pwc-archive/papers-with-abstracts huggingface dataset:
Plug-and-Play Quantum Adaptive Denoiser for Deconvolving Poisson Noisy Images
Authors: Sayantan Dutta, Adrian Basarab, Bertrand Georgeot, Denis Kouamé
Date: 2021-07-01 00:00:00
Tasks: Image Deconvolution

A new Plug-and-Play (PnP) alternating direction of multipliers (ADMM) scheme is proposed in this paper, by embedding a recently introduced adaptive denoiser using the Schroedinger equation's solutions of quantum physics. The potential of the proposed model is studied for Poisson image deconvolution, which is a common problem occurring in number of imaging applications, such as limited photon acquisition or X-ray computed tomography. Numerical results show the efficiency and good adaptability of the proposed scheme compared to recent state-of-the-art techniques, for both high and low signal-to-noise ratio scenarios. This performance gain regardless of the amount of noise affecting the observations is explained by the flexibility of the embedded quantum denoiser constructed without anticipating any prior statistics about the noise, which is one of the main advantages of this method. The main novelty of this work resided in the integration of a modified quantum denoiser into the PnP-ADMM framework and the numerical proof of convergence of the resulting algorithm.
SISAL Revisited
Authors: Chujun Huang, Mingjie Shao, Wing-Kin Ma, Anthony Man-Cho So
Date: 2021-07-01 00:00:00

Simplex identification via split augmented Lagrangian (SISAL) is a popularly-used algorithm in blind unmixing of hyperspectral images. Developed by Jos\'{e} M. Bioucas-Dias in 2009, the algorithm is fundamentally relevant to tackling simplex-structured matrix factorization, and by extension, non-negative matrix factorization, which have many applications under their umbrellas. In this article, we revisit SISAL and provide new meanings to this quintessential algorithm. The formulation of SISAL was motivated from a geometric perspective, with no noise. We show that SISAL can be explained as an approximation scheme from a probabilistic simplex component analysis framework, which is statistical and is principally more powerful in accommodating the presence of noise. The algorithm for SISAL was designed based on a successive convex approximation method, with a focus on practical utility. It was not known, by analyses, whether the SISAL algorithm has any kind of guarantee of convergence to a stationary point. By establishing associations between the SISAL algorithm and a line-search-based proximal gradient method, we confirm that SISAL can indeed guarantee convergence to a stationary point. Our re-explanation of SISAL also reveals new formulations and algorithms. The performance of these new possibilities is demonstrated by numerical experiments.
Secure Transmission with Different Security Requirements Based on Covert Communication and Information-Theoretic Security in Presence of Friendly Jammer
Authors: Pooya Baee, Farid Samsami khodadad, Moslem Forouzesh
Date: 2021-07-01 00:00:00

In this paper, we investigate joint information-theoretic security and covert communication on a network in the presence of a single transmitter (Alice), a friendly jammer, a single untrusted user, two legitimate users, and a single warden of the channel (Willie). In the considered network, one of the authorized users, Bob, needs a secure and covert communication, and therefore his message must be sent securely, and at the same time, the existence of his communication with the transmitter should not be detected by the channel's warden, Willie, Meanwhile, another authorized user, Carol, needs covert communication. The purpose of secure communication is to prevent the message being decoded by the untrusted user who is present on the network, which leads us to use one of the physical layer security methods, named the secure transmission of information theory. In some cases, in addition to protecting the content of the message, it is important for the user that the existence of the transmission not being detected by an adversary, which leads us to covert communication. In the proposed network model, it is assumed that for covert communication requirements, Alice will not send any messages to legitimate users in one time slot and in another time slot will send to them both (Bob and Carol). One of the main challenges in covert communication is low transmission rate, because we have to reduce the transmission power such that the main message get hide in background noise.
A Multi-task Deep Feature Selection Method for Brain Imaging Genetics
Authors: Chenglin Yu, Dingnan Cui, Muheng Shang, Shu Zhang, Lei Guo, Junwei Han, Lei Du, Alzheimer's Disease Neuroimaging Initiative
Date: 2021-07-01 00:00:00
Tasks: feature selection

