pwc-archive/papers-with-abstracts huggingface dataset:
Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving
Authors: Ryan Boldi, Martin Briesch, Dominik Sobania, Alexander Lalejini, Thomas Helmuth, Franz Rothlauf, Charles Ofria, Lee Spector
Date: 2023-01-04 00:00:00
Tasks: Program Synthesis

Genetic Programming (GP) often uses large training sets and requires all individuals to be evaluated on all training cases during selection. Random down-sampled lexicase selection evaluates individuals on only a random subset of the training cases allowing for more individuals to be explored with the same amount of program executions. However, creating a down-sample randomly might exclude important cases from the current down-sample for a number of generations, while cases that measure the same behavior (synonymous cases) may be overused despite their redundancy. In this work, we introduce Informed Down-Sampled Lexicase Selection. This method leverages population statistics to build down-samples that contain more distinct and therefore informative training cases. Through an empirical investigation across two different GP systems (PushGP and Grammar-Guided GP), we find that informed down-sampling significantly outperforms random down-sampling on a set of contemporary program synthesis benchmark problems. Through an analysis of the created down-samples, we find that important training cases are included in the down-sample consistently across independent evolutionary runs and systems. We hypothesize that this improvement can be attributed to the ability of Informed Down-Sampled Lexicase Selection to maintain more specialist individuals over the course of evolution, while also benefiting from reduced per-evaluation costs.
Lessons Learned Applying Deep Learning Approaches to Forecasting Complex Seasonal Behavior
Authors: Andrew T. Karl, James Wisnowski, Lambros Petropoulos
Date: 2023-01-04 00:00:00

Deep learning methods have gained popularity in recent years through the media and the relative ease of implementation through open source packages such as Keras. We investigate the applicability of popular recurrent neural networks in forecasting call center volumes at a large financial services company. These series are highly complex with seasonal patterns - between hours of the day, day of the week, and time of the year - in addition to autocorrelation between individual observations. Though we investigate the financial services industry, the recommendations for modeling cyclical nonlinear behavior generalize across all sectors. We explore the optimization of parameter settings and convergence criteria for Elman (simple), Long Short-Term Memory (LTSM), and Gated Recurrent Unit (GRU) RNNs from a practical point of view. A designed experiment using actual call center data across many different "skills" (income call streams) compares performance measured by validation error rates of the best observed RNN configurations against other modern and classical forecasting techniques. We summarize the utility of and considerations required for using deep learning methods in forecasting.
Scalable Optimal Design of Incremental Volt/VAR Control using Deep Neural Networks
Authors: Sarthak Gupta, Ali Mehrizi-Sani, Spyros Chatzivasileiadis, Vassilis Kekatos
Date: 2023-01-04 00:00:00

Volt/VAR control rules facilitate the autonomous operation of distributed energy resources (DER) to regulate voltage in power distribution grids. According to non-incremental control rules, such as the one mandated by the IEEE Standard 1547, the reactive power setpoint of each DER is computed as a piecewise-linear curve of the local voltage. However, the slopes of such curves are upper-bounded to ensure stability. On the other hand, incremental rules add a memory term into the setpoint update, rendering them universally stable. They can thus attain enhanced steady-state voltage profiles. Optimal rule design (ORD) for incremental rules can be formulated as a bilevel program. We put forth a scalable solution by reformulating ORD as training a deep neural network (DNN). This DNN emulates the Volt/VAR dynamics for incremental rules derived as iterations of proximal gradient descent (PGD). Analytical findings and numerical tests corroborate that the proposed ORD solution can be neatly adapted to single/multi-phase feeders.
Geometric Ergodicity in Modified Variations of Riemannian Manifold and Lagrangian Monte Carlo
Authors: James A. Brofos, Vivekananda Roy, Roy R. Lederman
Date: 2023-01-04 00:00:00
Tasks: Bayesian Inference

