pwc-archive/papers-with-abstracts huggingface dataset:
Geometry-based spherical JND modeling for 360$^\circ$ display
Authors: Hongan Wei, Jiaqi Liu, Bo Chen, Liqun Lin, Weiling Chen, Tiesong Zhao
Date: 2023-03-07 00:00:00
Tasks: Video Compression

360$^\circ$ videos have received widespread attention due to its realistic and immersive experiences for users. To date, how to accurately model the user perceptions on 360$^\circ$ display is still a challenging issue. In this paper, we exploit the visual characteristics of 360$^\circ$ projection and display and extend the popular just noticeable difference (JND) model to spherical JND (SJND). First, we propose a quantitative 2D-JND model by jointly considering spatial contrast sensitivity, luminance adaptation and texture masking effect. In particular, our model introduces an entropy-based region classification and utilizes different parameters for different types of regions for better modeling performance. Second, we extend our 2D-JND model to SJND by jointly exploiting latitude projection and field of view during 360$^\circ$ display. With this operation, SJND reflects both the characteristics of human vision system and the 360$^\circ$ display. Third, our SJND model is more consistent with user perceptions during subjective test and also shows more tolerance in distortions with fewer bit rates during 360$^\circ$ video compression. To further examine the effectiveness of our SJND model, we embed it in Versatile Video Coding (VVC) compression. Compared with the state-of-the-arts, our SJND-VVC framework significantly reduced the bit rate with negligible loss in visual quality.
Prediction of a T-cell/MHC-I-based immune profile for colorectal liver metastases from CT images using ensemble learning
Authors: Ralph Saber, David Henault, Rolando Rebolledo, Simon Turcotte, Samuel Kadoury
Date: 2023-03-06 00:00:00
Tasks: Ensemble Learning

Colorectal cancer liver metastases (CLM) are the most common type of distant metastases originating from the abdomen and are characterized by a high recurrence rate after curative resection. It has been previously reported that CLM presenting a low cluster of differentiation 3 (CD3) positive T-cell infiltration density concurrent with a high major histocompatibility complex class I (MHC-I) expression were associated with poor clinical outcomes. In this study, we attempt to noninvasively predict whether a CLM exhibit the CD3LowMHCHigh immunological profile using preoperative CT images. To this end, we propose an ensemble network combining multiple Attentive Interpretable Tabular learning (TabNet) models, trained using CT-derived radiomic features. A total of 160 CLM were included in this study and randomly divided between a training set (n=130) and a hold-out test set (n=30). The proposed model yielded good prediction performance on the test set with an accuracy of 70.0% [95% confidence interval 53.6%-86.4%] and an area under the curve of 69.4% [52.9%-85.9%]. It also outperformed other off-the-shelf machine learning models. We finally demonstrated that the predicted immune profile was associated with a shorter disease-specific survival (p = .023) and time-to-recurrence (p = .020), showing the value of assessing the immune response.
Deep Learning Based Dominant Index Lesion Segmentation for MR-guided Radiation Therapy of Prostate Cancer
Authors: Josiah Simeth, Jue Jiang, Anton Nosov, Andreas Wibmer, Michael Zelefsky, Neelam Tyagi, Harini Veeraraghavan
Date: 2023-03-06 00:00:00
Tasks: Lesion Segmentation, Panoptic Segmentation, Segmentation

