pwc-archive/papers-with-abstracts huggingface dataset:
Predicting seasonal influenza using supermarket retail records
Authors: Ioanna Miliou, Xinyue Xiong, Salvatore Rinzivillo, Qian Zhang, Giulio Rossetti, Fosca Giannotti, Dino Pedreschi, Alessandro Vespignani
Date: 2020-12-08 00:00:00

Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on real-time epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that provides estimates for influenza incidence in Italy up to 4 weeks ahead. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence. Our predictions outperform both a baseline autoregressive model and a second baseline based on product purchases. The results show quantitatively the value of incorporating retail market data in forecasting models, acting as a proxy that can be used for the real-time analysis of epidemics.
Classical symmetries and the Quantum Approximate Optimization Algorithm
Authors: Ruslan Shaydulin, Stuart Hadfield, Tad Hogg, Ilya Safro
Date: 2020-12-08 00:00:00

We study the relationship between the Quantum Approximate Optimization Algorithm (QAOA) and the underlying symmetries of the objective function to be optimized. Our approach formalizes the connection between quantum symmetry properties of the QAOA dynamics and the group of classical symmetries of the objective function. The connection is general and includes but is not limited to problems defined on graphs. We show a series of results exploring the connection and highlight examples of hard problem classes where a nontrivial symmetry subgroup can be obtained efficiently. In particular we show how classical objective function symmetries lead to invariant measurement outcome probabilities across states connected by such symmetries, independent of the choice of algorithm parameters or number of layers. To illustrate the power of the developed connection, we apply machine learning techniques towards predicting QAOA performance based on symmetry considerations. We provide numerical evidence that a small set of graph symmetry properties suffices to predict the minimum QAOA depth required to achieve a target approximation ratio on the MaxCut problem, in a practically important setting where QAOA parameter schedules are constrained to be linear and hence easier to optimize.
Model-agnostic Fits for Understanding Information Seeking Patterns in Humans
Authors: Soumya Chatterjee, Pradeep Shenoy
Date: 2020-12-09 00:00:00
Tasks: Decision Making

In decision making tasks under uncertainty, humans display characteristic biases in seeking, integrating, and acting upon information relevant to the task. Here, we reexamine data from previous carefully designed experiments, collected at scale, that measured and catalogued these biases in aggregate form. We design deep learning models that replicate these biases in aggregate, while also capturing individual variation in behavior. A key finding of our work is that paucity of data collected from each individual subject can be overcome by sampling large numbers of subjects from the population, while still capturing individual differences. In addition, we can predict human behavior with high accuracy without making any assumptions about task goals, reward structure, or individual biases, thus providing a model-agnostic fit to human behavior in the task. Such an approach can sidestep potential limitations in modeler-specified inductive biases, and has implications for computational modeling of human cognitive function in general, and of human-AI interfaces in particular.
Fusion of rain radar images and wind forecasts in a deep learning model applied to rain nowcasting
Authors: Vincent Bouget, Dominique Béréziat, Julien Brajard, Anastase Charantonis, Arthur Filoche
Date: 2020-12-09 00:00:00
Tasks: Management, Optical Flow Estimation

Short- or mid-term rainfall forecasting is a major task with several environmental applications such as agricultural management or flood risk monitoring. Existing data-driven approaches, especially deep learning models, have shown significant skill at this task, using only rainfall radar images as inputs. In order to determine whether using other meteorological parameters such as wind would improve forecasts, we trained a deep learning model on a fusion of rainfall radar images and wind velocity produced by a weather forecast model. The network was compared to a similar architecture trained only on radar data, to a basic persistence model and to an approach based on optical flow. Our network outperforms by 8% the F1-score calculated for the optical flow on moderate and higher rain events for forecasts at a horizon time of 30 min. Furthermore, it outperforms by 7% the same architecture trained using only rainfall radar images. Merging rain and wind data has also proven to stabilize the training process and enabled significant improvement especially on the difficult-to-predict high precipitation rainfalls.
Retrieval of aboveground crop nitrogen content with a hybrid machine learning method
Authors: Katja Berger, Jochem Verrelst, Jean-Baptiste Féret, Tobias Hank, Matthias Wocher, Wolfram Mauser, Gustau Camps-Valls
Date: 2020-12-07 00:00:00
Tasks: BIG-bench Machine Learning, Earth Observation, Hybrid Machine Learning, regression, Retrieval

