pwc-archive/papers-with-abstracts huggingface dataset:
2BiVQA: Double Bi-LSTM based Video Quality Assessment of UGC Videos
Authors: Ahmed Telili, Sid Ahmed Fezza, Wassim Hamidouche, Hanene F. Z. Brachemi Meftah
Date: 2022-08-31 00:00:00
Tasks: Video Quality Assessment, Visual Question Answering (VQA)

Recently, with the growing popularity of mobile devices as well as video sharing platforms (e.g., YouTube, Facebook, TikTok, and Twitch), User-Generated Content (UGC) videos have become increasingly common and now account for a large portion of multimedia traffic on the internet. Unlike professionally generated videos produced by filmmakers and videographers, typically, UGC videos contain multiple authentic distortions, generally introduced during capture and processing by naive users. Quality prediction of UGC videos is of paramount importance to optimize and monitor their processing in hosting platforms, such as their coding, transcoding, and streaming. However, blind quality prediction of UGC is quite challenging because the degradations of UGC videos are unknown and very diverse, in addition to the unavailability of pristine reference. Therefore, in this paper, we propose an accurate and efficient Blind Video Quality Assessment (BVQA) model for UGC videos, which we name 2BiVQA for double Bi-LSTM Video Quality Assessment. 2BiVQA metric consists of three main blocks, including a pre-trained Convolutional Neural Network (CNN) to extract discriminative features from image patches, which are then fed into two Recurrent Neural Networks (RNNs) for spatial and temporal pooling. Specifically, we use two Bi-directional Long Short Term Memory (Bi-LSTM) networks, the first is used to capture short-range dependencies between image patches, while the second allows capturing longrange dependencies between frames to account for the temporal memory effect. Experimental results on recent large-scale UGC VQA datasets show that 2BiVQA achieves high performance at lower computational cost than most state-of-the-art VQA models. The source code of our 2BiVQA metric is made publicly available at: https://github.com/atelili/2BiVQA
Control-Oriented Power Allocation for Integrated Satellite-UAV Networks
Authors: Chengleyang Lei, Wei Feng, Jue Wang, Shi Jin, Ning Ge
Date: 2022-08-31 00:00:00
Tasks: Edge-computing

This letter presents a sensing-communication-computing-control (SC3) integrated satellite unmanned aerial vehicle (UAV) network, where the UAV is equipped with on-board sensors, mobile edge computing (MEC) servers, base stations and satellite communication module. Like the nervous system, this integrated network is capable of organizing multiple field robots in remote areas, so as to perform mission-critical tasks which are dangerous for human. Aiming at activating this nervous system with multiple SC3 loops, we present a control-oriented optimization problem. Different from traditional studies which mainly focused on communication metrics, we address the power allocation issue to minimize the sum linear quadratic regulator (LQR) control cost of all SC3 loops. Specifically, we show the convexity of the formulated problem and reveal the relationship between optimal transmit power and intrinsic entropy rate of different SC3 loops. For the assure-to-be-stable case, we derive a closed-form solution for ease of practical applications. After demonstrating the superiority of the control-oriented power allocation, we further highlight its difference with classic capacity-oriented water-filling method.
Interacting humans use forces in specific frequencies to exchange information by touch
Authors: C Colomer, M Dhamala, G Ganesh, J Lagarde
Date: 2022-08-31 00:00:00

Object-mediated joint action is believed to be enabled by implicit information exchange between interacting individuals using subtle haptic signals within their interaction forces. The characteristics of these haptic signals have, however, remained unclear. Here we analyzed the interaction forces during an empirical dyadic interaction task using Granger-Geweke causality analysis, which allowed us to quantify the causal influence of each individual's forces on their partner's. We observed that the inter-partner influence was not the same at every frequency. Specifically, in the frequency band of [2.15-7] Hz, we observed inter-partner differences of causal influence that were invariant of the movement frequencies in the task and present only when information exchange was indispensable for task performance. Moreover, the interpartner difference in this frequency band was observed to be correlated with the task performance by the dyad. Our results suggest that forces in the [2.15-7] Hz band constitute task related information exchange between individuals during physical interactions.
Boosting 5G on Smart Grid Communication: A Smart RAN Slicing Approach
Authors: Dick Carrillo, Charalampos Kalalas, Petra Raussi, Diomidis S. Michalopoulos, Demóstenes Z. Rodríguez, Heli Kokkoniemi-Tarkkanen, Kimmo Ahola, Pedro H. J. Nardelli, Gustavo Fraidenraich, Petar Popovski
Date: 2022-08-30 00:00:00
Tasks: Deep Reinforcement Learning, Management

