pwc-archive/papers-with-abstracts huggingface dataset:
Russian Agricultural Industry under Sanction Wars
Authors: Alexandra Lukyanova, Ayaz Zeynalov
Date: 2022-11-16 00:00:00

The motivation for focusing on economic sanctions is the mixed evidence of their effectiveness. We assess the role of sanctions on the Russian international trade flow of agricultural products after 2014. We use a differences-in-differences model of trade flows data for imported and exported agricultural products from 2010 to 2020 in Russia. The main expectation was that the Russian economy would take a hit since it had lost its importers. We assess the economic impact of the Russian food embargo on agricultural commodities, questioning whether it has achieved its objective and resulted in a window of opportunity for the development of the domestic agricultural sector. Our results confirm that the sanctions have significantly impacted foodstuff imports; they have almost halved in the first two years since the sanctions were imposed. However, Russia has embarked on a path to reduce dependence on food imports and managed self-sufficient agricultural production.
On the Convergence of Credit Risk in Current Consumer Automobile Loans
Authors: Jackson P. Lautier, Vladimir Pozdnyakov, Jun Yan
Date: 2022-11-16 00:00:00

Loan seasoning and inefficient consumer interest rate refinance behavior are well-known for mortgages. Consumer automobile loans, which are collateralized loans on a rapidly depreciating asset, have attracted less attention, however. We derive a novel large-sample statistical hypothesis test suitable for loans sampled from asset-backed securities to populate a transition matrix between risk bands. We find all current risk bands eventually converge to a super-prime credit, despite remaining underwater. Economically, our results imply borrowers forwent \$1,153-\$2,327 in potential credit-based savings through delayed prepayment. We present an expected present value analysis to derive lender risk-adjusted profitability. Our results appear robust to COVID-19.
Color Image steganography using Deep convolutional Autoencoders based on ResNet architecture
Authors: Seyed Hesam Odin Hashemi, Mohammad-Hassan Majidi, Saeed Khorashadizadeh
Date: 2022-11-17 00:00:00
Tasks: Image Steganography, SSIM

In this paper, a deep learning color image steganography scheme combining convolutional autoencoders and ResNet architecture is proposed. Traditional steganography methods suffer from some critical defects such as low capacity, security, and robustness. In recent decades, image hiding and image extraction were realized by autoencoder convolutional neural networks to solve the aforementioned challenges. The contribution of this paper is introducing a new scheme for color image steganography inspired by ResNet architecture. The reverse ResNet architecture is utilized to extract the secret image from the stego image. In the proposed method, all images are passed through the prepossess model which is a convolutional deep neural network with the aim of feature extraction. Then, the operational model generates stego and extracted images. In fact, the operational model is an autoencoder based on ResNet structure that produces an image from feature maps. The advantage of proposed structure is identity of models in embedding and extraction phases. The performance of the proposed method is studied using COCO and CelebA datasets. For quantitative comparisons with previous related works, peak signal-to-noise ratio (PSNR), the structural similarity index (SSIM) and hiding capacity are evaluated. The experimental results verify that the proposed scheme performs better than traditional and pervious deep steganography methods. The PSNR and SSIM are more than 40 dB and 0.98, respectively that implies high imperceptibility of the proposed method. Also, this method can hide a color image of the same size in another color image, which can be inferred that the relative capacity of the proposed method is 8 bits per pixel.
High-Resolution Channel Sounding and Parameter Estimation in Multi-Site Cellular Networks
Authors: Junshi Chen, Russ Whiton, Xuhong LI, Fredrik Tufvesson
Date: 2022-11-17 00:00:00
Tasks: parameter estimation

