pwc-archive/papers-with-abstracts huggingface dataset:
Electricity intraday price modeling with marked Hawkes processes
Authors: Thomas Deschatre, Pierre Gruet
Date: 2021-03-12 00:00:00

We consider a 2-dimensional marked Hawkes process with increasing baseline intensity in order to model prices on electricity intraday markets. This model allows to represent different empirical facts such as increasing market activity, random jump sizes but above all microstructure noise through the signature plot. This last feature is of particular importance for practitioners and has not yet been modeled on those particular markets. We provide analytic formulas for first and second moments and for the signature plot, extending the classic results of Bacry et al. (2013) in the context of Hawkes processes with random jump sizes and time dependent baseline intensity. The tractable model we propose is estimated on German data and seems to fit the data well. We also provide a result about the convergence of the price process to a Brownian motion with increasing volatility at macroscopic scales, highlighting the Samuelson effect.
Despeckling Polarimetric SAR Data Using a Multi-Stream Complex-Valued Fully Convolutional Network
Authors: Adugna G. Mullissa, Claudio Persello, Johannes Reiche
Date: 2021-03-12 00:00:00
Tasks: Deep Learning

A Polarimetric Synthetic Aperture Radar (PolSAR) sensor is able to collect images in different polarization states, making it a rich source of information for target characterization. PolSAR images are inherently affected by speckle. Therefore, before deriving ad hoc products from the data, the polarimetric covariance matrix needs to be estimated by reducing speckle. In recent years, deep learning based despeckling methods have started to evolve from single channel SAR images to PolSAR images. To this aim, deep learning based approaches separate the real and imaginary components of the complex-valued covariance matrix and use them as independent channels in a standard convolutional neural networks. However, this approach neglects the mathematical relationship that exists between the real and imaginary components, resulting in sub-optimal output. Here, we propose a multi-stream complex-valued fully convolutional network to reduce speckle and effectively estimate the PolSAR covariance matrix. To evaluate the performance of CV-deSpeckNet, we used Sentinel-1 dual polarimetric SAR images to compare against its real-valued counterpart, that separates the real and imaginary parts of the complex covariance matrix. CV-deSpeckNet was also compared against the state of the art PolSAR despeckling methods. The results show CV-deSpeckNet was able to be trained with a fewer number of samples, has a higher generalization capability and resulted in a higher accuracy than its real-valued counterpart and state-of-the-art PolSAR despeckling methods. These results showcase the potential of complex-valued deep learning for PolSAR despeckling.
Signal Representations for Synthesizing Audio Textures with Generative Adversarial Networks
Authors: Chitralekha Gupta, Purnima Kamath, Lonce Wyse
Date: 2021-03-12 00:00:00
Tasks: Audio Synthesis

Generative Adversarial Networks (GANs) currently achieve the state-of-the-art sound synthesis quality for pitched musical instruments using a 2-channel spectrogram representation consisting of log magnitude and instantaneous frequency (the "IFSpectrogram"). Many other synthesis systems use representations derived from the magnitude spectra, and then depend on a backend component to invert the output magnitude spectrograms that generally result in audible artefacts associated with the inversion process. However, for signals that have closely-spaced frequency components such as non-pitched and other noisy sounds, training the GAN on the 2-channel IFSpectrogram representation offers no advantage over the magnitude spectra based representations. In this paper, we propose that training GANs on single-channel magnitude spectra, and using the Phase Gradient Heap Integration (PGHI) inversion algorithm is a better comprehensive approach for audio synthesis modeling of diverse signals that include pitched, non-pitched, and dynamically complex sounds. We show that this method produces higher-quality output for wideband and noisy sounds, such as pops and chirps, compared to using the IFSpectrogram. Furthermore, the sound quality for pitched sounds is comparable to using the IFSpectrogram, even while using a simpler representation with half the memory requirements.
Information Synergy in the Anticipatory Dynamics of a Retina
Authors: Qi-Rong Lin, Po-Yu Chou, C. K. Chan
Date: 2021-03-12 00:00:00