Dose escalation radiotherapy allows increased control of prostate cancer (PCa) but requires segmentation of dominant index lesions (DIL), motivating the development of automated methods for fast, accurate, and consistent segmentation of PCa DIL. We evaluated five deep-learning networks on apparent diffusion coefficient (ADC) MRI from 500 lesions in 365 patients arising from internal training Dataset 1 (1.5Tesla GE MR with endorectal coil), external ProstateX Dataset 2 (3Tesla Siemens MR), and internal inter-rater Dataset 3 (3Tesla Philips MR). The networks include: multiple resolution residually connected network (MRRN) and MRRN regularized in training with deep supervision (MRRN-DS), Unet, Unet++, ResUnet, and fast panoptic segmentation (FPSnet) as well as fast panoptic segmentation with smoothed labels (FPSnet-SL). Models were evaluated by volumetric DIL segmentation accuracy using Dice similarity coefficient (DSC) and detection accuracy, as a function of lesion aggressiveness, size, and location (Dataset 1 and 2), and accuracy with respect to two-raters (on Dataset 3). In general MRRN-DS more accurately segmented tumors than other methods on the testing datasets. MRRN-DS significantly outperformed ResUnet in Dataset2 (DSC of 0.54 vs. 0.44, p<0.001) and the Unet++ in Dataset3 (DSC of 0.45 vs. p=0.04). FPSnet-SL was similarly accurate as MRRN-DS in Dataset2 (p = 0.30), but MRRN-DS significantly outperformed FPSnet and FPSnet-SL in both Dataset1 (0.60 vs 0.51 [p=0.01] and 0.54 [p=0.049] respectively) and Dataset3 (0.45 vs 0.06 [p=0.002] and 0.24 [p=0.004] respectively). Finally, MRRN-DS produced slightly higher agreement with experienced radiologist than two radiologists in Dataset 3 (DSC of 0.45 vs. 0.41).
Malaria detection using Deep Convolution Neural Network
Authors: Sumit Kumar, Harsh Vardhan, Sneha Priya, Ayush Kumar
Date: 2023-03-04 00:00:00
Tasks: Diagnostic

The latest WHO report showed that the number of malaria cases climbed to 219 million last year, two million higher than last year. The global efforts to fight malaria have hit a plateau and the most significant underlying reason is international funding has declined. Malaria, which is spread to people through the bites of infected female mosquitoes, occurs in 91 countries but about 90% of the cases and deaths are in sub-Saharan Africa. The disease killed 4,35,000 people last year, the majority of them children under five in Africa. AI-backed technology has revolutionized malaria detection in some regions of Africa and the future impact of such work can be revolutionary. The malaria Cell Image Data-set is taken from the official NIH Website NIH data. The aim of the collection of the dataset was to reduce the burden for microscopists in resource-constrained regions and improve diagnostic accuracy using an AI-based algorithm to detect and segment the red blood cells. The goal of this work is to show that the state of the art accuracy can be obtained even by using 2 layer convolution network and show a new baseline in Malaria detection efforts using AI.
Robust Trajectory and Offloading for Energy-Efficient UAV Edge Computing in Industrial Internet of Things
Authors: Xiao Tang, Hongrui Zhang, Ruonan Zhang, Deyun Zhou, Yan Zhang, Zhu Han
Date: 2023-03-08 00:00:00
Tasks: Edge-computing

Efficient data processing and computation are essential for the industrial Internet of things (IIoT) to empower various applications, which yet can be significantly bottlenecked by the limited energy capacity and computation capability of the IIoT nodes. In this paper, we employ an unmanned aerial vehicle (UAV) as an edge server to assist IIoT data processing, while considering the practical issue of UAV jittering. Specifically, we propose a joint design on trajectory and offloading strategies to minimize energy consumption due to local and edge computation, as well as data transmission. We particularly address the UAV jittering that induces Gaussian-distributed uncertainties associated with flying waypoints, resulting in probabilistic-form flying speed and data offloading constraints. We exploit the Bernstein-type inequality to reformulate the constraints in deterministic forms and decompose the energy minimization to solve for trajectory and offloading separately within an alternating optimization framework. The subproblems are then tackled with the successive convex approximation technique. Simulation results show that our proposal strictly guarantees robustness under uncertainties and effectively reduces energy consumption as compared with the baselines.
Fast Cauchy-Rician Modelling of SAR Images with Method of Algebraic Moments Estimator
Authors: Mutong Li, Ercan Engin Kuruoglu
Date: 2023-03-08 00:00:00
Tasks: parameter estimation

SAR technology has been intensively implemented for geo-sensing and mapping purposes due to its advantages of high azimuthal resolution and weather-independent operation compared to other remote sensing technologies. Modelling SAR image data consequently becomes a prominent topic of interest, especially for data populations with impulsive signal features, which are common in SAR images of urban areas. A recently proposed model named Cauchy-Rician has manifested great potential in modelling extremely heterogeneous SAR images, yet the work only provided a MCMC-based parameter estimator that demands considerable computational power. In this work, a novel analytical parameter estimation method based on algebraic moments is proposed to provide stable and accurate estimation of the parameters of the Cauchy-Rician model with significant improvement on computation speed.
In-Situ Calibration of Antenna Arrays for Positioning With 5G Networks
Authors: Mengguan Pan, Shengheng Liu, Peng Liu, Wangdong Qi, Yongming Huang, Wang Zheng, Qihui Wu, Markus Gardill
Date: 2023-03-08 00:00:00
Tasks: Super-Resolution

