pwc-archive/papers-with-abstracts huggingface dataset:
Tilt-then-Blur or Blur-then-Tilt? Clarifying the Atmospheric Turbulence Model
Authors: Stanley H. Chan
Date: 2022-07-13 00:00:00
Tasks: Image Restoration, image smoothing

Imaging at a long distance often requires advanced image restoration algorithms to compensate for the distortions caused by atmospheric turbulence. However, unlike many standard restoration problems such as deconvolution, the forward image formation model of the atmospheric turbulence does not have a simple expression. Thanks to the Zernike representation of the phase, one can show that the forward model is a combination of tilt (pixel shifting due to the linear phase terms) and blur (image smoothing due to the high order aberrations). Confusions then arise between the ordering of the two operators. Should the model be tilt-then-blur, or blur-then-tilt? Some papers in the literature say that the model is tilt-then-blur, whereas more papers say that it is blur-then-tilt. This paper clarifies the differences between the two and discusses why the tilt-then-blur is the correct model. Recommendations are given to the research community.
Multiview Contrastive Learning for Completely Blind Video Quality Assessment of User Generated Content
Authors: Shankhanil Mitra, Rajiv Soundararajan
Date: 2022-07-13 00:00:00
Tasks: Contrastive Learning, Optical Flow Estimation, Video Quality Assessment, Visual Question Answering (VQA)

Completely blind video quality assessment (VQA) refers to a class of quality assessment methods that do not use any reference videos, human opinion scores or training videos from the target database to learn a quality model. The design of this class of methods is particularly important since it can allow for superior generalization in performance across various datasets. We consider the design of completely blind VQA for user generated content. While several deep feature extraction methods have been considered in supervised and weakly supervised settings, such approaches have not been studied in the context of completely blind VQA. We bridge this gap by presenting a self-supervised multiview contrastive learning framework to learn spatio-temporal quality representations. In particular, we capture the common information between frame differences and frames by treating them as a pair of views and similarly obtain the shared representations between frame differences and optical flow. The resulting features are then compared with a corpus of pristine natural video patches to predict the quality of the distorted video. Detailed experiments on multiple camera captured VQA datasets reveal the superior performance of our method over other features when evaluated without training on human scores.
Lippmann Photography: A Signal Processing Perspective
Authors: Gilles Baechler, Michalina Pacholska, Arnaud Latty, Adam Scholefield, Martin Vetterli
Date: 2022-07-13 00:00:00

Lippmann (or interferential) photography is the first and only analog photography method that can capture the full color spectrum of a scene in a single take. This technique, invented more than a hundred years ago, records the colors by creating interference patterns inside the photosensitive plate. Lippmann photography provides a great opportunity to demonstrate several fundamental concepts in signal processing. Conversely, a signal processing perspective enables us to shed new light on the technique. In our previous work, we analyzed the spectra of historical Lippmann plates using our own mathematical model. In this paper, we provide the derivation of this model and validate it experimentally. We highlight new behaviors whose explanations were ignored by physicists to date. In particular, we show that the spectra generated by Lippmann plates are in fact distorted versions of the original spectra. We also show that these distortions are influenced by the thickness of the plate and the reflection coefficient of the reflective medium used in the capture of the photographs. We verify our model with extensive experiments on our own Lippmann photographs.
Subjective and Objective Quality Assessment of High-Motion Sports Videos at Low-Bitrates
Authors: Joshua P. Ebenezer, Yixu Chen, Yongjun Wu, Hai Wei, Sriram Sethuraman
Date: 2022-07-12 00:00:00
Tasks: Video Quality Assessment, Visual Question Answering (VQA)

