pwc-archive/papers-with-abstracts huggingface dataset:
Hunting for supernovae articles in the universe of scientometrics
Authors: Dimitrios Katsaros
Date: 2019-12-15 00:00:00
Tasks: Articles

This short note records an unusual situation with some Google Scholar's profiles that imply the existence of "supernovae" articles, i.e., articles whose impact -- in terms of number of citations -- in a single year gets (almost) an order of magnitude higher than the previous year and immediate drops (and remains steady) to a very low level after the next year. We analyse the issue and resolve the situation providing an answer whether there exist supernovae articles.
Multilingual is not enough: BERT for Finnish
Authors: Antti Virtanen, Jenna Kanerva, Rami Ilo, Jouni Luoma, Juhani Luotolahti, Tapio Salakoski, Filip Ginter, Sampo Pyysalo
Date: 2019-12-15 00:00:00
Tasks: Dependency Parsing, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Part-Of-Speech Tagging, Transfer Learning

Deep learning-based language models pretrained on large unannotated text corpora have been demonstrated to allow efficient transfer learning for natural language processing, with recent approaches such as the transformer-based BERT model advancing the state of the art across a variety of tasks. While most work on these models has focused on high-resource languages, in particular English, a number of recent efforts have introduced multilingual models that can be fine-tuned to address tasks in a large number of different languages. However, we still lack a thorough understanding of the capabilities of these models, in particular for lower-resourced languages. In this paper, we focus on Finnish and thoroughly evaluate the multilingual BERT model on a range of tasks, comparing it with a new Finnish BERT model trained from scratch. The new language-specific model is shown to systematically and clearly outperform the multilingual. While the multilingual model largely fails to reach the performance of previously proposed methods, the custom Finnish BERT model establishes new state-of-the-art results on all corpora for all reference tasks: part-of-speech tagging, named entity recognition, and dependency parsing. We release the model and all related resources created for this study with open licenses at https://turkunlp.org/finbert .
One-Shot Induction of Generalized Logical Concepts via Human Guidance
Authors: Mayukh Das, Nandini Ramanan, Janardhan Rao Doppa, Sriraam Natarajan
Date: 2019-12-15 00:00:00
Tasks: Inductive logic programming, valid

We consider the problem of learning generalized first-order representations of concepts from a single example. To address this challenging problem, we augment an inductive logic programming learner with two novel algorithmic contributions. First, we define a distance measure between candidate concept representations that improves the efficiency of search for target concept and generalization. Second, we leverage richer human inputs in the form of advice to improve the sample-efficiency of learning. We prove that the proposed distance measure is semantically valid and use that to derive a PAC bound. Our experimental analysis on diverse concept learning tasks demonstrates both the effectiveness and efficiency of the proposed approach over a first-order concept learner using only examples.
Computational Induction of Prosodic Structure
Authors: Dafydd Gibbon
Date: 2019-12-15 00:00:00
Tasks: Rhythm

The present study has two goals relating to the grammar of prosody, understood as the rhythms and melodies of speech. First, an overview is provided of the computable grammatical and phonetic approaches to prosody analysis which use hypothetico-deductive methods and are based on learned hermeneutic intuitions about language. Second, a proposal is presented for an inductive grounding in the physical signal, in which prosodic structure is inferred using a language-independent method from the low-frequency spectrum of the speech signal. The overview includes a discussion of computational aspects of standard generative and post-generative models, and suggestions for reformulating these to form inductive approaches. Also included is a discussion of linguistic phonetic approaches to analysis of annotations (pairs of speech unit labels with time-stamps) of recorded spoken utterances. The proposal introduces the inductive approach of Rhythm Formant Theory (RFT) and the associated Rhythm Formant Analysis (RFA) method are introduced, with the aim of completing a gap in the linguistic hypothetico-deductive cycle by grounding in a language-independent inductive procedure of speech signal analysis. The validity of the method is demonstrated and applied to rhythm patterns in read-aloud Mandarin Chinese, finding differences from English which are related to lexical and grammatical differences between the languages, as well as individual variation. The overall conclusions are (1) that normative language-to-language phonological or phonetic comparisons of rhythm, for example of Mandarin and English, are too simplistic, in view of diverse language-internal factors due to genre and style differences as well as utterance dynamics, and (2) that language-independent empirical grounding of rhythm in the physical signal is called for.
Mixability of Integral Losses: a Key to Efficient Online Aggregation of Functional and Probabilistic Forecasts
Authors: Alexander Korotin, Vladimir V'yugin, Evgeny Burnaev
Date: 2019-12-15 00:00:00

