pwc-archive/papers-with-abstracts huggingface dataset:
Forest and Water Bodies Segmentation Through Satellite Images Using U-Net
Authors: Dmytro Filatov, Ghulam Nabi Ahmad Hassan Yar
Date: 2022-07-12 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Global environment monitoring is a task that requires additional attention in the contemporary rapid climate change environment. This includes monitoring the rate of deforestation and areas affected by flooding. Satellite imaging has greatly helped monitor the earth, and deep learning techniques have helped to automate this monitoring process. This paper proposes a solution for observing the area covered by the forest and water. To achieve this task UNet model has been proposed, which is an image segmentation model. The model achieved a validation accuracy of 82.55% and 82.92% for the segmentation of areas covered by forest and water, respectively.
Centralized and Decentralized ML-Enabled Integrated Terrestrial and Non-Terrestrial Networks
Authors: Mehmet Ali Aygul, Halise Turkmen, Mehmet Izzet Saglam, Hakan Ali Cirpan, Huseyin Arslan
Date: 2022-07-22 00:00:00

Non-terrestrial networks (NTNs) are a critical enabler of the persistent connectivity vision of sixth-generation networks, as they can service areas where terrestrial infrastructure falls short. However, the integration of these networks with the terrestrial network is laden with obstacles. The dynamic nature of NTN communication scenarios and numerous variables render conventional model-based solutions computationally costly and impracticable for resource allocation, parameter optimization, and other problems. Machine learning (ML)-based solutions, thus, can perform a pivotal role due to their inherent ability to uncover the hidden patterns in time-varying, multi-dimensional data with superior performance and less complexity. Centralized ML (CML) and decentralized ML (DML), named so based on the distribution of the data and computational load, are two classes of ML that are being studied as solutions for the various complications of terrestrial and non-terrestrial networks (TNTN) integration. Both have their benefits and drawbacks under different circumstances, and it is integral to choose the appropriate ML approach for each TNTN integration issue. To this end, this paper goes over the TNTN integration architectures as given in the 3rd generation partnership project standard releases, proposing possible scenarios. Then, the capabilities and challenges of CML and DML are explored from the vantage point of these scenarios.
Terahertz Communications for 6G and Beyond Wireless Networks: Challenges, Key Advancements, and Opportunities
Authors: Akram Shafie, Nan Yang, Chong Han, Josep Miquel Jornet, Markku Juntti, Thomas Kurner
Date: 2022-07-22 00:00:00
Tasks: Management

The unprecedented increase in wireless data traffic, predicted to occur within the next decade, is motivating academia and industries to look beyond contemporary wireless standards and conceptualize the sixth-generation (6G) wireless networks. Among various promising solutions, terahertz (THz) communications (THzCom) is recognized as a highly promising technology for the 6G and beyond era, due to its unique potential to support terabit-per-second transmission in emerging applications. This article delves into key areas for developing end-to-end THzCom systems, focusing on physical, link, and network layers. Specifically, we discuss the areas of THz spectrum management, THz antennas and beamforming, and the integration of other 6G-enabling technologies for THzCom. For each area, we identify the challenges imposed by the unique properties of the THz band. We then present main advancements and outline perspective research directions in each area to stimulate future research efforts for realizing THzCom in 6G and beyond wireless networks.
Convergence Theory of Generalized Distributed Subgradient Method with Random Quantization
Authors: Zhaoyue Xia, Jun Du, Yong Ren
Date: 2022-07-22 00:00:00
Tasks: Distributed Optimization, Quantization

