pwc-archive/papers-with-abstracts huggingface dataset:
Reviewing methods and assumptions for high-resolution large-scale onshore wind energy potential assessments
Authors: Russell McKenna, Stefan Pfenninger, Heidi Heinrichs, Johannes Schmidt, Iain Staffell, Katharina Gruber, Andrea N. Hahmann, Malte Jansen, Michael Klingler, Natascha Landwehr, Xiaoli Guo Larsén, Johan Lilliestam, Bryn Pickering, Martin Robinius, Tim Tröndle, Olga Turkovska, Sebastian Wehrle, Jann Michael Weinand, Jan Wohland
Date: 2021-03-17 00:00:00

The rapid uptake of renewable energy technologies in recent decades has increased the demand of energy researchers, policymakers and energy planners for reliable data on the spatial distribution of their costs and potentials. For onshore wind energy this has resulted in an active research field devoted to analysing these resources for regions, countries or globally. A particular thread of this research attempts to go beyond purely technical or spatial restrictions and determine the realistic, feasible or actual potential for wind energy. Motivated by these developments, this paper reviews methods and assumptions for analysing geographical, technical, economic and, finally, feasible onshore wind potentials. We address each of these potentials in turn, including aspects related to land eligibility criteria, energy meteorology, and technical developments relating to wind turbine characteristics such as power density, specific rotor power and spacing aspects. Economic aspects of potential assessments are central to future deployment and are discussed on a turbine and system level covering levelized costs depending on locations, and the system integration costs which are often overlooked in such analyses. Non-technical approaches include scenicness assessments of the landscape, expert and stakeholder workshops, willingness to pay / accept elicitations and socioeconomic cost-benefit studies. For each of these different potential estimations, the state of the art is critically discussed, with an attempt to derive best practice recommendations and highlight avenues for future research.
Minkowski gauges and deviation measures
Authors: Marlon Moresco, Marcelo Righi, Eduardo Horta
Date: 2020-07-02 00:00:00
Tasks: Position

We propose to derive deviation measures through the Minkowski gauge of a given set of acceptable positions. We show that, given a suitable acceptance set, any positive homogeneous deviation measure can be accommodated in our framework. In doing so, we provide a new interpretation for such measures, namely, that they quantify how much one must shrink or deleverage a position for it to become acceptable. In particular, the Minkowski Deviation of a set which is convex, stable under scalar addition, and radially bounded at non-constants, is a generalized deviation measure. Furthermore, we explore the relations existing between mathematical and financial properties attributable to an acceptance set, and the corresponding properties of the induced measure. Hence, we fill the gap that is the lack of an acceptance set for deviation measures. Dual characterizations in terms of polar sets and support functionals are provided.
Error analysis of a demodulation procedure for multicarrier signals with slowly-varying carriers
Authors: Dilshad Surroop, Pascal Combes, Philippe Martin
Date: 2020-11-25 00:00:00

We propose a procedure to demodulate analog signals encoded by a multicarrier modulator, with slowly-varying carrier shapes. We prove that the asymptotic demodulation error can be made arbitrarily small. The intended application is the "sensorless" control of AC electric motors at or near standstill, through the decoding of the PWM-induced current ripple.
Error estimates in Second-order Continuous-Time Sigma-Delta modulators
Authors: Dilshad Surroop, Pascal Combes, Philippe Martin
Date: 2020-11-25 00:00:00

Continuous-time Sigma-Delta (CT-$\Sigma\Delta$) modulators are oversampling Analog-to-Digital converters that may provide higher sampling rates and lower power consumption than their discrete counterpart. Whereas approximation errors are established for high-order discrete time $\Sigma\Delta$ modulators, theoretical analysis of the error between the filtered output and the input remain scarce. This paper presents a general framework to study this error: under regularity assumptions on the input and the filtering kernel, we prove for a second-order CT-$\Sigma\Delta$ that the error estimate may be in $o(1/N^2)$, where $N$ is the oversampling ratio. The whole theory is validated by numerical experiments.
A framework for studying transients in marine metapopulations
Authors: Peter D. Harrington, Mark A. Lewis, P. van den Driessche
Date: 2021-03-17 00:00:00