Visual perceptions often come with illusions whose physical origin are not well understood yet. The encoding of stochastic light intensity $x(t)$ into spikes with firing rate $r(t)$ at time $t$ is investigated in an experiment with retinas from bullfrogs to understand the mechanism of anticipation. Partial information decomposition of the mutual information between $r$ and the combined state $\{x,\dot x\}$ is found to be consistent with the encoding form: $r(t) \sim (1-\lambda)x(t) + \lambda \dot x(t) \tau_0$ with $\lambda$ being a system dependent parameter and $\tau_0$ a constant. This form of $r(t)$ indicates that a retina is capable of anticipation based on the synergistic information generation between $x$ and $\dot x$. Our results suggest that illusions such as the anticipation studied here during retinal perception can originate from the recombination of information extracted in the retinal network.
Smooth nonnegative tensor factorization for multi-sites electrical load monitoring
Authors: Amaury Durand, François Roueff, Jean-Marc Jicquel, Nicolas Paul
Date: 2021-03-12 00:00:00
Tasks: Clustering, energy management, Management

The analysis of load curves collected from smart meters is a key step for many energy management tasks ranging from consumption forecasting to customers characterization and load monitoring. In this contribution, we propose a model based on a functional formulation of nonnegative tensor factorization and derive updates for the corresponding optimization problem. We show on the concrete example of multi-sites load curves disaggregation how this formulation is helpful for 1) exhibiting smooth intraday consumption patterns and 2) taking into account external variables such as the outside temperature. The benefits are demonstrated on simulated and real data by exhibiting a meaningful clustering of the observed sites based on the obtained decomposition.
Contrast Sensitivity Functions in Autoencoders
Authors: Qiang Li, Alex Gomez-Villa, Marcelo Bertalmio, Jesus Malo
Date: 2021-02-28 00:00:00
Tasks: Sensitivity

Three decades ago, Atick et al. suggested that human frequency sensitivity may emerge from the enhancement required for a more efficient analysis of retinal images. Here we reassess the relevance of low-level vision tasks in the explanation of the Contrast Sensitivity Functions (CSFs) in light of (1) the current trend of using artificial neural networks for studying vision, and (2) the current knowledge of retinal image representations. As a first contribution, we show that a very popular type of convolutional neural networks (CNNs), called autoencoders, may develop human-like CSFs in the spatio-temporal and chromatic dimensions when trained to perform some basic low-level vision tasks (like retinal noise and optical blur removal), but not others (like chromatic adaptation or pure reconstruction after simple bottlenecks). As an illustrative example, the best CNN (in the considered set of simple architectures for enhancement of the retinal signal) reproduces the CSFs with an RMSE error of 11\% of the maximum sensitivity. As a second contribution, we provide experimental evidence of the fact that, for some functional goals (at low abstraction level), deeper CNNs that are better in reaching the quantitative goal are actually worse in replicating human-like phenomena (such as the CSFs). This low-level result (for the explored networks) is not necessarily in contradiction with other works that report advantages of deeper nets in modeling higher-level vision goals. However, in line with a growing body of literature, our results suggests another word of caution about CNNs in vision science since the use of simplified units or unrealistic architectures in goal optimization may be a limitation for the modeling and understanding of human vision.
Scalable Traffic Models for Scheduling of Linear Periodic Event-Triggered Controllers
Authors: Gabriel de Albuquerque Gleizer, Manuel Mazo Jr
Date: 2020-03-17 00:00:00
Tasks: Scheduling

This paper addresses the problem of modeling and scheduling the transmissions generated by multiple event-triggered control (ETC) loops sharing a network. We present a method to build a symbolic traffic model of periodic ETC (PETC), which by construction provides an exact simulation of such traffic. The model is made in such a way as to avoid the combinatorial explosion that is typical of symbolic models in many applications. It is augmented with early triggering actions that can be used by a scheduler to mitigate communication conflicts. The complete networked control system is then modeled as a network of timed game automata, for which existing tools can generate a strategy that avoids communication conflicts, while keeping early triggers to a minimum. By construction, our proposed symbolic model is a quotient model of the PETC. It is relatively fast to build, and it generates few to no spurious transitions. We finally demonstrate modeling and scheduling for a numerical example.
Emotion Recognition Using Convolutional Neural Network with Selected Statistical Photoplethysmogram Features
Authors: MinSeop Lee, Yun Kyu Lee, Myo-Taeg Lim, and Tae-Koo Kang 2.
Date: 2020-05-19 00:00:00
Tasks: Emotion Recognition, Heart Rate Variability

