pwc-archive/papers-with-abstracts huggingface dataset:
Fast Reconstruction of Three-Quarter Sampling Measurements Using Recurrent Local Joint Sparse Deconvolution and Extrapolation
Authors: Simon Grosche, Andy Regensky, Alexander Sinn, Jürgen Seiler, André Kaup
Date: 2022-05-05 00:00:00
Tasks: CPU, GPU

Recently, non-regular three-quarter sampling has shown to deliver an increased image quality of image sensors by using differently oriented L-shaped pixels compared to the same number of square pixels. A three-quarter sampling sensor can be understood as a conventional low-resolution sensor where one quadrant of each square pixel is opaque. Subsequent to the measurement, the data can be reconstructed on a regular grid with twice the resolution in both spatial dimensions using an appropriate reconstruction algorithm. For this reconstruction, local joint sparse deconvolution and extrapolation (L-JSDE) has shown to perform very well. As a disadvantage, L-JSDE requires long computation times of several dozen minutes per megapixel. In this paper, we propose a faster version of L-JSDE called recurrent L-JSDE (RL-JSDE) which is a reformulation of L-JSDE. For reasonable recurrent measurement patterns, RL-JSDE provides significant speedups on both CPU and GPU without sacrificing image quality. Compared to L-JSDE, 20-fold and 733-fold speedups are achieved on CPU and GPU, respectively.
Power Scaling Law for Optical IRSs and Comparison with Optical Relays
Authors: Hedieh Ajam, Marzieh Najafi, Vahid Jamali, Robert Schober
Date: 2022-05-05 00:00:00
Tasks: Diversity

The line-of-sight (LOS) requirement of free-space optical (FSO) systems can be relaxed by employing optical relays and optical intelligent reflecting surfaces (IRSs). Unlike radio frequency (RF) IRSs, which typically exhibit a quadratic power scaling law, the power reflected from FSO IRSs and collected at the receiver lens may scale quadratically or linearly with the IRS size or may even saturate at a constant value. We analyze the power scaling law for optical IRSs and unveil its dependence on the wavelength, transmitter (Tx)-to-IRS and IRS-to-receiver (Rx) distances, beam waist, and lens size. We compare optical IRSs in different power scaling regimes with optical relays in terms of the outage probability, diversity and coding gains, and optimal placement. Our results show that, at the expense of a higher hardware complexity, relay-assisted FSO links yield a better outage performance at high signal-to-noise-ratios (SNRs), but optical IRSs can achieve a higher performance at low SNRs. Moreover, while it is optimal to place relays equidistant from Tx and Rx, the optimal location of IRSs depends on the power scaling regime they operate in.
Secure Communication in Multifunctional Active Intelligent Reflection Surface-assisted Systems
Authors: Shaokang Hu, Derrick Wing Kwan Ng, Jinhong Yuan
Date: 2022-05-05 00:00:00

In this paper, the secure performance of multiuser multiple-input single-output wireless communications systems assisted by a multifunctional active intelligent reflection surface (IRS) is investigated. The active IRS can simultaneously reflect and amplify the incident signals and emit artificial noise to combat potential wiretapping. We minimize the total system power consumption by designing the phase, amplitude, and IRS mode selection of the active IRS elements, as well as the precoder and artificial noise vector of the base station (BS). The design is formulated as a non-convex optimization problem guaranteeing communication security. To tackle the problem, this paper proposes an iterative alternating algorithm to obtain an effective sub-optimal solution. The simulation results show that the proposed scheme offers superior secure performance over all the considered baseline schemes, especially when the number of eavesdropper antennas is more than that of the BS.
PMBM-based SLAM Filters in 5G mmWave Vehicular Networks
Authors: Hyowon Kim, Karl Granström, Lennart Svensson, Sunwoo Kim, Henk Wymeersch
Date: 2022-05-05 00:00:00
Tasks: Simultaneous Localization and Mapping

