pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Learning of Sampling Distributions for Particle Filters
Authors: Fernando Gama, Nicolas Zilberstein, Martin Sevilla, Richard Baraniuk, Santiago Segarra
Date: 2023-02-02 00:00:00
Tasks: Design Synthesis

Accurate estimation of the states of a nonlinear dynamical system is crucial for their design, synthesis, and analysis. Particle filters are estimators constructed by simulating trajectories from a sampling distribution and averaging them based on their importance weight. For particle filters to be computationally tractable, it must be feasible to simulate the trajectories by drawing from the sampling distribution. Simultaneously, these trajectories need to reflect the reality of the nonlinear dynamical system so that the resulting estimators are accurate. Thus, the crux of particle filters lies in designing sampling distributions that are both easy to sample from and lead to accurate estimators. In this work, we propose to learn the sampling distributions. We put forward four methods for learning sampling distributions from observed measurements. Three of the methods are parametric methods in which we learn the mean and covariance matrix of a multivariate Gaussian distribution; each methods exploits a different aspect of the data (generic, time structure, graph structure). The fourth method is a nonparametric alternative in which we directly learn a transform of a uniform random variable. All four methods are trained in an unsupervised manner by maximizing the likelihood that the states may have produced the observed measurements. Our computational experiments demonstrate that learned sampling distributions exhibit better performance than designed, minimum-degeneracy sampling distributions.
Sketched Ridgeless Linear Regression: The Role of Downsampling
Authors: Xin Chen, Yicheng Zeng, Siyue Yang, Qiang Sun
Date: 2023-02-02 00:00:00
Tasks: regression

Overparametrization often helps improve the generalization performance. This paper presents a dual view of overparametrization suggesting that downsampling may also help generalize. Focusing on the proportional regime $m\asymp n \asymp p$, where $m$ represents the sketching size, $n$ is the sample size, and $p$ is the feature dimensionality, we investigate two out-of-sample prediction risks of the sketched ridgeless least square estimator. Our findings challenge conventional beliefs by showing that downsampling does not always harm generalization but can actually improve it in certain cases. We identify the optimal sketching size that minimizes out-of-sample prediction risks and demonstrate that the optimally sketched estimator exhibits stabler risk curves, eliminating the peaks of those for the full-sample estimator. To facilitate practical implementation, we propose an empirical procedure to determine the optimal sketching size. Finally, we extend our analysis to cover central limit theorems and misspecified models. Numerical studies strongly support our theory.
Stochastic optimal transport in Banach Spaces for regularized estimation of multivariate quantiles
Authors: Bernard Bercu, Jérémie Bigot, Gauthier Thurin
Date: 2023-02-02 00:00:00

We introduce a new stochastic algorithm for solving entropic optimal transport (EOT) between two absolutely continuous probability measures $\mu$ and $\nu$. Our work is motivated by the specific setting of Monge-Kantorovich quantiles where the source measure $\mu$ is either the uniform distribution on the unit hypercube or the spherical uniform distribution. Using the knowledge of the source measure, we propose to parametrize a Kantorovich dual potential by its Fourier coefficients. In this way, each iteration of our stochastic algorithm reduces to two Fourier transforms that enables us to make use of the Fast Fourier Transform (FFT) in order to implement a fast numerical method to solve EOT. We study the almost sure convergence of our stochastic algorithm that takes its values in an infinite-dimensional Banach space. Then, using numerical experiments, we illustrate the performances of our approach on the computation of regularized Monge-Kantorovich quantiles. In particular, we investigate the potential benefits of entropic regularization for the smooth estimation of multivariate quantiles using data sampled from the target measure $\nu$.
Robust multi-item auction design using statistical learning: Overcoming uncertainty in bidders' types distributions
Authors: Jiale Han, Xiaowu Dai
Date: 2023-02-02 00:00:00
Tasks: Density Estimation