Fifth-generation (5G) and beyond systems are expected to accelerate the ongoing transformation of power systems towards the smart grid. However, the inherent heterogeneity in smart grid services and requirements pose significant challenges towards the definition of a unified network architecture. In this context, radio access network (RAN) slicing emerges as a key 5G enabler to ensure interoperable connectivity and service management in the smart grid. This article introduces a novel RAN slicing framework which leverages the potential of artificial intelligence (AI) to support IEC 61850 smart grid services. With the aid of deep reinforcement learning, efficient radio resource management for RAN slices is attained, while conforming to the stringent performance requirements of a smart grid self-healing use case. Our research outcomes advocate the adoption of emerging AI-native approaches for RAN slicing in beyond-5G systems, and lay the foundations for differentiated service provisioning in the smart grid.
A Unified Filter for Fusion of Multiple Inertial Measurement Units
Authors: Yaakov Libero, Itzik Klein
Date: 2022-08-30 00:00:00

Navigation plays a vital role in the ability of autonomous surface and underwater platforms to complete their tasks. Most navigation systems apply a fusion between inertial sensors and other external sensors, such as global navigation satellite systems, when available, or a Doppler velocity log. In recent years, there has been increased interest in using multiple inertial measurement units to improve navigation accuracy and robustness. State of the art examples include the virtual inertial measurement unit (VIMU) and the federated extended Kalman filter (FEKF). However, each of those approaches has its drawbacks. The VIMU does not improve the sensor biases, which constitute a significant source of error, especially in low-cost inertial sensors. While the FEKF does improve accuracy, it models uncertainty propagation empirically. If not modeled correctly, this can cause the global solution to diverge. To cope with those shortcomings, we propose and derive a new filter structure for multiple inertial sensors data fusion: the unified extended Kalman filter (UEKF). In addition, to cope with the multiple equal bias variance estimation problem we offer the bias variance redistribution algorithm. Our filter design enables bias estimation for each of the inertial sensors in the system, improving its accuracy and allowing the use of a varying number of inertial sensors. We show that our UEKF performs better than other state of the art, multiple inertial sensor filters using real data recorded during sea experiments.
A Resonance Model for Spontaneous Cortical Activity
Authors: Yanjiang Wang, Jichao Ma, Jiebin Luo, Xue Chen, Yue Yuan
Date: 2022-08-31 00:00:00
Tasks: Functional Connectivity, model

How human brain function emerges from structure has intrigued researchers for decades and numerous models have been put forward, yet none of them yields a close structure-function relation. Here we present a resonance model based on neuronal spike timing dependent plasticity (STDP) principle to describe the spontaneous cortical activity by incorporating the dynamic interactions between neuronal populations into a wave equation, which is able to accurately predict the resting brain functional connectivity (FC), including the resting-state networks. Besides, the proposed model provides strong theoretical and experimental evidences that the spontaneous dynamic coupling between brain regions fluctuates with a low frequency. Crucially, it is able to account for how the negative functional correlations emerge during resonance. We test the model with a large cohort of subjects (1038) from the Human Connectome Project (HCP) S1200 release in both time and frequency domain, which exhibits superior performance to existing eigen-decomposition models.
Latent Similarity Identifies Important Functional Connections for Phenotype Prediction
Authors: Anton Orlichenko, Gang Qu, Gemeng Zhang, Binish Patel, Tony W. Wilson, Julia M. Stephen, Vince D. Calhoun, Yu-Ping Wang
Date: 2022-08-30 00:00:00
Tasks: Computational Efficiency, Metric Learning, Prediction

