pwc-archive/papers-with-abstracts huggingface dataset:
Manifold Model for High-Resolution fMRI Joint Reconstruction and Dynamic Quantification
Authors: Shouchang Guo, Jeffrey A. Fessler, Douglas C. Noll
Date: 2021-04-16 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Oscillating Steady-State Imaging (OSSI) is a recent fMRI acquisition method that exploits a large and oscillating signal, and can provide high SNR fMRI. However, the oscillatory nature of the signal leads to an increased number of acquisitions. To improve temporal resolution and accurately model the nonlinearity of OSSI signals, we build the MR physics for OSSI signal generation as a regularizer for the undersampled reconstruction rather than using subspace models that are not well suited for the data. Our proposed physics-based manifold model turns the disadvantages of OSSI acquisition into advantages and enables joint reconstruction and quantification. OSSI manifold model (OSSIMM) outperforms subspace models and reconstructs high-resolution fMRI images with a factor of 12 acceleration and without spatial or temporal resolution smoothing. Furthermore, OSSIMM can dynamically quantify important physics parameters, including $R_2^*$ maps, with a temporal resolution of 150 ms.
Two-way Fixed Effects and Differences-in-Differences Estimators with Several Treatments
Authors: Clément de Chaisemartin, Xavier D'Haultfœuille
Date: 2020-12-18 00:00:00
Tasks: regression

We study two-way-fixed-effects regressions (TWFE) with several treatment variables. Under a parallel trends assumption, we show that the coefficient on each treatment identifies a weighted sum of that treatment's effect, with possibly negative weights, plus a weighted sum of the effects of the other treatments. Thus, those estimators are not robust to heterogeneous effects and may be contaminated by other treatments' effects. We further show that omitting a treatment from the regression can actually reduce the estimator's bias, unlike what would happen under constant treatment effects. We propose an alternative difference-in-differences estimator, robust to heterogeneous effects and immune to the contamination problem. In the application we consider, the TWFE regression identifies a highly non-convex combination of effects, with large contamination weights, and one of its coefficients significantly differs from our heterogeneity-robust estimator.
Fuzzy Difference-in-Discontinuities: Identification Theory and Application to the Affordable Care Act
Authors: Hector Galindo-Silva, Nibene Habib Some, Guy Tchuente
Date: 2018-12-16 00:00:00

This paper explores the use of a fuzzy regression discontinuity design where multiple treatments are applied at the threshold. The identification results show that, under the very strong assumption that the change in the probability of treatment at the cutoff is equal across treatments, a difference-in-discontinuities estimator identifies the treatment effect of interest. The point estimates of the treatment effect using a simple fuzzy difference-in-discontinuities design are biased if the change in the probability of a treatment applying at the cutoff differs across treatments. Modifications of the fuzzy difference-in-discontinuities approach that rely on milder assumptions are also proposed. Our results suggest caution is needed when applying before-and-after methods in the presence of fuzzy discontinuities. Using data from the National Health Interview Survey, we apply this new identification strategy to evaluate the causal effect of the Affordable Care Act (ACA) on older Americans' health care access and utilization.
A public micro pension programme in Brazil: Heterogeneity among states and setting up of benefit age adjustment
Authors: Renata Gomes Alcoforado, Alfredo D. Egídio dos Reis
Date: 2021-04-19 00:00:00

Brazil is the 5th largest country in the world, despite of having a ``High Human Development'' it is the 9th most unequal country. The existing Brazilian micro pension programme is one of the safety nets for poor people. To become eligible for this benefit, each person must have an income that is less than a quarter of the Brazilian minimum monthly wage and be either over 65 or considered disabled. That minimum income corresponds to approximately $2$ dollars per day. This paper analyses quantitatively some aspects of this programme in the Public Pension System of Brazil. We look for the impact of some particular economic variables on the number of people receiving the benefit, and seek if that impact significantly differs among the 27 Brazilian Federal Units. We search for heterogeneity. We perform regression and spatial cluster analysis for detection of geographical grouping. We use a database that includes the entire population that receives the benefit. Afterwards, we calculate the amount that the system spends with the beneficiaries, estimate values \textit{per capita} and the weight of each UF, searching for heterogeneity reflected on the amount spent \textit{per capita}. In this latter calculation we use a more comprehensive database, by individual, that includes all people that started receiving a benefit under the programme in the period from 2nd of January 2018 to 6th of April 2018. We compute the expected discounted benefit and confirm a high heterogeneity among UF's as well as gender. We propose achieving a more equitable system by introducing `age adjusting factors' to change the benefit age.
Decomposition scheme matters more than you may think
Authors: Anna Naszodi
Date: 2021-04-19 00:00:00