This paper presents a novel mechanism design for multi-item auction settings with uncertain bidders' type distributions. Our proposed approach utilizes nonparametric density estimation to accurately estimate bidders' types from historical bids, and is built upon the Vickrey-Clarke-Groves (VCG) mechanism, ensuring satisfaction of Bayesian incentive compatibility (BIC) and $\delta$-individual rationality (IR). To further enhance the efficiency of our mechanism, we introduce two novel strategies for query reduction: a filtering method that screens potential winners' value regions within the confidence intervals generated by our estimated distribution, and a classification strategy that designates the lower bound of an interval as the estimated type when the length is below a threshold value. Simulation experiments conducted on both small-scale and large-scale data demonstrate that our mechanism consistently outperforms existing methods in terms of revenue maximization and query reduction, particularly in large-scale scenarios. This makes our proposed mechanism a highly desirable and effective option for sellers in the realm of multi-item auctions.
High-dimensional variable clustering based on maxima of a weakly dependent random process
Authors: Alexis Boulin, Elena Di Bernardino, Thomas Laloë, Gwladys Toulemonde
Date: 2023-02-02 00:00:00

We propose a new class of models for variable clustering called Asymptotic Independent block (AI-block) models, which defines population-level clusters based on the independence of the maxima of a multivariate stationary mixing random process among clusters. This class of models is identifiable, meaning that there exists a maximal element with a partial order between partitions, allowing for statistical inference. We also present an algorithm depending on a tuning parameter that recovers the clusters of variables without specifying the number of clusters \emph{a priori}. Our work provides some theoretical insights into the consistency of our algorithm, demonstrating that under certain conditions it can effectively identify clusters in the data with a computational complexity that is polynomial in the dimension. A data-driven selection method for the tuning parameter is also proposed. To further illustrate the significance of our work, we applied our method to neuroscience and environmental real-datasets. These applications highlight the potential and versatility of the proposed approach.
Monitoring the risk of a tailings dam collapse through spectral analysis of satellite InSAR time-series data
Authors: Sourav Das, Anuradha Priyadarshana, Stephen Grebby
Date: 2023-02-01 00:00:00
Tasks: Time Series, Time Series Analysis

Slope failures possess destructive power that can cause significant damage to both life and infrastructure. Monitoring slopes prone to instabilities is therefore critical in mitigating the risk posed by their failure. The purpose of slope monitoring is to detect precursory signs of stability issues, such as changes in the rate of displacement with which a slope is deforming. This information can then be used to predict the timing or probability of an imminent failure in order to provide an early warning. In this study, a more objective, statistical-learning algorithm is proposed to detect and characterise the risk of a slope failure, based on spectral analysis of serially correlated displacement time series data. The algorithm is applied to satellite-based interferometric synthetic radar (InSAR) displacement time series data to retrospectively analyse the risk of the 2019 Brumadinho tailings dam collapse in Brazil. Two potential risk milestones are identified and signs of a definitive but emergent risk (27 February 2018 to 26 August 2018) and imminent risk of collapse of the tailings dam (27 June 2018 to 24 December 2018) are detected by the algorithm. Importantly, this precursory indication of risk of failure is detected as early as at least five months prior to the dam collapse on 25 January 2019. The results of this study demonstrate that the combination of spectral methods and second order statistical properties of InSAR displacement time series data can reveal signs of a transition into an unstable deformation regime, and that this algorithm can provide sufficient early warning that could help mitigate catastrophic slope failures.
PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information
Authors: Jianxin Chang, Chenbin Zhang, Yiqun Hui, Dewei Leng, Yanan Niu, Yang song, Kun Gai
Date: 2023-02-02 00:00:00
Tasks: Recommendation Systems