In this paper we extend the setting of the online prediction with expert advice to function-valued forecasts. At each step of the online game several experts predict a function, and the learner has to efficiently aggregate these functional forecasts into a single forecast. We adapt basic mixable (and exponentially concave) loss functions to compare functional predictions and prove that these adaptations are also mixable (exp-concave). We call this phenomenon mixability (exp-concavity) of integral loss functions. As an application of our main result, we prove that various loss functions used for probabilistic forecasting are mixable (exp-concave). The considered losses include Sliced Continuous Ranked Probability Score, Energy-Based Distance, Optimal Transport Costs and Sliced Wasserstein-2 distance, Beta-2 and Kullback-Leibler divergences, Characteristic function and Maximum Mean Discrepancies.
How Should an Agent Practice?
Authors: Janarthanan Rajendran, Richard Lewis, Vivek Veeriah, Honglak Lee, Satinder Singh
Date: 2019-12-15 00:00:00

We present a method for learning intrinsic reward functions to drive the learning of an agent during periods of practice in which extrinsic task rewards are not available. During practice, the environment may differ from the one available for training and evaluation with extrinsic rewards. We refer to this setup of alternating periods of practice and objective evaluation as practice-match, drawing an analogy to regimes of skill acquisition common for humans in sports and games. The agent must effectively use periods in the practice environment so that performance improves during matches. In the proposed method the intrinsic practice reward is learned through a meta-gradient approach that adapts the practice reward parameters to reduce the extrinsic match reward loss computed from matches. We illustrate the method on a simple grid world, and evaluate it in two games in which the practice environment differs from match: Pong with practice against a wall without an opponent, and PacMan with practice in a maze without ghosts. The results show gains from learning in practice in addition to match periods over learning in matches only.
Theory of neuromorphic computing by waves: machine learning by rogue waves, dispersive shocks, and solitons
Authors: Giulia Marcucci, Davide Pierangeli, Claudio Conti
Date: 2019-12-15 00:00:00
Tasks: BIG-bench Machine Learning, Combinatorial Optimization

We study artificial neural networks with nonlinear waves as a computing reservoir. We discuss universality and the conditions to learn a dataset in terms of output channels and nonlinearity. A feed-forward three-layer model, with an encoding input layer, a wave layer, and a decoding readout, behaves as a conventional neural network in approximating mathematical functions, real-world datasets, and universal Boolean gates. The rank of the transmission matrix has a fundamental role in assessing the learning abilities of the wave. For a given set of training points, a threshold nonlinearity for universal interpolation exists. When considering the nonlinear Schroedinger equation, the use of highly nonlinear regimes implies that solitons, rogue, and shock waves do have a leading role in training and computing. Our results may enable the realization of novel machine learning devices by using diverse physical systems, as nonlinear optics, hydrodynamics, polaritonics, and Bose-Einstein condensates. The application of these concepts to photonics opens the way to a large class of accelerators and new computational paradigms. In complex wave systems, as multimodal fibers, integrated optical circuits, random, topological devices, and metasurfaces, nonlinear waves can be employed to perform computation and solve complex combinatorial optimization.
Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic Manuscripts
Authors: Abhishek Prusty, Sowmya Aitha, Abhishek Trivedi, Ravi Kiran Sarvadevabhatla
Date: 2019-12-15 00:00:00
Tasks: Diversity, Instance Segmentation, Optical Character Recognition (OCR), Semantic Segmentation

