pwc-archive/papers-with-abstracts huggingface dataset:
Efficient Topology Assessment for Integrated Transmission and Distribution Network with 10,000+ Inverter-based Resources
Authors: Tao Fu, Dexin Wang, Xiaoyuan Fan, Huiying Ren, Jim Ogle, Yousu Chen
Date: 2022-03-10 00:00:00

The renewable energy proliferation calls upon the grid operators and planners to systematically evaluate the potential impacts of distributed energy resources (DERs). Considering the significant differences between various inverter-based resources (IBRs), especially the different capabilities between grid-forming inverters and grid-following inverters, it is crucial to develop an efficient and effective assessment procedure besides available co-simulation framework with high computation burdens. This paper presents a streamlined graph-based topology assessment for the integrated power system transmission and distribution networks. Graph analyses were performed based on the integrated graph of modified miniWECC grid model and IEEE 8500-node test feeder model, high performance computing platform with 40 nodes and total 2400 CPUs has been utilized to process this integrated graph, which has 100,000+ nodes and 10,000+ IBRs. The node ranking results not only verified the applicability of the proposed method, but also revealed the potential of distributed grid forming (GFM) and grid following (GFL) inverters interacting with the centralized power plants.
False Data Injection Attack on Electric Vehicle-Assisted Voltage Regulation
Authors: YuAn Liu, Omid Ardakanian, Ioanis Nikolaidis, Hao Liang
Date: 2022-03-09 00:00:00
Tasks: Capacity Estimation, Stochastic Optimization

With the large scale penetration of electric vehicles (EVs) and the advent of bidirectional chargers, EV aggregators will become a major player in the voltage regulation market. This paper proposes a novel false data injection attack (FDIA) against the voltage regulation capacity estimation of EV charging stations, the process that underpins voltage regulation in distribution system. The proposed FDIA takes into account the uncertainty in EV mobility and network conditions. The attack vector with the largest expected adverse impact is the solution of a stochastic optimization problem subject to a constraint that ensures it can bypass bad data detection. We show that this attack vector can be determined by solving a sequence of convex quadratically constrained linear programs. The case studies examined in a co-simulation platform, based on two standard test feeders, reveal the vulnerability of the voltage regulation capacity estimation.
Natural Gas Short-Term Operation Problem with Dynamics: A Rank Minimization Approach
Authors: Reza Bayani, Saeed D. Manshadi
Date: 2022-03-09 00:00:00

Natural gas-fired generation units can hedge against the volatility in the uncertain renewable generation, which may occur during very short periods. It is crucial to utilize models capable of correctly capturing the natural gas network dynamics induced by the volatile demand of gas-fired units. The Weymouth equation is commonly implemented in literature to avoid dealing with the mathematical complications of solving the original governing differential equations of the natural gas dynamics. However, it is shown in this paper that this approach is not reliable in the short-term operation problem. Here, the merit of the non-convex transient model is compared with the simplified Weymouth equation, and the drawbacks of employing the Weymouth equation are illustrated. The results demonstrate how changes in the natural gas demand are met by adjustment in the pressure within pipelines rather than the output of natural gas suppliers. This work presents a convex relaxation scheme for the original non-linear and non-convex natural gas flow equations with dynamics, utilizing a rank minimization approach to ensure the tightness. The proposed method renders a computationally efficient framework that can accurately solve the non-convex non-linear gas operation problem and accurately capture its dynamics. Also, the results suggest that the proposed model improves the solution optimality and solution time compared to the original non-linear non-convex model. Finally, the scalability of the proposed approach is verified in the case study.
Fluid registration between lung CT and stationary chest tomosynthesis images
Authors: Lin Tian, Connor Puett, Peirong Liu, Zhengyang Shen, Stephen R. Aylward, Yueh Z. Lee, Marc Niethammer
Date: 2022-03-06 00:00:00
Tasks: Computed Tomography (CT), Image Reconstruction