With the increase of content pages and interactive buttons in online services such as online-shopping and video-watching websites, industrial-scale recommender systems face challenges in multi-domain and multi-task recommendations. The core of multi-task and multi-domain recommendation is to accurately capture user interests in multiple scenarios given multiple user behaviors. In this paper, we propose a plug-and-play \textit{\textbf{P}arameter and \textbf{E}mbedding \textbf{P}ersonalized \textbf{Net}work (\textbf{PEPNet})} for multi-domain and multi-task recommendation. PEPNet takes personalized prior information as input and dynamically scales the bottom-level Embedding and top-level DNN hidden units through gate mechanisms. \textit{Embedding Personalized Network (EPNet)} performs personalized selection on Embedding to fuse features with different importance for different users in multiple domains. \textit{Parameter Personalized Network (PPNet)} executes personalized modification on DNN parameters to balance targets with different sparsity for different users in multiple tasks. We have made a series of special engineering optimizations combining the Kuaishou training framework and the online deployment environment. By infusing personalized selection of Embedding and personalized modification of DNN parameters, PEPNet tailored to the interests of each individual obtains significant performance gains, with online improvements exceeding 1\% in multiple task metrics across multiple domains. We have deployed PEPNet in Kuaishou apps, serving over 300 million users every day.
Goniometers are a Powerful Acoustic Feature for Music Information Retrieval Tasks
Authors: Tim Ziemer
Date: 2023-02-02 00:00:00
Tasks: Information Retrieval, Music Information Retrieval, Retrieval

Goniometers, also known as Phase Scopes or Vector Scopes, are audio metering tools that help music producers and mixing engineers monitor spatial aspects of a music mix, such as the stereo panorama, the width of single sources, the amount and diffuseness of reverberation as well as phase cancellations that may occur on the sweet-spot and in a mono-mixdown. In addition, they implicitly inform about the dynamics of the sound. Self-organizing maps trained with a goniometer, are consulted to explore the usefulness of this acoustic feature for music information retrieval tasks. One can see that goniometers are able to classify different genres and cluster a single album. The advantage of goniometers is the causality: Music producers and mixing engineers consciously consult goniometers to reach their desired sound, which is not the case for other acoustic features, from Zero-Crossing Rate to Mel-Frequency Cepstral Coefficients.
DigiCall: A Benchmark for Measuring the Maturity of Digital Strategy through Company Earning Calls
Authors: Hilal Pataci, Kexuan Sun, T. Ravichandran
Date: 2022-12-08 00:00:00
Proceeding: FinNLP@EMNLP 2022 12
Tasks: Cultural Vocal Bursts Intensity Prediction, Language Modelling, Management, Part-Of-Speech Tagging, Sentence Classification, Text Classification

Digital transformation reinvents companies, their vision and strategy, organizational structure, processes, capabilities, and culture, and enables the development of new or enhanced products and services delivered to customers more efficiently. Organizations, by formalizing their digital strategy attempt to plan for their digital transformations and accelerate their company growth. Understanding how successful a company is in its digital transformation starts with accurate measurement of its digital maturity levels. However, existing approaches to measuring organizations’ digital strategy have low accuracy levels and this leads to inconsistent results, and also does not provide resources (data) for future research to improve. In order to measure the digital strategy maturity of companies, we leverage the state-of-the-art NLP models on unstructured data (earning call transcripts), and reach the state-of-the-art levels (94%) for this task. We release 3.691 earning call transcripts and also annotated data set, labeled particularly for the digital strategy maturity by linguists. Our work provides an empirical baseline for research in industry and management science.
Hyperspectral Image Classification Using Deep Matrix Capsules
Authors: Anirudh Ravikumar, Rohit P N, Mydhili K. Nair, Vimal Bhatia
Date: 2023-02-02 00:00:00
Proceeding: International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) 2023 2
Tasks: Classification, Edge-computing, Hyperspectral Image Classification, image-classification, Image Classification