Hyperspectral acquisitions have proven to be the most informative Earth observation data source for the estimation of nitrogen (N) content, which is the main limiting nutrient for plant growth and thus agricultural production. In the past, empirical algorithms have been widely employed to retrieve information on this biochemical plant component from canopy reflectance. However, these approaches do not seek for a cause-effect relationship based on physical laws. Moreover, most studies solely relied on the correlation of chlorophyll content with nitrogen, and thus neglected the fact that most N is bound in proteins. Our study presents a hybrid retrieval method using a physically-based approach combined with machine learning regression to estimate crop N content. Within the workflow, the leaf optical properties model PROSPECT-PRO including the newly calibrated specific absorption coefficients (SAC) of proteins, was coupled with the canopy reflectance model 4SAIL to PROSAIL-PRO. The latter was then employed to generate a training database to be used for advanced probabilistic machine learning methods: a standard homoscedastic Gaussian process (GP) and a heteroscedastic GP regression that accounts for signal-to-noise relations. Both GP models have the property of providing confidence intervals for the estimates, which sets them apart from other machine learners. GP-based band analysis identified optimal spectral settings with ten bands mainly situated in the shortwave infrared (SWIR) spectral region. Use of well-known protein absorption bands from the literature showed comparative results. Finally, the heteroscedastic GP model was successfully applied on airborne hyperspectral data for N mapping. We conclude that GP algorithms, and in particular the heteroscedastic GP, should be implemented for global agricultural monitoring of aboveground N from future imaging spectroscopy data.
Emergent Quantumness in Neural Networks
Authors: Mikhail I. Katsnelson, Vitaly Vanchurin
Date: 2020-12-09 00:00:00

It was recently shown that the Madelung equations, that is, a hydrodynamic form of the Schr\"odinger equation, can be derived from a canonical ensemble of neural networks where the quantum phase was identified with the free energy of hidden variables. We consider instead a grand canonical ensemble of neural networks, by allowing an exchange of neurons with an auxiliary subsystem, to show that the free energy must also be multivalued. By imposing the multivaluedness condition on the free energy we derive the Schr\"odinger equation with "Planck's constant" determined by the chemical potential of hidden variables. This shows that quantum mechanics provides a correct statistical description of the dynamics of the grand canonical ensemble of neural networks at the learning equilibrium. We also discuss implications of the results for machine learning, fundamental physics and, in a more speculative way, evolutionary biology.
DeepTalk: Vocal Style Encoding for Speaker Recognition and Speech Synthesis
Authors: Anurag Chowdhury, Arun Ross, Prabu David
Date: 2020-12-09 00:00:00
Tasks: Speaker Recognition, Speech Synthesis

Automatic speaker recognition algorithms typically characterize speech audio using short-term spectral features that encode the physiological and anatomical aspects of speech production. Such algorithms do not fully capitalize on speaker-dependent characteristics present in behavioral speech features. In this work, we propose a prosody encoding network called DeepTalk for extracting vocal style features directly from raw audio data. The DeepTalk method outperforms several state-of-the-art speaker recognition systems across multiple challenging datasets. The speaker recognition performance is further improved by combining DeepTalk with a state-of-the-art physiological speech feature-based speaker recognition system. We also integrate DeepTalk into a current state-of-the-art speech synthesizer to generate synthetic speech. A detailed analysis of the synthetic speech shows that the DeepTalk captures F0 contours essential for vocal style modeling. Furthermore, DeepTalk-based synthetic speech is shown to be almost indistinguishable from real speech in the context of speaker recognition.
Causal Inference in Geoscience and Remote Sensing from Observational Data
Authors: Adrián Pérez-Suay, Gustau Camps-Valls
Date: 2020-12-07 00:00:00
Tasks: Causal Inference, regression, Sensitivity

Establishing causal relations between random variables from observational data is perhaps the most important challenge in today's \blue{science}. In remote sensing and geosciences this is of special relevance to better understand the Earth's system and the complex interactions between the governing processes. In this paper, we focus on observational causal inference, thus we try to estimate the correct direction of causation using a finite set of empirical data. In addition, we focus on the more complex bivariate scenario that requires strong assumptions and no conditional independence tests can be used. In particular, we explore the framework of (non-deterministic) additive noise models, which relies on the principle of independence between the cause and the generating mechanism. A practical algorithmic instantiation of such principle only requires 1) two regression models in the forward and backward directions, and 2) the estimation of {\em statistical independence} between the obtained residuals and the observations. The direction leading to more independent residuals is decided to be the cause. We instead propose a criterion that uses the {\em sensitivity} (derivative) of the dependence estimator, the sensitivity criterion allows to identify samples most affecting the dependence measure, and hence the criterion is robust to spurious detections. We illustrate performance in a collection of 28 geoscience causal inference problems, in a database of radiative transfer models simulations and machine learning emulators in vegetation parameter modeling involving 182 problems, and in assessing the impact of different regression models in a carbon cycle problem. The criterion achieves state-of-the-art detection rates in all cases, it is generally robust to noise sources and distortions.
A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems
Authors: Kursat Rasim Mestav, Xinyi Wang, Lang Tong
Date: 2020-12-09 00:00:00
Tasks: Anomaly Detection, Generative Adversarial Network