Historical palm-leaf manuscript and early paper documents from Indian subcontinent form an important part of the world's literary and cultural heritage. Despite their importance, large-scale annotated Indic manuscript image datasets do not exist. To address this deficiency, we introduce Indiscapes, the first ever dataset with multi-regional layout annotations for historical Indic manuscripts. To address the challenge of large diversity in scripts and presence of dense, irregular layout elements (e.g. text lines, pictures, multiple documents per image), we adapt a Fully Convolutional Deep Neural Network architecture for fully automatic, instance-level spatial layout parsing of manuscript images. We demonstrate the effectiveness of proposed architecture on images from the Indiscapes dataset. For annotation flexibility and keeping the non-technical nature of domain experts in mind, we also contribute a custom, web-based GUI annotation tool and a dashboard-style analytics portal. Overall, our contributions set the stage for enabling downstream applications such as OCR and word-spotting in historical Indic manuscripts at scale.
Disentanglement based Active Learning
Authors: Silpa Vadakkeeveetil Sreelatha, Adarsh Kappiyath, Sumitra S
Date: 2019-12-15 00:00:00
Tasks: Active Learning, Disentanglement, image-classification, Image Classification

We propose Disentanglement based Active Learning (DAL), a new active learning technique based on self-supervision which leverages the concept of disentanglement. Instead of requesting labels from human oracle, our method automatically labels the majority of the datapoints, thus drastically reducing the human labeling budget in Generative Adversarial Net (GAN) based active learning approaches. The proposed method uses Information Maximizing Generative Adversarial Nets (InfoGAN) to learn disentangled class category representations. Disagreement between active learner predictions and InfoGAN labels decides if the datapoints need to be human-labeled. We also introduce a label correction mechanism that aims to filter out label noise that occurs due to automatic labeling. Results on three benchmark datasets for the image classification task demonstrate that our method achieves better performance compared to existing GAN-based active learning approaches.
Compressed DenseNet for Lightweight Character Recognition
Authors: Zhao Zhang, Zemin Tang, Yang Wang, Haijun Zhang, Shuicheng Yan, Meng Wang
Date: 2019-12-15 00:00:00

Convolutional Recurrent Neural Network (CRNN) is a popular network for recognizing texts in images. Advances like the variant of CRNN, such as Dense Convolutional Network with Connectionist Temporal Classification, has reduced the running time of the network, but exposing the inner computation cost and weight size of the convolutional networks as a bottleneck. Specifically, the DenseNet based models utilize the dense blocks as the core module, but the inner features are combined in the form of concatenation in dense blocks. As such, the number of channels of combined features delivered as the input of the layers close to the output and the relevant computational cost grows rapidly with the dense blocks getting deeper. This will severely bring heavy computational cost and big weight size, which restrict the depth of dense blocks. In this paper, we propose a compressed convolution block called Lightweight Dense Block (LDB). To reduce the computing cost and weight size, we re-define and re-design the way of combining internal features of the dense blocks. LDB is a convolutional block similarly as dense block, but it can reduce the computation cost and weight size to (1/L, 2/L), compared with original ones, where L is the number of layers in blocks. Moreover, LDB can be used to replace the original dense block in any DenseNet based models. Based on the LDBs, we propose a Compressed DenseNet (CDenseNet) for the lightweight character recognition. Extensive experiments demonstrate that CDenseNet can effectively reduce the weight size while delivering the promising recognition results.
DerainCycleGAN: Rain Attentive CycleGAN for Single Image Deraining and Rainmaking
Authors: Yanyan Wei, Zhao Zhang, Yang Wang, Mingliang Xu, Yi Yang, Shuicheng Yan, Meng Wang
Date: 2019-12-15 00:00:00
Tasks: Rain Removal, Single Image Deraining