The distributed subgradient method (DSG) is a widely discussed algorithm to cope with large-scale distributed optimization problems in the arising machine learning applications. Most exisiting works on DSG focus on ideal communication between the cooperative agents such that the shared information between agents is exact and perfect. This assumption, however, could lead to potential privacy concerns and is not feasible when the wireless transmission links are not of good quality. To overcome the challenge, a common approach is to quantize the data locally before transmission, which avoids exposure of raw data and significantly reduces the size of data. Compared with perfect data, quantization poses fundamental challenges on loss of data accuracy, which further impacts the convergence of the algorithms. To settle the problem, we propose a generalized distributed subgradient method with random quantization, which can be intepreted as a two time-scale stochastic approximation method. We provide comprehensive results on the convergence of the algorithm and derive upper bounds on the convergence rates in terms of the quantization bit, stepsizes and the number of network agents. Our results extend the existing results, where only special cases are considered and general conclusions for the convergence rates are missing. Finally, numerical simulations are conducted on linear regression problems to support our theoretical results.
Holographic Transmitarray Antenna with linear Polarization in X band
Authors: Mahdi Salehi, Homayoon Oraizi
Date: 2021-04-19 00:00:00

In this paper, we present the design and demonstration of transmitarray antennas (TAs) based on the holographic technique for the first time. According to the holographic theory, the amplitudes and phases of electromagnetic waves can be recorded on a surface, and then they can be reconstructed independently. This concept is used to design single-beam and multi-beam linearly polarized holographic TAs without using any iterative optimization algorithms. Initially, a transmission impedance surface is analyzed and compared with the reflection one. Then, interferograms associated with the scalar admittance distribution are defined according to the number and direction of the radiation beams. After that, a transmission metasurface of dimensions equal to 0:26l0 is hired to design holographic TAs at 12 GHz. Several examples are provided to support the method. In the end, a linearly polarized circular aperture wideband holographic transmitarray antenna with a radius of 13.3 cm has been manufactured and tested. The antenna achieves 12.5% (11.4-12.9 GHz) 1-dB gain bandwidth and 23.8 dB maximum gain, leading to 21.46% aperture efficiency.
A Machine Learning Approach for Driver Identification Based on CAN-BUS Sensor Data
Authors: Md. Abbas Ali Khan, Mphammad Hanif Ali, AKM Fazlul Haque, Md. Tarek Habib
Date: 2022-07-16 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic, Driver Identification

Driver identification is a momentous field of modern decorated vehicles in the controller area network (CAN-BUS) perspective. Many conventional systems are used to identify the driver. One step ahead, most of the researchers use sensor data of CAN-BUS but there are some difficulties because of the variation of the protocol of different models of vehicle. Our aim is to identify the driver through supervised learning algorithms based on driving behavior analysis. To determine the driver, a driver verification technique is proposed that evaluate driving pattern using the measurement of CAN sensor data. In this paper on-board diagnostic (OBD-II) is used to capture the data from the CAN-BUS sensor and the sensors are listed under SAE J1979 statement. According to the service of OBD-II, drive identification is possible. However, we have gained two types of accuracy on a complete data set with 10 drivers and a partial data set with two drivers. The accuracy is good with less number of drivers compared to the higher number of drivers. We have achieved statistically significant results in terms of accuracy in contrast to the baseline algorithm
Rubisco function, evolution, and engineering
Authors: Noam Prywes, Naiya R Phillips, Owen T Tuck, Luis E Valentin-Alvarado, David F Savage
Date: 2022-07-21 00:00:00

Carbon fixation is the process by which CO2 is converted from a gas into biomass. The Calvin Benson Bassham (CBB) cycle is the dominant carbon fixation pathway on earth, driving >99.5% of the ~120 billion tons of carbon that are "fixed" as sugar, by plants, algae and cyanobacteria. The carboxylase enzyme in the CBB, ribulose-1,5-bisphosphate carboxylase/oxygenase (rubisco), fixes one CO2 molecule per turn of the cycle. Despite being critical to the assimilation of carbon, rubisco's kinetic rate is not very fast and it is a bottleneck in flux through the pathway. This presents a paradox - why hasn't rubisco evolved to be a better catalyst? Many hypothesize that the catalytic mechanism of rubisco is subject to one or more trade-offs, and that rubisco variants have been optimized for their native physiological environment. Here we review the evolution and biochemistry of rubisco through the lens of structure and mechanism in order to understand what trade-offs limit its improvement. We also review the many attempts to improve rubisco itself and, thereby, promote plant growth.
Comment on "On the Extraction of Purely Motor EEG Neural Correlates during an Upper Limb Visuomotor Task"
Authors: Patrick Ofner, Joana Pereira, Reinmar Kobler, Andreas Schwarz, Gernot R. Müller-Putz
Date: 2022-07-22 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG)