This paper promotes the application of a path-independent decomposition scheme. Besides presenting some theoretical arguments supporting this decomposition scheme, this study also illustrates the difference between the path-independent decomposition scheme and a popular sequential decomposition with an empirical application of the two schemes. The empirical application is about identifying a directly unobservable phenomenon, i.e. the changing social gap between people from different educational strata, through its effect on marriages and cohabitations. It exploits census data from four waves between 1977 and 2011 about the American, French, Hungarian, Portuguese, and Romanian societies. For some societies and periods, the outcome of the decomposition is found to be highly sensitive to the choice of the decomposition scheme. These examples illustrate the point that a careful selection of the decomposition scheme is crucial for adequately documenting the dynamics of unobservable factors.
The probabilistic rank random assignment rule and its axiomatic characterization
Authors: Yajing Chen, Patrick Harless, Zhenhua Jiao
Date: 2021-04-19 00:00:00
Tasks: Fairness

This paper considers the problem of randomly assigning a set of objects to a set of agents based on the ordinal preferences of agents. We generalize the well-known immediate acceptance algorithm to the afore-mentioned random environments and define the probabilistic rank rule (PR rule). We introduce two new axioms: sd-rank-fairness, and equal-rank envy-freeness. Sd-rank-fairness implies sd-efficiency. Equal-rank envy-freeness implies equal treatment of equals. Sd-rank-fairness and equal-rank envy-freeness are enough to characterize the PR rule.
New axioms for top trading cycles
Authors: Siwei Chen, Yajing Chen, Chia-Ling Hsu
Date: 2021-04-19 00:00:00

School choice is of great importance both in theory and practice. This paper studies the (student-optimal) top trading cycles mechanism (TTCM) in an axiomatic way. We introduce two new axioms: MBG (mutual best group)-quota-rationality and MBG-robust efficiency. While stability implies MBG-quota-rationality, MBG-robust efficiency is weaker than robust efficiency, which is stronger than the combination of efficiency and group strategy-proofness. The TTCM is characterized by MBG-quota-rationality and MBG-robust efficiency. Our results construct a new basis to compare the TTCM with the other school choice mechanisms, especially the student-optimal stable mechanism under Ergin but not Kesten-acyclic priority structures.
Pareto Optimality, Functional Dependence and Collective Agency
Authors: Chenwei Shi, Yiyang Wang
Date: 2021-04-19 00:00:00

This paper approaches the problem of understanding collective agency from a logical and game-theoretical perspective. Instead of collective intentionality, our analysis highlights the role of Pareto optimality. To facilitate the analysis, we propose a logic of preference and functional dependence by extending the logic of functional dependence. In this logic, we can express Pareto optimality and thus reason about collective agency.
Costlier switching strengthens competition even without advertising
Authors: Sander Heinsalu
Date: 2021-04-18 00:00:00

Consumers only discover at the first seller which product best fits their needs, then check its price online, then decide on buying. Switching sellers is costly. Equilibrium prices fall in the switching cost, eventually to the monopoly level, despite the exit of lower-value consumers when changing sellers becomes costlier. More expensive switching makes some buyers exit the market, leaving fewer inframarginal buyers to the sellers. Marginal buyers may change in either direction, so for a range of parameters, all firms cut prices.
Mechanism Design Approach to School Choice: One versus Many
Authors: Battal Dogan
Date: 2021-04-17 00:00:00

A vast majority of the school choice literature focuses on designing mechanisms to simultaneously assign students to many schools, and employs a "make it up as you go along" approach when it comes to each school's admissions policy. An alternative approach is to focus on the admissions policy for one school. This is especially relevant for effectively communicating policy objectives such as achieving a diverse student body or implementing affirmative action. I argue that the latter approach is relatively under-examined and deserves more attention in the future.
DW-GAN: A Discrete Wavelet Transform GAN for NonHomogeneous Dehazing
Authors: Minghan Fu, Huan Liu, Yankun Yu, Jun Chen, Keyan Wang
Date: 2021-04-18 00:00:00