Hyperspectral image (HSI) classification is used in multiple domains like precision agriculture, mineral exploration, remote sensing, and others. Conventionally, couvolutional neural networks (CNNs) were used in HSI classification, however they have limitations in exploiting spectral-spatial relationships, which is a key factor in understanding HSI. Even though deeper CNN architectures and use of 3-D-CNNs mitigate the above problem to a certain extent, they have increased computational complexity, which inhibits their use in resource-limited devices like IoT and edge computing devices. In this paper, we propose a novel method based on the concept of matrix capsules with Expectation-Maximization (EM) routing algorithm which is specifically designed to accommodate the nuances in the HSI data to efficiently tackle the aforementioned problems. The capsule units enable effective identification of spectral siguatures and part-whole relationships in the data while EM routing ensures viewpoint-invariance. Three representative HSI data sets are used to verify the effectiveness of the proposed method. The empirical results demonstrate that the proposed method is better than the current state-of-the-art methods in terms of accuracy while having 25 times fewer model parameters and requiring over 65 times less storage space. The source code can be found at https://github.com/DeepMatrixCapsules/DeepMatrixCapsules.
MG-TAR: Multi-View Graph Convolutional Networks for Traffic Accident Risk Prediction
Authors: Patara Trirat, Susik Yoon, Jae-Gil Lee
Date: 2023-01-20 00:00:00
Proceeding: IEEE Transactions on Intelligent Transportation Systems 2023 1
Tasks: Graph Learning, TAR

Due to the continuing colossal socio-economic losses caused by traffic accidents, it is of prime importance to precisely forecast the traffic accident risk to reduce future accidents. In this paper, we use dangerous driving statistics from driving log data and multi-graph learning to enhance predictive performance. We first conduct geographical and temporal correlation analyses to quantify the relationship between dangerous driving and actual accidents. Then, to learn various dependencies between districts besides the traditional adjacency matrix, we simultaneously model both static and dynamic graphs representing the spatio-temporal contextual relationships with heterogeneous environmental data, including the dangerous driving behavior A graph is generated for each type of the relationships. Ultimately, we propose an end-to-end framework, called MG-TAR, to effectively learn the association of multiple graphs for accident risk prediction by adopting multi-view graph neural networks with a multi-attention module. Thorough experiments on ten real-world datasets show that, compared with state-of-the-art methods, MG-TAR reduces the error of predicting the accident risk by up to 23% and improves the accuracy of predicting the most dangerous areas by up to 27%.
Graph-based Representation of Audio signals for Sound Event Classification
Authors: Carlo Aironi, Samuele Cornell, Emanuele Principi, Stefano Squartini
Date: 2021-08-23 00:00:00
Proceeding: 2021 29th European Signal Processing Conference (EUSIPCO) 2021 8

In recent years there has been a considerable rise in
interest towards Graph Representation and Learning techniques,
especially in such cases where data has intrinsically a graph-
like structure: social networks, molecular lattices, or semantic
interactions, just to name a few. In this paper, we propose a novel
way to represent an audio signal from its spectrogram by deriving
a graph-based representation which can be then employed by
already established Graph Deep-Neural-Networks techniques. We
evaluate this approach on a Sound Event Classiﬁcation task by
employing the widely used ESC and Urbansound8k datasets and
compare it with a Convolutional Neural Network (CNN) based
method. We show that such proposed graph-based approach
is extremely compact and used in conjunction learned CNN
features, allows for a signiﬁcant increase in classiﬁcation accuracy
over the baseline with more than 50 times less parameters than
the original CNN method. This suggests that, the proposed graph-
based features can offer additional discriminative information on
top of learned CNN features.
The Challenges and Opportunities in Creating an Early Warning System for Global Pandemics
Authors: David C. Danko, James Golden, Charles Vorosmarty, Anthony Cak, Fabio Corsi, Christopher E. Mason, Rafael Maciel-de-Freitas, Dorottya Nagy-Szakal, Niamh B. OHara
Date: 2023-02-02 00:00:00

