pwc-archive/papers-with-abstracts huggingface dataset:
A procedure for automated tree pruning suggestion using LiDAR scans of fruit trees
Authors: Fredrik Westling, James Underwood, Mitch Bryson
Date: 2021-02-07 00:00:00
Tasks: Decision Making, Management

In fruit tree growth, pruning is an important management practice for preventing overcrowding, improving canopy access to light and promoting regrowth. Due to the slow nature of agriculture, decisions in pruning are typically made using tradition or rules of thumb rather than data-driven analysis. Many existing algorithmic, simulation-based approaches rely on high-fidelity digital captures or purely computer-generated fruit trees, and are unable to provide specific results on an orchard scale. We present a framework for suggesting pruning strategies on LiDAR-scanned commercial fruit trees using a scoring function with a focus on improving light distribution throughout the canopy. A scoring function to assess the quality of the tree shape based on its light availability and size was developed for comparative analysis between trees, and was validated against yield characteristics, demonstrating a reasonable correlation against fruit count with an $R^2$ score of 0.615 for avocado and 0.506 for mango. A tool was implemented for simulating pruning by algorithmically estimating which parts of a tree point cloud would be removed given specific cut points using structural analysis of the tree, validated experimentally with an average F1 score of 0.78 across 144 experiments. Finally, new pruning locations were suggested and we used the previous two stages to estimate the improvement of the tree given these suggestions. The light distribution was improved by up to 25.15\%, demonstrating a 16\% improvement over commercial pruning on a real tree, and certain cut points were discovered which improved light distribution with a smaller negative impact on tree volume. The final results suggest value in the framework as a decision making tool for commercial growers, or as a starting point for automated pruning since the entire process can be performed with little human intervention.
What's in a Name? -- Gender Classification of Names with Character Based Machine Learning Models
Authors: Yifan Hu, Changwei Hu, Thanh Tran, Tejaswi Kasturi, Elizabeth Joseph, Matt Gillingham
Date: 2021-02-07 00:00:00
Tasks: BIG-bench Machine Learning, Gender Classification, General Classification, Recommendation Systems

Gender information is no longer a mandatory input when registering for an account at many leading Internet companies. However, prediction of demographic information such as gender and age remains an important task, especially in intervention of unintentional gender/age bias in recommender systems. Therefore it is necessary to infer the gender of those users who did not to provide this information during registration. We consider the problem of predicting the gender of registered users based on their declared name. By analyzing the first names of 100M+ users, we found that genders can be very effectively classified using the composition of the name strings. We propose a number of character based machine learning models, and demonstrate that our models are able to infer the gender of users with much higher accuracy than baseline models. Moreover, we show that using the last names in addition to the first names improves classification performance further.
WiSleep: Inferring Sleep Duration at Scale Using Passive WiFi Sensing
Authors: Priyanka Mary Mammen, Camellia Zakaria, Tergel Molom-Ochir, Amee Trivedi, Prashant Shenoy, Rajesh Balan
Date: 2021-02-07 00:00:00
Tasks: Change Point Detection

Sleep deprivation is a public health concern that significantly impacts one's well-being and performance. Sleep is an intimate experience, and state-of-the-art sleep monitoring solutions are highly-personalized to individual users. With a motivation to expand sleep monitoring capabilities at a large scale and contribute sleep data to public health understanding, we present Wisleep, a system for inferring sleep duration using smartphone network connections that are passively sensed from WiFi infrastructure. We propose an unsupervised ensemble model of Bayesian change point detection, validating it over a user study among 20 students living in campus dormitories and a private home. Our results find Wisleep outperforming prior techniques for users with irregular sleep patterns while yielding an average 88.50% accuracy within 60 minutes sleep time error and 39 minutes wake-up time error. This is comparable to client-side methods, albeit utilizing coarse-grained information. Additionally, we utilize our approach to predict sleep and wake-up times from a user study of more than 1000 student users, demonstrating results similar to prior findings on students' sleep patterns. Finally, we show that Wisleep can process data from twenty thousand users on a single commodity server, allowing it to scale to large campus populations with low server requirements.
Making Intelligent Reflecting Surfaces More Intelligent: A Roadmap Through Reservoir Computing
Authors: Zhou Zhou, Kangjun Bai, Nima Mohammadi, Yang Yi, Lingjia Liu
Date: 2021-02-06 00:00:00