Transient dynamics can often differ drastically from the asymptotic dynamics of systems. In this paper we provide a unifying framework for analysing transient dynamics in marine metapopulations, from the choice of norms to the addition of stage structure. We use the $\ell_1$ norm, because of its biological interpretation, to extend the transient metrics of reactivity and attenuation to marine metapopulations, and use examples to compare these metrics under the more commonly used $\ell_2$ norm. We then connect the reactivity and attenuation of marine metapopulations to the source-sink distribution of habitat patches and demonstrate how to meaningfully measure reactivity when metapopulations are stage-structured.
Do Picardy thirds smile? Tonal hierarchy and tonal valence: explicit and implicit measures
Authors: Neta B. Maimon, Dominique Lamy, Zohar Eitan
Date: 2021-03-17 00:00:00

Western tonality provides a hierarchy of stability among melodic scale-degrees, from the maximally stable tonic to unstable chromatic notes. Tonal stability has been linked to emotion, yet systematic investigations of the associations between the hierarchy of melodic scale-degrees and perceived emotional valence are lacking. Here, we examined such associations in three experiments, in musicians and in non-musicians. We used an explicit task, in which participants matched probe tones following key-establishing sequences to facial expressions ranging from sad to happy, and an implicit speeded task, a variant of the Implicit Association Test. Stabler scale-degrees were associated with more positive valence in all experiments, for both musicians and nonmusicians. This notwithstanding, results significantly differed from those of a comparable goodness-of-fit task, suggesting that perceived tonal valence is not reducible to tonal fit. Comparisons of the explicit and implicit measures suggest that associations of tonality and emotional valence may rely on two distinct mechanisms, one mediated by conceptual musical knowledge and conscious decisional processes, the other - largely by non-conceptual and involuntary processes. The joint experimental paradigms introduced here may help mapping additional connotative meanings, both emotional and cross-modal, embedded in tonal structure, thus suggesting how extra-musical meanings are conveyed to listeners through musical syntax.
A Classical Search Game in Discrete Locations
Authors: Jake Clarkson, Kyle Y. Lin, Kevin D. Glazebrook
Date: 2021-03-08 00:00:00

Consider a two-person zero-sum search game between a hider and a searcher. The hider hides among $n$ discrete locations, and the searcher successively visits individual locations until finding the hider. Known to both players, a search at location $i$ takes $t_i$ time units and detects the hider -- if hidden there -- independently with probability $q_i$, for $i=1,\ldots,n$. The hider aims to maximize the expected time until detection, while the searcher aims to minimize it. We prove the existence of an optimal strategy for each player. In particular, the hider's optimal mixed strategy hides in each location with a nonzero probability, and the searcher's optimal mixed strategy can be constructed with up to $n$ simple search sequences. We develop an algorithm to compute an optimal strategy for each player, and compare the optimal hiding strategy with the simple hiding strategy which gives the searcher no location preference at the beginning of the search.
Scalable Hypergraph Embedding System
Authors: Sepideh Maleki, Donya Saless, Dennis P. Wall, Keshav Pingali
Date: 2021-03-09 00:00:00
Tasks: Graph Embedding, hypergraph embedding, Link Prediction, Node Classification

Many problems such as node classification and link prediction in network data can be solved using graph embeddings. However, it is difficult to use graphs to capture non-binary relations such as communities of nodes. These kinds of complex relations are expressed more naturally as hypergraphs. While hypergraphs are a generalization of graphs, state-of-the-art graph embedding techniques are not adequate for solving prediction and classification tasks on large hypergraphs accurately in reasonable time. In this paper, we introduce HyperNetVec, a novel hierarchical framework for scalable unsupervised hypergraph embedding. HyperNetVec exploits shared-memory parallelism and is capable of generating high quality embeddings for real-world hypergraphs with millions of nodes and hyperedges in only a couple of minutes while existing hypergraph systems either fail for such large hypergraphs or may take days to produce the embeddings.
Moroccan Dialect -Darija- Open Dataset
Authors: Aissam Outchakoucht, Hamza Es-Samaali
Date: 2021-02-28 00:00:00
Tasks: image-classification, Image Classification, Translation

