pwc-archive/papers-with-abstracts huggingface dataset:
Learning Composable Energy Surrogates for PDE Order Reduction
Authors: Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams
Date: 2020-05-13 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Structured Prediction

Meta-materials are an important emerging class of engineered materials in which complex macroscopic behaviour--whether electromagnetic, thermal, or mechanical--arises from modular substructure. Simulation and optimization of these materials are computationally challenging, as rich substructures necessitate high-fidelity finite element meshes to solve the governing PDEs. To address this, we leverage parametric modular structure to learn component-level surrogates, enabling cheaper high-fidelity simulation. We use a neural network to model the stored potential energy in a component given boundary conditions. This yields a structured prediction task: macroscopic behavior is determined by the minimizer of the system's total potential energy, which can be approximated by composing these surrogate models. Composable energy surrogates thus permit simulation in the reduced basis of component boundaries. Costly ground-truth simulation of the full structure is avoided, as training data are generated by performing finite element analysis with individual components. Using dataset aggregation to choose training boundary conditions allows us to learn energy surrogates which produce accurate macroscopic behavior when composed, accelerating simulation of parametric meta-materials.
Triaging moderate COVID-19 and other viral pneumonias from routine blood tests
Authors: Forrest Sheng Bao, Youbiao He, Jie Liu, Yuanfang Chen, Qian Li, Christina R. Zhang, Lei Han, Baoli Zhu, Yaorong Ge, Shi Chen, Ming Xu, Liu Ouyang
Date: 2020-05-13 00:00:00
Tasks: BIG-bench Machine Learning, Specificity

The COVID-19 is sweeping the world with deadly consequences. Its contagious nature and clinical similarity to other pneumonias make separating subjects contracted with COVID-19 and non-COVID-19 viral pneumonia a priority and a challenge. However, COVID-19 testing has been greatly limited by the availability and cost of existing methods, even in developed countries like the US. Intrigued by the wide availability of routine blood tests, we propose to leverage them for COVID-19 testing using the power of machine learning. Two proven-robust machine learning model families, random forests (RFs) and support vector machines (SVMs), are employed to tackle the challenge. Trained on blood data from 208 moderate COVID-19 subjects and 86 subjects with non-COVID-19 moderate viral pneumonia, the best result is obtained in an SVM-based classifier with an accuracy of 84%, a sensitivity of 88%, a specificity of 80%, and a precision of 92%. The results are found explainable from both machine learning and medical perspectives. A privacy-protected web portal is set up to help medical personnel in their practice and the trained models are released for developers to further build other applications. We hope our results can help the world fight this pandemic and welcome clinical verification of our approach on larger populations.
Validation and Normalization of DCS corpus using Sanskrit Heritage tools to build a tagged Gold Corpus
Authors: Sriram Krishnan, Amba Kulkarni, Gérard Huet
Date: 2020-05-13 00:00:00
Tasks: Morphological Analysis

The Digital Corpus of Sanskrit records around 650,000 sentences along with their morphological and lexical tagging. But inconsistencies in morphological analysis, and in providing crucial information like the segmented word, urges the need for standardization and validation of this corpus. Automating the validation process requires efficient analyzers which also provide the missing information. The Sanskrit Heritage Engine's Reader produces all possible segmentations with morphological and lexical analyses. Aligning these systems would help us in recording the linguistic differences, which can be used to update these systems to produce standardized results and will also provide a Gold corpus tagged with complete morphological and lexical information along with the segmented words. Krishna et al. (2017) aligned 115,000 sentences, considering some of the linguistic differences. As both these systems have evolved significantly, the alignment is done again considering all the remaining linguistic differences between these systems. This paper describes the modified alignment process in detail and records the additional linguistic differences observed. Reference: Amrith Krishna, Pavankumar Satuluri, and Pawan Goyal. 2017. A dataset for Sanskrit word segmentation. In Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, page 105-114. Association for Computational Linguistics, August.
Deep Learning for Political Science
Authors: Kakia Chatsiou, Slava Jankin Mikhaylov
Date: 2020-05-13 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

Political science, and social science in general, have traditionally been using computational methods to study areas such as voting behavior, policy making, international conflict, and international development. More recently, increasingly available quantities of data are being combined with improved algorithms and affordable computational resources to predict, learn, and discover new insights from data that is large in volume and variety. New developments in the areas of machine learning, deep learning, natural language processing (NLP), and, more generally, artificial intelligence (AI) are opening up new opportunities for testing theories and evaluating the impact of interventions and programs in a more dynamic and effective way. Applications using large volumes of structured and unstructured data are becoming common in government and industry, and increasingly also in social science research. This chapter offers an introduction to such methods drawing examples from political science. Focusing on the areas where the strengths of the methods coincide with challenges in these fields, the chapter first presents an introduction to AI and its core technology - machine learning, with its rapidly developing subfield of deep learning. The discussion of deep neural networks is illustrated with the NLP tasks that are relevant to political science. The latest advances in deep learning methods for NLP are also reviewed, together with their potential for improving information extraction and pattern recognition from political science texts.
A Mixture of $h-1$ Heads is Better than $h$ Heads
Authors: Hao Peng, Roy Schwartz, Dianqi Li, Noah A. Smith
Date: 2020-05-13 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, Mixture-of-Experts, Translation

