pwc-archive/papers-with-abstracts huggingface dataset:
Lasso Inference for High-Dimensional Time Series
Authors: Robert Adamek, Stephan Smeekes, Ines Wilms
Date: 2020-07-21 00:00:00
Tasks: Time Series, Time Series Analysis, valid, Vocal Bursts Intensity Prediction

In this paper we develop valid inference for high-dimensional time series. We extend the desparsified lasso to a time series setting under Near-Epoch Dependence (NED) assumptions allowing for non-Gaussian, serially correlated and heteroskedastic processes, where the number of regressors can possibly grow faster than the time dimension. We first derive an error bound under weak sparsity, which, coupled with the NED assumption, means this inequality can also be applied to the (inherently misspecified) nodewise regressions performed in the desparsified lasso. This allows us to establish the uniform asymptotic normality of the desparsified lasso under general conditions, including for inference on parameters of increasing dimensions. Additionally, we show consistency of a long-run variance estimator, thus providing a complete set of tools for performing inference in high-dimensional linear time series models. Finally, we perform a simulation exercise to demonstrate the small sample properties of the desparsified lasso in common time series settings.
Wild Bootstrap for Instrumental Variables Regressions with Weak and Few Clusters
Authors: Wenjie Wang, Yichong Zhang
Date: 2021-08-31 00:00:00

We study the wild bootstrap inference for instrumental variable regressions in the framework of a small number of large clusters in which the number of clusters is viewed as fixed and the number of observations for each cluster diverges to infinity. We first show that the wild bootstrap Wald test, with or without using the cluster-robust covariance estimator, controls size asymptotically up to a small error as long as the parameters of endogenous variables are strongly identified in at least one of the clusters. Then, we establish the required number of strong clusters for the test to have power against local alternatives. We further develop a wild bootstrap Anderson-Rubin test for the full-vector inference and show that it controls size asymptotically up to a small error even under weak or partial identification in all clusters. We illustrate the good finite sample performance of the new inference methods using simulations and provide an empirical application to a well-known dataset about US local labor markets.
Is happiness u-shaped in age everywhere? A methodological reconsideration for Europe
Authors: David Bartram
Date: 2021-08-31 00:00:00

A recent contribution to research on age and well-being (Blanchflower 2021) found that the impact of age on happiness is "u-shaped" virtually everywhere: happiness declines towards middle age and subsequently rises, in almost all countries. This paper evaluates that finding for European countries, considering whether it is robust to alternative methodological approaches. The analysis here excludes control variables that are affected by age (noting that those variable are not themselves antecedents of age) and uses data from the entire adult age range (rather than using data only from respondents younger than 70). I also explore the relationship via models that do not impose a quadratic functional form. The paper shows that these alternate approaches do not lead us to perceive a u-shape "everywhere": u-shapes are evident for some countries, but for others the pattern is quite different.
Submission Fees in Risk-Taking Contests
Authors: Mark Whitmeyer
Date: 2021-08-30 00:00:00

This paper investigates stochastic continuous time contests with a twist: the designer requires that contest participants incur some cost to submit their entries. When the designer wishes to maximize the (expected) performance of the top performer, a strictly positive submission fee is optimal. When the designer wishes to maximize total (expected) performance, either the highest submission fee or the lowest submission fee is optimal.
The Game Theory of Fake News
Authors: Alexander J. Stewart, Antonio A. Arechar, David G. Rand, Joshua B. Plotkin
Date: 2021-08-31 00:00:00
Tasks: Articles, Misinformation