Emotion recognition research has been conducted using various physiological signals.
In this paper, we propose an efficient photoplethysmogram-based method that fuses the deep
features extracted by two deep convolutional neural networks and the statistical features selected
by Pearson’s correlation technique. A photoplethysmogram (PPG) signal can be easily obtained
through many devices, and the procedure for recording this signal is simpler than that for other
physiological signals. The normal-to-normal (NN) interval values of heart rate variability (HRV)
were utilized to extract the time domain features, and the normalized PPG signal was used to acquire
the frequency domain features. Then, we selected features that correlated highly with an emotion
through Pearson’s correlation. These statistical features were fused with deep-learning features
extracted from a convolutional neural network (CNN). The PPG signal and the NN interval were
used as the inputs of the CNN to extract the features, and the total concatenated features were utilized
to classify the valence and the arousal, which are the basic parameters of emotion. The Database
for Emotion Analysis using Physiological signals (DEAP) was chosen for the experiment, and the
results demonstrated that the proposed method achieved a noticeable performance with a short
recognition interval.
Robust graph-filter identification with graph denoising regularization
Authors: Samuel Rey, Antonio G. Marques
Date: 2021-03-10 00:00:00
Tasks: Denoising

When approaching graph signal processing tasks, graphs are usually assumed to be perfectly known. However, in many practical applications, the observed (inferred) network is prone to perturbations which, if ignored, will hinder performance. Tailored to those setups, this paper presents a robust formulation for the problem of graph-filter identification from input-output observations. Different from existing works, our approach consists in addressing the robust identification by formulating a joint graph denoising and graph-filter identification problem. Such a problem is formulated as a non-convex optimization, suitable relaxations are proposed, and graph-stationarity assumptions are incorporated to enhance performance. Finally, numerical experiments with synthetic and real-world graphs are used to assess the proposed schemes and compare them with existing (robust) alternatives.
SQUARES: A SQL Synthesizer Using Query Reverse Engineering
Authors: Pedro Orvalho, Miguel Terra-Neves; Miguel Ventura, Ruben Martins, Vasco Manquinho
Date: 2020-08-31 00:00:00
Tasks: Enumerative Search, Program Synthesis, SQL Synthesis

Nowadays, many data analysts are domain experts, but they lack programming skills. As a result, many of them can provide examples of data transformations but are unable to produce the desired query. Hence, there is an increasing need for systems capable of solving the problem of Query Reverse Engineering (QRE). Given a database and output table, these systems have to find the query that generated this table. We present SQUARES, a program synthesis tool based on input-output examples that can help data analysts to extract and transform data by synthesizing SQL queries, and table manipulation programs using the R language.
Low Frequency AC Transmission Upgrades with Optimal Frequency Selection
Authors: David Sehloff, Line Roald
Date: 2021-03-11 00:00:00

The advantages of operating selected transmission lines at frequencies other than the standard 50 or 60 Hz are numerous, encompassing increased power transfer capacity and better utilization of existing infrastructure. While high voltage DC (HVDC) is by far the most well-established example, there has been an emerging interest low frequency AC (LFAC) transmission in applications ranging from offshore wind to railway systems and mining. In this paper, we investigate the use of LFAC as a transmission upgrade and propose models and analysis methods to determine the optimal choice of frequency. The paper first presents an optimal power flow model with frequency as a variable, assuming modular multilevel converters for frequency conversion. Using this model, we analyze LFAC as an embedded upgrade in a transmission system using existing lines. We quantify the system-wide advantages from improved power flow control and frequency reduction and find that an LFAC upgrade achieves similar and sometimes better results compared with HVDC upgrades. Finally, we analyze the factors which determine the optimal frequency for these upgraded transmission lines, and we demonstrate the benefits of changing the frequency in response to different system topologies and operating conditions.
Helmholtzian Eigenmap: Topological feature discovery & edge flow learning from point cloud data
Authors: Yu-Chia Chen, Weicheng Wu, Marina Meilă, Ioannis G. Kevrekidis
Date: 2021-03-13 00:00:00