Multi-head attentive neural architectures have achieved state-of-the-art results on a variety of natural language processing tasks. Evidence has shown that they are overparameterized; attention heads can be pruned without significant performance loss. In this work, we instead "reallocate" them -- the model learns to activate different heads on different inputs. Drawing connections between multi-head attention and mixture of experts, we propose the mixture of attentive experts model (MAE). MAE is trained using a block coordinate descent algorithm that alternates between updating (1) the responsibilities of the experts and (2) their parameters. Experiments on machine translation and language modeling show that MAE outperforms strong baselines on both tasks. Particularly, on the WMT14 English to German translation dataset, MAE improves over "transformer-base" by 0.8 BLEU, with a comparable number of parameters. Our analysis shows that our model learns to specialize different experts to different inputs.
Robust Visual Object Tracking with Two-Stream Residual Convolutional Networks
Authors: Ning Zhang, Jingen Liu, Ke Wang, Dan Zeng, Tao Mei
Date: 2020-05-13 00:00:00
Tasks: Object Tracking, Visual Object Tracking, Visual Tracking, Vocal Bursts Valence Prediction

The current deep learning based visual tracking approaches have been very successful by learning the target classification and/or estimation model from a large amount of supervised training data in offline mode. However, most of them can still fail in tracking objects due to some more challenging issues such as dense distractor objects, confusing background, motion blurs, and so on. Inspired by the human "visual tracking" capability which leverages motion cues to distinguish the target from the background, we propose a Two-Stream Residual Convolutional Network (TS-RCN) for visual tracking, which successfully exploits both appearance and motion features for model update. Our TS-RCN can be integrated with existing deep learning based visual trackers. To further improve the tracking performance, we adopt a "wider" residual network ResNeXt as its feature extraction backbone. To the best of our knowledge, TS-RCN is the first end-to-end trainable two-stream visual tracking system, which makes full use of both appearance and motion features of the target. We have extensively evaluated the TS-RCN on most widely used benchmark datasets including VOT2018, VOT2019, and GOT-10K. The experiment results have successfully demonstrated that our two-stream model can greatly outperform the appearance based tracker, and it also achieves state-of-the-art performance. The tracking system can run at up to 38.1 FPS.
The Equivalence of Fourier-based and Wasserstein Metrics on Imaging Problems
Authors: Gennaro Auricchio, Andrea Codegoni, Stefano Gualandi, Giuseppe Toscani, Marco Veneroni
Date: 2020-05-13 00:00:00

We investigate properties of some extensions of a class of Fourier-based probability metrics, originally introduced to study convergence to equilibrium for the solution to the spatially homogeneous Boltzmann equation. At difference with the original one, the new Fourier-based metrics are well-defined also for probability distributions with different centers of mass, and for discrete probability measures supported over a regular grid. Among other properties, it is shown that, in the discrete setting, these new Fourier-based metrics are equivalent either to the Euclidean-Wasserstein distance $W_2$, or to the Kantorovich-Wasserstein distance $W_1$, with explicit constants of equivalence. Numerical results then show that in benchmark problems of image processing, Fourier metrics provide a better runtime with respect to Wasserstein ones.
A Survey on Temporal Reasoning for Temporal Information Extraction from Text (Extended Abstract)
Authors: Artuur Leeuwenberg, Marie-Francine Moens
Date: 2020-05-13 00:00:00
Tasks: Natural Language Understanding, Temporal Information Extraction

Time is deeply woven into how people perceive, and communicate about the world. Almost unconsciously, we provide our language utterances with temporal cues, like verb tenses, and we can hardly produce sentences without such cues. Extracting temporal cues from text, and constructing a global temporal view about the order of described events is a major challenge of automatic natural language understanding. Temporal reasoning, the process of combining different temporal cues into a coherent temporal view, plays a central role in temporal information extraction. This article presents a comprehensive survey of the research from the past decades on temporal reasoning for automatic temporal information extraction from text, providing a case study on the integration of symbolic reasoning with machine learning-based information extraction systems.
Many-Objective Software Remodularization using NSGA-III
Authors: Mohamed Wiem Mkaouer, Marouane Kessentini, Adnan Shaout, Patrice Koligheu, Slim Bechikh, Kalyanmoy Deb, Ali Ouni
Date: 2020-05-13 00:00:00