Registration is widely used in image-guided therapy and image-guided surgery to estimate spatial correspondences between organs of interest between planning and treatment images. However, while high-quality computed tomography (CT) images are often available at planning time, limited angle acquisitions are frequently used during treatment because of radiation concerns or imaging time constraints. This requires algorithms to register CT images based on limited angle acquisitions. We, therefore, formulate a 3D/2D registration approach which infers a 3D deformation based on measured projections and digitally reconstructed radiographs of the CT. Most 3D/2D registration approaches use simple transformation models or require complex mathematical derivations to formulate the underlying optimization problem. Instead, our approach entirely relies on differentiable operations which can be combined with modern computational toolboxes supporting automatic differentiation. This then allows for rapid prototyping, integration with deep neural networks, and to support a variety of transformation models including fluid flow models. We demonstrate our approach for the registration between CT and stationary chest tomosynthesis (sDCT) images and show how it naturally leads to an iterative image reconstruction approach.
ModDrop++: A Dynamic Filter Network with Intra-subject Co-training for Multiple Sclerosis Lesion Segmentation with Missing Modalities
Authors: Han Liu, Yubo Fan, Hao Li, Jiacheng Wang, Dewei Hu, Can Cui, Ho Hin Lee, Huahong Zhang, Ipek Oguz
Date: 2022-03-07 00:00:00
Tasks: Lesion Segmentation

Multiple Sclerosis (MS) is a chronic neuroinflammatory disease and multi-modality MRIs are routinely used to monitor MS lesions. Many automatic MS lesion segmentation models have been developed and have reached human-level performance. However, most established methods assume the MRI modalities used during training are also available during testing, which is not guaranteed in clinical practice. Previously, a training strategy termed Modality Dropout (ModDrop) has been applied to MS lesion segmentation to achieve the state-of-the-art performance with missing modality. In this paper, we present a novel method dubbed ModDrop++ to train a unified network adaptive to an arbitrary number of input MRI sequences. ModDrop++ upgrades the main idea of ModDrop in two key ways. First, we devise a plug-and-play dynamic head and adopt a filter scaling strategy to improve the expressiveness of the network. Second, we design a co-training strategy to leverage the intra-subject relation between full modality and missing modality. Specifically, the intra-subject co-training strategy aims to guide the dynamic head to generate similar feature representations between the full- and missing-modality data from the same subject. We use two public MS datasets to show the superiority of ModDrop++. Source code and trained models are available at https://github.com/han-liu/ModDropPlusPlus.
Widely Distributed Radar Imaging: Unmediated ADMM Based Approach
Authors: Ahmed Murtada, Ruizhi Hu, Bhavani Shankar Mysore Rama Rao, Udo Schroeder
Date: 2022-03-10 00:00:00

In this paper, we present a novel approach to reconstruct a unique image of an observed scene with widely distributed radar sensors. The problem is posed as a constrained optimization problem in which the global image which represents the aggregate view of the sensors is a decision variable. While the problem is designed to promote a sparse solution for the global image, it is constrained such that a relationship with local images that can be reconstructed using the measurements at each sensor is respected. Two problem formulations are introduced by stipulating two different establishments of that relationship. The proposed formulations are designed according to consensus ADMM (CADMM) and sharing ADMM (SADMM), and their solutions are provided accordingly as iterative algorithms. We drive the explicit variable updates for each algorithm in addition to the recommended scheme for hybrid parallel implementation on the distributed sensors and a central processing unit. Our algorithms are validated and their performance is evaluated exploiting Civilian Vehicles Dome data-set to realize different scenarios of practical relevance. Experimental results show the effectiveness of the proposed algorithms, especially in cases with limited measurements.
IRS-enabled Breath Tracking with Colocated Commodity WiFi Transceivers
Authors: Simon Tewes, Markus Heinrichs, Rainer Kronberger, Aydin Sezgin
Date: 2022-03-10 00:00:00