Objective: Endophenotypes such as brain age and fluid intelligence are important biomarkers of disease status. However, brain imaging studies to identify these biomarkers often encounter limited numbers of subjects and high dimensional imaging features, hindering reproducibility. Therefore, we develop an interpretable, multivariate classification/regression algorithm, called Latent Similarity (LatSim), suitable for small sample size, high feature dimension datasets. Methods: LatSim combines metric learning with a kernel similarity function and softmax aggregation to identify task-related similarities between subjects. Inter-subject similarity is utilized to improve performance on three prediction tasks using multi-paradigm fMRI data. A greedy selection algorithm, made possible by LatSim's computational efficiency, is developed as an interpretability method. Results: LatSim achieved significantly higher predictive accuracy at small sample sizes on the Philadelphia Neurodevelopmental Cohort (PNC) dataset. Connections identified by LatSim gave superior discriminative power compared to those identified by other methods. We identified 4 functional brain networks enriched in connections for predicting brain age, sex, and intelligence. Conclusion: We find that most information for a predictive task comes from only a few (1-5) connections. Additionally, we find that the default mode network is over-represented in the top connections of all predictive tasks. Significance: We propose a novel algorithm for small sample, high feature dimension datasets and use it to identify connections in task fMRI data. Our work should lead to new insights in both algorithm design and neuroscience research. Code and demo are available at https://github.com/aorliche/LatentSimilarity/.
A risk measurement approach from risk-averse stochastic optimization of score functions
Authors: Marcelo Brutti Righi, Fernanda Maria Müller, Marlon Ruoso Moresco
Date: 2022-08-31 00:00:00
Tasks: regression, Stochastic Optimization

We propose a risk measurement approach for a risk-averse stochastic problem. We provide results that guarantee that our problem has a solution. We characterize and explore the properties of the argmin as a risk measure and the minimum as a deviation measure. We provide a connection between linear regression models and our framework. Based on this conception, we consider conditional risk and provide a connection between the minimum deviation portfolio and linear regression. Moreover, we also link the optimal replication hedging to our framework.
Text Difficulty Study: Do machines behave the same as humans regarding text difficulty?
Authors: Bowen Chen, Xiao Ding, Li Du, Qin Bing, Ting Liu
Date: 2022-08-14 00:00:00

Given a task, human learns from easy to hard, whereas the model learns randomly. Undeniably, difficulty insensitive learning leads to great success in NLP, but little attention has been paid to the effect of text difficulty in NLP. In this research, we propose the Human Learning Matching Index (HLM Index) to investigate the effect of text difficulty. Experiment results show: (1) LSTM has more human-like learning behavior than BERT. (2) UID-SuperLinear gives the best evaluation of text difficulty among four text difficulty criteria. (3) Among nine tasks, some tasks' performance is related to text difficulty, whereas some are not. (4) Model trained on easy data performs best in easy and medium data, whereas trains on a hard level only perform well on hard data. (5) Training the model from easy to hard leads to fast convergence.
Classification of 12-lead ECGs: the PhysioNet/ Computing in Cardiology Challenge 2020
Authors: Erick A. Perez Alday, Annie Gu, Amit Shah, Chad Robichaux, An-Kwok Ian Wong, Chengyu Liu, Feifei Liu, Ali Bahrami Rad, Andoni Elola, Salman Seyedi, Qiao Li, ASHISH SHARMA, Gari D. Clifford, Matthew A. Reyna
Date: 2020-12-29 00:00:00
Proceeding: Computing in Cardiology 2020 12
Tasks: Diagnostic, ECG Classification