Software systems nowadays are complex and difficult to maintain due to continuous changes and bad design choices. To handle the complexity of systems, software products are, in general, decomposed in terms of packages/modules containing classes that are dependent. However, it is challenging to automatically remodularize systems to improve their maintainability. The majority of existing remodularization work mainly satisfy one objective which is improving the structure of packages by optimizing coupling and cohesion. In addition, most of existing studies are limited to only few operation types such as move class and split packages. Many other objectives, such as the design semantics, reducing the number of changes and maximizing the consistency with development change history, are important to improve the quality of the software by remodularizing it. In this paper, we propose a novel many-objective search-based approach using NSGA-III. The process aims at finding the optimal remodularization solutions that improve the structure of packages, minimize the number of changes, preserve semantics coherence, and re-use the history of changes. We evaluate the efficiency of our approach using four different open-source systems and one automotive industry project, provided by our industrial partner, through a quantitative and qualitative study conducted with software engineers.
Coordinates-based Resource Allocation Through Supervised Machine Learning
Authors: Sahar Imtiaz, Sebastian Schiessl, Georgios P. Koudouridis, James Gross
Date: 2020-05-13 00:00:00
Tasks: BIG-bench Machine Learning

Appropriate allocation of system resources is essential for meeting the increased user-traffic demands in the next generation wireless technologies. Traditionally, the system relies on channel state information (CSI) of the users for optimizing the resource allocation, which becomes costly for fast-varying channel conditions. Considering that future wireless technologies will be based on dense network deployment, where the mobile terminals are in line-of-sight of the transmitters, the position information of terminals provides an alternative to estimate the channel condition. In this work, we propose a coordinates-based resource allocation scheme using supervised machine learning techniques, and investigate how efficiently this scheme performs in comparison to the traditional approach under various propagation conditions. We consider a simplistic system set up as a first step, where a single transmitter serves a single mobile user. The performance results show that the coordinates-based resource allocation scheme achieves a performance very close to the CSI-based scheme, even when the available coordinates of terminals are erroneous. The proposed scheme performs consistently well with realistic-system simulation, requiring only 4 s of training time, and the appropriate resource allocation is predicted in less than 90 microseconds with a learnt model of size less than 1 kB.
A Generative Model for Generic Light Field Reconstruction
Authors: Paramanand Chandramouli, Kanchana Vaishnavi Gandikota, Andreas Goerlitz, Andreas Kolb, Michael Moeller
Date: 2020-05-13 00:00:00
Tasks: Super-Resolution

Recently deep generative models have achieved impressive progress in modeling the distribution of training data. In this work, we present for the first time a generative model for 4D light field patches using variational autoencoders to capture the data distribution of light field patches. We develop a generative model conditioned on the central view of the light field and incorporate this as a prior in an energy minimization framework to address diverse light field reconstruction tasks. While pure learning-based approaches do achieve excellent results on each instance of such a problem, their applicability is limited to the specific observation model they have been trained on. On the contrary, our trained light field generative model can be incorporated as a prior into any model-based optimization approach and therefore extend to diverse reconstruction tasks including light field view synthesis, spatial-angular super resolution and reconstruction from coded projections. Our proposed method demonstrates good reconstruction, with performance approaching end-to-end trained networks, while outperforming traditional model-based approaches on both synthetic and real scenes. Furthermore, we show that our approach enables reliable light field recovery despite distortions in the input.
Representing Whole Slide Cancer Image Features with Hilbert Curves
Authors: Erich Bremer, Jonas Almeida, Joel Saltz
Date: 2020-05-13 00:00:00
Tasks: whole slide images

Regions of Interest (ROI) contain morphological features in pathology whole slide images (WSI) are delimited with polygons[1]. These polygons are often represented in either a textual notation (with the array of edges) or in a binary mask form. Textual notations have an advantage of human readability and portability, whereas, binary mask representations are more useful as the input and output of feature-extraction pipelines that employ deep learning methodologies. For any given whole slide image, more than a million cellular features can be segmented generating a corresponding number of polygons. The corpus of these segmentations for all processed whole slide images creates various challenges for filtering specific areas of data for use in interactive real-time and multi-scale displays and analysis. Simple range queries of image locations do not scale and, instead, spatial indexing schemes are required. In this paper we propose using Hilbert Curves simultaneously for spatial indexing and as a polygonal ROI representation. This is achieved by using a series of Hilbert Curves[2] creating an efficient and inherently spatially-indexed machine-usable form. The distinctive property of Hilbert curves that enables both mask and polygon delimitation of ROIs is that the elements of the vector extracted ro describe morphological features maintain their relative positions for different scales of the same image.
Subsampled Fourier Ptychography using Pretrained Invertible and Untrained Network Priors
Authors: Fahad Shamshad, Asif Hanif, Ali Ahmed
Date: 2020-05-13 00:00:00

