small perturbation of the forward propagation, gives rise to an update rule which corresponds to an approximate Gauss-Newton gradient-based optimization, without requiring the manipulation or inversion of large matrices. What is interesting is that this is more biologically plausible than back-propagation yet may turn out to implicitly provide a stronger optimization procedure. Extending difference target propagation, we consider several iterative calculations based on local auto-encoders at each layer in order to achieve more precise inversions for more accurate target propagation and we show that these iterative procedures converge exponentially fast if the auto-encoding function minus the identity function has a Lipschitz constant smaller than one, i.e., the auto-encoder is coarsely succeeding at performing an inversion. We also propose a way to normalize the changes at each layer to take into account the relative influence of each layer on the output, so that larger weight changes are done on more influential layers, like would happen in ordinary back-propagation with gradient descent.
Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild
Authors: Jason Y. Zhang, Sam Pepose, Hanbyul Joo, Deva Ramanan, Jitendra Malik, Angjoo Kanazawa
Date: 2020-07-30 00:00:00
Proceeding: ECCV 2020 8
Tasks: 3D Human Pose Estimation, 3D Human Reconstruction, 3D Object Reconstruction, 3D Shape Reconstruction, 3D Shape Reconstruction From A Single 2D Image, Common Sense Reasoning, Human-Object Interaction Detection, Object

We present a method that infers spatial arrangements and shapes of humans and objects in a globally consistent 3D scene, all from a single image in-the-wild captured in an uncontrolled environment. Notably, our method runs on datasets without any scene- or object-level 3D supervision. Our key insight is that considering humans and objects jointly gives rise to "3D common sense" constraints that can be used to resolve ambiguity. In particular, we introduce a scale loss that learns the distribution of object size from data; an occlusion-aware silhouette re-projection loss to optimize object pose; and a human-object interaction loss to capture the spatial layout of objects with which humans interact. We empirically validate that our constraints dramatically reduce the space of likely 3D spatial configurations. We demonstrate our approach on challenging, in-the-wild images of humans interacting with large objects (such as bicycles, motorcycles, and surfboards) and handheld objects (such as laptops, tennis rackets, and skateboards). We quantify the ability of our approach to recover human-object arrangements and outline remaining challenges in this relatively domain. The project webpage can be found at https://jasonyzhang.com/phosa.
Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients
Authors: Sofie Tilborghs, Ine Dirks, Lucas Fidon, Siri Willems, Tom Eelbode, Jeroen Bertels, Bart Ilsen, Arne Brys, Adriana Dubbeldam, Nico Buls, Panagiotis Gonidakis, Sebastián Amador Sánchez, Annemiek Snoeckx, Paul M. Parizel, Johan de Mey, Dirk Vandermeulen, Tom Vercauteren, David Robben, Dirk Smeets, Frederik Maes, Jef Vandemeulebroucke, Paul Suetens
Date: 2020-07-29 00:00:00
Tasks: Lesion Segmentation, Overall - Test, Segmentation

Recent research on COVID-19 suggests that CT imaging provides useful information to assess disease progression and assist diagnosis, in addition to help understanding the disease. There is an increasing number of studies that propose to use deep learning to provide fast and accurate quantification of COVID-19 using chest CT scans. The main tasks of interest are the automatic segmentation of lung and lung lesions in chest CT scans of confirmed or suspected COVID-19 patients. In this study, we compare twelve deep learning algorithms using a multi-center dataset, including both open-source and in-house developed algorithms. Results show that ensembling different methods can boost the overall test set performance for lung segmentation, binary lesion segmentation and multiclass lesion segmentation, resulting in mean Dice scores of 0.982, 0.724 and 0.469, respectively. The resulting binary lesions were segmented with a mean absolute volume error of 91.3 ml. In general, the task of distinguishing different lesion types was more difficult, with a mean absolute volume difference of 152 ml and mean Dice scores of 0.369 and 0.523 for consolidation and ground glass opacity, respectively. All methods perform binary lesion segmentation with an average volume error that is better than visual assessment by human raters, suggesting these methods are mature enough for a large-scale evaluation for use in clinical practice.
Fast, Structured Clinical Documentation via Contextual Autocomplete
Authors: Divya Gopinath, Monica Agrawal, Luke Murray, Steven Horng, David Karger, David Sontag
Date: 2020-07-29 00:00:00