The COVID-19 pandemic revealed that global health, social systems, and economies can be surprisingly fragile in an increasingly interconnected and interdependent world. Yet, during the last half of 2022, and quite remarkably, we began dismantling essential infectious disease monitoring programs in several countries. Absent such programs, localized biological risks will transform into global shocks linked directly to our lack of foresight regarding emerging health risks. Additionally, recent studies indicate that more than half of all infectious diseases could be made worse by climate change, complicating pandemic containment. Despite this complexity, the factors leading to pandemics are largely predictable but can only be realized through a well-designed global early warning system. Such a system should integrate data from genomics, climate and environment, social dynamics, and healthcare infrastructure. The glue for such a system is community-driven modeling, a modern logistics of data, and democratization of AI tools. Using the example of dengue fever in Brazil, we can demonstrate how thoughtfully designed technology platforms can build global-scale precision disease detection and response systems that significantly reduce exposure to systemic shocks, accelerate science-informed public health policies, and deliver reliable healthcare and economic opportunities as an intrinsic part of the global sustainable development agenda.
Signaling Games with Costly Monitoring
Authors: Reuben Bearman
Date: 2023-02-02 00:00:00

If in a signaling game the receiver expects to gain no information by monitoring the signal of the sender, then when a cost to monitor is implemented he will never pay that cost regardless of his off-path beliefs. This is the argument of a recent paper by T. Denti (2021). However, which pooling equilibrium does a receiver anticipate to gain no information through monitoring? This paper seeks to prove that given a sufficiently small cost to monitor any pooling equilibrium with a non-zero index will survive close to the original equilibrium.
Multi-agent Reinforcement Learning with Graph Q-Networks for Antenna Tuning
Authors: Maxime Bouton, Jaeseong Jeong, Jose Outes, Adriano Mendo, Alexandros Nikou
Date: 2023-01-20 00:00:00
Tasks: Graph Neural Network, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Future generations of mobile networks are expected to contain more and more antennas with growing complexity and more parameters. Optimizing these parameters is necessary for ensuring the good performance of the network. The scale of mobile networks makes it challenging to optimize antenna parameters using manual intervention or hand-engineered strategies. Reinforcement learning is a promising technique to address this challenge but existing methods often use local optimizations to scale to large network deployments. We propose a new multi-agent reinforcement learning algorithm to optimize mobile network configurations globally. By using a value decomposition approach, our algorithm can be trained from a global reward function instead of relying on an ad-hoc decomposition of the network performance across the different cells. The algorithm uses a graph neural network architecture which generalizes to different network topologies and learns coordination behaviors. We empirically demonstrate the performance of the algorithm on an antenna tilt tuning problem and a joint tilt and power control problem in a simulated environment.
Observations of peak electric load growth in ERCOT with the rise of electrified heating and its implications for future resource planning
Authors: Matthew J. Skiles, Joshua D. Rhodes, Michael E. Webber
Date: 2023-02-02 00:00:00

This analysis quantitatively compares the evolution in summer and winter peak demands in the Electric Reliability Council of Texas (ERCOT) service area from 1997 through 2021. Weather data for the days in which peak demand occurred were also compiled to investigate the relationship between peak heating and cooling loads and ambient temperature. This relationship was then applied along with population projections and a climate scenario with medium to high radiative forcing to create winter and summer peak demand growth scenarios for 2025 through 2050. This analysis informs resource planners about how ERCOT peak demand might change in the future and provides new insight into how electric load growth and non-flexible electrified heating demand could have contributed to the February 2021 ERCOT blackouts. We found that historically, summer peak demand growth has been generally stable and approximately linear with time. The stable summer peak load is likely a consequence of fairly constant temperatures observed on summer peak demand days. Conversely, the winter peak demand growth has been less consistent, varying much more around the broader trend. This phenomenon is likely a consequence of high residential electrical heating load on winter peak demand days, which saw temperatures that varied widely from the mean value. Future peak winter and summer electricity demand scenarios indicated that while average temperatures on winter peak demand days will remain fairly constant, they will be more erratic than temperatures on summer peak demand days. As a result, winter peak demand will remain more erratic and will sporadically surpass summer peak demand between 2025 and 2050. Thus, resource planners in ERCOT should place less certainty on winter peak demand projections and an increased level of winter preparedness on both the supply and demand sectors appears warranted.