Recently pretrained generative models have shown promising results for subsampled Fourier Ptychography (FP) in terms of quality of reconstruction for extremely low sampling rate and high noise. However, one of the significant drawbacks of these pretrained generative priors is their limited representation capabilities. Moreover, training these generative models requires access to a large number of fully-observed clean samples of a particular class of images like faces or digits that is prohibitive to obtain in the context of FP. In this paper, we propose to leverage the power of pretrained invertible and untrained generative models to mitigate the representation error issue and requirement of a large number of example images (for training generative models) respectively. Through extensive experiments, we demonstrate the effectiveness of proposed approaches in the context of FP for low sampling rates and high noise levels.
A network-based transfer learning approach to improve sales forecasting of new products
Authors: Tristan Karb, Niklas Kühl, Robin Hirt, Varvara Glivici-Cotruta
Date: 2020-05-13 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting, Transfer Learning

Data-driven methods -- such as machine learning and time series forecasting -- are widely used for sales forecasting in the food retail domain. However, for newly introduced products insufficient training data is available to train accurate models. In this case, human expert systems are implemented to improve prediction performance. Human experts rely on their implicit and explicit domain knowledge and transfer knowledge about historical sales of similar products to forecast new product sales. By applying the concept of Transfer Learning, we propose an analytical approach to transfer knowledge between listed stock products and new products. A network-based Transfer Learning approach for deep neural networks is designed to investigate the efficiency of Transfer Learning in the domain of food sales forecasting. Furthermore, we examine how knowledge can be shared across different products and how to identify the products most suitable for transfer. To test the proposed approach, we conduct a comprehensive case study for a newly introduced product, based on data of an Austrian food retailing company. The experimental results show, that the prediction accuracy of deep neural networks for food sales forecasting can be effectively increased using the proposed approach.
From Simulation to Real World Maneuver Execution using Deep Reinforcement Learning
Authors: Alessandro Paolo Capasso, Giulio Bacchiani, Alberto Broggi
Date: 2020-05-13 00:00:00
Tasks: Autonomous Driving, Deep Reinforcement Learning, Domain Adaptation, reinforcement-learning, Reinforcement Learning (RL)

Deep Reinforcement Learning has proved to be able to solve many control tasks in different fields, but the behavior of these systems is not always as expected when deployed in real-world scenarios. This is mainly due to the lack of domain adaptation between simulated and real-world data together with the absence of distinction between train and test datasets. In this work, we investigate these problems in the autonomous driving field, especially for a maneuver planning module for roundabout insertions. In particular, we present a system based on multiple environments in which agents are trained simultaneously, evaluating the behavior of the model in different scenarios. Finally, we analyze techniques aimed at reducing the gap between simulated and real-world data showing that this increased the generalization capabilities of the system both on unseen and real-world scenarios.
Converting Anyone's Emotion: Towards Speaker-Independent Emotional Voice Conversion
Authors: Kun Zhou, Berrak Sisman, Mingyang Zhang, Haizhou Li
Date: 2020-05-13 00:00:00
Tasks: Decoder, Voice Conversion

Emotional voice conversion aims to convert the emotion of speech from one state to another while preserving the linguistic content and speaker identity. The prior studies on emotional voice conversion are mostly carried out under the assumption that emotion is speaker-dependent. We consider that there is a common code between speakers for emotional expression in a spoken language, therefore, a speaker-independent mapping between emotional states is possible. In this paper, we propose a speaker-independent emotional voice conversion framework, that can convert anyone's emotion without the need for parallel data. We propose a VAW-GAN based encoder-decoder structure to learn the spectrum and prosody mapping. We perform prosody conversion by using continuous wavelet transform (CWT) to model the temporal dependencies. We also investigate the use of F0 as an additional input to the decoder to improve emotion conversion performance. Experiments show that the proposed speaker-independent framework achieves competitive results for both seen and unseen speakers.
MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset
Authors: S. P. Morozov, A. E. Andreychenko, N. A. Pavlov, A. V. Vladzymyrskyy, N. V. Ledikhova, V. A. Gombolevskiy, I. A. Blokhin, P. B. Gelezhe, A. V. Gonchar, V. Yu. Chernina
Date: 2020-05-13 00:00:00
Tasks: BIG-bench Machine Learning, Computed Tomography (CT)

This dataset contains anonymised human lung computed tomography (CT) scans with COVID-19 related findings, as well as without such findings. A small subset of studies has been annotated with binary pixel masks depicting regions of interests (ground-glass opacifications and consolidations). CT scans were obtained between 1st of March, 2020 and 25th of April, 2020, and provided by municipal hospitals in Moscow, Russia. Permanent link: https://mosmed.ai/datasets/covid19_1110. This dataset is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) License. Key words: artificial intelligence, COVID-19, machine learning, dataset, CT, chest, imaging