The manifold Helmholtzian (1-Laplacian) operator $\Delta_1$ elegantly generalizes the Laplace-Beltrami operator to vector fields on a manifold $\mathcal M$. In this work, we propose the estimation of the manifold Helmholtzian from point cloud data by a weighted 1-Laplacian $\mathcal L_1$. While higher order Laplacians have been introduced and studied, this work is the first to present a graph Helmholtzian constructed from a simplicial complex as a consistent estimator for the continuous operator in a non-parametric setting. Equipped with the geometric and topological information about $\mathcal M$, the Helmholtzian is a useful tool for the analysis of flows and vector fields on $\mathcal M$ via the Helmholtz-Hodge theorem. In addition, the $\mathcal L_1$ allows the smoothing, prediction, and feature extraction of the flows. We demonstrate these possibilities on substantial sets of synthetic and real point cloud datasets with non-trivial topological structures; and provide theoretical results on the limit of $\mathcal L_1$ to $\Delta_1$.
Adapt Everywhere: Unsupervised Adaptation of Point-Clouds and Entropy Minimisation for Multi-modal Cardiac Image Segmentation
Authors: Sulaiman Vesal, Mingxuan Gu, Ronak Kosti, Andreas Maier, Nishant Ravikumar
Date: 2021-03-15 00:00:00
Tasks: Domain Adaptation, Image Segmentation, Semantic Segmentation, Unsupervised Domain Adaptation

Deep learning models are sensitive to domain shift phenomena. A model trained on images from one domain cannot generalise well when tested on images from a different domain, despite capturing similar anatomical structures. It is mainly because the data distribution between the two domains is different. Moreover, creating annotation for every new modality is a tedious and time-consuming task, which also suffers from high inter- and intra- observer variability. Unsupervised domain adaptation (UDA) methods intend to reduce the gap between source and target domains by leveraging source domain labelled data to generate labels for the target domain. However, current state-of-the-art (SOTA) UDA methods demonstrate degraded performance when there is insufficient data in source and target domains. In this paper, we present a novel UDA method for multi-modal cardiac image segmentation. The proposed method is based on adversarial learning and adapts network features between source and target domain in different spaces. The paper introduces an end-to-end framework that integrates: a) entropy minimisation, b) output feature space alignment and c) a novel point-cloud shape adaptation based on the latent features learned by the segmentation model. We validated our method on two cardiac datasets by adapting from the annotated source domain, bSSFP-MRI (balanced Steady-State Free Procession-MRI), to the unannotated target domain, LGE-MRI (Late-gadolinium enhance-MRI), for the multi-sequence dataset; and from MRI (source) to CT (target) for the cross-modality dataset. The results highlighted that by enforcing adversarial learning in different parts of the network, the proposed method delivered promising performance, compared to other SOTA methods.
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
Authors: Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier
Date: 2021-03-14 00:00:00
Tasks: counterfactual, Probabilistic Programming, Recommendation Systems

The development of recommender systems that optimize multi-turn interaction with users, and model the interactions of different agents (e.g., users, content providers, vendors) in the recommender ecosystem have drawn increasing attention in recent years. Developing and training models and algorithms for such recommenders can be especially difficult using static datasets, which often fail to offer the types of counterfactual predictions needed to evaluate policies over extended horizons. To address this, we develop RecSim NG, a probabilistic platform for the simulation of multi-agent recommender systems. RecSim NG is a scalable, modular, differentiable simulator implemented in Edward2 and TensorFlow. It offers: a powerful, general probabilistic programming language for agent-behavior specification; tools for probabilistic inference and latent-variable model learning, backed by automatic differentiation and tracing; and a TensorFlow-based runtime for running simulations on accelerated hardware. We describe RecSim NG and illustrate how it can be used to create transparent, configurable, end-to-end models of a recommender ecosystem, complemented by a small set of simple use cases that demonstrate how RecSim NG can help both researchers and practitioners easily develop and train novel algorithms for recommender systems.
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs
Authors: Zhiwei Xu, Thalaiyasingam Ajanthan, Vibhav Vineet, Richard Hartley
Date: 2021-02-09 00:00:00
Tasks: 3D Semantic Segmentation, Semantic Segmentation, Stereo Matching, Video Classification