We present a system that uses a learned autocompletion mechanism to facilitate rapid creation of semi-structured clinical documentation. We dynamically suggest relevant clinical concepts as a doctor drafts a note by leveraging features from both unstructured and structured medical data. By constraining our architecture to shallow neural networks, we are able to make these suggestions in real time. Furthermore, as our algorithm is used to write a note, we can automatically annotate the documentation with clean labels of clinical concepts drawn from medical vocabularies, making notes more structured and readable for physicians, patients, and future algorithms. To our knowledge, this system is the only machine learning-based documentation utility for clinical notes deployed in a live hospital setting, and it reduces keystroke burden of clinical concepts by 67% in real environments.
Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation
Authors: Yu Xiang, Christopher Xie, Arsalan Mousavian, Dieter Fox
Date: 2020-07-30 00:00:00
Tasks: Clustering, Instance Segmentation, Metric Learning, Object, Segmentation, Semantic Segmentation, Unseen Object Instance Segmentation

Segmenting unseen objects in cluttered scenes is an important skill that robots need to acquire in order to perform tasks in new environments. In this work, we propose a new method for unseen object instance segmentation by learning RGB-D feature embeddings from synthetic data. A metric learning loss function is utilized to learn to produce pixel-wise feature embeddings such that pixels from the same object are close to each other and pixels from different objects are separated in the embedding space. With the learned feature embeddings, a mean shift clustering algorithm can be applied to discover and segment unseen objects. We further improve the segmentation accuracy with a new two-stage clustering algorithm. Our method demonstrates that non-photorealistic synthetic RGB and depth images can be used to learn feature embeddings that transfer well to real-world images for unseen object instance segmentation.
Data-efficient Hindsight Off-policy Option Learning
Authors: Markus Wulfmeier, Dushyant Rao, Roland Hafner, Thomas Lampe, Abbas Abdolmaleki, Tim Hertweck, Michael Neunert, Dhruva Tirumala, Noah Siegel, Nicolas Heess, Martin Riedmiller
Date: 2020-07-30 00:00:00
Tasks: Robot Manipulation

We introduce Hindsight Off-policy Options (HO2), a data-efficient option learning algorithm. Given any trajectory, HO2 infers likely option choices and backpropagates through the dynamic programming inference procedure to robustly train all policy components off-policy and end-to-end. The approach outperforms existing option learning methods on common benchmarks. To better understand the option framework and disentangle benefits from both temporal and action abstraction, we evaluate ablations with flat policies and mixture policies with comparable optimization. The results highlight the importance of both types of abstraction as well as off-policy training and trust-region constraints, particularly in challenging, simulated 3D robot manipulation tasks from raw pixel inputs. Finally, we intuitively adapt the inference step to investigate the effect of increased temporal abstraction on training with pre-trained options and from scratch.
PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards
Authors: Prasoon Goyal, Scott Niekum, Raymond J. Mooney
Date: 2020-07-30 00:00:00
Proceeding: ICML Workshop LaReL 2020 7
Tasks: reinforcement-learning, Reinforcement Learning (RL), Robot Manipulation

Reinforcement learning (RL), particularly in sparse reward settings, often requires prohibitively large numbers of interactions with the environment, thereby limiting its applicability to complex problems. To address this, several prior approaches have used natural language to guide the agent's exploration. However, these approaches typically operate on structured representations of the environment, and/or assume some structure in the natural language commands. In this work, we propose a model that directly maps pixels to rewards, given a free-form natural language description of the task, which can then be used for policy learning. Our experiments on the Meta-World robot manipulation domain show that language-based rewards significantly improves the sample efficiency of policy learning, both in sparse and dense reward settings.