Videos often have to be transmitted and stored at low bitrates due to poor network connectivity during adaptive bitrate streaming. Designing optimal bitrate ladders that would select the perceptually-optimized resolution, frame-rate, and compression level for low-bitrate videos for adaptive streaming across the internet is therefore a task of great interest. Towards that end, we conducted the first large-scale study of medium and low-bitrate videos from live sports for two codecs (Elemental AVC and HEVC) and created the Amazon Prime Video Low-Bitrate Sports (APV LBS) dataset. The study involved 94 participants and 742 videos, with more than 23,000 human opinion scores collected in total. We analyzed the data obtained and we also conducted an extensive evaluation of objective Video Quality Assessment (VQA) algorithms and benchmarked their performance, and make recommendations on bitrate ladder design. We're making the metadata and VQA features available at https://github.com/JoshuaEbenezer/lbmfr-public.
Electromagnetic-Thermal Analyses of Distributed Antennas Embedded into a Load Bearing Wall
Authors: Lauri Vähä-Savo, Katsuyuki Haneda, Clemens Icheln, Xiaoshu Lü
Date: 2022-07-13 00:00:00

The importance of indoor mobile connectivity has increased during the last years, especially during the Covid-19 pandemic. In contrast, new energy-efficient buildings contain structures like low-emissive windows and multi-layered thermal insulations which all block radio signals effectively. To solve this problem with indoor connectivity, we study passive antenna systems embedded in walls of low-energy buildings. We provide analytical models of a load bearing wall along with numerical and empirical evaluations of wideband back-to-back antenna spiral antenna system in terms of electromagnetic- and thermal insulation. The antenna systems are optimized to operate well when embedded into load bearing walls. Unit cell models of the antenna embedded load bearing wall, which are called signal-transmissive walls in this paper, are developed to analyze their electromagnetic and thermal insulation properties. We show that our signal-transmissive wall improves the electromagnetic transmission compared to a raw load bearing wall over a wide bandwidth of 2.6-8 GHz, covering most of the cellular new radio frequency range 1, without compromising the thermal insulation capability of the wall demanded by the building regulation. Optimized antenna deployment is shown with 22 dB improvement in electromagnetic transmission through the load bearing wall.
Gridless DOA Estimation with Multiple Frequencies
Authors: Yifan Wu, Michael B. Wakin, Peter Gerstoft
Date: 2022-07-13 00:00:00

Direction-of-arrival (DOA) estimation is widely applied in acoustic source localization. A multi-frequency model is suitable for characterizing the broadband structure in acoustic signals. In this paper, the continuous (gridless) DOA estimation problem with multiple frequencies is considered. This problem is formulated as an atomic norm minimization (ANM) problem. The ANM problem is equivalent to a semi-definite program (SDP) which can be solved by an off-the-shelf SDP solver. The dual certificate condition is provided to certify the optimality of the SDP solution so that the sources can be localized by finding the roots of a polynomial. We also construct the dual polynomial to satisfy the dual certificate condition and show that such a construction exists when the source amplitude has a uniform magnitude. In multi-frequency ANM, spatial aliasing of DOAs at higher frequencies can cause challenges. We discuss this issue extensively and propose a robust solution to combat aliasing. Numerical results support our theoretical findings and demonstrate the effectiveness of the proposed method.
Trajectory and Resource Optimization for UAV Synthetic Aperture Radar
Authors: Mohamed-Amine Lahmeri, Walid Ghanem, Christina Knill, Robert Schober
Date: 2022-07-12 00:00:00
Tasks: Trajectory Planning

In this paper, we study the trajectory and resource optimization for lightweight rotary-wing unmanned aerial vehicles (UAVs) equipped with a synthetic aperture radar (SAR) system. The UAV's mission is to perform SAR imaging of a given area of interest (AoI). In this setup, real-time communication with a base station (BS) is required to facilitate live mission planning for the drone. For this purpose, a non-convex mixed-integer non-linear program (MINLP) is formulated such that the UAV resources and three-dimensional (3D) trajectory are jointly optimized for maximization of the drone radar ground coverage. We present a low-complexity sub-optimal algorithm based on successive convex approximation (SCA) for solving the problem, and perform a finite search to optimize the total distance traversed by the UAV for maximal coverage. We show that the proposed 3D trajectory planning achieves at least 70% improvement in radar ground coverage compared to benchmark schemes employing constant powers for communication or radar imaging. We also show that positioning the BS near the AoI can significantly improve the radar coverage of the UAV.
Studying the age of onset and detection of Chronic Myeloid Leukemia using a three-stage stochastic model
Authors: Suryadeepto Nag, Ananda Shikhara Bhat, Siddhartha P. Chakrabarty
Date: 2022-07-13 00:00:00