Using brain imaging quantitative traits (QTs) to identify the genetic risk factors is an important research topic in imaging genetics. Many efforts have been made via building linear models, e.g. linear regression (LR), to extract the association between imaging QTs and genetic factors such as single nucleotide polymorphisms (SNPs). However, to the best of our knowledge, these linear models could not fully uncover the complicated relationship due to the loci's elusive and diverse impacts on imaging QTs. Though deep learning models can extract the nonlinear relationship, they could not select relevant genetic factors. In this paper, we proposed a novel multi-task deep feature selection (MTDFS) method for brain imaging genetics. MTDFS first adds a multi-task one-to-one layer and imposes a hybrid sparsity-inducing penalty to select relevant SNPs making significant contributions to abnormal imaging QTs. It then builds a multi-task deep neural network to model the complicated associations between imaging QTs and SNPs. MTDFS can not only extract the nonlinear relationship but also arms the deep neural network with the feature selection capability. We compared MTDFS to both LR and single-task DFS (DFS) methods on the real neuroimaging genetic data. The experimental results showed that MTDFS performed better than both LR and DFS in terms of the QT-SNP relationship identification and feature selection. In a word, MTDFS is powerful for identifying risk loci and could be a great supplement to the method library for brain imaging genetics.
Epidemic processes with vaccination and immunity loss studied with the BLUES function method
Authors: Jonas Berx, Joseph O Indekeu
Date: 2021-06-30 00:00:00

The Beyond-Linear-Use-of-Equation-Superposition (BLUES) function method is extended to coupled nonlinear ordinary differential equations and applied to the epidemiological SIRS model with vaccination. Accurate analytic approximations are obtained for the time evolution of the susceptible and infected population fractions. The results are compared with those obtained with alternative methods, notably Adomian decomposition, variational iteration and homotopy perturbation. In contrast with these methods, the BLUES iteration converges rapidly, globally, and captures the exact asymptotic behavior for long times. The time of the infection peak is calculated using the BLUES approximants and the results are compared with numerical solutions, which indicate that the method is able to generate useful analytic expressions that coincide with the (numerically) exact ones already for a small number of iterations.
Uncertainty quantification of a thrombosis model considering the clotting assay PFA-100
Authors: Rodrigo Méndez Rojano, Mansur Zhussupbekov, James F. Antaki, Didier Lucor
Date: 2021-06-29 00:00:00
Tasks: Sensitivity, Uncertainty Quantification

Mathematical models of thrombosis are currently used to study clinical scenarios of pathological thrombus formation. Most of these models involve inherent uncertainties that must be assessed to increase the confidence in model predictions and identify avenues of improvement for both thrombosis modeling and anti-platelet therapies. In this work, an uncertainty quantification analysis of a multi-constituent thrombosis model is performed considering a common assay for platelet function (PFA-100). The analysis is performed using a polynomial chaos expansion as a parametric surrogate for the thrombosis model. The polynomial approximation is validated and used to perform a global sensitivity analysis via computation of Sobol' coefficients. Six out of fifteen parameters were found to be influential in the simulation variability considering only individual effects. Nonetheless, parameter interactions are highlighted when considering the total Sobol' indices. In addition to the sensitivity analysis, the surrogate model was used to compute the PFA-100 closure times of 300,000 virtual cases that align well with clinical data. The current methodology could be used including common anti-platelet therapies to identify scenarios that preserve the hematological balance.
Price change prediction of ultra high frequency financial data based on temporal convolutional network
Authors: Wei Dai, Yuan An, Wen Long
Date: 2021-07-01 00:00:00