Riemannian manifold Hamiltonian (RMHMC) and Lagrangian Monte Carlo (LMC) have emerged as powerful methods of Bayesian inference. Unlike Euclidean Hamiltonian Monte Carlo (EHMC) and the Metropolis-adjusted Langevin algorithm (MALA), the geometric ergodicity of these Riemannian algorithms has not been extensively studied. On the other hand, the manifold Metropolis-adjusted Langevin algorithm (MMALA) has recently been shown to exhibit geometric ergodicity under certain conditions. This work investigates the mixture of the LMC and RMHMC transition kernels with MMALA in order to equip the resulting method with an "inherited" geometric ergodicity theory. We motivate this mixture kernel based on an analogy between single-step HMC and MALA. We then proceed to evaluate the original and modified transition kernels on several benchmark Bayesian inference tasks.
Testing High-dimensional Multinomials with Applications to Text Analysis
Authors: T. Tony Cai, Zheng Tracy Ke, Paxton Turner
Date: 2023-01-03 00:00:00
Tasks: Diversity, Vocal Bursts Intensity Prediction

Motivated by applications in text mining and discrete distribution inference, we investigate the testing for equality of probability mass functions of $K$ groups of high-dimensional multinomial distributions. A test statistic, which is shown to have an asymptotic standard normal distribution under the null, is proposed. The optimal detection boundary is established, and the proposed test is shown to achieve this optimal detection boundary across the entire parameter space of interest. The proposed method is demonstrated in simulation studies and applied to analyze two real-world datasets to examine variation among consumer reviews of Amazon movies and diversity of statistical paper abstracts.
Covariate-guided Bayesian mixture model for multivariate time series
Authors: Haoyi Fu, Lu Tang, Ori Rosen, Alison E. Hipwell, Theodore J. Huppert, Robert T. Krafty
Date: 2023-01-03 00:00:00
Tasks: Mixture-of-Experts, Time Series, Time Series Analysis

With rapid development of techniques to measure brain activity and structure, statistical methods for analyzing modern brain-imaging play an important role in the advancement of science. Imaging data that measure brain function are usually multivariate time series and are heterogeneous across both imaging sources and subjects, which lead to various statistical and computational challenges. In this paper, we propose a group-based method to cluster a collection of multivariate time series via a Bayesian mixture of smoothing splines. Our method assumes each multivariate time series is a mixture of multiple components with different mixing weights. Time-independent covariates are assumed to be associated with the mixture components and are incorporated via logistic weights of a mixture-of-experts model. We formulate this approach under a fully Bayesian framework using Gibbs sampling where the number of components is selected based on a deviance information criterion. The proposed method is compared to existing methods via simulation studies and is applied to a study on functional near-infrared spectroscopy (fNIRS), which aims to understand infant emotional reactivity and recovery from stress. The results reveal distinct patterns of brain activity, as well as associations between these patterns and selected covariates.
Measuring tail risk at high-frequency: An $L_1$-regularized extreme value regression approach with unit-root predictors
Authors: Julien Hambuckers, Li Sun, Luca Trapin
Date: 2023-01-03 00:00:00

We study tail risk dynamics in high-frequency financial markets and their connection with trading activity and market uncertainty. We introduce a dynamic extreme value regression model accommodating both stationary and local unit-root predictors to appropriately capture the time-varying behaviour of the distribution of high-frequency extreme losses. To characterize trading activity and market uncertainty, we consider several volatility and liquidity predictors, and propose a two-step adaptive $L_1$-regularized maximum likelihood estimator to select the most appropriate ones. We establish the oracle property of the proposed estimator for selecting both stationary and local unit-root predictors, and show its good finite sample properties in an extensive simulation study. Studying the high-frequency extreme losses of nine large liquid U.S. stocks using 42 liquidity and volatility predictors, we find the severity of extreme losses to be well predicted by low levels of price impact in period of high volatility of liquidity and volatility.
Modeling Sequential Recommendation as Missing Information Imputation
Authors: Yujie Lin, Zhumin Chen, Zhaochun Ren, Chenyang Wang, Qiang Yan, Maarten de Rijke, Xiuzhen Cheng, Pengjie Ren
Date: 2023-01-04 00:00:00
Tasks: Imputation, Sequential Recommendation