Darija Open Dataset (DODa) is an open-source project for the Moroccan dialect. With more than 10,000 entries DODa is arguably the largest open-source collaborative project for Darija-English translation built for Natural Language Processing purposes. In fact, besides semantic categorization, DODa also adopts a syntactic one, presents words under different spellings, offers verb-to-noun and masculine-to-feminine correspondences, contains the conjugation of hundreds of verbs in different tenses, and many other subsets to help researchers better understand and study Moroccan dialect. This data paper presents a description of DODa, its features, how it was collected, as well as a first application in Image Classification using ImageNet labels translated to Darija. This collaborative project is hosted on GitHub platform under MIT's Open-Source license and aims to be a standard resource for researchers, students, and anyone who is interested in Moroccan Dialect
Effective Deep Learning Models for Automatic Diacritization of Arabic Text
Authors: Mokthar Ali Hasan Madhfar, Ali Mustafa Qamar
Date: 2020-11-01 00:00:00
Tasks: Arabic Text Diacritization, Decoder, Deep Learning, Machine Translation, Part-Of-Speech Tagging, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis, Translation

While building a text-to-speech system for the Arabic language, we found that the system synthesized speeches with many pronunciation errors. The primary source of these errors is the lack of diacritics in modern standard Arabic writing. These diacritics are small strokes that appear above or below each letter to provide pronunciation and grammatical information. We propose three deep learning models to recover Arabic text diacritics based on our work in a text-to-speech synthesis system using deep learning. The first model is a baseline model used to test how a simple deep learning model performs on the corpora. The second model is based on an encoder-decoder architecture, which resembles our text-to-speech synthesis model with many modifications to suit this problem. The last model is based on the encoder part of the text-to-speech model, which achieves state-of-the-art performances in both word error rate and diacritic error rate metrics. These models will benefit a wide range of natural language processing applications such as text-to-speech, part-of-speech tagging, and machine translation.
Feature-Based Matrix Factorization
Authors: Tianqi Chen, Zhao Zheng, Qiuxia Lu, Weinan Zhang, Yong Yu
Date: 2011-09-11 00:00:00
Tasks: Recommendation Systems

Recommender system has been more and more popular and widely used in many applications recently. The increasing information available, not only in quantities but also in types, leads to a big challenge for recommender system that how to leverage these rich information to get a better performance. Most traditional approaches try to design a specific model for each scenario, which demands great efforts in developing and modifying models. In this technical report, we describe our implementation of feature-based matrix factorization. This model is an abstract of many variants of matrix factorization models, and new types of information can be utilized by simply defining new features, without modifying any lines of code. Using the toolkit, we built the best single model reported on track 1 of KDDCup'11.
Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models
Authors: Aynaz Taheri, Kevin  Gimpel, Tanya Berger-Wolf
Date: 2021-03-19 00:00:00
Tasks: Decoder, Graph Classification, Graph Representation Learning, Representation Learning

Graph representation learning for static graphs is a well studied topic. Recently, a few studies have focused on learning temporal information in addition to the topology of a graph. Most of these studies have relied on learning to represent nodes and substructures in dynamic graphs. However, the representation learning problem for entire graphs in a dynamic context is yet to be addressed. In this paper, we propose an unsupervised representation learning architecture for dynamic graphs, designed to learn both the topological and temporal features of the graphs that evolve over time. The approach consists of a sequence-to-sequence encoder-decoder model embedded with gated graph neural networks (GGNNs) and long short-term memory networks (LSTMs). The GGNN is able to learn the topology of the graph at each time step, while LSTMs are leveraged to propagate the temporal information among the time steps. Moreover, an encoder learns the temporal dynamics of an evolving graph and a decoder reconstructs the dynamics over the same period of time using the encoded representation provided by the encoder. We demonstrate that our approach is capable of learning the representation of a dynamic graph through time by applying the embeddings to dynamic graph classification using a real world dataset of animal behaviour.