Owing to the ubiquity of cellular communication signals, positioning with the fifth generation (5G) signal has emerged as a promising solution in global navigation satellite system-denied areas. Unfortunately, although the widely employed antenna arrays in 5G remote radio units (RRUs) facilitate the measurement of the direction of arrival (DOA), DOA-based positioning performance is severely degraded by array errors. This paper proposes an in-situ calibration framework with a user terminal transmitting 5G reference signals at several known positions in the actual operating environment and the accessible RRUs estimating their array errors from these reference signals. Further, since sub-6GHz small-cell RRUs deployed for indoor coverage generally have small-aperture antenna arrays, while 5G signals have plentiful bandwidth resources, this work segregates the multipath components via super-resolution delay estimation based on the maximum likelihood criteria. This differs significantly from existing in-situ calibration works which resolve multipaths in the spatial domain. The superiority of the proposed method is first verified by numerical simulations. We then demonstrate via field test with commercial 5G equipment that, a reduction of 46.7% for 1-${\sigma}$ DOA estimation error can be achieved by in-situ calibration using the proposed method.
Deep Learning-Based Channel Extrapolation for Pattern Reconfigurable Massive MIMO
Authors: Mu Liang, Ang Li
Date: 2023-03-08 00:00:00
Tasks: Deep Learning

Reconfigurable antennas that can dynamically change their operation state exhibit excellent adaptivity and flexibility over traditional antennas, and MIMO arrays that consist of multifunctional and reconfigurable antennas (MRAs) are foreseen as one promising solution towards future Holographic MIMO. Specifically, in pattern reconfigurable MIMO (PR-MIMO) communication systems, accurate acquisition of channel state information (CSI) of all the radiation modes is a challenging task, because using conventional pilot-based channel estimation techniques in PR-MIMO systems incurs overwhelming pilot overheads. In this letter, we leverage deep learning methods to design a PR neural network, which can use the estimated CSI for one radiation mode to infer CSIs for the other radiation modes. In order to reduce the pilot overheads, we propose a new channel estimation method specially for PR-MIMO systems, which divides the transmit antennas of PR-MIMO into groups and antennas in different groups employ different radiation modes. Compared with conventional full-connected real-valued deep neural networks (DNN), the PR neural network which uses complex-valued coefficients can work directly in the complex domain. Experiment results show that the proposed channel extrapolation method offers significant performance gains in terms of extrapolation accuracy over benchmark schemes.
Efficient Gridless DoA Estimation Method of Non-uniform Linear Arrays with Applications in Automotive Radars
Authors: Silin Gao, Zhe Zhang, Muhan Wang, Yan Zhang, Jie Zhao, Bingchen Zhang, Yue Wang, Yirong Wu
Date: 2023-03-08 00:00:00

This paper focuses on the gridless direction-of-arrival (DoA) estimation for data acquired by non-uniform linear arrays (NLAs) in automotive applications. Atomic norm minimization (ANM) is a promising gridless sparse recovery algorithm under the Toeplitz model and solved by convex relaxation, thus it is only applicable to uniform linear arrays (ULAs) with array manifolds having a Vandermonde structure. In automotive applications, it is essential to apply the gridless DoA estimation to NLAs with arbitrary geometry with efficiency. In this paper, a fast ANM-based gridless DoA estimation algorithm for NLAs is proposed, which employs the array manifold separation technique and the accelerated proximal gradient (APG) technique, making it applicable to NLAs without losing of efficiency. Simulation and measurement experiments on automotive multiple-input multiple-output (MIMO) radars demonstrate the superiority of the proposed method.
Near-Field Low-WISL Unimodular Waveform Design for Terahertz Automotive Radar
Authors: Arian Eamaz, Farhang Yeganegi, Kumar Vijay Mishra, Mojtaba Soltanalian
Date: 2023-03-08 00:00:00