A deep learning approach is proposed to detect data and system anomalies using high-resolution continuous point-on-wave (CPOW) or phasor measurements. Both the anomaly and anomaly-free measurement models are assumed to have unknown temporal dependencies and probability distributions. Historical training samples are assumed for the anomaly-free model, while no training samples are available for the anomaly measurements. By transforming the anomaly-free observations into uniform independent and identically distributed sequences via a generative adversarial network, the proposed approach deploys a uniformity test for anomaly detection at the sensor level. A distributed detection scheme that combines sensor level detections at the control center is also proposed that combines local detections to form more reliable detections. Numerical results demonstrate significant improvement over the state-of-the-art solutions for various bad-data cases using real and synthetic CPOW and PMU data sets.
EvoCraft: A New Challenge for Open-Endedness
Authors: Djordje Grbic, Rasmus Berg Palm, Elias Najarro, Claire Glanois, Sebastian Risi
Date: 2020-12-08 00:00:00
Tasks: Minecraft

This paper introduces EvoCraft, a framework for Minecraft designed to study open-ended algorithms. We introduce an API that provides an open-source Python interface for communicating with Minecraft to place and track blocks. In contrast to previous work in Minecraft that focused on learning to play the game, the grand challenge we pose here is to automatically search for increasingly complex artifacts in an open-ended fashion. Compared to other environments used to study open-endedness, Minecraft allows the construction of almost any kind of structure, including actuated machines with circuits and mechanical components. We present initial baseline results in evolving simple Minecraft creations through both interactive and automated evolution. While evolution succeeds when tasked to grow a structure towards a specific target, it is unable to find a solution when rewarded for creating a simple machine that moves. Thus, EvoCraft offers a challenging new environment for automated search methods (such as evolution) to find complex artifacts that we hope will spur the development of more open-ended algorithms. A Python implementation of the EvoCraft framework is available at: https://github.com/real-itu/Evocraft-py.
Concept Drift and Covariate Shift Detection Ensemble with Lagged Labels
Authors: Yiming Xu, Diego Klabjan
Date: 2020-12-08 00:00:00

In model serving, having one fixed model during the entire often life-long inference process is usually detrimental to model performance, as data distribution evolves over time, resulting in lack of reliability of the model trained on historical data. It is important to detect changes and retrain the model in time. The existing methods generally have three weaknesses: 1) using only classification error rate as signal, 2) assuming ground truth labels are immediately available after features from samples are received and 3) unable to decide what data to use to retrain the model when change occurs. We address the first problem by utilizing six different signals to capture a wide range of characteristics of data, and we address the second problem by allowing lag of labels, where labels of corresponding features are received after a lag in time. For the third problem, our proposed method automatically decides what data to use to retrain based on the signals. Extensive experiments on structured and unstructured data for different type of data changes establish that our method consistently outperforms the state-of-the-art methods by a large margin.
The Counterfactual NESS Definition of Causation
Authors: Sander Beckers
Date: 2020-12-09 00:00:00
Tasks: counterfactual

In previous work with Joost Vennekens I proposed a definition of actual causation that is based on certain plausible principles, thereby allowing the debate on causation to shift away from its heavy focus on examples towards a more systematic analysis. This paper contributes to that analysis in two ways. First, I show that our definition is in fact a formalization of Wright's famous NESS definition of causation combined with a counterfactual difference-making condition. This means that our definition integrates two highly influential approaches to causation that are claimed to stand in opposition to each other. Second, I modify our definition to offer a substantial improvement: I weaken the difference-making condition in such a way that it avoids the problematic analysis of cases of preemption. The resulting Counterfactual NESS definition of causation forms a natural compromise between counterfactual approaches and the NESS approach.
A SAT-based Resolution of Lam's Problem
Authors: Curtis Bright, Kevin K. H. Cheung, Brett Stevens, Ilias Kotsireas, Vijay Ganesh
Date: 2020-12-08 00:00:00

In 1989, computer searches by Lam, Thiel, and Swiercz experimentally resolved Lam's problem from projective geometry$\unicode{x2014}$the long-standing problem of determining if a projective plane of order ten exists. Both the original search and an independent verification in 2011 discovered no such projective plane. However, these searches were each performed using highly specialized custom-written code and did not produce nonexistence certificates. In this paper, we resolve Lam's problem by translating the problem into Boolean logic and use satisfiability (SAT) solvers to produce nonexistence certificates that can be verified by a third party. Our work uncovered consistency issues in both previous searches$\unicode{x2014}$highlighting the difficulty of relying on special-purpose search code for nonexistence results.
A Deep Marginal-Contrastive Defense against Adversarial Attacks on 1D Models
Authors: Mohammed Hassanin, Nour Moustafa, Murat Tahtali
Date: 2020-12-08 00:00:00