Side information is being used extensively to improve the effectiveness of sequential recommendation models. It is said to help capture the transition patterns among items. Most previous work on sequential recommendation that uses side information models item IDs and side information separately, which may fail to fully model the relation between the items and their side information. Moreover, in real-world systems, not all values of item feature fields are available. This hurts the performance of models that rely on side information. Existing methods tend to neglect the context of missing item feature fields, and fill them with generic or special values, e.g., unknown, which might lead to sub-optimal performance. To address the limitation of sequential recommenders with side information, we define a way to fuse side information and alleviate the problem of missing side information by proposing a unified task, namely the missing information imputation (MII), which randomly masks some feature fields in a given sequence of items, including item IDs, and then forces a predictive model to recover them. By considering the next item as a missing feature field, sequential recommendation can be formulated as a special case of MII. We propose a sequential recommendation model, called missing information imputation recommender (MIIR), that builds on the idea of MII and simultaneously imputes missing item feature values and predicts the next item. We devise a dense fusion self-attention (DFSA) mechanism for MIIR to capture all pairwise relations between items and their side information. Empirical studies on three benchmark datasets demonstrate that MIIR, supervised by MII, achieves a significantly better sequential recommendation performance than state-of-the-art baselines.
Deriving land surface phenology indicators from CO2 eddy covariance measurements
Authors: Alemu Gonsamo a, ∗, Jing M. Chen a, Petra D’Odorico b
Date: 2013-07-01 00:00:00
Proceeding: Ecological Indicators 2013 7
Tasks: Time Series, Time Series Analysis

Recent progress of CO2 eddy covariance (EC) technique and accumulation of measurements offer an unprecedented perspective to study the land surface phenology (LSP) in a more objective way than previously possible by allowing the actual photosynthesis measurement – gross primary productivity (GPP). Because of the spatial, temporal, and ecological complexity of processes controlling GPP time series, the extraction of important LSP dates from GPP has been elusive. Here, we present objective measures of several LSP metrics from GPP time series data. A case study based on long term GPP measurements over a mature boreal deciduous forest is provided together with LSP estimates from remote sensing data. Results show that most LSP metrics are interrelated within each season (spring and autumn) both from GPP and remote sensing based estimates. We provide simple mathematical derivatives of GPP time series to objectively estimate key LSP metrics such as: the start, end and length of growing season; end of greenup; start of browndown; length of canopy closure; start, end and length of peak; and peak of season. These key LSP metrics indicate the collective ecological responses to environmental changes over space and time.
Hospital transfer risk prediction for COVID-19 patients from a medicalized hotel based on Diffusion GraphSAGE
Authors: Jun-En Ding, Chih-Ho Hsu, Kuan-Chia Ling, Ling Chen, Fang-Ming Hung
Date: 2022-12-31 00:00:00
Tasks: Survival Analysis

The global COVID-19 pandemic has caused more than six million deaths worldwide. Medicalized hotels were established in Taiwan as quarantine facilities for COVID-19 patients with no or mild symptoms. Due to limited medical care available at these hotels, it is of paramount importance to identify patients at risk of clinical deterioration. This study aimed to develop and evaluate a graph-based deep learning approach for progressive hospital transfer risk prediction in a medicalized hotel setting. Vital sign measurements were obtained for 632 patients and daily patient similarity graphs were constructed. Inductive graph convolutional network models were trained on top of the temporally integrated graphs to predict hospital transfer risk. The proposed models achieved AUC scores above 0.83 for hospital transfer risk prediction based on the measurements of past 1, 2, and 3 days, outperforming baseline machine learning methods. A post-hoc analysis on the constructed diffusion-based graph using Local Clustering Coefficient discovered a high-risk cluster with significantly older mean age, higher body temperature, lower SpO2, and shorter length of stay. Further time-to-hospital-transfer survival analysis also revealed a significant decrease in survival probability in the discovered high-risk cluster. The obtained results demonstrated promising predictability and interpretability of the proposed graph-based approach. This technique may help preemptively detect high-risk patients at community-based medical facilities similar to a medicalized hotel.
Noise Reduction in Medical Images
Authors: Sussana M. Florez-Aroni, Mijail A. Hancco-Condori, Fred Torres-Cruz
Date: 2023-01-04 00:00:00
Tasks: Articles