Hazy images are often subject to color distortion, blurring, and other visible quality degradation. Some existing CNN-based methods have great performance on removing homogeneous haze, but they are not robust in non-homogeneous case. The reasons are mainly in two folds. Firstly, due to the complicated haze distribution, texture details are easy to be lost during the dehazing process. Secondly, since the training pairs are hard to be collected, training on limited data can easily lead to over-fitting problem. To tackle these two issues, we introduce a novel dehazing network using 2D discrete wavelet transform, namely DW-GAN. Specifically, we propose a two-branch network to deal with the aforementioned problems. By utilizing wavelet transform in DWT branch, our proposed method can retain more high-frequency knowledge in feature maps. In order to prevent over-fitting, ImageNet pre-trained Res2Net is adopted in the knowledge adaptation branch. Owing to the robust feature representations of ImageNet pre-training, the generalization ability of our network is improved dramatically. Finally, a patch-based discriminator is used to reduce artifacts of the restored images. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-arts quantitatively and qualitatively.
XCloud-pFISTA: A Medical Intelligence Cloud for Accelerated MRI
Authors: Yirong Zhou, Chen Qian, Yi Guo, Zi Wang, Jian Wang, Biao Qu, Di Guo, Yongfu You, Xiaobo Qu
Date: 2021-04-18 00:00:00
Tasks: Cloud Computing, Image Reconstruction

Machine learning and artificial intelligence have shown remarkable performance in accelerated magnetic resonance imaging (MRI). Cloud computing technologies have great advantages in building an easily accessible platform to deploy advanced algorithms. In this work, we develop an open-access, easy-to-use and high-performance medical intelligence cloud computing platform (XCloud-pFISTA) to reconstruct MRI images from undersampled k-space data. Two state-of-the-art approaches of the Projected Fast Iterative Soft-Thresholding Algorithm (pFISTA) family have been successfully implemented on the cloud. This work can be considered as a good example of cloud-based medical image reconstruction and may benefit the future development of integrated reconstruction and online diagnosis system.
External Dynamic InTerference Estimation and Removal (EDITER) for low field MRI
Authors: Sai Abitha Srinivas, Stephen F Cauley, Jason P Stockmann, Charlotte R Sappo, Christopher E Vaughn, Lawrence L Wald, William A Grissom, Clarissa Z Cooley
Date: 2021-04-18 00:00:00

Purpose: Point-of-care MRI requires operation outside of a faraday shielded room normally used to block image-degrading electromagnetic Interference (EMI). To address this, we introduce the EDITER method, an external sensor based dynamic EMI estimation and removal method to retrospectively remove time-varying external interference sources. Theory and Methods: The method acquires data from multiple EMI detectors (tuned receive coils and electrodes placed on the body) simultaneous with the primary MR coil during image data acquisition. We dynamically calculate impulse response functions that map the data from the detectors to the artifacts in the kspace data, then remove the transformed detected EMI from the MR data. Performance of the EDITER algorithm was assessed in phantom and in vivo imaging experiments in an 80mT portable brain MRI in a controlled EMI environment and with an open 47.5mT MRI scanner in an uncontrolled EMI setting. Results: In the controlled setting, the effectiveness of the EDITER technique was demonstrated for specific types of introduced EMI sources with up to a 97% reduction of structured EMI and up to 76% reduction of broadband EMI. In the uncontrolled EMI experiments, we demonstrate EMI reductions of 37% with a single pickup coil and 89% with a single electrode and up to 99% with both. Conclusion: The EDITER technique is a flexible and robust method to improve image quality in portable MRI systems with minimal passive shielding. This could reduce the reliance of MRI on shielded rooms and allow for truly portable MRI with specialized compact POC scanners
An Unsupervised Learning-Based Approach for Symbol-Level-Precoding
Authors: Abdullahi Mohammad, Christos Masouros, Yiannis Andreopoulos
Date: 2021-04-19 00:00:00