Radio-based vehicular simultaneous localization and mapping (SLAM) aims to localize vehicles while mapping the landmarks in the environment. We propose a sequence of three Poisson multi-Bernoulli mixture (PMBM) based SLAM filters, which handle the entire SLAM problem in a theoretically optimal manner. The complexity of the three proposed SLAM filters is progressively reduced while sustaining high accuracy by deriving SLAM density approximation with the marginalization of nuisance parameters (either vehicle state or data association). Firstly, the PMBM SLAM filter serves as the foundation, for which we provide the first complete description based on a Rao-Blackwellized particle filter. Secondly, the Poisson multi-Bernoulli (PMB) SLAM filter is based on the standard reduction from PMBM to PMB, but involves a novel interpretation based on auxiliary variables and a relation to Bethe free energy. Finally, using the same auxiliary variable argument, we derive a marginalized PMB SLAM filter, which avoids particles and is instead implemented with a low-complexity cubature Kalman filter. We evaluate the three proposed SLAM filters in comparison with the probability hypothesis density (PHD) SLAM filter in 5G mmWave vehicular networks and show the computation-performance trade-off between them.
TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network
Authors: Muhan Wang, Zhe Zhang, Yue Wang, Silin Gao, Xiaolan Qiu
Date: 2022-05-05 00:00:00
Tasks: 3D Reconstruction, compressed sensing, Super-Resolution

Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable interest for its ability in achieving three-dimensional reconstruction along the elevation direction from multiple observations. In recent years, compressed sensing (CS) technique has been introduced into TomoSAR considering for its super-resolution ability with limited samples. Whereas, the CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity and complex parameter fine-tuning. Among the different CS algorithms, iterative soft-thresholding algorithm (ISTA) is widely used as a robust reconstruction approach, however, the parameters in the ISTA algorithm are manually chosen, which usually requires a time-consuming fine-tuning process to achieve the best performance. Aiming at efficient TomoSAR imaging, a novel sparse unfolding network named analytic learned ISTA (ALISTA) is proposed towards the TomoSAR imaging problem in this paper, and the key parameters of ISTA are learned from training data via deep learning to avoid complex parameter fine-tuning and significantly relieves the training burden. In addition, experiments verify that it is feasible to use traditional CS algorithms as training labels, which provides a tangible supervised training method to achieve better 3D reconstruction performance even in the absence of labeled data in real applications.
An Adaptive Incremental Gradient Method With Support for Non-Euclidean Norms
Authors: Binghui Xie, Chenhan Jin, Kaiwen Zhou, James Cheng, Wei Meng
Date: 2022-04-28 00:00:00

Stochastic variance reduced methods have shown strong performance in solving finite-sum problems. However, these methods usually require the users to manually tune the step-size, which is time-consuming or even infeasible for some large-scale optimization tasks. To overcome the problem, we propose and analyze several novel adaptive variants of the popular SAGA algorithm. Eventually, we design a variant of Barzilai-Borwein step-size which is tailored for the incremental gradient method to ensure memory efficiency and fast convergence. We establish its convergence guarantees under general settings that allow non-Euclidean norms in the definition of smoothness and the composite objectives, which cover a broad range of applications in machine learning. We improve the analysis of SAGA to support non-Euclidean norms, which fills the void of existing work. Numerical experiments on standard datasets demonstrate a competitive performance of the proposed algorithm compared with existing variance-reduced methods and their adaptive variants.
Querying hippocampal replay with subcortical inputs
Authors: Adrien Peyrache
Date: 2022-05-05 00:00:00
Tasks: Hippocampus