Objective: Vast 12-lead ECGs repositories provide opportunities to develop new machine
learning approaches for creating accurate and automatic diagnostic systems for cardiac
abnormalities. However, most 12-lead ECG classification studies are trained, tested, or
developed in single, small, or relatively homogeneous datasets. In addition, most algorithms
focus on identifying small numbers of cardiac arrhythmias that do not represent the
complexity and difficulty of ECG interpretation. This work addresses these issues by
providing a standard, multi-institutional database and a novel scoring metric through a
public competition: the PhysioNet/Computing in Cardiology Challenge 2020.
Approach: A total of 66361 12-lead ECG recordings were sourced from six hospital
systems from four countries across three continents. 43,101 recordings were posted publicly
with a focus on 27 diagnoses. For the first time in a public competition, we required teams
to publish open-source code for both training and testing their algorithms, ensuring full
scientific reproducibility.
Main results: A total of 217 teams submitted 1395 algorithms during the Challenge,
representing a diversity of approaches for identifying cardiac abnormalities from both
academia and industry. As with previous Challenges, high-performing algorithms exhibited
significant drops (/ 10%) in performance on the hidden test data.
Significance: Data from diverse institutions allowed us to assess algorithmic
generalizability. A novel evaluation metric considered different misclassification errors for
different cardiac abnormalities, capturing the outcomes and risks of different diagnoses.
Requiring both trained models and code for training models improved the generalizability
of submissions, setting a new bar in reproducibility for public data science competitions.
MedAI: Transparency in Medical Image Segmentation
Authors: Steven Hicks, Debesh Jha, Vajira Thambawita, Pål Halvorsen, Bjørn-Jostein Singstad, Sachin Gaur, Klas Pettersen, Morten Goodwin, Sravanthi Parasa, Thomas de Lange, Michael Riegler
Date: 2021-11-01 00:00:00
Proceeding: Nordic Machine Intelligence 2021 11
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

MedAI: Transparency in Medical Image Segmentation is a challenge held for the first time at the Nordic AI Meet that focuses on medical image segmentation and transparency in machine learning (ML)-based systems. We propose three tasks to meet specific gastrointestinal image segmentation challenges collected from experts within the field, including two separate segmentation scenarios and one scenario on transparent ML systems. The latter emphasizes the need for explainable and interpretable ML algorithms. We provide a development dataset for the participants to train their ML models, tested on a concealed test dataset.
Actor-Critic Algorithms
Authors: Vijay Konda, John Tsitsiklis
Date: 1999-12-01 00:00:00
Proceeding: Advances in Neural Information Processing Systems 1999 12

We propose and analyze a class of actor-critic algorithms for simulation-based optimization of a Markov decision process over
a parameterized family of randomized stationary policies. These are two-time-scale algorithms in which the critic uses TD learning with a linear approximation architecture and the actor is updated in an approximate gradient direction based on information provided by the critic. We show that the features for the critic should span a subspace prescribed by the choice of parameterization of the actor. We conclude by discussing convergence properties and some open problems.
Deep Reinforcement Learning for Time Allocation and Directional Transmission in Joint Radar-Communication
Authors: Joash Lee, Yanyu Cheng, Dusit Niyato, Yong Liang Guan, G David González
Date: 2022-05-19 00:00:00
Proceeding: IEEE Wireless Communications and Networking Conference (WCNC) 2022 5
Tasks: Autonomous Vehicles, Decision Making Under Uncertainty, Deep Reinforcement Learning, Intelligent Communication, Joint Radar-Communication, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Current strategies for joint radar-communication (JRC) rely on prior knowledge of the communication and radar systems within the vehicle network. In this paper, we propose a framework for intelligent vehicles to conduct JRC, with minimal prior knowledge, in an environment where surrounding vehicles execute radar detection periodically, which is typical in contemporary protocols. We introduce a metric on the usefulness of data to help the vehicle decide what, and to whom, data should be transmitted. The problem framework is cast as a Markov Decision Process (MDP). We show that deep reinforcement learning results in superior performance compared to nonlearning algorithms. In addition, experimental results show that the trained deep reinforcement learning agents are robust to changes in the number of vehicles in the environment.
The Shifting Attention of Political Leaders: Evidence from Two Centuries of Presidential Speeches
Authors: Oscar Calvo-González, Axel Eizmendi, Germán Reyes
Date: 2022-09-01 00:00:00

