pwc-archive/papers-with-abstracts huggingface dataset:
Multi-user Downlink Beamforming using Uplink Downlink Duality with 1-bit Converters for Flat Fading Channels
Authors: Khurram Usman Mazher, Amine Mezghani, Robert W. Heath Jr
Date: 2022-06-29 00:00:00
Tasks: Quantization

The increased power consumption of high-resolution data converters at higher carrier frequencies and larger bandwidths is becoming a bottleneck for communication systems. In this paper, we consider a fully digital base station equipped with 1-bit analog-to-digital (in uplink) and digital-to-analog (in downlink) converters on each radio frequency chain. The base station communicates with multiple single antenna users with individual SINR constraints. We first establish the uplink downlink duality principle under 1-bit hardware constraints under an uncorrelated quantization noise assumption. We then present a linear solution to the multi-user downlink beamforming problem based on the uplink downlink duality principle. The proposed solution takes into account the hardware constraints and jointly optimizes the downlink beamformers and the power allocated to each user. Optimized dithering obtained by adding dummy users to the true system users ensures that the uncorrelated quantization noise assumption is true under realistic settings. Detailed simulations carried out using 3GPP channel models generated from Quadriga show that our proposed solution outperforms state of the art solutions in terms of the ergodic sum and minimum rate especially when the number of users is large. We also demonstrate that the proposed solution significantly reduces the performance gap from non-linear solutions in terms of the uncoded bit error rate at a fraction of the computational complexity.
Using Interpretable Machine Learning to Massively Increase the Number of Antibody-Virus Interactions Across Studies
Authors: Tal Einav, Rong Ma
Date: 2022-06-10 00:00:00
Tasks: Interpretable Machine Learning

A central challenge in every field of biology is to use existing measurements to predict the outcomes of future experiments. In this work, we consider the wealth of antibody inhibition data against variants of the influenza virus. Due to this viru's genetic diversity and evolvability, the variants examined in one study will often have little-to-no overlap with other studies, making it difficult to discern common patterns or unify datasets for further analysis. To that end, we develop a computational framework that predicts how an antibody or serum would inhibit any variant from any other study. We use this framework to greatly expand seven influenza datasets utilizing hemagglutination inhibition, validating our method upon 200,000 existing measurements and predicting 2,000,000 new values along with their uncertainties. With these new values, we quantify the transferability between seven vaccination and infection studies in humans and ferrets, show that the serum potency is negatively correlated with breadth, and present a tool for pandemic preparedness. This data-driven approach does not require any information beyond each virus's name and measurements, and even datasets with as few as 5 viruses can be expanded, making this approach widely applicable. Future influenza studies using hemagglutination inhibition can directly utilize our curated datasets to predict newly measured antibody responses against ~80 H3N2 influenza viruses from 1968-2011, whereas immunological studies utilizing other viruses or a different assay only need a single partially-overlapping dataset to extend their work. In essence, this approach enables a shift in perspective when analyzing data from "what you see is what you get" into "what anyone sees is what everyone gets."
Dryland evapotranspiration from remote sensing solar-induced chlorophyll fluorescence: constraining an optimal stomatal model within a two-source energy balance model
Authors: Jingyi Bu, Guojing Gan, Jiahao Chen, Yanxin Su, Mengjia Yuan, Yanchun Gao, Francisco Domingo, Mirco Migliavacca, Tarek S. El-Madany, Pierre Gentine, Monica Garcia
Date: 2022-06-29 00:00:00