Objectives: Analyze the types of studies and algorithms that are most applied, Identify the anatomical regions treated. Determine the application of parallel techniques used in studies carried out between 2010 and 2022 in research on noise reduction in medical images. Methodology: A systematic review of the literature on noise reduction in medical images in the last 12 years was carried out. The observation technique was applied to extract the information and the indicators (type of study, treated anatomical region, algorithm and or method and the application of parallel computing) were recorded in a data sheet. Results: Most of the studies have been developed in anatomical regions such as: Brain, Bones, Heart, Breast, Lung and Visual system. In the articles investigated, 14 are applied through parallel computing. Conclution: Noise reduction in medical images can contribute to better quality images and thus make a more accurate and effective diagnosis.
Self-assembled neuromorphic networks at self-organized criticality in Ag-hBN platform
Authors: Ankit Rao, Sooraj Sanjay, Majid Ahmadi, Anirudh Venugopalrao, Navakanta Bhat, Bart Kooi, Srinivasan Raghavan, Pavan Nukala
Date: 2022-12-07 00:00:00

Networks and systems which exhibit brain-like behavior can analyze information from intrinsically noisy and unstructured data with very low power consumption. Such characteristics arise due to the critical nature and complex interconnectivity of the brain and its neuronal network. We demonstrate that a system comprising of multilayer hexagonal Boron Nitride (hBN) films contacted with Silver (Ag), that can uniquely host two different self-assembled networks, which are self-organized at criticality (SOC). This system shows bipolar resistive switching between high resistance (HRS) and low resistance states (LRS). In the HRS, Ag clusters (nodes) intercalate in the van der Waals gaps of hBN forming a network of tunnel junctions, whereas the LRS contains a network of Ag filaments. The temporal avalanche dynamics in both these states exhibit power-law scaling, long-range temporal correlation, and SOC. These networks can be tuned from one to another with voltage as a control parameter. For the first time, different neuron-like networks are realized in a single CMOS compatible, 2D materials platform.
CarFi: Rider Localization Using Wi-Fi CSI
Authors: Sirajum Munir, Hongkai Chen, Shiwei Fang, Mahathir Monjur, Shan Lin, Shahriar Nirjon
Date: 2022-12-21 00:00:00
Tasks: Blocking, GPU

With the rise of hailing services, people are increasingly relying on shared mobility (e.g., Uber, Lyft) drivers to pick up for transportation. However, such drivers and riders have difficulties finding each other in urban areas as GPS signals get blocked by skyscrapers, in crowded environments (e.g., in stadiums, airports, and bars), at night, and in bad weather. It wastes their time, creates a bad user experience, and causes more CO2 emissions due to idle driving. In this work, we explore the potential of Wi-Fi to help drivers to determine the street side of the riders. Our proposed system is called CarFi that uses Wi-Fi CSI from two antennas placed inside a moving vehicle and a data-driven technique to determine the street side of the rider. By collecting real-world data in realistic and challenging settings by blocking the signal with other people and other parked cars, we see that CarFi is 95.44% accurate in rider-side determination in both line of sight (LoS) and non-line of sight (nLoS) conditions, and can be run on an embedded GPU in real-time.
Grid-Based Decimation for Wavelet Transforms with Stably Invertible Implementation
Authors: Nicki Holighaus, Günther Koliander, Clara Hollomey, Friedrich Pillichshammer
Date: 2023-01-04 00:00:00
Tasks: Onset Detection