Although 3D Convolutional Neural Networks are essential for most learning based applications involving dense 3D data, their applicability is limited due to excessive memory and computational requirements. Compressing such networks by pruning therefore becomes highly desirable. However, pruning 3D CNNs is largely unexplored possibly because of the complex nature of typical pruning algorithms that embeds pruning into an iterative optimization paradigm. In this work, we introduce a Resource Aware Neuron Pruning (RANP) algorithm that prunes 3D CNNs at initialization to high sparsity levels. Specifically, the core idea is to obtain an importance score for each neuron based on their sensitivity to the loss function. This neuron importance is then reweighted according to the neuron resource consumption related to FLOPs or memory. We demonstrate the effectiveness of our pruning method on 3D semantic segmentation with widely used 3D-UNets on ShapeNet and BraTS'18 datasets, video classification with MobileNetV2 and I3D on UCF101 dataset, and two-view stereo matching with Pyramid Stereo Matching (PSM) network on SceneFlow dataset. In these experiments, our RANP leads to roughly 50%-95% reduction in FLOPs and 35%-80% reduction in memory with negligible loss in accuracy compared to the unpruned networks. This significantly reduces the computational resources required to train 3D CNNs. The pruned network obtained by our algorithm can also be easily scaled up and transferred to another dataset for training.
Recommending Short-lived Dynamic Packages for Golf Booking Services
Authors: Robin Swezey, Young-joo Chung
Date: 2021-03-13 00:00:00
Tasks: Clustering, Collaborative Filtering, Recommendation Systems

We introduce an approach to recommending short-lived dynamic packages for golf booking services. Two challenges are addressed in this work. The first is the short life of the items, which puts the system in a state of a permanent cold start. The second is the uninformative nature of the package attributes, which makes clustering or figuring latent packages challenging. Although such settings are fairly pervasive, they have not been studied in traditional recommendation research, and there is thus a call for original approaches for recommender systems. In this paper, we introduce a hybrid method that leverages user analysis and its relation to the packages, as well as package pricing and environmental analysis, and traditional collaborative filtering. The proposed approach achieved appreciable improvement in precision compared with baselines.
Fairness-aware Personalized Ranking Recommendation via Adversarial Learning
Authors: Ziwei Zhu, Jianling Wang, James Caverlee
Date: 2021-03-14 00:00:00
Tasks: Fairness, Recommendation Systems

Recommendation algorithms typically build models based on historical user-item interactions (e.g., clicks, likes, or ratings) to provide a personalized ranked list of items. These interactions are often distributed unevenly over different groups of items due to varying user preferences. However, we show that recommendation algorithms can inherit or even amplify this imbalanced distribution, leading to unfair recommendations to item groups. Concretely, we formalize the concepts of ranking-based statistical parity and equal opportunity as two measures of fairness in personalized ranking recommendation for item groups. Then, we empirically show that one of the most widely adopted algorithms -- Bayesian Personalized Ranking -- produces unfair recommendations, which motivates our effort to propose the novel fairness-aware personalized ranking model. The debiased model is able to improve the two proposed fairness metrics while preserving recommendation performance. Experiments on three public datasets show strong fairness improvement of the proposed model versus state-of-the-art alternatives. This is paper is an extended and reorganized version of our SIGIR 2020~\cite{zhu2020measuring} paper. In this paper, we re-frame the studied problem as `item recommendation fairness' in personalized ranking recommendation systems, and provide more details about the training process of the proposed model and details of experiment setup.