Evapotranspiration (ET) represents the largest water loss flux in drylands, but ET and its partition into plant transpiration (T) and soil evaporation (E) are poorly quantified, especially at fine temporal scales. Physically-based remote sensing models relying on sensible heat flux estimates, like the two-source energy balance model, could benefit from considering more explicitly the key effect of stomatal regulation on dryland ET. The objective of this study is to assess the value of solar-induced chlorophyll fluorescence (SIF), a proxy for photosynthesis, to constrain the canopy conductance (Gc) of an optimal stomatal model within a two-source energy balance model in drylands. We assessed our ET estimation using in situ eddy covariance GPP as a benchmark, and compared with results from using the Contiguous solar-induced chlorophyll fluorescence (CSIF) remote sensing product instead of GPP, with and without the effect of root-zone soil moisture on the Gc. The estimated ET was robust across four steppes and two tree-grass dryland ecosystem. Comparison of ET simulated against in situ GPP yielded an average R2 of 0.73 (0.86) and RMSE of 0.031 (0.36) mm at half-hourly (daily) timescale. Including explicitly the soil moisture effect on Gc, increased the R2 to 0.76 (0.89). For the CSIF model, the average R2 for ET estimates also improved when including the effect of soil moisture: from 0.65 (0.79) to 0.71 (0.84), with RMSE ranging between 0.023 (0.22) and 0.043 (0.54) mm depending on the site. Our results demonstrate the capacity of SIF to estimate subdaily and daily ET fluxes under very low ET conditions. SIF can provide effective vegetation signals to constrain stomatal conductance and partition ET into T and E in drylands. This approach could be extended for regional estimates using remote sensing SIF estimates such as CSIF, TROPOMI-SIF, or the upcoming FLEX mission, among others.
How should we proxy for race/ethnicity? Comparing Bayesian improved surname geocoding to machine learning methods
Authors: Ari Decter-Frain
Date: 2022-06-26 00:00:00
Tasks: BIG-bench Machine Learning

Bayesian Improved Surname Geocoding (BISG) is the most popular method for proxying race/ethnicity in voter registration files that do not contain it. This paper benchmarks BISG against a range of previously untested machine learning alternatives, using voter files with self-reported race/ethnicity from California, Florida, North Carolina, and Georgia. This analysis yields three key findings. First, machine learning consistently outperforms BISG at individual classification of race/ethnicity. Second, BISG and machine learning methods exhibit divergent biases for estimating regional racial composition. Third, the performance of all methods varies substantially across states. These results suggest that pre-trained machine learning models are preferable to BISG for individual classification. Furthermore, mixed results across states underscore the need for researchers to empirically validate their chosen race/ethnicity proxy in their populations of interest.
Using generalized additive models to decompose time series and waveforms, and dissect heart-lung interaction physiology
Authors: Johannes Enevoldsen, Gavin L Simpson, Simon T Vistisen
Date: 2022-06-13 00:00:00
Proceeding: Journal of Clinical Monitoring and Computing 2022 6
Tasks: Additive models, Medical waveform analysis, Time Series, Time Series Analysis

Common physiological time series and waveforms are composed of repeating cardiac and respiratory cycles. Often, the cardiac effect is the primary interest, but for, e.g., fluid responsiveness prediction, the respiratory effect on arterial blood pressure also convey important information. In either case, it is relevant to disentangle the two effects. Generalized additive models (GAMs) allow estimating the effect of predictors as nonlinear, smooth functions. These smooth functions can represent the cardiac and respiratory cycles' effects on a physiological signal. We demonstrate how GAMs allow a decomposition of physiological signals from mechanically ventilated subjects into separate effects of the cardiac and respiratory cycles. Two examples are presented. The first is a model of the respiratory variation in pulse pressure. The second demonstrates how a central venous pressure waveform can be decomposed into a cardiac effect, a respiratory effect and the interaction between the two cycles. Generalized additive models provide an intuitive and flexible approach to modelling the repeating, smooth, patterns common in medical monitoring data.
Two-Stage Classifier for COVID-19 Misinformation Detection Using BERT: a Study on Indonesian Tweets
Authors: Douglas Raevan Faisal, Rahmad Mahendra
Date: 2022-06-30 00:00:00
Tasks: Language Modelling, Misinformation, text-classification, Text Classification