Intelligent reflecting surfaces (IRS) are a key enabler of various new applications in 6G smart radio environments. By utilizing an IRS prototype system, this paper aims to enhance self-interference (SI) cancellation for breath tracking with commodity WiFi devices. SI suppression is a crucial requirement for breath tracking with a single antenna site as the SI severely limits the radio sensing range by shadowing the received signal with its own transmit signal. To this end, we propose to assist SI cancellation by exploiting an IRS to form a suitable cancellation signal in the analog domain. Building upon a 256-element IRS prototype, we present results of breath tracking with IRS-assisted SI cancellation from a practical testbed. We use inexpensive WiFi hardware to extract the Channel State Information (CSI) in the 5~GHz band and analyze the phase change between a colocated transmitter and receiver with added local oscillator (LO) synchronization. We are able to track the breath of a test person regardless of the position in an indoor environment in a room-level range. The presented case study shows promising performance in both capturing the breath frequency as well as the breathing patterns.
Simultaneous Transmission and Reflection Reconfigurable Intelligent Surface Assisted Full-Duplex Communications
Authors: Yiru Wang, Pengxin Guan, Hongkang Yu, Yuping Zhao
Date: 2022-03-10 00:00:00

This work demonstrates the effectiveness of a novel simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) in Full-Duplex (FD) aided communication system. The objective is to minimize the total transmit power by jointly designing the transmit power and the transmitting and reflecting (T&R) coefficients of the STAR-RIS. To solve the nonconvex problem, an efficient algorithm is proposed by utilizing the alternating optimization framework to iteratively optimize variables. Specifically, in each iteration, we drive the closed-form expression for the optimal power design. The successive convex approximation (SCA) method and semidefinite program (SDP) are used to solve the passive beamforming optimization problem. Numerical results verify the convergence and effectiveness of the proposed algorithm, and further reveal in which scenarios STAR-RIS assisted FD communication defeats the Half-Duplex and conventional RIS.
Data-Folding and Hyperspace Coding for Multi-Dimensonal Time-Series Data Imaging
Authors: Chao Lian, Yuliang Zhao, Zhikun Zhan, Wen J. Li
Date: 2022-03-10 00:00:00
Tasks: Action Recognition, Classification, Fault Detection, Fault Diagnosis, Time Series, Time Series Analysis, Time Series Classification

Multi-Dimensional time series classification and prediction has been widely used in many fields, such as disease prevention, fault diagnosis and action recognition. However, the traditional method needs manual intervention and inference, and cannot realize the figurative expression of multi-Dimensional data, which lead to inadequate information mining. Inspired by the strong power of deep learning technology in image processing, we propose a unified time-series image fusion framework to transform multi-modal data into 2D-image, and then realize automatic feature extraction and classification based on a lightweight convolutional neural network. We present two basic image coding methods, Gray image coding, RGB image coding, and their step coding methods. Considering the universality of different application fields, we extended the coding method and propose two types transform coding, Transform-RGB coding and RGB-Transform coding, to improve the multi-domain representation ability. By applying to three typical scenes of Parkinson's disease diagnosis, bearing fault detection and gymnastics action recognition, we obtained the highest classification accuracy of 100%, 92.86% and 99.70% respectively, which were all higher than the classical processing methods. It proves the strong classification ability and universality of our coding framework to different multi-dimensional scenes. We expect that this method can be used and perform well in other scenarios, and be potential to facilitate the progress of related technology.
NOMA-enabled Backscatter Communications for Green Transportation in Automotive-Industry 5.0
Authors: Wali Ullah Khan, Asim Ihsan, Tu N. Nguyen, Zain Ali, Muhammad Awais Javed
Date: 2022-03-10 00:00:00

Automotive-Industry 5.0 will use emerging 6G communications to provide robust, computationally intelligent, and energy-efficient data sharing among various onboard sensors, vehicles, and other Intelligent Transportation System (ITS) entities. Non-Orthogonal Multiple Access (NOMA) and backscatter communications are two key techniques of 6G communications for enhanced spectrum and energy efficiency. In this paper, we provide an introduction to green transportation and also discuss the advantages of using backscatter communications and NOMA in Automotive Industry 5.0. We also briefly review the recent work in the area of NOMA empowered backscatter communications. We discuss different use cases of backscatter communications in NOMA-enabled 6G vehicular networks. We also propose a multi-cell optimization framework to maximize the energy efficiency of the backscatter-enabled NOMA vehicular network. In particular, we jointly optimize the transmit power of the roadside unit and the reflection coefficient of the backscatter device in each cell, where several practical constraints are also taken into account. The problem of energy efficiency is formulated as nonconvex which is hard to solve directly. Thus, first, we adopt the Dinkelbach method to transform the objective function into a subtractive one, then we decouple the problem into two subproblems. Second, we employ dual theory and KKT conditions to obtain efficient solutions. Finally, we highlight some open issues and future research opportunities related to NOMA-enabled backscatter communications in 6G vehicular networks.
A Lifted $\ell_1 $ Framework for Sparse Recovery
Authors: Yaghoub Rahimi, Sung Ha Kang, Yifei Lou
Date: 2022-03-10 00:00:00