During sleep, the hippocampus recapitulates neuronal patterns corresponding to behavioral trajectories during previous experiences. This hippocampal replay supports the formation of long-term memories. Yet, whether replay originates within the hippocampal circuitry or is initiated by extrahippocampal inputs is unknown. Here, I review recent findings regarding the organization of neuronal activity upstream to the hippocampus, in the head-direction (HD) and grid cell networks. I argue that hippocampal activity is under the influence of primary spatial signals, which originate from subcortical structures and set the stage for memory replay. In turn, hippocampal replay resets the HD network activity to select a new direction for the next replay event. This reciprocal interaction between the HD network and the hippocampus may be essential in providing meaning to hippocampal activity, specifically by training decoders of hippocampal sequences. Neuronal dynamics in thalamo-hippocampal loops may thus be instrumental for memory processes during sleep.
Paired associative stimulation demonstrates alterations in motor cortical synaptic plasticity in patients with hepatic encephalopathy
Authors: Petyo Nikolov, Thomas J. Baumgarten, Shady Safwat Hassan, Nur-Deniz Füllenbach, Gerald Kircheis, Dieter Häussinger, Markus Jördens, Markus Butz, Alfons Schnitzler, Stefan J. Groiss
Date: 2022-05-05 00:00:00

Objective: Hepatic encephalopathy (HE) is a potentially reversible brain dysfunction caused by liver failure. Altered synaptic plasticity is supposed to play a major role in the pathophysiology of HE. Here, we used paired associative stimulation with an inter-stimulus interval of 25 ms (PAS25), a transcranial magnetic stimulation (TMS) protocol, to test synaptic plasticity of the motor cortex in patients with manifest HE. Methods: 23 HE-patients and 23 healthy controls were enrolled in the study. Motor evoked potential (MEP) amplitudes were assessed as measure for cortical excitability. Time courses of MEP amplitude changes after the PAS25 intervention were compared between both groups. Results: MEP-amplitudes increased after PAS25 in the control group, indicating PAS25-induced synaptic plasticity in healthy controls, as expected. In contrast, MEP-amplitudes within the HE group did not change and were lower than in the control group, indicating no induction of plasticity. Conclusion: Our study revealed reduced synaptic plasticity of the primary motor cortex in HE. Significance: Reduced synaptic plasticity in HE provides a link between pathological changes on the molecular level and early clinical symptoms of the disease. This decrease may be caused by disturbances in the glutamatergic neurotransmission due to the known hyperammonemia in HE patients.
BrainViewer: interacting with spatial connectome data at the mesoscale
Authors: Seth Daetwiler, Angus Read, Jessica Stillwell, Kameron Decker Harris
Date: 2022-05-04 00:00:00

Scientists construct connectomes, comprehensive descriptions of neuronal connections across a brain, in order to better understand and model brain function. Interactive visualizations of these pathways would enable exploratory analysis of such information flows. Current tools can be used to see individual tracing experiments which are used to build mesoscale connectomes of the mouse brain, but not the brain network itself. We present a connectivity visualization program called BrainViewer, which we use with a high-resolution mouse cortical connectome. This has the ability to display connectomes from other datasets when they become available and compare spatial connectivity across multiple brain structures. Our tool, optimized for speed and portability, presents a GUI visualization in 2-D top view and flatmap projections, allowing users to select and explore the connections of every source voxel to everywhere else in the cortex. Anatomists and other neuroscientists will find BrainViewer useful for building understanding beyond the known topography of cortical connectivity.
Evaluation of the relative performance of the subflattenings method for phylogenetic inference
Authors: Joshua Stevenson, Barbara Holland, Michael Charleston, Jeremy Sumner
Date: 2022-05-04 00:00:00

The algebraic properties of flattenings and subflattenings provide direct methods for identifying edges in the true phylogeny -- and by extension the complete tree -- using pattern counts from a sequence alignment. The relatively small number of possible internal edges among a set of taxa (compared to the number of binary trees) makes these methods attractive, however more could be done to evaluate their effectiveness for inferring phylogenetic trees. This is the case particularly for subflattenings, and our work makes progress in this area. We introduce software for constructing and evaluating subflattenings for splits, utilising a number of methods to make computing subflattenings more tractable. We then present the results of simulations we have performed in order to compare the effectiveness of subflattenings to that of flattenings in terms of split score distributions, and susceptibility to possible biases. We find that subflattenings perform similarly to flattenings in terms of the distribution of split scores on the trees we examined, but may be less affected by bias arising from both split size/balance and long branch attraction. These insights are useful for developing effective algorithms to utilise these tools for the purpose of inferring phylogenetic trees.
A Stochastic Climate Model -- An approach to calibrate the Climate-Extended Risk Model (CERM)
Authors: Jean-Baptiste Gaudemet, Jules Deschamps, Olivier Vinciguerra
Date: 2022-05-05 00:00:00
Tasks: model