Single image deraining (SID) is an important and challenging topic in emerging vision applications, and most of emerged deraining methods are supervised relying on the ground truth (i.e., paired images) in recent years. However, in practice it is rather common to have no un-paired images in real deraining task, in such cases how to remove the rain streaks in an unsupervised way will be a very challenging task due to lack of constraints between images and hence suffering from low-quality recovery results. In this paper, we explore the unsupervised SID task using unpaired data and propose a novel net called Attention-guided Deraining by Constrained CycleGAN (or shortly, DerainCycleGAN), which can fully utilize the constrained transfer learning abilitiy and circulatory structure of CycleGAN. Specifically, we design an unsu-pervised attention guided rain streak extractor (U-ARSE) that utilizes a memory to extract the rain streak masks with two constrained cycle-consistency branches jointly by paying attention to both the rainy and rain-free image domains. As a by-product, we also contribute a new paired rain image dataset called Rain200A, which is constructed by our network automatically. Compared with existing synthesis datasets, the rainy streaks in Rain200A contains more obvious and diverse shapes and directions. As a result, existing supervised methods trained on Rain200A can perform much better for processing real rainy images. Extensive experiments on synthesis and real datasets show that our net is superior to existing unsupervised deraining networks, and is also very competitive to other related supervised networks.
BatVision: Learning to See 3D Spatial Layout with Two Ears
Authors: Jesper Haahr Christensen, Sascha Hornauer, Stella Yu
Date: 2019-12-15 00:00:00
Tasks: Robot Navigation, Vocal Bursts Valence Prediction

Many species have evolved advanced non-visual perception while artificial systems fall behind. Radar and ultrasound complement camera-based vision but they are often too costly and complex to set up for very limited information gain. In nature, sound is used effectively by bats, dolphins, whales, and humans for navigation and communication. However, it is unclear how to best harness sound for machine perception. Inspired by bats' echolocation mechanism, we design a low-cost BatVision system that is capable of seeing the 3D spatial layout of space ahead by just listening with two ears. Our system emits short chirps from a speaker and records returning echoes through microphones in an artificial human pinnae pair. During training, we additionally use a stereo camera to capture color images for calculating scene depths. We train a model to predict depth maps and even grayscale images from the sound alone. During testing, our trained BatVision provides surprisingly good predictions of 2D visual scenes from two 1D audio signals. Such a sound to vision system would benefit robot navigation and machine vision, especially in low-light or no-light conditions. Our code and data are publicly available.
A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection
Authors: Zhe Chen, Wanli Ouyang, Tongliang Liu, DaCheng Tao
Date: 2019-12-15 00:00:00
Tasks: Pedestrian Detection

Deep learning-based computer vision is usually data-hungry. Many researchers attempt to augment datasets with synthesized data to improve model robustness. However, the augmentation of popular pedestrian datasets, such as Caltech and Citypersons, can be extremely challenging because real pedestrians are commonly in low quality. Due to the factors like occlusions, blurs, and low-resolution, it is significantly difficult for existing augmentation approaches, which generally synthesize data using 3D engines or generative adversarial networks (GANs), to generate realistic-looking pedestrians. Alternatively, to access much more natural-looking pedestrians, we propose to augment pedestrian detection datasets by transforming real pedestrians from the same dataset into different shapes. Accordingly, we propose the Shape Transformation-based Dataset Augmentation (STDA) framework. The proposed framework is composed of two subsequent modules, i.e. the shape-guided deformation and the environment adaptation. In the first module, we introduce a shape-guided warping field to help deform the shape of a real pedestrian into a different shape. Then, in the second stage, we propose an environment-aware blending map to better adapt the deformed pedestrians into surrounding environments, obtaining more realistic-looking pedestrians and more beneficial augmentation results for pedestrian detection. Extensive empirical studies on different pedestrian detection benchmarks show that the proposed STDA framework consistently produces much better augmentation results than other pedestrian synthesis approaches using low-quality pedestrians. By augmenting the original datasets, our proposed framework also improves the baseline pedestrian detector by up to 38% on the evaluated benchmarks, achieving state-of-the-art performance.
C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds
Authors: Albert Pumarola, Stefan Popov, Francesc Moreno-Noguer, Vittorio Ferrari
Date: 2019-12-15 00:00:00
Proceeding: CVPR 2020 6
Tasks: 3D Reconstruction, Diversity, Image Manipulation, Style Transfer