A great deal of empirical research has examined who falls for misinformation and why. Here, we introduce a formal game-theoretic model of engagement with news stories that captures the strategic interplay between (mis)information consumers and producers. A key insight from the model is that observed patterns of engagement do not necessarily reflect the preferences of consumers. This is because producers seeking to promote misinformation can use strategies that lead moderately inattentive readers to engage more with false stories than true ones -- even when readers prefer more accurate over less accurate information. We then empirically test people's preferences for accuracy in the news. In three studies, we find that people strongly prefer to click and share news they perceive as more accurate -- both in a general population sample, and in a sample of users recruited through Twitter who had actually shared links to misinformation sites online. Despite this preference for accurate news -- and consistent with the predictions of our model -- we find markedly different engagement patterns for articles from misinformation versus mainstream news sites. Using 1,000 headlines from 20 misinformation and 20 mainstream news sites, we compare Facebook engagement data with 20,000 accuracy ratings collected in a survey experiment. Engagement with a headline is negatively correlated with perceived accuracy for misinformation sites, but positively correlated with perceived accuracy for mainstream sites. Taken together, these theoretical and empirical results suggest that consumer preferences cannot be straightforwardly inferred from empirical patterns of engagement.
Fuzzy Conventions
Authors: Marcin Pęski
Date: 2021-08-30 00:00:00

We study binary coordination games with random utility played in networks. A typical equilibrium is fuzzy -- it has positive fractions of agents playing each action. The set of average behaviors that may arise in an equilibrium typically depends on the network. The largest set (in the set inclusion sense) is achieved by a network that consists of a large number of copies of a large complete graph. The smallest set (in the set inclusion sense) is achieved on a lattice-type network. It consists of a single outcome that corresponds to a novel version of risk dominance that is appropriate for games with random utility.
Neural Sequence-to-Sequence Speech Synthesis Using a Hidden Semi-Markov Model Based Structured Attention Mechanism
Authors: Yoshihiko Nankaku, Kenta Sumiya, Takenori Yoshimura, Shinji Takaki, Kei Hashimoto, Keiichiro Oura, Keiichi Tokuda
Date: 2021-08-31 00:00:00
Tasks: Speech Synthesis

This paper proposes a novel Sequence-to-Sequence (Seq2Seq) model integrating the structure of Hidden Semi-Markov Models (HSMMs) into its attention mechanism. In speech synthesis, it has been shown that methods based on Seq2Seq models using deep neural networks can synthesize high quality speech under the appropriate conditions. However, several essential problems still have remained, i.e., requiring large amounts of training data due to an excessive degree for freedom in alignment (mapping function between two sequences), and the difficulty in handling duration due to the lack of explicit duration modeling. The proposed method defines a generative models to realize the simultaneous optimization of alignments and model parameters based on the Variational Auto-Encoder (VAE) framework, and provides monotonic alignments and explicit duration modeling based on the structure of HSMM. The proposed method can be regarded as an integration of Hidden Markov Model (HMM) based speech synthesis and deep learning based speech synthesis using Seq2Seq models, incorporating both the benefits. Subjective evaluation experiments showed that the proposed method obtained higher mean opinion scores than Tacotron 2 on relatively small amount of training data.
Maximum F1-score training for end-to-end mispronunciation detection and diagnosis of L2 English speech
Authors: Bi-Cheng Yan, Shao-Wei Fan Jiang, Fu-An Chao, Berlin Chen
Date: 2021-08-31 00:00:00
Tasks: Data Augmentation

End-to-end (E2E) neural models are increasingly attracting attention as a promising modeling approach for mispronunciation detection and diagnosis (MDD). Typically, these models are trained by optimizing a cross-entropy criterion, which corresponds to improving the log-likelihood of the training data. However, there is a discrepancy between the objectives of model training and the MDD evaluation, since the performance of an MDD model is commonly evaluated in terms of F1-score instead of phone or word error rate (PER/WER). In view of this, we in this paper explore the use of a discriminative objective function for training E2E MDD models, which aims to maximize the expected F1-score directly. A series of experiments conducted on the L2-ARCTIC dataset show that our proposed method can yield considerable performance improvements in relation to some state-of-the-art E2E MDD approaches and the celebrated GOP method.
Image Denoising Inspired by Quantum Many-Body physics
Authors: Sayantan Dutta, Adrian Basarab, Bertrand Georgeot, Denis Kouamé
Date: 2021-08-31 00:00:00
Tasks: Denoising, Image Denoising