Through in-depth analysis of ultra high frequency (UHF) stock price change data, more reasonable discrete dynamic distribution models are constructed in this paper. Firstly, we classify the price changes into several categories. Then, temporal convolutional network (TCN) is utilized to predict the conditional probability for each category. Furthermore, attention mechanism is added into the TCN architecture to model the time-varying distribution for stock price change data. Empirical research on constituent stocks of Chinese Shenzhen Stock Exchange 100 Index (SZSE 100) found that the TCN framework model and the TCN (attention) framework have a better overall performance than GARCH family models and the long short-term memory (LSTM) framework model for the description of the dynamic process of the UHF stock price change sequence. In addition, the scale of the dataset reached nearly 10 million, to the best of our knowledge, there has been no previous attempt to apply TCN to such a large-scale UHF transaction price dataset in Chinese stock market.
Robust Replication of Volatility and Hybrid Derivatives on Jump Diffusions
Authors: Peter Carr, Roger Lee, Matthew Lorig
Date: 2021-07-01 00:00:00

We price and replicate a variety of claims written on the log price $X$ and quadratic variation $[X]$ of a risky asset, modeled as a positive semimartingale, subject to stochastic volatility and jumps. The pricing and hedging formulas do not depend on the dynamics of volatility process, aside from integrability and independence assumptions; in particular, the volatility process may be non-Markovian and exhibit jumps of unknown distribution. The jump risk may be driven by any finite activity Poisson random measure with bounded jump sizes. As hedging instruments, we use the underlying risky asset, a zero-coupon bond, and European calls and puts with the same maturity as the claim to be hedged. Examples of contracts that we price include variance swaps, volatility swaps, a claim that pays the realized Sharpe ratio, and a call on a leveraged exchange traded fund.
Market regime classification with signatures
Authors: Paul Bilokon, Antoine Jacquier, Conor McIndoe
Date: 2021-06-30 00:00:00
Tasks: Classification, Clustering, Time Series, Time Series Analysis

We provide a data-driven algorithm to classify market regimes for time series. We utilise the path signature, encoding time series into easy-to-describe objects, and provide a metric structure which establishes a connection between separation of regimes and clustering of points.
The Role of Binance in Bitcoin Volatility Transmission
Authors: Carol Alexander, Daniel Heck, Andreas Kaeck
Date: 2021-07-01 00:00:00

We analyse high-frequency realised volatility dynamics and spillovers in the bitcoin market, focusing on two pairs: bitcoin against the US dollar (the main fiat-crypto pair) and trading bitcoin against tether (the main crypto-crypto pair). We find that the tether-margined perpetual contract on Binance is clearly the main source of volatility, continuously transmitting strong flows to all other instruments and receiving only a little volatility. Moreover, we find that (i) during US trading hours, traders pay more attention and are more reactive to prevailing market conditions when updating their expectations and (ii) the crypto market exhibits a higher interconnectedness when traditional Western stock markets are open. Our results highlight that regulators should not only consider spot exchanges offering bitcoin-fiat trading but also the tether-margined derivatives products available on most unregulated exchanges, most importantly Binance.
Learning How to Demodulate from Few Pilots via Meta-Learning
Authors: Sangwoo Park, Hyeryung Jang, Osvaldo Simeone, Joonhyuk Kang
Date: 2019-03-06 00:00:00
Tasks: Meta-Learning

Consider an Internet-of-Things (IoT) scenario in which devices transmit sporadically using short packets with few pilot symbols. Each device transmits over a fading channel and is characterized by an amplifier with a unique non-linear transfer function. The number of pilots is generally insufficient to obtain an accurate estimate of the end-to-end channel, which includes the effects of fading and of the amplifier's distortion. This paper proposes to tackle this problem using meta-learning. Accordingly, pilots from previous IoT transmissions are used as meta-training in order to learn a demodulator that is able to quickly adapt to new end-to-end channel conditions from few pilots. Numerical results validate the advantages of the approach as compared to training schemes that either do not leverage prior transmissions or apply a standard learning algorithm on previously received data.
Fixation: A universal framework for experimental eye movement research
Authors: Mostafa Elshamy, Peter Khooshabeh
Date: 2021-05-25 00:00:00
Proceeding: ETRA 2021 5