Chronic Myeloid Leukemia (CML) is a biphasic malignant clonal disorder that progresses, first with a chronic phase, where the cells have enhanced proliferation only, and then to a blast phase, where the cells have the ability of self-renewal. It is well-recognized that the Philadelphia chromosome (which contains the BCR-ABL fusion gene) is the "hallmark of CML". However, empirical studies have shown that the mere presence of BCR-ABL may not be a sufficient condition for the development of CML, and further modifications related to tumor suppressors may be necessary. Accordingly, we develop a three-mutation stochastic model of CML progression, with the three stages corresponding to the non-malignant cells with BCR-ABL presence, the malignant cells in the chronic phase and the malignant cells in the blast phase. We demonstrate that the model predictions agree with age incidence data from the United States. Finally, we develop a framework for the retrospective estimation of the time of onset of malignancy, from the time of detection of the cancer.
Application of Hawkes volatility in the observation of filtered high-frequency price process in tick structures
Authors: Kyungsub Lee
Date: 2022-07-13 00:00:00
Tasks: Management

The Hawkes model is suitable for describing self and mutually exciting random events. In addition, the exponential decay in the Hawkes process allows us to calculate the moment properties in the model. However, due to the complexity of the model and formula, few studies have been conducted on the performance of Hawkes volatility. In this study, we derived a variance formula that is directly applicable under the general settings of both unmarked and marked Hawkes models for tick-level price dynamics. In the marked model, the linear impact function and possible dependency between the marks and underlying processes are considered. The Hawkes volatility is applied to the mid-price process filtered at 0.1-second intervals to show reliable results; furthermore, intraday estimation is expected to have high utilization in real-time risk management. We also note the increasing predictive power of intraday Hawkes volatility over time and examine the relationship between futures and stock volatilities.
Estimating Test Performance for AI Medical Devices under Distribution Shift with Conformal Prediction
Authors: Charles Lu, Syed Rakin Ahmed, Praveer Singh, Jayashree Kalpathy-Cramer
Date: 2022-07-12 00:00:00
Tasks: Conformal Prediction

Estimating the test performance of software AI-based medical devices under distribution shifts is crucial for evaluating the safety, efficiency, and usability prior to clinical deployment. Due to the nature of regulated medical device software and the difficulty in acquiring large amounts of labeled medical datasets, we consider the task of predicting the test accuracy of an arbitrary black-box model on an unlabeled target domain without modification to the original training process or any distributional assumptions of the original source data (i.e. we treat the model as a "black-box" and only use the predicted output responses). We propose a "black-box" test estimation technique based on conformal prediction and evaluate it against other methods on three medical imaging datasets (mammography, dermatology, and histopathology) under several clinically relevant types of distribution shift (institution, hardware scanner, atlas, hospital). We hope that by promoting practical and effective estimation techniques for black-box models, manufacturers of medical devices will develop more standardized and realistic evaluation procedures to improve the robustness and trustworthiness of clinical AI tools.
Optimistic PAC Reinforcement Learning: the Instance-Dependent View
Authors: Andrea Tirinzoni, Aymen Al-Marjani, Emilie Kaufmann
Date: 2022-07-12 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Optimistic algorithms have been extensively studied for regret minimization in episodic tabular MDPs, both from a minimax and an instance-dependent view. However, for the PAC RL problem, where the goal is to identify a near-optimal policy with high probability, little is known about their instance-dependent sample complexity. A negative result of Wagenmaker et al. (2021) suggests that optimistic sampling rules cannot be used to attain the (still elusive) optimal instance-dependent sample complexity. On the positive side, we provide the first instance-dependent bound for an optimistic algorithm for PAC RL, BPI-UCRL, for which only minimax guarantees were available (Kaufmann et al., 2021). While our bound features some minimal visitation probabilities, it also features a refined notion of sub-optimality gap compared to the value gaps that appear in prior work. Moreover, in MDPs with deterministic transitions, we show that BPI-UCRL is actually near-optimal. On the technical side, our analysis is very simple thanks to a new "target trick" of independent interest. We complement these findings with a novel hardness result explaining why the instance-dependent complexity of PAC RL cannot be easily related to that of regret minimization, unlike in the minimax regime.
Tuning the Geometry of Graph Neural Networks
Authors: Sowon Jeong, Claire Donnat
Date: 2022-07-12 00:00:00