Conventional sensing applications rely on electromagnetic far-field channel models with plane wave propagation. However, recent ultra-short-range automotive radar applications at upper millimeter-wave or low terahertz (THz) frequencies envisage operation in the near-field region, where the wavefront is spherical. Unlike far-field, the near-field beampattern is dependent on both range and angle, thus requiring a different approach to waveform design. For the first time in the literature, we adopt the beampattern matching approach to design unimodular waveforms for THz automotive radars with low weighted integrated sidelobe levels (WISL). We formulate this problem as a unimodular bi-quadratic matrix program, and solve its constituent quadratic sub-problems using our cyclic power method-like iterations (CyPMLI) algorithm. Numerical experiments demonstrate that the CyPMLI approach yields the required beampattern with low autocorrelation levels.
5G Multi-BS Positioning: A Decentralized Fusion Scheme
Authors: Sharief Saleh, Qamar Bader, Mohamed Elhabiby, Aboelmagd Noureldin
Date: 2023-03-07 00:00:00

Fifth-generation (5G) networks are expected to provide high-precision positioning estimation utilizing mmWave signals in urban and downtown areas. In such areas, 5G base stations (BSs) will be densely deployed, allowing for line-of-sight (LOS) communications between the user equipment (UE) and multiple BSs at the same time. Having access to a plethora of measurement sources grants the need for optimal integration between the BSs to have an accurate and precise positioning solution. Traditionally, 5G multi-BS fusion is conducted via an extended Kalman filter (EKF), that directly utilizes range and angle measurements in a centralized integration scheme. Such measurements have a non-linear relationship with the positioning states of the filter, giving rise to linearization errors. Counter to the common belief, an unscented Kalman filter (UKF) will fail to totally eradicate such linearization errors. In this paper, we argue that a decentralized integration between 5G BSs would fully avoid linearization errors within the filter and significantly enhance the positioning performance. This is done by fusing position measurements instead of directly fusing range and angle measurements, which inherently leads to a linear measurement model by design. The proposed decentralized KF method was evaluated in a quasi-real simulation setup provided by Siradel using a real trajectory in Downtown Toronto. The experiments compared the performance of decentralized KF integration to that of centralized EKF and UKF integration schemes. It was shown that the proposed method outperformed both UKF and EKF implementations in multiple scenarios as it significantly decreased the RMS and maximum 2D positioning errors, achieving decimeter-level accuracy for 93.9% of the time.
A Kalman Filter Framework for Resolving 3D Displacement Field Time Series By Combining Multitrack Multitemporal InSAR and GNSS Horizontal Velocities
Authors: Manoochehr Shirzaei
Date: 2023-03-07 00:00:00
Tasks: Time Series, Time Series Analysis

The availability of Synthetic Aperture Radar (SAR) data from different sensors and observation of the Global Navigation Satellite System (GNSS) has been growing worldwide. The complementary nature of InSAR and GNSS observations demands methodological advancements for integrating these datasets of variable accuracy, spatiotemporal sampling rate, and geometries to generate seamless maps of 3D time series that account for both observations advantages. Here, I present an approach based on Kalman Filter, which recursively resolves the 3D displacement field time series by combining line-of-sight time series from at least one SAR viewing geometry and horizontal velocities from GNSS networks. I apply this method to 3 overlapping SAR frames in ascending and descending orbits of Envisat C-band and ascending orbit of ALOS L-band acquired over the San Francisco Bay Area from 2007 to 2011. The experimental results and validation tests against independent observations indicate that the presented approach can resolve 3D displacement field time series at mm-level accuracy comparable to GNSS accuracy but at 10s m spatial resolution.
A switching Gaussian process latent force model for the identification of mechanical systems with a discontinuous nonlinearity
Authors: Luca Marino, Alice Cicirello
Date: 2023-03-07 00:00:00
Tasks: Friction, Gaussian Processes