We propose a free and open-source framework for experimental eye movement research, whereby a researcher can record and visualize gaze data and export it for analysis using an offline web application without proprietary components or licensing restrictions. The framework is agnostic to the source of the raw gaze stream and can be used with any eye tracking platform by mapping data to a standard JSON-based format and streaming it in real time. We leverage web technologies to address data privacy concerns and demonstrate support for recording at 300 Hz and real-time visualization.
An Incremental Algorithm for a Generalization of the Shortest-Path Problem
Authors: G.Ramalingam, ThomasReps
Date: 1996-09-01 00:00:00
Proceeding: Journal of Algorithms Volume 21, Issue 2, September 1996, Pages 267-305 1996 9

Thegrammar problem, a generalization of the single-source shortest-path problem introduced by D. E. Knuth (Inform. Process. Lett.6(1) (1977), 1–5) is to compute the minimum-cost derivation of a terminal string from each nonterminal of a given context-free grammar, with the cost of a derivation being suitably defined. This problem also subsumes the problem of finding optimal hyperpaths in directed hypergraphs (under varying optimization criteria) that has received attention recently. In this paper we present an incremental algorithm for a version of the grammar problem. As a special case of this algorithm we obtain an efficient incremental algorithm for the single-source shortest-path problem with positive edge lengths. The aspect of our work that distinguishes it from other work on the dynamic shortest-path problem is its ability to handle “multiple heterogeneous modifications”: between updates, the input graph is allowed to be restructured by an arbitrary mixture of edge insertions, edge deletions, and edge-length changes.
Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization
Authors: Saurabh Desai, Harish G. Ramaswamy
Date: 2020-01-01 00:00:00
Proceeding: WACV 2020 1

In response to recent criticism of gradient-based visualization techniques, we propose a new methodology to generate visual explanations for deep Convolutional Neural Networks (CNN) - based models. Our approach - Ablationbased Class Activation Mapping (Ablation CAM) uses ablation analysis to determine the importance (weights) of individual feature map units w.r.t. class. Further, this is used to
produce a coarse localization map highlighting the important regions in the image for predicting the concept. Our objective and subjective evaluations show that this gradientfree approach works better than state-of-the-art Grad-CAM
technique. Moreover, further experiments are carried out to
show that Ablation-CAM is class discriminative as well as
can be used to evaluate trust in a model.
Digital Media Copyright and Content Protection Using IPFS and Blockchain
Authors: Kwame Opuni-Boachie Obour Agyekum1, Qi Xia1, Yansong Liu1, Hong Pu1, Christian Nii Aflah Cobblah1, Goodlet Akwasi Kusi1, Hanlin Yang1, and Jianbin Gao2(B)
Date: 2019-11-28 00:00:00
Proceeding: Conference 2019 11

With the development of the Internet, the total amount of
digital media transmission and storage has been very large. However, traditional digital copyright solidification and rights protection are offline
processes, which are time-consuming and labor-intensive. This paper
focuses on digital fingerprint technology, Inter-planetary File System
(IPFS) and Fabric Alliance blockchain technology to create a digital
system to optimize traditional processes and improve the efficiency of
digital media copyright solidification.
In this work, we build an IPFS system where all digital media files
exist in the form of IPFS objects. Digital fingerprints, which reflect the
characteristics of the digital media files are also designed. Finally, all
files are recorded onto the Fabric blockchain, ensuring immutability and
provenance. Our system proves to be efficient and accurate in ensuring
complete ownership of digital media.
Essence of kernel Fisher discriminant: KPCA plus LDA
Authors: Jian Yang; Zhong Jin; Jing-yu Yang, David Zhang, Alejandro F. Frangi
Date: 2003-10-15 00:00:00
Proceeding: Pattern Recognition 2003 10