This article introduces a neural network-based signal processing framework for intelligent reflecting surface (IRS) aided wireless communications systems. By modeling radio-frequency (RF) impairments inside the "meta-atoms" of IRS (including nonlinearity and memory effects), we present an approach that generalizes the entire IRS-aided system as a reservoir computing (RC) system, an efficient recurrent neural network (RNN) operating in a state near the "edge of chaos". This framework enables us to take advantage of the nonlinearity of this "fabricated" wireless environment to overcome link degradation due to model mismatch. Accordingly, the randomness of the wireless channel and RF imperfections are naturally embedded into the RC framework, enabling the internal RC dynamics lying on the edge of chaos. Furthermore, several practical issues, such as channel state information acquisition, passive beamforming design, and physical layer reference signal design, are discussed.
Predicting Eye Fixations Under Distortion Using Bayesian Observers
Authors: Zhengzhong Tu
Date: 2021-02-06 00:00:00
Tasks: Blocking

Visual attention is very an essential factor that affects how human perceives visual signals. This report investigates how distortions in an image could distract human's visual attention using Bayesian visual search models, specifically, Maximum-a-posteriori (MAP) \cite{findlay1982global}\cite{eckstein2001quantifying} and Entropy Limit Minimization (ELM) \cite{najemnik2009simple}, which predict eye fixation movements based on a Bayesian probabilistic framework. Experiments on modified MAP and ELM models on JPEG-compressed images containing blocking or ringing artifacts were conducted and we observed that compression artifacts can affect visual attention. We hope this work sheds light on the interactions between visual attention and perceptual quality.
Generating Artificial Core Users for Interpretable Condensed Data
Authors: Amy Nesky, Quentin F. Stout
Date: 2021-02-06 00:00:00
Tasks: Clustering

Recent work has shown that in a dataset of user ratings on items there exists a group of Core Users who hold most of the information necessary for recommendation. This set of Core Users can be as small as 20 percent of the users. Core Users can be used to make predictions for out-of-sample users without much additional work. Since Core Users substantially shrink a ratings dataset without much loss of information, they can be used to improve recommendation efficiency. We propose a method, combining latent factor models, ensemble boosting and K-means clustering, to generate a small set of Artificial Core Users (ACUs) from real Core User data. Our ACUs have dense rating information, and improve the recommendation performance of real Core Users while remaining interpretable.
Unsupervised Audio-Visual Subspace Alignment for High-Stakes Deception Detection
Authors: Leena Mathur, Maja J Matarić
Date: 2021-02-06 00:00:00
Tasks: Deception Detection, Transfer Learning, Vocal Bursts Intensity Prediction

Automated systems that detect deception in high-stakes situations can enhance societal well-being across medical, social work, and legal domains. Existing models for detecting high-stakes deception in videos have been supervised, but labeled datasets to train models can rarely be collected for most real-world applications. To address this problem, we propose the first multimodal unsupervised transfer learning approach that detects real-world, high-stakes deception in videos without using high-stakes labels. Our subspace-alignment (SA) approach adapts audio-visual representations of deception in lab-controlled low-stakes scenarios to detect deception in real-world, high-stakes situations. Our best unsupervised SA models outperform models without SA, outperform human ability, and perform comparably to a number of existing supervised models. Our research demonstrates the potential for introducing subspace-based transfer learning to model high-stakes deception and other social behaviors in real-world contexts with a scarcity of labeled behavioral data.
Emergency Department Optimization and Load Prediction in Hospitals
Authors: Karthik K. Padthe, Vikas Kumar, Carly M. Eckert, Nicholas M. Mark, Anam Zahid, Muhammad Aurangzeb Ahmad, Ankur Teredesai
Date: 2021-02-06 00:00:00
Tasks: Prediction