Bibian et al. show in their recent paper (Bibi\'an et al. 2021) that eye and head movements can affect the EEG-based classification in a reaching motor task. These movements can generate artefacts that can cause an overoptimistic estimation of the classification accuracy. They speculate that such artefacts jeopardise the interpretation of the results from several motor decoding studies including our study (Ofner et al. 2017). While we endorse their warning about artefacts in general, we do have doubts whether their work supports such a statement with respect to our study. We provide in this commentary a more nuanced contextualization of our work presented in Ofner et al. and the type of artefacts investigated in Bibian et al.
Initial recommendations for performing, benchmarking, and reporting single-cell proteomics experiments
Authors: Laurent Gatto, Ruedi Aebersold, Juergen Cox, Vadim Demichev, Jason Derks, Edward Emmott, Alexander M. Franks, Alexander R. Ivanov, Ryan T. Kelly, Luke Khoury, Andrew Leduc, Michael J. MacCoss, Peter Nemes, David H. Perlman, Aleksandra A. Petelski, Christopher M. Rose, Erwin M. Schoof, Jennifer Van Eyk, Christophe Vanderaa, John R. Yates III, Nikolai Slavov
Date: 2022-07-19 00:00:00
Tasks: Benchmarking, Experimental Design

Analyzing proteins from single cells by tandem mass spectrometry (MS) has become technically feasible. While such analysis has the potential to accurately quantify thousands of proteins across thousands of single cells, the accuracy and reproducibility of the results may be undermined by numerous factors affecting experimental design, sample preparation, data acquisition, and data analysis. Broadly accepted community guidelines and standardized metrics will enhance rigor, data quality, and alignment between laboratories. Here we propose best practices, quality controls, and data reporting recommendations to assist in the broad adoption of reliable quantitative workflows for single-cell proteomics.
Neuroimaging Feature Extraction using a Neural Network Classifier for Imaging Genetics
Authors: Cédric Beaulac, Sidi Wu, Erin Gibson, Michelle F. Miranda, Jiguo Cao, Leno Rocha, Mirza Faisal Beg, Farouk S. Nathoo
Date: 2022-07-08 00:00:00
Tasks: Disease Prediction

A major issue in the association of genes to neuroimaging phenotypes is the high dimension of both genetic data and neuroimaging data. In this article, we tackle the latter problem with an eye toward developing solutions that are relevant for disease prediction. Supported by a vast literature on the predictive power of neural networks, our proposed solution uses neural networks to extract from neuroimaging data features that are relevant for predicting Alzheimer's Disease (AD) for subsequent relation to genetics. Our neuroimaging-genetic pipeline is comprised of image processing, neuroimaging feature extraction and genetic association steps. We propose a neural network classifier for extracting neuroimaging features that are related with disease and a multivariate Bayesian group sparse regression model for genetic association. We compare the predictive power of these features to expert selected features and take a closer look at the SNPs identified with the new neuroimaging features.
Malliavin differentiability of fractional Heston-type model and applications to option pricing
Authors: Marc Mukendi Mpanda
Date: 2022-07-21 00:00:00

This paper defines fractional Heston-type (fHt) model as an arbitrage-free financial market model with the infinitesimal return volatility described by the square of a single stochastic equation with respect to fractional Brownian motion with Hurst parameter H in (0, 1). We extend the idea of Alos and [Alos, E., & Ewald, C. O. (2008). Malliavin differentiability of the Heston volatility and applications to option pricing. Advances in Applied Probability, 40(1), 144-162.] to prove that fHt model is Malliavin differentiable and deduce an expression of expected payoff function having discontinuity of any kind. Some simulations of stock price process and option prices are performed.
Exploring Financial Networks Using Quantile Regression and Granger Causality
Authors: Kara Karpman, Samriddha Lahiry, Diganta Mukherjee, Sumanta Basu
Date: 2022-07-21 00:00:00
Tasks: quantile regression, regression, Time Series Analysis