Graph representation learning for static graphs is a well studied topic. Recently, a few studies have focused on learning temporal information in addition to the topology of a graph. Most of these studies have relied on learning to represent nodes and substructures in dynamic graphs. However, the representation learning problem for entire graphs in a dynamic context is yet to be addressed. In this paper, we propose an unsupervised representation learning architecture for dynamic graphs, designed to learn both the topological and temporal features of the graphs that evolve over time. The approach consists of a sequence-to-sequence encoder-decoder model embedded with gated graph neural networks (GGNNs) and long short-term memory networks (LSTMs). The GGNN is able to learn the topology of the graph at each time step, while LSTMs are leveraged to propagate the temporal information among the time steps. Moreover, an encoder learns the temporal dynamics of an evolving graph and a decoder reconstructs the dynamics over the same period of time using the encoded representation provided by the encoder. We demonstrate that our approach is capable of learning the representation of a dynamic graph through time by applying the embeddings to dynamic graph classification using a real world dataset of animal behaviour.
Topology-Aware Segmentation Using Discrete Morse Theory
Authors: Xiaoling Hu, Yusu Wang, Li Fuxin, Dimitris Samaras, Chao Chen
Date: 2021-03-18 00:00:00
Proceeding: ICLR 2021 1
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

In the segmentation of fine-scale structures from natural and biomedical images, per-pixel accuracy is not the only metric of concern. Topological correctness, such as vessel connectivity and membrane closure, is crucial for downstream analysis tasks. In this paper, we propose a new approach to train deep image segmentation networks for better topological accuracy. In particular, leveraging the power of discrete Morse theory (DMT), we identify global structures, including 1D skeletons and 2D patches, which are important for topological accuracy. Trained with a novel loss based on these global structures, the network performance is significantly improved especially near topologically challenging locations (such as weak spots of connections and membranes). On diverse datasets, our method achieves superior performance on both the DICE score and topological metrics.
Human-Inspired Multi-Agent Navigation using Knowledge Distillation
Authors: Pei Xu, Ioannis Karamouzas
Date: 2021-03-18 00:00:00
Tasks: Collision Avoidance, Knowledge Distillation, reinforcement-learning, Reinforcement Learning (RL)

Despite significant advancements in the field of multi-agent navigation, agents still lack the sophistication and intelligence that humans exhibit in multi-agent settings. In this paper, we propose a framework for learning a human-like general collision avoidance policy for agent-agent interactions in fully decentralized, multi-agent environments. Our approach uses knowledge distillation with reinforcement learning to shape the reward function based on expert policies extracted from human trajectory demonstrations through behavior cloning. We show that agents trained with our approach can take human-like trajectories in collision avoidance and goal-directed steering tasks not provided by the demonstrations, outperforming the experts as well as learning-based agents trained without knowledge distillation.
Investigate Indistinguishable Points in Semantic Segmentation of 3D Point Cloud
Authors: Mingye Xu, Zhipeng Zhou, Junhao Zhang, Yu Qiao
Date: 2021-03-18 00:00:00
Tasks: 3D Semantic Segmentation, Segmentation, Semantic Segmentation