Over the past several years, across the globe, there has been an increase in people seeking care in emergency departments (EDs). ED resources, including nurse staffing, are strained by such increases in patient volume. Accurate forecasting of incoming patient volume in emergency departments (ED) is crucial for efficient utilization and allocation of ED resources. Working with a suburban ED in the Pacific Northwest, we developed a tool powered by machine learning models, to forecast ED arrivals and ED patient volume to assist end-users, such as ED nurses, in resource allocation. In this paper, we discuss the results from our predictive models, the challenges, and the learnings from users' experiences with the tool in active clinical deployment in a real world setting.
From Toxicity in Online Comments to Incivility in American News: Proceed with Caution
Authors: Anushree Hede, Oshin Agarwal, Linda Lu, Diana C. Mutz, Ani Nenkova
Date: 2021-02-06 00:00:00
Proceeding: EACL 2021 2

The ability to quantify incivility online, in news and in congressional debates, is of great interest to political scientists. Computational tools for detecting online incivility for English are now fairly accessible and potentially could be applied more broadly. We test the Jigsaw Perspective API for its ability to detect the degree of incivility on a corpus that we developed, consisting of manual annotations of civility in American news. We demonstrate that toxicity models, as exemplified by Perspective, are inadequate for the analysis of incivility in news. We carry out error analysis that points to the need to develop methods to remove spurious correlations between words often mentioned in the news, especially identity descriptors and incivility. Without such improvements, applying Perspective or similar models on news is likely to lead to wrong conclusions, that are not aligned with the human perception of incivility.
Recommending More Efficient Workflows to Software Developers
Authors: Dylan Bates
Date: 2021-02-06 00:00:00
Tasks: Recommendation Systems

Existing recommendation systems can help developers improve their software development abilities by recommending new programming tools, such as a refactoring tool or a program navigation tool. However, simply recommending tools in isolation may not, in and of itself, allow developers to successfully complete their tasks. In this paper, I introduce a new recommendation system that recommends workflows, or sequences of tools, to developers. By learning more efficient workflows, the system could make software developers more efficient.
COVIDHunter: An Accurate, Flexible, and Environment-Aware Open-Source COVID-19 Outbreak Simulation Model
Authors: Mohammed Alser, Jeremie S. Kim, Nour Almadhoun Alserr, Stefan W. Tell, Onur Mutlu
Date: 2021-02-06 00:00:00

Background: Early detection and isolation of COVID-19 patients are essential for successful implementation of mitigation strategies and eventually curbing the disease spread. With a limited number of daily COVID-19 tests performed in every country, simulating the COVID-19 spread along with the potential effect of each mitigation strategy currently remains one of the most effective ways in managing the healthcare system and guiding policy-makers. Methods: We introduce COVIDHunter, a flexible and accurate COVID-19 outbreak simulation model that evaluates the current mitigation measures that are applied to a region and provides suggestions on what strength the upcoming mitigation measure should be. The key idea of COVIDHunter is to quantify the spread of COVID-19 in a geographical region by simulating the average number of new infections caused by an infected person considering the effect of external factors, such as environmental conditions (e.g., climate, temperature, humidity) and mitigation measures. Results: Using Switzerland as a case study, COVIDHunter estimates that if the policy-makers relax the mitigation measures by 50% for 30 days then both the daily capacity need for hospital beds and daily number of deaths increase exponentially by an average of 5.1x, who may occupy ICU beds and ventilators for a period of time. Unlike existing models, the COVIDHunter model accurately monitors and predicts the daily number of cases, hospitalizations, and deaths due to COVID-19. Our model is flexible to configure and simple to modify for modeling different scenarios under different environmental conditions and mitigation measures. Availability: We release the source code of the COVIDHunter implementation at https://github.com/CMU- SAFARI/COVIDHunter and show how to flexibly configure our model for any scenario and easily extend it for different measures and conditions than we account for.
A bandit approach to curriculum generation for automatic speech recognition
Authors: Anastasia Kuznetsova, Anurag Kumar, Francis M. Tyers
Date: 2021-02-06 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), reinforcement-learning, Reinforcement Learning (RL), speech-recognition, Speech Recognition, Transfer Learning