In the post-crisis era, financial regulators and policymakers are increasingly interested in data-driven tools to measure systemic risk and to identify systemically important firms. Granger Causality (GC) based techniques to build networks among financial firms using time series of their stock returns have received significant attention in recent years. Existing GC network methods model conditional means, and do not distinguish between connectivity in lower and upper tails of the return distribution - an aspect crucial for systemic risk analysis. We propose statistical methods that measure connectivity in the financial sector using system-wide tail-based analysis and is able to distinguish between connectivity in lower and upper tails of the return distribution. This is achieved using bivariate and multivariate GC analysis based on regular and Lasso penalized quantile regressions, an approach we call quantile Granger causality (QGC). By considering centrality measures of these financial networks, we can assess the build-up of systemic risk and identify risk propagation channels. We provide an asymptotic theory of QGC estimators under a quantile vector autoregressive model, and show its benefit over regular GC analysis on simulated data. We apply our method to the monthly stock returns of large U.S. firms and demonstrate that lower tail based networks can detect systemically risky periods in historical data with higher accuracy than mean-based networks. In a similar analysis of large Indian banks, we find that upper and lower tail networks convey different information and have the potential to distinguish between periods of high connectivity that are governed by positive vs negative news in the market.
Test-Time Adaptation via Self-Training with Nearest Neighbor Information
Authors: Minguk Jang, Sae-Young Chung, Hye Won Chung
Date: 2022-07-08 00:00:00
Tasks: Domain Generalization, Pseudo Label, Test-time Adaptation

Test-time adaptation (TTA) aims to adapt a trained classifier using online unlabeled test data only, without any information related to the training procedure. Most existing TTA methods adapt the trained classifier using the classifier's prediction on the test data as pseudo-label. However, under test-time domain shift, accuracy of the pseudo labels cannot be guaranteed, and thus the TTA methods often encounter performance degradation at the adapted classifier. To overcome this limitation, we propose a novel test-time adaptation method, called Test-time Adaptation via Self-Training with nearest neighbor information (TAST), which is composed of the following procedures: (1) adds trainable adaptation modules on top of the trained feature extractor; (2) newly defines a pseudo-label distribution for the test data by using the nearest neighbor information; (3) trains these modules only a few times during test time to match the nearest neighbor-based pseudo label distribution and a prototype-based class distribution for the test data; and (4) predicts the label of test data using the average predicted class distribution from these modules. The pseudo-label generation is based on the basic intuition that a test data and its nearest neighbor in the embedding space are likely to share the same label under the domain shift. By utilizing multiple randomly initialized adaptation modules, TAST extracts useful information for the classification of the test data under the domain shift, using the nearest neighbor information. TAST showed better performance than the state-of-the-art TTA methods on two standard benchmark tasks, domain generalization, namely VLCS, PACS, OfficeHome, and TerraIncognita, and image corruption, particularly CIFAR-10/100C.
Progressive Deblurring of Diffusion Models for Coarse-to-Fine Image Synthesis
Authors: Sangyun Lee, Hyungjin Chung, Jaehyeon Kim, Jong Chul Ye
Date: 2022-07-16 00:00:00
Tasks: Deblurring, Image Generation, Inductive Bias

Recently, diffusion models have shown remarkable results in image synthesis by gradually removing noise and amplifying signals. Although the simple generative process surprisingly works well, is this the best way to generate image data? For instance, despite the fact that human perception is more sensitive to the low frequencies of an image, diffusion models themselves do not consider any relative importance of each frequency component. Therefore, to incorporate the inductive bias for image data, we propose a novel generative process that synthesizes images in a coarse-to-fine manner. First, we generalize the standard diffusion models by enabling diffusion in a rotated coordinate system with different velocities for each component of the vector. We further propose a blur diffusion as a special case, where each frequency component of an image is diffused at different speeds. Specifically, the proposed blur diffusion consists of a forward process that blurs an image and adds noise gradually, after which a corresponding reverse process deblurs an image and removes noise progressively. Experiments show that the proposed model outperforms the previous method in FID on LSUN bedroom and church datasets. Code is available at https://github.com/sangyun884/blur-diffusion.
FewGAN: Generating from the Joint Distribution of a Few Images
Authors: Lior Ben-Moshe, Sagie Benaim, Lior Wolf
Date: 2022-07-18 00:00:00
Tasks: Quantization