The COVID-19 pandemic has caused globally significant impacts since the beginning of 2020. This brought a lot of confusion to society, especially due to the spread of misinformation through social media. Although there were already several studies related to the detection of misinformation in social media data, most studies focused on the English dataset. Research on COVID-19 misinformation detection in Indonesia is still scarce. Therefore, through this research, we collect and annotate datasets for Indonesian and build prediction models for detecting COVID-19 misinformation by considering the tweet's relevance. The dataset construction is carried out by a team of annotators who labeled the relevance and misinformation of the tweet data. In this study, we propose the two-stage classifier model using IndoBERT pre-trained language model for the Tweet misinformation detection task. We also experiment with several other baseline models for text classification. The experimental results show that the combination of the BERT sequence classifier for relevance prediction and Bi-LSTM for misinformation detection outperformed other machine learning models with an accuracy of 87.02%. Overall, the BERT utilization contributes to the higher performance of most prediction models. We release a high-quality COVID-19 misinformation Tweet corpus in the Indonesian language, indicated by the high inter-annotator agreement.
Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology
Authors: Yunlong Zhang, Yuxuan Sun, Honglin Li, Sunyi Zheng, Chenglu Zhu, Lin Yang
Date: 2022-06-30 00:00:00
Tasks: Benchmarking, Diagnostic

When designing a diagnostic model for a clinical application, it is crucial to guarantee the robustness of the model with respect to a wide range of image corruptions. Herein, an easy-to-use benchmark is established to evaluate how deep neural networks perform on corrupted pathology images. Specifically, corrupted images are generated by injecting nine types of common corruptions into validation images. Besides, two classification and one ranking metrics are designed to evaluate the prediction and confidence performance under corruption. Evaluated on two resulting benchmark datasets, we find that (1) a variety of deep neural network models suffer from a significant accuracy decrease (double the error on clean images) and the unreliable confidence estimation on corrupted images; (2) A low correlation between the validation and test errors while replacing the validation set with our benchmark can increase the correlation. Our codes are available on https://github.com/superjamessyx/robustness_benchmark.
PVT-COV19D: Pyramid Vision Transformer for COVID-19 Diagnosis
Authors: Lilang Zheng, Jiaxuan Fang, Xiaorun Tang, Hanzhang Li, Jiaxin Fan, Tianyi Wang, Rui Zhou, Zhaoyan Yan
Date: 2022-06-30 00:00:00
Tasks: COVID-19 Diagnosis

With the outbreak of COVID-19, a large number of relevant studies have emerged in recent years. We propose an automatic COVID-19 diagnosis framework based on lung CT scan images, the PVT-COV19D. In order to accommodate the different dimensions of the image input, we first classified the images using Transformer models, then sampled the images in the dataset according to normal distribution, and fed the sampling results into the modified PVTv2 model for training. A large number of experiments on the COV19-CT-DB dataset demonstrate the effectiveness of the proposed method.
Resource-Eficient Continual Learning for Sensor-Based Human Activity Recognition
Authors: CLAYTON FREDERICK SOUZA LEITE and YU XIAO, Aalto University, Finland
Date: 2022-04-30 00:00:00
Proceeding: ACM Transactions on Embedded Computing Systems 2022 4
Tasks: Activity Recognition, Continual Learning, Human Activity Recognition

Recent advances in deep learning have granted unrivaled performance to sensor-based human activity recognition (HAR).
However, in a real-world scenario, the HAR solution is subject to diverse changes over time such as the need to learn new
activity classes or variations in the data distribution of the already-included activities. To solve these issues, previous studies
have tried to apply directly the continual learning methods borrowed from the computer vision domain, where it is vastly
explored. Unfortunately, these methods either lead to surprisingly poor results or demand copious amounts of computational
resources, which is infeasible for the low-cost resource-constrained devices utilized in HAR. In this paper, we provide a
resource-eicient and high-performance continual learning solution for HAR. It consists of an expandable neural network
trained with a replay-based method that utilizes a highly-compressed replay memory whose samples are selected to maximize
data variability. Experiments with four open datasets, which were conducted on two distinct microcontrollers, show that our
method is capable of achieving substantial accuracy improvements over baselines in continual learning such as Gradient
Episodic Memory, while utilizing only one-third of the memory and being up to 3x faster.
Watch and Match: Supercharging Imitation with Regularized Optimal Transport
Authors: Siddhant Haldar, Vaibhav Mathur, Denis Yarats, Lerrel Pinto
Date: 2022-06-30 00:00:00
Tasks: Decision Making, Imitation Learning