The constant center frequency to bandwidth ratio (Q-factor) of wavelet transforms provides a very natural representation for audio data. However, invertible wavelet transforms have either required non-uniform decimation -- leading to irregular data structures that are cumbersome to work with -- or require excessively high oversampling with unacceptable computational overhead. Here, we present a novel decimation strategy for wavelet transforms that leads to stable representations with oversampling rates close to one and uniform decimation. Specifically, we show that finite implementations of the resulting representation are energy-preserving in the sense of frame theory. The obtained wavelet coefficients can be stored in a timefrequency matrix with a natural interpretation of columns as time frames and rows as frequency channels. This matrix structure immediately grants access to a large number of algorithms that are successfully used in time-frequency audio processing, but could not previously be used jointly with wavelet transforms. We demonstrate the application of our method in processing based on nonnegative matrix factorization, in onset detection, and in phaseless reconstruction.
Modeling the Rhythm from Lyrics for Melody Generation of Pop Song
Authors: Daiyu Zhang, Ju-Chiang Wang, Katerina Kosta, Jordan B. L. Smith, Shicen Zhou
Date: 2023-01-03 00:00:00
Tasks: Rhythm

Creating a pop song melody according to pre-written lyrics is a typical practice for composers. A computational model of how lyrics are set as melodies is important for automatic composition systems, but an end-to-end lyric-to-melody model would require enormous amounts of paired training data. To mitigate the data constraints, we adopt a two-stage approach, dividing the task into lyric-to-rhythm and rhythm-to-melody modules. However, the lyric-to-rhythm task is still challenging due to its multimodality. In this paper, we propose a novel lyric-to-rhythm framework that includes part-of-speech tags to achieve better text setting, and a Transformer architecture designed to model long-term syllable-to-note associations. For the rhythm-to-melody task, we adapt a proven chord-conditioned melody Transformer, which has achieved state-of-the-art results. Experiments for Chinese lyric-to-melody generation show that the proposed framework is able to model key characteristics of rhythm and pitch distributions in the dataset, and in a subjective evaluation, the melodies generated by our system were rated as similar to or better than those of a state-of-the-art alternative.
Microgrid Optimal Energy Scheduling with Risk Analysis
Authors: Ali Siddique, Cunzhi Zhao, Xingpeng Li
Date: 2023-01-04 00:00:00
Tasks: energy management, Management, Scheduling

Risk analysis is currently not quantified in microgrid resource scheduling optimization. This paper conducts a conditional value at risk (cVaR) analysis on a grid-disconnected residential microgrid with distributed energy resources (DER). We assume the infrastructure to set up an ad-hoc microgrid is already in place for a residential neighborhood with power sources such as photovoltaic (PV), diesel, and battery energy storage system (BESS). With this scenario in mind, we solve day-ahead scheduling to optimally allocate various resources to match demand in scenarios where neighborhoods, especially residential, are disconnected from the overall grid such as in flooding, hurricanes, winter storms, or operational failures. The goal is to provide an alternative framework to optimize power availability for priority customers and strengthen the overall grid against dips in power outside of normal operating considerations. The focus of this paper will be taking in renewable energy sources from PV combined with diesel and BESS while minimizing cost. Case studies demonstrate that with the proposed energy management system, microgrids can be implemented to be more resilient against new challenges.
Data-driven dissipative verification of LTI systems: multiple shots of data, QDF supply-rate and application to a planar manipulator
Authors: Tábitha Esteves Rosa, Bayu Jayawardhana
Date: 2023-01-04 00:00:00

We present a data-driven dissipative verification method for LTI systems based on using multiple input-output data. We assume that the supply-rate functions have a quadratic difference form corresponding to the general dissipativity notion known in the behavioural framework. We validate our approach in a practical example using a two-degree-of-freedom planar manipulator from Quanser, with which we demonstrate the applicability of multiple datasets over one-shot of data recently proposed in the literature.