We introduce FewGAN, a generative model for generating novel, high-quality and diverse images whose patch distribution lies in the joint patch distribution of a small number of N>1 training samples. The method is, in essence, a hierarchical patch-GAN that applies quantization at the first coarse scale, in a similar fashion to VQ-GAN, followed by a pyramid of residual fully convolutional GANs at finer scales. Our key idea is to first use quantization to learn a fixed set of patch embeddings for training images. We then use a separate set of side images to model the structure of generated images using an autoregressive model trained on the learned patch embeddings of training images. Using quantization at the coarsest scale allows the model to generate both conditional and unconditional novel images. Subsequently, a patch-GAN renders the fine details, resulting in high-quality images. In an extensive set of experiments, it is shown that FewGAN outperforms baselines both quantitatively and qualitatively.
Decentralized scheduling through an adaptive, trading-based multi-agent system
Authors: Michael Kölle, Lennart Rietdorf, Kyrill Schmid
Date: 2022-07-05 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

In multi-agent reinforcement learning systems, the actions of one agent can have a negative impact on the rewards of other agents. One way to combat this problem is to let agents trade their rewards amongst each other. Motivated by this, this work applies a trading approach to a simulated scheduling environment, where the agents are responsible for the assignment of incoming jobs to compute cores. In this environment, reinforcement learning agents learn to trade successfully. The agents can trade the usage right of computational cores to process high-priority, high-reward jobs faster than low-priority, low-reward jobs. However, due to combinatorial effects, the action and observation spaces of a simple reinforcement learning agent in this environment scale exponentially with key parameters of the problem size. However, the exponential scaling behavior can be transformed into a linear one if the agent is split into several independent sub-units. We further improve this distributed architecture using agent-internal parameter sharing. Moreover, it can be extended to set the exchange prices autonomously. We show that in our scheduling environment, the advantages of a distributed agent architecture clearly outweigh more aggregated approaches. We demonstrate that the distributed agent architecture becomes even more performant using agent-internal parameter sharing. Finally, we investigate how two different reward functions affect autonomous pricing and the corresponding scheduling.
An enhanced LBP-based technique with varies size of sliding window approach for handwritten Arabic digit recognition
Authors: Ebrahim Al-wajih, Rozaida Ghazali
Date: 2021-04-06 00:00:00
Proceeding: Multimedia Tools and Applications 2021 4

Many variations of local binary pattern (LBP) were proposed to enhance its performance, including uniform local binary pattern (ULBP), center-symmetric local binary patterns (CS-LBP), center symmetric local ternary patterns (CS-LTP), center symmetric local multilevel pattern (CS-LMP), etc. In this paper, the accuracies of LBP technique and its variations are enhanced using four different sizes of a sliding window approach. This approach is used for investigating whether the features extracted by LBP are significant enough or its versions are needed as well. Five LBP-based techniques have been used including LBP, CS-LBP, CS-LTP, CS-LMP, and U2LBP. They have been applied to an Arabic digit image dataset called MAHDBase. Support vector machine (SVM) and random forests are utilized as classifiers. The experimental results show that the obtained accuracies have been improved by 19.56%, 21.43%, 5.63%, 6.51% and 5.62% for CS-LBP, CS-LMP, U2LBP, CS-LTP, and LBP, respectively, when the sliding window approach has been applied and SVM with linear kernel has been used as a classifier. Moreover, the results show that there is no need to use LBP variations to enhance the accuracy if the sliding window is applied because the highest accuracy has been acquired using LBP. At the end, the accuracy of proposed systems has been compared against other state-of-the-art LBP-based techniques showing the significance of the proposed systems.