Imitation learning holds tremendous promise in learning policies efficiently for complex decision making problems. Current state-of-the-art algorithms often use inverse reinforcement learning (IRL), where given a set of expert demonstrations, an agent alternatively infers a reward function and the associated optimal policy. However, such IRL approaches often require substantial online interactions for complex control problems. In this work, we present Regularized Optimal Transport (ROT), a new imitation learning algorithm that builds on recent advances in optimal transport based trajectory-matching. Our key technical insight is that adaptively combining trajectory-matching rewards with behavior cloning can significantly accelerate imitation even with only a few demonstrations. Our experiments on 20 visual control tasks across the DeepMind Control Suite, the OpenAI Robotics Suite, and the Meta-World Benchmark demonstrate an average of 7.8X faster imitation to reach 90% of expert performance compared to prior state-of-the-art methods. On real-world robotic manipulation, with just one demonstration and an hour of online training, ROT achieves an average success rate of 90.1% across 14 tasks.
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
Authors: Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
Date: 2022-06-30 00:00:00
Tasks: Additive models, BIG-bench Machine Learning, Model Editing

Machine learning (ML) interpretability techniques can reveal undesirable patterns in data that models exploit to make predictions--potentially causing harms once deployed. However, how to take action to address these patterns is not always clear. In a collaboration between ML and human-computer interaction researchers, physicians, and data scientists, we develop GAM Changer, the first interactive system to help domain experts and data scientists easily and responsibly edit Generalized Additive Models (GAMs) and fix problematic patterns. With novel interaction techniques, our tool puts interpretability into action--empowering users to analyze, validate, and align model behaviors with their knowledge and values. Physicians have started to use our tool to investigate and fix pneumonia and sepsis risk prediction models, and an evaluation with 7 data scientists working in diverse domains highlights that our tool is easy to use, meets their model editing needs, and fits into their current workflows. Built with modern web technologies, our tool runs locally in users' web browsers or computational notebooks, lowering the barrier to use. GAM Changer is available at the following public demo link: https://interpret.ml/gam-changer.
Learning Iterative Reasoning through Energy Minimization
Authors: Yilun Du, Shuang Li, Joshua B. Tenenbaum, Igor Mordatch
Date: 2022-06-30 00:00:00
Tasks: image-classification, Image Classification, Object Recognition

Deep learning has excelled on complex pattern recognition tasks such as image classification and object recognition. However, it struggles with tasks requiring nontrivial reasoning, such as algorithmic computation. Humans are able to solve such tasks through iterative reasoning -- spending more time thinking about harder tasks. Most existing neural networks, however, exhibit a fixed computational budget controlled by the neural network architecture, preventing additional computational processing on harder tasks. In this work, we present a new framework for iterative reasoning with neural networks. We train a neural network to parameterize an energy landscape over all outputs, and implement each step of the iterative reasoning as an energy minimization step to find a minimal energy solution. By formulating reasoning as an energy minimization problem, for harder problems that lead to more complex energy landscapes, we may then adjust our underlying computational budget by running a more complex optimization procedure. We empirically illustrate that our iterative reasoning approach can solve more accurate and generalizable algorithmic reasoning tasks in both graph and continuous domains. Finally, we illustrate that our approach can recursively solve algorithmic problems requiring nested reasoning
Evaluation of Performance-Trust vs Moral-Trust Violation in 3D Environment
Authors: Maitry Ronakbhai Trivedi, Zahra Rezaei Khavas, Paul Robinette
Date: 2022-06-30 00:00:00