Decomposing an image through Fourier, DCT or wavelet transforms is still a common approach in digital image processing, in number of applications such as denoising. In this context, data-driven dictionaries and in particular exploiting the redundancy withing patches extracted from one or several images allowed important improvements. This paper proposes an original idea of constructing such an image-dependent basis inspired by the principles of quantum many-body physics. The similarity between two image patches is introduced in the formalism through a term akin to interaction terms in quantum mechanics. The main contribution of the paper is thus to introduce this original way of exploiting quantum many-body ideas in image processing, which opens interesting perspectives in image denoising. The potential of the proposed adaptive decomposition is illustrated through image denoising in presence of additive white Gaussian noise, but the method can be used for other types of noise such as image-dependent noise as well. Finally, the results show that our method achieves comparable or slightly better results than existing approaches.
Using convolutional neural networks for the classification of breast cancer images
Authors: Eric Bonnet
Date: 2021-08-31 00:00:00
Tasks: Data Augmentation, Transfer Learning

An important part of breast cancer staging is the assessment of the sentinel axillary node for early signs of tumor spreading. However, this assessment by pathologists is not always easy and retrospective surveys often requalify the status of a high proportion of sentinel nodes. Convolutional Neural Networks (CNNs) are a class of deep learning algorithms that have shown excellent performances in the most challenging visual classification tasks, with numerous applications in medical imaging. In this study I compare twelve different CNNs and different hardware acceleration devices for the detection of breast cancer from microscopic images of breast cancer tissue. Convolutional models are trained and tested on two public datasets. The first one is composed of more than 300,000 images of sentinel lymph node tissue from breast cancer patients, while the second one has more than 220,000 images from inductive breast carcinoma tissue, one of the most common forms of breast cancer. Four different hardware acceleration cards were used, with an off-the-shelf deep learning framework. The impact of transfer learning and hyperparameters fine-tuning are tested. Hardware acceleration device performance can improve training time by a factor of five to twelve, depending on the model used. On the other hand, increasing convolutional depth will augment the training time by a factor of four to six times, depending on the acceleration device used. Increasing the depth and the complexity of the model generally improves performance, but the relationship is not linear and also depends on the architecture of the model. The performance of transfer learning is always worse compared to a complete retraining of the model. Fine-tuning the hyperparameters of the model improves the results, with the best model showing a performance comparable to state-of-the-art models.
A color temperature-based high-speed decolorization: an empirical approach for tone mapping applications
Authors: Prasoon Ambalathankandy, Yafei Ou, Masayuki Ikebe
Date: 2021-08-31 00:00:00
Tasks: Data Compression, SSIM, Tone Mapping

Grayscale images are fundamental to many image processing applications like data compression, feature extraction, printing and tone mapping. However, some image information is lost when converting from color to grayscale. In this paper, we propose a light-weight and high-speed image decolorization method based on human perception of color temperatures. Chromatic aberration results from differential refraction of light depending on its wavelength. It causes some rays corresponding to cooler colors (like blue, green) to converge before the warmer colors (like red, orange). This phenomena creates a perception of warm colors "advancing" toward the eye, while the cool colors to be "receding" away. In this proposed color to gray conversion model, we implement a weighted blending function to combine red (perceived warm) and blue (perceived cool) channel. Our main contribution is threefold: First, we implement a high-speed color processing method using exact pixel by pixel processing, and we report a $5.7\times$ speed up when compared to other new algorithms. Second, our optimal color conversion method produces luminance in images that are comparable to other state of the art methods which we quantified using the objective metrics (E-score and C2G-SSIM) and a subjective user study. Third, we demonstrate that an effective luminance distribution can be achieved using our algorithm by using global and local tone mapping applications.
Regularizing Instabilities in Image Reconstruction Arising from Learned Denoisers
Authors: Abinash Nayak
Date: 2021-08-21 00:00:00
Tasks: Image Reconstruction, Rolling Shutter Correction