By recursively summing node features over entire neighborhoods, spatial graph convolution operators have been heralded as key to the success of Graph Neural Networks (GNNs). Yet, despite the multiplication of GNN methods across tasks and applications, the impact of this aggregation operation on their performance still has yet to be extensively analysed. In fact, while efforts have mostly focused on optimizing the architecture of the neural network, fewer works have attempted to characterize (a) the different classes of spatial convolution operators, (b) how the choice of a particular class relates to properties of the data , and (c) its impact on the geometry of the embedding space. In this paper, we propose to answer all three questions by dividing existing operators into two main classes ( symmetrized vs. row-normalized spatial convolutions), and show how these translate into different implicit biases on the nature of the data. Finally, we show that this aggregation operator is in fact tunable, and explicit regimes in which certain choices of operators -- and therefore, embedding geometries -- might be more appropriate.
Neural Topological Ordering for Computation Graphs
Authors: Mukul Gagrani, Corrado Rainone, Yang Yang, Harris Teague, Wonseok Jeon, Herke van Hoof, Weiliang Will Zeng, Piero Zappi, Christopher Lott, Roberto Bondesan
Date: 2022-07-13 00:00:00
Tasks: 2k, BIG-bench Machine Learning, Combinatorial Optimization, Decoder, Graph Neural Network

Recent works on machine learning for combinatorial optimization have shown that learning based approaches can outperform heuristic methods in terms of speed and performance. In this paper, we consider the problem of finding an optimal topological order on a directed acyclic graph with focus on the memory minimization problem which arises in compilers. We propose an end-to-end machine learning based approach for topological ordering using an encoder-decoder framework. Our encoder is a novel attention based graph neural network architecture called \emph{Topoformer} which uses different topological transforms of a DAG for message passing. The node embeddings produced by the encoder are converted into node priorities which are used by the decoder to generate a probability distribution over topological orders. We train our model on a dataset of synthetically generated graphs called layered graphs. We show that our model outperforms, or is on-par, with several topological ordering baselines while being significantly faster on synthetic graphs with up to 2k nodes. We also train and test our model on a set of real-world computation graphs, showing performance improvements.
Machine Learning Application in Health
Authors: Ghadah Alshabana, Marjn Sadati, Thao Tran, Michael Thompson, Ashritha Chitimalla
Date: 2022-06-10 00:00:00
Tasks: BIG-bench Machine Learning

Coronavirus can be transmitted through the air by close proximity to infected persons. Commercial aircraft are a likely way to both transmit the virus among passengers and move the virus between locations. The importance of learning about where and how coronavirus has entered the United States will help further our understanding of the disease. Air travelers can come from countries or areas with a high rate of infection and may very well be at risk of being exposed to the virus. Therefore, as they reach the United States, the virus could easily spread. On our analysis, we utilized machine learning to determine if the number of flights into the Washington DC Metro Area had an effect on the number of cases and deaths reported in the city and surrounding area.
Radar Image Reconstruction from Raw ADC Data using Parametric Variational Autoencoder with Domain Adaptation
Authors: Michael Stephan, Thomas Stadelmayer, Avik Santra, Georg Fischer, Robert Weigel, Fabian Lurz
Date: 2022-05-30 00:00:00
Tasks: Domain Adaptation, Image Reconstruction