Accurate understanding of electromagnetic propagation properties in real environments is necessary for efficient design and deployment of cellular systems. In this paper, we show a method to estimate high-resolution channel parameters with a massive antenna array in real network deployments. An antenna array mounted on a vehicle is used to receive downlink long-term evolution (LTE) reference signals from neighboring base stations (BS) with mutual interference. Delay and angular information of multipath components is estimated with a novel inter-cell interference cancellation algorithm and an extension of the RIMAX algorithm. The estimated high-resolution channel parameters are consistent with the movement pattern of the vehicle and the geometry of the environment and allow for refined channel modeling and precise cellular positioning.
Data fusion techniques for fault diagnosis of industrial machines: a survey
Authors: Amir Eshaghi Chaleshtori, Abdollah aghaie
Date: 2022-11-17 00:00:00
Tasks: Fault Diagnosis

In the Engineering discipline, predictive maintenance techniques play an essential role in improving system safety and reliability of industrial machines. Due to the adoption of crucial and emerging detection techniques and big data analytics tools, data fusion approaches are gaining popularity. This article thoroughly reviews the recent progress of data fusion techniques in predictive maintenance, focusing on their applications in machinery fault diagnosis. In this review, the primary objective is to classify existing literature and to report the latest research and directions to help researchers and professionals to acquire a clear understanding of the thematic area. This paper first summarizes fundamental data-fusion strategies for fault diagnosis. Then, a comprehensive investigation of the different levels of data fusion was conducted on fault diagnosis of industrial machines. In conclusion, a discussion of data fusion-based fault diagnosis challenges, opportunities, and future trends are presented.
Availability, outage, and capacity of spatially correlated, Australasian free-space optical networks
Authors: Marcus Birch, James R. Beattie, Francis Bennet, Nicholas Rattenbury, Michael Copeland, Tony Travouillon, Kate Ferguson, John Cater, Mikhael Sayat
Date: 2022-11-16 00:00:00
Tasks: Diversity

Network capacity and reliability for free space optical communication (FSOC) is strongly driven by ground station availability, dominated by local cloud cover causing an outage, and how availability relations between stations produce network diversity. We combine remote sensing data and novel methods to provide a generalised framework for assessing and optimising optical ground station networks. This work is guided by an example network of eight Australian and New Zealand optical communication ground stations which would span approximately $60^\circ$ in longitude and $20^\circ$ in latitude. Utilising time-dependent cloud cover data from five satellites, we present a detailed analysis determining the availability and diversity of the network, finding the Australasian region is well-suited for an optical network with a 69% average site availability and low spatial cloud cover correlations. Employing methods from computational neuroscience, we provide a Monte Carlo method for sampling the joint probability distribution of site availabilities for an arbitrarily sized and point-wise correlated network of ground stations. Furthermore, we develop a general heuristic for site selection under availability and correlation optimisations, and combine this with orbital propagation simulations to compare the data capacity between optimised networks and the example network. We show that the example network may be capable of providing tens of terabits per day to a LEO satellite, and up to 99.97% reliability to GEO satellites. We therefore use the Australasian region to demonstrate novel, generalised tools for assessing and optimising FSOC ground station networks, and additionally, the suitability of the region for hosting such a network.
Data Fusion for Multipath-Based SLAM: Combining Information from Multiple Propagation Paths
Authors: Erik Leitinger, Alexander Venus, Bryan Teague, Florian Meyer
Date: 2022-11-16 00:00:00
Tasks: Indoor Localization, Simultaneous Localization and Mapping, Single Particle Analysis