This paper investigates the indistinguishable points (difficult to predict label) in semantic segmentation for large-scale 3D point clouds. The indistinguishable points consist of those located in complex boundary, points with similar local textures but different categories, and points in isolate small hard areas, which largely harm the performance of 3D semantic segmentation. To address this challenge, we propose a novel Indistinguishable Area Focalization Network (IAF-Net), which selects indistinguishable points adaptively by utilizing the hierarchical semantic features and enhances fine-grained features for points especially those indistinguishable points. We also introduce multi-stage loss to improve the feature representation in a progressive way. Moreover, in order to analyze the segmentation performances of indistinguishable areas, we propose a new evaluation metric called Indistinguishable Points Based Metric (IPBM). Our IAF-Net achieves the comparable results with state-of-the-art performance on several popular 3D point cloud datasets e.g. S3DIS and ScanNet, and clearly outperforms other methods on IPBM.
Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild
Authors: Zhaoyuan Yin, Jia Zheng, Weixin Luo, Shenhan Qian, Hanling Zhang, Shenghua Gao
Date: 2021-03-18 00:00:00
Proceeding: CVPR 2021 1
Tasks: Deep Reinforcement Learning, Interactive Video Object Segmentation, Video Object Segmentation

This paper proposes a framework for the interactive video object segmentation (VOS) in the wild where users can choose some frames for annotations iteratively. Then, based on the user annotations, a segmentation algorithm refines the masks. The previous interactive VOS paradigm selects the frame with some worst evaluation metric, and the ground truth is required for calculating the evaluation metric, which is impractical in the testing phase. In contrast, in this paper, we advocate that the frame with the worst evaluation metric may not be exactly the most valuable frame that leads to the most performance improvement across the video. Thus, we formulate the frame selection problem in the interactive VOS as a Markov Decision Process, where an agent is learned to recommend the frame under a deep reinforcement learning framework. The learned agent can automatically determine the most valuable frame, making the interactive setting more practical in the wild. Experimental results on the public datasets show the effectiveness of our learned agent without any changes to the underlying VOS algorithms. Our data, code, and models are available at https://github.com/svip-lab/IVOS-W.
Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks
Authors: Despoina Paschalidou, Angelos Katharopoulos, Andreas Geiger, Sanja Fidler
Date: 2021-03-18 00:00:00
Proceeding: CVPR 2021 1

Impressive progress in 3D shape extraction led to representations that can capture object geometries with high fidelity. In parallel, primitive-based methods seek to represent objects as semantically consistent part arrangements. However, due to the simplicity of existing primitive representations, these methods fail to accurately reconstruct 3D shapes using a small number of primitives/parts. We address the trade-off between reconstruction quality and number of parts with Neural Parts, a novel 3D primitive representation that defines primitives using an Invertible Neural Network (INN) which implements homeomorphic mappings between a sphere and the target object. The INN allows us to compute the inverse mapping of the homeomorphism, which in turn, enables the efficient computation of both the implicit surface function of a primitive and its mesh, without any additional post-processing. Our model learns to parse 3D objects into semantically consistent part arrangements without any part-level supervision. Evaluations on ShapeNet, D-FAUST and FreiHAND demonstrate that our primitives can capture complex geometries and thus simultaneously achieve geometrically accurate as well as interpretable reconstructions using an order of magnitude fewer primitives than state-of-the-art shape abstraction methods.
Large Scale Image Completion via Co-Modulated Generative Adversarial Networks
Authors: Shengyu Zhao, Jonathan Cui, Yilun Sheng, Yue Dong, Xiao Liang, Eric I Chang, Yan Xu
Date: 2021-03-18 00:00:00
Proceeding: ICLR 2021 1
Tasks: Diversity, Image Inpainting, Image-to-Image Translation, Translation

Numerous task-specific variants of conditional generative adversarial networks have been developed for image completion. Yet, a serious limitation remains that all existing algorithms tend to fail when handling large-scale missing regions. To overcome this challenge, we propose a generic new approach that bridges the gap between image-conditional and recent modulated unconditional generative architectures via co-modulation of both conditional and stochastic style representations. Also, due to the lack of good quantitative metrics for image completion, we propose the new Paired/Unpaired Inception Discriminative Score (P-IDS/U-IDS), which robustly measures the perceptual fidelity of inpainted images compared to real images via linear separability in a feature space. Experiments demonstrate superior performance in terms of both quality and diversity over state-of-the-art methods in free-form image completion and easy generalization to image-to-image translation. Code is available at https://github.com/zsyzzsoft/co-mod-gan.