The Automated Speech Recognition (ASR) task has been a challenging domain especially for low data scenarios with few audio examples. This is the main problem in training ASR systems on the data from low-resource or marginalized languages. In this paper we present an approach to mitigate the lack of training data by employing Automated Curriculum Learning in combination with an adversarial bandit approach inspired by Reinforcement learning. The goal of the approach is to optimize the training sequence of mini-batches ranked by the level of difficulty and compare the ASR performance metrics against the random training sequence and discrete curriculum. We test our approach on a truly low-resource language and show that the bandit framework has a good improvement over the baseline transfer-learning model.
Neural SDEs as Infinite-Dimensional GANs
Authors: Patrick Kidger, James Foster, Xuechen Li, Harald Oberhauser, Terry Lyons
Date: 2021-02-06 00:00:00
Tasks: Time Series, Time Series Analysis

Stochastic differential equations (SDEs) are a staple of mathematical modelling of temporal dynamics. However, a fundamental limitation has been that such models have typically been relatively inflexible, which recent work introducing Neural SDEs has sought to solve. Here, we show that the current classical approach to fitting SDEs may be approached as a special case of (Wasserstein) GANs, and in doing so the neural and classical regimes may be brought together. The input noise is Brownian motion, the output samples are time-evolving paths produced by a numerical solver, and by parameterising a discriminator as a Neural Controlled Differential Equation (CDE), we obtain Neural SDEs as (in modern machine learning parlance) continuous-time generative time series models. Unlike previous work on this problem, this is a direct extension of the classical approach without reference to either prespecified statistics or density functions. Arbitrary drift and diffusions are admissible, so as the Wasserstein loss has a unique global minima, in the infinite data limit any SDE may be learnt. Example code has been made available as part of the \texttt{torchsde} repository.
Streaming k-PCA: Efficient guarantees for Oja's algorithm, beyond rank-one updates
Authors: De Huang, Jonathan Niles-Weed, Rachel Ward
Date: 2021-02-06 00:00:00

We analyze Oja's algorithm for streaming $k$-PCA and prove that it achieves performance nearly matching that of an optimal offline algorithm. Given access to a sequence of i.i.d. $d \times d$ symmetric matrices, we show that Oja's algorithm can obtain an accurate approximation to the subspace of the top $k$ eigenvectors of their expectation using a number of samples that scales polylogarithmically with $d$. Previously, such a result was only known in the case where the updates have rank one. Our analysis is based on recently developed matrix concentration tools, which allow us to prove strong bounds on the tails of the random matrices which arise in the course of the algorithm's execution.
A practical model-based segmentation approach for improved activation detection in single-subject functional Magnetic Resonance Imaging studies
Authors: Wei-Chen Chen, Ranjan Maitra
Date: 2021-02-06 00:00:00

Functional Magnetic Resonance Imaging (fMRI) maps cerebral activation in response to stimuli but this activation is often difficult to detect, especially in low-signal contexts and single-subject studies. Accurate activation detection can be guided by the fact that very few voxels are, in reality, truly activated and that these voxels are spatially localized, but it is challenging to incorporate both these facts. We address these twin challenges to single-subject and low-signal fMRI by developing a computationally feasible and methodologically sound model-based approach, implemented in the R package MixfMRI, that bounds the a priori expected proportion of activated voxels while also incorporating spatial context. An added benefit of our methodology is the ability to distinguish voxels and regions having different intensities of activation. Our suggested approach is evaluated in realistic two- and three-dimensional simulation experiments as well as on multiple real-world datasets. Finally, the value of our suggested approach in low-signal and single-subject fMRI studies is illustrated on a sports imagination experiment that is often used to detect awareness and improve treatment in patients in persistent vegetative state (PVS). Our ability to reliably distinguish activation in this experiment potentially opens the door to the adoption of fMRI as a clinical tool for the improved treatment and therapy of PVS survivors and other patients.
Speaker attribution with voice profiles by graph-based semi-supervised learning
Authors: Jixuan Wang, Xiong Xiao, Jian Wu, Ranjani Ramamurthy, Frank Rudzicz, Michael Brudno
Date: 2021-02-06 00:00:00
Tasks: Speaker Identification