In this paper, the method of kernel Fisher discriminant (KFD) is analyzed and its nature is revealed, i.e., KFD is equivalent
to kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA). Based on this result, a more
transparent KFD algorithm is proposed. That is, KPCA is ;rst performed and then LDA is used for a second feature extraction in
the KPCA-transformed space. Finally, the e=ectiveness of the proposed algorithm is veri;ed using the CENPARMI handwritten
numeral database.
Combining Frame-Synchronous and Label-Synchronous Systems for Speech Recognition
Authors: Qiujia Li, Chao Zhang, Philip C. Woodland
Date: 2021-07-01 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, speech-recognition, Speech Recognition

Commonly used automatic speech recognition (ASR) systems can be classified into frame-synchronous and label-synchronous categories, based on whether the speech is decoded on a per-frame or per-label basis. Frame-synchronous systems, such as traditional hidden Markov model systems, can easily incorporate existing knowledge and can support streaming ASR applications. Label-synchronous systems, based on attention-based encoder-decoder models, can jointly learn the acoustic and language information with a single model, which can be regarded as audio-grounded language models. In this paper, we propose rescoring the N-best hypotheses or lattices produced by a first-pass frame-synchronous system with a label-synchronous system in a second-pass. By exploiting the complementary modelling of the different approaches, the combined two-pass systems achieve competitive performance without using any extra speech or text data on two standard ASR tasks. For the 80-hour AMI IHM dataset, the combined system has a 13.7% word error rate (WER) on the evaluation set, which is up to a 29% relative WER reduction over the individual systems. For the 300-hour Switchboard dataset, the WERs of the combined system are 5.7% and 12.1% on Switchboard and CallHome subsets of Hub5'00, and 13.2% and 7.6% on Switchboard Cellular and Fisher subsets of RT03, up to a 33% relative reduction in WER over the individual systems.
ClueReader: Heterogeneous Graph Attention Network for Multi-hop Machine Reading Comprehension
Authors: Peng Gao, Feng Gao, Peng Wang, Jian-Cheng Ni, Fei Wang, Hamido Fujita
Date: 2021-07-02 00:00:00
Tasks: Graph Attention, Machine Reading Comprehension, Reading Comprehension

Multi-hop machine reading comprehension is a challenging task in natural language processing as it requires more reasoning ability across multiple documents. Spectral models based on graph convolutional networks have shown good inferring abilities and lead to competitive results. However, the analysis and reasoning of some are inconsistent with those of humans. Inspired by the concept of grandmother cells in cognitive neuroscience, we propose a heterogeneous graph attention network model named ClueReader to imitate the grandmother cell concept. The model is designed to assemble the semantic features in multi-level representations and automatically concentrate or alleviate information for reasoning through the attention mechanism. The name ClueReader is a metaphor for the pattern of the model: it regards the subjects of queries as the starting points of clues, takes the reasoning entities as bridge points, considers the latent candidate entities as grandmother cells, and the clues end up in candidate entities. The proposed model enables the visualization of the reasoning graph, making it possible to analyze the importance of edges connecting entities and the selectivity in the mention and candidate nodes, which is easier to comprehend empirically. Evaluations on the open-domain multi-hop reading dataset WikiHop and drug-drug interaction dataset MedHop proved the validity of ClueReader and showed the feasibility of its application of the model in the molecular biology domain.
Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability
Authors: Mihaela Curmei, Sarah Dean, Benjamin Recht
Date: 2021-06-30 00:00:00
Tasks: Interactive Recommendation, Recommendation Systems

In this work, we consider how preference models in interactive recommendation systems determine the availability of content and users' opportunities for discovery. We propose an evaluation procedure based on stochastic reachability to quantify the maximum probability of recommending a target piece of content to an user for a set of allowable strategic modifications. This framework allows us to compute an upper bound on the likelihood of recommendation with minimal assumptions about user behavior. Stochastic reachability can be used to detect biases in the availability of content and diagnose limitations in the opportunities for discovery granted to users. We show that this metric can be computed efficiently as a convex program for a variety of practical settings, and further argue that reachability is not inherently at odds with accuracy. We demonstrate evaluations of recommendation algorithms trained on large datasets of explicit and implicit ratings. Our results illustrate how preference models, selection rules, and user interventions impact reachability and how these effects can be distributed unevenly.