The initial Climate-Extended Risk Model (CERM) addresses the estimate of climate-related financial risk embedded within a bank loan portfolio, through a climatic extension of the Basel II IRB model. It uses a Gaussian copula model calibrated with non stationary macro-correlations in order to reflect the future evolution of climate-related financial risks. In this complementary article, we propose a stochastic forward-looking methodology to calibrate climate macro-correlation evolution from scientific climate data, for physical and transition efforts specifically. We assume a global physical and transition risk, likened to persistent greenhouse gas (GHG) concentration in the atmosphere. The economic risk is considered stationary and can therefore be calibrated with a backward-looking methodology. We present 4 key principles to model the GDP and we propose to model the economic, physical and transition effort factors with three interdependent stochastic processes allowing for a calibration with seven well defined parameters. These parameters can be calibrated using public data. This new approach means not only to evaluate climate risks without picking any specific scenario but also allows to fill the gap between current one year approach of regulatory and economic capital models and the necessarily long-term view of climate risks by designing a framework to evaluate the resulting credit loss on each step (typically yearly) of the transition path. This new approach could prove instrumental in the 2022 context of central banks weighing the pros and cons of a climate capital charge.
A collection of invited non-archival papers for the Conference on Health, Inference, and Learning (CHIL) 2022
Authors: Gerardo Flores, George H. Chen, Tom Pollard, Joyce C. Ho, Tristan Naumann
Date: 2022-03-28 00:00:00

A collection of invited non-archival papers for the Conference on Health, Inference, and Learning (CHIL) 2022. This index is incomplete as some authors of invited non-archival presentations opted not to include their papers in this index.
Angular Radon Spectrum for Rotation Estimation
Authors: Lodi Rizzini, D.
Date: 2018-07-12 00:00:00
Proceeding: Pattern Recognition 2018 7

This paper presents a robust method for rotation estimation of planar point
sets using the Angular Radon Spectrum (ARS). Given a Gaussian Mixture
Model (GMM) representing the point distribution, the ARS is a continuous
function derived from the Radon Transform of such distribution. The ARS
characterizes the orientation of a point distribution by measuring its alignment
w.r.t. a pencil of parallel lines. By exploting its translation and angular-shift
invariance, the rotation angle between two point sets can be estimated through
the correlation of the corresponding spectra. Beside its definition, the novel
contributions of this paper include the efficient computation of the ARS and of
the correlation function through their Fourier expansion, and a new algorithm
for assessing the rotation between two point sets. Moreover, experiments with
standard benchmark datasets assess the performance of the proposed algorithm
and other state-of-the-art methods in presence of noisy and incomplete data.
Keywords: Rotation estimation, Gaussian Mixture Models
Angular Radon Spectrum for Rotation Estimation
Date: 2018-07-12 00:00:00
Proceeding: Pattern Recognition 2018 7

This paper presents a robust method for rotation estimation of planar point
sets using the Angular Radon Spectrum (ARS). Given a Gaussian Mixture
Model (GMM) representing the point distribution, the ARS is a continuous
function derived from the Radon Transform of such distribution. The ARS
characterizes the orientation of a point distribution by measuring its alignment
w.r.t. a pencil of parallel lines. By exploting its translation and angular-shift
invariance, the rotation angle between two point sets can be estimated through
the correlation of the corresponding spectra. Beside its definition, the novel
contributions of this paper include the efficient computation of the ARS and of
the correlation function through their Fourier expansion, and a new algorithm
for assessing the rotation between two point sets. Moreover, experiments with
standard benchmark datasets assess the performance of the proposed algorithm
and other state-of-the-art methods in presence of noisy and incomplete data.
Keywords: Rotation estimation, Gaussian Mixture Models
Fine-Tuning BERT Models to Classify Misinformation on Garlic and COVID-19 on Twitter
Authors: Myeong Gyu Kim, Minjung Kim, Jae Hyun Kim, Kyungim Kim
Date: 2022-04-22 00:00:00
Proceeding: International Journal Environments Research and Public Health 2022 4
Tasks: Misinformation