We use natural-language-processing algorithms on a novel dataset of over 900 presidential speeches from ten Latin American countries spanning two centuries to study the dynamics and determinants of presidential policy priorities. We show that most speech content can be characterized by a compact set of policy issues whose relative composition exhibited slow yet substantial shifts over 1819-2022. Presidential attention initially centered on military interventions and the development of state capacity. Attention gradually evolved towards building physical capital through investments in infrastructure and public services and finally turned towards building human capital through investments in education, health, and social safety nets. We characterize the way in which president-level characteristics, like age and gender, predict the main policy issues. Our findings offer novel insights into the dynamics of presidential attention and the factors that shape it, expanding our understanding of political agenda-setting.
Inequality of Opportunity and Income Redistribution
Authors: Marcel Preuss, Germán Reyes, Jason Somerville, Joy Wu
Date: 2022-09-01 00:00:00

Opportunities, such as access to education or family background, shape income inequality by influencing the chances of economic success. Unequal opportunities create uncertainty about whether success is merit- or luck-based. We examine how uncertainty impacts preferences for redistribution, comparing it to more transparent forms of luck. Using a U.S.-representative sample, we elicit redistribution decisions in two environments: when workers' earnings are influenced by luck directly ("lucky outcomes") or indirectly ("lucky opportunities"). Spectators redistribute less and are less sensitive to differences in luck under opportunities. Inferential challenges drive this gap, which implies that individuals are less meritocratic than previous findings suggest.
The Impact of the #MeToo Movement on Language at Court -- A text-based causal inference approach
Authors: Henrika Langen
Date: 2022-09-01 00:00:00
Tasks: Causal Inference, Sentiment Analysis

This study assesses the effect of the #MeToo movement on the language used in judicial opinions on sexual violence related cases from 51 U.S. state and federal appellate courts. The study introduces various indicators to quantify the extent to which actors in courtrooms employ language that implicitly shifts responsibility away from the perpetrator and onto the victim. One indicator measures how frequently the victim is mentioned as the grammatical subject, as research in the field of psychology suggests that victims are assigned more blame the more often they are referred to as the grammatical subject. The other two indices designed to gauge the level of victim-blaming capture the sentiment of and the context in sentences referencing the victim and/or perpetrator. Additionally, judicial opinions are transformed into bag-of-words and tf-idf vectors to facilitate the examination of the evolution of language over time. The causal effect of the #MeToo movement is estimated by means of a Difference-in-Differences approach comparing the development of the language in opinions on sexual offenses and other crimes against persons as well as a Panel Event Study approach. The results do not clearly identify a #MeToo-movement-induced change in the language in court but suggest that the movement may have accelerated the evolution of court language slightly, causing the effect to materialize with a significant time lag. Additionally, the study considers potential effect heterogeneity with respect to the judge's gender and political affiliation. The study combines causal inference with text quantification methods that are commonly used for classification as well as with indicators that rely on sentiment analysis, word embedding models and grammatical tagging.
Molecular genetics and mid-career economic mobility
Authors: Paul Minard
Date: 2022-08-31 00:00:00

Reductions in the cost of genetic sequencing have enabled the construction of large datasets including both genetic and phenotypic data. Based on these datasets, polygenic scores (PGSs) summarizing an individual's genetic propensity for educational attainment have been constructed. It is by now well established that this PGS predicts wages, income, and occupational prestige and occupational mobility across generations. It is unknown whether a PGS for educational attainment can predict upward income and occupational mobility even within the peak earning years of an individual. Using data from the Wisconsin Longitudinal Study (WLS), I show that: (i) a PGS for educational attainment predicts wage, income and occupational prestige mobility between 1974 (when respondents were about 36 years of age) and 1992 (when respondents were about 53 years of age), conditional on 1974 values of these variables and a range of covariates; (ii) the effect is not mediated by parental socioeconomic status, is driven primarily by respondents with only a high school education, and is replicated in a within sibling-pair design; (iii) conditional on 1974 outcomes, higher PGS individuals surveyed in 1975 aspired to higher incomes and more prestigious jobs 10 years hence, an effect driven primarily by respondents with more than a high school education; (iv) throughout their employment history, high PGS individuals were more likely to undertake on the job training, and more likely to change job duties during tenure with an employer; and (v) though no more likely to change employers or industries during their careers, high PGS individuals were more likely in 1974 to be working in industries which would experience high wage growth in subsequent decades. These results contribute to our understanding of longitudinal inequality and shed light on the sources of heterogeneity in responses to economic shocks and policy.