Motivated by re-weighted $\ell_1$ approaches for sparse recovery, we propose a lifted $\ell_1$ (LL1) regularization which is a generalized form of several popular regularizations in the literature. By exploring such connections, we discover there are two types of lifting functions which can guarantee that the proposed approach is equivalent to the $\ell_0$ minimization. Computationally, we design an efficient algorithm via the alternating direction method of multiplier (ADMM) and establish the convergence for an unconstrained formulation. Experimental results are presented to demonstrate how this generalization improves sparse recovery over the state-of-the-art.
Radar-based Respiratory Rate Monitoring in Standing Position
Authors: Tassneem Helal, Fady Aziz, Omar Metwally, Marco F. Huber, Dominik Alscher, Christoph Wasser, Urs Schneider
Date: 2022-03-09 00:00:00
Tasks: Position, Respiratory Rate Estimation

Estimating human vital signs in a contactless non-invasive method using radar provides a convenient method in the medical field to conduct several health checkups easily and quickly. In addition to monitoring while sitting and sleeping, the standing position has aroused interest for both the industrial and medical fields. However, it is more challenging due to the micro motions induced by the body for balancing that may cause false respiratory rate estimation. In this work, we focus on the measurement of the respiratory rate of a standing person accurately with the capability of heavy breath detection and estimation. Multiple estimation approaches are presented and compared, including spectral estimation, deep-learning-based approaches, and adaptive peak selection with Kalman filtering. The latest technique is showing the best performance with an absolute error rate of 1.5 bpm, when compared to a Vernier Go Direct\textsuperscript{\textregistered} respiration belt.
Evaluation of Performance for Human In-Vivo Conductivity Estimation from EEG and sEEG Recorded in Simultaneous with Intracerebral Electrical Stimulation
Authors: Hamza Altakroury, Laurent Koessler, Radu Ranta, Janis Hofmanis, Sophie Colnat Coulbois, Louis Maillard, Valérie Louis Dorr
Date: 2022-03-10 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Assigning accurate conductivity values in human head models is an essential factor for performing precise electroencephalographic (EEG) source localization and targeting of transcranial electrical stimulation (TES). Unfortunately, the literature reports diverging conductivity values of the different tissues in the human head. The current study analyzes first the performance of in-vivo conductivity estimation for different configurations concerning the localization of the electrical source and measurement. Then, it presents conductivity estimates for three epileptic patients using scalp EEG and intracerebral stereotactic EEG (sEEG) acquired in simultaneous with intracerebral electrical stimulation. The estimates of the conductivities were based on finite-element models of the human head with five tissue compartments of homogeneous and isotropic conductivities. The results of this study show that in-vivo conductivity estimation can lead to different estimated conductivities for the same patient when considering different stimulation positions, different measurement positions or different measurement modalities (sEEG or EEG). This work provides important guidelines to in-vivo conductivity estimation and explains the variability among the conductivity values which have been reported in previous studies.
Optimal times to buy and sell a home
Authors: Matthew Lorig, Natchanon Suaysom
Date: 2022-03-10 00:00:00