Multipath-based simultaneous localization and mapping (SLAM) is an emerging paradigm for accurate indoor localization with limited resources. The goal of multipath-based SLAM is to detect and localize radio reflective surfaces to support the estimation of time-varying positions of mobile agents. Radio reflective surfaces are typically represented by so-called virtual anchors (VAs), which are mirror images of base stations at the actual surfaces. In existing multipath-based SLAM methods, a VA is introduced for each propagation path, even if the goal is to map the reflective surfaces. The fact that not every reflective surface but every propagation path is modeled by a VA, complicates a consistent combination "fusion" of statistical information across multiple paths and base stations and thus limits the accuracy and mapping speed of existing multipath-based SLAM methods. In this paper, we introduce an improved statistical model and estimation method that enables data fusion for multipath-based SLAM by representing each surface by a single master virtual anchor (MVA). We further develop a particle-based sum-product algorithm (SPA) that performs probabilistic data association to compute marginal posterior distributions of MVA and agent positions efficiently. A key aspect of the proposed estimation method based on MVAs is to check the availability of singlebounce and double-bounce propagation paths at a specific agent position by means of ray-launching. The availability check is directly integrated into the statistical model by providing detection probabilities for probabilistic data association. Numerical results based on simulated and real data demonstrate significant improvements in estimation accuracy compared to state-of-theart multipath-based SLAM methods.
A Review of Deep Learning Techniques for Protein Function Prediction
Authors: Divyanshu Aggarwal, Yasha Hasija
Date: 2022-10-27 00:00:00
Tasks: Deep Learning, Protein Function Prediction

Deep Learning and big data have shown tremendous success in bioinformatics and computational biology in recent years; artificial intelligence methods have also significantly contributed in the task of protein function classification. This review paper analyzes the recent developments in approaches for the task of predicting protein function using deep learning. We explain the importance of determining the protein function and why automating the following task is crucial. Then, after reviewing the widely used deep learning techniques for this task, we continue our review and highlight the emergence of the modern State of The Art (SOTA) deep learning models which have achieved groundbreaking results in the field of computer vision, natural language processing and multi-modal learning in the last few years. We hope that this review will provide a broad view of the current role and advances of deep learning in biological sciences, especially in predicting protein function tasks and encourage new researchers to contribute to this area.
GRASMOS: Graph Signage Model Selection for Gene Regulatory Networks
Authors: Angelina Brilliantova, Hannah Miller, Ivona Bezáková
Date: 2022-11-17 00:00:00
Tasks: Epidemiology, Model Selection

Signed networks, i.e., networks with positive and negative edges, commonly arise in various domains from social media to epidemiology. Modeling signed networks has many practical applications, including the creation of synthetic data sets for experiments where obtaining real data is difficult. Influential prior works proposed and studied various graph topology models, as well as the problem of selecting the most fitting model for different application domains. However, these topology models are typically unsigned. In this work, we pose a novel Maximum-Likelihood-based optimization problem for modeling signed networks given their topology and showcase it in the context of gene regulation. Regulatory interactions of genes play a key role in organism development, and when broken can lead to serious organism abnormalities and diseases. Our contributions are threefold: First, we design a new class of signage models for a given topology. Based on the parameter setting, we discuss its biological interpretations for gene regulatory networks (GRNs). Second, we design algorithms computing the Maximum Likelihood -- depending on the parameter setting, our algorithms range from closed-form expressions to MCMC sampling. Third, we evaluated the results of our algorithms on synthetic datasets and real-world large GRNs. Our work can lead to the prediction of unknown gene regulations, the generation of biological hypotheses, and realistic GRN benchmark datasets.
Local alterations of left arcuate fasciculus and transcallosal white matter microstructure in schizophrenia patients with medication-resistant auditory verbal hallucinations: A pilot study
Authors: Fanny Thomas, Cécile Gallea, Virginie Moulier, Noomane Bouaziz, Antoni Valero-Cabré, Dominique Januel
Date: 2022-11-17 00:00:00