This paper proposes an unsupervised learning-based precoding framework that trains deep neural networks (DNNs) with no target labels by unfolding an interior point method (IPM) proximal `log' barrier function. The proximal `log' barrier function is derived from the strict power minimization formulation subject to signal-to-interference-plus-noise ratio (SINR) constraint. The proposed scheme exploits the known interference via symbol-level precoding (SLP) to minimize the transmit power and is named strict Symbol-Level-Precoding deep network (SLP-SDNet). The results show that SLP-SDNet outperforms the conventional block-level-precoding (Conventional BLP) scheme while achieving near-optimal performance faster than the SLP optimization-based approach.
A Survey on Millimeter-Wave Beamforming Enabled UAV Communications and Networking
Authors: Zhenyu Xiao, Lipeng Zhu, Yanming Liu, Pengfei Yi, Rui Zhang, Xiang-Gen Xia, Robert Schober
Date: 2021-04-19 00:00:00

Unmanned aerial vehicles (UAVs) have found widespread commercial, civilian, and military applications. Wireless communication has always been one of the core technologies for UAV. However, the communication capacity is becoming a bottleneck for UAV to support more challenging application scenarios. The heavily-occupied sub-6 GHz frequency band is not sufficient to meet the ultra high-data-traffic requirements. The utilization of the millimeter-wave (mmWave) frequency bands is a promising direction for UAV communications, where large antenna arrays can be packed in a small area on the UAV to perform three-dimensional (3D) beamforming. On the other hand, UAVs serving as aerial access points or relays can significantly enhance the coverage and quality of service of the terrestrial mmWave cellular networks. In this paper, we provide a comprehensive survey on mmWave beamforming enabled UAV communications and networking. The technical potential of and challenges for mmWave-UAV communications are presented first. Then, we provide an overview on relevant mmWave antenna structures and channel modeling. Subsequently, the technologies and solutions for UAV-connected mmWave cellular networks and mmWave-UAV ad hoc networks are reviewed, respectively. Finally, we present open issues and promising directions for future research in mmWave beamforming enabled UAV communications and networking.
SPSA-Based Successive Beamforming for Mobile Satellite Receivers with Phased Arrays
Authors: Zheng Chen, Håkan Johansson
Date: 2021-04-19 00:00:00

Efficient and low-complexity beamforming design is an important element of satellite communication systems with mobile receivers equipped with phased arrays. In this work, we apply the simultaneous perturbation stochastic approximation (SPSA) method with successive sub-array selection for finding the optimal antenna weights that maximize the received signal power at a uniform plane array (UPA). The proposed algorithms are based on iterative gradient approximation by injecting some carefully designed perturbations on the parameters to be estimated. Additionally, the successive sub-array selection technique enhances the performance of SPSA-based algorithms and makes them less sensitive to the initial beam direction. Simulation results show that our proposed algorithms can achieve efficient and reliable performance even when the initial beam direction is not well aligned with the satellite direction.
Research on Resource Allocation for Efficient Federated Learning
Authors: Jianyang Ren, Wanli Ni, Gaofeng Nie, Hui Tian
Date: 2021-04-19 00:00:00
Tasks: Edge-computing, Federated Learning

As a promising solution to achieve efficient learning among isolated data owners and solve data privacy issues, federated learning is receiving wide attention. Using the edge server as an intermediary can effectively collect sensor data, perform local model training, and upload model parameters for global aggregation. So this paper proposes a new framework for resource allocation in a hierarchical network supported by edge computing. In this framework, we minimize the weighted sum of system cost and learning cost by optimizing bandwidth, computing frequency, power allocation and subcarrier assignment. To solve this challenging mixed-integer non-linear problem, we first decouple the bandwidth optimization problem(P1) from the whole problem and obtain a closed-form solution. The remaining computational frequency, power, and subcarrier joint optimization problem(P2) can be further decomposed into two sub-problems: latency and computational frequency optimization problem(P3) and transmission power and subcarrier optimization problem(P4). P3 is a convex optimization problem that is easy to solve. In the joint optimization problem(P4), the optimal power under each subcarrier selection can be obtained first through the successive convex approximation(SCA) algorithm. Substituting the optimal power value obtained back to P4, the subproblem can be regarded as an assignment problem, so the Hungarian algorithm can be effectively used to solve it. The solution of problem P2 is accomplished by solving P3 and P4 iteratively. To verify the performance of the algorithm, we compare the proposed algorithm with five algorithms; namely Equal bandwidth allocation, Learning cost guaranteed, Greedy subcarrier allocation, System cost guaranteed and Time-biased algorithm. Numerical results show the significant performance gain and the robustness of the proposed algorithm in the face of parameter changes.
Internet of Fly Things For Post-Disaster Recovery Based on Multi-environment
Authors: Abdu Saif, Kaharudin Bin Dimyati, Kamarul Ariffin Bin Noordin, Nor Shahida Mohd Shah, Qazwan Abdullah, Fadhil Mukhlif, Mahathir Mohamad
Date: 2021-04-18 00:00:00