Garlic-related misinformation is prevalent whenever a virus outbreak occurs. With the outbreak of COVID-19, garlic-related misinformation is spreading through social media, including Twitter. Bidirectional Encoder Representations from Transformers (BERT) can be used to classify misinformation from a vast number of tweets. This study aimed to apply the BERT model for classifying misinformation on garlic and COVID-19 on Twitter, using 5929 original tweets mentioning garlic and COVID-19 (4151 for fine-tuning, 1778 for test). Tweets were manually labeled as ‘misinformation’ and ‘other.’ We fine-tuned five BERT models (BERTBASE, BERTLARGE, BERTweet-base, BERTweet-COVID-19, and BERTweet-large) using a general COVID-19 rumor dataset or a garlic-specific dataset. Accuracy and F1 score were calculated to evaluate the performance of the models. The BERT models fine-tuned with the COVID-19 rumor dataset showed poor performance, with maximum accuracy of 0.647. BERT models fine-tuned with the garlic-specific dataset showed better performance. BERTweet models achieved accuracy of 0.897–0.911, while BERTBASE and BERTLARGE achieved accuracy of 0.887–0.897. BERTweet-large showed the best performance with maximum accuracy of 0.911 and an F1 score of 0.894. Thus, BERT models showed good performance in classifying misinformation. The results of our study will help detect misinformation related to garlic and COVID-19 on Twitter
Automated Generation of Diverse and Challenging Scenarios for Test and Evaluation of Autonomous Vehicles
Authors: Galen E. Mullins, Paul G. Stankiewicz, and Satyandra K. Gupta
Date: 2017-07-24 00:00:00
Proceeding: Conference 2017 7
Tasks: Autonomous Vehicles, Fault Detection

Abstract— We propose a novel method for generating test scenarios for a black box autonomous system that demonstrate critical transitions in its performance modes. In complex environments it is possible for an autonomous system to fail at its assigned mission even if it complies with requirements for all subsystems and throws no faults. This is particularly true when the autonomous system may have to choose between multiple exclusive objectives. The standard approach of testing robustness through fault detection is directly stimulating the system and detecting violations of the system requirements. Our approach differs by instead running the autonomous system through full missions in a simulated environment and measuring performance based on high-level mission criteria. The result is a method of searching for challenging scenarios for an autonomous system under test that exercise a variety of performance modes. We utilize adaptive sampling to intelligently search the state space for test scenarios which exist on the boundary between distinct performance modes. Additionally, using unsupervised clustering techniques we can group scenarios by their performance modes and sort them by those which are most effective at diagnosing changes in the autonomous system’s behavior.
Handcrafted localized phase features for human action recognition
Authors: Seyed Mostafa Hejazi, Charith Abhayaratne
Date: 2022-05-05 00:00:00
Proceeding: Image and Vision Computing 2022 5
Tasks: Action Classification, Action Recognition, Optical Flow Estimation, Temporal Action Localization

Human action recognition is one of the most important topics in computer vision. Monitoring elderly people and children, smart surveillance systems and human-computer interaction are a few examples of its applications. The aim of this study is to recognize human activities by utilizing the phase information extracted from the frequency domain of the video data as handcrafted features. Rather than estimating optical flow or computing motion vectors, we aim to utilize the localized phase information as descriptors of the motion dynamics of the scene. Phase correlation information extracted from each two co-sited blocks from each two consecutive frames of video clips were used to train a model using KNN classifier to model the action. To evaluate the performance of our method, an extensive work has been done on three large and complex datasets: UCF101, Kinetics-400 and Kinetics-700. The results show that our approach succeeds on recognizing human actions across all these datasets with high accuracy.