We consider a financial market in which the risk-free rate of interest is modeled as a Markov diffusion. We suppose that home prices are set by a representative home-buyer, who can afford to pay only a fixed cash-flow per unit time for housing. The cash-flow is a fraction of the representative home-buyer's salary, which grows at a rate that is proportional to the risk-free rate of interest. As a result, in the long-run, higher interest rates lead to faster growth of home prices. The representative home-buyer finances the purchase of a home by taking out a mortgage. The mortgage rate paid by the home-buyer is fixed at the time of purchase and equal to the risk-free rate of interest plus a positive constant. As the home-buyer can only afford to pay a fixed cash-flow per unit time, a higher mortgage rate limits the size of the loan the home-buyer can take out. As a result, the short-term effect of higher interest rates is to lower the value of homes. In this setting, we consider an investor who wishes to buy and then sell a home in order to maximize his discounted expected profit. This leads to a nested optimal stopping problem. We use a nonnegative concave majorant approach to derive the investor's optimal buying and selling strategies. Additionally, we provide a detailed analytic and numerical study of the case in which the risk-free rate of interest is modeled by a Cox-Ingersoll-Ross (CIR) process. We also examine, in the case of CIR interest rates, the expected time that the investor waits before buying and then selling a home when following the optimal strategies.
Memory-augmented Deep Unfolding Network for Guided Image Super-resolution
Authors: Man Zhou, Keyu Yan, Jinshan Pan, Wenqi Ren, Qi Xie, Xiangyong Cao
Date: 2022-02-12 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Guided image super-resolution (GISR) aims to obtain a high-resolution (HR) target image by enhancing the spatial resolution of a low-resolution (LR) target image under the guidance of a HR image. However, previous model-based methods mainly takes the entire image as a whole, and assume the prior distribution between the HR target image and the HR guidance image, simply ignoring many non-local common characteristics between them. To alleviate this issue, we firstly propose a maximal a posterior (MAP) estimation model for GISR with two types of prior on the HR target image, i.e., local implicit prior and global implicit prior. The local implicit prior aims to model the complex relationship between the HR target image and the HR guidance image from a local perspective, and the global implicit prior considers the non-local auto-regression property between the two images from a global perspective. Secondly, we design a novel alternating optimization algorithm to solve this model for GISR. The algorithm is in a concise framework that facilitates to be replicated into commonly used deep network structures. Thirdly, to reduce the information loss across iterative stages, the persistent memory mechanism is introduced to augment the information representation by exploiting the Long short-term memory unit (LSTM) in the image and feature spaces. In this way, a deep network with certain interpretation and high representation ability is built. Extensive experimental results validate the superiority of our method on a variety of GISR tasks, including Pan-sharpening, depth image super-resolution, and MR image super-resolution.
Overview of the IWSLT 2017 Evaluation Campaign
Authors: Mauro Cettolo, Marcello Federico, Luisa Bentivogli, Jan Niehues, Sebastian Stüker, Katsuhito Sudoh, Koichiro Yoshino, Christian Federmann
Date: 2017-12-01 00:00:00
Proceeding: IWSLT 2017 12
Tasks: Machine Translation, Translation

The IWSLT 2017 evaluation campaign has organised three tasks. The Multilingual task, which is about training machine translation systems handling many-to-many language directions, including so-called zero-shot directions. The Dialogue task, which calls for the integration of context information in machine translation, in order to resolve anaphoric references that typically occur in human-human dialogue turns. And, finally, the Lecture task, which offers the challenge of automatically transcribing and translating real-life university lectures. Following the tradition of these reports, we will described all tasks in detail and present the results of all runs submitted by their participants.
Going beyond zero-shot MT: combining phonological, morphological and semantic factors. The UdS-DFKI System at IWSLT 2017
Authors: Cristina España-Bonet, Josef van Genabith
Date: 2017-12-01 00:00:00
Proceeding: IWSLT 2017 12
Tasks: Translation

This paper describes the UdS-DFKI participation to the multilingual task of the IWSLT Evaluation 2017. Our approach is based on factored multilingual neural translation systems following the small data and zero-shot training conditions. Our systems are designed to fully exploit multilinguality by including factors that increase the number of common elements among languages such as phonetic coarse encodings and synsets, besides shallow part-of-speech tags, stems and lemmas. Document level information is also considered by including the topic of every document. This approach improves a baseline without any additional factor for all the language pairs and even allows beyond-zero-shot translation. That is, the translation from unseen languages is possible thanks to the common elements —especially synsets in our models— among languages.