It's well-known that inverse problems are ill-posed and to solve them meaningfully one has to employ regularization methods. Traditionally, popular regularization methods have been the penalized Variational approaches. In recent years, the classical regularized-reconstruction approaches have been outclassed by the (deep-learning-based) learned reconstruction algorithms. However, unlike the traditional regularization methods, the theoretical underpinnings, such as stability and regularization, have been insufficient for such learned reconstruction algorithms. Hence, the results obtained from such algorithms, though empirically outstanding, can't always be completely trusted, as they may contain certain instabilities or (hallucinated) features arising from the learned process. In fact, it has been shown that such learning algorithms are very susceptible to small (adversarial) noises in the data and can lead to severe instabilities in the recovered solution, which can be quite different than the inherent instabilities of the ill-posed (inverse) problem. Whereas, the classical regularization methods can handle such (adversarial) noises very well and can produce stable recovery. Here, we try to present certain regularization methods to stabilize such (unstable) learned reconstruction methods and recover a regularized solution, even in the presence of adversarial noises. For this, we need to extend the classical notion of regularization and incorporate it in the learned reconstruction algorithms. We also present some regularization techniques to regularize two of the most popular learning reconstruction algorithms, the Learned Post-Processing Reconstruction and the Learned Unrolling Reconstruction.
The Role of Bounded Fields-of-View and Negative Information in Finite Set Statistics (FISST)
Authors: Keith LeGrand, Silvia Ferrari
Date: 2020-04-02 00:00:00
Tasks: Object, Position

The role of negative information is particularly important to search-detect-track problems in which the number of objects is unknown a priori, and the size of the sensor field-of-view is far smaller than that of the region of interest. This paper presents an approach for systematically incorporating knowledge of the field-of-view geometry and position and object inclusion/exclusion evidence into object state densities and random finite set multi-object cardinality distributions. The approach is derived for a representative set of multi-object distributions and demonstrated through a sensor planning problem involving a multi-Bernoulli process with up to one-hundred potential targets.
Image Processing via Multilayer Graph Spectra
Authors: Songyang Zhang, Qinwen Deng, Zhi Ding
Date: 2021-08-31 00:00:00
Tasks: Edge Detection, Hyperspectral Image Segmentation, Image Compression, Image Segmentation, Semantic Segmentation

Graph signal processing (GSP) has become an important tool in image processing because of its ability to reveal underlying data structures. Many real-life multimedia datasets, however, exhibit heterogeneous structures across frames. Multilayer graphs (MLG), instead of traditional single-layer graphs, provide better representation of these datasets such as videos and hyperspectral images. To generalize GSP to multilayer graph models and develop multilayer analysis for image processing, this work introduces a tensor-based framework of multilayer graph signal processing (M-GSP) and present useful M-GSP tools for image processing. We then present guidelines for applying M-GSP in image processing and introduce several applications, including RGB image compression, edge detection and hyperspectral image segmentation. Successful experimental results demonstrate the efficacy and promising futures of M-GSP in image processing.
Signal Processing over Multilayer Graphs: Theoretical Foundations and Practical Applications
Authors: Songyang Zhang, Qinwen Deng, Zhi Ding
Date: 2021-08-31 00:00:00

Signal processing over single-layer graphs has become a mainstream tool owing to its power in revealing obscure underlying structures within data signals. However, many real-life datasets and systems, {including those in Internet of Things (IoT)}, are characterized by more complex interactions among distinct entities, which may represent multi-level interactions that are harder to be captured with a single-layer graph, and can be better characterized by multilayers graph connections. Such multilayer or multi-level data structure can be more naturally modeled by high-dimensional multilayer graphs (MLG)}. To generalize traditional graph signal processing (GSP) over multilayer graphs for analyzing multi-level signal features and their interactions, this work proposes a tensor-based framework of multilayer graph signal processing (M-GSP). Specifically, we introduce core concepts of M-GSP and study properties of MLG spectrum space, followed by fundamentals of MLG-based filter design. To illustrate novel aspects of M-GSP, we further explore its link with traditional signal processing and GSP. We provide example applications to demonstrate the efficacy and benefits of applying multilayer graphs and M-GSP in practical scenarios.
Hybrid Beamforming Design for Wideband mmWave Full-Duplex Systems
Authors: Elyes Balti, Brian L. Evans
Date: 2021-05-26 00:00:00