Human-Robot Interaction, in which a robot with some level of autonomy interacts with a human to achieve a specific goal has seen much recent progress. With the introduction of autonomous robots and the possibility of widespread use of those in near future, it is critical that humans understand the robot's intention while interacting with them as this will foster the development of human-robot trust. The new conceptualization of trust which had been introduced by researchers in recent years considers trust in Human-Robot Interaction to be a multidimensional nature. Two main aspects which are attributed to trust are performance trust and moral trust. We aim to design an experiment to investigate the consequences of performance-trust violation and moral-trust violation in a search and rescue scenario. We want to see if two similar robot failures, one caused by a performance-trust violation and the other by a moral-trust violation have distinct effects on human trust. In addition to this, we plan to develop an interface that allows us to investigate whether altering the interface's modality from grid-world scenario (2D environment) to realistic simulation (3D environment) affects human perception of the task and the effects of the robot's failure on human trust.
Sub-8-Bit Quantization Aware Training for 8-Bit Neural Network Accelerator with On-Device Speech Recognition
Authors: Kai Zhen, Hieu Duy Nguyen, Raviteja Chinta, Nathan Susanj, Athanasios Mouchtaris, Tariq Afzal, Ariya Rastrow
Date: 2022-06-30 00:00:00
Tasks: Quantization, speech-recognition, Speech Recognition

We present a novel sub-8-bit quantization-aware training (S8BQAT) scheme for 8-bit neural network accelerators. Our method is inspired from Lloyd-Max compression theory with practical adaptations for a feasible computational overhead during training. With the quantization centroids derived from a 32-bit baseline, we augment training loss with a Multi-Regional Absolute Cosine (MRACos) regularizer that aggregates weights towards their nearest centroid, effectively acting as a pseudo compressor. Additionally, a periodically invoked hard compressor is introduced to improve the convergence rate by emulating runtime model weight quantization. We apply S8BQAT on speech recognition tasks using Recurrent Neural NetworkTransducer (RNN-T) architecture. With S8BQAT, we are able to increase the model parameter size to reduce the word error rate by 4-16% relatively, while still improving latency by 5%.
Shifts 2.0: Extending The Dataset of Real Distributional Shifts
Authors: Andrey Malinin, Andreas Athanasopoulos, Muhamed Barakovic, Meritxell Bach Cuadra, Mark J. F. Gales, Cristina Granziera, Mara Graziani, Nikolay Kartashev, Konstantinos Kyriakopoulos, Po-Jui Lu, Nataliia Molchanova, Antonis Nikitakis, Vatsal Raina, Francesco La Rosa, Eli Sivena, Vasileios Tsarsitalidis, Efi Tsompopoulou, Elena Volf
Date: 2022-06-30 00:00:00
Tasks: Autonomous Driving, image-classification, Image Classification, Machine Translation, motion prediction, Weather Forecasting

Distributional shift, or the mismatch between training and deployment data, is a significant obstacle to the usage of machine learning in high-stakes industrial applications, such as autonomous driving and medicine. This creates a need to be able to assess how robustly ML models generalize as well as the quality of their uncertainty estimates. Standard ML baseline datasets do not allow these properties to be assessed, as the training, validation and test data are often identically distributed. Recently, a range of dedicated benchmarks have appeared, featuring both distributionally matched and shifted data. Among these benchmarks, the Shifts dataset stands out in terms of the diversity of tasks as well as the data modalities it features. While most of the benchmarks are heavily dominated by 2D image classification tasks, Shifts contains tabular weather forecasting, machine translation, and vehicle motion prediction tasks. This enables the robustness properties of models to be assessed on a diverse set of industrial-scale tasks and either universal or directly applicable task-specific conclusions to be reached. In this paper, we extend the Shifts Dataset with two datasets sourced from industrial, high-risk applications of high societal importance. Specifically, we consider the tasks of segmentation of white matter Multiple Sclerosis lesions in 3D magnetic resonance brain images and the estimation of power consumption in marine cargo vessels. Both tasks feature ubiquitous distributional shifts and a strict safety requirement due to the high cost of errors. These new datasets will allow researchers to further explore robust generalization and uncertainty estimation in new situations. In this work, we provide a description of the dataset and baseline results for both tasks.