Flow-based generative models have highly desirable properties like exact log-likelihood evaluation and exact latent-variable inference, however they are still in their infancy and have not received as much attention as alternative generative models. In this paper, we introduce C-Flow, a novel conditioning scheme that brings normalizing flows to an entirely new scenario with great possibilities for multi-modal data modeling. C-Flow is based on a parallel sequence of invertible mappings in which a source flow guides the target flow at every step, enabling fine-grained control over the generation process. We also devise a new strategy to model unordered 3D point clouds that, in combination with the conditioning scheme, makes it possible to address 3D reconstruction from a single image and its inverse problem of rendering an image given a point cloud. We demonstrate our conditioning method to be very adaptable, being also applicable to image manipulation, style transfer and multi-modal image-to-image mapping in a diversity of domains, including RGB images, segmentation maps, and edge masks.
The development of blockchain technology
Authors: Jala Quluzada, Sabnam Maharramli
Date: 2019-12-15 00:00:00
Tasks: Age Estimation

Blockchain technology is the first successful Bitcoin Network. It enables the ledger to become more decentralized and secure. Since it is not limited to bitcoin and controlled by third parties by government, corporations or banks, the technology is capturing the number of industries including cryptocurrency, infrastructure& hardware, financial technology, Internet&mobile and so on. Blockchain is used as a public ledger to verify all transactions of peer to peer systems and to maintain traded bitcoin spending from central authorities while transactions have been distributed by Bitcoin. Achieving high blockchain-based performance and privacy & security are global issues that are desire to be overcome as claims show they are still significant challenges in many blockchain applications. Thus, this paper provides an introduction of Blockchain and the process of this technology in a way of outlining blockchain types. In addition, recent advances & challenges, real economy integration and current situations of this technology have been listed.
Joint Learning of Generative Translator and Classifier for Visually Similar Classes
Authors: ByungIn Yoo, Tristan Sylvain, Yoshua Bengio, Junmo Kim
Date: 2019-12-15 00:00:00
Tasks: Data Augmentation, Domain Adaptation, General Classification, Translation

In this paper, we propose a Generative Translation Classification Network (GTCN) for improving visual classification accuracy in settings where classes are visually similar and data is scarce. For this purpose, we propose joint learning from a scratch to train a classifier and a generative stochastic translation network end-to-end. The translation network is used to perform on-line data augmentation across classes, whereas previous works have mostly involved domain adaptation. To help the model further benefit from this data-augmentation, we introduce an adaptive fade-in loss and a quadruplet loss. We perform experiments on multiple datasets to demonstrate the proposed method's performance in varied settings. Of particular interest, training on 40% of the dataset is enough for our model to surpass the performance of baselines trained on the full dataset. When our architecture is trained on the full dataset, we achieve comparable performance with state-of-the-art methods despite using a light-weight architecture.
Action Genome: Actions as Composition of Spatio-temporal Scene Graphs
Authors: Jingwei Ji, Ranjay Krishna, Li Fei-Fei, Juan Carlos Niebles
Date: 2019-12-15 00:00:00
Tasks: Action Recognition, Few-Shot action recognition, Few Shot Action Recognition, Spatio-temporal Scene Graphs

Action recognition has typically treated actions and activities as monolithic events that occur in videos. However, there is evidence from Cognitive Science and Neuroscience that people actively encode activities into consistent hierarchical part structures. However in Computer Vision, few explorations on representations encoding event partonomies have been made. Inspired by evidence that the prototypical unit of an event is an action-object interaction, we introduce Action Genome, a representation that decomposes actions into spatio-temporal scene graphs. Action Genome captures changes between objects and their pairwise relationships while an action occurs. It contains 10K videos with 0.4M objects and 1.7M visual relationships annotated. With Action Genome, we extend an existing action recognition model by incorporating scene graphs as spatio-temporal feature banks to achieve better performance on the Charades dataset. Next, by decomposing and learning the temporal changes in visual relationships that result in an action, we demonstrate the utility of a hierarchical event decomposition by enabling few-shot action recognition, achieving 42.7% mAP using as few as 10 examples. Finally, we benchmark existing scene graph models on the new task of spatio-temporal scene graph prediction.