Recently, full duplex (FD) has been studied in 5G LTE millimeter wave (mmWave) cellular communications for New Radio in 3GPP releases 15-17. FD allows bidirectional transmission over the same resources and has the potential to reduce latency and double spectral efficiency. Self-interference (SI) is the primary drawback. SI can be several orders of magnitude greater than the received signal power, saturate the analog-to-digital converters (ADCs) and degrade communication performance severely. Massive mmWave antenna arrays may provide enough degrees of freedom for spatial multiplexing and SI suppression. In this paper, we design spatial beamformers for the phased arrays already built into the FD basestation/relay to extend mmWave coverage to a single user. We propose alternating projections to design the precoder and combiner to maximize the sum of the uplink and downlink spectral efficiencies while bringing SI below the noise floor. Our contributions include (1) hybrid analog/digital beamformer design algorithm to cancel SI in the analog domain to avoid ADC saturation and in the digital domain on each subcarrier; (2) full-digital beamformer design algorithm; and (3) analysis of spectral efficiency, energy efficiency and outage probability. In simulation, the proposed algorithms outperform beamsteering, singular value decomposition, angle search, and half-duplex techniques.
Oversampling Highly Imbalanced Indoor Positioning Data using Deep Generative Models
Authors: Fahad Alhomayani, Mohammad H. Mahoor
Date: 2021-08-30 00:00:00

The location fingerprinting method, which typically utilizes supervised learning, has been widely adopted as a viable solution for the indoor positioning problem. Many indoor positioning datasets are imbalanced. Models trained on imbalanced datasets may exhibit poor performance on the minority class(es). This problem, also known as the "curse of imbalanced data," becomes more evident when class distributions are highly imbalanced. Motivated by the recent advances in deep generative modeling, this paper proposes using Variational Autoencoders and Conditional Variational Autoencoders as oversampling tools to produce class-balanced fingerprints. Experimental results based on Bluetooth Low Energy fingerprints demonstrate that the proposed method outperforms SMOTE and ADASYN in both minority class precision and overall precision. To promote reproducibility and foster new research efforts, we made all the codes associated with this work publicly available.
Virtual screening of Microalgal compounds as potential inhibitors of Type 2 Human Transmembrane serine protease (TMPRSS2)
Authors: Ibrahim Mohammed
Date: 2021-08-31 00:00:00

More than 198 million cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been reported that result in no fewer than 4.2 million deaths globally. The rapid spread of the disease coupled with the lack of specific registered drugs for its treatment pose a great challenge that necessitate the development of therapeutic agents from a variety of sources. In this study, we employed an in-silico method to screen natural compounds with a view to identify inhibitors of the human transmembrane protease serine type 2 (TMPRSS2). The activity of this enzyme is essential for viral access into the host cells via angiotensin-converting enzyme 2 (ACE-2). Inhibiting the activity of this enzyme is therefore highly crucial for preventing viral fusion with ACE-2 thus shielding SARS-CoV-2 infectivity. 3D model of TMPRSS2 was constructed using I-TASSER, refined by GalaxyRefine, validated by Ramachandran plot server and overall model quality was checked by ProSA. 95 natural compounds from microalgae were virtually screened against the modeled protein that led to the identification 17 best leads capable of binding to TMPRSS2 with a good binding score comparable, greater or a bit lower than that of the standard inhibitor (camostat). Physicochemical properties, ADME (absorption, distribution, metabolism, excretion) and toxicity analysis revealed top 4 compounds including the reference drug with good pharmacokinetic and pharmacodynamic profiles. These compounds bind to the same pocket of the protein with a binding energy of -7.8 kcal/mol, -7.6 kcal/mol, -7.4 kcal/mol and -7.4 kcal/mol each for camostat, apigenin, catechin and epicatechin respectively. This study shed light on the potential of microalgal compounds against SARS-CoV-2. In vivo and invitro studies are required to developed SARS-CoV-2 drugs based on the structures of the compounds identified in this study.