This paper presents a parametric variational autoencoder-based human target detection and localization framework working directly with the raw analog-to-digital converter data from the frequency modulated continous wave radar. We propose a parametrically constrained variational autoencoder, with residual and skip connections, capable of generating the clustered and localized target detections on the range-angle image. Furthermore, to circumvent the problem of training the proposed neural network on all possible scenarios using real radar data, we propose domain adaptation strategies whereby we first train the neural network using ray tracing based model data and then adapt the network to work on real sensor data. This strategy ensures better generalization and scalability of the proposed neural network even though it is trained with limited radar data. We demonstrate the superior detection and localization performance of our proposed solution compared to the conventional signal processing pipeline and earlier state-of-art deep U-Net architecture with range-doppler images as inputs
ANTILLES: An Open French Linguistically Enriched Part-of-Speech Corpus
Authors: Yanis Labrak, Richard Dufour
Date: 2022-06-20 00:00:00
Proceeding: International Conference on Text, Speech and Dialogue (TSD) 2022 6
Tasks: Part-Of-Speech Tagging, POS, POS Tagging

Part-of-speech (POS) tagging is a classical natural language processing (NLP) task. Although many tools and corpora have been proposed, especially for the most widely spoken languages, these suffer from limitations concerning their user license, the size of their tagset, or even approaches no longer in the state-of-the-art. In this article, we propose ANTILLES, an extended version of an existing French corpus (UD French-GSD) comprising an original set of labels obtained with the aid of morphological characteristics (gender, number, tense, etc.). This extended version includes a set of 65 labels, against 16 in the initial version. We also implemented several POS tools for French from this corpus, incorporating the latest advances in the state-of-the-art in this area. The corpus as well as the POS labeling tools are fully open and freely available.
Language Modelling with Pixels
Authors: Phillip Rust, Jonas F. Lotz, Emanuele Bugliarello, Elizabeth Salesky, Miryam de Lhoneux, Desmond Elliott
Date: 2022-07-14 00:00:00
Tasks: Language Modelling, Named Entity Recognition, Named Entity Recognition (NER)

Language models are defined over a finite set of inputs, which creates a vocabulary bottleneck when we attempt to scale the number of supported languages. Tackling this bottleneck results in a trade-off between what can be represented in the embedding matrix and computational issues in the output layer. This paper introduces PIXEL, the Pixel-based Encoder of Language, which suffers from neither of these issues. PIXEL is a pretrained language model that renders text as images, making it possible to transfer representations across languages based on orthographic similarity or the co-activation of pixels. PIXEL is trained to reconstruct the pixels of masked patches instead of predicting a distribution over tokens. We pretrain the 86M parameter PIXEL model on the same English data as BERT and evaluate on syntactic and semantic tasks in typologically diverse languages, including various non-Latin scripts. We find that PIXEL substantially outperforms BERT on syntactic and semantic processing tasks on scripts that are not found in the pretraining data, but PIXEL is slightly weaker than BERT when working with Latin scripts. Furthermore, we find that PIXEL is more robust than BERT to orthographic attacks and linguistic code-switching, further confirming the benefits of modelling language with pixels.
XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
Authors: Ho Kei Cheng, Alexander G. Schwing
Date: 2022-07-14 00:00:00
Tasks: 2D Human Pose Estimation, 2D Object Detection, 3D Absolute Human Pose Estimation, Segmentation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

We present XMem, a video object segmentation architecture for long videos with unified feature memory stores inspired by the Atkinson-Shiffrin memory model. Prior work on video object segmentation typically only uses one type of feature memory. For videos longer than a minute, a single feature memory model tightly links memory consumption and accuracy. In contrast, following the Atkinson-Shiffrin model, we develop an architecture that incorporates multiple independent yet deeply-connected feature memory stores: a rapidly updated sensory memory, a high-resolution working memory, and a compact thus sustained long-term memory. Crucially, we develop a memory potentiation algorithm that routinely consolidates actively used working memory elements into the long-term memory, which avoids memory explosion and minimizes performance decay for long-term prediction. Combined with a new memory reading mechanism, XMem greatly exceeds state-of-the-art performance on long-video datasets while being on par with state-of-the-art methods (that do not work on long videos) on short-video datasets. Code is available at https://hkchengrex.github.io/XMem