Speaker attribution is required in many real-world applications, such as meeting transcription, where speaker identity is assigned to each utterance according to speaker voice profiles. In this paper, we propose to solve the speaker attribution problem by using graph-based semi-supervised learning methods. A graph of speech segments is built for each session, on which segments from voice profiles are represented by labeled nodes while segments from test utterances are unlabeled nodes. The weight of edges between nodes is evaluated by the similarities between the pretrained speaker embeddings of speech segments. Speaker attribution then becomes a semi-supervised learning problem on graphs, on which two graph-based methods are applied: label propagation (LP) and graph neural networks (GNNs). The proposed approaches are able to utilize the structural information of the graph to improve speaker attribution performance. Experimental results on real meeting data show that the graph based approaches reduce speaker attribution error by up to 68% compared to a baseline speaker identification approach that processes each utterance independently.
Extremal learning: extremizing the output of a neural network in regression problems
Authors: Zakaria Patel, Markus Rummel
Date: 2021-02-06 00:00:00
Tasks: regression

Neural networks allow us to model complex relationships between variables. We show how to efficiently find extrema of a trained neural network in regression problems. Finding the extremizing input of an approximated model is formulated as the training of an additional neural network with a loss function that minimizes when the extremizing input is achieved. We further show how to incorporate additional constraints on the input vector such as limiting the extrapolation of the extremizing input vector from the original training data set. An instructional example of this approach using TensorFlow is included.
Deep Semi-Supervised Learning for Time Series Classification
Authors: Jann Goschenhofer, Rasmus Hvingelby, David Rügamer, Janek Thomas, Moritz Wagner, Bernd Bischl
Date: 2021-02-06 00:00:00
Tasks: Classification, Data Augmentation, General Classification, Time Series, Time Series Analysis, Time Series Classification

While Semi-supervised learning has gained much attention in computer vision on image data, yet limited research exists on its applicability in the time series domain. In this work, we investigate the transferability of state-of-the-art deep semi-supervised models from image to time series classification. We discuss the necessary model adaptations, in particular an appropriate model backbone architecture and the use of tailored data augmentation strategies. Based on these adaptations, we explore the potential of deep semi-supervised learning in the context of time series classification by evaluating our methods on large public time series classification problems with varying amounts of labelled samples. We perform extensive comparisons under a decidedly realistic and appropriate evaluation scheme with a unified reimplementation of all algorithms considered, which is yet lacking in the field. We find that these transferred semi-supervised models show significant performance gains over strong supervised, semi-supervised and self-supervised alternatives, especially for scenarios with very few labelled samples.
Multi-Tier Federated Learning for Vertically Partitioned Data
Authors: Anirban Das, Stacy Patterson
Date: 2021-02-06 00:00:00
Tasks: Federated Learning

We consider decentralized model training in tiered communication networks. Our network model consists of a set of silos, each holding a vertical partition of the data. Each silo contains a hub and a set of clients, with the silo's vertical data shard partitioned horizontally across its clients. We propose Tiered Decentralized Coordinate Descent (TDCD), a communication-efficient decentralized training algorithm for such two-tiered networks. To reduce communication overhead, the clients in each silo perform multiple local gradient steps before sharing updates with their hub. Each hub adjusts its coordinates by averaging its workers' updates, and then hubs exchange intermediate updates with one another. We present a theoretical analysis of our algorithm and show the dependence of the convergence rate on the number of vertical partitions, the number of local updates, and the number of clients in each hub. We further validate our approach empirically via simulation-based experiments using a variety of datasets and both convex and non-convex objectives.