Auditory verbal hallucinations (AVH) in schizophrenia (SZ) have been associated with abnormalities of the left arcuate fasciculus and transcallosal white matter projections linking homologous language areas of both hemispheres. While most studies have used a whole-tract approach, here we focused on analyzing local alterations of the above-mentioned pathways in SZ patients suffering medication-resistant AVH.Fractional anisotropy (FA) was estimated along the left arcuate fasciculus and interhemispheric projections of the rostral and caudal corpus callosum. Then, potential associations between white matter tracts and SZ symptoms were explored by correlating local site-by-site FA values and AVH severity estimated via the Auditory Hallucinations Rating Scale (AHRS). Compared to a sample of healthy controls, SZ patients displayed lower FA values in the rostral portion of the left arcuate fasciculus, near the frontal operculum, and in the left and right lateral regions of the rostral portion of the transcallosal pathways. In contrast, SZ patients showed higher FA values than healthy controls in the medial portion of the latter transcallosal pathway and in the midsagittal section of the interhemispheric auditory pathway. Finally, significant correlations were found between local FA values in the left arcuate fasciculus and the severity of the AVH's attentional salience. Contributing to the study of associations between local white matter alterations of language networks and SZ symptoms, our findings highlight local alterations of white matter integrity in these pathways linking language areas in SZ patients with AVH. We also hypothesize a link between the left arcuate fasciculus and the attentional capture of AVH.
Stimulation of soy seeds using environmentally friendly magnetic and electric fields
Authors: Agata Dziwulska-Hunek, Agnieszka Niemczynowicz, Radosław A. Kycia, Arkadiusz Matwijczuk, Krzysztof Kornarzyński, Joanna Stadnik, Mariusz Szymanek
Date: 2022-11-16 00:00:00

The study analyzes the impact of constant and alternating magnetic fields and alternating electric fields on various growth parameters of soy plants: the germination energy and capacity, plants emergence and number, the Yield(II) of the fresh mass of seedlings, protein content, and photosynthetic parameters. Four cultivars were used: MAVKA, MERLIN, VIOLETTA, and ANUSZKA. Moreover, the advanced Machine Learning processing pipeline was proposed to distinguish the impact of physical factors on photosynthetic parameters. It is possible to distinguish exposition on different physical factors for the first three cultivars; therefore, it indicates that the EM factors have some observable effect on soy plants. Moreover, some influence of physical factors on growth parameters was observed. The use of ELM (Electromagnetic) fields had a positive impact on the germination rate in Merlin plants. The highest values were recorded for the constant magnetic field (CMF) - Merlin, and the lowest for the alternating electric field (AEF) - Violetta. An increase in terms of emergence and number of plants after seed stimulation was observed for the Mavka cultivar, except for the AEF treatment (number of plants after 30 days) (...)
A Survey on Evaluation Metrics for Synthetic Material Micro-Structure Images from Generative Models
Authors: Devesh Shah, Anirudh Suresh, Alemayehu Admasu, Devesh Upadhyay, Kalyanmoy Deb
Date: 2022-11-03 00:00:00

The evaluation of synthetic micro-structure images is an emerging problem as machine learning and materials science research have evolved together. Typical state of the art methods in evaluating synthetic images from generative models have relied on the Fr\'echet Inception Distance. However, this and other similar methods, are limited in the materials domain due to both the unique features that characterize physically accurate micro-structures and limited dataset sizes. In this study we evaluate a variety of methods on scanning electron microscope (SEM) images of graphene-reinforced polyurethane foams. The primary objective of this paper is to report our findings with regards to the shortcomings of existing methods so as to encourage the machine learning community to consider enhancements in metrics for assessing quality of synthetic images in the material science domain.
Learning unfolded networks with a cyclic group structure
Authors: Emmanouil Theodosis, Demba Ba
Date: 2022-11-16 00:00:00
Tasks: Data Augmentation, Rotated MNIST

Deep neural networks lack straightforward ways to incorporate domain knowledge and are notoriously considered black boxes. Prior works attempted to inject domain knowledge into architectures implicitly through data augmentation. Building on recent advances on equivariant neural networks, we propose networks that explicitly encode domain knowledge, specifically equivariance with respect to rotations. By using unfolded architectures, a rich framework that originated from sparse coding and has theoretical guarantees, we present interpretable networks with sparse activations. The equivariant unfolded networks compete favorably with baselines, with only a fraction of their parameters, as showcased on (rotated) MNIST and CIFAR-10.
Informative Initialization and Kernel Selection Improves t-SNE for Biological Sequences
Authors: Prakash Chourasia, Sarwan Ali, Murray Patterson
Date: 2022-11-16 00:00:00