Deep learning algorithms have been recently targeted by attackers due to their vulnerability. Several research studies have been conducted to address this issue and build more robust deep learning models. Non-continuous deep models are still not robust against adversarial, where most of the recent studies have focused on developing attack techniques to evade the learning process of the models. One of the main reasons behind the vulnerability of such models is that a learning classifier is unable to slightly predict perturbed samples. To address this issue, we propose a novel objective/loss function, the so-called marginal contrastive, which enforces the features to lie under a specified margin to facilitate their prediction using deep convolutional networks (i.e., Char-CNN). Extensive experiments have been conducted on continuous cases (e.g., UNSW NB15 dataset) and discrete ones (i.e, eight-large-scale datasets [32]) to prove the effectiveness of the proposed method. The results revealed that the regularization of the learning process based on the proposed loss function can improve the performance of Char-CNN.
Risk Management Framework for Machine Learning Security
Authors: Jakub Breier, Adrian Baldwin, Helen Balinsky, Yang Liu
Date: 2020-12-09 00:00:00
Tasks: BIG-bench Machine Learning, Management

Adversarial attacks for machine learning models have become a highly studied topic both in academia and industry. These attacks, along with traditional security threats, can compromise confidentiality, integrity, and availability of organization's assets that are dependent on the usage of machine learning models. While it is not easy to predict the types of new attacks that might be developed over time, it is possible to evaluate the risks connected to using machine learning models and design measures that help in minimizing these risks. In this paper, we outline a novel framework to guide the risk management process for organizations reliant on machine learning models. First, we define sets of evaluation factors (EFs) in the data domain, model domain, and security controls domain. We develop a method that takes the asset and task importance, sets the weights of EFs' contribution to confidentiality, integrity, and availability, and based on implementation scores of EFs, it determines the overall security state in the organization. Based on this information, it is possible to identify weak links in the implemented security measures and find out which measures might be missing completely. We believe our framework can help in addressing the security issues related to usage of machine learning models in organizations and guide them in focusing on the adequate security measures to protect their assets.
Persuading Voters in District-based Elections
Authors: Matteo Castiglioni, Nicola Gatti
Date: 2020-12-09 00:00:00

We focus on the scenario in which an agent can exploit his information advantage to manipulate the outcome of an election. In particular, we study district-based elections with two candidates, in which the winner of the election is the candidate that wins in the majority of the districts. District-based elections are adopted worldwide (e.g., UK and USA) and are a natural extension of widely studied voting mechanisms (e.g., k-voting and plurality voting). We resort to the Bayesian persuasion framework, where the manipulator (sender) strategically discloses information to the voters (receivers) that update their beliefs rationally. We study both private signaling, in which the sender can use a private communication channel per receiver, and public signaling, in which the sender can use a single communication channel for all the receivers. Furthermore, for the first time, we introduce semi-public signaling in which the sender can use a single communication channel per district. We show that there is a sharp distinction between private and (semi-)public signaling. In particular, optimal private signaling schemes can provide an arbitrarily better probability of victory than (semi-)public ones and can be computed efficiently, while optimal (semi-)public signaling schemes cannot be approximated to within any factor in polynomial time unless P=NP. However, we show that reasonable relaxations allow the design of multi-criteria PTASs for optimal (semi-)public signaling schemes. In doing so, we introduce a novel property, namely comparative stability, and we design a bi-criteria PTAS for public signaling in general Bayesian persuasion problems beyond elections when the sender's utility function is state-dependent.
Using Drug Descriptions and Molecular Structures for Drug-Drug Interaction Extraction from Literature
Authors: Masaki Asada, Makoto Miwa, Yutaka Sasaki
Date: 2020-10-24 00:00:00
Tasks: Drug–drug Interaction Extraction

Motivation
Neural methods to extract drug-drug interactions (DDIs) from literature require a large number of annotations. In this study, we propose a novel method to effectively utilize external drug database information as well as information from large-scale plain text for DDI extraction. Specifically, we focus on drug description and molecular structure information as the drug database information.
Results
We evaluated our approach on the DDIExtraction 2013 shared task data set. We obtained the following results. First, large-scale raw text information can greatly improve the performance of extracting DDIs when combined with the existing model and it shows the state-of-the-art performance. Second, each of drug description and molecular structure information is helpful to further improve the DDI performance for some specific DDI types. Finally, the simultaneous use of the drug description and molecular structure information can significantly improve the performance on all the DDI types. We showed that the plain text, the drug description information, and molecular structure information are complementary and their effective combination are essential for the improvement.