An approach for the identification of discontinuous and nonsmooth nonlinear forces, as those generated by frictional contacts, in mechanical systems that can be approximated by a single-degree-of-freedom model is presented. To handle the sharp variations and multiple motion regimes introduced by these nonlinearities in the dynamic response, the partially-known physics-based model and noisy measurements of the system's response to a known input force are combined within a switching Gaussian process latent force model (GPLFM). In this grey-box framework, multiple Gaussian processes are used to model the unknown nonlinear force across different motion regimes and a resetting model enables the generation of discontinuities. The states of the system, nonlinear force and regime transitions are inferred by using filtering and smoothing techniques for switching linear dynamical systems. The proposed switching GPLFM is applied to a simulated dry friction oscillator and an experimental setup consisting in a single-storey frame with a brass-to-steel contact. Excellent results are obtained in terms of the identified nonlinear and discontinuous friction force for varying: (i) normal load amplitudes in the contact; (ii) measurement noise levels and (iii) number of samples in the datasets. Moreover, the identified states, friction force and sequence of motion regimes are used for evaluating: (1) uncertain system parameters; (2) the friction force-velocity relationship and (3) the static friction force. The correct identification of the discontinuous nonlinear force and the quantification of any remaining uncertainty in its prediction enable the implementation an accurate forward model able to predict the system's response to different input forces.
Heuristics for Vehicle Routing Problem: A Survey and Recent Advances
Authors: Fei Liu, Chengyu Lu, Lin Gui, Qingfu Zhang, Xialiang Tong, Mingxuan Yuan
Date: 2023-03-01 00:00:00
Tasks: Survey

Vehicle routing is a well-known optimization research topic with significant practical importance. Among different approaches to solving vehicle routing, heuristics can produce a satisfactory solution at a reasonable computational cost. Consequently, much effort has been made in the past decades to develop vehicle routing heuristics. In this article, we systematically survey the existing vehicle routing heuristics, particularly on works carried out in recent years. A classification of vehicle routing heuristics is presented, followed by a review of their methodologies, recent developments, and applications. Moreover, we present a general framework of state-of-the-art methods and provide insights into their success. Finally, three emerging research topics with notable works and future directions are discussed.
Enlargement of Filtrations: An Exposition of Core Ideas with Financial Examples
Authors: Karen Grigorian, Robert A. Jarrow
Date: 2023-03-07 00:00:00
Tasks: Survey

In this paper we provide an exhaustive survey of the current state of the mathematics of filtration enlargement and an interpretation of the key results of the literature from the viewpoint of mathematical finance. The emphasis is on providing a well-structured compendium of known mathematical results that can be used by researchers in mathematical finance. We mainly state the results and discuss their role and significance, with references provided for the omitted proofs. The discussion of mathematical results is accompanied by numerous examples from mathematical finance.
Conformational Fluctuations and Phases in Fused in Sarcoma (FUS) Low-Complexity Domain
Authors: D. Thirumalai, Abhinaw Kumar, Debayan Chakraborty, John E. Straub, Mauro L. Mugnai
Date: 2023-03-07 00:00:00

The well known phenomenon of phase separation in synthetic polymers and proteins has become a major topic in biophysics because it has been invoked as a mechanism of compartment formation in cells, without the need for membranes. Most of the coacervates (or condensates) are composed of Intrinsically Disordered Proteins (IDPs) or regions that are structureless, often in interaction with RNA and DNA. One of the more intriguing IDPs is the 526-residue RNA binding protein, Fused In Sarcoma (FUS), whose monomer conformations and condensates exhibit unusual behavior that are sensitive to solution conditions. By focussing principally on the N-terminus low complexity domain (FUS-LC comprising residues 1-214) and other truncations, we rationalize the findings of solid state NMR experiments, which show that FUS-LC adopts a non-polymorphic fibril (core-1) involving residues 39-95, flanked by fuzzy coats on both the N- and C- terminal ends. An alternate structure (core-2), whose free energy is comparable to core-1, emerges only in the truncated construct (residues 110-214). Both core-1 and core-2 fibrils are stabilized by a Tyrosine ladder as well as hydrophilic interactions. The morphologies (gels, fibrils, and glass-like behavior) adopted by FUS seem to vary greatly, depending on the experimental conditions. The effect of phosphorylation is site specific and affects the stability of the fibril depending on the sites that are phosphorylated. Many of the peculiarities associated with FUS may also be shared by other IDPs, such as TDP43 and hnRNPA2. We outline a number of problems for which there is no clear molecular understanding.