The t-distributed stochastic neighbor embedding (t- SNE) is a method for interpreting high dimensional (HD) data by mapping each point to a low dimensional (LD) space (usually two-dimensional). It seeks to retain the structure of the data. An important component of the t-SNE algorithm is the initialization procedure, which begins with the random initialization of an LD vector. Points in this initial vector are then updated to minimize the loss function (the KL divergence) iteratively using gradient descent. This leads comparable points to attract one another while pushing dissimilar points apart. We believe that, by default, these algorithms should employ some form of informative initialization. Another essential component of the t-SNE is using a kernel matrix, a similarity matrix comprising the pairwise distances among the sequences. For t-SNE-based visualization, the Gaussian kernel is employed by default in the literature. However, we show that kernel selection can also play a crucial role in the performance of t-SNE. In this work, we assess the performance of t-SNE with various alternative initialization methods and kernels, using four different sets, out of which three are biological sequences (nucleotide, protein, etc.) datasets obtained from various sources, such as the well-known GISAID database for sequences of the SARS- CoV-2 virus. We perform subjective and objective assessments of these alternatives. We use the resulting t-SNE plots and k- ary neighborhood agreement (k-ANA) to evaluate and compare the proposed methods with the baselines. We show that by using different techniques, such as informed initialization and kernel matrix selection, that t-SNE performs significantly better. Moreover, we show that t-SNE also takes fewer iterations to converge faster with more intelligent initialization.
Active Learning with Expected Error Reduction
Authors: Stephen Mussmann, Julia Reisler, Daniel Tsai, Ehsan Mousavi, Shayne O'Brien, Moises Goldszmidt
Date: 2022-11-17 00:00:00
Tasks: Active Learning

Active learning has been studied extensively as a method for efficient data collection. Among the many approaches in literature, Expected Error Reduction (EER) (Roy and McCallum) has been shown to be an effective method for active learning: select the candidate sample that, in expectation, maximally decreases the error on an unlabeled set. However, EER requires the model to be retrained for every candidate sample and thus has not been widely used for modern deep neural networks due to this large computational cost. In this paper we reformulate EER under the lens of Bayesian active learning and derive a computationally efficient version that can use any Bayesian parameter sampling method (such as arXiv:1506.02142). We then compare the empirical performance of our method using Monte Carlo dropout for parameter sampling against state of the art methods in the deep active learning literature. Experiments are performed on four standard benchmark datasets and three WILDS datasets (arXiv:2012.07421). The results indicate that our method outperforms all other methods except one in the data shift scenario: a model dependent, non-information theoretic method that requires an order of magnitude higher computational cost (arXiv:1906.03671).
Permutation-Invariant Tabular Data Synthesis
Authors: Yujin Zhu, Zilong Zhao, Robert Birke, Lydia Y. Chen
Date: 2022-11-17 00:00:00

Tabular data synthesis is an emerging approach to circumvent strict regulations on data privacy while discovering knowledge through big data. Although state-of-the-art AI-based tabular data synthesizers, e.g., table-GAN, CTGAN, TVAE, and CTAB-GAN, are effective at generating synthetic tabular data, their training is sensitive to column permutations of input data. In this paper, we first conduct an extensive empirical study to disclose such a property of permutation invariance and an in-depth analysis of the existing synthesizers. We show that changing the input column order worsens the statistical difference between real and synthetic data by up to 38.67% due to the encoding of tabular data and the network architectures. To fully unleash the potential of big synthetic tabular data, we propose two solutions: (i) AE-GAN, a synthesizer that uses an autoencoder network to represent the tabular data and GAN networks to synthesize the latent representation, and (ii) a feature sorting algorithm to find the suitable column order of input data for CNN-based synthesizers. We evaluate the proposed solutions on five datasets in terms of the sensitivity to the column permutation, the quality of synthetic data, and the utility in downstream analyses. Our results show that we enhance the property of permutation-invariance when training synthesizers and further improve the quality and utility of synthetic data, up to 22%, compared to the existing synthesizers.