Natural disasters such as floods and earthquakes immensely impact the telecommunication network infrastructure, leading to the malfunctioning and interruption of wireless services. Consequently, the user devices under the disaster zone are unable to access the cellular base stations. Wireless coverage on an unmanned aerial vehicle (UAV) is considered for providing coverage service to ground user devices in disaster events. This work evaluated the efficient performance of wireless coverage services of UAVs to provide the internet to fly things to help recover the communications link in a natural disaster in multi environments. The results demonstrate the line of sight, nonline of sight, path loss, and coverage probability for the radio propagation environment scenario. Therefore, the path loss and coverage probability are affected by the user devices' elevation angle and distance in the multi-environment system. The user position's optimum user device distance and elevation angle are also investigated to improve the coverage probability, which could be especially useful for the UAV deployment design.
Noise Attention based Spectrum Anomaly Detection Method for Unauthorized Bands
Authors: Jing Xu, Yu Tian, Shuai Yuan, Naijin Liu
Date: 2021-04-17 00:00:00
Tasks: Anomaly Detection

Spectrum anomaly detection is of great importance in wireless communication to secure safety and improve spectrum efficiency. However, spectrum anomaly detection faces many difficulties, especially in unauthorized frequency bands. For example, the composition of unauthorized frequency bands is very complex and the abnormal usage patterns are unknown in prior. In this paper, a noise attention method is proposed for unsupervised spectrum anomaly detection in unauthorized bands. First of all, we theoretically prove that the anomalies in unauthorized bands will raise the noise floor of spectrogram after VAE reconstruction. Then, we introduce a novel anomaly metric named as noise attention score to more effectively capture spectrum anomaly. The effectiveness of the proposed method is experimentally verified in 2.4 GHz ISM band. Leveraging the noise attention score, the AUC metric of anomaly detection is increased by 0.193. The proposed method is beneficial to reliably detecting abnormal spectrum while keeping low false alarm rate.
Spatial Correlation Aware Compressed Sensing for User Activity Detection and Channel Estimation in Massive MTC
Authors: Hamza Djelouat, Markus Leinonen, Markku Juntti
Date: 2021-04-17 00:00:00
Tasks: Action Detection, Activity Detection, compressed sensing, User Identification

Grant-free access is considered as a key enabler to massive machine-type communications (mMTC) as it promotes energy-efficiency and small signalling overhead. Due to the sporadic user activity in mMTC, joint user identification and channel estimation (JUICE) is a main challenge. This paper addresses the JUICE in single-cell mMTC with single-antenna users and a multi-antenna base station (BS) under spatially correlated fading channels. In particular, by leveraging the sporadic user activity, we solve the JUICE in a multi measurement vector compressed sensing (CS) framework under two different cases, with and without the knowledge of prior channel distribution information (CDI) at the BS. First, for the case without prior information, we formulate the JUICE as an iterative reweighted $\ell_{2,1}$-norm minimization problem. Second, when the CDI is known to the BS, we exploit the available information and formulate the JUICE from a Bayesian estimation perspective as a maximum \emph{a posteriori} probability (MAP) estimation problem. For both JUICE formulations, we derive efficient iterative solutions based on the alternating direction method of multipliers (ADMM). The numerical experiments show that the proposed solutions achieve higher channel estimation quality and activity detection accuracy with shorter pilot sequences compared to existing algorithms.